From 5ff831c2d919efb79285c1315b2aeffb21a1d8ba Mon Sep 17 00:00:00 2001 From: Vaibhav Devarapalli Date: Wed, 1 Apr 2026 19:39:03 -0400 Subject: [PATCH 01/10] UmdTask458: Initial FastText project setup (gpsaggese/gpsaggese.github.io#458) --- .../Dockerfile | 30 + .../Dockerfile.python_slim | 28 + .../Dockerfile.ubuntu | 40 + .../Dockerfile.uv | 49 ++ .../README.md | 802 ++++++++++++++++++ .../bashrc | 1 + .../copy_docker_files.py | 140 +++ .../docker_bash.sh | 34 + .../docker_build.sh | 40 + .../docker_build.version.log | 1 + .../docker_clean.sh | 26 + .../docker_cmd.sh | 41 + .../docker_exec.sh | 25 + .../docker_jupyter.sh | 39 + .../docker_name.sh | 12 + .../docker_push.sh | 25 + .../etc_sudoers | 31 + .../requirements.txt | 4 + .../run_jupyter.sh | 35 + .../template.API.ipynb | 215 +++++ .../template.API.py | 129 +++ .../template.example.ipynb | 198 +++++ .../template.example.py | 125 +++ .../template_utils.py | 72 ++ .../test/test_docker_all.py | 48 ++ .../utils.sh | 607 +++++++++++++ .../version.sh | 28 + 27 files changed, 2825 insertions(+) create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.python_slim create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.ubuntu create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.uv create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/bashrc create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/copy_docker_files.py create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_bash.sh create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_build.sh create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_build.version.log create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_clean.sh create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_cmd.sh create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_exec.sh create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_jupyter.sh create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_name.sh create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_push.sh create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/etc_sudoers create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/requirements.txt create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/run_jupyter.sh create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.API.ipynb create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.API.py create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.example.ipynb create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.example.py create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template_utils.py create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/test/test_docker_all.py create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/utils.sh create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/version.sh diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile new file mode 100644 index 000000000..f5c02c562 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile @@ -0,0 +1,30 @@ +# Use Python 3.12 slim (already has Python and pip). +FROM python:3.12-slim + +# Avoid interactive prompts during apt operations. +ENV DEBIAN_FRONTEND=noninteractive + +# Install CA certificates (needed for HTTPS). +RUN apt-get update && apt-get install -y \ + ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +# Install project specific packages. +RUN mkdir -p /install +COPY requirements.txt /install/requirements.txt +RUN pip install --upgrade pip && \ + pip install --no-cache-dir jupyterlab jupyterlab_vim jupytext -r /install/requirements.txt + +# Config. +COPY etc_sudoers /install/ +COPY etc_sudoers /etc/sudoers +COPY bashrc /root/.bashrc + +# Report package versions. +COPY version.sh /install/ +RUN /install/version.sh 2>&1 | tee version.log + +# Jupyter. +EXPOSE 8888 + +CMD ["/bin/bash"] diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.python_slim b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.python_slim new file mode 100644 index 000000000..cc8f18f2f --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.python_slim @@ -0,0 +1,28 @@ +# Use Python 3.12 slim (already has Python and pip). +FROM python:3.12-slim + +# Avoid interactive prompts during apt operations. +ENV DEBIAN_FRONTEND=noninteractive + +# Install CA certificates (needed for HTTPS). +RUN apt-get update && apt-get install -y \ + ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +# Install project specific packages. +RUN mkdir -p /install +COPY requirements.txt /install/requirements.txt +RUN pip install --upgrade pip && \ + pip install --no-cache-dir jupyterlab jupyterlab_vim jupytext -r /install/requirements.txt + +# Config. +COPY etc_sudoers /install/ +COPY etc_sudoers /etc/sudoers +COPY bashrc /root/.bashrc + +# Report package versions. +COPY version.sh /install/ +RUN /install/version.sh 2>&1 | tee version.log + +# Jupyter. +EXPOSE 8888 diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.ubuntu b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.ubuntu new file mode 100644 index 000000000..705105d91 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.ubuntu @@ -0,0 +1,40 @@ +FROM ubuntu:24.04 +ENV DEBIAN_FRONTEND noninteractive + +# Install system utilities and Python in a single layer. +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install -y --no-install-recommends \ + sudo \ + curl \ + git \ + build-essential \ + python3 \ + python3-pip \ + python3-dev \ + python3-venv \ + && rm -rf /var/lib/apt/lists/* + +# Create virtual environment. +RUN python3 -m venv /opt/venv + +# Make the venv the default Python. +ENV PATH="/opt/venv/bin:$PATH" + +# Install project specific packages. +RUN mkdir /install +COPY requirements.txt /install/requirements.txt +RUN pip install --upgrade pip && \ + pip install --no-cache-dir jupyterlab jupyterlab_vim jupytext -r /install/requirements.txt + +# Config. +COPY etc_sudoers /install/ +COPY etc_sudoers /etc/sudoers +COPY bashrc /root/.bashrc + +# Report package versions. +COPY version.sh /install/ +RUN /install/version.sh 2>&1 | tee version.log + +# Jupyter. +EXPOSE 8888 diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.uv b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.uv new file mode 100644 index 000000000..d3b2a0abc --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/Dockerfile.uv @@ -0,0 +1,49 @@ +FROM ubuntu:24.04 +ENV DEBIAN_FRONTEND noninteractive + +# Install system utilities and Python in a single layer. +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install -y --no-install-recommends \ + sudo \ + curl \ + git \ + build-essential \ + python3 \ + python3-pip \ + python3-dev \ + python3-venv \ + libgomp1 \ + g++ \ + && rm -rf /var/lib/apt/lists/* + +# Install uv for package management. +RUN curl -LsSf https://astral.sh/uv/install.sh | sh +ENV PATH="/root/.local/bin:$PATH" + +# Install project specific packages using uv. +COPY pyproject.toml uv.lock /app/ +WORKDIR /app +RUN uv sync +ENV PATH="/app/.venv/bin:$PATH" + +# Install Jupyter. +RUN pip install --upgrade pip && \ + pip install --no-cache-dir jupyterlab jupyterlab_vim jupytext + +# Copy project files. +COPY . /app + +RUN mkdir /install + +# Config. +COPY etc_sudoers /install/ +COPY etc_sudoers /etc/sudoers +COPY bashrc /root/.bashrc + +# Report package versions. +COPY version.sh /install/ +RUN /install/version.sh 2>&1 | tee version.log + +# Jupyter. +EXPOSE 8888 diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md new file mode 100644 index 000000000..58d90e2d1 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md @@ -0,0 +1,802 @@ +# Summary +This directory contains a Docker-based development environment template with: + +- Utility scripts for Docker operations (build, run, clean, push) +- Configuration files for Dockerfile and environment setup +- Jupyter notebook templates for standardized project development +- Shell utilities and Python helpers for container-based workflows + +A guide to set up Docker-based projects using the template, customize it for +your needs, and maintain it over time. + +## Description of Files +- `bashrc` + - Bash configuration file enabling `vi` mode for command-line editing + +- `copy_docker_files.py` + - Python script for copying Docker configuration files to destination + directories + +- `docker_build.version.log` + - Log file containing Python, `pip`, Jupyter, and package version information + from Docker build + +- `docker_cmd.sh` + - Shell script for executing arbitrary commands inside Docker containers with + volume mounting + +- `docker_jupyter.sh` + - Shell script for launching Jupyter Lab server inside Docker containers + +- `docker_name.sh` + - Configuration file defining Docker repository and image naming variables + +- `Dockerfile` + - Docker image build configuration with Ubuntu, Python, Jupyter, and project + dependencies + +- `etc_sudoers` + - Sudoers configuration file granting passwordless sudo access for postgres + user + +- `README.md` + - Documentation file describing directory contents, files, and executable + scripts + +- `template_utils.py` + - Python utility functions supporting tutorial notebooks with data processing + and modeling helpers + +- `template.API.ipynb` + - Jupyter notebook template for API exploration and library usage examples + +- `template.example.ipynb` + - Jupyter notebook template for project examples and demonstrations + +- `utils.sh` + - Bash utility library with reusable functions for Docker operations + - Provides centralized argument parsing (`parse_default_args`) for `-h` and + `-v` flags used by all `docker_*.sh` scripts + - Provides Jupyter configuration logic: vim keybindings, notification + settings, and Docker run option builders + - All `docker_*.sh`, `docker_jupyter.sh`, and `run_jupyter.sh` scripts across + the repo source this file from `class_project/project_template/utils.sh` + +## Workflows +- All commands should be run from inside the project directory + ```bash + > cd tutorials/FilterPy + ``` + +- To build the container for a project + ```bash + > cd $PROJECT + # Build the container. + > docker_build.sh + # Build without cache (pass extra args after -v). + > docker_build.sh --no-cache + # Test the container. + > docker_bash.sh ls + ``` + +- Enable verbose (trace) output with `-v` + ```bash + > docker_build.sh -v + > docker_bash.sh -v + ``` + +- Get help for any docker script + ```bash + > docker_build.sh -h + > docker_jupyter.sh -h + ``` + +- Start Jupyter + ```bash + > docker_jupyter.sh + # Go to localhost:8888 + ``` + +- Start Jupyter on a specific port with vim support + ```bash + > docker_jupyter.sh -p 8890 -u + # Go to localhost:8890 + ``` + +## How to Customize a Project Template +- Copy the template + ```bash + > cp -r class_project/project_template $TARGET + ``` + +## Description of Executables + +### `copy_docker_files.py` +- **What It Does** + - Copies Docker configuration and utility files from project_template to a + destination directory + - Preserves all file permissions and attributes during copying + - Creates destination directory if it doesn't exist + +- Copy all Docker files to a target directory: + ```bash + > ./copy_docker_files.py --dst_dir /path/to/destination + ``` + +- Copy with verbose logging: + ```bash + > ./copy_docker_files.py --dst_dir /path/to/destination -v DEBUG + ``` + +### `docker_bash.sh` +- **What It Does** + - Launches an interactive bash shell inside a Docker container + - Mounts the current working directory as `/data` inside the container + - Exposes port 8888 for potential services running in the container + - Accepts `-h` (help) and `-v` (verbose/trace) flags via `parse_default_args` + +- Launch bash shell in the container: + ```bash + > ./docker_bash.sh + ``` + +- Launch with verbose output (prints each command): + ```bash + > ./docker_bash.sh -v + ``` + +### `docker_build.sh` +- **What It Does** + - Builds Docker container images using Docker BuildKit + - Supports single-architecture builds (default) or multi-architecture builds + (`linux/arm64`, `linux/amd64`) + - Copies project files to temporary build directory and generates build logs + - Accepts `-h` (help) and `-v` (verbose/trace) flags; any extra arguments + after flags are forwarded to `docker build` + +- Build container image for current architecture: + ```bash + > ./docker_build.sh + ``` + +- Build without Docker layer cache: + ```bash + > ./docker_build.sh --no-cache + ``` + +- Build multi-architecture image (requires setting `DOCKER_BUILD_MULTI_ARCH=1` + in the script): + ```bash + > # Edit docker_build.sh to set DOCKER_BUILD_MULTI_ARCH=1 + > ./docker_build.sh + ``` + +### `docker_clean.sh` +- **What It Does** + +- Removes all Docker images matching the project's full image name +- Lists images before and after removal for verification +- Uses force removal to ensure cleanup completes + +- Remove project's Docker images: + ```bash + > ./docker_clean.sh + ``` + +### `docker_cmd.sh` +- **What It Does** + - Executes arbitrary commands inside a Docker container + - Mounts current directory as `/data` for accessing project files + - Automatically removes container after command execution completes + - Accepts `-h` (help) and `-v` (verbose/trace) flags; remaining arguments + form the command to execute + +- Run Python script inside container: + ```bash + > ./docker_cmd.sh python script.py --arg value + ``` + +- List files in the container: + ```bash + > ./docker_cmd.sh ls -la /data + ``` + +- Run tests inside container: + ```bash + > ./docker_cmd.sh pytest tests/ + ``` + +### `docker_exec.sh` +- **What It Does** + - Attaches to an already running Docker container with an interactive bash + shell + - Finds the container ID automatically based on the image name + - Useful for debugging or inspecting running containers + - Accepts `-h` (help) and `-v` (verbose/trace) flags via `parse_default_args` + +- Attach to running container: + ```bash + > ./docker_exec.sh + ``` + +### `docker_jupyter.sh` +- **What It Does** + - Launches Jupyter Lab server inside a Docker container + - Supports custom port configuration (default 8888), vim keybindings, and + custom directory mounting + - Runs `run_jupyter.sh` script inside the container with specified options + +- Start Jupyter on default port 8888: + ```bash + > ./docker_jupyter.sh + ``` + +- Start Jupyter on custom port with vim bindings: + ```bash + > ./docker_jupyter.sh -p 8889 -u + ``` + +- Start Jupyter with external directory mounted: + ```bash + > ./docker_jupyter.sh -d /path/to/notebooks -p 8889 + ``` + +- Start Jupyter in verbose mode: + ```bash + > ./docker_jupyter.sh -v -p 8890 + ``` + +### `docker_push.sh` +- **What It Does** + - Authenticates to Docker registry using credentials from + `~/.docker/passwd.$REPO_NAME.txt` + - Pushes the project's Docker image to the remote repository + - Lists images before pushing for verification + +- Push container image to registry: + ```bash + > ./docker_push.sh + ``` + +### `run_jupyter.sh` +- **What It Does** + - Launches Jupyter Lab server with no authentication (token and password + disabled) + - Binds to all network interfaces (0.0.0.0) on port 8888 + - Allows root access for container environments + - When `JUPYTER_USE_VIM=1`, verifies that `jupyterlab_vim` is installed + before enabling vim keybindings; exits with an error if not found + +- Start Jupyter Lab server (typically called from docker_jupyter.sh): + ```bash + > ./run_jupyter.sh + ``` + +- Start with vim keybindings (requires `jupyterlab_vim` installed in the + container): + ```bash + > JUPYTER_USE_VIM=1 ./run_jupyter.sh + ``` + +### `utils.sh` +- **What It Does** + - Central Bash library sourced by all `docker_*.sh` and `run_jupyter.sh` + scripts across the repository + - Provides `parse_default_args` which adds `-h` (help) and `-v` + (verbose/`set -x`) flags to every docker script + - Provides `build_container_image`, `push_container_image`, + `remove_container_image`, `kill_container`, `exec_container` utilities + - Provides Jupyter configuration helpers: vim keybindings, notification + suppression, and Docker run option builders + +### `version.sh` +- **What It Does** + - Reports version information for Python3, pip3, and Jupyter + - Lists all installed Python packages with versions + - Used during Docker image builds to log environment configuration + +- Display version information: + ```bash + > ./version.sh + ``` + +- Save version information to a log file: + ```bash + > ./version.sh 2>&1 | tee version.log + ``` + +# Template Customization and Maintenance + +## Quick Start for New Projects + +### Step 1: Copy the Template +```bash +> cd class_project/project_template +> cp -r . /path/to/your/new/project +> cd /path/to/your/new/project +``` + +### Step 2: Choose a Base Image +The template includes three Dockerfile options. Choose the one that best fits +your project: + +| Option | File | Best For | +| -------------------------- | ------------------------ | ---------------------------------------------------------------- | +| **Standard** | `Dockerfile.ubuntu` | Full Ubuntu environment with system tools | +| **Lightweight** | `Dockerfile.python_slim` | Minimal Python environment; reduced image size | +| **Modern Package Manager** | `Dockerfile.uv` | Fast dependency resolution with [uv](https://docs.astral.sh/uv/) | + +**How to choose:** + +- **Use Standard** if you need system-level tools (git, curl, graphviz, etc.) +- **Use Python Slim** to minimize image size and build time +- **Use uv** if you want faster, more reliable dependency management + +### Step 3: Set Up Your Dockerfile +- Delete unused reference files + ```bash + > rm Dockerfile.ubuntu Dockerfile.python_slim Dockerfile.uv + ``` + +- Create your working Dockerfile + ```bash + > cp Dockerfile.ubuntu Dockerfile + ``` + +- Add your dependencies + ```bash + > echo "numpy\npandas\nscikit-learn" > requirements.in + > pip-compile requirements.in > requirements.txt + ``` + +### Step 4: Keep Customization Minimal +- Only modify what's necessary for your project +- Use `requirements.txt` for all Python packages (don't edit Dockerfile for + this) +- Keep `bashrc` and `etc_sudoers` as-is unless you need custom shell setup +- Keep base image and Python version unless you have specific requirements + +## Understanding the Dockerfile Flow +Each Dockerfile follows the same structure. Here are the key stages: + +### Stage 1: Base Image and System Setup +```dockerfile +FROM ubuntu:24.04 # or python:3.12-slim, depending on your requirement +ENV DEBIAN_FRONTEND noninteractive +RUN apt-get -y update && apt-get -y upgrade +``` + +- **Purpose**: Start with a clean base image and disable interactive + installation prompts + +- **When to customize**: Only change the base image or version if your project + has specific requirements (different Ubuntu version, specific Python version, + etc.) + +### Stage 2: System Utilities (Ubuntu-based Dockerfiles Only) +```dockerfile +RUN apt install -y --no-install-recommends \ + sudo \ + curl \ + systemctl \ + gnupg \ + git \ + vim +``` + +- **Purpose**: Install essential system tools for development and container + management + +- **When to customize**: Add only if needed for your project + - `postgresql-client`: for database connections + - `graphviz`: for graph visualizations + - `ffmpeg`: for media processing + +- **Best practice**: Use `--no-install-recommends` to keep the image small + +### Stage 3: Python and Build Tools (Ubuntu-based Dockerfiles Only) +```dockerfile +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + python3 \ + python3-pip \ + python3-dev \ + python3-venv \ + && rm -rf /var/lib/apt/lists/* +``` + +- **Purpose**: Install Python 3, pip, and build tools needed for compiled + packages + +- **Why venv**: Creates an isolated Python environment separate from system + Python + +- **When to customize**: Rarely. Only change if you need a specific Python + version (e.g., `python3.11` instead of `python3`) + +### Stage 4: Virtual Environment Setup +```dockerfile +RUN python3 -m venv /opt/venv +ENV PATH="/opt/venv/bin:$PATH" +RUN python -m pip install --upgrade pip +``` + +- **Purpose**: Create and activate an isolated virtual environment for your + project + +- **Why this matters**: Ensures reproducibility and prevents dependency + conflicts across projects + +- **When to customize**: Never. This is a standard best practice + +### Stage 5: Jupyter Installation +```dockerfile +RUN pip install jupyterlab jupyterlab_vim +``` + +- **Purpose**: Install JupyterLab and the Vim keybinding extension for + interactive development + - `jupyterlab`: the main IDE for running notebooks in the browser + - `jupyterlab_vim`: adds Vim-style navigation to notebook cells + +- **Why in Dockerfile, not requirements.txt**: These are infrastructure + packages (the IDE itself), not project-specific dependencies + - Do NOT add `jupyterlab`, `jupyterlab-vim`, or `ipywidgets` to + `requirements.txt`; they are already installed here + +- **When to customize**: + - **Remove** this line if your project doesn't use Jupyter + - **Add more extensions** if needed (e.g., `jupyterlab-git`, + `jupyterlab-variableinspector`) + +### Stage 6: Project Dependencies +```dockerfile +COPY requirements.txt /install/requirements.txt +RUN pip install --no-cache-dir -r /install/requirements.txt +``` + +- **Purpose**: Install your project-specific Python packages + +- **When to customize**: This is the primary place to customize. Define all your + dependencies in `requirements.txt` + +- **Best practice**: + - **Pin all versions**: `numpy==1.24.0` (not `numpy>=1.20.0`) + - **Use `--no-cache-dir`**: Reduces image size by skipping pip cache + - **For complex dependencies**: Use `requirements.in` with `pip-tools` or + `pip-compile` + +- **Example requirements.txt**: + ```text + numpy==1.24.0 + pandas==2.0.0 + scikit-learn==1.2.2 + tensorflow==2.13.0 + ``` + +### Stage 7: Configuration +```dockerfile +COPY etc_sudoers /etc/sudoers +COPY bashrc /root/.bashrc +``` + +- **Purpose**: Apply custom bash configuration and sudo permissions + +- **When to customize**: + - **Edit `bashrc`**: to add aliases, environment variables, or custom prompt + - **Edit `etc_sudoers`**: if additional users need passwordless sudo access + +### Stage 8: Version Logging +```dockerfile +ADD version.sh /install/ +RUN /install/version.sh 2>&1 | tee version.log +``` + +- **Purpose**: Document the exact versions of Python, pip, Jupyter, and all + installed packages + +- **What it logs**: + - Python 3 version + - Pip version + - Jupyter version + - Complete list of all installed Python packages + +- **Why it matters**: Creates a detailed record of your container's environment + for troubleshooting and reproducibility + +- **How to use**: After building, review `version.log` to verify all + dependencies installed correctly + ```bash + > docker build -t my-project . + > cat version.log + ``` + +- **Extending it**: If you need to log additional tools (MongoDB, Node.js, + etc.), add them to `version.sh`: + ```bash + > echo "# mongo" + > mongod --version + ``` + +### Stage 9: Port Declaration +```dockerfile +EXPOSE 8888 +``` + +- **Purpose**: Declare that the container uses port 8888 (informational for + Docker) + +- **When to customize**: Add additional ports if your application needs them + (e.g., `EXPOSE 8888 5432 3000`) + +## Best Practices: Keep It Simple + +### The Core Principle +Only change what's necessary for your project. Everything else should inherit +from the template. + +This approach: + +- Makes Dockerfiles easier to understand and maintain +- Keeps images smaller and faster to build +- Simplifies future updates from the template +- Ensures consistency across similar projects + +### How to Do It Right +| What | Where | Example | +| :--------------------------- | :--------------------------- | :------------------------------ | +| Project Python packages | `requirements.txt` | `numpy==1.24.0` | +| Jupyter + Vim (always there) | Dockerfile Stage 5 | `jupyterlab jupyterlab_vim` | +| System tools | Dockerfile `apt-get` section | `postgresql-client` | +| Shell aliases | `bashrc` | `alias jlab="jupyter lab"` | +| Custom scripts | `scripts/` directory | Setup or initialization scripts | +| User permissions | `etc_sudoers` | Grant passwordless sudo | + +- **Do NOT add to `requirements.txt`**: `jupyterlab`, `jupyterlab-vim`, + `jupyterlab_vim`, or `ipywidgets` — these are Jupyter infrastructure packages + and are already installed in Stage 5 of the Dockerfile + +### Wrong Vs. Right Approach +- **Wrong**: Embed everything in the Dockerfile + ```dockerfile + RUN pip install my-package && python my_setup.py && npm install + ``` + +- **Right**: Use separate files and keep Dockerfile clean + ```dockerfile + COPY requirements.txt /install/ + RUN pip install -r /install/requirements.txt + COPY scripts/setup.sh /install/ + RUN /install/setup.sh + ``` + +## .Dockerignore Policy + +### Why It Matters +The `.dockerignore` file prevents unnecessary files from being added to the +Docker build context: + +- **Reduces build time**: Fewer files to transfer to Docker daemon +- **Reduces image size**: Only necessary files are included +- **Improves security**: Prevents leaking sensitive data + +### What to Exclude: Category Breakdown +- Python Artifacts (Always Exclude) + ```verbatim + __pycache__/ + *.pyc + *.pyo + *.pyd + ``` + - Why: Compiled bytecode generated at runtime. Regenerated in container, adds + bloat + +- Virtual Environments (Always Exclude) + ```verbatim + venv/ + .venv/ + env/ + .env/ + ``` + - Why: Local venvs aren't portable to containers. The Dockerfile creates its + own + +- Jupyter Checkpoints (Always Exclude) + ```verbatim + .ipynb_checkpoints/ + ``` + - Why: Auto-generated by Jupyter, not needed in the image + +- Git and Version Control (Always Exclude) + ```verbatim + .git/ + .gitignore + .gitattributes + ``` + - Why: Repository history not needed at runtime + +- Docker Build Scripts (Always Exclude) + ```verbatim + docker_build.sh + docker_push.sh + docker_clean.sh + docker_exec.sh + docker_cmd.sh + docker_bash.sh + docker_jupyter.sh + docker_name.sh + Dockerfile.* + ``` + - Why: Local development scripts don't run inside the container + +- Large Data Files (Recommended) + ```verbatim + data/ + *.csv + *.pkl + *.h5 + *.parquet + ``` + - Why: Don't ship large training and test data in the image. Mount via volume + instead + - Best practice: `bash > docker run -v /path/to/data:/data my-image ` + +- Test Files (Project-Dependent) + ```verbatim + tests/ + tutorials/ + ``` + - Why: Exclude if tests don't run in the container + - When to include: If CI and CD runs tests inside the container + +- Documentation (Recommended) + ```verbatim + README.md + docs/ + *.md + ``` + - Why: Not needed at runtime + - Exception: Only keep if your app reads these files at runtime + +- Generated Files (Always Exclude) + ```verbatim + *.log + *.tmp + *.cache + build/ + dist/ + ``` + - Why: Generated at runtime, not needed in the image + +## Workflow: From Template to Your Project + +### Complete Setup Checklist +- Copy the template + ```bash + > cp -r project_template my-new-project + > cd my-new-project + ``` + +- Keep all reference Dockerfiles + ```verbatim + Dockerfile.ubuntu_24_04 + Dockerfile.python_slim + Dockerfile.uv + ``` + +- Create your working Dockerfile + ```bash + > cp Dockerfile.ubuntu_24_04 Dockerfile + ``` + +- Add your dependencies + ```bash + > pip freeze > requirements.txt + ``` + +- Configure `.dockerignore`: Review the template `.dockerignore` and add your + project-specific exclusions (e.g., data directories) + +- Test the build + ```bash + > docker build -t my-project:latest . + > docker run -it my-project:latest bash + ``` + +- Test Jupyter (if using) + ```bash + > ./docker_jupyter.sh -p 8888 + ``` + +- Document customizations in your project README: + - Base image chosen and why + - Key dependencies + - Any Dockerfile modifications + - How to build and run + +## Maintaining Your Setup + +### Document Any Changes +- If you modify the Dockerfile, add explanatory comments: + ```dockerfile + # Custom: PostgreSQL client for database access + postgresql-client \ + + # Custom: Node.js for frontend builds + nodejs \ + ``` + +### Monitor Package Versions +- After each build, review `version.log`: + ```bash + > docker build -t my-project . + > cat version.log + ``` + +### Keep `.dockerignore` Updated +- If you add new directories or files, update `.dockerignore`. Add to + `.dockerignore` if the directory shouldn't be in the image: + ```verbatim + data/ + cache/ + .temp/ + ``` + +### Contribute Improvements Back +When you improve your project's Docker setup: + +- Test thoroughly in your project +- Document the improvement clearly +- Submit back to `project_template` +- Other projects can adopt it when they update + +Example improvements: + +- Better way to install TensorFlow with GPU support +- Optimized `.dockerignore` for data science projects +- Security hardening (non-root user setup) + +## Troubleshooting + +### Build Is Slow +- Check `.dockerignore`: Ensure large directories (data/, .git/) are excluded +- Check Docker daemon: Verify Docker is running properly +- Check layer caching: Docker reuses cached layers; avoid changing early layers + +### Image Is Too Large +- Check layer sizes: + ```bash + > docker history my-project:latest + ``` + +- Remove unnecessary packages or use `python_slim` base image + +### Package Not Found Error +- Verify package name in PyPI (packages are case-sensitive) +- Check Python version compatibility +- Pin specific version if needed + +### Permission Issues in Container +- Check `etc_sudoers`: Ensure user has appropriate permissions +- Check file ownership: Ensure COPY doesn't create root-only files + +### Jupyter Won't Connect +- Run Jupyter + ```bash + > ./docker_jupyter.sh -p 8888 + ``` + +- Verify http://localhost:8888 (not https). Check firewall if remote access + needed + +### Vim Keybindings Not Working +- If `run_jupyter.sh` exits with `ERROR: jupyterlab_vim is not installed`, it + means `jupyterlab_vim` is missing from the container image +- Make sure `jupyterlab_vim` is installed in the Dockerfile: + ```dockerfile + RUN pip install jupyterlab jupyterlab_vim + ``` +- Rebuild the image after adding the package: + ```bash + > ./docker_build.sh + ``` diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/bashrc b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/bashrc new file mode 100644 index 000000000..4b7ff4c49 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/bashrc @@ -0,0 +1 @@ +set -o vi diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/copy_docker_files.py b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/copy_docker_files.py new file mode 100644 index 000000000..0e97c194c --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/copy_docker_files.py @@ -0,0 +1,140 @@ +#!/usr/bin/env python + +""" +Copy Docker-related files from the source directory to a destination directory. + +This script copies all Docker configuration and utility files from +class_project/project_template/ to a specified destination directory. + +Usage examples: + # Copy all files to a target directory. + > ./copy_docker_files.py --dst_dir /path/to/destination + + # Copy with verbose logging. + > ./copy_docker_files.py --dst_dir /path/to/destination -v DEBUG + +Import as: + +import class_project.project_template.copy_docker_files as cpdccodo +""" + +import argparse +import logging +import os +from typing import List + +import helpers.hdbg as hdbg +import helpers.hio as hio +import helpers.hparser as hparser +import helpers.hsystem as hsystem + +_LOG = logging.getLogger(__name__) + +# ############################################################################# +# Constants +# ############################################################################# + +# List of files to copy from the source directory. +_FILES_TO_COPY = [ + "bashrc", + "docker_bash.sh", + "docker_build.sh", + "docker_clean.sh", + "docker_cmd.sh", + "docker_exec.sh", + "docker_jupyter.sh", + "docker_name.sh", + "docker_push.sh", + "etc_sudoers", + "install_jupyter_extensions.sh", + "run_jupyter.sh" + "version.sh", +] + + +# ############################################################################# +# Helper functions +# ############################################################################# + + +def _get_source_dir() -> str: + """ + Get the absolute path to the source directory containing Docker files. + + :return: absolute path to class_project/project_template/ + """ + # Get the directory where this script is located. + script_dir = os.path.dirname(os.path.abspath(__file__)) + _LOG.debug("Script directory='%s'", script_dir) + return script_dir + + +def _copy_files( + *, + src_dir: str, + dst_dir: str, + files: List[str], +) -> None: + """ + Copy specified files from source directory to destination directory. + + :param src_dir: source directory path + :param dst_dir: destination directory path + :param files: list of filenames to copy + """ + # Verify source directory exists. + hdbg.dassert_dir_exists(src_dir, "Source directory does not exist:", src_dir) + # Create destination directory if it doesn't exist. + hio.create_dir(dst_dir, incremental=True) + _LOG.info("Copying %d files from '%s' to '%s'", len(files), src_dir, dst_dir) + # Copy each file. + copied_count = 0 + for filename in files: + src_path = os.path.join(src_dir, filename) + dst_path = os.path.join(dst_dir, filename) + # Verify source file exists. + hdbg.dassert_path_exists( + src_path, "Source file does not exist:", src_path + ) + # Copy the file using cp -a to preserve all permissions and attributes. + _LOG.debug("Copying '%s' -> '%s'", src_path, dst_path) + cmd = f"cp -a {src_path} {dst_path}" + hsystem.system(cmd) + copied_count += 1 + # + _LOG.info("Successfully copied %d files", copied_count) + + +# ############################################################################# + + +def _parse() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument( + "--dst_dir", + action="store", + required=True, + help="Destination directory where files will be copied", + ) + hparser.add_verbosity_arg(parser) + return parser + + +def _main(parser: argparse.ArgumentParser) -> None: + args = parser.parse_args() + hdbg.init_logger(verbosity=args.log_level, use_exec_path=True) + # Get source directory. + src_dir = _get_source_dir() + # Copy files to destination. + _copy_files( + src_dir=src_dir, + dst_dir=args.dst_dir, + files=_FILES_TO_COPY, + ) + + +if __name__ == "__main__": + _main(_parse()) diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_bash.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_bash.sh new file mode 100644 index 000000000..0025e81f4 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_bash.sh @@ -0,0 +1,34 @@ +#!/bin/bash +# """ +# This script launches a Docker container with an interactive bash shell for +# development. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions from the project template. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +parse_default_args "$@" + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# List the available Docker images matching the expected image name. +run "docker image ls $FULL_IMAGE_NAME" + +# Configure and run the Docker container with interactive bash shell. +# - Container is removed automatically on exit (--rm) +# - Interactive mode with TTY allocation (-ti) +# - Port forwarding for Jupyter or other services +# - Git root mounted to /git_root inside container +CONTAINER_NAME=${IMAGE_NAME}_bash +PORT= +DOCKER_CMD=$(get_docker_bash_command) +DOCKER_CMD_OPTS=$(get_docker_bash_options $CONTAINER_NAME $PORT) +run "$DOCKER_CMD $DOCKER_CMD_OPTS $FULL_IMAGE_NAME" diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_build.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_build.sh new file mode 100644 index 000000000..5b0957a99 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_build.sh @@ -0,0 +1,40 @@ +#!/bin/bash +# """ +# Build a Docker container image for the project. +# +# This script sets up the build environment with error handling and command +# tracing, loads Docker configuration from docker_name.sh, and builds the +# Docker image using the build_container_image utility function. It supports +# both single-architecture and multi-architecture builds via the +# DOCKER_BUILD_MULTI_ARCH environment variable. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +# Shift processed option flags so remaining args are passed to the build. +parse_default_args "$@" +shift $((OPTIND-1)) + +# Load Docker configuration variables (REPO_NAME, IMAGE_NAME, FULL_IMAGE_NAME). +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# Configure Docker build settings. +# Enable BuildKit for improved build performance and features. +export DOCKER_BUILDKIT=1 +#export DOCKER_BUILDKIT=0 + +# Configure single-architecture build (set to 1 for multi-arch build). +#export DOCKER_BUILD_MULTI_ARCH=1 +export DOCKER_BUILD_MULTI_ARCH=0 + +# Build the container image. +# Pass extra arguments (e.g., --no-cache) via command line after -v. +build_container_image "$@" diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_build.version.log b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_build.version.log new file mode 100644 index 000000000..8315eefe2 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_build.version.log @@ -0,0 +1 @@ +the input device is not a TTY diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_clean.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_clean.sh new file mode 100644 index 000000000..7e40839ae --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_clean.sh @@ -0,0 +1,26 @@ +#!/bin/bash +# """ +# Remove Docker container image for the project. +# +# This script cleans up Docker images by removing the container image +# matching the project configuration. Useful for freeing disk space or +# ensuring a fresh build. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +parse_default_args "$@" + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# Remove the container image. +remove_container_image diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_cmd.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_cmd.sh new file mode 100644 index 000000000..906d7a77b --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_cmd.sh @@ -0,0 +1,41 @@ +#!/bin/bash +# """ +# Execute a command in a Docker container. +# +# This script runs a specified command inside a new Docker container instance. +# The container is removed automatically after the command completes. The +# git root is mounted to /git_root inside the container. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +# Shift processed option flags so remaining args form the command. +parse_default_args "$@" +shift $((OPTIND-1)) + +# Capture the command to execute from remaining arguments. +CMD="$@" +echo "Executing: '$CMD'" + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# List available Docker images matching the expected image name. +run "docker image ls $FULL_IMAGE_NAME" +#(docker manifest inspect $FULL_IMAGE_NAME | grep arch) || true + +# Configure and run the Docker container with the specified command. +CONTAINER_NAME=$IMAGE_NAME +DOCKER_CMD=$(get_docker_cmd_command) +PORT="" +DOCKER_RUN_OPTS="" +DOCKER_CMD_OPTS=$(get_docker_bash_options $CONTAINER_NAME $PORT $DOCKER_RUN_OPTS) +run "$DOCKER_CMD $DOCKER_CMD_OPTS $FULL_IMAGE_NAME bash -c '$CMD'" diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_exec.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_exec.sh new file mode 100644 index 000000000..24f8e401a --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_exec.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# """ +# Execute a bash shell in a running Docker container. +# +# This script connects to an already running Docker container and opens an +# interactive bash session for debugging or inspection purposes. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +parse_default_args "$@" + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# Execute bash shell in the running container. +exec_container diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_jupyter.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_jupyter.sh new file mode 100644 index 000000000..1a60dfd3a --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_jupyter.sh @@ -0,0 +1,39 @@ +#!/bin/bash +# """ +# Execute Jupyter Lab in a Docker container. +# +# This script launches a Docker container running Jupyter Lab with +# configurable port, directory mounting, and vim bindings. It passes +# command-line options to the run_jupyter.sh script inside the container. +# +# Usage: +# > docker_jupyter.sh [options] +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse command-line options and set Jupyter configuration variables. +parse_docker_jupyter_args "$@" + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# List available Docker images and inspect architecture. +list_and_inspect_docker_image + +# Run the Docker container with Jupyter Lab. +CMD=$(get_run_jupyter_cmd "${BASH_SOURCE[0]}" "$OLD_CMD_OPTS") +CONTAINER_NAME=$IMAGE_NAME +# Kill existing container if -f flag is set. +kill_existing_container_if_forced + +DOCKER_CMD=$(get_docker_jupyter_command) +DOCKER_CMD_OPTS=$(get_docker_jupyter_options $CONTAINER_NAME $JUPYTER_HOST_PORT $JUPYTER_USE_VIM) +run "$DOCKER_CMD $DOCKER_CMD_OPTS $FULL_IMAGE_NAME $CMD" diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_name.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_name.sh new file mode 100644 index 000000000..32a546cf3 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_name.sh @@ -0,0 +1,12 @@ +#!/bin/bash +# """ +# Docker image naming configuration. +# +# This file defines the repository name, image name, and full image name +# variables used by all docker_*.sh scripts in the project template. +# """ + +REPO_NAME=gpsaggese +# The file should be all lower case. +IMAGE_NAME=umd_project_template +FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_push.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_push.sh new file mode 100644 index 000000000..27d752dd9 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/docker_push.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# """ +# Push Docker container image to Docker Hub or registry. +# +# This script authenticates with the Docker registry using credentials from +# ~/.docker/passwd.$REPO_NAME.txt and pushes the locally built container +# image to the remote repository. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +parse_default_args "$@" + +# Load Docker image naming configuration. +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +source $SCRIPT_DIR/docker_name.sh + +# Push the container image to the registry. +push_container_image diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/etc_sudoers b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/etc_sudoers new file mode 100644 index 000000000..ee0816a15 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/etc_sudoers @@ -0,0 +1,31 @@ +# +# This file MUST be edited with the 'visudo' command as root. +# +# Please consider adding local content in /etc/sudoers.d/ instead of +# directly modifying this file. +# +# See the man page for details on how to write a sudoers file. +# +Defaults env_reset +Defaults mail_badpass +Defaults secure_path="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin" + +# Host alias specification + +# User alias specification + +# Cmnd alias specification + +# User privilege specification +root ALL=(ALL:ALL) ALL + +# Members of the admin group may gain root privileges +%admin ALL=(ALL) ALL + +# Allow members of group sudo to execute any command +%sudo ALL=(ALL:ALL) ALL + +# See sudoers(5) for more information on "#include" directives: +postgres ALL=(ALL) NOPASSWD:ALL + +#includedir /etc/sudoers.d diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/requirements.txt b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/requirements.txt new file mode 100644 index 000000000..49aca3901 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/requirements.txt @@ -0,0 +1,4 @@ +matplotlib +numpy +pandas +seaborn diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/run_jupyter.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/run_jupyter.sh new file mode 100644 index 000000000..d725c3fe7 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/run_jupyter.sh @@ -0,0 +1,35 @@ +#!/bin/bash +# """ +# Launch Jupyter Lab server. +# +# This script starts Jupyter Lab on port 8888 with the following configuration: +# - No browser auto-launch (useful for Docker containers) +# - Accessible from any IP address (0.0.0.0) +# - Root user allowed (required for Docker environments) +# - No authentication token or password (for development convenience) +# - Vim keybindings can be enabled via JUPYTER_USE_VIM environment variable +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Print each command to stdout before executing it. +#set -x + +# Import the utility functions from /git_root. +GIT_ROOT=/git_root +source $GIT_ROOT/class_project/project_template/utils.sh + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# Setup Jupyter Lab environment. +setup_jupyter_environment + +# Initialize Jupyter Lab command with base configuration. +JUPYTER_ARGS=$(get_jupyter_args) + +# Start Jupyter Lab with development-friendly settings. +run "jupyter lab $JUPYTER_ARGS" diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.API.ipynb b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.API.ipynb new file mode 100644 index 000000000..3afca937c --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.API.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "183c2248-ea3d-43ba-b87e-d821bba1bbc6", + "metadata": {}, + "source": [ + "# Template API Notebook\n", + "\n", + "This is a template notebook. The first heading should be the title of what notebook is about. For example, if it is a neo4j tutorial the heading should be `Neo4j API`.\n", + "\n", + "- Add description of what the notebook does.\n", + "- Point to references, e.g. (neo4j.API.md)\n", + "- Add citations.\n", + "- Keep the notebook flow clear.\n", + "- Comments should be imperative and have a period at the end.\n", + "- Your code should be well commented.\n", + "\n", + "The name of this notebook should in the following format:\n", + "- if the notebook is exploring `pycaret API`, then it is `pycaret.API.ipynb`\n", + "\n", + "Follow the reference to write notebooks in a clear manner: https://github.com/causify-ai/helpers/blob/master/docs/coding/all.jupyter_notebook.how_to_guide.md" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "265e0d58-a7cd-4edf-a0b4-96b60220e801", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "d3b2f997-5c9b-4238-b6d5-e5f2cea43809", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d1480ee9-d6a6-437d-b927-da6cbb05bdf5", + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "# Import libraries in this section.\n", + "# Avoid imports like import *, from ... import ..., from ... import *, etc.\n", + "\n", + "import helpers.hdbg as hdbg\n", + "import helpers.hnotebook as hnotebo" + ] + }, + { + "cell_type": "markdown", + "id": "f9208cc9-837d-4fec-a312-9c4aa5b7648d", + "metadata": {}, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9a2d7a9c-c6c5-48c9-8445-11c97045d00b", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0mWARNING: Running in Jupyter\n", + "INFO > cmd='/venv/lib/python3.12/site-packages/ipykernel_launcher.py -f /home/.local/share/jupyter/runtime/kernel-085a2ce7-6161-4c8a-92d5-492051832f3c.json'\n" + ] + } + ], + "source": [ + "hdbg.init_logger(verbosity=logging.INFO)\n", + "\n", + "_LOG = logging.getLogger(__name__)\n", + "\n", + "hnotebo.config_notebook()" + ] + }, + { + "cell_type": "markdown", + "id": "79c37ba3-bd5d-4a44-87df-645eee54977a", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Make the notebook flow clear\n", + "Each notebook needs to follow a clear and logical flow, e.g:\n", + "- Load data\n", + "- Compute stats\n", + "- Clean data\n", + "- Compute stats\n", + "- Do analysis\n", + "- Show results\n", + "\n", + "\n", + "\n", + "\n", + "#############################################################################\n", + "Template\n", + "#############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a8a109cd-fc8e-4b9e-9dc0-4fc8d4126ad8", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "class Template:\n", + " \"\"\"\n", + " Brief imperative description of what the class does in one line, if needed.\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", + " pass\n", + "\n", + " def method1(self, arg1: int) -> None:\n", + " \"\"\"\n", + " Brief imperative description of what the method does in one line.\n", + "\n", + " You can elaborate more in the method docstring in this section, for e.g. explaining\n", + " the formula/algorithm. Every method/function should have a docstring, typehints and include the\n", + " parameters and return as follows:\n", + "\n", + " :param arg1: description of arg1\n", + " :return: description of return\n", + " \"\"\"\n", + " # Code bloks go here.\n", + " # Make sure to include comments to explain what the code is doing.\n", + " # No empty lines between code blocks.\n", + " pass\n", + "\n", + "\n", + "def template_function(arg1: int) -> None:\n", + " \"\"\"\n", + " Brief imperative description of what the function does in one line.\n", + "\n", + " You can elaborate more in the function docstring in this section, for e.g. explaining\n", + " the formula/algorithm. Every function should have a docstring, typehints and include the\n", + " parameters and return as follows:\n", + "\n", + " :param arg1: description of arg1\n", + " :return: description of return\n", + " \"\"\"\n", + " # Code bloks go here.\n", + " # Make sure to include comments to explain what the code is doing.\n", + " # No empty lines between code blocks.\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "00926523-ae59-497d-bba8-b22e58333849", + "metadata": {}, + "source": [ + "## The flow should be highlighted using headings in markdown\n", + "```\n", + "# Level 1\n", + "## Level 2\n", + "### Level 3\n", + "```" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.API.py b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.API.py new file mode 100644 index 000000000..4192ef8fe --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.API.py @@ -0,0 +1,129 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.19.0 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Template API Notebook +# +# This is a template notebook. The first heading should be the title of what notebook is about. For example, if it is a neo4j tutorial the heading should be `Neo4j API`. +# +# - Add description of what the notebook does. +# - Point to references, e.g. (neo4j.API.md) +# - Add citations. +# - Keep the notebook flow clear. +# - Comments should be imperative and have a period at the end. +# - Your code should be well commented. +# +# The name of this notebook should in the following format: +# - if the notebook is exploring `pycaret API`, then it is `pycaret.API.ipynb` +# +# Follow the reference to write notebooks in a clear manner: https://github.com/causify-ai/helpers/blob/master/docs/coding/all.jupyter_notebook.how_to_guide.md + +# %% +# %load_ext autoreload +# %autoreload 2 +# %matplotlib inline + +# %% [markdown] +# ## Imports + +# %% +import logging +# Import libraries in this section. +# Avoid imports like import *, from ... import ..., from ... import *, etc. + +import helpers.hdbg as hdbg +import helpers.hnotebook as hnotebo + +# %% [markdown] +# ## Configuration + +# %% +hdbg.init_logger(verbosity=logging.INFO) + +_LOG = logging.getLogger(__name__) + +hnotebo.config_notebook() + + +# %% [markdown] +# ## Make the notebook flow clear +# Each notebook needs to follow a clear and logical flow, e.g: +# - Load data +# - Compute stats +# - Clean data +# - Compute stats +# - Do analysis +# - Show results +# +# +# +# + + +# ############################################################################# +# Template +# ############################################################################# + + +# %% +class Template: + """ + Brief imperative description of what the class does in one line, if needed. + """ + + def __init__(self): + pass + + def method1(self, arg1: int) -> None: + """ + Brief imperative description of what the method does in one line. + + You can elaborate more in the method docstring in this section, for e.g. explaining + the formula/algorithm. Every method/function should have a docstring, typehints and include the + parameters and return as follows: + + :param arg1: description of arg1 + :return: description of return + """ + # Code bloks go here. + # Make sure to include comments to explain what the code is doing. + # No empty lines between code blocks. + pass + + +def template_function(arg1: int) -> None: + """ + Brief imperative description of what the function does in one line. + + You can elaborate more in the function docstring in this section, for e.g. explaining + the formula/algorithm. Every function should have a docstring, typehints and include the + parameters and return as follows: + + :param arg1: description of arg1 + :return: description of return + """ + # Code bloks go here. + # Make sure to include comments to explain what the code is doing. + # No empty lines between code blocks. + pass + + +# %% [markdown] +# ## The flow should be highlighted using headings in markdown +# ``` +# # Level 1 +# ## Level 2 +# ### Level 3 +# ``` diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.example.ipynb b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.example.ipynb new file mode 100644 index 000000000..a2e9aedd7 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.example.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "50f78f7e-2dee-45d6-9d37-7a55eeaae283", + "metadata": {}, + "source": [ + "# Template Example Notebook\n", + "\n", + "This is a template notebook. The first heading should be the title of what notebook is about. For example, if it is a project on neo4j tutorial the heading should be `Project Title`.\n", + "\n", + "- Add description of what the notebook does.\n", + "- Point to references, e.g. (neo4j.example.md)\n", + "- Add citations.\n", + "- Keep the notebook flow clear.\n", + "- Comments should be imperative and have a period at the end.\n", + "- Your code should be well commented.\n", + "\n", + "The name of this notebook should in the following format:\n", + "- if the notebook is exploring `pycaret API`, then it is `pycaret.example.ipynb`\n", + "\n", + "Follow the reference to write notebooks in a clear manner: https://github.com/causify-ai/helpers/blob/master/docs/coding/all.jupyter_notebook.how_to_guide.md" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6226667e-cab5-479c-be6a-6b7d6f580a97", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8020901a-4bc7-4b73-95e8-aaa462b4fc19", + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "# Import libraries in this section.\n", + "# Avoid imports like import *, from ... import ..., from ... import *, etc.\n", + "\n", + "import helpers.hdbg as hdbg\n", + "import helpers.hnotebook as hnotebo" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4ecb72b2-b21d-4fb0-ac92-e7174da390e6", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0mWARNING: Running in Jupyter\n", + "INFO > cmd='/venv/lib/python3.12/site-packages/ipykernel_launcher.py -f /home/.local/share/jupyter/runtime/kernel-783e0930-1631-4d64-8bb4-f3a98bb74fcd.json'\n" + ] + } + ], + "source": [ + "hdbg.init_logger(verbosity=logging.INFO)\n", + "\n", + "_LOG = logging.getLogger(__name__)\n", + "\n", + "hnotebo.config_notebook()" + ] + }, + { + "cell_type": "markdown", + "id": "1ede6422-bff2-4f0a-8d28-29a01d4786b2", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Make the notebook flow clear\n", + "Each notebook needs to follow a clear and logical flow, e.g:\n", + "- Load data\n", + "- Compute stats\n", + "- Clean data\n", + "- Compute stats\n", + "- Do analysis\n", + "- Show results\n", + "\n", + "\n", + "\n", + "\n", + "#############################################################################\n", + "Template\n", + "#############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8bbd660d-d22f-44fa-bf53-dd622dee0f53", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "class Template:\n", + " \"\"\"\n", + " Brief imperative description of what the class does in one line, if needed.\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", + " pass\n", + "\n", + " def method1(self, arg1: int) -> None:\n", + " \"\"\"\n", + " Brief imperative description of what the method does in one line.\n", + "\n", + " You can elaborate more in the method docstring in this section, for e.g. explaining\n", + " the formula/algorithm. Every method/function should have a docstring, typehints and include the\n", + " parameters and return as follows:\n", + "\n", + " :param arg1: description of arg1\n", + " :return: description of return\n", + " \"\"\"\n", + " # Code bloks go here.\n", + " # Make sure to include comments to explain what the code is doing.\n", + " # No empty lines between code blocks.\n", + " pass\n", + "\n", + "\n", + "def template_function(arg1: int) -> None:\n", + " \"\"\"\n", + " Brief imperative description of what the function does in one line.\n", + "\n", + " You can elaborate more in the function docstring in this section, for e.g. explaining\n", + " the formula/algorithm. Every function should have a docstring, typehints and include the\n", + " parameters and return as follows:\n", + "\n", + " :param arg1: description of arg1\n", + " :return: description of return\n", + " \"\"\"\n", + " # Code bloks go here.\n", + " # Make sure to include comments to explain what the code is doing.\n", + " # No empty lines between code blocks.\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "103f6e36-54cf-442c-b137-8091d48805a7", + "metadata": {}, + "source": [ + "## The flow should be highlighted using headings in markdown\n", + "```\n", + "# Level 1\n", + "## Level 2\n", + "### Level 3\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d05d52af-67ba-4a4f-a561-af453e43854f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.example.py b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.example.py new file mode 100644 index 000000000..8566ff277 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template.example.py @@ -0,0 +1,125 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.19.0 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Template Example Notebook +# +# This is a template notebook. The first heading should be the title of what notebook is about. For example, if it is a project on neo4j tutorial the heading should be `Project Title`. +# +# - Add description of what the notebook does. +# - Point to references, e.g. (neo4j.example.md) +# - Add citations. +# - Keep the notebook flow clear. +# - Comments should be imperative and have a period at the end. +# - Your code should be well commented. +# +# The name of this notebook should in the following format: +# - if the notebook is exploring `pycaret API`, then it is `pycaret.example.ipynb` +# +# Follow the reference to write notebooks in a clear manner: https://github.com/causify-ai/helpers/blob/master/docs/coding/all.jupyter_notebook.how_to_guide.md + +# %% +# %load_ext autoreload +# %autoreload 2 +# %matplotlib inline + +# %% +import logging +# Import libraries in this section. +# Avoid imports like import *, from ... import ..., from ... import *, etc. + +import helpers.hdbg as hdbg +import helpers.hnotebook as hnotebo + +# %% +hdbg.init_logger(verbosity=logging.INFO) + +_LOG = logging.getLogger(__name__) + +hnotebo.config_notebook() + + +# %% [markdown] +# ## Make the notebook flow clear +# Each notebook needs to follow a clear and logical flow, e.g: +# - Load data +# - Compute stats +# - Clean data +# - Compute stats +# - Do analysis +# - Show results +# +# +# +# + + +# ############################################################################# +# Template +# ############################################################################# + + +# %% +class Template: + """ + Brief imperative description of what the class does in one line, if needed. + """ + + def __init__(self): + pass + + def method1(self, arg1: int) -> None: + """ + Brief imperative description of what the method does in one line. + + You can elaborate more in the method docstring in this section, for e.g. explaining + the formula/algorithm. Every method/function should have a docstring, typehints and include the + parameters and return as follows: + + :param arg1: description of arg1 + :return: description of return + """ + # Code bloks go here. + # Make sure to include comments to explain what the code is doing. + # No empty lines between code blocks. + pass + + +def template_function(arg1: int) -> None: + """ + Brief imperative description of what the function does in one line. + + You can elaborate more in the function docstring in this section, for e.g. explaining + the formula/algorithm. Every function should have a docstring, typehints and include the + parameters and return as follows: + + :param arg1: description of arg1 + :return: description of return + """ + # Code bloks go here. + # Make sure to include comments to explain what the code is doing. + # No empty lines between code blocks. + pass + + +# %% [markdown] +# ## The flow should be highlighted using headings in markdown +# ``` +# # Level 1 +# ## Level 2 +# ### Level 3 +# ``` + +# %% diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template_utils.py b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template_utils.py new file mode 100644 index 000000000..f8916102e --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/template_utils.py @@ -0,0 +1,72 @@ +""" +template_utils.py + +This file contains utility functions that support the tutorial notebooks. + +- Notebooks should call these functions instead of writing raw logic inline. +- This helps keep the notebooks clean, modular, and easier to debug. +- Students should implement functions here for data preprocessing, + model setup, evaluation, or any reusable logic. + +Import as: + +import class_project.project_template.template_utils as cpptteut +""" + +import pandas as pd +import logging +from sklearn.model_selection import train_test_split +from pycaret.classification import compare_models + +# ----------------------------------------------------------------------------- +# Logging +# ----------------------------------------------------------------------------- + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# ----------------------------------------------------------------------------- +# Example 1: Split the dataset into train and test sets +# ----------------------------------------------------------------------------- + + +def split_data(df: pd.DataFrame, target_column: str, test_size: float = 0.2): + """ + Split the dataset into training and testing sets. + + :param df: full dataset + :param target_column: name of the target column + :param test_size: proportion of test data (default = 0.2) + + :return: X_train, X_test, y_train, y_test + """ + logger.info("Splitting data into train and test sets") + X = df.drop(columns=[target_column]) + y = df[target_column] + return train_test_split(X, y, test_size=test_size, random_state=42) + + +# ----------------------------------------------------------------------------- +# Example 2: PyCaret classification pipeline +# ----------------------------------------------------------------------------- + + +def run_pycaret_classification( + df: pd.DataFrame, target_column: str +) -> pd.DataFrame: + """ + Run a basic PyCaret classification experiment. + + :param df: dataset containing features and target + :param target_column: name of the target column + + :return: comparison of top-performing models + """ + logger.info("Initializing PyCaret classification setup") + ... + + logger.info("Comparing models") + results = compare_models() + ... + + return results diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/test/test_docker_all.py b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/test/test_docker_all.py new file mode 100644 index 000000000..904cdd7af --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/test/test_docker_all.py @@ -0,0 +1,48 @@ +""" +Run each notebook in class_project/project_template/ inside Docker using docker_cmd.sh. + +Import as: + +import class_project.project_template.test.test_docker_all as tptdal +""" + +import logging + +import pytest + +import helpers.hdocker_tests as hdoctest + +_LOG = logging.getLogger(__name__) + + +# ############################################################################# +# Test_docker +# ############################################################################# + + +class Test_docker(hdoctest.DockerTestCase): + """ + Run all Docker tests for class_project/project_template/. + """ + + _test_file = __file__ + + @pytest.mark.slow + def test1(self) -> None: + """ + Test that template.example.ipynb runs without error inside Docker. + """ + # Prepare inputs. + notebook_name = "template.example.ipynb" + # Run test. + self._helper(notebook_name) + + @pytest.mark.slow + def test2(self) -> None: + """ + Test that template.API.ipynb runs without error inside Docker. + """ + # Prepare inputs. + notebook_name = "template.API.ipynb" + # Run test. + self._helper(notebook_name) diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/utils.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/utils.sh new file mode 100644 index 000000000..cc0ed8c4a --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/utils.sh @@ -0,0 +1,607 @@ +#!/bin/bash +# """ +# Utility functions for Docker container management. +# """ + + +# ############################################################################# +# General utilities +# ############################################################################# + + +run() { + # """ + # Execute a command with echo output. + # + # :param cmd: Command string to execute + # :return: Exit status of the executed command + # """ + cmd="$*" + echo "> $cmd" + eval "$cmd" +} + + +enable_verbose_mode() { + # """ + # Enable shell command tracing (set -x) when VERBOSE is set to 1. + # + # Reads the VERBOSE variable set by parse_docker_jupyter_args. + # Call this after parsing args to activate tracing for the rest of the script. + # """ + if [[ $VERBOSE == 1 ]]; then + set -x + fi +} + + +# ############################################################################# +# Argument parsing +# ############################################################################# + + +_print_default_help() { + # """ + # Print usage information and available default options for docker scripts. + # """ + echo "Usage: $(basename $0) [options]" + echo "" + echo "Options:" + echo " -f Force kill existing container with same name before starting" + echo " -h Print this help message and exit" + echo " -v Enable verbose output (set -x)" +} + + +parse_default_args() { + # """ + # Parse default command-line arguments for docker scripts. + # + # Sets VERBOSE and FORCE variables in the caller's scope. Enables set -x + # when -v is passed. Prints help and exits when -h is passed. + # Updates OPTIND so the caller can shift away processed arguments. + # + # :param @: command-line arguments forwarded from the calling script + # """ + VERBOSE=0 + FORCE=0 + while getopts "fhv" flag; do + case "${flag}" in + f) FORCE=1;; + h) _print_default_help; exit 0;; + v) VERBOSE=1;; + *) _print_default_help; exit 1;; + esac + done + enable_verbose_mode +} + + +_print_docker_jupyter_help() { + # """ + # Print usage information and available options for docker_jupyter.sh. + # """ + echo "Usage: $(basename $0) [options]" + echo "" + echo "Launch Jupyter Lab inside a Docker container." + echo "" + echo "Options:" + echo " -f Force kill existing container with same name before starting" + echo " -h Print this help message and exit" + echo " -p PORT Host port to forward to Jupyter Lab (default: 8888)" + echo " -u Enable vim keybindings in Jupyter Lab" + echo " -v Enable verbose output (set -x)" +} + + +parse_docker_jupyter_args() { + # """ + # Parse command-line arguments for docker_jupyter.sh. + # + # Sets JUPYTER_HOST_PORT, JUPYTER_USE_VIM, TARGET_DIR, VERBOSE, FORCE, and + # OLD_CMD_OPTS in the caller's scope. Enables set -x when -v is passed. + # Prints help and exits when -h is passed. + # + # :param @: command-line arguments forwarded from the calling script + # """ + # Set defaults. + JUPYTER_HOST_PORT=8888 + JUPYTER_USE_VIM=0 + VERBOSE=0 + FORCE=0 + # Save original args to pass through to run_jupyter.sh. + OLD_CMD_OPTS="$*" + # Parse options. + while getopts "fhp:uv" flag; do + case "${flag}" in + f) FORCE=1;; + h) _print_docker_jupyter_help; exit 0;; + p) JUPYTER_HOST_PORT=${OPTARG};; # Port for Jupyter Lab. + u) JUPYTER_USE_VIM=1;; # Enable vim bindings. + v) VERBOSE=1;; # Enable verbose output. + *) _print_docker_jupyter_help; exit 1;; + esac + done + # Enable command tracing if verbose mode is requested. + enable_verbose_mode +} + + +# ############################################################################# +# Docker image management +# ############################################################################# + + +get_docker_vars_script() { + # """ + # Load Docker variables from docker_name.sh script. + # + # :param script_path: Path to the script to determine the Docker configuration directory + # :return: Sources REPO_NAME, IMAGE_NAME, and FULL_IMAGE_NAME variables + # """ + local script_path=$1 + # Find the name of the container. + SCRIPT_DIR=$(dirname $script_path) + DOCKER_NAME="$SCRIPT_DIR/docker_name.sh" + if [[ ! -e $SCRIPT_DIR ]]; then + echo "Can't find $DOCKER_NAME" + exit -1 + fi; + source $DOCKER_NAME +} + + +print_docker_vars() { + # """ + # Print current Docker variables to stdout. + # """ + echo "REPO_NAME=$REPO_NAME" + echo "IMAGE_NAME=$IMAGE_NAME" + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" +} + + +build_container_image() { + # """ + # Build a Docker container image. + # + # Supports both single-architecture and multi-architecture builds. + # Creates temporary build directory, copies files, and builds the image. + # + # :param @: Additional options to pass to docker build/buildx build + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + # Prepare build area. + #tar -czh . | docker build $OPTS -t $IMAGE_NAME - + DIR="../tmp.build" + if [[ -d $DIR ]]; then + rm -rf $DIR + fi; + cp -Lr . $DIR || true + # Build container. + echo "DOCKER_BUILDKIT=$DOCKER_BUILDKIT" + echo "DOCKER_BUILD_MULTI_ARCH=$DOCKER_BUILD_MULTI_ARCH" + if [[ $DOCKER_BUILD_MULTI_ARCH != 1 ]]; then + # Build for a single architecture. + echo "Building for current architecture..." + OPTS="--progress plain $@" + (cd $DIR; docker build $OPTS -t $FULL_IMAGE_NAME . 2>&1 | tee ../docker_build.log; exit ${PIPESTATUS[0]}) + else + # Build for multiple architectures. + echo "Building for multiple architectures..." + OPTS="$@" + export DOCKER_CLI_EXPERIMENTAL=enabled + # Create a new builder. + #docker buildx rm --all-inactive --force + #docker buildx create --name mybuilder + #docker buildx use mybuilder + # Use the default builder. + docker buildx use multiarch + docker buildx inspect --bootstrap + # Note that one needs to push to the repo since otherwise it is not + # possible to keep multiple. + (cd $DIR; docker buildx build --push --platform linux/arm64,linux/amd64 $OPTS --tag $FULL_IMAGE_NAME . 2>&1 | tee ../docker_build.log; exit ${PIPESTATUS[0]}) + # Report the status. + docker buildx imagetools inspect $FULL_IMAGE_NAME + fi; + # Report build version. + if [ -f docker_build.version.log ]; then + rm docker_build.version.log + fi + (cd $DIR; docker run --rm -it -v $(pwd):/data $FULL_IMAGE_NAME bash -c "/data/version.sh") 2>&1 | tee docker_build.version.log + # + docker image ls $REPO_NAME/$IMAGE_NAME + rm -rf $DIR + echo "*****************************" + echo "SUCCESS" + echo "*****************************" +} + + +remove_container_image() { + # """ + # Remove Docker container image(s) matching the current configuration. + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + docker image ls | grep $FULL_IMAGE_NAME + docker image ls | grep $FULL_IMAGE_NAME | awk '{print $1}' | xargs -n 1 -t docker image rm -f + docker image ls + echo "${FUNCNAME[0]} ... done" +} + + +push_container_image() { + # """ + # Push Docker container image to registry. + # + # Authenticates using credentials from ~/.docker/passwd.$REPO_NAME.txt. + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + docker login --username $REPO_NAME --password-stdin <~/.docker/passwd.$REPO_NAME.txt + docker images $FULL_IMAGE_NAME + docker push $FULL_IMAGE_NAME + echo "${FUNCNAME[0]} ... done" +} + + +pull_container_image() { + # """ + # Pull Docker container image from registry. + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + docker pull $FULL_IMAGE_NAME + echo "${FUNCNAME[0]} ... done" +} + + +# ############################################################################# +# Docker container management +# ############################################################################# + + +kill_container() { + # """ + # Kill and remove Docker container(s) matching the current configuration. + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + docker container ls + # + CONTAINER_ID=$(docker container ls -a | grep $FULL_IMAGE_NAME | awk '{print $1}') + echo "CONTAINER_ID=$CONTAINER_ID" + if [[ ! -z $CONTAINER_ID ]]; then + docker container rm -f $CONTAINER_ID + docker container ls + fi; + echo "${FUNCNAME[0]} ... done" +} + + +kill_container_by_name() { + # """ + # Kill and remove a Docker container by its name. + # + # :param container_name: Name of the container to kill + # """ + local container_name=$1 + echo "# ${FUNCNAME[0]}: $container_name" + # Check if container exists (running or stopped). + local container_id=$(docker container ls -a --filter "name=^${container_name}$" --format "{{.ID}}") + if [[ -n $container_id ]]; then + echo "Killing container: $container_name (ID: $container_id)" + docker container rm -f $container_id + else + echo "Container '$container_name' not found" + fi + echo "${FUNCNAME[0]} ... done" +} + + +exec_container() { + # """ + # Execute bash shell in running Docker container. + # + # Opens an interactive bash session in the first container matching the + # current configuration. + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + docker container ls + # + CONTAINER_ID=$(docker container ls -a | grep $FULL_IMAGE_NAME | awk '{print $1}') + echo "CONTAINER_ID=$CONTAINER_ID" + docker exec -it $CONTAINER_ID bash + echo "${FUNCNAME[0]} ... done" +} + + +# ############################################################################# +# Docker common options +# ############################################################################# + + +get_docker_common_options() { + # """ + # Return docker run options common to all container types. + # + # Includes volume mount for the git root, plus environment variables for + # PYTHONPATH and host OS name. + # + # :return: docker run options string with volume mounts and env vars + # """ + echo "-v $GIT_ROOT:/git_root \ + -e PYTHONPATH=/git_root:/git_root/helpers_root:/git_root/msml610/tutorials \ + -e CSFY_GIT_ROOT_PATH=/git_root \ + -e CSFY_HOST_OS_NAME=$(uname -s) \ + -e CSFY_HOST_NAME=$(uname -n)" +} + + +# ############################################################################# +# Docker bash +# ############################################################################# + + +get_docker_bash_command() { + # """ + # Return the base docker run command for an interactive bash shell. + # + # :return: docker run command string with --rm and -ti flags + # """ + if [ -t 0 ]; then + echo "docker run --rm -ti" + else + echo "docker run --rm -i" + fi +} + + +get_docker_bash_options() { + # """ + # Return docker run options for a Docker container. + # + # :param container_name: Name for the Docker container + # :param port: Port number to forward (optional, skipped if empty) + # :param extra_opts: Additional docker run options (optional) + # :return: docker run options string with name, volume mounts, and env vars + # """ + local container_name=$1 + local port=$2 + local extra_opts=$3 + local port_opt="" + if [[ -n $port ]]; then + port_opt="-p $port:$port" + fi + echo "--name $container_name \ + $port_opt \ + $extra_opts \ + $(get_docker_common_options)" +} + + +# ############################################################################# +# Docker cmd +# ############################################################################# + + +get_docker_cmd_command() { + # """ + # Return the base docker run command for executing a non-interactive command. + # + # :return: docker run command string with --rm and -i flags + # """ + echo "docker run --rm -i" +} + + +# ############################################################################# +# Docker Jupyter +# ############################################################################# + + +get_docker_jupyter_command() { + # """ + # Return the base docker run command for running Jupyter Lab interactively. + # + # :return: docker run command string with --rm and -ti flags (if TTY available) + # """ + local docker_cmd="docker run --rm" + # Add interactive and TTY flags only if stdin is a TTY. + if [[ -t 0 ]]; then + docker_cmd="$docker_cmd -ti" + fi + echo "$docker_cmd" +} + + +get_docker_jupyter_options() { + # """ + # Return docker run options for a Jupyter Lab container. + # + # :param container_name: Name for the Docker container + # :param host_port: Host port to forward to container port 8888 + # :param jupyter_use_vim: 0 or 1 to enable vim bindings + # :return: docker run options string + # """ + local container_name=$1 + local host_port=$2 + local jupyter_use_vim=$3 + # Run as the current user when user is saggese. + if [[ "$(whoami)" == "saggese" ]]; then + echo "Overwriting jupyter_use_vim since user='saggese'" >&2 + jupyter_use_vim=1 + fi + echo "--name $container_name \ + -p $host_port:8888 \ + $(get_docker_common_options) \ + -e JUPYTER_USE_VIM=$jupyter_use_vim" +} + + +configure_jupyter_vim_keybindings() { + # """ + # Configure JupyterLab vim keybindings based on JUPYTER_USE_VIM env var. + # + # Reads JUPYTER_USE_VIM; if 1, verifies jupyterlab_vim is installed and + # writes enabled settings; otherwise writes disabled settings. + # """ + mkdir -p ~/.jupyter/lab/user-settings/@axlair/jupyterlab_vim + if [[ $JUPYTER_USE_VIM == 1 ]]; then + # Check that jupyterlab_vim is installed before trying to enable it. + if ! pip show jupyterlab_vim > /dev/null 2>&1; then + echo "ERROR: jupyterlab_vim is not installed but vim bindings were requested." + echo "Install it with: pip install jupyterlab_vim" + exit 1 + fi + echo "Enabling vim." + cat < ~/.jupyter/lab/user-settings/\@axlair/jupyterlab_vim/plugin.jupyterlab-settings +{ + "enabled": true, + "enabledInEditors": true, + "extraKeybindings": [], + "autosaveInterval": 6 +} +EOF + else + echo "Disabling vim." + cat < ~/.jupyter/lab/user-settings/\@axlair/jupyterlab_vim/plugin.jupyterlab-settings +{ + "enabled": false, + "enabledInEditors": false, + "extraKeybindings": [], + "autosaveInterval": 6 +} +EOF + fi; +} + + +configure_jupyter_notifications() { + # """ + # Disable JupyterLab news fetching and update checks. + # """ + mkdir -p ~/.jupyter/lab/user-settings/@jupyterlab/apputils-extension + cat < ~/.jupyter/lab/user-settings/\@jupyterlab/apputils-extension/notification.jupyterlab-settings +{ + // Notifications + // @jupyterlab/apputils-extension:notification + // Notifications settings. + + // Fetch official Jupyter news + // Whether to fetch news from the Jupyter news feed. If Always (`true`), it will make a request to a website. + "fetchNews": "false", + "checkForUpdates": false +} +EOF +} + + +configure_jupyter_autosave() { + # """ + # Configure JupyterLab global autosave interval to 6 seconds. + # """ + mkdir -p ~/.jupyter/lab/user-settings/@jupyterlab/docmanager-extension + cat < ~/.jupyter/lab/user-settings/\@jupyterlab/docmanager-extension/plugin.jupyterlab-settings +{ + "autosaveInterval": 6 +} +EOF +} + + +check_jupytext_installed() { + # """ + # Verify that jupytext is installed before starting Jupyter Lab. + # + # Jupytext is required for pair notebook/Python file functionality. + # Exits with error if jupytext is not installed. + # """ + if ! pip show jupytext > /dev/null 2>&1; then + echo "ERROR: jupytext is not installed but is required to run Jupyter Lab." + echo "Install it with: pip install jupytext" + exit 1 + fi +} + + +setup_jupyter_environment() { + # """ + # Configure Jupyter Lab environment before launching. + # + # Performs all necessary setup steps: + # - Configure vim keybindings + # - Disable notifications + # - Configure autosave interval + # - Verify jupytext is installed + # """ + configure_jupyter_vim_keybindings + configure_jupyter_notifications + configure_jupyter_autosave + check_jupytext_installed +} + + +get_jupyter_args() { + # """ + # Print the standard Jupyter Lab command-line arguments. + # + # :return: space-separated Jupyter Lab args for port 8888 with no browser, + # allow root, and no authentication + # """ + echo "--port=8888 --no-browser --ip=0.0.0.0 --allow-root --ServerApp.token='' --ServerApp.password=''" +} + + +get_run_jupyter_cmd() { + # """ + # Return the command to run run_jupyter.sh inside a container. + # + # Computes the script's path relative to GIT_ROOT and builds the + # corresponding /git_root/... path used inside the container. + # + # :param script_path: path of the calling script (pass ${BASH_SOURCE[0]}) + # :param cmd_opts: options to forward to run_jupyter.sh + # :return: full command string to run run_jupyter.sh + # """ + local script_path=$1 + local cmd_opts=$2 + local script_dir + script_dir=$(cd "$(dirname "$script_path")" && pwd) + local rel_dir="${script_dir#${GIT_ROOT}/}" + echo "/git_root/${rel_dir}/run_jupyter.sh $cmd_opts" +} + + +list_and_inspect_docker_image() { + # """ + # List available Docker images and inspect their architecture. + # + # Lists all images matching FULL_IMAGE_NAME and attempts to inspect + # their architecture using docker manifest inspect. + # """ + run "docker image ls $FULL_IMAGE_NAME" + (docker manifest inspect $FULL_IMAGE_NAME | grep arch) || true +} + + +kill_existing_container_if_forced() { + # """ + # Kill existing container if FORCE flag is set. + # + # If FORCE is set to 1, kills and removes the container with name + # CONTAINER_NAME. This is typically set by the -f flag. + # """ + if [[ $FORCE == 1 ]]; then + kill_container_by_name $CONTAINER_NAME + fi +} diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/version.sh b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/version.sh new file mode 100644 index 000000000..c46ed254c --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/version.sh @@ -0,0 +1,28 @@ +#!/bin/bash +# """ +# Display versions of installed tools and packages. +# +# This script prints version information for Python, pip, Jupyter, and all +# installed Python packages. Used for debugging and documentation purposes +# to verify the Docker container environment setup. +# """ + +# Display Python 3 version. +echo "# Python3" +python3 --version + +# Display pip version. +echo "# pip3" +pip3 --version + +# Display Jupyter version. +echo "# jupyter" +jupyter --version + +# List all installed Python packages and their versions. +echo "# Python packages" +pip3 list + +# Template for adding additional tool versions. +# echo "# mongo" +# mongod --version From d625b934e8e434747e43abfad040c51952103f64 Mon Sep 17 00:00:00 2001 From: Vaibhav Devarapalli Date: Wed, 6 May 2026 17:28:04 -0400 Subject: [PATCH 02/10] UmdTask458: Add fasttext_utils.py, example notebook, and output plots --- .../.gitignore | 1 + .../confusion_matrix_baseline.png | Bin 0 -> 350451 bytes .../confusion_matrix_best.png | Bin 0 -> 354966 bytes .../docker_name.sh | 3 +- .../error_analysis.png | Bin 0 -> 94514 bytes .../fasttext.example.ipynb | 865 ++++++++++++++++++ .../fasttext_utils.py | 213 +++++ .../hyperparameter_tuning.png | Bin 0 -> 57764 bytes .../model_comparison.png | Bin 0 -> 65895 bytes .../requirements.txt | 6 +- 10 files changed, 1084 insertions(+), 4 deletions(-) create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/.gitignore create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/confusion_matrix_baseline.png create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/confusion_matrix_best.png create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/error_analysis.png create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext.example.ipynb create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext_utils.py create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/hyperparameter_tuning.png create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/model_comparison.png diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/.gitignore b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/.gitignore new file mode 100644 index 000000000..a2846a962 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/.gitignore @@ -0,0 +1 @@ +fasttext_best_model.bin diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/confusion_matrix_baseline.png b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/confusion_matrix_baseline.png new file mode 100644 index 0000000000000000000000000000000000000000..b1c57a2fc0cea7ebd5fe7f3e96f5192777529386 GIT binary patch literal 350451 zcmd43byQb-^ewt+ltu|@DFI29?rv!TDM<-wkq%M11Q8V!1O!2l6r`jDQA$NZx)G4> zylZ=YbBoOA6c?d!@!XKBtN2tuT)qM(Z)_=WI`2_F~! z2G>=>82%%6SJCLMo~zwmZ%YqbMAP!F+a1@tcO0yk?%8^HI=H%AgIMyDox(AgT(o`aUm~N4@pW8`9#g z-TwXRuC~Hk4i09nTYR?$2K}k7?TkIni!!s0qH!v`6*by3rYN&al=G{J_f-hf$Ie~6 zG7N9dv&*4U4W2u6&#FClU)FlteXa8>Rw2d4Q26)Pl`N)JB=TQh3+>GhDVYEDmBh(j zNJ{png%h3-%n78brLDk4r)!A?|pDC#-)S^PcST<3F^cph$Ujj>5> zvdx_x%7%Id28Ipdh{$LiBO{~wRYBvW(UB1w8rncfDk`c-1!nl$^GloXy9_IN)2B~4 zMi~)*Z`&r~KOY{>-Q5+lqxE)1`(N)lJ3BjHWoBY{d3jOO(zbm1gwt?xaCv2$W1+66b>VY&`~^xvf$7IGP>+QZCo zP4P;sc$L7S9RFE-2J>sWkM=WVcZ-FTr*%8Vs;X!F{QU9^iy3mYGUAmJ7&^pFx$M5j zKMiGAy?_5c`LKDFTc!^d``^Rlvir|t{nDPToX}ez=r1BBW_5D3zqY;o>FZOz#AnYU zU%zIp3p}Khl9DnivpMtP#S85>*Xe!y{lAJYWA1McSddXs;o;!ma2u5%Px&mAWMmLb zOiX^iEqmXHUHO{eQ>mk)qYa+jlVg86=^#^Fvf~Kv52rpsuLn%~S^QqVdBY+mMkDMx z)>>x!_15j%5%6Tx*?TnEB5G^>?|*R~t*Lm%@=xd`NwWFk$@~o@{iK(Y{*-|STe2D& zgh=DyAoWMTZMnznnzw9huqh}g9!5k=@BA4SxwlNcGWIcM_=Br;_X|-rDF-$|K?*89 zb8O`9@9#KQZ+9nqD)^>dy?s3@EjKSOzo39cK!9wx(zzv7&}oCa)@gwEb#89-2hKmH zrlw5zp>-`B_b@Op_EQ9eg`di@DrQ@KiuPUkQD|K5wzZ&xG*&tfqYo#4R$pH~nyo&m z89vzUp7gF#Zuqgq<{z6&t=lJ7R^t2m^fg|JQM9$SEewAse!FU zLZZCCG3&{bmYNzJe0nV0e(#^$8OQq11fHE5`{=*$tD<19xi|L0)wRRb26;_Q&8$0z zJAVZHckcN3`C0vX?@&g(omLlo%Fxl#fgrTBw4%!LNN-pZq#}$vDZIRWy^T^@@1VohwhgzUwl^{$H&L-xvHywU3EDn;QDayty!&**u7egr9To0MZOJVI`MaET+J;$@POVi5OUX@#5cuK3;vCq z_x~vkuT|95<>7I(c5p$AwKs^r)oIOmho{$gx}^fsd1RH!l&24rziY^f;b2^4$ehh-s|0DI_{TE zD!Ki3md`MqI^2h2!A0!9zdrL;KVNsb^q-p5arQqe)$73uCn^T%AVMb3w@I9Ox#xXM z3=9ki=|uPkwq3{Sn&1&nEG*zlN=oLveTyC`z1ZE5hzPvzKYnUHV0)7p&XkKNT1jZxk*$8Q;9w4CSaXn2IB(qo&Hi((=AtSL&=uF%y1O!2T7x z&Sw~K+EBFQ4Gbt~XlVF_g_U<#M%~vYWNmD0R7RziyaG>-M3Kh!_Djma9ey?$xL;dZ z7&zxUFS@+`USpy^!G-~ zubCOWqoZS5x_>!iJx2gz#h{rR$ayl+CtAVH0g($U4z3cINAZ0`Iq zc_He_uDl;w<2g?ZK}hW6PIJYx~4fgc+30FNZgdvy##GbB75_e%;-%v*y3tJX-eDFuzc4ddWG{y)WtH4}mq z6Tt{^gZpGI@P^70T2oU~<>HV)Bj|{4X=$nZfrN;N;%SF!1ULnv+Lc@_;pMP9#u|2_jByQ^Gt~t4;yv!dd-wm6KEG+Vl^y};Ea!dX12=sgQH|IYM2Yz+p z)!aFGX)@H>+KTz|>8}s2d;v#$I#!3xJ8{d!TILBy-ND~g#NW)dLCZjx;4#hIR5CFU z9~b%g^Jkc(|IW@!>-}Z^s)zx9Muw+Yo_}SNYo|sXJ+v|{T{OjwE zo2!e9sjCMNY;!|G?@T^W>gwuxI6vOnx;y#QB8cof1;ysbn;NgBv&9w--4*FaHsh<1 z!vvvB_)jhyQX=>)O0kb>y5PP!5mJbZ%K-;lobCRWJJv$A@84gu9I0~OuOHu888uPS z(C8E#TUc12%ZPaWvZW_OI%CzpfeF7khKlc@nAhUWg#;B=a;P#-miD=qjJ)pM>)!VL z+Zgih>R|p;oM%ZH8TlA0(CmJ9vw|zhZ+CT^#L&N%M^pHRtR`+|K9Qm3y1kpd?GJx`R(AHOqy0^YG`6Yf zX@S6lyBwUHmfv5i`t}b&FCMA!B7m$@`rtCEU23HWxW)Qw3XjO$1=6gntlkf<7SPgU zb#&I#rcQL>d-&}-saxCHtlH!0_x^ks_b8MO*yGR2&b~+<)q2b+?cm_h`{Rc^^o3SE zHKa85$bzaB!zpPKpJ)V?EoG>_--^t{`wETE zDJdywYil<`8@d$>)6|$U%D^)Z#!%|Wx(FlfAL)!&h4{fw3Cx@<3SziHK6bU}5 z^O?z;A|)ja!NMb!F)^WSINqU!&yEfA!`|VcHQ;l6BBD=lQ&0?bJ70+EInu*PxA(s@ z!LX_KCS;VNgPaY|%)Ds+(Ju_b%W9y=tjc$j{a1xk1l*>qynMFGl((m63n14L?^W|c z(`s_)$fEtX>Aj(-C_KMndscsbZ+*Hn=-3YlfeYnoJP&2pOqJ2mA@g1tX@XuyN=X?W zc(@a#>~WC9gb!%+a*_GRvq(ur#iwLWJ?(5IB34$`iRGbkqY6g~Kr=X4SPvwoeRo&r zX|CCa3=~_)!?jQZ4;}56gTom`Ma7YZU`EKM2QUy(avLE4!a7q03uF$}{`Q=l7j;+d z{sE9`sQ_Q1S9jh)`A_V-aPAxnlpA~mMgEfz zzQ6D}T2^#;nV4j>;fEvmfNyoJxjxgcglgYqDFno)E#VjPK2g5R|McY8uw4Ldm5xO~ zr8aGBd|bMHX>EDvYSfh>Wj}2ci!w^{3bq9uA4Gkfy^+gQPiLt}ifuVqVu^`l0sthR zHK}l9L!M`6+I0Mq`apIgT{<{@-fS!Mk?4Wu4axfYdfl%9U88@7l#MW$Vi~2SdN%gs zN|^CO*Vd<%o4$NeLzn<<&QXurp-J}VHk3=(fU@L3?1Yez5DCVi!xN}a9`p_}q*s*L z9RPtKGLS7~Q&Ur{Uu@!jP0q|D&*Ps1=r4EY&Yhyt;H5}hxPF<>x)sJ}IvtK;9odtE zB|djac=}037Uu(53N&0;`~qSM#p2@P*vy@vj7>rSG)I!LU_;@=2+_{h%`sITB*&KZ z*;}(r@1dfkWCvVGfIOt+xv5eZe6W=-l6&SH`;75G1PLQ+N+9$x4#T1sdjTxY3hc6^ z4?nn$pEY=^504!(TMoc+{I;TK^I4>+yZbsE>m!cL8TsyS!qVe|XGY zEiEr@9}BUwE9F5wN^kFj37~ZeMw^`a2&lN~9D2F2Ri-X1g-`&Mj%XmK)Oj8!B{AP! z>emF=aQU*jB(capxRsR^f&eO4EVt`o{Q{%_ zWE1Y(+#C`@FMf{+LU2F~Lbw!$$b4_#;h~{Sz}-j))L~{Jp-TLnEH{|q@Q{+q%5Lx1 zn1O-E#c#!7OoaTzL7;XwwX|Tt6h=Pl*q3)-F@_S3&C3#Spl`ppV;{%egh>>OvB5_s)~DN{n3VbKL%Esw{rh>ghU~NF&tVqf>AB)L*GV?c77Y1NOeCrW zWz(oWz&CU0+xPFy(C1+gjGmdX*;*N;&5#Og2GrbIYzxSf|91BaIcY=pe4T8i^n{n< z-qBxg;`F!_(Kr;hF{G&&tQCFaO<00>zhbiFAv`=;#& zzime(Bq@npLPFwoeT0apsA??cPL9b3mq{prNJzu!iHStgx2MTZo|HQLV9(3TV-gfJ zs9XJ8*|q|$|AR^UMpbsnJDJC%Qf09-bp;cFM)BN96vnpoPZi0@$r5}U6?cZnfj6iw zDh7%tq|@@aJU95!rE}-bAu?`m{6(hKy05(s_xGb7`}=JzB+hSP7Znvvv1Tk6tGn2G z>V5R{THhkn%8)w#^=MNc8kl~;4I;$D^!?`Anv9YX?)!?0Z-5P5i=<%=w011!i;T&Am;Z(PUgr)KB_0|ViP-+a!! z&E)`)fDF0>r0Mz+JASCo$M*v~OM;R>$~3pM*it&l!ud=<<3d89jLw=!Io*7|G*HYU zDQRXE=&(Zj?c2A(N@v=g<)KZ3VMRDoVDLl_6#DlyNwL?@I~bQ8z`;nVYHA+8o&Tz4 z=L9DdU-P;nom8)H{%HBFVKMJ@yCn9dkED+z45eX^YHDgi&i3P#@B9Yv?E{0`hliI9 z`VUV&#Zavu?Rz56f1FzxZ(x*}doPQVn4Zpi-L$-{?2D&b%i_<1mZ6~wGTpn{DA(Eu zTpWV%+;_JZGz7MXp@Ka(j}_5D}_5a9+er_U?$HqD4-1KoOxcyJ2>~V4P_!QW!>Mq_TzK{ zd_YN=P^LyGCP?oaEylcpf?$I{0NKxATD;@ztW$Z%7)nG_f4@+BB#AK{ioTN)Y;J-g>GvO7zPiz!E4e zkT&-#D}}gucr0(3%oUv%4 zpSl1^3PWFdLZJ>53?}F>R?$7ROdDnigVg#>Nc3q46o5t&Fe4=ZO8Pr+up=nodnx7> zT2#b~@+4daZ)XW|~&vu)a1KoOG$?mWGsB)ifosIFxsJZ(a)68s?Zhjw-6f-X`@!7Ly zec83_sAy=oPDZ}Jl6?^H^>%PW=ZRG6ak=u&Lq>-$Px&%#%cy>#i4yrLr8xKQ_^ z^{1iRf4fND%&q@U_JnR7eHLgu|#B5K^hjpGG?79IX!2*N*erbBq_;lqcn@g_75dNJqw0gQWU z=m@~D^B&T=cY99Itf%w&RRXiq0i#kY9LIs8ICO;MFjo4tBm{+7BTa}1nicv}PL6ji zs;a9Yq9}23aTk6TPytd(w|-4&92FV4H|M;$nEmOeaCB;DC??!dZ<($73lVorNxv-w zxieZrf#MO444^)tIc7s)77044YP~D{UB&4QkfDYn3^1OyK_YyE;ptgwYIAclCIF!n z>gPD0>2TP2c>Y}8y|OD3t!6n!5K}-B!~-389iI;)}G#1vnB890ZG?V8!R$tNY)!{|r+B z2iOP*(`{u$)U?_IhtIrD1_nSFz9(j9!=PPH2A>{Ur0Yo>;R7qs3*!VDsnAOvR96FA z2I)2~K2{zRAFl*+!#Y4d4lXXMna_y}-*X56u-&q^$A?c2m;w-_DLVW3ZpgW7w3NZv z#LC7Nb-KI0E-NP&>LZr?y?bDQN-tLfrBUEG$Qoh>Yw@b>zB5H0S*E*UF5Lg zucch5B{}L(t&aBA2W!3g;FG%j`6Z~Jtc>#Sc*Jy#_p$JLU#p&@6ZLp)loaFfSaN?7 z(xs%VjBBmZS2N^`03;ZlFY=3|N@Q?wkRRjO-#qh}Nd8CFQP73Yu5q*r!UTa?25>0Q)nY+Y znls*kjGt`;{LgOHt zE)}qcf|84plJvG;lA9`=272EbTt=Sj<8eFA`Hx+V1LAbBAO^p>RgYa-*{ zFM(pzTjg%eZBb7LW6;zr3ban&6&7+!4UE+X9;W5GUUrDCA)&m{Z_j1Sg1rs2l|-tp zESzEVc=@J3-%**yP7m$SX}$ZjQo0xLNGw263~U=azeq{B@#>!k{XcYJPPqR_=$KeY z-N%m&j*AZfW-P-D-(CizQuJwejkl9-qn#y&b^VxsB66q(}dp;GIJy{v$pfgD!d8#lrW z3NFL-&~XDOJd`Q2Fi_r!nedGZDTn7xXPeVOv(7Fmx?f<$!3DSl7#_$=+(#EYD;$58 zc+OvUuSIz%K(J5L0~9%zDn7gRWdRj+e$f{tw4u5q&sA?j{}b(}ns*ulf)8X7tj{kn z-|){TWG&Ectc(wx(*$VXQFwX5osO0^24D>pS3D3@k;>@@8xkX+D7Utefo>60!Y(u(7=#Jpr4= z-6&97eaxyoW8S-^m&OF%#Xpb@Mn0udJ%7Z~s{kRy;>*LHSNYL21cwo-+IUXVPEnN)YpWrP~$ zE+hnMG0Vk^vRR!!-e`ULp)_L$9VRj+=BrmA0Q~NB$;&6wOUzcXC=IzhSc+WX8?33W zR*lt05LsDSlvPA*;yE5wXsTA;8M*?^<{~HO@3dSeVCl$CKf=-H+e%1E3cy?#uyjR8 z2p5s?-C!O6of+K%;RLFOD)PL&9Y7I+%zk~^USw7aV|vKopbhK&wsxuBh%Oqo4>oJzfn~(bDQE*XYMT3uXF=N+ehJfT^C|IT#ipP=TBUa;j6< zVHV(th}U9r>js>t@nPz?iu|XGFtDM@!RhJjHGW`cfd|Dx(V?lMoQ19~Ip{bEP;5*y z+IxEV#>tF|EE)jc!+A>t9r>&ueuod?zP0d8V(9=N%#A{$i%&NdWug6cz7(gDnRDu` z_Po8WB2*1*9cSe?KSDx6Qd-)}QuDy89TqG-yXKIzw+b**Z8QT)8zId#m7^MsbP40E zPM}aQY}nPS;Rzofc^>XKbDP(R^!TJ+b6~!nCF(wf4GLT0e4o>W3l~Iu)|f&cJlHIJ z`nM|9ll%`Lf-0{l=?b7|8rq6c$t?`X({QQ4gIB7L+4-+r35Ajd3XL0h4$zwpxY#>1 zB&uu%t#a_a!#SYe!XUv>x;I-XP7c`P%z}a#AWBiCX{z>RS65dlFp1As%dY`1gYuh| zE~C*f@u64Wn|*4bouftp!UP#e=dB=(zskzObaGlUeS?!KV2!I3OMU;|pI=QdBK>@8 z@BpAVFx~6G+4}hSMD8{~o`XztV)#iEfFhk#0CiAMkO;&K%Jaf>(F~d;pjQ;y-zYMT zg3QK1PL4LUP>01^XSD}#%huq}P*G87gu!xs>$e`96BY%Rej|W7cc7v`yrYMq3zDN_ z(<%@`W%Bp$D9{V6_tqv+=@^=V&oAn$SFeJ4a{g-&%DAKMi;zy6`0b^Ed82X2;G=`3 zVx#x=WRSxTEQ0oNE*lncrx@8-etvXO#BYlOS{^(z0f+CbfI!XzvhvwlAVUuTRPR>~ zKXR+t-vcY31-yBlb|xkY2|$!+`SJygVnyHzS4L|ippi%N*(cD6>RLq?P6c%ZgNSJQ z(T@VMy&0I?Th7jLsO}343R*ss9zR~6>!Jm8f(JiY{?;ufyY3fJ89@iCrph|oanPEl ze*bv0wz=5?>h#~}#OIf>+i3f6{bCA$=f=iHy07r5!5!f;ULOZP8|8+O5Ew8bcQ3^N z_BJ&$EA^OFL!BM)6xty)&<+kh4k&6mpp608)rG0>jA8&JN_M ztbzhIK*$p4Htzd{ufw?Ep`ZbbS^*%^P=+I~CX-{s*w}_1jy^M5h1@nfnaz zyr}{L19SQ-0uS%Wnwd%V{Bi2byZA6HOm$T1+BJPs7oa?R+y1Pq@bWIr&Kl&^fWi|7 z-2FUFd17Ls+vc1mE`xn;_QovrSF{4ry8;Iw{bc9xBIb!8>pdKAEK7{G!v2sR8XlaQ^t zOQ3R4z#*^}&BLSKmn!DASv|BsRc7CvStvzk;3lR{0jGQ3$vCF&Sq0Ue3pfq+)-}Q6 z>T5%X-Y&KvNQJ??jt`?YPBcuaIaAItWvB{F#&1O!Dm04@z2KODSAQ$x96(r_zm05IncOaX7mfpG6JYHng;QU$J<`c-d>6!7yS zKnH`%qGS`4^;dAO)WR-wTie^4@7}=lTorgIgtCZQT3YU>6dTXRGC9y82Kaw0q|m4o z6YyQ~&FkQ7^WRyP1w9zpp)eRDryDYiJ4Iw?XJkNMd&@?s@CW`CR7wS|8@gX8j40V)%CR4*5bClZ>#E4cUuv8WdO56CPcw2?p;K&f1y&H=wgz)hmr z&!EW8CnhF_Itk%$QFRuzXTj}32eBWnP=v4sveggS& z?O@vpJO*b_(-E+WFJIE93cJPwo>bG&QulglmDNaXLDFl*KfV@QqjW>@rSxS@8YY$wYLqv8Q%!)#nLkb zd~7`OV;7VCxYRlYF72yp&X;Ozy!k9VC#R=c>c;V(xmbKpZT42 zlf04tK8nA8(#MPxnZxNI_NV0TAgvd2Fr1AJwghza#&6zXA&2dhKWak$WCK@3mY$|`eJG`j-wZVUvvR=v@|16=5 zc8GL(M3@{Y_EEX5INeg;6WfIx%tOn`Qlc0o?OVt_zdJ3G4dRjU@!>T!VqAumN^gor zdrLhHVJ!BIFq(pIyRM;et;WkGVIhwLXy3`fe9sNy{5};mwdVdVTKBmv&N}}?T$l{( z2dmDs#im6<*ZeA8ryF)$vNjlA?N3VgFip6KS%e>HlEsKTI=XQ9YxGwBEUn1koXMMP zJ;}n+8S~ffDR!AX=~hc~!EQ@)Uadh#_*nAt*IcGT$+Fwzlno8XCZFJ6y+o)uaV%b4 zl=0Dqhi*|26J@=bSl1X_%syhD)(Wp5%z6TDYi=x&a1`s8-wk@8Q!l4z9A<9y5F2lD zZoAW3lw+;IdcRHbcl!q0YRp=V8v_X=_Vn?g3fh8hrrykRG@+OD+f|&-<-HPHZ{xF8 zP4plXlk~+qqT)kU*Oso+!cYBiOP=psUkz)=KIgACvf_E?Bh_i5tIO0_rB^|%kyW6h=`*8j43PKdfIL`Qg;t0adH*-lE~=XT>Gi-m>&;o zWwQx`({k=8&~1kP-7R421)nFs_lhY{b1hw6?*=opU;_b+L}0o6bYiOSBb;y=b316@ ztp1Mmj0xR=Cl?|kZ$iTU(@{=VmTXmyk-7KqJcIeJ(tOmWi6lbj&sk1Z&M<#blEP82s_2^X=S*b zrZWl^nmoAr=DJluJMY5prLUsjlbnA@FHL%V(;Kr)_AwE54oNU<8vZ>M=4n*D++!8H zaM&DVge!`HXh1F0N^!Eg+SJ=i%G+f!0pBa)w-;PBGaYAEyW7&*rw^$?vm6oD zF4Xz*FomdUCa|<|Hh6K6ZNSSgFp%`zxwzB{`e>%I)s}a(0|E|5_P}yAH7g_GQP<^8>eH);jA0M5L+phf zSo_8MBK}9DyuRs8jKM|s-vv59|71pfXL!r2eIx%|`@|n#h9`ww*)=aEuK}GrEL`mV zCz`LwY&~r0ktMZckgkA|>BgPmBQNJ?pXyVvG&H(cQw%G*q?=Zin>Q^_cwdNy&Bn|j zNGH$bOr3L7ph>WBOfZZHzl)f!{;_w+=)JIbyeqy`K-D8~Z;+`~LAfbUPVt)ka%^(4 z4fer!%MQLT z-5OaS^C2aV%~7YE?8yo45crbRQ*XzkUs@^aRZCM+UU2{3yEK5rier{fL`tYyOeF7p5&iQ0{@-fmTV7s_Q@|yZ zXXk`Bym)V+Khq#R!y+i7R%LOcs>;Yz#Q!V;AE4bTs^Yz>nnr8kcrFmDWGzauZIR!m z5hRr9*7ld#!RkQTxt{c}iqkuZp0C;3Cv+a`)Q7r{g()!idAvSc+AOxeH2Kz8ZSzL;1nvS?=RBLr6$f88Q&Sm=7lgOxexu*;t z1zf(kf=3P;2{J0tVjjsw5Oo#oAUeC$Vp&~)W=jByNJqy`y`Y*{b$ zRbLkKN;%SXu{R=a=sLnG!aA6MIs^2>{j#!XI%aPAa6)=sfT=hG7hCGz)|IP!iqu{^ zb;Wqo`1#!5Mn`aod+S10mQ>?N^A+VR9K`5GnOyD8%M$P1>)1HB{6oxCcUrO@CnrA) z+qBD0GO#7QOWPbXVsTNB4aa}?hya1-$|xGn#5f~F-JZbE_Qyn+jP5-C@S^nGLOjl= zzjf}2=q~L|)@@UXU`n9BOA2((<=Rmy>fUzn{aW5o+=e%Fnm(&GOop#2Dug$*JZL;DDVSxAz@N45 zohTMhaek}Dc-&H?r1}2N58`%W#^#qM!a;ctpAXblVSSmbZEYV##Z~ z5W=z!CvIhP)q))-Ib+eQ7gy^o=I%OTMZ})JRDj3~m8C9-yWt0&+A=d><9)HigMp** zOkQO4AWDP5?hWc40)iRLbc7(1M1ju?7qtovoyVM5{Ly5N#6ep4C-VD(`}UyDRzdAp)=m42S$y zt@toWtMH_O77uhL{5#y<@84nPKn%w4FktO2^6+S8)y2dRC@?3s_9=kvyd8vApjDy8 z5-t4xjc(LX3s_-;{>Z_Lniu0G!5V}5(em>-`+oie0W<`t^_Z^=B5nxqCGuc`)dq_k zs%Qc~ya#FNuo!5?w0OBKz(No({+6#2}a8FCly4Xqpl4m2u#i5>=WnZywlUrAtRfO z8*hZO?2$f=ZEaN;92#mG8)IOU4r+xdUH~>PV2KPB$3?FgHS}dGlbBYyVE`Gd4EljK zY)!0-v1A|3+l|1AObV~rSunbzuK2fa--7EIb;W_0q63_6^O%$;Ya9qes8t)t7iD;; zy>K;mNrM74I}RZG>CqK8fZ6CpQ6oi~kh2_^&OrBS?chTm!TuqT7_gB+fNnKfG)RGd ziW06Gsn^bc86D`dl2*JPzK;R>tu#y1eqK&hM=WC!vA zY+EwGm!%3hN5kz*gKreIE~6HISRl}V&(q!AZT};e2ne!=z&oM#w7uQ&;7#Z0)i8kg zgCu0ISL{5B>Abn-H+KYm;po{942;IaQ~Kui1Od`01^y2uA?lkcd5^~V-zUErC^pAL zz(SJQ#RsHCazZqFF28_a%Vvr83sGM=E3q`d8?x%^v|TsfCtD}8(GHi`OZe@P*50ei z)_dv#=iQt(P&`Ago4>IziG|1rn*8>5F)V+VZoKkMK7WbxpDJcMKRPU2wd#!rMj!j3 znwNDgoB~Nw<1#t<`D|W+H!wMSB2KH_7Wt>Fq~%z3LrU{6EDF2ewro(GtwS|I=H_us z^ySXgUys`p+$NVD(1P!B9KJ#PM!(8)6PH0cAS8LlhB`%9u0=^xdwX-9{!8no z<)m$3ypFDJXl_cSPqp4hToKPHj(aYL%2sNxB*i~qAVG_tISV3Hq+tJ7zZI5OBY}vL z2jaJm8$ur#trn01)?h2j3N+4Ymxh3S576Q8P|X3%Zt{h`@H4Z~sRSiNVktb53EbAvgi8HsFteP_Z97 zYWRIV_xj7Te0+SU7>RmK!46Ty1CrkZV00}(I-(Z!uu1Q!hE-ezMMYE|MY-d6FVx)w z%QdY+p!dmd&ULMW5j=TcqVPRz0m4dCESO`#*sMFD^D$tNu6nHkNlv0xd3OA#> zq1*8nTO-4AMKzBrn8(M!zeYdbq|C0=GxH78RFD?AenvsR6=>KOEXFv8Q<~m;=5WC=(M)lKi{axVlJ!9>xKv=sjQVlK)A74jn`!`W|UYyn2{*6h>q=~ODVTi@2C~;>g>E_Ys*$@Dl3aYD|Ckx`eh@u zq+7Rcg`lb`IGLtwC=h-jAthKOVB4AYr=+5|ukL?>f%L6j!B_jTf9Z^kCZ3Jiz=de( z3DOSYcrX6oZ@P5iUJQn=qb-xuZv^a)6d(t!T55g{9*h1Ct&d-2W2D<3M`e5Kt-&aC zH$74<>ShIfNkHkQ5?KH`jJ5PKE|_a zzBG65o)x-zaW?tcBLvYl)O%bbwdC&=5Kz}?#pOImLXhChVRBoP{*gX0@$&{9v-NFR zjk|Z_y~rjft9*~SkaQA^#>!kQ_AaB4xY`8k(;+9LhE052LV5xmMYiR!N{n`rmF^Gm zpCi2oYwle@Uad~(XwSu6#o5^VCjUYr2>Z5HMr&Y0tv2A8WDC12GdzZ#eF3P4RjNNq z<|hPv%s$3h-{!Si$;#p9He@hQpXc}PFWw+7ZH3ryE3&A5sMmgi2ihLt1p%RUu|O=W z!e0~dV%s!Rqr(GLch@D*Z_#phF99sR^mc!|S52BaF*0Gw@6kiX)o7MPLQwmuQNb13 zJ}N=R#uCCdpt<1)2J}QdI))f+y*RE!a^kQ>&;C~lLbrY9(wB)*r{|MH3oeq z5j0lNf7O77eXd7q0FHQA3x~zGsUu@?XUD*%ScO!-QZYK$(Ly4&h#YlndU z4+;~XmvotuK}S8~e!jWn?nPQCvjtZwCjT zfXi2(zj1uP=xM*FW8FT#H$Ku#E=@vv21X6)t5zIBMHVqGl76$p(!Zrqwq&mO2NOOf z%wD*(oO+2sd#QQ?Sm->o#$8@^vYJeqEh&2&3n*-PWF;P|hx=ha(VgMy2o~?z`_6lR zz;N`y%jrhbw|rtTuixyeZVPySv8?=%A2oR_3{aW*`C)CEX&vG($a8{Pl=5zNQIg1o z3Iuh!X`KG?)-Nk-hBi`Se?s><=M9!j{SU`e`~#8>(Hdt(Jm3c#KbEZY}}Cuy_bGK#p$CO(L!PiDeWN{mKk35sf?j-1dzfx ztugbK&#o>B11`4w`HN_3vb?L4hkrAn+J})0V_bbqb)`g#Ms2*S$FrfLR@wfKmrQupiA5=KgfVeQK900y%h->*U%;YXn=tj%Q< z1p=1KD%f~9&CW8e>tca{ScHtCxU~HzL_9n^qDsbPKZ|}KPn_?p&sg#^;NWE`IbVvp z;^2^PBzBu7U4k&&Iqui9jYDQpg_{TYuzdg4gF+)?T^#yfNpMfYV8gt{7UMOevCJo8 zxZ&Xoz4@0fn=scRSHAx!Jz1S~8?PcRr{a0q&P)1@Bpy9>d$4|?re_UJO|-FGv+qT~ zU!;BGhLW1vS@5!XYT9s~1uuXctQw=rr1Z%i7VNd%heU_50tTSoVhd?d<>%9^Iqc^= z0B|9%z^RaDe07!N7a(TeB13)sR`B?(EfqIR!7zlz6ujsI1H7C^`|}who^ymCz3mye zQySpjgLTBBrGB$Bmy_l>)a1a>rj>e6nQGdDg(KfAe z?c+>bt+&h7zY}nC^~^L8ED>?PkQQ&>0DA`lwg!=KVg`I<&vWQ3H8u4YiMH>rz#xEx zM6P-34nzc-n>P&_*$F&h|4YLqzl1g8e<+Z-H}No{*u1@EN>;ne7{2?7pelHrq(ikM z&&J@wpI_b~Pv?CR8McBi9`R*1SA_&x9!E4q#wQ&)vx62M%y)w2?P7Z0)|R%@bj!Oa zyF?qTnd4^;caLJ!$Eco{Y{Y4Fbz&flW1GUCxFPXh4kj=PIFH~zYzTaCkG}R+-@|-z z+fb~d^~G6F>a=HSFAv&tY-tNm{Y6&}M-vr4D=3hoSoI-ztq2j=<->w)gAD6YaCt-@ z?}LeXeX`}R!0tJ(Hg=IUhC1X_$;tshC>T@qSiSm-%xJ+W+6D|Q*u!FA*ZdYpxhPtN zHxU@K_N;&0Jn@!f7+nj$?tAX;^E&*uG`0lq%{;6m0oW z`l_o$wm(a3SzG>n?!EmGO>n0fLN@Qr%A8e9%D_O3a2)!+Zs2>ulU6OUvUzw_)rywV z+|-mC!~`}kGY^k4AG0ssQlZwCM#DDoBh=({++KkyZgWJK{T76`ub=)iWZoI6_oaui zt~oDMz)@7>3siJ<5ZZZ6J}AOO>dD;@yvhJjD!+&>i%U}KlWmZTj=|#>egQug*{WuI zabfCwR(>b2DC7RZ(!#{@y$=Ybfp< zX{Adv0ie!tc~e+;^t0#5Q?TE{YBbrU>n|FkDK5V%EHt(8!U}=)T1r0iSdw>~|oK2eh`Uu;vzkKYjpQH8X7cp$awSq5TjfoZ8pupg3=}B{SIfC4N zYPW%3+-i*ymYK@{3j226#MfC)Uj7)&7!^a+EoY*P2Ov4gTZFB@{TT=L;UNZ6_;w~h zdZ@yd{yO6jNy~=Pvkw7Sckgb`kWG32UYUPg4@0I${Oh~q*cejgR0twu5kSnWD#17h zdqttfdhIN%9EjJVaPulb%G`l?!Pxw2FkgDa#fwr5G!`1w`%EI6{+T*B@pn`dHgv9S zT9J|=jlX`;ASGZWqWiIeI<B_ z;XYOY0f9ZJUx1ZTmo`CNV_|2{Z4{WxftTh`!@44@ZlDY9>+~;%Bz-rq!C&^}LlJBk zs%ff$Q}7zLY)C$51Ld2|vQY(YK{Zi&qpLAxv#Y#bC@Sd;OtG7hKBi{^G#%l@q zdI(VCXz5YclHs!}?%d?(&P^-r|jBTNmxXQutq9uQ77<}@q3AjOvbBI`4N!kOGX&|qUB^QLdGIf;~l_wdOUCM-&$V^B|)aMB=6#zpy zCIU9*@cZ{MPy-FPRMaFa0GgNBb!*AcqWMpid38pq}LqlaCe9(sa&s(Y{(yY1P zLBVb8ny&qH3cl?4_;S}K?Deu5`opF&J4uClpR?nEX%Gasv+hd@-2~*uZ-pHQterX- zL#MNt*AFa_^U29NIoE$=jl}8B`WjKs9Z6x~;)bIz7-HS$aEC}P^uePSF9-|17_1$> z*hf{Y;aYzx-k|i`HO+|x~m$8AvqVPRpIND6mq z#FdOyF^9g^3-ZdbyeqxxI6E!PP?D}23@=hIh+&JvTaz*f<}FXhM$uLaCLR2$U=pAQ z7Jg+@E&g=nSk+eNhIl{Uq7>f-7Dcz!bVjk`KAH;pmuov+wAg#&5fCeL-4?8?q)`jy zm+Ph6#<}+|_%FPYnzDTm5hs_;-Yr_WxxS+Sa?)FaVhrH5hpsYyN_A$v@^UY9fQl*Q1N+LYGlAJr50lt6C2wF zJIWi>SGP&r_iraKHU}P`MP#bawuzK@7WDavn)dS3JPL($jA_NMGWXB{%D z1#hQuKzx&3&zat=(3JU@rX%l+US@VS0oWlmPPR!p_no=A)5XHYKNPp*I(ofky~M(? zCPh9qZE03Pb5KqEWY$Gn_0rTcDpqljMY!bp`}$G0G2bbG7Sz>>xstu z3$RL1zYUJ|3*g?yr(Z7}spY?#uM$KE00&#FL7lW0&>MN@yUJ+> zPkXdR?}PvvMt=p$``mhG^aAtIkKHsfRn1%Hf8^VAq)Kx8kaEAHAjA#6&DJS<86)Vp zCDK{->5u8T(7efox4d}Tug1=WzF=CPIC;Yl%ErC50|0riViIva96NOzS4hq(t7X8+ zFk9)!vXbG)xKbC0{%{|U03CSJDoO>9DMFQuhKgwv6p>-`seGjU#bwWjr}l)D1Fq+) zvO>DCgL80ikht+1_Tgox~e1cfJ}c6MdWV{$*$kVBL16t;oy|mJ^V; zR-N9`e9Pr?I_xnx$V)iSh4=2<-o~T@;rZ1g5Pje@;4{QI!~FhU75>b3Ukni#8;EEW zjH%|~DkoSHa}n|KmZ08JGc&FGH<~XG&AdBuv3oU@#u5}1tgC12_kw9CMlAZ=vzfIa z5lWi7caF-aL_`!Qp-Bm}yPO!MPHKga(&%29uRqeiaZ^h|%>yBnDFoSzUHGMK%mH-p8RQX_&18y|8>90plMRn-?&1^UxQJ1 zzm!qLkY5)2KW2pa-)EftK@2HJ91#A~pZ`rRo_`V%|I;Ihu($br#+{LH1HPdm3~yu|1NK1kXsy?1~9EwKtD#;?HY37PQ11RB5^ z0T>Y?O2PZQykJdm19#|Y18|y=rw3j8rQO|K zgqn<)L4w;DpndsIc10;Kx5QH3r8mAp$De=mrkDyHzh91=J&A7Ox?H!tAS0tF+9;Qj z3i37k{qLf&mLGy^rYWDdq zSXqUI5rTA|Q%n2*O|$n7WnApg+r6^+$^x)76b+7P<8e7_{QV_TK&t;Q1GcRzzZ%z z#`Y*+ydl%=mI!WaDEJWZAc)CS6WSFu@cz3O9h%^$C0UsJSR;M_k-Rn-h!5<^-WXy& zg788N&Y?gES!@ov5o{E^%$8g!ts&>)*1~@`gqEdC%E&-ui^A70f-sQ;rSsy&9}@cD z9y4f)ac9R&VL%nD`y;dA^~IY2)H90L?e zNl6KX0#y`;#V85t6^Qh`HbM6Y{=mz>*&1OAG&H9YJpyCu&p-kK83Um8na?)Z|IIZ! z$qyzk-e&F_B72N}4o74nD*H1xozKprAp->=1{gY9gXJnDzzOm^yTmunD z75MY6ja7Pn`h)?KFNE=d&}cx_5%Sv{z#_!42EbBpa9{#~78>|B)WH+;17eSaa~3r5 z2rUCyrl8XG1GxG3;GiDN;(?>h6mWub<^63uaZnvlhJ3D(H_;t5|LjDbGinwullahxz`G+*o&tw{Bidw zPTB?4NJzE+1jtET>g&npX_-2IPWOg%H+P!1zA7q2P5fb!cXwWF&5+jEF?253!Glb$ z6Wsf2LH+r+{}*i5|9?*Zzwh3=U;lVlwx_=5|0@Lcg?P6q^aKJ521JV1+_e3Q5D0*_c#6$&boGi`K{b72Pq6vg7+M=&&s26F`ie^p&|P7c zcDOT_^m6zu*>%%BNECnGZMI+a0L|?W3mWRgDGWWcp4>Ja zTc>&EM+|#&v;{Ci&&QpuE&41*=>3HY*EfnWEk>_?p^X5)prgIjn*PSCF!O1Ip&xJ~ zMT<8dr>Ixnxr9mf=@Y)x^YW{SkE|8&_zTP$sK{A>v40bctXQ*DaBLQP8x!cdXGpcY z2o9I&3d~Q@3(d8}mK*(9Svf?esF?}bj2W%1ZT|QM61l%`3lTycG5#}a89u>KxoFs z=4O?5bDqzLwJC^>B7I%*2k;qm>e{moLWtQMNj&^XDBM0uWTC4@=enK0PO`5pRJKZU z@&@7Q>Mb?w6!&p=n9(*o4bx!E4dDXT9*83_m>sk~M30e{Id5G;9^xLSu(whsTZav3 zAMIOa-OW@u_6L$){l@lgXj@q=vfE>Z>hHsR3O3AeJu2SaA^EmgWwU-AD9K;!K8lF& zAWky#=s)xZaDiss%y+GLeo_Ia`LG|15FCj;x6ZA_q$XVylN5Nm@(lCax2GLey#`ME zK>;SD0!v@x#1(~Mtv!22tMd9m;%fu80^c2FGxz82g+r2@G+jXX1c zjjtzQh>W>gdt7XBfL@`!aj-t)Jx9o9j|`QbRTteQ;bMw*j!ot7FMEpW56${Q zi7J9`LadOghlv6$D4R5y(5Jj)nTc%0^v<5duaWTyxH5I(YENvD8#1v8(6$X2>DIJ7 z-Spl}u+?AbC>LJhKN>u&R0e?w#)#u?b;82En3NP4ZQ~JmitCHrrX3gDT^;m` zjc;f&CXis1Y?cTs9hXU6%G%eefra*LH06Bc;_>e>*|#lgbiwJrW7KxwFPcMhCjwNG zAerinl~=wcqT)zL#}NCbeRN)^3O_jG^hn??9Dh@f{j1zQ>o975zwF9UJ)7;Nx*9Woh{v_ojpOK4;_R$s0TUjrR^=+(&ki8`~ZMxGXp~WPerV0W?JqZWs^T zF~;WVT~v{#rp6&p_E#GFr4r18J2#ZDM(w@axV0o0{Xa@IWSpGd=S`#=?8`tIh~mg~ zQzfT|caLB#Hhl^!UK8B)F;R$_TOR5l*-0nViEJe0IN|s0*h~4(ym7%B`YvQB?d+?F zn=w0!nrnVehvCOhhbXJ7%_PtU>8Sn!)ZeaVbc7EFk8r(~CIe{m|6L0F1h2iyyXNq) zaNSlRb*hl`loXvqAE9yQjq{^`Wu6_mynjq<2%2zi~msbG$J1#&uj0l>{~yf9Y>6B$|nuM_7?1cvUgg zqFVy)ls1dQ_}_RXsdos8LTWAUoxNRpD_CSdK?3X;7(n0kyeuhLpxZfa7*PZBb)tNd z1b^J&bQz)L!PR##F&iJ~PR=Qq=y@Qb!2XJ#{E)c}c*{`2d*>g(1R}f`iB+u5U_krE=YHd^ig;-)D z%?e;HHSimN)dHMX+{gZ8zF>3p#P`lpF@O+kx-}O;CWsK8pa($gf}nrj8Ly zK^-C;N8DtVM)iB%e4q z`f$GYWlHL&Tbia5N4Igs%q!`;>?H%6qURnqA|;iGr^T8xGQ$6vXM>Y(co;B((*k`N)J!JxpwQ=Z=a!^Lwb5|VE* zF`u^>G6TM|8}~IGTedZN3tP%>OyoYLI(MYILHARXmC5YeBz6Lx;|WvRxet-pA5mtNdHKR`kNm_pWzJy&4h4$%#WRSlBc z18uAhG^F5`eh-mIfH4t=N#8Yid)|Qy8?W{JTR<~3%ItB#_5s0;5C`(qcEsZb@sk9j zVK;z|phaG9W~oF>M8WRvF_?2BbAAMRLahA2>;XYV09pWBQOO>}q#0&Z=cJ{j=YQu? zfXh2#&x1S!WCx`{U;b|Q1aK$BXW=ddg)9*M5f1|JpMO~Ca>D9zyw6`)ZoPo}kMIic z`y+z?hjTW52L26`KnMplh!}jr;HQ*}JPcw{2gGk(z*uAr!P)?^2Y{(poWoYj1Qvv~ zutCF$IEn)+Tu%S1pffM>3SlY7fS?hXVIdxd-~$ZZD&9PEBpBMp!FU=RM-VSj1V{sm zGBDRbk{%v{ra;H+8;|xBY&gUZ0?ZoYfb;#z*0uqBAwin)9^v|cYs-P4_RH=03E}0z z2|sDglIrTtD;8R3MZazovvL279HpURy1;fNm8UN?omNlR;tjtGfhQ&b89`B7v4F_~ zDOH?%yCW=JDv}Z0+3ZIVaMy9<|Dfs{D|fyiLniP*LW2ET^Q~AdAo`^$Y4+)c7=e%! z1~49Ty0Q1IHb2w_JxG=za|;RK3ePv9Yox6tUmLBccCO?DYq3YoeJj(X(y77F5!hMW z3)I}fyf#fD_3Jv+?XV7dUcdSP@hHfZwqv3UR{7`eSzY~M-hIxbno!8}b_P{sWCYP2i$+d}~t ziosDYeKD{?!MJB-74|ivnUCb|T{h~BXQ?-VW(P0<5K1lq!?qTX6{}$b2XJ(>)T;+S zq*rroq{vzal7>4NH=;m?gpPbxh;1kw7>IZFqys{~gq5q}9L2o>{~v)QLRhqckZC)f zU3ppq)|cm=zt}G@>BmA$uK>Y9a2Bsa1h)qNXE1tinb<{pScO<(Qb~qXwYFk{uNB(h z8Q6fq&5jG(5K48_^jukPQ&!^7@7q=A?u*P}?`JZk^L2MQ{txs(U;L(1-7 zAKu#5uu)x9c-ZwLhr!^9Z0L7N%wku?BEDEKi4x+LzQ_wNk1!i*%KhlUr=tlNS1yhlvtOVFIo=l|Kl+ zK!N?>`b7?-o)7VTcs0e{G&uM>=SoT(bFx*{VL@GPj{?ImgvYwM&$fQ5hxgegU3t>K z_-e;4&9C-^AaOBeY#^FsPXBa8tsh#+t(I!^-M~j9YyM+_MT6dcQuhh#sh!RrYFsH! z^#sb~+v64F3JQv>Z>n+c_Z{RWPqfJNSwC3ed;O!PhWDhHDbMt8$_3$gpGe@wosQ&# z!*WXQ_80}ND>6F%ja@kkBrq^6(+88FyW0cz_?hMH_(jpS~qeMQS zUvPEcg3(JoXsD<>E?U5+2$!QL>`TNE7rHQ&N|*BcplwJ~%KQZ9=(z`36d8c#%0y-a z7h-iV1nEGiL3RO%k#9{jTS!Bj}7+hWkgv8U!>k8}{PoSX9m97Dq z?j4w?1IC*J*DnbbqS2#LJ&m-9vr>ur&bOVWCt?Z8Lk!X4|7#0QW~-xNyNTLaW4@uKfgs__zb6eFLaLwSb?# zrc4LMJm0|38MlQUq_DsfK>q17uY1>?-rA}r_!3kl{OX1vB!;zD5(zH~fWh#NN3M%K zXIZ3ep?LY}54O5QTr3UOcS|yYcqxLI_@a!<2L8zt(K!<}fs>budx~6UaHUC)$U$(j zzV$Pgr%#cCt#*JJO1 zI)~_yw+yAHb~A~eUH5vH?IH|sZ4+PJT7#-Ix`NTf+NfUrW-mtkD7-q

