ray-project--ray/doc/source/serve/tutorials/video-analysis/chunk_video_benchmark.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Chunk Video Performance Analysis\n",
        "\n",
        "This notebook analyzes the performance of `chunk_video_async` across different:\n",
        "- Video lengths\n",
        "- Chunk durations\n",
        "\n",
        "We'll generate a heatmap showing execution time as a function of these parameters.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import asyncio\n",
        "import json\n",
        "import time\n",
        "import os\n",
        "import tempfile\n",
        "from pathlib import Path\n",
        "\n",
        "import aioboto3\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from utils.video import chunk_video_async, get_video_metadata\n",
        "\n",
        "# Create global aioboto3 session\n",
        "aio_session = aioboto3.Session()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total videos: 20\n",
            "S3 Bucket: abrar-test-bucket-123\n",
            "S3 Prefix: stock-videos/\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>duration</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>query</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>kitchen_cooking_35395675_00.mp4</td>\n",
              "      <td>8</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>kitchen cooking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>kitchen_cooking_3992465_01.mp4</td>\n",
              "      <td>24</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>kitchen cooking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>kitchen_cooking_5036096_02.mp4</td>\n",
              "      <td>49</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>kitchen cooking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>office_meeting_3255275_03.mp4</td>\n",
              "      <td>10</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>office meeting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>office_meeting_3192305_04.mp4</td>\n",
              "      <td>26</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>office meeting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>office_meeting_4629797_05.mp4</td>\n",
              "      <td>13</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>office meeting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>street_city_traffic_35364266_06.mp4</td>\n",
              "      <td>12</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>street city traffic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>street_city_traffic_35364241_07.mp4</td>\n",
              "      <td>10</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>street city traffic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>street_city_traffic_35336575_08.mp4</td>\n",
              "      <td>44</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>street city traffic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>living_room_home_4009958_09.mp4</td>\n",
              "      <td>34</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>living room home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>living_room_home_5813761_10.mp4</td>\n",
              "      <td>30</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>living room home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>living_room_home_6158703_11.mp4</td>\n",
              "      <td>61</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>living room home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>restaurant_cafe_5498709_12.mp4</td>\n",
              "      <td>43</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>restaurant cafe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>restaurant_cafe_4426378_13.mp4</td>\n",
              "      <td>12</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>restaurant cafe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>restaurant_cafe_4428751_14.mp4</td>\n",
              "      <td>8</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>restaurant cafe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>parking_lot_cars_5607784_15.mp4</td>\n",
              "      <td>25</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>parking lot cars</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>parking_lot_cars_5607783_16.mp4</td>\n",
              "      <td>15</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>parking lot cars</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>parking_lot_cars_5607782_17.mp4</td>\n",
              "      <td>17</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>parking lot cars</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>classroom_students_1580507_18.mp4</td>\n",
              "      <td>22</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>classroom students</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>classroom_students_6209572_19.mp4</td>\n",
