ROSE_Composer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/spaceBearAmadeus/Student_Need_Navigator_SCRATCH/blob/master/ROSE_Composer.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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bgfbfTgPjInZ",
        "outputId": "247a94cb-8fef-4128-9740-a777314bb371"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  fluid-soundfont-gm libfluidsynth1 libqt5x11extras5 qsynth\n",
            "Suggested packages:\n",
            "  fluid-soundfont-gs timidity jackd\n",
            "The following NEW packages will be installed:\n",
            "  fluid-soundfont-gm fluidsynth libfluidsynth1 libqt5x11extras5 qsynth\n",
            "0 upgraded, 5 newly installed, 0 to remove and 20 not upgraded.\n",
            "Need to get 120 MB of archives.\n",
            "After this operation, 150 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fluid-soundfont-gm all 3.1-5.1 [119 MB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libfluidsynth1 amd64 1.1.9-1 [137 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fluidsynth amd64 1.1.9-1 [20.7 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libqt5x11extras5 amd64 5.9.5-0ubuntu1 [8,596 B]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 qsynth amd64 0.5.0-2 [191 kB]\n",
            "Fetched 120 MB in 5s (25.3 MB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 5.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package fluid-soundfont-gm.\n",
            "(Reading database ... 159447 files and directories currently installed.)\n",
            "Preparing to unpack .../fluid-soundfont-gm_3.1-5.1_all.deb ...\n",
            "Unpacking fluid-soundfont-gm (3.1-5.1) ...\n",
            "Selecting previously unselected package libfluidsynth1:amd64.\n",
            "Preparing to unpack .../libfluidsynth1_1.1.9-1_amd64.deb ...\n",
            "Unpacking libfluidsynth1:amd64 (1.1.9-1) ...\n",
            "Selecting previously unselected package fluidsynth.\n",
            "Preparing to unpack .../fluidsynth_1.1.9-1_amd64.deb ...\n",
            "Unpacking fluidsynth (1.1.9-1) ...\n",
            "Selecting previously unselected package libqt5x11extras5:amd64.\n",
            "Preparing to unpack .../libqt5x11extras5_5.9.5-0ubuntu1_amd64.deb ...\n",
            "Unpacking libqt5x11extras5:amd64 (5.9.5-0ubuntu1) ...\n",
            "Selecting previously unselected package qsynth.\n",
            "Preparing to unpack .../qsynth_0.5.0-2_amd64.deb ...\n",
            "Unpacking qsynth (0.5.0-2) ...\n",
            "Setting up libqt5x11extras5:amd64 (5.9.5-0ubuntu1) ...\n",
            "Setting up fluid-soundfont-gm (3.1-5.1) ...\n",
            "Setting up libfluidsynth1:amd64 (1.1.9-1) ...\n",
            "Setting up fluidsynth (1.1.9-1) ...\n",
            "Setting up qsynth (0.5.0-2) ...\n",
            "Processing triggers for hicolor-icon-theme (0.17-2) ...\n",
            "Processing triggers for mime-support (3.60ubuntu1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.5) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
          ]
        }
      ],
      "source": [
        "!sudo apt install -y fluidsynth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "heOWhygQkLRb",
        "outputId": "b78ee844-5689-42a3-b60a-a182a736d75f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyfluidsynth\n",
            "  Downloading pyFluidSynth-1.3.1-py3-none-any.whl (19 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pyfluidsynth) (1.21.6)\n",
            "Installing collected packages: pyfluidsynth\n",
            "Successfully installed pyfluidsynth-1.3.1\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade pyfluidsynth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEkdv3HqkNdU",
        "outputId": "e4d847ba-0505-470b-96ba-017bbd63d657"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pretty_midi\n",
            "  Downloading pretty_midi-0.2.9.tar.gz (5.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.6 MB 11.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from pretty_midi) (1.21.6)\n",
            "Collecting mido>=1.1.16\n",
            "  Downloading mido-1.2.10-py2.py3-none-any.whl (51 kB)\n",
            "\u001b[K     |████████████████████████████████| 51 kB 6.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from pretty_midi) (1.15.0)\n",
            "Building wheels for collected packages: pretty-midi\n",
            "  Building wheel for pretty-midi (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pretty-midi: filename=pretty_midi-0.2.9-py3-none-any.whl size=5591955 sha256=ce775d3c883fed996b5ace5383d098f0fe5131cb4f1ac387a2ce44d3d9254e4d\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/74/7c/a06473ca8dcb63efb98c1e67667ce39d52100f837835ea18fa\n",
            "Successfully built pretty-midi\n",
            "Installing collected packages: mido, pretty-midi\n",
            "Successfully installed mido-1.2.10 pretty-midi-0.2.9\n"
          ]
        }
      ],
      "source": [
        "!pip install pretty_midi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KY96cqKPkQX_"
      },
      "outputs": [],
      "source": [
        "import collections\n",
        "import datetime\n",
        "import fluidsynth\n",
        "import glob\n",
        "import numpy as np\n",
        "import pathlib\n",
        "import pandas as pd\n",
        "import pretty_midi\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "\n",
        "from IPython import display\n",
        "from matplotlib import pyplot as plt\n",
        "from typing import Dict, List, Optional, Sequence, Tuple"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eqi8y9HkkYbv"
      },
      "outputs": [],
      "source": [
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "AUDIO_DIR = pathlib.Path(\"data/maestro-v2.0.0\")\n",
        "ANNOTATIONS_FILE = \"/content/data/maestro-v2.0.0/maestro-v2.0.0.csv\"\n",
        "\n",
        "if not AUDIO_DIR.exists():\n",
        "  tf.keras.utils.get_file(\n",
        "      'maestro-v2.0.0-midi.zip',\n",
        "      origin='https://storage.googleapis.com/magentadata/datasets/maestro/v2.0.0/maestro-v2.0.0-midi.zip',\n",
        "      extract=True,\n",
        "      cache_dir='.', cache_subdir='data',\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JpTpKdukrt7L",
        "outputId": "4c679d91-8f8e-4050-ab17-190a5af8cc8b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New Bach Instance Created at file location index: 666\n",
            "\n",
            "New Bach Instance Created!\n",
            "\n"
          ]
        }
      ],
      "source": [
        "class Bach:\n",
        "\n",
        "  def __init__(self, annotations_file, audio_dir, file_index:Optional[int]=None):  \n",
        "\n",
        "    '''specifying file_index will bind a preconstructed pretty_midi object to the class\n",
        "       based off of the param specified at instantiation, else you can use explicit\n",
        "       methods to create a pretty midi object that is independent from any param within \n",
        "       the class'''\n",
        "\n",
        "    self.annotations = pd.read_csv(f\"{annotations_file}\")\n",
        "    self.audio_dir = audio_dir \n",
        "    self._SAMPLING_RATE = 44000\n",
        "    if file_index:\n",
        "      self.file_index = file_index \n",
        "      self.pretty_MIDI = self.__repr__(self.file_index)[0]\n",
        "      print(f\"New Bach Instance Created at file location index: {file_index}\\n\")\n",
        "    else:\n",
        "      print(f\"New Bach Instance Created!\\n\")\n",
        "\n",
        "  \n",
        "  def __len__(self):\n",
        "\n",
        "    '''return total songs in audio_dir'''\n",
        "\n",
        "    return len(self.annotations)\n",
        "\n",
        "  \n",
        "  def __getitem__(self, midi_file_index:Optional[int]=None):\n",
        "\n",
        "    '''returns both audio sample path and audio sample label of specified location index.\n",
        "       If no midi_file_index is passed, will return path and labels for all file in dir'''\n",
        "\n",
        "    if midi_file_index:\n",
        "      audio_sample_path = self._get_audio_sample_path(midi_file_index)\n",
        "      label = self._get_audio_sample_label(midi_file_index)\n",
        "    else:\n",
        "      audio_sample_path = self._get_audio_sample_path()\n",
        "      label = self._get_audio_sample_label()\n",
        "    return audio_sample_path, label \n",
        "\n",
        "  \n",
        "  def __repr__(self, file_index):\n",
        "    return self.get_pretty_midi(file_index), self.__getitem__()[0]\n",
        "\n",
        "  \n",
        "  def _get_audio_sample_path(self, midi_file_index:Optional[int]=None):\n",
        "\n",
        "    '''method will return the path of either an individual file specified by location index OR the entire directory'''\n",
        "    \n",
        "    filenames = glob.glob(str(self.audio_dir/'**/*.mid*'))\n",
        "    return filenames[midi_file_index] if midi_file_index \\\n",
        "                                      else filenames \n",
        "\n",
        "  \n",
        "  def _get_audio_sample_label(self, midi_file_index:Optional[int]=None):\n",
        "    return self.annotations.iloc[midi_file_index, 5] if midi_file_index \\\n",
        "                                                     else self.annotations['duration']\n",
        "  \n",
        "  \n",
        "  def get_pretty_midi(self, midi_file_index:int):\n",
        "    \n",
        "    '''retrieves pretty_midi object'''\n",
        "    \n",
        "    pm = pretty_midi.PrettyMIDI(self.__getitem__(midi_file_index)[0])\n",
        "    return pm\n",
        "\n",
        "  \n",
        "  #DATA VISUALIZATION METHODS\n",
        "  def display_audio(self, pm:pretty_midi.PrettyMIDI, seconds=30):\n",
        "    \n",
        "    '''generates a mini .wav player in the markdown cell'''\n",
        "    \n",
        "    waveform = pm.fluidsynth(fs=self._SAMPLING_RATE)\n",
        "    # Take a sample of the generated waveform to mitigate kernel resets\n",
        "    waveform_short = waveform[:seconds*self._SAMPLING_RATE]\n",
        "    return display.Audio(waveform_short, rate=self._SAMPLING_RATE)\n",
        "\n",
        "  \n",
        "  def fetch_inspect_MIDI_instrument(self, pm:pretty_midi.PrettyMIDI, show:Optional[bool]=False):\n",
        "    \n",
        "    '''retrieves instrument(s) in song, right now just [0], and shows data if show==True'''\n",
        "    \n",
        "    instrument = pm.instruments[0] \n",
        "    instrument_name = pretty_midi.program_to_instrument_name(instrument.program)\n",
        "    if show:\n",
        "      print(\"\\n*-------------------------------------------*\")\n",
        "      print(f\"{'            '} \\033[1;36mINSTRUMENT INFORMATION\\033[0m\")\n",
        "      print(f\"*-------------------------------------------*\")\n",
        "      print(f\"\\033[0;34m{' '}NUMBER_OF_INSTRUMENTS:\\033[0m {len(pm.instruments)}\")\n",
        "      print(\"*-------------------------------------------*\")\n",
        "      print(f\"\\033[0;34m{' '}INSTRUMENT_NAME:\\033[0m {instrument_name}\") \n",
        "      print(\"*-------------------------------------------*\") \n",
        "      for i, note in enumerate(instrument.notes[:10]):\n",
        "        note_name = pretty_midi.note_number_to_name(note.pitch)\n",
        "        duration = note.end - note.start\n",
        "        print(f\"{' '}{i}: \\033[0;31mPITCH\\033[0m={note.pitch}, \\033[0;32mNOTE_NAME\\033[0m={note_name}, \\033[0;33mDURATION\\033[0m={duration:.4f}\\n\")\n",
        "    print(\"*-------------------------------------------*\") \n",
        "    print(\"\\n\")\n",
        "    return instrument, instrument_name\n",
        "\n",
        "  \n",
        "  def midi_to_notes_df(self, \n",
        "                       midi_file_index:Optional[int]=None,\n",
        "                       prettyMidi:Optional[pretty_midi.PrettyMIDI]=None, \n",
        "                       midi_file:Optional[str]=None) -> pd.DataFrame:\n",
        "    \n",
        "    '''if pm is given, default to the instantiated pm object, \n",
        "       else custom index with same method!'''\n",
        "    \n",
        "    if midi_file_index:\n",
        "      instrument = self.get_pretty_midi(midi_file_index).instruments[0]\n",
        "    if prettyMidi:\n",
        "      instrument = prettyMidi.instruments[0]\n",
        "    if midi_file:\n",
        "      pm = pretty_midi.PrettyMIDI(midi_file)\n",
        "      instrument = pm.instruments[0]\n",
        "    notes = collections.defaultdict(list)\n",
        "    sorted_notes = sorted(instrument.notes, key=lambda note: note.start)#<-sort by start time\n",
        "    prev_start = sorted_notes[0].start\n",
        "    for note in sorted_notes:\n",
        "      start = note.start \n",
        "      end = note.end\n",
        "      notes['pitch'].append(note.pitch)\n",
        "      notes['start'].append(start)\n",
        "      notes['end'].append(end)\n",
        "      notes['step'].append(start - prev_start)\n",
        "      notes['duration'].append(end - start)\n",
        "      prev_start = start\n",
        "    \n",
        "    return pd.DataFrame({name: np.array(value) for name, value in notes.items()})\n",
        "\n",
        "  \n",
        "  def get_note_names(self, df:pd.DataFrame) -> np.ndarray:\n",
        "    \n",
        "    '''remember that this produces a FUNC'''\n",
        "    \n",
        "    return np.vectorize(pretty_midi.note_number_to_name)\n",
        "\n",
        "  \n",
        "  def plot_piano_roll(self, notes:pd.DataFrame, count:Optional[int]=None):\n",
        "    if count:\n",
        "      title = f\"First {count} notes\"\n",
        "      plt.figure(figsize=(28,12))\n",
        "    else:\n",
        "      title = f\"Entire track\"\n",
        "      count = len(notes['pitch'])\n",
        "      plt.figure(figsize=(32,12))\n",
        "    \n",
        "    x = np.stack([notes['pitch'], notes['pitch']], axis=0)\n",
        "    y = np.stack([notes['start'], notes['end']], axis=0)\n",
        "    plt.plot(y[:, :count], \n",
        "             x[:, :count], \n",
        "             color=\"m\", \n",
        "             marker=\".\",\n",
        "             markersize=8,\n",
        "             markerfacecolor=\"b\",\n",
        "             snap=True)\n",
        "    plt.xlabel('Time (s)')\n",
        "    plt.ylabel('Pitch')\n",
        "    _ = plt.title(title)\n",
        "\n",
        "  \n",
        "  def plot_note_distributions(self, notes_df:pd.DataFrame, drop_percentile=2.5):\n",
        "    plt.figure(figsize=(16, 6))\n",
        "    plt.subplot(1, 3, 1)\n",
        "    sns.histplot(notes_df, x=\"pitch\", bins=20, kde=True)\n",
        "\n",
        "    plt.subplot(1, 3, 2)\n",
        "    max_step = np.percentile(notes_df['step'], 100 - drop_percentile)\n",
        "    sns.histplot(notes_df, x=\"step\", bins=np.linspace(0, max_step, 21))\n",
        "\n",
        "    plt.subplot(1, 3, 3)\n",
        "    max_duration = np.percentile(notes_df['duration'], 100 - drop_percentile)\n",
        "    sns.histplot(notes_df, x=\"duration\", bins=np.linspace(0, max_step, 21))\n",
        "  \n",
        "  \n",
        "  def notes_to_MIDI_file(\n",
        "    self,\n",
        "    notes:pd.DataFrame, \n",
        "    out_file:str, \n",
        "    instrument_name:str, \n",
        "    velocity:int=100) -> pretty_midi.PrettyMIDI:\n",
        "    pm = pretty_midi.PrettyMIDI()\n",
        "    instrument = pretty_midi \\\n",
        "                        .Instrument(program=pretty_midi \\\n",
        "                        .instrument_name_to_program(instrument_name))\n",
        "    prev_start = 0 \n",
        "    for i, note in notes.iterrows():\n",
        "      start = float(prev_start + note['step'])\n",
        "      end = float(start + note['duration'])\n",
        "      note = pretty_midi.Note(velocity=velocity,\n",
        "                              pitch=int(note['pitch']),\n",
        "                              start=start,\n",
        "                              end=end)\n",
        "      instrument.notes.append(note)\n",
        "      prev_start = start\n",
        "    \n",
        "    pm.instruments.append(instrument)\n",
        "    pm.write(out_file)\n",
        "    return pm \n",
        "    \n",
        "\n",
        "\n",
        "\n",
        "rose = Bach(annotations_file=ANNOTATIONS_FILE, \n",
        "            audio_dir=AUDIO_DIR, \n",
        "            file_index=666)\n",
        "\n",
        "\n",
        "kita = Bach(annotations_file=ANNOTATIONS_FILE,\n",
        "            audio_dir=AUDIO_DIR,\n",
        "            file_index=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Afg7695hAqle",
        "outputId": "18e7e5de-6160-4077-a5bb-e4a7e72ba02e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "*-------------------------------------------*\n",
            "             \u001b[1;36mINSTRUMENT INFORMATION\u001b[0m\n",
            "*-------------------------------------------*\n",
            "\u001b[0;34m NUMBER_OF_INSTRUMENTS:\u001b[0m 1\n",
            "*-------------------------------------------*\n",
            "\u001b[0;34m INSTRUMENT_NAME:\u001b[0m Acoustic Grand Piano\n",
            "*-------------------------------------------*\n",
            " 0: \u001b[0;31mPITCH\u001b[0m=69, \u001b[0;32mNOTE_NAME\u001b[0m=A4, \u001b[0;33mDURATION\u001b[0m=0.1302\n",
            "\n",
            " 1: \u001b[0;31mPITCH\u001b[0m=81, \u001b[0;32mNOTE_NAME\u001b[0m=A5, \u001b[0;33mDURATION\u001b[0m=0.6641\n",
            "\n",
            " 2: \u001b[0;31mPITCH\u001b[0m=48, \u001b[0;32mNOTE_NAME\u001b[0m=C3, \u001b[0;33mDURATION\u001b[0m=0.3086\n",
            "\n",
            " 3: \u001b[0;31mPITCH\u001b[0m=70, \u001b[0;32mNOTE_NAME\u001b[0m=A#4, \u001b[0;33mDURATION\u001b[0m=0.2513\n",
            "\n",
            " 4: \u001b[0;31mPITCH\u001b[0m=58, \u001b[0;32mNOTE_NAME\u001b[0m=A#3, \u001b[0;33mDURATION\u001b[0m=0.0742\n",
            "\n",
            " 5: \u001b[0;31mPITCH\u001b[0m=64, \u001b[0;32mNOTE_NAME\u001b[0m=E4, \u001b[0;33mDURATION\u001b[0m=0.0846\n",
            "\n",
            " 6: \u001b[0;31mPITCH\u001b[0m=60, \u001b[0;32mNOTE_NAME\u001b[0m=C4, \u001b[0;33mDURATION\u001b[0m=0.0599\n",
            "\n",
            " 7: \u001b[0;31mPITCH\u001b[0m=82, \u001b[0;32mNOTE_NAME\u001b[0m=A#5, \u001b[0;33mDURATION\u001b[0m=0.8242\n",
            "\n",
            " 8: \u001b[0;31mPITCH\u001b[0m=76, \u001b[0;32mNOTE_NAME\u001b[0m=E5, \u001b[0;33mDURATION\u001b[0m=0.0456\n",
            "\n",
            " 9: \u001b[0;31mPITCH\u001b[0m=60, \u001b[0;32mNOTE_NAME\u001b[0m=C4, \u001b[0;33mDURATION\u001b[0m=0.0573\n",
