From 5cef625c1b9d4e5c1757842d536db37fd0691bda Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Tue, 2 Apr 2024 17:58:15 +0300
Subject: [PATCH 01/24] gradio app added

---
 README.md               |  30 +++
 gradio_app.ipynb        | 143 +++++++++++++
 gradio_app.py           | 437 ++++++++++++++++++++++++++++++++++++++++
 gradio_requirements.txt |   3 +
 start-jupyter.bat       |   1 +
 start-jupyter.sh        |   1 +
 6 files changed, 615 insertions(+)
 create mode 100644 gradio_app.ipynb
 create mode 100644 gradio_app.py
 create mode 100644 gradio_requirements.txt

diff --git a/README.md b/README.md
index 74713fa..2a5be3a 100644
--- a/README.md
+++ b/README.md
@@ -90,6 +90,36 @@ If you have encountered version issues when running things, checkout [environmen
 ## Inference Examples
 Checkout [`inference_speech_editing.ipynb`](./inference_speech_editing.ipynb) and [`inference_tts.ipynb`](./inference_tts.ipynb)
 
+## Gradio
+After environment setup install additional dependencies:
+```bash
+pip install -r gradio_requirements.txt
+```
+
+Run gradio server from terminal or [`gradio_app.ipynb`](./gradio_app.ipynb):
+```bash
+python gradio_app.py
+```
+It is ready to use on [default url](http://127.0.0.1:7860).
+
+### How to use it
+1. (optionally) Select models
+2. Load models
+3. Transcribe
+4. (optionally) Tweak some parameters
+5. Run
+6. (optionally) Rerun part-by-part in Long TTS mode
+
+### Some features
+Smart transcript: write only what you want to generate, but don't work if you edit original transcript
+
+TTS mode: Zero-shot TTS
+
+Edit mode: Speech editing
+
+Long TTS mode: Easy TTS on long texts
+
+
 ## Training
 To train an VoiceCraft model, you need to prepare the following parts: 
 1. utterances and their transcripts
diff --git a/gradio_app.ipynb b/gradio_app.ipynb
new file mode 100644
index 0000000..0d3f946
--- /dev/null
+++ b/gradio_app.ipynb
@@ -0,0 +1,143 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9b6a0c92",
+   "metadata": {},
+   "source": [
+    "### Only do the below if you are using docker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "270aa2cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install OS deps\n",
+    "!sudo apt-get update && sudo apt-get install -y \\\n",
+    "    git-core \\\n",
+    "    ffmpeg \\\n",
+    "    espeak-ng"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ba5f452",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update and setup Conda voicecraft environment\n",
+    "!conda update -y -n base -c conda-forge conda\n",
+    "!conda create -y -n voicecraft python=3.9.16 && \\\n",
+    "    conda init bash"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ef2935c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install conda and pip stuff in the activated conda above context\n",
+    "!echo -e \"Grab a cup a coffee and a slice of pizza...\\n\\n\"\n",
+    "\n",
+    "# make sure $HOME and $USER are setup so this will source the conda environment\n",
+    "!source ~/.bashrc && \\\n",
+    "    conda activate voicecraft && \\\n",
+    "    conda install -y -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=5.5.1068 && \\\n",
+    "    pip install torch==2.0.1 && \\\n",
+    "    pip install tensorboard==2.16.2 && \\\n",
+    "    pip install phonemizer==3.2.1 && \\\n",
+    "    pip install torchaudio==2.0.2 && \\\n",
+    "    pip install datasets==2.16.0 && \\\n",
+    "    pip install torchmetrics==0.11.1\n",
+    "\n",
+    "# do this one last otherwise you'll get an error about torch compiler missing due to xformer mismatch\n",
+    "!source ~/.bashrc && \\\n",
+    "    conda activate voicecraft && \\\n",
+    "    pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fca57eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# okay setup the conda environment such that jupyter notebook can find the kernel\n",
+    "!source ~/.bashrc && \\\n",
+    "    conda activate voicecraft && \\\n",
+    "    conda install -y -n voicecraft ipykernel --update-deps --force-reinstall\n",
+    "\n",
+    "# installs the Jupyter kernel into /home/myusername/.local/share/jupyter/kernels/voicecraft\n",
+    "!source ~/.bashrc && \\\n",
+    "    conda activate voicecraft && \\\n",
+    "    python3 -m ipykernel install --user --name=voicecraft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "961faa43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!source ~/.bashrc && \\\n",
+    "    conda activate voicecraft && \\\n",
+    "    pip install -r gradio_requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b9c4436",
+   "metadata": {},
+   "source": [
+    "# STOP\n",
+    "You have to do this part manually using the mouse/keyboard and the tabs at the top.\n",
+    "\n",
+    "* Refresh your browser to make sure it picks up the new kernel.\n",
+    "* Kernel -> Change Kernel -> Select Kernel -> voicecraft\n",
+    "* Kernel -> Restart Kernel -> Yes\n",
+    "\n",
+    "Now you can run the rest of the notebook and get an audio sample output. It will automatically download more models and such. The next time you use this container, you can just start below here as the dependencies will remain available until you delete the docker container."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f089aa96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gradio_app import app\n",
+    "app.launch()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "voicecraft",
+   "language": "python",
+   "name": "voicecraft"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/gradio_app.py b/gradio_app.py
new file mode 100644
index 0000000..956175c
--- /dev/null
+++ b/gradio_app.py
@@ -0,0 +1,437 @@
+import gradio as gr
+import torch
+import torchaudio
+from data.tokenizer import (
+    AudioTokenizer,
+    TextTokenizer,
+)
+from models import voicecraft
+import whisper
+from whisper.tokenizer import get_tokenizer
+import os
+import io
+
+
+whisper_model = None
+voicecraft_model = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def load_models(input_audio, transcribe_btn, run_btn, rerun_btn):
+    def impl(whisper_model_choice, voicecraft_model_choice):
+        global whisper_model, voicecraft_model
+        whisper_model = whisper.load_model(whisper_model_choice)
+
+        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+        
+        voicecraft_name = f"{voicecraft_model_choice}.pth"
+        ckpt_fn = f"./pretrained_models/{voicecraft_name}"
+        encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
+        if not os.path.exists(ckpt_fn):
+            os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
+            os.system(f"mv {voicecraft_name}\?download\=true ./pretrained_models/{voicecraft_name}")
+        if not os.path.exists(encodec_fn):
+            os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
+            os.system(f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
+
+        voicecraft_model = {}
+        voicecraft_model["ckpt"] = torch.load(ckpt_fn, map_location="cpu")
+        voicecraft_model["model"] = voicecraft.VoiceCraft(voicecraft_model["ckpt"]["config"])
+        voicecraft_model["model"].load_state_dict(voicecraft_model["ckpt"]["model"])
+        voicecraft_model["model"].to(device)
+        voicecraft_model["model"].eval()
+
+        voicecraft_model["text_tokenizer"] = TextTokenizer(backend="espeak")
+        voicecraft_model["audio_tokenizer"] = AudioTokenizer(signature=encodec_fn)
+
+        return [
+            input_audio.update(interactive=True),
+            transcribe_btn.update(interactive=True),
+            run_btn.update(interactive=True),
+            rerun_btn.update(interactive=True)
+        ]
+    return impl
+
+
+def transcribe(audio_path):
+    tokenizer = get_tokenizer(multilingual=False)
+    number_tokens = [
+        i
+        for i in range(tokenizer.eot)
+        if all(c in "0123456789" for c in tokenizer.decode([i]).removeprefix(" "))
+    ]
+    result = whisper_model.transcribe(audio_path, suppress_tokens=[-1] + number_tokens, word_timestamps=True)
+    words = [word_info for segment in result["segments"] for word_info in segment["words"]]
+    
+    transcript = result["text"]
+    transcript_with_start_time = " ".join([f"{word['start']} {word['word']}" for word in words])
+    transcript_with_end_time = " ".join([f"{word['word']} {word['end']}" for word in words])
+
+    choices = [f"{word['start']} {word['word']} {word['end']}" for word in words]
+    edit_from_word = gr.Dropdown(label="First word to edit", value=choices[0], choices=choices, interactive=True)
+    edit_to_word = gr.Dropdown(label="Last word to edit", value=choices[-1], choices=choices, interactive=True)
+
+    return [
+        transcript, transcript_with_start_time, transcript_with_end_time,
+        edit_from_word, edit_to_word, words
+    ]
+
+
+def get_output_audio(audio_tensors, codec_audio_sr):
+    result = torch.cat(audio_tensors, 1)
+    buffer = io.BytesIO()
+    torchaudio.save(buffer, result, int(codec_audio_sr), format="wav")
+    buffer.seek(0)
+    return buffer.read()
+
+
+def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
+        stop_repetition, sample_batch_size, kvcache, silence_tokens,
+        audio_path, word_info, transcript, smart_transcript,
+        mode, prompt_end_time, edit_start_time, edit_end_time,
+        split_text, selected_sentence, previous_audio_tensors):
+    if mode == "Long TTS":
+        if split_text == "Newline":
+            sentences = transcript.split('\n')
+        else:
+            from nltk.tokenize import sent_tokenize
+            sentences = sent_tokenize(transcript.replace("\n", " "))
+    elif mode == "Rerun":
+        colon_position = selected_sentence.find(':')
+        selected_sentence_idx = int(selected_sentence[:colon_position])
+        sentences = [selected_sentence[colon_position + 1:]]
+    else:
+        sentences = [transcript.replace("\n", " ")]
+
+    info = torchaudio.info(audio_path)
+    audio_dur = info.num_frames / info.sample_rate
+
+    audio_tensors = []
+    inference_transcript = ""
+    for sentence in sentences:
+        decode_config = {"top_k": top_k, "top_p": top_p, "temperature": temperature, "stop_repetition": stop_repetition,
+                         "kvcache": kvcache, "codec_audio_sr": codec_audio_sr, "codec_sr": codec_sr,
+                         "silence_tokens": silence_tokens, "sample_batch_size": sample_batch_size}
+        if mode != "Edit":
+            from inference_tts_scale import inference_one_sample
+
+            if smart_transcript:
+                target_transcript = ""
+                for word in word_info:
+                    if word["end"] < prompt_end_time:
+                        target_transcript += word["word"]
+                    elif (word["start"] + word["end"]) / 2 < prompt_end_time:
+                        # include part of the word it it's big, but adjust prompt_end_time
+                        target_transcript += word["word"]
+                        prompt_end_time = word["end"]
+                        break
+                    else:
+                        break
+                target_transcript += f" {sentence}"
+            else:
+                target_transcript = sentence
+
+            inference_transcript += target_transcript + "\n"
+
+            prompt_end_frame = int(min(audio_dur, prompt_end_time) * info.sample_rate)
+            _, gen_audio = inference_one_sample(voicecraft_model["model"],
+                                                voicecraft_model["ckpt"]["config"],
+                                                voicecraft_model["ckpt"]["phn2num"],
+                                                voicecraft_model["text_tokenizer"], voicecraft_model["audio_tokenizer"],
+                                                audio_path, target_transcript, device, decode_config,
+                                                prompt_end_frame)
+        else:
+            from inference_speech_editing_scale import inference_one_sample
+
+            if smart_transcript:
+                target_transcript = ""
+                for word in word_info:
+                    if word["start"] < edit_start_time:
+                        target_transcript += word["word"]
+                    else:
+                        break
+                target_transcript += f" {sentence}"
+                for word in word_info:
+                    if word["end"] > edit_end_time:
+                        target_transcript += word["word"]
+            else:
+                target_transcript = sentence
+
+            inference_transcript += target_transcript + "\n"
+
+            morphed_span = (max(edit_start_time - left_margin, 1 / codec_sr), min(edit_end_time + right_margin, audio_dur))
+            mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]
+            mask_interval = torch.LongTensor(mask_interval)
+            
+            _, gen_audio = inference_one_sample(voicecraft_model["model"],
+                                                voicecraft_model["ckpt"]["config"],
+                                                voicecraft_model["ckpt"]["phn2num"],
+                                                voicecraft_model["text_tokenizer"], voicecraft_model["audio_tokenizer"],
+                                                audio_path, target_transcript, mask_interval, device, decode_config)
+        gen_audio = gen_audio[0].cpu()
+        audio_tensors.append(gen_audio)
+
+    if mode != "Rerun":
+        output_audio = get_output_audio(audio_tensors, codec_audio_sr)
+        sentences = [f"{idx}: {text}" for idx, text in enumerate(sentences)]
+        component = gr.Dropdown(label="Sentence", choices=sentences, value=sentences[0],
+                                info="Select sentence you want to regenerate")
+        return output_audio, inference_transcript, component, audio_tensors
+    else:
+        previous_audio_tensors[selected_sentence_idx] = audio_tensors[0]
+        output_audio = get_output_audio(previous_audio_tensors, codec_audio_sr)
+        sentence_audio = get_output_audio(audio_tensors, codec_audio_sr)
+        return output_audio, inference_transcript, sentence_audio, previous_audio_tensors
+    
+    
+def update_input_audio(prompt_end_time, edit_start_time, edit_end_time):
+    def impl(audio_path):
+        info = torchaudio.info(audio_path)
+        max_time = round(info.num_frames / info.sample_rate, 2)
+        return [
+            prompt_end_time.update(maximum=max_time, value=max_time),
+            edit_start_time.update(maximum=max_time, value=0),
+            edit_end_time.update(maximum=max_time, value=max_time),
+        ]
+    return impl
+
+    
+def change_mode(prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls):
+    def impl(mode):
+        return [
+            prompt_end_time.update(visible=mode != "Edit"),
+            split_text.update(visible=mode == "Long TTS"),
+            edit_word_mode.update(visible=mode == "Edit"),
+            segment_control.update(visible=mode == "Edit"),
+            precise_segment_control.update(visible=mode == "Edit"),
+            long_tts_controls.update(visible=mode == "Long TTS"),
+        ]
+    return impl
+
+
+def load_sentence(selected_sentence, codec_audio_sr, audio_tensors):
+    if selected_sentence is None:
+        return None
+    colon_position = selected_sentence.find(':')
+    selected_sentence_idx = int(selected_sentence[:colon_position])
+    return get_output_audio([audio_tensors[selected_sentence_idx]], codec_audio_sr)
+
+
+def update_bound_word(is_first_word, edit_time):
+    def impl(selected_word, edit_word_mode):
+        word_start_time = float(selected_word.split(' ')[0])
+        word_end_time = float(selected_word.split(' ')[-1])
+        if edit_word_mode == "Replace half":
+            bound_time = (word_start_time + word_end_time) / 2
+        elif is_first_word:
+            bound_time = word_start_time
+        else:
+            bound_time = word_end_time
+
+        return edit_time.update(value=bound_time)
+    return impl
+
+
+def update_bound_words(edit_start_time, edit_end_time):
+    def impl(from_selected_word, to_selected_word, edit_word_mode):
+        return [
+            update_bound_word(True, edit_start_time)(from_selected_word, edit_word_mode),
+            update_bound_word(True, edit_end_time)(to_selected_word, edit_word_mode),
+        ]
+    return impl
+
+
+smart_transcript_info = """
+If enabled, the target transcript will be constructed for you:</br>
+ - In TTS and Long TTS mode just write the text you want to synthesize.</br>
+ - In Edit mode just write the text to replace selected editing segment.</br>
+If disabled, you should write the target transcript yourself:</br>
+ - In TTS mode write prompt transcript followed by generation transcript.</br>
+ - In Long TTS select split by newline (<b>SENTENCE SPLIT WON'T WORK</b>) and start each line with a prompt transcript.</br>
+ - In Edit mode write full prompt</br>
+"""
+demo_text = {
+    "TTS": {
+        "smart": "I cannot believe that the same model can also do text to speech synthesis as well!",
+        "regular": "But when I had approached so near to them, the common I cannot believe that the same model can also do text to speech synthesis as well!"
+    },
+    "Edit": {
+        "smart": "saw the mirage of the lake in the distance,",
+        "regular": "But when I saw the mirage of the lake in the distance, which the sense deceives, Lost not by distance any of its marks,"
+    },
+    "Long TTS": {
+        "smart": "You can run TTS on a big text!\n"
+                 "Just write it line-by-line. Or sentence-by-sentence.\n"
+                 "If some sentences sound odd, just rerun TTS on them, no need to generate the whole text again!",
+        "regular": "But when I had approached so near to them, the common You can run TTS on a big text!\n"
+                   "But when I had approached so near to them, the common Just write it line-by-line. Or sentence-by-sentence.\n"
+                   "But when I had approached so near to them, the common If some sentences sound odd, just rerun TTS on them, no need to generate the whole text again!"
+    }
+}
+all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}
+demo_words = [
+    '0.0  But 0.12', '0.12  when 0.26', '0.26  I 0.44', '0.44  had 0.6', '0.6  approached 0.94', '0.94  so 1.42',
+    '1.42  near 1.78', '1.78  to 2.02', '2.02  them, 2.24', '2.52  the 2.58', '2.58  common 2.9', '2.9  object, 3.3', 
+    '3.72  which 3.78', '3.78  the 3.98', '3.98  sense 4.18', '4.18  deceives, 4.88', '5.06  lost 5.26', '5.26  not 5.74',
+    '5.74  by 6.08', '6.08  distance 6.36', '6.36  any 6.92', '6.92  of 7.12', '7.12  its 7.26', '7.26  marks. 7.54'
+]
+
+
+def update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time):
+    def impl(mode, smart_transcript, edit_word_mode):
+        if transcript.value not in all_demo_texts:
+            return [transcript, edit_from_word, edit_to_word, prompt_end_time]
+        
+        replace_half = edit_word_mode == "Replace half"
+        return [
+            transcript.update(value=demo_text[mode]["smart" if smart_transcript else "regular"]),
+            edit_from_word.update(value="0.26  I 0.44" if replace_half else "0.44  had 0.6"),
+            edit_to_word.update(value="3.72  which 3.78" if replace_half else "2.9  object, 3.3"),
+            prompt_end_time.update(value=3.01),
+        ]
+    return impl
+
+
+with gr.Blocks() as app:
+    with gr.Row():
+        with gr.Column(scale=2):
+            load_models_btn = gr.Button(value="Load models")
+        with gr.Column(scale=5):
+            with gr.Accordion("Select models", open=False):
+                with gr.Row():
+                    voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
+                    whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
+                                                    choices=["tiny.en", "base.en", "small.en", "medium.en", "large"])
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=False)
+            with gr.Group():
+                original_transcript = gr.Textbox(label="Original transcript", lines=5, interactive=False,
+                                                 info="Use whisper model to get the transcript. Fix it if necessary.")
+                with gr.Accordion("Word start time", open=False):
+                    transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
+                with gr.Accordion("Word end time", open=False):
+                    transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
+
+                transcribe_btn = gr.Button(value="Transcribe", interactive=False)
+            
+        with gr.Column(scale=3):
+            with gr.Group():
+                transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
+                with gr.Row():
+                    smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
+                    with gr.Accordion(label="?", open=False):
+                        info = gr.HTML(value=smart_transcript_info)
+                mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
+                
+                prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.01, value=3.01)
+                split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline", visible=False,
+                                      info="Split text into parts and run TTS for each part.")
+                edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half", visible=False,
+                                          info="What to do with first and last word")
+                with gr.Row(visible=False) as segment_control:
+                    edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, interactive=True)
+                    edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, interactive=True)
+                with gr.Accordion("Precise segment control", open=False, visible=False) as precise_segment_control:
+                    edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=60, step=0.01, value=0)
+                    edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=60, step=0.01, value=60)
+
+                run_btn = gr.Button(value="Run", interactive=False)
+
+        with gr.Column(scale=2):
+            output_audio = gr.Audio(label="Output Audio")
+            with gr.Accordion("Inference transcript", open=False):
+                inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
+                                                  info="Inference was performed on this transcript.")
+            with gr.Group(visible=False) as long_tts_controls:
+                sentence_selector = gr.Dropdown(label="Sentence", value=None,
+                                                info="Select sentence you want to regenerate")
+                sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
+                rerun_btn = gr.Button(value="Rerun", interactive=False)
+
+    with gr.Row():
+        with gr.Accordion("VoiceCraft config", open=False):
+            left_margin = gr.Number(label="left_margin", value=0.08)
+            right_margin = gr.Number(label="right_margin", value=0.08)
+            codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000)
+            codec_sr = gr.Number(label="codec_sr", value=50)
+            top_k = gr.Number(label="top_k", value=0)
+            top_p = gr.Number(label="top_p", value=0.8)
+            temperature = gr.Number(label="temperature", value=1)
+            stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3], value=3,
+                                       info="if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1, -1 = disabled")
+            sample_batch_size = gr.Number(label="sample_batch_size", value=4, precision=0,
+                                          info="generate this many samples and choose the shortest one")
+            kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
+                                info="set to 0 to use less VRAM, but with slower inference")
+            silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
+
+    
+    audio_tensors = gr.State()
+    word_info = gr.State()
+
+    mode.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
+                inputs=[mode, smart_transcript, edit_word_mode],
+                outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+    edit_word_mode.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
+                          inputs=[mode, smart_transcript, edit_word_mode],
+                          outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+    smart_transcript.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
+                            inputs=[mode, smart_transcript, edit_word_mode],
+                            outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+    
+    load_models_btn.click(fn=load_models(input_audio, transcribe_btn, run_btn, rerun_btn),
+                          inputs=[whisper_model_choice, voicecraft_model_choice],
+                          outputs=[input_audio, transcribe_btn, run_btn, rerun_btn])
+
+    input_audio.change(fn=update_input_audio(prompt_end_time, edit_start_time, edit_end_time),
+                       inputs=[input_audio], outputs=[prompt_end_time, edit_start_time, edit_end_time])
+    transcribe_btn.click(fn=transcribe, inputs=[input_audio],
+                         outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time, edit_from_word, edit_to_word, word_info])
+
+    mode.change(fn=change_mode(prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls),
+                inputs=[mode], outputs=[prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls])
+
+    run_btn.click(fn=run,
+                  inputs=[
+                      left_margin, right_margin,
+                      codec_audio_sr, codec_sr,
+                      top_k, top_p, temperature,
+                      stop_repetition, sample_batch_size,
+                      kvcache, silence_tokens,
+                      input_audio, word_info, transcript, smart_transcript,
+                      mode, prompt_end_time, edit_start_time, edit_end_time,
+                      split_text, sentence_selector, audio_tensors
+                  ],
+                  outputs=[
+                      output_audio, inference_transcript, sentence_selector, audio_tensors
+                  ])
+    
+    sentence_selector.change(fn=load_sentence, inputs=[sentence_selector, codec_audio_sr, audio_tensors], outputs=[sentence_audio])
+    rerun_btn.click(fn=run,
+                    inputs=[
+                        left_margin, right_margin,
+                        codec_audio_sr, codec_sr,
+                        top_k, top_p, temperature,
+                        stop_repetition, sample_batch_size,
+                        kvcache, silence_tokens,
+                        input_audio, word_info, transcript, smart_transcript,
+                        gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
+                        split_text, sentence_selector, audio_tensors
+                    ],
+                    outputs=[
+                        output_audio, inference_transcript, sentence_audio, audio_tensors
+                    ])
+    
+    edit_word_mode.change(fn=update_bound_words(edit_start_time, edit_end_time),
+                          inputs=[edit_from_word, edit_to_word, edit_word_mode], outputs=[edit_start_time, edit_end_time])
+    edit_from_word.change(fn=update_bound_word(True, edit_start_time),
+                          inputs=[edit_from_word, edit_word_mode], outputs=[edit_start_time])
+    edit_to_word.change(fn=update_bound_word(False, edit_end_time),
+                        inputs=[edit_to_word, edit_word_mode], outputs=[edit_end_time])
+
+
+if __name__ == "__main__":
+    app.launch()
\ No newline at end of file
diff --git a/gradio_requirements.txt b/gradio_requirements.txt
new file mode 100644
index 0000000..949acc7
--- /dev/null
+++ b/gradio_requirements.txt
@@ -0,0 +1,3 @@
+gradio==3.50.2
+nltk>=3.8.1
+openai-whisper>=20231117
\ No newline at end of file
diff --git a/start-jupyter.bat b/start-jupyter.bat
index fc69502..eb98b66 100644
--- a/start-jupyter.bat
+++ b/start-jupyter.bat
@@ -5,6 +5,7 @@ echo Creating and running the Jupyter container...
 docker run -it -d ^
     --gpus all ^
     -p 8888:8888 ^
+    -p 7860:7860 ^
     --name jupyter ^
     --user root ^
     -e NB_USER="%username%" ^
diff --git a/start-jupyter.sh b/start-jupyter.sh
index 5888bfb..cb54572 100755
--- a/start-jupyter.sh
+++ b/start-jupyter.sh
@@ -8,6 +8,7 @@ docker run -it \
     -d \
     --gpus all \
     -p 8888:8888 \
+    -p 7860:7860 \
     --name jupyter \
     --user root \
     -e NB_USER="$USER" \

From 74fa65979d65acc74bdf7cb05c2eaa65c877acca Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Wed, 3 Apr 2024 05:01:55 +0300
Subject: [PATCH 02/24] deprecated .update not used anymore, better error
 handling, can use voicecraft without whisper

---
 gradio_app.py | 289 ++++++++++++++++++++++++++------------------------
 1 file changed, 149 insertions(+), 140 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 956175c..cab37c4 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -6,62 +6,63 @@ from data.tokenizer import (
     TextTokenizer,
 )
 from models import voicecraft
-import whisper
-from whisper.tokenizer import get_tokenizer
 import os
 import io
 
 
-whisper_model = None
-voicecraft_model = None
-device = "cuda" if torch.cuda.is_available() else "cpu"
+def load_models(whisper_model_choice, voicecraft_model_choice):
+    whisper_model, voicecraft_model = None, None
+    if whisper_model_choice is not None:
+        import whisper
+        from whisper.tokenizer import get_tokenizer
+        whisper_model = {
+            "model": whisper.load_model(whisper_model_choice),
+            "tokenizer": get_tokenizer(multilingual=False)
+        }
+
+    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    voicecraft_name = f"{voicecraft_model_choice}.pth"
+    ckpt_fn = f"./pretrained_models/{voicecraft_name}"
+    encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
+    if not os.path.exists(ckpt_fn):
+        os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
+        os.system(f"mv {voicecraft_name}\?download\=true ./pretrained_models/{voicecraft_name}")
+    if not os.path.exists(encodec_fn):
+        os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
+        os.system(f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
+
+    ckpt = torch.load(ckpt_fn, map_location="cpu")
+    model = voicecraft.VoiceCraft(ckpt["config"])
+    model.load_state_dict(ckpt["model"])
+    model.to(device)
+    model.eval()
+    voicecraft_model = {
+        "ckpt": ckpt,
+        "model": model,
+        "text_tokenizer": TextTokenizer(backend="espeak"),
+        "audio_tokenizer": AudioTokenizer(signature=encodec_fn)
+    }
+
+    return [
+        whisper_model,
+        voicecraft_model,
+        gr.Audio(interactive=True),
+    ]
 
 
-def load_models(input_audio, transcribe_btn, run_btn, rerun_btn):
-    def impl(whisper_model_choice, voicecraft_model_choice):
-        global whisper_model, voicecraft_model
-        whisper_model = whisper.load_model(whisper_model_choice)
-
-        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-        
-        voicecraft_name = f"{voicecraft_model_choice}.pth"
-        ckpt_fn = f"./pretrained_models/{voicecraft_name}"
-        encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
-        if not os.path.exists(ckpt_fn):
-            os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
-            os.system(f"mv {voicecraft_name}\?download\=true ./pretrained_models/{voicecraft_name}")
-        if not os.path.exists(encodec_fn):
-            os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
-            os.system(f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
-
-        voicecraft_model = {}
-        voicecraft_model["ckpt"] = torch.load(ckpt_fn, map_location="cpu")
-        voicecraft_model["model"] = voicecraft.VoiceCraft(voicecraft_model["ckpt"]["config"])
-        voicecraft_model["model"].load_state_dict(voicecraft_model["ckpt"]["model"])
-        voicecraft_model["model"].to(device)
-        voicecraft_model["model"].eval()
-
-        voicecraft_model["text_tokenizer"] = TextTokenizer(backend="espeak")
-        voicecraft_model["audio_tokenizer"] = AudioTokenizer(signature=encodec_fn)
-
-        return [
-            input_audio.update(interactive=True),
-            transcribe_btn.update(interactive=True),
-            run_btn.update(interactive=True),
-            rerun_btn.update(interactive=True)
-        ]
-    return impl
-
-
-def transcribe(audio_path):
-    tokenizer = get_tokenizer(multilingual=False)
+def transcribe(whisper_model, audio_path):
+    if whisper_model is None:
+        raise gr.Error("Whisper model not loaded")
+    
     number_tokens = [
         i
-        for i in range(tokenizer.eot)
-        if all(c in "0123456789" for c in tokenizer.decode([i]).removeprefix(" "))
+        for i in range(whisper_model["tokenizer"].eot)
+        if all(c in "0123456789" for c in whisper_model["tokenizer"].decode([i]).removeprefix(" "))
     ]
-    result = whisper_model.transcribe(audio_path, suppress_tokens=[-1] + number_tokens, word_timestamps=True)
+    result = whisper_model["model"].transcribe(audio_path, suppress_tokens=[-1] + number_tokens, word_timestamps=True)
     words = [word_info for segment in result["segments"] for word_info in segment["words"]]
     
     transcript = result["text"]
@@ -69,12 +70,12 @@ def transcribe(audio_path):
     transcript_with_end_time = " ".join([f"{word['word']} {word['end']}" for word in words])
 
     choices = [f"{word['start']} {word['word']} {word['end']}" for word in words]
-    edit_from_word = gr.Dropdown(label="First word to edit", value=choices[0], choices=choices, interactive=True)
-    edit_to_word = gr.Dropdown(label="Last word to edit", value=choices[-1], choices=choices, interactive=True)
 
     return [
         transcript, transcript_with_start_time, transcript_with_end_time,
-        edit_from_word, edit_to_word, words
+        gr.Dropdown(value=choices[0], choices=choices, interactive=True), # edit_from_word
+        gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # edit_to_word
+        words
     ]
 
 
@@ -86,11 +87,16 @@ def get_output_audio(audio_tensors, codec_audio_sr):
     return buffer.read()
 
 
-def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
+def run(voicecraft_model, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
         stop_repetition, sample_batch_size, kvcache, silence_tokens,
         audio_path, word_info, transcript, smart_transcript,
         mode, prompt_end_time, edit_start_time, edit_end_time,
         split_text, selected_sentence, previous_audio_tensors):
+    if voicecraft_model is None:
+        raise gr.Error("VoiceCraft model not loaded")
+    if smart_transcript and (word_info is None):
+        raise gr.Error("Can't use smart transcript: whisper transcript not found")
+
     if mode == "Long TTS":
         if split_text == "Newline":
             sentences = transcript.split('\n')
@@ -104,6 +110,7 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
     else:
         sentences = [transcript.replace("\n", " ")]
 
+    device = "cuda" if torch.cuda.is_available() else "cpu"
     info = torchaudio.info(audio_path)
     audio_dur = info.num_frames / info.sample_rate
 
@@ -116,7 +123,7 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
         if mode != "Edit":
             from inference_tts_scale import inference_one_sample
 
-            if smart_transcript:
+            if smart_transcript:                
                 target_transcript = ""
                 for word in word_info:
                     if word["end"] < prompt_end_time:
@@ -175,8 +182,7 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
     if mode != "Rerun":
         output_audio = get_output_audio(audio_tensors, codec_audio_sr)
         sentences = [f"{idx}: {text}" for idx, text in enumerate(sentences)]
-        component = gr.Dropdown(label="Sentence", choices=sentences, value=sentences[0],
-                                info="Select sentence you want to regenerate")
+        component = gr.Dropdown(choices=sentences, value=sentences[0])
         return output_audio, inference_transcript, component, audio_tensors
     else:
         previous_audio_tensors[selected_sentence_idx] = audio_tensors[0]
@@ -185,29 +191,25 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
         return output_audio, inference_transcript, sentence_audio, previous_audio_tensors
     
     
-def update_input_audio(prompt_end_time, edit_start_time, edit_end_time):
-    def impl(audio_path):
-        info = torchaudio.info(audio_path)
-        max_time = round(info.num_frames / info.sample_rate, 2)
-        return [
-            prompt_end_time.update(maximum=max_time, value=max_time),
-            edit_start_time.update(maximum=max_time, value=0),
-            edit_end_time.update(maximum=max_time, value=max_time),
-        ]
-    return impl
+def update_input_audio(audio_path):
+    info = torchaudio.info(audio_path)
+    max_time = round(info.num_frames / info.sample_rate, 2)
+    return [
+        gr.Slider(maximum=max_time, value=max_time),
+        gr.Slider(maximum=max_time, value=0),
+        gr.Slider(maximum=max_time, value=max_time),
+    ]
 
     
-def change_mode(prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls):
-    def impl(mode):
-        return [
-            prompt_end_time.update(visible=mode != "Edit"),
-            split_text.update(visible=mode == "Long TTS"),
-            edit_word_mode.update(visible=mode == "Edit"),
-            segment_control.update(visible=mode == "Edit"),
-            precise_segment_control.update(visible=mode == "Edit"),
-            long_tts_controls.update(visible=mode == "Long TTS"),
-        ]
-    return impl
+def change_mode(mode):
+    return [
+        gr.Slider(visible=mode != "Edit"),
+        gr.Radio(visible=mode == "Long TTS"),
+        gr.Radio(visible=mode == "Edit"),
+        gr.Row(visible=mode == "Edit"),
+        gr.Accordion(visible=mode == "Edit"),
+        gr.Group(visible=mode == "Long TTS"),
+    ]
 
 
 def load_sentence(selected_sentence, codec_audio_sr, audio_tensors):
@@ -218,28 +220,27 @@ def load_sentence(selected_sentence, codec_audio_sr, audio_tensors):
     return get_output_audio([audio_tensors[selected_sentence_idx]], codec_audio_sr)
 
 
-def update_bound_word(is_first_word, edit_time):
-    def impl(selected_word, edit_word_mode):
-        word_start_time = float(selected_word.split(' ')[0])
-        word_end_time = float(selected_word.split(' ')[-1])
-        if edit_word_mode == "Replace half":
-            bound_time = (word_start_time + word_end_time) / 2
-        elif is_first_word:
-            bound_time = word_start_time
-        else:
-            bound_time = word_end_time
+def update_bound_word(is_first_word, selected_word, edit_word_mode):
+    if selected_word is None:
+        return None
 
-        return edit_time.update(value=bound_time)
-    return impl
+    word_start_time = float(selected_word.split(' ')[0])
+    word_end_time = float(selected_word.split(' ')[-1])
+    if edit_word_mode == "Replace half":
+        bound_time = (word_start_time + word_end_time) / 2
+    elif is_first_word:
+        bound_time = word_start_time
+    else:
+        bound_time = word_end_time
+
+    return bound_time
 
 
-def update_bound_words(edit_start_time, edit_end_time):
-    def impl(from_selected_word, to_selected_word, edit_word_mode):
-        return [
-            update_bound_word(True, edit_start_time)(from_selected_word, edit_word_mode),
-            update_bound_word(True, edit_end_time)(to_selected_word, edit_word_mode),
-        ]
-    return impl
+def update_bound_words(from_selected_word, to_selected_word, edit_word_mode):
+    return [
+        update_bound_word(True, from_selected_word, edit_word_mode),
+        update_bound_word(False, to_selected_word, edit_word_mode),
+    ]
 
 
 smart_transcript_info = """
@@ -251,6 +252,7 @@ If disabled, you should write the target transcript yourself:</br>
  - In Long TTS select split by newline (<b>SENTENCE SPLIT WON'T WORK</b>) and start each line with a prompt transcript.</br>
  - In Edit mode write full prompt</br>
 """
+
 demo_text = {
     "TTS": {
         "smart": "I cannot believe that the same model can also do text to speech synthesis as well!",
@@ -269,7 +271,9 @@ demo_text = {
                    "But when I had approached so near to them, the common If some sentences sound odd, just rerun TTS on them, no need to generate the whole text again!"
     }
 }
+
 all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}
+
 demo_words = [
     '0.0  But 0.12', '0.12  when 0.26', '0.26  I 0.44', '0.44  had 0.6', '0.6  approached 0.94', '0.94  so 1.42',
     '1.42  near 1.78', '1.78  to 2.02', '2.02  them, 2.24', '2.52  the 2.58', '2.58  common 2.9', '2.9  object, 3.3', 
@@ -278,19 +282,17 @@ demo_words = [
 ]
 
 
-def update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time):
-    def impl(mode, smart_transcript, edit_word_mode):
-        if transcript.value not in all_demo_texts:
-            return [transcript, edit_from_word, edit_to_word, prompt_end_time]
-        
-        replace_half = edit_word_mode == "Replace half"
-        return [
-            transcript.update(value=demo_text[mode]["smart" if smart_transcript else "regular"]),
-            edit_from_word.update(value="0.26  I 0.44" if replace_half else "0.44  had 0.6"),
-            edit_to_word.update(value="3.72  which 3.78" if replace_half else "2.9  object, 3.3"),
-            prompt_end_time.update(value=3.01),
-        ]
-    return impl
+def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time):
+    if transcript not in all_demo_texts:
+        return transcript, edit_from_word, edit_to_word, prompt_end_time
+    
+    replace_half = edit_word_mode == "Replace half"
+    return [
+        demo_text[mode]["smart" if smart_transcript else "regular"],
+        "0.26  I 0.44" if replace_half else "0.44  had 0.6",
+        "3.72  which 3.78" if replace_half else "2.9  object, 3.3",
+        3.01,
+    ]
 
 
 with gr.Blocks() as app:
@@ -302,7 +304,7 @@ with gr.Blocks() as app:
                 with gr.Row():
                     voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
                     whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
-                                                    choices=["tiny.en", "base.en", "small.en", "medium.en", "large"])
+                                                    choices=[None, "tiny.en", "base.en", "small.en", "medium.en", "large"])
 
     with gr.Row():
         with gr.Column(scale=2):
@@ -315,7 +317,7 @@ with gr.Blocks() as app:
                 with gr.Accordion("Word end time", open=False):
                     transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
 
-                transcribe_btn = gr.Button(value="Transcribe", interactive=False)
+                transcribe_btn = gr.Button(value="Transcribe")
             
         with gr.Column(scale=3):
             with gr.Group():
@@ -338,7 +340,7 @@ with gr.Blocks() as app:
                     edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=60, step=0.01, value=0)
                     edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=60, step=0.01, value=60)
 
-                run_btn = gr.Button(value="Run", interactive=False)
+                run_btn = gr.Button(value="Run")
 
         with gr.Column(scale=2):
             output_audio = gr.Audio(label="Output Audio")
@@ -349,7 +351,7 @@ with gr.Blocks() as app:
                 sentence_selector = gr.Dropdown(label="Sentence", value=None,
                                                 info="Select sentence you want to regenerate")
                 sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
-                rerun_btn = gr.Button(value="Rerun", interactive=False)
+                rerun_btn = gr.Button(value="Rerun")
 
     with gr.Row():
         with gr.Accordion("VoiceCraft config", open=False):
@@ -369,34 +371,40 @@ with gr.Blocks() as app:
             silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
 
     
+    whisper_model = gr.State()
+    voicecraft_model = gr.State()
     audio_tensors = gr.State()
     word_info = gr.State()
 
-    mode.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
-                inputs=[mode, smart_transcript, edit_word_mode],
+    
+    mode.change(fn=update_demo,
+                inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
                 outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
-    edit_word_mode.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
-                          inputs=[mode, smart_transcript, edit_word_mode],
+    edit_word_mode.change(fn=update_demo,
+                          inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
                           outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
-    smart_transcript.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
-                            inputs=[mode, smart_transcript, edit_word_mode],
+    smart_transcript.change(fn=update_demo,
+                            inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
                             outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
     
-    load_models_btn.click(fn=load_models(input_audio, transcribe_btn, run_btn, rerun_btn),
+    load_models_btn.click(fn=load_models,
                           inputs=[whisper_model_choice, voicecraft_model_choice],
-                          outputs=[input_audio, transcribe_btn, run_btn, rerun_btn])
-
-    input_audio.change(fn=update_input_audio(prompt_end_time, edit_start_time, edit_end_time),
-                       inputs=[input_audio], outputs=[prompt_end_time, edit_start_time, edit_end_time])
-    transcribe_btn.click(fn=transcribe, inputs=[input_audio],
+                          outputs=[whisper_model, voicecraft_model, input_audio])
+    
+    input_audio.change(fn=update_input_audio,
+                       inputs=[input_audio],
+                       outputs=[prompt_end_time, edit_start_time, edit_end_time])
+    transcribe_btn.click(fn=transcribe,
+                         inputs=[whisper_model, input_audio],
                          outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time, edit_from_word, edit_to_word, word_info])
 
-    mode.change(fn=change_mode(prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls),
-                inputs=[mode], outputs=[prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls])
+    mode.change(fn=change_mode,
+                inputs=[mode],
+                outputs=[prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls])
 
     run_btn.click(fn=run,
                   inputs=[
-                      left_margin, right_margin,
+                      voicecraft_model, left_margin, right_margin,
                       codec_audio_sr, codec_sr,
                       top_k, top_p, temperature,
                       stop_repetition, sample_batch_size,
@@ -405,14 +413,14 @@ with gr.Blocks() as app:
                       mode, prompt_end_time, edit_start_time, edit_end_time,
                       split_text, sentence_selector, audio_tensors
                   ],
-                  outputs=[
-                      output_audio, inference_transcript, sentence_selector, audio_tensors
-                  ])
+                  outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
     
-    sentence_selector.change(fn=load_sentence, inputs=[sentence_selector, codec_audio_sr, audio_tensors], outputs=[sentence_audio])
+    sentence_selector.change(fn=load_sentence,
+                             inputs=[sentence_selector, codec_audio_sr, audio_tensors],
+                             outputs=[sentence_audio])
     rerun_btn.click(fn=run,
                     inputs=[
-                        left_margin, right_margin,
+                        voicecraft_model, left_margin, right_margin,
                         codec_audio_sr, codec_sr,
                         top_k, top_p, temperature,
                         stop_repetition, sample_batch_size,
@@ -421,16 +429,17 @@ with gr.Blocks() as app:
                         gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
                         split_text, sentence_selector, audio_tensors
                     ],
-                    outputs=[
-                        output_audio, inference_transcript, sentence_audio, audio_tensors
-                    ])
+                    outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
     
-    edit_word_mode.change(fn=update_bound_words(edit_start_time, edit_end_time),
-                          inputs=[edit_from_word, edit_to_word, edit_word_mode], outputs=[edit_start_time, edit_end_time])
-    edit_from_word.change(fn=update_bound_word(True, edit_start_time),
-                          inputs=[edit_from_word, edit_word_mode], outputs=[edit_start_time])
-    edit_to_word.change(fn=update_bound_word(False, edit_end_time),
-                        inputs=[edit_to_word, edit_word_mode], outputs=[edit_end_time])
+    edit_from_word.change(fn=update_bound_word,
+                          inputs=[gr.State(True), edit_from_word, edit_word_mode],
+                          outputs=[edit_start_time])
+    edit_to_word.change(fn=update_bound_word,
+                        inputs=[gr.State(False), edit_to_word, edit_word_mode],
+                        outputs=[edit_end_time])
+    edit_word_mode.change(fn=update_bound_words,
+                          inputs=[edit_from_word, edit_to_word, edit_word_mode],
+                          outputs=[edit_start_time, edit_end_time])
 
 
 if __name__ == "__main__":

From f9fed26b15515697965e7da9bad3d46eadc96816 Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Wed, 3 Apr 2024 05:16:22 +0300
Subject: [PATCH 03/24] global models

---
 gradio_app.py | 26 ++++++++++++--------------
 1 file changed, 12 insertions(+), 14 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index cab37c4..4124444 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -10,8 +10,12 @@ import os
 import io
 
 
+whisper_model, voicecraft_model = None, None
+
+
 def load_models(whisper_model_choice, voicecraft_model_choice):
-    whisper_model, voicecraft_model = None, None
+    global whisper_model, voicecraft_model
+
     if whisper_model_choice is not None:
         import whisper
         from whisper.tokenizer import get_tokenizer
@@ -46,14 +50,10 @@ def load_models(whisper_model_choice, voicecraft_model_choice):
         "audio_tokenizer": AudioTokenizer(signature=encodec_fn)
     }
 
-    return [
-        whisper_model,
-        voicecraft_model,
-        gr.Audio(interactive=True),
-    ]
+    return gr.Audio(interactive=True)
 
 
-def transcribe(whisper_model, audio_path):
+def transcribe(audio_path):
     if whisper_model is None:
         raise gr.Error("Whisper model not loaded")
     
@@ -87,7 +87,7 @@ def get_output_audio(audio_tensors, codec_audio_sr):
     return buffer.read()
 
 
-def run(voicecraft_model, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
+def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
         stop_repetition, sample_batch_size, kvcache, silence_tokens,
         audio_path, word_info, transcript, smart_transcript,
         mode, prompt_end_time, edit_start_time, edit_end_time,
@@ -371,8 +371,6 @@ with gr.Blocks() as app:
             silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
 
     
-    whisper_model = gr.State()
-    voicecraft_model = gr.State()
     audio_tensors = gr.State()
     word_info = gr.State()
 
@@ -389,13 +387,13 @@ with gr.Blocks() as app:
     
     load_models_btn.click(fn=load_models,
                           inputs=[whisper_model_choice, voicecraft_model_choice],
-                          outputs=[whisper_model, voicecraft_model, input_audio])
+                          outputs=[input_audio])
     
     input_audio.change(fn=update_input_audio,
                        inputs=[input_audio],
                        outputs=[prompt_end_time, edit_start_time, edit_end_time])
     transcribe_btn.click(fn=transcribe,
-                         inputs=[whisper_model, input_audio],
+                         inputs=[input_audio],
                          outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time, edit_from_word, edit_to_word, word_info])
 
     mode.change(fn=change_mode,
@@ -404,7 +402,7 @@ with gr.Blocks() as app:
 
     run_btn.click(fn=run,
                   inputs=[
-                      voicecraft_model, left_margin, right_margin,
+                      left_margin, right_margin,
                       codec_audio_sr, codec_sr,
                       top_k, top_p, temperature,
                       stop_repetition, sample_batch_size,
@@ -420,7 +418,7 @@ with gr.Blocks() as app:
                              outputs=[sentence_audio])
     rerun_btn.click(fn=run,
                     inputs=[
-                        voicecraft_model, left_margin, right_margin,
+                        left_margin, right_margin,
                         codec_audio_sr, codec_sr,
                         top_k, top_p, temperature,
                         stop_repetition, sample_batch_size,

From 1a219cf6da69df070cf03966f06433c9a0362de2 Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Wed, 3 Apr 2024 20:24:34 +0300
Subject: [PATCH 04/24] bugfixes, seed support, better ui

---
 gradio_app.py | 136 ++++++++++++++++++++++++++++++++++----------------
 1 file changed, 93 insertions(+), 43 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 4124444..707defa 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -8,11 +8,24 @@ from data.tokenizer import (
 from models import voicecraft
 import os
 import io
+import numpy as np
+import random
 
 
 whisper_model, voicecraft_model = None, None
 
 
+def seed_everything(seed):
+    if seed != -1:
+        os.environ['PYTHONHASHSEED'] = str(seed)
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+
+
 def load_models(whisper_model_choice, voicecraft_model_choice):
     global whisper_model, voicecraft_model
 
@@ -50,12 +63,13 @@ def load_models(whisper_model_choice, voicecraft_model_choice):
         "audio_tokenizer": AudioTokenizer(signature=encodec_fn)
     }
 
-    return gr.Audio(interactive=True)
+    return gr.Accordion()
 
 
-def transcribe(audio_path):
+def transcribe(seed, audio_path):
     if whisper_model is None:
         raise gr.Error("Whisper model not loaded")
+    seed_everything(seed)
     
     number_tokens = [
         i
@@ -73,6 +87,7 @@ def transcribe(audio_path):
 
     return [
         transcript, transcript_with_start_time, transcript_with_end_time,
+        gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # prompt_to_word
         gr.Dropdown(value=choices[0], choices=choices, interactive=True), # edit_from_word
         gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # edit_to_word
         words
@@ -87,7 +102,7 @@ def get_output_audio(audio_tensors, codec_audio_sr):
     return buffer.read()
 
 
-def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
+def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
         stop_repetition, sample_batch_size, kvcache, silence_tokens,
         audio_path, word_info, transcript, smart_transcript,
         mode, prompt_end_time, edit_start_time, edit_end_time,
@@ -97,6 +112,7 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
     if smart_transcript and (word_info is None):
         raise gr.Error("Can't use smart transcript: whisper transcript not found")
 
+    seed_everything(seed)
     if mode == "Long TTS":
         if split_text == "Newline":
             sentences = transcript.split('\n')
@@ -192,6 +208,9 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
     
     
 def update_input_audio(audio_path):
+    if audio_path is None:
+        return 0, 0, 0
+    
     info = torchaudio.info(audio_path)
     max_time = round(info.num_frames / info.sample_rate, 2)
     return [
@@ -202,12 +221,12 @@ def update_input_audio(audio_path):
 
     
 def change_mode(mode):
+    tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor
     return [
-        gr.Slider(visible=mode != "Edit"),
-        gr.Radio(visible=mode == "Long TTS"),
+        gr.Group(visible=mode != "Edit"),
+        gr.Group(visible=mode == "Edit"),
         gr.Radio(visible=mode == "Edit"),
-        gr.Row(visible=mode == "Edit"),
-        gr.Accordion(visible=mode == "Edit"),
+        gr.Radio(visible=mode == "Long TTS"),
         gr.Group(visible=mode == "Long TTS"),
     ]
 
@@ -253,6 +272,8 @@ If disabled, you should write the target transcript yourself:</br>
  - In Edit mode write full prompt</br>
 """
 
+demo_original_transcript = " But when I had approached so near to them, the common object, which the sense deceives, lost not by distance any of its marks."
+
 demo_text = {
     "TTS": {
         "smart": "I cannot believe that the same model can also do text to speech synthesis as well!",
@@ -281,17 +302,35 @@ demo_words = [
     '5.74  by 6.08', '6.08  distance 6.36', '6.36  any 6.92', '6.92  of 7.12', '7.12  its 7.26', '7.26  marks. 7.54'
 ]
 
+demo_word_info = [
+    {'word': ' But', 'start': 0.0, 'end': 0.12},  {'word': ' when', 'start': 0.12, 'end': 0.26},
+    {'word': ' I', 'start': 0.26, 'end': 0.44}, {'word': ' had', 'start': 0.44, 'end': 0.6},
+    {'word': ' approached', 'start': 0.6, 'end': 0.94}, {'word': ' so', 'start': 0.94, 'end': 1.42},
+    {'word': ' near', 'start': 1.42, 'end': 1.78}, {'word': ' to', 'start': 1.78, 'end': 2.02},
+    {'word': ' them,', 'start': 2.02, 'end': 2.24}, {'word': ' the', 'start': 2.52, 'end': 2.58},
+    {'word': ' common', 'start': 2.58, 'end': 2.9}, {'word': ' object,', 'start': 2.9, 'end': 3.3},
+    {'word': ' which', 'start': 3.72, 'end': 3.78}, {'word': ' the', 'start': 3.78, 'end': 3.98},
+    {'word': ' sense', 'start': 3.98, 'end': 4.18}, {'word': ' deceives,', 'start': 4.18, 'end': 4.88},
+    {'word': ' lost', 'start': 5.06, 'end': 5.26}, {'word': ' not', 'start': 5.26, 'end': 5.74},
+    {'word': ' by', 'start': 5.74, 'end': 6.08}, {'word': ' distance', 'start': 6.08, 'end': 6.36},
+    {'word': ' any', 'start': 6.36, 'end': 6.92}, {'word': ' of', 'start': 6.92, 'end': 7.12},
+    {'word': ' its', 'start': 7.12, 'end': 7.26}, {'word': ' marks.', 'start': 7.26, 'end': 7.54}
+]
 
-def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time):
+
+def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word):
     if transcript not in all_demo_texts:
-        return transcript, edit_from_word, edit_to_word, prompt_end_time
+        return transcript, edit_from_word, edit_to_word
     
     replace_half = edit_word_mode == "Replace half"
+    change_edit_from_word = edit_from_word == demo_words[2] or edit_from_word == demo_words[3]
+    change_edit_to_word = edit_to_word == demo_words[11] or edit_to_word == demo_words[12]
+    demo_edit_from_word_value = demo_words[2] if replace_half else demo_words[3]
+    demo_edit_to_word_value = demo_words[12] if replace_half else demo_words[11]
     return [
         demo_text[mode]["smart" if smart_transcript else "regular"],
-        "0.26  I 0.44" if replace_half else "0.44  had 0.6",
-        "3.72  which 3.78" if replace_half else "2.9  object, 3.3",
-        3.01,
+        demo_edit_from_word_value if change_edit_from_word else edit_from_word,
+        demo_edit_to_word_value if change_edit_to_word else edit_to_word,
     ]
 
 
@@ -300,7 +339,7 @@ with gr.Blocks() as app:
         with gr.Column(scale=2):
             load_models_btn = gr.Button(value="Load models")
         with gr.Column(scale=5):
-            with gr.Accordion("Select models", open=False):
+            with gr.Accordion("Select models", open=False) as models_selector:
                 with gr.Row():
                     voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
                     whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
@@ -308,9 +347,9 @@ with gr.Blocks() as app:
 
     with gr.Row():
         with gr.Column(scale=2):
-            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=False)
+            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath")
             with gr.Group():
-                original_transcript = gr.Textbox(label="Original transcript", lines=5, interactive=False,
+                original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript, interactive=False,
                                                  info="Use whisper model to get the transcript. Fix it if necessary.")
                 with gr.Accordion("Word start time", open=False):
                     transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
@@ -325,20 +364,26 @@ with gr.Blocks() as app:
                 with gr.Row():
                     smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
                     with gr.Accordion(label="?", open=False):
-                        info = gr.HTML(value=smart_transcript_info)
-                mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
+                        info = gr.Markdown(value=smart_transcript_info)
+
+                with gr.Row():
+                    mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
+                    split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
+                                          info="Split text into parts and run TTS for each part.", visible=False)
+                    edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
+                                              info="What to do with first and last word", visible=False)
                 
-                prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.01, value=3.01)
-                split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline", visible=False,
-                                      info="Split text into parts and run TTS for each part.")
-                edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half", visible=False,
-                                          info="What to do with first and last word")
-                with gr.Row(visible=False) as segment_control:
-                    edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, interactive=True)
-                    edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, interactive=True)
-                with gr.Accordion("Precise segment control", open=False, visible=False) as precise_segment_control:
-                    edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=60, step=0.01, value=0)
-                    edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=60, step=0.01, value=60)
+                with gr.Group() as tts_mode_controls:
+                    prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
+                    prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.01, value=3.01)
+
+                with gr.Group(visible=False) as edit_mode_controls:
+                    with gr.Row():
+                        edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
+                        edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
+                    with gr.Row():
+                        edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.01, value=0.35)
+                        edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.01, value=3.75)
 
                 run_btn = gr.Button(value="Run")
 
@@ -347,7 +392,7 @@ with gr.Blocks() as app:
             with gr.Accordion("Inference transcript", open=False):
                 inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
                                                   info="Inference was performed on this transcript.")
-            with gr.Group(visible=False) as long_tts_controls:
+            with gr.Group(visible=False) as long_tts_sentence_editor:
                 sentence_selector = gr.Dropdown(label="Sentence", value=None,
                                                 info="Select sentence you want to regenerate")
                 sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
@@ -355,6 +400,7 @@ with gr.Blocks() as app:
 
     with gr.Row():
         with gr.Accordion("VoiceCraft config", open=False):
+            seed = gr.Number(label="seed", value=-1, precision=0)
             left_margin = gr.Number(label="left_margin", value=0.08)
             right_margin = gr.Number(label="right_margin", value=0.08)
             codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000)
@@ -372,37 +418,38 @@ with gr.Blocks() as app:
 
     
     audio_tensors = gr.State()
-    word_info = gr.State()
+    word_info = gr.State(value=demo_word_info)
 
     
     mode.change(fn=update_demo,
-                inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
-                outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+                inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
+                outputs=[transcript, edit_from_word, edit_to_word])
     edit_word_mode.change(fn=update_demo,
-                          inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
-                          outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+                          inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
+                          outputs=[transcript, edit_from_word, edit_to_word])
     smart_transcript.change(fn=update_demo,
-                            inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
-                            outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+                            inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
+                            outputs=[transcript, edit_from_word, edit_to_word])
     
     load_models_btn.click(fn=load_models,
                           inputs=[whisper_model_choice, voicecraft_model_choice],
-                          outputs=[input_audio])
+                          outputs=[models_selector])
     
-    input_audio.change(fn=update_input_audio,
+    input_audio.upload(fn=update_input_audio,
                        inputs=[input_audio],
                        outputs=[prompt_end_time, edit_start_time, edit_end_time])
     transcribe_btn.click(fn=transcribe,
-                         inputs=[input_audio],
-                         outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time, edit_from_word, edit_to_word, word_info])
+                         inputs=[seed, input_audio],
+                         outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
+                                  prompt_to_word, edit_from_word, edit_to_word, word_info])
 
     mode.change(fn=change_mode,
                 inputs=[mode],
-                outputs=[prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls])
+                outputs=[tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor])
 
     run_btn.click(fn=run,
                   inputs=[
-                      left_margin, right_margin,
+                      seed, left_margin, right_margin,
                       codec_audio_sr, codec_sr,
                       top_k, top_p, temperature,
                       stop_repetition, sample_batch_size,
@@ -418,7 +465,7 @@ with gr.Blocks() as app:
                              outputs=[sentence_audio])
     rerun_btn.click(fn=run,
                     inputs=[
-                        left_margin, right_margin,
+                        seed, left_margin, right_margin,
                         codec_audio_sr, codec_sr,
                         top_k, top_p, temperature,
                         stop_repetition, sample_batch_size,
@@ -429,6 +476,9 @@ with gr.Blocks() as app:
                     ],
                     outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
     
+    prompt_to_word.change(fn=update_bound_word,
+                          inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
+                          outputs=[prompt_end_time])
     edit_from_word.change(fn=update_bound_word,
                           inputs=[gr.State(True), edit_from_word, edit_word_mode],
                           outputs=[edit_start_time])

From 3d3f32ba7e3bfb3ef2d7514c6081911f06d5fb2b Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Thu, 4 Apr 2024 22:22:27 +0300
Subject: [PATCH 05/24] whisperx added

---
 README.md               |   2 +-
 gradio_app.py           | 231 +++++++++++++++++++++++++++++-----------
 gradio_requirements.txt |   4 +-
 3 files changed, 170 insertions(+), 67 deletions(-)

diff --git a/README.md b/README.md
index 2a5be3a..5e78082 100644
--- a/README.md
+++ b/README.md
@@ -111,7 +111,7 @@ It is ready to use on [default url](http://127.0.0.1:7860).
 6. (optionally) Rerun part-by-part in Long TTS mode
 
 ### Some features
-Smart transcript: write only what you want to generate, but don't work if you edit original transcript
+Smart transcript: write only what you want to generate
 
 TTS mode: Zero-shot TTS
 
diff --git a/gradio_app.py b/gradio_app.py
index 707defa..4321a11 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -10,9 +10,16 @@ import os
 import io
 import numpy as np
 import random
+import uuid
 
 
-whisper_model, voicecraft_model = None, None
+TMP_PATH = "./demo/temp"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+whisper_model, align_model, voicecraft_model = None, None, None
+
+
+def get_random_string():
+    return "".join(str(uuid.uuid4()).split("-"))
 
 
 def seed_everything(seed):
@@ -26,22 +33,63 @@ def seed_everything(seed):
         torch.backends.cudnn.deterministic = True
 
 
-def load_models(whisper_model_choice, voicecraft_model_choice):
-    global whisper_model, voicecraft_model
+class WhisperxAlignModel:
+    def __init__(self):
+        from whisperx import load_align_model
+        self.model, self.metadata = load_align_model(language_code="en", device=device)
+
+    def align(self, segments, audio_path):
+        from whisperx import align, load_audio
+        audio = load_audio(audio_path)
+        return align(segments, self.model, self.metadata, audio, device, return_char_alignments=False)["segments"]
+
+
+class WhisperModel:
+    def __init__(self, model_name):
+        from whisper import load_model
+        self.model = load_model(model_name, device)
 
-    if whisper_model_choice is not None:
-        import whisper
         from whisper.tokenizer import get_tokenizer
-        whisper_model = {
-            "model": whisper.load_model(whisper_model_choice),
-            "tokenizer": get_tokenizer(multilingual=False)
-        }
+        tokenizer = get_tokenizer(multilingual=False)
+        self.supress_tokens = [-1] + [
+            i
+            for i in range(tokenizer.eot)
+            if all(c in "0123456789" for c in tokenizer.decode([i]).removeprefix(" "))
+        ]
+
+    def transcribe(self, audio_path):
+        return self.model.transcribe(audio_path, suppress_tokens=self.supress_tokens, word_timestamps=True)["segments"]
+
+
+class WhisperxModel:
+    def __init__(self, model_name, align_model: WhisperxAlignModel):
+        from whisperx import load_model
+        self.model = load_model(model_name, device, asr_options={"suppress_numerals": True})
+        self.align_model = align_model
+
+    def transcribe(self, audio_path):
+        segments = self.model.transcribe(audio_path, batch_size=8)["segments"]
+        return self.align_model.align(segments, audio_path)
+
+
+def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
+    global transcribe_model, align_model, voicecraft_model
 
     os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
     os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    if alignment_model_name is not None:
+        align_model = WhisperxAlignModel()
+
+    if whisper_model_name is not None:
+        if whisper_backend_name == "whisper":
+            transcribe_model = WhisperModel(whisper_model_name)
+        else:
+            if align_model is None:
+                raise gr.Error("Align model required for whisperx backend")
+            transcribe_model = WhisperxModel(whisper_model_name, align_model)
     
-    voicecraft_name = f"{voicecraft_model_choice}.pth"
+    voicecraft_name = f"{voicecraft_model_name}.pth"
     ckpt_fn = f"./pretrained_models/{voicecraft_name}"
     encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
     if not os.path.exists(ckpt_fn):
@@ -66,31 +114,78 @@ def load_models(whisper_model_choice, voicecraft_model_choice):
     return gr.Accordion()
 
 
+def get_transcribe_state(segments):
+    words_info = [word_info for segment in segments for word_info in segment["words"]]
+    return {
+        "segments": segments,
+        "transcript": " ".join([segment["text"] for segment in segments]),
+        "words_info": words_info,
+        "transcript_with_start_time": " ".join([f"{word['start']} {word['word']}" for word in words_info]),
+        "transcript_with_end_time": " ".join([f"{word['word']} {word['end']}" for word in words_info]),
+        "word_bounds": [f"{word['start']} {word['word']} {word['end']}" for word in words_info]
+    }
+
+
 def transcribe(seed, audio_path):
-    if whisper_model is None:
-        raise gr.Error("Whisper model not loaded")
+    if transcribe_model is None:
+        raise gr.Error("Transcription model not loaded")
     seed_everything(seed)
     
-    number_tokens = [
-        i
-        for i in range(whisper_model["tokenizer"].eot)
-        if all(c in "0123456789" for c in whisper_model["tokenizer"].decode([i]).removeprefix(" "))
-    ]
-    result = whisper_model["model"].transcribe(audio_path, suppress_tokens=[-1] + number_tokens, word_timestamps=True)
-    words = [word_info for segment in result["segments"] for word_info in segment["words"]]
-    
-    transcript = result["text"]
-    transcript_with_start_time = " ".join([f"{word['start']} {word['word']}" for word in words])
-    transcript_with_end_time = " ".join([f"{word['word']} {word['end']}" for word in words])
-
-    choices = [f"{word['start']} {word['word']} {word['end']}" for word in words]
+    segments = transcribe_model.transcribe(audio_path)
+    state = get_transcribe_state(segments)
 
     return [
-        transcript, transcript_with_start_time, transcript_with_end_time,
-        gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # prompt_to_word
-        gr.Dropdown(value=choices[0], choices=choices, interactive=True), # edit_from_word
-        gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # edit_to_word
-        words
+        state["transcript"], state["transcript_with_start_time"], state["transcript_with_end_time"],
+        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
+        gr.Dropdown(value=state["word_bounds"][0], choices=state["word_bounds"], interactive=True), # edit_from_word
+        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # edit_to_word
+        state
+    ]
+
+
+def align_segments(transcript, audio_path):
+    from aeneas.executetask import ExecuteTask
+    from aeneas.task import Task
+    import json
+    config_string = 'task_language=eng|os_task_file_format=json|is_text_type=plain'
+
+    tmp_transcript_path = os.path.join(TMP_PATH, f"{get_random_string()}.txt")
+    tmp_sync_map_path = os.path.join(TMP_PATH, f"{get_random_string()}.json")
+    with open(tmp_transcript_path, "w") as f:
+        f.write(transcript)
+
+    task = Task(config_string=config_string)
+    task.audio_file_path_absolute = os.path.abspath(audio_path)
+    task.text_file_path_absolute = os.path.abspath(tmp_transcript_path)
+    task.sync_map_file_path_absolute = os.path.abspath(tmp_sync_map_path)
+    ExecuteTask(task).execute()
+    task.output_sync_map_file()
+
+    with open(tmp_sync_map_path, "r") as f:
+        return json.load(f)
+
+
+def align(seed, transcript, audio_path):
+    if align_model is None:
+        raise gr.Error("Align model not loaded")
+    seed_everything(seed)
+
+    fragments = align_segments(transcript, audio_path)
+    segments = [{
+        "start": float(fragment["begin"]),
+        "end": float(fragment["end"]),
+        "text": " ".join(fragment["lines"])
+    } for fragment in fragments["fragments"]]
+    segments = align_model.align(segments, audio_path)
+    state = get_transcribe_state(segments)
+    print(state)
+
+    return [
+        state["transcript_with_start_time"], state["transcript_with_end_time"],
+        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
+        gr.Dropdown(value=state["word_bounds"][0], choices=state["word_bounds"], interactive=True), # edit_from_word
+        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # edit_to_word
+        state
     ]
 
 
@@ -104,12 +199,12 @@ def get_output_audio(audio_tensors, codec_audio_sr):
 
 def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
         stop_repetition, sample_batch_size, kvcache, silence_tokens,
-        audio_path, word_info, transcript, smart_transcript,
+        audio_path, transcribe_state, transcript, smart_transcript,
         mode, prompt_end_time, edit_start_time, edit_end_time,
         split_text, selected_sentence, previous_audio_tensors):
     if voicecraft_model is None:
         raise gr.Error("VoiceCraft model not loaded")
-    if smart_transcript and (word_info is None):
+    if smart_transcript and (transcribe_state is None):
         raise gr.Error("Can't use smart transcript: whisper transcript not found")
 
     seed_everything(seed)
@@ -126,7 +221,6 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
     else:
         sentences = [transcript.replace("\n", " ")]
 
-    device = "cuda" if torch.cuda.is_available() else "cpu"
     info = torchaudio.info(audio_path)
     audio_dur = info.num_frames / info.sample_rate
 
@@ -141,7 +235,7 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
 
             if smart_transcript:                
                 target_transcript = ""
-                for word in word_info:
+                for word in transcribe_state["words_info"]:
                     if word["end"] < prompt_end_time:
                         target_transcript += word["word"]
                     elif (word["start"] + word["end"]) / 2 < prompt_end_time:
@@ -169,13 +263,13 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
 
             if smart_transcript:
                 target_transcript = ""
-                for word in word_info:
+                for word in transcribe_state["words_info"]:
                     if word["start"] < edit_start_time:
                         target_transcript += word["word"]
                     else:
                         break
                 target_transcript += f" {sentence}"
-                for word in word_info:
+                for word in transcribe_state["words_info"]:
                     if word["end"] > edit_end_time:
                         target_transcript += word["word"]
             else:
@@ -296,25 +390,25 @@ demo_text = {
 all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}
 
 demo_words = [
-    '0.0  But 0.12', '0.12  when 0.26', '0.26  I 0.44', '0.44  had 0.6', '0.6  approached 0.94', '0.94  so 1.42',
-    '1.42  near 1.78', '1.78  to 2.02', '2.02  them, 2.24', '2.52  the 2.58', '2.58  common 2.9', '2.9  object, 3.3', 
-    '3.72  which 3.78', '3.78  the 3.98', '3.98  sense 4.18', '4.18  deceives, 4.88', '5.06  lost 5.26', '5.26  not 5.74',
-    '5.74  by 6.08', '6.08  distance 6.36', '6.36  any 6.92', '6.92  of 7.12', '7.12  its 7.26', '7.26  marks. 7.54'
+    '0.029 But 0.149', '0.189 when 0.33', '0.43 I 0.49', '0.53 had 0.65', '0.711 approached 1.152', '1.352 so 1.593',
+    '1.693 near 1.933', '1.994 to 2.074', '2.134 them, 2.354', '2.535 the 2.655', '2.695 common 3.016', '3.196 object, 3.577',
+    '3.717 which 3.898', '3.958 the 4.058', '4.098 sense 4.359', '4.419 deceives, 4.92', '5.101 lost 5.481', '5.682 not 5.963',
+    '6.043 by 6.183', '6.223 distance 6.644', '6.905 any 7.065', '7.125 of 7.185', '7.245 its 7.346', '7.406 marks. 7.727'
 ]
 
-demo_word_info = [
-    {'word': ' But', 'start': 0.0, 'end': 0.12},  {'word': ' when', 'start': 0.12, 'end': 0.26},
-    {'word': ' I', 'start': 0.26, 'end': 0.44}, {'word': ' had', 'start': 0.44, 'end': 0.6},
-    {'word': ' approached', 'start': 0.6, 'end': 0.94}, {'word': ' so', 'start': 0.94, 'end': 1.42},
-    {'word': ' near', 'start': 1.42, 'end': 1.78}, {'word': ' to', 'start': 1.78, 'end': 2.02},
-    {'word': ' them,', 'start': 2.02, 'end': 2.24}, {'word': ' the', 'start': 2.52, 'end': 2.58},
-    {'word': ' common', 'start': 2.58, 'end': 2.9}, {'word': ' object,', 'start': 2.9, 'end': 3.3},
-    {'word': ' which', 'start': 3.72, 'end': 3.78}, {'word': ' the', 'start': 3.78, 'end': 3.98},
-    {'word': ' sense', 'start': 3.98, 'end': 4.18}, {'word': ' deceives,', 'start': 4.18, 'end': 4.88},
-    {'word': ' lost', 'start': 5.06, 'end': 5.26}, {'word': ' not', 'start': 5.26, 'end': 5.74},
-    {'word': ' by', 'start': 5.74, 'end': 6.08}, {'word': ' distance', 'start': 6.08, 'end': 6.36},
-    {'word': ' any', 'start': 6.36, 'end': 6.92}, {'word': ' of', 'start': 6.92, 'end': 7.12},
-    {'word': ' its', 'start': 7.12, 'end': 7.26}, {'word': ' marks.', 'start': 7.26, 'end': 7.54}
+demo_words_info = [
+    {'word': 'But', 'start': 0.029, 'end': 0.149, 'score': 0.834}, {'word': 'when', 'start': 0.189, 'end': 0.33, 'score': 0.879},
+    {'word': 'I', 'start': 0.43, 'end': 0.49, 'score': 0.984}, {'word': 'had', 'start': 0.53, 'end': 0.65, 'score': 0.998},
+    {'word': 'approached', 'start': 0.711, 'end': 1.152, 'score': 0.822}, {'word': 'so', 'start': 1.352, 'end': 1.593, 'score': 0.822},
+    {'word': 'near', 'start': 1.693, 'end': 1.933, 'score': 0.752}, {'word': 'to', 'start': 1.994, 'end': 2.074, 'score': 0.924},
+    {'word': 'them,', 'start': 2.134, 'end': 2.354, 'score': 0.914}, {'word': 'the', 'start': 2.535, 'end': 2.655, 'score': 0.818},
+    {'word': 'common', 'start': 2.695, 'end': 3.016, 'score': 0.971}, {'word': 'object,', 'start': 3.196, 'end': 3.577, 'score': 0.823},
+    {'word': 'which', 'start': 3.717, 'end': 3.898, 'score': 0.701}, {'word': 'the', 'start': 3.958, 'end': 4.058, 'score': 0.798},
+    {'word': 'sense', 'start': 4.098, 'end': 4.359, 'score': 0.797}, {'word': 'deceives,', 'start': 4.419, 'end': 4.92, 'score': 0.802},
+    {'word': 'lost', 'start': 5.101, 'end': 5.481, 'score': 0.71}, {'word': 'not', 'start': 5.682, 'end': 5.963, 'score': 0.781},
+    {'word': 'by', 'start': 6.043, 'end': 6.183, 'score': 0.834}, {'word': 'distance', 'start': 6.223, 'end': 6.644, 'score': 0.899},
+    {'word': 'any', 'start': 6.905, 'end': 7.065, 'score': 0.893}, {'word': 'of', 'start': 7.125, 'end': 7.185, 'score': 0.772},
+    {'word': 'its', 'start': 7.245, 'end': 7.346, 'score': 0.778}, {'word': 'marks.', 'start': 7.406, 'end': 7.727, 'score': 0.955}
 ]
 
 
@@ -342,21 +436,24 @@ with gr.Blocks() as app:
             with gr.Accordion("Select models", open=False) as models_selector:
                 with gr.Row():
                     voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
+                    whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
                     whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
-                                                    choices=[None, "tiny.en", "base.en", "small.en", "medium.en", "large"])
+                                                    choices=[None, "base.en", "small.en", "medium.en", "large"])
+                    align_model_choice = gr.Radio(label="Forced alignment model", value="whisperX", choices=[None, "whisperX"])
 
     with gr.Row():
         with gr.Column(scale=2):
             input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath")
             with gr.Group():
-                original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript, interactive=False,
-                                                 info="Use whisper model to get the transcript. Fix it if necessary.")
+                original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
+                                                 info="Use whisper model to get the transcript. Fix and align it if necessary.")
                 with gr.Accordion("Word start time", open=False):
                     transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
                 with gr.Accordion("Word end time", open=False):
                     transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
 
                 transcribe_btn = gr.Button(value="Transcribe")
+                align_btn = gr.Button(value="Align")
             
         with gr.Column(scale=3):
             with gr.Group():
@@ -375,15 +472,15 @@ with gr.Blocks() as app:
                 
                 with gr.Group() as tts_mode_controls:
                     prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
-                    prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.01, value=3.01)
+                    prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
 
                 with gr.Group(visible=False) as edit_mode_controls:
                     with gr.Row():
                         edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
                         edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
                     with gr.Row():
-                        edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.01, value=0.35)
-                        edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.01, value=3.75)
+                        edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.001, value=0.46)
+                        edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.001, value=3.808)
 
                 run_btn = gr.Button(value="Run")
 
@@ -418,7 +515,7 @@ with gr.Blocks() as app:
 
     
     audio_tensors = gr.State()
-    word_info = gr.State(value=demo_word_info)
+    transcribe_state = gr.State(value={"words_info": demo_words_info})
 
     
     mode.change(fn=update_demo,
@@ -432,7 +529,7 @@ with gr.Blocks() as app:
                             outputs=[transcript, edit_from_word, edit_to_word])
     
     load_models_btn.click(fn=load_models,
-                          inputs=[whisper_model_choice, voicecraft_model_choice],
+                          inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
                           outputs=[models_selector])
     
     input_audio.upload(fn=update_input_audio,
@@ -441,7 +538,11 @@ with gr.Blocks() as app:
     transcribe_btn.click(fn=transcribe,
                          inputs=[seed, input_audio],
                          outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
-                                  prompt_to_word, edit_from_word, edit_to_word, word_info])
+                                  prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
+    align_btn.click(fn=align,
+                    inputs=[seed, original_transcript, input_audio],
+                    outputs=[transcript_with_start_time, transcript_with_end_time,
+                             prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
 
     mode.change(fn=change_mode,
                 inputs=[mode],
@@ -454,7 +555,7 @@ with gr.Blocks() as app:
                       top_k, top_p, temperature,
                       stop_repetition, sample_batch_size,
                       kvcache, silence_tokens,
-                      input_audio, word_info, transcript, smart_transcript,
+                      input_audio, transcribe_state, transcript, smart_transcript,
                       mode, prompt_end_time, edit_start_time, edit_end_time,
                       split_text, sentence_selector, audio_tensors
                   ],
@@ -470,7 +571,7 @@ with gr.Blocks() as app:
                         top_k, top_p, temperature,
                         stop_repetition, sample_batch_size,
                         kvcache, silence_tokens,
-                        input_audio, word_info, transcript, smart_transcript,
+                        input_audio, transcribe_state, transcript, smart_transcript,
                         gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
                         split_text, sentence_selector, audio_tensors
                     ],
diff --git a/gradio_requirements.txt b/gradio_requirements.txt
index 949acc7..967b3d7 100644
--- a/gradio_requirements.txt
+++ b/gradio_requirements.txt
@@ -1,3 +1,5 @@
 gradio==3.50.2
 nltk>=3.8.1
-openai-whisper>=20231117
\ No newline at end of file
+openai-whisper>=20231117
+aeneas>=1.7.3.0
+whisperx>=3.1.1
\ No newline at end of file

From 97b1f519478f39702e910d3fde86c8a88bac4d8e Mon Sep 17 00:00:00 2001
From: Puyuan Peng <47729801+jasonppy@users.noreply.github.com>
Date: Thu, 4 Apr 2024 20:26:28 -0500
Subject: [PATCH 06/24] install whisperx deps

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README.md b/README.md
index 2a5e247..5f587db 100644
--- a/README.md
+++ b/README.md
@@ -86,6 +86,10 @@ conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=
 