              "      <td>4</td>\n",
              "      <td>384</td>\n",
              "      <td>384</td>\n",
              "      <td>classroom students</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               filename  duration  width  height  \\\n",
              "0       kitchen_cooking_35395675_00.mp4         8    384     384   \n",
              "1        kitchen_cooking_3992465_01.mp4        24    384     384   \n",
              "2        kitchen_cooking_5036096_02.mp4        49    384     384   \n",
              "3         office_meeting_3255275_03.mp4        10    384     384   \n",
              "4         office_meeting_3192305_04.mp4        26    384     384   \n",
              "5         office_meeting_4629797_05.mp4        13    384     384   \n",
              "6   street_city_traffic_35364266_06.mp4        12    384     384   \n",
              "7   street_city_traffic_35364241_07.mp4        10    384     384   \n",
              "8   street_city_traffic_35336575_08.mp4        44    384     384   \n",
              "9       living_room_home_4009958_09.mp4        34    384     384   \n",
              "10      living_room_home_5813761_10.mp4        30    384     384   \n",
              "11      living_room_home_6158703_11.mp4        61    384     384   \n",
              "12       restaurant_cafe_5498709_12.mp4        43    384     384   \n",
              "13       restaurant_cafe_4426378_13.mp4        12    384     384   \n",
              "14       restaurant_cafe_4428751_14.mp4         8    384     384   \n",
              "15      parking_lot_cars_5607784_15.mp4        25    384     384   \n",
              "16      parking_lot_cars_5607783_16.mp4        15    384     384   \n",
              "17      parking_lot_cars_5607782_17.mp4        17    384     384   \n",
              "18    classroom_students_1580507_18.mp4        22    384     384   \n",
              "19    classroom_students_6209572_19.mp4         4    384     384   \n",
              "\n",
              "                  query  \n",
              "0       kitchen cooking  \n",
              "1       kitchen cooking  \n",
              "2       kitchen cooking  \n",
              "3        office meeting  \n",
              "4        office meeting  \n",
              "5        office meeting  \n",
              "6   street city traffic  \n",
              "7   street city traffic  \n",
              "8   street city traffic  \n",
              "9      living room home  \n",
              "10     living room home  \n",
              "11     living room home  \n",
              "12      restaurant cafe  \n",
              "13      restaurant cafe  \n",
              "14      restaurant cafe  \n",
              "15     parking lot cars  \n",
              "16     parking lot cars  \n",
              "17     parking lot cars  \n",
              "18   classroom students  \n",
              "19   classroom students  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Load video manifest\n",
        "with open(\"video_manifest.json\", \"r\") as f:\n",
        "    manifest = json.load(f)\n",
        "\n",
        "print(f\"Total videos: {manifest['total_videos']}\")\n",
        "print(f\"S3 Bucket: {manifest['s3_bucket']}\")\n",
        "print(f\"S3 Prefix: {manifest['s3_prefix']}\")\n",
        "\n",
        "# Preview videos\n",
        "videos_df = pd.DataFrame(manifest[\"videos\"])\n",
        "videos_df[[\"filename\", \"duration\", \"width\", \"height\", \"query\"]]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test video: kitchen_cooking_35395675_00.mp4\n",
            "Duration: 8s\n",
            "Presigned URL generated successfully: https://abrar-test-bucket-123.s3.amazonaws.com/stock-videos/kitchen_cooking_3539...\n"
          ]
        }
      ],
      "source": [
        "# Async S3 utilities using aioboto3\n",
        "\n",
        "async def get_presigned_url(s3_key: str, bucket: str = manifest[\"s3_bucket\"], expiration: int = 3600) -> str:\n",
        "    \"\"\"Generate a presigned URL for an S3 object using aioboto3.\"\"\"\n",
        "    async with aio_session.client(\"s3\") as s3:\n",
        "        url = await s3.generate_presigned_url(\n",
        "            \"get_object\",\n",
        "            Params={\"Bucket\": bucket, \"Key\": s3_key},\n",
        "            ExpiresIn=expiration\n",
        "        )\n",
        "        return url\n",
        "\n",
        "async def download_s3_to_temp(bucket: str, key: str) -> str:\n",
        "    \"\"\"Download S3 object to a temporary file using aioboto3.\"\"\"\n",
        "    async with aio_session.client(\"s3\") as s3:\n",
        "        response = await s3.get_object(Bucket=bucket, Key=key)\n",
        "        body = await response[\"Body\"].read()\n",
        "    \n",
        "    # Write to temp file\n",
        "    suffix = os.path.splitext(key)[1]\n",
        "    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as f:\n",