            "\n",
            "*-------------------------------------------*\n",
            "\n",
            "\n",
            "*-------------------------------------------*\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2016x864 with 1 Axes>"
            ],
            "image/png": 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AAKBZghIAAAAAAKBZghIAAAAAAKBZghIAAAAAAKBZghIAAAAAAKBZghIAAAAAAKBZghIAAAAAAKBZghIAAAAAAKBZghIAAE6sycYkywureX60nOWF1Uw2JkO3dCRO6rgAAKALLZw/tzDGLo2GbgAAALpy5eJanr16NuN6Pktrm/ns4y/moSfPDN3WA7v2wnYubT+Scc5n6eXN5OJann7piaHbAgCAqXRSrwtu5RrhwbijBACAE2tnfSvjOpdXM5Nx5jLavj50S0ditH094+yO68Zcdta3hm4JAACm1km9LriVa4QH444SAABOrJn52Sy9vJnxjbksndrM6LHZXLh8Yei2Htjywupt45qZnx26JQAAmFon9brgVq4RHkyptQ7dw10tLi7WlZWVodsAAOCYmWxMcuXiWnbWtzIzP5unnjuX04+eHrqtB3ZSxwUAAF1o4fy5hTE+qFLKaq11cc9pghIAAAAAAOAkOygo8R4lAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAs0ZdLryU8ntJ/iTJTpLrtdbFUsqbkvyXJG9N8ntJvqvW+pku+wAAAAAAANhLH3eUfHOt9XytdXH3+3cn+WCt9auTfHD3ewAAAAAAgN4N8eitdya5tPv1pSTfMUAPAAAAAAAAnQclNcmvl1JWSynfu/uzh2utn9r9+tNJHu64BwAAAAAAgD11+h4lSf5yrXWzlPJQkg+UUl6+dWKttZZS6l7/cDdY+d4kectb3tJxmwAAAAAAQIs6vaOk1rq5+/lakl9M8lSSPyilvDlJdj9f2+ffvqfWulhrXTx79myXbQIAAAAAAI3qLCgppbyxlPJnXvs6yd9I8tEkv5Lkmd3Znknyy131AAAAAAAAcJAuH731cJJfLKW8Vudna63vL6W8kOQXSinfneTjSb6rwx4AAAAAAAD21VlQUmvdSPJ1e/z8D5O8o6u6AAAAAAAAh9Xpe5QAAAAAAABMM0EJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJAAAAAADQLEEJdGiyMcnywmqeHy1neWE1k43J0C3d1XHsGU6Cadz3prEnAACgG9N6/j+tfQEny2joBuAku3JxLc9ePZtxPZ+ltc189vEX89CTZ4Zu60DXXtjOpe1HMs75LL28mVxcy9MvPTF0W3DiTePxwvEAAADaMY3XJInrEqAf7iiBDu2sb2Vc5/JqZjLOXEbb14du6a5G29czzm7PN+ays741dEvQhGk8XjgeAABAO6bxmiRxXQL0wx0l0KGZ+dksvbyZ8Y25LJ3azOix2Vy4fGHotg60vLB6W88z87NDtwRNmMbjheMBAAC0YxqvSRLXJUA/Sq116B7uanFxsa6srAzdBtyzycYkVy6uZWd9KzPzs3nquXM5/ejpods60HHsGU6Cadz3prEnAACgG9N6/j+tfQHHTylltda6uOc0QQkAAAAAAHCSHRSUeI8SAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYIScWSuuwAACYxJREFUAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAAACgWYISAAAAgCMy2ZhkeWE1z4+Ws7ywmsnG5EjnBwCO3mjoBgAAAABOiisX1/Ls1bMZ1/NZWtvMZx9/MQ89eWbf+a+9sJ1L249knPNZenkzubiWp196oseOAQB3lAAAAAAckZ31rYzrXF7NTMaZy2j7+oHzj7avZ5zd+W/MZWd9q6dOAYDXuKMEAAAA4IjMzM9m6eXNjG/MZenUZkaPzebC5Qv7zr+8sHrb/DPzsz12CwAkghIAAACAI/PUc+eSi2v5nvXfzcz87M3vj3B+AODoCUoAAAAAjsjpR0/f03uM3Ov8AMDR8x4lAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAAAABAswQlAAAA7GuyMcnywmqeHy1neWE1k43Jsa4DAAB3Gg3dAAAAANPrysW1PHv1bMb1fJbWNvPZx1/MQ0+eOfI6117YzqXtRzLO+Sy9vJlcXMvTLz1x5HUAAOBO7igBAABgXzvrWxnXubyamYwzl9H29U7qjLavZ5zdOjfmsrO+1UkdAAC4kztKAAAA2NfM/GyWXt7M+MZclk5tZvTYbC5cvnDkdZYXVm+rMzM/e+Q1AABgL4ISAAAA9vXUc+eSi2v5nvXfzcz87M3vj3EdAAC4k6AEAACAfZ1+9HQv7xXSVx0AALiT9ygBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaJSgBAAAAAACaVWqtQ/dwV6WUV5J8fOg+4AT6c0n+99BNAM1yDAKG5BgEDMkxCBia4xAt+qpa69m9JhyLoAToRillpda6OHQfQJscg4AhOQYBQ3IMAobmOAS38+gtAAAAAACgWYISAAAAAACgWYISaNt7hm4AaJpjEDAkxyBgSI5BwNAch+AW3qMEAAAAAABoljtKAAAAAACAZglKAAAAAACAZglKoEGllEdKKb9RSlkrpbxUSvmBoXsC2lNKmSml/E4p5VeH7gVoSynly0op7yulvFxKuVpKedvQPQHtKKX8493rsI+WUn6ulPKGoXsCTq5Syk+XUq6VUj56y8/eVEr5QCnlY7ufv3zIHmEaCEqgTdeT/JNa67kk35Dk+0op5wbuCWjPDyS5OnQTQJN+LMn7a62PJfm6OBYBPSmlzCX5/iSLtdavSTKT5G8P2xVwwv3nJH/zjp+9O8kHa61fneSDu99D0wQl0KBa66dqrS/ufv0nufnHgblhuwJaUkr5yiTfmuQnh+4FaEsp5UuTfFOSn0qSWuvnaq1/PGxXQGNGSU6XUkZJziT5XwP3A5xgtdbfTPJHd/z4nUku7X59Kcl39NoUTCFBCTSulPLWJBeS/PawnQCN+XdJfijJjaEbAZrz55O8kuQ/7T7+7ydLKW8cuimgDbXWzSQ/muT3k3wqyf+ptf76sF0BDXq41vqp3a8/neThIZuBaSAogYaVUmaT/NckP1hr/b9D9wO0oZTybUmu1VpXh+4FaNIoydcn+Yla64UkfxqPmwB6svs+AO/MzdD2K5K8sZTyd4ftCmhZrbUmqUP3AUMTlECjSimvy82Q5L211vHQ/QBNeXuSby+l/F6Sn0/yLaWUnxm2JaAhn0zyyVrra3fTvi83gxOAPvy1JL9ba32l1vr5JOMk3zhwT0B7/qCU8uYk2f18beB+YHCCEmhQKaXk5nO5r9Za/+3Q/QBtqbX+s1rrV9Za35qbb176fK3V/6QEelFr/XSST5RS5nd/9I4kawO2BLTl95N8QynlzO512Tty8z0jAfr0K0me2f36mSS/PGAvMBUEJdCmtyf5e7n5v7g/tPvxt4ZuCgCgJ/8oyXtLKR9Ocj7Jvxq4H6ARu3ezvS/Ji0k+kpt/l3nPoE0BJ1op5eeS/I8k86WUT5ZSvjvJjyT566WUj+XmnW4/MmSPMA3KzcfQAQAAAAAAtMcdJQAAAAAAQLMEJQAAAAAAQLMEJQAAAAAAQLMEJQAAAAAAQLMEJQAAAAAAQLMEJQAAQO9KKX+2lPKh3Y9Pl1I2d7/eKqX8x45q/mAp5e8fMP3bSin/oovaAADA9Cq11qF7AAAAGlZK+eEkW7XWH+2wxijJi0m+vtZ6fZ95yu48b6+1bnfVCwAAMF3cUQIAAEyNUspfLaX86u7XP1xKuVRK+a1SysdLKUullH9TSvlIKeX9pZTX7c73RClluZSyWkr576WUN++x6G9J8uJrIUkp5ftLKWullA+XUn4+SerN/0V2Ocm39TJYAABgKghKAACAafYXcjPk+PYkP5PkN2qtjyeZJPnW3bDkPyT5zlrrE0l+Osm/3GM5b0+yesv3705yodb6tUn+4S0/X0nyV458FAAAwNQaDd0AAADAAX6t1vr5UspHkswkef/uzz+S5K1J5pN8TZIP3HxyVmaSfGqP5bw5ydVbvv9wkveWUn4pyS/d8vNrSb7iKAcAAABMN0EJAAAwzV5NklrrjVLK5+sX32TxRm5ez5QkL9Va33aX5UySvOGW7781yTcluZjkn5dSHt99LNcbducFAAAa4dFbAADAcbae5Gwp5W1JUkp5XSllYY/5rib5i7vznErySK31N5L80yRfmmR2d76/lOSjnXcNAABMDUEJAABwbNVaP5fkO5P861LK/0zyoSTfuMesv5abd5AkNx/P9TO7j/P6nST/vtb6x7vTvjnJf+u2awAAYJqUL965DgAAcHKVUn4xyQ/VWj+2z/SHk/xsrfUd/XYGAAAMSVACAAA0oZQyn+ThWutv7jP9ySSfr7V+qN/OAACAIQlKAAAAAACAZnmPEgAAAAAAoFmCEgAAAAAAoFmCEgAAAAAAoFmCEgAAAAAAoFmCEgAAAAAAoFn/D/PugwYbLjGAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2304x864 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#rose.display_audio(rose.pretty_MIDI)\n",
        "\n",
        "pm = kita.get_pretty_midi(669) #<-index number of the sample we are inspecting\n",
        "#kita.display_audio(pm)\n",
        "\n",
        "instrument1 = rose.fetch_inspect_MIDI_instrument(rose.pretty_MIDI, show=True)\n",
        "instrument2 = kita.fetch_inspect_MIDI_instrument(pm)\n",
        "\n",
        "#these perform the same function but using different params to specify different files:\n",
        "song1 = rose.midi_to_notes_df(prettyMidi=rose.pretty_MIDI)\n",
        "song2_cust_i = kita.midi_to_notes_df(midi_file_index=6)\n",
        "\n",
        "\n",
        "rose.plot_piano_roll(song1, count=128)\n",
        "kita.plot_piano_roll(song2_cust_i)\n",
        "\n",
        "rose.plot_note_distributions(song1)\n",
        "kita.plot_note_distributions(song2_cust_i)\n",
        "\n",
        "EXAMPLE = 'example.midi'\n",
        "example_midi_file = rose.notes_to_MIDI_file(notes=song1,\n",
        "                                            out_file=EXAMPLE,\n",
        "                                            instrument_name=instrument1[1])\n",
        "#rose.display_audio(example_midi_file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rWFCbCANaJ7n"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "\n",
        "Feature-Finders listen for simple time-events such as notes or peaks or pulses.\n",
        "Measure-Takers notice certain patterns of time-events like 3/4, 4/4, 6/8.\n",
        "Difference-Finders notice that figure X is like figure Y, but higher by a fifth.\n",
        "Structure-Builders notice that three phrases form an a regular \"sequence.\"\n",
        "   \n",
        "  -taken from:\n",
        "    Music, Mind, and Meaning\n",
        "    Marvin Minsky\n",
        "    Computer Music Journal, Fall 1981, Vol. 5, Number 3\n",
        "\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GIOdXJEgItey"
      },
      "outputs": [],
      "source": [
        "class MusicalAgent(Bach, tf.keras.Model):\n",
        "\n",
        "  def __init__(self, annotations_file, audio_dir, seq_length=50):\n",
        "    '''class requires inputs= , outputs= (from Model class) AND seq_length from itself'''\n",
        "    super().__init__(ANNOTATIONS_FILE, AUDIO_DIR)\n",
        "    super(Bach, self).__init__(seq_length)\n",
        "    self.sequence_length = seq_length \n",
        "    self.vocab_size = 128\n",
        "    self.__input_shape = (seq_length, 3)\n",
        "    self.learning_rate = 0.0018\n",
        "\n",
        "    self.input_layer = tf.keras.Input(self.__input_shape)\n",
        "    self.lstm_layer = tf.keras.layers.LSTM(128)\n",
        "    \n",
        "    self.loss = self._define_loss()\n",
        "    self.optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)\n",
        "    \n",
        "\n",
        "  def _get_outputs(self, x):\n",
        "    outputs = {'pitch' : tf.keras.layers.Dense(128, name='pitch')(x),\n",
        "               'step' : tf.keras.layers.Dense(1, name='step')(x),\n",
        "               'duration' : tf.keras.layers.Dense(1, name='duration')(x)}\n",
        "    return outputs \n",
        "\n",
        "  def call_layers2model(self):\n",
        "    '''returns architecture of NN for the intake of data aka Model'''\n",
        "    x0 = self.input_layer\n",
        "    x1 = self.lstm_layer(x0)\n",
        "    y = self._get_outputs(x=x1)\n",
        "    model = tf.keras.Model(inputs=x0,\n",
        "                           outputs=y)\n",
        "    return model\n",
        "\n",
        "  def _mse_with_positive_pressure(self, y_true:tf.Tensor, y_pred:tf.Tensor):\n",
        "    mse = (y_true - y_pred) ** 2\n",
        "    positive_pressure = 10 * tf.maximum(-y_pred, 0.0)\n",
        "    return tf.reduce_mean(mse + positive_pressure)\n",
        "\n",
        "  def _define_loss(self): \n",
        "    loss = {'pitch' : tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "            'step' : self._mse_with_positive_pressure,\n",
        "            'duration' : self._mse_with_positive_pressure}\n",
        "    return loss \n",
        "\n",
        "  def _define_callbacks(self):\n",
        "    callbacks = [tf.keras.callbacks.ModelCheckpoint(filepath='./training_checkpoints/ckpt_{epoch}',\n",
        "                                                    save_weights_only=True),\n",
        "                 tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
        "                                                  patience=5,\n",
        "                                                  verbose=1,\n",
        "                                                  restore_best_weights=True)]\n",
        "    return callbacks \n",
        "\n",
        "    \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p-Ieoi_FglS4"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "class Amadeus(MusicalAgent):\n",
        "  \n",
        "  '''Amadeus is the class that inherits from Bach the ability to fetch and read music.\n",
        "     Now this class will learn the music!'''\n",
        "  \n",
        "  def __init__(self,\n",
        "               sampling_rate:Optional[int]=None, \n",
        "               file_index:Optional[int]=None):\n",
        "\n",
        "    '''You cant make a pretty midi object without a file_index, thus a child class that is \n",
        "     instantiated without this param will ALSO be devoid of a pretty midi attribute. Calling\n",
        "     the file index in the child class will trigger the 'file_index' block of the parent class\n",
        "     init!'''\n",
        "\n",
        "    super().__init__(ANNOTATIONS_FILE, AUDIO_DIR)#call here even if not required!\n",
        "    self.key_order = ['pitch', 'step', 'duration']\n",
        "    #custom sampling rate\n",
        "    if sampling_rate is not None:\n",
        "      self.sampling_rate = sampling_rate\n",
        "    self.model = self.call_layers2model()\n",
        "    self.compile_model()\n",
        "  \n",
        "  ##########PREPROCESSING BLOCK################################################\n",
        "  def _get_all_notes(self, num_files:int, all_notes:list=[]) -> list:\n",
        "    '''specify [0] for all_notes and [1] for n_notes(note count)'''\n",
        "    for files in self.__getitem__()[0][:num_files]:\n",
        "      notes = self.midi_to_notes_df(midi_file=files)\n",
        "      all_notes.append(notes)\n",
        "    all_notes = pd.concat(all_notes)\n",
        "    n_notes = len(all_notes)\n",
        "    '''n_notes is now available by specing [1] after this method'''\n",
        "    return all_notes, n_notes\n",
        "\n",
        "\n",
        "  def create_training_dataset(self, num_files:int):\n",
        "\n",
        "    '''create a dataset consiting of n files where n is the num_files.\n",
        "       ***************************************************************\n",
        "       num_files(int): the number of files to add to the dataset, \n",
        "       all_notes(list)=[] : an empty container'''\n",
        "\n",
        "    all_notes = self._get_all_notes(num_files)[0]\n",
        "    n_notes = self._get_all_notes(num_files)[1]\n",
        "    training_notes = np.stack([all_notes[key] for key in self.key_order], axis=1)\n",
        "    training_ds = tf.data.Dataset.from_tensor_slices(training_notes)\n",
        "    print(f\"\\033[0;31mTraining dataset derived from\\033[0m {num_files} \\033[0;31mfiles which consists of\\033[0m {n_notes} \\033[0;31mnotes!\\033[0m\")\n",
        "    return training_ds\n",
        "\n",
        "\n",
        "  def create_sequences(self,\n",
        "                       dataset:tf.data.Dataset,\n",
        "                       seq_length:Optional[int]=50,\n",
        "                       vocab_size=128,\n",
        "                       show_spec:Optional[bool]=False) -> tf.data.Dataset:\n",
        "    '''returns tf dataset in sequence form'''\n",
        "    seq_length = seq_length + 1\n",
        "    windows_ds = dataset.window(seq_length, shift=1,\n",
        "                                stride=1, drop_remainder=True)\n",
        "    \n",
        "    #these are both functions\n",
        "    flatten = lambda x: x.batch(seq_length, drop_remainder=True)\n",
        "    sequences = windows_ds.flat_map(flatten)\n",
        "\n",
        "    #normalize note pitch\n",
        "    def scale_pitch(x):\n",
        "      x = x / [vocab_size, 1.0, 1.0]\n",
        "      return x \n",
        "\n",
        "    def split_labels(sequences):\n",
        "      inputs = sequences[:-1]\n",
        "      labels_dense = sequences[-1]\n",
        "      labels = {key : labels_dense[i] for i, key in enumerate(self.key_order)}\n",
        "      return scale_pitch(inputs), labels \n",
        "\n",
        "    seq_ds = sequences.map(split_labels, \n",
        "                                 num_parallel_calls=tf.data.AUTOTUNE)\n",
        "    if show_spec:\n",
        "      print(f\"Sequence Map:\\n {seq_ds.element_spec}\\n\")\n",
        "    return seq_ds\n",
        "\n",
        "\n",
        "  def batch_ds(self, num_files:int, seq_len:int, seq_ds, batch_size:Optional[int]=64):\n",
        "    \n",
        "    '''last arg is a dataset of a song broken into seq_lenSequences.\n",
        "     batch examples and config dataset for interperentation. Takes in\n",
        "     the create_training_dataset -> create_sequences ds as \"seq_ds\"'''\n",
        "\n",
        "    '''batch_size is set to 64 by default'''\n",
        "\n",
        "    n_notes = self._get_all_notes(num_files)[1] #n_notes value from class\n",
        "    buff = n_notes - seq_len #number of items in dataset\n",
        "    train_ds = (seq_ds \\\n",
        "                .shuffle(buff).batch(batch_size, drop_remainder=True) \\\n",
        "                .cache().prefetch(tf.data.experimental.AUTOTUNE))\n",
        "    return train_ds\n",
        "\n",
        "\n",
        "  def _parse_train_test(self, notes):\n",
        "    x = np.stack([notes['pitch'], notes['pitch']], axis=0)\n",
        "    y = np.stack([notes['start'], notes['end']], axis=0)\n",
        "    X_train, X_test, y_train, y_test = train_test_split(x,\n",
        "                                                        y,\n",
        "                                                        test_size=0.2,\n",
        "                                                        random_state=42)\n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "\n",
        "  #####################MODEL BLOCK##################\n",
        "  def compile_model(self):\n",
        "    '''compiles model with learning params'''\n",
        "    return self.model.compile(loss=self.loss,\n",
        "                              optimizer=self.optimizer,\n",
        "                              metrics=['accuracy'])\n",
        "    \n",
        "  def practice_music(self, training_dataset, epochs:int):\n",
        "    '''fits the model with epochs and callbacks'''\n",
        "    history = self.model.fit(x=training_dataset,\n",
        "                             epochs=epochs,\n",
        "                             verbose=1,\n",
        "                             callbacks=self._define_callbacks())\n",
        "    return history\n",
        "\n",
        "  \n",
        "  def data_evaluation(self, training_dataset):\n",
        "    '''evaluates data based on compiled model'''\n",
        "    print(f\"\\033[0;34mEVALUATION BASED ON THE TRAINING DATA: \\n\\033[0m\")\n",
        "    return self.model.evaluate(training_dataset)\n",
        "\n",
        "\n",
        "  def show_feature_eval_dict(self, training_ds):\n",
        "    '''dict of evaluated features based on training ds(pre train)'''\n",
        "    x = enumerate(self.data_evaluation(training_ds))\n",
        "    return dict(x)\n",
        "\n",
        "\n",
        "  def plot_loss_and_accuracy(self, history, \n",
        "                             feature_to_analyze:str, training_ds):\n",
        "    #helper function block\n",
        "    history_number = input(\"Enter the history number: \")\n",
        "    nodes = input(\"Enter the node settings: \")\n",
        "    epochz = input(\"How many epochs? \")\n",
        "    opt = input(\"Enter the optimizer: \")\n",
        "    settings = f\"HISTORY NUMBER:{history_number}===>{epochz}E, OPT:{opt}, NODES:{nodes}\"\n",
        "    train_loss = history.history[f\"{feature_to_analyze}_loss\"]\n",
        "    train_acc = history.history[f\"{feature_to_analyze}_accuracy\"]\n",
        "    fig = plt.figure(figsize=(20,10))\n",
        "    #subplot1\n",
        "    ax = fig.add_subplot(121)\n",
        "    ax.plot(range(len(train_loss)), train_loss, label=f\"Train {feature_to_analyze} Loss\", color=\"red\");\n",
        "    plt.xlabel(\"Epochs\"); plt.ylabel(\"Loss\");\n",
        "    plt.title(f\"Train Loss({feature_to_analyze})\");\n",
        "    plt.legend(loc=\"best\");\n",
        "    plt.suptitle(settings, ha=\"center\", fontsize=18, bbox={\"alpha\":0.5, \"pad\":1});\n",
        "    #subplot2\n",
        "    ax_ = fig.add_subplot(122)\n",
        "    ax_.plot(range(len(train_loss)), train_acc, label=f\"Train {feature_to_analyze} Accuracy\", color=\"blue\");\n",
        "    plt.xlabel(\"Epochs\"); plt.ylabel(f\"Train {feature_to_analyze} Accuracy\");\n",
        "    plt.title(f\"Train Accuracy({feature_to_analyze})\");\n",
        "    plt.legend(loc=\"best\");\n",
        "    plt.figtext(0.5, -0.1, \n",
        "                f\"Model loss on the training set: {round((self.data_evaluation(training_ds)[2] * 100), 2)}%\\n Model accuracy on the training set: {round((self.data_evaluation(training_ds)[5] * 100), 2)}%\", \n",
        "                ha=\"center\", fontsize=18, bbox={\"facecolor\":\"orange\",   \"alpha\":0.5, \"pad\":3})\n",