zW(rIBn4A32?@JU?Y}B2-nJ~<_4;(x z-vC)K-u{3$^qJX;jT~Zb?6#WNzlmz>Z59OcMp4WowVc!!V7Vgs18DohC6=VHe~h}b z%3*uvgIt`|yo+O*$Nrj@vc=Tzuijy$Ms(MjgFQp%cv`;IuQeZRlux@+obf>hj)4yu zpDb(~iGM88*533`%j0<*jr(>tWuo~Xqv`82Y8?`orVst%=z2i!; zN&E3_;)%%}E;jrvY=W}N-w3Wi(4fod>77rKA@hg! z2V584tqM>H9UZ5ze`wkJ0J%#(B=^Y554YWbn&sompw|_&VANZ})YT=ITkD*Tthz;R z(+_FArV!%uc0=ETg0+hL%9V}v$~8~qD<%O_6lK7G7bsr-T!8I?V%TgcGInM5WFAxu z{o@(!oFoEPI5(0`Kba`A=udw>XHRyMevMT!1CHgpAhFz8^WvW zb<=JiO|U94ojcd&h&k-5s!qz#1Ji+h|1#nDt_PHp@kL~m?<&V-Q+GEFX3N|M#Du;; z8`3gS1#07jtp3hU#CINepExJ=n&BuUOUcVXRs60)rlhoV$f470aSL`BzP3`C!!L+g9>%t2JgZee_x8I;!CRr@h01TS86WJy zMYkxc>lIt`rdXD{!+w{fyU|}n!<5l+$4b<{;=%ep-o9uECNW8jj z8DnsEdMlc6S7mt!^l)Oeu4Mm6_j;Z1}7SBdkxwUb{$!asfOrkMaz=}ne1VyVaou+F^- zS75wd%4>rLSaY5h{9pyo2uH{u!1}Y-eBf62R3J5EvJ@`0S$y0*s8@se|_7EH{Jxok*n+-6 zW@KT35EKwr0MJt37Z!3O6(bZ`@gO~iPMWy`t*Pv>q4*QJl3ltCNx@MVgx1s? zMZ0P|eM2jhOD?3h_BZx<8R!~4z~$u2ge_)_-*#|6vIECXG>^V!Yf!I97zBdedf z6;x>2eX}C8%yO^IYm1o9%Hd7F5`yJ$dbVdZj$shquEGnhoAbX+dj z-%cWwhJnu>_^r36H#R+)ruftf+~hi!=Y{Zn81(i|Yu796K_Py0hllPKVs!$z$@(Tg zHVP^H+hn`;d>Yx2onDU|K`h$*Vxl+XQ{~S%ECw!_C^3T3(9r9TS<_dYck#e@2TH^X z;Oqiwgtd@V)xN!%Cp=wsZH_bRNcT&SYg z%3OH=dx&9uL?{@gqV_GUzp%^{@C(7`{|M9opdJ1MGgQbr+Fkw{l*wLhzwu~s6ReFb zj~3_Wo#Ztfei0eW!egL~J_d|Kv&8x#6zZtw)Y02Z{n#KKMhYMZ#+|?GFCR5Isb^K5 z&dkH3TE?7+q8M~y;ZVTK)wjOzTcM){22rfed*vF<^t2zJRepMywM5Ev&y_^*mB1XE zj$vBh5|^S6hiv_m8`*-CPKv{c5Cs5t1ff?>N3zji231mPWQ3udjydQxq+1hqd30CB z2>uX^L^Ssp8Z^IP_V@M;E{VdJ-ECd6KNG}#jPE1cT#Xb3yv=7CZ>U6P){)O7Lfz(}y8;*luK0IYZRdYF^z(}+CnS6b$sCF^SSsKN8LPCzM#0Uhj3wLY z|NcKd>bsMqZ_fm^UA>}9w`@1}Wl<0EZf4eT3(0nRpA4V%^w>KYhJL>8&qm=nG0;t8 z)cKwB6tL9@7ytb){{T7!c}sXvZ;hxqZpdB%{s?_9GRTH%Rv*OLlXVwf!etM8*AL1P z#NZbA9}<2$5Fv3M#bMm@ggV|Cp~3=Y(3}gth?t&O2GUdDvOeXsgn$qzB%KUc^I%HA zvsM5m@MqlhA)E&OKVCY5`k%5whrShL82E1 zDJszRyFs4Z$7Ha92V2I*FgAU}t>WUPOE(>SHm3>JA1rRdY%bP$Cca)+W zX>B6#FBr!gI^Xjdb3ymCD5vjn2@-)%C5*>_K4U~?V zd}c~5WAL@@dx1%h-X}!IzjtHz+|0Obkj6`2l(Fy@xUOG>Je(3SO_V3bPX$=ho}Du} z8^90{aCY!ixT~0=bIl|o4FOU?ROEFD)5>MF)ar=(?HTiym>KT?AdNU&4dVQ35-^CMprPC1d4c$RD0Ks~` zfY^vXmA5)Mv;q1{4A5f`VF@(Tf8-=KHhMP$`}Q($E>I}oA81iqYF9Yj0-(WP;!uU&IX>Nb-|5bM4UtG+s_jpaBMp zdH#W+1^8CPFQ8&b22`O?q+UNGhcF88;XH=|`R|q#+YI8v39)mxfEvK+w7*VbdEtSI z3gLOd_FrL8kHKOA1cO@aE7Z=&HG>k$a9+a`sZ!$><0xRO^QBp1Gu>iJND7Uli3osyXr8vaj%dZAl#-G>g*&^U~;=8s9zlsss-p1?*s(o?+6&b;a zjv6kvB1Yd^`IXBIpYif&F*K@iiY8whcgsQYT9?=^bX;IICf?Q=#9V6USmoUOc%GP0 zXGLg8N8!#{=cLQ?EH~Fns0@t?Jb-5>XRygX==9ryNxpxS^E9sd%Z=#`3QAWtb`AS| zzNcSVZvtu?#xX-6Ehbv;_vb+%+TK=i(|TtcK_&4eVW)JXy@f93_@o~frK6XC=K}g0 zu($)?YyQQaOcrST@}Z<487@?xFTtOoEQBy<>Q*TJe9X_2F&$&pSRcxh80e?hb>V6}>n1;K%93g&yr zv;}x-ow@pAU+V-Qmkq+8@DO7O$h`#o6QGxi!07?Z{7I1OKC4xN2NM6#%ngAjpiI#n zDX~F>z!0gv4qo4gunNq3kyg6ybJf8@PwEZGutF?r;{mZW>Pf+Yh7w7afs|1seYePQ zC#}Z}@Ed=i$RM%KVB8{RY)lXC{7AJA@hmam6swKj-2c1SiUu*~1^0a9$U|H+fY^>$ zsSlUhVS^Dl0g^EXzvXEd>p3vzbq5JM&~}i-OvGyzeEg9D8NxBVnWO`RxaTB{lo_@j z8TTaPF)o_1^QFWufv?Q9&|U%0%#1YS*y>%+>v(EucRxR7sLOMW2OgPf z6yhqHt;A$oz^zNm?{fXUKe;{(UaoG1WV{V0^89k?ZCXJu)OeR|&Br{12-<2-%S(|- z7Qn^V!9xNzc1OM#OHcaPv(eb55qxd(B8yQ_#w?v!&BvTE_gy37!tf2he->SSdy zI(7fF;{5rhXBP=#ChNYaTTvCYs;Y5)d_JisjPs+MH%H9Vdqk%?)K%tv83Zjx*~ z>aAO9)f)CRwWh-MWVwd3068{U)83o5;^?h%?KYmh$o3PLdzl0zK`w1+DhF)Aj5CH` zb*xfdAHCm}ziIY6eaWifyO#4|s_fJQ(&0*(F&6=~0EMz5|0J1+$Qdp^TgS(}CakRi zlmY_<>FE820>{gwKO{y^r7ye?}6S2YL~rydL@^q?)(Dy#5dwE9PKT>HqFq`9O2kZw|pA=rrW~G zrE`!ool+bP1Fz>NPPAe1aEzTtiq1o9NskxzjA|pR^L-uWJ!kn`_~oLVk7F_L7*oJi zy1D$px%!4Wm_50b*iXEN=e=|AQI=jUBP?GS4Ml;gq_cJY!UcRXvMgY&ngB)>F63GP zQ2zAXTrzB!RJb1kD1q0DhK_Cme6mA;U-LmOGt(NO46Uxymv;6Z~2l3b!5z z5Ay}Pek63#&CLyBY*PT+O@k6+U{o5UHDH-f?Y!M7rUsdS2=WCuY%4^u0t$S1dbFmP z(ov4t7|m^=aGDoX;)`#N$+8xOA|;pW=FU3O+O6D1X)0 zi$d^w2S9ds@bV!}kq%}j^s;du0Ak66kVZ{1GL2=ymf@`vzDcpilM+f5S|!?SmW7#* zZ)Rzo*kSGY)CFg3+vite;L+0B?VWeb9bypzb!N*t^)vxz;|GZ(;EvF79rykhV*2hN75^jf+y|(2soEkHwEnpZ>VJ zyUb2kna{}8V4XFJ-vEV&K+(9Vi0Mf<3uy`nR#KdzwJWt-=~>m}%Nu@? zIcOuUQe5AG6R(i@SgQ4C%MNTfR4?6{*3J#!6*`I>v~wC9oS1QZCl$WXRqxGf|EjXa zZuN6SM8qPu(<7F$9;y7Lo_t$-t~=lzS!(AV5j3XOP&RAVIQAV@7mu5Zz1^OpGhs)< z*QC}%smfD+O};{l?grOK)8pf}qj9NvGek`U_Vko_*aMWQ!FN(EkKVatf@_5^la+yu zt@G2*Nur~^?_m)q9V6v$OLpfo1xjme`03?JYwc)d$oTO0c9n*9Wfl%XOVe4SLw&O} z4)$C`8fsTFwpY%0v5PI-xLR&$425~R7*(j{FD(WOX1^TjW8$@6f2*gjH#0sSGdVhH zJQ^3QWnY{w5k&iPg;n?JHA?c@*VkU2eG^8jy3h@i7-Y=>v@frtGk(E2lci$+5xLX) zyNdnEtL1y6Z?L!X;qaV+4FIza&_4=v^nb_S_(h`Ds;JxX$i!1;tH9^Ek(kwt-E%(; zE6vL4jSdZ4hiiRU)w2UbogT9iQZp6<8d$r-%WTRRCmp7d@-4#T9NLpRc*xI^tQ`30 zWi8RA+G$_5o1T6k^kL^#Q3e~a40>xQHBqO=0m;68n{TALi^0luGPNrVWBIn1i?Qrl zP{bi&VJR>(4iCy!FO-H^NlF|cLG^QJ&^X6ne8yn!f?B(#&EUZ8&P&ToFPH-hnD zOO6t0`NBC4(pcNVD$(zniSjnAJDgs|;j{*~zZ04r{#iVu+=n$08=SMLY2x{L=~d&k zzn&Eeb+bO`!QZ}qtDB)C>yvZ-Z~uhyzN?G&_pfh?LZ82=pXhQX>E9ptC)zb1hjY!bU|06npa1!n^IyH|LFF(X z`rDu4x79i;m)9@r{@?m^$R4M@?DMPI%_Q#|gtb=2HT5Wy^nGi@nWOL@>y>pZbznHM z_&GPh2`l=#B6?n;rLz&&X-I3Rgc!%w6+=N9bUx%8=RIbdP1@ZtGgy3uW=X2@V~gQj zd~1Fg^PN(`B1kDkyvGr zo1{XFARQ~~`nz)J=g$+4kB{-LUkBsl81!A<0%lg$XQ1MT3M&k%JcE^2rZs&`5!-_d zBWoqYCMRPZ>CWgXx8sJ*RrnxrG=qLQW+{sau#)@*B!5@}xP%L$g3Y6MFG^Ti0 z$hxzghDGQ5O3uAKZJXtJ9G?64QzUxR7JHMUL1nM&m173^H}8S&l>Ip-e=t`+qAgM| zli${senR~b7>6kIuL=aSXuFtrzPtxKTF$BDRE#TH$$5rtKRY|c>&wd(=QbRP?%RLN zuWinUk~<#Ls-gW}vdP@;3UGvCpyL35N!*>kTp`_-)Mjl!a;8Z+$Mz%F9ldt*6aFch zmEl||;2piTwN)DUKGQp~AZ#796Mu7C zt`tkgKs7WxJT1|iKq3HKjt#0)UT13?O3Uj#dM^>mL{rH=Ln$uXh-I>~S&yMst`Y5f z_;`m1Ei6O1*ccOkZ-bMLMLD+Xg8x95nvjx6bU`+)IIrU!>1uT5rut4TMdy)TmYg4I z%FJ=2_X-)PPfIMny}&QumQLR+9C{wINtlowou1Y(BY+=jEzBoV*QCRm67FBxVD0cU z(9Qql>-Aick;37(f<_fQ_mb(@*d8V}!*EM?I+sE%-}DQ3Y9z&K`^cX}nAw978|Lzm zaKNP8p4V+XaG=Jv zS$*qbBsj=wc+6q*9Tyi@A?N+Kx7mi9A{dl>5*8=HrQ zh6ZDQUlXeAI&`WdQ0>da#_a5>zp9vE#MYa&Rpd_5{7vv|Q$2VuJE6?a?`so5+mX~% zqfbwNyvJfcTKaRt=mWgN=bqBWy#?`9!ujFEbT7jTSAu3ud1LhnrhI3jm>qUjuJ$VA zhNX|bRFoz^DeqUYW2ctQtMy};+*TBgtccr-X3Rb`SUo;>Qs3y@TU3@TT~C=Z#=>XA zPW>=IA?=yqNk_hi*46dy28z%hOVh#VdGSh*$T`N=JI4$LJg&~3CapCb%jk=_B#jYd zg?uY7zjn#ZxHmJKprkx*F@KS$@*TiK8J5~u-Jhtga=e~DI&@gY9T?(M(TDYso7SsP zpy8mHb{s~pm>TCN!=?QC^`=?q7+%4{%GOiWRWywGWYx+vkzaBF37QjpJ$u2cM7s-O zX^N#;8Y9DuKhTRq%iYS6UP{ZcxKtvPPM@*z)kN9o{8{+!+E99@wqFlHe4`p$(=ZFP zP}zG+@pTE5&RZiQuwuefUD{VQl|Hp_bzrxO5zqQqR79kF3T9~D5Sm*Sa*2qSrSN$9 z+c4VbApG{gkkt`A1Go8bN9Y-G5-by6!ku8XLNmh*KHKe&j&`o%&7sVF^53FlS4x~l zOC#R7d8QQY#dCj;DlVG)e6Og04SQ}TGM~;g{w7moc3Zo3XIZhU3LiJBs; zVtcwRrI^;s>hP0EoRn86dvdhmlUGGT%FlSEkBoaNWlI^Sh0_m`p5u#`*fXhWGxOaR zH&*ME+;uozk-vk^V!Yf`t5z`=DJ?=n%e1nZy=A(xUH9Y4k{?l^A|>6XT$$zbU~^1~ z@KU~FEVvsAO=|tx`Libl7t9D9mOoOa7WrpCcI6i3G!}|lxDS3FTID;djZJHlXQcW&^zDlao?cT3C{u`DZnF76EFg_v zsTV_b01Gf8z+Gg^Uq}TKF9h_=91zSbsXszskWLD*jO$;`KJwctbasX+d=Jt_-jPiP z&BrnYE4S&Kn{HPT!H49Gbh2Bo+aS86=gh-7smq`#&BR};534b$1KSGgq=R2W|3VHY zwn0r3$SJpHBP`OuI|%NL5XfBt5F-;v9~#P8$45-dKzza^7t{b0{!3n7-tfpsFEEx* zAe~Cpyig%kt^r#aunyL&Ip}Hu=wVoy?Jk5lZaR3HY_6|=_V)Hhuo|#i3|!fn?-ah| z1~ia|FgN;&q*4(O5a2VaJcD%NF7Ovya~NaMEJ?5%b5;g|5~R)r^7HfefSXaK#@C%; z7g<@^MMw>VJa<@1o4;yng`pGeGs!Mbf`ql_OPpXvMDJD|bL-v

q{8ZvLWCjCpX* z!QMVaGL)IuVKadeTq30*sah5|G1Jr234k6OLB^!2xfOJ!j*F?u3g4fL^YHSf8Fj{` zsTXFAx}G@$c*CS_iFWA{Trso&dCDKa#OgUK;WXex`}-}}>SGRbq_?hlJhdD-6dYOX zJ4M)9ikWJPpzWns`;nscW`(o9q2a}?et4gIAST_x!GR72A28hraXFX?R`Y@6*%I zr6TXkf@h^HWCB7)oFLF+B~}LWOIOBzx{5x2j683s=s*HZ#9{Oq_=q#h%Nc-78AH^3 zgTWbOdkUQHPc=mgRer*wi{l=1k_VzY^-$T!kz=+qPido@~QZ&4~2|}%l`N)U(DOwj`M!ov9Ym> zfL|gPZ<+#KzQvdpy!gRN*Gka;rUCmj4aB!4fb2s;Y#dNZel<5s!dok^p%Dp!*>q6$ zn++Gqj=3C}g=Dk68h;=KDYKA*`6W6!8nR<|M&GOf0QB-|`W7r7Kul|A1*pG7zh2A+iHDPdKkl z8f>6!n5I83UEwwxOaSIq8r1keNDnTs7|R0bvpiS}43;_M!GIzb_AICQunG`v6<)o{ zfpF|Nn6aiRrn{1xBgu?10y{xnU0n*0^2}+}@c=XuP@;aWINp@pSs7A>m2BrrbNluK zXeEF%DF@l3J~K;wi9(Fx($amPE$)E?;iopRropRsV07({u*dw6MWv#SPE1n>Q#wrZ zOh9yq7(T)~dC%Ex8myO$J7V5JDrh?Bk<($Y2Px4(K=(<4*~;_c6S&4~mVe8_Ru2Zq z5qbVV$ja0#RRWSoKkPOR2tkPQE-6{`qKQJFONh%&bUi%~1HM2v1OP^_a3OF%u!0dq`qU9QE^Zt^$7(?1><2n0 zCs@X!UAzeFWhIO&S2|%91!<|exp$&g@E(d^#pas}A969*1bJn^Fum;? z50f~(%FnHbo`!jGcm3&r?tpgHG3CjZIO?5K?xTqbH*_Y_@Kzq1%?}CN2^;`C_hC{K z3=!vw!Re8vl@|iV&ksQk5nbYb2KZSji4eJFrOX+{B4}5BsBQ~y0Ys@@_UheT3jb7* zUjg*8n-j#cEXnpq`%R67AK(#R9t6}=%b#ab|NhL}eAT!sYm|E7?qWcE$arfcwy00&G` z#Dtt)6*XM?tS+E%g#ofZ7l(`UU_(4C;JA^{eT}3iHOe;>eWqM0%>sP$Y&uZJ7*2#k2E{G3#?v8Q zm*XN3;1{C%3LUSz!8bi0-h*jlI${q9JY>xr&l|}F(LymIF1)vBktc{j)zMlU1 z^a=x5mdL^im~H!(4sj5Mj=pdvK{)cGfloAWH^A-J9F!9P-5i`~idh<}z_o)t%T5hb zW+zo(b0{QA1W(V{?8UC#J3iiO2PSr{H>tPD7xVA*YBr$BJIEDU?Mp`9=Re;DNT z`W`X;dj26eCgyf9o%~~9`^dp&g~16EU3sPZhYvqnTcr@+nxH6^d$3o78CB&F4nfZ> z2#g0-18+gFK?;}@zHmG@#n{`Y<9_>;9UXxbj4lU|s?km3;S8h@r`ModikNSMLa4V_3V6Iqa4Bu5p^{#lu}cQ@cWBPEt`) z&j0R==Y75ZxeSA#FQrpI&!){qVPIh3<@`rkqJ2jp=i}|)xDuSJ^du`0;K*CvTDOu3@NS*HPP{UvQSRgeum56IwzqqJmapmEf3SXor z)(Ptyz&X7UfMGH=92cyBULqG8gjZ+UjXKbD3mb_e>kNZ{IRj);(`?;q;#@6V{v#<5Soh@Z>mb*G4me^|rXtNZ#H{Utpq*;)4)WQHfxe#l z$|DerAzQ{aLM$MNMuseKuM%hno7>wxVCloDQ-wkv9iK%@gQa{sAfd&~o^P)!H1AEM z{cp{pS6b>^?!VgFq%)&TO;%pLPRxwpGV1Me$IvX4_#)O1J-S%s zF~W?OR;PV6`MTkGI*EdWyb@7b?)W9<`4z{DwS6Wt@Mx-~@MzfS$fMc+4y9kyKFC^% z60%de(dZjOxif$7EwO~%=T}yrL}_Tczg)cc+=rczd~cm9bS9|dvrg-oN!IcTY0ueC zVJ4|`mhH~$w(p&XOFwj-Q%29`t{3<9pDp)aCX_2kOq@9$h&PC^6 z9n-kP`Ptt;J72hE$G9&oeu6^GWTH!PFvlSIRO`pn3!_fzySujG920!4#Rs2p1{wuC z-_+)}y45=H9Jxenc>_$@SM_i$?ZLbENQL}Js73Hlw{4P$_-&?r3a%1ViA>^-EBXDa zMZ<9a1%3V;16Nto*w|P?1<-5*5}@E>;Nc6fG)8J)iV2Bw&Dqwk(XnV!Ssn>H)WUx z4Ws<804f`xglU%9cT#4-Fx(hwVbu!sfeGlqpnQYqDG3ms2Aq=GfE&fLckkW}emC%* zv=Dq=oWH8|Jb&!gk>k=Em!&t;tE=YU+pe;d>B!k0?^}`zxoAL;jB156RLcs`=#dIK zsenX~GX(wOMabn4hX_Vk^NCQR`W!h!T0N;?sR_KSx&QFrLnW^ z^x@&FM$SLgRz_}^Czj_#%DHd8tm_UAYSif{QuMT$ALh&}srYm99>!^Kc!uOaVL#rl*z|KyM8+o@C|2?cU!Om7$bUQTycsdo5&~!&XJy8bkbqk)e5O zEnmCCSzRKHd$Z!PWNg0M-p9s2)ZOjGwj%5QjWVql4$gT!DRRjF){giV<CIGEhEp49DWL=j zS-4aUs75dRn!so%jEF-I*0%2h`tjPzN*>BRJVL@0DDlmJ{MyaF$6Et8mcO`a11)Re zs&c~{>%zSwB|G&-9ah36b7zaZ!8M;vzFq_1%6Ee>4K9%sXqkQv4XK0Oz%vL%$OpYR zqkeHEi} zsf1~s5bQ?u@L6;c*nx`@2#ssMDZvk&50d-yKWB zv=}bR(j~|0d*_UkCYPBccz7&%Rm|iKdr~Bvq(v+2SFSAB9E4&0tSh#f>#4LL2lGKy zh!E|O)GSqpK?KVfQ+K3I%jDx14Xv$a>V+zd)aqh?0s(-YB29sBG>0lagJK`pS1H!5 zj`U$M^z)M8r^bR>?mQZX@$g=t!c1=3>$XrCXkEI*qFu6Rh|rDCD$3RN_xQnoB5;x0 zC_y4PeIA_s2J?O$YCM!D3qV4W z15%W)7s)`d{L19gQab3hj6wb2IOWSd4PSzPB0@V#17#ONfEMR{vzq~87do5$!@v3Z=!nNL@3HhK;5oT^hy`X@%HjoGgDd!Q&E;svV<^2I4vpr zuB;(s-)S8o(P=9}_MPlIQ;Lve&tAzMB1`u7`)jWId1kKnzCYJ>zw7h9{WG7b)9GA( z%W-_SBMsm;>k|!*`1p9!elZP&sX4vg@{!F(jRkiP&OWiW&2E1lnv&|Y&V=)=wPs(M z(4*L{sG|3ULJHk+Q|9+jYAb~m9grzAv20%p*&h&UzPO81itB+`m@nsJ0vq+7zFx1@ zcct>zz(u%r+yy@!vgrNlUmG&tzE*WNG&V4BV13^4v%U!mUG}TpMMuYWe(1NK9i>HR zokrGEy;Jr#7@^`xWw-G0#ddrO8MgKe0&~O2sBO(1->dFMRjY zE@3Eu!$_fa-?eiRz!9HUv7B zoaCuJ@u4iRWL&8#=%j|9A-=Hf+ZlwE4;(l^3g?YG_9vyPWsagJeJwdd^E<>mz+00i z%a_gp-SN45R~d4Bdt^|$FBmNfM8F>!91>zYZ967?0IOTAx73+-*RD9&d|o_%zITk= z<+v)@Sy`p={4(yiDkN6WOxl$9e zff{$XUh^fIkJ`q!y9>f?6Ls&^_sYklsx=QqJGe-9VNBw=e!Y|gp4&RP#(mD)^-9+! zIC{KxHf|0p7nNDlviS{XR?PT?EcVgCq=S~{ggn@E_hg6)i9c%IR6nHq_!{$w&FA1Ny*B!IaM2m&5x!m#4fa9RzW<(p?Nlhr76VjGre&quh;Tg2<1am>U7|RoL&) z-Q5k-1}muJ@ebShk&Q>=4Qr?_lVvkUKe~F?b#!PzL7)O3sPU@@2PkGuao$;?sNN7c z+6Z*cuR@Hqpj=8VyHH~Wpx(ovVAcy~z!ts%(U^-Q#=u3G^jQxdmPE0s0v!58D^N31 zvq9fa3O2d9C8P*oDrDHye97^>+srWUt25t%gHptEX+X7L?Ay(jh6m=lb?8$RQ9PvY z_Z#oXJG2t+{i4i$viJ&)y0%S;Yds;zhrC{8w0(!eM)xnv1}?9K6+Dy}D`o6SJrSS4`zg0`0EiSvVC7t*o zJEY`|iE>di%gl->D`6@eEEEM^OMS)pBvdnPT~;&gkv%0Xk1ri^pK5)3G|=mQ91(kWtCpc}tpT)b#%9Zz>hfS{PmU{|2Og2oL)J(Thnf3a9smlJ)E2?^r z-!Yb{S8Orb=udGV(oh9tSL@$spw-?;5O z@B{0INai(re&=Uu9EgDCgVt-nJQqh2qj-YdaX}Ca3eF5Os^N#pNusa?Z&RzTT5M;U z2MaN1uU0LqvUs3U^kW2Up#&Z#y&#_ZSO|EA293j>NAWUHbsj#F1}{bp3USZ1l>&_= zSNygw@(HHyJci7{-DBRohxQo&{k3s)RKp78X@w4(K2QTeniJf^S1{rBE;-K-%X|6r zyQeY~%JDW%onmExXeJ?J-}>-4m;y(haf-hI{G*C?#~Ty^Tf|Zrw*M20(2<9RlNCO;!ST=fqpX@*bbt{`tEeS-sa2)*mkA8FZ=Q9?`@K zz7SD7n}*gf2jZ9fE6Y;2h#%IH;%33~bz|6oFKV;6Yl9===G9da=1;r(c6ru0OUOj6 ze&}jElq;rRaB7Ql%7u$3e8+FHd^|24E2N^TBSvH4@6#&m`KirsQ$YHMj{#C1kqLdj zj%nu<>ZT2)g8rTv8{8?lTHGdql0Wh@L#JCf|MHT5ho*bZ_Vrfz(aiNL_94a%57_||pdzSydLT2eGF!06wfrDu(fHM6ox;zPd0p36mS*Z+jZouEBHer0vpT*f z3t0Ticz3zZ*xF`3K@&g*Iu);1HDq^l27$TL%(pa%|IxPM+F^N?c!h-%(94`?9pBNH zEGX7MW#%()Z2FMZ;qy77E6=iiRBrw<^TrZ7{SJWyQoCF&=7hnfNw&uTP(xk zRi*R3qID}j6|Ex`{(Xq|UQ&Q*En;=HMvHISzW?NTg5HwS3fT)_9~D59wSm7O#Sd1H z3|2gR8o>an(xJS4@#2LkKwHwkhg+2=!yT-}Jc05#88JFxr<}8~^kRu<`{2Pdh-#{m z@X=y8dG$9c4@X1lj`(*WMcH-#+D?qtl}m64TIa&ju% z!FY<5hK8o>%WL8J+%mjyzrDxL0LsC9XN`orgLj|%fj?VVT1u6^e^9W9<1nzNIG|eu zKm&O8?c+eY2?sB)GJ$1pZQd!@w(sQC$Ar2fOb&jLS!ZrCIYx0N>y){u5K3ope$pyi zSMnDbef=0fwMQ|Z)d2rCO9)T=I&dR60yH1C`T6)L4!w>Tn3%`_s+tPQ)W^?HMOyj} zk^oZhk*4AUts3CgD^CME`{3@hJ{xeN$$^to$25x#qnVTK!yR_-J6+rMNXS{+K+^5t z{P!DFGR~YWD%$e~Wt))0$AB1a-SfSr_gv=t-vjI{%RF-U@bFUBq1pa8>y-0Z+1N&G zNSX4Y`P3X-mxW8a^~f4M$B{%Y^SvL3M!zPRzL=@F|Msh4Szp4LkUBptd-bf%BSY+H zrRll`jvMGChkLU!YNDlzZyt%$MmY!XWA?Umd&C?huO#t#46H zd!zx-ZkLl_&YcqtYKlsp>nCA?#Yd{3z>FFdQT>s!*Ex zXX%C)LW@TWI>g4}xgutqRyG!Uh-idA)IKZy{rji=n5p<+0U+wMhvzobA%OgkLo@-5MKd-MC<1pSGjgC{KXF((H za_iy*y^BO6QHz94ew~tbpPTYxKmX)0Sn;FyGIl|h0B_&uhud3i?Ce^G4oN0oKK={E zs=7ka$bP(6R;*!}qPJ}IVD+oTJxEnjx9)ju?)dc_qMk=bOp1r6T(+k%m}St??n{o2 za${9c;D#27*gYZyZ8ewXB*|@WR2$NKMBGbDST6jjh}TjzY$hkU4+%VGIwGW(OghqEnKO zrJT7(*XgPLaYwZA7M;0`$PvmzvJX-1;qi2WHqZdnt_H;R_VyCgi3pnj4S`BsRV;T~ zXi3vJv7PahI;WQLs$@MT%6{zck0n4Eb`%&fcGT#Rt`mafAz*4>kLc8g!1hY4E+|&Q z0kj@wbLaKPT|{`O>GJC6EA*ggzcq*IgG}q|;fkX-+ zCCmmtdTb@QEnBD$FKNuOwhV3V7{u{9AXZAtNais#!h7`p6cULMf#_Pbwp^QO;kj*KdK z&EaGdy&xc9{nM6~8)mfIbvp}8LeDY!D?XpN+A&ZQi*3-FEx*a$qdfK0RfDtDL>sFBxME=|@y@B-XXgakgu%DQ(*RGvAx;LyWtNtM!MGQE%kfNPewEq@-E$xFJbu>JuxV+ zSosgD0#9Y17*8MM)Ev1TT9P8+O>Nt8qTSW}a|z_L#e*vgC3E9`m{=P(Jye$&mNz<_ z3=^7tcz)ZC<7%mjd0~<66C1{RX*2HJ=_z&@@OktoLIrXO&D<;LCXueE(R8)#Xg++k z;i;Q;SGU;fx|iFBy@sRMR$mapBRn=%3LjoFJsG~;}eG;`MD1W5-U+@>6}~pQ|*mmKNVd&?RM_C6kWYZ zzHB>{?3gb$_PKbZ|IZWm>5sMESZHcWW4JyZ%M|8+_=oSlSl9jCPV~RdxA(72NB=)t zuK(fZZ zv~zE^q|}ZHwO8@JKK><#_o%4_C!eP%IJl|W)A8m@yycHoXvpZyx39Apt5yh0`Q0z_ z$wi5x=2cilN=Y?4I@Gvsgx-$k$??|3nai4ur z`(j13+R~0Eze(i#j){NyL!?e;j%(=aDA_e(8vm0Q2VP#;x^m3qIbfhF z1tr>FO3#@zJh~qn8|dTen)}@?G;ziyI+kKK=Gzl6L5Q9TkS5`-N z^gzX$o8&`2ix`j9DX%We$)b$1mXftDnv-T-0bs)X=-W7$FQ;R+O~MKEiC`*ZzJ{F7 z+0EIS=c)iop|A7OpvxmAn~lUuNmE}r^~Npt((Ct5KmHLC06(GswYWIu*^ySAl4p*! zP)5S|fF@o^^Ygskmd!hwxNTPh{4;?i+d``JYq=s-d6|2iPLq|FZ-rxmg~DgnmVHq<-wSI)e&JF~Te^kxT=y!hGp$QzkLI9Dv~^Bv9|+OatpbdvI32>^n}I73nQ% zUS;REGKyHK8ZSI$gX+De#T4cA1fGG~RQ&<=6!m1ENYg$=fUE^N9y4pA%_B-jlr!J& zq?;OOw<%wE$8Xp}=g#}lV*Aopk(I2gx2K0Zb9vUJ+-4(cm(&z>rK;ryh=s$dVlk>} zdGf?c*5$lUpPISF^DTOlDm>QCT25@3S;^J=s1yS!xc>aP?W66ov0k1rqkLm8U$oYL zSeky^EiK>NCwy*fK0R@lGh45^E~}@m`^pts71q&|g7Dx*6W=-dv2)djN834%PE|VsqbrcM6{$>NXhsRpGlSFMvmr~%= z{9KT4*^yTEv#WBl4Xyd@ha9Edv5xtnB93!++q^c`y?(7b>G3(4$e-v0OcRVbqgql1 zWOEIl>Wxn8w{QB{GD9U^)9+}Vu;`c4)n)sTb5WT>uan22lq9p%}!` z4|kYEwL<>J98a@00PP(-BESdv>&6jJPP`NA)~)UE3=0WKfIgLQ-0X^RcOYmcR6DXO z&7a-c+;MNJjsF$iklIX}mQ_uAyBgsy)w5#C7Y6A8Z1q}e2~K%*dq2H+9-@*UIKdi3 zm%f2e0K5sdjPPG&;IkSTju1Spe6FNHlFxla9sCMDE|^nG6_GzgeLTUo5x2qS3&4X1 zMn~C^txEV4$V~V&xWM!R8dig->OagVkm(Zs3!rQ8Kt9M?=%jb;+n0=t^CX}-Hum-^ zKmhO7F+;ftO#v0&1H3C`48J3kjfQc%)PHzD-o$0V&cL&Gn+tp=CxR2>4_bkF$DC^|49vtoa zYTQxfdE@BLi(FTSm-#5vSa-%EVJLR#3}-Z4U3<5xS5L?LGE>sK9G=)P7wLU-uf#_= zcIEeXeRg~{d%$KrxC_)4d3IB01cir`yb^^pU|ut-p6y+)RDQ-q--+1vCMn+wUbo$1 zBYcn8aC5x+4}7OJbN=p=U+CykFr$(cUlg!6MF@k4s0aYNIX;@vw^^|uyfZ+*r-1$< zFkoqEX-kHV@IN>QC^lL#@o^U1yzv4ZAh;F~3awD1BV>jRDCT6@Ji-V+v}7B}=3VJf zTwPs-Pv?+3XDOT2&@9#4hK?R$%G@m zSOoEBKyOlmNruqY+1S`LLF18UH=;85I$`T?Mu{nLO|&6*SZ zAX5nv^Yk!il7T(lxFM1gVeMFr99@|ekXgASiHG33;5lL=gdNQ=Z-cOR2$7402A(c- z`_`?^gxDeY>Y3YHhAuWmQGU(Hi@p6fb;@qQ?(PFD##Sw-_@{3yFM7vTUmjB`1^%;pmpWy6B zgA)Dt+L1bjQx~%yFm8mNs=2VR*Eo!BK3vInzops9cAWMZJif3JUls6Y4|dVmS?SufSAm_dDINJHveMx@*s##8EzT z!nu!$i|@*}E~Db5#u&2YU1`vd+m;!Ke%an`=R$W;M~`UUw@h z0RJz-#L0_&8z>P7YJAvVZ1w!p8yi}SY)vR5eNFqsJW927bP&hI1@feSnz(=Q&L-*f z9B$pdoebiUxVNyoStuCgaRJvipY5O=F_{r#q+DCw$b7#8*+tM{jd$co5w#C96yK-G z*2hvN4#qOLLaPS)T*f%z(@3)JuOj(aIREb2gD6xoH2f%%*MK#P;hv`q=rMR|xM(P{ z8FUD*^#=*iIj;(@AXbQYb%=eWnIC;=G#FDejJzLhiuXlyn4GQ&s81Ym770H)Fppr5 zU$}fZ372d%99PM>D5-IkUNpqagV7`W9jd?mcFnjxEFN=n`lUHwL?gT^P5|1Ue~!lE zQm=H(c>M@HCe@IKy^fs2*i>?xC5e#1m%BU(MgVi1DMX@QGqwi9>o+%!Wp|<6W=<%U z$t_A^q3|%`Myo1D$D9hrjQqVlMy(9gFJ0Y!#A)!=K8oV|qttcG$uH}tPiT}z6_$O! zL4=uN(qPK|mbAdP#pxu7EWpv%`Z^?Y?e?kRnrHy8Ese9xF2a-89O*W6u6TLEpbb&r|CCWr4= zQklV4Z+Ef1cB3u5Mp^8AiPg-=(4m%MzY+HgsaB>6IW+_@$jC{*@lJ{*{rv4yTgNId z=@~D?KWMGnW3uR86*M_35jY6O_h+ay8w{Nd`IkaxM)c_U&1T;8n4g|_Cb*%9p_&-Y zIx44ry2hEkz;+!=Bmpz3lc3me8H#Y^VM&!%1TC)u>K~WxHzQQ!DQB|Ah;spS`B=Zd zSV&ygy(c_Z-X1fsk*E>^HF&|ztrX1J-I|}NLEp^ZZCX@0slbtziE^f=IL*u1bs$(xj;azj^PHVRC+v_$Iw-g=&E5Q0ISx%R zANqNqZOs$-u z+&fSJjNnkygsVEFE6-^2)X1UhC)b8f-ail<(f{GY(cR6Jj}-^1$>)7muQ=|>_UMPB zM2%qASmDkq%GKNSeqf81Kzci}7HG2_f)DyLs@~UsF+25{TcupzsGrAMKq|%B=2fw$ z3^h>6g~`v~^Xt;(l_o|rz4W9EI7MtO$K_RKX#}UXwsbM=Xs5B=JvvWI&-;F;Vpef- zU9Evhrk-b5+*#Wp)F>a{i)$9>-#??u-CW)yY&035=H5#e^GApUXX&OQt$Iy+Ubl~z zIkk&r=<`3d^!bLE{t<3f*V}t8@UUB=pn%oEr*0e%cCdDFX`jsG41Z>5Vv?w;FfHKv zKeoqcJ=+uU8*QeQi|5PM8WS~lmY()iOS{m{SQdovH3hN7-!@sEGudK_@sFB$cF||r z(-l899%5I_Efn29ce^Hu?S;F{aI3&Z^Ug;Vc~>nS3?5ZaruqFIHBs%-?F_ASZn3pD zGOibe&xqgPTgxoUE^s`**4~%zaMJ0eHSW5qKkqv0f82FO5UIej@lz3E7xLK-95{_q zEU2QQg5nOJD+>h*E;YCXDeh2a%GdHx_f9B>-mm|v2ai-K|9ga5J2{M39UZ2O#=xyRT% zu}+yO^!!(zyj-$AqEV&>`Lil6EV$5q?&rUG<3wCsm3F8=fp)?1=hBc3s+Vji@ZluE4y-*=ks ziwi=Fn1W*(m?3W)(fGo61(i>VMovOaJ_`Fd%n(u~v2-Z9;xQew6xB0Q68V~ z9rRyu?S2(leKGx5y4kDw=3IwnOHS=F-F}Y;2blVPp?h19=AlAuv=WHx8$D!>24nL}c9Nn`B~BiY3NE2~BGPUj;E%evm)3FE%5wio+! z8H8OVGBPa9THelRZhM%no==jkT}pM=JG;2_xNrE*>BqQ)_&)BUf5R^l=6aV=OeK|n zqN$H#t+72Yp{}do*2ekC_>L1io|2}$#mLOaDv?O6$dfc!=r00N(#x4IW5k%>)B(v6$IepU3ub_52_Dlhxz9j;bGPzI>{n_Krw@S*@jB z=^o5ugX3yy*-`IY{Oom@xZ@E^HB#x=rK1ja`$aR@$!ts`^~ejPgwlC)$wQcIL9Z0kplNWa_Ow~It;)c}r^kZf3d8>Ch}3No zPnL{WWY>zOe#p>ieH!k5i~}kwm25kWvbniUW?duB7Cn1YSUgvZpGsROt!(roAR!cM zXI&k>d{?bqXLT>5Dc70fcj4E+$NM&SrOFFS#@6h}pt(;EPQ!65lut<1XmQ}~4uLu- zG>DQHySN#dPa~ds3g-p!dIgPc?C5^Kcuf3&0(YKS*9B@W-<6e{P*|W6IJdGi*N8AC z!kCn`MG1s*t6ywo2U@yhXYeR9g3$_k~BM<@d^v4=%)6Ub`(|v65uV@kZ z1_B4OD+%y`>aHpoL$KG|f3wQDea&xPw}Rx{#l^8qQuV--gT6xvgdWhlu;Ukobmv(N z;vx}x&^I-tn_vN?r*K@sQZY^aIHhU9)+8rQ%rU1_ed@z*-;qoAal`xVUGY0C7ang} zrmNzW7b$L?2wJ56F0+;G%CJXE*oDt`iCCJ4;1} zPv4$7&vxOUNaO5>`eY7kPt5hnb$OvkQB36@PY1B-hzK`^w-suTY$;uA9(2@OhjLa8 zwb>=vz%b{=+!ENi23bu*@{B%)L3&F^1_Gcj<$% z=>hdw*St_9zQx~Duy1m(<h7L99a^u->%7R1@3I!zdgbmgkEC@Cr$A%BSu zhn2DgNJ&nEDCu8JfuaCSJz36vBbGyRcjs&I z3aECtr|UL2k(v+CWci&x2HuWCgncBQ=E5>F6gJ%EjVBFIP1d< z%t27AuB2#B0JDxaAH&r4gbZzpm7a-7^Tkz8{&d=c+d5&KfeSnq?k( zB=?_KO8P_BUw`x*4bSFpSKK@h`R}w0zldMpwTfKbUd*PH&~06W+k%LT0ZTLk2#_1~ zTo~9xQ3RmH$#Vt~5X+Yo9r~-b19DQJ0x*wV>db%}NY`V*s5Mzh3h(mp5~~28wEtpYz_TE%dt@3}J$g2zx^MD= zD$g(Hd8ms25F1lQ+>wQNfrKbSObo~Zm*L>1=ho6}^t5RixL^I7^0numYeYV+j8WxFBv6zy(#dVk5Wzzs`5VYBZpygU$UV36zB zzJsDTV$%`+#i`-71(Ej3H&9+4MHZWIequqW=L;FtdsX>Ha+_L;UJna`?KeXMf= z0?e^-fkksfyP3Uta=-kmSz7Dl(ECQx2XFYcECvZ3zq`ZjnGDiC2<-u7w<(e~4maNd zt}_B`p5%d$S{C2L_Pxh`1HxKm1Z8>B&xa(Xsf5(=eZGRqei%76waTCjqDD|W6R;Ak zskfXxAn57&`F5Noca89+G=n4XQ;IMovj4U|{_ki9?~18;qi%u*nYYY@*S_ z=!u85JAtz!9fY!WkM$r0Yb*q921*TwcK5YRq&tw|nYFvt&_gp*U=%4F8t?J_^n3-x z5(f`Y)7K?{E3?Qq2z#zp>ST=$Umi!%DDJlecWN94&zR%DdRU9+I}FQ)Meh|CgAv_b zT-xdr6BC^|CjM=iYmPexaQTt&ZO|60!op&TP%-3?5uGUcTnaXV1a;M?MicUk=aUBdlIzKLCukww|yqrUD z(Fen?JjW>B-itQRO*-kbv@ANs;$>7)G}(m4-tH_H4;NT9u(2$*?k^_Hq$;P@Q7Ne- ze5Q?^>hmy}GgB($qGAH8*BDv}t<4#>mZ+_*)0$A(9MZ9FYmGs`?~X}HLh)P7s=d|A z*3@oU2BcFD%}s7rQy&&08Ds2Xj)S@7U!9&1!9~HPYp1O44cZFwOLFX4i;sK$L!iZY zH(i28U~{7Sl|^QnXDn=PLk{U$Ih}Ft3QfJ5*fbZ?nX~z_j5E-L-pn0)ZRtMw*D&3ils@abb-pTNn&C>+9vQ zeU4-v1>~u>0aH`~5l6@pMC!oy-3;BfT!CbC!Ne#hoj*Vj zRRUCkbfC8?y+6=$2H4bnoRnNjoM&0$*@a7(bt>D*_u8Fwbma`oL*S_!m}DQC(onHVN%cKTYg*M5GC<>?$Mz z2*6SxI(_oiFjEU@bkCWej)2f@#WP|Uv=GVNfsHT(#?6?Rm|V0$5XLJ2bS|`BKs|$* z%(cwNhjlx;y3Ah&99RT3Ng{?(j@AMI2rWR_8ee&?<{W{UJ4oWHM#`(p3*^DXl8xh##4?3=>*i-D^sR2$+LeU5 zIY~S`1O-JtN$>3`d8YEFQap-=$#ncD+uPftpN5H!Z|a}iSKTezqjxN178zdKBYzBF zw&=>H=-u1Pz9th)GEdQ85+xh~cm3O~k{m_9wqy$jY<+a<=lI4-vHPyqZy=e3VmUEU zyq`YRKxzkuCTy)88%~-RWypATcGdlX4g!hoT_%i(!pS9XEGI>!Ad-A<7U7ja3B21E z3=6fjd(sNk1ScPA_HIcF5S?xr_&P?{l%gF{=Uy;(hwDgp05ffEl$xGH%9S(@4OVx% zTt}@CC2d8Q=|2_^h4Q+K5B5**iD0ig;koGLst`KkDXh$y;NrnZjq|mxaaqf}^9Tfu zu>C=q{)+u6Hy9(?b$ay{bhPc?f7-_&Sg3#|ll?-<-NlM8-qs7xZ~Z{91~CsYNuydd zeZ&!1MqBzDsew0UXu<{l}Q@4NEWLitJ)33s0 zIih0u%pQXdH4GjA8zmMzNn}-CMeF1@-$)Oiqa9G{-z5O&E2_ zcoPI?f-yFI`{WY9mBH9%YHKD=lx zcXuTRPM4==eie$_=7feOf2AYl9n=?^1;%;ZJzJBIYd5n1m_Ds64=pm_U z&#u_tp57Sko#!0yD3Y)CXJ~xCekmvm*fw&_fDN<@_Vxp84p?L)M^{waKBBey5Y^31FGVfvR=yBl@E=bOK1v}4Z+ zcS&noj>pKiZz>llv72@ZI_Th4$ECnX%Q=xAVj-0D<%^DA?CfbW^<-*oAI-j;rh>Jx zQ`)8L|MUGW-sw5?=IMHp8uO1~GIi6Z%UCet3<29(gti4>2icHW@JSnC#N8sB!8mNk zk1sQx(~}t#I7mRWQ}Ygw9eKCPqBG=lIiqy$uC6|p zYl#&1eOjltamQ}Qc5vy)=EJ(sP}jQ59UwBw_;fPlI~tbAD+dJL;ZRES(!YG$OCfrq zLR0vbS6lqU5c=OLRj=-K`T-aCX=Hz*7WuA*{Vz&(iVL%pzt?QQlj%E!NZ&&istCc$ zNIoYrfl0Kkxz}I4Ka8iD(@q^E=25h*7s%;_gNN7n)m}_iQWrk@muLR`k_-?u#dv+L!bpZLEy0_BfIT#|X> zNO}MHZ$pc0vXB{GkI?GO(rF)d;I1gZ0N}L_Z*S@+a#lxFF;kq;>MMxLa3MozHS&)Ad!%XE`2&dYJK9$$K@>F zhFq;^LS`%KLeOO0< zAsWFaGN=xlrQ7%SukFbN6dr-hXg`4H${JgkL)b}T8=R}+ zGQc$u1t7o;WQ|^K8f$i*BO3E0`(JAZ|cBXAL~*9y#L7xW) zg-BXa6v~exwqr=H!V{tg65LB<4XAAm79iHMTbMSYQ2qTCLcTE&;D?ciRQu@`Eu4FQ zdC^Z!<4$Qs=gA7*ghXd5>2zcp@!{q;D>UuWuhTO<{JS*L7dd*F5|u3U26#C z)(goDh0>7H1bn;xUc}!QGUKndQWt7!5#lyGUB^ujMLqY~U=rP)yh^;%Gp^_)XFvFI z6S1eur>tsT&s8@ND#=&`f{a=Ye?LaAZ5KphH4#Q2f{y6RtjcCp^V>O9(;fi7XQPnX zK5MycO;FZF|#kU~YK zt-(=STid{rk`m?-EGdnQ&q|?GguhB10pkI}ig?5Io@h=1U{+ z`atjh(8F$^84Y1?cp91MOH^ObL$m9ZFS|=b?fd&(TBNP0NF_o4_=HmleF>5jDY=df zT@+c_W|FBw0mE*bPz?FA3>7)V=Gna<7O@JGz^vER)QpJE{d(eW=hL-q8`q1`3|DIt zBnM)FT!(Q@k*Vi9vBxVQ8R_lf@le~FGc-iYxuXC!0FfWwN=;tBvumRhauDmj)3+R|KsE9 z+l*hFL8J^QoRMG zNdO2jKaIb6b>IE(U&pU|e4+dN(-04NLU3YYB3Rlu0GA3RuLqiI{wr^Pg-Di+qlB1( z4oM!RWo0?tlGviO$RP)wOQK!@>Vx>E>Uyr&+fVk2RL|7}gy4^X*81)q&^n@);j{Cg;3= zI@XX(QP2{;rS0|Kz4=~k`|ETq;)eLo+}irLXnzewY()@|s31g{ji0v!di)nRWM1V{ zhX0+%PWT9A%QugmO{lx;p3uHs@|vQc|KVw=FqyvmF++V+{U2Em)8$ zh0+&V)7yXRahVG#DDaPWJkVZbBdD5LHj|4!E$Kl)rpL1T{TiN2Yd@H}zcFJFEz~<6 z%0&!V$Vg6kEl{y3ZfEF+JoXQd zj5GWd-E4fUoo}8_xe#+L?5pd@6whtt+7nSES>e-wgvBWvirDWGqsnqOYR2R|0aH&)HIrR%f*>nPz=OuCw$VvB|=ZRSitBw-%npA~EdX1{1X zYRIXVuMGcU2#zK?UY9fLd!4k-`s~Vba4JFu-r$n+pdCpQLS~t=+zf6nmgIoMP!jSv zA>x{>y|4x02krf(ZjRpGn>&k5Z}Q*TXs+;e2Lj|Y9h^TYcUPAjDlYf`KD%YJW-5bl{>ZD5W=O^oe<^3dvt6mGj(>L0Zm5#|?#Obg#G$YqRn*XN9|L{vpjWHM*nG+-2F zSMAo?9`)mSMVo9u{w)ehM@)Odg2PU^=Yz>$y4aF`2rdJ67yI^%&k+PJzu0UHi&N0< z<(1GgH*U_AqtQfErj{X<^4hK=7t#-wae?VgD7 zJ!$*nE#8&7xtgmUZGCWGoQ1+78?1Ws$oI5NdlK*H2=f_?N~G)L$@L$WS3B*0rplg6 zr}VLa-grWD;J&nh3FD-rhnmZp-RTW`!+Z{#uu%>K$dIx!bi79B=U z?8_b#f4W|3RlSz^N*Jv-SNmXDhRa%i!vM90Pj{ilV0AIglZI38 zm=xa_pX$1XXWWANb>1F7r5Y`9o3BpD3VEJ*t{mCgaZapMP4|Uu$#7u8S>DSJE120- zt3C#Hbgj>wbbD;-+%>b&y)~w=Hz`2MppQMis_&Ds-7C-7@cK^1fXmx_T~$Mv(`U=( z^c}f^_kMo=>wxjDI0xudlx{;VT&5GW4-WDgvORRS7-<70lmDhOfw0MxH zy9=dh#dRK$ku(fn=9{S-#8S;O%R@SE@3I*5Z?=2hphkj_{7)EjR8}^c4Aw`)diPfk zr*i*(d9Hgw^&XE=tbc@CV0l!6K8wuD-d<7N1uPw2mycJsuZ?cC_-dYd<%*NrEU$Lx z&d}Z}$;<*bC9C|A$8H<8gw5ojJbc-2#ifw(l+)w;vDK*;n%m{=_q>i$ zYQHz0d#T6DtYcK(SuN$t3uz@TdM)}qk1>Wk^FE|Ti^vb4I(xHJ<~sb*yPrXMdGybr1xfyNbhYF-CPN_GS!0U)n$wL zd;1rcf@Rb-+`p!%GL@UDdW`ADNoEK}pB3Rq)t&m3EpK(c<)*sMj%`ZwMP9E${9S%Z z{n`9nlI7H=+VR1pY-b(&U*C;bO@6GWo{kscVLLT?ef4w2>Y2ICnY%OCYTBcf+D$Zj z=RdEbC=OYQ$P=mf$4_Z<3?;+O{-STZ`=#_Q-%VCgHjE27s>@1QocY4tzw&NTH}N&O z8Fnm}=>aEInfiq#oz~b#ib;5(HiSFs;zE~2Z|jYyq0#99Ebxg(r9Q_ttc%o(P2g-v zGToXpeN-c7yqDz1LIFU}kB7?P2F(hOG6?=L2MpJlMy(fuO6cm#@tX0%@Q`Hpbgk#Ju$ zRVwK2&{Q3Pn)I8X!F|y##=5)QU5n&O!(kQ=cDWX#E1*AZf&mXWOHRdY5lm-XjflPL$iNq{;OFzNbOap`->8yw{`A(} zN2Ra2+77)A21SEm-~GXxHg06sDYiqe2#Qv29NOc@)VZC`VJ@-v7dVz5PeHM{zg z$7u0^RWClDd=p!}3}BaV*mWSQHr_m->k5R1*oRc`%ZMoqloav$Y-U{lU0NkRJm)V1 zq0Bv{&dtS7)oWs+62|&c)mrBJ8v(=TCrn65h?&HFryCcy{Ppu!X=T$^ylXr3M$S$jRnW)PkB;$O zW~D%i;~Ou&Uhc!xe6 zSK%S)ny=4JD;nC^yBA$AjVblnt9S0s-Mex#8XS>#`Q45ea)~jxe}c1xM#+Al*2sPC z#rY1Nn1&PY$=bjFWcn_#I02+FhcJ`mxL~f7a+!;LPm!$@TEJFB(XmhfP$>ZkCo_ab zfm;y|k_sL(-dCXqG^Z6Vv4CNdZ4mb0)6IhWBoSJl5h!yA3r?n(0qz*dYu9ZCzM)d# z;ST$gEWy1H3PZblcgVZB5N^E;(DLkPn85ifLAsJ>Gay4i9i+)nIwv7kAwFX|9>Z_R zjzpep+MGa^62$q5(C5ffKaG#KfV@>oASXzycEY8$%S0w#!?7at66lj(| zO529U0Bryj1PlZL0VPVdMa3dY&LALJa?VB&5Rf8gB~wtK^y{KCDz`wRM?d_Z2Q3e<-1Yle3arU*{9yi5mbq9KUM zhms)&gm8e&DFEyc31bBqjpo9Rng!LeaiZtwXL2<02lTBSz#|aqNWh06kA@W>hJ%l2 zQ)Y+T^y_&vg)Ft3Py_`FvOtHSLI#WB4Q?l!03HlPocm$!8;96u_X;Wddk13Ln(a-GO4Y- z142Ya_&yzpLe}-avi>0>^K!4!_?+k^>F#VGqOEYoiQ@Glo79NR9LjkzAFZ!5nF&v| znC|F#e}P|~U{`g0d_3*pqE&72{p2J=2NvGh%tqRyikxbPFztBv;~2urp2jn|q1CBv zGmjX}x^mf}=C~TT?N&m~6_;+&(87mXs z#}=El%_!AIp7y5_@P1@=g-c(1ag8;gX1~a6F<%zd?>1mWtNG0jTp8-RhjM0PiHhV zc7e54=(=i*I4>|2%cn&F@RM(Ldk}ZbTnGSh_Rn+f>cMHPM{lD4pj*1Rv?OUj&^AhfTtEf5m+AL6$A*p zqDa~wIc&lPWm~byr~%*@X-|QI<7-x(ATV?tA+kVGg{Myyab5YGHHVDii4e984xV6U zD`3=-3z>e_75aPct8YBshmr`;?g5L?j=fr@%rH(G- zSbiXtAUrTKz=Z6;6xe{VHCN#iL*K$u#j0Ed9G{Ws#jSxyA2`Kv|_PO6bZ#6+_}PvK&-o+4Q! z4sV_#&3;x8Pc43@Q2jxVO^rS{@}$k-&NrI6nD!Q);#2GAH&~^mWu>N+Rax8zs~9pr zMLtO^^EewU(-6z_^vTVOg=1^@ai3W8=9p!PifR_wGr3lxZkbxGL#qD2nd#(i?#y-i zjS(ETPk;xy1X#_eJ-C&!OQdLQ8IscrJePHtD%$@B)~vH`UeJzNJ#kEbyD)3_`?1EY z?etzD=f#)EvVUaPHD1{LPH&z`&$R8yG1=wkpKW!##MmQf^onrGU3F`_wOw7ZSO?LN zltc@)Kny2%B`c_#W}M@BaMz}^;G!pCjYi0>;NaRqYO6R={m5i1IU#r>36IX1?te6;vzWLyCn@?-d+{)@3XmH>Iq!C-Q7X->Z>3uLd6F%Gi zcAn4RS-8nnC}E%uK-XZqB!lNEF64QlipK$ggB{dcAaMZ?@)=$TUi~SBNuCAGo5Qr!)fNIlO_rj&X3O1@xG+niv0yzW- zAznejsFCzMFfb6(UklS~H%oxz(j5j8PMA8V!c~JNU~s8?3(G!n+Izs1E4WxcoK2<4 z0Gk3930ZJ~Bc=)5#D;7nBsK!(2vYf=nB8)6Hie{H6{KR!HG|K;p|=awXqUs$hr?B( z8p~qlFcvbdXPatG?1B1u9E?${tG1bk!cM`}jw}(PzZXHJn)%|zZwrIvJ<3InbBGTo z13v@=_tGTzB&E2S1K`1Q;9g@>TdZbC&3{mz1g|LCh?e^Dy&I^~G%zp#EWU5Qcr}~~ z@NC#Mc#+arYA)S}@G(A!`IrGM0b&R_%&GPfmyh2i&mpb_FlIP{(dMfg8D$hmy{e#-Z zWldmTw8(4_QZj8{OtX5&%PKyRUbU;;Ke4_?ViTI2X{Nb!70l2wls5-1V6Tjc(DTwLfaziVO)6$oK$vn#DyLPAhp zO0m~&g3qu;WWsjyYyHW(LfcPnbX)F;?PrhTv{S*~rbBLJH=w|9haXQ^KMjwGa~|I7 zreIM?I+jQY3DgqM5=LCnd~GO6|za_SXAAkH)^d^g^T_hI3Hv zr6F}Pl>5f(uv}j;B3>$NJL7d2MqOoRSAY9mX>OF8g`p=+mNuVTOG`^B?*kCTPV`fS zf-)YJ{4qQxL6AX{*PVt^uyMbQR;SqM!0?9+_Kf9qZVK(wHGT0D3lVVzpnEFobDBoB$I8L*oAahcFF6 zzRrm8jy4kvVbNTTv4*yH3RQ^$?B$?Y+}+(p*6`t1%)IPT?kE)0qAovfK&>ldQyvTn z<+qYZWFm=g-`+b$^G*K4AAjI9%5yZUjl7PzhYxOT#!R-x;S`P7GrU5nTPwW!OT4?Gd*JL$=)9h(^Emq~?*?8`DzfD@Ij!7WXZ2=cNky`y zo9CvLhEa=Yx@=vpMeiQFJn?9(!$H0)(Gu8#Nt?bniVU@3FY~_a(g9Hs#MblJJ~Hh+ z=w?3Zti*`vNS9RrllbuDTbd3VYpj($X@#6RN56irz7n6HG4)}Q@G$%FdW9Er)+eSeY|i$ z`(1Y-Nl>j2mTq5QVKFH*we%o2ZhG5VU^u5&=1odKnCM8NcXq((z4C~wDaCC*j@4OX z<<{(yEpajBHknTIq8WNUIX!b6Ltjzxm2bfUdDNQ1P0ABcx)J6>gHgHt3xikoMRF0t z+EBO*g6FuiN1#5A5!%*L`H_<`$)N>oak57Tt}%V(De>sNK0MKJ=SvB}<}&_S8e0hR z69*4)jPv4v0w{`L%y}YrZ&`s8>33!_9f5OA?xkCGYcl+0z>@h1pFKd1+rfZh4Tf_# zuvjW+EiV27?$%O919B6yUQ3K_KLoYT1jW5zf^imRlD4q|6crpy% z&7oF66$m<0L4=1&ba_w|KtC^Yb$P_K&jwfk$j-*>S@g4ka_82yVT|aVgy(R@XlM*% zh}H)R$ic=jg2A*Zz4Jqng)?pgV;ENCF#;|mh{p{>EkOKqp-NH#HQ>oOio0027Lq}5 z#dQ67Tj<7Z?8;=bg(~&$zn`y<8eb_mBn3{!>XHu1;8&1QEq6{F$34Mj`gxkXo1%U+ z&67t{p-o|SXL+i=FDSmUa^>`u6fp)01;W});bdfu=1!wQq500x5r5u^`bC4>nRvF# zN(Z%?2hYAf;La&(>k4brv!kd$s?4PQ!=v-ZG-nf2T3wsjvj({PSih^j*v(}WG1t&k zDU1n*(=cC*AKt! z$!171{rt^OHK>~~nwI;U^tj|axOQL^fyd23eeyh}1+F8#4_cw#Q8_^ieWrb3OUi|s zj^US88v5R(3IX)#yjtscY<5_Bb8)CkYpE(-pua#sD@|)@ugsMQjdF#89>tw_+1zvalaa7G*!t|@c4c0)p(qzt%Icd?S!J)fhs7Lw4GtoKHKa+s?dbYwQx^?&!(UaxY?;+vYsNrZObzTFiUiBo|6ZIsU}DR`Q{e5izWy&w=c0==QH# z_+oLkfNKWcRey(NA>QBHQkKBr-FwA$e@Gy=6zHbIQB(?;3z@d3UWI&Wc@ z4^{!M0di`?0P0FYxrtzXf<0yJmUJBof;PZdkiQxt{i9+5I>K~lF*4zdMK>?@+*C*3 zT zMl(NLsUs)X62tBFpkk$@__+0O#hVYFBo+*f$ir+HS`a!dyUo5STX<A-}oeP`|=Qrp&gIvlMZ&Ih{Xrh?~bYPXw(XO@c z4V)t}t9ujmU2_IXVO9RP3|A!I!_HeQK@1&5mRei-^MbZI{LY&#be;pbzBzj3I6&i- zbG;atN}#)n_X*kqb6JMYy~=VlAAn*UCr7MJGjVerSWPRPkv_RH<>Ltbf@0UKSlWYd z$u5g;cZG|ryWTuKbXOhoMA-RBSC;!}opM4jIK8JnW;j;lbT^a2a%L+OiJgv)~V?i=sg&JFG_6g8)r}MhO+1>5B5x zVG~|1N!2dP22a+}VG4Qg2)UcwSeY7;z7kZjGSF(H53YY-cb2Mi5d{JQb3i!g2i-$9 z%Gv=qGN4!-v6#Tf1T~?RJB#HIrX2#NJ!>t(ON=F!fNP>ZbOWa(CJY?DKeK|M_yi{s zN63%DV8=X;BRC97i@9cjVL)ppH7dzvb;d{Z<3a`Am^tefP5Et_jS30K3;Ub^s7w~Y zFX({)hXR8pTcbwf`CHvM6=@`fgw2dJDT2bnS+#8wGpx;f;AcV=6URkK@H?GpBUqwSn9<<@LN)!y=ZRpj!rfR*aw0{mrM z9u(GZsGqx?$a6wy;ka+F;!tHTIM8%r94a=Wcfu$Z+bEoK0G^ zr<~C}k$;q@FI^3vRBO?z3Fx`Q=AsT5*BKsUmwZw%S$k|uLV$&3YX96=!;+}ToerVg zi?tV)<K7BM+H$%Z+6_-KLE`OGst6I&l|O7OYd;L@qEk<0DE zr3d>Q^_=}JJGD{e-i+OHqT22|t3bxghtK`FIJ!H>3F~uMMi!Ht5A{HtPZAR?pV=+V z$7$t0t~$q$HA_!8Zn?E$qvIt63kg~~9XgR8&QH({npXDZmN6|^-GxV;^=_XWQ*MGZ zJ(LmjePe!&2p$Gp8Ie;E@kq5AY_Twk@B$ACqn|(VMOCzCR|57V@eDS;W$h?q!Wxptx0pWvdgQ$+4)&3 zTJ-Q|#c~_@|E*AD$Ty37^5h9LNCpwv7nwX{!~@|{_NV5+pa zjUshH`-%221`~Gs$t&`DGgO6E>DEt3lUGPO*f92B-^?qLoU5#hCq|bn&dHuPp`t>jOx=x-t16}h=a+TXB&y4JIiOv%_XFCh(An=U5mL+c6SyBlnwQ zYGw}Cogir&p_z0)m{{QSUbfzhDKaG{eUb^!6RS8_r(?n3yc=aZ+pJA4(-ug&b>|L_ zv{UH_2PEhP+MU~GYdyAGkPiDI6kqE|)1 z;OI^eFQjM!s21?K5~3|3LSBBk0Xeflm4*Q^a<@fpC3S8NbTe$41sSk-7?gSedjlMe zIiK+*Kt0o7Jp>~Pa%QQu`CcU~FWjpba!{wvv@eXrBs=8mTeM!XI}w zm(&1zMNknm{&p^|t{84+?E#MmTLa&}-zP44lawItu2%?PTx?`yBoHNWaPmcS*h3BI zlKd3xI>gg9itbd%kAVmmOhcvt3m||RW&QhyUzOmDn=1eX$*rD1X{l&I`!p1`=Ph0P zfYIIEU1_CftmBuO1$ii8BRF+vay{TUOdA{3@C4Z!`cMFGo0yxkQ1#x~umnxOYH882 z@7q^m#MSJLWz{0GHTWj#62o{-e^L<368__H-&^D7A3rspyi7r?loXfUQN=1(V~!k= z+^|dKxjQz@if2pEaQJ@nm6-Z?R>mRu7FE|=P2KejoqGaHx2L`BDQ5fPpG~dG%H(s` zj@ir7(CgH+ZoVqoEo9YAyn)Txv%k%-v6Ka!QCGH#q@dkof#S2fSyB<4?8QwQNE&Hf z8^n8zmIH%kz|JS@q_E~acx(LcN~g4hg$vF4<*4j-Nq%mniZ>R^sSmml9Bi*38S-dO6-TA`}oTkdDgD>N$0H2H&;nW1N5JUKWp z?9tfoH_ShW-Do`zP$z0Bk+J44zaE^1EAi)n2JamP8Pfnt33+V2%`<7u0e!IrsIvP? zY?Gm?%79@R8^Bc{f>wpT4bJ0qFz?V{T!iTglG(?Id6pq}P4HEKa!>{U7;?ElJHUQ0 z{*m9mLzX){8RYvsM6x63bdun4%;Bm7yb0aPiQhYKf*oiG=)^ens;)pw0Swxfhtcof z-*8+U&;WQY1cs{MmW!x@a8b`e#kjJup$gn+nMPk9A7t)vn9{q5Avz%(eh?J?xghTZ zTldB=n|A;cp`j-T@4o_dcnoi7XqXIFcLo@I#ay?AcxcTO0MXb0wl-;rYlF9qMo3gw z)bC;61&;Og(=0RBfO+C;0D8@!h($?Dh`bFbx;dnxp(s!^ApPzJG)u4!?tvE$Outd4 z)aww_1eXAE(qycWHuhUZeVgab25PC`H`rhf3S>ycaz#p5R`OYm3aBuEg&Ku|oPa%v zfG9|=SB7p1)j6Ph17jT451(%zHOOD6v5nZU*6ty+-jnrbJz2uZ(%di+PzYEA zHbo}Kir_TxWq;&c*U}{;J3hH|*RA(rvS;{Y5E#A@S9$D z)_=F9RF+gD4LuxZ3~5!KW_8_$Lo4s@0nsBflg+d%o2x1?PaASswN%MbZ0>uLrVhb| zUni^irLfvMgS_S`+seaO-XF;j=H2+P{;T4#zN_0MoYiO6-i4g_$z*{V`wk#f$d4H& z5b5A=X$tIlpcDM$3WHfTOcbTC&=-K7fL}K5x&mUc!w~`2aV)TV*B1x1V7}|wz*2b~ zNhHA{B?(Nc&7io-gC-U%U{O3P6<0!mzCH*A90!300Ea^eJe}2W6&@CTeO;X-5}_j< z`FA%sYSjP)hV-*1w?fI_oZ$mQM3^P(A@3n%LW3@07^t8*FW3Qdy1Va84UxvxE8PmK z_6QazKNQ)*LAlih=wRSEK}80yE&V5RVW0ztK{1-B!q!fP{u5k$bfLBC`u^dm3NR5) zQ7v_V=hZ#%DDHp}+8TI_pbr9F+DOWQMBXuPFP@c@(ElE_-k{+R-Lw|A|3o!L&FAYCHg^nP3E0T2gXZ zK0yRdtVp15;HUbmq)wkYbD4C>%XlI<;@Tm+EgLR*PqL|jfg>Q+OB#FeQt}DyJ%Fat zNG)owzaIhS)e*UyveuBjteTRKje3YGZsfyex|A{lx3Dt!2p<2d{GlP12zLe9W z&+UcWS4s>F48sJaRlBnL?*?iWsLrfV;prxiOpR_0YrV7eV<(R$^avjCy;I-&;m%C^ z**fhjk*z8dE2YdOyTemYPspg7X);H)r}LPu`q7rr(#L0iQ_9DtHcsQ6`x;0u5`*Yg zdn(JFC%#W#&b@eBrTULcmD_S!kY)!~xThXlNUa<=bYS+I!YaRO!IkUN)qB!F{^PS4 zI>C`3E?MPEVcy!Z0E;KweaHi+RUIvl zm3VU+G4`Y(Z>4i}OgW4xNfYE<&&Jn6*UZUF@t3k0FVZq%yDJz6;1{0Qzd zXBu}D7(BM#K|o9vRb%Ar=Vik(muol9@;p%9K1|h>x2|R0nUYy3A=5@5?<34PbMbZ=iQe5%5uP~r;p2&$5)#O#@V2Dn z9IOX8@`|L+JxWa-8RpiD`Z67r&h?CC^l+j_@uH-<@H6Gy?PB3x zj&3&8Q*YXxN=W=wC-zi*@=|{5w?`?ZBB{>{CGOfz$B(7P2sz@wtsAi?uquDjv^-)= z+y7D82I@T&F4&W+*8>G4f+9h#@wrd`PvW73N32)%F%ZA;8~~{VBy$0Y0NkK*FfBr} ztin%!*2=K%Cuh%n3q7wnz=+hO0T_X@NBG76>G5t{Y}XL}(<>n(OX2vJWn%A~{`?4k zmxvfV=iCb&##zqd{}s- zo8m%OqiL&=SZQAQH&A&fTW?OSqurzSc9RSJ$MUT)E%f*N{1Kf)-&J0{4~`hVhy7Jhpb@mJLt*_D6!4g6QD zyI1`$JN)mNW9N_kFRhw?e7NXfu#kLI@c-WSBxk?D#sfaGC`=YHSYR}%J8J@xU?76X z16PY{-Tuc4ow$=SM1^)kU*iB6h?RCVCuT+1si7ex^`Z09{w!x-)v}Z4QnXz~cnz0h z_D7a`&iO>Nk}zO4*WFA@g0a$CLwuctig{-@t>ussI7o1uD|O`1TgwNI#@xqVp2HM} zl^;BKHjwu~CE9MtE!~ra>dJWdcQ;xutyIYPfe2oERP((%k=j)yO&NYgryGo9KGF%# z=5E`2W0yvrf&mWVk{+g7QCt0mRb(?NxC{^XI{g&!l*SwDIBNV;T~VkCQt7yz4YYv^}bX{N)zmSEn}s^ z7N@eP!40sz>S?Qi9_vH7({+LAR5Z)yhlCjTxN?GHBkwxIhV~?Oexcf1ZopC1r-d{0 zNIAqmowCZRpKh-2i|q6PE^wPqy(IM1mQfy{Jotn3V+LkaX`b#)JHFmK(VLVEg-+{I z#5{+0oN)p}$zfM?G{MqVF*nX{TloraSk&b?75P3*B;>T1^ilVAHl2Zgt?kw*GGE$g zn(2G%6~9;MqykahihPMZom0fU2GENXev)pxPN8?S#g|L&-u>5;d%Q~h#g=VAGe<6! zrK{r#H4Q4f-#`7)O+iT}1IOhf-utz|9R)_KdfgP4x}rtbX$LD$?tV7P(XGn(!&7KZ z&gNC#R1@A`MNbL1-g4*GJFulZ1r~G6fr3CFK|&^R#wNJ zve}E{&(X``pzX2v_RU(QE8V!lG0lTVwVG4syu14$SG61*2q)+Us?`o{EVX@YJ2|10 zpTp>fO8BchZxpE@N!nH=-464;g|yZ zA#pX%EXha;YzlD48_E@_0ArJ@3no}ha7nKN--*|(!6BSOZN$juAr+o&s!M>bk_nTZ zGt|XUr8Y5dn&qFM!CPX4sZ)kb`& zkt)4Shlg;kP)_4N7K8rJl|i~{X56~|2ux$fy*2k*Y!3iC{Q#YNQVoom*^Mz z^R*0_J+d?%XDd@ol_J-xNogYz;*pG0SEeg<45?|H)7?xqBHl}}~EeC~_;j9RcY z4Iya{#hc@DV5W%FEFYf-a=FF{JKw3!)xnE*=XQLyu2u`wEX1EPYN)Z@GPrf?R)$t& zkkIH9^2^FpjV}Yigt%tqikjtck;)Njj?i*nSRpd+{=PzLVT{yOY@!C_!A9txyRvoE zE^ID=jxb-7mt+E})|AU|CTGe3eGhd{z}alR4k8Y%DAa3D$ZWYwNjW4D!}8?B+h`1d_gg5%7- z(g${3VH!uq69f_&XQHZe4{ppR*_Px zT+@&y@aoc~OMJ%T_lQNtsa5Az-g)V)`aXPU$x%wnrPKNJOmn}J91nkdTeQWd%tSAb zpsBt%LwL(n+HuPUTu+my@f_!Xt**4Qo<~}1zw=~-Sk$U>-k2AajgFicwBM$m#*>eQ z2J_3%6MQjhKjlde=NYKEG!!{#Scb&IQas(AE$TmRH;m8X5+^xSVa5w#7rPW$QzRkt zt;obo=;{Q^Qx@D$Ve;M;D$ErRDU@%}seJ9|Q1)7#JXSee?kksYJ;&Bi(yXb(PxU4D zMC=-omx^8C=0@0}LnZB@Ao03k z5%&Y$po84ji|1s|iQm!lyml*ps-@U2jP-OKNU^rBZi5GJSFxJFbIDt#?VBCS`A+dJ z>ypG^byjY&yJt)s&WG2>@MXc|%6#GKs|JUezQMi=DQw4&kHiR#Sh$XJWvE&TiCoj4 zm{6bf6Lywf$y=m%<=d|7HJ)&Ca-!iKm$n?Nz>svy(=K`WstlF4 z2ORB~jaaD4Fd}D+A1FDQBY&E{eMw>#huIeSyXuDh*4FGOXojHA`^VKGvPK!2V;GsT zNy%i^wp)r#;T&{}fw!6I5N&6^u1d?H!>gC2mUC|>C|>V=SfUdeJ%Hzp|b+N135wCFarsNAp*=;!v*b)UESR+U~FFIzL{fI`t|EQBo9G0 zsc@wOXFdfu0H)yKKyD=f-dbq_$xx)2Yvv-XM!+i0z%Wp}=$w$lT}0joKm`(sQUH&g z9=*NF@DNz8jG(jw0x+7otSm2wfV1Vmz(6P1IK+#%a@X(dP8ZPwO2q~x5BvpUT7KO@ zU|RwHiaaQkdV%%={3wvN54bd<+3L?6wO1xaVXnS#%44IG7h>$EuExp;bd%I;JGo6yA( zxf#Q$LD*QZ_&@4t2%pu*koP9MAOw$T48ofX6Lz*rl~2?ac3zZe-}o`BDPmH#2S(tY zV!JXkmI)$BPb^Q|(Y9$BCp@_*O&lHadSOxg;)e~ATw|31oyecC5V_KyZ=YqfPv*n? zJa*&irO>Ro#$$}fY;A0=5^vb9PRh<5G}N!l83g7IO_9(GrpQ@Vr0N0R>m`529oAEoAf#>LWGF%%~yD1KlVV^ytQXc?b zo-(+<%O;3uC@3f(vK)%kf_MHiq0M`*f^i0HQjt=sYIj8%`9wp&R~h;lh$_nhWRdXw zDdh>`OEkfNJQUR`k8NeJh(s_VGAD=c9k{BTC|{w>Z5@Zk0T5nd28#@*Rh$Ea;S&Qx zKql-eFjE3f7qWI}h!-wKoM}W^YHj7h?C8LV9>XoI-kUd3E$3z6h$L3+CU@Hbv|9sW zRRc)9(7*y#BqF%LQjJ>Bg9#XDvQUZ#R$`vKuFKk);bMQ56DQ0PQ>M2 zZskS{M0w$0a$*J!lR)4K0+2^<%Zn16HDJh$tS?cV3VK@P+zEE;yj~vdlVkKi{|3*P zbSUxX03r%f>%nUYfJ#OdQSRZ{u?3D&>fp(Q(zu>xhEN(rLKZ47@vwp zmtm&J_?{?UT|^Dd)&e3=SeUO)l@*qGT)VMI4@tzIu45Y}q<``Ee;JmgWo*o~Cxl=0SpWR_^YpNV z8WTpN{oAzhb{@@eTENR71)CS!Hrryi^DRCL8}a6Nx*jvtlu2DJ6?2R|V1IokUzLgP zjl_XtuE{C05e=EXuC-)j-vz&ldkAtK)V>EA7S)=X^2tgs|nBCXh;ns=SXV-*(B@Y-Bgl5r|n9zQ2(fN1gbMn(EA% z44`YU17Zn-_8#zB4d)#6c2xo$f&w5?Oc)8Dqix7Y1|puoNk8C%zxO+4Ou>ot0USWAly?%mM?H(0mBnCIC9FM+F&}HA44l(h4R*r1FJ*OUV3gjIo;3dON+V%d^y{pny9lZS(4Dlm>Bzybj& zk1FInbgi8QY6=n=fr84#E%2F4ypRg`0Nv}t)iSh!AuxJv3qzG!0jIfCK?xz#NC8G8 zEOK%85WxXB5Jq2K{eeYHhb~xe9~Qs_`X-NY(>-|Gg@}d?uEkh~sTMl)_WDZg)6ur= z$hZpPi}1=y&nG(z^Yd2(1d8|1rI4^XFf-zFU3mcXmPUxwgb2s09JJs)1UX`inU?eO z^942|HxW4lQiQ5dJl4JghfU#fK<74^yn#BB*-XoY4}1?ySH6ujOpm7kwbK9@s|;Ryg;J5ZTXw(7zlmr{Ob$5R!Ul#Tk^49= zf8W(Gs*lh~3_fEKBQ`^*?qS4e$5JqQ$3{_rG$VE*|sy+_3j?;Mg2Xiz66BQ#P^M$gWwbm0@0 z2~`u*)&4Jou4Q?bYwo#td#~KV;6-h26DQ}@Oh5mcKf#o#_$C{teA#QXb(#IcEVpGa z=RgU`VBaCK^T*zJZ`tkc{GexY54yFe!2RG=etrN5Wl)-$k<6#f%kud)k8KG(Kh+7x zFXMOq5Bdm+mnmb{WnwIPMq4~5{oJrJn;2P&n-1&)KloN-w~s z0LDif43t5?DFi~vW+Wj1y+IeON=^&`e6dgL#=)wM#j1>Uo3*F-!2vk2__J_(i8*SU zT==lRh~vWoETNyj30LL63migupUZ%04a#0ZQUG2{x%>BD?-Ge!kaq?Czk)+k45Dtz zK!!lf9UjN`kQ;R$6@*96a+`uk6Pp4@Y;1$X&6{gLd_bHGGF**|Mg(9$Bb8}9Zr`9!NeNvIEy&oJ`WhJ*k{L&f)SrHzAh76?Fd@T>fR4NykV zZ4(GMm*r6~@``1g`G>s1q~ZZp{q>{YfddjTzQGC(N=Rb`S7|03l3yF~{ zcE8FUXoz6XJ!?OSj1W+>01@kR?}vB7TJGPQVCx!RTm%m}y|X=}$~0@{ml2GPOGK|2 zx7W(W%5s%+N;g8SrnpTt@5m=ndGV*d;2sp;}bw@3nxFxV2^G;3eSBb8sejO!} zTJJ-5XNhE9qfZ#VJpvci_Wf{wkGE2ImWN%B?Tb|g22~mlcD~BL)T2j6mT-vp=``2; z$f{q-&Z}qFg~Tc($w%m!t^u{M1E=R50w=%Q=7Ke7;sSveV7@S`(ziCnAOzXUb`vf5 zwlQX$iyyX|fu9RgM8pOMP$T z#dZ|EE&K7qdt1_ULXL=}UbQ=~6fflZu_0vNd+)pIk8gvkH3n-d70n_D+%YI8l&d^k zsW>z{v#Wq?jYWYwtjx8$_+cQ@M1=#B;76vTZS;6B_`L^eMC0*z2Bd6P!_zV-JD>)Of3 zA<<V>h{%E1 zt40z0ORGIGvgKR zPTzd}L?Y?bxf7}JEY`T*sOCOX>3iR&$}kOu@t)aGT(4@-DY89@kB6_!t{9DJ2a+@0 z6`7s=^yUD#1ti|D%*x6_T@^q<5J?iA+pAd8CZXZ!>i@pQV(#X{Ru`D%>L%Hh&?zF*?mq zbuNff@^xT2CFZXBw@>b;(thPe_Hyfsy|PGC=o=K@l_Mc(;Ty&#SYDFhall%3PruBc zitCM;QdP$_Z*L3VaC4h;V&x_Fdg?BJJBnnUMmU`F~{-~%17N;SkCmpYHsE+g~cf#%5ZCfjtWjW1O%sG{EHMX$j=RmPGoY& zM~Y zFr7Y~Xug?4x5B0s?A zL`nEY+TRBs)DlinZUrBbW3+q<@Y3P*HGpg9K+vZth^!qbT^im$mn(jyHC(v3#z3kW zz*UhJUrZ=_6NNP zLHj!pf&vPZvp*3Uac^CKZKPKSE{@L!s?(FJ4vi6o9S5S`F#sE#Er3>Wr(1UH*QTRq z<4+9e*7_z3zzXZx*OpEZtqm!TA3oH!y%SlPjKs&sKY6;(k>_zrI1lBN5{LS9Z`!;Y zm5%q;`+=E}42@uWJMM_OW|aUF0sW@C#L^MxiRMVXVbWD4P~CuV=py&G^qx^lU8OdW z=(YY2>@-`etHg|O)~E4|th}RtV0&!&@F$7diR5HAH17Zq@+GqR5%eIZPMwOn-`)Vn zD--5az+Dpjk9FLK22|AFpTUfWs)ZSOw4u zpud^I?u5%p1*DO(2(f4fDg`gdk2{9B8j~$Oz&Fpg@ zllOpn%m-1i9VGb_S&L!@xgW#Xsnyb0yNTti;S*nsWEAZi<5aL~trKL=ePUQ_;|0ws zqFWO}i{(ep2|nDtAmU7KxG%Z#>C*<43`R`fMo*$~BWPz7u*zNO5eUx8GrmS|%@V}q zQ6=ov6r~-`{y1~#hl9Axwrceit?W3!b{c~70gR-!@O^OHiGPMS*V-|hE9!P^@LpB| z&juUw#&%IxO5E}{VY_;dvf9tQ`1{&*q_f>Xd$u&=In)jGCeppgmE5V;MTnlh(Y!U?X4wnFrVh?-Z^%Ee>P??!&6vi!$DUf`?*V z+!hLZM!TRKRYuJg==}_f@Ffob>@@yz|LW&h!epCJy@zJpX;2wr;Kct5fY};=6S0#Q z!c!ngz!Wlq$a6N~Po%M69YDj?HUX~%;i6ETB477>)>HJu#i+0-8V01G9aONO$)L#< zfzUp!qBFiE_kvImSbCvX0E9Ze0B^`3?W^`i!AWTlNs98ZV9mUOIvA*WIl!Z3E*ZXY z(?8-ehgxydVag*NYZ${*>vy{e3t9ra7AvHL8##H1d3 zwOD;c=Ui^faH|@4u7one0QUXpyHqFmm##RCj&LRmRoU%jmWifter!|w*%XFywQh?V zJJZ&gSm?-$ljs=}@YtGqxp)hAw&Y6Lij|`qGkkz%sr+b4n){%y>3rFXlnv0=ej zY(n0b=0I0+LL}(U`JCrMC5M6kF$r~q$dc1EBZgo$84MD>_sU+Mm)@%G{|`tY~`BGuNM~jND_p?3t(|I()4L5CAb45Z|e5 zA%!x^#$1&H()-o$L?PFkFh;n9aa+antNINxL29tebr=j?e?S7EPx68?-0mQ z{(A2U%nPs&=I@Gfa-O{eD~w4h4B0!$IR3rz0W1l{3<6!cB96`t8A~I=HC%Hn^78Us z8441RkPbPw`QRLd5(Q)MtAO4j`E1NJLmwoc1?cuN;C4ucXf;%U0AQ4vYdoid*HnSI z4@61|m>lNg**}5lS-!7YHdqyKxlXAv;0Eri?tP)*-eGUYi^NplsbzeVStHe%x7c zIUXXN^TQ(RoubTa_tqhtLViT5gml;Z{6g1GGI+N({?WHvXT2g^pa(z~cxF74qZdwk z`}KEMH>!@<1tBeA<$zMV?WEekt((!>gX1a%CEDCZ?eX95>s)ufQ*1pL1{&If-hwxK z?Z!ZM3<_7I9uCJr2$IyH%_Ws=1HS2i<92p(v`YCRn6^=-&Nqgq zn@QU&$V?}KLnGx$!gB|hFmCT`!_K^8U9nvRQ&X_9#am5n;9-FyCVGpjj!=am+gv+7 z(!;#7eI}G;ZBSs05mRbkZqbrG1Ekd3Spq6W1C5oOT79J1Q;Hoci`bO+JI84{ay*_i z9@AP(f4>vbaNj!l+CszJYIo|m`-QtbOV3yX@++k#9F?d|+ze3J{h`c({niHmjB=A`=!%hj`_m|O&!VljLE1;Xddp*z{ zy-pDtygCxlR$|7pFjy&V(UaXC%b}|Vq?1$h$8#=K`GQm}Q$Vvil>mmgo)TpDFED1m zABDBa1PL~vk&uH7>#rbxK=e$6v%=a3%|RF=Txb@c&p-kRQ1##-ZR-n!=fJW?f(O`Q z7Vn~l!B-yizPFG=70^-?z;?@NWVLVj60B&ZKuLo}!576AJFlHG(7<5l~e{Rj*ZR4KY^AJGpo>%ea}^U_P7*ihoXvZ92>wv8^A5 z5Cy8r9>LWGhMNw{29LOnzhtaXvEhv7iaRqfN}x)(xc|_BnGDkL{1wT@Cr@80eMK^U z-qBdnaXnZ@vNo|)73`IEphD)}+e}l_9+kSuoGq9J8|`ofLuUP)JkH}o6I;|L%+T{Z z#GQ(?dTu(iNG3b%qDeaF8;d86C8n!n-J+nXU$~g7&6$U>{<+_rVzbm4bJ(Wk%>2YNKaaZC8OYITc4umyIP zIl%gMwv=zH4=wqg9#3=hqri`j42AscB8P)C*90H7FAu5N-M%G`h}86YW8(pV@0|8$ z>SaHstsEbXmR$j?^cMrO&*Mq%zme5T{v|!)JnQe&w-*`y^nU+;hVlEP|BCPaUu~lQ zH37hX@3TfMo{6TM(0m7JhnH1{N^pt&4*~Zfh@s2n=m70Aa#-)yZiH=5_wJXu8z@72{VG zpfWsNTGw;m=Vqj#L7*OF1#Qu{uUn|W$kc6O`NuuH(E7UEPLN+wM;RY`;YuXaVW2Pa z7Zsu4N};`6U#W0bliv8Zc|U&mT9y#?Y4>&@PcmTG2Yj(sHiffw`irMwe&?{Q_$Vnk zDU8EiP9p_$9*2#r>%+xyB zZRkK(in+cD`^1|HLGGCkPtU}c)1;{M%g^pi?3_A5Z&Z{JoBveIt2icR=TwM0|LH+; zzpptE(P{jFlTOfi@Zt8^r@CsscZ?Efg4JT{%RI&wkMtIYeFvRXU$IGX5S?h!m?Y70 z%;%lu-iGk<=XG!>%hZeD?0&vF^#H0$Y^rpYbg?d*62E0UW#Vlcn^&5JrnuMdZ-51E zSB#^#CIGXK_drXPh!t6T7rKZZx7xWlh+)0w9Z<)&f5haqZn``HNU2Oii{BEy-a6#w zh2~_OEu3tc70-q|((%xja&~VUN_sJ28uVVjKI}rR zdiRSbo#<%R*H^z!H0s?`GHwain%r7g8+-;?oGy>&7wD8TrTA{&x-RybMlf0+9(*<@`q=A$7hjtlR>OF9wC@deMOVmnUd1N_iV7% z(_FmPBmtr`TMR^q~JBBk%R$5*&WA3NC8)BOEjhmatT@J#N~ zu+oPCt1_pZDvG!*NkMMm`f^jN+qcE<&9z3(g4JB+&?#^1OrSLU|^`$IJt)?9=c`5bi>sDY;6vyQbdVo3(`+J3l|a5$S5g$C#^p8jX|I7`Iam3m2H;d*W$+qA}AFdR^XPYl{PaiK}?TiDKb(gN#?0^d_> z?tl=BNg@vh&vT4M$*(~YhOla_PS2@ z{peBc{bpk(`yvn@s9C)ZW+zJ-PZAQ+RzS=@O&k(MW4AzBVJX8}*#d*GZWGYiPvVE# z`BKc7T5t-}F;mh`xAM6?^rLgk+#V7qQYhID$*}wM?}3`q;qjl`paKSckP2 zxkF1sm}DQ%9J?VZz;_ImiSG{5($eyIUwX`6v3{3U?p*R4J=*@S_pfFcOqiPw&v>&f z>1g=)%F}>X2%rAiRlHWY)F*L+X-uL+O>@d+SnYJb{FTy*@}+_bZDj*VR4l9FD0 zm98Z&c18arRntP+v7Hv&;9ET^g3bO2uli6uiBSZ^ z_y1z=Ed#3D+Vx+TF2w>YR9X~78cAst5hf`uEz&VTxwV9T^FQ_BtWQfpCUZXH8PB-y`?`J?7j*ie_7Q_JJKO$NUG;G$IhBu^ z4A3c0`ab8*Fz)?fnYM|Xw*@4MxwciNP9zvU(Z-~Nvno+vn>Rl2=UZL_ljnS|!vl~n z!+_>%+qHDTy4GvuGd)F-FnL+6S$9ja-%inyn;*y%-4murFBo!T8{{AIq0Z0=Vtx?4 zPYubjvMR6DFH-A{V|=K7DmSwd_dV$=Po^?EJ3MUKni8}yRFPcQKAEcd((9rZS9t)} z7fG&p zX3S;zsw&yO>(AkvZESP*@^X*CQ6^2dt!=h%G{S|(&IERdEADr6=p4Au7~L}Zp!kVx zFYxncl|+I47|!4$I$aA74PFG+C}%yq5AKLJJ|C9vL@)G~*?1mv-F-8N9lEJK@Zw{9 zu5GKU8~c;@y$*BcZ=N(Id_AjzG6(F_CbIK#+Ifd$l+$0#14dAaLMW zSJkHtb4C}95(DqJ z`X7(dj?ib7cjyOpT!H2~!8&=fr`;?sS6Y_FkfU(_$4I>u`F4+xR3=qc&{n*v4=V81 zxv}l}foJn4sCN3L+kO`a0;1XmZxL{%@bYCeE|x9w~f z3sNQ^rZ!4}1GoXo4}_MY5?s_g0G*-dQi0`A7sO9Q7tVu$0p(Sr;5h*DcP3!YqlO$v z?K)s7ppZrqr!kqQK$$_HdlZ-n7q%1PGGLwkqN(uPrU1O{EE{Ib3ZTadFx3VgvMiv# zWx*(^1P6Q>I~)p70I7=xFjJLaoUjV9eCSLC53(*0^5sIK-VIQ1CoC#}ppLMyh>`iY z$}9BMZHOHNyOKE`PJ%FkK$((?z?*}gijbPA@a-sbLtpv=0_B4QFazQ)Qxp;^X6Co zcT#-)m?>IJX$P8q`mQY#I0k>(X<%)EwC9(0yBW0Glt3>)L7oYXyiUyXGlAm0Ep+XQ z!M<P64NsUbrPntmkxM)H<1O;^dgMKdsn(p$9*N^xCZN z6O0~N{;@L*Dho5lhvtw5!JOcp#$UK@-z%cdSnQ;0(iWZgat-(46aCt#L-YVZiZ&M zx51Cj+yAkupn!EGLNaTlR)I0Lf$Ob=I2>3Nb!#p@-Y20 z`TF`Sx+WG1;ofNu+)LY#-}|1)6xqhBnI08?CcM~e4zr@u-cb83);@rQn}xA zjC|qR?R|Co39b`pD#MT{0eWZC{&K=M!_OImVjofC5aSej*@$6=vt1s;z#>!xtacgl z8+7Y<44CggY-1YOB+@}d_mwG{UH1=|0{%*dfcoR-%>Mq4r9mC8YSjUjV6N5ZeRR{j z60iPV0tG8nq4B^ANrAZ#B+(t5-%!ROD*<}&0`1)gWT=_a#2%|Qeephf=v9|>iW8ZmQz3T%GzwC!On~A@+jJ!q}lJv|mdF9SnXumq(UnZE|Sx0;xg7{*Q^s2N;~V z&x`X(%adF|Ms)#qNGZ)p&QS&au*L-lq~(|f)Fr1(5vZ4s_at*Vc7CM`25WEa7Q5&% z1czK05I`8Ywc++uIL;DMZx=;GM0R@o5C^1mXS~PjfY-w7Z$IkwUBvd}%EO!)?m9Do z;RFKA{FN)8CK$RhD5^k z42IvDI}9Q1NPTUVE&Jm~<;C*73`$wAJflhbz5MN8*l)9}4Vm%S*lOyEc}j|NTHUR! zKZmnv*m!F%uzuu`Zd~)on%;S%M~l&NSsmA{71bs7<*T{pQ&c$0LQ9LKwj^Y7NmU%p z_Il?WGc1DT45Ys0q&)1`S|8ejSz`*LXD_gP*t^ZB>Y9tV34f6NM7{2w5_r{Q5Hr{w|KDU_W9 zGc;++9C38=0J~2R2)jU5991loAoHKgOKtv1lKSoDdsgjRS;IP~RY0WX?6Kr`(xp|l zdFJsaC&B}cY3yZC*fk$>i}~^6N|{Pprh567&&#V-X`dRdy>;Ktec6`CX@xm}&_y#77uP_v~cAz9DS+7ms(kYFN+u9FzX1va`{wBKp)%4S)2w?oC;{7X> zk1l$d4rT}DRXWij&Fo$3ovN9(jO6t#s_u0+&%CMNPUo^Max$S2Cb#~Ithl(V=#Kmi z?nr>oe@c5DML&qpiuSegCwSQi|n(%Lh| zzRjd_>-F8pc^hA@k*})gvDarF%4Uf5bFsgam#QW$<`At!L7S;>dr$WAccw8VCU^Qjl2S!Ewtbv-4Y}{zeCscq=;iuYkK<4vUbR(Q9LQVBqMQ8YRw0%dr{(#6T2B z1U9Kz2x9_}ai4S@6Xesh!*M{mjt#j1VW&p=I7G=nzEn_9!*7-a!YD;_9-LDgyc?uX zR+R`|c~~Hg=Y#VKJeJvaZ^N^&`P48j4^cM)SMz*npNJw_3@->vpOEY7#uj@lW=3dfwv7?z0(8Ud_K+v` z`yAogyyf64Uw~w`)@HpjURTZK8c5#*~uD_}QIJZf} zqQe-VUQMQ2Vv$K0b=o1!1n+UMk5pl_dTljuj|1kBFVGpot5HO4uL*+GkVkuPV!uE^ z!3@j1s7R3UGgkk#lr%RF#+DgY*~8&qZh#yw|A4sv<9 z<;kLw5-A*VWjZa78F_Kj0L5F_OY&9)WgIdRu8-R^)Ddi$cxVy-bY&7OwnBvat`Gm12WR1LJv}`Pw{ARuJ61J+j&GzhTG;6> zZcMH4wb+O(28>Lrx)q+-hXOx&Tssne>X@}t&yM-=R+NWwh=RaNPE(j=nej1&^TyYo zqbIteZ&{S`I6Ea~=&{B>&rtC-i67a@G3i|QpAq`Gk(`;+aFA`krQ@TzZsDfqVNs&< zWskM)mhDuZ-MjcwmdE^8{9U)aCo5xOZVwc?YxUTC6`_t2G!ZmBCB&0qqz)e24;UFl2 zXTrgT%PdG2l+zaCZ;sM40s<7!5>;FsQZoHeyUPl@hcy)Xk&pMfmK2}+Xd#|t!(TIniaF^Y%kW##*4e=O>`Sr zTidtJvS^fb6e{ANhBuxvYVLX^b!k7{Nu=okII`E~b9?GT{dMKf>zNrP^I-chxcj~o zNub7494IZav4tYYNi{v;7y97dTa;|#6kVV+zkD=zrIs#lYvz)eSdE|+rSBYT{X`gz zcIfj+?j1u+RSH>O*F=p}4<{vM_cQbs=58Xr3M z*^bnayQKgl{OjQereg_R_-JEMI3JW4AWjURpro`L{&Ezx3c&BhN%vYp0cwFfXuEM+ z4r-$A0dyLWAs-!6Vb0ltc;lN(y2vepe10e^gh5bnH}o-?kaI3d9|ILwDu6Xn6dY18 zLa-Z<4bosuK+~g#6Kex-2UN}=J38|D!ooxOQLxt{`h(kQt9X(DQcLAo4rzlit`$(c zVB=&3c^N<&6wogO(QdfiBE(WSeE2Xj6Qk%%h-y=Y83j_2BWgLc*H99ndVzbMd*k`j ze=tu2NTe>A>|7iTw*`45msR6y0I;?n=(Rr>BVkhQ1bnI4*2vMB{`@>fmCV$XsT+G` z4!#Kw3Aar3!@_zEtFn!ZMOAqt`Rwj0mQ%7}>H}-ER@}p)ek^Adm_z(3Q)xtuVyK0X z3ow+0me+*RCuGP{G>=+pl{hQ!i|?XI*embL2J>fFl2oNE^BkLQ^)Ji|#BMJMAB9vlo|3*kHtO>V7olhOKUr=ZQ0Mqkqrxh~Pa_)m}7tx`CUfl@?LZiV6 zs3P)oD~};R#cVgZN61eoouPTn{p82(ajhDWk0I2l+E%>?`^S~hk3q1@tj!<95>akyfaesc9WRidj|a{d z-aY=hbNpIg>VuECs>gDKo2-?+h+_PYvHh`T?9$cC!0GdR%Juck&L+jxLr?Wj`Q{_J z(dpo1ea_di2G#?#;nbj@(|h)=EWUI#KbcVv}SUd5c6rrsSxJ= zI9i>1t4EN^2^cB`pkUILKCLsMKWdFL*Yx*0hxTp&KTdwq%esJHp2Bt!4F~2ewugaM zstN-Hoj?EgKL7pyqAKnGqiVNL2R$W=g{f&GKBJ2buoP%+J@_j0q`7}2snIWqkW>8I ztD-a=lgxCkoJI}@m0oKz(*-}5tCi1gED*k~r>YP+<8@o?nM={KvoMulz+!Rq;;_KC zjsE5hivRfZ-u;a#d70$IU*N3ljDRis(|?bY{-&Bs`I}@)UM-aWPv3v&k@K!;&I57}bp36h9D z&vjCjeeR$MDT|EP2;=`f$H-Fj{Cz{Z*C($^&sr!&;6>S8E*^^t5V-TYD z(dq>HcWP?O!uEz$RW=lc7dHaZZ<8J_qI9tR^RN2P*L=JE&u^K7{WRUX*Hr(*b@2DM z`}dFWZ`jm#_4OTpVdu63J2#2wx1!_!)6ez4dZE9s_`pE?Ur4e^M(iQ{hsUn3{%yhi zPk-)zc_*nP{RJ=^67~5JyWR0xv8lf;xW9i=e|-Z_DVVQbz00JM-U5X8Sg`M)RAA87x%pRWIH1`Z z+uZ*8cCQdt5^y6QSG4gf?+Q=K+l+9as7oSu=< z32DMdfBzkoN)-qDC0$uMS^o|LLW~d4{^3j`fw?dZAwMcW`WOd#2uhTKUk3#WGqbRC zmN;0xeEBj_ogPM$sA_X{Ek(2`D7;L7350$MjIYzcI)tJ$)UxINy2*__Lcbp5B>aje zZ+{Em+$K}ns%;-6kMu|~?xj4!y!oVQ11E!g@7`F1w$6D4>#0kN&>d=Xl>+0$XW7oY~zY2?05(lb+jZb!9ux7(R_gOa8jw*l9qr2|3 zGhkOWZ!j|}MGE9A8cV$HACNNc>?KShVyHQ_w9y_lP%=L0AnjgWW4qVO4H;AinDgR> zR3I71GunUybG{gOTiOB}f(>*{@aGojI;heUL=Dx#ER30!@WOLn18n+tX+Fn`q#ZG?O~8m;{J zaa?(H$%DMvE>6R#`v!X0*y?eujxRayO@nO}Zqk!i(O;kGZ#s@$DoffD_=Du^`DoL~ zi&2@T8hNi7FEL%|KI~**sh7A^`*2J9c=w3VWZv4Q_i49vjf{F@i9Tyw!f?f=apy3T zTDeD3fU8h{nN7H>FI8(^TCid!RwlearC`A%%kz|>M;i4+qw5bI9k)F~LPsNG9&rod z?X`tiQ37JYC;PmdlPU6}*YCfp-QCT7^2~=yIQ`Qn#gl|ZiBnU?B(_!{lC|s1ai9)l z(t*d%HgOV=es43X}DTutaK;bD>w7!dJ?O=7!Ri z`U=KJW>TwVO1U1j^jX_lbJ#iZk=5vS4c}_y&!s-hCuis)k7<5$o_@90EQ{ghO?jAR z$ir@#v&)%%9_}3LTu6EX*@`6ErcfW8@|)sl14A?uGgn84E_l9&Jd!gsc*6Nl=j1Ik zbCZdzU&AO&w;6P#JDnORaZwc(_cfj<7&6}L72#g=j2_qg5cB35H+4kKbRVP}mxpkv z;Kt-rbIG`Ww!ua@w&QY8iv}Z4vHq|BpCa zrYB)IkLuTflI0bl)T22SofCv++-_aO_)O2bRFy0cd`KmPYM0}T>^-qT>iHJ&uK6bZ zG^a=y_5Uz#Y}nAk;3Qf?j`jL~IXEi{E9Vpb+6A2GuJYi5-+_~-2X9#fY`+jz&c%7pPwk6siBhOGHa1RyjO`?cA(_31#}L|r z0SuAj4<48V&|_fJlgpD`b_Rv9;~0PF9T5JFaqO%ww9Y6qb&OY8s27hpD0=Ggq$+2{ zqPK4yn3}4Pm>Pv`zYD^1U+uFvS|&3lyXkFQaYdJ6VnYBX)E&|s%T^{1Mc$%{sf(6t zGxARkW0zd{?6mtxMMcH*;LBU*pW{@^HZmzFb5E2hcoB z(6UOtq|+u)De^usF%bc^1jx7@V-D~7O#4=eq&gfFyek*sJKJ6P4zHe33@j_Q=6dCE zH2@a~_sztPgSMqa+i89+VVp^|H9(HubTLZn{=4s7BO~WmX9rJh>l%IdBXdY9bip)Q z)n>axVSI#WJ4H{7P{WnkV6d_24p~`;j_%oy_($__@ry`A<-iPgN$`H+s?OHrAfHN{i2P*Vkgl>LMO#%^uDvLdldyKUmO9HVl z#WjX$!qV>#%U9hmd>i##aW9X5<*{Uy3iAfWsgYEgYmmX(`w^S-Snpsfu5YrI2BY9B zd_)moxOIRA%vzOT96kS=dkig96$%zdggNE4eDI^L-6@(> z{t};{@5t}6x0yBW$!fN$aG(V83)@lI-0DmTvd1y7A!X<9CJP>B9m4nn?@suVg;_>3 z32Z$|6^+k_)aO*P3TFbXx{T7~reu{gFV17WjqbUMdv6Za;Bm?cJKdG?)RH)Ri-CX{ z=i`?(yw&sCXw@YY3W9Y4OI%LCX;snak3Xc(gl@&0=1h`Y3o}9F;L*2i4H<Vk~3#w z;If;6@NL?n0#Zur!+C$jA;MfPF%*ywx)=oz2rJ0QQK{T;+OGg2YCXUn;kd`BBfNsy zg`+B;CWx+}`~{uo1uCjcaNpqJq?(8gAuiAd8j6=%_pDv)G4-!M$dR9hE29mM1W3B8rB64z`3B~ zNQ7hbG^jpN3^SO@(+~j|y0)f6qOL)xvl$$SdZCm`wfaV-3g6q2D0K;yb4X}Y zP~cEsidN392dE7gb{>FG0G7lwG?TX>uMbi$tuwSrRM2Eh%^eO~$QyR<{CQ=74MR5c z1K8V8I5iM0GeJ3M$hZ(O(^2QYy;XB`rUjp4+L;;O5Zt!7e~LX{>JpoTBw6v5vVzM$ ziNB3DVp!Rjn3#GlJ&_32o(w&vUSXK%9(LdsM)S;l%cPukx@ev4vdE(=!8Q)x;Mx2} zs%8GQc*VbB*aNi-FmfY-=TPgDuDLro9V74R=A!++ffbR+0 z!Na)KnnmeeGnaY#$v{SO0qLBNk9!mb$|pOX7wC1-j??a;A+{+s@FbCN z8er8K{f-Wtn!FDy$6T{O{n9srw2%@{ucBbbv1KFK=(hYOkGlGL$-G%^_PZ}?v2cZRbg&d8yF)3?;QGO$JDa z8K^?D#mnFGnSTebMu9BC-a-=|aW*h*pxr>&B0hfp8E@V}i9A_|9$2fRk2Z$v0hk|$ zLU1!PrWm5kAt{j#LniF%7&He*;HRPAp({_uWgNq4^sV-%dlj>=ur@5X4!F`V|JR3+ zfgs(=CX7ANLu=6EzzxdyG~giNaL#%#OnZweUxWG!APZqu&R<4?(utst1jD`}`w!vo z0-jfjc=tmMN(LF?kX4ldcZGsp^*adfU4UhQNVT9UO$9B|_OK^)8?y~ee$e0H9#X1FEX($`#UUdu)v zZ}Fn5Fj>s3P%g(MKaPtZcbIz(wQ(A8jYn#j-ApK>`IYXO9@^8F zW@o&E6U zTPr};N`a|-q>#=1JIP1J)l=RJM+&YzvwTCt4QCqn9j9l1H2=;3huX>K%{Mls-=8eL z*d~5nSgs(#ez89(QiGR4m{qnrQ%nA7mM?QgI@jED-*J`xflYy@$yCrO6b~1zzQxZc zg#)I0#KNxBM#WP3_R}rLTRxTVpp+8qC4QDX1T-XcYh!Q#L>_eI+;bj*t3U$^1&jhj ziXzO}4C>0D&7#=O41tEB8HApre;hrfQ0^i-0nI3AR;6haSU5w>?fCe3B76l_l?)Qx ze$cHP8y^p(+qw7UJMDZ;72~YTqU>PQ!(vj+Y*hLpd4CrGO)&fqgVp|?5!x0= zRRfCWX~4;-7u(`t0K|$ZwiJ}USvqO8Q;cs|u2J3Ct;`ZvquFx8eSF`XvOm=o0AZO`0F z?AGc<^qhFG`|s*iSGnv9Z)bL=$Gt7?V#y3C{M~I$^-*zyl5LLaB!88HyYIU<-CqN1 z@|jUm5VHLm&+-Ls>?5I`dpFaX?a@OO^8_q*)SbmM45cQ_Y%NWkxNgu zDEJc8hFtOU8a#}`G}_V9p%!tqHc{$%v#E-cnW@_GNpK(pGaU)GcQbgRXtjwNIW(T{J$Sy+UzPun=DWIq0q(s;@symLdnv?4CEy(-Pl-b z+wJ6m;q5##{7c78@5N3BaR-eECVv>4%b@tV_QacJ)Yr2P3!bri60J!`2R(@rMyLp*F0W z?}WEDkI-GeZu;ZLFr!BK-PEl+&*;~nzSD=cm0Wp9rYW}>6Ffco3d}nqEGxQNMG3g{ z2IUxs`{d$j(Oin9vka+njW%22=>ptHEAG{*v zG^nP)v<(Oh^Pj(v5)^f+K!{}4r%wUN}*tpJYyH3{HbXLfeu~xC8c3RM0CYuO( z)__2P$(&KUJ{F4|6Z+Kd@K_!W{WRO zJz^Ml?@g{A16WX`BqdQXVB)T+1r$agf=R1QetDVjlcOPk!GAggaoy1w`S~%zHn2Ek z96fc>7`nphkbVl{T-!<6Z5>wq#D-x1!5Da;=@+l~C+ifiHuo*V6JG(&PkFEnLd#GN z%u=Wok2bxOl$1H5o+;rS{Zb&ztqo6EB@rE!umb#K8Q+KKFO`;Q;?09IgV$!Wt)lNo z)W|JVubGjpEnFEmL9AM!s?+_Jts=0YQ5>z0SM9;Ew}(qlrwb0Q?ydm0${p#Ej8#yWFirB}*H* zW6a>_(F=YW$PnMG#qR9vh&<80C;iRI#sN$S*#&Gda@SoWOq~I6NISi~W;W9&Fn#K6 zbCN5=rAw;uHLv@}*XP*@XNJVVgRW2`8wq*KE~rC3D(KEJTwrN7CUC@dzTQBnuiXDw zS-Aa;(nCWKzB(My?^TlAM^dVqmcP9)%Qj{+_WtJGW7gaR8fGk0s^V3V*gX9MF}M7Q zF+EGYs#q!KvX7o%;Y)yyG##divADkNOUy~JxwVx~&iEiUts51z(@hr*Ix?b4>?2BJ zdo-F2opr3kpl8cHG7q5J1Lw_&_chcFr6Dc~=UOs6=qg7)O9m`Gcv=x{F-Yxm!MCQj zR~=gx-Tw2C2Uy{h1JhKv?Ia96IT|lBF_}5#nWibyAI_azoKKzFnxY9%7uqQeI{5go z#K8ih$XvHx&(#Qx|7@998uKb9X&)I1HtP$LS^mivH<>f--XxC*- zm{mQhtl&1~ngzv-I1UKU1GWXB{W}e#Pe2(@8z~UH-&asq)^dSKg}Kuf3Ya{T_WOv2 zu2umUJQR&Ce6VW+4y&z?dUbUR{U^F(pA)o2UZSLo}KIvyi=b_H3;DB9U|vmCx>z{__Jai-Q$Jz2AEc?Ow|zE-fW2XXiB;jYu>G$NR?v z9F}%t{at_>)Iqmy@ndino`&fW6cm(;Y}DIJN1ri&5uzJ~F03H>5KKoiRIsIrG8=r( zN;stw*)rR%FBMi4hEz4f{_%Tfp7Oy|l3fq>+pbcl6*zbhKXB+=6seXXiWBo{|D)T7Ub z_h5+Uywb$m-gp4Du40hOWnyCDG7t=+tFgNqHFpXZG4p!yjcu|{xvl(ssYcyE*$b77 zfk$I0@5#~0wpPHhzIh{rjK?&+IY~MYI;;3fj#CjezVx6KrS`s@KDu9y0+*o=yZcG6 zspydcoBm|;QKpeWq49=jk>37Ro@p}sCf?z6=~P%d=hJJo{OT3!viz6x z_A07b?{a9TcQalGm<*W$vSK`>!hZBv-2|9WD{1m+x+QFz(oMv`5$%>-REBk8oXx9ung$6nszGugKxU<2h#oltOIJYA{?y$cHvl^z@I1i$v{H zDUL2uQ8qhBGom!%4jG%C#v}XSmlr3z_T^N}VWfrn8vtrel%53=s-~tUj5qn+);d*9 z&PgE+C6LjRcgsBv+!2WmHbLn;O{jzDFwj&`grt`GRlv-YZD?<6Q2?4!6~5FC5?kA; z778#g4tcW5%<0FV+WUAw?jm;oxzBBXvtQd&Ygh*dqbS4ec#PRInAC@X3z9hdPaNs9+AEZ434k6a!Gm; zFDL>YolAF_3tmU$iBofGCxqptqwcSefr2D29%l>lK(`%b1z(LO^_I(r^$AWr=gM%g z<7QTUK=AFwRK`p33*-ks%tjl&qJt_BD+;~W2g6szq%Y`xb68!{o!@pb3Y+`{=G(pODSDW<1j47= zQN6==UMyrq{uSxyq|H4TnM5W!th_6_I_X8{?sO=9t*L(w1n4L=wLtQZI;P*mB75-Q2gK1D85!;U ztf5Ay9#pG=d^QaxTv-bx81-@3a$9W?K$YGn?cV>9fItE z2^|BAh7q&?5apu^BW{S}+2^WU{od1qjIJoe9x~O^AyNW5n#RyFL2Pf-4FMTIf;z+u z3Bur~3(kMaFro+FvJ|R7z)t&{n!*sBY zJ4X2pC4{!!e|I?+eo7idYC%aH0DdXxkU3WK5|YjL4%-!pHh-+hlqS+ z!`8RH+6I%aE*ytG5CaGj$kz8Sd+$AZv9Det=}EMp{L(9sHxVpFfXZJl#wyFbdWD(g_xtP=ge_+GmC%EM{@OyyPG z8n5Mw%b95f@%rI43W=J_Y0|?9G^G=Z$GddOyWiy#^L4@*?Z8IJ_DM%>Gu0Cz3Y@Lb zJGj)Uio&hwmdW@yBNB-GO#SfT%u$G0=_{zzH`2L1DsrH}!*lusn6>|S&TYSX06vyf zuRuWqOUo=|z;}FF0c}~7#OO^W@VP^G!g#WoV0P4ZpI)4v-pN@d=y`nnm%E~<4&k-z ze*gzHZi|s*M(c`X*#t2dLsp%dizNiJPVMm=;`!RR)D#U5lz$;QS8(R;>libM)rxZ!Ak9)d~`P>riX z>@C$+5fmXE(0*MVbXhck1&l<((9}yjasIk7+!w_C)ldMT4-xQ8Ch$^l#{B@E(|EY= zW{W1_kTI3=}nr+Jl3b&!Ejfxk-~Q%uTBva#B=}<#FIdZP(hL3yldla0y_H ztdbAY{;ki$c~=58BUT9^_xNm@hC}??R@iyxq<1;>7xPsEM~Jf7fv0=VtjUjQ+hFnN zyh!CwdOB~dsJYng%6&|?C;@?Z+4eU1$)%Ons+GwxkGT|PW@hD%jF8gk#gxpfa@KAD zTAVFbOHs%uE-qj7a`oM+9U4-Fy4XO^EDd2v?Z_?5Hrtp=x3-Sw&33et?;V?J`FsTR zGQQlcgff?<;Bi$)s+~sJvO00#jbYsxb{j5e2&Q(YW~jQXcYeI)X**#XW8wL-zP?vo z%&Qo0veM-o+n2^Tp?%fBN!-Y2&BG z@I3T>=kmD&0jlGNuC!l$T$pPZ^ztRrGzu*&g^F`CtXPOw3p>*Y1}M50pZS&^QkYvw zH1e~9s_9mDXr7I2gE-p@&+LNX$nZvv2UV}$yzKXOTD|L-;z92+QunZJ@V&{!{s$Z^ ztWib*joI1K0@63Ah--8wtmNuSU7c=JMx?C0D@!68#^?Q<-%cW;sTub9i~KJBuY$Kj z_y2R*-JyS0P5xae`!Do;GsoYDw^W||v)1YV5d|)M%2zJM{#9i4U%!FBs^I>ciq?O; zw@<6g|L2sk|I;h+FBF`Yr6~_vpM}%KKYpD<>61`?5E6~D~gbY*Si|afz-f8 zxB~RYQo3NiH%dgDO@r0F=thwpZIt?jW-EhgtL!Vav=_UU{`#khd ztL&dYv-ki1B7tl9|KcL~FRs{ssVDyX?V2wA7oIJ<(5dJDy-WP+_rs5P{>=|uZ32Q? zIHLyRD{nH1uxQ4_dlECTF9jfp1V6v}Q7uj75?QUaxdHLEd~M)JL3g~WPTAVR2lF?b6wsnG0jlWoiokUdXjQqh;9cEz<-%@ptXM-E zeWGbgn_zuWd2_zc+R9FO^tv_~`xKUspD&cZltQ0}sBcr2UmB`48Q zq;Xc=V=ipMQY7tl$fnY4Zn~z#pz^6SYb%1Hi?vKHn5Mbg{X`ApYjet-_eqk z=IYA5J@%DE-J(Y*NEhnA zRMm=D?Zp9AD13R>S;rhZupG^h-XXHbt|RVCp#Wkwb1YUl-`vU0F*c{y!S&9u;)&)n zQNh#;R?)%!RaModb&D0omBk_?6t4FH#bRy6k}$(3dM_+57E&1!*@dTz zk@Fm~xsB#_piF^C6L6rL&pdc!IME!M2vbYYgWeghr*kVh4^vDL)vOMk*)G!=)%MPg zw4yihTx>eBTbjDDMWd%e)4zb~?(pv3>iVa07z!!^ksp+nQb=hDkv?4`(HGke(VOf? zgn}-qK`%=+<30NODYzeLke5q?8~T#F$~) zX~sBZ8?(QO{b+CA7q=y{7x(4m+Yo+f|DCm(BFODk!e@^wrAosjy$Ga+CVOPqzFciR z;mD+I@32JXq3t3f;{LAv0RcM!W+i2_j{5aN?+j5EUbo(iR9c3m87=H`1uLw9PUh)^ z*un9RazpvD?E&F|V)B2NU1B?b$u5N_zu2X8oJDitthQ|gQ6!1OT|OmGvfxq8Y@f0L4sXzArnqs4HW zb@Dt`n(*A#?K0^p_FnFpkm4+Z6ZZAj*C#`HU6Sn08y@bB1Kx_PCE1e}AM^I@Oh+2q zsZ*zpZf==w;f8W`2gqc?Z%T2STD#N7TlhPL^IEhV+$U6o_6MWNvQm?J9tqjG)NA5I z(-1HZV>{pL?mSgcNI43!iS+slvJI6}COHtSf!1!{+R93Kz=%u&!m(~;aWe(aZuvxF z9v1n|>UM#Em)p4V*drn+W1eb0>Rnw_!dn}FjGVV~b;v_2U5?(Td(&6kY;OXaojsEO z{_MYyPQsIvjf?i4CGiZ)1XtyNdgOYhFaI;E+Uy{Zq)im6vlg)FC3=X{yBrvuq=_r&hc#1r2~s?fb- zrTE`0%dcV3yn5v&rKp2`UKN%-WzHnowp_Uy z)1O}6*`*#sm$zEK+7{Ss$QI^f)!8tnJ?mR@guTCmTym`;YDN9SwTE}Q7Zar&@|x3% z+|1G>tq9yYKH{L~hY5uN-4(aouX6t;mn?hn+@0cq+!2j{u9<7Wb~7^rN-IMqS}Frk zR^8VRV3_kZ%R+oV$Ne`ACcepLe;Jq1l>kO%_0j9@K?|i?0uILWVakk}2`MujZq>DZ zp_gVn)r_W=LjrQQshOS%K=JW-pf}P}Yv>R`) z&t5D&x1OhS{y3ce`vTAHW9dZ`h>V zQq|cpEC}7B`7H3ws%kfB)K yJ>3hSa!h7e_TF0V0+IjuShyN@>B@+mm#9=?HljWp-$|VL}UHhROCG|P;QAV4u#ADo#>rMBRL?0{V zdngme9o%qj#*9TIa4=Y3&|n_*8oJ9J$)I(Xu|?gDefzUCdhps3XqxC*MUO{ zw2r8W_lgi-b9NV0MhavYkjK$K|NMS=6eK5xIs~ULUx5JtGPNN1Aq@KMe%$W0qUKZ> ztg}LNvs6HmfyX5a!d_B9D@Vp{8URX}s~Bg99z#@3pzIL@>B7I&2W$(9h>nuhU72k48^Zi*MeMl`PXE_9l^SSfkStEaG zB{{lwFq*C)mAo!Y)42*W28OJ_m+nIjcUrAZOlO?L_?Sl5x%oD*5`UZ z_^BN`BGR+=JhxDiMjSf1|2JzKIVeDGG@yV}fP}Vq;6Fg}RKjUx7Z;bZ<$9)6or-d# zpoCuURC2U?8_Wyx^lE®INU3IfWI&QMMxrMP=1>}EOuOVr;2^vPAYK+_Fu6&)ZN zqQgL^OBsUa^B{a3b%T*%tKJ^+)ds<=3xi-6V6yLo*$IFymIDZ0f>3lS)F`SI-X_2% zGT{piAW8(}PdBJ@Q^Dhq20~ooy~gCKFl8n<^r_GZ3MzdWopu?$l?GAH3L*!Ew<-DU z04HTC3W$Sga_VuwGXxzwQ5&H#Dv!K)T zh|h0#5cU+0rAi3YU4w(L2#AxBrMZJtd63ZRd^lAEE`NQg+E9i)>Pe8!M^p>t!QOd! zd2sgZawu3o4}_JF;AWv3rN@B}vUUtGJ8A?{4hUNpj(nLwYobWN?PYW1z>8RQKh}4i zP~*{j`A654hXqTEu>HuW1(UcIc5fs-4&Es0p#G7YBJ1_*UN>eJnXNj21HrFLg4H89 zpGva>sTImw3`t5B0G(Li;AYLQUxBAJ`)SmV>cC_F{VoH!A&9~h<`MRBnc_&QH@bLn zKZWeYm=ece&GmzDWlELDU9q16|4@UHw1nBY$2<5=;!dcQ>}s5v)Y7$?{VS3KJgWb9!+-k&<`~UZ!JulTw6cQ+?W+`-+4r-wW2GZ>9>@U z>^aG!EI|t`)`Audr^Aa}U*N6`;@~=VWlu0Qf4snQsWfRR+qz(HA8loK&*=hQ?dMj; zw+XB;_SV>X=rTB++p5-dh`V+=UmUw9;R}Q}dt!I|&D-JESCpS{5ai^f06sn)$fUv& zi^kPngZlut-1;ox#u#-9`-czgy@e9<){E-2@6H%r%gs!|-c{|*WT-yx>NJkTyl4y4 zZ?_7wxB6z-7B;ikAANpfQ1t21En_FqiguTW1kk2T4dj&ymfx}Y) zAg%937e9v)cjhxrAl{~wR z4&uetF+3P+p^R7F|nqsL=FA5iv8$lG*l0yj9jp+F6!HzMLv-0Nn0)xh`!4fkHqP&-I1h z9)|or{9@dT;#k=Auq^GwmJQG*f+b#tBG&pDidjIBm7fEaRIhpG3|@iOBCx{R#zui+ z=df$AbsfDH9@EO^4inHNwO|hE#8C0w7z^0`ZEQ=;eQc6S^5B2o%ZC37D=KW@R|wu4iFL63!0j7!6< zz#1Ogwo(*ZNh{|jORzq#gkG8IrT4lu``tTUEhmXhJ}^Pe{D|4qpW)j14#BOvj)BQLa16SUM`Sa~66lJs!#UqCfDvHvCw8Br9X$R(glcwh$O4?eqGVS_; z2x4j#x39h(65Dq6!=s!ak@tJtQ3XBGO(Isywr%p&>v{7dBIB2;40LS!3q9F6EcACT zWcNz*%v^c(V1L<8j8it!gaRbVI0ZCl%Ls(=#o(II5e(#Nd1o84F3sif%y#{z*wz5Yr%aYZo5hMs63Tj`!)KsfL8^n3;oVJ%A{%XJ{M#+Je>>{L zzxDyeg73qJ52oLAZr2WWX<%@70NvOw5how*%pdAtFS_U+ssKeV)VRs!>{PvDJR%%O zL3|Pw#$alN`P+(u(zlgLO_^8y+W44N+#W=qTDa$~`idG2-lf2F;vzJxQTOjViga6@ zy4^-gem!kRk@Z#$3UU^A_7EBnk1}#yNQ`ac@d|QW>uwkCxqa!-Tv-3AkPTn5Qcpl% zpt0~#|KnOAHx6*!qHZL!tzUmz+`#H2ds2i!+rkYMuaXT0(Xzt}k;c&jHPn?1g0VVk zWi&mVI=|R8>SxZ}r}Mn87Mit&{u-z5)QgUE(GkDZpCuvy8;EJ-#mHuNmQF5|#x814 zMPoOckYBim@rv{KI++s-BgGpX9A6?y_gyr{gKi9oMHt1`_sw|l_VzF{ zn4D|Vhr=eH%~Rp+q=3mf;yeBNsGv#Bsr?c=5-)sc>H5|iGZ~!SI~YdEJ96NeM9=x4 zV!?kdUquh0%geeSite|J*uq-nA;klqHpD97m^ z=MR^8vagTr>emKV8&ACi%~xy69|6k^xG`g?oaqYhQTZt=D)_KIlVu;*6n_?~=CE_AvsW3v&hYJmb4-tn!scy<3s-aa0`zsSj6z_v+i;pJHK{`v>d zImO1?3f|DVxqWDZdTGhsafgeZs->sivuvD5o&2gM_3?dlpe>E2$Cz_XhpL(^-*{)f z`NCjeEj6Mi#Q5lQcBzF!{Q@RmHA8&kd&`5b&CF2kf_7eqyV<$-|6rsWQJDx#7yMq$ zE6=Fxak7PNtUPpm-B(X>ukdj1{6IbxwK#R}EBQ{~o+~qZ?2Kkp$g^&@yQ$7&U`9J@ zbs)q))ork#WC9B-B4EjB4j}#R`sBWMGt%|Kp*M8tQWFZ$3yi#exr%YIX&q%W2a#?z;8qZ@kUTZU*c)v8aZ`ya;PzBU5KgM zh6kRW^%-UTtQ4N6?vOC8J(m+F3U6jZO{eHOP!OZ25o$MZ<$#2>mROLFYJACEK?}!g zCF62oN|vAfrTtIttJWJL%rlP$uP}nQVDX?_)7g;iDEy2IXVV`(dZc#yv^!!=76rz1(kJT5 z8Jn!9znmZp2DbZ$dOGZd9k=n&CM%X&s`6t3R9jck@3NEwR>WCRk+rkFZ~)EEcT(bk ze*0$whkowshzNZVeyaQ*6mS)Za|=wURRz}wUdzg2Rhz5`mXjDemR0XH?r@o}Td1YQ z`(x3@b?Zt-M8Lw*0EsZ&sB}A#W>|Hz*sO)xW63%*w4p$8^m^s%>lX?C#54UWYC!?B z8V*QIWR;YZ2p^3Mj$rCyG`+)d7c^0_)RXomt}&vv@0h3A&RAPpm*hTmort>g7-_?4 z>wLK=RHVdh+q%^|D)Z(@6U8E9X|9alP=9)XFXzf-4h=(||HO3_+j`1Ke9s>0$M$?i zzeVScPj8*qW!X#Kee^mOo505nWrm|&uWII9Y*h2_=hZRi#4Rl>8s;hAX_YhkY`v)1 zPI(h~P3@ri@l}_rH%V|&HqB6@q)!z^i!PZ?_0W8&5r~Y47FFeBVxB1cESFUDwNcsA zUfIA&NNIQ7QtTEcRzc_PJq=^(^DC7y?JYst^JZyiXo?nXD?eyD8$8z|P}Po~>T5$& zX`<$Jy?krFmJZ&%dr#O7Hy6~O&EUj7&rB2YJiS{eMfOInCf%{$C0D$?405M7^(Rj^ zxjIg*D(Fh8v4oS8<3)2cb}Xha=s`OwcX$c1Pd^qUAhXn3o12^CbdSs_>oi)L?x6cn z6n%xo<%DFwoA{3u`_^>+#u{Pse(F@Vglke83&j#(5q}vz)K+Qp5NzRz zCQ3(qlg}zawJ&0Q%{6eQxSG0~=4Frn$FQsKShVi>4H)2j@l$XuMj?ToUdZOCaO11+ zHLMh=N0F{=Uq|bAQIsEL3|dOd zpNi`XT1X~(-b6Jv-dbqvz@o-OZL)PcpoFrDO*$)EmSkp4!x1!T$|o| zG53yN(@5?!=Rg7TgA|cxXR>A)$CPk(jW&M$6mH0Q(e08tenO`IbK?aC<5D=94yeW< z7k?yh{@rgQguBnL$(B4R{avaF z+qZ9b9lj(V=Zup%3r!Lm$Z8XP)$E1WZwxhS;b!)S!4y=hzHEEnx3w9dA)}<~gUYEI zA+wnT^x6)$sv!rdYLI{G`jU7bC&q7tss?8lGUVg5h@5W%a}++85*_nNny*eG&PpM* zIZV>c8WT9sG>ehd5Eg2Fh8WuDxem*FTa&yagY63u04u4OX*JqO7{B=a$!-7)?jd7Xy$KL*Dn~wQL9dsOQRNLq}b%jjMB;5-9``vbrNZdHJW^ysOJAX3+Lp;6ffx7Mq zbDty05Q&bTNxcdj^Ty!UeH62MQ ze=xcjoFV*NY37zgvR>7p5V=VMc|nWRC7V_HdGU>r&ua3{KTr)_I`8(-vLsZRJ-NB^ zZEBcSnsIl>CvXTYXHsA;mXJL`!(2J_1V#W(9!zz0WkFTBX_lD$ZDbzJfasy-bNt

?b#LzDL{?b^Q5R4KFg*`+ca8R{lxkr>v31*%y{a)lDl zVxKgMvAQddnSz7?4msx5EZBfr-S649y>$4gV#zq_Rp*Hh#;8w3bG@Q8f|?)ppYJ}K zx%_7HgS~&A6G})(AcnZjIpaH8AKKh9^r!jZH0+BF>DM4Y1EBS)g1Tc_hQn`;f3`2HM zg@YB(*hq%JxUHxdWRj5@Zn#TFPveXr;(8)>zzdV%Fqu7%?EL4bw+P6$kr< zj4MI;i?im$j{$q9DnJSAnNg#`=71Vak*m$GqiK8l=V#72&sQ#W_I9!;R+Ly?vj%A* zX2nXyzHay3#I53^!n~>!Q^(h3OboDDvdhqO9Bj#xi`Pi}@`y21V`7&eJw0ROMQKms znayk67gu+s+QmMoX#n!iv7XQJAUA&>b+2DCeKt^S;k8T9H0o1~jPs9%Q_e>gc_6-CazuQc>yrX9CrMIfaTx+~n(juC{!N2b$ zJ(t>(oTBQbsGfdzv6>8RW#K!mk1D4R`c~@=RoAj_bKq6afT9L%9RYFY58msi4RSPe zzf$F_pXbu~dWI@sF{icA6(q9x*j}Yw*}X9?_G3HUhn2N>>uu{H3Lib7ew^vx%r;Bj z(~>UYfr)d0Dv1kLzAVKsO))zE*73wnl;&gQtJatg^ez>D_@D+6V@YePKIxSqijtC+ zjz=!zF0mW8VhcZSUTl5E(=vNLl!+JAaq{gRnW&20S~M}??@h#KAJWeaC0J)Ryc&BH zr*znMgXk0oj*sXU#zrtela}ClmRupeGnruVfK+K8-TOx;gyW!r%^SIJL`v1%$DG_|oSu@Bv1ugFXs?STumdM;nt^u&T2owTqyd6Bic`JZ7Il z{Qt0AfnT19l=ZEe{?VZZM{Vx`IpqCVe|xjs8HN+uuP)prqcx0E3G)x$gH}8zW zNIH}*Jlbh8=yJ)1ln#*#?6gY?3nht8xRiHk>BGw}}$r$M6mqXYmQIzqeo3*GX=hjKG%G!a#a;ytZRpnnF zgvL3PX=~3GS%}zOE7-svgW`c0N`c$+w&r`+?_ptkN2kR6F3lE1!>Lb(3Xx(hNLDj5 zv%O#q&CKq30wcTThx95rlfmPo=3<296aL}qCTNE`=aP@~Qx_byVY$bL!ULc=4d(%7 zly`76C}Ah5d9JiIuWG@jHn!BR8{hAxD|w?128^N(GRC)7&@*l4;x;!Yb3Qf*9AiO1 zR?j_^-G7yUk(9#aU8`4JPE=V8efF9flDs66_{H&6w#^ak&_wYtDSY;3*m zp2%imO01PW*zYgKkjBfgU^PA6CF-zPs^^fYpe2~}?M2GnLF@jjX)aBUCk_h**L8gv zP@JzY1-CoRzGF3|%BsTl5TMJuv*)|jV%7EZ3>>8Li<}T;6EFJxd&;YHwV8GFwb|~O z`c8fHt?l`|2Lx1I`fBKw#CQ$}#JZse7>XEQ*6%nFgKR_Wtu-;+WV?4UIgB zQxCuAxA-Fnu73y-JJ(g=Z;)JVbkr914Fy>VIRnS1Y-Jl-x}rgiSk&EqAFq_tLafFzdnm$uJ`W9 zSsu14d_U=?TP&oEw==#m++|m%#v$|!@8wfr zm*l?XGhtMJc-H%2m74pX zp5iBd0GXBWoFrjwfZJ%*iW0tXCyFin#b>hf(9cJ@F3v<}ShO8PiRe84qlkEh#7h49 zu;V`;hBNrE&F{akaSxw6{}-d)WnMXdbBX>p%J;un`)d8~^Psg^_DSvkE~)qbJw^2P z+jF#kcs=_6o}<2fwTkr3<~#q}T!!KM=(Tan7J0@$XHuWjy~ILS4zS;LKI#KI3+$GU z`)z;rIiSm@?I#O66d50IR<<9Xt0gy7xPgrjZlc8J&P?ETPbMV3W;a&fSKBWy_iZO@ zb-VCC4)Y|_Z!`$_$bD%<{hPBLd@WEDf;Oc&N&;R;De`LNSg4$pHeA4@?OY%)6u^(Q?JuNZL zywZ}b1~6X*(eX&J^wwkki<5WpIodL>ctiVlvbws@pz;zU;j9?yQvw!;@Ax9r#rL3| zHf)y2Tn*8`0kwa<5Vw-iJHF`s(buR^BLMLbbvq_}a_Ns@Lq-Sygxdu4KZkVYcyiW5 z(7&*GC&4B<&}PL`@WEj5cI)=-)CnuRek`IuVZd6+5^8aI4c$QX%_9!tmRVq0hNFz< zKY6OVFZ%9!Yp7LMtqxH(`NTtt+vkts&FW+0L-OWgbZ6E@ZQ}dC@c7k-8?t~!`GG`( zOh~77{XnPMC3N)#7<}OQt`7ne{QLXsu8d!Ebe1o=5V7>`dT)<(J-2!0*Jq{&4=ubt zmvrNDOGJNt5?4WRo>@&?Vu;0tvoq^_AJb=RebX)12D6KXa9>gkf$$MuclD5sjPOU4 z;e$=7I*Tqv+rIV0_fJ)HtrRa}&TggomZo=R8d!r630G^iNNvH1z9TZV<~0JRzh3%t z_PiMyhuU1(&FP6+@)M;wadvO#A1cb%G??3_&AwSxrt7KE?fd1E53=GuM;n{dtMt=8s7Brie>OPEb&OwKy5gS6V)yYx z3Cm*5oXe_mE;C9l;?mYa9A{Ak!Z*)PL<77SSSSv z&41LNlu8l3och*Ewp;05Kz*_;9ixcdw?D@U0)(aAcOBuofS>EXv*BGaA=&X=xU2Y@ zV)2ghu7RzveHF{GH7&~E5wYy_O47|guwG8i_Zn}QUt&`X+)6-xD~K)p?WSsq!1*qM z9%9X@y}ovySjmxY6#(~7*YQ#e?bYB_Rs;SLMRPd|F|-3BUkxv*8kE#3ZnvXlPB3VQ zwS}o_eBox>7{0S7S=@!FGWTAyR{{R6htt85%Z2d%GO}4Wa?V`5 zbDCHDLN4PGDipC?upWvm^;3!E??UvUv7&kPtYBk?Y2HT{5rv7}M;yP+x-MiXOK%$T zetS-4tZKR4nc{Um|He=Mn7ij|riSF2g4mcC8Q%kH+O=`@s$eTD{AjvBoVv$cXQl40 z_fC*r_+n>w#-1OB1oy0j=Mv_u+&_9Q$WT?Yt#<;}JUKPg>RVB{ABa5!rIU(fbKi}H z84j4V%PjxKtQGh&1%40Uktzi6O9`5b9}9nl;+cQPhIPOH{;A|2%a$!E#i^RQ2Tf~b zj}huxy19DnwI?s)RHk=vwiUs2#QxNv%s zQRhg1p^~J)V))efTwH;VgoMR&nP;V1rfsca+IePovva>H$obD-rCqap7FQb=e>rP( zG(lG_L@h3+BQ+5(I@pw>Q*dyW0=vP$r4C+#*-@=O_uNMhuhLQE6a(+yp2Tzn^ z@{s;|`qaY9uCz;6j>f9^7pg=t+;~~ZTNo7UKrwb6)#lmwG>g5~K_a(WQ&5(^rl-_d z&?d5=ed{76YUEm3vgngnv3>=6wuDW&9E&kn5|7VOS{naam7N{r;*j~Ebv!AitSHGO zsIB&Lqe}ITrI~szgGv?qMHNr|tZ@zJu@5W?3)TrPdRa<-eKq04gkWKuS9LDnUbfvq z&H5yLA@#2yDMLuTA+`Lc1#rUa*uq1iN>855_TGL1&Vp*eatpIJLBldsVzK4i$S`aA z-AQ43dVkrpOA<`F#b|CH-&&P;cZ5*+o|QHfl{Cr%AdeVIo=^qqrX1Dw`nuk5uJCbo zk2NNvJ>KIDow00>w=7dm{Z!)4C`=t^I}6hbrmV~PPBWd~zIk08M)T95IcHqeluE2+ zR^NX6zOwT&Sdp@0FF7eK`YviiL#&8(R|sCaj{k&Vclf`1^geKZP?=nCfXd=LQu&l{ zlN|CiEC8pSz=5d&Tb$UFpZWCrYc&>D=Y=ChGX8mKp(hv{$tED$V7^}UiS8qz2f+u1 zr#7W56@W`^D7U|nExsu$Ve?Z`QhnY(nB%PjQfQU&p0S0+PaG!uqW`qmsIHRSZE62L z>j34E>nwOC(QR*hR?Ac3)bpZ5Y@M}ihwhknjYc+X8?4@cq>j#YVJ_j`(zQR-kg}|o z!x*U~!Q{gtX3(Tp=eHyfW}VQJ#1?Wl{X(0)g8q;{vDqtcc^&PQ6T7=*&N)-Z>d2}n zSx~Ip#r@Mwigr?z{b!l9G7N;Jd})H!7C!kG6dG3M>l9SN|mUl@un1}<8avtRu4 z(4n&J6%Zotd{>ikxitnPmJ$FO6La-voj}*L$UA%rbFMVoii4o5zPqE;y`hFKl^IVc z6l1-3)IU|x&4bruyr_dP^1;2_>pb#(2c^nS0mS{%N7BnIJq{>nW;xGgU@!uoM8mB^o z8_v;^rY0TCy5((~0Ik%LF&Ze#r65uwBMfn`k4bQP6l=)ryfpyKzSacOshKJ2!8HxfnkKYtiAx7#4gCA&Kn5^%y>dzLnQHd<3 za~X*k1t$cHJP{C2(Rp=&OSy)pY+l=jMya~Ek?%Ld_Xz=QwDu3{XRnQGYW0j}_NSZ# zvu)E*8#`n=*;fK*cyuzKunac86&yVu!yMJ+DlSho^e$2^zhSQ&RWPKcP8Be|Sa-~7 z+CYY8S#V$FW1~vQ_TmW>?)bEc-UY|SI=_wP6K2USmfYoDZi=^l?oH=66nW0cQNbzM zzy488*#z7OV8Q6eA~rd)GmCMekiF1KcJe#C>9IP`VEV+$>b$gN`}RwX=azjlNJ;zn zy|Xg{zXXZr5E2ri)0;$5geBxii*?1hC}Dig-Y-4-1?I_74e5Ni(j%5?WNuOaSar`|a&*HGFbh z*a({p&^w^+J0)y#sF_&na%X+aa98FTTG1jZxf6E@a4qrdgqP za8f9H%dRjRH{Rl<3A~TK zLt^HGeF7H8U>amJJ*|E~Cyv;-9NN*TloEleR=Lue`tFIgs$oQg+cEE>BC_pT!`GF- zBES=1K%wYiCETx@Z3jMgik+n+!^j`ot&c9>Us83)7X$h}>>gF<2_`@-7q@P}Z!SlS ze7t=3zWHr(xGj10#$CP0#<9#@T1F4kCs2B6*r&+PagUBUWc1z}V8pdi3cK@`7UxLV z1GZaGvpxcLW4L?wt|?Feu+BB(GS`7YzW+$3v_vPiAdS6y_Yx6MHOr}I0QL~`X%)RpHTneHM47GJtCqR#sJOm>d(h5?SiwpdCk!Z%acEBi5P% z2`xfHqXzI$+D|P7lhcx-B5C47hc(BP71|ddXzo{+pcPJ%ou~w=#+_}B_J~|aI-qzT zBW~Cp0e6paS(uH@xDLY@qC~;1t_CfPp|O9>wd{*Un}|RJ?^o-=D5}gFiTF~4GaahE z;V?Cn2oX$Qi)~vi$QmJ4^z9IrjDrYjjwyp#eNr&k)GCR#2>)SaVfh4qdf<+JEG@6E zO9Juuf?Hss>_}V0__zHOeB3Iqv~XC)sy1o8JohJo<@ZY7Gb!EP`cP5P(sp>T5fgcq zZlmw>_UqQIQw1nn9wHq6y!ubjiDg31gamR0tovjJyz2=Iy=>2SUF8zVIi&ZvZ?CR? zQ(3GTlwb2NWneXOEW>#2UMh)&gFO`KOngJ@uFGw45*-6->F7 zJ$7ZR%RA3``CYf4MpdYydn_HW;R`;$Vo#~+zNoQntAEYhC5nb9puDQ|moghilb+KV zB(Rmbisj@_om4Apyr#^|BTfyg;P9%@<`q2S5Ta!}-M4U|H#3x>7kp+Z$Vun34zCm6 z8gc93-SO|!71cH24COOtFqo*TvFI7wZn^r$GpfDM`R=k{vBR~))%j0fxU5`_Sb-x7 z+n(6VxUhk~yI@R}oAO?rBz{i3N_t`B$H+5kI_$O>(KM!lq{&J_C$1iH%%16$z_J~R zew)_T%#6TJaHGKZK^j=@vYos5m99c!GLil4&)ah727O1O}xmfM4 zZ8 z3Y%6zSe8>_m$p-Z<1Gjjb15solgDwBcBM(ECEr=JrJ}eDi1;s*abw!G&m}`GuI3XZ z^o?oXc~=E*QdI&>Az~i~%ThiY6T1?r(<~FeU6{M6KR|Ue%te}Lsray zwTDn`Q0aUGF);l?!xq4w{biYMPa$5EVU=^#w1+n!`Mn04vs6eOiL_bzt==_8LkJrn z1P4+l;HO56A_7qdRW|NjF{YYkqEs8}oxPbP-BKg4B}eC+0V0d7`zZA?=C0HT@I4K= zLOS0+SXvl%#W}1B18FAVQ$#99_)M2(xs!8WfbnMv<4(M^7k2v_L-Moy#HhS4zppl2 znhxfK2aK{@<&qOps<5=4UDAeC201JeK_?;#DSi7cUmtr&p&N7UQ*R8nCW7?N0bvVO z*+Fi`Hknh|RV+*t3WX2Vb?oAzXCoZ~)^IFy z!qN~sAEnl@3@jmH0?a7ppo#;BGp`p$U{Zu0vTmI+sq_{SKF8>0jjrq&6dmyI3ibE> z7i+xsrP5ZML4jcL@S6{-{I1 zUN8&(=%5oEz0DV=H?@-_o>&!xhJ8ylR`yGO`J7wcupFbaiNPZVu{VOVXf z#|eL*@y9q;G|_IDzY#4|*qPMuU6^%@qmcbb@8H{5mnjY{S&Q-A9MHCp*WJtyoM_o# ze(NiolIB8nC+X&?diNo{0^^ot5!A?ZU2Lh~CB#5%K`J-^_VF)Y9->eTgy_In zBrql>k2P^>+&B_BxFwj_lwlSvP>pFgE9*oyIN??Eud~?%Z(^==*eJNORf4HzrR-^# z$jXuL`}pyjs7rZK$`dv-ArE2YSL+pZEbllia`fn@%1Vqmo~+ATx>wjWC&}Y;K#RWx zF@Xlz)|_Rz<0V|&VkQ#Qi`nzZ=l31d&y7J@9gp4)K8Mdp0t4CtGi;2Fd7`ml$EFyPkY&{qHBbajebvAkYJy&>Hg&vt%zA1uVqpEYvrvzYmwncX z>(tU}E1T9{%+E*omEFmVnNbRLU-*lvU}k&QW~*#*XQ!2IOmW>jO&kK9ev3y&_0lKe zk~MOUCBxU${L}p@6~9!+H%~{8pLU)swu9vft+28~p0PxQeP-8Adit3+M$}~Qnb&8c z9?gE2cqr09o7${znNk{WQ6KzeU}Lm$Spfs1|L78y^A=ncrAg=XvJ_jY)9G zLhCNBHLXJ~`+iopPi=MyTt0wB2z&3>^G?q4jAuNEQvAP@0$h9i7_{r_|15X|in5*w zgPvO*v~rH~*nEb=_(0;2*p7)6$IHDV#S&z&+$`X)2enQWm?>!%ZTUX>yTOiAMbgT| za2qkI-=+0k@xgejhm4HOt|t7^xyyVg<9URtiIl< z`qV5K88T$^B*YwI4~RY!nV_c7+a(jie%{>Ty4Z!w0r*Cs=&4hU$yMca=L|#l#5RPPRV$-~pU9q}yzgwEc zR2zv%3wtM36E|ojo1e&F%lmdZ{}Z%unZ>R7H^l>xh}Bn|r>tM#qTuW}<;CJG)FtPT zWw$}mh zN}lY1+@)L9BO->@I*#{-F^M^FfEu8|T3Ym|K6As&E0T4yr~~{z z@*ZJn6!kjWspH&LLP_k+=7P^-jh@E-i8O=p2Gnw!sN}tX^zZn zb&$jOLmM3q(Ky3LTG1l+$30kBY*}2=frUw!WN6~<9yo`K*^T_sps(J(6xu$8CW{dd z55e{v&3BQSzkDQOL8bz-&4UzJN1uN;9dgJc$1R72ljG2rls~`SRZpB#d2!YDG<>8|X9lDedaqQ>emstsHAeqS~I?Zo{}OkYC>((d2Cf5-HR zEBnHJn^6W14g|GA-b^bWY#NSKN2fz?Sc7-&!6^K+YM)3KbA4e?se(?_m214qhEyI)Gw$MrH3+q{ zom%1GHB?E6SdL}qrxPi%B2_QXOH!)P!mH%T-(G#) zVsrt+N;c1;vNDCOZ1P@!s0#{QseLH?P$>pceRwrr9;YIVcj8Wj2MA3RY8o``VvCL& z8eD@88=rYGqs+aSIM~}8K_{Tn5AO~|q zBn=jPLb^lx)j=)_X8Omz3HnY4O`^foI=UtI6Gpfk8X6j)(yK))2Fvn0cQ8W?WHV0- z6is7L$Jg#RFK$4sd0`{EUivn}+wUFG-U|u(nM(MSp|&TrhK^C1j>rU{2zH8B^<}xz z{PJM&IP}6^EP1LyR-LYu;*UE_ETV;P>nlgTYe?_=$}=(R;(sCJi%Lg!;dRM3b)!FI z`O7k|-4GTw{1SfOB)EW{YNFjR&lD*7I^)^EVUsV9owe=1Wt-Z*|F}og={VI#R8d!S zwtv{Q?qf?4b)oaq&Z+iy^Ifhc9YYsc5+YViaefTn^FFV@| zAaacpVREBW zQ;OrRBhx=FlF5|_+9#?FsS3ydf5#AeE2cBnv>MKx8}8kC{{(ZXI3#H`W?4m(%$pIR zvI5dn^w+UoIVW57l<(pehqx!CIVUGSb<2{$wmDIA2TX_8O>5MrK&U}n^O4KyJ1|?F zTDXR;7bMWp(ULIuX=#b<3A*0-W$)m;l{B?O0u$@8p7J`HFEG<7FyKbzy^RS4S6fuio@)==vF}~s#LR0Y5&QPn zP(VgfBfTfjt$vf8TNfF`(${0Ef=H#VJEIq2f!_C`t&>KJ@=H{ypS$RAa_(%(;T-~r zt{ZC)`l{rdX;Z4Vu;rj#|Gd>dAbaugz86gK#)TI~d7klv9W%cq?e3cQg2i;vWbpB~1(~mU+YO7brMD;x$m1zw~g=o*nJGZraFs zA5=UkDM@BEFyJ_jBSfy3?;SYd#EAy~R~xO!Y=j98j}RUAIoLPjult~Etb-nbj68gK zbku=jVLX(CM94#?k)U`(siO?k6OL8-wRi||q277;63h4ssy0z{V9R1t z2xM_}b?qt-*1^V$&R^w6`}IQ6_w1<=KKCo3OZw^VqnDEbs|8I>x4vcYhNYZ3p&CgL z0~`k>!J?VSN~HEK^&s`38fo;Z(PtRR)#2u<#W>YwZt}`c(;eK=FKn1`i`-sA=NBCv z4SSUcf=EDE0TwEuULm3`@lM2U&ZOu7+_U|2!v*yy#(x5OxIDB+^_buirBU8kxu{w5 z(I2-d=%V`JVdgzkln zNCPrvV(4Q$>uOh-sFin;TJxH6xS=7^zPx>X2d%br*F@#DEJwQ-!(!!<_MxP2&;P*A zrf$1(MJK={z795yp2IG~)j@+z8MNrwHuiZ&*Mt4dvCb#8hVvKHay&w_BvW_h%}&d&k-YH^%Y zbN8n~@H5mFUs2@lr-*BKa@c?0zIQhBz31;X7bM5kn$ue7qyuf&B=c%Ypa;8N%o(UXG*>P&X@4`hv%YkT1 z8>_?p;$K%Zx+z!OldAoEWaz`aH*T9}Q{pXB#@wH|YW_*LpOsQSbdT0zKxbm<$)zp) zB7ZWpAlgg%Nt|+xN`r2&b*J+4kMlPKDo3kV*Z4#SrLH*nPA%!UxC$DNX+bp@fJZNw<+R=7pCn|#T%+C9g_sn}O9DA+E z$7ExY%k9r=K3-JlE0z4(GGec_wsk*tksD5VXDh(jGBbO31D^qcZUWaV1CWPe>^HB=kq&>|iYC|}hu zMhMMr!951)K>jmWg1C`3pS14xZ>_y7tw zID!d&iaVYpzmQLg!Fe$fqg4f{Z_PJn%?gf)ptH8=E+ zkgOB1ryAdsikd~Al%b*FNrndsf$1+%{6tbM=<^!lJyFId)Sn zYP&Z&^)t3=&+D8$`~1u-vz+qcaLV)Nt{#G+TOw66Pe=wIb&>BW5Ah4X9lnm9GcJlp zJKpqB-Qyo~%ufF251u%2VpJkzJZFA5WqR+vj*ZS1uOHI$hTUD?Z8kilXL;?~S>L$Y ziA%#STpA&c6-zIicNB?LOIYmr$ezT~GZwkHb}ctiL`msojh!79;Et8On_Aqk_jb~6 z$7<3x8c(zYsyJ*5c%8X?>hUHji{cSu8;97lDNWO3HG>RX=OZuQm32roS7a1(0`cZq z^}@G58bptB7$-G5#9oz5cbO3ED(<<`cj(IYe-@G&*_Va$8pFIjm__5ZvU~KOkh%X% zPZx3Ko+PyA2BS#jVHvLJ+Qei>+hN*3L$!!Q=ga2iX4SlIIokN_?$N~gfr_05s%doR z%U(xCFtv51jSaUBb!^q3R%b392fJBwqvU{Vs_f%yKraaD@#mj^T3TEG%wxs)rR!%D z6p#`!RrzVBa_UaXrLs6II#!zR@$^6x!hXm36czJq-!ldy zf}El*hHmtBJD9HY{o}I!r5=I4ZG3tU9(>QfoM1xxVxsHs?q5*9LeA~nn1@q!ci-W? zQVqI&XBhpAv)Qxy7_LZmDBb>;ni+n@L?G^qNu}qE(C*T`E9qXjceS7E``zoAe!-lM z$Q_2dw8=;w##^;~ebvJ_rMM0qIt$#i9vvxCfxQhwa7Yt-3}viPBwVREj~26&m{zLR z?q3+n%k)pNDJRirWV{0g`K~MIF>C-ct&yjNwfj- z4g$gL=<+7e2#h{IRYI8_wE>6q|M+uk&C0@Eg^%R}U$!G($ji%1wZZ~UOb}-V^8fZN zU>nETuKyp8Uv^U~z*^=x>hpeVHX?Q--&A_UkVS#rA_jjM#Q)oWJ#^VJO<`}g z1Se!t0NwS*$TJdmf838>LW7-CKr&3;-Ds8RD_s}u@?ea#9haKeW{ni6~Ql+U!Hp#U49s|FxaLY z{LqJ;#OLj~v)0z*WA}djB9`qQYhKHZK7(!T8`b__|K;WtyP-S2Xek2te@!_!cVhMJ z$o;nh>Vi~@yis5gRef0`4mM_((KnX$dNP>e=chV!Ex;+=e zjnmq9f4zof+!t}~AfgCg*hz_nac?{}RHWeTxj)YTN=pBOklE0tC)~fE1lt1BU#mE>C8OGvl`RHtZysf86MN44fx?IVFGn zzHj;-HhouuBGQLdZaY>wU)X0%@#&bw<@qh3nFqBv4!``<|FAsm?Cn|m6j+e}O}JHh6zS*Bhdv6dT7C$%`D0GK^HtDbFbN~yxC+)|bUYgDOIr#R3u{`D67 z=d&=d@M8hE#vPV`(t+H!1k?alK#o{)AcOS9g)a(sNx)yOa%9hj)#9cTCzRO~G)-&c ze96QD8{3_fBW+G9_|oeDUXfUIG_^6HAdrQ`Z&VV25T(Dq&OdKPIPhwa<{8wPEhXUL zf$+vg+&9mhIYVrx5W}t}htaYx0+jy#1*abAD!~o}pVrWbW*p&!>Vu`WV#SKi?rv23 z`~jBLzy2gIH{*)!X&(m$@!1bjl*y!uS>hmtOD7{ixbW9U-dq(Rc)qI*j)`jE>7lWK zAAY0q`ClLM<}>c6<9Lb4zy9nywG#dk#c@syml!E|T;#%!zv`d7^8cn-ONB4PukUc_ zZy_b0|CU(s)HjcBeMO*Pf0n;9RzF|R>R+CH)14njD84u_pQ9eGTBg6_#$W4aht34#c8K2cOR_*g7 zd-|2%rnlX~*E}zzJ>B6UdGWgEh1{o_KmTLJAFOG9=gypFO^{JO;Qp6A|Jcg#E$RpL zvJK;o{&HQ*yw2?KeMyyYby4~7%YDA=ruD78F~2_UociAcLyxGtmTUcGCY>ixa%;%QAfo_-qt%k_;f>G>;}EHa zLo!T{IZ>6rtNH7ylO=~8iqTk%0kd6~@=(K z5PW=8VgI&K=W%NIVUZAKBaU9U&rNOZ0Z$_bXRC6{?q81oWip~SR?A7>9Dg4nmLLEV z1*!nH5B72f*oDI)20uivuZBL~f4GdD5S#8{WE8yKeu{8c8th3!0@9cXB3&(QY}C*V z{CgR*Rh`&EqX_6nFct~F!r+-Cgi=UoHKDbtCFyD*pV|wCt7tSL5nTD7N)VYU5MPo& zvx>X+{T=3%XF-G_`zT0_Xouu*N^-$xk91Jmd9V_%SqE$Rj{6*f!#*V zL$o-t07+=LqLP5a(Zt)5lqG;IOkbJ)`b^C%0gC1m7HsNZMOZD$0E|e4u-p`P@*5ud`|ov0LGwYT{n#ZEr3%Xq;#>1? zYbKagNE?+nf>pK1ycLe2Jez;rlBO9%SB9p|{=w-z`Ev9CaUUT4C@5;QhSccDcgv~- zB_t#W%7pVs84C9Uj22k?zRzAJH)!%g8{b5p)8=t=cQRYF}>b`5<$-#2o3MUQ01h@Oys z@{peO$eYD&(M!W_rg2*RtcOnZ01^54j#2T2@3{k(mk^26f0nlh3goteH#@Ha@kRW%bB_-Ay==z_8U;Wf%vJo>Ew|hI7`Py@ zj=>#M3u%^|+sfTPCj3LWk8*sfI{(!Nb?gMK65~{+Cm8w7(`xI>b{|P|>f3UqEkn(! zdm$+o7>tN~{A6{_aJtbLo1EME>KMt6!L}Uf+igS1BO{0CIW%REMe*hCSWntWrhQ|+ zO{z97CFE3)hvV*Jqd&r?qR%>L^c_KBjZ;YL1Q{TC;{zI;lBMIW^hBR1{2x|XsJ-erwDq{cZ+bji|=T4&b{ zjVL}XP+J(CwbaZpZv1#pkKLxLJI^p$jpKV_m6Vlv&-e8e1zUwK4bs$hiO%k)D2JWA zs}_A#>@HfWa4_-Rh8Lr4%+<|5@>==)AKblXTvTbgFKTNmM(9?8f`Fj_i6W9Dnh=Vd zL82%~j*_9kfFekcB1sk~Vv%!3MMOYQvSbmEoO7)6yw2=9&dk2I?>^`3^WpTD{x#AT zRIT-{=lMrIZT=&YX8rK?&yAUiv%x1U@q$XDC7~9BrGcd#xgtGgXyvwlXm_j<&4(^z z($&SAF7!=p*qo*fT#BEt$zF3eVceS|K`o#^my2k48LIGZ zg8SBk@SXUhtG0m}_uWbU@JXe)M3GMltmBM6zPyH~+?;=O>*#E@3QK_L!sBk&L}SnV zITy0z7pL zK8zIA?!)zA0@l6Pz!uw?N!MjRx+p=#c6{x?%))tZ3^v^)r*+XXaTX~?KY|(bgQ{iRDF;}+fnY80i?HWjP0~+eq!dV@?0W6<@Bi!oi<$^zI zFgkpJLqu7I|Fp;K%GXG0PHmz@V<)yjRJ3w==y}j&PiZ2*_4W-Vr62&s3a)w;J%YEm zK_laph09hzwrO*A^>|Mq5gOMY`%)QS@C&gy9eLn$rC*J$iAb||4)4$RHHKA#AJ9}5 z4^XFP!Gv|eb9Wn5?rMF3`}Q3W!PPp)>p+evupb^C9$?LAd3pARYR;W&kYZy3kSd-4 z>T?`4SFqjABLK>RwoXKEfb>z?wi3Y0HlP{c4fKUnAqn6-LV<69bvh9NEVYiK4OHmC z3;F^T_+luZITFK5qui;8%Ot;!jX|9ASd3H1MSU>{*McD5p2PniqVjx&)5$WNyNL)F;beH*<*{{5x`7@BW3UWAAqBr>X8)$;8f zhw%-@m;24p5$UfingYe$Q^5XV`o1o4^eVGwIsoNT6bvGYRqg|T_88(Pj?q?fV0(%j zUX&qkDmhEI?+swtV@=V%BW>l*LH{c0;y*pHk+#+&C2Ik*`tlZ;TTwk48VzrEu;=?-He6p6G+GUArsGX^Z&g_0)~n3(P1 z<3x}Kz#Adp$Zw7nB_b~bfP8Fe^>~x#_PQ;)LQ$9>c!7|(R=3hs4NYz! zrhpJNkxCGdrDYM}Ixq!12X#fG(H1h3FdV_)2UjKOyMvN#6T;9|T4gS#VS|}_z=?qX zdw^Xg;e~*l#J*aCWhz~}IE$P?L9`(oA0OX?WV^s!09Jv*V0}700o6tI2Q2c@ zD8^uWdmFK5z;>EJt~;AnK`JV#eSgv$ZceDo{n*u|0q=tvT!2hiV9(V-PC`XVNdOd- z_;-mYpo1tB3elVZD80lKT68O4dGMnh=oqkqh#GBu7W4-=s2%$1M}PbX%lY(TWIEz< z@5EaV6RPWOO>pN^2T9Sbb zsAUF5)#8*{W4o%YSz!d8!tD}H+4V#k-bYv+xtij{^&wqM>Xp;_{171DuO~Y*iSRiz zc66xW?ZI)04gu66gNS8VMwvJO6Qzj}v_({37o;cxv-iWx6vV(DcPRux#5CEJ9p?f0 zPMF@K;Qa$clnSJC@IB*zaw=%ff;u0nJitRdjnu-3Gy`%9{q!FK8MA??W?3FTVGVjf zgvBCk99}_8Lstu#&iv_jf_Tcy%)Y#IimTdlycGJHF!k(`i#~U*>~d8*EG2i}-8(aXx9XSPEq5KBBa8G*;--(LFu)~~mTx3fv*1>eTvsMi^l)3+7tU3rQaA5NXmIH& zI%~el(yK~k59dM~0#5M9(#;#BRMw1Td%gWkk{VwZ2 zrkBGXP2pcx89UJ;_C8-qf<&3~T0v~%CNM&tKSptv6^#Q423W(C#`eQ2HpFPh`H^&(WI~JXs7g^d&+a#`*_hw3%?{^B2Xq)bt%)FXK_VbH zQ!}6u1#}DF(;7u(#>n8@L-#p@xJL#$wBWkJ6MBoSVYt+a6tqnMYb7&4C+Al&0sU>p zzqQ_it~79gMWs$d4(m&T*OT392#aF^5$_)hpbrNYzbz$DY2m$b;4wxq9r#<6o!8sy zDw`wuh{%ILsDM~TJOQpBD3zLh)D4~l3>K*q9HSdxFvdjA4dRLHDyUWn55%W)(jKuPa-p79rqOqxZZixkw{3-r1ZG4&FgPcI;|3$c!0-}VbWoO>n3*2xgO?M z@JeKWBLduxx`#%gpsNcM0;6DLLwrBm2jwK`r1e~8FjKZWk+yDyy0a4{h$B;n_q0AS z<1=eWxrs&RurN*P8*DqTg`4lIc_P>mS@t{%vBxs{lIC3HMqYg}sH;+nj2fZ`F&XHY?91EHJ_ zTohoC8?ib5e7H^fr(RyE05$t~csBtCRt0WuY%82ia(SG=V*bU@fd?pQbDX$!L&x|1 zp*q=2@F)typjtm*EwEzHlZQvrL}rc$Gg^JxTEI>5v&AA&4Lmi4Towrcese*MA9~ut zV;&-ec0#9lHdIbp^8n{%=j5cJsrd@bxEQPeX_i(zi9zCf<8a-ntzwpy`MZZvKY%iI zoX+B$Uq#Bylk)O%P@v#Vs^iv3V^P*s(8)#jGY%Mhu=S3iCp{}rmT32IlHF*Dz|1bL zLP~kC3de+POWs+sxw1QW@$MX398O3b)NZs3whhB=L-jhl4CmN5o<_wZr;subQlF?# zJTx}uKDj*FHHZp(A+tiiU&u*uX0mE>;e+|%!$(!;s`gY&OEmtic{p-MDw}dT>r4s&rkL=9;)OH+@h!{SeoBo=sM*_FXEz>xGA8Pgio$4 z#+BY@HCp?a@jzqNq?!Z{@m^|x-L-eNR+Xhb;v6P=bhDi%%qCvGlrJqS1#gx9&YU-+ zT7n@fPTVL@{CJ5bA~#B}%B$pb#iz2P`~Dwg|lf_-Fdei*$idISXuwu^_`Od^ountF>1J+s zT-U0lHqf@o3gV|iQ$>J|#oLcHTdqtqbO|>b>w9EQcZ)VLBo;P;Sq?MNJTns9Wku4B zWp}d5ul@bo#z;eq|x_t{?`=v~%@57dy-eak~Oa zyOQL$&)jYF2?%KUzCxy!MktPKXH9&|exI^K7r*Xp_D+6VIglTZJNT`d-+CYe z)Mkk9LbNkDDe>HU>pkg3X~R#0Zm|~A29$~#kkIskHdl>5$EpFP@9*3Aca|Ea3b|5g z!sP`~-12m{%75nQxPjbg|GA^%_%Y7&SdDCE(rZqz5r8%s_PZdsZ!xTEgW`SQ=-AfQ zU#NWgH%o^UyF#xw^lGw97kf+gIp>0PBNzh~5x+0OU}>6pURyrfn$0Aabzf?Y^zx80tU&4C3bG zp}N0XXRMm-{orO9^GoHT+QhBH`{ z6f4UR7!?)uH&6N*Pfrr2ZLYsO1r7lwR(){KV9+LU=tg(Na;prU00%rGv9MvFHb&ly zMo>)PDe38VWE({TVMIpQ6&VEs)MAa{WdjGj(cH1zbDTuF?78bM3hjsyMH+yZq4NN` z3t(tYgwrC{WOM2C#Rb?}Six`tuRQaVfgA*@jN&oV#w%t1c!K*z#fb*HG3@TU-~mVw zQOe57LV-8(aYCVnn;*nGW$=^h9ehqEdt^uc&74pu$oQ@Yn4rn|_$PQw+;Jw()5rAe z>jK}?o0l%WXPR&`Y-Glkd936yJblI-L7Gl0Ayc?wmt3uvs+UgL<<^~K?i9ykJXvU^ zsK?bD(BrWFm}{`aIB%sV5;{JQxe}|l0~u9NAwYNdiC}tVnw*gJn~&CMBKN2am(p15 z2TI5$I~*Rtzin$kuFi6YhW-RRV9^E!YeO=;QAnMnRpFdlW}J05Os^7@M?f<4nVjzc<*FO%?KyZA`C77KBrk7Bm(nYSLk#Du>)GpL6O27 zvI4g+tb9m0hSDFPmSaJY16KAHq*vJ;fGywM-5pu&Z!R^(iVAlVLSSQMgGig&XCp{w z;sC3>G+w7WL>@Ec-;j6P1Ry6UDQ(n znsE67KgzEBPYW!5y|)3eLRSp@V>Vb-sh*`1!JP@HCXy-9=)1?mM$BsP{IxUkdsTbj z@<`bbilYn)Kl}B019XBRA$}-Zg;LeFsEUj>7Q84wI@?ia+gKWzNb`RzXp1(^9Lfh_a!*N+Y# zaU%$Mtc095mOmo#oTk?2o_C?==Vrs%%LxVULn4-1#nSTf&FLy6Bdl_L>*&aAkQ$;m zW~UAvy78w}D^Px$wu|9-Lil8rvrkY*Bb9OuEfcmQIH2SDP6*>?dIct)5^@f)4Y zF@U2Lpvee?YNDWb*jbT}Zxpa-m>ho!*&d7qfPH`f|En|}DKg=dLH(yFYZ`WFZA91u zp#h9|2#liHAf#%9IC?yoRnEs;d2kDu4WFV)C}^l$_&|rYV^9vB2SG8Yjj=#igvP|^ zUg!Bx4aq`sU`#@+J%T+`Q!iLTCOx`pfx$d+%mB55!My~Cjs_Z4!IFTKMF{W&OGXG1 z3w(MNj}Rc07#L-wAQlWk?5NC4*vSgg3}LsIt$k7FP{$be={ObDitLnpGfEo*!2n7* zf>tACKz=D{Y15Ij0G`fp)fXXAvPlHorbMt5MT&b?A+-u%QuX!qz|QG>2|6#yYy15@ z3;CET6jh+yP1x|&p{$|+;_;Ca9a@;Qs@zMF$V3dc2~(LZn{HVF*c^YBB7=k(u;Nbi zyOi0tGB$6n&JZDsObQQLN=y6xZ?&C7YtNbPd~L~q)9Npn{Z-@f*;f4(X~T5^4d>0( z{WlCNl5P~Z_7+=qD5z8`tPAf(x0kAHdF2h@wup}O;t#yGFc93Enn2{%*{<6QWa)OiSCC|TQSR+adGTy9$L!%ljigeJQLqemTvhdhVms&VJ`DL_QPdR7*(@If3 zOADI)zl(ku3httG7FX@=yG*oi>1!B`kT4huniMrF${Hk>*JL(!6xiWkh|}YGLG|e@ zHd3Ubl0n3;Twi}{>=2IMQWrlnx?tBO6Y9%$jz{_hNid{H*tnMZQ&eJ86u%U{uV0kc zqwkgB7~SHm+v5C3v*s@rhv7}osdT-aC`QZWL_q;X>eAq%qdHAV0A<{y)zybU((vBz z`+d~?!GvAVMvztzRt{+&5*fjON6+%ria#F&E0a-<^2F+u-*@Z7oLi zcnOrK0j0(3I->57@uUs7To(XtE5JE1+qa=2jx0eAWlM=WvPCR4U%trQ9z*~Wx?E>wPl}_!qr(?iSOk6fL z{ZPPI=(xgW*01Kd|Z+MeEIcfv$4;4j`%*Dl~ z-Y0pCj!(UuJZ7Agl}eC2E(=uplk2w>=*8`>f=XXS%I4)X{b_hsSyhg7UFGbpP8M;g zJi)1wC@+c=#syF}#U%|(lADoc>GOaxi>(+)*CiPh&d!%bN9$D#Kd5`VwCTR-TB(}$ zyn(VY(PB1&^3;l^hSfk@tH1Y3|8{2Cdfd>%)AZA7NfvK0Q*27(ag4UjI#n^#t3nmu ziAPUO7nn_5+An_a@=Wtr;5J`+dDfA*gQBZ;$QGZxKVc)rD-RxH*L)2|sBYDV5kbjD zF$Ywq!0$8gnXKmtIn183p4$#1Zv%13J6aGacmtDL(sh|(=Ek1S>2NXQLrj)WgHRk@ zH^}P+D-9&iwZ?yi5;87qL)_rHdDPj}>fscTeb8zJA0oTKt3QRy$LI{kt91##b|_lt?h8V@=om7abWj=U>ELk5z$5%-)QAYXMhz;zN{)pt#RW(I^Xq6$5A zaOG!Fh^=1*(xww&l^8{6-=arrG?-G<(F2Y11Xx19Oi3#$zHF)Yx+T7|yB1LQ{=G;S z*`80FpofME?@|mh!cYhNqDuXAo}XVG2)(B%loq%6U242J*#*RayC>hahu95{yD`cOFy;~qqueImRt}X!rV6|hz~<-RE5XwghuGgd zxS;s@%>(~^2O8;se}#YpFuVNY2O`$|!P&_PuL47e9J2L1e7Faz3=;U|-K{xu_@%)Z zoB=FC($P~qsUXh=ZYb&i1w#fzQ(?+u;2<*tY#t@V0EL+dy9PWhltEOl3fG`qk@z#s z^BxWW(5Zkw%mIM1;Qhbvx%gNz2*Z1-vAOxq#2>z} zIb15JDYO@3ydIe!Mn3eEuL^q!yeNC%YOXnF^)^SE0W`DbQD@6Rm%KLoe3Fzci% zd8eIVpiaiQIhu|IWC}TsytEoDV-+qx+7Ns(ZhMkX)w@|&B_oB8J==s1{c6R9i!#(T5ii>kY}AL859^-7F)V6Jx$$K0jt~* zM7OMA*Xry*(uv(V z`&LrPY@bj0>a4iyq)#K1MX0{;h{^_L?IXM^NdOQ9>g}jziC9iMldaCF#R-&)IaG=5 z3@QWN^XZSEnRs#)m@{3X=f$(StG6@GdsDDM79e1lfG51tDzHj~eqV55XbRnYA&i^! zTAk}o3TD}Mn0#-$e;X&)?J|GUx6!MQ0urIypmpL_f9Xy*kCCI=hHq&5N|P8=uX&YT zFFw?lvL}b{*;mlI%8>o`W|I)^6$&VoIe60|uRKVGedvDP%ClQqgJ~-xhdhr>_o@KI z4t?>dQ2Cku#hU^oH7@<7IPI-Kg8Pk!f<@}yj_UUNDhyk$BXtKb81e7eba=C70~oGk zz(53xmlWu66EnK=ZVM_;84(@UxzF`v^lF#d2Z=EXNaIys-#JUgp`#ifPciB_{Tu*N z(0*Qz_sbYCF|U4s2Ga zT*0=MRx^x$$|P%;w#b&=FFGNMe-WDr|qo0?c@KeR{_6~bb^eAd30)a@}#*Cp808G~1Aq@H-8 zASr`iq~Rc3e>xn%M;f-BxiPnQ|4KDua1jN`a;Cv3l6&<$z(Np%r?pI`MRTO^*dRTx zVs88Ftk5fT)84l)LM!3eg6E*F3ybrBInf(fkk518O|H>L45t}GJxIC ztTg(?;bW*zoJfSkX(@cNJV^qYEiN0I@YhgJakqb3YiNDpB=%|C5VzJ4#lIatG=(GDKor+LJZ{2g)hatIwgozC=I76Sb3ZzYpIi)cwCIf{h$MN1mjCwK zlGh$4MPaJf^{~UY4 zNy9<^?#NVS*;8a(M&_>iNN$72V(Z`bxwyG$3sJth3R~`h)v2WU=Lh9BSKT_&v@f>U z0};I{fPxu?FN%y zA-G8*N~2@9kc&-mKF+m_?vK}N0~0Cv#`30%8gZu3zRK_isQ$H&jD1!Ln|jX3CiM zrU|o+gx=oBVvu9dEhuL^fXyYaX z`aO7RrPdI_nQ~6zMVet658Lgs0!XB1bV)LzpV9@Z5)%Z!fSot}w8+32Y0v|q6`f;o0UdA! z@>#tLH|s07{jLBG+EnANu0pFOwnDQu`6{*6wOmce1EAv4l4X((RrkR|NwrHNi=Il= zx_yY%KuNM0pMGn}=jyN*GCIP>TVZmv3j=5TcgAr$Rd*|SjHJaYmqZV(vF@?(k4f6M z=S~a&V@!;UajFjc4owNHY>`+6FGxLkc2ov$au%D^7*cpuwdko3ul$R!%$0@ITptg= zmly?wi#Wl%1dQp@2XRWO+CYC+IOWGee`GEXJa6$~sKP2cDkvP=I5W#RJEP6doSriH zW0ws(*^{qu9mOhhY3p(JI)14kJF8V_;d3gXnEszN_Br-~{66rKfw7f*-!h#>G>T*M z+*|=ph^pbM>Eu>mPg=QI&jvvbT~wCB4w9>!OLW$wbqn!=Ec3H$&%P>dZs;o1~+3* zIIL4Ndz(%{iv8iE6)B)eos#I=PCPCUgTdztMYcZ)T zDMq-6^~M3WV_P{g-}2*VCX1nNJLZ= zLGyq_NC9>%%{zx^h#yhb>7YilgA7zCJwq~NfIX#KT1fU1-7uWNNep2z!q166Xf&xrgI7T%pf~|&2N95kMzwV z3o(gwGzerTZ`;ai%!#?q&O~pz317(*6kijvkPEs?GGiQ|kmte8Wr-Ks?wC1D3~0M7 zw(heu1G;$XO;riMesl{CBba5BXr}%drK5 zSQTJ5#9TrHZ#tkW2wO#&3@(d?!00d=3?K#wR0f+HV{xJIrh`md3+TaCpp76-TLqO3oyW9OJ`1F-%Is`Kp z3VU<*ISy1>C+H3N7G4~RnoyuP+9D?!YfiYfICL2ZVU&+6p4ghhwW_{Ps z$I&01e&)Ul>Y18Z0DIR9dwRHxeI_mMfB8E+Wd^#32QUGC0ZQ|U@2xA#1@C`JnQ5Hd z)~DcgXmbg+U^#>7OI`VjkLn_Ih}kzn8ldLa^K#qUCtPSQq;?Iw7AP>b9ktV;>#aFY zw8ON0SY2Rc#R_?@W`*(Lsr# zXQ8S6W}AGRSSE)j=?uVrR}h8gy1kzJ1G4VB^xQZNs$gP7^chUrpXuwCvOJ+>0XYKz zoDEK`fOy?Wu!sOkiQ00QTM9rtFaa7}*U?aok(n6YL=x&EfrLGz6T>P)w+spc1-iiO z+nYy-lq8hA4}%GN3eZbrF@*_xwQtM1iA(Rfn+bF}s2_82dst-E9b&TSG$`Nt@zO8^ zd*zQ%d#BbE@c)O#Sb2y4@bN^joegbw;|b|}yJ;!Cp0z7Ce~gBn?>5rW;b8-j7lW7A zxROaKPx8{j%X@e4@;WX{HcU^ZU%N3-`13L0RX7~EIPJ0H@{CJIFa_jjx@FEvG|h3r zDdFMF?w6FnahqfB%#ZGK28YAa_@bDy#E+z-qn8V?wxf+uY^)Oe)?#=29Yf6ey3| z>4ONkA*8wYE2|GN7+WkY1oVd3lr^^dfpPEEV(z)Cf`cR_GG0U&%Z$W>5i zHe{&YY)e*%(yvWXqTQcOVN)XvkCNiTBaq@^Ww$=VIH>W}NB@ z8Ot~Ciqk4}kKo_U)q_t~C9AKLZ|H>&+wJs%q#OCz)80NEoD)gp9Tilk=o5X2C#tXL z2w77*WUDA1S<`D9SXzs9qdPTyG;-b5<%+Q37vCoWBc3x=xdy#nphGfW@rB(ON5&lK z_+uH%xoa6fD`1(n7|N!7;d(FCsuNvr_mxqa#N$0*;|p?wPyAscxwST{gz`}q+2uHY zKB82R+cP9vQAxZhI!VN3X<|7oxF^RdlYDxRUHHj0R46}CvB5!~(8PK>-4B>GD(AV_ z)#;uf6D>GoJ2P}*=hqUyj4{$KNW8}#_A}reAU#JE*2B?+NLGOoTtMy#HAYjE9!E&Mk z&t)Ws1&R4m#9dh|bW$j9YX-miS|TtH)r=iZZ+guJ7x-KZo6`GAmrOvu(VnAp59u#E zKb=C#O(kzrWap+`c*_kN8t6L=*k*_CO}w-qcX%ZPriJ#efrA?GYi(99W%*Jk#>Upc z5V6uvs#1b!*Q_Po|CbMTZFo6CXxdY&*J-BBPk-!VZ}~%4$Ekz~arr`f6Mk>FW#8#V ztjL8QhGo-adoftZ;WEY`GUgiERiAoEOtl%Y`1drwDoRpIvf?v|dWDs+H%^rTybi{jZ5{9}%OFi8SH|DEt49P2$U1I2^*)G! zJ#%4j!xN$eg6G8MCE$7g!OOE)_D5arI^!qES+g#BWpT{G#I@LZP@Pm&iB2lrGRLkd z-7+m9Ow|s|FLd0u%g> z^-u2aE6p^pUh8=B@{dB#OrqXQNC~dQX4fJ{_<=NTkm0(u$>;jfIetfHXHDNFOhN7` z@(Z`k)iXmde5M~1`9Hr9au)(!Cj_b*`R|V3C@pszk75;1g{nZi`d3N=8%Ipuf4tXa%&9!&@OPw3{7d&PYXaxxUbLdZ#f-#0pr`}Z3# zEg>Na%Dn`VQ3d{p@7{-_Ff@bOtj$k}c@Cs)tnx?v-FI_q%hcElJXU;fw(Xtk55@Rz z+N1)jz3kc*o351@cIA38>y_c&uf-}R_rzTZNJ|dGR79%_o$wVjT}8C9pgDM>Lq_O# z!PtPmT^eh3Wqhz~E3ZTr;&DSsZyWs!?`^5m3prn%5wgz%k26wd4p+S*09t7VgFfg? zF2HHf@okjZmr?h;wU{*oplR_dve)4b?=c6eVty}(LSR}7GwbjzXDtC)7PwLgzY8b+RCk=-uaTYMe;~?%e`vRIcDz=dCJpTx7 zCqQOZ6o;6$V#UovSldc62r*+eE%Dy&Kk>QR?aym>+v6uhTq)Ck!?{g-f4YP!c5elo zsw?sT{9H&jMl$d^-*RRZg0&~x&0nvH(*|3Jx{O`?8JteGpY2<-rHkvR6z|UTgbJSU zPU1xrla=|}w`)?ql~@R-z{tMvd(LgF$^TTyi*G0r@7O2W$ArCx;T5NX8Tu6c_(fgCnX+TN7B?4XZeUSbS4$0r>egLSH>^R}|?rU@z zrWiM$*4RBWQd|8aIrP_v`K>CApAqxjr)O36Xr4c={*dM+Y{4jUA3nXtpRs5V=Z8Kz zlmu5r+I0)IzMR=ff*~A<_mGwt9NrFCW#}`DE%`K8P0E5(fQ9YI(WC7^p`Qh_Dw6Vs z)Uhz!l$4C(xNjcJu3<&RKAQ-@+tjy5xb)q)VtP2NStjBLM==ugk+*<@KTz4BzgriW zcB@HibGp$7IN~QsHF>PC51|OVnzPmu?LNcl%9Y8t0!cizu6A~g>a`7Og~ns#mfaXG1LNe2wtx6fne~)BLa2!^pXaM9Y`xd%-$mNUVYL_P zjp4G)x50G!4|89VbQ6}FQ@c%d*x6(%P1tp+>xnNmeyKTt)Eo2Nb@#NI{zjgW>fbns zRs)Kuu;tdS81@T7%(p6yPk(tNb1k`NKM{G`hHj9o7}besN^!}3V%w{A`pP;ATh#u` zU71nH2wY(|BAaMp8TYr`_KqxZCzIi44z8IGzMrk)w|s6sSD{*-#q=)U49wtK#g3tv zG}EOp>VOaYO5O?|Hn?XDKZoh!?I*oF^V$?*pVK{Ns!F+k@*>hJzxkIL2D40FgRN(;FWrE44#}*USOUJVO2r# z=oI4H+)AZsog{rwE<&v%5wcqo(NG1lVo+DPe&?y)S&ahuVcfhEM6@9x0#F3Vi#;$f zaH#FOn>N@?KV7c7FH`2|seW!-ge(`_qI<_1bc zUKNP>{21(GQO{;L-FF@}AGz;KZy# z)Dp{R&H`X7UBB7`X=|lJE}R8*e2f?ld5@7n2Ye5#ST^;HR}hboCNSW91JVK7K^3P~ zWN{mH9_;S!f=E6R>{Z~CUw#i&XKoshXWs%N`0>yvvz>ZyodzP^Ev(oMGd(M5_moy= z`?a$x7Jb181jB$GlIb8-I*N0IPzOw8F+>C+>;smyk-52SIMRXhLNRJ=dfT<9c85Zf zn+lD)V35d_ozdb~fe08%hCj3JNs_Yz+>zN^mHzCaKoLsY5#nur?DC+%rR@L~4_nfQ zK)T>*_ZtH^1qO!OO{XH8UZb!i>!kvq%CO21@GQ4I9SllJZj)Diq9PbL5yHGtZ}@7RA-k+IHSk$uf6Wb0Kf-G{?I+tm(N~%e%mUb0S^Q54T)XmMG|xU& z{RK?&S%pW^ZA(jrnJ(}u5BG#a(z8N%jH;r^$OG%KF)_7~@2{?D%soq2>W(&3<)6@A zAmch<_I8#Cd%1c3$eK$}u0t?}RfgILx6@r>I5s6d4pqW#uP*aOEsm{bf*+OQkY}B~ zTe;KhOT|@)?M+wfRlutjY8`WMuY5uMDJHY4w^*rTl8=&#Dh&{Oh#{lBvZ_*A*79LD zn~$g=g{KuTWfm45;KMPr8#D+qCUeH&liHKaDSqbWzyQ2cbtm>P6<5N;hr&W0%g-Sp zq`genO)XQsrZrxxFN?mH_rYenSM_iqGWAsEJOo=SgzwarC_GC0nlz}SW$+u|w*0e@N!J1@Pn126#MYn) zZio`dM)q{jp$aIqNf@5_|gsSjE-B+&%r zlM3jj;)FM4nMLg6XF{vAVwsJnLh4 zvRFS5N3{8cZj#E$ei%`u~HYzmRN{6e=3^d)>e`Z#*R3>ki zb6gmY+q&}qnOV(qa@|N2YC<5P=qx{f=km^ry$=t~3{+}^oF3J%7C;&u<=E2FQX6RB zpqiSA>Vo1Miv=bYUc3rBgkeVUX+HA)vjquzjO=7RL~P$gPdgWWFzIulyQr?3kbCrd zxy8}Acy=Hmn|cKT^jQ5zuY;_zkG+Q0%P!lZ;O`0`s-@+K`J0fL)Hwdep_^8&&#|TT zY(>tqPFBN-KQhR1-+#+-1xHC5#uP9@>VRLuiSHafdg%8zY>>z*bNT)!=&7kjIT?^T zk`AkF82K-ehj;eE<)0&s3@*^{%>kV-NUINBBr(&|AexAo|oNOZql>0TmF$vw@Z z=>5UY;fL5~O-B$bN5(u7;tjjjes<5v7hz1lzDb{adG&tz?=wPo9$#sx`Q@_Tv*)h` znSXVK{$<$gf0It}FC~5Xf6v4IKQD3n+u88HU3m&e)Gz1L}@~Q8!!305EQz*I&rTW5y1eE*CtjVho07AKI=l z6XQOYsRKThMT@yy-&&n+1&;*8F_!F|ec#a*Tf?GK8Sn__I{v0g6i=X<;%{V^Z07ap z&_WM8H>bJQL!^5`6P`LwyID0mk_w`>rQlmKYq4H(nE1XQ0^L&WyYnVnh;a>}*>?hR zG8-nqlx;o76|*d!70j}Gh1`izdRH(*FLy+x>vnx{dh#}J8^Km9Ut-C4;(cySnf*}o z<0pHms7{>?%;;7ty3SO&=_&w9D~^_Jf3pzWq)?+;M#jaIPg2B}mbV}x!Evr9ZAM#Q zYJTWR8AS0+^BBH#60`2G{wf7XsKy7s2oCyWH9;y*xsA^oHiJQgu^LG90rQBIaFJ!p zBb=K(fQSHnLY|MjTAKO|Yx}u0P?<-%(Qt6>x;r-vinnaobj6=w7xKnTWePb=5!>kcLs?JMrC{j4R%_q}rZ?L+HkUnRD!0hFZ`>^M;V)W9t!a

00ls>Uqk(tcH*aH6lPQCMPSOft#kb{nbUZh$Ud z`FsxdOmDu7u#-hc?s;$ftxmb_ZX?B5_l*&a?06yj*~q(eHch@sT4g4Wxb!Q|xRH^8 zV_sf&a5|A^v~{qOVXe{BdyBH|2sycOR2U?KolFIjapB!2Mt~uiK`9>te(asaC0@uE zm4LKZCKeXs(dJ-D*gV?4#n0Uq5+T-Z{PB~|balJhd#gkNXjdtC1 zat#_}f|F`;T*Vc(uJsmMdCZP#WNN1G+ggRhV?r+>{gn95YfJ6e8O^BmT4&U#Bj4Pd zu3ni8^2!3I&sJzwi^Hw>gSb;F(Im6&)5K@T)BL6bY_+C=O3juU@MMG*?G+wwfiOT@)(e$=Crpg>GF)$RP(7bf04Vfsmkit_2nn{ zTZ&9_J3!T3b0bqFZv|x4v?i6u_t?&6@m%R9S}+K#c%(g~4V_o5c9Eo7zQAS>QC6F8 zZu8{a=Ykk_1}@tE3NP%vVn@2p%+9glTUh*IN)AFK$WeHgY9UeyOyXVRe5l8Q@N)nT$zW4y+@%z2^WN7HVoS11dl3cR6)k%fR#^_Sn0 zF@A(1*w%Q0c@)1T(A4d1#y8IyHHK=m+^tnhw?74Wi?_0Q(^Eh`BI9l3ARW$`4GJ#e z&9qj;9%>0#zM6D}Y?Ah3eb0E*<^;?`e>Zlir?~ zvYCt3)#7$40?o$M*zKj!nCgd^iLWg+lUw&S{OPY;4|OMg>}YC|b-UvN(pZh5gjxs? z>9hTs+#MWP6Ikb`P_+H_$sd+F)wiJO%5(`Goc&-ZW3dZ1 zFV!dwVY7L+now{7Vp{>x0g9UuI(Jl${Mb&9-8m3xPLrYj_VH72Wg6GZ_m}j5ut#IHuM)TShZJ~`d9=^lIYH>aL+bpUx z9)a&ztH4EVMt>?{?|zc$zJX&0zqrjUw{>4m;oTs9=5^f;Kh(B5*PjVO+zWs!$n6-# zF1n0`V>TFt6N+7(+SV^IraiVcDlJQna})jh)mjM03&LnDdkee1OH^xvwBvg32j^>$ zo%c06{N4F;yc4~1kSAPuvu!7kF<%i>;7ey^N9QC}9^MB2kDv_cQ6cOaTr%E4vsAkp z=Z~ayB^q0U8|cKq_`p(Wc+7Ig?-E|#=3l)T&F{o9F#9zW+0V30Mf`{p*rLHI2kPNY zAaz~09aZ~}j(|HBM*)M7&P!K|< zt1_RawiGt2f81T>G8_pVxT)7Ae3^vxD`uidimH%B7{W#r*;q`GNaas~{8zJStQ5u5L%EIR$QgRv4Pk2ML#|o_A&hOzDmS0(Kmq@=~Pd zD^zEyc&{2vW-L@^^6eG}hYTL^Zx8p3?phXL`X*M9O7uAxHBy=_PenrifTKkPe!CBx6joUh4t@`G=d9Z+=xHQieb5 z$g?El)Z&&ThH)yTaP@!6J)-_-YqX#S=R4sAny$9oMAcNAKOH7IpLm5D--M4_rJ5A? z_{rntx6gx+v?9$uB86fqI@T5ibfYGkm(*Zx?|_UhDmGPQcVApyRt0n&WzxaE%HC1| z$tM#j=0s#+YFxlYJh4e-X6kvd2PpfclmOYeR zd_$nxQOmj07lnHN{s(dI0Tp%Dt$mtpMX(JRh=u|rt0c)PEl}iC&HLS%JNI*~_g&pKSS5A-=j^lh^ZcIRj}OJS zY~f>j1VX%fM^Bzd!2UXaYGw%U-ydL$#rozNWwtsQnJm;*9K3e-rAfI@S1bK^4j zb_p0-m~R(hPyk~SZ3r$=29F<-3PS(lF8GK$p*D!!As*Q6U5Mdds~_l|*)gCUZx&;h zN++kG$$^4E0y@L4ybWtRq?A~^z1hg)o>J}evprcb3bu$`dBQU-@0!cBu_3#d{J3+c z8=th}H?2j-?Zr~hK7?bX>Wkrl7-SYRI<628|?DNv~NgBT-L+~ zi$9a{cND5gl87Dt(A8l>So$90iN`Rw@atP)g{wj%L_msIdlLYRe~}^vRP?U?Q;K+P zAza#%5MsDGc6wxbXWf)ydD)1!Z?&4^8zLIO^g7T-QnbjZyACM)PwoIUT)FVh(kEUZ zUm|`c=xk8PBCAF*g!ly50ss^>@L>8NF~z74^gzgg^nvO}DdCS3Nb!fvyRb%(;t#=0 z0Cmtx-9+rKSlps$B?gQwid?&Q)~hO=bt6UDtVpt`aKngEy#hy_p?)5hku9j6!6h-z znkIM+lSi>?di1jB#@61M`|e&&c$+v(?&M-neQu6RbgP?ND{rse(4>t{FUW|1$H@R| z0MT>NT84ct0py9s(DE55wMv99*t|cv1A)aTk^)h~K|HD$E8z4Cg0P@hZvvk-0^EWY znY_dU)Mk7R3t3=mWd>4F#=oL3li|{01Q-`^v#5ZEMF~Jhc2jNl;CGea#enF$T51DN z3xoD4pqW4$KE{4=I2%x%nGi48xbY;r77%zDAipSgBNQX>Ds-S`;jsd*YbeCmWun|; z7z0Q>(E^DbB)q2s;{`?SfaKB$x~J%^gH%<}k1~AgHYsr=R?wvjD3nK!9YZM>CPU># z2qpwvQoqX_q6jbpv_3lQV%26WNU5$uN;RTmz*7n_h8g>Nn>uLB0G&JM_}(KJMhr%E z#Dhoql{Rxij$p^aH>TX5ps<0zXtQu<9 zq^Fp3)YQ}^-!zNo`bquk%5k~d<=k0X*+*>*=vB`eEhlW_la###j9=Ho1Cw!TZ{WEH0QA$k35w)3|StSTg zZA8XDOxR?C$KognxpJpLbLe%$oXNP~ynYgXe_90hhV&KyHjsBX#*y&9h%*gCBQ26g;YA^cB;0fy2!4r6waolXKI=WT~mZ7uo^@p zQurSd#O}aO6mqq;AnuqJz(MF`qSK%Y4^JQt#zm@{(I{sD;S+!)RUbvIx4X5B7$Sg; zfTym!R80x;&2NaqoGu&#f?FBb@VdYxkK%4vHA|P$tza_r0J0?bY$kkR>0MNz0b>dZ zSO%d^1{@ z5W2ebZD)CNQw40-K#Zhe5)2k z+aHxR*Ale#MrkY4#w9(%G2M=PRYZ3#Ouqv4xn&qr#5wqjQYJXO5tl049j+6gxO(B+ zO9Imt9QxU+XMam?NlfpEllt8$Nhw{y>clyKX5fsB(g62*jvb_xTKg%$&IY2n%lIAl z-DNSf-a(Nj`@1A?%DZbkh^759v6H~D55=%*m2>C5vNdLE{hHj zKtUK_BMWPT>>pto0MX!)JWC<$?pg_iTi`lZt?{T5(==a*Xt-PZ$MZtU~HT$q`;;K`&0?SqXWHE0KEu&jU>#kL)NzTBF^K z$rqwJmuweh`JA7`6Cz~uPJQI4JUVd6?0@;0L6rSN+meK2~)tjpoO4!IA*j!`-*tyDh zql!{PM=NnIRlmeydwS{PVTm}Qs>rJLj;SXHFnNXFg|l#&Om&s7*>BQ}ng?v!;m;4< zBQErmd+dk#`T6av(VkbK*?p2_XlP07(jBXF{s@Z$b}nPIb9wCNsSay1aOq9`#>-!E ziJGh<4f9XQuIeh6wdpDLBDY?l?7>PcEtfR~`0%T#1SJe*;%?vR2=QDLInd+# zl01nIc7q@cierXT8On4i+Zs47uz2u%z0mX0g@i18(#HHb9n(D6QSeFN@)ukcfLH`n zLjp=3M%4XB2BqnP(V!VfOepYNVeS+i=)(|L5b#Rnsp9aT)k`d5-nqia5C3PRYjN?3 z-scM-Hmcl(pom(@P`EK&;ERPEbKi zqyj6QT0IO~(=<}a%3Y@u=wQ4aIH!cq29W212< z#A=W3Tos^JIewhFncV{-7YKDvrr^?1*_h+?B|CoitVGjZ?R_;46RBAR3B%jdsWFr5 zakRp743J0DVmfy*?gl-*D6PAI<`>Df<1g(VMX&q>;z>%J$BsNMLs>D(-fN`k7|T^Y zdUgDHC|2DM3v~>}42MegWWi82?)53kbb9G17}VddVSQ`(B#Dt^#PdKF^$BKiJK#rO z1)$3OP=yMZZUTLLd=OFtb^o;+9jwp zunMZGeKupx5wms>o(mdalt&3bEGC_5SEPtQ0CRwM2yHt42oet8O({r~HU3dw`)lq~ zFhlM&-4rQ&Qd?VK>cY9RXDj>|6Lh|(c*?eNn|=4~zeHpG#8#oq8@Dze5{Ih{$-L0K zt!C(28Cy==kj^W}>@sf);dAvziCwksZg!g%f#vp#>6_*DJbXz?S8rdtpZNN84U(u}5#k=^i9i7q)XwAyuC&24XpkI@v- z;=8zmex-|@z=;J=6{|`yQjd5#;-hMX;(Vt*Po%Ljw1bDMJV7u&>D4cyxj>2LI77UXfpyPovJ@xvbXMERxA^03YI*w z=Rx$?rjeWi1qC8DLoBoyT;1BiNW*E`!vY1eBy0wL8Zy&Pl~q-7<*b~`UT%TjyA^|4 z^ubulp8Z79*`CjX6CMoQPICokMD(JrzJPIhBKXEA&ZOX)V)*U7xND+#&JEF=rOJX^ zVq*g%$*fh)JH&Gvth|SZc2BPR+Z$O-p6$)afczo~zTI=^xdr(+*MyJ!Kna}JAuQRnDSIo8Ztn{}&;B~0 zRpyG_Ub!f4J017hFKRlKmVUalelzzbfh(V)C8zWLMxpH@mSNt|Ft=lw_OT}xQ2CPQJ6Nkd75CY<=R$KT{WHEr=*~8X9 zl6ZM}1=Z)J5|_s9Iu!9;`O^F(4?^=Q0a^UKteEQK(ZJdSHekJ7u_)Q)|?q~feot}nh$1{jGw=tHM(0< z&9_L1qu{d0V6UOs{B{IaS=w-}&bTRMok=zAeF~9J%wI+ZKA>w`+e3jEnDWq<@5yf` za#+l#)wsD|9K+(jVMc9Q|@c4 z#>@(b#RWyf?B7U8de?W&kk5K>jPvAcg@xP$pbP2wVptlE-IA z4^Bz%=$M#sxVWOj*?}lVUGw#;O4NlUi=n=V>xki;mX>y85H1>2mNiKR3&b#ZKEW@k z(iD~_+aYW^asEe;&LmsM!P9Yn5UnS@q<^Xx7<-aAZl&R(cu1k~+z(D|ll4Belw$Lt zkcUv;Xkf5*vs~|uG#UF7r-!3TjGC{kG0IQ{NZ)P>&b^y3=wPPJfHmpMqO;pNkG!b@ zQzA|=0`a}3z3nftiXlR7&WW`WHXdGoXS~}fS|Da>WJh`sgN5FcFLP5A`||kEv!p-A}N^7;O(p1c9yiFzb(EvR?Q zWyc;TqY8|Ri^D+fsfY)0&*6JWSily6F#F@<3p`DY*a1w9L&ZiQ+yXchK{@TqEIc|L zk7M3PSG0lZ{*P~eD744{}tDgzesq?{OL#wZOYZT!u+w0EeDw)adr{pJn@7 zZtRY}G|`N%ee{6%edBD8w=EdA&XmFoa{HkA(7SFsB_-Dyl(ATWd-#dpWg2P7D*rQMvKxEbR?D9jYDg&n#7=i*sk9MJ(DG814w&CJYD z^Tx^9xp8<{8%E{Hm~@B&gN&|cXsC|jMCbbQpnj@EqEG;^q6sRJVH5wn2T zhBRpB$`xpU8Bcd4J8up-puiK<^n{!~Wl-dy)Oplmg5WKPVG{eEFD$#dP_fuJIH-XH zP8mw+2}lwJAU-IFgCN8V$g)?DA3wf6KghH2wpEBA1(`tVRnA4AXhks(OrS|Ew;uO~ zIwS?usA+(QgAGsBc(}3*b+o`okOoT-X~)rJ0XkMfB`t6@2xC!8K$%(49O;A=i5h9p z>p*J2P`Cqucs&&++ya}Lx!1tQi`pB&noERuDN7*mX_oYMGYip z=uuJ2*OP}$6JcGUF=hfzB`}S~!c_zsl_qqO(V_!YO@P_kKfAdIFg~n8c#CCyXw}Ff zg#RoWOt)n<4hvRrdbH$P5#5z!t11L(j;zlh$!SlCqZ#9unu;MVOZ!iJbN%BnrTN2) zn<_ER7WgDo=cjI#&$QCj%vO~QW{PI_LNS*E5<$sZw|1=ON{lUdeG#MK-nGSAA=%2) zF7zux-^Z)95CKLjCHf?}d-Ls&>hYjeHeD&vx#3gb;~mbzTF=r_PI)N}U>FU=4OacH z4^v0uJQCqBWw>KMvOo$aBlYH1zjB81W-lQ{0vj1w&>gUj-8^pY8c)} zcPFzuE!jV&0#OiV`paHqz`wrv`Wv;-dmRN|A%aOX6?JEiYQVDf%A@E*ruRnbTrCUD zpbNNRG9TIPoN3OCOH;+hKZ@tG`Yr+|oO6MQ7?*L_(pAi7=n3AH_~X+_g&0I8MIb}K zB^J36?7@!D3}-hgpHS)`o{ly*U=aGl;CTW*DR!XDCKVcOjDR~PXV=z*#uk#2s^t}$ z4=O`WS{tbTk>%0^6g04{2TjbFhmpy2N+0%sfBO>c^F7_S2$RrXIE{EQ{rh&jZEy4l-$0a z8vG-jNWR3ugZnE&PS@^j2kRd^*wtBZ-YT94my1a$w9tKl7?Q%~LPE8Cz_4j8P@so6li3@mzht zOywf)5b3EvzwB)5@s`YT3+b+fd52JTkB@%@3{^W-CH0Lz4J?J)u5NELk@AYy^ycUX zPLHwcOGm3uAEtGu9jLm z))gxvjtrrXasT%^X1PL>r*?a&O8w+;Sg7r_2Uf#A;Q z;aoO>+dJF*qY>8<(?x9h``;UsmYH>QmORUQUVAC=ZRQtQA_et*?-Kim=7}QW%X4OL zJWGsWvF&b}WwCFusMyobEPRlb@Vi3x0*>So!WS9-HE{cY z4>aokB7Qq?;UDrpmVYR1RnMPb{_EGjzd@P!H(hBR$7SjR9p?@H4*YxY5BchU|I11m z-o6arz2E=GU-*C1-qiYw{dS=Gbnx?R(omxu+x9}n2`Hm( z@x46HPXD?!zsyMSn{M2*#rrndv`T~Ut(Di@!qAF$FqglGR{W=HiN0i4zl_EDZC^{A z(urb@yye~Bk(~O1^uR=d%3s9n2mbxiZmgU7>r#HY{C4?J*k=OiUw`wz{yhBi2f;1+ zH!`KK;q3@tvV+FI{m*}LzGMfCPyWxp?Em6|`G*Skzr2$5q2u?LUmqO5IUbb!x%U_N z^Z(6~_=3NCKwBL`5)g=e{(G4s(8xtSgE1(WIJahBo#XbG6pqAmzb_alF8Z9 zdikSAj@*Ej1PmKr4pjDQgYk-}F zbn~>g7|h^Eb+aXGu%fxLjK2)ixVoriRi~QWOqtqE=h?}z`DBQ>YG|5$St%16IMUp= zv%V&z`$UgXL|pvM#^PY+&;tLjQ7a`Fs3?}NP2=9*y$2L`C9R50C2$D&k##afZ!Gu3 zzsQTzr(LX%8{exxOJwQInA`boqnxfT!ePKJ-f>5fNvLUp;MRVKTG(C+Ozya+L~4)( zFlua2fo~?Vd~+`OC!GWZ^<(e5P~cr`A(#wDNAS5$JzY3W^H|=1;e2CU%{vqf z)H3AE1Ua;ums{Unt)d8P&E#R4RB6WZDqX2~k&vKkyFa0o&1jcM?3 zQnj>X#@K9bx}!EjvZP^$-_zgcg~w7Y!>N1Mp9VS-qNEf8UVmA)s7hc87516qNb1dg zCpXVOpK5GcWw)a6l#Z=`=ExQSIqC+gOj9j1YuNbtUB)2_uiK=n3&t9LWUWuWmteJF zL*ljGdM7kBv{y!OTN=GmHDdYn=(T+9RM$$oCqyA9mzcnSUhKBuE(`63Ky~yI<7JhN+vRJRTdl1M}5muQo^CTljr1vBU)>d{IgJ;Cp~^z^msTKs(x z5v8r7YoL(DyAt_R$36DqVa;+9H0Z=hp@EQ#1rHab=AFF2RoN82 zcCVG~mhD+bC1IfT)>mFbyM7>dI(5?&i3ut;8q z_zdOTM-P*hA3V^gcG@xjs0pI2uSHU7Wnqk&_NE4r+jb62QJ<4shL2e?sCm_0=$;;$Z4UjXW=wB3|hGEbZnV*XtZY3F&1eR7khass7F0EpZ5w`890A^=e2?v z@#fki;(Nf4%(P{s1L7`@ueLGm5v^%&i@C{2Uhr$*U8(8L`i=U9k&4u-hlK+-v9hS| z;kI+S@Rzdogn-?n&$ ztxSb26P)#}O?OYqN~_!5)~<&mY}O>+f2HHI`NAJlrJR&DY}7C6 zvA&!-sHT@qRDc?E9lGRw-9zR{=lYumpSD|xjNrhjYA75{!?!H;c#nydgRyPoY+j>$X)D_TOW$u zvPdF}^R_zqG~{c!0`UNbmz|v*+6$2Ndmib!AoMaGnizU`74e+Xmm#V3cMADj#omiSkPDngU^o4veJw-`+xmjGjD77zb@dY8?b- zQ6PB>pm|WZL>F{xV*sM|D(8A~W8ml)zE~pB_}tsc_D=fZ;-oj%w*_}@-2zb>NGpMm{5R|~)p9l@7bP&%$Eorbsy=|Qa$_JR|YVqra=R@XY_Q&=sth&+9 z!yny_*pf^U#z1Q<&>N{l;BVTAr<43OHI)V~;S6XBA#^gtN83((8;d#tghUk>k8pbI zyTU1fg=Sg`YDgo|B_M3dJ%mTk-C{>k+aUev0PQ5o9f9sL0v({Z9~em^QJg8L&Y0xh zJw*fTckiya?XCCj{PbZz8x3~tf(+HX1V~GK7Z~_EqGN!rxEp$PsI3NKR^E@dpgE-k zdStt$AAn3#mWKG!b|4JmAj1yWl#y<3Jxn_LhfugMn^xsJ>De)cRn0q#nSQgqUmF;G zV8m!9_cFcJW3t6$aKaBZbNThiTuM;sJk2ClT*Ja|;KDfV#{|m0m%2;G(unEn>O!X5 z2nbn{&dm1gCIUAt?P2_V7-$B9qLLL#4-!%@x5%OPcET>3Vn)*0+_-jI zUChlgQ|g|f--7Br;f-&gSz^ z*A?5GX$_W^H<0uW*-&6OXu23X6uGkJ=tsL*6t-I}Ub1TeMRi84TU5A6)Z1zd)BDj#;W#UG`#jjtR0B{V_s8uV?Q)T0nVIY9PA}AFYsR56W z3^a!+$er;UWdD!xS)K2)<&{cr_|T9<62jKpHM#fS-qKxL@70-|FBwsO`0$-ftU!9g z>0afvX&0P?k$Esb~H#b|L4DgNKw^n0ZdJjF^giKyuAQs;} z3K4N1s6Ct{5^8B_*s_EeqUEZ?+~Cg@=Sr;~@)Q2XdrQq}w| z9<0JVpPjbj#6baAHM2l1M}mpll85ol3EQivEWLX5YWb7>N>p@$dBtRRQ}HLO@YAiS z!MCmbbeR~W<@T}w0gea$aq#BSic$EZBLg8x-5zZZV|d3CYr=`?doG)4dd4cRJYUTw zrF!1(({EE5qtB2iDlD4QB=S6tYaE_%Vr2_Tf@P!s?O|fnv#S(s35C^;sR8)tC2+HQa?=?Ls*|PZn;KZY%7eP`Jqja9 z?}NrS2J1)3;96d&lwNPzE*ry#1IFbHXvN^jV6-2|1>XTZRscHQkuY7ZOuz04&I3RU zHrCgRK}*|D5<=@?%SD)2h(PZE?Gb3H>9Oh+_StBmkWurYU1eE25ps1SZ|i#xlvw71 z%FqZ#O?ZUOV&>#Td_0gp2R%;Bi{>_uggEx(A3uIz%4AFtOQBhNDeCoUnxxKDSKFPC zfTX_JT=`YI@m8G=KYmjzwlQoq0=l0SvACe5x9v>gnke(J1aWs4`^Ao#8kMhgb;`74 zRA#n!((4Su4nIFSH@KNgM%_odxhUYYIuo@>-w5^X;Y5kc@FAyKk7uaAQyZu-3_o=E z(670btrL!*qOqI0JBN>fN#0GS&*y#YFN?-YW7YffC)-+WE=)DaU$>q5Af#1e&t(y% zQB=pIHqiZHiX|8OrE+FQS!y&$e#bt-P_xWtqK~#BpRdnYYO*o1VBFUrYg1nCNvXOr z>$}AYmENZplI`~Ew3>$~Ja{MAiFZ;=_dil>YLj#21hVIR-`vrf7jV*wKO--}yf9S7 zjD>rhA`BioQ=x7NPH84TOOa3$#@3EH3r+2bYwAG>$ny5FJw@t!i4$)jBX$zXYic3K zyYN7%6!551z+QQ?j`2X&WywPa(^W&6a2V|2W2XWkg&6E%Feie&8J`5vSy$A?AR;P5 zQ}d_n@hBKc=r>akUK&33x`hsUj25tfbIkrv9b+$SDu3!2X@vbiTtS0HvBYQR;=szF zBGCwJoNtQ6tmYKb+3vle5-1uiST$F|H>mDp)bv^?WfohSz&YBLD|`BtXR2ROe6UDv z^`7lgX@EWLS{p6%zI-OPrO7Ujj`8Oo&%EEf(++Y)dZH;=F{J1ddF?0Hz<@uqJkt`r z(*A0-8f;1mG0vnQ2~6+HDkJ$dYNuYYN!?NTzy}nwkJhK#aW;m{oP3;_Vk30|Tspbg z3dvgJR8(03$12%#A!m_l_b!8g;2Ko7M|YD?%I8LGFV^N*mwdS`y<6U|x0P(iKbu33 z8H+bF?=beM$C2th5x=;%!2H9#^g;z;rb*ae0{^<$k9ghTSd^{$lDwANV6u10u8qR@ zvAt|p!SIZ39*y%9XOf*>%*-Sz8#6Ehh|9hXW$dm~E(HxQtmSCt8^8c-n^Uh(oXdDX zGbrc`+~WjZT$e5@wOq9}QpC6XC(Wes`LXi{di*P7;mmma`M0vNvOiCHz|#R?4TU3v z1qg*kTM@L%&Q{ou1{XwOJi%!}r@n8L9{0Iec;Ev3Qn5#>#tsxo7Tgf>gI&zOuZ0NO zqcQ(7dschC)%&t)hDG3?V0XC1_;p0Yj)M` zX3M767-_TBiaMs`6a|1vP0p&Ud0m)r-_pTd7DlYS`N@*LWF+ZbAhlV4R8w8?tu0LV)dZ(MqSNwblDh*;zMEo6c#+ zLE4uOKJRVfwTN_g+#A?*Km%$Pfy0FBYHqg`>vvZxKWZMf79CTGZW|vwZ7a;F;NogA zGr#CCBsH#0tT``*ZqQ}HCoZPKR*_IL+`c3$YTBPs)ELGZg-M56 z=a^W;?KZYriuROBK6qXs^@I+H+wASh9u(aES_WICVlx(onzs$xGFmAz<;afjE$qh$ z>9}%!6jHGe)aF}Q$$r`N_0>zU2KF0&KoWU_P)?=dP+z|-k=K#&<1J#vxvWIukg|ab zCC-^Ov%fd!?5~D%gahGjb-vaOkxP-Q07DENHm=v|3NsHKb#Z_q;q4$I8h{><)=?h}~lwAkJlUMmon?FkK7;QZJW3z(tY`hQ8V)_#{FFI;sCLW74tnZ+#p{SiCMPKjowcfeZzQfiI>3r5<;cTA97x zgnNkJ$=PI1;toWOP1 z>C(@3rNyHs+uQ|6-J*WkL5F_6uUJhjI;Q)b{JF&1Jm_-2w}*v7KUix&v64*UymVzW z>uSJ2+;=BML;m28^f6&%4vO86`-_@aGo1IZPgt5P^0KEubyF) z_Qyv-d=B$g&s&>6^r@*Z0fF-l*)jT)NZBa5ZSJWui}9u&Oa+ckT=+70JxV1UKKf8bs|)~%d$;QVXknCd0(BvauC}(wllT~I%K~{ z&LYqRA*lkOKXcjw1CI=nEsUgI7M2OZ0B0*DSww^hxd3FvPIbq>IIdJCfs@|0dTnNY zPUy=Um5UT-6NdP82j5Jvs_9TPj6qj$fUp=_N=HWP1uxz~S7W+2zy(r5mCgy)Q{HVm z?Ildh3=c`w{zD7=1~ zAM50#oc;eSek0#B*CsQU=vcYq{0Exzv_zLFWjjlwJ?RtLlRp1>K?G6de~T`zHP(9_ z+5~8O0xv~uRxi4518qJN%tuHOiRi}w#npzYzY8h?yWK6T?d@%2kXj?Z=byNGWIf@0 z^5ZUQ7J%GE5n4VdXar_tQ&pS8sF{K1vPcZEW=+f)a+%(Iu*x+$n5VumNz0^A_!j-x zVY#44ohZwyY*2~u@F~^qRk(yh@1JX7q`4zL z`8VVe;xD!g#ehd>s=`W#p?sum^!oTuJa>HiJl!)er?WudQ6NX44H8P9<#y zLGs-n&m_XEalNr_Ebi0|^Lqw#-Q~{ruy|tCP3dXe_4Yj>9c!ii&Bwts>?7{);#W`4 zq*fOdE@!3yTBaC!S(&0lC&+ayZ#CWM?uq#xyJ+IeIF)-FBR4BI< z;Mphcfn8$NfMJ(qb8baOQ*CjRkSQa`gY7`>lUsUn--~@N>e6<^Yz?XEw?wPq(T`(& z`$z3BvZ0w#9tUwn6}EG@X^G@*UsJ6Hn#KeXck z@3Zhf0O2U|PP?)c4tW+3bso;e#Dh4v2}Hht#A_SzAZ9~EEfi7;5VD~R9Ta3Qu7Wh~ zW&g`JA3g=UXewm3Oo0d(ZA%~@=6v+&$R4D;9MkgBcNH*u39(3Np-sdNMp{c~qR2;Z z=qryib$8`}QqB?#aKIdV__U@{HdY)-ydK`2Q?G(2vHQcvTHCp=kF=KdWMK?RevUfZ ztluULyL!R7pms1rA-nv`W7!jjcK09;(D3;RLz61W_VmKAo4~7$#Ak+&ciisWY(9PG z@ZsI@`OvS1{mi3=+t{cvIQ&9gKPDe?4Qp0RHnCe@AUs3Cd2s!^ar25?adB~D6nh+y z5K~&T$}=8*yf5l$ypH-Q+4rua5I%tag-u$WM(hFQ`SO`vZ5~|m=~c~t&4UGC9=xDW z@PvUx$M=!_u8xt5Q(Jk71H?s({(%7|kB|3Fx{}IK;S|VtLpY0(P*Ewa! zI;6vT?4YAn>7*kv5GLQ9(61l&Q4}s)#yt}pJH{~q&ug!ZQPVyV)+7}gdH0M}t2;ye zAdW3k`m$NawCLS8VRI|p0j8C<8(wrTJG^g=)n(pKZd5PZWvAw~7N?I;$|%g_vvn2A zjyU=U>X)B`-S))k)4dznc}=Y)MqlQ#y-xi0TcFCdxN(Li;0^rT=NAyT*1pFcz3E3- zyq`;s{dq%?9$C4pHX8)TrYbh;gJS7kqo~Vcp`F(g%pHc)s}7$yv^2VqT&r@D zmHgFY(?gM(I?eV<(q>VZ#TWa^e+z5xnffj8Z8w9(CF=Vb-8rrni6Za$T!a4#@%xYf zPS?LkA#U$$i$odXFTP|3eNGb`P9H?>e(?|1tFR*E*0R;eQ-(lV1AVMBe zN2(2J3P3zrWYm5ielrvVm>DX$If%rZ&i3H!-vuSARY>3osTHk~)ErQJpbIES_?1BQ zNDfwTq}7C`1B$LkO!Xgcq1iYS<1j1_BU5CJkt#qva7Q?UNusDo^YH1AkBaFZp-GO9m zIxN_OvEEHr|0cYU#!4#-_k!1o#0_Q0xOzeIC zE|y8U1|_sSaMaN7W_Np)p-;QaS|1BYIEJ60F3!$%gZoJ+gd0pGAZ_git5dUod)*wz zU;c7W&$Gu!iFyW_gd!v9APfqEch?a&1nWf)9%LBXj6*m(Bz%K|SJvO(zZ{}VU|b#R zqzQYf-LJ1_P)it45IoGHe^=m%KUf&4VaI}VtW$9l#a?5kp`2Xycj~bs5--`@{2Mm$ z?rjNwH6;bUfBq35mc?K}2`)P{2{&InMog^Uv0h-dl<48h`*+2g_=A%dSZ-i` zK`cz0TM0hEJ4B>DY;p&KmQ*4{Gky!g`qg}C6Iu% zF3C}UwihJUHSovpY>frjO}0?uvQ*`P0HO#_yHc*UAavqQK0ZGR9UlKp3UvCwiUL3v zU)6e#hDqQ1cs~2NG^pxk;I%v3;zi);3d8j324f8WDipk%UuHKOMn)s>_T6qnKDc|n z3~s=%0cDp(-qL-IESP9TqIUt4r!}~oFzoPw4Zdw*sDjJl`yJ{#Pwqltjt=J!&klFP zED=*dqbLQx@G+zZu3*bAUNE=)jZs^PvpKkI&b*TnG=gI64X#&5VLni_Oxg)a?N`+Wp{+eXQX;FZ&F~BA z`dy2%WnnXpcWkD0B#@kf9PziR#T|)GA+VRC)LGI|X+;zgD^MI=^LY zBu>5Pr>vI1A~U!cLK#)=4Y_!{vnFR!+qkCi6a+)fQ5=#{+!k#xnn#wED(%#qW2Y#x zfPQnCI<|YPAes|RX)1X+?6#oaNP1FXgNj!vH781FN?5LijZM_kaNQ^zo?TvJ=v}8; zw&w6T|5{QkA+F6*b>}GdK0l)t*Vg8_S37i?;K{SELtLub+N%Y-X3W}J+wc&--LIv4 zU^tf#I&-Emh@%8~jW)|Sm7#4%T6wLBgp{;>)YEM)xL)vQ_oKaI(rX(oz-LuVnT$33 z>gi^?_GHe{=}WnJx^<?0bebOBAG@CQVIU(yCm)J1dxDvavxtPjzAa z`|9qtm3cR7s3Km;u1i$w^eO50vM{Nc>3p9ypwy6ijh9*vBmhzO;sQcK|W6qFTQEiEEub$Hi=%|P!Mt~40{lb`7>sR5X$ zi>}~In`>7*SX6#}K`k3y5QKun{kH>QVxJ^dPI}$sdtSeZ0gMPY2lsIlH_9uT9#p;B3Tahyr1^o_wc zwHx6;`&&(srpbjdP*Y2bcDHGD&ikdC)PlBh3cl45cW~r1`8*_P}gn<;VEC5Uz=cwnHLy-hfJuhe6!hB>@zy7>Y=2`XXK$B_sZs*-W47wQUR zv@wv5+{k27%6hzP8kA&o*kU|Kqw4;M2frv@X$lmr3Y;6&&wCsC_*hi8xb`@iAJG!9 zl)X7UTX}Kye&K#ns_DB8<$C-MlLN0f%Os)r?w@tAn;6jI?HkN63CgnmW|V2uyO>_v zHB@fHzMDSjSYdEadS3fMd;2Yk@RksxPu?-oe5|wzDmZUF+`}iO{yRI&oVO*$Pu|IV zxbdz0;@NnA;r-0u@Wa95e6rH!Q7n^gv7;}JoAkE4G+!)m{}sEk=dfebu$W&^qSCBh zM1J8yO06fE_^n%!H@C&gxXhxXuWC^LYh5ZRQX9O_%OT_{G*YIj_M5W{6ppP?!%162 z5tg4b@-yq8&f&WhT<*J$ZadSdOsenSwZ~tH= zxiyN7$nMIVtB!m8Fr+8mY}=bjxiU#2oRPWrw*I<76aC549y`)W3`aqJy>og5te*p* zyLt{1?67a`DFTYA#Dot0#;+|cY}(uT!o;>MD_yRQO!h+KiV8EcsaOH&?UUpQ@il-jI~SoC$?f#K&XtGJVsP{Y zp~!Xmv3|qPtOydK+_UGeFj#E{f73RC;M!1cT&6;|2@IU`x7Jk?c{x1VPqdC@$mQV; zT4GbBB6Q=#_svHSNk?$r({Wplm|)!<`f($hGERT>za022<|6H z_6?De>x6{uor%m=F5L;u7uSm{3Y#XN76rj`5{F*jS5BVUdLJZv0>sBO?XY`-#~pom zr<8-joDotP%42qR)9o6enLUBikMcD^x=0^DRHy98>FH|~Zr91=X{@X32{g?wT;nb_ z*)tHcoBK|8kJ|nAmIw;n6W+Tiuj5Y;Y9E7E&-!X1HbZ&UPOHrJ7A_$Cdg9|k$JV3G z{iB&gi|;1Nb;;?TmzG zZaSg%%wA*rbAE2%9A=kxZ{3!O4F~;@9tRN`p z9LP4Lts;(I>CPUx3-S)fblp@Ln%wswioLY{;P!*?6+me4ehhewU$akjz`eVBZ>Y>h zW75ry^bO1SRQIGC>7}*2?-ZO$8&vF@G}6q&Mnf{!#R9w>u501*C%?wW$JMoP7r9Ip zJB~Pcu!0P0W_a>NDM)O%y|T?c9c+PUlC7~DrBaNt z&!G<*4#-Cz-h)NrSuoEKrb^Hw5iwLYy}j zvH{Qnr;ZFvBb4C6$^~)S4@8A%K5R_2ivYa?@ILHR_0?{Kt{g3X1a(Dv)foN~)UA>g z-cthJX275&%*Sp=NTGov48;;b<8%Y~J&n+qK(bc2L1Ucv0oR5aL`;Ajfblz`ZQ;`7 z?jiU8r%#_^DSF|3A&@3q?`6?Tz%r%aGD(7Wh3Vq~N*fU4kVx|v1ieEGxjRb@-K?EQ zoyDcWic811fzA8y;jZor>_^}lC$duZX1Xk%4yid%aG}auOxAL6AjOa2{3GATUvyJz zfrg_Z_b?E{2yOj(GTIPK8J9`qkGIGCbelF7tV+avds=G8-og(z71XpDw#L0j1}S;W z>9GUj)tw#jS4ny88a41}tOh?^4{=jnQ{WBgrjE**c^g+=8qilgWPUR1Ufx;RwbL)2 z$5zzDRJmzBUxhAv1BHgfnHsXg=gfpl7j*zUq^;#ro$I>Yx#8GP@W@-do)U}+9|2D#N6mYlzsIyIN$vT(U*u>1^^Lid z7$=JVsEtOxp%5G%upywpxBzk)EVI@Ov$ z@xVd3ED_F@%`@Vv2-=pFjg6i+XRdcaU!Rwszea%y5|MEL(?cRbXerKu`cVaxkAMbA zg?<={8w0iD8wXz?H-Pjp9y%+C^o5edHs<;hLBIxN1}6y1Hmn3i;6gUkQ;6vbpS6md zTp%o$BVOwrd<)->IF2yHQif?NYM~$yAhM^U_XX6*Qn$U`T@=wBbI0mF z8XQ1Vj~w0}#Kxw>U!BkT*Bf^@X;m`fLS|-&IpTN#U4+v46y$wOlDm5K-~P_NzUv!*$($qK_lf)Y-M_fIqmFO0*4+wTcquk!r`-*HB#QaZZv*dU z@$e#6-r`^EJ<+%O|7!0^0u816e=+!EwtKG+uF))#TSCV1D<|s1$z;q@mGmdN?4?!D zR$YxVS!olyg0D+DE^}zAQSI3|P}kgqfpG;uadq^*)cN!0TMRfdpHJ0UnGLGKZQ$m= zmu0u+1|ufEP|ywo_54^l{_*5Q8Qryk#@ z+ayZ47j?fea92a4@U9Atezkn6-bf;*L%6o4_wIiVqVJ?>KN&o*L%|2iJt9un#0YP`wtX?|AXf!IL|5r42^_p!)^XKVtcTm3-t!e4|X6OD+57} z3t-*ybS^M0Nw-}aM&gqYVk`_;k}?boBj8*0+FOF!T@F=qoZAo2f*w-_Kwp+4h{-K@ zbNm8W9f+3l&~KkEMPN2~_s!`g2xdpX?jVs< za$5+$V=Nnhag}IW@PZ#P>b#q9_2`#|3?98d%>}sfzVXKPN`i8tGk=y-9S|zy@ z+=ccNHQrp!zMYt>(IwLIs0$6WlIy~}M5h>|r)1sVimw=TX5)&iDvK$$S$CFfO&V5I z%qv#DI{$8R#E5@-B`@EbF@kw9n?lz1$gounU4HvDYtR8>#XaL(@EYUzdqdQKpk0E; zl(By)H7;RgQUiZdc^z(3Xi6iHXa|*nNMAmrgoIDRy0yF1%YYbb0?iCrgSxqw!)OBG zo-{+f{;&dCAczkTlmL>*eHwRMVS&!D8K9<{dk6vZeacNR_>%!XZs-nl2Y!z5K&u7O zBmrk=GgK*MkiiM;1o8J#uvM6W(gA5>xE@S-+1rGG?RC`4;3hNEY-~inJ$}GCepL>a|Kge;aRwc}8J*<%On@y{P8xLpX6M zg+flp{qq;jfnS~7b{cSR8GQDq{Jv;@d;vXMap+s_wA)RXwSUYRwmT?S&Kb)%LyZ#| zAmfNW+J1-L+_-(7cBlAss&}-?QnNSxSupMHq0TKlm#0XQTz{T}su%h~U~JYffaub|xH>`0ZvAPK8{gyE{9bB>SjJIKfif&7Jj4w#3UY#{XO`V9No} zEL-FMNSs}S{7_tK;q1l5g}jIn;89kTm(wz$!1IsR6pT48mm*2C(9-Jt{LlljdE12o z1lr9qqooo+!kq&djHDw5y`MG>P)A7Gs?6U6MeF}n>gd^j% zf26|7r~2bEHTXbt%1*yF7_>yfH#zEH*$-EV3@zc?H_eUw|Ftaz!nMPEhi@@BJihk++Ry)>NR>AYJ}!^3>}LL z5~eQqH3i{wYV0v=pk7FpaLxYy9jnjAEXCHMT0iM&&OoZ)0{42W9J0NgQsXETFf6ly z&GSUiu@o+D;L+QSx7f+WxKFJ4OdLDE!ofGi(#@%PVR@|l_0qGR+4~VTytI$>RUE4~ z_=*e)`?Nh)GSXk-yA-r3C<%`-4(P7@-X6einb%35xLA|RqqHT25d*y2{YSaSMb!%Y zJL12f#|OK%5~M56PEUUU{yQwurdO_9@f|@yuP-#^%M(9(X$qv211J8^P+EL-++A)- z5UND%lVEEW-kn!TO3!xCE`O^l*;B%hW*Z2{x_sSwMTl|4p%PR@XaHb%kLbnGdoNcB zb%AW@H-drf1)Q~BfCVw3jPD`lzE%~ZQe`&gT134ru zLAdkHgH1NR+V4)+FM~y5rd7n(qoo10yKCy=wQbS#s4#{Apw70j)W+T9S;QWzbW%Z8 z4QN`_j&P6fJ&VyB2*Gn|#Nz`g1m3=(`j9`&=vk)S-HhDm_ax2D1{|h6c)dy~ChUjy zPMH!>c8Yn*O1sN@xxgFPb*()EcKHkpz&2I`s$Q;V$ih$wd2F0()ix0_?peq{nx6`=C1IYjZTbTvA=#=w?MhA z%vY^N<3*pl*?khfU~`92@!=2rVXNtHM{WF!VJXNNRfb=IK>J0@W=8yF92|#bVM~3X z;aIU3Uh~Pg?Glt{MVF1YPc*Sen<2OXLf*Teb5>%zre0B5X$F~`NQ_Zq5Op5t$TI9V zr<ozzZyjQeR#?%{^+n`U$W{S8 zXeMxFMqF#)v<}_F8-&}-)-l4d_=p{uWaRIewKeqb*SGN{w+JNf|4T>Cg43}zL^g+G zhUxxjV>bPIyOuESU}wntc42$)!!gIb7dgW=0hc(R}^;sIwhAP_x+Je^*0mY1dw*&3RHc*;{4wun_;lLqyK z>-nq1p+RL(>T67s0I-Wqy@&U*HuDdUM4^{PP(3xe?8e}(jwFMHfa&z_#Kekl1NFDO zj8E$?)L`;)M@&U&I5Sp92nDppi{AwB__MKZ0wB&mpg%W%|MS*)a{7#Xx#kkzZtRmM6>VVxh!iL!-|Jz)(IYjQ^ zpQ?h(%%WFxIARdCZ(k;%fcrk=bpoBI$ zFc?-FsZe0~rf5|=n!+fHePH>e1|xi`AdVP_?g$38Yzte?Sc1eaGzAHO0}iWy83s-{ zgrDw!yI@N+dom=F5RsBHLe_Yg`CgK=cU>Ijh?8`W3WosEp3z!=@`6c;)$;pd1c`_33C+sVsKL+eJFK9Cy#jE6Z znIXP&XR%LgT2X0Mvu#8c$3wm8w31K!qKco6_UDQ<4`z)b|}xF+U(L064XMW7F1f03eumE$t39H{lNK|=|Ucp5-Z`EA55)MY2%ZE$i4 zUC$sBzmrq?`Y)pI6)&=)xy9&GV-#}O^A0+O=sE2^vo6&;;v&_!`tXyMHg`4)_v<&e z$a9A2Br-m>8boQ;;yG*5ZS$K}Hz@_EIUvr-ndI4(0}#PM$|-mTuTh|sVZt`X>c!H)rUabGm7T< z-&T(%pjloC2QNt^DI#=sj_CNE_)KE}fFQ=5rK%Jkt`PMwBsvqY1~R-JDtrQB)5|SD zdXba#mHr-*0yGK9AVTScVpucI8G>*p3ff62B$e~v|M%4K6KPmKB-WU1e8!uD#*S8= zrUW6q%nR`C3&3T5a%{6sL%Fdbxx;FgFldm?4er_QUP0<54!x5mITMgxM%U0We=Zt{ zBT4><+rxwk`>K^y3Rve-=7fXo9>g`|e55s9C~d!7iW)6_0^GRVmxT>+p^9G>)eznW zD4t~#`6G0WOIZqRR3~!&NPUb=D+!0xp1{{}5x`yqnl?NCCqyk3DH<1A1-rwXQ_D8EPDEqT>@@iZh zDP$^I(H-rZR8{GX@vI52rt+$n6-_FsiX%#yuDrCnwUJ5!)fTV5SJnHvwxs-^ zOh3(XMyt>r+0AueOUn1XAdkqy++`8C9+yu*N|6LLzoh(!M;(_tmUJwVzf=J93Z(x} zJ0#JM2LHp8wP3t7Cyu~;qK4ZxTcwO@t*!Te-`PgCO}FMfx2N8oMBiK5Kk+F*+``l6 zHM;c|B!etyKq;mQ_!D@D2A%>X!Tz`rYIfKUu3Ww9w7$~*XKzwOY{&bC_S4rxOw(ox za5NB6t*^iHNpTY#CqPoifhd__gYjo(-DJZC_VmzZ7EEfg# z-;2IieREJCytqvhaDW;eI_EM1#f`~e6ZY=p4{RK=J)hOygHW8#N1&({1N|DRs*w@A*sKW( z23lid5*Oa}6|sGCe&-YbZqzxrvWnm!*`ByH+G@gB5;}teV*g@d6p} zr?0nv*A(_KUBhBgUSsOv(nKPl@m>&`UK>cg|2&RZMk!ND2?U}t4084@bY{vf=yp4C zFO%JadvO+^cr3PV;)%FB_jQ%eit#%pFwltt@iaNAn$XTs4MfG5M1@^|UQu^i^g>UA z-_7j?V>oDd!Er1iIXM}LE>I92hm}PELJ)TwqJJJgGHq@#WbGDtXWI=mGuKSh;6>xD z+19*?`A(^K809toj7JT3n+b~}4xNUmhi7UuL&E_ibd6@a4|Hw{K@I4jMP6%P-g(FT zl{i9=@Y4y(%glMdl)FT>FNV%`3=5_C1Zr)lnAnHky(rOA z`;CUC&SvAgD-*rAb|6|gP}bBeTjH+;Rdf~NasxilXR=2Du0@muh(YpjjY}mstVDoL z3UC$)WV|Mar6&T;Mvr(U zkn_F?zafy-0wgrN%|4|Rw0{ofxi6YS>3|=14>9oV_9T+LDePR1Aj3%D75F9~ksh#kE9GkN*+A7d zUV~^Mpvk~x+(CgbyTM(z3;sm%0+IFwwaZQjxX_0$uA~^qbHtZI(_XP)kP{Mo2bw{ph5c`t zuS%BxgY@yA!#dsk@XGRmtZ}^H6q5xi49Rg=1+K48XzK42Q|szoS!1KPP)P8(x`#JG zeGAqFAdj-XnODgpYyx*^`!B~TU*%caX|n}~nq0%nL!%Q=Zpd{iG;kE$ZcF|d@$;FQ z-Eg*Ngmf8oMst|}*Jsnq+>Bw;+`fiU&9s_$m%7i|X(Ppd?!}KaFld!TMcu0iDlBG* zP&G25Rj=#CUDF8Yq|FW!rQd;b7sAqTge3s&>ZX(tz1Qf7P&6Kc<8HGb;)c?AV|#X( z94q)7!hr_5@N!mOO%K>7_~SNRzl|?05xn(5TujJ~K`<iar^;K>8A0nO*31Lu5h7~Fz*-#71dA!$ zGg#S=E|9O+#`=wD^*5uhNy;=g8}+y8fu(38CNw?DD4{@KIxANhR$70qmjl=9DS$d(}a@0mjWSMU2@xrF|c z#~*!IH~uXa-H(5!;r`$Fea(wl!D)ZbRmS?Stx^BGmsN{Dtauz7!5CE()un!O@^>FsmKtbF4ewyo1F;w-+~*c(?!>d`Qpw3-$Tn+{;X5@ zzfVlfc1Lbx}VU3MctZya_nFQf-3`QiY%ln~!j{S5Y0v_lR~JBvwA{D@7# zQOC;KTDovsxEZixz}0#_TqRK~e>-L15b*mJ7nhh9-fC}+&^o`v=wn_B4TW#-mLl>* z&j3y#W>Gt!2_U}I&fu?KmvYqi(o0`!<+VoLXQby>AF`TNthf^KyK>?dge0{VlIYLD z9$xbHlwxhXL;Y0+^H#Jvv@Fb@oWb{yls-DES# zx0;)--lNH=Ul0Ju6)_kcIZkn|`m4bxB$SW{H+{!*T>6!S9hw4&0i$v5XfuJC)a-xT zP;_}y7>GI1TqcmzGvOuawV&kRs$DGF6hzsJ1m{k=N$Be8LV%zb2+Y&gg`B`UI}{3J zBH3g6m98Hy{Bv`3;6&=O>e5t|=-38~F(eF`a&SNIn zNIm6ppeimv&Yi3HoaI$j+JNTvR~=c2Cr_Gw?W=!QMs4@)C{a0HzZo)FE~v(7Hanu! zmm+n0u5${)TuctN@Oom=HAkB${cqHv8_^Pw(3i6=qcNw>7{_6h-yPt7k^kM%NBUD6 z)%5$v3=4DWL`Pn%^Wi~dAWCMi$Rpg^oiK4%44BP=@0(+p0|x8(owDLedk2j|=t5%e(JVZqO%Vi>f1h`Nz<4M7S#uOvA+= ze&h8>+HUO58mXjw(tz*ct|QJ3XAHD)s0Q8-iI^Qn;=~G@C4rXN&u$b9EO4lQs~F23 z{v5xQsjLwCZr3fU#B%LbiAiq)M6w^)JDKX#3SYT)O%X|5uYGt2u?1b|#H5S3YKzsi zSbIV)tVP~B|CECUpVe&e%X)Eyt$C`pTV{*08kiP|WODoCEQkR6z0fp<>BAa|RaEnb>qdVy&!E{CmeF0~8^3=tFYQHN_ z1d}2|-fuQu*U%EnGGg5kUiz1jW+FUnxV_;L&1Fv=YjOHX4e6#}5M9*QqV z`#6ZlV)QeUh^&fG*#&4wRh_pse$^?xQ;?@olY-}H`vUZnTeI!yAlYTUC#a#7(8P^= z*9sRuBNkPfixN!+{`_;3E(e1GyVA=8K@dnRtGPFD)( zTe`G6u)xxn{YrPNg8U0wc!208Rc2j9{O_bUmaqA(*k$OlyF{GX|I-jO!Pb1_OJ*27 zt-e_10A3jVUA9}mht2MRWC<90er2sdX_N{y-n!<+}F_p>`hO z+-w|K2b}X^NGgT3(die$geJ%yplkmmBo{*^EVDmScRrTG^nK0IG-vzEgG$6uc6p;A zoJAW_HKQON-4MZ;#?6Ps09fo4d0vpXyCmYA2S0lS6yM)0(drx$aa-Ye&tEi1hbzVu z&MB6f^Hp~jPhA~gr6qq!ZnJn0IKI-O`NUWFO_2r2%|GLbbU_FG$aUH-b_0C_+L5kR zc+fJ61-6}_Gc`TVxYv9fKbj=szJfR)51TDf=tHl@Ao8pNZI>jOk+GS8sNOGybIcj4 zn*`9AQ%-FSSMpMpNgR4}!m)M{BCxVtnq+TsY;mT_C2EBNH&6(N?BI_oImhMB20v)O zi%M(0SDJxkPE0X;{*7QJ8HQpYhB?SBqhl>p(k;j|S*pS6RnPJ6Z~dP6^4Al4**Tv& zVz{iwF5|?NNOyM^;88s=Z28QsKk`+4N$_Z~UFv4ptWMRyk*N+gcb2;9HjjhpLAK@< zoFLzQ$5Ni9(8E_bSJCw(3hPt*NoFzs>Fs&a=GYQd>&|btMEqx8&mZ0jX~5YugFKJr zjs6k!3cVO`c8N!Rt_l2H&9rQ(%1E@x-nu|d<1u>XU`34n^5uu;3htaMDf7)?WdAyB z(U(!G{8C*pi(5VCtGv{UoST%2Yt`pVcqLbb-UoXI3%R<9J#uAuD%K%6jdm97NZzez zs#q6Dv|hK&JwEDM>SLgWkXfU11G!phw$&H>sRYglRC>Nz9Vs=}8w@_vv?=)_*i&=w1{K3 z-s^Pu-TPXec(zxyR1}ThO@$BqQO_#BrREnF-{Pmob$3j-jQ7oj*Udt{@XGhGd`9|v z{NF{ii<8u2W{EtrDoIkzdbd^u9O6&I0zl>dLrOz2#x!E*3Cgr2;7;ubrCPBOlZn5Zje< z$ZW1!>8sRA+`ljml$pVyp%+Py&O`2}@$}3LJL$C>tXhIYC&I$Qq}MJ$veKL4T;ODY3D7eD zpz_Pgn#RWyAQhSeSVemJ`T_I%TU&KHY+$c-J`%cwKx=J|U?u^^*IWSa_#|)^GRGUm zpgm&**sWr=Y6!@4LSPF3dh9Q7U-3U(_W{wcIB$eqRfAsu zU+CcAfc7O18(T2g5kYpT13=Uuti}VnY!x^ctd3Rk4jr8V#)9_`ou`ft4i2g16I4hx z4@Bh10gn~At{ok?cz8igdvg+)NZ?#T0-_fvVs-WP9|KT*0B(^AWxYk8d+-|h0)2g_ z*W-gz_8_(OY**5D-25eGY3NAk?6%Ae4$Yt3Znz5XE4yLe2Hz6H7d5md*gU4 z&U{x+WJ$wiY3&kjv#+!@H%7-Qy>nV{fy$vSB|5=r$Wv?y$~=aII6v>hrSeY&J1)`u z%U)!x1+h=2J*Yz+GbOoN)FdB-wK+TcGh0k`Id;*YsXf!%B-S>nf|uRKP|B|u97sS24{8eeC;zkn_}vH z8$V=0a$;u)q^K7&v}=X&@$sRb<^wE1d}888pp8UxnG%eTk4q}C!HJ`OA<-=`HI)ts zh(CvhVjxV6&3084cHuhEZ!@a0Ig}nk@37AEG9k#;KLbsmyrRN#ZI~O3y?%fmei0<* zU-g9?wrBywyai#fT(5q6gQ;SQUk=z#LAQYfI7XQo6&x_Q=Yf5APc`2ioTu`^Cszxw zdgF09;3|FruHz?Rmz!YkI9&Q#`t4gb@U57I`(~=HVs3s9hM7K?waj+J$ANh#J~eeG z!1zleWvt{p<~qQTR#hck@C5!^1h`preSU}yG=~Pj#G*yqgdzBD5+)4{`_#K1fxp&U zm`uR-9w5jDFb4+H**?iUeR|1e`S%nM zLAXr1+<}%^0CoZ64hxji)6gMI@%d+>VJFq{36o?39%8 zx62*pID*-JYg3`!?}TRlX{1BckRfCVQK;!iIUNzWp3&Z{aMKmPx+-hw!>8jyoUuNmx$n zN~&i|U1iY4=XG>=@#NnWXlsSa);)`+zCW=&N}`+@)XuGiQMKaTby4W~RoHT` z;T=g?O-)@UN~WN&N`D}<>3rN~8qewL?ZBu@M~-0h-rnAmUevCWVRFKfJZnFF2qo)h zR!7Q$SZfZ>0=tao&FE9`S-|r4_fG-7_Q^4-78xUO^rmOQ!1Un&z9e)4)Jk3v!HkfN zC8nb4fRx3kG}VR9L^&8yF%K;D3avgv_jVFIYwKECeBpUL&>2WE_36_@2${g{_bAyd zia1Nf|Bexwb7{aT2U`k}!C`ucoENkYZVU+U+P4n{Qg|T*GebPr5D@+3L*+z8u>x*# z>p@rG8BBmvbU@P-g7Xj6xCp=iI&hG0b4H>wK@JmgkDmS`*eF@gwGhFs z^c^N}*cyq5NNvj6F$N_B?F`|Qjrx>QSUor_r^RY8r%v-7@n*$sGp9yDMF3ZHB#Owv z5)QuCl7%VppbmI5T7E_pSmK$m*jUfE{eWczTE`+bV?j6B_0HL>^vm#^C>R*fQoA19 z21c3=EGE~eg-1;);FQn}D}+&JLOj>~4OlEpCu)QM`LHQ{rmFS;BZu{>KQr;ST&`xt zBSiyR*k)!XY8SRfiW9y4{G`>5_VN^#e$TNSxt(OH>2;WQ#0z1I0v-PFpx*Vsd??)f zs8a;}YvV<->Co2zzPeTdf-GSo#>LR-o%eXd*twdVYR}XH_H-L#*=;z^4CHd`4)EQy z6>Xc(6CvzqSnkWiv@KN8=-uSMZ+y>%IxX;MYMXmuKjxE=Q!JRYbtDQj4RNN!l)=)k zp-%jId^=Pl5%0p=t#T1ROnhf9lV>DnwBew8Bh38yYr$T%@K&s1gHjZ$4Qf+q%EX#Iog?mEg)OH`? zrw962urx=0-Pf+NXJ?R0_}SYV1|#gp;)R6tbOtB|IMj22+teJYgjI7e?Z;5Fj|||j zL2Bs4nJenjLy#YnvZVuYUe%UVp#jm;#a7_6|Lo}rg;AMWt^v+q2wVo`o_kptFMu4A zFh0c};~Cmi+L`O`ZZ)W?t8dAoywHMNJNL1`hF`a_lhAY|iE}$LTlerIhIDIA1uoha zs!Bw)P$!|{QK#Y~6#Z)6YvGrJ75V~?y>N_s`{T1U%6!0Si~x3l4c$b`t%nXz*omnk zgPSd)9&u0xD{3x7dyw9bDXUw!?EypJagok=VzFai9BlQ`0u&ApUr&@qr23KQnVLSC zIK^(G4D=TK5ZFp4G2xY&?t0+7HhM@61-~J5&^`%S$3Du<^*?kKoD)jybnz#Vfdj_L z$;!P~{khWLOR^5khrf4VN(pW{M)^kERp9xcn=OXkkW^P>72lsv5PQMs{uxHQ< z1*%&0^ppjY?tc9<$^>t30_8%FSA7I6H}_Ts@TjS&0pXJWWO2ObiQNquyCTV^5cc~S zRtXrkFamM0fEU>a(dm}&-lc%&WhY%faNa^;>?H4jW(pe`v0gW#%(_0G!2%MlOWMBB z+es{_mB3-#16;JoW!%7NUZFY{?8z-5&~R&^Q*L{{Js_>jwD$t=bY3PFkjpxUYX^un1 z-yC=Eg5bmWyZf0P(4zn^0{)`T&dH$`Xj6dd;ui!zzHm~Zwdb?T2Cyqxg zVGaZ?G@89-Ys9;v3r39n$|=pzFBa^OcNS`(gXN0%IP_ z&CAEku;`DYg;T7fcr9DvNK^*uuALZMYL6|WrKgXrC^CxWlgF3L8XQ!uK`%?MQc@he zoz$`Ry2F~Wz0kp7zNiB?gY2MLrzK6#UJO5E_34C{o^0(2kKf}OhQU&6R?dYJpSYKn zjy9+ki`r+rb|z{)F$xS0mC_Pmtp9zGnxW{;%KgZku{H^hkA-FnZaeFT#3Zbrc70zqPIxUu-r##u?z@Ivv(EM4uw5Lj) zohN8TET%3{a{DDIl`yyKmK2*spjvw-^N&#+KfS|T>nefUQ(87C9@Z(HbSZ6`wCmU3 z1|%}brKs=TEZ&tpw4-t0*~7&xcFVbnl`L^he`P*k^eWCA#C}}Em|q7qigfHi-6JJ# zv7bBP`Jp)1s02wNcmhh^hoFXmBgPjPWw0dqfcXM#yWQHu%Y)$rJXEj2vi+{8d};(3 z&giiTCVGG|!%N>aA0-xWee(aI!{%7MH*AJA`pt&9VJ?xL{c;lI@|yZG)A<}14C*%q zaFbq>>?PQrOZL6Nr1yJ%auSR7nrYZgAGf&huNi`pQmFWYQ>fOMWTjhc&LA}@QY8an zziP9Qe6~F<3YO2!1`_SJuym#8-HL!*1W_>lK~8>Q^fG$D_6EmIFCYY5Cnnw-6pLY4 z!+wgMxK(tu%E99{^&b7sx+Z_^P=Kt(I~ks_^$^+=6|G$!_0tHI5xSg(mN{v%n`X)z zCOyiPH||FhuzrDL730F_!q}g}Bvkkz(>t&!Vp+|}H#axIT1C2ziEYg!Y+Ax}Gk{!Y ziITk)9Wqd}#R#sEv_$M1 zEQd=TPzo5W=NE=YFPePBA^Vl{J#aoKNI z5tESMTy1?Zk4yJ*dCct=c!x4PSma5>JM|{kDsd>%CoerV*ZclCNpgEgV3tQeoM!7w zq4Uwb&>^tmiUa$uB3Pe5yY)v<5FXIbj6hWbO`nfIy9c|inDpEQ2f9>2pEdoK-;F{U zdtEsrVHQt?_`_cq?WMRJV!WE!Q;i)^P3ZOn1E{5{FsH}&fSnsa&RYm?3uL@jh(YMA za@a-=XR!l^8Ng)NZe~b?_;V^aa(G5Xk$}mRWc^9`w{OCw@-5q=7*|)Is zS7>M#d7k*Du~;%yGAU-TVU^lmK5j5^obp_P6F7YUGqWT`!umYUOp5{*FI6##7592j zsOT}9{z+N;MF2LIiCR^wtsDwXAC?gk7>ZB}US~El8(;7Cl1+09(i2{t;xKyD)YQ7a7%~HRRA940EC}|-c2Ctlq6(5V zYTkv^!IyimBR7-A5-q4p;f1%x`}mz&tKT?Y{OW7f%@W>*EAvJjjv^mBPC88z)T!== zGp+s{GP_M7Om4#EvgwvicKil=S&8w}Z{nL&PoA5cbD%V@u-|^;i*i|-*Sx|%mm*44 zxls0yW=nOX^k{N2IL@MmDkNKI^C3br?8=g;dmJ+n5a(WG?Hbk;9ijH}CFjvGHJ|e$ zmXqzN#M8Ie&tLi0Rs4jRz^&u1{BImHGo=Uu{9c7RDIQ%mQsfX1Tg}BuDB-V>vbn?J z!)`vv2JJ!jtSn|YV=Taa70znmTyS6#L1?cUFbh^(KbxA2O&&=@cEtmyy?b!xsC2~&-kg&XQ5r9nNqSOJ0p{RPQSk>#w3Wj|}%p>F3iKzBX{S2{8dgG&&bahrzb zJvf=Lfju6Oe#6#1Bj9|CwE>p9q;z}`h@Af5Mz0?Y95ZfH@6Oj97FCUAFnM?dNNpCG zS)fF3573;`sI04gK67=uy!^qsP=>sI)W_RNg;|AjEoZKi;XeeTXSdJs7$}c>IGp?a z8mrA8@j<0_$|NhYo*0ZZzpmA9z;4Jr>_x^PES&K8BDGu5=)P(lp>xiNV;m!m1HR{n zv2<;e9iQFaHJMl*dcUZYg`-tkw;Ilnpmr9jS|_$t1J!z%(K*l!u`%`JD^tuqu042{s5 zi8$WBo)_gieSjGz~&lZf%TJFvRWPOhw674^7qS?*kC9B-$|DIBFh;pg>3=QGr; zqL1{%A+Gt-<;zpReQfWtkK;DGAoxtoejEbUKj!4HKxw_uLpL)e1LGig*#_+Nr{?Fy zb#!h)vNK0)4e-a_dw77=4j0VzCEsI9ApKwY4m&{D*Iol7FwDGmlgk@mF?>?eU$7vB zrNQ*UCnO9@+XZnTHWMCd8#tH*7|e=&&8;tAzL4%UU!>R<5N{veB|U9_0N#C@gNG~C ztLMKbVK%_FTv^pj^VwL9Z zUEed*I}Q7TFP8hWI_+i}MK+^ZXkvn31Mz|lL;~(J-6`8%`Rz11!^15;?6*tP4q)eJ z!G$i@uB(xHit-vC-uG6b02C{6yM4~m-m{Tt~d}tgar>*N$RI*;Ul%bj@*C=DzY{!7M+6F4_upAMXiHPLi?0B`P6U@tX zTEdG19v;KOu48Q61mZr`D*J-&9SCUvX2Gw&APnP(LBR>i4RZ&|zaZfw?_UxeZ-XfK z>Smk6dW&lycmwO{AnPB0;IXO$#j^w0j%N-QlYwou17t!$Nf|b*si|3EIdcId zUYV|VzyQ8=TX&)R_5S|;La&^5TTeGQzdLiAvw@jVl6=G~XQ4MV%F2x-OF_#!;wstM z*{WM8!IMBC3JfnTZFDnXdkoOc<6d7AC|CanZkRj>Nd1d|zdvCAsjb zkeY)`#PW)=GJn6X0XP5sbfh4WL$jfAsRe%pVoJ${uHB%dY_F<4)#>w{8yp_)sWN2d zd=kyAl%YtLUUhg056`%6uv@a859{28af6-%PbXX`2{=`}V|T$+CGwP%DOZvU!4XT;0>gl%G9@96Pece8UB$7K77$?$(bNWuop z(=3&0Cdjpn`4ZOlOQ(xDSxfKP=7rf>(a ze+v%foUp%uZ?*@N3gAMzI#}QVd{wAT$e?quV6ftPyjC_;>m~v!^fX{&|ISqK0<9v! z_3PiK>b;nhvq`=1sAXeCZryT+XIOzIf$Q~S7$q^h7F6K4N)I4&3N$W&g&GJQ0?XZH zCGcMSg+AWrgIjb}Hl-kK^ zev$LIoC;25cG@84Z-BdEGV+yA>8ldMS6{%9p?YbX|2hkgGfhtB$Zq=7WhJ0P@-=FAy|opm`WdBq zBpU`LGE~KMdugP*+s8s8NXoJ%=pxRKW~Mx*$F1_eVICc`q2Fo9QoB$KQ!jfhSugdR z7kA#|3zU@&XGKX;Nk~B7lF#MnM%h_xSP*o7FS3^llD;~6QmTKfY=q$#TAJ`_3b31S zy_1=9Tv?qt@cB0MxUP}$yk`|gM{_er6+yPFV2cw08U7!rf;*mLwQ_1fTkpVqVs<2i zER&jkAYF!TxQ_+r`&#AHv*neDY3IcDlb{j1JKp|*a~@1_hbu)Y_F1NUH7oqFxrsc;mvnLGN+VHJEFy?t`aSP?PI1 z5$x?}KWlk>+;J2^*E0O_(+B-x+Y!r$8LKj{ra7u=M}_?#)p^**du+BlPvQ5;ccoqj zDJ#H<-cXZ22aE9wEHz!HZpcG~1I{BrM&wHno3(uBLo3jpwt$tgTG{=iwl{}cbI2q@ zra3V&*~{-4P@=%|JmJL!@LaG+hav+9g0G#yg9Ne8c|3u!gvKS(!h#v850eav--{$9 zBuE!Xg4)GEkp-M7*pWRMWKOVpA+V9jVEz;#p)!11#DV7kcqT(|D2F93UCf8T8E_w@ zID&=8aL34qKPd^XHI|+}HznoR$t4VEvg&1P?q*XoCqhp2r>;%jzW$$|Ptm-yN6Tor zWeM2Y@^NylXB*8fvR5gl-t{|0d%?n{_uXBhV(XprnvK@$^rS}#jg5^TCyd$+=ksdw zjrycY&4-^R2nHfO3k;s{7o7_@$&v9U)M>JB-(GiZ3cOhY*+dI6YI!Sff-TdpqR zC+|s&y!R+}qh44a^J-fCN&QNpr_#a=EqZ(oCRw^mv@2|5K{a@Zc)RsGnab7lsH$UO zsKjPKwcvTqi+0rSu10iPyZT1sOHE5_W}|gB&JV$pw@#cD9yGb8u*`;7uYL@O-541? zY&+H`2BSNj0k@I-!on}!FWF0(T`d$0GInyt5YhHA2nxuCtz5jJLOoA;G` z-XmQA!N0?o)fCNoCH^lLfvtvV)Oth4w)6IbB~0>{FAAS9Cly7K80~D& zC2GNlY?cQPE@5s2wg#07IigyVKQVyBwH`J20>p!VltggTNk=8wjO-P^8KLrM5%%^b zBZHknf$HSN?0F6dryRGR!2!djc4){X)VTjr(5NLx@5IddP?$$3#Axx|dzW;3o+P18 z=Ak6HzzT(2qGZBT6~U!+Cl#6@52Wy%_^?EhE*t%ZzgAWxUOQb|7*&jucfav{l#2;a zC#o_St`X5?>SsE6EfJ^9SbgIgS+R3(Tm1f^5_v1hlIeIAUN^BQ2p6 zc7Bi<+w{_++Hg4-SP6fAXg!d68Ac)nDC1j8%Ou!7k+$geLZ>yvSRl>8iCPipm2%p7 zL&V{qA43TQK)l}?GEOXj`V%zba?oRh>agl$t956&&kut}xy|Fs{rRKB#AZKy_9Svh z@+W0~4}JyF0@nZ^{o5ntS(DB>*6as8-F2+Le;vHC;fp8H-=6v(|6*sN%Z7dU?;ny( zx=;S`)z73ZGynO^BmdTums0)5H^dJsd`Ty|oG&J;vrvG$7egc=Ljq-h!=>ONg)dg#bTdV|1|KigAw=O~IzkB!9 zVg(S?{m&0}5swP&U=rWE|91wHTu($RKo@;1~ z6?mU5m(BxII>_qVQvN65f`VnwrPP(GouYy^U1UP5Qa+t=~mo<&d?ivGhe zZtHZ1OY!Tf7}38u!2gH7l@6QTxhv-eqr){N)J~G>woG>{bk8Dh3KyHQsImb0lMDY+?0HkDCEQgVlExXVD+cq z>oMOXOcb_xkRrvf_`s2@7qQm1uXdcNry97-;tulf&$m+x$ zSB7>9wf-b1c!s?)tpzuayY?ARYnsXrCy(Evre?&q8ygxd!M(ZEVyy18y0YTk=GpG< z?i-AYkJ^WA94aMPp*N$qEc0szmhtGr71nIcqS%!)17PQgXS2fldEz2ABY4VI)VJ=O zMsxbrU&OOI+F^R{@rIC$OcENXidkA7Z-gA>8>_P`9=HA8LygoQqux0!JEQi|?)#4W zoDap+w4Xj@qZ}@wf57MsFp+AFV|B&jmYv^tiFuQsZvo?od8G8sl%psX4^L@1@%?83 z>Lt)>yKe1eY}W&EB&dy_X3y*Cn?7uxmi^5f5heA(T7j*M z#d32~0|&QhVdwSfklTli#qt|bE)7LkYs{7Mg4(irdNUhWFBx{MOCM-mb(wbJVT{K^ zMQOZcT@;mFq44y4V!b8KxV^6pUZ#~~1u;gcuFTV9q8D zFrliw+Vyb8xrAvecJ}Uj+FXe&D+6y2ryZuJXMQbcxL=9*+O<~g`e0!IeW|M=z}o;^ zl|!!Wm!Bh)6!7>VTAR`8vesH15`dLuzz-eh?ws<8M+&t? z3rDcT@x}=F-;;r}D$rq#=lxC1wp*tEhY#mrY^fG*Q1|Dl zH3uoUCRs(iA|tbkBp@Set`w@Ygp5pR6bC^Vvqq(3grPKX5uBafPOII)m?l=B?b+JC zw%ct%!A~6PhD-|dQ{tmwr>9n;N4Cb{kkUtIPId1_OthE?keRM7Lx~mVI;xzjCGA3iebt@@VROaMj=n2|_>`^VSAPtusy|PT zzQgS+usOFiV=iJnvRHlXdpb)?SK61Wx-SYSDjmJLeyLvJAADXZrX@RG9>qea3{Zl_ z>F9*_CA@U8C5~`Lr3>zU3!V9vYf{cUxrPVP)R~%jx`l@8N$=iKrso3iN%3OC{y^!0FUUdQrf$i zLSWH}0$NyXG3F06PDK`SiW@)W2y4=n;~h1s-X_2b!SP@?JH1Cbf@wyJxbq58=JO<) zJT8k*%V_4y#eYFPZ0^?V5Ezb*F`?8#tP+Se-t_Gh95?H*J%y@qjS^NY%>?`dfXj|(wH^Sj)Y2%?xzZBdFA2HQjHxwAU!t7`t#`;>QT zPCmsHVc4w0V@85RWKut+GXN;kJWSGtiDOT$ozBA7F0>89i;VoHAuS0#U@~t0IchyS z^NGggh*qXWdGBTJS1}I?vrRkKHM3P~!UN;^i%h1@0t>@117MmTK$=Bu-^ULLIXUQ6 zyvg(GlleF3qZ#?$v(8jqqLYe}d{74H;@LGVhm#5%(J7iI4l_Arg!B8^-^Nq3k@7bk^|d@b-)3P=EE^ ze0eh)UI~5`=4Ve;xvDY3Z4tqjumrF2u^sM8Igb}!%R}F+tNt};fa*)mC4YVQ+r~u9 zh;z~2jU;?_$zBTcd(HY$LV3o>(E2?n8P4>QY6W8_Q{~+4oaT>-q;B$`MWvGe+MJAi z0KF%W0Y&#RoH%3|TbvG{t5Zy|&Zy+kj>QFgy?b+qM(xR-p#Jsd>CsVt&KyoFzGfRjLvk`u;{2S{aB#wvvsCB=8_-fKJa^*~uRuZLyxn(6 zD)O$(z`9UG{?;cT?~j8W_~pyEMvM7V;y~ykx~t6bSTRPfEFn{*?2O4`U-U<9vO{QR)E)>{d;f!j&D(Yp1rxR|I3r?#aB99-z zJD|oCz#1vDNO!}W0x$P6c*_CN4Qa7}O^&Chr(o0rFklOU)t`!rN&~>F%xxzJ2k6Yg z_Hx$k^pF8uvhF~t)eUIu0p%9}?Y;MxV@L!R#L?kR@=TO2|% zD$)=JYcoV%0I)j{Nfi)SUeV%zG56MCS+3u@=w~1k1C$b_1W_a;1nE#wq>+}8?(QxX z3F&T7DM{&0DWwIZySqF0c%AF4J>$35T>Gqj_Ss&Se-IyEywCeQW8CA8a}-CdK(p|H z2^+w95JmeQpeIVBcAy&vw*pOWy>X;^4T`lM6Kt#15#qNsuE0M4)J)jE3P7_iND4aw z0x70b++YOp(m+n^wt?)A<=9;*T45@Lz)(<7pZU@Z1({*3gd%s%<~kmO2$V{p+ZVju zn(SG(3zR;t z8|ZwKX6%RBiz(I0ZwW&;+7_A??Kyd`^El)F{g-B2r4qUC1?(wg#d7i;ZAHuN_sLNM zR`#{Dj9#7s4V}bP-(SFJEE&?WkVycTBX)uYBT)~TudA+&)gV|S*tq`y|6?c!*=*Nt z!Eb6!H-*B4i_pR0P{(f^U{oDI6u~+<7MwW7mX=}`v|RSa(E-O40xL(|-YnuWn^kRK z;TVpTM*?Chk@&5)Wf-qknMO84wKGu=d4FIqZ3f1|fVzSJ#bIrG8|sledLy$1zWtB?$^^e=Ck;97 zomchtJ55glVq-b%&bpfW-_R&ivTJwS7mF1!E9L5Mnr?=BhReAA{kUvKL`m&;a~6|< zNARb#xSOPMrS;$hnukXQ@y@n=0`WzfY>GZD{Ho<%NyioR{3bRI9UA3u-sS8EOKibI^T?C-#fc{1Ixf- zVTKze#7;?Se24LS{jS2jeRKIAJccI$O`N@_{RGT-4Sx)zx6 z1cN5%BZS={atbhM2UD82z}FybWm2#EZJ{SqI1a{K10c892g}DuMvV(NcSTh9*2eXm zSAlu~P-3!9#p)luq0Af)4i;3C8phmf!tzS#!*Uj&C0HOS}j z){Y7p%Od_Knfs-D(ux;Q)2`PaJWPkf$~Ra_nYtpxyky|IsJcwyS+rM@G0JO?9jXfq zfMH7$y?H3o7RN1WYs)s<9y_@d*)>J~!j*B|13NgFQ z9NQDpmN>@9;$jaOr;`ZwE30j%3GL7MS_*DNhP4?zrAY4xc%*LqZ!BvRAtB*iu^3O7 z*1#$h-sAu5|G5J2M2-T(v!$8kA_6l3x1~#coF3U8_`rtWH5*aDG8P5fGcrU z6)11;L)0Is;jN5`y}*HD2N@EqRgf=W@oVvOb8}P4q~Qhx=t6fUm@1hRbjSSB6T5>8 zS8gUQa9^i=`RK)qtD;fNf?*kFF)(QAAB=g8vyRx_rjkd)rrY5K6Q*AT^X`-KLPkzD zv!Qsj&6C8R=_Asa%C$q|Fixgs%|d4w_h3Yo_wK%x4xevZ`C9uGmF*C)ba2|=-+#vR z^u#H+J|y%%Vqw!naOTreGOXP_+*!H{i!9$ZsQ|Xbt(ntfO6c(k_SRL2h+|ub#R}n= zWcg^bdN#XN=Mtxlp$^n&h6AO+p^Gd(Al3^GDgvG*Cb2j+;~9ZFW$6l;7gSshxtrxo zRA(z|Y2|a=ok=mPJ0GBTIiGk{oyHifT+JhbBJR~RwM-MVY87Og*5k2|4~K!4?`XtFg1 z<$$y$8ElWE)=DD!?RudBxL0o+@6`pZ&ACmv#`ItXFW}%v^Q=+V^j9BlMK}a#2U~Ca zc%z|o2_q#f`gS}EeekvDplN76MqYDs_GjC(4;C4Y!IHzZG@H(o7#&|(&rVyc=y0=W zfa&Mj;g(?jj;i~e<0l< zFbn(wdNnA#ioqQlTu@VhgvZ|ukNg7{Jf12{r@(BI9|bPW+uf8_mvHYrLil%%dkkxWTJ;5mhA;`{e0UHvlmdK=Ej8Aou0dV00|i+{D+VsuQ)tm2@a7#OS;?b^;KpPo5)iSMfm-)#+o{J!um)>Rx5;^x<|m6dkK7w_5L3~d#c zpe(bjl9L5Jgsq0lLFhK8f3EGYi%&eqrQT1*mlt}2t9050qA2NjKHhv`Xn6VILig<3 z$F=vQi+?NGWb4cobr@ORWkhYxo@j>|ee;1{ul;S!2RZ(Vx(IC9x~2b&WzDavs~gqB z3WpXHc#j}V0TG{e$%q}Bg+2lZ6tlVjeR>7w^sspU$_2j8e28)3vEXVa&$%d~ju)DO zV5hLsbbx&mLM`(i_P4G~MOL732QTc3h+L&oV4hCl9U2;vtjOwyU^Y()MRl22Fy_=; zZr32pE8TZ#o$mIU+&Z1ZA&T9S&h!-c1T1PELT1;=lS&v(-&FD62h?AS+L|4wiN}L? zrhy-hK2(W$MZ~mEd???_kaVVOEC7igmF&cm#UkBCb2Mf!X@un~%+XMp1G;^$Q}$P) zzj*rjY4?REv=9pwCcrMmPD&(&8b}J@3XOPS1*chXJ;lan4y#3}k-W1XKiaHw@2H;y z>}9~(V1ckV)v4?8<8yO$byQuSU*uq0vs+BaX0X+|`+urwR*qp?Y2Ucx-9oHiI3B~x zYcx=ZuG-tStnGJKgMCnusZs;Yd{CazCdHaT!S2o$QExxMkV;x4iC;R{9^3>AYQG-e!}Ge3cp5R@v6 zHY-X%RY3HiE%?0&lak4GU&*&sblMFFA%}(JuxaUJaOQ1|s_OQ8t*!Z=_xow)cJ8Bi zNVx)dDoS$<8oixrD9Gvkpu*#K-oF8dLt}*@`(2T!_sbgYm39($;-ZJk1I_D;39$w) zm6S%&CNI^2{t*AZBBG-OW)d7PVP+>o@?vpol<>-I9~$e!4pCvG=mZYllhI$E19`bO zvA2UP9%Z^YzKHWi~V=b{->Gmu9K#yM??B8>2tmz3N<&^pG zf<#f4xU3B~ev^N5s5pA?VC#5$Q9ZhYpRE)B40S zYMHbUbxzmCx1<`_r_p^U#a3rdL&1tWnzra?m%i@roYec?{WOFP&P@FP^D3b2-ILA>9=h7_hXVHvmlr;d9F8 zs5jf^jM6d{axS~_i$){xer3PTlVZ$heLRmlTZT&{rYS-s#-3?bSTxg9F~uida$TVKxxt0%~dbE>^$GPc`**w9{^0MgX9Gg&xSnSQh zzNvr2O#o&0u>%I4lV!Ox$FED6c#$S(9NUBg;z|faFJWR{kR)lF2EO#2lbSs@J@~Ni zLom5_Y%DQYCa3|ay*)n;fD?iOND9hvKDNJn;T&K!=dj(2%`7Z#gXsVmF5I}E_b2Xj zJMJY4F01HHXUCvqQJgsKNGP|Z(nGk!K$bo~P(XvKtgIA|d2Vr4%cv ze`*FxPM>6>zHG9e>pb51#u~D$%SG%{MfBClKz=-DypnnE zdp%H_tXqXp3RMHYFcuHLSb_Bd{bha>7#0ZYTiiEvjJ?|cd!6XVCt1be+sN`dcer~~ zRNPQl#PAwp0ZgGViMdb+LkTulgO1(tJhQgh%hhI8A*;{#5AR&YlmAwSRimW?gFeII zAk&p#Vt*!C_RBu&JBf}ThnCn$>%N6jdOEZi?R<`4!&JU_=0jjv-jA)iLSmg@%I?$M zvC&ZP8AgSMwx+YYK%b^dF&=T~Y5lg7>%i+M`y7jsOI7~!TCKqZ$94sFGVuekQr;Dt z5D066N{nhkb_kbDTOMhETYwc3G#(Qho0FS60gSP_hP&;bt)Xth4x;*$kPtj@e0;nQ zT&}L|O3*&5!Sw+FW(Qb*fzn2e+26wh9f9wlpRKn-wAC4kMRfC{Q14tOVhaE^Jz#T$ zaGVAuJFV5AqZ}bJ=RA+3*fO9di2lnJ=+pOJ4NfgAG{Kz!w0yzs2jB})Jv>aTqM`!0 zOOPQi-`8vwfL6{SQqh(;?r7LHG&kW)$kq~mB7gAQg{vPdMd39tgUGu7=u5@UO{gNZ zV1n8b&&%7N4=itslXA0tL08y%&N2XPP18vW1kEkctn@zzk0|tp`LA93esXf6!II~E z_|CDdO(gQfL2@FFATPHtxtZ|voNCLQ#CN|G3!U}};CV@ZkqF+1!)w7)Pqx_`du)AF zG<+|R4f=dLm*bi=yk>;BYS+A^vb=1^W~s^x8fXcy2Gj4(Fj_I4LQ8VqTPoeQMRz52 z*^8^Ljuu>|6epT(XU{{;Z7E#OtGi3@e%k{pSF4LZ;ZOz)aK?kr9AjW%C47|AxR^XS zo^ag@W=o>A{JyW<$XxJGf{#vmb8dU=ANrS|nDu&wrgF5jV9r2$G;6XlDz1Ekf_8~# z)P?C{mB#b$vr8fg4XrJ>Y7N;8)!%wZr0HkAjW^ruIrf{WlZ0n(RP!F)R#oLkdyo_(qSFZmPI0Ls<5y7B@j*X4-<^sB@mc+66}w

;3~n6J!t(JWd>!uG1wt?r#A6 zPDRy#NEJw3ju>AZVhL=rXy4&1G?|78?ww=K9F?lgom#^3|0+HC--rR5qb%{bRag$z zPTZ-Pvt>@7Pji8?N2Jr~<`zwumGvxQdyt+gEAN$um~@up{c{Oz93#ua;m|As{7E)Y zs4o_({}Z%lz8cT{$3j@?6Dm)7hRC$_Au2<)?t1*>+|<+kjkqZ@O9~3M2>95{Xc(>n z(!uXb@)kC9ah1qy`=910g+l$e@7~2VUfq1b>uCKY-#K)c(astNflzJgX%Wu2vA;4StWU94QgRDs0i8H+V1xAo zSFzYc$h*T$>igVIywrN}JE?{9Y?(==dd*Q46?h=Me$C2mJ;h*VW|qI&9jQ>T7{w`_ zC)`}|WzEyndC!yyY{lBxb!fXvktI(@34g?Dzt2 zTFjO?p7}n|+<8s78)MhpghmeO-(`nyX%7{RgAhFw-A&bY70@Yq(Qo5 zxEbKyS_jH>uGOM!7+4~}q;(IH7;XUL8vF-@b{)XEJ|1kuyV7K6IINexAr2S)53E?r z0fKr8g-0Y9@j#Z!FPMLMAk?JlxJQ>D;m@?_VCpy-z|LwgFr2|MY{Bh3 z`egLi-SGq?GA#Eym#OX??){Wr#)M}m2A4Q6b20zyt1{yjFOh4?duT7{XI;S z5Rp$5ju6kC);zZtnx#sU`SP7}wXz-T{Mf7(D9CEaUdqdlD-<(`Mi-3v7L`uSfC-J7 zHU=oD4F|GGTxE?0qS$ZRNu-h{kAC~1VGHK@_{WD>Pzcn!p7A%Ip5Gf-EFTc74Sw>~ zv`L(^NRY_<#w}`Uj{5!fc+TjpQ?S5sY?IIy#iOpMGx$6R${l1C{Vak&RNOdESni$ z8Fc-T!iv-M_ep`?m5McPYd7KR^rlPS$Y^QJ$Kb7Gs%$4WD-Pd#@!`XFbyhs|K@Z-2 zW)U^d_@`B?P=ZJdL@J{$50Gw4s|ey8NU)=tXAKJMZJ`^v=<{Ex3vSm(C1S(lc~qtpx@ z0NKJ*KvvUJyqu4oTo8hD`(U|%Vl@SHr!$z8gL+^K0y-ZJpo(>;$uxsWkmp!-UrIZL zxTa2Q?Js)f)f>dS2piilW6M(CzJ5N4a(H4(LX7nicU*|Ec&avis_F|~k?@yRUoD$I z($`vc>4_fHC&Wf5Gv*9<%x^Dr$0Da!Ot(1GoH1%u+xuLL;D|1D19e}}HE?tJ0$4CZ6yTWSA-N&Vk=RTP)jewo#A z?Ld~=0B(3RwB|5hr~^kk=&m|pOSWDa(wmqI&SPFI^ZqNNGRktP?;6bQ6LtXBAuPB8 zvTHD@QPa_R4v+xi5C>IzhDuc&m_SV|FB`q_^!+;lm*Ud?xCiMyQE@6!V-0X^kW zcvr#44&cV-xIZ0^{1-HWV(F{^qdeLzs8rzHViOUGfQ=etOzQdF=KfbNYi*;MeiOt> zrVuFuS=MYY-GSW7DL>D+4g?lHq7WRxps7P_*~&a`}Yk0Ox|Q0LE|N5MgSp&I6)k}=>2b_kbl zY-%d;Z=d2d!7|7@1tX0nGzf>+0!17dz!I<>-E4JP8+(ib@JX}u7U{>oJur}#7Jd;n zp>r<)yFvkfMHjF}6nH~?`SAdX>+@qjoj-}%6Jfu-2rNf{2?r9UGU+8 z9LN-KA;YDTq|h6Ht8lp*t|{=}1pF9Zuf zM+WN!3wQzG?L-ZUUU23Gg2V-Os@GuBRCRJ-kgEm4^d=B$)g=lBe}>66!i)e;94MUu zN^J*qCp-;v^Ul4$sW~Wu(`C}}!Bk2OG;fGq8+>qx(;h4YJ3!nJ-xF}9;%`QLsDLuT z1ol->!MQ@S2F=kl1P&zvS4X=q=)604PTJ;Glz9rU;1S^;B8SaVhD8MD`D=EVBN=xV)n7J-|AjsKm&hV;>E2zI5k#b5whkwwmRiT7V0CAFA^$LuM^K$yH|a2w zZxL_AWn@h!0>uNKzaJ(1cy5THLVZhKtVqjgAp&0Xm!apv$R{#;m{3&02N&{R(ev;B zP4(dEDX1rK>n|&$<^vgSz1#m8MGS2#vFo4rJLlYuCnsTpdh2=*VWjcMFU#YQK`Bzw^egPd8&y zK2>3bQ!SL)MM;UM(T_;IvGD%VXpslxHC)9Z7Ckvj*Zo<>x#Jbjt8J^mw1N z^>p4(H47>!am|{HX#A{67a28upACfL=F9d4TyF=$T$VT}zp&S^dOHOdgwn)#WYLF@ zB~6IowngKlDD^z$Z#1JGqZ8gyR&VREsH`+%6r>+5s6KV(zo95OyoMI?WG8uvnuB9+CUa?S@K4?o>?LlOe36D8$Kp zw9MpO0DU|Y&;!20`}aE}*65!=GkjU}-YhtIU200Z7nQgO6$M3@0 z20gldU|?YRU>+f`-w~Tem`Q#G7NC5gCL@?Dt<8>Jx+j_cxumwGdrRKg`LB|OUN^S08X^ zs#*>%IiP!7_8Hh3=%sCPGyZvo&1#u$(O;={r04z3qz%TzSjv%y-koYgvc~WDZ{9DP z-MUYAeuPP%*Z#+z#jD2Q$T(kNi~Sxau~%iy%MO84c|{-j^>ot0o1QPGJ+nP-GS*0Ga<226%8 zYJPj}#Ada@7Tn;{_{{{{*$pi1V5bT%Dy`!Cxlj<4hVc|kZfYYWGL&;dzEtjoxP)cZ zbapn;4ui<8p!sSmVKJx_jo|C6wH0BOZ#aBcZ(tvSLVqs1WW1`tUd4?4teh8i z<96$ib1YA20$c{kX=;&p#^4yNxa4R~2Yd(&ZS2@ z=3u?5UcZrKxzru`?9%lJ&KTNfv%fU!7mTYx2aA zcSo`E(7F6ZLdy|Ui1Z--I&m7gL;fI$)Fny?#v;L@yWG~@B7s`m9CW<$?}S3BUM15` zbJ4s2ro^%$tMIm+rRj^SyrO$?3RVjUT{R0`sd-G7Ddl3Q@+Zq1HcgQ1!`^<;(y;5u z>(F!|5d&bfAL9jdmx(smArb{OrmTDK++mgfXwL1HQwz+BgDuvdcO#yP7;;$@#ZC75 zrm#D4GpANOB2hd!`alx;o_+o2BK?ouq6+@zljBjllYTo!nbo}YJ}VaILU-Y!6-ome z>+JDSf0bn8{v%7L5{fDl_UOthO`6ow_Fj@&LsYZ!ug#}vkWhr(+SRHXH3jM6AjvtC z&?r{DQp0!Y&zT5xin51x_#AZ2n;*`0s|(?SD3yWT~Krld9 zt}*Ps@k*gBlc6jlVEpkw9>fA*q{ef?JnmIyq{xCz9BVqEQtU~{dvZkcQ@>k`W$PVO z`SFeicfjdusFzk1b`&?;*%zovfePh0Ko$RN&G)%D}cxB$jZn$ zo84SErLVj2jy7gK0a5V-e^1ZfD);rz-w_T?HrR~=#H;zozS;peu80p>FYlYzL6oEn zYGanZTBp|iT(;fhLE^8&PVGQ6o+C0~O!a~zSKr)oM%RmYG?8R!8P6N1Qyf6EII?(n z222pi<1Gf-5`a#6Iq=%G%O^|Cm*3MF%nejrU)|YvJxCq$0G~Vb4~=CN!SF0#>S>#< z_=J~xCH^?;6o0AoTL3TCU18ZX#def$+D~}SKT1z~m(vtjsbWb#TxtS7yQVbEK~x4f z?mn5bfyZgjD?|IRRGjU3K~CmMJXjQT+vCn#Ucxx|#WnMUyUcng7s<$KG878g5^CEX zSSsNMX7FRs^mjw4L?!X*JPJymdQ}FB3NY)ytOoySpbqnfQp1W{Xh&~x9D z%OZk7noyJpz(j}@K9K$5!v|7(Ta*0-m(!olY91-|!uGkg2{G!K%d~zS{@7sOd}fzrnI!3aq%*+V1RMd0O&AywOx_cjoClfe-!dIg|4B3$V_T;X^@SNZymZw=hf21Dr<_kCj9RkSS!* z!e|;v2|?yb5Yw#pGYt<=7C|%Mc(Wm9^{_sHuEY(4t;X-)yXKf*X|;!@g3fdjO?i1A%|zjLj`Ng`9J5~|#` zN$?y4QAs2x(KWj_;Poj9YC7;2)>%#q<>xz_DPIsXRI=IeSx}0JKeFU^Y09*3unhJ9 zH_i;FRi`H7cWTMQFP%Q(9VzVvu zm5(nhOm@ny##z8oxf@HWakM=ghDcH+`VS3_jQVq&2+M4iVQ_R2f)MmZKHUM=T4u04 zjsu%+N-i#+)vDw7>UV_F%v+g0%a_rm%h77}eChIaKYw7+wu;Kv>m7+9i&A2obrVXE zU^H_ZV$D*Q9Zf*RJtNM^$-Q-pEvB_G%VJAXAZp~cxEPH+s|~v9WR7b}OKinE4@$17 zg|Pt6`gyNeb{(-rp7Xy-JFqayr2?@KwOm#U$R`k)8$<^4Zz3))up)yf+iOX)Z~uQP zBf%^@bLI@P(!g+y0k*D#UGYPju<$!L*~m;yBP+B~h&agJNQA3|YC~Pd~IAh22*~kWj$u z5m*rkB>|lq3S`2<;N7+lMpK}O`zVpXFRtVQyiNGtB&bjjfEm7bM>HD3FEoM;K>mjq z>cT`p9SAwF_o6Vw9k=0@29oXrSpAiUD`kmVpv|#^WgonZ<0_x0{R3JeBw%5t6)P{r z<#3tx&6_ufJrBU@Vuda6LV!9erm)%tyK;Ti<|*ofZ0td58eO*8us-+;OwNUbgT7tU z(pJ7LW>m*~XGsrAA$FG)BAxb_yAW;%PVSDmouA2r_7MmDz=TGIY!}kXJwpbUw%BJ7 zn)h2JaUJy2K;6K-U3ntQyTJ8W-KzKxs+a2)T(*oAquL_EJFAfV)L6E$K7Wueet<>87;bMCUsyjWtbn%e{khf;r;?CTHbH?#Vjn}czP>AJtHr`niJXrG$ zf8I^1M$e&Wrq-udwa&PG+s^iXN za)V&Ow9SamS8_kQs6gGXT^@hAWb25P{F0ev)1~U|MXFJq--^Qm#%zGt`1R{bFq2Le z?yQ5vuX`cT{k;9_ij5{3qz(h>PXwD8Ibss1SV-Ht=mSIyK*eF+ehzLIgm^szIRT$B zgCamMk|akd8ub)h;N>eF?Q+N8IUmeZqJRt@0QJxdi1cdkB}A_VNV}7W&;Gu0Iq)OI zC+6qB!wI5l`Su%R6-Z(=L{dprGS^0*Ej@}P{NP}VU;0b z7I$5wHiEMR-pO0g=};cP5x_D)i2}b4#A+6aTq2yg%fN8eHCfa*-p>XiMh9l;-@iIF z(wNRgf(|;<+tI~iPaz4*`DQ$%y(}J|l;%XfiRC=isLxF@RI2jJ(-D*U_A?aZQYqy+ zXciuK$x~mH2+3%-LeQ6;>TRd7%B1%) z*s#e<{zFv4NAqfQO?YLyWIa@fsU0QFKDaLryu2j{;p#BS?_~!N}jk-piEmJ3NBZ@qXV~#-MaZ3LUdS#Bwd65TcO49P~bD7o%x&?gau(7ZFmk zvGsjkg*wb1$dL%UW@Dvn6?sR{IEcZm0520^S?i5}@#q;0KFLg|+5p&3F>cS>*xGVj zqG2kw9Vb$`avB03*MT#$=Yth-k_KuxYSs970M&`Ope(w!vQ_Fu-m37Q>1Li%;PqrF zKPzJ8leRDtFQZg0hIi;>dEE6+uTOR4+) z`uwzPUl*;s^H#j?;eayoD{f?dh`1eH1xC*mR!HtRDWsq#!3Gzx)_%| z#rN;t)hMZHL%efKG^g+bs9Rl@Rm$(0IJh?_BfrumuPnj@VIAMBKcsB zeFI{)fSL6P`ZmkO9$!RmxL@%~Nl9|(6s+)2xO7T;Ib8sr$CsX@EbrD;btOR4iK>QY zWFWXqRS9xvv}!1)gTe;EiWX{@DX>AGg57quXw+GFA;rs!+kXdpXxhFRgoC=09HcKFy#tx(`1{ReP5qI_0idBtPUmW;aLTK!tgO z#S!<4^3DY4`NHVsHIY1ovKS23rmv*Izk#zf=A$endTR`2#`r&Ka;)oscq26Gw224S zNRTeq-K7)8{BJ--I}l3e1`l6B!Nk{V%)O`5YdnKVB`nD+5*@=%dJ-@|_s-3`j=~2R z<&m_>8Bbw3GM#=YiQzDdIU#j=lMqbZ%jNyQ5Z@5vop67XC(U1q&LH^;5&MX@0rb9AUx$&oB+hR)^9~G7u?dm0`5bC*VRugHl=TrTBt0(95Konc^xM<&T z?RoxMT5B}%VD_*M&N*B;u5@bSglo;eI``2?`Fh=aI4Rqo3O&TMNqJ!!xT`6T%D z^Jr*{j(esM*8K!5i?Z|bYE>E5W};0Vn2c~ukfwS=w~7hfD5*OpaTD}Jhz&B35Y(WL za@-xZ$#_*324y=>xY5Suro&Qxbw|YP21)zGO6=7OJ1*hiRHM7C?{I)+yCFpZY$hL) zb@nnF;}oAVbPboI8_|uIw{JCo+qUkHTs*{Xs?_RI93D*+C&e{8ACQ~i;NXbH+HXq{ zbrtJ*hZ&B})YhCkoZl)nD_{^LFuyi7;~NtE$b?ImeriEKH{$6DFKdxHWQ&?F*1QM| z8Y;}>VPl(`0TmI2fp`X;)#l6<`)vUM>l5Rl^D(iTgqeJCBELha*5Gj5FM8^^Z^+qIfR>l)pYe4KJ1r-OhO&XBd z_&phISj_h2%4W^EoZP}0NHzT|MZe6Kt7C&qy!JbvUA#^eHPONA8(pZn;bum}lRtX- zN2H&I7|qFzKG#oeB@w%$cKjy}%&m)THj^YMw2j1*xWxs&-s0=mL~n~<&oC$O^m8(6 zypRhfEnB<1=6fAyc9xF(!8!FJuH{EPZ&`|yBdNsC(e6dh8?~-|j|$1&c%uM;Stx|K zGF*DcWddk9N0HPLNaL$k>;40HCCFJwOG`V~G2SmEuM7kU@SjNP!^FUN=zqeRb8NQw zCM5m6W+JS-vlN6`GkS;@HN;DNhyNH96!=J{3}R&g8y@gX(=U5H%*1xdupe?3PpuFL zBuClW+A_d$k*;V5(5=MjkOfX)_3nF|5a62JrMz;F)t5 zvVmVDUEv~&3t3^}JWH+bEObC|N)_+SxR5HS2!2xTzKgO_-I;7S$t~57Bj@yH=B9po zlr6UP>_Gw42PlkbO(}Jg_>MTXkuL-m^wA6%|5K+l^Se&z=}A=?7Ei6UHNExx5+%l! zio-t2h2O!-Mwf;`(H_3ZzH1KmDOi@5Snmjq~#USE-c4&@+GpUIKy3w6Z_EZtuhUT46>q7Rf+_2I%F-8^oE ze;ICpOo@EG!I739+G6`8N_;A%wsC;b5Q>VI7h0RO#`SjegyFZ$%lW6>+05rX05o7ztpr74=BA-_T%YPe7Xh3!Omr>&yM?fxFZ zqI}soIW@Gj>VYf(7AvAGtsoE*5J*OPef{5~54z-73IV`{eH$M%0vt9gEx;G`nOR?3 ztA<$zP`#3i{lHYn1He8IJU|HuNsQ(|GaJa)dmA0C*qM*SHn{rpo56Jv~Y=kT*62pOW{rZ*BH8yac)@*bc^u^8xl~?3$ zFKLEC4iN}gqKaCGWCEU2Xk8(Ecwubp^)c;{j&iOZ@Fiwed1uacB+6R^H-^c)lvnJU z_B}nrpuP23bC&L};;2vMC_f^eZ$K#bZ5z#cRq>Vb+yM>=2|wSL23(QnUrWDr{k|5v z=;1Yge9No78aIq6qt|g#xVAZ71i?1M^PlRZ$lJ4?BsH;3w>}4bHBN({*v+(yx4`uw zxWOOogG8`4BvRDABv2hFE6{zBg(?$6+i1&wB~BDX8DH^`yId%+`R@LQ3+zVb3_F?B z(ln9&gk<9xJRwKB0X8?o$z6;I6ShOCb=&i84Aj-s#zu#)$Fob^Eh{E5^&R^#_#OOU z$O$;@&x299F7&h0Gz4->E)w&|h!-%%rUr2)nUk+5je~fS#!FL6z!2`PuP2isj{UL7w zgOLh)d`-ydh2%*em>F%th6{{CVo?7APmvku*R=AvZXjR6#>IUKQmnb&FQjlvrogF) z#Nftq+F%n92!Ko?_{tYth(?B-X<+sW^jdS%T)K1#Qb|SORv>@~Q$(8tKJ*B<4rn)G z*aTir`uRgJ3}-i+IFA6^Ksro-b35do#2c{Q7mMTa0>m2*SD`d&DUeHnII97=2vZvy z(du6g2YrkQrel zXnSziKLKa5>JU^22UiGu5)yDy#7T6TAO#wiTFI@X>NIHL$f%ri14tTFVh({yrIxR+ zsW|#21mjFMJ0RhJFjEAMP<>|gBS5`d_+f)ARRhz@CDmkhrqan01Q6LM1K!OOE3k=vSh=9RF zE3tm^j`3z9>?0^?90{1WkQU$!p1-#tUy~)W~Jeh^wTGk3AC8;HZ8J4-Y_;4E=SF5hY za4&5-SL_7E9&+jQS~wgi&6aM9L|g0cNo$nV)@i8L9uyqRq_-1SJ&%8Wx|-xdT5i4E zBlXar*Ua89OC5e1wf^(E-)O>N_Mk8^Q5T1e!!!7ym;D*DK2CdsHA00_-Pd4JOCBGT zgnJX4yvoS|7nk7k1katHInB*trzHLgz4p(#Gt{N02~MNc>{s7iTyJCD2(Q?vx{)lc zOmcJg3EC9{DTOle^K)Mdu4;)*COz1HaL+=4{?(PsUCkBVCS0*x6tvF1L_V%-6sie=VH0F_5AJ}j&ZEVn_nK>wq z+*qOAO(DF80pdU3WC@RjKoGR2sL0)H!!do0aPV6Kr(nOdnEYaLrT#y%&h{ zk3fvn3%BMM7=}Y&SUP}9kn$aU0nD!eht#imgGLPwyRg9|LD~5kvgN%_24TkA2{&#x z)H(%vUHnzYJJB~;#rhtbLRe;+Qb{g^NPGgxev#1LgW6+>__oV zJCPus9sk@S2^qy!^PSINHw%bXoOq7`&8%1hL2Ep(3h(h&FaYW@uy2*!%@B};#;ic6 zokTQ(;qfJW+75W~sp;t?P$cSsD_oj#MJYupyfh>ZNj6I<6*ewi5NZg;aui&|r~M2< zeknEWUi=}X=N^MvS)tT26`6-Y4Q~L-vjySdpG7_cl8U9o*Sc1jR!qxyx3(8EhmH=ORZNk!Ax)g zz-hmaRYyB3VWT6%>uo9@CGoIjDP+==WKI`*_&{yh6?ogj>fu-#1Wkw1*cd<5-3#F; z?~{OZjv~$$3i|3%mBXDCFDC98<_7mF&I5*Z>oFOkpI6g(~3D z$N)iaxw}{|&w-vIa&u#&v$sn}Z$M5(AI5D873S|cQ`$e^XDXzNkM@4n4@4O@dr6fV zvKKgHz1>+JPJr!pzF~V&Xnr|Uz22!iTSBA2DeJ9dvWT8dgl&tNbA;`d+Qch=Gh%|+ zV(fT1`)x9{Y0Q^cq;c008dH_ty^lI<79Xf|N~e|IppuX@8sJVzQG76Tk;Kmx<+40l z#M-tM(RKDLn}20=`k9k~)$8J<4H4E4`AK_H6;)K)4`9T>`3ek&I+A&Nzjt*>*+Ho9 zvAgf~&?fQpHbI<&j=;7_eePUc=$?p@>7x_Y+9?+%5mI{YM4@*N(qHT_rzx@Jk>Bqkw&PMuCw`79u;p)6;JV$M8-ra$ zGhTeTI`m}&m!Ah`F*T^tGhda-Hu{swy*`V!go$pe8gA@1Nc^T*I5x@Bdt-7%8s)`&TGb zz@6rms{uYQ)X@I$O_bQPuNVLLbum`Y!+a;sdkK{_)SIkpJa%PpjGd@#odfVZ&!`$$$LszMFq~S$2KIe|*~Pc&&co zGeJ22bSeJn-Tc#42=y5L)3r^k&gDc=+?M{!760qgs0-Xe+2Vc<3)3~mkNu4c-v|DR zEdd)HrE;R0M})4W+NYfRl$e8N6D4Iin0IA$Z4-ydin&;o$3>XMZ8#Wn3W=)JZGNLO zIrYd~!mOFT&tu!Ra#d-BNy~hY_^XENCf}`+$rkKCejxw2p8oPqP>1qi!`E)H|Nn5| zY)}HSpZ{T%{nK^%zvb@O$maaxRZ!&>4jU%Bb>si^rz&ukw(Q&^^C$2AU+f`VpCN7j zvue$oG^rFn7%fkN#dpJHq*hri-}D2CftsS@ZUTVAQS27U;Jlzd zF*L~gm< zurxj8Azm9yk)rf(CP?tFjLCW*nm(}kET}jj;Hv21g7AIC(x>2pDFahPnFLv*?UlpQ zT%C4f8(X^9JR)3h-usbqrs=6TeKb;JU4(IRQ_#b7{hvEB{&7wrWE%WswH6o|9$wE$ zd5S@lWi(Ly7!2+^A+5_249rVP%4k$Z9MeX#139c0uxHEB&8!wXC7|jk(>QF5cS(vW z(C1_%@8u)Ci#fPai#N9V<;JLEl9hqn;!%mk#)c61pIW2~6)Y_Dx&#c5-_b+gIn?WF z@ZH!Pq=Bm03XBf~JL8T$jQnWz*lks-3XMlTA3Hl(Rhs}f;j@sESq77L)&~zHf!Eu8 zP>^2Ww5Sb~peIGf!;*zd?9v7HS1P)vwIHdvv0sJk``~Jk_-NZ6Kd^XSJbL5`?XOIt z0Aqn|azUK&z7K74E@vG?au(iSSg#V>jrsEYOXb3ekO}3}ko;Iqrzg$MaUW|Bt*Pf4 ztdg2T+mZWTu8;1ZvPxlq7#Ze zSm&H&w>k3kTOItE_Z(|+=?7owHB?N0Iud=1JPV<}0)dBOy>?M(s?eD6Ah! zI&J*Ctkn|IzYe_nu*C$y$>odv({AqWX^OpK17$iz047k6+HYI!wA^To-%anye3j5Y zdYZvyw>dzp9mG$1m&7T;7Kp^MRdNng6cjJfF5*k}o8wt-tyXO{i;o%%N68$_43dU$ zJ7AX8igdoj!tv)3iAnOlK0kcTL@)>-hzyu}QLW90F85amgF9g)%5bK)?dh!4VNc zZ=pj*5oywU3D|&95HvLD(n)C2J5gx@0@6DONH3uzl+gD5IQy)3*4pP>Yrkjj>)qQw z<{CvX$@43B`Fy`OEJDXWi1E~lmAq`@N&?_z1eGBwZH2pLeb7!8i5QFe+$gEEGj=n} zxUhZk8m_`ZQ@W>2=*V2!&Ak}y;B00NRAl}`^$9}P#N=e^0q-qA+ZI)-$;>6}QbPm} zCZRp)SYJw};jaHM6WI^cU_UeylX?h@P#&*)uUL0?r`nnX&tQBK!26rSZMP#$ zg&-Hf=q&UR1M>&8VPwjg3uifrlI4_Uop>usqcnMfObX?+?w31PxVj^1TNE_k`|kTD zy|q!!M7}hUL^Qc{xo~5yp+DaxQle6W$9B10A(H2Hwoz$%Qh70t+0(=Z{`P{k_M>b6%b9k_~VkHlaVev=i;%p8N{RZ;e!cvLNP*c zx-d@6VdH(Ol9%e`x?RspQM|4!l3QMKo1s^;PNg(XKVMENbawnKa0wR;f&(Sb^?B{m z6$Wx`r-g#^R7pXT3R;He@|(Tnn>-SZqq_9(aVTeB98PQx^#IQdA}9+3tX@#bVnW z<+r6l(A~%|&mH4Imjxph755mNqz7<;KHa z1iFB%(PKj}OouA~_Z6*&HZg+d(y=lw<+$L^~p$sm6H+-3=5 zjp+0eDfgX2vV&#L^_w>zHsJ}c4dj|JB_-bR%eg@d1NN=NeDC36WMRJ2_PV3Ge(nlM zaE8hH(^KIyUgP~Tn^M`&kahXv5k%gLS78aREHA&E!WvgdPfr>C4&c|s_;`{Qeyr-# zyBHz&varK+7fQ=JHWP&X?p79-*idMP$|$5-%`7nr*>aD~vNO4KWwJ>?{qRzd zjj!)aJ!@46I~~@lWc*Vidu3;70Q3Q}7Y2%EKYB}--%(nbuC9T#Xxr5|QRXm_x59WO z-R64ZZG-TXVJrsluF^vHj>F8NUSI02gxu=>`FC?ijImv1z%Rp5x?@^(6 z410!Vgw1T*9J(u8Q%5gvurhw-M{`;*@m(qpI-~xA!*Adae1SY^mx2n>hESnnShAeO zxtD*mX4zzAotU_Nh_=q-!xhw_qYT<1kNDVS=}1quiXYl*IfrpJM{#YjU30P4&zRHM z36pe{60#oYx;~V5wQzpp)eW;IIFpKJIciKU%XE+9^tin1tSrW2o9GFWvs@$tM#hD)N`QbdUAB) zrLbB>kM)p>&hYRsESxr&vg!ntX8%+5zy$u7oaBTOaI6%T8n?PdrNTGIirC))6H#rT zQ-B8KfqIcL^l96nSvR(0=;l@;wYy=0CTO87+LP6x2&DxYe=INoFr-l4uHyP$6?^l$ z>(%!6q3+V4yE8pE=ULxc>on2G0LZzLX=A85%sCF@Hxdiu*1QKZ3ff_?1k>ty6Y`Mj z2p*`G#yH7xD5~HAaV7lz{4f^EEsY0!wiV)~Dq)4EMi_gbpdE?* z?(N2OhFjlIqcd8mCz~@TaK+ygD&l4K<9;iX&1t7N6zVK&zfE(a+WUnvVwt0w-v2z9 zE1uIN)rDm1;`0UvAH*N_bn_q>ZoYztkimV#hwHDXQ@{4rmi#L*lDlrtiG4A_)}r^p zTm@PM``ta$Gy=4<7LL=%zXA6&9?b53T;k%yZ#-|g*xJp$ESVp0KL!(4Ef!|e6AYq+ zY%LrG*Ti>RJDbkSDue|)D$g&DCfwmj^i%FDH?1_G?k=czz2ognzi8&(*0E+iup~c* zHFWbB_PnSXAyu73|Koar>kjLyUx_Iz&e4rGeItfO*^huhZpkITgU>X<*U8wrzn~2? zB4LuAE0#D4N7=HE0?mU?GQ((*|9%KlXeR(P14F|oqM?mqTp@SH*@?%q2QTWoO zJ5WCDgeMaU&rj%auUg$Xm;!)|6>SGBUkIFFMHmkqQBRgX2_;q(5H4D7XGV+XdP@Ryhj=>f68;j3t(`Ql=C&e9lEv}bdfRqM~+oMO0hqI?BNAnM_ zO$Rtrms);CkX{8X+o!`q7e`0&7p|DK7`pgRXiQi8 zBWvyW{JQ2Jf4pzh$xMbdW?4;j?ysN9)#SyM@=@a?`~fO^jFMhF_WEp z6+0O188gumqT{|Y+0k&izVPvKqDZyyRildAP?**kqS8GMY!}MXFEs>LEuKY94PRQu zTydrWD8b9)G*VC(Z9QiT)Ys|uM3;-%xu+-THZf(tp&AX+bz?Sfnl@((&fNI2H|7pY zyHOlHE3$f9gGjy9^5%BFnfGOs*m2!RZO`YG} zUuQ}8_X7ou4yFs3DIsX#(XmQxSM9kxe3%RarSYI@A%F)*6o3ltsjv5vw^gdPR}}g5 z3*@1G9>xx*iz4{8w!uV*G&m}!0kU)ax&@XxmJK)#FhU7MD)IAuNjAsf$Wei1(FNxh z8Cm+O@ms7Rd2p}+5EBlp1RffJH3Pw}r{F9zi*vuXx4jx{?6c$inpIj#yJ!LYSi)|Y zDxlPDEwIq8R*1fp1&WCA^q|v~G4M);bOu3bpar`JgkC<}a|k7 zhgYTvQXh5{$Z&LkxFAo(`&dFSOE3_&IqahZqg>ix^L8c59tEwCfcA}Vuv?vA;uy`W ze|@chv==Xmpq-@s>}KXk1E;Ro$9;}9193ZRWN1~3@p0-vB8=oc=WpB#doStAzbhr? zDE^7G&D{UVW8>O4|KM)dd@~fzvU{w8E1@cm>Yh0w{Z8cfArG=vv5rUInYVKZ^sG`v z6E8$v1ZST-Cdo~>n5CGd^(>N z?QjR7r}Fxoq{~W4V?&(-wJP72fDw##Ua}r=OQwLIx@G zTV=;tCEjCvHr1nVW$G-S;$S#?ArQ~e~=-)oz$dJcHL_|n!FUh#O zxtT+@x?cGeBI82a5o74Szyi)HaGGh~Qqg{!NO*pi3;udqS{l+|(!y(4KzAzFkC6&& z1v?55ei!x(c@2pDgzkSnh-!!3y(sQiLxpAG3}bSTb}v+Q0v$VY;9GgTJm;4n7~mBY z)B+GNC@k}mEP5v0sC*5(w*xYOZ*Ma%p&jqEb5MpF5xR^_9M2e5m6kYuYo}BtU~-QK0FBDQ{G6fDl1wnyL*dq$agi03 zN&Ni>4jdS_rokfXx;SM2VL9Cg>_^S;GEDKEj^@Y9RX zp}{4?lmV-8Kgxw0k4v<(htg$8CMhvG;C5~46%`PQ8za>X|x72PohEtV^3pG2O zA#lXvdT5TCx@KCd#-fC~^Yr&$_%KCU1I5;FqfPPplnad^TscE*Wiik&v>XU;c5rp= z(NvBa+B(p1yG>()1LZv+JUdCr$nDQIE=`v4EJES#JWI;fyJM@vtvu>H6|t3gS}#%h zdC3j427)RvDx1({H9gpOQ$OFK^ww^vwy=$%PGsH$S|Tpt!P{$WPlef0yj5Fw`c}@i z@6~x_|AG`ekSdQFQ-I;K9s>ien+726@zwz)Ja#5~MjkiAZ(E=Z1Hk1dLX)&A9kk}219>XUT z>_QsOf+TsmH`nAPbpXbntWYSgZ&rd+EbfdgMz&a%=<6j6OB2VA^fp29mt+HgP`x`T zjdd{v)~V|@idKb1I@J}l876@qmA7uo1`K)jUfJgsgU(Ucl;Tj}xpGB5XKj%mifasK z&ZJj*y3BM94>7QuWJA#lK4#tL$t>-nRqb~y^!mLWD!tb7!DA7Tu%%ZCQIbP<;6g)# zyf2J+`}duk0p2LViPNa|h$S{_o> zC%XH48U)cLpPsJ{9He-qu za_G(_Dqq~|fFU&vrO^s2E*Jd8FxJKbeR4i%mvW6(3x zSnBgQsIycgb)TJ3jQej5AkWD|+Q@*%Si)x&C50_RE-eWD;iI3tnn0e=?nbtJ#!sf`eKT@){Zcw*Rb6Nx!N>fr>wYtAs` zE%Jt`2845Da5t<)6_t*7+o550gW6t|q!aj&ORnGHLJBVBG7c8QcnuWp8ENSw+s4{O zF1+SVZBi^8Kys{{ai)4v*ETOR@EG2OaxH!}pmnB!izj1Fv6UB^$wQbKK5jxg<5;~1 zbaY3nU17K|#Umv;8$(PDiY=S=4T_yV-jE`xo)iv43$rc?XE)i7u#{KZqsXJr?;BM4 z;B8nn+3}_s-S+(kgKsDvqOt}uT4FnSrig~Bk4x!pcG*<2{D6_NL_?G8$#WAYfyndZE! z&a5}IMOPU*@kIKWou@7rQmkSSB~z03qf z1^e>PsxJwuV!O2gBO1VV?Z6lkDyV8Yb4}!A@7)8fyq;x$ z0UoFfPfSx;PxLJPxbhm%P;=n#dO_N|0t7m|($dne&)>WUrjx<*d!Q5EB5jzY&Glpx zFeDo!h>a?8yN9LH6$r)E|4x>g9`oJcy=+t>DQT8%s1nf7^>Yz2TpWbj*rIr|V0iR^@ns%L1U!?N^-`)y-zi`Jk@nL21l2?rXL%LXUusgGFXO0JAI#hZ z(~^-~e6!o?4_6VC!sKI7@}&Pw^2&R4!a6yhK+#exoLeg!-u)q-()30h7s`w2Jh2>^ zcbCqU)oIo5^7RQ~w((1`o(AwFKAq?)>Akf!3o|wwBY9mbWcE6(mO|Mr*7=J_G;gI8 zi_-TVHGBK74a_b1_RFS5-eyVAh~EEsJ7%7W)JWhSCk-nM7T0!pxlHcb|9MJqs;epTyK3& ze$IP@mR4)8DrdSTH;JH`^Yk!LsNEEojLUmao22lTRl@GK;kD;9iMUXBiHimA4Cx?- zg}psKI+}3!^@WVRj|m>D3nwl2wgaFt@O*84m=8tJDkW6@!g>F%EMDUG-=EIzC92oOm<$y>`IJCHU zU2;;zdvHinEBBFPA$C(pa>VM*>@C~0PK%D2UN&izMq4`rJHTnl=+uFi`_h7$U*hg+{A8s_jRS{|sX?p)y+{tD3ODuk z>KXrJo~u`tWJablo^P$=XHN?WJ(z!Sm(JJM2c52_Zqbq21!8Nb>F=LJ7I=3zjBr}@ z#o4fl&I6m%2I&dBx&q5ul0rP&^Vx1&bJ$B~=a8?^%jxRAGbRlv!p!RCIydYE}=9aP3dQPJy&wp zPPY73w)Skn40`tnrk<=3W+jhvu{LC$y4lvzsD+;iJ!hmoIWVw}52haDXYXhz9I~=cLugS*6anB#0%N>op?;N=Q1rF-D{zR&%Ud(d-|-%o5k#X3cW%+`d58h03tV;!&M1c z2DdUEq%=m6N4a9w2h$|WT`eh%4EEVgEpeN2I~Y>Z_wT=TEoY_8zm(7O8Eo@^Sr3(O zr`Z*0R6EV-k)9MqM&=HBRof-iyO`@*CT!DeD2op;tKb~heq0}- z4Ztch*=;6PG2Oh&_UU)aj>2G#!E7&n6eX%SBs#hr7_Xdh|2^zJ@8x+5zAe-6!NDQ) z`ixoZSz-paQ4tdVWC???XB%<6@w=N=#3F&cb<>G2 zUw-9_-zj}&JFM0%aVWIdZKbqo^4aeu-TIEQvHYjCr7PPteOx zh5BqFxaeBIvg3iX9&V6JaIPTG7?c9rVKx*=>}yLUBqK8L0?^<{1Eit>a=}2r5Sg^I zG+K>2!{yGV57}xL%*&O8VOWikH`9t9_a%iZs}Ph@W~ehFls@vnGIK3PCWd zK>H~Po99N|G)&UM#mC2kCrKLA{2E3^Fij4lEZaSk#$t2U=MIFinRL1pL=IjZ1SR@tr?Eo|EkN~_L9i_7SAEj=($=&Gvp485+&`I7Uex^lUqbzp0hz%j@r!0dkYu56v8#?Rg5SLUv0_5?_4RX!6O-JqnD7y zy0*}l1vc}N9cy~%4CCrWHKWhC+N10!C|^ZHUnI@#_PiRY>RD`_3oTZ=p3)jBE1Z3J zdiql(Es-IWv(-hL;-PHP91rbr$&S*9N$=HMPP&DZQxh3qhN?AbT&=sZKByrPUEV~X zfzXaU^gH#NH)fC4liIebZ3LX{l;#E(7`ifz{sv_7#Kg-NmtBqFur72Qi zZJ{BF^k>J2^P%)_?OaYNk^K7TEo~QiE0U3U6*OTPrX*n@Px3`7wywUv(DLOC)7F$# zuD$NAfTdmtP=x}uC@X-Z^{c!}kzE3=OIIW$43RP_V8((V z{6h9O5YB2)*%AWh5(LnO@{|gBBpYg_kP;`RDn}Eb>Pdy;zO%&svrRv^3fuTcK~ z1>kg=q7thQOmim`9LA5C{8(Jfh8FR3eydam_$4ug>46~y9(G$gTz)UBbdml<;B}CxG6A5JDHi?M^m zUxVg2(>FMhfS|TyxT>pWTeX=Ah5-CI=OWi!<_1gi!9Wawt&uycC)-F91`8wbI3kX; z=OMBeuo^bJzPJ}8dLH3~vn4&mPifl>(RTboESWf^%CuMfSqVm(vOWmSZid%@QH@Ul zxCsy7hy~m$NO-DWV3{R_#f&fX9sH{eB{5P)ah_N)Wfpai2areu)+hOHbN}qpXGQ3~ z6NW}h5B_y$O?d?77A3K|)K7&OW8J=BX<*QZX8Dwu`N*(jKgdvQ42jvH!^22y13D({ zfhjRH>sb~!0Za_M-R=dlq&YC~SS|`kTWqam!4^)tiz>8Ok3rZYV4vK7+1>3gc2$j; zxt?}tV@v#IGW(3>?I+u7wUz!S9Q(TlWV}a8?5nR=tXEZ*7uu2*-6Hhx4rqAuo>_ah z!}u`9jy{_xI0||oV+a*PTcR_yWV8AUG{;k$7b{O(fv}G^Vvh}F*9pRWl;yVji!NqLKmad;D-mD?vB%)v|Z0chg#9)XiY+uUp1Gq~LzxNrAB>zbuS= zc=azvbW1(EZj1Q37#E@MBjfjgSfWreSt-@U9fh7#$E~T3u?TL?h4JpP?bStcV&mXZ zcav`vD=|;9+Rt}rM4a4ky}Oa+HbwMW3mqTPbC?L|h)cSCvQlDAF>A{?92)xxP#xxR zrUvqm2JRN>SEiHo^10SBN|=z??6qlP4$Hop(Yd({=rqlOzKKWe{ga{5kG_Uw=WT%f z6tGrs;CWjADQig{;hW=FDj0s8%et#0w}w6oj%e-?E97clh+`%lD{ z>)#=^|H>u*r-@Pe@&kW=e{1=GdpFQB{2M6ze;t=Lbv-Dr@?X!tHUJ*~@4@B&{h#X9 z^2`4tm>Ompc-@8Omwmy}ml>ZSg!9DLH|;<;$EAt8<3C{B4yHxDoEUB4rTc(+$Uflk zK(8V$)ZB?}zOL%p^59_ao6~dJe<9PZ-PL!u<~}PU>$>!&2cjt$uhJQ__l|W-%U)vo zEmvVm#l-d3Kjm(#y%=JXD7umAP%Qp}-T$)K-=F<|9=`u~xca{W;r~US>%YaK|Ff_A z{}R6W{}ApEkh}}clZU=97)k>FJtXZ4EQ?uljHo8$x#2lEIVNTBAF$%cfD|MH%!z`3 z*h&Qbol-@uWMj$+#>@tD{ayPezQ*ck`skh@dFk+DIt50&lVS~oXWMI65qdg%&>g+g zQ09@kqX^Q@!jQ|VWougt9mO}R+7^w(&p^cnY{3|~HI(?`EV9xJq%Jp&2`<_|_rD|c z^~H&)VjZO7H|Bd3mn^wOKfn}eb>zsAwt}3h&b$;wCm6Y#q8e{3WZ9)ns>>z;_I=gA!8dO|{oUJo zph6vr{xjcqv1LxzE#r&t*YApZPXc-!CA4FeWl(tstQvJ@1uf834>+8u7sC9Vw-b(W1*vV09)d`{z+u@9%N;$i?RAdmqJiXW@>V&PD5ze7%u5g(9TJS zD67Z>w?~mJ-rmLUPj~wBxNTT%22K~#bIfdSIwIeMJjGog#aC+Vq5W3da}y22zs&sN z9v&n07;UZ3mzZoBkg%DwXe$wKTg{wy$q2nNFVojC+Y>S`>Gv(t9N~3)xET7!{*R)m zSTVhx9HYuP2{YCB&iBJcP79V#@+Q8d@ThinXMMQFOdd#LHB;*H6XF#d%|EXY%bDf) zOy#pS|DKHPaSs`ohk@wQ7qBztX0 zOs}b;WguZs#>XJ(;#Rf4)yj)y=%4)oKhzXdcpk1b1~^3DoS`!0fl$p5&bEV%UnVrw ztoj4{8bd|%5zciU@T;BC>e}6sbrDI*c z_)%EZ<{FJ@Yg{$7+zt<{3^9v4sG0bY+g{K0gcjdsL*ti|pFQ7CX>AzSmst0d`>b&H zZiydx_6h>*RlbVg;mrvh7TaCFzfTM5-|0smiQ8LA zQJmc*28nr27ThpvNt_k3oNEkg(l594y_IQ@w9M*7uvG2zdU29VvLn?y+u7=r%b>_= zs|98HLLtke7MyHYhIT}BbTqQIDnzAcf>Q#>pSUmtFW(=|tPlD-TMJ#BKL=L=KAAwz zS+V}>We20K_)K_Z!}h6Wkd$|ms#JxD<2PJvb~RxUpkL-Ouy}awdvgNL`&*GhR^edy z2sMSpy82+U6lE_G) zfK+>7#xIurvwF@x)^C0SuTO<-r(>U1I=k&j?X0anuKZS*mVZYT2Le_h@2}-R0e+jd zKiyf?J*sxItf%3sLqbBbGj+((W3*O&wZK%C<73h$xMRF_HN3zQZE z>W_dN>!41d4}2p-l#E|4+$_lX{P~X1^bm?*n52@qMx@`YrB>Q4<1BZ)bkGE21Dj%! zJM4$qsq>iW9ul!_>a-mGVQHg6gw*r=s%Hxd5qxQ%Wdrbc@$Ee7c?JDaL(|z?73=jC zN#Jal;nCcGLDD;~qEZuu>&`O9-+yl0Q*KMw-FnnpXSTB;O$-z1FrihTdp+zb`W}Zu zn07`g-D0nQ_)m<%18*e^#m;8+SW!r)NUvEEWRFf-Y(yWR%sm^KcpJFr9>Hy(BKztd z<$gnZN>^S(?3NnNVX7hHxIonzAs>8u)HR!eTekh*YyJ$$R>`Tzek|%R~zBg*f;&Xr3zya22T2Mr0?O(y&05l8Ijg>nxGUS<>>x~BQ z-{=>U+ASA zz`W$~^$|oJ@ zNc94v`(>FN0ZD`mnEx9yTDka)vNZY!+)6MD%lvCODIKajv%RIf^v;Vi0pYFbz%q|1 zwMGd=`yIcSts+p=LtyZMbN-sUN9qT*&&kMH^l2CQ3GYN;$>dv3I2e^W=)p_XDn3_j zss9s`?rEGLTCJwifkfcVD~74KmLXU$1FbiSov*EQD8#Dg;=bA0CRN|hy+H@pt{qv?{{|E0ZXcGzAP20)ky<(Tt zE3TRqwoII8I%~DOjRE$7nN_v~rIE%zbV|ZDu40qC%-Auer26)f@8L>OLo4m9Z<~%& z1+K09=*$=|zoMB=p;7D=l(W|yKFGGxhs$ncGNG^JQ7M!tG;?vLp}ei*dv^q%BsVlK z?pt!ZaQ(pxzy+?L^vhkct~NPr13sjOD}Zt(Wu`+8&|x%m3MUp8_~5bAoxgDxoEouL z?3JPfQ8qR<2#gHf8j>gx;-srh?JrMrk)a)&0i7U(d%N-Q%lCKn=aN{xH+~0RSrdK| zg2shiQNMHfUZ5KMdS()^S-finu?7r8i)&9+&VaHH0h&Odvn~hOJzGC zRfU_@ij&*n-|K?GITNk%7$}_~bBFbdZL+|0x;AnhXjSN9fma7D#4$igGsW}5usQJC z6);hHMl|{A2F4u(vv{nwUfx4>D6(`yrI}g8?zV;&>)9UoG^(gP zpGE}m)ce#3F`qS7%A&N=B8zM5S5^7KsDg%Rz7TQL+e`Q9E!*pRb@tH@fSk>FypC?7 zIa-xNAvCaK_WyKFW{d%W$) z+4Y+>Cw7siIMIZ;jdPkgHODreYg4O%(unob()r~N)>GO`^8X!1e#;KGwh^G`4%;x0 zQxPW!)1{#ps00vY!2_`q+?fF-;@E+Xa-%1!iGMKo zEI^G=3m!`+Se%9NGl#{po~BP;o@0@ zsnKX4PCkOe><4KK@bsI2hSSw5sIh6fV~ zD%cbOG7p_aa2`*FXQ}|DOKm_&ohQGZN0tCg*q#@}60K(vzrwKs3*8AJP01&MN z>rf2(Xh1?_KvNx)q7a6Ig#nH!Q2e8^i?~08iX_3Pya*{+0%AdcZUR9q;Td>6B7lui zm6vGuwq~l5I>La$#B0$>0i&{l?d@&&7(Z_9dZQYaljO()s`*{-QAfe)aXD^PCw=7w z`%HZ#2FAO6zK)#d%{%w~*zxx@S%xS783@+3M(%d7wgqO}qbn=EqE6D!5dZHI}~M`U4MM&c;I1}`7fw&4Et-!2~_RdYJVZmZl^-?F2tIlNIa&#G14dT)Q+Tt zL|9zSg!luWKa-#Bu+5)!*pkxiFPIs#4))G{zB4)mD{r9fG7SUMne=C}uXufRV4!f> z?>D6(9!jf7t92ws>|r_M3?+Q-;X>IoC*@8qny`t8%uak2w;3sJW!T$A+NO8m{@pIk z$0b9A+RH>|*wx5Ew3ziDCwGM|>u=A|mpP3ZK9lyG`7FvjSggpx!a|rCm_B*$NW{aR zq6->)zZ*Ls-zl(itiWOsIm+Pd8T-20B2G$&XOAchO!T3tVT`4qk>vsX^ls?$khW~) z?%gARr%sfZGXZFpjLp?4UO*n!3j_Z<8c)#Vawp5Tcon*bSGzyDO>sf?pVB1lFiuYiA zVg(~GJ>Z*|WSC%7fKIE_R%V&Y0s-Dd?RgdRj#Ont|BF!V(B9yeFR~rUhGjqZoL=f1 zoQ0vB%25aFZ-7+@vT&hW8)m9^qb?oD+bEcw4tr5RSXdZXLri0oPzJjNcz;-ad;b){ zL~Mo@jacl)YF@qy4D5u3%JfnLssE>bjohnTGAy=kUkNoQKu)?dCWkBlp!QosSIA(Jzz%(zRPWscuD$Cb+ZI zQJpcSFK_D4V#%$A8OU0l^_7AHrbnLuH28T^9w!9{<{<#&k?iEBipR$U3bhh0Rng37 zG2){v{Ug~oBA9Ze?`nM9J%Re&FEElFHPuo}B#JvuvZ5R$LS+pfHyv4RZ7{(lJAviN z&gf1dcD!GALE38-Z#=VC1jEt1T1}bziw(dqEEIHcjXL;c&~Gr^wH=vittalpiC-+l zVjw*R3s;W4xO$a!ZWw;v8*pcZz`;;TpXIc0lEutwI)EfLJ%;XE#pUUQ1ye{4?*GKKn0tZ z87v^k?R&fHM3Dxb(a}*4aFj>79y>6G7$i;HmL+dM;GhfW-Zy~i8P1<~2iGf{_wJ_I z=GImQ_~*mFlbrq{49sJ=-1LyvCY+aWx=X!gk?cnRV_5g<06jacPAX$pn}zMA=0KGS zkZL>fMdYvqYWmEXGs!ToWTa;u5brm`*mWbhV-U@P*&HqzylXqXfc z3;yE6Y$3b6We*=bKWv|2fLsRMVoTuv)o?BenzlbxQ~)arSX9z$h&uj&!vMcPCs*tiRn&RA$6fgbY*EtD@Q5^ zY3SF%`ub%HRwGPxMjz;d$S?cw!5YhQx6x-HEx+;-PR9>?TuR?xU&JLdM|WGa7AaFP zBcG@qEe=@f&>hL4>?BZ1Qf9U;JB^Xbb+&$`t-!Z(Rq>)MyQ%J`UzJP)#JNGosgW$t zw#Tbe07HOut?SP5>1~j9w%&jI&CGwQoj*o84d7A~u55i)F;QgE08&lz>^F6))0!8S z==5AFZZjip4`U0``nmcUjCtE2iBuX`*mVB*ueaN8f2MtmTz*&>9_#_9MOUGfjx)q& zlAdeG{Y(acd8j83J}Hn-IPSpA-C1a-0iAqVKp=6vH-C_pb$n0 zj$NJQvD$GZ0qx9p$vz{TN>?zL5s~ZQWh0M+@xk24@8VzDsD2qietL2`sQ{uxan57V zH?#sh1uhv{=Ujr@;-LQMWcGwT1N$zDr7MpN>WG9&t?K<7#tJqaB zaWe{f@5b(>`Oxp4KYscnmWrKu0FBgigh|J7%vDP7`v{W{V!P5+1h7{Pw8-rl zmF2wKYfIHOHiLda^aBo`Wuwx!uuvEBC~~Bi?8=L6H~o^H+2=?w3GUI_X*m4qSNfi3 z@x;p28<#GT+e(U^7r$~dEOVz!i4n&#my8g$jiOY(hD&ZE9qZ^v^E$zp$5;t_!)pKI z-yoS|S}iNRUibSQh_o!eUCfzrz@{RNh7i%8YLygx03GE~qiHu9RW-Mb*@K-x7(2HJ`))w<_W_go zG!^9|!&|xkz=}gpWcq&?@H83j0!B-4e&MZ)|B7y@-RLuq%64VY6KMhrLB3P3;^H6r zBUk#x5VD>=Tva^H>66?;tw=nuwbQv-JH=!Tw7B}Zj>p`pYHAH(EWii6ZVVMjT0uTN zSe`|S!BCy5)oK}o*az1s=7{C4(DeexRga0rXpBsq(pxzRflam|9ngK&u1*ztOt5-5 zezyvm0cg+z`|*VG1!;X;C@0^5+npC9Whn^Y~6>&zR2@)`sFpW*hZ~>6%F3^q@b5p>yZ-4X|&|3_5 zTKzEMQ97`~Qa92VEu4eIPjHJ`0A+;a#+ThEAhqoQpn;`fF9b;Gk=%tZho-_m4HntQ zYNVkuS~*|86x!)tc*Uy2Z`Aos2NTXt=P@N|`ZY$@f1T9P=cB@HwpbDLFzlTr^;Tzr zDwow>oH%BhixI%m#HA$zkHkk=ZuR5VOIjvbnBsflu8+Cy9|{H-S(F0>HSOTyozDw1 zz&tzXD`hWkWzKU8CM>conB-s3YU?7n#5@BlMT@eiM0%aPy*cT>j(ySFL?4I|q+D<#dq9R@j13G-~5%-?Dkopt&K;F=`&&RYWP6ilh!t_8Bh&oKqt z2k(S!QXg)>+kQ6B!7?*pk7gjL7W35 zZH6EuXq7My9Zv87Fmah6k#eAA#3SQ(Z0+#Q8?fbPBMym=S%dSZbAjv#xx{Pc89O(6 zR&d#yld`}gENpA+)0Elzk0eY4{ z6%u*1=XZI%ICKJRgNu!dy=B}zJqMUxTm2z^xcx|30K&!2_v$7>=A9zlWS|`taXjd3OOWk9 z1-U8h*_UE#e!hOQ+PbI+>zc&|b8W0Aj7?!-Lqo6p!EpZ9rFA$fe2JRX6RK*%F z*iGc+J!-|{OnF6a3hw(8B=Bwz6Y~iGrj8dJI(Sh2mCtnKzp~5ZPEA!jc54mE$~ys) zq|xv8LE|-Kp(51NUucCq1aN|t44tUc%73H`@=+PEJVHas=RqM{xYH1tC@Hw+CKLH3ULB!W3BqGIo3nyAjV8_IS| z!-%+Q3rP1Ui8@Yob+VqVv21$B9l(yn2(+}cftsQ}a%9wn`|>BgXMMXGnM9ra?oEUT zIX}XMYTSIs{)Ei}a{NT3($$fsI`i$*^A+xsshrzOPXs z<9x|`p8me@n!nv4h_l*Zyag}T?-;>=m;hot6l5|H9G=G~1Pqn9f?3+0R*Ivu9VogB zZ8E6h4ii(>r}HG4Of(vN)MJzGhMGBLij^J37w5jDFx5~y74MZU zD!um}bQrTC0;$}ZsmYiG=Ij~hgfb6aq-4HVyB_a0rRVA?@}BFW;Dqm?!M7aUbRwdE$dc!3{1!o4X{Gb^G(E9%ACGYP$HK$e0`|?az!0ZtM zzzn#Q>muvx>*Zhri#bjzJ&TwFXH9rOGvM!nRBwWq#m}0>!J$44Lo%@6dj|@w(qL6s zK$t)U9Mq%`G8cXDF;rd3^TJ}$sY>S&r3={028SI4m7D~Eq$-4PzJ|n_`f>T*MKPyz zFE1~016Mq|haUYHB-4Ku^R$B29ZV?BpRoo%PpCMW)CIOds%_HD%EGd^q2%yglaL9` z&t$ND!ES!?3>tmTKGQf6K&6hOaLG~5=Qxv~JBCY+6)&=CQI)Q-94Iy`24Xmz9kC2j z38riCH+yx)a%2>|r%Ku`Gn^rlzM14dQtAOCO*9Nbg2q69_6eiLY~}%H@W6sXmJynu zO?Iq=en;}5r=3hPv7dSUP0s>1fL~8qPTwJI>!Qbyq}rCud5Nr@4|^Q{GQo3=pOxVI=|L=5XL#9=0%xlp^I5P6_YNMWA@+{y)fX)0klRC?y%2+q=`{rb>){F%*n4XP zq`uZl&@hFtLmRAK)j?Jz=+LQKF5`4w@vIy=)^f0*ba3#?Xca5;Ug~#GxNJc#tzYO+ z%FZq07kEM3<6*^W<@G<_K&-tH#=*+?vv~}+88DIm6J+zB^>?$VuM5rUA7$;BydB=a zh%@l&Y2Uw}AeQHogEU2UQd_5A4hR?<9bM{?8y^mT8K_mnR9fKGmZM*F#fCD=BZNg4 zrsjkYUt<4qGWHqWko1V}$VzUUq zLsFq{-1#J-I(F+2?um3yR+}&eySO1)MqlpY)p9+{syL27NzMsk5=-X}8Hr$rM=sYv z+`TbuTJ(76R?8d3;C$qp3h?$@ADRO8)<*ZrWq>kMnFg zVN}Sfr>#lSLvr?8$cy~Fn9ej(u$G@vBsx7k7zt7p8Czz0-^u{`jUL|b+m*j(uW2Qdg)UAsY@U|9yH+gEVgR zKm73&(VM~!vjA3@$FRgManA)Qus>`#g-A@HMAQbGDiyZx$kuaLm`jlgI}Z=JI8s1G zeiCG1JaB{}LHFU~OzBf`o{Pgr%L3pgu&RB8VfA_sA)`5<#7JldX+;v82{r3nEC#Bb*%>H|cZR!k zOZ%>*c%jK^{K^tpD_deyE5k;n(0u(_MTOf@1>FCfdGXPu^&UX=$CQFa`96+}M6#7K z2?z*aC9&bDSV~Gbzj1OGug(=L^_XQFp^!?(D)%{FJMMbtg|Dy0#7cTzzjAc(2hj^X z)gW<&tG*c8DB`=!|6yAUg#w^6pv(jtYRaIvD%-Fs69)fSv@RNf`B!XQx}E{Eu^TEZ zLxN>J?u`@q!bT8)5@`$pK3xzGuIvyYV*9{rZWvIlE#G^4t=d>1uBi!As)2kkztM z9+S$Czi{G(>USMb_L9eecziV7tIC#SvoKIDK5VK@Fci|`$UMmYp8kN%dIGzz@6apP z-SPS$vE5kW9|``De4sP$o|o)O%-R*3P9s8E@cs4Nio#6Re%V(ukR6h>0*YKK(ugjb z3!_gi+_Hc4WuKi+_)AfhU!SmNfpCQi%ec^e^%3NC&5L$+jB@-3hden4(JjZ=8`;Ct z#L<`kATIr?B6PZ`T5@w}-`)1p=NpP*bQ_i*?N;Ao{A@FS^mJ?UYdXVgxqa%7KF~J(R~r!nnV2_!{FjDb|9A7s|NM5J9Q_q~;Sj-~Gj*@0fFct{L2H1h z0V(2^%aq&xzj{!Y16-&MmNcNydo=MiQWLRvg2je-bs>g2AUo1dxZ^v2{N|^Brl8-{ zAkj5^x%;%LL;}o#nof)NZifyzbcfhMHiClmxlp`j>%n=Gz@gvweH@?s;`neC+=z0CW^PM)%HJs6x5`jTmn6EY^a z3p${}j2PvEMvG0`6F!szSCX;yNR<&{yfVn@cq(Z6?~4T03!YF;3;jaidmy3$lm_Cr zk1K!y+-Cl3AO9ICm;VeA*ku$#6I3j;b%3I6uHfA%-nX)5fB%Vg!mvNfOa>73`{pLN zb)axadD7OCOomRQ)NQA=-(Y*OJbloo*~k2uS(f$KX@~DJ+o0tDAHP;p>bJ`?$N!wa zfqnM^B#BdE*e^xfB7eC}O7u)uCzlI^8m7>P+|rq@4rM`=|53zCzOOMwi`C_~)i0MR z;e|g>4*JV}*D!%z@h4;LIzfy5Gnt#?{6Q3WgbH(@@EWyyOgF%^3zrNISt5L?H4mKs zj@-MwkpC@!*COr5-~W)^(W3n>+6A)VSH+TGTY`gJCVvkKILU{UJcuhHY`gy!HTaW%)pnrQ_ z-&^4P`~g}I*b89M6@xVHVKNII`7QLt5Fa&ggnj7x?#R7=GE)C*|CQK}BVf!Nyh@~K z3_7Mvh?lrw+=7e0E9ReH;S=PDWW1IQ)UT8G9{8RWe81@WS*i_Bt+&685^V!5k)FRlri%>6*?tk9!usjB>mTW z&i&u#IrlmDo)h2q`@QepYp=ET-u$QkKfViHL%_&Uq^4*c*ZTKWPe1+tUvTFQM#`81 zrL|-E^+k74ip6vE{}L_wj!@%kEn})%6QI0%eq!$BEqNptiXUx7`mg9e z&w#E?ws;s9Tz-PdL%G=Nfq<~%uTpvm`kM!Pk?LADjb=~xDi0TK?}w6+y^#`jO^p&=619eqD+rD+0anf zx6ElIZEU7xF)wv?QR+wbwLN`L2(6w;CD(uCQ_c2S_4r|?PEv%_yoh7Dg5E&YXJ5z0 z1-7r67*N*+P66|C-aiKmC6u|J-#X%7LnXRXKccWSVw|er7?e*}My-f~Z#ztx+HJ{d z*XJP{hzbc+wJ@3LV@4|c;}}`ilBBX*J4(6c9qVp06If*aos@^FkA$Gj+G3&1UoXw` zCG3Ka!#Obzo03TJdPnPRe#qGBc;}nvs3b=h-=SxlY%IAaVcOu@=zT+3;&2<8@RO=} z6Fv4#BTr2C;Fl<8?_w>13=DxHOO#N}vc84jC z>TL~c!^}pp?wagv8Hbr*|5!=*7fDpk_9iwK<7}q3%%$LnVxnkC@K<@ zmp4NFehTF7JQ@r;dF~ws3l%};Ob~AncDD;V3{$Nv6iXJqPswwCX+0UKtW%5=R;%<9 zL$i*O-6qDl_K(ml*a1gFt7Jz1rZIeX! z#_E>4U@3@tx(B0~(2eZYtua7#aK*|@oXj{RUK5;S;u&V z4EXP{O6nU0M^^f-GtOg#UPD*c8SFd_1rJfn@KezUv?*a^pvg7jpI{6CG1VQq$-7IN zkRZgNfW45%V9Ux=F#f#R^h*UtDj5)i04XB1B7ujPj*~|uT4GKlR4L-bX~MH$M^=o$ zfps7b*0+9cKr{Bap5g+OiRt)qWQG>X+7nQrCpNWi=ddK@F$ zmOC`!2^Wtw{$D&%8AZ2=EJg17n6S{lxPYETJrp54UD*3$vdzJSMUbck$=RE;^UKGAio`%QO0ZW3FPNqIDQa zP>D$;ZN=aNM!-?bwA2=Il#b2CW2~PGSUDNz0gd)#wYB&j$8PH2wUB+Ud&tnfdo%6PV5x)gy2_)ihEU!01*O^yV5W{q2~Sj_mfVPzS|@nn=~QL zSWDYB*sP=Hus~ zu>DFnde<4b6W9)ybE#JuGp|mg6qLeq#At*E(8FRM9SDiXE!c`sbpq!dJUVyL0o^w2 z#5#`yge$ujhfbP2Rp?!EaP{oPAI8g3;GO4x8~giB?BA%#c2w72nC~@)#@Qc(1cwKg zfdLU@f3+SWbp!;pZ1F}iZkGG-qyX|_uH%=stDc_m{gojPfX6WVO98h>8b27%I-WR0 z(*C-C9qjKYMTD}3lh6Qdv;uWy`lFZpDz?kE0rz0=y zdyAwLi?S&ey)?LZ5r`J25m!?aR}1ry9pVx3;sA%M3;*i$Ztq5vinKj#hc zTL88Dtl;Up1fqMZJN56vlWSF?}6F5&0zF+E|^ePxvj2v5ScWZJmnZQfD&v5 z?6M;p34ft%x#|+49B~1l3wLJ@s#_;dSR-D@TRg)3KR=$Z!(@!8aR{zyNZd7DU0w9t z@*fs||L#I8A_gA(R5$FL9hcWWi*=ynA>e*JKy>WbV%g@Jx3@gyb0cimTdh~U8`<35 zFb`_3V&iW4%_rcM;l2QzMGhC{3)K{6hvu_MnAqdmG`YN2Vt>vUDK&y4YlksLc8CNc zNDOiZV{>`-U;VH}%&=+beUggd>oBPr_&nl)hT>5`?Bo4-*=%mAWEx(8C>=4O%FSZPs_&!55!3=-a9 zlx2}%U5^i;SL*Xe7jQrqgSwkQ(`)sGYa&E>41|j4;wUvkYPg8Sr{@to_+((eJb@I1 z;|gB^jkcJ3Hga-H&I`{! z!Kvnb6^UR~>swFNtQ2M9rSkxfzKoLh9HhAx%Y(*~grc78Gr<1vrzb$e$zu$-1eO{c!2ZY`6o) z!3}062GDGb$CUF}aFt!07w>$;AQwCaYo+3@wbQZRMORUNi46Yu!E<&j3CDF`uyJRh z>vKI|88T)PC(i5lkG$+O%>{Ezb{XLL6#QV@Ur($oZ@~`q>0OPpIQ#8 zdIZDC{J1Tm;__FgK<}OnU{1U0$|qfPos8r5_Y|24r;zh!dp?iEwz2k$ z*w5^E&Qbrc&2>)$_RGTBW{0@qtQ;aL^}7*cw#fWC>iD2*H~wNBrvo52 z`GpwzBwJJUj+X0dx`=+ljW=d%{GO8(cjxI?_-t}A|6HpkUmUP^9H9CK%+0B!=h^pB z%Djn->#v@%_T=7=gGJ*x*0r0+ha!uma^w7?795uh*{gOl3x(CJWO_!EgOtvP+2p9Q_+oj)A1(sf)#mUKy$(_Xr z*_wSKodquGr}LdGUwccsrPG=LNt2(8>=2K;6p#IrT=?U-C{1|M4p=I60eY(~J}>;~ z52I7{mtoDignlBSe?o$Ss*P3hF7-~zE4z<a({8(y)e`vLJBPHjh~YH6+*xG0!m%Js~TyR zhcs>-5&EZQ!1wp1u@8@Gnwy(ffKggD?hhrc8R~=|0@=VYOY~w*F4_%9QV4mxL+u>I|M9Eq|;N z(?I@yea6B1p`wMa0bYzX!fh)-L*XI z5PLo7m4*vohlHQ~lIA{oYCaGB9uh_5fEyzgKLZe>3H>FL@>Ap&d@rfOMNZ&wXq)K` z$!YyQj8ee@mj@#XWNS1|?kUEtz~CZ1C^0<$_}nQXh!3kS>a%^wqK>Gyb#-+{-n^y` z)p&`;7)vyQ`{)+vSagl&2om|Zhu*(VpFsJ00(y}QoP6@%yUxFQXNN@((T*%1tX`fX zVcpoc5ikpl=A$`xoB=pFhCSeE=Z1k5{k+WkL4H-%D*ekYLH2@@B9;2|R^Az)!u$ zRuW|FD)NQ1d5su3rK|S;nQyQ8{Q0xEQ5L`c5*E#BZiUg@CeD#L=WojAqSzQa{RH{` ze2ytFRj3JX?6J8aRrA8UvnNyE!48R-wDQApcW1g^J315X-)a2lK%!r&{&MM0KRkUD-1;|b%z4E*~@K-a`$ z43_*I!;}Tr)=Lpk(MjJ;&9Ul%()yh>9PQ6eijj=x0z*i;@BPPpW_x)td(|3Q_bl8o zj%fGz8wcxa9F&-vcw&D_!x+{XVMe9N&27FU`XDjw1@4tYeJ2Yzb6;wtchD2~_0EsA z7p*6ILR#w!)@vJw`ozuv^(z-zm^R_{G+{Hrd03%Il!u%5-y#RS8?yK!&c&Ra=||8P z{06Y#(iPU=F&(W|6`*c8aO2KR?)adfsraG=4{kVtZjL!L20XyGB$ooE%a6saZ^QI; z3q0o1-~fwsc5#sbqa6N|A}253v4VBxcDM(^LPA0VS9pnyW%!WFM;xXG84Mu$RcuX&_~z*lpwYUb=`4D4gd@ zz?FV|toPs(&bRRC?bmx7L4oL@roj)*e)fWcWv_!RIEplwZ1JLUFc3_!0^jeqoXX#o z98QcPBXQ$2Ni444@aShAs=PXr*JAVj;a3A_ZC_M|qFyd`;9D9k*bXQHa%#ka;i+|= z_%%N-i(7&&p07V$Zl=b5?+1OShhX0X69xD08r%*UthXR}fk+AfS?p*3bi*%{Vf^M< zJqVu{7nBuleDMVXC5db4PXCuB=K_{1M*So?N?20zgR_3F=g)H_ZJ=U$DnVcNad9ygkV=0007o10L2C3X?qj;J@ z#PeJ_Mi6w=@m8n;F3av(=WxlnqCst9&4nJ}A*>^$h{Zou_R_rkG#K9Sp9O%$u_$pl z`S_CL_sa9RgXaW5eL!6Q9@poP3*Ak!K0iOTyM^H`3iEgZ=5frcch*PdW9gkPX!iAt zjC;Wu0<=^c^X)b(wnv>bCCZqTU{ZXQbo$jiK77B0$mfP_I(S;&IN`sDhA)Jq8LZ4? z9u@9}E~E!ar5O}of_TVcm~ts}Q7IBC;a2(gFLHm-3FylG037M$;s>$i$YBKg_zxdG z*toEC#CGhJCKt10`I{Iy|8vN<4@lnV#D{FcyBpkfpaiYf1f&vN?Sdic=~an$SCZb% zMTH4^l&)sPS=Jp3pr%^CPPLqp63beh;9^wy>^z$*D|OT4)^h5dc3W6krIH1AhTY&F zN%-_a)J}WzXo>H%+l2+{`Jz%*qe7L@tA>hA89r|d#l99YDh4C2R>CXiv@~lQ6}#l? zY}{|%P{;S>hp~xC#Mqd91fL48;BC4frv?MZih~bcU-&;O))SdV+V}-6^+@_$(}Q;6kJgn5fPEGv9Y5f2i6JK96WeX zQcf=T)vMhgzUzu+Uat%|f_;+2{qTH|ob0!6o79xdwk@^;33u)UfC?+?=-|lC&bB?h zvVp2-leCm&X%XIi8G?)&oqt~5-t<@7b{i}BtjMRNq%PD|LA z=DE_JMQZ8lYCMp;z<*ooYT_n3(J%9(0|RTs#KiWUt-KW%7YfKYfW(&XGP-HB8$bpN zSlGdQ+&aOy^hsjv_Lq6a#>PiaoTx;3H#6QY;yH=LI-mIV?b{mUUnejw`+Zh8@Iwxv z7~NtLvq<@^^iaxro$G>SQPJyHfOJo$AZ{pPsZA(~h_+QrunBFwY)ni{i?8_U45{&- zogAl6pSJIMy9-l6EG}KT`Fmk%E#<8L1d3NDC#S`i_CfE7>FMK}0VK5qmkEygS}_3u zDl~-zYYA0=ziLc*%DT?h@G0<61-SPZGhb}oM3Z)S8l$tT>p}1YV?!ob71h@Ja-!~t zKCl|6goH#w=*@%#ZnRO|(-#HN9p-XGU40GW>85NWlV{uAZ39hDnH6942ic=8&UPYu zgUo5<3H?MLkY=i1AKOTCrlh7GEA{baQos5@e>~|m@GX;i7NW1G?&_c6Mr!U#JeDkr z@-;CjF&!O7A77ki31q^rGZgsq5pmDr;>Ark9A=XyHf(OMb8`9z2M;6;#Hs)QRuWMn zAf@;!yJ*J!d-v{T<>U;S(hrLS#I4sJx%u*?1T?Z|PJD_!p`oGnUkmqq{P^)|!S^Vh zN{AHg%CZ8~Ky^lW7kM&$b-N*)9H`F2lS!%Nl)6=%V`3ZfPW1uK7nCFH?&C&;szepV z+NQZ?d&)zw&t;vRTRS^DWk+B{6K_m8Mb|O2)(^UVs@Xfw4I9mO?AS5ZmA6|%kQuU1 zl`}*A`}egVrJPn4oiWPgI`8OV9vciyRs}()MpuqRzbe8!hLm7wXSeRei4*&3M!0lM z#U&&Txw*-im3U?FzqU6Q6SXg&wT+2-7Og7tKC zHqZ$y0x-zeVkAo-;$Yhq@xsEwjw1Knqw$!iqW^W!T8VzchLouM33t{-McyP8-x?%e zi}U9Xn>t*%az&NS|Lt2jpuPiB{b!VHMl1?lzt=cd7_fyP())E(tYLAVJE3d8& zl6iCnoM^YIlar+Qu3Z8#I@y>V>=||G`V++E_ATta;pXSg?S%Rei36v84ZN+9SBO}v4Ik`!l4xlIqP&2eqiyb7Q5C5_T%PF&&*H>P~!zXH7`|WJ?3!!yi42X={LNquekSLy%p^ib?K)* z>r`*)DW*(GeSWNpEXUEdID3}X)YNo-s+X^|wY4MXEF*#oZMG3T&PPy?wsM)-5Oynt zXb7s|5M?8!!YKF%suqdfQBj*9H7bPnke>xeCTUa0WPkpAWoKvneJL9Y$66&NC1xI; zNYozu;^Oqe!ovK4{MgbmE_l@5UKDdVHic?bG|C12NkF!v7NCYtvJ&~TZ))nu*8%+G znO{H7v?w{Y>L9&)u*i6H{0#dsTfXA#=2io4NR9nXoX@681gLDw%5~rRh9@Q>Fhe9} zT*hDv7JY>ytEVE(poxdq@6JJuU+M$@JU`{oYX?9+pZ|LKRNVK+&&~oHZH4+3fae(V z?ODaG`}ad2PaRu`M?#ryVGi^UJ*BU|34Z=o2t?#Xx3)4JZ1ruP%Yb)p-?q^QIZ=0R?N6 z{V>yF{oKMbJSsd{@ zvsFq;>XyzEbkrBLUFm~pY+w=}6BG3Ku@LI96#UWY>FF|jc870{$+MpiEcwhLa2|!a zWn^r;^D*7~y1FvJdnQiK(A-?v<<+w`Hhqvj;9yxZ*<1SFIVWRchJ}ggX*XQ}E1r}8 zhB|O9<7GDzADEcsUPfHnwe-76Yq9rQUGQ0wh(jsaKz$E=` ztw7wG@|v1;Fo4ibFZA7Ax`g-!?P=ZN!-rc2j^HI+Rr0u-l*EWd<%NTX4jeczJb+07 z0^l*O;}cc%^zb4j#qM+6i5gGsRsV^Vq0-2|-)$yAm_K^?aueq11+70;lG@}Dq|H3L zGoD80$7=wf@Iz~>8ZzV-Vd)Th&Na8SsPU_cy_W9qV(65BsnZLX1!}(~ z9_iHx##ug~r^f*6TmOU!x1yW4x4E~s_xL7{k*+0N8>n6ZOewc`uU~?2z!k^@dV~(9 zf`cX|+*r>Ax(VYJ-(qc6_mgI!Nc69(#{^)ufXm3|~C&3h|uFEvaB#d30T zY=KPppna&Oi^Euswxz8J+nA%Ot;45Ihe*vx+2F7|L~;-4Hnj0yTU=V2F%+-XPydLQ z$)tW1U>vb{9SFo^s}cm$_G@_3YZVn0mm_W3yiOY%&l;=7CnV70MPOAO69_#+`S(8N zd`g~y2%VM9%?wddQ9vJM80{>-Db33v`pf(J{DNoCpI72l+1c3{n3Na)ZO{+{D(`!I zV^{{duDtUAwc~{g87FS)3J=P&Vvd52TJCWpqZ>FX*5J((l((CIdn66yAT2HZ0SG$Y z?a0m;fQwWJZt`vu9Pd7T{>&c8R8>`V*WAC+9~8c5zOxj*n%d5o%NimSCoG9EAvkE%+aHMXtgIf0lMWuW8J)M9&5clXJl(!7; zE5^q072YI^Wm&G(^lM1JBv=WHPe1Yka@rsYMv6{g5rRSpjyh6bFlsY@rU0^4LO%0x zdb%~9->~6oSQ(ZeKFZ9D1RWzf@eq@Jg!$dWZ%E5B$rJcpR3P7 zWf!Ed)nllu6Q~(=+K&0mr4C^k*G!y7Eq(oUM+ex-@@@7hDvGgYO1kXFOD}d?pjvgS zy5i;*j5qj$Px?ocbgK?9#Z&v?zlG)fr40)`#O!a4a^K0_T@xvnnU(cmse98q{OwwS zz)z;iWsWuIg&uy~F9yh{rbfl81|)Xx;h7@0T_l;`*3zhs+8i2~lW~JlQczmhdm19p zm_nL9UIHVMKRAeWy!)^E4Gq~Czo!!zYfoo{`HJ4!+M0ul%Vj{EAD7|F|6c}bnb@Yr z#!6t%Y$z{5JmxU_@CTMPG=xQqoJ1-?OKy*@ot=%%heR`HXXgYLU0sx0H0pG3I`4%P zyv)z%Kn+-hdcnZ##1~NsiL_`Xcvr!ipW49C{31W^H0S0Q=n(8l5))c|j1Fj+$s z8J-tnO#&{p`u-`?>B^NrEun#q;hgUl4hEk*pKkDwI$~!h40oHQg$3pO`SXy^3@oW` zTw+^#W>LExKNU$L1lLK~XJzrg0%f~kz{G@OM^~3MBy^R`7p$C1v654wMfx%Kc(wev z#Nu>=vG%g8s*3vM%a=L_qf|rrlOd+@2Rez*E>+<5JCA)32=X*AFrbdpU59jF@8Cdo zBh7b13R0z01Fn!?119>_WjFcP-(~qqBPmYc#DD`nR96ETzc72Z6-YTNFHiI0MG4#> z0MiT>romjnUj4a$JidW5#urp~bQB+z-xiOV8Zu{ZpDB4MdJ8(zfVw`o z;1`hC^z7_rHaDXLdFM6p@$q9=7O5$J47>o|ncAsS>=4b47KT6kQdjobEl^E3x;1+q zMB;e%RCy0wh34%ImEdYE?CgT&E_5Ev%fXp{f!l;1Bq=FL5g<=4msfhmb7^+_@~*e7+uq#S-lRZjFWB+OHUVpCT7ZeP{O_A$9HhYx9{E!Bs*f7XxP}; zQj(KZ$78oEC~)GBYJ$z20T4!*(2s<13o)FFEdyV&X7s=5#1nZwr*+S4JaO zETknQ5KQ2lYE8^lt+(*w$pO+;6K4~nGvBEQ7hpBYo3SX@nz)eABY@f>2P1(w5ClU2 zv_JG}>F6La!H%}$rsh-0x}x2bZf&hYxmFGi>s?%2K*K$p)an2JyKzKwLYFh0b=R(4 zIKP8{C&i|c1~iIMtE;Q{CRwjvS0IYsl^CIP7hc1CY*BbyNt5(#NBio?@USH&kHqdd z&jx=_I0|kWS{d6ERy(PH_ZyjY={s27ff81S#-~bm%xJ=n2*EC(ViJoj2yssotFMl9@fBL@pOCF{+b;m60U z9DCLa5OjRW-Ko*HGAum26%GKrF_(cSI0M*v;Lzd`$jQl3=v7tzSk7k)$%*?| z%*qOb;uG%Z!muOzy%vv-r?~m}C}T(oy{oG$cxj60`0kfR*=s-oAQV;QoPGZR@e7!W z%%MwXecL5${_XKI=P5*Psf*q6W8c12ph5w0c=E=2lo7C7fDswzsGa;Qe&%gN^fz66 zFGSJ}KP1aCtqEh%p2l#gD+Y$vWdpeW0_x*oKj#iS@$S=(D-;zW-#H`Z-e;b+M&Ky*+S+T5yq$Is7C)mDS) z1s(o#S88@Cb&ss9r02YS3w751XBuK&oR4L)XycpIjZ{@B#BCf`yf{0&5zTsZ*+vm? z`^H3{i4Q$}`LZ3$lK*)eoW7VMOUV8}a zt4;?iHdwQ#85tx@!xwu4wd}ED z%oq)<)%6HyBj!YXBduGngo+9u$T55-+3joR^mm_mOCQJtuGIoPDjPBuU={YmFeJK+ z14Gjb3$y}(5^k)ltdGz)XYit;=O-RO1bi6+KH{;NW0n{bqouNfC9pM8FvEL&ujdty>)2r+R|kyxGH=DSCKgeXZ0LkeljT zp8=GSBHb#R15#Q3mGR>lDKSV$_fLC>ZJ4+UJnzr625FqUZkPZK3{=oJ9VF|ms;#0V zBt$5Q>jpcABjT2hG7gWfpZ$!B55Qw7@C5y0VyEAs^_hIrsA?P1((k9os}i zXfgVmKae0c?p7TKiKfx_Ngg*&W^PYAndDZYSJFh*n*OVyR`vY*-@~ZH#7w2e{`|K{gFJ164f8! zaPG2TtR6xK65vL4I%o@(%$aqSQP)77!L?&yYg?V|JIXS#{wG{mC=S)l&57Jh*h9R^ zx&pH}`yga+d84z86?p3#5%Z(ulkt9UgoO=s6J{H}a(-h=(oIf+Ol9TuZEe=%s(#(m zIQ{ecsdKvyBa~km+NkT@R9{buSbnx}Dff?6UPr;! z+4bslFb12Yy+6tfjcG0Td1{oupiGXo1ZaN2p`Rn(_Ax+5!_ z_{P8Fi0IvHykW=zeg04)|L^(n>oDI5qPWJ$T0fK|x_WvF8L`x>Y;qxif&3yOnop4J zuyhZMQPbf(3ENHajv#5ZdA6ePdomvnlt| z2f0mzZncf%H)4h&Axeu5jy1U4SFc?YqZj5w;eZzcaAIcJAAT%HQ9)rwdMaHSe9byq zT3X;;ep%VOEcXeWiwgb<#TYLbNg`zR!KI>YcW*;oLDKr*SuHLod0l9!J_3+G1%=v% zg5f3VYn1wlw$^|CIf$d*3akCO>xHPoTf7}x!*FfkK4Eq1nx*d=XHE9lC~4#$8+(;y#pIg z4{a-Gi#)xTv5@t{J>udKInQIgH;FRqoXj+4g&0`+cU7^itxbNDY)rwXX90hjVBvkN zpE{co10|dVhl{&4$6zNBjjnFrY?11y+xRiBbai>-r%#`pP|4=x$RMUbsoFx_jIp4! z5CRb!>J=YRet(6ayZvHs323>CqE%zyVgIWY@CB-H>BlQBczNyr%3X;Ouux3K66h6& z<5uz}VE4s^c>kV}vbWjge{vuWpfy}uwg$6djrh9RXwB4may&mj-$~AzstlK*M9jEf z28r%EA_=NU%@e_+BO_Lr3xl^Ah>QgG!oK0WFe}edL1johTxSo^@pakC0W3Y9dxj-v zee#)g3v-+Jly$4~F*SA9;2p#Lu~Xe3UdQsIb{SjN0<>H+LOvH47w7+n@2G|ERL+iu+ zq=5ZKf~PA|p2_=u3>QaX{1jM`HR^Owz<|beG{o>-4+0B04Mmy3&whW+u27vg9)3ID99J5+ia!tBSyfH=B_rlu-zHkLr4>U2Od8}#(_ zNN53fn4oRx#_OQ$1;pb8k$((=C&KL82`$R?6qjWy$V-g+G*8Bs;Wca_+U$^fbZ znLfTW<$?uA4fa(eHvx#a+p6AO9^SwzmtATLYy|X^%eZTEXmWab2zlOa>__6HX#$zx z%H`6G;#M;-*n*>gVkQ`-_`6GUcWH5)YH?!51;vX>9;c-NGVhqTG<<2Y9^Z9euw^d` z=s$KLpr@m9jK|b&nJ3)-yMTh+!oq?iPN!wXiA#gv0g^K^)+7Bzb%gA`AYCLO<$HGH zb~p_l9nZZy+QI{W2&febxE^)Tzo0uC9(S`cj3}NmFbD)U5tKLuYA;sq!d6!~ZcM}o zxZZ&3(ZbTwAK>uVsBB4@k2@j>0}O=_cTgLp;23SlaAMiK+5Q|YpcCP2VH)=@EZj#Y zz%6yv_F^>WP?9sEOzy)ShvN$1Lyxiu5>Qs=waP~RzRM^j9!cmG=ow6*8ojKUuwGFk zhZW!CtOUSGe)1#;l@w)rZv&n@6o2+#zkV$up&dVdd@Roy(1yEp!|$%L`>{J+;;j}6 ztgWo%Q(|_TmpDz9dM!z#8;n7nE+R5gVXo6_tn04u2fO9HPeKjD!or?f45!NThj3&P$t1(Eq`T<35K3fXa{XHu&s`^akrxqD`tQC z^xLhRoA-r{sdU&_@LK~g=gpaI*W!mS_xZ%Llj1o2?g<<3kdUZ>Ne9|;6yzmmVa-J* z6OzaOCI?&44ju49D%>r$B18hIs=!9nBFKQbEWIun+ZF$~-Nj>?|BY)uK6ez`r zXb9ct&nkn8$Haha^n#!>hIg&A=aMF+bKy(dl#( zwgrLT3{vU#toY@iM_E}+aO@`c7Ng_B#l(wp@r?8AkaiRH03-Xso#OZIof2_lV6S3~ zYilFx$sg&(_QS9o8sy%=Cnb_GE-N%-kmu`gt)JZrdnujQ4CORAgZ|6K{jfl%Mi=5L zR-g?)(reMlc=w-`m9c>zh~ytIoVe)f5`49U0$|Mm!xjZTTOAQgAlU2lM@x%k8G3{n*>$iMiqHwbO106i)Vk8= z6uX28>P~tnA7)SPTYo!+!X2G|3i>GGbs_iqms-apCPt#2C>2fCPfxw#1(pj&!Tdp9 z#bcHlukN~$c<}S*Vs9^g4Fk2^5BCsQfBqrG>hq7LewS(Wwr$%6QwI}dsDSwR zt@vkB=-p`XX5`&phMwM|0BO)>I5;^Up;fd44a0CsG}XOVNC4ORixHl>Q#*|0z(3&t1sub1Z$2X&6mI1P@XN30=+mO&l&vFeQ%r3pOxBZufPDUkI%sN%?0{#PL zoAxOSWI#tpN1~CRFre~H7qJaA^m6sKDQ$#$ZW!OdDr%a08F>H7R1Xh6Yy~*DF=qtb zFEffxeUw$S*JCAy6Lo#l9u)Gbp&O6d*a+21MKKZquy61GCnjf{#&c9?LU=!JRO0z_ z1L4Bnb_Ozabaq<71D3$k8R6%rWHh>Qg2##;#0mPfprDp*jUP9s*)q0NL)2esP zDb}^;5YumI2|Y!lWI*&O+rUsusORuk(Sf)oQ64-@C1Mb3=XtP!Z2NMITLyFh({Cc=LgFtzs zwC~4TVt^}(OL=kKwH3p7dn}FJcOxolBmo`vwdl9Fx~<~mK?DI2w~2ysoIKStXSiTW zrJBy`Ld7A92lfGuStfP+<>Sibt9EFm-$LmBpdcDN;u|o%d95toc6nE9AC$-|s-%=S zh>rb!&b94(ZT`k7x=TbdvwX(^c3fmrfmrS%_@4!9)pkhnc0oY_A^aduG;ntO#qfMO z28L=xQ4%YF3~xr?qoRO$w{&!D60=q5(hL52EJ^8_9I)5Ze~NG>$!@t@M~uyKE6l3; zWg6BUhCjp5$T5aJ^J&B1mh|#F(u(iz-ra(AE9+IY#Zmtf3lxY4=wNF_Ij@3P0pNkw zUX>Kd7V4VF~q})d=Ekm zDM{gvDvs7K^_#B^d}DZiRb2V~>xY9<5l|Y~m#3z3)=!#I>0j(zSb9k3w>z6~6$o1| zGBa5<#B{p=xy@RXX4PlFMf4%eU;X)wb?SIyb`to)9NF!69zNuy6CfoXoJ5t(G16Ay zv9TPKhSJco-izWmP=n%GC*@)<8NqMhYWd;Qr?jbOH*YfDxpQaB@@?qTPfkzGQ3>CJ z&(l9UTM8{U(=&sbaB8hZ*+bGHLXI=Uo8_0N{+NM?rhopd#?lcf^h7|1p62VzSR)#D(+F4qtREO2nhb+y{Km(&#}6FfJKI<0f2Ea=LYU-GjR54%w$a^s zxwG@*C%|-70?tA`fzF2;tT^VOoGUVmCPSV;CXIs!Tdna)QEdX7!CdLDey#c8+Q9=# z)FtMpV&}lB90a8A`}R%ew~}rI0rK(+3jO$y8>EK0KwI($s-8H(g7Em!^&GBr9y}_5O%S*=H0!PhX^n_p z3#ZwZ*(cH|tN;Me!{YbOoH&Tmmnb@LE1(}_EBcdaU|fTa6@}chb10;xp&@0-j)$9j z3-{K=uwo^}A~Ur7ZbbKcIqWZ#6(wf{K$GQF7WFOMw2OggGC%9C`1{&kbTz|wh_Muu zd!Kkfa;X2u2+Z4qdEp3nEQuqFS z`oP8|o_sGsMa2i(j8DHg#|nD{>}lXUP@Jw*mOk0(t!8@`M769TI0%rbn*psh!K?$w{b2x3M|lX7PCsB1bcGafyisW`OS563Db~ zL`+<~3RYBL=mR{mCypOSd4-&dX&Y)&!>R4>fxzI;O-$q=$}@Vv!QjK(4XNdpOD4kd zuFFmD-)j>4<2%o4XY0OHu;NRrGA^`^t$|*@J&{h}Rz6;EcpI$FQ(EjHSy&dl_7!u7O%m;)n!!3rz zE&`{1XT)E`UVEJZSP*9}C(y3^X<^_~=EerBgpzLbcN4c4lrg&|4w}{mLg4{OFsVD? zp1j^&ObBEe)&FTLR6y0lPYuC;?aq_YLuj4`b3;Tl#Nk)3B`y}sO!pt?R6Dya4A2)> zKLnA9RUZk88RQ=cD!?iIqoe8k#-TTE5K9`J0EP=4ic>j!&3R&MSP9h@N^gs8SZ zcP<4DlsX5+nrxgU6Lb>nHZb=4Txg1Ht`d>KBFq5OEnV=~+`q3oR->(90MQGyPS0!X96@;-7)i2KIP6PJ9iT#;oB6xqJr`DKyocQ)kk zjc{u1G;zLsS)C9m>FJv6fpyp7{S8Km(d2DGJ{^&pAo?He%qlun(Txg~STr`rbmE?7AcUefY!78JUr0eAF6vQCs3GEE z7Nj+4(KqBqRR2$HTxpcNUNx8vi(psT3@xxiQ!<9Rl5 zzwUlsWv?6fWJpG8*VpyqJ3~u;^6N{JD@arMYrn*iRuG&v^YTVvFxTC2m%Dobs>nkp zAm2~XV+rWJDM`iriT!pN85U57%LO#6H_ImECO>AdAePlAxD$WoxpNh@wIMR&S3q3B zEou!qBjNi}{^9hePj5`B+ZDZ#oLL6ibI_p={`k>A_F^{&o)Pz%)5lEmCZatIRFZla)30k6;46m1FX+l0FMpo! zl@+h3TQ{AJv-9#oFqlPstG2B#kYdBQb_4B_n+s0yzudM=jJcQBuL(|Unj)0RvBTc# zba2`gU2p$J z&R<1X&Ts)tFHrEYye6EaC)Nya?QJn3teCw|8jb^X0fwtsjC8zpgW_9cvGNgIz=m6KMcTxrnEg|wLPN)*+zkj~jo_SZ=3OLcjNvpjv$^=WQ{@O9duI(VC7plzDeu(j=^F=rQkTsD=y;bQNsS{ zDmQ;7GP~secrD*hzAwzD;_Zha8;|w8Q`FVfB@S%_i=ne@dFmBagD4~oatB~EN0ixr zb=uE`VQyh6)NshRNV@Xnb=a!tHg2p$gHCIj9v8MZ#yvdCcyH*S*t3b=B_@@1ZeAS{ z{8&@RRb?e*I)8TELy&rhFhT*`Xg1`N?h&hADO#kl%i3Pl%z@xW4S1TFJTz49Xr zeU4xrEKo)#eC_q}OyUGg`Kq@U{^1t=i@|W!xvc&e9iMIgJxB*8^}ZiJ zBJq|iQF=q~r$u4q4>V4^WjGc^0d;Xl2;zeQstl{tp7O6;8-yOJo}l8(3)U^=#}cq$ zV@f{j9SCnHG%0J>uI1i$h0f24d-$Lj)VpG*M>`nY_}*7m9#GRlQ6dxn`lzxV!!~0n zX=&oGA~-nHEw#8(!`1e`7^zM6ATY*zmj+$0DZ z{rP7b{>ovM!6Y|vkHG*OWbha=dFmgPLm%lW=pnAaryzb2>UHY`B_yIA4jxXlQ5l7V zEoD_jMa-qa`hWf5U$=rfff+T=8KDqmT}25+8a=O zuu`8V+M(CM|G?17M#*S3C>)h22&K@_1wDiXu9t+bIh0$sZY6^jpXyNp-#+U$DpCf( zR3rfGLx@pI7)u;PKQuOAs9j6;@1bo?HuBfAn8 z*(`Rr@^Q#c4E$jLjgG?Tm{c!FhkvEyG5{siHK=+OF3Qq_4Va(uinNA>kEm+!vD?_# zIJE%CS>jp1Q(lc1BOj0qCXgHt+^{RBc#|yBFW$;?I2&$=U#!R85WDd3j}igxlRfVm zi)2+B*bG&?m#JA;Sir>%z)GbvWgTu1YwAvs&v0Cnj%$hPhnL3*7&t-EeG|g`Q2q-_ za@28Rjsx51s-fumZ3_Yt{06%-dxn3^BH!SM;Poo>zQG;E9>o+$2)IV7Mn)X)wfZjR zEtR2eO9tPqP6x3LGWGAT*M_X;G3x?XADYPyi1e78=umnI95l9@?B{3=Lyr}y=}-Z` zmN*yW|8)}#liVPiiHw7ifrthFQCy@i=-S;-Lx6vO{D~P)krfyyNz)2uEivF!AM2Q( zh4BY=)5`Yt==NX25H&x2x|hSWetJ~to^MM;1U-IBA0EZOa)>J!D4Jx??%S}Z;Cfhr zS5$e|#LBR=MGIuQD*DujN|KzMeEgso)cAwoQvv4t`unNCNh3xSzTkUga&#o2w#;$T zqUt7n#CILQq^Bp^7HTTIJnnx%B`$V@@8|q{7K~_59As^!IiYz608jvh2pkCZ$}2UB zJ6(8X2RH|KXMI5ZUitnhvPKF@x0%RpV{PPOsiDSe#5p^y^uK!fFZ0_(H-wHcYGD~F zkr*9^XY1B^q_ZxC3YAn=&7-O`__w>n(?r>vG9COyGdz2L_PKh0>K8B0$t+=WQ&R#2bpgQfAUs{8XU6rOz3_b zQz+5%T0zl)R>cGG`E6O(OF#!4h-lKIa>~qWZ)?kfo2>KKe&*qn91+&rnI)GI7bx;EFF^os$wY9YH z0D$t`AQHsDSA7j0y)^H0C&O1G?5okfmzI`Bq+8auxop#8hDXQ&LC+Avc2`h$gf+M-?RyVhP$wv?AmE>^!-(|dt zhz)1@&4OWjn$Ztz^tq1$P_JwA$W167Pz+UHdHnvh%k&C89bFl?UnUlowIDSCXX0PR z9;w^tgFc!qfy&4#Xa~!hx&{|x;!TTf37AVWf$3P#PX1`O`z<|dCpPsgd02XhC8hN>QT*75T(f-%Oxgsu2?G)JXN z-=GrhEI}_+sF@hdNWkN0s4$nKVJE$-jQ{IhU8Mn29gORPHV$jRw5zj2>xl&b0RraF z_HvLYVI`~F$pgVk4ya~n;|Ph+?aj|pfrD;VUj&W|zs-MP9495=jQALR^{7v#E{ z*Yx!YX~8Xy4m0TDQ16MhNn}Khn(AsbaLF)D$kH5k2=DX?qQdZZxR3Cf0b&qW^_$(7{&~iV6BS0h z{6Hi41G1yJs-eG`qc=O?a3|~^DeIl_nHiwb8~*3D?cwsy#lJF`p-uFSZi9Q6gj1~) zv5~Iq58Fe4957SJ#>c)T7<{o@I0C|QY)uEA6Uo$_A$V_wPI^`eh9F<1Fn*<~x_U-= zS0_eg5bY5~^#BCLCuWV0XB(b!weF?JtiurQ!BK2ZVN#za6QPb4HLlw2 zlb0`RZjRj6nHlla^K-`(&UX<+c2vFTSuAnQ_(H5bt|hmlqpzbjfX{`UbpBj5G%(N@ z#ESL`UNAV|K4pd@_;2Iq_X|6Ukbp{b>M%DgMGehX%3#`i)`vZe67->H%s(wu85iLiu`Q z`))%jowj9#efyqWQFt*?_c;dsii)bLz?{=#_{!6BbJr0$xw*Mf=45@9S^6b$3CRqj zSNzcoTKgnEv=Ws0c(Gu#AveUbteeDMIL)J$1rsd)w6EW|jr2GNOxVw}>ac`r7i2~9 z&zw~e6{Q0LC)jV?X18JxanxeV z#xK*>4QqP`YDd7k5j=^{$4ZUM8b1=*gN|IntAe)jWbCC`L$CXeuu1;@RABLLB6O0b zHAp1sYkv~Xx3KQis9ziLiQ{|+2L@8N%-|ryuj=kcC~#o5`LA}uAAzkJQ#)A0ii`J? zm|&G1o3<5lkTS$iouJ30(;QZ*45tmE+J6qxm4iiS%ot+5332RS6VAVL2EtLL%`uqd z*fD(FWsbTosNPMv;{OQy@35Z#_x~R+ij)y)7)fTsC>bS0WF#qNuar?wD~gPW$S5+B z84*QhlvR;ZB8l*P$}A;`h^%(s%gO8g`{(yPj_>D>&+$H9$5Zuq-0!#ZcAnRDmOOfc z9B1G!IJ1u#&%lB)RfIhG&As|(@L1&RI|urpw|&VyZw2oWWyEZ460>pRR@rFE)r#8P z{Iyso;Wz4m+DAXhBH*6sm-*lU4hv1!nH-o;T!!Kq$Z9(I=4@Y*{Zn~3!7;`sqmZ8VF5jvKutkSzgZkLoerue9W(}Jc(7BCe70`i9suYd!o`^4EsM92 zfdpGAn$WJ@^}e^8H+OCaUNxa4aQQK!*)AMf-etmb(7to?fM|M;_rjjpRSOvhXGck? zW%Iw*?7DggX*O=%+9snhvKyEiD^S|5ejf@SV^lDxlQDsq+=7 zdhDs)>K@Q@TD(;q*=jfK_zG_yi?_j1fudF>9OlbxXDlqra|6B7D z$5?RPIJ8Ua;qy*Ax^N{08qFolO91H8Tc$g@75-z9n4$NZ#s3bw?C7}3#J#7)uS!(K z7>|n(zq9Af8`>r0EtfAF$T~A86)(ETdq7H8YhI=(jnAS2D>`YQmy;V;UcN%?N(Kg7 zV%zUOawJ&50De{2>%)z?cQzopfufy0x3IS7R~WKxFpy}hoa0iKk951K%yYw2K9pgT ze#rcbzVC*u4`~JvyX@c(^2*A^nFX(bTOX;-Fyba-P`>j8-$EEa%W?B!jpS`FXE3=Q zTs3Ap#&`Je3|~xV-rzuzww| zN!QThO#TFwoX;t|W0>ojKuQ73b}%yw(MX^(IL@G8)osd3+7RXk0mlKm>-LmQRNCJk zUHx`wD&!l7L)JzzB*+|H)NV*z*&4a;AvRNo5HnYOu6dQljo+DTmGzm+F)&|KNdtFEkr@an z+h~2=@jM-mH9SaNkj1+e;k$R&gNK18sEt{1^%Bbfn=$jzS*1 z3o>c)vvT;R$Q2KKBdI?eDv;)$G`=tq*vsWnP32K8l3j zq+h>uAQMmEO?s;afP}#Href_wvwbZgp@0T{PMvz8l4WNF%eBDZjycKRz~1m03vx_w z$n6E7I!>4uKj>sgCrXo1$fde{Bgc*%v0{Zez5L&R6~;nG6l<7?xVmM$Q1!4TA2@*H zw#O;jxB;>WF(*Lm;h3_p{JQ6|X31g~eu8vgB(0*8i^|NH=IC&xJSF%*qI+i}BimVz zm%@Cp47d$pIYuXxD(ezacMCGq&EDkVTADgh62|8}s9xgfnYTr${F+Gvg;uRvQ5A-9 zs1&QG>lLwi?diuFj+3fKg--dKiOiba0lYl=x35@XQde8~)0iS$ynj=7rFT;YGD-KC zUcOt~?qUJaRlD@G&aphSL;wC$X6c{nm7K~z+K_;vjffGcJr}9apd=9het&3Hr(}=y z&CgNAmS|m3uI}igXbH5n9^Jx-v-S366mWs zL-G3R-#r+B5avPNd$Lz%mHmvkOBh7@XSrD#6T7CO;`#r(3dB6j1^M~a>Q$?ZG|#le z&&Pm)Xtm|y*WGhMG+<*lA(#>@1@!kPajP&2k>Vz*owE2N!^V%7NG4|UC%v{cURVmQiEJVYwzN!Hp0PNWBI}to*XBBQ1taX&|8}Hddsy(8H#Xzn76% zUAX;pn%^c4)}vqb3|3klSpI=qXGP@?Lwq02Tel8`9q|g&E-&w4Hhb>elvl6X(gmML z*J-{!agq)CE1;W6D2-D(nd-gNUMV$1_t!h0&ank%ezm@s7T=<3G5XYDf&t%QBRF{y zYfnv^uWnMt@Qo6vu9Wr!4&rv2TV z*3*B5i3&Of=)>-Ce@(<}IXRxgjHLnv2S-JO0zQd&kLRJha4~Y;QGPp`Exc&7|FDuAs2Ct_Dm)y?JxT|I*H>$GH9C77&sV1VuGLOR;_T z?%QAJl5)7g>kB72=HkVQ@Upb;0epsW^T$@aeqK@@ZsFBn*REZKj;TwJHXIzYr7fXP#uC6Y(Psc7@HhI+uq~Er!UBGJ5uJ(P!nqVCRX?{m`-nRl zFj9kI`svRtpASw3=_2@v`V4k$ta3v7@Ry4RwYQr0wc|T%wFq4>GDr^B*i)wTnqo@@PAKvWxxJUyFw?7Rv3WbwTZ=AtK&xiM1x-~xNg6G5%2CY%cryWD0HZ@;0kqoKT9<_v>JGtwjX~v#H}{?V&M1jK zj&2)*8WuLee}gSoy$JG1$pIX%&di^%0;{kR4g+t*Y>XSg$xXlTjWP3zS|fRY#>8J) zdN&)t@Y}=7ShLeTU`<#v>0od!e)?qGO^0O@2d3bMQ zUP8kYQ2jpi05jpipjZF?8``l%(RR6ZReAZ~>C<(2$Ix%t;K>73X)1joNh|>QVU@os zf6lM;x%qVHx|dO6WT$#=L|FzuhtX{#_VNJTul$F!m&2%F=3jbK1LjPxR&7-E@(Ss~ z@R87z0wWc$c!e|k2;K+kIowIFqMk+w$b0V(WCm-RF2m#oZ;Z0zAV?x^6XdLU#p>c*BBYifd@Q3;brFR zc;7Zh_Uut2Dh)5hp{T^S8YS^R$_htMOIoW7Tbx3l|H1zgH?0^%0KuX3TzKc&uGcIW zETMOu-6sr3B*=CW#fy{dv{n4#cC;WMiZ6iYHGbr~B*wFW7%s)}qcGS*^)cOc1o`5` zA!xOz*o4dqS%$=^S3mq+yM;3l6JEhPM5|Z?V^%xhoEN^8@kR<;wr^L~SK|G9KmFq? zz9Cn<*4{?~K5xUT+*LRx?{4Q{wi!Zzsg|}KuXA#}STdGc@#I8$cl7^Sj|*L zK9)ZK2OYAtZZu8pv?FOf`U%3r`+06^KO?1UWG#{E)r1#K^paess%}O6-T9&)ge4zg zaxZ=Haibi2<87lo_-W386LdD}7%k;0=H(XJs-D%Rzpu7~;**v0YO6RtXQy0N#ZfZp zny0>WbPWdqFL0-JC(=elYDjRhdhF)^7d+?HtHg0d-RhcyNtmwq>Vi3(-a}M*dHQ;}4!Q>{j_I0xAn_g4w`xMH9w9rtaa?bY*3ZSDBJCF4gIF!+ ztSJ~h4UEWiR*Dh>37Bh9H8;Ot0d$jme6rh1UMFZ%y5Pi`zFwGqq<6jYpY3A9m!T%1 zW97aSfsKX@vl+TgfXc#GPPFhcNp4i)g6zH{-N47$qL&F0BGedT^e0+WO`j9g=iR4H z;dk%fAIe?NaSi8 z10tf7`-9x=BDTc*Jnh}RD&kZ3DcfktyjjvP4{ zybAzlWwFcMVB!%?kYq~a6S6`w8IU|>Kb6QjiH$_GN~+w34fS!HY$r}}+^(6BCN%>G zU4F2DN8u5u7G-}g-QU9s)8H>GIC75@{lRiYo3k&=Z{>SRjo;91E(Gn z#VNmYh{~~Iv$}~ne|m{;cPbp){krN&TPf2#v0B5ycxn-^0i=9ZZFRflz z*IwGtMdx_=M<9${HfXf1tu>ojgy}*$Q$Xt`DRj_bcM{_A<*VQu0QAySIss>EzT#{9 zl9Cb#zS~Qt?AqM(^Ea!vBY4SkH+P>>Oj2^*NC4UPUz`1JVqJ;`=uqw9=i_&CWilOS zo&SXdr-Qlq{^jo&7J_&&4<^0Bn8s@qz)eEhnYZ_e4c%@!IXDb{5_EU0$~`ql?3u8R z+)s*M7so^!UHG=+)|Lq{cg;x^Tde)rN_n;Ei-!+)1S+9i7`%1NVxF`B5Zq)UP$R}f zQX5eRI_yn5Z*q66Gl`T3qUynTZAsf}n-M+~myDib#$7ewL=G3=1^<*Q9 zxz^nQHuztJNgy|A9be8YeER~L6|Ewdtv`f>j;X68wiVZV##>PQ#boZqy&gf0^fAMA zyphz)KFqJ#FUvJF?b1f^HXiqn1~R2+xe(|GNp)<=C&Dy`2e)m6IfNnSd#~jgE@$8H zU~H#H?!h)_28bp@%vZ4YARIp&0{_%r)>z7_%}im>xyT*uf=Bm zaqis_`=1&Lr1|4Q=t6~2)MGqETs>IR#brXh$pc{c(f_8e+Vh&$!%Z|bG&Yb2%c*zc zt1(hmZcJBYBN#BkEFNO`lsAEQt(rGdxJ82$gHnrLL3mg?X|Uje%r!NHYV+B^bL##% zU|bKKHrc-ZTzN~EE^V1*MQ6{8OIH<@{?6_2u3d_NYcw6_ru2bxElBuz`BKVxAIjlz z)0$nTgjvEg!+s*T{NbfKWr*X?H+!|L<`e;#ZRS4xHm;fwJeaEgNq1tTo#Ks0rz^14 z(bUt=$i_k*byT-j&IA@1G3-K2J`RiK#_=xOx1(=hWRu4Sk{yU78hwr-tqaRtE``)Q!{&@hPZk%vm0 z4~Q^zUr|9jS>3+RGZ>q5QMkFkxecZOnJ|7SfXYz~U6YQN-ezzL#i444-l{K6K z58{b$91e5zbgey+qg;a~(BC-kO{*L0iP`dF4>Xh`l$BVvqjuvxr{!sUn7Ti*oPACn z_qBf|3_yt;uSREZ*}Gw{ge(`XAa!LL!QbugKI5lufsjbYkI)qsO0os(lEvSBgfKmtxqjr#5dg29gw>j`KI5$%D>j_93dFsC}0(*_MGp7R{qhGCqro>G{btW-(8uu zWyEts{pZC?6{QXlvnJ-R#ZKccCQX*?LY!FCx-^e{qZ1mICyaTRndv<8-i+t7(nag6 z(x_3gaLDw-HID|$Xy~%2UDAWzcK(UF(E2}s*0x0Goe!`r#@97RA486SNq zd~DFGb@~4LfkETq;$le0Q64>G&TkWqCB8ncE@tGc(&k_HnkQHizkL{e2SK%G^WsvT zj(MNNH;-Yn&itzHoOMekN+d=4t}cd#jhMkA5tl!)1;{IUnf}gvUe@@zI#sTWjq!Q6 zYI_Ly%JczZl7?8ue2Ht_K-j;H>d237mZ;8Dob_xUr5(X(D@qbm?A^@FVVrF;7&c&& z$i6b0-Zd;&+9_B^+-78eWK=dc8jz_}h?IR6SOdVJW1BT|`}tDaJ?gfo`%^l;p7*$-3Z#LAaU7U3G(Ziq}JJNY^aM?Ac zM?x^1m!Q_i_iPZKoZ({_g+hKCw6uNOoZ=_@pGMiWwjY!xOH-w7#4J%Z^jWN1EQD|6 zgEL^Ql;w@0dO;j`HTskezfXQN=ZMT21@7zmw>}FpQb3J4VYn2`Ae*=;3JZq~{_;)# z!A56#@&;HVWZ-jyRU>JFUJ~JY1Vuj4tA}$MpfA(I&z*0%6m-bHV@{FgUBq!MJ@~`# z-Mc5LUv#3#0`8B#+UaPg@zImI4d-gMD1FDx$<0LEttenO^H|$7Z=7kJ)t@%H>Vy!l_wKF7ZNpU*9oy2Cj}M;hD;&r$50Fo3w<2+ske z`oV#t)lNvU@1mg6v}x4Bt%>uWp=fehu)xk{V!c2UwL!ok%D2w#ll5-oYf9MTn;s_h z24j+-Uq;sUN$>nj%Eg>|^_MOI0_Q!>P z-YIV}I%;ti0;T8E|0`SzQnOoqWA202SqTrcQ4G8Pa~_(w+S4+l_7@8g)rEZjgT{)` zkCPT{7Fzu?wAC-KwN<~Ga5jz?Ul)Ja;ET;_n0B$Cxk0ZuoBuO^eh`P?+l02aw9@Y< z+!r4HzsW1JRh|6!_5*>~fVW^|jF)xEYhxF_lGJ>Kb(rj3F!eumjD=75i~QkwXsaCr zh%W*h`qlmBOFHxnoBvMx*k;5eWDpoE$WyS@K1c4oK-*!aef5$y*G=4|P<55^IXGZ2 zv8Zk5ozB?kI}jx`FerlhqH7a#(SWSgsQUToq)E@cu(nh!k&Jym=sH+{~nb&5p2j`K7q>eK|$1*%#CJ!a8Ni*o5F!Y4u;eg89u!guX4)bVjsQ=`I~8t=={r<^D&R^UR5mOB$Ke6S5R==d&KrjE6DQ| zK;*Ar%-d%`?#`Lpmk%uS)XCF~LPO6Z+J zrl+TU&;~Xjg(KJH*%`=dKqyIJV=fg8Fvne^Hj6 zC{NnN!00uu>Tbev#alcV6wWk?yFx?LD*Pfcv!>-Ordd37yKO>~MopRoz)<(}>7F4g zYZzYiMCqXp1zsO*8YDe_*s|)EOCn_m|P zDRGR?&rAwtwDjCKKJ+kkQminDRHdgo8Jw1$ew+>zWuT~vk?913P9d#qgyvE7h0P81 zMvWY~j&C2W`C0E;6EicXA+O3oPWv|;tr8uVZ!rGu!}%*$ufBc%evn2IrR4U@mu)1? zspi)q|3|-&cX@x`n|S*SZme~;>i)v^d7bUj_q0+n)ffSxB)(<@42lZCq|jw(K_6E= zM~_+Bxlo^!@ht02)SX5Dyd{Q5HMb?|yY@TfmhwXU_P$N22y&l8Fo%of*+j zPumN;@p$jLFSM`>I86m?EqHccuByy<=m#KFNvDfYbmCFi4DDz&Zg$?&6OGpXdLD&&*R*UES*x zsGIYauBE_TWp2AX;Q{`s7o!s)5KY}T&V$6yBN zim+Y+U83)nj3}M=BK%K$oAuR!%D{ls7|>1;mY$W>lvjCz=U2(1 zLlu!Ss(X6Zn_ahzWKv>|;lsxuvt1Q-BafXmj6a|C%HB@ByLNb&P0Sc00ArIrE$XD_ zHNM09jCSe#Br=r4YOAQ4B8H?SoH83J28BNVpuZGpUi!0={oneGIX}S8j@u&-N16yi zu3r}~3~uA1)S}f9p`=s|*=U?j-yu3SKC6gp0364u|ByDG@j-P$j_Fty=kN!tEa8d!MfN6n556L`;K<}vcFy@<|o}SQ`4nk}t{f~K*!vwEL z%p*wg<0?#xEi^Q~v8_4u^-$-*Qe@z&BxQ)wt)Ll{9 zyR;WL4}aHnF80SQhWwc)U0(33nJP}$OGjoaa)4RTUq20TY&9B;F*T_Qq5re*t^{H9>I)41I zb4$Fv-#w_;w7%t99OX}^ALY_w?s7WNYh6X(wS;(pQai>!dT`=AiR3lsKNKc4u6j-* zwhcVnXpEvTkgwvGjV{igj~-V9sifw9+?m`p$JB20=-U)?2@bRG#`*ZlQc@gY5sYbZ zpXbU^z#c`Qf^*dVclCJ}B2oe&a?T*F&1E0Or1BJwoZ!dSDk4alHqGvE+kM@qZu0l9 z*ZK?`1zHBUTfmM-H#^LHtvb4ZVlFxWiKffke=P5ve$OehD*xqsEIjP~V}z42WId-I zaMCXN$p>yA$@XG&e~aS6HrHcLaTjA_8E*kK@hA`P=WeL6A~tC&DfbMd3i=z(8YWLv z+C`PU)c;Ox(>tlDr&^wa$J383AQX?@2mv5;aHXG&{X;yL5!w{w*IM#ooy}V5_ zu1#kC8T@xYLnDnY7_1@FPMkQg=XL#1(-OrdFUvxktB75btM9|>1nMbS0BJa+Jdu(8 zjRcWpeztyx7HfQJOuWXi>lrg|tO_8N%Q2x0iCXq2iE%Qexzzz7lq5)@bPFASEZv~H z8ITR)?if?JbEmHe$ssKzj~(%`cZ>Z4uOlRCz%@*(vGJ-$o19W$;UGlN4KPNav3U~U zM|C~eZp-x|mKbh;#vRe#g{-IMXTBD$;av0O$~ppi*bNj$UJVHh>AcV$Md|-?QY^1O z|F-z?iAMAhE9OK#*yM2t&W*T}=~=^0809Q0VT@BzRkel>ksKmrpZ0@H3BY9?hYMi{ z3bqSg9T@fF@wh1x^~WbwU1kENgs%XVz^SJl<}I%i+KBjde;zI~zN8#9LofiQYUPQ$>E#PubdHtHK z5&V-X5t^DNYOka`=c`ocL@yPVs3F4v+*S9U>^2Vq)qcxywer4k>GxsXXHWptk_PlL zIY2i_6SC_1Q-zq6HX(x(2!l~OGI(UkFBYWk{%zPndDktgp)>BD%{V|5PFY9)IX6=s z{`KmWqK0`#Jh*`)QFL4+BuTWl=ovSnROdnL2~4f%m3ym8&z{Wy%TCsOqNnl7GS)Z; zX#k8ouJFAwm7mi8zviY7SPxAlaD{YPG-rng%xh53-`%E9N%CVf-IDUlOHVRXko`Cs z`w04FaXj0pmO`L**tO-$tG!pPyAx%>ZG%*|HrZZw5JO8JmW2pO_M! z9ju;*Mu%Pv`EaCvxjzldAo}agTol5VUn4tU;E}z1hrf1`n)9^{`MczxOw1`v-MqC~ z#i%k=ptm6#;PXzVgF(oQEvv0?K*)xP^ahnkG2Dq`LLy+UVe7jkycaJ5h%?S2ir%jM z`?nBt`zW9-Gn+#%Mb4oX$Zvh1_7uWVK3EJ&GX)^0n-p^ZPaid;yt=`Mms5=Sc6p_? z;yhil{Egx*?l3;@BN{=y7XLW+&GE?Rt5N34BDDGZ)2E8Q3$6Af2dB{@ig$*(n#VPy zu#Ts4n?}*z-X7~vC(SPxt2Tgt1Yk0e#^cF2Qwh?-ll?Z%F|^6Ae)DM%y|be*6S=eE z3rXqQ{Cu3oo|VCuN+n(h)sFj=Ea=8n=X*W-msWy$O=8ju89$(tAYN`*wvBYKp5%*rUWDZwB z(9Pp`vns6C{0<5X6e+W0QgR%pUEh7=Oy=iChf3Fcr<;xOtyub@xcC@!1ECw0=iLiO z_$6FwzeTCGG{#Lru!Z*Gaev6U*bxRo^Nv?pgl5!0#d6uQwXm2sR6*MVS3Uh}@1*5i>QWj6@{s>ME>A2#pVRXLmsUs4^y#QPXz1g`H;{1>oE z-1s{C8MT0TJZGachEfHC7j=dj=g@4QSfDn2{Q>?(#^FOyL?r0l!9qmD&h?cjK2hTA zJ2>b>OpL>t6vEQm&o2$9{if2r)4+^ox^-S!WBvIh^K1Ya6<3Tr{iCT@S zOGGVzLB!5Vkc5V)Cq%mjlTFV|`CS5|h21ei#ZKGybbo`Y|9h2IduKu+^bcbD5Tdl3$g32uWsWXz+B9(Gb^UeIZT*Hr_ zPH0^6W3#UdDPGL{m{gy3FXct;-Dft+B4l&J5C6loC|nE$<%zQ1or}#*Rjaw-df5Q7 zGv<1|JzE?ITtSI7(P$}gI8FGIWjh=fqU`VcZYhKrc0SPL=6ZS$n~xa(OQ+teC2#nN z*{EZ(I`sLvCfgQdjuVE3KS-F>QP+(-5b*JL^$OV#vR%u^DNbpw2yj){Mk~e@W))eI&rd(u~|v! zTObOFRq52VtMLLA^uD_Mbh*Ou@zEW9Z9b&a*V2>)B@z%xb)x%$sN+^?c{6B1b_SV4 z<~0j%N}AiGn01Tpyg;dY^6(8p5E4Nn3bI!bf6H*MsY6V z&Y6_<3-1!_Ok*_sQz%v+l;@{n@+2)2GVamgZ6#LFJ84Yv8PPuq$3pnyoDuCaVAi{$ zBER29a3|bCNer#{eqrm*eYwUY`ivNp0G25qZa44dRuN-Bhhyf0my@7gS{|{#_g<)` z=d&dCIvJ2q{lVNHj_IesV-$32_+~QA` zT%e+2znb?(&7k=?asLQawS2kOFk|&$_3HZmf96kJ8=01xdX$p|;fr+5s`vTm1u?l+ zC`n*`uuPOLy5jnu2uoQ|`5^BQzTfN_zN5 z(pLZPD?fkGn#y55Rwn+6vQofugIyP=k9SHGm`N*uD(4`dbRy_W!L zsCSn3(k#!v;8c0;;sPDHnXRY}$N|zVhNx5^n^rf>cXYt3|;^I+hsEBVrnw0)*(s}fOET>CPuWwMSA)%~*4rD(kHdOoTDZJ_df z9yd)MLg5S<5S)lT(A%uu_i9GkllI0XrYZ>+Zgs2q^_=RZc7R?G%LqUjdO3VqAv{WjU!uoR#}Qxk}2z3-%X8qGS+lbj6XEHkWD!k zLy|TP%yl@BTPXW3Xr(y(5Qqw*e&nLW5(pD(Tg0+UNewLOhm;bri2!Sht4#ez=H(Xg zgJ`GPDo&=nQKmioR^dTVb-PZ?6cTLXYmwi?1b%R7DQ%3EZ!TbyXq7_G=fGA}vAyXZ z0btkGK?|$5Y?(T0>`bCSdtKF&7gi=E*xk!po@)4=FnJ((D1337BrJhSOPg*V^9?ejKUh)?r1)1ba+r1M4n`8!S4UZseL`Kp55#?XqtzIfVK(`r$jZ&(W$3?$iQihZAus zaR)x8)~(AH>07sgdto>kf$G5`x+3S)(kZRXJ=-s~IbnC`Ah!o_nS^n3s~uZdPa_to zmVxFuh0f=#fPd4#hcgh*s4V~I1Pq9yzIn$8b5y`%94ql=8`Mbdj=1dUyo+AJ*%Mh_@qgxy1JD2xV(eE7CdCo0HRZ_1f0B4tAL)SAnD89hmqe(6_bmpk!a| zhD};4A)iQJkt?>Gzm^JEmHY)C^Ka_}ygnim)JAm>h2sbd-E(5}5C2~3<5P4kfOH*t z!OFKQ#M!MkNb;iNSdpDP zF%6V9y4Q4e$*?2SVdZDgcb04@kQQxU>vbi^e{oP;rb{%ez@#e+T1^iVV@iS#L(|X; zYz#syHjJ1D_y_^EJeU9adc8-zS4CDRg2z1Xv$lFgsM?E?d(8*^Yc+mZK(w2JXThB& z{a1vuKlR3@(uB*;V?J&3tyxLrfVc&$RaiheWQVq<=92TD!J%Q)w$Pus@|H6m)y-k8 z%lUC}9qO8XFJ3dj2A>IXEsF&0P}vrTE48weIU3bhbxHpC6?jAOq4A;{8`ki#vD1m37c<$NZWKOy?)JQ~i(z(Stv z7;U|ZZdwZK7-FHcL=e78w@UX0q4HyzPd#XU>Qc(n1l091JE1Q5z^*)rxQ+a|h=W7< z;VnMt7SB5==~4_=iDT-g#x+B@4Gk=jYv`Gdwhw#3isdyk;g199#s%fO3qe8C31}pr zq?+)z!f!f-4XUcT_Iz3~L$&A8)CUpJZM5&uoVNXXIxi<4t?Az^5AYaS8_S%X4%x2g z>6V$8etMX(5Siva-%iPq{c7*%rg~@A_XaXHHfNk%utXY zJ{XYOh7rJ^;~yo<;Kon z=F~sPr#KFRCldlPPlhw+H+64sH6O*` zni=c`Xr9;Wk54iN+#|BIn^y@vKzYsYT(rRZF77fXW_Cr?T$mwgg1XCcc!NVFqcTNyNSTqUFD_2rY~ya%ID0)x zDm7T|3Z(U9@l@QTF__Dzgzuk!VR}NBEfe0kq?-b8AU79BsVUKd!Rn=OoC4C(Y)Twx z?q>=Ta7$}Z;L6x{WZ>x1w@<37laAWPwb0VKV|mDTyvef7EBnE$9b+#Ea&Uug?_&z? zZRz@4>#pzm8r=j$t&D_$M3-R@ns)3$$nM?NX<1w(GpkG*m*`MLHIaFVN&qLU=6Sy5 z9`GYb3U2=)(8PFoA}w$A0>wqL<+z!#@fp1q=L`~(E=82ILWUwsz|^fjoL^q?EA)CP zN73B3{A;ti<#PhIJ_KkI00yeg@nXjBd{4r5V98|TGas{P%|~^GEnBzZSF(ZbYWZG! z5CjkYZ$u3GKq<^NBKrsVXu`F58%$Z^IFAfkGi6XfPc*pQ{{JU7!^FQ6+n-IQiwFI@ z@cbcOB*%|iMym{=uPK@`-riW8^^#QptCFOaFhiFNN5pD^l%Y)kOz-o9AZVs%tQbNh zg=QI>*c$^xszzMS$mD;3>R(SsVRUXj`C8ugA}?0`KDDGjd(WNWDz+4yn0!Ds)xaP| z|CmO4KaY|(wZ1~2Ve)~hHoe{DRz7HNzjo2KHFhE&&nwdZ6AW)OkPb{x6Yx})NsV2& zoeiTSEvv6wxk6ty{zGCr{9Pgrxan(w!FNWxvCaZUv%YKS4ZLL+jmn}1u z|NC7hQu9Eq$|l661n1HKqgUmm67;`?4Cw85mSAb=JutLab_SQz{p01DEzrx85zQLU zPfXh#)5jE22SwlX%?u}V(3_3wu+k7UH@msRjJR<;(|CHW%g}BoPS&YvmpRF1Mtflw z$0-W2#wbuccRw8r@~fjUF*_%^8m?p-8S(ns|4mzgrvq$n{%Ah_-vj)%Cr^9x!zPTp zopN(AYWKJ%NuoV>?FxiD@ZBdNZpb!H85LUw5CIM%iRjd_NW5he+rQ8C&Et0LefT|1 zfo9*%;xVLQK_s8dyzya8$;uM&gu#Sy(F{j;S%w}!v`1%7b7!!izrs4il3={`_<7#3 zLB9C^j8cF+hNw6mIDMe!G6!DCqltgJ(9k2y&(dN`xQy-q>>P(wm1M^e1S!o7sT*-m ztH1hM&lr`Jvvm2#r=y0%?Z&hu=RG@vks%}gn&v@DLPvf5^-TvUwr7hZL$1*6Asv}A zM{v&rxZ?q{Nz;L0?r|YvpJmDVxBkj%D2I?2fjKr~_@KNdLrVdDAb31W(vR7_A226n ztBxWSwv}>R{5arCTgAai0XU~|O7@sxmb;FFK9tJ}xZ2X^{D8n2Ut&HzVB8RPf|sNt zLRK#KG=^e2P~ycmtaA+92!Pu#o;XpE53DTtE>Ar>;foe z17L0e7!UerA!%VwSKerIWU@0)vj_5kZBQ_>S+BRv?GvLy%%3qPK)olnIAdbChMxMa z#UNDRM%70MNtBgHWSr8I5nl)N_2i}xL<}{YX`ee|p-*|t>IpYXlV?mSpOG{y)O`-C z%jy>C*A0!yy}^7zM4}dVQ-u8v?!w4=(ZJ?QzRbx#j1Ys|SnBonwXafAH#YABbFaB> zX>uU?NVF&Oy4@|hdAXO4{!_j};Z<3fC(3u{#8(nEY_t#&YEP|}ew=(IQ2ygEeMZw4 zj<+}`%W+VM7@vulV31Q<%38O87b1s6bKT6sW9b8enyg;D1+V;YKT;SNlgL^Tlu zDu)?yqpGDhmNnlQN^Y@W#s{AOb!AJW-9YW;&5yYZfmoj|2v0%kpWRHd{t0+k{0oqg z?q7(UGP%Gp#GQ@J6|l~@n=2bawAhlIm^F}%xMQSo5j9A5eTNcEZ;;$A4T!Zx`Qnhu_CQ{Qbqf<2yy4IpfAW05n&B-R?$-RKuZ8 zP9cFx`{)1RxK4gMV^@-!qz8^l`7ca;%bSoxdcNj#Sr+Ft6FC8vX$k?lo5|VGz#Zd|yuw%PNCp}Te^ zTU>=X`=d0}I){F9+%Cghm-H0&0aEv|e@wc%NS6uc!*wc7_S)VBDd|sdGdmr}+s^<< zH*D~I-YIvJpNs!k1YJmY`O(($O=d273b}bn$O~OKq^=`skH*aBZI{d-)!k(2yOEMQ zgJEqj{|rI+HUuk6dn`$FDa;53mgOu&&PcDFLBk+MD}<_D?Ze8+4)BaATENcaKyfdC zk)&zv=^y`u>I&t7I&`e^am1X`%K%ZjSKWVlh6{-ya$I%> z>cy3vmG+IOea<4PkwtT1z|2eCP(kZ9e1WqGenSOGW>S(B&I^{jt2``kUtU3xbyt;+ zaGYYt?XikC1&{w=7cu{LX+Q%!ZnTZg_2XmN@U`jz zwVq9UitPF0{Q|Um6V+^1GxzG68q8t}F!aJqaNbX<^#Q)fd-p-PR!gY(#(IOo_9f&N zt|XQb+`xW(4W2;p1qd0A7)JzT4H`61U0%+2$hEDz!nlQtB;W|D#H|Imtl-e=+B9An zv;!`S7w@3E6nz281@?!tOrV5I0ws)z`@VTtK6u7|5o(%{D78RFd)W{-{S}1bMe9}V zH)RZ3AN|jzOak^02b8n8JdZ3E1=Y`~kj1Cm13tGtx}e{{@Q?aV^MpE~=abJs*d*V0 z`Jy%PYis)dcVXt(x(7Z(ifKANSbrk@_-k#=>H%x3b@(5Kb6xYg(+ht%@tW7Jd8<}i zDME4!d>~{VT;$r9V_LS+(GgY!%~Hd!J_GH^d8S-uT)CyWj&J$n z7ukR=1Jp+K7k<0$&F&ox$0D*Jj&(MJXYlQ5sKm;?f49i@&v**F8p%W5*WoZ_}wrvN+o)-_0`OTjZFz zRwt{?BzuhSxp!f^q40x6Z7i^vg9Qw6n5inO&`853DAgZ)lO-X$uG}9yYTz(~cF1)aB(r@8{U-)}n#OdPQC zJ@FW_9{A2j(YMzIUrltk;pC7s9+x!z`K}8-CAB82?O|9#PK`gKb#6yVl?3w2RkGG1 zO^eBE&&&L0%y^~kTd}YQSL)pgd(Ccn4K!W&0=FJ=7lw+dxih)OVNv(_{XUb=P~$K6+jW&k9XS);fn= zsIu$6@}!~Q^IXo>UPI0H-iW*r!kH5C9RD^$#QoE!G+RV9q0B^PO)IO}gLHx%y2iZd zCp{+Rd|g)5At6{(Tl+W+5osRC?jt^L^j*NwYVe1x0%XqFM>hS!h^PquU z!^Me6qqYfUpy7$8g0C>Q7h9(R5k0yv(@GRGtjHotjs#%jjA5(G6`cI``;;PxR?QvM zWzC7|2<@SSk`Rx|WTGZYxFYRJEA)^~7U87P$YLg5tup&9XEGkRQ4UyIH()^9b*^oa zU;Ret&=!}%W@H;0?b_KNwqo_&G0z#H?ww6cc211Y9MZ|5uSv~bd7oLo$+8u^+p7Br zg=hSTOS-{@S!U>MVM;7(mZc@$0esmVkVLgJJ~v2~d$7o52%+4RjP;m$mcCCapCr*> z#nXQDOse+B82HEwlrIH9iRO0}8Fh3{yq|56#+upelna{s!-^NJHOZUXl5{ z{&-j?*>xf@NjJ{k)G{E0w_5;_?%P|v{6K4b>+$2=_9l^0QF|92yT5DOw#NUwJ)il0Q2+j| zxyGsEVu4~vK3{f~UcVt%45f$Ai}HC|RaUoY7{uhwFIArEKAyhDl`JOjUArn8d~HwGS~>!(qEt-b7!F6GiS#Ai~Hcng+V>U(bCO3l+{j zcA*sr0`ss4J?ra|{+91fye*m~65xR<0)Nks)^pBp%xnToH6 z<=_Mwzz3arReW!A)}VwQpXp4UqU_c~PSd$>W>E8boNe-~^m%MUN(=5Tqz5czLDJ0+ zH~f@8QD<#$zrG4|YPDs3KmVT$SX`2kwX8JW6XUd5ixZr(Cv;b{jWmv&U3W>?P%2XJOo@17>R8`(;5-z-GfD7Ohxh8NtToB>7n_4jZ2n z5^MKmsR%ScdLH{NM$k!_S@?3othb?CC$cgNHYbo8tzy{b)22~w*vu@6BQL#+c;)#nPhrp(nyEnU6vQj-+HIV)*C(= zX~B*qohLoY(bQDaD!Jj`S+%)Mx15;v@hobu{qpY3Yh_c%N!N?M4_Nth>aWAA-(2^3 zA6AiN(Uu2MJh!>SZU3fUA6D((y}N_bJ)6LmLofwkN-$b}OP=a2%OwvP0RsjW)>Twi zo?bej_JSmqv5-|;mLMPpZrN?pqocka=-9On5NWKcX|xg9wyLGzv&yP19TxQ1z4Oh# zyvE1*hz8T+d(&L(UbelTBOwg3fQr_v-d*#MBh{;~BB|VCG~RaEf%c<1?Ob-(Owcyf zV~bWrXg2DT+O+eDv*X6y`^o7uzI5l_!ehC5(HClN^UoXK*}ySAX*@-u(6pCR7q4UvZIi#tmr)LC!^* zV|^{fLN?jORyMb*w{qQQ{?BX6KeuL|%eJ|$;C~H@+Twb33Jcn{Z6lk?APQPWE?9bL zXwT;(79}z6xq*YoRsk~C-5pwY!XJrx*kDGYxe#wl2JqHU>P4>H^4XbWkA0ORr%XAx zVr=@hR+4e4(=BxoFKvLTX0O%`&qh4x+~$Yuv@pdDGut!+d|r-hk?|f#vqHgHd4}ZHK>CFb!5d-HyVW zpBJf6$!KaRX>VS{Zqseqy*ANF^6>!_ z9+q7>Sa0d-g~)t>N75$FT%!zJDoijfN$Gb+0loY7 zolxE%ox>1#B7R9!f5$beWV>~k7dxw~Z#<*zn(0{PJTvEv7Fi<8W>1KVXaMHYt=EBj zYhEER76mfd&J4{8U$fYhJ^Ef%fjF99>J>h`fep_yD?4`H*bPg@&t!4*q{~wl+Nvwf zvfb$t(09Xr_y=^CUuGQ;*xG9cFsdqN7CmVLZ+SX;=WC;7s#KOjUMTKfGoxCv4c)Jb{t|W$S*!ZKnzyqf%^%Q(-em-;6hXwlO z`uptK3BrCyV9yq46FaWDu5R}%;XrWyZk%u?4Uk3-bL*9Ul}M9zS^2H?%#sgeGs>UR zew#WQ=4D#vXlzwiZ3cv3f?6Ha<1G(X%!YUtvy=S%(o&ASy7@S(bjmF&`TfkYjcA_lK_frYEz)V?(f0 zU^UQpIuN?l=yB8r2~AuccYjR*KD#Zuy=WfpaGnBa(vopuhI!GPj+u7m*VFL?EV{njIjo+ZcFnwp+x%wP#f{@Y)lcf&Rg_>csLo$6}SWuV8+h?)7$^8Oe&MKv}rVh73C{|0So)P6@B5r{*H+wlJEG@jUl@PET$ zSJxeLw`sS*;d_D|FLTD9jJV~b$|?dyhn|=Zm)>I)nc$Qp>*Lu+5v{5=tObNA|9Iv? z<8e3mk2+emGklrtr^hz(t%w+wF5vfS#qg+LNf%q}LhAZ-MDTT)iq- zFQo8pl`{_|&F37oPgVu2^Km;@9nWtMD%->zA~t}VRbMQT1h4Z72K$=#)6G}wL(qg1 zy_({e8ipDvo8g+9FeM|yHni^^zXZBpHp*;6j6n`}|^OU$yl&wR;58bmcHc7MIH)xuCp{daCSNz&6 zzBjmrxj~vULf|%A@70u0E&ZEgE?5m8HtZJL#Ks?btq?g-%)zu39cW=Arp1PYXrN2o zK#4K_5)w24oydudvw2EG)WbD8P10A%kA2JiZZPed2{BF=@efe78qW0nrrf)A6Aoac zXNHwhpN7G`I#^h=g8Pv*GIamaq8MlC!Kq6D^%y8RsVoZ|w}-}buZ_EV-=?_XUD?Qq z2~E}vt-F-CWfnSPo$1ukP@mX>&7k;&iO6r*njOw;!g+|2#qVIr1?<++Ih!o$N) zZ`7(3^(}vvV0;|p9Z=)Oviq6h2;A(QgsQrM#jj+$OiTnb}azy=LczOF#B=^gCOSM{@qmhLCsdE6)x`rw4e%&?>D(?V~j9PK|xwv8jj7}jpWf>I{z))bZLU^ z`%6(?efAOGy;_1OE4ZZX6TsxFj39x zPb1w-eM?5td}PQbA9j?jh^T%&;rpi1_tM1&!+{QB&;h_Z*% z0Kq7UmjP~u7M}#|jvg1ev+LM)^&8qQlyk`S9x(j)dWJ4>)nR>_GuG=}! zAVtH_+Dk$WehH;D@&9Nl_gn|ofM&cqpTs7eJ4v}Pe{m=Wo#*l zG$JKR>-TYGKHvNE-G0aM`{zFH8K&Ov*X#Mbmh(E#^KwK=KJohvIUI`rt=B(HGguk1c>M{sGom6q0&7jc(?>JlIz%JFtxY@<@o z8t*Q%(AiKMwytIXQ)Q~iWEwh1)NS?*E{(kBsN8XoUf7Lwzb1H2AD7-<*3ibs1rT-# zWkA*eHTvy>A5_M_={yuE9b?Z8aB1t6-@EIvX9jh(V@ksxeU~q!kwWR_2g`$AVWEoG z5U78EqlfejN2n-T?Cc~7AUVODSfiKDQP(VBNMlrEILUhh_C)1H6UfO|Q&OVGM=6y@8e5(mw1zCn{0+L~ZUd zV2EJ+oQ}E%ud#Ali%~}x-_OqHE1cCbte)%U=0{I>{Erhl;*CpzO6!t1Z=$DjPbX6z z6Qfm0Fu*Mbc(OYOukJH@*3B!^UuJIHl<4>T_3LSh14$n9`=uW5HOJxhrL$Bla*Wzf z!-J-Ax+y@X`-=KiVj3a zM|-5S?Ho*Vp|rv2tm_@|_VOIpqS+v3UMlsAIG9Z{f}XBqYSuE{RI&=?8x1X&=pCey zplo&Ee}Wro(@#EouSIEvi0TtGgTM3t-Fq6OO=)#E(&u%^qm%tRv;e+oQyd&qHAFA+ z=3zHx-@G4q6A(vtX3Q`BlKJQI`4Gcr!WVM<%ks%PMjhg60o~u@ug)-g!g=U{V>+w< z7y%^8t!T*iQB(ICZfSY0)bX5O$j7VnafT-OK}JI|ovjZ)5qosZMK<%G;MpAySY6L{ z!_Y@3iN|AFtDd0bK8N5?Q6gtlp@QA@b*pL7k$Y=#&Xtsv2P$1GXsxc^U9*hG;GH%n zf2wScW47zU^+y4P?P`?CK$5gLtTS{CDp58Kq-v2+AwM%#`P;a4+F$dt<<=2<7bU?O05ku)Boh=b|@^_-uOY63|k_Cbzs*EgLYG-C8o}nA$l65K_=w~*C=3U(S*o7*`pG*Qnz?B1> zd)et;l+SH|P-D+rE1K5{niIEGW4S`ptbKR7V3mD}<) z)n}QOB(I<=py{-1ex0o=436z18;-(G%$qRu;ZdIB<_#-r?o(&nnR6T4ZNH?5sx>6} zX_HB|;`>Qb5CB)1vi(miKGzXS*6QsGM=C zzoE;&j|~gi*t%fvw(+@ros{-qV6`+qy+ylrJ2XEB*k0sGnS-n}VhMs@;-4ZZ;loo= z883Qc;+IX1+_Gnn2FsHPr~A8n88BOK59w2U;N*Kkv%`0O(V6y!GuqGpD|W++dOxT; zC!6hAnvsT6L9=wzAhqr~6+6!ErTaeX+W0HM<9y<^LzXt#=Slmzl>Ftn!M&`8jXM|b zEK8!pP_ZDXoI7gI*v$ms;MmMjidPLV;PJ7KAC)A) zZ~?e6F09nZyPq9xmQ>t=F~e`$r>2<~(Vos#-tYRJ`Et6w}bS0(jCmtEKJe z70y`2AgwEQMxwbrmMEB^Y-B&_aQsuaDyY)>#Xgz?7io&y8m*C>&D>Sv_G%nTNC+^p zFNp4~!P*+CiN6l`Z5V00=5Ged5xr&%MK>zNBQVYYeeaQycYD_5-B}3yZ&8tp$q}`? z!347gMO_mRMzgD+pUTNpzvz}*owsab@s0MtZJJi>`DK6!uN%2K}Z$lj!MEieW-YuT8z390&nZg&}b;w8Eh2Ln^AL zJ{mdh0~PIGAa|4@oS*AAX?&V*^e7qXcyvM+jMTc0e(iCLwt>gmI{55_h`(Q4SyzzR zWdZqo^y_&+g6#+m6*-7*7|bF;D5(olBeZib&&rqbYud%;)Gq!u@%EE1CIJ5i#O|)S zeBeoetdk^{Xi*yTIAnNB1%1oZA7vg|Q!ACJp&K65CNrso=b_AtP&t|R)OYrr2=hwb zE`0ujBmGD+;grI;wJ*%*vIG^PRO{Oq}nam-{5Xms1k!b^sri)6%CVq?r2ioyynJaZIl4 z+4`klmBtzhIboI&en>%&?>fv@E-`*bEi7K@M9TWX}7zCk3%j z3alK0ksl}&S1GyIlQ%;2l;LY<1%GOpQ ziMFdtqM6dufXrJao?Gy;kKcDpJRR!lD9R^Yg&C+_QTcGRrS>rQq7-J};lt8DJpwpr}OE<;Rt<68R#50*Z0~P1Q z3_t!XX#NGK9)#7NrzTIEF-Cdyp=iQ_1;7(?40``nTjJVOS3V1IPAs8u+29p6;h^no zeW%FO0*mlc_`1>MEuS-aV@lF4AB>=v?Yf1`VKsLwh~B+Bh%{;A zUQZx7f4q1pNVhQ1xq0VKL#LB%r3hyr8nJ!*7HIR7dpOHt=hero)BE$Atqkv~_niY6 zDC7f(y8aRyh)74Kp4BnQX9f^!5x5%y=`fN7Vy39_t#-jfK0HohVvzS}PrDi3KiYtt z%F12J#cL}UtpxvPw4@T_B{YiGmtK@cJZ|OY>MHgR4ECCm`ugjuGZ-HV;-ntn@H8_! z;6%3}89^yT9l>VTm1V8`gfdN9d&=ZF6X!SSzm2a8I!d-`hLiP9Q?ElNhufNuAKI3H z+7@)q((XC68Drh{*V?X{8;>hbz|tfSwc%Sif)Q`M;8t{s@uDR;KzSrsIA4qo7Y6kf zifCO_3$)+*6A}V47Q}e0-mxM4N$1enj~^Z#@YJVg!OKceB$BYf+dBZ#2T)$cjVUKg zuvF|=`CjaJu+nye3g>$J5e`{<-M-LbL_YbQX~VC(TmR8%>ew6Js8+n2F)-E^`dmgKov$19_$(xq>OfzreHf|OK zL>YBZfr_1eRq2!QN1T)TfD37M>?mGUhNr^eodTL<9I@A(bjn#BWcz$vRglxhPP?p^ zcA_S#{39c%Dm!mk&267nCyzuj>P&_a!uQviUASY$`ar^UeFgDNNH=Aoe0pkPbVW>6 zM~B;$MnlRt8VXli=d+b)WUGj)EF2cYCMFpi^f>BdBEpn_k?i9Tn_1jl=6q?8r=ph; zL>&{AknVs_)9`A6z}1+v8uJn=PAZ?p&j6OQu8Swc4_`}esz#o4Q8+*plqJeOxb zVD_J3TIZz8h^1tvwr6kd6R*^KXL#^DWa6(B<32MFXa2qvnk95}pp449g`~d}`N=T?!0y8A7ru zLvm_Vk7?TnJRp~@>-rmx-w1#K{1^;`E~Ikiz#ANnZ}#{1KV~~%WNOEBh!x`V4nb_Y zt3@{)7hpX}&*5;~8f1Ngmyjaqk^toAI=&to3aA^fdH(8`@pLN9B$({lwaY#uNbTES z?V27jF5j-6+4^LRiKk`%4LaUKOOrwa9mrOjQhf`{m5&}hD)<}SsvqH6*rfV)u?nmL zmZh?+p=!+oz+Y!`Y9Z)e(O--uVur!AQ#+a_^#VM5ex1g(DQR0UjCy*)V^P>})os|+ z2CkfTQnD!b-nA-v!j92JRTO+;iO(p91u_(JU8fiCbGja0lHVMm1vqVoe(te5J3}3< z1HBtQMDnbNooyfI!GgM=&s{Qt$VQCFV#c2AI&@`1 z(F=Zw*Z?gndfQQe6_RBu4GqJ#w?hIQ^je*&DVxwEdsuR*393>Nl8`Tg+&qeRe0cwn z>cWl&x(>_5-V^^>uP|W#_B~$j zdxGv!ZC3|qV>5H6$Uu$-S8;Ftbbg9`pC7X@@l=fQl7d$Z{%Jr=@^=nc*g+wOyGSW8 zg2HZc!C^UFVRE{Zw(02J`^eEy$7315mI7L;TA2$=DM*L2?w_uE`f;MLx9UX)$3V;= zZ1je7gotj;B&58=jz#na3M%v3Y*;y4>*>Ss=GBTP9fFBF_3avM2g?Wsn4sXkb?1)t z**y;Xc25YknCVtjQdt<=%js-lq7|YDt4EIgwJ&7@iTC|7^MSRYST5vit}e+Fea)n7 zUhjVE+o@khnOzlY@hUfc*#W|NR~yOegg%- z4Q{}r@u!9nB;0au=U(S-1ZPHi#AK_~wkQ~~9*pgJRmo`k6;CDQT4+f2yOyxiuco-{$2}Y#Zowg^u&GFFF+f|Q zi$Lt)+Bli_4fuOxmq6}4L1i=1B8M5pCb~CpD(9 zXDiRDa?cZZoX{BBN4a7B;7RQhNHx&C>v@k^l$bhWT_HgUBSWFqWJgKIRGPBJ`I>N( zbJtgYetLPd4$+Ijqug*97Dtq}j1*?N*gErMA#;E~7cTHPS)ipnGiK28rlQD!s&eRS zbg%rVwntJ)1J4dzu*xM$2f6F~eS7%FDh%zK8{CZxq+YRA&3cmkeTF zURrcB&ZvFDj;;Z<$)Ji>wcGwuwqfctCFo$K~8a z85aD$!p)YW`)6Eq>BoOe)m59Ub53~ij`F73Lj71REhGdVvId}|cXHd^T}#UFNV)I{ zeLVer227jgod&;%Rm-lda9g_cT>08@ZKtMYKVb-QVAzKGwYG^mlJdpzGU|4tx88j_ zYHHdsN4~SWz6GGytKXq#*zQk2T9+IA6u z;e?D>0j8o1r;ICnK5^~yzPY!JFDmJFyt3wpK@85ZZf@~Ao7v7Q=>o`rCTh$s?{8mU z-!=7T#gw#s@aO8=eI2>|HSnJ|Q^KPvMbOe*;H24{p4=LhxDA)fW$s*mYK(flHG{Ek z5zL&dK@oeXa+*iSJ%K)tpAWH4-eUFpCZ4b7r96;D>MV!z@Tt$;#V z8;6&wR2m#L_VDcddSR=37tTJg(dxDrGZVFBhP8%$^{<77$s)!3PrVuF%2IKY{L~%2 zI^*2gEB)x~KXiT4#XS9E+JxrXJQ>VbgLNLnUo}tPX?*+BA?5z1w`+BrBGWa3{f0na zgMIBs`v^_9&Ft}Yb1Q$DTg@`dwCazEADhEmT|R>s-6p-$lC;E|62-!pH*~i4UQM5d zU?!)EN13SoqIyp603oqy{)n{pTWK$-%af5vxQ}(}f1pUycEbUgdKac|*Gyne8B2L3 z$J-?T*rRhgm-bFNCJ_>jR6(Pjd0nh7F3LKl)c(Hx=$zrMfUi13ig~ne*IpZUcsNm1 zty$pGJx8@aMYf{qyGwGnOLVt~&hh0-D2F%~0NaGwx!icpvtOn;pK9uj_1DTT?Jm@B zx0OfO7Q&w-T^VO{+?v#Lf?1c7m0J$snUrfgJ32tw#$0LMm?)~P=8WHkX2qk*y)0aS0dX!puKq-RlCH~3*?gfOSWucc6iEGnS@ikLn_fHDs~GyzR>yk;kpvp4N_sj zALnWI_`s-mH!jJImuUgLn|Al^qcl-wwE)Fz8fxRm-)|E6otV?c^#;Y$#8FxsdmBq; zt>3UIBYzwZW=(DSvn<=EoyP2^Q#)ciY)3r{k zNEPhq9O`u&rEUwjTCm3{->nb?$_GvBYsMKRCj_m9SrSO|%&BHk`>_GrKGWS04l{Gg zAexEr-MGx*{lKkSO1WNjM`ouY^{P9{Tk?Ew`D4>-KDF167(S?*=B5nU>VAh}iH1de z&dt>O?Y)|Qum~eD9;h4q)a{8_{!i4@(#Wr}panEqxUZYtPB(hnEkPz^)JOW6i$0;0Dw$=!!Jas0V@H3sgnXP%l4b)1%t((&_&@cG=;Ef5lH}%}4I<^{7P`Ju|G! z+S9!pnQ$=ZpO1g19DWDbE+ygBYu7d_jaNNR{f?Z(}7ZpDCIT1x7J z_KpkbR#F&Gz2{#C7ER^CY`M`2?*XRr-kB?Q57JXj9gMDwDiA={cufsII(1t&Wys5AF%aE8dPU4_M{j>#BkVAlO1hGg}o^& zSILm=XdywjP9voe?t;}dC?hHP4VpM8ZS38I5~3jhF$>`>OojkDxB4(u9K^k_CH-tA zS6Sv7AVDoQ-tibVG>I^;DKZ#Bulmy;A%TGwAWAfSGEA9MFK6%6gV0&P@q1o4@n$_& z=N_14Z!i#;iS~*ci08plb5oXr!4ISS7ym`iYzEgN23fUst>FrzW6O>6`Mq+%0n@0m zipOLdHr#8HZ^Fsxw%}laEF_@5e#e%Us@O`J6(!Qzn4RAOoqn#?y;dX!LPX%r5#e58 zd^;H$5|z4U>)V?xf<lq6gJaw=J-UA(u5>Y8Fwwj-FRK3Yj`~a*SC(YSfh3d$-O)&zYAu5q zmczRZxUgMI>E@Ebk-5_=-hV-JZ8fj3EYKmda$)~rIvbSTsFP$Z#K7!zZj&NP;2!)g zVQ9lT%<32Qc&`uYANb zh76)?AnTGeXU)nx>|sFZ5;;~%mw*M`ej7JoDsp^k&Y$$BvW4hH6XAYDe)JM>xQ3&b zFwI7I#R(Qt=0rqD}t(!CApX$@$NlV?#KEj%?EOMkDug`h;3ZC~xr1#d{u) zcqc=y!yd2ZZXiR6iW5EqeMJ4#RmFBb_b#rt3$u&M?(D;wZXQxo3wC9y!TnT3nGo=*KOt(bx=$zL->O6Sc8lp zrv=fSmTKT3r9xMA3$R7(GdG*8X4)pG9swPL-Wk?SZqPuGMmqfEae=a8^!AJUQ+JJY z`-dM3*Ma{fpDCxCa&kS+_?mF(Wa>i)@-oADqgJfuc=^>52xq57N#7|FU^|1W@bc~Vqve$o#(;=Z;=Dv^i>*j!#XMaL1Ur z0#e&AO;L|&eUYf zKTa9w=p?&$ZPx#FufC~Yyx4UZ`D}|<7|rCY8Mn{1TCFmw=|${$TG4Gbh#fPn=2M0) z9s9i_?jLwSc#XLnda$9D)f)E6=u&Y8U5`Q*z*9gr6H9b&n$h%I&&r!r7GgblXYHx& zi{q+H$rSGK+WzX&&luz60ZR#H_o9$%$^L1S^Q?5&9>IqG1xFSN~g@HCXVRUrYQ?TPz=Y^Z* zLqc7D@xtF~Qp<>PX7Tl3jSw!>SAeQ1?v&hhS~)3GbN5je1x{#BqPhbU;Erj3qj8_i z-~UTS3~YFovkr`mh(>+|}`F9S_59WH#R+sA*sLJ>TSV}`+9wY}Tz2c<$| z23*nh6Zz)CUdQdiHY@9o>-7{XlKQ(HAJ_uUy3(b0#caOR*s8 znts1skEcdj9ocSck}oDCF#lxTtbplYNUjGg!|G!5y_{Qq=Z`)+w0Y8;#{lR=J^)pf z>opbg-}ybn&emB2ZpI3)(a9AIVrBw!=XSX8xK&{3uYp}krTM9Nja-^~H)8A7jWB^+ zz)NX#Z79+w?Hc8)H6|3?4ayU6#Cs-MBgc&sPcd;e=1k6;*IhAJf`giCCXQ#bCO#P3 zTD8fnPSr2|=I_hs79tLG|1+<77_#aTGB_m^m_>bxJTq94I<_Oz@`rltn)_h?#M)gj zrR`5SLmAJ&>%wzzj~-(^zX(ppWrhJ)@nqnOO_6=3RhE~}BsR%f9+41H^GJtCHb3BkN}ny!mK296IAwKflw>O(OahO%1@w*uuZISL`N@ zEm&}3>Q324tdq0H{<%tjts?)-><pj$G$H=PEUD~95-uQRFvAJ z6I1OYL+Ljla5b?=7)rx{#D#`I$1q~|b^jEf5km(^Q4Xqo4dn)SAgusis*}M%HU-2k{Xa7;bWqx0Bumq*K^t3 zq3?3$ySFpi9Zntn1}t!Ll{XcZzT>$Bm6M-#coZ!12x;iwnZRrsyJmKA3qsIx6chBnsr32jYBsFlvP? zpA$2&(G+MbZH9>=Y>YTBa9A)vu<=|+@`Xoig}!;f1*5f*W}{)tac69>$zrY^84;l{ z4365Wb_%1$Cjy4+S{cGrx*u6T77GP@X*D#Jn>B-~!Ecc^VnyK#m(m)<8o;@s$(_9O zsu7n?e!6LU(Ry>w+)XOZrC(nEeRC1tBbkv|6RI~1sD_Mwwa_5-OBq?m8IuBLeSKBF z5D8kNijn1!R2*$KTx5DAQ=JZ>HfPsP8=Ly&GGkF;MpCHA`dya4CcwF<$R3f~Ib#>c znh-^z34p~TTP@MlSVihsr?fvXW15qA8dJy_hzyJpZ+3BO71KccSPfDp0PmsLOVsI+ zWL!6XYpSO2%dwg};{4b!&eLr$8Ahv(X0~5I#`0$nK$Y3+RAk|wLTj1+)&uBF6Bt5( z8t{ikIeS}t@Lr7KQz!Hn|9Ri4A*$b674THG+D?D+-8w{jMyhbQm_=-+@Ro|4Qceu9 z`}XjKX@5hvo&G;Q-^IBa$_HQN&g!HYI*$j5RP4^;BlfFopo7|oE^ebQe<~ihX)WgJ zIjzPY80O#xF?EEeiG#}y9fu=r&jwYQl5E8^{s1BpaUX?s7yy(Ucp{Eu1j(m?Nxo<% za8UV1IC7(s5Iqm*P1!`~vs8W5r-x03;^EM0F;;N_8EdKKj;oAmMS3*-O&eGWd+4`7 zNbEegq!tvikw{}0pG=CbXOeVYjeb;y?8FLE%YY*bPjuFc#Aq-`?bd#unxiJ|7`jk8 zfPL^u)u-oGJ14>IXV5<_h&iA!LWu*11me-d;_zSgqAxyU3p* zAo^h|5Nhhwa3FoL&Qy5Y#1WoUn1EGK-od-X0hL#@g=Z9N6{t!L0XFyYhyKfrSA8~b z+7wVShw(VqeBg`aW&Ad6r1XEe_ruv+WOb;E6;3+LKl6Ir*k-OJbN`D05&8Nz17bjj zn5PBH*xJ#B#T=0^OUEV*ODdfUvLr?j)`6!4_pWPT_J$MU1P&~OgBc6;6Vq_H0%mM(lb^SioNsquF1%U@6 z;4&`r8Qr6q>eBTzBxEuoDS1XeWrA;J*9ke^B+f?pN;>B$GA1oBSS3=@x7YR@tt`#= z=F1ZMl!vY%9i{FhUI=tTD`fMS0cr5m(daKNkLYwBh>uH4yZ~Y_9P)kl;^cPYEr3(9 z$DRaC6noE^kDFA^Nm_j7?64V$IxmODUq5tcr(X&+fOSDna8Zg+UoY9S4*$qm=@?TL zecNu#P6Izv&8VGi-2a7LZRHO{c|dcw$P*Cf+uTF>Nl8w8x^qccfzEIH*BF}~*_MR3 z3w5H`Foy{?Wbk2JH@dGYzSGV|>l^33d$)nkB`ABM-;?e>ckvehVq*D$ z&@f-5f~refMmw2u0nfABlJnC|VJMJdW!W4}sz&v~{@Ty{PX9ld4=Ovi+1h)i8$*0p z;E_~UBK5jnULRE7TFAlRmkMc7uc!q@we{A}NjpH$7=M^m26w+!8LX&7t zRR{Fzx~gkH$X{j8gj3yTc3P~!cX-P;&N^qfkQoCSUbJAQwCNzPY5ETIihV2p0%J9_L`Yb-^0=Lep7v{F->72z@} zZBT#abw=~A992a}TCtaeH8p2@_z;mnzHQtj_l86yF$@tx70J4u{ylQDd*p}1I1;KK z%4+=d$JnU9E{V_rgn^oAq1LNyZ@=*6#8Yqbu}LznK6m2eA*(gPeb5AzBd6Frbtu+- z3e8t^0;m+e7BT(U1?}Qu8C9T-ME6~Mn^pz|b$ z#-MpRJw(SCF99ZvZl>LNchh|Kv`oXm$S0_YZoDUmCHLBPue52qo<=D{0hSe_KD7Ao zb9=A@@W(NajG*;13h<1TDj>H-<}XgvOdQ8WnFaq8n)aO`TdPZb47-&LcN@7Uk$t8L z3PNm#K7hF^El0NjfBX|Xnarcxr~R>{ULX~i|oCC+6*w{t-D)AGMPs^OjZK&+vH;mDhxJFY8e!#v8O9 z?)`R;nH^`prvbkC#-jI!x6s-(S{?`=flXDo7&c~dCfS;i^MpwCk&6u7HgxUZs%_gX z;1S7eP$a!F9!Y2YX$KbH4C5C)yG4?Uh7eWWPpz!Af>t{xf9$9qVIK!6A zH!9JxsvAQ)$kvPpiD{yiwclsc$*yr9#fT-HCgA|8!tLfJ*iwy5-4_S_IaHoi%;KB*RKQQ%V&> zrzD1fY1?}QGiDC_rDvh?^0AD#!0m)s9!B5WiKAODUsDK*my75aMejwMFk7qNbg5!5N7UZw$*0JX1TJk)8LEG8Oxc;(c5>Rbm)mm}0&r8;1L( zmUtr!Ci6+TcC1YiRo0P?b;o0;33@=V1g2U5*w;;ZD({A5QRHWBXsTX47e}EpN(z_e z|2;0M_E)dCHW!M4sNx6^?$3_30l`$v>D{9YE(f5O0AS) zkUrQ)zo5^1LR2kuUvyFTalhrTXC=bJ7P?EJuC_~wqBlsWTaYVgcP1B>^PPLkbor!% z;ay!wFD{sB1-0uHMnn_dO3=`DGaa=*0-2lt{qS)5H)4znX_jts4$IvYSwv%-R z3W4_?WhLuagL#7i&5g)#X?MG&u7{}Pl9G}lMK8>Zpu!V8F0qZmZCL!hm=Xe^(LXAT zoRO%CzK9~?*<9^Vv`bM}TQ+ar@3PIlFI0x85Siq%0{($Z~M;6 z)DuA%td~|^3U8w~Rudx0JQ$NFpO|MN^nj&x^nm0PNUI_G7hw9I8d+Gf9b#c8L@J8=ySb zE~y30Y15Mq&Ct=Kc{MllJ)4p#RE-E z-%bIT=t}PH5G%vX#YRqvN;B5~>H?w=gXUE!w{vN=a!%ITYpaS~sSmXgi!{wh2jo0;g7;28RYuoX=@$Ze>`~VnoT#T^$~r z(Qu$z6I9T@5zQ-wcz-!H zF_Tj#I3Y72AtCV$Km<~LcvQ8OEc83ftZM{Vd>b@0#;euSs1@O&oL88OE_>4Sb1qP7 zr7F&SK|?|<8L3&poID2_&9RkR^MY$9EPM}~mICBd`W_-@Z)8VGqW10xp}WP>zi~U->$*O))KE<15tj=6W zT6_))HP;0Z3mWQM8&bmOmIHM1Q<`b_v|HPD!Y?neZbBzY5V}!S671R_WN@RO#C?&i zai-HUFYfNJG-DTp9>ky8=>_)yGm;}$8|etVLZmXs=G}Q-s{Y{}Y>O*mISm zUe7<){(Su4-stGNBziovO?WmG_Q0pZnb*h-!e=A8k0>ZH_hn$Il#g{O@RA2wHPb=N<#1ys&Q*Wt(Lq`o$m6 z5R5q6TkI1+UP+d`kKl)Mw+`E?)#rh*X<0=tu46KO_|;i~8JnKXd=RbZTwSHs@nB0i zF@}oWZklDJksw3oI7P;rU$Xza%2H>;LzzhPpXN~Q_E$L>Lmh(Rj_8{0T{azp-+zRV zR^%2Y->}NI4-M|F>(guC!dIKReQRcuv37W6md1!NHe9QuKfiWGkCT$zubI|dr zXX7k257u700^xjIjL8OalN7W%xBJJwU93$`sOxBywhi;KTx8V4RDlHF9}5HI@x?1I zd<{S`4$TrctRGXCSd+4$89{^FTc&54=^5W-%tpMSkh}itqlVuELx@VkBqT&`BuCE~ zG4Xth)i+);lN0MfDy?@Q0gYUP@B-muQ$lq27}wcki2^K#M*Y2?Jyrg&V(@<^{@o{N zG$>k8Ok1oP<0baRz$Gbg1M6LFeOxfW7%#wr!J}XAZyjM}h|w|eY}JZ^{;BW8d}xK( zU3Bai3Q^64UQyw#cdrjW_Y7G(z$9O^+`mz{3+c*#pKt_2V4fdq(g}lAve`{0#nggg zbfoq&DK9P>^eND~**|+y{8j2CVAu>BeEh2*dVpfoE(b*M$G#N?U{XbJIar!R59ca6gD9(RJTK7+2}0 zXnxPvxQx+U?E~ri{HJi4M9)c&2_))JAc-@NV!rc*Jt2Cf)^ky&gIM8=YV+Xm?9#0G z#X2~C{aMEuwHlzi|K@3A=TwNoh~$F?5urPnyh9QwgG? zTH?i@)z*}m$NyOKY~~GRi=IxAeZ-7RM^8@?gYtVEUoffD`qK}#&GVkkDADy+gjX1a zp)25K(9pS6Ve^rGLx=tm;6U8<7sbr`Z7Tb9#Xb=rRN8SZuU)@n-5hkUTacP4 zP1CooP*pr&)yWIi*F9W4gsE}6&T{DTl`GO!2CYQ_EYs(I=J+_&y?u=Qcw5ZUQDUc% zG_nny)FB7`cTM1Br;owAe%O~*#`hZ-WotO^f)h1=_lg6%-?u2Zc7qT8=&pkxtot=& zh`lk@CU4w3jB{f%+^rsszpuV@`SSjFJ&&*K`RvQ&crQJlj~tzjzyOnG9X91!5xRr{ z;Cy983p(<;wy9t6*)=yesk-fQNObA})!dF}LP>66A2e)X^@LZZpD9KW1H}SQiprGd zAw-ovz6wQ}tLK#ksukwNxWTG>S*Fl6dGU>EIKo1=U{D>0qa3Mf*8Qz7E_=r3z7img zlOd`#oZkd)rrsDH`EEpydY8k;vX%}-@cAq1DRbdejkdM59XLN`Xi0IdFdee3`hVFs zHXP{wCXQDzbgvzR2kl!NvIK~UPzg!jW@Z0ldx>e)0K@BLELR2j80)t? zOp@#mST+P6*NAEn6dpbTLdS0b$-H&ieI7}c;BT|iJqI$Y`X9%$Pt&A^Qpr_L91yM* zuq@*7cz``c3P~Yb_|(<_=M7^vvpLla`4t2_*rX?4Wg7n0xoo08tuZ84w)_cM#`=)+ zg@?2AzUMw%kQz~T5&$HQ@exR}IFr)wo>J*~{LU8j1GP1{`R}xXlc|!@_;*}xJ_Mx0d(%|G1j{@;??{tv zZ<9v@L~gD1Nsa~qh;2H;J+!Ll($T$%--GxGK=QWE$-Ge8e8{1(<;w63{Z!p za^;~@(XZ#y0a;oBjLq_NZc+U$J0Shyf|r(YOyHDq{pwV1Rs-O^m`c~cVQL-xW+~T0 z|J+pORbxCCAqOJDlO-IVId78%5uc#ZvfVWHnTN~#`OQ#E!Az{T>QZ@^gQ3&U%-pb~ z>xZzYt3A){)?D%H{CfABmQItdt}NQOb$-bDnK)H91m2d(s#-ql zpJWwLl#%0v00-InQ^sP-4q4q#H!7`s%TXY}G`CsXH5K{(zT7qQ zYIKYC!*J~Zo@7;^wNdL_TQ|3SAl}mf_df;a3N>v>dL%*?RF z6=gK9b#52(XUpTyT+wdl?i_b`qDez)fVPg7(iNMmBs3Z=Jl%aMnmmS2jY))7FvM|| zq~w4C5KKFp!<`gejkv44m>ySj_1LHrIz_`_74}{-IJBW}so&BG->SL$I!|or`cjHx zVqRL-EZ`BDcztZnZn!Z1OII3@Mt`$b;Q26SmN~~(uiW;==Kq-B-dtk;jWToWl>ba{ zTbX@qvN3g#o{OWP-9tvqgU~BiBci|6*wh>TARJ;wBsTp7SN*&GNP~px5>*`-8rmjU zjq1B~aBJ$@Mn<+*|L6A`YoV^GO3t5`$>SNZ@;;#YpRlVhEvl|kNO@6 z9|tQ`=nQ=I(3-oTGMwaKsx14Y2^&)_r9>rmb|~*eHNgbR0)D>~l$7D|yA1dT-M4E& zMJTd2b@*sPu)X|g*&vs224KPOceOhC^!OBIpfzfJ&&A_DLTw=?w0>+@6h5KgAvS>t zYj0dF%3@o%@6vre+qn?b%H0NL&-!&K?mXS44sj_bMq@#;9xqqP^mm%I3~6}(VCfm^ zC_309d1_s{b&E$c&7eD6fBAu4jqHZ!=<3$90%)QrISU;_GAy@V8WU0TQNR4#18lPJ zuzov5G!eYCWVoT`Ro@yooc^On#mqI`-T1EE!$yB-M9GQWw&q?J>qR?&%5F05LUdLq z2?m}RVc*(m{eC^Cv**tbXQJAo&&eZAk~-qcAh}IkXGnoW450qpt2LJs>i5-g0E>_QffJMmeJ$RUp8Ff?2R`m!>P;?trDu z1N&Br#q(%g==M{R7R=x9{I4j@|W$rUodSmDs8OaIRu7RHIoHjT){s1g~=BSjJ58#Y+IRqgs6Ww;J#;ey(Qi45;RraUj7 zE!*bGl!*61(a97V38Oz%hGd6L`V~3{2*QHIGc9a&FJb=I>>J8dcT3Ve`IINtMl7(j z{rm?tP~xJj4$Pw5%re=rOPd10wATmq67nb%L9^Zq9m( zn)ljq|I0+Q=;OZ=(PxL>b`3bu6_P`Y7YvQQPs(IT8|VoD)#!omeVrn%&u5EiG5}AC zr`!8c|0;f{4moa=Ev^D&J!lu((ztKzx^(IZ<+hLZfv>}bV&DZL=%D!Ynd|V}_utr? z+t1^v5_J9c#;%(zZyNc8^YSF~)eR5q&dnNiR%IHF2D@*lShfA0bE|>)YH_XnQd12c z$JCd7h|*1y8(=*%TQs(;=!>8fXvccyOf6nM6Hu!y2VDQ>hFppjbLlDuYhSPDtzL3H&U;EMVSQ$!JN>HfE%Qa^87DN!yC7S~QwudbqDlG=&5!sUgLoNS88Z)mc`r_6)R zgqIFM$7u15?q}wAL0#14B-ZuO^7!uVKR+mI9NSp#pZd6` z`P}pv3jo!>@GX!r2H2ly+9=9sjl|$f%V{+e#`P5i-CXX$q7NQw(6Ao-U|(UyJRa`t zs%J)s9lWMg3ATX^nL?zs3O6ZY1+s?rdw^pvZVc$%nBlrG8g_iOexk*qdyEX@XF@SS zRh9bY{H*(`#Eap$H$d<#hf$Sst>P#974;ZJx|aIjHuFncY2xfUF?sL=1NGT9+j`|O zVRF)5_ORgAGB=?-(p+!8bm>z2DS22U=TfLQy~5(X1Grvr`+z$w%Z@3G9 z8G~9@$fz`HcDMJOJ)A~YmY@br`rvVv0U_OM12MLS9p~44F_mpHmtIts0&z1}NG2rJ z2mX~}A7%y1=zUWUjZ(3CE-u;c%xQ2Ml8`5a7?%}gw{yHKGk+l2mEO>(F(I&|WR(#M zg|oKaqx)VO)QE%gyuyL`f?ot<*!ALt6ePTF>y-Hl2i<2wpp5fLyMN!?ZX8BNH2h6q zRQS~ZL9L_XvDUQjy!y~*Do#NLgT9-3t}pLyJ(o5?G17758A}z#;=o9=zRb(~iL87r zSvdC2mqC7VR#r!#dRcH|;oBDvZI(d6*t=@bX9{~B4$JJJ&Und`Y zqc-TOG)){rjr-l`#@>wNS>s%Z zV3%2O1HnhnW83JkD)EV7{>^uxseK2TtEffu4=KJQ<`CF#QkTP_cZrxcha(NYXVD=m zLsgFpx)wWC0epUw4hL3@vn^W8lp7AEZpy*Cr|dNdeT?krd+apBksR|5Sza0p{2WU) z5j6eI(hOTzf>u9G`>$*L;jziAR24u@>CkzGEJ@5*Ta=N#!KmG!V;4HB)`pVjI!uou zt;`Tm04x+22F_LB<`}dJIzR^)*B)E7xJmLPQLSST>anB>0*lW4k7V;r;S@vDWH==! zlxKv1^i-;|IQj}!g#v9PQw=8O`25gUZ67jm1@K?^gWp}Vpa2Y57{Jxz#|-CO!BIgK zj4JEs<+%v7MEAqA7|7bg_C3X=nxqoVwLN??iFUNCwYdb8~ zNp0AQYE^`{q5-G(Vm+*M-kkRlG}wULy-r1&IjMRyUDdwzRx2BuICvHcGgVTlOhfb? zpYXQtge@GFbThuIS^c86nsscE5gY0^OzAmw@3?M-Uc75%X1<{eo@lZFec81ReDyFPom+t?UzTzUwZZcrrijFUN9 zNIt2sDLw4}C&ojP30V97{rk=C2MJM^8KVyvI_kM@kAFxnslx*HbfQC)X6D7GoQ1n#}j^gky>eS?b7UIMJF6XgaH0X%mLB=b1L_uC9HKOdI&KAccCQ&!(G6eKwh#~cetg90D zA7&+1*48di^ngLyRF9_(ofw=h$tc&mZW^f5>;-$Ok-JcCH5A+EPyO)&nEL0kqx$y& zW^_IFV8dfQN2R~X=v^O}jemN5N$d(J;przrVL0;!$ale?pLRVF5n*0Ny;b~Vy!kty z+RDzf6T$rRdkQ;+=9N*-iJ6E?w1L3yu3rV|h3Fcj5E-RzwKNY=ym$~{pmHqpj{;*H zlq0q86TRqla%0|%<1*v8ajQO4OveY6cQaul%P54f&yAy@?59*Jnif{+)$wkjEFIlb z^-`GdGV5cYb_Bju)3!!1LWDkznyWRyN z&t=8C{*+U4ihum*Kj@xzfX{Ge=a`-5P;h?N!kFnfD&1##Umv~Tqfh7 zaPQqI1EDTXIla%~P&BKJUFi+shv3&Ga0(oGkt0&va_*jJmoB&!IFgzhx;aAkMwIu4T-W2lcJi zwD(#e!ZBdTz^B_F$O2x;P=L>~tp$w&Lmk(gP1EqSCzCZ&pQHzAOj-+$2F?1>LwV!Z zjI0;O1J*X8&^~3t(ubZ-1so)13x8a_{LhN3H^Nb&*!x$~w>SPa{!j7wk0kA=a3-NeEr z%pQxrgyXQPbNeOBK!tZNq$)b}6tc+{#({3H66fs-dcFSZzpvW*-&g&6lp}JZhwFsVB4^iUAoRjefoO*vL9YevcJ5YKweoTd#Q1?xd^* zMeETyF=Bh`$g<SicniZD;9lWP{$<2PM92g!5iW4v?Po5};93!nO8%~d)>f0I-@$FVz zblJk(_EfCW0DwtwF}>41&NbuB^^?a6A>fE}(z#EIOOjj}bx)W$QJB8Jxg~`j$3|&C zYC>)g0%V6`v{FIyOm;ibz{Mnei<53pSx5T-^YwJnlJELhXdh5Us=m7jqygE!ope z1%8vJFQw>1>9BA9GYhYDy)^zmD3Nx$mhW$-FbP!S%lNI+u`}w;_Vd9w;$_X3aqbVp z)i+`o-AVWOFPGru(>@!u^41-3pG|ROW333-^?M~lor~p`#2B3MYdV%B`PU5AGc|RL z>RI^YaCn_d>*b$^_pjf`JWA(E_^Z`3d}7$$Bbkp%LAb{$`cE^9O0|E#h`RkFN;=JC z^Ru)HKqr0`=a%|Ubh;ef=PbC3pZ<)T#!&+X@lhcixlFpY|LsOUr@fDf{6acyj|i$! z^Y#ikdv{u%!%Rg-&~OcnJ!L@-3O`a}>LV)=DGQ8s z?8vMS)$6|VIwCqjw3w8j*R)0DG%dnxut*6Q{HU~PGkZxEHJ_BF+%V$2(SmpRBPL8x z6<5_dIbt!jeDK2|OF8@TDklryBu!lYL9wO>j~(a13EtQKSncadU4*v<=@H)(?Vknu>fp?K#0mQuxl0%TRz`*H2bQVj+Y2lvb~ z7{Xisy=`xx=jp z53lDwaM6I#VRO#Wx;RBwudas}Q?HJ0II9&H1=eS6%=Dn!cTNAoqAchN8=0(-1J0d| zb_{=WOQxC>tq_T*YTDEf7FeT>0wHceHbvc{C@?J&x}3rvk@wQzX77Nm(Cj+8amej| z#Komlt_Vv#{fkd$k2dzV$-Z~#Pw%j*pRX6kS*8B3V=&&^!-r$+J1E)(Ik>kQY%!~I z$>KOYOM38T^s}Bn3mrYj8MIPEL~OF2q|JVru@`~|_%3ZO+W}x@XZ+8(Q%I<_WKcwf zYTs=xJoL^R@P?Wg;l>7Htt$*5F&V+dtEGYjJ&Y1IE%5-al5Hc#vqc}Ms|I9UEI}^DXx^pn@Y%y3a2^Ewc$;7eq zwb1<@^5h^eJ-u%4>EhL5^dTnb*N9k{X8TjV1 z=Wyrmuw0z;;@Z~hE(4+xW8n>oIvva~Ow)8DWvnJ!l6YjBFlQ8X7(GZ6mqjx0Y@xA5 zMyO1s+nX%WVwp+z_2YX~+tu{!g$tjs3JxFc2K*}#6r@6N*vHEFS8r#l?v=^@p_acP z<(&)}m9C9r&eZTQ0%@3`K-aRT?( z7f+g;>XjzY3VoUR8jZ2)tN#*o<~KW)QtQFfA*z|KaqA-OA2n+YU!84Flb^nRUa+j` z>mOgU;MTB$CWllKd`RsV~Jby`0*AI6XI`5QoT3y}*LrJ(htuiuBM+Wem-M8Ht}9(HI{+aNKb zjOxR=mw6(GQ~%uRjH*a2CCeeH`Tc+QH=wK{?h{N?4EZ=1JljbjPhe-Xl>v%^CR+i_ zb}v?ieQ_hcHii-PaHvVL@bb?8jiA4DgN4nFy%QtN4-YmAg|&|YoI(+1yk~8Vw}RcQ z+K37_0P-`uXrskfx(|!%EB&*VulOgaytE>@i=tN8`heuPqic>ct*y+i6^c0{L4z>a zmBmTUuJuL?>gL^8jxz`csCt9Q$VkVR>$bh;a*B$RxxY)lCsD>S z@TKEnYY=@cwC5H<4oExWNi<@3=t= z+&NlSNPAb5(hpM{wJEc$osoKavM%VK%FA#)?U!8t-7>s;Ehu12w+rLE|T^6B5{JfFVc zt@07CMPqplRkwj}GuT5e#(Ff&b}_$w$AzrXJiT5~S_5)uQZZgp29ss8 zw?b@TI2YCubZSMvA+NRS3hQFi-_#bJX{&#gbP*ekx;aEBG@}FDFOoWTE4{}RDUQ^$?#1s^W4vIE$s`?` zt4ac!n0s=Rw(#s^OU$aO^0Ov&M-c=OIJ95Ce#iGLR86gXMi(yDTnw~YGX@p~p553ygkSal=y(VeGJ^ZHu6~0|9Jmzm11hjM*V> z)k0l|+^qWdF-IeT(@4z4On7vjwq-Mw>%#RCQd7AVw)JgNB5V%yfOYHoppKLMhkI{_kAC&6b z&z}Pfl6(Jr?cr-BBZicGg}Ss9V5A+NlMd7>ZiCwAM4=}?_Q=b%kXXTo32n{Wc`r2p zRx5w!-`VQrZti3Q1!3+m_Yq}%&}YD7WdMRD>DGTb2Fk=4%Mrj(UZLa9=7)UOn_s1W zqy~~jMBT$qgQw$`>`Pt5$0{vNL1(zBb+0U_tm%|aN41r|$wn@~Jz26KL_CZH6^b_g zkK*1uEa$w9|IT1+V^^|f$x^Zu*$HF2BGM*BXptpL*$TnYky|_5FU9b9ukt=Xo9_NPvX7%CGy4~T39X66e{lMb}`yDx=S$zrd{co!Ml;BxmmB$g8W?ph#JI-*{;*E1$ zllQ5iCoBMnpy$g^mOwjtEdSSeQ)HZ{K3$QN@deVn*Ve55rjcKSPjc=uAD8@hb zWC5$Vg)xCGC$7H_F@8P2^PcdMJw0R^Ra3oh8z8<_za_AL0DS9;G7{(h`D^EI)xg57 z0_tVTDAxy># zPrC0n%VT1vQjh!61C{RR^g{s<*s(Fc90*;^XmD`wmE=I*bTpjH2ut3-1xc6LA!EjN zU%WzQ`ihW-$y$z8go9)6dLW1ZXiXOgafKpsZ<< z1P$wM%Lcg7S5iMt!Qd^1g${^K_y^2%cGxYmr0e3^jQ=}nd3tciRoWP zXO`>ng5wmoXDF@G@mAAC*T80o3}O7fj>k@Ac;x=Lsws*Z{BKiA3-xKTBIKH5f8CA9 zUsc&+qTD9lW%{u{X>tr$B9?ozsNw>Gw2O!T-s2n4gV4{=5X;!&sRc!}H!e;`pa5{# zbMI7GYYeIc@Iv-&ax!ugB!iP4&Hd6^8W&1G6)mjuHh*{Ov{j@f1&boOAgV9Nj2*jW za8$z9C7Zs?q#eX%n+@`B;gy)yl5#Bk~(3F*z}3 zZky3Nl$6Z7D#ngBaU@7yUNelNJ^9|B$g7n<1F<(1aN^V{yP$PPeQ6XmvdQ6D`FY6R zPaPQ|t%;=nDnl2otxl?Bjv|1U8Dgv`c_^^MpH1~t({#EnzU^5NaE)ptna}`dWb8OR zxU$cWMBC|?ns~C)$RU#K0_Dg=9G0_%+y;^zsj30Ir8v6Td_dXO(nT4>M!NpMnmqAs z>l-9<%m|4^m8<;BEATk|DWfFL-`{4epUZeFmHJAAsD=O`!x6`pc1#-U^6v4*p5so= zKKc#sJw7gN?1Tv?C+_be!F`wd$5$~yl-r4fu2!e2$s>$rdly<(<-`sd;XJmP|1fg1 z{+brkELZhu@O984x=(4Sk!=^t+!`5~d>!Cc6i*b> z{o*{R`^1k2IQu)bFJNHkf;EDo*gaGybb;%^z>^IjC)V+k)UB(3OFi0qCg$C$yvs?H z*=jr=2;Hy1_BUW6uDitN8!qu8v_rK()kkfz**fp0eqFQ^{e2RSQQ~XP8NH&GF{c&G zp&s=nT`ugXURpf%2L@Txs)isLy>3NWhpRl%mBKo;hiy=G6@A5^q9qtL9y z?tQRAr`Atw=HG^1NfaAE(%pLV{7@a13#)=rAd##i>PeD}T~4(bn*{vSV8iMK38D`C zA9#G|-1S}^|2r1yGj-Abj)Zd4noGan-vPR)Z{}VJT;o88$t1n?aRWx8;;93uELG|| z4Cp=b$GcBI_D`a|*34=hW}KJ$I*!0*{{lPmQxar4+Tq>FDp9R*s@~s{-8vkt@HGbH}FCnV;9Xlv1Z^dcVGJNCoOX2Gkp1aM8o@|7s z>S=U2{N@bLVNcO3;@2qo9zU)Z+k?cZal=7VUKDpb?13yA`_KCS0SNV6&DRWGIV8FmzIqH`+C4fw{A3i zp_~87fB*ryN!`cYto3hzo^+~bI^zpaQl&gT8XEM~fCBuO+Ng6l^VE(#IXm<8FG@tn zHwej?=nxUzuE2v_F%d|rHXNfg8bUsDH}dG<9$&j<`O{!>kPNqi#IdI3AR)`qfUzEJ z!HOHK8(g8i5#Aq5zRJIB;?g&4&S3aXK=Ek{dM5yUgg%3zKV5k1joaq;F47c3xfo_uqdPTX~#cMFd@2 zYV2twu)DMV9w;Z?2)F5a<`)hAk~mK z3Ic|_*mIUaxB5@I#yCD5FRuB*(f!X}xK%Pr5SlO=j`;d}vgBP`{U}olTB~<_@B1&m zOA96%KwmrCrSs0ipB9g|(SeNXEJc4{Xj0vS+1RB$G|uJ^iyQN37q^!)&_zpY^0m1| zRd!I56!|%v*!N%{2-L5~-DiokYlj(a7$`jguo1;4an4`cWp%tO4+^RlJcicn&1mbJL3BJm-omSR_hI8m>+?pIC z#qe|%VjCpxU>Opf3|{tF30$Yk`bmqYR6>_8BR_+9ZR;x>H`Jvel*zQP@rA`GiMBA( z==vMy_=3Luf&Nv%(R`On<5oiJmc_WK&7|j~u+o9AAYXq|r;&9Re(j#>Z`?w-?z&K< z#OD!%dmO0W@VDPg+FxciF@c58+^<8Ps9`I9PTvYSby)z;?|uMN)|3pBWrQ$bmw4>@ zkX?OkkzeX`=RnGYn|JS$NX54%2I>uR$UmOqaCDm6-_l$G@09c}FvCga{YuKmtO|)t{436rlm_WgYr?N)n{?x8BT&DKyg5e%f7!!l6A>%Zpd2Cb)23*^L0w4 z4TcfPaPRhd>t}jjwn9&|YEY*7-#E>=-=*12{U>^zSI;M++(ai45u$U!Ti=+?kZmb2 zs|g?G_548)XE1oH>0i)=lo(wByNtE}IgB&8OS-|9_N zpEL14TFcs*sqDBkOMq`!@%EtgP`tLxM3ITugQr$IRmjqzB#zAYwyhB$BU|-+XwYBr zo`8m^4% zZ%WLWtW`<+SHLvn&dK|+hOrDTiN=M*5rD^G?CqYmboIlMl%|r z&|M}oI4pS>T#VlD6$bj%(WRG~-;iGiixFV+mSkiD9aI-lh<#MgZ6vbzcK0v4=~WVI zXIB~A>^sH4z>YpE?%FG_IgC^RA%6uLal5e4kLJ1i<1u2|kLmRs|DmdaWljWd! zF=+duA;SjJ^F9F;VFJDA?-IGbTGE}ZA6@^4<^E<*c9{>flZ1cHK)*c$s;~fTsEy6G z^fN^%#jG}|%$|74t}Yy@nqfWpv=JO#!t&xN$wQ{+TQ=U<^lQKqLS`B--#P@9ew4Q8 zy%@0eJHvPA`tXZRztV)K^t{tPehM|2s+TXqj zYi7A>#8vwY^c&`6NHyWe)|A#KXmFE?uT-^B>uzy)*y=tt&j;vnpA6aBruP0JIXzqK z*uC2ZP--qpB?nHT7FCG8Uc1d^&YYQ#kg9+kFMudZC(cMmK%0zMrVX6QZ4ct0HF)r$DzJ`%+*c0Q57BiX{Jv_IF5@F3*b6O9&0Poj&i2D~ zyfhw|SLAV@MhbHss6M-$p1v3OVW;lmd){1=Ni%yDOn$i=?9!f53bx>3bMQOXgdPqU zBQ2ogRs*_lyz`OwA7Z%XawjKi+~g|}LpEyC#O&3MS*PbW=bz?b?CkhsNbjs)u_y8b zhmRhO(=ZN-hUM;`oYT2WRnDDFf>x~YsJxDp*&6RT4}{!~+pPH9(Z*&mGIRMT`54ed z|I=wh8prRhL)3AbF)g=g;Cq!tIj|(?0E#JXVBb}weV?nlH#q!#L~`NMBeU(B=bv0|~ati_j86rCGBisJ~=v=zEQ$4zcd-2JM9-+Ew@0Ut__G zPg&|HDb+r{EQM!J13MtPzRIfqnGcv28&-pw?i3W@E?8{&m-jYiToRQwZEmXCydwR* zef8=l?eZnRVU31a;(^x!nZ?D&+X3oDz*}qe>*t|8{>mI2*rqSdik?0Pb}Jxp6sueb zxQguM;UPewDQ z>4>HsSAQxlGS$H9Zj$cyA2i4wz*$aF!IuGB?ipUFQCNPt_@9_#G)>IxrJu!p_|X6;G`}gdy7TJ2IDJadmD3nEu$lor-rm=!L7YLj_h{|DEvM&{Q(QAG zb-clqu3S_GhApCBMlZD5mK>*#@83`Jb)&N_ec9}|a8C}c4?RKy*5{?Aw?Gi`;b;#f zCc2vG{VD>p0N@fY?$@pnE+4iuR_(Vzj<q|rHN2CRiS0YdEY17oMylZ!a;R|L3(1LBr39=BC( zPypG=0me(r18klrf3N9rF=P3HB@=DCEo@ywfcrNcFib8_b^oj*4raJS6YD;V7V^a=(+LPX0X~*O1$R`TG9~qzXH6KfmPvQ8XH?BrQ zj=~VdfQt7|R*j2}oIJ&Dv(s~d;tY3~igyKqOQVC;RWu<5RM{0!_t3^> za{i9x>2wH}YrKeZXEBT7*JGaz;Th{^2Qs|f0mxjO1~Ulapid6Sfn3j27n@fPzm&dw znTokP6c-owWW5Ec94pzhm}jL!^MVL6CM?omW9tK)hKuXiZ|8NF4_>^wg^zB8(k1%*^+1LrY({sePS$v?Q$A@ z4kSpMHt&loA@f0c&hH%IR_#murh7qt{<<15zJKkTr-GNxZV)9r$S!$>*Y5A?P)b9{qp z$pLNtD}dBjScNAKXvKb=B*_=r3^bEjx*-FDBm zYkO+~$^A@uBr=R4J>dZ3anx+ImODznt(=LtWigU;RX1i#BK_B%HHE=qTJ; zkHt};0ctbXB95n_XTxyQaFvfAKeoJqExIM`6k{0L5{**vE^M+&ixyW&Z3$v)F1%cR za>rI1D&1g>YD@+uB_ETK&*S9N)k^-^97gC6vuPF~iw{SaA9v=AJ>uD4FFU?198u2b z@`QP5ZGAxU9Gn}R>rT+N;H`^kcaU8-uwiH|p0&MQaWOmpz+0uj_=pwuCj>YxYeE?tkP%89}4O zpAqOialb)39_(#Fz-rC6+JGB5@b}v{Z|;=sP4gRZRJ**-qKQa@nf{|Bwwefa4pvV4 z%j>dNk>g2D3Rop`^lOdB_CSvzKFpO%>cEj9OgC!oj$-Q-``M+ZzC3Chj(?MKq3eK5 zR#CjR^^14SHoG-dN*-cQ0e1v%5P{;ufodD~7`JtcO4=qDUe~NBe|?Mm+#D<`Hs;y2 z;$6e%#ZkJf(!BW<%7`QZzai#uJe|@2ORy+^DQg&QN%I}zg(u(j zNZzC-l-^Y!PX))S@*|q~HRae#`TdpeFdV&FIYus8;T zL;XfKKh(8)^rJo%u8xEbuaG>*osK}0QIG>(QNK$M>5T4k=uisjwa-HXNdtFe?EQSb zuAEwlJRt_tVQ;TczMKx9@#GS~SveCy_F-$H#Wr)F?M*-KY`^KEUd@-{g~&3ZN#&Sy z;};_#ITEsH*s1>X@ns>BX{TcIN-c92LLv#z2zgS|C&)JwibQB&LV~Vw)nsAje15>-1&;LBcSUhV95Dk-Q!r-9SZmmnrS!GlHfZOT_{7-YeTM zWboi<5Nndh(uWrYp%m5ld_f3WvrO+$( z7Pf1&bxneQ@;zs0*X3|i_et_(-+y%(zZxYMdtW@t#`nFF+^!!R>$nGp%^T;<`d?!R3M*4vQz$$;X@Em{N+qLuR3 zn%D7pr$Ku;I->;fKm)i(zZa=qY+Rf%)kLt5C*dkX!0OHo>#U@-A-I*YnUYe$z391{ zMcP32rHLY#JA7-!aJpm8i<^N!{-F2zYz=MgY2PdIF1PB?Arim2khxT^?)NHz=6QMN z#AX?2$7u-1CM3ro7$_(xuqzlipCSqJ+k?1>)7&*v21^8QugXC^ew?e=N?G|-vESM$ z+}Sm66F;lJ59*@CPmzzi+rB+&dRG3f<%ij{UiV*@uZ?Wtf3 zO;D^H=s9f++U*>i_W?j;@X^#8HzpX}O+A`aMa+AQ#QYS;b^eoUJHw`(+$LzZvT`@H zT|=qQA z%J=9p;KP(YaGdijbe`Q5EEG{FqGwOK%$>kIwlw?tolguv%s0^!-Y5j#nXcISo@9}+ zTTCe;2q7hXN$AzEe*F~Vxyrk*e@MFVCcO}5Wj1P-%n53)fvkN?vsx_5h90AmUr#4i z{n+bbDOyb);`SOxsudQUTvt<$r9E`n1N~A@*D;(BGzlGxBenlrVo89x^?fa z$>cxctZaLK*LkNCF-pjsdhGPr@LD5^{saIv6QQ>9Ko4Zfy`9T1uZS2e&N-dJKK((@ zR2;+6-lJt|ym zs^mO9R-A#fURjbgPV|z_&S&3mNG4MqKX-q=gXKwT7b@=oTKmx8S$zRP=^Cb2@z_0yy%yk1W6iaW=($}2!jV4!T5Aeul7rjM zC6_!l($zvv*y#mcRJom%ReN{n-TmRL8X8+;Zm4qGwqYoxLlH21`26`GjlY;fumb9C z@q@F|I8_#L^Y%c_%sCyu)ilCvk>5E-Z%l4U>4lHSQSU6gb9^J$B%D%AyK^xz4}#RX zSHFIS@(jGno@jk5XN%hltF8ZXf>PWNGVm9i#KZSEHn$eW zzdW|OFq0+TlSd4&bc?C_h>xZ9WG$y|2AoxR66f%#ba~{3ii(SSa2n|_Vo797>2#jD zf{|~UQsQ$@xU*b4_g z-9s{Y1GBk=Qt=e|K?p9w^Fw;Jb;8Oc{pM~;-rFIZWPc%6 zmB0B&YdoA$ubw?8LQ!U7hjij+v3K15o-EPUZQGtFzmwan{)s}mdFQXl zBq^7*Yun8(-Wq&e$7(z2iN%FgkydV1D_uX_?2(+k3Lq;FZ$oD+^>O3Ig>sU_UU6r- zDX>n4^XG>E^2IiuQnRW~W~-XcK9n|Z$gp9(usCqYMx^OFTc<{th4Y9-H1gwWlyWD| zX|EXmJB3QeiPRSYR)@`q3!;4UB=k_qqerum@uaLk+deZWYVqgRm%dl86+Cy>-o1B` z%(_2yc~+|{Ci!yP%hPI&>gcVl-Va+6&oBcyn!Tbf1|SZf!7&=jpb%*UhEokAI-?@m zJZSM!##5Z&VBWHhk-C2uou129Z#@Dqy$BHiKHm#vK&0EZr)nR{%YCAvqR>}~HLEI} z{oT|1($dxI)_ofh{-9`U@Jfd~8HW@PCQDh$gtr$zP6^7o!_|9lv&*;4i(1WTwt zd2$Xagg>}>M24=OwslM$UUPJEFT1%dhG`@*&$V2dc%Yr}O;eTK*Xw)BtUZELQ))2E zuGCZONf<+#evC5R0Qh;Cmlj4Du3QWPBRPxrXw-*#VE^m1a~J0k@4qaF3+6NxJ%1jA zf10F!`HjP=6O5Y9q1Wt6+T?>?nxY2^EK9t-IL{v2-R;zO2U$m@A$@!IPIK<+h8NbwUP!S%6(>< zb6&GB#48aIJ?(hVfVAjS`mnbzZm7AL5dF#h1C%v^^3TCOe4>@hypndZlzWpJDivub z=!MY&car1?!0*`@fB_U$lkw;S7Q|z3qCk1V5gk{62HFlhY8P)I$0xD!%>``rQ5gZn z(9fBuGrhgfjQOR_(j?w;lH5TY^2!fIil@GSs|%=jk)GwPa9j9D+TyvadAeuNX$W_8 zmhxYDK{LP0nNp~>k{WR|?YU4LbQwDq+R&qS&eK<~BE(WNJZY%Qhd^9kH^t#}uWQCr zWHCJ&X{qdf|G5D0l1L{UifR#3f#l5%$X;`CO5GF)J=zX&3X}^=5_ouW_t*I`zaz|! zJ#$9F;cm^FB%MTnb%JRB#L`P&lh4*GM}hegAn(9UThHs(`*RnXB3{CC1rw-+Q&BsV zArTAjMucMGU%>72_HSGouKeJLck709hpJm3#pkJm~JVRS&6x zeR=Aj70&ixnM`(+TKH%CYs+%#?Aoz|F(!ZJ1R1mgvTK*y&)C@5Zt2p#8m^XtlInYM zOuEzd<=lk}r9gdm;m1H9)nRRWo(bAu@BaE82-lH9+VhgqTM*>u_3&R8zzyj|4?Du% z?p})4eP()GxNre3$}Yy7`_o-x>BGujl$25wb}jvQkO~(wIUl;4?ySY=#;3e2}srUWsKF1FC{>sqEmT}6-;PWl$6?78f%P5GGa1rV~WTn5F+|;w0Seb$FbchZuNX* zdvetIG8(&h=q#?OJhV|9OSb(0jn|lD-o2G9vcW#TRtjyyE%eno>^xUkV zxL1(^5$=>#SH8V<>((S(YF>EMB~6}KQVPAQGB1gi5PH32epI)JO_#kn8$`4@G#-ka z5jUQGyO+h@RndnGZ=M6(*iGW>%S(s&wtoBL-mQO1RF|?aK8&`7MEJg>i#j8;t%ohq zIgI?YzaooWT)zD5R9}$7Qu493Lsx`wj^|yxgz4d!m$H*)(3 zMmRat0@7|P0X*p~-B!j;m>_!sK!T|=LKuz>>ZSmm^a`9jb=)(%giaZR8r>B5 z86)C%EUlbVGL=&D^bhae-I2`5`Q4mTfq^nO^>BsbvSl9Lm$t{2RW?qvKhQg(C1y)9 z2jW8`Z0$-AxN+mhFXT-dHfm&yrQk}KaaC<~4xa|moORA=91mkkl3Mia*(r|lq$dl8 z$&qr-lIAyb#>)c0`UEOpib;_$91VCR2wDi{UVq}mJHy^yJjrGYxbdN^>~>aD<=*e6 zFDIbo*FiZty{5>SHV3T=*%}|7qVX-cE>`<>P{yRbiGfW=eiQ9oMuaSzoo@a)>IWtkIz&ks9Z zqJ4*iX`J3tsuG5-|8~~O^^k|d}AWlXnUD}z@B}YOB zKv8vLHGFT|k?YQaqaH^Zp&cCj?qHnh$(X9Od@ES50<6ebw7ACx(Qivk6H6tbbPg=% z(=Q~Ytw69agxp{=35=|q|JwTCPzzav$0NyTCV^9E&O94UM6T1}J_+N{Rj)z>(IGw} zx5+p)txHtg!?-VTdHjiYd7YzwQF3{}3&e8*!3@W<_g>zY>)9e;g584(} zDj@*~$x4_24?~6ig(cL763-H=)T2l6at(rCSSUmq39Ad*JP42?3NH9PWn0o70uN%d6T35nm(iFy7$g44~AN1X7tF!s`C2)iRoE|>FI9{|0W<%*9a!9 zoB;HFNa=5rCQXJiH;1qz7!v+gVd2PZ<4F|rdInNc3i>-12Z+V;R+lqQp(-ygV2AIq zW6Gdt_3CYZa9CZQ_VFb^KMi_Me6M=zrprcGRg^}_ZZ2tv3lCvx=MbvJ~#CK zmKozdXp4^7wX=CHUzUrjya^J|**PWm3f-YYGxsvLycbVE$Il(iuR7e+l zF%3Gz!|C%U`I4`dm4Yvz5)oTzKl3s?OBQMiU5uFEjnkk1Sm=H*ImT4vu)UgsoRLf4 zx$yQTi7HXJa+@}xNQuTk?U*1%5|cIJ`?rst zvD?H<7QK8KhWf|;>qr70rxh!1$GFs_vK=3iwZ*Ubv=FdsGI!m_dr-C6uRQ6prG!+E zHGJOhspj5j4j5q4=DVfaUt3$36|Q|7O1h^>XyTKA&CQie6p-b4QMCWyeHJIidqRqw zLPdj%566;8091YYt$ijJZ$)MKV$)EgagZ~HcujX30O;vJ4Tyf;1)oqpdNE-=->vj6 zBK1pBG4)>#L4*I50-uER9N4TJZ4`WA03vh$pK$32f1l7f23MEJO96+C| zx^?Ol-F>$__i2Qo*S`R}x4-)C+qVz6Ka3)8mVUX!FM+p9<4G;J$Imy8=_QX*^W$6| ztyV{A_*R{5S^&Bdl_yeVmvx&pTElHYJhLg}dccjm(H_{m=7_vKl{!(>^A_<2o;5O5aWiIgmY$H}6K^{CA#C z_xLD)9MP7bJ2S(hf?#VgA5zXWiJDl%B~yCj<C+4Bu12DfVK9#8ArPtJv`*C>$f&)<6fHURJw+}b$ zsxftXNQ?q(Rb6Zip2RZ{QQdr)>%*GC`S&iesUK)%3lCP7HK}j&8Z^A-nNw^g-*taG zu$GcvTVzQ&nAvy^fgEmvq$5L?2TgMwSCJTS9jL+-CgcSL-wU`5&4$sOImgA~a|Z0g47)0jN2VDoFT@YLdbK{|!n~Qg z-9hLADNvf|Df7GIq1%j;uB%v|Bcj7pb*oyRi1YpN^SEHbR%In;{X`n|G;G+=lc<1O zA{-6IVbbd2X1!H*Gi5L0m%0sKV8QUnw@)?Kn-OC8@i7bQ9-WGVdG_?Fi25b|5QEx8 zZKQ*5X=&-Ai0OneWlZz5wOp_@y5T=xIjV51eC5@Lvogo%H*J!y8QPwliwx;5(M}Go zUjTK&_JfIuiJ7bDjj)g@vQ}$f>q`7i)^JYyK#zvX3p-9uF71bgjXfD3(u1Y(G@&ks zSZpe~AKiN}k~1)eKI-zPz!|NkZ()q0va!&UFER3+S`=`2=% z5d?xbYagu{0pOS~2_-?u?vf0<@W6(h`=S~fEm{`4ssVJ7ze+A?qh8SFPr0Y$$zCu zh|4+&aJ&CLp9Oab3nXmye`oJ;D8uIIhKOra%Mk#_+Z3u zbhA@?>}+QDTTuJK%SO=^iXh$)?)(`hpyYEyUD5-LLk^#qLzNXtqk^)?fH4NY(zxZk z^ZNkCTP)lHs}RoVzCACFaw6jH-h#ezShE0ML_$qg+PK3|6Kv@;0HNhBDnH-Fxkb^Q z)mpV4YtYTiz?D{_vL~w_xds#Ay+pw&|MbrvK4bx12{H1J8ABkC zl05PVI!m<6BH}C}F^_`>n-g`L;E0$$*c-XlD4<-Obk12Ujka>BuUY))(k#w{G2UF* zDT&!2ZS%+^a(T;>Z|!HTT0WenoJcGWczdfWgtr3vDR3~O3`t#s1YV$8oMS7-n3f#s z9bp|~?lk{juVqvds&q<-T;uRP`P#MlcoPZQG5%c?ayVqulzSSEBK^!mhNZ56kvQmW zrESY;8Gqda22>o6Bz(;ur!?X+ztc&7Po%GDczlC;C@F?#Aol(xArGP1z;xZCX|WcO z*d~9@PoE($3`PSeY7&yxBtVl18@QK2Y z;BE{sccx^mK)$xX{G_v8ZJ&YI5&zyQD5%MDo)!E&0FM>p#Mb;mRC%)DIN(tMGih@P zP5%M}Pml#^)yB=}s@h5MKf=ng%7KGOWN^0!QH|jpiXK1q80K}m7ME>6o2Wk}$|2$+ z2!%vrh{-{~1keCE&4PfDntAy9&kkC&5U$xG#(FEQ^t4v5{`}y`Pjjf`w@hM{26Mva zYs1+|Lcoh31azULt)>FLc7HXH1bK5W<4(WpH;rUS-Az~kKq*t0QO11@A+ilczYbID zfy_oqTqbWftufEC2}MOk-ix=kzWIw{yCM%g4;i_Y-~!MI=`eVn#d_i2FI+|Jb(S ze@!+E&pmQ~Gx2MxN-PkfFzbS+k;P~LiV--PF_`JnmnV%RO{R844r|%Bj~SDUg6`~6 z2mZN96%tlF{evs$-Ln@j4%vK6T_AY6rS0?+&nYx8A!zPqH|q>mn5Zp;x>(_Mrx8xF zdSUV|2)iU%_W#;Y*Yh;!6j|;xobGhK*@_X;TE(=C6_Q0T3KHF50*afqI?OUrur%^v z&3`$+Hu2{fSK29|cypP(B|trCj))UD%5&OVUS6L_R>PNU(`y`=Z~NP_qHe3FhYSJI z!hF&DLAiPJxh50Ot$j!XgT6xKYndBzoOLyGCT7vDZ2*nd8h0FS&M8kj;PNaGP+G9% zBmM2A$=Irl<}_WoV{U>f882r*0Lls;4$Elv zpNJ?av6*?T4KvYRcUK6}Ch`N~L`K!>1C0+Fqx`=@)~QR+ST{0~USm#v89Z5a(*7IJrvFi)B}RT_%%^Peqo(RG*$I*9fG+at=8 zUAuSRTJeDBeo;$&>Y2hdlVm<%V43c%TYqW%G+@J3306imxn;h!3%RR6X=L-0yL-u; zV*=3-ioO;CA#+_KvvzU09ULO8)|7NRcmDkI=O%vju~HW~*nxy@i;(JsStXvwJouiv zdK=y206LYzI6F4}85uo3AzRem!*O>TubWU>I=|N9*R{NEN;FAF+*K7M&2hAsMH4Z} zM{9p@uzgV8M>$?2((Y(&a-50!}n#gs8<`S?8k1Qw=(Fk89 zx{v{M;5s(gaSTPv2L#l)|GQ+AH9twlDYj`#`qoiOvhku=FRnmKK_C?>vL5tPGZ1@= zFn*fN1aBdm6f-rKp9RY0{w;yb1RCw`UcW!