        "        f.write(body)\n",
        "        return f.name\n",
        "\n",
        "async def download_s3_to_path(bucket: str, key: str, local_path: str) -> str:\n",
        "    \"\"\"Download S3 object to a specific path using aioboto3.\"\"\"\n",
        "    async with aio_session.client(\"s3\") as s3:\n",
        "        response = await s3.get_object(Bucket=bucket, Key=key)\n",
        "        body = await response[\"Body\"].read()\n",
        "    \n",
        "    with open(local_path, \"wb\") as f:\n",
        "        f.write(body)\n",
        "    return local_path\n",
        "\n",
        "# Test with one video\n",
        "test_video = manifest[\"videos\"][0]\n",
        "test_url = await get_presigned_url(test_video[\"s3_key\"])\n",
        "print(f\"Test video: {test_video['filename']}\")\n",
        "print(f\"Duration: {test_video['duration']}s\")\n",
        "print(f\"Presigned URL generated successfully: {test_url[:80]}...\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "async def benchmark_chunk_video(\n",
        "    video_url: str,\n",
        "    chunk_duration: float,\n",
        "    num_frames_per_chunk: int = 16,\n",
        "    target_size: int = 384,\n",
        ") -> dict:\n",
        "    \"\"\"\n",
        "    Benchmark chunk_video_async for a single configuration.\n",
        "    \n",
        "    Returns dict with timing and metadata.\n",
        "    \"\"\"\n",
        "    start_time = time.perf_counter()\n",
        "    \n",
        "    chunks = await chunk_video_async(\n",
        "        video_url,\n",
        "        chunk_duration=chunk_duration,\n",
        "        num_frames_per_chunk=num_frames_per_chunk,\n",
        "        target_size=target_size,\n",
        "    )\n",
        "    \n",
        "    elapsed = time.perf_counter() - start_time\n",
        "    \n",
        "    return {\n",
        "        \"elapsed_seconds\": elapsed,\n",
        "        \"num_chunks\": len(chunks),\n",
        "        \"chunk_duration\": chunk_duration,\n",
        "    }\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Selected 6 videos with varying durations:\n",
            "  - classroom_students_6209572_19.mp4: 4s @ 384x384\n",
            "  - office_meeting_3255275_03.mp4: 10s @ 384x384\n",
            "  - classroom_students_1580507_18.mp4: 22s @ 384x384\n",
            "  - living_room_home_5813761_10.mp4: 30s @ 384x384\n",
            "  - restaurant_cafe_5498709_12.mp4: 43s @ 384x384\n",
            "  - living_room_home_6158703_11.mp4: 61s @ 384x384\n"
          ]
        }
      ],
      "source": [
        "# Define chunk durations to test\n",
        "CHUNK_DURATIONS = [2.0, 5.0, 10.0, 15.0, 20.0]\n",
        "\n",
        "# All videos are now 384x384 - select diverse durations\n",
        "videos_sorted = sorted(manifest[\"videos\"], key=lambda v: v[\"duration\"])\n",
        "\n",
        "# Pick one video per 10-second bucket for diverse durations\n",
        "duration_buckets = {}\n",
        "for v in videos_sorted:\n",
        "    bucket = (v[\"duration\"] // 10) * 10  # Group by 10-second buckets\n",
        "    if bucket not in duration_buckets:\n",
        "        duration_buckets[bucket] = v\n",
        "\n",
        "selected_videos = list(duration_buckets.values())\n",
        "print(f\"Selected {len(selected_videos)} videos with varying durations:\")\n",
        "for v in selected_videos:\n",
        "    print(f\"  - {v['filename']}: {v['duration']}s @ {v['width']}x{v['height']}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Benchmarking: classroom_students_6209572_19.mp4 (4s)\n",
            "  chunk_duration=2.0s: 0.48s (2 chunks)\n",
            "\n",
            "Benchmarking: office_meeting_3255275_03.mp4 (10s)\n",
            "  chunk_duration=2.0s: 0.92s (5 chunks)\n",
            "  chunk_duration=5.0s: 0.62s (2 chunks)\n",
            "  chunk_duration=10.0s: 0.64s (1 chunks)\n",
            "\n",
            "Benchmarking: classroom_students_1580507_18.mp4 (22s)\n",
            "  chunk_duration=2.0s: 1.75s (11 chunks)\n",
            "  chunk_duration=5.0s: 1.10s (5 chunks)\n",
            "  chunk_duration=10.0s: 0.68s (3 chunks)\n",
            "  chunk_duration=15.0s: 0.68s (2 chunks)\n",
            "  chunk_duration=20.0s: 0.73s (2 chunks)\n",
            "\n",
            "Benchmarking: living_room_home_5813761_10.mp4 (30s)\n",
            "  chunk_duration=2.0s: 2.03s (15 chunks)\n",
            "  chunk_duration=5.0s: 0.99s (6 chunks)\n",
            "  chunk_duration=10.0s: 0.68s (3 chunks)\n",
            "  chunk_duration=15.0s: 0.69s (2 chunks)\n",
            "  chunk_duration=20.0s: 0.69s (2 chunks)\n",
            "\n",
            "Benchmarking: restaurant_cafe_5498709_12.mp4 (43s)\n",
            "  chunk_duration=2.0s: 3.28s (22 chunks)\n",
            "  chunk_duration=5.0s: 1.56s (9 chunks)\n",
            "  chunk_duration=10.0s: 1.20s (5 chunks)\n",
            "  chunk_duration=15.0s: 0.75s (3 chunks)\n",