        "    print(settings)\n",
        "      \n",
        "\n",
        "  def predict_next_note(self,\n",
        "                        notes: np.ndarray,  \n",
        "                        temperature: float = 1.0) -> int:\n",
        "    '''here notes is an nd array of a sequence'''\n",
        "    assert temperature > 0\n",
        "    # Add batch dimension\n",
        "    inputs = tf.expand_dims(notes, 0)\n",
        "\n",
        "    predictions = self.model.predict(inputs)\n",
        "    pitch_logits = predictions['pitch']\n",
        "    step = predictions['step']\n",
        "    duration = predictions['duration']\n",
        "  \n",
        "    pitch_logits /= temperature\n",
        "    pitch = tf.random.categorical(pitch_logits, num_samples=1)\n",
        "    pitch = tf.squeeze(pitch, axis=-1)\n",
        "    duration = tf.squeeze(duration, axis=-1)\n",
        "    step = tf.squeeze(step, axis=-1)\n",
        "\n",
        "    # `step` and `duration` values should be non-negative\n",
        "    step = tf.maximum(0, step)\n",
        "    duration = tf.maximum(0, duration)\n",
        "\n",
        "    return int(pitch), float(step), float(duration)\n",
        "\n",
        "\n",
        "  def improvise(self, song_df:pd.DataFrame, \n",
        "                temperature:int, num_predictions:int,\n",
        "                display_audio:Optional[bool]=False) -> pd.DataFrame:\n",
        "    #gather a sample of notes from the song and stack pitch, step, duration\n",
        "    sample_notes = np.stack([song_df[key] for key in self.key_order], axis=1)\n",
        "    # The initial sequence of notes; pitch is normalized similar to training seq\n",
        "    #so = the first seq_len amt of rows in song1 samples / an array of vocab size, 1, 1\n",
        "    input_notes = (sample_notes[:self.sequence_length] / np.array([self.vocab_size, 1, 1]))\n",
        "\n",
        "    generated_notes=[]\n",
        "    prev_start = 0\n",
        "    for _ in range(num_predictions):\n",
        "      pitch, step, duration = self.predict_next_note(input_notes, temperature)\n",
        "      start = prev_start + step\n",
        "      end = start + duration\n",
        "      input_note = (pitch, step, duration)\n",
        "      generated_notes.append((*input_note, start, end))\n",
        "      input_notes = np.delete(input_notes, 0, axis=0)\n",
        "      input_notes = np.append(input_notes, np.expand_dims(input_note, 0), axis=0)\n",
        "      prev_start = start\n",
        "\n",
        "    prediction = pd.DataFrame(generated_notes, \n",
        "                              columns=(*self.key_order, 'start', 'end'))\n",
        "    if display_audio:\n",
        "      self.plot_piano_roll(prediction)\n",
        "      self.plot_note_distributions(prediction)\n",
        "      out_file = 'prediction1.mid'\n",
        "      pred_pm = self.notes_to_MIDI_file(prediction, out_file, instrument1[1]) #replace this with class attribute!\n",
        "      super().display_audio(pred_pm)\n",
        "      return prediction\n",
        "    else:\n",
        "      return prediction \n",
        "\n",
        "\n",
        "  \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4Zz78MIBd5e",
        "outputId": "2f3b636e-119a-4f10-ddb1-d19c92617394"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New Bach Instance Created!\n",
            "\n",
            "New Bach Instance Created!\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#example of class without params(wanting to specify pretty_midi object explicitly)\n",
        "mozart = Amadeus()\n",
        "\n",
        "#example of class with built in pm object based on parameters. Also includes other constructor variables\n",
        "haydn = Amadeus(sampling_rate=16000,\n",
        "                file_index=rose.file_index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABURTdHFY8hP",
        "outputId": "4e51d3f7-34d6-4bf0-a60b-95e23b9ab5dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0;31mTraining dataset derived from\u001b[0m 5 \u001b[0;31mfiles which consists of\u001b[0m 79822 \u001b[0;31mnotes!\u001b[0m\n",
            "Sequence Map:\n",
            " (TensorSpec(shape=(50, 3), dtype=tf.float64, name=None), {'pitch': TensorSpec(shape=(), dtype=tf.float64, name=None), 'step': TensorSpec(shape=(), dtype=tf.float64, name=None), 'duration': TensorSpec(shape=(), dtype=tf.float64, name=None)})\n",
            "\n"
          ]
        }
      ],
      "source": [
        "N_SEQUENCES = 50\n",
        "N_FILES = 5\n",
        "\n",
        "#create training data from num_files as arg\n",
        "X_train_raw = haydn.create_training_dataset(N_FILES)\n",
        "\n",
        "#create ds that breaks raw train into specified n_sequences, show_spec if you wanto to print that\n",
        "X_train_seq = haydn.create_sequences(X_train_raw, N_SEQUENCES, show_spec=True)\n",
        "\n",
        "#batch out data, ensuring that seq_len(train_ds) == seq_len(X_train_seq)\n",
        "train_ds = haydn.batch_ds(num_files=N_FILES,\n",
        "                          seq_len=N_SEQUENCES,\n",
        "                          seq_ds=X_train_seq)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "haydn.show_feature_eval_dict(train_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMsacOwdeodg",
        "outputId": "8671f194-6ffc-4148-b200-78f010be1346"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0;34mEVALUATION BASED ON THE TRAINING DATA: \n",
            "\u001b[0m\n",
            "622/622 [==============================] - 3s 5ms/step - loss: 5.4612 - duration_loss: 0.2267 - pitch_loss: 4.8470 - step_loss: 0.3875 - duration_accuracy: 0.0000e+00 - pitch_accuracy: 0.0211 - step_accuracy: 0.0384\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 5.461216449737549,\n",
              " 1: 0.2267370969057083,\n",
              " 2: 4.847019672393799,\n",
              " 3: 0.3874559700489044,\n",
              " 4: 0.0,\n",
              " 5: 0.02107616513967514,\n",
              " 6: 0.03843448683619499}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UQKIyziAps_0"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "history1 = haydn.practice_music(training_dataset=train_ds, \n",
        "                                epochs=100)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "haydn.plot_loss_and_accuracy(history1, \"pitch\", train_ds)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Cipdn_Bnh5MT",
        "outputId": "dea8bf93-4be5-436d-f3f0-de8d16eba86d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the history number: 1\n",
            "Enter the node settings: LSTM(128), Dense(1), Dense(1)\n",
            "How many epochs? 100\n",
            "Enter the optimizer: Adam\n",
            "\u001b[0;34mEVALUATION BASED ON THE TRAINING DATA: \n",
            "\u001b[0m\n",
            "622/622 [==============================] - 4s 5ms/step - loss: 5.4612 - duration_loss: 0.2267 - pitch_loss: 4.8470 - step_loss: 0.3875 - duration_accuracy: 0.0000e+00 - pitch_accuracy: 0.0211 - step_accuracy: 0.0384\n",
            "\u001b[0;34mEVALUATION BASED ON THE TRAINING DATA: \n",
            "\u001b[0m\n",
            "622/622 [==============================] - 3s 5ms/step - loss: 5.4612 - duration_loss: 0.2267 - pitch_loss: 4.8470 - step_loss: 0.3875 - duration_accuracy: 0.0000e+00 - pitch_accuracy: 0.0211 - step_accuracy: 0.0384\n",
            "HISTORY NUMBER:1===>100E, OPT:Adam, NODES:LSTM(128), Dense(1), Dense(1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history2 = haydn.practice_music(train_ds, 100)"
      ],
      "metadata": {
        "id": "VJ79pQjbtimI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "haydn.plot_loss_and_accuracy(history2, \"pitch\", train_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5K9n2_1ot3z4",
        "outputId": "99d61eed-df23-41a6-c587-8cf21eef8d6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the history number: 2\n",
            "Enter the node settings: HISTORY1\n",
            "How many epochs? 100\n",
            "Enter the optimizer: Adam(lr=0.002)\n",
            "\u001b[0;34mEVALUATION BASED ON THE TRAINING DATA: \n",
            "\u001b[0m\n",
            "622/622 [==============================] - 4s 5ms/step - loss: 2.9063 - duration_loss: 0.0709 - pitch_loss: 2.8152 - step_loss: 0.0202 - duration_accuracy: 0.0000e+00 - pitch_accuracy: 0.2338 - step_accuracy: 0.0383\n",
            "\u001b[0;34mEVALUATION BASED ON THE TRAINING DATA: \n",
            "\u001b[0m\n",
            "622/622 [==============================] - 3s 5ms/step - loss: 2.9063 - duration_loss: 0.0709 - pitch_loss: 2.8152 - step_loss: 0.0202 - duration_accuracy: 0.0000e+00 - pitch_accuracy: 0.2338 - step_accuracy: 0.0383\n",
            "HISTORY NUMBER:2===>100E, OPT:Adam(lr=0.002), NODES:HISTORY1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history3 = haydn.practice_music(train_ds, 100)"
      ],
      "metadata": {
        "id": "JEI_CqBQyeZP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "haydn.plot_loss_and_accuracy(history3, \"pitch\", train_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6LG57kRgy0Yq",
        "outputId": "70a956f1-a437-4a47-e0e8-ad2e2ddde38c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the history number: 3\n",
            "Enter the node settings: HISTORY1\n",
            "How many epochs? 100\n",
            "Enter the optimizer: Adam(lr=0.001)\n",
            "\u001b[0;34mEVALUATION BASED ON THE TRAINING DATA: \n",
            "\u001b[0m\n",
            "622/622 [==============================] - 4s 6ms/step - loss: 3.2618 - duration_loss: 0.0959 - pitch_loss: 3.1386 - step_loss: 0.0273 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1638 - step_accuracy: 0.0384\n",
            "\u001b[0;34mEVALUATION BASED ON THE TRAINING DATA: \n",
            "\u001b[0m\n",
            "622/622 [==============================] - 3s 5ms/step - loss: 3.2618 - duration_loss: 0.0959 - pitch_loss: 3.1386 - step_loss: 0.0273 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1638 - step_accuracy: 0.0384\n",
            "HISTORY NUMBER:3===>100E, OPT:Adam(lr=0.001), NODES:HISTORY1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history4 = haydn.practice_music(train_ds, 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8x9xWaS6JhH",
        "outputId": "c143d514-cc50-4cb0-f614-07b6ea64de70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "622/622 [==============================] - 8s 8ms/step - loss: 4.3023 - duration_loss: 0.1471 - pitch_loss: 4.1084 - step_loss: 0.0469 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0329 - step_accuracy: 0.0381\n",
            "Epoch 2/100\n",
            "622/622 [==============================] - 8s 12ms/step - loss: 4.0810 - duration_loss: 0.1285 - pitch_loss: 3.9149 - step_loss: 0.0376 - duration_accuracy: 0.0000e+00 - pitch_accuracy: 0.0395 - step_accuracy: 0.0383\n",
            "Epoch 3/100\n",
            "622/622 [==============================] - 9s 14ms/step - loss: 4.0239 - duration_loss: 0.1276 - pitch_loss: 3.8592 - step_loss: 0.0371 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0435 - step_accuracy: 0.0383\n",
            "Epoch 4/100\n",
            "622/622 [==============================] - 7s 11ms/step - loss: 4.0069 - duration_loss: 0.1269 - pitch_loss: 3.8432 - step_loss: 0.0368 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0445 - step_accuracy: 0.0382\n",
            "Epoch 5/100\n",
            "622/622 [==============================] - 7s 12ms/step - loss: 3.9919 - duration_loss: 0.1264 - pitch_loss: 3.8290 - step_loss: 0.0366 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0453 - step_accuracy: 0.0382\n",
            "Epoch 6/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.9703 - duration_loss: 0.1254 - pitch_loss: 3.8086 - step_loss: 0.0363 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0479 - step_accuracy: 0.0382\n",
            "Epoch 7/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.9400 - duration_loss: 0.1242 - pitch_loss: 3.7799 - step_loss: 0.0359 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0484 - step_accuracy: 0.0382\n",
            "Epoch 8/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.9109 - duration_loss: 0.1228 - pitch_loss: 3.7528 - step_loss: 0.0353 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0509 - step_accuracy: 0.0382\n",
            "Epoch 9/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.8828 - duration_loss: 0.1211 - pitch_loss: 3.7269 - step_loss: 0.0348 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0538 - step_accuracy: 0.0383\n",
            "Epoch 10/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.8579 - duration_loss: 0.1196 - pitch_loss: 3.7039 - step_loss: 0.0343 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0575 - step_accuracy: 0.0383\n",
            "Epoch 11/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.8308 - duration_loss: 0.1185 - pitch_loss: 3.6788 - step_loss: 0.0336 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0609 - step_accuracy: 0.0383\n",
            "Epoch 12/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.8013 - duration_loss: 0.1160 - pitch_loss: 3.6525 - step_loss: 0.0329 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0639 - step_accuracy: 0.0383\n",
            "Epoch 13/100\n",
            "622/622 [==============================] - 6s 9ms/step - loss: 3.7725 - duration_loss: 0.1133 - pitch_loss: 3.6270 - step_loss: 0.0323 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0674 - step_accuracy: 0.0383\n",
            "Epoch 14/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.7472 - duration_loss: 0.1103 - pitch_loss: 3.6049 - step_loss: 0.0319 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0702 - step_accuracy: 0.0383\n",
            "Epoch 15/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.7195 - duration_loss: 0.1079 - pitch_loss: 3.5812 - step_loss: 0.0304 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0747 - step_accuracy: 0.0383\n",
            "Epoch 16/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.6989 - duration_loss: 0.1057 - pitch_loss: 3.5619 - step_loss: 0.0313 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0775 - step_accuracy: 0.0383\n",
            "Epoch 17/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.6716 - duration_loss: 0.1030 - pitch_loss: 3.5380 - step_loss: 0.0306 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0820 - step_accuracy: 0.0383\n",
            "Epoch 18/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.6490 - duration_loss: 0.1004 - pitch_loss: 3.5187 - step_loss: 0.0300 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0840 - step_accuracy: 0.0383\n",
            "Epoch 19/100\n",
            "622/622 [==============================] - 6s 9ms/step - loss: 3.6288 - duration_loss: 0.0989 - pitch_loss: 3.4998 - step_loss: 0.0302 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0883 - step_accuracy: 0.0383\n",
            "Epoch 20/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.6075 - duration_loss: 0.0982 - pitch_loss: 3.4802 - step_loss: 0.0291 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0913 - step_accuracy: 0.0383\n",
            "Epoch 21/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.5814 - duration_loss: 0.0958 - pitch_loss: 3.4570 - step_loss: 0.0287 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0931 - step_accuracy: 0.0383\n",
            "Epoch 22/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.5674 - duration_loss: 0.0941 - pitch_loss: 3.4428 - step_loss: 0.0305 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0978 - step_accuracy: 0.0383\n",
            "Epoch 23/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.5460 - duration_loss: 0.0931 - pitch_loss: 3.4236 - step_loss: 0.0293 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.0980 - step_accuracy: 0.0383\n",
            "Epoch 24/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.5247 - duration_loss: 0.0920 - pitch_loss: 3.4035 - step_loss: 0.0292 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1016 - step_accuracy: 0.0383\n",
            "Epoch 25/100\n",
            "622/622 [==============================] - 7s 10ms/step - loss: 3.5022 - duration_loss: 0.0901 - pitch_loss: 3.3825 - step_loss: 0.0297 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1044 - step_accuracy: 0.0383\n",
            "Epoch 26/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.4804 - duration_loss: 0.0888 - pitch_loss: 3.3637 - step_loss: 0.0279 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1087 - step_accuracy: 0.0383\n",
            "Epoch 27/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.4618 - duration_loss: 0.0870 - pitch_loss: 3.3469 - step_loss: 0.0280 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1102 - step_accuracy: 0.0383\n",
            "Epoch 28/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.4498 - duration_loss: 0.0867 - pitch_loss: 3.3356 - step_loss: 0.0275 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1142 - step_accuracy: 0.0383\n",
            "Epoch 29/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.4329 - duration_loss: 0.0863 - pitch_loss: 3.3176 - step_loss: 0.0290 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1175 - step_accuracy: 0.0383\n",
            "Epoch 30/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.4075 - duration_loss: 0.0834 - pitch_loss: 3.2968 - step_loss: 0.0273 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1230 - step_accuracy: 0.0383\n",
            "Epoch 31/100\n",
            "622/622 [==============================] - 6s 9ms/step - loss: 3.3893 - duration_loss: 0.0828 - pitch_loss: 3.2793 - step_loss: 0.0273 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1240 - step_accuracy: 0.0383\n",
            "Epoch 32/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.3750 - duration_loss: 0.0827 - pitch_loss: 3.2646 - step_loss: 0.0277 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1286 - step_accuracy: 0.0383\n",
            "Epoch 33/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.3540 - duration_loss: 0.0803 - pitch_loss: 3.2473 - step_loss: 0.0265 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1322 - step_accuracy: 0.0383\n",
            "Epoch 34/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.3431 - duration_loss: 0.0805 - pitch_loss: 3.2352 - step_loss: 0.0273 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1347 - step_accuracy: 0.0383\n",
            "Epoch 35/100\n",
            "622/622 [==============================] - 6s 9ms/step - loss: 3.3229 - duration_loss: 0.0801 - pitch_loss: 3.2158 - step_loss: 0.0269 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1380 - step_accuracy: 0.0383\n",