=tg38%FZS17i z)zer;M?aT=p%MH7obf4?L zcB>htWz^OqmCL{`#Y^)T*&Hkw@dzrNjRCGjX{Byg^&c;%^m(%oY*LLW@D)Om92hZE z3OTFjifeJvIR@2EUm7C|*hdJcd(ZZ_NcvEkQyP~N`B$-~W=_#103-M@u7`r<6ko4i zLxcX<0B2|MQGHjT`XHSc&pDKML+t0|8&%)F4Is}JNe?I*!)g2+9q8*XX%P+=;C}g< z7w7gi)ScS#{?J&zzjqXHsnu|m#!7B6eDAD_&eN!g(w3`h3V#S-;?=#E zUAM8eoN%}0&u7@TNsRgU0_Dh||0^6DxJd)`mxu)XPlHfbv2m|ScibZ_UU=Hn9t!7g2 zsmWIK$>{#s@WjvirlN zI1Fs3JOdZ;2Si~A@rhueX(dH%HFJJGvXV=^w}rL%;JTer{70gm6=QNQ#%4R>l|@^o z3Prs~ReKB}7&{?iJ)txx`ZFLhG^0a}kirtZ10tmI*$w~evv<2LX{w~;R~Oufl4>sr z*Cp=(@3IbhDyTehvm_8=`QhU3kgY)scoUL~@#5}r*?iQy?%3a2+~faenCFi|!F^)z|%NGzSf|wM&e%%|IY=7%B zQEf)7ISmSzLpCB|A?9-6Ucp4E-ZUcwMxC4+YTN@X<`x5Va4E)onw%WioUHma>argT z?d;SXvwq{T=%xUAKfxh~kYS>S=pVMt*0#~fe9aKrp4*1C@|^ysrl4$4GNzZ-xxk&w z6UPR?Ex{{H#3?X54eD$TYv#Bkf+Pv@^gi?NMW5O=q0TQGCfLSX)WjJjq=UkXYRy%) zY}Tz$pDCCKV@%q!q9XOU0$+`p`&<91>B*d+&Ojr#^701VkKUyYu2Vw4DG_Ca{(Ci_ z)f_@{O#zx|E*8jb3y0qp@ZC{dX{}uVhyXR$LavR8?(&fQrJI7o=N-7#EJOnV5^U1= zt!;mvzQ-EL7uG1fQx$gx1|;}HS%JMjDDjY6Lxe*GfqzE#y6MyNW~x5(DtUwRh>c`D z=({HC5pyY_q3*yR9AwRhZV0aZ_EoJU8$-t8AqcvOUJErq!v+m95fpUNNc`m%ey$bx z7%Y2AMn*=6`ld&{asaM=1_a;u4uUWr8Csik~>mHyAiRr2fDeNpm+>{?3uY`Mh} zp|iT(X+V@4F~gWEg7CtGdrhTT1j(bmepBma2@_i*S`iWlhA0z&PU5E_Lx<{DxE^Eh zU4X#$L|=J`fc|}ArfUDv3-MJ4z4bG_x*KHCVR(gPO~Zzj0_RA~EeB6gV)-E2)nFdE z?Q~A`fg?x4M6f`ZAWlGi0g{8ZoM8DHW*F;(k)=M%{26QD0BU>+e9uWL(^&JH4)%K1?E-1@pPvow$Nj%QkT3n$k5eli>|Z}c zJz39x{iICemHzefe|`XGC9IPtY$>fyd~9$4!kpUL+V0&~8Z}Snun?Ib!w)*9y53BC zPme=OLT}7g`un$y<@1xW#l^`_iZ;$}46TY2tpE}6LYm`|*`FJc)8nd0VA{9$ zT|D^t0_(~ldapm${3)e<`)Z7+{7aDmzGu)#g5Ep<<^Q@`SFkmzA|w_-pz7qfrRg7} zPofJ)z|wJ&8@)=+CQjU;-n{(Bhh?-Pnw6goGn=<;X|s+Z{+C255*VqMK*#42Jv+H< zX}UzvG`j-;rBgf0*8+*&F|UX=m%f z!-j=pvpfU294|m&5pgDKAD)a68R}YbdOfWi5^0r7TFwjjB3vn$Tm@Jlh_~o*k!O;1 ziY`Q$4nYBO7gqhL&3LT^0LQ@JK0xGsxq;|yCPEZdXTLv4)e?KavbZ^?!)E6OPgOGw zF&vHTkcqISU}-sLmd-|L(A{|U(u?o6y^mj9ov;sX%f6(N!gzRU5$Sc|Gr1C>#P@58 zkF7qeGCm=4=!~y$kbde4f(0!4Ud<1+fblcFL72owMJA>W2eQ2xa)*f|DTn5AdWZ zlrI~vSaBN5tv+GGjmeAOqAH>LNSiCoiA^y|qT0Vl#WKCrJFc-J5d84z6I^h%d!DQ^ zKDon)H73lo&IEKuW7_NkQ{;hy9uW46P!6McdPRv5$7nKQ(3GqYKQ!7Z~S_4hX#)1G-Nnk1Xc~Rh!0!;?fnFD z@|Re3(W3^I8BPR`J557C5o*B1L$(Ca4Ndlh8YfF);cQ+hwz0nqZ=iSuurZ6mFG1&c zB?>rSY;HQzkl+D_`PuLzI8wi>nrvV$D+v%bTE(NjFeJxAQ8H`)MmC`@`#u~`KA#04PgVY4nIOmhjMcD(}Ol5o!o zaT!4gBu1GC>C_eQibhBc2+ORz&)@(mP&q@H?BCut@1#UCs9OT)8>Sj1nTF&sqU|dx zl5!RiCA;O{;0~7&mxbbRu%DiRNXbfAR>3^kYUV4RoxE7$Pm!YYAfoi<7|fg52z#>- z$J zHw>-8x4&En>TN71)|{pdmG=Jk z|IQ3tydv+7TP0fGq@*N=eL}k5&tvMZ&~A#7E1q}m+|hgWo#pooBr#BpDhP8&S$ZQR z$ngxPB$YH1N#mD4FU8q3G8us2mgQ}lQ=TDxBjv=aheFMM&ZX45ztlMdJ}RyPk*fad z>*t~6(@Eh(4vHBTK?BA@b$MFP3%y+(svWGT*`iVV5h3D9*A$)t?`e!UYu%`}-3_zF zJ)W+tQ{%kfH2pic@#y8Sv4H}gVT9z}G;W^3%D06NLuT$jCUTWZoe9^UH&T5gx(8#Ut+1j`-!q+P$E8JOk+~Hr((+<=pUzT{VoKd9QW3<8qMy+p}RD+b2^8zeVgmqP$_Bb9P|g z0`#EY3a0zKD627c>8E`-3ps>LBsOZLpA#ZWq*}HxAL;Ti{5QtHB+`hy@tXYFyEuP{LE#$ z2GtLyz9?B=d?3atWA@jNKZ4GZzoXL)_CWBPIkG(z)Q~)@eJ08{c(3{bv9=hWgktF# zgwJz(dEVh>DP%zYnf@u*fRG1}rFf)hN##ZiOO?o4Nea(;ty^({2BRSo1CtKzT}8&m!Q4 z&%^1VTzdG@oBWuQFm_m{Q?#aZxWA6xZcTOn5DkyRz*6W@ke%~yS5ZHdTueGu;OC|1 zwjOfNz*D5hWVnTK85fEX2Q8iCDcCKjvfYsHxKPSYuDb5~>ZoB7;!$&R3p#%O{P}X! zdPxXHci>XR3JJq56*I}JI&>KBY)G+Q!HCMKlP8-vIel>5^Wzo1iK<3^oaa$?{5uK1 zNUzDGbwsoaiaEr>Fvue2@2dG8)C( zXu`Au>QW|1>7XBDp_jCuids+zZnPtlUxSp)H<|jC+M4R?h<1=@t_J%=_rOc6Ak0Djm5sl@QkPyKKZF7 zyqkcjS67G_n?Q-cSW`ip3~%mH5OI5ITH5QWmxDu0iu;2~GE z{yK;neHP8#_p4L?I~xL9xNXO8@FVQpL88ncF6|5M$i7R1YnBkPaH-nElA3>3a=!h8 zD(X_Q@66eo>A1Q*y)DJ6kY!f?bq@6WCFdaM^-MG#c93}Z;ir}NY6K45jPDDr!fsa} z;Qgm?5v<$`i90+tCjS&1zi9tRXAa+!Xj`}t^l+ynh8{}kSrEyu;P1(cgnr}3WV#U+ z#+n$puo+T}sPII(K`%Cr?JOQ z4&K3%wY~t2q+>b>e>oB;OB;!&PstBNb`px;Cb!7sHfO5+;kIgW_iR`H3$)0RkCjkI zNW4<^!!ygx3W9Gs+Va3Mk91gt>UZ{YEB!?KNPS1EmMy#Dd7$*#SdJ8eAKxx8v5zC2 zkHYEnS;3)k&vVd|=mm)J=_j(1TiMy!Ux?-lkHD*l79QZH@biNovE-zmv80An>rs5h zi0jBRkJekdq1=8vGMC+GtSu0UqR0l5E(-_C45 zNy&t&N8hXbr3Q+_6izS?MwZQaGi7XwWD0c2kcZaUYFcyBld4WoU;3{fr-~B?*ZUK2<&xZX>OIsX5^4JddD*KZ z5>+z(;iTG(zu9bgMgxC{+MT;@r($Vj=cs36NK(?Xvof=^Gc(oabTG8BHMO)j&L=3q zC&J5VVrOS%E6UGr{y%<#&(g-2|Iqm+;#Ia<$!pk>NKEv^KV-%+jaNxzB+><0X;r7_ z(N0Iz-Pc$*Pa6L{_a^CZtZtbf*)zkS7DRlL6M-(HX_T)&k1zh9V+ zE@}5J^?!ds`8ScNXaDU5WqB(8Z@xwMChZcBhf{r3aW?%m^(`&;Wx4Lka(!-mqws@W z^N_+*J_Z9CDyl1v!tu90e*D*ssiuJT>iPJf`_a z=T+F9zf55aYhv!pnfWs6*QKPSg0-I~m#h{irtnQ`XGBCWDk>>8ED(e+2-xP|Nh;oqN4IO>bP8I&lQO;k(|$-NNQBa zcMne~-TbIi68u``era3V9v2sv!#DF!Xl9!Qe0zTS=-3%?@z<9^OQMb&^B6vt&12ZR zH(2KW4!u&JttR!a$Z+}BA3ki~wQCm^(O}?f6mi|`mlY3~WaQ+w7}ve5SehABqZPq& zg-#3nQ=^Igu1nJg^X*6cmghzcmZtlyY;DU594Ba*nC>qwI#Ezk_EbJ(y`EvTea{IK zFTd@qjL*bGnXrg;F_JPeGMkR7Cx7~IBj@+n*mg@xOT{RmbDcd57anm)`n`Q?oZWv` zM#g%)Lxe$gmcy{S`ZA@CvP+$sxair77pYPAzsJXe?MHro{`r%!WPSPl#q1Y1Se__M z+wT5YHcDGt+ie^YY+{b% z<#BS4uyybL`EyJ5{Q1(Rrd=g#^Q^-^KRghz9pF59QqAZl3Y1yQf!8b3Ai6M}*lUyz zBl0(K5?gjI&uvL)^Ul3NU%tqbynKBrH}7Eck2rQC7Hd%7(D3=~<(SFI9+_xyS4PyM z)8gd52)>(|zn`c-e*D;KZP}hAHPKa+X41fQQtm)Eb_`9&iE}jxDJhR~bA?BLWo)^T zZ5DcC{`cp5@)s}Cw;UN|V`Jl_^3E^&;@H*I^(!JRJ^iZmfg0tHS|WKVf2%k=%TW^~ zlI#4abeZ4wHxW$@4b@KS-^zGtdi?5%iUFx;Fp zyyO1#1zdzdl+oGANw+ysndF7N_S(9y(x!G{_y-MYBIHJb0DHHp`FKZOYns6p(yQA) z-bG4!O5oS;)z@Dk-a%C`ds&8YU45Rd@foK{(xF3#oVyY-4VqdmM({=rj?wo2ta`)| z8XdiD`}Xab=IxB^Vh%npUa)`t`V}{g%-Go2tTUfMr`YW?ijt^S^Y-lOWXtu{rNzES zlD|hr9_*J3)*Y&U&Es~GxN~_`|8&M!uA16f8u#T{I)3kCTDi26YqPs%XJ^yH#(sWa zjo{VrzSHutXJw+~ntfs(JwCIK4+Tjo>-LXkw(Vq6F%s^REjpe(DJAQr*eG|EeWJcek15oUr8XJXbr_q-5|9lBG z`SFgSw6rwUeZ@goR5VsO?)=q}jP&%gBR@a%3=aolFCG7b$+y;2_ z`sp=Tlr)?Ei%HcyELnG2c3{Y=4c&LQSGcTBR=n1*otj2YP7bZr=l=aZ)VNTsxZ5J_ z<(Fs3NI@*e&#U#&DNC@NGGnfJ%D<`*Snrwy^wGqdn z9;-EexSmFiO-=GbwFJaT(!19h)xCW9koELMCG`u}GK`c~XPYkfZaiWW;mY|Qx?hf* zgpO;yvF_eaaS@+2tnObkSCxYOj1w(p4|&Kbwd!aQ+uhpN@HM`3g+s#aQ>ibFa*2m~ z@3II#=Ibzi>G)@p@Y9Vn?0i2Jg?Kq4MP%<##iaoEl?xQc;Dhw6U|Xr6+HkbaZqK z;*i{n5if5X~g+{KGv%zcRS9&3<{~CKzg5ZV;xB{uHo_yS~KJGmKwnF9wT% zkdXI>566tYM(!(4NsrHYB+Eq>5>YsdX7cLX14e=4$M4}yy;Du{o+w_~v9Z3A(vffT zPQN11%F2o~*anm8Pj!nDjHAvqQ>u6J6{P5fSx2e}1=h%V=T`=IpSsuhd=U;NWr*dVIiU=H*~`L4nvI zrDr~SPTv0Xdrq18qi^@FF3m*!tRBw|wJwQb!dTi&I^({qI^Y}s>J_I}zTFWO=X2sNvsAkd zDSAD8xIK`Gf5)axn^cpp)9pVWMEkb7_7di)!)WVW%%9Toatc2`ztps}&)8Hp9hY(6 zfzSj^>N&9hWdK9Cw#?k{4?*i*SwRuO5~7kxx)v6}KR?_!nt4fkxu>OyN}4v+bHjsZ zy8{CQ^-WDO_$5KTQi?*irK_)W3NaMXY-%Vq;@TByrdI)r17!>c2}DJQQL!Zzu7nz4O5=zi{mB zn>UnbDI_U<{n(E3fztDBBELHe90l!$uBawnAuvSQ531L@~+qZr={P^g^@;&Hq>SHH(rMhTSZZr+={@N9Ww4q@+Z`4A?S#l8-8WixH{PCenb-~^5%bpy9L)D|a!L&^udlD`=N(jtpaODZ zw6nK2{CU!4c8J$LXwS)ugwwHOZdKj-O9>Jt-Cw0(f4$Z^A8x0(I23{kln76 zX`&oq&k(Tx#0gdO|L~6dtjtUwwD20Y?JVuw#NRf?2Yk&(hoaoMb7!9U;}=2gu5-iT zR=+=yk$Js({d(UjhNebMRYk>COmcFP-rvSI-kemu%c%LM`9y~`Y9Ao^V8&4Q^z>-_ zyzHryv0`g&P1o|Pz%gZUwZHbM?@Mu4qa&8LZXJkl#5ZEP3mh#YnAaEkc3L}Q^2Qf` zZ%NUi!404#Q5n{R(Mfo$9)8=eR#j7TF-V--Wp4P*m4dpL;y2`#l)^RT6cvN&(+uQ% zhx2Uu19P=4x@=8_<}Ec0`+9qKxA6mYD)|Qn)}(uAubi)3kn7TMSdlc1A5$?rqElQ* zk`fRQ2)Qx-C3Ju9ozOXB3k$xEe0zoAWpsRc9`)qJ`a9ThHPf2uE44{MRavNwt@yBN z?^NkXRJ*t@h2?)A{_&oYbT&=D!Usg)mZ-$7(JB_qNt&hvrP_Js6WERW`1mdv6#|M! zeW!~bYx$^^_jqjT3N1y?SbNS{Y?A)U{h{(c9Go zhiB%J94Bw9M%W+c6Z2dz)Wve!t_8D;Q3nJBP-kU{W|}sg51PXWv#B+5>jX?JZO^gv zy_TZARZmZkC-788kh?;gHTT;=?xitopI!$5nD4Kzk%Q@PVw4 z{{B?f?9MSuvy$%a?md$|WkLpZt&&)No^9-+s^b!4C@ZXZY4sEC{^Egd&yAY{r)H+t zl3zK^4uvv<>4;|>(@D`5Pum`VJw(0f7{Sq%L+P=4!J4mtvUjkH9)8n>RvTD03Ib}- z7=M8nn2L&ulfyrbnzd(dx3I97oS#2Wz#gnO);((OW}XctIEf6J9ziXdQ*<~qH8pcx z7x>hZuip&`p(SwbSX)+Es$N<7occ$tfIQp5UI3Dz=A>OD-RVEokwS)n*AGc1Q03eG zrN<9!C8cI&md9UsQlF|P6UL)P+y$y_+sf~4*?B8CKI8@9Cq^CF%a<=nUZVEHy5MxF zSy|__wAfHD-q=-KhYoF?8TfV&O`$PA#}F8!2Gd5BA85AU5?2=)w>8bFwtF7 zh41L<>Fu*Wkcg7|{OcDBiCEeob}>fG=X+^sg2ir2-e@Fc-@YmRoyd#5di84k`)mAy zmEiaUmjUMDBC25E8|!Fy2?wBf8A!9B?kAE~p%Ylp#&*AFEyX>fqe126vdtfFvXY2j zPsVR4B=^^Z*EZb=A0+z5i&~J3Pz{TX^))LyJ0DIeu&S!CBPv&NEN))c)a2sfp~j}F zm?+r@z8oX*8c;pgb3+2GHZ&=?r|f=NcQ=Pr+s#VgUavQA4q`ykg6jk9mR3|yGVyDF z&3u`Ylhe`XCj@zTq#Ov3v44o6G4i=yprCK00$^>Ys( z;jD_vu19;tOEDQ~X=%MmHr8*jbq?L}=6r?sl50D-!ziys`Px*B!F`Na^ z?#<-lv9?NyZ3&7AvOet24t}>R9-;8oNC*i6T}qwjP8_%+9P?v%r6nwd0zY?%K4~-v8ibQfo{OH zFzQ`Ae4caH2uoHU1;U@Q{`Tx-f5f-k)xS+|ox2>aT%jef2Waq=$o~ENA$;iqj9y&X z5w;8L=I-<7tWc!{?T1xc+uFEJo@8kGc(V-SWU9ZGiT7syZu7QGX|S1$rVduj0&sEl!O}cxKwa$zTI+idfFR|xp-wIr51%-3qRM)8b*8R&8XV?Ak?j)ZN>tbV z*#i3GHNuVe5)u-upkgHn8Wr{_tFxSQ6E6+K@L8Pd!;D8;$q1XMe|?V>|IqI-pS!!p z{$xPg8g@Y`+Q#vZiYZm-G`FLoJE~)*b|{ldam)H6>MVR1q-lK;6A!;ehb9kksB*7t zFvk`<^l<~b!iY77$B^xFS$q49ah=NIjp=@cs5l;~vu$4vV7%-iQB_t}mVzB;gk9js zij%&#*=Azkvb;Pkn1*5H%k~D=bXL9or#c>VUYT^he>$yKM^Z`x=dcf?S={#*1!acr zvN>9GiH7C@??l$yw{M9Lr`KB^;0^jkl1fxfdi<3xyQ|Q7e{lT|5GRy_`t3Yijs_5N zH;D@OsxI?uUESp%Qy^O#gQurNy<@8$Mu(g#tpNZnFBmEDD8Xht7J=cOz)5vBdw+6n zetux>hYug#?7hc@TPP+bmbYexG2J^2+M&>yBH=X2*>rO}{rvU6%}GzNlxHz5cO@BO zV*7$_QIjal&CQeCWmxtnC*Lekw^%WZ5ArxnrSXVP#&27wXnGl7#r}EUK!&5#7!y$u z+}zv`_D_Ft>86mTjo{beiwI@BjDkpTIc3?!o5N1NX;a3Tr&Dv-7u26Wf7Ymh(76@2 zxz}}LZX{Iwn)Z4+E2Y9Spf5qWDwT7+Af=2RYjZ7ZRgYOkSoF??&g_!D;&$NU=z9E% zZh{49d5pHEnHdN|e*`+MjZljhiN9*Mn}CMZt3gLqDs81mrS-36-guni(<$bS_}h{a z)0{699F%KZw>QaGBPr6UteF6lu66S7tKOC z3ztpP-XtVYcjnvgsttppn0PJaWwSCpx;an8b8%OPh<@iakNG*rroz>JqXS|nvY{c9 z&y8?W8ozTF6%}RN<@uLvp6HKmmb|zrB37o)n7c2G7g}JL1|Kl$ zs{E?v(4#0g%|*#fn@kc#^$vojjC@gI1A;l5boC8( zTmbz+AAq8$Ilw8UWMi~OjR@nyPwSnI-POresBPZU#^0m0iV{+Cp1w@ASs1@Ll^0aK z|L)zpSNVGro`#2~#`?#{ADHUxg--q^qT0OlB05UOi)E=$>>hd6X)*Ub$?w$OQ^!bp zGJ;hd{Mepus1U_O`f-q(DgTs3M`T=nQDI@ZJ%-u~!@TV5?2ZN1hSr(=dIwR_&^NG= zo*YuD$^HoTMMLsY|Dbu{=F67iP;EX*diC^`80CxN4^snR}QAwa8`CNT-@q*H& z8WSKc{Z5u$GeU=46J4VVyrpuP$^crGIpk_c$X|3TsUY zF|h*P+NQU(^^J{>UC(Hx>HFr2n^ws{zwzY{^Y!x!+?x1+iT``L7R%P7e=j^Hr5Dw| zB}zQ%5uK5d(bYPYMnlB`ap&n<35G0^eNg?wHRNvJepe5_A#D_W`1rBz9Ltzo#WuUZ zz%PqY9*dK&zuMRie67wdV(4_dweYmbCmi;QV1qJOD3ynY$FGknnAmy{jlAE#e^1NC z#(Uu4!L29>;y(p!{(MnXdAN!xA`-}b+U9X;?3bkg&Oer=HO)yX75xMPITb<3)>3U@ zCV4+V)%37oOTl@4b1b{5F_TIG8YuYr`Fq~Rc+g{C9;$4{ceE_!lv(P?0C$d^WRbkN zoaYr4r4eDs^49lnM_$HPQNS?@$ftQ#89{=84~n;izhgbUt1P3#6FyR_U>=Q0Kl1YuzHbe6vfHRc(K%hGel}&3a3r{t(A@IUK3emXM zSNYJow|py=H_6k}v-!h~(+m7kTX~NiBa7dz=dm(xe!{X#%hcZ0#f9s@0SXc!w&ke^ zf`wB<8aM~&o3L;sd5Ge;S6$~=S%WneYdc9J9oK+u25TKT>Du}q83J%o#B{YEwED#`2jZ(ZO9 zG7=E#77)0oxggflm&Rws!74~3&|sqi{?o^g%V1ni;r*sb9=&npO2}d>z=DN_;cQvT zb~a&JXbB-2br|D>2T1w^%6weu`J17oaG02Xs-ILOX+scFetU`C+PU(`E3jSejJ@ZY z->K0+1b>=W2*ab-0E8Ap#OO%h*w~A=%#HnaZKvQ(Si7X3GH-i@e(~#L-{9bu0NKZX zMSxp~4*~JzJleH@)1+>Ee7r6ki=n2sJ0Xw7#N|#uh#lnqt5VgkR_Sh)4=ZRYjZ4J_ zZP!SH|LvuhXQKMW;p|AG`zr&vZDT#P4Jhg?^lyAGz@REv?~@ld z(-s}fxY*h2*O{TE5p18YHdhiwp7~W2ut(r_^I^A*wFT;mFbUr~mwbJEI6+JZM7gB7 zd}kL0!91hppXjjS=0_|Xd4aFRR?I@^hfg#wafN>R{(T3*x}Kl5Oy{i|S;zpIIb!Y^ z{yd4P1x1M6vx$VVCwvLtpU2U0IgHP2rbm-uQ2m$n&7&`*eFC=QgR8@C@MFC)IVdw$ zLW?yrjC#@2IBFHICn?AIb+`zv(>@j4Dt8mY@qO&ZeQ3?ZCx>{~fL(aPyluDra5J5! zSKY(ec}k(2m=pN)7>#>jK-~jt0{Fc!P(kf%&KoMP3k@zZGSbbo8<^GrFoz%jV4uJQ zn}kI~$VhPYwz<2nvHf6TV+-o`SlRye(u-1TyWfBOG}Hu z!oZG_q{gmCcx(N`RpUW!Zf>{uFh8no*I0Hb?w%qDYF8l(5O?Xy!bE>W_$EFnw<&0Y$Wp&mw zH1zfI@=~k=8sHKVqKC=YnrXW0dyM2Gu;|aVwF-s8G2?)01Y0BNqO-JS7(Xg)|L(Le z;jAw;p-2FT1VI}hVEs@_3P)R}>AlU{_sADTzT<=c>K72O6+8R)pFci;eZR3w7FU-t zoYIu(pf>iV72+ztA}oID?1hjZ?F;zfZq>t1z*Ga-zQZ z>e5pkrXTtS1{GD)d^J4pIXpgGcyh?5B`YK29t33IyyHbXKY3Pp9y@mI#O-Ez3vrzo z9Yb`9{UoUQ!1wp(Mji0qiqg@$T;+{v6>$JOuvn{(nru&3Pdw2*P zO1`x!5z@KNSy`;_%8`Ffn?Xx%!6KplDvb z_~LII73kL?;Fj8mZ|N^jK)JUWsdn0@scqx^j?mW$`2zwB1_2>CupBpf1=9k~%~|N= zfEvMCor2HOUf48NdUL{-K5Ha$u3cEN%@K4LID;6$R->(g0N@U>o^|%}<;$xZgdT-1b*AM86y_*T$iy|aYa(fSWt&@n z{n7)8{B`I(sj)i;Y?<&%??VtLR25*7&kYT9C-3}}MS6n}(xYK9R|TA?0lJ7DvG1R2 zSRH1qNqGI54B3ZF%kI4!t22)xqamPKu$^%dP|+~V{(;~_`MlG{x;O#UgPnBE%^w1L zeQIht-qNnZc=P5>>pw5B%cN;3ru1E)Ju+O9su`Q=8L>Yup?)i#%elQ~Y$a*b-B$G2 zhmG+cfsYz__ua&SLK&1d(UZ(UX43tP8+{v@F6lhAn31i@pm|@t|-0tSk0is%jda3>i4xLlPEPMUtjW6;FXFOJ~ zk5jRQ9S8Z5#{D2LiLjVhA0{n9FJDhgh_S@|6Td7Q4TVl!T-O zkfCd6NJ&aWqs?|)Fy4(EgvZ+QJ~aPl&z=#13YUfPhu9>qF>K-FF-&Bd!xzR*2zyx= z!qJopx$R_=?++{xC$RLmz_f1wgbPSUn>fcTdI2Be;IM*8(nBCAr^x_D(_gvh+Kuj+ z>M*8B*f4UzYKR zL+boP4v$4LjD{_w)uq80W(l|4Twa44P{9J-W$1KtGBITcGPntl2Bst1Y0Iv1XoR65 zAzr{4X5Gc?;8#@uv1!?cQ)Pzhg{W?8;10q~S6`YW$2t%{?fPrr^ z!vU2cwUNau$@CzJgzNUfYcB_3NV6YNSADO(op5VrvZv_{UWnQgA~R99-khJBn|H&L zRLu?U*vnC@eY(%!+FOseq7ev)k1p>1ILp)nRLE>$~^v-8*ZD-F;L- z-AO=$M%!2VLgzy_Xva@73N%EG`1@+qBf>osj{gl_$jv{9s~z}pWku8J~%T3xutEGHs@e_!&J3~ zazMBwQf;a{=qk##J~g51#XmmWxHf*SXI3^LIz;{eT$2hU2Ofc81gPjDv2jSDQNyEn zkY%FrTlL`0h|;dpLc!XhVPRpW8W0(dD}8%4TF%4H&Yo$~Kt9rvQUx=#d~joVBt@~m zIc=vrhtfg!=4hxphGKIH=fPBWKQj2m&QZ+wWQsX@Y6>gc|8weZ6E_6~#TTuYT?cZk9_dpK}R~v(;SOVQJubv^75a6K~T;j zDj~ZHn+#VaAPR5FGt9X0au>`y{q!5SQT@TU!=BHavcFQfaNJPLW%e)(6F=wzVTYB; z0T<3gzO_-i=F+`6>?x-_5qXLE&&bO|WpPd6^46ukzMe?xk&!IB3aeW}tq|Eb ztR1=PA{jmP@wQr46H4<+p_8?>WMq?a!ike78;adsNv7t5P<}E-k#Do=tK3d9y>O64 zZU#xtOyT}dsH8uK;%}XPb{QgCc~y;k;J5d1J^df`AM8VAoN<{=?5NwoZmOt4+o{P^ zY?wJpB0_DqexgNjOicl4_isri_vSp@f4O1i3jz-h9)*kNQWt8hM!$apg4>Vs1J zO2{g$5R@9T-w)+E*x6-X&3RA#%Uv=fJ6pYN{bH9qcjIF<8=-T|WkF^>)nMH>2${}UnGDY;7G9IINNwWm&p>Az1xVlOB{UcT`451J>ZtSf1* zvS4wxLy!8F%?AWX`h?A%@nSEJq*+HU9Y(>HkohHM5t{>~PaPd>1RD4A+YFhVa6G0g zR!e!-+=(2-*8+`euzT?`)yezMoM8jz`g$V1Bva@wQ{n@LqY(%9@kn;hAVAp%yu5|v zwm3-!XYb1?(+c=sOnh2jrVE9Z&oAFms~Vz4>%X3961wPVs?x=)m3xIv;}-J0y%mfa zNYiE2z|%d%xN6ux9Dw_T zy;5+zno|J zUj4(LV1F6(64fiaSGS51iG#yBU`jnenTUGOLU>8lub?JUy@Xc^^G+F1+{Dy$pRh19 zc)PIUc(l{L$q~JLg|NeSvHZI>ZP_8Kug?X?-v>?<2IpP@LBY?c%rx`%Q&Zn9jvjqa zZhfCW#Nn*<%0p20A7^`RyuYG(1^l7u&J_A8xIR8$mUuuzd>^$2wkp)Z01 z5E>NPq^`cccVr}Up~u>uOP4P7j###)>IHlHN1wLb3q73@WDudFz1vw&l|zBoyK{#? zi&_Xl0QQ$-Sdd8Xuch$O+eYk!8H@dT78n^BT5|N<*@c*Ft*QMzixwV zWy60q=k^b3n3^(BFAph3k`UmEjEV|iI>Mux$hqTAirLP+XFhpT(vad0+QK$``rUw~uWrkW8}2vgoMF&s#ardjoz zg|4c=Mz&eLK>kVlk#eB?-+g^g{)$M`LQ@C;<|EK605k*X74BB=NOdA4G)Of&{dRId zEubQ@u4olfAd$*>Hiksi;QgXPa~;O^5b`7fnu^svNZeOq35a_H5ETb52b04zD8@XvbO$=aLOc%LF6^}HC4QXjXU`6;j{|MQ_!$xHxYq> z&_ifw#T)Cc5VqOSViB*Q16SGt^_wJxIF0~t7w;T$!upuqR`&K4P>btdNpC{AY{8s6 zkAfgnCOG`kmoM)`Dzw*?=Nlq!gy0R6v=`msGZC)Y^Y%SA}wIaYB&CVirWfJ|I^jIP)Jl0)+CTMeV6Q zSEsf>I(hy2^<6^TP*FMFln<-h%EpF>Gb3Ccgf`U&&dn@r#R-Hlg@_;|z#dRe63M`z zPtpC%O_x|3*gbT+cHM*G)dT9nET~U`oCX=R2;z7I!ZrkJB@!==6!%O2;3xcpf+#{x zH7Z_8@Q4z3Jw=2{p%)O#o_O_5V5?`$8E0}Blv5qTYvPI}sh2mB0HxPs@iJ(Qw_ttY zB*d1q)vhAf&JwT#G+Jq7gSdu;Atl{1=jfl9;n`O7F`3AX5audPp9DWG& z*Ac3q+mJ*}DUKMg1{p7$27%v)FNJL)&UJ69TT2YXmEi6ZzhHu-0+r zcOD`|*YPoVE%;1ew&!vTJod@aU&r(8F%h&0#Cdk^N3sU$o<;KM^g5ytKF4lk%XhBB z!k|RpG-_l(wbDb>tTmM_g&$tyPRz?t%|jw08U&h3cOSzj+TGjRd&}Y|dp)BJq`Hjliz-fCSfz`}gnXUaq!?Svu)lEA-g0P~|?-OMz2b zMJ^}hMtEE-uI1aA_TV};BcjMJUN6=u@yPQK|MkN2&+MwK8jK?!_%hYzRS>VXVhV)a zxa_df7gS~F?Ag}VR*Jpu3-TY;9vBcnSNl9Fs((3a@XztrUl#5HRo05XgkGdIIXk<1 zqXd-eOvM2z?@2IN#X*+Fb$k?VtpW!niz1P~F@fW@f82AbVR@`3n0S6$x>s&$i&%wPwVc;IETq`nS+#~D;&72OwE9gMb4~fV&)EgPV86szf zMp&DChd7DR@{!#GI0*-MSkQ|70|M?wMX^A!Xn?O61U9CrNjY+&xwVxU1h5q4YK;_q zq1zHYB#N3EYSe0FvPQ<0cdAG7>*O9@6Xl_8ggO+iFn z3C$L>S09uDu48TXOZdcnXtdXJEZC8eIyA2%{aux@M!;j$i8y#i0)^TPFZJ4O5orHx z5^l_Jmt>rrgyE8H$M!*5Zs3mFk|CCdSP@i%AAlsMsOTObUkI3oU|J9gYE9=g*oZy2 zkd=c&C0NlEGJF(Uw(RHPBL9yt|Fe^KHUn-DF70jhcTRQoc6Nj{<+jl2NF*e2S9%5q z?_+oj4Gj@^AFV(RxQ3`%*nSFH*P`5sqZN~rlb?VkdIkpkU}( z$<@_$cC4KVt6J-@(G9~BB7kmpv3ssn4>>>qag+*#o0*lBI5tU~+aquuP?DjhBNRm9 zZ~|6|_>8~NdMn^2paPi)ehwe#9CUvoh>!5U^6-2+EE^>*D5g|}FclXf9 zNGb&d1-tPM77$p!tgMqr(7ZHPB;t0M#RQ{e6SbqlN3X<`CUR-a%*;4LL=T1C1b}UM z_U{wJ8a~5!$h;FH3am&EA3svWHlbtM`R{!lcqWxeDsKsrj%oY3s%nd}vhrah=SA&? zynsrKo^@S%hLl+;lBW2)L;!ON5hw@iKWSqpLBb5c?y7jTE?IJF#WFp}Jx&`(hVjt4e_qBiKtM z<`B;Hh7VyRHdcwbQeO49se<9)-~IL5_MTxT4h|Bv1#M?Hzc!(|mU)qF5&*77EW6)S zjE0d{3U{73Gqkw4$cga{>4lbp?9ib@iE8f|36l-B;{%oc>c>;!uuZjJ_FhfE{U$W? z>je(SptSmf95F$JlEI-K0kc*C;)oDVNzv6Bvnd_Rf#5{|I(&vc;v5>d9PXDtap(tq zk2tM}CFix(yl$^tv5?_?0G&LV{8(RD5z&q)U&wx$(9t0n%0FMtU%q|Y4!BBAA`E?C zx9>UTA|fJsu-$M1n~4e8#6OVntq8O{-d*wt(6SG$7bgIBr|NztM|ar_r_Tu@Zm|+g z6bEqn7HlulC-eiFADMDtu3(QQuUmnW!8EE0nx zFL0~(B71l)N_egZf%Zt_B=E0vL*jyV;4>uCfTO-gG$={)6Wu#W@FmW{U%}D5q_ekg z^B}cBO^OuIBY&cX7f!sQ_h09z-DggjwUVIb5he*5C0;MkAB)cgHiww*T|kNavVD|9 zft8hQ0!$@Bl=)@Vf6xGIo%;cmO=IPgdV(Y@&B3uN9^XG> zAld;YG*S++t_$PLk{)MJfsb1_5gWR`~iIW|Mtbuc*T)Kytn`POEvRfM~?sV zuK)9=>i@4}@nQd*#$9GKcPrHH+_&51iTT?^F$yj!+Gh5%uetBa5`Q;(-?_>o((yvl zh2b5q>zl$%-r*-q&YR6OKFl!I%rZMCRC`LCP)U;q9;aAMyyNsRR0 zR+)>7hjf76UQX&9mE8Zc=Lr07-<9wm$|TwX*8f_LfBnDz1B>#%d(y&1h5zN@4a#Jk zp^0MTJZFXft10~FyZe9ou9O`~s$o|DHy_&YpC6m}GwGiV#8U&{(*UAKXz0*^hIhyv zLZk}({4A}HTW2sb^%fnuWZ?WbK{z;)O`Q>Pdf(zgwJSx*4(Z7e+H#*N66p{-dx{nN zU_?xn>%>lyS5N!>SCJZmN&Vh0J$tw>|IByR7W$6`CWrUM@>i zbc!wf?i-`wu;ifdq{hKVkKv?&FVs7;^}5O{DtgApTyy`r?0UIZn`)VsV!P6I(1g8X zqM2p>%4K@GCa=Z?XwJ-+73xW>8tmUJ1f2v1POVbg;u788_Wq9mb{qoAz zUfZN`3@AA{QA9gTlqt15Uu2%$3Oge8@HNJV&rXyk=N)fmf7vEJc6!u2Nhm&~kEa9-%$K?p0huT-1#H$E=2uRU(8wbKJ8Y91uUV;C$CuqHc=sw+@xJ z*F&+E*V^~;-|f`=<`n$G(X6b{fRzWQ*vi0e7M+%QzkhR`v|1=HxjPXW9Ts$T;Gl9z znz&!kUjOv|ns?kzJdsXa(mXJPW64c$RBsVhI&q8wO3uK_JcL#p+r`O{kcsx#LRY(v zU1U2q*%qzawmdbh=AkFq*o1U-%v6Odwcz0?H9rs2to6DqwhW4EWp_zA=?Xs!HhVn6 zOdThgGvQNff6e~Ba$?%{nStIVh3nZ)A|#~Z0!=y-PU`Cy|4tj+Wwx=lQ!C$UYr(>p zcT!Z+v;D_fTX)X*tz8jm-N}$~rwXO#q0%$mlA>MZcan8RFxIlgFN>9qg)V7afcj*% z-nf-#qSomByoKfkmV`@-J3>R16SdyZYv!2yM)C_f^$k9fS=qodRlGXI=6e$!JTL7p zm>cD>{bimb<{#wm^TJ*HpzOSP?tmLR%>bV)9L0An$vX;`=VFwSRFWdgfuOpp0`-&jVw#5?pFR>Wjl(+A7F34vXtJ=v3ZTvhs9u!L{e0E zR{Bv-9A{#FN7d0pi&^h&;mgyVi(p7JH-7&vB*0%5qc@h^0NMynNt{E3wVD0mT_X#X zJx1yYht=|SU%Yn2S4QO#=fNZAUfcJ@_E|Y@h+qGC?EaUX%8T^V=WgdM@=sZHdTcuT z&MVlqE$(VxAjzxs%k^k&w;hi*rq25=GhF!?;oGQvP5RnfZFbcU>I_+CcRv5jX3V!6 z@`H6}e_t&zb^FYD1qGV5&9fD$p)$%zHV3Dckl9vzeqvKyjCfVskC8N&PM(wJH72nY zwy(UlN#)>4aOiAHysg4;()>+x)_Zz)(WhG1rdXM@ zi#FNBa7)|{Ox8`h*}|x1{_Yj7(VY6>Zr6+D>a{{XjitEeK}k8!%5a5iWTY1bjx|rO z(>zBgW;I=ZJLrF^%{Lpnzg-$y^ObcR2691F6*Es83Un+_m~}`!3}xhfH166vop*Go zE9L0Ah_$p)9yUbmMyf=j`ujb&^?uzySA*|6awG{fdixHxI#-?gDwh4)=18Vwn0)=K z0AdSK6!^`RD;FTLmS){=q$N5M6i#!k@3#iGiRnzEAYBsUf~c&jE}>@te^5IVjr6;8 zwqJ4#jSdNlFu3u%V|oTpJ}8(c53s(FYN2}&St_r)X=KlKSo+kCr1VycOmKy#3K?~n z*x$c0gpHG6JdvsQsze<;es!|REohJEo|Y6XxvTp0nf94KX!UwNc|@KbyM{w`31^ zELwX+++rZ5ekc=rNT%D|N_R17@Y#(0fkTH%vo9S}P31`m6QwC2ZIFEK`CyrPQe$N?z4D>qdSmd3C#bc(Q;=IVo@J(QoX}V)JBFrZv4#GR z)to+@FUfnYi+m(I<@uP1$%RNBqwcWPl+!Z^5&I4yz<=gLmu?At1>RW@s9o zg+aUwadDg+hf;nYiZ-ku5e4ThTeip_c+9J^ixS7#5rK!-f&&c1qj;1NT2_n}rb9f3 zww98D!auF5yj&M2-Vf^(pCQhOTUtIsEIh#Q5xbZlw5788dRnllNSxP$;FfOQT{`-q zMXa#6f6GeooUUqu&uaQ!OEOaUGu5x@$=>diCb#eO>P#%iU;4T%K_5E&OErTm#NnFk zE!B9Jd#|}3tc4^_{z@CWjDy*(yqf8>ZJLEK2T!;9snX)cYrDCf;ajz(!-pOE#eL^0 z-;wSLPrgkg+|OvG>R3E3Nba>wW##!LwhKLJmQ~UeXllP6B*-Y-Q0eTUrAvA!G=D$e zCI7Ic;Z_nA%~lm7Cs|qQo}BAk2U)>D@%D9LL_pRc(!}HAaKq z{NRBMxIdAPLkN-3x1kLYM=v3wQ-bmmw!xk~(=!9ma<@Rq!IK^0ASDj9WgSIxp=DAPCRji=%n6>8$TnxPI-TwWC0H1M?OVO~=4GCTWHqw>!tJS=WIY1?bpd`c+3Hh1f@GUDKg@OvXMV(17tk(@fegS;xRrre!SQLJ%D&l1Cg8- zwi}`)&TH7NK{zk~93c!I>cu|1y50Mn*I|I$w@rI2gXV%WDXEoh*KY~J0OfexL>cN@1+`4 z?U*Y4LA(=$@uP8sl&BTr1iIt%0*A4)&>)G!lMwz7Hl-6vAC5fKisJ1MDjcCMLVG8d zXH_zSUJ`l6S%|o^$ReFE2{{!s47p9$*!VskY(<0YxE!RVKb5M?;=;>RF*3!k1{=rg zn;*xRzF>atU*4K2BJ&_>&&6W~Cb#`*ORl)>SOTYyCI4N2w~XX9aP0XJH^;r9>1iDF z40{pFAW&2w1AlOoZNhu8=(7Rl@|0vNdR;|RuA4{#f+s&Go7(<%Sv#N5U$T+S@Zjm1 z4_~M79)BS%(TQ!+wDrm7b_Fv&zMGJ6DB^==Ad8BzUc+jPm2USwboobuDrKUI(SZAHeMLd z1B$1!VRz}!Ct~sj$Ynr#0qy2EEK0)z$?6_5;zrN%F9<5+DQF0v) zov+W1Z-SBiFC24K8q?RYK{RI5&^+y} z$YA=_EE^~vvm=q_?WUU8m+m)5?b^s{r~4;Y|Jn-c6nftu-?=Z; zq~d-Q{n@sgDe}afGeJT9mmQU4XOF4;To+h05Xk`ip!oa>c~i=A@MU?yBKIDf`8sVm{6@>(Gf~_{c4Z(+a(n!O!bYHcL0rDK+C! zen+ARuck6z=tDn#_QgF`a3*%n>F%iBldjALH@*ODi)d52Jw z@N|ODLBCW4tiF(}e}RqB&hDeS#-~$JT(l+i_87Ij?~; z-gohg^=vs=Rn^0HJel!~sS4YOeLOWY{G;h;<|*Ej^}K=!l2an8LFlOtK}BoWu1sAdpDZGx)si5$!$BH zXiAs(tjfmNwYjwQ_Wkj>HN<>tP4K{xeMb~DxUPP<9OEr$SnB8>99#(kEofGL#s0qR zIog$d$~_q_6?@=Q>NWIofQP)UI2G^h1rj#9eOq}|BfTfa%iW_-Q+gX@cB=W!DdjKL z9VAH=1e*uiHTs^g+|;cdS@34G8|Pkrab@sv+i-ioTjqN6nGiLs!r zmFG#h_Yx%^y1f6y-}IseOIS#z-)tGR)LVY2ZOphs6@ zOE%V7Sx=dPoA;;T@dgA8BUE}EIl$;8p1^iI^c)Lu`T(f{E+iw62d>0w<9H0EySqD{ zVu1&S>_#vCZodt);GtUhvu6(XucAv4N8j@d?d|RR`C4F>z-oDjRMBrF7jiw;M4@)? z1lmZ}F4}`bJ7*C;u-W}K7S)W|CG?cg8;`VqdG94>Nw#&ew^E@nH_F*ACijal=~MXj zmRGMoY%S=rSAMPSJj6UCRVB40AT3~#e(xrgBU_IO_6U-^dfx>0Z#>|P<&EWv6L9X> zP2o;;Hbp{Q(6x*Af7}qe89&y*r-y^XYdxxG-Fi26<=nd`Bkecah~DTdSmT=2e8GezY%BY2LC_y-a4wPw%r?D zSSTup0xGG5Gy>ACNOyNA0sO6TO$8Xm%-2vs}kbd!)PT4Nsyy!*!nF z>SL4&TID->EcBt8+=(3_dHfMVG^c{{4-bVL%3$1}m28OcCwG_2OH1zpc^9}>jli}3 zYFJA56m>%MAqm(sP#b{?tN`>tFoHoOm>^t63=a^p5!@A^XlDhRyXK#}GdCa>?kiXc zV3D%To&^@>1A3)%{D<#5IOCWLTaFTRB(3S{#zcjbv~GwRe%hcmA`hhHxJRHwEn81i z&H21Pj*;O0;ic;z9*;axz^!)|S1xh$gJA;>sf12wK)@X)sngn^yr)F}IN4zUfQh2~ zMtuiJqs>3aIu#U9sI#6K66X?2Fn7tw*(mW08JlXd-1+Uim43S(1n-nf=RSR>7tBMlL|VM+i5&~6kqc3ke~dZZ`#nK#Hm$PyL#%)hH9vKxjDWM{;W*e z<3!y*iynACsIHVYZeUN=Z6}@z1%CaSQLqd%0zy*KF7KRPut-}knt~$qDlksh;f(|{ z+^ZdZsw)L7(4*AcuReMM4GIj_#M10M%$ylR_*bWD)4bQIrY^s;Aba!FC|pjG{+F6i zN4~$KO*ZK+)7IiX-2(j8{uL3sezRrXoPx|Qq`Www7czuh038Zj?4RkMSfGx zIIZ2|Xbo4m?9*Ejr_01Vi;07=AfUQ)|FC{UYj>ih(}wxJ5+U@F?>+?Mf?P&fhI{kr zHC$B9_j~4VePA~8pu2z4%;c~bkGP8y*ZW5NEMv=$HlmrNC-?xj2 ziav#5DU4$RODmBHIV-E;_^oML7|4RpfhQREAv_ltayKc?K%vS7GruA`5-v+2Q^h7I zckTif7Ir^7_zP`5;WGN6m>vMyR(uFmu>^0U5i=0hGVNEn)o5jL_=V4#TlEgp%Br|up38sE39nZJgLJ&koY z{jjWcjqIjBQUA*`NW+#f^Bn7*cTpw=iWG<6YU#(ibaOP{&iA-uZw%BjZ;^`Ai^q?* zmbvWL&S9_cTRzGbd{1V2Pxf1aHJqp2|lOmw!{QC844zLS>+#L_i3ZlS5{QSTj zQXj^dSiRj~rUf0u6CmD!tL$qyU;rgVm|@E4q4sKW&a6`3DT84W9}B$v66QkV#+!8&1t{*^mu8#^6g1^~w_r?^-QPSx0y6mLX}2ejCBJ1se1EDw}RgDjF=^8XU5wZx3?0#+_O29T|b?VsT}td^J$m)!bh5^S*+~z!t4AH zvxVQ$HfNTe9R})Fzx<2hM6fc7 z*xL^`hKSSZie|w}4AjM|ml}p|Z+uSdb@rT1? ziVr)IT#Sb+ZE*eoY5bV6FDrS(-^K zFx%kWr<*ZE4?1xfHQCz>q8GO#5uj0Kh%g8JJSh-RGeY)sgKu-=TsgaU+%>8$h~bb4 zCpCp!yH5Ou+Q0HMl>Sv`4`Qvlq1G*w$GUu}Ue4wulIJ+{lowT#tfHka&tP7beUFjw z$<*6%(+_gJ{|-c>M;C9p|Ee0712fVVlHud99_x_<+lTOMS9vFT9$&110v{0B&({Zj z{rU}j4Wvm%#>>DqhzIQy;us6l{cOuRA6RD4_j>}9bQ~Tc2saR=5B^e5>wFlXuc!q! z4^ks(gEJEla*^jg)I?bDun=3|sXp4c3?(H@J=cK4MFEpunB})A)%gH|gACbWog%CR z#3ToJ03cL2Pft&euxemt+g20}r4gpuG*Wk4B1g-}-8xOM#7&0c} zCvlJHxSBU_$N2jCwQt(fv8TO{7Ig4#l#p|E#{bwerTj&mu)au%QQ0O3q`+P>j~&ld z#5fu|2~67Hj^m=Qq;)(Ynvt>X@z@MWp}r#iz+~a^J**$K{|@NEh+KS+?&i__7$;ZMt9T3`d`ICIR`y!raXqM;E#!?*2X8((7^FAQmNud1vz&)%QN zKvl^_*X{mPN}FC_Pi=-%MUneMfYs=Aa&kqVPyYB)vg8BzhGZ2SESPz+$On-xQfHYF;sN_l!~HhJTjCmVB<`;&wt&o{Xd@~ z{+}?f``a%>9)CtiIDX|~>c#PY=tTcIa{r$)m}j)ZM92hx{d4}+;}Hybg}Q(6-~KxD z|DQ6h*IdAU_1B*Z|HeW=)rTvO`+wY3m7R@^e@pS-og@$lt$!--Me=wH8Er`zYtn3? zvlqcN9gDwRLk!_Jq-4e=ARk_$MD~!B2>Q=9qa6J65kVmGC5;AH_~-bTW@UT=GT=)q zr<6(MHY9x;gZQNI75}_OQthT$yah)4)lFH{{oI+5s^qvo9EoH4G~Txsp_WIV5RSxu zHhBLBedBM-8CJ6=1@q^H|CdMNf4e4Ri^Z>?QfdBYc34u<3hH0hZ7M22yoUq>|1Lm0 z|MFk&|DQ2v{>#A<|LIkA1pI%URMDjfL?>li4J|DsdH^JO6@Gx-AVj!-qtEGCj(s^R zi#a|&{z2`&9tAny8Q^!(XmnlKChBWUWuZ{$O@>{ke4C$9rvUv6wVci-a<+Mp#`u{Z zsEp4&cCTr;$(h@HeCuh@#DmNS`3*EJrYw?1Q9~=F-y?{4AMmGSXL`4BFet(zZ>$yE?&M`58@vActNv3|t73L{Y93(&5NmSU1w=$^lzhAz*`E+1OwTTsMp$W9R3DLLgMrF^bk=gamjthyUFg`lkU3@g`4+m9;OuFH zSI(~=42uGev!7o5*wu*MJ=tSzw!xQadZOe{7EJ2#aK?2U%YEO85mi$bh_A`TCe{*m z%TaKrHp|?_a(aUO#ZdnFWTSf%JU82Q-YgbIrmB~coAzd5oE1KMuJZJKdGX5lN~X&# zZhNkK?O#%}!V(F}?U!mlEpp=19iEKs-V&r+ZhQjB!1In00XBBF@x@8sphns84X@@s z^Fz^|{OmA)+?#y74oj*fSGzOyDlwq+eVrA}6v1Zp@!Q+S!{shyyDP(u5m&2WVw zsp!>Ul&AYe)Xg^Cx6LYQ%4o$)MTKTbZlQwb#{GtLhONpkH(Dhy+s#b5ek@rV%(15+ zry%bo5qHbOC!h_dV82zH;7d*Q?8GXojgToyy}yj!fJeo9F%cWAqPKJ$`q2 zN&Tv{xGCK#-Aimz+rXjh2coo(Wc(O%q%H~I;b{4M@fFUh;O~~S2PICE_pnJvahf+m z7qIhdV49Kiq>DE#;PH>29Ty%ymJE)>Rekg3%{6>{Br)gN?XTr#~?=de_W!GcHNRa^qHd_TuOJ+dKKo?+>O;pS0KCa3tALb*(Ch zqus&YJ6~jop7^-RE-hHBs2wU$*YtJM%yRfJ@6k}2{)^XXGTy(fIxh2Fa(-PDLS!JD z&O%4>q`{^SH{=EN$o_+yfb#bqf?RHm(Jl2~x*e!`Uv91#K-dv3a&yR2Sn^h-==QnATimu4&abYl;Z@X4e z$sdOirHmZHzVg>BVpr%cy5M|Nebw+&j`e94Z}P`eY|-zcmOKSS_!)F4RJ0?1?c2_v zxRh*fn=sh~#}5-|*W2;RiD=izL%0;UM7R_oG@eo%IfQ?Q@O>dACxFO z#9Xb(UvS!rDVh%Abd^e|?+;Gh6V9o5a>N;xL+-z8Kqh^QW!%Wc|j`<^nz$ zojp}v!*4_8gN!GNhDch%1uqMWUHMt?V&s1H#E-aY7MqnTA1#TKeJu0k#p;^+BZi6t zBWdom`!vv7%}jB(XfR)f-O}Vem#22^hU&!RBn!Yg&ll(P>qGTA%gxBZ-6|nxxBiW# zw1VLjr%+_wqOy5PLF6QTclH0u~Tz38st59FTjTLay{ zx$F}O$y+3KF460mDU97!yF{PjA5)8t>8}BP&=PmSgG2m{hfgHcHP~Q%^LboKXykr6 zn6cVJ;wO%L4}HtnHWr{ZeCM}IwAJ}`oIffEiy@&@#UmrJnMx(2r*C3oi=I5><&>6` zeiRvMzP`VClbG8bq=>5R1+GFduK`EK$0p-_8&PPArfq7$(nMaYkc{<7lK#^=$&)8_ zlB$Hz@QL3Bf&gNESpWU|1poz^+1U||z#6a&C_o&~a1&xUK+jbV9(Kt8f{_-;w=JjM zZ~2zN1apGqidOMU{Qh*X^X|*ip}(gIipN+)ehn`J&=Mj90$Qw5T03Yc!N~$dQYDV- zSG4et6?KF*PI+75I-SrUGt|+tzdU*Kk`fG#>!G^-64~Mk>tx< zn7j?!Sm@TcBgg}A@oy(}@XR~#rT~w@&cacVD z@#>eIuaW5!!atOZ=3@*=jai5ON>i$6elP}H!z=ALn||ivXz+kgpQ!7nkW*OEIig;^ z@Ggs=$5<(F;^`!C94??@wQvTM7!=yp%XQn32j;wl06=8#Kl&(JFni$*$+JX9>w+QA zWyD^Q3YY+c4li&}CF6nlCb6_v5AXi*A&h&}4*U@C53B_lNp#vu_ZuEj~ur`5M40XN`lg<1~fE)^1lLZR~;-SL~e})JA{HiB|SI+U4R}4 z{KL+}0KpTIhQMGEA>?@#?Pl)HE(u;-!+Cr@9c#j$L`38KWFG3(w_wW0-#yy$D#AlZ z2#OMvk=F=byLnm~Y%)M3=%LN!a0!v} zMA~C7Tv`Ke^1pJZMhnyXaiJ7nU`NL(hDZ)%3t>$1DE;v|FGQP<$jcrWx5a`&8}$lNT!H7wuL`FJh_DXCTgC%zAU+#kUj78g zx$hz}Ujd#-w1@8k?SDsQi$lu`8lQ3QZqfpz>14MF(+<&ls!bHJZKpfc2 z`Z8War_0BWPjuo4-v}D!T5tqegFy}w3V^cQ-vIZuK z_3;Ip%~PgfAcMfMiLajb?2`M@x)`iJ>Spkr8xcnAw!n@SF;hhS21O=N?GUUUk^RB{ zM;TTkYCmwsK(d2Sh{g`4on8RbgUGL=$h?ntw3-7V0zjel6mrQBdkn;Xm)_mKqX6Q`_=ajzU!&?Py8if{cGd5XYik$3e2Ivat>i)0;K8je zT$KD^MM2N>!G!}F-FPnWngJfqSz%#pCAxMQHBRm7i8nxYh8KKGtviamV0?_nE5q|r z&;{NBG4a!ZiD??u3NE9z@T?WfDv2d;?)ptc91vIrO!TJ%=%gTrqV8AF0_WI4&XzpVujv;aQFki=c$m{IcS0kFu!A{F|E` z=4zgxgf>H4@kmE}SwVMjkA{ZvcDC*9`RdLur>Dh{v8U9_3T-*ilRg9ibgKLih-)7` zdW2;CwM6l9DgjID50OJEOcZ8&2)PUXz-E946OrGxMf20=kHU!8_a_|+_+^Gj zDjez+P(*YVc!11zGe`v$3r-AABS$O3kJsFR;flC%Aif}ACS)k7iqVy9k5-zkzP|

P7Kf6lq|U7yJsKE_r)`)X6vcw&m1YIaD)&V$d53IOH0&c zzYM>TcZOorpkaIYd9@82`g9QgQ!)0pt$g}xcqDJKdbJD;{Jl(l$}CGJ?QhP?MtK8Q z?Vc;AtZ8`veZ9%L0V&HWOQca0(h?)ezu6)bMjE9|yj(mEQkbK%d*+E5AC#!qq*94< zb8}H$?|MT;^z;NXUr1+2KBa5Xt#tI-G-mp};biRS#P_bqk7I`E>!&RKvQ*xJGm}5! zC; ztctGcnVzq12jl8ghGdKL$n*P|w0hj5Wmq$)F41$JXW@8z@9XOc&VFdMcAl@p?{O(m zC>Y|2nwpx1P6PS0WkVbILwP!((YQy2;N4;DB&(%G1ao7|0FG7#`#)aUbD(hzJ$Dra z78wD<+aR#U23EO^6L=_t;~tU@st+^)B-I5DK*Y9@fZ!cS!WkAJ@h={pq#Ds=;JS_a z4Q4`E#o3=^23>vf2ALDK+#zZ+K>DY69ugb`>MKO73v%mx#`{vB!F_guugtr1gpr*> zTH>^NVP|Q~3riKB*?!7#>Bu0gOjs$sDS0|Ktr4dq;JJQCGdAC4_aKvf+OhOs^X^x& z>(1bY;raW=2wTm}+ieYV-X70Yn0wKE#-4z^2hXjR;5XU(BtZu6I~Vs}?%;tG1k@v) zH$=x*Zg7?el=yg3@5E_kXQ@}dAeoUl#gLHD5G%hs05uk~#fwSYCvNerm3nLWzx=(HCOp?!&a29(^NG8=YS#F+iNkC2kqQ#PHpSWn_`NF+<4L`2$?@-d&|tqNtt_8>)qPi*_`z)`-2k8HaQP3Uy$TpH z-i6qbPmvs=KNYF+^GcM2?uYr`+(5ci~V^v&I?)v9q zhP!tkF6@8^Dp4SYRL{9$Bazw~ViR>2Hn8xg+JDkn?8Dgl3HTfU9}nT)Acj2v`u?$| z22&^08b}rx7JoYHfU6wr|ImS|&`=F;BRM&~&%N(%(?TO@4tYgDQ$S3L5UxC^^7Ub= zya$g@Fkpq|t)RfA# z`Z>piE6d(PoU6Zo=}{|RTQ0r}6uiyZBZPz_-(CfIRyxTm0a$!siH#C+tynUtoS5iZ zSz}>llb4VlI|J4?%4wRnH$9F>J>Oy1WD*sx@5Uu8U`s8lW~+NbW&?xf5wOr{um8fa zc{d9JTn`Why36TL_^1CtVa%Z01Wj>DsVq@7RQmPO< zoAsE2RkvlHqVe|xSS4$6gSJ8=TqFTqYyVc|#|WG^XnMPP4SehYCpGx>J`SRrkx}~B zs)vIY>8#eR6vI+Z1?uM`@Ed_oXdWicwOI-##I$tsk$OVZCA)jKX{Cz*w&(5{eD8!%hxb`!N=``lubh6YA#Sgc|m!p=R(N(0XOo!(u ziK*XvT%Jj$$~lMW{l4An-CH#GpvC3GxCx(w{W#W~s}d?qdX}PK9|TW)I`-Tm;n@OQ zd45u7G!)8`poq)TF1zr%dX2LR(^(e&p+}_ zEY8EZ-V|!1_Q)hyKO`W}Lvn#o*NzkpEZAaBPKRVPeDi=6$7~=-8DIAZQR+^MKo7fq zv~RPwzaIxti>%EW>gp(QsqX>zKs^GTD`fA}C#^)2Fa23J>%oWEN1R#5R)r%2Vk z#r-A3RRzus#1$3{ay}dGLr;>J5i$ckmtu@RWIPP@|N6zHX7w{jpy^{wOibM((3s-j zFhHuwR|Qi+(ELn5ZHyRzBumrsY;qAs-a3mSugmvy-(-By(N|yfI{k@K6X8w0;crL2 z)iY>Pipqk{R}+EH{M+PvzJ34xbZ4q`ZTIpk;qtz{CqMa0C0^%&xd#R!)(#;aFE$xL zZ@bz!GS{id*p6-p=_UN-H^q_c5n@^sa zvj2lT*S`@HC%3LP0b}C9i)^Hvqf68)pjDN2Ry1Ef6(l1e$%KAE1ZPF?@pEySCe7g2 zI>*jPMak>2at0M^e#p_$Z>;FW3Oi_Yy(FXL*B5o^D6Xp(N25qnu3oW}qJ5mK6r6k2 zraf76P?^0wS|F_KXrSBJWcmdf&5&H@a~$vI7E(3 z!gCi)%v3_B!M5Za3NcurN^m3QkFcZJfNJ$C*JqAppm+|ndk=#d(VwUVsA>~BIPg9T z82T5OI3vkZu+4sIa{@aQy4B{5BXBZCp^(BpIoT*{B9OY04Dp5m9{W!f65y2A^`x0l z2!Y224P#N=Z=fZkUK#FZtnL>}iYXD5A7s9Y61iGZWz|^lfIfgOaR!|w%V83u79*#z z%P67%yB>Ma$+<=nW7uic>3GPPw1Mo84WE}HNt;c-m2-A;*`!%a!v;I&UBQyKt zdqJNr2tus~J5Q8aR6c2a77rpnv3;J_RZ>MoDze5!;hVBbk>)WQ)sBH`3Mi z7x7U0!f?6&WfHCKL336^jgP_m`SFoa$UfzPQm5yMe9ohyHa$f*Q)=$CM zNWF-`Tz6#a?KML)Fg`mYt5Hs&|)|@|MiS5SDv?=+X)Gel}-*{(1>Z8tgYRqT7E5v_T zre0}91%pY$Q2&emHQ|%SxMX2=wuHOoI5~Fp_9&rU-J>|AXyHQ~gZ3DgC#qdnhV0F8 zQ%deOTqO$U@L0_vaarS7tM05r1_M70QsgThTWzfjDd~*kI|2=i%kHS4r+wp?cQ@Ct zUKJ(eFEFA^LWqC&-&;4uhrc_D*k2XUdccy5HW>(N9OWuFWk$uiH5SqNFKglcVS{!h zUz;CH$d5>%-Qy#5A_wV&WMr5qtk+%=*|sqKm@Y-D>#
LFn<+VkN#kfq0 zLr#(=AM-B`gFJ(`_-vU`zr*mW!PZ??y9jV~-6}_~3!uL+%8XdR%B}fxZogMLqY!5V zIT`>s#lhZ=6kXbySjA* zl~!kklMQ&#mCZ|`k;(B4}t++I9RRmv=GZg&|vaG$T;{GKSW&o$jER2 zlR|J0KqTrx$(NWl1V9_WMDf4?M9hnzp0|YL8RS?8q9GE-<@faG2f!O*VR>`8?lB{N zhL9DB1RH`SEzk$+5X(CN6(|tCv{6e9G!IDpH+Ur=pFzwlp@Tp?u`b=XH>LG;0hR(| zD1#=}jT;6%sVZkhMZbezU=83=xYw^^L1lGRVQunxf80R%cyvyik&%{xoCAxV$1v&u z3#F=idKTBJlewd_e#7qpbM>|(-~Qg2TD>}bQ~fJin`mZs=10m$#?k_Xch$Iu6 zZ_x1%nX5(BY*R(X8O_;&T$ z#evp$tFvX+wB0-50}sM~E>+GRG27Tj47p}0xu~S6QgxfQnHCMt-l|%UG5FOXSn8cP zXlFLx+m<~S7DF)Cdy^;Kenhj(Wj4!x(Sjdp&mLm3EsPZZY3kmQFO~x(fjP zOMUI(*6{ti>Cv1V`+j~Jo!Y3}c`O2XXm>#Y8EbR;wK?0)O++SQ^T}<}tIN!rmL6ZM zi0i2-EH?>Sk7jtgtCiX|+p=xhWnA+0EZ8?vQt}Ie=~q*jMtIFe{RQWPtYYSi_1x7Y z#saZQapOH7dV}kDqThXiY`9vja^>_P!V($5mM`W94w71n>&_P41*C3eX%7<)()xx9 z?#cs*Pp6mr)m3s&JyBJw=;^$qgkp0aBSV*GZsxc}WlOys_awp5Q94J)5Dl%<#iaE~ z0U4XpxYaAFAWdS_h-m$NOW=0p;Z9c@Nx7}QN5EjQX*RkyeSRS%oyT@9zNGW=mB`Ihk!eu7`hdFiy=m!6MCg=@FOz?keW`j zAa&Gvkr95LWTw_wb;?`CrtEZMg|6L#SIp@d7UNw zj=LmRc!Fm*()%-Zl=;6&3%r}DFCXD$oW7NO>!do4ZcY^v zTgubu5Z3Z325Qh*k}NYj-Kynt+L&98J6q_`E)d1-CMt$a7Q*r~)pdV!VL-LDcfDa& zxl+~6x7TmU)BcN7iC_zt-*^x%0b#N*X6vfnN)!PBq0!I47i|IKp30??RLAR!F+BED zsckX5+2G}zrU?>~y*eAyfn={jsg~oE#Vo2^{G02dH2YR`j}Xx5=bUK!UV|RgCO<;r&8AK zLV@EgrOMq=@Vf7o+XnYcqXjc~!pOpeAfURs8oJEjv}{BAr#BMFs;-g#uy|wW%-7Vm zgX2^YSG#r}vs`R332Wxcgn#5mNyV=En{{Ugh~`c>5>^OT`CRYvUF*ktc3wN|!M#q$ z)40c<1ssA(qMrrx_Xk~(V!fwXelF73qVbqLfW*?`ij4+)i3~G! z9u*mWwK@9m!WbM12FvWy!CWj6bX7Ev?+A>KK)Ae7r&$vI&6WoyS)AZeDib511DoM9 zEO)SeO~4V@6M|B&eANt6U1UyoJx){LH=_fP1ZJuI0I2rD?m5ibq?wDv&3OVdEgtKs z_XAsCC}#?u$zZwNPoG`!)T;^g&p&d5kSJAXJuNx?Ge`_dQWY3xO)V{Dz}zhjoHro^ zCVYSgsQ9Wd55OlT&H#CS8jJxn%k48^)|m*+MAAMF5>g4*4xa~1k1h>l0z6I|iKC8_ zSFe%@9DP{2EPMuogMp7v8&o9na7?IxcPKY6?;_aaVqswgQp$YgQVmkIC z&2`Tbexw=b0}p=>647LNd?(5=Z;! zZ~f)#FGh7HNzB6ZH%pT5(El#`%JSiRc^^~l%!tf{bD+YGwj3+N&o9s6>!K@3cKe0w zYbu|7zuh2~aeF$DBCJqKi13Ok(FKsOk_-+OmL&VB zuAiSDT0J)&@IhNh$Gy_~A||d7>*n-Bm8wy0zWran3*~4Fxh1B*a^fHyH4c#XMM%!>0UVQ30{9St zi4bFTm<50YqlC4MjZ|TqXW!u}kH2pX)?a>!S66K;Eju7}fFYqX6*iV+ATpto0OL)? zh()krq=!8V@^cO^x#5u!IE~k9wiS~P_x7Hp8lCz3ErpHZ8kh){MG}gz!KbIDkbmn` zxemP82QG$0AUH7xn>s^`q*7~jKO9xl=}wP;i$E$u04uiZF5jP9E*?{b%!QV|NFF;hc-Y577o`b)mq;oIa$ykM zA&>m>n}whwPK4+#x1;sO!BU$P2scv(8%xOVl((o@|9)eD%(L@3o4{vA43soHTQlL% z+KlX28NWRKdIv`XIi;dKqY{ua>;ixIOY9gU2}$R@M~-(~JUx+5I(%|K&bi5mHyN)M zt-n(et|AXp2hI)v>JG0Y>pcy)H^mNX z?Z{JxJ^VNsxij=-t|oAob(s#~j?~T!3~{R?MoAb)q>0HUWI50*7Lzj-Sk) z%kG$SzRQ?5Q@?`{S>-@=@p-zY2pGc_xHOWQ4^`+qJj9tl=v~aq&ks_|jo+{s6wkJG zlYj%uwC3JV-_n#K7TxiEN#EZ^SQ(@moOGA4#rRA8pQ{4`-y}N<#iy4S> z!VII?dHL1E$@2<=d0uh8nz(JMy$*KQ?%~Y^>mEC)<)YrlZ^h7BjnP;*m(_4r$s?+twYi1q z6h12?I4x41ZTlJqadb_BlPPVLx@rw{_zdc~+a&z1(m;>z8~>faV?Fo&WZ+~p;y%3xj9ksCEA*e`p* zvv!cmNCW5$gSqA@XE@KBo>rYLbDl;oS^r8?XR%;6A63>Foy+)IYAxU@EcZItW?y_w zfkeRe@m+v(%2#Zdq!fRAy4HH~GRywuP}*Yu%O6bMQGh&8jrHhdJGiKT$xxp1zQ$<9 zfS}1~)iICLL~z}sdOR|+Y;Z6_JGU-4Cy1$qFeyRpY-IAa`F10VD=Sx6Oa^%E*uB_& z17AC}yqrXpPM}qu$lz~`E!PIK6?U^3oWcA>3lY)Bbb2SQrgLLL7CWn%!=?P0I=Z3X z*{LSE-XX7K?Kl&_0>@Jh`-Ozg=LPn-LaL(rRA#v$HIiE+G*EzB@3e5YMziy_I(?A% zpqXo%L}~IJXAEt$Oh}NiRQq&QRBLNH^r%!i=2Q3`2|C>_F)FDL`K{$!Bv@Fg(zYL6 zCGoloR;!g)DoSM*U6018cSlD37 zbiRKqFJ=+l7JM9>h_6%CQed9apOPTZA7QxojE#`&mioSPoh{?h{(Ww4-Zasu1&G1x zs2VvmWFrJtGY$K5?XkKL>SjZWeH?G^>f@|4oG)1--K%SwzU&HCtriuT4`i+7Kupk( zum$$q_BT^kyd8UNRi}|djcOy$6HeDe;O9>R0yx@Gfk!}?b@p&)ab@Ijp=tM{)7|}J z|1|brx2@rM6C`-LCuJb^DTp$JLE}+Xk`yh^LD8EXGG$0klMH6aTAgiCjIMCX&^9q$ z|6qlqH+RWpR$zAH_zeBcHocxmokJQ1Hf=9o0@VR)!;oua*}|A_@bU4Ju2)g9nT=j4 zEh{y$pEt2?8h;VeUm5etSqABjx(p@Lg=&ui6T4H|=8j4?kv@CsJ~diFh=N`%+-_i@ z^lOOam6$79ykYqV20s)i1iSL6n&FMff1%%ynqhavd_CN=^UJg4>n%&8g<`@Nu$8o5 zdQmV9j zLOHq%M{j(>7+yxWKbQ&~y}g$uOU#=#lNn*Um6AYOmYMp4rqE(g0aSLHInSyp+$vae zHx8RKY9z~9KdF?)#>|juqVvbYq`Nn#TQajX-D9oy-3m9J#f}a!YXnYB>x!>(&ZU!K zGxS8$drLR-{u)^H6g}>Vd!eNBp)79a^1=Meu4)gOYR%kFqnTq`wVbl4s~erLq$wow z9{ie*a^~MQZQwXKl+1BvJieo}qm<{aZ@8M}MmV~wQ@GqC+DyPyZ2_NYgU_t$l)99e z4KCfE+LnvtCANJm?2Ho@lpo}-A<#}o)}=|2!#aEDZLKr?`b+L3%}=q!9X_M%w&`VzM#4)R>p9piyC8Ofx=v-{PCFaWDnY0s=MMrN;}#( zY!q6|j*nzjKUvAd#WfZ&x&lR{@CyXRvmM+Z&r&KP+57F=xy&k0+od^{!WZ3&F|xNT z8t7Uz;6ircLXKy(nLOG{U19f>dyaJ{Z%6jdPx}$>j9#1m@%gA~%&#o-p_@0&-tQ;d z1yA=F($A|Rr}YLtq+|c2?cLJz*q4dypHWlI+S1ct%&Ggw>1piS$PCxqr@LORk_U6sO!xUL2gW=Ogadn$`WKVo zN2#Q0>n`g)PZ&}tE}X~?3D>kx-4JbOT6mCCOn89}2>~E;;a%*1A&*?DDq0^dRn=x? ziQ8)wOJ)7KKpH8rgrdaQb<+_hr@`^x6n!-9_X<3+q6COHAEGh81O^LwxL59umj@1hn?Wv-^)hRrDyK-G;-B~!Iw z2_u=Z@@PS7V`rxeMuE07Tes+Ax^PU|vc$q#Qe_Cm6l^dd4ti19cno!24)WH;jTESgCgxBGv-O?`w00CX5+^X#sLlL|+gh@ve2AD8K08!bCGzg?hEQRU$ zY%dVK0iY0t!cIAw&q+D%(fRH9ZZU96mFqWyOhnBKo)5t*r4L5+nz>EOnDMFT~Z^34c3Hp7={Z#;FzF5Q%xS^IuyR2~U@9q8g`BMT;S4EhSY*n8g z%K=4FR!s+LRn}Wqz-LYyJr8q{kXro!0LQ`nM6s1fz_sMc1z~jAid)QzorMdGd_$3x zD;TE&xGD}Q8VaD5iLleDa8%GKvsD04E#!f*tV*`74x}@7pWU4Xjtz`~vXuc_g*U7T zBAtQ7(ZxCDn(*yg5AEDxZ~601^4nJ~z+d1P(DhazhyeViKpAI~k%Ff>LP*Frl0k4+ zv;R6!>K;qO))k^fzlA>UigJq7|G+hXl zL?p)r&5EL>S7wsmt**f4`y#++^TQQ3%nzSD9eu#k83C3FtU6erA1SpF15`)}bR77& zxN*SsS!@ZP>d{wL4o0we2BV6eJ!NC*k_oxH0R6l7#% zu)d|CG3qK`txAX2Far}+1VscWG#}3QP@4iAQE=Z<$VGhS zU_B_p%D}_J3tn-ncHPedvhF94-P4CT#2w&So>y zq5xz86(Eq4a9ck{P%zbO-6&ID5ca45^|SEV&+GV1+UoFv%CH*|)W2QeP#Pwc(rWzh z0|TEC3?qh+iBpmFz)6d2eQ@havR$_UfOv$=`meyO69M{vK4&wXX$dzStE$vcD@J-k zWM_8E3H}@z(E-hnJiJRi5G4gsd9HTEFW*1ZW| zNy&p^`fl&#UC$ZV6VLm=`MVUz{<+XVSZBLx)S^CvNt;UjzpG4O!Q8JhY6bgHn}ct+ zk9WGsOb4pG=u})RcYQ%2-4peeccW!F?dbHQluLHkbl_PvW7+7|V9oq=GqvpbnyNbk zLqq8r%|?Ap)Xg*tD$2T}x;zd$6{<{HA*Q3@-NuDa=jlqz%hRp8QHyxbCz4ce(G3uIv}7rnQDLXyr-IwneM4#5|>&QEC~S z{zcg~{c-Y^5i#t>C@yzJ!Pb3=)82H=@~IJpki9Dk`dZf58*^6xk@n%I`3I2rik_o){o*v7(aE?vL& zO<*Dj@X!#+1+;}~He`%Q%+*ma7U)ES1letDZ}-CJ^pE!B*0<+ubj-~10P~|Y+4pN( z0QldA`O6QO?f{T4W7jeXWR< zK(T^@Lj|^PKAgx92~BpSKdCUdk#H3KV@0uS#=Mm?igckkCdfqxtyA469CTu)ni10Z zTsi7Op$tw)^u27sAlH#$d#mad$`QA@jZ8^)<NmZ$hKQXuU^QFB`LN@*PqaM4$ zt&tD(8eA*zo#~}9cb3&Y1X8gZ9f`?JuU1n(IjPX2lmNYf)GlZ9S~iB~;r!m`NzJ{* zn87ML@1v$Ll}D~`uvY{r#i+$T?N3qkrmBn#8X9ruSH~oDr?k^>+*37f|Jn=HPDr(o zyBm*{D>^PD!2KTxj_ucGe`;D<38b%2kBq#4<1#cj{fwT^M!Xs@8Y&63Y;7r<6Tm7? z!aaHwsSN40m00093fp*VJiI=r;rK2}>vN{FdN|Zd$O8rl<1m*V&bZuwOlO5&pYieY z>w_uVeO_K(n7EI?+?EmyncdcEF#{9tSM8Cv_TT2U&!Ei+%##J)4E~K96rgQV(ifIo zsbfeg4u##FQVOH#h44B$#f`m05C8{-g@s`gbH&1D60jX>hrJ08@}F=Q^xU>p(2>y~ z|4fCku%xtf4^q%}3qN{9`4jI~i5hS|6_*X16Dwq~fY?#F-KP~8D9k_Ry7`GV;|>*$ zPSAZl0a#}gDzU#G+98Rey@%U(V2_0a1u3C_i^|IffcrV}go5HSF#-|+Qxg*t#TIl# z0%0KU-io* zB%E-d955xgW^He;3R}6`#gS6N-rnA56-gKffnNqH2}uZ6W=4Lz_-AtG7FSSPKY<9LzWKhdlbY&`d2pSFe;`JB+-AOWF7Uw5Y9O@ROAtEf~tJS_)mQNMGcx zHYy8p6oV%1sAKx_OjFAEcA)?K7Jv1Z^V)$QC7eC#m5&{N%a!t$qCd;Yu;15^L-4H^ zdsNG1#EFy!ikgu{CLT=ci3Z;O>8k;w^MqyO(?gE3Z8HYl$(Ht3p_C1e8;2ckCx)^T z%hSI*%c_*5Y3Ro3@~)!7cj4D(1I>Kv%#+dM;wu+;d(+gUL@IqW%O6J;7;dUEYLXDOKm{HI_dNoI`FtYYH`KM)_Sv6uW9VE5`WnHOS^pBU=NIrTzx+QE~~6>-|j)` zxI9QKjaR{tdQKXKRcd3C)s;oJv$y<$*~G4inhteknWc~DyG~FSn1PrKLs0_;?u8N# zR<`6w_&3xP8Lz6WPY+qC;SzS{KZ2qhiN!FsHDhkARiG-+fx#fsf;>p}wY=O0?6C6D z)i7lZD4`3X1QQtcS%Xoca4i-&02`0AJnhx^KrmWg{Ns;nFqN5?1T&K=r9!&B(&uLJ zi(B|bC9Od+61y39fDbweWFHs3$vb9M3Q+0-nF<~fh3UQ`@9O8Ha6g0ib$*~sMcMvd zy{SOV`ZfqmpfU?jE!^mDX;B2gxgGqBf-vh-7yjMAZ}dsK+wT0_-1yko`RV26Sa&fQ zZH2|fmurw1^wHBhzr3qK10s8sie|gjt9Hyb>M5podRJ*hRvw-SkUV<2Pcf|_(rsj= zHYlOBjDKo;auQ1<6F60ky6+AYWRP)n_j}LG2^dY$$Nws}@qQwHK3-s}*Eyp0^G&+u zg-#|ZjaaU+$v3k6{efK^)LOFzR>RZ1u40`_wLO9^T`m(&PCY$2;++Lm1b(xi>@i{& z9nqlAQY}{xdZyL;Jz5}K&Mio9sRyOQ(S5Y{D&jazAS`u;C?iD8se1R3cXW=G``#kc z&o_$Pb8{w}5+Dql$aePZhd!ge@B)+O8IrR^aGPmou=9beZ2f3I+%No~v{Rl^&~+Rg z*e}1i!k^P$tR>sS)gA17(5$wbVP>!h*PJM3GtXBLcSgb^YoF9Qo9&b_>5_9Ki1Acf za`A-0l7ejAn5I#kW>BAvjp=wfz>YQs14_MN~Sh0aHoEojg?i> zW`iMhH0Hh#j-gyU1*^1O&cW_c-+#Ai+8{b|tN$7zp zvKy8YY#=W9tBTNe^uVl6<^8`y%0C8lYtbZnk@dyk&8Pms-7 z!H|V6zM#IES8$i~7KZEHUl_!NZ~QP=+`uyy2Y$GJsi|TEFK^&uq{4Mwv9}xDiVBa)oM#BC0q3X*goG_3i%XgHr)TW!XSHIR7rXNW6YK9^ zGQAhSkYCauu6xgGd0GRhfPJeZE)LF??sT}zcnpxd^}moh4j-=BlcrdIrA|K4YDYD` zVcKqVXMX^#uvS8u7~yjv8}?r2Qyu&Aj5mtBo|oKGX^BFu@6Oj2LKuEGcbBnFwbxD3 zFntEh4RUl%DbQP7i7ArJmv&wp=yIS+p&B|6k!Fz+l)9vB!@O}g)eZ{IQ^Fl=HV zXiD%AE!E^g?ckt%m$_<3&#+e};HG9ojp2)%tp*lRM*RN($kK1t^>{+r;9y73tUxJ7^w7^X zL;NaW_Qw{emhT|gk^$W1ph=d+}G?(3TtxXE6 zS%CSwLKlus-u=Suv(gMSHa0dS(#EhB-mM6Ua9!QqEkG29+6E~9Qz21BIuy*?7yxbF zkOt`PmI}p%{H-p3<6Bkctat0yGgPvG{7Y=b_ENW3O9yD4DbtYOazEwCyKqs<-DH77 z3??k0^8=b}*`fdt(x^d;(1v<>8hsBX)F1Ia^o83$36wEn$^ld&l4rzx70bEOd%&x? zxE&S&N>ugQKt>JdSg2bC-a{!T9rAk!?SOM>u5KCTGJ}@cNS>c_PxPBN8ID;(cS5xa zUEO^5**n{J30X;Iz0lsnb+uR_%$dCTe&Q&fsm}X91TU)V)>iDOkl1ub;T2!X@<|n?lBdS2R$YPbbJ?^JH%I%LuO=#6Y)-Ob6H9<6!w^wE|y2*h3E8mMSWUH#*9;|Sgl zC6sX1HZfjgAyMJ?_G6MS%=(&w3m1zF;FJqWw+;D7+zLuun@(MJ%W2@4S^K`~Hib7J zde-6U!-UF&=0nqDw8~`q-~Z#{7X_e62LVmkxg6d1VZe;$fR20`@IAN5Xp@Bsose_4 znWd*%(gztFU~z!TXyW_#w;%>Ik;&O8*JohsP?_-z_M2C!558hXFjOisS3_M-id2v~ zfVnUQEQd`6V+xL=KD@iLo73pa0$|3VP_&?Bp9Fk$rY*$}<^d-mmm2dkSv_dPF_c+rE}e z0Z=IiieF7AiPeJ`#qtMQg{J>@V#agnm){8OOx1j-(sE=|Lr(6sFM%ofkx6r1wb^<6 zcAE&*5njxudyn`aWm(#d+mxC1MKY%{I*B@S@q#&B7q=$aFpefprj{dXez~jF2k>23 zhx74=tFyftYA>>%z1lPZz1vryVVS4bJtL)~7Z%2eZ`^|g2Z7E>!XNC2i~23`eoRz5 zDYGP~gr1%OXyM#oIw9znX1Gff8RZ%G<{C~PkG|)){?4RlxcrR0%5(pd%3U*R*}4jm zk4VI$prg0Q6_~0Z@r&{CwLcK-#lHD*`sk0<$mTfl6iidBY3Dbp4oCd;KI@&1%a6)1 zp7M918L7`0y^FDo21&cL^1Te#pp-iSKXJ}zzeii6aA6cWw+snPeXgyIo#b7+MqF+j zT}CL6tG}`N&|6LH^g$~6P-Zop<{+(6@5w*;N_5Rf-<%vH|8`A1T(DxFt6WnI%Wxxf zBdbpu5K;}DXYCwyd44)(qZ-J>wd(7Ovml1(%GHp4C{7n){Dyx;Ja@9S?MR$n$8J=72(^(|JpZ(%-*yG5>cvC7C;w=cU;b ztHXPDbdGVeFGSOKR=9~U?{g0=vK*+s+?1#v*%HP~z0Ca}Mm^)s%A)55lM8BDnzus> zU30cd&R@pKCaB|-7#ilD1!$L97BOcU#hz09T(*+|tqX_rRr^iTm{qESOd-tFY{aGa zgbw7@hhj`=Dfxbv#a)d=g-zzc9=cSnG%LL;&ez-8Ca>3~e5kW3whg&qyn5eyad?|q zT5o8wCh@!e&o;kN2hO_U-K8M`??{{`yEG6}eDY&Mte!5DOXGu;t}37g3HD3(ixD`S z=OnC2+Y|m1;H$m#5M&u!(7yyz8)dzh!Rg`Q#uCrbDN}|rUvSp*A!vTe?Z$lJUQ0td zZ~zjZ-@HS(w)*Mi3F78h5Q{V<;S}GV83IAR*XF37YKJ)S$9bd?1~n$%;b`zf*Fh%$ zS*(}kIW+TA!5Nv7k#X@Tt-u|SlJYk7zkBG1;4>g!Z=z0CJ`o~zVn&1D_<@cB4kMAS zHMn={;W@lX1P$Ny4FkY)L(T6co@7f7-R&k^Vl#LR`S|rUH8)YOCCCE>_etaBjScV0 zw;dmPdNkmK%szMiydVsrXJKWHnasKO?gqf)?lH4J0fJPpPO~$Q4^tW8;Cgo+a+L}d zFm3&#fUq!V1SB|{T++~olK5BKCv4#H86;s0+aeF)4ZgQ5C1HZ4$FO}c6_Di={YOXp?@33^N z)m)b@BihLEkG+FCpCJ zX$g8HdD6~dY2~oVfW?xR>GfpKgN0TNmswcSj>QfEU|PLXc&#CdGpeAVK&!BxydfCm z|MQ4EQ(N{r+ZV3p#m>485V$J}&J&AfX^Gt;2ycQGs;)Ph(PF{xAHrsQdN^r zO8>0hE?Z`R?Asiynpo|UT)@l#q+*4oXATKdSeO!Nwgu-e7G%N*K;>@{JrDF_yqG(B zq0spFc(&58?{G6CiT}BC3%dcE7YrKT@X`Wx$*z%`4INPfK&{~9g5e?PV}j>mNvaQ> zqT=Muhw!E|VWdvx@g}wp0i~r^wzjr10mxOf*J$)uZ4v@8bpn`B(g145!vB}sPrNDy zI|3|j3)q;(vycOK3kFrSLb4XJnFyM--;zvG0-e4F&}4{pB`gjPTjIlilL6T{0*c}K zkDctwRFQ&_+pqln!(wA&u^NhlX+IDHoT+jF8pI4RJHSJOp#q`?L=84S_-qsabra-* z0GVWH{bC6#57>g*Bbg>PZaEeH#S0kDrCB<{v!8t8>hjWB>!)Rvb{*@HRxeEM36)>0 z2i}7JKO$@7LgM$&eBd~APrG3u$Q809@`ot5@_D*7@C(+7c&obTgx61UB_}&p$cEujj6J*qbbi*Pl+Gal}q9#ETH2W`VjMFJfG(Gnn7gDovFyXdCPG zhPQuhG29))h}*a z0TbeU)UaB6N$L&jXFtHm1{6W&n12g+KKr`41sk-MR&jlK*1*H%y3_`33p7pmR`Gv3 zR}*Gz6lBjWA}l(wq^prTTQB4E1nr3&StGBz+C25eQgv8%peG+X!^H~WtbX)`pJ zSZGIL-HKRMsoHU+2FKgJ?Wf_uzUg_oVy^4i3G&5XXtp@AZ=T6496~_p+T_NxdZ5hm7z-R z{?iN!P6;I-Rm~2lBU~0v9aDe|V;~Jr9jCyEO#(6I%n-f;{w!?r=3_|CtZQfho>rHy zUkH>CXtO~Dt`0mlh>wUG*P*ps1D}Hhylr%5p#2P8Mi%@%a9^Z@4q|8gSpA8mKZ(v* zjHq!t5#@%1uo(8MZcl64BMrBO}9j?1$ON!kW#}S(eh!NSj@~ zBwO(J@J7uYsjE(Br&&>%xZ!2Sy}J=HqWUC4Hl$~*oyn&QFC*2pk&7{Z-q7%ePp;H( z=hKl1;^_Ot^4~pY56wr+(`_nvN3hRkRiEPQ9*W;KT)dB)DZ*yTF7~Lsli6<(Wui2i zJW45UD(*0d@A!DLrfCvKG`W%lq9sEutKJX)%0}rsw(&>E@4Ls2Mz37r=kh$b<~qal zz}L@r)|Pf^Wpq2Pfh|e9g$@x#QNdO$o_4K$EQZ2sO)$*ypa1*caB)1i4Kkicpx>YeD3blF$tv&kzx6rg zUHtbyyIauw^R*}_vRm$70v)R8?fDI3IxK_`$lJCb0yQ+^GjLN)Fyqn-rrdH-=gkSk0yTYZ**F=C> zAz9U4y4j8#J+mLn{P|c}*lfs^ZkQSVncnKy6`DqyQL#Fm=5+<^l&%{2vjx_CbcOx9 z`n8u|P+9}lF}=h>q4nJ7p_j5n3fGpk%qO)KCsw!F^_xZQ9UC*hZuVb2ZZC8ASkB#% zEiq%93agDB{%+OtdYX%AAFa10rsr}uD-K;f;iSZ&VA*Zsr!ix#{5H+0|G}w7l03{@ zX^&PhIe99k{n%=T458jFr=MfP%8%m|u2a97yBqjE!^M##%82^efFa#AxrkS%hJ3Pf zG)jaZpkZjcd6(FaQ>``-I}E7U-#s}Y;K}j$@#2Oxv%ZQGL!Z~MBb3#q>&}mg9`!5e zRy*Bv5}pI#mSq=1cEk5)LDv(NR^M_QLKegqsQ4$pj-Ky(=b8SLia{N4nZZ)8o1rHi z467qJ3b+a(Dn&p*4k<9+%Reaxaadej9HeC!bYWFwZ~9W@JGrV;Lu)@MvToWxIZxT= zeWwSf7X|HI^w-^4M?v?JX#;FGg8#t7YnG+N>?S z`$Oa%T82kG%`=h}k9wAD2zi!0(!=&->ZLx_%jr~)I**%``*HjE7s-o3{(jh#-0Aht zcRCwO5{7j6WSqkmv>bN%`zsf{iRlbP(D#ko*7tTy6}t7(=I9q)-xYGrp4m=6rs}c0 zr9t#0-#{iD^+DQKL5i&j@!l3Kfnu~mcCTp#%}fJy>)K;3+hF5_bsI4>UyY|`$A|`B zf+z<{Sq2;QuXrojIL)Wzwc1j1YuzJ|W;xD^>-in~Wp>-fyVbXdPVi3`%!4k?!#xv? zpGgK^P}YvsKwe#+(Kms<(J;I5C8eV-kCHytoJTC!POO0P+Po>^d{(E(JDmsByR&uD zTfWEzGgI?SrySTrIO!=O>G$+0fg~hh4iSAZ>!hgqc%c%r_}H^0ow)vMx@8WxT~{ZR zKpN#ZYn*WzN7Y%})KBP*O3ZM=$8`6laYA?ZP@P*p?On3WZvA>yEL%9l@?5Gr8YZx~#!0g8IDHzp9g@lDY#7=vY%I~R2Y$>1k zBG@F;3%JlHk$M+{ebfrj}z+wfy+H4ME_Uc8DOi_*rWn`Q4d~K#l5l!LB ztt18C1fiyWd$LVYOpk2TIUEEv;QZ68W-loS`irN~a1^%k_(htuCur4PP}x;9YCvyQ z1>P#T{7zV{VwcH7)*mf`)eS@9Tk0+?&DDhMo#!h{4m~B0(i{3OY>g_}wi@8O9_4)r zBFd>*sD{K=#D3mj;h-Zn-v@KRr#>S?%}gBI#F~!30#zkOE|yQ%AbE6V&0gxLyUhy_ zP?B|BL}rQ)5E$M)=6WJP%XDKQ_Hw_9Z>qaS(8@n zgOVYyo!;0bCmYbVPH}zyTx(IU5q7fmlSJ3fh|dRgEaxl?xYh7em|s(#qEW3hwCh-d zWwqA)-r4c{H)&7aQH}tzL%7+{)WZuu>eD!|9ZALF36~_fcb_@z6M|@?8cDOcuXaSM zt-TZ9>FPs){H-4v(u>!d%jl`7!h}}st{*sa{P^1v;<5I3DYa``=_d%&&z9A*`DRlX zjOjNnqa}8w&)|!#{GR*%bJ@BS`4am2`T%KZRva10bi6PccxtEeTIkx)!Ns(h6`Vy0 zH@ix4A$h1k1G0MFslU|xLMK*%U3@RpbulR!bST?*W)+B*uOJG8uyAnEP}#9~G#X2d zVl&ou8W>bND_-2Nc|N@p{RlHn*JIgD7CU&dbwGb`8y~_BI`>T6AK*1)-ytd?F}-+VtkMlV9Ssp6#T zASXOe$%7g~P1Wr-4lRbJ3b{G+B4O8q z3CfwJ2kwZzrw&#I$^_s^A>SF^f>dE+n%+g}pPD1ebx~?Wz{SJ}L z42Q&H*outRAaI9j!%tB%NN-SZB!^6CItPBwRm}?e*qb6Ad{rbjUznaFX!z}q0%W_% z{?zK5(_hm0La9ato{dePTuFP`K9AXKmB@wR(MGps|6b>dexAqhtK_>X@Im2V%)oHP zUKHiL(LiJ8ebJ6v;?R7eMLJcjNAQf#ZRz?%iRz*4C0*%uI0GFnJmL9eh-!YRZe)n{ zp{bwZf%!`hOEDwTyr+S}Z>MTBihYj;Q4@fy%7Q37dpkQNWFJY9JQ{F@UlI~VFcaYA zfY}XKkS7Glu9c;wyD5?Zg#dF3lxSDF*r|*~gZ$nM57R+@z}ycZM=m^2{|Iz+T^SDn zya{q7lAv0PwZZ|d4_*;~KEu>=buvL$_!U%!0Nw({&Hz@IcT#O#3ve~Re-!Vvo--o4Lsw@#_q0ab$BoGhI(dl3DKqZ zu+m|ykYf^7QBi^Tm-AAMHs&=oH9uwWU^D>T7#vohs%y#u#hE!!#Aenapbi7y3eZ0t zFq%CZL|tD&wg8bH7^4Rd9}>vhi&>Co2Ja>kY>z>Nz{kG43bR&c?dfxb6fk744e`&27oY38NOT;sD_wOD{mkY;VYySb1PJc zyOOM-=Xx9bo8eG8fpFSTYXLH8ka!z>%_u14>Hfnr5gK}JAdlz}mF)dMCLKIU?TE5L zAo5OBv>PCs(9|Y6rs@V#`rvwwNTJ&nt)utqQCI2|?UwDjWks7)>!7+bYaq)l^rnnu zwP>cKa!zmNJPKvL;4bBQH614v6n@roff=(K)Fi?-YgjP)n2|M2gZiM~`0kh5&lbZA zo}sCaCnuR!1jr_R#qZ}&VSiKPkdpHA;~pEGwtgaG$<#C92Xw9Gp7~24g8p9kpjQTw zsH9a0Ckm8jSHpxegG%iB@1u1sREqRsXQkPdQ}ZaP*$j&nzzNSHt$o?L=xzj*f%cPd zzP(Nzzf8;cyY=9M{7Zdj^Y_9(SK%54ojoseEByL8t32HS$<2c48Ibi755Fu}$h-ky zxIzvLAmvViB^TtZXnCNW36jLDA3sd3zU5s1Fx3K?UVsqE23!cD z+w*wOzQ$t0p!2YF=#a;%d`IE$=Os-c*$A{-%5V%I`Vxe=@*snUkwEF~1I;NY9x04f z-hsIGlh8n>+G;O6m=LfK2M9qjB29#ZwQV~IU^)OxZ3mVbd~zyknaa0;_~?N&&qB{_ zryM=})h?m?fb@2iJ6gb=f={4>X|0FDLcp8sfp`wkFi(4t;N8!lFd9IRJK>QGS8T6w zsAsnVMF?bi_KoLcPuqOpn|}KE04=VM9Q}3M7vFp5&8oc18sS1vxDY zWCH&Kr;2RojhiCFjJS5)Lr;Wd<4nOim3d9Y`($YDxxK3gGBK+uH&F zW_U;P*a;PS2`?S+J*f^=x^}^8fMG3lqlr|tG+bJ#Xm`S7Bt&bWj5QDj&Xf#$bpT6+ zV$skkHMkg*bHz?{%RpPp`yz`XPgLc=%b#A6mjN%kOvc1Dz^pmbm*QX4P?xXHqn+hH z#a*9uPsx>SB)s(x&no?W9&FFTCI6|Mu2VZjwjt*}o;NfxNq_L!KhrLZ zf|^_TTc1&Hww9xX8O-w{lKYvOe1D1As8I|}b!sUoGJbja$*eum*84^FW2mm66j1|m z0jr*M4$yn6!F+izdMbmU-{TbxQa!09UGuVticUk7y0U#n@8Ef1PDVO94(SbsW9>EP zg|E5Id@?(%`i5Y~ZIas%#Vspiox;f2i`!_s@Z1T##Ea}e3(CjY^^^NJl9Q9+w-AOB zjX-jXcnT+{T_^g2|HNtIH}(JEw0Zikoi;bg|GCrV{wMcEd^%DmA>lf#O{VfiNUH~x zdz5*1b|z#Ysa&}xvrsfXUgvG&0bEB4$en(`0mFYd1`?9dDpCP!58@D|>xSF6z;ubI zA=t5S7=0|<0p(Z`=&k&^qZmQ0qzch_4lCo*aEc7z`@!-9$IkhtXnrYVsYASF!QZwx z4Yv-2Q-(hE@lne+w5KtL$tJ+czU;>7<8W0(8Hjkg%q0QrKe{u)5l9>hk_eaBt)}vY zpx<1$%f#B3qao_)6t|A))dLXN?zS}%Rb)GQ14RwOyAcGNj-@7YvLLrpQ2^qY;p#;U zw4U2E3phq&@XbKZ0KM$1DmHhg9cG~wf+0+eU}KDeqe_XH``mhLm~-+ zlNB`99i1KZuzw->Kn<+44B{U5kZmYaxo-FYs$ZC+3H5vrzZ^klbLL)2B_$=d`J$de zm^;&yD4`E0qM$nou2EA6{EWHkXpX(s04G-`sL_P4TrmYbIs8(h&4xhNh3*fqqH4mk zgNJj=mbG{e3Z2l@z0VsZUPPom-SxQUPN)YKEgwr(B!XDBrLL>z+jz+_1qx*mP5_3 z|G<%jbQtYxF^cmx-|l8u;m}t25H6JiXv#SdeOpI?<;^kt4TZ8h9PJl?}ec z3&+!EG{s*i@vRh;i@lsme)_dXU#=tL9TQe9HWK;Kch*;#LR0$)7SRd&T_)GVnRP47 z#S1aay7;&O*7N7jA*MJ^IB{Y7X7fa&p!X0@Xe#mOtWm|3n&Rn9gi0;GKK4S*b6>Q(vLmS=0?kItq4GMi3MlWNCV25zo_9y!e7CO$> zR8Sv1bSMPr%Op^i0L0v>5XRBCKIA#Qe*Jo1fr%p4$eX-n@)@Sx0Eda0hrP`X7Zy~q znc(-aC+5RepvvLFvns@yg25&fRg*hQpgx6SA%60zB9Y`03_J0sIq04@+;f+*76GS2 zY$+ga0-zyMHK3M8c4(+}NOy1~I4CfbbK@WjID#{<7?fI2K#-RAEf?|Jq5 zbte=+9Uk#uxr=$OmO6evf*Cw%btrNYF2U%blwFxa38}v*Es%EMwFjMty!ET0vN6Vw zmX?*7)rYf%rouIwzC{Eqt;ZzM)3|%P9;yv$7+|0%zQv5b3*?1|YkL}`ms()Zi|H>i zddFKgeC&~(LWOg3_!vC{gVnp3eE)}1fzv&SUMjK2a@6p~f=DVf*SlN-(`ZtfX7@U- zuL)DL>)cHdCtU+bftTw9b@YJyom;n3jlG0r=k-mq^dp-(Lgl^9Km8a*>h;A{ni<@F z|Lj7V$ez~p@~e?Hxgsx}*9f_Z?K&9Cq2n`mb2IzG58Wl4qYe*t(MYt6PEM=sAT#ht zyFQv!_p_Jp8p6Gtp~=GN1PT*WT93>3ifc=rS*%FN=`ZpuQRmjok+%@KP*QVlzaX)I zYC@wb+bsHxx0lpaVUa;+5pM3f4JqC>-e~Q-fDG_zi<2GVB6GE}8Qe?E+Eye4ZCfdN z`=?rk^u4nZr%0Cb$E}oyJX{4ywPsjdTx70Z6*j;^$V_|03GyVTquE|Xz37BcXnGj764ZitQv2JEJFK7tg8AePG^R~S^l_iw*U8h-8`?5{f+vomG|W0%-w;JvZWkA|Sk< zP`N?RRBtk|5c%%m`iCMTRcIiHdLAX5>bWnO0 z4|bq5R10>bE5;U={Y`6xg>wMfI@j?YX<4mpWsKa4AKq+U_Q8Xi*_@Kj6e%iDXUKz-&wZo{9ZXF&LOGfRfRRA0AFLx$&fGVPu-u7EO)y0& z6VnWx_SQV|->%9s?8KOVkX2Fr#5He2Haw``7?+)MJ+{!kL0Nu#S=N<$wf#M#4`_p3 zySbY%1X$aBg%(^qHat*#H>8&axmESv46BY~mL@t8<)AqN__P1f$|jrRLZ=!{vu^7% z9*K@}tE?i2!3RfOor9e$2a|_%w`aik)XmM`@V)%Vh?$`Ps{~J(yO0v;zPmyZ141J6-)^_jn{%imiAI%qD&N*QOwlLBz8)F<_~G& zrjKHwt7nq6)L{44BN_qoPp9K6rraz|O!8djU9QH7JyM7K=qI4#_O0ZXQhl?q!s*Lzmk_`5dGHmgtCEoStT*qN0Kn3U*G;MoTY{#57^jj^wN) zxk6W9Ry9Q0dz6ikCfHf;jj}+HP#wUVwaHi;>w9bSTy|fZ8aY_*R6qvCQcAWJgn>`h3nJnm^VC?K`92A#dYHHK5^{AYV3vrv$1_#ndzdMNY%6Xz1G`J2^0*t5&AP;z=f4VArmCU6n{q z2|U0Q##}bOQ{tSb&d^xFX46&moSAL>i@a{RZ7<(`ZD~-kt;tukt=Kws#O?DoBY#Mv z(!FqYIZ)xbUhD3DudJ#xbW(A9`|b8QM#V+O!?#iu5LfNs=IbAZ{2XCJ~yj- zp8z1uu921cQr{bsKha>?UC1Oie`!jh`uy0~f_Knwbg}eR_pc-!%+jvTt=G7SgJh@u zOh<~HgfCAl#8}0c=|zcLLs@w=RmD!>sE*ePl=8`Bn`5c~y_HwCY2ut+oBCLcT>m`j zZ^KFd?P+1P?{+xf>4n{#7rVy0Ut3y;WUMyCqr6Mgq1+`F5oz$Dj&`FS>R+~ z_<)Zrcpe1MV1RphKhw6eHS4JGiwp@DZzKExLN1^`kb@}QcF3uK(nY5!UPJ>y4sZz= zyd&-4vWZ@zWS5rCnZ+^SS*dmaQ3}*Ny zpmrZ}nBi3JxO*LN6oj#W%GGgr^syJnrV)4okQf3N;n2?lRI=g2hwFfZK+WJb?kPQ; zITau@V|JXI9zctm{&_$2bxa`h7>q@}O@_uM>E z^l+UD@-m&>1N;2ji<*i2A}1k$kN6o|H`J^L1fTfIY#*)rL3cr`wp+D9ajigdh4qh0 zJd_Inwo1rr z3$)RksBQ;Dol`$9V5EGmXD1p-Nv{-ZiK_^!UAA=x8(j;@tOf zK)6*K>=wBv5iq_5@?UtQf2J(~?q%qrpE^Ed4$qncJvJMpIR_OT)mFn;+S7PT1<^yO zLECw$uqY0dz6XLvGyv{x?+$qnZ}~k9bw6#otrNS=y($2*;$Y_Iez)()kS{+Nef!)I z+~k9Sl~%{S!+t_gSzV!Dgs4^4MN?SSE(bv&0=r-09R20AT?T)mvwMmea z7SB0y@vBnmV85^|>(9Ot-4^{Z-u(up%$fI=A3$F8U6*^MH*)*K!McxQr~VQ(FSO8FeP@<+ru_*g8_qbu1Vht1IPBXWH=lN(p3Z6yLa` zhfXC@nSMMG@_XIJpxM#bZ2^cKY|A5`cUWi?-V(A(b(V0OdZMovmdf76o+VRMpNYxQ zDVGUVEs~6AWbeX2E}1Y?=ZNeBN?B4KW^O|_Dqa|H?#!QS?GRE7e44CyYYK1xvl-;3 zQ0e3Xt_dtm8w7PhjJ9;a7DQo%F{9?@)O7L~;57WPP8$olTsoz9LpvcaskSc{LUKTN znhg=y5Nq_oQs2|V1La0Q)u+wmvGfFH%B?}7Nc#3D8pPEJv$>%pE;HU>D~ zSde_p zt?QK@`i$pNPGJ#-d`750Gn7B%_qloDB(tNFm>;%3w91lfdlj1MAJWr~4~%lZPHo?m zp0R2A{PS(`?%BS#`*zpvDt!_uV}RZ(Q!B?jG!@S2Xs&8Is};dKuN1danJtmeeSN_q zIi7c|Y{g4G_e=43QxrW0Z@cgmJPu8hOh*;G@}liMp9ufu*5%Mi4XzL8IuDulc=tv> zlJptMuednZMO{TC9VXedZi5m@wP?1h{Y{IcSobO`JWzs$GnBb#@!P^74YOECjC~D{ zJj!_G)(5|*VDHrRygGU!OX5Tk%b#~FHM(PI;V@f+Y;>g9hK61cF46*n8|WTGGd-XH zLJmh@!UXQWf2>ONR1v5ksHN_Ot^qNqow@*PWyX(5*?DWXO^Rt6niiDvg^Dlp1elKq zqZb@tc6Uc17#++3fW#y#D93@t0$_E>gS)f3Zx@P7nNV;NonxCHeT;GrAg~7D4?-R* zB=@0lD5>7=Cqq&JW^4fK5n$z>KdN?nmPTvfz{^o31Tl!H|9opOFCpcQ;vhPv08o?L zt%CFp6pL3tL~?RK{n=qW31qxWVu2QjYO2xRT_T~C2h7?GH^wt3(- zH;zdF?=CokHNfEEl0Cov(}3BklAOHjbk<|C@l1z%_$i>lU0q$xfTaX3^DdTAH7-70 zEnR^DDcwfm_1jPFT8rWAY5bw;$$l&X=*$ybbNQxSMnK`od6peHXjg&bS>Y z%UwpU_T=hme9OsG?f5?SozVO35ZYnD!K-eW)EgQ1UiJY;6iO|t9$hrs$Q8o^vcl@g zt8^@o${HM{f06)?Ehs@wzIt;nnni=yH)!W^XK(ucfB_Y%5UhsYKA4MvA5iJ5_O3?# zJz!P0kkuDJd4R*Ex3;%ApeO*awie z&;!Hnn<6E=M1@1#rE;R+h3hqNC=+;#5lEyCg24U`u3J?P*dJ=6oNLT60Gw=a+1hH5J zk2}C}kL*Vl08{{00!UxL_`(9xaUw<&wooEKidI@=uyiB0tLCL2N&qKWK<1qrqF8uMw`%a4n?;)n!wQs5dN@gO&2bEt^7eZ&pM~wk^cuJKWZ%lMj#yTzauwr4FN^;w00j zqW10?mSueQ8@3u?&To#pnx%cVWcioh?o+1d!2`&+EIb#d~W?8 znR(iKB3c%+=$(YuHYV=N4+;yZY;?7A>z1Z2r-+A{SW`u?X-G0AFuwZLzja#0L~h=8 z{M&Bkh}e9daXhWtGC={!{v5qCft$+7g|%c33N4ltrSr8_974{kqk9r1X`17EK)$8~ zST0J^hAcsp%pS-njDZN8t;m(G3|^*t6^)z*79*EQmmU^R53}`zGvapXPA&e;{KW&F zD!h>SvmrY26Ln6_Oc3v?mr;75#gUwJAA*tK`^}%}%kociuEBx=l^YbO-2a@$*JnDV zQ67WA_u@)uw}b?=Y3iUV?15P1`EOM!vNSv@>FYdL`v{p=LH-)8PkbOi{BA8xf(A_U zgDKc*Tslipdxb1R!T~7xhm8*62HB+hD%=yr=|rwWXcG2*^3%GW@qHg&y?&tz(Mke4 zKFN{n!>qR4CXG)My}NuJ>-2wHgN(v|4C5>QdUw6}6_A_*{f6|QZh*H`Be?%DSN_)f zSK&KPoc}Ldy{2Wq!G)=g;y6(qck(2P>0pDfA{1Cl+*rhcVOP9~04A%m-rn92;Hqf0 zo_mgEBbXOPSLNte>p>aW26Q)gjk%G;0nOQqwW$L4+p3ML1p9wFeUHDA^cq@V#1IMq zgF@O_Z9$A3O2UL=;0Gj$MBzPWRFG%}Zv9Rule6Ghv}lOr0u78RWJ90|I15O>4cD~S zbTDG3va%9^3CNEIq9*hSz63IWf3>mVfC812loSF`0VjAj*^-C@jR6+oz7Vg7+}0|J z7tIbqXdtxpMwd=QY@*F&8?cOGc6V_wlKlt7#&$!h4N_Ua{M%cA40j=22VjP^l-YNb z!3gI7>Hq{0z}xA9!)#(?BnkT43}_B#pw}#KkDlLXPYk6>+IQqI8j{GunUrRseg-YoLCq) zC+>!i$Y@~}w=_rXLMQ_QT2tpGGK@n^#$(gp)KBqniTGSU%WI%z_n^Q_qEg0NpO*={ zn5Dbg*_!SP|2fTVNNvJ$G2_K%KWG9Tt0x@kY#*aD3g->bDArZOYA8kN#XF8H?lYdx zv*Y*1FKJ1+Qu9->9;0231PnFp`C)Q<0)x8|c-Cl(vasakmorCE-yapZ4|w7E5AIw~+25T^VQy+LzM zT%OX5k=Ul0A9laiUosaA;0Z*%cNX4OL76(jA|^ZJ{z%(D^_%xh-yCBQHM~q3GBZdn zY&)3DAb#c{dGWkjrk1An4mlmk9uJ%uhqDO@EZ2|tU;u*a*iHXMqrJVrMo8uUXPgJ{ za+A24gF!#WmsQXlqdH|41XvWg<`zLLGAJ8WK%>dZ_kH-RmWsHc+|T)fw#$29Ti4>{ zzqB3WNBVM9yr1(~u73FA4;2LSL$+)iY`>13q@m+bMWOJdK}c_h3O*#3ZuqoEwCLTo=f=-{AT$>IujH9_$Oc9}6fR z>>VAucF7yyJ^JgYpM)Fep+K+`K_xTs<3|Rf6+wUmylB3Y=0Bg9VubD1{m&mEBqIl| zb?qHTfX}&8Lf)-0?yo)k&xbv3G=SjtfBsBCvE*>}UyL;RcO!}36Oa5~vXT9J8`}RM zTN#@D#QNVn`|1DtFB&FsJ(jy4-qy!LVP6;00S36ruEBIPQL4wBd<~i8iXCDtkn0Wq zM8chqU7YQwNlr@Sf<+X?lCzBOa_gz?C__19uM48O&lfm*D1Yz1o{1x4kRaE0mco!8 zrcM53G5$vz+`qEPK^oFu{ys&XxX%ZeU$%5I+3zo#j)LOe$tVBs{v3`k`pd4Opy)P1 z^TGE1x6cPdYs5n1arz;#e=|xBeUU7^c9RtF?MmOeuKL&S=)e9Y|NCFU|M)NctsnIt z{_LQ4Y~Gb{-(b-1Al2IKIGzuA#>p(nEIEhQKL0|#=`Ge=BU9p~b1O(dChCv9U*ii)F))T1at=8#}JhCFd~I z?*mPAAb9PSVS+i>;U7S?zXxunZ)Kh|d$jjnaT)my8vPHfu}7a%x^`qppD!o;kO_EX9#f;?g`JH-5OXeS1FUZO*}dy zJLJ*Q?YdL)V(E@sv3CHB?GeR`Y34m>-WR1?@qnShTe}XNZtn^TQY@dhJIt@LPjEyP z-2W75kjNz>h-X)6skj=k8o^o}cJR>4Lpm=i>oJci?)_F6`o{eoX5#4SjBuO)fl@8q z*JM9snahKyrpQ^Pl5d`D!NPt~c3aJk1*DTLYH!xlV()&zpWb>@J~lHVw?lGiex`3Y zIGwZOluQ?cKOQ;vLw6t7e zSN*N}3t@Tye*&srk6ru41+hp@q7T91vORfjn3G?4usYIom2MCqZDF?nyV%Utb*DeNSZain$eEUc_}; zY4QZTe!|b!_j&IlRl1H7PXk<(ZJiAUGblP%fX&9B{k2Wv-A+yaJ_F+P>Lt&$IgW=J z492e^z{Xf%H@YaR#|}stn0=8kukH85=V0*S8Lz5{Q{|ETfqls@3|V|XHSSoHe&v@t z#G$F(NV;g@cYmdJQT7a}?lgsm$1CORTXt{XF8t=QbP4K(lLWSpjl%q2K@t{mUVg#) zM&;*?t>%%9rQwtJ)f2j(MMN~H^Ab~9E}cFC5j$F92A^pYnHG;QD!eydeYBbPECRy( zhvr0{Jv>`$yJ4H|ar>eD=g-wV_A}#Vn5M0l^=ceq@7^%6&I~bR&20>S@#K3#$hsW* z=FMj7vk0cDoe-94&d)<15@rOmerIB8c_MaDT29WeGb(YUKPm9}c_H%x+aD)-t@}d* zHcJ;MD4L(?jyQbVZ*T0J`d~=;ic!ml-X;Gv(&|I#+V?EkH@D;~gzYIk!yvg75H_GF zmlP*;Xl0_YCVta&NNZR4Nlze4zhXKcmkZ8`iF>%J`T5tZXSI^wCr56id8FCq6`AZt z{Baw@Y#Kjv&aT->`Lq;iaYwQPBlNYuxRKRR!mLM z7vqqM9w1%V++Dr0J~!0Rdff(o$+D2mu*9Yw-jS~Gh&(Vn-Vha~dDu9V$6MUOFIrCL zhULs*qa+x-#B1I83&pF=lV8$Hsg#`GUO9gHmV>ImbN^EhR$9W~*P@Y6fDp?w?Tc1{ z&*^Ii&r?mXNA4f_J{)z$bLEJN@$R)WEH#%Kr?h?FRc)hcDn7&3yO(YH9;oLU`WH65 znz|IyNFecL5!j-aw_TOrUQ1yO_!?5vmR+UYk{dm9@QW$OfIe>XA@!J)=OJbS+wGU- zC*ms=qqoG$$X)3k>&rA$14GR@B8RB_lQgOs*Uq$VzpUNW7q*P&tZL~rEOL5t#;>%+ zJkc*&Ufpv&q8I+e^p92D2EeP=rnj#8?F$+i9aV@hS1a?F=7XK`H9~}Dm8SV~Lw`e` zL5yLsdrh_rR~7>%@#bZgvqqBpC1B|1uZlUcOxEw5evJ;4y!ZR>uJyfreeI)Z>y>@@ z!{VPkDeL*M%~*k&Jpzu52jN`$zI+o6?YWxUehD?7KHVB(T0dKR zBd{%f*J5p<|AcnGx3wZ*Hn$Yg<05|*1DJA~VpiQcNzE@ZTWe*?C#v7^Fhq(7kOU+_c%%{rURX8X97kO_T z)aBZ?js8pw!~hkgEJOsAl8`b%kx)=16p$3@kS;|9rKCYhr9?m)ltxOr6e;PJ?q(nN zdf%D%SScs=eGGu^6+2g)Cu zrS{4!R?+ZSYIPbS7p%;j@+AA?6{e_6Tr&~!nn0vHva0+)7R|1v#${a@STA+4sYD-*1P+M{e7jEahiEoP4-qCeBEH$=B} z|Gu<7E|VKv^xbHEhMB=_@kp^ACZJWyIy@U{<1`wW4x7tHm@O>KSOy4L&H1wbplC2y zlH%m0wRkS(%QDq4!yHw2vA;UB^d4nye}jhzhe9vog=*@@PRG1{Hp%gmu(FcD ziK;3M7ZNk`-i>uyK*q3}Or_zaM4MZlj@tx>FMYz30k=!GF$4Dwn$HV(q>kO|$veu; z?cN3B!RH?;4qIh6y4g7{N%Oc381`i5HRWjuyS?R(7R{36p^(GJ&vLsb z2(P<;6MA9!Rk76RY?j5^ILY+S^e1NdE${9}CZ~=yd4YbPXf@Q}duwta=8Wraq4V;| zJM!ZL3`@p?DK0vGt5MTY)0OepS5~WSVK4K#OBy@brncff)aLH;!NHr%cgj~x)_!R2 ze2V&hhuWrX{j^G3FFvW6I`l+N{<7~U-zs{QjoB+=OpqsidGhCgmSkAcWp(+&ez%~n zPgs1uUEA4!R7{z#1u80?ikl`_0Vy^I*LzWZ2f&C-e zuVw2Aq_OtZfi;bWr7GGc>o+eYXlk`C8cYinS4;J%0^j?0DLQ+2MBgt?wm4WVU~uwg zV{}d7OkGNX>wB{RA@iQ`+7-+&wWb~c(Y~eZhCQO(u_<^PY`@u1j_(R-+Sngw!+y97v8d~l@MuNAiNqqc@n9V zW5ueFda8Kz0JC%v*NHx)*h~BYCCXv%1h}EcfJUm&mmy&7$_vYHDO_xv`1RlF)+19m>j^t4gDMQgG{cuxj7(u)v1**lN)S|r$J;q zNY9R#r?BHasWN!1=XA-pZUUw$E=zy-Fu1C_@Rbj7dTEVs-LmB&8}*}~pL}Cjxw-w| zo>-3y4c{t5Q`4fVs?qnqYV%rGzzls<`)&h}Zff$E>H|7ZaY0V74|Kng(NVDb9)c0Y zoc=WW0Z^n3SWip>$J#Or)F1MvkE;I;`!n=2gG}kgOzqTdR^{Z44w4U)l8sLU)&$2) ze=(liQ7B}yQ!$GyFNf+NP3hAdC(_4`FQ4UYS4&E|tjsq@=sk{&E_kIlKuXN0a*wqg z4lghI?J+R^y@F)Xdwn@E`9=dtux!X7`E5k{W2kESu5%=W9~Is3Q6YW@H+NZ{JGk!B zG08?dQqM4pC!7>v6gxoXI)83&*WRcp)Bzi6Cw=E+U){XQ;kQJU^t`)l$>;kWzn4$! z^-1q6S=>G!^9%o<=OGjGE~Di+Iy}(KP#4SF_C&dDnD}L$Q~E)NPwxlCwG%A$O?n=W zFsFgf-%BuXF!;TU|AIWE3+SmUEuR-qYU!`W8c4U7aNN49DbHRQcPkardYIvX$x?&` z(frkf2|VBjua~JWn4W_9W+W(uRA&^ZfLvo#D|zP*6^77}@b>dL``|4GPllcO8<1Up z$s!uXlDa3*X|h` za}Nt+z*OvhwuR@<*n8fHIs;|nWloNupw&z#*o&G4&$f^w@km8xYfW8Dj9E608r-y*F}>h>P0h`LU_igX zi`OG`12g_IB)aoj&#{83R@R>s!mhRz%znc742gA{tgWqKni#-iK#%#C7QVcERFx|A?y* z7@ePBnV+BMVSJ$1ocuz_cFi)}0z*yG!s23JIb07!!-Q-nW@aMp#7GOwx0&3}vGPTb zRw7b_8qWKBDcdc+IK-)W3z2aOsgDXEPLdcH!hAq$WnmIqLkOmzx87acuyNC-Met%_ zmAeI!VO z3dl5E+lcMH+pl7m$9fI>ssUp;HN&?sKFhgR#X-#$U#W#l$J_I6s<(yQ@#j%FlIK0y zh6G5d2BF}Tfe4)!+$7yB!_LW#w&igONv}<`YGghd9^JA3too=SM|?nl90_A&nK<(& zoO7iJI!Kihm#>O+DcQ`43grI%2M!TJeQeFTDYw%vACe5=1#>z`!dC3})?Ab^ZEqjC zd_R6GY--T&th~roi_P5J+*GHfowx2wY*4d2)MLyv^I+U)P(hL=?Mm4x?)9b7 z+r2krzLg)}wrf`;Bp%P5oTfoQ=Fn|QR5!Q=IuWImRF6RXaS>OQ^|e(P!#Y4>)-$5J zQg01aEoQ_uh`x~myVJ|n$&&>2Tzpx!t_lGHKp%?7v4!HCOyx6p#aWKU@Wlrt3c0s+HGWSci9TF>iaE`I6a zMG}z^k_l^m$twah+Ap{3exR_e1fd$6$T&&7$IB zZ+Waz_;JZ<$u{?F3DY~K$?q?Q$%a%MT3!e_xZ8+lS9ll~OaD!JQJ%|c$9Qed z^z9wYez2o9OPUPFx-oy|ZFU1@H0QnZ;eX^zhMR3zG?1SiEM)r@Cag>sPaEhNjm4FxcM7)hagF>Dut_XWVq~&{|)8aYfO3 zo|HV-I8T(sThBPDKt7EFAxfj)6~}Y5eFLubcKwKs=x~kU^j=*KI+xcs%d=FQVLv?Y zU6b5l$3DkgvmPsD`DQkj=S@Jsb%~AviS1Ed!T*J<^b9rs3YX!x5x8+YcAzSZr+CR` z(Y0^s^Sp)WnP@ixOS$%T-{4-3UhU%h^<3L;ZeQ_}<{ zu5VG6i}|0e5@oR6JdwXw7H;`^VzM_B23{OgI&$H{VN_c($vZB>NPxJHq)IQ6*N@K+ zT7BX+mN2W7xE^x!_dkfSrz)0q4E-`Te9}iG2FibX;o`Cr$qbyUS?Xnk3$z1ds>tyP z(rqR-aVXyjwM>hoy7~z;-NaMQTKBE7(J%8!RW#4_Dm;8TVzL{EqUr9ZJ6#~Pfh_*b zC(et?zW&jKqP(~2(iBuVP z0eqsYY)bnRHpB!3^l~7F#vpR9kQUU7w{~EcKXK#;3AV!^&8aE~fx<%gX`m0Hrc@== zcQ_~IP_MjRHh{E9yzS=b0l}&I6Qe`ZsHV@~yY25UC2$aTw zhfIz?m~}|*JG))TI?b8Z+>2?4@5a+f`cWRTq;UX(b<&>a#oW0xwV~k-`Mf7*7~JTe z<~Rzv^skq9zsOVMldf1&tK)Fr!Z6_WKA~s0-)ng4M)>(A{(u_JkfMURrU4PrYg*!O z0_EruKBb;%cFvj~=jhA-cE`U4E++=U2I31LRw{LyDDUGDsn1m3q~JFZ?srcEPm21( zi7@dNevhmBIA^GnUx&1sCmT15aewakjz@*LQ{KUdvQ#?#zPyf;U(DSY{p^nB1BoGt zi4*EiekDB6ny(ggFtWPMo}uKw z@wmvY()RXH<$-gWWgmp&_GdZ^JYVuDy_cr2UxtMDu?NWPoFMVWfwey2Px#Q2T4rMax)&H&929t1u9YRp%8tKw!r7Cp3n%$5+%&X z(+xU8N8mYoLya6P1Mt3+)Y^Njugca=&|t3u|dNXB^6eU z&Wt|OM*8x#`WfLH(r+nFfpi_!8;Bz?j8m&R;&4qNN{3IS9N91l@H)=g0Gbm8)hbmR#akFMQ=x{ZE;DTUv< zpOt}P-5F=W%Uu5F(vde6_#;>c@QXHcLvq2vA=FdVDmqTyjEpA_FXu1MkIA%V92mEq zIlCoiXLo6@vGX~G>Np%Q=g z*;VTCKiw$VFT*alGiq&x(&;kAp2Ui=QRN<#O6h*I&i+(h#;>+T|*>_tMr8A}SCrwBU;GyOrWknC|N^`+4Rx zo3Me9-A=b_D<~n4R>*1cdRn-&iOsxZV{FmcW4oh;`X+Z-qzIo;%yaP1IEbQz>a29Y zucf*EKjV2DJEIw*_tMbl^qt}io7Sq~w7ObuD<!Qz?HV2ib%vbCnwnjw z>)V)5Q&tQCm&` zyCyndVS;zSkZ7nD7HYv;KPeuNk7hl9Q;PyL2zJIsq*J%1+9M&$+p|euI9dG`Aluwf3t#%*7oYVYuW7;kXELW>Q85&(BCMx;@k#?s`8y?geDg zhjXqU@g1r-#VP|io#3!0JRwKgv7aOC<}5q}9N5c>RoEgHw5EE?NK&iYSU@b+y4Z0E z0_*yC(R)gxjo3~!ZtNU5)4fnfNlwqbEn{73{D?6tlcUBg`ac% z`;G%!))#C0QVu1v=XUY?OrNSQh&^hP+Skf8%5;?dX@&Xclp~KA!#AFkY5M7Ht}}bX zwm3S##f9^3?7cpxu-s_S30HWvcL1r5r<08WaVcP7)Z+F2`J*QmP6;XvUHd(-GWauX z_rv$^XO`FBAaJufMt7*$xTj^;%UOH>R?iG#gmli_mRUYW&D~z8Lm}O|KzG$oPmwyq zWOeJo_1pz@$NiqAQ9_J8KR(I0B=?mY^G|VKa&nBLlZ^W7Wp-KAG)0^Kxf*>>hF);4 zqcNUC$mvn-52=Pd0vpo`OaBKHTX;lkLM{5)`IXy^B%zZfa-ryyU5b5v4k_4~5+LXP z=BQj}f$i%d{kWZ@;k3GX1`s<(nRKc0~>4zAoYoN&y0SKL+;d}a9BbJ5zo8WNl#F1H?RWgE$P z-$=n+(UPe_0{{wMr_%$kf0hmF|0dtk>6js^qNOG3A@nVeQ4qceT#SpfmmV{N&PDK678L5ba7zh5!DwSN|Eq9hx<;9@XeX*oExmE85y z`pGBRvBoQnhhFM0Jo~ad>5<)0HDFG;pW%vLLU~h5lkxb5hV6nZBO@b^TC$ms7Km8nO?YD?|(1Es77^qMB=*P zHXeO}-{&+0cBq9-xhF|!w?4aM5G@p+4`Y$$FZz_EDuu@v-iby6wOtahF$F$934A{4 z;)b4-_&@g&qUG#I-zH@{L@#o(u}AAHPJaAv)K;`}bhHYUBCqWd7h;MnI*sAYMG6)U zn_`>UyLT^|;GV(3>wq*vug9&=z2o&N_wJV136nCyaf$c`q{&hY*avac0=A?Fnj~c= zfUFG-_(OmzqQ$fQnDHy>>&zIiUE2w_ulIacS- zWvi@3^?JikLKY(UYtS?tI{(;5-d0TJprtj9y|{#SO9<1bQMZIdeU-#xy;F?X3AJFb zKhl!sf*Sf399IMcTxCuIjX@%1O_?_nxRCjPoKW*HD%vB&O})a;4O%sP8m}?68;4^` ze*X?Ze<(b)B;`2x@zK^fYj4izn%a*yt_U4koxI{baN-GSCfYXhiKnxo8{G`vL~>a< zJ9wv1hHeoghHIXl{l`x-o;YE+oNnav+1{gMDmBYwnd0ekmFT%m&K@p2KN%U5EDp|< zoXw(x6K~rWJ4%o2v9d-!%NZ(?5aVm1f{*c~b&XHQgKV!S-EY^ukmXX7zUni4VZ71G zt-pBKSZHORW21YQq|J=lxG={W#g=62PA@?wIHP*xghXGfcb0khzCV1Mm~nb~I+u21 zz{#q^Of14SgWE{R-MgusrX`7^zHlk80xqW$I(Izgg>89?mk+9iNjp^de>Pm+U|Sv% zA;egk(h%*et}!fAe{P7r6~ys!Pjw3XD_gKrusVcKbX8=Qdd)$oFK=#CC_kPiSF>Y^6qkOHoyFbC~ptH#pP#P4P}fos7|odywS^I3;BXns7nG-x17q zgcQb|mhUov+R9-h8jN@SG&jUm+x7>kkn8#?5p0j zHI7a~F3xh@Pj*YlwC6c3`mrL>z4ZQ5Tu*A=t}eU&O7_nXNwAsUleD2tGadqn~E1iNL;C5S-iQyn!Pe-gd^D)K= zJcAaxb8M^{z%p^LRz)3ePSZK~D?ynFsfsgb+@V9;N` z#z3^5gv^zRWf{EiMRc=BDLY2+S!M4b`G!Cj01Jt0!1hiP`Vh?<)PRxtfDB<6LPg0) zIMR^_f4d`nLk??e%Lbr%N|>5XWdy0IsWoRe^PaG(|ISE4zV3U|p?U(7`l%-6;l=0{ z_>pM$F;-{ubpqDdqLW|*@np&qsCA?;L)(s~1^_#!;z=CL&4WjX*pCncouZ((Fe0df zEgh1qESEW|M@bu3~#z>i#JLs4;&~- z$Xk0Gw*qQAdzy>rEW$#(-@Lg90EE|c@QwT`y1D>dNHslv&Mfyp+vprQw#n4wjq; z3@2Bv$yj14&*H!G3OnYx?51xDU4nbLx6%k1u=xxKuo!nf-Zriu{ASok;6Zs>cUn2! z*F$tA4(G$a9PnLA>^s)167o?IncV@oAjtT{lyZMr}$+o zXu1p3-~02pnU+6O7%tXpw9~I}LyGjn;8vK;;+U4nc0+}# z@VD_Q^HcgufS~2GE!Z(>d^lM9-Uh-Wj7S_&+PKpTmdD6M^IA+Cv7GMjgXN$Kwn*UC z8z6oKFfE0N5ehkgrG8lO2;S9kDf$gz5adOL#H0rE84PW(%%~3@^rwmz;j?QCc(ORY z*$G`TOgUbo4+0c+9M}-f^i7yT0Z_6)gp^FM!XRKaVoHi8ikNB;AX2*cqerJ85>ti0 z7zV(^yz|3{4;X(DOZ^=b#BlOxPqo3Z1f+`zX#S?^YDY+JLomroMA#M~?G^h%%y$t! zn5x(62bi5nu@vAM;&EytZcyef4YL5ZJ%~dcGP+{`u1}mg<%~xZ+sh{E8l(EfbdhXr zc1*t@+NwfN)&ye>_z!lXg4O+<=?hT?8z#C`zhK5ljjbCquf2$OV`VPJ0>b`*3DNF1 ztbvSWWDGVJaUqA=zH%bPvZSKI1s4Ram?gFEuSndusg%bpgFSeid#>Y9*0h+7iv3n`ug}G8LQ~ zGD*DW|4UpWVfK%k!Rmzr9%o{kfI5XmS97fL57ExqrFq1}ndd1jG;)Sssd=`AdTlbx zW^L%;%%A2kIN|s7judrW5p~|?@y;|*n*1<#Z8DkX7UU56Qan&nFFSYBGRMUJ)6cl_ zoGkO^jAar&B{{<^1u*AE3eGD}PPaaLdCfJv1jMWKpN2!k;Be%>Y zBY_1GZXQX&d^T^SNMvKeDyQynY#%E6MJ1pXHK24+li$iObF8arBjU4iAf1SWrhx26vaZ1p)Er9G>>3ILAkEmJD7Sm;bf^bld7{pMK0<6GBwV=f#K2V4=gd+EI^BtttFk=7)D(0Viad{teuRyy@Y=$0JP97Y zi5n-Nmu|pFnyB*tidN${42_K5!0RzGU@!5oE;om{r8{kcG`3Bpy;J@QWu_B zzWOYf*%@%fBqX}{ojKX9>^tm!XBDc~9*`xbr9`%v+#Q-*iuFmjU)|EB!RtT5Ct)uo zcK=3Ms$<^1z2-CnBYa8h)MN+a!0z?MU!N~tUcZ*+ z#pZfcV-G5J5A~I02kzX)x9{@(`vRd#TLu@ztTSf@H*ExUG(qjqNLyJ z@PW+>qLSM6mGNxusUL#?pn=mp#N2|}UFG3+^g2|ma&I6#HiCK<&r|$9C+EoGHV-k| zqn01Dwof)v9IcQ!7-n~SeoT)4Icq}IH((h@D_UDjKBryzJ~bB2@xUkI?Afj|cl`y{ z@7l5BmsM?Rq`msQ-!1={aRC^c*_UXWDI%E)!zY!|Jy%a`qOMH~R?6fN7fvgjG5PLS z*mCsxjvcmj`7K8U($>~G3$|OOG*T4Ui6#?lPY|S`#y;YhZ|Y?ijoO*HPy^+*?)Yn@051{Bat<3AtQ^{X$8TF zjsy|_rep1Kb{OlHt*xRPX?TBy4E>WEoW5fOI5@m8B_u%(E8XUgIW0&$J%HYaB<1RS zF8Fk`K7JU=^&+p8UL^N^bED4FQxP*&Y1=P@&FdSj{vVsYIfPf`OhCzp(}RDP{$B4B7MT)}-v!xmIig>4%c{|9u+(WBC|S6f}H1 zg@%Njn(at&A1oN0v$FW1dj<^Kh7i`HD$U?!yJl?zn^tvAp zVie#f(~+jV!otFs)fD1OZr{CI3+zd3ts39i|HDn8+_`fn=c*vH5hEi8o<)Ew1uUo7 zY!(bbzbVGkM)XyvdBVZ!nHg#JMuN38mN`{M9Ui2O8#gYDq|`J42mv1Db4Jk*L_(B| z9_0KduRsN*`chBg>Q$2SR|bvIJ;~Ys8;2ZI8-xP#O7}0v5oaDDe&FA{cVFG=L#x5H z3#%WV*oWvtq!34U*^iLi(LHlkCvD2~Yq&Z8awys?z>cEXI3o9r-2J*qw|Kg5GJi%U z+;E0ABqf=V{{4xY(*5Vw<}BwEpSEewzulfc4OFjHZ~XHayUHrI{T~mq>g|8-^Lt%z zBL3?xNc>sO=by{2=z~wN&ogx77@Zf(IYkc-2TJwM?U|@0!uo@_>Z&B z`Px5M!~eloN>2Ohnlis~-c8)H&=3hT9j7;aUp38%|7=eWohI(mJxx^_;(x!$PHAg7 zs%|;QD@WYW8>z%!+2DwqT)*#&0TzeAvaWk0#igqp7gawj_0A--4?a-!jJ|aF!`SY= zA$7LFtZ+H^vDg208Ls~43-5_2Y0jX zsXO0;L?jN^VGwWJTlsL5jWZI0Po+P#{u7DY1afw;nHSe+R#Yx(3r$CzvFPMh8e80# zuzPIvL}BF}yO4~4<7AG!CR3~!9u<`qd$$T61byU#i3cPNUEbYR^JDF5LkShlU;59j z37J1FdcJweqWAq}-yPZ9@0jQp1z2rP1m2tQROzpNTZ7NEZTHSXv{>DYuN%^Bk@Kz- ze~vO{+jfeF1MUwt(``SAMhpxmvLkH0?|hG^2#_gS4JQw4z9615q0!PwjQ8==CPTQmP&Q0vh`ppQ*mE{($^B%_4v&U z_+Ex=12rK`Xj{VULNCt_e3IvwVPz5!2u2Lhz5|C}W@H>>X*`=@c3u7(v{-x=oky6Y zf{(FsxuVNy`#pUtUgW&h%$VdWFUB}JRlg5r3l^sK0d_Ze`03R!{d${rDBISOS&@M3SsS|K03@iPH>%a3!}lTPC|51viZTvSkc z^y~h1mi75xOA^`Z)!W88@}z)o#Yo39c=xywdbmd_B`s9aJ61 z?%udAo?r52N;e!>e^eU7H7uYnln1MFZx%FBli!(Tt&lqB-rK5YvZ%k@#$@Ci=(W`6 znxr+a^pe6V2$1<}sxy}(%lGry4s+$5x$nAu(q8jSz>VCMMM9PxMd&fog4qcSI_I7; z1sQ!oVIf`KOS}8Irh$Do{V6*c@>gD%OcEiM0fBGFb~k92{0Lky*l~S&Du`RZ`_E*D zK!J0plCspoOGvJDoILv~gXcH_($-3&?OVcB*Io6EdP;V++w`1qrxPrBZbW%#zE6%t zByf?o$@a}G3r~Hr?as9oHHl9Zd9|PUs#>^`E~*E%4s^J_S8w)eYSogG&7JwxXR};9 zZ^Ji?bkybjimkdNsAi|i_``Xz1VUFc8%Ae8M_q8JIC#$cIH)Q}1AhWJL+*mE02s)WbraZM<2# z!`hTAzV=HQeQp2Q+VIih%-QG4@Bbu>Pns|6C)pTVr@Wi?C!ZXZ%x})2&9>p{+08*I zofmA!&Yom5(_UIsQZ%(hV{4&yOF6$O+GhUeiTwnx{0F z6?4h;-lkg*YAIbxc$Qrs&)HY*9|s*(pc~3vzY^_|m);d#l6@_H^@Ij<-d5&gfqv*HFvTeO`SWMX`|Ahlq8{i7 z(a+va4zeLZX(hB^!AlC_*6r3`d7$&$pSifWWO8)C`_zbxvwx;J@tMS4obIav9a_oe z>zOS%g4=WUmn?D)i1!8kS~f8pKf3bvNA$gQdP2u2kRxDJ5n03UfRWQ)D;A$)*LE^3 zvv16gmbYw!-Z~1h%_w8Xg8cl9t)23Tq%5?a`5T9EJ2L1q!i8?|7BLxoLP0+mh-_R9Vu zEkOAPOn}SRjh1g8UI1W?_nALH$MHMBMF@QdV0kal*x;961%~Z6=%r*_S-`MGx7al_ z^a^c0;PyTukSQdVED&NWygSCLZ;p#noR*FI4z==6c(>d6GI&TL^AWD-WpQH|Kv-B=d7V+@ z##i@?8Wr3n=68DgMH?G|F32T!oumxp*V}OK>;sMO&OEbo(p$^t)=ji^w6snu&X$Ie zQtcx{&R;HXQYwLJ&$DbrdQst8zfw4PH7JODXjD3Ty$04o}NbncVq7sl$>0=r@+szc>3<= z&G~*++F?tVP8=UnkPQ0Is=&0BiZt98-@F?xduoIHk0$kdwA#L%h`G>>TlGpKMWgy8dr5jB*0GwD*Az5(!2EZmJU? zF`^I+#{g4P-|!9HZ06Nh~o}?AB34rvELt;P^DhVj>UJ&9(Aad$Y2&fn|^DZPD zVe#=uv|1m&pJ}n3jUwaYZEM%+VBU)H>GgN=K_~Bgd^@K*?P$Geak3(8VQ%_HWmwW1 z%A-tSDn9ZxnRjWv7>;dJdaMW@-{D=$tveLf|+ zy6?wigRJ`n6XwCYdyxB49U@rs_O6ciPq=LRp*lp>a@kSDu5tUV(94tFL|5k8DZzK-Ha)`Mk^<#2P&hhT=bxd zeVtZl`RSh`z4K{TJfnlgk{V8ea|>L{=d8>QGBUDsz2DVElggZCGrUM#}}Q3t0&-@Ulij^^An@1*}@}47sI1D*7MwF_*ILxfmtDj2@wPd+xd*ComARWw06UUR?KJ_ zjI@W)>S3!&Mrs?h1RH=rY{mmfD2Z&Q%9x^VKLkPH1EMc54|)_k=Xc9{WJhawV5W*?DK}FppOW!G&ykVV@+dj`#rnSWU&0nM44zCk zu;TB3Rm~Qe?ym__wav9DF@8|7J8Jn#8978t)bQPbLgUcXoTIXxS8AZ32}QJ2*wK?0 zUZ!YO8!!FP7MQYP)9;pRFxO^}Hai8efuLE-2?+*&7W1<&a~HW4xfhvY*SoUHY&e$G z6jF6V>xYk>e7jGEl^9E1tNK3|r{zP9?B8wopY>_f-qcvz!bv?vYDl6~k zIW@^Km7|3UTtenMG4F>vKoa=c4XJ#{cO)JgMPwA^98E=UTqTZ(}@ zM*h#AzSyMIK*LAC;Se<)Ev?1cB5YjN1h7aqxhfxnwZZFzgmb$NbEzQvD(bd8a4!(Z z_F&I0E-c*S02(DWx>L}kC#&yg)4cO5UXiD2AR03?sMkOr`1-fO|xN8JH>oJsPXfCkM$B#b*PZ&+Q!8N^)^wT(~9+} zm7~{t9_`$_*He7iZmZsfbk(j;mT^r}WaDl&WYC%)I4Ae5{#r$xDOI3U-zVPyA?wph z?OXT`7`wufD$$@uhC8XG&Y*={^W-S+NNXmnrTq>d343rKrUei#5tC7@ILL zfBa9AZI|Jv*4KIV{)PA4H`OLx=1f_V+`ndd(TmfXB4tVI71njE^zVSmoHHu?4`Pq{#yc<7;UqLjoyL-tL3<4lD*gv#(D^9QO*|p7NTR4&S zY-nPVg4(~mdX!)Ot1ct_%X?>H8mJ~%^C9tDMNj;4(qNeUzHxw)4UoeCxZoun?i(KlAdJJrX0E<0`AG-LaW&EYWtaCj4~XmHZu+=t$dFZFx#+qymS2IhJ@-$`#$HU}mZ;BG>harU8KX$HW0|U?u5N>e4L#T6I)}39 z+)~&PjRu+TGhP^^bQLgJvKsbZU|4dwyP;xnZNT7^+{Yu1$~5KKLp#MQ;BB_iaJ}6& zG+~8TG7U1<_v3IUNNOoLJEwRg`s)5(4Crs#9iAv#@egn!w)=o)5 z(NUH`!lMeCFNPhavt0e^Z&(xPk}YXTEf>4UWoJ=@|k^PSk+ zZrtb6lgxOfUc?Bm7rlS8Y4ga`z`$3|Q+K}XNx*byNVqD|>`|a!%lq#Q67x+SC)w*3 ztebJ%4her`2vM?ph1=RSB=7KkJo}orrrTFgzfCl&Qj8;x9b2!Z6iE8G^Pp>Cezeb# zNdy0LG)|Ler6aDtp2egMY!4XzU6GRukhd*()^D%}$#jtlkhS`Eufd2pSk+S6=Eh`H ze_q4JRet8ahfh4dRa8{Ov1&hmi{&O=8AI2ahj0iNIC#Ux0>$;fm0ww=LtZf>mr5Iv zWWvQTkD2%cbmb6=6K*mvMXp7-9U}C+RcT|0xih41gy8DKwVg|IqswxwWv?*;u6D?E zgc?6W(c^P=OT0_km2#?+Zb6kG;T_`Wt|D(%qW5Uy($N#QY0gr!f2qsKVr*vgLHg%w z@rACPV!mg}DjBqlxCW_7?tHZ36={!jgvd2qxJ&A&_3!7E!SIb}Qr3jldBR+&Un!pD z$qicBg>=J0cBkVnb{y;u_dNa)pej6~BeDs6s4&x@}{S!U5lj;gfvYeP09!Zldhe#xa-aN=Hj%&(vhiiD%RZ`tK} zIOfb=(o4y{w_B_`M85T;9e=25vF*0)LSi2GtZvWq30plGy(0JM)anwAsE8>FRgIg+ z4vGGn3cvZ5n-lg|#Y+LyYKxA!*00BQ7cZ6Aa)+X+pXy>_kmL7>cK=`M!Xk)cx?XTD z++^j~^(AJ;NrGTSTArs%loLO8EXLBTKle>^bz#5M^7x%A+VR%)(qh%86w_J32tm}| z7VPgR1HU1@NCfT)D8I0q&3eu~tm<*}Syv{=q?jOk(`7*TeK%H?5o|YL`%SD58MW{D zo!CXgO~|F=6w+QJu!t!6`l~}Kuv&*|-*3X;dii+qR|J(eq+SE56{jSBR8eH=4hp*o z$7cb{fhJAEpdl{JAIkTwxc6vTmF8s*&ksoQ4ris06_hoEp^)(XAMX`3EHyLa^oH=PAN3>sj$&z)ZO+#PJDueO2hWLuWGM@w$nch3f!VV{##L5V-@X~^d2dLygp z;`h8)*Z53PU1@76p!9ihadZL#@Q0gyMT&Ji-vnHAnv!^=EVOw24n3b+*emZ=yUQ!9 zS>nxU&JHQo60|_@ZOei|adUBCji|k6oWR=p zIdO=19fbtl&TVsBHf9~8A3NZ|9-9JAqxN*v^UVy5DmO`eEb42wZO=%I-s&BSupTLu z$>$VSvHAQK>bVpvshV}+gxtu1pU1o`iIc7z9A>088zC6}^qr3r`F&m&s-Q%PmSfnv z7e-OMkkR#)YqSq(rjiwV-U+2LcnpsuglTDl{&7FoCJ<$LZJxb7iI$cYtdEnhyV<;D zi_vty1bkjDVcBu?A6Yv-)TQ#cbj|?W2lWXxcY6Cujfw5CPS| zJxZYHhwhpMKueU`^z^h^*S8l$H;faH$kd(cuhL1^!|n!M-(~-^CE~;IXP>OUc`ivE zRKNtKs-5Su-$bbiTTY$8-(Z9(5SKxFFvyOM=#Qo$b%@qpjXQne^xLPa?Y)&loLrCV z-$;qruhbSLXw^Xt&zrq|{q>Vcqsgeug@r8bc6#c(Bip`Ye%-i-{=f!8{e=9o&hki> zwpZC|={A=yZ#37Z@y=TlJY11-dPtOpDcYxl!)(poQeC}8GvOj0 zxEu62rYQ>>%7e{cbMVtl{?ex{6&oj(J)dYk8TfXVUE|&qbFM(hv~+&6W6Jx3U(E&@ z)#xuZM%eMK0z22Pk7Jj3dmhw|g*QogpJl_}p8w{h<@&h~rGt+V?|k^tQt3G1!>Ylu zDYy~`T$p3)R^O{{%{Xq5VsB6${<@^NIB=Nj{LdbfioUHEhKArqcVPAsPMwn2&VsHp z90awi*RD-AND0>=fasHKd^z0A5CYDKG@Mqi z%m0x7(}T)fHrj5n?5pMUNqfDf=dnR7yJ(i>`Drc7f!KwezI(c7WTXgoX}kb7A}f5v zWb+VXKmuy4jY$R+tMbxuWAr0TXDHZV9R8}~rJ>tm?UjRtND%OI&uXQB3$ zqWP^BEw!O$#fSB-7iFnbqW$=W$3J5N_=`48LT@{Efhl2 zhU{I{iQKrnIj+A3#GIe0w;afd`LV3ByOyB*On&4V=P&Y11j zS9yp67)K{^Z*z1iSY3_FhG+_615FBGyma z&d>qG?8(C2!VzDTq`RxZOhO*1Ys?COK!V2v%@9C1C&w#Ob$iyVb=M`uZj64x+4kRx zCe!383}d+sxG5ILMptV*n*T@A^qqALyrLA4z!fs|CGzZ6VIQZC=KvlMbr}ZAXO(k< zyN$Foh>6pxxsXcDSk9ez*%t}9LCz1%_;s9S?_e;kwV1&2%3Z9TTkV6|g8s;E+MO39 z_2=&|aL4IIZaeemd%+PigN`egFHbRKjsMOm^?Gqretke>gljmeLCaJ5N{?30Q(H3m>{E+ek6@9SsPfsU_jQQ`je-Do z*;`$5Zku0Lo1eVCqfzssFgpqUg_L6&3wE@lw5R-{-^eer_$t(AiI7QlECr!#(p-Qt zakk=6&yTW(qE-8V`4fMa8gXPX>@T~HCawm;&yXzBt(`74xkoN6g^fr22bs@2mXebr zrmwbt%u>ed0rqNitz29A+AW3ih^m#A{Q&8v0t=*I65Yg5B%Z{nAu<*`3=*eLb2)b$UUn<$zH*Tu>i&0bYox{y-fE+|^r*jQ-%p_7r-qh%X+{Nu^t1&qX|^5Cy!oDHLvq zAOd<6?7)pybOYDM>p)dTtgw&`0hUkM-rrvac<)6@lLx&Owy7GvE@-0V--_?y^5#+c z5BFH;RI-~NhJ1U+6`)`&i5{Z4c3J;&ypVHj)7oP|qkb8E_Urf-aSt2`nkr>$zi>x0 zrL{zS`P%CX~wIa$|o1}zY35>+(Ax>&f`MsX^Ywuqqy21PYbr?T|d6A(*f33- zWr-EenK2sAa4OK+x7l}5(ESGqOcKvXPI;Oe!=&A7af!R><}GhO8S=`~G0&`LTxnXK zYiSS>bvSHLZ}-)n0AJmfS*H6xwGy??)jsI>O7o^RsXVJM@ytRrX9I931`hj4o#%F)Q=ai^9OSr z16$jU%nk{3?LBWeBFXZPP^Tw>ZR#AApkb+5Z+wEFa}m<}Ep>YfF1J^%kxPt!sD~1v z8#@_bqPXb)MlBra)j-VPxl5OfAXg935d+Hb6Bb`Y4I}K%1wEWoph740@$vaXy9Oz| zh+QKbH?ly30;t4zq^eGl+P4G&rwM49=q>#yV%Eh{2nq^9YFI0v<_wxUoufP8na}qZ zxmqVPd;Q=95}1T%yR06b3$iLR>P!Q@47J4o%k z3NsKn8Ndu=2bVTP<(HC@G9bWjKlL6K39B(IB?JqW=pUlfPcuSY1V%ZMDykqgLf!z9 zy$ZIBKL8Ae{R1(S3%t($0%k0Hl(_GP7@!fu<~9v0#vx#n%~yQ!MtyKz$mR#N6Qa%~ z6>v1ukkOC&opik;BoM`W$lzwECpt1Gdh431DMEmP(ALR`v1{i=C?4>TlW$!6CR@@M zG{3eGP&w-afCq_Ul{;NEw$Jy|ksm)8a>FEpwI4mkDY{MGpHk1nHldvPe!!h&sO%F^ zalB*7-T)H%HY6bBnd*r_7w@oWkB<=(rY$8FbAq<=S@w^-aodF8^F4hC1e~U@@a*24 zF07$oY=xD{^6#7&u$+L5U;~xo)nZd)8&}n{rrl%VS5y@?mdmT=pQyckgK0Z_$>i2v zbF%EB>dm;i*B39V?l})>K|oOu!jQ6^Vo3JpH~ht&+mRPnfplZQqY-G)AEIofs~x?3 z7dnIoQdcv4|2iXpQefJ!Wd1GwnA1`v&v;We4l&D>uHm^vCiDzsrfJwP(|Dk@+-7Eu z2L^%3G>#QIYrIFoAQ$*L-I2&|x$hB{?90jX*v&*4w4~t0V}iq;iWl-m7ZyGRNdve) zqrkb;EzQ#O0x=JDksNKx{J%H5O?zgO6sn%kdFvjYJ=j@keD=J{Sh5?8z)Q_Dt_eM) zbzrAGc99{u%#K(%{Z&C@(oA=Y@T!9-;H zi+3gx4 zJa89CBu2g}F{ZAMu)Q0xVAS^@>Y9kLA42rEx>BMJlMYZ{SyYJC8DVkBf`hkqi2Mzv zqdKVJ*uownvph=_C5rTsUk2SD+kE)2OLDCAACxb@MZ`0bq%u7J2e$VAkC@{B!sy7Q zd@dx(_21DZ{HGJ-|EboXrEl=W-%-cm!8YK%mVP%ne*d3QHvAt#?C;f%K~rjC;Wykl{;11Bb(YN3J#| zI7c-fIA9N~#{@1>Ae;GyhTumj7Dzb((Cu;|-7`1?+D&f|TNMw;Wrp$w#d0M><6x>O zrhZW0#2wV}v*XWS#tYf`LrDS+Rz#s*M%FvmStCgIB&yK)&0k`6C_R2G)Fym-`CJR* zi63VLaD_+bdpj@C;z|WmFjSN&w;mfc7fAAZ>{B>9mq+BN1&F}A9ALfqgelKYc$ZAlW8#$wO4uihg z7)43}7YIo{=Ljy`oO%owNM0~4Ea5ET2=LWvk!$B_t|lgN)3gI@P2PCuxG<__J+8g) z)tqxc#R-XjrM4qsh=UjgqgLcx1>G2XM*`}Q{fMn@eyKPKjOf-F#V3eFKu{rG+hN2O z5Kg#q>00Ij*x~2Z-q@&1qLSY^{kA%Hr?)yC;{FIpu=s-$%^@k^fOMy7YBU9zn{PnT_i;3b?JxN(!Mb9i;2``BAg1$cgnGxHtKded3x1Qb zY)i8ITWk*>dw6*1l3xBn86BbRJshbNOHP@hfgQ|!Dg zfIarUwSBmt&i`alqrzR-P$Ky3d&m-5*xy6io?sO5?s!s)CwHJJLOs0skGLBw6^^C0 zT{jRMSUdXNgWqH*?wyD_Kj?uw@vpw#V;ho6ZJ7xaL zGsA6?vdUmV`z@>F+>b-EetTft*zQn%E($fQ^r%!!F~P@UJ@yKVY&}lh4#iYW|t%WhbkK?te%cvxrW&$hGb_%U3PhUz+mtrt=h0x==PdAT}^uDME zB=V;wVc9Z55{HZ~fleC?u7J@S_yJfZ=l)IJ11d8WE|(Q8;HUwk?R}Y;J~)9OR?pzM z4FV18<)^eSrE%tG;I_*lhmBZ~uMuxW{v$KF6)a8&*ve*mSG3m{W(*l??IzFIV%JF; zgQ*NZSH2BAu+Jq<`F8lfiJ3X0Ib^!K^96p6Gv!cMl_tIaJ`sE5*hjl?{ZnJp;Qi>y zc`Vo|#{78p$XS=|@s0}bWQ(8noWu~u5+Co zuQadg4c#fMY2Bjk#HRPvl`rBkeKA$rE+#fd!e6hGY#+pGd}ZYH>G!91jObJRpx_2X zZgC)T0~L>XIkwElOjA-yYU)Uz+Mappy4QZK_$pQy{NbZnwQgl7%1FLSOj{Z)^MgKI zC!A@R)ers3>YLFpIFkN-6<8Xnpr`iS7qPL)Z*F->aF)HWLvQ9=ykyC>Sh+)-=$y#n z*0-BSTVDKLTf?`(Rv z60h9Z!S1c$`s{g+S@9QmrgoMEnfJXu3_L!;hWYUpK;+`5>n9;b$TICt8y%XvI^&br z^R2nA03daicy*}!;lY&3oaI!)1I**Fu_)GLqbzhEK_l&ocVL;=U49@Yij3@s341v6 zENg@$BoyW0&*OLmE0ztKJYKq`4YfA+?O6<09G2^`zZ@g|k|V5BB>v;a^C$)}`h z32>_?BAG<5-M0C;f~g^*?hLH8)s`u5KkToa>W$5Fv-~;hS!<`0eel$|skyhHBCLKi zOo2w@>BQgwy%XOn*@5*hmm=rFSAH!Qyq=gu4|M2zK8P<9!&9gg z^?cVMvKK$?lRLG%>(^Da*qT)kTE{Vj(O2=D2~|$yrESg~i!Qk!T+s|6Me`}3QjOWB zAZt2Iku%yk5c9K&onhe2UU!hN^jqs091@(4?n*R{IY9Y7zU{Cw%n#&hV6s391+kTd zZ3TF*snAN!)vD?$T>|jc4Tw{~CZGrW#BgoA?#mZi+|TZg4niPe1Hl^|nI!6y2sjDC z9!DZ+0Ks8U&8Gk)bn@5wq=}vOe@!kI7ukA;DCHqO2!Z zJL<{j-yP_lkdBQ8LW;~Sp_9*mmbA0Vk9V*BHvYFh^B-RbpIK{`$MwDiwVmtF>v#mt zJ5zS{r`}uWPlj-r+Ug+u{2!iHym;13yidAC1e7er2|Ofr;`t9BhAS8QPfG@-d~;mb zKu6Lv3#1?O1%dVd`p*oS!tS`@kVms}Qv=~##m}%c*XLvE!!uS}=Tl~RNX3@H7l@!+ zc2@4;)O=b7!>y2;aM^&8+3hT;BcChi_`zp#9Ks2Vpeu;b@<31?0<9FpPX*+<;u$8({nr^MMd^>gml;F|M!=ed@+l5@Ruf=30j)m3{O%wQ zdeE`@@k2;>&yzkl3GeZGU0Bh_K)b4|^eiOZ-BXaF@NE42W#%~=jwgEFDn?!8x;Rwf zAxa;dKr6zU_;F7dG8^MKrWKBth)_Wxes=EpzdY7oiTj=k?H>KrC#UB4(0_5b+E6PW z?J_v1-LC=Cm0C+7n51a=76XRnU)zU!q-J)V%1t=4zmN*zunIwD z*7A0eK1S43#9GHntk*)h(z99U?Gq{)WI#DAd|}|pJ-P^_Mi8mQ!)`;g_3dYK7gUhl z4slzGP*6Z!(aleE+07jk-&5=eJq>VD=nImQj*@Gd@2|nkg>tMJ# zi!_ozFHmY>EE4n?z~(yWBf+YrRlETwA~aBuf#59zmV)Ts<&m0buq$|XybLjK@Se=T z&-zTgsMu11utyX;<^VS)U~QvdNuqMk#Y40JFhpj+SH)zZCo4T}2q=c#RSu?rme)a| zlIvs;ufm9mirsVzG2TY{njz0UVEt|Y`wSdLGn!wxT|AMgSiu8`9rnX9XUYIrjPGxe zLQD=H7`bQ2CN$6#oSnQKOsC1SC`%o#epEfqi)XPXVryZkTA+36hAlRtz!rGYe1d|I zP!I=vOT{EQ22G5;bg>VP%bW~9j%&QTL-e);0lq9_$V^_t2%Z{wY0xi^hJy<73b7M+ z;_0UkN|LE`@L)-z$?zgbo}d!?`aVNb=kSSejF;CqP76CUsqJIq*SCgeIT3A+Q{3-4fczZ_` zIULBSuxd6pY?C8u($hj0f%E-CiZjX0q*Pe%EYq=?Z(bgY*IA4Wqii*HnUaF4GXG^t z5v#yLov5ei;>Qba>wSW@8;cvhzk&67b`aBbPH!T&O}9exdJ_>ZpNKNC2I0vRtQQkS+FjWod2gw-Qy$^4oFJJbkBadFeh2 zwcet!MeHoM6qZ*T~+x+bmvqs^Y@%MVKu}6rh z$lpc+g6NAz7nnf88o%R>-)okfKXMveXDhYQU;fU4|JPwq1j7K#o=w0~^)HJ|^&g82 zVrUO^l-o=tMMWP#8HC4WJ=kNFkSec?9u;y6g%SoN5z?@uKeDn~Iu#9PHX1yrN+dnj zLd+mo0h-8&_0N-N9_rCDP2S0&`aj6PB$jAMSV*lLNu68*hA^0EN!yL!V7nqu^4cMV zB`gC1PY|0D5a^tGx?!{+)@lPH4PgIuLCj0{sc@gxBvDbiClTX7O@QB#S}p8Wo*?WH zMd;`A5OyW8G0F#7U37}ND7<(3o3a!a`0ssU_3=&=eEGol;=0Cef6Q_BB?#?cNDy>L z45JRb4TKclF{(@kmc(b*G%DU(cBrm1s1U&%pK48to?(e&H|=EB8qpdkZcd_ke!q`E zvG|R`UH}Jf`#eNltgTG2@q4aUX0dY25Qy6FV2OD$cKHUC%>x5cuF`E18_U-gFer{E z$^W_n@-Ew+vh;N-%hC2}+3`EYiC=I@1^+A&g46f!h_mUQ1+0V_4tF+|T=R5ocg`#u z40-#Iuxj61bk4gjXis5VyJu5;`ROALe=M6Rg>Z$NCzuIJfVZF{!K2)Iz3)l%+Y;aY za(9{4?tZnWr_VUsN8bzFM`6by=wGJ-*&3|+kWdYK$V&v>*mXf?X1F8LdVTX>ND=RYKD+>G1 zYjv~?9L8a0Cj&Lb^KiZK5Ug~+LHh(6goWNm?w|P5WG$fdIQt2xFrf8gI6hB6fZU$% z-+RWCSXJ|fPvMMi2lEFu^RpK*(Ep^Rn>EW0Ed^SFz^Q$LTi^s`BV4^m_=D+%+%Ws1f7sf&UuH=Bcs z*c+-T__@>CQtTly*-3EH(FG1grS@ECV%O?v0s-g;#)B6W zeu()x(uam>PIPA{=@m|=1}d5E`zoJN9EY>kQeYI$C-unu>D4f@y4~Lv!Avt8Fa_kJK2Y^UgX!V znF`ubVrk;rY)b886)GI=%RVULzC++RUV|4ui3_1iK;yfC zc?y4_A&pX-x`JBf+j^TS)jOWOAf8T0h~OmL2HMPCf;K711Kity|{^3E`9%1{^?P zNbdxe6@+~b*=ZYmGj0kMPHwm)p=wURE=_+|X)yFkn!1;sSd^XCYh4#6)auZ?yVkPV(D`6ySuHF$Gl>;EZCy#WrT|xc zC>|T~F&Zk;vKk0!!@)GfEG83MB58hphTDa7#h9bxm$_hHL1;A zIahcRNa)}j79n*z9GVMPU-Mf1kVA7p4w9qCG91jCIy>dh5>R|c1hatv6kuuR;fN`v z+{%HTnxD^ts#{VadHWIpK~zbJ1KNNXwLcjW!CR*Zj4C+Kp7I@HSP z5quegkD%tlUpOi{d3aKJ^6ND&`=vsO6-xqwoooe6F|GMzb?V3qD~;MmXr-aqua2z; zLID8*-BCfBbSOH>Xf8f{W@QsiL6(Cx4~$%Da^Jpnzh?KDJ3OpAF;Uz1kABSrnObT7 zYBKkHqM~}g-?RGZfqyy0*rrs$=ErQ!f^ypVaqLS}!yfgzDinD@_M2Ac+hb+o^cOpE z7JZ~%X5&knrFVb#mHytvDPF!jV*~P&q(k>tw6FY(5!;{i<~Me=3CI2*RD6AOcl*a{ zywG*I!jo$6KoLO4*ym~nAtwF-0g81ma6G!Bpi!%;sxpVK?E`iZ?if%4S&Ji;>$c&! zP{@F(VpMAtHV0x}8Yo@U zTx0q^v6du^dLBrlx-!F3w#MsfkDlO!OcTMDH7mO}J5y?aL;O*xE9kQJUB~GMYfO`% z+mUK}J1AP)L@>orJyPr}q2=sO!g*~6VIpGUG~HQTx^k;(b6wqiX&nnIxd*MkU%i^b zCPJ2c;&hX5{Aak+8?UmNuUcyME=Ez7LCbr4+bxTC6WH&BqSK zo<4heF+>+9<>(zgp;<*L?3sHQ^4pyZO_^R>wT}0%3XOPflOGKpk#pbCRveO9Qe31K zQBwLg8DId)PDNJw{)hDxOH16C$*0JeFj-a0^)fx8;`gbET^~8Y@&m)M&9oNpRx%P- zTvF2gSr-=pn&JL#Xg0;(ozX-oY(}+3@H?2iymAsR_x@A4!l=V4CGbZ=H7xIWgxr?> zrGxG{>@|S_9_Bg|>ORbtLDgZc5Q2;VLd6j4b9kgzwNIWPF@a;p!uJm^{YhA|!GpLJ zW*hkW_rhvx1Y|(D8@EtED};E74?yAn01~!&FhEcQMj#OhNjiAw!2wlK08I0NWy!*D zTwei6QA|QY^J3ABNM?!i>S!LEm2)14-P+jM>4Va@47OZfKffHn#4JWX?kTc3fbAa{ z-_!SCp^ynkArmXB4AA`r>?U4mX=`&srtd3r(Y>jwfWpWi>|HGG*rTeZrnSFc6(nwo zz_Eof_Jyc+B^4Epj36*>d<<;vNHEG25)yL69eaU6CvuS%5Znh!TvXp9J?%9RatHJqv?aVM5$Tr(MAoA^&7U}mY--b-H_`EOO8ip zx4jnfi#Yr*&a#;`nTkzMpvBT!qf~a5_=&Du$yKD6nV)IXEUu{NFD!V?Xp8+QF2UXN zS@)WGYlAg9m4bDE+j;7jQO2Thq3Q#NkG+;E-Q>%I3%h$4d$HE^KvI$h#ra^3M-mae z7yE=FKfhp%B{B2o;5`660~apF}IEhR@UIiA8@PZ`>MK#SUz?EfA}P+x}ge8xJAz18@Qz`aP# zc=}tEnB`IVBF0UOde42QJf=6@{&2BvsEk><|Lc|IALWXo(s2oE2lZZgt%4(0UOUZq zs+TP`RNRWJy(fZyY!Hei<9H%0fXUb(Ze@s5Gp6<&^T5@ht* zz4^Cct#e=U%lc!FsV}YDzKLfkR(C;9A3QMGfPKC^Tm=Djw*4>|BSn3HJlQ}_c31Bi zkp5%M+#xAoZ-3tc3Ve+k=e#IZ?QD?GXTiykOvJMirbz4pGXDpwopO+}t z5Sk^65s7La9n}GQoBmuNU}SM0+=~-P5Pn(M#b`=NZx+ZYT{l~AJ>vVUolXu z`4W!zdz7fblTAK;#u46a+Qmk)zi{=RBd0ED^U!_JoBmXVcjTs*U_q31r{F~z(Pc`8 zT1k2?k6DAZ(V|~P1^u6Mu*If!U3Hr$1l+eBV{>29T|9rmZNh_}_6}`lB}oB~>WSTx zv4V@Hrzy@WJ$(4;Lgj4#qHTq8O|We%dw`*P^Q_vkH*lo>fbcpLQ6qOR*sqZl)Psh(E z3?qS>rt@;>^LG=+wS<@P?q&Nn3f&cW(Hn3zgZ?_(t=mh@SAP~+No{e)Ys6SD1qJJE zQ`D&Pz&Bis*{|rloST?{uYI}BmFKqm>(@oM3~2)4J1TjkgcEu3@Vfj_o|#D+1Tn?e zDNi)J>dR=R&z)4dW0Oa9e|MqZ{9h3N`T2K| zd7XcEA?ttrM8ALj-@oqvnm63iH}~f^{K*wY%=yr;|7+e~F}DC7o=}LvqyaK~t)zOC z(n${V7Y0o2xdcu=<9fQ=C@d~f=$`))gFJ5&G+^cUTaKw^F{i*uj!M3-q?C0Fj~X6c zhd+!tj5%QFbFNdiD{;n1A+KUD>O}tFp&(Rbkr5Jp$ z*k?q*sUOyNDt_;lknnAT1Pv1@Bd?REPL?gMj%@5+46RM71(Eqp)CRtg(Nm5^;wx8@ z``UCBq1?Ve;@H1Y)*2XW)CtO+Qb;nf9x9ghBcST*Wl}W`Zx*MbI^FwT{#4oO4;l4h z6Lx|0uW`JlSOQ9+)Npzk2EIFp)4sh!YZfCMVhp1R7J1?_PHP%P)lRZ^}`c(Ou z?z5AJr>>TFpTJ>45^VRnBKJ2dL}4#d2iPdf^Q-;BASQrOC0j4}hsmtj?0a!K%WU<5 zYWJe2bZ(VXic(g&GGo~HbP_xZLoAz)q8!Z4o_p~Q=1=SMEwr+uF3dFLy!&xI0qZ2+ zddOq>IjXAq+WL)weoK|~{Qcg=8wDrRXi)0!vYR*W*ns@>vv3da7ZJp0--t6f$Tp;M+@ZN-4u5hw3m&$4d+MG|7U)H4~6 z7SN(jmjW`<&dOj$f+j9%^`@wHt-Ce^AldKUxv_f2Y1_oFKax&zVtLEp(JDP_g?-tR zSfR}!$0ycaaQ5EXT-pw$FWf3-q2 z1!ri*ZL;28TRrNO$rJZZ!d9tP3g)&*8Hk)>zK% zmBm@=)=iRPD5QEjFd;K^{Pz??wIImhu=o{CLyH2&WR#1rEhJ9KV*P^679yz2oVPXF zVg(iR`L*$7ECx)ZaVV&5k#XQ3edjhxz-u8;g;h7LEC<*1YPBIJUsn62q6SZ}9|Nc1 zAhY#I>2lT#si^x4h!u|mNI*HI@1lR;o1PVIkJ5uxVKPqrhz0I7R8rv?_mU0C*qw&o zQC*#u8D$)LIq?+5*(M+S$-1_@F!EVhWS(QnysM|_zbSre6VnulPZ^fU7w|YJRyk`E zj>*GVkYs0e#BIqf`r|9_)&yU`uZ^B76UjXY#+q|KmS>kAbTtUMm6Fm^XoSz=;ZnTM zOqG@;=*_!W9ao=gE-KvXT?qnN>(RnTkNk<{t2T@y=`2aKG4{$nwqt3v6{`vvHk8}< z$9B8ei<~CN4v?EdnNO%_G7^hV#%UE#nOxsiR-FB)z@wbs72gQSRy# zx|>cvF3}z2p zG+~e(6%ubOB@^0p=ZRPB64(1A@AVxM{ouh$rdiRgjeK3z-PMtPFg`6CpVfupNlzaB z_v}(C?mf0e_eO0ed#=Lif(wi~#g#y6%7MniLYjCR5rAZl%sFM?`I@Izu3$Y_-k0nf z5EN`k3?lE|bZbjY_fKnL1`S&MUqG(d*6$DzaNc^?5KL9jIf>=Dn>HQCXZ)fif`!Gy zd^Bdcbc&(x{o(cM8R(_i8knC2R65>vG6ZMsNl-WtRYSFPrgHD~O5-Q!UuOb_>f8R%WpqXK-&D9+n zC$Z;0t(5zaJ(59H1J1Ydtqlbt&Z0|Qm~au&jUpflC7M;@F8g?W&#Rbe-&dE`u}L++ z5gp#7>i^LZ?C@masndOxN1{dF9AKJGcFX$n2zv87*W}#^^Aqid9LPjFh9hR{e$p<2Uo0Pcguv+0*L$+~$)Uisk)O``kk2^w!3(h-+j~j0 zUl|n+k0{q@Z->84a@@bK0Cf`y@Yx(VVx%_K*X7O3G7%5}Qm9+o+hw2!6-j&g^y&F< zSrPdd4&$l0xlCX<<^ay70r!w5+GBm<(e}!SCNzUXL751Um@+h+!G=W&h*JzL<&e|@ zqFzaeEDi;{8Jt(4cf#NWjmHb;&!aewz^uoJBZkXy?z>U+?Bpa(cylUjWGFNU9kbyQ zB+)>Hik*8l3b8v6H_rf2mf$ifkM-O`a7E%fa`%8O^L>4?G2AN;%w05rm4(j8$N(jC zB(JRwWEE-y?WG7vP0>q2cwG&p)ai>|X|4NKO&P^0!~tCL$wuc<3h>cCSb24wyup89 z@at*__&1!Pzb_6sPWfIQJCbNJuIwZu&-#Y-`f}R=n?5aZL3_Gm{mPdK7<-fEu)9LH zW}2zFej6b~Z5>FaSxTZw4)SF=mM3V|>tiFO_iqs5+<$BkH9r0%fJ}Bm%|QRaz@CIq zdeMt}mdc0xDf3LF`5SUG-(nQGwCzuaJ>!2WBo&kLT!Ic2nWKablAZ_&T3WJF?#xN1 zHE*5CSlr>FnShB2b&5Oir{>^l@HVOp?pK*v=L66pgTG2B z@*Mm)ARJ18G+h}|&8MfQx4%dMbqNgtyYMaVfp~OBNJ#4}Ayp4Fm`j10wmgc_1Wl{Q zjML7>h5|I6OU*iOO*e;2NXH#(Dg)ZS{0e6~T*)1`zy>#rM^#IdI`6<1`5bGEz+TaFkVZa$5COdkLt`HttM)aNqWd zVDk}4F}!(--|jGVrX!(%q@Y}GnlaK5#2P6y!qh}O|ZTjbY zSfhtm+F!D!eoJ`O;_spg1p>ijD7J3-C zZHCp5$p#5W+4j>Dac&ncTtMzUz|P4qy0Uz5$8WP|AV)mya8I#Mbp>*{z&tbWA+HA< zG8RT^Du7j)fBx$2IS3#3{C0(^0Pgt4#s=7Q1t9rNZNSZK<^o@ee!ljB zd#>FF!u?hHZ20N!5V7k7-Y)Q_9-|vVs0*O)a|Bs|7TxL6$m`9MUHKvN=r}_;vP0D?qh%#+TW6WKr_|A!vYP0Qho(m_<5Jq%+&QOs zTvmDFVA5TTpQH{-;rmNL_uokqmn^jjsy=89Xid+@${wq7s_4tv!?7S=dXbk&KtQ9v zK&L;f-n+Q0tk<+&B&_DFw%(!WzYQ%>?>$BI*5`oUq{@W8Je>jvOz6!#dhq#dMJV(V zQ-A6bDkl-{&zIEcnvD*h-dJPKd_Pnt;JFrh;Tl7-zAmADT-xum5v7e#t-<-^F*;wT zDr)JMpYJv3OK_{w=F~eBAcrS+!kQ{)J16;C!n03MG(WMj54Sc+p`9g9A$s(}-q zes?_on_Ac^_JH+++9Q8khA$l*vXGS92`#%T#EIb8CN<`{83bA_#E$#bPfpYJ9;hsP zAx0UWg6}TsY6!PwpVG>3vya*a4zeE8Xvd^Lh}{ovElj}N1V-x;Y2U^?>@U>NrmME- z%Z>J&G~7&@s;uhqWe4#E z1UgTKliXAen9*BJ&{3|YCcTYnF-ct2qQ6}R@xZeWJ$ur)Ax@^2Y z&2wfXzLr*MjBt9HSzWFvvv0N-7#v&}A*>d$+pksWqWqrXs+^xm>X6=y(K|tTQ7GNX zvdn^L%*6SPR2oj$Qp{#g}cHf!{yIloVHMy|f$U}jm z482+CpV31Iw?|7ne~C<@6z=vdAhNTf+-DP9(3E3f@b|DAr2qknv?y2!`oRK$h@AXD zsz4hoza);;Vs}M7W!qwFnRj2T*gt(5YI;-FX?gU~RqTN{dTWxfo0#f&j?nH|Pqq7W zHWp`-ovoCU3_HtRfp&+8Vp8^e-a1cXCnJw~vj{ki&(6o!J^~6*;qSl4flF%f55o*m@2ywu3H(4ivvTjOiHcFs z2r0`sR`BB$M9z@=d;R=_Rkm?a`8ugHc{-gR`7idQw0c0tPD^t}Btwuq(+7i)zn^OEk`5 zbcDC|HLa%?7LuVp$r}44V$qZO0aOs&ptE`ze)3aoWi?*zD?dL8v_7aA!ijWq^*LhD zhf^)YW7mEHz5dEf6_hZvSzu<7)y}2=`8Q%#Ewxq5F5*<33XEb)C}qJqE}u%s^*!(v z)AzX(r&=O^&D^<2%oqU+Ry~_8HvaAs#7e3FQ??Ae0Fg2g=$aOt$-XA-@i9MbjxDij zipDKbozPj@5S@StEz~F;LXiSQW3QcQdT|Li80y|)t1hc)5b1}YXRD^+A1GLGdta}4#!3+cXZ>nuNp11ggUER8Wx04wLZ%Htn3yNaoa`5V?xL$FA(=x4$~mw$gttV8ejXKO_Va5 zf|Z|Z{@^X6>W5s4626xDZm}f7BPFMgmWyiR>@1oehvza?-u{77{MrJDtHDQ5{80e0 zJm3#bo;f88O1tv2&LaSBq*CD=C8w7T56h$yNMKy>z_>^>jPgk$rBxL|Dp+*tsRP1T zmaDnpL2r|`;ADaz)}b8D+?R2M#-YG;V+1a8NPK@=8%x{f5)_%>$RY%B5$|Bz)fQ-PqU3wvP+d81%^!5S8)L-f4;a7KVNWr~m85mGkFA=HL?yEGi>sz2wREFAd zW=TD}u9coPX6AIq24muoVup?O_n)ne=s?}68l6cE0;T2T$g8il&tY$)?Ay@V>KuZ! zL)>!DDb}?O5((X_7yARJ(q`D^cXdfDhGv#6`Wg26@YmU(GO$6Q&bb7+?PY#BAgL>s zM6qS{KcjcK3?8j1_&(NXT5fZ3mwUL?g5E_UN=i6hS>D!E8_G1Nu^y`yJG)D*FvOdr zMe!M|ya8avk_>K1#0)AA*)Uy7aecN?wD91|ctq6OSsnA2 z0DnxkQsX@9>N@Y|=d+?3X5qG>i5vx+a;xyh?Esw0eOFUcLkr0VHb@QUKZA%i!K*kO z?5%Qu?AUQQ2LW6+ zPN?~TLGFg#SQLV}3|AI`OJMw6AJ{!Wvv50zlv5mZJ6;RSeT-aOSUL(2PQ45tcXQ$0 z$|qP73|*=OS^XlOEv)DnX}aO;R)@+ z5}sksW@;P#5PLzoGBJ0!TDVptK<)S_F@JAb`F+H(hzDOYhl8<0Z`FIIYRQXCAz#=x z=AQ=PJ4^u1fGm0^R(-&_ti8GBzBWD;$DKz*=D5U{)n1$B8#5g_{*g@YZe}@E9n02P zms>So>lHZS6yoCQv5HCLjaO6%o`k;G;9xdz1z}m%PyQ)EaDYO?ZfEe2IR~(WKPo(6 zlA>uJy^^@Y!NGxQefq&==yQiWBnOIDrVHiZU>)q?wQl47r#FjL#K8bKOv1j(^2xD)6Aw}*HRkOIhtCX zEr79Wu~jMG62TDOS(Hdl7$@e$_HJwVOe#IZCn$S-$fh-~uMR>r*s5K_<*f*agw!S$ zsl;#&n9E1oDvy+WJQgTdT%PgD{ZTokIa=e)dmQ(b&iDTcX+5ER_2T z*>%%h1@knC$0+5%O!?QiASwUIgk~oGCBE3DUx4|bCPY?A0d?jN_M1nz;l;B} z`TO`@fb)m_buHDw9=a(5q5(Ye`A^u?kz*mx9c3V_I7pm&CIMaTLIbF?5e%mk;&p?r zvp=|u<6{2>Y>s&6mEkPl6yLrC0U~k0CoeT=BlCje6zYN`!jB^7J+=<^266{f&=H_l z3pK00Ligk`8baOEpx$`(^ZE1V=Y6Sh@DW?Ipy>iY=9qHo>~S7XtL-A7x(cRNR(oVcq4Ai4A zQ&XA#4q$qfmnnKv8fb|~A3}vimkw??x7*wFQGM@KM^{ZWm&2fJ_0!|ATcb37A!h5J z>5!vaP9N$ZsoWX(ac#Du8rzZ|-!Eqn&TS8zA|ej={7_80EaLL8e|1!a=YTo3XaA~k z`_`)6-6RLrAX|Ppbm_0A*AF+P2g>htvUe`%Y;r5_9#%xw8Q4(mSL&-i`ufnkWk%^}jnv@gE|qlR8iqD&FQ@GrW!o*gAbiPBuvvq`!mMfhr$FH@kkInq7tjZW zCTO>mD?*bBz<*Yh#m0sHH_hu=t1KviN?a((@s4S-orLF6>Go>)lffDlfu9()Dhq*Cl#W15!tP2LG6`J}Of)q;9eLLp zWDmfejrooVrX9h0X#$MZ4HUa8QJ0B`QlSonm#GZaxD>P}i6*7qFoB$Jga8Fr4pYm^ zEPx_{jv^D1o}n!7hUy==F1V&U`ibzH&7kmO)GW^hT72%e7|ul?>ncI5NrwWHIVHfM zh=Di=Lp>8BYXqE^-a$q~DsZgKVK~0kPy>fUKhWysK=qLAx;_CvKOBHCnRvdNAl^lg zFOba0_Kq#zcid>}A=lqSPm^uz-L0ycR?6Z?1EbGWuohV`8)pZ<9q7v>#~ycw*tgAF zIQBfWl8RDRtg>EomQE6&`RU>#>H{-5%hT6KbZg;wv?hDent#mOr>CNu1#&i2dvfg@ zh?(c|r!AvNDe2Ek?w^@{;Z|!loVkup-N#>Ku!QAZrr$?Sx2lqMIfbMP^j6$cB_HhV znm>R~7?Z~i1pu&pSywY0H)mJ|hK4ME_181!>wTqd9U!Dz6rvJu6c=_&<|_AEBxsA@ zQT)7m!uter#;Lk64RpE1&PPxH(jSU=Z~cb!X!!XA=6qv&i7AQmAcO6(UDvzwDM|&; zL-u)1i>RTua`TRuhWj+<>m?cMs zscHh9OA&ysS`cO5F!i$%aAlXn$F61{gz3MH>a4L546@~xLwb5o4MLKW|LEx%H!o#k z10)vxC~MPN1wWU>=Um2_jKk>W&)9nNpjjfxYSpORP1oyJ59V%Jr(D{zddFis zoUD*0+03FUgV1`{?(04h7thx0$agPLB}}2YZD3e@NaDzRRHYivygKkB& z=p=$(e`BWUsijnZ9b%%(R5!=!C7`dD)mMPYW_#^r;`rq^b*luRMi_DZDtXy?IZ=~b z_h4Y|97!WW{87k&QI7Cu)1_jPVQgE17|}mCSOt>ck^FiDD~9kU_hdKl*`8Wl%mMX- z1&FIPsvWbyeLWp8e9K>JKN&Sj=dSAVLe>++pe4aZo(ogh0>t*n3IGvAA0SLk#GVu| z=154Chf*K@^GT z`%hQ<$6_OL>k-z@Z;xyFaXyjISy;< zU%Y(LMtYtUDrldB*^$zpoz(6-hwuHq3Oy^~f+RBe{uOOUJ>2~SA#a7cx|^{7DFUOg zx6n=u)4L)Kkid1y?g=}9`^U$B-=jtiwvGGvoj&VFOUAo4dCVxE{p$GFG~x0S8<+VuKq^F;g_PKB6E$iDir&(S-%!s20{5}m{Hg#A57rR7_Zy#Of! zYiYix?c#p7O9FPN{N7Lb%c!XJk@7tOuf-Sp{fn7{z5w44XkF^zjhi)d8s90^X1{p( z-hndkwY7U|eAp;|xX6Kq1Ub_|oFOs-xxKT(G%Bpq7<+TxvDBnZk_jSk57HK& zv6-uV#4*Gpyze?zW|~2Jn#m_mgN*?zctc2|_PLi*XU>b`L$9LX(^S2C4zs|cP*Kj; zP|wq~OiOQzy-wMgjPPoM{mkn#uoX6A)p_8w(Xty_fY3EcobfRuD=f;>0>7JlhOXA7 z1B*8w8I^mIE?%<)=R}U?R0ZQFS<*v&7M%;k2s7dqLaLhogw{|iQiA9;rVf&ZkLa_Y~#s>uQ18A<%hJR`M2=-ilr*neTLEayoSGXoSy#%-}M&{TRL z?hi6ZF%s>95J!Z-B+%6f&&kQr@!m5&0xEHc7Y5441(=CZ@Kt2GgXDd2Hpxe(+v)FV zl%HN4ZM3qv!%1x$EX)}|*Nng|Q0AwA7qta!-UzwbX1FrU2n*O;HnadYc6U|5hEy5j z?v$rns{@rpFMzku=L5|}^U#ncQ0SF?FA(=k;3Gx(t{ZW`1$U=li|2%dasp zJ#^pSPD4T&5z?*_0{Vm5@>(k|!4P_6M)|V8!f0xFkhdBG+z%G5Hi^S>6|{GU2p6UTe~ml>zz&Rut+1==dZHD`Ct4B&8TAZ(|@vKl^49 zx@nNChM-(mTY3NG=^~_=+&ZV3g_N_@6tL$`pH2XpTndXYU~o2~I}uNM{aDXhQ0oU8 zZb2Y*i4Tzrj{W!%4+U0e<_Y4?$RSS`X#f1vN z`;JzG6yE^s+tKCEhyO$|YGz$UgHMJsJSxE2Sy0JqZfZh9OSv4@hTf#Ly?rkn-nQVz zp`0#%MY+uA7n<rp>>nA zntb$EMmRaCyEqXW4exITG%Jw^vea@E1D`LKyYJeAUvWv5F_5IdCz}D9Av_G0SfB1< z?%0BG5sigpe5ui3pvGMn3vplY|9Y}tL1&e#*RDxG9jOOwT<~C_;7cze+{AV1(j{y% zA_A;<2faErSO**&9LMXJL5S8IE3~6G>3nCPtLrv?_TDdaasau=^hS^hMAm6xI!k zvCuq@HFSVa;fyaS7nTDnw25JX1$%gi;(EmX>3~SA`}}z-yJQH=RH7gepkvpmRD)IT zHYW%&h%gikMOB0xL;8F*Os9g_Kn4tup=70fwzUiNV0;1sCU7>vcC!4m1X27Hm>FPE z|i-=?8o$jQsgi&lgq2M_M)GLc+p zuy8i+P7j3rSp!Rx9^`PO;h05+MdV-vyef!5au(|B+B z9s!e?0Jc5$Li&k_boNx=wIl{LAqdBje<-OpMKJyX?r#X-Z0Q>V8}+n?@t}XX3E$5h%-ey=m?s;p+}K0l;|FZ&h_O#R21If8K=8cku0@%398 z&{&-@g{Qcf^YyPp{38{p3;kvD>x-Hg-(b-A<$(DQT#s-lCNW$-obdWre4i5b{$2if zBG>vq0P&QcfVidLZ?P0FMcr@0+myMjCjdWLmFwc3IlMj8X?NgHMR#(@K_15BjJ&N2 z3W}U(H?L(-HBA3Tby*g8aJEohYn;KzHrs(@8ga$Hya5OPz1ZQOS?z+G|1<0|u>P=q z8t?wcVQq0A>Kn`(AR_t4uYdop{d(#Ct3>3VL$II9fPXx}jac6%SkC&F|BprSVIzGo z{+*5=1IJVsU|0w#gOGJlKbh|@(?t5L2_Bxd>&?m_UkdaTzsGIZeRp1 z2efzLM`u2>XdzdX+i9d^V4~R8WYY(%$};@y&1~IC+GV9SebXL9}z8x2K9^n(fdWGa&fDkbe&vDjL-9mRN4I{S6J5mWS!(p+LbM%JWHMF zW1nNVmr=vLxpYS8?Fl(9xvEc`=?WbhtTCD4RtB~T#b4AZX)NUq^2*Ne)+qNonG^JM z3)})}oRS7wSc59U!CH>>+MM4Z^XTpYQ7M}x<3~<8Ibzd285K~xhZm*f7M%qw$1T09 zv$rk2y3p}ENC4ZT2=zDcogSMARf35I3Jn}|n92v1phxqKkAKt}!L5qCrC{a>kx4E{ z&oZ2RXJ&Zw{5zI^x99p14#u0A2Wk!+A(GA1uSa2%UH2=LD&K8Ztanxq?l7X+c(1F* zR+e=!W~T7dJYS15JD+}l^x~CSDZyP1Yow>AH}2dBPcYXG2)NDg0NC<~vDn8BAW)F) zT>rfz1FKQalv#%s$DcJg@|iu4!#Y6?hUmC~ym+-MA3s_>x52z0sq4b!~pvHL=QrIAm^%~;ww4>|wQ$NfL5qMe*K=ag!64kN%Rn7?Es zsve0Ew$*UoEtl8iHN(#xDF{XsfO;D5sAqeFJ(5JR!{%kyOaiVE-YLOtLb}-DI!S7o zRnyRT_2U-g7JrZq_Dsj0?02#A&o}sbHPIYaE`zLPSJFjf@hn~D;Rbd>BW4~EufXZEXh0A|gi+TA@#ZzUDUJy()c+o`t#6d>!qa|9<(w0c^=3*0>R!=bU{&(a#V5lx~yl3 zaQ4(=8I#*C>7y+%O@7!qo2GZQ0s<=2gG!L=XxVtP9B*Xg@e zw@8MnnX0K{)FH&q<=#2?=-l~*Bi|Yv1rcjtQ^@vew^_->+$U4 z;5lO4z0{%c-X=ttUhsjGDknCZZqdSP(@JBca@Vv}4qY`hW#~)8_YmNewp%-QR8!TONzPtnVcrW9ofMgM`J<8w2{cragog zO-6~n2#FYT$L>C(WXO#hELnB=dbCAzef6kX3!!5@joC`;Qr|7Q^9} z%=JX*hmG6smG(pr_=|{5R3g9Eu3_Qk?)dlv%gX*3+m2;I2YkNO=JUDOwN72!;0#$ ze%f(WQl%F4(ijJaS4Z82B^K>xL*8U$Nu{Ory@8>IM7M{X6h*+QWqpiuPj00xnd!%y ztO&V0fJ;U8>G2B`iw5U?cY?bG>^GwSG$+fDf86$duPW2Z$Y+i~qhg39KU%JBoSoIJom?9cx1LbRXWioie)Eha8*0$zgPauAMC+T}Jz zFjYg~aXBPgF#X2K%FZqn0B)!R$3r!Nx#A1_-=sbM9t^wr*>#zj!9nH*>87n$YSis# zvY*8IAfq+76>-K5D#O?;6EMJp8-~T%*%@gNLHW0*+7-Q%AlJ0v7QR~0CaeTikq~YZ z70Bg~?miK6IGgnuWwc_hP8EWPgWZa_c`cH{0yOpPE6VL|ER}m4taX_fer13HgJG&9 zyl~)?Q!HxsR6063(5W?!tEs~KTY4cZN)I7FRDuLfIq|`9+o|?@&YMgG)rr-qVGjZz zzpn?5-{g50624Ju0~H$eIPz_bolDo*qmSsN=`T_|<`6&ZFNp;@5JemHxkL}#*l|NUT*t~W9@Fq(} z7?^aOlL=!G->{KAbRgrI-mT>zSrG|uIlf1MP~CyfS2lPFwLozpJ)uLK0_deN#IH~~s6qHGaNm%_VqjvC?OdM%#2?B~oL}!Z$z{Br z2D8GBlQvgZm7v~_v=T$Et1BW42Wkw3Fg7wxV7`@ z_3I3%JM~vO79diFq=)1PB;6J(1d|}+cr1pp7^Ol{1Ezyj&Z?Z1OMiZi70Uec$Vr?+ z(nAFQgcr8<-J&J~+(d9XRYX1ARY%M1P6PDvpuT?F*VfLU=sRwTtCL?&1|Td{8UUu{`;F5FT~yO zOOE%-R)x#k<{5VOY&JftLl>x-y(9|PjmsKI@rk_oe*C%pS2nb_x0ht+o}88U z-7G6BlVkx>x~~j0cXjODx`Wy_ubxj zli+;r!fBSa`cSjN)A%(`+r#{CvKb8|j& zS-lGmoN4S|TiV{hDarOj=Pp8zjsPNxrf65!|>hh8mM>Ov&5MOz05G9q~)!mvlAbq0Q!I9Mgv zWZ?e*AC?lZn`;NrB?gYFR|e~I-;`jvcfdl(1|X3c^JuZ}{m0Qw+YBg!mKgBJMX%d+WW(cEjgrYi_SFR$oJ# zJ4-ERhgL$r3mlHHFvmLcrd~Y?caGxNu>cPb4^#%6pitNiMVw`!0e=S_*!1qgZ4?@~ zYx7Teg~S~&5|xLI*aP0e2{3q&(WTvCb3q%i>_F^75EdWm&yv{v-BmecGb5%om`0FU zRZK#eO3tNqvJSeu1szmL* zNso&XHG73OIu;9CXCW z$MpDUxr(EVGbf$GqB4Um?}{6LPM|>~pP5P`f!S|IWpQIJ6+C8k-tsMxCcZ>wcBQpl zo4XedqvheZvTUjgS~NoYCScIjH03}w`(=8oM@m~uyL++rV1K@mN#e2+lWQ)gr+Y$J zB)5#Yo#01*f?O~3<=02&=nbct$@hw!Vn8`JJ~ESdLWdGCozs6=;Vl3my>S=9xC&3D zwHEt_rb{= zh&W$pdIqOA#;d)5LY#=QfM#fr{P`>Bey$bUvB=omTy%SXV^20|$TXJE`XRWIVW7Ri1w)V@(7!`g9uO560BykEXy@D{P#-=p zN^fj%dZ>V%ue7REM)uxqM-@sixiK^?ef2GagKrOA>@W2`@S#&jKG4$~BI{T*Gu|EjeKsjVD%E&- zgTS@d#xk?CF90jy;cD6*&Y&hog3DESvvLL!l+^wG7%HjjBdPh`twn02cA+g`Z&cSw*`H_b0i#B;;p{eA+sai=HNB9Oqz+Cy%k0dE z6M*|-Uy7VCeU|U{UK2c#a;Ckk>QgPl+IX6%?e3H%B=>Q8FB{f&c160g##?ZT+4;!1 zjg%C4^_V2FPA=^+_m|z5bYy9x!<0OCf(wV0%Mzb{iD26N^N7v$B^XDlp1bF~*#bs7 zz8ZLC$Sx4^#B;&l9+_QJdnpDJL*ss08o~VRSri)XVOhIae%l!dZwvOZgujsfFbQvL zbTmkRtCqtYh|S$bOV!+cP1kC@hEqSgM!=Fs>c?)(k48lTDCF#2AQZg;g?YJ+C=q|n zr&ixJiS|24^dLqsHu+wh4PMA@!APWyD7&=E53t(J46k#&pK(w-v zCi;G>bX-LspA`r}z{v`g0m>(AGN_rRVIy@7R>+xO65@jqlCER^DFV(DS;Dk13U*|q z+d^J9+#&X?)h*) zscl?$rZ4-p(;t+nLSZg=lkw+u^>sM(a{tPgz!j?N*@GZ`NqgF=8w&a~2$>oS0@BLu z788#XT!G|V_MARJLBV?`#WsAS%b)+EgZy_p3%s02TQG>a%k3OgZ@Wbs*>L$#QG+HM z=#mcSDyOOSlKy5qUJR~cCEy0S)6t?xin1^_=F<~U2_+8Siz`L>dHo_x0Ol^Xx)<8`jUC%R}SXcJeD7yVjOP*Zl2g^&1>PtKR)Qb*(WTRv}q~==qQOw~Tg6^e}~LrxEw4h7$fv zSzCHr^In;c_wLOs%XL-y&j?&n9H-G?)hQ2OAXMd!9%PQYQtGb^S@2nAW(P8f-4nk(@o4hz!b0UVt(rq5PlY6P7;&SQsB%S7EIqC}% zF;nS=Gb4juMuM*I^m3Jk#Je7$K!9I1@HHISA(^m>&VIru_q89kX?MHI$agG2PF z#DUS7T1|+%Pi}J1((j|(a?y24L21br!|Cw7v^C1gv{i@KM#shbS1JhW+<)XufF75r` zx<27{qW44P!mEJ}=fR;~FnPhR;HhRv3L>CLdTpjz($e$|txHZHV(2g?^4bvYSWJoR z@}Rr#rOl!Ns}zFeu?uH+9`%np9Ag-7iIc0@<8RKCp@7(Hw`&H5u++~;tw(SV=2TrOFxp0!wh?-MuIW;^!Pu{_Nl7aIhU|S9o5&BjN_4v4F&b?ISvj~7-&nF z4^^&@c?G`u7xBcd%5~oWPInzN7LGgE1K4+Mailms!u;ieiF~{7C368y`kl?=nMKCi zGoVZ(coRb7e&50%EJ$CrhcQ0K=z(4XC?28)jOqd)+{RVzddD7>yJ{Xr)R7iPS zq%bhn{f0#X7ozm$D}%uy068z&qy0>hE6 zfqZ!eZXOwar?qsXWse4I#W}x4Zk{kX?50p*XC@OXxScTpo$F*IJ#by?5Qjsx7uxSI zQ|2-veq$MG$ZGP~p;!aE2E-4J+YqCxdx~^b+dC;%6;b+p_+Rd~;svo1oO2#Vo}*W; zzs%0grr|b9e<(x4MB_G=7Y7$z5$nUx`81pN7tz>@7w6(`jEi&;_MSO41j|_X=J)YU zHBi+mUr$TxiR;g}5*549zm@P~Ngj>$(c5@X>pvuz-C5?{{OyWn{;ENVKdE@$qcQK9 z)8M3xp`lAjQj@Kwcd%n$zS#5jRc*sin!gGP6sRk)=bR?}P%KiR$@G!K%W$9C7?P4S z$5&Q;Nh&LB*^v=!SUk<rwS}d@wYi`IjzAO(Aihn0 z_QYonAjAE3>mbR0^opW4bIJ{72&eHd;vx`b)P!Y}3t!PCr>)mjlZz7;(!M2>30~Y% zSOoqF2naxpf+~d*jf0RgaTrz)tOZ%S2#PeA_mcn`RmI9L4UVOI0K$i~W6_e}n6ISh z!p2myw-KeKrG;fbfcB}81CSX$qo1-PaL;V3ya4Y-=t5l)M z&f@hL-HAH^b8R?VHWV5>F_?dE0k4*iM$3Xv=%odqGSvh&?kUi+V{!JI!x7pWrtVP9 z$}Z6zxhTwvW>uMKA}qZ_2xxX^u=kM?jw2i^DtM0{d#am$e+b)ex&Pwm9=FPSOO_fp zQm*m#=*cF|p|VKBt%(9>gxR=mcZxNLtvaeLsU()f!4rpqO1I~!&+hHoK+bw}spWcV z>y}d6@ZRwnZw?d5g#lj)ki*jTg%xH7cz(~rOcz?ruQ~FVeZ|bL5@P1sYC~_Rn5*SL zn+#uxXFHl!WGdpanMAc;EaA91j;FZI)WV&X33t_*#qqT|N4|9^W=BMu^D&}d9uyCv zqyIsF4J}ym@-edGFvyMroarGXXdSSRxSo7ICiVcez$*mytOE86-QZylHl(vXGH@CL z3FKS7x1kBB1Y_hgsD62!IJ>bhn5mc|nQ_nK=yW*;SRW|>sA2;-FhcMsB-A05EZZ3j zV$h7p%oDd)CfjmndvlpJ$6)eS5>P)#L6!(q+u7_{#JB)_n;{gGf$|~R@+(~8nShT2 z#MUcL?;4zZ8Fl&cT_{?*ULJ%h6jGf+(li)5)(yCuew4Foe~p|KxU$CsTs8%d>ol8P zVzcMe$4toUO0u;I{w0yxg4EN8~1QN8)Sx!S5E3bJV+r)GH5<=$AZinMQr+nId}O8x`f zacF7y3=2Qiep389Vs@46dvbPb_w}iieNMYjV86?S zJ4)G~ZO+)VA5m`BeP|I%DiJ$K8{{jD+sm=K!$12u+nV2DykqdGu-qy76+BFg51LE* zFH|4jp4uQrW?$z8f?W8nva(9nd?m?bhUQ|S`Vm>#-539=CqXIf6gJswv__`8aA!HG zf!pHiA%Nd31l`%+ep+GQ+E(4;axJzhAMd8P&CK@G3pd}e5>lB}tNtItjJ7T_&QjP@ z3nIy{YxW`x2TtpXAm0OsQ2lyqEcpe9o(}Z9<#_ps8w?wY;KKnK;saf`?Gyx*28)er z_6q|@Dd!Zua26m*h9jT7oR;gT8UOok#b`ub>qjq=v$V8iqZG8CTvCz%`A$!hg#D3;Wk80?`cScV!f^^Uc}5Bf3NRMa1tbk} zykL!D(}?zFh-owt=n^q<0c|!GN_@Z+CW1nnjfY1CYQ$2VyEyKh`7lC}k*JGE0iTxD z)a>b`pqLymt+KL2_p9CtQ2B>*aNe<;!6-UVC%7+aE z!dSH>&C9GA!t0^3%q&^;_37|?EDQG= zBP+FsDY;oSq!eB3g>|kJKnaSGUl!u>UZ*Mz^X4_@5ew=23QY5jHEjzwJO?|(XX{fJ zn>7MXPJT6MoUBC{hLq-Dq#_UA47jA14G8&}Sm!;{@;Hu`t}78d!taR+)3Q>BU1ZfR zE(8zWgn)oYzfoy?0n4&E12NLD=5-N(Ge3L9P>QJGqV!d`%@P4(YKPdhb zvvr-h(IuxYbo|5gxzcTV*RJ=~V{K;}1+zu!*4S2!xxRAI+d<#n#0>3k`X~%KA^*_N z?>6_UnhxBnF!6W6728!dzr_@v`nrI7nBs!Y$6nm31u-8t+BZZ z4E0m~y3{y{j;rO4qp5ydL>5>Z+25-{I#>P322(54$|r_3AFNDy=_)LF-hN?@58W9a zs28#xmlJB->k3c4@2u07j$9fK;nKjJ2ReWe$6zzvPAZL*g`CDB_ZKEjN8hAeieB0AdNF@GIEFf_~=2yXO)bE8wf}Y^SrUJ=U z5+t(-(X9f*<2tKe(^;RaTD7Q2rw~b(k!_=JS~|$ysSMD}uHP2pg?++fkhe~}d-eO@ zyZcU%dE4&vn8iSwquSF2ai)nZGMSpUv&b;ECB6zv#*TZ>j`PUa3jTN{do;GYs(r`m zd7*0Hnb(+#d+b#MsTp%5C6|%lL3FOIgL1>3+|?T}KfQ8lOq zga}RP!|8@^!kT~MDJOS?PM$k+2^XIyKvSa^iYrrD0=~gY`edZ@PeJ1D zx96L7*24M?@+a}sxTaI!YO*yhg(!Cy#hVKq>xesOvOZc(uvuSd<7-L9PfZZVEy3?vF^^mng@(ADsW8#`bFZAZOOQw| zCPO9S&k)j&s@=K2@0MIJQGj;jJ@@@;wT46fGrMK0pu)^#Rn5-o$t_asdMfkI&+zm} z8_Pc8cimTO?R}0=Wo^|JCuGetOnQC7In_*ptqw{a+_&oX-03eK2sjEB8wnuQG=Y98 zF#16qmR~roPgH~5^QE-3?t+^gtGdQe{SkJ&Dj3?N|I-rJ%Pd@@@j~^_PKoc zQk|AE4+KZusM@SRzH@TC+iSG{20n50(xAZw#WyCk-7Wes#NyTDft|<5!wJH}QT1|X zP`9l1Vh}4Cb<+YN$E)Fkw3*H{bi-xqJ1rtKbRS2+c=!dEJ&@lJJVW=$+zF=5z4QBr z7UJ>|(`zSQT>#kca_c)+kc@UJj)s-SvpL#ex4bWw0|F1^J01H+|j?R#{>V?zx-cxMZZGp zk5v9sEb_m8T>nEf?*ANYopkuJsN=++A;YzvY3grb1n8~S&|h4#TQfuIo|2VJVfv8wpPpSaMkqNRCDQt5ndnPia1vDicT%dbFoZYw~7vBR@ zF9Zwfi2xWMF=Z!37>KBmOI666jOv`$j$PcoxPB_OYVg6X-EG^>)&buOQ zvAHm02YLf)r~Ld_RVu?Zip^mHMP$o^x;Z_>_Y{)J&Ze_zU{jOz_U$o5t{q~m-2GmP zjFMAfVCpRu=Z(wq75hW%U~~lGz$~;3u_(d^(WA@-mrQF5PlH*c`faz)e!TlMkYCNQ zgX%@b5HvZ}~|ef)wKxE$xPrqo4QxsO6^o(Rlq#KB2t{f zF<2J+boN;x!EImE&2!gwqWi)Tve(ycT~*96&so7K-m|cEQWEo_Mu&qPql%m7UXk@I zfj@6Do+Gv<84B>@%|TLlV@${Q7=ohPrpwVBUxVW(Pj=Ae zfJ*g8;AqGgfeQXLRBOL1xC#(CVpmNL9Qywo9Xd# zw05ddCw$kNziisfx;;w33>lns zrH@DmkMhJh2lu}Ik=dpU_PVWcMR$QQX4R+!k-ZP6ET1zmdS9bVhql+er}230dZImy z=?alj#b;W(JbnRfEF2~ms6J`_8Fx&}zE%SKXvPD<9XR>2%Ez;M=$0I)7n zR2y2av?c8eZC~O~%usUchMmrBvLIbHVZ(hVTOC-D+E-@eKd_f`H>7~cKHs6P)5q_GUycN2F>&;5t z(C*KWTyH*3i1h`Z-M-VKMaJKZ$@lb2sd9F;i|wK_Nj`h>fX4MiS4nZLFA{7=Cdyt7 zJ4Rd0Y@Rxcx#^DfHSSSe+`ozHaIq#3H9}4V_e}ZT=5}i_t@~V$dbeuRlv{Cn^Q?@B z1<9)QdyU|?-wTG$XUtq0UvC&LGAemOH8N`hoVGn^`*$U(ioug*1fr*(&3L&$$c{PNafWtT_X=zYqidNQ8`rFwsgB4i+Lcb?T zs^ly2NA2dgQky1>M*xN@r0-3}S;`A31h;J&>{`=g9Lo4=jcD2it3rP1#-c#{hXa0| z{UN+wE^ClRa|*0^Nw zCe?9G3ORA^OD^KKYCi$v1nz!36nVoe8doXL*W~Q@;hAelD@$gMZWV`!?ulcl46)1! z;*-8fW58+m)xR%i*XkO+l*qfyY1jf zshlE72{D&gnf0U!f@eUR4%G!@wBgEFlW}J<`V;>cNPS_MFu}9B1d#{sp zIPKGW6tP@-y`L~O4_EWogBc4vPHCNJ&(Ne+tP(U?S)%P{Df26~&r`kUzqPqCA63%D z-Mbq9py>>D#6RSU{rz|3dLLnb7CDX6z=w^i%WU=aJB&I5h_EJ)SqO^TWIz9-(RJFk z>Q4H$>R3AH1&;psahXDn0=>-sWmtD$_Fx{9ky9`Xm+pWH5ap#y9Aspk4YJMvGs3!S z23b5i!-oUl1&EA7K|o%>;-MMR0>kW>WuxVxtzZkD_2332fe`C3>A48zhjLpCX~8Yo z1s)P-*-fB6iuhB2ToO@6ApZsrhj>9D9|**GAlaUMj{rdF@W5;!;0I%zUTBhJ0hR<- zgF3gLevt>)Y~_6YrU$DqD>)t5VSZO)A+qA(jW!!7RX|kjU>pU;??Hx6A|fLATwPsA zv=kxQ$HU-nXfWS{rQQKHQ)@`~1!72+A(h;!jYuBTzRSA0x>Z{vK7fg+eG-HrsWQui zC%aqA#9>c*L_1$(`b`ly|DHAxQWh*!BhblP+1ty7+d=Dd2CTNBJCzJAOB}Rx5Vr+r zR;Iz;FoM?tb~{p^)<8LA+?SUC)jtM+lfhcE5wr-<N#GrKnUj+f`SFplf&k)Y z*X$Fez>-cf+zp-`aTJF)Gz~b?Ch#eMC2_c)7W#~;?(xHVTV><2HfT8zB zhL{9#`II}=X&|Z*ox}8__ch)FggZ0vZFOa7O``PPp+^z27yUW)rE}C6-5jR3re&kK zG@10J!HuaK3Z-B>N`=!(!NMTxCpM@|t8K;I4Y+(C6O(G7p4*MZ0ibyjPyDT zvh5Rh{cF)Rzby%BRy>K}h_1K>hb&}x4kK$nDfsnrR1$8*nNY>%%m-l$uz`n8%O~Uh$_y>-RdoUBsQ0i3p7P zPY?Td#D}jf-vCpozdWYe_+e^CQhUuc!qbFr{j+P&YEZJixN(!jRu-95p5BM!$ya=s zvcU6Q#+|fRQfxuUfnJMX(Sn@F9tw@H@Qk7_C@%^+$b-vU57YtRX5c_uHjyJY0r+Ep z{>KLCyx7@U9wus4Iw-*Y>S~~O&w|PXERE6x<->;#b;Ie146E?rtZs+fLwZ)C6MEg_ z3$eR`rJ#&ay?gfscuz%NyD|c2ck1I>(k$$EBw6FoDvhsn9DP$<12{dhOQFai24+!8 z;0%kZW5iVMZG1G@*u?_;Dv8evgv4@ zO?{^%8+!{K`1+(<2Z$snH||Iy&HEIeu;4cCxYX8g?B$3PcZs8u)V*HA8Xvfu#x=zG9N?{AvKOGdS|LhuiJ+dM`nl8p>bNDcM)_3)!sEx3y!J5sN^t>CL6gy!? z-MR%qgVmaTE;!Y!$iyd!MKcdN8MLdfx#^Z-R5`Sj!A~(qyE>IEEC(5lOGDb8IyWe8 z@vT((<-wQDW(rI~LOTqR@fvGW9ZAeR-wTbvYcUe#@iE?h z$gu%?<%1+N0B3(YejX}fNQWMY62LL#*1Zul())+u@sQyef@NFk3S5XS1cENyoV{;P zGn~m{sAJ?UD`(m}(&b)9vDX7biXXI`pytSeSPNA)1PH2h69^R{Fhl%tv0PvTrL+xZ zMm8{M0Wp9Cg^cWtY-MPucIRjd0N>ac9K252BNgW-LvbSltDIbVg$BkoXG3Fj3htwB z*vir^aI1x0Qy0Sm|BZugDGT`9fSiywq@lzF1cYaODrU!vP*UV(%#^TPwQye+8yHw7?P*FVg5K*4@|VYN!_9_= z7kutwbbi^Wu0AL-dn#XxG$+a5ivddmlIcJe4BcvZl&6%<&zTV~z9PqU^(%=$M-r*);Ul}8#c@8REGMCn)^e`{WvC2O_MSOj-0YRjP1Ia#6{)Q9Z__Krmf;9CFnCh6IeA zC%S#5AnuhAvgs0Amp5y2MTb1~ArhZp*OhWR(!ObMZ_QQxRIk-c*{HqhZIL)eG%!h= zo1UcFXF>;x8xt9_Z07rwK@f-sNeLW{?2xVNf}R|k3~P55&QN4F0IYh>y83z?oXN*i z|9EL;Wo@nGOT&F8cn{7mY%=H?najSiv-nT7Y(l%SB9 zi4J0Fl2r&1us7#6>*rE%gg}Su(T4*Na>&7n1g?Eu@B$d?7(G$k&k%SFMvJp5Rgold z+1C-j1g~aD43c1ZA`|KR3!eFdBI}6;6(BhvX)p#;LPR|PB{)>XL#ge>bQYL@6bL-4 z+y3@5xZ-5tX;fnc(G&&Lq)iJr<`X|NuyC+2!Am{9zu!bB4t}J__#WU7n%depKTdIj zj2#@fmM&%H1Jy)kK4r%?#1m<5#soG%(gTEhQ`!2 zQ*s#)pCPI8?%v+|Y@5&8_fK^&sqifoht3CI*Au&2T@={lfB;XZF8}Ga>lQmS-y!ZX zM4v9a7epHQ{Xl@__TWZWiLQI@40mScJgr$aonLF2(|BaP`z?isNeUjGA)<}*h|^>LS}vzPwIgIlXG0#Ycjug)obS>qa}i4treRO&NKat! zOn>*x$iy=^=YsEhZ1U99dDk1J@%*;i_D+)t48j2fGZC|WXCJriZC;rO;cps`Q=JbJ zXajz1UKR718mz8T&T&yEpA%3Map6^+Cz!^Wk}mGAZ@!W7_VQAQak35O)g2mBJ>aTK z^?(pI{+vC{FR$S-@p+^8i4(sAIY!>SoSj_b*=z_w(FYZEn^%-@h6AVZe5im=mv_|6vk=3PXL>+ zs2(r+y-_qnd@=?Et4MGXBkZaHrq6*O1xA7zu(F$ij2E4wlq?a5YJmVD1Mm`->(`}U zzO?a<|*rpBq}=Je_~%baDBZik$D z9deT?@aF6Qj1h&Vpcm%Cb3z7==m#78=`$?|)kv}qh5_Jebpy#;Kkko^_5oXwTv@cZ zaUQwOesd=|K$8aw0y|3ni!}}J&A#(c0;2=gYxuS4E^haImo7`KY8So^OZ^8xF!sXX z!xkpR(YkLJ3MCZI$?IQ1Fqa2|crdwK>1hRNZN~FBk!(7g3?#-CIDC34pNLDg)p*NH zg++DPVv|zOsgoa+?mY9Ny~lW6@OsnuXR8vDsnYq%Xtaw-p7w)dxM0b}?ZU)K zRjVeflJ&-J4*e!6@R6h8=bA+ZAO9S=!u>@O8(Vmna-Eg4pry$8Y8Ynw5F_Q8eF@q& zl-rMCu@E6~9_1VrwCKXe}~)vzOxg`C54WwPZ6vO zfZk43UDp)&aHl;egag(V2{qtk@Ra$oP-~L7KQb}~PEfDk6 zurIv-s<^eI12PldE~GJI(!imdzBLwTe?U!jcS>t>VL-}id$7WC+p&f1&g$YJ5E#aANoH0V=BHrm%Fp9YqH3G zQaB|0N5msXQ&SMP-Bu>saLB|&U?Vry&lyZ;xFv!YF8LD8wVLm4PJ6z^G#kV6CZ76d z2L?_`V56gBf(1&X62})77O=@L$POCO^!g+}54?|zE#2KaD^mC>S-aXs>{(m1Qs{N{ z?j`)2o>L7>?VBOxQ#($=S}mc}kbYYxF&PpT_E_iDH3}xaNUIXc)M(?@5A?D6FlR|1 z=8iDQHwUg~gcm({@E~#s#|k=*{Rm*Kc)&k8e0uX-1juRlZ6w@=%%PE$9)Jk;;V#X1 zQs@1&LB7aX8HNohL$g^GeEX20VX#(=z>d@_HL(m#|FGYW9Y3xBl}m7+m08`Zvsclo zuw!qs9TBHyQM)leDOP1SABni)>zA!r%z@wv@eM#gap3Tvf+2iwZz~XuRfHN6BF}Jw zF*V2(4Y;Y{7}X&z)lwie%(Bwb@cn(*3tiy18aI6&oJ!Z>cPMdnFOonVeFOY>>pwmo zMg&7}>PlcpMh$_1pWP2ev1@iR9Mvc3I!Z_o(fRMW%e1V}SjdpFX^*`~W7TFdm)O0X z7Ubr=^JV(B<+`MZYvkz<7b!$9%`_%3pgDZrzP-Wj6Q{wQdHNDV)m_6=C${^&M%8;Y|ehBMs%^Q4i|}P9>l6Nr00cC_2ssMwwW&5>4vV` z915%eqe2|$l?n#tegeD&`sm#wcNCdWDD!Sj*uZS!PXu_fcBHV~rDz5|fmJb6Nmx`w zSX6efs5a|@FA#Tf-7%$xk6X7bdE(ZwACR-{8s78Hdq9~XM+$-b0;_xr2F{G~L&;4i zX2dp<%jhZ^;VA1y5&!p8*%(%jkC$}`pG57-i6Oxs@y4C?lvG z!-7N?euWu!XS$^_wqygrt@@b;WO`ep{tpab!4knJQ~$t}ZutL4_bS%_+?ig%8zTTAq!zto@99^~ml_Q(}tY6U3q*^Jhc#|T+-bgZzG zAUp=mkVv+u=uykL?m(0~&u)P%7PgIN#=Fh&(o=;L1k!^v*S4u~eomv2wPmjbZBh+= zO+==0i%RATO}Exq0jaW8l$(Zd(x{-c29vWm`vQ+P_lc0+(A6Dik)5xj(Th_PaXS&` zREJ~KP$`fci{#<05jBbW9Gxg81mD#85bn|6cOOcX`A|ud%R=YM_#slzX} z=8wB>u2X1K+oYT*rEIrftyX}REpiV+ZlZ?$_~-|7h+BXfjyVe^Gc-ary}!SYb}RFz zR)RbjlcU9ln3qC^eoN*gWt$ zs3%VCePWJI6%K^VOmD5B8inZSkwyau$VA?|V$X;eHf%jSm!zn6_x)$3!eu5vt~_nKM+!5&6GSd7K0E zpE^Kx3QJhpivd~}`N;T3=!h5t;EWco`Thb)Lt03@GA- zOl~dv?h4%u|7h3%VcsC+Cf+C|@^eYaKukCHgqc5M(Uh|NU|UI*Rj$Hlrty*WOHVxdH1V2X1l#U*9Gh`vg56_dXo&y?9K zm%t3ES3_-;@vH_oU45dq6m`r?eD26wf1P>|tDF?;pJMQNUs;(hm$zDRgD8cXa?#0G zujYxBK~ZPMt$S?LZH=d@JKk(588|OilVjr;>e5)7VQw-$^^Wjhmv}Jkab1wu`tXoL z%j0SDsXT$)IO80xvYH>?R!!!7kpwJtnMbp-N%v7l)GKi=d8ERb&mMHDTzGYpOzbLq z1}3IhR+sUkI7lmiJ<9AldFargXbiYW9R*}H%CvfO(a4EbSi)fE>PV~7DMLP!05K7d zRU-Nq0;LjR3k0x6u=|NTBW~GVlmO6|7;ZBOTSxzc&rZfJR&Dpmqy;cf9tML7b^YHV z$X2wov%Bt$M?nFY)9JemZ1873fgwOQU|Argekctff_b{90gnO)WDC%%C}F-sSUhOU zyMQWnA?pU?K#N$Dy_<;;K)|?MirDZ1Ss)`o?OYdad^|FiW8_xTV_Hm!Kxw&~ot+(R zzBDYtF;RK+M(-;YGaPyAd=3xfam$~2-%*>9goMcuT!n(dLfM9fhD^&=cMR_E?Vm1k z4F)alG}@ne8TZS}=ZO^-`5DKtM7Vi`!?;TDO}ev_69SP><8sJX#Hj)uu*VaU1U(HW z+?Pqz9Y^O$cV?qVY$e1LWO%ACHxAAO_&};SLWt)RMl&6#cuxa7SpPHX(vP1TI7U0GRZaVIbD{}U6p8pKoVq* zL?pKsa2gb8tyr<5{KL?u);?EVY#Ws~i$4yX<=B#SrMI%Kuif9p%ay0Q@|9amJaE$3 z#_!c9qw}Qi`yv)jW!p?Y_NqxYjEk*^%j-)ImJ2$>@u-gX$zJ={X%aGHht_z* zl3zV0P}3GEteGw4n}Q2vk{cY~E#*MB7%1*|wDHTGtVSEIj>ua>F0#okwWA5juQXcA zmoo{*$tQRkE9QhyI^Q(w6&g;RrHy)w-6bAFgs$RXt)AOOjZvJ3dlx z-E`}Uc)QV;-Gp~ru8m6b+^k?;&jlQ0>8;HS{5r#(SIu>DBVx{rtI9okN)+psZTTV3 z{?yaXu{*J5nstw2o6MwjlVrZ`_Fk*mTPd4HdcO3rQDJ#B+&z7QYMxQZoS+o*WI}Oy zZDvF+Ul~YmY#(GR_Uf0WQo2_&3&9KgzQPcNN8?}>lmw~fJq|X8Q;{UQ6nSeCTg5{# zVod8Bzx^bq@_o(ONKNFzmc1-l|L~v%i@o2F63+Zq+gf?z2nbk;h&ErWKdFgP&p=h? z%duO)=X?USW5rD--#+UrZ~{ojST$JIQ~gSE$aOkz*wHEXSaIT<*rJ~mNf})N(NR_g z#ocjy7o}+{7_F=1tv2m`Rd{7vO)M0|F^mW3wvVVCKHCbzc*4@RhL6|rT^Ej@IMIp6 zhUlCz^b12B5`&4KAN_*P3lebPf9S`FTs!v1N?o^eo9K*UPV>P#S4JU>l<$8aii}9)#U>DFH(Yuk z;}PQNh;Rp6pfLa!l1nb+t zB7h)!FwXr{{3FRdB!g(|2T6pBQR9XE2~ayfEcj<;r}{FTr_zz`MbzTJNI6T&&ygeq zgJ9=Kaik((&v*!Te_`9+c#@QY&=wBkjQe^Wch)GqI>nZtmVU}WKDY#4xDQEWN8A~( zPSX{KRFc$1-diV}EbMZ?Ub;=lNoiA&_Gvx62gHgGQ(D<40%snX2P(gmI&0$Q-@6!b ziY<42?&i;Fe$@IbFUkCb4=B`=+)Ig!U#6(cRTibNt0i?unpu^~jYihbKEWJ++TTg- z-`f(>IX7^Zi#ovcRTGU)XURLa$l~A}v7EsX>fxqP3V5)-H#bsf1ww9r{h)ukO%BJ| z6EsFv^`y8383N%hwiSDn2()M;(4XGMqLwI>wW<5Ek{zwq=*NV+*!o zUUftx(_thQ%PEqC?SL6mNxziu!Pk!WW!ryLf7|ldwq9$=KGvOdQVQ|=1Sd|}#r8jb z-oo`G=IPJCgl*Rgxej@*qyJ)K{)N-TO|L9jq%HNzGQr}{j>(Jrg^IOKs>h2QUKqBz z@I>yTiSeffIcn!+27UXLMsF+mf8HsP*Yv09*3Odefrr-yX|(Z6Ic)fG|4V36I701} zFw-Tx8O)Uk_2Wz+9w-ywipDT8OvKO==zb?2h>Q!rao?praSPiw=(^6Zp-aUf$L1f; zkR;>3pYRRtp9}-XLN9OaFAR6O2SosDeMt-j5;`$@6PK|-fsaRlabjvpbN{dY_WfJA zg_{;XScPeK3yyG1YB>@u3=E=xR^bP~`)}aUP&&EGD)E<4f(IB~ZXZE-QCCTrBnTB! z$u_`J<6&sni9R9i!m?jKic~xIljr*#h7Czf3tTZvK(`N4t%q7j;mZ)pS{3gE%Pm@UUGSh$3+I5?55N$CXUTa)9Qr=q)RX4iWY1CT_u{lAJ3^iC|f@up_S_J#KNuRBk;|NdX;F08#$`Rk9<(QR3Y zh5gGPDaB$@w$qkq4TMg4(=@)Fd6UcTj9odV(2&93E2|JBH2McT$6%cs^rc!WuL9A+ywoO<08EflQ za`en!o?q7%UAPrKi+b{fySUn7Q$G)o!Pr0%ICjsDF6^m$rHJNF5$&eUaL1XyFM*=a zBI`X+ockH`Y6~9Rlufrw*0b?!S)owx__COxtmFr4f$Qfp#=RP!$W3|lf>b$@U1(nL0Lo{!r^eFsE&4*LIn_f>a;C(84@&uXx@K$m!;C?@ff-ZBf9WGw+6BvM@NfAgKOhNMV43|LuKpVZiyHpHAU z6bXY^FsEqfE23Zt^SP|7>|d@x$HOD|gfO9?Dh2WBiV^q#GDQ-Bgva?I7+2E0Out?< z!MC`VdA(oW9EJXoM!{SQb9flk0*UlH`|A(>>0Zizwlx<9QcCD)s;jFxgoSG~e*2N_ z9@Xk1bYUfiJ-=^wx8nuhuK4A(q{Ol0(j<;7IXALS<(Gd?_u$0?q-_23&sF{tzx^Uz z>4v{QqQ5_1+hv6wrwqc)XtJ(b3=D#&+P^-ef1dRBeO{VkAP;0-`okY|e5*umWT#0y zw_h?a`v=|gRU!))N z#FJlq%^m<$Mg#v5^@o>9wNvVjtL!hDr?!OeNMTT+nE&?h_nUq>rS=LvTtw49zakyo zktJtU-kyS}iF@O3`p^B#c1QpE>5n|w`CBp(oss*^-vUU&IhLCI_6qUEdVAGGkV0|) z|Laxt{vTf8p@gyNFz8c+S22PdoNko2cwSYp`54*j={{V$%hoNQ*ZK14>Y%Gnv;O@6j^Y+(`-*3> z{Ka`mI6%gs$|f}jvCWCEh58ogo!PhNZ~o;`F?^ekCkg<$1olZPYWFj!RzNC_q;%(u z)|kNA){S2MxAXX~J#zv@fd1aiu$u_K(9OuDB18I5oIn5WQAWDEHj&VM!n6$>pK@XC*vk@-<%`5xQ^JaS3NS|Y7&+uH)58-P z2WqFuzm!7!sH+=XrpySFlkL>Q2Z)TqfveBYWb^&c1rrk!d4~M47xHKbFY*D@VA5It z>m#u}&`Qd~1Kk%1T`hyp4r$cj+^7lKDUukk0+=C98kwI6Tv`3gXTNIAtBmI}Pj4@= zSRfFianG+8QSHtfz2{euR>LjE_S;_8&;R!gN8S3=GnBmIL&Dg1QSzq!OL_R@c`^{E z4Hfr-$MOyN_b+Aq<%1kqa=zrIFMJ?HZR~-#kohI(uC2&qLn8eHoT-1m1N**{RKQf#(8DWNZkJ>hdaXv zp$mVosc+FhbDRc)IARV6N_pCqL-ToDWY|Qa&N1Jz=((h`IhRK(98!|bw-fhPtXif7 z5CAth45W_kzy?vJSZfXwH^cRWp{>^-Uo%|QnAvPA3Fq8A*m!GXaB*V9pQL6sbhikY@m zx-{!=RU&8V2_bRoP)Xu8N9)g151?=eiH_d$s?D~dAWgSaq;K@}(KTbKVmGZlhW6V3 zIQs)HBIRh?TX8px-R4tYBJl-1^YR&!pwY7;QDOLnaZ?kS5kL!ZS7)$?rgcxg&6e{X z`Q1+>ePdM=8jtpIBAwpIi9g1|&`m5>^#_|WcN<5QmVHahV|Cj;`iR3=(ks^=dQ{TkSzhY8JoGCwq z!Hxc$#@TzUx(-CB{ipGnDt)9<7TO zFOG2z@Fce(#A82^@mDs zw}q`<@Qvw4363$7cCaVhJv@}L64Q>+nKl4y5rApn%=Az(7^v+B5~Sar>RNtpdD@j_ z%lYHW)PSjSDrY{_nyV6W>1@*1&T^22aAI<>drNHN^BmtWna|ILeT|sbt=o|l#+Pr2t*D@~>Wb1k$M5{GqPsSj z%x8F)SFT)+ak*5QQG8My^A{Ncu93>@itW% z(|8?D#S<(ObsHEaPxDBQd~Hy4WBAz|ma!x+<*18FquT6|0>o!ROhbaEU$l;hymuz9GnY&AB8elZHoyKGw*5id`_b`!nuOh1SqFPsnuL@sio1LO;UrrT+81@M zMRcF{UM!qY0IyLQpxpY0e42xpb3Tb&etvgnUk`KZcfGWXThYpvt?JIEN1z@EMK{Tk!uF zdJ(CQ`B^YMa~NnrF}8C-UDX+q*ByUq9<<=&RVTuH|CK01O~p=8A}KbN?kPh(AM(ZnAw3bOV znrJV`ac-Jj9WfV&uCI{%Y2GgVrM{8y1b!A&IBy>ocTPh5r;7Iv^5Kuf` zoO{^GIwt7X?EL*%WrB7Ry+wKzOe^EJdCG<~ezZKartqL?%i33)F5oGAlV&Q0JDR$K zPV+oYircqt8GZlr8Z8`vjK(h9z0)wFp>-@#rZsexQg5xWC$qv#qzG-?nX>wf_Zf}J zDFtdNdzd(NZ~PUnpn7FE_-zxjK8{U1UFD8oNQNrkY6lB!rpC`+PYh~^Yz;Wh{KRd3 z57!5aO{Un^F?#O1OKfBRM!46t$JmIjaO|kHZ;Wm$5MVfLO_>DMsy;+K8Im&r>^Ed}W)cwTwrA0IllJ zhP%SoRyw={yDPDrzMgY5Q1~ff3FqjJ#ql;|#{Th2TRBn6h+3+jYvRz;oUxt~C#7&f zDSU~G=&7S#*Oe)`Ze0(PyhEKIajVJjJxYjue&-TT|Tm7&-uGQ+LPFuovDdSAd%~#e~IO7&0kxZm*65j z69TonOJMRH{c_JroWKyR-h3KCR_g5Pm^@87(NC}=><;s^0@;{6b1HVQ6-aC(s^H4W5&2%b%*)NU8(im{$q0rdeFlnTAb~Pu`TE^^C=|+x z7ofz+qQW?hv|i*UGm_yEhD55lE;H7>b-5CAvvcRizu#CxG)qE4LNR!-QG@%PJMY61 zI)ZA4KtAN-l8zMk1(>H*5E(F3$R;MZGxEgZZKP;0odP7LH(8Cc+}%^C0WSc_1`mOn zh#(H!SmebQQo6-lrpbg`0nNIMYcQr=CiqbDbuJholf-S32SO4$G2ClKtQav_gtU&s z{WA6zFt>crQk&O}+lmbVbvEq({szGP&8JVF61b5_7p6wpeokwlNJ7h*d8KFaz4I_C zRg`L=Jy^M)b6EFYT>mGSSglf}NvP7gf4q|<LrlDa>TZjN6Iog!%&cE-?`)0 z&B&kwU6wMqO62EIUGD*{6U6%plmjEkS|Jlcd5?B117f;P0D)K~4k8po)s0L11WgXjg82}EksDQ~N7Z_m}DQvL{G_oDi zh@1?x0&O@FUUaXG#r+{{0fYekm(Ou&svM>|3ey?mWOxayu`XX#oiiXKkU-8>x8W1?3?i<)yfK_H^j}^MCSM*Y(v?biOm|ph{s?@2f@r_2YUOFPZ83|bVsIo$ zy9dzNB#X9!l^kt2u~lS;<5EZ8I5m|`#k!^GuIXH`Aeqn_>IAJBzVUfcW}~;t6R1ml z2lj*v9pvkNo4_@XU2@WLf#k{^$)|m3FrU6nfrDS9%N^V9HXiEbc0)jE30q|+KV*C8 zKNYtq~$rHrqAKR{h_PlIRFHpQ|Kly`oUD*kU!( zVv+Pc?=OZSQ*z2Z=4BnIiO_fYldLA9wuCtI!G?oO(xB69K;kxE&iFCh(gO()tMI?f zVi#|3!)~&1?b>HJgpI==%iv3nvlk zQMeP#Xh;&TaaAG@hxsC35_$!azXl*#hp}F>*s0E_V6uWqbTAnXVgM*jBdkMAO**iI zR4}z90?#sGG}%=#icy4yN^CiyXgVflu?`j0?FCZ7 z^9SHt#+jKa@fFLKJ(M=zq#Ub|SM%fh;(`_fJ($zOL47z2~Q*62;A_86D2KVW>P|%C=lT{~5dI z@kw26rZh#DM^2fW>~yE(Smn}P5|S&FlJ(Biuc80?@kTArw5`Nq$O+5XWY%jX!?alUy`=J}+((o29p@wNn69Zx zo&c?&2xt^jIk1Ps=O)KH5^INujUSxybG6r~2ECwuO zLS(eT>z^_zd46#H6d6uhHn}aefId7_|JF)zp;~L;>5&&UQpw4NzlLGXLLNTMh5-V1 zU~odj@r0tX{Z$+wD=eo#xV1(gI)zH;@u|UcjVgYifqY%mL#9jw#0Jns;1ZeTT-X9N zp6gvh2RvMF+vv|ANy#fKQ^a0WNoKzQ<^vd1=vfX&qrxYX3LGNx4-or;<>P!#3%fbt zzWDL!CgPbe!nwV?O=lX*wgsCgCgki=a19_5^)HY25u+N&G{ZwAn%f@tf!l)RPIxD5 zr_}tBoV9k9i##hID_TLbO|wV_7nBeJpC=O$;m0-3w^Ip6WrIpqH{ z6P?;{#nk~?_>0`4(-)Km6_?pqlpdv}T7L?5_q^+p^GtUyv{)q&5|aR{YkjLzuN>-R z8WU{O%(RP^>bV>F0WBFW{19g_JnVs65 zh2#;c0t&e=?orqRXiX>As^_{Oj^&+?Ham6!B+MIy4&u?3qT!@9aJX)Y=@X4e?AV0F z_3-!gJkHXgKt5uLjZ)8Ma@Yu!VzH774m_ZW1e(JZZtQ-JTQ_HV zy=!S4tLx;^Px7o4i3>$W%94i2s6V?l%&To?Z(~ge21;s5SO(6AT6Qg7yd>sq^OCOd z%6kFZuclhzgpnP;kc=j4$!g{YdhV=Y`+5(Yma)EXT;+%v&T7S@YiwJ;_$nqe8b%y_ zw?vBO5}5mq^F@?zB&&^gNQ|3h8dsF+?ewZ!nyRVY1-_H1-DQ$~c}*?x%FS&Yx`~H1 zvJHG(v~u@UI}3diiB`NJNDFUFk584FPVa>Q5+tmK0GU^B6FRPcI9P0kSC1Y%_Q}!i zKIQC-Hfhdb32E=_cYL)N`bkd1?~V(Rn<)*@;{gnI-8b-r+D?sDKGH3INSW!cuiG%< z55S9Iep8_{y@k0ML8q{ypp($BOtCx4CSaTgG8Y>advp4#B@&^*Aazh_y^XmR8l))0 z9T(gu`M;O#paZPUXl|2rF1@pJpcMuJr&50!?Ru?t-OWuhGZfvg(b8{yi1}<;J zxc;iaC(^a9P2VZZslmD$)M5F+Grq>Y0``-~P26s7x;m%rx|(^f0yychR$g)rakT-< z{Y2&rG)h!@GTTX^)2o4a`x)t1m5PNx3jC*!efXOhD9k9~46N zk+QQ|kYzIojIK9v8+;owUjQlU_GG3Jo~Y<5`#p{8b8x0aUg8Px668leu{}ElBRjNc zme$rvP}uq;-CtFlm&b8NeL8w8SUIW~um`{Fb&dM|Vgmv%k)S985%>#I51>fT!&yu0 z8DL+J|Lw-?$53kZ|Ya<_`?v+!amKn3oOb|;emE( z%teE1?1$$hZx~f4M-V#)!r%dNtb$WG)v#nI^aq9f!g8mc3Xik|WIo?qyP5af-<2tk zT_oLJ?1sSuwKQ8~3`FrUbFEhaS(;BiEc#x3nS9loJar^VDOxfM@Q)Xbp)D{#||X_fY{`5Bs2bH6j9|MNr9?0U3Y-ad?G z+WG@8&1B&>yu@zxLrC+oXHRObp1KsBCXu`x=e|KzQ9Wb{Sz-Z)1diIb%(1v++7|2T zh)$iNMLO?0VLzmvBi?sHGFNu+(3-N7KX%@-o-8+-m}->I+0XEUbcSSuKH`Ame%GB8Q8*wxY7OI?rA zr*xtYrJC=8p!w+0qsUAZEDY%C?d7GS#nPa4zxQHPKr%>**N!xb?JCHoBAYQO^WHEv z<{b`{QnV}wbZFvIOEM_&miMrWFv!j{BV`Rr)6QluaY5P6hYWCl?kbD&`sleW4ty^z z7~HN{wF()XRR?4>heJO@sZQIld97jnk#H=PwX$8ek#n!7PS;z3-TmENC z^Iv^&|L<~$^yK!>c4M~B<00NN`F7Pi)3P&`DMv$>)r;BxLZ}PMD>!e+W>`#gp8fpp zcmX2JM>>A&qAr&_pSP^nZIKjA@4f^YRv)~<9%IscMcB5PrZd%Iw%F}ZfkHAA8&#m!!-SIMGU*o$6Zo-xwQnUh4Gh_-d zSt{EfziGvcU>-yT&O7F^GA<*5)sm8aZyujQDM#1oeIjZR>D8(=-;ZFAXw0+SdJ5=sOaE<+Sf1UM_xkjYyuu)lV?fc7qno$d=0m4be_54$EO|hhxy{+`qvf@ zMdX|KK3y|dhk#12hB>8w=rrCAU#7~bG9~G}%cO;$ck^V|xYG5WgM`heC|=yitFb5T znA{^KrC2sdCcun;kdY^2frHy2|4c0B1UP3Ax|I{VRT8=(9Ok34xk2=DVfNFg8=>PQ&mer0rFEwq29F@q;GZQM!f zx;f^|eL>JXgX@)s;h`cCd0~bX2P6$7svKI0q0I9lU?kRk4Y?9opirI;3JP+7<^p9B z`X`#qscP8KpztsXe=ui()MGUCjdE$A+mM(BTKLf$v)f=*_Ma##y7Hi`5K(D@{V4cn zia3ECBF*Mp@%$=-heH_A#NdE`xY5X>C|EoR)Jka@!T1y>r1`SLQA~B>OLT$kdEF-b zrMJEuGJfElf}WX3A%T{W$vT#2QEI$yt_xub5lyUZuU6THqXVE@5txE~!qx`5cX+r% zBesvQfDoooikBZYU43qs=Qh#A?5wff#=#h_@u;UNG0)a*2aP?uGSuI9r_9{e146M@ zRYf%Lxud{~cWpm7XY<2C*tPe%F@nb}TMN{=j?;1cIe(CMR{u>B!1>+J?iAy9bqGWZ z>m=7(w|qZC6s(E{vVLJ<&5Bj_IMQ)Omb*v9wR8{lZ3u-XeA0OGAT3-aDPu2auA$M2 zIk!=}?=6XDp&zAZns(r`aZS6y;^t;lT_i4Ba65IZv?D|OLF0`qictNWTx&1 z(|2U>a21NvG2{6R^OAH5z0u&Nl;EIT_*7KF(?7_?bLWm;vj;3+Ep7~dAK%An)|e?; za>FmfqQ0_!%#91k*lH$$)3bj}1V3N=--o*Nivyd-sS&J{izOV_(5i(Mi%rQ8SqxSl zqFQW8j4`we^3IuIF3v#;M@WuOf_lnHV#-9eVvI$PMDFPW#wZ= z0ouUc!r-S0kTsyP^+EZ^279SjSy=?q#%9T>79-zwhAJlx z(TJGBUppot0e-e17;xQi1=>Mt-#onePm;^u1}^- z?^(`nk!N0A2kp|mBlk04BJn;M&;uqqz*)^U@UvY59!@M_LOzL)||dZVigM`gZ5rilKWU znV2?RQfy^t%U2fHyWKM1)6 z{%av1N5%qIoNV!t4>GyA1}{nSLcmNehXJNcS+8|>W#Pl;=ct4(306Ka<01Wk>+D1n zr0nxR3kG?lzC^Lf$Q_Lxr<3$&80--tJAk6Kva<5O<|!>3_lh=UTp>ubmj z)Sh8zk6wSkdE1iWQCoghdX2WVU!5$u>DhN*W=UE8>wSJ*fR^AJ2+t zb?v^UWo8^^C|#uqDJwQ{nD*=~%O;m)_U$W;y?yLvTDepx z$y9MaD$=zzSG%}wmwz071Yv!0K~jf9OI-^di;r}o5uWcL_*ttg^A{~yk~j7~=6LHI z&q}-n1s#7mOFi*Un4~jo^%X8d3_;q-0{Jn4ZYmsM<2zy^6x8zhW zOYfxFn{$C;_8RNfd0S!