 # to run ipynb
 conda install -n voicecraft ipykernel --no-deps --force-reinstall
+
+# below is only needed if you want to run gradio_app.py
+sudo apt-get install espeak # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
+sudo apt-get install libespeak-dev # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
 ```
 
 If you have encountered version issues when running things, checkout [environment.yml](./environment.yml) for exact matching.

From 0d19fa5d0334eb527de642d15b2ae21fe6ba4bfc Mon Sep 17 00:00:00 2001
From: Puyuan Peng <47729801+jasonppy@users.noreply.github.com>
Date: Thu, 4 Apr 2024 20:31:07 -0500
Subject: [PATCH 07/24] fix whisperx loading issue, update generation
 instruction

---
 gradio_app.py | 41 ++++++++++++++++++++---------------------
 1 file changed, 20 insertions(+), 21 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 4321a11..15f77e1 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -1,3 +1,6 @@
+import os
+# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+# os.environ["CUDA_VISIBLE_DEVICES"] = "0" # for local use
 import gradio as gr
 import torch
 import torchaudio
@@ -6,7 +9,6 @@ from data.tokenizer import (
     TextTokenizer,
 )
 from models import voicecraft
-import os
 import io
 import numpy as np
 import random
@@ -64,7 +66,7 @@ class WhisperModel:
 class WhisperxModel:
     def __init__(self, model_name, align_model: WhisperxAlignModel):
         from whisperx import load_model
-        self.model = load_model(model_name, device, asr_options={"suppress_numerals": True})
+        self.model = load_model(model_name, device, asr_options={"suppress_numerals": True, "max_new_tokens": None, "clip_timestamps": None, "hallucination_silence_threshold": None})
         self.align_model = align_model
 
     def transcribe(self, audio_path):
@@ -75,9 +77,6 @@ class WhisperxModel:
 def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
     global transcribe_model, align_model, voicecraft_model
 
-    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
     if alignment_model_name is not None:
         align_model = WhisperxAlignModel()
 
@@ -443,7 +442,7 @@ with gr.Blocks() as app:
 
     with gr.Row():
         with gr.Column(scale=2):
-            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath")
+            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
             with gr.Group():
                 original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
                                                  info="Use whisper model to get the transcript. Fix and align it if necessary.")
@@ -496,22 +495,22 @@ with gr.Blocks() as app:
                 rerun_btn = gr.Button(value="Rerun")
 
     with gr.Row():
-        with gr.Accordion("VoiceCraft config", open=False):
-            seed = gr.Number(label="seed", value=-1, precision=0)
-            left_margin = gr.Number(label="left_margin", value=0.08)
-            right_margin = gr.Number(label="right_margin", value=0.08)
-            codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000)
-            codec_sr = gr.Number(label="codec_sr", value=50)
-            top_k = gr.Number(label="top_k", value=0)
-            top_p = gr.Number(label="top_p", value=0.8)
-            temperature = gr.Number(label="temperature", value=1)
-            stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3], value=3,
-                                       info="if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1, -1 = disabled")
-            sample_batch_size = gr.Number(label="sample_batch_size", value=4, precision=0,
-                                          info="generate this many samples and choose the shortest one")
+        with gr.Accordion("Generation Parameters - change these if you are unhappy with the generation", open=False):
+            stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
+                                       info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
+            sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
+                                          info="The higher the number, the faster the output will be. Under the hood, the model will generate this many samples and choose the shortest one")
+            seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
             kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
                                 info="set to 0 to use less VRAM, but with slower inference")
-            silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
+            left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
+            right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
+            top_p = gr.Number(label="top_p", value=0.8, info="0.8 is a good value, 0.9 is also good")
+            temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
+            top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
+            codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')
+            codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
+            silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
 
     
     audio_tensors = gr.State()
@@ -592,4 +591,4 @@ with gr.Blocks() as app:
 
 
 if __name__ == "__main__":
-    app.launch()
\ No newline at end of file
+    app.launch()

From 94e9f9bd4233b6510f4ff45c2cd291a9476957ce Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Fri, 5 Apr 2024 04:40:57 +0300
Subject: [PATCH 08/24] README update, gradio_app.ipynb update, debug print
 removed

---
 README.md        |  5 +++
 gradio_app.ipynb | 88 +++++++++---------------------------------------
 gradio_app.py    |  4 ---
 3 files changed, 21 insertions(+), 76 deletions(-)

diff --git a/README.md b/README.md
index 2a5e247..41f1654 100644
--- a/README.md
+++ b/README.md
@@ -96,6 +96,11 @@ Checkout [`inference_speech_editing.ipynb`](./inference_speech_editing.ipynb) an
 ## Gradio
 After environment setup install additional dependencies:
 ```bash
+apt-get install -y espeak espeak-data libespeak1 libespeak-dev
+apt-get install -y festival*
+apt-get install -y build-essential
+apt-get install -y flac libasound2-dev libsndfile1-dev vorbis-tools
+apt-get install -y libxml2-dev libxslt-dev zlib1g-dev
 pip install -r gradio_requirements.txt
 ```
 
diff --git a/gradio_app.ipynb b/gradio_app.ipynb
index 0d3f946..7b13660 100644
--- a/gradio_app.ipynb
+++ b/gradio_app.ipynb
@@ -8,84 +8,28 @@
     "### Only do the below if you are using docker"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "270aa2cc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# install OS deps\n",
-    "!sudo apt-get update && sudo apt-get install -y \\\n",
-    "    git-core \\\n",
-    "    ffmpeg \\\n",
-    "    espeak-ng"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8ba5f452",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Update and setup Conda voicecraft environment\n",
-    "!conda update -y -n base -c conda-forge conda\n",
-    "!conda create -y -n voicecraft python=3.9.16 && \\\n",
-    "    conda init bash"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4ef2935c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# install conda and pip stuff in the activated conda above context\n",
-    "!echo -e \"Grab a cup a coffee and a slice of pizza...\\n\\n\"\n",
-    "\n",
-    "# make sure $HOME and $USER are setup so this will source the conda environment\n",
-    "!source ~/.bashrc && \\\n",
-    "    conda activate voicecraft && \\\n",
-    "    conda install -y -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=5.5.1068 && \\\n",
-    "    pip install torch==2.0.1 && \\\n",
-    "    pip install tensorboard==2.16.2 && \\\n",
-    "    pip install phonemizer==3.2.1 && \\\n",
-    "    pip install torchaudio==2.0.2 && \\\n",
-    "    pip install datasets==2.16.0 && \\\n",
-    "    pip install torchmetrics==0.11.1\n",
-    "\n",
-    "# do this one last otherwise you'll get an error about torch compiler missing due to xformer mismatch\n",
-    "!source ~/.bashrc && \\\n",
-    "    conda activate voicecraft && \\\n",
-    "    pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2fca57eb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# okay setup the conda environment such that jupyter notebook can find the kernel\n",
-    "!source ~/.bashrc && \\\n",
-    "    conda activate voicecraft && \\\n",
-    "    conda install -y -n voicecraft ipykernel --update-deps --force-reinstall\n",
-    "\n",
-    "# installs the Jupyter kernel into /home/myusername/.local/share/jupyter/kernels/voicecraft\n",
-    "!source ~/.bashrc && \\\n",
-    "    conda activate voicecraft && \\\n",
-    "    python3 -m ipykernel install --user --name=voicecraft"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "961faa43",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "!source ~/.bashrc && \\\n",
+    "    apt-get update && \\\n",
+    "    apt-get install -y espeak espeak-data libespeak1 libespeak-dev && \\\n",
+    "    apt-get install -y festival* && \\\n",
+    "    apt-get install -y build-essential && \\\n",
+    "    apt-get install -y flac libasound2-dev libsndfile1-dev vorbis-tools && \\\n",
+    "    apt-get install -y libxml2-dev libxslt-dev zlib1g-dev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "598d75cf",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "!source ~/.bashrc && \\\n",
     "    conda activate voicecraft && \\\n",
diff --git a/gradio_app.py b/gradio_app.py
index 4321a11..5e349fe 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -75,9 +75,6 @@ class WhisperxModel:
 def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
     global transcribe_model, align_model, voicecraft_model
 
-    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
     if alignment_model_name is not None:
         align_model = WhisperxAlignModel()
 
@@ -178,7 +175,6 @@ def align(seed, transcript, audio_path):
     } for fragment in fragments["fragments"]]
     segments = align_model.align(segments, audio_path)
     state = get_transcribe_state(segments)
-    print(state)
 
     return [
         state["transcript_with_start_time"], state["transcript_with_end_time"],

From 6f71fa65fb8d6efaf54cde474009e9d78bebfe94 Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Fri, 5 Apr 2024 07:00:07 +0300
Subject: [PATCH 09/24] smart transcript fix

---
 gradio_app.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 310e6f8..d14433f 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -235,10 +235,10 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
                 target_transcript = ""
                 for word in transcribe_state["words_info"]:
                     if word["end"] < prompt_end_time:
-                        target_transcript += word["word"]
+                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
                     elif (word["start"] + word["end"]) / 2 < prompt_end_time:
                         # include part of the word it it's big, but adjust prompt_end_time
-                        target_transcript += word["word"]
+                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
                         prompt_end_time = word["end"]
                         break
                     else:
@@ -263,13 +263,13 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
                 target_transcript = ""
                 for word in transcribe_state["words_info"]:
                     if word["start"] < edit_start_time:
-                        target_transcript += word["word"]
+                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
                     else:
                         break
                 target_transcript += f" {sentence}"
                 for word in transcribe_state["words_info"]:
                     if word["end"] > edit_end_time:
-                        target_transcript += word["word"]
+                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
             else:
                 target_transcript = sentence
 

From 21b69ad676235c1ee6f0004cc17349896ef5940e Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Fri, 5 Apr 2024 07:42:11 +0300
Subject: [PATCH 10/24] configurable tmp path

---
 README.md     | 1 +
 gradio_app.py | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0626bbd..1f7ca11 100644
--- a/README.md
+++ b/README.md
@@ -111,6 +111,7 @@ pip install -r gradio_requirements.txt
 Run gradio server from terminal or [`gradio_app.ipynb`](./gradio_app.ipynb):
 ```bash
 python gradio_app.py
+TMP_PATH=/tmp python gradio_app.py # if you want to change tmp folder path
 ```
 It is ready to use on [default url](http://127.0.0.1:7860).
 
diff --git a/gradio_app.py b/gradio_app.py
index d14433f..32b3e72 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -15,7 +15,7 @@ import random
 import uuid
 
 
-TMP_PATH = "./demo/temp"
+TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 whisper_model, align_model, voicecraft_model = None, None, None
 

From 1141775763e7e2399bc57ed2613a926b37aafa53 Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:38:22 +0800
Subject: [PATCH 11/24] Update README.md

---
 README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/README.md b/README.md
index 1f7ca11..c0dbf31 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,8 @@
+# VoiceCraft Gradio Colab
+Made for those who lacked a dedicated GPU and those who wanted [the friendly GUI by @zuev-stepan](https://github.com/zuev-stepan/VoiceCraft-gradio). Potato programmer brain here so all code credits to @jasonppy, @zuev-stepan and others who contributed in their code. 
+
+COLAB LINK : HOLD ON I AM UPDATING
+
 # VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
 [Demo](https://jasonppy.github.io/VoiceCraft_web) [Paper](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)
 

From b9ffda940503cf07c1d5f370eef1f3611f03a5d5 Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:40:48 +0800
Subject: [PATCH 12/24] Adding share=True

---
 gradio_app.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gradio_app.py b/gradio_app.py
index 32b3e72..4b816a2 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -590,4 +590,4 @@ with gr.Blocks() as app:
 
 
 if __name__ == "__main__":
-    app.launch()
+    app.launch(share=True)

From 5c92b2864f5492399961ed99a36eab3a891d0feb Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:49:00 +0800
Subject: [PATCH 13/24] Update gradio_app.py

---
 gradio_app.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/gradio_app.py b/gradio_app.py
index 4b816a2..11203fe 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -14,6 +14,8 @@ import numpy as np
 import random
 import uuid
 
+os.chdir("/content/VoiceCraft-gradio-colab")
+os.environ['USER'] = 'aaa'
 
 TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
 device = "cuda" if torch.cuda.is_available() else "cpu"

From fcdd1d30af11da333d8cf57e7b8cda78b159578f Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:53:38 +0800
Subject: [PATCH 14/24] Update gradio_app.py

---
 gradio_app.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 11203fe..9183e66 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -1,6 +1,8 @@
 import os
-# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-# os.environ["CUDA_VISIBLE_DEVICES"] = "0" # for local use
+ os.chdir("/content/VoiceCraft-gradio-colab")
+ os.environ['USER'] = 'aaa'
+ os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+ os.environ["CUDA_VISIBLE_DEVICES"] = "0" # for local use
 import gradio as gr
 import torch
 import torchaudio
@@ -14,9 +16,6 @@ import numpy as np
 import random
 import uuid
 
-os.chdir("/content/VoiceCraft-gradio-colab")
-os.environ['USER'] = 'aaa'
-
 TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 whisper_model, align_model, voicecraft_model = None, None, None

From 0f5451bf4ef2a33766a45d6436767e95b68c62ad Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:54:24 +0800
Subject: [PATCH 15/24] Update gradio_app.py

---
 gradio_app.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 9183e66..d0b442a 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -1,6 +1,4 @@
 import os
- os.chdir("/content/VoiceCraft-gradio-colab")
- os.environ['USER'] = 'aaa'
  os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
  os.environ["CUDA_VISIBLE_DEVICES"] = "0" # for local use
 import gradio as gr
@@ -16,6 +14,9 @@ import numpy as np
 import random
 import uuid
 
+os.chdir("/content/VoiceCraft-gradio-colab")
+os.environ['USER'] = 'aaa'
+
 TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 whisper_model, align_model, voicecraft_model = None, None, None

From 38e8fa0776fa7ce277813947d5870cdff03178a7 Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:54:59 +0800
Subject: [PATCH 16/24] Update gradio_app.py

---
 gradio_app.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index d0b442a..72df9e1 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -1,6 +1,4 @@
 import os
- os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
- os.environ["CUDA_VISIBLE_DEVICES"] = "0" # for local use
 import gradio as gr
 import torch
 import torchaudio
@@ -14,6 +12,8 @@ import numpy as np
 import random
 import uuid
 
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 os.chdir("/content/VoiceCraft-gradio-colab")
 os.environ['USER'] = 'aaa'
 

From 47937fb0dcb4ab04caeb572899fcc3a3924cc460 Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 19:15:10 +0800
Subject: [PATCH 17/24] April 5 Colab

---
 voicecraft.ipynb | 122 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 122 insertions(+)
 create mode 100644 voicecraft.ipynb

diff --git a/voicecraft.ipynb b/voicecraft.ipynb
new file mode 100644
index 0000000..f8b9c85
--- /dev/null
+++ b/voicecraft.ipynb
@@ -0,0 +1,122 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyPEhMt0mIcJv2BbaCwogF07",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Sewlell/VoiceCraft-gradio-colab/blob/master/voicecraft.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y87ixxsUVIhM"
+      },
+      "outputs": [],
+      "source": [
+        "!git clone https://github.com/Sewlell/VoiceCraft-gradio-colab"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install tensorboard\n",
+        "!pip install phonemizer\n",
+        "!pip install datasets\n",
+        "!pip install torchmetrics\n",
+        "\n",
+        "!apt-get install -y espeak espeak-data libespeak1 libespeak-dev\n",
+        "!apt-get install -y festival*\n",
+        "!apt-get install -y build-essential\n",
+        "!apt-get install -y flac libasound2-dev libsndfile1-dev vorbis-tools\n",
+        "!apt-get install -y libxml2-dev libxslt-dev zlib1g-dev\n",
+        "\n",
+        "!pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft\n",
+        "\n",
+        "!pip install -r \"/content/VoiceCraft-gradio-colab/gradio_requirements.txt\""
+      ],
+      "metadata": {
+        "id": "-w3USR91XdxY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Let it restarted, it won't let your entire installation be gone."
+      ],
+      "metadata": {
+        "id": "jNuzjrtmv2n1"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Note before launching the `gradio_app.py`\n",
+        "\n",
+        "***You will get JSON warning if you move anything beside `sample_batch_size`, `stop_repetition` and `seed`.*** Which for most advanced setting, `kvache` and `temperature` unable to set in different value.\n",
+        "\n",
+        "You will get fp16 compatibility issue if you set `whisper backend` to `whisperX`, for whatever reason, setting `forced alignment model` to `whisperX` doesn't do anything.\n",
+        "\n",
+        "You will download a .file File when you download the Output Audio for some reason. You will need to **convert the file from .snd to .wav/.mp3 manually**. Or if you enable showing file type in the name in Windows or wherever you are, change the file name to \"xxx.wav\" or \"xxx.mp3\". (know the solution? pull request my repository)\n",
+        "\n",
+        "Frequency of VRAM spikes no longer exist as well in April 5 Update.\n",
+        "\n",
+        "# **To those who want to voice cloning**\n",
+        "![Screenshot (438).png](data:image/png;base64,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)\n",
+        "\n",
+        "Don't make your input audio too long like in the screenshot, 20s-30s is fine. Or else it will raise up JSON issue. This one is due to how VoiceCraft worked so probably unfixable. It will add those text you want to get audio from at the end of the input audio transcript. It was way too much word for application or code to handle as it added up with original transcript. So please keep it short."
+      ],
+      "metadata": {
+        "id": "qQ-And_w2vJV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "My tip on voice cloning is just \"get a good dataset\" that contain plausible amount of variety of tones. I guess that's it, you could always try experimenting with other voice that are hard to be clone.\n",
+        "\n",
+        "The inference speed is much stable. With sample text, T4 (Free Tier Colab GPU) can do 6-10s on 6s-8s `prompt end time`.\n",
+        "\n",
+        "I haven't test the Edit mode yet as those are not my focus, but you can try it."
+      ],
+      "metadata": {
+        "id": "nnu2cY4t8P6X"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python \"/content/VoiceCraft-gradio-colab/gradio_app.py\""
+      ],
+      "metadata": {
+        "id": "NDt4r4DiXAwG"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 8b21333bef69a42f0db7ea0f735aa9243012256d Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 19:21:47 +0800
Subject: [PATCH 18/24] Release access for the Colab

---
 README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index c0dbf31..5d3a3f5 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,8 @@
 # VoiceCraft Gradio Colab
-Made for those who lacked a dedicated GPU and those who wanted [the friendly GUI by @zuev-stepan](https://github.com/zuev-stepan/VoiceCraft-gradio). Potato programmer brain here so all code credits to @jasonppy, @zuev-stepan and others who contributed in their code. 
 
-COLAB LINK : HOLD ON I AM UPDATING
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Sewlell/VoiceCraft-gradio-colab/blob/master/voicecraft.ipynb)
+
+Made for those who lacked a dedicated GPU and those who wanted [the friendly GUI by @zuev-stepan](https://github.com/zuev-stepan/VoiceCraft-gradio). Potato programmer brain here so all code credits to @jasonppy, @zuev-stepan and others who contributed in their code. 
 
 # VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
 [Demo](https://jasonppy.github.io/VoiceCraft_web) [Paper](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)

From 9ce26becea9f198ce9e74aab24d66f9e6e496555 Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Sun, 7 Apr 2024 00:21:28 +0300
Subject: [PATCH 19/24] gradio: added giga330M_TTSEnhanced model, changed
 default top_p to 0.9

---
 gradio_app.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 32b3e72..564af54 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -77,6 +77,9 @@ class WhisperxModel:
 def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
     global transcribe_model, align_model, voicecraft_model
 
+    if voicecraft_model_name == "giga330M_TTSEnhanced":
+        voicecraft_model_name = "gigaHalfLibri330M_TTSEnhanced_max16s"
+
     if alignment_model_name is not None:
         align_model = WhisperxAlignModel()
 
@@ -433,7 +436,8 @@ with gr.Blocks() as app:
         with gr.Column(scale=5):
             with gr.Accordion("Select models", open=False) as models_selector:
                 with gr.Row():
-                    voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
+                    voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M",
+                                                       choices=["giga330M", "giga830M", "giga330M_TTSEnhanced"])
                     whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
                     whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
                                                     choices=[None, "base.en", "small.en", "medium.en", "large"])
@@ -498,13 +502,15 @@ with gr.Blocks() as app:
             stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
                                        info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
             sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
-                                          info="The higher the number, the faster the output will be. Under the hood, the model will generate this many samples and choose the shortest one")
+                                          info="The higher the number, the faster the output will be. "
+                                               "Under the hood, the model will generate this many samples and choose the shortest one. "
+                                               "For giga330M_TTSEnhanced, 1 or 2 should be fine since the model is trained to do TTS.")
             seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
             kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
                                 info="set to 0 to use less VRAM, but with slower inference")
             left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
             right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
-            top_p = gr.Number(label="top_p", value=0.8, info="0.8 is a good value, 0.9 is also good")
+            top_p = gr.Number(label="top_p", value=0.9, info="0.9 is a good value, 0.8 is also good")
             temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
             top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
             codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')

From 9148e6020e265b81f8f946d4e1f86e3310fdb7ce Mon Sep 17 00:00:00 2001
From: pgosar <pgosarsteam@gmail.com>
Date: Sat, 6 Apr 2024 16:42:50 -0500
Subject: [PATCH 20/24] remove trailing whitespace

---
 gradio_app.py | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 564af54..80d96ec 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -90,7 +90,7 @@ def load_models(whisper_backend_name, whisper_model_name, alignment_model_name,
             if align_model is None:
                 raise gr.Error("Align model required for whisperx backend")
             transcribe_model = WhisperxModel(whisper_model_name, align_model)
-    
+
     voicecraft_name = f"{voicecraft_model_name}.pth"
     ckpt_fn = f"./pretrained_models/{voicecraft_name}"
     encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
@@ -132,7 +132,7 @@ def transcribe(seed, audio_path):
     if transcribe_model is None:
         raise gr.Error("Transcription model not loaded")
     seed_everything(seed)
-    
+
     segments = transcribe_model.transcribe(audio_path)
     state = get_transcribe_state(segments)
 
@@ -234,7 +234,7 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
         if mode != "Edit":
             from inference_tts_scale import inference_one_sample
 
-            if smart_transcript:                
+            if smart_transcript:
                 target_transcript = ""
                 for word in transcribe_state["words_info"]:
                     if word["end"] < prompt_end_time:
@@ -281,7 +281,7 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
             morphed_span = (max(edit_start_time - left_margin, 1 / codec_sr), min(edit_end_time + right_margin, audio_dur))
             mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]
             mask_interval = torch.LongTensor(mask_interval)
-            
+
             _, gen_audio = inference_one_sample(voicecraft_model["model"],
                                                 voicecraft_model["ckpt"]["config"],
                                                 voicecraft_model["ckpt"]["phn2num"],
@@ -300,12 +300,12 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
         output_audio = get_output_audio(previous_audio_tensors, codec_audio_sr)
         sentence_audio = get_output_audio(audio_tensors, codec_audio_sr)
         return output_audio, inference_transcript, sentence_audio, previous_audio_tensors
-    
-    
+
+
 def update_input_audio(audio_path):
     if audio_path is None:
         return 0, 0, 0
-    
+
     info = torchaudio.info(audio_path)
     max_time = round(info.num_frames / info.sample_rate, 2)
     return [
@@ -314,7 +314,7 @@ def update_input_audio(audio_path):
         gr.Slider(maximum=max_time, value=max_time),
     ]
 
-    
+
 def change_mode(mode):
     tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor
     return [
@@ -416,7 +416,7 @@ demo_words_info = [
 def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word):
     if transcript not in all_demo_texts:
         return transcript, edit_from_word, edit_to_word
-    
+
     replace_half = edit_word_mode == "Replace half"
     change_edit_from_word = edit_from_word == demo_words[2] or edit_from_word == demo_words[3]
     change_edit_to_word = edit_to_word == demo_words[11] or edit_to_word == demo_words[12]
@@ -456,7 +456,7 @@ with gr.Blocks() as app:
 
                 transcribe_btn = gr.Button(value="Transcribe")
                 align_btn = gr.Button(value="Align")
-            
+
         with gr.Column(scale=3):
             with gr.Group():
                 transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
@@ -471,7 +471,7 @@ with gr.Blocks() as app:
                                           info="Split text into parts and run TTS for each part.", visible=False)
                     edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
                                               info="What to do with first and last word", visible=False)
-                
+
                 with gr.Group() as tts_mode_controls:
                     prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
                     prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
@@ -517,11 +517,11 @@ with gr.Blocks() as app:
             codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
             silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
 
-    
+
     audio_tensors = gr.State()
     transcribe_state = gr.State(value={"words_info": demo_words_info})
 
-    
+
     mode.change(fn=update_demo,
                 inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
                 outputs=[transcript, edit_from_word, edit_to_word])
@@ -531,11 +531,11 @@ with gr.Blocks() as app:
     smart_transcript.change(fn=update_demo,
                             inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
                             outputs=[transcript, edit_from_word, edit_to_word])
-    
+
     load_models_btn.click(fn=load_models,
                           inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
                           outputs=[models_selector])
-    
+
     input_audio.upload(fn=update_input_audio,
                        inputs=[input_audio],
                        outputs=[prompt_end_time, edit_start_time, edit_end_time])
@@ -564,7 +564,7 @@ with gr.Blocks() as app:
                       split_text, sentence_selector, audio_tensors
                   ],
                   outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
-    
+
     sentence_selector.change(fn=load_sentence,
                              inputs=[sentence_selector, codec_audio_sr, audio_tensors],
                              outputs=[sentence_audio])
@@ -580,7 +580,7 @@ with gr.Blocks() as app:
                         split_text, sentence_selector, audio_tensors
                     ],
                     outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
-    
+
     prompt_to_word.change(fn=update_bound_word,
                           inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
                           outputs=[prompt_end_time])

From 623550f8081c6851d9ff59e0f3986affb0752bfa Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Thu, 11 Apr 2024 11:43:11 +0800
Subject: [PATCH 21/24] April 11 Update

---
 voicecraft.ipynb | 31 ++++++++++++++++++-------------
 1 file changed, 18 insertions(+), 13 deletions(-)

diff --git a/voicecraft.ipynb b/voicecraft.ipynb
index f8b9c85..78c816a 100644
--- a/voicecraft.ipynb
+++ b/voicecraft.ipynb
@@ -5,7 +5,7 @@
     "colab": {
       "provenance": [],
       "gpuType": "T4",
-      "authorship_tag": "ABX9TyPEhMt0mIcJv2BbaCwogF07",
+      "authorship_tag": "ABX9TyPsqFhtOeQ18CXHnRkWAQSk",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -66,7 +66,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Let it restarted, it won't let your entire installation be gone."
+        "# Let it restarted, it won't let your entire installation be aborted."
       ],
       "metadata": {
         "id": "jNuzjrtmv2n1"
@@ -79,27 +79,32 @@
         "\n",
         "***You will get JSON warning if you move anything beside `sample_batch_size`, `stop_repetition` and `seed`.*** Which for most advanced setting, `kvache` and `temperature` unable to set in different value.\n",
         "\n",
-        "You will get fp16 compatibility issue if you set `whisper backend` to `whisperX`, for whatever reason, setting `forced alignment model` to `whisperX` doesn't do anything.\n",
-        "\n",
-        "You will download a .file File when you download the Output Audio for some reason. You will need to **convert the file from .snd to .wav/.mp3 manually**. Or if you enable showing file type in the name in Windows or wherever you are, change the file name to \"xxx.wav\" or \"xxx.mp3\". (know the solution? pull request my repository)\n",
+        "You will download a .file File when you download the output audio for some reason. You will need to **convert the file from .snd to .wav/.mp3 manually**. Or if you enable showing file type in the name in Windows or wherever you are, change the file name to \"xxx.wav\" or \"xxx.mp3\". (know the solution? pull request my repository)\n",
         "\n",
         "Frequency of VRAM spikes no longer exist as well in April 5 Update.\n",
-        "\n",
-        "# **To those who want to voice cloning**\n",
-        "![Screenshot (438).png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUoAAAMSCAYAAAC8sv40AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAP+lSURBVHhe7N0JQBXV/gfwL/u+IzsiICICgiiLoiKoiKi455aWlWU9/5X1qme99+pVr15vaU8rM9PKLM3MPdz3DXFXEEFF9n3fl/85c+fCvXCBC1xA4fepkTvLneXMmXNmfnfmjJqBiW09CCGEEEIecl5+QcjISBP72qe+vh62tvYd/j4hhDzMbGzshL9UBhJC+iJeBnau/KuHuromDA2NUFZagoK8POHcknQdNTU1mJqbQ9/AECUlxairq+FDJSM7SV38SwghhBBCCCGEEEIIaRc11NXWoKysFHr6+jC37AdtbW1xHFE1nrY8jXla8zTnaa+qIClHgVJCCCGEEEIIIYQQQjpKTQ011VUoLy+HlrY2LCz7CXc86urpQUNDQ5iE32VKXfs7jqchT0uepjxteRrztOZpztNelejRe0IIIYT0CvToPSGEdAw9ek8I6cs6/+i9DHZOqa6hCS0tTWioa/IBrFNtIK/vkqRlbV0NqqtrJHeSqjhIytEdpYQQQgghhBBCCCGEdJaamtBeZkVFBSoqK1BdUy0E9urqa6nrRCcER1la8jTlaSu0SdoFQVKO7iglhBBCSK9Ad5QSQkjH0B2lhJC+TKV3lMqpl9wEKflEOqEhJCp86No7dOmOUkIIIYQQQgghhBBCVEqN/S/p+Fvaqet4x/6RdF0cJOUoUEoIIYQQQgghhBBCCOnzKFBKCCGEEMLwx+/V1enUiBBCCCGEkL6KrgYIIYQQ0ufxh3hqamqgpaUlGUAIIYQQQgjpcyhQSgghhBDC8DdoamvriH2EEEIIIYSQvoYCpYQQQgghakB5eZkQKNXU1BQHEkIIIYQQQvoSCpQSQgghhEANtbU1qKqqgr6+vjiMEEIIIYQQ0pdQoJQQQgghhFFTA0pLS9hfdejpGYhDCSGEEEIIIX0FBUoJIYQQQgRqqKutQVlZqfBSJwMDQ3oMnxBCCCGEkD6EAqWEEEIIIVJqaqiuqkJ5eSn7qAZDQyPh7lJtbW2oq9NpEyGEEEIIIb2ZmoGJbb34mRBCCCHkoeXlF4SMjDSxr5Pq66GuoSncWcrvKtXQ0BACp4QQ0hvp6UnaZuYvtSOEkL6Gl4FU/hEpCpQSQgghpFdQaaBUUM/jpdDQ0ISmJg+UqvMbTgkhhBBCCCG9FAVKCSGEENIrqD5QKsVOlcSzJTppIoQQQgghpPeiQCkhhBBCeoWuC5QSQgghhBBC+gJ6KwEhhBBCCCGEEEIIIaTPo0ApIYQQQnoH3qAoIYQQQgghhHQQBUoJIYQQ0ivU1tZCXZ1ObQghhBBCCCEdQ1cThBBCCOkVKivLoaWlJfYRQgghhBBCSPtQoJQQQgghvUJJUQG0tbXFPkIIIYQQQghpHwqUEkIIIaRXKMjLgZaWNjQ1NcUhhBBCCCGEEKI8CpQSQgghpFeoqqxEcWEe9PX1xSGEEEIIIYQQojwKlBJCCCGk18jNykBNdTX09ChYSgghhBBCCGkfCpQSQgghpNfgd5XmZKQAdXUwMDCkx/AJIYQQQgghSqNAKSGEEEJ6lbLSUuRmpaKqvBQG+gbC3aX8JU/q6nTaQwghhBBCCGmZmoGJbb34mRBCCCGk19DW0YGeECg1hLaeHjQ1taBGwVJCCCGEEEJICyhQSgghhJBejQdMtbR1hMfw1dQoUEoIIYQQQghRjAKlhBBCCCGEEEIIIYSQPo9uqyCEEEIIIYQQQgghhPR5FCglhBBCCCGEEEIIIYT0eRQoJYQQQgghhBBCCCGE9HkUKCWEEEIIIYQQQgghhPR5FCglhBBCCCGEEEIIIYT0eRQoJYQQQgghhBBCCCGE9HkUKCWEEEIIIYQQQgghhPR5FCglhBBCCCGEEEIIIYT0eRQoJYQQQgghhBBCCCGE9HkUKCWEEEIIIYQQQgghhPR5FCglhBBCCCGEEEIIIYT0eRQoJYQQQgghhBBCCCGE9HkUKCWEEEIIIYQQQgghhPR5FCglhBBCCCGEEEIIIYT0eRQoJYQQQgghhBBCCCGE9HkUKCWEEEIIIYQQQgghhPR5FCglhBBCCCGEEEIIIYT0eRQoJYQQQgghhBBCCCGE9HlqBia29eLnLmVgaAQ9fUNoaetATV0N6uoUoyWEEEJ6g9qaWlRXVaK8rASlJcXi0J4hnG8YGEGbnW+oa9C5BiGEEEIIIb1JXW0dqvi1R2lxl1x7dHmglF+wGBqboa6+Hux/gHXsE9TU1CQTkIeCjZ09CgsLxD5CCCFEilfuauy/etTV1aG2uhpFBbndHjDl5xsmZpbQ0NKEmrqGeL5BCCGEEEII6U2EaCL7p76ull171KAwP0el1x5dGig1tegHHV091NYBFeXlqK2pES6i6oWIKXmYDPIYgoyMVPaJAtyEEELk8R8/+ZMiGhoa0NLSFk5aigryUJCbLU7RtcwsrGBkasbWQQNV1VWora2l8w1CCCGEEEJ6IdlrD2127VHHrj2KC/KRn5slTtE5XfZMmiRIaoDysgqUFBWhqrJSuHChi5aHGO06QgghCvC6ndfxVVVVKC8vQ21dHUzNLYVzga7Gg6SmFpbCMsvYsvk60PkGIYQQQgghvZPstQc//xeuPdj1AL8uUIUuCZTyx9/0DY1RVlaGiooK4a4O8vCjS05CCCFt4XV+ZWUlqqqrYW5pJZwTdBU+bzPLfqhkJ0l8mXS+QQghhBBCSN8hvfbg1wP8ukAV1x5dEig1t7RBWWmZsLKEEEII6Xv4L7w1tXXoZ20vDlG9fjYOwjL4sgghhBBCCCF9U8O1B7s+6CyVB0p59Jbfz1FeXs7vh5UMJIQQQkifU1FRDi0d3S65q5TPU5vNmy+DEEIIIYQQ0rfx6wJ+fdDZaw+VB0pNzCxQWlqKenr8jRBCCOnT+KMw/OVK/NxA1UzM+6Gqih63J4QQQgghhIjXHuz6gF8ndIbKA6W8bVL+4iZCCCGEEH6yYmBoIvapjqGRMSrZvAkhhBBCCCGE49cH/DqhM1QeKNXW1kFtbY3YRwghhJC+rKamBto6OmKf6mix8w0+b0IIIYQQQgjh+PUBv07oDJUHStXU1VFf1/m2STU1NWBhbgYX5wEY5jsUUyZPhJ2tjTiWEEIIIQ+D+vp64dxA1dTU1IR5E0IIIYQQQggnXHuw64TOUDMwsVXpVYbnsEAkxN0U+9rHyNAQI4NGQF9fDwbsc3VllfCK/6qqajg5OeDu3WTsiz4kTk0ajJuDlV7Apc+34jBM4TN+NOyKT2DvuQJxgs4b5DEE6empnc5whBDSl+npsfrNwAA6OjodLk955V9ZWSm0By68OLENqlgm197lyrKxscO12DNin2p4+QUhIyNN7COEEEIIIYSQzl97PFCBUvdBAzElYiLOXbiIiooKdiFW0fB3xIhh0NRQx7btu8WplTECj708AVYZB/CfH2PEYV1tAMZMGwzc2IfjieIgRcY9ilVjHFBxczM+2npXHNhBr36FM1OB7WOfwb8QhX9tWgiP1F8x/ZUt4gSdR4FSQgjpHBMTExgY6KMoPw8V5eWoZ/91hBr7T1dPD8Zm5igtLUNhYaE4pjlVLZNrz3KbokApIYQQQgghpDt09tpD9c/CdQK/mCstK0PMhUtCd/1GHBKT7iItPQNlbHh1dXvbInNBcGQkpgW7iP3dYTimP/oIIr3E3haET52JSWFjMW/6JLiKwzpM3wCGRgbQF3pu4czJMzgR27FgNSGEENXjd3UaGxsh7f49pKfeR2Z6KjLTOtix7/J58HnxefJ5K6LSZfJOyeUSQgghhBBCyMPqgQqUcvyxPn4XqWqoQ0NLHVqa0s20gbvPYLiaA3ZBEZg3ZwbmTRkBO3EsYApXLy+427PxfuMwi4+fPg4+bHopM5fB8Bks21Yqn6fkOzAfAE+fQRjkYgd7Oy/4iMtqbgKmDzNC1tm7qHYJwFTZSKn9QPh4DYCZ2CtdJ08XU7FfQvH6cxlIvHYVF+JSxH6Jhu2ZE4Egvq6EEEK6ST3MzEyRn5ON3KxMVLI6rjNtawqPwLN58HnxefJ582XIU+0yOeWWSwghhBBCCCEPrwcuUNq1wvD4i0/h6WUr8dqTUZg6ZToWP/U0Xn48SBw/DHOXP4vn/8TGPz0TUZPDMXfJUrz80qMYIwY8A2Y/hZeXhEl6BHyez+Lxieyj7xQ8+awvbDR14RzyNJ5/dhFm+EqmkjM5Ar4GWbi0/hTu1vVH8OQB4ghm4gK8vDwKAWKvdJ1WzB4m9ptizOMr8cafFmLulHBMnf0YXvO1FMdxwzBl7izMndA4fdC8FXjt+cWYx6ef/gheeHUlnhwtH3glhBDSRep5G9xGwl2ZnQ1WyuLz4vPk81YQJ+2SZXKtLpcQQgghhBBCHmIPdKDU0tIcc2dPx+KFc+Hu1ukH1Jn+8PQdgZFehri29Wd8u2Ejzlc7IWzWBIwRxlvDbagPAjxtkPzbNmzYuAlfn6yG88S5WBQlCWbaunnBz6O/8FmCz9MHnnx00hns2Z6JEtQg7/YubNt+ACeSJFPJmjx1CAzSr2FL/EGcSKzDgJEyj98PcIffUFfYir3SdfJ1s5b0+s3E4wunYmjtGXy9YRO+/eF3XMuTbZKg+fRPPDYFg0r/wDo+/frdSLGfiMVPLUCoZApCCCFdTEtLCxUV7XsBkjL4PPm8FemqZXKtLZcQQgghhBBCHlYPRKA0dNxoLJg3G0MGu4tDJCaMH8cuxioRn5AIS0tL9Osne+dkR2miImMTfvnjKA4fOYz1iQXQMu0n105oXX48fuDjj55A9I+7cbvMAkODhotjW3H3Ag7sSxMCpTlx0di97yjONntP0wREDdFHyqVfkYB07D59F7UDRmGGjzi6Da5hY+Frko3TH29B9JETOHzoAH65XyyObY5PP9wiHxe+2ClOvwdfbk+Ghud4RIwWJyKEENJlhJsu1dQ6fGenm5srHBwUt5kizJPPW+yX6uwy29LScgkhhBBCCCHkYdbjgdLgUQFCAJS/tMnG2gqWlhbiGMDTwx0XYi/h3PlYZGRkoq62ThzTORWliZC+qze5slr81KiuqgyZ4mdkHkNGEWBgKnsXaSdMjsBQE3WYeM7De+/8Fc+NtIK2vgPGRrTx9ifR0AGW0KnKQcKlbHEIUFhfK35qTjJ9Hu7dkN5VVI7EP1JRpGkJ58bn+wkhhDyAHpk7G4GBgZgwPhT+w/3EoaphZGQkfmpOTU0NVlb9xD5CCCGEEEII6Rt6PFDqN8wHcXEJOB8Ti6ycHGhpNz7KdyshEWPHjMLo4EDhgo0HTLufGwz1geqyfLGfUdeUedlS+/DH7k0yzuE/G2Nw5izrtn6FQ0n1cPCfCGVuKr1fUcufp4Su2N+W+2UVwvSGsi+VcuNvyK9BdYnYTwgh5IEzdcpk2Nvb4cKFWNxLvg+/EdK2pzuP/yj5xuuvwtHRQRzSSF1dDQvmz8WcOTPFIb3TsJDJmDmtlS5EuR8wCSGEEEIIIb1HjwdK09MzMcRjEDw83GFlZYmLF6+IY4ADB48iOzsHujq6OHDoKG4nKmjwswvomthihPCUoyl85syFt0kpbp8/JIwrrqoBzOwwSRg/EJOfcYOVMEaWLvSEtwE3xR+7N0bapW+wY9e+hu7L8xnQcBiOKTxSWsHmr9sP7gH8+/xFTP7oLxMVvXHmDvK1HTF6aZAkWGs/AosGtPxipht7byBN3RGjFo6WvB3f3Avz5rnBtOQuzkYLkxBCCHnA8CCpk1N/7Nq1BzdvxsHO1hZVFVXi2M6rKK9AXl4+Xv3zSgxgy5FSV1fH4kcXYsKEMFy9ck0c2juNmDgD82aJ3ZzZWDSfdXNkhk1U9DbGdho+E//38pNYoETrPYQQQgghhJCe1+OB0uMnz6CoqBgD+jvi+o14nD13QRwDpGdk4sy5GFy8fFUYXlXd/DH5rqBmMgTLX/0r3n7zFby8ZBCqLmzFR7uShXFnT8QjS9cFi177K977x7OY51uKzAphlOgE4lMBp7BX2Pf/ghWTxcGM2ZQIDDXKwNW9ceIQicRfryFFzRaBkeyibMcVxFdaY+SKV9j8X8EzE6qQLW0ngCk7+h02X6yCy6Kn8Cab/3uvPoYpGlWQWwUZZed+wuoDWbCY+iTe+Mdf8Pbrz2LpoEqc+WUNtjdrP5UQQkhPkwZJo6MPIOF2IkJCxgj9h48cFafovJLSUnz/w09ISU3DK6+8BFcXF2hoaGDpY4sRFjYOP/64GafPnBOn7p1O7vwJ334vdnFVMLe1gXauzLCdKtj+AX6IiAhDkOR9kIQQQgghhJAHnIa2rtFb4meVsLJ1QF5OjtjXtlJ2sZaTk4uCggIkJCSisEj+xUTV1dUoLy/v4AspSpCTdgNnz17C7ZQC1l+AjNRLOHP2Nu5KGyHNz0LKxYuISUxDPrwwfWkALDJPY82xfJgZVCH1zE78suMoTt4uEyYvykxDckIeqo20UZF+CtF7j+PI2Ys4HyOdZzbS7iejoM4E2pUZiLlwGffE5kTVdUqQfvE0jpy+jyzZmG9eJu6n3MTFG3eReCke91KyUKaljbqC2zgYvR+HTlxm8xe3oSwTyfdScCuzBoYGdSiKO4E9fxzH8TMxOH8rGRlNt7k6B8l37+NuYj60zPWgXpSI3dt3449D53G7VFy+Eiz69UNJSbHQbh0hhJD2sba2ZuVwgtjXMkVBUr9hvti7Lxq3Em6LU8kb4OqGrKysZuVzW8ssKSlBUtJd9O/vICzX1dUZgQH++P77TThy9Dhqa1tu/5prabmKGBoaISs9RexTDX6+weuljspOZXXuHbGzGYVF/jYouPQe/v2DOCw1VzKhrS+mRU3ApLAxCPQaCHv1LMSl8wo0EHOXTkTwAA0kxaWLP1j2x8T5MzFpqDkM9J3hN8IPIQMtoVGnC4sBvvC2qcCl28qfIxFCCCGEEELap7PXHmoGJrYqfWmt57BAJMTdFPseNvOx7tgKDIn7HCNX7oetmSaq8zOR0yygaAxrBwOgOB2ZMnd7yjKxtoURSpGSWSQOaQ89WNqaQquqAOm50pcwNWFg0cr6KSKZp25dx9ZpkMcQpKenUqCUEELaif/Q5+3tjSPRe8QhirUUJN1/4BCuXbsuTtXcuPBIXL16Va58VnaZnL29Pb5b/xWMDA3xzbrvsP6779sMknKKltsSGxs7XIs9I/aphpdfEDIy0sS+TprzT0Q/54OcfVOx8N/iMG5gOJ59LAKhLjrI4292NLSEaW0idm9Yg+/PmmHuB2/gyf5Z2PL+G1jHWw7yX4ZvXw2FZsxH+K0oEpMCPDDUyRAlqfeRUwWU3PgZT//viGTehBBCCCGEEJXr7LVHjz96/8AqzUV6SktByCJkprQcJOUKM9M7GCTlypGTnt5ykJRrdf0Ukcyzo+vUsTt6CSGEKKOjQdLO0tLSRFTUFKSnpWPr1t/g7z8Cbm4DxbF9XX/MnD8HM4eo4fg3G/HN+h/wzbe/4Y7RCDzySCS8cBdHNp1HlqEHouZOw0A4Y+7ssRhQdwd/bL+Ks0d34ccsXo9XIj32O6zhQei9D+sPyYQQQgghhPQNFCiVcwbr/vku3l6v2rteHnZa2tpCEwiEEEJUb8qUyfD29ur2IKk2K9uXL1+GYb5D8eXX67Dh+024cuUqVr6wAl5eQ8Sp+rAB4zAlyA4VCVux+fBZxLB9EXP2EL5NKobFkACMYpNkX96FT49mwMgvCgsfn4VHvNWRuPt77IuvQNqNCzieyx/Ir0HR/bM4fuosTt+QtvtDCCGEEEIIeRBRoFTOXZz6Yx/2nqa3HMnS09NDdXUV6KF7QghRvYnjQ3H48NFuD5L+6dln4DlkCNav34jTp88iOzsbP23egkuXr+D5//sTfIZ6i1P3UYNdYasP6DqEYPlfXsLfhO45zHdlA3V0YSFMlIaYLT/jfIEFRs8IgFH6UazeGwexdVNCCCGEEELIQ4YCpaRVvO05QyNj1NAdpYQQonI2NtbQ09cTPi9ZvLBbgqScnZ0tHBztsW7ddzhz9nxD8yp5eXnY/PNWXIi9CG9vT2FYn2WoCS32pyT1DGJiLzV2G7/CO//6DjslUwFa1jA0ADS1daCjpQsd8QWOhBBCCCGEkIcPBUpJqwwMjaCjo4Oq6irQLaWEEKJaNtbWMDIyEt44z8vZo0ePd3mQlCssLMR3G37A+ZgLzdqgzs/Pxy+/bMXZczHikD7qfBYK6gDtmkqcjj6EvXLdWVwTJnLGzPmT4V59Ez/9lohya38snOMljCGEEEIIIYQ8fChQSlqko6MLc0tLlJWXoaamhg2hSCkhhKhSSkoqvl77rRCU3L//MC5fuSqO6Vr5+QW4elUS6lOEj09IuC329VH3duNoYilMh8zF07N8YSMMNMGAwHBMDjQR+gZMnoV5QfpIO/g9tm3/DtHJ2vCeOxcLhgqjgaxSVEAHpo6DYcq+a2Mr+R4hhBBCCCHkwUSBUqKQnr4+LK2shEfvy8vL2F9xBCGEkHbjd22qqTWvcnNyc3HkyDHcvp2IXPa5trZWHKMcPs+md4RKtbRMVWhtub3Hbfz+5c84mmuB8XOW4GWhjdI/4cXHpiHczwWwDcP8RwJgkx+LjTvikJt9FVvXnEK6oTfmC2/BZw6fwtVcNmngUrzGvvvyvCBhzoQQQgghhJAHk4a2rtFb4meVsLJ1QF5OjthHHhY8IKqlpS08Zm9sYgIjE2PU1dWhorwMdfzCnSKlhBDSIbz0NGFlamFBPqoq+VvQVceIldfmlv2Qn5cnV0535TK5lpbbEkNDI2Slp4h9qsHPN0pKisW+TirNR3rcJZy5cAfJMm2MFmWk4F5KMuLiS6BloQ1U5eHm8ZM4ffUu7udWoT4vAWePn8LJuHzwlryLMtJxP+02btxOw53kLJQVZiElLRelrBqtrSvEvbMXcSmFpRkhhBBCCCGkS3T22kPNwMRWpbeEeA4LRHFRkdhHHiaSu4PqoaGhIbSVV1VVxQcqdRFMCCGkBawc7WfVD/V1tYi/dpX11okjOoff1enu5Q01dQ1kZ2XLl9VdtEyu1eW2wMbGDtdiz4h9quHlF4SMjDSxr6sZoZ+tPrTqqlCQmY8ycahydGFhbQId9RqUpOeCzpAIIYQQQgjpOp299uiSO0orKspRjzrW0X8P03917IKad9XV1UInXPpSkJQQQjqHFaPV1TUwNjaBgYEBampYGVvF7z/s2O+UPFBpZGwM+/4DoGdgJLypnpfdwoKkVLxMTqnltuCBv6O0TVUoKylFcWmFcOdo+9SgvJR9t6QcleIQQgghhBBCSNd44O4o9fILREZ6WicuxUhPEy55KUBKCCGqU18PXT09GBkZor62VihnO1pPSr+rpqGB4uISVJSXKy6zVbhMTunlKvDw31FKCCGEEEIIeRh09tqjC97ywC6a2IUTb/OSuoezY/+I+5IQQohKsHKVBxYLCgpRXlmJqtpa1NTVdajj3+Xz4PNqNVipwmXyTunlEkIIIYQQQshDqgvuKKU7PAghhBDFWJUr1rodrXwbwpPCB2WClZ1fJtf+5TaiO0oJIYQQQggh3eEBvKOUEEIIIYqpsf8lnaI7+pXp2D+STulgZeN3FM1P2Y79I+naGSQlhBBCCCGEkIcFBUoJIYQQQgghhBBCCCF9HgVKCSGEEEIIIYQQQgghfR4FSgkhhBBCCCGEEEIIIX0eBUoJIYQQQgghhBBCCCF9HgVKCSGEEEIIIYQQQgghfZ6agYltvfhZJbz8gpCRkSb2dQ9LS3MM6O8IO1sbWPWzgKmJMQwM9KGlpSV5Uy8hhBDSx9TX16O6uhqlpWUoKCxCVnYu0tIzcDf5PnJy8sSpuoeNjR2uxZ4R+1SjJ843CCGEEEIIIQ+2zl57PLSBUh4c9fX2hKfHILg4O8Hc3AwmJsYwMjSAnq4utHW0oamhIU5NCCGE9D01tbWoqqxCeUUFiktKUVhYhLy8fCTduYfrN2/h0tXr3RI0pUApIYQQQgghpDv0uUCpU38HjAocgaAAP9jb28LWxgpGhobiWEIIIYS0pbikBOkZWUhNTceZc7E4dTYG95JTxLGqR4FSQgghhBBCSHfo7LWHhrau0VviZ5WwsnVASUmx2Kc6JiZGmDZ5IpYsnIvQsaPg7eUBC3Mz6Ghri1MQQgghRBm87uR1qPOA/hjoMgCeQ9yhp6uD1PQMVFZWiVOpjqGhEbLSVRuI7arzDUIIIYQQQsjDq7PXHg9FoHSEnw+efvJRREwMhZfnYBgZ0R2khBBCiCrwOrW/o73QjI2bm4vweH5aeqY4VjUoUEoIIYQQQgjpDp299njgH72PmjIJc2dNFe52oTZHCSGEkK7D2zS9fiMeW7btwo7df4hDO+9hevReW1sbmppa0NDQhLo6vRCSEEIIIYQQVaqrq0dtbQ1qaqpRVaX6p9l6bRulmpqaWDhvJubMnIoB/R3EoYQQQgjpaneTU7D1t13Y9PNv7ASmRhzacQ9DoJQHSHV09MBDo7V1tewErg719XWSkYQQQgghhBCVUFNTh7q6OjTUNcADkpWV5SoNmPbKQCkPkj752HzMnTUNVv0sxaGEEEII6S5Z2TnYsm0n1m3Y3Olg6YMeKNXT04O2tq7wq3Z1VaVwZ20d6+qFUzdCCCGEEEKIqqix/9Q1NISnxrW0dYSnuaqqKlBeXi5O0TmdvfZQF/8+UBYvnI35c2dQkJQQQgjpIbwO5nUxr5N7Mx4k1dHWZSdmpSgrK0VlVaXwKFBjkJTfY0odddRRRx111FFHHXXUdb6DcJ7Nz7f5eTc//+bn4fx8nJ+XPwgeuEApb5N00bxZwtt4CSGEENJzeF3M62ReN/dG/HF7XV19doJWgsrKCtTV1Ypj5E/mCCGEEEIIIaogf57Nz7/5eTg/H+fn5fz8vKc9UI/e87fb//31l6hN0j5G0WUoPexICCEPDt5m6dvvfYiY2MvikPZ5UB+9NzMzZydmlSgtlb49v7FGkqubFFVUhBBCCCGEEOXJBHrkYz6SPgMDI+jo6CA/P0/o76he8+i9iYkRnl22pE8GSfn1V5sd+0fomvYr6mS+9yCQrofsegkd+4d38isv6Ro+ykxPCCGkZ/C6mdfRvK7uLfiv1bwh+fLyMnGIpKYR6pyGyof909hDHXXUUUcdddRRRx111HW0kwn0iH9Ekk/8vJyfn/f0XaUPTKB0WmQ4hvl6i329G88CDZ1cjzhAUSedoGm/ok7me8IfcUx3kC5L6Ng/vOPUpD2yXatrJo5rmF4yhBBCSM/gdTSvq3sL/ob7xsftJTWM8G/DP1TrEEIIIYQQonriubbkf5Faw2P4/Dy9Jz0QgVKn/g5YMHeG8Mar3krMBmJ8kOcGsWscI3aqxuYpE2hUxRKk81HUKewRtrOzJPMR50gIIaSb8Tqa19W8zu4N+C/V/LF7aa0i/Nv4DyGEEEIIIaRLsfNuMc4joSacn9MdpczUyRPh6GAn9vUufIc3XHc1BEZ7AluuTMBUWXzapp1kPi108lOqnrCcLps7IYSQVvC6mtfZvYGmpiZqaqqEz0Kd0vgPIYQQQgghpLvIxHj4+Tk/T+9JPR4otbQ0R1Rk77joaqrhmqshgPggYOsh+b9F0rUVppHtaQiG9jTJejwIa0IIIX0Nr7N53f2w403C1Mu1Ik+1CiGEEEIIId2v8Tycn58LTTf2oB4PlAaP9IetrbXY13sIu7XxH5Xhc2uraxubSsGEct8XeqRD5MaoTNO5N+3apNREhBDSOdeuXcOB/ftRWloqDunbeJ3N6+5eheoTQgghhBBCes4DdD7e44HSSePHiZ96o47taf6tljqBohEynWxvy+TvyGz4LHyx7W93lHTOzWKwCjrpx5bRXaWEkK5RU1ODW7duCZ+vXb2Kd995B39++SUcOHBAGNbTfv75Z/zjrbeEvz2hd9fdhBBCCCGEkL5KQ1vX6C3xs0pY2TqgpKRY7Gsdf3TvxRXLoNNNDbV+s3YtBjg7Q0+va9+gJQTvWrhVOD09Hbt27sS2X3/FxdgLqKmuhq2NrdAGQ7PAoMyAanbRfvbMGWzd8guio6ORcj8FpmZmMDY2EW9LlnSFBYXYu3cPfv11K5v+LIqKimBjYwNtHR1hPnLY92QWIXw4d+4cbsXfwoABA8T5tiwrKwu/bt0Cewd76OsbiEObky6DBx6aboNZk20oLy/Hph834Rd28X/wwEEcPNjYWVpaCtsiR9wGQghRFV5Wff75Z8jIyISfnx+S79/Hxg3f4fLly4iLixPG+/j6Ql29Z35rPHBgP955+x84fuwYrly5DBcXF9a5imO7h5mZCXbsjkZZWbk4pHWGhkbISk8R+1SjPecbivB14t8X6pA26jtCCCGEEEJIV2qM7RgZGXf6PL8z1x49ekepl4c7jAwNxb6ud+LEcbzy8ss4fOiQOKR7paamYvXnn+PixVgYmxijtKQUP2/ejD17dguBUCFXNHSNPTW1tfhj31789NMmlJSWwdjYWJjHmtWrkXzvHptGIj8/H999tx7H2MWzrq6uMIzPe8uWLW0/MsoWVVVVxS68jwvByraCpNnZWWzdfxICniUlJeJQedIt4OS2gW23dBtWN9mGgvwCYZ7Z2dnC+vCuWuzKysvEqQghpGvwIOgXrJz+kpVNWZmZwrARI0bgo48/wfTpM3Dl8mV88snHQiC1Xr6By25z6uQp3IqPR2FhofCX93c3XnfzOpwQQgghhBBCepMeDZR6e3qIn7oHv6j97bdtwp04H3/0EZKTk8UxqiMEBhXEGPnF9+HDh5Gdk42ZM2dh0cJFWPjoIvgO88O2bdtw504Sm0oaWpSfAQ+wbt2yFd5e3li0iH1v0aOYO/cRFLGL5D/++EMIJvJtO3/+HC5duoTJkyOFaeYvXIjQ0FAcO3YUV69cFucmj8dDJTFRNeTl5iEtLQ1ubm7COEVqaqpx6eJFfLlmDUvL31BRUSGOadRsC1iP7DYsfPRRhdvA5ebmory8DAsWLMTyZ5+V6zwGt5Bf5BZGCCEdw8tp/gPUmjWrhbv/paysrDBr9my88tpreP6FF5HBxvHpeJneE4YMGQINDQ3hM//L+3tCd9fhhBBCCCGEENLVejRQ6jbQWfzUfWpra3HhwgWsXv2F0N7czp07xDGq1DxyxwOCZ8+cRlBQEAICAmBtYw1X14GYPHkytDS1hABnS3cnXb1yRbiAnxQRARdXV1hbW8OfzWPM2DE4w+aZl5eHyooKnD17FgMHurHhY2FnZwdHR0eEjZ8gPK7Ot5nPo7nGsOb9lPvQ1dGBFZt/S65cvoKPPvwQmZlZCA+fJA5tJLflDbNWk9sG1xa2gcvIyICRkRG8vLww0M1NphsoPKYvN38ZLQ0nhBBl8PJp9RdfCF16Wpo4VCI+Pl74EWfw4MHCjzZzH5knTPPpxx8JdUp3Gxcain++9z6efuYZ4S/v7wk9UYcTQgghhBBCSFfq0UCpg72d+Kn7ZWZk4I99+/Cv997DP995Bzdu3BDHdI2cnBzEx8Vh0CB3aGppsiGS0F4/KyshCHgn6Y7QXinvtm//DevXf4uysjLh4j0hIQEOjg7o16+f8B3+TX4X0WAPD+Rk57AuW3gEM+n2bTZ/N7k2WPkj7vziPikxSXikviV1dXW4eeMmnF1cYNhKcwg8WOA2aBCWLl0q3K0qSxqs5I/Ir1+/nm3HdrY9NQq3gWu6DaivR0pKihBETUxMxKeffIx33nkbv23bhvz8AvFbilCYlBDSPgcPHMDOnTsbOv44/ZrVXyAtLVWcohF/3P6vb7wu/KDDf3h6/oUXYGBggNOnTwtthHY33l7zkscew/PPvyD85f09oSfrcEIIIYQQQgjpCmoGJrYqbWTNyy8IGRnyd+O05Nj+bTA1MRH7ut5jSxZj+2+/iX2N+J2K/MUc8xcswKxZs6Gj6MVHShJCdgra97x86RL+8tqr+OTTz+A+mLfrJpmG30XK72BKSLiFd979J7S1tPDBB/8SHlX/97//I7Q1+u9/fyAEL/lFMX/pk1RiUiJeWrlSGO460BUvv/QSXnxxJUaPGSNOIfH779uxdcsWfMDmZ2trKw6VDy8Wl5Tg/ffew5QpUzAqOFgc2hx/5LS2rk6YDw8evPvuO/jv/z6E84AB4hRAQWEBXnv1Vdjb2+O11/4iDJPdBg1xG/jyZbdh9OjRwnQXYi5gwsQJwn7gL4y6d++eEGBevHix8AiswgzL0rGjGZkHf0+dOoncnBxxCCGkK1lYWmLUqGBYWFiIQ7rf7FkzWXmWIfZJ2l3OFNsklfrLqtex6vXXse6bb7DqL6/hlVdeFR6/5+X2nNmzcGD/fvz1738Xhnc33tTK7du3MXDgQPj7B4hDu1dBYSHGTpwl9rXOxsYO12LPiH2q0Z7zDUX4OqWnp7ZYbxMVcg1AlI8NKu7sQPRFcVgn2Lm7AvGJ6Pjef3iNmBAFV9zDzwe6/0ea7tKx/WsKV3cT5MXfQ744pDsJ+0UnAzG7zyFRHNZlzJ3gblKI+DvSmwh8ED7LCVDR8UUeVk4YM8UHdpWtlQ+uCJ0fDF9zdq1bm4mYr3bguDhGNZrPP/FhLq+bHWt9R++uaxTVFwqODTtXuLMSPb5DmfcBLZc7tU3dRIzr2Nrad/o8vzPXHj0aKI099Ydc4K+rtRQoleJtc4aGhuGRefOEx8I7oqULrpjz5/Gv99/Dfz/8CM7O8o8r8vY++YuNeCCTP3Z+/do14W5Sv+HDhcc633/vnyyj2OHZ556TC27euXtHCI4+8cSTGOwxWPj89zffhJ/fcHEKid27d2Hjhg3C/J2c2AErkqwr61gOSLpzR3ik/i9/+Qvs7O35mDbxtkoVBUqrqqsQe+GC8CZ8Ty8vof1R2W3gpNshuw1jxowR5qeupo4FixYJzQfwNlAvsuXwt+tHREzG3EfmQkNDQZ7pRKB08+afcP7cOZS28FIqQohqGRgaCmXs/PkLxCHdz8tzCO630U61bKD0pZUvsjLJHpevXoW2trbwwqfXV/0FU6ZOxaafNovf6B48SPr3v/5VeOkdv0v/7Xff7ZFgKX9awG9U8yZYFOlVgVIVB/36hGmrsOVJb+QdWIhnPheHdVTIEnwy34edhOzGC1/1zMs5e9KLn21CBA4j4v/WikN6mQ7uX9c5L+G18bbIPLoef9vctU+JKSLsF7Or+OrR99HylYYqOGHeqhUIt8/BobUf4EchhrEMX20PBVRxfJGHWATe/2EJhuW3XD6MWPg2/rLAAyblpSivuI1tS97Fd+I4VWg2/3XJGPbQlteKjjXVsAuMwrwQd/TTZ9e0tRXIvn4M3227oPBHHtfgcIyw1kXR/WPYe75pwNYUQeFj4WRYgbTL0Tiuwl9penNdo6i+aH5sHILuB7Phr3ENW/+8HnuFqdrjQSyXR2F5p7apmzwggdIeffRe+jKKBwV/PPyHH77H3//2V3yzVrWFAn+LvBDIYzteiEy2gKfJUB8fBI0cKVyM80s3yRvo2XeE7yrGg4vCdC1P0qobN64Ld4DyO706i683X3++HXx7FG2DotXU1dPFvHnz8dSyZRg6dKgQAODtrE6YMAGDBg0S2jLlb8xXNQqSEtK9+PHGj7uHhbT9aP5YPr8DneNNmnD3798X/nanQwcP4tSpU0Kdxf/y/p7woNXh3WbIeCxeMh+zW374gjRl1A8O9rawNRf7O0PPHq5DhmBQ/44//fNQmLIUb7++FFFir5S5jS0cbEzFvgdA4CN45fVn8Vig2N9ZHdy/hf0c4TFkIFys6sQh3UvYL3b90K7n5FrYx60rho3TQPi4O8G64WTaFLaqOr7IQ8wYVnatlQ9DMHl6AAZrp2PdF2vxkcrvJlUw/64sr1Vd9jSj6FhTgeBH8PLyuZg4zBa6lTqw8wvDvCWPYVmE4v02dNIcLGXnHMvnR8BVHNbAPARzl85n4+cgUsXv9Xzg6hoVal5fKDo2dNDfZQh8XPujsVHD9ngQy+XOblPf0qOB0qrqavHTg0NHR5cdOENU/hZhfudsfl6ecKeoLN42KH+TvImJKbQU3F2rrq7OvquFoqIi4e4d2XKa91dX8e+aCBes/PsFBc1/i+J3dBroG8i1XdqAzbCyshLXrl6Ft7d3B5sdaHI3Z0OP5INak22Q4mNlt0FHWwd+w/2EZgQkgVUJfX194fFS/lhscXGxOFR19HR1xU+EkO7yMB130vKIl7O8ORSBWEQ1llTdh5eXspr2d5cHsQ7vFqoM+pH2i9mGD979D/616bw4oJfyHYPp08ZgmNj7wHIJwLRpExHsIvZ3Vgf3b86+DfjHux/i0133xCEPgQ7t4zzsWvsh/vbeN/i9y5/xJ72LGxwt2HVecRp2HD2JwyevqLiZCAXz78ryWtVlTzNddKyl38aNE/vw4afrsGbjRnz835PItPDElIlB4gTyTPrZwsHMDPbDQxDRJFJqFhGCIHszWLJzEisjcSBpU/P6QtGxcRk/fvQf/O2/v+CkONXDrzduU9fR0NY1ekv8rBJWtg4oKVEumDV/zgwY6HdfPJs/dh8XFyf2NcffHPzyn1/B7Dmz4e09VBzaPsJFs0yQT6q8rAyHDx8WgpHOLtJH79WER8v37d2Lfv0sETx6tBAYlcUv0G/evInk5GSMHj1GLpDJ7yY6cfw4ps+YLrSzeur0KZiamgl3Y0ov7Pmj+8eOHkV1TTUmTpgILW1tYbgUn4o/vhkd/QcmTYpoV5uB6RkZOH7sGMInTRKWyzVsuUwSKLsNpaWl+OBf/4LTgAFy68GDyefOnkNGRrrw+D1/iYo8uTBtu/FH+Xnbq02D2ISQrsFfPjQxfBIGyDTZ0d1Wr/4CRYWFYp9ivL1n3iTIxdhY/PHHPgQEBODJp54Syundu3bi0KFDGDHCn9UZc8RvdA/eVnN1dTWMTUwwYeJELF68BCam3f+rf35+ITb+uEXsa52hoRGy0lPEPtVoz/mGInyd+PeF6kpBvd0i70l4aqQtym5twE9HxWFSPpFYPns0XDUv4bp0c13D8OSCCRhqkoTLSRXiwADMeyYSQTaluJjA71K2xogJkxAVMQ6R40fCb5AD9Mpu4q7kBmYBf1Rv0fSwhvHGNelIzJLOT9Th5bMLLu8QzJ4ajqmTQjDS0wU2mlm4mSrzFIcwb28YnU+HSUQU5k4LR8RIDwwwrULy7WzIrYn7KLauEZg6MRj+7jaA+gBEBDRPs4Zl8um8BsCsKhW3pdvUsLw82MyajQWRY+FhlIqLV20xJsIdxqUncFW8xgmdvwwRLhWIKe6PeXOnIkq63Mx4pMhmEbvhwvjIiSEI9vFGoP9wjOSdtw3KLyZAvpViRUzhGhyGWZHjEMHmwdPJUv0+4tMat166Lona3pg3hy0r0AY159h6sHGtbq8M3jZcaFAAxjqw8x0tIziydXTVjxW2N/SRx+GBBKw+Wdn6trI8FRQ1HTMiJ7D95A0POzWk3UyHdBKfqCVYOMwAZ/Ks2HxmICpsEIxSr+I2KxaVXU/+SGh44AiMd7eCZq0u+rkOh68t2w/x2S3P38AH4VNYXp8YhvBgtl5Ouii+dh8NWb3MvWP718kP4b720Mi8gJvCjmR58LlQuFVdRuEAduzMDEfkGB942Mqng6Dd+cIUQVNmYcZUnraSY8DKbwrc9TNwasMfuCRO1do+aG0fmzkHIHIGO77GhyI8yAMDLTRwNyG94Rjr7z8O/vbqSD0nXbdgLHjaDZA9vmS2KWTYIDgZVCPlbpPjFGyaZ6Yi1FkT527Iv8iQp/lMX1OkXbkDXlO2WT6EzMLKKf6wY+ktSX+O74MIDBW3q5mWjvFEnkKt519Ouk78WOTbaK+WgesN62QKn9BIzGT7KHLccHi7WUE9K0HuGJHPozL56h7LV3pDMHn2VMyMCEWwV3+YlFzH7YZMKs1b96A9TKYsNC7G5cR8SVktk98S2XrLpTvbN7Nms/IxnM3bZxAc1WXXux3lmdCm4jTMnBSG0OF8+9mQCcPhUM7Khy2nxGmkeHuJgRg10g39NGqhbmzbcKzKpbVwTBqhOu0eMmXeAdxauSbRwvwvmne8vJbdhywtvV1NUZlyR1ivlsuefpj1xAxEuOviDCtXpIR9HWzfeDwrWZ81O9aUrgfZuvPxEWGIYOvuN0wsU/hxXrkb2w/dw83bKcgrzkfGfRdEPuYHm5I4fLPzqvj9Rr5TF2KUWR5S1WxhXXkCv56Xnrs6YcELSzCiPg91JrrIPvMTdjR8vZVzCpY/V0wfBWfZ8wSB5Hj1M8tk+bi4oa75KU4X82ZGYPp4f/i527W7rmmqtXMZuXzGlzmRT9O+vNFIXK+JrL7mZYCzceM0cvVFS8dGMryCJ8DLpBgHZPKS9PiVls8Kz4EECsplpq36ta3yv8Vy03CMEnmzvNk2NZSDaUbyZV6zer+VPN1SGd9JRkbGnT7P78y1R/c1EKpAFm9fzbLnb8ngj3fzlzhNnzFDeKlT59tN5YE7+Ysuy3794OExBJcvXxZelqSry4OF9Ui+dw/x8fF4/PHHFS5XXUMDQzyHYP/+aCHQyNv85Phdorwd0P79HdlFs7XQtil/FPTatasoLioSLqA5/jb5K1euwN/fHzoK7+Dib5q/Dz1dPVhZW4vDOq5hy6WxS9ajoaGu1DYUFxcJb94/dvQYnJwGCI/wczyQe+FCDDw9PVt8+VdnQqUjWNrwwGwJPX5PSLfgL3ZzdumyWwCUMn78BOTk8AsEicTERFb+tNyuHS9jX3hxpXBXKX8Uf/fu3cJw3sxId+vv5IQXXnixoY1S3t8TeB1OmshhF66zJkDH+jp+Pi02LxESicVzBqP2Sjo2HNglGRY4HgvmBqLo99+xDvaYteJPmDXKHsZVRchTN4b92NHsAtoQeGEjDvPpRy7Ey89MxVDdXKTm10PLewTGehuhaOUGyLW+1KHlsxPz4IV47fHxGG6lgXx2YqzdLwih7ATbyfY7fLRdvJXGLRiz59jgZt1gmI5yhpG+IUyM9VHPpjMufBWfnZZMBvdIrFwxE5GuRqiprEV12VAMqzNH0zMQ6TJ9TKqRnVMFDcsAjPO1gd7Hn2JbPJtAWJ4lYsu80X+yG4w11XC39Hu2vn6YMicC0F+LH8WLj8BJ0zG+ph/MxtpguK0xDNjJsaFuJQJsa5D9zz2Su6Zc2Xo9OxXjndTZscNO/o1t2AWdIcoLclCcex23vt2DtpqgC338OSwIHQRbrVLkl+rA2nY0xgaxdX73Q/wsJpOwLqVGsNDwxihrbagXG+AcdrS9vTI8gydiigM/36mD9dhghLJPSVWN2wtDV6x6ybHlbYUry1OPY+44F+gWZqMQhggKHgY3Yw28+905oR08D5Yv5lleQ5G3HWYOMoSGegqK1iuxX2Q4+4/DVI9+0GKXEyZObD1tgeKrSfhyxw3F8z/I9sGSSIy1N0B1SSHqDG0wTj8AvvrA8m+lgZ0O7l8hv7giLmsjtgk7ks9nNOrtTBBiOxQOhvowMjaAXuUw2NTl4K3t4lVdu/OFKcawfPDc1OGw0q5CbVU5hg7zhSFvuUruZLT1fdDiPq5j6T+PXfhaaaMkr4wdy1YIC/aHg14O/rFVss5C2g5MRHqLeTYAT/6JLdtLH9mpBajX9cSooMEwLnsda86Kkwiy4BA4BbOMbHBr6zlEi0OBMMxcOB0edzbgux+ULB/8x2NepAXON6Q/13xfymnxGG87//J1emnJeATZ6KAwvwB1BoEIGdYP5an/w44UyT5aFu7DjtUCZJZqwZKlcbCXPezWrm3Ix0I62t5hFwIsj/TXh46OJF8NM9OFp2EwJnsaQU/LiNX/wOiB7HrklV8QI3xTzFv2tqh1HgwjbV1JWRjkDE2r+xg6fhSc9bWhK+Q3b5buifj3PrFtSV4+LpuKiQN1kZtWxPLbcIQEOMNQ8xOsOymZRqn8LqTR01g4yQUmNTWoqSrF8GH1sGzxYZ1BGBvhhn5a7KNaP4Sy9JAcq5WStA4bBLOqfORU6WJMSBBGedhj7SebcDxP8u2WyrVGLc3fo2PHs3icLQtn137Vucgr10DAqFEYZq+Lf3+wB44tlj2GCJ0+Hf55Ovhoc2OwuNkxo2R91tHvjVnC1n2qB4x4/q3UgdUgJ5jXlSCroEIsy9m+F41YOALOOqW4E9vKPX5q+Yi7a4YxoyfDZ/WXknVxDUU4y5dJp/Lh4ij7Y3kb5xSZdgiOmgwj+yR2niBzYEZMxuNz3JCwdgs2iINg6ok3VrhigJE+jPU0ocXyV4CrIbT+J80bbR+rcto4l+F5Y4q2GwaWmMPNRBt6ujrQ0hnZZJmt5w3J4c3W6zm2XhNYvq4uQE45KwNGB8BDvxB/+vpCk/qipbx7Q1ifGZDJS9Ljd7ApaviTwtrmCA50g35+07JVsTbr1zFtl/8tlpvGyuXNptskKQcTUO1pjDHsvM1Qj5V5BqzMY+VfyfPrGprnaDtPixP2Ij0aKL2XnApPD/4G+J4zddo0LF6yBL4+vrCReSN8RwmBwoZoYSN+F+TYsWPxw/ffC0G5gMAA5ObkYs/uXbC2toavL3/wph7V1TXCy5fy8/OF9jr5Y+dDPIbAwcEB361fjxkzZwp3Y8VciMGJEycwZ86chkfvR44KxheffYaNGzdgXGgYqquqsH//fuHx9qCgIGGapurq6nHjxk0haMGDFx3SJErJ78z8+eefhbtc+YtOeJMAPEjc1jbwbY2YHIE9e3az9NIX7tQqLCrEH/v2oaqyCuMV3BEr6EyUlOH7xsvbW+wjhPQFz/3pT0IZKcWb9njrzTdx5Urzy05bVje89Y9/IDQsTOg/efIELl+6BHNWlvMyricYsiu37Jwc4W9P4XU4aSL1LDKqZmGs+2h2mi55+3W4z0AYVldD0z0I4dglBCNcxw6Ek1Ep9v6RxfpM2cVEPuL3HMDpxALk1ehg0IIX8JeRMzEzgl3U7GMnqBFTEOZahb1vrMfWwjromrrAzfpu8zvdOrT8UVi6dDYmOWVj03++xcmccmiZu2DOn5Zh3mNVSLv6jiQIqK3H8ps9PEemY98PGxFTUQcNz8V484lhmBI+nJ2Es4sP/vKLJQsxz08Hcb98gQ0Xq9n5kBbsZyyH/HM6kmWOt4jDJ5/+jvi8KmiZ+eOFVdPx1Pxr2PaPQ+LyHBEYpYHzW77BntRqlKTzQAIfzs7i9SVz4vQNDWBpPhgeR3bg653pqGaXzzNefIOdN4RjvHjh7TMlCvOCTXH5ww/xza0KdhANw5/fehyDas7ibXYRnCCZVatS6sqRcewHbE0oQH4JYDLqKbzz2ATMnbIPP38q+aFFWBfHQATePoLPP7uCiooCXFVme2Wc2rUZLm5/xRCbIsSvXgv+uriSdMk4gYEZPAq3tLitrnMW4qnZQ1D8+xf49GQOu3g1wKC5f8LL8+dh/sVzWMOKOV19Axg6DcNcjUv4Yc0hpNaUIiWvfesZd3A7vnVxxWfOWkg9vxZfsOv8mvwkYZzC+ScbYGpOIrbvjUFSQSGqtJ2x5NUVGD13MiZ/e0p8sUTH9q8kvxhAv+E0kfdbwNLdDXe2/4jP77O8qB2BV/4xmp1rjmkIlLY7X4TMw/L5oXArOoYPPzqE9FpAQ3colr02FA4y5/5t7YMDLe3julqUpp/C19sSkV9QChiz9P/bQkyZORnbt0oCJELaGuk1+/GhwcjxmDvOGZUH3sXn24pRp2sENzdLJDYrMO5jR0wW5iz2w/TJpojeKwbz2DWEjw3L7ztOsZJpFF5SpnwQ1kk2/bnm+1JOC8e465z/ayP/SvJp5MBiHPryK+xILEatvjmGupriDr8xU9xH/TO24b2vziOnTAtmg6bixWdYPq7MxOU3dgh5RkhHRzeMzDiIr9awY7XOEnNeYvkxciZ0485h+9fnkFqljtHLX8O8kRMx0fUXxAiZTZq37HHktw3Yz/KWxtDFrCwIwOIZFojZ+z0+TahErc10rHp5uPBI9b/ZtUxD+ehdgl//tw7HMotZfnPGwj8vx+JH7yLm5AZh/yqT3yX5yxe6Cdvwrx+voZpdC2k5RuHPkubTFbiA33/oj8HvDkS/okR8tPp34Vh1nfMUm08AG70WH/6WhPwaPViOXIzX587G8py7OP6ZJJiiuFyTpXj+PKjcoeNZ2IdjYHZtHT789TbyKjVhzsrbv85eiEcv78FXLZY9Y6DPlmdYJf/EarNjRqn6rKPfC8OseayMKNyD178+iMIqbZgvehH/HaOHvN2fyQWUeOBs+SJXVF/9FR/tuisOVaQGiSeTMerxQEwZxsqBiywPTAqEa30yNiTVQP7Wg9I2zimicTZ7KpaOmIQo86PYIQbDwyd6wlYtA1uPi+UAp60FvZsb8WUsy89Qh+20Z/DquNlYFH8SxzfeU6qukdXWuQzPG2YWttDbsQFfXCxn+br5MtvKG39jh5qwXnMCoHubHR8/XEB+OS8D3GCXy895GLn6oqW8K1kfQ3a8S4jH70jx/OZMAco0TeE22Bo5zcpWRZSoX9PaLv9bKjfhr1yelt8maTnojuDUw/huLTu2a9XhufQlLB8RhsjAdTguBICVz9O9SY+2URqfIBSFPWKwhwfeeOOvWLXqdeFxblUESVvDH9XkwcqIyZOFC+xPP/4YP/20SQj8PfbYY0KwlAf8+KPyVy5fxpnTp1FVVcm+Wc8uxs2FO04NjQyF73zCvnvhfAwmjJ+AMWNDGgKgXl5emPPII7h37x7WrP4C69evR0FBgfBmaWdWmSjCg5r8jlbeJquG8Ng/jzoqG3lUPB2/U5S/eIlvB98ezsJc8Tbwu7qk28Db/psydRpCQkJw4vgJ4TF8HljmwWMezB4svjyFEEI6y93dXfiBRNqFjR+Pt995R2i6pCl+1/mChYuEH3Pibt7Ee++9J5SdixcvhrOztCmV7pOUlIiVL76Av73xhvCX9/eEnqzDH1xXcSypAFp2rhgvPDATgHBnPaTGxCHHdCDChRuQTTHewxZauUnYc4v3F+Dstk1Y/9shHDidhMKyQhSl5KGUnYA7io0X6rGzeS0NPZjbaaLw/iWcOroXW3bH4o5ktIwOLD90IqZ6miM79ktsjD6JU7GxOHpgN744cA/1DiMxY5LsHcv1yL/yPZvuOA4fO4kDW2KRUqEJC3sPyWjXiZgRbA/N+6fw8U9HhGkOHz2CbVeywM9oGgjL1EfKifXYdvgczl9m23TkN5zN0ED/EWPR2FKbFsrivsSXOyTzOt9KNFOrIhWfbdyNaN6+GFvmmgR29WdoCTfx9MdjkC0Mq7NweQ9f3hWcP7kTZ9MqoKVVg6zT12UeIW1Zwr5f8M3WA4g+FoPU0jKU5GWhgF2Q27ALMFla9bnYsXoL9h5m68LmnaP09kokXDiJO8W8TfdyZPI0bLrtxUmtbKsTpk4bif4Vt7Bu0yEcPn8FsedPY8c3t1Fo4YmQUcIcJLTKcfOz77HtEF/GJSS0cz1Trp7D4Rz+HGMNCu9J1vP4VZmIbtP558Viy8bN2LL/OI4nFgpP8tzPK4OGhT1GiF9pSVv7VzFNlKV+i3Xbxbx4YAO7OGVf6+cM6dfamy/CIyfAy6IAF7/7Htv5/mXzPRC9BUmNN4gxbe+DFvdx4kFs2LAdu1mancooRml5HtKLaqBvPRDiUdY2A31oa2tC38Qa2kXJOH/2JLb/Fo2zCuIwCTtOI6nCFL6RIZA0osW2cbI7TEuTcWxvajvLh45oeowrkX/FdcqN/QFf7mR56TwvEw/hp18P4Gq+uI/McnHuU3YMHo3Befb96O3rEX0P6B88GVPl8gzbl1+Lx+rRaOy7WwQNY3Vkfb0Z2w7w/XIcG+4UAjoWcJJ7fQXLWynfYq2Ytw78cgMZVTowrLiBr7ayspzv093XkVGpDlPbQZKviOVjWdxmIS3Psvx29mQ0y8tFsPQOQohkKkHr+V1MI81UHPn8V8k0bHnR26+xdRC+rkASzh+7j0Ke5SqyhemPX9WWzAf3sOdrtqyTsUJe2btpMy7mmcMrfCLCJV8WNCvXxOESiuYv++uOvLaOZ2EfGqTjOFuvA6cvITY2hqVxLNI17TEqZHjbZY9S2qjPWtTW93wwoJ8mCu78Ism/l9m6b7uHAk1daJTIluWSwJlPVSw+/3wHzrZRCZXt3Y8bpbYInOTD+lgeCHJC5a392Nms5bi2zimSsO14MsrMPDElTHonagimuJuwquUUdsuuR2ESVrPjUMjP7FjYvvYyKxPN4TdmDBvZjrpGpNS5TMldtkxWzwr5uuky284bDeuldg97+fHB6mtJGbAdvx1X9AJWJfOuePzW3zksOb85e4kdv+z8ZuteHGstxi2lTP2qdPnf0rlRR/N0Ea6sE49tluabYzJQoWUO+4YyT9k83bv0aKD08tWWH3PsKvxx7nnzF+BfH3yAZU8/3SV3EgrhQwVvqOd3H/HH+xcsXMgO4GnC4/6LHl0MH99hDcFOfvflDDbN0qVLoa+nL8xMQ10DPj6+Qjt0/DtTp01l81iAGbP4nZm8LU++rHrhLswJE8bjUTbPadOiEBUVxeb/qPCov8KXNLF11NTSwvTp0zHE01McJnb8H74NCrZDij8e/3//97zQrIAsPX19PM7Wn28rb06Az4E3IaBoG2Y22QZ+1ylPn0WLFmEaW3++HQtZ/8iRI1vchpbXkBBClMPbUg4ZNw7/ePsd4UcnWbxNUF7+7I+OxuuvrxJ+yOJ1x1NPLWvWrnR32Lt3L35nJ3z8zlb+l/f3hJ6owx985Tgak4JiPScEjGO9PuPgblmApD38ZNUC7mH8rDMIfvZ6yE86jVjhO+wCyMgJIY+txNtvvoTX/vQ4Zroag58VaPBHwZjYH39G9G1gyMIn8Nprr2DVMxMxVGFLNB1Yvr8jLDQrkBF/SeYO1XLE/Z7MLrf0YO8ue4LNLkrT4xunyylAGb9Rz1BsRmnIINgbAHl3fsMl8cYNrqyyBnLvIheWqQkzn7n461ur8J7QLUOgtSagb4zGsGMVsuJjkNjYfF/LyvJw/h6/cJaIK+Gf9WEknujfzi5CjaYFHH3FC0Nzb9iZaaKGXUSyS3Tl1NjAd/az+Pubq/DGisfxxEhr4U4jDY0m5yfFKdh+RSaMoPT2KqnVbfWAh60eoOuIyOeky1qFvy4cCEPoQJ8/Ii5VlYMbZ++i4Tpb1evZdP4sXxm4jsQzr76C91atwItPRmCQMZu3mjq77GtDG/u3JWV5lyR3GQpuo5g3uWZo1HCHc3vzhb+jOTQqsnD5qOx25aCaX2Q3aMc+aKYGdj4z8Mpf2Hf+/DSeWxQIO21Wz7C6psU7SJu6tAXfHU6E2uB5WPHyS3j75aWY5mUsjmwiJRq74wpgMmQ8ZghBqhBMGWKOolv7sZs/NNCu8qEjmh7jSqSddJ1unJQrGwoLJdFqYR9VZuFWrMwxWHob25ILAQNreMjmmeoipMdJpyvHaX4XF/ubdzm9Yf8mV7JCjuVQrSYPcMjlraxcFFax2RVnI0HahGRpuZAvNLTE8kEsH/X7h+EFcbvee+tFLHFjA3X0IJctlDnG8+/iR9lyprRSuLNUeeJ8CpOx/VbjspB1FnGZFdCwdIS/OEjQtFzrjDaOZ2EfapjAd+GLDXngvef8YMsqRn0TVf1A3UZ91qK2vpeJQpYvzKyHQ/rcrPtgc7Z1FSiSqRcxMgyTPXVw58AG7FMmXVOPYh/bTw7+4RjjOhHBAyoRt/8oksXRsto6p0jesx838o3hNVF8k37oeHhbFCEh+qD8j0OVRbguW5ffSkMOy+f6JvwGs/aXc0qdy7S6TGXyRmO+/lnu+CgS9kuHicdvwb3f5c9vWLnTLFatiFL1q7Llf0vnRh3M03LlIPtaPiu7oA2DhhCPknm6l+nRQOmtW4nIylJRgasEdXYi9s4//4k/v/IKQkPDYGbeVkHYWc1rK/6I+fARIzA5corwGCd/m7s0mMg7HlD0HuqDwKCRwt2m0uE8kOri4oIw9p1I9l1//wCY8RcoSScQOx5cHerjg0kRERg/YYJwp6jCAKPwBUng2D8gQFgv2VnxfyRTMEKwtGFMA3OWfjyw0PSRfT7PILb+fD1kH/fn28Yf8efb3dI28KeW+ln2Q9CoUUKgVEgjNzfFj9yzL8ivESGEdBwPevKX+r397rsNbSlz/KVzr77yZ7z77js4dPAghg8fjnfYND3VNii/C4u3k8rxvz3RvjKvu3kdTprL2X8d9ysM4BwYANcwN9iVpuDEpRO4lsJOgIeEwpUPN65AyvUzkpNr8xAsf24pHg13h0n2NezasQ8//5wG2b2acykaaz75FO//cgGFxt4YP3M+XnhpKSYrOI1p9/J1NdkFVA2q2YWInLJaoY7V0m4hwCKjIRBgpCkEvGoqZR7dU0RYZiVyb17AmfON3Y+f/Qevf7AJjS3L1aGWxyg6rDG4ceOXfbhSbIWRTz+PVSuexd9fW4JAvVTs27QPyt2H5IpFzz6JZdP94Vidij9278PWrbeQK3lwRh5babmzW6W3tzOk22oMLXbdhZJUHJJZ1pnzG9lF2fv46Hc+jai+jl0MyVD1ejaZv1noEqx8ejYihxgj+8p+bN+5A9+ndqb8ah68Ugo775Zem7c3X+hosUun6kqUtHrB3Y590IT73CfwwtJIBDvUIHk/S6Ptv+NmftODsw05V/D72i/xzw9/xZlCY/iNm4alz67AighFL/1LRfTWa0gzHIjwiaxOEwMmt/cflQRMVFA+tK7pMa5E2knXqUImuCdD2Ec1NfJ3sTPFQhSx9TyTUyv3k047VKO2rYsSsXwsun9CZrtY982neP2ttdgmmaoVTY7x6mrlf+RRSJxPbS3ks3M5yniElx0ncleQTcs1lWvcN8I+rMzHNdl0Or8NH/7zfby9QdJSbFdpqM/aqfF7R7H5RCo0B87Hq68+i5Uvv4JXZ9mj5OJOrJNtx9JWHWV5SYj9/bZygTa2tw9vj0OhrS+mz/XHgPLbiD6iIAcocU6BlKPYdCkTJu6SN+mHT/aARcld7DnYVm0o5nOhmZH2l3PtOZdpJLtMZfJGY77u+GuAFBCP3/rqDkZblahflS//23du1LE8rS60SSuhZJ7uZXo0UFpSWipkkO7C7yDldzQOGiQ+AtGFhLqy8Z9O43NRtmsbm0ryf4uk4/hfoZP90NDTcQ2za6FrG5tKuQkJIURpPFjKf0jjd5a6uUl+3718+RJ+/OEHxMfFYf6CBcIj+uPG8Vdv9Izp02cgLGw8bO3shL+8v7vxupvX4USB1H24nl4Dc6fRiPC0R9XdCzhamo7fbmRAw34QQoKdYFmXgbi94gXOqPF4JMABFRe+xpqfd2LHPv6YW3aTC6ci4VHdvb/9ii9Xf45/na+C25gZiOJ3jTbV3uXfKWQXE7qwcuGP8zUyC7MS7nDKyxBfCqWM05nIqwMsbRof41VIWKYm6isSsWN3tHwXHSNpo07FyqysYa5VgLtXc2HsYAfD7DP45oPPsfaIpC2yNrmyY22SB0zTfsEXG7cL+yn6qPi4Xlu6dXvPIY3HqTWrUXG6ybJYd/iKZCqFung9QyZPw8j+lTjzxQZ8vy0au/cfFx+f7TntzReJeazc07WEi9gshmId3QdOmDJtAryNMvDdV5vw094D2HtI+oh++xQmXMLh6J34ft1afPBhDCpdR2FB5GhxrLycM5tw8r4WXANDET5hECyKbuMPaQCmM+WDu65MS3jKUiLtWlgnKcX7yBSTbMyBugKkSV88193E8lGnuhLHm2zXjt0ncUmcrG1iGplZYVKn7vlpaT4+cOmnyy7UC7ukLFaGsA+16lF1u2k6RWOvojYkWmUKIx0eOesumrA1NUB5xk3c1rZAfwt1xO34FO98vguyNznj3K/47wff4tc2HrmXlXNmFy7nWiEwzBEFl3aw8lMcIUupc4o8nN54HslaThg9MQThg/ld5EdwoK1I+LABsGJZoziP74OOlHPtOJeRklumMnlDVcdHE8qe37SkzfpVdeW/6imZp3uZHg2Ucvuij4ifuh5/BJ2/uKe7CHG8xn8eEJIAY0fWiH9H2kk+sH9UEDRtP77c7l8qIaRv4MFSfvf+mLFjhX5NDU2hGZD//Pd/ePnPr2DkyFHCND2Ft9f8t7//HR9+9JHwtyfab+7Ouvvhcxc/x2VBy8YToXZquH/tsHAXTuKvt5Cu64jJAbbQyrqFn6XXembGMNBkF3G1FcjMLYeZdxiefNkTVuJoHjyZ9dz/YeWs4bArzUPi1VvILqwVmuXRUfSwRXuXf+h3nM0A7IIWY3nEEOECwC5wFp6f7QL9kkQc+kU6oRLuHMbFlBrou03B0sk+sGOD7IaFY/kYJ/nHxoRlaqB/6BMNywSsMWJyJCZ31Ts+/YfAUb8SaTeO49vvN2Htj79iywHZR9d4Or+C9978PzwmtOXaRKElDPQAjXpNZKUUsQ0bjnnPBcNVbsNa0IHtvVbA7/8xhV24NftrDTuemEq5i82HE1Fs6omnnpmFIPF7Zs4BmDUloPULvI7sl8xSVLC9a+7Ep2fra6forkUJM2N9aGjUo7oiBzmlpvCJWIhV3gqez+xObeYLeQePJSFfywqBC2ZhjDPbVnMnjJn/LHzkNkO5fdB8Hxejnz7LZGr10MhNRSEbFjRrGUKdlclkrK5Sl6S964xlWPVcFFtuOXKSruNaTgnq1NShrtlCAwelN7Hp0B3UOofgcU8LFN061hgwUbZ8EIOX9h4hcDXn00Rh5fLBMGr3DZpKpN2hI7iSJa6TWM7w8YueW4JZrmwf/RqLNDUrBD+xBJO9eZqwdJz/BOa56aH41glsbt64c/e4sx2HbpXC1Hshnp/PynNhoClcgyMRFdzycdPcXfxxKRU1hi6YvTgcI/iM7HwQ9ZSS5VEDMa3ZfOZJ09p8CCY/tRjBNkBazO84KJmw2wn7UMMeE56W7kOGb2NUWMOjv4rLHjFIxo7LecPYccW35/FnMNquO8/ZPODvZgq1gns4vmczvt2wCRu3HMDhJo/XuwaMxeSwiZga1Y6nk0pj8e3FTOjp5SF2V4ziO3zbPKeQKLv+G6KT6uAaOg9elkWIO3S8+fzMXbEiKkA4pvl8lj/hDzu1HFw5dIKNbG9do+S5TKvLVCZvsPU6cw8VYr4WymmhHluClY8GCFN0CDt+TydVsPOb6ez4layfsNwnlik+ZxBJy+W269fOlf9dS7k83dv0eKD00hVWgd+IF/t6HyGM2PgPH9RzhMCmatZCujVCJ/2nO7ZRhdtACCEt4c2GCC/ZYwIDA/HKq69i3vz5DXeZ9jS/4cPBmzDhf7sbr7N53U1aFhedhCx9ezgb5yB+p/go262DuJVngoH99ZCVeBBxkqHA8dOITauB/chn8OZbq/DmikcwyTkLmbwtRYE+rNyCEPXok3hDaNPqJbw8uh9K4o/hl+PiJE20a/mpp7H2m524WO2ORU9I1uGNZ+dhknk2otevxpab4nRKuYof1h9GfJ0jZrKLB76+bzw3BxPr8+Uf+xOXeaV+aMMy33vrBbz4aCRCfezFiVTsQjxSq8zhN3s+ls6dgaXPPCNpJ+xvK7FiMm+lzRy+YycgasoUPBKu4LjKOYqz8QXQGTRbsh9efQwzhhcjU9oeYWs6sL03os8hscwIHk++IEz7wvQ2GuOUcf231fjqeC6s2AXwC6/yZbF89fITeHTyCLT6s0pH9kv0MVxk10r2o/j0L+GNxYrvWuSOnLyKtFpbjHn+JTZflo8XhmBgZg4asnpPaDNfyEvZ/y02ny2A6QhJ+5+8rdXlU4eitEh+K5TZB833sQ2iLyQiX98FS4TvvIDnonxRmMUjP61JRR47wCx9n8OTwYBBv4EYGzm/cbnPB8G6LBEHtp8Rp28uYcdeJFQ7wMOqBHFHTjYGTJQtHw4dwVlWjlkHLMFrq9g0y6dhZM09pDd9/l0JbaZd6lGsWXsAcfV8nSTljDB+gh/c+dv6T/6I1duuoXbITCwX8hpL20XjYJZ5FF99sgs9V3vFY8sn3+NgjiUmLWwsz197eham+A8Up1FO7OaN2HerDgOmPIoXeRq9ugyLx9Ujt53PGgtpfSQbZhPEtF71DJbPdUftlZ1Y/e3pZi8z6y6SfXgD9UOl+1CyjUtnBMPXQZxIYdnDg2TxKNZzwdIV7Lhi2/NkuD3ikts6hlQpDufvFMDAOQRLFszFggWP4oWVknz895WPYIx4l+OQ4ImsvolAZKCjZIBSynF106f42zuf4vvYFu7Gb/OcQioJ23YnodLRDdbFtxF9tPlj/BXFagiZ+4RwTL/5/GIs8gJu7/kOa8RH9NtX1yh5LlOv1+oylckb17d8jh9O5Qv5WiineT32OKvHPB0UBHCVFY+fPtqG47kmmLRIsn58ucumB8PHUQyGypEvl9uuX/M6WP53B+XydG+joa1r9Jb4WSWsbB1QUqJ8KV1VXS0Ev8aNVfBqtF5IbF5DQuiRG9IFGqOK3RVcbNiihu2TLrmj29r920AIIVJGRkawtLSUa3O5L1vz9YZ2v8jJ0NAIWemqveRq7/lGU3yd+PclVVU76qeSXKRdv4AT5xJxt/HtJvJys5B87zpO87ennk9BtjAwDxkp93Hx7AnsP36efVd8nKogE8n3E5FSogltzTIkHzmGfUeP4+iFKzgfw5dRhdyMZMQn5EHdXB/qdSVIPrsbv+w6jhPxBfJtTEq1Z/moQXZKMu4k3saNIsBYh63SnbPY+tsf+ONILO5IAx3Cdl/E6Yt3cL/hvL0UOek3cfp0LOLv8aXUICf1Pu4lxSO5VA0amlXIuHIYO/Ycx7HYWJxqSDOZZWbXQN9AHXWl93HowAmcv5mC1Dx2RadweVwhMtIu44xM+udnpSPmnHQdRPnZSLl4HifF74+JWIBI5xIc2X8DFTpsebU1qKk1wOCx4xE8UAMp244iJisfrv4jYVZwHBuim74iIwcpyfdwNacaRjo1KIg/iX38zbJnrrL9dAW3UyUrqXBdlNneJqqzM5DMLkzK1NmZT1UWrp+8gmtpJUptK4rSceduChLj4lGsbQ5ttQpk3zyB6JNXkZCcDf7Km+KcTFyNiUFMYhryhZlw7V9Pnn/vp+SipLYetXUFuHPyAmKScxXOvyAjFcm30lGirQX1slQcPngEBw+fxrlLl3E+4T4yhKk6tn+b55fm8+GEeZ0517BebeeL0/KPH5ey/XIvFfHXc1FjwMqN/LuI3huNg8cu4Nw5me1VYh8o2seHYuKQxOZdbaSNmvwElvaH2bxjcDH2UsO8m6dtHtLLK1BZVom8WzE4dy0LSQm3ka3G7yyrQ/G988LxfOBMAvJbateuqAoBM2fCo/YS/vvRLtxpeE5XyfKhOAv37ychu1wdNdUluHPwEHYdOoETl/nxIb8PGrR0jLeZduXIuJ+Mu0m3cK2gDoZsnYpTr2Dn7qM4G5eG7IJcJN+9j8SEWyhQM2LfL8Kd07uxdRfLbzHJDW2XKjwGhHwVizMN+ZFpmtcU5i1JWXj2bGNZ0Lx8ZN9Mu4+k5Lu4caMUWv202X7PwbVDx3D8chLuZUl+TlImv1fnpuEOK4+upZVBXUMdVenXsHfvfhw4eQlnm5U/Us3XpyGtb2aiRp/VL5UZuLx9F7YdPIrjN/Ib6hfF5VpTCubf0eO5unEfplfrCfm45O4Jlucu49r9dAhFUQtlT3ZaCu5nFKGKHctVmdfxx74D2H/sIq5dajyGlKvPFOQRZb7nOgrzpgfAJGkvDqVos/zHy5QaaNkMx8RxXrAqO4ddlwpRXaCO6soknNl1GpfS5X5KlNNsHXJScTsxFdnSndN0ndo8p5B8jSusHoY5c91QE/sl/rMtWe7xfL6fzp84iZM37qJGzxxqBYk4/OtO/H7wFGLTxfMHJcq5Rm2fy4Q+8jg8aq7irW/+aHmZSuUNdnyw9Yq/kYFqXVbf1OUhbl80dp2+inuZRahuth8V5d3meTU/lZU7yYm4mlEttJVal3sbe3YfYOVcCtKbtcMjXy5fSWu7fs1iebet8r/FclPJPN10mxSWg8K8ZM5zlczTqmZkZNzp8/zOXHuoGZjYqjT25OUXhIyMNLFPOeZmpvjo3//AMB/5Nw33Ba1emsmNlPZId5fsyCa7UKZXpTu3A9rehKYDRQ/QNhBCCJG4ePkaVr76JvLyZc/O2mZjY4drsS3fzdQRHTnfkMXXKT09VayK5GqrHqFvYQVz3Tp2wZ/DLi0V0YOlrSl01WtRUZCFnBYeDe4UE0s4GGqhtqIA6bmdaztSsj1oe10NzGFrqgON6lKkZMlfVqlOAN7/7Z8YnfJfLPjredQasuUJw/XwyHtf4IkBifgiZAUOhT+DD16bgLQ1z+C5rc3vrhEIaaTe8X3Qru01hrW9AbTq2IVWep6SL/yQZ2JlCyOtDuSZdq2nNG9Wt5J/pcRpa7tyfytLuXyxRhjWlLhvlEif1veBon0sGabenuNQ2F+aqJZZhuQY1FDqeDYLfhLr3p0Pw9NvYcbrJxXnNSXKB8m2KpMPlNNm/m1jnTqc/7tc549tgXicsg3sZJndnmO4e0nzcXVJuoI7+FtYb5WlSwc89THOLDHH3qdX4OM8A5b/JIO1XJ7Hug+DUB39Eia9cZEN4XlAGxVdlN5tn1OYYsxz/8V/Fhvi+OtL8crhltNJOI40Ws+ryh9rLZ/LvL3lMGZhD7zm/kepZbaeN7guytfK1o8KymVBq9/vQPnf1ZTO0ypSL3lht62tfafP8ztz7dHjd5Ry5RUVKCgoxPjQ0XTHjhL45RzPPNK/D7PesA2EENIXVFVV4d8ffoGb8bfFIcrrVXeUdpHq8lIUFZc1e1NzoxqUlZSwaUpR1tJdYZ1VWcbmX4KS8qZ3RrSfZHuUWNfqcpSwZRaVduAZXaUNxORFYXDVLkBKcgby8+6g0sgdnoHBmDjaA5oJu/BpnAuW/ikMWhc344utV5DR0pWZkEad2Aft2t5KlPJpS8oV3zmshMrSDuaZdq2nNG+2ln+lxGm7dH8rS4l8sfsaFIfMxX2jxHa0vg8U7WPJsHYdh8L+kl+G5BhsfT5mEx7BsxFjMWn6JPjq3MIP//kRZ3JamF6J8kGyrcrkA+W0mX/bWKcO5/8u1/ljWyAep50vs9tzDHcvaT5WfKi1sN4qS5cO8J+G5cNNUZWZhXsFBUjP10A/96EIGxeMQMd8HPnuFxy5wwNgPA90XXq3fE5hivCFixE1YRxmTfGCdvxWvL8+tvEOVQWE46iNvKr8sdbyuYxwRykSsHrLKaWW2Xre4LooXytbPyoolwWtfr8D5X9XUzpPq1ZP31H6QARKucysHFhYmMPTo+vfSE8IIYSQ9tm2Yy+2/LoTNTXtP3mjQCnpWcXIqjCDk8cQBHp5wM/fD6OCRmOkjyNqr+3CpxujcSGnFnU5N3Ds0ClclD7iR3o5JfJFZofv9XsojJq3HAsCB8KyNgkHf9yIn46n4YG68ZKQh01eFvSt3OHl7wXvoT4IHOGHsUG+8DLPxLHNG/DDocSWm8DoFiPw6PMzMGqQOWpuHcf6b7bjWMqDUc75hE2FQ9l1bNgdKw4hD4QeytP06L2M/o72+PtfViLAf5g4hBBCCCE97dz5i3j7Xx8h+X6qOKR96NF70uMMHOHt1R8OpnrQ0lQHautQXVmEzDtxiL3b04+Akx7Tx/OFs89IDDBRR01pJu7E3UYKRUkJ6TRrN18MdjSHoY4mNFCH2toalOWnI+FG/ANwjDnCL7g/TDSqUZJxH9dvpXe86Yd2+nDSZPGTYm7Dg+GAdBy+kCQOeXi89Mde8VPv1K15+gF59P6BCpRyQ7088LdVK+Hu1vxNk4QQQgjpXvEJiXjn/Y9w5Vq7Xn8uhwKlhBBCCCF9190X/yx+6n0GfPxf8RPptAckUKou/n1g8Auxjz9fizt3m75tlBBCCCHdidfFvE7uTJCUEEIIIYQQQh4WD1yglDtz7gL+9+lXwl0shBBCCOl+vA7mdTGvkwkhhBBCCCGkL3jgHr2X0tBQxwg/Hyx7fBG1WUoIIYR0I94m6drvfkRM7GXU1taJQzuOHr0nhBBCCOm7Znl4ip96n203r4ufSKdRG6XK4W2Wzp4xBVMixkNbW1scSgghhBBVq6qqwu59B/Hr9t0qfdyeAqWEEEIIIYSQVlEbpcrhF2rrNvyE/33yJS5eviYOJYQQQogq8TqW17W8zqU2SQkhhBBCCCF9kYa2rtFb4meVsLJ1QElJsdinGoVFxUhMuoeEpLvIzsmFjo4OrPpZimMJIYQQ0lHXbsRj+859+GXbTpw8dR65efniGNUxNDRCVnqK2KcanT3f4OvEv093lBJCCCGEEPLgMDIy7vR5fmeuPR6KQClXU1ODjMws3E68i7hbt5F0NxkV5RUw0NeHgYG+OBUhhBBC2pKVlYNzMRexfdcf+H3nPhw7cUZ4wz2va7sCBUpJjzAfjqh5EYgc5Y+RHqbIvnwHyvwMMGJCFMa46OJ6UqY45GHlhDFTQjDSqX3b0q3b38F9RIhUs/zqGoCoUE9YVSUiuasyk7CM4XDSjUdihjjsoeKD8FmBcH1o178DzJ3gbqOG3IIKcYAiHSszu0wb5WPvqat6QHeUE72Nsmk2LATzRrnDvJNp29OB0gf+0fum8vILhJdLbPl1J1av3YB3//0JPvp8LX7bsVd4+QS/0MsvKBTaWauvV2nzq4QQQshDg9eBvC7kdSKvG3kdyetKXmfyupPXobwu5XUqr1sJ6V1MMXnpY1g2fzImjg1GaIC70ie9o6fPx9LpAWLfw8wDkQvavy3dt/0d30c9wnwIJj/+LP7+t1V4781X8PeVCzHZXRzXDA9ERWFe1CgonMR9FGbx8RN8xAGko5rl1yHjsXjJHEQOEfu7grCM+ZgdLPY/dAIw+6Fe//ZywrxnVuDVl5/BolYPuY6VmV2j7fKx99RVPaA7yoneRtk0C56Cpb0gbR/4lzkpw9DAAObmpjA1MYahoQH09PSgra0FTQ0NcQpCCCGk76mprUVVVTXKy8tRUlKKgsIi5OUVoKS0VJyie9DLnCSinnkFI3AZf/8qWhzSBwU+glfGWyDr4BpsOCsO6xJTsWb/Svjn78dfPj+O2oocnD0XjzJxrGDKUrzNLppj3luPHeIg7u0thzELe+A19z/ikIfVI1h36FkE5rZvW9rc/hbSrf2U2EcPDHtEvfIyngy2RPHtm0hWc0bQMAsUX/gZ77zyC2LEqRotw1fbQ+FUV4jTnzyLfxwVB4tGvPQR3h5rDfX7hxHxf2vFoaQjmuXXhR/i9Eo33PhoGp7cJBmkcsIyhiF7Zyii3haHPVRewY7zkcBDu/7tZY4XvlyHxa7p2PTyc/jwiji4mY6VmV2j7fKx99RVPaA7yoneRtk0+/t3uDbNAmc7mra99q33wwKRkZku9hFCCCGkr7OxtsW1i6qNij2MgdI3Nx8ULmp85v9PHNIHLfgvjj3vgrhPZ+Hpn8RhXWIlfjsXBZsLazDy2V/EYU288S0uTwW2jXwC/xAHcRQobWP7W0i39lNiHz0wTOE9YwqCde/iwsVk5KAfRrz8N7w5JAc/PrYM7yeKkzXggahw2JWpo+jU3xC26pQ4nAvAP359G1Os6qGbcYiCHJ1EgdKO6GuBUsB1+BgMMi7FrXOxSGzxt+IHKVDadvlIgdJOoEBp+/WxQKnK2yi1sLJFRUU5PfZOCCGEEKirq0NXVw85map92qTH2ij1icTy2d4wOp8Hm1mzsSByLDyMUnExUR8jJkxCVMQ4RI4fCb9BDtAru4m7ucKXED4rGKNGBaG/fj10jG0x0n8A9M6z8Xy03XDMmh2BqeGhCPYZBEf1DFxPbd9dv2beIZg9NRxTJ4VgpKcLbDSzcFN2Hi2ut3wahs5fhgiXCiRqe2PezAhMn8i3xQbIjEeKzKRmzgGInBGOiPGhCA/ywEALDdxNSIe09TefqCVYOMwAZ/KsMG/uDESFse0yt4WnzwiMd7eCZq0u+rkOh69tBWLis4U0mDd3KiInhiBk2CA4GVQj5W52w/yaanV7hXa0AjBmjD2Ma+uhb+EMV/1YXL0nGc3xtt1CgwIw1sEA0DKCo//whmlCH3kcHkjA6pOVwjpFTQyGv3vzNACsERQ1HTMiJyBipDc87NSQdjMdCnNlk/SfGxmG0OGDYF9/D9fTdOETEYW501h6jvTAAONiXE6Ub9irzf0rcGX7bxpmThLnzbK164ThcChn27JFJlDXRn5r2H7Z74haSze59Ahm6eFkhOq0e8gsF74qr8V9NAqLnguFW9U9aA+LwsLpYRhpW4szLF2VSu/Wtk3YB+Mwhq3zSNnOVRdnrt2XTNPi9yuQdf8eEvh7EtKLUJCbDo2g6Zjjro5723/DQeE4lxWMBU+7oT49Gxa2xkj66SgaYqkhT+CNKGMUFRrCtCaxSTq3nYZyx+gcdswE2qDmHMubMIVPaCRmTp2AyDE+8HY1RWXKHcXpL2DT83wXEYYINr3fsMY0UbhPFaR5w3GeZoTJs6diZgRLN6/+MKlKxe0s2aO3jXVrsXzSgGtwGGZFjkMEKxt43rdUv4/4tMZ5N8uv3pPw1EgLZJ/5CTuusnLlmakIddbEuRupkvEino4zfU2RduUOCsVhLXJn+XI6yxfSskB9ACICbFF2awPYrm2g3HHKtWO9ZPKkwrIxZBZWTvGHXdVl3GxoqpIfRxEY2qTcayTJn2Drf7QiCotmhgv7xcO26TFl2kr6t7QNpghfuBhRPo3b0JAuPP28BsCsWf6Q1doyJaTHQExx/zbK6Eb9/cfB314dqecS0Niip2rKzLbqQ0U6W4dxHaqrFJQrQp7zk28DdcycpZg9yg41sQmQnr25RizE4xPcoBcjnrvIkCuX+LnDeH94OxujkOWBXJZ+cut3j62fTLmk9PlEi+VMB47zhnJiL+7Zy9S/plVIvi1/7tHmca3kMSiXRrJlN2/WhW3XtIni9ltpsF3Y/Fyi4/uprXplCGY9MQMR7jJ1ISOke7A9yi+Kx4xc2SpMIjDzD8ciln7TxH1eaeGD2YP0kdpkuvbqdS9zMjY1EyLAtbW1kgGEEEII6bO0tbXZv/XIz82WDFCRHguUjnkc7z82FAZq7pg0IwDuTo7Qyz8P3aBlWBrlDy8bY+haOWP4CF8MddVH9r7L7ER1LJauDIe3gwUMNdRhamENZyc95G49jAvukVi5Yj7mjnKFsYYerAd6I3C4C/SyL+Pi/dYusxqZBS/Ea8/MRORwJ5jqmMLZxxf+3q6wQjLOxImn0wrXezv2xMgvY/7Lb2O2jz1cfAMwevAAuA5wwZChnvBy1EHWpatI5ifWYxbirWUzMMGXXcDqG8He0xcjfQbDuu4ajt6QXIqEPr0KT/kYoH5wCOYFucK5vy606gZgmLcz7M30WJIbw8qhP2yQhG3H++HJvzyBRWNdYaShC0tHN/j7u0In9SBi5K95BG1ur990vDDDEwNsjaFXz9Lbyh76hduxL1acATNx8QpMG+yAfoaaUDc2g51T/4ZphItPwzqYsH0xYagb3Aa6w8fXA0PMy3HpeIJ4geKKWSuexhPTAzHIRAv6Vi7w8/eBm342zl9KbX6BLKS/H2wMPDAu0geDBjjB08cb3gMNoWHOLqhmjcRgZycM9vKGj4cN1BIP47L4khWl9q+4Pk/ODGL5zBaODnZw8x4AB1tz6BfLXPQrkd9aC5S2nG5ieswMho+tAfT6ubB9OBTeTobIuSLmG1kt7qPpWPXBeAzQMkfQpFD4DHBk+eoSthzTaju929q24TOwMmo4y/v92fHHOrchCPTzgqv2PWzYd63t71dXorJaWHvAPAzPPRmGQeWXsfrbY7grHd6AB6IGofZKDMrdfNEv7WfsuS0ZE7r8/zDb6AauVLpggFw6K5eG/Bid0l8D/QInImKoEwZY1iNm+zU4PP4cVjwSAm8bPega22Ho8BGsPKpG/MkENIvjMmOWvIQV84Ix2FQb+kYOGBocCP9BDrCytpPfp62kuXCcjzCHrnswpge6YZDzQHj7sPLCSRupey+yHMqZsuzfxrq1UD6Vez2H5XPGwt+FlZ2Gtux7fvD1tETdtdO4Lmb91gOlmoj40wt4xE8fKVuONQarEYaX3luKUfq38OO+G82PV1lCvliAuaMHw9HWHv0dXeDtbAt7Sz0UyQRKlTtOpZRcL2meHOsJewM9WLn5YKTvYLgY5eGCNN/PXYGPp/tAO2kr9t4QZsLw42gqBpVtwq/ND2NGEig1qjWG27CxGObmDDcPT/h5ucG84hqOxInl+OMvtZL+mpi58mXM9NHFHbYNDYEz80l46W8LESRug56YLuFe1tDXNoWDlw9GefVD1e2zuKkgY7a+TMk0wjHgZgAHv9GtlNHyhLw6yhCJmw+BHe2MispMJerDplRRh3HtqqtaKVf8l76KZWHWKNwbjQtCOTMcz/7tGUz1t0f9lV04Jgb/5r74Fhb7aODEL4chFmcNJPvEBI7+ozGOnzsM8oDfUHfYaenAY/osTB3uCldnyfq5GefjyOk7kvyr7PlEq+VMB45zoZywhpq6NYaNGI7Brs5i/esI7ZT9OCfG15Q6rpU8BhWX3fcxbMVzeIqdF9hqa8HQlpezXrAru4IjCfLnuB3bT8rUK6Pw3FsLEW5fim9+PyfMg2t2zCgIlErSZxamDGPndnb2cHAehOGONmBZ96EPlKq8zfSSogLxoogQQgghfR0/J+DnBr2Gth4MjRwRGOWIlO3f4L+frMZ3B1PZBUk+4vdswKdfrcenn32JdXH1GBgwEzMj+Jcu4PcfbiG/jn0sSsRHq9eybhsO8xdMLFmIed7qOLx6HT7/6lt8vmYLEk2CsPjR6VDuNS+jsHTpbEwaUI3oL9bi4zVf46PP+TwCMO8xNm9XcTKF6918v+gbGsDM1hZ6NzfjizVf4f0PP8faa3XwGDcbi6Y6SSZKq0Vp+il8/eU6fPH1N/jwk/3INPPAlJmTG9ZZV98Ahk7DMNcpHZvZfP79yS/4aed2fJvBz8orkHqep8FafLmDXVWMHI+545xReWmtkAafsTT8ad9ZxDbe9iNDie2NO4qffkhDCftYkXlBWM6PTdqIPLVrM24W1rBPBYgX9keTaQzM4FF4DF9/vRb//vAzxJb1g/eEcIwXR7vOWYinZg+B+ol1+PTLb1m3DhuTjDF2/jzMV7TjhPS3h0eQBmK//RYfffI5vrxaCRvvcCycYI2E3SzvfPo53juQDX2XAEwLFdNayf0rWR9fmKcfwH8+/hz//mI9vj6Uhyq5B7w6n99aSjfJ8gNgkrQFH372NT5h6/rhoWo4T5iN5YtGSb4sq8V9xNPJAh4j/aB2Zj3LW6vx2fYbSqS3Ett2eQ++FNf5o9WHkKZmAkONHJzexx/Na0/asAvPZQsx0SwL+775BWcV3TAoqi38BbG5pvCNDIeZMCQE07zMkXktGgVNHr5TNg35MWo5OBCBaufw+Wer8e8vd+JyyDwsnz8Glmm/SdLn6/X4+HClcGfRo0IZ1FQYZs0LhVtNLJv2O3y6lqX1pQLommgh76TsPm09jwvHuaM7gnXj8N3adfjvR5/il1RNuI4IQ2SgZBoos24tlE8pdeXIOPYDPvtyPT5b8yX+sy8bll4TMHeKsm8JuY8dMVlQd/DD9Mmm4jAmNAw+NkDW1VMKg2qNxHzhZ4rUP77Evz9ajY++/hHROdWSH9kaKFkON1BmvcRlBxgj8Sc2vy++wceff43oqv6YNP8xLA0Rv8P3gZEB9OUuv3l6suH6Ym8LDE3dUHzoR3z+xWq8/7+rKO7ngYjJY8SxaCP97+O3WwUwcBiKadJ1YczCguFrLd0GSbqMt8zAj2t42f4tvlh9DBUu4Xhqfpj4DXnK7HPhGHAZ3GoZ3ZSQV430oCv2q6zMVKI+lKeaOqyBUnVV6+XK6buF0LQcCD9pMeM6BoP716FabQB8wqR1UQj8BpqjPi9OQZvM0n0yCE5p+yTnDv/dixSdARgzfxpG1JzBV2v4+m3EjSpb+EwIY3MTKXs+0Wo509HjXBtm5vq4+sNGSf37Wzq0nYdhSvhwcbySx7WSx6DCshtjEDXZB+YZP+Ozr3k5y/L978dw5V7ziqUj+0m5ekUH+nxdDfXEfommx0xzYvq41OH8D2vw/kdfsbrkF1wtEkc/5FQeKC3Iy4GWljY0NTXFIYQQQgjpi/i5AD8n4OcGvYsWyuK+xJc7juDwsZM4n1CAs9s2Yf1vh3DgdBIKywpRlJKHUk1TOA7j0yfh/LH7EOJLFdnCdw4fu4QE14mYEWzP5rUZG6NP4uzlKzh7MhprEopg6R3UeCHRmtCJmOppjuzYL4V5nIqNxdEDu/HFgXuodxiJGZOkJ89c0/UWBzdVcherNx1A9FG+nsexfe1lpMAcfmPEC+jEg9iwYTt2H2LLyyhGaXke0otqoG89EB6SKSS0ynHzs++xjU3Htzfm6jkczuGB0hoU3uPDTuL41XR2oacPbW1N6JtYQ7soGefPnsT236JxtumzfZwy25t6HcePZQsvvaguvq9wWxMunMSdYr5DypEp7I8m0xQn4bONuyVpcPQI2yd5gKEl3IQLIydMnTYS/StuYd2mQzh8/gpiz5/Gjm9uo9DCEyGNMa0m6pF7eQNb7+PC8nYcvI8CdWNoZH+H9dsl++TAprsoYBctVk5iSiq1f8X10UzFkc9/FffbSURvv4aMKslsBCrIb4rTTVw+7mHP1yzNTsYK+3Dvps24mGcOr/CJCJd8vVGr+0gTdVnR+JSl7QE2/PhVo7bTW5ltE5YpWec0aycMMKtB4uEfsO4Yy2jtSBvhwjPCDClbP8faA228fKr+Jr4/lwoDjwmYZM76Q8fD1zgd5368BPln79qXhlr1udixegv2Hmbbc/o6/CInwMsgHcfZdw+cvoTY2Bgc+CUW6Zr2GBUiveiX5YMB/TRRcOcXSXpeZtNvu4cCTV1olMjsU6XyeBGurBPXhZUXm2MyUKFlDnsxrhWu9Lo1L58S9v2Cb7aysuhYDFJLy1CSl4WCKj3YDHITv9O2hB2nkVTBg9UhYrCardNkd5iWJuPYXgW3rMsS84Xm/VP4+CfJevHyYNuVLFSKkwjaVQ5LtLle4rLr7xzEF6x8OHr+Ek4dPYSNP1xDttlgTGXf66zCO99inVj2HD6wAfG8mOvnDGn8p630v775AlLqzOEbIc0QppgU4g6TwiTs5tsgpIs+Uk6sx7bD53D+MtuGI7/hbIYG+o8YiyDxW7KU3edaFamtlNFtUWGZqWx9KKWiOqyBMnVVG+VKyq5bSK0ygutIyRv0zULdYF92F6fT6tHfe4wkf44MgatJBe5fOYyWzui0Ku7js+/EdTn8Gy5n1kDHoBKX122XlA9Ho9mwCmiY2sBT/I7y6dd6OdOx47wKGRe+Z/tBUicf2BKLlApNWNi3p/5tn6Zldw70oK+jDh1DG5jVZwrlbPT23dh3rfkP2e3fTx2om9tDTJ/8qz/gy52SNDx8mJVR9xTfSf2wUXmgtKqyEsWFedDXb+MnLEIIIYT0avxcgJ8T8HOD3qUKWfExci+EKDNyQshjK/H2my/htT89jpmuxtBgwzW0JOMVGjII9gYsnfqH4YW3VuE9oXsRS9zYQB09WIqTtcrfERaaFciIvyTT7lo54n5PRgE7Abd3l73UaL7eClUW4XqW+Jkpu5WGHHbxqG9iKw6pgZ3PDLzyF7a+f34azy0KhJ02O6Xk7dGKUwiqcnDj7N3WA0ncpS347nAi1AbPw4qXX8LbLy/FNC9jcWQT7dreTijLw/l7wjNpgrgS/lkfRsJFmQc8bPUAXUdEPifdb6vw14UDYcjvzGhxx9WgMD2+Yb3LzuaD74qy/CtIke6TlErwp7i1tMXtV2p7xfXJv4sfr8hcwpayecneHaWK/KaQuPzCZGy/1ZhmyDqLOH5RbOkIf3GQsoru78OlhjyoRHq3Z9vcZ2HZ4pGwzDiKDzaelBwP7fj++MnswrPkBtZtUeJYYq7/fAEpeu4IjzBlF+4eMMm4gB+uy6SToJ1pWJyC7TL72t/RHBoaJvBd+KK47qx7zg+2rBDSN3EWp5KViUK27mbWw+EuDnEfbM5yeAWKhHRvRx6vLkJ6XOO65OSXszysDYN+kn7l101B+VRjA9/Zz+Lvb67CGysexxMjrYUyRkNDRzJeGSnR2B1XAJMh4zFDCB6FYMoQcxTd2o/dbcRJpfki785vMvmRHbOVNeAPCDToSLnU1nqJyy5gx0KcTJpknr6NjEp1WDgqvl+xPcryLuFOw7xvo5g/m2xohKGSAW2n/63fcTalDhZeExHK+81DEDrYGPm3DiKab4OQLpow85mLv0r3/VvLEGityXa+MRSGu5Xd562W0W1RZZmpZH0opeo6TJm6qq1y5c7vrM5Xh6X7OOEuzrDhTlBPu4lvk3Kg7+QLfu+v6xgnWNZl4PquVl7aLbcuOcguYScOrHzISpDeYliOsuoa/is6K0WklEy/NsqZjh3n8nUycgrY+gHahvxXLaYrzjealN3AQfzw2wXkWEzAshUv4L1Xl+GxMc4y6SOj3fupnfVKe4npk35VrEdFmdXN2qJ5KKk8UMrlZmWghiWQnh4FSwkhhJC+iJ8D8HMBfk7Q+9ShVvY8kF0cLn9uKR4Nd4dJ9jXs2rEPP/8seWyuVUaa4HHUovsncOb8hcbum0/x+ltrsU0yVet0NaHBTvarZe+C4cpqhTbjGwJugibrrbRq1PKZic+aus99Ai8sjUSwQw2S9+/H9u2/42Z+0xVg6uuEoF+bcq7g97Vf4p8f/oozhcbwGzcNS59dgRURMo/QSbVre1VNi7+/iDGGFn9wqiQVh2T32/mN7CL6fXz0O59GCTksXcWPLVJqe8X1YcdbHh/XElXkN4XE5dfWCoHfRoouipVTWyu7JUqkt9Lb5oPHnpqHsSZZiP5wM04liRePSn/fFCY1pUi+eQknZAJnrUr4HSeTtTFk3DxMH2KM9Iu/I04c1aidacgOZNlLbR0tdklXmY9rsut+fhs+/Of7eHuDogdlj2LziVRoDpyPV199FitffgWvzrJHycWdWHeWj+9sHleHlhjlUH7dmpZPrlj07JNYNt0fjtWp+GP3Pmzdegu57X4NRiqit15DmuFAhE90Ar+r19uiCLf3H0WbrdeJ+aKmso3mYzpULrWxXuKy62tkAhxcaTl4ltDQau9RpSSW10yED8qkfxI2n72PaksvyeP344LhZVKAuEPHJflTSJdK5N6U3fcX8ONn/8HrH2xC8+ZTO7vPpWV0W1RXZipdH0p1Sx3WpK5qs1y5jT2JOdCycUYIO58ZN0AHqXHRiDt4F1lGLhgXCowfZAutnHv4+Y7wBaVU18r9nKBQu9NPTmM506njvImGY6sr9lWTspsfQwc2rcP7//kKu5Pq4TpyEpY88QxeWqjoh5D27qd21ivtJaZPBSuTeqMuCZTyO0dyMliWrKuDgYEh2wd8DxFCCCGkt+N1Pq/7+TkAPxfofXeTKjBqPB4JcEDFha+x5ued2LGPPzIseWyuVaczkceuI3SqK3F8dzR2yHUncUmcrFV3ClEMXVi5yJ9Um4VZCXe75GU0NszfYcMGwIpdjBTn8WfhnTBl2gR4G2Xgu6824ae9B7D3kPRx7I4rTLiEw9E78f26tfjgwxhUuo7CgsjR4lgZ3bG9bTqHNB430axGxemm+y0ah69IplIJpbZXXB8zK8nj3S1RRX5TqKXl+8ClH8s4JYUyL9foCCXSW8ltG7FwIZaMNMD9XZ9j3TmZth2UTptK7Fz7Md7//lTbx3eDJPxy7B7q3SLgq3MPB35NEofL6lwaJuaxy2CtelTdbrru0dirsA0LTdiaGqA84yZua1ugv4U64nZ8inc+34VY4SpedXm8/esmch2P6ZM8YJr2C77YuF0oV6OPik2YtFPOmU04eV8LroGhCJ8wCBZFt/HHkVZDZBJivrC0aXycV6EOlkutrldLy5Yrj1vgrgs98WOHKZn+Cb+dxO0Kc3hPDEB48ECY5MZh81FxG4R00UR9RWKzfb8jOqZ5nlbhPm+dqsrMDtSH3VqHKV+uxJ5MRr6OLYZMDcFg4yxc/5Xlr4unkVRoAteQcHjb6SE/+ZyCH3k6Q7XnEx0+zlvSmX3VjmOwLO0mjh84gJ82rMd/P9qEDJOhiJweJtw12lT79lNn6hVTGOm0EcNrIX16iy4JlHJlpaXIzUpFVXkpDPQNhDtL+Asd1NW7bJGEEEII6QG8bud1PK/reZ3P635+DsDPBfoEM2MY8B+FayuQmVsOM+8wPPmyJ6zE0RLxyOe3mBpbY7Id+2tnDbs723HoVilMvRfi+fnDwQfzk1PX4EhEBUvvpgzAY39ehff+tgyzFLW9duh3nM1gswtajOURQ4QLarvAWXh+tgv0SxJx6JdWLqZbYu6KFVEBcGUn1nxblj/hDzu1HFw5dIKNLEY/fXb6r1YPjdxUFMIaQbOWIdSZnXQrI7MUFezE2tyJr6sp7OzY9s5YhlXPRSHIrhw5SddxLacEdWrqUNfk9/I0ocLtvVbAdwhbh3Br9pftD8kOUMJdbD6ciGJTTzz1zCy23pKhZs4BmDUloPWASnsptb138celVNQYumD24nCM4Otj54Oop4LhKrtblMpvInXNFrejebqJ6cGWP0+aHuZDMPmpxQi2AdJifsdB/sUOUyK9ldm2kQuxfJEPTDLPIfpGGQx9hsCHd55OMFM6bYYheMJYTJ4cisZX3rQtcdcJJFQZou7eaWxT2NZg59Lw4K+xSNOwx4Snl2Cyt7i+PA9EhTU8Wi/PA/5uplAruIfjezbj2w2bsHHLARxueCRUdXm8/esmKrSEAStqNOo1kZVSxL4zHPOea5KnpRTlV9lLztKb2HToDmqdQ/C4pwWKbh3DAXFTXaOW4e23VmHVo5J2/+TcOYyLKTXQd5uCpZN9hHxhNywcy8c4sVJMRkfLpVbWS5qn9d2mN+TJxvI4E2d/5eUxn04SrLD3CBHKbLvAKKxcPhhGdZLRHaZs+t/Zh+N3y2HpNRvz3E2QdnkXTku3QUgXDfQPfaIhXXiZMWJyJCYr2vnt2eedoqoyswP1YVfU2S1SvlwpO34Bd0uM0H/SQFgWJ2PPLTaw9AzOsH1r7TUHbqaluHuBv/hOlTp5PtFUa8dTRyi7rzpzDLJ66ZWVC4WysTDlNs7fLkRZPaCjoYFccRJZ7dtPyu5/MaBq7oR5w1i9zqd5/BmMtmshbicdzM4JEwo0YTdiLhYFs3pUODZm4RVvHkZ++HXprZ78Aqmmpob9LWYXT4bQ1tODro4u1ChYSgghhPQa9XV1rL6vRlV5GYrySlBeVto37iSVOn4asXPcMXzkM3jzLXZWbesEu+osZFY4ybwZ+RZ2n7mHcdPcsPzVVRjDzjQz1v8TWz75HvYvPYJJC5+E1eBwdqGgB0tnO9RfzMKOk/yx1AEIDg/HKJNS2KWuxbamP/+nnsbab3bC8ImJWPTEMxgclAGtAb4YZp6N6PWrseWmOF171OshZO4TGDpmPMpMB2H4QOD2nu+w5qCkbbLoC4kYPcUFS9h2DC8wgsMAExRksbNsB2F066KP4eKMQfAZxdOqEFrl5/BV3kCMjQzF0EGeiGKz0bf3hnVZIg5sV3BRpsLtvRF9DomjpsLjyRfw3ih2cZ75A15bc0Mc27rrv63GV47P4fHQeXjB2l2y3pZO6I8rSN99DqfF6TpNye2N3bwR+wY9i/FTHsWLzsORDHu4WdYgtxjCnS8S8UrkNyCZ3wHo7o3XHg/AX75rfseMwnSTpscEMT2qbeA51AW1V3Zi9ben2/3oY1Ntp3fb2zZr1mwMt9IB8pwQPPMZBIvzRmkCfnzhc6XSBvDDpKhwDKm+ifR/b8JxcWibUg7is/czYFJ8k+0bxRq2sQNpmHLyR6zepo/nwmdi+fMDMSa1HDC1h4dxJjbHHkJ8sy/H4fydAox0CcGSBfbIkflNq6IwEYc3/ILjKsrj7V83Uc5RnI33x/RBs/HGW94oNLWFs1ERMgvB1qFRs/wan4m8Gm+4TnoW8/aswc/iDWUJO/YiYeZz8LYqQcz3Jxsef7X0DsaUKU7APeDwD+cgX+pcxQ/rD8PjhTGY+cQyuAamAvbOsKnORwlkbtHqRLnU0nrJHa+LJHmy2p6Vxy61uLjtO6w9KbYVeegIzs4ZhJCAJXht1ShUW7qgX849pFfagbeT3WFKpj9/5HnnH0mY/7w3vOuzcXx3bOPd1nLpYiikSxmM0N/NGGm/X8be+CaNRyq9zM5TVZnZ7vqwK+rsVihdruQcxuWUxVjgbIWKG3sQKwzMw74rqVi+wAXWtUk4sL8Td2cqlNe58wkFWj6eOkDZfdWJY9CsnyP8Qv0weshgsWx0wWDtTJzZu1Nxmd/O/aTc/r+LzWfiMWGGC5aueAE+eQYYZK+G68kF8GTnfw2alq2ph/HZr4H45wI/PL58BfwnlkPfyRpGpSWogIH4pYeXmoGJLW9ioctp6+hAS1tHeCRPTY0CpYQQQkhvUV/PA6W8HafKbguQevkFISMjTexrPxsbO6Snp0oCmWqN4cw2OY/A5CHGKLxxCKca2uqyhHfwUPj5eqK/cQ2yL8UjPi0dldaO0MlofExV32UEQsL8MaKfLmqKknBo++84m2oMN38fDLJ0xhBfC+jWliD5aiLuptzEUf5GeAzAqImL8drfx6Jyy0Q88qlkXnIMbOHt6wFnD3f4CPNOxZUbt3ErNhYJ0pePKlzv5t7echizNE/gT6uPw8PLE/202PpcvoEbcbENL2nQd/HDqEFeGDbMAprCspJwt0gHrhaVuC4+TukaGAZPk6KG/kaW8A0Zh9BARxhqVCD7xCH8dF8Hfm426D/InaWfZsP6x52LVfyyHGW2F54InWIP3ZxbLT/ea8DSdtRYBI+QpHvCoV3YHJPO1i+cXZSnYsfR6+KEzNBgRDnqIEcm/Uzc/ODvYomBQ1g66dagJCUecXdTcfX49eYXOArTX7KOuC/7KLOC9VZqe/XgOmIEBnsMxlAHQ4BNc+lqHPLUbWBdL7stbeU3oP+I6Zgd5gKN5EP47+bLwjA5LaSbkB6uA+E91F6yb9lxcPP+TZy/lt7CY+qK9pGiNJFoO71b3zbfsWy/Kmq/sDpPzKdtpw1fv1l/CoN7XQJ2rNkHmRzSRMvbIaUonymThgrzJ6Nv5wF/T0cMEI+jiuzruJGYg1uXWD7JFyeSco3E+x8sx/CUrdh2z4Klp2Swnn0gQodq4PK3f8by7+61meYKj3Mhr5ujQmbb21w3hceHmKd9veDXT5Mt+yau3rqPQl0nWFY05plm+dXAHSHzwjDWuhwXP/0OuxrKEBe8v20dpumdwQuLV+GgGEHhgdKIF/6Mv9jexAtTXm9+5y7L74EBAzF4iAdbd6Dk/nV2bBVAw8EStbL7V6njVBHF6yUh5snBkuNasyIXl68n4M61S7iaJm0X0Byewb4YE+DD9hHPMzdx/U46avs7ydU/8hTnTyFv6UiPB+XSX2DpiwmBVtCvK8adY6dxVbbclqaLiys8pdtw4w4yk67ibEKBOJGUcstUtoyW1TyvqqbMVKY+bEZFdVi76iolymYhjSzZ8Zl5FdGsTBeIx7NWQzmpmKJ1UVQ+SJZRiWSxSZMOn08oKGckWjueZCgsc1jdFj4IlpUy26HUvlLuGFSURvp27vB1d4Slq5swf0iP8UvsOGqhHez27iel9r+zL8JH+WN4f8mxcOX6LdzIN4C3tUw+VlS2WnkgxNcRHp68jmDzvngNV7JrYGKj1+b5Zovq64U2YG1t7Tt9nn8ttuN3QXdboJQQQgghRFV6LFDaCn0LK5jr1qE4NQctXhebWMLBUAu1FQVIz5Ve6HLGsLY3gFZdJfLS82QuXkwRNHcV3nmmHw499wTe549ZtaTFeStPCJRiD7zm/gcmVrYw0mi6PlKS9VXv0LL0YGlrCl31arm0kqSfhvLrr4LtbTndlSekk1YtKgqy5O7MUzllttfAHLamOuxiq600aW27JftHq6q1ebT0fcX7VpXaTu/O7tPWv8/zqQmKO5Hn2tK5NJQeR9Ul6cKdeAo99THOLDHH3qdX4OM8A5aeksFaLs9j3YdBqI5+CZPeuCgZyKgqjyu1bk0J+V69lWUryK/CdyCXfmbBT2Ldu/NhePotzHj9ZON+dQ/DS2++iKmZ6/DIyt9bvANNsu780GojDdpZLrW4XrLE41qjuhQpWdI3iMuT7KMuOO7aTH8lKbENDVS1TGWopMyUjGt3fdjOvNI5XV82d1wH008BpY6njlBiX3XqGBTnD2WOjw5RYv8rcywI6ylftnIqLX8oUEoIIYQQ0jEPYqC0S4Q8ia+fHIHqs+vw4Rfydwl0BdlAKSGkl3rmC8Q8boWrX63FV+fjEZ8O2Hl6Ysy4BXhiQi32vv0S3jyo6sdse4bZhEfw+CALWAdOwHire1j70hv4+qY0COCEee+swiMWd/DjmrXYdrX7trn19SKEtAcdT73IAxIo1dDWNXpL/EwIIYQQ8lCwsnVASUmx2Nd+hoZGwvcf+ECptgYqky7i0JEYxHf1nTWMT9hUOJRdx4bdkpavCCG9UF4W9K3c4eXvBe+hPggc4YexQb7wMs/Esc0b8MOhRORXi9M+5EbNW44FgQNhWZuEgz9uxE/H09BYlGpApy4XV04exdErmejOTW59vQgh7UHHU+9jZGTc6fP8rHSFLb0qhe4oJYQQQshDp8/cUdrN3IYHwwHpOHwhSRxCSKMPJ00WP7XfS3/sFT+RB4G1my8GO5rDUEcTGqhDbW0NyvLTkXAjHim9KMLg7DMSA0zUUVOaiTtxtx+YbXtQ14uQhxEdT70IPXpPCCGEENIxFCglpPvdffHP4qf2G/Dxf8VPhBBCCCEKPCCBUnr9PCGEEEIIIYQQQgghpM+jQCkhhBBCCCGEEEIIIaTPo0fvCSGEEPLQoUfvCel+szw8xU/tt+3mdfETIYQQQogC1EYpIYQQQkjHUKCUEEIIIYSQXoTaKCWEEEIIIYQQQgghhJAHg4a2rtFb4mdCCCGEkIeCla0DSkqKxb72MzQ0Er7f7jtKXQMQFeoJq6pEJOeLw3oTcye426ght6BCHKBiw0Iwb5Q7zFtKv65efke1td6kx7iHzMLcKaMxxt8HTpqXcL2su/KQKVzdrYHcQrS+JB+EzwqEq248EjPEQR2lbPnTZceRstvc1VwROn8aZo4NwEg/B2hdiEeyOKbnOGHMlBCMdNLF9aRMcVhbVJg3CCGkFzEyMu70eX5WeorY1350RykhhBBCiLKGjMfiJXMQOUTs71WcMO+ZFXj15WewyEccpGrBU7C0xfTrhuV3VKvrTXrMsEfw7NPz8EhEMELHjoSfc/flIdc5T+C1F1fgpfltZYoAzF4yH7ODxd7OUKr86bo0UH6bu9aIhY/hT4umIXIc2+/BQ+EqDu9ZHohcMB9LpweI/cponjfsAqOw8tVX8N5bq/De31Zi5azhMBPHEUII6R4UKCWEEEIIUZZRPzjY28LKSOzvVYph4zQQPu5OsO6qFuzNrVpJv25Yfke1ut6dNGUp3n59KaLE3gdC4CN45fVn8Vig2P+A8pk4GWMGaSPlh7X4aPV6/HxaUR4ajsdWvoJXFg4X+1WjsJ8jPIYMhItVnTgEiHrmFbz9TLjYJ2UKW5Z3bM3FXiUpnJdS5U/XHUeKtrn7DcHk6QEYrJ2OdV+w/f7VDhwXx/QsY1jZ2cLBxlTsV0aTvBH8CF5ePhcTh9lCt1IHdn5hmLfkMSyLaM88CSGEdBYFSgkhhBBCCJOHXWs/xN/e+wa/J4qDulVPL7+H+I7B9GljMEzsfSC4BGDatIkIdhH7H1Ae/c2hg2Lc330Sh4+dRuwdRXnIGcGTIzBtlLPYrxo5+zbgH+9+iE933ROHAMNCIzA91Fvs65yOz6vrjiNF29z93OBooQMUp2HHUbbfT15Bryku0m/jxol9+PDTdVizcSM+/u9JZFp4YsrEIHECQggh3YHaKCWEEELIQ6fH2ij1noSnRlog+8whZLnNxtzIMIQO94CreRWSb2fLt9tnNxyzZkdgangogn0GwVE9A9dTS8WR8nyilmDhMAOcybPCvLkzEBU2CEapV3G7kI1UYj5m/uFYNC0c08b7w9vNCuqFdhi/MASuVZdxkzeXFzILK6f4w07aLxiFRc9FYKh+LK6KcY/+/uPgb6+O1HMJECbzicTy2d4wOp8Hm1mzsSByLDyMUnExkaV9e9fL2RiVFj6YPUgfqWd+wo6r4kQyWl5+OkwiojCXzStipAcGmCpIbznWCIqajhmRExAR7A0PJyNUp91DZrk4mgmdvwwRLhVI1PbGvDlTERlog5pz8eAtWim33qbwCY3EzKkTEDnGB96upqhMudO4jNbSTjRiQhRCgwIw1sEA0DKCo/9wuDbsD5n5jxsu2a9ZCUhpKdu3J61k9l3IsEFwMqhGyl3JNK7B4QgPHIHx7lbQrNVFP9fh8LWtQEx8tuS7clpbR3tMfnQ+pvmaIvvyHcg2qTlixhLMC3ECLkjSu628pGhf2Ru7wzMoCB6WmqitZ2nnbYPyiwnQls1DQpueIxA8diCs1QF9C2eMFKeTbUWSz3+mn/x6jpmzFLNH2aEmNgHS9+66RizE4xPcoBdzE3ed/BDuaw+NzAvsmOJtTQZj1Kgg9Nevh46xLUb6D4DeeTYdgrHgaTfg1gYcrYjCopnhQn7xsFVD2s10NN+drcyrofzZi3v2Le/jZscR3088T0SEIaKtvGQ+BJNnT8W0iaEID/LAQCsNZMaL6ym3zaxfyHPjMIbl25Gynasuzly7z7/RrnJQ0ErelKRNIEaNdEM/jVqos7RRmDebHHuScnoQ7Ovv4XqariQtpGlnXIzLifINvpp5h2D21HBMnRSCkZ4usNHMws1m6yy2kzpJnDerRlwnDIdDeQJWbzklTsO0uv2NeeOno6w3Px1JSfdw83YK8orzkXHfBZGP+cGmJA7f7FRQYBJCSC/V022UUqCUEEIIIQ+dng2U2kPP0BEeXkPg4eIMF/ch8PN2gWX1bRyPEy+43SOxcsV8zB3lCmMNPVgP9EbgcBfoZV/GxfvNw3uhT6/CUz4GqB8cgnlBrnDur4vsXw/ifP+252MWvBCvPTMLU4Y5w97OHg7OgzBiqC9GjfOEYdJW7L3BJpq7Ah9P94G2tF8wHas+mIpBZZvwq3hdL6zHKEMkbj6Ea3zAmMfx/mNDYaDmjkkzAuDu5Ai9/O3YUxzWofUa7mgDW2O0GChVvHw/WOoNRFjkcAx2dcZgL2/4eDhCO2U/zik8B3bFrBVP44mZwfCxNYBePxf4+w+Ft5Mhcq5cRbIYyJz/8tuY0l8D/QInImKoEwZY1iNm+wkUKLXepmzVnsOKR0LgbaMHXWM7DB0+Al421Yg/mYBcPklLaRfTuP8nLl6BaYMd0M9QE+rGZrBz6g/9wv9n7z4Aojjz/oF/YZfO0qX3pQiCKNhQESt2TTSJxsQkpieXS70kl7v37vLe+7/kaqoxiUZjcokxmhg1thC7ImJBqvTeO0tZFljg/8zsAAsssBQV9fcxE3eeac88ZXbn55T9OBbbuf4FmOZpBkMLF0yaMglB3tboyItFCr+BXrQtq862OWcCnEyMYOsdhNBJ4+EpqcbVuCL4r30CDwSzfbc0Yl3DDLbOrrBHNvadyxFW0GmwPIowbdOLeGKeE+QXj+FyVywqCE/88Tnc6ybHwX0XUKJFX9FUV+2SSfDzsoeNiUhVdrZAxs8X4KTehoJX46V7JsPLyRzGHbqwsHWChzBfBr9mlamb3sBT8+0gOxqJq3z7CMFzf3oGK6Y6oSPhEM4KUdX7X34bG4NEOL/nFDL58p4OCd+n5mDTKxEIdLaGqYhtx9oOHm5GqPrhFK4KwTBJmxm8J8/BZG8PePtNQHCAN6wUSTidyv1riLoB1sUff+ygo2uHyVP6r+Pe/Ui66mn87tFFmGJrCD0TW5aPYEy2lePq+Sz03Lob6zvP48llQXDQ14OpgyeCpwbAUZ6A0xnseNtjn9nsIffglVUhrG27sjyywdsf04MDINXPw1fH2JaHeBwcrG0q+LKZhvH2VjDRVZWNxrYp9AV7Ez/MZfvi4+6GCUGBCPQyhchqJh5aE4rxHm5C2dlDJ+sU4oWXKamOW/diWYgbLAws4BE0CVMDpbBFPi52Ht+FY8wT985g9eQAF2dHeAe6w9nBCsb1aoHSQfe/V6CUaW5uVn1gpmx4AutZPeYf+xw/XKkVUgkh5M5HL3MihBBCCLmtiKGnW4/InV/i/Q83458f/oJKyyCsXL8a8/jpblj3yAasC9TFqS3bsfnzHdj86V5kmc/AxodXQ9P7VQyNTWDqNhn3u5Vg96efs3XuQWS1NuuZiU2b1mKxZzsuf/Mp3n3/c3z0+S7EmtvBTmICY31+JoBbv/o4z4hPMzUWRhk+HxIjGArj0OfmccH0VS4o3P8F/v3hFuw8YT68fG3bg8Q6fmK/NG/fCRNCjZH4zdf46KPNeOenEuh7TMbyCM3PnJTetwFPrp0G8+y9eO/jrfjwk21472QrPBauxbMPzRTmYkViagKb8dMxXecSNn+8Bf/87GfEa5vv8HV4dn0YbIp/wkef7cBHW7/EB6ea+SsOH14izKOx7HoGOy4c2o0UmZJ9qkXaFu45m9vwLRcw4dc/Dx4tp/He5m2qffguD+bTVuPJR1ZpfnmNVmUltKlpZsj6bive/+QLfLB5KyJbXLF4/aPYFA6kntiPHaVctFCBosuqPH12sCu63m3QPNbi7MVytNr4YPYiN2EhZvJ8TPGwQF3uYaRq2Vc01dVeVnbJNS1sqlB2O46w+uvVhlLP4LtvSiHXYXtTdpXfl8751EXnyiC28UJwZ/OQhmG8aztaddwRNL8z7+EI9rJCR3UqrnCjfHl39qmrOPBNOmq4R3fWZam2s2UfTnGTBKYW3qg/+S02f7IF7/4nEfXj/LBkaZgwVd1g69KHpdXA/aF3P1qwPAJTzCuwk2urn3+JzZ8dQmRKgYarWcOwamkQrEq/x8db2byffYmPD5xFQp5wBWSPfWbij+AzPn/ccBLFOuYwFVUi+thFNnGox8HB26aqbFJR1cY+CmWjsW0KfcFvhgixO3bwx+nPEpthHxiBDQvtkHH4K1XZHa+AMfeYiXmddSz0f/dWRLL2/MGnLB+buTxPw7pHWd6Ejqc6xkyCVclx/OsD9h3wyZfYerIaLT2eCTvU/e+JC9g++5AUrYn78P6hXCGVEELIzUCBUkIIIYSQIVGg5Mo32Hecey4iG47/iJ8y62DqNg1LuJffSBfhnllOkKfuxteRUYiJT0BMVCQ+zaiDTeAM8Of7mug1IeXj/2LfSW69ccjQZj3zFmHFBCvUJH6Dz34+p8rPqZP4Or/3VWojocfy8Bk+O3iaX//l9hHkK284+epATcJ/2bZU6zm+NxaFCjGsnfyE6ercsGJlKFyRhyNbDyMyKhaXY6JwdNduXKu2QkDEIqi/HkevowoHt+zF0VMsf9HJqNQy3xHLFiLApATn2DaOR8chNvYKju+JRYnYCTPD1QO4vcpO/TJGJuNqFHLquUBpE8q4bQnz8Ou3rMKlj1jezlzB5cvRiNz/JSLzANdZS7Gi39d8D1JWQpvqyDmBT/afxpnLcbhwhu3fN0mosByPFcvCUZh4CacquUCpErI8VZ7OJZaollejTR4LT5xAarURpNPndQV3pyyeCGedIlz6NgXyIfSV3nUV17vsWFqfa0eKknHubAXk7GNrfYGqPjXMV3goHUUtEkhDVW8st5znDSd5LqKLO+AaGKZ663hoOKTmChQknEIlP5e6bFw+WwA+5q2oUG2H68OqiTxZzg5sZ2XOTzv+FdKqAdNxHhqC3oOtqwWlV7XtDyrGhgYQGUjgaNGB0vhYnDl+GPt+TenxOAQVIxgb6MLA1B6WHWWI5ev0MI4l9XM1I1++XP6iUGznBndLJbJOfYPtZ3OHfhzUom1qKhtNbVOlA1XxX3WV08ETBajVNYOoYie+FOrh+K5c1MIAtm5C2Qn9vyL2Mz7PF2JVZfXJ8Tx0OIfinsVcQFU4xoiLcHrzj4jknpPK1hW5PwmlXNy+01D3vwdVwDaoJRabNx9EzPAviiKEEDIMFCglhBBCCBkSJeTVBXzwRaUSx8vrAbEJbLiX3/j7wMkEMHadj5fefgvv8MPLeMSbJRoYwUa1UF8tlbgek9u9Xm3WM9UF1mIFShKjkKX2CL2y1lbh02hoQXnale713/R8KSErSet+pmRlLeRsNfqmml4j7gc/ByNAlo/96Z0PC2XKY5BapoDIxgVThSRefSH2J6iFvbTM91QXK4hE5pi04WVh/9nwfDAcRKxczNVfGtSr7LTEr7+5HOmxanlrzMQ+LgBuYgc/fyGtj0HKSqi72oJjSFXfv+hMlDbrwtplsOvcummVx8pziEyvhbHHdCzhI4JBWBrsAFHhVXyTwkaH0ld619VoyjmA5HJd2PjO5a/0mx/iBt3iFOzIroSx2yTMZ2nSMDfYtJci+VB/gbmByavjkNNV5pmo5+68NpVgoiphCIbSH1QO7/kZF6sssOLxp/DHv7yCVx4OhbeBMLGHE/jmp6uotF6Ip154Ce+88RQeDfOAxlnV+a7BUxtDYVN6Bv/4Wug7Qz0OjmLbVOlZTvKYGnCrldckoLBz/YXN4Hq2nr6Zalzo/6Vpcd3liyakHshHLYzg5MsFVIVjTE0uvlVvj41sXepXlA51/9WFzsfSCQbIOf4Vjt2oNk8IIaRfOibmDj1uErhRTNgPASMTCfT1DaArovgsIYQQcqdob2tHS0szmhrr0TiC5wkNRUDwDJSWdr5iZejs7R1RUlI09GeUbngP0a944/r7K/HELiGN8+edSFppjRguHdw8k1Ef/S42/yJM79KI/MNRiBPGOj326c/4nW8G/j3/VewU0lTbGmQ9ndv9kG33G2ESRz0/XD57j/Nex8HLy4Cf52HVX1UpffKhaX9HM1+9aLV9PIDtJ5/D9KojCLj/X0JaJ2FaTSTC1r7b44o5ft1TqrBv6mP4Mxv/695TWINe69Ay3/yyNln4/t97+tzG3VqZjqMxuf23lV5654vDr9/6Ws/2wFi+/Q3OLTfXXH7alJVQdzWH78Wyt9WvEuw13yD1xNE2jzYL3sWevwehbPtzeDB6DQ58sgyiQ89gxd+ztWtL7JPGumI0lV2fNiS083FXPkXoc3v4FE1m/3svPptRia2rdsP72/+B69mnsPrcEzj53gQkv7kGiQ8dwYvjLuG+1W8jlVugT3n31y779jNOf+Wn0s+6tKljpk8ZmDggcKIvXCdMxZKwYARYK1Bw+Sg++t89qscIqDF29EOIvxMmTJ+HeVN8YdNahAv7d+BPu1hL17j9IDz6rz/gN9OacPz1t/GHS8Jt4lrWbRdh/kHbZr/lrEZjPjXVQ6+20V//t3oLR36JgCU/H1Tb73OM6ZUvrfZfc9vAmj/jwBM2iH7mRfydriYlhNxNOjrABSgdHJxG/Ds/KZZ7DMzw3PCIJRcgdXTxgJWtPYwlZhDrG0BHpEcDDTTQQAMNNNwhA/fdzn3Hc9/13Hc+991/d7HAKgcboL0WxdFsNLoM1e2AQWszzh2OxMEeQ6/gwEC0WU+ODPUwhK3nUK+2YnwNYSR8HJIbna8RuYRiLsZiaYvFPS6wC4LnOEOgQYYsIUUjLfOdVd3I3QuOlsze+x+pCpKOEL9+Qxt4ThYSeBZYbM92qrOdDYdQdzb24arbyTtNdoctK576au3zrm0eKy8eQnypEaRTw+A73x/OHQWI+jFbNXG0+sooiI3KR42BA/xXhGO8WTmSf2RlcS0a2TJzSMMjEOhohJr8S6og6e2msQSJ0adxeO8ufPLRZmwrsUDwoqVYqqGZy4tTcO74cXz31Zf49/u7UGo+EctWz+/3mZpTNmzAI6EmKDi0Gds7g6ScodbtKLbNYeun/1vOt+WvAK0uvcT+398xppeRtO1LP+Lf/9iBHylISgght8QNDZRaWtvC2s4R+oZGaGvv4N/i16RoQlOTnAYaaKCBBhpouFMG9t3Ofcdz3/Xcdz733c/9BrhzGcJ16gOImGzHPtthxvrHscnHBIrsK9jNvXw5Zz9OpjfCInADXlwfAkd+GQtIZy3DqlkW/JhWtFnPyfPIqBXDccr9eGiWGyz56WvwemCvGzuFAICTXzik7OTecfoqvPLseEjYifyQjWa+Rl0udp/KQr2pJ9Y9swYzuMxZ+WPpkxsxyx4ovnIAJ1QzaqZlvk/8GItikRMWPv0IlgYK++wYhFWr5sNXNaa1pNoG9n8LOEao2pMjyzO/fh1bzHq8c/2qdrbO2wj16edV7Ww4hLoz9l7dVXeWgfPx7ONT4ahThpgfz6vmK2uEgrUXKzd/vgwcHfu2W63z2BiLnxLLYOw5A0+FOKElJwp7Oh+4OVp9ZUCV/G3uhpbOCGJt39LRrmcgTiA/dxW5DRK4LvaCTX0+jqSzxMaLuJjbBLuA++Bt0YjcqwNdnZKGGq4qzeywlNsRth3V/gzHaK5rGp545Tk8sYTVpawIqVczUSvvAPR0Iep9V3foBrz+yga+PmWFmbicKQM3q4FIhCphlh7Y/M8+FATzskuIvC6HaZA/grhhAus7Q61bbdvmjXTyAGJKWXHP2IhnufJiSY7T1+DFtZ4wbsjCyT1csDYXv8QVQcmOMWs3RmAKXz+s7z85C9LOt2dxhrD/Yt2e49Jpc7B0/iKsWKX2EjRCCCE3zQ0LlHInSBbWNuykqR1ydhLV0tKCtrY2dHTclDv9CSGEEHKTcN/t3Hc8913Pfedz3/3cb4A7N1jagGbJIjz5/Et45+2X8NJDC2FbewnfvH8Ayfz0NOz98L84UWmDxRuewB/5Z9O9ijefXoPlU734ObSjxXqKTuHjH6+iyjwYjz37Av7CTX9yMSZ29HymJk6eRkyxEnbTHsGbb72FPz67EqHKPJQ0C9OHZPj5Cm5sAPdoxhsp+act+Px0BSwXrsNLb7C8vfUMnr3fF20JP2PLjmgMeJGWlvkujPoWW/ZdR8fEe/Hsi6+y/WfbeeMpbLpnFiY5CzNp6XrkJWTJJfB7QmhPq/2F9Sehzb9z/Vw7mwvLsjP4/MNDQjsbDlXdRZaZY/FDqrr7y4sb8ZB/G67t24ltUcLzNyPP4lol4DTzGb4M/rhxtipdjfZ5bMKVb68hX98Tc9zakX3+mNpVvaPVVwZyCscTKqHjOB2vsbb/l9+t55852kflKcQXtsDOzRaK/HjE8onVOJZQhA5HD9i1FSH+12o+VbN0HL6YB4WZN57l2t0bT+K+YV9QPYrrsrKB+6QwrH+Mq0uufF/C4z56KL4Uib1FwjwCy3EuCJ63Wq1Nr8d4/TJcPPqzxn6zZs1ahNgaQGTqhln3PoPXfisMT6/EtCHXrZZt80Yqisa2L37GtVZfPPS4qrz++Nw6LLaqQOSXW7CXe64uE7v7axxLb4f78ofxMl8/T2Hj3A5U9XjyjDb7X4TqBsBm0vN4YpaQxPjPWoRVy5dg2XQXIYUQQsjNJNI3lLwtfB413C13NvaOaGYnTNxJEwVHCSGEkLtDZ9CUIzEzQ0uzAq3st8Bos3VwRsMInodqyn6rcMsP+RmlDVUoTr6CU2fjkdoghqWJEkWxP+P7A6dxPCYLne8IkRUXIDs/F9evN0JvnD7/oqakk2dxLj4beeXcpWI91VeWIfHKFVzJKu7xXM3B1yNHcX4B8jNSUNFmxHalFlnHj+OgRQBWexih6OJ3OJjIZqsvR0FBNiqadKFsbUDOiZM4dPI8zscn4vKVLOQKby7pkw9+f68h+loOCtQeGzjcfP18KhoX2Pp7r6+TdttvRGVJCqKjY5GWVyGkqakrQU5uIbJSyqA0NoZucyni9x/CvhNncO56Df/yFk5NeQmuXOq9Di3z3VqF/NwCZGWko6TVCCbidjTknscvp+ORVFCCai6q2k/Z9dZaUYr8nFrIddnv5ZZyJEclICkvt2v9tToS6OvUISf6MH44dBonruRDY3xby7LqqrsyJYxMdNFcch37fjqCYydikFTNvU6cqS1DQWEVGtpYf26vRU7UVVzJ73VNoVoZDJbH1ooy5Bel4fKFczh5PhFFdcIERpu+ormuhPZy6RIuZhSgVD2tR1+qRUlhMcrqW9He3o6a3BhWRoXo23IaUFZcguuxMTh+9hJSi1XbrmP1U5B6DWdPReFsnFr/7FPeSpQXFSK7tol74ToUJWk4H5uGonoZSovjcfFSdz/j8Pt08VKfPq/Sz7pKtKvjHmWg24DyvGykpjfBkCvftmokHeD6wwVcruh+JR1Ht76Stf1MpMn1YW3AclGVqmobZ+NQzh3ceu2zQXMtrkWfx+mYZFxPzegxpLFt5w7xOKhV2xys/3M09gVN9aBKi7qQgMwibkYlKgrzkZOVieusjZqxMqjNicEPP/3C+nYscoRG3VpVjJz8PCQVy/l3b7SUJOHo0V9xPCoOMWrtdPC2XY2SJgWa5c2oTr+CBOFxfK21umhtzsbFQ9GIK+lbToQQcqeTSMxG/Du/vGT4zy+5IS9zcvfyA3cnl1zeebpACCGEkLuNsbEJf+tKbqZwGc4oumUvc1JnYgUHCwO0N5SgTCak9WEGOycT6LWzE+GSarU35Q/V4Osxt3WARK8V9UWVkPXzMp4e8whpIzPEfAlpN4cRbBwsYKg7vG1rm29ja1tYGYrQOmA7GEz/5ajKRxsUteWoHM2f1kL7FbU2orBcLXLZRfvyG508jlZf0Wx06kkL5jZwNtVDm6IWJVVNQuIwjea6OssXWvQHYbvot20M1RDrdtC2eRNoU/ZCPlnDH6R+Bth/fh1itPboO9z8+lDc9GMmIYTcYmPkZU6jHijlriZ1cvNCrayG/1dbQgghhNyddHV1YWFuiaK8zFF/G/6YCJSOZVq8tZwQQgghhJAxY4wESkf9GaXmVuPQ0tJMQVJCCCHkLsf9FuB+E3C/DchNVluF8vJK1Iz2ZXmEEEIIIYTcwUb9ilKfCZNQ31CP1tZeLxEghBBCyF1HT08PElMJ0pPjhJTRQVeUDsJ7EuY56KMm6xLier2wZaxauekvwieijZ+//F/hEyGEEELIHeBOvfV+wuTpKC8vpRc4EUIIIQQ6OjqwtbVH8rUYIWV0UKD0zvOHz4f/g/Zu9M4zM4RPhBBCCCF3gDv11nvuhIiCpIQQQgjhcL8JuN8GhBBCCCGEEDLWjXqglBBCCCGEEEIIIYQQQm43o37r/UhvhSOEEELInWWkt79oQrfe33kCZywVPhFtJF48KnwihBBCCLkD3KnPKKVAKSGEEELUUaCUEEIIIYQQMqA79RmlhBBCCCGEEEIIIYQQcruhQCkhhBBCCCGEEEIIIeSuR4FSQgghhBBCCCGEEELIXY8CpYQQQgghhBBCCCGEkLvenR0odfCAl4PwWVvDWUYrAZi3cinmTRRG73Q3rBwJIYQQQgghhBBCCBl9YzpQauE/C/dueBjPPfEYGx7GxpWzEGApTBzUdGx69km8+PzDWCSkDG44y2grBCvXr8XK6cLoHe1GliMhhBBCCCGEEEIIIaNvzAZKvZY/hteefhAP3bMQ82ZOR9jchXhg/YP4zW8fw0ovYaYBGcDFbTwC3J3YJ20NZxltmcPewR72AwR6lz72It56bL4wdjsbfjneOWVACCGEEEIIIYQQQm4nYzNQOvU+PPPgUoQ5NSHmhx34ZPtOfLp1B3ZdboLTzKXY+Nh9mCHM2r9E7NnyId7Z/BNihJTBDWeZ0TMxbCGWh/kLY7ez4ZfjnVMGhBBCCCGEEEIIIeR2ItI3lLwtfB4Vtg7OaGioF8aGwxxLn34V6/0ViP38U3z2yxUkZRchPz8P2bnF6PCZgQVB42FQdhInspvZ/NNx/xNzIG0tgH7gMqxdFo6p9m24kp4Lv+nz4GfWgDMpRapVM/YhS3E/mydiTjAmBgRhavAkfgi0VyAus7DPMgFLH8TaiSa4UirBolVLsHxhGKb7OcO8pQTZFdz2O9licvh8LGXTI+ZOw0QvRxjJ05FfLUxm+Vz7qBTI3IUfo4SkLtzzS6dj2rSpcDIC9CX2LE8uMLrGlu93/5rgPj0cKxeFYcG8WZg63g3WOkXILO3OU9iax7DAne1XozPuvWcJls6bjsnettCpyERxgzAT01UmQr4lyjLkqu0b9wiElYsX8NuZNdELDjrlSC1p5Kd1biNXfwLuZeWzaIot2mITYd677AMisGmlPyTXamG3chVWLwrH/Cm+cDVvQWFOJRQDlgEhhJDbmampBOUlhcLY6Bjp7w0uT9zyOtyIDv9/QgghhBBCyC0mkZiN+Hf+SM49xmCgdC5+81I4XGWX8faHB5AtF5KZ1oYKFDUGIGKhF+z18rHrRA5LXY6X/zccbmIrTJk/BwEuThjXnowDFwqw5oW3sMpTjv8eucov7770Mfxm/SJMczCCnokVpBOnYWqAKxxsbDEOefg5OrfPMrMfexUbJ1nB0Hs6loZIIXXzwISA8Rjvoofi4wlQFb0DVj71LB5aGozxtmYwtHFD0ORABLgbo+pEkhDoGyhQOgsbnp8HfycrmIh0YG5pCzcXQ9QcPIe4fvav2n01Hl81E5PdLWFiagf/oEmY6GeNtuuXkFqrWiu3LxGeJnCcGIrwCZ6QenohIMAHvpYKJFzIAj/b1Pvw+8dXYbaHKUQGFnDyDsQ0qT4KTyahmE22mH4ffvPoSkRM9oCNqSFspQGY4meF+oRLyGTVzG/DSQybkHlYMMEFrtbtuHb4IoJ7lSNmbMCfN0yDl70vps6eAC9XF7h5+7A8e8BamY3ojAkDlAEhhJDbGQVKCSGEEEIIIdq41YHSsXfrvaUvHMwARWUhrqsFSTtVxeagVAFY2HoLKYasEKzgMy0IuPINPvn8C2w7nMJPMTIxgamJIf8ZGI8Vq5YizLIMu3fuwtYvd+HzX4qhZBXQkrsTXx7VtAxgYMzGnbww3TAdu77+Gp9s+Rz7S8TwmByOiBBhJshRixpk/MrWu/MbbN22Hd9kdMBjygosXyDMMqBrOLonC7I29rE+G59yjxrY/jPO8dM0719xexPKovdg25ffYuv2L/HxiUpY+c3F6sXj+aU43L7YuPnAp+48vv5qJz765HPEy23gHz4f4cI8MxYuwRz3FlzbzvK94xt88dVhHLl8HWX81Ol46KHVWOili8QfvsRnX3yNT7ftxOFL+cgTrkjlt+ETghCdq9i29Qt8tOMYkoR09XKEPrcfdrCX1uPEt//Fp59/jg8+OYlKiwAsXrscYQOWASGEEEIIIYQQQgghN9bYC5R6m8FQB1Aq1W9rVyNvRLOSZVysLyRwxGivOInPfziLMxdiEH29RkhXJ4W3gwlaKlJw7HIi4pMSEXUoHsXNYhgqq9kyqtCgZnVI/u9+HD8Xg3MXLmDftTIoxBZw6IpJynD15x/w7SG2/cu5kMnr0VBcC7nIHM5BwiwDysW1C4Wo44KEiiq2DW47ichSTWT67l/Wif3474HTOHnhGkoamyCvqYCsxRB2XlJhGRVxczG27o7EySi2zqjz2JHNysbEGp7uqulGxnoQi41gaS+GrCgRl6J+xYHIeNVVsGHzsdjPEjUJ32PnsQuIjuWmn8WPB0/huoxfnCfuqMbR7UL5XE5FlZDeVysq4vfgwOkLqn08cxCHsuth6hKMBSGDlQEhhBBCCCGEEEIIITfO2AuUFjSjVfiomSHEXK7bVWOd6oqOI6lCGNEoD+X1SoitHTBRePO8hb8trMVKNNUJ96r3R1mP0vTu8F9VbROU0OfijV3kpi6Y9eBv8NYbv8HLT27AMncJRCxdV6yaPlJ99k9pi8BVT+CNN17Fa09twEPTbPkAs0ikHkBmmmpwrUAhjAAZjdxnY0iEIG/C3p9wKhvwvW8jXn7pRbzy2DxMMFdNw2RHWImaUZ4agxy1q3vr6npdAl1fhCPJ/YdHu7GyrilE96qqcLaygWXaBNZC4JYQQgghhBBCCCGEkFth7AVKSzJR0QgYWrtispDUw0RHWBsADbUFQoJKe5umq0jVZeLHyDQ0WE/G0795Bs899xu8+WgQjArPYdfRASOs/dCFuPPOcstZ2PTEQ3hgvhfMqlLwy7Hj+OmnYqhedzQ6eu6fB+5//BE8uiwYTspinPz1OA4eyEK1Upg8KDH0TFWfqhJOYMdnn+H9/XGQSfwxZ8VaPPP8w1jEBZMNxWwvlWht7g60atSuHOAq0oFVtQkRb3o8HCGEEEIIIYQQQgi5hcZeoBQXcDZLBrHtRKwM67y0sZMHVq7xxziRDGnnLghp2lJgnI0F9GuLkFhtChcHY5Rf/AH//GQvzmQLswzXtLm4d4oTmuN2YsdPR3H0OHeLfJXalZOjzD0cS+f7wKz0J2zbfZjf3sko4bb1IatHVlwMjh86gJ3bt+GDay3wDF2OJbPZpDwZGmAAG/cA1ayjzhxL7ayAdhlKLwtJhBBCCCGEEEIIIYTcAmMwUFqGE19eRG77OMzYuAlPLZ0OLwfA3ms67n1iIzaGWKLp+hl8cXagZ4pqFuLnAOPmcqRc+BHf7NqLXfuO4uTV/JEHNC0kMBaJ0NHWjIpqBSz8w7HxN+NhI0xWJ9btHfztlIFa7hJUiS0Wsf2Fgy3s+XQN6q1gbASI2kWoLK5n805iZTMD7mrvTtKOK1Y+8QyeWzkJ9vIa5FzPRFVdO8ujLgy4O/jPRvG3+9tPeRCbFgXw+bFwDcH9TzyIlcO6Vd4AzsH3Yt5EW/bZFlPXbMRDXiZQ5MXixzxu+hDKgBBCCCGEEEIIIYSQUTQGA6VARfwBfPDFaRSaTcfaBx/Ciy++iteeewgPrZwAw5yz2Lr1EOKHca/31cwyKKz9sWbdGqy9Zw2efvp5/On3r+Ktlx7GUi9hpuGIvoT4kjY4TNuEN9n63nzqHsx3r0RljzvWS/ggoFXgk9g4XUjqIRORl/OhMJPiMba/f3rxEazu70LOqii2LzIYeK3Ga2x7f3pxA5ZPbkC52guWtGMEG+kULHvgEdV6fv8b/GaGDRoyL2A/d8FuyXns+Po0Mjq8cP9Dj/HzvPnCRjwwNwhedqo1DE0bxHbz8MgTz7FtPYdnHpgLG9lV7NlyBKn89CGUASGEEEIIIYQQQggho0ikbyh5W/g8KmwdnNHQ0OtlP0MmQ1F+AfLzsnC9TMFf4dhUk4+YI5E4diYK5xOL1F74JEN5SRKuXM1Bfq9HjdZWlCIuNg6ZBZVsLARLH5gDN/llHE9uhr6oA8q2NihNPDB7bjB89EuxL7qg1zJAY1U5rrPxuNxSdL3yqbEGJalxuNi5TVk5CouyUdwohp6oCQVRUThx/gKi4pJxLa4zX7Uoa2pGc1MLarKuIbmUS1OnRGVJMXJlTdDp0IGiNAMX4zNQ3KBp/6pQUliI69WtkOi3oTbjIk6cuoBzV66z7SUju1gVMe29L7zaShQnxOJiQh6KZC2oLitAZnYNdC2ModveiIIrv2D/L9GIyZCxMlagvKiQ1UMmUmTtMNHXQT3Ly7HIKFzNKEWlvJ9tMH3S/Rdg41QLFJ/Zju9TxLAwVqIk4Sj2Hz6H01dyhKt6+ysDfiIhhJDblKmphH2XFQpjo2Okvze4PHHL84/I1qEHZRNCCCGEEDIWSCRmI/6dP5JzDx0Tc4cO4fOoCAiegdLSYmFsFBhbws5cH6L2FtSW1Qz/Nvmpr+KH/wtB4Qev4s+X2mFqIFxMa7gUf918H7yyvsXcF79TpQ2TsdU4WBi0o6GkCnVCWg/8voihlFWgqr8dMbOGo4kYbc0ylFUP8hIlfl5dNA+0Pq0YwtrOHAa67f2vayj50uS+vyHyeS+kbVmH3x5R1Wl7YxkqNBXUSLdFCCFkTLG3d0RS7EVhbHSM9PcGl6eSkiIKlBJCCCGEEDIWdHSAC1A6ODiN+Hf+SM49xuSt9z3Ia1BWUobikQRJOcZi6IgNMM4/GEGOpmgv6YCxoxdmzfeDnU41EqOjhRmHT15dgeL+gqQcfl8GCWrWVbF1lGkXIOTnHWmQlKNAVRkr44HWNZR8DUaoU41BUs5obosQQgghhBBCCCGEEC2M/UDpaEk6iJ2RqVAGrsRjjz+J137/HJ7fuAZLg9txadcn+Px4rjAjuSGaZKisqEZtkzBOCCGEEEIIIYQQQsgYMkafUXoDNFWiML8QeVm5yMrNQ15pPjJSUnD1UgxOX7iGjBphPnJjNFShOC0JsSklKKdnjhJCyF2FnlFKCCGEEEII0QY9o5QQQgghdzR6RikhhBBCCCFkQPSMUkIIIYQQQgghhBBCCBkbKFBKCCGEEEIIIYQQQgi561GglBBCCCGEEEIIIYQQctejQCkhhBBCCCGEEEIIIeSuR4FSQgghhBBCCCGEEELIXY8CpYQQQgghhBBCCCGEkLseBUoJIYQQQgghhBBCCCF3PQqUEkIIIYQQQgghhBBC7noUKNWKOdxCZmCS1FwY10DiADcXS2HkDucYgBkzA2AjjA5oKPOOccbSEMwI8YSxMH730IO5uyesTITRm+pWbluNuScmzQyB2wCHAM0c4DNzBnwchdEbuT9DOgaJYOzoCQdLkTAOGLpMRljEYixbMhd+d0KHJYQQQgghhBBChmhMBkqdpy/G3Im9z9Rt4Dd3MUI8hNGbSgKPyVMxWSoRxvuyCw7H/HlT4CyM39Ec/TBjqh/shNEBDTivOex8nW5N4NHCCR7OA0S9NEw3kU7CjMlSjEaMS8/WE862BsLYGGc6HjPnzkHYBG2jZwaw8vSElaEwOhibAMxdwgXoeg78MWDI275BzKSYPHUSPMyEca05YsLUqZjQGSi9gfvT+xg0cBtzRtCcOVg0pfOA6oSA2TMQMK4NFY0NkDcJyYQQQgghhBBCyF1kTAZKrb0nwt+1dzjKBC4TJsJHq+jcaBPByMQEpobdV1/1Ji9IR1JqPhqE8TuaviEkJobQF0YHNNC8hlJMCQuCkzB6M+lJp2DeJFdhrC9N00WGxmxfjFhrGDmrCaEIHX+bXIHcWoGc66nILtU2emaP8TOnw8dKGB2MqRv8g3xh2SFDRW33UFXfMoxt3yBiI5iaGMNILIxrzYAtx44dnR3gBu5P72PQwG2sDkXpqUjJrxXGHSB1NURVymXExyahqFFIJoQQQgghhBBC7iJjMlCqKxJBpCFnIrEIYrV0kY07PHx94SF1grGekNjJwBYO3DRfd1gZaAht6RnD2NxYLeglgp6pOQzV12NgCmOjXivmbm/lt+kAY7XV1hdnIz2zAJ1hB4hMYSPltu8LB/WrujrXqWcOO366J2wkvfOnB2NnT35ZN2dzjXlU7bs3W1aY1KnP+t1hzu+C6lZbPj82vfaJEVk4qfara/6eusra3UYtP2oGK+/euPJ3dIC7rS2szc171oXIAFbu3LrY/juaCon9UN8uvx42qNVZ136ptRGRESsbRyc42Vj1mZ8z2PTuutVQd2p571HvXVR16OTkCCdrS379hnx56cHQ3BR6rO2r6onVAx9c624Lfdp5Z10PkJ8h9xGuXkxZvjvXydV3cxXysrORXSZEz7rmUdvXzjbF0gzNHeHmZCPUK7dPqkn90mH9XdSBmpw4xMd1DykF9YD6tvl1a+ijXF406dq/3nXRWdZq5aOpXffT1zXqd1u99C5LTr9tWO2YpEVeuo9B/bUxdXUoYflIKahhn7nyMIGRSInGJkDc3galaiaBlvkYsO2zddh2lrVtzzYxwHL9tl89G6HMPGFnoT6BGbTe1fqYREP74duZFu2WEEIIIYQQQsgdR6RvKHlb+DwqbB2c0dBQL4wNj8Ok2XBVJOFSmkxI4VjAe0YgjIvPI6GAnfJ6zsKS2f6wNxLD2METEzzMIMsrQkMbm+YUggXhk+DpYAUrOxf4sJNs3aoslMuFVXHMA7EgIhAm5Rko5dIN/TFn2XT4m8iQVqTKv0PoMsweV4P0IhG/bTuxLswdXOA4zoGdZHvBzaQRWfk1aOdWN3khIvyNUZRejGbYIGDRAkyTWkBHzwKePt5wFFUgt1wB8+AILA10gb2XFG5242DvJoW/px10qnJR1tjBrQnS8PkI93OEhZU1HN29IbVQIKeA244NghbPh5Sd2EsnB8HV1gTy3ExUKvjs8vj1+9jCzNsPPs4OcJN6wcdWjBbz8Zg6wQ02Du4I8HaETk02Suu57Ykg8Z+NhaH+cLO1hKW9Oyaw/KA2H+V8fkSwmjQPi6ePh4uNOSzs3ODtZA8HiQLp5xJQyuYYsLxdghDujq55u3hOx/LpXnC2kMBEYgFHDws0pOajzsQVk8LnYIaPA8ws7ODmKYW7hRIFBdW9gjeM3WQsmjcZ7uZi6Fh5IsifrW+cBWyMm5FXUgfj8WGICB0PRysrWDu6YYK7JeryC2A2dQnCvOxhZWoEiZUdK99mpLJ67OQcqnm6qe90hFjrQYfLl/M4OHiMh5+zCWpyWb65zBk6YdK8cIR4OsCSLcfl3UW/Etl8A+tkA/95YZjiZgsLIxOYj3OEPTdPiRNmrQ6GjcE4BE72hp2lLmrS2uC5aDamejnCimsLnj4Yb6+Doswy1sbU6trHD+M15GegPmLoPQsRMydC6sjq3JbVmaclGnPyIZPOxj0hthDbTkCwjyMsdWuRnm+DmSumwrHlOvIq2YZ92DxTvWHvIYXUyRY2jh6sTTnDtLkQBTrjMTd8IrxtJTAyMoOdiyN0K3r1vd4sfTE90ALV8ReQVqlEm1I1tHMdCz6Y1bVtMRxDF2Oeuw6ycyqgFLHyjgiDl04u61ut/Ko6GbK6XzpjApxZm7S2c4WvtwtMG/NQWMN2nlsnK2tnC2dM9Gd9wp5N9/GALcqQJdQVXz6zJ0FqZw5zW0dInZ1YO9JFWdIlZHb9a4jKwNtyQVCYG5CrOm713B82OmAb1uf7+3jWL338fTQed9R1H4Oa4amxjfVsh0GLwjHBuByZxbaYsmwyfMcJdWbasz/w8w6WjwHbvjHcZs3H3ElS1q4tYOviCV+rVmTkVaNjgOX6bb96npgRMRsBjkbQNbFj5e0JE3kuSmTtWtV7jz5Wbomp833QkZ2Nms4DjEcoVk63RWVqAejC2tFjaipBeUmhMDY6Rvp7g8sTt7wON6LD/58QQgghhBByi0kkZiP+nT+Sc48xeUWpNlwmhsDHsBxJySlIT0xGRn4l5NwZu8gdU+ZMgatuDq7HJfBDoYkXZod4qxbs1NQKiYsfAjtf0OQxHhPsxsFzog9UKU7wCfSClX73zfT6IqAy8zqS4+MQV2WM8SGBcBGmGTl4YDw72TfiRsb5ITjIFvLkBGReT0Jqai6KqrnQljCftyNQloG0xAQkxaZCbj8Rs6f6gL9gK2AW5gVYoCI5EdfjE5CcrMC44OkIGMctbQx7Ty+2bjc0pcYjKS4Zpb3O5Pn1T/CEcWUWUhPiEJ/bAafAKQh2bGZ5Yeu8loh6mwmY5uegWsBiAsJmTcI4+XUkcuV1LQml5n6YHRYM/imKbPqs0Imwb0lHPDc9MRV59WpBKW3Lu7eaAmQUNaIFTShNS0dGWgHqIYLDlDDM8jJEflwc2/84JGa3wzl4NmaO73uVnvPkGQi0rMX1pBRkJSWiwsQN/rYdKCitY/kOwJywAEiqU1h9sXzFp6DRfjJCJ9qgviAdeVy0UFHFtsu2zV9V123A6WIRlFXZSEvg6q4SRl7BCOIf88jyPnUOZrjpIIeVO5f360XG8A4NhluPK9PkqMxOR6kCaGlgZcDWn8cHk6zg6jUe0/0tUJjI6jY+EzXKBtTXVLD2rWoLSddbYcvq0l94VkFXXWvMzwB9hJVNeNhkOLM6S7qmqrOUnDLUc9OsnDHebzL8zYtYOtuHzHJ08HnzgmvnrfTcPOM9YCHLQ3oS12auodDQG9OmB8FGXo68tCq2l22oL+LqNRuVAwVJu+hC4qi6CpAfPG35/qAqF2HbHS2QlbbCasIMTPU0gGTCNMxyFaOsTNW31CkV9SjPSxba5FUUG3hhRrCv2jrHw8+hDXkpSfz0DKUdJgf7qfo+a9PBM1nZGZciniufhBRksp3oHZjsNPC2eutZlgO2YaG/T/Qw6/e4o677GNRfG1OnWreXA/eE4BoUq9dZr/4weD4GbvuSgDmYH+yGtmyuXbN2mpCKzJJaVp4DL9dv+/UIxAxvQ5Sy419mcjJSMvJR0aCqHW3qvUcfkzXA0CUQUwI6n0csgvOEiZBKlOD/nYgQQgghhBBCyF3ltg2UtrGTWAMTK9ia6aCmMB3JyTmQcVcEOfpgkms7chPzUFBRjbKKMqTl1kHi4SkEQAWKbKSXd8DBVfUcSgdvBzRnp6LU1AV87NRGCk8zGXJSu6PYzbVZSEhOR3ZOJlIzqtFsYtZznZ1aWOZ0DGBpZwMDZRkfLM0oVgtUdNQjKz4VmTk5yE5LREy+AtbuHuw03gCuE8fDvDYNSUUVKKuuRnFRCgrbnOCt9pYoXXke4pPY8pn5qO15IZ2KbiNyE5KRkZmJtKuFqNUzQltBPFLSMpGTmYyCBhFs7Oz5WfWkAQiwkCHhQjLSMnPY9Ou4FF0CkafqebDcdD/Talw9lyhMT0WSenRW2/LurSoPyfl1aIEcxddTkHw9D7XcG8L92Y4Wx+BKCssrK+e0+POIq7fC5EBPYcFu1rZsCzX5yMgvRXl+BjJrWlkJNiGnuI7leyImmtbieloRilk5llUUIbFcCU+pM2pzU5DNNZamCrZdtu3camGNKgNOb6tFRryqrDKvp6OszRhW/GMgWd4DXNFWnIjMUq7uKlCQnYtaEw94WPNLChpRmpGC4ibWTGSsDNj6M4o7y1MMZdUVJF7PYG2sFA2QIS8hDkk5VZDrGsHYhHVXPWu4CjFuHlfXGvPTfx/hyibIug5x5+ORytcpm5aYierOK+rESlRcTkRKOquDEi5gp4GoCcXX1NpMWgUMHL3h1FaMjOsVLO9KVOdz9ZrRJ5ivmRiWruPh7ycMrD/2fRdUB2SZ0ThboI9JofMRPtUOlVfOI72iM+PdlKzfXGP7VMBFu4wlMNTVhcTOGd1PquDK+hoSuHbG9v9aYT30JZaqf+hgbTrIoR0ZFy4L5ZOBlNxa/ipeTQbfVv8GasOdOhrztDvudBmojWlSjXz1OuvVHzr1n4+B2j53TPOHXX0Kzsd1HvOSkZRZwWpz4D7T7zFeySbomWCcnYQdSguRlpyM/GruilFt612tjzXl4GqOAm4TxgvzuMLfzQDF7Dh3VzxvmhBCCCGEEEJID2M2UCrSGfgBcaWXf8WpzA44B83C8uWLMXeSiyqwYmcNSx12csxdJRQSzA+BlkpU1SvYKbK6RmTlVUNs78FO153g49SB/OREdoJtA6nUAHoeLrCVlyCNuz1WoGxu5GOgvGZl31vBO9Vdx5nIOFRZ+2DG/KVYvWg6PCzV9qdDieYW4TNaUFbNTslNzGHJ/thb6qNDbAP/YFXepwS7waihFnVql7O1NFSjoTMfmrQ1Q64QZmhoZltoQ4OsM+CrRBtbl0iseruMxM4CBq0ylFZ3rrAD8twK1Ist4eDQPb2ovCvD6OhQ27jW5a0Ne9ib6aC+qqA7KNVSgaLaVhjbOPYJPJWW1EDX1gs+DuYwdvDHBFsRykuL+Gl8vtv1MM5nspCvyZDqNaCGvyRtBNTLtqMZSlV8hlHlXcfYBUFCOUzxt4SypgEtmi8t1KihtlStXRnDPmgOVi6ahSCpPfTbmtg0EfTV34zVb3767yOa6pRVqvCBI0d1qdo0jVgblne3mYaKeijEprC0EJKGrA01OQn8lbP8kFKkOTDZWonM5HLoOk3ABMNSXExSLy81Vt6YuWgx5k3xgZOFHppaWMGIxT1eKtaiqGM5V2luUVsL16Z1G1CWq3apf3tH17x9aLGt/gzUhjtpfdy5wfrPx0BtX3VMU9QWoaqrSbGV8OsZuM/0e4wvvIKj5zPQ5joRcxYtw7LwSbDrfPefFnXRs48pUHwpHdXWE+DvyEbd/eGjX4b4lH7+gYAQQgghhBBCyB1tTAZK6+XNMDIb1/PWVZElJAat/MtGOM3FqYiNuYBLVxKRUWsB79AwBLuzCfJWNOvooKk0FwU5wpAcg1Nnk9H7CQcNSTkoNbSHh4sH3PRLkVFShcySFrh6uMHO2RrNhRmo7Dc6MoCOauQlXsbF6BjEXy9Fq9MkzJs1AdxNrpqYGhsArU1ohJyd2LNdVTZ05z0nB4nnTiA2V5h5lDU1NbMN6vd8MYuFMYzQhmZW1k3Nrfz0vje+C4ZQ3oNr5OIvMDDojHpwRJAY6QMtLegduqssrkKzoR2kQcGYEuQFg4IL+PWa6kmoqv1qhayrHHORFxuFXy/foIIU8t7RXNJdDjnXcfnkOaRWCbMMlfsUzJvihNbcdGSkXEdGcjWE5q+V/vqIxjofKVND1kaa0Tj0She0ob4sBzmsvfNDfmWf+lYxhpt0HFqKs1CiYwV3F029yhx+oTMx3rIKOSmpyExJQV69WgR5MKzNt0IMg/46bA8j29ZAbfj2MVDbVx3T9PT7Xh88WJ/p9xjfVIy02BhcvHgVSZkySMbPwIIp3ITh1UVbyTVcqzJDwHgH2Pi6QK/0OrLVH49NCCGEEEIIIeSuMSYDpcX55VCOm4BQH+GN5yJTOEzxh1N7GbJz+ATY+fjASF6K/PQUxMcVQyFxgBP33L/8FGTLjWBvJ0ZZvhB0KaxGWZWGwEt1MjJlZvCc5g7r6nwUt3agMr0IbXYTMclWF0XZeRhCeKWbhTs8nHRQXZCN1MQEJNaK4ORi331Vk9gUNp1vapb4Yqq7BPX5aShHPVKzKwFLO0gUhULQKB9lNRUov0En7k0pOSjRtcekSQ6sVBlW1h4hbrBoLEB6IZueUYxqfXsET7bl5+feNu3jqHbj72DlPVABdnATDWDcdRViPlLy5bD0mAIP4e3tIodJCLLTQUlOWp8rDE1tzKBTlYHktFwUpMXjckwccipV14rx+6VjCWdLBfI7g2+VLF9lqivFuNt6YWjU7+3Rg03vS5V3Mxs7oDJfqLtCVneVqFZ72VYnbv2GhoO80d/OFW6GcuSkpiOnoAKtNpYYZAk1/feRpqw8lKnXOfRgLnUf5Hbu3oxh6SSEz/UcMGmCHXQqcpDB3+Hdxv6IYWiqFokV2UI6MwwT3fsNuWvF0CcUc6QKJJw9j3M5IgTOCoWHelydJ4GLixXaq9KRmpmP0lpjjDMfwvXNeXkoajGD7zRv1RWM3PHH3aaff+gYbFtcWfRvoDY8Ulq1sVExUNuvR1ZuDXQcAjDRQTjmsWOIGyvPgZcb4Bjv5A0P0yaU52YgJSEO2c2mcHPkJgyz3juqkXatBEbSqZjhKkJuQobqHyRGqc0SQgghhBBCCLl9jMlAqTw1CmeKRAiYvRirV67A6hWLsWiSEXIvnBfeOG0EK9+piFi2FMuWLMaKcCmMKq4jgbtYsCEL0Wcz0OE7E8v56UuxetlcTHHXFDCoRFqxAo4eNqjNS+cDcW15mSjSd4GvaS1ysjREuLRh6YYgLu9LF7Ptz0OodSvSk6+j+ymlJhgfvhgrl7K8LZkNaWsqTl7IYdvvQPW1U7hYZYkp85ax6Wz55cuwJMwPwrtfRl9ZHH69WgHr4HlYtXwpVi5bjHluCsSevYA8LuhVcgWnU1vgGrqA7Q+bvnQ2xus2oatkBivvihrIYImJM7grvnqpyEZegxkCFrHlls6Fn3kjcqPOIqnNC/OWcPu/FKsWTIZpSTR+jasQFurWIm+GyMIVkwMmYGJAIKaHzseyhTNUjzng9utaNWxCVPu1jCvrRbMw3k61bF1OEWSmXljKynjl3IA+QbDBpvelynsKxiOcyztrlytXLMOCEA8Nz9oE8gvLoeMQwu/jkukayobD2mJ26zhMZWW6bOkSzJugg3qtLykdoI+U9Kxzro1xbwq31T4qzOjDcepCPv8rl8/DNEk5ok7GQfVC+BzklgMuIUtYvhcjhNs9K29MnxqCKT5CwH04TNg6Qv2hk3UG8QUVyI+JRq6hP2ZPFwKaXWqQmVsDI/dwVg8sfxFTYdPSNGDAsoe6NJy9WgoT/9lYwfWJpfMxzVzZz9W8g22rAlUNgI3PDKg9ZrjLgG14hLRqY6NioLbfgcprJxFdbonpC1R9euXyBZgxwZH1qYGW67/9Gtr6YPp8bl3s+BgRDn+DClzjG/bw670hIwGFhlKMNyxDUpbwPNfRaLOEEEIIIYQQQm4rIn1DydvC51Fh6+A8otf485plqKyqgaymCc0dbZDLylGQmYrrqbldLy9SshPgFkULFG1KNNQWIOP6dWSVNKEdbLy6EjWyejQpO6BobkB1WQnKy6tQK+97yqyoq0FdSR5Sswog4yIhykbIZNUoycpAdlHnC1yUaKqrQGF+MSrrOjMgR115EYrLa/gASpu8DhUF+SiurIOyvZVNlqOplW2/tQEVLO/J6XmQsZWZ+k5HiEUNrlzKgKy9HY3VeUhPTkZaQb3qrdpNtaislqG+sQXNba1s36pQXlqGiqp6sLX2zUcvPfLBpyjYOkpQXFSJeuGS2paGWpTkc1eqcqHbJsiqqtk+N7KybENTQwXy01KQlF4IvrjaG1HD6qK2Vs6mN6O2ogj5GZnIzi9AEb/vg5R3Ux2q6xvRyNZbVNHrhTKsbGpq6tDY2obGukq2n5WszqtRwaUplGhRKlBTlImU5FTkVvTeXwPYB02Ba3MKUopZWbUo0NikhMQnCBOMq3Etq4jfrzq2X83cfjWxNlVSirLKKu6xrWirr0G1rIHtkwK1leUsvb5HQEXT9JY+Zauqj+LCcnBF2VavynsDy3trawvqaspQVlKOSlZ2vZ+MqmBtrLZOgVbWjqpKS1FeW8PqqRgF+ap18RpZnmsb0arDylhWgbL8LGRms3IvErY3SH767SPtctT2qPMqlOYVoKSqFoqGBtQWF6BAaNcqXBtSy5tLEMLd25F6OgElrNCaG0qRxdZ9PauctVEO6xvVtaiTs/bQWM23X5miHe3N9WwfClFW2+vaYL4vsW2qtdFu6tsWQdRcjqzUTFSwzHXIa1FVW4d61kcqWMa6y7gV9TWsXze1o7WlEVVlhcjNzEZ+QSFK+LLqtT8cBdvvEtaf+enNLP+sfKrroehQor6qGIVZmchkfaawTx4H21YT6lnfqGPHg4qCCjamvu1B2nB2+aDHHXW920PfNqa+RO9jSd/jRDcNx53ex78B2n6bvIY/pjWwjqfgjmnVhSgoKGF1J0fLAMu19Nd+2XwtTQo0sbarYMfLwvTrSM4s5dc95HrvpGyF9eQZcKg8hxPxlapjgc4AbZYMmampBOUlhcLY6Bjp7w0uT9zyOtyIDv9/QgghhBBCyC0mkZiN+Hf+SM49dEzMHYbzFM5+BQTPQGlpsTA2UiKIDfWg297KTprVw1gCHTHEBmJ2ktsMJfcm5F509A2hp8tOmtnJ9qjupJY0bd9+1W/xlEcetn34MyoMDKDTpjnvg+3b6NOBiOVH1DFQWYvQ0dysui1dg37LW2wAfV2l5vUK2+1TDiJ96OvpoK3f7flh+UuLoHfyMxxI02Xb5dIM4f/Ak1jSehJ//y6Wn6trv8C2zz0QUd1g+6TFPmvE510X7a2KHi9X6oOfD4O0T1UfGHY7GLAdDVLn/Zm5EX+aCxx657+I4+ocmpfn24PaNB19A+i2DrEsR4Jrd+KOEfR/oewHq0fOQNvi60AXbX2maduGR0CrNjaKBmr7nW1R07T+luu3/Q7Qr4dR7yKH6Vj3YDBqD3yJI2p3Etz0NnsHs7d3RFLsRWFsdIz09waXp5KSItaaGAqUEkIIIYQQcmt1qF6i7ODgNOLf+SM59xiTt953a4NSoeg/iNOh5Kf3F0DqaGHL3qwAgQYDb78Dbc39532wfRt9qvwMXNYDBwz63V9l8wCBuH7Koa2FrWug7XFXfHbAbkI4Zk+ZhODgSZgeNhvjxUWIvsI/yFYg7FfvYApnsH3SYp814vPeK+CjCT/fYO1T1QeG3Q4GbEeD1LkW+DrvZ/ne0zpabnLAiWt3I+r/QtlrUzwDbYuvA03TtG3DI6BVGxtFA7X9zraoaVp/y/Xbfgfo10Opd0N/hK1YgVULJkKcGYXL+T0ft3LT2ywhhBBCCCGEkFtqbN56fwfre7s0GZ5GyKplaGzRh9jMDEb6rWioqkJhRgpSs8vRQsGNG4e7Tb3P7flk6KgN33I6Ihia6UNRnIGU5AwUN2gTFSfDQbfeE0IIIYQQQrRBt94TMgI6+iYwNTeFPhSQyxrQNIKrIwm5FagN31p0e/3NQbfeE0IIIYQQQgZEt94TMnIdLY2oryhDVYWMAkzktkRt+Nai2+sJIYQQQgghhHSiQCkhhBBCCCGEEEIIIeSuR4FSQgghhBBCCCGEEELIXY8CpYQQQgghhBBCCCGEkLseBUoJIYQQQgghhBBCCCF3PQqUEkIIIYQQQgghhBBC7noUKCWEEEIIIYQQQgghhNz1KFBKCCGEEEIIIYQQQgi561GglBBCCCGEEEIIIYQQctejQCkhhBBCCCGEEEIIIeSup2Ni7tAhfB4VAcEzUFpaLIyNHn19fYjFehCJxNDV1RFSCSGEEDJS7e0daGtTQqlsRUtLi5A6euztHZEUe1EYGx0j/b3B5amkpAj8Lwod+l1BCCGEEELILdXRAS5A6eDgNOLf+SM59xjzgVIuQGpgYMSfyLS1t7GTuXZWdu2qiYQQQggZMR0dXejq6kKkK+J/nDQ3N41qwJQCpYQQQgghhJABUaB0cEZGRtDXN+SvcGltaYayrQ3tbFAVHSGEEEJGgw77oysSQcwGPX0D/g6OlhYFmpqahDlGhgKlhBBCCCGEkAGNkUDpmH1GKRckNdA3ZCdpjZDLG9Hc0szfFtgdJOVOamiggQYaaKCBhpEN7DcJ+8N9x3Lftdx3Lvfdy30Hc9/FhBBCCCGEEHK3GJOBUu52e0NDY3ay1oDmZgXa29uEKT1P7AghhBAyUj2/W7nvXO67l/sO5r6Lue9kQgghhBBCCLkbjMlAqYmJKX+Sxl3ZotJ9Aqd+OsfdKUcDDTTQQAMNNAxzUPtOVeke476Due9i7juZEEIIIYQQQu4GYy5Qyl25wr1UoqlJLqSoTtj4U7fO87fOM7yuEzoaaKCBBhpooGHIg1rEVPhLoPrEfRdz38l0VSkhhBBCCCF3Nx8vT4TPmoHFC8P5v7nxO9GYe5mTRGLO3/bX0FDHxlQnavz/u/9HCCGEkBuC/SRQ/SfogKmpGXR1RaivlwlpQ0cvcyKEEEIIIaSbRGIKFycnWFtZsN/bJjAwMLilES/u939zczPqGxpRW1OLnPwCNDaqLmDkAqNzZ8+AjYUhrCUiGBnooqm5HVX1baisVeD0+Yv45fgZft4Robfea2ZtPQ4yWS1aW1v48e74KJ3EEEIIITeF8COFo6enD3NzC1RVVQgpQ0eBUkIIIYQQcrezsDDH9CnB8HBzg5mFBSzMzWFmagIjI0Po6+tBV+fW3fTd3tGOlpZWNDUp+GBpdU0tFAo57GwkCPS2xwRXfXS01EDU0cLy2cHm10Gbjj509C2RlNeMmIRcbP96L8orKoU1DgMFSjWzs3NAWVkxVz4UJCWEEEJuCfYlrPqPjyHa2Tmy7+YS1aRhoEApIYQQQgi5W9nZ2iBsZii8vLwg9XSHk4Mdf0WpiZEB+xmqAx1dHeiOgd+j7Vygsp0N7G9Fcyv0xB0w1y2FbmM2xG0yNoPqgsYedPWhFJlDoeeCyIsFePvdD1FX3yBMHCIKlGrWeeLCoZMXQggh5BYRfqhwbvWPFU0oUEoIIYQQQsa6pRELEDJ5Mny8pHBxcYSluSn0xCLo6Ymhry8W5hp7WltaIG4phE7tNaCl+xFcl06f5m/RnzxjBkzNzYVURt8czcYB+HTXKXywZaeQOERjJFA6Jt9634XOWwghhJBbg76DCSGEEEIIGRbbcTZ47OEHcc/KFZgbNhP+4z3hYGsJC3MTmJhwt9qP3SApR0/UBp3ahB5BUk5uRgaifv0VzQqFkCJg8xnIU7BmxcLb/iVPYztQSgghhBBCCCGEEELIbcLNxRmbHnkIEQsXwG+8NxzsrWFlKYGhob4wx62nLKuEIjENDacvou7oaX5oOHWRT+toagLk+UBLtTB3T3W1NcKnXtj8DmYKrFq2QEi4PY3ZQKnqQha6nIUQQgi5NXToW5gQQgghhJAhcHZ2xKMPP4ipISGQejjDbpwFTIwN+OeQcjeW8386uJchqYY2fmjvNXRP5+ZV/zMSbTUyyA4eR8mf30fx797l/y57ZwvK//E5P3CfubT2xkagIYdfpkEm6zEoW1v59MaGhh7pnfSaC7Fg7kxh7PY0Zp9Ryp+c0TPDCCGEkFuH/2FGzyglhBBCCCFkMEaGhnjphWcwNWQKXJ1tYWUhgUik2xXgZD+tuf/zf3eGPdU/c7hfpdzlCtzPU/XP/CfhJyuXNlQNJy+g9odj/BWjLfnFaG+UC1P6mpB/DjrlPwHtzSjJz8eJnw8KU4Ci3DzIaqrh5e8PfQMDPs3e2QULV6/mP0PXAE1298IvZL5qfCjGyLkH3XpPCCGEEEIIIYQQQsgIrFt7D4InTYaNtTlsrMz6BEm5z+pXkirb29nQhta2ngOXxk3rcWUp90eIpnauUxvK8kpUbvkGZe98Ctn+SChSMgcMknJ0DA3ZRlRXjiqVSjTU1XUNSqUqXc5dUSqklRerBTXZcgYGbPnbGAVKCSGEEEIIIYQQQggZpmlTJmPB/HmwsbaA/ThL6OiobrXncAFOVaBTCJCygQuIypoVOFGch63p8Xg3KQbvJsbwn0+ytLqWZlXQVFim6xb8IQRLlRXVKP/XNlR9+i2a4q6jo0UV5BxMW309IDLiP1vZ2mLJffd3DY4urnz6nMVLutJmLlzIp/HYco0NbPnbGAVKb6ogRKxZhYjJwugN5ugrhaPwmdwNbm77GnssIPV1g6UwNhjprAisWz4NUmF8JEZzXTfM5HCsWxOBsDGdSW25IWz5KqxbGCSME0IIIYQQQsitwQVF16xeCWtrKzjYWcHAQK8rkMkFNlUBzu6rSEvl9fgk7RpejDuDv6Zfxke5idhakIKthSn8Zy7txWun8Xl6HKqa5F1Xlw4lWNomq0fFB19C9sNRtJaUC6naUSSmAgbj+M8mpqZw9/buGkwkEj7dyd29R3oXttz1lDRh5PZ0xwZK+cDFmnBMEcZHjwXWvfgW3vndAxj6Kfo0rH1kPdbOEkZvpPBH8ObLz+PNZ4bxXIghmLJwVc8AkdUqvP72W3hrg7+QQG6em9i+xiDpfY+zNv8CXl2vXdubuPg+bHpwASYK4yMxmusanCogvmqWmzAOhD32Ct75yyYsFcY1mrUcmx65D8vuiK7ph2UPrsem1dOEcUIIIYQQQgi5NWZMC4HUUwprSwmM1N5szwU0VYHN7iDphbJCvB5/Dptzk/BjWS6uyGuQ196MKt12fuA+X2ZpP7BpH+Uk4Y2Es4irKu03WNqf2r1HULvnMNrqG4UU7ckOnECHXvf55lAo9N3w87FTwtjt6Y4NlPKBi0eWY7YwPizLN+Gvf9iEVcKoSjMmzonAqtk+qBJStGcBBycHOFgJozeSkROk/v7wcVU9XPdGmb16fc8AUbM/5i6PwBz/GiGB3Dw3sX2NQbJxLvDz94KnbbuQMjDzcQ5wdhwHc2FcK9MfwOt/eA6PThfGBcNalyb9rL8nVUB842I/YRyQTp+PVQsmQ3UTRD+sbOHM2oet6h8Ah0arfN1MZrB1ZGVubyGME0IIIYQQQsitsXrFMpibSWBpYSqkdOMCmvwzSds7cKWiBG9fj8GB8nwUtjejXV8M6LGB/W1kZQ5jNnCfuTRuWj6b58fSXPwp6SKu11Ty6+CfVzpIkFRxPRNVW3eDe8v9cNQdOYPmgkbApG+wlHuJ0/yVPaNkXdj82cWNiDx5Tki4Pd2xgVI+cOFkixHFjCaFYfXKMPS8k7kJbW3sr9ZmFKoSxqYr+/CP//cv/H3XZSHhxrCy7xUgamwDXzwtJapxQm6SymNf4X//33v46FCekHIDeE7DypWLMMtTGB9tWq1fFRB3HmcmjANKLjasVKJZNTr6bvR+E0IIIYQQQshtyHacDX81qZWFKfS5oKeAC2aqrvxUBTe5Z47+9XoMLtZVQinWVQVIxSJAxD7r6uJP0mD8j3Qy/5lP4+Zh01vY32drS/F/yTFQtLUKgVJ+zf0GTKt37EFLdr4wpsH8x+D4yV/h3M9g/+cXITZvQYcuOwE0chIWUvENCkLoggUwNe91mRCbr00SgO9+PIryikoh8fYk0jeUvC18HhW2Ds5oGMGDW01NJfzyOtyIDv//YZm0YgNmOjYhZdt+9H/Rrx1mrFqNe5YtxJLQQPg56qA4pQRc7rlbyufNmIY5ziasAUvgMjUEUuNYJOYBE5auR0DLZWw9EKdajWMI1t2/AssWhSN8sg/cTFpRmFsBhWqqmll48GlvIP0Arhutwv0rI/pst1v/eVOxQNASto4l87EkLAjBk0MQyvLIDXw+U3wRtsQXZo3n+Txz5q1/Cks8FbhS78rnd9WiWZjqaw+UpaFwmFU274HH4Gdcigtf/QJVafhixSO+aIn+Fvuu8gld283SD8S6e5dg9YKpCPQwgywhB1VC2XXlJY/lpUm1XF+D7DO/n2rlNouVm5sErcV5KFNbp3blMBMPPT8P3i3xkLmvwkP3RmAZ26afQ996kM6ajzXL5mIJq//QCZ6w0S1AWnHf2ucFLcOzawMhuVwN+zVrcf+y+ZgX4gOnjjwkFxuq9o9vF35wN6tHfJb6lbl2rF0uxqolc7FsQSiCfZxhJE9BbtelzZ3t6yt8d0ZI8o3AE+vmY7pTG66klqnSWJmvWbsEKyLmYVaQD1x0S5Fc1PdyfNeIB/D44iBYViciUz0b/D7MhlQ3ji3HJQxW5v5Y8/g9WOJriItJBUIaW82qR7BhlhOarmVAyFkXro7uDbZARXwOOjcddt8mrJ3pCGVsBjrfqSddsgGPLfSG0RVWDm7BiJjkBFHZVaR07up0Vner53eVl5myBFnlqrrhjxE21bjwcwEc1q7AvUtYeQS4wrylCJnCPOq4x3lETJ+CBb62ELcZYpw0BJMcWDtKq9B+XY5BiFjO6nDRfETwZWWIelYmXBUOtP6eVPVsUnwF2w8n8yn1s1dhg0Uh/sEqvrO8LKdG4CHWllYK/a3ZOghrfYxRdPE7HEwUZlJn5Y+lLO8rF81DxAw/eNmKWJcoge1A+Rpgf8DK/oXVM+EhZu2kx78qcX1rCYIty1j75noS69fzluHeFQv5PhYotUBzYU6PPstKh7WJlbh3sdBf2FeDdGEInJsysGXvBWGeXtT2Z15IIKZO6TxeuMPoMmsv3DxqfUHzsVvb4wCjZb+6XUkkZiP+bi8vGd1/XhwrvzcIIYQQQsjda8G8MEwNCYGLow3/lvtOXAyTC2S2s0/cLfPb0+OxoyANbXoigBu4IKku+w2qq4uZLu74t+skhJhYIUpZh4J6mer3KT+o1pPXIIOXkQT+Fjb8M1H5P9zfvX7HtuQUoPTPH6C9qZ+YBGfJs3D+7VwYB/jAsJ9BV6cEDf/6GaKQKdDRN4JOB1tfRyv09fX5QSwWgsIiY8DMmw3jUVIrwrnoS7iemq6aNky3+tzjLg6USrHmhafx+Orp8DHXg7GtJ4KnBsHbuAKX44oQtvEFrBzvjHGmYuiaWcLRzRXGsv04FstO670CYVZ+GYejucjcNDzx+8fx0BwpJCJD2Lh4Y+pUKQyKTuAKH0RSpwpwWBrYwc9vMsZLPeDl5YPgib5wM1RtV9WUB84bN0/YI6/ihXWzMN5CH8YSZ0ycNR1TfZxha+co5HM13vrHCvjId+FHIY6w/rW/Yrm3CZyDZ2PhRG94e/kiaJIf/K2aEHcuoyvAMhR9A6Xm8JokQdnF44jK5hOE7ZrDZepszB3vDqmPH7/PjnoG8Fu9BitCpJB6qPLibVaD09E5GoLM2uyzUG73zkKQgwmMxnmyupiIQDdTVCYkIl8IvGhXDlz5LYCfmQOC587BZG8PePtNQHCAN6wUSTidqrqEfd5jr+LZ++Zgqqc1a7sOmBgSjEkTbNCeFI1kTQUa9hjefTQY9iZ+mLssCD7ubpgQFIhAL1OIrGbioTWhGO/hhvEBgQjys4dO1inEl3ILOrF9+y02rZqKAHszGNp6IGTKJEyUGqPiWLwq6NM7UGrF2uYrT2JjqBWKTv2C87msX/ouwysvrMf9M6UwExnBjrXl6SGeMKqIx7WCnqWuO/5+vP7kXHi0xGD/5e5L9oMeeQ2/X+mK+l9+xukSbcp8Jp5/ewMinBrxxYFL/Do4855+C0/ONEXW7pNIEtI6Td30Bp6abwfZ0Uhc5dcRguf+9AxWTHVCR8IhnBUCofe//DY2Bolwfs8pZPJlOx2S7B9w9DqbGLoBbz+7BvOkphAbWMHFLwgzfQyQ90s8fzU4f4yw1YXIcQKmTfeGj4cXAoMmIMBNH0VHr6H3v7+FrH0CDwR7wMmSfUnomMHW2RX2yMa+cznarcuVlf2r63HfNB84WrBjhcdETAvxh49RFQ5dKxhw/T31DZTWOvtgVksOvjilioBaztqAN59Zg+WT2focneDs4YMQF3s4mKGfQKkbq8fn8SRrkw76ejB14I45AXCUJ6A+8AHN+crzG3B/oDcPr7y2ElMlJdh1Uu0q3yVP472npsAo5SccTjJkXeJ5vPBAOALtjWBo5sj60BTWxluRFpUhPN5E1caeuHcGAp0d4OLsCO9Adzg7WMG4vr9AKXdc3oSHwr1hLjaCpbM/Zs+aBD9XG9g7GqHqh1O42tkX5kyAk4kRbL2DEDppPDwl1bjadSzW7jgwlH51u6JAKSGEEEIIIX09+vB6uLu4YJxNzyssuQAp96e9A2htb8MLV0+ivL1VFSDlg6S6/G9Qsa0FNntMwyQDMxjrimBrbIIfmsvRLm/mg6Q8LuDK1lHd2IgN7uOhq6PL/3zlBt1ev2Nl+45BdvCEMNYPAwnELcVQxF9HU+dwvQFiHyeI+PinAs0HtqLkg+OQZ5ZCx8QOBr4TWTYk7DzPFDoG5ugQWwFGbtAx9wFMPFi6Bcor61k+O3Di9Fl+M8N1q889usPddxnpfRvw5Fp/6J7fjo8+28GG7fg62wxz1q/D+iDgwqHdSJEp2Zy1SNuyDe+z4VvhKr3Y/V9hy34h0hC6APfP9UBz3DZs/nwHPv78S3x3LAaxvS+RU6MvNkTiN1/jo4824933dyGuwxvz16/FPcIbkQbLGzAfa9bNg7cyFh9t3YmPtn2J9+JqYWiuh+qoznwawVRiAlNj7rOKsakJbDzHw092Flu3bsM/3/sYsfJxCFwYgQXCPCOXiB+3fYUfhYttOart+sCt+Bg++fRzvPvvoyg0cEfY+pWYoryIzz/l8vI1rrc4IGjhfIQLy/U0+D6rym0azLP34r2Pt+LDT7bhvZOt8Fi4Fs8+NFNYj7blwJWfNTx8vVF/8lts/mQL3v1PIurH+WHJ0jBhHqCwvQmlZ7/Bx599iY8//Qz/OlYBm4CFuH95P2/M0efW6wS/GSLE7tiB9z/cjM8Sm2EfGIENC+2Qcfgrvl28c7wCxtztzvM6nwnSiBr2J+0Im87a2Ecff4btqR3wmnYv7l0izNKDG9Y88Sg2TjFC7tEvsecSF7V3w7pHNmBdoC5ObdnOt9fNn+5FlvkMbHx4dZ+Xk9XEnEBmoxX8Zy9VeymaG5ZMcoelPA8/pmlb5gYw5tqiqZEwrmLIvgBMJUYwFMbVRefKILbxQnDnKqRhGO/ajlYddwTN7yyTcAR7WaGjOhVXuFG+bE1gLDw7O2zJcsyXtuDyFlZerB99svUA9kcn9rx6lR3grQxTsXPbdvz7/Y+wp0gM6ZT5WKbhWZypJ/ZjRykXtVWg6LLqmPDZQS4iKxhsXQ0yoDIL+79gxwmWn/c/+g4p7e4IvX8p/xKmQdc/kBN78Z/OAxRmYtOmtVjs2Y7L33zKjjGfs/6yB4l1wmSNwrBqaRCsSr/Hx1u5Yw5rzwfOIiGvsf98DbI/yI1ETIUILlMWY5XaM1AiFk2Ag0414s/Vsipch2fXh8Gm+CfVsW7rl/jgVDN/pfDDQrtWtbFJsCo5jn99sBn//ORLbD1ZjRbun2j7w+p+40IplDHb8ek2ru4/R6zMAGb6Mtb29+FUZ1+YZoas77bi/U++wAebtyKyxRWL1z+KTV0HIW2OA0PrV4QQQgghhJA7h6uzK0xM+n83DBcsLWioQ2YTOyHrvK1eVwf6VhIscJXiH27BmG9sLcwN9tmGT+Om6Vua8fNyQwcbrtVVoqlNya+zP/XH+7njTl18JKo+/gqVncN3mdBxsIWuiJuoQPNPH6Pk4zNoqm9E3ZGzqPhoNwp/vxNV316F7GQJ6i7U8n/LInNVzzEVmwA6uvyLrFxdXPhN3M7u0kCpG1asDIWrIh3bd53EqcsJiL0cjYNfZEJmPQHhM4GMq1HIqecCpU0oOxuFU2y4nKFaujI7BYnZ1aoRE2Po64thbG4H/bp8XI6Jwv6fIhGjusRPI1nmV/g68hy/zlOnjuKTKyXQsQ7EfP7kfPC8gZ16u48TozZnj2p6/BUc35eHWrEhRA3d+dRET1GEj78+jMgzbNtnTuPTDLYfpjbw7npt/UhVIyspBVm9HkmhpyjAxzuF7Z76CfFlShiYNCN++34cPcXlJZKlKSCysMcEYZmeBttnodyQhyNb2XaiYvm6OLprN65VWyEgYhEihDVxtCsHMeRFO7B9/2lVXR3/CmncbOM8ut7yn3FsD7744Tgiz15BUaMcDdXlqG0xgr2PtzCHJh2oiu9uAwdPFKBW1wyiip34UtjW8V25qIUBbN06X9hTi5h9u/DlTydxPDobMrkMdYXVaBRbwKXnQ3R5vvesx6NL3SE7uxMf7L2CLO4OYOki3DPLCfLU3WzbUYiJT0BMVCTb9zrYBM7oG6CuvIIfEstg7DodSzu3wdYR4mKA4oQfkNg4tDIfisJD6ShqkUAaqnqrueU8bzjJcxFd3AHXwDBYcomh4ZCaK1CQcAqanoBiZKwPPZERrBzFkBXE4cKZo9h7OBY9rs9UVuDS9r2qNnj2HHZfKYVCzwpOGuLchYmXcKqSCxgqIcvj5o/CuUS1Z/EOtq7qWOz9ejf2/noO57JkaGhoQEG1HCJrJz4QPej6B1KYidg04RL2eYuwYoIVahK/wWc/dx5nTuLrvO6rgvsygrGBLgxM7WHZUcYfcyL3H8axpNr+8zXI/oC/GjYfcssJWD6/86VL4Vjua4767As4XAhELFuIAJMSnGPt53h0HGJjWb/eE4sSsRNmhoew+YU2Ji7C6c0/qvor237k/iSUtqjWqNFkF9joy5C5S9XOYy+x+siqg8hQFw1n45Ah9IWOnBP4hPW5M5e59sHK6JskVFiOx4pl6r1hkOPAUPsVIYQQQggh5I5hYmICPbVnk6rjbpnnhkJ5HZTcByECpycxwcfSafib2yQ8ZeYCYx0+QsnjPj/J0rhpH7F5dLkLjriLRnV00Njext+C37leTRSJWtz23lCF1txitHBDozvMH1sNyUx3iMRCkPQ/B9GQKhdmZutMzoBs3y+o2nkAFZ/sQ/kHe/m/q3b8xKZ2X9HKxca48rjd3aWBUj/4ObDGZuiCZc+/hXfeVg3/s8ELptzVbzbCbNqI24udp7KgM34dXnjtVfz1tU1YGdD9khVNFPVpale1NSG1sBrNMII5/4xcbfJWBlkjYGkXAl9ulPEdbwVjKFBXLiT0R16Ny3lc0EMltYH7bAxJPxdAjpoe261ERUML0FqH8ozOy9yaIG9VAmIx20tNBttnodxk+dif3r1/KI9BKheAtXHBVCGJp2U5yKvjkNP1mMFM1HN30ZpKut/yr7THpLXP4c9/eQt/fOExPB5qx18hKRL1/y9KfMCppLsNyGNqwG1CXpOAws5tFTajlf2lp9/dluQSN4Q/+gr++pdX8eZvHsO9UjNwh1ORnmp6J7HFBrz4aBjcJe2oTD2JuM424e8DJ3bMMnadj5eEdvXO2y/jEW+WaGCEvs2+CVe+vYx8HQfMXqm6tFO6eCrcRUW49G0aGxtimQ9FzgEkl+vCxncuf0Xe/BA36BanYEd2JYzZF8Z8liYNc4NNeymSD2kOJsZ++z0iM9lub3gcb775Ot56ZhEm9n4tPWuDJandYdbKmiZW7vowGSckDMWg62qCiTQUz7zxOt556wW8/MQS+JixL1QdXfSqwpGZ6gJr9gVXkhilCpALylq5FtWfE/jmp6uotF6Ip154Ce+88RQeDfPopy92Gnx/8o/8ius1ZghYtEQVVJy3AIHWdciIPME//mCqixXrK+aYtOHlrmPdO88Hw4E1bGNzDzaH0MZqcvFtglo4vJH1j35+GPCK61EPczhP6/wnDSkCbNh65A3gn2Qh9IXagmNIVS+j6EyUNuvC2qXndaADHgeG3K8IIYQQQgghdwLu+aC6IhHEbNCk8/Z4ETtHUsUT2UkMS2hvVcJYR4xAAwkkun2DrFxaEJtmqKuLDi5Owq2EwxaXK5Vd6+2jowPK6qE9VNHkyU2wvscPYnby15ZzHfKLiVCoBUnVKcsq0ZyewwdOub+bU4XnLQq4chCpLku9rd2lgVIz/gVjaCjCyctXcbFr+Jqd4L6L9w+o5tJKZQIObPsMf3vvR1yUmSF47kpseu4FvLCk8woqLbS2g3tptarjaJO3M9h9vghir/V4443n8Mprr+ONNU5ouPYztsdw04dKj3tf1U3V2sbv8RAMts9CubW18UHHboMFYNVpWQ5sXap4mxQPPfcEnlo9FS6tRfjl8DH88EM6qrjX/g9FZSsGXcQqHM8+vwkPR/jCvCIJhw4ew/ffF6NBmKzOxn8tgluTkVRtCOnMld23zUvEfACrruC8Wrtiwxcf4Q9vb8M+1Vw9yJN/QmS2EvZTF2Eed3XfDBfoF8fjmxRu6miUeX8ycSSrEnr2Hghn+z7X3QBFqZFIPZGLcokn5s4DFvg4QK8yD9/3foSnoDIuEp9++BHe3XMVMrNALLh3PV56dROWqt0Grpku9DQ9D2BYutdlOe8RvPL0WizzN0NFwq/Y//NB/LdIUw2OkKEYIiihaFQLXg8qG8d3bce7//och7M7IA1djEcefwavbuj/xnGt9qfwDHbFlcHcNxxLpEDEUj9YN+TiyAlVcNtAj30FNdcgSb09Xt6H9/72Lv76FfdABaGNtbZCuIZfOycO4HS+CN6PPI8/v/IU3vrD83jAtRFX9n+HaG660Bc6lL3KiJUZ13RFelq03M7jwDD6FSGEEEIIIeT2x799ng1itZc4aeJmYgYd7kIPfuhAm6IF/8iMxQtlSfhZ3vdqt6NNFfgtm/avzGvoaGYnKNzlo+w/UYcOnIwHDlrwgdUhMJzmDbFwzqprZg+j+56H47tPwTxQlTaQ9paet/lx5cC9lf92d5cGSi+huJb9JW6FIjoSBw/3HE4lqObSliwjDqcif8Z/t2/DP967gmbpTDy4bLYwdXBTpDYwRCOq+YCPNnkTw8HCBE2lKcjUt4artS5SD36E/9t8CLGa7kG+Iwy2z0K5WdpicY9AWBA8x7Fe3yBDlpAyaqQLsHqxHyyK9+CTr/fj4LGTiDxTAP7RtqNt5gI8MM0Ziqtb8en3P/PbOn62Apr+nUe/NQff/2cH/nMsDwZB9+PR+4Wr6qLLUN0OGLQ241yvdnXwcJTwMq7esrHvcDrqbAKwcl4YprvroTD2J6Ty00ZS5haQGHARsP7FRuWjxsAB/ivCMd6sHMk/5gLXopEtM4c0PAKBjkaoyb8k5EWTOv4RGkd/+hGfbdmMv19ugXfYPVg1V5h8k4UvXYlQ12Zc/OQr/HdfJA7/ek64pX2U5chQz44otp5DezqmvDgF544fx3dffYl/v78LpeYTsWz1fP6KXk20259qRH99Gfl6bpi9KBwR461Ql34ax4XjVFZ1I6DXgZbM3u0xEkf555f018YGYWALG1MFSpJzIbJxhrU4Ez/+8wN8sD9B9ZgGoS/Y2IerHuPQabI7bFnTra8e4NkpvQ2rXxFCCCGEEELuBNzLi3q/eZ4jvJee/zPOyAQeBhLocG92amcnDx0dSKqpwLc51/GX1Eu4oOBOelTONFXjTyzt65xkJFdXqIKk3DbYIDWWwMbIWG3NvbbL5WOIgUrFsf2o/uoHVO89A0WzFQynzoDZxnWwfWm9xveJDITbPU1lcbu5wwOlYhgF+SOoxyCFI3Kx+1QW6i0m4Mln1mCGo2puS49pWLN8WteJc1Itd3WUBRwj7NjfdnAU5lMnvecpvPX8KraOJlRmJyOpsgHtOrrQFfd/M62t9yasmeXGtmOBoCWP4NlQW+iUX0fkSW6qNnnzw1RvC+jU5uHckd3Y8dUufL33OE6p35o6XKEb8Oe338KfH1E9G3LsGGyfhXIz9cS6znKz8sfSJzdilj1QfOUABnnv29DJbGBixP2rjhjlhXWAYwjWPT8L0qEeTbRhaQYTsRhoU6CsqgmWgfPxxGsTYCtMVleZth3fxaQhdu8WHC80xfSNG/AQ97yCnP04md4Ii8ANeHF9COsHHAtIZy3Dqln9XwGdf/wnXKu0wqSNc+EpKkHMD5nCFG3LXAh2Wblh3WTWl7h5HnsGsx0HPvzIz11FboMErou9YFOfjyPco1YaL+JibhPsAu6Dt0Ujcq9eVM3chxvWPP9bvLKG7WdjNbIS01Eha+NvAzAQXvY0LGWNULCvCys3f77/Ojpqf+W4pZkxRKIOtCoqUdnI9f0NeCuw143ZI1h/l5PnkVErhuOU+/GQcJyRzlqD13tvSx3r96+/sgFLAy0gK8zE5UwZ5OxLzkAkUr15XkO+tNofRnVVcjuk89YhwKYOqSfPdT1T9sSPsSgWOWHh04/w2+Y5BmHVqvnCIzZy8UtcEZSsja3dGIEpXBvjpj85SD/zD4KvlS5k2Rdx4Ovd+OLrvfjuWFT3YyiEvmDsvbqrL3B96tnHp8JRpwwxP55XzacNrfrVNDzxxlt4540N6H4VHCGEEEIIIeROprpFXgf67Lxqozs7w2lrUwVK+YBpB1rqG3Gtugz/rMrgn2Ha1NGG/6tMw9WqUjZNrgp6cnfj8su0Y5OnP/R0uTfe6/DrHg1NP+1WvdDpvc9R+sYhKLinIxpawmDRvbAYJCyka9TzTjwl27/bP0x6xwdKrTDpt8/gtR7DA1jMpiT/tAWfn6uCLTt5f4k7gX37Lfzltcfx8NIpGK9aGNcjLyFLLoHfEy+x6S/hpdV9H+RpMs4Lc5at717Hi7gvdj0AAP/0SURBVDNgJ8/C8f39BXAUqGsPxcNPv4C/vP0qXnv8XgTp5OLQtq8QKbyLZfC8peJyTi1MPMLxyIP348EHH8ZLr6jm+/MrDyBsKFde9TZxNlYuj8DK8AAhYawYfJ/5cjtdAcuFQrm99Qyevd8XbQk/Y8uOaP6ZiKOq8gxi0mph4LMWf2T5eOeNR3FPSD3KBnpnznCdi0ZssRJOoc+wdsPawwusHXuUo4x7VmIv8roU1fNPC2Ox86szKLEMxWOPrIIv0rD3w//iRKUNFm94QpVn1gbffHoNlk/14pfVqPIKtl0ugfl4NxgWXsNutZeFaVfmudh9MQ31Rp7YxD3/ks3zRIQTUvO7/9VMo8pTiC9sgZ2bLRT58YjlE6txLKEIHY4esGsrQvyv/d2QbQxb7xlY9XD3fr42exwa0s5izzlhluGIPItrlYDTTK4eXsUfN2p/5fjpqEQUtzkg7MVXVfnZEA6vskp2RFAzgvV3KTqFj3+8iirzYDz2rOo48+aTixHc2NBzW2osx7kgeN5qPMvnjWvL6zFevwwXj/6sqkMN+dJqf3jcVcnZaHbxhl19JiLPdNdZYdS32LLvOjom3qu27aew6Z5ZmOSsmid299c4lt4O9+UP42WujbHpG+d2oKpeNV2jpDhkVxvDY8ED7HhxDx557Cm8ya37L6/g9Q3cPzip+kJkmTkWP6RqI395cSMe8m/DtX07sS1Ky5do8bTpVwFYwI6rq5bPxiQhhRBCCCGEEHLn6gxkql5ar4sHPfzgb2QOKNvZoFQFS7lAKBuO52fh48pMfFqZjXMFuV3p/DxccFXZhiATS9zn5suvi1snZ3jB0hBY//XPcHqPG56DeUep6qVOGWloPHYBLZ3nWSbjYDhIWEjPseelW21t7WzXBno3xu1BpG8oeVv4PCpsHZzR0DDQGezATE0l/PJ8fY8gRF5fWYbES5dwKTUD13sMmUjLKEBpXQlycguRlZqGen0r6OsoUJFyHpHs5D8jvwJcEL21ohT5ObWQ67LG2VKO5KgEJBX3fAaforoc2RmZqNDhrvhrR33eZfzw0y84fjEDNX3ahwylxfE4e/oyErOVMBqng+r0C/h+3y+IPJ+I4s75B8ubdCbWrZ4G8+yjOFmoz6YrWb9RQs8+BIvmBsBWfgmH4vL4bV28lIVc4a1BNeUluHIpFml5FaoETk0FCq9dRtS1HBRwcavGNhi1lSMh6jRi0vhryQbEr/PiJVzJKkZ/jwzWtF2+fq5c6bFcZ51d5OpHSOuizT6fTVWVW0oZlMbG0G0uRfz+Q9h34gzOXa/hX47E0aochLpSLz9Oz/2tRGF+HhIrWyExUKI2LQrHuDfZX0zE5SsJyCzSEAhsqEJx8jVEd22Ho2lbqrSoC8J6asuQX5CFwgYx9MVy5J8+i2NnzuHM1QS2rc7l+q6npqSQteEMJGWwcmGJxcUFyM7PxfXrjdAbp8/adSWSTp7Fufhs5JX397xMJcrd5+CJUCsUHHsPW6LVarqzrQ5S5hXFhSgorUMLq7OWsmT8cuw4fj17DUlxcQO0nQaUFZfgemwMjp+9hFSh79WxflmQeg1nT0XhbJzasj3KtgVVpfmsr1dD14rlq70B+TGHsefQOZxPq+XzpakNqtZxFed71XsXVg8FhVVoaOtAW3stcqKu4kp+lVbrqi0tQn56CRr09aArL8KpE6dx4lQ0LsXF43Jnm+9n/T31aht9yFGcX4D8jBRUtBlBR6cWWceP42e2rQssjz3bnopufSWbnx0b5fqwNmA1XpWKfT8dwbGzcSjnHkCrIV/HYxIG3x+BrHUy7rvfG8rYz/Cvffndj4xorUJ+bgGyMtJR0mrEH0Mbcs/jl9PxSCooQbWCm6UYOayfJRXLoSvSRUtJEo4e/RXHo+IQ07sfC6RzVmHtdDNkHopCPncJMWt3yjYxnKbMwfxJ46CIOY5TyUJfKGPHYhNdNJdcV+3ziRgkVXc+P0Ob4wCba9B+JUebUQtKkqJx7Ar7vuDXcnuRSMxG/N1ezo5Ho2ms/N4ghBBCCCF3r3X3rYW1pQT67LyoL/YbU/iZKdHTh6uRBFHlhahrY2ekqh+h/LTW1lakNNYiproM1dzvW/4q0g4+QMoFVl1Fhvj35DBMtLSFSFcXuro60GXLcj9he99+X/7PrcKn/ohh+dc/wGquH4wCvWFg3oa2FkBk6wbj1ctgsdAbIu4uzLYKNG3ZjboC1VKaSCLCYLZsnjAGVNc0oKS0DAcOHRFShudWn3vomJg7sNIfPQHBM1BaWiyMDZ29vSNKSoqENtOzwm8Uc1sH1mjboKgtR2XPt9IwZrBzMoFeezOqS6o1PhOSY2xtCytDEdoUtSip0ua5g6r16g4yv8a8PfkBLj5ihaNPv4APqk3YdFWynueL2P7eDLRGvorFf7ymShwyI9g4SMC9Sr5vWdxCQ9pnbh8sYKjbivqiStyICzx7MLeBs6luP+1ndKnaWfso7Jd27VpFijX/eQd/Cq7Bl48/i480vjxJizI3sYKDhQFYQWnZR0ZDZ77669/DMZL2JSzb1ojCcu6fYzQZvfarOn4MYT18W2adq1VT/jTlS5v9sUDY8//Gvzaa4twfNuH1U5rrvvMY2tpQovmq7CG0n6c/P4RnLE7hkae3odKUtXM+VQzPV9/DljmtOPbCBvyu88V3wnpFGvd5qPrvV9z+maP+Jrb9UdLRwT9z3sHBacTf7Umx/d1pMTy34+8NQgghhBByZ9m3+1v4eDrC1NRISFFR/YpWXRjaxv7X3tGOlrY27MtLx2/jTqOeuyCPe7wed3mo+suguMXauVv02QdlGyTtutg8aS7WuPvwt91zV5SK2G/Xzp+vvQOlSTYhwqf+Gb36GdzemMpvHg3lUKQXob1VD7qunjB0MGaJSigvfI78DTsg7+96KsZl579gvmK+MAZcTyvA2ajz+Pu/PxRShmiMnHvcpS9z6klWXoLCov6CKHUoK2LTBwkmyavK2TpKhnASrFrvYPNrzJtID2I9I7jPmAG/cfpobNKHuedkLJ7rDFN5Hi6ezBNmHI4mVJaMsSApZ0j7zO0DV24jCzJpTVY5QPsZXap2Nhr7pU27DsKaJ5/CK797Gk8GmyL/1B7s7+cN81qVeWM1SobUR0ZDZ75Gs35G0r6EZQcMyI1e+1UdP4awHr4t95c/TfkaaH8sELGBtZ9XnscLq5wgj/8ZOy/1X/edx9B+H10xhPajryeCWOKMGTO9YaPfhEZ9M3iHzMMiNxMocq7iqPpbxoT1jjxIyum/X3H7d9sFSQkhhBBCCCHD0hnA5AKanbffc88qXe3qhYedfaDTogQ6B+7N9tzb6rlBLZ2b5zHX8VjlKu0KknLr6i9Iqq2m3VtQ/PkFKLhn+ZnawjB4MoynB6iCpA0VUJzch7L/d2jAIKmBtwdMZ3YHZeVNzfyVseejooWU2xddUXo78piCl597HAvcxaiplqFapoSJlSWsRNWI+2Ufdh26gqyxFugcqbtxn28110fw/jvzIdVvR3Xqr9i6cz8uZFOgh2hrNv6y7UmEWHagKfcavv96F/aNxgvntCCd/hR+89xseItkqJTVQqY0grWFJXRrk3H4u5+wPyZ3kKuoSRe6opQQQgghhJB+9XdFaSfu1zR3VSn3m7pduLI0S1aDzWnXoNThxlXTOoSfoyxJFVxln8Us8UW/EHhIzIUgqSo0yk3vL0iqzRWlPEc/mAS5QM/MFgbBLhCJlWjLuY7mwga05magKa4Y7cKsmtj/36uwee4hYQwoKatBbn4Rnv7Ny6hvGCDCOpAxcu5BgdLblJ33JIx3sYKpgRgi1nzb2pSQ15Qg43oaCu/QgOHduM+3lJUvpgfYwLitGTVF6YjLHY0r7sjdwwXBs1xhLmpFQ2kBktNLbmJw0gbeId7wsDaBoR77idHWjtaWJlSXZCMx5Wbm4w5AgVJCCCGEEEL6NViglKP6Ra26m76DD5Z2IK9ehja085/Zf13zcAFQ7q323JWjYh1duJpyQdLutM55+qN1oLSLMcQuFtAVsbxUlkKpRYzTZGYIXLa+A7G9DT/epGhBTl4pTp09i3+9v5lPG5Yxcu5BgVJCCCGEaDZGfqxoQr83CCGEEELIraZNoJTTGQjlgqLt7PO/rpxHcm0V+x3KpesKU7kgKKedzaiDAEtr/C5kdteLmzgDBUk5Qw+UDo2eswOc3vsjTOeHCilAQXEliorK8Mf//T9kZecKqcNAgVLN6MSFaPLeaxOFT7eXV/+TIHwihJDbEAVKCSGEEEII6Ze2gVJOV7CUDX88/yv+ERuleoAp/1u08/com8rN0N6OP4TMxl9nLeyaMliQlHMjA6VckHTcq0/Act1y6Bhwr8YHZPVy5BeW45fIX/HJ1h3s9EG1j8NCgVLN6MSFaJJ7ZKnw6fbivuyo8IkQQm5DFCglhBBCCCGkX0MJlKpLqarA45H7kFRVjkZla484qYlYDwHWtvhq8Vr4WKlub9fWjQqUGgb4wvrp9TBbMR8iM1M+TaFoQW5BOXLy8vC3v/8bhcUlfPqwjZFzD3rrPSGEEEIIIYQQQgghN4mvlQ3+GbYYXyy6B18tXoOvIoSBfebSuGneQwyS3gi6RoYwXxMBh7++DPPVC7uCpNyzVQuLq1BdU4vvf9g38iDpGEJXlJLbwpoFTsKn28u+E0XCJ0IIuQ3RFaWEEEIIIYT0a7hXlN4oo3VFqXicFUxmTILJvFCYTA+CgY9n129mRXMrSsqqUVklw54ff8DPR36BQtHMTxsRuvVeMzpxIYQQQsYICpQSQgghhBDSr7EWKM1/9HfCp4F1tLWho7kFHS2tQFs7+z0M6JoYQ8/RDnouDjDw8YC+hzMMPF2hY2ggLKV6JmlFpQzVNTIc/SUS+w4cgqyuTpg6QhQo1YxOXIbByg2+5jKk5dQKCWOHdFYEpljU4srhS8gS0u4ebghbHgTH5jx8fzxeSCNkrLKA1Ncc1Wl5qOHHgxCxxg3IOYjIa3zCoP35tuvvjlL4spymDf8r685HgVJCCCGEEEL6NdYCpYrkdOHTINrZ73ylkg1t/G9+DveCJpG5BCJLc/5vdUo2X129HOWVMlRUVuLH/Qdx/sJFVFZVC3OMgjFy7iHSN5S8LXweFbYOzmhoqBfGhs7UVMIvP7QTF+6Efjqm+PkioJ/ByTANWaXC7HcUN6x7+QU8stgfkqIoJJYJyaMpMBxrZgfBpVcZckGRiKnOMErJQ3cT5oKD4ZjiLEJKbgXmPvUmnpxjicJ955EqzHHTSKdh1bwQuHXl2wLrXnwFD4U5ovJCMm5EUfUUiuf+8hCWurXim6OxQtpY5I+Ie0JZnRkiObtXqUwOx7qZE+AkzkBWuZBG7kjS+57HmxsWYKKkCKeSKljKKrzx7kr4K3/EoUuqeQbrz7e0vw/ZTDz7P4/igVlOaI2MQ6aQSjSTSMxG/N1eXlIojI2OW/N7gxBCCCGEkG7r7lsLa0sJ9PX1hJRbS2xrrd1gZwM9B1voOdmxwV412I+DyMIMumpXkHI6ryKtqa1HXEISdu/9EVHRMaN3JWkvt/rc4w55mVMwVq+7Hxs7h4fXY9Mj67vH2bB6ujDrHace9m5eCPJ1g92oXhusxmIG1rLyfHiRv5DAscCCtQ+zcl6HlaFCEm8q1jy6HuvCHfgx83EOcHYcB3N+7CbzX4CNLN9rZwnjaMbEORFYNdsHVULKjWUGW0e2//YWwvhYVQPp3Pux6fF7sUYqJPEssOq+R7BpXTikN6fAtDf9Abz+h+fw6JD7dQgefeV1vL7hxrwJ8HYmG+cCP38veNq2CykWcHBygIOVMMoM1p9vaX8fMgO4evojSOqKrn/7HXa7IoQQQgghhJA7R5OiBdW19SgqqUJpWTWSUlLx1be78fWu3Yi6eAmNcrkw553nDgmUXsSeHV/hU2FIarWEs5MeytXS9pwXZr3jVOPQtvfwp3e+wIEbda9rSg10rR0wwXeykMCZjWlBLnB28EbwfLUAapAf/NwdYKy8LiTcQpJxrB2oB3qa0NbG/mptxuhe13S7K8HxKzUw8ZqCdSuDhDTGdznum+EJE9kVHB9rtyZ7TsPKlYswy1MY15oHZi1dgpUzPYRx0qny2Ff43//3Hj46lCek3Oni8e37/8Kf/r0HUULK8NsVIYQQQggh5G5VK2tEaXntbT1wL2figqIFRZXIKyxn41XIzinAmfPR+GLnf7Hty69x5JdfkZKajvb2zotr7kx35DNKH/v0Z/xuShX2TX0MfxbSOPPWP4VJxkU4ueMIO0UWhK/BKxOAuC37cIpPmImHnvdCU9TXuGi4CqumusHWQIHynFgc3HdV7RZzjh1mrFqAKVI7WKEB5Xma5lGnNr8um78oHdGRZxCv9kgHLo++8ih8dsUI65YEw9PGEIrKPEQfO4iL/aw4aNUjmG9T2b1fQcvw7CxDpG05ieolEQjzc2L5q0VeSgwOHrsuPH9wKNzx7/1fYonoDJ5c+Tb4Jz2Evo0T749HQ5E1nOt3Y+Fj21Xrfe5TXH3cHlf/9148fUioC98M/Pu+L1C2bDoCHC0AWRGSLkTiaKL6M1UtEDRvPkInOPHlXV2WhXNHTnaVDb+Pxpl4P7IWSwdcj5oN7yH6lcmo+HkeVv1VlfTC9qN4TGcPpjz+pSqhq6yi0LRmMUI9rGHYzA4IMQfx/WU2Wb38ks/iq8iBotFSVn+zMMlJWMdlJSLefQrTq44g4P5/CfMAloHhWDbVB662plDWlCEr7gT2xajd8j6U+nMMwZqIiZDasvJoqEL+1V/wvfq6tGUzEf/v8/9gqe5JvPXEu4hk5T7v99/iHys7EPmbF/E/cVxFqNWRsRLV5Xm4EnmkR7scWh87iCyHCMyfYAfkXMD7rO/0xLbH7b8X6y9q26vwiMCUiHX4/TJXlJ3fg18ygKZC1mcOcsF5O0xZOAtTfOxgayZGfXkR4qLYdtPYJO5RDEELsOnVCLiXX8LXxzMBhZBXPo+myGP9fl9Xxrl8sownb8O3Z4QkVt5cv3S1MYVYzso7+QqOnOqvT1lgxvJlfF64+ivKSsBpuRSrXBu6y0fb7TKDths1fD3oZ+Hg1ye7nhUadt8mTLEow7kvjuCKkCZdsgGr3BWI+4yVEd/ubFDelZfXcfDyMkCt/3T15/mvYqcqqYe+0y0Qdv+9mGLdiOQjexCZP5Rj3MDHy2Hto5DWiV8HkvH+7guq56v21660rvf+27alxzTMC58AN2sLSNpZe0hPwL7DVzX25855evf5rjbAlZmsDKkDHf9GCz2jlBBCCCGEkH59+K93IBaLhLHbV1t7O5rkTaiplaGsvAKlZeX8rfVVVdUoKi5BQ2OjMOcNRC9z0uxGBkp//+Uh3GMVjw9W/xG7hTT87hNcXAbsn/8b/J1PeAG7Ts5Gx4UoVDpMhLOpMSRmJjBqLsaJnf/A2/s7r7aSYs0Lj+H+uZ4wlFVABlPYmrcg5dBO/L+dlzScQAvzz/eBZUsNKlsMYWfRhpJrJ7Dtw104J5z8c3lcoLyAkzJ7hDiYwcRUAlPDZhSe/Qb/87cjGl+Qsv4/u/GyV1b3ft33N/z6vD1S9mTBYqYHJMamMDczRkdtGva//wY+juYXG5JHPzuE1wPL8N+VT+AfLK9Bf/waO8PL8FGcK14LKcBfFvwOP7L5wv7zAz6dVoEd9z6H9yqFuvCrQcy5GphK2b4YSSAxAWpSj+HfL27HOX7tFgh77Hk8FTEBFq1VqG4SwWacMcou7cU//3EEXIyL30eHDHyTZYawftfTi4ZAacSrf8e9OIrn3hMiUEJZZfxQDMs5zjDRM4SZmSEUecfx3XlTLIgYD4m+oar8auLx9d//jK+EF9v0xNXv09iw2BPmSiWULY1obOiAjZ8XbIq6A6WWszbgzccWIMRWhJpyBfTHWUK/Nh2/7t+J9/cLtatt/fkuwytPrcAiL0NUFdcBZjawVGbgp20fYnvU0IMn3o9twY5nHJD+6ct44pAvPvr2TUwu+gKPP/kdMrrqKAgOerUoa9SDjY0+K/8z2LttG/ZxlcQMpY/JT1yATuBUtj5dNMR9igf+r+dl39JVL+IPD4fCqYkdpBt1YGIrQdPVXTjcNBcrZgZgsocE9QV5qGgB6hP/i4f+lsrq4DdYM9MJZi11qNY1g5ONIWQZh/HJS1/j1ILnsWXdDEya5AIzWQmyq7gFk/H5U//CYT6P1ri8ZT1++4OQAT6fS4AjK7Dh39z4NDzxN9aHA4xRUVSLDkNTWOqX48jHf8CnMfwCalTl9fyKENjqt6CtpQmNzQpU6IzDRJP07vLRartaths197P+8pp/IXY89Dts5Y8tIXj3uz9jnnUJDr/+LP5PCMo+vflHPO6QjLfX/hnH+HYnRWpXXkYeKPW950X8+fG5kKR/hzfe2YtUlhftjnGDHy+HtY+qpC58e2WpMzZtxsKX38GjGttV/RDqvZ+2fdoVf183B362+miolsPAzhY2bZU4tfsf+N8fhO+Uzv483gLK6mrI9a1giVzW51Xb6WwDQeatqKhsgcjGEqZVl/HVBx919b8bggKlhBBCCCGE9CsocILw6fbW0dGO1lYlmpoUqG9ogKyuHkqlUph6k4yRc4875NZ77RibmsBUYgRDYZxnzKWZwFgYBYzYuDU8fL1Rf/JbbP5kC979TyLqx/lhydIwYR52Gn/fBjy51h+657fjo892sGE7vs42w5z167Be7e7lTqr5p8E8ey/e+3grPvxkG9472QqPhWvx7EMzhblUebTxHA8/2Vls3boN/3zvY8TKxyFwYQQWCPP0Zsjvg9p+6XP74IQJocZI/OZrfPTRZrzzUwn0PSZjecTwns14IqsMSn1LuPGLW2CBry3aSjJwJLEU9RJXzOKfU2qBMAdzoKoEByq5cYGBOawMU7Fz23b8+/2PsKdIDOmU+VjW+RzA8HV4dn0YbIp/UpXl1i/xwalm/kqwh5eoZuH30cUXswZajxZi93+FLfsThTFGKCu/GSLE7tiB9z/cjM8Sm2EfGIENC+2QcfgrVfkdr4Axd1vuPDdhwZ5U9TsJViXH8a8PNuOfn3yJrSer0dLjnyFmYtOmtVjs3opIVv8ffLoV72/eiyzzaVj36Aas63w+qFb154Z1j7BlAnVxast2bP58BzZ/yq1rBjY+vBoamuCgMo7sQXSpKQIXL8XS++5BiKQE0bt/RQY3ka+jefBoOY33Nm9Ttd/v8mA+bTWefGQVOrM+lD7mFxoMnYtf4r0Pt+Dj/X0f1bBgeQSmmFdgJ9cmPv8Smz87hMiUAsSd2I8dpU1sDgWKLm/D+1u2CVeTNqKG/Uk7wuqMzf/Rx59he2oHvKbdi3u5dpR6Bt99Uwq5Dluy7Cq/3PudV3Z25lGfG+nE5ZOld2Y8dAHun+uB5rhtfHl/zLbx3bEYxGq6qFMoL+/2OGx/fzPe/Wg7PvkqEea2Nj3LR5vtattu1ETnyiC28UJw56FFGobxruyLT8cdQfM723A4gr2s0FGdqrr6km93vfMyAqEb8OKjEfBSXMHOr0/xQVKONsc4bY6Xw9rHXvj2KrydMrW/djWUeu+vbRe3obHkAray74lPtn7B0n9FmaUflt+7VOirQn8OHYfyX1n9fvIF3mf7/d9f45HDb0fVBhbYlOLbT7k87MAnW85C4RmBJ9fP59dACCGEEEIIufniE5PviCEhKQUpaRnIzS9AVXXNzQ+SjiF3VaBUe2LIi3Zg+/7TOHU2CqeOf4U0dpJvOs5DCAi5YcXKULgq0rF910mcupyA2MvROPhFJmTWExDeHfcUCPMjD0e2HkZkVCwux0Th6K7duFZthYCIRYgQ5uToKYrw8ddsvjNs22dO49MMbuM28NYQEOlfB2oS/ouvI8/x+3B8bywKFWJYO/kJ04em8EABKiGBQwgXgJiNQGcDlGVHouxkCgqaLeHNP6d0BjxsxKgvTel55auyApe278XRU2x/zp7D7iulUOhZwUl4tGnEsoUIMCnBOVY2x6PjEBt7Bcf3xKJE7ISZ4eqB3TokDLAebVRmpyAxW4jYdOlAVfxXXWV18EQBanXNIKrYiS+FNnB8Vy5qYQBbN03lJ9SvuAinN/+oqje2TOT+JJS2CLNw5i3CiglWqIj9jG0rChdiY3Hm+GF8cjwPHc6huGexehB2kPqTLsI9s5wgT93NrysmPgExUZGsrdTBJnAGwlVzDU15DHYczECz2wI8u8oTullnsCNaFfHm68iyCpc+YuV/5gous/Yeuf9LROYBrrOWYsWQ2iZHjPbySHzE+s9xtn/nEnvXCWBsaACRgQSOFh0ojVeV1b5fU5CWeAmnKrmAlhKyPFVZn0ssYeO1iNm3C1/+xNYZnQ2ZXIa6wmo0ii3gwj1etygZ585WgHvkdGt9Ab/cqehk7Z9Xa2IMfX0xjM3toF+Xz/fh/T9FIiZXmK6GLy/rWlzb+V/s59srq8PIvcgezov7htRuVAoPpaOoRQJp6DR+3HKeN5zkuYgu7oBrYBgsucTQcEjNFShIOMX69iizmoYnHlmLaYaZ+O4/u3AsoecWBj7GaXe8HO19LOyvXQ2h3lU0tO2sE/jqq/04fJLVX2k9GpuqUVKnhLGdF/geLfTnjpxT+OA7dsyJiWP9+TT2/XAUZ7nt8G3AGIXnv8S+U5dwOT4OF07/hJhSEVynzGFHXkIIIYQQQggho4ECpf2QV8chp+sRDJmoV7C/TCWYyI/7wc/BCDB0wbLn38I7b6uG/9ngBVMYwNiGn0mNML8sH/vTuRNxQXkMUssUENm4YKqQxJNX43Je93ypDdxnY0iGEBDkT/ZL0tB10VNlLeStgL6p2iushyI9GYUNYti5BgOhU+BqWonME+wMvugY0irEcPQOhaU0CHYSJUqzer05q7UOJandYYrKmia0Qh8m41TjU12sIBKZY9KGl7vK8p3ng+EgYnttrvbSnUHWM3w9y0oeUwOu6uU1CSjsbAOFzWxbgJ6+mWq8B6F+a3LxrXpAqJEto35F6VQXWIsVKE2L664XNCH1QD5qYQQnX/Ug7CD15+8DJxNWPq7z8VJnmb39Mh7xZokGRujTBLXC8nJ4D67V2EBqU4ur3x5BqrD/fB01lyM9Vn3/MrEvXwaY2MFvSG1Tpa7gGOLKhRENDu/5GRerLLDi8afwx7+8glceDoW3gTCxH3KJG8IffQV//curePM3j+FeqRm4p8WI9FTTRyRuL3aeyoLO+HV44bVX8dfXNmFlgKb2IJSXohzxZ3L5wKxKJVqH849yQ2o3gpwDSC7XhY3vXP6KxfkhbtAtTsGO7EoYu00Cdw2iNMwNNu2lSD7EBZlHkyF8n3oUGydaQbeuCJdi1MtAMOAxTsvj5c3axyHUe6e+bVsJx6B78PrvWT/93dN4/qHpcNRnX7+6uqqri4X+XJt3oMdyclmdquz4NiCGZdD9+J+u/v4UptuJWbGZwZufmxBCCCGEEELISFGgdCjYiao5/8EMeuz8FA1FOHn5Ki52DV+zk9d38f4BfiY1wvxtbXwArlsT5FzkhK13kPgPowc9ifBxBER6g29Js/PIKlPCxN4PYWEusGosRiz/rM5c/JJdCQMHH4SHOsJeXIfCc9oEJXShJ9x/bKDHmmFzDZJ6lOU+vPe3d/HXrzTdMKuuez2jprIV3MvxtSfUb2sr+l4XqcZQDBGUaFW/ypQjb+Ofw6E5CNtTV/1JxKxFcAGZ82plxoYvPsIf3t6Gfaq5hk4IRqGlHNejioREoY6USjQL453q+Ujw8NpmW9uApYWM03vx4b8/xH9+yUO75zSsWr8Rr/32AUwRpvdhFY5nn9+EhyN8YV6RhEMHj+H774vRIEwescoEHNj2Gf723o+4KDND8NyV2PTcC3hhiYUwQze+vFqb0dCzww/PsNpNJo5kVULP3gPhrFzmuhugKDUSqSdyUS7xxNx5wAIfB+hV5uH7HGGR0WLohNCFNii6mgfluAmI0FA+mnW2I22PlzdpH4dQ7516t23f+x/HS5uWYZazEvm//or9+w8gpUatQoX+3NHaT4Ph20AzqlLU+jobvv34X/jDP3bhgjAbIYQQQgghhJCRuesDpb6Gw4myXUIx964ccSsU0ZE4eLjncCpBNVc3YX5LWyzucUFnEDzHse03yDS+pGlsKcHxwjqIbByw2ssBbYUpOCGc08fGF6PezAnzJo2DsbwcqWqPANVGVjVbkV4HWjL7luXR/u9vHUP6q99ecmSohyFsPXs+QdRyvi1/BWh16SVVgjaiy1DdDhi0NuNcrzI7eDgKccJsQycEo5RKyNViNnwdGdrAk7uFvYsFFtuzHW6vRfEALwgbXh9jGkuQGH0ah/fuwicfbca2EgsEL1qKpf09gHXmAjwwzRmKq1vx6fc/4+Ax7tZn1a32w+JrCNXTK7vJMuJwKvJn/Hf7NvzjvStols7Eg8tmC1O7aS4vLfXe7jDbTWxUPmoMHOC/IhzjzcqR/CPrS9eikS0zhzQ8AoGORqjJv4RUYf5RIxKh6uLX+M+n3yCm0QGLHl6HiCFdyK798fJm7aO29a6ZG5avXIhASSl2fr4L3x09jqMno5BTr3Z5sdCfbezDVY8M6I1vA2J0KLJ69XU2RF65Db4/CCGEEEIIIUSzhtoKfhgr7qpAqSp4YYeAeW7sZNQOM9Y8hRf9h3OZZi52n8pCvcUEPPnMGsxwVKVaekzDmuXTNJzoCvObemJd5/xW/lj65EbMsgeKrxzACdWMN9E0PPHGW3jnjQ3ofkXVwOLTy6EwdMV0DyOUZR7rerajnHtOqdIGwf+fvTuBq6rcFz7+Y55HEQVEREQERRxRxAkHBFScUskph0zzeirrVNdz7r11z31v3XNOp05mNljZaJ0sNcsycp4RxAFnAZF5EkEGkUHetdZeGzfIsBlMs+fbZyVrftYzLPb+s9bz9HaE/BR+1Amw6WPXt/FkGrkx7okFhPurT2m5BhAZOQYfzdwDLoWfT2ZQJZXvjPmhDJLLV07/48F46cYId39HTLa0auh8lof5KfXEdch0nprRHcuSJHZ/3YKg8JWt7L5Uir3/HJ6KGoimCtrjFRxBZLCah0Fz+K+XV/NfCzR9OLaFUkYGzgQv1paR1H6iFjPb24LiSwf5Sn1ir/3amFQ/Vz3JEjmfijK4cDyRwrIaMDHESH77P6eUcsxx9JDz0R5XVylNDrZYGRtDdTk5127i4D+GJc/1xllzQFW+0o2GuUMXAqTq6uDaSdNe1WCkm+8ovKTlrkMiWbW8Fza3lZ0UXlOXsnpFpNR+b5KffJYz+SXcNjDE0Pju9/p37U/muokzQx6dzghPKW2OHoyIepKA+n0i6HHe1tabsgPHSSmxoeuEHjgVp/LjJWlh6VGOptykU59H8LYvJeV48yMBGhuq9UlftzI4smE38ecP8d43Z7nVLYzH5zcSAGyQ/vfL9rrGWg3Uq5aUe8OK6WhpAQY1GF3LoEhtFyGeOjcHqT0fSS7H0nuK1J4Dlbqg3EMWL+UxeaA8pQ4Y0TVkcW0dkNvgoPAIwn8bN0lBEARBEARBEIS7yAHSWzdLlOlB8bsKlO7aHU9mtTNDlq7kpZef5unpA6lOzpG+GLfc2S3reO/ANZxDZvO0HHB8eTUvPbeYeeGD6KVuo0vZfm8eDuPU7VcvY/lMH6pPf8+6j47oP6BMu+mjjCoeOXE4/dQlzSmLTiGnxhYHu0JS5P5JtZR+Ss1w6GDG9YwTtPQt1/RDX7Bu8zlq+k5j+VPPavrfe2Epi6YG06+LutEDLv6rT9lx6TbdJs7jGSUAvZT5o2u4pjt4T8YR1n/wPScqfZi7eJlUB1fz5ydnM8Exj+gN69h0Xt1OLxfZ9OZn7Mp3YsKcJfxZ6bPwWV58YjoTB/fQbNJ3OJOlMp48qo9mvg00ZXSGaj9tGUntZ+5oHHL28d6bP3BW3a7d2pijE936jSBqoSaf5PMt7mlC5rFoNsk9AkTv50Q+uA2T1z/Ln+cPhwNHiM+swi1Is89LK2cxwTMXuSeBO/aw83Q+Bq5DeG61tM0fo5S+LNm9lxhp306BC3hRWv7n5ZMJqrpKlk5fA1YdezAyIupOe39qKJ3Kkti59e5AXPovH/FVTCH2gzT9Wr6yeiXLJ/lhVP/1eT3O2+p6k7+HU+kVdPJwpjz1FPHKwgJ2nM6gxtWTTtUZnPqlqe4PMiiQflc59VvBkmDNkjPZhVSZexD+VGTjgc+qUvKT5b5Fb3J2+4dsOgc+E2czTxvA14Pe98s2X2M9DdSrlpR7wwqIPp7EdcvuLFCO8TQrIvtRlCs/Nqt1kS/f2MyBa3ZMmLtYqQvyPWTplGAC3KV8U+vA6Zq+tXVAPs4z8yIICXBTjyEIgiAIgiAIgvDboQ2SzpvzKIsWLVKX3n9GpuY2L6s/twtnly6UlLRmaGcNa2sbZX8DecZA+X+LFefnkHDsGEcvp5GtLpPdyMkk9fI1SoyqqSxNY2f0L/yy5xjxp08RW7ttEdmZpzh6LImUOyOncD03i7ijx4hLyuS6vOBGFldS0km6cJFiU0dMDcrJO3+Q6EMJXE7N44aylw7t9udzqLK0xPBWNqe2/sDmXfs4cO66MlCQTDnPsXguXtV57Ph6HuknYjl04gpput+tVcr1xsXdSVvJNTLPnuBIne1Lyc86z5Ej2mOXUW1RQdaZI+yIS0Svh5wL88nISODQ/gPsO5pKljbRFJKbmcWJowfZeTCO5Kw7A7DclTaZkr7jHNTmceU1UlPSSLp8STqmBVbGtylJOcjPe09xJi2LgnI9j1Nfc+tlDeZVQ3VAs+zQ4dMkZtxdCJXXMrmSepUzmWUYGhlSkXWGn376hZ2HThJTW55V5KWnciUpkXNSBbE1k3LuSgzfbPlZutZ4rmiDY3qVn5SizDSSU1M4d64Uk46mUJHPmd37OXAqmau5JdIu1VhU53L60F5iLl5T9tFHg3mtU0aFBjZSfb/BlSPb+eaHveyKS63tu7QtbawOwxJyryZz4dJNzOVrqy7gzHdyezlMbF6ZlHE5pKVL56muofp2IVcOHSfu9FlS05KUQcdMjctI3bufHfukunr8NLFx2nMVkpWeSU5xJbdv3+Z6SoyUz+nkFeeSlpZM3k1DqipLuLJrNz/sPsjBUwm1+5YX5JJ8WWorBvKTq7cpvhqrlN3Oo5e5XtsWVKXZpF7N4OLZa1RZSfex6ylE/xSNfcAEvCyyOfzJz5ruEfQ4r9715i4l5Ejt8lx8DDv3H+NCpuYvdDfyskm7cIL9ew6x/2T99qRb7wrIulnOrbJbFFyK43SmdN7cbMorblN2M5MDx5Nr71tad9WdsjzSUjO5dP4iSVdSSLtepd89Ts/7ZYuvsZ670tJAvTp44pz+5d5I3c7NTCdZqguVNqZUXb/M7p172LU/jhPxJ2vz6npGKimpSSRkVyp93N6+lsiP23dKdSGdrKKbd+pAXhWWVtJ6qX3t3nmQ2PPpZMg3yXvMxsa2zb/bc7Pa90+Cbf28YWlpxc2b0u+rmppWf94QBEEQBEEQBKHldIOkw4KD6drVg117D2JoaCR9TrektLT1T5i29buHgZWdizweSLvpM2Ao2dnSN+pW6tzZlaysjDYFSptmSyc3K0wqS0nPvSuc2Sp2zi7YmFRTXphLfrOvnVvg5GKPuWElxRn50tfq+8eygzN2FJN1TWdk6ftMTpOjuRGVJVnk3M/MaS0rR1zszZAqQ9P5audEF2sTqpvbTi9qnb59i4KsAp0+OeW6ZgM39KmX+mu+vrdnG1OPRUPtpeG2pKlDt5tsX43VM821Nd02tfvqV3Z18+Ivm/YwvcMJXhvzLB+rW8j0Oa+iXeuNHpT6bEylTlkraaX97p9Nux/3y6bqlb7l3hBNXTBsbn/1HmLUWPtpbn17q6lRBg1zcXFr8+/2M/Et6AZBD239vOHo6KQEWm/dKlc/bohgqSAIgiAIgiDca/WDpEbGJmza8j3xJ89gZmauBDoLCuQ+91qnrd89foeBUkEQhPujsUCpIDywHuJAqfwBzNDQkKKiQhEoFQRBEARBEIRfQUNB0g0bNnDi1Bms7Jyws7NX3v5sy5tjbf3u8bsf9V4QBOHXUpCfT25uIa2/5QuC0F7k1+5NTU0xlgeBa9c/GQuCIAiCIAiCUF9jQdKjR48qy+XP5fLnc6V7rPtIBEoFQRB+Jds/+Cf/8+a3xKjzgiDcP9XVVdy6VYGVlbW6RERLBUEQBEEQBOFeaCpIKi+TB3OSP5fLn8/lz+n3kwiUCoIg/EouHz/EniNn74zaLgjCfVVWVqq8fq8ES5U4qQiWCoIgCIIgCEJ7ai5IKi8bPny48rlc/nx+v4k+Sn+nXn+ur/rTw+PZf5xWfxIEQRDaxUPcR6mWiYmpMrJmdXU15eVlVFXJf8EWnz8EQRAEQRAEoT3IQVI5WDpmXBizZ05j05YflCCpvGx82CQenTWdbdt/4WhsPJWVFeperdfW7x4iUPo7lfJjuPrTw6NbxE/qT4IgCEK7+B0ESmVysNTc3BxDQyOqqiqkD2iVSsBU7kheEARBEARBEIS2kYOlZhbW9A/ow6mEc0p/pLerbmFl40hv3x7EHj/VLkFSmQiUCq0iAqWCIAhCs34ngVKZkZExJibG0oc2E+njh6E0GYiPIYIgCIIgCILQzqSvGNIkfc+ouU1VVSWVlVXt2i+pCJQKrSICpYIgCEKzfkeBUi05YGpkZIShoRwoFV25C4IgCIIgCEJ7kgOkt2/XKF1f3YuBm0SgVGiV6WPd1J8eHpt3Zag/CYIgCO3idxgoFQRBEARBEATht0sESgVBEARBuDdEoFQQBEEQBEEQhN+Qtn73eKjeKXP18cJV/fn3ZtC4SGaPC1Dn2pdXcCizJwbipc435tfNf3u8fDxwUOcggNDpkYT2V2clzaVb3+tqDYfBoSxZtpRVK5ayJMxDXSoITalfp+8lL0KiFij1c9WySEaoS9vH3W2xQY4e+HjaqzMSr0Aip4cyor0apKsXPr/XXwiCIAiCIAiCIAhCqzw8gdJRC3jxmRW8uGyMuuAh4ehH6PQ5rJQDGtK0cmEkof46wQXV8ClRLJoSqM61r74THmHRo2Ppq8436FfOf69HFkvnW8mzUX7qkkBmLIhiRrA6K2ku3XpdV2s4jmHpsnnMnzyKkJHBDPM1UlfcD5qg1eyGpnsUWG93SgBNj8Dbb9zddfreGTTnMf5t7mQiRgcTEty3nf9YcHdbvJsHs5et5IXnljFXWw39xjJ/wSNEtMvlD2P50yt44dlFPHy9MQuCIAiCIAiCIAj3ysMTKLVww8vPj55dzdQFDwGfCFY9v4wnF8xg2rhgQkaOYtqsKJ586ilWTa0b2nDs7EKXzncHUFtmII+tep7n5wxU5zXsOkrHdu2InTrfoF85/4s6uuPr14PuzrfVJfa4uLng4qjOSppLt17X1RqjQ5ni35Gi4+/xxrr1fLgjS11xP2iCVovmzWT+7HpTxAB1mwecEkBrLvD223d3nb5X/AifEkgv0yw+fHs9b7y3jQPqmvZxd1u8WzGdPXoQ4ONBJ23nLzYd6SLt52yjzreJGV27+xHg1RULdYkgCIIgCIIgCIIgNOfhCZTGbeav/+/v/N/GWHXBb10gS56Yw+yRrpQd+RevvbWeN9a9x2vS9ZV1CWb2Y4tZEqRu2m48CQ4PY/IwT3W+BX7l/M/f8Qn//f9eZ80PV9UlDxAfZ+wMq8i/dIA9+w9x+PxNdcX9oAlaOVWe552PPqk7fdu+/QXeM2oArenA22/fr1envXHvYAbFmWzbd4g9h06TpK759RTww/rX+c9XPuC7e3LyU3zxxt/5z9e+5pC6RBAEQRAEQRAEQRCaY2RqbvOy+nO7cHbpQklJsTrXctbWNsr+LR7MqcyHEWE+2JYeJEEbZ3AdyOyZk4gYP4pR/XviYVVJekoe5epqXSFRSwnrXk6SqT+zH5H2GdKZqmNGBC6eSpiPOUfPpKlbQkDkAuYEu3HzxGVylAURLJ/hj01sFnZhkcycHEpYkC/d7CtITWz4fM1xCH+CV+b4U3Z8Hf+z7mcOJCSTcjWVxKSrpBh4ExIygH7mOXy3M0k5fsishfhymXWHbinXHDk+mME+nSHnIun6FIf8enPIIIJH9qCTIVh28CTIv7NyjS6T5jDMqYDD36fhMmMS08JCCO7TFbuKDBJz1atrKP8d/QiXtp88PoTQob70cDaSkpNF/eTIeT9tgD15p65wXV024pFFzBjmSlX8ZbRDdXiFzWHhOG8s4s6T4jGA0H5uGOUc57xSCME8+oQ3XPqEL/cpm9NPm+5PfuakZlEdtet/ycdHm2d9uuGge10qB/9RzJgUyqQmtpHJ/Z6GDg1klLstVJnT0asbFrHn8ZSvMVCTn0pyZaOms2qir7I+RVkwjLkrQvCuOEVRt0jmTgslYkQAvi4GZJ6/O9/0o8kX86RoXnzzey4nJd+Z0vKl9X5Mb8c6ruzX34qjmTZK2d9VV4ZEsnLKMDyNT3I2Xd1JIV97GAMccjiVVO9K/SfweJALZTplW0tJ13C8dI/nNYYlj46jr10yp5K1KQtk9rIIhnYu5cTla9J8JwaNm0Bk2GgixgYxoGcXLMqkcpBXqVyltM6dMqZ2vW1VFkkNlLlC514THODPkMEDCZIntQ3JeVhbhyaMIqh3dzob53I+o1Szv+yuOn3nvhRX3LXpdq3H+TXkrhiGMCzIm45G1RjautDPRTr+xTxpnT0BIRFMmzSOiNED8fd2xjD3cp3z1JZvgbN0vqlEjumJTUYCiUXqBoo7bXFfeeP1uOvg0Qx2MyTjmJo+pZw7kHf0S7YlKJtImi+nhu/dJ7EPHkcfu2J26tRrOZ+mzwhjUmhIw78TlPo0Gh/L+Dv3MbWNjHLX5pNEz3vbw8TGxrbNv9tzs+o0+jZr6+cNQRAEQRAEQRAePm397vHwBEqZwuq/TqJn2Ua+PSzPB7Lk3xczd6QXNkbmOLl7M3iwF2YZu4jLUHaoI+q5vzCxqxEdh4wnrK8H3ZxqiNt6m/CX5xDqVsoH3x1Tt5S+mD+xmseHWZP01W7OyAtGLOTVxwbgZNGDMRED6eXlSa8+/gT4umOa/gvHWlE+E59exUT3Qvb91z/5LFnnicTKErLSShgcNg6/zsYkf7GLC9JiJVBqfRu7Hv6M6+uNdw8fAvr54ud4k5MHLtcGIBs1YApPT+1PDzc7LGsMsXd2w9MZLn9/GCs5oOhsiJFrbwKHeNPTswf+Ab3p42FKxk8nSFUOUD//PZi+cgWPRwTgYmqCtUt3Bgzug2vZafZerls/Bi96gaVjOlH0UzTHlUsdyJP/uYxJg92oOf0D+9Uoz8xnXmZ+gBEHv95DopLnQ7BJ/oafzslrWxkodbXAyqMHff2lPOvugY9/XwK8HahIjOG8GoxxCJ7Di8umEdqnE5am9nTpE8CwPh3rbKM1cMYSZvXpioudmVR9bXHuYsG1b/bgLdWvR/qb3qkzspkr+eeU7pR88iMHlQVyHo7F19aFAaNH0t/bE2/f3gzo441j+Rn2XqgTjdKTJl+sMuP4cPtZdZmuYaxoxzqu7DfIEXOfYKY0VFdMQlj13GQG22SxcbfOk5NhT/D60kFYnN/C9jP1gpFNBUqNw3j+TxH0M0vmq71qw572b7wRNZy+drl8uuOSZtmQBfzvH0Jwyd3ElmP2Ut38A4siB9Onsy3mzp4MHNSPvl6W5O04pQlaB83h5eXTCfGyxtjMEXdfqcx7mnH151Pc1Zy9Ilj1VBRTB7lhZWpNxx59GT2kL16uTrh20LShfLUORQz0wN7MHs+Afgz298JZaj1HL6it8646rd6XvK3oMmB44+1aj/NfVo4mG8miVYH06uyIlaHUzjt0ojPJbD5wXTr9ClbOGktgdylP7N3pJ+VJgHcHaq7G19ZzpXwDrKjpNYrZQ73w7GpO3re7iK3z0LSmztlU2+Ldv/F6fFcduytQ6tZ8OUkavncfJFBucz106rXclcnKKGaO7I2blQXO3gEE9etFd5sCjp/M0ARLlTIYhVvtfUymaSMjbVP4LPqiNK//ve1hIgKlgiAIgiAIgiD8FrT1u8dDNOq9BdY2VlhbqrNBY5k52pNbJ9ez9r2PeOu9DXy5I4b4O49W1WFpbYVTryEMMTjG2rfW8bd3v+cUZljKx7Su28uduaW0zMYCc3UeU/ncbvQOsiTh809Zs2Ytr2zJwtSzPxND6/b3qR97+na2hVu5XDnbwGvb+fFcyJa+1ts701tdpLBywLdoP++/v56/vf4W8WUd8R8Xylh1dZMu7OPLz7MpM4DynONK35pvfPSjlAcqMzsczS/w8foPee2NNXydYYzXoDFEDFHX189/RhAZHoBj9r946/2PWPPuBt76bj+nr+o8Qac6klKEsVMPBgxTF3iNoFfX21QadCNgjHbE+FEM6OFITcEF4uRZJc+tsDRVVraesTEG2jx7Yx1//+kajgNCeWyaNjHDWLRoBmOdsvniHbkefcTb6/ZT3j2Ux6PuHrjqwq6tfJRRIv1UTkas3F3CZvZIc3L9qlNnZEo9kq5BndXkYQc8fbwp3v0Fa99ex6v/SKC4oy9h4W0bl9zYplvdgZxqR/tv3zqu7OfuQ3BjdSUlmpg8I9wHTSBS51X60PG9cTEo4NSBQnWJnjJiyK6wxcNneO2ARKEBPbCurKSDz1BC1WVeI3vgYVNK+s+50lwp16X/Lv74CWuk+8Kat97lwws19AicxrQwzfYjwiYyxquC2HXS+nelMn//O7YeSdB5MvOOgIlSfgY7kvH1R6yT6se6d37iarUV9lVJahvS1KEJ3SqJfns9/3znfd5Yu4kku0BmPzaH2dqEN1CnlftS915Ntuvmz6/rON99foFr1dKPN6T1Ujt/d9s5qXnNZnlUCJ4Ve3l97XrelNL5+pdXsQucwuMLImvzVilfj/7M9Mjiq3fe429vfk10gbqyHmv7puvxXXXsLs2Xk6zhe7fa5mrrtQezF0h5HWhL0pdS/r/9Af9c+z7RFV2ZEPUYi0apm6llcOc+JlPbiKW2/2X9722CIAiCIAiCIAjCb8tDFCitx8oSU1NjLO06YXojldiYQ2zdEk2M9jGkBpjUXGPbuk38tOcQe46cRX4xWX81XD/9GZ9Ga/ql3LkpnvRyYzq4+arrW6IH9vL3+6oqbmkW1HOTkltVSpCvztBJxcm89el2ouV+B/ft5Z3LBWDthHfdcZ8alnGWA/vzKJN+rCxOU65BzoPaGHxVHsc+VPNm/wG+isum3MQRt0ZHqLbA0swQM+vOONTkEB97hOit29lx5u5AWPoPl8iosMErSDNqv0OIN25lKRzJrKGr/wgc5IVBo/CyKyft9J4WlkszKnKJ+1jNM+mao7/8icQbVnQbMoah8vqQ8UzqbUn6wQ1s3nOM2FMnObx3CzHZRnQdNFKzjY70hGPsyZeD21UUXZWPeVLnaT59GFOW8REfbt2rKYOdn3BRLsaOnm0amdzUpX/dgZymjablg4vrW8dvcLrRuiI/vZhKmUNvJo7RDj42iok+dlL1Pcz2Fv/RJ4H9yYWYuHoxVgm8BhLqaUFG3AXy7XsQqvTja89YXxdMriXzo/KAaSExmzeyYctudh5JpqisiBvpBZQa2+OujqxvYWmKiZEFjq7GFKVJZb7vJzZtj+eKZnUdvj1dsK7M5dSPcv04Teyh74nJLMfEpIpcuQ0pdciRvPh3pbw7xOH4ePbt3M7bO69S0yWIqRO0fwxomEl5RpPtutnzazZTJRO7P40i6fZBeZ5SjgcSsgiNGEcfh2scWyOV2744YpX2uoHoq9A1OJxJupXP5Cbn3/qMzbubrt9FV9paj5svJ61m791e45ka7EbNlV28LaVpX6xcprv59PMz5Dn0YlKENlKqD/3vbYIgCIIgCIIgCMJvy8MbKD25iY/3JGHQazYrn3uWvzy3iMl9bNWVjShOZ+vp1obhqijKunjnibP8QsoqwdS6NSPQZFAuBzKaYGFsrP6ko6yA2Kt3nkC9UCL/bIlNyyNid6u8QdaFO3mTf/0mlZhi1VFdcJddfL7lOPkdxrF05dO88sJSHhvhWTewq3XlO87mGuLkM5oAaXbMQA8MM8/zUXI+lh79kJ/b9BrhgdPtbM7+0M4jyFfdpEAnz8g9QHYxGNk64i3PD3ang5TXDgEz+Y+XV/OKMi1lSCcp/y1tNdu0s7KCk1ypfTgtkWL5nWBrG/pqFrRKZX5C3YGcvth5pwsAvelZx5upK6k//sK567b0GR+mCZqFjMW/ww0uR++6+7X2Zt1kX1w6xRYeBI6WZgNG4+NUSPKPchC3Az5j5Mo/lAFuFlxPPkK8so+UxzYejHpsFX956Vle/LeFTPOyxUhabmSiWR//xb+ITgS/OYt58cXnWb1sPH3tNOvqS8y7QZVxB9z7qYFfR39cHYypKiuiQJ5X6lA52RdP6jyRepML36VSiAVuPs38MaWZdt3s+fUw2N0Ro1u5XIrXuf+VJrI5tQisOuGrew+pyOdcTIryR5WmtEc9bq6cajV37/briZsVFKbt4ILOg585RxLJvmVIB3f5zqOvFtzbBEEQBEEQBEEQhN+UhzdQmn+a79a/y/++/i1Hi2wZMHoyi55cycow7VNsDaiubN+nFSVGJq35+pzFuRzp27y5E93rPTmlEUDXDqZQUqTHaNUmmNioP7Y7Q0wafW82mZ0bP+TVv7/H9uQavIImsGDxMp6d01BAIpEfk/Ix6ezJKMdRjO5mRsaFaC7sSiHXpjujQ2BsTxdM8q/yr4Ye6WtXBVTKryUbSGUnz5obS//e4tr54xyNvTN98dbf+dNfN1LbjeG9ZmxMI3E6vVQWJLL95513pj0NPx3ZGvrVcZ26kr6PjSdzsPMZRZgXhIb70qEkhR93tS4Inv/LWdLKrfAcEojXGG9cS9M5ePIgZ9KrcPULwUtebltO+tmjmuCeVMeWr1jEvFAf7PLO8MO2HfzrX5nIHSZo5Z+M5p031/Dq18cpsvVn7LQonn52EeEN/N3j3Nc7OF3sTNATT7F65ZP814sLGGKRwY6NO6SWLFHqUBWVFcrmd5RVUyP9Y2LazB9wGnSnXTd7fj2YmUi/Chp4gr24Uklh3XtIzW0q1R9brCX1WI9yqtXcvdvGWLoKKelVOn8UkZXepLKqpffpltzbhHvNztYOWzGJSUxiEpOYxCQmMYlJTGKSJvn7QVs9vIFSSdHlk+yJ/p7PPlzPX1+P45bXMB6NGK6ubS17bMwaeJqzne3an8x1E2eGTBmlefVch8/UaQxwNuT6hb3sUpc9iMoyz3Ng506+/GQDr72xkWy7vkRMGaM8NVpf/KFUrpu54DdpFL1sczn7bQqcOEJykR1eo0Lxd7XgeuoxZeCqe8oxFFc5GFaYwwF5/koRxRhTU57Etu3RdafoOD0C1Y3zMW+8d8b7617X8QKOfBpLqokHw8ePIrSXIzcu7WVna/9KkbGDs1lVOHoMJ6y3GxUpx9lXmsWWc9kYufVkVLDmaeQLP6nPVw4by6zALpQff593/vU923bsZqfa7cQdN7h8/BA/bfmWd9et5f9iK/AeMZVI+anVesqcO+FoUkhKwjVsu7hinXeUD/66lvV7kzUbKHXIHOfudWu+wxhnnKR/C7LvDKLVGs2eXw9JBQ39YcaeCZ2lxnC7kMwj6qJfk17lpKcjORTcBqfO9e6n/bvhLDXD4oIm+mRxtMai3m/KltzbhHvL3MISCwsLMYlJTGISk5jEJCYxiUlMYlK+H7TVQxso9Zq6lNUrIhnqepP85LOcyS/htoEhhsb139lsyjEy5W7nHD2Y3b+T9K8f4QuXMdy1NdkWyJIXVvPKC3PQZ1ie9F8+YndSDZ2HLeDZZRGE+Ejnd/UiZPpSnnpsEE43z7H9vX0telXZK3Ipf3l5NavnafoCvVu+8nqsuUMXAhzBwbXTXUFavQXN4flVcwj3t6coPZHYxCLKasDMyIh6A8Uryg4cJ6XEhq4TeuBUnKrpS7L0KEdTbtKpzyN425eScvyoZuMmGBs28cRwQ8zdGDpvFINcpZ9dBzJ3WRS+NuUkHd2qCYLu/k7TH2nIYpaH+an50YlB4RGE+ygzetEEojrRJ8RDOkYnhsrl6NeaR309mL7ieV75z6VM17+zxya0Zx3XX9nZLUQn38YrZDZ9nG5wYfeBNjzNncK/LuRi0rk3Ia4GpJ3R9GOb9O0lsszdCQ90wST3Ev/SxsIcbLGSu66oLifn2k0c/Mew5LneOKurNXn8B1ZNH4hraQFJCZfIK6rGyNAIs4YGDxvsh7vlLTLPHeCjzzay/otv2bQzjiTtK95KHZKq19D5tXXIdch0nprRHcuSJHZ/3USQTh/NnV8Pu76NJ9PAmeDFC5Q2K9fxoVGLme1tQfGlg3x1z5/k1qGtes2WUwtc2cruS6VYek/hqSipXKVF8vGWLx6Mq0EOMd8e1GynBlSdu0Yq9wTlnP8WiJvu3w2aubdp7rPPsyqy6b5nhfZRVFQoTUViEpOYxCSmh2AykP5raLmYxCQmMYlJTPpPbR874qENlFp17MHIiCieloOTL6/mpaeG0qksiZ1bmw+23ZHCV0cvUmzRnUVyX3Srl7Ek1I0Lqa3J+D6MnRhK5MTh9FOXNCn3JJ++vp7tqbaEzJjDvz0j94W3gn9bMIUBplf56b332HhS3x4INZz8g5kopeGRqWPvGoRIYw87T+dj4DqE51ZLefbHKKV/0NZw6OjOgJApLH/qWU2/ni9E0cs0h6M/fd9wcDd/D6fSK+jk4Ux56im1L8kCdpzOoMbVk07VGZz6panrzaCgRLrGfitYEqxZcia7kCpzD8Kfimw84Ft8C6uIOTyjBLGXsHB8B4oOb+avmy5q1mccYf0H33O6pi9zFy/jJaWP0qd5Zl4EIQFumm30sGt3PJnVzgxZulI6xtM8PX0g1ck5yN02tow7QaFhRE4aS1A3dVGbtGcdb4lkNm9P5pa7N52KE4ne17K6XN+F6GRyLd3wtM3n4vfqC+eXdnGpwI4eXS3ITdp152nkA0eIz6zCLUhTni+tnMUEz1xyagvDEmfvoUTOW8KflfJ+lueGd6Tk4n6+Vh4zruf4RTIqHBkwI4pFM6eyaNkyTX+2/7mKleFetXXoRKVPbR3685OzmeCYR/SGdWw6rx6ntZo7vx7SD33Bus1nqPabprZZqY7OHY1Dzj7ee/MHzqrb3VMXcyioMsdrwpPMlp/qbracWuIim978jOgcOybM1ZTrS0/NZ65fNSc2f8z6Q2qdubKVIxdKMe8RpdwTXnpqDpPcErlapFkta+7e5jlkLFMmhhExxF2zg3BPlZffFJOYxCQmMT0kU01NTYPLxSQmMYlJTGJqydRWRqbmNi+rP7cLZ5culJQUq3MtZ21to+xvIM8YKP/XUxHZmac4eiyJlBzpy1NBLsmXE8kzkJ9Kuk3x1Vi+2fIzO49e5noDHexdz80i7lg8F6/mqUs08jLTScu+QUV1FRU5Z/l5x05+2X+CMydPEpeUyXV5o5JrZJ49wZETV0irjS+Vkp91niNHtMcso9qigqwzR9gRJ6VLs1GTrmekkHz1KufPZnHT1AjDsgKSYn7k2+37pDSc5qrOdTSY/ut5pJ+I5ZCarluFeeR3G8Bohxvs2rirgT4qC8lKzySnuJLbt29zPSVGuqZ0kvNzSIiLu3O9MuWaj3NQze/6+W9YnE+qlP8Xy0zpYAZV1y6wecuP7Nh/ktwGn3QrISczi3PxMezcf4wLmZqeCG/kZZN24QT79xxi/8n659fN8wKybpZzq+wWBZfiOJ0pXU1uNuUVtym7mcmB48l39atYLF/X0YPsiU/ihpEdVlXZxG3+ns0793MoUfuCbxV56alcSUrkXF4VllaG3C5NY/fOg8SeTyejoIGoTb18l93IyZTy4xolRtVUSvvvjP6FX/YcI/70KWIvp5GtbFU3D7WUsj16TM3/Sgql+nzrygm+23eWzGabmuaYhw6fJjGj4eBn+9VxNU+brSsaRZX9eWSmN1Xx7/L3zamNv1LdyP51XMsl9epZjsijuMemq+2rgOz0NE7EHOSXA7HSvuoIaYU5pKYlkV5ijKlxGal797Nj3wH2HT9NbJx8jgquZady8XIBho6WGN4uITVmO1//cICDFwvvqkcjwh4lwrOEvb+co9xMqh9SPlZVW9Fr5FiCexiRvvkAR7V16AbYSu2h8EqMcj/6eW88V7QdgzaQx/q06+bPf6ReFxF3lxuV10hNSSPp8iUKDWwwNbjBlSPb+eaHveyKS63tu7TB8r2LPvW4gWPdyCfjVjm3b94k9fBJLuU1V07yTo3kkaT+8qLMNJJTUzgn1QMLqR3fyjqnuSftiuFMgXb0vGtkpGWSdqOC6qoK8hL28sOOfeyJu8DxOE0bau7eVlFkRE3FFY7+cISTWQ32qPqbZGNj2+bf7blZLR+urSlt/bwhCIIgPFisrKwpLX14fncKgiAI90dbv3sYWNm5yKN1tJs+A4aSnZ2pzrVc586uZGVltCJQ2jDLDs44mhtRXV5I1rVWRpatHHGxl74Rt+UYEjktdhS34hgWOLnYY25YLSUhl/wWvFJbh88Ynn3pGSblfMisVd81+qqzNs8qS7LI0XmSqlXsnOhibQKVpaTn3lAX3iNKORlTqZNHds4u2ND8uTXXfJvijHwavWS1Hhi1+lps6eRmhUlb80LOU7OK9s3Pdqrj+rNnxIrX+Pt8aw78aRHP7/k1zllX82WuT7sL5NUt/8vw9Nd49D9iqbaW6oey3IJZr7zN4m5JvD1qJe8oyyRqe2jT/aiOFp5fD0qbMWnjvaa1lPyhTpno1TZbQo92rDmn3ByayING721yOzelvL3Se7/V1CiDjrm4uLX5d/uZ+Ja80dG8tn7eEARBEB4szs6dyc3VPD4gCIIgCK3V1u8eD+2r91pl13JJz8hqW1CitICsth5DIqeldce4SX5WlnQdbQlceDB73iyCCw+x9qOm+4PU5lmbg6SyonzlWO0a1GuMUk5186goV79za665mcCGWg9afy03yGmPvJDztL3zs53qePPsCZ2zlFWrVrAy0o2yU9/z8bF7fc6GNV/m+rQ7C0yMjDHr2Ifgvp2xrcnitrUrfiPGEtAJcs8dYqe6pUJtD+2Xzy08vx6UNtOme00bKPlTt0z0apstoUc71pyzmTxo9N4mt/OHJEgqCIIgCIIgCILwO/MQvXovNM0Is9vXOH1oH/tO59z1+rAg/DoGMe+pqQzr6UjVpQNs+GAr+9NbNY75A6KY3HIHPHz9GNLHlwGDBzBs6HCCAtypPvMDaz6N5njOvby++31+4fdCvHovCIIg3Gvi1XtBEAShPYhX7wVB+A1xZ0BwV+yMKinJTuPspazG+yb9rbByx79PV7rYW2BibAjVt6m8dYOcKxeIT2nnJ38bcr/PLzzcxKv3giAIwq9EvHovCIIgtIe2fvcQgVJBEARBEBomAqWCIAjCr0QESgVBEIT20NbvHg99H6WCIAiCIAiCIAiCIAiCIAjNEYFSQRAEQRAEQRAEQRAEQRB+90SgVBAEQRAEQRAEQRAEQRCE3z0RKBUEQRAEQRAEQRAEQRAE4XdPBEoFQRAEQRAEQRAEQRAEQfjdE4FSPXgFhzJ7YiBe6jwEEDo9ktD+6uyDziuQyN9Ieu/O64eUUiahjHjoL7QBjh74eNqrM/eGq48XrurPrdVudbGBsh40LpLZ4wLUuYZ4ERK1gFUrlrJqWSQj1KWCjl+hHj2wfkP3dEEQBEEQBEEQhN+ShyNQ2kXzpTEy2ENdoOo/itkNBKOUIEV4IF3V+eb0nfAIix4dS191HgKZsSCKGcHq7IPObyzzH5D0KnnfRPDp7rx+wDlG8vzLq1k9x09doCelTB4hooW7/fZ5MHvZSl54bhlzm4oTtsWoBbz4zApeXDZGXdA67VYXGyjr4VOiWDQlUJ2726A5j/FvcycTMTqYkOC+D/8fDlrsV6hH7UH5HdTA76a2eoDu6YIgCIIgCIIgCA+ThyNQWtqLsNlRLJ06WF2gMWLibBYtmMfsEN2njvwIj4piUUQfitUlzbHr6EIX147YqfNgj4ubCy6O6uyDzqYjXR6Q9CoBoiaCT3fn9QPulh+jJ4Yy0u+6ukAyZBbP/+lJHhuizjdELRNnG3X+YdRgPhTT2aMHAT4edKpRF7U3Cze8/Pzo2dVMXdA67VYXGyhrx87SsTs39jSkdI+aEkgv0yw+fHs9b7y3jQPqmt+l+1WP2kHolDnS7yDpd1PU+PYNdj9A9/SHgYFNL4y6zse4xzMYez+r3yRtK+8j7ysIgiAIgiAIwsPj4QiUXj9HtbULnj19ufNwkR/j+3nTxcWdQQOHq8tk/enZ0wU7o+vohLaEX4kSIPotBUKbU1pNtfRPZUWWZl7WPZDJk8cT3F2d/71qMB8K+GH96/znKx/wXZK6qL3Fbeav/+/v/N/GWHXBb4037h3MoDiTbfsOsefQae5VVv0m3K961GajmDioB6blFbgEBDH1QX7y9XfMsGMIxu5zMOwUhkGHERg4DtdvkraV91H2lY4hCIIgCIIgCMLDwcjU3OZl9ed24ezShZISfZ/VvJu1tY2yv4E8Y6D8Xw/X6T5uBoPdq7n20XZi5EWOkSxb6k1xWhmdOxqRsHEvqfLyITNYGdmFa4fe4l+HS+Qlkk4MjZzC1IhxhAX54+tqQOb5rNonTvtNmsMwpwIOf/IzJ5UlwTz6hDdc+oQv9ykLmhUStZSw7uUkmfoze1oYU8YOxt/TlqLTV7jmOpDZMycROT6YwT6d4epF0m9C19BZLJ4QgENBAom6Ud2ACJbPGI6X4UnOZqjLdPkMY+6UMCZpj2fYjbBAF8rqpNeegJAIpk0aR8SIAPy97LmVfoUc6bwa9ngFj2F6xGjCxo8iqHd3nAzTuJhZrq6vd02PTCJiSGeqjklpV9c3JGTWQnwts3Xysq7avP4+DZcZk5gWFkJwn67YVWSQmHvn3LgGEDpxgpRnYwgNlsrMw5ziM2lck1Y1mm/9pXybrptvTZd7QOQC5vS34mimDeGNpsWHSQt8qDjyBZuPa/q1DB0yiLE+zhhXm9PRayD9XMqJu5inbq/yn8DjQR3IO7qbXO8ZzIwYQ8hAX7wcK0hNzEPnSqVrHcj0GVJ5hkrnD+iJu2G2lP5SdaWGg/8oZkwK1ZR5n2441EmjVB9WhOBdcYqibpHMnRaqlLmvS93rvUsTeXyHmofjpXoyeqBSp+V6ZD2g8XzoOng0g90MyTh2GTO9y6q5+qqjzIcRYT7Ylh4k4apmkX5lWZfedbG5tNWW9ZdsS9AsUtoBl1m36bBmQS25/+MhDAvypqNRNYa2Ljr1R+c8cl57O2OYe5l0nQKsvc4CZ+meMpXIMT2xyZDytkjdQOU6RKoHU8YQMTaIAT27YFuVRVKd9nWnzo3q3xMPq0rSU3TrpbZOXcW0fyQzJ4dKbciXbrbFnEq6rjm+Tj1LkupZnVzWo07LmmpPuvUoR95YuS/6YxNbQOfp2jbVE7eaq5zNNCcg7O506mq6DbVCyBJemGzNuTXxGIzuh0dlPF8d1r0PtKBd6nVPv7dsbGzb/Ls9N6up3w4t19bPG/LToMbu88HSU8pTE6ipkRZqH1GW/21sui39I/1raAymHTC06EJNWQpU5EvrBEEQhNaysrKmtFT7/UwQBEEQWqet3z0ekkBpFfkBYczxs+PGua/5KU1aFD6XPw6r5sDPRXgN6sTtuC3skb9Nz3yUVX2NOPPuh/yk5JsX01c+weIpQ+hpZ4Klc3cGDA7A2zKP2JMZypf79giURj33FyZ62+E+eDije3XDq6cvA/r64Gpihu+U6Uwa6IWXpw8B/Xzxtr3O3iNXqOg1k+cfH41nRQxbY+9EOgIWPMe/T+5K8c/fs1fnQUaFTwSrVj7KzOG9cHdxo6t7d/w9XXBzsuBGbXrtGbFwBStnjcK/swXmtq70HTiIPp0ruXjoshIIC1n4LMsfGcng7h2kMnGR1g+gX28nbp85wlk1vqBcU1cjOg4ZT1hfD7o51RC39SBXNKsbpFeg1NkQI9feBA7xpqdnD/wDetPHw5SMn05ogt1dpWt8NopHAnviam+Ok2dfAgf60dPiGj+cSMNw0Hz+tGA4XculfIu7k2+DFj7H8xO1+dZ8uYc8sZrHBzli7hPMlMbSgh09+tmQc3Qnh5Jh4IwlzBrgiZuDhVR9bXHu0pXOJLP5QL1cUYJnblhYu+Pbxw/f7p509/FjgH93nCoTOXBBzWSlPKOYOcwLWyMLOvXwZ8jA7ljkneJEmiaI4xA8hxeXTSO0TycsTe3p0ieAYX06UpEYw3klqjmF1X8di6+tCwNGj6S/tyfevr0Z0Mcbx/Iz7L1QL4omayaPNaQ8XCHl4fRgArrYYuHgTr9BAXQ3Tie7W0Sj+aDk6zBrkr7azTm9yqr5+lqXfL2T6Fm2kW/VOKR+ZVmXXnVRj7bUskDpSBatCqRXZ0esDA2x79BJzbfr6nnGEtjdFnN7Oa/7EeDdgZqr8Wo5q9cZYEVNr1HMHuqFZ1dz8r7dRaxuQDloDi8vn06IlzXGZo64+0r1pacZV38+pfkjh7bOjeyNm5UFzt4BBPXrRXebAo6rbaO2Ttm5MXR0EL08PejVx58A386YmPdgxqxQAnt40F2pZ55YFx7hUKIadNSjTms11Z5069EZeeMRC3n1sQF0tvJldEQAPbt50DvAH/8e1hg5DmPu9LrpNEjaw6ls5TR6tKGWC12xgikdUvnkr59jPGEaAzpVEvNNjCaoq9CzXep1T7/3HsZAqXG3hRjY9JZ+kj5ryB83lM8c8os2ykzTk/yPQvrBxA4DY0tuX6vfnoXfBnu8fDrBtaK6f9ARBOFX1y6B0i6Dmbd4HnMjxxExdjgj/Tty6/J50hr647ogCILw4HlkBa+HdebnI5fUBS3X1u8eD82o91fis7mOHV2CNP3+jRjSFeuCNKK/SyHHyJmew9XlXTtgUpbLBTVg4fXIHB6f4YfhwQ9Z8+5H0vQhnybbMjJqNlHt+KqkpbUVTt174pG5g7ffeY9XX/uJdLNujIiazKCqo7z3znr+9vqnnKtwIWDcGEZJ+1yP2UViqSN+w8MZpDmMxIOwft1wKLvKtxfVRbU8mL1gDrMH2JPx87v87Y11vPH+F0TnV975TicbNZvlUSNwytyiueb3N/DPPbfwCpvDvDDNJum3b5K9/3PeencDb73zLn/fkYdTn3HMnHhnRBrlmnoNYYjBMda+tY6/vfs9p9R1bWJmh6P5BT5e/yGvvbGGrzOM8Ro0hghtH4UlRZCfxNYPPpLS9xFvrPmS87e7ETQznHBp9fWDMWRVS/k2JESnX8AAwgfcyTd9yt3c0gprdx+Cm0oLCXy7/hO+VaO+F3Zt5aNs+ZNYORmx63lj3Xre3XZOs/IuxpgYFhP98QbeeHMtf3vzZ/IdApgcNQXNi5xqefobsmfdh6x97yPWvrOJJLuhzJ83Re1mYhiLFs1grFM2X7wj5Ye0zdvr9lPePZTHo7SDGVlgbdMBTx9vind/wdq31/HqPxIo7uhLWHgj46k3k8cyJQ8fCaRT/m5ef3s9b0rT6//aQ2yq1L6ayAclX20sMJd+1qes9KmvdcnXK53DUp2V6FeWDWiuLrY4bc05znefX+Ca3J/DjaQ7+aacJwTPir28vlbN6y+vYhc4hccXRNbmnXKdHv2Z6ZHFV9J95m9vfk10gbpSNSJsImO8Kohdt0FJ89vvf8fWIwlqAE+tc4G2JH35Pm+8/QH/XPs+0RVdmRD1GIvkG5NCW6fcSN/+CWvWrOWVbVlYdg9k/tR+VB78jDVyPdtwiZsuvZk4fqi6nz51+g5965HCVE6TG75DjYj/SKqzUpt6N+EWnf1DmTOuE5e16dyZp6Rzcoh2gCV92lBLjWFKgCM3EvezrzSRL87mYObWj4l1LlCfdqnnPV1oFUP7AZoflACpNBlIH4n0mtRtlX00JVF7rBaQn2Keu3gpq1bI0yKWRA7EVV0n/Hq8HlnMi8+s5Nmo393oioLwEApi6arlLJnQH3fzm1i4DJQ+V89j1ROhiG69BUEQHnSTefHVl3j98elMH+OvLrs/5E/6D4ej50kvM6aTh/yF3I/xXg4UZ57l1JU9JF4zo1sfuZ9SD0Jc7SA/hR+Vtzw9mDQ5iK7ll/hw4272xJ4mPvYI2z5IpKhDb0YNk7dpPyblabz18Xai5X4H92zhVE4VZla3OPXhVn7aIy3bFy0tK8fIvjPyMy7kx/FNQg6WXYcQ3l85hPSJfjwD3c3IPP0NCfXfVJXWTQ12wzjtMP/8ci979svH3Mvm07ncUjeRhUaMo49VFgfe387OIyeJj49j59fxZBm7MWzUQGWbyzu+5oNvdhK9P46M0jJKCnIprLCgc09vZb2WSc01tq3bpEn/kbO0y4uHVXkc+1A95v4DfBWXTbmJI27a7zAF8Wz69Cs2/XKAA0lFlJSUkFZQhlEHN01AOWMfP14qxNJzCGHaCFL/MQxyNSI1Ts63lpT7DU43lRYKSDpzniT1wtMTjrEnXw7sVFF0Vd7nEAcS6j/2q1VOVtznbN6p2W7Pzm/ZkngDa49AwuRAnFqeZRe+4tPoQ8ScOk3MoWjeuXwDJ/+hSjCdkPFM6m1J+sENbN5zjNhTJzm8dwsx2UZ0HTQSbXhKDsqWZXzEh1vVerHzEy4WgHVHT50ApY7m8ljbdgyu8tPab5V6EivlYfTWrWw5kKZ/PjRbVvrVV/00V5YNaKYutl/atJKJ3Z9GUZX0Y3lebb4p53G4xrE1Ulr2afN6A9FXoWtwOJN0C9HkJuff+ozNu+U0n+SyuljLwtIUEyMLHF2NKUqT6su+n9i0PV7zJLha52qu7OJtqa7si5XX7+bTz8+Q59CLSRG1kVKJVKfSP2K9Wqd2fn2O7AozrMvP8d43u9kp17PtZ8m+ZYi9S0/NLvrUaR0ta0+yGq6d+kQ69gFl22270ig0tMUo72M2aNO5MYVCzHD28NXsoncbaoGwUPo53CQl5oByTzy74zyZJi4MCa1f2Zppl3re04VWMtaOsKYNfGqCoAbSz41NFmaG9Pcyw1D6uTZYKqs9ln6Up5ifWsDCaWMJGRlMyLhw5i9cwp//OIeQLupG90nksuf5y7JQda4R+gxa+BtR1NEdX78edHe+rS7R00OUB4Lw8Mjm7PmDfPP6Gl5753Pe/Oc/+CXPkf5h42jtnz4FQRCEey2IJ196iddfXcC8KeFM72qtLr9/Hp5Aaf4vJErfSK06B+DlGETPzlWknTlIGQnEp5Vi3XUQIxiARwdjrmecUF8P98XXxQLM3YlYsZpXXtZM/zGnB9bSF2lLJ2Wj9lNWQOxV+Uu/LJ+8kgqovEHu5RvqspuUVVZJX7iMpbNr5uO+iCXVwIXhkzXRO68Jg+lmlMGxL+56nBT8euJmBQVXtnAyV10mKbtVJfeoVmuwuyNGRnb0m/NM7TW/smIALkZgaeep2aiqM/1mPMl/vbSaP69cyOKgTsqTW0ZG9UYSL05n6+l27pdNypOsC3eOmX/9JpWYYtVRXSDli5VXEMteeJ5XVq/kmSVh9LQ1Vr60mijrC9i5O5HrVh4MH695amzQhL50McjiyFdyvrWg3JtNS1tUSVUiTaqjWvnszC2Wyl9++liaVcvTsusYntaW08vPsMBbWmhmgZLMwe50kOqLQ8BM/qN2m6UM6STlh6UtumHtsoKTXKkNridSLL9jaG1DX82CeprLYzUPi1L5l275l96g6O6uJpvQXFnpWV/10ZqybGafdktbM5Tz3MrlUrxuXieyObVIuul1kr7kq8tkFfmci0nRqVd1xX/xL6ITpeo1ZzEvvvg8q5eNp692dDW1zhWm7eCCTjnmHElUAp4d3Os+81mnTuVeo0i+pRXncVnbi0KplF/SLc3IRL1v6FOn26SKoqyLta+3l8VcR05e2fXTpGvTmX5LKkMwMbXVzLegDekrPMIXu5pqLLxnaJ4WDLLjdqUZXQaNv+up2SbbpZ73dKG15CCn9qlQOUAqTU28eu9oY8SLM615/hFrRgfIdVparu6rmfTlwSNRM5jQy4iTG99VnpR+4613ef1AMU6eDpTptL37oX9IGFNCmvkr/kM0aGH+jk/47//3Omt+UDu11tdDlAeC8PC4wuFvvuWLX44QfzmTC6cOcbWoBiMLGzqpWwiCIAgPjtnPygHSZSyeHs70Ke607NGDe+fhCZSSxrGMQow6uhI8TPpyaZLPxR/kJ49usutMBuUO7oQE+eJiXU76pTjNLthiIn0XpiSD3bHHOVo7fSp9UX6VN77TbHWvVFY3/1W37OwWopOr6Dx4PCHyU3xD3THNPMXn59UNdNkYK0GsqluFmvlGmJlIxX7rOmfqXPNmXv/fV/nLJ3LeeDH3ySUsnTIY98oMft6+g2++uaR5Hbi+6sr2eYq0WYaYqO/YOoQsYNUTM4jwsyXv9C9s/X4bn2XU7c8of982EvIt8FJe6fYjfIALRukn+Erp5qKt5X4nLe0tX1sn5O/canneSDuok0Zp+mANf3p5PZvl7cyNMeIW187rrJemL976O3/660aa7THP2BhtjExX83ms5mF1deODQemp6bLSp762RWvK8s4+9zZtdyjnqaq66ynC4kp5YBkTTHR/o9TcVgKBjck/Gc07b67h1a+PU2Trz9hpUTz97CLC5XfS1DpXU6X9g46qfsCzQZVUy8lpij51uj3lS2lSf2xUW9vQXcYQ6WtLVXEZFl7BmqcFBzpSU1Kuef1e+3ZAU7TtUs97utAGSnxTEyRVZuR/G5ic7Ix4/hErpg2zYFBPU1wcjaTF0jplH/kYLeHLQB9HTK4n8+429UnhPbvZ9sVHvPb+dhLULqrlwdlWRQ1TBj+bvXgpq1/4A6sWRzJUbquO0r1y3iKef2EVzy+bRbiPZp9a8mB8UdL+q1bxX88tZbl0nLqbyIOJLWB6gD0BYbNY9Zx07OmRhE6fjrudIUYO3mqXANPVrmDukAdZm93LQaqeNnQN1HQdsDxS89caeZBH+WcH/zE8tuIPrF4ppVde0Vx65MHYlHNp0rNS3m7VIh4Lk46lbqJQ80K+7tUrFzA3pN56ZYDBOaxc+Qf+64UnWTlvDAHa921rzyFtM30Bz0vXvCTUTfp15koPb0/cbLT3vbp5o6RFTvP0O10jNJUHgiDcZ4U3qP2tOXAOIzzNKE4+RbS6SBAEQbj/Ri99gddffYkno+QAaS86yl96HiAPUaAUjlzKptyyI4OHu2F7LYWf1fFz0n9IJsfIBa8IFzoZF5Lxo7bTvmNkyr9JjSspPxLNtu11pz2nNVvdX8ls3n6JG059mBwygiHdTEiP38IFdW0dR3IouA1OnUfV++JQV1JBqfzOPBWJd1/zTzEp0jeAsUyZ4It95te8/elWtu3YTfQ+9XXgB8Co8MkEdb3F0bc/4bPN0Wz/5YD6eq6O/Dg2ncrB0nMgY31CCHCt4UrMd+pryA9qudsT6eIEtwvJPCLNquVpVnmLA/XSuG37Ic1gWFeKKMaYmvKkeuulKTqOJHmbVmg+j9U8dHBmQls7fWqyrPSor/fRr5U25TzmTnSvE2SzZ0JnKfO19UVvN7h8/BA/bfmWd9et5f9iK/AeMZXI0dKqxu4h/bvhbA7FBW28Jn3q9K+tvdtQWCh97cq5smctr8lPCqrT3765yHUzF4ZMqP9MaRP0vKcLbaGNcqpBT4X2Z83UUQ2STgw0x9LckP0JFRw8K/9C1N2+JbK4Lv/dyaYr40foBPpyLxJ76s7T4L6jIpgdOZlVzy7h0bBgRgaPZvrsKFYsmsWSPyxjSeQIRgcNY/LMmSx/fNadvszlwfj+uJQnp4UQ1McDv8CxzJq7iFWLdfuVGcDERyKImLSApxZOZvywoQT19mVk2AQ85T+8mHfUBPlH9rvrqWrPwaOZ5NsRE6nd2Hlo/hgwwt9FWTdkwhRmhIaxYsV8Zo0bysjBfljpkx7vYGY8MpFpy1fw3JzxjA0OZHT4VBYsXsi8IHUbAlnyb0tYNCWYvt088B0wkqgl84iqffVdHmBwJU8vnMKkEX3o6TeYSbPm88QjajcoyjkmELZwOU/PDmW0dM0Dukv3VmV5BCG1FyrnzTTmPf4Uz80LV9IyfPQE5i5YwnMLA5XyaioPBEF4QHQI4sllj+JbeYpP3th+V1dEgiAIwn0gD9T06ks8N38y06eE0+3+v2XfoIcqUJr/YxYFBo70C3CiLO008epy6Rsridcs6DGoK2YluSQkq8tJ4as9SRTb9+bxZdMZqj4q4OAZyPSJmg/DTTE21AwQJX94f+yPq3nlP5cyvcEOH9smdecWTuRL1zV/NN2Nsoj5JlFdU490nSfSq7D0nsii8ADlyQfX/qEsH+FxZ8ATya5v48k0cmPcEwsI91evwTWAyMgxmic8ipywsgCjGmNy029onuBYEYxXs0/eeUhfUp7nlZf+wGO1X2zan4OtJUZGNVSW55NfKj/xMYfV/vVf2r1JzPbzZFp4MmrhALpWXmXPFm3Bt63cm5VTSrmU444e8hdge1xdtfWkPnO6Dp5FaH/5ZaBODI1azKKeVpQnx/GVHOS/spXdl0qx95/DU1HaJ1ns8QqOIDJYPebu7zR9KYYsZnntkzedGBQecfcTRi3QfB5LeXj0KuXW3Zkt5eEITzk98nkXsGpeoGYTvfOhqbLSo77eRy1KW/27raGx3nVNOY+BM8GLtefR1JfZ3hYUXzqoqS96kduo/OSYVJ9KC0hKuEReUTVGhkaYmUqr1Tpn6T2lts7JT4YtXzwYV4McYr49qByl1fSp0/XpXY9aSZ82FDSH/3p5Nf+1QK3bTVBeu7+VwZGNmj5Va6etu0gsNKPLgDE6g/M1Q897utBGypOhYGNhwMwR5nTrZKTMyzo7GPLCI5ZEDDbD3NSA3acqePO7m6Tmqc8qtzRGqrjExu3HSJXa49zFy3jpT39g5fRAvOr90UkzCJ03QYYJtQM+ni7vSEDENCY4p7D1fc0gc7/kWuAVNJ7x2s8fegzGpx1MzDdoAAZHN/D6m+t4a+sPfPf5Ja7fllarg8m9sW4zezQ71GpqkLUGB3nUJz2mmsHYegdZkvD5p5qB17ZkYerZn4mhaqAzaCwzR3ty6+R6ZSC4t97bwJc7YohX+9poaoBBhXIOd4ZEupO+9QNek675412F6nIrLOV7oEKet8fFxZyEzzSDwL36+kZO1ngzJmoGU6V8btnAjYIg3A/jFi5hccAtDr29js9j0tSlgiAIwn0xfAH/qx2oaUo4/k4P2COk9TxUgVKSz5NZYoZDhxrSzsn9k2pp+im162BLVU4Ku9SlsrNb1vHegWs4h8zm6Rc0/dO99Nxi5oUPope6zZnsQqrMPQh/KlL9Ep1BQQk49VvBkmB5vhvBoaFETppCZP3RSNpDfhzrY7Ow6yV9OZZfSW70T6IJfL5hDxdvuzNt8VL+LF3Ln1c8wvia6+i+NJ1+6AvWbT5HTd9pLH/qWU2ffC8sZdHUYPrJg0jk7yPmYiFmPWcox3jlhceYOrCYHG2/g41ypN/IcUROnMgs7Rebe2DvoQQyq10YoaT9WZ6bM4oeOfnS15W6yuK/5li6Gd7BblSnxLBVJ5ikT7m3WvR+TuSD2zDpC7CUvj/PlwcSa0gJt2zG8/iKp6XzP83Tc8fhXHiMz9/4jrPK+otsevMzduU7MWHOEk1ZSMd78YnpTBzcQ9mCjCOs/+B7Ttf01XzhVrZ5mmfmRRAS4KbZphX0yeOzm9by+eHrOIybzcrn5O2k8y6Uztu7i6ad6J0PTZdVs/X1PtIrbRdzKKgyx2vCk8xWAyGp8hOiHfx5cWHzgTeZ5jxnqPbTnkeuL6NxyNnHe2/+oNYXfVji7D2UyHl36tNzwztScnE/Xx+Q12vqXHSOHRPmarZ56an5zPWr5sTmj1l/qKmBlPShR52urwX1qFX0aUN9hzN5YiiTR/XRzDfGUfo9IL92n3Gab+sHr/MP8XOidF917XtncL5m6XdPF9rHhIFmLAu35E9RVvTpZoyro9wnqRVhg8wwM9EGScs4c1X39YrWREpvkvD9R7z6v5+xJ9eKvmMn8ugCuc/gVawMr//X1kJOvK8OKLcvmh0pNzCyNST3/a/UgQAP8MkV6ZezWQc8tG9+NzsYn5Yxt3OjWbNRM/jagYSzdw0m19CAcM0NsnbXII96p6eG66c/qx2MbeemeNLLjengpg68ZmWJqakxlnadML2RSmzMIbZuiUbz8H7TAwzeYULZhXdruzyIbeIRs+Irn/Dpz9quEX7i7bgsDKT79hjpc17LB5oTBOHXFcTkMD/M03az5scEstWlgiAIwv0QxJNPzGbWAzJQkz6MTM1tXlZ/bhfOLl2kD8Gt77XQ2tpG2V/56qE+5aG/62RlXCLm4EF2H0ogTe3nS/4gey07k3NxR9i5L4bjadpX7yU3sriSkk7ShYsUmzpialBO3vmDREv7X07NQx5mqTA3m/KK25TdzOTA8WQqKSDrZjm3ym5RcCmO05ml0vFt8RvuhWHiZ2yK0Rxa1/XcLOKOxXPxap66RPoQnp9DQlwccUmZUsp1lh07xtHLaTq/1KvI7TaSJUGOpO14nXVHai+sniryM9K4mnyR1FIDjIwryD69h20/HmB/fDyHjyWRIj95UXmN1JQ0ki5fIqvSAivj25SkHOTnvac4k5ZFQXk+6alXScivxMasisKLh9ghf3E5mkBs3GkSMzQ9/9x9TRXSsut4DQ7CoVD68hadqi6/Q9nn6LE616yroTyh5BqZZ49zUE1/YXYGqZeyKDE1wbAsgz279rJrzxGOnTwlfenRybfKfDLTszh7/Cg79x4lPlOnXupR7vqkpUGFOaSlX6Okuobq24VcOXScuNRr6kqVcpw46cvVKS6UGONgVUVG/Pf867u97IxJUgagkRVlppGcmsK5c6WYdDRVBuo5s3s/B04lczVXDpVUkZeeypWkRM7lVWFpZcjt0jR27zxI7Pl0Mgrk0GYR2ZmnOFovzU2VhV55XJip5OHFc9lUmkvb3S7gwo5ofjiSwNWcG1Q2kg8N5mtTZdVsfVW3q3X39bamLPXaR5+03cgn41Y5t2/eJPXwSS5VQn52KeVVNym/lsjhM/VPXkp+1nmOHNFpWzrnKTSwkerrDa4c2c43P0jlEpda23dpg2muo0K6V6Vy8XIBho6WUpmVkBqzna9/OMDBi4VK36a1dS6nCgupPt3KOsfmLT+yY1cMZwq0AaKG6pQm3TExd+4RDV1L83W6Hn3rkVI2Jzhy4gpp2tM3mE7NskOHtenUow2VVmNRncvpQ3uJuVivLesyNKUk8zQH9x0lJr1+CdwkOyuTSydPczo5hYwb+rRLPe/pvwIbG9s2/27PzUpX59pHWz9vGLnPUfr0RfqsIf/n4WzMrFEWdHM2ontnIwZ4GzO+vxmmapB0zXdlnE9tqA+aGukQRlSnbVTn9VAqtenkKyRdTSLh1FUKrLoSFDyUfj2dMUjfzQkpq/pNmsMwhyyi/7FF7ZaiioKgSSz0riT+fz6v7W+vaHgkK3ysyYr/km0J8pIqXPqH8ujsSUROCGHs4B507NgJN/ubXFq/VX1CNJhHn/Cm8vgrvL5dt1uV3kx5bDBdbl5m3aYmeugdNVU6pyUZR7Xn1AiZtRDfypP8+5qfdf5YrUd6/CfweJAD6b+8xUbt8cq8iJg/GPeKq7z7rfTB6sY1atx60K/3IPx79aSvbzccylM5myH/xgxi0YrRdC05yZ/e3CG1clXlLW5pO21Wz5H602tsiFWXyZTlHcirvRZN3lQl/IMP9ilbSKS22H0UiwK7UJX1CV/ulxY1kgeCILSelZU1paXt8afAQCbN6kV53Ou8vUs7aK4gCIJwf5hgcSuZw3v28bNDHya4WqrLm3D9Iv/cqDzJ0ypt/e5hYGXn0tzwGy3SZ8BQsrMz1bmW69zZlaysjFYGStvGztkFG5NqygtzyddGqlTKOulLf3qu+svWyhEXe2MqlW3tGTpzNf+zrCO7VyzmVXUQmvbjxfR/vMJ/DrjOhsXLWaPHa7aWHZxxNKfBa9Gl2c6IypKsu58YtXOii7Vhs8fQ5RO6jL++OI7Md5ax4hudgHS7s8DJxR7zap0yaaWmyr311PQZVlKckX/nS1tDlLpkxu2GyqCWLZ3crDC5fYuCrIKGRzRXj2NU2fY80dA3j5u61hbkgx6arK/3WfNtCZ080OSLSUUhWdd0gxTNa5/6qi2XJo7T7vWpPj3qdK32rUeNavSa5fPbwI32vEe0jL739HZXU4P8IcHFxa3Nv9vPxB9V59pHWz9vmA77gZrbldJnDUMlUOog/b5bEmbFkgkWGBlCVTUYG8G+hApe31I/SCrlipI38r+3pUOYUHF4krqupaT61d0b/4gVvPKYF0U//Dth/32che98zx99LvPamGf5WN2S//qYM5Nh8+CF/Je6SLOsAzFvTGbJRpTB+F59fAJelcn8uOsAl7JvUjZ5JW8F3dTZ73m2xUbA9yFE/kVZoJrFh7ufZMi1H+kz8+/qsgbUO6fWXzbtYTp199UrPXNe58gqb87VOd7dabHz7scAz570GzWMsX27YnztLD989Q/W7gjVbHs9mhEzXm34D0UNnkNy1/JG8kbZrj952uWN5IEgCK3n7NyZ3Nz2eP7TnQEjPDHN3c/Ri+oiQRAE4f7rP5LpXa0Y/fiLRDbVt2PyNrpNekWdabm2fvd4uF69b6Oi3CzSMxr+Eqqs0/3iXFpAlnbbUTNYPNmWS1vW8XW7BkkDmP74Ulb98QkeH2BN6p6v67yS3JSya7mNXosuzXaNBJ2K8vU6Ri2vSBY+OpjCvR/z/t57GSSV3SQ/q16ZtFJT5d56avr0Ceoodam5wN8NcqRt0psKKKnHab+glr553NS1tiAf9NBkfb3Pmm9LunmgyZeWBkll7VNfteXSxHHavT7Vp0edrtW+9ahRjV6zfP72vke0jL73dKH1rpfW8OnOm3z4802qb98Jkr65tbEnSVvLnhGjdPvDlupX8mn2HM+lFGOsHDqqy1tHrwEPf0XtmZ6iyyfZE/09n324nr++Hsctr2E8GiF3x9GOAww2YpCXE+ZSCRXo+TlMEIT7yHsw4eEhTJPuP/UHpBMEQRDuoxP72fzdT7z16v/y7Or/JkbtSv5BIwKl7SHzFN9s+JB1m1o/yniDugYwYnQwIYEdyT3wOX/9/Ah3v8z+AClKIXrjBtZ8upuT+eoyQRAEQfiNyC26rQRLX99cyofRN5Ugad0+Sduua8hsHn9iMX9etYDpI7xwlQc06z2MueFe2FLIldhT6pato9+Ah425qBmR37YT4croYZ3UQdfqacEga21Lzx1eU5eyekUkQ13lwPJZzuSXcNvAEENjeTAAPQYYbCFn70VMD/ZQri8gbAHLg5wxyD1H9G51g3s90JwgCK3nF8y0KeE8Eh5Id3WRIAiC8OC4fPgXJWD617/8N8+ujubyXd3p3V8iUNoeLscRvSeOs+0dDS+J4av31/PGm+v450fbOZx8/54I0Uv+afb8coj4qw94OgVBEARB7p+0AXKw9Kt95Xz8y039g6SNHKshxRlXuHDNmH7h03h82Qr+LA9o9tQiFg41J23PF7z1S9sGBdJ3wMOGXWK7HHC09Wa5PNDhC4/zSIC6SlcLBllrW3rusOrYg5ERUXcGYHxqKJ3Kkti5VfNaVbMDDLZQjUUQ855YqVzfc4unEWCQwg/rPyE6Q93gXg80JwhC653+Fx98tpmP/rWFc+oiQRAE4cETv/snNn+3gb/8hxwwPcGvNPxCsx6ywZweMjevkXE1jZS0TLILtcO1CIIgCMKv62EczMmw03gwtkTuZtRA+bwhTZoPH1RUQWl5Y124y/2San9UdoaKa9zO+k5d2LTya5mkpKRw9uw50suMlUG6bmRfYNu2HUTvOsyxNM3v+wYHZ7ueR/qJ+LoDPirLYjmkDmSm34CHDQ8mJg9alJuRTnLhTeUay7MucjD+Ihn1s7mRQdYaGrhSr/Q0OBhb3cHgygtySb6cSJ6BrTJwXvHVWL7Z8jM7j17mujxgU3MDDDZ4Dsldy9WBrk79g3e+r8KiowEFlw7zr80/E30wgUzt4FD6DNwoCEKLtNtgToUZJCZeISHhEpkP2FNKgiAIQn2FpEqf8c5fTOVq+gn2ygM+WaSJwZx03c/BnARBEARB0PEQD+Zk1HUBRq6R0vUZ6wRK9fzcIQdIpT1rpH8NqKI6cxvVqZ9q1rWAdhA42n3QNHUAtNYOeKgMQGdCdXlTA861ZJC1NqZHhzbPGk9bS9LVEN3BnDSDzhnes3MJgqCr/QZzEgRBEH6z5AGfHK6zeXeCuqDlxGBOgiAIgiAILXQ752duFyVgUCO/Xi8HSGs0AdDaSbPoziQvUydlgYGyr3wM+VitoR0Erv0HTVMHQGvtcZUB6JobcK4lg6y1MT06tHnWeNpakq7maAad+3XOJQiCIAiCICgDPrUhSNoeRKBUEARBEITfnZpbOVSnfUl1bjQ1FeoIhPITpfpMEnkfeV/5GPKxhIdFIbm5+eTqvp4vCIIgCIIg/G6IV+8FQRAEQWhYzcP76r2WgVkn6cOQBxhZyXOahc2ScqW6lJrSqyJI+tDpzuCRLpB1iNjL6iJBEH4V4tV7QRAEoT209buHCJQKgiAIgtCw30GgVBAEQXgwiECpIAiC0B7a+t1DvHovCIIgCIIgCIIgCIIgCMLvngiUCr9NXoFETg9lhJc6/zviMDiUJcuWsmrFUpaEeahLhdbxYMTESGaPC1DnJY4e+HjaqzP3QgCh0yMJ7a/O/gZ4BYcye2Igv8PmJgiCIAiCIAiCIPyOiECp8NvkN5b5Cx4hwk+d/71wHMPSZfOYP3kUISODGeZrpK4QWseXiEejWDQlUJ33YPaylbzw3DLm6sRO21cgMxZEMSNYnf0N6DvhERY9Opa+6rwgCIIgCIIgCIIgPIxEoFT4bbLpSBc3F5xt1Pnfi9GhTPHvSNHx93hj3Xo+3JGlrhBaxxZnVxe6dNY+QVpMZ48eBPh40Klde2/WZY+LVHddHNXZB8pAHlv1PM/PGajOa9h1lPLItSN26rwgCIIgCIIgCIIgPIxEoFQQfkt8nLEzrCL/0gH27D/E4fM31RVC+yjgh/Wv85+vfMB3Seqi3xVPgsPDmDzMU50XBEEQBEEQBEEQhN8PI1Nzm5fVn9uFs0sXSkqK1bmWs7a2UfZv3aj39gSERDBt0jgiRgTg72XPrfQr5NyErqGzWDwhAIeCBBKvq5vL+kewfPpwvAxPcjZDXtCJoZFTmBoxjrAgf3xdDcg8n4X2igIiFzCnvxVHC5yZPXMqkWN6YmPnSVjoMDyNpWOkqxsqhjF3RRgDHHI4laQ5goP/KGZMCmXShFEE9e5OZ+NczmeUKusUAVJ6ZvhjE5uFXVgkMyeHSunwpZt9BamJeZSrm8lqjzU+mMF9uuFQkUFiru4WdzSY7gwpL4qaO458DSF4V1zFtL9OemyLpWu6juuQSOZOC1Xy29fFgCQpr+qkwHUg02eEMSk0hOCAnrgbZkv5fOd6Q6KWEta9nCRTf2ZPC2PK2MH4e9pSdPoK16R9Z8+cRKScLp/OcPUi6dq4oP8EHg/qQN7R3eR6z2BmxBhCBvri5djSfKp7fXOmjCHIpZqj0nUo1ybNR4wNYkDPLthWZZHUUP7WllkBnadr09ITt5qrnM00J0C3HNV806VvOcr9RIYODWSUuy1UmdPRqxsWsedJqXf+RyNG4muTwQm5zunk/6j+PfGwqiQ9RTd/2li+tVpzHDfC50UxuZ89eaeuoJsrSn0d7QHHpTJXl2nVqTOPTCJiSGeqjknbuQYQOnGCVF/GEBostV0Pc4rPpHFN3U/DS9p/MtMmqGUk3WK8xg2ky83LrNt0WNmi6+DRDHYzJOPYZXKUJTKd+4JybBsqM68q95aGSfciudzDxhA2eiD+3s4Y5l4mXbkNBPPoE95w6RP2ldfNF917jaLJ8tPcXwI7XOOE3JAVgcxeFsEI92riLmhT70H4glmE9TTn2Lk0dVkD5H5/QwYRPLIHnQzBsoMnQf6duXniMi6T5jDMqYDD36fhMmMS08Kk9tynK3Z31dXG78ENuav9j5fbmtTWc6TyrM2IxttonfPdlc8ade990v1Eufe2Iu1NtbPfARsb2zb/bs/Nqt+a26atnzcEQRCEB4uVlTWlpSXqnCAIgiC0Tlu/ezxEgVJ7RixcwcpZo/DvbIG5rSt9Bw6iT+dKLh66zPVB8/nTguF0LY9ha5w2qACDFj7H8xO7Uvzz9+zN8mL6yidYPGUIPe1MsHTuzoDBAXhb5hF7MkMJToQ8sZrHA6yo6TWK2UO98OxqTt7W2wx/ajKDbbLYuPuq5sCysCd4fekgLM5vYfuZchyC5/DismlEDPTA3swez4B+DPb3wplUjl5Qw0QjFvLqYwNwsujBmIiB9PLypFcffwJ83TFN/4VjallrjxXapxOWpvZ06RPAsD4dqUiM4XzdyJCiwXR/u4vEAc0dZwqr/zoWXzs3ho4Oopenh5qezpiY92DGrFACe3jQ3bc3A/p4Yl14hEOJavDBJ4JVK6OYOcwLWyMLOvXwZ8jA7ljkneJEmmabqOf+wkRvO9wHD2d0r2549fRlQF8fXE3M8J0ynUkDvfDy9CGgny/ettfZe+SKJkikBErdsLB2x7ePH77dPenu48cA/+44VSZyQM3P5vNJc33dTBwZOiGEgG7udLp9kk2Vw3l5+XRCvKwxNnPE3Vfar6cZV38+dVfgTltmna18GR0RQM9uHvQO8Me/hzVGjsOYO71uvhkk7eFUtmbXlpTjwBlLmNWnKy52ZlLTsMW5iwXXvtnDceX8fbEy8GHC1EB8PNyxuL6VH4vHaPJ/ZG/crCxw9g4gqF8vutsUcFytz20q3zpac5xKfOeu4Mlx7lQc3UFMbaTUjyV//jdmdCtj2+bD1O9cQKkzXY3oOGQ8YX096OZUQ1y8LVOfjeKRwJ642pvj5NmXwIF+9LS4xg8ntMFBTfteMm0o/l1ccO/iird/N7q4OGJZfCdQqrSVYdYkfbWbM8oS9b4wLZgAFyssOnZn8OC++HtYk386gdQGgoBekU/wx8fGM8jZHBMrZ7z7D6C/cxnHDyZRpAZKbaptpeUj6e/tibeSL944lp9h7wX1/qRtP42WXwDL/nMe412K+W77aU15DlnA//5hPEPcIX7LUTXQO5kX/+8R+hscY+OuFGVJgwZM4emp/enhZodljSH2zm54OsPl7w9jJQdKnQ0xcu1N4BBvenr2wD+gN308TMn46YR0B5M1fQ9u4LaklOWMADe69wtkuNz+u3XHr690XHczck9q87aRNrr/hnq+sQR2t8Xc3p1+g/oR4N2Bmqvxte1HKc/g7rj2HkjoAOl+4uGBj78/fb0ddNqZHmlvrJ3FNdQeHj4iUCoIgiDcayJQKgiCILSHtn73eHhevR81m+VRI3DK3MKadz9izfsb+OeeW3iFzWFeGFw/GENWtSN+Q0J0Rm4OIHxANxzKrvLtRfB6ZA6Pz/DD8OCHmmO8+yGfJtsyMmo2UerALuaWVlh79GemRxZfvfMef3vza6LPRROTZ4T7oAlE6vQ7GDq+Ny4GBZw6UCjNDWPRohlM6FZJ9Nvr+ec77/PG2k0k2QUy+7E5zNYmytQCaxs3egdZkvD5p6xZs5ZXtmRh6tmfiaHafgM1xxrrlM0X73zEW+99xNvr9lPePZTHo8ao29TVYLoL9DmOnJ4OePq4kb79E016tmVh2T2Q+VP7UXnwM9a8vY5XN1zipktvJo4fqu7nwewF0nX5G7Jn3YeslY699h35eocyf94UKec1LK2tcOreE4/MHbwtpevV134i3awbI6ImM6jqKO+9s56/vf4p5ypcCBg3hlHqfhrGmBgWE/3xBt54c610TT+T7xDA5KgphCjr9b8+36ABGBzdwOtvruOtrecYETaRMV4VxK7boNSFt9//jq1HEnSeMNShlpnvUCPiP/pIScu7Cbfo7B/KnHGduKzNt515Sr5NDtGOVN+ycrywaysfZcgfHsvJiF3PG+s2s0deoZzfnSGR7qRv/YDXpGv4eJedJv8DbUn6Uqprb3/AP9e+T3RFVyZEPcai2oxsbfnW15rjFHIk+go3HHsyarLOyEleIQzobs+NlO1cUBfpUupMryEMMTjG2rfW8bd3v+dUSRHkJ7H1AykfpfJ6Y82XnL/djaCZ4YSr+2nadz8cs3by939K9eXtDby/u4CKen2RKm3FxgJzdV6zXyB2yZt4/a33eVNqv6/vrsRz3AyWzx2mblXX2ImhDLLL42P5PvLeBta++wPR59PqPC1qbe9N8e4vWCvnyz8SKO7oS1j4CHWt2n6aLL/jpBQZ4eTlX9suvEb2wKO6Ejz9CNPeU0L86eUk3QMvnlIXNOLCPr78PJsyA6mG5RxX+sB946Mfqd3LzA5H8wt8vP5DXntjDV9nGOM1aAwRQ9T1zdyDGyKXpYOLCxbnv9K0/9fXsv7MbXxHz2DuJG07abiNas4XgmfFXl5fu15TLl9exS5wCo8viKy9zyvl2bkzPYv2qveTtfz1p2s4DgjlsWlq+emT9gbbmXxvFwRBEARBEARBEB4WD02gNDRiHH2ssjjw/nZ2HjlJfHwcO7+OJ8vYjWGjBkLGPn68VIil55A7AYT+YxjkakRq3DcklHowaXIQXcsv8eHG3eyJPU187BG2fZBIUYfejNKNh5jc5Pxbn7F59yH27D/JZZLZfCCVMofeTByjHRRmFBN97ChOPsx2OZAdMp5JvR3Ji3+XT6MPcTg+nn07t/P2zqvUdAli6gRtUEBWw/XTn0nbafqh3LkpnvRyYzq4+WpWK8eyJP3gBjbvOUbsqZMc3ruFmGwjug4aSWOhrLvSrfdxjClL/4j1W/dq0vP1ObIrzLAuP8d73+xmp7Rsz/azZN8yxN6lp2YXr/FMDXaj7MJXyvXGnDpNzKFo3rl8Ayf/oXUCniblabz18Xai90nH2bOFUzlVmFnd4tSHW/lpj7RsX7S0rBwj+870VvfRKCcr7nM275SvR5p2fsuWxBtYewQSJgdvWnB9t3OjWSOVu3wtBxIKsLA0xcTIAkdXY4rSpP32/cSm7fFcUfe4Ww3XTn1SW2bbdqVRaGiLUd7HbNDm28YUCjHD2aN15ZiecIw9+fIjdlUUXZWvWa57WiZSXr/Lu9s054q9rcn/miu7eFs6/75Y+Rp28+nnZ8hz6MWkCN0SaEX5Nqjlx8k/+iMJORZ4DJPaorJEqjoT+tLVJIf4L85Tpi6rz6TmGtvWbdLUjyNnyS+IZ9OnX7HplwMcSCqipKSEtIIyjDq4qcdV27dxBnvXfqupa1J6oreekdKobNAIdT+u8qN0b4k+FE9szCF+2vgVJwoc6RM6nlB1S12W5mYYmdngal9D9ilNW9/8y/k63QsUXfmID9W82rPzEy4WgHVHT02AT20/TZdfFlvOZVNl68HwIHkne8b6ulCSeJz0GjcCQjT3ohHjPLAtz+DUL9IJmpJxlgP785Q8ryxO06RLytvav8NV5XHsQzXP9x/gq7hsyk0ccfPTrG72HtyYkhTWbdyplskBtq6Xn9p2ZMAIbdBYdncbVc7ncI1ja6Q07YsjVrpfR2/dQPRV6BocziTtfV5WnU/8x9+raZfK/cufSLxhRbchY5R2pn/a67WzOw1QEARBEARBEARBeAg8NIHSwe6OGBnZ0W/OM7zy8mrNtGIALkZgaScPTFLAzt2JXLfyYPh4TVBy0IS+dDHI4shXF6U5X3xdLMDcnYgV6v7S9B9zemCNGZZOyi4aFfmci0mpE8RJ/fEXzl23pc/4ME2gI2Qs/h1ucDl6lybQMNidDsblZF88qfNU4k0ufJdKIRa4+ajBM0UVRVkX72yXX0hZJZhaq4+rKscyxiFgJv+hvdaXlzKkk7F0sbZ4a7a6W/10t+A4ZQUnuaLtWjT3GkUVcjAlj8vaXgxKb1JZBUYmZpp5v564WUmH6TqGp2uP/QwLvKWFZhboZqd0cGKvat9fzievRD74DXIv31CX3ZSuXzq4lFb16Koqadc0nXLIZ2dusbSd/JSqNNuC67uRtoOTueqMJP6LfxGdKF3GnMW8+OLzrF42nr5NDvldt8zKYq4jZ1fZ9dOka/Mt/RZSMWJiaquZb205NqiC3ItxJGnPpeZ/oXRdF+50CUvOkUQlUNnBXecJTkmLy7cRLT5O6Uk+PpaBkWsgM5TYrQeThsrdTJzgs/PKFg0rTmfr6Xx1RnYTK68glr3wPK+sXskzS8LoaSvlo4EhJsp6tX1fT+EL3f1KpTJpcnR7db+iVLZe0nnHPjeGC3Lw3smdweoiXdu//p6j1+yZtHgpf35pFavmBeFdL+vq5BWJFMtvcFvb0Fee1bP8kr69RJZhB3zGyNHK4QR6GJB+7isS8yzoPnC4tMyDEK8O1GRdYovSB3MbSG0y68KdvMu/LpUlplh11Mw3fw9uxK0bnNVpe2WXMsmX6o2lnYu6RKN+G1XOdyuXS/G65ZnI5lSpsll1wlcN4CqqblJQe4+R5B4gW7pVGNk6Ku1M/7TXa2eCIAiCIAiCIAjCQ+WhCZSamUiXcus6Z2KPc7R22szr//sqf/kkTtkmf982EvIt8FJev/cjfIALRukn+OqSvNYWE2Ppn5IMdtc5xqfSl+ZXeeM7eRtVzW0l4FVH+j42nszBzmeU8sRqaLgvHUpS+HGX2sOiuTFGVFFZoZmtVVaNHKepDZ41oTa4pBzrFtfO66bzOF+89Xf+9NeNaHpabED9dLf2ONJRqpsMLklsjJUA1Y20g3WOffSDNfzp5fVs1mzVoMrq2+pPLZev3Vfu3rYF11ddXfdpu/yT0bzz5hpe/fo4Rbb+jJ0WxdPPLiJcp2uFJuVLeaT+2KhW539DbiO/cV1Lzf+aKp3gkEyvgKce5asXfY5zk4R/7eNSpTNDwoYpT1IGdzMhPf5bzqpbNEi6WN0wqUPIAlY9MYMIP1vyTv/C1u+38ZnSTYGW2r4rK2nmucp61P2qq5XA9x2NBe81Lu/dxJuvvck/fr7K7e6BREbN57k/zKp9arZR0vGUeLy+5XdpF5euGePqHSTlwVC8zbK58GMiPyfnY91tKKGMoJerMfnJ33FvBvE3xETto0Cfe7B+1HpTr4vq+m1UOV9VldSC6ipWIt8mmNho5htWIN1npH+kcxhJ/+if9nrtTBAEQRAEQRAEQXioPDSB0qSCUvl9XCoSo9m2ve70U4w6gEl+HJtO5WDpOZCxPiEEuNZwJeY79fXlY2TK3c0ZV1J+5O5j7DmtbNSEAo58GkuqifzE6ihCezly49JedmqjOVeKKMYc5+51n+RzGOOsPF1ZkH1Ms0AfyrGMqSlPuiud26Lj9A+ItNdxGnIkh4LbYFZ5iwP1j739ECfVzdqXPZEuUm7eLiTziDTbpuu7weXjh/hpy7e8u24t/xdbgfeIqUSOVle3h18h/506j8JBXaTo3w1ncyguaGJQn1/b5R/ZeuEGHfqMJzSkP92MsojZlKiu1M+o8MkEdb3F0bc/4bPN0Wz/5YDaTYGW2r4dnJmgb7Bb0dh+AXTvKGVkSVHD5VSaRcKRvWzftJG316xlfZY9A8aHE163+TdO7/JLYH9yIWYuPZk6pgf2eZf41xWIP5TKdftuhIT74mJRSMrRluVna+h1D9aHnnVUOZ+5E937qwsU9kzoLBWU9h7QGMdQXOXyLMzhgPRPu6VdEARBEARBEARB+E17aAKlu76NJ9PIjXFPLCDcX+0n1DWAyMgx+GjmJDeJ2X6eTAtPRi0cQNfKq+zZkqyuS+GrPUkU2/fm8WXTGeqqWergGcj0iYF1gxWNKDu7hejk23iFzKaP0w0u7D5w56m33d8Rky0laeh8lof5KcdzHTKdp2Z0x7Ikid1ft+DLuHIsI7qGLK49FnRiUHgE4XcutnntdZyGXNnK7kul2PvP4amogWiy0x6v4Agig7X9uLaVOV0HzyK0fyfp504MjVrMop5WlCfH8ZXcmWirr8+D6Sv+wKrpUrpLC0hKuEReUTVGhkaYmaqbtIdfIf8tvafU5r+D/xiWLx6Mq0EOMd8e1Gz3QMgg+psz5Dj2YeEYD+W1e81T3vpzsLXEyKiGyvJ88kvtCQibw2p/3Q4eUvj5ZAZV1t2ZMT+UQXKGyPeHx4PxUp+IbJh6X5D2m629Lzj6Ef74fII7Q2bcd+zSbKgjkCWrnmSJXKZFGVw4nkhhWQ2YGGKk+xhsU/Quv5vsi0un2NaNib06cOPKLmUArLIDx0kpd6LvnG7Yl6RzVN3cK3Ipf3l5NavnBWoW3CVf6QLA3KELAY7SOV07qfWyefrdgxvg6MXKyEC85PPVXmM+p3c3XUeV8xk4E7xYez7NPWC2twXFlw5q7gFa5m4MnTdKLfeBzF0Wha9NOUlHtyqB7lanXRAEQRAEQRAEQXioPDSB0vRDX7Bu8zlq+k5j+VPPavqYe2Epi6YG06+LupGkLP5rjqWb4R3sRnVKDFt1vkyf3bKO9w5cwzlkNk+/oOmn7qXnFjMvfBC91G2alszm7cnccvemU3Ei0ft0XhXNOML6D77nRKUPcxcv4yXp2H9+cjYTHPOI3rCOTU31x1ifeqzTNX1rj/XKy0/zzLwIQgLc1I300F7HadBFNr35GbvynZgwZwl/Vo79LC8+MZ2Jg3uo27RVCbdsxvP4iqeVdD89dxzOhcf4/I3vNK9tt/r6LHH2HkrkvDvpfm54R0ou7udr+fGz9vIr5H90jh0T5mqu46Wn5jPXr5oTmz9m/SG1S4gHRP7RjRzLtsW3mxnpJ7RPeetv76EEMqtdGKG0fam85oyiR04+crefWvFffcqOS7fpNnEez8jtW7o/zB9dwzXdoegboNwX9ubhME69L6xexvKZPlSf/p51Hx25M9iRlqMT3fqNIGrhnTJd3NOEzGPRbNK7n1D9yy//l7OkVTnh6XKLlJMJ6sI9nEqvwdXLmer0s+xQA7RO/sFMnBjKI1PHNjLo2x52ns7HwHUIz62WzvnHKMaoa5qj7z34LjUWjJq5mBfl88nX2AcSf/yYd7TdljRCc74zVPtpzyffA0bjkLOP9978oW7XDdXGdI6Yo5b7EhaO70DR4c38dZPcP3Ub0i4IgiAIgiAIgiA8VAys7FzapTdCrT4DhpKdnanOtVznzq5kZWVouqczqNdJXTMsXX0Z3Nudbj196GprTHneWc4l5XPpZDyXdYab7jVsHD0dDCnPPUt0bN3IhZ33AAZ3d6KHX286mldRkn6RCykZJBzQjP7sNWQMve1ucLaxV6N9/sT2z8djcei/mPWMzhOlMisX/Pv54unrQ0BHc6puZHD6XCKX4qX0aQe78RxEuJ8tRed2c7g2iNuNYaE9cbqVwbZ96td/7bG6e9G7izXG5dc4de4KOckJxFyW3xWuq9F0N3uc3oRMdIM03e4HNOmxK7qk81pqA2nEFu/BAfR08sSvXwfMq0tITUgiJf08+xI0QZB+o0Lpiu4+DadVWeZ0i1Tta/tKPllQkiudxacXfd2tqZLK+9TZq5w6ojMQVquuzxbPAH96ubrRy98NayO5Hpwn4Xwih+PqDuKlaLDMGjquZpl5vk6+tbAc6RtMpLsZ+brnavD8MjX/e0n5oz322ctcOXOShEzta+ltKV9dbT/O0ve387RvDl8sXsyrTURKG6ozOPsyqp8/A/pJ5VUpX2cS6Zm36ORhRlZtVw8WeA0aRC9fTX4gtb+TCRcoMOxMp5o7x2uo/in3Ba8e+PeV60M5eScvcj7tPLFnsu6uD1KZ9u7Tg26Oar1H3T71NPvOyneEhvJKvS6zAp3z6lN+MjVPTSrIObmXGPWWpmkzxlTq1Dc5UBr29B/5d5fzPD3xTw08DQudpDo2fsQAPOV7aO5RfvzwCOUN3T+UeudIuc516HsP1vrLpj1MNz7Iv607gG8f6Z5rIt0jTp3j3IV4nQHeGs4vmXK+Pu749tber6V2eiGZ2NhktLfUhe98zx990vnqf74lzc9XSleVVB7nOXvlDAd0BqdqNu2NtrOHXE2N0o+2i4tbm3+3n4k/qs61D/nzxs2yUiV9giAIwm+b/K3PzNyCW+U3xX1dEARBaDX594mFpVWbvns8VIFSLcsOzjiaG1FZkkWO9ttyC9k5u2BjUk15YS75dUdxaYI9I1a8xt/nW3PgT4t4fk+9gVi07JzoYm1CdXkhWdca2UZfVo642JthVFlKeq52lPhWaK/jNMiWTm5WmNy+RUFWwd2BpbZS0367qfJu1fVZ4ORij7lhS+tBK9zL/L+nZdtOfCJY+49VDMz/inkLP6wbzNebWl7VzVynmh9Sobaw/WnrQyXFGfm1gbjGqfUefbdvRHuWn88Ynn3pGSblfMisVd/V/UOOjrbeQ/XdXwmU8iN9Zv5dc881at09oqn7tSZQepnXxjzL10q6bjdZHu3x++Oh8oAHSgsL5Si2+EotCILw26f7vU/c1wVBEITWMsDe3kEESu8/e0LnzKR3p44MnTCCjimf84fnvuDsvQysCcLDoH8Ey4PccPAdxZQ+Vfzy6p/5z2i9308XWsSD2f+zmlkdrvDFO+vZnFB3FPn7QTdQeq/oBko/VpcJLfCAB0rbkiZBEARBEARBEB4+bf3u8dD0UXp/9SEoJJiQYG848xNr1/0sgqSCoIeu/YcwfmQwwzrms/vjtXx8SARJ751iLu/8mrXvbWTHAxAklRXk55Ob30AXE+2ouEA6R26hdPWCIAiCIAiCIAiC0DQjU3Obl9Wf24WzSxdKSlr/ldTa2kbZ/7f1RGmN9GU8iROxMezdd4Qj53KpVNcIgtA4w5s3SD13mkMHD7Pn4CmSRTTrHrpJ1tUrpOTceGDuT9eyMzkZf5qUrAY6MG0nxXlZnDlxitPpeTygHU/8JtjY2Lb5d3tu1l1Dn7VJWz9vCIIgCIIgCILw8Gnrdw/x6r0gCIIgCA0Tr94LgiAIgiAIgvAbIl69FwRBEARBEARBEARBEARBaCMRKBUEQRAEQRAEQRAEQRAE4XdPBEoFQRAEQRAEQRAEQRAEQfjdE4FSQRAEQRAEQRAEQRAEQRB+90SgVBAEQRAEQRAEQRAEQRCE3z0RKG2L/qOYPT2UEV7qvMTVxwtX9WdBaH8BhE6PJLS/OqtyGBzKkmVLWbViKUvCPNSlwt3s8fLxwEGday/3r917MGJiJLPHBajz99+gcfql50G5V4p7tiAIgiAIgiAIgqAlAqVtETyRRQseIcJPnR+1gBefWcGLy8aoCwShvQUyY0EUM4LVWZnjGJYum8f8yaMIGRnMMF8jdYU+NIHX2XWmCEIHtn8w8UHg9chiqY2u5NkobaNtB/e13fsS8WgUi6YEqvP33/ApeqTnQblXinu2IAiCIAiCIAiCoEMEStvC0Zkubi4426jzFm54+fnRs6uZuuDXNpDHVj3P83MGqvPCw8ceF6nOuTiqs7LRoUzx70jR8fd4Y916PtyRpa7QhybwuuiRcCaFjZemcB6dM4cn/20lf34qkkHqVvp7sOtgUUd3fP160N35trqkHbS03U9cxF/+tIhIdbZtbHF2daFLZ3t1/lc0ZBbP/+lJHhuizqscO+uRnvt+r1Q9KOkQBEEQBEEQBEEQHggiUNqe4jbz1//3d/5vY6y64NfmSXB4GJOHearzwu+CjzN2hlXkXzrAnv2HOHz+prpCH5rAq9PtRD76/Ctp2sh7H3zK4ds9GD89irktjuY92HUwf8cn/Pf/e501P1xVl7SDlrb7fiOYMnkE9XpP+O3pHsjkyeMJ7q7Ot8R9v1eqHpR0CIIgCIIgCIIgCA8EI1Nzm5fVn9uFs0sXSkqK1bmWs7a2UfY3kGcMlP/raRhzV4TgXXEV0/6RzJwcSliQL91sizmVdB3XIZHMnRZKxIgAfF0MSDqfRbm6p8J1INNnhDEpNITggJ64G2ZzNqNUXakh9wM5Vzru5LGD8fe05VaHAGb0tCTj6JdsS5A2KPNhRJgPtqUHSVDiMH5MXzyVMB9zjp5JkxcoAiIXMCfYjZsnLpOjLIhg+Qx/bGIL6Dx9BjMjxhAysCduNVc5m2lOQNjd13MXr0AiQwYRPLIHnQzBsoMnQf6d75xD5/pG9e+Jh1Ul6Sl5dfOgvkbzRMrr5WGM8jTm8rkM9RgehC+YxeT+ThSfTCKvgWsKHSql376C1MQ751Xyor8VRwucmT1zKpFjemKTkUBikZTf/qOYMSmUSeODGdynGw4VGSTm3kmxUqZTxhAxNogBPbtgW5VFkna9lPbZMycRMX6UXtdbe64Jowjq3Z3Oxrmc1y3/2uvJwk63POpdj6xN6ZY0vX8wjz7hDZc+4ct9UrEHh0r5Gsgod1uoMqejVzcsYs+Tom7dPM3xzFN28R/rdpByNZXExCSSrvYj4hFf7Et38sVeuT3rUZebq4MKqVyWTSJEqjvHpLqjKyRqKdP62ZN5+gpS8TdfJqOms2riYFwrTnG+9gTyfSCMvpbxahusx2MAof3cMMo5XruPfN6w7uXEFXdV6kyknO8+nSHnIum1tzJ7TTsMG0PY6IH4eztjmHtZs/6udi9ppP71GRdJiFReI7tYgYkN7oMH4lWbVukcIRFMmzROuU/5e9lzK/0KOXXi3l5SeiczbYJ6j5BukV7jBtLl5mXWbTqsblOfznHrp11V2w4zbQifMYlpYVKb79MVu3p1V0upd0MGMdbHGeNqud4NpJ+LlIcX8wiZtRBfpPQcutV4frYgz+4+u/Zef4qibnXv65nSfV3nspRjNnnfaygdjn5KHkweH6Lcs3o4G0lJz8IhdBaLJwTgUCDdn3Rvwcq9YThehiel+6O67CFkY2Pb5t/tuVnp6lz7aOvnDUEQBEEQBEEQHj5t/e7xEAVKp7D6r2PxtXNj6Oggenl60KuPPwG+nTEx78GMWaEE9vCgu29vBvTxxLrwCIcS1a/LPhGsWhnFzGFe2BpZ0KmHP0MGdsci7xQn0jTbOATP4cVl05nY3xM3Vze6ePZkoHtnXGy5EyhV0jCJnmUb+VaJWQxjxctzCHUr5YPvjskLFCFPrObxYdYkfbWbM/KCEQt59bEBdLbyZXREAD27edA7wB//HtYYOQ5j7vS612OQtIdT2cqh7hgwhaen9qeHmx2WNYbYO7vh6QyXvz/MZe31jeyNm5UFzt4BBPXrRXebAo6f1AY662kyT6oJWLCIRaO6cftCNHFyWoLm878rw/As2c23+9IpUa5pGN4uvRgW4k9PKf3dffzo37s7TpWJHLigiTQoeRFgRU2vUcwe6oVnV3Pyvt1F4gA5v6cR2qcTlqb2dOkTwLA+HalIjOH8Nfl8c3h5+XRCvKwxNnPE3Vda39OMqz+fIp1Alvz7YuaO9MLGyBwnd28GD/bCLGMXcQ0EMjRlO42IgR7Ym9njGdCPwf5eOJPKUTWd2jJysujBmIiB9PLyVMvDHdP0XzimtkHtsVqXbj32rxcoHThjCbP6dMXFzkxqLrY4d7Hg2jd7OC5vqhfN8awy4/hw+1l1GXQJnsasEY5cO/YFXx+Va4gedbmpOqhuD8aE/dvTzBpgSfqm/SSpS2EMz76yiGGWl/hixzks9CmTmSv555QATJO/4adzmkV3t8F6lHIcgo3OPlHP/YWJ3lZ0GTCccX298e7hQ0A/X/wcb3LywGXks3lFPsEfHxvPIGdzTKyc8e4/gP7OZRw/mETRXedsvP45jFnJ5F5d6GhtjKGtA64eXbEs2sqOeHspaStYOWsU/p0tMLd1pe/AQfTpXMnFQ5dRih4vpq98giXThuLfxQX3Lq54+3eji4sjlsWNBUq1xx1LYHdbzO3d6TeoHwHeHai5Gq/WKbUcBzli7hPMlCHeUnvtgX9Ab/p4mJLx0wkp1+tS6t0A6V7oYKHWu650JpnNB65oAqXWt7GT7hmN5efd5dSSNqve621dGDB6JP29PfFW7uveOJafYe8FOcwu0eu+Vz8dHlIer+Bx6T7sYmqCtUt3Bgzug2vZafYbhfH846PxrIhha6x6DknAguf498ldKf75e/a2pNeL3xgRKBUEQRAEQRAE4begrd89HqJX7y2wtumAp48b6ds/Yc2atbyyLQvL7oHMn9qPyoOfsebtdby64RI3XXozcfxQdT8PZi+Yw2x/Q/as+5C1733E2nc2kWQ3lPnzpqAZu3kYixbNYEL328R+/g6vvvEea9Z/TcINZaUOOQ1WWFuqs5hhKc9bW6jzGuaW0jIbC8zVeUzl/dzwHWpE/Ecf8caba3k34Rad/UOZM64Tl7XXszNPuZ7JIQ2Man5hH19+nk2ZAZTnHFf6qnzjox85pb2+QFuSvnyfN97+gH+ufZ/oiq5MiHqMRaPU/etoLk+S+eXjo2Tb9OaRedPxwYu5j4bQ/XYyP3x9EiVWoFxTZ9x6FrNjg+aaXn39Z/IdApgcNYUQ5TxqXnj0Z6ZHFl+98x5/e/Nrogs0+T3WKZsv3vmIt6Tzv71uP+XdQ3k8SjPoyoiwiYzxqiB23QbWvCutf/87th5J0Dy5GDSWmaM9uXVyvZL2t97bwJc7Yoi/81ijDrVsu1US/fZ6/vmOlEdr5WsNZPZjUh54qZupZdQ7yJKEzz/VlMeWLEw9+zMxVNsfZxvTrcf+9V3YtZWPMkqkn8rJiJXKfN1m9mhWtYh5p4HKiPny9PwLz/PCAlfKTu3k0+2F6hZ61OVG66CuNLbF5WLYZQBTwnX6sQwZQ0BnyE04zHV9y0Q5txWWpuq8on4brEcpx7r7WFpb4dS9F75F+3n//fX87fW3iC/riP+4UMaq24ydGMoguzw+lspsjVSf1r77A9Hn09SnF+uds4n6d/iHrzhfVCVtVMhFOX+k6Yt90uyo2SyPGoFT5halXqx5fwP/3HMLr7A5zAtTjorXI3N4fEY/HLN28vd/ruVvb2/g/d0FVNRo1jdIOW4InhV7eX3tet6U8vP1L69iFziFxxdESi1XQylHdx+CzS/w8foPee2NNXydYYzXoDFE1OuDVKbUu2z5UVdtvVvPu9tqo9Vg5dBkfrYkz+4m7yvf670p3v0Fa+X7+j8SKO7oS1j4CHUbfe979evLCCLDA3DM/hdvvS+Vw7sbeOu7/Zy+Wsr1mF0kljriNzxcp+9eD8L6dcOh7CrfXlQXCYIgCIIgCIIgCL9ZD1kfpcaUpX/E+q17lb4ad359juwKM6zLz/HeN7vZKS3bs/0s2bcMsXfpqdnFazxTg90ou/AVn0YfIubUaWIORfPO5Rs4+Q9F+T4dMp5JvR25nvA5736v6Qdyz57dfHr1zlNFbVfDtVOfSGnQHH/brjQKDW0xyvuYDdrr2ZhCIWY4e/iq++jIOMuB/XmUST9WFqdp0njkLOnq9dVc2cXb0nH2xZ7k8D4p7Z+fIc+hF5MiGoiU6pEnOfFb+NvubOwGz2DhslnM62fI5S0fsq1O/5gV0nafs+kXNc92fsuWxBtYewQSpht8MbnJ+bc+Y/NuaZv9J7ms5Lcl6Qc3sHnPMWJPSWneu4WYbCO6DhqJHOK2sDTFxMgCR1djitLka/qJTdvjuSIfz8oSU1NjLO06YXojldiYQ2zdEk1MQ++jq2WbF/+ucq2H4+PZt3M7b++8Sk2XIKZO0A1K13D99Ge1ZbRzUzzp5cZ0cFPLo63p1mP/+tITjrEnX87zKoquqvmnWdUiJjbuyoj58jQ6aBB+HW2wtLfDzbkFgwQ1Vgc1a2td3naE5HJ7+kl1Tzuyfmi4D/alqez/KaOFZdI+TMozeOvT7UTvk9K8b69U1wvA2glvNZJoaW6GkZkNrvY1ZJ/SpGfzL+fVpyPraaL+XT5+iCvFcqD0Jjly/khTrFRgoRHj6GOVxYH3t7PzyEni4+Ok+1c8WcZuDBslB+I9mDQ5iK7GGexd+60mndK+0VvPSPc45awNUo7rcI1jazbx0744YmOPSPtsIPoqdA0OZ5I2Uqq4wekPpe32yMc+wFdx2ZSbOOLmp67WcXe9O8SBBJ3HKYuTm8zPu7SkzSqke33GR3yo3hv37PyEi/IpOnpqgr+tue8pLLA0M8TMujMONTnEK/m1nR1nCiE/jm8ScrDsOoRwbQez0nkGupuRefobEur21CIIgiAIgiAIgiD8Bj10gzmVFZzkivYLa+41iirkoE0el7UxzdKbVFaBkYk6yrFfT9yskL78juHpl1fzijI9wwJvaaGZBU7yNoPd6WBcTlbCIZJ0vgznVFaqP7WHKoqyLtb25VgWcx35VGXXT5OuPWf6LeQzmpjaaub1oV5fYdoOLuim/UiiEjDu4K55ZrYOffKEDGI+/5zD150ImRmMbfou3th2lnxlnVaVVB5pSuBMI5+ducVgLD/Bpy6SVeRzLiblznZKfhvjEDCT/6g9/1KGdDKWEmWLt7RJ/Bf/IjpRSuqcxbz44vOsXjaevnaa3Tm5iY/3JGHQazYrn3uWvzy3iMl9GskztWyzL57U6UfzJhe+S6UQC9x8dIPSdcuI/ELKpAIxtVaHoG9ruvXY/16pfQJUmuSnCV/6y2aSLPvxyKLZtU//tpv0aLZfKMTObyxTlajWKCb6OXLj0i9sl1+zblGZtJOyAmKv3gnyXyiRf7bERg0Sbv/6e45es2fS4qX8+aVVrJoXhHdjA6W3pP6pBrs7YmRkR785z6jlLk0rBuBiJKXCTh4YyxdfFwu4nsIXp3VaWal0T2jiiVLluLdyuRSvu08im1OlG6JVJ3x1g6CVN8i6cGe7/OvSvRJTrDqqC1qimfy8SyvyrM69nkSK5XfprW3oK8+25r6n2MXnW46T32EcS1c+zSsvLOWxEZ5oivomcV/EkmrgwvDJw5QlXhMG080og2NfiMdJBT10G0j45HBClEoqCIIgCIIgCMKD6KELlNZVSXVTr6XKbIwxkf65kXaQo7HH70wfrOFPL69ns7yNuTFGVFFeeueL/z2XL6Vd/bFN1OurqaqX9voBY1365InMtBO2VmBsZoa5qQXmueryJuRX39b8oNv9bM1tJQBcS8nvW1w7r3Nuafrirb/zp79uRO5KMP9kNO+8uYZXvz5Oka0/Y6dF8fSziwiXY5b5p/lu/bv87+vfcrTIlgGjJ7PoyZWsDGvg6Ui1bCvrP5VXVo1cdfQJStfmYVvTrcf+90rtE6DqFP3zJv5xqZROvUcwtrGYUqtlEP3NGTKtexA63gNCxuLf4QaJv+zTPH3aDmXSPkzk8ZYUl/du4s3X3uQfP1/ldvdAIqPm89wfZum8gq2jJfVPZWYi3YpvXeeMTrkfjd3M6//7Kn/5JE7awhYTY+mfykoKlD30oxy3qkqqVXUVK9HVO9fXOENMavsIaasmzteKPGuQsTHK3x1ac99TJLNz44e8+vf32J5cg1fQBBYsXsazczSNoOzsFqKTq+g8eDwh8lO+Q90xzTzF5+eV1cJvUP9R4UybHMqIHuqCOjwZES6vD0b7EHGbyH1xR81gcgPdWQiCIAiCIAiC8GB4yAOlejiSQ8FtMKu8xYHt0WyrMx3ipLzNlSKKMce5e3tEjOyxMZMjHr8S9fqcOt95zVnRvxvO5lBc0MC7rfrkCV7MXhCJX+VZPv7XZcpchrJQDSY0zp5IFye4XUjmEXVRQ5T8NqamPKneuaUpOk4dAOiG8hrzT1u+5d11a/m/2Aq8R0wlcrSykqLLJ9kT/T2ffbiev74exy2vYTwaMVyzUlcjZeswxll5crYg+87ARc1qa7r12v/Xko+dHGoyNsCk7mPCOlpfl/OPbuRQmgleQ0IIHdeTDjcS+XmvGgJsS5n4mFO3F9V2UppFwpG9bN+0kbfXrGV9lj0DxocT3kiV17v+qZIKSsGkhorEeuUuTT8p758fI1PuLtbBmQnqA8z6UI5r7kT3OlEeeyZ0lg7SXDv8lbU0z5rUmvueqizzPAd27uTLTzbw2hsbybbrS8SUMWp/1cls3n6JG059mBwygiHdTEiP38IFZZ3wWzR04gzmRj3Cwoail33HEvWovH5Cg12ftJh1B1xdOtO5TqUUBEEQBEEQBOFBIgKlV7ay+1Ip9v5zeCpqIK7KQnu8giOIDFafZtp9kMuFxrgOmsncYA/pi7e8fjrP+2teQm+cGtxw9GB2/07Sv36EL1zGcNd7le35yuun5g5dCHAEB9dOOKjXZ+k9pfb6HPzHsHzxYFwNcoj59qBmV1165IlX5CzmB1uR9vOHfPX1+2y/Yka/uXN4rE5Axpyug2cRKl87nRgatZhFPa0oT47jK6VTzkbs/k7TL2fIYpaH+amBjk4MCo8g3Ef+2YPpK/7AqulS2koLSEq4RF5RNUaGRpiZSmmbupTVKyIZ6nqT/OSznMkv4baBIYbG8jNm9SjnAteh82vP5TpkOk/N6I5lSRK7v248oHKXNqa7+f3vMDZs/Ek7r8il/OXl51kV2YK+PI2tCQjw00xDRjF78ZM84WtD+ZU4diijjutblxuog+qaOkrPs3H3Fao9R7GwdwduXNrPTm1AVt8yUQOqbr6j8JLO5TokklXLe2GjPrTcfgJZsupJlshpKcrgwvFECstqwMQQowaCyM3VvzOF8uBb9riGatqFqyvs+jaeTCM3xj2xgHB/tWxdA4iMHIOm6FP4+WQGVdbdmTE/lEHSPsr6x4PxauKJT+W4Bs4EL9YeV9MOZ3tbUHzpYNPtsDk5pZRL+e/oIZeRdD2ujdfJ5rSozeqjNfc9WdAcnl81R8mrovREYhOLkIvazMiIa+omqTu3cCLfkX7zR9PdKIuYbxLVNcJvkb1zZ5ztO+AzZAzaocC0hoYG4ufkgKNLR6mGC4IgCIIgCILweyACpVxk05ufsSvfiQlzlvBnpX/AZ3nxielMHKy+i5exh7e+Pc41uwEsXL6Sl+T1j09gQGkJcrd4jUvhq6MXKbboziK5v7vVy1gS6saFVO1I4u1tDztP52PgOoTnVq/mpT9GMUa9vugcOybM1VzfS0/NZ65fNSc2f8z6QzoDsNRqJk9cQ5k/LxiX68dY/81Z8nNPsvGf+0m3DmCBMgq+VhUmLuN5fIV07S8/zdNzx+FceIzP3/iOs+oWDco4wvoPvud0TV/mLl4m5bd8/qd5Zl4EIQFu0gaWOHsPJXLenbQ9N7wjJRf38/UBsOrYg5ERUTz9grxOvt6hdCpLYufWo5rj61LPdaLSp/Zcf35yNhMc84jesI5NLXmlto3pbn5/WQYFJeDUbwVLgtVF9XgOGcuUiWFEDHFXlzTP3GMEz/1hmWZ6cgHzp4Rgn7aTd/6xhRhlC33rckN1sGGXt/3E5cou+DqXcGHvoTv92+pbJrv3EpNZRafABbwonevPyycTVHWVrPrvmbeVoxPd+o0gauGdMlnc04TMY9FsUoLIdTVX/85FHyOpzAbfJWq7mOJH+qEvWLf5HDV9p7H8qWeV/eT+MRdNDaZfF2U34r/6lB2XbtNt4jyekY8trZ8/uoZrmqH3G6Q57hmq/bTHldvhaBxy9vHemz803Q6bE72fE1KhuQ2T8+VZ/jy/lU9/SlrUZvXSmvseOHR0Z0DIFJ0yiKKXaQ5Hf/r+zqBk+XGsj83CrpcH5ukn+Ko1o6cJDxTDG9fIdfAlrE6kdAgRA525fq2Eu5+bt6PPiMksWrqMF55ewtI5oQx2UVfp6jGEmfMWsurpZTw5L5zB8h/EGmDvF3xnu4XTGO+n7bhaEARBEARBEIRfm5Gpuc3L6s/twtmlCyUlTXxzb4a1tY2yv9KFpYFuR5bNKSI78xRHjyWRUjsCTCn5WeeJiTlNYoY2oKNZduRIPBev5ilLijLTSE5N4dy5Ukw6St9kKvI5s3s/B04lczVXfvqrjMzUNFIvnyev2kJKViFJO3fy/Z4jHI6L48iJK6Qph787DXmZ6aRl36CiuoqKnP/P3nkAVlGli/9Heu8EkgAhhBBaCDWUCCGU0LsKoqLYUJa3ll31uft21/++t7q7urKLighixYoi0g0ltNACoYQWQgik997rf2bu3OQm3CT3JgERzk8Pc0+ZM+d833fm3vly5pwL/LxrD7sPnub8mTOcjE/V7JpdnEPqhdM69cjo648mLfKIbn90ySctOZWMoipqa2vJu35cqjOZq9r+ZVRjbWtCRdpFNv24g117j3M+V96B+2ZalEl2JXXZl4mMOMiBC7nK+qIFqSkkplwh5nIy8dfTKQmYwlOjnUneu5ovzpvhbFtNSvRWvv1pP3uOxysbVckUZWcQI8mwXhYK1WQlJ5IQf5WLWdXYSG2uLUli357DRF1KJiW3kJz0RGLjcjFxscGktpjE49v5btshDsfmU5SbybW4q2R1csDWrJaiG1F8/+PP7DkWR95N+2/pXKsQHCwlKSYcV8r/vD+aBK3TTa+OmtpS+9pd1er5sls+l7SycipKK8i9cpJzqVJSXhbJp6OIVNtWWWBKXWUCx7Yd5UyabL8tobGpI8fPc/FynCZcvMCJY8fYt+8A+04loq3BIFtuxgY1I60JhZUEzZtHv5ozvL1yGwn1u3kZqJOiTJKSrpFVZkJ1VTEJe/exbd9hDp+NIeqk7rjRQY8e8zLTOHmi4X6goCvTwmIyb1zj8pUyrOSxUJPL+Z+2sWnvEaKy5EY3HqvlrdhfVVY6iQn5lJrUScrK5ELkOc7fuE7i9STi466QVmWtnFd8/bDU37OcT0pDVn1VTioJiTc4n1qKiakJlWnn2blzN3siz3C8afu1VOXU15vfyR6LToUkHN3O99v2s/dkYv3apXrHoSKrUxxudA/SIT+DpOQcimvqqKnNJyHyFCcTc1qXpyJ342TWGH33RlWPx07U96H+Htbifa9xXSZF2dJ9/iqxpRa4SnZXnXNZc87BM2TWbwpVTWbPcTw52oWkXe+w+mjDnetuxt7eod3f7Zlp9e7mDqG9vzdkxs5dTN/ay5yo6MVQt1S+3HdDkxG8hBen2nD+UiW+3eu48tl2NPOQHRm1+CmenhfC0J72WDp2I2BIAAN7uVKXdIYr2kWEe4fx3NP3M2dUH7p17Uo3z54M6NEVD1drCq9+xQ+RmmJOI+/n+cdnEdrXHWtLBzz7BRDUz42qaycb6hIIBAKBQCAQCAQG095nj062jh7yrh4dxsCho0hPl703baNrV0/S0lLa4CjtCBzo4mWLeW0FuWm5Oru1N+Do7oG9eRVFKdnSI7aB2Lrg4SQ9dZfnk5Zz6zeEsnF1x8XKlKriNDJ0G6m2w7SqhOTMQjWxNVqXiV4Wv8PRF/24uHIWT/6kuW5t0/YYQotttsbNwwkrkxpJtJlk1zsyNGjlUGOo3B3d6GZnbnj5lmhHuxVaOl/JM6OquXMVnVlQboyNGorartZsuVkb1ME5+EnW/98i7I6+ztw/ROq3LQN00qYx2SbUsYBh12rZ/pofV63KzkAdNEUjpxZsrk1obblj5G/0mDWEFsdiM6h2h95zfJn/rzf409A8PnniWVa1Z/mCXwN1dcomah4eXu3+bj8f3dYZwvpp7+8Nmdc+3cYs9vDU1UF8NCyR/33idXbmQejrn/O/fS7w6tmevDUFtk54jjflE4KXsvbFOXTL3Mo7X0aTU2aGs+9Unl06EI58yiv/t5Pr9GDe//wPz91nRdzmtXxzrkr6PWOO54wn+M0od5J3zWTxP+XKRvKbVStY4HyFD9fu4EpeJeZOQ1n24nTsj6/mwX8ekAsJBAKBQCAQCAQCI2jvs4d49b4RhWSkpJHcgkOwIFPKN9YhUJJLmlRvhz34t0JpTqbURj1OFrUdBjsLFFqXSauo1zXaSSrTYpvLyE6T9aHf8aOVg8FyL8g2rnxLtKPdCi2dr+S15OySdXaLnIZqu1qTUbM2KOE86UFeXP4crz4zne7lMXz/RXTztmWATto0JtuEOhYMvFbL9tf8uGpJdgoG6qApGjl1pJNURmvLHSN/o8esIbQ4FptBtbvG5wQy/6mnefH3z/DUUDsSI75j893uJL1nqOXixmiSrfwJGye/9h5MWD87Us/8yOkmf0oOnRxKf6dcoj/czJ7I05yOjmLf9i+JSIJuoyYztadUqOd4ZozyxCzlOB/8cJhDR45zKPIwWy9kUampRsPYCUzpZ0PqsS/ZeugUp8/HcOLwNk5lmtFtyBhGqMUEAoFAIBAIBALB7UM4SgW3htJ8MjOzyWuzd1VwtzJkxFhCx42mH5fZvHY9P13qQKeYQHCr6BHI2PHBhAZ1JvPQBv6x4SiJapbgLiB2G1Fp1vQbF4LT2PEMsMskatOVm/6QMaSbEyYVWVw9q93eS6L0GluTC8GmC336SvG+vnjYQF7iVs7rrEBRWlFNo73mhnjiYmqK48C5/O6/X+JPSniM4Z1Npbrs6KUWEwgEAoFAIBAIBLcP4SgV3BqifuDtf3zAhig1LhCoJOz4ipWrP+TtVZ/wWfgF9C1/KRDccRQf55u161j5n9X8++PtHLkmHPx3F9fZdDIZiz5jeDjMH4eMc2y6qmbpYGkm/Wyqqa5f31dLsbKWrhnmdtLBTjpKh5qKVuZZW5lJP8IqyLtyhpPRDWHj2lX877+/54RaTCAQCAQCgUAgENw+hKNUcGtIucChgyc4o2dXcMG9TcLZo0QcjOTQqaskd+hr4ALBLSQ3luOS3UZEnuTMdSNe4xf8aojffpzr+DAjwJzrkTuIV9N1uZ4n3bSsXPEZpCYoODLB3RlqC0iX/zgYlUl+Lbi4B+OkKaCfGwUUY05dRQI7w/c1DhGnESs7CAQCgUAgEAgEtx/hKBUIBAKBQCC4sZeD8dXY1SVyeOd1NbExB7aeJR13Rj78EJP7y+uZujNi/qPM97WiOP4YP8ib5t84zNnUamx8p/Dw5IF0lZK6DprA0jE9sJIr0XJwB6cyTPAa+yhLJ/ZVnaruDJkcxuTeSkQgEAgEAoFAIBDcZkwtrOxfVz93CO4e3SguLlJjxiNv4y+f/8vsei8QCAQCgaAp9vYO7f5uz0xLVmMdQ3t/b8iMnbsYf+JZv/m4FCuS2pjO5eiTHL2QiTJvOHgmT/aGK59t57BcIiONYlsvBgeNZKBvTwYMHsnEicNwzY9iwwc/EJEutyeP9NIuBAzrz+A+PenddyBjxknlaysw83Cm/OpX/BApFSvOIKXMDu+AIMYO8KF3v0GMuS+YiWP741IQw/7LxXILBAKBQCAQCAQCgRG099lDOEoFAoFAIBC0yN3qKM3PSudM9BmuJmUr8aKMJOITVSepTH42qefOEBWfQqYcr8olJTGZhPh4CkzssZBK3oj6mZ9+PszB00nq2qXV5KalkHQjjuTSTpiaVpFx4SC7dh/lyNmzRJ1KIFHZ5Kma7JQkEq9fIza7BmsbE2pLkzi4/xinY1NJzWu6EqpAIBAIBAKBQCBojfY+e3SydfSoUz93CAOHjiI9PVWNGU/Xrp6kSQ8YwlEqEAgEAsEvTF0d8o8EDw+vdn+3n48+psY6hvb+3mgvDp27YGdWS0VBFjlNt8dXsXHpjJMlLZZRsHGmi6MFptWlpGa1z/krEAgEAoFAIBDcy7T32UOsUSoQCAQCgUBgJIVZGaSmtewALc3NarWMQmkeGWlSfcJJKhAIBAKBQCAQ/KIIR6lAIBAIBAKBQCAQCAQCgUAguOe5Bx2lTvj6e+Osxn4tDJ80m4WTAtXYr4AhISycH8ZYXzX+i/Dr1PVdjW8Qs2+hXSjjZEYQHVW9b3DYTfV5+vviqX42mg7qv/OIMJ5c9jQvLn+aJ6d6q6l3CoaOu0DC5s8mbIgaNZB2yV8gEAgEAoFAIBAIBIIWuOccpb73P8GrL6zgpUX91ZRfB/fNWcTSOUFq7FdA8AyWLrmf6b+gmH+tur5dOAeE8PATGmfbi8uX8uTsYe12QCmOxfkhDFfjN9F/Io/eQrtQxslDExmkxtvLoCn3N64vZIlkU8t5ddkENcFIOqL/LhN4etkjPDorhNBxwYzpZ6pm3BkYPu6CWLBkEQuC1aghtFf+zaE4sGdLttsQ5k8KxNdFzRcIBAKBQCAQCAQCwT3BPecoLejcnX79e9PLvVZNaY1hPPbiy7y8eJga/2Vw6epBt65OauxXgIs73bw8cLdX47eY2cte5q/LwtSYBuN1/etAX1+NxTl4Ma/+dgmPz5uoONtCJ03j0cef5I+/X0xoN7VQG1Aci0tmcJ8avwn7zrfULpRx4tkZRzXeXhw7N6nP2gvf/v3p08NSTZCYsZS//mEps9WoFr166oj+jw9jTkBnCk59yMrV61i/K03NuDPQN+7026wTHpIsPIxxRhohf6NQHNiLWLr4AR5dqAlPPfU0r776Iium/aLT4gUCgUAgEAgEAoFAcBu55xyl2bs+4//93zus2nZDTWkNH4KnTWXWGB81LrgTGRI6lTmhAWpMg/G6/nWgr6/G4c39ixYwpa8pZ75aozjbVr67hncOFeHm40xpiVqsDSiORS937tqJeCc38Y//e4u/fxWlJkgMHsucWWNp+gZ5+/XUDP7uOJpUk33lEBEHIzlyqUzNuDPQN+46TBZGyN8oVAe2RfZnfPCxJqzZk4JjYBgPPfEETwb/iv5IJRAIBAKBQCAQCASCNmNqYWX/uvq5Q3D36EZxcdt3bbWzs1fO7yRHOin/GsgYHl4eil/lDSyGzOaBWWFMDQ6gn0cnUi+lUd8i76GEDfbCNOMUlzLUNJwIDJ3OvOnjmT5xNEP7dIWMWJLdg5gdOpzgcb3pYgI2rj6MDuhK2ek4MkLm8+KMEXhWntWpR27DVAbZRBOj+ghCFz3N1F7lxFsEsPD+mUwf2ZXqE1LdUp786vOCmWHMnBzMiIE9ca5M4WpmuebEJoQ++Dj9iOPry1YsnDeVORNHMNTfU9PORuJW+zJzEtPHDyPAzx2TzLj6MqEPLGXe2F6YJ1wiUfWv+E5dzONhAbgXxRCbrUlrwAnf4AnMl2QzdXIIowf0ws0kidjUxu2U10x8WJL5LKldAT4OVLgGsqCPDSnHvmaLzWxWzBmDj9kZLsgd1+ISwmNLwxjsnMHZeE0DW5RJ4HSeXRCAfVQuXecv4KHp4+hnb03nwGGMGTOKHjZ1WDp4MHpET6yjLnFdj649R87m4TkTVD13w6E6jXhdmXsOY/6CqcwMCyU4sA/dTdK5kKL1HOqxsdH96OlQJLU/T1P3vDCmjw1U7C5esrvGUurCqNlzmDt9knSeZJuejW1Taysni3qw8IGZzJZl4K/aolJIXtMxWH9flRoMZTTPvDgZn+LT/P6Nz4i+msT16wnEXkvixpVLnL+eT5VaUlceIUP64G1bRfL1rCb9amDwzMWM8Szj0rrNRKhpjQiYwlOjXck6tpMbXjoydKok8apuvV0YPmkKs6dqx2Q3rEulfuao2QpOjJoxn7mSrWvrcB86A3+bdI589jNn1FKNMaTeBpT+uOU21Ffqz9ip/jiUHFbGuLwmauioIMZ1swVze7qPGIavTQ0ew5rRU33/pXERo1xCwdB7gby0QZh0vZDuDlBtRWdfTb0+ku3MC1LvTWpZlHtUP811lQSt/Z6loGdjW210j9RBtsl5Q53IOptAnpo29v6lLBjjSXV0HKlqmnIPmeSH9UnpWo3GXUs2G8xDz/jBlc84UG5YewyTf8P912AUvXiQf+ov/PXja8TFXyM2/gY38qT2ThnJ8M6lnN4R0yBbg8eFF9MeWcSswY1lKBM4ewmLx3vDKc33Qat1Kve/8ZJ96/avP/OfmCvZg3TfiM1S024N9vYO7f5uz0zT/QJoP+39vSEQCAQCgUAgEAjuPtr77HEXOUrn8No/pjO0uzfDR46gby9vevfuw9BB/nhbZRF1JkXzwDn2cd58bCT2175n50U5wYmxS5axYuFkgvq4YGfVmT7DhhHgVsSpkoE8PncIvb0csakzwcndCx93iNt6hLgHVvDvOYFY1NcjI7dhJn1Kv+KHI5qURb/7KzN6mNJ55GSmDvKmp1sdJzcfJl9+9XnZPMIGdsHGwoluAwMZM7AzlVePc0mP00ZxlDpZ4Onnz8g+vfDz8aF/4EAGdrck80yM6vSU+vL4clY8OJGgXg5YOXVn8PDBBPq5UncjWqnXfNDD/ObRYHrXxrElWn7sD2LZn59mTs9idmyO5EaT2YShj7/Es/ePY0QvV0k3HgwaNpTBA9yoPX+UC+pTv/Ia97L5zBjig5enF918+jCse1c8HNA4Si9P4+VXpzHMOpGvIpI0J0k4z1jG3x4bhtWlH9l+vlytpwWZKLobhG0nf6bMDcLfuzvWeTl0GRtKQDepfaaSjly74ONtTc73EZxqquvRi3n92fmE+tphZulC935S/X0sufHzWY2jwn86L65YxANjfHEwtaZL7wBGDuuFddZZTifJ1iPrdyL9HL0YNX40fX286TswgMB+XTG36s2CB8MI6u1Nr34DGDrQB7v8o0Re1bo5fJm/4hmemDOSPo7m2Lj3YuiIQPxsGmxTsRU/W7oNvY9Jg/zw6+1P4OB+9Hcp48yhOPIYx9IXw/T3VXMRA7EleN5U/B1ryUm6SrzWQVmSQ2qGjpNUK49xA/CytcbdL5DRg/vSyz6XU9rx1ATDHKVd6GTShSHDh9HX10eVYXcskndzQlGElySr/2Lp7BEM7CrZsbsPwyQ7HuRrQ9aus6rTT2Pr/7VwIsN7d6W7lyc9+wbg7dEFB5pzlBpSb2NucpQ2GeOTH13BrL7d6GxnhomDM57ePbApyMUjpBk96XGUtmr3Ogxb8CQPDuyBh6OldHt0wL2bpl4/yXbuH2JB/Df7OK+WRblH9aL4sx0cVhJU+3XwYOj4cQzx88FPsVU/XMrPs/9ygVJKlxFLX+HpCV0o2BnOKeUeM4zn/rSMmSO8qDu3jYOq5/CBF17n0UBTDn8XwdVG464lm9U4Su1rHPAbYlh7DJP/ZnZFq8UNRXWUll75jK8PqGkVhaQmJtFj3FRG9XGi9OA2IuV7nlHjwpR+Dy/nuUndqTy2i+P1ntL+PPnH37CgZylbNh0hzZA6FbmG4KXz/SI7v5e/vphxDtf5IjxWTbs1CEepQCAQCAQCgUAg+DXQ3mePu+jVe2vs7J3w8LAi5ovPWLXqPd585yvO1PkxYdEC5mqXmbOQy9liY6HGQxby7EOh9Dc7zyfvrmPlB1J4fxeHEpIounyArzekU9oJyjNOaV5R/ngHZ+XzbGwb16OgqdvORo1K2NjZ4tZ3JCM7neC9d1fzzzVbpfPHsHTpAia6pfPlBx/z7ocf8/7qg5T3CuOpRS1sUmJhjvWl71iz5kPeXPkB687X0m/8Ah6eqe56LfdlUSg+lft55711/Of9dbzz9Q0cg+bw1JLZys7dcfu+5GC6LYPnPsDD/tIz/wMLmNqzlrifN3OyfrpUA8m1ZaQf3MC7az7h3Q/W8NauLNwGTuKBGdqNWjR9mdKrlqgNH0jt+pBV674jplDNlrm+k9gCG7oPnkyomiQ7uqaEDKBLp1zOHsqX4gbIRNFdd0bO7k7y5o94+z+r+XTvIX7acIU8eTnEwniNjlZv0jjpmuh67NQZTPCtJGr1J6xaI9W/9ic2H9XOEvNm4ZLFLAwwIWL1et6Trv/eBxuJdxzFo4/MIVApI9fnio+/F8nbNTb2xpY0bHoF8ejcwVQd/oJV76/mzU+uUOYxgBmTRylnyfjev5inFvTH5PB65dqr1qzn82sOjFu0kEWayjW20qsv/QoOsnbtOv75zrtEl3YmYFIYE5USp5rvq1Fc4avtJ0iU+vzwE8v4yx/+ixXzg5psXKPKI8iB+K/XSmPiI/793lrCK3swZdFjLA1Ri7UJC5xdbIjZ8LlGhj+mYeEzhBlh2nWAS8iT/ovdIcn4Q0lX765h/eU6egfNY95UtYhq6361Z1i/Uhrrq9azZsNlClpcjtaAelul8Rg/su0bLhVUS5/yiVX0sY4vDxijJ+PuBZf3bubjlGLpUzkpUQ31yrZjZ2+NlVJKRXuPUqMN9utHkXQfeE+21X/FUNS5H1OnjVXLNObo9QLM3HozdIya4DuWvj1qqerUk8AJ2t32Qxja24W63MuclKONxl3rsrBzMrw9hslfk9chlFzluxvS/cmqMz7KLc/YcZHP0fAECl36EDJLHegyvqEM7eVE4fXtXDa0TlWuut8vYImNkqazZqtAIBAIBAKBQCAQCNrMXbdGaVHCZ3z+835l7b6IiJ28fzKNTq4BTGjGsRM2fRIDXfKJ+vQLNkec4Hj0SSLCN/PdzkvkpVzg0MEsSqVyVUVJmjqPXtDMPjQC87octqzeyM4IzfnZoZOZOcCG5MOfsEm6ZtTZMxzZ/yPH003pMXwcDe61JhRcY/VX+9gjt+PgITavk2dCujB0rMapoPTFOYcTq6RrHThJVNRRwjd/QvgN6BE8jZmypzTzDF/9+yDJdkN55JGlPP5QAKZXt7Jqe6zSz6bE7fqOj77fQ/jBk6SUlFKcm0l+pTVd+/hpCih9cSEvZgNrtmrWTIyI2MfnN3Rng8WyQdJDndtAZmn14BJCaF8H8q7sZrssUINlYk7p5TWs2aLRcVTcNaIOJqH4SsqzNNc/eIY4TeFGWNtYYG5qjYunGQVJUv0HdrJxezQJcqbvZOYGe0l1f8Pn4ZEcP3uO45HhfBBXiFvAKBrMx4zS5I9Zt1lz/T3fXSS90hK78ot8+L2qm+0XSK8wwcmjj3qONzNnjaZH+RXWS/qLiDpHtKSbLR9dpcB1ACFaJ5SEeXkK736+nfADUj0H9kvXzwU7N/wUR7/hfW2ZMmK2fsybf/uCiExbBk2cwUNLnmi8cY0qj7qEvbwv9fVAlCwvSa8bzpPl3JeZ05sZUAZRSfqpLyQ5a+xlz8ZoksvNcPXqp+bnc3zTV3zyoyTPo9coKC2gMDmXEjMnuqsLUSq27prPaWXcynKQ6gnfyDVdB/1NtF6vscSdiiShSFZIGRmKPoyzScPtXkNyzAkisuWpndUU3GiL/iX7TfmY9ar9Ruz5jFjZxDr7KH9IaUrytiukVNrjOzpIiTuH+uFVep2jqXX0CBiLs5w4OgRfx3KSzkVw08odBthsQYLh7WmKfvlr8jqK+LIK6V9z+c3+No2L7GM7iMmwxnvMBIarab5TBtHDPIPoLy9RekvHmkAgEAgEAoFAIBAIjOGuc5SWF8U2rCMnPTxfTs6lAmscvdSkJozo7oJpRSYxB67rOAoLKdD31mdbKUpm8zkdF8KI7riameEc+AD/8/prvKGEpxnZxQxsHFBdkDdTUciFTPWzROmVVLIrpVMcPZS4ti9XonWuVXKVTYlSZ2y70E+dBJp48jvWRhXift9cQh1T2f7eDs7c7OHQUN2VwQue489/eY0/rnicJ0Z3UWatmZqqM5iUvpSTFhNJvM5r+xlV9S9wK1z+/jTJtS4MnqrxCjpPCGagYz6Xdx/QOJ4NlkklmbEnG13LUKK//Jbwq9B/sewUfJnXlk1mkHY78/598LKVLtVjAs/XX/8FlvhJiZbWuKnFZEpzz5CgvX5mDgWSDqqKsojT2kxJGVXVkozMtbO8+tHPwxqsujN9ubbu1/ifxb2xk2eENa6cqBsNm/NcLpY/22CvncDbUWTGcmjPZj76YDX/+/eP+f5CDb1HT9dsXDNaylflkZ+0i8u6ej16VXECu3bXmR1nNNUUpOmM0+x8SiVzsbBrmNJaau9NyGMv8te/vMSrv3mceb4OmErppuaafMXWyzM522jcZityb4nW6r3ttPVe0A4a2S9XKZLf67azZ5AmoTEJP0n3HBPc/Mcrs6onDPPGJPUSH1/LxsZ7MPKcV9+x3rjVpnNhW9t23zeqPb8AvhaSLmTkWbFtGRclZ/j0RAqmnkEsUHye3swcJS81cZovLknRWzrWBAKBQCAQCAQCgUBgDHf/rvdVtcrzrWbR05uxNJdEUF3drp2+W6WmqvFMKyszTKkg59IpjkU1hC/ffYs//OMr6pefa5Uqauqkg9o3bV/k+U+6FFXJhdQZUQqmeDpYg5kFVpaWWFs25yX15eHnnuTpOSPoXpXCz9t38f33V8ipUbNllL5UU17S4NzTS9xPRN6oxHWg5vX7CSH+OOZdJXx/ribfYJnUyuJsE9lnwvngP6t487tTFDgEMHHeIp5/aSnTZP+cvZkkIShMOtzo+sc+WsUfXl/HJqUGfag6aBEHzGVfS3EK+3TrjvqcN15/k5U/aUo1j67uOpJCEi9EE7F7K1+s/5D//S4ek55BPCC/Aq/Ko666iV5vcgJ3HPV1uoTw7PKlPBIm2UjWebZt2cW336Yiv3CuRbH1qgqKjRm3BtR72+mwe0E7MTND+zeDxlxlR3w25l19CJHkN76nJSmXw7m89zqZ9r0YLw3miX08MM++wbfK1OwOotn23G68mdpNukFUZpN4TIq2aVyUEfPtAa5UuTNS/kOR72SCe5qTHP0DF+TsX2CsCQQCgUAgEAgEAoFAP3e9o3S4rxtWlJDbzEN8fG4JWLnRq42v3jbC3wpr9WOLJBRQhBl15fFs2R7eOISfJF4t1ipDeuJuBUW5mm1o9PfFiSldpQf92nxSj2pS/Ocv5sE+lZz9ahMXy7oS8siD9a+ENsJ3InOm9MMp9Tve/3wzW3btI/yA+hqtFqUvVrj3am3W0zW+O3iDUrd+TB0dwvjeDmRe2kKE1kfbUTJpkULlVd2dP/7AmtXv8feoSvzGzmX2eCnraAa5tWBZVcGhptffHtnMDuqGcoJUeRlWsyrKjzatO5yIc5pStw8nxoYEaV6bVigj+9o5Ik5lSiPFDFvnzvXycOsaolNOoonN3RLGTOTBoG6Un1rLB99uVexuj7oEhpY2jVsD6r3t3EK797dqtGJpm4mOTCTP0oP+M0Po65DJhR8k3Z8+yrUCR3zlTas8rclLPMFltfzdhGfY/UzraU35jWi+lU2+reMibgebLxcqfygKCx1CT9M0jm+8qslrz1hzscP67v9zp0AgEAgEAoFAIBDcNu66Ryx3v6XMD/aWHjidCJy6hGdHu9Mp8yLh+9QCTdi77yKZndwJfmIJs4d0kVKc8A2ez4tPTVfXyMtWXgW1cu5GoAs4e3bRPMyqDkKvfiHKJjieI2fz4rN9sVemr7bCvp80axCGPsGzU/urD8ddGD5tOtP8lYh+XHxZMVuz6Y5zwASefWIEnp2yObdPs6f13h+iSVX7Mi3ASUrpwqhFT7DQz5qiK4f5RnYW+07n8cWjsUuKYNXGH1m58waWgQ/w1GI9js4CN2ytwbTOjMzkQqmTw1i4PBhfXf+LdO24fDM8hz/Aw6rcZfm9HKD7PrmG+G3HuVbuSuBDsxngkMHZn042zLRtq0wUYsmTpwU6dGGap3SUdCQfGuPN/OX/xYvzh+FZkkt8zBWyCmowNTHFUt50JmEz+66U4BSwmN8uksoo58h9mc7sYFmW7eE630TEU+Q0gKeWzWeU2jhnnyDmz9B1WBpCM30dvZg/v/4af16iWUuyJXqELuSpZ57gjy8uYf5YX+l8qZ8DxvDwNF8cyCch6my9PGz85tTLo8HmMjj+g8bmbgnODtiamUFNORk5Zcp1n/zdANzVbJm9B6+RZ+7OyIfmM9ZH0o+LN2MXPUfgzWbXgAH1toXz+bJCnPAMk+8fkj4UhbRik9o7b7vsvgGN47gLA0PlMSiN+/lP89v+HTMNufTQKa4X29NjSm/cihLZcUVKLDnGsetldBl4P35OJVw/JU+3bA5Dxmfb0S//IJ585TXeeGUxzW0LpQ8zi/4EBkphZAgLn3iOV58cR/e6G4Rv2KpxBLd5XKQQ/v15MlwG8vgEb+W1+29kOcoYWqfqUHXvMZvhUiHFfn8ThJe6MoBAIBAIBAKBQCAQCNqPqYWV/evq5w7B3aMbxcVFasx45G385fOVt8k7NfO+vF6CeegZP2xKbOg1KJAhg0cwMSyEgZYp7Fy7li+j1JljAVN4arQrWce+ZksMFGakUuTUm5Ejggjo40vA0JGEhQUzpEspp7eeIIl8HIffx6jeXvj6+DHyPn9Mfz7OpRxrAsYNYYB3L3z6DCB43Cj8i1Mp7dodk2uf8bW683Log4/TjzhWb9R5ibYonaRSO3oNGUPoIF/6DBjCuPHjmBI6ELf8s+y+cLP85Hp8qnNx7D2EYUMCCA6ZwJS+FtwI/5SV30WTLnWuMF3qi113ho8ew2CpL4OGjWHqlCA65x5j/b+/YXeqDbNXvMCSQRVEvvcen8dkkJqUTM9R0xg/3JmSU/uIyVEvKFNaS++QoUofe/Xqz30T72N0zyJKLXrgmH+S9dsvSH3JJNnWl9HDBzHEz4f+gSMInRBIzyoTHN0tyFDlrFCYhdWYSYQFdMU27wzvrNrDDe1r9IbIpInuGiikyuc+Jgzywr+nD4OCh9Ej7RDH3XXLexKyeDELxvWnX79B3CfVPTXIF1up3Jr1kVwtyiElqYYuUvvHD+uDn7+mTNikIPxMrrHtWKp0HY2NcaVBvzCAOY+NoHOOKg+dtG5lDXrPSk2mxmMgwaMlefbxY9iosUyeFMpYP1Ou7IhS1mnVayshc1nub0NKfZ+b6euw53h7kWSD1ums23JaObU5TCxc6RkQwBipLYH9ehMwaDjjJo0lNMBektVXvLPhBKklGnl0HjRMsg2NPMZOlmyujynnt37K+5vOkKVnCYTBMxczxrOMS+s269/lXa8Om8ir0JFh9w3Cv4cPvv5DGD9RGo8OuZS7edFJHVuFGWnY9B7OyMCB9PHrw9BRwUwa1p0q7Ohimc2Rz36+eRawAfU2xWz4dGb27YyzbTl7j8dSrscG8mt7MCE4AO8+3eg9cBSju6Wx52S8fj3lDWb+1D50cbGj7MBJLmQafy+42SYgx7YvE0b0pWfvHvQJkO59wb4UJ5fj4d2JuHpd6LNf1e5s0vXLTKY0G99JMxnV046Kqz/zj63nJTmXkdkzhEVjemFfk8juf27kiLapN+m4GZvNaEt7DJX/UF5+Yz7j+khy+nQ7JzRFm0dpswd2tt3pM3A4Y0aO4L4RA+hadoWdGz7l890XyVPsvW3jQqZU0rX/5DmM6W5OyoG3ee9wnppjYJ352fQaG8qgXn708u1D0NgQguyukGnZB9cC3fvPrcHe3qHd3+2ZacZuhdgy7f294ejohJWVlQi3MFRUyIsOt47Qxe0LQiciGBoMtRWBQCAQCO402vvscdc5SqvO/osPtlZj3bkTuVeO8O2mnwk/HEOq9uG1OIfUC6c5ejqBJPl16NIsbtxIJv7yZXKxxaJTMamnd/PTntNcTsmhhHzSklPJKKqitraWvOvHpXOTySrKJCnpGlllJlRXFZOwdx/b9h3m8NkYok7Gc13dqSYvM42TJ6KJvZGlSVCoJis5kYT4q1zMqsbG1oTakiT27TlM1KVkUnJv/mEi1xN1OJLIi9eptnahU348ET9s5ae9R4hOU9+Fr8oh8XoS8XFXyO9kL/WlkISj2/l+2372nkykAlMs6nI4f+wguyMvky3LpDCNxMQ0Ll68wo34RFIbrfmYTXLiDWKkgvaW1eTHRrJL3qn8mNzHc1xNUQRIamISiXGXyKqxllSWT/yePWyNOMqRkycb5KxQRFpqKvFnTxEh5R++mk2DT8EAmTTVXT3VZKYkcy2/DOqgPC2Ww9GxpKTplq8kJz2R2LhcTFxsMKktJvH4dr7bdojDsflKOwpSk7iWeF2SRQnmnS2UdQnP7zvIobPXuJEpz1orID31LMdONOgXyUKy0y5x/LhWHg1pR4/q6F2Sc8J12c5iKbJwkXRTTtalw4RHxhCXmIW8WbteW8nLIvl0FJH1fW6urzVY12RyLnI/x2N1vd03U56TyvXr17lw4SLJpWaYmlVSmH6ZLVt2ES7Z04kkzSq39fLIkMaTpI+KtIts+nEHu/Ye53yu7voLDRRlZxBz4gTH4pJIV9MaoVeHTeSVn0FiUjzJxWZYmJWSuP8guw4c4sCpcw1jqySdxBspxF7IodpWuk/kXSd8Zzh7D57ixImTnIxPReuGqseQepuQn5lOeWUtpWWpHDp1TbKTm22gKktqS0I+pSaSQiozuRB5jvOpefr1lJxNivTgUVtWRuKRM1ypMv5ecLNNyI7jVGkM5lBsWkOVdP6e8N3sjjhB9LmzRNXrQp/9qnZ37IR+mSkUk5Eq3SOij7Pn4Akup8pjQbqm1O+ky6c5GBHJwTM6596k42Zstqgt7TFU/pnUWFeSdv4ou05eRffuqxelzaeIPBXHxctSuHiBqGPH2CON/71HYrihc19sy7hQqJIdzg8xxjWV7X//lMPq8swyhtUpO1RTSSqspKa6kqyY/WzbdYCIk5c5VX8/vnXcfY7SOpydXSktLaGmpkaEWxA0v+fkb7fWfssJXdyuIHQigqHBcFsRCAQCgeDOo73PHp1sHT2kp8uOY+DQUaSny7Pv2kbXrp6kpaW0wVH6MluipsPWUGb/1YEuXraYlOeTltNkg4wWcHT3wN68hvL8TLIbOQzBxtUdFytTqorTyNDZEV9zThVFKdnSI3wbsHXBw8kS06oSkjPlHySto1zTtILctNxm11dsqS9G4+hGNzuTVutqtyy0tEEmCko7zalpUe/WuHk4YWXSkmw09mNe27KM20qH6Oamvsr9sodC4+rU2jUtybqt+mgnmrbVtmJPqq6MaJth9Tag6IvW6m/GZvTZpJLGzddvt5yNl8VtxaDx2VZulr+sZ0f5jzMdfi0VY/XlP533/vUiw7K/4ZHH1+tff9aAOjX2S8fc2w2hrk72b+Ph4dXu7/bz0S0t0WA87fq9IfWrq9Qn+feG4Nag2Iws39Z+ywld3DaETgSGYrCtCAQCgUBwB9LeZ4+71FGqJgkEAoFA8EsyZDrPjvbCuV8IcwZWs/vNP/Kn8F+R8+EudZTWSf3SOgLk/sm/NsSxY49aR1unVn7LaXWh/e1naP3iaPyxXs4G6kSMj3v3aOj4FQgEAoHgTqS9zx530WZO+WRmZpN5a98+FAgEAoHAYHoMGcnkccGM6ZzNvk/f49NIMUPrTkJ2COkPLeXJQeTrT9cGVcBG0PT8xvGmQeTrT9cG/fnGoq8OTfhl2t8QRL7+dG1of75AIBAIBPcyd5GjdC8f/uPffLhTjQoEAoFA8AtTdOQn3lu9jrdXrmPd5pPE347X5QVG0EmZPXXzUTOrSuS3Pd9Ymp6vv16R3758YzG2fpF/t+QLBAKBQHAvcxc5Sq8RdTCSqDg1KhAIBALBL0ze5WgipO+mA8cvkNCuxZsFt4I6+T95dpU4dvjRWAytVxzbfjSWpueL471zFAgEAoHgXuYucpQKBAKBQCAQGEnTKVQi3pj2xo2lo68v4u2jo68v4o250+MCgUAgENyDCEepQCAQCASCexL96/OJ0BHBWPTVIULHBmPRV4cI90YQCAQCgeBeRjhKBQKBQCAQCAQCgUAgEAgEAsE9j3CUCgQCgUAguDfRzp6Sj/K6fCLeYXFjaa0+Ee+AuLE0PV/E75m4QCAQCAT3MsJRKhAIBAKB4J5FuySfOHbs0VgMrVcc23c0FkPrFce76ygQCAQCwb2MqYWV/evq5w7B3aMbxcVFasx47OzslfOVL+pOt+nr2sUb/66dyMkvVxPagW8Qs0OH4W0VS3y6mnaH4unvi31OHm3XVvMMnzSbsb2suHAtQ035hTFIx96MnRHCaO9W2q1Hx7ezv/4h83lgxn2MHRGIt9kZLiSrGQqBhM0fie8vaX+KfAbgXhlPYp6apgfnEWE8PGsC40cOY4BrDqevii3B9eEbHEbYIFfK41JoQZyCX5ib9XQHjMUOxN7eod3f7ZlpjW5W7aa9vzfkPhUW5KuxjsPUKQBT9wmYdhmPWedxmLqNwcRlKCa2vRQvRG35HfK9eItxcHTS/J4z4LfcrdKFpaUVVtY22NjYKsFa+iynmZmZS6roRHV1tVry3kDoRGAoxtiKQCAQCAR3Gu199hAzSvFm4bIVvPK7ZTwcqCa1h/4TeXTJIhYEq3GX/kx7/Dn+/KfXeOMvL/PnFxczzV/Nuwn5wXo2s4O91bhE8GL+/PprrJiqxjuKkCW8+sJyXl02QU3oWO6bs4ilc4LU2C+NoTrux/SHDGh3Ux1L3Lb+DnmQ555ZyINTgwkdN5qhPmp6PUEsaNK2244in/uZ3l+N68NlAk8ve4RHZ4VI/QhmTD9TNeOXRXZ2LZwfwnA1rovsDF84IwhfNX67GDTlfpY+NJFBavyW4DKbl6X7zGuLW1KaoCVu1tMdMBYFtxVTpyFY+P0XZj0WYeo5S3GWmriNVYLiOJXS5Dy5jFxWcOuQHW9OTi7Y2NhgampCTXUNlZUVSpA/y2lynlxGLiu49QidCAQCgUAg+LUgHKUU0dW7N4H+3nTpiCV57DvTzcsDDxc54sXsJ5/i2bmj6eMoRR38GD9rAc8+9aBeR4z2wfrRKf3UuETP4UybMY4gTzXeUVh74du/P316WKoJbWTkg7z8h+d4bKQaV3Hp6kG3rk5q7JfGUB074O5pQLsb6VjD7epv4ORpjO1jQfKGdaxc/QnfHlUz6nHCo0nbbjuqfNzt1bg+xocxJ6AzBac+lPqxjvW70tSMtjN72cv8dVmYGmsbirNryQzuU+O6KM7wW+2w1INjZ8m2PDsj30JuGRX9GT8jjHH9xZzVtnKznu6AsShoFc1yfPK6fO07mnnNxsx7EaZdJmFi349OZnZSRicp1Kqhk5Im58ll5LLyOU3ruZuOxmJova0d5VmKdnZ2ypqL5RXllJWVSaGEstJSTZA/S2lynlxGLiufY2j9v+ajsTQ9v63HtuvEsPrFseOPAoFAIBDcywhHKblsW/cOf3rjI36KV5M6jBIS4k6x85sP+c+6Daxbu4YPrprgO3oyk/VOS9M8WHfr7KDGJWrkf6qp6oBVARpxchP/+L+3+PtXUWpCG+kVxKxZkwnupcbvSG6ljm8v/Xq4YEkRSdsjiTh4lOgENePXhr87jibVZF85JPUjkiOXytSMtjMkdCpzQgPUWNtQnF1e7ujzbSnOcM9b7LD8pSipUW41VZXtd1gLBL8+5Bd+NUcNxsUVJ2nXaZjY9VHidJJnyNdJRylfNyhpmtnzcln5HPlcKbFRfXdP3Fhaq6/1uK2t/Cq3NWXlZZSWlmhmK9ZUK843pZAU5M9ympwnl5HLyq9/29raKbXo1qfhVsV9GDd7Lg+FDWnId+lJ315ODXH12LFxY2nf9WS5yvI1XifWij6b1ifitysuEAgEAsG9SydbRw/pl0rHMXDoKNLTU9WY8XTt6klaWormS9qodXHG8PDy3pRFbiHeI4yx/bxwIZ8bFw7yWXg8niNnM3uEN+6W5WQmRPPtplP16/0Fzl7CBLds9n28g7NKwnSeDbYidvU+cqfq1HXpOFt2XWy8TqC/dN3gAfRws6I8+wZHa8ez8jdDyNoayuy/Svm2DjhSSEGJpnjA377i67Bqtj6yhNdiNWkNvMyWqOl0PvkBo5/7TpPU88+Eb+zHuQcf4veKU6zt/WyMXM8AuLCOLw9oUkIXPY1/aSRrTlqzcOpQemn7tGsLx/SoVH5NeXjYQv57eg8yDn/Hz3FQliydv+Uif90YwXx2MPD5fa3U1YVRsycy3LeL1I9iMm9Es0Vqs34L6kAdK/hKfQ5msJcrVhU5XIuqJuzNpxmZI7X7gbfUMhKt6Viivr+65xnVNxmd8iZS+ZQrHA0/wNlcTa786vfsx59jvn8FFz7ZzrHylCb9kdHYEFvn8acTWhnpuXa9jUdSNn8KY30cyY3ZxPrwFPAcxvywQfi6O0FxDomnfubb4zpr6nkGEjYukAEertgr7bxAxDdHqDfnxe9w9EU/Lq6cxZNfaZKcg+fzcKAjRVfCOVw2gOELn+BPo11JVuwmhzOrNxEh9X/4pGCG9+mCu4MZRZkpnImU0nXGiaLjYV71+Rei9hEe403YfF8efPq/GGUSy/qfTkkltXUax+MfbOX3w3PYNOJx/qymaVF07Hqatye8xKdqWms6VuzO5iorw/OZNn0kAz0lmRakcP5IODtjGtZ+098vTb7SJv843r7/IzJaqEP+Q0tg6ARGD5DqkcZAbkY8h3bsq7cfZRkQ6fy+XZywr80nJf4cO7do27qULw89CBum8fCHSoIeWrNn6fry/bK3lC9dv0jH951Zf59pvUxLsmiKs08QoSED8HZV+3TlHJu2Nxn3svwPVkj3oSB6ORdwZuNX7EyUMluz8ya0dq16PdXbh3YsNtwnfpXIs4ukg4eHV7u/289HH1NjHUN7fm/Is6bkPiXeuK7E5V8buj+GDI2buQzFovdzdLJwBRMzJU/JlQto/tFBOkOpRK2ptpq6yhwqr35ATW603vq16MaDwubRu6W/2BQkEFXmwYiuLby+XJnOiZ+OclX66BIYysyRfXFXildTlHSG7T9GobuqU0vtkdEX7+7dU/N7rpXfck11IdOW61lYWeLo6ERJSbHicGugues31GBhYak49AoKCqgoLzfu+r1HM29IV6zVqC5l6Wf48VBCM+fP4K3vljIsby8Tl30gxXuy+E8vMKVbJnvXvMHnpzVlZYxqj0Rz8R5t1Elbr2dpZSXpxLHdOqmUdGLU9SWdzJV0UnbtR3ZJPwlaLS+F2xU3ZPx+fc1aab8+m9JQTvLpnRyUB3C3ETw4ZTA9bDX3n7Kcy9Jv7Qiic5SoQlvaa+j4FQgEAoHgTqS9zx530WZOc3jtHxPp5+jFqPGj6evjTd+BAQT264q5VW8WPBhGUG9vevUbwNCBPtjlHyXyqmaaZugzr/HUGDviv9nHeTlh7OO8+dhQ3Kx7M2H6MPr6+qh1dccieTcntE8P/tN5ccVDPHBfX7pLPyh7dO9FgI8HXm7WFF75jK9lx0BVBRVVmuLyuozLn5xAn7KzrP74INe16fUE89AzftimnmT99guapHwv+o+sIO7zw0QrCW3vZ2PkembSp/QrfjiiSVn0u78yw8+WbkPvY9IgP/x6+xM4uB/9Xco4cyjuJofrsAVP8uBQH7ycrSVVOeDerQdducYm6aEg9MHH6WdXi2PvgBbq8mX+imd4Ys5I+jiaY+Pei6EjAvGzySLqTIr0M7ApHahj9dpPzhtFQDcPunfzxC+gJ908XLApimP1RlUohuhYQukvOucZ3Te1/LxgAj1sse7cixEjBhHgbUf2uRgSy2Dyoyu4r3dX3GxNMXFwxtMd4rYeka6qi8aGnC270K/fEMV2e/fuw9BB/nhb6VxbsfFB2HbyZ8rcIPy9u2Odt5kdRROk/i7igTG+OJha00XS38hhvbDOOsvpJOnMHpI8XlrE/UF98HSyws1nEEHD+tPHOodtp5OUFhAwhadGu5J17Gu2xEhxSYa/++3DzO5TS/TmA5iFPMyDA3vg4Wip2o01Od9fwXvFf7F09ggGdnXAyt2HYcMHM8jXhqxdZ1Ee1UYv5vVn5xPqa4eZpQvd+wUypo8lN352YPKLYZIeXbEzNcHJtQs+3nKdEcguU2MYPHMxYzzLuLRu801OVkXHNukc+exnzigpretYsbvhLlj5BzNnpB99fHoTEDiAgd4WpOw8jeyva75fZxVHhdImdxNMPQcQ1FwdOEkqXc6KB0MI6GqNlYMng4YNl2RZRWxkHDl4S21dzlPTA/GwMMfOQ27rQDxLz7E/Tr5fO9J7sD0Zx/YQeU2psAmt93XskpdYsTCYvk4W2Nh3Y1DwSEb06YZ7F09sCjazS7qBtVrGsmVZNGKsVPbpuUwa3As3G3u8BgxmdGBfutSe58BFzcZgivwDbanrG8LCUb749LAi64e9RMl23JKdN8WAayl6csvVsQ/NWETnPvFr5m7fzEn7a8PYo2XfFzGx7SklyC/oSKnyUftZfmlH/h2j/JZRQ31cjppoXse37kp1+m5NkvJvy8cHnv8L9983gMEBUggMZMSwAOk7rx8BA9W0rhVc9wjl6cnDNHEpjBgeyNBAqcwAtYyvNdd/PEzW2Ed4bfn9iqO0u6cnvfr0J2io9F3dw5GSG+e4XthwXWOPjk7GbRxUIOmipfpaOzq7uFJVVUV5eakmUUnVlNB+0sS0NKTU1MhrZJopa2SWlpTUlzPoOG4pf39qEqN1daCGXjZJ/LA/vpnzh/PoCxPpb5LIe98cluImTH7sKab6m3Jtx1aOZMjlRvDE7x9jqm8Fh8+lNlOP4UdjddLe8eHi4tJhOpHRlm31KOnkTUknPtUb2SI9Ixl83m04GjJ+NxQN4M9LJjBaa0tDpXJDAwjo30+1LW9sU7azr9Msfv/bxTw4MZDeHh707NWHYUOl3wd9umCRcZIL6t/+DGlX06MxtiIQCAQCwZ2G2MypHmvs7F3x8fcieftnrFr1Hm9sScOmVxCPzh1M1eEvWPX+at785AplHgOYMXmUeh5YyWsn2VtTP/fCQq7LiwGjbYjZ8Lmmrh/TsPAZwoywYWohbxYuWczCoU6k/LyGf65czcq1XxKeXVX/Q6Mxvsx/ejGTnTPZ9dF3HFdnmLbOAT57dyPhaqw9/WyMXI/Ubxs1KmFjZ4tbr770KzjI2rXr+Oc77xJd2pmASWFMVMvocnnvZj5Ol6eElZMSJa+ZuU6ZTVqPrXOLdfnev5inFvTH5PB6Vq35WArr+fyaA+MWLWSR3k2XOk7HmmsPxiVtD2/9+z3++f4nrN2XS6Xun9SN1nEDxvZNUz4Ix2sbeefdtfzn/XW8s68Kn0kLePbhMUqZI9u+4UJepfQpn1hJ1itvmk3agIWZVb3tvrnyK87U+TFh0QLmapd8UGy8OyNndyd580e8/Z/VfLrXUdPfABMiVq/nvQ8/5r0PNhLvOIpHH5mD0uziAsiOZ/NHH/Ou1K+Vq77mUm1PRj8wjWlKxU0J4slnFjOrZxlHv/6Gn2PzNXaTUizlae1GnvlZQp70X+wOSa8ffsKqd9ew/nIdvYPmMU/dyGzs1BlM8K0karWUL137/bU/sfloDBmc4qcNV8irlQoVxit2qKmzrTjhv/xpXmwS/HVWxJAxRMeK3XX3J9jqMp+uW8/bK1fxXYoZvsMnMF1d17f5fulg6YhLC3UQspBnF43FLfVHTVvWfsK/IyrwnbqYRxT5jWX2tEBc0r/l3bVyWz/h3Z8Ocu6G9kYUww/rPuMHjYfvJlrv6wTmLwzFrzpauvanrFr3Ce+cycfK0ZzcSO1s0tbLGCQLLak1lKQdYa3UlvfXfsQ7/9lNhnM/ZsybprFVCUX+3kN4wDuNbz74kH/+5zvCc9Vx3ZKdN8WAawl+nSjzZZVZs8YfTR0HanayV74RpKA4FDTHTp1MGDvQkifCbJQgf5bTdMsoRynIdch1GXrdiI2SzUrjWAnnK3Ht5olFtk7axkhidnzTEF+bhYVUxrXqUkPa5+HE1PXkwYcfZHp/U6I/f0/6HviAt6Xvw7cOFEq/BZwpLWp8XaOPxmJovXqO8mxSU1NTysp0HXKqhOtFLf2j6Eb9KBdQ0HySz5XrsLS0vKn+Fo92nenWVAdq+GTHpRbP16CN57BlzT/5w//7kB+vasv5MHbmdGbf59PovLYejaW5egw5ynLsKJ3I+jX0uspRXitd0omni4Hlb+PRkPFbFxPOJ9q4FDKsPOnmVsm5+rRv2HqujikLF/HQOE9yd0njVh6//3mPf3x9BRPP7phmN76u0UeBQCAQCO5h7iJHqYwZpckfs27zfmXdwz3fXSS90hK78ot8+P0+9khpEdsvkF5hgpOHupZYs9SRd+4LPg/XrKG4Z2M0yeVmuHqpGy35TmZusBdmSUf499ea60Uc2M+mc5novlykRXE0THUm+fv3WLcnFu3PxtZJ4fLZq+qsMS0d2c/GmJen8O7n2wk/oOnPB3G50kOAG3561lRNjjlBRLbsKK2m4Ia8ZmYkh2J01jgsutZCXd7MnDWaHuVXWP/VPiKizhEddZQtH12lwHUAIRrfoB46ou/qtc1S2P/eD5r2SeeFbz4v1aUWkTFSxw0Y2ze1PDfYsVaSV2Q0Uccj2fnVN5zOdWFg2GTkLYriTkWSUFQtfSojQ27L0Qs3z7JTKbj6Wb3tRkTs5P2TaXRyDWBCiFpAwZzSy2tYs0XTt6haTX9LL38jnRvJ8bPnOB4ZLulNemgOGIVyam40Gz//ho27D3EovoDi4mKScqUHGVcvPRuUybMcH+bR4ZZclu0+/ILi8LrZbs4QRz7HN33FJz9KOjx6jYLSAgqTcykxc6K7ujm0tY0F5qbWuHiaUZB0hiMHdrJxezQJXCPqYBIFsmjKszR9VupsK9Z0GRes7MavG7o0eovVGB0Xcm79RnZGyO06xDcn0yk3d8FL3WC++X7pUJ3FiRbqCJs+iYG2aRyS7GfP0TNER5+UxkY0aWZejAmR/7hjjY2lCZZ2XXGuy1DaGr55O7vOa19pzyX+/CXis9VoIwzpayA9O5uRn/CdJv+sdP1NN8g3s8K0WLItRRmtlzFIFlri9/LZZ5vZvi+SI+lFlJTlklZYjU2X3uhshyeZeRmX3v2CTVI5xS7Ucd2inTfF0GsJfp00eGc0GBg395DuzKYWmoji+TGRDlKQjk9MseaFeXYsnWyjBPmznCbnKQ5T+eeXco780QIzuS4trVz/7KFwtu6Uwg4pXM5BfjmlKEWNy+mHLnLtxP6G+M5U5Hm3VTlX1LgU9pyUxlV/RvR1xTw3ntXSvXfv/kPs3bOHzZ+v5e8fbOWc9jWSVtrTatxY2nA9eeOfiooKamvl1ZY1BZR/6//RpGlQ45r/VTop58p1yHU1QvdUmWbiig608pWDJP/wEzd0yvdmwuKl/P7VV/jTC0t5aLR2qYYG7L186Ofrgb38N6zeo5h7vz+dbU1w8BrJ759/jt8/M4shBrannqZxY2nj9W6ZTnRPk2lz3Ikhkx7gNy++yP/743/x/JOzGOMlJeuUHzJX0tfDY8F1IDMff5r//uMr/Pfyh5g5SGfzTrm8mv/7VyTdvijp6bdSkPX12weY0OT6hoxfEk4SXj9+w0mS7aEqh0tqfOvO/RyVvhiDAnpgX5XK8S/2EC6P34h9bP3hM9769wZ2pyiXM0IeKk3jAoFAIBDcg9xljlIozT1DgnaSVGYOBZXSb4uiLOI0b0ciPeFSVS0925i3ttt7NQVpsQ0zmbLzKZV+zVjYqdu89O+Dl/S7LTfhR85kapJkSiuqkSe2NWXitNH0KL7I+o0niTd4NmnzdFw/m1CaS9SNhoUDLxfLn22wVx0yRtFiXf3o52ENVt2Zvvw13nhdE/5ncW/ssMTGTS6jn/b3Xb123nW+PKfjGSqpoEr3j+hG6rgBY/umli9IZPOVBnmReZzLGeWYunVnhJpkKOVFOrZLGZeTc6nAGkf5IaCeSjJjdexR7a9Njwk8r7b5jddfYImflGhpjabZZdj6jmbZKy/zxmsreOHJqfRxkB72pAd/cyW/AWv/JTy7qD9uZiUkRrdu96X23oQ89iJ//ctLvPqbx5nn64C87YmpWnH0l98SflVq5uInePXVl3lt2WQG3ZKdldQZu01CrPoKqgYjdFxVSNrlBjvLzpPsEwtsO2viBvWrlTpGdHfB1NSRwYtfqG/LG8uH4iEJ0MbRRyqxlw0/niLbdRJPr3ieN155msfG+kgtNQRD+pqhrMPs3GUY/so54N/XRRrt5RTWj53Wyxin42o8A+fy8n9L7fn9Myx/eCSeFtJXmolJw9sBMpXZXDx+veGPUwbZeVMMvJbgV4dmh+e2BRMneT6xfJeSPQvyrDjN8b6BFjw+2YaAnmZ0cTZVgvxZTpPzdMsqoZOpUpe+axgSNOjP0wS1iETj9BRy5cn9Dt5MGTcQZ216+iWOR1+jpFFZ44Ox6KvD0GBlaaU41DQyVf+tl3FzyLLXLdFJqcPKykrvNVoKCnrSG0Jv7n9xOc8/No+ZISMJCQll4XOT8FVvINpy/UJn8dD8++gnx31GMH1GP7pIJmNu34OJIWOZOGYgXvV1Gh+MRV8dhgZZjh2mE0m/+q7RUtCgP6+uzolxTz7Py88+xP2hAfQdMJLZix7lxZef4/6+DeUUfcybwfMv/4alM0OYMCaYOQsXsfy5BxlXX9connnpNyx/IIQhvt74DQ3lgYXzeGC6pK/xQ/CvL6c/tNxOTdCWkSKN0tOLpW82M1f6TxyOpza9+AZRJy6QrlOuLUEgEAgEgnuZu85R2pgqajr4u77e+WZvpjiGqiv0bzLSGCccq0tIvHSGwzoOt46j4/vZGHPpR7r6sd1o63LAXJ5MUZzCvqhTHKsPn/PG62+y8ie5jCG0pe/qtauq0O5zoxejdKyLsX1Ty9fU0NiXWEap7PE1MzPQodUCVbUa526j55NaanTXyVX7W5h0WKfNUvhoFX94fR2bpDzn0CW8+MwCpvd3IOvcbjZv3cIXymv0TbHCe+QEuiRHE1/tRuCkCTirOXpxCeHZ5Ut5JMwfx6zzbNuyi2+/TUW35uwz4Xzwn1W8+d0pChwCmDhvEc+/tJRp+raobxfqjN0mIaPR0pXttV8TzNUH5Lb3q6EOS3PpVl6Rx/lGbdnEO397k79+dlIqcY09X63nzbc+ZPu1OnxHT2HJE8t4abEhL44b0tcDfHM4BbPei3jlled48Xcv88p8L4pPb2X9cTlfpvUyxsjC/4EneH7pdIK7VZO4W7LFzT9xSVmaogl1tdJdQgcD7LwpBl9L8Oumqf+mlbiJpZtiXw3OH02YNtySrs5mUrLGIaoNcpqcp1tWOVeqQ6lLTtKlo+NN6RTLF1uOcqOuJ48+8xv+9/WXeOGB0fR21earRy3tvV5rtON6ZubmVFfrjEmD2yIV1Ckr1yHXpdDC9RSaxF38lvP753XCI2PVHOi9cAnLHhyKa+rP/P2tlbz577Ws2ZPdeLkfqT4rWzvstcsEXYrgi0/TKJXSyzKieHvVat5eu5X6PZ6MbN9NcWMxsv4O10kr12u1ft380IdZ8chEelVG8NbKD/jXv1fz1hcJOI2ex7Kl8+ktl5HKK/rw7stYq4usX7OGf/zzHb5OMad30GRmat8cmT6bx6b4UnVkDe9+8CH/Wf0+UflWOFjks+c/G9mrFjO6/U3jTZHyd327h/OFzgx/bBl/+b9X+P3j0wjS/kG8o68nEAgEAsE9xF3uKL2FHM0gV3o+cusa0rIDSKGCrev+zZtfHDHilfu7nROkyv5HsyrKj4azZXvjEHFOU+rWoF7b2Z0pLTmjjNKxLsb2rbn2BNKrs/S4VFxAvJrSVob7ukkPXiXk6n2HWUXtr2VVBYeatHnL9khlg5qQabMY3aOCY+9/xhebwtm++5D6Gn1TzDDJPcHqdz9m7cliuo1fzNJQnVfVmjJmIg8GdaP81Fo++HYrW3bJyyhkNRkvhcryAzt//IE1q9/j71GV+I2dy+zxavZtpSPtt/39is8tAfM6Kq/e3JadxzW7Fpe8vR1HAAD/9ElEQVSmXuLQnj18/dknvL3yK9IdBzF9zgQD1tg0pK9meDjZUpZ+iasWrvRwNeHyllX873vbiK6fCGtIGUNl4c2MWZMIsE/n0w+/4uude9i5T7s0RSsYYOeNace1BHc+dfIkLfkf44+aXe413gXNDFH5A3RxkmeZ6kfJ0xbVniMnSHUZet1GRy1N05se6wvpppdwdvNa/vqX9ezNsGFw2GweeeIZ/ud/XuGFmX766zHiaCyG1qvvKMtSjspopKqVrSF0qi8t16HopUn9LR7Vc206j2VSiE4Y0Ust15M5c8fgbZbE3pXfsmPfQfZEHGLH9+fU5X7UeuT6tMjxpHMcjNB8D1YVJrI7QjrvcAxJrbWnpaOxND3fiGNH68TQ6ypHLU3T1ePUWVMIcMnm+Ntfs23fcY4fi5T0sY5d0m8k73EzmOOrllco4MwaqdxuWW/7+fJ4KmXmrnQbqNY3rAedLQq4+vlBjkafJepIBF/FFWBqZUpxRDSX9Vy//qilaXrTo5Ym6Tf2f82bf13NF2fq6DN+GosffYzf/+EPvPboaGWGeLP1GXAUCAQCgeBeRjhK20pCBKeTq7Hxm8HSaYF4SkmeQ8J4dqy3nlcxhxA8aRzTpoXSML/gLiGjhHKpxy7e/XHGCU/PFpxhjbjONxHxFDkN4Kll8xklC1DC2SeI+TOCjHRMGst1fj6TQrVdLxY8GsZwRXmBzH4quP41OAWjdCwhPehq2m1s39TyUnsWasu79GfaU48S3BVST/7UMCPBQNz9ljI/2FvRSeDUJTw72p1OmRcJ36cW0EfCZvZdKcEpYDG/XTRM6a88G9o3eDqzgzV6dXawwdS0jqrybLJL5LoX81qAvpeVy0k69hW7zsYS8dFmzpZ3Z95jCwlrzjHt7ICtmRnUlJORU4ZzwASe/N0A3NVs2Vk1f/l/8eJ8qV0lucTHXCGroAZTE1MslSUCY8lTXiHtwjRFUV007R+9mD+//hp/XhIkxzqQjrLf1vplGHt/iCbV1ItJzyxhWoA6BmWbnj1B85q7JIeXX1ys5BUkXyXqagGl0oOQpakpOUrhljCkr/0Y4edEp/wbHNrxDR9/9hWfb9xDhO7SFq2WMUYWRXS2sZaenuswzUmRHmO7MGr+04T66B2ZjTHAzhvTjmtJmJkYek8U/FI0dd8YGq+T7lcozs7GJa6kyGsy6ufmPOlc2Qkk16XS3PW0NI03xajz0y9zMPwHPly1ij//9UO+ianGL3gmjzyzjGXBmiJG1SfRNG4sbblebU0NJtK9oj7BWNRz5DrkunQx5PoyuXGrlY0RteGtDYeU9E4MoL+njbyOD5+fyWo4v6SiyQaShmFoe7Q0jRtLW693q3Ri6PW16IsHebtgWpFB7EldfVxh4/UCsO1K/4FqmkxVAakXs9QIZOeWKkvf2KlL33RKKZS+JRzpNtJPE8ePQZ2l74zSQtKVeGOaxpvSWvlG8ZJETuzdwmcfvsfr/+9N1h/KxD5wCouWPMF/zZOX3TGyPommcYFAIBAI7kWEo7TNxLDhkwhia7sz74mn+ePrr/HH5fczuS6v0evCGoYyZXYYs2fcx2A15a4h/CCns8FrzDL+8vpL/PHR+9SM1rnw42o+PJSDe+hCnn9Fs1bgX373BI9MG05ftcytIvqbz9l1pZaeMx7hBfnarzzNo+PryJF3vKjHcB0nyjP6XAN49XGNQ87Yvinl92fhPEkt/9oynn3An5pzW1n98dFmN23STzmFtaOlB90Vik5+98Q8AjtdZ9u6zwjXLu6vl1g2/ucL9ma7MWXxk0p/35DOf/WZ+cwYobyIxv7IGFJrPBj725eUvN8tDqF3RrZ0xaZUU5StWRey9FI4q36MBb+pPL6oGSfioaNEp1bjNVq2I0lWKx5kik+mzuvuNrj7jWL2Iw3t+t19nSmOPch3yrPoFbYfu0G5gx/PKvp8ivvlqZKD7mPWjDBmheg+9XQMHWO/rfXLMJIjv2T1povUDZrHs4puZBk8zdK5wQzuBs6duzM0dI5O3iL6WmRwbOdWg2yr9b5eJiohH1ufEJY89AAPPfQIz7+oKffnFx9krOIgb62MMbLIJfxUPHk2vViitOd5ls8eTEGmPPW1NVq388YYdq3z6flUW3kz7bezVRvXrP/oNng5T6oOJ8Gdhzx7qq2htiwVeX3mBjQuhh8iy4lNvnnGsZwm52nQcUdIddSWpem9RutBrQN9edqgFpHQn1/A9Zgodu/8kU/XvMefv7qKic9IFk4boaes4cFY9NVhaKiqqsJc+8p8O5DrkOvSd43mg+bckqwDyqzP3RHq8cRVNd9BqlcqUFVJjp7zZOrT1Kl8DWW0hRri7QnGoq8OQ8OdoJPm5GYlL1dTXUVZk/RCZaF6eYkoNU2PPqT/JUwws1Lj4d+z97oZ/k8+z19fXs6fpO+IRd6lnPjhcw43Ok9fUKqX0JfXELToy5NnhmfGnWFf+C42fLSG/3v3BHku/Zk7K0RPWcODQCAQCAT3MqYWVvavq587BHePbhQXN/I2GYWdnb1yvvIIoczUMJQC0lPPcuxEPNfrd7EpITvtEsePn+NqivahVpN29Gg0sTc0fyEuys4g5uRJTsanomzyWpxD6oXTHD2dQFL9s3DT86rJTknixrVYEks6YWpWSfq5CLbsOMTB6GiONGlHjVU5qTFH2XXyKg1/l26Kpg+RR3Tb25S297MxN9eTl5nGyRNNyudlkXw6ishGstAhP4Ok5ByKa+qoqc0nIfIUJxNzDKurMI2E68nEX46lyMIFi07lZF06THhkDHGJWTTaO0eh43RclZNKQuINzqeWYmJqQmXaeXbu3M2eyDMcr2+34TrOTi+hvLqM8pyrHDkvJRrbN235SxlU29hgUpHO2c3b2LT3AIcu5tWvsaj048QJjsUlKTMVbkYjo4P7o4i5Vo11507kXjnCt5t+JvxwDKnaivTauHR2ahLXEq9z8WIJ5p0tlI1wzu87yKGz17iRWUx+egqJV9IotjDHpDSFiL372RtxlBNnzhKlbdNNdZeRnpxC0tXLnL+WSLykp6qmtiDZUWJSPMnFZliYlZK4/yC7DhziwKlzRJ2U5Sw9YKYnEhuXi4mLJJ/aYhKPb+e7bYc4HCvVJ+kqMyWZa/ll8vMG5WmxHI6OJSWtBuuaTM5F7ud4bOtzJ1uSr2LTx0403CcM0PFN9xYZRT6nOKzYT2v9MqQOKV6VQ+L1JOLjrpBWZY2tWS3F1w/z8/6znE9Kk4ZeNolxV4kttcDVUrLsnMts+nEHuw6eIbPxwrj6aa2vvmNYOCcIx2s72ZdsIeVXU11TjXnXYUwePxD30hNsKxrQSplDfLbrXIuy0CUzVdL3hRyq7C2ozotj354I9h48yenoM/Wy0is7idbsvCmGXCs/M53yylpKy1I5dOqa1N5c0srKqSitkMbgSc6laur6tWJv79Du7/bMNOP+5NMa7f29IfcpP0/Wnua3RsO/hsVNrFwxtfeTopr1SBWkY15xnTQuazAz1aTlFNZy9FIVH+4s41yC1oGq44ioraAqfTc1ufLqkw31N/zbQjxwBsvu86T08kdsUN4YaJKv/DuWR3/jj23KcT78KUYnx4mQCUMpSkiW7tJyvIrS3AyumY7mkVm9sMk8xbrtcTrltf8aFndydtH8njPgt5ysi4I8edXwhvMb/m09bmYmfXdYWiob/2gup8k1Fltbeaf2csrLyxvVr/2kNx44XUcHevIpZuSC++nvUkiydN+NKdPmBDD/qZF0L41l1VeHlJJD5y5hbOccDn+0Q12LdFy97tbW666hZmPjxuqkPeOjo3VSUa6xUt0rNRu/SSeN83uGLWJst06kR33P3jRtjhPzH1lKqEcex9/ayF6p6zfrQ0/d7qNZ/GB/6qJPkWHjgWPtDQ5+/QPf7j5OvPTbUffKmn914gaN305MWvwUA2xS1XY05ARNGId9QiLyexlyvLwgi8SE3sxeMpgeFddY9e1RNUf3X8PixtiKQCAQCAR3Gu199uhk6+jRoX82HDh0FOnpbX8i7NrVk7S0FM3X9K/ky9nG1R0XK+kHSn4m2c04HeQyjhSRlqNvPcdfO9a4eThhZVJFUUo22s3njcHR3QN785oWZXhLsHXBw8lSVl6LumldxxoZmFfeXI9xfWu/LBtwoIuXLSat9E0/mnPNpQf43LTcJmuFqm2sKSE582Z3dnvQyLm2hb5r5dOMPB3d6GZnTk19n+Xy9sjbqt9Ku2q//bbSLyPQyNCUquI0MpoKUZUPVW3Xnd6+PvVvji1xYeczK/h3rq2Ur0k27/Vb1r8ziqrwl5iS8FjrZf4oP4oaI4v22LhMS3belNavpcgGHdkq9xczqtqp018UeXaRdPDw8Gr3d/v56GNqrGNoz+8NecaU3KeE+KtqivGY2HhiF7QazGwbnAzK7xZNzNfDFHcnzYzTzPxa4tO0rw9LElVmbGlkS3UJxSeWU1vahr489j6nXx1O5o8jpfGjpt3EH/n54mzcj69iyNIv1TTwnryCfzw3gszjBzl84iCREVlYBwwi+NEX+P1MWy78cymLPm27zn18e2t+z7XyW64jdCHPOuzi4UF+fh411dXG/36U2mBqZoaTkzMZaWnKDEaDUXWQt/0pfv+1mqalLJvoS2mE/O0H1s51IPbTlfzt6x0cZyjzZj/Dq8uG0DlpC74z/6YUf+qTPbzWP5Y3R/6Gj5SUZXwf/QQDkzax+Il/cMPaA5LTDFgyRT9CJ5JKMmMoHvR3vvz7GEyPf8bf39/E1jNWkt0/xv8sn4dHygYevf9dZLf0zfqQaDrm5r/Dif8bSMK7f+Gt/YWUVmSRei0Tnb9BN49B4xfe3HacBzufbNyOgPl8+Jd52J4/xu6jR4k8GU2ZexBBoc/yh2f9Kdr6eyb8UXaUtg1DbUUgEAgEgjuR9j573EUzSn85qspKKCwqobSF33BymeKyu3Xzj2pKi4slGZRSoaYYS0WJfH7LMrwlVJVRXFTcqm5a17FGBvrqMa5v7ZdlAxWUGNA3/WjOLSwuu2kmX30bS9rfwqZo5NxS37XyaUaeFaVSnm6f5fK33q7ab7+t9MsINDIslpe/uxlVPu3Rnd6+jpjFs8OcqMzI5EZ+Pml5pnT2H8SE8cGM7J7H/k+/Y7/bpNbLJMgOSGNk0R4bl2nJzpvS+rUU2ejKVrm/3Hr7ux3cvTNK5VmMbaOuqggTGy9MbXpIv1dMVIeCFDQ/YJSZpcnZtUqQP2uQnaTqpzrpQ20VVZkHqEoN1yQay+AZPHufJyX1M9L00TArUTOjVIOphRs9AwMZe98IBg/oQ+CQkUyYMp6Jgx3IOPw5//j0KCk3T7A2GGcXY2cvtl0XtbW1mJlbYGVlpcw+1OrAMDQKkdtQUVFJibE2perAzq4HfQKCCB6tE3qb8v3uc2SnFtMtcCTDhvejT9/+jBw3iUkDaygz86BzhWZGqYzuDMZoJSUXx5HjCfbzwtfXn9Eh/TDdfoQLSp7xCJ0E4We2jY3bkimy9yZo7FiG+PsxOGgsM2aMxj0nkjX//IJdKZo/dt2sD4mmY67SjdApY+jt4UYXLx8GDhvBuEnjCZs4imFedVw8m6TM2NaLQeMXJi1+mgG2mhml9e0ws6Rz72GMnxDEsAH+DBo8hLETJjE5pAd157ez6oOtXMpu+5ePMbYiEAgEAsGdRnufPYSjVCAQCATtIzcTG3d/Bo4YSMCgQEYOH8q4UYMZ6JLBwW8+kx4A48lLN6DMXeBQvFuRHRbt/W6/Ex2lebltnZunobY0CVOnfnQys6eTsgu+jPTbpf7ni/aD1juq/adO2cCptuQa5XFrqa1s4/sDRdkknzvBgaNxXEtT024in7TkaA4ePs2VxIZFKMqyU7iWcI2YczEklZphalpBQdpFfvx+Gzt/PsjR6zevPm0Mzi6uBjtaOkIX1VVVWNtYK698V1VKNxNDf0NK6rCxtZWKm5Cfm0dNk82cWkXVwaHjl7lwoWmI41JCBpWyrK8nEJNcQidTEypSz7Ltp52EHzzFkSNRShmlqqw0zh47wYm4ZHXWaB4piUmkF1UpjsechCMcjkqkfvUhIxE6ucz5S9JYSczmxrUbxF2+TH4neyykMRJ/aDPfbN5D+LGE+nXXb9aHRJMx1zt0AYuCHYjbfIAEeefB6mqqqs3xGhnK5GGdKY/cyYnmxGjQ+JW+YtNTOHH4aJN2pJNwLZHY8zGczq3FulMdxTnXidz0Ez/+vJcD53OpVIu2BWNsRSAQCASCO432PnuIV+8FAoFA0G66+A2mb3cX7CzNMKVWerCtpjQvjbiLsSSrr50bUkZwh1F3l796fzVO6Z/8c0Oe4Cn/6jA2buociFXvpZja96GTqaWUokXKlQvIyCdp/lGoq6mguugKFVc/oTrvbLuu3xFx285dcbEygapiktILO6R+H18/o17zvibpoqX6DInLsxcdnJyUVHmd0epq+a8vcil91GFmZo6VpZVSQUF+PpXSOS3V3+64rSseLpZ0Ks8nJavU4PO1+qkqSiU9v/XyzcV79TZOJx0xPiwknTjKOpESyisM1Il0jlymUNKJsl5sC/W3N+4o3ZvszWspz01XlkhprXzT+PLP97HCZTcPPvI+WfZ2aFaVMcfvv1fz0YQqtj85l98eNby+NsWd3elmZwbSfSU3NYfS1sobEDd0/AoEAoFAcCciXr0XCAQCwS9OifSQeSMhgbir8cRevUZc/HWuSw9shTqzRA0pI7gzuatnlMqeARnFQ6D5qKCNt5JfW5au7Fovz34zse0hpVfrzC5tjDyLVPqHqvR9VCZ+rzhJW6v/duRXlRZTWFDUeOkII85XaJJv9OzFHEkXRtSvL1/eIK66uhpzM3OsrW0wNTVVri87/pSlDiRMTEyU9TMtLa2wsrKmurKKoqICKsq0r4er6Km/3flVpRQVFlEkr8ehL7+Z87X6KdZdNcWI8xWko7NrG2aUGlG/vvyaKnXjPlknNgbqpErSSYGkE/mV/Vbqb29+eZFk94XFlMrTL9tw/n0Ln2KkSyXJqUnk5GaQY+pKwLDxTJkwHJ/cg7z78UGuyn8IbOb81uo3KL+8RDN+i0sblpEx5nyZJvliRqlAIBAIfs2IGaUCgUAgENwi3vndIPXT3ctL/zqnftKD7MyQDnfrjFJ5FmNjj0Hbj6a23TG1742pUwBmrsMwsXSjk5m1lCeVqC6jtiKb6pxT1OTHUFN0lZqSROW81ur9tR6Nnb147eoVKdZ6vYYczc0tsLC0UBxvZuZmmJhonHMy8vXk19hlZ5y8I3tlRTlVVfLaw4bX/2s99urdx+hZvi3VZ8zRXNKDhaQPS0tLSSfminO0sU5qJJ1UK87RyopKSSdaz6Vh9f9SR7/g5bzw2/H4mxWQlZdLfrUtbs7OmOad46cvvuP7yGuUGFDPnXY0dPwKBAKBQHAn0t5nD+EoFQgEAoGgGa7vmKZ+unvpOX2n+kkPdXe3ozQ+TnbO6XcUtPVoauOpbPLUycwOtDNLa6upqyqmtiyFmtIUpZyh9f1aj75+xjnl4uNipVjr9RpzNLewUNbHlB2lJnI7pP9raxscpZqd1A2v79d+9PXzN1InHT8+zC3MldfrTeQZ2KZyuqqTmhpl5mlV5a/DQdpw7EzfIH96udlhZW4id4aqijJyUuM4ez6VEoPrubOOho5fgUAgEAjuRNr77CF9owsEAoFAIBAIOgLZEVqVHUWl/Hp96m4phEufI6jKOSHltd3ZLDAeeQOhstJSSoqLKSoqVF57l3dQl9M0MxYFtxvZEaropKRIswxBYaGin7IySSfyhk+/OjK5fOIwO3bsZNNPO9i0dSdbww9w5HwKYultgUAgEAh+nQhHqUAgEAgEgnsSeeac9L/8QTmKeMfFjaXp+SLe8XFj6ejri/ivJy4QCAQCwb2MePVeIBAIBIJmmD/RS/1097Jpr/wqeDPID87S4a599f5KbCsvoIpjW4++fYx7zfuqpIuW6hPH9h97G6kTMT7u3aOh41cgEAgEgjuR9j57CEepQCAQCAQC/dzljtKrsZdbdRiIY9uOvf37GuWUi5N00VJ94tj+o5+ROhHj4949Gjp+BQKBQCC4E2nvs4d49f524xvE7PlhjPVV47eVQMLmzyZsiBq9Rxg+aTYLZwTxi4i8IxgSwsJfzGbuITx98fdUPzeHMn47Zgz5h8zn2eVP8+LypSwcrSa2xi96/7hDcPHG38dJjUjokYky5icFqjE9GKLrNuIbHPbrvt/cg2jdAOLYsUdjMbRecWzf0VgMrVcc766jQCAQCAT3MsJRervpP5FHl9zP9P5q/LYSxIIli1gQrEbvEe6bs4ilD01kkBpvyh3vSA2ewdJfzGbuFcbw7PPLeeWlpbS4x7kyfjtgDA15kOeeWciDU4MJHTeaoT5qemv8ovePNtCBjmUN3ixctoJXfreMh7V+UD0yUcb8nCA11hQDdd1GBk25v8X7jeDOQp45J8KtCcairw4ROjYYi746RLg3gkAgEAgE9zLCUXrLGMZjL77My4uHqXEV+8508/LA3V6N31ac8JCu7eGiRu8RXLp60M2zM45qvCmtOVJ/cVzcf0GbaZnZy17mr8vC1NgvTTNjziAs6dGrP4G+PbBWU/Sijt/2jqHAydMY28eC5A3rWLn6E749qmbUcyfeP9pARzmW6ymiq3dvAv296aJ9jtMjE2XMd9WZddoIA3XdGiMf5OU/PMdjI9W4imPnlu83BtNM/YJbRNNpVCLemPbGjaWjry/i7aOjry/ijbnT4wKBQCAQ3GMIR+ktw4fgaVOZNcbQqWKCX4rWHKmC5hkSOpU5oQFq7JemPWPuLF+ufIs/vf0dkWrKraRfDxcsKSJpeyQRB48SnaBm1HOX3D86yLHcQC7b1r3Dn974iJ/i1SSj6SBd9wpi1qzJBPdS4x3Nra5foKBvJpUIHROMRV8dInRsMBZ9dYhwbwSBQCAQCO5lTC2s7F9XP3cI7h7dKC4uUmPGY2dnr5yv/DHT6AXEnQgMnc68mZOYPn4YAX7umGTGkaw0x4tpjyxi1mAnss4mkKeU1zB87hIWhnjDqViS5QTPYcxfMJWZYaEEB/ahu0k6F1JKlLIyoYueZmqvcuItAlh4/0ymj+xK9Qn1XBn5ddPQ4QSP600XE7Bx9WF0QFfKTseRETCFp0a7knVsJze8ZvPArDCmju5HT6dKEq9mUa5WIeMcEMKCmWHMnBzMiIE9ca5M4WqmbokGAmcvYfEQW47lurPwgZnMnhLC6AE9cGx0TjAPPeMHVz7j6wNqkiQz3+AJzJ8+nqmT5XN64WaSRGxqk+uoMpk6MZSwUU3bqyP3sYEE+DpRkZxARpmSaQCtt0Er85NFPTT9k2Xi3xUyJLk3MjcnRs2Yz1ypLVq5ug+dgb9NOkc++5kzaildQh98nH4t5Rt87S6Mmj2HudPlawfQz7MTqZfSaFSkFduScR4RxsOSXcyaOIIAHwcqXANZ0MeGlGNfsyVGLaTQvE0r9jBev02HDOmDt20Vydd17C1wOs8uGC/JKZqYG2oa/Zn/xFxCukt9j81S07TI690GM2bMKHrY1GHp4MHoET2xjrrEdSVfRxbBkiy87alKvdGyTUhtlOU7XbKB4MAARo4YJtUpBe3YkYo0OyZaGnOa2uvRP37P4BQ8iYGORew5n6SWlPAfw8NzJLlpdW7Sk6lBHpQ2GkNNabnv8lIP940aRT83M2rq7OnetJ235f4h9Wt5KH6VN7AYolOPQxFn4/PwHDmbh+eFKeO5n0cn4iU71q3bYJT2NicvuQ1TCXLN4fTVAjUtiIXLpjO2ew0nL2sl4s20JQ8ytY8VJy4m0WPEeEZ4mZByQpVZvUwaxocypolj9cYjmoRGlDGwia7r75+p9kxbMJN5U6XxObDp/bMBeR3SsJHDmejvjlmNFZ19hzHYQzNOBs9czBi3XI5sTcKjpbo8pTE0Y4p0P5lAmGInVhRJ7cmRslqq/5fC3t6h3d/tmWn135IdQnt/b8h9yslWZdrk98ZNG5iIfPWDBkPyXd06a37PNS2rh0a6kLgd7WvEPZLv2rmNOjGwfi0i/9efb8z4FQgEAoHgTqO9zx530a73Tox9fDlPhwXiYZ5PRok5bm4W5F0+wMZ169gU68TilWv4bd90vljxAu/Xz0YK5E9fvM6MTpEsf+Rtov2n8+LTM5nc24qc1EJwcMO5Oo4f1/2H9ZH5yhn//ck2JpbsY79pAGO6WGBSFMPbj/2dvUquxMTlrF44isGDu+NQkMa1nEoousCHT7/F9sXvcPTFfmQfP0Segw/2NnY4OthQlx/L5pWv8K76Cq5z8GJefXwigY5VZGVXYurmjF1OFJ/9e5XUF00ZXRb96xte6JnFjhumDO1mg6WFJRZWZhQnHNI552W2RE2HraHM/qtyGqGP/4GHQvtIMishr8SSLh7OVGZFsuH/3uFbrYy0MunrRHVuLqUWLjhzXWrvH/jguFbuA3CqyiG3zBS3zjZknNjIP/+xAz1NvQlD2qDIvPoI+wq6MszDAVvJ8O2sKkg+uIH/+dsONMU0bVk+cxjuFpXUVJZRWFqEXfdBdKs7zdsTXuJTpVxj/roxgvmuzecbdm1f5q94nAfG98KqIIsC7HB3rOTStk/5v09PaJyYBtiWovenpjKqsxmVVdWUlJVSZNONoV51HF85iye/UoqpODHrH+/xx0HZfC3Z9H/qbbo/f/j8b8w2aWLTkv4qs3OptJWuW5XBqfAv+Ye2bff/jd3LA8naMZPFbyuVSMzh3Z+epu/VdUz+3U9qmpYH+L+1kxjdvw9danK5liY7KxLZtujPrNXKYkIfnCvzyK60ootTDWmn97LuP19xKFdTQyN8pTY+N5OJ3iZkZZVLsulKP087yvKzKcrRjJ0jLY2Jbi2MOfUSWpobvyOk9LnsYtTS9zQFZbmtmMd0X3uqK2qoKi0lv9aFgL4uJOmMoca03vfH/vIus4L609e9muz4NAqbtvO23D9W8NW++6g7epoan77YW1hp6sk7y1dbkxg0cQw+NhZYOdhiXZHI1jV/5p+7NDZqFEp7h5ClV14zeW/LswxM/oZ5yzdo7HDka2z+WzCuaXtZ8ehKzirlHmHdzkV4nPkXM1+L0Nzresfz7zl/5Bs5W7mGHxd1xocyptnBwAfe0iQ0QbYBXV0rdXrEsSHegbGSvu2s7bG3Rfr+2MXbv13PIaVUA5NeeIPHxgxkiI89RUk3yJJVFPMFD/9tL49/sJXf98vj+KE87Jqrq4dkWy9MZ5yXLVXFBdTadcXdpogrP37Csx8fabH+2448u0g63K273hcWaJ30go7GwdHRqB3WhS5uPUInAkMx1FYEAoFAILgTEbveawlZyLOLQvGp3M87763jP++v452vb+AYNIenlszGl3wOHsukyq0P9032Vk+SGDKB4T5OFF7fzmV5o5Ali1kYYELE6vW89+HHvPfBRuIdR/HoI3PQ7h1iY2eLW9+RjOx0gvfeXc0/12xVH+hVLh/g6w3plEq/LcozTrFy9TpWfrxDp4wFzi42xGz4nFWr3uONH9Ow8BnCjDDteoRjWLp0ARPd0vnyg495V2rH+6sPUt4rjKcWTVDLNMbKxha7rl3pU7CfDz9Yxz/feY9/7MzBZWgYj80bo5a6meTaMtIPbuDdNZ/w7gdreGtXFm4DJ/HADO3OKKpMRncmc/daVr7/ESvfXcsXu8+SIE/nUuQ+FrfUH1m15mNWrf2Ef0dU4Dt1MY9M1dTQGq23QZV5r770KzjI2rVy/94lurQzAZPCmKiW0dqAX+0Z1q98jzdXrWfNhssU1Kr5bcSQa/vev5inFvTH5PB6jRzWrOfzaw6MW7SQRYrhGGJbGr1P6VVL1IYPeHPlh6xa9x0xhUqmHvI5Gp5AoUsfQmbp7PDtG8rQXk1sOsiB+K81+vv3e2sJr+zBlEWPsTREPcfCGjt7yYZs1LiCJTZKmqUa1+UUP224Qp4s28J4jY2v3kSEFNXIIgjHaxt5R7IVZSzuq8Jn0gKefVi/LQbOmM3CYBdSvvuY1ZJsVn+wkxs1tjhVS3UrY6eVMdHqmGugufErp9vZaVetVOU21ImUn9fwz5WrWbn2S8Kzq1pcusuQvh/Z9g0X8iqlT/nE6mvnbbl/yPp2xcffi+Ttn2nq2ZKGTa8gHp07mKrDX7Dq/dW8+ckVyjwGMGPyKPW8juQU1wtMcfMNQGuGvuN6411TBT79mardXS00gL5ukBerkYByr7O3xkqJtY3Gulbr7O5PsNVlPl23nrdXruK7FDN8h09gup41Qi/v3czH6fIU4XJSomTbX8eaLRc1mTKWjri0VFdxAWTHs/kjST/S/WLlqq+5VNuT0Q9MUzaYarV+QbuRx3F+fh61dTUi3KIgy7el+6UWoYvbF4RORDA0GGorAoFAIBDcjdw1jtKw6ZMY6JzDiVUb2XngJFFRRwnf/AnhN6BH8DRmSg/dyXv3cjnXGt+RofU7nA+fMohunVI48eUlSn0nMzfYi9LL3/B5eCTHz57jeGQ4H8QV4hYwqv5hXsa8Loctq6VrRUQScfQC2Wq6QsoFDh3MolT6WFWURMRBTZmGib+VpJ/6QrrGISVvz8ZoksvNcPXqp8kOnczMATYkH/6ETREniDp7hiP7f+R4uik9ho+jWZdFTTbRn27VtEmqN/zrnVwttKXnyAnNnhO36zs++n4P4QdPklJSSnFuJvmV1nTt46cpoMqkLiGCf3+9n4jjZySZ7GfT9zs5eF2Vu20ah9ZuZ8/RM0RHn2TPd9GkmXkxJsSwTXVabYOKeXkK736+nfADUv8O7Jf0kgt2bvipylTa4prP6U+/YLMqgz3hG7nWrKPRcFq+tjczZ42mR/kV1n+1j4ioc0RL9rflo6sUuA4gRPaPGWJbit5dyIvZwJqtGtuIiNjH5zean9GRfWwHMRnWeI+ZwHA1zVey6R7mGUTr2HRdwl7e37yfA1GSLR2Q6txwniznvsycrmvVxnCNqINJFFRLH8uzNG09eIY4rSy4wQ7JJsIjo4k6HsnOr77hdK4LA8Mmo2/rp359PLCryuTsDtnezxEVuZXjqeWYm1eTKY+d1sZEq2OuMS2OXxlVbmZJRzR2L9cn6X3TuUwq1CI3Y1jf405FklAkC66MDH3tvG33DzNKkz9mnWQXSj3fXSS90hK78ot8+P0+9sjX3X6B9AoTnDz6qOd0JGn8eDGdagdv7hstx52Y2M+D4qunSK7zIjBUsxnT2EneOEjj7+xufVORO5JCzq1XbeLgIb45mU65uQteOrvpa0mOOUFEtuzIrKbghlw+kkMxaZpMmeosTrRUV240Gz//ho27D3EovoDi4mKScksxdfVSxnGr9QvaT6dOlJeVUlZWJsItCrJ8ZTm3itDFbQtCJyIYGgy2FYFAIBAI7kLuGkfpiO4umFZkciVax+VRcpVNiQVg24V+8gNq9iHCr+Rj4zNSna0UyLShHpgmn2LDJSnavw9etmDTYwLPv/4abyjhBZb4SYmW1rjJp2gpSmbzuZvcKwYiPfymxTasSZidT2kVWNipO56M6I6rmRnOgQ/wP/XteJqRXcykxjnQ2H2oQ3UZuTfUhRBlMg+RXgSmDi4tnNOVwQue489/eY0/rnicJ0Z3UWZqmZqqswhVmeTf+IkzmZokmdKCQsWRo8jd1JHBi19Q2ymF5UPxMJWa6mjgRjSttUFLaS5ROv27XCx/tsFedT4obSnP5OyB60rbNGRTJfuk2kuL1+5HPw9rsOrO9OVafb3G/yzujZ08K1M2HENsS9F7OWkxkcTrLFuaUSUZR3OUnOHTEymYegaxQPF5ejNzVHcskk/zhY5N5yft4rJunUevKg4w1+46M1E7BFUWBYlsvqJri8e5nFGOqVt3RqhJulzNKqTazJXug9Wdyl0C8HQ2o7q0AMU91tYx0RytjV9VbrkJPza2+4pqmp+g3La+G0/H3T9Kc8+QoLWLzBwKKmXnbBZxWt98SZkyfkzN9c0qbj/xP1whzcQV/wnyQLqPIO9OJF/8RrIHa3oNu09K8ybU15W6tCv8mKKccuuoKiTtcoNNZOdJfccC285qgjG0WlcZtr6jWfbKy7zx2gpeeHIqfRwk/XQywVwtIbgNdOok/S/CrQrSP6qgDaDJuSLcmiD9owrcAJqcK8K9FaR/VEMQCAQCgeDe465xlFqaS12prr5ptldRlby6mjnm9nIsl4jNl8m09da8fh84geGepiSf/onLcra9mfKQWph0mGNRpxrCR6v4w+vr2CSX0VJTdfMstHZS74ywMsNU6knOJZ02SOHLd9/iD//4Cn3bk+gnl6oa6SD91jHVJDTBl4efe5Kn54yge1UKP2/fxfffXyFHPkeLKpO6qsYbDmlR5F6Rx3mddh6L2sQ7f3uTv352Ui3VEga0oUW0ulXbUlVBsf6m3gK013bA3Ew6FKewr5EcPueN199kpby8pyG2pei9mvISHSdbq5QR8+0BrlS5M3LqGGUmZHBPc5Kjf+CCnK3VX3WTOm+ZA0yVRU0NjdVQRql8QTMz9F3x4ne7OFfkzuhnfstrK57jz68uYaR1Cru+2oUyj67DxoRKa+NXlVt1hTHrcrat7x1F++8fVdTc7o1ur+zlSo4Znn6jcQ4dhZ9lOpd3XOXna9nY9RxFGGPp62lG9rWf1LWAbzcmmLfnHf9GNNTlHLqEF59ZwPT+DmSd283mrVv4IqVYkykQCAQCgUAgEAgEgl+Mu8ZRGp9bAlZu9BqiJig4MaWrC9Tmk6pucpJ9bBtn063xHTFWmcXUrS6JyB+uaTKPZpBbC5ZVFRzaHs6WRiFS747ot4SEAoowo648vkkbpBB+0nCHgUsYnvIks/yMmzYjUfCdyJwp/XBK/Y73P9/Mll37CD+gvk6tRZWJW9cQnNUkXRS5m9dRebVJO6Ww87hm//MWMaQNBqLfBm4HJ0iV/WlmVZQfvVkOEeekPENsS9G7Fe69jJzlGbeDzZcLcR04mbDQIfQ0TeP4xquavOb0N6Qn7lZQlNuCjlzssDb6DqHKwtmdKeoERw2B9OosXbC4QK/9lrp3wcU8n+sxOTh088Qu6xgf/eM91u1Xx2ZHjQlDacXu9dO2vnc4t1tW7SKGg9fysfTow9wJvXHKusK3CRAdmUieU09Cp/XDw1qyi2OqPd8lhEybxegeFRx7/zO+2BTO9t2H1FftBQKBQCAQCAQCgUDwS3LXOEr3/hBNaid3gp9YwrQA+fXdLoxa9AQL/awpunKYb6SHb4WSaH6MycCm1yieHuZFZUIk38WpeQmb2XelBKeAxfx20TA8lUQnfIOnMztYfSXYYLIpKgcr524EuoCzZxfDHS77ftKsJxj6BM9O7a+e14Xh06YzzV+J6MfKi1GPhDBcbrjnMB5etoh+9uXEH9vcyDliZqL2pcANW2swrTMjM7lQOWfh8mB8dWdQSTI5eq0cG785kkyC8JUdQJ6BzH7iaR4brcrd1ItJz2jlrubPnoC2qb6zn+avr7/Ga48EqSk6GNIGA9l78Bp55u6MfGg+Y32ktrh4M3bRcwQ2WjPhVnCdbyLiKXIawFPL5jNKYzg4+wQxf0aQRn+G2Na+w8Tlm+E5/AEeDvaWzpPz5/NyQGsdSCH8+/NkuAzk8Qneymv331xRs9TravSnua5zwASefWIEnp0yOP7DYU051THo3mO2Yj9ymSd/E4SXPEOyWWLJkyfBOXRhmlyxZOOeWlnY9WKhVhYu/Zn21KMEd4XUkz+hd9/uEf3pblNB6sVDfPzFV6z78gc27jnZsASBQWOiHWOuKQkRnE6uluQ2g6XTAhW5eQ4J49mx3i1sItTGvuvlF7h/GIQ385e/zBt/epr52oWe20UZB04mU+TgxYy+rhQm7FVm95ceOsX1cjcGLe6JU3Eyx1QzbZGm32YmZm3XvyFklFAuWYOLtyxjJzw9Df+OcHawwdS0jqrybLJLnAicupjXmo7zdtQvEAgEAoFAIBAIBIK2cdc4SpMjv2T1pvPU9J/Hs799iTdef57nHx6Pc8YBPvzPNs1ryAplnPzyNIkWvRjnXcu1w7t0nIixbPzPF+zNdmPK4if5o7K230u8+sx8ZozorZYxlAj2nMumk+dIfvfaa/zl94vQv1+9HlKOsu6jrZyrG8TDTyzjL0o7nueFR6YTGuilFtJDjRldpy/mhVek8q88yeOTXSk4sol/bIxVC6SQWwxug5fzZLAUzT7A8dh8LPss0PT1lceYO6yIjEZ7B8Xy9cpNHMpxZMrDT/Cq1Jc3Xnmap+cEE9jdSZX7ReoGaeWuyV86N5jB3TQ1uAUEM2NGGPfPnXjzplIGtcEwknd/zDfH83EavpAVv5Pa8toKnp05iJLCcrXErePCj6v58FAO7qELeV6Wv9SXv/zuCR6ZNpy+SgkDbCslgnd/OEWO41Aef3aFpHcp/6kpDC0pprUeZB/7ihPpDvTraaksJaH1/WuvG54h609z3b/89lEe7l/D6U2fsi5S3SBGdohfLsGq9yLFfv7y28XM9LpKC/tISVxh+7EblDv48axic09xf6Aqi/1ZOE9SZfHaMp59wJ+ac1tZ/fFR/RssnYolpdKFoQsWsfSBuSxdtkyzvuafXmTFNF8Dx0Q7xtxNxLDhkwhia7sz74mnFbn9cfn9TK7Lo6UXpNvUd738AvcPg+jO6LCpzJ45kdE91aR2kr37AknVbvh4VHD9TIyaGMHZ5Do8fd2pSb7ArpbWSYjNILfaCt8pz7FQncmbKM8udw3g1cf1/HGmowg/yGmpXV5jZBm/xB8flddUNYz9kTGk1ngwVrlnvsTvFofQOyO78ThvR/0CgUAgEAgEAoFAIGgbphZW9q+rnzsEd49uFBcXqTHjsbOzV85XlhA3ZiHxqhwSrycRH3eF/E72WHQqJOHodr7ftp+9JxMbrV1alZVBYkosUUcOse9wDCk6u6IXpCZxLfE6Fy+WYN7ZAiqzOb/vIIfOXuNGpsZFkpeZxskT0cTeyFLi+sknLTmVjKIqamtrybt+nKOnk8kqziH1wmnpcwJJ9csflpCddomjR7V1VpOVnEhC/FUuZlVjY2tCbUkS+/YcJupSMim5N7vNBs9czBjnNMJXfs9pU0dsq9M5uWkrm/YcJPKqdmujXNLKyqkorSD3yknOpWaTnHiDmOwq7C2ryY+NZJe8k/axGKJOnuNqiqaBeSmJXE+MJya9SlkHtDbnKju27+Hw2WTSstLr5Z5WZY2tWS3F1w/z8/6znE9KQ25qRX4W2T2HMt65kL1f7UU7uVeDgW3QJ/O8LJJPRxGplWWJ1JYbKcReyKHaVrKdvOuE7wxn78FTnDhxkpPxqeRpzmyEUvexEy3nt3btwjQSricTfzmWIgsXyf7Kybp0mPDIGOISs5BNrHXbKiU1MYnEuEtk1VhL5p9P/J49bI04ypGTJ5vYTBOqsvGd9BBjXFPZ/vdPOayzQXj9dTOqsZZsqSLtIpt+3MGuvcc5n6td4yCHlKRUkgorqamuJCtmP9t2HSDi5GVO6eihMdVkpiRzLb8M6qA8LZbD0bGkpKiyuJRBtY0NJhXpnN28jU17D3DoYh76tqYaO/UhpvsUs3/3RcotJRurqaa6xpa+4yYS3NuU5E2HONbqmGhmzGkuUU9z47dxejXZKUncuBZLYkknTM0qST8XwZYdhzgYHc2RE/Fcr99NSQetHbTS96LsDGJOnOBYXBLpalpjbvX9o4D01LMca9QPTT3Hj+vqu2ndVeRLY7wi4TQ/HbhAamu3eqW9pzjcnLxkirIlm7mi3I8jjl4lRamzmIzUNC6cOsae/ceJ1rE/RXbSeKgfr4XS+RXl1JaVkXjkDFckIWenl1BeXUa5dK86cv7mCze1gZvqlGmt7fkZJCXnUFxTR01tPgmRpziZmGNQXfnpKSReSaPYwhyT0hQi9krfU9I4P3HmLFFam2im/l8Ke3uHdn+3Z6YZ/qcCQ2jv7w2BQCAQCAQCgUBw99HeZ49Oto4eHbp9x8Cho0hPT1VjxtO1qydpaSnGO0p1cHT3wN68hvL8TLK1r+4ajQNdvGwxr60gNy1XZxd147BxdcfFypSq4rQ2zZLE1gUPJ0tMq0pIztTx6Dbh8Q+28nv/ON6e8BLfKdespSglm5suqdRnRpWubBzd6GZn0rq8WmlLs331n8BLf3mBmRnrefDFn/RvomNoGwxC1V0rMrtVtG5/rduWpo4q/TrUh/903vvXiwzL/oZHHl+vfx1KA2xJo0OM04OiO3NqyvNJy9FdZ9EaNw8nrExa60cQb/74N+5LfpuH/ieKGjupjUq6NQ++8T5P9Izn/ZAVfKCkSbTVDttIm2RicN9b5nbdP4xC1rdl5S8ytppFsUF0ZK2Rv3llU5vsaNqjZ/XcmpZ00zF21C7q6uS/g+Dh4dXu7/bz0cfUWMfQ3t8bAoFAIBAIBAKB4O6jvc8ed6Wj9F5E11H6qZp2Z+DNwv99jQddE/jyg3VsitGZ6ihoP0Om8+xoL5z7hTBnYDW73/wjfwpPUTN/LYTw9pbXGVe6jX+9v5Mz8Rcpsh/GwCFDefjx+XRP+Jxlz32ts5yAQCC4bQhHqUAgEAgEAoFAIPgV0d5nj7tmjdJ7naLcbDIz87nzXkIsIm7Pd7z34VfsEk7SDqfHkJFMHhfMmM7Z7Pv0PT6N/LU5SWXk9UDDOVcTwEOPP8Urr7zGH1c8xuMzh1F3YgP/+8Fu4SQVCAQCgUAgEAgEAoFAcMsRM0rvEroFBOFnV0Lc0QtGbBgj+LXj3Hcog92tqa3IJ/HyBRJ+kXdzOwDb7gQM7EE3J2vMzUygppaqikIyEi4Tff0OesVbILjXEDNKBQKBQCAQCAQCwa+I9j57CEepQCAQCAQC/QhHqUAgEAgEAoFAIPgVIRylKu/8bpD6SfDSv86pnwQCgUAgaAd3uKO0rLREaZ9AIBAIBAKBQCAQyF5Eaxtb4SiVub5jmvpJ0HP6TvWTQCAQCATt4A53lObn50mfhKtUIBAIBAKBQCAQyHTCyclZOEplhKO0AeEoFQgEAkGHIF69FwgEAoFAIBAIBL8i2vvscdc4SudP9FI/CTbt/TXufC4QCASCOw7hKBUIBAKBQCAQCAS/IoSjVCAQCAQCwa1BOEoFAoFAIBAIBALBr4j2PnuYqEfBL0IgYfNnEzZEjUr4BoexcEYQvmq8Ka3ldyi+Qcxu0r5WUc4JY6xuAz198fdUPxvI8EmzWTgpUI3dJoaEsLBp2+8ybpKrPn21iZtt+dfLre3LL2LbKrf1/iEQCAQCgUAgEAgEAsGvDOEovYUoDhFdp0TwYv78+musmKrGCWLBkkUsCFajEoOm3M/ShybS3B7+reV3KP0n8miT9rWKcs79TO+vxhnDs88v55WXlmLMKrL3zVnE0jlBauw2ETyDpY3a/gsQEMJ8PU46xcE1P4ThalyDN2NnzGZ+qOENvkmuN+mrrdxsyx2Ky2xelsbOa4tvh3JubV9+EdtWua33DwPQb9eCxmgc97ODvdW4QCAQCAQCgUAgEAhuFcJRegtRHCK6Tomew5k2YxxB9bMrnfDw8sDDRY1KOHb2oJtnZxzVeFNay+9Q7DvTrUn7WkU9x91ejWNJj179CfTtgbWaYgguXaV+dnVSY80ze9nL/HVZmBprJy7uTdrehJEP8vIfnuOxkWr8VuA0SnHSPTJZ1yHoxMQFj7B0yUJmjVaTFEYw/7FFLAzxUOOtc5Ncb9JX6+iX+c223Fb01l/Rn/EzwhjXX97h+lbT1r4M47EXX+blxcPUuH4Mte1bQYfeP2Ys5a9/WMpsNdoWFMftkhncp8YF+tA47h+d0k+NCwQCgUAgEAgEAoHgViEcpbcQxSGi65Sokf+ppqpcid0jnOXLlW/xp7e/I1JN6UiGhE5lTmiAGrvF9Api1qzJBPdS47eCS3mYuHowwF93Sul9BAV2p5uHH0Mn6DhQA/vRr6cHNtUX1YTbw62Wud76S2qU4VNVmaaJ35H4EDxtKrPG+Kjxu5zBY5kzayztWaFAcdx6udMB/vW7GI3jvltnBzUuEAgEAoFAIBAIBIJbhamFlf3r6ucOwd2jG8XFRWrMeOzs7JXz27aZkxOBodOZN3080yeOZmifrpARS7LaHGefIKbPDWPqxFDCRvWjt6sp1+PSqPdbuvRn2oKZzJqs5rubSqen0dAbtf6Zk5g+NpAAXycqkhPIKFOzmxD64OP0s0nnyGc/c0ZOKB7JwgcdubHyB8Lz5YRgHnrGD658xtcH5DgMnrmYMW65Dec0oT5/dzb+D8xk9uRgRgzsiXNlClczG3tgnQNCWDAzjJktlGmE/xgenjNVU95fkp1JT6YGeVCq075WZRAwhadGu5J17Gu2xMgJZQwMnsRAxyL2nE9Sish4jpwtXWsC00NHMDRwCKNHDFPCYI9yTsZmaWRHHKsjK1io7afcpnp9yq+jBjNmzCh62NRh6eAhnd8T66hLXFcuMIz5C6S+hIUSHNiH7ibpXEgpkXPqcR4RxsOzwpg1cQQBPg5UuAayoI8NKfVtb0B+RThs5HAm+rtjVmNFZ9+GtjaSyfhhBPi5Y5IZV293RlGaw+g5c+lrX0T01/tJltNGL+b5WdZkpNjgZVfAxs2nNTY7/yFeHGrGlW8/YusVOUGilX7Xy3XjEU3CTfpqiZZk3mDLB8ol3c4LU+yjn0cnUi81HkO+wROYL43RqZNDGD2gF24mScSmyj1qqX5/Zi7xp/Lol2w6pamp3oaUsd4Nh+o04pux71bHfiNuHpcKOrINGdIHb9sqkq9naeqQ13oNHU7wuN50MQEbVx9GB3Sl7HQcGcrJDdykA5nW7FXKl8fBdElmN1271ftWA/X3j61JeEjnzJsqXW9gDxxvuje0bNPysiKho4IY180WzO3pLo1dX5toYm5o8g1FaY9nGZfWbSZCTWuM1I6ps3lg6gSmNm1H4HSeXTAe/0bX7c/8J+YS0l07NqVis5eweIgtx3LdNfeSKbLd3dzn0EVPM7VXOfEWASycN5U5k2/+DtFgwHhvVSddGDV7DnOnT2Lq6AD6eTYdJ7po7NE29STrt19Q024/9vYO7f5uz0xT7mgdRnt/bwgEAoFAIBAIBIK7j/Y+e9xFjlInxi5ZxoqFkwnq44KdVWf6DJMeYt2KOHU4noKxi3n96blMGtwLNxt7vAYMZnRgX7rUnufAxQLpfG/mr1jOU9MD8bAwx86jF0NHDMSz9Bz74+T+SPU/vpwVD4YQ0NUaKwdPBg0bzsCuVcRGxpGjtKExNzlK873oP7KCuM8PE62UaKOj1NMaW+/eDArww6+XN/4Bgwj0c6by6nEuqQ1xDl7Mq8vmETawCzYWTnQbGMiYgZ0blWmE/3ReXPEQD9zXl+4eXvTo3osAHw+83KwprG+fATLQ43hb9Lu/cn/vEj766YQS9539NL9bMp0xXtZY2LrSZ+gYRg/qiWfnrrhzjU2HEjSys6vFsXcAkwZJ/eztT+DgfvR3KePMoTjyGMfSF8MI6OaKnakJTq5d8PG2Juf7CE4pfVnEA2N8cTC1potUx8hhvbDOOsvpJI1jRCOf+cwY4oOXpxfdfPowrHtXPBzQ6ygdtuBJHhwqlXW2lszSAfduPeiqtDVPlclEgno5YOXUncHDB0v6cKXuRrR+WbdIPh7jHyC4pykFm7cQWQaBjz3BQx6pfHbJgrF9LbjxeTiXpJJjH1rKnC6Z7P7Xdo6WSgkG9Lt9jtIWZK7asn2NA35DxjHEzwe/fgMYOtAPl/Lz7L8sjzHp+o+/xLP3j2NEL6kOOw/JfoYyeIAbteePciGvpfod6T3Ynoxje4i8JlU0WhrPz84n1NcOM0sXuveT7LuPJTd+PqtxLuvS6thvih5HqVa24wbgZWuNu18gowf3pZd9LqfOpFA+dA7Pzx1Cby9HbOqktrt74eMOcVuPSNJuzE06aFVvQTz530/w8Dhf7E2tcOvux4gRvlim7OVkSmv3rcYo9w93E0w9BxA00o8+Pr0JCBzAQG8LUnaeJlEppR3nzdv05EdXMKtvNzrbmWHi4Iyndw9sCjazS3NjM5jWHKW+s5/h949NZri7Fea27pJtDWWIe6l6T3+cNx8Lwav0K36o9zmPYfnrixnncJ0vwmOVlNBnXuOp4F54DhhG2FBffL3le2YAg5rcM+X71IJAL3oNDuK+vj3x7dmL/oMk2XS3JPNMDInKH4Nal03r3yW+Uv4zPDFnJH0czbFxl/MD8bPJIkq2JbmKRghHaXMIR6lAIBAIBAKBQCBoSnufPe6eV+9DFvLsQ6H0NzvPJ++uY+UHUnh/F4cSkjSzdFJrKEk7wto163l/7Ue885/dZDj3Y8a8aWj2nx7L7GmBuKR/y7trP2bVmk9496eDnLuhzuqS6180FrfUH6U8KX/tJ/w7ogLfqYt5pH5zptY4wGfvbiRcjbUZMzM6FRxk7dp1/HPlat7amYPL0DAemzdGLTCGpUsXMNEtnS8/+Jh3P/yY91cfpLxXGE8tmqCW0cWbhUsWs3CoEyk/r1HqXLn2S8KzqzQOay1tlIGNna1kqNoVSvsz9/45jHdN4/MPP2OVFP6zLZkqB0cq49exZovOa+S2zvTT9vOdd4ku7UzApDAmKpmn+GnDFfJqpY+F8axcLel79SYitH0JMCFi9Xrek/r+3gcbiXccxaOPzFF1rZHPlF61RG34gDdXfsiqdd8RU6hk6uXy3s18nC57SspJiZKvpbZVkUkoPpX7eee9dfzn/XW88/UNHIPm8NSS2W3aXXxvfAbVFs54K0tdOimzWGvS4tgRk06RfQ+ClXVKnRjr4Qg5afyULccN6Xd7aU7mDdg5+VG070vee381b/4rhqLO/Zg6bayaC8m1ZaQf3MC78vj6YA1v7crCbeAkHpghLynQUv0x/LDuM35Q/3owduoMJvhWErX6E8UW31/7E5uPxtw0e1Oh1bHfGqpsgxyI/3qtdF/5iH+/t5bwyh5MWfQYS0OkIpcP8PWGdEqlAVOecUrT9o93cFZTQQsYoLfRE3lgvA8VZ9Yp+e9++Alf7zpOtNLZVu5b+rB0xMXqMp+uW8/bK1fxXYoZvsMnMF279q4BNn1k2zdcKqiWPuUTq+hpHV/qzr7tICbOCGO4Yxafyvcbqd/vrdlG+CX1nm5hjZ29dG+xUYqqWGKjpFmqcbCykeJdu9KnYD8fSt8L/3znPf5x0z1Tc59y9vDA+tI3vP/Bh7wplVt3vpZ+4xfw8Ex1IyWDxnvLOvG9fzFPLeiPyeH1mvuoZJefX3Ng3KKFLOqYgSoQCAQCgUAgEAgEgjZy1zhKw6ZPYqBLPlGffsHmiBMcjz5JRPhmvtt5CWX7l/i9fPbZZrbvi+RIehElZbmkFVZj06U3mi0yrLGxNMHSrivOdRlERx0lfPN2dp1X3pHX1G+bxqG129lz9AzRUv17vosmzcyLMSEtb97SQAqXz15VZ221g8pMTn66nfADkUQcjCT8651cLbSl58gJjJLzQyczc4ANyYc/YZMki6izZziy/0eOp5vSY/g4TRldfCczN9gLs6Qj/Pvr/UqdEQf2s+lcJhVqEZmOkYEf/p62VGVcYItch9S2A5uiSa4ww6o6m0MxOmtQFl3j3c/Vfkrt+SAuF+zc8FO8EdeIOpiE4qspz9K0+eAZ4tS+lF7+hs/DIzl+9hzHI8OlcwtxCxiF7NPSyMeFvJgNrNl6SHNuxD4+v6FvdqGG5JgTRGTLjtJqCm5o5C63VZGJcw4nVm1k54GTRCl28wnhN6BH8DRmtsFTmvxTEtnY4zFMds7cR0A3SzKuhZOx7xJJFc74KeuUjsLHzYyi9EvEyycZ0u9204zMNZkKBQkfs36zakN7PiNWVllnn3qHcdyu7/jo+z2EHzxJSkkpxbmZ5Fda07WPn5TbUv25xJ+X+qo4haXRamOBuak1Lp5mFCRJ9n1gJxu3R5OgyW5Mq2O/FVTZ1iXs5X2pbwei5OtJ9rLhPFnOfZk5XZJuygUOHcxCnthbVZSkafvRCzfPbm2KIXqztcHCwgwbxy5YFCYSdTySzT+Gc1xZY6Ll+5ZeqrM4sV6y1whZvof45mQ65eYueKnL3xpi03GnIkkokhVVRoaip0iimk6d7QBsrCwxtbTH06mO9LPRHNiznU271Xu6MdRkE/3pVrXPeu6ZWoqvs/oryT6Ve+shNq+TZyi7MHSsxtlv2HhvSSfezJw1mh7lV1j/1T4ios4p+Vs+ukqB6wBCGvy2AoFAIBAIBAKBQCD4BbhrHKUjurtgWpFJzIHrirNCQyEF9b6vajwD5/Lyf7/GG79/huUPj8TTQuq+iQlWSv5eNvx4imzXSTy94nneeOVpHhvrg3ZeklK/qSODF7/AG69Ldchh+VA8TKWHecfbvHlLdRm5N7SLgkpkHiK9CEwdXJDdTVJjcTUzwznwAf5H29bXn2ZkFzOpsQ6aMrr074OXLeQm/MiZTDVNorSiGnlyn5aOkcF1MgqrMXXzZJi6g4tzQBfcTKspK2ji/ijNJUqnn5f/P3v3AVhFlTZ8/J/eKyWFEkJIQguhBQghQOg1NBFERFCxsKyu+qrr7rer676r7+662Csi2EAFkSLSe4cQek2D9EYS0nu+mXsnlZQLxKU9P73kzrlTzpxz5mTuk5kzuep7a+xqPhC+Lm1frNsP47mqff8Dc7yVRAsrWqrz6MqnkKTT+4mqceFdSkmJ9s5wle3uUrgWwVPlRbI6Vml4Ni50aSyvDbl0lvhcU1za94bAvrS3TSdy+2VI2MTFNFPcvQNx8vLHxa6U5Kh9+mUM2e//gvyME8RUlWkkOep9xLZ29NAnKG3XlZ7TnuGvr73KnxfO5bFAF93xZ2JSfQWgIcK/+4Etkcpuz3qMV155iVefGkmPBh/l3tSx3wStbLPiNnGhZns5GElykTEt2t3CZYCG1NuJlSzbGYVR5xksfPEF3nhxHhO7Vz7Yp/F+q14l2SRdqG6v6ZkFlGCOTSv99G/Spm/Shh/Xc+iqIxMem8+fX3ue52cH4n1jTUWvqT6zUlE2Z2v2gZcSSS9W+zc33bRhZdNYnXShi5sVWLZj3ILK+n6V/zerE7bq1bD/rQNVCCGEEEIIIUS97plAqYWZsiulpeQ3cMep7/THeG7eOILalhK7dStr1qzlfKbyDbhKNNuWL+Gtf3/GhugKvAJHM+exp3hhlj4Iolt/USZnjh7jUNVrNYv+8RZvfBWmm+f2yaBEfSS4kfLFX520NFV+FnH1fM28HuO7D/7Nn/65nBqPkNGzM8VM+VFa1MhVaIrmKYNLfL/hPLkt+7Lwxd/z/PPP89r83ljH7mTp+hoRigaZqc+OaZi2L9lx+2rkUXl98T5/en0xq9V5dOVTSmFejcDJTapsdzWvvFXllFQo/zaR1wbtIyqlFBvXLgQHt8M5L5Hw42r6ZTZHp2Ph5sOQQHdcTbOJ36tdgWvIft8upqboY5hePPzM48yfFEC7kgQ2b9jEqlWXuKq23RuUfmILn7z3Pm/9eIxr9n4MnzKT516Yx9h6Hp/e9LHfBK1sK0rrtBel/ZSUKsec2c1E7jSG1Fv6KdYu/pR/LPqJQ9fs6T10IvOeWcjCMY7Kh433W4YzxkyLGv82bfrmROxayXtvv8d/Nl+hvGM/Qmc+wou/f5C+2uc3r06f2aASytTd1sYgMaxsGqsTe8xMlR+5CeyoWd9Hv+bN19/inbXq8kIIIYQQQgghbhflW9+9ISojDyxb0rGXllCLB+MnjsDPLpllny1nxcZtbNxReetotfzE8+zdto0VXy3l7XeWk+zQg3GThunGCdSt36yC4sgtrNtQ+7VRfw/s7eM8Cnc1QJSVwl51OuYaOZhSURh1XV7XbQnT36pd08EUMsqhpesQnLSk+jRPGRTQupUTFllxnLhqR3t3G1L2r+Bvi5azrTlu3dX2xaKkiL118rhuw379A7J05WNJ6463cCWgpv5258hoV6VCyrNIPKgl3ZAktsVnY9LSjUmd3CiLP8927Q8A4ScTybFvQ0jPVljnp3Kh8gFMhuz37eY1nEmju+CY+CMffb2GdZt2sGW3dqv9DcvW3f698eef+PTjD/m/o8V4B08mdKj2cRXDjv1GNXR89OpAa0vIybiF49/AersWcYKdW9bzzZLF/HNRGEVeA3lo3CDdZ431Wzfjt2nTNykvidMHd7Fh5XI+ev9DFic50nvkWMY2tHPOtlgZ8lutbp/ZkDp1bGjZNFwnR0hU/x5lWkLhwbr1vYWdp/TLCyGEEEIIIYS4Pe6ZQOn2HedINWpN0GNzCO3loqQ44hU0leefGIcXObSytgKjCkyuJnANFwZMnU+IZ40bbwNn8dLzsxjr58i1+EiORl4jvwIsTEx0T3Pf/lM4iSZtGPHkHN08Ou7+hIYOw1c/ddNMjbX1GcqyDQNmD6Gvu/LevQ8PPzWTLnaFRB1aow+C7lirH4805DGeHtNVC+640HfsOMbWl9mYnRyPL8XaezzzxvqjW22vUTwd7FHr1uQbKoNGWlZ/P3esC1M4s/t7vly2nGXfr2fLkZpDJhjqIpm5yg97F8bqMu2Ce8wadlzKw9FvFs/O7KPbF31bGEdokJbnHfuIyDLFve90Hg7yUMpH31Ze8mvivteUPAqVEnH2UMvUEXd3R32ZaO1OXyZK25r5GDO8rci5tI/vdYNmejB1wUu8+Zf5TDVwzNKTl1IptGxPf08rUiI3VY11ma+OU1rakt7dnCH9Mr9qAVQM2e9KxqbXB8Sr6qsfj/7Pq43ktZ4y16Ub4FpLbJTD0KTClNT4bF3bnbEgCK+ajcyg9avl+Xuen6rsZ14GUacvkXatDBNjEyzMtVmqGHDsN6DquNTK1tp7UlXZOvkN4+nHAnA3SuHwT9rwB6TrhhqwdGqLv1I9TkreG/zDQ2UdGFBvXpPn8+qCUAa4F5AefZYz6bmUGxkrqzBrst+6GYa1aTiTpVaUchyMUvtbpZ50me/H4y8r7eflWVQ/wutmKet6/hkeV/uwawlcOBZJlrpzZsaYqHe+a0Hm1u1DdX2hWieP/64fbdQrNutqqs+s5OzFwtB+eKn1V1XH6ZxS+gyVQWXTaJ1c5vudUeQ4duOJp6YqdapbLU6e/Zg6vl/D7UUIIYQQQgghxH+Fibml3eva+2bR2q0tubm6ZxLfFPUx/uryujsdjbT7HQ2QnZKofPnsRP+Afvj5eOHXuz+jRgXRyyWf4+t3c9krmMBubfHx9KTHgMEM79+Ba/kmeNpnc+CrzVzpPY4npoUQ2N2HHn0GMHx4MH3cSzi5+jO+PX6V7GRl/bbtCRg4kJ6+Xto8Qxnp70z6sT2creeJ6SEPzqWLdbJu/fVf0deB0bP64OHUgvK4vRyPA9O+45jQuRVONoVsP3wRdYjHmnpOmMVAm1yutu1CYE8/+gYNZfxAF/LD1vLWl1uIU69WykkmLt+Wjr0GEtLDC59uvRg8dDCjQ7rTMuskW8/WrZ8MEvNd8e+vrK+LJ77d/Bk8PAj/siLM2jhTeOkrVuxWytiQMjDvydQxPrg421KwO4yzBVo5EMHHK/U3/Zt1H8Wwbq64urSivVd3+vdX1jM0mCH9vLFLP8HFq9cvozNkMgt8rUk4tIJ1uispsynxHMSwHm3w7aDUa1Af2if9zOqjZbj4BzC0jw/evj0YpOz7qBH98DaO5pdDiUr5pBJv40Vg3x708vakqzJvyDB/OpQY49DanJSq9deRYUdASG+823jg5duboV1N+P6nXUqZtKNvoFImSrvr0WcgY0b3o1XGIZa8+z1bE9Wy9uOxP89nQh8XCo+tYmu9TxyqreRqdyY90A1XyyxOfvoZv1Y+ASwnnR4Tp9HL1ZTM8ytZtLEyzHOVhLgm9lvhNWI6A9o50po4tp1IqKe+gvjd6zMZ0aOjUhfLWaO75b+m+sp8L4dTgnjoSW/Q2kqlWsdAfjmdhvSmm0dHOnbsyqDhgwjskEO+eXscssJYsuGsskRD69evT8+dIbNmMW1wV7p00e/nmH5e2CjzfbpkP5G1mncB5U0c+9cfm3WPS33ZturRh6F99WUbPHIYo31MOLN+GR+tPkGabnjbLBz6DmJApzZ4eXrTf5AvJpsPc163zmq16+BUk/XWLngGT0wMomcXH/oMCGbkyD742qSy/ctvOOEwuNF+qy5d/9Eyo/Z++43miUA38msd5021aWVvy9szLMgPD5+2dOo+gMC2SWwL681Lb05lsI9ynC7bwBHdnA3T5ce9gPOL17BTS6vi7M/UuRMZ07cLXbvr+7AR/m4Un17Loh9Ok5aVTsfgEHp09Kajlw/9gofQz/YSqRY+tLhW2Z60bbSqoNjFi94N9ZkKXVu1zMemvR99evkRNESp487mXNmyjHd+DCc537CycWrid0laYjxlbt0JClSOBR9vfZ2OCCHY24RLvx6t5wFg+mPLJrF6n24HOzv7W/7dnprU5OPNbsitnm8IIYQQQggh7j23+t3jngmUkp/GlSvxRF24QAY2mBvlknh8K2u3HedCwlWilS+n0WevUmJnTmlmBDu27WT7njCOh58gLCqRrJx0YiMiuZhvTgsLKL16gdU//8qmPSdIVa/aK7lK7OU4oiIukVRihY1pObmX97F510nOxCWRUTeiqchMTSLs0BHd+ut/SnMGSQWFFOUXkXEpjFOJkJWaTGFxOfkFiew9Fk3dxwvlpKdw+tA+doZHkW3igE1pMmGr17N62x72R1Zek1lKWnwsMVGRnEsrxdrGmPK8OGWf93H0fDwJ12W2lPSEOK5EXyQ2zwgT02KST+1k3a972RMezoEjUVxWA1WGlEF2OglFhZQXFBB74ASXlB3QlcORcC5eSVNm6MfE2cPomHeQjacKMVfWUVpWSqmNFyEjA+hslsQPe6/UWUaTmUb88aPsPx6jBTdKSU1Q6jWrACqgMOki+8Ivcu5iHNGxlzl3Lg+zVuZQnM6ZHXvYezKaK6nqVXD5JMbGKfV9nrQyK6WZZRG1bRvrdx7kQFgYB6vWX0dWCnHxV8ktq6CsPIuY/ccIi1TKTCuTLCM7pd1lE3NwA6t+2cX2sFhtLMMSspT9LIo5ztrdZ9HiTI3LUsox4TT79+xl96FYpby1dLJITUziuNIGtu0LIzqpetzMa4lN7TekJ+dRWFpA4dVIDpxRKvW6+srjarI9XQd5YRz5DSsP6xarof4yT8i5RnLiSQ5VthVN7WMgnfjYK5xOL8HOopSsi/vZtGUvOw+d5mjYKSIT1EJvaP369ekVK3mM5WJEBsbO1hiX5xJ7eAM//rKXfRezrjtmUps49q8/Nq8/LqvKNqUUK+V4Kko6p+8fth/mTEblbfxZJMUnkpJTQnl5OZmXDyttKZ4aLVinbh00VW+FGalEK31TmpG97pjLuXKUVT9vZtuhCLIymui36tD1H0obr7XfuVdJPHuMffUc5w23aWW2tGRiY7LIN1YqqjiVs/tPcSYxlTKrYpLOHGRTmJJnbd6G6PJz5AiHIuJI1tKqGOeSeiWaC5cKsFTLpSyDM2t/YfX2AxxNU/s6NYCdSFx2MWWlxaSd3sUvm3azM+wCx6rakxYodUpiyzurON5gn6kFSktP8/oXmym1csYoK4qdP61nrbK98CStjg0oG+OmfpdkJxFzWf1ddZEcc2dlHYWknd/Hlv2niYhN4/q/uemPrf0HqvfpdpBAqRBCCCGEEOJucKvfPYxsHNyUb7nNp3vvASQn669euxmuru4kJSXceKC0BofWbtiZlVGYlUp6rWCBPS5tbDAuzCLpanWAqRaHlrS1NVO+EOcRn1rPZaIK6xatcbY0oSQ3iZSqp+rfJBtn3BxNKamRV13+aXj7lfT5KCcnIV35Kt0A3fotMGlkf2rSr5N6yq62RstAV4bUn6/AV9n0dj/i/m8B/3OgDDtlHTqWE/n30ofwubSMPk98pU8zlFZnZdfVq76+zcqLyEjKqPfWfn1bKWm8DGuxoqWbI5bG1y/TcLtTqHm0KDaoDm5dY/utz79ZcY2yqlVfjgyY/ip/f6oVOxY8xluX9LNcp8EyN4BuWePG25hB66+siwbKvBYDjv2a6jkudQw4npruH+qpA53G22vleustE628Guu3bkajbVrn+jyr+XQg58bbRYO0bVD/cdpUnzX3k/X8j28Ebw97gR9189bfZ76xcidT+ZXu0/+t32+ThvsNVZNlY0CdNF2+d4CKCvVvFri5tbnl3+1nwg9pU83jVs83RE3tGTC6G26Fcfy8+4yWVptrJ0+IjLn+jxpCCCGEEELcQW71u8c9GSgVd7ARf2XLPwaQv+ZT3vv1LBdP5mLXvzN9+0xl/kx3or98hceWRWszi/+6IY/z+eN9KTm8hEUf1fPgLyHuMjUDpcu0tPrUDJSKGu7xQGmvIWPpYJnK8c3HuM2PZbzNRvLXz2fQI2svD7xczx8rgx7irand4fQWXl1WY3wVIYQQQggh7jC3+t3jnnmYk7hLnPiJT389R2mvKTz5zAL+/PpzPP/4DCb1q+DAsnd5/1cJkt5WiSdZtXQJH6+UIKm4N+RkpJOamkVTN2hnpCvzpd++W9vF7TFg/DQenjaEbtp0s+szhd+/+DgP9dGm71i2tHJ1xb21gzZdh4Ubnp074932uifm3X/umjoVQgghhBA3QwKl4r8r/SyblqlXK37Dt2s2s/noLtb/tIrPP/uSxT/t50SqNp+4PSLC2LIzjLNSD+Iecfj7T/j7ez9x3XC7dWz44l3+/sV2bUrcLxxbu+Lu2gJ7bbrZdejNmDHDGNBBm75bnVzHe/9+j3dWhWsJ97F7pU6FEEIIIUS97p2HOYm7Rsm1dOKuxBARGcXFiCgiomKIjk8hq/IpMUII0UyyUxO4HF/fQ5Jqy0iK43JS/Y/dE/fuw5yCJ8/C1yqFIyt2UP/InODYvg8jxw9n+OBghvX1pWMLY2Kjkql6LKJTZ0aGjmH0UO3zVsakRibTsv8wQvr2ZkinlpiUW9KiQ0/8XAs5EZmuLajqz/THR9LHOYNTMZUj5/ZhytzRBLYp5URE5V+t2jNy5hSGd7Ig/EKCPsmtJxNDRzB6WDBBPTrRzqaUxNj06nzp1j0Yr5I4zP3GMW3cEAJcywi7pI4y6knw1LGMHzaE4J6dcFNOtzyH9sS9IIola+r5s0KBN4HDfbDLP8i5OH1S8NS5DO+g7E9eW6ZMHsPYkP708m6NUVokifrnB16n+9iHmNbDhrCsVvplhgcR0LktDsVJRKdVngQ0lm8HugePYvzoEEYF96SrVytM0qNqbe+6bVTmK07Jl5W+rsaPCKZ/F2W7uReIztAWrLPdSWOGM6J/N3xcIVnZtrqJDgbVqRBCCCGEuJ1u9buHBEqFEEII0aj7NlAa+ACvPDqRoX4etLCywa2LH327+9Cq/Dz7L6qBzfZMnP84s0d2w8XcFBvXDvTo1RXXwjPkdp3K5B4euDlaKqczdrRyb4sLV1h/sOZoqH7MfWk6Q11z2bjlrD7I2ech/vLUUPq0MeLkL0fRh0rH8NxrofgZh7Nq9xXoNIpn5k9jUpAvbtaWtPLqTt8ePnSwzeTk6SQtWDqeP/xtCB6mzvQdNpju7doo+T7L2gOmSp7nMntiAF3cXWmrvDp2bY+7qxPWOQ0ESnXrGoVXwSrWH9GnTF34KqM62uDeI5Ah3Tri1bET3ZWy8XUq5NSBKOobyGLQ3Bd4pL8nbp17MaxHBzq0b0enrl3p1tGRkugwLumClg3lO5sBs55g/pQh9O5gh4VDW/x6+dG9Ywsq4k5oy2rb6NkK2y79GeOvbKODPl9etiY4BU5kSqAXnu086dajM51djYjZeUF7QJV+H/3btKNnn974eLSnQ0cv/Lt5084ynXClXH1D5xhQp0IIIYQQ4na61e8ecuu9EEIIIUR9ksvJTznEsqVf88VXX/PRpztJdfRh1IQRdNfNEMiYkd1xSl3N4q++5fOl37L4132ci80nYvcvfJdaoMxTRFL4Mj5ZsoylG8/rlqp2nNgcU1p4dmWgltIhqCPtykrBw5dhlbd3B3fFuwVkRZxWJtozZeYDTOljR8wqZb1ffM0ni5exs7gtw6c+xMNB+kXAUjlJdMannz+EfctHn33B4g3n6RA6nUdC/XBK3smHn37G+198y9d7Mykp1xarl7ouG2yttEmFlY0NLT188Mnex9dfLeP9jz7jZH5Lug4ZxhBtnrosrJV1uLTG69peli7VL/P+tgyc/EN4aGJ/ba76803QVB6bGoxH8T4+XvwVn36xjI9XxWHfZyyPzBxLZVHpttHGiwDjs9o2VnC2sCXdR01gWKtYNixT6vHjz9iVbolnQAhDq26hV7frgKuLJed+WM5nn3/Gux+v5HR5RwZPDWW8Mp9hdSqEEEIIIe5mEigVQgghhKhPzG5WrNjAlj2HOZKaS35BJinZpVi39sRHN4MlVubGmNu44FiRxsnwo+zYsIXt56+ReO4Ye6+q13aWkh13mL0HDnPwXIpuqWopbLiYSql9OwYEqNMODPFxJTfmOIkV7nQP1j9cacDQdtgXJnFmZyZ0GMr4Ae5wZTdfbNjH/vDTHNm/h+9/PEe6ow+jR1ZFShWmlKft4LNVe9it274dY8b2o61JInsXr2PHfn2+dmw4T2qJtsgNMC1K5PPvt+jXs38fX0Yr+bNpQcfGxu8svcrJFRvZtlfb9k/biM6xoX2fYHRFoFM335mEjAyhq2MG4Z+tYdv+4xzXlfV37IyDtgNGMqbWNrM4/ZUyn7qN/TvYHpeDsZ0xactWsX63ut0DrLiSDRYtaNdZW0STc+U7vt+xT5e3vXu38sXJZHDuRrBSrIbVqRBCCCGEuJtJoFQIIYQQol6luPlN4PfPv8BfFs7l8Qf74mqunDoZG2Oh+3w3K385QYZzCI/Of4a/PDeXhwI7cCPPho9ZF0GqkTPeg9WI3QD6tDMi8cJqYq5a0qHnACWtPcEdnClPiWRDkjLZ2Qs3a7iWsJWIfHUNemlHokktMsa5rZ+WopedsI0zadoEvvi4WCoLx7Lq7FUtTZFfREmF9v5GFGRyPK56VNSIPPW9NXZ1go+1lBWQWWMZ0g6QmqMUqZ0THbUkVe18Q6+2jhgXpRF5sma+o1kfn61s0gWfmtsszSH5UuV8hRy9phZUIZlnUqgssviiUuVfU8xs9dOVinIjqd5sIREJWRRjib27liSEEEIIIe5pEigVQgghhKhHp8mP8NSskfR3KyV+1042bNjAxWs1L728zO5VX/PO+1+y+XI5HQKGMXP2PH73gP7GfINE7iYiwxRXrwAcg/vR0TyViK3RbL+cgW37foQQiLerKRmXfyVGnd/WFDPlR0VpjWCjKr+QkjLlxM60dpi2vKzmQ8psMTNVfpSU8ts9uuz64GPjtNv+jWqflNbON1iYKp+WlVL3uY+5uupofJtXyxodV6BxSqHqlpZh84UQQggh7gsSKBVCCCGEuE57Ro0ZSlfbFFYsW8VPW3exbc9hYnPVKxGr5Sdf4uDuXfy04js+/HglKfbdGDFuiDaGqSHOcuDKNSxcOjF+iCeOVyNYfQVOHY4jy7E9g0f64GZ1jdij0frZj6aSVQ7OrYNw1Kfo9WhPS0vIzdQeS1+vYySrz6BybMkwJ33Kbec0DDc1L9dSOaRPqdflzDywbIFnDy1Bx4FhrZWFy6+RfFRLama9PFtgSR5ZSp0IIYQQQoh7nwRKbxt/Rk0NZVQvbfJ+5dyH0LnzeH7BfJ6fMwwvLbkpfUeEMmOEvzYl7gjOHvh61vrafpfwIHi8tKd7mVPAKB5/SuljlH7m8TEeWmpTpI++f5hi170z3Wu+OrfHkRxaWlqCUQWmmYlk05qAiXMJbq+/6V4n4AF+/8wDjOzqQHZiNMdjssmvAAsTE3QPYU/NoxALHNt1VtbngKubfszR2go5cDyRXHt3RnZyJvvKbiKU1PyDJ4gtbEHXBzxwyEvkaOXD6K9sYHdUHtZe43hyak9clSTHrkOY93Bv5X0qx9Yf0M9Xr1i2n06i1MaT0BnD6OWmJLl1Z+ycAXRQdvW/wtKdgAeDtG33ZPrcqfjYFnH56Ab9FbMN2L3+JMlKHfR/+CFdeaPWx9RHmOplSW7UIX5qpkBmy46zmdhfrX8Hug9/iHkBrSHtAjv2aDMYVKdCCCGEEOJuJYHS26Yf0+bMZFrNZy7cdxwZO+9R5s8cy8jBQYT08zW4QQ6aNJN5k/ppU+L282DGUwt5+cWnePiuizd2YdxDd1J7ksBts3IexvynZvPIxCGEKP3MwC4m2gdNkT76vmHZliFPzuN3NV9zR9ObTHaejCHLypMHn32Bv/zxGR4f052c9GxtQfXCzDb4B49n7pO/Uz5X5nl2Gj7mqRzd9iuJ6gw7D3D6Krj1n8crf/wdL85Qxxy93tWd50koaYGHSxGxp89qiXs4k1iBm2dLShPPs71qaM5I1n76AzvTHBj+4BxeVLb7ypMzmN65jFPrl/PVocYfLnRq9Qq2R5XTfvSDPKPu17NzmTGonMwcbYbfWqkprUdO17Y9h4dCWpB9ZB3vro3UZqhf4qEfWbL+HGWdJ2jl/QxPPTgIh7T9LPt0Exe0+W6ZVQDTH52vq6/fzZ5Ad6MrbP56BTvV8WFVBtapEEIIIYS4O5mYW9q9rr1vFq3d2pKbe/Nn27a2drrldUNBGd0NA0L14dHnZzOqYxEHTleeRRtiOE++2Bu7uK9YsVtLuu+M4MU3QvG9tp+/f/QzB46d5XzCVWo9eLf/g7z06GC8i8M4maClKcY9tpD+tnF8vLKxK2cM0MD6RSPqLTMTRsyexyhvE2I2b+Dgb/oQ4Js95hrSm1kLQ+hq0gztqVkE8sxrDzPWo4RvN4Zrac1g/DzemN4Lu70nuKgl3RfGPsW/pnYkc++7vPvjMY6eiiAuvfat0/WXjfTRNdnZ2d/y7/bUpHhtqnnc6vmGKistmRNhxzl1KYqLtV7RRF5OJiI5kZiLyu8lW3NKsyLZs3svuw+Ec+rEaU4on2fnXiUuOobIQnOczY0ozbjEL+u3sP3AGdLUpwZdSyU+8Sp5ZVBWfo0rh49zIl53rWltynqSEiM5fuQg+45Ek5irJuaRlpzMheNH2b1f6W8T1Xvm9bKT47kSH8uF1FKsrI0pSr3AL78o290dxvnMyvZ9TSnzM4QdiyG2xkORSjKSiI2P53xSAUbGxhQnn2Pb1p3sPnxamfcEkXHp2pw1Xb8uXdmF15k/K53EU+EcOnWFhOrsVvEbPZ3+jsns+GQtp43tsS5N4cQvG/ll1wEORxdoc9WfbyXjJMTGExMVxTVjO8zJ5srRzazdvI89x+Oqxi7Nu5rKOSVfav1kaWn6fJ3gaFQCqVrS9Xntz7RHvSg58wFfbi7FsoUxmZGH+fmX7ew4eI7kypMTQ+tUCCGEEELcFrf63cPIxsHtZp5z2qDuvQeQrHyxuFmuru4kJSXcRYHSB/h8yzN0vvQJgxeu0tIM8RLrjo6D9SGEvqEl3Xee5+cjobge+4TAZ37U0up46G32PNuRC+9P5ckVWprijZU7mcqvdJ/+by3lJjWwftGIBsrMq08wPvZ5XDoSTlSelvibuNljriEPsmTHM/S/2gztqVn8Rvn585ecnACrAx/jb1rSfeHVLzkztRWH353I499paXXVWzbSR+tUVKCeJLi5tbnl3+1nwhsbgfLG3er5huHsaOVmjXHRNVIy6jxAqZJ9C9xtTKE0n8S0usFbS1q4OGBhXEpu0lWqr0dtBtZOuDiYY1LvdpugLUtj+9XMHlr0A7/vFMkHoX9mrXMrHC3Kb6pM7Fu5YGtarmQ9jas1nvx/a55l+Y5RsGkCs/7VVJ3/hnUqhBBCCCFuya1+97jHrih1xD9kHFPGDWXc8EB6+7hCykXitew4efZj3ORRjBkewqgBXejUwoTLEUlUngL7h85hVi8bDiXaMXbaBKaMCSGoe3scihOITK3nRNmrH6EhfQka3AkXY+U7RwtPAv1cKTgeQQou9B0xmtAxlXlpi1X+eS5X3ToXxENPesOlGlcr+Y7i8RnD6N+mjLAL2iV57n2YOm0ME0YpefH3oZ1xMmcTGolCafNX7mMHx2JiI9O0fXTEK2gYU5XyGTNyCIHdOtLSOI6LidX7FjJzPmM6FhJl7seMByYwrr8rpUeUMqws2wkjGBfsj5+XI0XxMaRUXgBSDye/IUybMIoJo/XbcjVN5Xxl3nVl14/g4DbYl1Xoys7LOpzTNcYY8woaxaj+fRnu2xrTMktaefWhp1shYRfTCHlwLl2I4OP9RcyYPoHQkUEE+Naub1Vjdd7Y+q/nwoDQSUweN4IxQX508bCjJPFKrf2vLLuwnPaN5qmmWuU9ZQyThgfg52nPtVMxXFXqstZ6rijrUbfXP5SFkwbiaXqCs7X+SDKQhxeMobdTCiej6tlgjbY0pJcPHjYlxF+ubBsK/3E8PW0ovrXqoStTH5vMkHb6cmmszNoHDCWgjTEJR9T2r9Ctzw+7o0k4jAll+kSlHgLrtkmV0rbUz8cMY4zStnr36kNggP5Vt000fsw10eaqeCnlPpEpo4cR0seHNko34zWiD20LlPZUdUVpY8eKUi9PTSDE05Qj52pfhqzW55SejiQq9VfPxVQG6sakRwNq52fIVJ4fH4B78UnOV12tq6/vHjXLSGsz45Q816zj7iNCCRnQj8FtbcDMjnb1la3Kuauu75s4UjteWpsozTeJ6tZUox8Y2gc/79YYp0bUat9V/WhG6+vbr5V+/VV9a+5ZIqv6RNWN9jON50fXXpX9HtLOHkrV9toBq6NKP6z/WEcd77j+sqnuo3cXhvLwlFG6PHVxMyLxfM0yUdXoHwKV/sG9vnnubvfqFaWGKSY/N4+8gjpXItdUVECOMk9OfrGWUFMpBXnKZ7kF1z2x/ZaVFJLX4HaboC3b6H41M90Vpc4ZHFmxg+MF+TddJkX5annmU1DrFpRbpb+ilMjl/LS/qTr/DetUCCGEEELcklv97nEPBUodCZ7zFAtnjKSfjzO2lq3w6aN8cW6Zw7F9UVwLnsXr8yczomdHWlrb0aZbTwL9O+NSfobd5/QhjZAnX+WJvs5Y+gYxqb83Pp6d8PPvRncPcxI2HidWN1cNvSfx3ORedGrjgHWFMY6t2+DZGiLWX8Fv4e+ZFxpAd1d7LFt70qdvT3p4WZO26aT2Jb1OoNS5H48//wSPBDqTsHMz+y4rZeg7jucXzmT6QC/sTaxw6eRH/z4dsUo7yfG46jBTlcr5B3fDw8EKR4/u9OvZHtO47YQlKPs39wWefmAwAR1bKOXsRo8+venZrSXlZw5yNlO/ipkvvsH49ia06j+SMT086NCygrA1Z2g7dwELHxyCn6sVlvbuyrJ9lX0r4eL+CGrFOTROQbN45akpjOvjgaOFI57+PQnw86K1UoqHLigb05VdNzq42WOllZ31tTVsqnGncZ9pj/Ngb0/aOFkpTcGe1m3b40o0q/fG6AOltuU4KGUyooc33p188e/Zha7OBZzYG4Fud5qo88bWX5sXUxc+yWNTgvB3s8GqVUcCAnrg52FL+qnTxGpBHF3ZedvQtveghvNUh34ZB9oFDGJo5w54+XShdw9f3M0s6DJpKhP6eOHlqV+Pt30muw7GUGgWwvMvTiTALonlO2pEusY8yaL5fbE6/zMbztRpHzXaRhsbK1p7+xPYszMd7TI4diJBH7QMnstbjw6hTb7yJbHqDvSBLHh9FoPtL/PNlouN14l6/Ay0Jer7HZxRF9WtrzctrToxbFwfOnt50rm7H/5d2mEev5UjWr8VPOcF5bgNorOjOdZ2bekR1J8An7a0dnG/rk00fMwdIL2pNqejr8vHpwzAr60b7dq64+3XgbZuzljnVAcmGz9WTBnzu+eUcrAmfuUeonRLqIbxwpvzGGh9ie82nasRCL5R9QRKpy/k3Un+mEevYuM5fRJM4tV/TsCnqr6UPuSPj/HwYC/sTCxp2c5baadeWCRsx2nYQiZ2bksrW1OM7Z1w92h/fdnioZTNAp4Y54+buRm2bh3pHdAd9/xT7IpQ+3Slj9X1A8Pp11Hp1xzb0VPp1/y9W1BxJZzzWkeg70dbY9c9sE77NcE5eAoPBivHhofSt/ZS+lZ3IyK3ntWP5Vi1fkP7mabzo2uv3dvj5mChtVcrrq7ayTFtDaqRjzRUNvo+2q7MHu9eg+nl7Yl3l2707u6Nc+EZdl2oDIVr/cOk/vg4mGHdWi03f7yt0zhaeWzdA+7vQKloLp79htPFPJlD6/ZzSUu7c3RmyDg38pXfoRtrdhJCCCGEEOKucqvfPe6dhzkNmcHTD4XQ1fQMSz9YzDufKK+PNrE3Jk5/VU9iGXlJB/j80yV89PkXLHpvKylOXRg/ZSyVj0yxtLbBtp0vQZYXWLZ4CW+/8z4/Jpji1XcY4/prM9V0YTcrvk0m3wgKU47xzsfKNr/8lZPkkan8d/HXr3j/s6W8/8GnLLlQQad+U5gyRlu2Fg+mPv4oj/S14vLGpfx4RL1KzYMZc2Yxw8+YnR8v4cPPvuTDT1YS5TCAR2ZPqspzNW3+wFakbv1c2fcveOeDz/lm60litCvQ4ssLSN7zLR98upQPPvmUf29Ko2X3EUwf31U/g8La1oaWnfvT3+gIH37wMf/6dD0n1bKdGUzLxJ95/9Mvef/zpby7swivMbOYXe/+DGTevGmM7lDClo8W8+4nSn4+VPPejxmPKnlUH22vK7tE1GHYKsvuuzrjAF7YvoYvk9UoZCEJR5WyVeb5dF1VlAhsnOhybQ+ff76Yfy36gPD8VviNGMVw7eOm6rzJ9Wu8HpjFE9P64RC9kkVKmb6n7NOiHSV4jpjG0w8P1ObSyq5j58bzVId+GR88Ejfx0Sef8dbbG4m36EDwzIn0LT3EZ0o7/teirzlX7Ib/iGEMURe6vIXDaSa06zuaUGfdanRGjeyGm1EGJ/dWjcqm0dpGP3uiVujbxrsffs6W4vaMnvko83QrVZhbYWunHAPW2rSOBda6NP1TnhsrM93xY2dF1YOTdetrQ7dAa05/+zXvv/8hb/6chLlnL8aP6qPNNIypM0LwLg1X2tUy3l+8lEUnsrB0MCNj//VtouFjzoA2p9DXZU+ck7bx73c/5F8fLeXzHRkU1xmApPFjJY51YakYt+3NpLE1nvIfMgx/V0g9faDeoPgt0ZWtDdbm2rROnfoKHM70oZ4UnVis6y8+UPqeFZsOE64c/wd++Z7z19Qro7K4qJaZ8rqubAkmdKw/zsk/8MHnynGu7vvaPZy6ol2Rq+sHQvAs3sWiDxfrj4MVV3DoN4kn5oSiFbHWj3oTaHy6qv2eKmyF/7gpjG59mTWf6/vWralWeAWOZGTlgjfazxiQH117TdD1Mlp7Xc1O3cLVmiobW0dvcnZ8x4cffcxb/zlNTqsujBkbrH1a2aa6YrxviT7fSn/zdbQ9g2fOYKY8j0uIWo799AX//nRdrT9W3Dl2s/S9j1i6TZsUQgghhBD3pXsmUDpq3Ai6O2dxdNk3rNl5hMPhYezcsoYfN57XBy2itvPVV2vYsGM/B5JzyCvIICm7FGuXTnTRraFSNqeWrGTjzv3s3LOX78OSKTRzpk11LLFawln27klDHR6rJCdOmV9Z5uBZ4pUv3IdXL2fpzzvYdjCaa/nXyI7PIM/UkXa99IvW5Dt5Jo+O7cC1Pct4d2WYfnxHr5FMDmpD/oXv+XrLfg6fPMXh/Vv4JCKbln4D9AGzmrT5K2J28u6KXew8fEKZfxerV21kj3afacSmH/li1Ta27AkjIS+f3IxUsoqtcPXx1s+gMau4yrqPtTJQ9qe3WrY2Sez9fIOyPycIV8p224/hJJm2YeCQyoBXDSEjmdDNmbTwT3V5PxAezu5tG/ho2xUq2gYyebRHvWV3NEK/eKX400fYma4G5Uq5dkWtj/3srfnwnpxoPvh6A1t2K5/t3qWUTQbYtsS7MvDSRJ03uX4dDyZMDKQ9V/hV2f8t+8M5eng/G5d/z/EMZ7qPGskobU6VWWFC43mqh1lhHB8s05bZ+TMnU0qxsCni5JI1+jrYvUVJK8TE0ZVuuiXUKzhjyXfqxvhhlYG6IYz3dVCK5AAb6v7hpKptbOejNbvYffQEB3bv4Otvz5Dm1JkJ465rTQ0yrMxqqiDz1DdKO9irm3fbynDiC01p0abyqPOnQytTsmJ+ZOfRU4SfVNrW6itkmVpiknt9m2jwmDOkzVXWpWkCuz78SV/eyvJb1pwhuc5dq00dKxHrDhJd6EhPpeycdClKHzTWF8e8WPZsvE1PBbOxxtzcFGsHF8yzY3XtdM3PWzisHP8Rx/YTk6MGAwtIUctMeV1XtlhhbWGMha0rThUphB89qJTNBjad0QfedX2s01WOvK/0DbvDOKr7fClbrkD7oLFMqNXGszj+udaHKO130+VsTOyNSf38e1ZvU7e/l69iroFFCzy0vlW3/hvoZwzJz/Xt9QR1d7upsrkW8yVLlONG19a2fcVF9ZBu5akFhrU2VXiJJct36Nuwko91X0RyrUU3hlT/HUUIoUg8d4y9Ry9oV5HfaS5z/MBhjkdrk0IIIYQQ4r50zwRKA9o5Y1KUyundl3VBFL1srlUNFFiKu/9kXvrjq7z5P0+y4OH+uJsru29sXH0FnKokm6QL1U9wTc8soARzbFppCQbKt/NgyKPP88ZrL/DK7+YyxcseEyXdxEz/eSVTx1k8+2gwHezKSb+wgxOVj2Pt6kMbG7BuP4znXlfyrHv9gTneSqKFFS212apo82ddWVu9DkX+tezq8ih1pee0Z/jra6/y54VzeSzQRbfvJib6qwWr5MSz5lR1GejK1sSBnrP+oOVDeS3ojZuyQ9YOntpcNQS0o4VpIckXT+jHqtQp4MLaWLKwoo1v7dD0TcvP4OgVNQiidyFXfW+NXVVQ28A6b1QXurhZwbVY1lyq3haph7mgBi9btiNAS9JpMk/1qLVMOmm5xbp2mBpR+XiIAvJLSpXGYkplTcX+upVzmfZ0HzlGH7AJGY5fi2witmznugvMK9tG3CYu1BiuM+VgJMlFxrRo91te9lbKtaSL1e0gPUvZFzC3rbwUNoVrSp6cXPrgq6X4dnZWSqyQ7BrtuEkGtTmtLjMv812N9k1eESV1H2nX1LESv4UNF7Jw6DqcyboKGML4rs5kX9rKhtsUJ+XESpbtjMKo8wwWvvgCb7w4j4nd7bUPDbGdb38+RnqLEcxf+BxvvjyfR4M9q9pcZR97Kbxm2UWyOlbpZG1c6FKzjdfqRws4mKU2vAIyTiZV9UexRerggmbqsKA6N9rP3FB+bkF+xgliqo6bSHLUe+lt7eihm9balGU7xi3Q8qy8/t+sTtiqV2Nf11ELIYQQQgghhLiT3TOBUgszZVdKS8mvEQiqyXf6Yzw3bxxBbUuJ3bqVNWvWcj7T0IcfGGNmeGQNnIfw9IJ5zB7li0PaGX5Zt4kfftDfZl5Xy67T6F1yljMZlngNnEhfLR07U9SYanbcPg4dPVb9+uJ9/vT6Ylbr56qmzV9R0kAB4MXDzzzO/EkBtCtJYPOGTaxadYmrZdrHNZWVUCP0oC/bokzO1MzH0dUs+sdbvPFVmDZXDZammFBKSd3izS/TPT3ZzPxGgjc3qjrwcmt1XskeM1PlR1kZtUv2+uBlw6rzZIiSsnLtXSPid7P8RAoOvkMY46VezdiFFrmX+XV7PVd3VraN0hqBXlVeAeoumJg1vQfNrXqbu/l+XwKmnWby8svP8PyLL/Hy1DbkHl/PksPaLIYwqM1pdVlSQob6WYMMOVYS2LLqDIm2nRg10qMqUB25dff1ger/lvRTrF38Kf9Y9BOHrtnTe+hE5j2zkIVjagwP0Khoti1fwlv//owN0RV4BY5mzmNP8cIsfSC9so+t++CSHF2UufE2nm5Am77RfuZW8nPLlOPeQfdGa1O5Ceyole+vefP1t3hnrW4mIYQQQgghhBB3CeWb5r0hKiMPLFvSsZ5b29XbI8dPHIGfXTLLPlvOio3b2Lij8nbL38DA4TzYry2Fxz7nkx/Ws27TDrZptwvXZV4Sww//+ZL/bLqChf90Hp2uv6GTgylklINFSRF7N2xhXa3Xfk7o56qmzd/StfpW4Fq8hjNpdBccE3/ko6/X6PK0ZXccuqH5mqArW7MKiiPr5mMLG9X7euuKuUYOlrTuWPtKRadhrXVXwmYkH9En/Kaaq86PkKjeeezUmtE1xgNVbxnv2MoScq/VeJjPf1MGB78+SqyZB4NGDmFUZ/Vqxl1sqxnhrtRQ2+jVgdbKLuRk1FOHlZxtsfpNewlT3BxtKEg+T6R5C9q3MObCuvf5+4e/UPNCwSYZ1OYaqss6DDxW0g8tZ3+cGV79Qxg1wocW2ZFs3tV4CLZ+LvTtpR33NTUVW/S1xEp7W+laxAl2blnPN0sW889FYRR5DeShcYO0T5uWn3ievdu2seKrpbz9znKSHXowbtIw3Zi+9fexjox2VQqzPIvEg1rSTbrRfua3zo9htDZlWkLhwevzvfOUfi4hhBBCCCGEEHeHeyZQun3HOVKNWhP02BxCe7koKY54BU3l+SfG4UUOraytwKgCk6sJXMOFAVPnE+J5I5eJNiRddyumpVNb/JXv507uLjg52WNjagplhaRcLcDJbxiPv9iN1toSNaVfXMKKwxcJX/kx2+Jt6f/ILB5W70GOWcOOS3k4+s3i2Zl9cNfNre7TOEKD6rlCTJn/YHQh1t6TlPn74aUGgtz9CX1sPo8GKu+vtcRGKQKTClNS47OVz/owY0EQXgYUwfafwkk0acOIJ+cw1k/btrru0GFVt0vXsmMth5OVWQY8wtNjuuqCc+79p/LstI5Y50ax48dGAnN1peRRiCXOHup6HHF3N/TqOAPrvMn1X+b7nVHk2HZkxlNTGaBWhHNXxj7xCEGukBi2lu36Gf/r8s/+zJbocrxCZtC9ZTYXduytdSVwFa0t6duGvi2pbfLpxwJwN0rh8E/79PNpAdXW7UPpq8yka7e/60cb9Yq5mm66TurThQBvR4yyrrD31+/58qvlfL1yGztr3hp/nXqOOYPa3GU2n0igVKnLaY+M0u2jrh0/Uec4MPRYyTvP8h0xlHkOYW63FmRf2lMdqA6cxV9ff5W/zumnJTQseM7T/OHpR3l8rD/uzh4EK3XUXtlWZvJZbQ6FFghu02WI7th27x/K8093xq5GMNVr8nxeXRCqtNEC0qPPciY9l3IjY4xN9eN9nMlSr2lX6muU2j+6KPWmS66m5Pml52fpjvFr8ZEcjbxGfgVYmJjonjiv6we0PlbfDyjH1MzHmOFtRc6lfXwfo1vLTbvRfqY589Nk2TRI6x8cu/FEZf+gcPLsx9Tx/XTtUAghhBBCCCHE3cPE3NLude19s2jt1pbcXN1z5m+K+hh/dXkjdcJI969BslMSlS+rnegf0A8/Hy/8evdn1Kggernkc3z9bi57BRPYrS0+np70GDCY4f07cC3fBE/7bA58tVl3hWbPCbMY2DKjalrHbzRPBLqRf+krVlz3lGhVFg59BzGgUxu8PL3pP8gXk5UXsBrUA9/2nnj59mLocCUf9hkUtmyDUXTleoJ46ElvSk6/zRfqdHYSsXntCBo1iADHHI7sOMDJuDJc/AMY2scHb98eDBo6mFEj+uFtHM0vh+o+CuEqCXHWtO3VnaDevvh08SNw8FDGDuiE2ZVdbD6aT6chvenm0ZGOHbsyaPggAjvkkG/eHoesMJZs0AdlQh6cSxci+HjlAd20KjtZKVvb9gQMHEhPXy969BnA8OFDGenvTPqxPZytHEqzUk4ycfm2dPDvR0gPL3y6KWUwcgSDWmey++uP+XJ7EvqhY/VlYJNYvf3rZNgRENIb7zYeSln2ZmhXE37df6nefDJkMgt8rUk4tIJ1pwsoN6DOG1p/TWmJ8ZS5dCNwkFJ+3t70GTiUccO8MbmwkY8/W8uRdP2lhk3nSUurob5l6muHujT3As4vXlPjqd2ZJFoMYNIoX5xzzrFs0TrO1XfZsq5tlNGqRx+G9tW3peCRwxjtY8KZ9cv4aPUJ0tThIrPS6RgcQo+O3nT08qFf8BD62V4i1cKHFtdq1FEDZXZdvnXHTgvSau17NyY9GkDbgsp9LqV10FCCO7rQqrWbsr4eDAgMZvjQYIJ6uVN+6SyxdUYMqPeYW7WWLQa0ufSkXNp070vvXp3x9ulMX2XbIV3LKTR1pWWRlqf8coOOFVVGkilDJg6nm1Mup1Z8yA/ntcyOf4a3Zw7A1yqZxeuO69MaYOYewLBh/Rjk14nOfkp5DuuOS94pvntnBUeTtfVdtcJvcC9dnjx9uhE0WFl3biL5ru0w1vqUdsEzeGJiED27+NBnQDAjR/bB1yaV7V9+w86oXLLK2zMsyA8Pn7Z06j6AwLZJbAtL069f4dR7HE9MCyGwu492jAfTx72Ek6s/49vjV7V+oB19A5V+QOlje/QZyJjR/WiVcYgl737P1kR9v19vP6o7DqjdfuscGzfazxian6aOQVX9ZeOj65+o0/frjlnr5Kr90/UPbkq/G6i0GR+lf1DLfkQIwd4mXPr16O0biqGZ2dnZ3/Lv9tSk5i2NWz3fEEIIIYQQQtx7bvW7xz0TKCU/jStX4om6cIEMbDA3yiXx+FbWbjvOhYSrRCtfZqPPXqXEzpzSzAh2bNvJ9j1hHA8/QVhUou7J+DnpKZwOC6ua1sm9SuLZY+w7EsXl6qfE1JBFUnwiKTkllJeXk3n5MAd3KuuIiyI+1xRz03xid+1h0+697D52iqNhleu5RnLiSQ7VWG+mUpGxMRGciVD2Q0lMTIwjOvYy587lYdbKHIrTObNjD3tPRnMl9foRTzMTYrkcG8Xp5BLd+H3lVyP5dcM29p2MJ+laEvGxVzidXoKdRSlZF/ezSX0S+aHTSp5OEZmgf7J1ZmoSYUfCuXilOoBCyVViL8cRFXGJpBIrbEzLyb28j827TnImLokM9eEmtZSSFh9LTFQk57LB3kIppZjDrPp5s7JMODFVgwrqy2D/gertXycrhbj4q+SWVVBWnkXM/mOExV6tP5+ZacQfP8r+4zHEKatLNaDOG1p/LdlJxFxW6uR8CqXW1hgXJXNyzS+s3r6bvecyUWOMKkPyVFd9y9TXDnVpR45wKCKOZC1Nda2kFw9M96Y0/FP+vTq23uEdVNcq21JKKVY2xhQlnWP1z7+yafthzmRU3lOuBlQTicsupqy0mLTTu/hl0252hl3gWI020lCZXZdv3bFznIO19j2P9KTzHDyo7bPXQGZM6odD9EZ2xJsrx20ppWWlmLn2YeTQ7rTOP8IvJ6qeyKap55hTtnHWgDZXcjWRGOU4OJOYj7GJMcVJZ9i4cSvb9p/gcFU9pBt0rOgoZdVvyhS6lJ3g7Xd+IaayAvLKsCpL5dT+XRy+WKc91ZGZFMdl5bhPLDDGpDxPKbO9fP/TJrYfjaseFzcnlbi4aNKUeUpLconZvoNfduxTjm01T/o+pDAjleiISNKM1Cvay8m5clS3/9sORZCpNNKStGSlf8ki37hC6UtSObv/lFIO1f2IcU46scryF/PNaaGUX+nVC/o2sucEqWpGavQDWUZ2Sl1lE3NwA6t+2cX2sNiqsULr7Ud1x0F47fZb99i40X7GwPw0dQyq6i+b+Ov6aJXumD10pHr/KvuHCxfJMXdW8lFI2vl9bNl/mojYNOr+HeluJYFSIYQQQgghxN3gVr97GNk4uKlPvmg23XsPIDm57tWOhnN1dScpKeHGA6U1OLR2w86sjMKsVNKrIg0qe1za2GBcmEXS1esuU7sl1i1a42xpQkluEilaXEefVk5OQrp2BeXN0ufbrLyIjKSMBoNhVWyccXO0wKQkj/jUOl/THVrS1ta4nrIxTH372Sjd9swou6Uyt6KlmyOWxiU3UZaG1Lmh67+VfPwWHAle8Db/fsSWvX+ax0s7DSjfxtqGRl/HNNFGmqksnniXQ3Oc2fjkQt7NsFGOW32yWcdnWbJoACVbXmD0n+u/IrPBtmhIm9PKQdnJhucx4FhxCnqcJf87E9uDrzP5T/trHJtq+dihPrrf0OOscn8ay7e+b2u8zBtfjwF9iVZ+NNJGGu5jm8eN9jPNk58b7Gfr8VuXy21RUaF7IJqbW5tb/t1+JvyQNtU8bvV8QwghhBBCCHHvudXvHvdkoFSIe5sjo2ZNp5tLKwaMDqbV5W/5/YvfcfZuDMw89RFhc1tz+rPFfHb0IheTwL1bN4KHPsRjI8rY+MYLvLb9Zh6Q9NtyGvEgc31a4NJ/BMNbX2HxC3/m88rb7oW4l0igVAghhBBCCHEXudXvHvfMw5yEuH90JzAkiJAgbzizkQ8/3nx3BklVW5by7d40Wo57gAW/X8hrrzzNH+aEMtIzjvUff8jXh+68IKmqV0AwIYMD6cIF1ny+hLUSJBVCCCGEEEIIIe56ckWpEHeddvQOao+DSQm5yXGcvZR0U7cJ3ylcvHvSuZ0zthammFBOWVkp+ZlJRJy7SPwdGgD29A+kg4MxpXkpxFyIvGPzKcQtkytKhRBCCCGEEHeRW/3uIYFSIYQQ4g7n5tKajp4e2Npak5ubT/TlKyQlp2qf/obuw0Cpubk5pqZmmJiYYmws5yFCCCGEEEI0p/LyCt0FUqWlJRQXF2upzUcCpUII0YBFL/bQ3t3ZXvjPKe2dELXZ29kyY+IY/Lt1wTY5HauCIgqsLMh1bcnJs+f5Yf0msnNytbl/A/dRoFQNkFpYWOnOP8rKy5QTuHJl98v1HwohhBBCCCGahZGRMcbGxpgYm+i+axQVFTRrwFQCpUII0YDLv47V3t3ZOozbqL0TopoaJP3jwvkEFJZheuAEpUmpGBUUUmFlialba0oH9uSopQn/9+Hi3y5Yep8ESq2srDA3t9T9VbukuIjSsjLKlZd+74UQQgghhBDNxUj5z9jEBFPlZWZuobubq7i4kIKC5nn2x61+95CHOQkhhBB3oEenT2K4mTVG364jb/12isJOU3g2QvdTnVbT1c/V+cTNU4OkFuaWyolZHvn5eRQVF+luBaoOkqp/tJWXvOQlL3nJS17ykpe85HXrL3Tn2er5tnrerZ5/q+fh6vm4el5+J5ArSoUQ96ypw9to7+5sq7cnaO+E0HNzbc2KD/5N6YLXdMHRhlh288b047/x0MKXSEr5DcYsvcevKFVvt7exsSMvN1t3olZNzj+EEEIIIYT4bVWHIy3MLbCxtScvL+eWb8O/1e8eEigVQggh7jBjR4XwxvjxpDz0nJbSMJcV7/HXDRvYuGWnltKM7vFAqZOTM0VFRboTMr3q845aZyByOiKEEEIIIcStqRF9rB2I1E+pFzBYWFiQmZmhm75Zt/rdQ269v4u4+3rhrr2/p3n1I3TqKIK9tOlm1ndEKDPG9+OGVt9rCDN+wzzdOTwIHq+Uzwh/bboB/+Xy0NVZU3lS/LbHiBchM+fw/IL5PP9UKMFaarP6jdt+0wys//udofXk7oXvTTRItb2PD+lL+YUoLaVx6nwd2rfTpoSh1KtJ1YHkCwrytRR9NFT9V/d32so31RPykpe85CUveclLXvKSl7xu9lV5bl39Q6N/p56Xq+fn6nn67SSB0rvFkDm88ocFvPLUMC3hHtZ1OI/MeYBxXbXpZjZo0kzmPTScG3oeetB45v2Gefqt6AKMNxR468K4h5TymdRPm27Af7k8dHXWVJ5+42Ok76xH+d3DExk3NIiQoB43Fmg31G/c9ptmYP3fpOC5z/Pma/O4Ox6x1QiD6mkgTz+3gJdfuPH9Vdt7/+7tqDA301IaZ2plgVlpiTYlDKU+4b6oqJDy8jJlSn9ypvu36h99mhBCCCGEEKI5aefa+v81RrrzcvX8XD1Pv50kUHoHCn3qJd54apQ2pbFqg1fXrvi0t9AS7mF2rWjbxo3Wdtp0M3N2daOteysctGmDOLf+TfP02xjCtEdmMm/ObB4e7aGlNcWe1u5K+bg6atMN+C+Xh67OmspTfcfI+Hm88ad5hGqTN68rYyf1o7N5Eks+Wsw7n61jr/ZJs/qN237TDKz/m+TVfxihw3vRXpuGPjz6/Eu8NKuPNn2naSB/BtWTBe07dsXfqz03+mtebe+2pkVY9PXTUhpnHdiL7i1TtClhKPUv1ept95WnZ7p/q/8RQgghhBBC/KaU824j9Rn4lYx05+dyRam4Tq+QMUwKqfMFOWw1//zff/N/y49qCUI0IWQ4/X3MKCpuRe9BY7nnb6au7xjpGcykicH00iZvnjftWlhATiLrdu9n5/5TGHZTtKiptFz9p5TqR+Z4EjR2DBMHemrTd5pbyd9Jvnvn3/zl7R/Zr6XciPKCLOJLi7EfH6Kl1E/9vNjVja4dnLQUYShTU1OlOeoHipcgqRBCCCGEELeJGi/V3qrn5+p5+u1kYm5p97r2vlm0dmtLbm7lQxFunK2tnW55/feVG/nCMpCHF4TgXXwF816hzJo0jEC3Mg6dT1I+c8Q/ZBxTJoxgXLA/fl6OFMXHkFKgX9I/dA6zetlwKNGOsdMmMGVMCEHd2+NQnEBkaqF+Jo2T3xCmTRjFhNFDCOzWEVfTVM4n5CmftGHs7JlM7OlI2skYMvWz6/SdPIcZQzzg2EXilbx4BQ1j6rihjBmpX0dL4zguJqrb8WfU1CAGDhxAe+sKLOzdCAzogNXR81zO9yV4jC/2efs4fUW/XlXD+dH4j+PpaX7YHU3CYUwo0yeOYkxgFzo4FhMbmUbV3rn3Ycb0CYxT8jSklw8eNiXEX67xuY5axmPo1+IqxyOvaWn9mPHUOILblRF2ofKKJg/GznmQMT6WHDkXp09S1j912hgmjFLK1t+HdsbJnK2Zz5r8RvNEYAvSDm3kSptG8owLfUeMJnTMUMYND6S3T1us8pWyuqp9rOPIgPFTmazUfeU6Wvcej691Mge+2swJba66nAJG8bCy3YnDA/DztKeohT/TfKxJOLSCdae1mWq2q6F98PNujXFqBPE1mr/hbavxNnozRi1YwCSHS/zrsBGjAtpScmwte6+76Ewdd3MiU0YPI6SPD22UQ85rRB/aFkTw8coD2jyGlYd7/1AeVo67yrqwL00iqs7xU8VdaevjlbobOYxRQX508bAk50wctaquhpAH59KFCFZcsGTGlDFMUvLRWx38MUU5pirLu84xog47EDKgH4Pb2oCZHe0C+uBlHV7r+KlSo31e3/7V47I/AwO9aWVShrFyXPZ0KyTsYpru09pcGBA6ickjleNbbRNqWdWoxyaP16q2X7Od3chxnoHr1Gk8NG4wXewSOB7VVF9sWP03Xj4KA/uZnEFK3+wYzz9X7CZTHeczpC9BgzvhYgzWLTwJ9HOl4HgE1c1U6ZeemkCIp6nSlyRoaXohM+czRelvE0/FoPZGTZbRkKk8Pz4A9+KTnK/agL5P61Ffu2gsfwb1UQV0DxpBd4cctiltW2Vof6Br7xUXePOLTQyfNRXT/EKKIi5rn1ZTg6Tmc2eS5uKCbeZpvlhfo9E0Mzs7+1v+3Z6aFK9NNY9bPd9Q9yknR7/8jZ9zCCGEEEIIIZpH7fPw2/3d4x4KlE7i1X8Op4OZMwNGh+DfoR0u5SdYuSeb4LkLWPjgEPxcrbC0d6dHn750dy3h4v4IXWAm5MlXeaKvM5a+QUzq742PZyf8/LvR3cOchI3HidVvAKegWbzy1BTG9fHA0cIRT/+eBPh50VqZ49CFa/Sb9yyPh7Qh/9AmjlZFSv15/M/PMMUjn3WrD9B57gs8/cBgAjq2UPbVTclLb3p2a0n5mYOczRzMvOdH4ddW+czEGMcWLnh6WHF11U6O6fZvAj75y/lJi180nh8tA8FzeevR3rS06sSwcX3o7OVJ5+5++Hdph3n8Vo7o2k4/Hv/jYzw82As7E0tatvMmIMALi4TthNWKTfjz1F9mM9Ith7UbTumDAf3n8I/fj6R/Owj/+ZAW4JjIK//3AL2MjrB8u/Ll3ncczy+cyfSBXtibWOHSyY/+fTpilXaS43HVwYEquiCEC0bGLvTq21Ce2zB14e+ZFxqg1KU9lq096dO3Jz28rEnbdBJ9SMFRV/e/nzGcvp1cadfGnQ6d/fBwc8GehgOl+nKdyvhenrRxb0NbTx/6tHPFzZ4agUH9uhc+OJx+HZXtO7ajp7J9f+8WVFwJ57wW8TOsbVWuq+E2euOG8OxzY3CO+YG/LzNj3APdcSsK5/sDNYN7XkoZPsnjUwYobc6Ndm3d8fbrQFs3Z6xzqgNlBpVH4Cxef3oqIV62mFo4066LPwN9LLiy+STXdU/tlfbwwkwe6OeDu6PS3jx70K9PV3ysrvLLcS2wXocucORojru3L/19OuLt6UlX/+50b2dB6onTxOoCkbWPkZGPLGRi57a0sjXF2N4Jd4/2WF9bw6Zw3SqrVbbPwd1oY2NFa29/Ant2pqNdBsdOJCjtXD0u+9HZ1RkbY/1x6Uo0q/fGaCuopJTngid5bGoQ/m3tsXJS24Q/HU3j+fVYkmHHaz2BUsOP8x7YGPkyenI/fD3aYZW5hl/D6jm+qhhW/02Xj8Kgfgay2voQVBzDFzuVnes9iecm96JTGwesK5Rybd0Gz9YQsf4AEfrZFaaM+d1zPNjbmviVe2pcxTuMF96cx0DrS3y36RxWhpTR9IW8O8kf8+hVbDynT6rbZmppLH8G9VEw88U3eKBTHl+sPaKbNvR3TeUfBv70/moyjezp++BEWvTtiqmLM+bt3DEL6IH9zAmkdvPiqEs3fF3KuLLxM1aFZWlraH73YqC08nxDpT/dkECpEEIIIYQQt0WNU/Hb/d3jHrr13gpbuxZ0CeyN0aGlLHrvYz5Yo3wbHjKDp2cG0zLxZ97/9Eve/3wp7+4swmvMLGaP0S9paW2DbTtfgiwvsGzxEt5+531+TDDFq+8wxvXXz6NeeTRv3jRGdyhhy0eLefeTz3nnw5VEOfRjxqOzmOGVxZ5DqZS09GHQyBrjQfYaRl9PR7Ivb+CCMhlfXkDynm/54NOlfPDJp/x7Uxotu49g+nj1qSDHWPvtJTLV21Ozo3jn48XKazU71fXo9k/Jp7VuQtFUfrTZzNXl2tAt0JrT337N++9/yJs/J2Hu2Yvxo7Rx9wKHM32oJ0UnFvPhZ1/ywWdLWbHpMOHXXX14jMvXTGjp5ccQLcVrcCc8ykrAsytjKrcZ4kfnlpB58aQy4cGMOUp+/IzZ+fES3fo//ETN5wAemT2pkdvBzXFybiTP5JGp/Hfx1694X8nv+x98ypILFXTqN4UpWr3q6z4E7/ITLHnnQ956fwmffnuBa2r5Nkgr147lHP32E9565zPeX/wjp7O1jytp6/Ys3sWiDxfznlIHi1ZcwaHfJJ6YE0plURjUtgxoozdszGh6t8gmctch8i+t5mSKBW37jqxV3l4PzOKJaT1xTtrGv9/9kH99tJTPd2RQXKHNoGNYeQSPGc8wr2KOfqzUhbIPH32+ljUHT9e4MrCG3GuQHsWaL5S2psz7zvsrOF/egcDpYxt/6I25GVbnf+TTTz9T8vEJi8+U02XoNB6eUHm81T5GDvzyPeevlSrvsrioO5YW891u/WfVtPbZz56oFcox9NEXvPvh52wpbs/omY8yT9fQ1ePyAlfV571ox+Wn66oibVV05flAP1zSd7BIaQ+6NvHDTo7GpiqfGni8XudGjvN29A9tR/yaL3hb6f+WbW88aGZY/RtSPgpD+hnV9pX8p7ISLuxmxbfJ5Cu/EAtTjun7uy9/Re01qsWxLiwV47a9mTS2xripIcPwd4XU0weUXsDAMlKPRaV9WNca7qZuv1pDk/lrqo9SNmmrrNu2eoRSw37XVCssLOKMRTvMuyv9s58pezq4cLy3L/uUn19eTWFJdgvauJhRdGo17/xy/RWnQgghhBBCCCFuzD02Rqkp5albeH/5Drbt2c/e0xmMGjeC7jZJ7P18A9sOniA8PIxtP4aTZNqGgUNqPqAjm1NLVrJx53527tnL92HJFJo506byqcYhI5nQzZm08E/5est+DoSHs3vbBj7adoWKtoFMHu1B/PbtXMiwwqt/SFWgrO/oHrQ1SuDId+fJV6YjNv3IF6u2sWVPGAl5+eRmpJJVbIWrj7fyaTRH98Shi+0Upin5UPNyosbVVTUYkJ9qFWSe+kaZb69undtWhhNfaEqLNl30H9tYY25uirWDC+bZsRw9vJ81P2/h8HXfu5P4+VwypfYeDApUpx0Z3sWN3MhjxFe0wT9EH8gIHuGBfWECJ7dmgNdIJge1If/C97p8Hj55isP7t/BJRDYt/QZUBVyvV0zysUbyTBaHVy9n6c9KXR+M5lr+NbLjM8gzdaSdNiClru5bZHF82Tes0dWrsp4tK4muG/SsSSvXzNPf8ul6/bZ37tzB11cqhxrQ063b6SpH3lfazO4wjh49yJY1S9lyBdoHjWVCrcBX423L8DZquLHjuuFUGM/+XUodcJFfzqRg0aYn46sipR5MmBhIe9MEdn34E1vUcTeVfd2y5gzJ+iH79AwsDytrc8xMrHB2N+Va3AkO7N7Iyg3h1L3mUicjnJVff8/KrXvZG3WN3Nxc4jLyMWnRhr7aLPW6Fs3H2rGtluOaxerVqs70Dg7WZqgt4th+YnLUg6mAFN0y+zla92DS2mdFzHY+WrOL3UfVvCv79+0Z0pw6M2Gc2kKvPy73nlaH9KhJK0+jK2xUy1M5vvVtYg0/7427weO1hhtazkw5zj7l03W76t/XWgysf4PKp1IT/YwqPpLwi9pl6gln2bsnTdcvluTE6ZbZefDsdVcgR6w7SHShIz2VbVWOwjlqrC+OebHs2ais62bLtilN5q+pPqohTfyuqcl3HLMeGIJJ4h7eeX85X2zdwae79vDl9h18vy4LH7+OdC08xocfruNw816seV/R//FariYVQgghhBDi9qn5UKfb6557mFN23CZOqBdwaQLaOWNi4kDPWX/gzddf1b8W9MbNBKwdajygoySbpAvp2gSkZxZQgjk2rbSEgHa0MC0k+eKJGlfJFXBhbSxZWNHGV/lynL6XLZeysPbsr11d6c/Y3m6YxB/j2/O6BaDUlZ7TnuGvr73KnxfO5bFAFyyVZBOTG3yavSH5qVLKtaSL1fOlZ5FfAua2zvrpEytZtjMKo84zWPjiC7zx4jwmdrfXf1ZH1E+XSDJuge8w9Vv9IPp5GBF/7nsi06zo2GeQkuZBiFcLKpIu8bMaD+nqQxsbpazbD+O5yvJ//Q/M8VYSLaxoqa60Xk3kWZFv58GQR5/njdde4JXfzWWKlz1KtWJipv9cV/eFqZzcfVkX7NBLp0QNeDVEK9ek0/uJqjG8YUqJsvEadOsuSuVSeHWbIS+S1bHXwMaFLjWDHk20LYPbqMGGEdpFqb8ySzo/OJ/nF8xniFM5pRZu9B9VmbEudHGzgszLfHeq5j4UUVLzikIDyyP8ux/YEqlU96zHeOWVl3j1qZH0cNA+vE4BNl6BPPXyS7z56kL+8PgYfOxNlX7RGK3q6leUzdkax3b+pUTSi9UyctNSboLWPrOUfuNCzf07GElykTEt2hn6CCytPK/F8kOt8szmmrreGzpea7ih5YpJvRhWq54aZmD931D5NH3M3pT4LWy4kIVD1+FM1vWrQxjf1ZnsS1vZoPYxN1u2t+wm97ep3zWVjB15/MlZDPewoTwtkg1hcUTHxHL+YoTuZ3a3AMZ2syBm21dsqlmHQgghhBBCCCFu2j0XKC0rU6+gq2ZhpuxiUSZnjh7jUNVrNYv+8RZvfBWmzdUQY8zUKKbK0hQT5YtxSc2rrVT5ZahxBTNzNbCYwc41F0i18dDffu8/jL7uJsQfX6u77R68ePiZx5k/KYB2JQls3rCJVasu6W/pvVEG5adxJmZacDb9FGsXf8o/Fv3EoWv29B46kXnPLGThmBq3ula6tJ1LV01x9w7EKWQA3hbJXPg1ks3R6dh2GMAogunsbkp69Fr9eIJ2prrgV3bcvhrlr7y+eJ8/vb6Y1eo8N6Aqz85DeHrBPGaP8sUh7Qy/rNvEDz8kkqv/VEdX9yVF5BoUONJo5VqY1/hTlHTrrvX0br0cXZTJTH12UBOq29attdF6jBlFD4dScnIt8BkcRIjy6u9cQVah/vZ7/VWb9pipD5IrKVFabSMMLI/0E1v45L33eevHY1yz92P4lJk898I8xtYTM3IKmcPzT05jXFd70k5tZc36dXyTULPmDFVCmVrct/JnJ619VpTW2T9lf9WAelV7a5JWnmVl1DuSys0erze0XDnqKBiGMbD+m6F8DC/DhiSwZdUZEm07MUrtV0OG46cOK7F1t/7qzmboC5vTze1vjd81lVp05pEepRw/nYVlx/5M166Ur+JmTH5GNOFrI2v8IUgIIYQQQgghxK245wKldUVl5CnflCsojtzCug21Xxuvv7e8YTHXyMGS1h1rX2HmNKy17qrIjGT9wzrSD/3CyWQrvAKCdVddtq2IY/9P0brP8BrOpNFdcEz8kY++XsO6TTvYslu7pfdGGZgfQ12LOMHOLev5Zsli/rkojCKvgTw0Tr1CtK7T7InOwsLNh8nDOuGYdokfYiB8fyyZjh0IGdsFN6ssLh+K1M9+MIWMcrAoKWJvnfJft2F/g0+db9LA4TzYry2Fxz7nkx/W68pym3abbCVd3Vu2pGPdAENjGijXuupftyOjXZ2hPIvEg1qSAZqtjWrGjuuCQ1Ec2xZ9ph9XUX199DErzin11qYnY3V5PkKiOoSlU2tGN3YBnIHlod5OrN7qvvHnn/j04w/5v6PFeAdPJnSo9nENQ8ZOJLB9EYc++opvVm9hw9a97ExvPBBbr14daG0JORm3MDaj1j5bulbf1q1zw+tuojxv9nht5uO8moH132zlc2vSDy1nf5yZbliTUSN8aJEdyWbdsBKKWykjX0uqRxC9w5iVELnmQz78YDvRlt2YM3sqvtpHOkd+4u1/fslPcsu9EEIIIYQQQjSbez5Quv2ncBJN2jDiyTmM9dOukHT3JzR0WO0vnU3ZsZbDycqiAx7h6TFddUED9/5TeXZaR6xzo9jxoxYwyAvn59MpWHccwPw+bSiO2c+PlWMFXmuJjfKt3KTClNT4bGUFfZixIAivWlcSXSRTvbjO3oWx7spPdxfUH9cxND8G8Jo8n1cXhDLAvYD06LOcSc+l3MgYY9P6boQuYHdYPDn2bRjfuQXZMdt1V8vm7z3G5cKW9JjVAcfceA7t089NzBp2XMrD0W8Wz87so+2LI15B4wgNqueKVUM52WNjagplhaRcLcDJbxiPv9iN1trHqu17osk0a03/h6YS7Klsy9mD4JnP4K9GTxqyYx8RWaa4953Ow0EeSrmqeZ3KS361F9K1K6PWBD1W2a5cGDDzMWZ4W5FzaR/f1zs4Z/0MaaNeofN54/WXeD60qfEW9bfdF8YeY5k6pmKN109bLpNp4Ubf0WpA6TKbTyRQatuRaY+Moq+urSnbfKJOezSoPDyYuuD3PD9Vqd+8DKJOXyLtWhkmxiZY1Hpwjp6TvTUmJhWUFKaTnueI/5hZvFqnfOvl7MXC0H54OSvrUOr76ccCcDdK55SSx4acyVIPJkfcR7koP5VjSd8Aq2nt09p7UlX7rF53Cod/anjdtV3m+0NXKFTKc8ZTWntTttd37Byen91PKccbPF4re+ZbOM71beZVXlW3fx0D67/Zyqch6eQUgqVTW/zVelX6u1oB2Up551m+I4YyzyHM7ab0O5f2sK3ybnNDy0gLqLbpMkTXhtz7h/L8052xa/Thbgbm77eQEcF7K8M4e/Jn/rUjGYeAGcyfXj34sVe/wYwdNpIJTfYJQgghhBBCCCEMdc8HSuP3f8fHq89R0WMKTz/7gn78x5fnM29yED3bajMZIuEgi79Yz/ESXx5+7CleU9bz52dmMNo5jS1LP2Zl5RikFBD23XFizTsy2KOc6H2b9Legq9J3c/hiFhY+0/izLh+PMrlPDim1notziQ1qwMXem6dfVud5ggfqu5jP4Pw0zaZVJwaPm8lz6vaU9bz27ABc8qPYtuaQNkdt6VvPElfaEk+3Ii6fOK0l7uRkfAXuXq0piz/Lpqoh8y6y8r1v2J7ektGzHtfv9+sv8MqTUxkf0Emb5ybsPUh4YiltAvX7/trCBxntmUpKofa5In7rl3x/OAvHvvqxV9XxMJ+e0IO87Boz1ZWwkw9+OsZVh97MfXqhsm4lr0+MpndeLjWX0rerM5R1rWxXz/Hcw0NxStnNZ+/9wlltPkMY0kY9+w9n0vgxjOvfTp/QAKex+tvuE05tqG53mvTdu4jMsqBt72G62+/Dv/+aTZfK6TB+Nn/QtbX5PDK0gqs17x03qDysae09gNDZ1fX74qBW5F7cw497tVlq2LX/NIllbgTr9lWZd9YQOqWk1yrf+hTmGDFk+mO88qraRh/h4e4Q+esyPtle98FK1c5tOUJUvh1dHn9OX0eT6j4xR98+t6Q4MPphff516+5axvHVy1i8v+F113V25Yd8eyATpxFae1O294e54wjp1hYnQ4/XiylklFriNfoZZqhXet7Ccd7SL4jx40fxwOThDNDSajKo/puxfOq3k22n0jFy78+Lar3+z0yGaZ/UFbFuIxElbenSOpcLu/ZT1cUYWkY7dnFY6TNc+s3RtaE/Pz2RwNIrJNUdP6MWw/PX7AqzOa0bkzeBw99+y5ZkewY/PIsZ2l9PugaNJNSAPkEIIYQQQgghhOFMzC3tXtfeN4vWbm3Jza13lD6D2Nra6ZbXDTtodCODD14jOfEkh45Ecbn6iR5QcpXYy3FERVwiqcQKG9Nyci/vY/Ouk5yJSyKjEHLSUzgdFkZYVCKZ2mLkXiXx7DH2Va2vlLT4WGKiIjmXDfYWkBVzmFU/b1bWFU5MjS/bJWkpxCZc5OiBvezYd5qEqqespxMfe4XT6SXYWZSSdXE/m9QnJh86zdGwU0QmqPfClpKaEE90VoH6EGkKky6yL/wiCTl198/A/Oj24zgHj8cQp65eJ4/0pPMcPBjOxStpFGakEh0RSZqRepVmOTlXjurWs+1QBJn1jXmYk05CwiXd/u08GKnkTU3MJSUxibPHDrFt12HCdfuidy0xjujYy5w7l4dZK3MoTufMjj3sPRnNldR6xqY0IM9kKWUcF0V8rinmpvnE7trDpt172X3slFKWWhnlJRN7JYGLZ69SaqO0pczLbNm4he17jnHkSJ36rpJPYmwcsRHnSSuzUppgFlHbtrF+50EOKG2kKk812lWWkR3mRtnEHNzAql92sT0stmrsUoPalgFttPiaCRXFMRz65SAnkhoez9PYIo+kk0eUdhdObN3BJ/PTSIyP5tjx80RFJ5F+NZEYpT2eSczH2MSY4qQzbNy4lW37T3D4iFbOBpVHMVeTY7kYkYGxszXG5bnEHt7Aj7/sZd/FLOo2oazkBGIvJZFrboZxfgI7tytlpqzvyImTHI2II1mbr6bM1CSO7tvP/nOXKbVyxigrip0/rWft9gOEJ1WOXXF9H1CSprSBmCzyjZWDqTiVs/tPKftbu/yq2mdKKVY2xhQlnWP1z7+yafthzmRUrrtO+6tPllKel+O5eC6ZEktl38ozuLBpC78cPM2VFKXODTles5Vjq6iQ8oICYg+c4FLJzR/nRVlppHfozVCnbLYv307di5xLDKp/A8vHkGO2XlkkxSeSklNCeXm5cogeVtYRT71zZxfTb8oUupSd4O13fiGmapwNA8soJ5W4uGjSCowpLcklZvsOftmxj30n1f63us3U1kD+DNxftd2G1ShLw37XXL8cWUlcjo3lwrloLkfHkpSn1F+WMSVF0U32Cc3Fzs7+ln+3pyY17zgBt+98QwghhBBCCPFbud3fPYxsHNzU5100m+69B5CcnKhN3ThXV3eSkhJ+ky8u1i1a42xpQkluUp2rOG+QQ0va2ppRVqh8ib56E2Mr6pY3pjArlXTlC2+9bmQbt5ofTWX53Op6GmaPSxsbzMqLyEjKaJYHkOjzXE5OQjoNV6m23ZI84lOrotZNcmjthp1ZSRPrrpyvrPH6NFDDbVTdB3MKm8jLTbFxxs3RAmUHGq33psvDipZujlgaG1IW2rxlN1YnKl0+TG6kDRnY7rRyMLnBdnK9ynJooKyaOl51n3P9sjd6nPsO44XX/sCElCU8+Pza6isw6zKw/puvfK5nSN/sFPQ4S/53JrYHX2fyn/bXX48GlJGhx3VNzfa7o1n9hn1CTRUVuodiubm1ueXf7WfC679L4Wbdyecb6lAvfXt3w8u+lNSzJ9l5sd5IfC3uvgPp260lVgXpnDh5gIv17JqTpz8Bfh44lTQwj7Ldbt186d7KkoK0s4TtjeLmS+hO5IhXH3/8PWzIvXKWo8eu1PMHz7pc8A32p6eBZeLu25VWlkWknaw9n5OnLz07+9LaqtDgOhVCCCGEEDfgDvnucV8FSoUQ4v7gwYy/v8qDLWL47pPFrD7d6LPt72hOIx5krk8LXPqPYHjrKyx+4c98ft6AQLFoHhIovWFOQQ/yxIQA/Ns5YVleDnnpnDq5ix8+3cJFbZ5anLsSOn00o3t2xd3ZEjMKSUk6x/HN6/hwY+UgKo4Ez3qISQO70d7BQjdu0rWrtefxHTWL0JBedG3TglYWZlSUpBJ75gybf17Kuno3fLfxYuqChxnT1wMXGxOKctKIPX+UtUt/ZG8DXZyT3ygeHj+UXl1dcTGkTHxD+d9nh9PeIpmdT7zFUl2iF2PnjmJ4Xx86tGiJpVkFhWnxnDqh1OlnDdSpEEIIIYS4cXfId497foxSIYS4/+QQse1HPvxsOZvu4iCpqldAMCGDA+nCBdZ8voS1EiQVdzQPJk+fznR/a05+u5zPl33P+gx3xk6fycwh2ix1tB/xAPMm9sbi8la++PIrPll/Dssu43hozjTGavPgNYaHH55IX8uzfPX1d3z+zVYy3Ifx0OxJhOhmcMSj7yBGeJYTvWU5nyjrWXXakh4TJjN/5n9tZN3fVN9Zj/LEA/44XNzM519+y1dnyuk2ZjpPzxvWwEPW2jB6xkym9DPncp0ymfdgfWXixYw5MxnWqyu9/X3x1FJxbsuAocF4lcey/iulfr74hZNmXZgyfTazx2jzCCGEEEKIe8Y9NEapEEIIvQKSrsRwOSX7ujFi7zo5WVw4Hc6+ffvYdfgiSXf9Dt2dZIxSQwXy5B9G0TZ5C6/85ydOxcQQcbkNIx4IxDXna348oM1Wg7GRLaXJe1i7fg/7TlwiIuoKlz0GMj2gHcZnV7JZLbZBD/PyODcSN/wvb/14jqioaKLbDGZmcCuyl6xjn7IXRdmpXAo7wLY9Rzl2PpqImBwCxo7Br2URu3/YV//Yv+59mDF9AuNGDiHI34/+AX0IVF9+rhQcj0B3c7kyz9RpY5gwKoQhvXzwsCkh/nJajQfwDeThBSF4F5/kWodQHp4yinHB/nRxMyLxfBK1aqjGuoL8fWhnnMzZBEPGrPHg0T8+TbDxMV75x1J2qeUUHYdFr1GE9m9J2i9bOHXd31BMMLIvI2HHZtbuPKKVSTydBk8gwMOYs8t3EavNqfKdupBXp7UnLjoXt9alnF+8hp3qB8bFZCdHc2TPHnYcOsW5SKXsc/0JndCFVoWH+HbXVd3yQgghhBCiedzu7x5yRakQQog7VszJg+zcs5+9xyKJv8UxgIX47RVQXAomVva4OutT8rMqKDcpp6xYP11X5pkdrF5/kNOJWqQvL4nw9FyMjK1w8NAnUVBKKaZYOrXWrp4sIMOoDJPyUi1gWUC8cqxsO3yx6jjJL3TG1BxK869R73XlXuN4/oXHeWRsH7p28KDrwOHMeGASoSOCCAnsRlt1Hl/9PE88MJKgrl70GjKOufOf4pW5/Wpcxdmb8Q+MY9r03/PKkxMZGTyAQSPG8/Ccx3lycuUOKCrXNTmYHsr2egwcxSPPPM3jQY7aDI3pgkcrCwpTojlaWU6pF1l3PJkS+04MGqhPqi2L01s3sGbv+eoySTxFcp4xJpb21MiZ7pb7+bMDsYlay7txdSKuan3s3cuB80na2MgF5DmbYqHUSMG1pkdIFUIIIYQQdxcZo1TcURaNrrrR8L/mhc0btXf3hkUv9tDe3Rle+M8p7d2963a02yZ1T9DeiPtJsx9vMkbpDXKm95N/5e2ZbsTv38bm3Wcp7fMkC4dm8NWzL/BFhDZboxwJff8r3uyTwfKH5/HmZSWpZQ9+9+ZfeNgtkT0bd7PjdCkBzz7G8PQfeOqZFVSv1oPg8f60b+VCe99gxvQp5uB7b/LHDdHa59X8n/2Uzx5y5OSiRXxxqRBse/E/r8/FJ3MHL7yzmoiDuQT94x88HwxHP/uCVZeyKLF2Jmje75nlmsg3b/2ORbvVNb3EuqOjaHn5PDvWrGZ7XAmYj+Glvw3C+cwSBjz1rTKPBzPUdfXN5af/fMOelBxle57M+p+n8U/+nuee+YqT6qoa1IG31yxleOlG5j/wL8K0VN8/fMa3D3ciduVwpv1LS2yM8yg++eFVAtN/ZspD7xOjS/RS8vZ3XuyTyVevvMZHk97mzERYHTCXv+o+13j1I9S/La1c29BlSAj9S8L499/eZJ1BdSqEEEIIcWeYPXMqXTv7alP1+/b71Zy7cBtGYpeHOdVPAqX3t8t/+B/t3X9Ph3ff1t7dGy7/emcF7TqMu7cC0fW5He22SaPOam/E/aTZjzcJlN644OdZ/b/jcCu4Rm5WJmWt3CnY+x+efn2H/lb2JjiFPMWX/+8BHC58zlO/W1kVBB38xy/490RXijPzSM8qw61NITv/+TKvbkrX5lCN5I0v5hDg6kTrVhbkXNjMB//vbX6K0z6uYeaHa/l/Pa7w8dhn+Vh3xaUzL3z1HY/Z72HGlLc46/UEK5Y8TIcrXzNvwVIuaFdluoz4O9/8YyCmu95g2CtqpFQNlI7Dev/zzP/zCWJ083XinfWLGVmyg0lT/06Uti6Xk3/joed2aeVgRed/LWPVwAw+H/QM7+vSGhby6jL+Pd6GM2t+ZWf0NUzdPQgI6E+vri6krA8h9A1txgY5Muq5Rbw2w46LH7/CY9/qg8e+0//Ce0/3IPmHv/PK56dI+euy+gOl41/m6zn+uLVoQWvrPM79uoT/+d9fkT9JCSGEEOJuEtCnJy899zRdu9QfLFWDpF99+wNJKalayn+RPMxJCCGEEHei3Kw03UvcIOdhLHywF+bnVvHavz7mg23RXC0ox8knmCkhBtxi7juOBfPG0L74NCs/3V4VJHUaM49Zvcw5//3b/P29JWyOziS/3IHOQ8YxSrvFX+8CG777ig8/fJ/F606T5dSNcbPGUd9pcGRaNqWmLWjXU8uXsx/uTqbVt+p39aGNDWTFbaoKkqpSDkaSXGRMi3b+WopefkZlkFQVSY46JoCtHbp7HLR1WbcfxnOvv8qbutcfmOOtJFpY0VKdpwmHf/iQResvYd47mLFjhjOkkwnH9qWibrK0esDUBvlOnsMT49tQdGo972/SrrD1ncr8RwKxSTjCzphSXP274m9uqnxgipXyvptnjTo7u42vv1zKu/9ZztoT12jZYwRzJ3tpHwohhBBC3B2OHjvBv9/7lHPnr79i9LYGSe8gEigVQgghRBU1QFpUkKt7iRvjFRrKQ/7GnPv0J7bs2MG6ld/yn3+tI8VlEA9PH0+jYTXfYTz95CwmtrvG1s+XsPp05ciiHjwwdTIBppd4/4ddbNm6hRVffsbff07DdcgUZoTWHG0zjsM7t7FhkzLPV0tYluhI3zGhTKod09Q59+MmTuW0JvDJZ3l14TP89ZU59LdKYNPyTSSpM9iZYqb8qCitO2ZnASXqOKxmFlpCI0xNcVB/auvKjtvHoaPHql9fvM+fXl/ManWeJuRHh7Hmm6W888ESPvt2OYuX/sCPBRXYkkdWzacy1cN31ByefXQUHlm7+fDjXzlReRHukOEMdrPCooUPw2c8xYu/V1491MizMz2V9wun9tTPp7oczrbNStluXMMXn/xIkoM/E6eNpp6iFUIIIYS4o9UXLJUgaTW59V7cUaZ26aa9++9Zff7eukV56vA22rs7w+rt9/6Nibej3TbJPUt7I+4nt3q8VQZJZ896CDMLS9Zt3Cm33t+AEf/+kXf7pfL5kIU1biVvyV9XruRBu6O8OuZl1muptTj34/E/P8PcHmacWfMuby8LI6rq6sxBfPjr3wlM/pY+jy3R0hStX2D9+ok4HHiTwc9vxdfXi4sXo7QP9fz+sZwVoyzY89o0FvyqJVYa8gK/vBZI+q+7SGrpikl6LGdPHGffQW3bnq+w4fsxtDr+LqOfXkvVY4t6Kfvw0Vhanv6YwKdWKgn6W++pc/v7Gyt3MrXFcd4e9gLLtHXZ7nudyS/url7XLdHGcvVL4JPZC/iogabvFDSL/312Or3NL/HD/33C54cvaw9lUvQIIrSdjTahmTSfN3tB2OuL+SH9EhsPmyhlG6WUrfa5Tmf+s/YTRlsc5pUxf2SDliqEEEIIcTepvA0//OTZOyNIKmOU1q91a1fS0lKpKC+TQKkQQgjxX1IzSDowKAgTUzP+9NpbGBmb0KpVa1JTk7U5b9z9EihtO/9jVj7mRsxn7/KXdbuJygD3/lN5839/T/eUVUyf/RExgbP462gPiN7OG18f0QdJn3uMR4a5cHXHOn7ad5izleeohVc5edGY3y1ZzJPtYnn/rX+yeucVMnFhwMxX+c8LviR99yQPbA7g7ef6c/XwdrZu2UJYoiNeQcN48sUnGO90uf5A4v98wckHLNnwxr9YeqUEMlJISMqoDiLiy4vf/Id57qmsXPwRi78/RoHfMGY8+Qzz+5ax97Xn+cMW9dpTAwKl2rrmuCSw+svPdetKRM3jQLpxgHX71T/sKOXw8nC8uMLGfy1nr341VdSA59zuWexd/ythhR6MGjeTZ+aNwOLQWzz95x2oF5V6hc7n0d6OZIb/yDvrruiWeeXpaYx2zWTr6g1sO3CxapzYwrRzXKyv+dQZo7Tb9L/wwuAsDvy6h40bT5Lo7EHwqNn8acEIHCO+ZvrjS4nXLymEEEIIcddRg6Xx8Yl3xpWkd0ig1MTc0u517X2zaO3WltzcHG3qxllYWFJWVkqp8tJ/b5FgqRBCCPFbqi9IuvLn9SQlp2JuboGpMl1QUB1Cu1G2tnakJjVvOOlWzzfUPKnLN2egNDs1CRuPnvQd1IOuPr4EBA5h9KgRdDa5wLpl37E+IgvGP8PbMwfga5XM4nXHCZ73Mn+Y3I2W5hWUVZjj0rkvA/trr46m/LzzECmJlnTo48/gXj507dqTgSFDGTfaC9MLW/h06TYicizwDhzGuIE96d61M30HDGbUqCEEOF3jyOpPWLIjiWtaHqtY+BI6uCvuLi1xbduRXn37MnhoMMODA+hik8aRyAgS4spo1aMPQ/v64O3bg+CRwxjtY8KZ9cv4aPUJ0krUFQXx0JPecOkrVuiegq8X8uBculgnc+CrzZzgqm5dLv4BDO2jX9egoUoeR/TD2ziaXw6pJ8KjeenNqQz2sSZh2QaO6FdTpe2IR1g4dSD+nX3pO3AIY0N6Yhm9iU8/W8uRq6W6efrMeZlnx/rjXhTONztcmf+X+Uz3c8asopwyixZ0qSxX5eVluont9T1qf8hkFvjC+cVr2KlMmlp2ZNCo4UrZd6Zbd38GDRvCmGH+OGadZOV7y9maePNtUAghhBDidktMSiY3r8aA9HcAOzv7Wz7Pv5XvHndcoNTExAQzMzOKigolUCqEEEL8xuoLki5dupTDh49gbmmtnGjYUlpaSnFxsbbEjbtfAqVkJ3H5cjyXLmVgbGuFcUUxKae2smbDLrYejiRbDSzmlWFVlsqp/bs4fPEqZsbFRB07wPbdhzh8JoJzF2q+Irl4JY1riXFcjo3mQqYRttZGlBelEf7rZtZv38P+S7mU5CUTezmBiEsR5Jo7Y25USEbUYVb9vJktu48RWc+5b/CYhxjnmcuurecotDCmXPdHahs6Dx5OUCcT4lcfJFzZbnTsZc6llGJlY0xR0jlW//wrm7Yf5kyGPjgJ10hOPMmhI1FcrvFY/8zUJMIOHSEsKlF3q726D7p1ncvDrJU5FKdzZsce9p6M5kqqOh5uPmVWxSSdOcimsEjqPkqsLO8qsZfSKLOxxljZ/1PrN7Bm+272nc9EF69VFF8zoaI4hkO/HOREUhGmRZc5sncf2/eHc7JWuUZw/mLt/FbJTCP+eDiHIuJQr6HOVdrt5bgYLlzM1+e7IJPogxtY9ctudhyN0T1MSgghhBBCNJ/bHSi94269NzExxcnJiczMDMqUL2bN9uVFCCGEELU0FCQ9dEh/q0rrtp2U38nOyu/kTN3dHjfrfrn1vpoVLd0csTQuozArlfRa0TT1Mzv18tM66QawccbN0QKT8iIyat0mX82htRt2ZlBWmEXS1ToPYqrSj7d+/geD4t/mof93lDJbZZ26dCsefPMjHusQxUdDFvKJLk1Rud2SPOJTs7XEm2WPSxsbzOrZB+sWrXEgp5F8V5ZrCTkJ6ddfJatbtzmF9X52q7R8U1+dCiGEEEKIW3aH3Hp/xz31Xv0iVlRUjI2NrZbSrHFcIYQQQigaC5KqafPmzdP9LlZ/J99KkPT+VEB6UhLxCfUF1NTPbjLQlpdBUoKy3gaCpKprqep2kxoJNqqsMDMxxaJVd4J6uGJfkUS5rTtdg4fj7wKp5/azTZtTp3K7txwkVWWT0sA+5F9NbSLfleXaUCBUXfdvESRVafmut06FEEIIIcS94o4LlKry8/MwNjbWB0t1cVIJlgohhBDNpakgqZo2aNAg3e9i9XeyuNec5tulWzhV5sdDc5/g5Zdf5c8LH2XuhD5UHPmWv3+ylQhtTiGEEEIIIe4nd2SgVL1yJS8vD1MzM2zt7DE1NVVSJVgqhBBCNAczCyvdz8TUTO3BTb9w5kKULi0lIwdLK2vW/7pN97tYria9F2VwYsu3vPf+Yj778VfWbj3G5g3r+GbJl7z/5Xp2nkrX5hNCCCGEEOL+csc9zKlSeXmZ8irXPdjJQvlCZ2pqgpGRERXqmAXKSwghhBA3x9TMXBcgTc/MISMzi5Onz2FlbYON+oCjglKuZmRwJOwEJSU3/wCnmu6bhzndTUqySU2IIzIqmosRUVyMjCbycgJJWUXaDEIIIYQQQvz3ycOcmqA+3MnMzBRT5QudkZGxLlgqz3cSQgghmo/690f9HyLLKS0toaSktFmvJL3/HuYkhBBCCCGEuCF3yMOc7vhAaSU1YGpiYoKxsRoovSNHDBBCCCHuSmqAtLy8grKyst/kVnsJlAohhBBCCCEaJYFSIYQQQtwPJFAqhBBCCCGEaNQdEiiVSzOFEEIIIYQQQgghhBD3PQmUCiGEEEIIIYQQQggh7nsSKBVCCCHEfae8vBxjYxNtSgghhBBCCHG7qefn6nn67SSBUiGEEELcd0pLSzEzM9ONg4T2rxBCCCGEEOJ20I9Pqp6fq+fpt5MESoUQQghx3ykuLsLS0lKbEkIIIYQQQtxu6vm5ep5+O0mgVAghhBD3nYKCAszNzTE1NZULSoUQQgghhLidlPNx9bxcPT9Xz9NvJwmUCiGEEOK+U1ZWSlFRMTY2tlqKREuFEEIIIYT479Ofh6vn5er5uXqefjtJoFQIIYQQ96X8/DyMjY31wVLd+ZkES4UQQgghhPivUk7B1fNx9bxcPT+/3SRQKoQQQoj7kvrX6ry8PEzNzLC1s9ffhi/BUiGEEEIIIf4LKnTn37rzcOV8XD0vv91Xk6okUCqEEEKI+1ZJSTH5+fkYGRlhY2Ov+2u2OjaS+hdtIYQQQgghRPNSz7PV8231vNvGxk53Hq6ej6vn5XcCIxsHt2a9dKJ77wEkJydqU0IIIYS437m6unMm/JA21Tya+3zDxMQUMzNTTE3NlJM1Y90Jm/K/EEIIIYQQohlVVKivCuVVTmlpCSUlpc16Jemtfvf4TQKlBfl5cuOaEEIIIVBjjVbWNnd8oLSSGjA1MTHB2FgNlMpVpUIIIYQQQjQnNUBaXl5BWVnZb3Kr/R0ZKM3KylTeSahUCCGEEEY4OjrdNYFSIYQQQgghxN3rjgyUyhcXIYQQQlS6G269F0IIIYQQQtz9bvW7h9xTJoQQQgghhBBCCCGEuO9JoFQIIYQQQgghhBBCCHHfk0CpEEIIIYQQQgghhBDivieBUiGEEEIIIYQQQgghxH1PAqVCCCGEEEIIIYQQQoj7ngRKhRBCCCGEEEIIIYQQ9z0JlAohhBBCCCGEEEIIIe57EigVQgghhBBCCCGEEELc9yRQKoQQQgghhBBCCCGEuO8Z2Ti4VWjvm0X33gNITk7UpoQQQghxv3N1dedM+CFtqnmo5xsFBfnalBBCCCGEEEKAlZX1LX33kECpEEIIIX5Tv1Wg9Nq1LG1KCCGEEEIIIcDBwVECpUIIIYS4c/1WgVI53xBCCCGEEELUdKvfPWSMUiGEEEIIIYQQQgghxH1PAqVCCCGEEEIIIYQQQoj7ngRKhRBCCCGEEEIIIYQQ9z0JlDbKk6GTpzJ7dC9t+i7WwpMuXo7ahBBCCCGEEEIIIYQQoqY7LlDae+REZj84jP7atF4DAUvvQKY+OJGx/tp0s+tG6COzeeqBQG36buXJ7IXP85dXFzL3Hoj5CiGE+C96YAGLXpioTQghhBBCCCHEveuOC5Q6Dg7lqcdnEFozoNdiCLOfnM1T88YyVEvSGTSJ3z0+lVHttelmZ49rW3faud3MlZgBzH/5z/x5ToA23QwmP8U///YUU7XJ+tW33WzcPH3o1aUDrhVakhBCCNGoibzy1mssemIqU4f5aWlCCCGEEEIIce+64wKlp7NMaNmuM51rXlI6vA/927vTros/42oEUHt38cGrjTVFJ7SEO4oXg0PHMyXYS5tuBr2H8MCUIfTVJutX33avsubj/+Pl1z/lpwgtSQghhKhXIM+89hqL3prD7EljmdreVksXQgghhBBCiHubibml3eva+2bR2q0tubk52tSNy08L4KGHfHDIPMmSzYm6tKHzn2GGdSqXrd2wvbaG7w8V6tIffmwB/Syi+PLdjZzXpYCz/zBmTBnL5PEhBPl54W6awtm4XO1TRa+JPDuzJ3aHruL24IPMmTiM7nZxhEVmKx96M2L2ZKaPG8nIfr60NVJSxgTQLv8i7y7fq1++Fkd6j5/KrImjmDAigF6+rTFOvUScSyBTR/Rn8DAf3IwrsG7lxaCerhSEXSJJWcZ78EhmTBzBhLH6PLYyvsL5eP0+qUbMXsCEToVEmPdk9kOTCR3oRhv7LvQJGsCw9jZgZk/7AQF4Wx/lRIy2kEodiqCB7Vr0H8aAdsbEH1TzoApm7h9G4FsUg0UfZR+UMpsQ1B0v+2zCIzNoM3Aq86aPI3RIL7q7Q8TZRAp0y2naBjBjxkSlnIczuLcvHsZJnKpZzkIIIe46M154jcdDRzA5NJC+XR2w0NLJbOj3oGFsbe1ITYrXpprHrZ5vCCGEEEIIIe49t/rd444LlJLhypg5/fEyyea7Hw5RgB8Lfv8AbrHLOWE5EH/LOD5Ze1GZMZB5vx+Pe9IO/rbqKPlKivPgOfz199MJ7eeJk4UjXr36MKCnNy5cYf+5DN3qCXmCRU/0xM6oM+MeHEBXz/ZYZfzEuiPtlC+Iv+PpB4Pwb+9Oh3Zt8O3RkXZtWmCTXf8XRO+pC/nz/DH0d7XEzMYF374B9HXN40ieH/Mf6INPO0esK0xwcmmLlytcWL2XdvP/yHMPDaO/lzN29u707BdAH7/WlJ/cxykti7P/9BaT2pviMnAME3p2wKt1OeX2fQju1hYXOzOM7Z1p4+mBddYqfgnTL6MTMJWXGtru7/7KM8G2RHyzlVO6mafy2ruj6ObUjqDhg+jq5UlXf396dXfDzNKHGbPGMMDHk07d/Qjo0Qm7zL3sidCCuV0m8srzs3locCccTCxx8/EnqJ8X1qnhhF2pDvgKIYS4Owyd/zLPTh9BaOhYAnu0xMZE+6CSBEqFEEIIIYQQd4Fb/e5xBz71fheXkksxa9UO3SOUWgyhu3sZcee3cCwuF7sO/fXjlLYYSDsnuBr3K2nqNME89dQMxncsZsM7H/PP9z7i/xatINIhkNlPzGG2t24mMLfGzr49A6e2J3blJ7z57/dYvCUT75lzeGZmb1ombuLNf73D399ZzEdbr1LcyJieoyeNob9jKp+//xlvf/g5/3l/Db+eiSX73E6+XppEvhEUJB/ln+8q+fl0PeHKMvEV+STuXMqiDxbzn/c+5B8bUmndYxSzJlWP/2Zja0vrboEMNDrIokXv8XdlvSvWfMvZrDLl0yzOqetTXst26Oev0sh2LW1slf22xlI/p0Ith5Z4dW5H3JrFvP32O7z2cwI2nQJ57IFelOxZyn/eeY+/fXaRgjbdmTQuSFvOk9mPK+Xpb8zWdz9l0Yefsei977nkEMRj86bRW5tLCCHEXUB9UNNbr/HiIxOZOmksHeQueyGEEEIIIcR97A4MlCayIjoD7F0YqAY3h3fFwyyNS+vS2Hw6ngJnL/04pcHtcTPLJfGsdu/5yDFM9mtB6tEPWPLrHvYeDWPH5nW8s1n5vH0Q08d76ufTMSfv3Id88NMOtu7cw6GLnkyeEkQH0zi2LfqBDTv26NI3rDpFUrG2SD1srCwxsbSjrXMFieH67f2w8SwZcafZtTOVPGWekuwrunVt3XeaOGX6wi8r+GTFZjbsPEJcbgE56SlkFlvj5uujW2cls4p0Vr+3gvVb9cseO7KHqOwS5ZN8ktX16fKtn7dKI9utnxn5cZ/x0Sp9OWz67iyJxRbYFp7hwxVb2aQuv/Y0SUXGOLl31i/iPYbpQ9qRf/4bXTkfCD/JgT0bef9iDq16BjFMP5cQQog72aA5/KPyQU2TxuLX0kz7QAghhBBCCCHuX3dgoBTOnkokx8wFzwAYOrAtNmnRbIiCuDWRpJi44jvUEe8+rbEtTuHSNm2h/u1paVpI0oVwkrUkNah49qfLZGJNmy7dtDRVMSnnjxChRhR1utG9jTVkxLDsuP76VJ28okavKF373Rr2pzkx+akF/P3Nl3ll3iB8qy/ZrF+JK71nPss/3nyNv7/wOE8PctVd5WliWjUSnF52LCtr5uU3knc1nKjKckhOI6tYDbKmcSFLS8vLp7hUyZ+Zlr/uvrSxAWuPUbysfsnWvV7icV+l/CysaaWfSwghxB0rkGeenMGD8qAmIYQQQghxlzI3N8fa2gY7OwccHBzldRe91DpT606twzvRHRkoZVs0icU2uHcPZKSnEzlXTnJUTY/azsU0C9p1DWJwBxdMMhPZXHm5pJUZJpRQXKRNV8oro1z5YW5ur5/WKaNMvTizij1m6sU0JcVc1ScY5MK25fz7zX/z1obLlHcKZNoj83j1xVnUfGB/bd7MffZpfjdtAB4lsfyydgPfr7hIunpHfV1KBn/7MGldJZQ1EhjWsTdFbcrZsbvZd+hI9euT//DCqx/zg34uIYQQd6xkznz/Ia+8+jdeOJaupQkhhBBCCHHnU4NraqDN0sIKIyMjSktLKCoqlNdd9FLrTK07tQ7VurzTAqZ3ZqA0bjdxmca4eozD262MK6d36W4nh5McvZKLQ7tgBrhaU5B0STf+pk5UFjlY4tJJvS+/mvMoV91VjhlJB/UJ9TpIQqbyw9mV8S30KQbJS+TEvh2sWf4177z9Dh8lOhEwdiKhtbNQzXsU08Z3wylhOYuW/MTqX7ayYccVskq1z+8G+5LJKAeLkiJ2rd3I6lqvPdX1IYQQ4g4Vw95ftX570b944dW/sS5KHsQnhBBCCCHubFZWVsrLhvLyMgoL8ykoUF75efqf8rp7XlqdqXWo1qVap2rd3inuzEAppzmbVIhlh774WKVxfk2ilp6vG6e02MWfPs6QErNFC6Aqtv7E/iQj2gQ9xrPj/VA+ps3AB/mfGZ2wyYlgy3JtLNN6xbDhWDyltp2Y+dhY+rdVktr2YuqCYLwbvJU+kGdefpZn1G1lxXH2yCWy8svBzBgT3QU66WQr3zutnNvRuwU4t3VV5muJrTWYlJuQEputbCOA2X9obBu1ncrKVf51pO1YV+WnK23UfF6nnu1qnzSLqFVsuZCLU89H+Z/ZAbTRJTriPXgiUwc76qaEEELcJY7v0QVMP3jrH7qA6eFULV0IIYQQQog7iBpIszC3pKAgj/z8PIqKiygrK6VC+U/PSF53xQtdnal1p9ahWpdqnap1e6cES+/QQGk+62LSwNEJx8xY3fiklXTjlJo54WyZRdzRK1qqIm4/H328hmPFnZn79ELefOs1/v7cLMY7p7Dx8/dYfkabrwFHv1nCLxfK8Zw8l1f+32ss+n8LeHx4BenZ2gx1tWhFxz5DeeRJ/bbUcTqf7GxGwsGNfKcbDmArm06kY9QmkFdfe403/zSbUWk7OHA+C8vOM/m7usz/m8/0fjkkqVezGuDMhkNE5NnT7ZmXdNt7aWr1k/Kr1bNd7ZPmcYHl/17KltSWjH/0af1+vPVH/rrwQSYPqP1AKiGEEHeHiANbdQHTf77xN154dQsRcoGpEEIIIYS4Q6i3ZltaWpOfn6u7dVu9ClGvdgBO3A1q15lal2qdqnWr1vGdcBu+ibml3eva+2bR2q0tubk52tTNy0xNJPb4ITZv28ee6BSqhhTNTCU27iS7d+xix4HLxOdr6cocqbGXiY6M4HR2BQ6WyqxRB/l+1a9s2BZGZOWXvtx04k8fY19YNLE1ApQl6YlEXYnhtLJCIxNjihJOsX7dRjbtCWf/gTDOx9S5zMY4h+TLkZy/kIdFa6Uiy65yctVafti8l8OpaqaySIiNJzmnhPKKcq5GH1C2GU6Yso2T6SXYWZaScW43v/y6i637T3LoyAkuxemfoJSRnMDhg9dvsyQlkctRGeSZVEBRCqf3HOdUQt2yrm+7cUSmJXPi8BGORCSQoc2XFH+c/Qcjia56+lUuqYlnOXCgOi+Vafv2VucnK+GKrqzOnMnBVN33ojRObdnJzvAoYlJuve6FEELcHkkxyu+1i5e5HKf0+zsd6DnCDd3jnjIv8u7yvbp5boatrR2pSfHaVPNorvMNcet6DRlLYGcHCqOSlLOLG+DmSSe7LDLUG2aEEEIIIRpgb+9AUVERBYWVAaDqQFvNsJuR8o+87uBXjbrSq54qKyvD2NgEKytrCgsLdGk361a/exjZOLg19fieG9K99wCSkytvlb9NHFvRzs6MsoIsEtOrIqmGsWmBu7MFGLysPa7tbDGjhJy4tOu+INi0dMHZyoSSnESSKz/U5c+YwowU0qrGDjCUtr2yIjISr1YPPVBHvdttdoblRQghxN3Ij5GT2mL3wO9Y5HSADhPe1NJvnKurO2fCD2lTzaM5zjfUAF+Hms96VJRmx3H61BkuG3i3h4Bn/vUFwx3PsfTJRWzQ0prWn3l/C6WX8XnW/+VbtmqpQgghhBA1qVcY2trak5l5VbuSVB9Y0/1bFXGreiPuCloYUvlRHZCs0AVKnZxakJubTXFxsZZ+4271u8cde0XpLSnMJzs7h5z8Wo+2N0xJATk3tGwRucr82dn51HenYkl+ni4vuTU/1OUvj5vJXtX2cgqqr7KtR73bbXaG5UUIIcTdKJXoi5GcvxxP7ImT199ZcQPu1CtKp/zuj0wa0In2rm3o2NGbXr160KtHV7r5tsOpMJozCUXanHewPlP4/awgvIqPcyZJS/svG/nwk/Rtlc3JFTuod6Sj0bN5dXIP7A6eIlJLAi9C506gn3MOR3/ezyUtVQghhBCiJmtr26on218fJFX/kSDp3UerN/3/GiMqKsoxMTHBzMyc4uKbPw+/1e8ed+gYpUIIIYS4I6gPfNpxWpu4tzi2dsXduZSIH3/iux9XsfSbFSzdfRX3fmOZ+cBoumvz3dE69GbMmGEM6KBN34n8Ahk/JpAe2qTeaX78+D3e/PBnDmspQgghhBB1qVeUqrfdV4bUdP9W/yPuakodGhnVqEkjXV3f7nFK780rSoUQQghxx7hTrygNnjwLX8tYtv7razbExREdc5nImHjaDhxNgJcNV7/bwjH6M/3xwXiVxGHuN45p44YQ4FpG2CV1gG8HugePYvzoEEYF96SrVytM0qNIrDHmZvexDzGthw1hWa2YMnnM/2fvPwDtKM67f/xR7xWBekMIBALTm+jVFAM27hjcseMSJ7HjOPnnzZvkzT+O7TgusY0BF3AD27iC6b130btAvffepd88O/vdmX3O7J7ds+fceyQ9n2TOZ76zZZ6Za67uXR3dS+eediwdPnkf6jJ3Bi3oM4XOuvAcOv/Mk+jYA8fQoHWv0lv2B4kb0vNedM4ZdOaxU2n/EUSLzNw8xYRjT6fTjjqCTtlvGHXb0Zv2mnAYHTJiEz07Y5m9RYp96Ohzz6XzzzqNzjza3Gekuw+T1LloQLqmLQvpraX+3+gPoqPffgGdfzbf5wAaN2gLDTv0bJrcZzE9EXhHKf94g5OOOpKmjepH1H0AjTriMJrY91l6ee4mOvDY0+jAgevo/lfmR+c2vleM97E4/hA6aOIg2rJgNi1p6b+sURRFURSl1fDPJ1271v48wbZ4SDpqEp12ovl6ZPJetG3DXFpe4MvRIRMPpZNPPpaOOmAE9dmymhasyvkCZeh4mrr/aNqn71ZamjpvOB1w7FF0yrGH0oGje9OWVYtpZbUf5dk+8PPSuLtz5/boY75+feM/xL7q9x76oFRRFEVRlJbS1g9K5QO+zato4mnvpmPHbKe5P7uZHqLz6W///RQa330oHXX6yXTw2NG0946X6M+PrKHjLvkkXf6uU+iICQOo16AxdMjhh9DB++5FO+c+S6/HD/FO/OgX6bLD9qb+Bx5L5xw6gSZM2I8OPnh/mtS/Gw05/gJ61/GTaOLYiTT1bVNoyoguNPPeV8n+jkWe92w6dPRYOuzII2j/8eNowr6T6NCpk2ls72U0/YWFdMCFH6Z3vm08jRzcm7p0GUB7jxpjvoSeTTc9Oiu6g2MiXXD5R+lD5x1N+w3sTr33nkCHmlon9bX34S/BbZ1DqffkY+ncIyfRpPGmpoNNTWN70IK7nif70RsUrflT7zqVDps4nEaNHEHjDphKY4fvQwMo/KD0tA98it4+eRQNM+vtMmAwjRg7hvqsvpnufp7o4s//E1247wb6xS1PR+c2vle2rsvfdQIdOLw39R4wgqYefjgduPdWeuPxN+NfYqkoiqIoyq7IgAEDae1a+zWffUbaeQ9Jh5xwCf3dJy+ii048ho489BA67LD9aFyXpfTEjKwfbj+cTrv0w/SJ951DZx9/BB3+toPpsEOn0AGDN9JLL8wP/PjGwXTSxz5Pn3vnCXTE3qvo5vhruiFHX0if+fB76F3nnkDTDnubmXcqHTZ1PA3dNIOem7s7/K1w+mPKH/MqX+frP71XFEVRFEVpEoMPOpcOH92LaPmC+KFfb/PF1lDa/5hDiZ76Jf3gqh/Tj25+heiEi+njF59E47c8RFf86Gd05Y+vpSt+N5cGHnkuXfaBcwn/Er5X337Uf/QkOrrrS3TNNdfS//7genpp0zA6+Ox30Ol7z6Gbr/05/eCKq+i+Zb1p4tGn0anJP6HneQfRiOG96eXfXEdXXX0VfeeKG+iFHfvSyRdfSOeb8964/y/0qyX8VoLNtHD6tfTDn1xL19xqahNMuPC9dNmFU6jLYz+nq675JV19zc/p17MG0LSLL6Z3xz9fwNa5Hx3b+3W67ue2pj8t7E4TDz+Fzj7SnoM177vzefrVD009V/+cfvqb12n1jvh4gCdu/z29tpZ/8cJqesPUxzXe8LA91qefmbNfbxsMDe9VVNfxtNfCm+jqn5r1/exX9MMHNtOEM99D7zsjPkdRFEVRlF2fzntGSjT0dLr8k++mi/bdRnf97Gf0w5/dSYuHTqMPfvwj9JHD43Mkx15AH37faTRuwxN0zU/NNT+9n5aMPJbe86H303vMl5aSISe8mz554VF0+CFvo8P22yceJdrrwOPprOMG0eKHfmO+1vkZXX33Mhozzdz70osocJtdk8782Ar0QamiKIqiKHsuvcfRaV/6Av1T3L7yqXfTYf1W0JN/uYEeiU8h6k47lt5DV/3uAbr/kcfp0ZdX0mlnnUYHDV5B06/6E9318DP0zPQn6Z6bf0X3ziUac9xZdE7qZ4auohd+Zs578HF68OF76O65a6nrgK609Nrf0U33m7FHHqHrZ68h6rUXjZ0SXxKzdvav6Nf3PGTOMec9eCf9+LlF5gv1qXTSCUQLXn6aHlzO7yLYRmvm8n24tsX2woRxdM65x9CYTTPoV6b+B6e/RM+ZWm/7xVu0ZuiBNO2Y+LSINfTSL+I6TU1/eGYxbeo+mEbGNUVrHrqGXvjVb+jm6JzH6f57/0RcehZvPvs4zVm3zfQ20VJeg2nPvGWPhSm/V1FdfRfRoz+7g+5/8gV67rln6P4/PUeLu42iY048zJ6kKIqiKIpShWmn0/kH9aI5d/yUrr/tLrr5tpvoR999ilaPOobed37qCyrH4ul0+29+Rf/zoz/RjdE1f6If/nEmbdhnKp12YnwOGHoMXfqR82j/NW/QTPF7jJY++nv61reuoh/dcAfdfLu5z+9+Tg/O60LDDzyKMmZWKqAPShVFURRF2XPZsZN6jhlN46I2nLrOf4iu+Z8f0lW3vErL41OYNfPvoheXxsFw+JjB1HXzUprxnHfWhrfopnlriPoOp/39B57b1tKi13HeJnpy9YbIK19cTNxj5m3mh4ndqUd/m8HmdTPITbuJ3pi/irZQbxo4Kh6qywG0//DeRL1H01mf+CL9yz/a9qX3TKS+1JP67BWfxqTqJFq+aiNtM+f0i8+J1rxpCb348JykbnMWbeU3jDaLBvYqqqv7IDrkPZ9N1vcvnziMhnc3H4qB4+1JiqIoiqLs0tg3HHbi2w4PGkGDu66neQ+9RqujgY008+kb6LVlvWjUwSfSpGhMMGs63finO+jxN/C3ymvojYVrzNdyvaj/kHgoYjCd9IEP0bv3W0v3Xvs8reAvdTxWv/EU3fXA8zTTTmwGBlCfXuYLnQ3r4h9DtDvg/1KnzkUflCqKoiiKsueydQk99ZOf05VR+wX9+Po/05/vfZJe9h6KMju2p3/2VK/u5kuo7dtI/IU/rdvKr7UPPH2Wb8/5t+r12LqdoqsLfyXZn3qYr6Np3UJ6YPqz9FTSfk3f/tq36Ie32LOy6Urd438dH61522Za556StpwiexXVtXkVvZJa3410xTe/Rd+4/pn4LEVRFEVRlAqs3ExbaQAN2X9wPGAYOoy6dyPqMXQEvS0ekmxYn/6NS0cdNZIG0ypa4P1gd/tP7vel1Q/+kn5yf/Y/1TnqzAvp/Zd+mL78j5fTCYOX02N/up4ejY8pzUMflCqKoiiKsueyczMtf/Elei5qr9Ib81Z675bMZtbK9US996KJqa+KB9Hp+wwh2rGaFj0ZDzWZwyfuRb1pPa2aHQ/U5WlaxO8+6L6NNj15D916R7o9+JI9qwjhNXc+UV3dd9Lmt2rXd9dTc+KzFEVRFEVRKnDnA/TMsh60/3s/S3/30Yvp/Zd+jP7pSx+iCT22ma9DupuvAgtwwMX0kVNG0s43H6KrH4rHhp5CH/sY/5P7h+nqnz1Mb5ova7I47rz30mXvvoDee+FUGrRuJj3+xPO0LD6mNA99UKooiqIoilKS+296jhbRPnTshz5IZx3EXxrvQ0dffBldPKk3rXvzMfp94QeZ+Qzb91K64NhxNNh8+X3wGR+kjx29D9HSV+meB+ITlqynTdSLBo+dEp0zYqT8Mn0O/f7BmbRu8BT68EcvoKNH2tHB446kC95+pLmmOPc/MotWdd+HjnzPBXTcODPPkHF03MWfoEP8f74f4NXV/BX/IBp5Gv9Sgn1MjdFw04g+Ft1G0akfxcfCMPJgOvfcU2g/mxRFURRFUaox6276wdeupftX7EsnnX4GvePUt9HwlffTayt3EG3bRnV/R/vQY+gTn3o/HdtvEd3xs9/T9OgJ52A6+7JL6F2Tt9Kz9z1PKwZMpEMP7U89+FD3/qY/ifyftnT/739GP7zyKvrBrx6hWV3H0+nvvYROGhofVJqGPihVFEVRFEUpyYLHfks/uell2j7lHfTRT32O/uUfP0Offt+JNGjpw3TtlbfRq/F5lelzNL33I5fTV/7xc/S5S99BB3eZTbf//Hq6d2F8/N5H6IXlRCOP/Vh0zpfef1x8wPHqX35M1z6ygoaddDF9+gv2Z3h+5fOX0XvPPIz2j88pwoL7fkm/f3oVDTrsYvrU582a/+5y+vjbp9L6NfIHEKR57e6nadbG/rT/ZZ+x+3Su+I1VFbEfC7PjU/GxMGv8wkfpQ+cdQ28bHZ+kKIqiKIpSiWX07P030Y+uuJK+++Pr6KfX/IJ+dN0c2tqzO9GaJfR4fFaQoUfShz79EbrsqK708m+/Tz95YFZ84DA6+4z9aEjPPjTmqLfTZ/760/Slvz6JJvKPlx9/kum/j94en8m88PBd0S9zuuHX19C3ntpCk099N118anxQaRrdevYe8G9xvynsM3IMrVtX91m6oiiKoih7CP37D6AlC+fFqTk04+uNVUsX0bNPTqdnZy2iVfFYLatN7S/SU0/PpDn+zy3duoLmz5lHM998k1Z3HUA9aQ3NfvJ2+vPtD9EDz8xNfnbp+uVL6OXpz6bnWLWMFjz/LD355nxaEg/Zsen02POzaX70g/qPpXd/ZBJtffF79NPbt1HvvbrSyhmP0x//cjfd8+jLtCj6WaiG1Uto3oLltH470fYdq2n248/Qs/NWxAdj1i6m2XMW0MzX36B1PYeYWjfT0tcfpXsfe5nenLcsegdEsM71K2nhq8/SY1j7BjPX3AU049UVtK1vVzP3bLr3rnvo/keeoelPi2s9ti5bTHNnr6IN/NfzW5bSq4+/QK8sWm/338w5Y679R2MN75X3sVi8rQ/17baT1s15lO55+EV6Ze5i/pFiiqIoiqLsovDXkfw1X/Tj2bt09q/72Uyrliyg2bPn0qy5C2npfu+lL7z3QNo0/Sf0g7vnx+dIJtHFX/g0XX7WKFp2zzX0zZ/fT694XzCtW/4qPfrwkzT9lTfo5Ve59aMDT5hI/ZdNpx/++j567Y251O+ASUTLV9Km+JqtG1fQ3P1Op8uPnUA75v6MbngsPrCbMGDAwEpf51f93qNLv0Ejd8b9pnDwEcfRokUL4qQoiqIoyp7OiBGj6MXpzf0Krp2+3hi493Dq330HbV69lJY37RcdfYGuu+dsotveQZd8YwDtPbIvdd28mhavwJfIPr1pr+GDqFfXbbRu4XLK/hUAzarV1tNj2wZasLToF7HxNTu20KrFxX4ObCP0Hbo3De7VlbauX0xL8zZCURRFUZRdAv46cuHC+W3woHQSXfzp02nA63fQjffOpj6Hn0cXf/Jj9LH9l9NVf/t3dPVL/EubjqFP/MMZ5szZdOs3rqMH+ZrPf4o++e7DqPtLN9Fv73iMnpwdfxW0bT3NeWk2pX9dKPM++sk9n6GDXvshHf+Z35p8Cv3L9y6kfq/cTzff+gA9OHMVjTr8bLrwc5+hz0zdTLd++QP0j/h5p7s6O3cSP6AcOXJ0pa/zq37vof/0XlEURVEUpQJrli6mBQub+ZBUspaWLlyc8ZCU2UTLF3MN+Q9JmebUausp/pCUia9p4UNSZsOKpWZ9+pBUURRFUZQmM3QMHXzC2+nDl3+e/vXf/on++fOX0Hsnrqb7r72S/hw9JGUOpjPOP5suPP9EOozjO99Hn3z3UTSuf3fqN2IKnXzhZfSl6J/Xm/apC+iY6Jp6zKfnlvSmI877AH3+S1+kr0ZzX0ofmriNXrzlWvrFM/FpStPQf3qvKIqiKEpLadd/et/eTKFTzhtJG175I936dDykKIqiKIqyh9G3bz/auHFj9G7DTn1HadcttGL+LHptXTca2Ito1ayn6A9/vp1uv/8ZmpP8mJ8NtL3PFlr44qN021MzaGnPnbT2pSfo7vsepoeffS3+p/WuvfbmgsA7StfTsoWv0OOPP08z5vO/0V9Bc2fPo5kzZtCSnX2oX/cdtG7BS/SHP95Kt9/zGD2/dJu9bDeha9du5mPel9avXxePlEf/6b2iKIqiKG3N7v5P71vDBDp82nCiRY/TM2/FQ4qiKIqiKHsYQ4cOi/5yfPPmTfFz0k7+OaWDhtGY/j1o+6ZVtHA53knq6LvXPjSI1gaPVYXvPbR3N6Kt62nekt3tn8/sjJ6F9+rVO3rQuWKF/Rn2jaD/9F5RFEVRFGW3YxY984g+JFUURVEUZc9my5bN1Lt37zi1AauX0bz5CzMfhG5YvqQlD0kZvjfPvfs9JHXwx5o/5p1JS95RunHD+ugHsCqKoiiKsmfDf+ffp28/fUepoiiKoiiKUppu3brTkCFDaOXKFbR927bO/ef3SuvYuZO6deeP9VDzsV5J27c3/iMFqr6jtCUPSnfy+2X1UamiKIqiKNTFfD3bRR+UKoqiKIqiKA0xYMCg6PnomtWr7N/Cd/Y/v1eazM7o/wcOGhz98/u1a1fH443Rlg9KV62q/XG0iqIoiqLsmQwePEQflCqKoiiKoigNwe8qHTBgAG3dssX+kh99WLp7sXMn9evXn3r07Elr166t9G5Spi0flOo3LoqiKIqiAP1lToqiKIqiKEoVevToGf029O3bt9OmTRtoG/8zfH1Yuouzk7p370G9e/ehbt260YYNG2jr1i3xscbRX+akKIqiKIqiKIqiKIqi7LbwAzR+kMY/0qlfv4HROxB79uxJXbvqY61dDf6Y8ceOP4b9+g2IPqbNekjaDPQdpYqiKIqitBR9R6miKIqiKIrSDPif4ffo0T16J2KXLl2jh2z880uVXQf+OaT8u4127txB27Ztpa1bt1X+5/Y++k/vFUVRFEVpa/RBqaIoiqIoitJM+IEp/3Ptrl35Qam+q3RXgh+Q7tixM/oxCs18QAr0QamiKIqiKG2NPihVFEVRFEVRFKUjqPq9hz52VxRFURRFURRFURRFURRlj0cflCqKoiiKoiiKoiiKoiiKssejD0oVRVEURVEURVEURVEURdnj0QeliqIoiqIoiqIoiqIoiqLs8eiDUkVRFEVRFEVRFEVRFEVR9nj0t94riqIoitJS9LfeK83goCmT6cD9J9HIkcOpb5/e1LVrV9qxYwdt2LiJFi5cTK+8/ia9/Oob8dmKoiiKoijKnkjV7z30QamiKIqiKC1FH5QqVTjumMPphGOPotGjhtN+kybS0CGDqVfPnsmD0s1bttCKlatoxpszaf6CxfTw40/RY088E1+tKIqiKIqi7ElU/d6jW8/eA/4t7jeFfUaOoXXr1sZJURRFUZQ9nf79B9CShfPi1Bz0643dn+H7DKPLPvAuOufMU+iMU0+kiRPG0bC99qJ+fftSz549o9ajRw/q06cPDRo4kMaMGkkHHjCZRpjrxoweSXPnL6T16zfEd1MURVEURVH2BKp+76EPShVFURRFaSn6oFQpy8TxY+jTn/gQnXf26dED0v79+0UPRvldpNy6dOmSajzGx3v16kl77z2MJk+aSCOG702zZs+lVavXxHdVFEVRFEVRdneqfu+hv8xJURRFURRFaRv4naR/9YlL6ezTT6a9h9l3kOIBaZHG5/N1fD3fh++nKIqiKIqiKEXQB6WKoiiKoihK2/DhS95NJ594HPXv35+6d++ePADFO0fhvHG+jq/n+/D9FEVRFEVRFKUI+qA0l4l06jsvpkvffnicO5Zj327mfufxNDnOhRkzmQ4cE/cVRVEURVF2EY4/5nA6+YRjaED//tStW7fo4ScaHoLmNf98vp7vw/fjXwilKIqiKIqiKPVouwelR5x1AV36vtPp2DhbMh5YTj6eLn7fBXTuoXFuOlPpwssupU+/5/g4dyynvsfMfdnZdFici3ESfeHLf0P/8pVP04XxiKIoiqI0zHs+S9/64gVxUJTW8qH3v5NGDB+eeiepfBAKo/njsvF9+H6XmvsqiqIoiqIoSj3a7kHp4JMvpE9/4v10of9MdK9T6NJPXUqf/ti5dGo8FHHiRfS5T1xMZ4+Lc9MZSCPGjKKxIwfHuWMZOtLMPWZvypz9nZ+mr//7p+niOFp60YT9DqbDJ4+j3vGIoiiKopTnAvrKf/0rfeuTF9PFpx8SjylK6zhoymSavO9E6t+vX/SQUz4IRWPysryW78f35fsriqIoiqIoSh5t96D0hVXdaNjYKTTFf0vpGUfSseNG0dgDD6XzvAeoRxy4P00a3Zc2PxsP7GkccQq9512n0FFxtDxD13ztP+kfvnodPRCPKIqiKEpxjqfP/Ou/0rf+68N06UXn0sXj+sfjitJaTjv5OBo4cGDuQ1I0n9BxeQ++L99fURRFURRFUfLo0m/QyJ1xvykcfMRxtGjRgjg1wH7/f3roxjOp221/Tcd/8clo6NTv3EjXHryeZg0bRat/dhFd9O1V0fjfX38/fX7sc/TFE79Af4hGiIYeejpdePwUmjC8P21dvohmTL+DfvPIovio4fAL6Aun9KGXv/MAbXzfeXTqvkNoxXO/oR/eOs8cnExnXnoSHTlmGPXZsoxmPLaNzvv2Z2na0htpwju+aq+vYQSdcPHZdNx+I2goraPFs56k3//2SZofHz3i4o/TWX3foK/fuoouvHAaHTJmENHKefTCg7fQjc/ZdVgG0wnvvJCO25/vs5pmvfQIHfiZH9HFez9F/3ns5+lH8VmAf37pgR/4OP3bsd3phatvpIfM2OLnr6Br7yGzhs/SkfQCff2XD0bnJjXct4kufcfRtN/efWjj0pn00A1/oIfpkHRdd19HN74SXRYzmI4462w68ZCxNLznRlqxeAbde+MdNH15fFhRFEXZbXj/F/+Vjt17Ip103hTau0c8yLyV9+dgfUaMGEUvTn8sTs2h6tcbXXoOo677nEFd+oyOR5TO5qovTaJjDt+fevXqFXxAWpSdO3em2o4dO2jz5s30yquv0wuvvBmfpSiKoiiKorQbc+fNp1tuv5uWLF0Wj5Sn6vce7feglC6h3z7+BTp8/q/p2Iu/QyvoEPrGzVfQmfOvpMcn/TWdOOe/6JCP/dmcdzz96L5v04mLf0Env/8HtNSMDD35w/R/L387HTOiG61YvJF67j2Ueq56lW694cf09d+9Ed2dPvgNevhv9qGnfjKPxl94AA3q1pXeuvWT9InvDTPfIH6OPnz+fjR461bavmUdrVuzk/Y+ZH/ae27WN4iTzTWfpA+esR/1WbWYVtFAGj54M730px/Tv/zoUVM70WXf/yP9w6g36NoZg+iUyQNoQB/T+hOtfOUv9J+XX0X3RfcZTKde/rf0t+86mob32GLm3khrNqylAeMPpbE7ww9KP/nVq+miYw+kQ8yHb+kbC2i1GZtx2wfor35I9H9/fRe9l/5Ch3zgO9G5toaZ9Mv5g+iMCf2od68B1L/vZnrr5t/R3QNOoQsOGUB9epi6Bpq6nr6e/s9fX0ePR1fauj533iFmT5bRig1dae/h/WnRI9fRf/zHTZR6nqooiqLsspx6+T/Qhfv2oSPOOJcmhN5Auhs+KO0+4RPUde/TiXoMikeUzuaPf7+O9h3vfj6p/4C07INSmB+Ssrdt20aLFy+mnr31HdKKoiiKoijtysqVq+nWO++h71/5k3ikPFW/92jD33p/H72+aBv12HssRb9Caa9T6OBR22nuK3fQ03PX0YAJx9qfU7rXNBo7hGj53Fuih6T8S4w+/en30/n7bqGbv30Fff27P6Cvfet6mjHoeLr0kx+mS/FjqXr2pQEDx9G0i8fRnBt+SF/97+/Sj+5YSZM/8GH6zAeOoGELbqOvfuPb9B/f/hH94M7ltCXnMbK95mDq+sCV9M3vXWXaD+knMwbRaZd9iC6Lf0RA7379acD4KXRyn5foR1dcSV/9+v/QdfN60ORjzqYLp9lz6PRL6QuXnUEHbH+arjRz//s3r6T/veYlWrk9Ph7goT/9kl5axSesope/Y9ZrGr+blOnX38zZv68NBlvD/nRi1+foe/97Bf3H135Kz27Yhw6/8L10/vA36Xfft3XdtrgPTT7xHDoXexXVdQrtPf8G+p//Nev7wY/oG3dtoskXfJg+dn58jqIoirLrwr+o6b/+lb502QV08UUZD0l3U/QhafvRu8eO6AGppMxDUsY/H32+Lz80VRRFURRFUdqXIUMG0blnma/TO5E2fFC6gK5/awXRwOE0jR/YnXEQje+xlF6/cSnd/sI82jh0kv05pSeNo5E91tGCl2ZGV9FZ59A7D9mLljz5PfrJLQ/Qg08+RffcfiN9+3ZzfNwJ9N7zJ9rzInrS+pe/T9/7/T10570P0GOvTaR3vusEmtB9Lt31rd/Qzfc8EI3f/LvnaeGW+JIa4ms2vEpX/uxOuvOx5+jJxx6mP/zwdVo97BA6/aT4NKbLKnrmyuvppjv5vvfRLx5fQBt7DKWxB9vD5154Dh06bDU9ffU19LvonAfotluupzf5baIZvPrEA/Tmmq2mt4EWmfPtOuyxIF1W0NNXxDXccyv9ZeZq6jaoGy36wS/pN7fbun7ME/YaRhP9uvovoPu+fyPd9tB0evLJJ+i2Xz1FC7qPpZPOONqepCiKoux6nPhh+k/8oqaLzqVDhvn/zn7PYOe2dXFPaRd2Uvrdo2UfkErkfareT1EURVEURWk9a9d37tfpbfiglOil5xfQ2h7DaeLRRKdOG0P9lr5FN79JNPdPM2hxtxF0wKmDafKR+1D/LYvp9bvii44dR8O6b6KFr04n9xNJN9BLv59FK6kvjT5wajzGbKHFrzxBb6yPI02lg0f3JVoxk659xr4/NWL95px3lMbX9B1HF/4N/9IL2/7jI5OpP/WifvvEpzFb19D8l9x9l67YQFvNOf3jc44bP5S6bVpI0++ZSUlJtJS2bIu7zSBVwwZ6cBXPtIGWT1+QzDlrMz947UE9Btoc1dVtMB35kS8n6/vW3xxNo7oR9Rs8yZ6kKIqi7GIcT5/51PvpfXv4L2raPv/3tHMjfqK40g5s2Nwleddnsx9q8n31QamiKIqiKEp7M2fefPrVr38fp86hDX9GqWHsP9DtN72Tet3yJXp46jfowpU/pOM+eh2tN9/cXXXvt+noGf9BP+j9JfqXMU/TZad9maJfWfSfv6ZZ79qLHvnvs+iSa6K7WPb6v3Tfg+fR0Mf/l972seuIPvJ9ev4rB9CLXzfn/Sw+hy6h6x7/Ak1bcQsdce7/i362qCUeD/4yp/jYukfpi/97RzzmWDvnVrrzGaLLr7mT/vmg19I/ZzSq4Sha8sfj6Mx/Jvr6Xx6j9wd+aVPWOIjufexy+s1BH6CvxGNMdB25moM1RPtF6Wuxh/He2PnfoF9+9TqaHp8Cti59lW56JH43r6IoirIL4f2ypvd8jr515DA7nMdu+DNKqXs/6tp/iv7z+zbiO185k0498Sjq27dv9FAT/wy/kQec+Dml+BmlGzZsoMeeeIr++Bf8DbuiKIqiKIrSbqxYuZpeeuVVWrfOvY2wLFW/92jPB6V0LP343u/SiQvupOcnn0R9f3MJveN/+J596ZPX3kj/NOIpuqfbqXTi0p/QUZf8yL4j8uNX0gt/P5WW/PELdMY/P8MjEUM/eAU9+C9H0NI/fZBO/f/NzHhQOpH++7br6b1DptO/nPdZ+kXyG93zHpTG1/R5gD5z8T/QrRm/Bb7Ig9JPXnsX/Z/Dl9NvP/5++oen43MMnf2gNKrr0AX0k0s+TP+hv7lJURRl9+Pwk+nicf3o1E9+hS6c1DseDLA7PihV2o5L3nsR/dUnLqVRo0Y25UGp3xYsWEhX/uSXdN0N/AtBFUVRFEVRlN2Vqt97tOU/vSd6gV5auIl6TziK9u+zlF75E74R2hD9nNItww+lI4cSLZ55R/LPxunO39PDC7vQ6BM+Tl84/xAyh2n0tPfR379/P+q39g2647q8dz/OpJufnkfb+u9HH/j4uXTsGDM05nC6+LMn0eTM7xtn0s/vfIPWDjmEPvf599EJfI1h6KTj6f3vPD6avyi33/cmregxgqZ95H106qTBRHtNpFMv/QId6f/z/QDPr+Kf2zCYxpw7wngEjY5raBa3//Ypmt9tDJ37+Y/ThYeauhjel4vPpgNtUhRFUXZlnnmA/vDnW+l7//Wf9MV/+nd6fEk8riidwL0PPELrN6yn7dvtb7PkB5zNgO/H9+X7K4qiKIqiKEoebfqgdAPdOHMp0eAhNHjlnOjnk4Lo55T2GEJDe6+iuU/OjkcNcx+mH1zxJ3p6yxT66F99nr7KPy/0by6h84cupluv/i5d92J8XgZP/uIn9JdXd9DEd36UvvJ//pW+9X8+S584YyctWxOfEOCFG75L37t3GQ0/+xL6Ml9j5vzqP32aPvqOo8n/iaj1mHvbVfSLR1bSkGMvoS/90z/St/717+gL7zyM1q3aFJ8R5sWbH6M31g+kqZ/hnyH6ZfryxYfER5rD3Puvpe/85iXaedh76a//3tTFP6PU7Mun33MiHTkuPklRFEXZ5XnjkTujB6Zf/3//Tl/8pzvojfw/fhSlJSxcvJQeevQpWrFiReohadkHpvJavt/D5r58f0VRFEVRFEXJo1vP3gP+Le43hX1GjqF169bGqXFWLllAc555jG6/6yF64K3FxL9mKGLlEpoz9zm6/5776J5HZtG8DfG4OWPJnFn01ow36IU1O2lQb3Pqm4/Sr393C91811M0A9/0rVtG8154mh566i2aszIeM2xdtoDenD2TXjA37NKtK22e/zzddOOtdNsD0+nhR56iV2YG3maz2lwzcy698dIrtLrXUOrVZRMtfvE+uuX+5+nVWUuIn7GuXbqInn38CXrijfnuZ59GNTxB9z86g97i3zy1biHNMvd55flltLV/Vy6cbr3JzH3vE/TIo+Jaj62LF9CsN1fQ+m7mG4LNi+mFB56h5+evpRWL5tPj5hsC1BysgffxqSfp4ddm08J4yI49Tvdjb7Yuo9kz59Drr75KC7f1pX7dd9Cat+6nW+4288xaQMv0G2lFUZTdioUzZ9Arr82iWXNfoofuHUSHnTmSol/3tPI1+s510U8Eb4j+/QfQkoXz4tQcmvX1htJezJu/kI4/5ggaMngQde/ePRrjf3pf9p/f8wNSbps3b6aZs+bQ/3zvR7Rqdc7ffiuKoiiKoii7BVW/92jTn1FakcF709gBPWj7xlW0YFnyJLUY/faiUUN7EZW8dvCIUTSgx3batGIxLW34Z84OpBFj+1OPreto7qKiX8zH12zfTCsWLHc/iqDJ9Bs2nIb26UZb1y6gRaviQUVRFGU35hA666IxNIB/4dOQR/RnlCodxnsuOpe+8NmP097DhlGvXr2Sh6RFHpbi3aR4SLp02TL63yt+Sr/7863RuKIoiqIoirJ7U/V7j7Z9R2klNm2gNWvW0toNyftQi7N1I61t4NpNZs1r1qynRqZ0bKZ1Zu416zbHuQjxNWs3unfdtoCtG9ZHe7pO30WqKIqyh7CE3nptBr0yax7Nefa58L+sKIi+o1Qpw7wFi6KfKzp5v4nUs0eP6J2l/kPS0ANTPCBluL9p0yZavmIF/eL639ONt9xFm7dsiY8qiqIoiqIouzNVv/fYPR+UKoqiKIrSHBbNrvSQlNEHpUoZ+KHm7DnzafXqNTRm9AiinZS8szTvXaX8gJQfsC5fvoIWLFxI1/7yBrrp1rv1n9wriqIoiqLsQeiDUkVRFEVR2hp9UKqUZePGTTRrzjx6481ZtG3bVtpn2F60cdOm1M8tBfxwdMuWLdE7SPnh6u1330e/uP6P9MDDT+hDUkVRFEVRlD0MfVCqKIqiKEpbow9KlUbgd5bOmbeA5sxdQM+/+DItWbqMtm/fRjt37IgejPI/r1+7di0tNeOvvPY63XXPg3TDH/9C9z/0uDn/Ff3n9oqiKIqiKHsgVb/32D1/mZOiKIqiKG2D/jInpRmMHL43DTdt0IAB1KNnD+rapQvt2LmTtm7ZSqvXrqXFi5fSQtMURVEURVGUPZeq33vog1JFURRFUVqKPihVFEVRFEVRFKUjqPq9R9fYiqIoiqIoiqIoiqIoiqIoeyz6oFRRFEVRFEVRFEVRFEVRlD0efVCqKIqiKIqiKIqiKIqiKMoejz4oVRRFURRFURRFURRFURRlj0cflCqKoiiKoiiKoiiKoiiKssejD0oVRVEURVEURVEURVEURdnj0QeliqIoiqIoiqIoiqIoiqLs8XTpN2jkzrjfFA4+4jhatGhBnBRFURRF2dMZMWIUvTj9sTg1B/56Y+PGDXFSFEVRFEVRFEUh6tOnb6XvPfRBqaIoiqIoLaVVD0oVRVEURVEURVEk+qBUURRFUZS2pVUPSgcM6Benjmen+eqpS5c4GLJyGQOZd0ewbhDaj3zvNO6SsjnCd6rJ2Qb18p4A9gX4+9SogcwdD/53A5CreHcG6wR5+1DUQObG4BuYGyUgVzGQWWk1+N8JCP3vp6zbCy7IFJaAXMVA5j0R7AsI7Zczvm6QOW1z1ByuzWEDmXdH8vahqIHMzWTLli36oFRRFEVRlPalVQ9Khw0bar7ACn2Ba22+zDHNz8CO707w8vgLToCc7ex9k/uXbVAvtx+8PN4HgNy4TSdat8xVDGTufLL3obiBzBYeMAcSsnIVdxxYNwjtR1lXg29gbpSAXMVA5vJgnSArlzGQuTXwBGaiphrIXAvWDUL7UdZA5o6BJzQTJyBXMajN9s9BM2qGGzWQuT3hAk2hCcjFjK8b0tkkE93XFcjFvGvDCzALSUjn8H6Vtd0nGMi8O+L2AesN79OGDRvb70Hpxg3row+7oiiKoih7NvylYJ++/Vr2oLQo+IIKuC+w5BdaMmfbrq42OwOZdz14eWaZCcjZxr7InLdfZd2+YN3A7UPjBjbzgOkkIFcxkLnjyduHogYyF4MvMBdWMqiXq4N1g9B+lHVz4RuaGycgVzGQuT5YJ8jbh6IGMrcGnsBMlJCVqzibvH0oaiBze8AFmcISkBsz/txLZ5NMbNS7NrwAs5CCDu+ftDlqDtdmayDz7kix/apn7J/NQOZ2pi0flPbv19duYLTB9T8QDnxA2wcuz5RZyrsXvCCzsFIGMjefIh8P6TQ8YA5UcvMoUr+04sMbYjamlIHMAfJuk+U2pkz5nbOcvMqM8R9CKiPKnOFOJa+w/D835Z+fWG47Ea2Cl2PWsnXrtpY8KN1rryFxaj346ADeb+x72EU+fnzH2pxtIHP7w8szy0xALm7sk8umx3eqYCBz54N1Arfuxg1ktvCAOZCAXMVA5taTtw9FDWQuBl9gLkxAbqabB9YNQvtR1q2FJzATJSBXMZC5FqwT5O1DUQOZOwcuwBTSVAP8OWhGzXCjBjK3J1ygKTQBuZjxhlVAdQAAgRlJREFU5186m2Si+7oCuZiBzLsmvACzkARk6/D+1bPdl9psvTuDfQDh/SlnfDxcbj0bN25qvwelw4YNoZ07TIj+h1Rk44DdwHaCyzNlekbdMvvrwTqy3Hm4uovadOquRxrI3Hzq11/rNDxgDlRy8yhSv3Q+fII5MQG5jCtQ5PbSHmVOt7nIFdJA5gB5twk4+jiZLqhzetBpcATkXZlljzKnN//yAuTd2Rj/IaQyosy1buTjA2RujLyZjLG+PAMMx5Hho5zzDGRuCqYg/nORa636t7oh0g9KW7KCDgUfVuB/mMPO+nrEZdPjO1Vw+4J1Arfuxg1s5gHTSUCuYiBzx4N1g9B+1DOQuRh8gbmwqQYyN5+8fSlqIHNj8A3MjRKQqxjIXB6sG4T2o6w7Fp7QTJyAXMVAZpNM5HWCvH0oaiBz58AFmEISkJtj++ehSSY2aiDzrgEXbApPQA4bXz/InLY5ag7X5rB3Z4rtVzkDmXdHeHlmmQluH/C/nyL7ZfdfZuc2fVDaTt+48L6Z/UtAdm5s4y0ytx7UDZCzbTpRnQC5jFtH/fprDWS28IA5UMrNA3WCUP3S5eALvAly1wUDmetTpH5pIHNj8A3MjUo5m7y6s9xaeAIzUSkDfN4yo2a4qDsWntBMnIActv952NkcNYez3F7YdcDh9eSvr51xdaPevHVZg6pfrISo945Snp7rBMjOWetx2fT4yng8O2d718XtQ6M2nWgfZK5iIHPng3WD2v0o73z4BHNiUw1kbj5YJ8jbh6IGMheDLzAXJiBXMZC5Onn7UNRA5tbAE5iJEpCrGMhcC9YNQvtR1p0LF2AKSUBujvnzNa8T5O1DUQOZ2xMu0BSagFzMdv9kNslEfP3gcthA5t0DXpBZWJ39K2e7T8gA47sz2AcQ3p9yduDjsutR9XuPrrGbCm+wbS7n2fTquHGSKWKQnW3HN3f92mzXN3fk+jDOWMuc7eIkt4xxNWTZ1olmXuM+6q/vdGPKGshsRuKhMkYD6XOKrEfab0woF7Odw4GcZ79hLNtuDmRLntEYP/tjYafnrJ07na3RQOicPKfBYBn7jXG2c5Sz36LRks4HJ5UxmnmNxHXaMZez7T4nRIPxseIuh7wIOWxXk2+uF45GU+ZjcDRa40iBbN1ccFPMVcTp9XEfzZ1T3K3EzRUpswbfaJ2BnBbZGXVGCmbuu/F0Nr0o55s7WWasZS5uUC+XByU1brsPyKYXZ2vkcuaOy+nGhHIVA5lrsTU5kKvYb3LMvJa2pTY7owE5FsqN2dbgQK5iNCDH8u1qSucy+yeNxvg5NMYUd1aNaZtejtFk9seqGNgsBpNcxWiMn/0x63QN9fYty6bnZb9Fo4FcxfnIk5CbY3yuRXOfe2GjkvY/90ev0ZgdR7aWuZiBzI0hb4JczFhDOvP6Q9nfJ5e578ZdtucUcaRAtgYydwyYNOz6awsZ+8fZjmHcjtnszqnmdkLWVK/2eja9qG+byxzS58qcbT43ehXZGcjceXTr2XvAv8X9prDPyDHUp0/vONWSekBtsE+u7bg1srPdsDx3HlF5wliPzG490RHPcj3SQObm49cNp+uvdRo5wNmv289Zbh6oz3e59dQDF/jGOrIMZK4P6vPd3PVIcAPfft1+znI2qM93a9cjkTdE9o11ZNmB+nx37HrqgQl9Yx21xucrK/xNo83tsR6JLIAz1sP12eyb/8BO266jPdaTj11H2rXrSdvh9qUj2LZtGy1ZOC9OzaHe1xudTWq7Dci+/f+d+Tns+ESDy6aTfBzzcpaBzJ2Pt9wIZN/5+1VrYLM3EBHK5sRk3M9F3Xlgeb79ffBzloHMxZAXIPs2Ny5lIHN15PqQffv74OcstxZMkGdTSCkDmWuR60P23eh+ydwxyAmRfZvCgjnLoDbj8w/WGXJ771dZZIGcTeHJeF7GfslsUiR8nYNsnbVvu8Z+1UMuIJ3T++Wyb/d1YTo783W1+yXz7ojdFwey79r9Spv31ZzpZYvMuyNYnm+zbC/zvkhj/2yu3T+bsw1c3rZte6XvPVryjtI8ePE+vHjrSF525i5segFzB7bn+pn7+QYy1wdTFLetGza9AuaOy+nGlHU2do5y9pt5TR1zOdsW335jyhrIWorZbxjLdpH1SKMBOZbt8Jyml2O/Yay441DJfmOcMUe+Tc+z3zBWxmnkIHIZu2bnYMucbdeiwfhYcZcDFxWzW0coZ63HORqtcaRMV0PeBNk6uybfbn0Y4xg+N+RISW4l9WuptWvRUHwMa669ho9FryI7dy5F62/cjZOUEIPcuO3HDdn04oyPZyPmjsto6f1DY6oayFxLUkIMchWjWbBujGXlbFtqs3NeY6oa1GZbk+lVMBqQY/mWNWTvX3GjMX4OjTHFLWsK145czH6TY8hlDGzGYDONBuSYy7IG5HI2Pc9oQI5VNZDZIgeRqxiNSWdbA++DHXO5uOXnfmR8/o7VsIHMjSFvglzM6T+TkHm9oZzelyzzuXA0GshpRwrksFuLnCSdUSPGs9eUZ94PZzTOjM04txFHSjKQuTPIqqmMuZvO2L9oJO67sXDONp8bvYrsDGRuPSgp27aTZ+4im16S7VDI3EG251alJe8o7du3D/GDX66v2BNhIHPrQZ3A1V3UplOznnruOOrXH59okNnCA+ZAKQOZy4M6Qaj+es6HTzAnJiCXcXFQF8irW9Yvc2PwDcyNSjkb1AlC9UsDmZsD39DcOAE5z8D9TRYI1S/duXABXHDYdj3Stu4sA5k7By7AFCLWI3N4fTa3M65u7HdoPWljP1xuH7gcU1bQu8I7SlEvyFtPMZtO9PECyFUMZO58sG6QvS/ZBjKH4RPMiZUM6uXmg3WD0H6UNZC5GHyBuTABuYqBzNXJ24eiBjI3B76huXFCVq5iIHMtWDcI7UdZdy5cgCkkAbk5tn/umm5M3j4UNZC5PeECTaEJyMXsf93ibI6aw7U5bCDz7gEvyCxM7JfM+bb7kpX3JLAvIL1PxYyPh8sAH5fdB16eWWYCsjP2oV7O2z+bs9162vYdpbyJ1vbJLltmOySfAPs5Goj60avI2QYy15LcMga5qE0v6qfXk2+04uup52wwRZ7RQHqsyHqsXUYDcqy47T0dyEXN5J/j5kC21K4v235jsh2ew/Ti8XS2RgNyrJ7TYLCM/ca4bOdw48h5RgPoFzWQ2SIHkfOMZl6jm7pm56i3HudoNOqnc56rgZuE7WrwzfXC0WjKfAw5erUhx5ESNxfcND2nzGljfZztGJodK+dW4uaKlFmDb+4im17cL74+PjdtIHN5MEWWW0vz6gf11lPf+HhhzH38/FzO3HE53ZhQrmIgcy22JgdyGaMBOZZ23j6FbfEzGiMzI8eaZ1uDA7mK0UDonGy7mpAttftX3GiMn0NjTHGna66tPZ3DRpPZH2vccTDIbEGuYjTGz/6YdbqGrP2rZ9Pzst+i0UCu4nzkScjNMT7Xotma2DIXt/+5P3qNxuw4chUDmRtD3gS5mN2afPP6Q9nfJ5e578ZdtucUcaRAtgYydwyYFLWFc76xX6Fsx9DsWNn9y3c7IWuqV3vI3EU2vXjMjiM741yZs83nRq8iOwOZm09SQgyys+3Uz87cRTa9KOebO1lmrGUu7up0+M8oLYt7cl2bw+YTTYdfUznPQObm49crc5bz4RPMiaXcPGSdofrLrUfCF5gLSxnIXB9ZZ6j+auuR8A3MjRKQyzibIvVLNxd5Q85molJ2yDpD9bd2PfXgCc3ECchh27+JM8lE/oMgncNuL7ggU1hqPfXczutJU2w9aTuwL+0Dl2fK9OzXH16vW0eWi1H1b3VD8NcbvXvbrzdQf/166xnI3PlEy/PgbNfduPPhE8yJyT74uVEDmZuPXJ9ct5+LGshcDHkBZ3OjSgYyVwfrBqH9qGcgc2vgCcxECchVDGSuj9wHPxc1kLljkBNyNoUk435u1A65vtB+lHV7wwWaQhOQi1n+eWf/3DaJo8hFDWTeNeEFmIVEZvxc+3WOn7Nt98VdZ8H47oy/Xia8P+Xs8D9Ouwep5Rk4m2Un4362xr7I7O+X3SeZsw1kbn+wDwC5uE3HrHv79rZ8R6mpMGUgc314seVsn1Qjm17czzN3XE43pqyBzGYkHipjv2HMOW9d1hbffmPKGmAO0ythv2Es20XWI40G5Fi2w3OaXo79hrHijkMCchn7jXHGHPk2Pc9+w1gZp5GDyGXsmp2DLXO2XYsG42PFXQ55EXLYbh2hnLUe52i0xpEy3VxwU+vsmnyn18d92J2T50hJbiX1a6m1a9FQfMyOuezMx6JXkZ2bB6Zwtp282rjL2Q6FzB3Ynuubj7USngZToU6Y5y5v7riMll4PGlPVQOZakhJikKvYb3LMvFa2xTcakGOh3JhtDQ7kKkYDcizfrqZ0rt0vmbONxvg5NMYUd7gm04vH0zlsNJn9sSoGNotBr/bGjcb42R8LGzWVs+l5GQ3IsarOR56E3Ey7ZmuqzY06ejWyzWVrmYsZyNwY8ibIxZz+MwmZ1x/K/v7I7MzHkKPXONtrQo4UyGEDmVsDJgk7e0155v1AtmNu3GV3TjUDETuF2prCNZexa9FIagznYNzl4jY9YSBz80lKiEHOtu3UZmfu+mtDtkMhcyeUo4Ea87HoVeTiBjKXJykhBrm4se5qtPwdpXiyC7Kf/GbZdKINL2Mgc/OpX398okFmCw+YA5XcOKgThOqv53z4BHNiAnIZN06Z+outpx58A3OjUs4GdYJQ/dJA5ubANzQ3TkDOM8DfjMXREKpfGsjcMfCEZuIM2/VI2zprszWQuXOw64CxDpnz1tfOuLqx36H1pI39cLl94HJMWQnIztnryXbH0Lp3lPaKU8eupwi1H5/yBjJbeMAcSMjKVdxxYN0gtB9lXQ2+gblRAnIz3ThYJ8jKZQxkbg08gZmoqQYy14J1g9B+lDWQuXPgAkwhlQxqs/1zxoya4UYNZG5PuEBTaAJyMePP5XQ2yUT35zZyMe/a8ALMQhLSObxfZW33CQYy7464fcB68/YpbPvx8DOw47sTvDyzzATkbBffv+IGMrcfvDzeB4DcuE0nWrfMVUzt+o5SBy/WB7moTS/q8//o7FgRcweZsdk2pqyzwRx5RgPpsbx1WFtqs7PfmOK293QgFzXDfTRkZzcHsiVvPdJ+Y0I57PxarNGAHKvnNBgs4+xm54heIiPnGQ2gX9RAZoscRM4zmnmNbuqanaPeelyOXqMxO46c52rgJmG7GnxzvcjRa8p8DDl6tSHHkRIDmRsDN8Fc4Zx2en3cR7Nj5dxK3FyRMmvwzV1k04v7xdfH56YNZC5PMkUMsrPt+OYubHoBcwe25/qZ++Xcsbh6o5S0dP1oTFUDmWtBCVWMJrMdw7rr5WxbarNzXmOqGqAG4HIV+02OIWdb1uD2CbZUsd+YUA5b1hDOphePp3PYaECOVTWwGYPNNBqQYy7LGpDL2fS8jAbS55Q3kLkYuKiK0Zh0tjXxuu2Yy8XN1yJHr3HG5+9YDbu5yJsiF3P6zyRkXm8ou32pzc58DI5GAzntSIFsDWRuDXKSdEaNGM9eU555P5zRODM249xGHCnJQObOIKvGMuZuOqNFI/ExGOfKnG0+N3oV2RnI3HySEmKQs207eeYubHrBnDZ3kO25brw2c7+aGycpIQa5cdt1I5tenLEfjTjqVKIl7yi1PzMs/US3VUQPnD2QffM++QYyWzDgG+vIcvOQ9ci6/Ryuvx7yAs5yPdJA5vr4dcNF19PY+iR8A79uP2c5G1lPqP6s9bQGTOAb68gycH9DiDpl3X6W6+n89TF+dn+D51vWn7WezkEWgGwdXk/++toZW3faWE+WHWaBNfvVuaA8Z/nx8Ndjs1tHljuPqDzh9Hqk4xOTutlYR9pV/1Y3BN5RijL8+mTOcj44wbe5MJizDOrl5iPXh+y70f2SuRjyAs7mRsl4Xs4ykLk6WF+eW7tf9ZA3DGUzcTLu56Iujlwfsm9/H/yc5c5FFoDs2xQazEXtkOuV++Dnevslc3uCAvNsFpJh+ecp/jyy8v/cNamggcy7JliAb7OwOGP/fLuvu+T+hTPA+O6Mv17G3ydkf1/8nGWH+7jsLqSWZ0B25n0IZ2t/v2zGPrn9sznbuy7R8uo4vV/S8YmGdPZtDpQykLnz8ZYbgdyW7yjlDwhvIpxuTFlnY+dwIOcZDaTPyXsybW2ptz6Z8wxwTwdynv0mx5Cd3RzIljyjMX72x8JOzynnDhsNhM7JcxoMlrHfGGc7R21O2/S87LdotKTzwUlljGZeI3GddszlbPO5yNGrVWGXAxcVs12HNNcLR6MpYz3ha9mRMl0NeRNk6+yafKfXx300d06eIyW5ldSvpdauRSPxseLr43PTbh6Ywtl2arMzd2HTC5g7sD3XNx/LN5C5PpiiuG3dsOllupXYORqz3+SYea1s1xiZGTnWPNsaHMhVjAbkWL5dTens75e1zNlGY/wcGmOKO1yj6QVz2Ggy+2ONOw4GmS3IVZzXGOd0DVn7V8+m52W/RaOBXMX5yJOQm2nXbE21uYz9z8XRazRmx5GrGMjcGLhJY5Z/JtrM6w9lf59kduZjyNGrDTmOFMjWQOaOAZOGXX9tbOxXKNsxjNsxt4/IVdxOyJrq1V7ErkUjqTGcg3GXi9v0hIHMzScpIQbZ2XbqZ2fuIpteku1QyNzJsukJ87HoVeRsg3q5PCihcdt9qM3WpteAueMyWnq/0JiqBjLXkpQQI3OjtOgdpfiZYeVxT8KLOw0PmAMJyGXcPIrUL10OvsBcWMpA5voUqV+6GnwDc6NKzqZM/c1Zj4RvaG6cgFzM+Js2EKq/njsWntBMXNBYX5H6O2c9Ei7AFBJj6+dsx5HTtnXXZut2AnWB8HrStvvh5/aByzFlJSA7F1mPzc5A5taDugFytk0nqhMgl3ExWveOUvcz0YvVW9YdBz4uIPvjVtzV4BuYGyUgVzGQuTxYJ8jbh6IGMrcGnsBM1FQDmWvJ24eiBjJ3DlyAKSQBuYoB/hyIoyG0H2UNZG5PuEBTaAJyMePP0XQ2yUT35yxyMe/a8ALMQhKQrcP7VdZ2n2Ag8+6I2wesN2+fwuZ9N1d6Gdjx3QlenllmAnK2i+9ftkG93H7w8ngfAHLjNp1o3TJXMZC588neh+IGMlt4YJf5rffS2fBiy5jhPpp5Fef4T8LDtvj2G1PWQNZSzH7DWLbjkMzlcrbRgBzLNuZI2/QyzHAfDbm449CwGe6jMc6YI89o0YiX/bE8A5ktchC5mPkPI9u3zc7Bljns6NXINpfzDGQuBi4qZreOLNtzouhdg3HktCMlBjI3RvommBPj4ZqwHpndGMf0OdkGMjcDect6tbC5i2x68RjWGL7GN1+T78ZJpohBdrYd39z1a7Nd39xBxrkYZ6xlznZxklvGuBqybOtEM69xH/XXd7oxWW4Ntg64eN2wpTY7+40J5SoGqMGBXMV+k2PI2XY1pXPeftUzGuPn0BiTbVlDuEbTC+aw0YAcq2pgMwabaTQgx5xRQxWjRSNe9seqGMgcRp6EXMVo5jUqwjVbE1vm4vY/10av0ZgdR67i5iJvilzMWFM68/pDOb1PIUevJnBO3yPPkQLZGsjcGuQkyNaoUeZy5v2B7RgyY7M1chkDmdsBV2ukmtqLmLvpjBaNxMdgnCtztvnc6FVkZyBz80lKiEHOtu3kmbuw6QUyd7KyPdeNIzvzsWpunKSEGOTGbdeNbHpxxn40Yu64jJbePzSmqoHMtaCEKkaT2Y5h3dXo0m/QyJpnsFU4+IjjaPDgQVGfC8x6wlvOzQNPooH/ZDrL5eALvAly1wUDmetTpH5pIHNj8A3MjUo5G9QJQvVLtxaegAsqY4C/WTOjZriogcytgScwEyUgh538TaH5P/MpKM7mqDmc5fbCrgNO1pPrdl5PGlc36s1bl7UD+9I+cHl2HVkOrQfryHLnUVt/PZtO3fVIA5mbT/36nbds2UIvTn8svrI52K83BsapPEXqrmcgczH4AnNhAnIz3TywbhDaj7JuLTyBmaipBjLXgnWCvH0oaiBz58AFmEIqGdRm+3ndjJrhRg1kbk+4QFNoAnIx48/BdDbJRPfnJHIxdzyH0gc/dzyND/3DxG2bafXCV+mBOx+ll1bFY7nwAsxCEtI5vF9lbfcJBjLvjmAfQP4+hW0/Hn62yLw7wsszy8xxI/tnc7aBzO0PL88sMwG5uLFPLpse36mCgcydD9YJ3LobN5DZwgPmQAJyFQOZi7Nly9ZK33u05EHpkCH2QSmTt+FZBjJbeMAcSEAu48ZBnSBUv3Q1+AY8YRkDmWspUr80kLkx+AbmRpXsyKs7y62FJzATJSAXs/3EbpKJRd25cAGmkNz1SNu6a7N1iuHj6ICeK+m1uWvjATO072Tai7+o3rySXn5rmR1sGViPJbye/PW1M7ZO3/XXZ/fDN5C54+Hy7DqyXGQ90kDm1lNbfz2bTlQnQC7j5rB5c6selPLXG0Xqr5erg30H2R+X4m4ufENz4wTkKgYylwfrBnn7kmUgc2vgCcxECVm5ioHMJpnI6wSh/ShrIHPnwAWYQhKQm2P7ed8kExs1kHnXgAs2hScgh40/J2VO2xw1h2tz2LWcR9/89SV0eD/T3bGdtm23o9StG3WnbbRx9WKa8dTd9NP/vYOejw+1K8X2q56xXzYDmXdH7Lp9Y19kzrY5k+9Uk52BzLsevDyzzATkbGNfsrPp8Z0quH3BOkFWLmNgMw+YTgJyFQOZOx6sG4T2o6yBzGH4BHNiHVd9k0YH/dZ76eaRt+FZLgdfYC4sZSBzfYrUL10NvoG5USVnU6R+6ebCNzQ3TkAuZvsHh+nG5NWd5Y6FJzQTFzTWZ+uV2Rp0zno8DjybPv+Rt9NZRx1I47rNp+lvrY3qfccnPkfvPOFgOnJcN7r1kde9dWQ5vL52JH8d1vxxNGd6GdjxzoTLMWUlIDsXWQ/WkeWOA3UD5GybTlSnzGXcOrLrzjaQ2cID5kDA27fvaOlvva9Xt6xX5mLwBebCBOQqBjJXJ28fihrI3Bp4AjNRAnIVA5lrwbpBaD/KunPhAkwhCcjNsf28bboxeftQ1EDm9oQLNIUmIBez3T9pc9Qcrs1hA5l3XSbT2e85hEb2JNo091H65k9vpocff9q0N2jl4HE0Zdw+NHLMCOo27w56cE5o/4rup817EtgXkN6nYjY7x1d6Gdjx3QlenllmArIz9qFezts/m7O96+L2oVGbTrQPMjfT7QPWDWr3o7zz4RPMiU01kLn5YJ0gax+qfu/RsneU+oXmwyfwSssYyFyerI0N1V9sPRK+wFyYgFzG2aBOEKq/npsL39DcuJSzKVN/a9Yj4QnMRAnIYds/IE03wmW//noG537k0/S2oXHIZDPNvv9a+vX0OFaGCzCFpNZTm9MO1y9zaS78B/rj5W+jgWQ+6T30v/TBrz8VDf/9D39J544xnXn30Rmf+XE0VpRi60kbyNwOcDmmrARXN/Y/tB67fpmdOw9Xd1GbTt31SHccqBPU1l/rNDzg3SB3XTCQuTpF6of1n943At/Q3DgBuYqBzPXBukFoP8oayNwx8IRm4qYayGySibxOENqPsm4vuCBTWAJyY7Z/TpluhMuhfShqIPOuARdsCk9ADtvuVz2bs83ptdkayNw63DtK1z1/HV30z7fE471pxIGfoG9+7Xga2XUHvXXzh+nyK83w6e+hLx002Kx2FT33/d/RnfZkb3ylGf99PH4ifezzU2jolsX06G9n0l4XHE0HDesZHaFta+nNR2+k3z21zgReaL19K+bdGV6eWWYCsjP2Qea8/eIb1mZnIPOuBy/PLLOCTafufpU1kLnzwbpB7X6UN7CZB0wnAbmKgcwdD9YNQvtR1vnwCebEhKxcxUBmR9V/et+y33qPDczb4OIb7cPZ3xA/Z7lxZJ159RdbTz1wA99YR5azkfUg+5bryVpfc5A35Ix1ZBngDwKHrNPPsv7WrKcePKG/Dj/X/sGG9aFONuo+/+N/TWcdPJYmjrNt8qQJNGniOJo0fjSNH4vxobTzrVvortfs9a0nLjR2aD3+H+A22/W48YJs3klj9h5CW1fMpqfufZCenL0+Gp72jnfTZH5WsmYW/ewvT0djzQL1ocz0etJZfqFSen0dgCyHsykzGbfrSBvryXbn4ep2Tq/HZbceS/n1ydx8/LrhdP21zodP8Ov2c5abB+pj79jRmneU9urFP3cjVH/r1gPkx8HPRd1aMIFvM3EwFzWQuRa5PmTf/j74ud5+ydwxyAmRfZvCgjnLQGaT4pjn9t6vssgCOZvCk3E/11p+Psfneyv8OY1snbVvu8Z+1UMuANk6vV8u+/a/rvGzM19Xu18yF2cyvf29b4veUbpl8fN0/d1vxOPbaN3WQ+miiyfQIHPzNXN+T3/i730v+DD9vzMPof327U+rr7+LHrUne+MDvPGz6PP/fAodte8oGr73JDr61EPpsAMmmq+jx5tmvn4e1YuW3/48zYzOrY/dF7dOl51r9ytt3ldzppeB+7jsLqSWZ0D2bbbBy7wv0tg/m90+ZRnUy+1PtFwPzun9cjns+ERDOvs2B4I5y0DmzsdbbgSyb+yPzFkGNnsDEaFsTkzG/VzUQObWg+Xludx+xaEw8gJk3+bGwexc9XuPFv3We7spRcxwHw3Z2f3WKmSL/1u86tlvTHHn12KNBuRYPQObMVjGfmNclvdEzrfpxWbkWD0DmS1yEDnPaOY1uqlrdo588zXI0Ws0ZseR81wN3CRs1JDOXC8cjUa+/RdX039/9yrT2I/R7M18jGjTvMfpG/H4N777c/rtM/692JESA5kbAzfBXOGcNtbH2Y6h2TG7Xj9Hnj2dfvOr6+nqn/+B/vjkkmS8lbgaItXWFDB3kU0v7qfXl2c+N+3mkUwRg+xsO765C5tewNyB7bl+5n6+gcz1wRTFbetGNr24n2fuuJxuTFlnY+coZ79hzDlvXdYW335jyhpgDtOTh5qOXwMa4+fQGFPccs/S66tdbz0z3EdDrmJgMwabaTQgx5xlDcjlbHqe0YAcK2sgcxh5EnIVozF+9vdB5uLG5zyX0aLB+FjjBjI3hrwJcjFjTenM6w7l9L5kmc+Fo9FATjtSIIfdWuQkyNaoUeZy5v2A7RgyY7M1chEDxMHjDqWLPzSFhkV5LS1+NRo238V2pa7d4hYPRQTH49xzCE05eiitfOJP9LVvXUk3vLzWHOpNwyZPo9OnxacWwNUcqWYNRczddEaLRuJjMM6VOdt8bvQqsjOQufkkJcQgZ9t28sxd2PQCmTtZ2Z7rxpGd+Vg1N4+kpBjk4rbrRja9OGM/GjF3XEZL7x8aU9VA5lqSEmKQqxjNgnVjLCtn21KbndEYP/tjzTRATaZXwWgy+2PZljU0sn/Vack/vR80KO+fwoWf+OYbyFyfvCfdWa4G38DcKAG5jLMpUr90a+EJzESlDPy/GTSpoDsWntBMXNB2PdLmqDmc5cbI+udJlrMvuZzettcWmv3wH+jN4efRyfsPod7dttPiJ39Et6w4kU4+ejKNH9yLunPpzMaV9NKjt9BNz60xgQd30hEXfYhOH9uPVs+4he5efwSdfehoGtwjOps2LX6RbrruIZqB9Q06mC686Bg6YIi5Z/xV6aY1i+ilJ++mO6J7xkyaRu87eQqNH4TzdtKm5TPogdvvoqcXER35Tjvnslevpwc2n0ZnHTaKhpgi1824nb5342j62F+bL5RNfctevpquuTu6I/39lb+i88aY+V67g7752DY6YvQAe+8dW2j5q0/QDbe9SPgFqdHHY8QhdOHbj3H/5GrHdlo1/0W684ZH4/WkP34Ouy/tBJeX/t8T6pbZXw/WkeWOA3UCV3dRm07d9UgDmZtP/frjEw0yW3jAHKjkxkGdIFR/PYOtW6v985cQ9uuNAXGqpUh99Qxkbg58Q3PjhKxcxcXBukFoP8q6c+ECTCEJyFUM8Hk1joa8fShqIHN7wgWaQht06M8ntx8yhw1k3jXhBZiFJCBbY59kzrfdl9ps3X6cR//zmw9FX9tuWzmT7n1mXrTaPkPG0AFTxtM+fXbS+rceoG/824/pwRXmwN9+g+45c7TpzKdb3vEP9E2+BRMY37nzcrr25lNpvOkve+5b9B8/fJVemLeehh34Zbrivw8zX1vuoDdvvow+eUX2fuLj4TLAx2X3gZfH/zsByM7YB5nz9svm4t51cfvSqE0n2geZqxjI3Plg3aB2P8o7Hz7BnNhUA5mbD9YJ8vahqIHMxeALzIUJyFUMZC7O1q3b2u+f3vfpw/8UziyL11Xj7CfC2UZj/OyPhR2e0/Ry7DeMFXccEpDL2G+MM+bIt+l52W/RaEnng5PKGM28RuI67ZjL2eZz4WjUqrDLgYuK2dXkm+uFo9GU+RgcjdY4krD7gfdbFr9Av74H/zzJctGn/5bOOWwcDe3bl6Yefwodf8gkmjxxCG2ZsZn2P/8ddOZRB9Eh+42lCfxP+Pfjf8I/lsaP7k2rnnqBZm7iO3ShaR/6DH3g+Mm074i9aNwhR9OJh+1PB0wal/xz/6FbZ9H9r/NXqgPp9A9/nC57++F06OSxNGbvvWj8fpPpgH3H0Nh+K+mBh2cT37LLpFPp8o9cQBccP5UO2X8CTRg7mibuO8HUtTd1mfM4PTJrazLn6AG9aPyRp9Aphx1g7jOaei/4M9349An01/98Ch2573gatO6PdOPjXKf3T++79aYJB0ylI/nefN99x9G+40fSgM0z6fE3V0fndpl0Gl3+0XfQO048lA6bPM7+yAJz7n4TRtPY3mvoyRcXEL9RF3vP+2A/Pv7HjDuwOzfLfG7azQNTONtObXbmLmx6AXMHZqxlzjaQuZbkljHIRW16UT9/PdLcQWZsto0p62wwR57RQHosbx3Wltrs4D4aU9z2ng7koma4z611v8yJf3kkg0mdbR3SphfMYaMBOVbNcUjIylWc1xjndE1Z+ydtehkZfTTkKgYyh5EnIVcxmnmNinDN1sSWubj9z2XRazRmx5GrGMjcGLhJY3Zr8s3rR45evfHazH037rK7Rz1HyjSQuTXISZCtUbPM+cZ+yezvV/ocjCOXc6Qkl8f90/uuvQYmX6NNHDWE+nVZR6/dfyNd/bsH6MlZa2kbn37cWfSRffkLwLX0xnV30iM8xgTGu3Q5kt55yQQabPpLX/w+XX3HVj6TNiybSudF411o7Zw/0J8fz1pbem9MLx6z48jO7tyiNj37KrIzkLn5JCXEIDvbTm125m4o26Ei5g5seiJzP3oVubiBzNVBSY3b7gOy6cXZGrmcueNyujGhXMVA5lpsTQ7kRs1wHw3ZOW+fwrbUZmc0IMeaZ1uDA7mK0YAcy7erCdlSZv+k0Rg/h8aYWrflP723f+uxs5CLnhPKtpnRUi5yjrPpRf0so/m53jXS+c2cVcroy2xtegFzJ5TT/SyjyVysmauCuZjNa9TPMs71s+1nG03m5jRz1xwzadtjMM7l7COv6RL9k6OeNOqQE2jfrrPpzuuvpK9d8zu6+bG5tK1HD1r/2kP0s+9cRd8w7cq759P6Hn1p5JST6JyT+f62de3WPfpnSv1GH0wHdDH3+Jm5x08fif7Jf/eBI+mYM46lcdG5R9FZp+5HI/t1oaVPX0f//f2f0H9/51d0y0sz6c2nZ9HmqKYp9N4PXEAXHjaahvVcS6/e+qto7q9f9wQ99/J0umvG1mjOLvGcQ/Y9kQ4fsoae+f0v6Wvfuo5+8+Qyc4/4n01x6+Kv19J9wBDa/spN9PXvXElfv+Yuem1Nd+o3Yj8668KzaFpU5wA6/cLz6MJjJtHIbXPojmvMeXzujW/S5kFj6Mh3nEnnjHPrt/cOZ2dXgz2nNttzYXcM5m6+W9fMDLmOulHfvIoctu3X5jyjyVy/mSuCOctoLptu6hxkZ5wXdpFzpLObOSOYsyz7frY2EtmeV9RFzpFGc3NzaxWYq5jRl7mYTS+QuRPKtUbzc71r6rlaM3epZPRltn3T87KJcS5n7lijL3MZo8ncnGbuGsyN2bzG/XQuatzLz7Zfm4saTebWNDNLoRw2k297bpb9c2V25mPZGQ3j7dVkjcjWyEVtWxSTjH4Zg03zHrNfo90/P/rLdurag7aveoXue24ebTDn8vmpS5KxrPE4M8lY3nh+MycHszPfLtvcldn0knF7zB/HuTI7czeUs915zVRQ0qbnZTtkLXMx235tbqab18wdg7mo0fycPscomLNt75Nl2fdzo0aTubaZM4K5qNH8HPVlzrSRyNx34y435iLn1DNabTYjqTHkokZfZvT9XNRoNptXb8zltKvSkgeleU92peXT53S2RgNyrJ6BzRgsY78xLst7Iufb9GIz3EdDzjOQ2SIHkfOMxvi52Hr8v92KXqMxO46c52rgJmGjhnTmeuFoNOXa9dRzpMRA5sbATTBXOoN0Tab+KBH16b6SHrryt3T9Xx6iO299hJ6Y+xr9/qfX0g9+cSP9+b5H6K7nZtHrL86gJfw2yq79acLbDvXmiNm+hB65+gb6za0P0h23/omeXLAlGu41bCQdGPX2pkG9+VNJF/6xULTmzSfp3vvuod/88ga67qGZtJFPOfIkeseRI6ifOb74ud/RVb+/h+66/2G6++Y/0E9+dSc9PSf6Mjipu3vvzfTKTb+ka2+8h+645z568BVRU2gfVs2gK667k+6836zrtr/QlS/ah6v9xryNTjvcnDl4Gp173EhTw3aaP/1X9KvbzHn3PUx3/ek6enWlOXPQJDrhOHurRpFbh+xsO765C5tewNyB7bl+5n45FwdTFLetG9n04n6eueNyujFlDWQ2I2IIOc9+w5hz3rqsLb79xjRuO4fplbDfMJbtIuuRRgMyN5dw3c4gXT9ArmI0xs/+mHW6htq9LWbT87LfotFAruJ85EnIzbRrtqbaXMb4HMU5eo3G7DhyFQOZG0PeBLmY5Z8R7nNtKPv7JLMzH0OOXm3IcaRAtgYydwyYNOz6a2Njv0LZjmHcjrl9RK7idkLWVK/2LINtq2bR3ebrubt/+yd6esl280Vabxp/5DQ6ZXB6j33qjSckWYwnZI03j6ySnG2nfnbmLrLpJdkOhcydLJueMB+LXkXONqiXy4MSGrfdh9psbXqVjZbeLzSmqoHMtSQlxCBXsd/kmHmtbItvNCDHQrkx2xqAy8kZyCWMBuRYOWfvl8zZRmNkZuRYcWfVlLbplTAaCJ0TchVa9KDUPsnNcu0TXzSZa1t0l1RO3ztki5+zm336nGcjkblfzq5FdwtkZ3uNb0ttdkYzr9FJfG5xp5sZLWHG9tM523EoaHdP3zx/tvObvUeWmfqOlBjYLAaTbF1sDlsrjOZIj0ctPkKr59Kvn3id5q0xYxs2RQ8tZ81ZR32nnEQf+ezl9I+fu4w+ctZ+NLSbPb1Xn9Hi3oZta2jWMzNpIV+8cT4tWxv9gyei7t3J/jTix+jBV1fSNvPpZMiUs+gTf/VX9HefOJcO67+WZm6w99p5+HgaEf1IUDN282P0/KLo8SntXDOHXnljeVw3ryRmyzJ64dYX6Y0Vm8z4Rtpg7pMqKzqfB7zBTSto+sx1dnzDEnr2reX2XQjdBtA+k82ZB0+lCf3tA91eo0+i93/icvryX3+KvvzBU2hk9NNCetOAEWwH5nS2ndrszN18ZzdzRh3be/iZ+0WNS4rbXlPOaNEdon4ZpxuT7eiSGpuel9GiES/bhnOK2fQC5k628xruYXG5jPObPAc5fK+wueNcpqXv0RrcHGFb/IzG+Lm2ybWXt3k1L1EUuXGjycxzhLOzvSbqpXK2LXlGM6/RRa4hVzGazebVG3O5MTO2n86NOw6GdC5m1JDOXG8oF3O5Zq4K5rCZ+o4UyNZAZoscRLbGPWUuZ7tWGE3mxpqdA43xx5GruAruXpEy58gzd9MZTeaiLbpVQjQ252n6xR2zop8132/0sfT+Cw6NxqPja7fYf4JvvhodNm2AHR9+JH1w3yHRKMD5CUkW4wm141m3yLbt5Jm75Ywmc20zZwRzcZueMB+LXkUOOTklwmVrmYvYXlObqxhNZjMQ1VjNaDYz/jiTbXtJPZteMFujyRxu9h61uZhNL5C5E8rFnN8Yl6NLArmKXZO5SMu6h5wjncuZO85oMhdptfcw8sZdzrZ5TWfzku/Gm3mNLbN0NVr0oDT/6a7fMFbGaTBYxn5jnO0c9Wx6XvZbNFrS+eCkMkYzr5G4Tjvmcrb9v1WLRq0Kuxy4qJhdTb65XjgaTZmPwdFojSNluhryJsjW2TX5Tq+P+zhu8c91jtiyiWbHMRoffCh98OMfpA+/6yw677Qj6Ygx/WjbimW0brs9pzFm052/uIZ+dvuLNHvLUJpy9El07rln0yWXXUaXn8M/Nt/M3b8ndY9662j1C5u8Gu1abIuHmO1baMNKO4BzU8cN7h4ZbNoWfwG9k3awhvUm+9OTu9LQ8SfQmae4NrLHdtq2xTSxD5jCOV2Ly87chU0vYO7AjLXM2QYy1wdTFLXpRf389UhzB5mx2TamrLPBHHlGk9mO5a3D2lKbnf3GFLe9pwO5qBnuoyE7uzmQLXnrkfYbE8php+duJdk11BoNyDGXsUdVjBaNeNkfq2Igcxh5EnIVo5nXqAjXbE1smYvb/9wTvUZjdhy5ipuLvClyMWNN6czrD+X0PoUcvZrAOX2PPEcKZGsgc2uQkyBbo0aZy5n3B7ZjyIzN1shlDGRuB1ytkWpqL2LupjNaNBIfg3GuzL4jefDAJnr9ruvpoblbiLoNpMlnv50unhSfOH0hLYm+wOtH+3/gY/Yvvz/9bnr3yF7x13/NQ9aGnG3byTN3YdML5rS5g2zPdePIznysmhsnKSEGuXHbdSObXpyxH42YOy6jpfcPjalqIHMtKKGK0WT2x5yxDzJn21KbndEYP/tjzbGtAbhcxX6TY8jZljW4fYItVew3JpSLWdYUrh05bO4iM9FY3JCrGNgsBr3a065GSx6U8hPc/KfIaNHZUT/LodbINdnNXJVrND9nHTMlRDVIc8c61OQ5Ra4p3sxdgjnL6WZGjY0ynH2NsxxvbUNNYaMGxo7bY3Y8fS5sj0UniWzdsc3WwM3h1+YM/GsOOuc8evdJB9C+A1fTw7/8KX3n6l/RT65/nZZ7X1Hi3NQMGMsYX/ryU3TjDb+lK35wFf3Xrx6k1zf0o9H7H0EXXXwOnc7nJH/r35+GHO7uZe6Q9G122DFrmyMlZF0DDx7Wl+yvedlCW/gdtcu22HeY0nZa+NRV9I3vXW0a27Tv/pC+9p0f0o/vcLU0o5lKUo7+z89RudYyu3NkxrkyZ7nxZu4QzFnjtc0cSl1jXlPjLrtrZJPnFLmmWDN3SFmO5x0Lj5vXKGcZzc+NXGObGQlmOe63oteE72FGgznLrt8q3DyNNHl9Vs4ymsx+y76H6QXMHf885HCTx9L3qDWazK1pZpZgLup0M6PG7hhylu150mg223OzjSZzezRTVY6ZdOa+G3fZd3Q0yrbZc+HoaCrb8zDusjWazO3Z7BrQGH8cOctofvb7fi5q12Ru/2YqFsbacA6ytc18FHAvHl/6Mv3uttdo2Q6i7kMPoHdfeDwN4gOv3kq3vGT/RdPgfY+hc846mc6Z2odm3Poa8a8aZeRcwI7b5nBjHd3k3MhZ5q61zM723Cyjyew3c6SU0WRufjMzBHOW0WT2W/qYial7IDvjvLBDTR6rdw9pNJnLN3OHXKPJ7Lesc7OMZrN5jXJRo8nst6xzs4wmc20zZwRzlou0ovcodk9zVjAXNZqfs47Vt+nF/XTOMprMfss6N8u2X42WvaM0+8kuGw3IMWc8Nc636Xn2G8byDGS2yEHkMnbNzlHc0auRbS6XcTlwUTG7v42Do9FU5r4bd9meE3KkxEDmxkjfBHNiPFwT1iOzG+OYPifPkZLcEWCuyVMn0NAeprN2Id10++P0yDOv0sLeg6g/j8XwuVGLM0jqj159BtIxZ5xGk3e+SdMfe5Tu+Muf6aonFtAm8+ml36h96W18yj1v0sLoR5v2o/3feSEdw792NGIAHXncoWTfd1pLvS1K7WGPXjR1SFzjkMPo3YcOt+9iXb+EXn7WrOflZ2kW/9sts4J+Q/aitc89HP2MUv4ZWHfd9zq9+Ooj9Ohr9nj0mty7nosjr8AU2cb/tmw2vThbI+c53ZiyBjKbETGEnGc0kD6nyHqsXfYbE8rFbO/pQM4zGpBjabs5kC15RmP87I+FnZ6zdu50tkYDoXPyDGzGoDjYRHgetKxcxfnIk5CrGM28RkW4Zmtiy1zc/ueO6DUas+PIVQxkbgzcpDG7Nfnm9SNHr954bea+G3fZ3aOeI2UayNwa5CTIYRdbI/ZLZn+/bGZstkYu50hJbidkTdlrKGbTi/q2uexce009m559FdkZyNx8khJibH6Orv/+D+m//ueH9O0bnvPGiWY/8Dv6zrf42DV0zcMLaQsf2PAm3f6zn9LXf3Uz3XjbPfSXm35PX//etfTTP/+Gvmvu8V//82u61Zxn73Ef/ciMfdW0H93OGfuI8SvMOGe3v9yPXkUubiBzdVBS47b7UpuxX42YOy6jpfcPjalqIHMtSQkxyFXsNzlmXivb4hsNyLHm2dbgQK5iNCDH8u1qSufa/ZI522iMn0NjTHGHazS9YA4bTWZ/rIqBzWIwyVWMxvjZH7NO15C1f/Vsel7GWBW69ew94N/iflPYZ+QY6tUr+mGEEfwwlwstYyBzc+AbmhsnIOcZdDH17IzqAqH6pTsWntArMHddWI+0OWoOZ7m9sOuAw+vJWp/N7Uz2OibT29/7Nhpp/lPbsvgFuu6u1731dKETLng3TeYfILpmFl1709PRvZjh086hU8f2Maf0oO59BtGUQ4+kM88+gqaM6Ec9zL7gXszUMy+ko4f3INq6lJ664UF6KRqtHZ8/7d30xQ+dRkceMJ723XcyHTplPE3Yb3+auk9v6rp6Ft34h4fpjZXLqPehR9GhI/pS38FjaNyoETR23wPo+JNPobNPO5xGbniaHp29pebeL8brsR/fo+hdH5pA/Ix19Vu/oz8+asenveM9tP8g0+3Rl8aOHkWTJx1EJ5x+Kp1x0D7Up9sWmvXAr+nHd86lNRuX06JRh9NpkwdR/8FjoxrGTTqADj/sUDrltJPp9Kk96c3H7c/BKgqXZ7a9hE3H+99rMQOZm0/9+mudhgfMgQTkMm4eReqXLgdfYC4sZSBzfYrUL10NvoG5USVbduzYSUsWzotTc+CvN3r2dF9v1Ad1gby6ixrg83McDXkfl6Jub7hAU2hCVg4bf57JnLY5ag4XNZB514QXYBaSgGxdbP/yDUw0OQ4GmXdHiuxPPePj4TLAx2X3gZdnlpmAnO3i++e8lhbMnk1vzpxDMxeuMRmY4+ZruDkz+dhsemv+Ktoa7++GpQvordnzaNaMGfTiy6/Ss6/MoWUbltPc6NwFtCQ6i3HXz1kaD0VgfI4YrwYvj/cBIDdu04n2SeYqBjJ3Ptn7UNxAZgsPmAMJWbmKOw6sG4T2o6yrwTcwN0pArmIgc3mwTpC3D0UNZG4NPIGZqKkGMteCdYPQftQzkLmZ8OfJKt97tOgdpWbFcePF17fpeRkNpM+p73zkSch5RjOv0SSu2TnzzdfA0ahVYZdDXoQctqvJN9cLR6Mp8zE4Gq1xpEC2bi64KeYqYqyPsx1Ds2Pl3Eqy546U4B8PH7Pre+GP99Jzy7YT9dmbTj33TLrwvNPomGGL6YV59jfZW8QN6rB501ZavbMf7XfkyXT+eWfSReefSRccMJho9Xx64tY/0WPRWXPpzl/cQDc9PZ9WdRlMB0w7hS4w551/1tF04MihNGwfftJZC9ZTuy5keDstfnMFjTryFFPDGXTetIk0tNsWWvzMrfT93z9D9lPkanr+zz+nq+96g5ZtczVcdL45//SpNHn0eNonOq84qKm45XqKmDsupxtT1tnYOcrZb+Y1dczlbFt8+40payBrKWa/YSzbRdYjjQbkWLbDc5pehhnuoyEXdxwqGa21oO585EnIVYxmXqMiXMMeyv+Gy9j/nBGNRv10ruJqyJtk5bCxBpnT5vVn2UiYjyFHr3G214QcqUC2BjK3BjkJsjVqlLmM0UwydmOccU4VA5nbgXq1FzF3kU0vHrPj0UjUwZ5m5WzzufnuOJKSYpCzbTt55i5seoHMnaxsz43y2pW0cOESWrh0vTce9Sq6cZISYlzNjdquG9n04oz9aMTccRmtefsnDWSuBSVUMZrMdgzrrpezbanNznmNqWqAGoDLVew3OYacbVmD2yfYUsZojJ9DY0y2ZQ3hGk0vmMNGA3KsqoHNGGym0YAcc1nWgFzGaNGIl/2xKm4GXfoNGtnUZ7gHH3EcDRjQP05F4On9FSGXsSP0xLqeOxae0Exc0PZvXk0ysag7Fy7AFBJj6+dsx5HTRv3h9bYz4fWkbffDz81gDB118kQa2p1o26qZdM/09N+WTD3uJBrd13Q2LKI7HnvDDkYMpanHHkhHvu0A2qsX0ablb9BrMxbS/O170/jB3Wnrypl07zPzTJ1EE488niYP7kZdtq2m1+5/nmbH9U88clpqfFbf4XTAlPE0dvAY2n/qkOjngm5bu4hefn0uzX3jeXot+Rv63jRy8v6035ixdOCUEdSfazfnvfrWIlo08zV6fu46mnDENNp/SDdzYDW9zveOr7TsTyecMZz6md76BQ/Sw6/Y0anH81p30NpFy6nrsEk09SC+92Za9tJb9Pq8V+mx11dE6zHlR+47dgodPnFvGjNpXxo9gOfaSPNfnU3zVy6i1555k5Z6//0Vc+tA3cBfR5aBzBYeMAdKuXmgThCqXxrIHIZPMCcmIOcZyFwf1AlC9UsDmRuDb2BuVMrZoE4Qql+6KNu3b6cXp9u/NmkW9uuNfoXqzDKQuT3hAk2hCcjFjD+X0tkkE/Hnk8vFvGvDCzALSUC2Du9XWdt9goHMuyNuH7DevH0Km/fdXOllYMd3J3h5ZpkJyNkuvn/FDWRuf3i5vC8AubhNJ1q3zFUMZO58sG5Qux/lDWS28IA5kIBcxUDm1pO3D0UNZC4GX2AuTEBuppsH1g1C+1HWrYUnMBMlIFcxkLkWrBPk7UNRA5k7By7AFFLJoDbbPwf5e48dlb73aMmD0oED+wc/QB3zgeEJeMMActjYSIvLobqz3LlwAaaQ3PVJ27pl/e2xnjSoO389WeuzuZ3JXwfqt+uX2bk8A4ftQ/26E21dv4SWrY0HG4DLMWXF9Kdhw/tE/4R/+6a1tGTVJm8d9jxbfx8aus8A6t2Nz1tDi1duNmP+eqTL0NvceyD16rqN1i9aQf4/3JL0GTKMBvcyc+zYSquWrKKNZiy9HpfzDGzmAdPJXY80kLk6eXVnGchs4QFzIAG5jBsHdYJQ/dLV4BvwhGUMZK6lSP3SQObG4BuYG1WyA3UCv+7WPSj1/2LWr6tRA3weNqNmuFEDmXcNuGBTeEHj873MaZuj5nBtDnv3ghdkFhZTbL/qGftlM8D47oxdt2/si8zZNmfynWqyM5B514OXZ5aZgJxt7IvMeftV1u0L1g3cPjRuYDMPmE4CchUDmTuevH0oaiBzMfgCc2FTDWSuDtYNQvtR1kDmxuAbmBslIFcxkLk8WDcI7UdZdyw8oZk4AbmKgcwmmcjrBHn7UNRA5s6BC+hS+XuPDnhHqS003wB/AMfREPpASHcuXAAXHHboC4usdcBA5s6BCzCF5K4na302tzOubux33rqwHrcf1u0Dl2fXkeUi65HuPGrrr2fTieoGyHkGMjef+vXXOh8+wZxYys2jSP3S5eALzIWlDGSuT5H6pavBNzA3quRsitQv3Vz4hubGhlY9KO3fn99j3q5g/Y059PnafbxkDhvIvGvCCzALSUC2xj7JnG+7L1l5d8auMw4Gty92PL1Pxcz7bq70MrDjuxO8PLPMBGRn7EN2Nj2+Mh53ubh3Xdw+NGrTifZB5ioGMnc+WDeo3Y/yzodPMCc21UDm5oN1grx9KGogczH4AnNhAnIVA5mrk7cPRQ1kbg08gZkoAbmKgcy1YN0gtB9l3blwAaaQBOTm2P55aLoxeftQ1MDPO3ZUe0dpC3/rPTDV1jWaeeXVeWNRrGO+Bo5GrQI57GrgJmG7GnxzvXA0mjIfQ45ebchxpMTNBTeVc+YZ6+Nsx9DsWDm3EjdXpMwafHMXNr34WPH18blpNw9MkW3b8c1d2PQC5g5sz/Uz9/MNZK4PpihuWzeaeS1g7ricbkxZZ2PnKGe/Ycw5b13WFt9+Y8oaYA7TK2G/YSzbRdYjjQbkWLbDc5pehhnuoyEXdxwq2W+MM+bIt+l59hvG8gxktgQHmwrPa5udC3U0aiBzY+AmjdmtyTd/3JCjV2+8NnPfjbvs7lHPkTINZG4NchJka9Qsc76xX6Fsx9DsWNn9y3dnIktwtUWqqbWIuYtsevGYHUd2ducWtenZV5GdgczNJykhBtnZdvIy9924y3aoiLmTZcZa5uIG9XJ5UFLjtvuAbHpxtkYuZ+64nG5MKFcxkLkWW5MDuVEz3EdDds7bp7AttdkZDcixUG7MtgYHchWjATmWb1cTsqXM/kmjMX4OjTHFna65tvZ0DhtNZn+sioHNYjDJVYzG+Nkfs07XkLV/9Wx6XvZbNBrIVZyPPAm5OcbnWjRbE1vm4vY/90evVpVoyW+979mzR5w6AjxC9s07E8rub2x980ZyzHLnIgtAtg6vR9quoz3Wk49dR9q160m7nUF5zvLjEVqPOZD63y2QueOJyhNOr0eaT/Tr9nOWO46oPOH89djzHBjwjXVkGchcHlkPsu9y65HIEzj7dfs5y42D+nxn1V9sPfXADXzL9UhnI+tB9i3Xk7W+5iBvyBnryDKQ2SQT/Trz6ufPBa37rfeYiI06ay0/v9psklXkeuvJWt+uiVwAsnV4v9L2/9yyma8zKR7P2i+Zd0fsvuRb/vkvzftqzvQysOO7E6nlGZCdeR9C2Tf2z2a3T1kG9XL7ES3PA9l3er9cDjs+0eCy6ST7kJezDGTufLzlRiD7zt8vex4js0UOhLK5MBn3c1F3Hlivb38f/JxlIHMx5AXIvs2NSxnIXB25PrkPeTnLrQUT+DYTB3NRA5lrketD9t3ofsncMcgJkX2bwoI5y0Bmk+KY5/ber7LIAjnbrwfa7rfe8xcqvKlsmyMVdjlwUTG7muDoNc7pJ9Nw7XqkI2W6GvImyNbZNflOr4/7aO6cPEdKciupX0ut/YYx8ypytvnctJsHpnC2ndrszF3Y9OJjoRwNRP3oVeRsA5nrgymK2vSifno99cwdmKnqbDBFGfvNvNa1xc9oQI4Vt72nA7moGe6jITu7OdK5dn3Z9htT3OG5ka3RgByrZ2AzBsvYb4zL8p7I+Ta92Az30ZDzDGS2yEHkPKMxfi62HvfffivBBPmWn1PSn2tCNspx9GoCZ3tNKEcqkK2BzK1BToJsjRplzjevP5TdviAzNlsjl3OkJAMRO4X6tdc3d9MZLRqKj9mx7JxtPjd6Fdm540hKiEF2tp3anG3uwqYXyNzJyvZcN47szMequXGSEmKQG7ddN7LpxRn70Yi54zJa8/ZPGshcS1JCDHIZo8lsx7DuejnbltrsnNeYqga12dZkehWMBuRYvmUN2ftX3GiMn0NjTHHLmsK1I9c3w3005CoGNmOwmUYDcsxlWQNyOZueZzQgx6oayGyRg8hVjMb42d8HmYtbfu53LRqMjzVuIHNjyJs05abt+I5S+wQ4y/ZvimU2ycQsA5k7By7AFBKD9eQb67EZYLydsHXGwZC/LqyHL/DdPnB5vJ5sZ6+n/vpkbj2oGyBn23Rq6i/r1lG//lqn4QFzIAG5jJtHkfqly8EXmAsTkPMMZK5Pkfqlq8E3MDdKQC7jbIrUL91aeAIzUSkDfN4yo2a4qBuF52rFO0p79LBfb9Srt2r97QEvwCwkAdnafjxrc77tvtRm690ZrBO4fcH6Q/uVb953c6WXgR3fneDlmWXmGPsgc95+2Vzcuw7YB4DcuE0n2geAXMVA5o4H6wRZuYyBzGH4BHNiJYN6uflg3SC0H2UNZC4GX2AuTECuYiBzdfL2oaiBzM2Bb2hunJCVq7g4WDcI7UdZdy5cgCkkAbk5tn8Omm5M3j4UNZC5PeECTaEN2v86wtkcNYdlbrt3lAIusDy4KGxevMXP/KQbjkZTmfsYj17jbM9hRwpk6+aSvqmbM89Yj81oPI6xMgYiNgV5zyI1cRc2vfgYnD43ZD43bSBzeTBFtm3HN3f92mw3tD7OOBfjTFkXJ5kixtWQZVsnbHqlnW5MWWdj5yhnv5nX1DGX89Yj7TcmlPMMZC3F7DeMZVvOEYdcozF+9sfCTs9ZbH1+w1hxxyEBuYz9xjhjjnybnpf9Fo2WdD44qYzRzGskrtOOuZxt/3NCNGpV161gx/btyRdJZqZ4rnC9nKPXOLv66zlSphNkbglZk1qjZpnzzfsRyv7+pc/BOHI5R0pyZyJLyKqxjLmLbHrxmB1HdnbnFrXp2VeRnYHMzQclZNt2arMzd0PZDhUxd2DTE5n70avIxQ1kLk9SQgxy47b7gGZe4741cjlzx+V0Y0K5ioHMZkQMZeUyRgNyLO28fQrb4mc0RmZGjjXPtgYHchWjgdA52XY1IVtq96+40Rg/h8aY4k7XXFt7OoeNBuRYNcchIStXcV5jnNM1Ze2ftOnlZL9Fo4FcxkDmMPIk5OYYn2vRbE1smYvb/9wfvUZjdhy5ioHMjYGbNGa3Jt+8fuToNfo+oQotekdpz+ibFy4w/YQ37M7F1ulAtsY3Yflup/Xk49dddH0O7Ev7wOWZMj2jbpn99WAdWe48XN1FbTp11yMNZG4+9euvdRoeMAcquXkUqV86Hz7BnJiAXMaNU6b+YuupB9/A3KiUs5H1hOqv5+bCNzQ3TkDOM5DZJBOL1t+a9dSDJzQTZ9j//MTdZr+jdOCQvah3795m7TxHM+B6fbLXg5z8+SJzYnO2Ob02WwOZd0d4/Xb/LOH9KmcH33f32sDU8gyczbKTcbsP/ri/PzJjv+w+yZztXRdent2H2lzMphPtg8xVDGTufKLledTuR3nnwyeYE5N98HOjBjI3H7k+uW4/FzWQuRjyAs7mRpUMZK6OXLefixrI3Bp4AjNRAnIVA5lrkesL7UdZA5k7Bp7QTJyAjEL83KgdWDcI7UdZtzdcoCk0AbmY5Z93nN2607mogcytZNPGDbRy2ZI4ladl7yjlL8Kc8YTXPen2zcfgaLTGkTJdDXkTZOvsmnyn18d9NHdOniMluZXUr6XWrkUj8bHi6+Nz024emMLZdmqzM3dh0wuYO7A91zcfyzeQuT6YoqhNL+rnr0eaO8iMzbYxZZ0N5ihjv5nXurbUZme/MWXtsHMUN8N9NGRn1C1z3nqk/caEctjhuZGt0WT2x/IMbMZgGWc3eU/kfJtebIb7aMh5BjJb5CByntEYPxdbj//ffvQajdlx5DwDmYuBi8Ku/ZzZXNatWUnbtm2LUzOQdeavx+1xnvnjE8r+x89mxmZr5HKOlOR2QtaUvYZiNr2ob5vLzrXX1LPp2VeRnYHMzScpIQbZ2XZqc7a5i2x6SbZDIXMHtuf6mfvRq8jFDerl8qCkxm33oTZbm14D5o7LaOn9Q2OqGshcS1JCDHIV+02OmdfKtvhGA3IslBuzrcGBXMVoQI7l29WUzrX7JXO20Rg/h8aY4g7XaHrBHDaazP5YFQObxWCSqxiN8bM/Zp2uIWv/6tn0vIwG5FhV5yNPQm6mXbM11eZGHb0a2eZyFQOZG0PeBLmY038mIfP6Q9nfH5md+Rhy9Bpne03IkQLZGsgM+GEuf59QhZa8oxQ/M6xz4EfUbsfsE2/Odhw5bftkGxlgvJ2wdcbBEF5P2nY/fLcPXB6vJ9vZ66m/PplbD+oGyNk2nahOgFzGraN+/bUGMlt4wBwo5eaRV3eWy8EXmAsTkPMMZK5Pkfqlmwvf0Ny4lLMpUr90a+EJzESlDPB5y4ya4aLuWHhCM3ECctj+52Fnc9QczrJPs99RunXLFho8dG/q3qN7VA/qhFGnzGUMTDQ5DgaZd0fcPmC99fdLmved999lgI/L7gMvzywzATnbxfcv26Bebj94ebwPALlxm060bpmrGMjc+WTvQ3EDmS08YA4kZOUq7jiwbhDaj7KuBt/A3CgBuYqBzOXBOkHePhQ1kLk18ARmoqYayFwL1g1C+1HPQObOgQswhSQgVzGozfbPQTNqhhs1kLk94QJNoQnIxYyvG9LZJBPd1xXIxbwrw+vlf3a/YO4s2r698TdUdNBvvZeOlLi5pG+KOTEeronrdRmNxzFWxkDmZiBvWa8WNndh04v7WGP4Gt98Tb4bB1Nk23Z8c9evzXZ9cwcZ52KcKeviJFPEuBqybOtEM6+lnW5MWQOZzUg8VMZoIH1OkfVI+40J5WK2c5heCfsNY9mWc8Qh12iMn/2xsNNz1s6dztZ+w1gZp8FgGfuNcbZzlLPfotGSzgcnlTGaeY3Eddoxl7PtPidEg/Gx4i6HvAg5bFeTb64XjkZT5mM2N58tmzfRyuVLaOvWrfEIJrJGnTKXMZpJxm6MM86pYiBzO+BqjVRTexFzF9n04jE7juyMc2XONp+bbyBz80lKikHOtu1krs2Yu5ztUChzJyvbc904Yy1z426cpIQYV3OjtutGNr04Yz8aMXdcRkvvHxpT1UDmWlBCFaPJbMew7no525ba7JzXmKoGqAG4XMV+k2PI2ZY1uH2CLWWMxvg5NMZkW9YQrtH0gjlsNCDHqhrYjMFmGg3IMZdlDchljBaNeNkfq2Igcxh5EnIVozHpbGvifbBjLhe3/7k/eo0zPn/HatjNRd4UuZjTfyYh83pD2e1LlqNXEzin75HnSIFsDWRuBfygdNWKpdH3CVXogHeU4pG0NRdutijO7gl42nweb6TN7Qzq8+3X7Wc37tZv3T7EZXv2Px5+dq5dj3TnEZUnnF6PdHxiUjcb68gykLn5oDzf+eux5znkAGe/bj/744zM1UF9vsutpx64wDfWkWUgc31Qn+/mrkeCG/j26/ZzlrNBfb5bux6JvCGyb6wjyw7U57tj11MPTOgb66g1Pl+hzlD9nbueNM1+Rymz2XwR1Kt3H+rRsyd17drS309ZGvfniaX24+V/XZDOWeaPuzkzdZ3Fju9OpJZnQHbmfbDZGtk39s/m2v2zOdu7LtHyhO0+1Oaw4xMN6ezbHChlIHPn4y03Atk39kfmLOeDE3ybC4M5y6Bebj5yfci+G90vmYshL+BsbpSM5+UsA5mrg/XlubX7VQ95w1A2Eyfjfi7q4sj1Ifv298HPWe5cZAHIvk2hpQzk1wfmaBx9N7pfMrcnKDDPZiEZll8fYD+t/K8rTMowqJd3DWTByL7NwuKM/fPtf92V3r9wBhgvw4Z1a2nxgrmV3k3KdMB3Fbxpzrx4SzqnzU+wke0Ymh0r51ZStAbf3IVNLz5WfH18btrNA1M4205tduYubHoBcwe25/qZ+/kGMtcHUxS3rRs2vQLmjsvpxpR1NnaOcvabeU0dcznbrjF+9sfKGMhaitlvGMt2aD31jAbkWLbDc5pejv2GseKOQ8NmuI/GOGOOfJueZ79hrIzTyEHkMnbNzsGWOezo1cg2l8u4HLiomN064Gg0kJ35GByN1jhSYiBzu8B/W7xo/hxavXIFbdmyJfniCuvpTGQNtXuMXNzcRTa9eMyOIzu7c4va9OyryM5A5uaTlBCD7Gw7tTnb3EU2vSjnmzuw6YnM/ehV5OIGMlcHJTVuuw/Iphdna+Ry5o7L6caEchUDmWuxNTmQq9hvcsy8lrbFz2iMzIwca55tDQ7kKkYDcizfrqZ0ztu/ekZj/BwaY4o7XKPpBXPYaDL7Y407DgaZLchVnNcY53QNWfsnbXo52W/RaCBXcT7yJOQqRjOvURGu2ZrYMhe3/7k/eo3G7DhyFQOZGwM3acxYUzrz+pGjV2+8NnPfjadz9GpDjiNlGsjcGuQkyGHXXxsb+xXKdgzjdsztI7Jvhr8HWL92DS1eMKfyu0mZFv3W+x5RoVx41SfCrYbr8fY3VXdRtzNcninTM+p22fT4TG89NjsDmVsP6gSu7qI2nZr11HPrQF0gu25nILOFB8yBUgYylwd1glD90kDmMHyCOTEBuYyLgzpBqH7p5sI35ALKOJsi9UsDmZsD39DcOAE5zwCfd+NoCNUv3blwAVxw2O7zsW9bd5aBzJ0DF2AKyV2PNdOKd5QyW7duoc2bNtKOHTupR89e8aiF5y8Kl+mfnreeovb3xxrIvOvDyzXLTkB2xr7IXLtf2fsnDWRuf7APALlxm060DzJXMZC588neh+IGMlt4wBxIQG6mOw6sG4T2o6yrwTcwN0pArmIgc3mwTpC3D0UNZG4NPIGZqKkGMteStw9FDWTuHLgAU0gCchUD/DkYR0NoP8p614ILNoUnIBez3T+ZTTLRfZ2BHDaQedeEF2AWkoBsHd6vsrb7BAOZOwKuZ+eOHdE/t1++ZCFtWL8uPlKNLv0GjWzqUg4+4jjq169vnELYDwic/wGwBjK3A1yOKSvB1W3Hw+ux65fZufNA3QA526ZTdz3SQObmk113toHNPGA6ueup5+aBOkGoful8+ATvhrnryHLjFKlfGshcDL7AXFjKxUGdIFS/dHPhG3oF5K4LBjKbZGK9+qU7Fp7QTFzQ/N9z0XXAnQsXYArJXY+0rbs2W3cWXMuL0x+LU2vo2as39enbjwYMHET9Bgyinj17Ubfu3eKj9cE+AX/fGrWidBz8PzjzP7wE5CoGMnc+Rf77q2cgs9IR8IabjU9ArmIgc3nwvxMQ+t9PWQOZFUXh/yDMfxgJyMWMr4PT2SQTs74elgYyK+0L/8KmLVu30MZ1a2jd2jW0ccP6pryTFLTkQWnfvn3iVB38D7+MzbL4Si+3D1yOKcsz6pS5dj3Z7jxc3UVtOnXXIw1kbj716691Gh4wBxKQy7h5FKlfuhx8gbmwlIHM9SlSv3Q1+AbmRpWcTZH6pZsL39DcOAG5jB1F6pfuWHhCM3FB28/DJpnoPi8jh925cAGmkARka//PlWy3bj2tflAK+IFpz169qFs3/m347fVzSxVFURRFURRFqcbOnTuin0O6ZfPmpj4gBW3/oLQq/jd81vW/UeRvKM2ZntsHLs+uI8tYh8umx1fmGMjcemrrr+80PGAOlHLrKFK/dD58gjmxlIHM5SlSv3Q5+AJzYQJyGRenSP3SzYVvaG5cytkUqV+6tfAEZqIE5DwDfB42o2a4qDsXLsAUkmH/zxVnW7esvz3WkwZ1568na302+3TUg1JFURRFURRFUZRGacmD0m5du5pvq9rsOz6lLbHfUJdzGh4wB0pZ2VMp8r8v6ebCNzQ3LuU9hyIfD+nOhQswhZTynoj9gez6oFRRFEVRFEVRlHanJQ9K7btI9tRvCBVFURRFceiDUkVRFEVRFEVRdg1a8qC0f/9+pse33fXfYVP7Dqbaf2qIdchcuz6ZWw/qBshlvHvDCzQLLWUgc3mwzyDv4yA/HjKH4RPMiZVcHNQJQvVLK2XgDeMNLmPg/mk0yPu46MenI+AN5g+IdejPl1rj42NzO+Pq5t9Mv1UflCqKoiiKoiiK0va05EHpsGFD41Qf+w1fHAz+N1b2G8LabxT5G0pzxMsA33B2HqgbIGfbdGrWY3O2O47surMNbOYB08ldjzSQuTqoE4Tql86HT/BuGFxPPTdOkfqlgczF4AvMhaVcHNQJQvVLNxe+oVdA7rpgILNJJtarX7pj4QnNxAXN/z0XXQfcuXABppDc9UjbumuzdXuB9VjC68l3R7Fhw0Z9UKooiqIoiqIoStvTrWfvAf8W95vCPiPHUJ8+vePU+fD3geb7wRyHvnHkbzzz3HnU1l/PplN3PdJA5uZTv/5ap+EBcyABuYybR5H6pcvBF5gLS7lxitQvXQ2+gblRAnKegcy1FKlfurXwBGaiUnYUqV+6Y+EJzcQZxufddDbJRPd5GTnszoULMIUkIFtjfXkG7bGeNFwP1wWKrEcabNu2jZYsnBcnRVEURVEURVGU9qRr7KbC3yDxN4nWyNnmc9MGMpcHU2TbdnxzFza9gLkD23N987FyLg6mKG5bN2x6BcwdZMZm25iyzgZzlLHfzGvqmMvZtvj2G1PWQNZSzH7DWLaLrEfab0wohx2e0/Ry7DeM5RnYLAa9WrKNxvjZH7PGHPk2vTijj4ZcxvngpDJ2zc5Rzv7nhGjUKtNA5mLgorBRQzpzfcjRqzeObB29msDZXSMdKdPVkDdBts6uyRnNJG8sfG7IQOZmIG9ZrxZp04v6LiuKoiiKoiiKorQ3nf6OUn7DCX8P5Yx3orjM32yZXjzusjOQufVE5Xi4uovadGrWU88dR/364xMNMlt4wBwoZSBzeWQ9ofrLrUfCJ5gTkzr9XNTFkfWE6q+2Hom8gLO5USlnI+v0c5aBzM2Bb2hunNTt5yyD9DvomFD9WevpGOSEnLGOWrvPx342R02stx6ZOwcuwBTirUdmrM/Z1u3W3b74dcucZX/91h2DvqNUURRFURRFUZRdgZa8o7QM5ns3YdvxM/cxbnpJtkN4t0ooRwNRP3oVOdvFSW4Zg1zUphf10+vJN1rx9UgDmWvBFHlGA+mxIuuRRgNyrLjtHA7kPKOB0DnO2euROdt+Y7IdnsP0cowG5Fi+45CAXMZ+Y1zGHPk2PS+jgfQ59Q1ktmCwjNHMa3RT1+wc+cZ/+/jvOVZhl0NehBy2q8k31wtHoynzMeTo1QbPkQLZurngppgrnNPG+jjbMTQ7Vs6tpGgNvrkLm158rPj6+Ny0FUVRFEVRFEVRdh9a8o7Svn37EL9xhb+vKvaOFmkgc+txdRe16dRdTz23jvr11zoND5gDCchl3DyK1C9dDr7AXFjKjVOkfulq8A3MjRKQ8wxkrqVI/dKthScwE5Wyo0j90h0LT2gmLmj7edgkE93nZeSwOxcuwBSSgGzt/7mSZdAe60nD9XBdoMh6pNsZLs+UGVnfUaooiqIoiqIoyq5Ay95Ryt8cWduOb+7Cphcwd5DtuW6cKeviuDmL2taZv558pxtT1tnYOcrZb+Y1dczlvPVI+40J5TwDWUsx+w1j2S6yHum8xmQ7PKfp5dhvGCvuOCQg5xmN8bM/Zo058m16cUYfDbmM88FJZeyanaOc/c8J0ahVpoHMxcBFxWxr4vqQo1dvHNk6ejWBs7tGOlKmqyFvgmydXZMzmknGGAufGzKQuRnIW9arRdr0oj5aNBJZ5vQ10avIzs0DUyRTKYqiKIqiKIqitDlt/1vv8YYZ3/xNlzO/syad4zM983dpee44UJ7vdP21TiMHOGMdWQYyVwf1+a6pP542vB6JPMG7QYSfs1wcWQ+yb7memvVVAjfwjXVkGchcC+rznVV/c9YjwQ19+3X7udbyHXOIvuV6stbXGuQEyL7deqT9z19MXv0ds556oABr+/FJr0farSf98WyP9aTx62Ns/a5uP7txu365vnYA5fj2992uI+3a9djsDGTOZ9u27fqOUkVRFEVRFEVR2p4WvaOUv4Hy3Tj8zVu+bcfP3IdNr67RcC9/zFLPQOZaMEVRM9xHM6/inOx1wa4xfvbHythh58g3d1PZaxhzrl2fxV9PPfuNyXZ4DtOLx9O51gz30ZCLOw6ljAbkmDPmyDMakGP1DGS2YLAx2/8mXbNzFHf0aoSGnGcgcxh5EnIxY33O0Wgqcx/j0Wuc7TnsSIFs3VxwU8wVzmljPTaj8TjGyhiI2BRq5wjX4Ju7yKYXj2GN4Wt88zVpA5nLgymybTu+uevXZru+uYOMczHOWMusKIqiKIqiKIqyK9Dyd5TyG1L4+6XiNp3kGy0/5xnI3HxQJ6itv9b58Al8wzJuHkXqly4HX2AuLGUgc32K1C9dDb6BuVECchlnU6R+6dbCE5iJStlRpH7pjoUnNBMnIIft3olnkumkc9jtBRdkCkutp57beT1piq0nbQf2pX3g8kyZnlG3zP56sI4sdwz6jlJFURRFURRFUXYFWv5b7/mbt3K271RBNr24n2fuuJxuTFkDmc2IGELOs98w5py3LmuLb78xjdvOYXol7DeMZbvIeqTRgBzLdnhO08ux3zBW3HFIQC5jvzHOmCPfpudlv0WjJZ0PTipj1+wcbJmz7T4nRIPxseIuh7wIOWy3jlBOrwPmY3A0WuNIgWzdXHBTzFXE6fVxH3bnFHcrKVqDb9eiofiYHXM523xu2s0DUzjbTm2OB8THCDlt7sD2XN98LN9AZkVRFEVRFEVRlF2PlryjtHdvfkdpx79jhXHvrCnuNDxgDiQg5xnIXJ0i9UuXgy8wF5ZycWQ9eXVnuRp8A3OjUgYy11Km/uasR8I3NDdOQC7m9DvozKiJ9dYj3bHwhGbigrbvrDPJxHr1d856JFyAKSRGviPQf6egs60bGbTHetLIesLrSRvrl+trB2Q5nE2Znousx2ZnIHPricrx4JxejzRf4Nfp57S3b9d3lCqKoiiKoiiK0v605B2l/A0Uf3PkzB33jhX7jRMaU9bZYIqiZriPZl7FOf46wnaN8bM/VsZA1lLMfsNYtuOQzOVytv3GZDtrjrRNL8MM99GQizsOpYwG5Jgz5sgzWjTiZX8sz0BmixxELmb8t4lm5yju6NUIDTnPQOZi4KJiTn/u8et3mfsYj17jbM9hRwpkayBzY6Rvgjkx7mrwjfXYjMbjGCtjIGJTkPcsUhN3kU0vHsMaw9f45mvSBjKXJ5kiBtnZdnxz16/Ndn1zBxnnYpyxljnbxUluGeNqyLKtEza9YGYriqIoiqIoiqLsCrToHaW94lQevFMFZL+TxTkfPoFvWMZA5vIUqV8ayByGTzAnVnJx8urOcjX4BuZGCchlnE2R+qVbC09gJiplgHeumVEzXNQdC09oJk5ADtt/J56zOWoOZ7m9sOuAw+vJWp/N7Uz+OsL298O6feDyTJmeUbfMeeuR7jxc3UVtOnXXI10MfUepoiiKoiiKoii7Ah30W++lgcxmRAwh59lvGHOufWeLtMU3GpBjxW3nML0SRgOhc5yLrKee/cZkOzyn6eXYbxgr7jgkIJex3xiXMUe+Tc/LfotGSzofnFTGaOY1moTrhKPRXPO5cDRqVdjlkBchh+1q8s31wtFoynwMjkZrHCmQrZsLboq5ihjr42zH0OxYObeSojX45i5sevGx4uvjc9NuHpjC2XZqczwQ1W2zHQqZO7A918/czzeQuT6Yorht3bDp1TUa1pVuDKwoiqIoiqIoitL+dMA7SuU7TuQ7Ufyc5eYRvWFGmL+/y3M5cIFvrCPLjYP6fDd3PRJ5A85YR5aBzLWgPt+tXU89eAK/bj9n2YH6fHfueiSY0DfWUWv7jjOTIuGddTa3x3oksgDOWA/XZ7NvfuCTtl1He6wnja3bgey7dj1ptzMoz1l+PPiA/Xi69djsDGRuPagbIPuW68lan8XPWe44/Ppl9scZfUepoiiKoiiKoii7Ai37GaVo5jW2zNm2+PYbU9ZA1lLMfsNYtousR9pvTCiHHZ4bOWy/Yay445CAnGc0xs/+mDXmyLfpedlv0WhJ54OTytg1O0c5uxYNxseyDWQuBi4qZluTqXPwaNr/wMl00EH70vjBXHc83mUYTZo8msxQlKNXc4wPu/VIR8p0NeRNkK2za/Ltr8+Owe6cbAMRm0LtHOEasmx6UR8tGoksc7YdMlcHUzhj7khR5r6rxWU7hHWEcjQQ9aNXkbNdnOSWMcjZth1k04v6PG7H6hut+Hqkgcy1YIo8oymKoiiKoiiKouwKdPBvvZe5PHinCgi9g0UayByGTzAnJiCXcXHy6s4ykLkx+AbmRqUMZDbJRK4LhOqXbi08ARfUmGvf4VXfQObWwBOYiRKQw7brkdkkE7Mc5Kh30pfefyIdd9j+tNfKh+mJOTw4md51+Qfo4tMPp0NHdaNZz82i1dHJzcTWDWM9+S6wnjbB1Y1689Zl7cC+tA9cnl1HlkPrwTqy3HnU1l/PplN3PdJA5uZTv/5ap+EBcyABudbbt+/Qd5QqiqIoiqIoitL2tOhnlDK173Cx+NkfK2Z7TwdyntGAHEvbzYFsKWO/MdlOz1k7dzpbo4HQOXkGNmOwjNGAG5P3RM6z36LRks4HJzVmfnjDfedoNNd8LnL0alXY5ZAXIYdt1xH17GuUuV6YRwfS2Zf+FX3l7/6K/tG0z79rSjSOayPvNZamHngAHXLgZJo4LBo245Po2FMPo8MPPphOPuVQGmtPbzK4qVdLXfvrSzd3TnG3EjdXpMwafLsWjcTHiq+Pz00byFweTJFt2/HNXdj0AuYObM/1zcfKuTiYorht3bDpFTB3kBmbbWPKOhvMUcZ+M6+pYy7XWlEURVEURVEUZVegZT+jFO88yXtnClwOvoC/6SrjxilSv3Q1+AbmRgnIeQYy1yLrDNXf3PXUgycwE5Wyo0j90h0LT2gmLmj7zjqTTOQHJukcdtMZfBJ96m8upJOmjKNJE8fRvsO20rM3P0tL4sMRU06iDx6xN/WkrbRo+k10x2s8uJl2jt6b9tqykmY8+yDdMX0+rY3qT79jsNYtXk8TyV9H2O0Ml2fK9Iy6XTY9PtNbj83OQObWE5Xj4eouatOpWU89dxz1649PNMhs4QFzoJSBzOXx69F3lCqKoiiKoiiKsivQsneU8jdtRe03jGU7650qefYbE8phh+c0vRz7DWPFHYcE5DyjMX72x6wxR75Nz8t+i0ZLOh+cVMau2TnKmR/4wNGoVWGXAxcVs62J60OOXr1xZOvo1QTO7hrpSJmWDJp2BB0wwHSWLab5W4l6DZ9Cpx9oj+Uzlx75w+/o6mt/Sz/7y7O00Ixk1+Q7vT7uw+6cbAOZW0G9WqRNL+qjRSORZc42n5t288AUzrbjZ+77NSHbIawjlKOBqB+9ipxtIHMtyS1jkIva9KJ+ej35Riu+nnrOBlPkGQ2kx4qsx9plNCDHitveU1EURVEURVEUZdehS79BI6u9ZURw8BHH0aBBA+MUAu9cAchlXBy88wb478TJMpC5MfgG5kalDGQ2yUSuC4Tql24tPAEX1JjtO9hMMrGogcytgScwEyUgh4135KWzSSZmub3YSef/+1X0xSP60+pnfkWPj/wQnT1iC836y1fpE1fNiNdj1nXRP9GNn5xK/WkDPfPjy+lLf+L1dKGzLrmcDh1qbrPiFfrmdQ/G6xtIU089lU6eOpoG9+Js2LadtiVrX0nPfe8GuuPw8+gzJ46m/mtm0m8f2kAnnHYwjRvY1eyimW/zUpp+2+/ojjfjSxokqT/Zf+RsmzP5Ss/tA5dn15HlIuuRBjK3ntr669l06q6nnltH/fprnYYHzIEE5DJuHkXqlwZbt26lF6c/FidFURRFURRFUZT2pGW/9d53GjmInOUQRa+pfUdLfm2Wotfk3cOBk7IcQh5z19g5ZTY9cYmf5TlZ1zQGbpJlEJ7M1uDWJR1C1o0sx5uDvCly2OHaaj9uvl39Mlt3LNPoxP36G2+kuS88QvfMWmX6PWnkIdPoINNLakvWwLj+wdNOorefadq0/U2y4xPOupg+/v630zvOOp6mHTaVDj/sCDqNz+F26jQ645TDaAqfOO5tdOaZJ9PbzzmLPvmRd9LF55xEZ59+Ep11+ol09lln0Yfe9/aohirU7jEy1iDtnwvbTt41FpmbT21tabsanO0x2J1bew1AznLzyKrJ1SaR66ldnyXzBgYcy3LjoIYsh0ifIz9OuDDLIRq5Jky6llqHyDumKIqiKIqiKIrSbrTkQal9l8/O+N0ktl+0uWthozrmc9NGk7m2mdfStvg5u9WuR9pI2NVd1K5Fd0lle07US2VnS55d4znL2byaF9eQi5mJVdBxKGg7B88XytZoMoebvUeWmVpHCmTrNOlBXIPx7Dl821rhVDvlRDqQ/9n95kX08gOr6IWn5hM/Ku018iA640A+x96D/8/h7t21W9ekcebfhH/OeSfQoWMH046599M3r7iGvnPFj+m22Zuic3YseZq++b3f0R18gy5dqQdf238EHTmJaMY919N//c8v6Z65W4i696exR51Ep45zc8HcTWc0mes3c4Vw+t4h87lpA75H3I1BdradPHM339nNnFHH9h6++ViW7Skym16m7Tm1uYxdMyMlzdhsG+NsT6nNaZueZzSZbbPXFLXpRX1rmcPOa7iHxeU8l2vyGuTwva25EwVFURRFURRFUZS2p0U/o5TfQuK/jQQZY9m27z6pfVdQlhnuoyEXdxwq2W+MM+bIt+l59hvG8gxktshB5DJ2zc7Bljns6NXINpfLuBy4qJjdOkLZroP79nA6p6/xHSkxkLkx0jfBnBjPrsm3Wx/GOLJPP20SDTD9bUtm0m2Lu9DGJ16keevNST33oUOmjY7OL8d4mjSib/SJZvXcv9D9jz1Njzz2OP1m7troaNdtq+iu+5+hV6MU02UHLXzsF/TT399Dd957D/3y6YW0icd77UXj93c1w6jdYteG8Wgk6tgxl9PXRK8iOzdOcssYZGfbyauNu5ztUMjcgRlrmbNdnOSWMcjZtnUim16c/frznW5MKOcZyGxGxBByntFA+pwi67F2GY3xsz9WzPaeDuQ8+02OITu7OZAteUZTFEVRFEVRFEVpf1r2jlK8i4SFbBtyUeefY3qBzJ2w0fxc9JpizVxVyvnnmF7A3HHnYQwtfU6t0WQu1sxVwRy2eY376ZxlXOtn2892a5utIctM2vYY7B+T2ZmPOaPJ3IrG2D77eDprMv+ze6I164jeds4ZdOEx/Wjz+u1mpCeNmnoijcM10asD95Ls3LmGNm62/V69R9CgaK4BNKl/32isaxf7czNRg2UbrZrzPL22aKMZ20Qzl64zI0x36t4P54YbV5bOqE1ma9u1ltnanQtzN9/Na+aOwZzlqBv1zavIYdt+OaPJXL+ZK4I5y7VjRsHsbK+D0Ze5jLObOaOUZd/PYZtX81LcRc6Rds2MBHOWi5zjrCiKoiiKoiiK0v607GeU4l0nTPodKPUNbMZgGfuNcZb3RM6z36LRQM5zPjipjNHMaySu0465nG3/3VzRq1VhA5nDyJOQw5bvgnPrgqPRlLEeXFvrSIFs3VxwU8xVxOn1cR+ZSZ9b3w0z7Tjan//ZvaHPqEPo/e9+B73/PcfSRH6LKY+NPYTOmhDPFb06subu0uU1uvPp+dE7QgdNfi999pMfoMs/eSl9eFI/M7KFZr/4gFd/pIT666l3vD6YItu245u7sOkFzB3Ynuubj5VzcTBFcdu6YdMrYO4gMzbbxpR1NpijjP1mXlPHXM62xbffmLIGspZi9hvGsl1kPdJ+Y0I57PCcppdhRVEURVEURVGUdqdF7yh1zb6bJBoWOdsWP2e3+u9kMRLmY+XsWnSXQHa21/i25Nk1nrOczat5cQ25mJlYBR0HQzqHbefg+UI57Pxm75FlptaRAtk6D1xjevY1uUeeba1wuWbvgebuWdxl8K89+dR9aRA/0Fi7mGbOX0UrV9i2aOZCWrXDjPceSUefMjo6NzVVlF29wI6toqdenEur+C2hfUfT8WecRuefcTSN2r6Qnr77evr+X2Ym14nLa+5Xi6jDgOxsO3nmbr6zmzmjju09fPOxosYl2bbn1OYyds2MlDRjs22Msz2lNqdtep7RZLbNXlPUphf1rWUOO6/hHhaX84wmc7jJc5DT98w3d5zzmjy39h7IznwslJ3DTVEURVEURVEUZVegZe8odZZvJUEuY78xzpgj36bn2W8YyzOQOQxOKmPX7BxsmcOOXo1sc7mMyyEvQg7brSOUw+viY3A0WuNIiYHMzQBzmp59TWrIs1sfxjiGzw05UpJbiZvzGDpz/0HRJ4RVs+6iq6693mu/oGeX8lm9aezhJ9AEc02qtChgzQ7k4w6dQAO6bqeFj19N377y5/T9K39C//2Dn9NPfns/PTU3OqVhxJRJdradLJtedC5nOxQyd2DGWuZsNw6myLatE9n04uzXn++k2QHbTxpTz0BmMyKGkPOMBtLnFFmPtctojJ/9sWK293Qg5xkNyLG03RzIljL2G5Pt9Jy1c6ezNRoInROyoiiKoiiKoijKrkBLf0YpmhnJdbmG+2cZzc/Z15he1LeWubbv56zxas3cJZjDNq/emMvpcZnTzR7LdmsbagobNcA4ZjPOsXbZnufGkV2/o5qrAbWljZbO4WMwd9O5CW3aCXTgEP50sJZmPfgwPf/Sq157nm6duTKar8/Yt9FZ48z5G7fHPze0Jw0//O00LdnbaDAC+YB9R1C/rl1o4N770qQJY2nihDE06YCpdNzxp9K5J06h4fF6vEv5btG14fHmteiOgSzHXYurMJY5fY2f3blho8lcvpk7BHPWuN83L/z/4hoezrPs+znvmmLNXJHrUJPHsq6x2bxGOcuhVvSa2nuYkWCW436T5xS5xjVzVq7R/Jx1rNamF/Wz7DdFURRFURRFUZRdgZY8KKXMd6rUvkOF4T4acp6BzBY5iJxnNMbP9dZh7b97K3qNxuw4cp6BzMXARWHLd73ZzPXC0Wimo1cbchwpMZC5MXATzBXOaWN9nO0Yxu2YXZ87J9+tRM5RrxY2d5FNL+77a843nxu9JpnolFP3oyHdTGf9Qnry0VV20OOFJxdQNNp7JB118miiZ5+nN1bwL3nqTvtMPZNOP5IP8j2tAed7nnyDVu3oSoMmn04XnX9m0t550fl0yQc/SJ/72Gk0yZzoX8rJ3is9zti63Xq5X87FwRTFbetGNr24n2fuuJxuTFkDmc2IGELOs98w5py3LmuLb78xjdvOYXoljAZC5zgXWY80GpBj2Q7PaXo59hvGijsOCchl7DfGGXNgTkVRFEVRFEVRlHanW8/eA/4t7jeFfUaOoZ49e8apGfA7Ufi7rDIG9jdq8zdp/IaWou5YeEIzcUHb9Uibo+ZwljsXW7cD2Tq8nvz1tTPF1pO2A/vS8fTYuJpenv40PfTI0/T4a0tpXTzO5ZkyaevyZbRg5sv06KPP0PQZ82n2W/Np4cIVtGrNUpo9ZxHNnv8svbSgC61atpiee+opevDJF+nNuSvMHcbRMeeeTNPG96L5Tz9Ij706l2bONm35Vuo1eBTtO2YfGjtxCG198W56YvYqWvTaC/Two0/Tky/OpfmrbQ20wY0/YcYXYLwBsJ7iNh3vf6/FDGRuPvXrr3UaHjAHKrlxUBfIqzvL+fAJ5sQE5DJunDL1F1tPPfgG5kalDGQ2yUSuC4Tqr2fA/y0tWTgvToqiKIqiKIqiKO1Jl36DRnrfylTn4COOo4ED++d+45T1jVRr4AnMRAnIxWwfaJlkYlF3LlyAKSR3PdK27iy3F3YdILyerPXZ3M7YOn3XX5/dD98dRW8aus8A6tW1C21dv4SWrY2HPXYe8RH6yT+eRZO2vkbf/L9X0dPr4o9Hr4E0/LxP0tfeMd7cZTk99O+fp395Ims9WeuTuflwOVwvQC5u04nqBMhl3Drq1x+faJA5DJ9gTizlxkGdIFR/PZeDLzAXJiDnGchcC+oCeXXL+mVuDnxDc+NSdqBOZtu27fTi9MdsUBRFURRFURRFaVNa+I7Sct9I5RnI3DHwhGbiDMsHO/YBlkkmugdayGF3LlyAKSRnffUf1FnaYz1pbH28HkveOtx67Prl+toBWQ5nU6bnIuux2blRttHG9etp7br1tGFzPCQ59Cz6+EmjqPeObbRh1UpavWYrdenTm0aOPYAOP/oQOmSf3rRj8Qt0zXWP0ayN/jqyzPVy3TLnGcjcfKJyPGrrr3U+fALqLurmIesM1Z+1HpnD8AnmxFIGMtdH1hOqP2s9jcE34DoBchlnI+sM1d/c9dSDJzATBcz/reo7ShVFURRFURRFaXda8qC0Vy9+UMrfHDEhu8bfvNUzP+BBjl6jMTuOnGcgczFwUdioIZ25XuTo1RtHto5eTeDsrpGOlBjI3Bi4SdjZNTmjmeSNhc8NuZXIOerVwuYusunFfRjHZHbmY9FrkoHM5ZG3RHa2Hd/c9WtDtkNYR5aZsvZYt532mrAfTRq9N42fMIb2nTSZDj38EDrmyMPoqHH9aN2cF+kvP/oD3fLmyuiXQ2HKbNu6kU0v7ueZOy6jVV+fzGZEDCHn2W8Ycw6tJ22Lb78xjdvOYXoljAZC5zgXWY80GpBj2ZZzpGsJ228YK+44JCCXsd8YZ8yRb9Pzst+i0ZLOByfVWh+UKoqiKIqiKIqyK9CSf3o/YEC/4DtZ4I6FJ8Q3awxy2PzNHD9ASdscNYez3F7YdcDh9eSvr51xdaPevHVZO7Av7QOXZ9eR5dB6sI4sdx619femUVMOoimjJtCBB4+nSeOHUO9ta2je/EU0/6W3aMbyhfT6szNpae56pIHMzad2PfWdhgfMgQTkMm4eReqXLgdfYC4s5cYpU39j65HwDcyNEpDzDGQ2yUSuC4Tqr+fWwhOYiUrZ4de5Y8cO/af3iqIoiqIoiqK0PS16UNrf9PxvnLJtHwCZZGJRdy5cgCkkdz3Stu7abN1eYD2W8Hry19fO2Dp911+f3Q/fQOaOh8uz68hykfXUczPoT3uPGErDhvalHjs20cpVa2j1ohW02tye6wS19dez6UR1AuQybh31649PNMgchk8wJ5Zy46BOEKq/nsvBF5gLE5DzDGSuT5n6G1tPPfiG5sal7ECdIFS/NJC5NfAEZiLj7dv1QamiKIqiKIqiKO1PSx6U9u/fL04h3DdORWwf8JhkYlF3LlyAKSQB2Tr04MrVH15vOxNeT9pYv8sA+9J5cDmmrARk5+Lrq12PzB0Pl2fXkWWuj+uUuYxbB+oEtfXXdxoeMAdKuXngrqBM/eH1SPgEc2ICcp6BzPVBnSBUvzSQuTH4BuZGpZwN6gSh+qWBzM2Bb2huXMoAn6fMqBkuaiBzVfQdpYqiKIqiKIqi7Ap0jd2BmO++SpgfRnE/20bCfAyORmscKTGQuTHkTZCtwzVhPTK7MY7pc/IcKcmtpH4ttbW7Fg2ljoVsevZVZOfGSW4Zg+xsO3m1cZezHbJrw3g0kmQ7IM3H8t04roYso36bTS/OqL++0YqvRxrIbEbEEHJRM9xHM691bfGz35hQLmb0gJ0j32hAjqWNul225BmN8bM/FracI78WazQQOifPwGYMlrHfGGd5T+Q8owE5Vs/54KQyRjOvkbhOO+Zytv3/9qNXq8IGMiuKoiiKoiiKouwKtPy33vvvvHMOv5MFBjJ3DlyAKUSsR+bw+mxuZ1zd2O/QetL298O6feDy7DqyXGQ90p1Hbf31bDpR3QA5z0Dm5lO//lrnwyeYE0u5eRSpX7ocfIG5sJSBzPUpUr90NfgG5kaVnE2R+qWbC9/Q3DgBuYwdReqX7lh4QjNxwFyL/jInRVEURVEURVHanRa+o5S/OeJv1kLmd6zA0WjKfAw5erUhx5ESNxfcND2nzGljfZztGJodK+dW4uaKlFmDb+7CphcfK74+Pjft5oEpsm07vrkLm17A3IHtuX7mfr6BzPXBFMVt60YzrwXMHZfTjSnrbOwc5ew3jDnnrcva4ttvTFkDzGF6Jew3jGW7yHqk0YAcy3Z4TtPLsd8wVtxxqGS/Mc6YI9+m59lvGCvjNHIQuYxds3OwZQ47ejWyzeUyLgcuyrKiKIqiKIqiKEp70/R3lA7bZyT17MXvKG0V8i0y6XeuhN4xWGtztjkdbmeKrSdtB/al80iVY+BsyvRcZD1YR5Y7Dy7PriPLXB/XKXMZt46oHI/a+us7DQ+YA6UMZK5OmfrD65HwCebEBOQyLo6sJ1R/tfXUg29gblTKQGaT0jFYf2vXI+Ebmhs3aPvfsyNUf8euR8ITmIkSkMOWn584F11PPXZs305LFy+Ik6IoiqIoiqIoSnvS9HeUbt68Kfrttgx/IxVyNeRNkK354Vp9u3fkYIxj+NyQIyW5ldSvpdauRUPxMay59ho+Fr2K7Nw4yS1jkJ1tJ6827nK2QyFzB7bn+uZj+W4cTJFtW2dt9uvPN1p6PWhMPQOZzYgYQi5qhvto5rWuLX5GY/zsj5Wxw86RbzQgx9JG3S5bateXbb8x2Q7PYXrxeDpbo4HQOXkGNmOwjNGAG5P3RM4zGpBj9ZwPTmrM9r9J1+yc9dbjcvRqVdhA5jDyJOSw059jkLleOBpNZawH19bmSNHXBoqiKIqiKIqiKO1O099Ryj+ftF//gdS9e7d4pB72nSuwfQdLbU4b72ixuZ1xddtvGMPrSRv74XL7wOWYsnKcvZ5sdx619dez6UR1A+Q8A5mbT/36a50Pn2BOLOXmUaR+6XLwBebCUgYy16dI/dLV4BuYGyUgl3E2ReqXbi08gZmolB1F6pfuWHhCM3ECctj287BJJrrPy8hhtwKed9XyJbR+7Zp4RFEURVEURVEUpT1p+jtKVy5fRlu3bKEdO4p+x2W+O/PM38xZ0jltvKOFsx1Ds2Pl3ErcXJEya/DNXWTTi/vF18fnpg1kLg+myLbt+OYubHoBcwe25/qZ++VcHExR3LZuNPNawNxxOd2YsgYym5F4qIz9hjHnvHVZW3z7jWncdg7TK2G/YSzbRdYjjQbkWLbDc5pejv2GseKOQwJyGfuNccYc+TY9L/stGi3pfHBSGbtm52DLHHb0amSby2VcDnkRcthuHaEcXhcfg6PRGkdKMhAxBf+ze/7aQFEURVEURVEUpd1p+jtKt2/fRj169KDeffpRt25F31Xacdh3BKbN3/Dl2cHfCbboLTcNgvKc5TuG/PXY7NaR5c4jKk84vR7p+MSkbjbWkWUgc/NBeb7z12PPc2DAt1+3n7PcPFCf73LrkcgTkH1jHVluHNTnu9p66oEb+MY6spyNrAfZd2vXI5E35Oyvw8/+OCOzSXH0nVV/a9ZTD57Qr9vP+PNDZpMi+Z+XTcpw54ICfGM9XJ//56Rcj83c+N2kq1bog1JFURRFURRFUdqflvzW+2VLFtGaVStp29ZtUeZvmPIdKcmtpH4ttXYtGomPweFrfPO5aTcPTOFsO7XZmbuw6QXMHdie65uP5RvIXB9MUdy2btj0Cpg7yIzNtjFlnQ3mKGO/mde6ttRmZ78xZe2wcxQ3w300ZGfULXPeeqT9xoRy2OG5kcP2G8byDGzGYBlnN3lP5HybXmyG+2jIZZxGDiLnGY3xc7H1+P/tR6/RmB1HzjOQuRi4KGzUkM5cL3L0muno1QTO7hrpSImBzI2Bm4Qdrsmtjx+Sbli3JvqaQFEURVEURVEUZVeg6e8oZfhdpVs2b6TuPXqZ1oO6du2afONUFftOlTgY7DtY8E6WsPkbN3Oml9sHLseUleMi67HZGcjcelA3QM626UR1AuQybh316681kNnCA+ZAKTcP1AlC9UuXgy/wJshdFwxkrk+R+qWbC9/Q3LiUsylSv3Rr4QnMRKUM8HnLjJrhou5YeEIzcQJy2P7nYWdz1BzOcnth1wGH15O/vqLwtRvWraXF/18799aTMBCEYXhMUJSKxENI/P8/zcQbNRLkfFHcoR2KC2221tI2vk9CPqfUMksbkt1AX19ktVykWwEAAACg3S6i0XNtU7lBdCv3j2N5eBpL77K3+ym+Tr700RSb8JniCWKShxPL09kcbU/Hk582jqx2f+l/ptuzOj/PJ+szPH/SDe6JUmn8urqQ/v0spju4HfesLpO/F9K/n9XoAdyBSqXx62Mh/fv5t/SA7sCVMhPSv5/10hdwL7RndVgmn1+ucmVoNksbcI0UjsfPpO+8bJdkHObUeHaPOJbJ57tMPt5kMZ+lewMAAABA+9W6UKqu+tdyM4hkeDeSaDhydb/Re5fqxFMnoMbqokSX6AnTE1wmjV9XF3J9+YlD+oa4N6ZUGr8+FnI+/ATy6QXiLpScPLWwmHedWXZFHMey2axlOZvK7Gsqy8VcNutV+iwAAAAAdEPtC6VGF0yTRdKemwDWcmtUAADQgO02Tm+7s2aBFAAAAEBnnW2hFAAAAAAAAADaiq92AgAAAAAAAPjnRL4BpjYlw2SdM1QAAAAASUVORK5CYII=)\n",
-        "\n",
-        "Don't make your input audio too long like in the screenshot, 20s-30s is fine. Or else it will raise up JSON issue. This one is due to how VoiceCraft worked so probably unfixable. It will add those text you want to get audio from at the end of the input audio transcript. It was way too much word for application or code to handle as it added up with original transcript. So please keep it short."
+        "* Nevermind, I have observed some weird usage on Colab's GPU Memory Monitor. It can spike up to 13.5GB VRAM even in WhisperX mode. (April 11)"
       ],
       "metadata": {
-        "id": "qQ-And_w2vJV"
+        "id": "AnqGEwZ4NxtJ"
       }
     },
     {
       "cell_type": "markdown",
       "source": [
-        "My tip on voice cloning is just \"get a good dataset\" that contain plausible amount of variety of tones. I guess that's it, you could always try experimenting with other voice that are hard to be clone.\n",
+        "Don't make your `prompt end time` too long, 6-9s is fine. Or else it will **either raise up JSON issue or cut off your generated audio**. This one is due to how VoiceCraft worked (so probably unfixable). It will add those text you want to get audio from at the end of the input audio transcript. It was way too much word for application or code to handle as it added up with original transcript. So please keep it short.\n",
         "\n",
-        "The inference speed is much stable. With sample text, T4 (Free Tier Colab GPU) can do 6-10s on 6s-8s `prompt end time`.\n",
+        "Your total audio length (`prompt end time` + add-up audio) must not exceed 16 or 17s."
+      ],
+      "metadata": {
+        "id": "dE0W76cMN3Si"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "For voice cloning, I suggest you to probably have a monotone input to feed the voice cloning. Of course you can always try input that have tons of tone variety, but I find that as per April 11 Update, it's much more easy to replicate in monotone rather than audio that have laugh, scream, crying inside.\n",
+        "\n",
+        "The inference speed is much stable. With sample text, T4 (Free Tier Colab GPU) can do 6-15s on 6s-8s of `prompt end time`.\n",
         "\n",
         "I haven't test the Edit mode yet as those are not my focus, but you can try it."
       ],

From 6f0aa2db577aa1b77ee1a87d50ea447a8999932f Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Thu, 11 Apr 2024 16:28:52 +0500
Subject: [PATCH 22/24] essential gradio app args added, colab notebook fix

---
 README.md                                     |  16 +-
 gradio_app.py                                 | 313 +++++++++---------
 ...aft.ipynb => voicecraft-gradio-colab.ipynb | 106 +++---
 3 files changed, 222 insertions(+), 213 deletions(-)
 rename voicecraft.ipynb => voicecraft-gradio-colab.ipynb (84%)

diff --git a/README.md b/README.md
index 3bad691..a9b06d7 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,3 @@
-# VoiceCraft Gradio Colab
-
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Sewlell/VoiceCraft-gradio-colab/blob/master/voicecraft.ipynb)
-
-Made for those who lacked a dedicated GPU and those who wanted [the friendly GUI by @zuev-stepan](https://github.com/zuev-stepan/VoiceCraft-gradio). Potato programmer brain here so all code credits to @jasonppy, @zuev-stepan and others who contributed in their code. 
-
 # VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
 [Demo](https://jasonppy.github.io/VoiceCraft_web) [Paper](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)
 
@@ -105,10 +99,6 @@ conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=
 
 # to run ipynb
 conda install -n voicecraft ipykernel --no-deps --force-reinstall
-
-# below is only needed if you want to run gradio_app.py
-sudo apt-get install espeak # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
-sudo apt-get install libespeak-dev # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
 ```
 