            "Epoch 36/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.3074 - duration_loss: 0.0796 - pitch_loss: 3.2013 - step_loss: 0.0265 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1396 - step_accuracy: 0.0383\n",
            "Epoch 37/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.2972 - duration_loss: 0.0788 - pitch_loss: 3.1914 - step_loss: 0.0269 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1422 - step_accuracy: 0.0383\n",
            "Epoch 38/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.2813 - duration_loss: 0.0794 - pitch_loss: 3.1746 - step_loss: 0.0274 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1449 - step_accuracy: 0.0383\n",
            "Epoch 39/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.2690 - duration_loss: 0.0793 - pitch_loss: 3.1627 - step_loss: 0.0270 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1473 - step_accuracy: 0.0383\n",
            "Epoch 40/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.2545 - duration_loss: 0.0784 - pitch_loss: 3.1495 - step_loss: 0.0265 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1512 - step_accuracy: 0.0383\n",
            "Epoch 41/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.2392 - duration_loss: 0.0791 - pitch_loss: 3.1335 - step_loss: 0.0266 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1543 - step_accuracy: 0.0383\n",
            "Epoch 42/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.2292 - duration_loss: 0.0785 - pitch_loss: 3.1242 - step_loss: 0.0264 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1558 - step_accuracy: 0.0383\n",
            "Epoch 43/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.2122 - duration_loss: 0.0768 - pitch_loss: 3.1095 - step_loss: 0.0259 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1586 - step_accuracy: 0.0383\n",
            "Epoch 44/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.2029 - duration_loss: 0.0763 - pitch_loss: 3.1001 - step_loss: 0.0265 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1626 - step_accuracy: 0.0383\n",
            "Epoch 45/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.1776 - duration_loss: 0.0752 - pitch_loss: 3.0764 - step_loss: 0.0260 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1658 - step_accuracy: 0.0383\n",
            "Epoch 46/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.1732 - duration_loss: 0.0745 - pitch_loss: 3.0728 - step_loss: 0.0259 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1654 - step_accuracy: 0.0383\n",
            "Epoch 47/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.1561 - duration_loss: 0.0741 - pitch_loss: 3.0561 - step_loss: 0.0259 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1706 - step_accuracy: 0.0383\n",
            "Epoch 48/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.1504 - duration_loss: 0.0746 - pitch_loss: 3.0497 - step_loss: 0.0261 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1686 - step_accuracy: 0.0383\n",
            "Epoch 49/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.1501 - duration_loss: 0.0768 - pitch_loss: 3.0471 - step_loss: 0.0262 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1718 - step_accuracy: 0.0383\n",
            "Epoch 50/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.1290 - duration_loss: 0.0754 - pitch_loss: 3.0275 - step_loss: 0.0261 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1776 - step_accuracy: 0.0383\n",
            "Epoch 51/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.1090 - duration_loss: 0.0734 - pitch_loss: 3.0095 - step_loss: 0.0261 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1785 - step_accuracy: 0.0383\n",
            "Epoch 52/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.1079 - duration_loss: 0.0751 - pitch_loss: 3.0069 - step_loss: 0.0259 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1805 - step_accuracy: 0.0384\n",
            "Epoch 53/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.1074 - duration_loss: 0.0743 - pitch_loss: 3.0066 - step_loss: 0.0265 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1812 - step_accuracy: 0.0383\n",
            "Epoch 54/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.1016 - duration_loss: 0.0767 - pitch_loss: 2.9976 - step_loss: 0.0273 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1807 - step_accuracy: 0.0384\n",
            "Epoch 55/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.0832 - duration_loss: 0.0742 - pitch_loss: 2.9832 - step_loss: 0.0258 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1848 - step_accuracy: 0.0383\n",
            "Epoch 56/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.0641 - duration_loss: 0.0733 - pitch_loss: 2.9649 - step_loss: 0.0259 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1895 - step_accuracy: 0.0384\n",
            "Epoch 57/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.0609 - duration_loss: 0.0744 - pitch_loss: 2.9599 - step_loss: 0.0265 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1933 - step_accuracy: 0.0384\n",
            "Epoch 58/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.0548 - duration_loss: 0.0738 - pitch_loss: 2.9554 - step_loss: 0.0257 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1942 - step_accuracy: 0.0384\n",
            "Epoch 59/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.0462 - duration_loss: 0.0733 - pitch_loss: 2.9468 - step_loss: 0.0261 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1944 - step_accuracy: 0.0383\n",
            "Epoch 60/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.0355 - duration_loss: 0.0751 - pitch_loss: 2.9341 - step_loss: 0.0263 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1978 - step_accuracy: 0.0383\n",
            "Epoch 61/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.0263 - duration_loss: 0.0733 - pitch_loss: 2.9269 - step_loss: 0.0260 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.1985 - step_accuracy: 0.0383\n",
            "Epoch 62/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.0115 - duration_loss: 0.0724 - pitch_loss: 2.9136 - step_loss: 0.0255 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2007 - step_accuracy: 0.0384\n",
            "Epoch 63/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 3.0044 - duration_loss: 0.0723 - pitch_loss: 2.9060 - step_loss: 0.0261 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2020 - step_accuracy: 0.0384\n",
            "Epoch 64/100\n",
            "622/622 [==============================] - 7s 11ms/step - loss: 2.9926 - duration_loss: 0.0706 - pitch_loss: 2.8962 - step_loss: 0.0257 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2056 - step_accuracy: 0.0384\n",
            "Epoch 65/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.9792 - duration_loss: 0.0721 - pitch_loss: 2.8808 - step_loss: 0.0262 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2069 - step_accuracy: 0.0384\n",
            "Epoch 66/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.9691 - duration_loss: 0.0711 - pitch_loss: 2.8723 - step_loss: 0.0257 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2088 - step_accuracy: 0.0384\n",
            "Epoch 67/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.9742 - duration_loss: 0.0725 - pitch_loss: 2.8755 - step_loss: 0.0261 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2093 - step_accuracy: 0.0384\n",
            "Epoch 68/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.9745 - duration_loss: 0.0716 - pitch_loss: 2.8769 - step_loss: 0.0261 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2082 - step_accuracy: 0.0384\n",
            "Epoch 69/100\n",
            "622/622 [==============================] - 5s 9ms/step - loss: 2.9633 - duration_loss: 0.0718 - pitch_loss: 2.8643 - step_loss: 0.0272 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2101 - step_accuracy: 0.0383\n",
            "Epoch 70/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.9432 - duration_loss: 0.0700 - pitch_loss: 2.8455 - step_loss: 0.0277 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2154 - step_accuracy: 0.0383\n",
            "Epoch 71/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.9395 - duration_loss: 0.0716 - pitch_loss: 2.8409 - step_loss: 0.0270 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2208 - step_accuracy: 0.0384\n",
            "Epoch 72/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.9309 - duration_loss: 0.0708 - pitch_loss: 2.8337 - step_loss: 0.0265 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2207 - step_accuracy: 0.0384\n",
            "Epoch 73/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.9148 - duration_loss: 0.0701 - pitch_loss: 2.8187 - step_loss: 0.0260 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2250 - step_accuracy: 0.0384\n",
            "Epoch 74/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.9109 - duration_loss: 0.0708 - pitch_loss: 2.8143 - step_loss: 0.0258 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2252 - step_accuracy: 0.0384\n",
            "Epoch 75/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.9011 - duration_loss: 0.0709 - pitch_loss: 2.8043 - step_loss: 0.0259 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2251 - step_accuracy: 0.0384\n",
            "Epoch 76/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8964 - duration_loss: 0.0695 - pitch_loss: 2.8010 - step_loss: 0.0259 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2307 - step_accuracy: 0.0384\n",
            "Epoch 77/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8974 - duration_loss: 0.0699 - pitch_loss: 2.8011 - step_loss: 0.0264 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2295 - step_accuracy: 0.0383\n",
            "Epoch 78/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8828 - duration_loss: 0.0689 - pitch_loss: 2.7882 - step_loss: 0.0258 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2312 - step_accuracy: 0.0384\n",
            "Epoch 79/100\n",
            "622/622 [==============================] - 5s 9ms/step - loss: 2.9449 - duration_loss: 0.0699 - pitch_loss: 2.8489 - step_loss: 0.0261 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2206 - step_accuracy: 0.0384\n",
            "Epoch 80/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8838 - duration_loss: 0.0685 - pitch_loss: 2.7885 - step_loss: 0.0268 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2322 - step_accuracy: 0.0384\n",
            "Epoch 81/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8605 - duration_loss: 0.0702 - pitch_loss: 2.7639 - step_loss: 0.0264 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2381 - step_accuracy: 0.0384\n",
            "Epoch 82/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8594 - duration_loss: 0.0696 - pitch_loss: 2.7635 - step_loss: 0.0264 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2361 - step_accuracy: 0.0384\n",
            "Epoch 83/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8622 - duration_loss: 0.0705 - pitch_loss: 2.7656 - step_loss: 0.0261 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2365 - step_accuracy: 0.0384\n",
            "Epoch 84/100\n",
            "622/622 [==============================] - 5s 9ms/step - loss: 2.8460 - duration_loss: 0.0679 - pitch_loss: 2.7514 - step_loss: 0.0267 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2400 - step_accuracy: 0.0383\n",
            "Epoch 85/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8397 - duration_loss: 0.0684 - pitch_loss: 2.7452 - step_loss: 0.0262 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2381 - step_accuracy: 0.0384\n",
            "Epoch 86/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8213 - duration_loss: 0.0673 - pitch_loss: 2.7281 - step_loss: 0.0259 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2435 - step_accuracy: 0.0384\n",
            "Epoch 87/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8108 - duration_loss: 0.0669 - pitch_loss: 2.7180 - step_loss: 0.0259 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2467 - step_accuracy: 0.0384\n",
            "Epoch 88/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8256 - duration_loss: 0.0703 - pitch_loss: 2.7288 - step_loss: 0.0265 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2430 - step_accuracy: 0.0384\n",
            "Epoch 89/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8131 - duration_loss: 0.0682 - pitch_loss: 2.7189 - step_loss: 0.0260 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2471 - step_accuracy: 0.0384\n",
            "Epoch 90/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8067 - duration_loss: 0.0672 - pitch_loss: 2.7135 - step_loss: 0.0260 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2452 - step_accuracy: 0.0384\n",
            "Epoch 91/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.7965 - duration_loss: 0.0687 - pitch_loss: 2.7014 - step_loss: 0.0265 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2500 - step_accuracy: 0.0384\n",
            "Epoch 92/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.8007 - duration_loss: 0.0679 - pitch_loss: 2.7064 - step_loss: 0.0265 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2480 - step_accuracy: 0.0384\n",
            "Epoch 93/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.7749 - duration_loss: 0.0661 - pitch_loss: 2.6829 - step_loss: 0.0259 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2534 - step_accuracy: 0.0383\n",
            "Epoch 94/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.7711 - duration_loss: 0.0665 - pitch_loss: 2.6782 - step_loss: 0.0264 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2554 - step_accuracy: 0.0383\n",
            "Epoch 95/100\n",
            "622/622 [==============================] - 5s 9ms/step - loss: 2.7825 - duration_loss: 0.0677 - pitch_loss: 2.6888 - step_loss: 0.0260 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2535 - step_accuracy: 0.0383\n",
            "Epoch 96/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.7911 - duration_loss: 0.0710 - pitch_loss: 2.6937 - step_loss: 0.0264 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2544 - step_accuracy: 0.0384\n",
            "Epoch 97/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.7706 - duration_loss: 0.0700 - pitch_loss: 2.6736 - step_loss: 0.0270 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2572 - step_accuracy: 0.0383\n",
            "Epoch 98/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.7570 - duration_loss: 0.0675 - pitch_loss: 2.6631 - step_loss: 0.0264 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2608 - step_accuracy: 0.0383\n",
            "Epoch 99/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.7605 - duration_loss: 0.0675 - pitch_loss: 2.6666 - step_loss: 0.0264 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2573 - step_accuracy: 0.0383\n",
            "Epoch 100/100\n",
            "622/622 [==============================] - 5s 8ms/step - loss: 2.7491 - duration_loss: 0.0665 - pitch_loss: 2.6559 - step_loss: 0.0267 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2616 - step_accuracy: 0.0383\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "haydn.plot_loss_and_accuracy(history4, \"pitch\", train_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nHDY2qX-6S4A",
        "outputId": "0f084ebf-a91c-4e0b-e989-a11c62bf7715"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the history number: 4\n",
            "Enter the node settings: HISTORY1\n",
            "How many epochs? 100\n",
            "Enter the optimizer: Adam(0.0018)\n",
            "\u001b[0;34mEVALUATION BASED ON THE TRAINING DATA: \n",
            "\u001b[0m\n",
            "622/622 [==============================] - 4s 6ms/step - loss: 2.9941 - duration_loss: 0.0743 - pitch_loss: 2.8924 - step_loss: 0.0274 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2120 - step_accuracy: 0.0384\n",
            "\u001b[0;34mEVALUATION BASED ON THE TRAINING DATA: \n",
            "\u001b[0m\n",
            "622/622 [==============================] - 3s 5ms/step - loss: 2.9941 - duration_loss: 0.0743 - pitch_loss: 2.8924 - step_loss: 0.0274 - duration_accuracy: 2.5121e-05 - pitch_accuracy: 0.2120 - step_accuracy: 0.0384\n",