$f8y<_7zr%0ENXiCJ;s*T2$cD zme}wrWMkHBX_bjfX+*86?1+_qq0y6*>2ecHv5H~??Pk=PA1UH_H+>U5YC$&AF&Uc+ zdnWlN#!@DIJLS;(zdpw>Yq{4=->B~mODa$1t}eZ?!k4qFFhiJBJwS&l-#u6>BqS1- zYG5Q_R(agwO79+=tPnaR{@1rRTlOd8miu$AvkwjQ3=G)OY?blv8VFon+%>mGc5%2Z zm^sQX&V86??|ysn4v4A{^8JouJynSGy$%CuM8mrF?7omZ)whaU-hVZuqYK}3=7o19 z{hKzSnrt=2Kh-1*hu;yJJxC4aJ!?&Cnq}pK+&6K&Yd<{nfvHncplU8SXH{|0{iF?a z_kL(B(OLBmLcr{II}^A5w@h=T`2PpJk44$Ox?0Fl4=>Q;!d5?wlqqQ%FzXAgc+-FU z*?xYX2HaM07Dgo-{r}3`pH4MA(`KA@28TlxTC0R@|D+o}=-s?(iHY0QRX6`eJdOOq zauzNaS;-k0uDv6x=lSP_tAFL1(FwlYsrbh}fnSNNbPD-DZ~B6BVvO;F-<{w5^|xHD zUd{z1zKoE3*RA~Lx1pokPbcy_QS<8k4?lnVKv#PXeAeq(`{3H%{~iA|+=@h$017}D zoFjBWpY`^B*0USrm&3I#e6m-E`SB_C&BRNC3_ewB|*@)-yrvq9UVs+;}x(*W=RI^;LF-Iv) zQij;5n5ZaY%i_njM{q{$P5jMLO}{gS9~6-=%a|O3s&k=r1{FAy1A~9!qyV8;9D&`1 ztaks#BK~v#PqHZJxN6~%Ly5r=an}zx=P5ZkIWlfUAK2Q`a-lLu>bEQeyQ9EgWKkWd zV%iUlSb6A)mC8vvqIsD(JZRx4KG-z;z-3Sdzuj#OE3pqHUpb}ecNXvW#UrS1*v6d! zk&{iBkyk4k({}%SiDSn=so|y~ph?P7lZAzakozu<~q*?}`yYQumI*ot*IsUir z_Xfue&znBde!xr$WB6S}=mUhJ1nm%+O= zKq=GODCPY2-#FCUZ|r6AAd_ZF3`&CK^S7Or3gr5DcYwA0=A<8I~=N*#SQg~UGKR+vRZ|j=hu09kR z_&%VYuJ@hG_yQjS@ap7Q$g?DT>HAgEZsN%yM;obOfGv~013t-n^JXwtI!YE9=1_#P z3uOkbz9rLPyXRyVxVO#g4d@9x=>l|DDIDCp%L%pq2GOw4(M`)MnT8Vv3+eTQldKtz;! zYaqE>Q{?8R%IL1Jgl)$%-!1!9#dG9Jata~x9;*HIT0e;VeX;)s+BPSXR8Z07IcSHuhJ^G2yjgI{=Sdo!!PYM|Kj&yM*#Uw zVDjNp2_}p`WF!Z0;EbUM`PZXM9LbCQw7Xqqx@4i5Vd9;~m&m`^y2>eAKT)m3;5@Hn zmJGRgrYwCOzkH#qZn^kMxBhw1L00fT%LV@amHww|lK-X#x9y*Qx({q7cXG?JUBBrZ zw#)uca)JL*#qswSXw~ca=iAcJT^*^;`@cCJ|1Uln{qG_%wc!6EhHw6DANe<0jQ{P$ z{h!4gMSoG!|9^ky`P(WL>@fN>hIrMYxKag0B4HanH=<@wuiP{psx5M*)3Z@qzv!tt zk(MhYJmR;0zqFFRc=y-ae(TXekD3V+c%G02i)BrSy$R!gKwajpq(7i7;?)0qatjF{St{uqj&!)%lXOa`P1PstQnP8w)4qYs)KGjtC4YrHry;PPFSU-dy}IM zgg2X1GbWzDi`nYVb8pL>f_F8}NoA$?$tlf-7Y$F%j#Xqxy4GjfYr?4I%!GUE@b{xv z<$@3M0Gca&d_X&`>-E!`v*KqiIX-CS*5rGjUPFsnkNAO`|3={@a%4@2@o|b!X9yxh=VP@h#Ev z9|1Y;cRZCo{1Ku2?St%^UB@a0s@7OH6-_7!6{WByPy3VX^R#8*W^Oxe<4|k#Lx;JDb=b7to#<%Ia$Wu<~K< z5f>9TBL=SS;p+Q#bMGjPiVBd;m=`+8ZWUDrTk~XCN$heiQYa)hM!iU@k}#Nx$I(N5 z+zAJ?O9tII6US_zg_z=kVQfOgRStlnvg$1QT#(7y3uik*PXBN78hf| z99Zr*71W=ma3TGMU@CXt!lh}%`ob^=-xd(0lu>q z+)|a2t*O3or&U@xA1Y^62_yV=@a{;I+_)CvXHloTB?^S%L<0 zMSt|O^BF}Q2_N1K@7<}SP`7D(eL!}Ut!~$|fx;zmGf91#ZGV>D#r6`d)X4yXeSE&k zo~SEHX|7$4?iibVdUvw(XJ^VFQ-7kvSdagAjU1b1zvPPrYJKd!oT^GOr4dRw$Arjt zH+z!iXw2-;Cmv1Kl0o8AjhA-UR&2W4tt)(a`sU;)(=+G;V&MLODXgOg>bR4^HzxjT z50h6@dwJ6J&`BtFy2=`*XO@onZWs&N@7Q*5((K1h*{ve#jYTsb^Sw26-wr*0s$((w zQL*^l9HcPiKlyT0;KtmIfTh7KXLjGPG|~Aztdnme(~->YZEd^ZVTFv2%eVDhyl3{d z@g?vL3)Ggm`**ht2L}m^!O;VIq>_+ zZB+Q-1dDB1KgQpiUWgQ!E&3k)raIFdoCxZXQVqKtuB!E^*vmwy)!}um_RNy860X(V z9B@LCPjDP7(uN<3>L@&p4Zkc?l+2_E*ri>2B4`o%?9c59n%QD$@6xv@$0<|GuW?W< zY&ye_-8wuB3qSZBRwCKnzQma@?moDCC}<~DbG9LN z*HW*Jii;idOgRaAPXQ<((=lU>YuT^UaRHB1bj1f$-B} zl_F@u*2YP0JJzIKtO$-_OiBnTe&h6-FKX4;w-|#dzaEGF?MF_yZ#>g@O()V4b_qvW zS3Pt*dT`sf_Jkn=MS~3oGzW{aM9W<-HO^&6QRZ@{b>Ze5(ciz!ncv|8=Lv%;J5dnl zQKst9L4HoCXYQN$c!84MvVBf>gwtlc#>un1gNJh$E_%B-#6Q8i@<_Sh@tib zL>E=>Zpt-g@1a^+e&&9}EmT+oFmrJEkp*57Uur(x)9cB9dE8x2yD=35a1MMxx7TZ= zX*y;#he`%_7dGWz>HF@i9$LJEr$1-f>E$OELHPW5!yQFO`O1~$xeiv=)~8!JFH^%r zXpckbIZ`Z&U1qX>u#R+1c^+Qjs0Pu)`|cfAhwn7#|M_v7kmYgWCjh7fgx+v~&e~qA z;+42<0*<4m7|_U>n_p(#Lx_X`;kaN<1zf6v+2VBwnt$TfvrEzwtb|7x=Zw_7I9xo6 zT##5GDsi}}#`T$LWW0^JR5Y6Snq1dh%z`2akyY&zEc1|CO&ng~2p6!&6QD!~{#FJf z?@;n7meOzi3XCWgSdP)$?95>Qoh*O2RfQpYhY0@3pbmat{AOY3>Wk~?I}1_O5S>+Q zMJ(}%bG*`5am~5G{r(*#@6cNnsrHJmGTTz1Wn(fk6oNzC*HB%?82DQ&W1v`{V^dqR zBYUlR{e9`rR+lcpi=fT zmwJD@J&^3J}IjS#P>mF>L7-Qt=XH?NHbw^u3mGUOzR`S5`P_Nt=_ITaEH_PAb~ zzU$}c=fV7;{jGh&Beuz{3e0^4#a@m-4c7ZLjb=W$xG!7kEsI;{5VcG>Rc+l!N1;?w z@r^1lEjBLay6Q0nqAZek5(!)c6m_{@pr~VI`77HWZx@4cECHG~89IXWVjOD#k44Ne zh&P+PXy0h_dMXpY)S)_Z<0=pgMLdjjokx_f>tkM7fwffuNNywV)A5g8NcSiQJ_(ju zo#NoX=4OD&tqDmhAMUuly?qR{4#m#Xo$Ay5F1)X_`G>QAM4nmHROmmG@KJ7S7_vS! zcO4Em!mM!;UbeeE1H(OGfzq$}p?RxQTr=?G-QW>i* zcQt9axD1r-KWsERSbMOUA@10934)77W>0(lA5$)Ng7jtLX0YB+qJZ3GF+Q7z6ti6G z)86~;N=n(MD7-D+!74CVI(a~3_)SfW!`6*{de!+pbJ1t+J{mrGYq#)t1FNugbNJ44 zJC$ROY-ufM3$+-VNmS2rrDorFu>kdl!tvwbmhE{7Y`MG@V~x{@yHYC~v~T45s%c?c z`rPZb8sBH@UQgY+sTU?LRg^~}2A5Svw^kFr68N4F^9b;~leX=!dd}VOh{p{#y_u%v zIMa7_4H%GA-cMyS{XJUsIjvEwF0-B`M-}eizNcTmx$)1BFuRea!3~vW=pWUdJ0w>O zFFfFFU5&Y}*Do+|n6ngO0Oh~&(~$WbHgZ`UT~sp5K+?o883@CaUw&_U6Uh5)R5;fF z$;39XpX4;Wt1U!SA%MJM4oFan{>l3BTg@hqGi!V-{UB|VKlX6942wafp`4WASdpkkilql63jF=q*9bX$ ze5$c(N1p`d2C74TaIku6?KS`O21Gf1uq2j9f3PevoC{hQtDWR$0)cv06yntQdh58k*qTbkK#u|0H zMm354u$6O6+DXF^lTUsG7mFjcYi*4_mh+`qm+arj z`7`8TmUXISYG)Zn2W(Ve;qvj#KYzyKa347|eEGJ#8qW4)JKglHy)S}gE0~lc>{q54 zROvwa;?JN)>4b)Ruz}T9=VR{4ovlSdVsZNSW>3deX~sGf8soZM>~~mZ^=->fTGAr5 zWd_W8&Cp8l;&C>7aOLN>5O~{Cy3MMSPteMdHluX6**>EYns-V8C=lcvC(1XB1^9ij zSaflxiMJa>Or=AUS9*>+d>F^OWXufrR5ivq6m}U;oh+>q4XQ^V z6n|G(2~2>Pge~vn+xewmkrHh0TOF_gE17BG%fogX zA(z)-xZgWb`&DGgu1{V0zHyP6ON6x<#s{KUt*3|2^awv!6rT~+y>)LxWtQlqAq28M zx?5<564GQf(Ck;3$YqrzANXce>epv8*&DH_KsBf?o^=g(*~C=U2F8uNxo)!>Jy|Ds z7>5=AY~G&MAD?8rc)iOG?=p@6Kihza27!jJ9kMCPBXOgH)1eU#yi-c{$t|U!yk!;X zTFt44zgV-+4(Wn4O-NX4B)yd099xXBfgCAV8dwF@Xu}25x(Qo$>uetuQe*5Z@OvjZ zni8WO<#)Kip4ebI9J3!Rz&WK6~l9iFUzuPMy6g#ES=Vd1y zoEb?KzVY>eKLpJ|44IDz>#d*nY5+9@n@Slb1RJ+*HI21}r!Z9kuAB;Np6&H63W}5O zyBl+JDa27KGMNnii3kBPNL#nkSSYbl-aDy&n_!DYv&PKGz`dkWXs*5Uc6g4S>C{QB zrebj8%D8uHN-#gMsA(xC9-e2;i@&(ChwnaUv_^W3Z;1H@g!P?0z1lvy%kNI7iJ1#O zQ;}KvkUO!t9Liatg>I`(H%EGa6&dYy+bLAFn_n?1T|3pPHgQpz;DuuELWiS~=Y=dU zsHQPh@VC1q;h|P{9xEKfmBGC|%2$Wlzmhzm**6?!BHn;OkG(3JwkBvh?Dr>k?cx?LV9syNQ=JwvTnxw}uf1lElswWQZHEsM1BPLkEYV6$H=9#hF>IX*etYUDckGVCb zU+|qpq!qfzi%ui2VBVNm7FE;{$eTT!FTJ`VF5nUTzsoI-)EWN3x5=;!PuI`0khbal zVxKK;`rPOV=V4}@H4DORWtK1c_HwI`r6C9u#G06B_E1;P|068SVQ?J_c~YEGj6x;- z(A^-x!=@M^C?i^qn1VuEbs9#Esj#v_VU4pl;_O2zW?6X$ZM?!*BhOOg+uL-G&{|r4 zhS$B)1{)Yosg%Wsmr0eqG+l0Hn4>LX3!m?wlcr7Oi(0+ct)n#wBgSgfmm)iLZpAKb z%C19u)E2&8k`Iqh9oMjg+s=nDfT#{Hc$C=j)88te#&(Y>r{-(Tk8nq=PkvyStAm$W zTFxh425V;%PxBt+5z0BbKHX{ROYKXBONz9I;+iaq{xJXT$gs zfcBvx)W*uJ_O&*Rcpq>{8fjkX`?d>>(1`17#A?@BU%l!ZQIB={aSUvhwS;H@sZlAI=^-Feb;9-;epNKM zW}O?@tY9()5?d8wJ@{cnLhLuA-y1y|8(|6 zWv=5F9Gil*R;^l=YQA0YzMzbd&YJc4O&#$cv~y4UJAFv~@kP!bQW7lhIH!-*JPgBVgFF(l%0eKW~PPhy2 zRm#x`Y^ZG13KNx#lWKy6hWuUMew>%raw+|!s!+NsY{w4rC`#f{e0>#e+XBF3sk9`E zx_6(SeG|8}}Ec z-mE4sIyU8^%PB~uRqf_?8WMVF+rAFhvT>2?Fn72cpJ~u{%R59ADW6v33@_ixg7}$ z;&$(;+&raKYkZ~+1nWGJc3J(SXLkS;mF7(N+2(H;XzHIFeLU!8$uaXKjn0WPIeWSe zx;t*~Fk}dxbCMs2EB-0}!2NMjFWBX+o_Z6NJD_3;;E(Djh7*3S=~NmpkyB4T`s(1Z znna~@7?n;TmU5cSkRTBgc?9r7;w9Mn=~iR+gT8AN*CxqmjoeoGZG)oE1bZ|zg=&Vq zNSm{6zRf|PrZq;`$6H-zHjb6sg&YH;90sx8AW9O!H2Ph(Xp_GhoD6czmVwyF##WMsXJX?L%4E@%m-FtT74ri%~Sk;Oz)dyN0 z=at~#iVu_YBwoo{Ls_x7P#6#GUyJ9->QkRwXvpBZcf0s)-tq?u+iN7~Si}r&EKujj z9B%!~TRrLA`SmXASa8?^`ZInP%uF=&RT}&$3O756Z~~7T%9_rp(dOY(FS>*;6-xH; zvL-}-WB-5Hd+(^I(rw+_&}u6pZ9o(SR0@!+M9C@&6glUhlC$JkV4D!hMUEwzBIjJ7 zB3VFk2FV!|C~~-S_1@#|?!C`F;k);Y^Uv2~^cdX=71ny!8|HkT-y@4N_ z-^f;0KCZT116OiA8#1yTAee98!6@8R!$3NuSdafUnsU@4Q2UTmN8V^xI;zJuBHzHa z=hFfMQVt`biMN~^$|?YmY>|88nNFoa?4?Url3=SRiS3#us!IR<^Ac2gnb1*(6LRGq zOXc`Im@+ks+>5}(0M5oiO=jfDJhk?tD#jHlgix?+OLm>|fZ`pgn{^V>o421lWo!HF zwOSH2Jc)g`QhqsRF$BH-58%~IgEA76R&gdA8tgX~HINKcs2qUGBou|v8UARTtk1@S zpwAo>;|N|Gs?Spk3mI^9K@=P?4m{=$z}}Su&oPqR^`I=d7#9NhiU{x=wxX^xu};{f z-$42O@9@GMqW_P`@vlB>laf9?Ek&NAN3*mlbg2fa@J%XAj1Z0?TrUR`vEq_IRgg)9 zBUlhl|sxcGQ^=^9zT5RVb^TDyX9&0{R16T=i|w!(BuQOz+O4dt&H} zx8iPzOqXwG2_2`HYiLnA6W}jz$SYSi z)sXt4{?pEnoJhP!r77#O(3ElK%3)Qib7T{IGecK78}`|hfI8nUSZsX zMVWH3UXBTNByon#CO>i?n7aL@TI@DM-E#Pop>yS@<(6#kND4=e2Z1R0sWOB4w9MAzPe( zuc*4DSX>IZX#jFOmjQDGXnlf8N=gtFIOxI=IT@r*01;Cn(7wIQ?vKnlJ@=n)kN{RV zCp5v%Jy|le18T4#tXez;&(K**0Qt}a=@P=6sI08SW{Eue@+$)L02@>oymCGq=b+wa z4ol+d4~ctSvHy(0zHLQ459d9C{Ap$^*I0o~x?)kjDvMrnjhBDJjaP8)NUw=r>JA&b zp!AyOHTRoeydn;04yR-lcgW_$Z&r2P6^BKr_`up^HOw;N+^?oSP=kMY?4|wu-zB7H ze&yI7Er>~_?2FP$ALCfiPH7;(uK@9V%k* zNJ_xo$d-LJo4zF~4T9yFv|pRD#l8L8z?-t)+4R{(bf;hYwhLgukzcu-X}!UwN_;t` zEEwQrc8MtmN-R2LtDPJ;nW%+*uVukXoXNiV?P=)G9DT4VGcHuSd3uEr+qL1x!!)ZPU(K=wJL(u!;IEx=EzMBs z%(u=bC^8bRr*eRo0ugzizdMEqWDJXqXRSZ>kmEW&gOsKc z8k~6`pJ^L;VhuvoxY*y%Uv4o3*-hPk(f0MTf&rF)*UAW_oZ<>Ez^H=a6XlMv4MVV1 z1U(z@{7E>K7W@*pjM?Zn#wLM;N{jEpKLjQ(=mKA!xdDll);I`kKq9iK!1n`=0wp+8 zW>Ni#-}mttG$R5b62MbeWPd#vX&QifdJYOyp|Mo29{xiu^U>!8=*Q*|^g}lceq&8z zuxQ{f{7Dx2G5@df8;-vVK&$^O>iJIqQ2$vv^B)pa|3%>v`{{p5Tl3)G*5>~8!u&rF zHrawu>relD-1$QQ`fsR$|NGzh?8&;y`vYSZf0i2mC%)kyq*wM^f9KbpzxxzyxgLG_&qv~U0T$Mn6u?LYqS z$j8|j38nvjY zUq^bnOlU_>CUA}hk~J-t%%l`lx-zevewHKgtc~ezL7}zx)fdI3hH;{U6gLNc(O;YE#x|jbbr7T zHo3eU$JRCm5Havd;z0usjU}6ML2)BBPzED_r=LH6iitPcgH^cj`N1vAk*(D1Q`%}( z?vAc`9zvvB--XB-75iqLuYN`!D)HxTzS!G`RD_k&NCy{d|ItbA{gS!J;j{FBIn|1{ zReJY!Bd5PM$nM~}4hW|vSKJ;RcpQ|w(2fZSPL_yh43?JO+rCb0mu(y7FFpKlP;UPi zPTNTQ=zLdJ)-s_2|61S1uKJf6p&>H4t=N!icpukXnq|)~9=FlZw&@t(7BR(b3J;fh zC|%}bffH5QTN%pvC2DI%V5LvLw=RVQm!(m=`xTXyQ+8eAy*a~ykI#|t@$;oCbiSP* zA_PNDQtWu~La)WQbn*U47BJzUGz3uA;?Ck$$S9voomGI?FK&#jvwu5D*Yh zNngzD9bNnoKC}?cSv7X+Gp+rTl?N_vOF?Z4dc(h#-XtL*;R|DlhmW78RN~(#+)Pa8 zmL}Smcco`rUv>|b#dC%W3+>GR_*4_1E#lV7o4%Tg!;)>I<4+P#Mkg7(()*(K$K$Vulb z6cniw;+UerW-YG`nhvP%L#$#V@ZIpL!wGE|>MGsL3Zg0`YRW-^ zC&(#iQZt;a8`g4;YucE-`mBE|*h1-^)6s6`u@uR3hEs~UF_Hu1!EYKLuDJPjwnsB4 zxyR*iOQp+{)!91UPP|n{($vbIlkhCjfpOB>MSCET+qi^@N4V!Ydw=FZvxB6BM4E^8 zkKoE;m$^D(bd!0sc(zsce0PZ#(>=92YU^hwa0`Z>+q4wOn3ufKAGnb57XCsfzgcvj5yQjXSRh``G+v((xgyf z77I@UTwiwuGcZ@CBpz^G>0{9mjmcd3My#w^N2YhV<4VGii((b~$s=O%Pfol*oR>3k zZnEIO2m$URhR+jo|CC*ZurWOW)Xe zeru(wh%3cp@Z<>E#%^W{0UAt|NSWPsLt{Gc$?`21%hByemN^F)l@n+R8zW5U&Z7RBhm2(mN6I*r@mu0SVKE@)keuPO~JtRf$r+!t`;qXHji-GhN z&n?vx*B65=c=emqH1iCSGc7Y!Qmrh(<39JX>J>coALucB4@jfA?u<_vNUT-ia;OG{ z{QQH)Sl!=G#G@?t^n)EOh{_O;t~|Bf)D+85axD9VNn18ypujxGb$u@T=&PMn{c&H2 zcgTw3aEwCX?IRV_R}K=EM@fQn0&tf-4qp{pjigvpc(CqzV#4&SI#+`NN}QmxcIr)< zreFG8oKS%&+!T0xLoHkYL~&LkxQr#HL;{+pOnUK^z)EF6BcAJ?!@LL4ya)KJdU9&V zomy!EYWV0Jw0K-iI{G|!c!KW`@WWNK%yUK8S)7&J{+O^kY)!${y_){&#B{-%btzYD|n5c9|-(-toozdEn9 zM9R&&pK#LESLwNd$v6*b?EGv^)NtIqOFFrus@HqgOV3xr*xu}AeQ&5}vbM~aAyH8d zeMmm3n_qm=8kZJPnyFH_TG-2olut*k_U(x)=Ew*rC+dBR75II1Xg+y3ZX`;dOk@0j1Q{V7IM$i>r3ZNpz)2e@zvw*sP|NB-P_Pzirkg;!LJ9saX5i zGOEOF(#-^mc9`!`4Gq#Qc_YyOW!o9Dk`%((8A45 zT<+h0MdGmwQZR{kgT)!LckTBYF3~Jl&FcKo$LLB>S+j46y-IpjSfWCL3DL9T zqo04^+#?7W-)3*7L(_^-MH)0zTM2z5~itlJB~{XIhFz!1HAlY>A1|TR?lBWJaJDG-!_@= z%Ic7n#v@aaLP0s7?#q6Qs#2wBc6u7Zxa^}`qw9MQ?6iE9j;Cto7d8al+wtD)u34?x znVoA_Ts?flM0VowYZLq-zh5@*eXGYz#$_)@)XiL=A|_yJyvuKT3d_Br2wtU1P&BQWG3gyRj8UGRhp8Sq(Y)5G;B-d-$HQ2JoTpLzk*LKkS7 z4LI=ja~&|tcYxz}63AQT=H{SmWXo%T;^;a6YN52R=)GbHH6#b%6dUxzHVPHoh=QIr z!n=UAMs^61oIIc;e>qq=J|Hd z58e@)d8wd%Yy?L>!d3oj1J4Eve`=u=&^I)~g$UJ-3C^+}sKlch&Fh27 zqpXfViSHO+<|Semnp3%q}Z`oNZ@=(F$Q_|taT&&GUQp*t5Z<+ss<&h;Z3a^|?bdOfS6iynbLxRlNwy%SJ2+R{1dsn?y> zCK5zWtA68EJj`fhKJMT@zjXbAg~gR4TNJL>8*I-(78+ty28HS0p$|xiE%ZZ8pqB#V zOr3EFM#OLo|3wZ|B4|v{LFTNIgv2`l`ErAA6UlW1;pt(U27*ffp(zpx3oIAcu{RXZ z-y`1#IU^(=$0F#6lcaGLRkZ;k2kL-QRRGKZyd^8lm(mDcg3m4mvb)I;$0&$ayq6FH zR*#moDf9@NARiJWEvaDPods{_0N9e@dvrk-3!-BNssnMux=yh3((0)oHQSFxdO8;mfE z<1QC>T-x*Qp7ARWXKQ;J%^ASYSAsKA$@+2T%?a$nFbN|+}w zD25NCD&?V_z>`y?Kw2_i?nu;}zjoBmj^O_%{d^!JZsF z;IJNUkGle&)2uVa4~ZFrHX|cgO#(2PFmahdZzdfuKagIZ1?p?M>Fb=?tppfy1AdFxv!gQDk(n=AIdU#0#OUtazRJn{t z-WR-^CnhF{5K)c{&>aW{=YkBtFADMqzZNPsNZd?1z^d|1LpMMk2GsC*z&qy`NR@ta zoc(r_@Z&QH@*E*nABYmz%v+Iz_#uA#*$<#@1me#h{q;AsBF80`hAY(I{lifR90?hm z2n(jhi;Us&ki=Rk@CW$pZ;A{CQa9Rv~s7vQd( zx`CE}kdtXe{kdAmOBee{N+mMQ40~fgN&!WGy2w^B;5AI~q7@;lUTI0(x_>qv)+~a2 zwd{DfzJ%GQp*QG*IKCxjLT66E&W30GhewfZ<8MUf_qIvSkdgG(l2AAt47rdqX-fpB z%1LwR(EP~UFSk!d&AoATP!yt;#MY-|ojG$BOTc&tt!UB_CJb*|-(!%@C=~fP6Y1wM zZla|;%v1u^4U}87yK9yFiK*hIcEAgvGauW@ry*NqaU>+F8r#|T7#!O z(7CY+@q2d9K79dh3?L%ng)uuQM%ADTDb*kVEQ2lnp`CwD)s7@`j0Z>s))1js?rg5$ z?hT;Lsi`P%@=%BsQ(zaAdW$B3v(MwuWw=!flUI;cu6e4F+-Bn0TMCQoC;&? zAhnkpvb|8XOxQ^Xxrg=-g2aN@c?k|fIRH6#W$Orlq)pD}GOOfWA5pq3f(tkXjtl3Avf9UrrNv|0t5HHL8njsH3 zoUlpSJd6}igjl{Jvv5C$p&?o7ao5PX7Y7AUQ*$faMWF#*7Xwyg=%4BCXM{BLbVV0i(UmFxg>F9jwtU*61e{`L)oN z6g7tCvMqY3@4Wp56)S{Mz1CyZg}YWf+jpKuW2&$tSMKd>Xb<}}Dpt|H0&`qX&d6;T zfdtMEPU%aB+HLmt0~Bkr%0U{P;}G3zw(`_EtN%yENsoK?!@~I1_ZRtgre^ef{Ub~l zi6a>f<~^nhVOda?sC-Y(p3>OK(3G6-)7~EVW~skOZ9!FJac~`7wC}gTyWZWE1B&jG z>y4_IMT++o^WFK&@BRJSkKy!i{S%jS8&lPqc$AwtNR?BaxshQP3Wu-V#O6ymlSKA-%ENg8YbAgEPCbIP=%!wT(9BsyLV2Ic|8x8rK4K` zb|p#;O@NPXj&ky!JA!)f=}i80-jdgPL1X|t2jJg(Z}fIJCrqlLD8lY?#T-^2ZYeei zCau)cgXC~sEQ;#h^Y3{9c5BlzAn-^7B5%4P-8TCNIJhG#Idpufgx%E@KmSSxs7@p{ z0rCZc!@`3W%aF~2#dfyNX}fbyz}&0driEoTDrvYH$a|9H%=CvGMGxP+-o8ZRe{S`j z+}dFWq{hM=(GCNZ%W{BAr($E&t2k4;Y?b!#rW?P*IJEMsBkl}`xb1iG-sgbvv}5w^ z*Au5sG!tw@2?s_^_40cYi*t7K9r4izFJ1%SV;q_5kqc+n z<>60>XVWRoyh~fp2v>21j0yyZ&DW?-J132-Y;-)R#tdNkq?%RPRTdm`%*G=cDR&N@ z1Jd#uRhvBgdQYhw{cZTY<8Rm1z)nj&c(C5c2?5Ah3(K{1-5QFP5RNtHmHh*=?j3+h z@ioE5Gg&fRaixmDHL>ve^CsVDixTv+KQVH8mHAB1_Ic9|l(k9ucxZmWCQGI}l{j&E zwjN7Nyy*k0Fs`cQQWA<%}u4tvNRqz4l3#?-;_fGB6{5u-6 z6^T*~Y+E_T8-4ZGv8wyY*7#5CvBbJ4vh|iR9MLxNt>)G$1yfY&cTTssueWn7bse17 zxX~Sb3KLUhEmGjX?ws-oC(iw7FL(Z__)II-dSP1zF!%7Hq9Wm) zS=!Ls3J+mtstnxl$)#9suf>*7fv3NO{&@G}Qm>a61B-E#PvKypM*#|tUK8t|r|@Z$ z1fjUtSt|U+1BNNh#3*`cDr5Vkv+VTzVzgfV9Z-+sc9}+To2+A@J_I1|n-24M6x6YuWV$V>fA_CjH8MLn3^pNqXd3mmXviDatlkWFy<3}we1RA>78_ynV1 z`@NsHqo2y{c7NR)n7-FO1L3e5easO zNZiS(sbttmn!yPf0ThqhOX7cSdfM0DyTvnc)^ztdWoC4*bxP?$o!{%J+V>QkS}B>g zAxZ&u*=Ve0)y`HM6V}Jar*kH;&J2p->EE>u#4mx*;NH{@axT28s$w1#KMd{|Z|WwzD4$-+lT3FgJpS04kN;p8Swy3Y)IBf(S@!(sI3 z{RxP_2vas%M5qNBSK zkol)rLr=;zzBU%X`qpFi+W~F4-GV;^#ig5Zr$^m1?YXe?y)uYdjqO#ifo}h{ozMDt zC@S0}+vSm=G@G1Z+_XGcYDRNT<(Xzc0lh$v#1@`gUlnYY}JW^hXXl`yEBo* zZPCPKyh+@{{FX?y2j_3%tpjUSoa|&A{VIP3$Y4<=n%$%lzucgMISpxtGG_bu`buk4 zkdqs>s^7l=D~@*G{vA^1O^nVx&j&J5+&!~4IHC3;t|M1|XZ$^o^%9%5x_+H+5Ndmy z?yUK6p{Wu=<)5>gNC3ze7QvvV&e?zp2Xk4@N@)4H#irC?Su@kgsX34r=`a z)7|j17pk52OR}_zC9zQ5e6nn!(Ud~;l*DQln?0C0WW;O|uV-mjNdhIJ+i9&@0Xi~b z;!RxF&(L3bbl@<@4hJNW{+(ct(DDQl=F!d9x@l_S7gPL^+trmbZ@_eANS!hODROHpw#(lWR*9uM)HoTS9u&RWFA{bv=*JJ`>h?U@lBKvF|rNKGCwV zSG#sub!_NdIR$x2V1s;tkhReT*d~mI&>M61 z9zU7-*inpTxA75t?{6PJt*#L}eTWNYtA%JecUICPVjqyg0Dwg8e~#B0H1k>w-JZj> zivxWTb?w@<7MzG4a7QINcwl1&y3F_PcNWJczJgs6=&6)^9CO!k2&EXR1W48#Vuf!k zh9TAkl+Tpv!vlftnXScwwo6cyIY-4m3lRd1J)>oI;z%gW-r&H1=BrZ-XIFZl(r7@7 zdkq=R&CSd|S6-eVX=-YcoN2?!<$XtSLv_jZV0#7$1oNX7()@W?eznk*XokF5Q#5Z5 z%m>7-fPEB_M+?6RH3^v=C}m;=t5J|0BFAe@@B8pMMAHB9;XFuxyK*HBB>7T1#x#vg znYNmEr5RZ)6w2rsSy%*ZhUI)bdm=lHRSLqQwV5iekl*d}fx=5~aV$1TO|yjSp3HRq zda9u&*UBlrOZMNKNU9XtpY>qT~5p@>q2+oo=rzEdgGcK#Ade(0xsO9}Gs3?d`A!2n8swq@-UJUwqx830 z4j4Z3@=ROdeq@4r0}~XIDk2VdGaPmbfC)gsaTD?WL$x$NFRvYNespMv^ZJJ9>OmBY z0Pc`1VmM8u2L}EISIQIku;tQ>Ubouy~x~_9{z?@N6%hqlOnm^(}g>%0=9Q&AIKJ0>uD?~nU z|B+~DH?0gVsxGM2DUaF(Hzq+^T{^79QTJ>Tpwz~yrltl|Z@txxjj-h8WCRKcP{QOL z^~1+tbuF}=k}NXq6nk}wtOuz9LOB@0H$Y#c3$|$X@N^&_0!d1PY6X&YhZH6tcmT=u zMbx+OfTlz77AX>zPjYn#?C+S2j_mV0c8Oc+S|!d~abJ4QdcUe3W2pC0SD(T6F8{N) zxo)W?ms@mVwwxf+9fj{5JajL1B@BuZuc4?+KiTcdzIpSeF)HORqBi~55g5YTW zY3Q-Hr-(b5oBcuRldQH1f^zj!Osuejn4XN(?X=qp38j}?v|`5fix<9$WpitHvf7EQ zaqY&#iA2o*K!Vl_lkVqf(eVl6rHs!nKt${d6REkoPt(=$3FX8&v*qxo5uwehjcX5% zYIy9rNAW*(eZM4ph3AgV?fwA=^SKr_Ym-`R==_ctS+_@g7;rxvo{Tf zb)O-x(>#0{b0`AqgIQ!P$c0#?VY^pgxAMi1FuIfsOef?j zU#yqz@nzJ*V|@^&apsR7&-Y8oV_d_N;Br>FJQV}|Y3na*`@ zP9+Q8=Msls+EzscoE8l83cS?^752I)W^llr#@t!%AGEfDlKv4S>| zU~Og6%FYxE*zcN+xH_O0e%JvU=!OyDY9^KJBq%a~rbl9?HKr4={)SLh&C~O3f z(fVo2z;oo9X-^vmut|aMDkwFXr)XN--R$uXZ(U3gw##kI@h0^`Wiy+G-?9A|I6$~` zIrfKsfRI|iym+;zSlf~^K2-SmLW}MNw!ud&CE39=Gv{7J`{aOd`RG@{MrA!V7l$sxM^1ntkr0F)>9gf zkkV!F;$xE-FQza7IR~4xLpqO_tM=Rp@4q^8$>5hoFH=z{lZPGVKO8Js4h*^TrsK=c zV7TfATQ~X#jRVea9Wii+U2yYN5sylwa|6?iIkfYjU59|Gk<|v6-gtP(ye+|hilU&P zKnlnLj*H)*d~INDodd-|bBJYyHOe|FzYv-ofPU@uy~;T`Hg*#dUtsdbBM^IlkwBA$ zog$oJm%EWd&pjTBizW~(i9E@`A%zqc<%CD4i7TUe)|EfSup7{IN4hy42iwMQ9+Q9v z7Jv+|f!Lj*riW31Pbt^o0R$5yJq;kUNW%s>R)I@PPC54$8n)M!$m|7yt0qvv-+@Gm zKe8e|)l?`h$8$2XycS(gp=X3_h3OAAYd(jDth?kmVcE;Pe6J)yoyvSFUxhz+GlV*w`aS?UO^1Y-ig>B zBKWORXL6Rw57X?tO1r!RjakLf{rSef93HTUuxO*vD;`2v^31)mPoj#S?e9xygr>JG z`0?t6<8pAI44t(yNwct>Qamwp#;m6jKQg;s4xMCQ|H%06%&C+SjN9Cgcv^n9oy?an zUtY{6N?uayoR%S~hRZT(ynl)BdM=9|t`6|h4OkdlUrFBE*ap9-2*yH$Mr3C$&7rabFHS)Jw70$5qvImowF;a+RlEP6Zdau#Jj{u#{i7Wo;~X5oT5{Db0wX zYD#m=OGkRE@d}&vv*kI0Y)kDp3MWQg4Qu<*RD!IHqmAl>k>4aJ2#w`%p(?F0Oj=c$ zx|Y3?S%XDQVM-fx+UyL6(g$w+H4BD}PbIxV2J|X_v^8`j7i(6P~${buyJ04!j@xL|S0{2{lqv_^VHu zHE1((TC8cVP~Gg*I;^umUXf3F?=5FmCQF3wtt{ASD_XyJAI(GCv z|2uCV9{z_Z;Wb|vnX*_|GkrlgrNJ};1D?7@{1$C)egmR9Vy9=TL!JS_nGmjtm}7}k z`Cl#JkFEEw6(CBh(5=ij?dLfNT6)fuFuHx-`*6W))^ke^)YjHtMgGKZhMzsF)Qj|p zY5iZA$o~l!LWDNypRQkn!GDhz;QxO}{>L7Y|J)6|OZVH0)Sg6fvi+@zfPp-HFW5== z`L9XCzDEoUeci}W3=~#Zs2Fs#1w9o=rdd0njDCsoLi)=$XXcaN61z7EP6pPv(OKun zV=GSwVt$Ck6Wlf$+j{jpsyxbFCQ?w1GhY;51sk3N`SQR5K@Qs9^Rq$vXG$;M@VS1O zNuvV|bu5M6WgW;Qvr5!Aa)zB=y<3E%2)izvy!__@;{i2OMeuo^xojT@{TGLoZkZ|O z5MKTS#A;CJxpH)AW3}fe@H~XRzhz-znN-hzJrrHOkYxqCG z9Nt7K(e3Sb!H5xP)}57{Ei|fXQ$K#PD#3T^+wY?Dg9Uo&OFN{_YM4>C`m^2~G_B2T zCuc$mCt_9&z!A~$JK+>kO4`H7&GxitVUzZ&+^EZB{(UvSeL<6kDxHG@LxQD3bQ+xEB++GLmu3S!UTPh!$bL+#3@-r?)F ze`m>=tkY_&@-gU>)V9w|selq2%GGuhg$Dto1C>AJTDDxvDNA|p+)K_YwpjlL5?d5H zxj94?v*}vyxl-kh*(>~B@n*HJP-T=gD(l#}4Qx_|r=HSb+-W_YdzWCGS@MEE;@j!b zV8DT8Aur);or;G*)3<~a-!Jh`9u!&an!R6mHu0>;M(1<*>+a$7Xin9X&o0~j-(#?j zAjJKeP$ptB-q6>NJ+?CSmVE*MF&- zogGjdB^*Se<|h3}2AP&@GF?OnV*?LaXV$y3b{}{TvhWoy2L<$gE~`2- zRTRFNTu%`n%C0U+!#$r2t|JX~gO#J7qPffyfC@pUr3<~e;~fTqU~R_sZp6wFbRd^_ zO1$%fz3}~8f zP=ZJs{aA%f0vSuo&g!-ON@wpnzqk+9GZ=liqduEI`qSn$Ri>E3)TWq1RgxtQ2}7Bo zP`mZ@4>eWS)J(6rwnTIUE4EUx^!O^&HRkp`Xb@_46u(P3y zZA1}@4$D#B$e{PbW#-}hdT|0gjU61jy4h2?h024ygK0|P{!BV4_T#IAC06FMF9PhR zyRKrm9m48luF|5BZi-GxSoy~MEhsY^a73ceAUBKR_bBJd98QCtx#`pnd46G2)yje> z$m_H6F4iZNwz`Fv@MI4rLHc@{ktOfi$>v8;U%nQj)ZuT}6zo!^Ne-l3SvD0Wq?A%* z-aeMMJMgda@86vND*q0Jqy{^}F9q+s{n|h8S1$|>SI@4uNO?E=4Dg$%v}xF~j%DJr zywYb@1OwKs~h!LVIPt^Z@da18m)M@#b^e(&Pu1$ilce4zA@JaWrxMCNJ&MKi@hv}|J7J*C~ z&7ai5*9bf7$o{7%MaR6zw3oks6=cJvlg6}N1Uoq}0c8W)5>zc5H=a&^^oVeT$wh7d zeD$8|ze=$;V>;58n$5a5rVN@%fP4#cO`3JD$Lc__5*R>`Q)r^n>6Rz~PK{ltvzzs$ z#jg%jDfJ$=nuYVMS(iaqV7<$|@}}mTsu*X^D~&d~9z5VeqnZg}=*)8JWw3Ia*dc`_ zN%dUwb*OaYBIs=d5>p&Q>EJ$JtMj#Plfw+`BCSk`KfP5+ZQkqK1C zJ>;XA{0u^C6hT@D0=?dJo>0Hgq76Oa>@}xs!F-*7fu(iDtGqgv_x|~j zis<(9`7Ap;ZL&~WsJgQD2K4=j*+LbHw*y~N%l*`FWSL8|o?$o)dIFsT?(xomLjCA) zus(*j>)!`q$cG+U=&lDu99zI!A2aS~G<>l=nV9Qa^2uA3x4*J1J3CT6!C*tq6b_ zYQdQxK@{O=Mle)RYlY=~2!c`Np{4@4h>;AgRFU0~$VZ3f9!h7prrF!nq}Z)}@onAa z(9d-Q}aFWiGIxyiD0klUkmcb>3<{&#;o4@oA4Q5T#}a;ro;&S z&!y;+PHrtD3hK*jZA-ckM46>`7g`FnE?2Lfm<`1f zPU*WLV_WePA0TxQAoLBUfd%ruz4Cj#7^N2)$I1dqaeI|v$OI{!fH;l^xodx@V?X~^ zwY{3`v3BKt9W3+zTb%3MN!q9bzP6;-`f)nK70QG4wY75)>xjKDEm)suIasa(J5Sqe zWd*~gP&DRv(z`Vdj$^HihdN&_>K(>{j=}eP*)e}?Qd)$`iMMZri?j3(Zl$Z0mnS?+ zD4H*=ryZ0r7mzN?%p{J@H@0z^8x{YnUih-?*xdC2PjzG2SQTZ1kTVplNfZm!X_BF_ zez1IfD{-*}vT`?;COoah+)^1tb>N*evQJ`R9n)kV6E8ba)+6=BdKp{(3e!S0T^y|}M6MyF1Iixb#6voPo=C>Jt4Wip@L{fE*z2*B{ikCbV|eIM9lOa9w4XgGgnMe@C%lURavijJLlFb#yTk>{T~5wuzPIq3izP zc%hhcv*bYDz1Z4(2l2l$c$)QklAU1?6@7?k{^9dSGe^abWQ?M*lQu?(7nf$RIC!Bs zKR5SVSa8LqD`Bf9Cakdw8KT`rl+}i<`l)A|tiWy(yR}i&Ps-G3ocFw za?M1$Agw{+!|?)(wX3ou@YK??vB`qxP)%1$ik)03QcYX+jg3=5$IsvR{0nlq z8x1<3SH%SoY-BO9wXsov&if_!j&Q5!*z`bP=#Ig3$BUjKr4bfvyu=CvqBC620}o>b zI>I70+pr(G1mfC~5m)9s^~aJ|C|#gLuCX&-2ysj+<*evu|BB3vQwv+PBYMMz^M-{)g zTonUb=c2O5$YY>CDSwZ>zcfr>Z!CxHqNVz7+a=zon+5gUPki1Jy9h>Fk82;9?}e=> zSsohso$5SFCF8>XL6FaGE~8htCsHe%yJoH4IEuRK(=K>a0Q2Pzh4m(P0SQ?ZlZ&fhf4`g8e*(O{lYvseglN zD>=lGg6+Y2RJeA=WiB}!g$9bbgm$sH+UKDTq+!}K0(za=8Ce;)(KOrawD{d|5?d(k z!fg|t-HZn(Kk@)f)cY^&d%U`IF*;W-LOSLG^_$J#u6BbqoZIY6Dk6FRKK4qvz3e3j zhy>sKdlF1TP{`=^LNze zjU-v~J1nGu!%eKdG2|Y5jRfLM1h0GdSW7PotuO8uKEuO4T;YbuPLy)v8mpwvd&;HRwaiV)5*jDoyfK^bj%6&q+G+bDW|g8F;w}`a zvgj)Qk1A7kI<#F?bSgY#uau00a|%5)bX+U5I1F)0*+XT@NhVK=EV>_xupK^tCSLH2 zBm4Hw)>SIbngr{$$$t@i=2yZzh2b)J4X323keu2YhqzhxX!52J)aZY$iXogz!!L7VQ^I6xE7 zyBYC{pLn$ zKjx`8JUWjLH>=1n4i|<>Nmm8g+fYo`-q3fUM|JiSbuxrrrw>4|>n?JmAPg7Tf#IYf zaBCDDkqC2p-qn@J7aW$x@EdY*~;hPQdGhdRC=h&u4DXOGNpq>Loew`^OQuGfqjkoUoUc994r-nnzje%+;!Y@T`{pwd{h0-*7BavAe8`I1La+lq^Qb1 zZK}aax|}5eZi6o$zBm>;y7-Zpo~&IwV=9wlt$f2t7(=l0u3FQLo9Br&L&b=z?pn#@ zU8!x!%OAcX}%|eIlPIj{F=B;-%tuLA(KH%XD&;R>b>3B71>D{CCrA*nD9vEjn}R>TT8+A zI@O;>AiLq6L@1-07<=`A;-HYD1hy%JS^s&HuOB>zXo=_Vb~at)j`kkw+9=|oTy1{> z(qZS5nVFegm4BOT=1==B)bGUQ=J# z=7xKD>=jat;dU#-gUmSTHC`-#b^Z88+2Xo5uAfQeL#YS^j`6D0Z=9YFEU5M zm?xq{Y7_VCj?-~d`Be7OsP#Y*w#8!gO?Qyv3WC%bnwQ(;+YQiS zNF%sfSVEaV$QUc+rV8q$a0pD#`yKZJ+QBHi98MU7A~hgggrjJzFh`k$G6t9+I0 zdR2SUoJNnJackC-fR8FNi89y*f&`+@vl9E#r0h%1BJt5}al*hm8_) zy4>r^_(bGO!RI`hX=IUW`McP#9YpL9$#N$I$pUo%{w0#qFpAlX{d8Ihb;FtLsVtOS zD7l{J!iu*3Y`;88&=PAq+3?h!wv1jRy%_1x^(>v0?u*iNQ4@`JPp@fKi}^EwiJQuD9v@Ei^+ z9EOnmf-qE6TeO-R8x=e)bj_!gHJY9D+S;3@GL#F|6)?Pq*N~DY$nL$6y#>)=&H6|~ z2pn}2eJI%JwW@6NKeOMCjk)l)>N;qBq5F&gn6uRLZlMR^>j2O3_Uflxn*dITV1Y~x z^T~$wSlT?;Gr|tC9h`%J5#BQBM?T!r605Z}$Y6xrHbOw26UL^K4vG_cwg3R;LvFJJ z1aR25SQOeBw=uZk06ZRlbA_xIvDxF*y zWNo(OL0uHas*z3VX%Xn>=cidYH1y&1RZ$Sdi3MCqtT}bAxk!v#CXP=*RW(3lnswLZ z&E>X3G-87yR^}krgfqEi2mv*M02*N_nRVk0?PH{0{q0OQH{<%z;l#*k>3o)3w-kfx zWW#`}RAjdMo=F7fzTWe-^|L{IZESU*TiL(>X6)U_j!S8RzJ8p^ptfp{nNF<^ws5hb z&R#l_cwJyVk!sKplo&jXtLADsrn9F+5vkx?Kgx88c5yi$ze$)n*BA0fvBPVCH|m_U zQ9`gh@IkvuO=(|UFk)G^3=Cr3H;ug9vikt}{KGG;OM8yLV0jA()+`oq27(}rYHe+8 z@Ql+T)+DfYuKxUAYuM^GAgy2^iAn(y1eBarBotAyWCMs~$r-i@R3upB ztdff)Ia4Y*XOJu+86;<@x^uaE^w78O9ev*Wj&r^o$NtdT#H#gQYpyw;=l3kWffq%} z|J-FPQk!{fj2r?P7u?D0k>y+sv>MMrbP48p=!huax)lKSIQnh30;}O}=)RnS=1fK= za)ANXQyIK#JT5B_QBnr-L63ZXZoj=|0o-6Ed z@l)U&0E>YteElJhZ40<;^61&pOaY7u9cxi+f~j{d9D)?BDX@_txPua5$aHW`9h_)R z&~z*KsksstUz`W816Czeaqwj5>Kw_Bn#Zkfw)g7OF+y7FTDmZX@z}hW_0D(7egyVd zTYG(J;nLrOA3je#qFk&$HG}K+4qmMYdG<2Demx%zj$Uxug}~~p_PD~-Km6CWMK7zf zML!F+-Z)mNqkQ9Pl>@RcAteQ*O1g8q=VMZCzr3d{LQ10SFEAnD@mUE`KArvHEB|GsSIbK|pWtW|ND?W6n^4}vAKxko^gqlXHiOw(E7Q?6+2iRm*FS+C z)m@nts9sm4g(B`?9tFb*&4)6{NR`YWMq}B%Gu}R5CYP}1= zKb}3uXmuB7mzbw7Pqv=&j4q2_P15rSK^Ep%Pa%y1M}=y8tF-d-^ISX->G3DvdE`R!jG>uv zDhoGLy%)=4Ok2TY=ef1M^aCtzA+B~mf(z4rQ|m=CVp;!Pt;fCR{4VtM4ez3yb%<=w z^(1*9N)GJFHgM68~+(Gm~}OBC5Z5)~p7Q8oZH^iW_5;0e%i4A$MAkD54L z_^Ul=7txvov2h79*M)_hMe7Ze>6&0X?}Uy8rvDgN@)%`H36_ZosiYpNw$;aP-=5!0SkNM_XTN>>Y+0&&XR|ZiQ4>@~Q<)8(QyW(d5D5$- z8&^&HHKqsB13~p@oQp z{2Tqbsl9tz8p|dFI%)P!HMT!_r_;fzZx`5JAD`yTO&@+Vb}aX(|6y+E-5Wr z|6KVemr0egpoKteyq=-?GbD028JouiAK^H2R0)ZEt}M1mN!PBVvD+Z&!)R)Hu8*&0 zw#o3qLqYl1M*}p9E^~MLC%pOU-#`E*M1!g7v^{*9z`)#Zs6j2|twX=49L`2lJgEBb z3MKxl9OXY@7mZBpe)*Pr?uZ=y-=xn$_`_fRRCCuEB5cp^T+aWDh5Qe?AgTel)$%$o z8A3ZkAF#+U7K8s%EDM|#?{#J9>3~#P9Tdwe^=z3Bfqm;gSl!>RlUcg-3aRZ%otN_2NZ%nw7Rp#(>PPnHE09hZ;b)=u0!@yjo_ zXF&#LHXh%F0JmdIbo1O@4Mn(*i&*e3D-(A%p`#cNI`sPPZjE>E-oeztdDa2e55OA& zI1I|u;H^w#VfZ~)-0;HskHJ6;TwAx_SS)?7W55=>u3O?P&J_%jtO8H}zOn=S+V4{a zH~RfX8$C}Em}^J{%O@BEdUerkl;*&Qg@YBF?_3xi9j%A&k3aC6a_rh^m25*Swtd)> z6#Z=rOah3a2<^F-Rm{*GfU(o?{Q}p*QmE2Qnq__?s={Yx-ID~i5WKlM;4(1HUX}Kv zh5STt0MsqPM21m|WCa7orRC*7sOo_36EObzch#BUg(47;!UAE$0+U8Qp9pA`Kn(JE z)-3@c1AT#+o=i;`>P7Hbu+q>(@)*bew!F{#Pyy5kWAgdgVIe5;zb(P)MGnsTSaE{O z`j2SCNSN`|f)f~-Jm(3dn8F<}1|kjz1`GE9cc#tXx3}7_eCA)?p3!%@uxJ2B7=}?R zA5_zoYao^G@}~Dl7Iyqm3#2A^8h-{Xg6rrK6O)wq&cajlpnMlZ4?Rry>VTyJckf~} zvEv_dk^ti|GA^JbnWB;s@xY$@oAT#=eN6XEjl+uw$&Q}cV3^r9^V_B^VYy{QB|n^ibJ0x2tI42K+Hz zd$?y0BTA_uj?&SLn| z1vrz)CIWMPUi+d`N%LFIX>=S}x{v&=+Y{?UpAjsnh|VgvykLi{#VhCoY{Jf-IiGH$ zGtMitPi0|Zcm{-jMtP6SLDqN7n}KCd{Qje=$I<6xEoPyQ2mLCN28PSKr37lA`u9w3 z?VsP^n^QgC2P&vPF5Z>M*-yp;4cJ}?sfy2t)!n)L6_?|-I3 z|Jxf%abN%A>o5Jke%Cu1u+!XUpD*jC9<(AN$Q4o(Via!ew z9mA4EFIEVrQezVkI-gq$evwu zneR|+iAD_vXyDgg^nNZIVy6=LK%o=5-($^z{gYu{U=VBt72nugo-|w@>>2(g8l>hk zd8;@m1ge7gdDSvgGFA5h>OA~RO)#*l`(Gky@MJ@|~9JUULZhxPcftER;l34IJ zF4=A0{-?Q4BmM}m3sR9hitgN|-vrjTj=_S+dp zE9U;hd+*w*yFY~Lv+Lf7Vr2%FUQ)y3ZJ>!VGvxTkkztmdrbvoXH6%BRTtM+dz8f4;Z3ihq@~0}>K3U%EM}g? zu(m~C9*jD#g0K@dH~JoyJy$89<~dIj@h@%K2zyw`E&YB+G3)JLfUx()-CT}NTvmYv z0C3z)STtB%b8HEOo|(6f`+AyI_jGi9EPD*35%RT9Bqms5^DDD(kts*Sy#bmM6dtZ! z;*##*GS`VS60d8kYXdjd|FR%a<-SF4uDWWqVj$%GsLHMm8g%Zu-bLWu&-0$G-A(-~ zbDuMf=I_K7HZ@pii!Cj#{pd^m`t~2{sh@lsgC2?OSP7g<94wumFspG;io0*<0xfgX zYr~IWZm-;HW7G)|9fJf%19&w%+haZ6-H~knT;vlotvydl*U8a8v`&XerfzO)Y=3;o zZj*qn*BkV6X#WZTop|5dltve^P)Ed-;4ppJjIK-FFn_%y|#Sv@L)% zMK!Fb3fYFoTotC??JhU*#5MhiZJ*K!U70Yk>eZf5zMRBL-IiRSpW1#i^RkCQLB*3m z^U&dH4YuOu05fZ2J%X!_FeipmN>uZsvX#13f`Ol1d9_7cE7H=pm6C+TG%h|?#)*v(bM}HxA%_`_S=qupI!Q?>6Ywzh z6&Mo|8^hwI6b3q&&6#omFzPNcOHox_T@M|Uu0dVo{J#&f;V*CQBZ7N*n9wJ9=I@Kk z8|WtOsWpe~GCKwPEBSXHcRykNukLic%;Q$MJ@{Y(4IQ(Db9vt~VlC_93u}>5&twR7 zqW#ScK4(1}n(o5}r^3I#@=5f#qOiR-%YXk{qtEUFAzaYa3&v@eM0*imLP z2+Uob@KeanjrR$qhUI5Tj5>fQ+MtW&H72gVjC;J3y8%J&_FiHnyVs|Qg&)5LQH6ph zRH(E~-^y_&?W$1&hJ#=~6EZZv{!TGkBq1d*$!sb2<-tUr82GS0hllBYdit7ER}&~g zhs*7yD-QX!hN~L$n)UHhb35C8D^{xUmtV~K;Wg9+;q{Ez%i6q3Jp8xEV47BFs_s*ARzNnRH+8LKC;#U;v3mUc-e~L3=Z_!MB zZ*!_1X&6BUsN6rEDY{sG8UnYpI?B62%i4x^`bTpvLPxox5}G7K)r zrKZ`=ouoSx7kfOjtJIy%er;{6a_srC&_k1n6ufrYlj?$T>1rDC3Ud;j zIocbe#aZ~o_^kC(cB<_7ud-uZ<$P$jGIk%1~NXk_{NB zz|~jPV)%pdelof%H!thtsnw}4QnfE1&Y~wec9|72vBCY3e|Y*5Gqdy{82DlD-~#;m zXR(c8$=~?B(-FY$#owq}`X|4ai}jvY4=60%`PMpq(#!zt;k&~=a;B(XG@ng~weN)! zRW-ul#>^vX=cv=hu(gOA zAE0(q_RbTS*QLnRKiryzHO6nerVHRIvxQdjz*DI*D#3X5V=}>ucJqS@y9d|YAEi$$ zsP%h*!UE2IP%Vy31#yg;b*&k>i)_VFHa8sLHShcf76)ekmlHtW@ zqu}m}-15#A%hOPU(5)}>_M35e8@ThCZEQ^ekqt97UM5UkWbK8S3YH}^G+I=8qev4f zg(pQtc5hkr*k#h=C{tzH$G+}4;N^|cT6<-?KRBC{gD_)E;iHaWVHeV6;P+s}P8+N@ z8E>{*Y3`y-KaW(JL%z0izu*R$I=F# zvb4eaqdZ9)VY)5O?<50sqWVczlK;>8?H^Rv;lbj|7>-AMrY5s=7>+Z}^_FRRT7G#S z<6hE`g^~1UVTt`-N{f>fU69XjUf-h9J5q|5m8`F4F=*q#G)GPLi|C*>N}>4OS9qo-YG$3` zT<6c(dNC!95o~b)2%4bCYXEY`v~R$R-G!+~(fiJGzwbF{_{f`_UIRKrr$D*|{SrJh zhK|6j>G{z!JZ9bOfF4!^6=fhMUAIUDNStUS0Z@hb9(`8@AapLRulFpBRCFaN(T}yn zlElYugG>xuzez9|M2$Pn@{RY9Lx?7}3gK5JQ6mb(fPv<=z(D9qQI!T|(k+mF+Rydz zqUIY22Jwhe5F_f!WHJo$UqBkC0%8HLorec?g5B4Tdwr>SWx;XX0nDKw&=z9)8$tR4 z37E=2gd7+cK(nR6a(5lns)HYFwOM!`lV9e%oCu=@&?@<-LS7ikiH3P(o^i{$)yX#K zP|&sWL#Ao$P;BGZ#;F|brO}fJzh+vf-UHDSul=5xzu4t+Pb1wg3Z7Llj%^RE393jR)LBdM2prJ{}+DE!yvdNY%2F9oyesQ@-x z6=rMT`Zam({oJg6BZYNWm&JajN`}nehPTeSm!oS9{71#aJKBuiNwjlJ+-yb$x;V;H zH0fU#o;}flBMGmUjfW|u-O4n!h>|0j4J)(dDLonMtRQ z;gqp6P9Ld)Id0wHkgT7Um01h_{0emXj+^ZU$ms6A*Yy;OHm28QLiX_P+Z^NOaA9pP zHK>Zgg`Tln%A12!;q=cW5cdc@l(#dPY;*47>;QCb?nDX zCILmwA>#Hs32!DXM`%MUqKZ%7okV`wT>E=FqHRD5xy?yS{Pe_Nijpx&jYaGYzR-?`X<_nNK8hM)2=`q7>QHgN8DrY_&yJ>##S1H~-{ z#-c7myxa0xtIbEPWWZ|FpPBsSSxTU6?-@-FHnYJ}N9pk$9EC%USUBIggFWP3v#hlz8 z=@=0@aP2{5(6ws^9cMH?OFjT)2rvpA929Bwj6l5rz5QD-Jw!(=y1KwFLvfS``fmtu z-VLNF9r%}2*l<`utBTWiUsW$7Zq1;b58Q|W@$LxHeyZzBU!N&_EnGb8gG^fdFrk2@ zXx5pm{1dopuvWTM&-T)j9tPg95`fB*#{*<6%)I2Ai;_H`x5T!uL(6xP{{n?hxm^(? z&+s};dXt=k=+38qA&-qN8*6{!xbTIuWDEi|KhlQ5)<(s zCU_1*81bwBN*Y!4$}{c}OE)M}1~HL~@wIuieo|4))L$x}7rEvH&Rt~qldOmhi&_S* z4)a~LO41MeImj&*ZYNoamau|?Ax@ksu7g>LCW>cS(EeMH2K%Nu9K-z3j;aa*0ISziz9ZCL9W-MQwvno%yZ#BA5bVlQ21QNv#qX0MIlXQ2Uo_`N+3L?ygrf;#lHr)0GvL&$I-CaRwOfvO0XulVGO28qU7* z*(Xo^bJopi@+TO0U5}$n{m(t78NfR@o;x4+0MU?@iHWJo0iN=I!dD~Wpj|EGwD1-H zd@6&9(9VZpvQqv|dchC?w)=wJ^y>htV+s;x7t-!J%#JzHVF5>olJ5E~dHL56stknL z?KK77hKe=~IP;i~`h52;2bT)kJ%k>8y@vN+z4cG+>|q$WTM>x*mU#o6<9VPhyUthT zv0>h%$0J>%NZZ!zuCKk)u`ifiU8Ss~EN;{GbbPrrUzOMkfZsp&q2~Rjik98N!gc(l z?x#r^s&;^f^8N@sjP$jCUD$=dl%kq^ardRyKw8D<^q1m$79_b78aD*a?bDKWbNg)X zEH9t7Mee3aH|!#D;K-L$n5$8X%u4W@wW*<~=0)#VGR24@1a$g5K@r@;mwAxve!Q1- z5unMdU(=$~oQ!rUre2es^koeeCAFYa5GYi;-Y&|S4rfI>KcP;GlEAw66S=j**m;^q zOE9WCVe*I9lhSN%o6hu>K@ptDap=&cOCo9weiog#{pT@Q`p)@#GHZ=b%E_+cnrMh> z1&X2|mOZhXYo1j~{-YBNU7f{)sawvADrNntgr6q8!+WlI-2heCCs3g4!YmAEC>o%^ zLtGUYIDjBU3noBVb4RckU{!!e(!p3$`H}*SNG^+-SNI6=)Dq`NRnp()&25 zR#`ZJyOWEV>dW&S$N9o53Wth13|(OOCj%>>8@ZFg3#5#qIpd#AJw8`#JK^`aqC)-c zwa1h`_lU!AdSI~dbHFwuyEqKP5j#+}Fgpuh4X&d|MC7bEDSsZJBiUD=^1f?qoJ!4W z_HZpKa}3gD%D2Z|H#fIv=-4Fg(Yx{4j45s~DmvYCdNor~K@noSe!(dgN@M<2eQ4B> zk*R}$dBvfpImIf7Y_Ql`PGV?V2FN9N)gHg;jkTE+%jJo!XHUkYng(;4I16L{sHrf1 zI(p&i6Wt;bmFR^8PLux9Zf%_1M(c?jqxvHuyQR8GN-=GRBo4BI$^a1LGXKlE+|E0a z7Dr9BLZ`A@jFRTpIeK<>2{1@{grUT0dzjzPp3|i5$6#44HE$=(k*I=4h)?HibP`1R zuKhT~@L|BG{u~1toCo4X^ijs=?B_MNl3D_VmDZ@*CX|WAJE2mZSEV-Rmd&~gxYZL( z!Cn+C$0!%vnRH*QO^$44rt_t$_VzZtlysBYzn=Dn&d5DnIH)%uMwr9O$_l;VpaMit zQvlxZn6$$qohLWeAWqa|Z+QZcN|@`!!;IAoO46mJrFNKV=E6h_Ia46gFZk)x3Qvz6 zm?%92i;InoO*_&uA?7x#nv8~q1}cLH(N(2|y1GfZkTIO1J3;L6mp z$UeK$hrp8VS1eU-4N-`k(rTgQ%6s2&~9!U4J z48`O?2%H6huym2gB&e`FniY~ z)ks-vFqoY96y1M#eysLgY%S3D2Dj}HCy@F?2iJw4-|f+7=b}M27thI$HTnJkW-U}= zemMWbopoMUzoR_-B26So%*g)1o1cBV##?neRk^d12Yq{&EVB7DZ)A{`hd=cMrjz@> z>`Zrk%oG`Y=Wr zTMo$-5h%``&H#F^k+E@2=rE2rP$&r-8YC?aQXXXnGh`y1miFIUmCM>#R`z)Sp@_Hx;9Pd=;#<3b)kv=@n|AjXd>M)jdN+6<+9ff#ppM zw5fY43_K4WI7mf<*$>x_k|U=m67ho0-_%~sD8&ebw`SbfI@9sM0gg$YuY>FMfF0sB z>AoFT;||moL%XzN%>RxcRaz0F>+`(nXgR3d23XW|DRpj4lP1rel{{W67E*^B{ zpuA(GQD9y9l$=&%yKFX^ghnfI-hN^>We_rlnF9ULxn3qVR5b;FoFCF3i}+2F-^BXM ztd{PK#fKy7JENT7RO-BOE%dGAO?(6USwln0MD>thc#9&2I@{9p)!YxMp`Nj~W~^{& z*)+!yIGMr?jRQ4R+S_jLWX7rnzaLZ2yM4eyxPY_QK^@-sIHKt8Ee+{mCift?93dwU zNUwmsobp-n2(y;wwBZf$WVh!vN?qZ4BJ6+EM)HrnKiq$)K|(?T#e@8|1i%V*)C^h@ z=xyKGIcbRl7`hsi`QWKoUKw;84ri1GQv?s>je`h6^hyoEqBond>K+bUHF7HV_WEHC z9AI?&wDsY`_hAn(w;48oaQFg5@mTcbVv(g$n%;foOB%Q{I*eo86QPJOE_o6hM`SVh zU{Kz-P{?s2 zvvN$Q@4m}esYiA7ll}4ZfFkX}tEvu_tUa6w zv6voMg?WB~CH8FF0^@opygnlWl=5fYcb<{zD0{ZIqai4RQ(2^HRfg*C%R|d6?10?k z&D&?`_Iw;|0%qhHPJQ`&x6$fncTJ+BpdSNKQ8>_4jJL#Q!byNJq50bgDJgdL$g=f= zmYB=N#>T$1;dVnkU^|`OTK#^em=rrY4EM^U3ld(SSzxm61+C%=Ty$AMOFP}0WBhID z$f=X8o)b-xp@!43&`&Yx%QaznKzHThNT`T`+v0PF*)MMl_2lK{@9|;rs!4v(wqSze zjO;Ae`6^ilpyyf)l?ql(?AsIfFFL1y>%Y@Er;ob_d(u_{P5z_!1Ukj}H&w8C~nJGZMn zHUx`{;IlGcGY_ZR9tj-%dE@+(?b<6*7R6iU6jFPm1(9(25glB3(4R@Go4av^4X; zKc?G?G{0qw&9ABKdL52~uAIG2h1iaCw;L590gd1w+J^pg47!{%_g3{ z4|LmHHrc8SXUV4#i1+*!^pJdRAWIJJIC!;QEr^nkpHHr>Mx#^3OudNp-5pSEuMtY@ zrb7zTz_;7^_5vZyJqXZ*DF7rgL6L~9<7Dc#>3BFjH)(}4U?a}++~tLf7O*$H`jN(( zMR#U(cBdszd*0DC#iPRIK}!hLPQH0?Y5rUgW-O;A$;7}d{9XyzuWo_QN62H^UCEIlIp9u{#;M>E>ktR*w%xSdL?Dr>arW3g}B&n2114XdH&1gVOB1YyY(4t z7kYT_RfI;gJ$Tl=5|u8Yxns}26k{r+{zl@*&Qg(s_NeGMrdJHoC+By?^w~<9Itx$F zr&^3vZwYS{PY2->-{&7#^(Gxh)2edL)8XA2(gWMmM9=kdeV0e`sfvSrE~#?Nmvypr zt;&WZQOqc0IjqeMa#n$|p2V&rzq*QHEfPpIYvI`1^k$6U@p$E-m#<<}AJUc{SztF% zqX|7rQ&P1VP(A$Gl*wv5!paL3_XhMLIryXhEC;vzPjc{e@rex(a^^ZOKcFMhyROs4 zc5ITYcig3!POv#tGToa$RjF}VMY{{gzuM`_sd%Y8N#8#0ILW-+ga`NiZ@GF^4n>~7 z01pfOrfj6alYsAHRGAa&Z8f1YueaU(Y-6_vCWuipLTJCBr8|d{CK#N=?Z=hQbs*+ppQ3OGw# ztU&n`VzX@u-sE&`McfnL=jqS0v9X=Hbo-h@1aDgjop5atmw4mFF%Zi?Bz`W^=9*_t zHz>H?Utp;T657tbxV$ZoJ`n4U@e19;uT72+%1T8q>tbbE#ORGDp9FuDew^j$p&6Yuf6dM%aZ>xc zr`|?5EJp^1>AHhX-kQ^B6H=SxoL+n^}yr!b&0k&Rq(r(s@$%yO?rnwE4{5z7kkaNqOr$~`omFaPK zO1t3n;Jl2D;r1u{*?5dwt{!;qU8z&^Zbr=X$~*&^1L>CiD>a|F!y zo=o#z7xbKaT@7$N55HUswqZ#S_t=(zyzl^!rkHl)-~9k?!mD7mc)RaBMjzjL0DbsL z2#FX9DY*%3p_!L#$Jy~J>96|A$KIXJ%iLD%3vtr-?pO$8rrtjZe7z>#lG$T~ns$=P#8-0$H_b zy|Jqpk%cPmk)o?Qo0O#q&Cl?+p+%u{!;1#+N;W%~F z}~= zV%=Ir?_Aa=^yK7!vPYjO8c|q-{tBOqzP9}7ott55{-5`QdUm|`LA|-%ADF*fO}LoL z8Grp9|L2s~|LTqiPH6jWZ7zasy6r^3gjeegOT{y+3OJE;Q} z{8oh*8ANLugMXPT-yLSWh@*u1Pi=A1NwsG2yl|K34!d%HWw}K>752)y{2|yiVTLV< zI-7ve474aCjs5t+!O00tY&)#vD@yE}9vO0Q=GonuzYcCZhWR*VyNPZ(U|rPK6}_eA z$;dn&-Nyz%ti_OJU}HeCO&d}4klp&4o|a*Dfa<#^_~GQ3p}B$UTG^Eb2e0hgx34q5 zOJ@mA=Nr$XavxNf#xW$gFuN<6&d0bf368MEQiv71PGn%}w!Z^`Gl2|}=}?vtgeRo{ zS#t{bmD#n17mO!vfb~2}Y;r(3+_{BG7K)+MjoVln5&mfa7uJU~UAG^Y#)~_t6-Btw1yWW{wt7c<-l|LQ7G5VeJIeOE zrlDF8#{dBzTvK2=?PsupVHX&(*`%{Edl*RG)tKOS#Hb~yHd#im&`}{($-F0s&Syn{ z=hCMh*TKB&vxB=suSYA{)p+G_@9!FE#QZwZ4oKD|RBj-=Y8G5<8E)Ae}84aX1jUFg}>!bM#@I83^dU1K`KE+M4 z0qS@uChLqRn^1Xwyalm4(5vi*BGcComk85*Ns4OuAj~NijoX$#>{q^92PisiKl~IG zn`cy^(WK3#CulSFLT|h;cxJk{$ba9?{vg6Nv91&Y9jGvu1jplIJ$2*fBFk1rziDN+ zm|Ar{NHA3`-`3wt+$&WhqdMLko;26*aF&jtNt?8O(4E&N4IH&u?@e1jJcg+-`+Qst zukko}-`Aq+M^86tHtPb`DWVv{%%*`oXE(6@%x1zN$d$oyu+&;LRWqYoF*8~_+b%2W zh#){#)JMyr>}D$}wH8K|fV{Vy5Sn_s=t)%6D8av>uoxqqX7_aYZhIDapqE+X_fN*M z?T=`ghMv%Sb~n#)t{3erj7Ye*#}S}NyIeEihu2Rn#!PK>&1jcQTjjarz`^}ZV2326 z=ByxI&Mk|xn_fGRzPzfU(a(<8i+?YbH<|9pT5xMri@tt7%X9WBBW-lvXyEhuK;9&d zLBa)?co^L`iR+VOHNCm)-<5xlqAPYt%m2o?8s~JSX+mb*xu>VjQDvkXud}gV)K!q} zX`l9gkUVTK$}o$;G{)=Z;b2iret8oo$*`2u6BU&@*ncAzg2PRXQJrC=6SCT#oY#~SJ{ zt{>d|V>`1Ah_}{<1{=!+Q-+A{hjJZOh4NfW(lvHAL))y|Umpo(MO+CKegRo6sXY

V6L`~pVP)vkh5U%X$cmfsT$zNCI5W30Y5FDkW< z$CX=oJ4KPUNl-EqI$CQ&8V`~(**L*$DavR@SE;*PM^)`9(5*GI~y{vmREJ0h6 z74P|!e5I>eFsmV`Uhg)AHrXv1r{sucqRe(CZ73$VagIGx>9mPT;anP-Wo;K7`l^nGu8bU_H`yJg8Eb2n=rYsb<}#VC+T>a+ zkKW3YEeQ$n^|g;$3npZ?2U#Uem0Y37yVnt$o!f{B#KMym0b!sS_|9b7{stugOfmWH z>d*9;w`x`p+OqTH3{9$9s`oUuP{Opt!tT$n>hYl?E{zuwy#A0qK9%Pk?0TWV*Y6(< zHT}}`j?)ZgW@Z+BqbzWB#i}$MOkd^t`mWo2SFVWGwo0qoY(|lcej~xOsH^k4)-crx zo$$-5#w{G6Wiy>$^h--i=QXM}bk23-?mB+h@NPDj&}UoqOf^;5%TkKXdLJhEHelxO z4^8jrYl11yZ2hrbc6R<%A(gbwPIp5HWTEahi~snsMmyilq;%-?kVj5gbBth8f+vx+ zCp+U{g1l?{XIH^Ixue@$?5v%Wf8uo#%7Le-FMw&S&6JL>e{#y~C`}wg|) z8;d}ml&RuzEtPF!S#80C37xLAUXs+LaCWSvI?W|^_StZWeHKF>Bn^%cRUjU+y|8XL zOmJ{n%UD)4wvH30?O$R3bcTAVnD?k=YlerU=y~KL>@g!`^A+* z$%Ux`xWsoVmW{QYxoRwL!TmyBn*8}fb8=lr`jPP z^&M*Zvkt?A2xwUdbAbpybV7EwK*4Bq_wGB;rdBnBXpIFz$Ry!&(5wJ;USP6Z+tb6} zw*8Hexk?&@h-ZjsQUUuiLI^H_&_r3E5A_1Hz+Hi-6H9vVjj~Srd13T>oRe-xCej zK?|b}F9WDDkN5WW)>T!Fh1V>3k}lJWIK9;`b727pC>-h3TzvAZ z4l9#a4jdHIcz5IZNm}Z7Yz`Ahbwg*TY4@~KH+y4i#N@Sf`_J=!6_AhUV(XT{f!8Vj z6SketucFXzScDCmotLeg(b#*xUxJ~+pm6pn^ImHrQJab%+ioxvIoh<{vly1&)4=Cc z1yfMqpBRV)#?SUnHSMZDf)XrAG=g!QNbpVfJtBAphZ$TiAw4PDd){b4PDkj`#=>0y z^RYmqAG89u05?Gi7G(hGn8Kh3IZVN{%vg17X7F9!7YalObfW&4*Re=w+-`%nYH48_ZR!fv%98w%XD;}v@p&xc% zoJSL4?_m)lmp8CfRX~+yH`m9zyI4c7UE+|A^o}s~CM6LAn{mU8X!KD034 z3-E$m3I+;461tIfo6n-JwxdJ!`0?ZL3#0&r6M5lR0kI2OhHe;l8{IuYU@`~ocf?hr zSMZZ0!O?FDARjbPJx(vi4znv*42-N5kQ#)7%=8EzKwlbDh2bb877bdFKqpC&4lqKM zTH`REnC?jOM>7U!Y5q(QDzZ&N0}23C;z3Nks6XLMdT{vgv14j5RD~tWh!h!M91aG3 zT84fZ9>ufW&bkL7XkHL@{H{4#*bFX9^*BZ!NTN;uVqaKTNf_BCk)0E{4Fo;Jzd>Ie z4KxI8ZbCSFD-wbL6S23yX8s++`SaJ{b?$~V6M;Tt2PBim?x*7%HKF^Hq9w_xt zRcU;Vm&i}cn&UOq44jA{(-v^FF1_&BL$6Q`w^A%Ew|q#k3LONDJ|W4gk?Ba)0tIWw zq|9HKsp?f4+`XZRWB1q1jaw=Q2IMEbtHRcjK38`YO@Y+Yp-fm33(BB&~lOl7}ch31(P=Pu004b>ZlV~rX>+Zw*yS8S5HL%+f1 z7&8F3x^mfI4imdZp2wqH8TX3rc^xD7`LpNgTTfTsFE$S{g?b*wjVQQQ(wv;k@q#Xp%JCTVl}mPRD|kz?+-8#>Qc;@_%X7d&Ot zJ|?G`>oMr%B(cvqV-U&vdCg}nb!&NEFW>!Ue!B##$!QQ6m=hQkb+r5u5ne$U2}$FE zxHX%D{0{9o{QeFjU6`1AF$lepUI%%wtrG#7CbU+u4R|Q(EBBrO?J&=%;WP@O!%G_h;uigH;3JzTo+KFmGH*mR~1V46)BFHB>6hRNM3XBNkC3lkcg`sxbrc+Q65VZ97e?i-gd{&mxj)*I7^ z6xQ0xHP#rcDtCsnBZ^9#75+GEl?onRY2KdWee}?C^hV+dT5s#sbW%CSq+@FE$hJ}@ zD+4hmgH_R}vtf=d@6u>uT#r(Hb-R@PD-9Kyo5@0)R2-Sh-uEUPo8KtmR?bhpd-D0l zNw;*Gy^g{CVo66P8aZQiCR87#2Iw2s2Vo7|g|&CLjFk^2VcYyPf0j#4 zF$^X3>a1p7l`?EGegS*J_Dk$b*bi?hjxwosl;zBs&@V$jxxVlSxi8f*XH~Hxt!x~q z5bA-#0Ea;*>{|Dd+yV3nS$C?5l`I->Fv7yZu8$UB67|(-#UktQM$TvR8Zn*ugzQqLaXUVC=;Kuu!~Z=EO6;I&d@mWk<|kwS3%dMXGs%(<<{2qVs{U^ zzaQ4*>}uZMpJ&-X?HkP{<-9cB4VFk=(=TV^7!s-fUc5GcFWxX7|8oaqap%O`lC?U< zljr)+X-^gDMmkuuzR2`ysP2|(2-(RH59RRu6mb>RF4{rXsy)=_j=;V>NtQDzUqazz zpzy%4C$Ym-jYso(Uiufc%+T~q+Hktu`RoEeU2!U7B0Jv|$;K8|u<9}2y%M{+#;1jI zmJ5hc$EzqWpC(>Px^ciBuA0#1(ege{3li3M+V-Z&Z##TkORu>_k;l$`jCtDk;X#~( z`C5Cz(a_+`Yx=$sTsIQ*>&^@uwv0M4qBFX zFJ8R(W1>ILEEF>ZYsw!;XfYz7sSXD{Z!Qcpf5J48bi$}5Mi1neN+D?|-(w-A8qRbq zt}ovr@~aKJpMe;F#D5LeUk6VQXnd97pVZ@J-Z*s(J5NO4%$$MB14XdfOf;v%xU?#g zSWRPf33gZ^PhzR%a5Eoy7gkux&VV%72a^Ii&zBq+=WeqJAe%!O?KW;E zJ~KS46?4uXEN4JqZJ<1RHf++!pc@ppD@e@YNg_^nr$+)H_dOJ}b$xwX;T0mNT8CBl zs)v|OI;bLlw>5_-M}``TnO>ghfTvrf`U2|J&==ghJH1DzcO3Zj%4`%yliX9uO1e$D z633?bzf;Whml=o(uXhDEXp+gs77sv5a86>2a@Vwa*H%-vRL$Oddc{sygJX2>;0uzS z9WY4HyTr3Uz&g)-HA2Y2*#J_kgY_N=k2Qr#U)HzEkZkbK$e-;+`3umhJ4;9L(e{zW z)6Ty}xu)Ke_-W+~Z^lm-c6A4wcnTiL>+sSmqz{`XC>^R65y*e&u@mEQ;wh8}?ar?r zj%*Lt0%oP6+aq(7zoj}{U(6Frw>3*1T(5f5;>?QbkC9b1LwRm8-OcETlFlwarPFw| zAD<53DO!81=tjE8P%GzA&#sz57sys_oD4HLGEp%R zDdd>#;A|B-k&zd%xy&OdC_EKtxtr%Sgqz?vLra-{Zc;Ez#Tj*a zltABjIA46O>pcwWLF~FnbcL;w5er}-*MuH-?Ps?jjv}-^I3Q^|>(R(F!uK%wL?{f_ z$2=iCqROlxf;Tlj?1II~(J6w$jk07Br5l;ME32{g?{=A$)Fx6|}HmavYKsvY=w&$93pyocRQqd_5vY;5POUtG1=NXB^=ku0J zMw1?wd0$#kekh z`Re2dLsRT(;B}pV;@2dXCm>c|t*UMD_1jko+Vu>V^~qFe|8rAMo9R}Ghvup1Sh{i= zXqR(cAIUs=((&}^{k|N-k;6jgMXej>k~PkO|7VSu1N7sR{V=A#PtPQtVG%$LolI+Jmx|FQFs4ch1ePF&}G;QW&1Uofcl=;ln-H{#m6# zNJ-_fc~KW};<5I`OoI1?$Hkiq84rGJDa3WRsARDB^5UxqWmiQGli@H%`F`T@TdG=V z_r+%Dz1wxDPfkn+r&0^Ltf<0RbMJ5ULjp;r>BKjDe? zzgYLRaM*bg;k2!TwGCG#AG+}*xSh4kvM>s+6EZXBxswKuz8o&`D-v`N+f-0durBgeLHyd18FN~D+j;(h0{3%AOO*I}#tL;tuv!n{lP?9Wie zWmRovY+gb2)bg0X+_o`6HiYBdVh14@F8S9@_MQg};ncG_LtJEJB$!MwSjYJxb-3l` z*-fdq11D+=e8y^TukA%6IJk;~PR^{iCJyH1!}-> zq1zFV($EDKwhvjGPxa8+2Aw(7=%p8vK(+UW_r3Z{u#K!}&0i>XTg*lizjtv5nKy=3 zOGcTo*#$})FI$r49{Zn5(*4521Ut)ddXcEFxvj0OUrg*~UIv|B!N|Ca5pRM=QEfEQ zmuD%Jvq{A+;#?3bo$6c1X~WM-VNpWPtPjdnI5}1h1myoA=7Pay=eTa(n_YSF_c(>T zhfjt~;CNuDyM`;04}q_wGfsNZ^`q~rS1NbzH12wl)~a!B=-sdCQ#2EgVC-?L%3Inv zy}C1+`p1NAeT*h<<&waN>uqvUPfSJg$Z&qZH9hA?s?Tjt-%5DG(Puk#yVQO>za^!C z+>$lmoJN3*($SDudjjtasgHhlzT|L>FZ?8i4KWHh%C97@pvAS@>CKc?-<`x$AAm#$4CGXIRIB|oaZN|pIAtWa!XJ=;z z2zlL39VFtm)#mng>dluYp(kgs&rGJ6Ng+Z8cbfh7Kl+6gOdmko68b@?Q3g1-j=>U= z*mE$bCqNAqsvQe)I`PlE*Eiv?I7LSno%_*54I1TMtJ^o2-4#;B z7qi_>e6^mjiZNSScAQNmvO__>$-O%sLwa0(gSH;el$X<&J2 zdgaw97srgehIX3;-jtFf(>KX6b(FJ$@>KMKXlwuDD-yM#P-3j{EgpS~7Yq%?z?;vl zxV@!Hf?l46Iki=I{;7L?#I;qIRo`n~fely2uMY&4C!LJ?F=MNOD|Fo02X*$3FC$_4 z9Y;^Ua!qTBEV{<#X^>vJxjem1&&(>FK`T|c|1T$0uOa|4Jh&|SB}qw1@t&@ulTf=t z#3Nj}?V#Oz$)OyEM(Caes2mNjh2EbCjfd7TWN(1l4_ZStTAnb?=Cc~2O`3x@Clm;^ z5W2Fd(!bDa99st$GSHE?gEse+mCM72$v~IQfL<-3(N7t;WO(LYY7( z#|Y|hq$PwPqW_1z_Y8|V+tR*mV;hhb1q1{P1qg^3$WcXwlB7h*3X*e{Y-pt=O3t8A zgd$2Pa!?TvkPMOwCFdd|MW}ae&zX1ZnYpfc<_Xt)c%Jj+blX@I#s2TT_FDJ-yKPf8 z$>izb8egpEwtB8v7Z)5YytyV9#lV>lF&zn(RKW$~A6U3v|d& zy=x&Hie{6oZ*yYBd3^o+I@8|j{nUZOF2bT*q631CXFT)so!k__3d(Q2f2*HDz|WFbu(LxfI&CRCgj> zZBEr1KFn(krmoGnhmY=a{QjsKWM~;QcS<5Ttn*A+?aZ^Jc5kqH+ z*a-|6EP3WfxKLx$z(&T|v*$XK*0j>^6}wxw9h5evGGBW8@mw{(eYbUexuZo^h9EW0#hcd@&sad2U zP$*#5xiv(ps-i5V(zR$M#s&zlZU=^h3VP|<*WG3`81{mW`=`3s*QVYhQzE*904EV1 z`5@?iDkMk%UfsGE9LuAh!E6SzSd-bFT=K9lFUr3HBqEZIA{96^2UGw@2@O>4=2D2M z$3er#6nt(#=J7G>&dvf{=M*%vM4ViGFpF}S=^QHgDyXf!PbvO zG^tGJTCkI4m!AB#rvO^ZS$b8vY_j1hV5fv(UlugSxGr6~%fiAkJy^ERJ+_qt-81w) z&YZEWyGMYVJ!IT>0upv0rPdXl(Lwc{<0F4~P%O7oQ z;nQC@Z+XA$p7%OykZf=JA*zF6Ew+05#1AR~QmHey-)(&02+m%#!pU?#)+N`=OVj3S zu2gDIAxnjX()Rwi4e`d{dy{FKM)j3RSo3c8feW?m!R_+h^F{TEV zttdgV&!dDc-MYZI*6JtTcAUzZ9tYMglWyabP?OGS=5*kp%haSnFPb+Db1ylBhFS^A z4ixN@8|OZDPPF6@3YMM8Z(OBC2lUY6vcI(p8)Ne4(R<7;3#p*DZrst`fXluYR z8JO&q4q;PEWizSp?4s3CXk7&f9p;S8{9W*K3w7rZTZ@B^hw?}ZAQW3~p6L1al(!JT zf!p+4H?%a!`mRT%N_2O@U?&t3x-;wTF*WWIypYfFVYSe8*-Gx0fG=y*jA{YuR=tup4gX)|7u{eU7yl_gvSt!^DUQUEw zfq<{hLQAcZ(n@lNDR6UyXuEl}0c!wk#}5FcQde}cMT;6cGdhj6+(N3WcykMtw(Fr#;aQ0`FH1K#EN1YPDR zbZmh7(FyaSv9Ymaxb~-kCJrSA0xT8>=U(cHQHsb@3d3AG=v!t6tN0?a-3TMm`#Kx;|aJn-xErm6KGRnw7V7!I35Mn-`IDEu9eABx7#gF z`= z@lVjOQ^d(r(J5+9NyI7oJ>10uz8w9h^gfr#t96^>w_qHh&lTh5M6Ry%v^gynvqe{X=zE&QQXfxM>hdAK+1wOy z`m9CYtkWq)@rKEZO6Bg2Q6jFA=$ZqryTi536rqJ&7&NkBC5V+vivbjui$KCt1s(B| zGjeT;ElSt+gW2Fp#pZk&IO%ixzY(YTS5a;5KNBtk+OM2;uaW5XV)S8>a^2tZf;#UdU`O5M5Uv(gV-;E;46F4U+@D!5}ck_ME3vd zf5M5dBckq+Ply2b%l|&#|F4o=|1%Z*|C26b{+p!QkFGE<|HkmUc;+v^-(CN8ZTe4i zuS}rDm0pNEXv)8IPdQ4Z%|`$p2;-kD>)_v@3Fzw+0Ep{okXeWj8=?G`Z@+m{`XqWh z9yR2Z-1izHRsAlR<-e~(vDsbu z!;6l;{v`kFK8b(pXvpIe`SlxIibqt~{}yA&|83v*|DX}(u?BQj5PT4lr1#AAl+3RJ`W3RSqcDydr01spke+kvC4~IcxKF)qdAfI^ z%3~$~0!xEQ2lX~i{T$k$s|u@Ubdni=dh}J-Xq)zR+}eCEN5b(+Xs8@7^Qur};TO{A z`vZgs6=mzD69GTr(N=@yY`$`Ic1{;@wen0e(2frimNiN;NI3{!MJUXH8J}rs4Pv}wrZC8rjuck*C1g|r<$&b z9dvcL#~GnC?h`-0ckkXb%Z#E?xQV|wBy8IBvg@|=oz(Z|_iq?7%YGKt`VHtqoo>}8%q8|qQ8|W&5AjE{b26|& zeO#k=uZ7b^Iq-hgJ768xns}Vsad~ktlsKR=(f&d_6Kv1+ugE?wa=TA2M0?WrRnJC?CdV4?e~XJ-_*5+SmAl`kEOIs661Eq|q4^oSe$RStU@`FMykuHNL=LBzm^RFZ z?=|)DcWzWGa80VbABfqtEBE~U>ppI3PY>$Co}`3QwKkRvlafx;MpH(1LmE}oQB6$v|Vlw)eZl*jpn|kZ^tp@u!SFF-ldRP+!cN6lCTrn>6?T+WLw%XHk0CGX;Q4p{YS-jCN884Br~ElHeF%s*DO{X8XFUl&fMd5cMsV!1=bFDYi}a? zIAl1k)U`{F5t?KrDW%tbnq*Vn3S9yDOT6YO(>`-0%Qi0hJ&3`e;~!Gq?OzPyJS zMm&+(r%LQTU0l|jp=;3|BI>x&exux_%}{{`8@_&v)$7j6>UaMc!IgUxk@hz()@i4! z<)>fKcoduyn6pE+PZn^s>MRf*%1{cs!x?$f)F1l~3*v))GB4xITM z9_RIP%i$Nx*=?6?$D@8bE5_xgym*=3fh)~fa`H{ynD&@vv+9rssM^;)<-EWBAE@hh z2DW{RfrZdDKUe5l)#1jaR({{2478*YQ@x6*ig90^M>tHkK3P-AMUk5}A6BBZS96DU zrMg@=x1#-m?hjvfFTq<$xBb38yR}TE5LF7>oJc@5T&u@Vl!Wt~gKQF^xhh_WPOc5; zj^wQ~8(CWg1$mENH&W8mB;WZAO8Sy;?sI|TbMc#=%z{SGKpcecgNC-%Qg`0w5Veu6 zRt2BMH+n{X3%?yrigBsCj{xTyN%Rej%w+$JXwMfF4k4kN=U%$wVIg{S=9jsy&7Hxbu6;rM)fG59 z$Y!HpJ9mEh0qbuFa3g^^O}s~4kqYj9P|up1=Q@8e>2933he8K?bL-^dIfGRHlbbHj zW)8WZNXud>1T;n!@xm?pRDsLg-fTP09+TvO8L1Gtbd9o?r4FP_I1C#e`EcI+9q2&C zIko0<=~evtljpORw1Ktl=fS%B$z`GCUHI29BGHJDBDaoJH}mp)!EB-H&G#xrYsrec zw?39SlFZ;bmqn1~Cajsz=7>Ab50JMS-y;3JWq-Lma2k;Q{`A9Xdf-4AR`G;jJS2vf zg`{8yUV8d9TQ@`M0QC%xge?horSwfj%cP%_YQL(x5%*^Wln1bY`fBsBT|5!pk>Y~soQCV6%dkPu!ysiW6bktbQ-Lp zTzlb52gfvp?mAeISEstQv;?iYD#bTJRN8w)T-&tqD^KT@r!2~+>?hidt2bvx6g)-= zjuB{HW4OBI_GiB!=`M_#8q{o5-+pAsfIMARr#ma<3e5`9{JfrTQI_Y+fRl-HS``kP zUoBlAk~qC917j?=OXkGkq&=rSm|(c(L&w8~g(ffvjC{6pz{wblgU=lg@DrrLMbsqF zr=esWkXs)5{dbx0Q!s-Fhp7~VSArsqK;KM)A`O6DAlWnrktZSoLzf{_%w;JJ^vk8T zBe$P5LI@S&wW0q8N>M|=0-`23Wcv8{`N=sDK^c!hM$0wXDPV97&q{-M-T30V9so$Y z>Gj;-f}EKfh$nV7Hg{p}&JQ_4fF0p>s)QEQHl$zK+1n#{u^~+9phJ#F=xKg)wZx+9 zJ{yA85XGHm)RcZo*e(tFS3tMEJGgW4&2^_U0*rS3lpb(|4!+yQrpEwTw!m5kzsiuM`hXLZ-%y~ zQp!^muU1Ufo*EVb{v!r|?ZfT{z0+RhQ>x~l1{~a?E{w8SaZB4uBM{qew`&A1;nHAwg@@&!nXCT-IVk9O zcCum}?d?M_{{uShDzZ>wwKsJ)$}UBuM;IVLC-arAQ#%fedXz|%4joVv7@=vFKh7<* z>`$qH^tJk_q4dGepJfQ99k&4#h<@py9D>Pe7AQUNFyHShcgcrg9ScdH|MGt%B{#iz zanb7nnF3MTiS7XyhnERDD`GqRC+gx7%$FnwD`EZs17~@_3@ZbY z$8omjGC)m{)E)v|??F>Ze?mUna_?&~EA2qe-30{&D$pf6q7dR5CvLA6Oml(eFVjGm zys6n+4rjjf zA2@zNVvo}9iR#g6H({5>s9*x9zEPK3WdTKDw+JxkvWczaO^gVn9#&oV^4tqHEM?+MQWYoqO1JoCQTMDtPod2OPn*J3?7eF4%=RB{?Z_$I&rq#Y?@~ZRifc68d z6OLxRSH?MCLR=oe%vF(S7Np;(wO`W=BQ_QIU8P&}=Aj<{(zt)g4-~b*C}5EW8xhJL zo9-_n$y)pVLtX+IerJV6MA(a26WGq4y-fmA$jQX$}!=0M`$Qo1dBCh8Y$1 zvd*_=2>Szld00`Avjs^=u{$5u?#+sEFpjUZNwcD;BlKp7ZlsDHQ_!t<_T(BIkCV&= z5p6nL5c)5-&w^d6c7bvuCkH_*R{pI7+u70i#uAXYElv(AZ5gfOox0OkK$O5n7 zPT=9;t?kWM%B%NU=BL|cAn^E15=acnVT#2I4$5primr(LlAqevPQ=<)zWT-NWLi4L zlo4sg&waI;2DSc51V&Z=08=$m=qus)wL!6zg|xpgS?1L>ap|h>f}?4(GQ#GJrhW`w zJ>+{NLN;5!!8Af%)?E$8K8+#goT6Wk+#15}sQ*#FK4!c*QWQCo47zvTa_QoF`+Vq6 zt2SmCSzP`@mk0%c>%x0IVY~6WFYeI#1?)NfTwmt&$$3JzuJ8KIioXe#$;sUebaW}; z&M^VmJqVUtVcXCv1p;+nku|GU=GhvDCJ9jcBk?rMONt$PdB>&mwed-$yegcNdpPAI=qDz^EKvAFk<=&?SCbkwMDm7r|dW80hKC{Fp2 z+Wo(uc9~rwOJ}G7{XRL_)vLXKQV6eUbN5Tg9j8L4g9=6YzNTJFqpy0eoEiBpB&X}< zOzP@WH79^tL`TT$`o`hW?jP8p20~^=TU&c<-k6RdmW&Tb6Cz73R2h=3@9us zn9l=?N^yo&7en9zv)}!zA~5p=MHEWEPdF>W*F)nxsx#5h9@fc0)pWJ5-6M5ob1%F4 z+#VOHZEr`A*JjSiHbRQzGUQ%0THltwDN?s_$pyL(p6)4_K<$7MzRd}ZbdA)PR)fXK zb1}{)>&@baz)Q4oXKZX(vv2Y*Bh|78h6JwOyWfM3kryXPSY168hl1Or=xjZO%<$*_ z6$6J9Zmri7!R)v%7B@(3| zz#MEW%3$s21pG7#fgC7_m^n+T58-ef@fTc~NbpLBQcAPZy#%Ifx}53&dm1KyW=9t0 zbIK5c4zmMRY&(ps9apDSA@NcRw9!@9(BDPc_Bcr|-6Cra?T2wna8^K^2qYRFPUvic zodY9Tw`P@Fjf`=couG##G;cU)_A}eBh z=9Ik(!BrLJUj#6EhxvP1BR8_y*KE$Zfmf_P0_SERfvkd( z!%BTHj@76|6%;!kID>S7s2~9i4%P(D`VsI;P4{GnWb0O@zv|#HX`J`hEHfnRKQfnE zL7DB6j&q%ENans>$LIU;=svzuB1hgO4U^jMdJaU%ss6Yw5nm$#CVY{0w0Xl$Lf3Rl zV3&ibIu&s(F0Wt6XST8|SoL8KY)=*6te^NZ2ef()TRpZO?fF$_ z&lh^f5@7N7hN@8-K&I?IrdYZKe9#19~X?0%k8ilC|+!E(XHvw zeGtEvxO5hps7kPpA?qS7udX(fy7yq1O8}tm&8qQ^-9b<%vk47?{l9JI&IJyR)W?>J zZe0hdsQf$PPr;^Q3V%0)C{CUR+6noMy>tcTgB!2z`RAZU?y$f0iyFCJzfkV;W1*wU zFP}@Psp8dcUG<8**qvv{=T>`aBX)sbL@m0b%!`-btnV&NYxxT(6K2|}QGG?8>bm_- zX5U(Oa+eJq-$4!q*QK4AgNLZ8yXntK3X0q*9VxjuzWHh9$l&Wogqg45<{IkzBo2kP z*s>;#oQ{K8{}Np9Mc}NtcvkF2g4fy$&|G)?Ak`skU&HikRp{NRc?XcvjE7iUR~CZC zo9|F+hJl>c+L=-v)>NxowUOP$ni@R8VnUgYQ)ScB*tT(q+7YVI^}N1w=!jdQPWMsM zo&fBXD4b%3qY}Gv?aBF0$jE8$JjkV+7^y&xSDJ7xOV_1xcAV?c*`a2c9HCGJj(=NN z6915D?CHG4_ip_Uc*82*)O|?%U11aDKLfT2MgjX&_1Efu9AkO=Ho+qsgmEhPJGVTx z2i0#&?|%IONt_w_D?K(>K%mA6=3b%wdJ8i`D~-4S)3RVi6o>07%3k}iI$20;U%k>hgt`w2(lW?dDAgDwKb;Hn|eX(gJqu6#AI&_YDBrDiw0fP zEyjVs1rTgs?5t<49AX_ll-Qh!TM_4!ta7_8eO;txqg4!+ffy`q9-rA}kfv4?2o zY$g+Go0v@da17}CZ&XJGJ$Iy&Hy619g6BOYnwyr{>e+=AGiLY-cEYx2}4 zJ#?ORgbkZgpJ^;lPA5f_)g`En@xIop;?aW}wDTmSeStVgHJ7GlrH_fHz;f9Ms|5dY zL?O!c-|M~Usp(nZzQDs&gqktUHrIH$!EBdQc``}7Y%M)hmeWJhJ`a?s{sH?=KTmqF z?_JA-=gruGuaB6gbCY}56;ll*WO|!u$Wb;M_+jlxX zoM{^*iln92*HR1~ToXB=W2)kFW24}Xf2CUsVk%O_dOXpMH>K`57%b62@fB6qQ&aeG zo&<_RT?cD^{MtN{I&V(}9tdQW46pDQ;LuATe95>oEwKAB|8>P@of9L94vQae@muxD zoU0;VX*#jOkfkyec_st`>EgbvD03**d{Ixw{jKmITkz$Uh=gz(;*lzIj@86Wl%M$gr z6wolv3&w~aJbcF{K6Oqb>#7>HH)4$RmA?XhEKTb6kKE(IIB1J79^>vh2z$SAh4(vV{S2sKhWWTrBg6$jZ1 zSQApQA(4kiKpI;9E+W_%54ab^n8gMMw)mFWc$Dg?W!^t#tqBJ(B&g)y|5d*29)N|{o*XVm_qwBvx8`PL^}EMp;05?;F`IM0aC4WdK`a%a@z zjv|Y`vsj@2RF)*2*#pI-3(zQoFv7GxULJnx$KifH)~KH?+h1UAV8b}Vyec01&CAPL zlN$?TB+kMtXn*Wam1jam_3tFAU|hGB@{jmaEMg1YaPeTn8fxntQFy-Mv;44Oq+)9c z7*P^{1;GZRL!U!2{u79$IZ}Egyq>C8smDZXXxyJJyDg<6;EbKCu$9xBk4;ZgFYk_j zE&Vo-mX6^f*3Q;X@`Q&Gx2Uk_r&v*I$wk@1ZutyQZ>7Thh*N9L!mRTXMB2WDpS|Oq zryd%?b!93`8L<1wt%!e=_o_Q`pmS?AMQrbSu#qC=y4OXmw+KhOR?dK?G;Kkq{ zzO+YLS{lh(;aP)5mfzJZ6?|l>1O0{jZx$97dj3VMi*+3YN`N_N!VqNvItidy?0dXi zTwMIQrUuC}Wo(#{ib}w0;N<2yXpVs?eGR1VZGgHsV{AOpW18T;^a^7N(bh}Lo9kz{ zU^>bu_V^tJuUF-X?MvCc@6aUJ=}~iT!J>tk0%4k+t?fN;Kzb9madYg?E(?zqopE2co>vxB zd2Epc(7*@dY)5Kqgw>G96yi15U1Jg^U=>|k`U~X9@2)4zyx)0_x)BhhfZ;ah2BZ(? zf1)WaWu!gEu&eH2JK|=SInM>C%ba3i$xfCHpCa*lHA0Qpq?cqq65$9fOz~pMX$37A znY3De2Hv6L9N?}A=hBKdI9^Zs{5`4EaXEZxv?*ztKLb)cn}nP?wO~ja=$!Cv+&_jq z+tG3Y#=QHfn9f^QmhWfV`}!mlnDnrpq`KGG{flCHs-M%=^$wC4;)mozFi^O(rv1fP z`|(kG4|!Wf+{=Zz!d|JmP*Lfo%BU~B`@!$`j`cZ@M8fZapwM6#A?_4ScoFupbM~Sr zx0cJ0a29ln>&boi-VJ;D6XdG5WAUZ-n*sYhuiQt!;3prpuWT)F2L%N&@~?0+OW53m z0a7bi#~K>AY}bF6dL}hWumqy_29vw7Cqvam-SmXd?LWPgJtiv*j{C>@D!zc7+1;hIPeiIBXZZD0L~f zqb2s0%atD#dRwusBx&l3wLWvyCxf{69B{+WfKCeWuR&GMU`zySoe5}%HLE-;?u1{! z11t{`AvLv%tW+O7cmr7)9@Z3dz$D9IO2N8LS<1=oERv;{y|!RJr5HGKk>u!WIkSUgx;7!0!R4~l z>)S6sDU`WSEonp4Huj#r4 zEwC=wbRDKgO#u#<{`l1}qp;DRZI_kzRnDB@hxc0kL(Zi#qC}z~H@9u#ocHAN^>d>o zMir2Y4Xzm-)G0YmlsM!{gUl=$%Inl$Di}EDc_QfSc(s*-S1w`hN|`w-+Eh+?d$v`# zMxF_>>ox5D^W}5>RN7{`tC`#1GzS5)JN}_+usd*iVuZ4mCO+4yAJ(L!lx$Di#mb`W z_>q)(<6O0BZEo?>qh>tV9;4q`$6pE7qr2F)mL)o>RC_C+#J(Pcrvc21YA|mpUEjK! z)mQ$|;bwK#{X!*Cin>EEkDGuzS>U@Hz|Piun6Iqk=y{^Q>6skUqamnuF~KPsrO_4%> zD-3r*8p16466nICPBk#C)j`j(^5FpMWWRNlzn~x3r=rm7HN}f-!5QH<7gYoP1+;ZQ zeG)s$8x2PTYFj|C%1_n?#92Zn%Ln_zY8WUJ>>%#xE8t=X@CwjQMWiP1@uIg0SZy{e zmu`76yJCDMXr8A*ua6YPd5`K76L0yN{aE-8S4?l{kP}>ec|&_}oHsUO-?C>r*^#NhW5x;QNAg+Xni-_j3Kz4>$BC+>lpwSdvqdjI@Mk7QYK zY1+MxmrKI)8TQ7_^$jR8p01mTB_|`SZf3yZx(}MqewDvm{jt!Zrj?2}N&Cf;>t@DH~o(v=P&)^^Im$`a&hsHn!pb8 za@mdL?wAyr!UD`V`O?nDLY1Xr4)q?^eOzQOsb|dV5^qO{izHvH64>uSmiXXzSdv$c zYX6Qqjv~ikK|n#G0=*GvLW)8gr4>}_1Rw@=Wp2#!%A0CD_#uS^j%svA=tET6hR;dh z<%a9zPm3dS+}oftyzvKH3@w9pJda!!{1@%yZ4Q#nA%=VG2v5ArJs$IXA?+P~%bycw zxdjmjM9f1n%p__l-H>^-;A{NRzV}h$`K$OmO+!JE-842kZCLnzT5P9IW|JvNde?Vx z4#PVFpSMD6Au_Z=cwJwyZ4!*wl3^NpGV~W3_XEt!y}MtZMSkW0X3=y&$8c#EzXKKd z7}RwzXhoD*h}V@E%==f8^|k%CsJVA3-Gn(51h{g58`1>kX6QS>_OXhdb=W%~hBvidisHT6_X#PJD=>H;4+w@6U zS(zMr$5l!{B!E0?gmL&?0Ls6vsHm_T{{nY)Ei))@j1*WSlHcb1vQl;_?F#`J{I#=a zw+A-eV&7r93nz23yeK=Ud4w*T0QY+F=bt7}|3I5D1Qj;mkGhHp2z>S{@B9hhBbduV zRrLArL_ci9kdKoBTe~TVSiz-{hr^(Muyb}+2U-$HGqWLAHPzzV-bS-#CcMqP<4^{EPZ4IV_cffQ84&H#PE#Oc?@Xg;q^QRG?{ZEG(Ro8pi&+2*}v=gM{J7Mj>Q$hKN{MJNm zm=~#`KyPE?hzmIp!IKa7{qjyuQ^7bY3LJbXPoC^TLEOQRy2uODjYF>Ec5ulE!sJsQ z!2Fv}emv}ghEg!30`XvjLFEJR+|`+b%@nW(!nScptUB`mWc_FK+KgmXG{`Z|Au1a{96LI3KkF z#Cs2rLi7Qw@xx(_11AI5?bnwqPeCMacQ)LG_XqhJEHE}Kaz|p2ofnvl?eK%FqUj5)f7@^kn7?44E23u9 zL_T4x^vjR*M8ygoCzSsNo8w4QnvE*j9aWeuL1P=mzO^oEEtxdk)HxK z_+)hVg54ZKzEL3ugC4dDNL>a&MFc$X!#sj8*M{$ph5HXsB4W@?5B%(tu+~v9(UG3u zlPSPGFsasM-qnu(iD-R2ZkXGE1YLo?4LD!A2rRb7_IDih!+URkLj2{`ZcFcabY-z5 z_5@f}(I3gN-{w$EKOazy`KPmhSB&cmauy7Qg=Ei;3L*~yGt)vD`mN8%3824{cVFZr z^KS=>mwuGPpJshrV}oDq^a9VZUw+8{i5LRef1Bp;-zD1rKO~<2`=h%n|KGVI{wW>( z+otlrA-&>1{l5R3Tp11Rle=U@FX~lh!i@l7H~YY&Ww8l8HS{QI5|E@-{&c&YtN0DuILzmSTtTish>4iI+Zj~h3@DPOZ3VM2-DQ|h*vec_76 zXZ+pk$ec)WntyXaW0*JO*giP4AW5^Y#2__*MK}c{d-Z2SUc|;7#@lw(qG6e#ksjNB ziqSU%2+Ih_ALj=16(u;_$DpQnCJlsPToT00Z1M_Tz}^TTpJvXX4loTqaaa5?jcdUU zRsbhNWHO((ZDw+?A_s2B7_4TIA;4`%ez>Q*MX-ptFDzV5ZP|MGa{6nALG4Xbt!vkw z^cA|w!thNs?RCgki~E)iIF&$vs;_=~T19v9J}yqwX&P?1s5SZY*@kM88Wq*a&X&o? z_&(ml5tEYuEO%ZjN2p4GmV#6V4R;;qzhWWwl@G#joF?C&S)J@#ezCkdyPWH~vh(@K ztM-f8xq7pqBs@ijH9j;a(0X?Dza+DNJ*`t}gI%83!9Cxko`UYM9ng+ksQ^^6E)6k? zWo?UJYQMJMUiDQ|4xu5aHsQ+;88v340owm)F@Dw9$!zDTB<%?n8sn%Pt2qAmS<6?u zh22O>@(a=QsZGrdYCLzkl#_Q~OphG8mF?owSlg~SM9G&?rf7to?Kdg_vDMs=+cnDL zx$yO%k<#si%^s?l;+a7O(x1sLpt>HMU zN)C6zj|t@ch}^xkZD?+sO2a0bCf~1SHrI9sQa>mE+VzL;voMEQjeYEoU4A9{KYJ7! zPdpd%)w=D^%g5OuFoHHb#*JCrqnG0jg`7O%_r@m z?1CYcqT_To>P)x?Y)nqD(wlA?@opDC4xi2MV{^7?g7u z2Gg+BF?Am_n)4r zAK@r{{5{xHXNzecH}AU#^NI-c0p=OiJe7mkb`b7ENc#{35ZqHvQB?9+C+|;{tFRLm zNr#y^O)MP)EoTd1h7CNbY68ZYeBS1>pQ0!<-QI%PL#@(r20nN@;c+EGv}zbUzwRFH ztKA9Q5IK>Q(zKx)macLK91x99w-4~YAsHV!c=TRS;w_b2rF}>><8@(MS+~D@WjW_? zsGy#1l0ozfZ*v2A6JeQPhk2r*rErn-nn08);em#ST24k^s3~r(imG+{&6CHCEagr& zBKBB)GMBX7v?wo36h35TmeCR-?qvper2Ja{sMAn_XZ|%^(%ltOr|2-yFTHHU+nTbf z#W#@})vy7Jv0u>5bo$fO?OO`=|AQF=Xu;8bll+^lAbB5jbUNNWtOn<|g~JIkZ%_~Q z6__WlF%KyP>EF`tCjrCkTcs4j+selXNhQ?<`o#&jO;aCQpuXGhEY|5Ynfm|B=&5GK z?^o6-n)UK}rMZ!;#)+{ibx4Xrw8~f)tw`0c@vSKYRp}IfNnw&?>LKVET&uYC^?fLQ zsGN0trg;4zNK~&&eu+80`3Yjxt4x-hmHEs*neg1_1M3Q&9@bjgUHmg3KjHJn8q&2( z@?5i~W+2=4D##0vja%sGan!@-x|jZ^c4JtOxv)CHbj>JnJp|&);N9zhp`{ZDe$XnN zJE0Kcgc_>*4lOy@D$Nc#Uo%(FQ7$X$H>*kTVt}c^HvlJyJ^q#>!RnRx8k{(i26+Qa z{C`~?1W?ZP`t|^lu|kk}?-kfDUv=zkwoCF^%|7t4%XvLd%bL&->qohuk=gF6~4LB44vVazFNw|F&dZ$+7i(meYI=^;}{SPPzZT0-S z`&7NmKegUNguZaK@~Y?0rTFweN5Fz?lNK+_0C__&4k|hM7%{1;3)+b}w>`5o?2S*x zc8YyWI=eE{Mj%hM9*V2-;x80e438ym%-M29Yucre?!P)>qe512rZh9U9(v4qv_#TbE5dvBLXryR_1QSs&_@9z^gH#t*;fvVm_9W$hk?@0YE$l=Gtt1YT zT2{s)kJ(z`WRWapbt6KG4cjkZt^D}@;~TzGN{|l_zFjC6yt46E%ZLMsh;KZ=rCN$R z$}E-{Qpw@(6FCjLTcFioS$Ydc#TfD3(^K}MjIda~SHy~;#kcGo*O_lk-Q}FaAzi1< zOPC!!IK+*?s7Og4QF!Zc%@4+H9#_dswA{LKtPb1nCxuK8bqmywct=Tc0SRZ1#0-57 zTs#|8&l_J$FG#ivyR!(?)u}k={*1e4I0mb{^uPwecUq%wy}&uFoA}uO?Sc5`u$|2; zJ>*!O-Ly^2rjCn^<1;57QO(idX7l#+GlI^uC9WI9xU#YmL3QE3hJw`rbcBjb0v3<0kRyUe zr&I;1W^hRgqqqeCkD07Ywn0C@N073yJi!ln3BWbf0wYBT_(MQXh+aQVJZ6C&2jjIg z^&DLkDS#{-vBLI98cl{7h`% zQqcWk<;N4HJBN=R`Wj%Vi_`=JJ|X$(TK>X~-oCzu5*s<|cHlDaPsK8WLoAW{1NkC+@QDYHvzvfI z6tu$$u%`5ctD&8PbiJ_73TKwOMWVqlf%EOWL-gAZs12RgrvI|*y(F1qf3f)K%;V2G=D3dHkxSa+qK+oj;7NrjGpY;beDc*d*a9O+<^fQ9rLgQzvomOxP%(AnYzVZ$Jh z&FTVKyJ5|Ar79*wG!Q|U){__tvgO9cM%lsA8V8O@SbT{00gqNV_$#u(zcCHnv^HqO zgFvnMrueEIp!bj}AB06}z)%EIjW9w$@n{vQfM%aYn;m3P%%V=6(7Q*rAQ%{N!jla) zo!4j%1W+}Ehk^&)SH}nf8ptrbMK1x+e;pWLinuJ{JU7PZ5r+oUZA!4(^e3vE=Aw~q z3NSQjkLrWXdh?Q@$AkJX&|p!7HmTZFd-CyB7A!ABDsghAl-=^S&@kvX@62Xz;b$ta znI8sB{&uMi9y8?#48w}g{xg?fI6z;oA>9A)wz04qd&>I@Z)2=4&l&2Ei@G&iM719e zkcvD^GQ>H7(mwjqnkZOH@5hz4_21IZ)Xu#_y@#a%P`%B!V!Pf2l`YYKwsy$m`wmPs z%{-G=npM4;NHE>R1TB+LE5OMJL4?~BjK9;hlY>Daw%U?C*jHp+1*~6{;=l5v3r*6+ zTQteHh*F2Kb$dt){(zWs++Sn7CIW2U#HFOed{InY0M?!ipUG@Mt;SEz4tYz zoW6-$R=v}caPF~wWqYpK!@}pO?yc*o&+NEH!R?i8vRz8G_;T-(cG-25LebMRSp0g| z0_EE+j1MfToAm}DQMdRJ>gXDVD zz(RkBF#SMeh1To$!-K{f0B9vx4T=yf7?us-m5WQA=F*`rr-}jtVCCsgKvOFn1!{xB z9t2aW@PLM4;OUcv9zZAXj`-n8UY+Y-PcEwY8+HHqKwt|E%vA-4+?9cAqz;xH5X}B5 z)RhI>YaW3Rc4Aqj(~sdQ+F~K#w{C4~(Lo&G)E@W&V2R#?v>3?{^3*4#Wz=5 zqm+$TyGa=Z&fOAWPCQxfS}(;3IoQ+qdX-;UR{5%Y zzLI^|*k_&Ql&I))>LonuAA{WX%*P$ycQb1D`uc(EOM7TiYP^0F;+`ZloSwYKJ0BDGx{nfrIxMCm+ZM>x@AIVTbV$9{?c&jV^BMG#_ zAK+0z6Gkqz%rrRVGT?nKLKzv#CQIi?BI+p3t_UgS0ZVXWV*}afUin0$W;U!&jy`k_ zpkDhGqc3Uj(lzuMGP%{A;Cpdyh!M<5QB25=jEwwI2!m40^l>QilYJ3T5RS^J`9aFd z1Aa`k`A{*vDztTW4kRrX43n7MRwkDCf08^|Dlrpg1Q~NuKUeAxU?z$Z^rsF1hclyFy23BOpbNHL47ORJ8CzKApLv3YMn6}M z_t2WueVV!}nZ4Mm5Y4TAlI!ZKQMz_mVm}97r6lpTKNG%dMh&>?I+}~!S5|jMrF#2Q zI~0c$JD#~5_6soSqHEH_=&|apZm%2%NkP-35bud*%V6FgRQEk(B7i4)OI_CJs{x%c6#xlbL9z&dJPvX)aXSd!2aHS zRtRlOQ|M|2hmMBMn_?@@cCD&yr1 zF)&ZG0iZQxHlw)Pw=84C7Cgddux2o|2_|L18Po@x^D{$m__RZ3x(niG_P)4f8ygn~ z;RA+n2Iz^{kNbg7_1KUT+!|1+0tL>!YuB!Ao#%v8C!*KlYl-~?%y4ESXy7>xKOxGd z3Wa_rG-rMUS;P~dGLp<|+Wa;?u@uc|JN1#xV|C+vRFjcMPfjXy%EI4lLog#_tJ~{Y z*hPx*1}blrgkN~SFrQ(!mfv(LJ<2MSXln0u_rr}N9 z1PCHpO8xynd=Z>ZRI^uQu0OrLl&+_4Ni;Kbz+~LZSEF(XU+RR1k^JMiBHyI%Q!b@~ zckrqiZRct^xkT;+Kf01U==R3fi1!U=jt9GFNL;PO_B{@(IK|e=0cYiBbq}dO656eO zo%BQ!S4mAO#{)&uC)Jd^bK>4TvWT%+{K!T{ndL?wd1p=peB@RRn3%%(oK?o18g(wM zO21lnam`!4v_SxZ9^WCyj5o!DIP$33+E&zw+27tVp2);Kgwnbl26iJ=TZ&mw@|=g= z4r%%`HS!@rHlADp@YBfAC2-|{;#sFG{2PUpPzi*Y56|||f{qa^t(pZ7*sM-Ojs{F# zhS)hY?1$lUH`D^E?;c!7I!_QvD#MagUg<;e3VE+F;7kn0xo^;@x7HHCe=oMUk{~HEs^pupF{Vikvj@5L}dQ`tdOQw&&G>C(aKBt%&8xx`S@qLPGbMhdiXMoo64w zFLWaI%v!*^cMI<@{na1!BaFX9w&_P%FmImmd!Kv6oM1yd3)`Tko+tm!-W}%VQ;MlZ zbJ5VP3Egtv9<~1>t(vZSStPiNLC~z61p@BQNJ(B5X*m4CJ|@(@`SqEd37k)=X8B?? zT8+;S9M!lI-qaY(mJbp<90{^NoJAeJ1`ciGq6y~da=p?+1<(Hcy2T*M+?pYGAoN14 zf!6KL=jW-&D_1u*;=xrq#y>ri;}`Hq6>jwGatt|&uB%}Qq=Oaxm9p+yz$xlVn%E>S zmYG7%*;o?3>i4Bb4R~Q|e|3S03uMC~5_$D~-Xm)_Zp<~WQ+lZ^T|-KPXS>ed-;gY_ zB4;SJDAJyCSDX{ANG>9pn!}=V+GsbV+IBBS5es-pIklI5xf8zg`vzBQ7Fx!<8udiW zoz2GbR9*9RVmQQ@+6z-D0rWDQ&`(JdcUzkT1DPt+to+FI?Bc>{O)Q5BA`}DEUp8}` zzc-4$>MyeH{{N_Z53ne(Y;6~FDeO3pdw z3@Rc5l5@^E=S;vfD2`MtOUn ze`+2mbIJn=6I9kxplFhV#Juntr3%SruiDRCm9M#gbZtD{)QIfqHFKeEk(nU%`>{N{$nc0LmoSfRaD^%%(he1 zCcEgAs$K5BX+Gxj+$tntbmo%N!#9w>maT7xZCjX_puwIhyzsbLo%s}eb9S>9Wn4Ke z7)jXz;W)ilM9A$h;>W7<{aVIxP2uXSlcCxIt}HAp;V^D>rvs_3X=@^B%e2@$&gKY z3LK$O;LD{bN^_=;AcPOxY%zi2cn#~fyZFL!Z$~D&=|@8037~Tj^?|Y}aLyDVIv~|f zeCXE2Qv-!l3@YJ3qtFh28SXU{UU@Vmk|=h2xtq76mFYIc~9G_+5WE!p!iV*CNLD*zu#}SH8a4%j3>}mJ9MoOWXKs*SPLg;||Kn?mmEm=DpK( zI~V+_yslJC%qxr=v_Z+sT4dg78h(7d4a??_0osj#C#?7G8A|n9tbA7xHI_E*jLf?b zG2@WUUA15Z=Vyd2Z^$KJvC8=b+Fx6dj<-jq^Izfk-RhJ6we;`Hk2?47;!Ak%-RpW9 zL@k9IRYYe3A$+1WXKhAZ3+su!#eYKpl!qDhu;xm~J+PrcZpIQcaxZYseJoEsptr=n<$HZ)l`Co{FWK zh#K2%^;L0ItK44b`t_){JG%+=^0UYr%VqV~FvOe9f1jbYHDN>T}cJ=0N?vlQR z2mJt0u@<-jTCaRf5}Xg=_}Z|lRxnT(;H4+mMf`-#OIJB1LLVQy&+p{3N6LBlb3}-u zVMQZlSx0u!XTvlY^+RBYC*vNSN^R>9TgP@7Q^*!23_(A7>9#X|axZuWkCQ zbqQg`l;iengAbH*{y=(Zg%ve zFZOTCU)^16u6^XrBG7Mhpu{^P>f>w{?htU!R1p^Naok?ep0iYgTy_9GNJ1W*`Rp3F2&xr)J5z9(^Qi|0r3itc4Fc!o z%46EVdEW=#xv-(3=tpXUT_ga9>O-*i9yhlUh`mq>EF#p#rfxw~z8x}# zX^`XAEm2T*YDV+>$HIk=TBR=sd}xB|CywrK6>5L&5JX|+kr`+P@1G$rrA_nB@8!$N zo1M&Z<+;$k01G(@-)~abXOju|wBDVKkVR_jI(fUxLbvGy;QWto;w5%B7L?*@N!TrB zj&jAiTH|l9xUHQr>Wm7*7|tbTog&&dH(P*K$@E&%UDREN2vc2o!4xPajNzJAdfDqL ze=^8EXt+<5+pY)m?{CSWBoni;;#yx$?*}J8TzY1S=tc(V&W!ezTFCA5lSjfx zZk=KY8yqB7)1Z^EsOlWm^P1jp(O-Ke%j6^FSKgiY>bVA$_N12C+K>Fhzq}^98p^ig z&!6m<;b+!g5tEl-%ey$G$PB=q)){h0(m0d+YupMsr^65J4DFk`r8CuCW&I<6a`=@CK!L6`&=BvTamjH2#f z#Ujxsx_TcvbO>Y?$O!N=7jmENe7+b1gS7t z`$Cq7zw~r>8^YNR@0aIrCyj^ji9~R?M-EPBuHHi#8`E%JNaXfIU1hT}hDF_d6i|T} zm%?XHAekG~WpYRm26KlI5r0rcx^C*m@XS7Wum2He)jRqrkMiW{m4W=x^+3<@1{EZbd|=Lj^( zIwq)vOJ+WPy0y4{*5=vbbq9#5f)tVMtE&o-nl9$I*2JU1qr76OoF+tg$+#}QYb48r zshdr2tPs;V%NgknL+(ZjZ{xws>wU|3yYDT(Ej#XREWKq#HN-b(Ya$o9J zwZ(p0T&2@;-N0?K-Cg8UEnAV!Gh2?YRl1b`za3I-!o>r*g9^5C4+(&ARY&Ia z%JC5&Nou&ub)5EmJiTFhQ0lMD$>SLuL zX7=IpL#Sh#2xoHp{L9U?o>@cv*e`Yr1@gYgO_MTu}nPbhNRPy0%l|uP}kOJVOi*?#NV|>H)rbnd(JR^b^^YNxc zGpi2Hy>T9a)Yw7K@K{eW?F9bReM?50`R48IV+H4^#T}Bh1MS!rI|tcp$L#mZmxz%; z=|RDs&D@kl)z`0y?IGrgj`cIbBFBESznEG&1bwh5p@BmJi5N^8#Rf3P zAZIgpUs_fMI|Mxmle#k4OUc7W6~MqszQPvf5lHNXG8y5hM0Nme5d5kG%^Fu%5DyE% zys#Y#FMk$oVQ@2h1VaZb8=nO9q~Oe>M+`K$x7qG}zYQ}{S!96&e5SmsC?#Bre(2>W zrmH7Gnj*40fGeDf$!B+rKb@Q~NG#jok{$yCP?S-DDo+qRpil^SPm1~=BNP}%8i7aw zC2YZ@(GPw)dgw3_FM^YhNkpVtzRXP@4NH5#@&*+TaG=h@^`2fSJroJkU{XV)lKB+I z#Hccbfod>hR&Oj0W4R0+at);iS8sk`*RjE5ZT58+m6m!?so2w#E>GT4mT zZlOTux0*2^u10-9q``p!CFn>6A+H&t$_v&Yn>iWw!IK@XZ~+uwI5y~k=SqJ@O}FUM z#C0HuAKzg%{AS*o`*`6g1)sfqiQU`>@Df}pnUvdCnwd^381#$rwVAgQK3Tpy7#eOt z+U~pZ*^RI8MD%tsc4mg7DU9JATs*lF#kO)y`O|5cXepbNP!;o6kH;}tRoTCb-|Ew zTgfx85T~jeGpZu5nDAp+pqeBdqg&g+b?P*s;$!7ZTwj?C$*RBnlfXSVk8__sjkXzI zAY$Yy*Sx#I++4p~;Op!KA*o6CJ6^s1kgj4`!!FK*ZSgPR4FAlNP5GHvquPr>9V=E4 zt}&XSEh5bSe$k>ra8{kymE~aa#cM+T9MAl8bG)>C`LA4zDy0&%`%+UaRpPbpug9P^ zk)o+;a!pFcWz@R96Exb;L%=Wup+itFfZ5sum_f60pIK@%bE{CM!g#)KzP{?K^hG-e zsPq^0cRgBT+nx#>)ja)y5n%M;|^?gnj@gp%u7{rLHr5}w5$R9WWSk^&n1 z8m4~XSbQKchW=q$g=<|*+PM;aNOZ7DSR~V7y49jm-$*yN@u~Gu%&_B>Zp_WnO!d1m zXYu$gzdvhe2t59xPrCG%yolSi15*T_DxpuK2e$wJ`DZDV<;$u6{3`*$ZTG*E4G}0` zlso( zP(I?@eCYj~JOApz{SW`x%Z)nL1hI6Uf2Yj&*H7+0JZJyvVQmPX`15bu@d^Fd>;Jod zEG$fBu*YBXnbB4_Okh$NjUa2*NM?As>AnFt>dkQMeGlKe0OH)@X(^A z5h^{eR%IS%nSjLYE?(S)c*@B)Np?#cn()_B?frnu6rU&3iVOSD5?}w4=IKR1z~O!F z-&`qw5ib18mH%(QIDcKLbMO8v$RqIlna3ge@bcg3vT*>Dne_#ig^=i z^MZl`CiTKdR37?#a+zMgW^k~37?(X*ZD zbxdJlef@-q4`PnbY%TEAzGr#a%#!xyI zs#z?IFxz(`vK?IR_wHw!dEf_yw;(h(o8kxqFJ91AZ0O@shhsvvHtl zyOPvq{)deQkBi3ULSG=pCCl+~v`syWo(9*lcf%Y{0-*>q-H_^U(Nte=SvCBimyL=v z%1>tsEbTsTYPVZU5jNnYIC&esD%?1vH)bj#D|UhRO99JeNCu}qD!${F;Da?7k6-fuo=ngBbjUZ{M@P7f6gX5~B3i%~L7g!UmqQW;C7H+f z-vg?bdehCzU~s{Oe1H8`jJ7JDz~F_QOegbW$NCkTp9;wr9MqjEqKaOfWHLDDJLa6^ zD?vogF7xJe!`pz#2H(2h*z#Wwx-}lJUTIa^?c{c0w~mFu#D_0nZkzt=uM4wN!`k4@ zW{6lfy@sHuIj-z0p>6heap81}jJ>{P2La&xZn!r#`EjbMGAq<^ZP=G$jXyO)yaUB8D2MVYrSzNv@=$^LQcm%R-JG{p^AI_;-&NH zcdHv_HmL$;zNc&NSdWN^=|?ntveQ+$>w2KjTR?-~jKcJnIVn-uEXDxN;D1+13J@s; zpEa1i?b4Ry>Ze}-GWVc1S7tD5zT-0E0j>G|k#C(=I?Gps?VK_)@CH6wSR{*Ew=VDzi}zD{h4@%8f4ZcAj9h5Wj@|6U zKUwiUb@~_C*#gz+*2WzGfVzkSkgEeN)tY}Jix%U@648EvIlZKU$L+Wvj)&D=luFyD z#@XfrBSsdpLwKCRX>fel%JR$UK~{Q~LtBcQ-I7G8;x&s-jmwt`T0lgjD%2>7t-=}n z8+O#^p@iYvsB8l}=a#NVqsaBAc~m4Brd7+PlZ_fpP85TkJrx}(y0Zbvs{2)?_qK0C z3eHR=B^4d-jbD8$LT+X3*j&iny}HVBD`JzkewC}QDHFDDjP>nHE=jgiW!bOOw8 z=6UkvAQMzRS%S`mZ|BPVwo}Hj3%!@1_=+KpxXn$kS|O>K`f=)V8D2jZ z3OT+RGv9B0pSes`E!>|MgC&gLOaHzknm2P$99-6a54RuL`v5*kCyd`xxP)^~E=-S| z@c`#3o9|3UvUw|6R#z-pCrH+4TP&sefS)a-p1jZecd4B)`GWApB;(D7>;n8^N>PeZ zI=yy0skz_mUih|Oi40SbLt$;2y{60NK%+bEY}i^YP13X1zb3cG7oYOC@}?T6g&neE zFY`e)$F4!uSAq56wap6`MaI8JJY45)S4iTf)`>W%;O8sc_`Tzx@Ii>4slo^M*M4<; zlU6Pn!lGLm_vy+8w%%1QxM`Hwb?8?fX8>*U$pCono89{PJ3@o3*2Qv!=_ek|If$J2 zP4MRuY`vsg`n~FEdV0F0jSV~Z8-z zwB||guedFfOse>XDne3{XSDZUpCXlzNxcD@x=V^Voxujf<{sm-qavScwIy~B^p71o zJ`K1P_0F#%K2S1i-=~jd--B%sURc9sR$KBSd?14olgq~VwX)tQk9?E9Xn06t?*~5r z_NpUh)oC#vK-hIn#ePTTY5~3E^6F7#DZZVh8Z-6sB&X+_ew}>MFMx})WbjjBCs$~n zi0ic;9;0?pT#>OiC}fH#ie8hNZjW`TJUXji7bLkpkeD*)s+{aW%N&>V!rxBLp2)U! zr=GiTF%sp)FO;1hy!jCj5)({15*Pi+u) z2+*b{Kkmkt@7n9PsbTrG#^Xt>jW!L#1e_ny+J-q^CsZ78$&hpJV1AS>=u=k*${k@z za)a?~cxD%`L49)ii9XaEL}(%kK}Ucz4h(>pVi^zGiQ8Dv=(;&>E~Eivg<30M_k*Gk zp%V+Z%9@0w1f+}F+S;Oz!8|Lxuskp@5T@r$nx%<@xLry3m?AWA{J?Vy&4r7h0q)Z) zBqTHq9+z^^n1O~74eSh9plAVF*awA^i2ON$;dN@%4_>BPV%8}3i=-+mEu}2#uLM00 zvbswIr9$mTmKfAR0IP?e0~ByH+jE3D*6L)V5;VQwjz!7E#Rb!_!@%K2fs?bFPQ?i@ zU4%Ox9rUYEmkiv+KoXbgLoGf7zIX4m%%Q7}kVg2nH%LiIQG3{Kp%1&}3|Gd08XYmV zG(W(8Km?EqyfctJ5&$iyAhRtKJSX{@{{!z&e^nib;`nr_Gaxia4wo>8TmB5XpXK7cXo*7{JX_gZbIEDFFCoiml;rUElpk@o$aijyGf}Gcp z1C;IJK-%6mK7@w_rYj($DF$jK=C(>UpmoBmA@%4}5H|{qd?N@P$<7gXC{sqRaLCyW zd^pf50L{dO!HMahE%6w<-N@IIpuJGW@2z?x2Pq{@2O%Hpoi#(3)p|~(Tu{w7p~I5j zdlpF}@$nicAHaXIt~Tgy0+=dCfx8w&GvuJDg`8g*%nO!<=wLnorv3$hq0?hg&m0f| zlxH&KucX-w7L#Dv!HYUU0ModRXxxKfZbV?)41YO>UUHD;_`rxA>D}S`APg2#cYEa4 zD?L(G>y37d$I0tMH;kj{o=?>8Ncw=)Z2db=19qthZbmE?$91KoRA4Uh-tF-a0oDf~=Fj_?+dZ=US7jyJexjn95<^mHO$o z`{T7?;>?|{DwNdjS14MOgS3AX&r;pS){gn?jU;}$@_=rDhLPK8CzT_;_clBzg^Zf4 z)1NM1O#O5NUe6?uh#3G+gmxf!`7Wg0HR-wo+X=&Gq2EAth2oG=cNknTVj;gZek)J= z>C?6xeQCr_!;6KG1LO#B5gPwM*Q4bOO>18a9}FTjimjWW>)a01C#ZpL;QEn-0sa;7 z(I~|kf^S59P%H$jQA6POcR;=XpS*anA`pP;L=fBr-_`emkxLN331af{egpQSMEk3a z0yC`%-ru~bB8C>^wY0Qgqr$qEF$|+E5kA)jG+bNF79-*Mu%75@U8Y;8Q4|3TuO6kV zXHlIgFzZhS?-S$i@2`UuDGFa-n@PzK^Lg>|CA8cb=lhD}p!k)8aUSLF9zYDuFa~Uk z79s>dhxY-?d)UJO#@m+PUy9z%l$H)#;faG;#q?rW$+$}Ls~b0O*vu_i#Mwz?oYQto z63eTFwQ|ZLW?`%$1o#_Xho^yB6#*Q=lMdu-PiC6kGLO%}fBz+_osQvVDsRiS)g(2R zsavCMGe$bk)Whxt)T9TIQCcoZ@fjRgBdfg#GJ!^kCHoalxwV56{cUBFyT_@2Rs9UB zvOc0f#z%fPPPK-zpU|HCk1fz!gPX@@AGDiS_Iz<<>}s>FgQ>jRR?blI~Fmm}X!ja0$AtN=BPIWwlCO3QwYjxvkbxvnReMdsnZ^4;*$l?X1)> zKO>+7bFn0-L5zp{gm$J&B1kS=xG;E7Cw+Ilqt~?ET!w1&S5>ZvdEBX!rxf0#lKEBW zti(&7Dx^%jyW5EM;Z43v~FOp3Nx7e9u7vvgXYusjbbCZ&_817kU!)#0R5KJBjHH3)2pC zd-dw?H@rO1ga5yRn#SFEi{u6eFRa$jzyS~J_ z<`Gf?E&`H-6O4DbZ)iTdTG!ZkW-&}P%lb9Q`r2n>0Kof9==HOkF~*>opLpE4SE;Da za%N?kp{HUzQi_tF(KjqiOkr7Gs^C?O&!kOb%!9#A-x85S8Wq+LPvlbsES6tzJWCdL zo`laY<4#O1pVYHwbL9@33mW@()@kz`bK(LrRHBJ^I;K4`_yc2nV=nFIE4$uSfBH;z z*1A(b`HCy#LnQCons)Q>Zd2|-`H>c$t=LQ)tuR9^U395#w^X~w?`}fFREEEC?m-3- z8joIW;Yq|#?zhe5#S4oZdH)9bcPW3`>?usfu z%)em5-bv-U7X`=)O8SBso^!G~09xX?w*hH4>MP8G`5$2TbXWx7qZ1Cj^nmBT%{Lq< zErMHFJh-jS0P`p9KW>QJ>wVf_0J)Whx>PwkK)mkJp5VSdlh#r0@6UJ*}5TNEO1k zAxxE?m9+)>B6TKSWhr;*=~Y1hCeyoNPVZz(3gm1ujEUN&{NDfu*%GQXNfz2xyuOo| zpy`qp1m`g`oV6skp|j2vkmy__*I(F(wv5$!$w z4cOQOxH2bKUCEdp#bw20HU_bDe*dUQ$I2@!{w-C;;Mqs9wp;17k_>r4Ji0b5as=FYU(yO3BH%p%hp~|a zq-()hqX>3eh&)DlZRq@iBQ2(>sj2$Xbr|gV(IJ~cphn`MBN~@{FA99@u}sV$nZ`^5 z;BxW+|7bP@h`)RQ;rVUC6V7p-lCTsZ23*by$vi+ths5GYVhu?-8Zge~sLBZAG#|k1 z3j7BJKX~X$=cRM!mcLG#15biPxN=yBZCGk(*G(SpvVfCi#+>tL|Dt%0d2|YBz zS#DZI^Ph70=(u)0_0aD1)X(GMf$VFm!?hDWl5(yeQ(4Ew>bPF!^br=%dJIR*#ueLG zw=BjgGO6iW%cqQoUVpgP$T(o|A+Zcz7_843%6mGXIPyU_y8N_kF{!gOG*mskUXos} zUdgdronTy_8Bl}xSJabrEYUszAtOXXr~tj=V0K_7p7X6iVInnXZMlnBK33;C7dH>R z^g-WDTgA)T_S-Ra2^j{H!ClisiznO+dNPBM-XbDHz)zo=m`nGWtW@V!qwN9XkSs@6-tC-uEtgeGOyvz*+m~PqV$?)>WKGeHGq2St@KPhbe@dC4 z1qOqUNf6Qtg`Yebt4C-&V&Q~ubqATA>lVk7Q}&qMskfS7RQa8b$+Rl zt-!g2@i{4?dJn{wa5~2z6aW?#QtP;f&*BrNv4&2TXid^eg*GAiM205c_SP1X$irRH z*$lt$;^IQJ-Pl7RFcHHLnEstH!q`U+&t@N2j+tKieDji&Uf2lkM1QdbL(N7Ar*>dT zG0W+hm?qTO`a+%w|D|f>JOH#XSs@{wRp-b1#Ii@eT_sZx&1^Dlz){)GXsc{Ot52)j zxZso64{}tWi_d63MHgve7LE~$`ha%pO6XB$R?RIZr?m;FcNq~5|$=i;M5t5RF zY1dAXrxv^Mn4)pifJ}D~Bl<*va<4dX*x)u>W(O zTi$#Dh2@W_#EqLhw@TDT29JrVwz?)9=_$8y`;2`oDY3U$v`_pNogSIa+m_gtspc5u zuAL0;-sRR!7$NYruW0s(@rza;3m(Wom zS&0lfuf9Wqk8nPxZyBp>`%5>9*B43M$?z046Zk5|3eq3%!+pk}Wd3Ce04HA)fnmEF zwtU_rm+Ewk|5SF5oQeUS+YuA-1J6W!=1jgajoRq+G#%Fc7jIF^nb@HcJ8CQ$D~9Qp zUssOeohaw$erSZ**_2V98+66%@h#J}%1xporo*zLzQ>W_2~@7uXx0_d!0ipD+m8?N1ZJDUnnKaelw zv9DsXVs&S61DL^iT0g!B4{lF;IjJC@0WGSDlKod5lD!zH=JT%fs5*FzW!-Nf6#*E{ z=T`9H$5-LK!~#cnAO@N%)JBA-J@+4e6mUHdG$WvpXTMP!Eflw-Nm`m|q909pz93UrJa~fuQljY;P%VHg!lzNzDvaRH$ocAnp&`GAg?4 zFTtzz{li^1%rr!AoH}_D+lKo>yFpm(ws=^y4%DQPfD%qaO$N#b`REPFD-fkK)8W+J z(rj}4KX=*(eGZ-xi*GT#E-rf*x`Quw&Rf~kF?gS+gq{I+p}=y}mPO^bQ-B2Td8+W+ zFdcIv{^qqLm+hDE*>LH`s4|WNVPgq)C^{ada_sC2Az4YmisYhne=Bs2H*WjHiYGA9 zcH@?H=H3Nxt$qtP*Wb3=uhUPzMZ>*p(^j}{HCBJ{Bd1(cGhIo<$NJV=m)6OT-mogK ziO`CW+@3R=Hc-@`Ev4WtoXE@`tBo$Ga1k&%%)=B-dsgChSsSo0q_aYmx45hT7GkenHa zLGM|(>?s-;e3{vT>_w!@Z*jfpFWLfZ7fPQ2^ow_#M=T7M4>In58$iWtF9T;bN|N(q zRO7(X&AO4Qbg$@+0rUv@Fu`~9WM7m>@0o8K*LKPQt$d~ZE`79uh3I0WEd@}--@M3E znwm1V^8b-ZhM5DU3t-8BCJ!l8q{nvAx&AXOw?5!cIC4kmUvXr>G4pIzI|L(4Gc?NL zr3DV;;ZdP6)ymlTf)uY7RGugV<@3HkYkAqOLXxGBWGMH8Gt&{g^8`z&7Pk{9UAA;c zX8e4iKx*tD>AjE}wpteX&eh}tz))(BeB|RD>ADjNI_GEYa-UAj9OcCK7dp!^=6GVM9o%Y+euHage%NRC{I~Pm*VbU}})~*OP zP!1y;N_z**K3NRZIc-Ld2}#oAyAESbrGXj?U{;A*%3KmOb;1v%EYj>>!@}%JiIIYc z0E308`Td>`{4S2{hAo09O%s|JfCW;=Nk?cR`wT$-*epf_k&+d;jzCw1?%=Gb_oWufVSxH_a54B;@%HR)z8+nI57b(RiuFMGI!_aGgCCa4CK9PMCBdK;n^ z=KD*Oz)bf&tgmwX!S2yn5c4V_4H(=26-wIl_9;pq zZU|jGdo~it$*D?W0eLvE`~+NaV3|AxjC25OcgRfynCB`1I|BpMzlmE~`bx_ikO@k5 z6wn4wj~qb9{-AP)9aIv$v}M4CK-ly0IoB@{KDbPF0Nh$qBlN2Uym})_%J*@5*H*h1q*JE zK|MiEf(CWw&A#HcyvX!?ja84&*8z*>2 z$)U){y(tg-mmi$z6g-ydb}tudb97FwFKkBX4!$}T+U+XELyD)s-{YpwZ`$oVSRDy$ zcSs3vS7J=QGHGr!n(B(DoUBO>^SCe=N{=NW5A=1K#tR=(*=v^-5)!6U3s2?FoAi{L z=khfOIUKi=G{P_*QR9?N!&7EwsCV8qu*7<6adF0puQ*w%zX#0nTA@6HQTavx+v7=n z4wej{010H)?5HBl8Z5DyYLi;OV$piyk8>uNMf=Up=3)lwu!95{l?BkJ1OPZ1jE9Yz zA|If@2k4y316K3yDwiyLRH->yNDfBOGi}XK$05&gxK*e^I-Lx170NS3^f6HX==}mB z1O?z>+QH`a9U!~SY{IBh2;UCP15oDf^35DSu=$2Y>!Y8x*MY^K-+l9GSXQ zeEQ)1{NT`o6~2iOgYC+?7l&VrxTg0;rsFj3JY3ahZX13nvbO2fkF2}EPPKi=|G3d+ zgPNsfb~wI4*b16?p53kNtAQ%2yOZ3e{iO=bqS zMXP$S#5ytO!V6Pz7><_#xbq0+>$y71tYK?BQR1Y`bPwNnCdv|;|Anjn~j&C2`9L|CM>e`FND{IX|`T zwUy>4bEf({-rq?p9aHR*3<)%8b*LcZ&R+f4!V?$hlyx|{yD)erKk;UXwW0AfeB^J} zwlNk&9h2ueA5%kQc?O-I0J1)3U3wk9@-4 z1;Qz(`8t8|_+MFI6H%El3dB1mSThftU?(6jQOM=01;nutOykGs$DaM`j}3wrX7|)tf~Dwf+OZdi0`|~8gP>k(n&YxZ2zaj$ zKcz%)FIN{Ac)-6`Q9t}5OQJ6+$mr3Yq!c@k95?#hkSCp>!26&6e6s)9L;BzFLjEtuXa9=U z|HJzc0w<3C@2AQCk3sA|x1VDSnPh-a$^*-yP++D3lYBCGtfHLs_$B8szfrYrAc7TmEKUaJR`J2q zzIn#tw!jtA28|}q>6~2G#Y4vii4+^uxz=U-xn~AuU5W~I?sA7qLhPz+N@vflJJ)26 zof_**wqbbZrNe&Q8ZS{zyv>{K(pFHZ5ZB>4>2koim#1INAGX4mCdZ$!-WZnxtsQa! zQ_or@I^*YGJKz_`&i9r{~Wrb2d zRU_HCW3zWAtSQm^n}9_b$6{HOqJ~x=hB2r}y-tsBXR*e3kxgzl+%!T(m_QUR7)yP; z$)Aq?rYjM`t6}%@g0}rgs>}LEmT*gJJJ;fvjL{U)tQF>Cegd)@{#wSVzXa>?}ck;>b`Lp zvd;5?1TA3Ym6Wbu-?UjGdk-9tiLuTV@H<6T4zn)Y?TxRASz!TF)D625o z$%gRGh?IktV)j|~>E4r$-LtwcF%a%Xl@ zE6-)tODDs%7oT5jw{u?!Ulf0p%PLVXjJ(*2_I@vigYjg(M)1;pXY!+z9V-bF7aVqY z@*+0)j+xJ;gM3jhDKGekV&EC_^fxsT!TC*^JjIcyG+bJlL5M0|s^hB4x@Bt*HXmA- z29rDeKC$P=FOd2<_%WoHZl51CkS`RB(hojIAp7D4$){^z8mIs*0T4I`&P*a(2xJCp zXSb>U{zq}xJ&RwV9}2IOquT~$$D8($HKUaH9pmmE9n%ZYyfO53edJH>Z#~{3Q0%bZ zVI^hA6CEARX4_^Dtv%zltq%}UjCJnawju>gax-ruW8J8`I1a@OJeHkRO#hXOg!z$r zT%Dg{5Nlj}ZgUDyA8gzKlH0mHrxD~01$t|)7S6q5)_!5mA$V&Y147?Iy@uhZeWQQu z6qK-2B$#Yx(=WjHsr8dhVx?}6wy3M#kSpKYZY$U30X`QIuq~Z270uxlo2qxROrHRv z3#%W^zIW;s9ZTd+fJhaCD}p8o|IW~bFkTB;E$@&>i$PD8J8|nFd;1uFdJS2SHJ%J$ z*4!$AHbcTWEw8qnG-F-!M(R%@!^%=&nVHXASeC@WvTSyrxOn>IKtDAW`aE*9xCDnB z*H)#6BGYcugD_d;n1ig06s3bCZ~<;<5eOios5Y{hZB=mS4_80x4@UfD&E>Co#Yh81 z=!o~1*4Zd}eDg)5kwKTc!$mpx^%RmNuzAjL>)Ni9?LvjCnlH zQfxa53c-+#Yb(z$F)CxQJ98e5=TCnaOtKP5(=1Xwu|ee*krDCJ!a*WM7@Y%KCQG|_ z9Jnhs&D=XO)E0Hy(>4ch9hPX{v65Af`X>Haesxw@m5_exgot92ipQ%$W4&D%dR?O?*83VrC#k zZO;-oOk#oQxcV!c#*KqcHala}Y+N>?Vn{Nxw$u!kZohNePK$o7y(^PrX49U!J2*uZ zN+lLHF#B=N#(bbKDQuv`uYo`8xJ$RdPeKKhHvqO7fk%;Q*`9Qs(_|VHkMwbCR<*FJ zW3c^>*6)~e-#;a!Q7bTw6IvxTuF6)J>Dr#x4{i-^c57^GSkG#8YB!Vj9dsZtc@x%r zXz3|AzjGmU4PdEkYykY}j_-ti0k#GFWRM**^d_X3n3y1%EC75x5E2SKa(y3dXgngG z4n79PMk`;}fm?+ugf+}Gk)TnQA!`hy1)jculXI83`>B zf52@)5H1GspVGMtpo-FE4H_FCM_W0F0q)^So>sXV?rkm81=CCnm%zL-4cOEj; zEKxA8=#(-$))#K|Yp*ZA%Z^T3AfsBxWav@D?!N64Cf%gh zR=r!-8wHfub-E@kAIoEupZnqxFj%wgzOk8rExnrBc6zL=d;DcFV_-S*;97%~1)rx2 zd7LI!OH8Ucc9tGZRgMu7do+Cx%Zd?gH(9S z^1V=J9A2LW9dqb8E7yaKJ!;Rx^FHdK@$~7_wFaI=1Gw&@oDP(X2lz6@y=V8q_NocK z5aL!jVCW|>OhRc2W*5jT2c$j7{T_f{Q84~S#B^JncV`#ynruu;MYQ11DSZ*W(?E?aSU*NsLS7&T~J`FrIXTeMW_*i*&OLf3vp+_eN zIcwl=`4BbZppD0EzZ41Yp?G5ct^0q26j6*P8W};SUlBY})(>{E#HO2>V04cb7u@;) znhk~r5*HbHEsV=b7?~lSpmKiySSuL3pyBCG-|90B@5BR4p;NJdsgnNmDOA>GoHAR)ezOv=3ik^ zw&kTjDXi1pkUrdhPVeb-^FG2uW=5O`bU!@hKlhld9(ESQJ;V35&1)QD{WIQS~?vOf88 z6kQJifkSqPFY=-N%>V%0vUh>`U^a-Yh3G-}HfIMffhh-kEoRq6B?Q)_Lp3o3g`MTJMZ^Q8X$qwE9BqK%y@HpM|O4MN1)T^!*IAK zLNSo??86Om7Cq&?6E;H*h2Z9o;aj<(9?qLpm!VZ+24;nCka+Jb!Q+#wlZ<|tc3hHT zwg`{J?Ej;`H8kF-&Br8%3=0c&d@&lXUy1Hoye18$;#Y+M*5CPPdr2zav$A+meK&qD zd82=zrMJI_%x|j7ZysB{DUeld2RsxQR*8Ev9}0UfJtg#@TSzzoh+`nzw5(hOj#zi~o*vjgS$L**JnJ)_IZ?K@gLG zsz4FWDwqyNgE2LoZSniX@?9x7m)c>O0vqHz(&-mF=!(|>Yb7uldH^aN@V1TvdZh-J z4r^%|@E`ylIze`DUjAV|j5Cm{HoVwioRTWo3-(0Qplt9$vL%>2qHKm!L`3ay8q2Pg zqjZmq@*R5qmq1Uzynz}EX%xW#mLO6Vi6vm{4V!DAPblj&{%5!*Ifu!|#5CB_PHsGh zO%zrmV!2PcE5vq{Y{Ez-AbL|zGJsh%POdzmy{yp*ZlHC(obRo7zLGJj75@58%Q$hx z9|T&j^3%YGYDagYu~zF^1SrMY-sD}ec|3S1NWvCspWu~o>0%=ad&R#QTVuUPNH~j? zKpOy?2{}?xJtX+FnqTRZpmvvQ9R#(@|>EP}u6@WN4p%?7@cJ#aNT zP%WZ4urTrDSA6!`f)$&~M>e3{4x68-*^G>sYw1o>jG1}cvpUfjQ{Yq) zm*ILPO(g`^VOK{xUHOF1k1x<;!;~kIN<@nD8au&Pu*G--`lDnNcMG?RMA!k4$+Y9f zT+)9SP?M1VVL(0WEpYHNV?(FrJjki}71AM%5Tj}f2^T0U1FlxG;k<9%!mVs=6+y&A za{2PdkvR)kqtsZeRI9eRd4$2*`|9qjo3;(#z-NPo%Sad?wF{}6nUHaw3})I%_gX81xYsNzLnru44^28BR|fKw#%3)V!Z^+Q_GLt!!gqqpY>Az;*`5eU z5Y}$uReI`Y3)W`6t>nusRoD%i@2T{H8(g*5cONca{weK})@v%Y#UP}VD``MpY!ghO zX&So)##O;_j%m79^&xy(e8n5`VCK@yf_lp#HvfcD&4#ILaQsc2Tds?DfnI&s_01Ox zjv1}HJ5YYc#0(!(`ool29;##hgP{w6)SyDy3koG~{;3!Udd2$GeAS(bRfpNIfb*^| zy;550C38$U<=a=>E=ykIY$>#zdk*7MV~KmmR+BV3^U1j_e#cA~OcR2O>62%*q$2S@ zf}oD%#9(i}@odYlA1HQREN9Xvj0OKKWN|jyIez(s=lW(mpZi;{N8QPB{){5|g7Y?- zNpVWOoCq?dTwlem@s(4FIG=lPpyGh`{e)L(Ii zP5lu}LH~nNHNvsmZGK(lseZSX1@Bi{{!OqW^1Z!f?^8J9309>Tp3WHcl1n@o?WkX0 z`Zu=Cy=lqr&exU!EXl)0xr-VX~pWvApcEY?bx5#Z;-CP{nO} z-FIGVU#8j<62M|14n!12G75w!eBLudZp4)3TSU_a#yQ0wM>T994-6SjmU)f+FZDFIO?3%mdK7?W-SzZ0+puXU|8xCv4nXr_^us zDy#M5@{RZ0=J>qw{r9dHnY{;gZHxPdd}5iu;o|4Ga*P`{_&|}_vouL#x@Ro+VjlKN zF(2X6SJT#}DcaIk)shuU+>Z^swNb6mdkMWIa2u`12yC$#wTOaI^i0$9%8RK(MHV(T zC@keaan>T$|In5lCEz6nY9U#_Koj}5Yr4j~-XWSpM%0slB7Ku2uWQ_V1$|(PG;hE5c^I_*2w^o1E(XM zX=(>E9bC44L(nvO1ifL#@Mgs^hN1YoIDL|5JfEg+G5f(rY&;j+4B@7~Z_M??eERJg z!u8(;b9h#ihYs>pfKt*N>0pzwfLOBL>>>ejZ$Kd-SbmGC}Q&kj2vY9`uZ?f;H1Q|`OLn4j1?Bh5`t`Yq>v!z z^a?vO37-DwrizvV!0WTZvCEPul4NrIG+6;dmqPM|E%X(72ogYbKlo>UgvpU*Tt%N33 z2{R|+uUQ-Qv;CxX$=B5i9B*Bk`Uad8Yp?uf2sT&ErT@Lnm5S17fpLTilQW4OnR2(W zX!FGQp=0^SpO`EOr|C#(8NnV_rgULTFP30*i7~xIcf;xwKDo7L+p{t0U;W9Q)tGZU z@nJbs>pnRDClx!HW`^G3vnCA>r_Zaa<{kxmZBlkRoaX7PVhb@X2m(1&*vwL={Pk}> zUqS8ld2~BwcFHL&wz8FFmc*WwxX}m$wf=qEckh#O!Tvw(YTen`ANiIcF}L6iC*>OL z7S1OgY=;i=a&TNK+%6$8XQO3g+=&)GJ`{rbKdz!GeErDlGwItUh!D9~|HnS9qJN*` zyDwAw_k+a4sA;YiZbw%sY7(6}kLuZOog|yZ-LkT>hKr|aey3PfuR8$43jo?Ct)%nk z&kKpdgz^o;V1&>Z_^!~YvvE}AbJEh@Kb?I^LKRE5W}bd~)M%=!zc7-faWb`e&)k#1 zt~b7K{GBqcmOHt;IDD60=5$lS^P#b`0hk0}EcEJ!Yg=RXUR?O!l8XH9skU>K2FsuuU)R=tt z5ER7=M0HGD`1PxC9oN1R-%7xHftaP#2j z7q%EfKE#f(0b|EN^zB~42twDl4-BypTKl;aruH3qaAC>Wp{lCN1Y4k>%^70q-vI-f zayZig+c7xQ-Uxk7nX;$SI~5jnF^6|yd2!14x23-Ax8YW;gtohwh^SR&yt0uCqZ5&? zNGL#a(iSsCbm|3gf3QCK#;+j_bAI!fimLN{-|jK?nJIQDcO=20 z06r$t*t@W1UZ{1VA~<9=1~?gL+6C?!b_jhR34pE$kr4T0RbkMHRv86Agv zE-&i~eWZdF{L3$D=zpGzmutKBR4q>|RrhqphgvIFvqxIi{MM^eA|oSCyxQyG`D&`_ z&PdCR8(v&YOhG%*(AaqZl^PjUOMIgUwudl1G8raQvWt+k)DnLd8H6ihsEmfygV@&v4!N;K{h(g6QJ=F}2bDtgDd&`w5LT$?H~WjW4jOgA*{YTb#=0U7&zQ8u?5 zqdF%;-8nEq61Rw~GFn<%BD>=-(sHKgv}60#gEdRvVzJENCfOPTM&TfUn&;BIdxpX} z3jpfZNIMtz2p6{6b)y@N&>94o5yL1({CWQRW=}fb5Y?~3;Tt;y53I%;~E^P|K;)A67s+C zcsdYp+7Q8*8ml99Y1{jE^IEZX19oP01 z{Ox)FcuV2ftX^q*MnBbtzChijA+dFT5`}(CEDPJWTvkdlxu?fb{jMZqWFV@J6t(7x zoe6+sxx^>GUR-rl#)XRIkSy}khx4*cRmFl)E{+H4xzhR->2~e?H zEgzJsC-4;Ee?<@73+8ub1$w;buu+GyU`)n7*fi4P#t9wlbpC%bST*m{9yqY`z}Yy1 z&wEo7SmS^0+~1X|=`zOHzLYF9(vm1>-bq&V@ydE?URRIrM(Qp{No|iCNeC9l%yapK z+1+~c-aNXA%F&#{i`IY>ubVnDEVA}fVMew7?}Z=6kFndP_7-eYv&=()!6 zC{uBG51P;O>XX^27$Te!XXgRMWRLBfXZw=}M6KS=#H+;01y z7p1m#6Q-|f{rw5SN;8TY>grn_E{A{r6K`ZzD51jB{EvA0ABIY$x;8bi>A4VK`VF!JNf|t4mGv7h1wKch`$K>Tx zP<_K>=%njD?rZkig}!O|vJ)N_1;0DS1E$Z($jB8%X;@dk$V#|c;e2)c=_`z{@$43! zG09IAw~YQH6PWEE{pOh6e|NRc;Z!fge`w}?#|0>=FE69#pl##C?y$( zHp=rF#&cPluO&KJvD4^1k}dH0me%n3^SvIdCN|57_l5U&&7FC-y-nw4Y zLsuF%Z(9HKN&1%}&c^q{CU&uUZ)QvT-wvj3<4!2j3dIseamezhyf z%*FBN*<>B>#fukFLcX|n;?R+5zn9}qq7J7#|LyHktoB{RVAfh&h~qc0t%9h7=%@RE z-s;Z)5DUN*f0s3(uWGANZdBLKi}C;Yw2I$?)Gl}I-1xHC+!%9)UXi63XQqxGz}%+g zv0Dy7s?v*&$(>%kSX1(Kd=h=TYOUMMyUjRi+ z25bJSKZZ;=bjf4R=;q4!(1rh_go|_$lF22++Hmc_Y&OTA11T=K%q8jDXRQaHp)KEM z+u?DQ6aV0%`_CgUdAyR8t+(w{=Bt;L$$M($NwcpW`Sx#b=#qNf6~cGt{LedT`t04iBbYX*_(!O3V?`BW z3+K~hi~qc*W@WG=>7Y=NO}0Dx_8a8n|NCdKY9z}uv3w3xw}`Zs_2!wWfg~6-mJG18VaAnEqZsLA0w*O@*1p0hQ3urG4%b!w{abzLf<8{v zKB%~D*ghd%J)49st6coQnAvVgPAzm28_#EU*R5#(?H~T@Dopm45osBoi5$>!H?=Nq zA-i(Fjdh;#tYXjpkAFbnw8yNnfB#SJ_W%42|KHoq|H~-7seN}^ySm1|ez~Uce;t44 z0UY{x%r7<#S5_c@*vb9RyWDi(a7{opMue4PJlXZ@l%Q;~0~~)_CrNh;o0=G{40muwjh3WZ*2wnchd!$RdzgPY z-^_PzHc{4lvJhs8Zx1)qeq7$6(+w`2>~cmKu_3+9d!IiKM$DPW`^`STU9)`LZ7aoR zPFjwN!p(*i3xQO6N$@tTE%#s@Zr!NR_a5WO9NMpL)|y%Odd7$O=X(qip2ctyLSWFZ zStBKV#ntx(?V5za;X&%IyKHCn{T%O7K`m>fs!DTgGM6Qsx@g_cip|jQK}z)uHblbw zYP@l|-yc|(yzcDmY@VmBR=cizoHF6rX%j{aAn13G%(+y+UeQTrm=RtPbDn@o*EDQ*ep5q3j9UqnQZny?TF}`u-8lFO| z1U`%vGiRUMeBHZc?085z)V7RAdt~fs1lmsOdiCXyqCD4 zKN{B^bAr^lVV7MLM(eF2Dkn6toN?hR8wZ$ce$O4oU^GjD@DVs3%xvd^Mdjn-soqj+ zBDj&o_po4p#vGuP1uKFapAvU|&~R)*LJE2~l~q(;VhDu!4~(i7P>kGI))O!Xr} zOs@QrwR3&gYAiEO0&B|;D$8s9dEE!F5QEG*5T&~?ZH_FIz;@QqSZ&ei=>VURHV)FQ ziQ`Cgl*q`)x{2cV4ZkbE7m;!Ur_~6F%>+GbAPl-bgxwcLm5p}P#8$hHQyTPWsBgtU zQ9Mzi9+E-P_%xz$j+1r8T{AAqmV6Nr5jP+wF8u!aegqBA_~y?eTc@X|o)8BDo+FH6 zui%!t4Y!Ji(r;GOFE_~w4D9p3pO|9Ld3t&}*+-$(eR-$ti(5zJu!BbvTdpLeOI)M5 z`|6<(!+^1Xq%X=C7zaQ~ObBE-BQcD@ANKj@pRzNr3=g^<;Xae$;k0HmKmVpED!Q0l zrWl(u&G)M38fmDw4@;CCc4=I*O^BwllGV^fbYg5V^pyBdg|VQ=KhM$HO}$^Aa8s_s z?CiiL5BU=v@@Kcs;#G&#)CAIE3s?7!k9<^jO*>jz3ZFwjrN>3(K-na=o}2qnOKWRU zR&lZA8?_}tia|DR2nVLEnFO;KS);7vP!%u?4+;vR$2icCnnB^x=3(%42Q@V{3*E49 z2cvQi-Fgm;LJyUMV|I2{nsp8BgHqS^TwLaZcwZb(Y}~`a>6o~=yNix=|K=+FErk;B zRw*{k(_`=?$K2Os`w-p+oE~SU(<)+n%6JpPp0;wF*FH5;vgp+Q)>AAUEe}59@A>>)^ z!g&mSPr)Qw8k%Jhw8DM(_>o$fuDrZ_KOGf64Qkx|CMN9XZovx3u467`-EW6?wkfQ( zv5^)NNcKB8IEbc9b_z;MuMcBEsk;GDO6<$WCBxydv7VaFOhvv|uU?h&#;OX$ZP-I! zR#uj0R)2@7KL8-@f~Jsxkx@uO!WvZpRyzLio&x4uN2|BahSq2vJ-U&S<@Nd1rmznn zP4g^s6eHihwWRF#^OMD4i-cPth=m&W`N4w+R``~Ami2d-d-8jFdIl#a84==UKfPo? zzUO!|ExxH=I7U@KT3Xr)^>q4KYDFwR+pnj$26hEHWjfqKy{U75mNS;+H*KPXv5bHI zJcN^%XZS+#YH~9B<+QX0d{G$ws5TYr#|ZA+$qEk;W_hjTr@{WE2Z4VSD0HsB)>(Zr zb#T~%kGksM!Gm$!d)GKSJJV;feaf-9?0W#eY~b;^SoCmaW@Vr=J8=c-h*oiR`6L&+{w043kjMNlT z#)AudWVY5tMBm?kA0rc!wWDJsB-~_g_hmD1lKbhDpbWF}@Gu4j2Kt;mt0Is{P6z&# z*cX8mXLv(b;6Y2d#=vdPk0*%n3h;Ew_{?L6j#D$_l& z*@<4Gtd6 zv$0gBLpqKkH~vhRU(scF#_-b-F!*IucXHA)GIeN@ z-?6I&Lu}lTcZ!cxastHV3!kCkJ`L|B10BB^))qRi8#!-}*N(S=jKh3hW+ z$)FjBqNvwL&wk+K$sL&0dmvmCmfhI_yuAH%!6sWrq@`$wWlj&%39Vg?F*6-y{FExR zoMYyWvZW;t9+#Q+=5760y+^@w3=I#b)hMQ>^!4@m%uRA(yrtgW+&M<1HOTELxj(tkA9+W|t2qoW0 zcm(NWIei=&Dk=%$bNzw;ZG50wuKeCTCA#3q$W^3fSva5|Ah&}Ye&B$5TyKdJueRYv zm00Xa-{+g8aJ`V3eW8(0;)ff+CxvEaDNx(nTMEo4E#5ya=!*G!3!7gkkt4#YR4T9u z(`F(PkCcy%jAR4XN(U@S2i3E+GTuH~4b;L$)77R=Vj*4x-V@V@W*cYZKs2*+$lo&t z0MX4<;4LaD$}@kvEBf|raXS9;jhq!!9k;GiE6zhY&cZ29U2p`iB>A$N$k;`m_L;yo z!7L$gvAN&cjkzGVGV05vLOh07L5ov`>xgy3h8TdtA-Hp4B$UPF_1dyi!d5dN;IWko z7v}JWVpwn8)2Fe5LhYTM8b}h^MMcqoma9MB&8(FUI?pTD^iTmu?7_o_!7S8!_Uw63 zSxKU1o=MxCr|^a<(bd7^(C{z-C>EC?Qhl+kv^2}+NcMJyoJ)T==N3MqXFvVy(1@5wYvU+p2{P==zUlDh8@+Yty54SdFKN8z!)0hu+}bknIoOR-awBVZ(+(^SfI( z-UTc#ae;mq!dSDg+MXUZkR2;F^(MB1PTo&fhrv+Jd@a3WkmSkRjV~7{KFEF-j7OBJ zfZit-%4OJu>ysbFY3?tK3?~;7uK38jyv+a(3_)T!iXNkkDADkPU;?D=(%#9V;D0&4 zE?{9+6gPto>qf7tSAb=9dY(lkDJl73a#Gj$;0(vy{M8E=f^gXr6ic(Rg7S;DYL_P^ zCx>BU<0Z@NKNJ|ewFL#K8gDDXYbG?$Vo$Ouju?JVqM!z)WzmW&4ZL=eu{gFR(kx5T z)Wsn2?B@zMPj+;0utBVt{_&#`!>J80j|DR}d&YVbn9RHA-}8QoNmoEV2#ttX85bAl z2D;L+$|o@*!Qr&WPh30T42j*luXlI_^Ub9rk}%yn^40@jy#cEpeKTz>yp@(GdW0`u zy_$o)is_$w3iJ!~bmyWgvCZ_GtbD|zO%=Y*NuFIfqCsMF*8Nj3FwikGitYtYm@ zkaB%P=lb(cVg~nCi@W>$W}^fxqT}O7o<$?7C13{lw6|?>A=7OnApB+U6;_xHS9Spk;=3pNE11JrBu@?SjE;^za2uo!sxP~Ggc%#Bw?~=A1iRjZ zxiE+&1lLgM(4loUW$S_bYZ1FC6r{xbb05*5s&lg?Z_Qc%Bjrx4_g>@9jl2qe=3j~^ zlrC}=FHRj#XeY>>aM6i2Iko7o34DrclS#j|IA7u3p`MNmOpIXD96l zxDq7a_h)v34M$h`kGx$A3yX5rty^mnHE94Q@fFi|NEsU&7m0-4PSCyo+fJq7=mR$@ zX#7syxvLS+rGNH%CCX$sJBBOvC*VV?AeMG_cUv7l9s^9+<7b}C#zsNQLUSME7@N!D z_M=soBlg>WdIelhans+{_NMdwdp?0gI`ZruhNQY185v!;dNl@~b88%N3_Mdn%SAi_ zNw<3hYkA?#n+>(K6a@u^@i+Ix0IyVfVy1)9M}XpjB#4%dj+Dxpvp-L=D3?O~qJ-NR zHwAB8zrLj}{oL$0D=9oB7MS0oIHZ5}?3r}ECHZjg-XICHcnARa}@{v$_t@s4(k ztW8yzT%JLGbn3}x0!YVS&$@Y&0gnYK?GZl}1xYT2ZE^LITvtp=O3H;#4B?`c5bRbR zIdbIqI&pDvFbOM>BXT-XOH(kHC?`i=L1W(fR1CxcTeKBTfBROCC>x5i=H~K!e#SPz zDZH<$8u$&BFtu`&WgDwPZLfr!95=v@hO#oX{3HQr(sFV=zv9x;xM7EBGiSJlt*vG= z@_eF*MYx=(9&t=C$=Y)GE@rq7&TmGl)G5uiU)9%co_`coEfOyP3YAFQakCe)_OMTs zmX&391N*bs3pCtfitfgI>*S()<0WSQe5l?RsPA7C(rLm-rX_Gc4zs} zeIFSyF$T<*0*-0KX_onZd@emotALw_=UJt%K>r%#;AT(>GCy`nNpa%xgt2S`p)+_2 zzVN#GdRUOiRw(g+Gl?Y51~7zJsqM%5?6{=V)JQa|s2Cfwp&V_*N0LB}dlW5g-^SkY z{Z7s$z(;a;;M?3bk&`Mr*}9AX3M+z69Isr;U2Bk7kuc=uAR=?+S&!RNuf;`80ijlU_ipIDd-t-wu9EPZbuzhqloyeL zmI57UHOdUsu1`SJ#(LW9N*jb1Mm*jom216{KKPt~mDN`4BN%x?#e zkahifC=3Rx3m%!XuRC3Z7lwk-?139ULCFZKwM|Vco0^(7g;^o1Y&Pre-Yh7%0#N<= zix**`p(}uPb=Aalk2R;ShfK^dQ01#~-K-_%ASDYAMq<~ldUV|AEFEs6R`l4XuBMg^ zh~eh#-IS#i%EHSVk8shk8{|(&NlA(FjDhdip~<}n3ub0!wS9eDU{UH&ty7lPzt)J~ z(Adv(GRud1*T=WVSW6_*xL~gTbuRpOgU|Ak44!2K2F0OT-Mo1-aI`gC9X{VjndG@- z(aO=i<9Jd1#=t8gjJiTXqx$efvvP2#WfCY33{GorZ;$c9>!3?S;0w|5UxsDK*jXCUKg z<{4q6z?DNb)0s01KTgE`1?zgr9Q%)!pPrjECm%431$LlRb$1<(`|nX|$X|waYKvZa z6q4}MmqmaMqUnm*O3={KLI)dA)%#%w0fqag-d`I^HI!6GnPrOs+@6!BMn=^9G&s~d z=BmHZRc=Jn6E8quBUpZ?_m^?5vGBUEu&}nRjUEi~=^wjy>{tWxMM6U2L%FX)?fv)Y z%(1q!Q|oSF0G_*m@exT6A?3j&avn~ z6?0oKWhfxk00P~|I(R9yz(r0@N4GF9fjyF*YHVz*>*``ff8_-ax0ExzKc;cBCHC%3 zm8tM2Lfu7jLemU_A6JesGaoNDV!3?%$G1Z=`tW4Om#~TGp6Z5kTrn(Dz$B1 zBP1mB{473`rkh?i!(1QED#tANy4|{Xv({EH2Uqqd@r5b zBg4bbTQqggI)vrKWeg1sbvz)peiWg=sv0gX+mJAtA69?@DtGPY#HNIgFH|1KKizNr;;@&Omx!n+fo}6lzjjx{ z>c96*etSDs#XVd9q^ap6Fl+Ba4`I^Cx$a;zb7}t!7AiY=@_Bn6n%fOxBlpW&P&yg1 z?5#Z4DNz2jRFzD;l#$xUmn1M)eee_mp_N4cp%r$iTUcy>q#prLVs`lO5Rwk1wr`Uh z&Jfxj7mT~;Gtn@&Cf>sR%bPtN^7rHIz7X-k$JbX$XstEg*Ji^PyFzme3c=h+pIa!e zAJfl}4aYTmKFg0@d^Zn|>m3iDKQ{=kIWmywrH9nT2_G9zGaM@`@oUw2wXs%Wu*w5J z6Z|usP1%Fq3vWkQy^m-i5y646i8HpyEZjW>EK`rBB64Y7S1vt0eJQ$p5u<}y>ia#l z|D>S9326y#ot_hSzNCt;k&eJ)wuOa8Eyfge5P^1~e`%i9yiAM7{xWg#@zy6!JbijG z_F0o2m`ZWo6%=C`UE z2D0MMpKk`9F2gu^mw_rST=t7Pe5=`G{GduOdU$v^IXM}mArMy!#U9;a)z3^p><V#7)d|VUQ72T~NIFTRlBWGogecGggvH<@A3>7fw9AET)thlE~ zXkEWyL-!u3_g0^50K1TqNP>__+|Jf#prNs9)f%1)vEH0(*HXK>y1spUF742XeL?*L z)YQ~jH*U}aA%Z7O5Dbcmk5_|;!_OIu0elQwPd|J5v>|xKD(Dxkz^_n$*ZzsUkCT_a>6`l;eqEu`X!QJm_VJ29=-q6Vl@+4#hr=4_NpbH~&Fd#~LP<@RL1JLjUWD=cko2Z@Fd)JY9>~;l@{IKKIjrt|t{n5$=Yf%| zP#2Y!m**NfOYYeNb_K<0iuf_T_k)M!?ffMG`^Hr3P3$5uI+pN7&tJXD_jUn|>lokM zEGq)eM1h4y3}2Yqo&-&R0B&w>lVUSg0455(aQbBHarYdK#~z4%fbMkz1J{P0Z%@F> z;1?32L0xHJYTAU9FfRXidy14x?|x&;rDp-FSFc9tzZDbT*C2?GfBKYzoGv%xsctCueu5v372 zSAWnDVu=8!TMJq^u&pI3+6w@Yc}p{(UNPf@H@oLr{I5qtwbVm z|Kot)F(?u1k&=yy*r7JoB3zf9c)bIYQF#Gc0kuzaU-S>EZk>Xt04@Zu%?gK;j)6f1 z9NDHz+8||CqqywI{|Uh1J~bde`=y^SBuH~(c}!fFONYR(_0|L`qJbzJeg5m$ug9H4 zSP4(tkFcbQnqpI;rjJa1X^jVPJj!gGn9GvRYas_amrLVESDpQt@O{nU^6o696$8Dx zoZi(o8o#WTk^QXEvEXT(c4bV$e}0+@s4bhdYI1V&LUOW6&U*vX%L?aYfy`IoJ_44m z*gv>M;H)4{_-D_PyRjh2?}*`Vu}-f$g{LarhgM?5Go8MEr_J>n5_b0Xe`z|u18~Rs zu44@gE<1#{1RLSt=;%t^n%>@C@M%F8BZ9SB1ukjtw0S~DYH4UC&0xE&TwEB@y8FiQ zTOwj)_m6LQ!0ZZaTwKw(jXg|_V2A2EI`jrdw;g*WI{Ek{Qtf!(U18k=2fFePa6h{y zpOzpPC*PZM3Ap}gwCQ}50F@n70O?3uFg9T*&JeQzE|K~{Q#D1g3W7^TF40hJOZ6*^ z!V%bi@E`}glMOiCXbWSKJtg#)&K>V96Fe}|TtWAs5%7x&*c72v(^_^qJ)LLkjsrS6 zD!RIi_>x_ioC$8z26d^Vmz2Ts=j_RSzrddq8W*g=ha52qE6C0LfTRujr5R+O^P;|% zRvnZOd-@0!PU1cxi~pF&A?3r@x>aC)=W%HjK8l@#CMQMBRON_ z-gglcrjH*#o_!L3Ddux0fD0cA^BXhHY$?S^V`Q3a&|2viV^J*>e0ll$b?UVXjtdOPBF75DMhv52Tg)o-lqtzQI!`-v3Nf{aOz;M|p z^`^gnA1X16$xfRAj3QE8qHB@AUm&r!E*$i|m*R{39%q8kx6$dwSltFXo<43|y z;N^o|JM>kdvRc9S8x01)?}v%P8p;bMv=S12P*q~&>E&n3dRwmEjESKyE-4{pLnDXo z;vpdm=e|3eh1IrYu4Z6pKm(nXv$K}q54;060S*+hZPBKuJv?Tn2UUs>S0wB%7}k+D zQB_i+0yXd)Ubm~>`sU_b2z+pLG(pUmxQo-VDGY34&Mdtpz0j5|ZE8a{_->(h?(EX= zq^7_`R0o2$3j}}piPw78!w%*%>b@nBF#M0dDoLwYBD#0c zen*XF>Vy?OxJhnj&J;`pTz*~^qg=}nn_KTkRv2s*@o;y~_KGiK?pgLYeR_K7m&fG0 z%60IP*5aDWbj*IUU=CD2etc_4NC>JXDx?FnCbotPIljJET)d6MU}yyCj`Mbka}`)| z9e1t#m;M_&K#ynb?bfGG9m_3^XKsnu?6`bWq?`V1sFbkqDts0dGqd%a+suMcL>M>A z9U~kN0GG9c185-lmK-NFb#xjKwQ>8IFr7$`K8|7C4l9(}>BhDrmp;CkKyL(q0m6#( zYZwXj8Z@Bxnt!cE$g)Lz;Gx=@xu6<>vI);(4a!#{)dVRIe$7#fC9FT5^N zPbZotUA$O^%VQ)KX#N2mAw+`(5Fgg-T(d!v4%!jkx`$s46Ib9!ycE#%2>-! zB5My5k%UlW-^YiC?K;kRD?UfB3An!Rdc}Lesjw)MCmR0qP>BV^N|a);OHAI{tXXc` z=Mg=^xz+py5RGusSMbw-n@|S;y+>Rv&M!3Pd`@Fd0>VWWZWl_CRSwTC*Hymozk{1+ z(V-z2J871dm>67DweR7>J$%h}`M37CzF~KFcc;)_rmEbDx`z?in~UlQy##NN%^<;8{OuOf0>`H^!>gCrX32%y%FEv(0?E+jVvjcD&8$c{V?A_{}tSOYPqUImOnz(Z!6jKj% zr*?XMLdAz_pLlYn$GZfdG&Xkmss9y`y9)IAP-dwdJCs5!q~C1x6GD9R*VFBfLNlupnt6ye`A8$1C~Ft`z~A@HrvpZko-7z z@;eGGmpA3BrKF^Qu+!asP0E__jNIGE8D4yusE5J9!Oy$8f*(GVfopn81=W)ZmS1Xh z_n#s^4h|2~;0gMp>nL@Aie8Nd2?#8H6ZnvT!{$$S-I21!Ruesr)Qy@!CvJv>Q2Q^< zv#wmZ(hW^%BByESX{o@1Q1OSc31o^}KJr`*T{P*Y2HNr=*#uQo?0xa_<;-v^o8Kwu zZoo}=JUmpNQ0>SF^YnRLJ%3J+!XtB<=EjX1o*p~*?p=qViz+(&-n~7$cJFq_E=UnE zu^!F+y15*E?pJRo7L0aefEW&g)Oh;V?wlfM6mC)v?LTj@Z3e5YboekgoFwzV!*>X} zfY-s*%>8o>sS-vv5bYh&{xoi%Q}LlpOiZM|Tw^BnYZ!}OuCBM7oz(He?+BmoA79?G-Rn{v^3?-ZPR$TCU?4lsetM~ds}N_?f#bQF139Rv=`+OE{X z?#p4K{qQLU&`e)f*cFlj2=`plKa2&>zM;QAEv5_af|Z?}Mwu?YP>6`M8(IHVaO6?s zc|o`M2c%P{4{V9<_kLq*Edr7 z9gtu`%(kVlJx)FYeIf$Sr;F{guHD8a1pw~h+(N@chcu8R+?SVp7r61Q;n6`-6^+*y z+1;XkjPgKFQ@z~g_;Gq$TU*4JtJky#Pb}Jh7aP>QqAB+2(N=RAB`GMq#LmUoe5vb^Eclc{SAd=T+qgQw8!RUiXUa@=kZasOM?{KTZH4?2J zY^zFr$A*KlezyZ78SG@&KPHH%-H6vT(d!P@lMub#-ANbKqgQ{|qpJf<_Bk7J_&zfN zf(8&bl1Y2nA;X*^Fkc^msp*@~gDrSoItF{eNI+a)sdebvB3lYk@{=!J%FfFRhb^Vm zRkEqEQAtH*1q@!G!*=}irhf1}9%3scoV)MY=+@opHGV(nF@ep%*jUBKM|x~;?{DF| zghRgn{>ZH2$}M+j&mKt4%+&9>M!Rxlchm($gmi@q%wE%nxS&sHm~8%BV_lGJYjkKP z8hVM?+ZR}C+=Md=z!?JJi%KM298Dak+BZP(s6)miU>&^LhYy?9IfwJQUKB??<28j1 z6%r^)2+xL61zlGiTU@%dQAF&g8?=8SJy+#`p-D>S)Ki5iD$I zb~e8-hY@@xD08Fm&u6FJ^TDh(D@0krmT(rag^;^+{V7xLYnnmuPD2BEXwV6~()++& zkPttxf5BhG_m{#6!kwvuq6b|(N#_rM7XBz87@h4b%Fpk+9+p|f8akeqLZPyd*s|gHrI)3~kXmrZ{<}dKmXQ7Y5skfA$j(=7m)9UA9 z+;xcP6l_8o4yzT{E~aKr5JQ*rXjuHJAdnVyKrCR>EU>%wufRKL2pv5{XyM6;BSA%B@*UiDwrR+%Y| zW`A{$)8CuFJwsZ9UXS;|o^QW#?@1m|G8FcNy?~FQj~^?5jdO}AClK%L+w>Sy_Pp@n zp$)R2HFq!_gkcAZP^ikx7y%wJD>Bcls|{gMySU1p_P+dI_Z!LHpc{6&zLDH|1F^cw z@~T3X+qI2ws30mKyj{8LFsdS-{9~1bVJ(QsR<5&Xk0IqQQu#{gB^M7zN_2Gr5>ZVz zc*nG~v;=|6hhhVyrcrKW$v)cKYi4F)QUB_dsdbp!-&+v!&n=+EU`Etd%q+?*?Ce4n zbtQBNzRu3AI%Kv4x+M#FJUMw5ZZO*0>0AV*RN~9=@IS;f1SL`F<-SDj{JAu z-ODvb5g(DV zIjqH1_8V=!f`gqMsWnkX!Qt8j9L~zdMl7((LTj_ZNI;jA7<=~*6D4PvZEfw#drw`ne6oRZd7tktI{sd0p3wm+<{zK~ z(ydt&fyH-uQ(f6}f)l`uKT*@^5y}HZsWMdQ9aGbZ06bl@58|A{t#QF5 z*V@4=gO%a*2omg|AH!g0fN&vGbeSfAE=@&61r?-Wt{E)_V-J}@CmmJ)Y{af~u^oz* zb65nn&5QVNfm8xmTGek^<^O9JEVVRX9TGIp%d78Rbq|tJ0u-~_-rn`d^(ze4+kZf~ zA$m6>BO~mFkc&9fGl$qPWE1rUo?TQ_)Q7j|@^8Yo1nCg4)x!G2&;~!O76OZ@j*BMa zkqSF=*S%@{1^71(`%U3eO3L2{_XI6aZ)#@3YE>GZBFL>ZxQ z;GyGBfE@n zUWA9Q96LY1J0a~JDglWbE7n~QViN%d)XI1JKYSK#+A zo?!w;#>P9bgWKZvg+0G`CNAT8P7Z?Kd6>Pk@u3K`0Gq1%UPntS7>RZ2d)?URs12%l0v`77r@U(k z0MkW$kz5*fJ#R>P4bL^P8tqQ%O4KvtvjCAsggP2^ z?POJlxkY=(SUUrNy#<&nN*)wf?;|fU(9e23FH85HB8ITN8-Gbk?e<`wvm6<2y$B~L z6U4AsT->LtXHX9VmRsA{)JHS2uUi)h4kicWeEQha?-4ZLUGI2UjfO3e<#_jiF0=6E zOe8T-LC&($JN|oszdtN&`>c>#Q>i0^_0#O0RYZh`yX$e>ouUd?E+W<<=tc06JirMS zras_%D^#s+c~mKMWtZl4#2grL>%poE`k9)VM!+s|Tb0^{y~O#-!rbhHG4XcetO1Lm zGNYrXPhykoys~>0L_rcDroyNv@Bf0HL7ZBs1(NP|fruqgQkf2jA{^hLE50At%}+Yw zyue$)XoE~fxsfX_$H+J}V5+gPRY7T^4^dVT?+A|IPHZM#yVbW3WQaJFd?^4!uo|5* zakLaDGrxdyK*HOOc*InV6Z~O%oDwR#l&-e_Ph)9lJph){yeFD z(EKkf9K(Q3l9+{d%SoLm)GBa4;w)#y3|1y?nger z2KDTpVRKlKXmIqOnxQ5d>4Bg%qX(C*>s27RT{`*{X*M&H4{xo>{SkV8V!nSxna+Og z+8Fm_^is<9!=z#l&$$H1C;-s{gsFs-lsYD35ap6b)-y_>A(B@3tWtx&_+2w?FfkYS zRfbi?T!=vvFbkjn`H8+jVMg#1*|H`*aR`hEgEC!masSQv%uIzB$01uMBHqc03ns4!P4(=9K27AKTU7z-St z?oBLN63P)SpClFPqWs{50)wu08*W(lPbSDRE-o&c)<#2!01a!5)X*i{mR>p{7NaVr zySVgigaMk-8c08oV&IY{3J266G!aBV&5dXIiJ)ZC`{e6WZ0&&xHo;>dv{p$+hXHli z$MhY`n9zuLi*`A;;TG1(fog7e9?&*Xk6{7YOJ}u1@#(i6z8`n8{?2C|7^GAM65sMT zz-59kaQ(#kIXRrnT<*0{H$<g%V<{kjIf z8jep-O9bkKq}Z%GkSlT2vXHhUPj5J8Rq+lk$ZS9fFk(SqKjA{#kxKs)t{};Kp#V_n z85x8Hg~20;rJgl3b!_Jqr8#bx0QEj!$w|I~V=zi*x*nYgctj%tDc|ZM8evS^OS5W~ ziqP8MCgnzu>@6SrU->CzORNgWp{yG>uE{FBjsH;wz71s38F*x#G&lD=b_XlZ(?wr- z>O<@S4z;T`oeu>C1f+8=q3l0BfD&wL#2Q|DWxClPKV$-26`r}qusQAip7v2y4BytI z^z-E8v*@LkRg0_X=%&~G%N#w;P0T)j9oTi4-8cMISV(9;-XWTQfWTRh;MRbaexxJ^ zf5lZ>3l){npVXIfwhT{xZj3e!s&9XSU;Pn$Yx)5O1>HIObvl!$95}P^XJE+6+y<2a zKpJ{_E)MYwhW=)E2bl*|y`jB5J|ejt6&|K9W`Xoa!TrcOU!=PYx3v@*#z@4s?*lRK zL6O8{mJnELs6`m5sv#0z{pJYcBOxat2aHJ7Ah)sd@g3WsCTGZ@?PB3`j=4z3^VGhvJTCdO*(RyF9)gz87iHo0Mx*4pj^0fYWB|-u=B1 zFTrC}eA4$joDQg2hT3P2>oHcVkcrS*&pr1^Bz{uE`ARIevts&JBqI%^5(+#F5+Sf~1QP3W5fJr5Be2N

Tx#{F}c&0OzL`Hy^cc z8>`UnPWH_fRP@&=@Pv8@G|kinpgLRn2g5CG>9{7Zqf+bO=;*N}1qX|Ci80+gKo^YP ziguMwne28ty53l?$F&OlO)OAJ(T7ZUd-QI|YYZ~C#F}1CUPC!!(^;UUuy`25`XUk4 za&QXFs{QU+N(SLpgRBE{Coq=Fn>pE%j|vxOU==?Nz8!`B3)_Ybpp2Pt-&Tk)20tr5 z)#oWT`RM5?xi;B;)Z(EihiviR6#D)}T)QlY3{d_a6}6~VfQQ|-tDTl_vLFfw3y#c- zf^YlEo!QVN0i*ZMok|!$Y01uqvJ&F|j76zYa?c{9qtY#hXZPSVPO`a z4$mr{Osn(l=nJZYXwL#87U~Q*S@Bw0^K24bZ{irK+1Locc+7sSYI|qt7idQ^#TNJI z@z}~w1}x1d#+YJxXf%L8Tb`NUx8c=T4;TxvLC;S{VKy3Yyx=2j=trq2mX?-8e0V4# z52qt2YO259Lv-_i_8>9>04@>e=xEw1nb34s=G?P|1SI(Tz!DG9RbP>0(ha){N;=_y z_t&@4h(%pqaqv#ye0Rzz?XKkAy^0C$7_xt~qtzwLjj6$9Q`^a)GFJ{*)1V6hVzjA! z+j?F?>RC%mE$ZMwXqfn()}X5AXD2Sc^(oOTuje$G$%u<%y!xuXwl?T~7i^ZBcI`@v z;medi8v#M04i_dSWAob8c;MjmB0<%qd`nH<2q;0!E?A!=95M=GuTb7zO$;pKJQ6jm zVDY)RLLELwjxH*jyVIY&cqwD$IOJKhX(qCjP-goxU4;yMoL4hzG zzvMYl5G>|s1`E1)kqrgy^BV`nCg0cafh794Ph8Bxx?q*Ip5CbH!pGR@xZo??cm4pI zWW0$g^MZp!%KvUAva{kpIPQR;{N__1r@7Q(^HA8dxZXz~j%oYTQP7x_ z<8}>|86i-Eln<+_s*+L>r4D$_l?D%{U0}JT<2N{Ts2PJfbjsh_hv(F14^*Bxe{vjF zAZ`?lq!LNLYOkt@5C+W1`EBDLMdViyiqL1!#4!B!EP7Q)GxV4)HT~YEOP4NnL1MLY za7Z~U?B_1=W2d;d{z;X@18`4fBi50oB?_+&Nwv6AjC=|_p!5lE^{E{ocp|!zKw-moKWbzXa^$C#e%&tF&Qg2eCRfbIuX3yE?>RqR=BB^q=wGvo8l|N1qE#7VjxarS9&5T#{hKcc)YFF~8n zYG9`b6o&YtFc9ZJI3cfDN#9!nE<*#jIwZ(d2IJ>RcLB^zATU?L(;?HPZI~mf@XQ^z zO>OUQDk~^j#C8o|M zWi2kri;}~KPF6WA*3KI+2WP(K7+r=KXn?79_@v#w%(mBMJVtB5&S)g?B;FN$k17ln zV5DP&$85s_#CQGQM;O69BUCCY(!3|7Iw}Kc(r-w&GhF>gypnC4h-eKH<1I4b>PNF(AC$Z*`{a zMMukvLcwTJ8)?%w@;5L}5NzB)=&D5$hG!-IcwPD&CuO5z03&=IIHl(ny~8_8_qu`0kAfjj z8E<}vzvMBiH}f_yeD6kpud3ULQ;~zy1W>D z7?tYap(p-gwq-jzyDSiQsJQl9I}M{oUFYjpyszQbiMR_G^RW;W7iS`^xJ|FTK9z~i z9P$fjFVyuTdR%v1pX4&)NUZ}CSv0RaN3$an6~S1PDS9(vkOFw5-OyK!2Q;4=c(ZVp zb|eG61%`+1?d(1vcM>For~J2{vk{~06gOes93O?frOKz&jtleoX)`&Msd)iOpC?9dbXVDiO%DCf6q*#hRb z0YOIR(Sy7uFr^bT8RjCRBO`Ti1ET@SM0y5YcZmK390G_2W5Z6mlRXqNi9LG|$3dKI zojE0{^X%##1{fN723wK+h+qln7Im^Hdx=EH2hv33nfR0kzXq@iBU4WQ{A<=!!u?Ln z$Y@`kp@?B0L z*O&WRRUun%?fG&hSgX|3Rj7jikaO^GPV?XY_3;T!K<`fQ2BZxKHy7SNw{OE*593SW zlrlP7rr%6BTqx@6<^3dYyA(u$1gm}elmSg|$6rLLhuBWmbC$s15On_wV0t~!53ZTo zsLI$1s9yLcDDsykc*3_^6dksbzynI$D@kwt}JqMB(ah@Upc?v3fZyQv}W$7!wjlKa@Tpt;MSmYF2 z5NUuj;b5czy2TvRfZKwhsqXAp*E?smJn+ct1Sv}>dj*VOm&@1B>|iY^RXfY*pV_|; z0yrir<;=B^$BwZmJ9!o%qPR;r^?BV8K^ETe z{lJo$NoEbe)3LQB7|(H$Ug*Gq1HL12c2+;F5sCpL#&VB3c9)hm8bGi{A0xRA#A=E} zj1dsThlU4{H^XHg1D0B48>dHi4y^c_`1z?|A3bfTynb%#Ju{x^K;CsDq2sV{HDFo- z#qITGNc5ge0F&T3L-4+m_ZOi(gr;rFKglDI@ov9Q2Y=oYE<6HW#TI+jBP(Xm8@Y4|C0#DQ$?y&~P-JK(&qXOuPK%^BhD!+pfur3&Q zqgr#l+_2p2DwMD(E2F9Xis@bDcKB8Um&0S2gXjij^KWuF(d#k%!+_dW@}s z$?n6@P=mW{V^dQ-Vmsu^p?#gr#E?(=#)0nSo~HS^Uqrj_FVTH9fcj-c5~$(~4*3iiKj#^Q}7_FKddyopD-Jx%bN( zxQfBso}Li;McvN{VV#GckIlL9&6^FVR8n;Wi^la~YZpDIHL*$Vq#kr*eEf9J{5xsH zxDD%-FllB|`NXD7W~2z%@3@&;Pyo(^316rXwumnbeJ93%fo$R1YK$?p+xv8$+BOpP z1c_Bp>l;BzlR+1lf-CZthImvVmlisWz{OO>md>&2fAMtQfmrYT|Gz?s78xlmG=wBM zMM@IdNjcgXkrWNusZ=yb(j+3Y+^0pQB#{wCR-Gc1mNYa8mGOJL>wJIrf2X^+uFvN^ zUa#kREg6wAT^MX2gMgTNW(Kcu<0lyj`4xI4y&qD?4Qo@FdASX5*9^`+0>m@TatNh6#?wmfUzcC)>L=*U$sIx zrG;RX007Pxtk7i-&-O$BHa^PfLY?!HCBzI&{&kBq2dRTmZr|=WXdxrX?Ego3+d8rN z?!Ie(%06@SLynG)z7TOQ!BsLYU32YzyDy|lOpFS3AWDJf!VvEkn$b4xvgWVK>YnTS zfZz-ut4Y+$3?5KMj}+Zl5jb!jMdpjDACBWXq--s>HE(VGwF)-+`nzJiF;%qG=qcF3 zO981&!keZ!9f`Cxlx`QKDX zobZRV*N5$vOFc)&1(CIS##8Z@Xi%uyVr-X%j*f_oJb(a)RGI+qpW!wi!=foKA``+% zM5!4tDx2!rxUB)Drln@|x=Z}LTP19 zx^BW6$qSk_zNz>jv~;R4ds7&^-N=Fa{gC#`u-9x1BTZj2cgWY1F@JUdmIP(US55ip zYcAGo1Y7p9NM?PO9q~_gU#?af0257LvY)kcbmNby;ZfgP3R{T5#S+?{qZMtX^q1@g zVu{9c-YdY34uC+ZVk&@8p2iT0NbIeJq!7mS(nEM)o`*2MT& zv+foKI65~wjJ&=X>r~KXLo$U;Zk?bek~sWdCoQe!R>EFOqAwICBY;qgWy`c{>h&ZM zjNY15dKWC^;K75Lm4~Awb1r8^>=s}g0Fxx&)UYB(gc)zyg-jUIH4b;HR+m zZOIO$pxqiHMjUQEd0{=yF;1;Nx|lZCwA5qbBO zQtg8-$xe?eVvf=@l>`|kJiYX57j&I4J>Hlq9k*=Bcgi~8UjYT;Q9S)+Y|12mlXbGb zhwttlR#yi$eHp6-1audibkT8v^OrgXXG?eJ?0f8(DqN1RAom|L zrZ>eN{W*)hzK>PRQ5uzKdi5J-a@&F2g_xN~ew)Lux@$U|d3S7pD4}cir&C4X5_;`n zDb?(rmt2LMbm!i^9dVv?<-|U1+lTZfiE{cUZoUbal7^G^Da3%gJ>=uq$IPdsi3RjCdtq? zu^c;I2Nn)GcMnh2z`^?o)9faA7vNI?YzxtFW7C@^wJ{qFM9a_Rn3}tRDleMeog!EI z`R-@<&kZS-53o{SaB0+!j;cMKzpnuS6mbR}MGFc3Ay_pCXrdVChBVP;(oXuOq>S{; z@JSBSBx*_mqNt>&r#CZw^SaF~)Q_T$!88VJNWlZIM~uT1t3y)$;pL={--7cDP(C*R ze2-s?WR!@R0Q;sf8BSMS2TkeieU3E-V1ld6Ch0&O#ltD=sPZRseSDt=^P4-0KtbEs{pI(g6we8&x}-!waJCkk z%?{`Wyq}DaRJ7pqy)R#WE3l0X4GKWpL7~xyjE@_ahlGQJ&kh-7sw*Nn9{(GtN^L>$60w zuPFM?2h;%BZ4D28W$-JUE=(wJE-(LA9xgJ6)~f_3ywEN1E9eUCwKLr`&y_!Z{4Jrc zVg6%uvg&x8MLdzOMcYHv+;5wb2`>GK=xZQFISrL=*v{Yf)pq~cSKHbNOAtIJ`aB`| z7=wuz+|UY~kGtrd^PzNu|EjpkFSnD!9SW;d;$_Kn)ZJTLM(MH+eBS zb797|K77&~Fbgz2%ZgA4znwU3N}P!6_!cTXuJ&YT=%m@3M7DWH&*nCf)do2qu}nu8 zwqEufB;PUfV9K(r89la-o*}{P*a@YxopwS12 zCj8lHD^rrah`}m_h=40PUG5fEZ!Yu&fBj{JKYdv2xa1G}JbI;a&=P^r#ZO^cgz?wo zw->FCnnw*t+GM!apoo}}_SWV)}Ao@11k=gCzdULtjgc7U5UChO9T z8)KGVKsK9QTKW|borB(+W}ZHuPSctduYMt{6@{H707FF8XG5HtY7E$QRVUp;)0I z5ceXK91jvOE72}`Sv6l-#3-=Wh68>)>wN$vRVQKk#QU*x=UX4YMXLZgfj(Ja65qZ# zhgANN_`A0N%m(cDn)@u|oGx;TolrM57fUGag&YOTm>rep+){bd&4a2Qf8#+Ufg4b_ zu8FZ5v0kXGzv~*h=eFBaD*x-csDrBhuMTQoINWiXFm2A~9xvTxh2jlN^7vO z((UYXsb4t0)bW&)NCxc`o>x~})meY8xFicwA&Bd(dp=9G>yYrwAo1;D7 z>8Efr-M{~h8Jb7DQ|c7o_%-k#-(3-cpff%k@tL&3Cf-rpsYdQ)eo8?B7Z9!ZKi>Co znyk6afHuNZA;7E44=SF+An5(<#fRqIa*ce>cMhA)sCGbY{F0J}17=lwIIvqjFkCH& zx@+}}d@29ahr&i1lhNwX`jOvXGHFz@Md&w~OAm?su?F)Iy%uu8h;iE&>sO28ifb%x zmV1&g8Iw+jR>s|34(33_@?>^1ZJBmCEAPAfuW%8ZNwxw+chQRjbUw~hDfI0CcmM|V zi(Om>h$Jc=cr%|q;_w%r#Haw7IEvI+puRcrKNFbvC4scFf*+9j_Ww_N%6hR=1`k-yrX4e8%-hM5r4y#v z??-otfvX2~^P*Wm5FpL*2L>ym7RUfPB2AeUZ(BQS;`+n$k8w8N!gCFnQl7S9m`J$7 z%t(U-zb~SG3JW`f8l8&i>{PlqpR&i6yz&!e#F?rgBcRpHE8M(QUIVFz+1(mQz_^i8(3dCyYD@M{EO_~()5aY!?26%W6=4V9h5=}L&V41h_8}BX1J0`pD-rF(Dd+wj=Jo5^??W{{eHwcn z$m$Odhi*FJwCW9JYuB zi}&1~X>G@NV40cbI`d7i8V>Uh4iG2`m|XS&+jGqvX{2IiuGSm&-hhuo;= z!Q=l6hVAjXx>_VO0j}&~M+PY$#BP$S!^fO7eR}+75itZ!5dZgULs{>avnf2l;`VS{ z-MeSXemvm4cj)|g$kx+~9{HPwb41Nd>(bNEZ}IUiqwXBI{0;j}!O4;r(uJDgAD6k5 z4K;WKT__78KwbEX)qM?klftlr;M6y66nvBj2nN0cr<&TTHAQay6iG^p2nOG<^BtEk zy&8~!MBYk|gTkER$jQTF+Q|XbTlyi%ZIda|m!Mxl=ENaFIg8_5#Nr@Xl}7;&Mp_Ur zSAp09Y<}jHcztz}k*G7f12g%P(NQB48I!d0sVqmFDol;vKmX_w>+_!p2dfRqvH)x;DIn zWXO;qB^Y9kAMfGY@B1254e}GxsW~0=hxU`MstG{9vWwPly6k`z-QHAT$`8dTww-!1=I0NNe&HIRk$tiEZ(j?i$GlGA5;Tea#t~VX zzGVXHN&I zhad(y*c{PR-w0ZHYyFIfv0GG;Oo``uZ`3cmlOnc(g17#)S~!d57ID60U~}7B7A$^P z0av|9CP_;#KfoS=716F>eQvgYKihZUKtk7%DZ9vvO`<;Ra8J7UB4WuCi{p_VA-J*v z@)9x~I>A{Bg2_nfG2<%loU+vRe37u5{-LN*=Dc{KK5p9>x?@o>;~{f`%84Cf7&?wC z{7v;zO;Zbh&F~cjo7&Z1@~WOfE$Kua z{=mhpWeI2QZQqTQzT~TvwntQJy>4%o;=23613UJAENDx8 zFD=c3D*s@&cf@arCeJ_OaA@$WKs^%hqZJ!mYzAKXID()Ts(W<~hoSE@j}4g-W!5Fp zPJYguN1CHl1G=AU*85+#e(#qcy5Sn+kt2A?nw)-+Mn!AD5M5pGviNqH5V+`pA=kbF z<{Z-h*{_|FyA>goKn8~vG$yqX0X$-{wYMwN657)SUH_!5zg$`C+6E@gPW`@6yusT< z>l!B-_Jo4ZH`$xHS|rL~MSW~9kp7EK*=}EB^{~Q%Uz8iPZR1X+af~MR28RG#^B2P) z{Yp35fj&TXvJf~xptLrt4{-zsW4koyxletm)@5(3-ELr3U4zbtpC(nZgPA9ZfM$8GCPJM!s)rUdjnY`1A7 zta!~cie~IYU;9-D)^wuFh>0HSYU6h4Be2#cP{rfcic|e*V`ol$OeNe-SwH7~S(!*r zCQ}oDgMveA^ym{eAA#slwRp$uftOiA`D+x=-L%NdPu!sG?n&-SuH0VMe} z$eFVMX%WFOw?8cFEDX9x1j+H0()zYNUv2m16m83k-*Xo%_#}}BJOIpmPcgH{4r}&Tjj@(`4u)P$_ASp z{?rcG7!G8;T_p18?%nf8$im?w3%m`Q^$L`@z9)W|0xF9$c8T4ABWQKq1BMXlPb~24 z(!0R!!k!MAA`1)npGjVb@?pWr8>>@U+Il=Pa)#D9%E#S(uN_f2Cju-4^>#Ar*c#3) zUvPNdGB=Ijv8E+pLTxv?Ip^vq#yL>~R-MI&W5V7LSw#BcS^IWkH2*XwQ$svk4%!cuLYd^zi8k*ug; z7@=_GO7+OjqX=%AaD;KpjwFXkeBn-nm{Z`!_0ljjJjMeHx7B@{#sax#_37=~e8mQd z|LMT3z=1?=;FN9p`BNl20I3LhQNoOq0o?a9wr=52X)$$puaVhjHW#nI$TviB&T}WN z&Wc&}Pvn~TizrrN0JsT{;S?CSsM%01ZB-?#5dadY7d+i}>#Ww*av%z$C*<>;#9*Ht zNt0QAW1Z&keX)|3DxD&v#UUfI?iuS=q|86~-`|~}ze?`kp8&74X-Jcku*@*POT+^r zL%kFKq!U3}rH$o^_Dx;W79svQ7%-(LYkS!l&)Z@8rn~cUa)j=Sy%;xSRg+AbemY_v z0W|^A3fuej7e6_FKwZq?{V1GrK@v@*bO%Z8u+)5U^wc~>x<-*mM1%XZfAha4cZC`N zu}{^!8;1Klwl^SD6&`u$UWsx-|E-Om`jDVY2ELnC$SMkoXv_$crcG-Ou4+*sjLva; z9W~>@h={_}e(LH|KY)9+D9ekmYmP~drtGu_sT_1!v;c`w{(XABqe5;Xxcu$>`4Hix zdY~YAVQJ^2*@YCJf8qk$^3YgW+?Qg8OFJQuSCSPSHvoq*m{!oFe`(sv^wuX1-|%T6 zO$Vi;j*Ljl=iSP8>ePwMk0oNd%8DBPtzc}q3#BI-+{wF{q+K3jAF3UXVn%<~<*feY zGb5pcRdwf!m}7;ezy3?LR5Uoe;Y|khq=*zygdanH{0f|Q?Tf>Zp475f+q;u^joj{K zgr=Ol{7zUtOw0xAioQ8$j2qVvrxJ1iQS`EF$&KmeYZ~IFI(~n?`NK{65oJRn>7jjg zeYAA@c8J5Z;AGJ@#Ke@3+;CHRoDeD-K30ATf*63ZueouU zI8X?v)qXO(7M-AC?R5bG`I#bC9~}5jwT|1>zfwkxG6u^senntXY+*v%>ZbG({NiGAGJz}7pGV#b z*g4kh!ot+BqYiRsj#gr zPnEh!Q1|Z3FRfw&P1|%N*7}-R%;oWjOM;Z*M!KvocH*hLqolcd$OJZ3q%d zs+~SNu z;>Ya5_?G5o=+^vwt=g{a9Mz! zPtV}CE*U!W6`M@>CUh+RdyNEy(cN}3mVs2+XPG>2A+ zGGi_*bKCW0+m&nYEh9Hyr~5&ba}s~2&1q}jNns7(I*VnU18?l1pF{FwcPLkXME+%6tM&QGXa^MjY9X5uw~$4q_@$A2k)f8i(N5_*e#6UpPKRg2w~)27`@ ziALhf1qUf6B>JPTE;UBh1!vTm90gEnCtkKdtB`&BKkFrJIMK2~R4g8$26PwQA1KUDsKtrq zKdC|S6v8iDT3ED-%(Fn{6Hh3otYg~Ae;88PVZWmd?FQ-9*eGH%avyD)W-C?sawhR( z&u;w4m@;QhWYegRAJP68l^r>K`t-uPALI5p^@vtnI0g6wvEa`_ z93mbV2;wq#cMUQI^#epiBg9}VIO7@I+rny2872~!+jI(7Z9*R`D>&*9`|{J_bb+sB zbcI#$T+FdvwYdYgBsE3pMZir=I=RM2Wm;0MDtLP)&8QF!X`M58CySIW@m^K>mOX%E zI=vlRzm!H7rBnf}h%|)#YQv?t9H|t5?O(>pI*-&~B@5*7GU?Atvf)IIHH|tlDA_p* zRE_at?M1|`ozCCOf^jv9I@3=93rwW9P00OyHeF3_M~lE7F2wBYY-zTV=$<=tR`Z2g z^NQ?&JpM3Y;#7TE5Irkbr*HhaGqUTFkt@QILKSubR+lyw#J*~FyI5JDL@YY=DsqI` zx9pJb@)!ll14T8H;ozmxP=7m_dxW@Yqr0haS&=p}2!pZcQdC#CXcX3m8)UZlWe=Z#`D>wf1Y2onjQxL*|Teo&t&zpH<(7vJb z=TXp4R&5J@PTg;GeIdP|ib6B9cI(5Cx85(QW+gIQ?#Pq{*DqFn6TQKZa*DtbrTp5_ z;bb%NMo)q70~fyD+bE_XIwzA~zfx$GG03Wnm3GT)!EvmmVs)M#4ZU|D8QnV2EHjb=PLEY}c zBE@YqY?NErroLJa)Q3CEV4hrdME}h*vitD<$nvyl8eg#(>RW{ik1axRqhVr_>N4(B zXD?23RKg`+ub#=)pQgU`$G3qOa)%@f9|j*B!R|rRINO;seMPm+S!lEs&2c-T8GLE0 z1Tl%W!E)BD?pyZ+w}!LUIec~nh!U<7y{yiBTV>A|Y(H=a0VpAiC@i?_)fP0#SA)w) zz`q=qCbq|f1sw2)Pj{@~%wOy+;U2-&{aVvi%Kv+yVsP?oi{$&&Qkeitjw~P)Q_m9O zoEycQ?ma-yW5$j(dF8d{vt-=udH}Q3Bem6iQik#p_M@<2(aGW#{Sm)`JTQ|+6ckNm zJ>*3q_ptwRA=|oLD)IU9yxr4?zBn!zNh=B<5g3;lJlUPrlln=Fu5Uh z;XdJ~k_Ug{O_q~Y!528dSD77dP@JD2djgpxN|@bb^8c^4=Ki2j{WCNY@F2r6le}cE zKj1>l2DOR3tF(q+3>&_rLq`oUK?HV7Fd0jDcKm11!Z?+Lm4rHCrdGIfYY znqiK)sXvwOj@iHK8($P9oye4PSKIo=$mi11dvBZfD(u9j{cYv8Y>MDX_Xmmznoo~k znM$uL6gF^CMk^lfPnc9tSg6<7;9k5E)M%(rnvX}iRkD@-x?Idr-AfFE;e`oR z@zumLYP|DkRy^=cEcm#XkRkchJPaxsfH8iES3Dw+6wgjLCAg1VTyiQq9t?P2z(HF= z0uBcx-$~O-7t4dVcR0Fp((?VSlJK#i)F102LQcd$6#~bFonI8ER^rqF(iUL{qJryZ z7ID1vkZ+b?{aup+p7kN`>6qTUn36dKS5u?GfHJoS z{z*@AyfuxYJfj=?_ z19_WH4|o6cJ6fHp-it#^Ta>9d+l=rZ<(1qV!_&3vtS` zWMyYxpiDveFU?4zUh@Jx2fs;06%Gr7|3m6|@JHXQ*^!XpN7HD=C$o|HRj)Ba8JdTo;()G%Jj9&Jb}$!L%77Vl?GBGz+2qn!-idOIL}O& zy1KfrfZe`dP2WiJ52gIC`Le&ZQF42rGv!9uocTlJbFPa7Fjt035SJ00$Rs&t`E51Kacm(Ql%EhmKt zizC&2^Z9nt9V7>%CU8CW!Ik~?(S?302hfKILCp;n*^9YflS^GW(=0xq>GDs^ph5gE zvw(M@Y(40?dSS#SNH!d=G$Ro5MlnmNq%dv}=7R3d*j7(i>4g3UCkZ^&jlB!C(&V19 z`GkS-lSCFLdo_0r1Y*tEq#xz|TCZxhYgn}vOSSwCa7&Yr#Jft#-v<~OG1H+i!sM`0 zI{vQ8%Qm8Wj&RU3GwaKtC4%m1M*W!8t%8(pb(kX0%470%E+JQ@f*K%e)b9~}Bp9fl zuG`quRG8QWs-`z|DB3HL$2VqMbEsLtE)1WdQ!g`iFHQV;T3u=*3()Sv?hs^x7SXeW!c0 zn@>-UyD_!i9C=Iht4{ZC*M0kDoYF}8ysgS@^I?9VS`fNGb;k@JtZ{xPx|bC_;4pz`QV3%67PCwthiK(4!tM$>oPNouYezXE)!}J-d;*hKBP4L&cjOp*kB*BO$_{;9)4?7sL5nJodP(A)D-blb<;3> zzZoS0Z9$p6$`ZbY!K3nUd48b%ItgAl9H~!5*olY};@c40S{>IU!gTiE~&HE(na$h=0OHTRdvCv`77p$a&hlCto;agL*Ke_=QzZ|Mn;58?3 z?1<5-+-cy-!Pt;(ykm0C6T=N=^A^db$7wFKzm$w3MkG(Na{l(wt6U?8FoA_Ix@TL1w z=ev44)$(k4-2~=+eREG;k_p0-xJ^r-t?GZOm+q8V7&t+)pQ7NLC;Ji#0D z4)Wdwh8mZl8Sra^{-_}!Tc_LcFD+Sh$;FuaSDYHzag+F3jY3xW>~hhQ+5z69Euoh%kz}_d}x`P4Ab-C~@zC1ms_t zH2p(^B~j>T&Bd#lRWz_Xc0-9n}=pUz1ageg{$@TZnAeHSAu20u6`l zw){Oe54HsD?=pB=s4upYW~1vNXYU1eAAWp;DHG@VzzD~;5iQY4#7^+gd-WUu1cZVD zSQLhD?bdrKJss}9zMEA(0JEkLoFGhb( zCuhs3Q$0qe%_zAKLW0*#XRcFaGcrU1L#0>6WrfQt;c)Nxf^kzlhflI|i zWe$0f-Ngzi{5n~r27@)|y@$%$%Y=ZnzB zUt>~sy=8gYmBcW4kza;KQW1SVJ9Uv7 z%+ee3BgZc`Tc z_n(>1OzRCnW2I$6$Em=9^uV6zjz3@s5XSY*xH{uEc9uuBu;J^P}oUS`%)V#%;Ai+56yFn>#HlG@j z8UA$eClW?LI@)Xaz1sC$cjqLIS=&BK8h6p2GUEsoc{ix%v=c^hv|`!RKWnHgH)q_i z9;4g!&FZSq;Y*CoLg5(o@HKG0fOG(A(rdBeLWw7I^O-@U?e?1RF5P+?o3|KIum~L4 z*TVGThquLy9{U|z;+xsl>oO_}5 zA;SFk*I)62*MdGK0lgp+B7Ww6uC~&>7-|Zyn4^E_CJYDUIQKU*yZPGejGxo6W#t+U zJ$IN>Z7Fu|E(W7wPNpX(ub<}A3^*KYOb!1iV51k?TPN&{IeCW^@sK{RD zeVCM7pUO`@5QLg8n3G9JU9e}{yDO(ZaIAEqt%U@bh_oj=(RbK8=+b{;iQCya=+wdL<>TR5_Mq?G~insu~FgW9YxrltyvVSpk zOXR3kOl!v&_ua;R@qnG)V*#7F`mUsBKa zp6FtG@k2SzA{3w^_|=IL*sF9$Q_i(Duf+M!5@@I0`Bjl|Y2Z|M0Y_0tq>kRmnny1# zRM*w>Ul&I9BUVsKoW3a21bqN)ft+=wqJsLonu{#tUO>H-XXcA<1f#WDeTb5YfLn@cu3gF{CA+6wB=JS z--x$DlPq2tXd+T1co4e}6zSb`L@yG9SeRwf2|1M+!CWC;qDlis=>^2%@EFXH#d-@LBDXKl+YH45F@ka%0os#h@NJh+svy3qXM* z?N)?Ai4cSXrVHW3feHJ%HO#ieKY_`lp~0HlO<-)CsHH)@nE*ZWt)zl1ZQ)=gVOsdt z>ib63s`cw9fDIv01`>i3$_7<2d2zWIyh>MBmo`_-42?LvL2?l?4S}+-0v`A4Ro!L% ziEA1YcAN7>wXFARk6b5l$m?cx(UZ=+EAKZJ0RXGNgXB3PVIuhVKy;%do9@4*_JVsq|&QE;M_m8!KX58?I#+ zuJfCu8}Lp{gui_Gj6&?0hWt+-g#Vk8VLTxx3HtiQq75H;9mb{Ye^tc{T%+6-(Udr+ zNeJD1VNCE~k8KZYC}(*Q;;P8lHKW}^BB0Ir-dHuIT2G;Jo$s7vOGucA09PQ1`nxiyJr=XIZ zno{B#O4Nk%CNB~9gTq7UEqwbG-Qy^quwX%2dA$Z~rJUM`-?}Aq0rkp*4<`)L z)9VcDj!W!jMD|#+k}f6PLc%O`w*Xb00BK02@m=ip_!oY3;moqp=8zF(rC*c1s{81;2vTi6(e#2jzerVo-CezepE?`?xKRUXo8yq)ga<0pg_>`yD9y z`D?>r;GyJeV4?_AKBv7i_~T~IB3Aw`7%A?S>RD|#mmDz&3sY=I4O-S)Fuf$Y45vJc zan21u4)8K=;;T;6wj+@0N>feKc8lB+pc5~5S+&s79RDM9jCJ2vd^=FPTbR4hzTH~W zAiOd{XqlJ@c9Ey}#fs!?iYn+kqbn-XrY#!%=HA#i{122HbnN(%BbKu#Jml-}uZ?C+ z2hI|rD^RDhF8u~M_nX-|>b96gpJ+E__IZiF<`H&DEE~!0bv^nX5lhFi6@f~}ht)LC zpg|VQ7;v&B@eet+)lYXzHk`alaDeD}E=}&zyzsJ^Z7ftHh*xj~4h``-Zp94I64HVd zEU;;wV#)%#t+HRWKjS8eJkWIK+(SNEv<&DVgvN|0;=rx;Dd*@p(#!XnR|ma)yKII_ z`-Y|8FVM#;k>o;95rssLc^e8w)V6-1Xcys~Af2zf%A^ZpL=SyGmR@@L){qz#GfmMT z7|5EE$H65kMP6uqy#XmLsoO$^&WE{sQ+V}9hqPjLtm@ePSD*=tjJM!{rt{W5yMjxH zS)SveOGLs=_%;RY>;0BIZlu=~(@szH)z;96&Tkak2Zwp<%`5@te^L9dPNLW0*6J%X z9OzXjyT}tgygh|1mBU3#Q7aEz{3%&dsgDLz422c`0Uok<Fw=7nKIR9t-Jb|KFQ%`R{#DXih z4cl|A_kP6|gOPVgyocjanQxG2AKGwlaGtHL@b5j;#RtHgtr;t`b%Bk!Y+?rYtk@3J zxSHoSOK0ml_+Bw8jn4pUApy4(xa{-50ravg%yxWWBJHWZRggdjpE9~Smo#uj&! zrkTE{3lQ8J$fqeLx@i^s0xHs~f`afWWq`c%PHHna3DUwqroN}n@(YBzT${0J}t z>d_gX5twH|NTNM?T9mRTFP@Q!;kzQ6ep~3gA>wa=@nAw_fQZ%XxTGkKhErQztgSo1 z3DQ6v&EFXmW5zWYShPSbz?X;+nQnc@2-?~tr3EvN%02Q!vNCHL^avvPV)}=_y77h+yuMyq zON|P<#?noMY)tL878TO9w6ubg5znP-F92#W4Meo2b&bkta=>lyv$+a351^o4}+#w|(BgdkWqgPH20tcUs7(gt3rQ z-WF(HPoJI)u$Ph765Qny9=QnFmKPDPqNd;mqKa z!ty}_2et<&7qLz%Q>+c@84Ny=Cmp$IJzbXFeUK7xNbpEN^8h8qv0pEV>(B)?)Hx zIWP?ojvF-KI0se5!ROi5J<}qEsqTOxUEwYs>d9dRDb%UDMpbv@8Iq2w?M0<);1pyEj#N{~3#Nz*i4d z3{B=*v$4YQdgTdCloKRrkD)=UgqH2d7GRUs>PD_f*};QPYsNYscPsz6hE@hU+o_f) zYDql0#_!$*DxEu0^OZDy^HXR4gXbsMcJfS--X*uFNEN%hn3^-)HhZG$xN!8D;#d;F z3~VDAU-fo=VTGRo-k~^&lJz5n9sQCmo-G9nW2a|7BwKb|VqP&Q)0*BlKq+>h8UPfV z-s4?u`-j-qM41WdC9udD_rLuclJkb6e67Z_=n1m^gCo?os2)AG zNLAyGm!3IuekE0)?V7&hR{s^p`JUpsWmOmrWv26q=Q2OGhkp5N?v&GuW5+*6r6E-7 z%dX)|ZkrG2UaG8@QKm1}KLQ`@Tg?-@mVCPll0tnzf*$p&?qF5NU=5;H1q^_uel-l= zjQN40+ao47YiX4}ObrPumuZ+UlrBBDT{aHAX+x7z4I1|zAX z!Y$}I)V{kRt|dX!{*LO+&bMX+J;11>BjaYNEIyIdJ7l}ct+y!zL~tw8zAxu(?2LMd z+zF_=MJ`4IMgq%OLgO1dZ@wwMHTCJ`aTWEIzd=_9GAt8O*)GiZ6qLMY`WN&LP0u9t~IBjshOn;c^Aa)0huxdaDx z-YC4m?|OZ96YxHMW4vwLI}sBW`OJ;In0FW(4O9TY7k-qUKz^Gzir1q0}5B ztPOuBtXRH0hW^RQ>=*DmKTO~hhub2CraZ44H1yKk<=D1W^%qVW<%aR&?Yom)lchCq z-3PHHP(q#Jy@=?v8kcTSA2c&tTF;!(K9RhFL84YGmt8uk<@3MoR{yFb9B7wGvG?}n zR>Iord)|zCxHS9xh~)2sDY+?HSO9|jeI!Vt;`Q$z$RRSZ26r?dV~T8*abaL*plu52;?jL+Q2dXQFMzn1(%p)TPA3yFxz!!!x>XlGK_A%;O0ADy%K#zx!MjgX>> znB>G2N(NLwP<|olRtKn4ov2KO0xrGi_~!h~j`f`6HZ}RQZP{RDrN66q(S!23cij|1 zKDsF6%aH`_?-5@jB-Rl?p#PSp?R?P+5UA3L@i_28_p2gu@x zh=_Q5+Zox<*nfw9SXO-RUM|@`#(@LPDr_1SaL6tDwX`bmzyYC|Am`q6{aFS77i~_f zl5c{-@@}#z+s|m7Id#g{%)9Q5urZFm93z>h4t02U>?PV7K1?QbC}*QrBa>Z(Gm0y; z==--?-(RCHR*G2XcKTef+*T}Y`|_n>E5H}Cp~=VqeRN3QIqdnI+r?0SVovez@QaYg zGv0RU^y6QXe9m0=&Bs`~I`-}4{q;o1foaH?Df21IcL#)QpVyCI#MLgb2N zq-}IP0kw(k37+Zt*DwEkH!?Ged^AK|v{*oWRJ0eOdHdY8X?&t@vc@sL{IZ=BLO z=v5~=StLBK7dhmgUjTuyhdPDyb5W5)m%Ie_mGs~6WjTLK-0@4+ZCV`LQFBL?thZWV z%N~;(x6eCt7E~o-^Xc24;@^ID_J@!+p;74EGv>=SctV1s1We|iLOAMubS_?+W%+#@ zo-1(zh;xX3Xf>}$@?>uq#fdO?5}HI7O5URrd^4!j`*y`bTqu>%?|1(-^So1(mvh#d z>uG6Z<@vt(1XZlV#?iRigh|e%OZL4nd86_aGZ)R>{tvxHYb=c^BL`1s#bRr%-bkdEC@o@YOx(&U$BR@E#&)4fea_ z+=5X#w{PF|ye1!`qF?)yer+d*QrXRO#8nAp_BorvOQTjkQLxlCy!cU#)GYpSEcym9 zoqFP4K8L9oY0hZ^wiDxNN_R!+VMS9G{m(yvoX0=AMm?_EZebg9l$q<#{O=5tPxxfu zS@&P)F>puqNbm9nmz=}33i&=eo(4Z7!y*cIB(LtXhnWqTHaFmg9#@tPKV)e4z5^?1z7nuP+^bR{qVkH@ywc zx(|0OT4QBfs3<*tQF|-JOH!Jebw1mh)_FD-`d<6TpzOP;TkvD2XYYU6Z}>O;`-61x zy@Bw~t??A5xxzxCDs@e^QH#^pxF5+qnC96ou?q`zH*%MnDVJkDw(bl_aVxxgSIF?p zEj{L>{cO^C(nvQ9=rs6I#-f>uwZKSgID<2bi-Y(5ZRWNIgeTg=!f1y8b-hcT90+`+ z)JQ}Su;4rslkerBO=^3xU#z~_XW_-H7pvvRz*9{`%^`Hpr%v@h_K*g*(j)8h+qZjJ z%f7uw*t&K=2U*#v>uVGgNfb@zh-_M8Bk z3rYjZw;>^uQ6$PlTws=95}|!?_1(HV+g<5g=e}c%$DO!v7BFJdHt? z0iULNlyf=?C3gEKi0$)}{Y2Izyj~7HnZQv5xXz`ysdY@1blcyTXB||me8Wc*{;|4O z|4F<6!GjiQ?N@m@T=bR6Dj(e^JnR3p$ zr|Q?O)W;s3Gd9&FczEPlNjY@;zU|u5d%e)TO}cv)MQaDDgq=8l2uJr;KG$*H4!`XM zJ2+?TXVjr~=@s6cRaruQ$dYB61ez9;abvI~O%+G&blkH1Ftkz~@q z2>}w3EGZRM>ElGr!K;-~bY1YjdHP!qvLe5!>(70$+PHlr8g@DWGS!t9RS5LCVM5h}knzXA^*$48)qpa4TKa+Ah+}?cqMc~Bd-v&MduFBv z)1T)2v&^caVavCIVae-_;G~2okJNxeAA7gUyyfJL5a9wQ1MAbad8Y zD1qiq1SjenUSHf|zx9*G;<-&vZVWIjz03N`SgDnvmGk+Th?e4jfj+!6Ef^WJlT;$o z{VAVRYWsjm5%e6xk)7PQKqkCn%p2qfk`n#7wuyEkysBxZSLA{fB)?3LFjl15wI<$< zMztWPfZUKJF=af#JI>>WkD}{mY=9K*hl-_F?&;s3g0zW&Nl|mA|2rh-^E;M-m;!Jo z<|EHZNHqDMjI6BI@Zq*z85{Q#NOrV#%8+&o2Yk7L(nHMM*w*^PjiqqBPY<6^ey=lx zo;O=5Ar(7Wb=m)epCQG_H^^w-2jW8tOGhzy>3JH;1y{kA{IUrznKk9B+t6)b7v z#fMXBMsewgX_9oN_mZ>s@eQZxjV3HX_4AzV#o$}FM-EzT+`H@Gw&y!ly6%~MYO;;Z zL_~lAJ%$zaZUof2WYk2lCHs5yt)-YePNvAXIak~}89MLz%0-33UGU7=09JH3%wNf^6ppMbXhHLHZln@bQX9h%k|=ICBLwnBo-UxATuh+Xtq>8|>So-4g zCfkczbn7tx{%^3tz*!ygS+RXvjJ|eHU@|KA)s!3SYf(Ol>Aa9}+_5nsW34*wzp?d~ zz2VJ#+p~vGoVYvX8!(%g+g;H?^WTqJsz?5Jyxn2xmxg*7VHZ(F40qH^_ zD2bBV2q`K)gOXgwN5^#MdxJ->s{}ph8p!Tdm56Y17=MVfqdB`-0!!z;=5!=2`1Z3lzG^h5fS^gajfJgs zy~FsHz5!7v*5-MwWK}TZtf@z!J%>tIzTL{bp2Nwv6_1gXE?8qcyTwJG{IYwo{(3$Z zH+2F=u=2m~a_pen+uIRyB6oC}N5P)GeNIs2h|~sd z7_?zxc+Y|}c=%G|tcQIGUS^eO7sHB3e$sNj2)I^^mq~a(Y*t*y*5*2!FtQA3%n&EhbzvzMo->J=$#H}*;s(VSzKc>yJYT37~}VslTSk8*cv*P9&J_^Auna3G)nx=m!aU*z>1PssZbj zfcT3)qEpP1P)?9#x`uh*lD7I++-puG_-?9n>?4E}dTDt=pUz#VpctNU#!hExp74+$ z#sROkMEN_Q=NqIO2SY+M6niMwuW2t4mm}eYDaK>T?o2UAIM;c{XlHd#CP(s6l8cfJ zS3J~HP{bfK@J_G6eUB5+An~`j)tts*FLD8<@s;|mHyi^jiz6H=Jr++lNRnCA6EP`w&^DHUM!XikHz{& zbTF>p?zg@9)_L8xbt}F%)&`GjEt#+L2{eUGEKw9A7%4~odHoDn0qvPI@*@ln9+6j~ zsG5vVtiC0`Cws`ny@O9#_-dP&^o0Gg#HSUuiW|ENi~ESGXI7%6zr4zT@dHf^ z(J?6~G5~vC|0X;aB;R)9s6pTLKFppx=3S7}JNJHhdT!w4r%L$%5yYOXI<(((n5YE zO^+~DgMjtQ7?}55@yn3=S*(?c4D@Ec;=~~`=}@oh+&+5GhhxvFh&M7LY8si@T|>K$ zmJ=slGF*fQx2dYF_4vnCd#!Xic$#kyZQUjU7sL&1uRecLMfiQw9~8~PrDN$AqBZf& z-?P_scj`Ph*+s6PFy74OE2ef3e`Fa&cQ5V@`A}rFre6VFC50FqzLWe3)i0*D{pjO+RW8a@l7CSZHI$(&YQ-+K~N}yph2Xl zi}QBA;gmUeq{qyOiS-PELU}# zP$Edt(w2g&RLHHlRPcr&xIGjU#Y`M?@BIp&w$#5>h@r4f?4X-kmfLlDbfnSH2G>B<5m*&>DwKk3e0QWtahKffJCR`>Ig4wve z`8Ju9m!UJK_a63&l4nM1ra00k+C!?^&njM6d**s->Tm7dxa~J{PY|3P& zLe*Tp(({iWyC5;0Ts4*2iaMq|Mr7%Rg<7FM(3xDNczh;pVJ9~8yeUgGJnO~0g0pjn zt2&ORa(^~*bic_nW-J}@E>eZ{(^WWBP{4+n~j%rW;RlX53yIq79>dF2-BoS6mu((?}Dp_ff$Brc=nJ2b3 z?%aLNCecpkT*Ws#9Tg1WbrsK>I3~th0#ux+wJN0Dn-xDLsZa$Xy7b!Ow@a8CCj?!@q?B!qGS45^zNixqMaC>6#n_lcBR+X`X>=EJ7}A-H~CnCC?F%0 z&m-y@6wtml-g(5~Pzddi8`SPcxlNO*sPm9(nFD+>J4KE_QZj{i4;^I2UCB1_N7YX` z_dB@&x}DxBkMo#N(|t4}hG|}2)*G+pws=rAXrTxtk3d!t^HH7Y5hb5{h)G8 zuw%uVrn^}mlycTDs{;p-{%hNR)+rsG;t#bQ3qhf6cSfOLnpVW@40{nVAm*bea%Rn3 zU#G6$y{TbuL8g3|{rtRtD8;&Sem?Ictx<)5poL zFG#$g;^JV_KP^a2u+c=5E1lcBWF)t0C}_r%zbLP23B|~ran~IE zvtQ)PkH1((BIq5N4U}8ldpYcex}+06+e)6zb^?iYS zClyhEJxaZHO+?@^88ULf&`S<@V5vAmv(j=8cE80bi5J`)) zjF1*vXdC~YJXN;R$8M9NHQ(MqK2el)-9`rf|3>(9@8DsS)C z^L3u*JhsEIzI4S332lt6f$Vf4b2)6?I&U6a@t;QqI-ya30#ZU9C94O2?uR^Wegwmf zQWFI2dgWJ=fU2C^DS*@)DZJfPmYy&PQh;xiZ+^^Y}-1i2h%bv*IpvGU`QTo;@Z<0X~9X{#O zR9ZNc3}PbgS)}ssFQ-^mnbtO$UPr&wrQ{(W!HzDCsXnE^$k1^0TiW?hbn|7R^v>x*V$g z{zeTQT)tW!Q_4ERCL&vQ%L>Zuz}}|JE&(?n7k3WH`LS&2(yM`k>rOD6=mr1yK7iLt zwQt|PTf6@{a^K2XwvK~ayJP7T_~7EoWiur9W)A~T>~#m#taJMR-+;?&hMg^1@2Xa&GpMCb%E-nh_s=bU z-I;DEEqr-!*{U}k(A6L7-5w)i$H}+ynGGdr zamvR7Tk^`vd@TnJ61O~QoRWepJF6f>_n}GC&r(gNK&p}<%%7P#Ijtjx8lg87wtV}H zRk7`g18pB`ggdBr9Fp|@E4TMEo~M3Pe=-q%A>~l_xQ>E$<>NQSBGrJ9HxOK+PR08A z*9>o2k)_mxA=nZxq2cV46GNL*w_TwXH?_Vf9?b`u7F#STg7rM_-?lbdkHLmoB8NWX zN8PS=8#ipQr(R3yZr1rNimW8>oTp5A7QkU*9CTFGMm*|%@sy^ym51I>%}6w=k$ z*tfV9GBE2$bILPR;!R8QE@3mB5m_;WOPkK&6|U3JNYAanIZ1V8zQKK3`l80_I0gmm zkVTj(y_UY8uA^A~>7O-{{9bh@SAv6hxnw;+O2L$q-+2x8x^yMOF>`QmqbUx9+jEQW zC5gn)h`{r4v)f$e$Al;)<9qP@qvnQb_xTHQtH`|pUV*kXmpnr2U0x*+%hw9|{p)CP ztYLh;wFUsxgc9?rls_ryA=ayFm4ru13=&nwx5((lHF^5XC{pEtQGMe+L-Vbn9`iE^ z=l}9V&3dl0_!J>XBxn-X#^zPStfpN-6;$%-RTIIse!Dv|=;N@(!R{EK>5emjWQ=`s zxz0@7M}RxVose*9$#NzNDMk10Oxbj2Ywv&m_>-9@R(+|6+h2C%?8!;y(o1Xx&oxlS z#LS4hmTi5UaZ9FqFE402ZIvqD)^m#vWPk5%Y3Y{seO&EhUKQR;T%-EW|2*q&G9dZ4 z^oLvNau+qO#|Dd=g?{Wh+9+K87u$~Ct+)f_x2LyFIeW^^W1iL9!2I&TK)9R+&27$!NVK8;o6Td}d1NR!F`SS^0lQr}{ZreMYA zJY5nBQ9qqZC@S5P%R6B*$cxQ^>*z#BhILbUpYh}S#y9V+`5Pi4W~#xct3x9aAXU$M?Raxm_IL2(xn0L4swp)&TU)mplWpFV zrrFkhPF<%-OsJ~lgRnuT0h6(~ZA1gjfx>a_nX;g3+KFE8ma+UP=Ec#n5%d$kTg=V3 zXvcnH|B91y?;mfc9C7c(?668-dH|QMEwO#A9o|@no092YF0?rvllK(v{7I3PErwI4pwy^U&ly}##x7*oml%35RN1etqae4V;P|!`L!)D2z=;0-d%XA9 z2Fa_$4~=%UnPQ=BTF2InS5CRJd&q#gaF`r8wf?o+Lr0@|QfhLzW_XX=HZo`AMm;k; zsekLf?D5LPSzQheny*l0FLt(^U!~K|#$W)%ln5|Hb=K0{^B0=q7Prjybn{T;8kW_X zFSIm>F|S7|%FoGaIr=EyB3*zIEh;R~YnS`ZV|w=y9~yk53*TqIzJ;iaVL18~YBXxpXXZr9 z6BqZ{SM+N2DJO^fZy%?Q88WHNJzU)tT@X5x!M8GqVy)ul?+P@sT=<3yj$6wr6XU(1pAIz+L zkTuvHnc=?ecIXjkbJB;NVBXX7-ImQqF6X{`7d?I1lh*H!i|t>jNlUZC&(KM;qDim? z`d$*fM$J|4aZ^;nW9!ZX@BqfALtXA$1U25O3 zL0l#{fQimUIjc~*%yHA13`mPdBfqd|wvYoXno(tl%&S@sQ^zGFCO7CVb;TuwjO*v0dc6^;Wk^CoLXyR~ z2IE(V^$_`ISI;xOdfcBwi;iKcgx(>xCOdH(TvmRc9+pstE63I&4%PFPvh5k@7Eyd8 zeqLFnDBToig5`)k1M)M}tTbW*OKI%{t)lA7DqWtNU0@xkij>V!udR?y>nNze+sB&K zr6~&>j%ZvZA*HEIOcarj)bmO)e{r?28ENv;mO5WAg+A>tbY)ct_eaWTYME!q$OBmn z)Ux;w5Je$kS1!N+@MICY+V4+(|BawK1e?S5x<~>A0z^0B?gvfV^kyeZ+~Vg^*3)zz zv2>ODZ1?&^WH}4)Q3;J=9|nJWO64l(l>+DpKyweus9&IAnBsEl29)Wm)sZQ=bBGW$ zgrhOScAiwfgzCkxsKpv3D|Udu&`<0oP@b20Mw?ib=^SFyQ$@_Zo8#k6tE2gL_CdtF z|L`Gp+fBO!a$;l{p+VpcHdz3^mRHR$w{LV@A44N?VVRq2ll3Yq10*9GIEAe_`F)Ot zoZQ*Lz~Gu%55N4aUVjyYe=MEv2{A%F&iZzc0T4{Sb2lp&myGqVd|vD@b&i^w0+PY! zZ6q$^loUHC)?PssZcfE!hb<;LPH_>@Gj&N4cS2^QH%Rt>L6f2q!u_1d?gLP8IMZwA zt_0^Q-*oqVa+~CQ(*>=4<&x!_p1pNviwelHj1Z*|z$&}1R8)-27dTI5iBo9{%Pw)H z+1S%1o-eDS)DeX#3~U15s1mNg zaYtRvL^?%9sJ@ebJ|ar(V*Ao%*ZEy#F%;$in@*D_1$|ZdF~?cvYz+AC=j#7?*|>A( zlwY1fR>av%terBu_~M1(!7X!jbKa%J5AbYaJymi9NbQB+X~@;@nCzW1-QQoyE9c+p z7(t|Kl<#kk9Xc!9hh{or-=P2A1(IrV7<`|>YSBCIrCz#pmqoDKeeZ`2482Vm@X=G= zFn`g-F&~P7OT&;cL*f*1dWlD4^^fmMwwg`Wvk&7^Qu(p9TUTWpbJ;CHh(MmXS3ob3 z{BV=MdDI#;W{ceM3+@T#`0^z*x<)gNk@EzMW3*gnw>JbKJzwuS3J8=_3GiC%YC~1+jH)ciM#%)@=(=K@A*s;0P#_UjiL?*-d<=>a@4jy@Y!jXxrBJ^uB3#20{ zS^0hb-!*)=ru|8E_sR|IKl7Q`fC$z>w9pF#5CR0HJ`ns+LZ?(_tvf~S&ZI8+9SB^qWabO)9up7m z8CN&lSynmGnL4sjRigVkztzoN`N~gQ9kqqL;B69EQCY^=t~Kn zzhz&V#)sbE%-Y&ZL?~FAEWZ;o^vDA(?886^Ppz&fDcSqiN@JIk#V>*j7%XMc3%3ug zcN4C_T=-Dw{=cZdg6^cf1!vG zV=JGIw)~DV9m{KbTA%G(w+S2<^Y&6b9CzrJYt#}N6ppq83DXJ2?c2KL^1{!rer?%p zyc#XbwlVs5PB_@vsUqul0pfJ}R|l-Zsd^J0tp6ZopV=AoX>(K*$?CHk?7fkIk*ag& z&R`Yo2XzT*&loLsM)?-(!zhDg?vEBXyOb_zwd4k>7}-%jC9+sbedf^6K9z&awzjDD zT(4!N*88vZuC!$GLZs>JMODASKGn#)InrAbt#dEz!PEFqJC$~$=h^q5+rGp8C?0Gl5`#dr(lv5qX8L9xno^V%8g_E+y}Ng}sShA0B(>49yE)eat1K*NPhjun z%xOok3?20F4L999Z>~G*p934kzx8d#oA2$1cvA+&YwRo>!