 If you have encountered version issues when running things, checkout [environment.yml](./environment.yml) for exact matching.
@@ -117,6 +107,11 @@ If you have encountered version issues when running things, checkout [environmen
 Checkout [`inference_speech_editing.ipynb`](./inference_speech_editing.ipynb) and [`inference_tts.ipynb`](./inference_tts.ipynb)
 
 ## Gradio
+### Run in colab
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zuev-stepan/VoiceCraft-gradio/blob/feature/colab-notebook/voicecraft-gradio-colab.ipynb)
+
+### Run locally
 After environment setup install additional dependencies:
 ```bash
 apt-get install -y espeak espeak-data libespeak1 libespeak-dev
@@ -130,7 +125,6 @@ pip install -r gradio_requirements.txt
 Run gradio server from terminal or [`gradio_app.ipynb`](./gradio_app.ipynb):
 ```bash
 python gradio_app.py
-TMP_PATH=/tmp python gradio_app.py # if you want to change tmp folder path
 ```
 It is ready to use on [default url](http://127.0.0.1:7860).
 
diff --git a/gradio_app.py b/gradio_app.py
index 162b4b3..a632f11 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -12,12 +12,10 @@ import numpy as np
 import random
 import uuid
 
-os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-os.chdir("/content/VoiceCraft-gradio-colab")
-os.environ['USER'] = 'aaa'
 
+DEMO_PATH = os.getenv("DEMO_PATH", ".demo")
 TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
+MODELS_PATH = os.getenv("MODELS_PATH", "./pretrained_models")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 whisper_model, align_model, voicecraft_model = None, None, None
 
@@ -94,14 +92,14 @@ def load_models(whisper_backend_name, whisper_model_name, alignment_model_name,
             transcribe_model = WhisperxModel(whisper_model_name, align_model)
 
     voicecraft_name = f"{voicecraft_model_name}.pth"
-    ckpt_fn = f"./pretrained_models/{voicecraft_name}"
-    encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
+    ckpt_fn = f"{MODELS_PATH}/{voicecraft_name}"
+    encodec_fn = f"{MODELS_PATH}/encodec_4cb2048_giga.th"
     if not os.path.exists(ckpt_fn):
         os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
-        os.system(f"mv {voicecraft_name}\?download\=true ./pretrained_models/{voicecraft_name}")
+        os.system(f"mv {voicecraft_name}\?download\=true {MODELS_PATH}/{voicecraft_name}")
     if not os.path.exists(encodec_fn):
         os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
-        os.system(f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
+        os.system(f"mv encodec_4cb2048_giga.th {MODELS_PATH}/encodec_4cb2048_giga.th")
 
     ckpt = torch.load(ckpt_fn, map_location="cpu")
     model = voicecraft.VoiceCraft(ckpt["config"])
@@ -431,146 +429,131 @@ def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_wo
     ]
 
 
-with gr.Blocks() as app:
-    with gr.Row():
-        with gr.Column(scale=2):
-            load_models_btn = gr.Button(value="Load models")
-        with gr.Column(scale=5):
-            with gr.Accordion("Select models", open=False) as models_selector:
-                with gr.Row():
-                    voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M",
-                                                       choices=["giga330M", "giga830M", "giga330M_TTSEnhanced"])
-                    whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
-                    whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
-                                                    choices=[None, "base.en", "small.en", "medium.en", "large"])
-                    align_model_choice = gr.Radio(label="Forced alignment model", value="whisperX", choices=[None, "whisperX"])
-
-    with gr.Row():
-        with gr.Column(scale=2):
-            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
-            with gr.Group():
-                original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
-                                                 info="Use whisper model to get the transcript. Fix and align it if necessary.")
-                with gr.Accordion("Word start time", open=False):
-                    transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
-                with gr.Accordion("Word end time", open=False):
-                    transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
-
-                transcribe_btn = gr.Button(value="Transcribe")
-                align_btn = gr.Button(value="Align")
-
-        with gr.Column(scale=3):
-            with gr.Group():
-                transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
-                with gr.Row():
-                    smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
-                    with gr.Accordion(label="?", open=False):
-                        info = gr.Markdown(value=smart_transcript_info)
-
-                with gr.Row():
-                    mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
-                    split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
-                                          info="Split text into parts and run TTS for each part.", visible=False)
-                    edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
-                                              info="What to do with first and last word", visible=False)
-
-                with gr.Group() as tts_mode_controls:
-                    prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
-                    prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
-
-                with gr.Group(visible=False) as edit_mode_controls:
+def get_app():
+    with gr.Blocks() as app:
+        with gr.Row():
+            with gr.Column(scale=2):
+                load_models_btn = gr.Button(value="Load models")
+            with gr.Column(scale=5):
+                with gr.Accordion("Select models", open=False) as models_selector:
                     with gr.Row():
-                        edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
-                        edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
+                        voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M",
+                                                        choices=["giga330M", "giga830M", "giga330M_TTSEnhanced"])
+                        whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
+                        whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
+                                                        choices=[None, "base.en", "small.en", "medium.en", "large"])
+                        align_model_choice = gr.Radio(label="Forced alignment model", value="whisperX", choices=[None, "whisperX"])
+
+        with gr.Row():
+            with gr.Column(scale=2):
+                input_audio = gr.Audio(value=f"{DEMO_PATH}/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
+                with gr.Group():
+                    original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
+                                                    info="Use whisper model to get the transcript. Fix and align it if necessary.")
+                    with gr.Accordion("Word start time", open=False):
+                        transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
+                    with gr.Accordion("Word end time", open=False):
+                        transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
+
+                    transcribe_btn = gr.Button(value="Transcribe")
+                    align_btn = gr.Button(value="Align")
+
+            with gr.Column(scale=3):
+                with gr.Group():
+                    transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
                     with gr.Row():
-                        edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.001, value=0.46)
-                        edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.001, value=3.808)
+                        smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
+                        with gr.Accordion(label="?", open=False):
+                            info = gr.Markdown(value=smart_transcript_info)
 
-                run_btn = gr.Button(value="Run")
+                    with gr.Row():
+                        mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
+                        split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
+                                            info="Split text into parts and run TTS for each part.", visible=False)
+                        edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
+                                                info="What to do with first and last word", visible=False)
 
-        with gr.Column(scale=2):
-            output_audio = gr.Audio(label="Output Audio")
-            with gr.Accordion("Inference transcript", open=False):
-                inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
-                                                  info="Inference was performed on this transcript.")
-            with gr.Group(visible=False) as long_tts_sentence_editor:
-                sentence_selector = gr.Dropdown(label="Sentence", value=None,
-                                                info="Select sentence you want to regenerate")
-                sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
-                rerun_btn = gr.Button(value="Rerun")
+                    with gr.Group() as tts_mode_controls:
+                        prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
+                        prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
 
-    with gr.Row():
-        with gr.Accordion("Generation Parameters - change these if you are unhappy with the generation", open=False):
-            stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
-                                       info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
-            sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
-                                          info="The higher the number, the faster the output will be. "
-                                               "Under the hood, the model will generate this many samples and choose the shortest one. "
-                                               "For giga330M_TTSEnhanced, 1 or 2 should be fine since the model is trained to do TTS.")
-            seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
-            kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
-                                info="set to 0 to use less VRAM, but with slower inference")
-            left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
-            right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
-            top_p = gr.Number(label="top_p", value=0.9, info="0.9 is a good value, 0.8 is also good")
-            temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
-            top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
-            codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')
-            codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
-            silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
+                    with gr.Group(visible=False) as edit_mode_controls:
+                        with gr.Row():
+                            edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
+                            edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
+                        with gr.Row():
+                            edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.001, value=0.46)
+                            edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.001, value=3.808)
+
+                    run_btn = gr.Button(value="Run")
+
+            with gr.Column(scale=2):
+                output_audio = gr.Audio(label="Output Audio")
+                with gr.Accordion("Inference transcript", open=False):
+                    inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
+                                                    info="Inference was performed on this transcript.")
+                with gr.Group(visible=False) as long_tts_sentence_editor:
+                    sentence_selector = gr.Dropdown(label="Sentence", value=None,
+                                                    info="Select sentence you want to regenerate")
+                    sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
+                    rerun_btn = gr.Button(value="Rerun")
+
+        with gr.Row():
+            with gr.Accordion("Generation Parameters - change these if you are unhappy with the generation", open=False):
+                stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
+                                        info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
+                sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
+                                            info="The higher the number, the faster the output will be. "
+                                                "Under the hood, the model will generate this many samples and choose the shortest one. "
+                                                "For giga330M_TTSEnhanced, 1 or 2 should be fine since the model is trained to do TTS.")
+                seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
+                kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
+                                    info="set to 0 to use less VRAM, but with slower inference")
+                left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
+                right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
+                top_p = gr.Number(label="top_p", value=0.9, info="0.9 is a good value, 0.8 is also good")
+                temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
+                top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
+                codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')
+                codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
+                silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
 
 
-    audio_tensors = gr.State()
-    transcribe_state = gr.State(value={"words_info": demo_words_info})
+        audio_tensors = gr.State()
+        transcribe_state = gr.State(value={"words_info": demo_words_info})
 
 
-    mode.change(fn=update_demo,
-                inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
-                outputs=[transcript, edit_from_word, edit_to_word])
-    edit_word_mode.change(fn=update_demo,
-                          inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
-                          outputs=[transcript, edit_from_word, edit_to_word])
-    smart_transcript.change(fn=update_demo,
+        mode.change(fn=update_demo,
+                    inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
+                    outputs=[transcript, edit_from_word, edit_to_word])
+        edit_word_mode.change(fn=update_demo,
                             inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
                             outputs=[transcript, edit_from_word, edit_to_word])
+        smart_transcript.change(fn=update_demo,
+                                inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
+                                outputs=[transcript, edit_from_word, edit_to_word])
 
-    load_models_btn.click(fn=load_models,
-                          inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
-                          outputs=[models_selector])
+        load_models_btn.click(fn=load_models,
+                            inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
+                            outputs=[models_selector])
 
-    input_audio.upload(fn=update_input_audio,
-                       inputs=[input_audio],
-                       outputs=[prompt_end_time, edit_start_time, edit_end_time])
-    transcribe_btn.click(fn=transcribe,
-                         inputs=[seed, input_audio],
-                         outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
-                                  prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
-    align_btn.click(fn=align,
-                    inputs=[seed, original_transcript, input_audio],
-                    outputs=[transcript_with_start_time, transcript_with_end_time,
-                             prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
+        input_audio.upload(fn=update_input_audio,
+                        inputs=[input_audio],
+                        outputs=[prompt_end_time, edit_start_time, edit_end_time])
+        transcribe_btn.click(fn=transcribe,
+                            inputs=[seed, input_audio],
+                            outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
+                                    prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
+        align_btn.click(fn=align,
+                        inputs=[seed, original_transcript, input_audio],
+                        outputs=[transcript_with_start_time, transcript_with_end_time,
+                                prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
 
-    mode.change(fn=change_mode,
-                inputs=[mode],
-                outputs=[tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor])
+        mode.change(fn=change_mode,
+                    inputs=[mode],
+                    outputs=[tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor])
 
-    run_btn.click(fn=run,
-                  inputs=[
-                      seed, left_margin, right_margin,
-                      codec_audio_sr, codec_sr,
-                      top_k, top_p, temperature,
-                      stop_repetition, sample_batch_size,
-                      kvcache, silence_tokens,
-                      input_audio, transcribe_state, transcript, smart_transcript,
-                      mode, prompt_end_time, edit_start_time, edit_end_time,
-                      split_text, sentence_selector, audio_tensors
-                  ],
-                  outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
-
-    sentence_selector.change(fn=load_sentence,
-                             inputs=[sentence_selector, codec_audio_sr, audio_tensors],
-                             outputs=[sentence_audio])
-    rerun_btn.click(fn=run,
+        run_btn.click(fn=run,
                     inputs=[
                         seed, left_margin, right_margin,
                         codec_audio_sr, codec_sr,
@@ -578,24 +561,58 @@ with gr.Blocks() as app:
                         stop_repetition, sample_batch_size,
                         kvcache, silence_tokens,
                         input_audio, transcribe_state, transcript, smart_transcript,
-                        gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
+                        mode, prompt_end_time, edit_start_time, edit_end_time,
                         split_text, sentence_selector, audio_tensors
                     ],
-                    outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
+                    outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
 
-    prompt_to_word.change(fn=update_bound_word,
-                          inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
-                          outputs=[prompt_end_time])
-    edit_from_word.change(fn=update_bound_word,
-                          inputs=[gr.State(True), edit_from_word, edit_word_mode],
-                          outputs=[edit_start_time])
-    edit_to_word.change(fn=update_bound_word,
-                        inputs=[gr.State(False), edit_to_word, edit_word_mode],
-                        outputs=[edit_end_time])
-    edit_word_mode.change(fn=update_bound_words,
-                          inputs=[edit_from_word, edit_to_word, edit_word_mode],
-                          outputs=[edit_start_time, edit_end_time])
+        sentence_selector.change(fn=load_sentence,
+                                inputs=[sentence_selector, codec_audio_sr, audio_tensors],
+                                outputs=[sentence_audio])
+        rerun_btn.click(fn=run,
+                        inputs=[
+                            seed, left_margin, right_margin,
+                            codec_audio_sr, codec_sr,
+                            top_k, top_p, temperature,
+                            stop_repetition, sample_batch_size,
+                            kvcache, silence_tokens,
+                            input_audio, transcribe_state, transcript, smart_transcript,
+                            gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
+                            split_text, sentence_selector, audio_tensors
+                        ],
+                        outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
+
+        prompt_to_word.change(fn=update_bound_word,
+                            inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
+                            outputs=[prompt_end_time])
+        edit_from_word.change(fn=update_bound_word,
+                            inputs=[gr.State(True), edit_from_word, edit_word_mode],
+                            outputs=[edit_start_time])
+        edit_to_word.change(fn=update_bound_word,
+                            inputs=[gr.State(False), edit_to_word, edit_word_mode],
+                            outputs=[edit_end_time])
+        edit_word_mode.change(fn=update_bound_words,
+                            inputs=[edit_from_word, edit_to_word, edit_word_mode],
+                            outputs=[edit_start_time, edit_end_time])
+    return app
 
 
 if __name__ == "__main__":
-    app.launch(share=True)
+    import argparse
+
+    parser = argparse.ArgumentParser(description="VoiceCraft gradio app.")
+    
+    parser.add_argument("--demo-path", default=".demo", help="Path to demo directory")
+    parser.add_argument("--tmp-path", default=".demo/temp", help="Path to tmp directory")
+    parser.add_argument("--models-path", default=".pretrained_models", help="Path to voicecraft models directory")
+    parser.add_argument("--port", default=7860, type=int, help="App port")
+    parser.add_argument("--share", action="store_true", help="Launch with public url")
+
+    os.environ["USER"] = os.getenv("USER", "user")
+    args = parser.parse_args()
+    DEMO_PATH = args.demo_path
+    TMP_PATH = args.tmp_path
+    MODELS_PATH = args.models_path
+
+    app = get_app()
+    app.launch(share=args.share, server_port=args.port)
diff --git a/voicecraft.ipynb b/voicecraft-gradio-colab.ipynb
similarity index 84%
rename from voicecraft.ipynb
rename to voicecraft-gradio-colab.ipynb
index 78c816a..a13fb2e 100644
--- a/voicecraft.ipynb
+++ b/voicecraft-gradio-colab.ipynb
@@ -1,28 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "gpuType": "T4",
-      "authorship_tag": "ABX9TyPsqFhtOeQ18CXHnRkWAQSk",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/Sewlell/VoiceCraft-gradio-colab/blob/master/voicecraft.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -36,11 +18,16 @@
       },
       "outputs": [],
       "source": [
-        "!git clone https://github.com/Sewlell/VoiceCraft-gradio-colab"
+        "!git clone https://github.com/zuev-stepan/VoiceCraft-gradio"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-w3USR91XdxY"
+      },
+      "outputs": [],
       "source": [
         "!pip install tensorboard\n",
         "!pip install phonemizer\n",
@@ -55,25 +42,23 @@
         "\n",
         "!pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft\n",
         "\n",
-        "!pip install -r \"/content/VoiceCraft-gradio-colab/gradio_requirements.txt\""
-      ],
-      "metadata": {
-        "id": "-w3USR91XdxY"
-      },
-      "execution_count": null,
-      "outputs": []
+        "!pip install -r \"/content/VoiceCraft-gradio/gradio_requirements.txt\""
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Let it restarted, it won't let your entire installation be aborted."
-      ],
       "metadata": {
         "id": "jNuzjrtmv2n1"
-      }
+      },
+      "source": [
+        "# Let it restarted, it won't let your entire installation be aborted."
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "AnqGEwZ4NxtJ"
+      },
       "source": [
         "# Note before launching the `gradio_app.py`\n",
         "\n",
@@ -83,45 +68,58 @@
         "\n",
         "Frequency of VRAM spikes no longer exist as well in April 5 Update.\n",
         "* Nevermind, I have observed some weird usage on Colab's GPU Memory Monitor. It can spike up to 13.5GB VRAM even in WhisperX mode. (April 11)"
-      ],
-      "metadata": {
-        "id": "AnqGEwZ4NxtJ"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "dE0W76cMN3Si"
+      },
       "source": [
         "Don't make your `prompt end time` too long, 6-9s is fine. Or else it will **either raise up JSON issue or cut off your generated audio**. This one is due to how VoiceCraft worked (so probably unfixable). It will add those text you want to get audio from at the end of the input audio transcript. It was way too much word for application or code to handle as it added up with original transcript. So please keep it short.\n",
         "\n",
         "Your total audio length (`prompt end time` + add-up audio) must not exceed 16 or 17s."
-      ],
-      "metadata": {
-        "id": "dE0W76cMN3Si"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "nnu2cY4t8P6X"
+      },
       "source": [
         "For voice cloning, I suggest you to probably have a monotone input to feed the voice cloning. Of course you can always try input that have tons of tone variety, but I find that as per April 11 Update, it's much more easy to replicate in monotone rather than audio that have laugh, scream, crying inside.\n",
         "\n",
-        "The inference speed is much stable. With sample text, T4 (Free Tier Colab GPU) can do 6-15s on 6s-8s of `prompt end time`.\n",
-        "\n",
-        "I haven't test the Edit mode yet as those are not my focus, but you can try it."
-      ],
-      "metadata": {
-        "id": "nnu2cY4t8P6X"
-      }
+        "The inference speed is much stable. With sample text, T4 (Free Tier Colab GPU) can do 6-15s on 6s-8s of `prompt end time`."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "!python \"/content/VoiceCraft-gradio-colab/gradio_app.py\""
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "NDt4r4DiXAwG"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "!python /content/VoiceCraft-gradio/gradio_app.py --demo-path=/content/VoiceCraft-gradio/demo --tmp-path=/content/VoiceCraft-gradio/demo/temp --models-path=/content/VoiceCraft-gradio/pretrained_models --share"
+      ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "authorship_tag": "ABX9TyPsqFhtOeQ18CXHnRkWAQSk",
+      "gpuType": "T4",
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From b818145ad90f848fe508d1a907755000d861a3a7 Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Thu, 11 Apr 2024 17:22:41 +0500
Subject: [PATCH 23/24] queue added

---
 gradio_app.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gradio_app.py b/gradio_app.py
index a632f11..0f82600 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -615,4 +615,4 @@ if __name__ == "__main__":
     MODELS_PATH = args.models_path
 
     app = get_app()
-    app.launch(share=args.share, server_port=args.port)
+    app.queue().launch(share=args.share, server_port=args.port)

From ad6c2cd836ab99e1c6c1b6dc508f9ebf151980af Mon Sep 17 00:00:00 2001
From: jason-on-salt-a40 <pyp@utexas.edu>
Date: Thu, 11 Apr 2024 07:17:28 -0700
Subject: [PATCH 24/24] a little massage

---
 README.md                     |  26 +++++++++++++++-----------
 demo/pam.wav                  | Bin 0 -> 770082 bytes
 gradio_app.py                 |  20 ++++++++++----------
 voicecraft-gradio-colab.ipynb |   2 +-
 4 files changed, 26 insertions(+), 22 deletions(-)
 create mode 100644 demo/pam.wav

diff --git a/README.md b/README.md
index a9b06d7..629461e 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,5 @@
 # VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
-[Demo](https://jasonppy.github.io/VoiceCraft_web) [Paper](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)
-
+[![Paper](https://img.shields.io/badge/arXiv-2301.12503-brightgreen.svg?style=flat-square)](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)  [![githubio](https://img.shields.io/badge/GitHub.io-Audio_Samples-blue?logo=Github&style=flat-square)](https://jasonppy.github.io/VoiceCraft_web/)  [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/pyp1/VoiceCraft_gradio)  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1IOjpglQyMTO2C3Y94LD9FY0Ocn-RJRg6?usp=sharing)
 
 ### TL;DR
 VoiceCraft is a token infilling neural codec language model, that achieves state-of-the-art performance on both **speech editing** and **zero-shot text-to-speech (TTS)** on in-the-wild data including audiobooks, internet videos, and podcasts.
@@ -8,20 +7,22 @@ VoiceCraft is a token infilling neural codec language model, that achieves state
 To clone or edit an unseen voice, VoiceCraft needs only a few seconds of reference.
 
 ## How to run inference
-There are three ways:
+There are three ways (besides running Gradio in Colab):
 
-1. with Google Colab. see [quickstart colab](#quickstart-colab)
+1. More flexible inference beyond Gradio UI in Google Colab. see [quickstart colab](#quickstart-colab)
 2. with docker. see [quickstart docker](#quickstart-docker)
-3. without docker. see [environment setup](#environment-setup)
+3. without docker. see [environment setup](#environment-setup). You can also run gradio locally if you choose this option
 
 When you are inside the docker image or you have installed all dependencies, Checkout [`inference_tts.ipynb`](./inference_tts.ipynb).
 
 If you want to do model development such as training/finetuning, I recommend following [envrionment setup](#environment-setup) and [training](#training).
 
 ## News
-:star: 03/28/2024: Model weights for giga330M and giga830M are up on HuggingFace🤗 [here](https://huggingface.co/pyp1/VoiceCraft/tree/main)!
+:star: 04/11/2024: VoiceCraft Gradio is now available on HuggingFace Spaces [here](https://huggingface.co/spaces/pyp1/VoiceCraft_gradio)! Major thanks to [@zuev-stepan](https://github.com/zuev-stepan), [@Sewlell](https://github.com/Sewlell), [@pgsoar](https://github.com/pgosar) [@Ph0rk0z](https://github.com/Ph0rk0z). 
 
-:star: 04/05/2024: I finetuned giga330M with the TTS objective on gigaspeech and 1/5 of librilight, the model outperforms giga830M on TTS. Weights are [here](https://huggingface.co/pyp1/VoiceCraft/tree/main). Make sure maximal prompt + generation length <= 16 seconds (due to our limited compute, we had to drop utterances longer than 16s in training data)
+:star: 04/05/2024: I finetuned giga330M with the TTS objective on gigaspeech and 1/5 of librilight. Weights are [here](https://huggingface.co/pyp1/VoiceCraft/tree/main). Make sure maximal prompt + generation length <= 16 seconds (due to our limited compute, we had to drop utterances longer than 16s in training data). Even stronger models forthcomming, stay tuned!
+
+:star: 03/28/2024: Model weights for giga330M and giga830M are up on HuggingFace🤗 [here](https://huggingface.co/pyp1/VoiceCraft/tree/main)!
 
 ## TODO
 - [x] Codebase upload
@@ -30,9 +31,12 @@ If you want to do model development such as training/finetuning, I recommend fol
 - [x] Training guidance
 - [x] RealEdit dataset and training manifest
 - [x] Model weights (giga330M.pth, giga830M.pth, and gigaHalfLibri330M_TTSEnhanced_max16s.pth)
-- [x] Write colab notebooks for better hands-on experience
-- [ ] HuggingFace Spaces demo
-- [ ] Better guidance on training/finetuning
+- [x] Better guidance on training/finetuning
+- [x] Colab notebooks
+- [x] HuggingFace Spaces demo
+- [ ] Command line
+- [ ] Improve efficiency
+
 
 
 ## QuickStart Colab
@@ -109,7 +113,7 @@ Checkout [`inference_speech_editing.ipynb`](./inference_speech_editing.ipynb) an
 ## Gradio
 ### Run in colab
 
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zuev-stepan/VoiceCraft-gradio/blob/feature/colab-notebook/voicecraft-gradio-colab.ipynb)
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1IOjpglQyMTO2C3Y94LD9FY0Ocn-RJRg6?usp=sharing)
 
 ### Run locally
 After environment setup install additional dependencies:
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diff --git a/gradio_app.py b/gradio_app.py
index 0f82600..113f0ab 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -13,7 +13,7 @@ import random
 import uuid
 
 
-DEMO_PATH = os.getenv("DEMO_PATH", ".demo")
+DEMO_PATH = os.getenv("DEMO_PATH", "./demo")
 TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
 MODELS_PATH = os.getenv("MODELS_PATH", "./pretrained_models")
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -371,20 +371,20 @@ demo_original_transcript = " But when I had approached so near to them, the comm
 
 demo_text = {
     "TTS": {
-        "smart": "I cannot believe that the same model can also do text to speech synthesis as well!",
-        "regular": "But when I had approached so near to them, the common I cannot believe that the same model can also do text to speech synthesis as well!"
+        "smart": "I cannot believe that the same model can also do text to speech synthesis too!",
+        "regular": "But when I had approached so near to them, the common I cannot believe that the same model can also do text to speech synthesis too!"
     },
     "Edit": {
         "smart": "saw the mirage of the lake in the distance,",
         "regular": "But when I saw the mirage of the lake in the distance, which the sense deceives, Lost not by distance any of its marks,"
     },
     "Long TTS": {
-        "smart": "You can run TTS on a big text!\n"
+        "smart": "You can run the model on a big text!\n"
                  "Just write it line-by-line. Or sentence-by-sentence.\n"
-                 "If some sentences sound odd, just rerun TTS on them, no need to generate the whole text again!",
-        "regular": "But when I had approached so near to them, the common You can run TTS on a big text!\n"
+                 "If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!",
+        "regular": "But when I had approached so near to them, the common You can run the model on a big text!\n"
                    "But when I had approached so near to them, the common Just write it line-by-line. Or sentence-by-sentence.\n"
-                   "But when I had approached so near to them, the common If some sentences sound odd, just rerun TTS on them, no need to generate the whole text again!"
+                   "But when I had approached so near to them, the common If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!"
     }
 }
 
@@ -602,9 +602,9 @@ if __name__ == "__main__":
 
     parser = argparse.ArgumentParser(description="VoiceCraft gradio app.")
     
-    parser.add_argument("--demo-path", default=".demo", help="Path to demo directory")
-    parser.add_argument("--tmp-path", default=".demo/temp", help="Path to tmp directory")
-    parser.add_argument("--models-path", default=".pretrained_models", help="Path to voicecraft models directory")
+    parser.add_argument("--demo-path", default="./demo", help="Path to demo directory")
+    parser.add_argument("--tmp-path", default="./demo/temp", help="Path to tmp directory")
+    parser.add_argument("--models-path", default="./pretrained_models", help="Path to voicecraft models directory")
     parser.add_argument("--port", default=7860, type=int, help="App port")
     parser.add_argument("--share", action="store_true", help="Launch with public url")
 
diff --git a/voicecraft-gradio-colab.ipynb b/voicecraft-gradio-colab.ipynb
index a13fb2e..4a5953c 100644
--- a/voicecraft-gradio-colab.ipynb
+++ b/voicecraft-gradio-colab.ipynb
@@ -18,7 +18,7 @@
       },
       "outputs": [],
       "source": [
-        "!git clone https://github.com/zuev-stepan/VoiceCraft-gradio"
+        "!git clone https://github.com/jasonppy/VoiceCraft"
       ]
     },
     {