            "HISTORY NUMBER:4===>100E, OPT:Adam(0.0018), NODES:HISTORY1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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KbtJhidB3z93VGDgCAEPvYWWqmJR8e0neAI/TrgC8vfwc5Sf7qGJS8v2HgirDYhz67rm7PNb6OMvh9alt2c/28DisyoZ934+HUuVInP7M1/6BIwAQKo0n8eY/fSu0YXWW41uHO6uLDC2VGZE9+QQAuM21bZq/xm2tV1tP7Y53VJwK+mW4butXWZAeReTAqfvC+o3a47E1RJt2f9cjWNqGfSu6FL957z0Ff5v6zOl/3Pzb8s+eneK2mTSB6aTHLSr/87dRxW/dd/fpf9z824IXb/rD6bm3Pl624PfXB85F5D9nUu2G+QOKXr/rwYK/TX2m8JVfzKlZ9V42ANhLj0aVzvvVLaf/Pv2pgpd+9rvSj38z3d1Qc9Z2zYfXpXrsDVG6zulNepzYTu7MgELpjBl791k9SSJzb9gC6VGYdi1Jb+k4tqUM077lGWfW+YkZe/cuKJRO67Etmf7Ltcm96wIDIKFIl00LAOrYtLN6xamjkxs/+84K6CjDYhzqmJTT9pK8/q0pv3ltuz7Urv0ox2Otj9P3uHpLQMACABDWb1RJWObEH6TTZqhd/X6rrzdx183Zoknps99jNibWbvy0xdcumMbrp77X0FPB1msSujb4/9/S++hcOStPxQGANqVP0PqoY5KtCrW2TdfDRuf7dRBKtdQkds/3r7dQaTxxkx9dCI9HWf3dP38mpQdSelD17T+mQnoUcdfNWeh/jmviu5gDh28SEbUVg0dE1GbS5VA7a4oNwX5ak18d37kBSpXDWVPc1Zof+o5ve5gPruoHAIbeQ3eFupOv7zmwUhXVqdBtqkq2l+S1OJ+Sq6Y4FgAU2rAzAS2lPsIVO/7+hdLp0Fd987cbLUc3J1nyNo1WJ3Q7Gju+bXMIRA39eZ4qOum05fi2YRf67mtE7vW7pcuhq9/2dd/GZfXbvuonXQ5dRM71u5vL67HWaxtfZ1vhgZiaVe/m2Ar3Z6uik07reuQ2GZ4R6jxpbdDDVV/hPe668LMCiTXfv3FNxed/eNiSt6lJUMJekhfVcGD1+LB+Y9Y217PB47QrQp3DLZ3XHZVX32NgadiAsas8DqvBtH3RTcVv3vPb0g8f+3ndli/6NAZzzgdH8ZEUANB2HtBkOIU6vnMR3E6t/fT+OACwntzZCdKjVCd0aZJWm9a/CABshQea9PzyP5f8f9xWU7t6RJsPruksHdZwTaeeeeq4zuZgaRS6MJeuS8Y+eNyqhn3f92qpTHvRYe/5p9FbWkrrz5K3KbXi8z88XPP9G0GD09aT23NUMSn52tS+tZEDpx4Tap3ZfGBVTpB9Sq1e9updbkttnKHXsI1h/UZvcBpLUmqWvTYtMK102JTWo5tGKMPjqg19R26MyJmyVJPQ7YS98EBu2fwnZ3scVmWT/Ss50rt+28LrtGkDDodnXrtCKDV20/aFU41rP8wo//cf7hYqrT0889oftMl9DjpK8jIqF78w2T+/7dSurgCg65JZfFZdPG7hMlUlqyLiyxS6MJf/OkOfkcUA4KwqaLY3YFvLcFYXpgBCNq5rpNCFuVQR8WWuuvJ29z7UdU4/AQCVC/863XxwTaqj/EREw77vu1R///pNQqNviBp2a5Phz+qEroXSYQ23ntod397tNmrL9cFWsLc/AEQOnr4zVJroUXfugVB47CVH+rWlHuEZE3f5ttHkvdOaz3pVZGINAJh2LcmWLkeLn1ctvY9CfYZ4rKZWXV/U8V1rAMCWv6e/q65c15o8rXUhXgeXqdr7eaSPOnM91Pe4ujJswLhVLmNp15plrw6t+f5fg1zG4u5hA8at0nfPDTqcjojoXHDYGhG1meXI+rGWI+vHtje/EAqE9R+7xrx/5bUV//7Dw8rI+FJ1bFqhJumq4rD+Y08F3oFsC2dtaSIAaNMGlDaXThWTUuqqK+9sKzzQ6awgg5Si8Uu7x2rSWI5v6WI+uHoshPCEZ0w84F9G2ICxxea8DRusx7aMqvr2pc5CqbbH3/jEN2iHqBG/WFm95J/31Kx8c2zSL/732/aU0RqGXkPLlRFxpea8jTnRo2ceAADzkY05yoj4UkOvIeW160LPVVyz4l93BS5TJ3bP63TL818HC9SFOk8CJy0FACndisbj7m6o0ZkPr+tpO7V7EJQqR0TOlCaTHIdSvfTlG5T6CGPc5Ec2N5fOfHB1l+Ynej2b/ySoHZVXFRlvj7/hNxsAbLAc35bYsHd5pq3wQHrt2g/71238zKpN7XMwLP2avcHm0GgLt6U2AgDUMSlNeiopw2NNAOA0lkTqgUpXXXkEACjDYpukVcekmADAbTZGBq4Ldi4BQHOT8TfHUX4iEQDUcV2afd9rkq4qtR7bAmfV6U6B61ymKj0ASJddaSvY16luy5fXAoC+56C9ba1PKOaDa1I95tqE8AHjNgHe3gPatP77bQV7B7rqynWqqE62xrTG9R9NBiASZjz3vr5rVjUAeBzW7SXv/dc9geUKrd6V+vBHLyn1EWcFWmp+eKfQtGPxTfXbvu4bPfKOg/7r3GZjfNIdL77e+PnnHDj1QMl7//Xr+i1fTA/LmPh9/JRfnXkPlXwwR28vPpzlNhuXNfZGc9WWJQCAJqWP0b9cV12FDh63SqGPaHJOKLQGt1DrLG6bqck5cS5leGzmCKHWWhRaQ5MekAp9hMlVV97Z47Aq2zp3GgBEj717j6uuItpyfOuwqm9fuq9xuTIyoThw/q9GqugkIwDYS44k6LvnnPOX+NZeH9wN1YlQqh26Lhkh591S6iOdSkNUldtsTHSbjRr/3oXN0XXLLvduo6bJDZ/WfNZHj7x9l+XYliG2kzuGFb5ye6Y6NvW0Or5Lsa5LZmFY+vjCwDnzWhLqM6S1wvqNLqnb/PlRZ2V+7+K3H/i1Kjq5UB3fuVib3KcoPOvaU4G9F9viXF8H6XaqGq+HrroKQ8O+7wc4K072FVpDfXjGNfn+5cRdN2ezvehgn4YDqyYAQqqik07HTX50C4iILgAGj4iozbSdM3Ya+o44FGydccWbM1tTRvyUxzarY9OqzQdXDXLWlHR111em2PJ3o37Ll1Kb1n9PwvRnlran8SadDi0AKA1RtubSCY3ODgAeW8NZdyk9lrr4knd++YT/MoU+siZ61MyvA+c8AYD4G36ztvjtB/p4zMbEqGG3ftXewFd4+oTC+u2LjtgLD+RYC/ZubvyyeCHoew7e3bBn2XX20mORAOCqKe4ennNdi3NzhOdM+U4d38X7JdZq0tpLjnSxndo9qHzB729JuusfCwK/mIU6T1QR8U16hriqi3oGHndlRFxpzJi7lwT2JPE9yarJ06xq132c7qw6fVXc9Y+/39KQFH33nLKYiQ9+3OwOX2J5/RmuGlxhuGrwSik9K837V3ZpOLg60158ZICtYN9A46p3jLoumfsSfva7Ne0pW7qcagAQam2TL9pCpXEBgHTa1N7fdm9albppWo3Om9blaDK80P9c8qdN7t2u897jsGoBQKE1NPu+V+jCve97p61J74SKz555xP9/odLYwgaMWxk78cGQvQeCicienB+RPfnPwdaZ9i7LhULljBw8/cz7IiJnyh7bqV1D67Z8mRE36eHtAOCsLgxz11WkaTr1POT/WaDQ6N3hA8Zvrtv87xln1VUo0Bg4km6ncJuNWulyKgx9Rpwy7VgMe+mxVABnBY80nXoe8Q+cq+PSLMqw6Cp3gzExdsL9Z02ArU3qWeCsONnXXnY82tBzUAUAeOzmMAjhaTJnna3B+3orVMEDNQqlC25X0CGn7S7D7VJDoQyRVuXylmlStyd4JIQCCkN0gzo6uVDbJSNPFZFQ76g4kWQ5tnV4xZfP35Z050sfqSLjzzoGSkO0BQDc5tpmJ6ZvrdZeH6TbqRUafYvXIKHS2AHA3VCjbW3wSBUeawcA6XI2ee+05rNeFdXJljzz728Z13ww3F58uJ+j/EQ/R/mJfuaDq2Fc/V5t1Ijbv40ceNOJxvTNvY+A0J8hjrLjncz7V17bmn1Kmvn3fxtXvTvQenJHpstY3M1VU9TDenQzajfMd4T1H7Mmfspjzd6ECOVcXwd70cGcknd+eVZvRFVMSn7spIe/DQyQCoVSxl3/+KLyBb//L0CKuOsfX9TWQBwRUWsxeEREbaaKSmwy0XQj44o3W11O1NCbj0YNvfmodDuF9dTuBOuxLT0sRzcNsRcdzKlc+L+epNvb3gNHqH2NMUtds93QpcP75bHxy2QjhS68NmrUnd8AgNtUFW45vH6gq6G6ExTB5wLyzc9UajcbE/U9B51Tj4+Ysff8UPH5c32Mq9+boPeb9LV9RMjGY9TQm/c37F0+qX77wmxICSgU7qghM/aHSt9I1zmjOGCS48NV3/69wXxw9TW1az/MCXziS3PnSSBlZGJx5JDpqyAlXLVlUeZDa4d5bObI1s6l5Kwt09fvWDxZ2zl9V3j6hMKW0quiOtlaW7dLJW8wQigQnnnt6fDMa09bT+3aUr38tanuuoo0S97GMQDWtKtMldoJANJpbzLUSbocKgAQap3T+1vrTetyNk3rsHnTqjRNgsBBzqVzotDovUEhu6XZ931jsFih1jWZay1q1J2fK3ThdiGUHmV4rFXXLbuyvXOeBOO21KntJXnp6tjUfGd1Ubiz2vtxodCGORX6yBrr8W258AWP7GXHYwBAFdWpSa8VTXKvoE/wqt0wf0DD/pXD3KbqZEjPWV0BpcOiD0yviog3Bi4Taq1NaPWmwC+oCl24DQDcpmr/coJ+xih04d7X2+Nqck54l7tVUKqavTHQ5jKUKicc1iZzQfnKUHnLjGhXT5KKhX8dbzuxY3DSrLmv+d0cyKvfvqjYuOrdO2p+eGtE4rRnzp5fT0oBAEKcvwc9tub6IJRqe7DgTiDp8t1k8QWEWsPVUKMFAKFSN8nT2s96dVyaJXHGH1YCWOk0luotRzd1thzdPMBRciTTuPq9WzWJ3d9srreOv1CfIaY9yzzm/StbUwQUaq0nbtLD2wBsc1tNKuuxLSmW41t7WU/uGGzev/JaVXicqbGHbluc6+ugTuyeF541aRs8buGsLoozH143wmNriFSoda5g5ejSBhgVar258e+21peIqLUYPCKiDieUaunrSVEROWTGvtL3H5ljLzqYLd3OJcEeqd0cdXRyhbP8ZD970cHkxkfUBuMyliQDgK5z+tm9iZQqp38jOGrIzw+VvPfQfbVr592s65Lxuiaxe7uH1LVE3z2nStt5wG574YFc88E1QScZFirvl3WPr8dHoMY79kKlCtrIBLwBDE2nnkds+buzIQFNpx5H/IfLtIWh9/AT5oOrr7GXHOkOoMncH62l0Ogt/sc94uobj5R+MOeh6mWv3KrtkvF64HCcQDXLXx8j3S5NRPZ1u2yn95+Z4Fi6XSpAwnZ6f6xQ61za5F71gHeCZVddRZMv1KH49yjrqLzBOKpOh5l2LE63ntqd4a6vSIVC6dJ06nnI0Hdkm54A5k9piP5xaFrAvBnuhpqzhrSpojr5hqbVNBmG5DSW+Ia0xbT56XBtpenUswIAnNWnk5tL5yg/kQwA6vguTXoRhvUdVeD/tLXzrX7rlwPgdmqcVQW9yhf8LuicS5ajm5P8Jx5vrdpNn/Wr27jgZmVkQnF49nVLVVGJ9UKtdcHjFsYf3rmzMZhxFr/HqJ+1GMGXn1nto9CGWSClwlVfpfXveaOKSrRBoXR5rE2HpnnsFqV02gxKXWp+c/vT1jIUujCTy1yb4LFblIGBL4/V5B3S1o5eRx6nXWE9tnW4Or7zicD3YuSgnx03rv3Q4aw41TUwn9tapwcAhSEq6Pxb7dGa64MyPK7CVVva1XZ6f2yoIIzbWq92W+riFbrw2tb2OgIAW/6eTr5tnJdeseqYZGvUkBlHo4bMOFrx9X/XWY9tGWXaszRd1yVj3fkov62U+ghXeObE0+GZE0/X7/zmlHHlWzPNeRty2xM8OtfXQamPqve7Hp4I6z/mWPlnzzxUufiFm1MffPe9y+3JjER05WDwiIguKerYVIvCEFnjNlUnO42lhsBH2LYkbMC4w5a8jWMsR7fkxIy/L+ik2daTOxNcdeWdlRFxpS09LlqhC3NFDf/FsprvX59Vs/Ktcf6PKL8QYq95YE3ph49n1J9HWFgAACAASURBVG6cP1GT2DM/cL06rnMtALiMJUEnYrWXHUsAAGVEQrN3H8MGjNtt/OHtAd6/b2/3HEvS7VQAgHTag9/1byd1dJI1PGvSKtP2hVNrVr45LOHGJ9Y3l95tqY2Gx6Wu+uZv9wVbX77gd48qwqIrOz/yyRsAYD60tnO75y3qoLyNXKZqrWnHon6W49szXDXF3QEpVDHJBREDb/omctD0g4FDaNpKk9q3xHxoDeyFB9MQ0JvAWVWYBqXaru2SUQ0A+u45FRAKt7PydFpgOfaiQ2kAoEsbcN56GIUS1n9MoXH1e2ZH+Yk+zppiQ7AgkMdmVtkL9mdCoXSFZ048fqHrFMhydHOO0BhMkYOnLQtcJ91OZf3mL6aZdi/JMfQetlTTqacRAFx15U3e545S73v8rLLzNmZCoXQlz375Q//hvudjwuZQVDEpFfbiw7AXH45TRf7YA0QolFIVEV/qMlUlBQZzLHkbUgFAHd+l2XOirWWo4zqXuKqLelryNqSGZ157unG5x2ZWuUxVSeqYlIL27KPLWGKA9CghZZMLiZQeQHqE9HiarHMZy2IBQJvar6I92w2lpeuDrmvm4Yba0q712xfl6rpkBO1+U7fh0yxIj0KT0vdwW7bdsH9FrncbWUfbVflmaFP6FlmPbYHb7J1vraOF9RlRZFz5Fjy2hnbV53y/DrrO6UZDr6GbLHkbx9Su/yQjZvRdLfYUJiK6EBg8IqKLzm2tV1vyNqVEZE9u0qC3FeyLdZtrE4Raa2lPLwBDr6HlmuTeBxylR9OrFr8wNnDeF2dtmb566SvTASByyM2t6tsekXNdfv22rwvsRYeybUWH1uvS+te2tV6tpUnsYdL3HLTVemzLSBlkXhBD72GlQhtWby8+nO6oOLnBf7JWj8OqNB9cNRgAwgaMy2tuOxG515+0lxxZDQgZkTMl6KOKW8N8eF1fAFDFpTU7UXF7xIyeuc+8f+Vo69HNw12m6m2Nc6s4Kk6Fu821Ok1Sz7rGL8qRg6ZtcNWVN+ltY9q1ZKzHZoqJGn7bwsahN8DlOeeRvex4ZM2yVyc5KvN7w+NWKQzRlWH9x6yKGHjTfm1y72aDoG0RnjExv3bthw3WU7ty3Wbjlsa74pZjWzq5jCXdtGn99zQO51KGxTg0id3zHOUn+1mObelk6DXUO6mu2aixntqVq9BH1hj6jy5ubnvng0JrcIdnTlxt2vnNDZVf//f0Tne8+Jl/bzXpdoqKr/58vcfeEGXoPXxdqCeynQ9ua73aUXYiShkWbWvsqWgt2Bvnqi3rouuWsy16xC+CzhdnObIh1150KMNjt3yvie9iVkYmFjkqTvW1FuyN85swW9lwcNWwwLyisReRx32mZ5CUHtRu+GT0BdlJAPoeA/PNB36A7fS+tMDhQ7ruufsb9iztbFz9/tVxkx85M39S/a5vh0IIT0TO9WfmX/I47Qp70aFYhS7M6X8et6WM8IyJB6xHN4+q3/XtUP/gkXHNB7nwuNX6XkPa9WVbHd/FLFRaq7OmpKut6FC0/2d/3YZPB8DjVqtjU5sEwpxVBWlCrTPru2Wf17nrWro+RI++a5fl6ObB1hPbh9VvX5QfOehnZwVJzXkbkxv2fT9BqHWWmLF3t3pi+uplrw5xlORlKMNjy6NH/OJgyzmaajiwKk3XJb1KFZnYpJer9cT2vgCgjkk5MyQz2PvofLIV7IuFEAjWM6h+x3/6AoAyIi7oENGWXIjXIWb8vVssx7cNbdizbEz0iF8caGuvbCKi84HBIyK66DxWk7pm+WuzazfMr9AmXXVcFZ1UAynhNJbE2wsPZMHjVoVffeN37Z30MXH6s9+Uffq7MEvexjFFb97TU9c547BCH2F1GUvjbAV7s6XTbgjPuX5J5NU3tnrumYhBU9cZV7w5s3bth6OT7njhP+2pV2vFXvPghpJTu69211c2eby0UGk8UcNv/bZ2zbzbyj5+4iFdt6xdqqhORre5NsxWuD/dY65N0Pcaur6lLy1CoZQJNz3ZpuEBlqObrnJUnIgHAI/donWUHe/iKD2aLrSG+pjRd20NTO+sLkw2rp2XGaysyIFTj7Q0ZEKoNJ6w9HEbTDv+c6Nx1btDEqY+tQ4Aar5/4xp78eGs2EmPfBiRPTkfAEI9Ycx8cNVgj60hOvAL++U455Gj7His01jSRdcte0d45rX7mhuWGUz18tcHe+xm75xA0qPwWE1Rlf95cTQAaBJ7lEUNvfko4J0HJHLQtKV1Gz/9eem8x+7W9xqySzqsWsvRzUOFWmuJveaB1f7lxoy/74eKL/7Yo+qbl2Yaeg/bIjR6u/XY1lxpt0RGT7hvftAn8fmdS/4Uar0javitZ56uVzj3tsc89oaoYD2wAsVe88udrtqyWOuJ7cNL3r7/YV3XrL3KyMRaj9kYbivYm+E2GxM1KX32xd/0xJq2HLe2suRtSq1Z/tosbWq/vb7J3WHa8Z8cADD0Cf6gAQDQdc063LBnabf6bV/1ix4180D0qDuXV383d3blV8/fo+8xaLtCa7BZ8/ekI0hPF33PgYcc5Sf6lX7061n6HgP3wuNS2E4f6CvdzmYnpj4Xhr4jShTfRxjtRQd7AThrgu2Ycffssp7YntOwb/kkl6kqWh2bUmU7faCXs+JkX0Pv4eu0qX3PBGGcFaciKz7/w8OqmOSC1AfemdeeMgxXDa7QdcveZsvfM7jkgzm36rqkH3PWlMTbTu0coopJLogeNfOs4JFp7/KutoK9XQHAaSxJAYC6TZ8NFr4J1xs/G4VCKcPSx69p2LP0uorPn7tf3z13hzIirt5ZVZBkO30gV6i1lujRM8/68u82GzVOY0kXXZeM3YHHrC3ncyjNXR+Uhihn/I2/XVC5+IU7javeu918aM1hTXLvfKFQehzlJ1PtxYczhVLliL32oc+CDYl1mariGj+zpdOudtWVx9hLjvb2WGoTlBHxpQnTn/0s2MMIWvNZbz6wKrN66cvZmsTux9TxXYsV+kiLx1pvsBcf7uUylnRTGKIro0fPPHPMgr2Pzidb0cGkug2f3qyKSS7QdOqZr4yIq5cOm9pReSrNUXpsAJRqR/SI29e2p+xzfR2CUUUm2vQ9B26zHt08qnb9/IyYsbPbNDTZXpIXVb9jcRYAuGpLEwDAWrC3t+s/L0YCQOTAqXtb6olNRMTgERFddKrIBFvEoGmL7UUHetpL8vpY8/dEwONWCbXOrIpNK4jIvm5rRM51+e0tXxke60i597WPjWs/yrIe35JtObJ+lHS7NEKja1AndD0RNeTmzW2dVyQy94aT9Vu/LrIXH8qyFR5Yr+ucfsEmpVRFxtvD+o9e17BvxaRg66MGTz+mioh/r37rVyNsBfuypcuuFwqVUxmZUBqRfd3awEdyny+WI+vHnflHCI9CE2bSdsncETvh/rXBenI4So+mO0qPpgcrS9/96leVYTEtTowaPXrWnob9P4y2Ht86zFVftfVch2Rdzgx9RhSFZ0z4R3vvOFsOrx/usTdENf7vsTVEWw6vGwcA7vrKvY3BIwCIHnn7IaHWLjDtWjK6Ye/ya4VC6VLHdT4VM+H+FZpOPc96NLmuS0ZNwvRn3zOufv8ay5ENI6X0KFUR8aUxE3/5SahA2Vnnkh+hMZj8g0fS7dQIjaHJo9BDSbz5jytMe5YdM+35brD11K6rpdNuECq1TRWZWBKRe8Nq/7IvFul2CtvpfVlCrbOEZ048HSpdRPbkww17ll5nPrIxJ3rUzAPh6eOLpNv5cd3mf19jObpppFBpbNqUPociBv1sR+WXf37IP2/0qJkHPHarxnx43bCGvcuvFWqtVdOp59G4KY+tLHnrvicvxH4JoYC+56Cd5oOrJziqTof5DzFWaPTupDtf/Kj6u1fG24sOZthO7dIr9JHG8OzrlsZe+9C25sptbxmJN/9xWfXy12utx7ddbdr5bS+h1ll03XO3xU95bHXgjQjbqd3dLXkbxvgvs57c8WOPLr/Aetykh7cpw2NN5oOrhlhObB8Cj0st1DqLJrn3gZhx96wO7PVXv21hP3jc6oiBU5s8pa+t53MwLV0f9N1zq1Luef1fxtXvDbUXHezXsPf7XoAUCl14nb577rbosXdvChWwcNUU9ajf8mUPAIBS7VBo9A2qqE6l+pwpa6KG3nwk1FMsW/NZHzFo6g6h1duc5Se7WY5uGiaddgMUCpdSH1Vj6DNybcyE+zcrDVHtmtS8PcL6jixw1VWscJQc6WE7tTvH47SGQXqPkzZtwO7okbdvau3k3cGcy+sQSszYezZbT2wf0rDv+zHRo+7Y35ZrgaPiVEzj530jZ/nJfs7yk/0AQNcl8zSDR0TUEiElez0SUfO0Sb3mJc+em9/R9SCinw7Lsa2dKr/+y4MRg6Ytjh1/756Org815TJVa0ve+eWjuu45u5o8cewnquj1Wb9UGKJqU+5+5d/+y3k+0+WidN5j3exlx2Z3dD2I6NLD6fqJiIjokmM5uqmnMjy2PGbs7L0dXRcKThURZw/PuGaN9cSOIU5jaaufInilqtv8eV+32ZgYO/6+FYHreD4TEdHljj2PiKhF7HlEREREdOVjzyMiCoU9j4iIiIiIiIiIKCROmE1ELfLYG06XznusW0fXg4iIiIguHI+9IeTE/kT008Zha0REREREREREFBKHrRERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhERERERERERUUgMHhFRqwkhlgohZnV0PVoihOgvhNghhBCtSDtKCJHXzu3ME0L8d4h1WiHEESFEQnvKJiIioivL5dKO6ghCiI1CiJxWpj0ohBjbjm2MFUIUNbP+70KIh9paLtFPBYNHRFc4IUSD349HCGH1+/+OtpQlpbxOSvlhO+uRL4S4pj152+EvAF6SUsqWEkop10sp+zT+f77qKaW0A3gfwNPnWhYRERF1jEulHeVXnzVCCKMQQnsu5VxKhBA3AjBJKXe3Jr2UcoCUco0v75+EEJ+cp6q8BOD3QgjNeSqP6IrC4BHRFU5KGd74A+A0gBv9ls1vTCeEUHVcLc8fIUQygHEAFnV0XQB8CmDWldTAIyIi+im5lNpRQohuAEYBkABuutDbC9j2hdy/BwF8fAHLbxUpZSmAI7jIx5bocsHgEdFPVGPXXSHEU0KIMgAfCCFihBDfCiEqfXe1vhVCpPnlWSOEuM/392whxAYhxEu+tKeEENe1ox5aIcRcIUSJ72duY7BFCBHvq0OtEKJGCLFeCKHwrXtKCFEshDAJIfKEEBN8RU4EsEtKafPbRr4Q4ndCiEO+un4ghND5Hwff3x8D6ALgG98dxSd9y0cKITb56lEohJjttwsxQoglvnpsFUL0bFwhpSwCYAQwtK3HhYiIiC5dHdSOugvAFgDzAJw1/E0I0VkI8bVv29VCiNf81t0vhDjsa6scEkLk+pZLIcRVfunODMdv5/7F+tpYJb71i3zLD/h6FzWmUwshqoQQOb5ePuMBrPVb/ychxJdCiH/76rxLCJHltz5fCHGNEGIygN8DuNXXbtvbXD388v9GCFEhhCgVQtwdcIzXALi+hdeB6CeJwSOin7YkALEAugJ4AN7PhA98/3cBYAXwWsjcwBAAeQDiAbwI4D0hWp5nKMAz8AZXsgFkARgM4Fnfut8AKAKQAKATvA0EKYToA+ARAIOklBEAJgHI9+XJ8NUp0B2+dD0B9PbbxhlSypk4+67ii0KIrgCWAnjVV49sAHv8st0G4M8AYgAcB/DXgGIP+/aLiIiIriwXux11F4D5vp9JQohOACCEUAL4FkABgG4AUgF85lv3cwB/8uWNhLdXTfUF2r+PARgADACQCOCfvuUfAbjTL90UAKW+YWq9AHh8N9z8TQXwhW/7nwJYJIRQ+yeQUi4D8D8A/u1rtzW2t0LVo3GfouA9RvcCeF0IEeO3nu02ohAYPCL6afMA+KOU0i6ltEopq6WUX0kpLVJKE7yBkDHN5C+QUr4jpXQD+BBAMrxBnra4A8DzUsoKKWUlvIGYmb51Tl+ZXaWUTt/8RBKAG4AWQH8hhFpKmS+lPOHLEw3AFGQ7r0kpC6WUNb79+kUr63c7gJVSygW+OlRLKf2DRwullNuklC54G3PZAflNvjoRERHRleWitaOEECPhDdp8LqXcCeAEvG0UwHvjLQXAE1JKs5TSJqXc4Ft3H4AXpZTbpddxKWXB+d4/4Z024DoAD0opjb42U2Nvok8ATBFCRPr+n4kfh6mFarftlFJ+KaV0AvgHAB1a0ZO7hXoA3rbl877l3wFoANDHbz3bbUQhMHhE9NNWGTC8yyCEeEsIUSCEqAewDkC0745WMGWNf0gpLb4/w9tYhxR475Q1KvAtA4C/wdub53shxEkhxNO+bR0H8Bi8d9IqhBCfCSEa8xgBRATZTmGIbbSkM7wNtFDK/P62oOn+RwCobeW2iIiI6PJxMdtRswB8L6Ws8v3/KX4cutYZ3kCUK0i+ltoxzWnL/nUGUCOlNAYWIqUsAbARwAwhRDS8wZ3G+aJabLdJKT3w9kRvTdstZD18qgOOU2Dbje02ohAYPCL6aQt8Gtlv4L37MkRKGQlgtG95W4eitUUJvHfSGnXxLYOU0iSl/I2Usge83ax/LXxzG0kpP5VSNt6FkwBe8OXfB++wtECdg20jiMBjUgjvULf26gdg7znkJyIiokvTRWlHCSH0AG4BMEYIUeabg+hxAFm+uYAKAXQRwSe1bq4dY4F3eFejpID1bdm/QgCxvuBQMB/CO3Tt5wA2SymLfcuPe3dRpAakP9NuE975LtMQvO0WrN3WXD1awnYbUQgMHhGRvwh4x6/XCiFiAfzxPJevFkLo/H5UABYAeFYIkSCEiAfwHLzdmyGEuEEIcZVv/H8dvMPVPEKIPkKI8cI7sbbNV2ePbxsrAOQK34TYfh4WQqT59usZAP8OUcdyAD38/p8P4BohxC1CCJUQIk4IETg0LShfQygW3sktiYiI6Mp2odpRP4O3DdQf3uHx2fAGOdbDO5fRNgClAP5PCBHma2ON8OV9F8BvhRBXC6+rfPM5At45HG8XQih9k083N8Su2f3zPalsKYA3fBNrq4UQo/3yLgKQC+BX8M6B1JjPAWBlkG1fLYSY7msrPgbAjuDtqXIA3XwBptbUoyVjfPmJKACDR0Tkby4APYAqeC/Qy85z+d/B2+ho/PkTgP8GsAPeHkP7AezyLQO8kyiuhHc8+mYAb0gpV8M739H/+epZBu9kiL8DACllOYBV8E606O9TAN8DOAlv9+3/RnD/C28wq1YI8Vsp5Wl4J3b8DYAaeBtarZ1I8XYAH0op7a1MT0RERJevC9WOmgXgAynlaSllWeMPvJNV3wFvz58bAVwF74M/igDcCgBSyi/gnZvoU3jn81kE740twBvIuRHeYVp3+Nady/7NhHdOoSMAKuAN+sBXDyuArwB0B/B1QL638ON8l40W+/bB6Fs33Tf/UaAvfL+rhRC7WqpHc3zzJfVHy8eB6CdJeOeeJSK6cggh+sPbPXqwlFIKIfIB3CelXHkR66CFt9vzaCllxcXaLhEREdGlSAjxHIDeUso7g6zbCOARKeVuIcSfAFwVLN0Frt/fAZyQUr5xMbdLdLkINi6WiOiyJqU8BGBQB9fBDqBvR9aBiIiI6FLgG+Z2L5r2MAIASClHBFt+MUkpf9PRdSC6lHHYGhEREREREV0QQoj74Z3IeqmUcl1H14eI2ofD1oiIiIiIiIiIKCT2PCIiIiIiIiIiopAuuzmP4uPjZbdu3Tq6GkRERHSB7Ny5s0pKmdDR9aCzsQ1GRER0ZWuuDXbZBY+6deuGHTt2dHQ1iIiI6AIRQhR0dB2oKbbBiIiIrmzNtcE4bI2IiIiIiIiIiEJi8IiIiIiIiIiIiEJi8IiIiIiIiIiIiEK67OY8IiIiauR0OlFUVASbzdbRVaF20Ol0SEtLg1qt7uiqEBERXVHYRqLmtKcNxuARERFdtoqKihAREYFu3bpBCNHR1aE2kFKiuroaRUVF6N69e0dXh4iI6IrCNhKF0t42GIetERHRZctmsyEuLo6NosuQEAJxcXG8I0pERHQBsI1EobS3DcbgERERXdbYKLp88bUjIiK6cHidpVDac24weERERERERERERCExeERERNRO1dXVyM7ORnZ2NpKSkpCamnrmf4fD0WzeHTt2YM6cOeelHs899xxWrlwJAJg7dy4sFkuz6f/0pz/hpZdearHcbt26oaqq6rzUkYiIiH46rvQ2EgDs2bMHQggsW7bsnOt5OeCE2URERO0UFxeHPXv2APA2NsLDw/Hb3/72zHqXywWVKvilduDAgRg4cOB5qcfzzz9/5u+5c+fizjvvhMFgOC9lExEREbXVT6GNtGDBAowcORILFizA5MmTz0uZwbjdbiiVygtWfmux5xEREdF5NHv2bDz44IMYMmQInnzySWzbtg3Dhg1DTk4Ohg8fjry8PADAmjVrcMMNNwDwNqruuecejB07Fj169MArr7wStOzw8HA8/vjjGDBgACZMmIDKysoz2/zyyy/xyiuvoKSkBOPGjcO4ceMAAMuWLUNubi6ysrIwYcKEM2UdOnSoxe0Fk5+fj/HjxyMzMxMTJkzA6dOnAQBffPEF0tPTkZWVhdGjRwMADh48iMGDByM7OxuZmZk4duxYG48mERERXSmupDaSlBJffPEF5s2bhxUrVpw1+fQLL7yAjIwMZGVl4emnnwYAHD9+HNdccw2ysrKQm5uLEydOnLWfAPDII49g3rx5ALy9v5966ink5ubiiy++wDvvvINBgwYhKysLM2bMONODqry8HNOmTUNWVhaysrKwadMmPPfcc5g7d+6Zcp955hm8/PLLrX+hQmDPIyIiujI89hjgu8N13mRnA34X39YqKirCpk2boFQqUV9fj/Xr10OlUmHlypX4/e9/j6+++qpJniNHjmD16tUwmUzo06cPHnroIajV6rPSmM1mDBw4EP/85z/x/PPP489//jNee+21M+vnzJmDf/zjH1i9ejXi4+NRWVmJ+++/H+vWrUP37t1RU1PTpu0F8+ijj2LWrFmYNWsW3n//fcyZMweLFi3C888/j+XLlyM1NRW1tbUAgDfffBO/+tWvcMcdd8DhcMDtdrf5WBIREdG5uYSaSFdMG2nTpk3o3r07evbsibFjx2LJkiWYMWMGli5disWLF2Pr1q0wGAxnyr3jjjvw9NNPY9q0abDZbPB4PCgsLGz2WMXFxWHXrl0AvMMA77//fgDAs88+i/feew+PPvoo5syZgzFjxmDhwoVwu91oaGhASkoKpk+fjsceewwejwefffYZtm3b1oZXKTgGj4iIiM6zn//852e6F9fV1WHWrFk4duwYhBBwOp1B81x//fXQarXQarVITExEeXk50tLSzkqjUChw6623AgDuvPNOTJ8+vdl6bNmyBaNHj0b37t0BALGxsW3aXjCbN2/G119/DQCYOXMmnnzySQDAiBEjMHv2bNxyyy1n6jVs2DD89a9/RVFREaZPn45evXq1WD4RERFdua6UNtKCBQtw2223AQBuu+02fPTRR5gxYwZWrlyJu++++8zQuNjYWJhMJhQXF2PatGkAAJ1O16pj1bg/AHDgwAE8++yzqK2tRUNDAyZNmgQAWLVqFT766CMAgFKpRFRUFKKiohAXF4fdu3ejvLwcOTk5iIuLa9U2m8PgERERXRnac/vrAgkLCzvz9x/+8AeMGzcOCxcuRH5+PsaOHRs0j1arPfO3UqmEy+VqcTvn8gje9myvOW+++Sa2bt2KJUuW4Oqrr8bOnTtx++23Y8iQIViyZAmmTJmCt956C+PHjz+n7RAREVHbXEJNpCuijeR2u/HVV19h8eLF+Otf/wopJaqrq2Eymdq0HZVKBY/Hc+Z//6FvwNnHavbs2Vi0aBGysrIwb948rFmzptmy77vvPsybNw9lZWW455572lSvUDjnERER0QVUV1eH1NRUADgzjr29PB4PvvzySwDAp59+ipEjRzZJExERcabxMnToUKxbtw6nTp0CgLO6ZLfX8OHD8dlnnwEA5s+fj1GjRgEATpw4gSFDhuD5559HQkICCgsLcfLkSfTo0QNz5szB1KlTsW/fvnPePhEREV0ZLtc20g8//IDMzEwUFhYiPz8fBQUFmDFjBhYuXIiJEyfigw8+ODMnUU1NDSIiIpCWloZFixYBAOx2OywWC7p27YpDhw7BbrejtrYWP/zwQ8htmkwmJCcnw+l0Yv78+WeWT5gwAf/6178AeINadXV1AIBp06Zh2bJl2L59+5leSueKwSMiIqIL6Mknn8Tvfvc75OTknHPvnrCwMGzbtg3p6elYtWoVnnvuuSZpHnjgAUyePBnjxo1DQkIC3n77bUyfPh1ZWVlndX9urczMTKSlpSEtLQ2//vWv8eqrBJ5H/AAAIABJREFUr+KDDz5AZmYmPv744zMTMD7xxBPIyMhAeno6hg8fjqysLHz++edIT09HdnY2Dhw4gLvuuuuc9p+IiIiuHJdrG2nBggVnhqA1mjFjxpmnrt10000YOHAgsrOz8dJLLwEAPv74Y7zyyivIzMzE8OHDUVZWhs6dO+OWW25Beno6brnlFuTk5ITc5l/+8hcMGTIEI0aMQN++fc8sf/nll7F69WpkZGTg6quvxqFDhwAAGo0G48aNwy233HLentQmpJTnpaCLZeDAgXLHjh0dXQ0iIroEHD58GP369evoalw04eHhaGho6OhqnFfBXkMhxE4p5fl5Ri+dN2yDERFdPthG+mnzeDxnntQWas7JtrbB2POIiIiIiIiIiOgKcOjQIVx11VWYMGHCeX1YCSfMJiIiukzwjhoRERFRU2wj/ah///44efLkeS+XPY+IiOiydrkNv6Yf8bUjIiK6cHidpVDac24weERERJctnU6H6upqNo4uQ42PtdXpdB1dFSIioisO20gUSnvbYBy2RkREl620tDQUFRWhsrKyo6tC7aDT6ZCWltbR1SAiIrrisI10ZZISEOLcy2lPG4zBo0avvw68+ipw+PD5eTWIiOiCU6vV6N69e0dXg4iIiOiSwjbSlenPfwbWrAGWLQO02ou7bQ5ba2S1Anl5ACfaIiIiIiIiIqKLyONpfn1tLfDPfwLR0Rc/cAQwePSj2Fjv75qajq0HEREREREREf1kNDQAGRnA00+HTvPyy0BdHfDHP168evlj8KgRg0dEREREREREdJH96U/AoUPAiy8C27Y1Xd/Y62jaNCA7+6JXDwCDRz9i8IiIiIiIiIiILqJ9+4C5c4HbbwdSUoAHHgBcrrPTzJ3bsb2OAAaPfsTgERERERERERFdJB4P8NBDQEwM8Mor3p+9e71D1BoZjd5eR9OnA1lZHVdXBo8aMXhERERERERERBfJBx8AmzZ5h6vFxXmHpd14I/Dcc8Dp0940c+cC9fUd2+sIYPDoRwweEREREREREdFFUFUFPPkkMHIkMGuWd5kQwKuvev9+9FFvr6O5c4EZM4DMzI6rK8Dg0Y90OsBgAKqrO7omRERERERERHQFe+opb4+if/0LUPhFZrp2Bf78Z+A//wGmTvWmee65jqtnIwaP/MXGsucREREREREREV0wGzcC778PPP44kJ7edP2vfuXtabR+/aXR6whg8OhsDB4RERERERER0QVSWwvMnOntYRSqR5FaDbz3HnD11cBf/nJx6xeKqqMrcElh8IiI/p+9+46yujrfNn49FBWxgWLFbvwZoxF1VGwxdmPvHbuIsQaxF8AWOyqi2AtWNPYYO2iiEYOJmGisRLFERURjo+/3jz28DjjAUGb2mZnrs9ZZZ8453zNzsxJw5p69ny1JkiRJ9SAlOPRQ+PBDeP55mG++aV9bVQVDhzZcthlx5VFNlkeSJEmSJKke9O0L998PF1wA669fOs3MsTyqyfJIkiRJkiTVwd//nreXpTTja19+GXr0gB13hO7d6z/bnOa2tZoml0cp5TPyJEmSJEmSpvL443mY9fff5wHY110HrabRsIweDXvuCUsuCTff3DjrBlce1dS+PYwdCz/8UDqJJEmSJEmqQHfdBTvsACuvDCeemAuhXXfNRdLUJkyAgw+GTz6Be+7JtUNj5Mqjmib/rzhqFMw7b9kskiRJkiSpovTrB8ccAxtvDA8/DAsuCMstB0cfDVttBY88Au3awWefwfXXQ//+8PHH0KcPrLde6fSzzvKopoUXzvdffglLL102iyRJkiRJqggpwTnnQM+eeW7R3XdDmzb5td/+FhZdFPbbL5dKa6wB994L48fnQql/f9huu7L5Z5flUU2TVx45NFuSJEmSJFV7+ulcHB14INxww0/nG+2+e64Udt4ZPvooF0q//W3e2tYUWB7VZHkkSZIkSZKm0r9/3qx07bXTHoy92Wbw3nt5Ck7btg2br745MLsmyyNJkiRJklTDZ5/l+UYHHQRzzz39azt0aHrFEVgeTcnySJIkSZIk1XDLLfnUtMMOK52kHMujmtq0yTWi5ZEkSZIkSc1eSnnG0cYbwyqrlE5TjuVRTRF59ZHlkSRJqnARsU1EvBUR70bEKbW83j0i3oiI1yLimYhYtsZrEyPi1erbww2bXJKkxmPwYHj3XTj88NJJynJg9tQsjyRJUoWLiJZAP2BL4CPgbxHxcErpjRqX/QOoSil9HxFHAhcBe1W/9kNKqVODhpYkqRG67jpYaKF8mlpz5sqjqS28MIwaVTqFJEnS9KwLvJtSGp5SGgfcDexU84KU0qCU0vfVD18COjZwRkmSGoX77oNtt4VPP53y+S++gPvvhy5d8pSb5szyaGquPJIkSZVvKeDDGo8/qn5uWg4F/lTj8TwRMTQiXoqInaf1pojoWn3d0JEjR85eYkmSKtDEiXDiifCnP+W5Rh988ONrAwbAuHFuWQPLo5+yPJIkSU1IROwPVAEX13h62ZRSFbAvcHlErFjbe1NK16WUqlJKVR06dGiAtJIkNawHH4T334dTT4WRI3OB9PbbeVD29dfDeuvB6quXTlme5dHULI8kSVLl+xhYusbjjtXPTSEitgBOB3ZMKY2d/HxK6ePq++HAYGDN+gwrSVKluuwyWGEFOOecPBx7zJhcIPXvD//+N3TtWjphZbA8mlr79vDDD/kmSZJUmf4G/Cwilo+IuYC9gSlOTYuINYFrycXR5zWebxcRc1d/vAiwIVBz0LYkSc3CkCHw4otw/PHQsiV06gTPPw9zzQW//S3MPz/stdeMP09z4GlrU2vfPt+PHu1ELEmSVJFSShMi4mjgCaAlcFNK6fWIOBsYmlJ6mLxNbT7g3ogAGJFS2hH4OXBtREwi/yLxgqlOaZMkqVno0wcWXBAOPvjH51ZZBf7yF9hxR9hhB2jbtly+SmJ5NLXJ5dGXX8KSS5bNIkmSNA0ppceAx6Z67qwaH28xjfe9CDi9QZLULDz+OCy0EHTuPOXzH3yQT1nr3h3mm2/K15ZdFoYNy3OPlLltbWo1yyNJkiRJklRxfvgBLroIPv982te8/z7stFOeYTRgwJSv9e2b7485Ztrvzwt3BZZHPzW5PBo1qmwOSZIkSZJUq5NPzrejj572NT175gJo/fXhgAPg97/Pq4n+9798ktqee8LSS0/7/fqR29amtvDC+d6VR5IkSZIkVZynnsorh1ZYAe69F55+GraYarP2v/6VVxudcAKcd16ea3TaaTBiBKy4Yi6Quncvk78xqvfyKCJaAkOBj1NK20/12tzAbcDawChgr5TS+/WdabrctiZJkiRJUkUaPToXQauskk9KW2edvPVs2LB8Stpkp50GCywAp56anx8wADp2zFvdImCjjaCqqtyfo7FpiG1rxwH/nsZrhwKjU0orAX2ACxsgz/S1bQutW1seSZIkSZJUYY4+Gj77LJdB7drBFVfAm2/ClVf+eM0LL8Ajj8BJJ/24PqRFC7jwwrxiaa65crmkuqvX8igiOgLbATdM45KdgFurP74P2Dyi8EiqiPz/LssjSZIkSZIqxsCBcOedcOaZP64a2m472H576N0bPvkkzzQ65RRYfHE47riffo6jj85b1n7zm4bN3tjV98qjy4GTgEnTeH0p4EOAlNIE4Gtg4akvioiuETE0IoaOHDmyvrL+yPJIkiRJkqSK8fHH0K0brLvuT1cNXXEFjB8PJ54Ijz0Gf/kLnHVW3lhUm5rb21Q39VYeRcT2wOcppVdm93OllK5LKVWllKo6dOgwB9LNgOWRJEmSJEkV46ijYMyYvF2t1VTTm1dYIZ+8dued0LVrHoh92GFlcjZV9bnyaENgx4h4H7gb2Cwibp/qmo+BpQEiohWwIHlwdlmWR5IkSZIkVYT33oOHHsozjFZeufZrTj4Zll02b10799w8ylhzTr2VRymlU1NKHVNKywF7A8+mlPaf6rKHgQOrP969+ppUX5nqrH17GFW+w5IkSZIkqbm74YY88Prww6d9zbzzwl135XlHe+7ZcNmai1YzvmTOioizgaEppYeBG4EBEfEu8CW5ZCpv4YVdeSRJkiRJUmHjxsFNN+Wh2EstNf1r118/3zTnNUh5lFIaDAyu/visGs+PAfZoiAwzpX17+O47GDsW5p67dBpJkiRJkpqlRx6Bzz/Ps4xUTn2fttY4tW+f70ePLptDkiRJkqRm7LrrYOmlYZttSidp3iyPajO5PHLrmiRJkiRJRfznP/Dkk/nktJYtS6dp3iyPamN5JEmSJElSg/jkE5g06afPTx6UfcghDZ9JU7I8qo3lkSRJkiRJ9W7QoLwtbffd4fvvf3x+/Pg8KHu77aBjx3L5lFke1cbySJIkSZKkejV2LHTrln8Ef/BB+PWv4bPP8muPPAKffgpHHFE0oqo1yGlrjY7lkSRJkiRJ9eqCC+Dtt+GJJ2DMGNhnH1hvPXjssTwou2NHB2VXCsuj2iywQJ7GNWpU6SSSJEmSJDUKX3wBCy8METO+9u234fzzYe+9Yaut8nPPPw/bbw/rrw/ffAM9ezoou1K4ba02EXn1kSuPJEmSJEmaoXffhaWWymXQuHHTvzYlOPJIaNMG+vT58fm114YhQ2CZZaBVKwdlVxJXHk2L5ZEkSZIkSXVy2225NBo4EP73P/jDH2DeeWu/9o474Nln4ZprYPHFp3xtmWXgpZfg44/zIG1VBlceTYvlkSRJkiRJM5QS3H47bLklXH89PPlk3or21Vc/vfbLL6F7d+jcGbp2rf3ztW0LK69cv5k1c1x5NC3t28N//1s6hSRJkiRJFe2FF+A//4HevaFLF1hoIdh333x62hNP5HLp1Vdh2DB4+OFcID39NLRwOUujYXk0Le3bw+uvl04hSZIkSVJFGzAgb1HbZZf8ePfdYf75Yddd89az8eN/vHbZZfOco1/+skxWzRrLo2lx25okSZIkSdM1Zkyec7TrrjDffD8+v/XWMGhQLpZWWgk6dcqFUbt25bJq1lkeTUv79nnK1/jx0Lp16