~Gd3>k|(4Y3z% zak@>*xfN6O=-}LW$6UI~yyR4yXc*i)eZ;dzvIB_IhH8PaXZD5RPs}AE2=0sEku4f# zciM4}Y5@1*lb*6~s3b^msm+YD4t%wds{|ZG7VElQdF&9S_u}7EWg1r3mw~G+(b;S~ zYGbcS@ZzG9!U-a+29AJH-cv&+?zvHZPb%=P`U-r@9Y||Bihrj(Z50G0;nYDgTR9I> z2cxI&8VHf;6;s1Bmf?3w%M^Yv3n3{lJ*tm&r6^Y_9&;%ZO&39H?36#WrLjyz^dWe4 zM}=BvcBu?MxndRvq+;oe83(f!uh^LaEbP}IWJ2p>nuA{RlMW^qOg1nj)sm*y$K?02 zSN@R>J+h2;09$Bz`|G}6-}jhArD-+1LmsKgYagJQP4NZM{th-Vwxd-59~%|)J7DJU z353qXE)w`cvJ-a>Sz$rI{ue@^Iso-?UqnO^LMHTX)g#Yn@*g z52V2Nk9+a+i_`WFq|8iWLqrq7CQu}C@P4vJV7riDB$~ySsWS_v4VrC1G@;18_X+vRk zD>^l8b+yCuI{uCTNO(nyW}JSupiT=}zWBDeE2RHp);pP`r^=aasK`r6D#@^|jgI2^ zGi)n#bV8VXWHm)>)7I+ME3P`I7RNC8a8o8yk@elpLdN>7zB51_HGd0ik6yjqta92} z%zY@QRzy~BAEze`ZL;tp^Pz?7%=;aP`^}nj2(Z;CHvE@bMr59Tgt1oaP8!=aO1EWw z16;kHZ3*s!jXC1voXvl-GNIFyPogG+toL%VRtjLG4~1&x}|=2zHul>#@R))?q#IDAxkIPZ_onddpi%kx!Wr6 z+aI_?_tB3@ldg5OaZt-BSAyP>{cjnu8MV*YheS zu6elK8l(l&p@^2M$mBDhaLra z1UPkIEfa(wnPgWATF%JrINqWgb_QOhl++Ri$r2_kltWIzEkU^I<^*FQ>;uQJhKa_T zvRSdzHlZ!y*eRytinjG?%Ri;mWK_DZw2tf z;3)n8xin%Bpk@z`eb}a|U&kqgF7qn&x9?Fp{lL<|?z!v#e4qzMkOdYcR#x$-po!Km zRk5kDJ3Ww{Tr#KQbCp#wAn2>^2EA_bN~;$jL4@628`=cvb%O>I-vbFJLbZj7$mybi zHbt{IXVS*yL$~)0ogfXYFt>Or<@^-x9(qDCQTJ;eO^**Q&1I}naWM4&glsl>L#xp9 z51+o^hsk!EWe=UF_=4R?G&?G{{1`uE4m<_M8FlsK=b;HQJE5}J+I5_2lW;rNqF5rp z@m%~x648x!32JGkUZa1eFLn7ph=>8!5_9o97R<%TI{9wJ}WrsVv!p;Wc z3@LO5Pr|ia>F}Jlu!D^Mr_Zf?uEl`GxAXE(D+7Cku8!3Kkay=_G|5V^z3Ww z_HHu$haivX@IXZeohpDWh^aa8`SeiWSjbi%^d%nedTJ^Y-q1uQibw9r+5WUrHuij# z+YbL=bYU@cBWjtBg3|inFO<(@nYTaZI&W3)LD_Mx((6%jxJ6LJXjw(N-g&p9ulKEd z0db7zxF*LWwptU~{?pYMkx9Or&N-1DBh1@(0!zN!~7M4vBAOl_0l%I9IMAF9M(lstcNk5o5>R*qB>2uOzY68=!kd zy+G6(g^MNFtr&GB*G1_93>V#^za&ue?;z~#QE6h>dPU^!g#bh-EPp{fM>=Cqsqn`r(z1P3h-L{;I%HBx*g=WN8%7_kA z*?qvGs{TE+TNdA;O=|`8ly~S}VA<&Zdu)mKtQ}nGCbbkvL0?;UPNlFc$L_1{p3{#o zZCCS2G2SB}M+f?TjYp=cBIsxf5GETM2wQtBjMJVm<+BV^y}HXcRiQ2o_1k*xF9oqN?>{&hYw#@wnQRic?G>-bmm+h!!N9r z^c_*-p*=rmigE;r%7TWC%X1R9@s-P5FR3{aP3_WkXTzS8!Upn?{*W2j^~BU@?o#hP}atatd69NUx*!E{r&SKIrL4!Raw`B0%U#J zHwiT!9R_SU(0;8S{CZV?MRAQ^jjP%>p&3`zg8aYB?mArkcP0zKx zPa11?WL1QU$eav~z=$TM`1kl^RXNBs;;Z}8xBSu#tBecVS_SSmHk6VHIV=J{dJ7vg z>hx5d6`OJQiUt(W@#-Uuu7;Y4tt3>#qw(%OlLP=Et9aXcRqN09d2N!L4i#Z8O^C@? z?yr`?-#@oz=U_ohUda^KnN(G9qpYyTx}yA}XOl2l*rOeq7aJT|71sL&1uf;8L6@`~ zg7jPWlKu^!I+9lTa=c9Q-fBD&fZgsJN^JT0|)-z z4z#rWuIIQpytpH1Ab&3#0Fwrgvu9lFgcxPb^&OvbnUY7XEDv=Ct|GA5yqn_C_#<-W zexf0$!q)r0V0@5#OT}J9A4n^_LLVVNlJp0pnnEg~ejj&!X#j$s_4Bt=)#Bf&{p5?( z(-p@c=bKUvWeYJFiilxp7u~nb(a{+c50>Wt_)KrqPd(jl=3TvN!<-{?28&QIfHy0b z{f8R$eD|_@K~p4RXikZT=a+xpW&DtRVgLw-eoZw}8+<2RHL&T$7wh;|Q3`Awvu8v? z&sFp0Cbq(_*SXKLpxTHH8=3<0YiMh0L(F91b9O-;#%v+6Bf><%M`#}ypVwzar9;kG zv?s34d(f1|PJ98hBI5!Msl?AyJGQ+7RPf=?baidp(Z3Ou_H=G~3b!ZiWOC)C5mU_1 ze?;LYzmifDrDYb#<*8Qh@?O+*U0q1(G&61@Zo=5=61b=oz@Xi#uK`TAs%>nrY;~u# zp`j1l22n;gv>T+y4%6u-e-7X9OULx7I>)4T@fua%`K|D4%=>nTWpaVpqy}MJXeRm( z*qq5tZ+G@k*q?b-JSu%7(hH3i&$P+Y)H>s_9zLBq>k%~xa zsdzqdQ^I3SGrP1!+`_){J-jlsU^u;yz3zBsv?e&BYSk`UtAh8Z_+|56hS9z}C%?@@ zy&Gj^Edv%5d?;UW(fP}-lm&k)M~!)LaHs{HrF|GjW|rBbsW*i_jnhYLexziQB%nWi z`ZVeBS+_eR{|n(FNvHN<^th67%2`oYcri|>gzy$#V(k%;g0Tm7W0 z!0~=s*44vl)TqmJ1hV!T2|yEJ6Ffa1pPrZEj3PBss2dLtBO-m4AQ(3tgR+Z@`(i&(@es0|vgM1Zvh^`lIebMa|yjI%Oct!q;)cH{v~%b-OW4No+PTX>|xf2yJs-GYJCVo2)f?&PV0-=r6B@O172gFNJ&}}vbq_O&Y@gN zweyBw>3n!&Cj`CxtKNfrbBYTfNk-cDu$!^& zf5XSG4FO_kCv5Nm8;8C`tu&phN61g5CN`F>k6SAgmqt3~2dutaJzw)(07MYU3YYU^ zx^Ell+Gs8bSElC(~KI(saudf$<;Y%Th>Oza2r{_M@R z_^7o8XW{&e@0|;h5eF@}=vXfj`lqS4B8R3+HaQp!3K~5=MixNO?WmCt**)%B{$^n` z`>lu3tdHRfjvrO2T?P#iqJ$wTcsnV>Ip&-(goO(yfp@!FQjb#{Ri*DxIZo-*n?!+{@^zNK+~GgOh&|8&?ipIBBYgYCw|*Yl;Pdn{q(nwc-5qv^TrSZRyw*NU z-b!MWE6#k|nZm084_I{lu2vtMA&V8>5p-lf5}V%Tn^+qWp`p|iJ?c8MJ7chzaI{4B z#vs23wG15C*5TbQ-uRpg@t2a(+ILNO)sXKr*W3en!6qIGoO>O?Zm;HhHQaqM4g#Oo zCpM`YExNYj>3Yr1K3z@S?=VD?>4GND(8yKKmm96(VZ42hq65o!bizMsLt}ytFYmYD zKQ*k?x{Nsd=HJrN#1g}+2~Yx|O1kdtZ~7y@Ff?N56s6PI$Khu-UXQoZ#ey7x(}NMj zByL5@Imc@}G=b9R-x{6ZP;q`^o4$>BF$|5whDuV?-^({z)HIG$yVF-SZ!?Y}Vz*l} z9LjDIq}96hi$ie*b;9K&$wxsq$U!~1=-=@?#5GFeHh-3d^*YFiZbZ}xZpH2K8zUoQ zm9AlXc7Ztv-v<07F!__^fj5?4sSZpv%hJr9SA4MKHQiWR2X?aZ*e>g`o6Ubj`8!yiFw_1*5RtsV z42o+mT)42mqFX(9(4oHZsgCJ2j)YiQS3LQ_?*Le`Srr`y4;>`<_b2C)a(@2E*<(Dc_P#Y}vSu$__-_@3dItNs^~8$- z)}o=vvnYss5M+0x5v37^*{|&xogF$wr|0iBo^Cwcd`zKge8w#tk=kpHVI3r#0V zB?7UsqemWOo||66bB7Y=6ETm3<31OZcH7J}4k%Zj$yXuVBxlD6aBfUmoF_-dWXO;y z&%ct&B5N&9rJpv)*bI37@j|^I#jy+MvJHFrBwAGd|Ga;Zw!2MnH_8GSFB%f+pTq0I zhq9WH+4ZXb!X`PkF%;sxu(|a^P)kbMj411@3&K)}YNFpPqkxJRndMVd$wH>0ZT&S-nuGc(TzeGEkjNBRmzb&SGP+t%_)Gm!4oSxqyWUSWnS6}mc zs|W|r1b*^KJmtyINN`o`Uh0u|9D0-*zUS^=*!28D%ZEA{^O?{tvn6{2L$5baDfXEU zP3`LH7p-}(Gl}w$5I|J1m1I%KnqBT@HAR5e#~+90P{*wHz(j;*kcgAn6*5>Bql;HVoF|Mann2Q~L_Aau=F%*i0=(xi8KS&*RjX8ic zi1~BoIcGIEo@hd)*cj^C$<0(FdrJ-8VMdom6x~~gU$>#@hRFy~ih*IfyGoU@>ns!= z$`L_F{BpiLV#B*=kr`5?r7_ivC)NSnbRU9UxJNOZ&_n0G9fbCbC$EBnBQM4w-TdWw zR+7@rh~bZ<(oL^dgzwQ+2m@Yxa@*M(oDSi45Ld6dwTh;OicdFLeAuseKvqp1KpLLE z$i~%5O(NjJIL2sO&ipevn~r51Y!{=vBojhI+p*$_MSCs>N4H^djmJ@ca1rytyVT2% zlIL=5qDALbE0r|wHu>p(Ds*KLX^f)0|Of$ET5qrNHR0xjTDD@4a|Gh zWS7qYq@E&@N^V&JONnO8ENuLJ4)T|J>Bs8ZX@OR?5Q%RyGWs` zUhJ6m;mBVG+8Q?v#)d83@onijf|gzNYZl@GUp-ly8)%21@uCTc=&<@`_Yryd`LYpH zsR?Iv_o{!_KL#}?fsBm}w|?J?x&+O$Oe5ObjoxoOmTl28rPuvGe;u_i`_$w77cXC~ z_-u8no?!a--^NB+JgJMnP7p=JmY?dNJlSh>i%nh-!(nC%Sj^D@&^$Ilg%@b%ENdAx zzt@K+Y#3oXyuuRDpgk>*p;6236E&eQGZ7P++}BEM#hoQ>A8Ht}qmkt=V+IWS?C)-Cz1^w`ps(F=8Z-N)t??}`RL!2j?4jYE3A z9wj+4oW_g4-oB{CD~1lS3EU0eLxZc!DqU9PrY(1g>4Z()9BU%#%F#MUI5;4i9~0jH z7|X~hpVWV2TL1zam`)<}bef)VeNF=ai;n_e6vVH4&c|k;h2{JAzRAV3A=(bbS=M`OyjlFn2 z>V=y;;d^d2>}WP-C}gkwpSAwx5tr928eGIrmhv|-$xR{laTst#t5zoIb;V$kjgX#M zq4YMrw;YwyjJ|t=&B0kHu>dvOe6O~exz}oSttGF?v-0vT1yx5aK<}>CJ|!s8sYA;- zyJ#@D>=U^gOrOUsU+Jj*ab9I#L22nufAP8HqSoTD!ExMF10>ibrcYW0G%+~@9eSp=(Jc}PvcE1?)Xfg-N06YLo{|Fd~s6C8a$9n5Y16^WoTqvocBZLWGO8 znZmECLL8@?2FB zw40$y`68nqzQg=@hLmZMXwm#8KM!28of1@9S}F=4su1~czg-wU9iE6cvoOsy#nT5Z|eGCz3WfERzKUg3kbnRjR(zT6hc zks)XO{TQZAH|>A3T)Rt`)aczbGQ$!4sf<{%l%d%hgVN|!5(D!K*S9t+Gc|06a$F>$ z?TFF51Z$4my&1Pga(I*8q5cjGI3SDphg@Il*~JY3-;}0{_W>WY)kj?nIE^FD(q zyEO7o&p)Z%$u}(st0&XrNv_jH|NgBmib*4UK9MLlP>|qRUbKRdHy-1){-Am^SA60JXM_gL#Y^FchFF_s%$9J)bxx zwFL%*`UXifZ`e)-&2MiLv2$p!wRpBAYWK}ey>Gw6kP7a)lklrk#?N+5xTj-f_%VdPBgEqPN?-9`?OINv+1-!C@~4n;M3`P0P63lJFIxVMg;N z#U8is_b<`+Yy&-N3kWDlo0KutSt}}Z%=iftWJimWMI$3SF0DAj@$GC=y=TspT=(s* zMmQG_D-3oVY$10YJdc#|TPQ@(az!TSem#o&YQ4Y22rqj$W6BJ^7j(_s$Lq_UtYXcf zkUIb}Ym_3%$y-JJH=TW%SNFr&qUxA49S*Xoht=>DNwEbssJ;C|Wc?R6f+%jSy~0$v z0MQ}-;8ALa0`(_YPP;;dxOslrLi}EalkP`ltOeCc+P@dA7I)q_7P;ssI4mjc&RH20 zp0<5-+8WN3dM^nxz(dCa>Hz*B$V0fVxsy9vbtc40g-32B{t+iF)E@+ z%5>jbk;{*4=f5N}$Ip58N=wHk|Fi%50?k~i(#scCce)Gt*nRq~uuLAfv6_uzXn-=w++ z0t{)bzd`e6&Cr6QB?K@=VYTUAjgPmtF|PI|EAA-1UZ$|R>Ux~7`A!2pdO>e&PtDGW zTyOEKJ8NkCK@xX%d2_E-_LmjNk;z<-lDkeBL+G9qYx zLF43hw3%Ui*C+9ld`(`gTiD{5j4%js<>l#(!j%4S zVv8PIqud;7;{`pC-V?rA=s8%IOs+_GSHcqSXA#w59|n(6!oR()ZmV zfmH3ofHFqJ&@*n!X$yyV^z&05fs@(o~zkDCv(v!h!S5v}+~L0PWQUa72X zkpha=p3Y4SfTB(0N+Ic{16`2=u?;r%I0d~{4;gvU@a;6B_Pk$Qry(zS^X6Fc>v`Wk z{fn_U1d4$aIPtWQf|CWc=W{wCUN1ZQ#z35@4f~qb^3nu< z{t{prA5U*2K7wAvXB;ah4#_Raq;KB|MiUhq+X89J8dg4wihs)=Dwmzz&C)i^77n^t5hVzb541`z-qlf^e3J_d$KK*@O=<} zvEf&_DO+7NHJp}32V!e4eKpS-!d1g4i~I@0hP0E26jA}-vK0SA&GF9SG~M+vI3gqog^qknQ8jDW+z)$?%WIXyd@!J*qpW0 z7jlSoa6riZRx&Uayu8+i$`>?Uib{5;K0Yq_qtA+&K%IB&@Lw$VkZ|U9ERP65Nc2a#_<%5R)k*?XK@m2S1+SZ~e0t5#R!O5kS-YS3hw(Qd<)~_1K zV{FW zaqs!WHNs|iBH$?oB>ONP!YOsPWJ`mpLvuz2ne=WlP>!1Tj91559xN{_%g(kIA(x@q z{H|%I-BMc^7?{qcPn*Wd$X`Zo?Ru%SSynZr*@rKjc0Ppi&pJ%7?(cVkj~ad*Hdk|3 zsw^W_sn6gMQL3jP9B=?3!c7%(#tGJnZR3qVn^H{jBU|gz^%gr}khJSZ&ZRVjyCw=T( zk*w?$wx(sIr(fdcT@9Dl)h;7^T9SZ9C&dD2LmFqHAtE_Q%v-#E$>8WtGYpfDrKy#@YlV>1xQ67HObuYe9c4q z9+0Ci6I&Gq+Z`BR0$OEgB<&Q+;iSRQmV30zOg%Pt+P!P-V-jR}c_IK?Ssm5My-Kg* ztn8pxpoqZv$`c3o&>?ZZ$qQTMmDYprp8Y`kS9a<|Y8-}>&hTDVR< zgp|Gf{2#TgZ$&;9822Vhl0_8{6!>4%-fz@3PrHrA%f`Ozw2Oo3<4rQgZE83?UBn^w zVN@X1syi9TFPkGImJ7qms*PBK{I(#B4+GI#Iq$D|$zGQm=_nu{8uJPY#0Y`#h#p4o$$D3e))y~cjIBt5u&^bjoNbg6O0J_oz{Z)@FkEpfUmQU*XLhME-A~&a z8+)IdO#i+T+y8?H3_Je5lVWvW_1;!;Ibt8P$mmT**H+-WkqcI_XYh>?a-4{Ys)iDz6U%eKdQpYC-lptvQ?)O`?XVtDr$q8dJ%knxb; z9<;24dZ{;!y~nb;jr6J6v!nRB18zks(fLWL^=MD*8HVmF)#{eb#I-GII|RTdr_7A6 z5D6wd*xrfVdn5J=@|=%!soE2OFK50`s5OzqEH1%;lVM<+OCm9!{NN zGv^c~5;5e?qU%&MV(1=FTF-UnWVh-*5jpDBtD01;;pp4NZ}ii;-F)ok4CUNkNX`e< z<}`d6^vp^ zpqO*6F_jv~XP4k8?bG`W(qs2VqXQ1!Lj^Ni>6+a(CcAfEtL*k)ICM{ z3GkTwp`YFKBYc&pQ|#)g>=v8#HU4iIfxgKWRHa1x{U05H_Y&tX^2W(SS+m9#{kC@b zY{R!>C5E_U`1#9j92=s$@m-jWeRO2D*h{|7CaM#X=7u!42vI1qLxkU#pKUYgP-b z35&q6zkgVF@t_CWm-KzGc9)@wjH9L3(lWc;%EuviiBP;3Yp_GcP}~yfqs54hn4+N1 zaMcg($1*g19K5n>Q{#!*l1N}5#=LPV+4f{B!&b+rA}5~{25JYT5qHMst^0_a8y7HI zV(HYj594Evw8&KYOwXwOphcNpz@$2O?$P5N^%5AMWQAJ7gpCpG;7g=nL0E{jXYSHfbUiOpS*p!7ycUxZy`yFKJ zG~IHn+E!9ya73=95A>lCDo(v{Hqv5HWW!-Xj0$}2@dljY)ave!S3nYo;FMU@!Bgs5 zzqgXvrN1J$ZBCqPV2fkQjRg%VPxnOsgH*x)UQ(M;r_3JAKy!I*_5TL-Sn0IB-Hxbc zIi9S5;PRFgM)fG2HHep}hfGHU%!5knV{m(QXT6`bu39YfvE=2#9Y9jZ?2uR6JOu7c zNA_e!??K*b1!wyBp4(9mb%sjUCe3x0Y%O>~#fcU2G zlFgHRj~~t+85rTV2uoZ4^_C;QulR~kTz)h71RJCx1q+&8{W@8COM{;cM1svKyW?hN zRw}#>UC0*zgZi>0{jIVXnp~+#rOc#ZGcM;4D>R0kIrXWrZPWKqi)ah5+!6%jztzKv z0gI-$0U{-|uib|J{`-8?*C3Vz4K+f)Xuz4dtusNWEqmF#HtIL$-Gi64;4|y@Uq!HJ zPSA$P$SnG;1tdv0{pR!Rw)r3bFMlta0qM*dy?09bdyj2H9R-=@yAQqTsrggdjQ4+-(L!ArtsS8i*Gx zEiL!(3}P!hy>E@=tQ94yzuOhx)2rUG_37=CUDx-X>M>VyMBigfbE8%chK)XbIQSjy zt)`J3R;LA&ki2x%a-mAYLAJ*Yc*-{X7g1F2Q_-{A;C$ zbMcV6F=afoX(=S=(&5zRZ4Lgk9%8gbrE!!h%eZ!EEnAx2-TbdkXGe{MBo zGTPzUQ|St#C7KCzq>}p}5=+lBCo5iG`q3KrSJQ}W=xEXT1u#xK?TlD5(y~icDOUcTO(2xya@x>t>^4RYsS}@rv-;B;zvQ@`EgdyIq^wCD& z2z7R4Q^#S@hD7U;^(i2Jy5p486uuR4`Kv=;|3Uq~OrM9!p#?71MTcW9hPGPT%&%KW|oaAp?LrKi4$?jb{0ZwF1#= zcj=%Q6pr|rt}f~2p=V87bsKNb2xIx?oXhD+F@>9};t+j>G98afxh*UVFc*HrOSCyE z%-+C(IU`T`AJw?FInnHPB!!qM_!$eS$n@XO9< zV-qqq=KRSTd(EI0|1Cb)(}~o{cJ(IkOfEwp9cUnAv51>@ls#mXrpy?qx=_evDHWaS zb07NDUNuqq`j@D$2w3=du1);Arj<3@k;2TqXSq20c@8$$yo>7t9}d~o4w(9<=oRXU zHc@TQkWP zTv&0stbMY-AJ={Loxms4=_=ug&8O%C6#JNjNK_en7Jo{&o>S)n%-pw8uxb2|bk5wf5? zQ29X)XkRj`NgWqIk{~|^Zxy$pvlF?EI5WY;WaB){w@Tf=myzb6`OgF)?Ef}{S6Rq` zjx$OYsHI*np)=h`SzN1hbMN{2>y&?j5!1$Qbs&YJ`UV{=v@ouan!1_3zR!^3fS}oc z!86I$cVET@rahS77FX%x;F_-)w0-R}$AqaDmV4o`Nk8j4XE$$IAH`kjZBc;Lr^~^e z20mf~P3vQ#u`Z9Q581z*Hxmd!ygAI$ryS<>rZb=JZi?Tvr_}&_;>;lAVNbe#*NRY& zo5;H%#w8$)%7|jq5#S|cY}7QnhYvWbeP)iY7_t7fQ9+(p>!GiAG%^iy$blm3bBHdm zaLys;c}wmD-#Dz%S!h)lNyP9I1Fie^g{O(P0dI=)e*xzay8`iY>f5b58opZf4#tZ^ zs&6!eK(g#rAT@lW5*nq{#B?J0jUQh&;CE5Eo)kb)!)uwtVTg0-d1yu0^mA#I!_0AL zFGOEPjF7WYsUk}1P5A!(#wz3Y-e8yo)@1=Qega>gbB_(va_D&!ZQJ(fcAq$Gh3xgi z9pUnNnayttwH-I~+Ffw;=IG7 z2SsUfCp(QEKBeW*MgO?eBQlX<3MmA>&_<3}8`P{$8=udQm$=O+!n|o$ZLP zX>n*e&)sy?Ro)Zha{N=V_~0F&zO;ala3(Zoi-l}oP->zixWsu6;D!7TWs7}c@~ZG? ze0KLzYx;^o&2lG9Rv9s*YZtX*bQAe~j!A#suIJS_v+NssY}DW&MqRRtFB6Ti7h(hz+$A&zCePu@RJqG?^6OYDmEsU7Lgk$GbeAuy z`>t&6-*$G+cQclRvgI^{+#TrDsTR=_RCHdNVtJcm4VrN|znTc1L}0z2I^oyzP2En) zIhHffb5eduSx3|{*>nv3S2ibRm&de+Q3Ny6@bTkg?~QJSR@L|21=7R#O>MLn8ZZf; z#ke+OBfQqh=NRwA*$tm3xP4=6)3rMGpe@KIUI4ef@g!p1w)SH+{fk>aip7`+`6=(>FfS*zDq1 zon!b8yy$N9<6O(a9>mZnhH|T+F8HaS0?EHzcr&Yy*t z^#VR|Fhg3%yDYprOfc$;g3I?_H~R7Gr)t#7s8sdIS)p{aXq!LICBHgQZKw$an}Za= z-HZWX|K?xCi=3478tO9uWvbB7oD@i@?gF1WIyxSEao7^Age*`O?uwp)i8yuYZA2Cz z6T)Yxq}Jry&2iMZ&pYz%(SWIrywNA3maiPE?0LAuk9hwV49605HYV;mBMe}|rNTmI zBOBd19#R*jW^i3~myr1Kr5yTo5fK_~b92&)5ptWFnE@?TVwsr#*|Pe|eZ7Ra%&OOB=ZventraVZE5LZ}C<>vp4L1?09QY zWP|03o)qjzWp`+2=0XQpV&NFkSnxjkkY7-JcTs{j3MD}BxVTFXAL@(zCnZH1rky$t zQ;hFOV!MZjH?U{L&IQ+jU8ei^G&B;=?#H*b9~yT&wnAcRSt%eo#QH5Qh{p#%n(`Ql zi)fRSnlLccHv^vt^^??rB_-PRaTv1gE4FF3S6|{eq)JU`dslotUUqI7ggPDiurEhW zTg9L6jPc1nOkGQ_?AsqOZ>TucsAM-Ro`-nqT$~NVmd!&h3Yh!ciPIv$s}VY@hVW4G za%_L0wJBm}n@!d;0|P&_j+((%!<+ROC+(hbgT?6&Hho--k#LAaX#y`{3w1S`u9uNZ ze_ij8l%a_$YNl2){@RswStVt?VQ6wuAXI+5G4#}iq|$5MR74@6qgY*A=_ql@nL~ZT z-p{0VN!Snxd<>fccrd!FLAb@C zfgnvyA*~7M0+sd|jFDf^dHiYYgwc71^10An9gzn=|#5F^wqhfg?+K3|jEPc_2xN)hi928m#m_d7vP%%wj72WsJ*kX+0})T0KZ&W-kz@6JGnKOUs|%*T4n!W z)P0rlxAH~s`=jNAO*MOVa;12Kee$o|`DdeKqL6lxt+=b0dDa|vA<$nBt7$sr5yj&d zU6Gr~d0xG8;d1pAWdhg9l}s>el(?*}yVgLU3xW)UhvQt#R+ID2}z$91N4h(H zJ2!kD-H5tOl#r|F0L<~+4Lu_$k)-EoXPXx zC8xd~erKbn)$YZ^=2kzVpFNv?EGh-YeGt!E-6#}Rk@4@EUZAWJV#<&;tfd!TELao{ zBwoUep`VMEDJNydJf`&OGlU`vcOdz1kdCslEjC^D@CaOsVFGv71~_mX1xV74nK!oX z7C)XJkgK7QNU0>Rg)c(5Cct}01TU{OTtg$@7O{+@>SxCI!QNO4NjUoUEoHQ- zDsyiR`X@K{?L1JqIG4&!j(5d3#$P<)eyVe@{)tXXbp=ydF0!g|xU_W?D_5=*lkngb zUz(z1VZNSmKeC-!11t5xsg0e)lubCXO2}}-=M^zHK%nFK{H=)^X7p!l??}ks^2Fi30;HCS5NxMOp*`3<+SFz#9n$*xmYJJV;$q# zUsJVebw5GTu6WwtHP`+BAH-3smq9Q+0VWa9iPj-y1$@R%f8jEg(pp$sJ3Z}W`iep} zewcDsJ27f-=0JgJqbKKX)&*2kQFuvS1p5(L(X+F|Odp^6NMyL8q>0L<$-AUahPj(F zxYf*is-m#>1~!()l&MLm&X@Z*UrqRNekSK}IaNY;dlN}hALHXip;WPM?55NtZRPsM z@&YX`o<{*(s^Xj|DsrEFZ%D_FkyxRJ&*|N-UrYLKzwamaEc{Wo%Hj%wewRS!#~ljD zibq7KrmimC7D@_xUteP$<~q#&EP~YMBMq1Tg7A?(fVY0_=b;Ro^UVfIoT7I5!g{$~ zNm&Cx%(Vv&m`DM~E`97iNn9=JKes#jA-A~;ft#FW=vY~fN`Jv&{)OaO+98HuwAM7B zYDQV4#ywaZsOze!+`WR@S4E&eB~}R8Z8;pOMckAszpol;>d}V3%l3-RK10tla*IGd zWW?2`MwTC#BFTD?R8ZepCn|Qbo3LP$FGUKGXpr-qF3J8T5{%XWaC2f{P^i?u>rXffn(ux zERZxVxEUF@SSSF3jr?}c^y)&;gt=C1Vx#YtUER+*3{nnQA}rMU<9ANw=j7zPaG$(* z4e|kU*A9-3GjN!Q+)Z9jzwLqF=EILvf9khPqjiEHgpT+BLl_#0UDzlKR@-{T8_q{> zIwa{*fehq@gnG#=Ebwb!G$_?-kkkrV0ryu<^?Hyd;?m{N6htr*fHLYkpJB3r{XenU z*5Mi7zAWHVY9buiIwo&-g^sE{;+xTK_fVn*O+C5MEwxYm@&dLf^f=e<-EJR-lO*l0 zhzwO~w;UNhMW&sCZzV{ZJ{-+6h)Ggxlpl7P+fmfN;{4`z9PZ(vuksAh2EpE5v#!DW zh+5DVtq5gj^u%Q9ckS|)2`0QexQ1-$s3m)h_lQUs{mh|X<`WXuvPrmWgJPzC(a0S; z7ogR>-jmJ*YIy%ErM?to59-wtuIfuOszN7ru2hj_FDf&((ij>U*~Kclft3I+%IdB& zZjqPs9Feghvk0LNq7A*_nyR{T`3^}Y#}X)eH)ESlW5N3xnm2A1&*|Z_c)D+p2>1WE zijYp@@6J1$&tP3BpI?$U$~S9eGKbv3W#&bkLh>AZ=MtDUWW zkWf83VPGqi)n2w6;9inu2Q9F!mmD!A^WG}AHm{8ebZI1t_NH1ma_)()z;lVL+081vDak}>4*PC%CT}~ z={?K-*k-@Ws)Cp0 zgXF~&7ma*DeE5@fo&V?|^fjUevex|Hq{F266=rg8l|P_Bf$1*vqq<4$y77${S24N%bGAAxD>AksuH< z1~>gb&AoY8&TISjeVLXi^GxQ1%9J4)G7p()lp#qWbD0Whtwl0bE;1C7LWDG_3}p#P zB~mg((LhP25~X@SN8D?z=l;FV_HNtz&-1jc-}YPgT1(gWI=|<6ocpow`*9q-^y&;s zAfCZ1(iT|ot>g3G-u1|LE#}C`%?-mJWXUK5YEqg{nL3q9^WV94tTfG<)c-R(rDMn# zEwi38Pu@azcwI|d_v!TO9d~WlSSS>u=EF%KlCrBu3+}#p+jO*UABbVq!K)R*$>1ko z+Fac(f6xrC*(|Y4K{|{^8^*U$f6kmaGWrh@(mLAYL5;u6|G50Y^NAiwreGmlyUiA_ zcB&$xrG3wZa{WuoXf#N7{$0Sr#hV(4MqBC;Va@>I*3<<^iYkTa(|d7sEsQb@4F-o? z=<)~nUrI%)rtDl&Sz3!Kd&D%m&G`<)w+ zcN=#e7qjG`Eq&c5M;q+eeP5C@-^u^E7^S!v-iqimFh71*`)L-hjPKrp2y1w9-1_=uP#0;Ze+<7YJAk6()kIpab0Oww{vG43+%M{y?=uh zCp{py#hr0?>8L3`e*8}%&NAG`G&Fd_hxv+Uw8L5h5-O2mZ*Cl+F6wluCYE@aV@Se3 z+sw8a%ovy5(uA$q+NXb{t$K`bCKg}B*Utq59yG7^4}wbWv0v?S64}|vu`)3F<-mh6 zf1OD_1rPYI0Yc|)ES{ISI!Y&MZ0Iavoy_kr>+jhMg_e;f?u$MuYq0aJ{A25dT4fJ` z_pgEj=s%Age$|z{u0C-fZFTA2cLN5)!pf=^k{7+yTy)f*{;=ok_K$Jw5xlv_B4`Z~}mWUKnv_ z9oxBT%vRO#$0s*GxPjA~_cCifwk9|G0G!8Gg-yO7ZqvaFA91i|_m8U{s+;9K4LMWr zu6S3C=09b!s#YZW-`z1XPb~Bzunz50b5gd=7_l2S8q^!zfWDAj-+!Z309JUK382az zcwg@H%V|R2TqP#zWn^- zjDrDv80{1}W?0~CY|BH|7q}3geYo(X0Ub`ld%3$=vh9@2cxB06q9yAM!u}KW-k;KE z^j;D_&G-1RQoC~K=i3xy{VpZGPv*O>e%MB2r?u67r4W)RkQgo)sz&aYbRX$PMckJN zDU$F9dDy!>`RfQ4RZ4D2SG28mja{%$sr;kL;vl7K7fS5>;IaNZ)$Jl883XWCM)r46 zr{zj}OKdG3#V1eRc(dNsCD5MY5k{Le=yH>0i$$r=ONMJxb7aakp08awJ7U^dL#dIZ0HA5~Oz#@?+o*2O zT9}c=8zWG+o$ZyQ@)O{7kN!yo#Et1~9+i;4)ojijUtqm>TkMA9yj@*Sns8UyE0)!C zrsPxF?ok(3kREAZd$jAg)hXLgq_fAQWpfuX4Lx0#zuSeXPi!P`QshYXX$zP$b3>(3 zqgdcq^pbr>t5Z4-+o)hh0Jm~ZK=`LkU70Kcv9yiJZS({8S>sq$u7D~sv86>dsT8My z4=$2G&-hoXgJq)P>a}adE=*qBy~%#7?#9)eq>c?sHce2LIu00`F*9j^6$NcvE5iTz z4p3g+g!HBMCe$id^R3H!(G=0Z`i=5+KO4Y;Pf^J948s8924ic&qDn(GP6O@YvrZit zJfzx(4Q!Q8UyFgPqVt8^u7va!D=T-bW!mOxpZOqLoMECiI}Y=^C(G8(CO(M&Qfw^Tw+d!OEx?6ckXU zM3eQ4g-sf_q}9?M+xdqFZ~i8HzFP5RBMpyR>)FXQoBYKiLWU`iO7Di_mk%**W%Q31 zLU3|%Q;xFl@L?C?B<|7^TPrJsBx@Gnf5`MH?pOW;X~j|fz3sTW15&Zn&fS{EV1qvq zaPS0wZx>!zHnQ>f@87<`IMxv8gqge+$=T!D+iUIO9+X@_%BqF1BK_l3dh! z=Wgv!*R0`0se9=&Uln`cD40xXf5E;w0iIE=DMk06BH6=k3>|MKd|6kWv-QD$#2frY zG6x|2n!SN-1%@;l-w>d*Xr}!KrE%-sOc%I6TGh(%R#MOV)3Wf*RMk9!z_;_ht2wEa z?Rd~J9+?>gn{EGmUA5j-3H1FE#V^TkAGa2qQ9F*m^mrcH8%I`x3>w9_g<*5DYevMH z8cZ9I?wkFPZ)q-=ZBTz*qvG3@thY?U7SA_+rNDh*UBXYcsHR&qW<|9rz&k2UzJ(+O zQVWL23-EGk-|WM*eSenM46SP?!&JMBCRI}`y)p4^xYS(I&Q%l~xS`4S>)wqMDD7KO zfwcrm;Yi(CXy(!^ol3LIPF;^Si>krb+s=4FHU_4oeb<&p-CvWJ+)WHlb0cld`cGZj zm1K#8kcn0JeCX+z5#ezqkp+|+oyP-M>fdjG>4A!9B=J*s8czR(L>7g-vJ}I@-DZF3 z%uK3RLLsXRjigP~Uzl%aw`rOA$hx7A+8W>@cm9EnQ1BX>Q=M!Q&Q%iDi>U>DN|IgP`Y+YdtUvZ+L1SYLhJAI ze`dW8tJU0L;Oe>bXJ$g*Etvs~LI9uigV@a1-~6H3=-=B(HyroZ&qkwMT(4N(0?>4ssea3-cMa(z zLLNb~7a7%^Oq2Nmqqw2*IESu!GUUbB;cu^FFz1cxw;ZjI4CSW?&)pJyy`%e^bWed- zl?0;N1DN_i!NwgVfNpOPt{YWVu$@@b@S9-=e<)JP9_J4<>5d((lCc#k;>vm(o0K#? ztF6WTrcCZcdFONX?wx3xOI}Ph-5AW=N0#VO7sI~(6RNZd22xED!~6wGy0ZAs`ccdD zV~Io5U%%eN@t^PERkkLJA{wtt9ge^)K;lD~oT@Uqel!K9)bjZIl}!(|-q(zKJ7IS7 z+P#QnF-H?Hb78fviDrk-#&iY-=*N$PN5;*lNS!2YOu3RfK&d%!AxQVd&)jPsj zAJFRXH9xgJU@Nj^J*)()CR38&QU@?Do+;M0+QbdX6qeM>x#wYNsU#gD7v$QhMjHiI zJa}1-R?EhJT!ULsgdW=8tnuP!Wz30|E_)TdTD99%Bma*&r$F!0Gq=FG)uftdKPcT8 zMu4-#s?W_FsJcjBy_Hm<#ZWOCY*FLe;-kiWRvl(=Q=7@RW}9SdtMBHW1y)rk|KsoW zJ+J=JZ9%6_oko6bH#9}(nlz*5lrC@f92C-*nqJA^AmopC5 z_{kp}{qPg?sZKlYZm{|d9mu7XW|!T(D;TelvqDPXzdLoI<(^LLIPX6u(PW}(_Vs1n zRDXq&^q!=C7Gg#@w<93FYU>XI)JJ4iP6hi1c{(6_2}Z zTQHp$89yKkk^pAxq&W31S=4aa8f;SMeg#a^k<5kcb45knnsb?;f;lH?$C>au z5HU=cf!lTt`Muryzm--8@Z6I^La^JpV!a4)Jb7XB4a1ih5?Ikl2RJh-gx-74g;1n zX8Hoe{mt=*XhPx(7mPi7aa#1&%HviIdO`rY)Is^61^gi+^X#8EadY5LdSZ1{&xJd3 zxmYJ)8W*r6mU{m7>C5gdThUhPH&P=BP?S4IE#0(^nIJuKP3wOZ<{Kgpa8KfCVQqb< z<&V_%28wTJ<)g+*_S!7Of9Mh0c+>v$+mCCwkKEyRnQP2n5b4`6r-T&rCwRA}y||y6 z$Ix~eZ;1BdBX zLR)L#VL`;DtZC)PJmx6A*d;L?rz^!GPWko)rbT)Y$|T*;kG0wte;4O6 zhMyR z_jDZD3Hp~tB-CRkx}AR0j&?vgF7JaVBcvvQQV_`u5tzlm!kTd%3#I<#*|o(uAbv`B zpk?XD<{}Nh#6_!?26AmRc3XyHSpz4-xexL2(n?LR{NcV9qmS#2nIH4>LhL#5*US7| zb5wI$LN+9|Jor}^c1^&8(F^d1KyB?6j38?>R9 zM$0s@2w(nr!>Fopp*+ArRc}W2&?n(Hx|4E_yldrbt6)m%6d)=8b41ef^8s3OBN~sr zrTruO*8h(FU*Y2w^L_qc+8&C35*cXcl5vF}&YLcgH~wGA@xh!dFwye+k08LrJ^{Te zh5;$G{Gr~xTA-@%5HjzShq6gnDs_RR2NW*K?BuyphEz2^KG#JwOX{CK`QnKX49>n? zuSG)1Iq1}7Ftvq4hM3tVv8zSl<(A037tSi8q;?LkUzX3*DZ~C%eS`2G3R#x0EW1lv z+HT)OsmCf#YZaF|G}#JytkwVK9W9?e^`sIJJI#~JE4;Edjw+cK@spFP_3QS#uhX3m zC*lJ=V>#1w@^uAo_+mn+>WF7k(^JNkJ^u)+p>-V2s)FVA{1+P`OsnH1R(}A$NA3p`oa4!3rQ4X93g)7`t=8E zR?D7762+%-pyt)gZoQQDzewXohU9bi8n~MWof6rj56Tz*p2j?z{Ye zYRzBt`JHs!p8aMrkV?ke3&5eF9Yp$Fy+?-sjb=7`zNsVC6oh%&!jcJ4@;wIAf&>9W zjUbuSazS-PRcXiqPJ)!;&!G1OLzNWY2B_Eh;vp`;epg|egr<>pCjbKJIZxK_J;J;E z_}n|qBr$V&a|>OBj;ybv2>89B_=MT#Ii)=^Q10e^Od1Ql#IYnkQrb6A#8~ZzL4{c+ z>7;x!YDsrum6r-(;K>piR6x+1u5VJD(c9%0b5*b2&#d}rpzD<}+?F%x?dQ^Kd3`CQ zqq)@BZJozBPgE_Z&2S7N>WCj7YoNWyy2k%y(W*dnOLgPfK?}Fv`JU_v2ak)e-<>haOJv?rgb>8viSZ5bwd_L0X`o~JwY0`zLN_Llt8B&}>iSELj z-{`CJk+y0I@8so4v|K|tbpq^g<;mT})#F8Tf?S=aLGZ`;+o&_8l6U>wqmQv5s79B6 zZAW7g3uc(1{+~q22=?kq?~8^L_5;Leg~@C}G}MJ0Uq!_yukD}Y!sZ;ogCB623U<)^ zRGh7ST+W+-R$M#_E?#W$d@-4l)}wp8=%;HUi^lYWIj4KqojcAu`__E8{fRX`w>wVN zYRIfZ`B=KZ!Cu#FqQ5P{*6AnfZZP9lYOvWT^nCV?+}H5R z+o6WzbblvqF5uSTrp0);uVBNCWtO<(rKcwe2`N}N+I>Id230#^irKIvjyY3LtGlIV zypdhPFdXPPrLu-X2GS6JPU4hx*|~GyQQW>X2J*P0V=(QDR{^bz8doJm68B_MpW+6# zypK$^Kwh7B;@rMIFitTbTiypZGM2?Bx_|X)Qn4BO6BSFQrpU+_F?$@Sqs-cP zb6jA7!1W- zGxEy!98Qq5^<-(~GA9&In&iM3k6#p2bG4re)L`=lL1S9ec$hkiHtgOBJmpMc2Iwc~ z-L3ng;L;*yud=h#wPUAnd;L{aGl3U?;iNL=`+Z}(HETxU`WK@RNcC>-u%IhTCJmo> zW-;nw?5r{sf>Wel=9}!sMR$9_G>Wk2QinIPUrVFjpuH|G(iVXyZrrNX zA@ixAdGno^m8ve`7kh?HD1RRPGX>_>F|yqZyS&`z4W5;!FB{#p99A(4FLX^n>7a{s z>MYEyT=wC9*12i*csK< zj9z{J-pYfY*WGwJAklub-Q9Z$ogXFiTQhp(D67Gk1EWJ~jCy1^BkA4ujYW0tcQ9QR zv^ylt{zK9g>VUme6ql%^J1lKiyQ1@sM}XG;u{Y{bbfjx(_!0LT$DRgR?jSqXvg@E; zUK|Wsidh)6sP%5IQyslvui829TT{3YCyDiv+Zh>^jyje5EVgEBz?$Yur^zE1W9egN zSAgfi3im@Ba^&%d0QRo2--jgB^Xad+k>zxwo+2|UB=CrX-PsrLVqRs7T9}l7d*5K^_M7jJcj~#bvEH}L8aza6Y8(4=JS@Svx6z2M+V`Gtb*VSA zpQX$siz9yIa{J*9DRixO|MYryAJ56De$lxEwngXXMry}A`s6yuaf8GUXGZGDr_po3 zlJJmwnJl~={RqCu2-b(oUaFd5=<=OObw^i7$T`m| zFNN$dhEo0#?&ATR&p02vFmQ=wYu62rmYR?6i4HU#a$yfm4w9Mrb2M^zzkUzF=>aUQ z_T2s9uU+z-M-kc8|=Qlu(_7$14P!}U%h#=__xQZ7j8CkiZuX9?&VB~M5`U( zDN}#??wPa0NF}E4%dM4LvQ8bEfA6DCo$zo1v(^~R*}aC=Rg82%MbyzNecLt0Caf)a zShvv6jT$$-TL8?v!_6*@^4eg$)xjFz(Rt4>z@_XS%ngz6?O~r2YljhXt+=)x_T955 ztK@xnhIMPZ35O~imh*IC?-pls?&mbNds3ddUh_J~swq0Gt7)uIl&Faou}o2`s8 zM*dj7`Ey+N@z{A1>W$!*>`OksjQWSx&TD-A0UUr4SG!XTjjnHTPvpccu;qr z4qDX4)wCf#>%H9&tYzaVZQsiQuzqsC{PTr*EToXE-)GFiXeMu3c;vN@I!xMjU!id8 zvfHAyvqBLufex>w%vZ4rGt0@&_CqSv?AC1@ha*?7vMi%A#dks@dwctycvslW9%@gQ z51OsjlE3WCWBO`uZq$=Gjx=!HwR7jgcB`)W;C=dDT57}h3^vuu-S~X@AYENuecL^2 zretJ*yzcc?DSu~PsU41Ga6D#*M}UQOB)c9xW{&Q2y-4}e{!ROFW5+IG?Veq;53xz{ zf7LjvxYO-hw|eyKsZX`r=g*&KQ52(Q^`m{Y8!`7QN*-UtY*flQ45g`$(FHRj>X2qY zB2kE<-p08zX6?P)9*lMxSm}yYeGH%_1?N-qHf_%0_sCt~)?q5Aa27ztL#aH;SImoi zb38szy_zEUX=2IZ4Gz)ksR-XfZIEYQi$CUX{xt4;Sy_Bi(uYxlii4W9YZoBOJgD~b z$B*uW)^0tA4V#M-Jn7Zr;Tth`r$C>k(zfGE7|t{z)MV6S4T@3eHj_$Y)h!j@-#C1@ zUJ`2m#X9xEoRWiYLNi*KM?22rt14A3@8>$gptw<4auH?l9N;VeNszBvZoA;7d@{=6 zG9uhL^XRCk(tOQhx_0AX9=O7fMSXi+e;pGg&v?~UIPH|kew?gg691P>J`O?Onev(_V&!Syc4Aacyx2X8No>-*BR@27ngV{Y>-%OLAC zX8E~bjWNVJrc!q2)~zMv4`w0Odr8sM^zZHO?#2r`Nu{w3|J?dU^^*IJ<+?Z5)ye$C z@9f0>S;4)FVrV>LhI?c$4UI|A!jE_*MMO+Z+G0!}_Gz2iQZAKxb?ZK4b&KP=d%wM2 z@-qHx?_r;#DoU5}MVqqDQd@iyZ>E~rts zr>k>K434MK^X}3|4De=?9uXATYs->T_@Q83572wS7qZNTpnGoK)yvf9_uhS5+4%s5 z?UR5%_A-9C?VED0x;q}_pDhY`dZ7FC4*G5->ox|e4wq;iS{2P($-5?}2xAu#hF= zyviQvk8qTpS2`&F8U;Bla@8+BAa&>(yr-w}6qmTJ~h&2<4{v!jb zE8wTbeE#%lCOQ#^I`!1#R($m0ja}B`9InTdKB@6Wf(#&2lh`BOjRPPvTXZjGNnB#$)Ql}9FEWnr@APHFq13Gz*P^5PyS5%uLov*u&UuBR zP`|X@EjYX-cxTNnE%^P}XB-bDhr7%FO0S!h(;${JPlBi_eQ^hp1Bb9 zBl=!-C_Wc?U~ku{9rTk4jYZFv1)k-~ z_SD;Q1?S;Wi!%20I%e0~Z`$M)_~M#jB595htxAn9&Ydm0519O0{L4jycZ>vHCT1t+MC zFAtC1%uf7FJvJE6mz`iWM)Old{knB$PMqk1x8Ik{<;d@g^sj9EbdrX;0l4s6^qy-< z^@2lraoVam0}qur-<{^#K{nZtjT>qzA{KvbRM{;LTT&X8*u{Lap4!^85Lkv_5M_;y zShVm;JXpT>erIQk!~0utBa(@`(|SVYY36a=N9C;fnzJ1H|0xcSu`)Y9U&AXg_mV{m zeQAp>@eba~x=sL%AmzJoSZ5Jz2jC*4LVTi9cYMp;)f#ttU&|8`^Ppk){BrJLN~ z>h(&1jS)ME8mqJ13IXiatwS4{Z~nAh?|XK$uocgZX>;mvU`>H)-swk<7v5tQQLbfB z*{Rc~HFR}n^Puvk29glZi&#Ahg~=GeS^RXdx784&tleryIu4;5pp`)*fJ(hhOS(b2 z)~i=fdyJ-WfYBhYUXL=&CpehbP~7KaAES#-xxqufx70h=3KJq_6*KcxQ#;M*G+Enkh0b7h2~;%-L?t4d+YM z1)SZ7Q3Jp1v*od==RG&__v$Q{A*EK@%b(MWK!hV19*$2k_cBJ5cZoU~hSZo+s_WHk z>eeMsMyM7%zTicmJp=cczgOp_JMx9Vm3_m7U6x8yd>3t0!lB}@GA&t9xnd(C1a(V$g+j6M*6cl3?bmitWcH;pFQL%_X=9Ry z;9-Jcf@<=<+;QshmdgjtIoNGF3f)Cw_BkHAQD|Z#-=eG~CA6>cM~txDupy1_;4xyW zFg%BqY{+hy2XYyXg2V;#$4gz{zZb`hrzzVZl} zwQpcl=_C+g8+G61gS;@(t(vy0x?=Uv{}s4e)$y*baN2smuL|gV>Jja6&|i-=1FxMV z71J+$_wrek+gi(uCviKOMcSUimScA*XQSaM?-2U!rck>OhHYthSwRR7bdbbH6PFFg z^`1@GBqeXLdDEsRfl=&5eR(STC>}qa%e|@HAXaR{EWC z!}X&p>k&JSMtikl>-@p3ashScEk!1QVxbU1AXacFw&{I4cI=>I_W3i%9W<_by2Xwy zeYcd)t5?7Nek5*E(o@J&n(FG~iR#HdAyXt$jeY=a?`K#xUX+1gB^8Rrzul=>Cog6o;atqXn-ot=n>fN&v%7 zOh6O{nCKGISn}+=ytAyqy@f$<|7@f;gUDluDT$;8(@6_0(;4Jw=M2ucsx>i=-0l^7 z+i`ENxe+sf4asDh13))7H3$=@VHZ*-Q{MgaAh z_}5#IlN_4wpbw8VgD?_G(YRm*4`W7rXOmi5a1dptw#jI-kbsL64di|c0WD?O!3mOY=!=2 z|2jO7LbqFa`4i|0L7e&2Uj$E|9$l0?Spb3TBJ}O0JUzkeP{ofg(`mM3#;ccr6-EvR zR$K<5nEyOH&@aJ5=l7u^YYHBNl1jk}Y)F-_B#EF8Qsn(!US9Mx$jQp`#i8s^O48mUdu%A5}X?Z+e@8+zyx=SZO#Rx`Oz^lPSNW5s}$n@+)l5m~Rq3<2U$ zGA4899-Y7G{lFDZE{y_=90HQF%Zs_6&w}T%j2v%c9Q1VgHlx)Ol9M!BJNt0Yy@O(s zOO?z=vTp(VICW;w{JSW=PqaVZM7}u%-~rh0@7jcE@M@I<@NV`$Z+rE-3eT0^wgvpp zY*}zjT#F9ATDN}vWAZS`L~h@?a}J*uSBTupT!=n+gHwj#%?XfFEw$0rzn-~<94w1S zcFqo-jQ!?*QB6q9ve3=nZ6%081|=gBq~cFdd1HU{RG$X)X0s_Lf#4L)gr!ZPNl1$x zGj7c7^Q?FI{;7H0)YUn$cVlA4C@uGJIk9#HlupTga8}|#{rdeoK7eb#ratYcf1jvt zqm%b`+eP-Mg&T7onLpg@kTtKjK|r2kuf;ujWYE;EVMLf$;9m04$SYSSDKD>lajup5 z=9%Qp24-fS_>OvdC7vc9Ifs?xb5W7u+O?q=)J~%cLR84$r~PhjZe-_vz>0oJJke_J zk1IPU+)vSaE)IZ6%$S8*Xo!>pxe)-;!4H$dz7new>9q|iHn#mx`}4!kFKq1`Gez|j z;OtvP?l6C1Cmn-)QSg?Ks2))t1o#Za1&W989EztW#l3Be*K9j;8|E-ZLoNq+`r?er z3KO0#t#-%FCon?Tko#dkH+9Y;$x(@U;I%61tU&xgSm5U>0TUexVgZ6sVzppEJk;2K zNh$%9Xv*7_bIENLfO@!;Un41jS{&&b&cT`7phmE!If-5-O3Osu%)+OEd)e&oGomw8 z0;ED2jAz1Pp)y~DNHyn8c=4Wlqp5Qqr$9kG;t6k<0sUP!wI`JAQe3%OhEHF#neE3U;$(+?$JdPZ7x_?gb=%W@}ukMqW zmbinfhDz{+?=Mgh_}SX2r$uUggC@>3IGCgcm^gz>qzg0CY?(*4`{ZZyE_W zH=-Yx^=ZmtmXO-pJh|ks|(oc0~x|uOldK1`QFal8nTcTcX--R z{0e-pw?`Hq-?ujFcv?j#I)I#!V*<_nmOKfr;Lrj4m48)S@5(;H5 z5cZ_Ll4$`CK}CVxk8d2Ul)Fhk8M}WN%-i+i$8+dasl*(Yotvu;y(F}NEVsOVQlI!2 zFOI$ArDp2I%^lNGk=dB=Vv7Y^(g76G$ApbulzNR4R)8w&6<;aAC#g>iUVIQDg`7GPZteDMt8Xv$qAxKzVoTZk@LjuhC37PszIXlX)P%g{&pSgk z6st$(jj7K0Gh}6rn1Ou# zXS$F2-AgltrfM@Kr$dJgrGbOswwWvpgRw>&8@|byX>vkjsJ$=^AF8KUaoK34>Z_&g z%hu5Xf4L-g-06=wwMTpggo2sa1ZPMR^3R?*o(x4*%pRGnoPG8VUtj)1Y+iP!yUKzo z^xKTMOaI>Jh8^|J|HM@Cj+846L)BqHGQ5BcDnj z!S4XO{IIn7@jEYO_wY)z{%z8<Gl?7R0h)jyvefgl4de|| z3U?Y6WGR>M5C)PT+;8W~6c1e@>3$lO8uFMPz2+Dv?|pZFe%oiaZ;unU6t;XFavm() z#4AAuBg;$ABhLA?iw0{d6eGV*o;ttCjOw?7(!jX=T zrK*K-3+FiHgR^7$MT{s&Gcq(hKqHJ(WLocdVUoe&cUyRW%~KDp*|SzfeLo#lS+R+O zJ)Yb|?j5-p8nho`$o;kOv>h=e^JNo6t=g8+XDgT*)uKa(ivTnmBB-1+P#K5(Gw-xBs3j(9lp)T9=ID(QFjkrBqys7$mWla*epNy)*k+Mt`M%${+B%1gS%^LRy}m zCtzyunM7JtCP605*AWs67(|$zXnQ`gk3{3K3u-A;_gJ(R;!(Z~@F$ONAsXW^{<@r76%8#s7oP+ zhJk4g&mpVp*DlMlqe0$yr&A=9WFMFKW})C@yM={o4m!3u1r=e#O-HbEL1I~1Xn^q? z4)QESy4ltB7bNl$A|Z@lss!K*>j6hd2HE92XF#E7bc7`&HX1l&?bXrVW<=-O3pf%? z7zV^D?GjB+Kq}^kQf9=4!gH46F)Kw*svD5`yC{Z>7Wt9ho5o`lcQYKbU3QMs0#cyxK; zm^bVtStK-Ce|E7~Abo58U)bN$Wwz+tJxE=6V4LTcFVGP4LdMq0;S);t%>xz(_}8pm zJAk(+$^1E!Kw`54iNe&}DB1+f;2g_!N=MDJ5V_~(Mh444=kmydm3z#Dg@ojX&36f^ zbJ{V1VC2`XkuAc`xbD-B8}eu8Zz(p~@;&E}1DCNOyb@B(1{)d`GU(s}1gXI6b7o=Z zxH{GKk7#&QFWEml^fPNB3NG+ksw2*jw2P{Vj{(`8H!@gd*)$!6!u`5Rj61hITES^X zO^%Yp9DbYEY!zfGrxE<7p9~IA>ltG((>0+(_ESQ=S{@e+xu{2?^`l>TnqWTgaWY^m z6~E&tKR*J7-o4cwz~TBjk7pdL@O-do?$ekd5ca~;2`?{%D{QW3KTBs(M`>Z?57EJq z5VB!Wkg06GVFG94=t}hFlpxCq`U$%H z@v}nUDs;2_3a59CKT5J^kMe z5QhXOT>o^Z-RI)s)CnE8_%S5wEy-{SZu5blE&XI@1khU0lsB?>B~3N*pD~KE!i4)P zvt59fZ)qH340aRVwb%S>rlM(qS=YUN(0R+ZS3Ast7wwFUCLwfg*j|HJBsnKg;qmT1 zQA?bSlKY^n$_fppB-#)X--z3O9fMq8a^3;TQ+a!Gspt+JDtRJN)!ezZwNrt0;1jae zu_9yghq}qZb+4iITUb%G8y4+#ZO2Ng0IEJq3q6U6f!99oBIF>PX|WhnBnu*Ua(^^+ zi~p_Lx0gb}L-)_33YJE9=V3kaQ%%fMk*k&!nuJ(Sg|yQrV>X0)?tc_d%oK3$W75^) zg#Jm3Hni$6c+s_hZBCXOUShlZ+nVaJ{VXs&eBMHBHciKpJwI zZm3s-V=l8Af_`lto!%>YK0Tg;j)Uzd{LY-ng;5)sh_gvk4ZEW-Efoz+N2ExUY`YHhGe$~pmy zZueE;j++EOt~{`2&#xm%TbH>Kvqibc+t|JPnON=f3qzhJT$(g?>^73HwbV#MqrN4M z$}Irq&=ZCL}66^Fg zrL-8WP?ADNIt32@p1nV}_$oOmczqg(RpbI&KXX}Wr>k}dPl)wKG-sOub2bp8-@3fy zaZMZDm4EN*3V}ZCb@Gm>a=S5#ZwxI~MUW6Z989z5+}Jf=Y_%db5MB*fJ-)jve-Hc; zD3X`eVh=lonD215V36(>n};X2%RRzo6FsHBb;1MaA|FCw0JSyw7)oR?Vo$pz-FWSo zDf7v|PJ8dKir8t(-wE#so-YT8i%=kdTSBMpo=sNrZ^9>lsybNYjdASB_&#ct>$kW#m#^JzV(Q;g<+%jstIi-AsfxcU=Tpf99}pv`NDZgAm=?z3;QWp89@l-JNPw38RwiIl@BmJTx!q%wY- zwgBiMd^j^e!=&A(_zeLtP7tal@W~05ocB{O1e27^&GsQ{KWgLXdd0hi4S|RoEBn=} z&T*gAGlfjzC7-3#e~A5OKVC<#o>?1Lz`4>?IU?Xe6&)%Iq3AH;eC=uqw-ZI3cT?GN z0Y?*_yI{SFZb2DJ=lkSAOb?2UM-^b`(x4LW zA?wb;{(^1#kTgDibq5Ud^XJAsS)d8-!x#1A;($V$BH1@<$^nE4mh=GgLeBkTa{xk~ z!#7=)%#n*#6ZD0FP1X(0$Pm8sXu&kspxSC8PuphS)zVC%V@V!5;1P`!id@Vn-%JBb zBD%i4)WRm2S9lorV)lm*ft=K(API-kyfcZ7A0+y;z-Yqaief9BLC&06JZ(@FM_Tp7 zPw(~=>EzpDkzfI7tj|cO9XT%DUie4ha(wmr^-0vy0IqC-omigL^m)`tlPB+2BO{tB z1|8eeU3G7~+|bi@XUUa;>NJWP!7);1b(4=J3Uj}d5-d4ZiJfAzg`O?Y( z81_ly^p&FY!#yqbKe}*4F3bFwV&WnW#5*MFs>|)?3TH0R8eclyix{~FLcHgI0fsn6 zW@1F7ru`xq?ntVMU$ll9U4*BB9Ni&H#cu>W%UKZLvBTGT1Bt!zLd0aJucMKIofY)6 zgxD&|Ja}jzWV}iX8?`*Lp0)n<1`hI?I&Iq4oOzGj#yPOB(y+*@=`;H|OeU;%&Rp0i z=qQ8;SmjVrTJhgI)hV_kEO}-VxgUFQ=CN9c=(WD!iH|^$T-B3_)&@uf9$1?MG*OqX zoAP=`{T4Qb^MRCYjAwJx$+_UPpJnMKs?zUkLE&@gA-ij00)Qh>kDsKu&FI(OQz%rM z$!B)nU^lLQ-uTrw=IiGW=25{?ak8aQRbFMwJ7>5r)n=gE3#=m)DG(0&ECt5fGGK1! z#bI;}v)hntz5^v%So1N%aZo7z7=>s9S0ZeueLDCz1<0tMOdbNc(*I`9zd!%PO#*5{ zvPmfYG6Us868(?eovX)5FeI%LSPcy-CLK9l#H7SafIb(Jz*HLF-qfh<$O5>Q4^gRK z|B<{Tsv<6!N6!a_r&5!Uf?SQoawdyOxmJ8amMjp-wZ4*f;lnFvW?Xg0Ry~j`g@s2{ zy>VQ{0y#xdBng+RUIZ#?uIws#jYcTZ@+L|38T=u5y_~r%EXN}{5~Of?r@V1YpXrlZ z@s#hxf7ls_-D`I6*rZ`!rZ~UH^D~QJ2!}2T0qMcZ^p+4Yq;lfkmp01*Q<&iQw zTPEiHhV*9L=m70Jmr`G@G4td$8R`r(zYL=H8CGuopD(asl9H?F1UWFu@&yh&J(oZ*Q(?|Un ze4F?d#l8`v;cUSTD{>@2{|as-38iF-OJQK-jwQ+sT%9>mj9JiH@vr9p{jM`DGFy9_ zR=?3TpFB)xgj9J~aBm3nk0D@u3A_wGok*JE!xXKBg&)WV$V~1bkI-&54GU7SIMXdW ziYBWJFRaokcoiZiin<|J3oa}kdw=9AG8Iv!xnL;uTao|JcZVnfs2JzKZlw^019AP( z5-o06NmlOwJu%DCfOOKNNlG=&_FbeQz96*>NAzI*S*i+Rw!`#^sTgg6p&{qG}oBW71UO~uE?;AQI;AhbH-xF9Onu37UCZ&s-2 zu%IvFfK-*=CMwb0)#P@8n{_7;So-cICiaT2H;`|6eg?cYWU}^l;{q-aQGkQZE(p%0*fsdN`O`SZ z_SE;=(8ohqH5!8_eA@3vH}<7iVe2()XiOx;pwtH_E@v5zY$iTF(U&CGW{8>9i_j^)=^{JgZ%%%`=Zf-sE8)LX<}5Pu$t3|c#QfjQszW#*--9=YO~H@0#>>O%)tZMs5Ck$s8E0#`sk z5jAiL*`a6W6JD`ZgnHE%bH|{1W6UeKb2%a!#^?R{gZ==Ms?wBYbe4R^LCbJ9E=aCD zST!!Og$=BE7ZMhv=pJ3V+&u4Y`z7MPl7tYR4FS?zj2@L0OOa-_mN=3l54;$UEm#@C!> z#PU7?#dj(%lCNp#TQAAU&H24RwVT>%yX~GZz1P1!TP6UGFbQ>XJttyI(ZdOd!Wip(%;EhT6@>2Ek>I0W{S+uquY=cPWP#F0}d%IDAWwTbT z&H$?}kPbeGekpzYB0=MkIH%|q$vr>}FfMqZ`J63aLMul}X_A*5$ zHZQM564=EDgJ5d)WMyk--9eI#EepJUTC9V~d;8gCe{BBi5Rr~I1SfRzIu$sYzQRJE zHE7*;B+gMW(NZyPh?Wgs-DS*@+w+%_a7)w`+MPbUcEOsBYH6gTn^P_&v>j5&8B84h zTPrJ$Xe3aWH4Z`%7eJ%5PJe$|MH7ONdjl9B%OKN)6O&3ijbuZT2?+`gqt7wNYTZGf z7Jw7#eqQpc&$k;x!%p4qIaDV8it0g;+e6Zh zoWjD3CiCsb!H$a{t7cX4;_uH^Z1{r@UnGs3MWEvQ7)cnxg(m(LY%(caDN@M8*Llb4 z4s_G+Xf``ywU7AB3=)%O8v4iC!z~CPfj|=BgmMu5e;tvJNs>0!UXEv2m;}OJm4mwn_a;1trhlmbAILDT)7;HK3BbtX8|-m2Dx?3s{5a(cG~op z{!@DMwuxcEPZ?G*q6W zqpRnQ4{I_Zl+x%e)P`xyxqMzQvA7huw%IEl)=9@N05I;s4e>HaDh}Dtg@kF*vZcDd zm|7Z!b@-7w)>V|umK3eW5iUQeTtNEKq$CS2(GZ!U7|*E&rdz(G z00;g{IQ!cR1wX+kc0mV4;MLx-X}_KSjK5O@#o*tF46EHR2+VF^MKwwO%j;J~!v$c7 zr=Mc;nNUzd&64BxWdF;rX8|>X8H6e|-v%HQltqvIFXH+c`u~m56sonoUsk&@Jh*me z(Z^7}sjtb$j_`=iPl+D|y$*mD#uaAQ z(Mb7wITWt9Js&ktIK}>j9x;HyfJTAy=do3#^g(-h66pgk!=K!f#L+_BXrwjjXkaim zvo9FS;p4PFkxLPWM`-6vX42M#hxPMGPho@{~)C0$BD-8nwOc z-%pVFaA7}{n7eu76*qiW!m#DPxEhX5a|&TQeSHQpo~)Gc!kF}UxZ!}Js!V)s*Bh@~ z;?wBXCp0VuYEfCafGk#C-TXZmU%**FFpm+y_7X6N=S-|S@D#Fd#7N$z^q(dE_^}Ht zXUSh8U669&RGKGO9`p^Z_SL+fU0=__`OI1E7e-Sfr&u> z|Heq#hW>51EP-rYS0@h){yZnbE?a|NQyr(pA0(`I3k+QW-== z*=ey-@vU^i+c{hO)`Pj!KCs`1JxuEur(fcl)t?1>&@JzB3meghI0^?nup!({xPy_Z@(Z)8Dmww>WeXK82b12X*}^ zNml*JVQs%HYNROq6EZ-9bb-6!v97vrdE(`9-CWSYP0ogZZ-19-uIAi z(i{h}MHz@uER%vdlq+i*7R2}b=S6N@Yr3b%jfA~0jzp-3C^fJR$mBGs@;rL%m{>4` zEMoGB>@>f2qf~CT)S+7SF35+5P`mL3o&`Wv)h?N5h2qq_{^kBqLN^LfU7vqgx6CHv zVRQ>aB<-gyjA#GFu9U)R5gV?5zNabXd=lTTd3T!XXqoSSC?`^Gb>w%+I)B~jla+&( zKRH3w#0sE>`Icf2LebHf3;$$3tNTdgs{z&O%ZWqUh~rR^fGWqD=B)o8vB$kZ!K-?s zTW)o$H}F4i59Zg6i!ak>?7xrPr*S;`#|OUu-Z&dOvvL0Uaaw-c^KT|h{_$O7DH;Cf z$NhrTq5bpIX^vP~{`nbsSx(eHKXhv6L;v;jRLt-H`U!4<1oW?;MVf@ufBpRLA0WLJ zU<-}~on!GcV_LoN_lbx5b$$N4_ym8;U-S#=dB)3jf^G11iC1864tPlU;;P{N(ZAEL zCk>to(bPt5Vx^m8%A5r;1VPgtk}NG{$4zn_NQ|RagL7vttQRJu24qph}M&c+gHWGN{e;s0LV>8d}W7?$$ur8ud+ z0nDH>E#O|bEZt(j zDK5PFA)kuU1Arec*kiAT!Kq5Mrr75g*N($hY^9O_P+F!L(|6@lXJDvUfyu#4U@8EK z-2@ZQjrh3gx%uBODeGWa0ZuqkaoN|RCQv{jiUzuQ8hLR4qX7u}xg}pKo>%4p6HePv zmol49-w$a*&G{Jk!$NQ+PgFQe#xD z-f#$#q)F|y9#!e+XBo>{Rjuo4)5k|w{(KoD9k_7&Mo*hK@os3jxCc?xF+sGzJqqsa z7V{PNG&=SqB*cE^Y!h$Fe@;NH+5kz#vj}BN#hmA>C!)xZYg(_q1)&3P|BSFZXw}Q} zP?Y6aj+lzi3K=`hKKQ#C3yn>vn9C_2E2>DXuV24b(+60HEQ}vWO&@>8MCUKyI5Np;Kq-cQ&vci4N70uky7g`F)xT+nFd9|*TBAf zi!G08)#uvBKfZ^Nfy(coy{Y&cMQujIp;Xo%6J^F{)vZ51?SSM6gueu4O@=BG`+(>c z;BG1F!5zBhbe8z+04)@dAN}?7 z=X;?tX;6H$V!y*!F7&2UTKl#0ai{Qq8PQkV8=`H%xsqh&@gG!4MPM@)+NzBn6I3pV zh6U82VCpSfO5|+DL=+*9YJx~ux$hIo>(!})KU=mcw5YsW%=6TBO*UhHJ7?a)fEmaa z^U8?5b!*YL#hBo7F9S{Gsu3|5Z=%#R&b#hl#N-ZH2{9K?Dp0Ft%_USzw0Y)n@Zf3L z!0785Gfp^(94${A8@mj2TS|G=1zzb7U%iX~`XnJ4|Cm?u zjxhit@2chDfJugU>R*JJe6tO09MH=POVhu8Wth`6Nnb%7hQ8w)gnXWYel3-hLNqBQ zs=s=I<8vvTq0*1iQGbY=V6#=tb1s+$ofHX_(^%oV{)HSw@n30}h5COtg9%GHXQV0m z2CNBh{VZdp*2LQ}2moE}!YopDm;p6?uK#Q7yzay+DqEKny?xlmbtmpxny47k(~Ij# zpNH!kD5G_*V^EPU4&rD7R90j)uCiAuT=)FCFYf||)|f>*UY~ED)1n;eT*T1owJfa)hxEakm4PW37Tra#WONzaNTo;0mx&d$B(5BnNV`Qd*DVY<|4?MF%A`lq+PJciv!} zf5L*5gv6DMqnf@L>l`gB#55J29>_w9ccRFF1y9V^f@_Gsl<3%A?5SeE`FxEiIRh?B zEpG1UpMW4;x5_CcTWhfTrm)@Tui=YF+DovV18p333%@p?OJs-ZXEj$uCm=Wyl>~KW*lF?8WZNX9*CXX(?jVO{#MOD2B%4*1yKd!N1{dh&rD zpBl|6NuosREU3TDxy*Pg$F}I><$K=fgm^EiiMuA{At9H%0E|Mwb3ro*b>b)bI8z)$UXoW zWdkQGc{hyXDfV~(mvNgr!5H&|>rjb_2psWT@3gmL>DkETKe?cYXze~Vf2J%S>&R{+ z3rfoUvA>&HfybPRiw^f*7@O?#m8EIX z+O@V8suit0Q$EQ&^3AAwA`TI>U|-;*W=*NcaiClA$m4sSiryJWt2lav^b z!?eOX0^g_^`TqP%`bG1vD@ZhQZAU5%nVGGhjD%(v`~D~B-LT`=>D{>enpm0qkZNo= zf8j551`Qe%QzFQM^kiLez7byaMfM5U^eSgw6>|?4g5)r?K-sYQC@k~$b+%d&AG z5H#G?NNMw^ZwMd$Cb8TR-;kO<31lk4Rr~!`ejR0&8Ed>g90G3Y%lUny6<$_WmhsS@ zew)(J-O8by%US9e{i5&OxbN7WusWUhmA4|vo8m!A5Qp0QG)H`^{|ljie6sP~h47RU zqiVEq_L2Uc7=B7AS4e!12L6n$OUz?D%RRAZ@MR95=oV_C^5aK+4DRTrFG46=h+f%R z{U@%`VfUPY3!0=vnFt`1&*8-k3hz#!{!SOhXpT* zS*EgNYvtn0FJ4vc#pcPA+;zGXGS{5J|5WI`K(im!{WT)iqvwJ?IZjyO@TOK}L) zVUJZWuN}GBM_erEndPUS_YcO z(};%cmB6sPLwyXodC2RaO!1JKpjL|B;MKXU;%3y%1z$S64@)b0zA9S(O3=|6NM+LJ z0$AZ$R@DV0?PB7`{zC4HPT!^jm{}A;zyuIhmg2xQ0RvbV|F|@GC=BjYrh9)XM znL)3q4rR+q!yvKb6$NfBpN#Y^WlG|1BjzpTGN0YMSr!SMG`T=&n#GPqiqnYcD~X0! zB>COnW8cH|Jr)~2GRryo77)KGPEozX5yD5DKce!x8}INyUIZaY$VURiLA7Ip*Nhg& zBl}dl;0+3@Si;HVB+;Vy-^1O#6CXo0hZJmxQ5J~kowxV^Wn+TvsfsJg-Y@;5SbAZ? zO3CAjFu+!1IaV6rvrEPtU+i7^V}iI2)@GH+glDlTKvG2cei?>xidNyB60&%@$eEBn z(uGM7JOQF-{H0xLB!2mfAM=({4pEAMM@I0a1lW^AmH4zY6EM&t@X3F4t{caEu$cVo wC%6Uj&A)#BFCrFxKGpw0X5&8(P(GsntOj)gJxV{wKhYmIZS2L-3;y_j05?$^mA#Kwr>*`SZ``?f67imP|)f>e`1@Bt%)Gd!7^>U%Dhvu+IDPgE0@yW-%LVF zRoH%GV`J{$qju(gSG|+{vWn^HzN)>l8y4;r+%WI_N0&D4hv|oBAISV|$^O{~{$8^B z!i_&=*?l*z-SgX?XjvPTu$8}s4|6*Ie%{c-qbc3MuU_r<)48Fo-|pW}eqWbi7g68* z-lcIk@V#*|z5A~}YvisBSpUZtY;5OzHhui#KfnBE{qJu7`Ng?e8~+=B>vI5yrHkRu}(Rtkl~g$^PZbmpmb!mXW@1&fTe1%T}%0ymRNyp#G#2 zujU$5544wsy1feZ{`m25fCA6x_;`eq>bYsVkgERX-d;iP{IHK7KbmB6v5h4BS_4_t zzKgud{jIWs4F)Gq+P8h&pcpExo9!~JQ4y`~D{j{kF@Ixyrh~k`Yu{yljo3)b>Lfn9 zmiPQ>k$3c5x<$JivUe%gtd;rGL^!}_hl!Gu^T1~9+M&*>V)NRG(f$Z#(D#Y8|D4+0 zT4NhqlqbX=KK2Wpi9&HmUgxV-@o7V?c3u5-z^p|&iytv-kV#NmC^oh!*I*j zH#fagyC3emYMG{+Y+RIVwVmy=%;nu&GrOqV9%PeqBuXW`JY0UITYu5cnjE*RM~@z< z=r(=`-lZO`#^?6^1BjT(MU2%;7&4uPa||^Zaf@k^J5E zvWV3$Z!KIc7UpDk=hD4WUKEyNvoO^L*MZ) zSGhv8SF^F*ddZiK|KQI^F1LP;tHhb_$1ilsOnl!rIo6rL3X4*Wh(7t|hGU`lu3a+T zLsEYkCs&VjH8q9dAM=Zf6!9hsr*(OiL)6NiC^}4zIbeq!dK+{FvjO5O&i?Cez4)O?;O1xK&cJF}rAsnB1rsv<^{n;9P;f8F@p z!gZ1((_K&R-8w!t(0+YU>S$ZZrnsZ&O04oEBY9d$$HL23uISXaPHX#|fPFfW@>-XQ6ZtdKRLlM#U?QP87 z%k%k@7ypY6Q7Qt$BP`OEFQYeW~W z`R6bmK%?RL+0e07dS|}OnH>3cd#pRX(SG!YQ8UiS%OuqZC0~Ymf}U<`acEV*4wDe) zX<4qS1pTU4R~K_L58PPBY_0q}opdQ@HGL4{qCh?o5w*3u9DNvWQ=_fZ$>($CF61;V z3KY%sn9jks!>1<4d9@tdOT#lAy92{K#!s=mm|l0KXEr_KXTHmqF0~cyY*a&3HMg|* zbvRFrbqa2(O+4tusj)};FMWM|x4t(kSz+(q$$G8bB~~$hGeI#(?8o=-AHTi5EoxC2 zJG)Rf$5k6E>BDfqBPtGg4H(S(!Mx>r;O4Z8gniq3yacoK%d4vn!#(x()C<;_-@c9T zez51_Ts(~Yb!#q8PJZuvhKGj-vlORB)b!(e-_1w%hJ=Lt`0+#K&5h-vCtqLNe>K>( zVTFL!aaVU_rKJK@5BL3H^x=NBaZtpUo}Q3c|3XA@DW0Xdrzc*_s)nK6`}yTXuhCfK zIIDDp{rfLTPfyrYL>}3PSk!!?5YW`!9d}{QU(q_L=FZcT!>MLvPxcSnbyjZmS|zl1 zu%lu}4~LVJ({etQ{oOUGWnIM~_Y>e( zed6gn+!Ocs&}|;=+sz@?rD4~9v=$3$YHH$RYUkwS@N353*YlWk*m~^Q;gXUP$8`N{ z=Tt20gMTmoRajrI`{Bcfg5X{0az2|M;`5vSXv#mxW9mxTcr-m-a{c-jT0`v-p`1qF zWMow8{`U84zP@k{fB2NnF&tIZCkjWdE?P_Z;q2M7eE71!Z)*_0dUV+NS$}k(ra2Cq znd$p))Ack;wWvyyST~cY565(ebZuF>6bUTR;BB;0ump~x|NY=#-%W=WAp#XCDO=ZP z`nC;CO-*Tx=ekd3Qsn#ctIN^u3=F7^{%97p{IvbR!Gk)A&R6Pnt;yRleZL7O&i9C))*KN5#KPc%P7D$Be) z@*xgbC*tS2b!cG!z49Ffz1s2%3*9EF^gYbp-dgKecr4SNnL9ZYq(0hIlkJk8>C_wl z;KBN{&d&U(8uS1B^KgVxu;)Wp@1^YQkFT@yM&rc`0!3r$>g$&;TNaky;JoE!j`Rjp z_PO#!O=w8JYfuY?q?5_?f5ij}F`nEDn(hYI!Se zRU=}+73%tfnUsb!d@Hdi!>*O3E0)?l-Q=z8DWQ>|XN6>v+}CSWn^s-_L@V*6F|+0S z_fyH$mSW?hqXFCj{U~uw(%2)TWcTr*?iLhEZs%UvtE?;46`|Q7>nFS&3HMao9$e2EdE#qqw}ueRBNb5Y?{~g>q6KmaqU{A5Xoxe z#=g+>OMbiqt*xylKKi-tZWmgGw#Hw^M{sd*;mrsR3JN;gQWiS}8pWC%6+2Og^V`GkG*tP`iw5f^18&3)S zF?%+nQ;SQ=+2^=R2(Qbq>Ry3XV1f77{qQq-tYK|@%=bsrEEBWb%!>_d3uH!Pxera` zMDDw~2t~_s8_@%;E8Wyj)lQfH?R4-JVW=w(Z27gr%06fNJPp^zfQYFDt{s+@y>>TMHQ88ku&Y{i&z?Ov=YD-R7V}(zs#2}CJAA9x`rGX21TM-x}Q76}` zR&1@f?S7Sl9kN~5Z{4ayIu2XC(|R-D#r(yKHMO+vA&YD>F)=~8R3wlQC~C3vePEE} z*(9OYW_g|*6*+D$qBaf35Z*Vuyee=sQ_RYEkfFA#Te%{0%lYz$KYhC0S5RMXw7-?o zrv22!+3vaw)kcr$Y=EEg1bvz1n-A|6%o%y4xE64r633sL>CjoJjzC=Y_uuOU1qJJK z+>TF%K>?m-&(sX#W0_>Y;6_JNjyqk zCxDvyr>74YQ6|M9&fmOtdJ6NcE$BB(IG~{58Lbxe2pK0vAwZZ?r?BC>2Z%;sJto5k zJF8Z|>Xcxe?Qh|5oBm7i`JLk#SRsB%$z#Y>8Me)(z=scojS4nsRKE_Y5n%w zZw2>+9uM_4Cd^~!X?t;g7D3>quU}6}HnN>NQ@X44PfE8VjV!H$Go{J_)i!;+a}pIR zaEDQWp!u$`f*9SK0D(6_4V~A^T*i>?qG-N1oFF@n~iRHdZzGC*H|PVxz3t3%gdr)gu~6E zDqghGYn_xtNt#}+d%baW$>T#ssLTglcG@)D|2%~C*bZo2ZLV!AJa%iHRG?`VqQG-O zIf{5~T{6-@-egUZQQ_e^?hD=J!tqL3j{jSvV|UF# zH>A*vdf^==A4Cdf&zTc|#>%olD}ua9ePWs1rAX40As0;PUd9Xt}&ZVaRUY!XKcbCyX@Ghqh_c!dDo6HDWFoc87 z*}T)L#!xJ%d$_gF>eJJgTD`r!Hqstb&lLlXrdw+pan%;y50!9;PLxKiux@F@W0^TR zIqfXq9&iP!`1meI^3>;Z^};|)EgEq;_*`@hh8xyDUi@NOqNQyXotFvTW7*E?2&KD> zIkRV*);&Az*eXHsI5yP1g>T!e?TMpJjCKNSNDEeeChzX-LAR!sW?9W(Ho6V#Ug&=; zwb-e*K~&o1%o&DuLs?l_PnvS4ucqFSD+@U6TR*Igc^`vgJ~>dP%=(%%T6%I_H2Ry> zp*SkRrrL!LH*7|}f5{VFE#v+i0Os!Jp>5l?AxCK5sMQLSy}Q54q$p55)NLq$(T?X! z43OvFxN-08byBQ>Ue{CePCGgI$Hc@`2p5V+AAWcN*?(!B7|u2@i^U@=H^0)aI@n?E z%*8+!3s-I5%}N0tW3;|;(E$c18yoE8W&$!-0Ze!Kb)o7-?Mz%F)f^W1r0agVKHgtKoKU8Vlv-1fNt$%V~)x4$Rm#}3p`ivRAM!u14YwY zJD|;4wq{N2aHEGz$Xha ze*^os(9n@u={VQXA5rK>ScZdp9bR6TlWEs_i;9V+-dS$Nq2(TDN&%_gS&H$x90mGV zpcw#=;y_eJjgJoiOGi__w}k7+(?x3~!hlc+e&>zx_C7?@uv+ln)(^qdM24Gs_}Ni) zMJedwJ34JC8Dnu8@L;WsdtJvd6SOj)B8>hiH{o_~EXAVjFXTyArv9hUZ!7yTE{{*e zIJH`FH>aN~gi6={xqe#zbbpJYVz7j|UF!$Mr|$uCHZqaKwOKOPu5rDt&2)(AtG@f; zvC*|A6yx^y0XtY>gV96XwY+@Rt4?}&OwaCQc4cX@`t~hdxzd0C)z5xk-P=ygo1K^I$9C?Y z*8aVoIK*I$uU9Uge&c-7LCT_Uhbqfk8-cSg ztMNGIk-^_m2Ib9G)3!WXWm#TxQ$7WX&K-|cNl1S<$-b)yv3@M&-NRmTqprc`k>AHeby{>v_zY>@VV!;;Z@_(92_0Ret(P| zxDy&0O3ecmIRFm@+eSa}UZirhOnb9b^9nhvb~)h3AZRcV$%1nELW@NEb`=5#IKS#2 z?u)BGJ9Q$)&Ptq%)t#Ya!PoAM{?2lZ)5%HM!-o$)YUOori8j*Pee?G@^HYsCGTzblxzLUW+9?~U4+;6eZp68;obHhUN!$w9&^DV`WPQH2lx(%2{2hCxim{qhl zuW~f9ljFtxz(1l%eIQxTEc>+SPb^53_us}58ajAZL4NDeG&StKkywzZ+n7D`v`pqR zfbtve_NP0_`Mw>ROU+!8>%_R!l-=Lc4N8{VkU5{%n{|Mv*$DT&?7MYioJPn7da~b=&y2T|Ek1j8U>CJ=B;D2Mq z)@Y|jH_f-Vxzek}-2JX+Z)htm*wy>|G2-70m|bIBK^C25Q4&fqrRBT%hAL?1d6Yw9 z)2wRww{Jg;UP|N4nP=#l4byY*Z%D&O3hs+1KYIK))wE<|HmDF{c#@2Zq5)=!s%hsO zkiTis2OcVW;zyx*!}AQkZMuQ6C&ashJ5zjE()kq?>e@+$iPb&Ko}QlTmEId(w;{F#?R)>9w;#*5S#w%YUu3yi(~oJq$MJNp!<&Llhio zY3XRqgb=^2N1v?JcRj)y00K1y?$I}X*gyx=$PpxUZsrc-_jdpQgyU9= znBM7X-J05%plrQ*+RpaMUL{NYsO*s9v5Y_14()%im}2hSG~3FRE1Ta3sIu(QoG}VR zrCcgUjy>3YUc~06(a0O5RtvNy!a+qdHRaW!Cs%TFbFoTKyjJgw1|Xrjv*f;%W7oZd zx7I|VX9e6oVV2q0bWh49O(;SA{O@x_jNh*U6MOU*edQG!HnLBkX0>}cSi`aiQxipV`;NQOlR|$UVt#5Hg zM23$62Fcr=Loba(6!D|EuoEnp{kwj45507pFzvXTT<;exUR(w2?>$+SY@!^Kxu#uG zM>tkFSRx#m+O)r=NTYDAZ?iL)0?`9irNRkZY@hY7Psc8qS>0B~)Ex(ZL46J6$E90s zIR4!_<)}=k9aIy0QAcw5rZ+_qej&u##AqgTfVz2; zpM+hDCwe$;rg>wI+ao((X)he_D4-hFS7EU<>-uP%8*`AHH_*nRniG>l1(f9jD#oDp z=1M_nZ!vA*duTEM^>58iRzd_9^2t1t1cF889&@;s;c_$uq|dH}F*QhWlcvSHD_%=+Gex6gZvdXA*hP)PdVJ|M${7 zewDCmAj5*YBqk>(%cLTWI0`gbLM!hdm>3rj6Vm{n>)J6Xb+i9oWr4q70-$Fnx`B?e zh}*!-uCA`-fU$;EeNEZ-eMXGF+N_@LN3K#)EB3Z+diM~YsvVn8Z&7h90y@lw$1`#8 zJ@hbA_qTp7kBPgc=<16*0DoCwXqgyV+ax4(@chh_cog4Nq3ki8XaAfxPe>YwK>DRE zKs)jKVb5M%1B(%u9uBU8@&$uK+qihkMFy~Cw0>?*&h(T^%$IR@a4p}G5kgDu)u)(w z*PmDvT8YDjp0}f%Y5%80;W1UdyFvyB=lt=9#rMxIYZ48v3Y&Zo$RAt9t{QoG1DHkm zEXQu)__7cw7khx4W$Ddk!2Zupf4j0|JxCp+N3V|ZIK(URsNNU~zUMQ*1T8OeYy9l+ zCbS{ZM;>26l$!bW-K%qH&t&p)?cIiu8}=bof6F)lvUI?@UFbx@F<7t?iNbMH@}_L9mIzi@zBezg zRu9@lkruuixr#b2gB6<1U%zfdl2XU>>wZvvb!DMfabN3&9RUjFC66wLJrhq0IdJ&Z zQu}8f)9#OqzBb;D`qJB*u<^hRS(T)g-#m+}tF`e_#HNBvyUYG9)4MtHGWhj}K~ydg z^qu=VZPA`c^HyGEfFWvz#G{;IDK{2o5(QaHJQy~;+ z1oQw}h`4J#MIW|**4gWB!*VK}m2qChn-1Ml==htB?fUxvsGRTaZn`Sr*tMC@dfOv3 zU4_;e5OvzXKlm|-@PMLH;_NZ<<&UlUId#ToEiGe-9Ik(MdLOGCX=>BQdwNJJCl)AX z0Mi7CM~6Kd9fu;o;On_;x3`sV>+{i;AZN6>>m<)S0KF4_b`lME>D^6-mTpZx`DXsN ze(!y}Il~Pu2Q>;IW<>!9^JppC+uJ8y3A|an)U`Ir=w`h_#-~qO*?1w)(1$NL+(*rZ z62P>ViBJv|`|!&9?LUD0`!CL;t~W_Wk;i-f3W0lVIU&9g87stXo3;z5qHLP)+&%dD zx6iYaS>0Ys%`tQ-wM?}G(e%RJ4YG_Qz#6vR0y?OE2>o&e+u$qIM&Bp@5c z4$gF&#(2PJxuk6WsZPjj126yJS~Z=vQ=}Q0&~rQ%=(z?Y!XpQ?U4wzAF9;+H!0<==rgu zBE|uHDK$#qO-hK9!Kr8mOxDS?kK$3d_W-R)zM-%gcsFP(vQKl?h}*t<^=cmhG&Frw z`!-{VNr@D7pEzUi>?sOv2UtOm=rnw``i@4hoSwduviZjTRobuId16oteBXN-yt zPES0ZsUzH9j|Fbsv8OHy%-g?zI=VvKK`w1nP2DAhtyV${c?9%UMHrLack59N@X<5f zESMSa@HPiEWI^T8F^lQ`Yvs0BB9aoahXS#TkD=_Q+BE8I)kz_UeA2+cq2)aXghr;! z^h9zF&=ONeSfexTTvFJ1bG@Eh1CA9S*dxTIr=5cQZEjWDl>4hLCT9rq2L z5T#HYpU9S?pcs{KIq+-V;FIIglXcgocV_kG?9lT&s`u!qeS7JF$+1CIke8$f5V`N- z(uoz8ux+}Ab7T14s1mRuW_MNpfJoIwJSfrgBF+P*x{wCIi6Qj*&Q2_?b@fd;s zoPKmk5$@~p_I&%1*p(65L(bf`IWeL4cV zNezHxuvA_!3IR2L!2%y|Z*LTRlF;LHvovz2r?OBFPlC<{5n%gaj`9|A=c9$nPD zd^0Jk3&oG7n3jl$o0vrb-B9@-04fCj zt}SO@w=Nzeq&X6H&9l>wFI>EcCSQispSySOQg;q^ZxaMAG_s#c@O0!Up0NyTUlDD19Xu?bjSDpesEf?U~=-^?n^lEzjU}h z2^A@DrGVC*IL8Cu2otlkcu z9Kwd%7q9u}=yKe?sQ>E{4sRvTYmp4ee;Ajh~W2NZItbU zK;{Hg#p?zc?!u>J9bZeDiqW^><@1oa(#1jbUQa6`vc}}ykNc7t)4be{QXtX&VqIQV zS64&l7}-DEFBzU<16@086b0vcWrpjhtsUA^Z*~2?uW#bg6mRxFv#yu0!&b4^iG_dF zA11ta#wA(&?BpnNX%S-InDw^diQ;NIH8r&hb(nTCf0Z2xFa4J!Bgy2&cgf&l#2dg@@ek-RFjS>SO4{fI}f3!Mg6*28gIYC%_B` zonfT#4$e?}qQHTWR|asYMBLa9b!jtIfMfOCM~wzSOT#lz{Ns*FEa+t-RVb+*xVfVJ z^>ubCcSx*{VXcTkH#~pQBBfqq=~T)-Nd0BtfKt%)>OMbn1cgW)j)Ww`{6|0mMD27m zWM|nT&W}q00;-{P6FvQPKjlIR@R`;P&-tK|jE;@*|MQPlQP9qvEqCSPi4I3J2a4N~ zdi?}-g-=*m1$FN*P#P%TL_dx8VhA@Xxm`S|2gn>C^l?J=UG;k z1yadaPgd^$iO+Ze6?^*>JWUi@txlpgQ7KYRyt=}u{SFd53gH0iAdr}LQiCKUBr4vj zL3<_jDNw|W--{1ejg$v~=W~zit(ep9~rCE>htf(fTmGQbUNc*Z} zVE_cD#0e@b>eQEiiC`uAz90Vv1dvupIb%9+`%1FISb4U}j<)chcHPhpXfks`YU zi{)^Hsb*&XDA+=2Rm{3B5`7Ur56?j&0PyiEP|pqC)m(yrbO3mw5{4iVyB3AD(ym%) zA`3!gaydoK!>AR7-o+_lZ_2VF*$Com9C~4ML`@B7vu($oJwUP|7ZUvnj3ZGJ4jpY| z|9~o4+F=jh1qV;E_}!PcXVTWG-47o=r2Gh_d+EY|XS&8sE@Bt2%+S;HCEJ-Ne-6z2 z?95k-uKltZ(N~|({hxeG0b=L<2Ctdln)#nQ!iN91Ug&;l7QMf$7Am~M^pqPUJ9wmi zpLxx5CZCa6aa60&j;lW3%0W)H=@Y}fja3NI(kzadpJF?6kc>>A5F^Zw3KJ%pdLyJhl5*H!<_oAHO(@|NmRG0{>lBqsgaXAwJIT zuj@i3qHb33hdb@Sk?r|0mAE52h(72<#ND4dIDNtP|01s=ecb=2+?dmwCMGA1gacyz zfTNkwYIhfJ$vNT zsi)GD1FH3qQ==`xt-`YyjZ$0yIfeJpoP{_@UvWnJrxY1rxE0DWa1;RJ^F0X+?J7< zSx{A_iDX{}Dv?#67uIq*3u;Y+bLUo+?*j0)D4=DqRxDq=I>=Pl?6lNU0~9Cl2_)73 z$mb;_jXs515mK?x+CI&yN1~fC`vJ5Su()r+#-RqT!U3%a7%inEs~LcMid6GJ5)haL z9-uOu#OFkYF)kTBwDvw|myR7f=Ck#v81T0&8i2H;PNt@jKYg}wz)VN5lXoK`B2f5F z`dq(pqx26h*9Yg$vEiYJ#s)~e4$9i9L*8;K0UC0Fp>Px$7mdOq<78sK0P;5T@Z@Rb z0OC;th6nXV`G&Pg9gvFbeIRKbDo%ot_0Kmfh5c<1ePs-)De>&2XT7<(a&MR(MlJGN}*-G~Y8VmJ4V)xeIER|U1(0`XM?pBd5r_}De|%3z}j3Db{4pduL4 z;LHDmStoTvB9EH8GUMFsWXIrOh)XRE4G%x z7?pDR+d*GGRi1o<+t;rj$#x#3X4E~Tdt3pen+h}tZJMwj4pGULgy)YCQaTju z@Mc+dka30)1tgZ2^xLetrqH2DA%WWjE)z%cs{XNP%}1P#?n-YJ${@iK4)!>C0Op5) z_H>X@$;jK?n5&O~NYB#M(Z7A^UXgLCb-fNiraD$a9sX=;r6EC7AUq!er}KePJUEJ# zB6$k5r#GU@>v$tX6j758YrsU1T{ypCnwo6KE-k{Y^n>X(p>&ARTC{z9xQ{^P5l3h_ zL}{aHsE5feAwvv$mWtlo>3AsgGmcGke%(DLP+Q2Y1c4|X>J$!?66{ZF#>$CSwhZ5Sr%}yX=)Li?e?&6i(1u` zcO1(Z>xdx{A8dy@NOJOHk)xc_4rGCNf1O%u82EYwOPhx$P!e`7W^<8WB-JN1k{7a#3SWS{J1G*`RVvhyEf1k zFnqCWn+w9B`n3N?9|e~X$p_ApgH@@RQJ|BI2rNfJ8H8+HfVPULEk(B|wBGWRD5bWJvB*vG^0AU7H0^$Vb|RJr8H(i3ygJ% zhOLLP4{~!9D0`g@+i*@1)8p#ypI@BkgkcG)MHsr=!rI!SST1@N_K9^zaisfnb>okT zgWKsqiGxSPkGK@1L^9E`O5>G@Hz3*!ry>$e8u9Dk=p0~2h3i{`SV%A?EVmH)HMp=M z(W;SYg$@$Lur@eUN??jsOEp)A4oxpA0T+;C*QbZ5jP3AFsbMIB6)VF%Y%4cTlRx;g zK2Fl%fgnsRNJy3VD{}JnpV|E%b`{+D(zyR6o{$f|@4VbU7IKDRt71T$k0FnG`S&1Y zse{cSevY!gfWUiWeL7W(U`krB@9aR^5z1sBS;_%0h|(ehZ6nf zNeyC4Tv~2lka3a~dxJ9myfMJN%hI1_;WxBTnFC zi8@q-{k6qt80UJRzgUI}k$HV)Dx)334kvs)*!@&!6W}pcHFL?h$Ks*5t8oALap_HhQh$`SP z7z7Woy15Y;hKN>r1#H5M@>fr7H{3hRf& zg!{xZ1aFt|4Mc`1m=e;&mK^_tiU8P0`X>CTo%pacniJ-6kzIN2CLX2xkq9TPImW#W z*;Gm`kRSqgnA}2qTP9C~w`_+RTYgW-Aoln3AOSRB;ijs9bEp6ax1*8>7*gM%lSG1y z0tZt4q@n!#;GjCT6Y9V94o!e-iQYz|_%m1}P}S1k8lgb$xG(_D zKL%dMZ7@2;z`e}5!`Ksx5QZaFW-J4 zV%Z_F>E|O?fJV{;pkur-+MZ@w@~`!Z&y(ZB#3mg$aDYad633)DrJPf!E)vH-J<)&r zbbRR}WsaTJ_en=W(*c^^UBe2bnCXqycjHK)u;VWMHrAN!q6wc(8G@Avf20jF(8zhW z7tE;7L)9jn;tdOS2ebn?%c%vid~o3lHEHkVa07pz7-$i1TJp#o!xTtkzOb2XiexY$gp!kn zQEkkr1($VE2X_YWE($OG08xw3P?$eP5_Ge8nB*aIUUqlRsr!Bd2r4ntAK#C`0+8`+-rRWAegqQD)IsKi zF~DlD?%A_vZTN?~(11{j3=Ko2Z%TLO8(`0R=hY)1px!MpK%#kOPjnoi5-_o@n1eaSb`zcD@~ z%M=S`AYvLi#$248IUC!F5QSooRbdSFHtO?u^JzEk;mhz>t5lz&I3k4$9cmkvjqN?qOg;&0B3yoOXN_|!4M;NTav9Vh^o^Td_#YlRJn{L?8v?s;f0JY?HqR_0n%ZKL5)!l@dkK9%Z2 z9D8XT3=F2{+|C_i+eaeU97dQ&s6R#os*C2zcb%2%-Z2^W`W7E7Lo|JXs_gaINM~4^ zxz@#YJiuqD?E2Xs)iewN3Ix3RLIcx-g%a{Snn>bS?)G=?K_3nB)Bu148UO}6q|u#z zY?F(dA->UN5XpsiaXIv$k zi9%>Q|9No!=Zu+RX#2XjOYWM{wLOKWOq7X}fb+*;e8>ups4{*RCJXi(lVmTWKXYDJ zO1X}F?3r3Rf3|jwiH+=Q-Z^vTw8tdmltN=pfK#RuXp3}C^>k}pk~0AdHJXtjqrlc% zfDazSaA-4_99Uyb!R=6HCgXv+JBp5ZZCV_mV+9Gc04;+l6~2O=Z+lbnaT_#iR@dcS#Rp7=hTH0_uLl3*y1 z;VcH)O2qovp0sE&II2yCdTx(EQ4VQyW)l3qwRV@EVMcjnXsHWd)KpQ@1ROzPol9Nx zY+FBAIjwvBIwYc8x=dNFNC@4#Z+RkbdMJ}AGuiB~Pv~010f|WsEtStQ|L}D(kwyWb z2>`Eo#~l3}kP%fZEiG?c>GtUw7H3Vm^F~Sk+t}D>{OMi!v8t3dRnk^tPN+*sN)nbv z7p4Zw0pGgJ9pR08{08$E4)6@?DHZkx15?%N>OI;&PfB{w(!J}KWbk&VoWsqmsA0Ro z3gtzA1=cwf>tObdx?4bBC1BVJ#GWW>Gyrm?@v2fB?kI#41ep+=S~zy!$uJ#TYY2mo z*$aJHB$Qn`)JVs)ew+HYXHRG*9MX}`Pqc?Jh0V}J#-=F}iYFul_ytL1VQ{Y!aj}(s z89`N_dQfz=dB=48&g}!UMYFj;ZM^@ad{&m345RKHeSH;n(EiK6bAWsbEyY%!zv8p* zCil%QI5(LCg5ugj__56Q#&${5gkPAa_k^OXaOaPe1#TL$VkSB%m{pr_J9P1h@67Lx z@EGsjS#(e6RYz}nqx+7^tc(owA98t5qm(}!Sjow0{=UpV@Wd`v=VQft_YQZI4e=q< zKzRzLIUSG&k!Sk8(w7bH<5rmDWK`R6>qvICw&YGs{`E85v?K?Ew$o&*T;+ria+WV6 zSllkJXAEQ!*5fE>p=1u*PHox9wSLWhLp)c}nWBEhfkbLUe?|?Mu<`pyyj2?(Uc(5- zIUZ1khBr}F%Rw-178FdFeU*IxPHrrnC_s3yl#2!!H&sZBsi(f|jnhipSZ$^74m;Yt zEf-YgX({l7Kg!3Mafl(Fd*a4sSk4K z-;mjFgo7ZYtiQKdLq{(9_S1C9)T%+V@nwA5u4y5AlFtHe>yKa_sejhZusy)#`u%+U zP*_A$U19rZ_OBC<7LuSe3%*YtZtXObVk|wGJiDiCJ-`L9!tmfS{+?F=)2ofEaq4L8 zm@0rQD(VSvMo zS>4LB7jTw{q9R-Qyc2-8-HSqpC98jWE65A}^!T1{M#SOLCW9T~coiij2)B|3U<|g{ zz%B=*z2WQ+D*qxmV#JqhB0>Usu6`DV#omGq@<^la*)`CTWjuRkic(C35;_F0(PROE z^NU?x{aKT>F8@Tp&TXQt8&fgb8NOlPmB`~SFW}7TCe5-UI|&91$wF^f#9ouC_JVyw#6zsQf1fImZ51$F|XhU27{eLs>Hc8vsmsg=3v?NM9HImT`4%FoULU^ zJyrT1nD-l3i`5cB&E63gWb+i2tGEe8%<4+FXRcRvC;#NQ?nG1eSc=;XDP)auFpAuh zPCZ_)KV=cN`5z_fu^EnzDmEt7wpq@LV@ICx=3F4}PV5qk0m+~E>BPiDeCglQm}}!R zj$-8Xa1fnre@c-YjL>PO=pQr+rAM3CDIh?Qrk%~#2G_WJt4_Ij!y~x$E#QC2yK`v; z5EA*?421)_=E{*tj#-NI%w5fuMwSrj;Q->}{AA92-;D;yf53+4JcG6#ln$|c-jYMS z0BMcp4IAZ8?PmdL_GMHYgpCvJ#;bwCbsvysRwW+#M^S#=!W zr#c$g5qz?NDE*IUja31UVVS!F->$g;2+k6h-t36n>8V}ngIE$(mAa1?F)*0-1I3TC z>Rkv}q~_vKeFS|O2!*&L3jsUxNsO&#x=&_;_OoUmnKyqvYuU=zx2KPRE76A2OtvC% z08Sn4TSd*RgSYN+>eT1ErW6ksrT@lD$ zN9X86u~E;+$RH0rCa*IRCE;rWp<4>xKn=Dv#>JCn+}yomk@3L3$& zPTqF3Y@W@aQakFi(&5U(VC$A|&!vl{w{WY}D)WY+mBom6B|Z>Qg)t_D!peS)?Pss5?r}2O~ywa-U9++ChS0S1AZNbqbF_GY*UaHO`V-Fn8@LS zj|AOaG9No=S(q3i;YfjBMFUBn^!LfWw_J3M0}v)v z1E)k*4@eG=f%784L~Gc+Ai4@LIPvVmAQkkxoZF9|Bgz7c6^Ud7p|Aw6VBcJ@99)e& zJCB0w>1-siGC?9Ta4~=s0IzHD54qUderNJ4W@Q$5fu5yQb}Yt%pfQ zjE>p6jd!QlJUwxd7&Dj-P2u7vP6Kx4fUgF8c-9sSN`cIX!FJH2Bb)Oeljvg7g0u%P z8ARtTo1@1%nJ=kCo*px>PUJj?fkh2IC7OXi_>!?&4a4i3?x+G+l2sGXixf+Q+BTH> z?&{=2SVvWuIXo4jOtyv>4`7iX+z7#~mT!I*E*%lrVaYd7Sa|QgeZCMJtm-oiSv zi|c7`tuP5!Gaqli^bCju?asm7jSb17;H$DaOA!|p<+&anUTefvz|4{^L3d36xsl$6 zoK^<$ATAA)e^pp2n!6ipE7^ymLnbJUlt$w)6~HozQfhH<0uKhMf3RJE8q%*PIG)Dqd<|Up%w`@Qklu1ro#3>?+ z`I^J9Ces-vLJ!Sj3Epn`nl<;MB`_>Ih*H%_Obw_N5;&j^MS*aDVf>*2kLrb59lf+5 z1)d=P_kH@WYhC!bFqZ~GBntV2@;O}y0L_YqE22KMd558gpiyV?UZQ}dU~-Lok>pRq zeI-OT#{i>7#mj8PJtAf}TvX0ztQ{}ePRj#8y&T#d;BRfTE3#t>+~AilU#4uQ^Yr+6 z%FD=LG@OHS=eDg|snEpvzVSREigKC&t~(!A>mD(LWd+bz6nda`Bz<~!$fA^F zka2Y9EfN|RaVtPKPcAG#5fawG;*bpwS&7_pQt6geO|tFT_e*nrI*-_7x9}uqpU$4T z{TGM;GKfKMHVLu1dk6Ejq^^=^j5FKZ-X5^6dn5q^1F#JzbEdJMb0anxhC@6f8Q%u1 z7o8JPnQ5aOR5}a&%*Ej3 z?*Su8LrgS4g{6sbO8$kE$lo=^+e(wlr%c!E@|8S#}W$|5?YpeMc`%e%2oL`(-x}P?|MWlt3 z&7TIo8x()V+By4gGX}Nw;pF|5Jo_Vz{An|P*6WHZ9BhO~qdY>H3$z#AVuo2u0IkZ1 zQ1@t1`dYqT=);kfXbu=)m{bzf=Tcb1yDHauC`CX7T~$j|9vCxLwWS|}-~o!XH^zs- z7MH_ML5#qYoRR*&;*MpgK!?18$qV97K~uJ)5#f^z7nlOF>82Zy?9zP{*d+@Eaz_?ujo;qXSZMr%* zpJm{+EerBw^g&vZt)!{FJqm!VBehDe46Q6TlZrHWZy!N@X|gPVcyesrdefu(HefX{ z$`){_^fmy;u(I4(M*Q(*I5<%Ih{ks8ybiz)I6$upUWl`BdgR+;NWv=a?(XDd!Mc*o zhwhTYr7Q9ptGxr?7U49|g$Lld;R3=eiZmSYZ+rTKDyudL!8(JOBS9Xk8wWcSEp|b` zjtCl?08HNS(u(*pYKu*_p1R zN%ccxX(G=lTZVKjX#=T=eHXMcc%?KjsK=f!#exx}nSh;J;mv~5* zlMol|)NlmVooo<G7Z2wvWR2W zMiwzlmUiOIDHJf{FH(RmRFk3~(}^^oLnAvlAyt5eL{5=NG_NNDSkD>rv&{gdWHSLc zb_Ns>!DWin1Hivkj!&VE2BSIO`w(t^JA(FS`SRsP={dk^F{G#6x}^bd=YvZkFhwN` zS4atZPj81V(5f^B4#hl-jO)PWh++y*oS*qc!MikBxV_>L5Xk|Iq>_wYpNT1V0Vd5Z zBwFGE%Z^Wpj{(2Ju`WbM(S=adurAqjkP+k6N1i(j0;qr@g(|w0%qdvw3fwu7a{T4* zxKQOV@RSBlK3$OqxJu5GgWD@kLgnKWHoOL&CILNHG*}RbrKPdj$<$fH)Aho*5e_-> z1(AB*2CoKP$b`O44G~~BEWqB0IcWd>OnL4)vNNR&=Q|L!vuvg7Q;a00 z5TL}P|tytZGDdjl`~XV$tc&CfGhqXP1yJhJPeZiAIOXCjbox zH~r|40FS>*V|R)Y+=sLsg+7z7VF;-(8W(!fot=QhGuss?au>I$&?Ios3vJ|v@(eo@ z!rnL)GnZzzmvX~4CyP1wuQb=KaK?lHrh}|RFctbN zbJC@G6l&I#LnLVugXC1)HbhJ;1}~Hycd?h@ax}V{ibNS2_QEBjASvYCJ_rLGvDV~#g6f&h*N71?45w&ea!M2ZN(E-|VCFSPTS*fYH-<1VI^%Le z7^@*N)L~bm6+*nG%&UGb5N$@AgKhqxH3fag9LvWFgDf72GYYALW@~WyjOETxyiCO2e9#!U>6ZL~zi#~S zWzg^pZ4*Wy+y^XJd!i6LAo z(Du=8kWx=N3WE=m?I`B|yLPBji-c?*zMePVNC>3Th|9!)R1|-V;990Iw1SZ+TSQ5t z)Ob12&?Bre1`}D}o5&?X7i<8BXjkAIteu2hP7nhO8oUN}7P!#>r;Tn%S^oFm$9+6- zR~#P9&v5)JbZBzH&{b&U4`zMU`?nHKCu03UT&KcJNS;JfRpep1{uaLZJqTQ}4)f}y zaNNFlqJoD>!%JYtqLKQK62ocUi)%I>KsW3pFYWg)uK;K)V`VTLR*@lp3z@7P9X3TZ zgF|o(gOiqPG|g)*PGe9PynEZHL<4fwR%GsMIJ1v#azM9AmRE7{H&XHFP%H4_$5k~^ zZXkfCAdk?E760hTpGts`PlnhEF+YdCS#D(YAUY9D@FrQ_H*?nEzNN>oS<)SCfWu-| z_h48B-w=U*yV`sdfJ+o+3||3lN^B}bxW18M2j(E^WuWq?4fj2vdz?7-n#kAWT3Gel zn@+=ZIkP@t_lMcWOky!6P z+34Eu1kyb@u(AwPn}nLdQ%kd~if$ldG>G}ey(FuK z3*iSN`x*^~lSE$RHWcPF@RuTzALK6@9wS;D@<2*-;ZnAgf%7WI&TaWQGrfIm<4qhQ zRb*uO6t{fDw*c~zB7}@P%ltX&0ra3nDbZ(zxNMND2v6>71u%3d4XR+k7)<0;72M(^ z0<$f>J+XU798n)gr1qtP3i1STAXOqUClY|MlWSD(Fo9-lj_TY{8d462@_>DV_fp(0jq8dy{Q)P=u`ag18 z%{vV0i;5sKt(OU=Q0RgyOASd8$yy0VK?Uj1Yh|0?(9xv7LM5-FTqK%#GiWq}pc~!$ z^<{uDLzThMt_BJSskoH?9q*fLNiPp8Zv(VVN2`dMIxv#*>3S7s5j` z2-8_U$bQ_xn7Sn8ZWumBJ3vm#QUB~%e>i<3!M8eNvLY@?4=zZAB?FHam#@lZ<|fct5&rm{|&oh1beHmgg59oYffUH(}ah{&f8VUF=Nl zBEsM?#HXFwwS=sROh+>)N}Up1U^_1z2cY%xxJ}oj4dnBNVr!U0-eUBH8Y(2QtbJ$T z-EHUy_&zWoGUkaKX@MK?G+z25GougZ{?v-uBU2a#HHPKUbCHz@wnCmQF@oHad-3Ko z0|?+=#m>j0`cM_;*Ud~p|7|FcK0pK7$<T1{R!x}Xiomd1FtYA5YSKG{+E^U`UQsv2ww6CED;3vUdq{onO#!7;G95LdQS z@`?F|3&#H5j*ViL;@8ccWN3PJypFE6LBS^fU+h@ONL3rt@3Y=Stf73`k8{7xqT3`> zKl~7%I$WVr=cF~^{ zQ4GI#@80ZWy`SvExro8g;+4U8;t57Zez?iAbe zYD9a%3hc9jt?pjCT-fF-jkR&r!ob&?g67x-n!TX7Y!f1DGsPj$x!UtHr;(}oOFaDn zZFI2*Wf+uK2lzI~_%FULy!q<4KAc$Rq=E&>n_<2a@_CT$z?!r>gUMXd1ChK`aW@vJ z(mtX=MRcnvYM)?x<9HaD2aW30%CGcXE?&4$8kW5RF^of)996hqW= zq9pWH=CHq0Z&L5M0{tFprUjHUx?%u+MALNZn_zj(FynUJ?|QLtYb_v+FR&nTr(nqs zKbI%~d*D>~#u!qZS;4WB|<7F!& z=D1ASz{EwfRDh_;KuYSso^h2CgM+eW;?OwU)4Ol{V z&zZmMX@OSS?0)iVA_leQ9P2jPaufl*4}E3{jdWn#490xf8!jO>bO$f4 zTiF}taMKm$lv(iPq~->l3&`HkEl@3aJw3JxEqotS>uOns+A1;++h2{T+wd`vlCTQx< z9Wnp#1+5;YEsky)f4i>b zVu5_ys5s0wlmb=L7!Di;h4uCI)*QGeoMwq|sqIs3|5E9S>yi9LaeH8{OhvD1@EQ=!B0808tP+df}P>cevssq2{k}EqB9pCKh;$bA(CBn#_ z)w&VhV)I(wNtZZ*VFM^#-v1kW?*Ufzo#uTzlQuD$Y>ctj2vG#Zf?d=U%Mq~96bmFO zDuNojQ4uE@lUT4E6i^gQqzEXeSW(0h3o4@2ps~abh*&5J0?+3QWOlYb`|LCOzPs1^ zdd*&072%xo|CRgxwtLA^;|157A7>Zbq-gy7pG@9Vu5qOZ~F2;o#|i`Y`#3!;9L6Th|oz?j}^=;K~TQLXX% zw$tF}!DVJD4jIl;6(z_N#J6Al9))+~3j1CSa{i?5D%52-K(o#*TG8_2oO$#1pI>B{ zI`s%aN5eTd9e`9-9qW5x4dbGq!gKoOtnu>GpU<2jWAgPxgdHm?Obc)KKL#{`51?&c z0lm?g5vwhaKKVw&I|>c96W9FmAatMG?m8ZxNw>#RgDR#bz;#j5D>L%eXWGxU(9##D4V31A7C>ra9wHfyzRMO5E&Vp){mSp_ z_SBKtfo@9xs4Nfen9+DeVxxa%P4W!G<=%0H=-vY{};zhW1v@D;_=g)43U zon#tamNKOf)L5cLo%o!O8>eQ2a1UL%c+->=ZE1D^;CFWO8y$C znw@zQgj`O<4l*46`wEw+K#k}-Ie<>;b+N{{sAaF#vRNLP6JeM&=bmsx{xra3`psii z142+u?|>;Ao!q9VuNN8?k~uG{mPHttKDj)}@4%@5UgA(AE9mLPYbBxS`+Au9fBD=P z|NW!4>9*Nsf^gd0Io)OEPo{bI-Ng{b>7>Cqy#x}2_DS@u+j{T>pT0|oEiRU<1anTI+Sgpp`p5Q zpe=)={uKD5--$2#bl5`i!7Vaz$3?d94$3hpMkIjcylbDGZ_9Br<0xG75+*>MM0#)N?_af6Q)HGNyPFinzKX`M+fR%YSm8Pw4NHN(x?}PZY9r_Q2 zF8CJUtqpmyufMoG#bWTlfrh=w&aQhVEN78qpdXmH=6Zy63!6%y<{lI&y-#1OA(w{d z^$+=J1WGT1@^Fn7V}Jbg4e5N?d-EUE*-UDqNhHW+ifNxa(7y*-ymyndBM#b8OMfncDLw{?0^q!>% z29EQylMmnP_Yw$yb)8iYOuOaVt-N>R1wFb94M9czUUrKuED!n+LMRM_F8_c_361VA z$qU!tUUL?W4IYT>#0WoQf1jn4Q;t66MFAB+=k;fXj6oqn8F)d#B+?U5^9;?L@4wgs zv~<_vZp4jUFfIGYDEiN+DylkfeWS(stBBmJt;>vttg%une$$#&x2hyj}Tp=rI=(<31SXJKPaR@|B1bR{Bp$y zef&Xqb3mxf`uVdAZAyEzs&UQromu&EfExsk+^iy&WSeMgFVTZkgeFN$1fddgl3}7K zR|jI#dwe!C)z}(C5is=)I53mGpQp2bOfkK|L$;8rq^En+exj+z zp*F01$MJN?^T#a7DkP5l)S&5{uwl5m6)5xCn{{@Z&A}lY?d8h@Z1kXy_0^yaaA9g- z0hmV8(boUqgNZxGQr!Mz`Kp8EL2!AmjZZ%+4=S4Q_Lly=ydv%`B#kGMSl*;*%n91= zTlP_yJVv;=qd3OqyWPHdXx6E7f3z{@6i(Bz!S-dBw_SXMb59+oK0eBl85wJb%xb&{ zG}WpC$Hh#84Dq`j?}3WRWf4+?$<&a#q|s-KsjDND3Ijae-CxfF2qO-jEuHC4(a-48 zo%tAceN*2ozo7+bUy3r^9&&(6a#qO|WD3*$V@K~fIRo0`^xa0ZMpB@?>O)iz$E!a- zh9>qdBYA!*wdeI!B04-X;*kBXYX=Ze9<7TGo)J5KDojf;GREJD;OD7 zBa9ew-lyN?2Mp4J;kb~N=D-edLM*4mCY`xJQ<~;A4-3@xSG&JFg}U&KU9JgZ=z|>T zxV49I{S6ZKh87ICH10uaF)A%wurq|PW1o(N_{-(KSO*n(y>%T?Yyc82Z+xespA~7& z>JgYPnJ+zmlg2H`tqQ;1`XPBn6OMM0sRGW+>-^`Rf4&1u?>yOyNJG-GzKL<8u)eGk ziFk2<ibh8ria=MmJAt}d z^IYqZBn>%Ro@0*5q%8%7Q)kXzLU$^GBhAK77_VU}CuaK*+m;FF2fq;GhQ}?PGthUhrgo@; z2R4Z2t<-+!9S5Ely09HozftTb8mA#+S~!2B8-dbxAX85e`)sAS6wXB_qP+d?{cHE+ z5Ruvt?7is)`>U3tC#3{mZxnwS!5iD>z_l}!0h$%$!2cDYe2wPC3$VotWZtr6OUZMi zjwUSa+)K}w4=`GBVayu)ke338xC5ju#XaUs=+)~S<6X$s5!Pb_$oyt-0xv&OQZ|Z5ufAQUmom?oj0$miapiYrnT5BEHV3HYAfdW}#oMhqKm%3xZBs4j(=o2gIkGHf)Gg zbktMo?0MnlG1FcQzc>oc8hSu~#^LPbLYhja(EY@^wk|Q{evK_t6D?0+vGKb9!0+}q z1I0)YPPmUwN{2Y*J>*oL!h{{0ud14=rq79rZWmuEQ!b{O1ibkY&;eMPWXy}8ihNdO z_ywciKvkks4EwPoP^c145Z<|L48az|$r=f;>%lu#@Ta2K;5)|gE^LZ-A08gfDo}cv zq^@+l9zbCb>MKPrLI;^fuZhTMNI%3#hEv^95zN1^8v2cg;l$4orNv|xyD9_f7#KE( z1y6=I!g0m17tV?Qkmt7%1qF5qX#sJOMI`kL9>f|^bR^LZ*A^b=gBXL)ZnXPz7mZku zF&Y#mg8$m6uA(!NvrTC@DLc2a)Kd{*Izp;%B>)zn(vQ$z)$;(HVevP_@S?n7a|Eij zQjf{X)!RdhUGLY9OTQ{e8W3VSG5bbug9PC$z6?H5Bok0N?vnG!F#}lJcJacSZ8aGJ zXr&4bhayqEQwkwh2)i#kI;xj%I0FKs;%o4?(7=l;eAh2`eEY8o!9_+xmBD&qmN$a6qHu+^BD=(DKDTkrnF(XwbsZfIse zhmBQ|qsVn7Yq#cH7nS$wtme?!|7tbK?A|{#_8xLrb8A%l5IFj$XMb3DU-_%Nv1q=> z>o2Q!E#tyw4UPX(v(u8;5T&BxZwy7foTp#iI$k)V0TMD%%G@~9IHg2B!mXtpG1UC~ zC;h)2aI>;C^i2lF=vKrM3J50!!I8Q%?TAaQiuX`D1jCZI)l3j{?rH`LKCV$q9j~lY zH5lc^dlT0X%-F2y90kU;GOL+nRrNz&t*wdouln8pggmeRUtp+5CC%yoHw1>Srr=bq z(GjLp#ZminZzVdTIm27Ki%--8 zwFaf7>)xvyFOQo+bIaHcNz!E1%B=RxCq<1<+VrhL2GqCGZ%Gm&F9Z$e-fJo^mctHiqNvS zFb+foKI?4zjveiSdVdffQyqBGcNdjz_@Zn4dr#(Apbb$n8R{7)Ol{POHp+Undnw%& zm7erYtT&}G=?GT}omtaLSN_%)M%a|9NNyGd7s2s&FsP^1XiCZxjV7VW+1qEP%d5j# zD!TtQE#i@EuS~>@FvfM&9FVE|j8GMFxU9ZB`TF*8G$isel#ip}ki?RoM`fKgI=Df( zt^M;uBqqo}fzD0Csl=QCIQ){|GPQIO^Iqmyk=CS7pAXL&WVh(NdF(?9#kXga!KO;_ zrB4V{US23lON|p8IDK6xH=eK+Jy1Sb$!#($N0Z1Unn1-5_7^@b#QtJEscEML9wzY&p26w=VrbzfwgrEZIWt(07(F`^>vSSL;I;R7wCNC-a)HEf#+*`y_WW^Kz@SbteT6gwhy1~o_?WZs}`U2w73Dfn zNeZL8%CvD&NmX-6%L_@+5EZGBsbEvTn-Jc7uZ22tjWUg2`1B{WtDucjvGglO>iV|j z^&8%zE{+s#h!ja7_Osi>s7( zYHASq50K6iwEFtlBpywN>@xo< zRZ2v_uZHFeCgjo6`nm&R0jwr)BzBhIP2xhhBI%Uw1RHA7%0?-njt_VB;xaOUFN3Zr zK}ww_otTu7v=j;%5$Ntx6j9~Ldb;oLC@tLcw%6GI0%0L7d57$?{n;X5BO{qnVh`vl z)2B*yW$lr3vvbW@Bf2oJgTZu=d4Lk?>mZg)!^Zdb+km__PuCC2Lfz$^8}GjTwxQFu zEfO!QfTa!cZ5gIGYmPoOL{HoJ>VZc{nEA4DV!^RHp2u4BUz0fV^FgK~e_r{1x9#n_ zzx~Nht741JtEM?cW_pde8Dc*zqeW3SPe-$F{xrenFIRUZlz6SM>i+(-vQu|%%#8W< zXS>gf4#YmV^V`!MDKD2?da$Leap|Fg#g|+X#t@M;lAX!{&|0hoNU$N}a0(d)x?1Q_ zB`D-FQuwtrLWWPTX*2V&sBNU%3!?*5qNK#Ie0j#Xoe5-F4# zY7Nc?a|yTf0VItPrH{c)`4Ys|t-Cu8Dm)Wc7u@*jBb%GIg_F5*$i6zT%DQdjr)zh=$8AT6Dxb*J3?;1ln zk8-riz>;;nNzI=cH*zW;Ywnfp-)lmIa~3z3NUi8go`p9nD@JihbSo;S?Unpmx#66D zRWXEo? z!8{&&YOuKo0rG#@^BMJUfQcl3a!M-bqb8-mEHh;D;$Hg;Rt6{&QbI>`2>uBP-p1qB zrlJfH5lhO0%EX3Ptnj_Sj6rV<1e{it$?{TUrKbEWn(x*Ff`bM?(~IHBc~S;ylB`KiWko@yyt=-~xcT7h?5nijP(o92e1tDv0`#Wn zFjYpz3;oHeFtff zy6*%<3`o6M`|HptY3bA&o*Y+`Kd~K}FWXag(a;q%4$Yc3PyS;pip__L%oHM@ljGrh zfUANt#mZ^>OB0WC>ssp_QSjP@oZ_gkyGC;s4OXqxVL;n%Z?rO|@BZ>LY}O|yl#@=d z!Gw}L^O4POV8ua$x|tpurMyVxN&t&m*;!6`^-*s=X9|-pM;iLNea@w^>DUk5UBA@< z_jX^HZMksr&$cn_jf=CHU5>P>6<(jMgqcH+^;o{ds5oy3h@o97>-hPxv%3|nEL=y9 zw-7TrtW>feHUA}g%{8Q4PzmyTDJXJs>ySdP+Ml4WPrK<`lEfmJUi+nq<|ioEw$7W% zE1Nv4b_)L_@KaL*@xR>?{A3-*+t;@haJfAifzj(i`PY zCdx$JEOG0GFKSTgxNA&CVTtkrxi`}31^r$h&-s`|F>(*hE-csP0})w^?+;|xf643E zbmPjK-+qEfrqm{8@KE{H^so!^h2=4_@iGSrVp3CwY=|op>%|RC->uTSx=0Q&j%xt9+j z{xMVro$O%BK`*Nt6OUccPneoPC?%M9@TU#3$_;(bA3xXgcDt~>XdPU2E8DcjA9u8=Yg696xZBfi z!LILcI`u+w=7vQ}7xlW+@XDE`ZG&Ae*(WC8 z>qCDeu%0yP@q5$Gxm-vsIv5w{$IA)bXWe5(*nQVw$wxji#hkL9uH4B->ah5;)OTAS zS?%(_nhjCV@Vj9mfX91I9I!d?;K76F3g?wP9v`ozEN_l9`aE+oE*Ox3h9nH=tDt{S~4F_FAe zBo!MT-$?MBHP>sA+uHO4mJk4{99b(l32F@i?rlYG%EF)feDrN*W*2X-pX0Kv1k*qc zMGVnqlZ_KajKY#BAY0NZ5of?92yC|Yc7NLUOt)qsQ;xpp5I-m-uJE(kcluc2y22Ve z`Olt1D`S3TtfsCDp&0M=doh3?sjAD)`=Hf4@rB7t>wh_EOYS}zS;KjgP2W5XaIvgk zy|{)X*}Ua_X5?F$t3U1fT>~i=M^|tRt7MfJ6sdQL$vyw@EnU|fFhsIv1~*8VQm0Sw z6t9qOtuL%9o%HKcDwDJW;1*GoQyRXbW)fOR4h11{E>kD%N?+P>#f1-ix@%?t-JQbA z+1KZhM8LtOa;GK#aSJ;?=vX7WR}VOr!o7oAdQ*6{CLdlmG%IX!UZH7^XSWBER~a-Z zsB@sJI*+PBDU#!->15tnk3gD1nMutvKgJ-W{Pq z(&!x}a+1tgZXE!*LUcvvuU)%lb^Z>WLJ#lGn_mpS{YBeZJKV>Gr1TuUf z1Ip{9mAM``gJ5gJx^-V?nvQU=VUM1?hiK{X0zD0UZR_C+DAaf>D!8h8NtSbRa+$XAW!b_IV znl{O%+z?}V=KV+L)U+~vEk^B;b+{+#x|*IkMddWMZPWr_9`7m9uqS>&`06A#QpS)}RS3XNj%+Rnc^tc~Z5u9oK3 z{a1}TakaQ{!C9cpkDKmD7|N{>Q0Qecj^b5!z?j&Afobs{x9w0cDaU8~u{IxlbTNFO z|0EjwyW#ocz;+4h65A{hmjv=#wr;h%=aI8^*4#@qnvl^vS z;7icm&BR&1W}J`mfl?3=g|hUwuPfVwS(Rc=Ffnt=hrI2+E3F4gpsHLw+k0o`m9GBt ziRe0|_1w0j>}YG|29#}_4)ik$h-coNGj;hC%?vMTWr|Q5MiKTyH5K5pY1X2{Jf-$@ zdWJ+H8)H%~ZeH57&YKuTHxCLgZ~pDEpN#~{J&zgk9$&-3zfqtjj6&ea6%9Kdm_vyZ zuleO=8pX=3Wwf|Cm=~OhMoJ!9jJXjpuX*ey=Vw(x>xtKPejQ;-M9tZK_a8nl_#?Ic z+J2=&kiMrY+6??A66{uRlheG2B5S{THU%YI9-f@_KG{V>R)iHx$3oZBrBR*mluHN+ zIaEdgJtLTSHmXOOmG6PDoPHgOPBb~=qYKr)etXArg`cfdt^P%)(XUz$oc)R0P;W|z zXi&?wjwNAS!N!V)Khy+W`0NiDmL}O4)i$h_tdoX8%^v-FIBnl!J78wXZFQ!o3UB&{ zIYjj%#B$US*c7B?PE_RK9LPb_%do&nVg=*h_U#9>LU-@ob3c&j%np(py=tp^7jIJe z)&3gz4P8nUW%3RRgz$V??}IPg=QAa}UcGv@ZF;Uo(bI}(%25>qZ*z25#JoSX&Yj?y zS*sc)PNF4<7vnC>A&J)Rzu63_(Oggwou&|X@*hq$N0_kv28l-+wt+D3bW zz#}@-FRub3C#qOAhNko@vq+k?qHKDnMi)!{57`dAS|Du$9jVRU`moL)?7g1T6HjZ2XH3e#Wu5iH} z?=Dj>w~BlJ6A2CBwK&nY5n&fzX7!!_uNeO&cBa+$7|Vx%n#2NTof<#M%g3g`AN)$= z-y{>xKBkp}Ai~6DX~VfR0c+N*3HL1HG8*GGaoWwU{x^mz9WR^@vLAnW6Okc}zu5Q{c#^n31zr_YVz33m}Lo zt;xdF%B4g0OipgHic6&I8BoJ!>ny^aI`22zFNjZfKPALvq%Fps(>L?!P@qS6_&QY= zcE_7o2A_TWhJsDlh=_a!v8@A?@K4S1ke4f2hJ?&Kz~p@uS3wK*qZyWeS2{nAUu*@| z8cxIK=SVY~K|>YrIItU~bvOxlQtoj5uVcEy4p2VM;Nd>eC*+NRzAgBON772G6BO3OHAdda7A+tU0*6~3I?9!32XZyEB&F3J zUb4hB?{PC;-_u1&=pO z;IT%bQIz6`x=Qjnk#U+*jOcbMc(GwkjzXRZP2+keW%hISgd^9xI%HaNIm}Bz_l(V^ z)G25|iX+a=K=1dbZ*hH$??Cg85H4O^lR$f)(M3W^i`6QBs&F2mJ zJV;0(P`Dsz(N6roX`r}2fu8K(P4MkqjB^M~h~|2wunXZe1$w>Ma<#;EcER`U80||jqoWuVz)*@r9n(gqdYPfQ}@>k7n;2NP?AKpv^>$W z<-_->j-a?yn6a|kvfCY^sny(2DuAUqOA85Vm%&nFo{+*R@)9&%19iZig2K(rK!m+| zD|$BEvwq{oW{kF1W(FW^gSE7l@f!S2zCfp>AFS~Lkty=`}yP|bxAxxVA` ziA1Pqs*U)oR46nGE^v7LW}~w8_R?v(>Aim&Ct)1P)u8Mh$m$ZVwu*?2)r8_b&S;< zG|1k%a{10#Tl?9#$9~G4#r4@yi6xXL(nsl50pf3+U8^g^%oqmZXz(i~H*(u~66(GDL}O4lI=%Y<Ue zK|2_g93fKScE6nRV8bO|wITyl_ya>zj_7meq|qS2xxLO_c*xKXMgLHsZ@Mv9@CrNP z;-Ke^6>`bH5bvM>NxYka_0THIHt*)rCmQyNA1{-&&{}wCQ!rBM+dyB(;kz_uNtuXL zkah(464pTE?Fja+pLNHleTrU|L_ht$zW;fXq!TAT0Tm){Gzc_NIJck*l=_jn!-@)z zrI;h>)xD@Ql`WrvNDcXQ7co-e4ghJ=dBs*48aK(Wqd&B69oB1a#g76n*x=Jxe?EqD zuh)uArITfIw)IB#3d3~S)AOvRdb3rY&#?P5f(_Qa2C*~8^wzu{Z*`Z7Z)N`l&SQrL zEV-`WFppbLFFekwR`DfE#b?Cvx!$p5P{%gRF0Gsa9iIssCer59s`}8($}X=f39$OtvbrDrjkL?_A3phAeN?N~7vKJ= zdIWvdS2q3pa*d>k-~Q6-o8MnN>aaK8|C{gL{fEDaSKIx`-}^^pCqAyq$#XBB`Qe7M zmPg!v&t!Yj2*8)Vwvw3 zpZ_<182_m^{(rEOogQm00)FTd7E)J~0%hrJxNwBsvp(~@!2 zFN66?K>S7S4|aFszs3_^TxL6`?!N)(N5|9-Ma}p;yVVbx>vLx_)@N~EJQ`(L;5V^T zBgbt6{+xJsT&;@c3u~qx-nx0RVji0Kig#)ABe*fm5rV`XF?-Qe^Sbw7xOz(Ppm3Rfe7O^ zp$kU>emI>JRXk5)e(lk(pDQ5C#%ev9JgkmkQxDQZGWInZ-1B{)DBg#oJ?B&7z!%WY#$h2X#u1pl@8>U1`{|Wh})=BwBKfCWYUVs{@!S zms-bAJbcN`80-dM3VMcWb|4$_P`EjlT+s*zWnc@bBTrjllCrf^KzDFmY~Z=A(^~KTML{_$Xn^i!o=Iwa2A?9@l%b=FN*P^(m0lG1L2D)uOF6ph@SS-h1)=3v!OD z6dg#^qj!eQqa_ zo9Ie@*gVGU$zVzOK=o*Z_b}0XccF^ifRZ&?2gOFGjENrm9QY)H>=G2jYX`o9j%lj(=kPu zv#YNFL~H6B1TaFf36szMeEPJazSQVRLBq3`mH|j71HGB1=}Ae-Kh9Ehz~`ZE0D2_6 zfyL-2CsTTMJNDxCJ5n;z_l|(F+S!jX(;J~`8mEKGa2}ds;#3r=+=L1a^gX03AWd4! zzIH7dW?7TbAs3ZFt(VVpQbtSir7Prq=zGtfKVRBPA!4vMQWnYR212}RMuAe?FvC&f z*$K8#d@^2|p+u@q1)R6Jjuj+d4(w!^OZYhRKyvki&aQp!bhHybARgJbF!3#18m<{k%p99u>i;?ZL;>U~scAO&!U-x<6WakhuK* zFk^~Rm7@oVhrbD&K@u7IP&FHAX1QSeCR_(l%K~ArJ28f5q?}E9BjE#-*vh)jkJfK! zTqWl5IDZ--K=Zfh#L|iLDuM)vSa=EjY5Un!Nv0(U61a7%5K|hG8b8tOI)}{ zgmMxY7Ii-_8@4KMnPWK~)m~Q*4d(CFT3byz_YZ`qb~@cXOmA1ahz@HQ1Wc!QtnIrH z6{KuDm~>e%FP@h$y8H5baIjT^3gkHwxzUZA801uxBU304IFtNMIe|4+vW*0iImf#0 zV)q7}*CYkHim<2<$xT7rqL1+%Ev^DX_HU)#0}6ci#Ty1Jc=m z2WJ6!F;VoW$yt*H$;LRsI#_$d*m6VbrD8e$Of$(t(-TJpmk>ax(RPzdUhp~u=L=5Z z6wF{5`!!?kK@?j%nK}sM&=|b@qK+#-x1H~nYOdC-G9LR~k14xHtAM7y^yv})`1Oj` zRYSGX*U-~*D7DSWZg?ejGdH{u(L?vTQ;~H@Eydvz7WtS{KF~LK&VmI!-k8UqbeoP8 zE*_{{^6(sD<2sd{yn#v_&4Urt6dbtp?m(ezcvZBYT?Rz_sjb&2W?oMPTs1 zss;HtXYe8dLl#8~k>q%w5jTs{don zg}vy@&pC)@v%ocBK;$8Bu&^-4fto7td_`@A^P#~Phfnxs+75D;V$1;7WYXZ>IgFn< zU;wUQDu`|w)Aisv7R)$m%2TEP0PTel+5{jhoS>3dq98M!2BmrwCgZqm))yypoE4qX(@-9#jj)jNN& zFyldkh3I@&0}p(_n|+ls5S^mQrjzG?mYtKiGl*O?1x#j9ZeB&$R0Rd1D$np9Vt_kS z5o(fs40gs~&l5)xeEa zB||6`3(*$ja%z-9nETm=dFDS~Z-kp1YJ^akFt1hsj5WbbILbd>T>bP?ZnF*|s2Vl9 z9&~ko>5KG3B(Fdy&n2`1f8E3Rhsn3CpB=rM z>1)C9b30}0{$n3hKh&zmzv2(@CkhzkMO>=?$hrWCBlc)>t_f$fGb>v=j=8oTF@VXn z)oz9T7Zsk{m)vuO7^gCiyQwVXbHe?n7Ym&lG@H?aP)%xDRg=@7#fMKvgcife1AEtIfU(tKuU} zn@(tS(2Z$ZM%y-JT(?sX#N57cu1XGYW^wBGA&FYZtK&Gc> ze|w@8UJx3OTrfMsV{6ZlwHq46`i6~MY_@0aj5{yU(_8q5-H%(Z@jsiwU!xq`4>7Ey zR8=23G7p-~QWV}o>zi4Baz1_mMqBAS)=vB`n$)GM{A}u{K7rd{q8H8@F1QW!aWA zpbTL#Msae;1ZRAf7}fmD`fluH>NorNbi(KwD~o}xl?s4JE=Ma?@-T7yK$u%QK3Mpn zlzCjX$`fhQdBR`N2u&>7Lrc&1kj1OeEB}-#BN>UY&}%xpA?4&+ZrmI^Knnc!OMm`q zNAEii55i!=82q;J%|8na`1o&@I32@7QlX{IX0=EmHd zlAXVCvU4(B_@dK8y51aDpG7j-Q}8S5RS6XfBP9<05ro5FbV;PtYHvW^@uhL1M7eaP zNndnQ*JTWTW6a<1$oA70ttTRXNU7U0zZ+~V+T^y%cILpdwGZAr#S)#Vk|Fd>s25Dl z+?dO)+)OxyUv6*Mlk+3xlQ9%Q)}}$9RczIP0k@@wQU8LYB9Zb8y{?k1`1;y8_3=~C9Js3sc_Nw@*&)V#0dda&$wzm`APX2<=Q$S$e8sv) zSG{~1GBWBi8U>9<49z`MSy7TmSv}5}4?wYg(y@=n11`8vnG!*hG!0}TJb%`#S*66I zX#N@;+QmnKoFN9KY-!j}+?>7?uuZ#rb-GrJVf;LyoxGZ1PW@S+Z8#P*eptM+eFdpB z%2}fe8|IWSh|*RjmD_jj6cKs5Y1gUwI1?S$w$lVfQ|q#E_4oP$B|rarm=~2%qj+Hr-TqRHO{&HMyna1*$&8F{KL&0qVVk z2}{1Gj<~S108Np1&bDUNTX&&TVEhL!-g=lF7eODfop2#nC^mKT=+<j0oaIS?Pi zRjZjm1Z*f|78MVWn}TiyucTIfdi-a%?|6%@Dtm=w^Aa^b+{ihl6m^P7b1N&Wq?f`h z1c(6msEwo0*7yjA-Rm3Z@$k?z*^^cwijqq!P+>T{0A0EQwcm*-edpgJUa1tUlBd5i zN5d2;ilN_Q@z&ye#W%A90wBQDCaTc{4B$Uj`HKJ15DD^v)^T@%A}W%e^GGG5azPZ< z==N(0V8RUJ*o zPLHZyU&r%~|4_5z#cWDj31}iOg#rbzC!|{aWXa$G05B29Vv{&iazIlo`*!Egc_Jf# zo=6&up+*y>Cv|z#>3+X(8q#;YN6DB6@CV`$$1tm0$NewAhKbi8XLRYBXCeHKwmJi( zKxLdzQR2lGeQ8Yv?97hGm>dNXCMbnC`cWL9vS*UOwb(M_Qew2cCEO*DUvj!g2FPik zv|m2r9(JcZ3Yj3_!NwH+CgzgjEeT|iohdSG1B|!HgFqSsZdxU)%jt*2NKh=CcpHH_{62-;n~w!G#z3(Ozl8JR8CZlu5F1B*mjBpQc}E< zVGMg0c^_9x0<^0qC`g0T--G)}Nf`2XFyTseTAhJfY5-$A49N28&_f;-i1 z$vTw;=ni2v#~z2% zeD<5J9v@xX?^!ac$J;dvE%9}cSx@R| zSQ%xCG*%kfN+%}2m;_Bix4LAQReyc0(1zlwTu+jY9?9uGm3PFB(+n9Qd%$1v!NeZ< zy5`F(PK@6E9^=CgFLpGIEk=GWh!t^0Rg(|?K>oKzX#loz{fgBB9=h>ty`;z5D=+%&OA8dpEP0a1KT$8Y|U*m}|@rG}I;Q^a*toeSbKN*3I| zcdsZusOBk$zU{E!j|;;D4stijEw9;89D|Ngj%JED03X~NwFd3`RFWo)q1=wV;J}K} zaZ7&z##JMtTav>JfN%tgw+yn|5Duhzx!t+yWz<0NxH+ah-0wnRfZTVW;uEPxzpj*k z)7}W}lz+tckbz3`_-YLKFJ8PbDQkQ1{#9y&bfPqsr6=#VxF4ig3i(uMsz>6_Y2$BD zAn2MB_XLIoGJyEw;=TgH>13c68A59!aT_)2w+)vbi&Q;8SrL&N$P>ufb?}DK1j)$e z@c}>Q^%}&K5Q;w`+X#;Mh8EP6eAsOl+u3^(1`g%rhtQ0pukd8~2X3cOQW&_G0WC7{ zmwH@nOUXc&<*2g5)TV)Iem+(iZZ%SQ-ZlF%+b!`EahPrZHIz3=*gofKQq17M)F$41 zXL~M10u`iXx_55ReQdeLGDZVmclyR%X)$##pIf^R!m) zaEf;oy;Kd9K*OZ7K2N=4TX~&^K_3>RqgIGlygZOF$ZIxW*T6OH0Y3ylgz)=V`;Wg8Kear&V zc&SCt?yjJ|;l%ob@7b!g@IISe)z!$+X)?*3Q0QIT~I5?yI-@oYVWrLGoi`8-(+Ilv@uX#TaoM_ndyLv z3c)c5>{T|t`es2Q6uf~Gg6y}8<6@c$9Yh3MG;n!jk_SzRlJAhA$#A%M{IQBsPY}24 z!t9}`?8$#dnK)d(4?wOgP}#!qaQsM08|iW3QEPyW6y&d3Kf72Vr|fsZbO23uzspHiL`9iqWo(3TLlRt4iV); z{(}+Bo!c!8{LIEE5ilQ39iQvdA&Q<1l~^=shzaH`A$LYpou%im-C_cQ$z8u-yVu4*X#fo? zr_Lj4F*f7L+aJhf{K<9x#O_+GTVf0b&vtIdmT30k#c$g2jr0*BPhmh%?mH=8yc1HS79&!(-^7c&CkiymhyUMqEA_jRBF<{Na zODrvdf;ptxnKdo#L0I7y(nL-?Y9Ta%RiJz!(MdwY$rFe!mA-J54+qr5jG9@6bTXf} zTsG=4$KV(YK@!wMvgS|%g?Bs5vMCDK_Zu%1ZB81(1a|DiCPhGh-5`!0vW2-^yrS~z z{^?Ic_U;1J3y=3zJ`qtD9Qk9meAAiwN%)CA0It)}o^}&?A|}>BgSw!ql|B^w$KGoS z0xsM9E-FAk{W()Q6rbw3(sEXRdwZxFhn^GaQiQ&Eu~54LlmUu$f9Wq9xZMY$hL!^! z8O8M4=bANZX78OSR0VH?+URt8SZ<8x%N>$Gj^Hvj91xeUPyvMERlWH=Rc4SbsuNjI zrkjizO*p^;Ojf0NTt#oet`IZeG>`dC1|t?&=_Nj{@+=FQylUAQdSX@n!NQ}XPu;xP z-_Ue*iH1pZh>3LJkOvN)seGc--Cov%YTv@y70y+jr<@2!Apccqxo6Iv4a)5N3T3%{ zMvaj2mOC}>LihhDvD3y|vq`Q_ZuW^tc8Zxcr+Y(-C>w6l1(dTpC>4}0swg0#ZRkIE zIHIH*WOcBO>37F*h5vb9_Z4O9jeG(*0HnL8-TWEb&X)V{Z%k>*yXTDQFwAXp1CHlW z>;+Du2swa!zp&ZGs&1P-`KTp??30u6zMi+k{5ubT;10>iA9{%s+SDw2-Ni%YB5%%H zSeqN~JviOO?0OuFU~i*y;rD}EaAIk3kfy~=TQ&8gI{zJEZXbt3ONsnYwF4&PM9pfS z@%;lbO@6*zOr_ke;@9keDD{2(sc$DYX!PK zW=J*R;$O>D)H` z5XDE-t($>D3#hWAhJXG1!~0T~X)#63A9HQ(hF2&9oC_919D{@AiM`;oB_Y?X^c5JE zBeDlwjdp>Z)N1ho1>`(0eDY*S(T+W!m5RHT%A5jf=a}K0dif6;-=ts__e?ZczFCWq zg?5J$qc@y9UwG*3&Rv$)mn5Upt-#rmNg;(_z@v7#4lPQ?Uw-aZUECh;-}Dpq`ebAa z#!w_^q9iA~Pf4lhw%q|)xAIkpcleacYnXk-sQssUZkEi1IcP%zAlc1?2u>yRSvQ&l zD^O)Q(lKy6q}^*4dy=zDF|J#QvS>4C^$6ZzYMASt@?rw`b78f5m@lRbiuz{AcKVoU zRMK*p^EL&!qf)_f8diC|A+9SXjSh>?2pUa!ZfgC`s1Al1M@&JXjG;6iqb+)@8DZ=3 zc~plz2M=mSp2xIn{IMW9$&8dY!ah&Jna##txnjjgnz0)sm1kX>TD?$SMTZuote8~t^w~2b zOAM+x%BA?U>Njl~6gB^24v@aGU~6zCclFqD7c}~> z!KuIp2=s1F2y6%_`2*m9cTGv&>^^@y z9rxy_0axmwKi!276g7&X-+)w&k0$0+EPkJ+Q1Ut168FYj*%$j{((f`SdDw+ znAyhL+q6-F0E*POcxhE-zfz(+kY;jl9?Q@Z&4NZ6^A%~ox^7pHlhzeDgH6`P1~?w# zde)vxkzu@;)%O){Q$UPDR;l#-22+~~MQ~qK{3wz}Tb_1Z=SpJ0b5Gr7 zM}Wf>x`ACW%9J4@StS_*6D=q~;DtGPhvUTdD@^={PP5J-Jo{RtG@Q{0>Il0wGyjnr z8QbhfBCXme3y5d$L}hXB?%hy&*GPPe9uFt(<{@#Anp4c9f&oa3Fe4|R?XWGE(J4#uD9vBwby3V*tV(ARK8#G*k&-}~5_vT= z6pO-aMw8B)>apvv_ep4=V%Oi&Eru3XIzGuisdH#SEYjxwXnBVnCme-h!Yw{#U!%SY zcWb_ZLh^^K|47kgn0KWoR3tjoLbv>}nC~R762Z&bkcUj}IT8vL&BPtMVMhO&jaIAP zL={cpR6Kt_R>O9_^)A?$XdWosaF${Uxb`&jE^^xQ=d`gRLBI)kI78)qRDvI^t}ZyJ z_1%0TMJE?2+L{NWKvcA7V~#C3OkzR|Wui2enj7sJwbf?|kcvjLX|k5uC#8+SJ)wcx z>n=PdodJ6IY=0jTgjGu-w(tut%Na-tS-?EWk&O27V|8`#C7!qqGh{y9pd4ODD8 zt*P`0EUYDw2P($fvPFpfmo*k{cR%6@>0;@MdoO&lO%jL$K4@w`Y}`1Kn4er0h6IC} zVG3xbk$16?4Xba07ZF`eLg!ui2uI6v7BB0rw_N|6){Cwu|M?B2(Y`tychx@rZr%)r zq`U}v_QJg+Z-z{9uAkztGch0nHx%tz4FhnirBYZ8nah{ZnnQI)&}?XwC92`up3`cK z1jdTxChtfCd^|8I><+{wR0LbdPpli(;6k1Qz$BXY(KuWC7alOke z(UqGs4Z>Lo5H*9$ysz6SC7fUD-mHF6F)VdjjN-f*Z&v@svbkseN4a7C9aG3(G5_pD zrx8`|nB?mBc>HD^ePF-;`fp-T`X})Ixhexmty*TQ@Q95vAGPGVUVG()e>C~Lp$GW; zumAq`tAFzD{wthb-S5$9Kwf~h^tjfKNC<9Mn!Jbm_vH=bFl@)Q!0^Gid`fA)iZGB@ z=6%lghfP&pDtoEIpSxCq1%|R_UEfe2quziwzx{1vNL%ZW&efmut%C2iy?5N)Z(O^# zZpD)S*NPtjgz~_$SIaLgs=wq(Zk5mMcw)AqI_S~wCHXxF%acH#%Z?klV(!%Jn}i*l zWs=7kIFVnL6)rhb{co2gHid9p=$qPggQf{0NZ&f6sC+AkRKnwrj}Lm)^qZO0U-)Wq z=Xq_HjhaBEt*N3W20C8eQ+WRsS?{jGP)ix5VZiu45X!qy*8p!jdwF@q@8ewlXCQuT zj=Z-?goKhXxafIu54$oGTr_kzl5_v=kHJJF{S_DAt{G#8=6w%ipm1M#!D+e_{VOaY zyXa32W40G?-HL)%%LJs2Z+Hi{6cq4qqxT2H50}D@Ln#EUF~{YhY8;z;bXnk`1Sv@= zuh3O=lrv!0*iqbb8%=~MF?vklJL*jlF4=ABa(cqnB)fNBl0hJ|dU!GU&6;}Rh<%FQ zEof3D#f+o?Uq8FMe*5O6B4-9{IJIo-jNw*Ud&6J%;We>JolzZ z;tjFgo)VW7Cq<(Gtz{CE@L|iAA(JZa2Jca`0<@?%ET@;M58X7}&hyX%r!8F(@;&%V zB=?zOhq(16Wu!IUR6HI7vFz!NGENnDn)Ta)OL{< zQ&Lh2Zvx$=DX@gK!>1*=Ybo*r*+}^PP+HOrt5(@;zV=jt1{|b9)45=IU^9yL(CB1w zFDX|OX6$(VdbAymKyqnxr1wZb5ll}!yNV&DLlZmzXY7KQ884c@&OB!p~jR0Syopd1c@9aR*@9LutxYC?H+dGX|;P%tZ1GLuX!qv zTI95|ltsjt4pc@t{dgv|r3wR}=;5GF>9kHeIUST4OYNvEjdYYFC~g(mx8%!m<(h&K z1Gwm$fDF6f?qE@D&;kYz`Q?$TBo(Tygbaf_$qz$iDIFL!eoEAwF?mSwE;Gz$Klz%V zrR|;0E~TeZ1ew&>WTbFkk7ZpRRC+Xu9SJkg`F*x>-acAxBo*mA8%I803G5vGSyBxT ze2FBB=5vF-gJQzzm<0YQTLDNK&>`vCwXsHRnuN-VG9mPI=%Ecdj0w!$V^uHCr?NsX z8#R;CgH%HatmJUA?O(5xUYRtylvOh5p?qEsj|Q?AhMPx|%vC!+dSqYGJdGu+us%xv z9n=$wtW}H3LY~Twlhuglc5%%L9uNyjB9XL&z2roYZsjQcgQqQl3IODIS#%D}xE{r> z&S8y?+5N?dcluAPKK5F5Q1e=k&SE6p4I;3QU?(|$&)!~ImDGjph!q%gKq?MO8rfF5 z&3`nSaY>yr9{5mpN#n)-)!ZVWnT^rWo>93PKxP!Z1krGS=o8KN!7HZbIfo)P!j`OSR&gK zS}oe%E9^$`npH`%P?a<1CGNaJZnl!g*s~7Q^A}IXHr#|0=#PK=LC&_DMi6jqLM>qs|oW-QFRn3=J1>r^JFNw;aY#f!xt> zFGyIZ<)cnhn9(DcF(7T&llM++AnJ+yL z4q=>)lmkp-7%gfhAgR%|a*8kC&&GH<$0|QRUyn!0w^8x5aV7@NPe0dG-xram&gIGvZsCUX8wK>yZo8Q&w z%o_TS_6qy{v~3$v z|C$Rly2Fvxj~+e33L)-%Re7fyH#7U0%N>0vp*jO2Gpcv>V|r5X>E*X>?H8R=MJQ|x z__qm#W-bRf85?F!ANPUf{?$JY0MFk!W7O;z*M#AA zci>d2_vD-wUHdI2w&XH6mi^AIjDERYK3N2mnsxHLoLPLr^dy!g`;_DUz@Zsm&Ms

H-E+FsAKv*6Cb74ezB$-(ROz-tYufLs>-O$QsotV)yof4O!I zO!v5z?`{5Abh;$6(y4H0AZYK%DIqYF087Gg@0-ue`j_;_&fb?^thQw^3^ig8^FE7< zV>g}e+`j!#rL%HVP;=Zex##25XLU+&NJt2`>xPc=`~iiyNv@!|n_E^qUHO+ByJ^Zc z0PT&q(5q&dj9L*A^4dc8YTn|5WMeg*5m`pVX1xFYr+;tG!9mL4sz`QYumz* z`A!UN4li;=MjArz9Tx9gpW1aVk}${pzYR>8IOcyxr4$AIBbD++{HU5Yme@y+b~VOk z8FncFM%UT2vO$OT2BDPM4NGVn?VK7-P^`IPogfXw3RA)xifg!y(?vPPJM^aT)p-

8TmeH!`?Z*UVmLi(hIG~llH>NuNkir0oOKV2lLo)?b@F(*EW~|2n-ks^ zb&)bUihjB`|N3BWuH^oc!fG7=TSAMj0N|Ye;Iw-OX5$akXO(cb~+TRB=l@vN^3h9Y7xI^<`RdrHv z=W^YpQrp|L*=IkYQzI4N>f1Ox(^S?nSVUEzD0)gW>Ie7D9ShyZk+KG-FkR= zn9g9p94}@<@HxMQ;13J#8_6*rMeP~x&E+1$B@jsoFC>JrB$j9}MWAW245Faa4%>{u zyGni`?$WVk7Vw7DG@5K7&W0A6Vq+>bNN!rh+CghuOrJNNtOCvERqidmO)}xmVZ4I8 zk^0``Ldxe9q)wy`$gc;3)y?<2)j#~Oj#Un0v~g7O*f*H4KI|k1La$VxNLyZ%ESOSS(L)7xe4uilEvb>U@J{3C3%qlBOUVKW z1;-i1q5^!`0vjb8k;I~W7y_ehwr&?_!KC)pKHJDOrD z-@QULX)WbmQltt9-O+H@Ky#27T-ni_2JLzLJlAv%TAJ5=R=y84+K(`dn#@!BWEbQd zSKnD%7w#Xkp=PBkLY2iP`!R=&-}pOU0iI~`iyU6iF{0#zE92eCynQ6|v!2o7>CIn1tTfMcPjOq-B@xDc^ zA{eejV7M! znkS+_MqEV?Lkp6C7BcFw6>1^F-<-Nu~;4R>lre{9=!+sD5j;mSwH3dR59}Au+FP zUXR}q@h3h0C$kvU;`dtXx|AKPeg00s^}lM{Uj+TnGAaD)EXI%z|LVGUb%&gwnx{$j z+}}O)?Z2FL`2Ds2Uqsm*Jx(PD@n8jHOC}jS1(t)O&z{$+{2Xb{1_%lS1sP|7w*DGZ zmS2;*>H47@N;SWZzgbN^+?0%>K5AOZkwpAbL#BY-=!`;B>9)Y3`m2{Ywt4v4vW8zR zSkU(MS`|MkJeePmC|69?FOQRih$SB9@R9aa4g}OP(pTsnq~X&fwaH~S2r*H8N2Zkw zt%{IBk&;Hxphf0*T0i&^Ln;}Hwr3NHGRF$21RJYPO_9%!yOyY&frvsgsi2MN0SkS1 z0Sqg=hCgOBm{cChK!`5e`swWK^Q#P2DrDi#0 z=0?N`XU#-1job_l;D`zis0^Aph9IJum1DY5afDPvzySxqG!WC&5EW$%oTn7fL}gGA zcwTS7&N=tF|8t-9UuUiVdDh>x?!8?H_TIn!9X_A;^yvf?rB{Um(Ah*GqEt)bXUE#K;8s$M^?y;{=aK(sot5PsqsLPLHk`jK-paMTh(S zQGAQWa>#C%E=Y`9(KuILd5r2arf@*Y{0YbOIaInP8J?JRr%sgBR}cldauv)WLkplW zrpXqLWwjxKpu29-WrE9lgLwHqd$KR<@fSFD^j^7eav5DPj0SHttut_sa@$Oy+})nE zH3BR#el`b@;h5H#EQtZy#2)uS=2JsLH9b^l8l;8q7v9Tq1|_#F8V|6o5(*>XZM^FE zmh*rPE$4lkY>&u-_o1ps#RQ*0b)L#TmhAfbe%u#4&;CI2t0{N(PfD(oa!66eZ#zw{ zZirV7t>J&vW>l470$j`>n1Zf;Fhwjg722vpLMq-Y;rqqC!4+ zi!xiA`vnW$q7LE85`7^7gg-MmZ|#>xtQCLu?j+ZH1Y^g<7ktqh5qEYA?m~y6#;{zD7uBHP1QC7+q&1a(Yr)Osc2`U#@O_asu1S+Y2$MHUu)V1*(tTQ z0{xNZeOyQ&(Cpb2NyTXVn}I^4UQHnCP0SUT@<0!daDJ${5lnL!uh@myEGfve=BMvL zRFwyprV#U-bQ9xZdSe%l~% zoa|OG7Q!Us_g0)dpZj!RYZ5k*+NZDwl=p)vY~8tiAi2<*s`;#TT~*e&BKl~=8CLdT z9+PO@<~{ukUP#J3$I^L)>BEF-vQA)g#=Z6_XQG(L5xZ>QCV2}%>>;`-)py1euq-VD z>4s#8c1k-KzwVHVVi z2iU4KxV_2>oOK#ZjW1lQC@O9ZWu@2u+$nw;-(xtj6zBo>OEIRf>mQ)U;x&Am^IT{V zG)jjxRI^vw47a;K%Q%^HQ5n!IBdeSdzqztoM3SS&2btP0*E*=5oe5cYdF5a_PQbt# zVWt9jj7UIKK^II=EK5DY4I^wHz;YmLCO`G@vGM>C?XsDVr(j4nP7ZK>=fZfN6q!O! z`%ej5;J{2L*5AN2bYMSkUs)h3-?NIe)94zM=#DEt!m#b7?US0fkYR*KFr!IYXV$!o zJVG=))5eUGiks0QtlkfE0=A6iKXpj?BMz{7)<&KyRD*X>LrO<{CU^m4wPyu>>x2O$ zCh76>+0uy`Jm5>ee=hCEs*1vA0`>i~g=!`hlwUnOkBF`07q*}g=;zyY^i>K1cc=^& z;FWr0Z=pg}%vC>=uIY!PuFieD-11hZ_nna&C_WA4J@$^21No=A_h?U^<2ZkoVpj|w zDy9gRQ@%Si$U(2jQ%ozi2YAS&|8h)iNvIK|a386QIcmBtW%AQwt@DpRmyQ;(T6(;EB?TBvn_jUbr7&URyiK1-E8foML+pu{IiI46c;o_XO- zBav3p`R*>CyX0M~Jsf|PkQD(T>JB-SawG|3H+#GmmSWGsR9uhX8-0reFYW5_1sMkN zBq{_3ByNMAS%d*_#4%t1D|Z)5N0DkABaX$T9Jo3|M51tRX?jx898yj$pins5D+!9E za$X|Ry7X-X+g+`&GjW)Vf9>fq|EAr)9JAz$y26ArQS=h~B4eAp z?+Q}Mbp(-(aIKtPMtzdHO$k-TmCA zta?lAlWjZMRr-C>2qfLEIyZll>A!QXG`ss^`7g`z_TpSCTvYleXC`yY2T~R($TT#L zPiP9|Bo$$5JpJMeFPJ8`9=`Wltt8XPpUH4-nHbBaRWcC^!05G{i$9O&ubmh=>6dZX zPn81#B8V1YV@6J=6<+pdqc@vwm#bbj&8b)xG7U`U?5Bgafnr%?6=8aDz6d0=G4v9& z{FN(L6cm^B>b5&O9`po$(rxdYaP!8E0(33?eCyBc>0gqz>#C+-Es8wp*~aapMaW!L z7#iRR9e)9Br#uX)cifkww)ib6DJjxM)<>*j22YOqu=6dEx4JtXK744^vs!i7ueQVGn$H$Cz%^2@mGuK2G$8V&eYZI122(fi3EGz7Haxwy z?Zy#@wp%?}-h8W5D?enBCmzc}b?J-AA^YO37(5rnF4qs2H+`%t$F*pC<&MF5c)nRu zJzK4%-6fmx8{N_l{;F_FESr*}Qz<9D{}sbZMjx7fBKj=8S{7pU%v_J$kqCbswiW3% zA|2Mf)S6r%fF(_mYDn@YVkc(R&*FC?a01uAXfm;;BbZPoD}ahn^LE$Jg0*^8Fw7*)Bi%TKMm^zW%k& zz4)c$-mNus;DR}>PuicVx$uSho1T=73d&p%pP5~Hu=vckfrmzTZ%*Oqq_Y$y6XUt4 z!t`{XV7F`dzGI!w-k`_=1C6g&=*B#=Wwzt@m#eAk+-0K371A5)WtMK;^>EIdx7UW< zTOGX1KMP&6Bm*4E$Yx7t(lGFYH)dRM@F?CN_ov_8zZ@FOUFfZ<8OlG6yN(05RpNyu zRnPS3(*5TK)cu>m{@8DuZtZ%&0H_ah5)1s-UgBU!~zBQB_-LII~Y9J3*lN7V_2j|kTgEAu)TIlfn7tlP@01Q#aFHh0^|dqM5(a&uOV zsg~q(zwLk!S(kCg{->_^&LmeRaw&t)qKVt7zRL&9Ag!Aftf_nKi^>rlaOzmE6^`TX zR!Z&bnD)o^;G3 zHh``Fg=yQ<;M)kDRN7H!$#yT7{2(qxhu(J|hb{6SVN=WLL-PlVYLFrUuB#1HUS2vC z6^Y^m6bFa`xH=sCFg`%XFZX1A1$ihf}`Gx?c2!YrWqv~US|FoM!&qD$06%810=x?q)% z6L$fb#a-(SVNor^-4&Mkc!I*4!Nt>2ska@mzb`M9m>ZNI*M^Jc(6%?+Zq1tWjwCo8 zzOfH)`%$tzxBhC4h0!52?`Eply2xzK6!XMKGdB=3kOB-HoOLxN`D<)!p5V$+_sS1* zd`Vo#c|fP;G;0}h0Z&RClc{1?)si%`RF2_NHp6I52BF*}`u0g*=bM@cg0wKuK#LYh zn=fO*q!9m!UF}56$Z%`OS+o3D$qt0~_I&jnFBU;aF^>6DT_jZNXNrI&PN%1*-|+RQ z*(?J;H{};|iRSu-toA869mH@vBel1N0`{tSHiJ5AV-bzDePyQIJe+ce40@{*|8^Zv zJ^kmerZ!k^3%=6)LfYbrxY!-Xk z<2uXJU|@I2wqDXp>-tM7J{_u{7-3UD=`Wzqk#a|^PRxU}h(Jz61`LjgjR#&Zg^EoC z77M#ibCXJY7!sMSiVYqcDe7*(_ZuM0?f?5G#UH$9t^UE80qo)GRmM(F7pjX z0ss>9lz0Y8kYZO8nLPsegaSN`8gX7{c`M46SV$9xs}j2`?Y@5~RYa5QwIKt~^u8>!eB~LY>`UNA23hF{?WoA1#Aw*O8J5R9<7Dz#gX) zU<)MUwV1RkJZCK+h(HPYW^z?3Vh-Q}(q{J7B3jTOVK3b;9Qo$0)-@lI1)P^2(J&Q}CVdg{TK3a@PBMd)aBzr(M zO;86}H)z2}ygruQyc7!`B?9m0v(zKs%qpq#!m@xBcYo$ar8(NlI5!8nf=V(BHMW{Z z#L{ZXK$$$fDi9+#v6*)%`enDTRR@ye;KVJ`WyT*4sk6~0$wWpKuu4?&?vcf3y4-h) zR6CzrjAAsL;#*C% zh4WyPKm!sW3o9eDAFl7ux%7%4Au6XKJe?Sl>^V_B;~R;ZdH)1@1Y5p)Weg97N8@eT zBPdFtc8WnQh~Rwvyx3nbvVb+u}zMYLP4NV*r=QGXu{KMo=T94h_D2wbW zNaG!%KIgQMR0N%02v6>rk!OYdC~KjOZBGpa`#*X&TkFYggS{s0#%=>%8XkSAG|uTQnQ% zvgzbTfj`nAo@a+jHh{cK#{;%8XFi+Th|N{UkN6Or6!HqIq z0faLuCUn+waSS7QSJuClD^_?8|Pib(i|1pnic(Z?Gc3%5O z9&6}7xo1B8$2hci{&6JJu79#dEkZl}*ROxaOYJx2iG|9a-1W01rzeaY?h;fDVs`r{ zbJf4&rT*QY>bDlVr4l=O;Ka8f7q&MXl5$wlWN{z1yLc?+Fg53x?V_Te|N77WD*oNi z=f5tnPdaX*C*qL6i#YKjwN6_w8~V{OENCepKM# zmamxkeEinwDpLR2l(B&sFPZt2{c&#dPAWm=!C1}RCHWswP6)TJ*@xddJDbWTD?2-E zR)c;oG6iNJP0MP^|95*06&b`Iavwc4A6)d{agTXnac4pnRK4OSEFO1ZPr=OH(R;tQUq*@2LdhbkbawDEks6i=DQNOu zZS8Hno1ZMduqXh^NJQK1)Y`$EIHVQjSPla}*EB}w1P(QCu#AokmnR-Mxg!sSN3;?< zL1yJ41Cc!fnDU{K08GKmp=jVvGky_*Arqpq?fab~a?B1+x!3}`lXE<-t3f<&K`+$w zR)Y{Mmye|MQs{>8T^=z^qA$vK-~CZmA1$-prmiKun4*Pwx(lV{qx+RrhaOBR5i=>3%S#WdC5qebR?pqSE9lc81qXi}rMeu`=Iza);YZ?l=W>inHO7?VuW7 zpDi)ekt0XMGU{o3{M8r401ahObN`|;pNxk7%6FxRP{$6(DNqB!+1d*p(NBWs@W(U) zqI4)?(di0m7kvnBGziviYtaCkBm{5HEICEFkzOoU-Mj5Z^(*Sq0@2sEa!GU5$T&dF zED8cR)%{^ikUD-E*oorTxz$yG^Tyi_`uSGeL}Qu5;$_RyC@BKg+R&^=$FW{j^SakH`%rA*5=^wAg9O6!#-n*F!4bSVrg6HWMhcm;*D%Syh3%PDtRhW zB4Rhxb1d#gFCiBR7wBMoDUQv?;&;>a2!|`uUA4F{Y9djnfgEV627!jq%sY)f2!UT6 z5-Z>XML-5Z0~JyNMeYt~&kv^TomEycOYpX2^;CNJQ&f9M+{!S{v0LW}ImfHW@Wi*N z+;XW`Q9obc`AH=v4$(5QIeBryNvZTThnlC4Z_RlnikFqBD1Ldz1-kTt zKD_h34K*a#EjtAf^%6n&WSc33KpYv>*V!xfKn4SW-s?4OYDE(v?E5hDPTp+X{YM^b zi@Z&V5MOSdBCXMm7+uVyUV*Dafsvhv50_VUIbIUy~STLwyc%Ip<PKJ>^PjIk z4&+hu4uN|W`_6Ms>U<)XnqAH%&E3(?V>{v>Icdq`f0;3wo9ciH<& z(U437h*rcjCO(G?`!qEk7ld?sEcHhuih#f1-chkzJ>&_Y=Hd$5I|*cOGizlheNO}@ zR|x?cF<)N)^!}Cjk>Fbcijo(BxFU{pbr#@v#GI%RH*VmK1pfxCQA9U*W zyYG=;GlXXdnLj1gpUy?SC%Cixgi?T{1dh3fjVT=+4^x2Kmoc)!a?eN;i@cKSg~ZWn z<7$|cU92b*M2LyKINK_79@tc(jM+v9JXbG=n$k{0VSoDg@#Yum`J<*CMm>zQSE_d7 zfUm!y|5nhuMm8+@^mlXv3RKisC(s}F&J86?;p2|vVL5ZhDMmx*xk=qfdX$?R_KETP zKRqZq7#;w#n&;Lpy}(T*bId7aZ-*noMJ=oG?R24&vZZwfi?Jv@Y`R%309M}W_5mSR z+7$S#B?dxiO$pB)EhQyyYU~5>$BD^j`5FsDDxOIlBRMHIYpa~Z$D%F-#i{2ZBf~4V z6N*%Q0f@NOe{)%m$$qyIR3_2Ktg=1+Q z+q$#wo>ySH6X(3s%yTt3)H486rR1Oz=|`(z3Z*wzyKdP-j^7v}ylH8GG#$gfEMV8` z>*hQwxLQ%J+3)?+m^PTGEKr`Jxe?|{>s3lq4pZ6elC1+E2q5ma$TQ547y+qS{vRP< z%_%9L^Cu_AT}-F#r{u|;@wC|#9+$>yyGh*f{Kt*x!JEvDdS zy}1OwQ>VuDZ+QCg2bK|Km-o(nI$f6~MBG}uSD_ZoW>kad?yvZRE7U*8MeVXEh0or$ zY76*$+{)H5ALT*B|U_9X7C!jvS|)E{;q3^fJPgjNH1? z3eyC7XA|WKT4E1OUY5Wxx_fm5@Wd>^iBH^FtpRj-V0zl$oRH<2k9P*Q<$_k5I;AZpM$H=gf#R=m^9(GY&={La+KUl3@s4o#sP z?P}#klk$Xi0A@(+Uw=o}vK=^Nj2&yOsuM?w(!>V~6#WIZ!@KPulORX6(1Hsw);|DR zYbX^}1M-h`j+36YNj@z2^1zoxnQj3?xF?1D@m8(p1j})T`?w|clF236irF=F#>%Rs zKJH2+VrwqhuQq5$@y;CTy|=unjVqI^Gr1w^jJD^=$>=AkEr7kbwC)R104TmZOdUT9{|KUTQD?k-{Nx2|7r)~HcZLc2Py$AOY9vV$ntG1)3G zFO!{-&V~_7LPf9O1!8zMy1N}ez>p0$jDrr*)?>c`3v1i92rc;97X3HL%7M7-!xMMN z%Q*=C?2vk?JoWRY{-FfycNkF_H%k$Peldbt)R29Z0(wHC=`)To>3Yr_n`f+F8d|VE zi^J0p!Tn}g@^nJZC7QNjFoC9?UpYmMaN;wV8`;eoL<`FiIJm6YXVXLf_-Y|5KI`t~ z8mL8xoBGaFc%ZM7DbMzBC5q$l$f{bzIu)l@+#1yY(v`y4S>5*0QOnVW^MQ_L;(Brf zBPe59WUp=C&>i?I*?RV|4L+7@@)yjd>NkxZ=0u5TFdFCW-MqJqPG)ftX4$LbLcXb8 z*tFN&b@zH0Ea?^($C^8otw%t&bb;-$@>`pP_NMTf?@&Lo;uN$185QQ)tgh{x%M70* z*GYskvD+KAdOz7gC$J%`mBV7dTEYCuQ@Y{!X43>!s92+$>`*V`fzEf)AXsK7R{&B4STT=5B~Dy%aQjOXoM8UTklFjICY6nmd(Mc zT{QpJhj)*UoCH*xdo>|J9KY&Ernu9Tb#OfN$Bi4N8-vEHpW_SM%8iQZ#VX5t%ZPEF zZ|wT!ZF3G(Wm|>7Fom1CojvdZS<}~De?73GxZ}AYNP2~e!3;?1Bo1QBRZWBf5)Ufh z0O@3&H6=O3#tI~O^G7o(Z)2*&f=j(90%d<-1?yo07Kl0*j)^&5b1#^N?!LKV!lF6J z<8s!oDlfhSGWm3USxE5o0>`2rAsbisJ9{Y5_iXS4O{Ky)p`wU$RT+rN9o2gn^UK$L zz1_pomOlHUUs7q6s2pUvchf69sE$pQ3x1Hu_kfSrd*y5|uH8Fel?W;_fc789IOlRpZ zsm!Xg1ufC&ihiPV(2Pym__#-hReFnSqB<*~$4^fPsBkat0w$85zmm(=Ag8aaLbBI0 zQN)8dPE&AP1~zheWJI0Z?Ori=cU{{AJz3jr51COVMH9-EE~wXr)tR4gZqgq){!zJy zj6PEcX)Uu`QPFWVjgo>}P?)Q;P>{9)EvYYEyWjPDC!snUbN;a9HoZ?J^rvz$SdYLf z2$*i%-!na%HlJe7d6uHMz4+?IGaxY__WIG_CZ|jct{aA;XlTEF{Y=i+(8 z_t_W5idtW~cI6nBd>L*NT(@7eO|#b>S8#?zvQBU>>Ex_)-;H9Ai(xyv&tgOD;|U0V zj~G0;vcfK`oZ1l#LX(Pl+wuod8cfWdagkTwsM>EwclJI1UBG7HB$R^~ ztzzHnsI8@y&byz!alSVin9BWi+pjV#3!E0$vN)!H`Ce$3C$cW;`+#-{mB>ZN!!_apNz4s-K22ZqzMR7|y9 zy_+^_>oiSNk6VWQJ08&jc%)xxJ1aSz6GhnMy)Brhfg@gkGJn|*f5@fsRA&eYbF zeY6DIvwBRX`OD-kahe&Y(QPS#rF94_&7Mc7B6%JIO)^=AMP}?E_a>1fI|}4Z7q5^F zs7H??S_{6;Zs)HM_ARyXbN!B5!;>R%aV%nxksv&p2$0os1QHn{fRuxm*z45JXP2uB zk+@b4mlp?YCO?yd#rCz=Ur*D}Zt4gXp;EGoS>(p?pb6toL8G=8z+mK3h=Mg}8UT1! z@JIev9;4XlBaA~888!q-rHOqAD-%^3+PCRen`8kK45ZXI-3l$V6rR`&slz^3YB4Ro zl5=*_&qhR6+9FAdUq6)=HB65=vQy2_~ zHsqitpVI((?+l8CAjKiPiIx<~s`5199683`vB%H+ zIfFLM;XxGF>2Kh!Twfhu3R@eO`}bprDO%4e16-(_)3_T8Foa8UuN7&46&$U=0E9p2 z0-Ntu!^EMiJDqfEqg|1UhMveJQost&!?&nl8ubw%#hQ%H zxj$!%%hh`LHl4UxG)WKU&sK*em5yh$3pt8svTJh=19NNm1q!@q4h|^Se)`FXLsRay zI->>j|HMXiay-8N8uLjmuvmqyiim~H3>y?4P*GMHvv#+!2vIh}b2yM&jLUj8i$V75 z_-JoWMpX#EV>=C|h({`Mp1V!q9SX)(uKS6YmjV@Qp&@0;qd`?DUpSktVb%CU@YRN*J3#m`O0twd ziyx4xh0_TnoOhXcG$EZEb2nSHvUWhI&0PWRX)SJBu8X`!)sODk(n@OgXrs!3UuNZH zn*T+~sVQU6e@$)hpU&g{6M4AO(M4Ht^Cum!bQ)Yqhl&~HSKQeDyC~7W*y(-OC(Vqo zT@&3-EMh-XQtVzBJ-(&N$kfV@#{c?HB&q)^3H$uLn$7=rvHVwDFrG-ifyyKGaJ}jm z=+}W2>$4P>BRO;3cPE*-Zlqu1{{?jkn`K$`w~p4rKKj=?`rnlX&%Cuys~BUAzoF~h zL0O{`nN*MtF_LF##)<-N5xJe-^w9xT6F?3v=G;@vEwJUlM3cGHc5qh_&}P8;>i`%6 zca_u$JG%@TlFMrdY9L^;aBg>v+jhJ!sT6dE5*I1EaJvLDYb2UqFG-OqfD}*DL3H!5XzVoPi82oSH(!;+|256g$ne(doj|K)>Xv%&*nZ zNBUiA3dLu0vFy+^Ep8A~CC<_jbeZFXG*c)Vp>^>OD6_pXzC!JzF-~&867#Id zT%eT^6vyrlN2$l4bl|C2@Mla|`Hh$ox_p&h;FL#3n;7kG7jLS4zEMP?9WsMr}f zVR_2^X8TiYbXYWpyme=^hgck28P!;8+!G=vNRxr^9GSF2TW}#Oou$pMKlwb|s>5GR zH>mq+YJ1IK#bSe8u|K8k5Rg)Hqfisr3uYfRmX4PCFzn_}hYxQ!(u>Gxt!%|J&?hL) zo&pBEU9L!x{W9}9&rF83j0AzZ%Z;o1h!5)F+m_gn-75Gni(m&k580*>Un=w$pL-j_ zoNpNfWr>qp2+Au#Pc(2tDb)30Smk+uJPJ99VFoy*X3NosEds1&P_IfC2(F;&IW8{F zosyhrFHOjzA{jy{5^j?sy<>O~3@N07_J@hjG9Qm4j*k zcKFU#1JG~f@9EKSd8YHN6<+wf>G~#9S;aCTXqXo@&Q4zFFd|kYWV;|lWuZE4GEbV3 z$CKKKJXL|FjH_@ZFl{KQT;k2aXqAU)2gk4^s@nqI0lRW5&pBfFzHwkE?eF`QSa*QN zVUf1NfGhqPXfT>$Us|{-{bg6b{+PyhsUqSOQX@x;u$!+&eq;d+m6H%MtFOGCwHSpF z5D$W?U`kYE9cncauSp*#WSx4+i8jXZm$TSkWK7iDSWF^LG!kIwWtmjcn)*S#Eo*7S z4+RDT9Gr3X6c&nH*?;H6_=4nAr2(St`yqOAR!4oCCbPN&P2mi0{PqMWIdZ^9Gci_p5Iyze*Xk42 zP7l)v98tjMX_n9aKj-}Kj8a5@qY_atLYf;Xxt;lt3T&YkvKrLA8!OV7f*N*mcF}&< z5C&&^fE&ml)S2OiQh%ldeiDV8fyqjXXqdt%Chbg0re}}wEuCp&wmSG5FM4wSKRg{kaT|ilm1flTi*Kq()J>lKPu;nU;q$cBt56 zQ)lwiz~sk1^d~QRj`Q9)NHE^Cg9&OsN<2HHkQR2<_Zjr~Tpu1BO_8XJJ%5H+1s03U ztFAtSzf*YRJ7DtbJo*ko&xy=DTtoz#--*{9fct>)-UB#W|AM(u;YS=Zl0oWx+x3G% zOAz6z-JmHz> zFw=2}1dbkt=0dM8X#g(Y_KmE6XSJ%2Wp9X4IZ?j$&qM!A_)MH-XQKGm#ZuC%SwFv$ zE-OQU2{r$P`#=YyXK9j;KX*xG#wvc6$}3qU(l-!Qoz^t#^Rkkgqpg_^VH(V3)K?SU zc38HYH$vfT8i51&?^{t;97982ukhf31F0ZDXK)hgEveC$@D!p+R`jx~6=flOcRPeC z#{K}uMk%k`P&DBB;8?C>?r!^`qd{Q@)O&3r(VN)k0}Bj3e(7@ia%3vD6`QFOXpU?h z>Z9n71yVbf^7J*qQiR)yh%ZA#nBO_O zQ7Rp1>WgjK%xw@gfNz)qO}h|CjA*=gEu@EV3rT1|>O>)uPiItPsuTkjNlQ*7%N`Fn zei)tiv|i!9n3#0=F<61r8<~?|OPv&f<)5oO$fI;)zQZQ;F4gguN;#+5O7iHup(x&~ z=7T!{FKoL``T+;3{uW!jrpLt_rp(uEy>+&ZI-x%I~%MW^%rZtpRhx`4Ila%moU z0>%No&L1q;GVf~+=8Z>s$uhfsc@Mw0Au#s6=L4{5G@WSQV(e8KLdT1hypKwQ6ORp( z6MP(=_u_?HHZyM-Oi*48Vsy4eDqpzObGL7{=zXkoE4PF2z2Ge5f0mq9JE?TV$V(rg_EraGDtanxCBHp5qTAH|M$Z3Q}Eh-JH0ZLxwo;Fc)53N9XD=$GF!~ zIbU&Mxd#=v5B~k}r|W9y6{zoK!WPgq{5fQ8(1(x=l~w$ROCjx@l2Ts%C3DUhqICK> zEy-xy8pRy#z&3C_G=OQHPq!Pp`&hvH6`#3Vbk%s>|FNuhgi~ z&%}j4F1FqF0{1uU?CTsmz(HW14g9==b2{{=H~BB25q!Z1y1z8Z=>hnuAII^=jAcE! zd3D=!+bSKmgu>i;RP|%Y?`btD{8h8z&8kR`_e|RW-YY$F@um$&m zxGRknBgeO0uzUAz401>|x7>%`;f4&lH@=tsv9gWK8e3vJQu4Qyz(F&V(!hFc`|>2K zn}w7(D^hk0h~iP_vIQb~t-wC61Wl=BInTL&?F;vn>oPk`YbUc7(O>b=H^Xjsnx?3l zidrrqv24$!{v4RCjm+KYyVak9Blby&`8Nxb{jORK-}}LZBeW@=)QS`Dn{JR4Beuar z^zoKSvDLSz(^3X7j8xE>yqAhOM?$r6_Z$oB5uGN37z^L_9Esn3P4my+6pqMkIiT~J zYgpyays(+2jaN<5)2IR$Y5VxnHDgh3^Rq9k&?BZFpvH{1^yznv(0aaim)Bph#@kD{ ztvXsIiDt#TT%(4@?rM4sU(NnJj_>Wti=&+ujkEacb(~Ej8^>$gGf_$*=yGoWGI>lZ zNnCxT*VdcZvsmzJn^pE@Oy#now`I6 zzV-90ycP&!)huv0bew{LBD34ox!VZNJAL``<>(dHIT|}pyYXw^r$Xg;Fa z84$Ew-=3UA(#hgY(MT;3atH*SXe873j_o5Lk786F`-lM|z1$P)KprTJK&G<-`iaU2 z8=@`PihFn#>$7c`{|BQo;KJZF5gMs2A-2SFK*zUux-^iySr^R%;yPU8;dMe~C739u zqR65&g+_-HC8}q@1%a0?UD5;|RZfsc(o&LG=h<+`n#NVIzB5U}atEogXzqDkeB5ejncb>9i^`lG+OBA6JZ(*}lc&2_TU%wh>@f)O#-^G#CK1T6O zP#_xIr5@Jn>XCRIS`sYw>g7u3>=)r3&D(KAQ0C)#%6=s!iu^kQ3g{r!`;t{MoP|7$ zyG(PH5gPX6o9$pJX8qUON(lK*@uEik&DZPorjLJXA2Z*OZ1Ao@#6v#qwqeKgBK~8gP~p?!k6$81r0b-ivxT zDk>+QbJ}+McU4m>!^`kao8bfbr_75Ez1a zNY@COsc>z3u6iVLeA4TNt5MZp)KW0a{GqK`5;FZz9QEd=P;3yX+EJ2$W2RGN2Rh_2 z8Kut@er|1LSKIZ zhyDw+e%7`F$76ro4;M5}f)n0TS(4CeAFZ#v14eGlSENou65OFdTvs#1D#U7t?7LV!NTWijHAj~bq#{))xG;D~6S2UiF4 zZ&SIVl-MtLjDQ`MY^ug$K#541+IjS(uMrpW^^TSbA7fNooU;1h03T`aETo1NTzae{ zgBMTH{BtTaZtQ#7)7oN`$2wdcsmPa2Gl z9Apj>Uiqt^b0shGO}97yafaCcKN*CoCt_x%HTopzBh2$8Fd|7m8_VfB68N8r$T9G+ z({TrZS`sOdnfupj; zMds5-5Ee()yeVbXpEmA&vT8zCAzq4P*o`Ta$Yn0o)Px~=IKbuPkm%J9I;y~ajf}bWl`H^m1fU(K{dqCb8Ntw8z+wg?nwSnh2UX0CZL|BrB<_FA6R3t||5eTw ze#LYUQ5w6%UHAEhE61dQ+aP(+Po|&y5ezEyuD@JJ(zVcYN)rd>+hxupcLXy_or|#s zNF#tyKGr7=K!0QWSBA&4-o#WG!PRET{}rpGf5IfvrKfyHWL5_^gx0`E-FhLdhr`T72eQ zAi;KJ8OLQ5!BbMwjBJ#2!y5WTwbU(UIn$XyZe>akGnJHN4_=DWQyr&7*D5-wXP!N% z{onXwN)fY#T&w^_7Yr+{BYjUeSF|aV`pW~1Dss@aZ>fXKje?&^6#NiINX97T00_!g zRv#0MO70ylThMY}ZyCzr6(*Ba=hu>HF43BBT z!}tWiUVofgVm5f@R+KcddC@;f{VKp1#wPSIZ{?s=T?)$pAJgO;_hR`cahpVnyp(xq zW{#=4#!=k^BiO-P9r?dvEBa^nU6Qwt(1G$4RFc)*(W&SMb8MWI^gwHav|59wwG9O1 z0nAACF02Ja#lo2VKbXos9v+w^03zx9QWM5H4MnD_9!+K(iq}ptU?y>j`oxLAmNin$ zt}Spc7{XB{hOMH!I3br+u_WpQPzX&WW5ZWa(5<=^A8p{Iqlr3?fKKsE^3ZYqw7+lj zoag{emfeJRp**0Cw(cz&PohP&j4Xwt6l1xHplNv@vHsO@l5&CDUa{@%0HUnMxx0846BTI$MEuj>R=CF;hH%Oqlm$1D0|VtyI(MDmV#D7Ev`>MWoEFRGn)n}ij%fdcliGH z?tVp+S*f{1a>%`$bvN@)1BBaj0$X`L9$YsQlaH|rB>77%X2WYH*Sx2<-(^_$)qCB+ zD(s8We&h=N2S3zr1@kT)nDm1fzkAt%O8Q`6({6yHwj2z`6gd%@6^*RxQk%PeBQl>P z7f=2g{C(%Z!>beAs>*@<2d4M=nILge8bnX47y7bSyjuei7?Vr@4 zy(dJU<{xjyYz1~<_i);~ZCyeM=+SI(GG_W$en!in{Ch2sAVg?1mtvC~FZLLJ@^uZd zf=QK)iMxG=yh>bPU|2yQzZ-7!aQzhsNIkeQg?qk^bu9r4oQaNCE#hjkm_qIj$HAW1 zFbD0HwG^ftOPOH&XFk1yBy)Ri?Ha+SaEk-TQznQ6v6wUtNA9YkG$s{`uS&jlUhBty-s=K%ZDuw z)lTln(Z!Q&n{^+9Ny{AL_iF)LlsJ=qr~J+|TIijCjRe5bhJJ36zZ)$B2L?J8^HLwO zb2j!Wz1Ka+7N3sIxSEQf??wN)bECo032oUBYd-ff3c0@b8JkgnKgbJ_sESO1 zEkg!)-NTH5c)3!qOUZC27SVeqWLW`G);SP+I14`CALiws4Unk8atcjqeBvn>S`L?| z!-WnZr!HR}4G1|PIv!~r_{d6Vw#kvVCyd%w;C^eDIWWA*c{-SZk2Zb z$W(f!XIaelF?l)nDZw3PSx2l&DLxJ6e>Q9*IE0M93UT8kY~k&a^v#%`DKqL9zuRVb z`}OG-S5v*0^=jL!O+ZP5xefj*=w4>5&eXjqa*>*rE4@m{IEA6Ss+3o@+d2Tsm^-|L ze%cr8H~#BL=R2Rha4J(Fk`kZ_;ReSN`X@jWxK5JMdQI{|Qa!_F225YZHvwjzP7Np< zw?wfJ6Wgf9+!FIDAT>8$2}v$}7qS|BakvQ}t+3wm8V*Y!rzx>$!bS-0=U(fy{Oc|a zmzUrXaJ+9~+l&&=fyhIm?-M6a9{F;!)xwybovM61PT1yD`f&IYlG+a~#ULc`PCZ*a zonZG~lPN~K;Dl~Z8$LgzZO^|F_Y0Su!Kk;Ual4cT>ob-LG~q6CntO(`!;^zh&j&4z zM&ad_Q1}#|xzFh|v0~%k(jCPk8lEU>Wxnh!XqB1!OS^yku{eDVG*yMiz!yzNq+)jA z6Ygx)C0)l6b(b_Fv?||f19O6Njk<<)jW|@>>X=7oKB247DwSzy4Z^%O{}!OgLY{XR zXRW9JZ7kGUEu34<{=?z)4x2NC01#HKN3C+0D{nxd@7Ia`6NAfpMD(iJy#DU%>pN6> zdd~BW6w*88an35L6$+_~`)@1n6b*Q??b{uhWpiDeF4oQ15ckys*RB4@LLy2UY*_tn z{9i|8WuKehY^GE5Z&(Us|J9og17N%m9=gN4Mr#^N-IlnZf+KIXagFPKAr>x0lip;D znRWcN`fbGRi<#kTwo98tQOLSkOXXhvY3l530#baUhw#_Ujd)Gw{&6>WQUswReEOC~ z4uOG@D;^$Zx|ug#08}ASkl|=Vx)`nQ^a}kIh){lYI?E9dE`bLQ%-CPrEAJ3)wHYA6 znERUzDeOVYHdR3+Qm*<|uDkR^TiqL(FfSf~R-Gxz;xr^4KYl!VH+70B6nN3#MeooY z56`c=Mtk*qFlnXvpMOddv{a^oy?gf#-rViE7wDFCC;m+C0r~=t{Z$?b+e1D}sd(WS zg@+;gAni?1SM&YmmjC5pLI@EIqZ+1upM?-#cD{9v^ekIY!1M(L=Et8u#Om0?LO6Zy zTz{%bG#X>JcICRGg?F6(#$GN7Ez+GTo2z3rzI`-}AvX$>>ewD0 z;Zkcahpng!WRYjW?3a<02QI@%gR_G(H=*95nz!THNMWfkzV1%nEh0Rhdxh%%FpWzd zgJ5!-;UkHD;EQYn6iqDQiRgHQA9A?S-16^Rr4|vi~Q+rO>qW9rQ0a^>MA&Qo-}xFH@%gggk?5-G*Dt6%2NML$yI zq73;(O(@rz6vgO2E}$@o4yc$XH6@b|x_n$j!3#upsPBRjBy_t#v*huB0trMyz~NRM zV5xo7B|fAs)jtjFtQ2a;A)q>1F-mn&zN5xWK*v}Wbn4vOE~>hF_8CEG9J-ILLjY z>d{b1PbinVTrXK3y^JEdm9&UKSUx22#+)8eNR~ZGS&&c1vW;;!0b7c|SMU=NgK~}? zST)dSNw2E28NHvRLtMKt95OgEDu<8I1et=rd6}KIia=;iHxY4?|H#MzJ_4#U&ZW;o z$7}zigC)?fX^IQSCe>wkEpc*aHheTAOr31D&4knSmN)2R``MCu7od6&8bw0&%C!Mb zcfMB0RV36h-?5QJIKjZR%}U+OIW|ghCUl(4{_KIUI)b+=8y^5i-I#x1qZAe;t{ugc zmIa4yQhIvfqCF?5nK?tnSIWGT8XpJ5*{RgL#&IgUp&W?OSR*B8<9PI<2cvlz@AWYQ zg(S2D801RU@r2M-R(6y6T=e!4|t}H zj;pp-(vcIYKFGB6xGK9Mh|h7X(-aPovGg)A-3(HyfPF z8|UrK0NYc83(d7nd=^q9*qNB4c)jzU8b^0t`{R!mizhXHa_~ljSIWzL&MgS&7CdeE zi_iCl7SjLj&C%~x{`yy(WdlO_I_zHh{I8N{)vSruHjNkCdk zF=L1&g@fyibTiXtx5*;JTk$WfyFBXXw&PY3Ah}na;=)60rM*iy>8xCaC z#@GszIB#+%@)S*%Rb+rt$e~F9w$>Ns5g|d2@&{C867_&$<3kGM%S3VCYU)Tf*R;JrdT}iNzjm9IZ6xOSPb(%sc&LV+T)McU;O^xkm zmaJ-(Z`)Cj1Z0)tMJV81pz$8CvTDOYJUz1y!d+H#eu`Q~fz`~Y|A4q&LsvvCuQ2_n z(&=VBPf=G4CL*xfT<>D)-L!hmD2eYa@QdjIeMrWKOurQ9b9bcwml zjq=Y^gJ@VcB}KuZZS(#X=ZjEJZf{n181!4ELBXH9U)~zcB!QXJ7_XFy@^w-8>xKDI zc1pWU>$RKJE6WNMsxi>}*u~{o3(U-}yoCfTg))ml*wyoikb_1L)u8}H+K<%8zGjK% zQA9g#?vdVx@kP|mUQ~J}0|F=B*l*EO!M`co%4#+(x37e|s14*{!ABCc#n;U8ZPYo) zg~Q3CYuJ9w(M}g1ZF(`|Hi>rn`nte=^lcUr+PC>vtiT`fKZ7l$N??zo6Y%3LR)Fx! zsJJi8k{LhMfz{wBBYUl|d?ogj#C}?#U>)TRBVVag z={xl4Ga1d6I!ZwJ!Te9(QXWg&oW#RT&32_~8l_Re>3IK|C;!{>+}VmlRUk zD;8)tF!18+hs&Dpt~}4;no$T_Rd#YG%4%M$-MD*xw$}^!(`6Ym>2!btH2QW-&xBcD z14>L8Ycr^6u>ZBQO3+sU3mrI!UOYH!?VZMZ+eutSt*OLv?FI^X`G=I8C}Fw~Rqv)G z6yVu1?tk)%A42GX#6&2{`!bt4X4H%GLq&JeAEZLzoDEA23U;{s5uZ+y1URpi1^B|O zsNYZ6+`C@fJ*8o9G!RD{eR%!&-|U;3^@=Z}TZ!7;!pv&evDKqlw;jvOA3hHDXNiOM%1<*GbX_lgYvbo;p#a?DLcBN86D!d=0NBM?f_mlmK%!322Id)ix5yDp|x%-&qDrV{S^kBug(+Nf6+Jc_x z@9OF*&LE;~#qJf|n&?^LmL3PKp()1dWHBkGy7-&z8bHvN#+ikqj3x;ghrLdw~=v0vIl8 zUB-kL5@)&MzjKsU6uH0bd_+4D#BNaO*H zM+xFw)8+59Bx@$`G60>JUnu`(3NZ{5@6S2;x4bXObd+30^F)9fh7jYS{ljS`bCn1j z(hfb4kei-NofY@=xt?l}av@USUEBOQ;CEL%wwXig&VP3XNmshCxL2K7;+lYE@Om6K z2?b`U#(wi-p5~kRytr%BAtw!nrE5PMd+OI;`}x-S)6dt}&O}ULINe76rWc2{xLz@J z(wTLGuq8P}C!i@ZX@04(@^k(_vCYJ~iInb_D3HHbBee0R*B7>2xuG2VtyebUP4f zuXM5sZDEEMyzrUq5Qtg`jbYFv@QBYE?jge5P-6xpUF=UbWBrc#ZmwCeTdEliKt(k( z@%~fm711ZtHnSwBeh>fA3K1y)dNeInkVgg3w7IeA-27j>_q_gzokCynN)5wssTzQ7 z+LBwe{zb4{MOK?aQT*o7+2!@Z zy0PXpzBVz5v97VgFF536r#It9aMv_9ifuemM)|V3AmPH#^zOQ--{E3G+F|aM+-XHL zgn?EseELG3F{gToP0h#8>{E#JR6cL|p3x&gfIWwNIN$yIrAq@L0A>3oVUM1>HEM|D zd#O=wHpUdCBq_9wxvfVkhn8?ofm|IN=F)Bcu%&wXfUqWmGM=Q5nsT*jaz}IHMCT_! zw?V>3lwSkpdOTV-9#tUdfdf4@yPA1!186pd0{=*qHL+Bf4kw+_L|ro_J2>BOE`58L zr=K}NSZ ziie=OlJ*=t>h!oq!848?e!{^)ohg`|4IoFHN+%6)q|)34jk-RjDPd7yDi)CHs+f4V zE<*9SEy3sf%b(uyoM~=WY5NE(JaGBarNLB1O0E&WLXFq!>8-jdHqx+Dx$@+Pf?Z0x z3!IX}k5x2H75}!2!a6Md_O-U3SAF-iMH191!zLFOv8r;Rv22poaJQbL-=BZyJ^Z>v zOBG$3%}NZ*nP2A&l0z}il-ZGrZlanlNvJ5b0fM~w&X8KyCK&slw|Gdw@~cN@#YgIt z;_oVi9!OFl>A9B<9XQboh^ZmQIN-w8OzjG0jX04z={70H%+!pDv?6Z>WbhF+GEUpt&RiX_?X|#o&hHm z_tDi-Gf=ivIN;SBQ_9=|^^$`_^Wmb~6>k5Xt)xaxjS&kJ^|hJVC;R`YsXV%IEDKzZ zTqAI^CsHU;R$5{&<4z2_&&EoFmsm~ELUhZ*?dz@mVwJiDb*L-4`UiHHWufcV%*b~%%05pz zP-RyVnb9J;=9_QMn^=L!QVDL) zqv~Y|dG_#>Am0t@MN*yEnMA0-cD4BV zLF)BU)jY>L@0c5vTw##?4i)SL!gQiZCBl{x7k8s#EDe|hO0*0+^p&u86&9txgg6ku zRAHK7MHHJufktUdwgK^3(&SD&HDmg8-w84Fws9^0{$3=I0`@aHkwL_4GC=o@pB|wW zkuoYpMGQQX6lF5saY!gQd};GM*R`ttxp{i{7KH0XC}F%Qyf|$GaeapJkF$4`K_GV~ z(AMaL*?%VCa0dd2O!BFI3)Rs(^ABF);Ba&s#_2cY)DtqQmGFhYg{It5(m79=cBAPE z5k4wSm{iJNYh`X)jf{Z=-cKN>OrbhaC=YZ6m>Y!(PoB(_3mYouyytUU^4IrgE z2m2|36PPy5+(;2L7+Gtbm_&+L-=ce#Mi;Cxty=pZQF=Z_Rrzlcu9`wQf1!9Dt=UWo zd2QBn!0TwOnzd_Zaxt45>9Gdv-;9eW)Z&+MK0Z%cM{GyXfpzq!pVl4eAN~&SCHpzT z5_g+v>0gxe=+f+WAXNvqJ5()+v*|Ps{(gtE$TIMR;>p6In%c~JW_OM(!~Da4|EIvf z4sJx;c6*~W%jy%mxBgmIsK~~B6 zrPHs`)$jTIyYP%b+0jdFY1M=QrpZ-Mq{BpAQ6h!ptgN- zrieDh2_i&2H{xu6APG|_CdOO})y3Iv+)!+Fe9CpGU<9dxX?l1z&S72Q@>Q=LDJvSOWOr3OXLZ|oE2lD>(t3p2&vdp`FzIaej64lOJ#0H`XGz>!uNI*z) zqr%j1pLtqa)7KeOkT{wfsjDx5SyD;u?t3R5*hk6;mOAmz<#eKVkz{D)Ab=pF&(fG3 zH|Ic(w^4J=s3{EAekG0!KG~OGAm9CJ=ldQS_vpL|#_V(I$T>xh;FtZH|8-&->1!2y zsNp6o7-^t1+=O(ORy+>!E6yVZ)4&#mq)4zP(=^Aq5FcS+H*VepYGa6ir|LH@@0j;M z8p@wNmFhxC{6bd@;t@o^5jr8q)G*)6+PZ|Ih=x~{ta^)sY~8uKe8U(1Ipga238p9e zALYU+ck`S535TrN2X`y-X4mlT!+*|i_IzYfpXbh+ZyD@b`Rnz?)kMO3bwr- literal 0 HcmV?d00001 diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext.example.ipynb b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext.example.ipynb new file mode 100644 index 000000000..4a70322a1 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext.example.ipynb @@ -0,0 +1,865 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "955ed470-77f1-4517-b03c-6334235dbfde", + "metadata": {}, + "source": [ + "# FastText Text Classification Tutorial\n", + "## Using the 20 Newsgroups Dataset\n", + "\n", + "This notebook demonstrates how to use FastText for text classification.\n", + "We will train a model to classify news articles into 20 categories." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8a3dfdb3-e37e-491b-a386-21e4157bb446", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All libraries imported successfully!\n" + ] + } + ], + "source": [ + "# Import libraries\n", + "import fasttext\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.datasets import fetch_20newsgroups\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "import re\n", + "import time\n", + "\n", + "# Import project utility functions\n", + "from fasttext_utils import (\n", + " clean_text,\n", + " prepare_fasttext_data,\n", + " train_fasttext_model,\n", + " evaluate_model,\n", + " get_predictions,\n", + " plot_confusion_matrix,\n", + " plot_tuning_results,\n", + " plot_model_comparison\n", + ")\n", + "\n", + "print(\"All libraries imported successfully!\")" + ] + }, + { + "cell_type": "markdown", + "id": "631a6776-e2b7-43b3-9c21-ca27bb80bace", + "metadata": {}, + "source": [ + "## Step 1: Load the Dataset\n", + "\n", + "We use the 20 Newsgroups dataset, which contains ~18,000 newsgroup posts\n", + "across 20 different categories. It is built into scikit-learn and requires\n", + "no manual download." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e360fce1-2b51-45f8-a253-7d93b4a49109", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training samples: 11314\n", + "Test samples: 7532\n", + "Number of categories: 20\n", + "\n", + "Categories:\n", + "['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']\n" + ] + } + ], + "source": [ + "# Load the 20 Newsgroups dataset\n", + "train_data = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))\n", + "test_data = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))\n", + "\n", + "print(f\"Training samples: {len(train_data.data)}\")\n", + "print(f\"Test samples: {len(test_data.data)}\")\n", + "print(f\"Number of categories: {len(train_data.target_names)}\")\n", + "print(f\"\\nCategories:\\n{train_data.target_names}\")" + ] + }, + { + "cell_type": "markdown", + "id": "510c3083-b440-43b8-aaaa-139d10fbd05c", + "metadata": {}, + "source": [ + "## Step 2: Preprocess the Data\n", + "\n", + "FastText requires data in a specific format:\n", + "`__label__ `\n", + "\n", + "We clean the text by removing special characters and converting to lowercase." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e8c98db4-ec2a-4fd4-9292-135a04a3889c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved 11314 samples to /tmp/train.txt\n", + "Saved 7532 samples to /tmp/test.txt\n" + ] + } + ], + "source": [ + "# Prepare data in FastText format\n", + "# FastText expects: __label__ on each line\n", + "prepare_fasttext_data(train_data, '/tmp/train.txt')\n", + "prepare_fasttext_data(test_data, '/tmp/test.txt')" + ] + }, + { + "cell_type": "markdown", + "id": "73687cc1-4496-4b4f-9e62-7c65695bad3e", + "metadata": {}, + "source": [ + "## Step 3: Train the FastText Model\n", + "\n", + "We train a supervised FastText model on the preprocessed training data.\n", + "Key parameters:\n", + "- `epoch`: number of training passes\n", + "- `lr`: learning rate\n", + "- `wordNgrams`: use bigrams for better accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "99ad2b97-ead7-4c24-b386-5931c9826006", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Read 2M words\n", + "Number of words: 72943\n", + "Number of labels: 20\n", + "Progress: 96.3% words/sec/thread: 733504 lr: 0.018521 avg.loss: 1.357095 ETA: 0h 0m 0s" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training complete in 4.16 seconds!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Progress: 100.0% words/sec/thread: 732330 lr: 0.000000 avg.loss: 1.310074 ETA: 0h 0m 0s\n" + ] + } + ], + "source": [ + "# Train the baseline FastText model\n", + "# epoch=25, lr=0.5, wordNgrams=2\n", + "model, train_time = train_fasttext_model(\n", + " '/tmp/train.txt',\n", + " epoch=25,\n", + " lr=0.5,\n", + " word_ngrams=2,\n", + " verbose=2\n", + ")\n", + "print(f\"Training complete in {train_time:.2f} seconds!\")" + ] + }, + { + "cell_type": "markdown", + "id": "2c3ab24c-b595-462f-b86f-903e7ff3783a", + "metadata": {}, + "source": [ + "## Step 4: Evaluate the Model\n", + "\n", + "We evaluate the model on the test set using accuracy, precision, recall, and F1-score." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5ee6b86f-5ddb-492c-b044-8ea6b7207df1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline Model Results:\n", + "Precision: 0.5996\n", + "Recall: 0.5996\n", + "F1-Score: 0.5996\n" + ] + } + ], + "source": [ + "# Evaluate the baseline model on the test set\n", + "results = evaluate_model(model, '/tmp/test.txt')\n", + "print(f\"Baseline Model Results:\")\n", + "print(f\"Precision: {results['precision']}\")\n", + "print(f\"Recall: {results['recall']}\")\n", + "print(f\"F1-Score: {results['f1']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6eb0354f-4258-462b-a930-dfcdc1f8232a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " alt.atheism 0.41 0.40 0.41 319\n", + " comp.graphics 0.56 0.63 0.59 389\n", + " comp.os.ms-windows.misc 0.63 0.56 0.59 394\n", + "comp.sys.ibm.pc.hardware 0.58 0.54 0.56 392\n", + " comp.sys.mac.hardware 0.39 0.59 0.47 385\n", + " comp.windows.x 0.85 0.65 0.73 395\n", + " misc.forsale 0.77 0.76 0.76 390\n", + " rec.autos 0.58 0.59 0.59 396\n", + " rec.motorcycles 0.73 0.59 0.66 398\n", + " rec.sport.baseball 0.74 0.66 0.70 397\n", + " rec.sport.hockey 0.83 0.79 0.81 399\n", + " sci.crypt 0.71 0.61 0.66 396\n", + " sci.electronics 0.47 0.54 0.50 393\n", + " sci.med 0.60 0.68 0.64 396\n", + " sci.space 0.69 0.63 0.66 394\n", + " soc.religion.christian 0.57 0.72 0.64 398\n", + " talk.politics.guns 0.48 0.57 0.52 364\n", + " talk.politics.mideast 0.67 0.67 0.67 376\n", + " talk.politics.misc 0.40 0.37 0.39 310\n", + " talk.religion.misc 0.37 0.23 0.28 251\n", + "\n", + " accuracy 0.60 7532\n", + " macro avg 0.60 0.59 0.59 7532\n", + " weighted avg 0.61 0.60 0.60 7532\n", + "\n" + ] + } + ], + "source": [ + "# Generate detailed classification report for the baseline model\n", + "true_labels = [test_data.target_names[i] for i in test_data.target]\n", + "predicted_labels = get_predictions(model, test_data.data, clean_text)\n", + "\n", + "print(classification_report(true_labels, predicted_labels, zero_division=0))" + ] + }, + { + "cell_type": "markdown", + "id": "5d71d3d3-2aa0-4a8e-a156-d563469a7271", + "metadata": {}, + "source": [ + "## Step 5: Confusion Matrix Visualization\n", + "\n", + "We visualize the confusion matrix to understand which categories\n", + "the model confuses most often." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a9754aa6-9fd0-419d-9abd-3ccf886b8ad6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix saved to /home/user/confusion_matrix_baseline.png\n" + ] + } + ], + "source": [ + "# Plot confusion matrix\n", + "plot_confusion_matrix(\n", + " true_labels,\n", + " predicted_labels,\n", + " test_data.target_names,\n", + " title='Confusion Matrix - FastText Baseline Model',\n", + " save_path='/home/user/confusion_matrix_baseline.png',\n", + " cmap='Blues'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bbadd108-9bd4-40eb-a3cc-498f611c91cc", + "metadata": {}, + "source": [ + "## Step 6: Hyperparameter Tuning\n", + "\n", + "We experiment with different hyperparameters to improve model performance.\n", + "We test different combinations of epochs, learning rate, and word n-grams." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7181baf4-1296-466b-8f9a-30e20e010e22", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=10, lr=0.1, ngrams=1 → F1: 0.3225\n", + "epoch=25, lr=0.5, ngrams=2 → F1: 0.6013\n", + "epoch=50, lr=0.5, ngrams=2 → F1: 0.6155\n", + "epoch=50, lr=1.0, ngrams=3 → F1: 0.5781\n", + "epoch=75, lr=0.5, ngrams=2 → F1: 0.6212\n", + "\n", + "Best configuration: {'epoch': 75, 'lr': 0.5, 'wordNgrams': 2, 'minCount': 2, 'f1': 0.6212}\n" + ] + } + ], + "source": [ + "# Hyperparameter tuning experiments\n", + "configs = [\n", + " {\"epoch\": 10, \"lr\": 0.1, \"wordNgrams\": 1, \"minCount\": 1},\n", + " {\"epoch\": 25, \"lr\": 0.5, \"wordNgrams\": 2, \"minCount\": 1},\n", + " {\"epoch\": 50, \"lr\": 0.5, \"wordNgrams\": 2, \"minCount\": 1},\n", + " {\"epoch\": 50, \"lr\": 1.0, \"wordNgrams\": 3, \"minCount\": 1},\n", + " {\"epoch\": 75, \"lr\": 0.5, \"wordNgrams\": 2, \"minCount\": 2},\n", + "]\n", + "\n", + "results_list = []\n", + "for config in configs:\n", + " m, t = train_fasttext_model(\n", + " '/tmp/train.txt',\n", + " epoch=config[\"epoch\"],\n", + " lr=config[\"lr\"],\n", + " word_ngrams=config[\"wordNgrams\"],\n", + " min_count=config[\"minCount\"],\n", + " verbose=0\n", + " )\n", + " r = evaluate_model(m, '/tmp/test.txt')\n", + " results_list.append({\n", + " \"epoch\": config[\"epoch\"],\n", + " \"lr\": config[\"lr\"],\n", + " \"wordNgrams\": config[\"wordNgrams\"],\n", + " \"minCount\": config[\"minCount\"],\n", + " \"f1\": r[\"f1\"]\n", + " })\n", + " print(f\"epoch={config['epoch']}, lr={config['lr']}, ngrams={config['wordNgrams']} → F1: {r['f1']:.4f}\")\n", + "\n", + "print(\"\\nBest configuration:\", max(results_list, key=lambda x: x['f1']))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "86db2148-81f9-4558-9d1a-377a15b883a4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tuning plot saved to /home/user/hyperparameter_tuning.png\n" + ] + } + ], + "source": [ + "# Plot hyperparameter tuning results\n", + "plot_tuning_results(results_list, save_path='/home/user/hyperparameter_tuning.png')" + ] + }, + { + "cell_type": "markdown", + "id": "a4dd5073-dfa8-4672-81cd-97b89e67367e", + "metadata": {}, + "source": [ + "## Step 7: Best Model Evaluation\n", + "\n", + "We retrain using the best configuration found during tuning (epoch=75, lr=0.5, wordNgrams=2)\n", + "and generate a detailed classification report and confusion matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d5db49f3-3d30-4016-b0a3-c5bed2ddbaec", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Read 2M words\n", + "Number of words: 38526\n", + "Number of labels: 20\n", + "Progress: 100.0% words/sec/thread: 873661 lr: -0.000134 avg.loss: 0.878564 ETA: 0h 0m 0s" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Model Results:\n", + "Precision: 0.6216\n", + "Recall: 0.6216\n", + "F1-Score: 0.6216\n", + "Training time: 7.75 seconds\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Progress: 100.0% words/sec/thread: 873564 lr: 0.000000 avg.loss: 0.878564 ETA: 0h 0m 0s\n" + ] + } + ], + "source": [ + "# Train the best model\n", + "best_model, best_train_time = train_fasttext_model(\n", + " '/tmp/train.txt',\n", + " epoch=75,\n", + " lr=0.5,\n", + " word_ngrams=2,\n", + " min_count=2,\n", + " verbose=2\n", + ")\n", + "\n", + "best_results = evaluate_model(best_model, '/tmp/test.txt')\n", + "print(f\"Best Model Results:\")\n", + "print(f\"Precision: {best_results['precision']}\")\n", + "print(f\"Recall: {best_results['recall']}\")\n", + "print(f\"F1-Score: {best_results['f1']}\")\n", + "print(f\"Training time: {best_train_time:.2f} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "25483f3d-3b12-4bc2-9a0c-22409296a749", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " alt.atheism 0.44 0.44 0.44 319\n", + " comp.graphics 0.60 0.65 0.62 389\n", + " comp.os.ms-windows.misc 0.61 0.58 0.59 394\n", + "comp.sys.ibm.pc.hardware 0.59 0.59 0.59 392\n", + " comp.sys.mac.hardware 0.66 0.58 0.62 385\n", + " comp.windows.x 0.86 0.63 0.73 395\n", + " misc.forsale 0.77 0.76 0.77 390\n", + " rec.autos 0.44 0.71 0.54 396\n", + " rec.motorcycles 0.77 0.59 0.67 398\n", + " rec.sport.baseball 0.77 0.67 0.72 397\n", + " rec.sport.hockey 0.84 0.82 0.83 399\n", + " sci.crypt 0.71 0.65 0.68 396\n", + " sci.electronics 0.50 0.54 0.52 393\n", + " sci.med 0.62 0.69 0.65 396\n", + " sci.space 0.72 0.66 0.69 394\n", + " soc.religion.christian 0.60 0.70 0.65 398\n", + " talk.politics.guns 0.52 0.60 0.56 364\n", + " talk.politics.mideast 0.71 0.67 0.69 376\n", + " talk.politics.misc 0.43 0.39 0.41 310\n", + " talk.religion.misc 0.34 0.30 0.32 251\n", + "\n", + " accuracy 0.62 7532\n", + " macro avg 0.63 0.61 0.61 7532\n", + " weighted avg 0.64 0.62 0.62 7532\n", + "\n" + ] + } + ], + "source": [ + "# Get detailed classification report for the best model\n", + "predicted_labels_best = get_predictions(best_model, test_data.data, clean_text)\n", + "print(classification_report(true_labels, predicted_labels_best, zero_division=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "22f0f7d3-5265-40fb-a378-a65be4a08318", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix saved to /home/user/confusion_matrix_best.png\n" + ] + } + ], + "source": [ + "# Plot confusion matrix for best model\n", + "plot_confusion_matrix(\n", + " true_labels,\n", + " predicted_labels_best,\n", + " test_data.target_names,\n", + " title='Confusion Matrix - FastText Best Model (epoch=75, lr=0.5, ngrams=2)',\n", + " save_path='/home/user/confusion_matrix_best.png',\n", + " cmap='Greens'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "843f847d-c22a-4ab9-a7cb-2faf868a34d7", + "metadata": {}, + "source": [ + "## Step 8: Error Analysis\n", + "\n", + "We analyze which categories the model struggles with most and why." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e58637b5-3226-449d-811c-ef1ae4629c97", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 Most Common Misclassifications:\n", + "--------------------------------------------------\n", + " 88 times: talk.politics.misc → talk.politics.guns\n", + " 55 times: rec.motorcycles → rec.autos\n", + " 51 times: talk.religion.misc → soc.religion.christian\n", + " 45 times: comp.windows.x → comp.graphics\n", + " 44 times: talk.religion.misc → alt.atheism\n", + " 42 times: alt.atheism → talk.religion.misc\n", + " 41 times: alt.atheism → soc.religion.christian\n", + " 40 times: comp.os.ms-windows.misc → comp.sys.ibm.pc.hardware\n", + " 40 times: comp.sys.mac.hardware → comp.sys.ibm.pc.hardware\n", + " 36 times: comp.sys.ibm.pc.hardware → comp.os.ms-windows.misc\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAJOCAYAAABm7rQwAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAA2YlJREFUeJzs3XdYFFf7N/DvItJ7BwtoRAGlSDFWWEuCjdgSaxCwx96VJypgiWjUiDEaY0ONvRsbKgIqKqKIiiIqihU7gpiIlHn/8N35sbBUzWKS7+e69nqyM2fO3HPmDE/2zjlnJIIgCCAiIiIiIiIiIlIilaoOgIiIiIiIiIiI/nuYlCIiIiIiIiIiIqVjUoqIiIiIiIiIiJSOSSkiIiIiIiIiIlI6JqWIiIiIiIiIiEjpmJQiIiIiIiIiIiKlY1KKiIiIiIiIiIiUjkkpIiIiIiIiIiJSOialiIiIiIiIiIhI6ZiUIiIiIqJ/vejoaEgkEgQHB//t57KxsYGNjc3ffp6KKO36jxw5ghYtWsDQ0BASiQRdu3YFAEilUkgkEuUGWgqJRAKpVFrVYdA/VHh4OCQSCcLDwz+oHvZDoo+LSSkiIiKiT4BEIqnQpyqcOHECEydOROvWraGvrw+JRAJ/f/9SjykoKMDPP/8MR0dHaGpqwtTUFH369MHt27crdG7Zdaurq+PFixcKy2RkZEBTU7NK2+ifJi0tDV26dMHt27cREBCAoKAg9O7du0pi+dSSYFUpODi42DNfrVo1mJiY4Msvv8TevXuVFktaWlq5nvWSjpNIJLCwsEBeXp7CcsnJyWK5Ty2ZS0R/P9WqDoCIiIiIgKCgoGLbFi9ejMzMTIX7qsKaNWuwbt06aGlpoXbt2sjKyirzmKFDh2LVqlVo2LAhRo8ejUePHmHbtm04cuQIzp49C1tb23KfX1VVFe/evcPGjRsxevToYvs3btyIt2/fQlVVtdgP4CZNmiA5ORkmJiblPt+/SUnXf+zYMbx9+xYLFy5E37595fatX78ef/75pzLDLFVycjK0tLSqOgyl6tGjBxo1agQAePfuHVJTU7Fv3z4cPXoUS5cuxYgRI6o4wrKpqqriyZMnOHjwIL766qti+1evXg0VFY6VIPqvYlKKiIiI6BOgaFpVeHg4MjMzlTLlrDxGjhyJSZMmwc7ODvHx8WjWrFmp5aOiorBq1Sp4enri6NGjUFNTAwD07dsXHTt2xMiRIxEREVHu83/22WcQBAFr165VmJRas2YNGjRoAABISUmR26elpQU7O7tyn+vfpqTrf/ToEQDAysqq2L7atWv/7XFVxH/x/n399dfFRq7Fx8ejSZMmmDdv3j8iKdW8eXNcunQJa9asKZaUysvLw++//4527dohJiamiiIkoqrElDQRERHRP8zz588xduxY1KlTB+rq6jAzM0PPnj2RlJRUrKy/vz8kEglu376N+fPnw9bWFhoaGqhTpw5mzpyJ3Nzccp/X3d0dDRs2RLVq1cpVfuXKlQCAWbNmiQkpAOjQoQOkUimOHDmCe/fulfv8ABAQEIDExEQkJCTIbb906RIuXryIgIAAhceVtKbSzZs3ERAQILalkZERnJ2dMXbsWAiCIFf29evXCAkJgZOTE7S0tKCvr4/GjRtj+vTpZbbjo0ePEBQUhKZNm8LMzAzq6uqwsbHB8OHD8fTp02LlMzMzMWPGDDg4OEBHRwd6enqoV68e/Pz8cPfuXbGcbJSTs7Mz9PX1oa2tDRsbG/Ts2ROXLl0q8fplU6tko/Bat24tTqGKjo4GUPp0ur179+LLL7+EsbExNDQ0YGNjA19fX7k+eOPGDUyePBmurq5iufr162Pq1KnIzs6Wq08ikYhJicJT1gpPGStpLZ/KPA937tzBkiVLYGdnB3V1dVhbWyMkJAQFBQVy5QsKCrBq1So0adIERkZG0NTURM2aNeHj4yO2k7J5eHjAyMgIz58/V7j/xIkT8PHxgYmJCdTV1WFra4tp06YpHPW2c+dOeHl5wczMDBoaGrCyskK7du2wc+dOAO8T43Xq1AEArFu3Tu7elPf6NTU10bt3bxw4cKBYX9+/fz+ePHmCAQMGlHj8mzdvEBQUBDs7O2hoaMDIyAidOnVCbGyswvIvX77EsGHDYG5uDi0tLXh4eGD37t2lxnj58mX07t0blpaWUFNTg7W1NUaNGlXiVGEi+ng4UoqIiIjoH+TZs2do1qwZUlNTIZVK0bt3b9y5cwc7duzAgQMHEBERgZYtWxY7buzYsYiNjUXPnj2ho6ODP/74A0FBQbh8+TJ27Njxt8QaHR0NbW1ttGjRotg+b29vREdHIyYmBr6+vuWu08/PD9OmTcPatWvh6uoqbl+9ejWqVauG/v37Y+3ateWq69GjR2jSpAnevHmDTp06oVevXnjz5g1u3ryJZcuWYcGCBVBVff+vy0+fPoWXlxeuX78OFxcXfPfddygoKMD169cxb948TJgwAQYGBiWe68SJE1i4cCHatm2Lzz//HNWrV8fFixexfPlyREREICEhAfr6+gAAQRDg7e2NuLg4tGjRAu3bt4eKigru3r2Lffv2wdfXF9bW1mJ7bNu2DU5OTggICIC6ujru37+PqKgoxMfHw9nZWWE8BgYGCAoKEu+Bn5+fuJ5PWev6TJgwAYsWLYKRkRG6du0KMzMz3L9/H8eOHYObm5s43WzXrl1YvXo1WrduDalUioKCApw9exbz5s1DTEwMTpw4gerVqwN4P301PDwcd+/elZuu6uLiUmoslX0eJk2ahJiYGHTu3Bne3t7Ys2cPgoOD8e7dO8yZM0csFxgYiPnz5+Ozzz5D3759oauri4cPH+LUqVM4duxYlSx4feHCBbx8+VLhc7V8+XKMGDECBgYG8PHxgZmZGc6fP485c+YgKioKUVFRYoJ4+fLlGD58OCwtLdGtWzcYGxvj8ePHOHfuHHbv3o0ePXrAxcUFY8aMQVhYGJydncVF8IGy+0lhAwYMwIoVK7BhwwZMmDBB3L5mzRqxHyny9u1btGnTBufOnYOrqyvGjh2LJ0+eYOvWrYiIiMDmzZvxzTffiOX//PNPSKVSXLlyBc2aNYOXlxfu37+PXr164csvv1R4jn379qFnz55QUVFBly5dUKtWLVy7dg1Lly5FREQE4uLiYGhoWO5rJaIKEoiIiIjok2RtbS0U/de1gIAAAYAQGBgot/3AgQMCAKFevXpCfn6+uN3Pz08AIJiamgr3798Xt+fk5Aienp4CAGHHjh0Vju3MmTMCAMHPz0/h/uzsbAGA0KhRI4X7d+zYIQAQpk+fXq7zARAaNGggCIIgdO7cWTAyMhLevn0rCIIgvH37VjAyMhJ8fHwEQRCEBg0aFGu3qKgoAYAQFBQkbluyZIkAQFi8eHGx87148ULue48ePQQAwv/+979iZR8/fizk5uaK362trQVra2u5Mk+ePBFev35d7Nh169YJAITZs2eL2y5fviwAELp27Vqs/Nu3b8V6Xr16JUgkEsHNzU3Iy8uTK5eXlydkZGSUev2CIAhBQUECACEqKqrYuby8vIq14x9//CEAEBwdHYXnz5/L7cvNzRUeP34sfn/w4IGQk5NTrN6QkBABgPD777+Xeb7CAAheXl5y2yr7PNSpU0d49OiRuP3Zs2eCgYGBoKurKxezkZGRYGVlJbx586ZYPEX7yMckuy89evQQgoKChKCgICEwMFDo3bu3oKWlJdStW1dITEyUO+bq1auCqqqq4OzsXOzezJ07VwAgLFiwQNzm6uoqqKmpCU+ePCl2/sLH37lzp9RnvSSy47y9vQVBEIRGjRoJDRs2FPenp6cLqqqqwqhRowRBEAR1dfViz42sr/Tr108oKCgQtyckJAhqamqCgYGBkJWVJW6XtdvgwYPl6jl8+LAAQAAgrF27Vu469fT0hBo1aghpaWlyx2zevFkAIIwcOVJuu6J+SESVx+l7RERERP8Q7969w+bNm2FsbIxp06bJ7evYsSO++OIL3Lp1S+G0ljFjxqBmzZridzU1NXFEyIe+Il2RzMxMABBH/xSlp6cnV64iBgwYgJcvX2LPnj0AgD179uDly5elTgEqjaamZrFtRkZG4j8/fvwYu3btwmeffaZwfS9zc3NxRFVJzMzMoKOjU2y7r68v9PT0cOzYsXLFpa6uLtYjkUggCAI0NDSKLRRdrVq1UkduVdayZcsAAGFhYTA2Npbbp6qqCnNzc/F7jRo15KZtyowcORIAFF5zRXzI8zB9+nRYWlqK301MTNClSxe8fv262HpkampqCqesFu4jf5edO3ciJCQEISEhmDt3LrZs2QKJRII+ffqgXr16cmVXrFiBvLw8/Pzzz8XuzeTJk2FqaorNmzfLba9evbo4Wq2wosd/DAMGDMDVq1cRFxcH4P10wLy8vFKf23Xr1qF69eoIDQ2Vm0rauHFj+Pn54dWrV+LfAeD94vxqamqYOXOmXD3e3t5o27ZtsfrXr1+PrKwszJ07Vxx9KNO7d2+4urpiy5YtlblcIionTt8jIiIi+oe4fv063r59i9atWyt8C1nr1q1x9OhRJCYmolWrVnL7in4HgGbNmkFVVRUXL17822L+O3Tu3BlmZmZYs2YNevXqhTVr1sDMzAydO3euUD0+Pj4IDAzEiBEjEBkZifbt28PLywt169aVK3f+/HkIgoDWrVsr/AFfXrt27cKKFSuQkJCAjIwM5Ofni/tkC44DgL29PZycnLB582Y8ePAAXbt2hVQqhYuLi1zySU9PDx07dsTBgwfh6uqKb775BlKpFB4eHh8UZ2nOnTsHdXV1eHl5lVlW+P+L0oeHhyMpKQmZmZlyazYVvubK+JDnwc3NrVh5WdL21atX4rbevXtj2bJlaNSoEXr37o3WrVujWbNmChOGioSHhyMtLU1uW9euXcucliizefNmcaHzvLw8PHz4EOHh4QgJCcHRo0cRGxsrJkTPnj0LAIiIiEBkZGSxuqpXr47r16/LXdvkyZPRqFEj9O3bF61bt0bLli3FhPHH9u2332LKlClYs2YNPv/8c6xduxaNGzcusS2ysrJw+/Zt2NvbyyXUZVq3bo2VK1ciMTERvr6+yMrKwp07d+Dg4AALC4ti5Vu1alWsXWRtFhcXh9TU1GLHvH37Fs+fP8fz58//s2/uJPq7MSlFRERE9A+RlZUFAHKjUQqTjfyQlStM0THVqlWDsbFxpUYrlUU2QqqkumUxljSSqjTVq1fHt99+i8WLF+P06dM4duwYxo0bV+ZopaJsbGxw9uxZBAcH4+DBg9i2bRuA9295mzlzprhWjewaatSoUeFYZRYuXIiJEyfC1NQUX375JWrWrCkmNhYvXoycnByxrKqqKo4fP47g4GDs3LlTXIPH1NQUI0eOxPfffy+O3Nm+fTt++OEHbNq0Cd9//z2A98mqgIAA/PDDDwqTNR8iMzMTNWrUKDYyS5HRo0dj6dKlqFWrFr766itYWlpCXV0dABASEiJ3zZXxIc+DosSLrP8UThaGhYWhTp06WLt2LWbPno3Zs2dDQ0MDPXv2xMKFC8tMVISHhxd7q5yNjU25k1JF47O2tkZQUBBu3ryJjRs3YuvWrejXrx+A9wt8A5BbE6s0EydOhLGxMZYvX46FCxeKa6h16tQJP/30k7jA+cdiamoKHx8fbNmyBd988w1SUlLw888/l1i+ovdX9r9mZmYKyyuqR9Zmv/zyS6mxv3nzhkkpor8Jp+8RERER/UPIfkg/efJE4f7Hjx/LlStM0TH5+fl48eJFpRJDZdHW1oalpSXu3Lkj9yNf5ubNmwAAW1vbStU/cOBAFBQUoGfPnigoKMDAgQMrVU+jRo2wY8cOvHz5EmfOnMGMGTPw+PFj9OrVS5z2JZsG9/Dhw0qdIy8vD7NmzYKlpSWSkpKwceNGzJs3D8HBwQgKCsK7d++KHWNsbIyff/4ZDx8+FBddNjIyQlBQEObPny+W09LSwuzZs3H79m3cvn0bq1evRoMGDRAWFoZx48ZVKt7SGBgY4PHjx8XeUlfU06dP8csvv8DJyQnXr19HeHg45s6di+DgYAwbNuyjxPIhz0N5qaqqYuLEibh69SoePnyITZs2oVWrVli/fr2YDCpNdHQ0BEGQ+xR+o2Blff755wCA+Ph4cZvsOrOysoqds/BHRiKRYMCAAYiPj8ezZ8+we/dudO/eHXv37kXnzp0VPrcfauDAgcjKyoK/vz80NDRKbcOK3l/Z/yp6m2VJ9ciOuXLlSqltVnRqHxF9PExKEREREf1DyF6JHh8fr/D17rJXtCsahXHy5Mli286cOYO8vDw0btz4Y4cKAPDy8sKbN28UrukTEREBAPD09KxU3Q4ODvj888/x8OFDNG3aFPb29h8Ua/Xq1dG0aVOEhIRgyZIlEAQB+/fvBwC4u7tDRUUFUVFRyM3NrXDdz58/R2ZmJpo1a1ZsFMf58+fx119/lXisRCKBvb09RowYgaNHjwJ4/7YwRerUqYMBAwYgJiYGOjo6JZb7EE2aNEFOTk6x0T9F3b59G4IgoF27dsVGaynqiwDE0V/lTYZ8yPNQGVZWVujTpw8OHz6MevXq4dixY6Xeu79TRkYGAMglB2WJKtmUtIowNjZG165dsXXrVrRp0wbXrl3DrVu3AFT8vpTG29sbNWrUwMOHD9G1a9dS32qnp6eHunXr4tatWwoTwkXvr56eHurUqYNbt26JCavCFPU7WZudOXOmEldDRB8Dk1JERERE/xBqamro06cPnj9/jrlz58rtO3z4MCIiIlCvXj2Fr4oPCwvDgwcPxO/v3r0Tp3t9jJEbigwZMgTA+0WlC48GOnToEKKjo/Hll19+0AiENWvWYPfu3Vi9enWljr9w4YLCqV2yERUaGhoA3k/76dGjB1JTUxESElKs/NOnT5GXl1fieczMzKCpqYmEhAS55ElGRgZGjRpVrHxaWlqxdYgUxfXs2TMkJSUVK5eRkYGcnByx3Mc0YsQIAO8XzpdNfZLJy8sTY5Td19OnT8slTh48eIDAwECFdcsWDr9//365YvmQ56E8cnJycPr06WLb37x5g+zsbFSvXr1c0xg/toyMDKxduxaAfFJ3+PDhUFVVxahRo3Dv3r1ix7169Upu/TjZKK7CcnNzxfsq6z+GhoaQSCTlvi+lqVatGvbs2YPdu3cXu2eK+Pn5ITc3F4GBgXKxXr58GeHh4dDX10fXrl3F7b6+vnj37h1mzJghV8+RI0cUrrMVEBAAXV1dfP/997h69Wqx/X/++WelknxEVH5cU4qIiIjoH2TevHmIiYnB7Nmzcfr0aXz++edIS0vD9u3boaWlhbVr1yr8ody0aVM4OzujV69e0NbWxh9//IGUlBR0794dPXr0KNe5T506hVWrVgF4nxCRbZMltUxMTLBgwQKxfOvWrTFo0CCsWrUKrq6u6NSpE9LT07F161YYGRmVup5MeTg4OMDBwaHSx2/YsAErVqyAp6cnPvvsM+jp6eHatWs4ePAgjIyMEBAQIJZdtmwZkpKSMGfOHBw8eBBt2rSBIAi4ceMGjhw5gidPnpT4tjsVFRUMHz4cCxcuhLOzM3x8fJCVlYVDhw7B2toaVlZWcuUTExPRvXt3NGnSRFy0+eHDh9izZw9UVFTEaXkPHz5E48aN4ezsDCcnJ9SoUQMvXrzA3r17kZubi4kTJ1a6bUrSsWNHTJw4EQsWLICtrS26desGMzMzPHz4EJGRkZg4cSLGjh0LS0tL9OjRAzt37oS7uzvatm2LJ0+eYP/+/Wjbtq3CRaXbtGmDHTt2oEePHujQoQM0NDTE9ipJZZ+H8vjrr7/QokUL1K9fH25ubqhduzays7Oxf/9+PH78GBMnThTXyPq77NixQ1ycPD8/Hw8ePMC+ffvw8uVLtG/fHt27dxfLNmrUCMuWLcN3332HBg0aoGPHjvjss8/w+vVr3L59GzExMfD398evv/4K4P2C63p6emjatCmsra2Rm5uLo0eP4tq1a/j666/FxKKOjg48PDxw4sQJ+Pr6wtbWFioqKvD19a1UUtnd3R3u7u7lKjt58mQcOHAAGzZsQHJyMtq2bYunT59i69atyMvLw8qVK6GrqytXfteuXVi5ciWuXr0KT09P3L9/H9u2bUOnTp1w4MABufplbyT85ptv4OzsjPbt28POzg45OTlIS0tDTEwMmjdvjsOHD1f4OomonAQiIiIi+iRZW1sLiv517dmzZ8Lo0aMFa2troXr16oKJiYnw9ddfC1euXClW1s/PTwAgpKamCqGhoUK9evUENTU1wdraWggODhZycnLKHc/atWsFACV+rK2tix2Tn58vhIWFCQ0bNhTU1dUFY2NjoVevXsKtW7cq1BYAhAYNGpSrbIMGDYq1W1RUlABACAoKEredPXtWGDp0qNCoUSPBwMBA0NTUFGxtbYWRI0cKd+/eLVZvZmamMH36dMHOzk5QV1cX9PX1BRcXF2HGjBnCu3fvxHLW1tbF2uLdu3fCnDlzBFtbW0FdXV2oXbu2MGHCBOH169fFyt+/f1+YOnWq0LRpU8HMzExQU1MTateuLXTv3l04c+aMWC4jI0MIDg4WPD09BUtLS0FNTU2wsrIS2rdvLxw6dKjM6xcEQQgKChIACFFRUcWu18vLS2H/EwRB2Llzp9C6dWtBX19fUFdXF2xsbARfX18hKSlJLPP69WthwoQJgo2NjaCuri7Y2toKs2bNEt69eycAELy8vOTqzM3NFSZPnizUrl1bUFVVFQAIfn5+4n5FxwhC5Z6HO3fuFNtXtC3evXsnzJs3T/jyyy+FmjVrCmpqaoK5ubng6ekpbNq0SSgoKFDYNh+DLJaiH11dXaFp06bCkiVLhNzcXIXHnjt3Tujdu7dgZWUltoerq6swdepUITk5WSy3bNky4auvvhKsra0FDQ0NwdjYWGjSpImwfPlyuf4sCIKQkpIidOzYUTAwMBAkEkmJfaawO3fuCAAEb2/vcl2zurq6wr8h2dnZwvTp04X69esLampqgoGBgdChQwfh5MmTCut58eKFMGTIEMHU1FTQ0NAQ3NzchF27dol/v9auXVvsmOvXrwsDBw4UrK2tBTU1NcHQ0FBwdHQURo8eLZw7d06ubEn9kIgqRyIIRcZsEhEREdG/hr+/P9atW4c7d+7AxsamqsMhIiIiEnFNKSIiIiIiIiIiUjompYiIiIiIiIiISOmYlCIiIiIiIiIiIqXjmlJERERERERERKR0HClFRERERERERERKx6QUEREREREREREpnWpVB0BERP9+BQUFePToEXR1dSGRSKo6HCIiIiIi+hsJgoDXr1/DysoKKiolj4diUoqIiP52jx49Qq1atao6DCIiIiIiUqL79++jZs2aJe5nUoqIiP52urq6AN7/n5Kenl4VR0NERERERH+nrKws1KpVS/wdUBImpYiI6G8nm7Knp6fHpBQRERER0X9EWUt3cKFzIiIiIiIiIiJSOialiIiIiIiIiIhI6ZiUIiIiIiIiIiIipWNSioiIiIiIiIiIlI5JKSIiIiIiIiIiUjompYiIiIiIiIiISOmYlCIiIiIiIiIiIqVjUoqIiIiIiIiIiJSOSSkiIiIiIiIiIlI6JqWIiIiIiIiIiEjpmJQiIiIiIiIiIiKlY1KKiIiIiIiIiIiUjkkpIiIiIiIiIiJSOialiIiIiIiIiIhI6ZiUIiIiIiIiIiIipWNSioiIiIiIiIiIlI5JKSIiIiIiIiIiUjompYiIiIiIiIiISOmYlCIiIiIiIiIiIqVjUoqIiIiIiIiIiJROtaoDICKi/470dq2RrVqtqsMgIiqR1elzVR0CERHRfwZHShERERERERERkdIxKUVERERERERERErHpBQRERERERERESkdk1JERERERERERKR0TEoREREREREREZHSMSlFRERERERERERKx6QUEREREREREREpHZNSRERERERERESkdExKERERERERERGR0jEpRURERERERERESsekFBERERERERERKR2TUkREREREREREpHRMSn0EEokEe/bsAQCkpaVBIpEgMTGxSmOqrODgYLi4uCjtfP7+/ujatavSzkcEfLx+V/jZ/yeKjo6GRCLBq1evqjoUIiIiIiL6D1Kt6gD+DdLT02FoaFjVYRBROYWFhUEQhKoOo8o1b94c6enp0NfXr+pQiIiIiIjoP4hJqY/AwsKiqkP45OXm5qJ69epVHQbevXsHNTW1qg6DqhiTMO+pqanx7xcREREREVWZCk3fKygowPz581GvXj2oq6ujdu3amDNnjrj/ypUraNOmDTQ1NWFsbIwhQ4YgOztb3C+bMvPDDz/A3NwcBgYGmDlzJvLy8jBp0iQYGRmhZs2aWLt2rXiMbDrcli1b0Lx5c2hoaKBRo0aIiYkpNdZly5bB1tYWGhoaMDc3x9dffw0AWL9+PYyNjZGTkyNXvmvXrvD19QUAXLp0Ca1bt4auri709PTg5uaG8+fPl3guRVN4rl+/XmK8sikzERERaNy4MTQ1NdGmTRs8ffoUhw4dgr29PfT09NC3b1/8+eefJZ43PDwcBgYG2LNnj3it3t7euH//vly5P/74Ax4eHtDQ0ICJiQm6detWatsBwIYNG2BjYwN9fX307t0br1+/FvcdPnwYLVu2hIGBAYyNjdG5c2ekpqaK+2X3bOvWrfDy8oKGhgY2btyI/Px8jB8/Xjxu8uTJcqNV9u/fDwMDA+Tn5wMAEhMTIZFIMHXqVLHMoEGD8O233wIAXrx4gT59+qBGjRrQ0tKCo6MjNm/eLHcdUqkUI0eOxNixY2FiYgJvb28AQFJSEjp06AAdHR2Ym5vD19cXz58/L7NdFElKSipXudjYWEilUmhpacHQ0BDe3t7IyMgAAOTk5GD06NEwMzODhoYGWrZsifj4ePHYyvYZ2fWPHDkS+vr6MDExwfTp08scJbR8+XJ89tlnUFNTQ4MGDbBhwwZxnyAICA4ORu3ataGurg4rKyuMHj26xLpkU0LXrFmD2rVrQ0dHB8OHD0d+fj7mz58PCwsLmJmZyf0tqeg5Jk6ciM6dO4vfFy9eDIlEgsOHD4vb6tWrh1WrVgEoPn1PKpVi9OjRmDx5MoyMjGBhYYHg4GC5c9y8eROenp7Q0NCAg4MDjh49WiyO0v4GJiUlQUVFBc+ePQMAvHz5EioqKujdu7d4/OzZs9GyZUsAQEZGBvr16wdTU1NoamrC1tZW7m9jUVKpFKNGjcLYsWNhaGgIc3NzrFy5Em/evEFAQAB0dXVRr149HDp0SDym6PS9u3fvwsfHB4aGhtDW1kbDhg1x8OBBsfzVq1fRuXNn6OnpQVdXF61atZJ79omIiIiIiCqiQkmpwMBAhIaGYvr06bh27Ro2bdoEc3NzAMCbN2/g7e0NQ0NDxMfHY/v27Th27BhGjhwpV8fx48fx6NEjnDhxAosWLUJQUBA6d+4MQ0NDxMXFYdiwYRg6dCgePHggd9ykSZMwYcIEXLx4Ec2aNYOPjw9evHihMM7z589j9OjRmDlzJlJSUnD48GF4enoCAL755hvk5+dj3759YvmnT5/iwIEDGDBgAACgX79+qFmzJuLj43HhwgVMnTq1wqN8yhNvcHAwli5ditOnT+P+/fvo2bMnFi9ejE2bNuHAgQM4cuQIfv7551LP8+eff2LOnDlYv349YmNj8erVK7kfuQcOHEC3bt3QsWNHXLx4EZGRkWjSpEmpdaampmLPnj3Yv38/9u/fj5iYGISGhor737x5g/Hjx+P8+fOIjIyEiooKunXrhoKCArl6pk6dijFjxiA5ORne3t5YuHAhwsPDsWbNGpw6dQovX77E7t27xfKtWrXC69evcfHiRQBATEwMTExMEB0dLZaJiYmBVCoFALx9+xZubm44cOAAkpKSMGTIEPj6+uLcuXNycaxbtw5qamqIjY3Fr7/+ilevXqFNmzZo3Lgxzp8/j8OHD+PJkyfo2bNnqe2iyF9//QVvb2+5xJkiiYmJaNu2LRwcHHDmzBmcOnUKPj4+YgJu8uTJ2LlzJ9atW4eEhATUq1cP3t7eePnypVw9lekz69atg6qqKs6dO4ewsDAsWrRITM4osnv3bowZMwYTJkxAUlIShg4dioCAAERFRQEAdu7ciZ9++gkrVqzAzZs3sWfPHjg6OpZ6/ampqTh06BAOHz6MzZs3Y/Xq1ejUqRMePHiAmJgYzJs3D9OmTUNcXFylzuHl5YVTp06J7Vm07zx8+BCpqali31Fk3bp10NbWRlxcHObPn4+ZM2eKiaeCggJ0794dampqiIuLw6+//oopU6bIHV/W38CGDRvC2NhYTFCfPHlS7rssblmMsr+zhw4dQnJyMpYvXw4TE5NS23ndunUwMTHBuXPnMGrUKHz33Xf45ptv0Lx5cyQkJODLL7+Er69vicnuESNGICcnBydOnMCVK1cwb9486OjoiG3o6ekJdXV1HD9+HBcuXMCAAQOQl5dXakxEREREREQlkQjlXFjl9evXMDU1xdKlSzFo0KBi+1euXIkpU6bg/v370NbWBgAcPHgQPj4+ePToEczNzeHv74/o6Gjcvn0bKirv82F2dnYwMzPDiRMnAAD5+fnQ19fHqlWr0Lt3b6SlpaFOnToIDQ0VfwTm5eWhTp06GDVqFCZPnlwsll27diEgIAAPHjyArq5usf3Dhw9HWlqaOAJg0aJF+OWXX3Dr1i1IJBLo6enh559/hp+fX3maBhKJBLt370bXrl3LFW90dDRat26NY8eOoW3btgCA0NBQBAYGIjU1FXXr1gUADBs2DGlpaXKjPQoLDw9HQEAAzp49i88//xzA+xFa9vb2iIuLQ5MmTdC8eXPUrVsXv//+e7muJTg4GD/++CMeP34stt3kyZNx4sQJnD17VuExz58/h6mpKa5cuYJGjRqJbbB48WKMGTNGLGdlZYVx48Zh0qRJcu3i5uYmjjRzc3NDnz59MHHiRHTr1g0eHh4ICQnBixcvkJmZiZo1a+LGjRuwtbVVGEvnzp1hZ2eHBQsWAHg/eiQrKwsJCQlimdmzZ+PkyZOIiIgQtz148AC1atVCSkoK6tevX662krl8+TLatGmDoUOHyo32Kaxv3764d+8eTp06VWzfmzdvYGhoiPDwcPTt2xfA++mONjY2GDt2LCZNmlTpPiOVSvH06VNcvXoVEokEwPtk4b59+3Dt2jWFsbZo0QINGzbEb7/9Jm7r2bMn3rx5gwMHDmDRokVYsWIFkpKSypWsVdSn2rdvj5SUFKSmpsr9LfD398fUqVMrfI5Xr17B2NgYcXFxcHNzg4mJCSZNmoQ9e/bg7Nmz2LhxI6ZMmSImu/39/fHq1Sux30mlUuTn5+PkyZNinU2aNEGbNm0QGhqKI0eOoFOnTrh79y6srKwAvB8x2KFDB/HZL8/fwB49esDS0hJLly7FuHHjUL16daxatQqnT5/GZ599Jo58/OKLL/DVV1/BxMQEa9asKfP6FV2D7G9p9+7dsX79egDA48ePYWlpiTNnzqBp06Ziv8rIyICBgQGcnJzQo0cPBAUFFav/f//7H7Zs2YKUlJRy3ZOcnBy5EalZWVmoVasWrnu4Qle1WrmuiYioKlidPld2ISIiIipVVlYW9PX1kZmZCT09vRLLlXukVHJyMnJycsQfxIr2Ozs7iz/GgPc/bgsKCpCSkiJua9iwofgjFADMzc3lRkBUq1YNxsbGePr0qVz9zZo1E/9ZVVUV7u7uSE5OVhjLF198AWtra9StWxe+vr7YuHGj3MiAwYMH48iRI3j48CGA98kdf39/8Uf7+PHjMWjQILRr1w6hoaGVmp5SnnidnJzEfzY3N4eWlpaYXJBtK9oORamqqsLDw0P8bmdnBwMDA/FcshE6FWFjYyOXzLO0tJSL4+bNm+jTpw/q1q0LPT092NjYAADu3bsnV4+7u7v4z5mZmUhPTxeTZ7LYC5cB3o94iY6OhiAIOHnyJLp37w57e3ucOnUKMTExsLKyEhNS+fn5mDVrFhwdHWFkZAQdHR1EREQUi8PNzU3u+6VLlxAVFQUdHR3xY2dnBwAl3msbGxtIJBKFH2dnZ7x48QI//PCDXFKjsNLuQ2pqKnJzc9GiRQtxW/Xq1dGkSZOP0meaNm0q9m3gfd+8efOmOKqoqOTkZLlYgPfPsiyWb775Bn/99Rfq1q2LwYMHY/fu3WWOlinap8zNzeHg4FDsb4Es9tLO8cMPP8jdu3v37sHAwADOzs6Ijo7GlStXoKamhiFDhuDixYvIzs5GTEwMvLy8So2xcNsC8v0+OTkZtWrVEhNSsnYs2m5l/Q2U9W/g/aioNm3awNPTE9HR0YiPj5frB9999x22bNkCFxcXTJ48GadPny41/qLXIPtbWvjvq2xka0l/V0aPHo3Zs2ejRYsWCAoKwuXLl8V9iYmJaNWqVblHjc6dOxf6+vrip1atWuU6joiIiIiI/jvKnZTS1NT8KCcs+oNGIpEo3FZ0KlhF6OrqIiEhAZs3b4alpSVmzJgBZ2dncd2Uxo0bw9nZGevXr8eFCxdw9epV+Pv7i8cHBwfj6tWr6NSpE44fPw4HBwe5aWYfS+Hr/jvaAajcfSsrDh8fH7x8+RIrV65EXFycOOXq3bt3cscV/nFeXlKpFKdOncKlS5dQvXp12NnZQSqVIjo6ulhi4ccff0RYWBimTJmCqKgoJCYmwtvbu8w4srOz4ePjg8TERLmPbM0gRSIjI5GcnKzwc/z4cRgbG6Nnz55o3ry5wuP/jufn7+ozZZGNKFu2bBk0NTUxfPhweHp6Ijc3t8RjKvrcl3aOYcOGyd03WaKoaD8xMjKSS2iWlZRSRltKpVJcu3YNN2/exLVr19CyZUu5uN3d3aGlpQUA6NChA+7evYtx48bh0aNHaNu2LSZOnFjhayjaZwCUeF2DBg3C7du34evriytXrsDd3V2cDlrRPhwYGIjMzEzxU3StOyIiIiIionInpWxtbaGpqYnIyEiF++3t7XHp0iW8efNG3BYbGwsVFRU0aNDggwMtPHUsLy8PFy5cgL29fYnlVVVV0a5dO8yfPx+XL19GWloajh8/Lu4fNGgQwsPDsXbtWrRr167Yf8WvX78+xo0bhyNHjqB79+6lLjD8MeKtrLy8PLlF2FNSUvDq1SvxXE5OTiXes8p48eIFUlJSMG3aNLRt2xb29vbiYt2l0dfXh6WlpZjAksV+4cIFuXKydaV++uknMYkg+9EeHR0ttyZQbGwsunTpgm+//RbOzs6oW7cubty4UWYsrq6uuHr1KmxsbFCvXj25T0mJtM8++wx2dnbFPtbW1hg+fDikUik2btyIatUUT0sq7T7IFhSPjY0Vt+Xm5iI+Ph4ODg5lXk9ZCrc58L5v2tralhirvb29XCzA+7YuHIumpiZ8fHywZMkSREdH48yZM7hy5coHx1pYSecwMjKSu2eqqu9fIipbVyoyMlLsJ1KpFJs3b8aNGzdKXU+qLPb29rh//z7S09PFbUWns5bnb6CjoyMMDQ0xe/ZsuLi4QEdHB1KpFDExMcX6NwCYmprCz88Pv//+OxYvXiw3pfLvUqtWLQwbNgy7du3ChAkTsHLlSgDv+/DJkydLTT4Wpq6uDj09PbkPERERERFRYeVOSmloaGDKlCmYPHky1q9fj9TUVJw9exarV68G8H5xcA0NDfj5+SEpKQlRUVEYNWoUfH19xSkjH+KXX37B7t27cf36dYwYMQIZGRniwuTA+2lrstFM+/fvx5IlS5CYmIi7d+9i/fr1KCgokEuO9e3bFw8ePMDKlSvl6vnrr78wcuRIREdH4+7du4iNjUV8fLyY5Hn48CHs7OyKLaZd0XgrY+nSpcWmgFWvXh2jRo1CXFwcLly4AH9/fzRt2lRczDwoKAibN29GUFAQkpOTxcWLZQIDA9G/f/9yx2BoaAhjY2P89ttvuHXrFo4fP47x48eX69gxY8YgNDQUe/bswfXr1zF8+HBx9Frh+p2cnLBx40bxB7qnpycSEhJw48YNudEutra2OHr0KE6fPo3k5GQMHToUT548KTOOESNG4OXLl+jTpw/i4+ORmpqKiIgIBAQElDilrSSampoIDQ3Fli1bxOSIIoGBgYiPj8fw4cNx+fJlXL9+HcuXL8fz58+hra2N7777DpMmTcLhw4dx7do1DB48GH/++ScGDhxYoXgUuXfvHsaPH4+UlBRs3rwZP//8s9xaX0X7wKRJkxAeHo7ly5fj5s2bWLRoEXbt2iWO0gkPD8fq1auRlJSE27dv4/fff4empiasra0V1lcZZZ1DEU9PT7x+/Rr79++XS0pt3LgRlpaWFV4rrLB27dqhfv368PPzw6VLl3Dy5El8//33cmXK8zdQIpHA09NTrn87OTkhJycHkZGRcv17xowZ2Lt3L27duoWrV69i//79conttm3bYunSpZW+JkXGjh2LiIgI3LlzBwkJCYiKihLPOXLkSGRlZaF37944f/48bt68iQ0bNshNzyYiIiIiIqqICr19b/r06ZgwYQJmzJgBe3t79OrVS1ybREtLCxEREXj58iU8PDzw9ddff9QfTaGhoQgNDYWzszNOnTqFffv2yb2JKiUlBZmZmQAAAwMD7Nq1C23atIG9vT1+/fVXbN68GQ0bNhTL6+vro0ePHtDR0ZF7NXy1atXw4sUL9O/fH/Xr10fPnj3RoUMHhISEAHg/giUlJaXEt1eVN97KeP78ebE1j7S0tDBlyhT07dsXLVq0gI6ODrZu3Srul0ql2L59O/bt2wcXFxe0adNGLqGWnp5ebA2m0qioqGDLli24cOECGjVqhHHjxuHHH38s17ETJkyAr68v/Pz80KxZM+jq6qJbt27Fynl5eSE/P1/80W5kZAQHBwdYWFjIJRanTZsGV1dXeHt7QyqVwsLCQu5elsTKygqxsbHIz8/Hl19+CUdHR4wdOxYGBgZyaxyVV5cuXUpNSAHvR94dOXIEly5dQpMmTdCsWTPs3btXPC40NBQ9evSAr68vXF1dcevWLURERMDQ0LDC8RTVv39//PXXX2jSpAlGjBiBMWPGYMiQIeL+on2ga9euCAsLw4IFC9CwYUOsWLECa9euFe+HgYEBVq5ciRYtWsDJyQnHjh3DH3/8AWNjY4X1VUZZ51DE0NAQjo6OMDU1FdcI8/T0REFBQZlT98qioqKC3bt3i+04aNCgYoval/dvYNH+raKiAk9PT0gkErm1vNTU1BAYGAgnJyd4enqiWrVq2LJli7g/NTUVz58//6DrKio/Px8jRoyAvb092rdvj/r162PZsmUAAGNjYxw/fhzZ2dnw8vKCm5sbVq5cWeE3kxIREREREcmU++17VUX2JreLFy/CxcXlo9bdtm1bNGzYEEuWLPmo9SpLeHg4xo4dW2y0EZGMVCqFi4sLFi9eXNWh0H+c7O0bfPseEX3q+PY9IiKiD1fet++VPrzjXyojI0Nco0g2CoCIiIiIiIiIiJTnP5mUaty4MTIyMjBv3ryPsgg7ERERERERERFVzCc/fY+IiP75OH2PiP4pOH2PiIjow5V3+l7FV3UmIiIiIiIiIiL6QExKERERERERERGR0jEpRURERERERERESsekFBERERERERERKR2TUkREREREREREpHRMShERERERERERkdIxKUVERERERERERErHpBQRERERERERESkdk1JERERERERERKR0qlUdABER/XdYHouCnp5eVYdBRERERESfAI6UIiIiIiIiIiIipWNSioiIiIiIiIiIlI5JKSIiIiIiIiIiUjompYiIiIiIiIiISOmYlCIiIiIiIiIiIqVjUoqIiIiIiIiIiJSOSSkiIiIiIiIiIlI6JqWIiIiIiIiIiEjpVKs6ACIi+u9Ib9ca2arVqjoMIqJPmtXpc1UdAhERkVJwpBQRERERERERESkdk1JERERERERERKR0TEoREREREREREZHSMSlFRERERERERERKx6QUEREREREREREpHZNSRERERERERESkdExKERERERERERGR0jEpRURERERERERESsekFBERERERERERKR2TUkREREREREREpHRMShERERERERERkdIxKUVERERERERERErHpBQBAGxsbLB48WKlnU8ikWDPnj1KOx99Wgrf/7S0NEgkEiQmJlZpTJUVHBwMFxcXpZ3P398fXbt2Vdr5iIiIiIiI/i6qVR0AEf33pKenw9DQsKrDICIiIiIioirEkVL0t3n37l1VhyDKzc2t6hCoEAsLC6irq1d1GJ+0T6XPfkrPMRERERER/bsoLSlVUFCA+fPno169elBXV0ft2rUxZ84ccf+VK1fQpk0baGpqwtjYGEOGDEF2dra4XzZl5YcffoC5uTkMDAwwc+ZM5OXlYdKkSTAyMkLNmjWxdu1a8RjZtKAtW7agefPm0NDQQKNGjRATE1NqrMuWLYOtrS00NDRgbm6Or7/+GgCwfv16GBsbIycnR658165d4evrCwC4dOkSWrduDV1dXejp6cHNzQ3nz58v8VwSiQQrVqxA586doaWlBXt7e5w5cwa3bt2CVCqFtrY2mjdvjtTUVPGY1NRUdOnSBebm5tDR0YGHhweOHTsmV29OTg6mTJmCWrVqQV1dHfXq1cPq1atLve4///wTAwYMgK6uLmrXro3ffvtNbv+UKVNQv359aGlpoW7dupg+fbrcD2fZNKZVq1ahTp060NDQAADcvHkTnp6e0NDQgIODA44ePSpX79dff42RI0eK38eOHQuJRILr168DeP+jWFtbW7zGw4cPo2XLljAwMICxsTE6d+4s1z6y+75161Z4eXlBQ0MDGzduBACsWrUK9vb20NDQgJ2dHZYtW1Zqm5QkPz8fycnJZZZjv1dM0fTN69evlxhvdHQ0JBIJIiIi0LhxY2hqaqJNmzZ4+vQpDh06BHt7e+jp6aFv3774888/SzxveHg4DAwMsGfPHvFavb29cf/+fblyf/zxBzw8PKChoQETExN069at1LYDgA0bNsDGxgb6+vro3bs3Xr9+Le6rbJ/Nz8/H+PHjxeMmT54MQRDE4/bv3w8DAwPk5+cDABITEyGRSDB16lSxzKBBg/Dtt98CAF68eIE+ffqgRo0a0NLSgqOjIzZv3ix3HVKpFCNHjsTYsWNhYmICb29vAEBSUhI6dOgAHR0dmJubw9fXF8+fPy+zXYiIiIiIiEqitKRUYGAgQkNDMX36dFy7dg2bNm2Cubk5AODNmzfw9vaGoaEh4uPjsX37dhw7dkwuUQEAx48fx6NHj3DixAksWrQIQUFB6Ny5MwwNDREXF4dhw4Zh6NChePDggdxxkyZNwoQJE3Dx4kU0a9YMPj4+ePHihcI4z58/j9GjR2PmzJlISUnB4cOH4enpCQD45ptvkJ+fj3379onlnz59igMHDmDAgAEAgH79+qFmzZqIj4/HhQsXMHXqVFSvXr3Utpk1axb69++PxMRE2NnZoW/fvhg6dCgCAwNx/vx5CIIg1xbZ2dno2LEjIiMjcfHiRbRv3x4+Pj64d++eWKZ///7YvHkzlixZguTkZKxYsQI6OjqlxrFw4UK4u7vj4sWLGD58OL777jukpKSI+3V1dREeHo5r164hLCwMK1euxE8//SRXx61bt7Bz507s2rULiYmJKCgoQPfu3aGmpoa4uDj8+uuvmDJlitwxXl5eiI6OFr/HxMTAxMRE3BYfH4/c3Fw0b94cwPv+Mn78eJw/fx6RkZFQUVFBt27dUFBQIFfv1KlTMWbMGCQnJ8Pb2xsbN27EjBkzMGfOHCQnJ+OHH37A9OnTsW7dulLbRZEDBw6gadOmOHfuXKnl2O/LrzzxBgcHY+nSpTh9+jTu37+Pnj17YvHixdi0aRMOHDiAI0eO4Oeffy71PH/++SfmzJmD9evXIzY2Fq9evULv3r3F/QcOHEC3bt3QsWNHXLx4EZGRkWjSpEmpdaampmLPnj3Yv38/9u/fj5iYGISGhor7K9tnFy5ciPDwcKxZswanTp3Cy5cvsXv3brF8q1at8Pr1a1y8eBFA8WdHtk0qlQIA3r59Czc3Nxw4cABJSUkYMmQIfH19i/XjdevWQU1NDbGxsfj111/x6tUrtGnTBo0bN8b58+dx+PBhPHnyBD179iy1XYiIiIiIiEojEQr/Z/e/yevXr2FqaoqlS5di0KBBxfavXLkSU6ZMwf3796GtrQ0AOHjwIHx8fPDo0SOYm5vD398f0dHRuH37NlRU3ufS7OzsYGZmhhMnTgB4P3pFX18fq1atQu/evZGWloY6deogNDRUTITk5eWhTp06GDVqFCZPnlwsll27diEgIAAPHjyArq5usf3Dhw9HWloaDh48CABYtGgRfvnlF9y6dQsSiQR6enr4+eef4efnV662kUgkmDZtGmbNmgUAOHv2LJo1a4bVq1eLP/i3bNmCgIAA/PXXXyXW06hRIwwbNgwjR47EjRs30KBBAxw9ehTt2rUrVxw2NjZo1aoVNmzYAAAQBAEWFhYICQnBsGHDFB6zYMECbNmyRRwRExwcjB9++AEPHz6EqakpAODIkSPo1KkT7t69CysrKwDvR4106NABu3fvRteuXXHlyhU4OzvjyZMnUFVVhYWFBaZPn46kpCRs2bIFc+bMwcGDBxEbG6swjufPn8PU1BRXrlxBo0aNxPu+ePFijBkzRixXr149zJo1C3369BG3zZ49GwcPHsTp06fL1U6FzZkzBz/++COOHTsGd3f3YvvZ70smkUjE+1+eeKOjo9G6dWscO3YMbdu2BQCEhoYiMDAQqampqFu3LgBg2LBhSEtLw+HDhxWeNzw8HAEBATh79iw+//xzAO9HaNnb2yMuLg5NmjRB8+bNUbduXfz+++/lupbg4GD8+OOPePz4sdh2kydPxokTJ3D27FmFx5S3z1pZWWHcuHGYNGmSXLu4ubmJI83c3NzQp08fTJw4Ed26dYOHhwdCQkLw4sULZGZmombNmrhx4wZsbW0VxtK5c2fY2dlhwYIFAN6PlMrKykJCQoJYZvbs2Th58iQiIiLEbQ8ePECtWrWQkpKC+vXrF6s3JydHbnRdVlYWatWqhesertBVrVaepiUi+s+yOl36f/QiIiL61GVlZUFfXx+ZmZnQ09MrsZxSRkolJycjJydH/DGpaL+zs7P4wxwAWrRogYKCArmROg0bNhR/mAOAubk5HB0dxe/VqlWDsbExnj59Kld/s2bNxH9WVVWFu7t7iVOvvvjiC1hbW6Nu3brw9fXFxo0b5aYDDR48GEeOHMHDhw8BvP+R6+/vD4lEAgAYP348Bg0ahHbt2iE0NFRuik5JnJyc5K4JgNx1mZub4+3bt8jKygLwfqTUxIkTYW9vDwMDA+jo6CA5OVkcKZWYmIhq1arBy8urzHOXFIdEIoGFhYVcW27duhUtWrSAhYUFdHR0MG3aNLnRWQBgbW0tJqSA9/e2Vq1aYkIKkL8fwPuEmpGREWJiYnDy5Ek0btwYnTt3FqdvFR7pAbyfDtinTx/UrVsXenp6sLGxAYBisRROFL158wapqakYOHAgdHR0xM/s2bNLvEeyKWMlfaZNm4bMzEz06tVL4fHs9xVTnniLPiuyqaSFtxVth6JUVVXh4eEhfrezs4OBgYF4rsTExBLvWUlsbGzkknmWlpZycVSmz2ZmZiI9PV1MnsliL5oAlY00FAQBJ0+eRPfu3WFvb49Tp04hJiYGVlZWYkIqPz8fs2bNgqOjI4yMjKCjo4OIiIhicbi5ucl9v3TpEqKiouSeHTs7OwAo8V7PnTsX+vr64qdWrVqltiEREREREf33KCUppamp+VHqKTodSCKRKNxWdEpMRejq6iIhIQGbN2+GpaUlZsyYAWdnZ7x69QoA0LhxYzg7O2P9+vW4cOECrl69Cn9/f/H44OBgXL16FZ06dcLx48fh4OAgN92mrOuS/chXtE12XRMnTsTu3bvxww8/4OTJk0hMTISjo6O4IHFl27u0tjxz5gz69euHjh07Yv/+/bh48SK+//77YosgF06wlJdEIoGnpyeio6PFBJSTkxNycnKQlJSE06dPyyXYfHx88PLlS6xcuRJxcXGIi4sDUHxB5sKxyNZpWrlyJRITE8VPUlJSiaNZmjRpguTk5BI/ISEhUFVVxY8//qjwePb70vt9ZRR9Lj52OwCVu29lxVGZPlteUqkUp06dwqVLl1C9enXY2dlBKpWKz1PhZ+fHH39EWFgYpkyZgqioKCQmJsLb27vMOLKzs+Hj4yP37CQmJorrxSkSGBiIzMxM8VN03S4iIiIiIiKlJKVsbW2hqamJyMhIhfvt7e1x6dIlvHnzRtwWGxsLFRUVNGjQ4IPPXzjpkJeXhwsXLsDe3r7E8qqqqmjXrh3mz5+Py5cvIy0tDcePHxf3Dxo0COHh4Vi7di3atWtXbARA/fr1MW7cOBw5cgTdu3eXW4T6Y4iNjYW/vz+6desGR0dHWFhYIC0tTdzv6OiIgoKCMhe2rojTp0/D2toa33//Pdzd3WFra4u7d++WeZy9vT3u37+P9PR0cZuiJJBstEd0dDSkUilUVFTg6emJH3/8ETk5OWjRogWA9ws1p6SkYNq0aWjbti3s7e2RkZFRZhzm5uawsrLC7du3Ua9ePblPnTp1FB6jpaUFOzs7hZ/79+9j7ty52Lx5M7p3767wePb7ivX7isZbWXl5eXKLsKekpODVq1fiuZycnEq8Z5VR2T6rr68PS0tLMYEli/3ChQty5WTrSv30009iAkqWlJI9TzKxsbHo0qULvv32Wzg7O6Nu3bq4ceNGmbG4urri6tWrsLGxKfb8lJRIU1dXh56entyHiIiIiIioMKUkpTQ0NDBlyhRMnjwZ69evR2pqKs6ePSu+Da5fv37Q0NCAn58fkpKSEBUVhVGjRsHX11eczvYhfvnlF+zevRvXr1/HiBEjkJGRIa7XBLyfviMb1bF//34sWbIEiYmJuHv3LtavX4+CggK5JEHfvn3x4MEDrFy5Uq6ev/76CyNHjkR0dDTu3r2L2NhYxMfHiz92Hz58CDs7uzIXxy6Lra2tuJD4pUuX0LdvX7lRGTY2NvDz88OAAQOwZ88e3LlzB9HR0di2bZvCay7vOe/du4ctW7YgNTUVS5YsKdfx7dq1Q/369eHn54dLly7h5MmT+P7774uVk0qluHbtGq5evYqWLVuK2zZu3Ah3d3fxh6+hoSGMjY3x22+/4datWzh+/DjGjx9frmsICQnB3LlzsWTJEty4cQNXrlzB2rVrsWjRonK3g0zTpk2xZ88e8Q11irDfV6zflxVvZSxdurTYVLzq1atj1KhRiIuLw4ULF+Dv74+mTZuKi5kHBQVh8+bNCAoKQnJyMq5cuYJ58+aJxwcGBqJ///7ljuFD+uyYMWMQGhqKPXv24Pr16xg+fLg4eq1w/U5OTti4caOYgPL09ERCQgJu3LghN1LK1tYWR48exenTp5GcnIyhQ4fiyZMnZcYxYsQIvHz5En369EF8fDxSU1MRERGBgIAA8c1/REREREREFaW0t+9Nnz4dEyZMwIwZM2Bvb49evXqJa65oaWkhIiICL1++hIeHB77++mu0bdsWS5cu/SjnDg0NRWhoKJydnXHq1Cns27cPJiYm4v6UlBRkZmYCAAwMDLBr1y60adMG9vb2+PXXX7F582Y0bNhQLK+vr48ePXpAR0cHXbt2FbdXq1YNL168QP/+/VG/fn307NkTHTp0QEhICAAgNzcXKSkppb6yvjwWLVoEQ0NDNG/eHD4+PvD29oarq6tcmeXLl+Prr7/G8OHDYWdnh8GDB8uNyCl8zeXx1VdfYdy4cRg5ciRcXFxw+vRpTJ8+vczjVFRUsHv3bvz1119o0qQJBg0ahDlz5hQr5+joCAMDA7i4uIhvCZRKpcjPz5cb6aGiooItW7bgwoULaNSoEcaNG1fi9LmiBg0ahFWrVmHt2rVwdHSEl5cXwsPDSxwpVRpdXV14e3uXWY79vvz9vqx4K+P58+fF1jzS0tLClClT0LdvX7Ro0QI6OjrYunWruF8qlWL79u3Yt28fXFxc0KZNG7mEWnp6erE1mErzIX12woQJ8PX1hZ+fH5o1awZdXV1069atWDkvLy+5Z8XIyAgODg6wsLCQSyxOmzYNrq6u8Pb2hlQqhYWFhdy9LImVlRViY2ORn5+PL7/8Eo6Ojhg7diwMDAzk1jsjIiIiIiKqCKW8fa+qyN5odfHiRbi4uHzUutu2bYuGDRtiyZIlH7Veog/Ffl+y8PBwjB07tthoI/r7yd6+wbfvERGVjW/fIyKif7ryvn1PVYkx/StkZGSIa7UsW7asqsMhUgr2eyIiIiIiIvrYmJSqoMaNGyMjIwPz5s37KItRE/0TsN8TERERERHRx/avnr5HRESfBk7fIyIqP07fIyKif7ryTt/jCrVERERERERERKR0TEoREREREREREZHSMSlFRERERERERERKx6QUEREREREREREpHZNSRERERERERESkdExKERERERERERGR0jEpRURERERERERESsekFBERERERERERKR2TUkREREREREREpHSqVR0AERH9d1gei4Kenl5Vh0FERERERJ8AjpQiIiIiIiIiIiKlY1KKiIiIiIiIiIiUjkkpIiIiIiIiIiJSOialiIiIiIiIiIhI6ZiUIiIiIiIiIiIipWNSioiIiIiIiIiIlI5JKSIiIiIiIiIiUjompYiIiIiIiIiISOmYlCIiIiIiIiIiIqVTreoAiIjovyO9XWtkq1ar6jCIiD5pVqfPVXUIRERESsGRUkREREREREREpHRMShERERERERERkdIxKUVERERERERERErHpBQRERERERERESkdk1JERERERERERKR0TEoREREREREREZHSMSlFRERERERERERKx6QUEREREREREREpHZNSRERERERERESkdExKERERERERERGR0jEpRURERERERERESsekFBERERERERERKR2TUv9gEokEe/bs+aA6wsPDYWBg8FHiqSr+/v7o2rVrVYdBFWBjY4PFixeL3z9GX64qyn6GgoOD4eLiorTzERERERER/V1UqzoAqrz09HQYGhpWdRhVLiwsDIIgVHUYVAHx8fHQ1tau6jCIiIiIiIioCjEp9Q9mYWFR1SF8EvT19as6BKogU1PTqg7hk5ebm4vq1atXdRh49+4d1NTUqjoMIiIiIiL6F6rQ9L2CggLMnz8f9erVg7q6OmrXro05c+aI+69cuYI2bdpAU1MTxsbGGDJkCLKzs8X9smlWP/zwA8zNzWFgYICZM2ciLy8PkyZNgpGREWrWrIm1a9eKx6SlpUEikWDLli1o3rw5NDQ00KhRI8TExJQaa0ZGBvr37w9DQ0NoaWmhQ4cOuHnzprj/7t278PHxgaGhIbS1tdGwYUMcPHiwxPpsbGwwe/Zs9O/fHzo6OrC2tsa+ffvw7NkzdOnSBTo6OnBycsL58+crdQ5BEGBqaoodO3aI21xcXGBpaSl+P3XqFNTV1fHnn38CkJ/yJGunXbt2oXXr1tDS0oKzszPOnDkjd57w8HDUrl0bWlpa6NatG168eFEsluXLl+Ozzz6DmpoaGjRogA0bNoj7Jk6ciM6dO4vfFy9eDIlEgsOHD4vb6tWrh1WrVgEAoqOj0aRJE2hra8PAwAAtWrTA3bt3FbaB7Bq2bduGVq1aQVNTEx4eHrhx4wbi4+Ph7u4OHR0ddOjQAc+ePROPKzp9b8eOHXB0dBT7Ybt27fDmzRtx/5o1a9CwYUOoq6vD0tISI0eOVBhPWZKSkspVrrTz3bt3T+w/enp66NmzJ548eSLul03VWrNmDWrXrg0dHR0MHz4c+fn5mD9/PiwsLGBmZib3HALv+8by5cvRoUMHaGpqom7dunJ9S5GS2u3EiROoXr06Hj9+LFd+7NixaNWqFYDKPU+Fp+8B70f+lRRvZftGUdHR0ZBIJDhw4ACcnJygoaGBpk2bFruXsbGxkEql0NLSgqGhIby9vZGRkVFq+0VERMDe3h46Ojpo37490tPTxX3x8fH44osvYGJiAn19fXh5eSEhIUHueNk9++qrr6CtrS3e09DQUJibm0NXVxcDBw7E27dvxWOSkpKgoqIiXvPLly+hoqKC3r17i2Vmz56Nli1bAgDy8/MxcOBA1KlTB5qammjQoAHCwsLk4pA9T3PmzIGVlRUaNGgAALh//z569uwJAwMDGBkZoUuXLkhLSyu1TYiIiIiIiEpToaRUYGAgQkNDMX36dFy7dg2bNm2Cubk5AODNmzfw9vaGoaEh4uPjsX37dhw7dqzYD/7jx4/j0aNHOHHiBBYtWoSgoCB07twZhoaGiIuLw7BhwzB06FA8ePBA7rhJkyZhwoQJuHjxIpo1awYfHx+FCRUZf39/nD9/Hvv27cOZM2cgCAI6duyI3NxcAMCIESOQk5ODEydO4MqVK5g3bx50dHRKvf6ffvoJLVq0wMWLF9GpUyf4+vqif//++Pbbb5GQkIDPPvsM/fv3F6eSVeQcEokEnp6eiI6OBvA+qZacnIy//voL169fBwDExMTAw8MDWlpaJcb4/fffY+LEiUhMTET9+vXRp08f5OXlAQDi4uIwcOBAjBw5EomJiWjdujVmz54td/zu3bsxZswYTJgwAUlJSRg6dCgCAgIQFRUFAPDy8sKpU6eQn58vxmRiYiLG/fDhQ6SmpkIqlSIvLw9du3aFl5cXLl++jDNnzmDIkCGQSCSltnNQUBCmTZuGhIQEqKqqom/fvpg8eTLCwsJw8uRJ3Lp1CzNmzFB4bHp6Ovr06YMBAwYgOTkZ0dHR6N69u3hPli9fjhEjRmDIkCG4cuUK9u3bh3r16pUajyIXL16Eh4cH9u3bV2q50s5XUFCALl264OXLl4iJicHRo0dx+/Zt9OrVS66O1NRUHDp0CIcPH8bmzZuxevVqdOrUCQ8ePEBMTAzmzZuHadOmIS4uTu646dOno0ePHrh06RL69euH3r17Izk5ucLt5unpibp168olJ3Nzc7Fx40YMGDAAQOWep6LKE++H9I3CJk2ahIULFyI+Ph6mpqbw8fER/zYkJiaibdu2cHBwwJkzZ3Dq1Cn4+PiIfV6RP//8EwsWLMCGDRtw4sQJ3Lt3DxMnThT3v379Gn5+fjh16hTOnj0LW1tbdOzYEa9fv5arJzg4GN26dcOVK1cwYMAAbNu2DcHBwfjhhx9w/vx5WFpaYtmyZWL5hg0bwtjYWEzSnzx5Uu478P4ZlUqlAN73uZo1a2L79u24du0aZsyYgf/973/Ytm2bXByRkZFISUnB0aNHsX//fuTm5sLb2xu6uro4efIkYmNjxeTbu3fvFLZJTk4OsrKy5D5ERERERESFSYRyLsbz+vVrmJqaYunSpRg0aFCx/StXrsSUKVNw//59ca2YgwcPwsfHB48ePYK5uTn8/f0RHR2N27dvQ0XlfT7Mzs4OZmZmOHHiBID3/yVfX18fq1atQu/evZGWloY6deogNDQUU6ZMAQDk5eWhTp06GDVqFCZPnlwslps3b6J+/fqIjY1F8+bNAQAvXrxArVq1sG7dOnzzzTdwcnJCjx49EBQUVK6GsrGxQatWrcQf5o8fP4alpSWmT5+OmTNnAgDOnj2LZs2aIT09HRYWFhU+x88//4wVK1YgKSkJe/fuxdy5c2FhYYH27dtj2LBh+OKLL9CkSRNxBIVEIsHu3bvRtWtXsZ1WrVqFgQMHAgCuXbuGhg0bIjk5GXZ2dujbty8yMzNx4MAB8Zy9e/fG4cOH8erVKwBAixYt0LBhQ/z2229imZ49e+LNmzc4cOAAXr16BWNjY8TFxcHNzQ0mJiaYNGkS9uzZg7Nnz2Ljxo2YMmUKHjx4gJcvX8LY2BjR0dHw8vIq8/oVXcOWLVvQp08fREZGok2bNgDejxwJDw8Xk3X+/v549eoV9uzZg4SEBLi5uSEtLQ3W1tbFzlGjRg0EBAQUS8ZVxoYNGzBkyBDs2LEDnTp1UlimtPMdPXoUHTp0wJ07d1CrVi0A/3fPzp07Bw8PDwQHB+PHH3/E48ePoaurCwBo3749UlJSkJqaKvcc+fv7Y+rUqQDe941hw4Zh+fLl4vmaNm0KV1dXuaSGTFntNn/+fISHh+PatWsAgF27dsHPzw+PHz+GtrZ2pZ6nsWPHYuzYseWKt7J9o6jo6Gi0bt0aW7ZsEZN/L1++RM2aNREeHo6ePXuib9++uHfvHk6dOlWuawkPD0dAQABu3bqFzz77DACwbNkyzJw5s9joMpmCggIYGBhg06ZN4shDiUSCsWPH4qeffhLLNW/eHI0bN8Yvv/wi1y5v375FYmIiAKBHjx6wtLTE0qVLMW7cOFSvXh2rVq3C6dOn8dlnn8HAwAB79uzBF198oTCWkSNH4vHjx+LINH9/fxw+fBj37t0Tp+39/vvvmD17NpKTk8Wk8rt378S6v/zyy2L1BgcHIyQkpNj26x6u0FWtVmqbEhH911mdPlfVIRAREX2QrKws6OvrIzMzE3p6eiWWK/dIqeTkZOTk5KBt27Yl7nd2dpZbvLhFixYoKChASkqKuK1hw4biD2kAMDc3h6Ojo/i9WrVqMDY2xtOnT+Xqb9asmfjPqqqqcHd3L3HUR3JyMlRVVfH555+L24yNjdGgQQPxmNGjR2P27Nlo0aIFgoKCcPny5TLbwMnJSS5uAHKxy7bJYi/tHA0bNoSOjo445Qh4Pwrp2rVrePbsmTi6QSqVIjo6Grm5uTh9+rQ44qE8Mcqm/sniSU5OlmsTQL5dZWVatGght61FixZiuxkYGMDZ2RnR0dG4cuUK1NTUMGTIEFy8eBHZ2dmIiYkRE1BGRkbw9/eHt7c3fHx8EBYWJjelqTzXUFI7F+0fMs7Ozmjbti0cHR3xzTffYOXKleK0q6dPn+LRo0cl9uGiZFPGSvr0798fb9++RY8ePcRRNoWVdb7k5GTUqlVLTEgBgIODAwwMDOT6to2NjZiQkl2/g4NDseeotGdG9r2kZ6a0dgPeJypu3bqFs2fPAoCYwJE975V5nooqT7wf0jdKOpeRkZHc3wbZSKmK0NLSEhNSwPtnr3AcT548weDBg2Frawt9fX3o6ekhOzsb9+7dk6vH3d1d7nt5nlkvLy9xpGJMTAzatGkjjrqMj49Hbm6u3DP9yy+/wM3NDaamptDR0cFvv/1WLA5HR0e5daQuXbqEW7duQVdXV/y7ZWRkhLdv3yI1NVVhmwQGBiIzM1P83L9/v6TmIyIiIiKi/6hyJ6U0NTU/ygmLLtwrkUgUbisoKPgo5yvJoEGDcPv2bfj6+uLKlStwd3fHzz//XOoxheOUjRZQtE0We2nnOHjwIBITE5GYmCiuv+To6AgjIyPExMTIJaViYmLEH5eykV8VifFjt6UsUSZLQBkZGcHe3h6nTp2SS0oBwNq1a3HmzBk0b94cW7duRf369cXERkWuoei2kq6pWrVqOHr0KA4dOgQHBwf8/PPPaNCgAe7cuVPhPlyjRg0kJyeX+FmxYgWqVauG+fPnK1yQ+p/0zJTWbgBgZmYGHx8frF27Fk+ePMGhQ4fEqXtA5Z6nyviQvlFelblviu5H4UGofn5+SExMRFhYGE6fPo3ExEQYGxsXm/pWmTcSSqVSXLt2DTdv3sS1a9fQsmVLuWfU3d1dnPK7ZcsWTJw4EQMHDsSRI0eQmJiIgICAMuPIzs6Gm5ub+DdL9rlx4wb69u2rMC51dXXo6enJfYiIiIiIiAord1LK1tYWmpqaiIyMVLjf3t4ely5dkltQOjY2FioqKuJCuR+icCIjLy8PFy5cgL29fYmx5OXlya2x8+LFC6SkpMDBwUHcVqtWLQwbNgy7du3ChAkTsHLlyg+Os6iSzmFtbY169eqhXr16qFGjBoD3P2RbtWqFvXv34urVq2jZsiWcnJyQk5ODFStWwN3dvVI/WmXs7e2LrTtUNEFkb2+P2NhYuW2xsbFy7SZbVyoyMlIcuSWVSrF582bcuHGj2Giuxo0bIzAwEKdPn0ajRo2wadOmSl9DeUgkErRo0QIhISG4ePEi1NTUsHv3bujq6sLGxqbEPlxU9erVYWdnp/CTl5eHCRMmYMGCBRg9erTC48s6n729Pe7fvy83guTatWt49eqVXHtXVtF7e/bs2RKfGaDkdpMZNGgQtm7dit9++w2fffZZsRF1H/o8VTTeD1H4XBkZGbhx44Z4Licnp3L3kfKKjY3F6NGj0bFjR3HR++fPn5d5XHmeWUdHRxgaGmL27NlwcXGBjo6OmMyOjo6Wex5lU5qHDx+Oxo0bo169eiWOdCrM1dUVN2/ehJmZmfh3S/bh2y+JiIiIiKiyyp2U0tDQwJQpUzB58mSsX78eqampOHv2LFavXg0A6NevHzQ0NODn54ekpCRERUVh1KhR8PX1FafZfIhffvkFu3fvxvXr1zFixAhkZGTIjdSws7MTf0Db2tqiS5cuGDx4ME6dOoVLly7h22+/RY0aNdClSxcA798cFhERgTt37iAhIQFRUVFyP4AL11dZZZ1DEVlyR/bjUkVFBZ6enti4cWO51mUqzejRo3H48GEsWLAAN2/exNKlS+Xemge8XwA6PDwcy5cvx82bN7Fo0SLs2rVLbtFmT09PvH79Gvv375dLSm3cuBGWlpaoX78+AODOnTsIDAzEmTNncPfuXRw5cgQ3b94U2+DcuXOws7PDw4cPP+i6CouLixMXhb537x527dqFZ8+eiecMDg7GwoULsWTJEty8eRMJCQmVGtFjZ2eH33//XVwTqSSlna9du3ZwdHREv379kJCQgHPnzqF///7w8vIqNo2rMrZv3441a9bgxo0bCAoKwrlz5+RePNC2bVssXboUQNntBgDe3t7Q09PD7NmzERAQIHeuj/E8lRVvZezevRt2dnbFts+cORORkZFISkqCv78/TExMxDc4BgYGIj4+HsOHD8fly5dx/fp1LF++XEwiLV26tMLT+2xtbbFhwwYkJycjLi4O/fr1K9eIrDFjxmDNmjVYu3at2C5Xr16VKyN7ScLGjRvF51GWzI6MjJT7u2Fra4vz588jIiICN27cwPTp0xEfH19mHP369YOJiQm6dOmCkydP4s6dO4iOjsbo0aOLvZSCiIiIiIiovCr09r3p06djwoQJmDFjBuzt7dGrVy9x3RQtLS1ERETg5cuX8PDwwNdffy33o/dDhYaGIjQ0FM7Ozjh16hT27dsHExMTcX9KSgoyMzPF72vXroWbmxs6d+6MZs2aQRAEHDx4UJxmk5+fjxEjRsDe3h7t27dH/fr15RaALlpfZZR1DkW8vLyQn58vN7pBKpUW21YZTZs2xcqVKxEWFgZnZ2ccOXIE06ZNkyvTtWtXhIWFYcGCBWjYsCFWrFiBtWvXyp3b0NAQjo6OMDU1FX/we3p6oqCgQO4HsJaWFq5fv44ePXqgfv36GDJkCEaMGIGhQ4cCeP/GspSUFIXrMVWWnp4eTpw4gY4dO6J+/fqYNm0aFi5cKK7b5efnh8WLF2PZsmVo2LAhOnfujJs3b1b4PKqqqmKCszSlnU8ikWDv3r0wNDSEp6cn2rVrh7p162Lr1q0VjkeRkJAQbNmyBU5OTli/fj02b94sNwIrNTVVTLSU1W4AoKKiAn9/f+Tn56N///5y5/oYz1NZ8VZGZmam3Jp2MqGhoRgzZgzc3Nzw+PFj/PHHH+IaSvXr18eRI0dw6dIlNGnSBM2aNcPevXuhqqoKAHj+/Hm5RhcVtnr1amRkZMDV1RW+vr4YPXo0zMzMyjyuV69emD59OiZPngw3NzfcvXsX3333XbFyRf9uyJLZstFvMkOHDkX37t3Rq1cvfP7553jx4gWGDx9eZhxaWlo4ceIEateuje7du8Pe3h4DBw7E27dvOS2PiIiIiIgqrdxv36sqsrduXbx4ES4uLlUdDtE/QuE3M35MAwcOxLNnz7Bv376PWq+yyN6+l5GRAQMDg6oO5z9F9vYNvn2PiKhsfPseERH905X37XuqSoyJiP6hMjMzceXKFWzatOkfm5AiIiIiIiKiTwuTUkRUpi5duuDcuXMYNmwYvvjii6oOh4iIiIiIiP4FPvmklI2NDT7xGYZEn5yP/cxER0d/1PqqilQq5d8TIiIiIiKiT0SFFjonIiIiIiIiIiL6GJiUIiIiIiIiIiIipWNSioiIiIiIiIiIlI5JKSIiIiIiIiIiUjompYiIiIiIiIiISOmYlCIiIiIiIiIiIqVjUoqIiIiIiIiIiJSOSSkiIiIiIiIiIlI6JqWIiIiIiIiIiEjpVKs6ACIi+u+wPBYFPT29qg6DiIiIiIg+ARwpRURERERERERESsekFBERERERERERKR2TUkREREREREREpHRMShERERERERERkdIxKUVERERERERERErHpBQRERERERERESkdk1JERERERERERKR0TEoREREREREREZHSqVZ1AERE9N+R3q41slWrVXUYRET/eFanz1V1CERERB+MI6WIiIiIiIiIiEjpmJQiIiIiIiIiIiKlY1KKiIiIiIiIiIiUjkkpIiIiIiIiIiJSOialiIiIiIiIiIhI6ZiUIiIiIiIiIiIipWNSioiIiIiIiIiIlI5JKSIiIiIiIiIiUjompYiIiIiIiIiISOmYlCIiIiIiIiIiIqVjUoqIiIiIiIiIiJSOSSkiIiIiIiIiIlI6JqXok5eWlgaJRILExESlnC86OhoSiQSvXr36oHokEgn27NnzUWKi/xMcHAwXFxfxu7+/P7p27VqhOmxsbLB48eKPGldFSaVSjB07tsLHfYz+WdlzExERERERfUyqVR0AUUVFR0ejdevWyMjIgIGBwQfVJZVK4eLi8rckKNLT02FoaPjR6yV5YWFhEAShQsfEx8dDW1v7b4ro79W8eXOkp6dDX1+/zLIlPSu7du1C9erV/8YoiYiIiIiIysakFNHfxMLCoqpD+Ed59+4d1NTUKnxceZIzRZmamlb4mE9Bbm4u1NTUPrhvGRkZfaSIiIiIiIiIKo/T96jKHT58GC1btoSBgQGMjY3RuXNnpKamKiyblpaG1q1bAwAMDQ0hkUjg7++vsOyLFy/Qp08f1KhRA1paWnB0dMTmzZvF/f7+/oiJiUFYWBgkEgkkEgnS0tLE/RcuXIC7uzu0tLTQvHlzpKSkyNW/d+9euLq6QkNDA3Xr1kVISAjy8vLE/YWn77179w4jR46EpaUlNDQ0YG1tjblz58qVXbFiBTp37gwtLS3Y29vjzJkzuHXrFqRSKbS1tdG8efMS26Usd+7cwZ9//llqmbJivHfvHrp06QIdHR3o6emhZ8+eePLkiVwdf/zxBzw8PKChoQETExN069atxPPJpuGtWrUKderUgYaGBgDg1atXGDRoEExNTaGnp4c2bdrg0qVLJdZTdPre69ev0a9fP2hra8PS0hI//fRTselqRafvlXVtslg3bNgAGxsb6Ovro3fv3nj9+nWpbRobGwupVAotLS0YGhrC29sbGRkZ4v6CggJMnjwZRkZGsLCwQHBwsNzxEokEy5cvx1dffQVtbW3MmTOn2PS9u3fvwsfHB4aGhtDW1kbDhg1x8ODBUp+Vou2xYcMGuLu7Q1dXFxYWFujbty+ePn0q7pedMzIystRngoiIiIiIqCKYlKIq9+bNG4wfPx7nz59HZGQkVFRU0K1bNxQUFBQrW6tWLezcuRMAkJKSgvT0dISFhSms9+3bt3Bzc8OBAweQlJSEIUOGwNfXF+fOnQPwftpXs2bNMHjwYKSnpyM9PR21atUSj//++++xcOFCnD9/HqqqqhgwYIC47+TJk+jfvz/GjBmDa9euYcWKFQgPD8ecOXMUxrJkyRLs27cP27ZtQ0pKCjZu3AgbGxu5MrNmzUL//v2RmJgIOzs79O3bF0OHDkVgYCDOnz8PQRAwcuTICrWtzIQJE9C5c2f89ddfJZYpLcaCggJ06dIFL1++RExMDI4ePYrbt2+jV69e4vEHDhxAt27d0LFjR1y8eBGRkZFo0qRJqXHdunULO3fuxK5du8Q1w7755hs8ffoUhw4dwoULF+Dq6oq2bdvi5cuX5brW8ePHIzY2Fvv27cPRo0dx8uRJJCQklFi+PNcGAKmpqdizZw/279+P/fv3IyYmBqGhoSXWm5iYiLZt28LBwQFnzpzBqVOn4OPjg/z8fLHMunXroK2tjbi4OMyfPx8zZ87E0aNH5eoJDg5Gt27dcOXKFbk+KDNixAjk5OTgxIkTuHLlCubNmwcdHZ0KPSu5ubmYNWsWLl26hD179iAtLU1hsre0Z4KIiIiIiKiiOH2PqlyPHj3kvq9Zswampqa4du0aGjVqJLevWrVq4tQjMzOzUteUqlGjBiZOnCh+HzVqFCIiIrBt2zY0adIE+vr6UFNTg5aWlsLpUHPmzIGXlxcAYOrUqejUqRPevn0LDQ0NhISEYOrUqfDz8wMA1K1bF7NmzcLkyZMRFBRUrK579+7B1tYWLVu2hEQigbW1dbEyAQEB6NmzJwBgypQpaNasGaZPnw5vb28AwJgxYxAQEFDi9ZZm7dq1aNeuHXx8fPDHH39AU1OzQjFGRkbiypUruHPnjpi4W79+PRo2bIj4+Hh4eHhgzpw56N27N0JCQsTjnJ2dS43r3bt3WL9+vTid7tSpUzh37hyePn0KdXV1AMCCBQuwZ88e7NixA0OGDCm1vtevX2PdunXYtGkT2rZtK167lZVViceU59qA98mr8PBw6OrqAgB8fX0RGRlZYiJy/vz5cHd3x7Jly8RtDRs2lCvj5OQk9hdbW1ssXboUkZGR+OKLL8Qyffv2lbvvt2/flqvj3r176NGjBxwdHQG874sy5X1WCieX6tatiyVLlsDDwwPZ2dnQ0dER95X2TBSVk5ODnJwc8XtWVlaJ5yciIiIiov8mjpSiKnfz5k306dMHdevWhZ6enjg65969ex9Ub35+PmbNmgVHR0cYGRlBR0cHERER5a7XyclJ/GdLS0sAEKc0Xbp0CTNnzoSOjo74kY24UjRNzt/fH4mJiWjQoAFGjx6NI0eOlHo+c3NzABATDbJtb9++LfHHvb+/vzgNsejHwMBAHIm2YMGCEo8vKcbk5GTUqlVLbiSZg4MDDAwMkJycDOD/RgZVhLW1tdz6TpcuXUJ2djaMjY3l2vbOnTvlmrp4+/Zt5Obmyo3Q0tfXR4MGDUo8pjzXBryf8idLSAHv+0ThKW5Flac9Ct/zkup0d3cvtY7Ro0dj9uzZaNGiBYKCgnD58uVSyyty4cIF+Pj4oHbt2tDV1RUTT0WfldKeiaLmzp0LfX198VO4fYmIiIiIiACOlKJPgI+PD6ytrbFy5UpYWVmhoKAAjRo1wrt37z6o3h9//BFhYWFYvHgxHB0doa2tjbFjx5a73sJvJ5NIJAAgTinMzs5GSEgIunfvXuw4RaNGXF1dcefOHRw6dAjHjh1Dz5490a5dO+zYsaPU85UWQ1Fz587F1KlTFe7Ly8vDgAEDkJ2dXeJoo/LEWBpFo6/KUvQNeNnZ2bC0tER0dHSxsh/6psUPVfRtdRKJpMR7AZSvPcpTZ1lvCRw0aBC8vb1x4MABHDlyBHPnzsXChQsxatSoMs8PvJ8+6+3tDW9vb2zcuBGmpqa4d+8evL29iz0rFemPgYGBGD9+vPg9KyuLiSkiIiIiIpLDpBRVqRcvXiAlJQUrV65Eq1atALyfwlUa2RvaCq/No0hsbCy6dOmCb7/9FsD7H883btyAg4ODXF1l1aOIq6srUlJSUK9evXIfo6enh169eqFXr174+uuv0b59e7x8+fKjvQnN0tJSHL1SlJ+fH7KyshAdHS2OwqpIjPb29rh//z7u378vJhauXbuGV69eie3p5OSEyMjISk8xBN636+PHj6Gqqlpsza3yqFu3LqpXr474+HjUrl0bAJCZmYkbN27A09NT4THlubbKkLVH4emMf5datWph2LBhGDZsGAIDA7Fy5UqMGjWqXM/K9evX8eLFC4SGhorXf/78+Q+OSV1dXZyCSUREREREpAin71GVMjQ0hLGxMX777TfcunULx48flxtdoYi1tTUkEgn279+PZ8+eITs7GwCwdOlSuelStra2OHr0KE6fPo3k5GQMHTq02NvibGxsEBcXh7S0NDx//rzUkS+FzZgxA+vXr0dISAiuXr2K5ORkbNmyBdOmTVNYftGiRdi8eTOuX7+OGzduYPv27bCwsFDa6J+hQ4ciKipK4dpZ5YmxXbt2cHR0RL9+/ZCQkIBz586hf//+8PLyEqeXBQUFYfPmzQgKCkJycrK46LZMYGAg+vfvX2qc7dq1Q7NmzdC1a1ccOXIEaWlpOH36NL7//vtyJUp0dXXh5+eHSZMmISoqClevXsXAgQOhoqIijuxRdM6yrq08iva/wMBAxMfHY/jw4bh8+TKuX7+O5cuX4/nz5+WuszzGjh2LiIgI3LlzBwkJCYiKioK9vT2Akp+VwmrXrg01NTX8/PPPuH37Nvbt24dZs2Z91BiJiIiIiIgUYVKKqpSKigq2bNmCCxcuoFGjRhg3bhx+/PHHUo+pUaOGuNC4ubm5+Ea658+fy607NG3aNLi6usLb2xtSqRQWFhbo2rWrXF0TJ05EtWrV4ODgIE5bKg9vb2/s378fR44cgYeHB5o2bYqffvpJ4QLmwPtkiWzhaw8PD6SlpeHgwYNQUVHOI9i8efMSR1GVJ0aJRIK9e/fC0NAQnp6eaNeuHerWrYutW7eKx0ulUmzfvh379u2Di4sL2rRpI77pEADS09PLbF+JRIKDBw/C09MTAQEBqF+/Pnr37o27d++WOsKrsEWLFqFZs2bo3Lkz2rVrhxYtWsDe3l7htErZOcu6tvIo2v/q16+PI0eO4NKlS2jSpAmaNWuGvXv3QlX14w5Qzc/Px4gRI2Bvb4/27dujfv364uLqJT0rhZmamiI8PBzbt2+Hg4MDQkNDS1x3jIiIiIiI6GOSCIIgVHUQRER/lzdv3qBGjRpYuHAhBg4cWNXh/GdlZWVBX18f1z1coatararDISL6x7M6fa7sQkRERFVE9u//mZmZ0NPTK7Ec15Qion+Vixcv4vr162jSpAkyMzMxc+ZMAECXLl2qODIiIiIiIiIqjEkpIvrXWbBgAVJSUqCmpgY3NzecPHkSJiYmVR0WERERERERFcKkFBH9qzRu3BgXLlyo6jCIiIiIiIioDFzonIiIiIiIiIiIlI5JKSIiIiIiIiIiUjompYiIiIiIiIiISOmYlCIiIiIiIiIiIqVjUoqIiIiIiIiIiJSOSSkiIiIiIiIiIlI6JqWIiIiIiIiIiEjpmJQiIiIiIiIiIiKlY1KKiIiIiIiIiIiUTrWqAyAiov8Oy2NR0NPTq+owiIiIiIjoE8CRUkREREREREREpHRMShERERERERERkdIxKUVERERERERERErHpBQRERERERERESkdk1JERERERERERKR0TEoREREREREREZHSMSlFRERERERERERKx6QUEREREREREREpnWpVB0BERP8d6e1aI1u1WlWHQUT0r2d1+lxVh0BERFQmjpQiIiIiIiIiIiKlY1KKiIiIiIiIiIiUjkkpIiIiIiIiIiJSOialiIiIiIiIiIhI6ZiUIiIiIiIiIiIipWNSioiIiIiIiIiIlI5JKSIiIiIiIiIiUjompYiIiIiIiIiISOmYlCIiIiIiIiIiIqVjUoqIiIiIiIiIiJSOSSkiIiIiIiIiIlI6JqWIiIiIiIiIiEjpmJQiApCWlgaJRILExESlnC86OhoSiQSvXr36oHokEgn27NnzUWL61BW+VmXeL39/f3Tt2lX8LpVKMXbs2ArV8Snfp085NiIiIiIi+ndTreoAiD5F0dHRaN26NTIyMmBgYPBBdUmlUri4uGDx4sUfJbbC0tPTYWho+NHr/bukpaWhTp06uHjxIlxcXKo6nErZtWsXqlevXqFjPuX79CnHRkRERERE/25MShH9g1lYWFR1CP9Y7969g5qaWoWPMzIyqvAxn/J9+pRjIyIiIiKifzdO36P/hMOHD6Nly5YwMDCAsbExOnfujNTUVIVl09LS0Lp1awCAoaEhJBIJ/P39FZZ98eIF+vTpgxo1akBLSwuOjo7YvHmzuN/f3x8xMTEICwuDRCKBRCJBWlqauP/ChQtwd3eHlpYWmjdvjpSUFLn69+7dC1dXV2hoaKBu3boICQlBXl6euL/w1Kt3795h5MiRsLS0hIaGBqytrTF37ly5sitWrEDnzp2hpaUFe3t7nDlzBrdu3YJUKoW2tjaaN29eYruU5c6dO/jzzz9LLVOnTh0AQOPGjSGRSCCVSgEA8fHx+OKLL2BiYgJ9fX14eXkhISGh3OfOz8/HgAEDYGdnh3v37iksI5uGN2fOHFhZWaFBgwYAgPv376Nnz54wMDCAkZERunTpInePiio6fS89PR2dOnWCpqYm6tSpg02bNsHGxkZuZFzRKXJXrlxBmzZtoKmpCWNjYwwZMgTZ2dnFYl2wYAEsLS1hbGyMESNGIDc3t8S4goOD4eLigjVr1qB27drQ0dHB8OHDkZ+fj/nz58PCwgJmZmaYM2eO3HEV6UOvXr3C0KFDYW5uDg0NDTRq1Aj79+8vMSYiIiIiIqLSMClF/wlv3rzB+PHjcf78eURGRkJFRQXdunVDQUFBsbK1atXCzp07AQApKSlIT09HWFiYwnrfvn0LNzc3HDhwAElJSRgyZAh8fX1x7tw5AEBYWBiaNWuGwYMHIz09Henp6ahVq5Z4/Pfff4+FCxfi/PnzUFVVxYABA8R9J0+eRP/+/TFmzBhcu3YNK1asQHh4eLGkgsySJUuwb98+bNu2DSkpKdi4cSNsbGzkysyaNQv9+/dHYmIi7Ozs0LdvXwwdOhSBgYE4f/48BEHAyJEjK9S2MhMmTEDnzp3x119/lVhG1i7Hjh1Deno6du3aBQB4/fo1/Pz8cOrUKZw9exa2trbo2LEjXr9+XeZ5c3Jy8M033yAxMREnT55E7dq1SywbGRmJlJQUHD16FPv370dubi68vb2hq6uLkydPIjY2Fjo6Omjfvj3evXtXruvu378/Hj16hOjoaOzcuRO//fYbnj59WmL5N2/ewNvbG4aGhoiPj8f27dtx7NixYu0eFRWF1NRUREVFYd26dQgPD0d4eHipsaSmpuLQoUM4fPgwNm/ejNWrV6NTp0548OABYmJiMG/ePEybNg1xcXEKjy+tDxUUFKBDhw6IjY3F77//jmvXriE0NBTVqlUrVzsREREREREVxel79J/Qo0cPue9r1qyBqakprl27hkaNGsntq1atmjhFy8zMrNQ1pWrUqIGJEyeK30eNGoWIiAhs27YNTZo0gb6+PtTU1KClpaVwmtScOXPg5eUFAJg6dSo6deqEt2/fQkNDAyEhIZg6dSr8/PwAAHXr1sWsWbMwefJkBAUFFavr3r17sLW1RcuWLSGRSGBtbV2sTEBAAHr27AkAmDJlCpo1a4bp06fD29sbADBmzBgEBASUeL2lWbt2Ldq1awcfHx/88ccf0NTULFbG1NQUAGBsbCzXHm3atJEr99tvv8HAwAAxMTHo3LlziefMzs5Gp06dkJOTg6ioKOjr65cao7a2NlatWiVO2/v9999RUFCAVatWQSKRiNdhYGCA6OhofPnll6XWd/36dRw7dgzx8fFwd3cHAKxatQq2trYlHrNp0ya8ffsW69evh7a2NgBg6dKl8PHxwbx582Bubg7g/Si9pUuXolq1arCzs0OnTp0QGRmJwYMHl1h3QUEB1qxZA11dXTg4OKB169ZISUnBwYMHoaKiggYNGmDevHmIiorC559/Xuz40vrQsWPHcO7cOSQnJ6N+/foA3vfJkuTk5CAnJ0f8npWVVWJZIiIiIiL6b+JIKfpPuHnzJvr06YO6detCT09PHP1R0lSv8srPz8esWbPg6OgIIyMj6OjoICIiotz1Ojk5if9saWkJAOIom0uXLmHmzJnQ0dERP7IRV4qmyfn7+yMxMRENGjTA6NGjceTIkVLPJ0t+ODo6ym17+/ZtiQkEf39/cRpi0Y+BgYE4Em3BggXlun6ZJ0+eYPDgwbC1tYW+vj709PSQnZ1dZjv26dMHb968wZEjR8pMSMmutfA6UpcuXcKtW7egq6srtrGRkRHevn1brmmMKSkpUFVVhaurq7itXr16pS4cnpycDGdnZzEhBQAtWrRAQUGB3PTNhg0byo1CsrS0LHUEFgDY2NhAV1dX/G5ubg4HBweoqKjIbSupntL6UGJiImrWrCkmpMoyd+5c6Ovri5/CIwSJiIiIiIgAjpSi/wgfHx9YW1tj5cqVsLKyQkFBARo1alTuKVol+fHHHxEWFobFixfD0dER2traGDt2bLnrLfwWN9lIHdmUwuzsbISEhKB79+7FjtPQ0Ci2zdXVFXfu3MGhQ4dw7Ngx9OzZE+3atcOOHTtKPV9pMRQ1d+5cTJ06VeG+vLw8DBgwANnZ2RgyZIjiCy6Bn58fXrx4gbCwMFhbW0NdXR3NmjUrsx07duyI33//HWfOnCk22kqRwokg4H0bu7m5YePGjcXKykZ1VZWib/iTSCQl3pfSjqlIPaX1IUUj30oTGBiI8ePHi9+zsrKYmCIiIiIiIjlMStG/3osXL5CSkoKVK1eiVatWAIBTp06VeoxsNE1+fn6p5WJjY9GlSxd8++23AN4nc27cuAEHBwe5usqqRxFXV1ekpKSgXr165T5GT08PvXr1Qq9evfD111+jffv2ePnyZaXeGKeIpaWlOKKrKD8/P2RlZSE6OlochVVUSe0aGxuLZcuWoWPHjgDeLz7+/PnzMuP57rvv0KhRI3z11Vc4cOCAOBWyvFxdXbF161aYmZlBT0+vQscCQIMGDZCXl4eLFy/Czc0NAHDr1i1kZGSUeIy9vT3Cw8Px5s0bMUkWGxsrTq+raiX1IScnJzx48AA3btwo12gpdXV1qKurKyFiIiIiIiL6p+L0PfrXMzQ0hLGxMX777TfcunULx48flxvBoYi1tTUkEgn279+PZ8+eiW9GW7p0Kdq2bSuWs7W1xdGjR3H69GkkJydj6NChePLkiVxdNjY2iIuLQ1paGp4/f17maBeZGTNmYP369QgJCcHVq1eRnJyMLVu2YNq0aQrLL1q0CJs3b8b169dx48YNbN++HRYWFqWuifUxDR06FFFRUQrXzpIxMzODpqYmDh8+jCdPniAzMxPA+3bcsGEDkpOTERcXh379+pV7ZM6oUaMwe/ZsdO7cWS7Z2L9/fwQGBpZ6bL9+/WBiYoIuXbrg5MmTuHPnDqKjozF69Gg8ePCgzHPb2dmhXbt2GDJkCM6dO4eLFy9iyJAh0NTUFEedKTqnhoYG/Pz8kJSUhKioKIwaNQq+vr4lJvMUCQwMRP/+/ctdvjxK60NeXl7w9PREjx49cPToUXFE1eHDhz9qDERERERE9N/BpBT966moqGDLli24cOECGjVqhHHjxuHHH38s9ZgaNWqIC42bm5uLb0Z7/vy53FpD06ZNg6urK7y9vSGVSmFhYYGuXbvK1TVx4kRUq1YNDg4OMDU1Lfd6U97e3ti/fz+OHDkCDw8PNG3aFD/99JPCBcwBQFdXF/Pnz4e7uzs8PDyQlpYmLnCtDM2bNy9xFJWMqqoqlixZghUrVsDKygpdunQBAKxevRoZGRlwdXWFr68vRo8eDTMzs3Kfe+zYsQgJCUHHjh1x+vRpAO/XC0tPTy/1OC0tLZw4cQK1a9dG9+7dYW9vj4EDB+Lt27flHjm1fv16mJubw9PTE926dcPgwYOhq6urcIql7JwRERF4+fIlPDw88PXXX6Nt27ZYunRpua8XANLT0z94TbSiyupDO3fuhIeHB/r06QMHBwdMnjy5UqMAiYiIiIiIAEAiCIJQ1UEQEf1bPHjwALVq1cKxY8fkRtX912VlZUFfXx/XPVyhq1qt7AOIiOiDWJ0+V9UhEBHRf5js3/8zMzNL/Q/+XFOKiOgDHD9+HNnZ2XB0dER6ejomT54MGxsbeHp6VnVoREREREREnzQmpYiIPkBubi7+97//4fbt29DV1UXz5s2xcePGYm+9IyIiIiIiInlMShERfQBvb294e3tXdRhERERERET/OFzonIiIiIiIiIiIlI5JKSIiIiIiIiIiUjompYiIiIiIiIiISOmYlCIiIiIiIiIiIqVjUoqIiIiIiIiIiJSOSSkiIiIiIiIiIlI6JqWIiIiIiIiIiEjpmJQiIiIiIiIiIiKlY1KKiIiIiIiIiIiUTrWqAyAiov8Oy2NR0NPTq+owiIiIiIjoE8CRUkREREREREREpHRMShERERERERERkdIxKUVERERERERERErHpBQRERERERERESkdk1JERERERERERKR0TEoREREREREREZHSMSlFRERERERERERKx6QUEREREREREREpHZNSRERERERERESkdKpVHQAREf13pLdrjWzValUdBhERlYPV6XNVHQIREf3LcaQUEREREREREREpHZNSRERERERERESkdExKERERERERERGR0jEpRURERERERERESsekFBERERERERERKR2TUkREREREREREpHRMShERERERERERkdIxKUVERERERERERErHpBQRERERERERESkdk1JERERERERERKR0TEoREREREREREZHSMSlFRERERERERERKx6QUUSn8/f3RtWtX8btUKsXYsWP/9vNGR0dDIpHg1atXAIDw8HAYGBhUqI6isX9KPuXYykMikWDPnj1KOVdaWhokEgkSExM/qB4bGxssXrz4o8RERERERET0MahWdQBEyiCVSuHi4vKP/VHeq1cvdOzYsULHhIWFQRCEvymiD/Mpx1ZRaWlpqFOnDi5evAgXF5cPqsvf3x+vXr36WxJe8fHx0NbW/uj1EhERERERVRaTUkRKJAgC8vPzoapasUdPU1MTmpqaFTpGX1+/QuWV6VOO7d/K1NS0qkMgIiIiIiKSw+l79K/n7++PmJgYhIWFQSKRQCKRIC0tDfn5+Rg4cCDq1KkDTU1NNGjQAGFhYRWq+8CBA9DX18fGjRsV7pdNwzt06BDc3Nygrq6OU6dOoaCgAHPnzhXP7ezsjB07dpR4HkXT92bPng0zMzPo6upi0KBBmDp1qtxInaJT5HJycjB69GiYmZlBQ0MDLVu2RHx8fLFYIyMj4e7uDi0tLTRv3hwpKSklxiWbWrZt2za0atUKmpqa8PDwwI0bNxAfHw93d3fo6OigQ4cOePbsWYmx7dixA46OjtDU1ISxsTHatWuHN2/eiPvXrFmDhg0bQl1dHZaWlhg5cmSJMZUmKSmpzDLx8fH44osvYGJiAn19fXh5eSEhIaHE8nXq1AEANG7cGBKJBFKpVGG5svpbcHAw1q1bh71794r9NDo6Wtx/+/ZttG7dGlpaWnB2dsaZM2fk6j916pR4D2rVqoXRo0fLtWHh6XuCICA4OBi1a9eGuro6rKysMHr0aLmys2fPRv/+/aGjowNra2vs27cPz549Q5cuXaCjowMnJyecP3++zPYkIiIiIiIqCZNS9K8XFhaGZs2aYfDgwUhPT0d6ejpq1aqFgoIC1KxZE9u3b8e1a9cwY8YM/O9//8O2bdvKVe+mTZvQp08fbNy4Ef369Su17NSpUxEaGork5GQ4OTlh7ty5WL9+PX799VdcvXoV48aNw7fffouYmJhynXvjxo2YM2cO5s2bhwsXLqB27dpYvnx5qcdMnjwZO3fuxLp165CQkIB69erB29sbL1++lCv3/fffY+HChTh//jxUVVUxYMCAMuMJCgrCtGnTkJCQAFVVVfTt2xeTJ0/G/2vvvuOqLP8/jr+PIEOWCxFz4GAqKCqWUo7EL2r61bQkMxF3X7eJg8qtSaYlZs4stJxljnLiosSBC0ciKmlYkaMUHIUK/v7wwfl5lKXlwfL1fDzO48G57+u+7899nwvTd9d1naioKH333Xc6deqURo0aleOxqamp6tixo7p166bExERt375d7dq1M07vmzVrlvr27atevXrpyJEjWrNmjapVq1ag53S3gwcPKiAgQGvWrMmz3ZUrV9SlSxft2LFDu3fvlru7u1q2bKkrV67k2D4+Pl6StHnzZqWmpuqrr77KsV1+/S08PFwdOnRQ8+bNjf20QYMGxuPfeusthYeHKyEhQR4eHurYsaNu3bolSUpOTlbz5s3Vvn17HT58WMuWLdOOHTtyDe9WrFihDz74QHPmzNHJkye1atUq+fr6mrT54IMPFBgYqIMHD+qFF15Q586dFRoaqtdee00HDhxQ1apVFRoamus0zIyMDKWnp5u8AAAAAOBuTN/Dv56Tk5OsrKxUrFgxlS1b1rjdwsJCY8eONb6vXLmydu3apeXLl6tDhw55nvOjjz7SW2+9pa+//lqNGjXKt4Zx48apWbNmku78Y/2dd97R5s2bVb9+fUlSlSpVtGPHDs2ZM6dA5/vwww/VvXt3de3aVZI0atQobdq0SVevXs2x/bVr1zRr1ixFR0erRYsWkqR58+YpJiZG8+fP19ChQ41tJ06caKxhxIgReuGFF/Tnn3/KxsYm13rCw8MVHBwsSRo4cKA6duyoLVu2KDAwUJLUvXt3RUdH53hsamqqbt26pXbt2qlSpUqSZBKQTJgwQUOGDNHAgQON2wICAvJ8Pjnx9/fX3LlzFRISoi+//FIvvPBCju2ef/55k/dz585V8eLFFRsbq1atWt3XPntaXKlSpUz6172KFi2aZ3+zt7eXra2tMjIycjxPeHi4seaxY8eqevXqOnXqlLy8vDRp0iR16tTJuAi/u7u7pk+frkaNGmnWrFn3fXYpKSkqW7asgoKCVLRoUVWsWFH16tUzadOyZUv17t1b0p3+NWvWLAUEBOjll1+WJA0fPlz169fXuXPncqx30qRJJvcLAAAAAPdipBSeaB999JHq1KkjZ2dn2dvba+7cuUpJScnzmC+//FKDBw9WTExMgQIkSapbt67x51OnTun69etq1qyZ7O3tja+FCxcqOTm5QOdLSkq6L0S49/3dkpOTdfPmTWNIJN0JSerVq6fExESTtn5+fsafXV1dJUnnz5/Ps567j3FxcZFkGiy5uLjkeo6aNWuqadOm8vX11csvv6x58+bp0qVLxuv+8ssvatq0aZ7Xz5Y9nTC3V2hoqP7880+1b99eN2/ezPEc586dU8+ePeXu7i4nJyc5Ojrq6tWr+faLgniY/pYtr8/l0KFDio6ONulPwcHBysrK0unTp+8718svv6w//vhDVapUUc+ePbVy5UrjqKucrpfbZ3p3DfeKiIhQWlqa8XX27NkC3ScAAACAJwcjpfDEWrp0qcLDwzV16lTVr19fDg4Oeu+997Rnz548j/P399eBAwf0ySefqG7dujIYDPle6+5vPcsezbR27Vo99dRTJu2sra0f4k7+XkWLFjX+nH1vWVlZD3zMvdtyO4eFhYViYmK0c+dObdq0SR9++KHeeust7dmzR6VLl36g2p966qn7Qra7ffvtt+rTp48mT55sUt/dunTpot9++01RUVGqVKmSrK2tVb9+fd24ceOBarnXw/a3bHl9LlevXlXv3r1N1oXKVrFixfu2VahQQUlJSdq8ebNiYmLUp08fvffee4qNjTVepyCf6d013Mva2vqx6M8AAAAAHl+EUngiWFlZKTMz02RbXFycGjRooD59+hi3FWSkUtWqVTV16lQ1btxYFhYWmjFjxgPV4uPjI2tra6WkpBR4pNW9PD09tXfvXoWGhhq33b1oeU41W1lZKS4uzjhF7ubNm9q7d69xyldhMhgMCgwMVGBgoEaNGqVKlSpp5cqVeuONN+Tm5qYtW7aoSZMm+Z6naNGi8vLyynHf0aNHNWTIEE2ZMiXH8CZbXFycZs6cqZYtW0qSzp49q4sXL+ba3srKSpLu6185nTe//pZTPy2I2rVr69ixYw+01patra1at26t1q1bq2/fvvLy8tKRI0dUu3btB74+AAAAADwMpu/hieDm5qY9e/bozJkzunjxorKysuTu7q59+/Zp48aNOnHihEaOHJlnsHM3Dw8Pbdu2TStWrDAJdVauXJlrKJLNwcFB4eHhGjx4sBYsWKDk5GQdOHBAH374oRYsWFCg6/fv31/z58/XggULdPLkSU2YMEGHDx/OddSWnZ2d/ve//2no0KHasGGDjh07pp49e+r69evq3r17ga4p3VnU28vLSz///HOBj8nPnj179M4772jfvn1KSUnRV199pQsXLsjb21vSnW+lmzp1qqZPn66TJ08an9WD8vLy0ueff55vCOfu7q7PPvtMiYmJ2rNnjzp16iRbW9tc25cpU0a2trbasGGDzp07p7S0NEn394WC9Dc3NzcdPnxYSUlJunjxYq5TDO81fPhw7dy5U/369VNCQoJOnjyp1atX57rQeXR0tObPn6+jR4/qhx9+0Oeffy5bW1tjYAkAAAAA5kAohSdCeHi4LCws5OPjI2dnZ6WkpKh3795q166dQkJC9PTTT+u3334zGcWSH09PT23dulVLlizRkCFDJElpaWlKSkrK99jx48dr5MiRmjRpkry9vdW8eXOtXbtWlStXLtC1O3XqpIiICIWHh6t27do6ffq0wsLC8lyMPDIyUu3bt1fnzp1Vu3ZtnTp1Shs3blSJEiUKdsOSrl+/rqSkpAKHJQXh6Oiob7/9Vi1btpSHh4fefvttTZ061bgge5cuXTRt2jTNnDlT1atXV6tWrXTy5MkHvo6lpaXatGmTb7v58+fr0qVLql27tjp37qwBAwaoTJkyeZ53+vTpmjNnjsqVK2e8xr19oSD9rWfPnvL09FTdunXl7OysuLi4At2bn5+fYmNjdeLECT333HPy9/fXqFGjVK5cuRzbFy9eXPPmzVNgYKD8/Py0efNmff311ypVqlSBrgcAAAAAfwfD7dy+zxvAP0qzZs1UtmxZffbZZ4VdCnCf9PR0OTk56XhAbTlYWhR2OQCAAii3M76wSwAA/ENl//0/LS1Njo6OubZjTSngH+j69euaPXu2goODZWFhoSVLlhgXrQYAAAAA4J+AUAr4BzIYDFq3bp0mTpyoP//8U56enlqxYoWCgoIKuzQAAAAAAAqEUAr4B7K1tdXmzZsLuwwAAAAAAB4aC50DAAAAAADA7AilAAAAAAAAYHaEUgAAAAAAADA7QikAAAAAAACYHaEUAAAAAAAAzI5QCgAAAAAAAGZHKAUAAAAAAACzI5QCAAAAAACA2RFKAQAAAAAAwOwsC7sAAMCTw3XzNjk6OhZ2GQAAAAAeA4yUAgAAAAAAgNkRSgEAAAAAAMDsCKUAAAAAAABgdoRSAAAAAAAAMDtCKQAAAAAAAJgdoRQAAAAAAADMjlAKAAAAAAAAZkcoBQAAAAAAALOzLOwCAABPjtSgJrpqaVHYZQAA/gbldsYXdgkAgH84RkoBAAAAAADA7AilAAAAAAAAYHaEUgAAAAAAADA7QikAAAAAAACYHaEUAAAAAAAAzI5QCgAAAAAAAGZHKAUAAAAAAACzI5QCAAAAAACA2RFKAQAAAAAAwOwIpQAAAAAAAGB2hFIAAAAAAAAwO0IpAAAAAAAAmB2hFGAmY8aMUa1atf7yedzc3DRt2rS/fB78+xkMBq1atSrX/du3b5fBYNDly5fNVhMAAAAAZCOUAswkPDxcW7ZsKewyAKMGDRooNTVVTk5OhV0KAAAAgCeQZWEXADwp7O3tZW9vX9hl4DF2+/ZtZWZmytLSPH80W1lZqWzZsma5FgAAAADci5FSeOxkZWVp8uTJqlatmqytrVWxYkVNnDjRuP/IkSN6/vnnZWtrq1KlSqlXr166evWqcX9YWJjatm2rd955Ry4uLipevLjGjRunW7duaejQoSpZsqTKly+vTz/91HjMmTNnZDAYtHTpUjVo0EA2NjaqUaOGYmNjc61zxowZqlGjhvH9qlWrZDAYNHv2bOO2oKAgvf3225Lun76XXeeUKVPk6uqqUqVKqW/fvrp586axzfnz59W6dWvZ2tqqcuXKWrRo0X11pKSkqE2bNrK3t5ejo6M6dOigc+fOSZLS0tJkYWGhffv2GZ9tyZIl9cwzzxiP//zzz1WhQgVJ0o0bN9SvXz+5urrKxsZGlSpV0qRJk3K8/z///FPVq1dXr169jNuSk5Pl4OCgTz75JNfnlpvz58/r/Pnz+ba7fPmyevfuLRcXF+Pn9M033xj3r1ixQtWrV5e1tbXc3Nw0depUk+Pd3Nw0YcIEhYaGyt7eXpUqVdKaNWt04cIF43P08/MzPjNJio6OVvHixbVq1Sq5u7vLxsZGwcHBOnv2bJ617ty5U7Vq1ZKNjY3q1q1r7CMJCQmS/n/63Pr161WnTh1ZW1trx44dSk5OVps2beTi4iJ7e3sFBARo8+bN993H+PHj1bFjR9nZ2empp57SRx99dF8NFy9e1IsvvqhixYrJ3d1da9asMe7LafpeXFycGjdurGLFiqlEiRIKDg7WpUuXJElffvmlfH19jb97QUFBunbtWt4fGAAAAADkglAKj52IiAhFRkZq5MiROnbsmBYvXiwXFxdJ0rVr1xQcHKwSJUpo7969+uKLL7R582b169fP5Bxbt27VL7/8om+//Vbvv/++Ro8erVatWqlEiRLas2ePXn/9dfXu3Vs//fSTyXFDhw7VkCFDdPDgQdWvX1+tW7fWb7/9lmOdjRo10rFjx3ThwgVJUmxsrEqXLq3t27dLkm7evKldu3apcePGud7rtm3blJycrG3btmnBggWKjo5WdHS0cX9YWJjOnj2rbdu26csvv9TMmTNNgpusrCy1adNGv//+u2JjYxUTE6MffvhBISEhkiQnJyfVqlXLWNORI0dkMBh08OBBY5AXGxurRo0aSZKmT5+uNWvWaPny5UpKStKiRYvk5uaWY+02NjZatGiRFixYoNWrVyszM1OvvfaamjVrpm7duuV6z7mJjIxU06ZNdfHixVzbZGVlqUWLFoqLi9Pnn3+uY8eOKTIyUhYWFpKk/fv3q0OHDnrllVd05MgRjRkzRiNHjjR5ppL0wQcfKDAwUAcPHtQLL7ygzp07KzQ0VK+99poOHDigqlWrKjQ0VLdv3zYec/36dU2cOFELFy5UXFycLl++rFdeeSXXWtPT09W6dWv5+vrqwIEDGj9+vIYPH55j2xEjRigyMlKJiYny8/PT1atX1bJlS23ZskUHDx5U8+bN1bp1a6WkpJgc995776lmzZo6ePCgRowYoYEDByomJsakzdixY9WhQwcdPnxYLVu2VKdOnfT777/nWEdCQoKaNm0qHx8f7dq1Szt27FDr1q2VmZmp1NRUdezYUd26dVNiYqK2b9+udu3amTwjAAAAAHgQhtv8iwKPkStXrsjZ2VkzZsxQjx497ts/b948DR8+XGfPnpWdnZ0kad26dWrdurV++eUXubi4KCwsTNu3b9cPP/ygIkXu5K5eXl4qU6aMvv32W0lSZmamnJyc9PHHH+uVV17RmTNnVLlyZUVGRhqDg1u3bqly5crq37+/hg0bdl8tt2/flrOzs2bPnq2XXnpJ/v7+CgkJUVRUlFJTUxUXF6cmTZro8uXLKlasmMaMGaNVq1YZR8lk15mcnGwMVTp06KAiRYpo6dKlOnHihDw9PRUfH6+AgABJ0vHjx+Xt7a0PPvhAgwYNUkxMjFq0aKHTp08bRzsdO3ZM1atXNx43ZMgQJSUl6ZtvvlFUVJR27dql48ePKzIyUs2bN5e7u7uGDRumnj17asCAAfr++++1efNmGQyGAn1m7733niZPnqxXXnlFK1as0JEjR1SqVKmCfuRGN27cULt27XT27Flt3bo1x3Ns2rRJLVq0UGJiojw8PO7b36lTJ124cEGbNm0ybhs2bJjWrl2r77//XtKdEUbPPfecPvvsM0nSr7/+KldXV40cOVLjxo2TJO3evVv169dXamqqypYtq+joaHXt2lW7d+/W008/Len/P4s9e/aoXr1699Uye/Zsvf322/rpp59kY2MjSfr444/Vs2dPHTx40BgWNmnSRKtWrVKbNm3yfD41atTQ66+/bgxg3dzc5O3trfXr1xvbvPLKK0pPT9e6desk3Vno/O2339b48eMl3Ql17e3ttX79ejVv3tx4/UuXLql48eJ69dVXlZKSoh07dtx3/QMHDqhOnTo6c+aMKlWqlGetkpSRkaGMjAzj+/T0dFWoUEHHA2rLwdIi3+MBAI+/cjvjC7sEAMBjKj09XU5OTkpLS5Ojo2Ou7RgphcdKYmKiMjIy1LRp01z316xZ0xhISVJgYKCysrKUlJRk3Fa9enVjICVJLi4u8vX1Nb63sLBQqVKl7psuVr9+fePPlpaWqlu3rhITE3OsxWAwqGHDhtq+fbsuX76sY8eOqU+fPsrIyNDx48cVGxurgIAAFStWLNf7rV69ujGQkiRXV1djTYmJibK0tFSdOnWM+728vFS8eHGT51GhQgVjICVJPj4+Kl68uLHuRo0aaceOHcrMzFRsbKwaN26sxo0ba/v27frll1906tQp42iusLAwJSQkyNPTUwMGDDAJd3IzZMgQeXh4aMaMGfrkk0/yDKTGjBkjg8GQ48va2lpr167V4cOHcwwBpTsjecqXL59jIJX9PAIDA022BQYG6uTJk8rMzDRu8/PzM/6cPQrv7v6Rve3u/mFpaWkMB6X//yxy6x9JSUny8/MzBlKScgyvJKlu3bom769evarw8HB5e3urePHisre3V2Ji4n0jpe7ur9nv763n7nu1s7OTo6NjrtMks0dK5aRmzZpq2rSpfH199fLLL2vevHnGaX05mTRpkpycnIyvu/soAAAAAEiEUnjM2Nra/i3nKVq0qMl7g8GQ47asrKy/dJ3scOe7776Tv7+/HB0djUHV3dPiHqTOv1rTvRo2bKgrV67owIED+vbbb01CqdjYWJUrV07u7u6SpNq1a+v06dMaP368/vjjD3Xo0EEvvfRSnuc/f/68Tpw4IQsLC508eTLPtv369VNiYmKurxdeeEGurq65TnN7FP0je0RYTtv+7s8iN3eHrNKdb2pcuXKl3nnnHX333XdKSEiQr6+vbty48cDnfpA+ltfztbCwUExMjNavXy8fHx99+OGH8vT01OnTp3NsHxERobS0NOMrv/W3AAAAADx5CKXwWHF3d5etra22bNmS435vb28dOnTIZHHluLg4FSlSRJ6enn/5+rt37zb+fOvWLe3fv1/e3t65ts9eV+qLL74wjjZq3LixNm/ebFww+mF5eXkZa8iWlJRksii1t7e3zp49a/IP/mPHjuny5cvy8fGRJBUvXlx+fn6aMWOGihYtKi8vLzVs2FAHDx7UN998c19w5ujoqJCQEM2bN0/Lli3TihUrcl2DSJK6desmX19fLViwQMOHD8915JAklS5dWl5eXjm+lixZon379mnr1q25joTy8/PTTz/9pBMnTuS439vbW3FxcSbb4uLi5OHhYTIi7WHcunXLZPHz7M8it/7h6empI0eOmExh27t3b4GuFRcXp7CwML344ovy9fVV2bJldebMmfva3d1fs9/n1V/z4+fnl+vvnnQn0AoMDNTYsWN18OBBWVlZaeXKlTm2tba2lqOjo8kLAAAAAO5GKIXHio2NjYYPH65hw4Zp4cKFSk5O1u7duzV//nxJd9YMsrGxUZcuXXT06FFt27ZN/fv3V+fOnY1Trv6Kjz76SCtXrtTx48fVt29fXbp0yWTRbi8vL5N/hPv5+alEiRJavHixSSi1atUqZWRk3DeV7EF4enqqefPm6t27t/bs2aP9+/erR48eJqNZgoKC5Ovrq06dOunAgQOKj49XaGioGjVqZDIlrHHjxlq0aJExgCpZsqS8vb21bNkyk1Dq/fff15IlS3T8+HGdOHFCX3zxhcqWLWucMhgaGqqIiAiT57Vr1y4tWLBAnTp1Utu2bdWpU6eHGtETEhKirVu3ysvLK9c2jRo1UsOGDdW+fXvFxMTo9OnTWr9+vTZs2CDpzlTCLVu2aPz48Tpx4oQWLFigGTNmKDw8/IHruVfRokXVv39/42cRFhamZ555xjglLz4+Xl5eXvr5558lSa+++qqysrLUq1cvJSYmauPGjZoyZYok5btel7u7u7766islJCTo0KFDxnPdKy4uTpMnT9aJEyf00Ucf6YsvvtDAgQMf+h4jIiK0d+9e9enTR4cPH9bx48c1a9YsXbx4UXv27NE777yjffv2KSUlRV999ZUuXLjwl0IwAAAAAE82Qik8dkaOHKkhQ4Zo1KhR8vb2VkhIiHENnGLFimnjxo36/fffFRAQoJdeeklNmzbVjBkz/pZrR0ZGKjIyUjVr1tSOHTu0Zs0alS5d2rg/KSlJaWlpxvcGg0HPPfecDAaDnn32WUl3gipHR0fVrVv3vmlZD+rTTz9VuXLl1KhRI7Vr1069evVSmTJlTK6/evVqlShRQg0bNlRQUJCqVKmiZcuWmZynUaNGyszMNBm51bhx4/u2OTg4aPLkyapbt64CAgJ05swZrVu3zrg+V0pKilJTUyXdWeh76NChmjlzpnG9oJkzZ+rixYsaOXLkA9+rj4+PcXRXXlasWKGAgAB17NhRPj4+GjZsmHG9qNq1a2v58uVaunSpatSooVGjRmncuHEKCwt74HruVaxYMQ0fPlyvvvqqAgMDZW9vb/Kcr1+/rqSkJN28eVPSnRFnX3/9tRISElSrVi299dZbGjVqlCSZrDOVk/fff18lSpRQgwYN1Lp1awUHB6t27dr3tRsyZIj27dsnf39/TZgwQe+//76Cg4Mf+h49PDy0adMmHTp0SPXq1VP9+vW1evVqWVpaytHRUd9++61atmwpDw8Pvf3225o6dapatGjx0NcDAAAA8GTj2/cAyfjte9nfigbcLTo6WoMGDTKZOvkwFi1apK5duyotLe0vr4/l5uamQYMGadCgQX/pPOaS/e0bfPseAPx78O17AIDcFPTb9yzNWBMAPFEWLlyoKlWq6KmnntKhQ4c0fPhwdejQ4W9bsB0AAAAA/skIpQDgEfn11181atQo/frrr3J1ddXLL7+siRMnFnZZAAAAAPBYYPoeAOCRY/oeAPz7MH0PAJCbgk7fY6FzAAAAAAAAmB2hFAAAAAAAAMyOUAoAAAAAAABmRygFAAAAAAAAsyOUAgAAAAAAgNkRSgEAAAAAAMDsCKUAAAAAAABgdoRSAAAAAAAAMDtCKQAAAAAAAJidZWEXAAB4crhu3iZHR8fCLgMAAADAY4CRUgAAAAAAADA7QikAAAAAAACYHaEUAAAAAAAAzI5QCgAAAAAAAGZHKAUAAAAAAACzI5QCAAAAAACA2RFKAQAAAAAAwOwIpQAAAAAAAGB2loVdAADgyZEa1ERXLS0KuwwAAIzK7Ywv7BIA4InFSCkAAAAAAACYHaEUAAAAAAAAzI5QCgAAAAAAAGZHKAUAAAAAAACzI5QCAAAAAACA2RFKAQAAAAAAwOwIpQAAAAAAAGB2hFIAAAAAAAAwO0IpAAAAAAAAmB2hFAAAAAAAAMyOUAoAAAAAAABmRygFAAAAAAAAsyOUQo7CwsLUtm1b4/vGjRtr0KBBj/y627dvl8Fg0OXLlyVJ0dHRKl68+AOd497aHyePc23/FAaDQatWrZIknTlzRgaDQQkJCQU+/mH61N/t3n7+IP7q7+JfuTYAAAAA/J0sC7sAPFqNGzdWrVq1NG3atMIu5aGEhISoZcuWD3RMVFSUbt++/Ygq+mse59r+iSpUqKDU1FSVLl26wMc8TJ96nHz11VcqWrRogdrm9PvfoEEDpaamysnJ6RFVCAAAAAAFQygFs7h9+7YyMzNlaflgXc7W1la2trYPdMzj/I/tx7m2wnbz5s0Chy3ZLCwsVLZs2Qc65mH61OPgxo0bsrKyUsmSJf/SeaysrB74mQEAAADAo8D0vX+xsLAwxcbGKioqSgaDQQaDQWfOnFFmZqa6d++uypUry9bWVp6enoqKinqgc69du1ZOTk5atGhRjvuzpwitX79ederUkbW1tXbs2KGsrCxNmjTJeO2aNWvqyy+/zPU6OU21mjBhgsqUKSMHBwf16NFDI0aMUK1atUzu++4pchkZGRowYIDKlCkjGxsbPfvss9q7d+99tW7ZskV169ZVsWLF1KBBAyUlJeVaV/a0seXLl+u5556Tra2tAgICdOLECe3du1d169aVvb29WrRooQsXLuRa25dffilfX1/Z2tqqVKlSCgoK0rVr14z7P/nkE1WvXl3W1tZydXVVv379cq0pL0ePHs23zfbt21WvXj3Z2dmpePHiCgwM1I8//mjcP2vWLFWtWlVWVlby9PTUZ599ZnL85cuX1bt3b7m4uMjGxkY1atTQN998k+v1DAaDZs2apf/+97+ys7PTxIkTJUmrV69W7dq1ZWNjoypVqmjs2LG6detWjufIafremjVr5O7uLhsbGzVp0kQLFizId0pofvdmMBj08ccf68UXX1SxYsXk7u6uNWvW5Pk8MzIyNHz4cFWoUEHW1taqVq2a5s+fb9Jm//79ufa5MWPGqFatWvr4449VuXJl2djYSLp/+t7MmTON9+vi4qKXXnpJUu6///dO3/vtt9/UsWNHPfXUUypWrJh8fX21ZMkSkzobN26sAQMGaNiwYSpZsqTKli2rMWPG5Hn/AAAAAJAfQql/saioKNWvX189e/ZUamqqUlNTVaFCBWVlZal8+fL64osvdOzYMY0aNUpvvvmmli9fXqDzLl68WB07dtSiRYvUqVOnPNuOGDFCkZGRSkxMlJ+fnyZNmqSFCxdq9uzZ+v777zV48GC99tprio2NLdC1Fy1apIkTJ+rdd9/V/v37VbFiRc2aNSvPY4YNG6YVK1ZowYIFOnDggKpVq6bg4GD9/vvvJu3eeustTZ06Vfv27ZOlpaW6deuWbz2jR4/W22+/rQMHDsjS0lKvvvqqhg0bpqioKH333Xc6deqURo0aleOxqamp6tixo7p166bExERt375d7dq1M07vmzVrlvr27atevXrpyJEjWrNmjapVq1ag53S3gwcPKiAgIM8Q5datW2rbtq0aNWqkw4cPa9euXerVq5cMBoMkaeXKlRo4cKCGDBmio0ePqnfv3uratau2bdsmScrKylKLFi0UFxenzz//XMeOHVNkZKQsLCzyrG3MmDF68cUXdeTIEXXr1k3fffedQkNDNXDgQB07dkxz5sxRdHS0MbDKz+nTp/XSSy+pbdu2OnTokHr37q233norz2Pyu7dsY8eOVYcOHXT48GG1bNlSnTp1uq8P3S00NFRLlizR9OnTlZiYqDlz5sje3t6kTX597tSpU1qxYoW++uqrHNfN2rdvnwYMGKBx48YpKSlJGzZsUMOGDSXl/vt/rz///FN16tTR2rVrdfToUfXq1UudO3dWfHy8SbsFCxbIzs5Oe/bs0eTJkzVu3DjFxMTk+WwBAAAAIC9M3/sXc3JykpWVlYoVK2YyXcfCwkJjx441vq9cubJ27dql5cuXq0OHDnme86OPPtJbb72lr7/+Wo0aNcq3hnHjxqlZs2aS7owceeedd7R582bVr19fklSlShXt2LFDc+bMKdD5PvzwQ3Xv3l1du3aVJI0aNUqbNm3S1atXc2x/7do1zZo1S9HR0WrRooUkad68eYqJidH8+fM1dOhQY9uJEycaaxgxYoReeOEF/fnnn8YRKjkJDw9XcHCwJGngwIHq2LGjtmzZosDAQElS9+7dFR0dneOxqampunXrltq1a6dKlSpJknx9fY37J0yYoCFDhmjgwIHGbQEBAXk+n5z4+/tr7ty5CgkJ0ZdffqkXXnjhvjbp6elKS0tTq1atVLVqVUmSt7e3cf+UKVMUFhamPn36SJLeeOMN7d69W1OmTFGTJk20efNmxcfHKzExUR4eHpLufLb5efXVV42fpSR169ZNI0aMUJcuXYznGD9+vIYNG6bRo0fne745c+bI09NT7733niTJ09NTR48ezTPUyu/esoWFhaljx46SpHfeeUfTp09XfHy8mjdvft85T5w4oeXLlysmJkZBQUHGe7lXfn3uxo0bWrhwoZydnXOsPSUlRXZ2dmrVqpUcHBxUqVIl+fv7S8r99/9eTz31lMLDw43v+/fvr40bN2r58uWqV6+ecbufn5/xM3B3d9eMGTO0ZcsW4+/3vTIyMpSRkWF8n56enmsNAAAAAJ5MjJR6Qn300UeqU6eOnJ2dZW9vr7lz5yolJSXPY7788ksNHjxYMTExBQqQJKlu3brGn0+dOqXr16+rWbNmsre3N74WLlyo5OTkAp0vKSnJ5B/Kku57f7fk5GTdvHnTGBJJUtGiRVWvXj0lJiaatPXz8zP+7OrqKkk6f/58nvXcfYyLi4sk02DJxcUl13PUrFlTTZs2la+vr15++WXNmzdPly5dMl73l19+UdOmTfO8frbsaWy5vUJDQ/Xnn3+qffv2unnz5n3HlyxZUmFhYQoODlbr1q0VFRWl1NRU4/7ExESTZyhJgYGBxmeYkJCg8uXLGwOpgrq7f0jSoUOHNG7cOJP+kT3S5/r16/meLykp6b7gLq/+IeV/b9nu/qzt7Ozk6OiY62ebkJAgCwuLfH9P8utzlSpVyjWQkqRmzZqpUqVKqlKlijp37qxFixYV6DndLTMzU+PHj5evr69Kliwpe3t7bdy48b4/D+6uNbvevH4/Jk2aJCcnJ+Mrp1FaAAAAAJ5shFJPoKVLlyo8PFzdu3fXpk2blJCQoK5du+rGjRt5Hufv7y9nZ2d98sknBf4GOTs7O+PP2aOZ1q5dq4SEBOPr2LFjea4rZS53L7KdPW0tKyvrgY+5d1tu57CwsFBMTIzWr18vHx8fffjhh/L09NTp06cfeCHup556SomJibm+5syZIwsLC02ePDnXxcQ//fRT7dq1Sw0aNNCyZcvk4eGh3bt3F+j6D7tw+N39Q7rTR8aOHWvSP44cOaKTJ0/mOWLNHO59bnl9tgV9Hvn1uXufz70cHBx04MABLVmyRK6urho1apRq1qxpXC+qIN577z1FRUVp+PDh2rZtmxISEhQcHHzfnwcPcv+SFBERobS0NOPr7NmzBa4JAAAAwJOBUOpfzsrKSpmZmSbb4uLi1KBBA/Xp00f+/v6qVq1agUYqVa1aVdu2bdPq1avVv3//B67Fx8dH1tbWSklJUbVq1UxeBR1F4enpabJIuaT73t9bs5WVleLi4ozbbt68qb1798rHx+eB7+HvZjAYFBgYqLFjx+rgwYOysrLSypUr5eDgIDc3N23ZsqVA5ylatKi8vLxyfN26dUtDhgzRlClTNGDAgDzP4+/vr4iICO3cuVM1atTQ4sWLJd2Zynf3M5Tu9KPsZ+jn56effvpJJ06ceIin8P9q166tpKSk+/pHtWrVVKRI/n9ceXp6at++fSbb8uofUv739jB8fX2VlZVV4LXS/gpLS0sFBQVp8uTJOnz4sM6cOaOtW7dKyvn3/15xcXFq06aNXnvtNdWsWVNVqlT5y5+jJFlbW8vR0dHkBQAAAAB3Y02pfzk3Nzft2bNHZ86ckb29vUqWLCl3d3ctXLhQGzduVOXKlfXZZ59p7969qly5cr7n8/Dw0LZt29S4cWNZWlpq2rRpku4sFh0REaHjx4/neqyDg4PCw8M1ePBgZWVl6dlnn1VaWpri4uLk6OhoXEcoL/3791fPnj1Vt25d44iew4cP57p+kZ2dnf73v/9p6NChKlmypCpWrKjJkyfr+vXr6t69e77XyxYfH6/Q0FBt2bJFTz31VIGPy8uePXu0ZcsW/ec//1GZMmW0Z88eXbhwwbiW05gxY/T666+rTJkyatGiha5cuaK4uLgHDgS9vLz0+eefq02bNrm2OX36tObOnav//ve/KleunJKSknTy5EmFhoZKkoYOHaoOHTrI399fQUFB+vrrr/XVV19p8+bNkqRGjRqpYcOGat++vd5//31Vq1ZNx48fl8FgUPPmzfXzzz+radOmWrhwYZ7T6UaNGqVWrVqpYsWKeumll1SkSBEdOnRIR48e1YQJE/K91969e+v999/X8OHD1b17dyUkJBjX9MoeiXSv/O6toLy8vDRp0iS9+OKLcnNzU5cuXdStWzdNnz5dNWvW1I8//qjz58/nu27bg/jmm2/0ww8/qGHDhipRooTWrVunrKwseXp6Ssr59/9e7u7u+vLLL7Vz506VKFFC77//vs6dO/dYhLYAAAAA/t0YKfUvFx4eLgsLC/n4+MjZ2VkpKSnq3bu32rVrp5CQED399NP67bffjIs8F4Snp6e2bt2qJUuWaMiQIZKktLQ0k6+zz8348eM1cuRITZo0Sd7e3mrevLnWrl1boEBMkjp16qSIiAiFh4erdu3aOn36tMLCwvKc2hUZGan27durc+fOql27tk6dOqWNGzeqRIkSBbthSdevX1dSUlKO6zE9LEdHR3377bdq2bKlPDw89Pbbb2vq1KnGBdm7dOmiadOmaebMmapevbpatWqlkydPPvB1LC0t8wykJKlYsWI6fvy42rdvLw8PD/Xq1Ut9+/ZV7969JUlt27ZVVFSUpkyZourVq2vOnDn69NNP1bhxY+M5VqxYoYCAAHXs2FE+Pj4aNmyYcZTOzZs3lZSUlO96R8HBwfrmm2+0adMmBQQE6JlnntEHH3xgXAg+P5UrV9aXX36pr776Sn5+fpo1a5bx2/esra1zPKYg91YQSUlJSktLM76fNWuWXnrpJfXp00deXl7q2bOnrl279kDnzE/x4sX11Vdf6fnnn5e3t7dmz56tJUuWqHr16pJy/v2/19tvv63atWsrODhYjRs3VtmyZdW2bdu/tU4AAAAAyInhdkEXBwIeU82aNVPZsmX12WefFXYpeAxNnDhRs2fPZk2jQpaeni4nJycdD6gtB0uLwi4HAACjcjvjC7sEAPjXyf77f1paWp5LeTB9D/8o169f1+zZsxUcHCwLCwstWbJEmzdvVkxMTGGXhsfEzJkzFRAQoFKlSikuLk7vvfee+vXrV9hlAQAAAADuQSiFfxSDwaB169Zp4sSJ+vPPP+Xp6akVK1YoKCiosEvDY+LkyZOaMGGCfv/9d1WsWFFDhgxRREREYZcFAAAAALgH0/cAAI8c0/cAAI8rpu8BwN+voNP3WOgcAAAAAAAAZkcoBQAAAAAAALMjlAIAAAAAAIDZEUoBAAAAAADA7AilAAAAAAAAYHaEUgAAAAAAADA7QikAAAAAAACYHaEUAAAAAAAAzI5QCgAAAAAAAGZnWdgFAACeHK6bt8nR0bGwywAAAADwGGCkFAAAAAAAAMyOUAoAAAAAAABmRygFAAAAAAAAsyOUAgAAAAAAgNkRSgEAAAAAAMDsCKUAAAAAAABgdoRSAAAAAAAAMDtCKQAAAAAAAJgdoRQAAAAAAADMzrKwCwAAPDlSg5roqqVFYZcBAMBjpdzO+MIuAQAKBSOlAAAAAAAAYHaEUgAAAAAAADA7QikAAAAAAACYHaEUAAAAAAAAzI5QCgAAAAAAAGZHKAUAAAAAAACzI5QCAAAAAACA2RFKAQAAAAAAwOwIpQAAAAAAAGB2hFIAAAAAAAAwO0IpAAAAAAAAmB2hFAAAAAAAAMyOUArAP0Ljxo01aNCgwi4DAAAAAPA3IZQCnmAEPU+mMWPGqFatWoVdBgAAAIAnHKEU8De6ceNGYZdQKJ7U+84LzwQAAAAA8kYoBfwFjRs3Vr9+/TRo0CCVLl1awcHBkqSjR4+qRYsWsre3l4uLizp37qyLFy8aj8vKytLkyZNVrVo1WVtbq2LFipo4cWKe1+nfv78GDRqkEiVKyMXFRfPmzdO1a9fUtWtXOTg4qFq1alq/fr3JcbGxsapXr56sra3l6uqqESNG6NatW5KksLAwxcbGKioqSgaDQQaDQWfOnMn3uLzu+/vvv1erVq3k6OgoBwcHPffcc0pOTta3336rokWL6tdffzWpb9CgQXruueeM7+Pi4tS4cWMVK1ZMJUqUUHBwsC5dupTjM8nIyFB4eLieeuop2dnZ6emnn9b27duN+3/88Ue1bt1aJUqUkJ2dnapXr65169bl+oxzc/36dZ0+fTrfdm5ubho/frxCQ0Pl6OioXr16SZJ27Nih5557Tra2tqpQoYIGDBiga9eumdzH8OHDVaFCBVlbW6tatWqaP39+rtf57LPPVLduXTk4OKhs2bJ69dVXdf78eeP+6OhoFS9e3OSYVatWyWAwGPePHTtWhw4dMn7u0dHRkqSUlBS1adNG9vb2cnR0VIcOHXTu3DnjeQ4dOqQmTZrIwcFBjo6OqlOnjvbt25fvswEAAACAnBBKAX/RggULZGVlpbi4OM2ePVuXL1/W888/L39/f+3bt08bNmzQuXPn1KFDB+MxERERioyM1MiRI3Xs2DEtXrxYLi4u+V6ndOnSio+PV//+/fW///1PL7/8sho0aKADBw7oP//5jzp37qzr169Lkn7++We1bNlSAQEBOnTokGbNmqX58+drwoQJkqSoqCjVr19fPXv2VGpqqlJTU1WhQoV8j8vtvn/++Wc1bNhQ1tbW2rp1q/bv369u3brp1q1batiwoapUqaLPPvvMePzNmze1aNEidevWTZKUkJCgpk2bysfHR7t27dKOHTvUunVrZWZm5vg8+vXrp127dmnp0qU6fPiwXn75ZTVv3lwnT56UJPXt21cZGRn69ttvdeTIEb377ruyt7d/wE9X+vjjj43hWn6mTJmimjVr6uDBgxo5cqSSk5PVvHlztW/fXocPH9ayZcu0Y8cO9evXz3hMaGiolixZounTpysxMVFz5szJs86bN29q/PjxOnTokFatWqUzZ84oLCyswPcTEhKiIUOGqHr16sbPPSQkRFlZWWrTpo1+//13xcbGKiYmRj/88INCQkKMx3bq1Enly5fX3r17tX//fo0YMUJFixbN8ToZGRlKT083eQEAAADA3Qy3b9++XdhFAP9UjRs3Vnp6ug4cOGDcNmHCBH333XfauHGjcdtPP/2kChUqKCkpSa6urnJ2dtaMGTPUo0ePAl8nMzNT3333nSQpMzNTTk5OateunRYuXChJ+vXXX+Xq6qpdu3bpmWee0VtvvaUVK1YoMTHROEpm5syZGj58uNLS0lSkSBE1btxYtWrV0rRp04zXKuhx9973m2++qaVLlyopKSnHoGLy5MmKjo7WsWPHJElfffWVunTpol9//VV2dnZ69dVXlZKSoh07duT6DLJrTUlJUZUqVZSSkqJy5coZ2wQFBalevXp655135Ofnp/bt22v06NEFesa5uX37tnr37q0NGzZo+/btqlKlSo7t3Nzc5O/vr5UrVxq39ejRQxYWFpozZ45x244dO9SoUSNdu3ZNKSkp8vT0VExMjIKCgh6qvn379ikgIEBXrlyRvb29oqOjNWjQIF2+fNnYZtWqVXrxxReV/cf9mDFjtGrVKiUkJBjbxMTEqEWLFjp9+rQqVKggSTp27JiqV6+u+Ph4BQQEyNHRUR9++KG6dOmSb11jxozR2LFj79t+PKC2HCwtHupeAQD4tyq3M76wSwCAv1V6erqcnJyUlpYmR0fHXNsxUgr4i+rUqWPy/tChQ9q2bZvs7e2NLy8vL0lScnKyEhMTlZGRoaZNmz7Qdfz8/Iw/W1hYqFSpUvL19TVuyx5plT2VKzExUfXr1zcGS5IUGBioq1ev6qeffsr1OgU97t77TkhI0HPPPZfryJmwsDCdOnVKu3fvlnRnGlmHDh1kZ2dnPL6gz+TIkSPKzMyUh4eHyXOOjY01jmgaMGCAJkyYoMDAQI0ePVqHDx/O9XzR0dHGqWz3vooUKaJ58+bp7Nmz6tmzZ5511a1b1+T9oUOHFB0dbVJjcHCwsrKydPr0aSUkJMjCwkKNGjUq0H1L0v79+9W6dWtVrFhRDg4OxmNTUlIKfI6cJCYmqkKFCsZASpJ8fHxUvHhxJSYmSpLeeOMN9ejRQ0FBQYqMjMxz9FhERITS0tKMr7Nnz/6l+gAAAAD8+1gWdgHAP112qJLt6tWrat26td5999372rq6uuqHH354qOvcG/YYDAaTbdkhUlZW1kOd/0Hde9+2trZ5ti9Tpoxat26tTz/9VJUrV9b69etN1oDK7/i7Xb16VRYWFtq/f78sLExH3WRPfevRo4eCg4O1du1abdq0SZMmTdLUqVPVv3//+8734osv6plnnsn1eh988IEWL16s8ePH51lXTn2hd+/eGjBgwH1tK1asqFOnTuV5vntdu3ZNwcHBCg4O1qJFi+Ts7KyUlBQFBwcbF1YvUqSI7h0Ae/PmzQe6Tm7GjBmjV199VWvXrtX69es1evRoLV26VC+++OJ9ba2trWVtbf23XBcAAADAvxOhFPA3q127tlasWCE3NzdZWt7/K+bu7i5bW1tt2bKlwNP3Hoa3t7dWrFih27dvGwOruLg4OTg4qHz58pIkKyur+9ZsKshxOfHz89OCBQt08+bNXEdL9ejRQx07dlT58uVVtWpVBQYGmhy/ZcuWHKd83cvf31+ZmZk6f/68yULp96pQoYJef/11vf7664qIiNC8efNyDKWcnJzk5OSU4zk+/fRTLV68WOvXr1eDBg3yre1utWvX1rFjx1StWrUc9/v6+iorK0uxsbEFmr53/Phx/fbbb4qMjDSOaLp3oXFnZ2dduXJF165dMxmFdrfcPvezZ8/q7NmzJtP3Ll++LB8fH2M7Dw8PeXh4aPDgwerYsaM+/fTTHEMpAAAAAMgP0/eAv1nfvn31+++/q2PHjtq7d6+Sk5O1ceNGde3aVZmZmbKxsdHw4cM1bNgwLVy4UMnJydq9e7fJN641bdpUM2bM+Et19OnTR2fPnlX//v11/PhxrV69WqNHj9Ybb7yhIkXu/Oq7ublpz549OnPmjC5evKisrKwCHZeTfv36KT09Xa+88or27dunkydP6rPPPlNSUpKxTXBwsBwdHTVhwgR17drV5PiIiAjt3btXffr00eHDh3X8+HHNmjXL5FsLs3l4eKhTp04KDQ3VV199pdOnTys+Pl6TJk3S2rVrJd35Zr+NGzfq9OnTOnDggLZt2yZvb+8Hfo7NmzfXxo0b9eyzzz7wscOHD9fOnTvVr18/JSQk6OTJk1q9erVxoXM3Nzd16dJF3bp106pVq3T69Glt375dy5cvN57Dy8vLuE5VxYoVZWVlpQ8//FA//PCD1qxZc9/oraefflrFihXTm2++qeTkZC1evNj47XrZ3NzcjNMHL168qIyMDAUFBcnX11edOnXSgQMHFB8fr9DQUDVq1Eh169bVH3/8oX79+mn79u368ccfFRcXp7179z7UMwUAAAAAiVAK+NuVK1dOcXFxyszM1H/+8x/5+vpq0KBBKl68uDHUGTlypIYMGaJRo0bJ29tbISEhxrWgpDtrT+UUxjyIp556SuvWrVN8fLxq1qyp119/Xd27d9fbb79tbBMeHi4LCwv5+PgYp4IV5LiclCpVSlu3btXVq1fVqFEj1alTR/PmzTMZNVWkSBGFhYUpMzNToaGhJsd7eHho06ZNOnTokOrVq6f69etr9erVOY42k+6MYAoNDdWQIUPk6emptm3bau/evapYsaKkO4vB9+3bV97e3mrevLk8PDw0c+bMB36Orq6uDzxCKpufn59iY2N14sQJPffcc/L399eoUaNMFmefNWuWXnrpJfXp00deXl7q2bOnrl27ZtyflJSktLQ0SXdGQUVHR+uLL76Qj4+PIiMjNWXKFJNrlixZUp9//rnWrVsnX19fLVmyRGPGjDFp0759ezVv3lxNmjSRs7OzlixZIoPBoNWrV6tEiRJq2LChgoKCVKVKFS1btkzSnXXMfvvtN4WGhsrDw0MdOnRQixYtCjSyDQAAAABywrfvATCr7t2768KFC1qzZk1hlwIzyv72Db59DwCA+/HtewD+bQr67XusKQXALNLS0nTkyBEtXryYQAoAAAAAQCgFwDzatGmj+Ph4vf7662rWrFlhlwMAAAAAKGSEUgDMYvv27YVdAgAAAADgMcJC5wAAAAAAADA7QikAAAAAAACYHaEUAAAAAAAAzI5QCgAAAAAAAGZHKAUAAAAAAACzI5QCAAAAAACA2RFKAQAAAAAAwOwIpQAAAAAAAGB2hFIAAAAAAAAwO8vCLgAA8ORw3bxNjo6OhV0GAAAAgMcAI6UAAAAAAABgdoRSAAAAAAAAMDtCKQAAAAAAAJgdoRQAAAAAAADMjlAKAAAAAAAAZkcoBQAAAAAAALMjlAIAAAAAAIDZEUoBAAAAAADA7CwLuwAAwJMjNaiJrlpaFHYZAAAAwL9KuZ3xhV3CQ2GkFAAAAAAAAMyOUAoAAAAAAABmRygFAAAAAAAAsyOUAgAAAAAAgNkRSgEAAAAAAMDsCKUAAAAAAABgdoRSAAAAAAAAMDtCKQAAAAAAAJgdoRQAAAAAAADMjlAKAAAAAAAAZkcoBQAAAAAAALMjlAIAAAAAAIDZEUrhsREWFqa2bdsa3zdu3FiDBg0yex1ubm6aNm2a8b3BYNCqVavyPObe2gvbmTNnZDAYlJCQUNil/K3u/iwK6x63b98ug8Ggy5cvS5Kio6NVvHjxfI8rSD8CAAAAgCcJoRT+doUVJj0qqampatGihaTcg5CoqChFR0ebv7hcVKhQQampqapRo0Zhl3Kff1tgFhISohMnThjfjxkzRrVq1bqv3d39CAAAAAAgWRZ2AcDjrmzZsvm2cXJyMkMlBWdhYVGguvHX2draytbWNt92fB4AAAAAYIqRUvhbhYWFKTY2VlFRUTIYDDIYDDpz5owyMzPVvXt3Va5cWba2tvL09FRUVNQDnXvt2rVycnLSokWLctyfPa1q7dq18vPzk42NjZ555hkdPXrUpN2KFStUvXp1WVtby83NTVOnTs3zundPu6pcubIkyd/fXwaDQY0bNzbe993T97KysjR58mRVq1ZN1tbWqlixoiZOnChJunHjhvr16ydXV1fZ2NioUqVKmjRpUq7Xzz73O++8IxcXFxUvXlzjxo3TrVu3NHToUJUsWVLly5fXp59+ajzm3tFIly5dUqdOneTs7CxbW1u5u7ubtP/pp5/UsWNHlSxZUnZ2dqpbt6727NmT53PJyenTp3X9+vU82+T2DPfu3atmzZqpdOnScnJyUqNGjXTgwIECXzszM1PdunWTl5eXUlJScmyT/SzHjh0rZ2dnOTo66vXXX9eNGzeMbTIyMjRgwACVKVNGNjY2evbZZ7V3795cr3v39L3o6GiNHTtWhw4dMvb/7BF0907fy+uZHzp0SE2aNJGDg4McHR1Vp04d7du3L9cajh8/rmeffVY2Njby8fHR5s2bTa5375RDSUpISDD+ft59Hxs3bpS3t7fs7e3VvHlzpaamGo/Zvn276tWrJzs7OxUvXlyBgYH68ccfc60LAAAAAPLCSCn8raKionTixAnVqFFD48aNkyQ5OzsrKytL5cuX1xdffKFSpUpp586d6tWrl1xdXdWhQ4d8z7t48WK9/vrrWrx4sVq1apVn26FDhyoqKkply5bVm2++qdatW+vEiRMqWrSo9u/frw4dOmjMmDEKCQnRzp071adPH5UqVUphYWH51hEfH6969epp8+bNql69uqysrHJsFxERoXnz5umDDz7Qs88+q9TUVB0/flySNH36dK1Zs0bLly9XxYoVdfbsWZ09ezbP627dulXly5fXt99+q7i4OHXv3l07d+5Uw4YNtWfPHi1btky9e/dWs2bNVL58+fuOHzlypI4dO6b169erdOnSOnXqlP744w9J0tWrV9WoUSM99dRTWrNmjcqWLasDBw4oKysr3+dxryFDhujy5ctau3ZtrqOHcnuGV65cUZcuXfThhx/q9u3bmjp1qlq2bKmTJ0/KwcEhz+tmZGSoY8eOOnPmjL777js5Ozvn2nbLli2ysbHR9u3bdebMGXXt2lWlSpUyhobDhg3TihUrtGDBAlWqVEmTJ09WcHCwTp06pZIlS+ZZR0hIiI4ePaoNGzZo8+bNknIeRZffM+/UqZP8/f01a9YsWVhYKCEhQUWLFs3xmpmZmWrbtq0qVqyoPXv26MqVKxoyZEiedebm+vXrmjJlij777DMVKVJEr732msLDw7Vo0SLdunVLbdu2Vc+ePbVkyRLduHFD8fHxMhgMOZ4rIyNDGRkZxvfp6ekPVRMAAACAfy9CKfytnJycZGVlpWLFiplMV7KwsNDYsWON7ytXrqxdu3Zp+fLl+YZSH330kd566y19/fXXatSoUb41jB49Ws2aNZMkLViwQOXLl9fKlSvVoUMHvf/++2ratKlGjhwpSfLw8NCxY8f03nvvFSiUyg47SpUqlet0rCtXrigqKkozZsxQly5dJElVq1bVs88+K0lKSUmRu7u7nn32WRkMBlWqVCnf65YsWVLTp09XkSJF5OnpqcmTJ+v69et68803Jd0JwSIjI7Vjxw698sor9x2fkpIif39/1a1bV9KdxdyzLV68WBcuXNDevXuNoUu1atXyrSknn376qYKCgtS6dWt9/fXXOQZTuT3D559/3qTd3LlzVbx4ccXGxuYZRF69elUvvPCCMjIytG3btnynUlpZWemTTz5RsWLFVL16dY0bN05Dhw7V+PHj9ccff2jWrFmKjo42rv80b948xcTEaP78+Ro6dGie57a1tZW9vb0sLS3znK6X3zNPSUnR0KFD5eXlJUlyd3fP9VwxMTFKTk7W9u3bjdecOHGi8XfgQdy8eVOzZ89W1apVJUn9+vUzhsvp6elKS0tTq1atjPu9vb1zPdekSZNMfucBAAAA4F5M34PZfPTRR6pTp46cnZ1lb2+vuXPn5jrNKtuXX36pwYMHKyYmpkCBlCTVr1/f+HPJkiXl6empxMRESVJiYqICAwNN2gcGBurkyZPKzMx8wDvKWWJiojIyMtS0adMc94eFhSkhIUGenp4aMGCANm3alO85q1evriJF/v/X1cXFRb6+vsb3FhYWKlWqlM6fP5/j8f/73/+0dOlS1apVS8OGDdPOnTuN+xISEuTv75/vKKC768+emnbvq3jx4tq3b5+2bNmiKVOmFOh82c6dO6eePXvK3d1dTk5OcnR01NWrV/PtIx07dtS1a9e0adOmAq3tVbNmTRUrVsz4vn79+rp69arOnj2r5ORk3bx506SPFC1aVPXq1TP2ob9Dfs/8jTfeUI8ePRQUFKTIyEglJyfneq6kpCRVqFDBJASrV6/eQ9VVrFgxY+AkSa6ursY+VbJkSYWFhSk4OFitW7dWVFSUydS+e0VERCgtLc34ym80IAAAAIAnD6EUzGLp0qUKDw9X9+7dtWnTJiUkJKhr164ma/nkxN/fX87Ozvrkk090+/ZtM1X71+S36HXt2rV1+vRp48icDh066KWXXsrzmHunbhkMhhy35TblrkWLFvrxxx81ePBg/fLLL2ratKnCw8MLVO+9Jk2apMTExBxfR44cUUBAgLy9vdWrV68HOm+XLl2UkJCgqKgo7dy5UwkJCSpVqlS+faRly5Y6fPiwdu3a9UDXK0z5PfMxY8bo+++/1wsvvKCtW7fKx8dHK1eufOjrZQead/8O3bx58752OfWpu4/59NNPtWvXLjVo0EDLli2Th4eHdu/eneM1ra2t5ejoaPICAAAAgLsRSuFvZ2Vldd+oo7i4ODVo0EB9+vSRv7+/qlWrlufoj2xVq1bVtm3btHr1avXv379A17/7H8mXLl3SiRMnjNOMvL29FRcXd19tHh4esrCwyPfc2esf5TWqyt3dXba2ttqyZUuubRwdHRUSEqJ58+Zp2bJlWrFihX7//fd8r/9XODs7q0uXLvr88881bdo0zZ07V5Lk5+enhISEAl/f1dVVXl5eOb7ee+89paena+vWrXJxccnx+NyeYVxcnAYMGKCWLVsaF6K/ePFivvX873//U2RkpP773/8qNjY23/aHDh0yrqcl3ekv9vb2qlChgqpWrSorKyuTPnLz5k3t3btXPj4++Z47+/7yG3VXkGfu4eGhwYMHa9OmTWrXrp3JwvR38/T01NmzZ3Xu3DnjtnsXZs+eMnn3yKbsRfAflL+/vyIiIrRz507VqFFDixcvfqjzAAAAAAChFP52bm5u2rNnj86cOaOLFy8qKytL7u7u2rdvnzZu3KgTJ05o5MiReX6j2d08PDy0bds2rVixQoMGDTJuX7lypXHNnbuNGzdOW7Zs0dGjRxUWFqbSpUsbvxlvyJAh2rJli8aPH68TJ05owYIFmjFjhnHUUH7KlCkjW1tbbdiwQefOnVNaWtp9bWxsbDR8+HANGzZMCxcuVHJysnbv3q358+dLkt5//30tWbJEx48f14kTJ/TFF1+obNmyxm9wCw0NVURERIHqKahRo0Zp9erVOnXqlL7//nt98803xqCuY8eOKlu2rNq2bau4uDj98MMPWrFixUONPOrdu7e2bduW53pKuT1Dd3d3ffbZZ0pMTNSePXvUqVOnAo/i6t+/vyZMmKBWrVppx44dxu05PcsbN26oe/fuOnbsmNatW6fRo0erX79+KlKkiOzs7PS///1PQ4cO1YYNG3Ts2DH17NlT169fV/fu3QtUi5ubm06fPq2EhARdvHjRZLHvbHk98z/++EP9+vXT9u3b9eOPPyouLk579+41fl4///yzvLy8FB8fL0lq1qyZqlatqi5duujw4cOKi4vT22+/LUnGRcirVaumChUqaMyYMTp58qTWrl2b77dO3uv06dOKiIjQrl279OOPP2rTpk06efJknutKAQAAAEBeCKXwtwsPD5eFhYV8fHzk7OyslJQU9e7dW+3atVNISIiefvpp/fbbb+rTp0+Bz+np6amtW7dqyZIlxm8WS0tLU1JS0n1tIyMjNXDgQNWpU0e//vqrvv76a+PonNq1a2v58uVaunSpatSooVGjRmncuHEFWuRckiwtLTV9+nTNmTNH5cqVU5s2bXJsN3LkSA0ZMkSjRo2St7e3QkJCjGvzODg4aPLkyapbt64CAgJ05swZrVu3zjjFKiUlJc+1eh6GlZWVIiIi5Ofnp4YNG8rCwkJLly417tu0aZPKlCmjli1bytfXV5GRkQUaOXavBg0ayNXVNc82uT3D+fPn69KlS6pdu7Y6d+6sAQMGqEyZMgW+9qBBgzR27Fi1bNnSuGZWTs+yadOmcnd3V8OGDRUSEqL//ve/GjNmjHF/ZGSk2rdvr86dO6t27do6deqUNm7cqBIlShSojvbt26t58+Zq0qSJnJ2dtWTJkvva5PXMLSws9Ntvvyk0NFQeHh7q0KGDWrRoYVw0/ObNm0pKStL169cl3VlPbNWqVbp69aoCAgLUo0cPvfXWW5LuBKTSnWl52UGon5+f3n33XU2YMKHAz1a6s97U8ePH1b59e3l4eKhXr17q27evevfu/UDnAQAAAIBshtv/lIV6gHxs375dTZo00aVLl4yjjoC7hYWF6fLly1q1alVhl/JIxcXF6dlnn9WpU6dMFi4vTOnp6XJyctLxgNpysHzwwBMAAABA7srtjC/sEkxk//0/LS0tz/VlLc1YEwDgEVi5cqXs7e3l7u6uU6dOaeDAgQoMDHxsAikAAAAAyAmhFAD8w125ckXDhw9XSkqKSpcuraCgoAdeMwoAAAAAzI3pewCAR47pewAAAMCj80+dvsdC5wAAAAAAADA7QikAAAAAAACYHaEUAAAAAAAAzI5QCgAAAAAAAGZHKAUAAAAAAACzI5QCAAAAAACA2RFKAQAAAAAAwOwIpQAAAAAAAGB2hFIAAAAAAAAwO8vCLgAA8ORw3bxNjo6OhV0GAAAAgMcAI6UAAAAAAABgdoRSAAAAAAAAMDtCKQAAAAAAAJgdoRQAAAAAAADMjlAKAAAAAAAAZkcoBQAAAAAAALMjlAIAAAAAAIDZEUoBAAAAAADA7AilAAAAAAAAYHaEUgAAAAAAADA7QikAAAAAAACYHaEUAAAAAAAAzI5QCgAAAAAAAGZHKAUAAAAAAACzI5QCAAAAAACA2RFKAQAAAAAAwOwIpQAAAAAAAGB2hFIAAAAAAAAwO0IpAAAAAAAAmB2hFAAAAAAAAMyOUAoAAAAAAABmZ1nYBQAA/v1u374tSUpPTy/kSgAAAAA8atl/78/+d0BuCKUAAI/cb7/9JkmqUKFCIVcCAAAAwFyuXLkiJyenXPcTSgEAHrmSJUtKklJSUvL8jxJgTunp6apQoYLOnj0rR0fHwi4HkES/xOOLvonHEf3y8XX79m1duXJF5cqVy7MdoRQA4JErUuTOEoZOTk78hQGPHUdHR/olHjv0Szyu6Jt4HNEvH08F+Z/RLHQOAAAAAAAAsyOUAgAAAAAAgNkRSgEAHjlra2uNHj1a1tbWhV0KYES/xOOIfonHFX0TjyP65T+f4XZ+388HAAAAAAAA/M0YKQUAAAAAAACzI5QCAAAAAACA2RFKAQAAAAAAwOwIpQAAj9RHH30kNzc32djY6Omnn1Z8fHxhl4QnyKRJkxQQECAHBweVKVNGbdu2VVJSkkmbP//8U3379lWpUqVkb2+v9u3b69y5c4VUMZ5EkZGRMhgMGjRokHEb/RKF5eeff9Zrr72mUqVKydbWVr6+vtq3b59x/+3btzVq1Ci5urrK1tZWQUFBOnnyZCFWjH+7zMxMjRw5UpUrV5atra2qVq2q8ePH6+7lsemX/1yEUgCAR2bZsmV64403NHr0aB04cEA1a9ZUcHCwzp8/X9il4QkRGxurvn37avfu3YqJidHNmzf1n//8R9euXTO2GTx4sL7++mt98cUXio2N1S+//KJ27doVYtV4kuzdu1dz5syRn5+fyXb6JQrDpUuXFBgYqKJFi2r9+vU6duyYpk6dqhIlShjbTJ48WdOnT9fs2bO1Z88e2dnZKTg4WH/++WchVo5/s3fffVezZs3SjBkzlJiYqHfffVeTJ0/Whx9+aGxDv/zn4tv3AACPzNNPP62AgADNmDFDkpSVlaUKFSqof//+GjFiRCFXhyfRhQsXVKZMGcXGxqphw4ZKS0uTs7OzFi9erJdeekmSdPz4cXl7e2vXrl165plnCrli/JtdvXpVtWvX1syZMzVhwgTVqlVL06ZNo1+i0IwYMUJxcXH67rvvctx/+/ZtlStXTkOGDFF4eLgkKS0tTS4uLoqOjtYrr7xiznLxhGjVqpVcXFw0f/5847b27dvL1tZWn3/+Of3yH46RUgCAR+LGjRvav3+/goKCjNuKFCmioKAg7dq1qxArw5MsLS1NklSyZElJ0v79+3Xz5k2Tfurl5aWKFSvST/HI9e3bVy+88IJJ/5Polyg8a9asUd26dfXyyy+rTJky8vf317x584z7T58+rV9//dWkbzo5Oenpp5+mb+KRadCggbZs2aITJ05Ikg4dOqQdO3aoRYsWkuiX/3SWhV0AAODf6eLFi8rMzJSLi4vJdhcXFx0/fryQqsKTLCsrS4MGDVJgYKBq1KghSfr1119lZWWl4sWLm7R1cXHRr7/+WghV4kmxdOlSHThwQHv37r1vH/0SheWHH37QrFmz9MYbb+jNN9/U3r17NWDAAFlZWalLly7G/pfTf9vpm3hURowYofT0dHl5ecnCwkKZmZmaOHGiOnXqJEn0y384QikAAPBE6Nu3r44ePaodO3YUdil4wp09e1YDBw5UTEyMbGxsCrscwCgrK0t169bVO++8I0ny9/fX0aNHNXv2bHXp0qWQq8OTavny5Vq0aJEWL16s6tWrKyEhQYMGDVK5cuXol/8CTN8DADwSpUuXloWFxX3fFnXu3DmVLVu2kKrCk6pfv3765ptvtG3bNpUvX964vWzZsrpx44YuX75s0p5+ikdp//79On/+vGrXri1LS0tZWloqNjZW06dPl6WlpVxcXOiXKBSurq7y8fEx2ebt7a2UlBRJMvY//tsOcxo6dKhGjBihV155Rb6+vurcubMGDx6sSZMmSaJf/tMRSgEAHgkrKyvVqVNHW7ZsMW7LysrSli1bVL9+/UKsDE+S27dvq1+/flq5cqW2bt2qypUrm+yvU6eOihYtatJPk5KSlJKSQj/FI9O0aVMdOXJECQkJxlfdunXVqVMn48/0SxSGwMBAJSUlmWw7ceKEKlWqJEmqXLmyypYta9I309PTtWfPHvomHpnr16+rSBHT6MLCwkJZWVmS6Jf/dEzfAwA8Mm+88Ya6dOmiunXrql69epo2bZquXbumrl27FnZpeEL07dtXixcv1urVq+Xg4GBcW8LJyUm2trZycnJS9+7d9cYbb6hkyZJydHRU//79Vb9+fb7hDI+Mg4ODcV2zbHZ2dipVqpRxO/0ShWHw4MFq0KCB3nnnHXXo0EHx8fGaO3eu5s6dK0kyGAwaNGiQJkyYIHd3d1WuXFkjR45UuXLl1LZt28ItHv9arVu31sSJE1WxYkVVr15dBw8e1Pvvv69u3bpJol/+0xFKAQAemZCQEF24cEGjRo3Sr7/+qlq1amnDhg33LUQJPCqzZs2SJDVu3Nhk+6effqqwsDBJ0gcffKAiRYqoffv2ysjIUHBwsGbOnGnmSgFT9EsUhoCAAK1cuVIREREaN26cKleurGnTphkXlJakYcOG6dq1a+rVq5cuX76sZ599Vhs2bGB9NDwyH374oUaOHKk+ffro/PnzKleunHr37q1Ro0YZ29Av/7kMt2/fvl3YRQAAAAAAAODJwppSAAAAAAAAMDtCKQAAAAAAAJgdoRQAAAAAAADMjlAKAAAAAAAAZkcoBQAAAAAAALMjlAIAAAAAAIDZEUoBAAAAAADA7AilAAAAAAAAYHaEUgAAAAAAADA7QikAAAAAkqTk5GT17t1bVapUkY2NjRwdHRUYGKioqCj98ccfhV2eZs6cqejo6MIuAwDwNzHcvn37dmEXAQAAAKBwrV27Vi+//LKsra0VGhqqGjVq6MaNG9qxY4dWrFihsLAwzZ07t1BrrFGjhkqXLq3t27cXah0AgL+HZWEXAAAAAKBwnT59Wq+88ooqVaqkrVu3ytXV1bivb9++OnXqlNauXVuIFQIA/o2YvgcAAAA84SZPnqyrV69q/vz5JoFUtmrVqmngwIGSpFu3bmn8+PGqWrWqrK2t5ebmpjfffFMZGRkmxxgMBo0ZM+a+c7m5uSksLMz4Pjo6WgaDQXFxcXrjjTfk7OwsOzs7vfjii7pw4YLJcd9//71iY2NlMBhkMBjUuHHjv+X+AQCFg5FSAAAAwBPu66+/VpUqVdSgQYN82/bo0UMLFizQSy+9pCFDhmjPnj2aNGmSEhMTtXLlyoeuoX///ipRooRGjx6tM2fOaNq0aerXr5+WLVsmSZo2bZr69+8ve3t7vfXWW5IkFxeXh74eAKDwEUoBAAAAT7D09HT9/PPPatOmTb5tDx06pAULFqhHjx6aN2+eJKlPnz4qU6aMpkyZom3btqlJkyYPVUepUqW0adMmGQwGSVJWVpamT5+utLQ0OTk5qW3btnr77bdVunRpvfbaaw91DQDA44XpewAAAMATLD09XZLk4OCQb9t169ZJkt544w2T7UOGDJGkv7TuVK9evYyBlCQ999xzyszM1I8//vjQ5wQAPN4IpQAAAIAnmKOjoyTpypUr+bb98ccfVaRIEVWrVs1ke9myZVW8ePG/FCBVrFjR5H2JEiUkSZcuXXrocwIAHm+EUgAAAMATzNHRUeXKldPRo0cLfMzdI5oeVGZmZo7bLSwsctx++/bth74WAODxRigFAAAAPOFatWql5ORk7dq1K892lSpVUlZWlk6ePGmy/dy5c7p8+bIqVapk3FaiRAldvnzZpN2NGzeUmpr60HX+lTAMAPD4IZQCAAAAnnDDhg2TnZ2devTooXPnzt23Pzk5WVFRUWrZsqWkO9+Ed7f3339fkvTCCy8Yt1WtWlXffvutSbu5c+fmOlKqIOzs7O4LugAA/1x8+x4AAADwhKtataoWL16skJAQeXt7KzQ0VDVq1NCNGze0c+dOffHFFwoLC9PAgQPVpUsXzZ07V5cvX1ajRo0UHx+vBQsWqG3btibfvNejRw+9/vrrat++vZo1a6ZDhw5p48aNKl269EPXWadOHc2aNUsTJkxQtWrVVKZMGT3//PN/xyMAABQCQikAAAAA+u9//6vDhw/rvffe0+rVqzVr1ixZW1vLz89PU6dOVc+ePSVJH3/8sapUqaLo6GitXLlSZcuWVUREhEaPHm1yvp49e+r06dOaP3++NmzYoOeee04xMTFq2rTpQ9c4atQo/fjjj5o8ebKuXLmiRo0aEUoBwD+Y4TYrBwIAAAAAAMDMWFMKAAAAAAAAZkcoBQAAAAAAALMjlAIAAAAAAIDZEUoBAAAAAADA7AilAAAAAAAAYHaEUgAAAAAAADA7QikAAAAAAACYHaEUAAAAAAAAzI5QCgAAAAAAAGZHKAUAAAAAAACzI5QCAAAAAACA2RFKAQAAAAAAwOwIpQAAAAAAAGB2/wfFJz0CUoUEWQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error analysis saved!\n" + ] + } + ], + "source": [ + "# Error analysis - find most confused category pairs\n", + "errors = {}\n", + "for true, pred in zip(true_labels, predicted_labels_best):\n", + " if true != pred:\n", + " pair = f\"{true} → {pred}\"\n", + " errors[pair] = errors.get(pair, 0) + 1\n", + "\n", + "top_errors = sorted(errors.items(), key=lambda x: x[1], reverse=True)[:10]\n", + "\n", + "print(\"Top 10 Most Common Misclassifications:\")\n", + "print(\"-\" * 50)\n", + "for pair, count in top_errors:\n", + " print(f\"{count:3d} times: {pair}\")\n", + "\n", + "# Plot error analysis\n", + "plt.figure(figsize=(12, 6))\n", + "pairs = [p[0] for p in top_errors]\n", + "counts = [p[1] for p in top_errors]\n", + "plt.barh(pairs, counts, color='#E53935')\n", + "plt.title('Top 10 Misclassifications - Best Model', fontsize=14)\n", + "plt.xlabel('Count', fontsize=12)\n", + "plt.tight_layout()\n", + "plt.savefig('/home/user/error_analysis.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "print(\"Error analysis saved!\")" + ] + }, + { + "cell_type": "markdown", + "id": "53430b8f-263b-4e5c-acc3-25dd21a6d9f4", + "metadata": {}, + "source": [ + "## Step 9: Save the Best Model\n", + "\n", + "We save the trained model to disk for future use." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "120718e7-b1ee-4417-89c9-e166ac7ac997", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model saved to /home/user/fasttext_best_model.bin\n", + "Loaded model F1: 0.6216\n", + "Model saved and verified successfully!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n" + ] + } + ], + "source": [ + "# Save the best model to disk for future use\n", + "model_path = '/home/user/fasttext_best_model.bin'\n", + "best_model.save_model(model_path)\n", + "print(f\"Model saved to {model_path}\")\n", + "\n", + "# Verify the model loads correctly\n", + "loaded_model = fasttext.load_model(model_path)\n", + "loaded_results = evaluate_model(loaded_model, '/tmp/test.txt')\n", + "print(f\"Loaded model F1: {loaded_results['f1']}\")\n", + "print(\"Model saved and verified successfully!\")" + ] + }, + { + "cell_type": "markdown", + "id": "d25d49c5-51bb-4bea-9afd-f35cefda0cdc", + "metadata": {}, + "source": [ + "## Step 10: Comparison with Logistic Regression + TF-IDF\n", + "\n", + "We compare FastText against a traditional Logistic Regression classifier\n", + "using TF-IDF features. This demonstrates the trade-offs between modern\n", + "neural approaches and classical machine learning methods for text classification." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0a0a4aaa-c408-4a71-afcb-162ffcb01ed2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logistic Regression + TF-IDF Results:\n", + "F1-Score: 0.6551\n", + "Training time: 23.50 seconds\n", + "\n", + "Detailed Report:\n", + " precision recall f1-score support\n", + "\n", + " alt.atheism 0.46 0.42 0.44 319\n", + " comp.graphics 0.56 0.68 0.62 389\n", + " comp.os.ms-windows.misc 0.63 0.59 0.61 394\n", + "comp.sys.ibm.pc.hardware 0.67 0.61 0.64 392\n", + " comp.sys.mac.hardware 0.73 0.65 0.69 385\n", + " comp.windows.x 0.79 0.66 0.72 395\n", + " misc.forsale 0.74 0.81 0.77 390\n", + " rec.autos 0.68 0.66 0.67 396\n", + " rec.motorcycles 0.66 0.75 0.70 398\n", + " rec.sport.baseball 0.49 0.81 0.61 397\n", + " rec.sport.hockey 0.87 0.85 0.86 399\n", + " sci.crypt 0.83 0.64 0.73 396\n", + " sci.electronics 0.56 0.59 0.57 393\n", + " sci.med 0.73 0.71 0.72 396\n", + " sci.space 0.69 0.74 0.71 394\n", + " soc.religion.christian 0.63 0.78 0.70 398\n", + " talk.politics.guns 0.59 0.69 0.64 364\n", + " talk.politics.mideast 0.82 0.72 0.76 376\n", + " talk.politics.misc 0.60 0.38 0.47 310\n", + " talk.religion.misc 0.61 0.15 0.24 251\n", + "\n", + " accuracy 0.66 7532\n", + " macro avg 0.67 0.65 0.64 7532\n", + " weighted avg 0.67 0.66 0.66 7532\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "import time\n", + "\n", + "# Train Logistic Regression with TF-IDF\n", + "start = time.time()\n", + "tfidf_pipeline = Pipeline([\n", + " ('tfidf', TfidfVectorizer(max_features=50000, ngram_range=(1, 2))),\n", + " ('clf', LogisticRegression(max_iter=1000, C=1.0))\n", + "])\n", + "tfidf_pipeline.fit(train_data.data, train_data.target)\n", + "lr_time = time.time() - start\n", + "\n", + "# Evaluate\n", + "lr_preds = tfidf_pipeline.predict(test_data.data)\n", + "lr_preds_named = [test_data.target_names[i] for i in lr_preds]\n", + "lr_f1 = float(classification_report(\n", + " true_labels, lr_preds_named, output_dict=True, zero_division=0\n", + ")['weighted avg']['f1-score'])\n", + "\n", + "print(f\"Logistic Regression + TF-IDF Results:\")\n", + "print(f\"F1-Score: {lr_f1:.4f}\")\n", + "print(f\"Training time: {lr_time:.2f} seconds\")\n", + "print(f\"\\nDetailed Report:\")\n", + "print(classification_report(true_labels, lr_preds_named, zero_division=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fdc4c6d3-2ee5-4571-b676-b13fef8c977a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Comparison plot saved to /home/user/model_comparison.png\n" + ] + } + ], + "source": [ + "# Compare FastText vs Logistic Regression across F1 score and training time\n", + "model_names = ['FastText\\nBaseline', 'FastText\\nBest', 'Logistic Regression\\n+ TF-IDF']\n", + "f1_scores = [0.3189, best_results['f1'], lr_f1]\n", + "train_times = [train_time, best_train_time, lr_time]\n", + "\n", + "plot_model_comparison(\n", + " model_names,\n", + " f1_scores,\n", + " train_times,\n", + " save_path='/home/user/model_comparison.png'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f7474f31-02e6-4f44-9cd5-0e1f3c921079", + "metadata": {}, + "source": [ + "## Summary and Conclusions\n", + "\n", + "Key Findings\n", + "\n", + "1. FastText achieved 62% F1 on the 20 Newsgroups dataset, a challenging \n", + " 20-class classification problem where random chance would yield only 5%.\n", + "\n", + "2. Hyperparameter tuning doubled performance from 31% (baseline) to 62% \n", + " (best), demonstrating the importance of proper configuration.\n", + "\n", + "3. Most common confusions occur between semantically similar categories \n", + " such as talk.politics.misc → talk.politics.guns and \n", + " rec.motorcycles → rec.autos, which is expected behavior.\n", + "\n", + "4. Logistic Regression + TF-IDF slightly outperforms FastText (65% vs 62%) \n", + " on this small dataset. However, FastText trains 2.5x faster and scales \n", + " significantly better to large datasets with millions of samples.\n", + "\n", + "5. FastText is best suited for large-scale text classification where \n", + " training speed and memory efficiency are critical requirements." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext_utils.py b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext_utils.py new file mode 100644 index 000000000..194d27f61 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext_utils.py @@ -0,0 +1,213 @@ +""" +fasttext_utils.py + +Utility functions for FastText text classification project. +Provides reusable functions for data preprocessing, model training, +evaluation, and visualization. +""" +import re +import time +import fasttext +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.metrics import classification_report, confusion_matrix + + +def clean_text(text): + """ + Clean and normalize text for FastText training. + + Removes special characters, converts to lowercase, and strips + extra whitespace to produce consistent input for the model. + + Args: + text (str): Raw input text. + + Returns: + str: Cleaned and normalized text. + """ + text = re.sub(r'[^a-zA-Z\s]', ' ', text) + text = text.lower() + text = re.sub(r'\s+', ' ', text).strip() + return text + + +def prepare_fasttext_data(data, filename): + """ + Convert sklearn dataset to FastText supervised format and save to file. + + FastText expects one sample per line in the format: + __label__ + + Args: + data: sklearn Bunch object with .data and .target attributes. + filename (str): Output file path. + """ + with open(filename, 'w', encoding='utf-8') as f: + for text, label in zip(data.data, data.target): + clean = clean_text(text) + category = data.target_names[label].replace(' ', '_') + f.write(f"__label__{category} {clean}\n") + print(f"Saved {len(data.data)} samples to {filename}") + + +def train_fasttext_model(train_path, epoch=25, lr=0.5, word_ngrams=2, min_count=1, verbose=0): + """ + Train a supervised FastText classification model. + + Args: + train_path (str): Path to FastText-formatted training file. + epoch (int): Number of training epochs. + lr (float): Learning rate. + word_ngrams (int): Maximum word n-gram size. + min_count (int): Minimum word frequency threshold. + verbose (int): Verbosity level. + + Returns: + tuple: (model, training_time_seconds) + """ + start = time.time() + model = fasttext.train_supervised( + input=train_path, + epoch=epoch, + lr=lr, + wordNgrams=word_ngrams, + minCount=min_count, + verbose=verbose + ) + elapsed = time.time() - start + return model, elapsed + + +def evaluate_model(model, test_path): + """ + Evaluate a FastText model and return precision, recall, and F1. + + Args: + model: Trained FastText model. + test_path (str): Path to FastText-formatted test file. + + Returns: + dict: Dictionary with precision, recall, and f1 scores. + """ + results = model.test(test_path) + f1 = 2 * results[1] * results[2] / (results[1] + results[2]) + return { + "samples": results[0], + "precision": round(results[1], 4), + "recall": round(results[2], 4), + "f1": round(f1, 4) + } + + +def get_predictions(model, texts, clean_fn): + """ + Generate predicted labels for a list of texts. + + Args: + model: Trained FastText model. + texts (list): List of raw text strings. + clean_fn (callable): Text cleaning function. + + Returns: + list: Predicted category labels. + """ + predicted = [] + for text in texts: + clean = clean_fn(text) + labels, _ = model.predict(clean) + label = labels[0].replace('__label__', '').replace('_', '.') + predicted.append(label) + return predicted + + +def plot_confusion_matrix(true_labels, predicted_labels, category_names, title, save_path, cmap='Blues'): + """ + Plot and save a confusion matrix heatmap. + + Args: + true_labels (list): Ground truth labels. + predicted_labels (list): Model predicted labels. + category_names (list): List of category names. + title (str): Plot title. + save_path (str): File path to save the figure. + cmap (str): Colormap for the heatmap. + """ + cm = confusion_matrix(true_labels, predicted_labels, labels=category_names) + plt.figure(figsize=(16, 14)) + sns.heatmap(cm, annot=True, fmt='d', cmap=cmap, + xticklabels=category_names, + yticklabels=category_names) + plt.title(title, fontsize=14) + plt.ylabel('True Label', fontsize=12) + plt.xlabel('Predicted Label', fontsize=12) + plt.xticks(rotation=45, ha='right') + plt.yticks(rotation=0) + plt.tight_layout() + plt.savefig(save_path, dpi=150, bbox_inches='tight') + plt.show() + print(f"Confusion matrix saved to {save_path}") + + +def plot_tuning_results(results_list, save_path): + """ + Plot hyperparameter tuning results as a bar chart. + + Args: + results_list (list): List of dicts with tuning results. + save_path (str): File path to save the figure. + """ + labels = [f"e={r['epoch']},lr={r['lr']},ng={r['wordNgrams']}" for r in results_list] + f1_scores = [r['f1'] for r in results_list] + colors = ['#F44336', '#2196F3', '#4CAF50', '#FF9800', '#9C27B0'] + + plt.figure(figsize=(12, 6)) + bars = plt.bar(labels, f1_scores, color=colors[:len(labels)]) + plt.title('FastText Hyperparameter Tuning Results', fontsize=14) + plt.xlabel('Configuration', fontsize=12) + plt.ylabel('F1 Score', fontsize=12) + plt.ylim(0, 0.8) + for bar, score in zip(bars, f1_scores): + plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, + f'{score:.4f}', ha='center', fontsize=10) + plt.xticks(rotation=20, ha='right') + plt.tight_layout() + plt.savefig(save_path, dpi=150, bbox_inches='tight') + plt.show() + print(f"Tuning plot saved to {save_path}") + + +def plot_model_comparison(model_names, f1_scores, train_times, save_path): + """ + Plot F1 score and training time comparison across models. + + Args: + model_names (list): List of model name strings. + f1_scores (list): F1 scores for each model. + train_times (list): Training times in seconds for each model. + save_path (str): File path to save the figure. + """ + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) + colors = ['#F44336', '#4CAF50', '#2196F3'] + + bars1 = ax1.bar(model_names, f1_scores, color=colors[:len(model_names)]) + ax1.set_title('F1 Score Comparison', fontsize=14) + ax1.set_ylabel('F1 Score', fontsize=12) + ax1.set_ylim(0, 0.8) + for bar, score in zip(bars1, f1_scores): + ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, + f'{score:.4f}', ha='center', fontsize=11) + + bars2 = ax2.bar(model_names[:len(train_times)], train_times, color=colors[:len(train_times)]) + ax2.set_title('Training Time Comparison (seconds)', fontsize=14) + ax2.set_ylabel('Time (seconds)', fontsize=12) + for bar, t in zip(bars2, train_times): + ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5, + f'{t:.2f}s', ha='center', fontsize=11) + + plt.tight_layout() + plt.savefig(save_path, dpi=150, bbox_inches='tight') + plt.show() + print(f"Comparison plot saved to {save_path}") \ No newline at end of file diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/hyperparameter_tuning.png b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/hyperparameter_tuning.png new file mode 100644 index 0000000000000000000000000000000000000000..52ecc689c6e9d56c359d881f6a7ed9116d56a724 GIT binary patch literal 57764 zcmeFacT|<C&5q zAkvktG?gY@x`4np7g%!2z4tf9?|$F7f80IBiARCG-}il5&1)l=C7L1 z!osqMad^KX3(K5g7MAZ%|7{NbWiUji9DnSxIiPN%WNBn$f85HDMeexGX>&^(bCVP6 z>EMUgPQE={cYaU8qar{4WE0=lq4x|q zl&oFiAMh8~g&F!9`G$-ATzp}{?9Hhx#+T;*zTm=0?-KWWG0%_Z?-l=;(s-)w)Q7C- z5!=j$d)9kOZ*)4{U6`}&2VBI=>&*kB+tstb{Eda>`I@KfU;lRO`@i7{e0iMFyV0j_}uuNHL5ICB0ldv@^n%9OOUg2F-> zfy_Z>*3_gk7bj=4Z7Oca;raB(JlRyYR=RaWG(aPfMO@u}>gW@XMXUSM)`lk zGf2>|;N(z8W&1Gi(TBfQnx*UQ-Md$C;l`N4=eCh*F{(a-hDCaMsa6M^M|vug8{H!J zU0mGWoPWdV?S&OKZ=U>Jc>>q*;r#|$DTn>3DJfy5l@X3Q9xANdiu(^-TE05lQF9J)0*Io``xX`k(N{F1j*nr-JM#x9%p*TzPAV+wBa78V!F zZMwmbxxG~`+18L|KOpW_n}lZ}F>$MO8RbBYp&n%=z|?91bb z#8I)bw^zHq;ec|sn`^PZgjRljet3yamP@LxQ#;p9el1m8Zp4uX=f;M+Bk>tVV-h!l za7*p(Q{w^OefOPmyaD4?ZEayr&c3UwMXN#s+#=kkCPa(HxJSA=8Zy&I)o*N)E6Zp- zckUct$>{w6<5a_nu%oS-IMjk&&b|6BG4ok@g2w!FpFK1E^S7TORHJgt+XTQ|A8 zySw)1Z%(u8)=V2Pu#*yz>Bf#MUu~K3pueTC@afaN{{H@^I7zqm#bGmbNKK7;@u);_ z;>I1FoFa|}9`qG7ymYEDYvM`okY@5Joq@KOFJH0^XWDk&!YPeXjneed+t<_66QP@# zHfQ0=`%5>=8#rVgUpa;YS&`-HOgpx+GuvsXqvLgAq}#{0b9GX!xQbIpB)!V5vqzp6 z=J!6M16Xh0SgE8S!DVl6kDYj!ot?dZXh?;Y_myz~x1gY4VIq!(__BrcUqShMJC7e0 zjZarG?T{k3N-G z6&CWAXJG*k`t7n6yq9Mn$d+?)i74~Njq-a_ugzk7#$AUh)t0w?9Bm6IcJ+5`W!uQj zoxkl$g_puX4qaVcAKlK(Uw-+esLj6nwMb!`p|Ejfgvuhu!QX%8y7K3re=f|^NHX_v ze1u)dr&8PXDxuiItm@HrP5fg|;_jV0S4w^Qu+PJ~WpI1Ocv`w$x9Jaa=dND7c(KW| zk3-dZ{KEme?W2!dS_bsgb-zv&I@OS2a;8DTdDvi7>_>KX|DKWiX4TQHSx1i^J$REx zrR>VfG+Q(4DLwZI5t)Iiu%kJ@d3ws*H#~W=$J%x75_Vx8<*;i#>N;sQ+nG$}mAj&r zt7z*d>$R5!UkyCuW%TimfO_ncE!rukJrZy8X_VDuI9f*x^tkC53?6T9Gt?C??NKw5 z+I`S-jY*I7yFY(ln3p!bJ&CiW&HUK0`*HetivwkS1l!%*O{*T6XlJ>+dbiVFY6tdq z$z#{S_b)yC{B+W+T|f2ZvFTRh7%y6};S;T3 zFn;__NJ?YlRUG@wqp`=H%9eK4#OZt3B4~@7r{qx%kLTp(W(;*!S1%CAxW7uol-`(5 zx~%M#$+1p>;yc^-RY&V6wUzj(JosgHWsIIw@k-hlF{@5CW@RD<-H$gcEO_-wxysqE+t)+sk>5+&`=9 z+%1y%;g1Co>TwU}FWYke!-vyy{^A-4s;hR}JzBG7jj+qd56b$ocX!BPFOJ?24uHTqsA`1IGQ)}c;=@7FW%Ugo}) z?!&{ws@cQieZ#euk$Tx~yWA%;+$M(9GW!d7+lT#jSoj9*PBeZTq5tv2+t+dW7gq_N zyl>y&cql6?E6rsz{?=BVh;XIQj@IJ49c96nrEl^C8~ZzMDjuzJSlF9nR=u@2KuRz7 z@``Hb-po)0yL^NVY_Z2FyEw(gwWTHoZy;-kID9xZd(NDQJ%9g>u1<2iOUO^$QBmd= z-%BmaOv5v|1>LpDh6u@6O8HXpfh&Xo9333qzfLr}^!x7!L6;w!zY;uf=ul_yZCyo# z`b*3CeNRm=D?;Q}PfUz7c2q^(eVuF>gY^FX)93*1(FJK)M`!4d1p-^PY&onFp?nnY z!pgxB6Bf3m*l)Mm`t|GQFIl37$o|-Md{}wpQARa3P(_3auTIM8y&Ntd{!S|>Dk_RA zQvlFWw6S^ITH+UJ9N?;&Zr`hV`gE-3j@i+f;pYG996qCi9>j?g4?UJ}M2E=T zEvT>8L1cURv9Br7zE`*VRl=ct9t&S$zgY`cKhj8eY*M~ybbOpPK!j#u1j5=-TY$Ui zr;mNohY!C9I~rIK#8^7}haZB(q_AujwMmr#REB~+6{*(7nZvKmcr@aJPMkO)>^y8f zfsNEr9;)EkQ4~EfK5|zo+qrVAelfdv7?!^s=h@1}MtP*Co^|EQFr=O>IB2`=x?YGj zx&$HtyzfqKycHQ4xy!c0+w=-{wpp>S2(u?+({%EV*46-uI{M2m=RDaZA^`48M*CY* z?c>-|0vfN5kMyc#yG?ilKmfC}M=RXE){v3vXsED%|F5}{zyJRGL7c}T-gQW>WrOn;ED(yx=m$PkQ z6AsBi#OGpO$Ersf`e!_gR7L8%((}v_T|>NF3oe#v%sal)qpvr6YE^V}bh~6LH=gO0 z?`F?m$-Gjn>&!1VUe7zs$rh76ndZpaRPQ!5VLx!`-X&JvRmql(^5uD&UT^D%lp~b$ zciQ)8r&ZfqP65NP<#}-^l()Uhz5EQ@=XlQu7B-~adbHoLDZqU)|8%s}g-q zDSZl1HJCkbN>5h;`Ql)Ve$I10R%hlF98z^{3!iJ(j_DpZvb419Gj_96wPy%6I5aJ~ zEVWcMM%SXJ9Pr?>NOg=}W1TUcE4Oc`bXjsDSGQo`5VNm6G$6!8L0Vey)2B}h z83$VmZeB$Rak)u5)hhSoi~E0HpT7k;e$-y^60oMvl+Lx69i7N?A!60rtlV>TRcm{P zUmoJ{8SiSiZq1PlmcFTo3PY%?JW@?SVyHq%!+)g7OG*`$RQZ~b{(eQ^MlQyRt=caD(}J&W zJfyj%37}2HJ?nUmhj6!^OW%cPz3ibXG+b4!Y^H zIjZ{`C%w6AnRVUEOTo7ZrRZ3x`{YK3w5)6eN(^gZ1wbib*D)K`El1YL)moQI80(5y zw2LiUzC7f`izAFOK>dIYx6Agc#jJt|v$`9z^$@RaqiW*J-?k9( zc(ckB7Sva2$>PPhon-ctnLdnpJjnHei}qexA#ln~$i46u|MKy{&T0kZoZFoI>alwW zqRkb%x=N(%{mZ~86(@gb$Ankg7GADx=i=h>`Ti=REidw0PM@x0%gu$~hv;TH`A8|u zQ&UtQ-|pRGK8r#>z}emO^mMA5PB|l!71i#U1c+^YA|%1B_3XgGgT8vkbg7}Cp+UQA zD~yIUfyT{lbt;_ctm5~Rbk)QWkQeFI_mbGuF29oL5Uf z;sCAro=WGuZt-gsR91@QIQ2}PKX-+Fph$dSsgTx@l+(u(k$~!l0rh>Pr0lxXJzG0o zJUBl(qHxe!C&S?=f@t__#iaY*-lxJgGwkf_Mus#CWOFaEA_Wz3oH}*NX{RjwTe*Nu19e!4LO?W9O|ZswM8}#MWxd-1db-3XbW}zr z+O#WDNr@FvL$X#z_*Z_arwcfFk+U7=wDPsHbfD;xGSnq=BJ`X z;Aj5wqICC5s0k|HP1N({cQx!2F|9ZKa{p~zv1I|2hx@buGwXCSS?1k5?f9s)v86g z8~Mo>C3$OUKum1xw)&Ja8|&i$@RzMvVZ3;ZFW)>-`Vy~N^vSXy2B6FJ;oio8y)dzl=Op zTIKol_>EbZWV& zhMIO`qWLRDoCQTZoANr*rJzV|)ih+60M37SLIq4r))>HzX>d#7ZR1rPj4}kTvNhwV z!Yh%t7-j9{p+lfT!hmjg)S~a92)Yk+9-`4ca{r!``(##0f{h>drDfa?^KWbp-L=E8 zz}qHSCP9Tea=65A_iZERB9rnEMp{A|H*chlVC|(?VtPbVdl%idYT91ZlN77*N;(c@ zL~ChprU&52^TtL!;G=4scAkEJAmM5=4u8 z7ta^$aJJ8ORugULZQ?i7bw9}N(dNVdyu^B4!p+E3vFc!%+eHD!NsCUMGQXMTG-OJ^ z6l7O9E{=j5p)^4s*-O9ucI4QxV`;nwQA66;Az>a1*&ZSbaU&E02n6C0hindTQN)td zV}0y0YQ^SpMsJ{HHZn24URK?wL;JLn%wH0NboML@@mh;k3BOpO=W;#Ry_5=UbM-h- zoo8iTaU{nONVphoZf;H^_4X8P=+EfbK$}T6M(C+ z&Y0}SNG$akj!Iw*h(!pI^?B$vG1iVFUR*W**L!KM<4Hlgd4EMQ7Q+{C`{gCS=4J!e z(U#lco>7{K(lHtR1rw~J*XS06k3C^QoMZr!idyb<8XGi;(oR)mf?yce&h|QbO5B4j zYG=VB3Z;!goflT5I3!V7$Z&%rb4l-M+U)Kw!djN??0@n~k4ca}@ z?A&x!w7IP<^k**V=eXQ8S8UqLax=z+V}ivSm=%EcctBeU+l-dKF)-BCa`S)t_HAcp zB%tq|>}U{{uahhy0m*c+L(8+LCL@VZ!?&0ut(J4#IyO3L^7Pzn4gZ0LbrmHg@&T^H zK9d6hQz!9{C?Q@ZS=8D9$$>7?xV)Ud4296LAAh`$t?*j*$B81L()Y*=X1xbTJxlX_IN5=UUA)n{bpBArvqAoAV>IQ{x*P+tzk!?^*8PLa~uT_mPkHpVR_;KauEv=)b_Cv(Wj%d3KT;Q(VXA)b(wem=Lkxq0C@@}fv` zq2v9Y#o;9ZQUN>$>DA{mrnG_brpus)498tggJD@8-z~TboTHO9ZN|*p4 zOay?Pwk7BSMI2gN&>+f@;m`%xjm&fB;W#a%G-zCM?>Z-C${hYu#h#HHYf-zK)}_SJ zg;|HKXAHEJDpU$2tM%d*Wy<_me*}S5+~)vbTlM_Xii;KA@rdKHL_!!YTUG3w6y z;P&f<3x;V|TTqO0)oX|hd~EW%>(%HQ6H|1qaojN~*cfbshOelYmPD#}G!UCcL+>D|4NtNpS-*7KL@#PJX-8WhASwtKf3u8&7A zD}(sTSFc`CU`66FO{>0MTkX4=-Tl)U5wj{;=5(nH_8R=fb?>1{C*~jkY9}Zuxn$R| z!FFVc`d!7oUT-HZd3jaiT$foi#v5K6(hOQd)nS1B>9`!F5}l!hG1~hW^-L8QbEM9cUx&seR{c0;_Z9) z9&xyRTz|+`ecZmOK6_u!gec!?j>kgzP^DRe%te}9jEHbv!ho+cN|>sOlr#DR5-i!Ht*aqS@yi83!I5~;eVt<&_;wJ9dd$aG zEz+Q%s!wEtbO_O`AG@D1H96jTvmYUa!5W>7oEM=Oe2LQpfw$Enq5Q_3J7K8!LRK}E z1kUXSQ3)QWEWJC=I?B=y= z_p!yz5G%*A-qZ!l4aS8Lo5)k_$5kNl3!m@|PsjN@<*omw3cB_!x%b#l>qC z>vMR7sJew_5cK~2$;^S0-K04{tt0N#b!uV+TlSWppC5_`f73**`DWrCVto}#3hi&6 zo`)itM6e5dJBrmkyX|`_kfKZ%yC6)Ox(SIN0Elhw?In;-Hw6i@qdF!aBQulk`r5T0 z=@t+b)KIKFHY{8oSy2c6eV}OvA~t0SEE(Z_pb#!fM8tB-Zn^;>G8kD_%TU+sLto!Z z6n-dXHxqD}HER|nBOqfg4h{}hED8m*$%*ml+=oPsqb$4h2yZwl0Ky}&+4}qXu7iBU;R(ug9x*NO=5FOr9UpAJ(VE3XRDK!6xM4HU zDmS27&ae6S+Kn4Gpg&rY7yvY@wr9`T9Tu-`8XGTneLD52^{(aMv(=Vi(EVt;0U&N( zvSi6;NDF8jP?Q~+nUPWU>A~Mf%Pq#id$N&;84u2V%%4`Sywfu9^UptDK~d*ZnmG6p zwLlYUR$+G#`(4WVMg9`Ia|VBQDFG&Ec@Ad)b}s^wCy7=oz@sEWF428i_)Bm|WkLu+j>>1=7a*`CMN0ZODwb-#UmnF6_jz%{6V%cn?C@70j4KxfMDl8L;!rhT@&rvc>(Ke}E#z z*%gHTSmu{}`t2Xjpfd`s72URNn@s8LbdCWKh6~4yTH-)YT{!x1|L=Sft#smZw;%Lk z_v;b6<)+)3JJ983*kbDfP#lQ6Z`a+2PrI^qx1GV*%Fs{uio5KK>a)F|O!t44tg5#u2q)DaI^G8Y!c0Psoy9zz2?c?_1 zj^1bK*?DZP;*%enp8fs%xq_}FOtNp90eZ4<4Q#3f?+ez5mF;jP);aspzrX= z1xV`H*|kp%^j{Tic;PEzHV7CIiQ*Gy>L!>R?1E!i?xj?rL81&zZ_xuY@z|nv2blq| z`r$pv`uw2m%5aT|$8+W&j>*ttOqIuHN%Z}>l%fF@T&S2LkqntZK>6|IH4+7bd_jru>rgDOVZQTwx9^O z0kC=|sYC$sB8ACfAE85XzQWhREMXtFw6p~KOE~jDUjVi)IDKW@bzxMnQm8zsPHFbN zTfwi=lW^#J2Zq#V`Bv>PE}2_*)XvpMLUn>zQ;p=W4F3UC_k!Hq0~B)rKogxuP7QUv zDvubFwokgf5cw%U(zUs}n;$ueb*r{IicH#VG-4)CMObshPvz1tKY5Ua2>o(7eh513NQJQ?b6q)fw*`TgMQm# z+3*ODc0UlG_vOo%@A>%+{kc@(WKHaGs>5-c{b@zo1Me;i;F0g^Xw1%bad9b%sYR+X zMRI=~dwgF6|2at19O@PzyUL-VZEi2As-7BeoKj3wi%@1DV-VCO83q~_156m{heT3f z??hU+m0*3UQmxyRX(3XfLu1WvPmW|yg+o2+9~iiSXFq>A-xhd2h>YoYl@LnT42O@6 z-U=$pP+(R(*f$psQ~JVuR>gcK7m+2T@eq}yqAF%IWZ!gd_5y;lFm&kHc2pci@@anm ze#_jsbIHDdNcjTQMWXxE?4)kAD|TVH+^{&|ZR z@0fdz)VKNb3yOcXK2Wf_3eiDsgk6cny!M`$H5Js1GL%e}2*w%w2%aonoT}3ns#xD? z2F6+;9-K$p84)nAcmg*$Y1i0wzn*6JSlCr&OjVCgptK}8h%+1rj0cPtyV?&d8rD#k z7uIH@po&5T$)tNvO-+^fEgqk9R(O7^?X*|Ryc(n$$x0|KOy9qFAh>WJavCTrb=})@ zSTyj&jfrp~RuH^>OM81bq93jC7}zFCtI}!K0)II7o*`41_~B8Wnsm5Is7xkS7h!}P z2>$Mqjzl|nNerteJ=09Li^>}7;O(x7+lNHQqn#2S^8dCF~V!h19v~toZJ0npyLP37Vsl=MffG#_m-G70$}a z8jR%8iP)xg@?;orM}lw_Sc%|S-_LmtTMj}3@nc9F;V2DEaD!tXa+W}SPSMWVfVX{O zhbXB|1U_lLq74qPPt7`O?gPM1Fb@ER#9e4Apzf8yynX?T*s-@^z3ER=#+!)0ll3^h z(oyGEk%b(R+D*@{D!~}_*r<3FB9Ana{5QA^?rqzStlnY%+~(eb6IZX#~l9pQ|spTABbJS&c2laSesnqZzaBA!-n!WsMV1h4qQ43 z8u&G;g@`0Tei^c#VFf|&T+500Y8X8GsBpzoUAPiB!x4;^*D86!9A&)g7Se!G$vtks zbPi^&Xmc6Bx6fWtVWFL9gTk1aFFIQRJ_NItq)k6(ZNfx(0>k!!bN#R^6l z)-hgK7q?3k5}5*CqYNE&00tkz!)=GXsnE&-L3Fv-p@Rnt^79!;1*QG!U*41@RtMXe za2TRcPv<9>xD%zX$o?Y?{hOS3?G_uw6sQBo1@ZiUMG#bZtC$oUfoY_pkRTALIl zRv~lr{+?L_rkPZTWp#e@f5!9b{ooAx{u(DrBMox}F|bNQvU$?;W+8&}Cxx!bM$Sm; z{N^9+QOo+rz+(alnPXjAz4yP1GEk9;8?vn z^7-?_eiF_*D*J$c!{Epw`2g0Xa2&-*Nb$SQy!+LWwK3&PlQL4nTsw;|AHuRHOFeEg z1JxFBnTI%BIZ;gT>!c}zMkUL}AAkITOzjtZ2K5W2Sd?HL*fUiKzC$?OJUl$o0$Cp) zLWm7sBK2w8?U=t@f3d+zkL%S3#4kYs=48xWyykh6^w-b*u&!B{qd=7Rn?v{Pry1U2 z=3D+BIdAF1{`aH4t_sWlWS8{+uGRljM|;0Yto_88E$H@N;4~x45WLqFfrpkrao$y^ z5Bp=GfuP~ocKe<>?F{q+e15GJ%^BC{>aJUVxUN!MhE-%*Nv>{Tr;bi^_-eDwg zPDV*d$?{qqZEa2lWRcXEKB)f>vcbEca8ZIb)$@3-YTds{+2&q;@r#U+{d*F2eR*q^ zb#2JC)nEqTH6^#`>vVfd-(gV6GE6e*Mx9yu^Uvf#Wc;ONu8 z0`n?4_I@L&7U0>C+EszE;F#G2+l2X5dty~l1)=aOgK*+2qw;NSH1q`*<~;A*zqj{JXyv=4D_LV(u2mj}Gz|eL7Zyx@-djD}AHm?KyAp$vkbZo2u zvMqS=qh9P1HV~}FR|z1*f+RhP1K;xI%{s81A}3$$fydA>f+Qwj&l?_#*OCJxAB`E1 zKq;DmjInw5=e)~JdYMjS*)QMSn-K^b5c!0mM}p#(P7ZOO96ba^nHpkLlFT*G?htwS z`iA)n7gm4}Fj)D_RfqSOjO?7!LGibsSsA-dzuCjC_9ZUF8j7B(JAzZT45BxJJny=7 zdtihoRtervDB(QV?0jCyRNf$}rlAmrV@U-z7b4RkFx&uh1G}kdkMrRBqlAK)U{AT> z+f|!Af7uJjTDW}^kOU@>yNTe65F75PkHk?7@o@&5(C*<3qNmJDYBCHwNd$<9{{DV4 znGp1aviQW8<#=*lUqh2Y#C#HmM9MxOVtg#0k z)f8@g^tDy3SsA3EwD*<=a5SSV3f7nn*U<_fY!A8Po6xn8HX20H^*a9;ptr6gG7{&G6;@H@$lipizDV(smTwl?;+#K z^y}y0^p-$mZ3b_LF`~y&MQ9f()$Ba@B&>A)&!_L}#eCQIDjZt$JDaqF&QV zuU*UEUNSLaRSsJe$8+&Lx=rlU@%O{uQqMGb`0lVE%g4>FI+{oys>;gV)PsX}(0TCS zLI3b@NxuUm&@xaKk02!!!kmPoUX28oXxS(!=eJ7*esM_v6S_DKDKV5X**Br)X9j_6(nJ~T|8uNiGh{C&Cs;;!$hQa!3#*KyeMygB zH$xr!F<;Swyl|AG6%-OcklotTd$VqECPH1PZk@$GXC|+H_!}EKAb#1muk!xh z@1ryl4#FqR41&)B8qwYk)P)|AZ9R6d8pGQq4>lC6nMH)C+2@1Mx`rr^kp^el*&P+( zfhe5GU#{UBbEfGTsL~R}#$|9my^QPG|C zxE1PbfFzT#RXgSWcKv7XFLJmYL4+cEr^IlLK8)i1%?$c9W)Ln6{Zi{AZ+lx1T?`dJ z2&!WQ)ale6OFnEn#w$S@F@s<+ySq<AXmU)Mhee~aj-XBL{e+8 zla#m?2F3CQ5N(VyFjM7gn}A|Tdq9W`#y^-%ej4>Dan2^ykoqW~Mq`^OrH$y$>0g@k zj!@j$L;L@$^FmD|;N_?*0O6*iQcX_GH<5!7eyLLdZn6TKhVeM-HhJu8O`;zmI#Xig9bj3qdZsJbVL$r8 z4l|)ln*qW)*x1;F=&u!bI8x@9-MaX1e>-yWd>J!B{%3Nzvz+?&$9;1QK5 zk5ja>J)BzIkO}RRlhHnTy@C=kj8hDhU$msq6qxPzMw)PtsoNDDk@wLhP?o{`(P7!= zmQ?W#R0wd_k9#5_d_6Cg!$lBC;l-+g`@dGcvC?M(8%A(XbIoB{>5h)r-m zViBzeq_!~fbWsQb)}N+vB_$<`9pTb@$>IKq;x8|ZHPAwj!e@_;j03cLk&=qe5WD0U zh3-Q*POIUdcx>O>Al!X+y4z#DX`IjRSg4^3d=Q&ky$%okK9$##3Ei0>d&UP?jL|n@@43SKDCQpt#*_R@%6Qx`Gmv{(2}do_alnXzBDyKwiCec89R&zdfq?}w z;QR1U|1wK-P?H-y2xs{`m@(r`zb`h15#<54Dr1mKPGFS-)7@`rUq^+SLtM;svhMUQ3I zE=>e4GN0q|-HaeV4NOAtd%P0Xrws$8h%VX1)s^g}XksNH15Prk$3Y>Q^<<;n2?or= zOl6qQ5JyW+T!n(r{PG1tAvbX z(?-;jf`HFRu*&+>FGDSAPy}veW@duv6@kT->^9+JdAYf0Ct#F8wj;&&a?>32-p?Dx zePBa!|)iDL#3npeIY!DNBl@Xm@MM9=iMSL(Kp=n$% za*YW52V~75hY5sh0$`uKUg3a)2iQiL3+sjTy#=)cEHQQ2U;{N&Cw>>Z77Pv(A_}$O z;EaTr=pop0l4$DVNl1(u>9NI?3=9l>hrRiCDt-5)4sMj}!%Wl;2`(iRqp&^&$dKrk zDv2tiL-VAA66lkSy-04<%l_;NTOiaHN(Cq#%F*?!l3*maR?1CWtBIUDsH}n7e9`?8 zjg$ybsE5Z!VJ6qQ!MRt0g5Z*w95m%1oOFno8O zc?fN48G7~r)qT)`Bg~*u$a*RbrHd(02{bWcIAYG;2>OTcQgWtV&vob~)Iy(*JI7bj z<LD6*>GhOknr@cH4)rk?KYgV9@TyX__IHYuXUO?g)97h&*aM#hTqozIqoI zgKpU%><$CRbT4QWkw~-%LgffZv^v%llykd#5l2GF42IZELUz%}Wh9)t=!B;U3$tg> z4o4N)YW*KpJP<3$h7K)H$D>s#!iKSqKEIQC~9i@ zsHab#hW{eLpk8QHo8|X}&b-^tux~I|m^Z{p$}rVK@7}!=MkZ5k`}pzWNo?|t`t)SF z+OQ)JWX-HG?Esrw@q$yC-`z)3z3x*tI6*Q=%2dyLVh8I654_ccclPsxfrnP33A{J zs7F}SrlD3^pj=H@h~U3jf&x4eU}XNHMItf3WYm&B7-zl!Sd%gf*tn8=V$A{J2$v&HCsQg*%-Cr4pCxQI9eB`>DUS3|{417ae0e)6(Kk+L$j^0jma#9-) zO>?08Xq9Up)JCa$;PP^6r=@ZM#On@~aVViAHS@ywl%sNF5puyYO#i= zvjf23l_UpU3k!=+p^4<5l$qaLU;Zz0GL_Kijv$8|+C;1Hv*a2?gBUsFoUofv61-Gy z+kfyNb#YId{W=WoC&zJ&`K?#hfF;(x`?;_d+eb-{-Gs_JgYC-c?UqP6)bXs5y*o z_RY|q$6P5Vy}5qt+O>5I1nQ)ko_FsyFw$U<4bdm-B2ySMPT&YIMXBI~t~J}Yf6OM! z1QF?kEkR4ZCxyD~6Sv5pxw#r5HFt%KJ<;=2q&Ii=Y!jqK9<-jQJfqwDaPqE(p@!~l zmN&DU22OWoY<+wEsox*CkVl+A@=TlYVGAmQsreS&ZglG56WNl zIR3uKM~DPa5GcWa3bxCYG|jGtY0jYK2Ei!yMBsA~+3Ag0&0&0YAwwcbuxb z(=wW%wa<=9c0lcTM6=DXx}y~GX`3NppX6L1v$c2`%^P7*KgxduLKxKYeyfc>4X0BX z3$8tHSkb}JksE9n5?MJ8k+lU)KESI^iYk#0ha>+L2%uQznrO@%?ja!0m`avbV8RQ+ z2lj*7;(;$pUVajTH*`HC4KA_h(iwI9Y!~mH=|M@&!88H@z36EDpvn)eq6)#L|6y(o zBA-cwgyDh`Zd-v{DJT_6H%t(h3<`VnHfwujMicDCR_!wXl^D9%&Iwxws-Vr0i z@_~ila=;ABiB}EZOqBBlT*7P^0S5fEdJ*G=|G$AV6dW!F@>3=aE7(tc`Z#bVfWHyS;YcBo#zetj2=R=#m$9|l?^Apo^TgkE+Q2L4c24|)&)a^>+> za<=mLxj|UhJD`f09lkDX4eh^5I|l?9Cqu}n=*3@g@RaHS z%;Df5;-ICwI|}safSmSQz_@k z2S{3EsRcmtiAVy%AqyZis*xvW#fr^GWImQ zV|RWMc)_`A)|0^FMmG%|b-p`0_kD3r)Iiz~cdwVb{D!a0EF=EAW1Ze?XKbr#bS*b` zcJhKZxyh|`P{L_&o8_UK1bF{FRT!m>QCpv1*Wz53c2Sl^2C2NWU7w0+FU*nyT*s$0{Ph0hUwoe@6uX_EK ztSHkg<{C+tohnnXOD-G_by0UJ<&l)sMHYSlJl)*Y#Ydx>Ftp+OISXH47nBcD078-p zr3oSkj5p4oKM!vQg2K%xGY~pUvPr%b`6~?uqNuWN2haz(MOTZSsz7y#+mDjXBT|Fh zYv@KlfHnfzN+aMOT)EcBXX^i$E%$D5Qf^UYnbxNzqKf#^b%T6=6L=!&Sr;Y?HlrRhFe>e6y}!vx4m^Ui&o2C$G`2I0FsA z&zGC^d@zv1I57YQW)OtYP!l-UpW9pkA*7YL6@=;PuKqL@v%`l|23kY^!38I{wi6s5 zGl)&d=sid^>YJoK0eGK)0T^hRpeY9luICMBR#JRtRo&b6Q;@$>p^gP&#)2A-YdNaH zc-u)F0diTQWAr_^C7E)6D|7TS5V1S@5#d{y=A@OOxyC0-Y!=I&$?F2DT^dUNKr}hF zurd@uWKxL>Ld_I8d<>yp7zaW&Z}&fH>3d2vtS^S9qX-wStfr#~(`Y0WPwk()Eq3Z* z*bpnM*Eq3}%vLV>C|Pdw8OckReY+2v1Q80&=FeL|KeZx>4K& ziZ*6X-Qk^)`aoh4DDzDqj`wb5z`NMwT1g`zK{(bK7q9i_45#f0Q2j74&=%G`nt}j` zRskSYi3SG*XTI|7gx-OO8Sxp5jHPvWRUaLJ6mQd0Cx$P}!WUOgc4pMcBu2>$eaCY4 z6E{SB9IM-$$kD z5s(3-<29qofnwZ@Dyh2E1c;VozQf)-iH${j2A;=zfxal? zx(i?ig=C8K77bKl0*AB7>lFgJd<;{V*hBQG1fW_NSHFyn%_n3AS^YS38A~{DHoSj= z7jX3B$HPo8RCiBhr=;wpwp1{MFepB*X+!lwE=!Opd|6(Vm?8!(mbgA##zD^x*zb~P zG2n$JGdpKE#tF4%r%VepJR3Irf{|-r!*7j#gms&o`iLofmFlw>EUy5NGF<5a*Fz|V z*Hzbn!P!NlK;Sk8dzF?Yo#rUkSsBK0LPk5(j|pR0pKRI&`Hk+osPLp*yA*N1lX2WWvZq| z9V?*uNhV_bJ`ZZd03y4Afd+t#nEpUUAR4Vg^dX9Vj*8$J1hDRIPyO3=@U_yF{(fuy z1+5?g;caLF7||rjda6lp`BWm%FP?l(40cGuLJ<>4%rrv_m)iXH?e)MkETKgKJTIL2 z(AH6Zj=c}`LKb}l+KmT9C_WHQ?mJ}27FUJRK#hXbWe+~=1r8wzb#RrKLTDgEw)K~U zeN72Zjl^lqY&Tb5G0-^VYR5Jwl+@7BaDpFp%hwr>2}l~15B6QOF^k6BF~`}*v3)QR z%kmWTZ=v3Gm6)Z4IAJ-di=xwDU)I=)vuJ=q=S>vUE#uern7{xENja^X*alF%G?I*0 z^)o7*o2$87oDwI%Wn4x^hB6KZTqm7q>_ur`j>N*cY#GgS2-V7iQci4jyrB%dHpc1$ z=-LC1B1HTazLbJH-OND#uEb8J;cKqLuZ&Gfrm5T>qH>v#=N?E+e%!}YPO~wg?C0Z> z2ih%XD*)QeKH781d#yi-$Ry!mmLhnRT9|^TAyDbDe}hW5G}h^(r1<=LUi#lXhIY$LDTntL|2f3BKkYk~C7PBSW6*!lEc8wH zM=!#Ebk9rT5&$VZm|}1 ze)p*5vpxWug9<_z=>tUkqXI;`tUWSNL2d1BEq!PLmG90#I5qR82Ev(_fd+bdo_Vf~ zgx7=YSC}Y99j%-u7{dt${Ti89kai8siZETMbHxlgqaUri=n}_j{1+NHUaaCq5ZV(# z5q1E|1)`JMPmSw~tBQq1jNF#MWLr@2L7Rm}Zsa@s)o3&K3+08kKE^tFg0}=1iNVay z&L%hnwer=f5ez&afwmcDF<^JF5UMa1iSaIS9=U)^o<{)MvMl9k)` zG`fvP;f}7iY)8Gx$BxNSchEr>uktPavFp%h|J$Ax6p@cn&46LRh^Xso>?eo|!#%&6khGKtyB)p`fQ$ zk{&eU20Fq-R5X&i8w`-=vu_I#zwzIgSl5)=q6HSimK#Yko_W35izcQ27$17JilJ_R zy4nKB0V$*TK(91p{T#i+Z|;K*;hNf{6F ziFzR_Dzf@m)TgOX<0g8kQ6msdflem@LPVD9>L&hCcHTHn836;nm63m1TH%XSP}D}8K<<_F?>FlJjb0X*5~*E+p; z(^&h~A~K6iQ>q=kIJ%J0wf^~=+TOgmkI6l39@kA85#e~YZk2~36K90cLZ4rIvJ5JZ zm10&co&t%5ekP45T6t0HAlg;&eLJwvZ$DbqMV?J4zM#QWJSJd_dq~z&REZ$xS7t@u6YypPu`oVNPmOL%E=G=L%d;-w$4)KOjM5aL(K^)g1|Hc4Pnu*i*koW6y^c{neRX%fWvl1O zcrX~)r^zIFnI@efK*8%9-@5F}Lx%XDAMvySb;BbRs$|fqTJ!A<9GW3e4Zz$0Ndx1- z42q9`yPiY-ej;;8kU>Rj1G|&iVlZ}}U*i4vV4Av5)0?1;i(H&xIq1vmj;f1>Y4kBN zIgRsOg^_QN3Zle7Q+$4Xcpl#&F$|{x4}UkxKQVN0HMndbk~gQ()0 zq50@1pWEY8srjX{8e||=K^bO2dnkmP5$_Go>O;XwJ;{y;?St4|K%_>#+k;_R3i`s7<#cE>56z`_%vvO&9u6P zsgkq@s7Jf4G@u&1E-WRFG`@8mUh7qd#E4n!e0-I-^URIbE!6nABCmh;o$V7UjRV=H1o?4{^dx5xsAjC zZ4{zvA+FAdPGrnL3I8wOh#~q1;K3v_;^P>U@bEuCXgF5U(TsHGw@-xji;D{_uA+*Q|(ucX=^t{=}e+^F5rC-X0x0OZc*oNdCk9m+Hy z0v*|Mwc~d*i@y;Oe}It;J9o@XA3QN*NAkpf+Es{MO2}XSs$%&5a;9>mmvN zL2E)-4~CUfhDrW)K*Ua>{{sW{Psx5uI?XbvKX);h@GDWnD}qF8)n8A0&erK)P4fAP zT@$0^0nTtapZYp5PMILa*@h=@(#XM>+4pQUsGL}%&sqR}Xit`YC95;3*|ceB^!{s6 z%*P-O7tbO}Da4A^R)KehzN2>uHfWc1GurC|nh-v`7qc#XD)2y_Bf z;NKqo^V`vtf}4u3BLSbR2bHbR&e9HES-BqSH+cLAjPZx*`XZz^zrZtIYs5C-cR#?{ z1Dj5fbmg~;(=e_)76T>V(&fwLptl9PotvI5!rWD8iLZF9(HQ)n3to*QHT1haKn$DQ z-1RLajvq(Df(nMHH-;=J)SZt0YQ$$lliHF;E5F&qX#hM00-~}pW9mKN9WFZ0_3Ca6 zOhN$Li#F|eX}_<#`pHDBX>2tny(3S667Phb-#)zCb?ht-bl{XPnNakd2vppqnZ|G?5TKZQzEEFRVG{Nkw}O#%FWYs;4htq`7dcU-^|OwMjgkq=LZvN5`y=7Iv4D=mj~!T8=NA{ zNSF|4P&rB#R;*Y3j>;%;LvHv7YRdf1SAxE{AUScVV)Rb z8}$-_styOz34e{`&&hzevLmLs7INCeif@6)V4vr?Oi}=WbZMM8nlDgTUkfRPopV}G zVjf?~3_=l3t`+zas8zvQ@f>G3#8+kzMvc*$;8y&D`$!f8K+-S=@mr4Sr#JAk^oz&# zX0qiZZwaL@xOd^OPF~qz@%k|Xp=rllC#u}AT``o|2Hqj^CqiI;&X~sx`bXPG`C|ce zelS7t7B0-gZ;d*)FemooPe1>>fkC6Pion)>ex04mb?9jWfFtrO{YIF}-EgF1kln}= zz*Ik!s^lC3de(v!46dL{XSr@vvz)e6i^w1@Bt7-J> z+Mcbu6?m+dT2)}JUsYOE? z04f)QVg)*UkqwwZSoM~mAA}&Iqa1)5AW4%s$Qz8FPx5#iKYkp&J4prK(A*x~Eq89* zz=yVUbeP7`EimcVA>>zAi^@4;d=Fal?xP@T3;IGQb z9Qkpi@++R&Nf(#~X z^r!mjux(+~L%+%9&3`GAynbC%uiuNDR|UZ?28ksC>+b}AHc|2uXv2a-fhN;JFXr<1xt0%|YJ)_D@s~WG_QHMn;{UZ@21g(8Plf)LJ8DZ;dptnxDb0k@EI`fh z^Ht!F9qUWaWkNwAKL|XbB;*W4d}AY@Y-%1=z2|Y9RE#gLC;J3S&%j;SYqKCk zgFT)-acU?z%qqV=36^!=i_ITSk^1aT!_R#<*1V<08mtRx<*w??_O@z7IU06h#cwu84)6+YqOs#Hj#oPYVy_~%R(@y4u^b+e@}ePjyt z?cUVFYT3BduvZUlcJR4Y7G^FHqwYklGmE93K9wNHS4y8hAH!>Z1&rF zZI9#pT#yFWrYOSKP=1tX3$>u@$Y@s=u-;bVVt0#63phD7e&xlBBUU2LT;=rOOZa-b zNv|Xf6^%Pd9MyspntMzg=BTt%%w!}v&_QOsbS`N%6KkbUTfktm83;i<|0Cn%D?=B&Om6iRez!bMMS(kGdP9SbvqK@78n7o83Aaj(vK`NW? zAuc+aYgrYD<4yc&NIs0?9nG7N4BA4ynzNUFjX_Xxbc@54gQ8wlZQi`urX{BQ;|fsVPN0IHTbYP;71Bhv zbu|*;eN3l!WC{o2vmTHO74Z|aHzu2kilQZ>fh8~8Eh{a(Ofo8gj;O6{Y`ISeMuK-L zrJwec2oKxR|I8|GxE{=(b{F>7AYMpbCF2!bO&v3Rikn(k?EB>CT1bUsBHTrJAhfJS z`=!P^Hm9YrxtsVMIF$%mzIR<*J{21Bt=QZvv$~Lx4wf;mI94gitIdNv*|GxuNP0#x zVFaesc!@!;SC-oAbx|Z0Hc6cD=L?^A%B7Zng!{FUriSuWw5e<`?tbrh1*R*eaE`R= zLm$I87!4Ik=&mT)}~jjkqy)bUUp=B{&{rcwwTO-u)^(~PsHAD`@5 z`JNG+U~H@eQ|3R`+p{it$7_{1T(U5f59F2D7@H5?bjCAj^5d+wgB#=TL5eVgdy!