TSSJEmSJFWcRx/Ns426dPnpa+uum29q/NxhOC3t2+f70aPL5pAkSZIkqUINGABLLAGbb146ieqT5dG0LLxwvnfrmiRJkiRJP/HFF/DYY3k4dsuWpdOoPlkeTcvklUeWR5IkSZIk/cQ998CECbVvWVPTYnk0LZZHkiRJkiRN04ABsPrqsMYapZOovlkeTYvlkSRJkiRJtXr7bRgyxFVHzYXl0bRYHkmSJEmSVKvbb4eIPO9ITZ/l0bQsuGD+m2B5JEmSJEnSFB55BH71K1hqqdJJ1BAsj6alRQto187ySJIkSZLUrHz6KaQ07dfHjYPXX4fOnRsuk8qyPJqehReGzz8vnUKSJEmSpHo3aRKcfTYssQTcdtu0r3vzTRg/3kHZzYnl0fT88pd5ApgkSZIkSY3cgw/CIovAOefA999P+dp338Gee0LPnvnx4MHT/jyvvprvLY+aD8uj6dlkExgxAt5/v3QSSZIkSZJmy6WX5tLorLNg5ZXz6qJJk/KPvBtsAA88ABdfDNtuC0OHTvvzDBsG88yTP4eaB8uj6dlkk3z//PNlc0iSJEmSNBvefBP+8hfo3Tv/iLvkknDggVBVBeusAx98AH/8I/TokZ974428Gqk2w4bBaqtBq1YN+2dQOZZH07PaatC+PTz3XOkkkiRJkiTNsptuymXPAQfAxhvDSy/BHXfAqFF5K9vLL8M22+Rrq6ryiqTJ29NqSik/75a15sXyaHpatMh/qyyPJEmSJEmN1LhxcOutsMMOsNhi+bkWLWDffWH4cPjnP6fcgrb22vm+tq1rn3ySC6dOneo/tyqH5dGMbLIJvPcefPxx6SSSJEmSJM20Rx/NB4kfdthPX2vZ8qfbz5ZcMt9eeeWn1w8blu9dedS8WB7NyOS5R64+kiRJkiQ1QjfcAEstBVtvXff3rL127SuPJm9l++Uv50w2NQ6WRzOyxhqwwAKWR5IkSZKkRufDD+Hxx+GQQ/Iqo7qqqspDtr/5Zsrnhw2D5ZeHBRecszlV2SyPZqRlS9hoI8sjSZIkSVKjc/PN+f6QQ2bufVVVeTj2P/4x5fPDhrllrTmyPKqLTTaBt96Czz4rnUSSJEmSpDqZOBFuvBG22AKWW27m3lvb0OzvvoO333ZYdnNkeVQXk+cePf982RySJEmSJNXRM8/AiBFw6KEz/97FFoOll56yPPrXv/JqJFceNT+tZnyJWGstaNs2b13bY4/SaSRJkiRJAmD8+HyS2qefwpgx0KbNj7f+/aF9e9h551n73GuvPeWJa5601nxZHtVF69aw4YbOPZIkSZIkFffqq3k10YgR8MUX07/2uONg7rln7etUVcGDD8LXX+cB2a++ms+TmtktcGr8LI/qapNN4PTT89/MRRYpnUaSJEmS1Ewdfzx88EHeGLP44nmL2eKL59VGP/zw423CBNh991n/OlVV+f7vf4dNN/1xWHbEnPlzqPGwPKqryXOP/vxn2GWXslkkSZIkSc3S4MF5U8wVV8Cxx9bv16o5NHuTTeC11+Cgg+r3a6oyOTC7rqqqYJ553LomSZIkSSoiJejZE5ZYArp2rf+vt8gieYva0KEwfDh8+60nrTVXrjyqq7nnhvXXtzySJEmSJBUxeHA+BPzKK/PahoYweWi2w7KbN1cezYxNNsl/Y776qnQSSZIkSVIzMnnV0ZJLwuGHN9zXraqC996DQYOgRQv4xS8a7murclgezYxf/zr/jR0woHQSSZIkSVIzMmhQHsF76qkNt+oIfhyafeedsMoqeSi3mh/Lo5mx0Uaw9dbQvTs880zpNJIkSZKkZmDyqqOlloLDDmvYrz15aPbo0W5Za84sj2ZGy5YwcGCuW3fbDd54o3QiSZIkSVIT9+yz8Je/NPyqI4B27WCFFfLHDstuvhyYPbMWWAAefRTWWw+22w6GDIFFFy2dSpIkSZLUBNx9N9x7b94eNvn2zDNlVh1NVlWVT1tz5VHzZXk0K5ZdFh55JA/Q3mmnXAO78VOSJEmSNBvGjYPjjoMJE2ChheCHH/Jt3Di4+up8CHgJG2wA998Pa65Z5uurPLetzap11oHbb88rjw48ECZOLJ1IkiRJktSIPfQQfP55/lHzvffgk0/yrKHvvss/dpZy5JH54HE33TRflkezY9dd4eKL85rCrl1h0qTSiSRJkiRJjdR118Eyy8BWW5VOMqW55oJVVy2dQiW5bW12nXACfP01nHNOnlx21VUQUTqVJEmSJKkRefddePrp/KNly5al00hTsjyaE3r3hjFj8iqkueeGSy+1QJIkSZIk1dn11+fS6JBDSieRfsryaE6IgAsvzAVSnz55ePZ555VOJUmSJElqBMaNg5tvhh12gCWXLJ1G+inLozklAq64IhdI558PCy8M3buXTiVJkiRJqnAPPggjR8IRR5ROItXOgdlzUgT0758HaZ9yCvzzn6UTSZIkSZIq3LXXwrLLwpZblk4i1c7yaE5r0SL/zW/XLp+lOH586USSJEmSpAr1zjvw7LNw+OEOylblsjyqD4ssklcg/eMf8Pvfl04jSZIkSapQDspWY2B5VF922QX22y+fs/jqq6XTSJIkSZIqzNixeVD2jjvCEkuUTiNNm+VRfbryyrwK6cAD8/h8SZIkSZKqPfggfPGFg7JV+SyP6lP79nDddfDaa3DuuaXTSJIkSZIqyDXXwPLLOyhblc/yqL7tsENeeXT++blEkiRJkiQ1e2+8Ac89l1cdtfAnc1W4evu/aETMExEvR8SwiHg9InrXcs0yETEoIv4REa9FxLb1laeoPn2gbVtXH0mSJEmSgHxId+vWDspW41Cf/eZYYLOU0hpAJ2CbiOg81TVnAANTSmsCewNX12Oectq1g6OOgvvug7feKp1GkiRJklTQd9/BrbfC7rtDhw6l00gzVm/lUcq+rX7YuvqWpr4MWKD64wWBT+orT3HHHw9zzw0XXlg6iSRJkiSpoLvvhq+/hiOPLJ1Eqpt63VkZES0j4lXgc+CplNKQqS7pBewfER8BjwHHTOPzdI2IoRExdOTIkfUZuf4suigcfjgMGAAjRpROI0mSJEkqpH9/+MUvYKONSieR6qZey6OU0sSUUiegI7BuRKw21SX7ALeklDoC2wIDIuInmVJK16WUqlJKVR0a85q+Hj3y/SWXlM0hSZIkSSpi6NB869YNIkqnkeqmQWa6p5S+AgYB20z10qHAwOpr/grMAyzSEJmKWGYZOOAAuP56+Pzz0mkkSZIkSQ2sf3+Yd17o0qV0Eqnu6vO0tQ4RsVD1x22ALYE3p7psBLB59TU/J5dHjXRfWh2dfDKMHQuXX146iSRJkiSpAX31Fdx5J+y3Hyy4YOk0Ut3V58qjJYBBEfEa8DfyzKNHI+LsiNix+poTgMMjYhhwF3BQSmnqodpNy8orwx57QL9++V8OSZIkSVKzcNtt8MMPecua1Ji0qq9PnFJ6DVizlufPqvHxG8CG9ZWhYp16KgwcCFdfDaedVjqNJEmSJKmeffdd/hFw3XVhrbVKp5FmToPMPNJUOnWCbbeFPn3yvyCSJEmSpCbr009hk03gnXfg9NNLp5FmnuVRKWeeCV98kbevSZIkSZIalXHj4K9/zYdp77xzXiPQpw98//2U1/3739C5c75/6CHYccfaP59UySyPSuncGbbZBi66CL75pnQaSZIkSVIdjBwJO+2UB15vsAGceCK88QbMPTd07w7LLffjj3nPP5+vGTMGnnsOtt++dHpp1lgeldS7N4waBVddVTqJJEmSJGkG3n4b1l8fnnwSjjgC/vCHvCXt7bdhyBD4859hzTXzIdvLLQdbbgmLLw4vvQRVVaXTS7Ou3gZmqw7WXRe22y6vczzqKFhggdKJJEmSJEm1ePHFvOUsAgYNyptJprbRRvDEE7lIOv98mDgRBgyAdu0aPq80J7nyqLReveDLL6Fv39JJJEmSJEm1+MMfYLPNoH37POeotuKopvXWy/ONHn3U4khNg+VRaVVVsMMOcOml8PXXpdNIkiRJkqqNHZunjeyxB6y9dl59tNJKpVNJDc/yqBL06gWjR8OVV5ZOIkmSJEkCBg/OJ6j16gX77gtPPw2LLFI6lVSG5VElWGutPK7/0kvhq69Kp5EkSZKkZuvzz+HAA2HTTWHcOPjTn+D226FNm9LJpHIsjypFr15521qfPqWTSJIkSVKzNGwYrLIK3HUXnH46/OtfsM02pVNJ5VkeVYpOnWC33eCyy2DkyNJpJEmSJKlZmTQJunWD1q1ziXTuua42kiazPKok554L33+fz3SUJEmSJDWYW2+Fl16Ciy6Cn/+8dBqpslgeVZJVVoGDD4arr4YRI0qnkSRJkqRmYfRoOPlk2GAD6NKldBqp8lgeVZqePSEiz0CSJEmSJNW7nj1h1Ci46ipo4U/J0k/416LSLL00HHVUXjP5xhul00iSJElSkzZsGPTrl+cdrblm6TRSZbI8qkSnngpt28IZZ5ROIkmSJElNVkpw9NHQvj2cc07pNFLlsjyqRIssAieeCA88AC+/XDqNJEmSJDVJd9wBf/kL/P73uUCSVDvLo0p1/PHQoQOcckquwyVJkiRJc8TEiXDttXDMMbDOOnDIIaUTSZXN8qhSzT9/3rY2aBA8/njpNJIkSZLUJPz5z1BVlWccrb463HWXQ7KlGfGvSCXr1g1WXhmOOw7Gji2dRpIkVZCI2CYi3oqIdyPilFpe7x4Rb0TEaxHxTEQsW+O1AyPinerbgQ2bXJLK+OQT2Gcf+NWv8slqd98Nzz0HK65YOplU+SyPKtlcc8GVV8I778Bll5VOI0mSKkREtAT6Ab8BVgX2iYhVp7rsH0BVSumXwH3ARdXvbQ/0BNYD1gV6RkS7hsouSSV88AFsuCE8+CCcdRa8+SbstRdElE4mNQ6WR5Vu661hl13g3HPhww9Lp5EkSZVhXeDdlNLwlNI44G5gp5oXpJQGpZS+r374EtCx+uOtgadSSl+mlEYDTwHbNFBuSWpwH34Im24Ko0fnLWu9e8O885ZOJTUulkeNwWWXwaRJ0KNH6SSSJKkyLAXU/K3SR9XPTcuhwJ9m9r0R0TUihkbE0JEjR85GXEkq4+OPc3E0ahQ89VSedSRp5lkeNQbLLQenngoDB8Kzz5ZOI0mSGpGI2B+oAi6e2femlK5LKVWllKo6dOgw58NJUj36739zcfT55/DEE/lUNUmzxvKosTjpJFhhhXyW5PjxpdNIkqSyPgaWrvG4Y/VzU4iILYDTgR1TSmNn5r2S1FiNGQMPPZSLo//+Nx9e3blz6VRS42Z51FjMMw9cfjm88Qb07Vs6jSRJKutvwM8iYvmImAvYG3i45gURsSZwLbk4+rzGS08AW0VEu+pB2VtVPydJjdbYsfDoo3DAAbDYYrDzzvDll/DYY7DBBqXTSY1fq9IBNBO23x623RZ69YIuXcDl45IkNUsppQkRcTS59GkJ3JRSej0izgaGppQeJm9Tmw+4N/JxQiNSSjumlL6MiHPIBRTA2SmlLwv8MSRpjvj2W1h7bXj7bVhoIdh9d9hzT9hsM2jdunQ6qWmwPGpMIuDSS2G11fLpa1dcUTqRJEkqJKX0GPDYVM+dVePjLabz3puAm+ovnSQ1nLPOysXRHXfk4miuuUonkpoet601NqusAoceCldfDe++WzqNJEmSJBXz97/n36l36wb77mtxJNUXy6PGqFev/K/i6aeXTiJJkiRJRUyYAF275mkev/996TRS02Z51BgtsQT06AEDB8LLL5dOI0mSJEkNrl8/eOWVvPJooYVKp5GaNsujxqpHD1h0UTjpJEipdBpJkiRJajAffghnnAG/+U0eji2pflkeNVbzzw89e8Jzz8Ef/1g6jSRJkiQ1mGOOgYkT8+qjfKCkpPrkaWuN2eGHw+WXw8knwzbbQCv+ZV6iAAAgAElEQVT/55QkSZLUdKQEw4fDRx/Bp5/m25tvwkMPwYUXwvLLl04oNQ+2DY1Z69Z5Mtzuu8Ott+ZT2CRJkiSpkfv2W7jzTujfH/7xjylfa9Uq/+78d78rk01qjty21tjtuiustx707g1jx5ZOI0mSJEmz7O234eijYckl4Ygj8olqV1wBTz8N//oXfPFF/rHnT3/Kv0uX1DBcedTYRcB558EWW8C118Kxx5ZOJEmSJEkz7fPPYd11YcyYPAS7WzdYf31nGkmVwPKoKdh8c9h001wiHXootG1bOpEkSZIkzZQzz4TvvoNhw2DVVUunkVST29aaivPOy1V9376lk0iSJEnSTBk2DG64IW9ZsziSKo/lUVOx/vqw3XZw0UXw1Vel00iSJElSnaSUh18vtBCcdVbpNJJqY3nUlJx7LoweDZddVjqJJEmSJNXJQw/BoEFw9tnQrl3pNJJqY3nUlHTqBHvsAX36wMiRpdNIkiRJ0nSNHQs9euStakccUTqNpGmxPGpqzj4bvv8eLrywdBJJkiRJmq4rr4T33su//27lcU5SxbI8ampWWQW6dIF+/eDjj0unkSRJkqRaffYZnHNOHt261Val00iaHsujpqhnT5g4Ma9CkiRJkqQKdO658MMPcOmlpZNImhHLo6Zo+eXhyCPhxhvhrbdKp5EkSZKkKXzxRf5xpUsX+L//K51G0oxYHjVVp58Obdrke0mSJEmqIP365VVHPXqUTiKpLiyPmqpFF4UTT4Q//AGGDCmdRpIkSZKAfL5P376w/fb5lDVJlc/yqCnr3j2XSKecAimVTiNJkiRJ3HILjBoFJ51UOomkurI8asrmmw/OPBMGD4YnniidRpIkSVIzN3FiHpC93nqw0Ual00iqK8ujpq5rV1hhhbz6aNKk0mkkSZIkNWP33w/Dh+dVRxGl00iqK8ujpm6uufIZmMOGwV13lU4jSZIkqZlKCS66CFZaCXbaqXQaSTPD8qg52GsvWHPNfPLa99+XTiNJkiSpGXruORg6NJ+w1rJl6TSSZoblUXPQogVcfjl88AGcc07pNJIkSZKaoYsvhg4d4IADSieRNLMsj5qLX/0KDj4YLrkE/vWv0mkkSZIkNSOvvQaPPQbHHgtt2pROI2lmWR41JxddBAsuCN26OTxbkiRJUoM588z8o8hRR5VOImlWWB41J4ssklcevfAC3Hhj6TSSJEmSmoEhQ+Dhh/Oso3btSqeRNCssj5qbAw+ETTaBk0+Gzz8vnUaSJElSE3fGGfn32McdVzqJpFlledTcRED//vDtt3DCCaXTSJIkSWrCBg+Gp5+G006D+ecvnUbSrKq38igi5omIlyNiWES8HhG9p3HdnhHxRvU1d9ZXHtWwyipwyilw++3wzDOl00iSJElqglKC00+HpZaCI48snUbS7KjPlUdjgc1SSmsAnYBtIqJzzQsi4mfAqcCGKaVfAMfXYx7VdNppsOKK+biD8eNLp5EkSZLUxDz2GLz4Yh6WPc88pdNImh31Vh6l7Nvqh62rb2mqyw4H+qWURle/xyE8DWWeeeDSS+GNN/I2NkmSJEmaBePHw5dfTvncpEl51tEKK8Ahh5TJJWnOaVWfnzwiWgKvACuRS6IhU12ycvV1LwAtgV4ppcdr+Txdga4AyyyzTH1Gbl523BG22AJ69oR994WFFy6dSJIkSVIjMGFCnmd0zz1w//25PFp55Xw2z69+Bd98A6++CrfdBq1bl04raXbVa3mUUpoIdIqIhYAHImK1lNK/pvr6PwN+DXQEno+I1VNKX031ea4DrgOoqqqaevWSZlUE9OkDa6yRC6SrriqdSJIkSVIF++qrvKJo4EAYORLmmw922gl+8Yu8RW3gQLj++nztqqvm31FLavzqtTyaLKX0VUQMArYBapZHHwFDUkrjgf9ExNvkMulvDZFLwGqr5el1/ftDt275sSRJkiRN5dNPYZtt4PXXYffdYc898+M2bX68ZuJE+Oc/4YUX8gqkli3L5ZU059TnaWsdqlccERFtgC2BN6e67EHyqiMiYhHyNrbh9ZVJ09C7NyywABx/fD4SQZIkSZJqGD4cNtoI3nkH/vhHuOsu2GWXKYsjyGVRp05w1FGw+uplskqa8+rztLUlgEER8Rp5JdFTKaVHI+LsiNix+pongFER8QYwCDgxpTSqHjOpNgsvnAukZ56Bhx8unUaSJElSBXntNdhwQxg9Ov/IsNVWpRNJamiRGtlKk6qqqjR06NDSMZqe8ePz7KNx4/I61LnnLp1IktRMRcQrKaWq0jk0Jb8Hk5qnwYPzCqO2beHJJ/McI0lN0/S+B6vPlUdqTFq3hiuugPfeyxPwJEmSJDVLY8bAgAGwwQaw6aaw6KJ5hpHFkdR8WR7pR1tumYdmX3IJPP106TSSJEmSGtDo0dCjByy1FBxwAIwaBZddBi+/DMsuWzqdpJIsjzSlSy+F//s/OPDA/F8LSZIkSc1Cjx5w+eWw+eZ5ttGbb8LvfgcLLlg6maTSLI80pXnnhTvvhJEj4fDDPX1NkiRJaga++w4GDswrjgYOhM02g4jSqSRVCssj/dRaa8F558EDD8BNN5VOI0mSJKmePfAAfPtt3oAgSVOzPFLtTjgh/7rh2GPh7bdLp5EkSZJUj269FZZbDjbeuHQSSZXI8ki1a9Ei/xdk7rmhSxeYNKl0IkmSJEn14MMP84yjAw7IPwZI0tT8p0HT1rEjXHFFPl7htttKp5EkSZJUD26/PY86PeCA0kkkVSrLI03ffvvBeuvBqafmTdCSJEmSmoyU8oaDjTeGFVcsnUZSpbI80vS1aJHP6/z0U/j970unkSRJkjQHvfwyvPWWg7IlTZ/lkWasc+e8AunSS+E//ymdRpIkSdIccuut0KYN7LFH6SSSKpnlkermggvyKqSTTy6dRJIkSdIcMHYs3H037LILLLBA6TSSKpnlkeqmY8dcHN17Lzz/fOk0kiRJkmbTI4/A6NFuWZM0Y5ZHqrsTT8wl0vHHw8SJpdNIkiRJmg233AJLLQWbb146iaRKZ3mkupt3XrjwQvjHP+DGG0unkSRJkjSLRoyAxx+H/feHli1Lp5FU6SyPNHP22Qd+/Ws44QR4773SaSRJkiTNpJTgsMNgnnmgW7fSaSQ1BpZHmjkR+UiGli3zrykmTCidSJIkSVINKcFXX0379WuvhaeegosvhuWWa7BYkhoxyyPNvGWWgf794aWX4NxzS6eRJEmSVMM558Bii8F99/30teHDoUcP2GILVx1JqjvLI82avfeGLl3yf5n++tfSaSRJkiQBY8ZA374waRLsuSdcc82Pr02aBIcckjcR3Hhj3lQgSXVheaRZd9VVeRXS/vvDN9+UTiNJUqMUEcdERLvSOSQ1DQMHwhdfwAMPwHbbwW9/C7165a1sffvCc89Bnz7523hJqivLI826BRaA22+H99+HY48tnUaSpMZqMeBvETEwIraJcC2ApFnXrx+sskouju6/Hw46CHr3zr/vPfVU2HZbOPjg0iklNTaWR5o9G24Ip50Gt9wC99xTOo0kSY1OSukM4GfAjcBBwDsRcX5ErFg0mKRG5+WX8+3oo/OWtNat4aab4OST4c478+lq11/vdjVJM8/ySLPvrLOgc2fo2jWvQpIkSTMlpZSAT6tvE4B2wH0RcVHRYJIalX79YL758mjSySLgggvg7rvhkUdgySXL5ZPUeFkeafa1bp1/lQGw774wYULZPJIkNSIRcVxEvAJcBLwArJ5SOhJYG9itaDhJjcbIkbkgOvDAPF1ianvtlTcNSNKssDzSnLH88nDttfnktd69S6eRJKkxaQ/smlLaOqV0b0ppPEBKaRKwfdlokhqLG26AcePgqKNKJ5HUFFkeac7Ze+88fe+882Dw4NJpJElqLP4EfDn5QUQsEBHrAaSU/l0slaRGY8IE6N8fNtsMfv7z0mkkNUWWR5qzrrwSfvazfJzDqFGl00iS1BhcA3xb4/G31c9JUp08+iiMGJEHZUtSfWhVOoCamPnmg7vuygO0Dzssnw/qcQ6SJE1PVA/MBvJ2tYjwezRJP5ES7LMP/POfsNpqsPrq+b5PH1h6adhhh9IJJTVVrjzSnLfWWvlIhwcfzGeDSpKk6RkeEcdGROvq23HA8NKhJFWe666De+6B9u1h6FA480zYZRd4/nno1g1aWTtLqieWR6ofxx+fN10fdxy8917pNJIkVbJuwAbAx8BHwHpA16KJJFWc99+HHj1giy1yWfTee/DNNzBkSD5lrXv30gklNWV206ofLVrALbfAL38JXbrk/8L5qxBJkn4ipfQ5sHfpHJIqV0p5IgTkU9UmT4WYbz5Yd918k6T65Moj1Z+ll4arr4a//jVvY5MkST8REfNExFERcXVE3DT5VjqXpMpx/fXwzDNwySWw7LKl00hqjiyPVL/22SffevWCv/2tdBpJkirRAGBxYGvgOaAj8E3RRJIqxgcfwAknwOabQ1c3tEoqxPJI9a9fP1hiCdh/f/juu9JpJEmqNCullM4Evksp3QpsR557JKmZm9Z2NUlqaJZHqn/t2sFtt8E77+QjISRJUk3jq++/iojVgAWBRQvmkVQhbrkFnn4aLr4YlluudBpJzZnlkRrGppvC4YdD377wxhul00iSVEmui4h2wBnAw8AbwIVlI0kq7fvv4YwzoHNnt6tJKs/ySA3n3HPzkRDHHpvX4EqS1MxFRAvgfyml0Sml51NKK6SUFk0pXVs6m6SyrrwSPvkELrooH2QsSSX5z5AaTocOcM45+aiIBx4onUaSpOJSSpOAk0rnkFRZRo3KhxXvsANsvHHpNJJkeaSG1q0brL46dO+e1+JKkqSnI6JHRCwdEe0n30qHklTO738P33wD559fOokkZZZHalitWuU1uB98kCf/SZKkvYCjgOeBV6pvQ4smklTMBx/kMaEHHgirrVY6jSRllkdqeL/+Ney5Z16L+/77pdNIklRUSmn5Wm4rlM4lqYyePSECevcunUSSftSqdAA1U5dcAo8+Cj16wH33lU4jSVIxEXFAbc+nlG5r6CySyvrnP+G22/K3yEsvXTqNJP3IlUcqY+ml4bTT4A9/gKuuKp1GkqSS1qlx2xjoBexYMpCkMk49FRZcEE45pXQSSZqSK49Uzkknwd/+BsccA/PMA4cdVjqRJEkNLqV0TM3HEbEQcHehOJIKuf12+OMf4cILob0j8yVVGFceqZzWreGee+A3v4GuXWHAgNKJJEmqBN8By5cOIanhvPpq/nZ4k03yocSSVGlceaSy5p47b13bYQc46KD8eM89S6eSJKnBRMQjQKp+2AJYFRhYLpGkhvTll7Drrnm10T335MOJJanS+E+TymvTBh56KK9A2ndfmGsu2Hnn0qkkSWool9T4eALwQUrpo1JhJDWciRNhv/3go4/g+edhscVKJ5Kk2rltTZWhbdt8+traa+f/gr79dulEkiQ1lBHAkJTScymlF4BREbFc2UiSGkLv3vD449C3L3TuXDqNJE2b5ZEqxwILwP33561r++4L48aVTiRJUkO4F5hU4/HE6uckNWEPPgjnnAMHH5znHUlSJbM8UmVZaim4/np45RXo1at0GkmSGkKrlNL//41J9cdzFcwjqR5NnAjnnw+7754X3ffrBxGlU0nS9FkeqfLsthsceihccAE891zpNJIk1beREbHj5AcRsRPwRcE8kurJf/8LW28Np5+ey6NnnsnjPyWp0lkeqTJdfjmstBJ06QKjR5dOI0lSfeoGnBYRIyJiBHAycEThTJLmsMcfhzXWgBdfhBtugLvuggUXLJ1KkurG8kiVab754I478q9nunWDlGb8HkmSGqGU0nsppc7AqsCqKaUNUkrvls4lac65+eZ8sPDii8PQoXmRvVvVJDUmlkeqXOusA2efDQMHwoABpdNIklQvIuL8iFgopfRtSunbiGgXEeeWziVpznjrLTj6aNh8cxgyBFZdtXQiSZp5lkeqbCedBBtvDMccAx9+WDqNJEn14Tcppa8mP0gpjQa2LZhH0hwybhzst1+ea3Tbbc43ktR4WR6psrVsCbfcko+lOOQQt69JkpqilhEx9+QHEdEGmHs610tqJHr1yocIX389LLlk6TSSNOssj1T5VlgBLr0Unn4arrmmdBpJkua0O4BnIuLQiDgUeAq4rXAmSbPpuefy4cGHHQa77FI6jSTNHssjNQ5du8JWW8GJJ8K7zhCVJDUdKaULgXOBn1ffzql+TlIj9dVX+dDglVaCPn1Kp5Gk2Wd5pMYhAm68EVq3hoMOytvYJElqIlJKj6eUegA9gUUj4o+lM0maNSnBkUfmQ4PvuCMfIixJjV29lUcRMU9EvBwRwyLi9YjoPZ1rd4uIFBFV9ZVHTUDHjnDVVfDCC3kbmyRJTUBEzBURu0TEvcB/gc2A/nV43zYR8VZEvBsRp9Ty+q8i4u8RMSEidp/qtYkR8Wr17eE59oeRxJNPwt1353lH66xTOo0kzRl1Ko8iom1EtKj+eOWI2DEiWs/gbWOBzVJKawCdgG0ionMtn3t+4DhgyMxFV7O033550/iZZ8Ibb5ROI0nSLIuIrSLiZuA/wG7kOUdfppQOTik9MoP3tgT6Ab8BVgX2iYipDwAfARwE3FnLp/ghpdSp+rbjbP5RJFWbNAlOPhmWXx569CidRpLmnLquPHoemCcilgKeBLoAt0zvDSn7tvph6+pbbUdlnQNcCIypYxY1ZxHQvz/MP38+fc3ta5KkxutxYAVgo5TS/tWF0aQ6vndd4N2U0vCU0jjgbmCnmheklN5PKb02E59T0my64w4YNgzOOw/m9sxESU1IXcujSCl9D+wKXJ1S2gP4xQzfFNEyIl4FPgeeSikNmer1tYClU0rT3dcfEV0jYmhEDB05cmQdI6vJWnRRuOIKGDIE+vYtnUaSpFm1FvBX4OmIeKr6pLWWdXzvUsCHNR5/VP1cXc1T/b3VSxGx87Qu8nswqe7GjIEzzoC114a99iqdRpLmrDqXRxGxPrAfMLnomeE3NymliSmlTkBHYN2IWK3GJ2wBXAacUIfPc11KqSqlVNWhQ4c6RlaTtu++sN12cPrpMHx46TSSJM20lNKrKaVTUkorkgdldwJaR8SfIqJrPX/5ZVNKVcC+wOURseI0Mvo9mFRH/frBiBFw4YXQwmOJJDUxdf1n7XjgVOCBlNLrEbECMKiuXySl9FX19dvUeHp+YDVgcES8D3QGHnZotupk8va1Vq3g8MPzsRaSJDVSKaUXU0rHkH/h1of8fdH0fAwsXeNxx+rn6vr1Pq6+Hw4MBtacmbySpjR6dN6qtvXWsPnmpdNI0pxXp/IopfRcSmnHlNKF1SuGvkgpHTu990REh4hYqPrjNsCWwJs1PufXKaVFUkrLpZSWA14CdkwpDZ3VP4yamY4d4eKL4dln4YYbSqeRJGm2pZQmpZSeTCkdMoNL/wb8LCKWj4i5gL2BOp2aFhHtImLu6o8XATYEPIVCmg0XXABffZVXHUlSU1TX09bujIgFIqIt8C/gjYg4cQZvWwIYFBGvkb/BeSql9GhEnB0RnuqhOePww2HTTfNxFh99VDqNJEkNIqU0ATgaeAL4NzCwenX4//8+KyLWiYiPgD2AayPi9eq3/xwYGhHDyCvDL0gpWR5Js+jDD/M4zv33hzXWKJ1GkupHpDps94mIV1NKnSJiP/Jwx1OAV1JKv6zvgFOrqqpKQ4e6OEk1vPcerL56LpEefTRvaZMkNVoR8Ur1PB5VEL8Hk34qpTwc+6GH4O23YdllSyeSpFk3ve/B6jrzqHVEtAZ2Bh5OKY0HHDKjyrDiinmN8GOPefqaJKlRqj6hdsmIWGbyrXQmSdOXEpx4Itx7L/TsaXEkqWmra3l0LfA+0BZ4PiKWBf5XX6GkmXb00bD99vm/4K++WjqNJEl1FhHHAJ8BT5FPtf0j8GjRUJJm6KKL4NJL4aij4NRTS6eRpPrVqi4XpZSuBK6s8dQHEbFp/USSZkEE3Hxz3mi+997wyivQtm3pVJIk1cVxwP+llEaVDiKpbq6/Hk45BfbZB6680qkJkpq+ug7MXjAiLouIodW3S8mrkKTKscgicPvtecP5MceUTiNJUl19CHxdOoSkuvnDH6BbN9hmG7jlFmhR170cktSI1WnlEXAT+ZS1PasfdwFuBnatj1DSLNt0UzjtNDjvPNhyy/zrIEmSKlBEdK/+cDgwOCL+CIyd/HpK6bIiwSRN0/PPw777wnrrwX33wVxzlU4kSQ2jruXRiiml3Wo87h0RDpZRZerVCwYNgiOOgHXXzQO1JUmqPPNX34+ovs1VfZNUgT7+GPbYA5ZfPh/w64QESc1JXcujHyJio5TSXwAiYkPgh/qLJc2GVq3gzjuhUyfYZRd48UWYb77SqSRJmkJKqXfpDJLqZtw42HNP+O67/DvK9u1LJ5KkhlXXHbrdgH4R8X5EvA9cBRxRb6mk2bXssnDPPfD663DQQTBpUulEkiTVKiKeioiFajxuFxFPlMwkaUo9euTfR950E6y6auk0ktTw6lQepZSGpZTWAH4J/DKltCawWb0mk2bXVlvBxRfnqYbnnls6jSRJ09IhpfTV5AcppdHAogXzSKrhjjugb1/o3j2vPpKk5mimzgZIKf0vpfS/6ofdp3uxVAl+9zs44ADo2RMeeKB0GkmSajMxIpaZ/CAilgVSwTySqv3zn3D44bDxxnDBBaXTSFI5dZ15VJuYYymk+hIB114Lb70FXbrAX/8Kq69eOpUkSTWdDvwlIp4jf3+1MY4HkIobPRp23RUWWggGDoTWrUsnkqRyZmrl0VT8jZgah3nmgfvvhwUWgB13hOHDSyeSJOn/Syk9DqwF3APcDaxd/ZykQsaNg912gxEj4N57YfHFSyeSpLKmWx5FxDcR8b9abt8ASzZQRmn2LbkkPPQQfP01rLsuDB5cOpEkSQBExDMppS9SSo9W376IiGdK55Kaq5Tgt7/Np6rdeCNsuGHpRJJU3nTLo5TS/CmlBWq5zZ9Smp0tb1LDW2cdePllWHRR2HLLvJ1NkqRCImKeiGgPLFJ9wlr76ttywFJl00nN18UX59LozDNh//1Lp5GkyjA729akxmellfLco622gm7d4OijYfz40qkkSc3TEcArwCrA36s/fgV4CLiqYC6p2br/fjj5ZNhrL+jdu3QaSaocrh5S87PggvDww3DKKXDJJfDhh/kkthZ2qZKkhpNSugK4IiKOSSn1LZ1Hau6GDs0rjTp3hptvzueuSJIyf1pW89SyZV6TfPnluUjy7FVJUgOLiM2qP/w4Inad+lY0nNTM3HUXbLppnm7w4IPQpk3pRJJUWVx5pObt2GPzHKQzz4T118/fNUiS1DA2AZ4FdqjltQTc37BxpOZnzBj43e+gf/88GPvuu2GxxUqnkqTKY3mk5i0iD87++99hn33g1Vc9i1WS1CBSSj2r7w8unUVqjoYPhz32yN8GnnQSnHsutG5dOpUkVSa3rUnzzQf33gv/+18ukCZMKJ1IktSMRMTCEXFlRPw9Il6JiCsiYuHSuaSm7KWXYK214D//yRMMLrzQ4kiSpsfySAJYbTW45hoYPBh69SqdRpLUvNwNjAR2A3av/vieoomkJuz776FLF1hoobzqaIfaNo5KkqbgtjVpsgMPhD//Gc47D371K9hqq9KJJEnNwxIppXNqPD43IvYqlkZq4s44A959F559FpZbrnQaSWocXHkk1dS3L6y6Khx6aN7GJklS/XsyIvaOiBbVtz2BJ0qHkpqiF17Ih+3+9reekyJJM8PySKqpTRu46Sb45BM48cTSaSRJzcPhwJ3A2Orb3cAREfFNRPibDGkO+eEHOPhgWHbZPONIklR3lkfS1NZbD7p3h+uug2eeKZ1GktTEpZTmTym1SCm1rr61qH5u/pTSAqXzSU3FGWfAO+/AjTfm81IkSXVneSTV5uyzYeWV4bDD4NtvS6eRJEnSbHjxRejTB448EjbbrHQaSWp8LI+k2kzevvbBB3DqqaXTSJIkaRb97395u9oyy7hdTZJmleWRNC0bbgjHHANXXZVPYZMkSVKjMmEC7L03vPce3HILzD9/6USS1DhZHknTc/75sPzycMghbl+TJNWbiGgZEUtGxDKTb6UzSU3B734Hf/oTXHMN/PrXpdNIUuNleSRNT9u2cPPN/4+9O4+7es7/P/54d1VosmQka5GyZSmyG4x9N2VniLGPJUOMGFIMkSW7iSxjp+yMvjS2QUiyZSiNiCjZsrW+f3+8Tz9JFy3XOe9zzvW4327ndrbPda7ndW6n63r3+rzfrzeMHg2HHQYx5k4kSaoyIYTjgU+Bx4FHCpeHs4aSqsAVV6QJ5CefDEcckTuNJFU2i0fSr9lyyzQD6e67oW/f3GkkSdWnK7BajLFdjHHtwmWd3KGkSvboo3DiibDHHvY5kqS6YPFImhunngqdOsEpp8Azz+ROI0mqLh8CX+UOIVWL11+HffeFddeF226DmprciSSp8jXMHUCqCCGk5Wsbbgj77APDhsFyy+VOJUmqDqOBp0IIjwCTZz4YY7wkXySpMk2ZkhpkL7YYPPRQ6kAgSVpwzjyS5tbii8O998KkSbD33ml0IknSgvuA1O+oMbDoLBdJ8+iyy+Dtt6FfP1h++dxpJKl6OPNImhft2kH//rD//mkh/VVXpVlJkiTNpxhjz9wZpGowdiz07Am77Qa77JI7jSRVF4tH0rzab7+0bK1PH1h6aTj77NyJJEkVKITQN8Z4YgjhIeBn23nGGHfPEEuqWN26wfTpafaRJKluWTyS5kfv3jBhQjq91awZdO2aO5EkqfLcUri+KGsKqQr8+99w113pnN7KK+dOI0nVx+KRND8aNIDrroOvvkrL15ZYArp0yZ1KklRBYoyvFK6fzp1FqmRTpsBxx0Hr1mmDXElS3bN4JM2vhg3h9tth1wGQ2/AAACAASURBVF3hsMNSQ+0//CF3KklShQkhtAXOB9YEFp75eIyxdbZQUgWZ2ST74YdhkUVyp5Gk6uRua9KCWHhhuP9+6NgR9t0XnvbksSRpnt0IXANMA34P/BO4NWsiqUJ89JFNsiWpFCweSQuqaVN49NG0wH7vvdNWH5Ikzb1FYoyDgRBjHBNjPBvwv8HSXOjdOy1b69s3dxJJqm4Wj6S6sOSScN998P33sOeeMHly7kSSpMoxOYTQABgZQjguhNAJaJo7lFTuxo+H66+Hgw5K/Y4kScVj8UiqK2usATffDC+9BCeckDuNJKlydAWaACcA6wN/BNyFQfoVV1yRztedckruJJJU/SweSXWpc2c47TTo1y+dCpMk6ReEEGqAfWOM38QYx8YYD40x7hljHJI7m1TOJk2CK69Me5WsvnruNJJU/SweSXXt3HNh223h2GPTLCRJkuYghNAwxjgd2Dx3FqnS9OsHX34Jf/1r7iSSVD9YPJLqWk0N3HEHLLtsmon07ru5E0mSytPMMwyvhhAeDCEcFELoPPOSNZlUxiZPhksugd//HjbaKHcaSaofLB5JxbDUUvDAA2l0s+mm8PzzuRNJksrXwsBEYGtgV2C3wrWkObj1Vvj449QpQJJUGhaPpGJZd1144QVo1gy22SbtxiZJ0o+WDiGcBLwJvFG4fqtw/WbOYFK5mj4dLrwQOnSA7bbLnUaS6g+LR1IxtWmTZh21bw977gmXX547kSSpfNQATQuXRWe5PfMiaTb33586Apx2GoSQO40k1R8NcweQql7z5jB4MBxwAHTtCl98AT165E4lScpvXIyxV+4QUqUYMwZ69YJVVknn5CRJpePMI6kUmjSBgQPh4IPh7LNTMUmSVN85b0KaC2+8AQcdlIpGI0bA+een/UkkSaVj8UgqlZoauOYaWH116NIFPv88dyJJUl7b5A4glbO334Zdd4V11kmtI084AUaPhr33zp1Mkuofi0dSKTVpArfdBuPHw1FHQYy5E0mSMokxehZB+gUHHphaR/bqBR98AJdcAiuumDuVJNVPFo+kUltvPTjnHBgwAG6+OXcaSZKksjNsGLz6ahoynXkmLLlk7kSSVL9ZPJJy6NYNttwSjj8e3nsvdxpJkqSy0r8/LLRQ2m9EkpSfxSMph5oa+Oc/0/VBB8G0abkTSZIklYXvv0+r/PfaC5o1y51GkgQWj6R8WraEa6+FF15IM5DsfyRJksTAgfDVV3DYYbmTSJJmapg7gFSv7bdfWtB/4YWw+OLQu3fuRJIkSVn17w+tW6cV/pKk8mDxSMqtd2/4+mu44IJUQOrePXciSZKkLEaNgqeegr//HRq4RkKSykbRikchhIWBZ4CFCt9nQIyxx2zHnAQcDkwDJgB/ijGOKVYmqSyFAFddlQpIp58Oiy0Gxx6bO5UkSVLJ3XBDKhp16ZI7iSRpVsWceTQZ2DrG+E0IoRHwnxDCv2KMQ2Y55lWgY4zxuxDCMcCFwL5FzCSVpwYN4Kab4Jtv4LjjUgHpoINyp5IkSSqZadPScGinnWD55XOnkSTNqmiTQWPyTeFuo8IlznbMkzHG7wp3hwArFCuPVPYaNYK77oKtt06n27p3h6lTc6eSJEkqicceg3Hj4PDDcyeRJM2uqCuJQwg1IYThwHjg8Rjji79w+GHAv2p5nSNDCENDCEMnTJhQjKhSeVh4YXjooTRq6t0bNtsM3nsvdypJkqSiu/56aNECdtkldxJJ0uyKWjyKMU6PMbYnzSjaMISw1pyOCyH8EegI9KnldfrFGDvGGDs2b968eIGlctCkCfTrB/fcAyNHQvv2cMstuVNJkiQVzSefwMMPp8nXjRrlTiNJml1J9jCIMX4JPAnsOPtzIYRtgTOA3WOMk0uRR6oIe+0Fr70GHTrAwQfDCSdAjL/+dZIkSRXmrLPSMOeww3InkSTNSdGKRyGE5iGEJQq3FwG2A/472zEdgH+QCkfji5VFqlgtW8KTT6bC0RVXwLXX5k4kSZJUpwYPhuuug27dYNVVc6eRJM1JMXdbWxa4OYRQQypS3R1jfDiE0AsYGmN8kLRMrSlwTwgB4IMY4+5FzCRVnpoauPRSGD06FZHWXBO23DJ3KkmSpAX2zTdwxBHQti2cfXbuNJKk2hSteBRjfB3oMIfHz5rl9rbF+v5SVWnQAG69FTbaKC1nGzoUWrXKnUqSJGmBnHEGvP8+PPMMLLJI7jSSpNqUpOeRpDqw+OLwwAMwdSr84Q/w7be5E0mSJM23555Lq/KPOw423zx3GknSL7F4JFWS1VaDO+5IjbQPO8wG2pIkqSL98EMayrRqBeedlzuNJOnXWDySKs1OO0Hv3nDXXXD00TBtWu5EkiRJ86RnT3jnndQou2nT3GkkSb+mmA2zJRXLKafAl1/C+efDBx/A3XfDoovmTiVJkvSr3nkH+vRJM4+2tQOqJFUEZx5JlSiENMe7Xz94/HH43e9g7NjcqSRJkn5V9+7QpEk6ByZJqgwWj6RKdsQR8MgjMHp02olt+PDciSRJkmr1n//AfffBaadB8+a500iS5pbFI6nS7bBDGok1aABbbJH2upUkSSozMaaV98stByeemDuNJGleWDySqsE668CQIbD88rDjjvDEE7kTSZIk/cS996bhSq9eadmaJKlyWDySqsXyy8NTT0GbNrDrrvDww7kTSZIkATB1alqq1q4ddOmSO40kaV5ZPJKqSYsWqYC09trQqRMMGJA7kSRJEv36wahRcMEF0ND9niWp4lg8kqrNkkumZWsbbgj77gt33JE7kSRJqse+/hp69oSttoKdd86dRpI0PyweSdVo8cVh0CDYfPM0N9wm2pIkKYPJk+GEE2DCBLjwQgghdyJJ0vyweCRVq6ZN4f77oXXrtIRt5MjciSRJUj3yxhuwwQZw882p39EGG+ROJEmaXxaPpGrWrBk88kg6zbfLLvD557kTSZKkKjdjBlx8MXTsCOPHp6HI+efnTiVJWhAWj6Rqt8oqaQbSmDHQuTNMmZI7kSRJqlJffAHbbgvdusFOO6XZR/Y5kqTKZ/FIqg823xxuuAGefhqOPBJizJ1IkiRVofPOS8ON666D++6D5s1zJ5Ik1QU3ypTqiwMPTH2PevZM255cey0svXTuVJIkqUpMmABXXw377w+HH547jSSpLjnzSKpPevSACy5IzQfatYMBA3InkiRJVeKSS+D77+GMM3InkSTVNYtHUn0SApx6KgwbBq1awd57w377wcSJuZNJkqQKNnEiXHkl7LsvrLFG7jSSpLpm8Uiqj9q1gxdegHPOgXvvhXXWgffey51KkiRVqEsvhW++gb/9LXcSSVIxWDyS6qtGjdIIb8gQ+OEH2G47+Pjj3KkkSVKF+eILuPxy2GuvdH5KklR9LB5J9d1668G//gXjx8MOO8Dnn+dOJEmSKshll8GkSXDmmbmTSJKKxeKRJNhwQ3jgAXj3XdhllzTvXJIk6Vd8+SX07QudOqVV8JKk6mTxSFKyzTZw553w0kvQuTNMnpw7kSRJKnNXXAFffeWsI0mqdhaPJP2oUye4/np4/HHYaSf48MPciSRJUpn63//g4oth112hQ4fcaSRJxWTxSNJPHXoo3HRTmoG01lpw880QY+5UkiSpjPzwQ2qQDWmnNUlSdbN4JOnnunSB115LzQsOOSQtYxs/PncqSZJUJo47DoYNg1tugTZtcqeRJBWbxSNJc7bKKvDUU9CnDzz6aJqFNGJE7lSSJCmz66+H/v3hjDNgt91yp5EklYLFI0m1q6mBbt3SqcXp0+H4413CJklSPTZ0KBx7LGy3HfTsmTuNJKlULB5J+nXt2kGvXvDvf8MDD+ROI0mSMvjsM9hzT1hmGbj99nSOSZJUP1g8kjR3jjoqFZFOPjl1yZQkSfXK0UfDJ5/AwIGw1FK500iSSsnikaS507Ah9O0Lo0ena0lSViGEHUMI74QQRoUQTpvD81uEEIaFEKaFEPaa7bkuIYSRhUuX0qVWpRo0KBWNevSAjh1zp5EklZrFI0lzb9ttYffd4e9/h3HjcqeRpHorhFADXAXsBKwJ7B9CWHO2wz4ADgFun+1rlwR6ABsBGwI9QgjNip1ZlWvy5NT2sG3bNAFZklT/WDySNG8uvjiNIk8/PXcSSarPNgRGxRhHxxinAHcCe8x6QIzx/Rjj68CM2b52B+DxGOPnMcYvgMeBHUsRWpXp4oth5Ei44gpYaKHcaSRJOVg8kjRv2rSBE0+Em26Cl1/OnUaS6qvlgQ9nuT+28Fidfm0I4cgQwtAQwtAJEybMV1BVtjFj4NxzoXNn2GGH3GkkSblYPJI07/72N1h6aejSBa65Bt59F2LMnUqSVMdijP1ijB1jjB2bN2+eO44yOOmkdH3ppXlzSJLysngkad4tthhcfz188w38+c+w2mrQsmUqJr36au50klQffASsOMv9FQqPFftrVY889hjcey+ceWb6My9Jqr8sHkmaP7vtluayv/tumn20ySbw4IOw3Xbwv//lTidJ1e5loG0IYeUQQmNgP+DBufzaQcD2IYRmhUbZ2xceUz337bfpT/vQofCvf8EJJ6Qm2TNnH0mS6q+GuQNIqmAhpFFl27Zw9NEwahRsuGHake3552HRRXMnlKSqFGOcFkI4jlT0qQFuiDG+FULoBQyNMT4YQtgAuA9oBuwWQugZY2wXY/w8hHAOqQAF0CvG+HmWH0RlYepU2HJLeOGFnz7esCE8+qhNsiVJFo8k1aU2beDuu2HHHeHgg2HgQGjgBEdJKoYY46PAo7M9dtYst18mLUmb09feANxQ1ICqGHffnQpHXbvC2mtD8+bp0qoVLLdc7nSSpHJg8UhS3dp2W7jkkjQCPfts6NUrdyJJklSLGKFPH1hzzfTn23M+kqQ5sXgkqe4dfzy8/jqccw6stRbss0/uRJIkaQ4efxxeew1uuMHCkSSpdhaPJNW9EOCqq+C//4VDDoEPP4Qjj7QHkiRJZaZPn7Q07YADcieRJJUzzy9IKo6FFko9jzbdFLp1S3v8nnkmTJiQO5kkSQKGDYMnnkgrzW2KLUn6JRaPJBVPixZpVPrii7D11vD3v6fum4ceCtdemx7/7rvcKSVJqpcuuihNCj7qqNxJJEnlzuKRpOLbcMM0C2nECNh/f3jgATjmGNh44zRqbdcO7rknd0pJkuqN999Pu6wddRQsvnjuNJKkcmfxSFLprL469O8PEyemUeu998IZZ0BNDXTpAu+8kzuhJEn1wqWXpgbZXbvmTiJJqgQWjySVXghp+VqnTtCrFzz2GDRpAn/8I0ydmjudJElVbeJEuP761CR7hRVyp5EkVQKLR5LyW2456NcPhg5NxSRJklQUM2ak/Su++y7tZyFJ0tyweCSpPHTuDIccAuedB88/nzuNJElV5/vvU+vBa66B44+HtdbKnUiSVCksHkkqH5ddlpazHXQQTJqUO40kSVXj00/h979P+1NceGH6kytJ0tyyeCSpfCy2GNxyS2qmfeKJudNIklQV3nwTNtoIXn89bX56yimp/aAkSXPL4pGk8rLZZtC9O9xwQ9pDWJIkzbfhw9Of1ilT4Jln0l4VkiTNK4tHkspPjx6wySZw+OHw7ru500iSVJG+/Rb22w9+8xt48UXo2DF3IklSpbJ4JKn8NGoEd90FjRvD3nunDp+SJGme/OUv6RzMLbfAiivmTiNJqmQWjySVpxVXTKPd11+HE07InUaSpIoyYABcdx2ceipss03uNJKkSmfxSFL52mknOOMMuP56+Oc/c6eRJKkifPABHHEEbLABnHNO7jSSpGpg8UhSeTv7bNhqKzjmGHjrrdxpJEkqa9Onwx//CNOmwe23p5XgkiQtqIa5A0jSL2rYMI1+O3RI28VstBG0bw/rrpuul1469UZq1Chd19TkTixJUjbnnQfPPgs33wxt2uROI0mqFkWbeRRCWDiE8FII4bUQwlshhJ5zOGahEMJdIYRRIYQXQwgrFSuPpAq27LLw2GNpf+EJE6BvXzjwQGjXDpo3h8UXhyZNUqGpaVO44IJ0ylWSpHrkww/h3HPTDmsHHZQ7jSSpmhRz5tFkYOsY4zchhEbAf0II/4oxDpnlmMOAL2KMbUII+wEXAPsWMZOkStW+Pdx4Y7o9dSr897/w2mvw+efp/tSpMGUKDB0Kp50GAwem49u1y5tbkqQS6d0bYkzXIeROI0mqJkUrHsUYI/BN4W6jwiXOdtgewNmF2wOAK0MIofC1kjRnjRrB2muny+xihHvugWOPhfXWgx490lYzDV2lK0mqXmPHpv0lDjkEWrXKnUaSVG2K2jA7hFATQhgOjAcejzG+ONshywMfAsQYpwFfAb+dw+scGUIYGkIYOmHChGJGllTpQoB99oERI+APf0i7tW25JXz9de5kkiQVzQUXwIwZcPrpuZNIkqpRUYtHMcbpMcb2wArAhiGEtebzdfrFGDvGGDs2b968bkNKqk7Nm8Ndd8Ftt8FLL8Euu8C33+ZOJUlSnfv4Y7juOujSBVZaKXcaSVI1KmrxaKYY45fAk8COsz31EbAiQAihIbA4MLEUmSTVEwcckApIzz+fGm7/8EPuRJIk1amZ+0Q460iSVCzF3G2teQhhicLtRYDtgP/OdtiDQJfC7b2Af9vvSFKd22cf6N8fHn883Z46NXciSZLqxLhx0K8fHHwwtG6dO40kqVoVs4PsssDNIYQaUpHq7hjjwyGEXsDQGOODQH/glhDCKOBzYL8i5pFUnx1yCHz3XWqkfdBBaTZSTU3uVJIkLZALL0znRM44I3cSSVI1K+Zua68DHebw+Fmz3P4B2LtYGSTpJ/7859T36NRTYZlloG/f3IkkSZpvn3wC116bzomsskruNJKkaube1ZLql1NOSfsZX3YZbLEFdO6cO5EkSfPshx/g6KOddSRJKo2SNMyWpLLSpw9ssAH86U/wv//lTiNJ0jz5/HPYfnt44AG4+GJo0yZ3IklStbN4JKn+adwY7ror3d53X5gyJW8eSZLm0pgxsPnm8OKLcOed0LVr7kSSpPrA4pGk+mnlleGGG+Dll+Gvf82dRpKkXzV8OGyyCXz8MQwalM5/SJJUChaPJNVfnTvD8cenxtkPPJA7jSRJtRo5MrXqq6mB556DrbbKnUiSVJ9YPJJUv/XpA+uvD126QO/e8OGHuRNJkvQz550H06alwlG7drnTSJLqG4tHkuq3hRaCe+6BtdeG7t2hVSvYemu46SaYNCl3OkmS+OADuPVWOPxwaNkydxpJUn1k8UiSVl4Znn0WRo2CHj3SKP3QQ2HppdPStjvusJAkScrmoovSdbdueXNIkuovi0eSNNMqq6Ti0ciRaV3AEUfAkCFwwAE/FpKeey53SklSPTJ+PFx/Pfzxj846kiTlY/FIkmYXAmy6KVx+OYwdC888k9YKPPdc2h/5qKPgiy9yp5Qk1QOXXQY//ODGoJKkvCweSdIvadAAfvc7uOIKeO89OPnkdAp49dXTcrYYcyeUJFWpr7+Gq65KE19XXz13GklSfWbxSJLmVtOmqfHE0KFp7cABB8DOO8Nnn+VOJkmqQtdcA199lfZzkCQpJ4tHkjSvOnRIvZAuuwyefBK22CItb5MkqY58/z1ceilsvz2sv37uNJKk+s7ikSTNj5oaOOEEGDQoFY422wzefTd3KklSlbjxRvj0U2cdSZLKg8UjSVoQW24JTz2VThFvvjkMG5Y7kSSpwk2cCOedB5tskv7MSJKUm8UjSVpQ660H//kPLLIIbLUVDB6cO5EkqULNmAEHHwwTJqTV0SHkTiRJksUjSaobq64Kzz0HK6wA224Le+4Jb7+dO5UkqcL07g2PPgp9+8IGG+ROI0lSYvFIkurKCivAiy9Cz57w+OOw1lpw6KEwZkzuZJKkCjB4MJx5ZtrM8+ijc6eRJOlHFo8kqS4tuiicdRaMHg0nngh33JFmJV11Ve5kkqQy9tFHqWi02mrwj3+4XE2SVF4sHklSMSy1FFx8MYwcCdttB8cdBxddlDuVJKkMTZ0K++0H334LAwdC06a5E0mS9FMWjySpmFZcEe67D/bZB045Bc49N3ciSVKZ6d077bvQrx+ssUbuNJIk/VzD3AEkqeo1agS33QYLLZSaWUyeDL16uSZBksTXX8Mll8Af/pCWrUmSVI4sHklSKTRsCDfemApI554L33+fTjU39NewJNVnV18NX34JZ5yRO4kkSbVz2ZoklUpNTeqCeuyxqR9Shw4waFDuVJKkTL77Ls062n576NgxdxpJkmpn8UiSSqlBA7jiitQR9fvvYccd0+WNN3InkySVWP/+MGGCs44kSeXP4pEklVoI0LkzjBgBl14KL70E7dunGUnffps7nSSpBKZMgQsvhM03hy22yJ1GkqRfZvFIknJp3BhOPBFGjYLjjoNrroH11oNXXsmdTJJUZLfcAmPHOutIklQZLB5JUm5LLgmXXQaDB6cGGBtvnJppT5+eO5kkqQimT0+/5tdbD3bYIXcaSZJ+ncUjSSoXv/89vP46dOoE3bvDNtvAxx/nTiVJqmP33JMmnZ5+elrJLElSubN4JEnlpFkzuOsuuOkmGDo0nZL+8svcqSRJdWTGDDjvPFhjjXSuQJKkSmDxSJLKTQjQpQs88AC88w784Q8weXLuVJKkOnDVVWmDze7d0wackiRVAv9kSVK52mabNAPp6adTMWnGjNyJJEkL4IUX4OSTYbfd4MADc6eRJGnuNcwdQJL0Cw44AD76CE49FZZfHi6+OHciSdJ8mDAB9t4bVlwRbr7ZWUeSpMpi8UiSyl23bmk/50sugRVWgL/8JXciSdI8mD4d9t8fJk5Ms4+aNcudSJKkeeM5D0kqdyGkwtGee8JJJ8Ehh8Bnn+VOJUmaSz16wODBcPXV0L597jSSJM07Zx5JUiWoqYHbboPVVoMLL4SHH4aLLkq9kGbu8xwjjBmTTmuPGQPjxsEnn6Trxo3hmmugbdu8P4ck1TMPPwx//zscfjgcemjuNJIkzZ8QY8ydYZ507NgxDh06NHcMScrnrbfgqKPguedgyy3TjKTnn4f//Cctb5tpscVgmWVg2WXhzTdTAepf/4L11suXXZoLIYRXYowdc+fQTzkGm3cTJ6aaf8uW6df0wgvnTiRJUu1+aQzmzCNJqjTt2sEzz0D//qmR9tNPw3LLwe9+ly6bbw5t2sBvfvPj17zzDmy/PWy1FTzwAPz+99niS1J90b07fPklPPWUhSNJUmWzeCRJlahBAzjiCNhnn/Q/k5Ytf1y+NierrZZOe++wA+y4I9xxB3TuXLq8klTPDBkC110HJ58Ma62VO40kSQvGhtmSVMkWXxxatfrlwtFMyy+fZiytv37aL/rGG4ufT5LqoWnT4Jhj0q/dHj1yp5EkacFZPJKk+mTJJeGJJ2CbbVLfJPuXSFKdu+YaGD4c+vaFRRfNnUaSpAVn8UiS6psmTeDOO6FFC9h/f5g0KXciSaoa48bB3/6WVgnvuWfuNJIk1Q2LR5JUHy25JNx6K4weDccfnzuNJFWNbt1g8mS48sq5W1EsSVIlsHgkSfXVllum0+M33wy33ZY7jSRVvMGD4fbb4a9/TZteSpJULSweSVJ9duaZsNlmqbPre+/lTjN3hgyB6dNzp5Ckn5g4EQ45BNq2hdNOy51GkqS6ZfFIkuqzhg3TrKMGDVL/oylTcif6Za+9Bptskk7tS1KZiBH+9Cf49FO44w5YZJHciSRJqlsWjySpvmvVCq67Dl5+GVZcMc1CevLJ8pzd8+yz6fqZZ/LmkKRZXHUVPPggXHghrL9+7jSSJNU9i0eSJNh7b3j4YdhqK/jnP2HrrWG55VIz7Q8+yJ3uR88//9NrScps+HA4+WTYdVfo2jV3GkmSisPikSQp2WUXuOsumDAB7rknFZL69UsNPE46KT2e2wsvpOsRI+CLL/JmkVTvffMN7LsvLLUU3Hiju6tJkqqXxSNJ0k81aQJ77ZUKSe++CwceCJddBq1bw9lnw6RJeXJ9/DG8/z7svnu6P2RInhySVHD88TByZGodt9RSudNIklQ8Fo8kSbVr1QpuuAHefBN22AF69kwNqz//vPRZZs466toVampcuiYpqwcegJtugr/9LU3UlCSpmlk8kiT9ujXWgAEDYNCgdJp9l13g229Lm+GFF2ChhWDzzWHddS0eScpm0iQ47jhYe20488zcaSRJKj6LR5Kkubf99nDnnfDSS2lp29Sppfvezz8PG2wAjRvDppvCiy/CtGml+/6SVNCjB3z0EfzjH9CoUe40kiQVn8UjSdK86dQp/Y/pscfgkENgxozif88ffoBXXklL5iAVj779Ft54o/jfW5Jm8eqrqQ3ckUf++CtJkqRq1zB3AElSBTr88LT72umnpy6xffsWd5uhYcNgypRUNIIfr59/Hjp0KN73laRZTJ8ORx2Vfu2df37uNJIklY4zjyRJ8+e00+Avf4HLL089iHr2TDOBYqz77zWzWfbM0/wtW8Jyy9n3SFJJXXMNvPxyqpc3a5Y7jSRJpWPxSJI0f0KAiy6Cq6+GJZZIxaN11oFVV4WzzoLvvqu77/X889C6NbRo8eP33nRTi0eSSubjj9Nky+22g/32y51GkqTSsngkSZp/DRrAMcfAM8/AuHGpF1Lr1nDOOam5dV30JIoxFYlmLlWbadNN4f330//oJKnI/vKXtHr26quLu0pXkqRyZPFIklQ3WrRIHWQHDUqXiRNhww3TOo8FWco2Zgx88snPO9POLCbNXNImSUXyxBNw991p5lGbNrnTSJJUekUrHoUQVgwhPBlCGBFCeCuE0HUOxyweQngohPBa4ZhDi5VHklRC228Pr70GW24Jf/4z7LknjB8/f681c2na7DOPOnSAhRZy6ZqkopoyBY4/HlZZBU49NXcaSZLyKObMo2nAyTHGNYGNgWNDCGvOdsyxwIgY47rAxlYp0gAAIABJREFUVsDFIYTGRcwkSSqVFi3g0UehTx946CFYZplU8OnaFe69NxWTfvgBvv0WvvoKPv8cJk/++es8/zw0bQprrfXTxxs3TkvjLB5JKqLLL4f//hcuuwwWXjh3GkmS8iha8SjGOC7GOKxwexLwNrD87IcBi4YQAtAU+JxUdJIkVYMGDaBbN3j1VTj7bFhySbjuujQTqUULWGSRVBhaYgn47W9TgenZZ3/6Gi+8kJa/NWz489ffdFN45ZVUhJKkOvbxx2kvgF13hV12yZ1GkqR85jASr3shhJWADsCLsz11JfAg8DGwKLBvjHHGHL7+SOBIgJYtWxYzqiSpGNZa68eZQ1OmwNChMGRIul1Tky4NGqSG2zvsAPfdl66/+SYtf+vefc6vu+mmcOGFqYC02Wal+3kk1QunnAJTp6ZZR5Ik1WdFLx6FEJoCA4ETY4xfz/b0DsBwYGtgFeDxEMKzsx8XY+wH9APo2LHjAnRdlSRl17hxKvrM3sMI4IADUtFot93gzjuhWTOYPv3nzbJnmvn4889bPJJUp55+Gm6/Hc46K20iKUlSfVbU4lEIoRGpcHRbjPHeORxyKNA7xhiBUSGE/wGrAy8VM5ckqUwtvTQ8+STsvDPss8+PBaaNN679+DZt7HskqU5NnQrHHQetWsFf/5o7jSRJ+RVzt7UA9AfejjFeUsthHwDbFI5vAawGjC5WJklSBVhiCfi//0s7tT37LKyxRuqVVJtNN4XnnoNptsyTVDf69oU330zXTZrkTiNJUn7F3G1tM+AgYOsQwvDCZecQwtEhhKMLx5wDbBpCeAMYDPw1xvhZETNJkipB06bwyCNw1FFwwgm/fGznzjBhAtxwQ2mySapqL78MZ5wBnTrBHnvkTiNJUnkIacVY5ejYsWMcOnRo7hiSpHIRI2yxBbz7LowaBYsumjuRFlAI4ZUYY8fcOfRT9WEM9vXX0KFDmsg4fHhquyZJUn3xS2OwYs48kiSp+EKAiy+G8ePTzmuSNB9iTJMdx4xJjbItHEmS9COLR5KkyrfhhrD//qmINHZs7jSSKtCNN6ZNHnv2dPNGSZJmZ/FIklQdzjsPZsxIzUokaR68/XbaXW3rreG003KnkSSp/Fg8kiRVh5VWgq5d4ZZbYNiw3Gmkogsh7BhCeCeEMCqE8LOSRwhhoRDCXYXnXwwhrFR4fKUQwvezbGhybamzl5PvvoP99kt9+m+5BWpqcieSJKn8WDySJFWP7t1hySWhW7fUwESqUiGEGuAqYCdgTWD/EMKasx12GPBFjLENcClwwSzPvRdjbF+4HE09NWMGHHQQvPEG/POfsNxyuRNJklSeGuYOIElSnVliCTj7bDj++NS4pG1bWGghWHjhNJ3gf/+D//43Xd55BxZbDB56KM1akirLhsCoGONogBDCncAewIhZjtkDOLtwewBwZQghlDJkuTvtNLj3Xrj0Uthxx9xpJEkqXxaPJEnV5aijUufbnj3n/Pyii8Jqq8HvfgePPpqanDzzDKywQmlzSgtmeeDDWe6PBTaq7ZgY47QQwlfAbwvPrRxCeBX4GvhbjPHZOX2TEMKRwJEALVu2rLv0ZeAf/4A+feDYY9OKV0mSVDuLR5Kk6tKoEQwZAp9+Cj/88ONl6lRo1QqWXRZmTr54+WXYdlvYZptUQGrRIm92qTTGAS1jjBNDCOsD94cQ2sUYv579wBhjP6AfQMeOHatmLeigQalotNNO0Lfvj78SJEnSnFk8kiRVn0aN5m4m0QYbpNlH22+fikhPPglLLVX8fNKC+whYcZb7KxQem9MxY0MIDYHFgYkxxghMBogxvhJCeA9YFRha9NRl4LXXYO+9oV07uOsuaOhoWJKkX2XDbElS/bbZZqnv0ciRqYj05Ze5E0lz42WgbQhh5RBCY2A/4MHZjnkQ6FK4vRfw7xhjDCE0LzTcJoTQGmgLjC5R7pIbNQpuugmOOALWWAPat0+rVx95JF1LkqRfZ/FIkqStt4b77oM330z9kHr1gs8+y51KqlWMcRpwHDAIeBu4O8b4VgihVwhh98Jh/YHfhhBGAScBpxUe3wJ4PYQwnNRI++gY4+el/QlK49RTU9/8Qw+FgQNhlVXg/PPhuedscyZJ0rwIscK2Mu7YsWMcOrRezKqWJJXakCFw7rlpSsIii0CXLvCXv8Cqq+ZOVq+EEF6JMXbMnUM/VWljsGuvhWOOgT/9CU46Kc06auBpU0mSavVLYzD/hEqSNNPGG8PDD8OIEXDggXDDDel/nHffnTuZpHnwf/8Hxx0Hu+wC/fql/kYWjiRJmn/+GZUkaXZrrAHXXQcffACbbJLWvLz2Wu5UkubCW2/92BD7jjugpiZ3IkmSKp/FI0mSatOiBQwYAEssAZ06wedV2RZGqhrjx8Ouu0KTJqkPvg2xJUmqGxaPJEn6JcsskzrtfvQR7L8/TJ+eO5GkOXj1Vdh9d/j0U3jwQWjZMnciSZKqh8UjSZJ+zcYbw1VXpUYqZ5yRO42kgu++g5tuSv9E11sPXn8dbrsNNtggdzJJkqqLxSNJkubG4YfD0UfDBRf8egPt776DW26Br78uTTapnhk7Frp1g+WXTy3JvvoK+vZNEwQ7dcqdTpKk6tMwdwBJkirGZZelqQ1//GP63+uJJ/58C6dRo2DPPdNxrVvD7bfDRhvlyStVmbffhj594NZbYcaM9E/tz3+GLbaAEHKnkySpejnzSJKkudW4MTz8cNr/++STYbfdYMKEH59/+GHo2BE+/DBNg5g2DTbfHM4/315J0gIYOzbNKFpzTbjzTjjqqFSnvesu2HJLC0eSJBWbxSNJkuZFs2Zw771w5ZUweDC0bw///jecdVYqJrVuDa+8Al27wmuvQefOcPrpsN12aU2NpHn25z+nlmNnnQVjxsAVV8BKK+VOJUlS/WHxSJKkeRUCHHssDBmS9gLfZhs455zUfOW552DlldNxSyyRpkn07w8vvgirrgp/+hO88ALEmPdnkCrE00/DQw9Bjx7Qsyc0b547kSRJ9Y/FI0mS5lf79jB0aFrC1r9/uiyyyE+PCSEVjIYPhwMPhHvugU03hbXXTj2UbKot1SpGOOUUWGEFOP743GkkSaq/LB5JkrQgmjaFiy5KBaJfarzSti306wcff5yumzRJDbc33jitw5H0M/fcAy+/DOee+/O6rCRJKh2LR5IkldKii8IRR8BLL8Hjj6di0sYbw6uv5k4mlZUpU6B7d1hnnbTBoSRJysfikSRJuWy7beqR1KhR2mt80KAfn5s2Df71LzjgAOjSxR5JqneuvRZGj4YLL4SamtxpJEmq3xrmDiBJUr3Wrl1qvL3zzrDLLtC7N4wbB7fdBp9+mpa3ffdd2q3N6ReqJ776Cnr1SvXV7bfPnUaSJDnzSJKk3JZbDp55Bn7/+9Qd+IorYJNN4N57YeJE2GADOPVUmDQpd1KpJC64IH30L7jgl1uJSZKk0rB4JElSOVhsMXjkEXjiiTTz6L77oFMnWHjhVEwaNw7OOy93SqnoPvsMLr00bU643nq500iSJLB4JElS+WjcGLbZBn77258+vtFGqe/RJZfAqFHz99pjx8LVV8PIkQueUyqi+++HH36Ak07KnUSSJM1k8UiSpEpw/vmpuDQv/6P+4gu47rq0HK5lSzj2WNhjD/j+++LllBbQwIGw8srQoUPuJJIkaSaLR5IkVYJll4WzzoKHHkq7sP2SyZPhxBOhRQs48kj4+GM4+2y44QZ4+20488ySRJbm1ZdfwuDBsOee9jqSJKmcuNuaJEmVomvXNJPoxBPT8rbGjX9+zIcfwt57w4svwuGHw9FHp8YxM/8n/tJLafnb7rvDFluUNr/0Kx56CKZOhb32yp1EkiTNyplHkiRVisaNoW9fePfdtPxs4MCfLkF7/PG01mfECBgwIBWa1l//p1M4+vRJa4IOOcTd21R2BgyAFVZIGwxKkqTyYfFIkqRKsvPO0LMnDBuWpmcsvTQcdBCccgrssENa3jZ0aFr3MydNm8LNN8P770O3biWNLv2SSZNg0CDo3BkaOEKVJKms+KdZkqRKc9ZZ8NFH8MQTsN9+8MgjcNFFcMABMGQIrLrqL3/95punwlG/fr/eP0kqkUcfTe26aqt7SpKkfCweSZJUiRo2TH2PrrsOPvkERo6EW26B3/xm7r6+Vy9o1w4OOwxGjy5uVmkuDByYerxvtlnuJJIkaXYWjyRJqnSNG0ObNvO2PdXCC8Ott6aeSeuvnzoVS5l8912aedSpE9TU5E4jSZJmZ/FIkqT6qn371Dupdeu0+9rpp8O0aT895vvv01I4m2uriAYNgm+/dcmaJEnlyuKRJEn12corw3PPwRFHwPnnw/bbw+DBcO65sPXW0KwZbLJJ2sXt9ddzp1WVGjgQfvtb2HLL3EkkSdKcWDySJKm+W3jh1Dz7xhvhhRdg223hzDPhiy/g2GOhf/80A2njjdNSN6kOTZ6cVk3usQc0apQ7jSRJmpOGuQNIkqQyccghqVvxiBHpeqmlfnxu551h333hoIPSMrZLLkm9lqQFNHgwfP21S9YkSSpnzjySJEk/ats2TQGZtXAEsMwy8MQTcPLJcNVVaX3RRx/lyaiqMmAALLZY2jxQkiSVJ4tHkiRp7jRqBBddBHffDePGwdSpuROpCvzwQ5p1tNBCuZNIkqTauGxNkiTNm733TrOTXLamOnD77RBj7hSSJOmXOPNIkiTNOwtHqkMh5E4gSZJ+icUjSZIkSZIk1crikSRJkiRJkmpl8UiSJEmSJEm1sngkSZIkSZKkWlk8kiRJkiRJUq0sHkmSJEmSJKlWFo8kSZIkSZJUK4tHkiRJkiRJqpXFI0mSJEmSJNXK4pEkSZIkSZJqZfFIkiRJkiRJtbJ4JEmSJEmSpFpZPJIkSZIkSVKtilY8CiGsGEJ4MoQwIoTwVgihay3HbRVCGF445uli5ZEkSZIkSdK8a1jE154GnBxjHBZCWBR4JYTweIxxxMwDQghLAFcDO8YYPwghLF3EPJIkSZIkSZpHRZt5FGMcF2McVrg9CXgbWH62ww4A7o0xflA4bnyx8kiSJEmSJGnelaTnUQhhJaAD8OJsT60KNAshPBVCeCWEcHAtX39kCGFoCGHohAkTihtWkiRJkiRJ/1/Ri0chhKbAQODEGOPXsz3dEFgf2AXYATgzhLDq7K8RY+wXY+wYY+zYvHnzYkeWJEmSJElSQTF7HhFCaEQqHN0WY7x3DoeMBSbGGL8Fvg0hPAOsC7xbzFySJEmSJEmaO8XcbS0A/YG3Y4yX1HLYA8DmIYSGIYQmwEak3kiSJEmSJEkqA8WcebQZcBDwRghheOGx04GWADHGa2OMb4cQHgNeB2YA18cY3yxiJkmSJEmSJM2DohWPYoz/AcJcHNcH6FOsHJIkSZIkSZp/IcaYO8M8CSFMAMYU6eWXAj4r0mtrznzP8/B9Lz3f89LzPS+9unrPW8UY3SGjzDgGqzq+56Xne156vud5+L6XXtHHYBVXPCqmEMLQGGPH3DnqE9/zPHzfS8/3vPR8z0vP91zzy89O6fmel57veen5nufh+156pXjPi9YwW5IkSZIkSZXP4pEkSZIkSZJqZfHop/rlDlAP+Z7n4fteer7nped7Xnq+55pffnZKz/e89HzPS8/3PA/f99Ir+ntuzyNJkiRJkiTVyplHkiRJkiRJqpXFI0mSJEmSJNXK4lFBCGHHEMI7IYRRIYTTcuepRiGEFUMIT4YQRoQQ3gohdC08vmQI4fEQwsjCdbPcWatNCKEmhPBqCOHhwv2VQwgvFj7vd4UQGufOWE1CCEuEEAaEEP4bQng7hLCJn/PiCiH8pfB75c0Qwh0hhIX9nNe9EMINIYTxIYQ3Z3lsjp/tkFxeeP9fDyGsly+5ypXjr9JwDJaH46/ScwxWeo7Biq9cxl8Wj0i/2IGrgJ2ANYH9Qwhr5k1VlaYBJ8cY1wQ2Bo4tvM+nAYNjjG2BwYX7qltdgbdnuX8BcGmMsQ3wBXBYllTV6zLgsRjj6sC6pPfez3mRhBCWB04AOsYY1wJqgP3wc14MNwE7zvZYbZ/tnYC2hcuRwDUlyqgK4firpByD5eH4q/Qcg5WQY7CSuYkyGH9ZPEo2BEbFGEfHGKcAdwJ7ZM5UdWKM42KMwwq3J5F+mS9Peq9vLhx2M/CHPAmrUwhhBWAX4PrC/QBsDQwoHOJ7XodCCIsDWwD9AWKMU2KMX+LnvNgaAouEEBoCTYBx+DmvczHGZ4DPZ3u4ts/2HsA/YzIEWCKEsGxpkqpCOP4qEcdgpef4q/Qcg2XjGKzIymX8ZfEoWR74cJb7YwuPqUhCCCsBHYAXgRYxxnGFpz4BWmSKVa36AqcCMwr3fwt8GWOcVrjv571urQxMAG4sTFW/PoTwG/ycF02M8SPgIuAD0oDlK+AV/JyXSm2fbf+26tf4GcnAMVjJOP4qPcdgJeYYLKuSj78sHqnkQghNgYHAiTHGr2d9LsYYgZglWBUKIewKjI8xvpI7Sz3SEFgPuCbG2AH4ltmmR/s5r1uFNd57kAaNywG/4edTe1UCfral8uYYrDQcf2XjGKzEHIOVh1J9ri0eJR8BK85yf4XCY6pjIYRGpEHLbTHGewsPfzpzKl3henyufFVoM2D3EML7pOUAW5PWgi9RmFoKft7r2lhgbIzxxcL9AaSBjJ/z4tkW+F+McUKMcSpwL+mz7+e8NGr7bPu3Vb/Gz0gJOQYrKcdfeTgGKz3HYPmUfPxl8Sh5GWhb6ArfmNTk68HMmapOYa13f+DtGOMlszz1INClcLsL8ECps1WrGGP3GOMKMcaVSJ/rf8cYDwSeBPYqHOZ7XodijJ8AH4YQVis8tA0wAj/nxfQBsHEIoUnh98zM99zPeWnU9tl+EDi4sOvHxsBXs0yvlsDxV8k4Bistx195OAbLwjFYPiUff4U0w0khhJ1Ja5NrgBtijH/PHKnqhBA2B54F3uDH9d+nk9bc3w20BMYA+8QYZ28IpgUUQtgK6BZj3DWE0Jp0JmxJ4FXgjzHGyTnzVZMQQntSg8zGwGjgUFKx3s95kYQQegL7knYUehU4nLS+2895HQoh3AFsBSwFfAr0AO5nDp/twiDyStL09e+AQ2OMQ3PkVvly/FUajsHycfxVWo7BSs8xWPGVy/jL4pEkSZIkSZJq5bI1SZIkSZIk1crikSRJkiRJkmpl8UiSJEmSJEm1sngkSZIkSZKkWlk8kiRJkiRJUq0sHkkqqhDC9BDC8Fkup9Xha68UQnizrl5PkiSpWjgGk1SXGuYOIKnqfR9jbJ87hCRJUj3jGExSnXHmkaQsQgjvhxAuDCG8EUJ4KYTQpvD4SiGEf4cQXg8hDA4htCw83iKEcF8I4bXCZdPCS9WEEK4LIbwVQvi/EMIiheNPCCGMKLzOnZl+TEmSpLLiGEzS/LB4JKnYFpltyvS+szz3VYxxbeBKoG/hsSuAm2OM6wC3AZcXHr8ceDrGuC6wHvBW4fG2wFUxxnbAl8CehcdPAzoUXufoYv1wkiRJZcoxmKQ6E2KMuTNIqmIhhG9ijE3n8Pj7wNYxxtEhhEbAJzHG34YQPgOWjTFOLTw+Lsa4VAhhArBCjHHyLK+xEvB4jLFt4f5fgUYxxnNDCI8B3wD3A/fHGL8p8o8qSZJUNhyDSapLzjySlFOs5fa8mDzL7en82MttF+Aq0hmyl0MI9niTJElKHINJmicWjyTltO8s1y8Ubj8P7Fe4fSDwbOH2YOAYgBBCTQhh8dpeNITQAFgxxvgk8FdgceBnZ94kSZLqKcdgkuaJVWBJxbZICGH4LPcfizHO3Cq2WQjhddKZq/0Ljx0P3BhCOAWYABxaeLwr0C+EcBjp7NYxwLhavmcNcGthcBOAy2OMX9bZTyRJklT+HINJqjP2PJKURWG9fccY42e5s0iSJNUXjsEkzQ+XrUmSJEmSJKlWzjySJEmSJElSrZx5JEmSJOn/tXffcVWX///HH+w9RVDcuFAcqLg3mpp+zFFqaaZlmTa0b9anMksbNj4tM7PhtixHmmaOcm9RHIg4AFFARJHhAARk/P44nh8H5CA4sk+f5/1285a8z3Wuc73P+w12nlzX6xIRETFL4ZGIiIiIiIiIiJil8EhERERERERERMxSeCQiIiIiIiIiImYpPBIREREREREREbMUHomIiIiIiIiIiFkKj0RERERERERExCyFRyIiIiIiIiIiYpbCIxERERERERERMUvhkYiIiIiIiIiImKXwSEREREREREREzFJ4JCIiIiIiIiIiZik8EhERERERERERsxQeiYiIiIiIiIiIWQqPRERERERERETELIVHIiIiIiIiIiJilsIjERERERERERExS+GRiIiIiIiIiIiYpfBIRERERERERETMUngkIiIiIiIiIiJmKTwSERERERERERGzFB6JiIiIiIiIiIhZCo9ERERERERERMQshUciIiIiIiIiImKWwiMRERERERERETFL4ZGIiIiIiIiIiJil8EhERERERERERMxSeCQiIiIiIiIiImYpPBIREREREREREbMUHomIiIiIiIiIiFkKj0RERERERERExCyFRyIiIiIiIiIiYpbCIxERERERERERMUvhkYiIiIiIiIiImKXwSEREREREREREzFJ4JCIiIiIiIiIiZik8EhERERERERERsxQeiYiIiIiIiIiIWQqPRERERERERETELIVHIiIiIiIiIiJilsIjERERERERERExS+GRiIiIiIiIiIiYpfBIRERERERERETMUngkIiIiIiIiIiJmKTwSERERERERERGzFB6JiIiIiIiIiIhZCo9ERERERERERMQshUciIiIiIiIiImKWwiMRERERERERETFL4ZGIiIiIiIiIiJil8EhERERERERERMxSeCQiIiIiIiIiImYpPBIREREREREREbMUHomIiIiIiIiIiFkKj0RERERERERExCyFRyIiIiIiIiIiYpbCIxERERERERERMUvhkYiIiIiIiIiImKXwSEREREREREREzFJ4JCIiIiIiIiIiZik8EhERERERERERsxQeiYiIiIiIiIiIWQqPRERERERERETELIVHIiIiIiIiIiJilsIjERERERERERExS+GRiIiIiIiIiIiYpfBIRERERERERETMUngkIiIiIiIiIiJmKTwSERERERERERGzFB6JiIiIiIiIiIhZCo9ERERERERERMQshUciIiIiIiIiImKWwiMRERERERERETFL4ZGIiIiIiIiIiJil8EhERERERERERMxSeCQiIiIiIiIiImYpPBIREREREREREbMUHomIiIiIiIiIiFkKj0RERERERERExCyFRyIiIiIiIiIiYpbCIxERERERERERMUvhkYiIiIiIiIiImKXwSEREREREREREzFJ4JCIiIiIiIiIiZik8EhERERERERERsxQeiYiIiIiIiIiIWQqPRERERERERETELIVHIiIiIiIiIiJilsIjERERERERERExS+GRiIiIiIiIiIiYpfBIRERERERERETMsr7fAxARkfuvjo/Fu+5OVL/f4xARkb+vSxnERV8oePt+j0NERP56Co9ERAR3J6qHvs+Z+z0OERH5+wqaRM37PQYREbk/tGxNRERERERERETMUngkIiIiIiIiIiJmKTwSEZF/nFd+ItBiGJO/33x7Syy+30xNi2FMfuUnAu/1a/2vqfsyIz1H89L9HsfddqfnVZ57TkREROSvpppHIiJyV32/mZrPzmEEQPdG7N/wBmuLtzmegFPj13g5rwDLOj7ERn3O/L98oHLPvPITgZcysJ/9DHvv91i+30zNjUep+elQ9lb3Iut+j+d/zZ4o3L9YR+BjbTkxoCXn76SvzRFUmLmBJqGnqX3xKh65eVh7uZDW2Z+IaU+w19uV68Wf88MOqn6yho6xyVTOzMbB1ZGrjapyeupgdnaoT5pp2+0n8Pj3T3Q9eha/rOvYuztypWtDjsx6hp3ujuTeanyHzuDy2Vqa7o2mTtJlKmRdx87TmUtBtYia9gQ76/hwrbTnf76WOhMWMQxg8YvMGtKGc8bHsnKwfHQGXbcco+n1PGwCqnLqx7Gsre9Lpmkfi/fg+/jXjJo/hnmPd+DsrcYsIiJSVpp5JCIi94S1Jbm7Iml85RpWxR/7z+80KQAsLci/D0OTe2xVKIEr9tPmfo8DYONRai4LoXNCGvb38nUOfcAPZ77kq9t9/lOdiU2bxdQPBhN2N8d1v4XH474shM67oqh0p319uZ5mqw/RppIbqY+3Z9szXdng607yz3sIDnyDUanpRX8pOm0ddUZ8y1OJl/B6qDn7nu/BulZ+nNwTReMeHzH60BlcjG03hOPV8yNGH4qlfrcADo/tzrr6vsQt30fndlN4NL8MP6nmbKX+4j10cbHn2iOt2T22O+vrVyZ+XRhtgt5kzNF4nM09N+kKNu+v5F+21uSU9PiYubRZfZB2XRsSNrQd206co+ag6fQzbZOVg+WERTwU3IhQBUciInK3aeaRiIjcE81rcWLfKRp9thb/dx4mwvSxdWE0a1SNqBMJ+N2v8YmUJDMby5w8LMsy08SUsz15d/K61lYUlPc1/9cMbc+xGSPZUa0C2SaHQwd+QeqvoXScuJTm3z7FPuMD32+mjaUFBbunMKdupcIZOi/9wMUv19P36w0EGGfHvfoT3bOvYz9vDHNHdCT+RtMDj88kedEuur25lCYfPsqR0sbXO5DYMd2Y1qga6SaHD770Awlfrqfva4tpt+ZV/izpuSO+ITgvH8tuARxYF0bb4o9vPU6DNnUJX/kymwDcHMn6Yi0PXcrE2njfPDObdhnZOCwYY2gjIiJyNyk8EhGRe6JBFRLjU6j4SwiBpuHRz7upcuEyFcf1ZPM7y0sOjz5Yhf/sLbQ7m0olC6CKJ+efCWbXGw9xsnjb8Qtp/vNu2qVm4O7uyJX/l1fpAAAgAElEQVT+Qexzcyx5eVJ8CnbPz6Pj7igaXsrE1c6G7IAqxHz+OJuLL2G5U1HncRw7ly6hMdRPz8LZ2Z70ID9OfjuKLabLVy5lYj16Nh22HqPRpUzcrCzJc3fkcotaRP/+KhuM7T5dQ91vN9E+MQ3vnFxsnOzJrFWRc58NY2NwACm3Gk9Z31PP0bxUwZlLM5/k9//7kR5R56lhAQUBVYlZMIa1xT4Y38RzNC+lZeAGYDGMycbj341iwehgzhi/PhyLy+jZ9AiPp05ePtZ1fIidMZJ1xc/lyjWsxs6l3aYIGqdcxdPaity6lYidMpAtA1uVvgyq/Tv03x1JU4B2UxhvPD6oNduWjmPr4Ol0WRZC5zWvMnPGnzTfFUnA1Ws4fzuKhaODOfP2LwSsDKVJXAqV0rNwsrMmp24l4t59hC0PteCC6WvVfZmRKem4p37PtOLHNr/JnFudq3G554Q+rPp0KIeLHysogEW7aJd8FU8Xe9Ifas7+BWPZVfycn59P0LK9tLnx/XD5kdaEONuT89ka+hW/BiVZe5iKby2jS/QFqmVk4+hgS1ZlNy6O7sbuV/oQVZ7r8spPBH62xjA75rM19DP+3XSp6r5TuF3OxKZ9PVId7UqfiWi6jMvUk505+msoHSMT8TY9npmDnbUVuTW8ii4Xq1aBqwBOdoWzfE6co6aXCykmwREA//4XhxftotuK/QSWITy6WNLx1/py9Mv19D19sej4jJbsxffPcFpPHsgvR8+W3OZ6HjZuDoXn4eXCtQKwuHIjPNp2HM8le+k8dTDLKruXPHtJRETkTig8EhGRe6ZPMw7N2ULPQ2dwaVbT8IFt1haaOdmR8fKDRL6z/ObnPDePlt9spLe3K8mPtmUbwB9HCJy4hEcT0/h9+ggOGNs+PYs2c7bS09eDC8M7sOlaDjbLQmjnYk9G8X7jU7Br+Raj0jJw69KAQ/Urc/H8ZZzXHqZlz495etNEvm9Th8t347zjU7BrN4VRKVfx7ODPoUZVSYw4S6XNEbRsP4Vahz9klvED3kOf0nvHSZq1q0tY81rsyc3D8vRFKoTFUcvY36zN1Pj3TzxWyZ2kwW3Y4e5IVuIlXEJP43coFs9bhUfleU8B0jJwGTiNkS39ON6lARuOJeCz9RhBg6Zjd/wTfijttV7pw/ov19MtPRvHcT34w3i8fb3CD9bZudh0/4An61Ti7FNd2BSXjMe6MFo/NoNH479ipq01BWCYBRT0Jo/HXKRau3ocadyKfVcysV8bRvNHZzDqh+eYZy5QABgTTGhmNnaHY/Ef1YU/KroYZp+0rVs0+HlmNg/bWHH9kVbstrAAP2/DvbpoF61c7Ln2YFMO+LiRfvoiHhvCaTFoOk+tf43vujYktbT3ojznWppfQgi6cg2nnk045OZI1vowmizcSfeqFbgydTDhxnZPfEP7H3bSvaoniU90NHw/LNlDO2f7onVxzIm+gMPg6YZ6ZQ82JbRaBS6nXMUxIgHfvVFUBUN4VNbr0r8FsacusGNlKB27NOBAmzrEAfh6FAaQw75mQPQFauyewpdt63KpLOMsLuo8rgAVnIsGm61qc2pZCFU7vceA1/uyq4YXmdtP4P2f1fTwdiX59Yc4amybl4+1rfXNNZM8nAzHziRTJT8fLG+j4MOROMP4PBxvDl6zcrB8+Uf6NvDl1NsDOTZ4esnhUf3KxO88SaP52zlW1ZPMWZtp7+1KsrGO1+jZ/KtpdU6++i8iyz9CERGRW1N4JCIi98xrfTkybxsPfLqGwEXPsyM1Hes9UTQKDuCgve3NswzOXMR+zhYeqOBM2sGpzK7iaViekpBKaJM3ePa7zfR4pQ8R1b3IikvGfuEOgr1dSQ77kDleLoYPeQdOc7jdZF4o3vfTs+iachWPZeOZ3T+oMDzYG83hLu/x3IRFdN01mZV347zHLaR98lU8x3Zn7cwn2W88PnYu57/dRO8X5tN++UtsAQg9TYOAKkTvmmL+tX8Nxb8ALDZN5IcGVYoEY9tvNZbyvKfG56Sk4/n2QH4xnTHW/QMKNkXQcuNRKnRvZD6smtiPE/O20eZ6HjbmZmpkZuP4SEt2m86cGfktGQt28MA3G/Eb34tTAC8uoFXUBWpOG86PxmM3xr4/4DWem7SUHkPamC+2PrwjZ1cf4sKN8OiEuXDC0Zas8I9YWPye3PMOPxYvwvzHEcL6fMKY91fStmtD1ph77fKea2lS03GL+A8zjMu1kq9yqMZ4Xlq0i1bG8CgmCYefd9OlsjtJ4R8z17iU6Wg8B4Mm3fz9UJJfQqiekY1T8WtfXFmvS4f6pB1LIGZlKB1b+HH2VjN3bkdOLhZf/UlnSwvyn+1WGKQBzH6GHSnpOG0/TrMBX9DYeDygKlF/vMZy0xk6ldxJOn+JikfjcTadXffDTkOIm5OL7ZlkHPy8Sy96XZI3l9IV4PEON9ezGj2Hthev4LV0HEtK6+PrkWzt9TG+T37HUwAu9qR/NZKlABMWERifSuX97zGjvGMTEREpKxXMFhGRe6aOD9eaVufkn+GG7cc/XUODrOvYvdiDQyW1n7ed2jl52AxsSYgx5ACo4kn2wJaE5ORiO3+7YanbnG3Uvm5ou88YHAG0qMWVdvWKfkjNz4edJ2lStxKxAVW5GnUeR+OfCs5cr+XN2fA4at+t894dSQMnOzI/H1Z0Rs8Xj3PAyY7MXZE0MB6ztyErIY2Kaw6VPOMAwNneEOzM2ECDrJzy/dtdnvfUyNWBq8XDg84NOA1w6AwVyvP6JbGAgi9HEGJ6rE8zQ//HzxX2/2c4TbxdSe4dSKLpNcvMwapJNU7FJFG9eJHk2/FkZ/aWFGYag6P8fEhIxS7qPI5+3mR6u5ISmUiVsvRd1nMtTbdGHDKt8+PlwvXa3py9eKXw+fO345ebj/WAluw3rZ3UqBrp7esXDVXMqeBsuM82HaVOQip25trdzesS9TnzCxbxzu3OOurzCb3OXKTqY+3YUjzUtLWiwMeVK/6+xLzUi9/eH8SS3oHsOXEOv54f8UhmduH30hMd2ZObj3WfT3h01mZq7I3GbfJyAj76jT5WNwr7p2VgU97xPfkdbQ+cpmGXBhwY291w3Y22n8Bj8R66DG7Dtvb1Sj//BlXIOPUFs9e/xtdLx/F93Fd8OaIj8ccTcPpmIz2eDebPgKpkvPcrDaq+wLNuT/Nym8kMjEnCobxjFhERKYlmHomIyD01tD2HX1nE0HnbqL58H82qVyChV9OSa4OcTsIdILAGScUfa1rd8JxTF/AAiLnx38bVSC7etk4lLm49Xvj1yUScMnNwOH6O2vUm8GpJr23BrZcPlVVqOu7VvThXPJCwtyXf25WUs6lUNh579V+sf2c5A//1KWM9nUkLqMqZB5ty8tU+RFpbGcb0wRD27Y7Ef+YG+szZwgN1fIhrV4/oCb0JL75Vd3HleU+NvFxurv/k42aYcZF05c4/jLo6cLV4ceiqnob+U9ML+79wmYrX87A2d80AYpJw9HTmyp2Mp3nNkmdSrdhHpcnLCY5MpGZOXtHgwNOpbGFHWc+1NDW9br4eLg5cy8wpfP7pJMM1bFT15nPx8yZ5s9l5RIWeCSZ2/nbCdkUSWGMcTap7kdCiFjGjuhBh+j37V12XW3l4Gl03HqVVlwYc+PE5dhZ/vMtU+kclUi3qc772dP7/1+DEC/NJ/XoDfSYuJXDacA4CTB1MePJVHBbuIHj0HEYCWFmSN7AlO0KiqReXgm8ltyKFum/ppR9ovmA7PRpVJWrNq6wt/vjo2fzL05m070exuyz9WVtR0LNJ0Z93I76lV1UPLnw2jEOLdlFl8i8MHt2NdZ39SXh9Mb0f+pSBR//DovKMW0REpCQKj0RE5J56sQfR767g6ser6Rx1nlrP9+D3v3oM+TdiofqViXmhx81Fhu+nNx7i5JA2TJu1mbq7o6gRHo/fjhM0m7OFuMMfstDZnrw6PlyLmcb387dTY10YfkfiqDF7Cz1/2EmX70exaHjHu7stt6WF+SCtACzuuH/LUoO6/99/QQFUdidpYr/C2knF1alUtno+pXFzvLnWzb5TuD32NU/a25A9pC3b/H1JcbEnx8IC3l9Jr5xcbMvSd1nPtTRWVncv2LyVXVNYueYQu5eFUOfgGWr8doB2K/bT6Zlg1ht3MvurrktpBk+ny4r9dOpQn8ObJt78M2XfKdxComncvRH7TIIjAF56kIivN9AnJJoaYAiPAL4bxb5PhnJgXRg+13KwCm7IxepeZDk/RSsXe9JNZ+7dyss/0mz6evr6V+HUriksKV4MfOpK/E8m4vdyb1btjzEEvACXMg2B4KkLuG47Tlb7eqRZm7n+n62l7qEz+P/5Bt9YWsKsLTSvWZF443VKSWfTiwsYfiQO5ybVSy90LyIicisKj0RE5J6ytaaga0PCVh2gg40Vua/3LSxSW5yft2GGxeFYvKHoEo8jcVQEqO1jaON347/h8XgVbxt93tDWqH5lMu1tyLqWg90LPYi5G+dVmgoupF24jFdWDpams4+ycrBMukIFT+eiM0n8vLl2ox7Mkfx86P853Vcfov2na6g/5WGOgeF9HB3MGeNuWb8dwGfAF4z++Hc6De/IT+bGUp739G65W7O4KrqSmp6F45hunDb3AboMbut5s7bgn5OL7ZdP8POYbkV3KJu4BAdrK/Juczz3RM2KhplQR89SgWLXOSYJr/L01acZSX2akQTsjkvGPmgST8/bSveZI9lnaVm+63I3Z/QZGXfJa1ePsC1v8ltJRaxPnsMFIL/g5mWe2dcNx/Lyb37M1YE80yLsS/bim5GNY0f/kpfaluTlH2k2bR0P1fclZvcUFrs63HyvnL5oCIw+X0u/z2+akwRvLmUIQORnfFK3hCDuwmVs3/+VPoPbsN1YuD35Cq5eLoVF//19DX+POIubwiMREblTqnkkIiL33MR+hA5qzbb/e5DfS/vt/VOdibG14vqK/bRKvFQ4syPxErYr9tPK1pqcJzsbCvQ+2YlTNlbkrthPq+SrhUuKDpzGdXdUYXFcMCz36FCf8LgUqry7goYlvfbxBJzu/EwN2tblREY2jq/8RHPT4xN+onlGNo7t63EcDMV+45KxN21jaQlNaxi2Ok+5sawp6jyOxV8jOIBkGyty07NKX/pUnvf0brG3JSczG/v8Ujdev7UeTQi7moXz07NpW9LjEWdvfc2M27EnpJVvuZ3VjRlDBQVFZweNX0jzq1k4l6evv8KITpyytiTv1/20vJRZ+MvBo/E47zpZ9PvBnJgkHHLzip5vdS+yvFy5dD0PG2O/5bku7k6G99/cEr19p3DbEI6Xaf2h0gz5is7LQujcti5Htk1ilbnwqn19UiwtKNh/Cv/i32MfrzbUYGtUzfxOfQCXMrF+YzG9rC3Je6t/0aVl5sb9yk8ETltH33qVOb1rMj8XX7JoNLwDke88zLLif1rUMoTFT3Rg4zsPs6yye8k/L0d8S7CDLdmzni6cSenpwtX4lMLaabsj8QGoV9mwe6CIiMid0MwjERG551rV5vLScWy9VbvqXmSN6sqGbzbSO/ANnunVhMMA648QmJKO54s9+N1YOLhmRbKGtWfz/O30aPoGo3o2IexaDjZrDxNU0YXUhDQqmfY9dzSb2kym2pTlDFp9iIim1Tlra03e2RTc9sdQ18+HxLu129r0J9i18yQBMzfQ++hZKgdUJTHiLJW3H6dZRRdSZow0fOA7fwm72v/HhMAanPT35XxFVzJik3FfH0ZLB1uyRnUxbLs9aDp9U9NxbVGLU9U8uXztOtYbj9IoOxfbPs1u3sHpdt/Tu6VRVc6Gx1Ovx0f0blWbeCtLCoa243SxneJuaeZI9u6Lxm/Bdh4IPUWtoNqcdrUn+2wqbgfPUMvWitzIz1lQWh+dG3B2/naYtJTue6MJd7Aht02d/z+zxqyh7Yiau5Xury9mwK5I9rk7knXgNNXC4qhbwZm0kma03E91fLg2pC1bF+2iW+PXeKpHY45cy8FmXRgtvN1IiU/B91azgD5YRdPl+2jTug4nalYk1caKvH2nqHk8gdpBtYgwLv8qz3XpFsBFO2tyfjtAyxfmc93DiSxfDzKMxaOHfc2A6AvU2D2FL29VNHvsXFou3UsXd0cud6xPzFvLioZivh6kv9jTMLPQz5trvZqyd+1h2gZO5NkHm3LQw4lrB05TLSSaJhWcSXvvkcIla2sPU3HMXPq3qUNkFQ+uJF3BeUM4TVPS8Xz1X6x8oHHRWkMljfuj36j/+RoesrMhu2cTjn7ye9Gg2sWBnIn9OAHQuQGpnRsYZgyZOnoW7wOnoXczTpvOgDL1826qbAyn5YKxzDVdDvd4e448O4dm7aYwoHE1EhbvoUPdSpxpUeve1p4SEZH/DQqPRETkb2Xmk+yv4sHV2Vtp//MeOgNU8eTC1MEsMX7wMpr3LHuc7clZsoe2C3fQzd2RK4Nas9vNkazP19LPtG21CmSHfcjcF+bTdttxAg6fob6lJfnujlzx9yVudHDhB8k7Va0C2TvfZs5z8+kaGkP9HScIdLYnIziA0G+eYqtxi3BPZ673bsbew2fwi0jALycXW1cH0pvX4uS7j7AjsIZhxsAjLTmyNITAzREEZmTjaGdDdmV3Lr77CEvfGsDx0kdTvvf0bpg+gr1nU/HYG03DzREEFYBFtQosKG945GhH/sGp/DRuIS3XH6HJT7voAoYi1HV8SBjavvTgDGBkJ+K3RLDx90MEfbGWvvkFWA5qzbZbhUed/Emb+SSLPlhFt2UhdLS0oKC2D3HLxjH/pR/onZJeWKfm7+LH59jpYk/2L/tos2AH3d0duTykLbsLCuD7zfg625c8C8aoTyBnjidQKTSGepsjcLa0oMDTmbSRnfjzyycMdXSgfNfF05ncqUP45Yu1BH+3iV65+VjV8SG2+M5jZXEk3rDD3aVM3P7zO/2LP17Hh1hjeASwegJ//t+PJK8MpfkvIXTMzcfKzZGrwQHs/3okW01nQdbwItPTmSt/HKFFRhZOdrZk1atE3JdP8Otj7Ugoy/gOnqFyAVhkXcd++h/0Lf64hxOX7/T7LSsHywmL6NutEfuHtS86rtHBnDlxjt8W7qDDwdPUr+/LmR/G/vU15kRE5J/JoqDgL6vBKCIif1NBfhbzQ98vWtdFRP4ZHviQBzcepVX4R3zWqJpq38jtC5pEzdCYgpH3exwiIvLX+1tNuRYRERGR22Na68joSBzOO07QtJIbSQqORERE5HZp2ZqIiIjIP8D8bdT8eDUPdKjPcV8PrsSl4L4hnBY5udhO6MPG+z0+ERER+e+l8EhERETkH6BZTVJ93Ej74wgtMnNwsLYkt4YX517syc4XehTWAhIREREpL4VHIiIiIv8AnRuQevhDFt/vcYiIiMg/j2oeiYiIiIiIiIiIWQqPRERERERERETELIVHIiLyX6X9O/S3GMbk233+4Ol0sRjG5D1RuN/Nccl/r3/qPXE3zstiGJPbv0P/uzkuERER+e+jmkciIlJmg6fTZVkInQHeeZhlbw/kWPE2U1fiP2kZQwAGtWbb0nFs/YuHKXKT7zdTc+NRan46lL3Vvci6n2PZE4X7F+sIfKwtJwa05Pz9HMv/qsHT6RJYg/MT+3HiTvo5cxH7qatouvMk9RLT8MrIxtHDicsNqxL76VC2BflxxbT9kTicJy+n1clEfGOTqZyZjWO7eoTtmszKsr7mL/uoPG8bjcNiqZWSbggGK7qS+q9mHP50KAcc7cg3bf/mUhrP2kzny5m4Vq1A4iePsW5gq6L3XUIqdgH/5rl+Ldi3YCy77uQ9ERGRfybNPBIRkXKztiR3aQiBJT22ZC/NrC3J/avHJFKajUepuSyEzglp2N/vsYTH474shM67oqh0L19n4Ri2p81iauvaXLrdPtJmMXXD6/x2N8f1d7AshM5rDuN/p/2sDKXq3K30tICCh1qw74UerG1Ri6jdkTTp+C7PrQ+jomn7vdF4rQyl47k0Ktby4tztvObHq2m/9RiBfj4kPtWFTSM6scXZnmvfbKR30CSG5ptER4t2UeXDVQysUZHzzwTz5/U8rEd+x7DES9ia9jniW7o525P53Sj23M6YRETkn08zj0REpNya1+JEaAwBR+JwblKddOPxo/E4R5ylTks/IkJO0fh+jvF/XWY2ljl5WLo7Ksj7b5efDxevYuvjRk55nmdvS769bdFZKOWl+6d0reuQvGUSX3XyJ830+Iw/iXpxAcPfXErXXk1ZajzetznnOvvzSX1fMqPO41hvAq+W9zWff4CQ/kGsLHZt9rWZzMCQaBp/tpZ6r/6LSIClIfh7OHEp5B1+sbSEx9oR3eEdxv8SQtUXexIDsGAH1bYeo8XPLzL7Tu8XERH551J4JCIi5Ta4DUcOxNDw0zU0XWiyxOGT32lqcePxksKjrBwsR8+h7R9HCExNx8PGmpx6lYl97xG29G1OkmnbS5lYP/41XbefoMm1HOx9PUia0IfN5sa05RieE5fQOeIsfpnZOLo6crVdXSLmPstWb1eu3855/rybKjM20DIykWqXM3G1sCC/igcXRnVh95v9b17ucjQe55cX0fFADPUuX8PF3obsqp6cf/4Bdhk/qAFsO47nm0vpGB5P7YwsnBztyKzlTcKk/mwb1JpEMNSaKWk5yys/EfjZGvp9N4oFo4M5A4XLCde8yswZf9J8VyQBV6/h/O0oFo4O5szbvxCwMpQmcSlUSs/Cyc6anLqViHv3EbY81IILxc9jxT4qTV1Fx5OJ1Mi6jr2zHRn1KhP36TA2N6/JFZ/neNnHlZSYacwt/tyR39JuwQ4emPU085/uSmxp7+9vB/B5ezldIhOpcT0XW09n0no14fCsZ9hta02BsV37d+i/O5KmcdP5aMS3dN93iobZ17GrVoFz7w3ij2HtSSjtdYzPB2g3hfHG48WXVWZkY9X/c7ptOUbTjGwcvV1JntCHTRN6E1W8z7d/IeDHnbROSMOnoADLyu6G+6KkpZymjNcP4LM19DP+vY4PsVGfM//7zdR8dg4jJvRhVUYWNiv20yolHY+BLdm5dBxby3NPGu+L3VP4sm1dw+wj47ENbzBjxp8E3upcS7oPjcfGBBP6zq90j0vG19qK3CA/Tiwbx/riIdeszdSYuoruCalUsrclu11djo7vxYE+n/BcWZa2xiTh8Pw8Ou2Lof6VTFxtrMmp4MylbgEcnT+G3eW5LnuicDfeA7sjaWoxzHBfABQs4h2AqPM4nrmIo78vV6tVILu0sRnf1+Je6EHMv3/mWnwK3qbHK7uTU9m9fCFgcSM7EV/S8YFBHA2JpvGROLzBEB5lX8fGwZYsyxtrDapX4BrA1SzDzKP0LKxe+5m+PZsSYvzZIyIiUhKFRyIiUm5VPcloVJ3I9WE0g8LwaP0RAhtXJ7KKJ5klPa/juwwMPU2Avy8x/YPYn3QF5/VhtHpkGrV/foG5pnU4urzPw2Gx+DeuRmT7ekTHJuP56iKGeDoX/Q0/wLIQKg+fyQgHW7J6NeVAZXeuHEug0vojtG79NtUjPmZe8TogZbF4L/4JqXh1rE9EtQpcSk3H8c9wmk5axpDMHFZMHUy4se2eKNx7fcxT6Vk4t6tHWONqnMvMxuZIHFU3hONnDI+W7MV3xDc8kV+AVecGHPT3JSktA4eDZ6i58SjV7uQD3DOzedjGiuuPtGK3hQX4eXMVYNEuWrnYc+3BphzwcSP99EU8NoTTYtB0nlr/Gt91bUiqsY9PfqfeG0sYbGvN9W4BHKxVkdQLV3Def4rauyLx7uRPWpcGhK09TNuNR6nQvREppmNYF0azii6k3Co4WrIX3+EzGWllSX6vpuzzdiV9+wnqL9xJ95OJ+Ox9lxXFn9PxXYa7OpDxeAe2pabj8NsB2o6ezbDgAKaV9oF8TDChmdnYHY7Ff1QX/qjoYrg/29YtGpw9PYsBVpbkDWrN7uu5WP0aSpvXfuLRdnX5yjQkGPgFwb+G0rFhFaKHd2CLpQUFW4/hP3k5gxIvsfabp9hvbiz9WxB76gI7VobSsUsDDrSpQxyAr0fhDD6An3fTJiMLh55NOejjSnrNilyG8t2TpSnruZpz5iKVnp3L0K4NOfxgU8JDoqm54wTNhnxFwdZJrDa2m7eN6mPnMdzBlqwBLdnp5kjWpqMEPDuH6mUZJ8CDHzMo+gI1ggMIbViFC9dysIm+gFfoaWpCYXhUlutS24eM1/vy60erGVDbm7hBrTlQ/PXeXEqrZSF0ntCHVZ8O5XBZx2kqPgW7nOvYuXoUDcXvpdgUXAG83QrvpVa1if/zCK1fX0yT/kHEvrWMdlaW5PVobFgyN2oWHa7nYrNgDFv+qnGKiMh/J4VHIiJyW4a25dBri3lswQ6qjehI/IIdVEu6gtcrfdhQUvuv/sAv9DQBQbWICHnXsIQCYGUoEQ9P49lXf+bBga2YBzD9D2qHxeJffMbDB6uIfXOpoRi3qfEL6efqQHrYR3xvGiJMXUnMpGUMeXs5TW7nQ+Csp9nu7com02PJVwlp+CrPzt5CJ9MP6s/Mos+Va7hMG86P43txyvQ5uXlYgGH50fiF9M/Lx3r5S8wqNutnp7Hd7XK0JSv8IxYWX3qy5x1+LD776o8jhPX5hDHvr6Rt14asuXFuNlNW0M/ehuwdb/Nts5qG8OmGbcbxTejNgbWHafvlepp3b1R4vedvN9wDT3Rg463G+sZieuXlY/3LeL4zvg/5+exr9TaDQk7ReOYGDj33AKdNn1Pbh8RNEw1jBZiynIvvrGDQB6to/NWIm0MAo+EdObv6EBduhEcnzIUjLvZkhn3IT8Z788E9nH5sBs98tJoWq1423Ae/7KPyr6F07NeCnStfLnJvhAS+waPzttH97YGEmQuzOtQn7VgCMStD6djCj7MfPsqRktqlZeB24H1mNKhChunx8tyTpSnLuZYmMQ2fH55jtqMshBUAAA+WSURBVMmsrwONXsNuxwkCL1zmD+PsoynL6WkBrPs3czrUNwS/mdnsb/QaI8syzvgU7CLPUys4gNANb7DOXLvyXJcPH+XIR6sZ4ONOmrn3/049N49OeQVYPtScsHvRf3EXLmO7eDft7W3Ifv4BThqPTxlIxKYI6n68mgEfrwZrS/LGdGN9kB9XNoTjtWIfHT97nJ+9XG5vdqaIiPzvUMFsERG5LeN6Eu1iT/rcrYbC2XO3EuhiT/r4Xjcv8QFYeYAGAG8NYIelyb8+/YO40Lg6J2OSqH7yHI4Aqw4YCtm+2a/orj8T+3GiokvRmS6rD+KdeAmfrgGEp2dhHXUeR+OfR1oRZ2vF9R0nqH0752gauCRfxSb6Ag5pGdg0qsbppCt4JaRiB4ZlNccSqNOwCtHFgyMAayvDEqyVoVS6cJmKHf05VNJyMWO72/VkZ/aWVLPEeB75+YZdlaLO4+jnTaa3KymRiVQxtvtuE7Uzs3Ec2JLdxYKjIuMLDiCljg+xW4/TNCun8P8l5myluaUF+f/uW3pQdzwBp9MXqdakOidN3wdLS5jUn+0Av4Ya7hdTk/oXLeb7eAdDuBSTRIXSXq+sRgez1/TefLQt52ytyTmbUtj/3K00tgBe6MFh03st6jyOwQGczM7FdlkI1e50LJ38CSseHEHZ78lbKcu5lqZmReKLLxds6cfp/AIsQ2MMO4BFnMUpLgXfwBqcMAZHAI525A/vSEhZXsfDiVxrS/IiE6myJ8rQb0nu5nVZOo6tBYt453ZnHb27goZrDtGuYRWiPx3KodvpozxycrHo+REDUzNwn9Cb3/28DUvTwPA9tWsyK/e/xxc/jGXOyc/4bMZIQvPzYexc+gb5ETGuJ6dWH8Tb/xWecB3FhHoTGLH6YNHldiIiIpp5JCIit8XelvzODTiyIZwWJ8+xKSSaRj0as9+0Vo2p85dwt4CC7o24WPwxv4pcDIvF/+AZPOr7kpl4CQ8LKOjSoGhQBODrycWLVws/4IaeNuxmtHQvXZbupUtJr30pA6fbOcfjCTiNnUdwaAz1M7Jv7iMhDfsqnmSHRONZANStVPq262HxhnE3qnpvtmdvXvPm9wsMNYwmLyc4MpGaOXnYmD7m6VQ4Cycy0TC+IL9bj29wGw58sIqBn6+j3sR+nEi8hO2+aAIaVycyoOrNoYepg2cMIYCf981Lero3ItkCChLT8Cj+WPt6RZcs1vExfEi+nInDrcZbFk1r3Lwk0smOa1evFfYfl0zFAuCBD3nBXD/n0m7vfjPl513ytSzrPXmr/styrqXxcbv5+RVcDNfjXJqhj0NnDNewhtfN59KsBslleR1ne/Ke7cb6bzfRq90Uxvu4cbFxNU4PCOKE6cy0v+q63Mpna6n77q8MrFqBc3++XjjD8l7JzcOi6/v0C4uj/tB2bH5/MEdLahfkx5UgP64Yvx7/Ay0SL+O17jWWJF7C9rEZPNG8JpHvD2bD9D9o+dgMnoj6nOl3Wp9JRET+ORQeiYjIbRvXk0O/H6Jd/y94ODsX23G97v1v2YsruBFV9Q5kz4NNiS6pjY9b4W/iyyo/H7p/yPALl/F6sCkhzWtxztOJLCtLCn7cSWDIKRrn5d/ZMrPbkZtnftawm+PNS0/2ncLtsa950t6G7CFt2ebvS4qLPTkWFvD+Snrl5BbdsrusXuvLsWnreHDxHppN7MeJj1fTKCcPm8fbc/B2+isLc8FkAXfnOtiYmfll2n8Bhi++GM6PVpYlt+/kf+d1bhztbr6Wd/OeLMu5lsbSzLmXp4+ymjGS0NHBnJi7jXoh0dQIiabhxqO0mrediP3v8cuN1/xLrktppq2jzus/M6SSGxe3v8UPZQnx7kRuHhZdp/LQ7iiaDmrNtkXPs6MszzsSh/PsLTwwvhdr61Yi882lNM66jv2K/2OdlwvXuzRgXaXnaPL1n9QzF0aJiMj/HoVHIiJy2x5oTHLNipw9cQ6/WhWJL1482VRld9KOJWCx8SgViy/ZOn3RMHuoeU3DbIbK7qQdT8Bi63Eq9A4sOlPpXKqhrVGT6oZiz1YW5L/Qo3BHszv120F8zqXh80grti0bX3Q3qAU7aG76des6pFoA0eepVFqfgdUN709EQuntABxsS54FcubizTNySjNrC/45udh++QQ/j+lm2J3NaOISHKytyDN+Xd/XML4DMVSCm5ffmXJ1IK9zA8L+OELrw7G4rAylmasDV8f1LDnAM9W8pmG2U0zSzUtjNkfgVQAWlT1untlyh+5oSaBRFU9SjyWAvy+XezYp2+yZ4ixucyzluSf/DgJrGK5zbPLNS+EOxeJVnr6aVCd92nAOAgdzcrHo+C4D952i0eI97H60LefuxnW5E9PWUefVn3jU243k7W+zsGZFsu7l6xmDo50nCRzYku232rHO1Mhv6V2zIgkf3aj3dDYFV0dbrhnrHnm5cN3RlmvxKbjdo+GLiMh/IdU8EhGRO/JmPzYOas22if1KL7Lbr4VhG/H3V9Ih36Qqz5pDeB+Jo76fN3H1fQ27YD3U3NB26iram/bxwSr8TZesAQwMIrGSG0l/hhO08+TNwUpWDpYxSeVf1mScmVFQUHQWxZpDeB86Y6jJZOTnzbWGVYmKSKDOjD/xK96X8Xz7teC8jxsXtx+n2drDRUMw03YA3q6knEqiavLVwmVmZy5ivznCUGOqrIyzMIqfx/iFNL+ahbPpsdHBnHK0I3P5ftoeiSv6WPHxAYzvxYH8AizGzqV7bDJVuwVw2NzsIFMNqpBRqyLxR+Kot+ZQYYCUn2+4PwAGBHG8POd5K052huU3CWl3tsTtqc6GAsivLaZbTu7NM2wizt56aZS7k2EsqenlG0t57sm/g0bVSK9WgXOHY/E3/d7MzMbyhx20LksfyVexMf0eAMMMtHqVDAH0hcuG97C818XWmhxzS/SizuO4IRyv+JSy1Y+a/ge1X/2JIRVdSd72FgtMaw7dqQ3heG0/UfTnWn4+BE+l786TBPYPYsfyl8q+U9oHq/A/epY6s58p3BHP14OrGdk4Gd+jiLM4ZWTj5Otxc90zERH536WZRyIickee7krsrbZlB3ixJzELdxCxP4ZGjV7HoZM/kUmXcV4XRktrS3I/eaxwJ6XxvTg1ZyuRuyNp2uR1HDrUJ/rMRTw2HSWokhtJ5y8XBg6WljDzSX4dNpMR3aYytnNDDtWvTFJmNjaxyXjuO0WD0d3YVN7itw805qKPGxdXHaB9n0+wqeND8qkLVNh4lKDK7iSdTaWyafvvRrH2wY8ZNX4hw5aGENa4Gueu5WATHkeVyu5c+u0VNlpawrThrBz5LSP6f84zXRpyyL8ySZczsT9whhrt6xH93Sj2AQxtx74Pf2Ng0CRGPNiUsMuZ2K8/QgtPZy4XD31KM7QdUXO30v31xQzYFck+d0eyDpymWlgcdSs4k5ZfUPiLJC8Xrr/Vn98mLWVwm8k81z2Ag7W8SU2+ilPIKWqP6sKeNx4q3MmpZxOS/byJ2xtNEwvg5d5lX7b44aOsHz6TkY98yZMPNmW/txvp209Q73gCtVvXJrz4Tmt3qnMDzs7fDpOW0n1vNOEONuS2qUNSn2blW8r0aFvOrdjP1mUhdKkxjjGdGnCssjtXz1/C+cQ5fI/GUzf3R94rrY9uAVy0sybntwO0fGE+1z2cyPL1IGNs99LPubz35N/BW/35c+xcnnjwP4zqHch+VweyNh0lIDcfqxtNSg0bd5ygwmMzGNnCjxN1K5Hk4ci1yPNU3BBOkKcTl4a1Jw7Kf11q3Zgx+cQ3tK9egcsWFvDeIMMSrTeX0mpZCJ0n9GHVrX5uLNmL74QfeRSw6B3I4TlbqFu8TfEd3R6bQSeAazmGUCz2Ij7GY90Civ487fERz3s4cTn1e6YZj/X9jB47TtLM14ML/pVJfmMxTUz7b1iF1OEdOVt8HAmp2H28mt5D27O1fb3CWmfPdiPyyz/I/tenPNo7kLB1h2liZ0PWs92ILO3cRUTkf4vCIxER+cvseJsVo+eQ+McRAmdvoYetNdfrVyb2vUFs7tu86If4rZNY9vhMgnecpMnxBPx8PUj6ZBhLluylsWl4BDCgJec3v8m3E5fQMTSG+puPEmRrQ3YFZy51DeDwo23Kv5zN1pqClS+z6Pl59Nh6nKYbwrGt7E7SWwP4NSyOSstCin5Qb1+PS9ve4vsJi+h88Ax1d5+kqb0t16pV4EKvpoXbyD/alnNeLnw/aRmd90QRsPkoQY52ZPp5k/BAY+KN7T4YQvi5NFxWHaDVrC30rOBM2shObLO0pOCzNYU7pN1KJ3/SZj7Jog9W0W1ZCB0tLSio7UPcsnHMf+kHeqekF93B6vWHOFnDi7kfr6bjpgiaZ4dh62xPhn9lYjvWvzloGdSaAx+vpnrdSpw23VHrVoa04ZydNXOmLKfr+iO0zMnFpoIzaU90YOOsZ9hd1n7KamQn4rdEsPH3QwR9sZa++QVYDmrNtvKGRwBLx7Ht0zWcm7uV1qsP0iYnFxtnOzKqeJI0prv57eSNPJ3JnTqEX75YS/B3m+iVm49VHR9ibxUelfee/Dt4JpjY3Hx+/Hg13Vbso6O9LVnt6nL0iY6EPz6Tpx1syC3t+Y2rc7lrQw4diafWwdP45+Zh5ebI1eAADv7nMXaabjFfnuvyzVOseW4efZbupVP2jbpfxvCoPPbH4J2bb/j/6Tlb6VlSm+Lh0eI9dDX9OiGNSov3GJay5uWz7VZhfGQivgDn0vD5aDUDij/erh5hJYVHT3xLd1cH0r8fVXTXwpoVyZr1ND9OXELv2Zvp4ePOxe9H8dO9XnonIiL/XSwKCu5KCQAREfkvFuRnMT/0/aL1cETKYvJyAt5dwSNv9mO5iutKWb33Kw3e/oXBk/qz/HZCG7k/giZRMzSmYOT9HoeIiPz1VPNIREREbtuiXbR0tCPz333vbo0i+WfIz4dLmUVnumdmYzlvG20tLcgf0kahtYiIyH8DLVsTERGRcjmegNOiXdTaE02NUxeo8Xh7Nrk6FO7aJmJ0JQvrqi/wUid/wmv7kJyajuOWYwQkXsLnoebsalSN9Ps9RhEREbk1hUciIiJSLjtOUnHqKh62tyGra0NCvxt192sUyT+Doy15zWoSFXKK+huPElRQAN5upIztztqZT7L/fo9PREREykbhkYiIiJTL6GDOjA7mnfs9Dvn7s7WmYMfbrLrf4xAREZE7o5pHIiIiIiIiIiJilmYeiYgIlzKIC5pEzfs9DhER+fu6lEHc/R6DiIjcHxYFBQX3ewwiIiIiIiIiIvI3pWVrIiIiIiIiIiJilsIjEREREREREREx6/8BQ1z4ZlWz03IAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 848
        },
        "id": "Q7l7eiZZ2-2v",
        "outputId": "583a0c0e-8a17-4979-82af-4cf273be2110"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2304x864 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#make a pred based on the original song df...temperature controls the randomness of the improv\n",
        "pred1 = haydn.improvise(song_df=song1,\n",
        "                        temperature=2.0,\n",
        "                        num_predictions=128,\n",
        "                        display_audio=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "_OoTequuCDtL",
        "outputId": "f8145ab2-df79-46a9-9246-8ca603937d5d"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-b510b2035a6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhaydn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay_audio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-6-7e613e7280ab>\u001b[0m in \u001b[0;36mdisplay_audio\u001b[0;34m(self, pm, seconds)\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0;34m'''generates a mini .wav player in the markdown cell'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m     \u001b[0mwaveform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfluidsynth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SAMPLING_RATE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m     \u001b[0;31m# Take a sample of the generated waveform to mitigate kernel resets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0mwaveform_short\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaveform\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mseconds\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SAMPLING_RATE\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'fluidsynth'"
          ]
        }
      ],
      "source": [
        "haydn.display_audio(pred1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "j9MnCy-fCXBY"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "authorship_tag": "ABX9TyMfDmYfTMKFUDdFFjcunS4C",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}