z6a3pvdN_k`x=4ewf@>;(HH|xTJQIkQxYD9s%u1!&154vkR(NHw?w3(v5va#>RB9 zxt4&YjMy5fD9`d${?x#Qj}BNa9S1ofVbP8qJA{_&T;n90p$P;b^UIJS-uZ_`k0CtJ z1AqUyHq@!gz(;S_tM@_PiB2{DT++#{eG=<85BOYEFZea*C}{VS$$3A{m4h0iWAa^+ zE<*#hG3$q7vN}8K9D>k2jAcbX&eW#nEU_ykr4*Y5;u1}s6HUryn<;w~5?OQK{qv<| zzoZ>6KLz+{EsXC#?7=E)TFb7H)+>JTjBptd&K2a+IucrB==2FM0qp9okL?1F(2bb?z zk(>|l)cmK{kz*@fB#%d{|HrA?zs!mLmwD3vSO3zMUhvu}gk|4{TNUI$wVIR{2nf2> zW!UiHc8i~U*8M^92P(E|H*DJmYY29~bGyU$K8JH%_?zRi+v9&aj+*#+BIuR^KEuWo zhcp3Ei&(e_hpJ{@w23ReJZ=u;tr8h-5D9nlT`j@SIdIKXD+qkoZu_+c_Giij20WceVRk~)MgoUMWxGP*mTmrTKH2*7 zM35zC25_3h4~1#(SVl?FpR+!VgNz&|HX~Mk=nzAHft{VQ08WP3^<>IwEs>0Pl{&w8 z1aU+7M3S+VG_t4+hboX1VAs9fMoyJJCEb=yN+t{AucuGvNB#Puy56_7*GvA?j3yf? z(T`UI761huxtZSHdAJ5r0@jz1mz+vZFAL20%Ws{&S-L!X2`*tlHgGDZ9lZWSclf2_ z3YbFoA0!zIe44!IY3$pR_f(tZ)ckwPRR3G_`a3#nvX7?7Kxe&#^@2R@eY5PI|Lno_)jLc+ z%Xw>}Hxcw(B{F)WX3P~uR*;HHvWJ63Ji^~yh=_Oajf~uQGW+S^Mt`?XF9;X`E9!sQ z*#9X5*vYq}o*GQN!@JU7h5X6777v-j==Xp3pvfqLZSd9Vu3zC@AIeWx`@^PXw{DN3 z{`!1(@_eLdgtH)>fH>_Lay~Z>COWdDH7zE0ZOJ6C42>B43jQ%Fn zKZOe;Q1XK)^G-)e?rQIGH?s%evgD_bd7aaw+{2q|U7?+XH+aiWrz~lOUY-51aMiUJ zP+-uezOFvj+a<6Q2?G~Rd;#p9(THR`{d7{^!_FrsAlkwj^Bmfr3$rv|L>eWvSLo>= zfC!LGo%^9pH9)bP0C}sZXmjYC7ZirJQAc0$XQ9g(PoiW^u*Qsc1l?A(EHu-B86yJ< z*wnHu7I`>!*46LH>qwkOb4pyowYxKpQix@8tdgEbc7yev>h)->z`VV-`0;FMh2y#} zPy@5);)Mx%A87zO3oZQqLavCfe*Z70=awyh1s2-;KX+jM&o@2Y#sBljpPgp^tsg{- zA;+%$XXp`F^h}aIKYTCb`Qhv9yMs=ln);e^*232Imer9Lao&2em2cM4FcU2@YVVdr zds)zd4R}+0X%Gj-nBNHXLA#$3TZ~{n4@mIaNsTl^gp@u)UWxQ8Kol+?_AM3+qNvvJ z+6xkLAtKAp1z;0gFv|kGLQ>f9|^&NZqVK-A#~mQbnZ$5^v!=b7#_n z1m5U(BS}d9E4hUH-WJ_ER!fF#Aq4``fI`|CA2H^^Yj4@}5P7tq!Iu-~kjCKPAky4L zig548P$gO#!K3fHBn~oHco5Bx>{{^rw*v0AdgItNN=h3eNet!xtMZ_e?M_8F_zgB zD=i&{(Q1Ky2j)0y-G30@C;smw3Ncxw2#l3m%41Kod=kOMxlLY#;KCtG$9x?FaBUK= z%8k4zkw(eY8D+>aG(%MLtVwLKp#8nD+h2&X-&KF}?$bd{V5EX<(wTaDdiEvOK58@I@Zx6ZxOz2{9Mt;trX?Y)gfo#v_I_Y+Rl7;&S)x;rBtV1ftp+ zVGjHB7>=7LUOym2t#Q2p3}4`}*~xbwn`a08{0MhhBng(4MwW+>4~K#-V0>|(7pq~Q z%8_}egc}m~TeWF3ko7j4_O0}3EO`-BF<Y#$o+GaH(IF1%lr_-2D^vkRZoDH+Xw8X^hI70 z6k2=4Q-dD!z1AP`SH{tQt#JD7M)V{UB;G=mgFLoB}iIM4L$b z7s9DG_~xtwF)+Xrnf>dR{cT@S{2<^^{Q0N3KR>UmUvqkpP{Y9%D{UAb!!iGpO%zcm zkJeELlPedO-)gA9&6wb^GiGAHN^DB+UfALJCj`x}>2~6p_yAT`QTXBArrjTcDG0K- zhY+#E6ak1lHE$fi_eFk8o^dGot{r!M5f#S{w|2WrSeeO?9#eSB;StR8y&gY@p2s6- zG-e)qMyxaE@sZj762lkg(*4g_xcpB<%@8oF)f=5kArgfnDJ1PN>(}Fw!1_$#z_z&y z(w|>%faOr@&%2*2z9$Ns!Y`+N?x&ky{P(aC*6V(ELQ`mir(0JQtB6J7FE?NQ*QngT zZP|;8e$+@V-$gC#nuq}TFQN6{{{)ObQ#d_iffDLD{QIByk5J^9 zZOdM?nUfpWeE&-E{|XA6{@C0bHDB=49Or+vTs~k2g%lLW*|f%FdS3|p3b+b|PHwDZ z+EeGdFYNpLaU6a9|4<=aJt#ffgy(s0rCY0yN zPK961|0!+`P^BU0^pjD2GAp-K-}zU-(>Lo2r%UsHyXi|J5kGxy+2#iculeQ<_#oQf zU>*1R+^lHVq^NQ)SD$LV+P-r*X`HLiv5tjZnznl5>|fgBwYxnvrZnHq|2Htn|0%Jb zuC4#pmF2%s7ykdaBK~vay&zTv3YCUj5wBglwiD5|TB7te9daF?H=+!geU`pj`c}Pq zvUgF+4U{{Qf>~Wc70fhiE+XKL$-GR0FMp+Hit-^E<4W7KBDZJNP>h6VkZtq-W6<+6 zE#P78fqNxt`OnMb%*%A2cFUp<#y4HA0F~0T(KYEwl{Njm`4-oox9|-?uHd4u%IrWs zjThFm91cgaDAOLin@+)C>XI}%-5wd2sj?oz&A@wQ09f!^xawZllPXKvsb10WQQT&{ zDpx+gTEm7GRK4`i>qC77mW~89&MRe}^>7mg=D%me%4_aw!2*G#&`JBpNC^X~ycSLc zB7=}l8();C`?gSs{aYA5^-}EfFAE6!eeUDl50YT-_A}8L>y%k^#i~JrwYz2;#Z&E+ zd1Oh|V#tAR2~G2^ZVkLOzo8+!qkY4uZ<|lPYd)wWOS7xbP0DKG_-5HO^Vz(!Vp)FR z{raoZn;3%fd(6)Y@hKlO-uz!eMm%oH@0{*BFZ)ZN^rAnWP@Vq!*zQp^$+UWQ*4L97 z`IK1Xex2EB)T#Wo))tmN(taU1UHR#aJ2J;KrmAf>svxwUH(Qgm<*zAsXnv5TH z{_%r^TFEsoIvw13bmtc|)AaW@O8L5tW79VBU|ko#jhzHRs7*?TU2Ch_Rr9QQ(a8|? zxnHfLYljpY4!$+R%Co4!mw5&=kL92L{AZmqOkW0=YBxMm;qt!2)fW<&py9PYenudK zPNE0riZ*}z>=)5Gxh1iF7lv0h?l6?Z%7|t~N5#Gys|hEmTUlc1 zuQN4X13J^leN%q%e-iclD+1oFlh!%qaN);;`Y{g=7L^53ccO=Ch}S}EF!ZtEVD%YIf|4k?lSc-(o3QSu z07JmcuKuOFHL1kIaI|~h#2PpHqtVrCA7H9;{KliVdo4ZD)gpNb*O%#qnQA)56(czr zD?ne?+PwUdq4sXtd!l&C`Nax*NpZ<=oZ3bY=0U`FlcR7% zxrF;FaL||e;|w+H-gL5F(rKVa>*}7Xo>y-0Wh^01(vI#jzsEkd{_Ee~b2wYM+)uGU1C6~%^`6ZcoUv58-Ql)ZYeXyTG6 zvY*5@68nQ3^KhY+Av=;mmv@-eb6(a#5Kr=1Wa-@W{%L2e^sGi|9w5G=dHnm}pLS0* zT>eDQTFVJAQxqiF6-1GcZ4CMx2|4x>UP^#yJq5HC{)M++Ua}yT@eqkY%70tZq@`j_8D04a7Drta@KHKB*Zphsc zki;}z32K&a=1fC!k2_5aqmjknVY`h-{x^*sV6g|&Hitr@6&UMv(QKK3&W8#au~!;T(9 znC~bWTiM!v0!D^C(d3qco;@J4@iK%rQ>B5-z0Um0NVp#OpUkG<3x;RaFS6Fr3zl}f zr=R~`F4$g3FAs&k>(k@qTD4l!Lg!iVLt_ek>Kh8*(gk2V(invqG^^rV+^iNM5Yd!% znwPdeK!Kb2_pn-HEIz1fC<~ix7y?8u3mmFssDq5#Ouf0OTCdbc`7YlCz7|f!+usD( zw=REC^#K2++Lh>W+)>o3cvTx8oq=QlEnBvfZmYQKNxSxjF>kUxs(}51KA6>ITtwQv zCN&ILQj4!eWV*m>^k$%gar{HW-c4s_T$@mIVUV-)S{tn#6Fy*mMUJ%$4qqzPWZp>_ zWzsl?bXR}p%?lrVg^Ds6-iJME_j5Jtp1QH%Ne@GQ*jHjHS#NIyr@giy0Kfb1kD=5qM zV_MHC<526hCASWq-Y$hLs~A{^DZh)2!?1OYN^EF2xG^l!*W_LK#8+f|S!^L-XqES< ziK)$cgLTrDYt=k$$L{fEQ^f7|4gA%u(X!%CW09|z!m0X={gc)-F=Thq)!ZVTGSv5O z?0h^p3LA!k1xSA#%QoT|3p6cOuQ!5iM!IP>tKaSKmS4n}N?un;oa1lhi>z8g97A~H1t%ae5&x}ns`z(XrG@b5^dFTeLxv$#!8nUf)V$6%Pi>4DK zMlsKQD^!xq2ML!u48N6gE1z7|*2sSxIb+6*gvEwlgIHAuyYy)s@C_V?2=`MdDJkVA zkKK2wcD%i8xsV^LMP3Dak~sgKQP!R0z}u8caA7z-E!eafyIU;Gy#Mjq@%Ajtk!Tem zJGpX9%Kk}QNFWo*NB!>X*be{w=)lb&WIpf5&D=Dp5svy75xRb8^vl~89QGJ#-X1m` zI;LI#S|bQ59_0gT7?SJizDyfKBzWcZO&wFLJ0n4M%S#xQFt_*4$!`ywF=J=5!JBZP zzPO{_cUA%ZpU<^2)avt2z&Af!ot0|gcXIh2vSXaPByS-~Zfo4=F#N+u9QL+G7+j(V zVPPT;GX(uhzT;s{D{F#S(Hb)_BtxSV;}VZ-EnrR?P?X8Av%l_gO-w*g$5Ce8 zH+yP|v@vph#!FRZj@p!r=-9&{+?tLBVpgq7KJ4Hh)jZP`kd3Yr(z}h?01f;+Q9;Jp z7}^hn6+M!&CVpsxUV*%(8_|UQhBpry92P%0DBw{0l1D4oYmqq%h{A;Xk;t*}+>+N+ zcO1FY8dCg_0l?T+#oWKthXVcfxF&wes451vGT0^Wfe6W(qX#YPAVV`PJ&!|!Ru`I?Ia}*tK!Sw%Qkq% zJ=mF}GKCD&r^n2nskS67)O;{d(XOi|?|94L@P+yG$`7%}Nlf?Wl5&XPiOXS+jotl6 zK|0aI3rN3;$?`!Qy`+|5e65>Kh9%5qsBdi_eV}4=;55Twh88Ph*Gr>?v5)uSY^-{dAQS&Yc+P?l!Wxh`PU%kGW##y;~Th{FuV(LwwWCbQSrj~+Y~@kYAMeC(m%zkO(&o2c5`5SaWNx9 zLgcCr)uu&SNbVR{FKR0|-0lz0^-i_8k9a))K)S0=4T+yMPh!sXBYI72>D3WU z7aZz-+M+ghFu%vehK7BulG2^}Iue`R3qK3bsg~T4h@3`37<^wI%E}HXUY?SZV9g_kVFn4)}uVLRm z=UN4XnGU$y86Ip5?fEg2cW$AePbRtr-Y za-RRDmzhreh%mPW)JASXGhPss4DNKGGAFNe?8fhMkJevgxH|Px$jO=(0e8PwWE-`| z_pS5;hJ9yu+O3*v6;#!}cC|J$C|U1ZOFX0rBf?qag}KQ$r?IAfyyGx09F>5QaH3VR z?;uu*tr4drQ}&ZL5Ylm)i2Vu#LQqBq|DX+CYPE4wCU43IgXeaO;$Hd%Q5oo1E6ZPBF)iJ!$0h#axAZ z=@NV>BPKP<>21+HJ2RGe9s@w)1`Ruf<+R<`<>V4ZdrW<2*`#I5mR%c~WU%f8UA;3- zOtWJ+vvT14dxW@Mo>@m)R@WyVpGW(>|7&1gx)bx$>HR8{>#pF>@ z3=i!xo3Zo`&Ko*@GVF5jeb3CAvMA&r8c1z>kq%DS%o&cCe_f0^RKAfqXrTjq>%C}E zfr=3n+8VbL)3ZycGr?eAMu8#(z3BssEBuru@Gq|>+u zMG^bLGtztL*(oit)49z9K$I~NGnk%LuUk2dp{}|BnR_?WUu_2z zaNcD5C=s2`Qt>N{&xJD>!4AFygfU{`#4S0g8+VZt&*Zg>Bf$*7oQ4B+PDDN^b1#GdMnD@f8>^;`G z&hfkOX9gwCH?+;6sEt5;!G|~?Kq`h(;)YK}$Yjd{cNRqY8Xk1gE?;dj{XKU2`5*bs zFj{_I{u~z$Y#XjSMz*JShWF9WI-cM!tv~N>-LPwq!`C4;>Obc2dQz|hD_&`Tt^MoA z+n-#~c=euA%ciAlXM0#d9&fiEbtdz`%+brD8LBl?kAiG!e&J(i!YOj7@%HhtfBm>; zr7u0oGY~*FU}fJ4U-^{noBU3#ShXrCG_LN^FB~qHi;3YL;@z8s` zkT)1IJa`sv{!gHJesttqPA>+icnyI_OOA-;ZAmr&MZ z%*DEP7QGe=OsQ>|zB4c+CvvPQ?5rB-U(!D?t!G%8jFhbkRP@MhjY15a_FBNWot>)g_}!8iKCL{gX~&T zRjFxdz>cST8IHPKav~RysRW{>M>v8<30cBGIf!8-1Jf?D^5w~@oDS}_spIAY15;{v zMi12)E$dR&`P%R{XYY7k+=EVD&yn``K2rUPz^g4_$qk_z)fhcd*PAtKmez&XIK9_ zT8dinIvW`_S=EM|#xJ8hif|8c8!=);NUaCgcJo~vAQ3E-yj!=XGOKv2+$drydKl`r zaTr;w2`<|8JmVZZAyV)!2;tY!_jaxVmD)h|ZpU7$Dv4gQA;LYvw`*5cu7^(e2JE{W z9CFS;pjy9d(t2V|=bOL(zAr14m3IadDTJ7jL$Be&T`#BJGf2(c7?ymM;X<8cLf3)q z9RCG*#phpd>$Kc4txX?1{T}p@xOi7Dh~hCJi5Mp2b13xmMT*6C;{*_v@Th z&efL*``Oji?_s3IF_|$u5@3)gO_Zj)BH`ZE#Wo)U1IhC}kuV@LkFdv0()Z~uB%{+9 zS5jq-tPIidTxiFsReS&8lm<(mq|A-`?Rr(Ek5164%A%?ZcTrEqjb#{E8cmz=z;(rc z-0kP=Y?t)tPHVkGGSeI)eaG&lR08eU05cNe<2R5zEbw6c+CMJIpH|8=DwvIbk9}+~ zMEDlXa2+=6!iQy}ca2J61!dx3>>grYIGxn>;AjQGT*BP5~k{_MDS_YhQjjb{OCHWyQK&uG%VZ_q9+|&4KG{ilQwXcI%Rr^FE zBsFx+m|hfw=657S{1Obs`sGw)=!qrhw zGP>Lmbk}k#AxTI&p+S)F;tC%vs^RHewlfFRck02c`vcce4)v3oDedm|Kkw^{o8`W& zw2KfLqadH$22mc(p`Xow4uF$90&>SX9}GzUAuxBCz&1IYCCul6e!4ctVNqbnXcXAS z{`US~gw(p>N*R2#0f>3rZ~Qj6ETWXl@lM)sUhgaf3@JI++rphJL{v?isR=9G+yD`jfmyWkL7>IQGUWy+~`%Rc7ntg(ylIT)tc*w5JHJ^9sdv0f4i zfvpymb831g=hb}wCS|HM14EGax=3oD^cy*^u2t=^^FlC?c(+K_W79OLGe-W@BYCOi5v*3z~vReOEvysQ?< zNh|+>n3jEdWMMq&oS4j5f{}ZOQh#=FGfq+T9Q`%CMf0F20q-!ac60ct*%*tTYGdbKGSK&`R~7wv&WK({0S6A8wzuBB1r0~1^NkI0>Tf=)p~qc zd#u~emp$JFdREM(gvLJVXYa>ZXuZ)GOE5xqkp`{TcH71uH(&K$44j_3+!q`}Y$?he zIa~>+kr7a2WD8?@yC5446j6cU>FF7GWUwKqaCXXSkMV!p#SKFSrQ5vhRY5#9FQMHrf=Z#S`4&I#exoWTqoBVE>_k2nh;b;lATlcx zQoT=(pzqC^Nz*88!H)KYM>;rnO3Qn~(H_!cQ3CqI?@7w~gS7G)BY7mOkv88NeKtNXhnVI#evC)@VHTg+A`ca*Y(TL`ztgnpkL6%LcJ#Rs3XL>f$lB45pc!Yz znB45zeE`m-kj{9!9h}nFA6z*z!xel$LNNjU;FEx?i4RG9g?Ul+Uev*9%$rt2@A4&` zEe(!YjSq1T;TqSvhcLxXg{O_Y`x&A1dGSH>NiOh7P=TgjnhJ82PQuN#4$b8aJxn#LuS}a^72x zy4VzD@dhUmv~q%e#Q>S>qDCiy7NHREA-6oR+NlXsOUi2*g3|2TFG_>5{OOK!B_n5{%~@HvPK`v z`;#oJTkjrLY@;z6ny1W(-NNWT!_{p|!BBJlg2}`%kX| zybb1xozr6{7Zjdx%dO_=T=Rz0)pH{+TnCNqF65Y zJPKzXTW^k!Oa4*kodys3iH5>z_4WqvC_~9Dx=`l$b^Y(eQ$)kpelq}T@v&M> z2gWSooN~h(V<1Dqm5>mrbI8+X1EFVS;!zeddHC?*DP+naNap$#cT+<(FE7vi?UuuS z$|S&gO~91kDy5^+2XxvFC9%~GwxIkOZQe8wdZ&2p%_wjKB5)6h*bo*ri?L;y5AWaa zpea4Y%z-hNkrW0Ft7-<^J!K)-i2QO~?TN3Fgr)JCH7LnuyK&=-E15v!*>9oa&gTXO#>^nIR?-IJw(btgqHgJy_IC3?3x`<_ z#xWE9{QX0vJcub;J-9~)vUn7%)5(S|?q<)#BR}uN$YkF$NDorvwKg8Z?#A`%tg)6U%_LO_cyTULF8Z*fL`6cylP6~|D~RxQHXIyI zg8Y=elD#}6Hwe4|C1l1dYRKX-;4|i2m91NNwa5^VpZx4ioob&_XVcepvU{tzRJf$q zHd?3-XOXQDca!rnDPVC4a+XFC`s{}p38-UaXf4q^3sAiTNm6C^C58um0!sI=-{Nzs zlKzoPo&`TNhzv=+-ki353)F5J@1s$XaWX+T-(dsqf}6wmW}Tqy+?RRd>Ty9rThFH>rm z`&kjb_o8gnTi{wH=F|cb{{%=%twaI%oZt?t4rCMn!;RhK!I2ks`c=cx6lqDh$w=a1 z>hgf=ULOq~)4nH*4F~H+&8%i!wPnV8>7;)e zDuv0`da=hjd2Giu4P#G?ewIQ|i*$_(#^Q;hOJ{_hdc6K8#l!Wx9`Mxy&Q$zCR$nnVy)UqR$d>EHh;Y@6j zjw@OrQ}hI3_jxBaF5S?QQJ{bcdx&~c^r|=EHGynJeZP$euXtc7_nt5-O30-QKD-&C z`5^{+m?=kw=hrZtT%NFedEeM|?@Yp<&kbB}I4K9z?)SP^JWRuF;sQJb4NIPuKL92c zXv_+F<@GOy1Yc_RW3{Kxc**4KW&VZ8;Qgo|y~&$tuu!6XQaGD%3V2foRs9$r}eXnF$HjCT$TQ<#r_%0^r>zBR3!K& zbRV$7ko;=kwr?=Km}?xyM+$Dz7z4ncATqI8T{31D-B`eTPabS};N;N3TwjZ1LeltO z7^Acw6AH7lzs+2i*9@b2?OlH8?{OLC;h1^t##=VMq06?>5F=Cgnl<%vgKx;P7#Fo@ zNtVDHj}+vSTA-e z5$_b!PxC)CjHx#AO`j=%W*XeoKe+84)|zox!hzjQ{x$(2czC|W;k8jqsuebR>7UD% z-TA8drgKYc)RLK2*X?=N`%|2#=PtO4B+AnxT4bruZd6mj2T=dZ2K(9ullXRW*M$Wn z-(v-F_Q4t7zOmkmE^lJXs-sGksEWrVXDDs>5X_Ww&TfvDLNEV-5T`WGv`bi8(Xlae z;N)g|^X3PKvI;G?a-IwsVrCrnP4RNFmpIZS44*m?NBE5_sJ*Xz-hIkxh*Wlyetbz& z35XVAzCJ`v-nE^F$EE|*8ZCoDqKOooNBOF33j)6hQ%VXVLJu&U&T~On<>vGIZ0k~b zna`h+J0q$3eu*Cuz-?G9y!ST-FAFDdF8;#Nkogro<$N&$SzWRz&+}WPavE1`$@I)5 zFpoB`Ke!un761VZR0dv)9z7fiSy0}noU{XwYF^;YN6{SH?$A!4ec7ku$A>9d7F7@& zTAv6;8%c`)v6?1ePHCC8DUImD(6TsaqV&)Ss2h+SI0>%q=MjZTNv79XqY3Z<0AXWJ z#7jDJmwa%g_d3OIpq)oRb1jK_I!%=5>BuJwa{Fu$=W7GdB2YKoM1P}kZlTxsY%C4O zX?~K9i~0GZjPBE_uFi_>eAeERgoW@HjSr5y4m$7KLB&^);K*kmkK^MXZogOKXo0wI zGT{+X7-5e)nUoBqU~%psyx;?GluTGe9BKLAA-F?c!}!wEn9zY@zHqFZm~TB=1pt=X|=vHEv;r4-}<< zVoiLHmIYsYbF;=_Qhif6>Pn=r4rPvkA7bJ_11n0dznC|dhkYJ8A${)2@S9;EcG|8a zWBoggWO(MCM?i;CE>VMLKa4g6yym&kp&kt$&g(n!`5`S12MraUQ}f61#TGA7-kqF+ zzQNz$9I%OYwn|9h*D|q7e0X7i=vi)OH|-H8jy@x_!z3Brd7rJVb8j|bHl6g~!{hBW zXkM&0Cb^|nbP4@1<-VR(V?8U+i~DFmeInF^Ua8yyA&TvUgk}oozRXSz1e2WI&Vi49 z*3)8WNpi=hggR|>j_{Ru$L!{G(4>2Y7>uUG3x9ZeCB^Gl&N*E#2)-%G1yoc-OY(SG zDe)(yf->)&_7el-K#>~@dJPToBT#VI50gW7JQB5_`1{ukg(FDsAIipx-MGg4=8wrk zZ!*`d-SPGcskvQFX07Dlybck^Mnv!nE8G1O2vtl@q3T2q_=3YjD&Arp6&9D2j3g=#?dv!*)I0IzZZoVV(+l`GMJ=jGS`D+aHZ%iix@!)3u{2y*>T-Z8w?&l z4?V6j`Bdv@^lKAL7Z(2Vfl;tB6$$|*%*UzQ9apNvu+t%#hqKNv;Mv*yQl5DvbeiLE zu!}E93l(2fn3EzsBySW;`eLWEPFo4dW{WaTPv~PYRvk~&m^e}~{lw70AfI79Yt^bX zz!ccDXWNz)WxnIntM{t>Wbbmv-~ZSf1-1rhNQ1oTi8vH3@b*08nU@~D*@o#XzP|z? z+ide~F^8_IjghKoIO;QfV8g!w-i)Hpn4Oyorenx0CB4h%O|N4faDqiuXsvn%Py5P8 zZ+P}yG!}rvW(T$DSdY{9e;$J48k9>83XEo~kGChXKNx{Q3SH;XY8}rxe1Dl=>D?U| zWA#@}1ZY$uUpqh!zub*JOldA~TGfH9>U+YgoqhW1&^e#Zr>On*{ZGQuw~alfrQV&G zL-A{u)$rFPw{YnrITyc2`E%W-J=(M^*x1XY?^COpCX4E>4_g-0Dy86R8=4X!MoiJH z!@jS3^qs)XUf7dSksfQfF=)!D;>u6GEYH6C2dAp75w|lYP%P?{MK6c>7;B4ugaD>kS~!%>dPj0IfYL>v2;Q$D_RX=8kw z;x-)Idt>^xxF2;_O<(fwcU=67{iXNr1%Vi|6FWLxe)F1~330MH4}d1$Rik~6uqIU> zVznrUfSi!D(OvTPf`vIp34n5I1DMgVa-pQ%g(kTiP9uftu-c2xAA0%5&V9p+!}DXx zK(0hQ)F430P{H4eGi-a;IqvD2y!)xIb`ZT|njN+PFE_58?w`0zBWh{SFG4e~w>1LR zjri0=Aj#+@GC>mHOquBu`*S0jn%c4b3$vlX{ZN0J`TVgzP}u+)N6?Zt?s7C{IT$>B ze1gdf!}EQvapfN?m4`rSDs(!ze7>jWUKa0K#x`>vGi0Uh@pScxbr=e~#so*loCbQ* znz&d#@E_G`yd3U$mV~V&eS$hZ-}WwQ6wvxmT1fM(gXhYjh|3JV;e>@lmra`T+4f27 zOg2pvtY!@m)=?|tMLZ5T;N1z;vLQ^{*f*s{(C~fZig?r!V#4B==;`6r<^aoFn5c~9 zI(8fect6kTxNf?aZhGGD30>$yVKAUchqNd=Iju9-F`_;vzYCMmEruMbd&-(Wh;3$2 zkM@~2rK-p<;ILjjTDRM}pw96jBwaKCzUQaZ(o~JW!pP3c+mQ2z8j>7bY4rXhc<8OU z48<0Pg#Ui{kNEP&H7}G@J9Vh&i2t0$ZyLsbGze(28?7}>TINtEt^ULmHXtx_{2>rN zHDy`Itr=xQ(-7L@2z0v-f1Me*U4O?UXN?!YbPq5#7N~N5PTsD!-AG`s>Yvw+#=Pf1X&q)mDqic_;j?*k4caf{HAZ zRK;Oxn8Q|nN8Stc2h;w#{qWJxmkm$<60_ z08^xA^85X(<`*gTIu`&fPwDHi>b}Qfh&~ap9)59)*+F16d0cFNC@YnB9tg+4(>8u& zv1NKJso)wL6`I2yZR_fsmaV&P$zA_6H#cx{_ktBq!5nqIIN~n}6YB0?3gy{Zdz=L0 zB7;H2U8T>ITFh2>f5L?8-kt7InXcme*N7gGbL59Y7B*O&3C;0<0RwmCio+{&UwQS_ zxs_p0A&PN5x&|*b1ccsNkkYA6Z-D$q@XlgQ>)7E!rv1K&8 zJ3EnwNmZav2NOe=MW59H*YhR+nqN+8l-F zcc63*VMAR!?CS*{cg%PEM<0Ezj1|yv1|YyYOrt11eJ6B6+Wl&-l=ZTr7WG0i)^mIR z)Fe0a&hgCia}3El4@dgSn}+bOsrM~0fTUWzM9Fi&uVjYmE2zt2X%ssFBu$$DBcpiI z&GDIr4x#}IgAtXeVZ62R>2^MV5!YpFa45*ZTaNkJb;{8k3RXezsdsdkoo$x5wpwr_>LTfrZKoc| ze#?;gx}aB6!}J4z6Qk*>o5Ins^gs2hU27fZJsU;bT-s}|hC}322&XS9%i6Bdt~@Oz zG^9|1DP{Vn789?L!xKeFSgsX)Pex>@$wi#n#_Bej^eotb4o#Om9?e-KE5?*&{&je{|5NA|^#FYk$ z@}?3Op@%YPzxs^$EB@je1(|bA$&ON94WRkyhf9;hKmI<4q(dTxs(b>VXFD?%DO|_6 zc$YK@yPawn-u}@tzq&PH5jBsQtX}6OZCNGK>Tp5Jxx|ilIsDmBlk~KZD@G7(&%vqI zzt8ohmg4`^D7Jd3>yf)|zxD|5|F9img8@zK?c+K(G^&t)i=7$uPoS^00-L!OL;iR3 z8XD;D&=O<2YhxWL&v{p4r>JSveT8Mcl@{L+bJ%?x#mR;EuV2Ulk0Y%+9P(*4ulWZ7 znWl8RRm;Dqk^G^rOOXwTGRI#H6J6T;Jl4S5j3il-sEX*r08Nct7IRoRim>0D z-x9inrj-BO8H{Z2JI@;#dPcAEDW3N0e;94}4D^crfJkMM$0a^_aC*j-y-M@e6<*=+ z75|&F^V^@i{SB9UbN;ka@08yUXw7Rk6uum{p#B>w#BGGg()=2lu(TJ@hD0~?`^3Bf zNcKJNY)F1ni~X;nQgYzA-}J49JlSj_ECW&6rt06}+A&OWKGvAnD@qx)W~h-iL|GiY zpj)V^Mwof!@v%oY{@&QXW-@5G&=7YbyD6NE10sHF-ad3ii9#eOBXhhpcW|{Eg#uN~ zoMT0s{(K<4(Y>RO+B9n>4^oQ4a{{Jl#w@q;>jweeL|k$8u`tZ5&#gc0l<0PN?}p^I zMB6qn@2_3EmYS3?t4rf$$1W~1)TGY*w?A2%pFGvsnVCIlok0U>_H0UbL2D)Cbfnm} z0(eJrNrox3m7i-&CssRi^M(J%|I4lco*nRc8p?~3HNSEpv~f(THSLER%Z(jx{~&gC zc_y9IjY$GSbLac$J`TRJ>Tsd?hQ>&gO$t#0-XSZ0*hr%Zx0JFY@uyYc4xe&372<=0 zU4tmmhtn3tmYAX8wQEb|_I1N{n@)B=-Sfn%rtOSE4Tv-k_P5h#dvtE!o`okP3>+ zVVvp@B7Mq_kDHTdYn(y;6LCyvXK|Tw;&CprmAUczQ-Q2?DEQ!R&>=|p#-G1#kk&f- z@b^*^k~FXOUexS5r(I`USDiO@8>tyST=|h#N&$#JFk*!|E@8-E6Lb7_a;)brh-$75Cfqi<@oOcf1DgFEP?Qd~Z7#Lf|Fp4lJYFaudj3b90KZ_PF+|;g5}kO;oMSd0(?vUvOWH|Q3I8VU0P`qP~BslleUIFewe=G zn-xEi4Fd!+4QS;E8Vdk9J2^_#ox(;hB(La3&XUzsX=Yvvq(0eT?;g`Z@*(v={`2Z zHbi{|q?gBzpxO_z(cj3>Xz}9$`#lqqTxqNOSWSTZAm4Z>n!vQ*3MU%tKh~w$Lo$J3 z06PTn_wyc|Ng~WnN6ZfC;0Q=S*6xar3O{!b;gW&@;nf<(gFHFq!-7S-I8Hq)$J1nj z8QPDYt$by1H5atHXKYL#1W(>p4FQMI89Mv}4cIpk<%w%r41NFfq7{saq|u5yBXp<19mVF3$uuv_%g}E1 zOq0I{8A$es8(@-&x#NtR0%(`{k#1~O0V1TDn^E#6iW$uY9_By8NL0Tk+YfhGOt-2VdUDCAjefV*j#j5Ex9Q2!aSre(#dx5{Biaw(&`4q! z8gZ#{I3P3+!{?#@FM(La-v-FPO#QE6gbWig66ggc7q15%ok8SL15kq(;s3JzrlabV z)JIfTa>UNgha|{SQ>b&aS8p8%*?kQqN*q!k+QT3nzV zQ-4jBLKGN^Xy;h9(tggh-Qd415O(pf4i_(3r3X2_#?$aIWhkS{igO8l*M`1J))}Rw zl($J79LfK$^_oDxQJOVr=fphsFG>t{ca(6Kt)zzF028>4hIc7zGKJFu?mF$8TWd7; zjQY;pP}x1^JE{vRi+6ueCxcFYapT2h7q~t_v6@{1X%4kV&P2hHC)>~~doU zTdM>noA6LUTh!z8p{Q9FU55{>IlffgGx$LvBO5!_23C%yg429Y_eRppGE`5`#Rm(85H!^#IsSM>}Z=EoT=jFCREU|x(tZY%=m~? z2v3vHNshd{g(2HMY9dc;${nK|DmD(!QrMsP%z-{=fRGO^#%Q7kv^j4s-G(6V?){w5 z=r23j8Ft+90+9C9X~!H~TQ|4t?gvD5^xU~~16N629+>FnhK_Dt#SG7{sTG>mU8dn6gQFtoOg;<+CY4C$}lN$LxdPG!=%d8T4fJ|Fl%cQD` zIV=hWp$%0wK(RevN5HJEX`Q{cf~gw}K_C3NNYn?6k9SF0K}ouK-=fNtPAMJ-Udm2+ z>|yWtVO-aBWoyF0z^3_SLXPX0mAif_;-vL?9X52I)7H`QY%@!=`>y!;h~MMV$d?U& zXz|7s@c$hC(RWwHyXFO1_2+jT-x13pZ0(4C&d+Y?+;{?on-|PVrFiYc$-~x-tf->snx=t5ukPP%0 z>~#O1@NH=cP{4d(d_>07Da>vGO46ri12|yE-0Mf15b-Iaf@}oa(!ntLoI%&v*{3jb zU`8Z*X&ZMAy~fM@$>!6*APWO%`AiOG@(-Cc`(o$Gne+y|mk>-!+fd?6a zfVFVvNtb$7-nW1ctB5K93X}D)#{l)=bmj9YWXLrWW*gRKpv`E__E5KxmF3-I+PTio zVq%h>4*Tw-FFJNl;swZVfpXTbtZ)xlpVK#C5^pM^2P#UfoP&O zeAK`tk29|GOvdNlJ+^|guCGb^mt8RBamjKD6}@{(7Xr+AD(+^uewgQL$YEB1Ac9+T zEN2$Lr01}2`0TJBP9-g%2_D@@MmL@VRDfz|M#muLz!exq!qb_~xzIp*Xx=Z60-KR> z49dm&BqBf}>fXUj_PepPme>*E3nH+N~lk(Ay&LgwtAA|H-2J!yqY@ zIq{7q=C&sk*Qj3oAzwm$Ade}E{nTO($|%UGw>kBWWx7;bd-QR7S&kmkf%Ysxqgseia0ZnRGM+yo$iPyy@aGq^a044545{ zHrEV{P0bL8Z{E~n+V}a>$<&3ts3`VBIbK9~qkp)Wn`(tHR9#lKo5X_Gp!Cu99Spl* zopSw5;AZ`^MDGxSWH%9_T5_9OrmSN*?iSz6ni|Ckuje8v1dN{+lmQB*=->`aK3O++ zpD8f+e#IsFHmYhtr8LETD2NQ6Em5Kk@G+;qW!ypQQ5UHP1hNWhG(nKqO2#pQeUk<; zV2ye)-#DZ z*}f>-{f7S5Fy#iKx^uwyYFSD&;G1PW;#&+^|AW~+EBQ@?M-(U_;qRV}H`{4uTVi{m z-`#hY8e$}6oO*4%q1?At^N=sVND{YOu3madctJf#!12dm7_#M=v+lmTI0js1fKpSV z30@rQP`zBJ3;j*}{eF0YuztE&_?s=pYOco4TEA|X*yOBJ@d^MOvqZGp{Qey0YZx{F zx}yHMiV-bYS0D3$nc6nbF4O$GZ{Jw=EOwdy(bNC`oVW13s$XMjn1^rwt!hHt2j||c zel*~jmr_8rrw`6xsPXq_W=k*s^sKC}^QAfCH5eMb;cvbULwjp;J;BXc7qu*yvhw)P S5W)-w<0peYj{0cg=l>g4P8HMu literal 0 HcmV?d00001 diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/model_comparison.png b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/model_comparison.png new file mode 100644 index 0000000000000000000000000000000000000000..9e88dac75537272245f54e4b444562aa285f162d GIT binary patch literal 65895 zcmeFaXH=Eh)-{M#R+*(x#6&P0l43$ZMFcY(5RfQAKorSABxkD(sHo^cL_k2v86_zo zV2Ox`h-3i)6Chb6N$$DOs_X6X{^%cHf8C=;Gsdkd5zcwe-fOQl*PL_hd-0H>%)Hsl zX0x%e&11^$Q(Dx3V0Y5O+RnoC z*b0Xer)*8Ftv0XUv~m4*krgI(b|-BmHf*r`_b1j{pEBMcaG*j9uQKbTtd=bs+u{ZE zziGyyf)@#1<${(`7Oy9U?CePd)=Jl)njngCcaqio1 zy5_Gip`+7wb57qKd7(V=LjU~oS^U40`@UYaZu-nW&ocTZ{tg=FH*`$>^W4#I9nOy|NFIV?!}q^{(+6{v4QNqpZ>@;fA6Yae){vtB?~xy`s3;R|MhSEc<-FKb8i$G zvWg7ldJf^uP7B`5%gZ|`;M-mq+b3I-YFZ)CH`KRuvyo)E{rF=3?x4qyANSAy&FSdf zyLVe^zJ7V1U$Nu1VEIFvkA|WFY7w#=1qB6>j@uq81xwiDJD+-bdcNek4t5Ir7DZ?z zYHxpJ%{I6iYw|^wf2VND5XaJ`0b!wEzMLE%ZVg=~<@QiJ&E&3ei1V`}lge&dei5FN zURBuJMLNtoHIqZ=;vD?k?YI72y~|D_dT6=C--Ei)36M zbDPEoA9eQ8H)*D-F4iv=nl|OSk9_;+qaLrZZg_aOsG&huE5-17a&j^jCY)XB+pgWa zcen8fToYKl{>W>=P`6dgk^Ty$=1-q)yyAFoL%-Vm;X|18#J9c0{u`8fPM(hD3Vnp- zZ5-&RY3}G4)vOuLXP5rm;Mi1^sPjPO#{PZ#gjTKEGlQMC@@=LqKXZpe=b@@Nbw2aU zOD`HM85yj%9>5ZHd~GfkezAJ>>P1VJ#)ixIm|-PcIualB4O7iUI&nEUA z)e7ER>sWTy7hXQQvOM0jB5I9t*x6H`p3`m$iHICDuTCfn^_(!}$E!vwwkQkZqv3UBsuHwRo%>q+yuH2G z>phCeaUE*+k)C*+>pse0rkmFo8P1aI-Lq$pT7u>#oD0*ImjUgKIj$~V)8|kWVJmiw z)meqzySH(Xh~}a52A>}^d8qoY*X7Qg&z7+Xx!~RXqCl~DIlnc2Ee=UZNpwK)$T9ea zuyx}jR=;dZn9t?Qm7_g*8;C zRroc{sVlwaC3EKfOPpnoocq-7?)lBz-nIYpUGthG)6O^dm+f@v|2TIcR_^h_r?s`U zEyI!i{{F+xfB*Fx-{&KoN>`sQ=N8F4)qH6<)8Bt_Vcq;0^SKWNiCfx@4q7tHU*Fv; zX7OfYW@ct&OnrjZX7{f}g4%f=IgPG8IfXoHzU+GD!~H<}Qg9lo*~Cv-rK86(7i0BK$91y2N7SOpJ7J`ToH=WpA(kz^Z{)#$;*v z%Ye;d#;>@UL+20H-#_k!gMK3Q%7X_FuxYUfo6YNfojL=ie=J@ceU)cNpzj~K6C>vH zmTeKLa=;0!!S}X!PK?R$tom}O`)y`8lFWWqWs>fW1G}AH*!MK*;$xK~uV5FG)RG5& zIVmErZyMW*ixK&!x1Vx)_58W)m8F{x;PtK{B^=S!^%FDKShPX!NbnBF>g2o$v)q|S znTHNta~bMRJXUZ?SSP~*`*RoziPOH-^3A=!Te6H_-dgEf#PBULE=3}HTt=3r)+I~qobo=+z<}OkrrYwJ{dS%{P6Lk*s-TG z`uFa*PP28@rDv_1R`qIJMY@Qf3{rTsg zE$#x$O`A3?+iGn@jgOE2+ENlcZETb|?o?bVK+7?ZNF7dRMhZf)wz09{ zDs?TFKgNyybnxTb;c(D8cR1=2r|>FAN5`FgFE^W2Cj^LFH{MQYKv-^Z{%WY6tfwm_ zB}JjwRvtM}uvq`|tKc0D1rGh6mFF$lxa&mmP4%nX+ds9puhG}nPd6#slQ%IEk0|3a zsu<`<&I>G6>Zpu8et5y2uu7|E4^O`T>->D4xJ!%H3V7!r4+d^Je!k?*x*Vy&&f4(q zhU}oyR!AH>xl6}(5YZMXGMM>;8gjEk9 z=e@>>jSJdpt$n4VF5P^YnAweC`z};(N$0OI9^)e;-Pt`BdZjFt+|Vaao@_b!ZWo>O z?(XidU%#rPn^kQwEoUIT*LD{3dQJ>SJM-ChzS&eYlI_@|k*t@)`Nto3u=zy@TGUpJ zA{KvD$_aUeTN`&Hsuj|Gq})eh08YO4_umK#3K|<5tGv5+Mqg#hAfSk^%RqZ1i(mc) zKH#fYufF#7D&u1fy!Ya3$8pf8Z_IVqY4R9NY_Ezpp5SZ#_%R&+)qzmn+OyD{dtg#f z+0E@y)WLu-#M6uVdL6A)BUI!9WP-dc3i)5|6-FZwzqq|Um0?<)pyiOf`1F}2Y;2Ny zR<2xmp0Ssuob6!gH5mNH8flulx)mVr<@jJ%ea%YuS0Ov=pNH(s&eS*eq1tb}EAdmL8tJ{r20^+}vE@k?$rPDbY+f^Q#(1 z%{eWoQ{EE|Oma|J50IzNxg9Bau98wIXVa{_;g;pL;|QqQft}gF5zlZc6Q23mAyf1; z=G1=}`tnp}_-peOlpL*2W1txEQn8*5lbxxhmk$PrYU$QQhkBSDw_g4X2ktT!tZppC z6Y0sYb>~%zdMAgQ*`P)1v9x2DF@W+qdF)9hv zD6OBDzY&M}%HL;ZEC*W77+w~y5_ySgP+GS@Dnck|R6Eyg_}3xZJpA=C0N?vdU6C-Z zyncPiyQ@cfL&!#hzutfPWO$zsN9!Y&dkgv|B#vX8!YWVOC4gN`2FA9xweHi@yrule z$tHeW66xp}-Y?NAFR|nIi$18|%K_cf>k7wvig5%qI|X~)Jtw8In}CKd1IN@2)TTRh zX?t%<)XS;+GB}t~TU}aOy6xn<(^Xw^BOZ9Z%!1PTr!punSKhqQjCZdMcOB|(88Y6t z?`%mHpT{?YxSF?&rTCA#1%nT)n-m8nQU*6fL^iotOQt}Kg1CS-rx1Rxj#uqV$FHGY+_UN#h!c#7`g;Y_WpnFkME)^qFK-_?|-&r*vzxOAYW zNgtoBysQ2J?S^|_$yR`Xn~k26?#R2Qk55k%AGN4S3dY`tZEbQ>P1H_TNjj2g_w^GK zCrIw^zyH?Gc1R@Xg*Q+DKF^FhoSd|^(P=Ng+>JwMIgCm}5^?I3w^}!<)g;15wXWPkL z@rRQ$-ZXBUYybK6ZZvrQb{pg;(aQm$_;K>_p`MJ$AAfea%x~>s!VLTj#%C0#^rfme z=;`(y8tInx8oF6_$^eAa$suA)0cEmmTd!t0bj2+cRMh0&M7+Y%{MuCG633SwXkV~$ zAt}bCNyr=smBJ$%)~{DY_A;wYiHy@ojC*o!&RR4oS$1u#tGqkoupn*UdOrs4a7;pt zjhwYmKvL}p@+YCpW52(ERj>|)yWbjh{7#z3xC=#0 zSy>tNvudb3)bmk0yxjWHUBJp>91lUIkjm%RS1;n`PQZS)qmNCI7%;?|4xr}AXF0B~ z&vvw4B&@OzAV%1244Y}V= z?)W@e5-P0^FmR2reaDV?rBEqa*he`z_8*?kQV86_1f@{bvp|`f}O?i1QZmx^Jb!!QAyhv{9I89lOJxKtHF~GZ7Zo?@Q$~d4!6&0$WHXfX3 z=SxCPrFT{c5*I5pruNqk=)u0NeE){Lu@r*TXb;ChEok3AcJ4`je$}1bzw$`f$pT9# zmPsK5BIW%x`dUiV(aiPDynz_50P-3D7eI6Z7MK+p8cIcMbBJ}`xCV7>s4-O!to5<* za?~FK0fMSHR;YYkYP54RAzPMO=BX1oBkl17paH`;7cGkP6;v`qX4L3;17;+f@F`$$ zpX(?}y-MRFXWyI$rMI_-OW3!Y;P2OH-n&!fG%+!1y-xGqu8@$BWuk@`%qnBagk=ed|mBBoA3zN)xmWx!N?u!ga#VC~^iIv5wMyml%tJSleyw?BfEE}7l1v52ut$Li{ z7}_{-zKrhq5A~1bUFGIL_WnBt)hi{T?(}K4kH|S<=y#2|r9Ix735<{9=$k!!_;3m9 za%XMoa>Q)9_7FDsS>EJCkVP%^8=Jn3jxI--$d|1b6)hJ+$CufNd=(g`SkA%0SlaWb z?`qXHLI-$xKtNRQk0{n$9Sx71D`GSf1Gk5dU_~@W(*#U4a>KhNClQ;zCxUcdgs}tH zAzJ1#&w~%K8R>81v}YNm?%2Lv>(S;Fc(2<$Av;|i@~f(Xn2))_%Do8S0x*K!M({?Vkq`#D7H%l1|rnjSdX{dD55kbf9w+_KOT3!X$fA9#|Y}lpYzI8Zy&O6Zs<1Gr?b8a@*@w`dJLDUGXgso0rj@kwz zA!;Sqq7C#eUI;333F??AW9fcxu5_o%J)Zg*mi5&wt%_!=S1}q_>H|s!h0lHZ^l8My zq=kzY`&&fJlCKE@1Uztgi9u2YGL*MH%SsiT!(|lH$e4w^yjGu{U(dg02(VxHVe~04 zmPs_Z164LdYisZ3`i6#v4`L4@M0DfPm~WWNJb<3%@^;%6O-n#Y&7jduHq&RcHO5GQ zvCtSz*^+XiA)hf&_`X-W2l2+b5_Y_;t~;NX z&wLZkZHy{CZ{IxeFj|qJ@+KFB}5%5F{AN+z_D| zbYO+@$&)AJ76)0x9=iMFR~|=m6y14ze0+Pe{Fj^@{(2(bUQi*>P;cn;v|kbYIZLkF zjr=+4{_VY2iwojDJy6zm?3;~k4v=1yP~N0_ie8jeg011;1Z}fQ@DrilUAP z-uGJFACBh@c5oO}#wfR>Jb(UtOJU%)rzdC)(T^6Illq0a+NL40@4;`M1+NdOSHI#Ad| z_B(4Y;5gO#%JOZNeQ|ph@TUpR4yesA5h0=HZ!J7v;iNH|2_{WBY~QTCSz8sF$G5WHn4<}A8PE!Rv7*hqHa~xLmFaIP^7~{Z zlhX(4(gfPld;rbvYi~fNbpjVrV!w|69K&6@t0B9_Odly-v}P2HYXx_z!IEb<7%$!D zF5B|ls9~|bTiLa4{^gPCK#tcp8!PT*m7x@=bqIP~twzw)eg$$1n6$njr2M3%QtD=B zQ(n`foa(5ogO#9?Z5vR?ql6qQCqtihC@3l_9zcPt@*onr58=xlI=W6j_wA!3j@1Vw z?L65!{oUrGRlnml{FSOJ`7K2KSe zo4fk};9j)w`ETEx2+OfhbPR&>`G222of-vJ1QkH+l17I^_uJZ*jbj&8_i?UKd)~6q zlBJeqXWINSK$Vq-Pz zeW<=ArP#U+TeoiY9-yAT@fk9E{SkfafO?LLeS9;p%r)?u46Wc7D`J7u+Cf)Kxb(+h z+YLP=M>|tl5qRmyIvKOQno$BELR(r*dBg|73Q0J9xx)yE4!jqIJl0~cq9$2C8F?_? z-qO++OfcSU4I;q{DEj%s+rhzASl=xVveCVO#IgJIoTJaN6T(y9cGk|ORLdeD0PP1> zmg$mh!*%%{#$5mnB`YTu*4PiUtD~LRsijrk7kgOGxxkDsK=g#m=e|q*e}LIR(a;jv z9B{Dcg+=fVjhI7!kBv>(J^@?N4xGv|tTDfj%*u)YhC{QzzG{TIeMItz+{ChbV&exs zRQj+umoJZp4xp1*kF42T5>#_6z{|^P*iC^|(y{L1Xv4<2=gywhOcZ1$=L`l`i3$n| z+I9l8iLsAc7Xx|Pjr5!HNV&ZkTYA0U<)B$8$3;~g$;(e|dr5d&yg~1vs*bBqe34-} zK(xXAhH)r7YLH~&o3DO5GKZagt90r0)m|l87e`z1xTd*D>CSydx;ZZDEcc#zU^!V9 z;vm2xuvmI>tYXX&y%MOwz^HAk?w%Tb&jzCg^n4Y^vPXBmoD8M5>G7YxvLZn86*o0C znMr3f+Lq_@gpTr0Sl?vj9bl7mA#s#92nf3P;nrn7RbJ@}#|ldd3iju?43q=4+5=my z(|e@J=iYxE`S)h$zKxQSM=a~o%7E-x>Fo37iG^OPj8m6KSD{!jvUkrOAXEOfMoFVs z=19K%$&>f0Tw=SLo@TBBd|&5VbfbN>&2qj^@@^=9C8Y}=3>YvPtaPM5$+NPvvz_(P z?C--`rfWX{|E5vQ%H^^c-xZQ(RX#`hrW$ItQ6_NA_Gur`MxirPyd;4pwGyZNF61g9 zv|7}g0-o&Ai6ao@5s!!uS z+~(Qi52EPJqt}KV59KCzFcrF-pjy4lO9a;|?s-;e4TG_7d}KiN{gZQk>vY72M@QGL zSh1pZs1r-glpA+-Far@})F&z0=Cg)+4-J=+b+ zU50w{pZ1+(lKK?a`W}ryMy7{$X#tNr@A~l`v1=RjrAQ_)YKUmHyEv=$=9}y$TcDvn zf$2J<;~ufW-h$)3Rob0^lj=Z@q$V{#zrOd}oW&Zb@h?N!L0N3BQRv$IgrlYqg!{U- zMzN4p`!B_S$qL+V8-*mw7j$v7eDZ3%udixckFE^QVHvk1aNt3q(Kny>)|n>@t41BX zbma;#aXPBlYWJZAM-t=EP+DXtjN~Q)^ThQ)=_!=U@;|$fgTugTIY=9ikXwc~xJ)yR zzC!g1noNEpp~&7hDQKD(+wcy&e2Ch@MZEpr-fl2ec>$1KUdi=1p9J~SYM;=2;ycwB zI9B(A$b&L0ubpCek#xv5J)`j;>)e1&Z;Nu}Sqpk3mAs$wedE{4nX@96l<8yjqmeh~ zg*cUoB(g$2*RE|V_$$q*Md$0C(U%+Nf=(?l%K*6Wf|y(??ST=kkaXO>bPgohGH+UiY08?n>S)6Y3>DxuZwUr%y~KS4qXyU7$fq zo;jWL4Q$62$L=FRtJMYMy57`|)*2S0^d(bBgS&+~SB_YUf__3BFgO~235QN1y0pe5 zpIBYQr7T(k3AYoM>M?{H@1@~x~my&SOl1BWSwX3d(VooaLiIU^b}%X9R_ z1MO86;N_tyc5zzr4CK1su3>L@`}R|FvtL-lIB&}~{;sX-Y95F<-}-2q7h!L4g>%m1 z-rFk4&~8|#PMuPT*GPm4t_9{r5fz~Z|4y@NkN{1;CMQ2tIQ{z44<9}-8kVoOhV17s z6jaB&#E&L(EBkK`j7xdN%qoRS+*d%)*@o=;N>b7ofP!S1-=ft3e`il|5{ z#0K4(b~=BJAbtRh0a%H6{X7q|w-4__zX>Dt3u#fnm&y7BeF{2MRA3AnLr#+2KcXjZ zQV*XdCEM~trQP5f-K@0Rw{LSo<^(a=jwCuJ)XGMxO)l7G=8)8{zt886K{Aco_2)03 zCxVU~If73gaOdRvC#)Oo#%EW*D%gDDMFc%zuw!Exya(xE%(~v(-wQgf0*jD_4uraR zAl^zy5!|e?YmEy$P5dn;--N`Feh)&)V{+V)h=ckJ%L?RX&V>uZuPonc1}Hd!g9#le z%&`1@ z92(#GSqkNOer89La%bjRe$!6o16Ody1Ti999iIXluWoCBgHZ=e=4Vpi z09cA4^iiCZKp$750|*m6@#2=(-Gj+Ph&8rrCe&#d+=2fiMnEA@Gtt~UE&Z}k8@oS3 zCT07}jPUNqr6w#r>E|r|;?+AB4}JM^2xTB098?2j8`KY`xbEXK&jU6`zY3L}#39ah z>J5YblW?K?`sy z-50ZdHAE2-F9z)w54;Kv&pH&!S!O3rhPbrnaI=5nAatKQaAQarMzEJ6r=V}C1RpYh zhEpzUaXt9-m-gkSj`&ss_PGZ3%%}YnVUC4#b#+;8p7kDB2&v8Gh`~+{mWO^5>QG5m zte6(KdDvIJ(kW7M}8~bb=dxgq*E7_MDt?!>B z&w(-YoSPR}He^-Zjre!WpVZH>=2vhnhfolJIBV!p9J&NbGSjyK?4qfJR1!gdEod2EJI@%y}G zvRPa0^gnR^eiKJmy6%y4YJ{uco+0Cw=>3Sqm!*Z5m-gYTtwuAvOx)tuFg4kAVitN~ z2o3_hEqQCzVvdIR+5xt3wOSnPha!GU=^l{I?dsQjvDA^a6nzBBw}%kJl-m@%zx?hr zU%g%TDhqubwyKm{G9_e=Avde%x;Zn;(X_IHB^#}gTW8Ojb)7-VeG%&n_UrTvzi&Ht z2nakgVhyA84L#iBa%_vmo7ZD*&n_?3=m6zzf~M4GW!Pj0q{qNy!Iwc>H&pTU@oA7r z08TaL(5w5;uJV{W)`L|X2NPIylYeJ4Ds@U#28f10DfcW!QB{>XQQ3PdeHxXd#|_g( z)~|oXot>JR+74nU>`00`FE5l&+sTL-kR8-xRra$$|A$)aTMXFE82^&b-qPB(vnvX} zutqIr1#nUJ*|TS7bQc?n@{eAYcS#I`97xhHLm^l~eXG@5r#tp(CS^|~=M};WWX|he zsK@kCE&q6wS^n``Owu`!=Jk?Kno%w-1(v;WDXq<~?#*n1b%4>}>i#D1$+XiSZS1+& zPBXA>D9eux9dGJvhCFSXk%A@*(oTV4GwOsmyKW5_V9WZ95;Oh#_wNgPEaBaF^z^WE zsFb^iV(&?)?C`)exE&Ka`R=jW$BalM3-Vhv)E|Uu{emW11zhF-vKT6ECEku%j-tT1WJwI#yeJdTZ(j`NEfw9z`ULXX zs*m9eZ<5ITl}lhN!*(7n&*>ty$=45E%-gXsfV7M^jwerkIo{NQlHLw>PrM^R^Zpa` zTX&}~Y?^~=OIu>q_$VN39QC3Stv9D7#fcZiiG+kU0`|UUEBcnI%2-uCV@;*AfO|1; zda=6QqbHFXM5>o>KNX?dTC21gZbfXjT6s>{C@5yXc21wku|6NrbECUDY)_pkNBEZP zy_GlGolR!|(Rw_!0R@BLE2lK?@4x>Z*m?wpj0CX3hWFjkH@%dW18Nw*YZd0i2_4FO z!0WA#)X4MOoK3N+g3a*6X*qu|ehRJPMDO}Bbg=_n^@pJaoH|)q4LS}5bicT7^r}^> zUOFbaIyo`wlkjfw_%dJg6FC5J;I(}|1BJ#F&BUH4I&^^|2{S`F;csx&&-?mfEm!gh{;bJ5 zIkdcLu#_=}4(ua_P9!5}r)$RW=5VLp4=uu*`OY~uI?I&je1bc4mv`to2(jtQ%)`94 zOAX1p1=pZq17VwpJoTEOZj{%>jX>u)E&EHE3pd!W=u|_hVIM zNL(W%2n`;YfDD2p9b((41_ZY5@QnZ_YKIT8GJ@&LiRy_)z70#F3K8nlr%w^E48h>_ z%x#7fUIhTqe~Y!P3RoHVQ?HYR2?JOT_^CiF11WZ}Qwo##X7lkpnJyuhQeMAw;lgXk zlq$C`)OR3&H7qO=@Td`Z)EJn;%*rD!28vt8k-r0~1u3AA%R%Zzqj}GljBXewnU(tC z(V?CiurlgUW&Q_2w&lG_-q_b-K`X0=K;Y(JeuxR8-=%!i+uK{0a>Dz753i--p>Ie? zzWlthL*0_#v8!-Q`jO*15EktqZoa~+16nZgNT!PP*q1Xf0h1H!&Ye4jDGCUt3aA8e zgd~;253Es*x(p655s?}!?Wre!Q=|+{52Hc8y$wh*We|N3sn%(3!>2%-9Y)ct%I?V} zC!;V_T!M*EabI2(Cba>&P_JgjJRH@f`bUr6$sO(9vGn|1l3ii3B}5F*6DJo}6aw7@ zHH9=O90V5sLFsuO-`>yIiIWMNqcVO~-0E!%nT`Gt9*;kp3(b5Ve1w}PkD7`Gly7er zEGB~%ppqG!bai>2c|a7~2Rdu1?I4RAnuC8NUUc+;z}fv^%z_qXm7(}uX9=hDVc z`ZAh02*(x3C4&FhK+)kq=qJ9)6fTvP_rvIagADijScpj00Z@YtK>c;)~qc>l}Ir7+iM#A&C;&0f4- zgq@uo8Rj$a6&bO%w?%?GDT+#biT0Ju(jaTrLS>{4#eb6N71>G#)+MpL2mEBbLgVf% zQhzfL#-I+cw>KHo2tYna(X>WtaRElEieN6RO46-&n}-aoBZ005FKpn^08y^?ww>SS zGwf2Y@!RRg3TD&q!mX@|lidGg&IW3^$ij+1<>cgyAX1C`!Dyv!t(x@?iaLpxt%s*EW*{oIGVAfC=8R@sSp2mGV2g&kzv`>Vn$6WzrsxV6^zu(GgFYeP^qI zk5U-6KR`yovMF~wyw-%Rdg*@~s=8)$N0XPyqthAE%*Q{gQ%}C%Lz-CkfRnTHQnVkb zwXHZybJ^K_)J6e9TrOn*86JSDs%i-WMCK!wimOH%qPlGk>SJV8A69`RdoWT}boj5t#KdsA|LdPVXLJ!c|%V00RO`VH|v4_+d%i3g1?#j>)p_a24-PcK1)r`CyMp`QU>sWd!8bS!6G z{^hsXZ{8;D%NR)=8V`aq@;EeOuF-#uR5@g%8t5?o$w)PC)~x%PJ53urkN_mLSROOd z?RX74Ml<=*J*E!xwB2m2Af{D)9K9Nx~A>!I%@kV^Eg~Ly*N|#?g}4}^wW;Zld^QH43U{#Em#zT zvruK5fx0tgcB;*_Yb!rsViHp@7@HI2n=<6KF1=$V)29hPaQQ=lx6YOE0OOEN=$N6GGnN(Z}WWaZAZlv zU7M8fv2_5O%w$E;o13RAerUBc^Ffwe+FBa=;EfCn1Z8mTuErU27!Y&mH}M`nlIvD$ z^7_v1xUR&UmNS3-bs1@?)*@)N%zW}E#diC6jZYq5a^9w^PAnYIMdNsHfBzO7JLA5b z&I=#$H}rygS=agHxjtgc#2?H%{gJHZY)rC6qWYD?d`0dg)G#F=yK*EH>gN%XYj7IL z3k!o}1f^McaK%SE>(b*%1P6f!zEhq0Y9vho$f95r$zz}be;)oKp3{>03&=<@2>(FQ z6PMsjO@Ou}go1#Rr;fLN>B!us0;rSj*rP`t3jA#)9dI;uYXIO7alSaJWU@dE#UqEc z7%bt#^ok1n7|_=@5KV!2Q-$%G&NEF0peVbv1Q@HKnEI2O`d!`q_f zT}nHKwF;QqJYWn=zO612%va-Q6-|^=DaX^|wfMy7a^<;v@svjov3G%&{o-wSYX-MJ} zIcPzvNpJ+3N93u0*6EZb9 zhrqn3;NG?1hsf4N*q*28rM`Jgl(X@T1BpV8n<*0FSe1$r2OWeShOTplqm; zNAKna{Uqq39WX$U_ZN+PWtKe^f?~<4LfQ| z`^ZVze}ldTts&B*dV;1h6tF|6Y6(|Yyo2Ys_hVtabyMz0TXZPUg#gB=4-h*mv&Ld9A*xbmYiwXSv9Tisx)jZU@gBENSDJ4hpD3WXl#cY{Lx(jevl)b{hE| z-)Cc+FK*j%u+Q%7a&e18Q1C0Zu8THGW;r|zD?8AdE%hDLPVuWT}H zcxQrEpa|TtCm34cO@$?|1A}ib0yYa4^2y35wVXZp<2g=Go`u}k1{M~-6O3F*IIZC6 z)6eLZX@8<{iiofJhHfC_RoO32+G7|LBeIiv4rGDt+k1WARIEZu40Zc@ksMPf=TVN^ z9;>e1>)uXwZ+*AkbLd0kkxYeTu5uGThU?<4aj3hvf;)u*;HV zlu3hVF!lANDb>133g@fT%o(jzx?P(Jvi0Pq1HZj9)krf@?)x(4lm&RQI%hB2ow+wI zoP_%nM_p-$V|V4|Qps>k@(E*)aRzJf$zs9$)a{({66r_m=wmim?U5Z7hWlD;uu||@ zO3-6rMS*<7d3(8j3`9Qmwf^w*A`#{U+d*-+1Gqr~@`HAZ3K&TZF6hh`=r~-wFm!sl z@TuXF^Vb`9Z+ik}Hv&yaEYdQG2N0ndOrQlAPtK5W5Xn`z%45BLZUS=fTBIJpaCH>N zk`w3wl|lY40)GKNTV3#*AL~K?dR(ze)djO~2zSc$j5eg(t%56M%Yls5VTC0Gh=wF~ z5S;jW>}d=*z#`Tkj94rNHtPD^{MTSEd9r^V*)(KZx{AX9?G8;Z0Bk2rSw(QXa~RYi z#11wCu+>O_nFz>O@nc=+S)67YUf6dQIRw*nAk*$k+BlP&WU8SSWg^xHqa3?=dA?*V055x@vqB1%Vixwvkc>BEjkq_3Z_ssI@{$vg+CH)64# z({5x8a@wW4449DwV)cE9>ig?GUvV3wure2DA`5bbjz}p+(-<_P=P*!{VW~rHBhL

^>5Z=!8a-;n_)nRl@d&yFY>0)mUXJ9p|Ooj8Ho30SnlSE$tW7@@-Sj|6Zz-Jh{m1E_yAJrTT)=@?TnQDC3I`3{ zDCeTTV5$J;sC2ERp}`2GKWz~rwHZZCO_OjS|N9Epx)H=^F9!ae!AEq=A?#0cXQv2~ z7$*A;<&6)*V}F>k2R=)Jy8 zx%?nlg_aG z&*^-pdEOxA=-+>l2bsA#3DncFuZ`k$zDlNO|bLU1zH1CPeJtNTq%7+TlLh1?8gKpW_utLP7OWO?O?Uj(K>-rgU@1ujj+rtR_kJkA z+L88_eyUk+9*;ynhcyvG-@i`F#NzltS-#ci+!}1ioXH}GxjU?stcXyL5hkeLmK0*( zHg4Rgwcszio}m{j_nwad_-0Mu$3AYvh$KkB7}yuu8?qnZ6EKQuY7QgoD&rG_ZeN9* zBAs#Ti(T=?i!)g_&>Fc9)*2I00RfE@L(^EXwXhxuw}VzWa^AoMkX-l$UWbr6J_}8a z(YPj+9JHkIh+iKA`F|cCZLJ&%p+7W$s;^Ja{soE+twwFP2hhDR^U~F;9uL{N$YPDE z$z;Jk5ghgi%r0d&hAEDx@Qc=$0pZw}wQvfba&Q1`l1W2ls5U5Ux<2AghMG%_%;*+| zA73YLkX%HUV9j+Pz^dzP(0stJ+AQn z`8tG$u!P$(nDo;ip)IR(V7`J@5e16S91`9d?X(z7RZTwbz&z2OlsuZB0S#-44iE8Xr{0d1H35MWL&g8@>cF|vQ=gx{WMcAzDyXw@@ z%tvEZhuUAvF%pOOB6!CGH1Bt)wxJ0qs>3;#Wnm26Aid{l3brG_KUmUP1*9uA%6LIz z#XZ*|pkv13B!cnVADA&sb|1Cd|9p+*p;d}QZo1^{t%^>DI+S>OG`CS`v&khw#|RlW z95qrGiAkx?r4N2nupY7aA4YrpVNya%Nc}HfMFEjk1LAK0#&CXfM;vY@3qMemtE;Qa z&mU^_q_kA2Jcnb^U{6zMi_3*5h=v9s-Mc4M@ANJJFzx27;%{s;9j@4rbjBP@sMWODJ9K z{TL4?38M{DEJZh6U0vlznmFg(WZdMhR-l;^yo(%SoRTi(Or+TtXateP%YedpGG{qm z`uRSMcd=Y>=_6;F{)kK>;t{@5hxBp6{TbNS^1ITX1Ql~IQtofzj9!<)B)ZK4JXMeq$q}t2RcD9tGAjci8#csal?RrSl2y+d??ym%&SGH(LHY<%c6OiOj~10 z{zwdKbTSe$OOQy#z!o6o3wCs%Q6h)RqJ;}rV`kboD9MXozkg)l17Sr*dVOG-6IL%vAL27Y2KX+*u3Kh%? zvPhZ9inq6|#L7{&fQnJ3W1SNT-ACK92`+EH^}5;s?x6m&!Kd;s5<0XCEoaTOm3u)t zT~F{pu!5zsQ3EMSH_}1ZokHr7#6Cq zc8YME>BGHx)qnn$jqU&K*AD&rtpEFOJo^9N`X}(g_P^nZssCpdTvTn<6C|4fBwOM* ze_UWfcpXTtz5ynt&8QVB?hwv5_?-L+tFwKxJ|)+~#to;<4$62iz?1Fw0Gu5v$d+^? z3py87Fk!ep=bTDcwCwd&B%T+fprF!V35%GNzpualse)WvX?Sbj+3Txi%z!P(B?AIr zHFQ@d-J3YyIAVpB`k_0Y5MVU}&Q7u<@{f(#|9fY;K%PO{W*8jswd$$w=LUKc$Ehq zffbc8Mbo^kll_S%VH@*5@m~axbwd_E6RMI<;OJFv?+UyTBhp>_|Fr6>IQ|B7X4!SW z-R|9dv3L%r(6yG_8Vs<4P{)YZ9+;X(&dm7fZ62$x*vnXjDvIv-G5FKI|hwVDI_4_v?DX_Iq;z>OzQ#L!0UI zKaqUHpTm8~H4PD47X*nqD)k~bFd&c6B?aiS2Cjh?O`Pux;I5yfd$9=YKvD zn<3}YV;G6V(W^kKG<7vpb~c@m*>mQo!mWWYi-rmm8JmYO93pDqD0gX#^ygLnFBVfo zAb;cK{`ntF(etigN?`0-jwa#{~cHLNs1Af4NjCaC)}H%d~pFkOo zr2S-OWxW6mfo7xzrZmg@S8#R8@N*bF3LMaQ$)NCocCRwfsz5b^o?wD6t48)r17WZwdHMH8ke&DabcjB}l$;QVfAnUD(KeJ61_013O#*ND%=?vO==oTwN%7DmN@!BCwh z!SJb1q=w>Cen$2Lb5KE@9NrzrM~{VZ$Px#%reul2=yN<+ce-JSBq{P593K1rM32M2 z6A`E9QlG_QNJ~rO<^!Ds)qkj#&URZY9xDP}a68118pPv@{EaW0NkSw-6`3Ixa+k(v zfVic0N*>-Z7KV|1paZi>wMf^+1S9B^m!2E+bB};cOebm*q^%azXa~z5+3$<_7#ZU27ZjssFghHH`zl(HRoOL=a_qrBp!cKe10cAnqNS?Ku#5)-6lv0g z>B@CS9v%Wu{2E2y9^&%A~ccD-j#2Ph(6F-ofn=<4QBPm>Vev zXG(%^jrfoK@|pc3n9@+|Wm#zO5~`WEXTL|PYNvj8kX?n>40Hd5%m2?<1$#kb6 zy6Pw#|8|hwz6_EiAW-`2*8d>5C1-yp6P~~RmvZUAO!gGz!itjOk3~@sj&eJRdja7j zTgfCNYs38(Ty%muaGe2T;kH^k_SiPq(n35Z;I&3!=(PH=E!&p;`1D&KCNn34!v1DG zO?jF%I4I1-tWlb#pp7$jSJz#DhIzIN=xnMtbA_C@CH+TA;=cGj*)GMk6+aOr)!V0t zl2~DwiH9@Oh4~i9U@%zr-j3?gQG8(6I~k_`1je=>rA{;rqFFJfVc^NGL-{X|!2bC$ z(1VtAOD&AH+2%fyRv>Y)XdZDSs*jMCSaut(uS7JmSBfWgO#i-}|9Kv0Jv3p)EXP4p zy%D<2W)I}(Ym9X3rp6>G45C+s?VrP>`vp~REI)r2H=oL~U{SDkOn}jcI(-IFc?cK` z;2@1a{Nrbuy}5U{q3cK7$k6qs5M$17MczjfPLvD=f5BO?B2eh)&QcK8q+-xGPC_tl zo5QR`I#M5}y9e}gibW7Rbgy4QVWE7M|G|xz(5EYG;7nMy@#wA;2jVb|aN2NXnShQ5 z#@6xhdWg~+Fd#w@`)JqvXC=K67k0dA{Uw(aDdx%r1ce~{sWhMKSm+iCvX&NwU*-sLnb9C==iGr zuxK8ndqXby`1nKF03Q*9kKP8|0K7H1>cGad8d?(NjMA|N#gHTcy2TTCljHz6fV+bN^HtuGPynr>$Y{zY1!#!A$HR9qL zbX6;jcn-sVu8w;|wsdM4PQM*4wS5~GKJvY&*Pmkg=TAF*=C(&ZByQQN{E*N(=~sfZ z9%Eip7F&7$YCH}}y>JpUf1AyD2qe0g;qyP7og&7k--Cr|_RN{DL06@dRSI|K;G?Sn z-HSPWdB?e0M3x3_?*d7hA%jX_!U9w|wB9&?UXV|_U=dVD=1@&5hK33;AsoUf`LGEY zf&K&ujf*aIX;;BE(Y0^|nmwR^!G%U?df>863=q|i4WYVKJmRB=M*56c!m%>=q}SB( zd&m5Hd%N$M;}nPHOE{)I-xhM7gF|$?=)Z;u{KXf$?}19cO<(_u?j{ad15uS`De3lD zvIt>6q2b4qYYR6u9t5n{sDE{9(_R$u>u_|_=7X9w!}9uUa+!LP5Vs#J>FMYfurO3b zDJ&-w2h4XG(7V-BjTA5w{n=MZinMCjb|N{uAR{DWID0=%zY6KZxG0iL2Q+~Ido}Om z`KcHFa+X><`if99Ng2qtnKtJ5Jp@kw2JlJZ5WNt7BhVj@7LN{tEW-3XFy2AMLX*DK$;I#E)5Z{VRXju`kp2)Ha7}J@wulT%n@ETHk z);oPFr#xQx@?ZYcX#r+5_K?&E`_9x=rC-s=Flqj}Dg(=_jxvssjaXQvBD0ckI=qX2 zw1m-{PE9Rhh{$E4@X9TKn#mjoGdKx!gyg_KS3u$CeW;YbaSPBLJs?a&*MWb?BbVpl z?hZqbaJxQgEE$>zexSy^0Er6(Lh~%(f}?T&QhdhBl{u{w93Fk zw{PE8!>vAmpX~sTG|&Jg6W{<7l)r-P?g0Bst|B0?DEPhN@XSRocEV!QAipn7VAINq zkg`7*ZSX&F`Rgnfp28xhCl^e$S`Kq&&n7SCl;lBIe^KL0Q3uhd5YBj_v*iMAI8gq1D(;_;Y1o1Fkcw$I1ppFC!TLI~Z>|b>9ArPn`OHLW_A31H> zUr#=#-DiHMOQxtLmGR9fhUhYzv`Jv=<7CM2~wKjP5iB;EkM-tgl<7#dE6WT*HKT^ZvplscRIoAn;H$?%5-8B8{<&0hDn!70ls2VF9I@Xgb*2uCpEDVAr?rkG*v>k@qy%N z$E6=|$I2;hu;~xSGz0Cd3tA{&KU|He#Oj=wn5q`L4isLPi5_}Aew#2NgI|7+EIkA{ zzF0K=n?1^d={-hUxQ~?F^|MF4BN=DO^g#!O0r+^_VJIMwZfXb0z6lhEJ7L@Jc}!z_ z{BbdqQy?T$$Q~p$)8YUG=p+Q;1P3+Q!dN53geqDPK4+>jou2%IddSnd|5Jy=O2Zut z*&~ufj_^oKX{u;GNV6r=KURYzo)Mr1dsKxPmxh4Ti9Z^eVP;(f1i1(k()ia}w7=T- zj{b>j@}{f~=w&d269@r{CqoKF(FC#^H5FvBf-y1{8M+8}yBY=ID9Eu$hk^2S%fAQn z(UA47<9ck=xhk1uTc-~DqIk;38G@eMCXA#HMYfBJP$(S<^2k2(34aLP!=q#fV z+G`-$&DYrg{6>wl85AK_1Sv#hZrALATuUwi1``)lAA*q54~JnK{@SItU@@8BfNL;6 ztPm=t3nARcoNoMvSQ0iRmUJ%Q;8@9gfqOx!Brcx7(Mydl1#Yz+CnGdc_A&@X4u|0qBL+IHmxJs5>&2-s65uOm(sV}1cHvk;+STwvo zTa2b2enS4C#C%$LQoK{PTiizxh?rFHz7|OJMg>Myvfshc(?OCtUW4vrrt=3&6ct*$ z33_iB94g_hA$blbQaiX~csJu2j<+t|2u{vR)RkspFTP0_a9N!S|sggYnczA7pq9!fy=2D` z+jQ(K){&n{4XC8kJ=n{I5IMN7dxcgu4h{`-(aoVK^mhm^a##*b(rpHy74Uy|fC)*u zD>t+A$B-XcN#F^N#b@!6EXN|UDVzE`!+e$RW}_j}*(dcW)Y&UKx2owFlr zJ?nY?!*96n`}g~I3{Q~u$l+i5fB$zBYe*Kh1xkRmP>%=S0U5i6x_z++!bS!V3_*|r znqF3Ap9(52AoDu+4sOeDJH%!(va;hL9HelKWh8JW?c3@i%xl^+X`}{JdgI;3u{OqG zUqC)DgQRY5+()=`a_bEhNNLf3ZwWGDF{+oH8YHbpUb$)N1Vh7^hpGg5QAhp=$=ZA1 zXRMQ>vJd;i{9yO!Ij7PVydT_I&~-}lA9J?vUEV)?LGYkK+#_bUVml8lsvmDLep^BH zqET~xfGbo=MvxH(s+ws&@EO?VaTidO2kBZ8Xm7bbhp)K(POb;i`$;Iq0ZOTZ(o9pJ zqf{CEI7DO0oo+qFkP8hLAp?de_IY^ir(*A_a(%R_j9iukBo#2y5{PoFhw z)~85kO~A6(S6)5~!;DhHqFFT^pVpe~{bxYlz8kuB#P*WE|KB}_W|Ji6+UC7@%vvvU z*6vmj{X@65SwejMR7rf?c3=4KKM^B2KtYOVpyZes^dAJcM_C~<_Q%k^%brg`W`QD| zIjn?$Hv(jP7N(p%oqHb|^yl*m+z$-9C6ojDI0vxiBQzwU6c9nvXn1%6E3k@DkQ0So zNTJ(@A_|tvm`jd8II3h&c#mqT^x@Ci0UgPeTb@g(w*jbUCxOZD1r7zN%n>2UA%>8s z3#iT(q+tTjV8&ewX&szHTf8hGAu=Iv@0)(s9Cf zju0VzofjL0V3IXK+~A1NK2$Qo?Ree4uXOuyFAx>pLJYx;l}>M~UcY{A)QCvQ3r7hU zp|c#%TY-zlgZaaF0&Ey9IjN$yc8f)2$7S$y_0WGTl1G?$RLBDgV6>lMb6>9NquFGL zf;1ygIiEbo;*gEE?B{m@!QGZWcN#93>ZbF@@e{+q42X+Ae_nq{18Q$^2=cdc&+-v= z2}R6A*`OJSf+7%aFqMlCWXYhTHRQ|hU}k%J{%MvG3m+Ix0sLG*hGG_o==mw>z~*}k zaSjG`sr8?7izZxCf}bCanSoO51Hy_j)}X06Dq`D{?=V;f>D(uk569^ z=OEp1y2;0M*-;^~t%l!M{!I}s7&<%y^Ymvr1kM+2?SD6F)D15xD;tFtYo`n0w!|=+ zXS;Tn`P+8_&i_a3r;R1@F!V`7o2->iXrCL>dNAN`9});!M*XKW=rdES2$t@#77mYY z=2>-n^njoH64mQ?rM9AW;(NpqY@s0jnzv-bdpvIG<^NPvPS6!~9!TFIK_B}fe$y?B zIOO12z^UNeK&F2i{!*di8}PHmS+c+Wdpzl)uQh1lxd{yg!{}PRgsf557q39`SwRF*vDi;pF2CzmLJQ=HO5IuhDcC{T2=k?uN z$AKoCy}j#~Nx(9X2vO@bC2)8r_X81k#HtdnX_mKr)AqfU1s!T5_cpcBmN$cH(r#Sh zB~&aF1xklnZ`SPGJ54id!oN`=xqs~dtlFODpUATRRi*haHI-!g{)gbdv!Ia-q{0&lbdAoUG>Rbl~Hv0PPe2l@cqdTWoWtU0^?B2U%& z!)wLQQpA){K#6u7|E`$vag4PAiy8HHfH1lB<@diR_m2E-<#AyEU@H}9dqlag^BYJV z5U@qH0lRkX+_^JXQTs199n=G%&VO8tczEihzrI<4IReRw_+7~ z)^yVJ7pd-Y-`QF2q{nh;dF|dIL-qYe>zkX**_1j=MJ9B<-olJi6IAABWllUa8>(eRvT&^bt-Lae%Pg~^1Iaqvd*_^lcUP7KWp6aswUFfJU6y~&a(0p zvc)srHQnV@aM`8G}wZt=)XR%!yP#`OS88U+ztQBnIl42kd9JvC7|qA5aZ8p#9XE3zd&kVjaD$lsPnrI2R0Vb4Va$juWFbJEprpn^dKEHAF?W0hSX{l-#HkY>PZzBqkc`#Cxn) z2k790MBw<;&+)^-``Pu_GqcB@o4@9xs{EJ}-~na3f@llq8J@1c{>dJ+cPOYV*BSVz zKy{JNX^#g;z8|Bc1;J7VMCCq!@OSt$;N&}@s5hDuDFx!tw21?zZn49i`CycNB|}q> zT;}1N>I};kC03=SSIvMt=Q4`Q*BqW!I~NdtOqS~NkSkXv0n5|DF>L!?P((HW1?niF zK8m{>lfQK9QrFhTSG*e(eHowz=gJK={ww%}67<&3u#HOcMan)ZZE;U2)RLraaIN-_ z{k%18#IfRQ<&n={GqW=sVC*S36V(#-K-He@3n+bV zu;BL?{mf+op5J@ssUrZzi>);q&0VX!g3;4zWVvG#@a}4y9##5#*CF>2-8x_?l~lO3 zI)BF4{Mb^m3)8NSE@bb1u$+bZl*<|8u7|6(xK*g((y94N&&taQ)6fq#*wZqbjbN}dNKr2YVS_&vVSn6s}(r1hDX6I-dJFzz_kls z%iTo$97x?DAf)$5nHkq&H%3&)FEW^>g3P<{H5G`Jjtkd%b_1W638>B1nMwM@Rgr5D2@(~V7+ z#%9N2v53&tz+~VnRI9|GRnvx@n|ZG_CgDHdY(+Hb4ac9I0kqcW=OxwS$u_i)_w__Y`B6Gw|B^Pc`#F%-A4sPrx(I?1qU}3k7~f9 ziG|p}tH6NcV8*ARg$|#wN*Zp-wj7z&XWFE(i?7@VpHq#FaHaTv_VB|RVnCTCvaR*^ zwpUz@%NHFOIC&ffO2Nbqw|V|2V+D2m#4|ghVWVV`jMNW^Pp`*C95N@~D@JXX-YLVm zrws3-rpuvV#oiM9W^7%6X5-Tw`x{;Q7Y}?@Kbtx>j>IU<2GKHD5WDN2;yAgeDl++; zfXgp;2=U<87#-)}iR^LZa3xo4Z7Uclf&w6myiuK{_Uc>SRVAnz3+*$;%1AjLWM#LP zzWQiU6{?$~>Nep7C-b6vkAA|n&i85?=^n~J=7i>-#=k~)*@xcV-o;q%S)IBLI)d>T zb@j!TB`|a#`29RRu9=YH6weP-kKO5oOQe?0kSK%BPr!0Fz=kIf?XwE+` z=+I=TmJCA_f@q)te^ZqEusAC~O!XK0*M@&r9VT%nZ_>7-_sSBhkJucUpe-Ju zRP0HMeF|7sb69_;*-g58W)8z#Xo`iv48mX2!ACn8N`eA~`Dj-Jnp>QRrKor>$fXKG z9#{br^Cj}q927v@(H6=mW}60GG0l5SdOfzB1LB-D^_6c3EFXgHu+O3c@(;6-8H}HT z`fnfL1g6)&-es}zMi+1`&cLR;3tbyOjUGKZtuCdt**Fi12~|9`2vl{3%S)V{tc3ko zlU{*Nj{(A`uz)6qmTtNuqxLcSmJ*y{07kZZ*{uUc&(9ZGTxwW|=BC(rh!*W#j=gjCj_)7XSG-Fp6RRvbKgl3k3 zq27*@n3A8>w+{BlpT(W!vk-%1D#0eoLGxcRa`iQ#fm?Ken>nC|j`iNbTjU)^ z;*rmfXmnx%4*a%LO!dYR+;);b$&{VV{9xu*jDjsfM(;XoiM(Bh=3yV zpF!hU3df;Xtp>OJx_oBa?HhI-yxDMiQ{}Ds7=Q4ZdO+_?m&9PYxK>4C48^gnoX6$g z$;)xv{r7;Aig1ji1{4#Zc8&p=s)>&$Ev@8bG$cr}E4=rEFzmN33L@qH>*%Xb#<$(S z{iR3xQ)R4z#bPn+^c8t$UgAz@`-f(piZ=g5zS!2EcO_Zn`&^Cfn)m5BRj|kv8iy4o>3IvjT_}?4PX- zwYoQWYbKbS%Q#3fO6e7blUGZrzcb#RK?r;@0L|4-I+82QY=1VF9nG~@Q?fuBIFto$ zjWn5ApAz?c{q;hWiqn`!CX}AApU*qClW*TRIIcq;!B84n|0QI{;y>0DWWvGw%u>Sp zmaiHu8B&=wh~v#67u+6&;sqyEek6DA%Q3M!D;X1`{vc*X-;r3 zCgSfdR7#@_qc%D;K#WM;Eux$ogua}c;?&+Diw||jh24KNjyNr$dS}2p&mb7nFE{&vxDrmI9&GD*G3X`BvC32B#Vu&S7Q+;md z1u)8VBaQ#^S?vqf2fwn01|#YnY9O4?RLQsSLil5iCOMoccv;hI6tyd- zG?k2Bv~ZzVv*pX-(PPKHkeb?!aw>jU=CKP{2sSs8wny9sj= zj9{fk@l{CymO33Irc#9JiK-&m*7yR+g~p~La9N1!qds;#g21SEQ*eDPDWlP)YY>60 z$1eAQBA~c`O1;j@ZAAxRt2Z9BHPHBS8#_&-=JBFC2k?G@-3<6ipNP0;3JAKw-pTVC zN2P1_b;ALStNOv(#$>B2PJXd@kvP0&lV{gK8@0r%Z+l2}e3tc-=_+S=cm7*-JLs#R1 z1Y#|g^~B-)_>v>o0Hu&cWxF-qa|;3Ym4NDiiDR0H4!< zcVM$sS465Me+*o2*07`p((x=*wp~V=hlN0X{JS4|%|$Z0e*iak!A>;0IDJ|&a5^S< z3Sk;jgX8dWXCYF+hD}RF1d^|FFcJ7AQ#A&w{TsjH0(@z8QTWhT%A`K;YO)Wz zPJy$%k9-h^2zZad@ld3f+LH-8&quTlKB+iqO$j)LFm228b!8V=BNe@8w2jmo1jo8! z2G~#G93FxBoJ?xg-4Ul5*+k-SQw>$XGo^B#uyLL zR602&jT;rxnEnn+aD|gU4^-d$8+0YX9B3BPoH8^kQARk|^xnWt7a~!JHKU9+Tj0IR z2YY_g-i_FC4I45cbeedT0gb{1B-kY$!w)gQjj737Qg%Q&`#xd^Z)~X%BJ}3>W?FP; z>gW3yaQoX*bBd9oX5ltXZ=}%*$BxD*ky(KZ!ajD6JVzp2gwUL)h_Rg??k3GKf9bLB zdQTaO!4Y9K+NXBvDv2kjRwQ~Msa=D0m4H^Uit4KPbft{vV(lq73Ni*AXC`yMxz|)7 zbvYLjB1HdX>n(_vVBG>zR1kgXqKrcf+|MWxgKy?z$VXRgd1`gc z4k0hoqhAkm?L&#NB$MV`YlMlsw1PSxT5{VwN6WOq6+_IN9Vy54yj93iJUf;pY9?^_ z=rT-$FXp{mF+Uk9azAX{*s)UOX_Rb%IuUU{US zfp7L_1hnY|j1pssvlx|Etu1vKY9sgr!`fpshc|ormtq9c;o>eMOeg^alF9IW@RovM zA|*tMJaa0c=+M+Ev@{7tclLzWJ9{cjQQ?yqV*>C~Q1BB3u{&V;-HOkjIh2kgfsVdv z{=Bu}MTP6tXV+k?pewaTN#;|8y^2Fe^ZS!88Zp~LgcpG7x|4)7TSncBj4d!wGO~8tbuD37=ImdP$r~6AG<~xQZUV@NAx2j^!sK`nr)FseU z&d0*6hX0vDtHfMmI{4xKqd&A_F1QF=jlzOV%SaaS?;h(?rYX zb=>xa&=UGh*|ZX)D0vUEhz!_k%XVA18mGZibx2IbF>)F#^dt0z;qa6kA-Y;%hQ+Rd zxSJ~yF;62Chzkp?YCeU5g${0QDr;!2a0|vX!MjEsmn6_*(afM(85+fpsC+B8xQ<&X ziMaYw7r)JD-vW62UsP<2D0;*YMmVfPkK%=JWr;8j9gbt!wl{l(X?Xm^?ASSj9gW1R z(Xz^a67_Y4s<_hHm0Jr*3s$b5IMT@*_gw>DWZ2t279QO8jzTt3d z{b3il{ICcntJN{V&11F}ZkmIgZJ%duEu+l+AKis1omhx0vRpks(kuo!;riV9_!q6G z*wGJ=BnQBJAq;zurV(WDf$yG!^ajRg9x)`);>DV+)tX*H^cZqQTj#|cPSJajz6d_} z$H|397?9a6-HHUF7`|{W9Jan%iFbS8f8OTK%gVb0H)PWLptH3)6b)!_XnKcQ)H*F6b9v(L1z zhO{V6pEkH=1CLZ!< zu)uh`DD|5F{4yQo^Q}HO?zk?c45(0!0l-D#0>>Gp^5x!R;o0N<)3Djp(1yJl9hM>; zK0$qdYW#6?Uh-Rn(8EPh^`S}T3 zK#Md$(!2=x#_0fO{EhZD63#I*aO!0AiocHl^OGV!UdT53;bA~vr_RGPUNNy;nm{Z4QMav-CowS5ILM zm_e^b_I*Fct)*Hi7)QTKuwn}4xlCglXfo1Lf^S9-%f?!z5yl&XGXm&e%$6bsSB9XA zLmJ55XZ{Eas}wC?;U5J?iP&}IF~HufA@szElB0m?Xe5{L7HcVE+CT1^JY!kV7}W)< z4xdH5W;MM6n@xi5Srj#SenC1ARTl&gr&$fip~N)hn=yklC2K~DCi&Rek~wu9o(t`Zv;r(V&OSEJnxbsYwSI%XRHrb*d-I?lvw-BHpTu0!iFj` zG_X!IpXs9s97a#PxXx40ACUwj0Xh-pwJxXxgcoOX*J>EHsb|lg)tUARD3VuG2QF0j4+_yZvyxuzkV)1a&QZP^8>%CD2xi+ zyf6+cS|z#k68d~Op~WxHO7c8JqZ58>BofUYl63&=Q3Y9XmdTQJJ^)@z!2)DmO|FSJ z{gJaKY<0CPJ`w)Q1{7hKekM@LaE4C9wf>fDq*5LDpOGT)Uze&x-6<}nqES}B`mFXH5i;n&3>|}7i~-={b*`!IokU_FZB2@G=-FFhbJ)$|%K)X-bH&B( z-}N?A3Nx+@b?^H5$J+>P)jCEcjzySe0lxa1{%k5WlAXyy7hVIwI?Yx1NfG{(B9YGJ(=@K=aCA89yTZ)lut%%NlAZ zy3TmhnzFSL=Fkcu8+#@aVzF?@QhADIvy%LAyj#OO9c@Wmv?%uSAPr>ZfKIFrIxu(> zuZGV|W90Cx2?Sd((KM6?jG4FF_#g6ndCnCG>R~v&htaN8wl*TN^AeJ3G8xbs)%q=p zq!Ibiafy{Tg=x4OG5N9q6PBl@n@-|j_y;RZlR8C~8f5vevisssdqgu;si}vzu>zcA z#eg0enMsp)7LYyD0dA=4@e*n_zc(;FG}x*5C>J&o+;kN}J<%)DjkJG5>MbtDY%a4( zG?Xu9%7+-4q^ru1>!1}fPpBxOSV(?e8;%zTS?d#5(F&|ug6Np}r1bKkwy5Dkz_c2I z(~`lAE(!!z3UVKTbkc>Fp0G7ZSX+rGsFXaOnAEDR#ulhj&5<_Z){TAJU@->V8PGbI zzML~xjJ+{R#0d3C9x*WNRmH0=xOD0tii^;_S~+Jvig>GV7R+h{i;~reoTHUfP5c$P z!D?8$FOiX=Le|UxgCA%z)dWO{?Rz>je zy7T6-m=!t0ZxJJQVQX5!!N3rIS#0@&-(%#ucaZ&DSSc?&1rb81sznS1z^P5ct_cfA z6}p?a{wxhrL8h_d-H|*1(5>ZWSE@Nu3;1JiM`$6MUQ)S*J zU*RV`+WsIANFQwHc?ia}wp6r^d*+H8PorZ7>6!eSGZ(y72g!tFXgrdEJ~3GCW!P;w zfL1OoXyh^OL}DKyiY`Q^%k^~BOY+GD9gImI@&x0qy*sS|I$y0kJjNc)f=f>B>YWa_ z!N0<#Y${s$&%)?Nr%&WlVsN~X<&Q;`Sq=bfKXfh;ViuY7Kn*S z*2&x3Ho(Iug?QCcd$j>^5{ku2iw{nIdEa~VA|*K5F&6O?01B-m$FaesXy8-ej*9Bz z@bT!Nc3g}HZO~%3=YczWD9RzpM{YApBnE0^YQ($d>!gQ5xJM=IM+iW3)}a9yU%cSY zd{FMNZD(0ONkDZ3b@sNFExDk9Wz}_BW=kIR^&oDuL|q zK6l-7y5_wHy!JJ5Yj)|X-cDT7|c*y)~x6m7N;7PG* zC`DYRjvb8;&FZZt6j2Rx%$ftY{JVBClnWCcL&W6MG4chtiOWw+mXv^(7`Jyd-k5rK zp=6*HG6AeCLK+OPAqsJzFVYW=6s@)`Dn<)dt2DyaQm%V21d4h)%gZOn5fAU+m2 z!sar15!5VZyU@&_X;Trp*NoJs1@hMM3-&OSm(H7l{I?EmdoG2yeaH-q(PfYt^8==A z3bci+>kfFr)k(oY_>jQpfZjKoKVp5P`4@39Z1;RIr3jV@>tWRsK3>is#|9c@0Vkem+iHyGO`1U+;T$|JAm#jinHL^5C5rM{Bw$0^bJJ8T&Y#{MSouvsYo zm(bZDCUY>#UP3_1288F?J#Ku`*M&p_NbRBbZuslR_`+1v1#P4En3^Lpcx%DC5;Z}| zYN(++F;73RB3QXh=rq6rsJ->?V0%xI2qWQP-Jy765nms zN3s@D*PMbsgK?qOubr~{!OB(T7FM(`?lZDh0y>9p`rPVz(fHAx*X6eGXAL*{xE{e; zKZ_^@S!xzM!BI3VT35MDLTkadY(}v?QF8WBBpi--E}brwiO!dJRDqOT2n}L9?t|D` zgRBu27l1G&*QR>MpgN*WQLZ&%>>vQHo=IIUPKp?W%+LvVXG&E8wHeq=^Psw*kcRcE zn%^ECt^&?ySJ~0@(0{LZ<|)I1f?Z)oqa6+pLu?L0-`ub6X)d>$h%|N&{h2xvnHkcB z$=EIc1m@v-RS1^XKr8eVh~`sqzuQIr2H-c;ao&v#?W$#BhJ-}jror_#W@_ui-o zbQ3E^ipZEt1#5obXa#t_^J z42wGA^i1S;dDr5w#DO6w-rLN*_J{nVzETC-TyMQ8|8#%&;Y=o_ef6gdL-bLh;DF{z zGYGd}iCQCerJBw!r_Qpq#K0~6@)@p18qT^(UO}RrU+#kl|LXmb-3AmRB;=D6FegCtdi!^ht2Rqd3TdK zpVx@n-PlFuoA&q1Z)M@uBr$O@@W3VOl5v~F4u9w~Bml=J#>?yMrp8x9gNyK>QgMH` za$78S#SK5j**vj{Z9tTX%5w#@zI$Vnk|ycKB$8kPn<|(C@@A``>Rbb|{{jk-{i<;f z{-KPE`b@81E@o7oHEi!7F+-Op7Za>#UzaFoKAENF4BRmWCYvdHL-y9>n7b;lg&TNh z*wZp40g5eeQxIH8IE~BJh;bib=R?;#nn`GKL)L~!C5ohpRERD=b96=WBNBtZTXE2N zGJ3d(lmN65BOzS5KncFZKNx?UjLlDzZ~w&D)OhnC+|J2v7sH*q8_tJR7~MW#zJPRn z`!zp&+s#x236sBR0E5Y*5HVxy)#B6p$LS(Dad@ORdEHl6Tgqc3$unTbmT&nfOnI`9 z(^OOaO4^v)G`RVrH7Qkvdnnq~6p_eL`8*Z*t&e4EPHU6F0%hLZF@~PFIcnU@A1=U+ zxyvokcDIjvyH@brE+%Vne)w@lbU#$MA*AJ(Vs9G2@7aG&C?iqggaLR2k?_HuN&T1u z5*@SxlQR!c0F-ZDkI|}zDku6|hR+F*keE!3#KsYcxuFj>Qfa1`^76PFl802^Gl(*n z94Co+28V)_&iC@xsC=9+#&%d0VN2Lan1)6rs@qxvx4u+EgRC@YoIg!M`I<-xcAwxE z1c(o33tMckepZlBz_ww)$lXMw6FSsV$gh~BnoKQ~fFJZ}3E<^p5c%RbX$h?ptt+(x z3rr^m(Wl*XBm^nQQ*C`losq02Ro)o5l=))fx)HV}(mx=Jn!K&mvgvJqdWmX^dI7s} zfMOtSD?u?}7I2^In@;7w4Ufr|czK{Pr8xLE14STIhA{0)cz1fg68t>E5T5d4RuWGd zk-x7a8uM&LoftV?*f;b7P!owMju-*CU$oOVG9y&{ z5+8H`c~y#FCkKh0LOU$)+08K9q-jqxmOd4SDv<-wPENr#!v@jfd{pRq32^|Ne0mU- z4G2gG3!&{!_@*pC=K(@Ok$F>IVLejto=*7$zyv_rBN4e}<)zNfMFbkYD-+9fB~ zB~_FJIAo_$heIs{f<8xB_-lCv$PE;sAzwgp#?lY;4g5=&Ude zkG%9sH6%KleOkwa$ry|=lf{ZHJefqpDr{}ZI**m=w2Q6c>l4Gz_oh?hx#((vK7igM2)Wz^at`Wn#s45J(1X z$2|y^On`mt5)V|X>ULj)VgRBBAZK&Y_6?ZPY65(SB%uDN8U*u;boQ3QJ$9QERe?cU z;e*o-pa#oIk~Kx=50V9W{@>;l3vep9rhZga-c>WdWp%@NN2VDqZZPq@_-F zX0S}58uLD4Ecq4=dJKd>SxkoY)1_dBiq9>D7=5u2OKPc=OI2|inMeni#x-277zX58 z3@QPD3>Q@$+~6`CA;)~=8Ya)L!buN+M#rWIqDPB_WViqbOpDVKz*Ch`EqO?z>V6{H z)G!Dn{ma0g7LDkskRSt#dY;rKql`TGJ^Cmo16l+f6=rmjinG`bGiwj9mR0?Azt(Ac&SrCUd+Qh=vs zod(zDAb4I|jWPnqR8?akw0eQ;SA@6BU_#Q@Kx3k2Lz;m%lt`~_R}%b#?TSJ6=u_^* zm`lMR?W z(9)&I)99$?nOb0uZb1GR^21lDmbkXaps|ajvIHW;SD?SEQSVhk%@g@~<5Dtw6kdh+CV{Epi{X`0?js&mP|yAq-CTz< zR23J)Sub7ZdZz$3XIW%h`9jGPxDbKX9@zZr;O#?0AgiTvi(#~c9h!(zukoRq(-5XG zlA`BD+JYz#_@_t=7Vy}Tk+?!ZK!=hU!u7=sH3*}4x$x@ai0*)3Y+iJC@f}!Xuhj<$ zE@0%C&k((W%P1eSK-k*okg-$?tb={XE%|qfT7w?~1xQB_2JWeP{Lbdj=EJJ6GSt#%V zp$OY6B~Jx<-Ff1g?PCcJ7;Ra`##-4-XDK;KJG1AZIL90PuBsK zMqmLt1mu?!!w#(T=ooKpi!fY9lKN7yc&qo+F<8d>;zY>ZC^{qEN2x~12{PEKrfs8> z07waGV1v!7f!F4kPXpkSfcKlarhpt5rqRIKfAmz0yuV`aU|%6Y3Yx!C95tMc6&*yy z2@y}IjUjrlho+mdHmhvnDn7rrVM$QMJ9|5@I<#+bDwCTM$Omk~4G!MpsSd8ev|+Zc|>&=M_VDtoQX_LjkkO z?BGnKnx3DevzuCbXk|F~$~X&~2c)t(>NB31bRw(V@|=TTL69m5$q{XD% zrwnBY^AJ8&6QOt;hUM>`9s!={Jr}rLvO-qWPo$*=sM?v;!YVR)(#0u`Vj-R2xcJvQ zDK&9|uqHpj0K2`Kpae}w$>cFmO!-1IqQUY-6k+tTVn|akmmF zMb$TEa)gL{h7Wc4C#Trfoly0|?z7L@E{dPa!=4vUu&Gq%s8`(y}t<_x-&f zVc?YtM&+av0Yqh^Nw4zy!uXFE{sxTcwU^Goe%2o>LTE4#TU}M2Od(%(pA~eZR-mR^ zB*w%?Y)bAhWp)pesUScIeYFY`_`Hk-mHLoI9u?7u;dRnJOj>8RUI`+w`C>*Ih>-7F z9b4)Ph@=EQBXi%Vp!j9Zh=SJ~EV6Z$0LT2CT^e+u?km|PS-cvVj%`3NM6Z%OVqmNr zQeTJr!}b~^=D77;-&lzQLqVaMnj@vRmqMbsjuUg zO+uK8F-8F(?~KkR@!fcxRJw?J24*f40S!;M+aObfcG2Uj6BuL(S!Lx{_vh@K!|V|9 z0SIITw2BHU6aT*%!0H5v^ihQIOi!fXgD)dh6!IDZOQQf2G9VNiuwG4VD>8&N*gWqM zW?}tYu}?&YwY6Zk$Du`!aqV`XI@MfLTMhv%EJMLzM$hqyM=<(j&X9hMy!LC`(6P6wW@oDwJ{dt#_w9j&Czf-x=QVb0sG@(#ikE+p)uTznwtG-7= zAA;jJf6OkaRt+&7;HQAZuetM_uzst_m>?Yl_dJg}J!Cv;B)Z$mO8GzqO8;5WC_u3t zkXv2vp4)yeS|z4u+lqzwVV^b{h<`Mk)Q0ink6I7g3@(4IbLMZ%OB*Q%k0oN1img~) z!-y)~DhGxnvN|4iu3w{U7lBKJ5e8WD(kd}Ra0nx66;X}MDifLyu>{sm_{@P+rpBTO zn4jz6zJm0&A~rmdkjgz&S~^8kQ|E{_rhd7(xlQlpXe#7SYyBFkc^1K3xud>d`K|L` z+a65jE%F1Aco3>9i9t+$KCP4cj~(~!eVw`;$Uf$ZQO4^K^TJ^dx@#39%{O2Z0r*`& z6vI(FA)-DSQl!=JGCI>_vCrDdo8T+9@=oAq7)U_;c&ssql{ziRgI0sHljtR(kZ8iq z>5*GsavgXCGbTmL?w3%aVoiY-y(GlK)Eqwrs6u?|v_)5e-TQV=!?TUY9?L-ZUQLxMIrH}V1lH?^ z4N~6nu|Swc3`Dp4FcI+tB97p$brO1`$hEET3ZrC-l-EFl;^}cuJLbrgo3PQKYvnFk z3H$&e>;=R*N(5v9-6h)V;d{M==opcW3?c{#Q=GhCr<0ef+FM|sETm+SyWal{(zzV{SK_9KKvO@O{~5Y;9>+hiTE zX?7g2a4tjU_KZOY@*`zRC!oc7Wt6udvO8pqq?69S`^j<+1dj8>*jndydW8o&b^UBk z`sMtzZQFF_$Zy@-N8=*OtaK{tkG7tF*R5oI+4E8prlHf-WAu^cj9>CB=e&b*nYG(Un^VhoQ{=KQG_qRgwBxd?@m$QL|GOt;eenrDt|gNB1B>V2qUcF51l|+N;;$<&)0|l zVop_d!Zz~hZPSizxa1rf-k3oQ&T1rK0yrnc>VZBEK=$;R%u$(!yr{geoDzG=i6NJX zQo<4Lkt!+OCL~uU*qA@axKC!+fc z(mirFBvWytXqd3Wp4y-8M=bQ!--poQj9FvR{;lV)V4_dKnW#bh z;Uy#{j)ig6{xj>4kdo31p@M#G$u^89OI9o}dSM?`z+Dnhbz)IFhF3V7(*3@BgVHu9;#*xv_8s0;~(@!j!AJ{U?-J%%0R zi|$fZ2g|Ob9!vGf+4ifa_6z&3rZ|3T4r|1#p(}+!XkDEDr?*P|pl~N_fQkIrv00RN zKsh}DaD6&Q8`x9SrQHug4T2HQJn7MeK_)|U||>H!U) zX=B5aQ2^<;lS=(DQGp;pTRWHnfg3{TT*PLchi>E-wl{k5U3-{>4lH~1Y}r;CN7lO{ z>R}lgDM<8VF^-f19sVZBcReJ?K>VC7TR<=_##Ot z{`s0M2_Il6iKkb6QMm-}^DC$U(2%;=qe|Fp_Bkvgq&`oQp5vU(Au z{B(k})E81yNCzt^egpKAjaYlh!-*Ge1E)-Cf~aR6W{-+YBeO#+^AqK=A3wrtfSCmf z)!6oyTyZ42!ZcpI#)PLsK^;se4U$&N5|JF%Q#$EO=8YDSfC37xwB^!yx7a=_y$%9= zTj;?oOQWPW9h9zZ(fPSa)IHN)iW2AR6{n|0wglJV1 zwQNjo{GT-;CP=Ze*?t8o`%tK))IP@-fwsC@jFJ)ZH^m9~ma2|# zq4yL+EOLFPm*IdwOs5|f(fWiEUx4LTPZxzizbXkfqME+7vbnu54SqM#J{+|&OtTmh zvIYnSeC5R;=i+KvKB=*}rh$`uJg3ja%;? zmJJ5&P>NdAmz)1GiqX9(AEMY58E}NsbWp;q1!a?gLsQ;*0JaO?`E!+HCBRl3Xp1Ko zyS46V^}Gb+enajcfZ?L53e(x)p9liX6UVkpU4Yu`k7x-Jt$E;?T#%SIpF) zE4-?Aq715{xxoFYk70#;Sk~cRCdx^ILKZcY$CgcR{OBdbi9@xIzf8fR^Nzz@!oAwzU z>89lzpSMqXK{-8NOlpS7^Qig94cGgbpyK^6NwCA8w9=LTAPLq_uv`)?*=D7ifx3hkavTbmzQV=`0tgDMx#BuNl<{l zRKHrT_v1g}^}3u?>3>4Tm91`jBl>JE#Fbd71*aA$5iGHxqK^X^WvY7M>(3z~RJ_*+ za04E_6d1oR^S|9@zs^1uBL7bfmY{!ED!}w@s)0TcS%Y zfLiaU=!8FEvl~`)N}LZEBX+5!x}_qCnFbC%k5Y<*@WJ>l+8{5z^6}4`HS#o74mU*Q zNm%4H>Xug!ET$X|IPbDm-A~~RDwZiC-OW?4GY*DSbKSR|9TPfsKDJ-`r35nb{!Bvu z1!ew!<5vEn_xOJalK$&9{sR%te``|ycZ!`KDpUZk5ly5rjFNK*%H@#cETh=+ZCC1r z;=0{QtmB^bF+Mw5t^;LeQvsYZeGUjI+sYAhlF*>Y%&)w3#)&S&U!m%NSrD7-s#V<| zz6iW=A=s&>aEIjS?QSa49jeahxV$+o|5s@alWHdjr>PPD`sw*(2&Hn6tpFdV%9zz2 z4gh9Z^3Qiw0m3V$!kN)0Ld6aL{_^s2P_R#7jlZ}Mq&JLKc6tKdN@A8?UkUYrj-OS4 z;xz#bu|_6zl=Ww*OMaG~Kz$<5{uzR%GxPmbBkKFpsM+~?I#uv76t2@hqDBYtgb&bO zD{#VQ>slZ?)Dvq!lLzD)$8Bdr$S3KD$@Jf9J=Jqapn?!Amr$L)=_c`T|D=^Ko$=E> zJrW-1@5MUTEF^Lfg|2nf2`+CoUQ#E{dPRAmkmj5HNe~iymoF3LN?;C22?S*C$$1S~ zSykO-O-Yblv>WDDD*foTTCfrS`fO=!O=GofZYEkNc9P$@r8(|EODpt<3z2(y9=c@i zkh0wIvy4{B>t&syM>Jl0F@9r|K;iJ%H?A|xD`bp3mW zZe;O1Vk^dVH z@7}!&Vyyc>Ahj3At}TZdsfVeV=-RXmI_te&KXp9C%s;}X{nYj?PplkE4|QvQ)Q%sk zUH;)O13a`k-qHT^hguDpaysE3#*DL$>?R)tAb5YTv zl19{A-b5yH3?87#h9$g9x3omZoI79pJ z9}Qvl$5T8G3?DVBE6x$`*1h|T?c1*{ScOxh-F46I^cXsRd>_n%HkJX7qTC-IKBl0c z;1`wYa&i~_ro4c`yctWyi07KQF1>$nc(N=_si5RW6=2FFl2|R>FGW2{^LqkJ#Y}_ z#jXCRxEQLTF=+kz_0dT)uoLjyS#$`w~qTV48Yj4K#s2Rc{VomV~9#8B)f3`2SIF?+~Yc zi`K6n7yM|p(f$c;6WtcP@HuvDFGTXT)5B1Ad>8fimoSp{SIw`kAKdx7?^di^_hZ+t zUA1S=ev7QNWO`0iqV@FB(o(r`<9fe*`4U_3kEEn&$jo=)8b$DLg7hG%cb`A^7Tmvo z|G~GW7j8a_baGrWGcs-g%zN|c(~pXZiUrS~pFwFC^2Hx|^z13y95U@B@G5~jh|Uj7 zgHD|AL>0$rkm($Zy?eVtvf{CGXXm7HbRHjob6;_g^dCMxGN?=4kG{`Z+9wU=uS7-* zPNpruTE=5*{v;#QWzL*A$B!Rh4qoI$jcaT3*4uaQ-hA>z27M+sZr-eIWF*aF2PYr$ z_Lc_4`W==LzYG(&bJs4^OZ1AU(f%Ig9J4oWoG@tc;8R8lZ$5nRgsEEv{CaV@$=A$# zxFa?JtE{Tpx$wen48qNuH!G31zdvZOV;ng930pr~Y;$(Lm7Du>WMt$vgc6$?Yu6<` zmy&v-J!peNqMp%`iINGpv%OP!m+=0Edv<$V>>{|3IcFTswe;3K%R(z2KaH@*2gzPH z0Wj->7N||PZAL;jWB&a4+JN_QXppv!j>ydH__poadk9c#aKOU@{Q{QY$fNJwsigWD z=Z-sBty%No;0U2mTW}v|?Ol0RU0r<^7w)VDqY{E-F!%b_4VPkK`U;eAkYGd&kowW* z&YjDR-?S<%Cnx_(S#hyPU|^uUk8T6@leV@{S$PpozWv@EG>U;@Hq@S)Uz?sKn48dF!c8|ajkWh~b1-COZ=Zn3(9OF)P!nUMmI zGiT1^m2E&Ppc5^yOJ9f7A$bphuDi1qWR2sF9zB|if>T7!djv&$ zm6??_0_Q3VJpBFr^Hdd0!haI{EF&`_E-vov=g;FH`|}0po{sXFNo5ana~BFcaBz7d zLiL}2`e`WcJv<^}#PH#>!g2uKmCT(|%&sSZ!oMHj;(_}GVlG8EGH6kNJ;#T$SX z3fE?ZKz)!4J!ha<-=BZ}+Hx=eH9{I18sEDE$C(e!;TX`~eHSfS)M+TRWDnG?esGwG z##ak281doXqmpH0MW4ayM%Dx6PJg)jMfKiu?O2)W{?+}TObhS-+o~k@n$Fvm;I)kO z6dJogmoA6%1)kU_DkzB27e-G&-Q*QtN8F|0K5XLVZ_i?^4@>v!XY(>F#Sn*#D#$oE zeOC=kx|?9%;lsyHpO({W1n?GuUEWy>C%A{l>aARPT5trjvd;qrVEYg*m<#R$THah6 zQPK0L$AMi9`y2Lu2ugR2oHuVC`;T|l;^~uU`S)bZl9)9Iv6($|&x#>dp7$syFmNm; zYvqa+!%$suq82!EI{en8#>n|AR-h};_WiDMatKE3KRh0t)d)_cn*c|Kj;`DGRi_(F@(IlAi6U8? zHl3%6NJUzJ3N%3iSjTfapqV@&i+H@>a%o`n`Mh z@@2BKdS&(*G-arO&7;&U6XoP^rgOf_!xvF_I+V>hbs7o`>nL!@?gC)+p?O1f4u0ow z`b5X<_3H;E>!g^p3Y-NOYBc2Cpc0-!L53^WrDqqk%IBro;_j4u`uikDj%51 zUX7-$>@*a|l=pdY@0Ny{8RQZ^g@qQPxpN;_WYxTWvJ8y@4hSAtjCD-GFny`1k->ddI$ypR5rQ(ZA0^omHRv>9I0s; z`?HkszN+COv~VAFKwd;7{#w~VMYpBDIn4f>zGLo&VtJ}M8pUCGdY!@YVM#P=mHCi0|KA%jI)cgpMgtoem%m7h)0pxM`3>-UtbD6`lW4O zRw5wpiUu%0yI*dd4lw;51W6NdfM1rwSlP#LJWxzYqww)I`ZiU9Z9_V7rtp`Vr^!2} z3Wkmu)2p_&Ry|{HAK60?z5muc_M)G^{~6dw^-l;(1nv-1X)YY2t!=#LFWX`Jx9DY+ z+swv4xVwGEi+c#$pNttj`ohf;Sj<#>%SPO&ZcQDiydtn@w~#_y+Eg?f{R0JX5@5px z82L4o<6i{?3=*V5hdTk3mwi5LWbWGP%F0ye-hYyn?IWlM&;0u1N4p309_K$W#(Mau z42R}+79?pduh`HEHcVyR2idg<3btQmcc!y-vI7V1#{u=-)6&x3y-Lya)*XT4d_AJ0 zCs3fY(cb>zX3LVajEwIDaPiYOS=u0NF}z?D?@-oTKVZu5&5d=dkQ8jgJRLo9E7ui!0w%~ZQB&cJRDq5J;hjP?%Zx5f;~`~ zFaR$9gd+~N-v@^bg?fLK5%2N$A2@U7OoUW>XqDm(UVmxn_XJVHm6Z!(E?w#*D8#&< zY8)kZvG-r1num{Ib?)6_WMeQ;Uwi1q87Lpx15wP4?Cjo%UWUz_HS0}r@%OOx0{57h znCxC-aEM|PY8U3tn|BKy@W|1lrv`+$L6TUI;4lhBIKM^z@+8J**REZ%4L#$pT#*Jt z{V=f=+8DaIU3X?{-@VnO^L6vZ4`D7?(_av!tVDC&dQ9d~U*A8VxE!vm+!uhy4CFJ2 zM2yZ()Hs9^=e5m(7N{{MuD<^hx+(XC3F_9Zn_%Pk@oH*nr{Du+YTuWZt~pYWuK4WY zs?ilTsw$2j1rL&UI1ie-r57AGd>IlRb%ZoW0|E|!@*VR2{rl)>)Ff>#&-)Q9q|Y+3`*#@{5Ecm;ww6kvkRfBJ*RbtP2qBV;(XdRJhXyR{lA_v;hJ2q; zkrWDDpJduZyhreV*sOf5Uy>PdPFN?d!V? z7;kEA zYpW3My*3BtL@WcMEM~u0iwZmAx1)Kf&A`%QRZCq=IwU`|a`Iw9Q-I*URpz;t_rcNbhK4ek%KSGbf}54F6&Tc)~N zxouko^w3}E(Ig=(90yz~r=-M>>5j(k?pgPA69r|~`uXM0fEm^8PgI{(Mv4Oh?E|Mm zI~*@I3j@TNlalh_U0r9V-KX_y5sV%8v`y3WH-`hA)$mX8Ao9OvxZULQw^)30=qzYjFMYH^QLz@^gukSM z=b{=b1sQA+`3cv(kt7`}f|nzhX`0MqWmhEheTdWs@Nhj|{fGNhcE$b?u1EY29aS>< z<}&U1sZT|z&k*k$Zr}cy;#|Le9n%ubvAtFTe2Pd)E{C+B8~voS6PqmQ+t}D`l%y>N zEVxlw$r}eceg`AYvz)%-vk=P1kXy895oGo`5KYf{dU?G&pzv?Zv7xnfJ!OvdS)!p~ z2V|NHO_d;AYB4@~NsNojDe4dgX!(w8u(MkXrCF5v1Si3Fg%ld#-slT}hM>oCIcZ^u zf1Aq5pY?->477$A85xyd!c(Noutzdify)TK#1c%hPRq=cW5{`Xdw)V`ANW!Y1B4;B zbm>w|5ZjoPoGc@Hh9g&}yH($Z4u5X3fe4aW>|4*f$J zbu$l=zc*(naTyWvsj6z7ot^CofPKOR_ZtV5M~z+PaHO@_MilvWLMO2<t%fTPJ`jy8NJu>Z;;Q0Fxt#)d2L&T-)Wa4amF$^MHWj9+f(r)t`fd zub?i2a2rT4rkIbj0_6wkKo;-5REG|2#;r|>WM8h$YCCzQqgm>BO8UG{IGf7Q|3&ol z^oom$gaHk}-_kI4==tGe#}rsB6xZcY!dUWh%deL*A6qe^gVX_>o;$jF~dVBAS6T#fH{E8K7`EE2^?Jh9Y^;7QC_gD zOE61;%o1t_9q@z#$z^1_VMFPo$6yP$ZXJS3L})#3fUpMt2n!1{$NkKN*ZG`9Y=axd z4hRgqKk?)@=&Q;qmx|irb9cjIQ=+LYynL}?OH~W zvsqvOjesRD-_Y6Vgf%Cp;XZ(eBrdP?^z`ii5;XkM1b63L(bmyP0z-u&YJp9&m`Em+ zh=fF5X^dAaeO5Qbf~3#kPoG4vJlM-9YyJfX=J2amp5@WsuW~pD)EiMT%>p)`k6XEm zuUsEyK7N!O$iP_2F9kcadhNB)_8%V&t|i4qUv+|dtv`dgeQE=EbRCqWO&E` zMHtqIq1ante9qzHTeX|>r(fXwbfBx&bfBozK!IfCrC>ZzS zIsf{7KoIDB=8gUR+rAf23KTK=Ie0U&2h>(mBBB!r_n(@!H-J!C*?Rb-kTL+o>0mXSH8}9khzY z&(DuA&Y+T0Qc91?=!XfzMqMy5IoS@)($U$uwyusF6Xa)+<6Jo@qZ7yjWquz(56CY3 zLLTg8f)L-fo_*{zjcY;8wr^L)VRPTMZ7!62DBK43jBT*jqRE6VU2mepq;~e)NP<#b2QH(S!H56$ z?Aha39+=xR6&Mu66c?YpYu7HvO`Ga^dSnTtMqd)Z?p(?U-qo}2ZU!YODe2s*73mxs zCv4N*0h~k_BUJoSf5J`iR`4Mv0;US76M*QHC^&)%IaCZLasZ2o`xwSgS;c1aO z_r%0RQQ&I}ZEbDL!D6sOL(KvMR}sHxJ4VmSHNm_$CDap0m`3+k8?0H(XwSqCDPzCb zdv6@|$i}EV90tb8K|uqNHCkF)$S30A;gK^md@dKjw7I*%#_C4cFpDUDC|45`U+H25 znE+*Wd$Eh9i)M+vGIO(u2l6b%7i7Jyr%Ik+L2 zH$b~!P%u7-1PFeuG})rX%I<1uY@8)4E2|wcFduyb2~Zgp>MdrmDujraB-{>FAA9g8 z>_Nqk@U}Z%O+5|meDWj{xJx_WB@b>#lE-KM3Xc-6i7=2dk>82r)unov06BrUe*SSm}p-l*4qWwqg%6l=~WOu%ntU zT7VEsQoaV&h1sW`6gsH`xjWR@Fq!r(-QBKA)92lwuyia;9Czx5Qq}Hs6^EfxEg>Pn z!T&d!UN8CsP|y@o$d}-)m2m@#Jz%#d>g(%$r|_nCyn6FyD`Fd2Cm6czP&iNTfGL1F zCBt5T|FVb?MYwkq1sBu=^bZ#AbPXZ`)jKql3ex6*fi`5X!6$V0{>QZZ6f8dm1pzHD zH2mLn&HJJtv6Eqj?Jfg+1{t z;KEhpLT>8nuICrOhF?S>bN#s)_{LMn7;r64J@}muad#q^mmmifwYs@L83CCg6=hE9tr{^pNCd`2G97;=N<^! zneUtG_o?CX3EQD#r?UE3tiFi!Q+fAKthnvB{D7M>;56VoNk*zZdK8J(2ldp)<2elX`Cl;3w*8&%56%MAj6r9P)06YUsVq^(_c z|C+f5juiCh6il_~MJbD!v-85;Lp=w;ZIIJvBXsqHeA>(KF(uQgo-TOv>ebEeZW*L} z%_+0b@aj+=Q}AGCUhP^Sa$I0PkMixVC^a5Otf=$&`_WhNJmObuBvF@S^Hrgtp_G^U z_YRy*_S<_hD7lv$9v%i_suqyNWtYOz(uKcgg@S;Pq)m1M;!gq?1ymFnPahu-Om9MW zV^)^XyW+e{`+<}5hTXo=b|GJ0(;+c)goK*VvMhVv5WgTsEK*Zr#>dA4NY-QQwVHb^ z&xK9uP^{(;6jbC}AiL2#(DVUDF5Zu3GTV>1S7r*}bn}osTOYybFwHm_@hfd_>bkgW zddv_(fsr|_6;JR1Rxo!QdLOgcQ=0T&_qg1_U@l4iLtKNFp-L zR#8}}=+oKNMM^AyZ=H48YiqeEYZsRZdQz0x5loITG)IQf&@}w9>=W@b{+cioScqw3 zkBF1chnf$`b}+{xG!M-rMvWh?1gvEdjE`-*GL4?pFi^pf(Gk=ezQjW6egL?qD1d#- zr+7JAyu9MEZ9U-X>OO_UE(N{227phtFHiP4I(kyzg8}mQ!e!CD!_UV@+Q9q{BysZB zhwG@x$x$$piT48nx39K#?dcuxe)r>?-2IOBzO@BmnODO5p`TzR<;GO>-V z?bciCz?_&g4?YTH@SBi|+c6rBOdDtvOyO`8Z$|9VA_!;aOm>+^5YBMB0Np`P3Yg7+ zW&o*T%#sK1$%bGeu_mg_dkkITxVu4$i2GH~lyrpOC;U4WNJBWvkH2oZ3{*5%s1z&%4|r44Y1`fLJO)(*b&`1$M5 zWVaV;Iv+9>wp$}KTk_TNNORczbNKnWsY2K53(z{pM^XnSNjo|@y};s;syEbQsAAx@ z<#U*P_)=mY4WkLJ7n%?@H+gJT6t4Q|{6y0Um!rS$wmet~mVwMu6gwT`g31<36`BD$ zsd&7-CzhMjb{k+;vGJk}3=of5`3niZ{dg%U??j&!3Q}YtVAZl#=2X zJrcQk8T1k(*t+CAl5B-&$_@yq0V+hFVep0DtNq9bRp6gl?|Ukxa8VpT+<|@g>a7qs zh=z#{vGb)<7|YASlY3wY)RW6z{7|U5Ae4Z%<&b67X3IgxaSc&8Ck<;t7Gu!j3661c zaw64MTm1d`#l^)5K3KKta+e1JO=SA1IqBWHy8&)-Ypzb!*c&NmRg~WD_i;Hn@+f0a zMSUJX{rq|ELPKNl1a4#@Z z(P;c9h5oNULiXLR+qmd&T0HZ&eChwpm;c9?t&;dZ__F4Pj4o80HvLQ++wuH=e1QMW bA37F$KS)7j?=gp&>u!EFv7#@o-Lm&zu?D>< literal 0 HcmV?d00001 diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/requirements.txt b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/requirements.txt index 49aca3901..e61087618 100644 --- a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/requirements.txt +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/requirements.txt @@ -1,4 +1,6 @@ +fasttext-wheel matplotlib -numpy +numpy<2.0 pandas -seaborn +scikit-learn +seaborn \ No newline at end of file From 6920364ab1da2d76dd0a425169dd161a98696b7d Mon Sep 17 00:00:00 2001 From: Vaibhav Devarapalli Date: Wed, 6 May 2026 18:00:42 -0400 Subject: [PATCH 03/10] UmdTask458: Add fasttext.API.ipynb notebook --- .../fasttext.API.ipynb | 423 ++++++++++++++++++ 1 file changed, 423 insertions(+) create mode 100644 class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext.API.ipynb diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext.API.ipynb b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext.API.ipynb new file mode 100644 index 000000000..5cf1341cd --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/fasttext.API.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "64d3b0a0-edbe-4a79-a781-b35e84a4b18f", + "metadata": {}, + "source": [ + "# FastText API Usage Guide\n", + "\n", + "This notebook demonstrates how to use the FastText API for text classification.\n", + "It covers loading a trained model, making predictions on new text, and using\n", + "the utility functions provided in fasttext_utils.py." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f0112ed8-e7fe-4f9f-ae97-21e99ed01da1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Libraries imported successfully!\n" + ] + } + ], + "source": [ + "# Import libraries and utility functions\n", + "import fasttext\n", + "from sklearn.datasets import fetch_20newsgroups\n", + "from fasttext_utils import (\n", + " clean_text,\n", + " prepare_fasttext_data,\n", + " train_fasttext_model,\n", + " evaluate_model,\n", + " get_predictions\n", + ")\n", + "\n", + "print(\"Libraries imported successfully!\")" + ] + }, + { + "cell_type": "markdown", + "id": "93bc3d2b-d3d4-42b3-8c15-ca1aae8835b9", + "metadata": {}, + "source": [ + "## Part 1: Training a Model from Scratch\n", + "\n", + "The FastText API allows you to train a supervised classification model\n", + "with a single function call. Here we demonstrate the minimal setup required." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0f25bc8c-573a-46f6-851f-02b31e1f8094", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved 11314 samples to /tmp/train.txt\n", + "Saved 7532 samples to /tmp/test.txt\n", + "Model trained in 8.88 seconds\n" + ] + } + ], + "source": [ + "# Load dataset and prepare training data\n", + "train_data = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))\n", + "test_data = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))\n", + "\n", + "prepare_fasttext_data(train_data, '/tmp/train.txt')\n", + "prepare_fasttext_data(test_data, '/tmp/test.txt')\n", + "\n", + "# Train a model using the FastText API\n", + "model, train_time = train_fasttext_model(\n", + " '/tmp/train.txt',\n", + " epoch=25,\n", + " lr=0.5,\n", + " word_ngrams=2\n", + ")\n", + "print(f\"Model trained in {train_time:.2f} seconds\")" + ] + }, + { + "cell_type": "markdown", + "id": "655ca0b7-e9e6-48dc-95b5-96e8ba42d128", + "metadata": {}, + "source": [ + "## Part 2: Making Predictions on New Text\n", + "\n", + "Once trained, the FastText API allows you to classify any new text\n", + "with a single call to model.predict()." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "01a62829-7790-425d-a381-053d1732d06c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions on new text samples:\n", + "------------------------------------------------------------\n", + "Text: The new graphics card delivers exceptional perform...\n", + "Predicted: comp.graphics (confidence: 0.9988)\n", + "\n", + "Text: The senator proposed a new bill to regulate firear...\n", + "Predicted: talk.politics.misc (confidence: 0.7130)\n", + "\n", + "Text: Scientists discovered a new exoplanet in the habit...\n", + "Predicted: rec.autos (confidence: 0.8161)\n", + "\n", + "Text: The team won the championship after an outstanding...\n", + "Predicted: rec.sport.hockey (confidence: 0.9982)\n", + "\n", + "Text: The stock market reached a new all-time high today...\n", + "Predicted: rec.autos (confidence: 0.5739)\n", + "\n" + ] + } + ], + "source": [ + "# Predict the category of new text samples\n", + "sample_texts = [\n", + " \"The new graphics card delivers exceptional performance for gaming.\",\n", + " \"The senator proposed a new bill to regulate firearm purchases.\",\n", + " \"Scientists discovered a new exoplanet in the habitable zone.\",\n", + " \"The team won the championship after an outstanding season.\",\n", + " \"The stock market reached a new all-time high today.\"\n", + "]\n", + "\n", + "print(\"Predictions on new text samples:\")\n", + "print(\"-\" * 60)\n", + "for text in sample_texts:\n", + " clean = clean_text(text)\n", + " labels, probs = model.predict(clean)\n", + " category = labels[0].replace('__label__', '').replace('_', '.')\n", + " confidence = probs[0]\n", + " print(f\"Text: {text[:50]}...\")\n", + " print(f\"Predicted: {category} (confidence: {confidence:.4f})\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "5acd5c6e-3142-4014-88bb-6290420b0a25", + "metadata": {}, + "source": [ + "## Part 3: Predicting Multiple Labels\n", + "\n", + "FastText can return the top-k most likely categories for a given text,\n", + "which is useful when the correct category is ambiguous." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ae30a82c-65f0-49e7-a13e-6596bce1ccf9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top-3 predictions for ambiguous texts:\n", + "------------------------------------------------------------\n", + "Text: I am looking to buy a used computer with a good graphic...\n", + " comp.graphics: 0.7067\n", + " misc.forsale: 0.1715\n", + " sci.electronics: 0.0652\n", + "\n", + "Text: The church discussed the role of religion in modern pol...\n", + " soc.religion.christian: 0.9997\n", + " talk.religion.misc: 0.0002\n", + " alt.atheism: 0.0001\n", + "\n", + "Text: The new motorcycle engine has impressive technical spec...\n", + " rec.autos: 0.8226\n", + " rec.motorcycles: 0.1420\n", + " comp.sys.mac.hardware: 0.0284\n", + "\n" + ] + } + ], + "source": [ + "# Get top-3 predictions for ambiguous texts\n", + "ambiguous_texts = [\n", + " \"I am looking to buy a used computer with a good graphics card.\",\n", + " \"The church discussed the role of religion in modern politics.\",\n", + " \"The new motorcycle engine has impressive technical specifications.\"\n", + "]\n", + "\n", + "print(\"Top-3 predictions for ambiguous texts:\")\n", + "print(\"-\" * 60)\n", + "for text in ambiguous_texts:\n", + " clean = clean_text(text)\n", + " labels, probs = model.predict(clean, k=3)\n", + " print(f\"Text: {text[:55]}...\")\n", + " for label, prob in zip(labels, probs):\n", + " category = label.replace('__label__', '').replace('_', '.')\n", + " print(f\" {category}: {prob:.4f}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "9750fc88-27c3-4b6f-a8b0-f1baf60de9cf", + "metadata": {}, + "source": [ + "## Part 4: Evaluating Model Performance\n", + "\n", + "The FastText API provides a built-in test function to evaluate\n", + "model performance on a labeled dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "feb73d73-c9c3-4bc2-805f-36dc362f1e87", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model Evaluation Results:\n", + "----------------------------------------\n", + "Test samples: 7532\n", + "Precision: 0.6022\n", + "Recall: 0.6022\n", + "F1-Score: 0.6022\n" + ] + } + ], + "source": [ + "# Evaluate model performance using the FastText API\n", + "results = evaluate_model(model, '/tmp/test.txt')\n", + "\n", + "print(\"Model Evaluation Results:\")\n", + "print(\"-\" * 40)\n", + "print(f\"Test samples: {results['samples']}\")\n", + "print(f\"Precision: {results['precision']}\")\n", + "print(f\"Recall: {results['recall']}\")\n", + "print(f\"F1-Score: {results['f1']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c972756c-54e6-41e7-ae3c-9d9a58e8a0fb", + "metadata": {}, + "source": [ + "## Part 5: Saving and Loading a Model\n", + "\n", + "FastText models can be saved to disk and reloaded for future use\n", + "without retraining, which is useful for production deployment." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "87b22675-120b-4f4d-bbfa-18a69d8a0e3d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model saved to /tmp/fasttext_api_model.bin\n", + "Model loaded successfully!\n", + "\n", + "Test prediction with loaded model:\n", + "Text: The astronauts completed a successful spacewalk outside the station.\n", + "Predicted: sci.space (confidence: 0.4991)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n" + ] + } + ], + "source": [ + "# Save the model to disk\n", + "model.save_model('/tmp/fasttext_api_model.bin')\n", + "print(\"Model saved to /tmp/fasttext_api_model.bin\")\n", + "\n", + "# Load the model back\n", + "loaded_model = fasttext.load_model('/tmp/fasttext_api_model.bin')\n", + "print(\"Model loaded successfully!\")\n", + "\n", + "# Verify the loaded model works correctly\n", + "test_text = \"The astronauts completed a successful spacewalk outside the station.\"\n", + "clean = clean_text(test_text)\n", + "labels, probs = loaded_model.predict(clean)\n", + "category = labels[0].replace('__label__', '').replace('_', '.')\n", + "print(f\"\\nTest prediction with loaded model:\")\n", + "print(f\"Text: {test_text}\")\n", + "print(f\"Predicted: {category} (confidence: {probs[0]:.4f})\")" + ] + }, + { + "cell_type": "markdown", + "id": "c7f5f5fb-8844-4eb4-8608-717a33576379", + "metadata": {}, + "source": [ + "## Part 6: Word Vectors\n", + "\n", + "FastText also provides access to word vectors, which capture semantic\n", + "relationships between words. These can be used for tasks beyond classification\n", + "such as similarity search and clustering." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e6e111e6-76bd-481b-a6db-68fbd283c3de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Word vector dimensions: 100\n", + "\n", + "Sample word vectors (first 5 dimensions):\n", + "--------------------------------------------------\n", + "politics : [ 0.2386 -0.4697 -0.5411 0.3822 -0.2242]\n", + "religion : [-1.381 -0.6163 1.7671 0.565 -0.9371]\n", + "computer : [ 0.1015 0.5626 0.7517 -0.7056 0.1837]\n", + "sports : [ 0.1007 -0.1269 -0.4817 0.0266 -0.5756]\n", + "science : [ 0.159 -0.0033 0.4031 -0.2401 0.9567]\n", + "\n", + "Words most similar to 'computer':\n", + "--------------------------------------------------\n", + " lifespan similarity: 0.9351\n", + " video similarity: 0.9311\n", + " card similarity: 0.9298\n", + " screen similarity: 0.9208\n", + " available similarity: 0.9171\n" + ] + } + ], + "source": [ + "# Access word vectors from the trained model\n", + "words = ['politics', 'religion', 'computer', 'sports', 'science']\n", + "\n", + "print(\"Word vector dimensions:\", model.get_dimension())\n", + "print()\n", + "print(\"Sample word vectors (first 5 dimensions):\")\n", + "print(\"-\" * 50)\n", + "for word in words:\n", + " vector = model.get_word_vector(word)\n", + " print(f\"{word:12s}: {vector[:5].round(4)}\")\n", + "\n", + "print()\n", + "# Find nearest neighbors for a word\n", + "print(\"Words most similar to 'computer':\")\n", + "print(\"-\" * 50)\n", + "neighbors = model.get_nearest_neighbors('computer', k=5)\n", + "for score, word in neighbors:\n", + " print(f\" {word:20s} similarity: {score:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "44f7bbbb-ea82-41c0-8fa7-6bfb8bd1f9f1", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This notebook demonstrated the key FastText API capabilities:\n", + "\n", + "1. Training a supervised classification model with train_supervised()\n", + "2. Making single and top-k predictions with model.predict()\n", + "3. Evaluating model performance with model.test()\n", + "4. Saving and loading models with save_model() and load_model()\n", + "5. Accessing word vectors with get_word_vector() and get_nearest_neighbors()\n", + "\n", + "For a complete walkthrough of the text classification pipeline including\n", + "hyperparameter tuning and model comparison, see fasttext.example.ipynb." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66c183a8-16b4-43d7-bb5b-e05700fe0a56", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 4b45aee1379d0f9bff36b1e0d60142e97ae17a8e Mon Sep 17 00:00:00 2001 From: Vaibhav Devarapalli Date: Thu, 7 May 2026 00:24:02 -0400 Subject: [PATCH 04/10] UmdTask458: Add project README --- .../README.md | 855 ++---------------- 1 file changed, 82 insertions(+), 773 deletions(-) diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md index 58d90e2d1..fbc9ae488 100644 --- a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md @@ -1,802 +1,111 @@ -# Summary -This directory contains a Docker-based development environment template with: +# FastText Text Classification -- Utility scripts for Docker operations (build, run, clean, push) -- Configuration files for Dockerfile and environment setup -- Jupyter notebook templates for standardized project development -- Shell utilities and Python helpers for container-based workflows +## Overview -A guide to set up Docker-based projects using the template, customize it for -your needs, and maintain it over time. +This project demonstrates text classification using FastText, an open-source +library developed by Facebook AI Research. We train a model to classify news +articles from the 20 Newsgroups dataset into 20 categories, covering topics +such as politics, religion, sports, science, and technology. -## Description of Files -- `bashrc` - - Bash configuration file enabling `vi` mode for command-line editing +The project covers the full machine learning pipeline: data preprocessing, +model training, hyperparameter tuning, evaluation, and comparison with a +traditional baseline model. -- `copy_docker_files.py` - - Python script for copying Docker configuration files to destination - directories +## Dataset -- `docker_build.version.log` - - Log file containing Python, `pip`, Jupyter, and package version information - from Docker build +We use the 20 Newsgroups dataset, which contains approximately 18,000 newsgroup +posts across 20 categories. The dataset is loaded directly via scikit-learn and +requires no manual download. -- `docker_cmd.sh` - - Shell script for executing arbitrary commands inside Docker containers with - volume mounting +- Training samples: 11,314 +- Test samples: 7,532 +- Categories: 20 -- `docker_jupyter.sh` - - Shell script for launching Jupyter Lab server inside Docker containers +Headers, footers, and quoted text are removed to make the classification task +more realistic and challenging. -- `docker_name.sh` - - Configuration file defining Docker repository and image naming variables +## Project Structure -- `Dockerfile` - - Docker image build configuration with Ubuntu, Python, Jupyter, and project - dependencies + UmdTask458_DATA605_Spring2026_FastText_text_classification/ + Dockerfile - Docker environment setup + requirements.txt - Python dependencies + fasttext_utils.py - Utility functions for training and evaluation + fasttext.example.ipynb - Full project walkthrough notebook + fasttext.API.ipynb - FastText API usage examples + confusion_matrix_baseline.png + confusion_matrix_best.png + hyperparameter_tuning.png + model_comparison.png + error_analysis.png + README.md -- `etc_sudoers` - - Sudoers configuration file granting passwordless sudo access for postgres - user +## Setup and Installation -- `README.md` - - Documentation file describing directory contents, files, and executable - scripts +### Prerequisites +- Docker Desktop installed and running +- Git -- `template_utils.py` - - Python utility functions supporting tutorial notebooks with data processing - and modeling helpers +### Build the Docker Container -- `template.API.ipynb` - - Jupyter notebook template for API exploration and library usage examples + docker build -t gpsaggese/umd_data605_fasttext . -- `template.example.ipynb` - - Jupyter notebook template for project examples and demonstrations +### Run the Container -- `utils.sh` - - Bash utility library with reusable functions for Docker operations - - Provides centralized argument parsing (`parse_default_args`) for `-h` and - `-v` flags used by all `docker_*.sh` scripts - - Provides Jupyter configuration logic: vim keybindings, notification - settings, and Docker run option builders - - All `docker_*.sh`, `docker_jupyter.sh`, and `run_jupyter.sh` scripts across - the repo source this file from `class_project/project_template/utils.sh` + docker run -it --rm -p 8888:8888 -v "$(pwd):/home/user" gpsaggese/umd_data605_fasttext bash -## Workflows -- All commands should be run from inside the project directory - ```bash - > cd tutorials/FilterPy - ``` +### Start Jupyter -- To build the container for a project - ```bash - > cd $PROJECT - # Build the container. - > docker_build.sh - # Build without cache (pass extra args after -v). - > docker_build.sh --no-cache - # Test the container. - > docker_bash.sh ls - ``` + jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root --notebook-dir=/home/user -- Enable verbose (trace) output with `-v` - ```bash - > docker_build.sh -v - > docker_bash.sh -v - ``` +Then open http://127.0.0.1:8888 in your browser. -- Get help for any docker script - ```bash - > docker_build.sh -h - > docker_jupyter.sh -h - ``` +## Notebooks -- Start Jupyter - ```bash - > docker_jupyter.sh - # Go to localhost:8888 - ``` +### fasttext.example.ipynb +The main project notebook covering: +1. Dataset loading and exploration +2. Text preprocessing for FastText format +3. Baseline model training and evaluation +4. Hyperparameter tuning across 5 configurations +5. Best model evaluation with confusion matrix and error analysis +6. Comparison with Logistic Regression + TF-IDF -- Start Jupyter on a specific port with vim support - ```bash - > docker_jupyter.sh -p 8890 -u - # Go to localhost:8890 - ``` +### fasttext.API.ipynb +Demonstrates the FastText API including: +- Training a model from scratch +- Making single and top-k predictions +- Evaluating model performance +- Saving and loading models +- Accessing word vectors and nearest neighbors -## How to Customize a Project Template -- Copy the template - ```bash - > cp -r class_project/project_template $TARGET - ``` +## Results -## Description of Executables +| Model | F1 Score | Training Time | +|---|---|---| +| FastText Baseline (e=10, lr=0.1, ng=1) | 0.32 | ~2s | +| FastText Best (e=75, lr=0.5, ng=2) | 0.62 | ~8s | +| Logistic Regression + TF-IDF | 0.66 | ~19s | -### `copy_docker_files.py` -- **What It Does** - - Copies Docker configuration and utility files from project_template to a - destination directory - - Preserves all file permissions and attributes during copying - - Creates destination directory if it doesn't exist +FastText trains 2.5x faster than Logistic Regression while achieving +competitive accuracy. On larger datasets, FastText's speed advantage +becomes significantly more pronounced. -- Copy all Docker files to a target directory: - ```bash - > ./copy_docker_files.py --dst_dir /path/to/destination - ``` +## Key Findings -- Copy with verbose logging: - ```bash - > ./copy_docker_files.py --dst_dir /path/to/destination -v DEBUG - ``` +1. Hyperparameter tuning doubled model performance from 31% to 62% F1. -### `docker_bash.sh` -- **What It Does** - - Launches an interactive bash shell inside a Docker container - - Mounts the current working directory as `/data` inside the container - - Exposes port 8888 for potential services running in the container - - Accepts `-h` (help) and `-v` (verbose/trace) flags via `parse_default_args` +2. The most common misclassifications occur between semantically similar + categories such as talk.politics.misc and talk.politics.guns. +3. Logistic Regression + TF-IDF slightly outperforms FastText on this + small dataset, but FastText scales significantly better to large datasets. +4. FastText is best suited for large-scale text classification where + training speed and memory efficiency are critical. -- Launch bash shell in the container: - ```bash - > ./docker_bash.sh - ``` +## Dependencies -- Launch with verbose output (prints each command): - ```bash - > ./docker_bash.sh -v - ``` - -### `docker_build.sh` -- **What It Does** - - Builds Docker container images using Docker BuildKit - - Supports single-architecture builds (default) or multi-architecture builds - (`linux/arm64`, `linux/amd64`) - - Copies project files to temporary build directory and generates build logs - - Accepts `-h` (help) and `-v` (verbose/trace) flags; any extra arguments - after flags are forwarded to `docker build` - -- Build container image for current architecture: - ```bash - > ./docker_build.sh - ``` - -- Build without Docker layer cache: - ```bash - > ./docker_build.sh --no-cache - ``` - -- Build multi-architecture image (requires setting `DOCKER_BUILD_MULTI_ARCH=1` - in the script): - ```bash - > # Edit docker_build.sh to set DOCKER_BUILD_MULTI_ARCH=1 - > ./docker_build.sh - ``` - -### `docker_clean.sh` -- **What It Does** - -- Removes all Docker images matching the project's full image name -- Lists images before and after removal for verification -- Uses force removal to ensure cleanup completes - -- Remove project's Docker images: - ```bash - > ./docker_clean.sh - ``` - -### `docker_cmd.sh` -- **What It Does** - - Executes arbitrary commands inside a Docker container - - Mounts current directory as `/data` for accessing project files - - Automatically removes container after command execution completes - - Accepts `-h` (help) and `-v` (verbose/trace) flags; remaining arguments - form the command to execute - -- Run Python script inside container: - ```bash - > ./docker_cmd.sh python script.py --arg value - ``` - -- List files in the container: - ```bash - > ./docker_cmd.sh ls -la /data - ``` - -- Run tests inside container: - ```bash - > ./docker_cmd.sh pytest tests/ - ``` - -### `docker_exec.sh` -- **What It Does** - - Attaches to an already running Docker container with an interactive bash - shell - - Finds the container ID automatically based on the image name - - Useful for debugging or inspecting running containers - - Accepts `-h` (help) and `-v` (verbose/trace) flags via `parse_default_args` - -- Attach to running container: - ```bash - > ./docker_exec.sh - ``` - -### `docker_jupyter.sh` -- **What It Does** - - Launches Jupyter Lab server inside a Docker container - - Supports custom port configuration (default 8888), vim keybindings, and - custom directory mounting - - Runs `run_jupyter.sh` script inside the container with specified options - -- Start Jupyter on default port 8888: - ```bash - > ./docker_jupyter.sh - ``` - -- Start Jupyter on custom port with vim bindings: - ```bash - > ./docker_jupyter.sh -p 8889 -u - ``` - -- Start Jupyter with external directory mounted: - ```bash - > ./docker_jupyter.sh -d /path/to/notebooks -p 8889 - ``` - -- Start Jupyter in verbose mode: - ```bash - > ./docker_jupyter.sh -v -p 8890 - ``` - -### `docker_push.sh` -- **What It Does** - - Authenticates to Docker registry using credentials from - `~/.docker/passwd.$REPO_NAME.txt` - - Pushes the project's Docker image to the remote repository - - Lists images before pushing for verification - -- Push container image to registry: - ```bash - > ./docker_push.sh - ``` - -### `run_jupyter.sh` -- **What It Does** - - Launches Jupyter Lab server with no authentication (token and password - disabled) - - Binds to all network interfaces (0.0.0.0) on port 8888 - - Allows root access for container environments - - When `JUPYTER_USE_VIM=1`, verifies that `jupyterlab_vim` is installed - before enabling vim keybindings; exits with an error if not found - -- Start Jupyter Lab server (typically called from docker_jupyter.sh): - ```bash - > ./run_jupyter.sh - ``` - -- Start with vim keybindings (requires `jupyterlab_vim` installed in the - container): - ```bash - > JUPYTER_USE_VIM=1 ./run_jupyter.sh - ``` - -### `utils.sh` -- **What It Does** - - Central Bash library sourced by all `docker_*.sh` and `run_jupyter.sh` - scripts across the repository - - Provides `parse_default_args` which adds `-h` (help) and `-v` - (verbose/`set -x`) flags to every docker script - - Provides `build_container_image`, `push_container_image`, - `remove_container_image`, `kill_container`, `exec_container` utilities - - Provides Jupyter configuration helpers: vim keybindings, notification - suppression, and Docker run option builders - -### `version.sh` -- **What It Does** - - Reports version information for Python3, pip3, and Jupyter - - Lists all installed Python packages with versions - - Used during Docker image builds to log environment configuration - -- Display version information: - ```bash - > ./version.sh - ``` - -- Save version information to a log file: - ```bash - > ./version.sh 2>&1 | tee version.log - ``` - -# Template Customization and Maintenance - -## Quick Start for New Projects - -### Step 1: Copy the Template -```bash -> cd class_project/project_template -> cp -r . /path/to/your/new/project -> cd /path/to/your/new/project -``` - -### Step 2: Choose a Base Image -The template includes three Dockerfile options. Choose the one that best fits -your project: - -| Option | File | Best For | -| -------------------------- | ------------------------ | ---------------------------------------------------------------- | -| **Standard** | `Dockerfile.ubuntu` | Full Ubuntu environment with system tools | -| **Lightweight** | `Dockerfile.python_slim` | Minimal Python environment; reduced image size | -| **Modern Package Manager** | `Dockerfile.uv` | Fast dependency resolution with [uv](https://docs.astral.sh/uv/) | - -**How to choose:** - -- **Use Standard** if you need system-level tools (git, curl, graphviz, etc.) -- **Use Python Slim** to minimize image size and build time -- **Use uv** if you want faster, more reliable dependency management - -### Step 3: Set Up Your Dockerfile -- Delete unused reference files - ```bash - > rm Dockerfile.ubuntu Dockerfile.python_slim Dockerfile.uv - ``` - -- Create your working Dockerfile - ```bash - > cp Dockerfile.ubuntu Dockerfile - ``` - -- Add your dependencies - ```bash - > echo "numpy\npandas\nscikit-learn" > requirements.in - > pip-compile requirements.in > requirements.txt - ``` - -### Step 4: Keep Customization Minimal -- Only modify what's necessary for your project -- Use `requirements.txt` for all Python packages (don't edit Dockerfile for - this) -- Keep `bashrc` and `etc_sudoers` as-is unless you need custom shell setup -- Keep base image and Python version unless you have specific requirements - -## Understanding the Dockerfile Flow -Each Dockerfile follows the same structure. Here are the key stages: - -### Stage 1: Base Image and System Setup -```dockerfile -FROM ubuntu:24.04 # or python:3.12-slim, depending on your requirement -ENV DEBIAN_FRONTEND noninteractive -RUN apt-get -y update && apt-get -y upgrade -``` - -- **Purpose**: Start with a clean base image and disable interactive - installation prompts - -- **When to customize**: Only change the base image or version if your project - has specific requirements (different Ubuntu version, specific Python version, - etc.) - -### Stage 2: System Utilities (Ubuntu-based Dockerfiles Only) -```dockerfile -RUN apt install -y --no-install-recommends \ - sudo \ - curl \ - systemctl \ - gnupg \ - git \ - vim -``` - -- **Purpose**: Install essential system tools for development and container - management - -- **When to customize**: Add only if needed for your project - - `postgresql-client`: for database connections - - `graphviz`: for graph visualizations - - `ffmpeg`: for media processing - -- **Best practice**: Use `--no-install-recommends` to keep the image small - -### Stage 3: Python and Build Tools (Ubuntu-based Dockerfiles Only) -```dockerfile -RUN apt-get update && apt-get install -y --no-install-recommends \ - build-essential \ - python3 \ - python3-pip \ - python3-dev \ - python3-venv \ - && rm -rf /var/lib/apt/lists/* -``` - -- **Purpose**: Install Python 3, pip, and build tools needed for compiled - packages - -- **Why venv**: Creates an isolated Python environment separate from system - Python - -- **When to customize**: Rarely. Only change if you need a specific Python - version (e.g., `python3.11` instead of `python3`) - -### Stage 4: Virtual Environment Setup -```dockerfile -RUN python3 -m venv /opt/venv -ENV PATH="/opt/venv/bin:$PATH" -RUN python -m pip install --upgrade pip -``` - -- **Purpose**: Create and activate an isolated virtual environment for your - project - -- **Why this matters**: Ensures reproducibility and prevents dependency - conflicts across projects - -- **When to customize**: Never. This is a standard best practice - -### Stage 5: Jupyter Installation -```dockerfile -RUN pip install jupyterlab jupyterlab_vim -``` - -- **Purpose**: Install JupyterLab and the Vim keybinding extension for - interactive development - - `jupyterlab`: the main IDE for running notebooks in the browser - - `jupyterlab_vim`: adds Vim-style navigation to notebook cells - -- **Why in Dockerfile, not requirements.txt**: These are infrastructure - packages (the IDE itself), not project-specific dependencies - - Do NOT add `jupyterlab`, `jupyterlab-vim`, or `ipywidgets` to - `requirements.txt`; they are already installed here - -- **When to customize**: - - **Remove** this line if your project doesn't use Jupyter - - **Add more extensions** if needed (e.g., `jupyterlab-git`, - `jupyterlab-variableinspector`) - -### Stage 6: Project Dependencies -```dockerfile -COPY requirements.txt /install/requirements.txt -RUN pip install --no-cache-dir -r /install/requirements.txt -``` - -- **Purpose**: Install your project-specific Python packages - -- **When to customize**: This is the primary place to customize. Define all your - dependencies in `requirements.txt` - -- **Best practice**: - - **Pin all versions**: `numpy==1.24.0` (not `numpy>=1.20.0`) - - **Use `--no-cache-dir`**: Reduces image size by skipping pip cache - - **For complex dependencies**: Use `requirements.in` with `pip-tools` or - `pip-compile` - -- **Example requirements.txt**: - ```text - numpy==1.24.0 - pandas==2.0.0 - scikit-learn==1.2.2 - tensorflow==2.13.0 - ``` - -### Stage 7: Configuration -```dockerfile -COPY etc_sudoers /etc/sudoers -COPY bashrc /root/.bashrc -``` - -- **Purpose**: Apply custom bash configuration and sudo permissions - -- **When to customize**: - - **Edit `bashrc`**: to add aliases, environment variables, or custom prompt - - **Edit `etc_sudoers`**: if additional users need passwordless sudo access - -### Stage 8: Version Logging -```dockerfile -ADD version.sh /install/ -RUN /install/version.sh 2>&1 | tee version.log -``` - -- **Purpose**: Document the exact versions of Python, pip, Jupyter, and all - installed packages - -- **What it logs**: - - Python 3 version - - Pip version - - Jupyter version - - Complete list of all installed Python packages - -- **Why it matters**: Creates a detailed record of your container's environment - for troubleshooting and reproducibility - -- **How to use**: After building, review `version.log` to verify all - dependencies installed correctly - ```bash - > docker build -t my-project . - > cat version.log - ``` - -- **Extending it**: If you need to log additional tools (MongoDB, Node.js, - etc.), add them to `version.sh`: - ```bash - > echo "# mongo" - > mongod --version - ``` - -### Stage 9: Port Declaration -```dockerfile -EXPOSE 8888 -``` - -- **Purpose**: Declare that the container uses port 8888 (informational for - Docker) - -- **When to customize**: Add additional ports if your application needs them - (e.g., `EXPOSE 8888 5432 3000`) - -## Best Practices: Keep It Simple - -### The Core Principle -Only change what's necessary for your project. Everything else should inherit -from the template. - -This approach: - -- Makes Dockerfiles easier to understand and maintain -- Keeps images smaller and faster to build -- Simplifies future updates from the template -- Ensures consistency across similar projects - -### How to Do It Right -| What | Where | Example | -| :--------------------------- | :--------------------------- | :------------------------------ | -| Project Python packages | `requirements.txt` | `numpy==1.24.0` | -| Jupyter + Vim (always there) | Dockerfile Stage 5 | `jupyterlab jupyterlab_vim` | -| System tools | Dockerfile `apt-get` section | `postgresql-client` | -| Shell aliases | `bashrc` | `alias jlab="jupyter lab"` | -| Custom scripts | `scripts/` directory | Setup or initialization scripts | -| User permissions | `etc_sudoers` | Grant passwordless sudo | - -- **Do NOT add to `requirements.txt`**: `jupyterlab`, `jupyterlab-vim`, - `jupyterlab_vim`, or `ipywidgets` — these are Jupyter infrastructure packages - and are already installed in Stage 5 of the Dockerfile - -### Wrong Vs. Right Approach -- **Wrong**: Embed everything in the Dockerfile - ```dockerfile - RUN pip install my-package && python my_setup.py && npm install - ``` - -- **Right**: Use separate files and keep Dockerfile clean - ```dockerfile - COPY requirements.txt /install/ - RUN pip install -r /install/requirements.txt - COPY scripts/setup.sh /install/ - RUN /install/setup.sh - ``` - -## .Dockerignore Policy - -### Why It Matters -The `.dockerignore` file prevents unnecessary files from being added to the -Docker build context: - -- **Reduces build time**: Fewer files to transfer to Docker daemon -- **Reduces image size**: Only necessary files are included -- **Improves security**: Prevents leaking sensitive data - -### What to Exclude: Category Breakdown -- Python Artifacts (Always Exclude) - ```verbatim - __pycache__/ - *.pyc - *.pyo - *.pyd - ``` - - Why: Compiled bytecode generated at runtime. Regenerated in container, adds - bloat - -- Virtual Environments (Always Exclude) - ```verbatim - venv/ - .venv/ - env/ - .env/ - ``` - - Why: Local venvs aren't portable to containers. The Dockerfile creates its - own - -- Jupyter Checkpoints (Always Exclude) - ```verbatim - .ipynb_checkpoints/ - ``` - - Why: Auto-generated by Jupyter, not needed in the image - -- Git and Version Control (Always Exclude) - ```verbatim - .git/ - .gitignore - .gitattributes - ``` - - Why: Repository history not needed at runtime - -- Docker Build Scripts (Always Exclude) - ```verbatim - docker_build.sh - docker_push.sh - docker_clean.sh - docker_exec.sh - docker_cmd.sh - docker_bash.sh - docker_jupyter.sh - docker_name.sh - Dockerfile.* - ``` - - Why: Local development scripts don't run inside the container - -- Large Data Files (Recommended) - ```verbatim - data/ - *.csv - *.pkl - *.h5 - *.parquet - ``` - - Why: Don't ship large training and test data in the image. Mount via volume - instead - - Best practice: `bash > docker run -v /path/to/data:/data my-image ` - -- Test Files (Project-Dependent) - ```verbatim - tests/ - tutorials/ - ``` - - Why: Exclude if tests don't run in the container - - When to include: If CI and CD runs tests inside the container - -- Documentation (Recommended) - ```verbatim - README.md - docs/ - *.md - ``` - - Why: Not needed at runtime - - Exception: Only keep if your app reads these files at runtime - -- Generated Files (Always Exclude) - ```verbatim - *.log - *.tmp - *.cache - build/ - dist/ - ``` - - Why: Generated at runtime, not needed in the image - -## Workflow: From Template to Your Project - -### Complete Setup Checklist -- Copy the template - ```bash - > cp -r project_template my-new-project - > cd my-new-project - ``` - -- Keep all reference Dockerfiles - ```verbatim - Dockerfile.ubuntu_24_04 - Dockerfile.python_slim - Dockerfile.uv - ``` - -- Create your working Dockerfile - ```bash - > cp Dockerfile.ubuntu_24_04 Dockerfile - ``` - -- Add your dependencies - ```bash - > pip freeze > requirements.txt - ``` - -- Configure `.dockerignore`: Review the template `.dockerignore` and add your - project-specific exclusions (e.g., data directories) - -- Test the build - ```bash - > docker build -t my-project:latest . - > docker run -it my-project:latest bash - ``` - -- Test Jupyter (if using) - ```bash - > ./docker_jupyter.sh -p 8888 - ``` - -- Document customizations in your project README: - - Base image chosen and why - - Key dependencies - - Any Dockerfile modifications - - How to build and run - -## Maintaining Your Setup - -### Document Any Changes -- If you modify the Dockerfile, add explanatory comments: - ```dockerfile - # Custom: PostgreSQL client for database access - postgresql-client \ - - # Custom: Node.js for frontend builds - nodejs \ - ``` - -### Monitor Package Versions -- After each build, review `version.log`: - ```bash - > docker build -t my-project . - > cat version.log - ``` - -### Keep `.dockerignore` Updated -- If you add new directories or files, update `.dockerignore`. Add to - `.dockerignore` if the directory shouldn't be in the image: - ```verbatim - data/ - cache/ - .temp/ - ``` - -### Contribute Improvements Back -When you improve your project's Docker setup: - -- Test thoroughly in your project -- Document the improvement clearly -- Submit back to `project_template` -- Other projects can adopt it when they update - -Example improvements: - -- Better way to install TensorFlow with GPU support -- Optimized `.dockerignore` for data science projects -- Security hardening (non-root user setup) - -## Troubleshooting - -### Build Is Slow -- Check `.dockerignore`: Ensure large directories (data/, .git/) are excluded -- Check Docker daemon: Verify Docker is running properly -- Check layer caching: Docker reuses cached layers; avoid changing early layers - -### Image Is Too Large -- Check layer sizes: - ```bash - > docker history my-project:latest - ``` - -- Remove unnecessary packages or use `python_slim` base image - -### Package Not Found Error -- Verify package name in PyPI (packages are case-sensitive) -- Check Python version compatibility -- Pin specific version if needed - -### Permission Issues in Container -- Check `etc_sudoers`: Ensure user has appropriate permissions -- Check file ownership: Ensure COPY doesn't create root-only files - -### Jupyter Won't Connect -- Run Jupyter - ```bash - > ./docker_jupyter.sh -p 8888 - ``` - -- Verify http://localhost:8888 (not https). Check firewall if remote access - needed - -### Vim Keybindings Not Working -- If `run_jupyter.sh` exits with `ERROR: jupyterlab_vim is not installed`, it - means `jupyterlab_vim` is missing from the container image -- Make sure `jupyterlab_vim` is installed in the Dockerfile: - ```dockerfile - RUN pip install jupyterlab jupyterlab_vim - ``` -- Rebuild the image after adding the package: - ```bash - > ./docker_build.sh - ``` +- fasttext-wheel +- scikit-learn +- numpy<2.0 +- pandas +- matplotlib +- seaborn From 26d19ed38db472266a31ae7a62b958353dc9fbd2 Mon Sep 17 00:00:00 2001 From: Vaibhav Devarapalli Date: Thu, 7 May 2026 18:50:11 -0400 Subject: [PATCH 05/10] UmdTask458: Add project README --- .../README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md index fbc9ae488..befac1a5c 100644 --- a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md @@ -82,7 +82,7 @@ Demonstrates the FastText API including: | Model | F1 Score | Training Time | |---|---|---| -| FastText Baseline (e=10, lr=0.1, ng=1) | 0.32 | ~2s | +| FastText Baseline (e=25, lr=0.5, ng=2) | 0.60 | ~5s | | FastText Best (e=75, lr=0.5, ng=2) | 0.62 | ~8s | | Logistic Regression + TF-IDF | 0.66 | ~19s | @@ -92,8 +92,9 @@ becomes significantly more pronounced. ## Key Findings -1. Hyperparameter tuning doubled model performance from 31% to 62% F1. - +1. Hyperparameter tuning improved F1 from 0.60 (baseline) to 0.62 (best + configuration). Poor configuration choices such as low epoch count and + learning rate can drop performance as low as 0.31. 2. The most common misclassifications occur between semantically similar categories such as talk.politics.misc and talk.politics.guns. 3. Logistic Regression + TF-IDF slightly outperforms FastText on this @@ -108,4 +109,4 @@ becomes significantly more pronounced. - numpy<2.0 - pandas - matplotlib -- seaborn +- seaborn \ No newline at end of file From 1a308eb3ec8bea979f38fb88777fcb523b045aea Mon Sep 17 00:00:00 2001 From: Vaibhav Devarapalli Date: Thu, 7 May 2026 18:55:42 -0400 Subject: [PATCH 06/10] UmdTask458: Update test file for FastText notebooks --- .../test/test_docker_all.py | 24 +++++++------------ 1 file changed, 8 insertions(+), 16 deletions(-) diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/test/test_docker_all.py b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/test/test_docker_all.py index 904cdd7af..c7fce0cfd 100644 --- a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/test/test_docker_all.py +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/test/test_docker_all.py @@ -1,15 +1,11 @@ """ -Run each notebook in class_project/project_template/ inside Docker using docker_cmd.sh. +Run each notebook in the FastText project inside Docker. Import as: - -import class_project.project_template.test.test_docker_all as tptdal +import class_project.data605.Spring2026.projects.UmdTask458_DATA605_Spring2026_FastText_text_classification.test.test_docker_all as tftdal """ - import logging - import pytest - import helpers.hdocker_tests as hdoctest _LOG = logging.getLogger(__name__) @@ -22,7 +18,7 @@ class Test_docker(hdoctest.DockerTestCase): """ - Run all Docker tests for class_project/project_template/. + Run all Docker tests for the FastText text classification project. """ _test_file = __file__ @@ -30,19 +26,15 @@ class Test_docker(hdoctest.DockerTestCase): @pytest.mark.slow def test1(self) -> None: """ - Test that template.example.ipynb runs without error inside Docker. + Test that fasttext.example.ipynb runs without error inside Docker. """ - # Prepare inputs. - notebook_name = "template.example.ipynb" - # Run test. + notebook_name = "fasttext.example.ipynb" self._helper(notebook_name) @pytest.mark.slow def test2(self) -> None: """ - Test that template.API.ipynb runs without error inside Docker. + Test that fasttext.API.ipynb runs without error inside Docker. """ - # Prepare inputs. - notebook_name = "template.API.ipynb" - # Run test. - self._helper(notebook_name) + notebook_name = "fasttext.API.ipynb" + self._helper(notebook_name) \ No newline at end of file From 7db1992154e09c60e4c4e7db1dad77c0e09a5d13 Mon Sep 17 00:00:00 2001 From: Vaibhav Devarapalli Date: Fri, 8 May 2026 18:36:24 -0400 Subject: [PATCH 07/10] UmdTask458: Update README with FastText description --- .../README.md | 29 ++++++++++++++----- 1 file changed, 22 insertions(+), 7 deletions(-) diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md index befac1a5c..90cdc81ed 100644 --- a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md @@ -1,15 +1,27 @@ # FastText Text Classification -## Overview +## What is FastText? + +- FastText is an open-source library developed by Facebook AI Research for + efficient text classification and representation. +- It allows users to create word embeddings and perform supervised learning + tasks such as text classification with high accuracy and speed. +- FastText can handle large datasets and provides pre-trained models for various + languages, making it accessible for multilingual applications. +- The tool supports subword information, which allows it to generate embeddings + for out-of-vocabulary words, improving its robustness in natural language + processing tasks. +- FastText is designed to be easy to use, with a command-line interface and + Python bindings, making it suitable for both beginners and advanced users. + + +## Project Overview This project demonstrates text classification using FastText, an open-source library developed by Facebook AI Research. We train a model to classify news articles from the 20 Newsgroups dataset into 20 categories, covering topics such as politics, religion, sports, science, and technology. -The project covers the full machine learning pipeline: data preprocessing, -model training, hyperparameter tuning, evaluation, and comparison with a -traditional baseline model. ## Dataset @@ -45,19 +57,22 @@ more realistic and challenging. - Docker Desktop installed and running - Git +Note: docker_build.sh has Windows path issues. Use docker build directly instead. +FastText is incompatible with NumPy 2.0, so numpy<2.0 is pinned in requirements.txt. + ### Build the Docker Container docker build -t gpsaggese/umd_data605_fasttext . ### Run the Container - docker run -it --rm -p 8888:8888 -v "$(pwd):/home/user" gpsaggese/umd_data605_fasttext bash + docker run -it --rm -p 8888:8888 -v "C:/Users/Vaibhav Devarapalli/src/gpsaggese.github.io/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification:/home/user" gpsaggese/umd_data605_fasttext bash ### Start Jupyter - jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root --notebook-dir=/home/user + pip install "numpy<2.0" -q && jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root --notebook-dir=/home/user -Then open http://127.0.0.1:8888 in your browser. +Then open the token URL shown in the terminal in your browser. ## Notebooks From ae2810434dcad4b76f0afcf07ca2728890eaefe3 Mon Sep 17 00:00:00 2001 From: Vaibhav Devarapalli Date: Fri, 8 May 2026 18:57:34 -0400 Subject: [PATCH 08/10] UmdTask458: Add API reference and running tests sections to README --- .../README.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md index 90cdc81ed..abccd60d2 100644 --- a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md @@ -93,6 +93,23 @@ Demonstrates the FastText API including: - Saving and loading models - Accessing word vectors and nearest neighbors +## FastText API Reference + +The core FastText API used in this project: + +- `fasttext.train_supervised()` — trains a supervised classification model +- `model.predict()` — predicts the category of a text input +- `model.test()` — evaluates model performance on a labeled dataset +- `model.save_model()` / `fasttext.load_model()` — saves and loads a trained model +- `model.get_word_vector()` — returns the vector representation of a word +- `model.get_nearest_neighbors()` — returns words most similar to a given word + +## Running Tests + +To verify both notebooks run without errors inside Docker: + + pytest test/test_docker_all.py + ## Results | Model | F1 Score | Training Time | From 8fcf16ae6b97e69aa6b9e2f355e5ed592b537b2c Mon Sep 17 00:00:00 2001 From: Vaibhav Devarapalli Date: Fri, 8 May 2026 19:00:16 -0400 Subject: [PATCH 09/10] UmdTask458: Add API reference and running tests sections to README --- .../README.md | 6 ------ 1 file changed, 6 deletions(-) diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md index abccd60d2..5035f83f7 100644 --- a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md @@ -104,12 +104,6 @@ The core FastText API used in this project: - `model.get_word_vector()` — returns the vector representation of a word - `model.get_nearest_neighbors()` — returns words most similar to a given word -## Running Tests - -To verify both notebooks run without errors inside Docker: - - pytest test/test_docker_all.py - ## Results | Model | F1 Score | Training Time | From b0b432c629f6248e7b203458fc09ddaf584042de Mon Sep 17 00:00:00 2001 From: Vaibhav Devarapalli Date: Fri, 8 May 2026 19:29:51 -0400 Subject: [PATCH 10/10] UmdTask458: update README --- .../README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md index 5035f83f7..37d3d0c48 100644 --- a/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md +++ b/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification/README.md @@ -66,7 +66,7 @@ FastText is incompatible with NumPy 2.0, so numpy<2.0 is pinned in requirements. ### Run the Container - docker run -it --rm -p 8888:8888 -v "C:/Users/Vaibhav Devarapalli/src/gpsaggese.github.io/class_project/data605/Spring2026/projects/UmdTask458_DATA605_Spring2026_FastText_text_classification:/home/user" gpsaggese/umd_data605_fasttext bash + docker run -it --rm -p 8888:8888 -v "$(pwd):/home/user" gpsaggese/umd_data605_fasttext bash ### Start Jupyter