            "  chunk_duration=20.0s: 0.78s (3 chunks)\n",
            "\n",
            "Benchmarking: living_room_home_6158703_11.mp4 (61s)\n",
            "  chunk_duration=2.0s: 3.96s (31 chunks)\n",
            "  chunk_duration=5.0s: 1.96s (13 chunks)\n",
            "  chunk_duration=10.0s: 1.19s (7 chunks)\n",
            "  chunk_duration=15.0s: 0.99s (5 chunks)\n",
            "  chunk_duration=20.0s: 0.90s (4 chunks)\n",
            "\n",
            "✓ Completed 24 benchmarks\n"
          ]
        }
      ],
      "source": [
        "# Run benchmarks\n",
        "results = []\n",
        "\n",
        "for video in selected_videos:\n",
        "    video_url = await get_presigned_url(video[\"s3_key\"])\n",
        "    video_duration = video[\"duration\"]\n",
        "    \n",
        "    print(f\"\\nBenchmarking: {video['filename']} ({video_duration}s)\")\n",
        "    \n",
        "    for chunk_dur in CHUNK_DURATIONS:\n",
        "        # Skip if chunk duration > video duration (not meaningful)\n",
        "        if chunk_dur > video_duration:\n",
        "            continue\n",
        "        \n",
        "        try:\n",
        "            result = await benchmark_chunk_video(video_url, chunk_dur)\n",
        "            \n",
        "            results.append({\n",
        "                \"filename\": video[\"filename\"],\n",
        "                \"video_duration\": video_duration,\n",
        "                \"video_resolution\": f\"{video['width']}x{video['height']}\",\n",
        "                \"chunk_duration\": chunk_dur,\n",
        "                \"elapsed_seconds\": result[\"elapsed_seconds\"],\n",
        "                \"num_chunks\": result[\"num_chunks\"],\n",
        "            })\n",
        "            \n",
        "            print(f\"  chunk_duration={chunk_dur}s: {result['elapsed_seconds']:.2f}s ({result['num_chunks']} chunks)\")\n",
        "        except Exception as e:\n",
        "            print(f\"  chunk_duration={chunk_dur}s: ERROR - {e}\")\n",
        "\n",
        "print(f\"\\n✓ Completed {len(results)} benchmarks\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>video_duration</th>\n",
              "      <th>video_resolution</th>\n",
              "      <th>chunk_duration</th>\n",
              "      <th>elapsed_seconds</th>\n",
              "      <th>num_chunks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>classroom_students_6209572_19.mp4</td>\n",
              "      <td>4</td>\n",
              "      <td>384x384</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.478574</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>office_meeting_3255275_03.mp4</td>\n",
              "      <td>10</td>\n",
              "      <td>384x384</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.923646</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>office_meeting_3255275_03.mp4</td>\n",
              "      <td>10</td>\n",
              "      <td>384x384</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.621022</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>office_meeting_3255275_03.mp4</td>\n",
              "      <td>10</td>\n",
              "      <td>384x384</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.642984</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>classroom_students_1580507_18.mp4</td>\n",
              "      <td>22</td>\n",
              "      <td>384x384</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.746246</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>classroom_students_1580507_18.mp4</td>\n",
              "      <td>22</td>\n",
              "      <td>384x384</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.104896</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>classroom_students_1580507_18.mp4</td>\n",
              "      <td>22</td>\n",
              "      <td>384x384</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.675051</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>classroom_students_1580507_18.mp4</td>\n",
              "      <td>22</td>\n",
              "      <td>384x384</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.679118</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>classroom_students_1580507_18.mp4</td>\n",
              "      <td>22</td>\n",
              "      <td>384x384</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.732817</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>living_room_home_5813761_10.mp4</td>\n",
              "      <td>30</td>\n",
              "      <td>384x384</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.032840</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>living_room_home_5813761_10.mp4</td>\n",
              "      <td>30</td>\n",
              "      <td>384x384</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.991365</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>living_room_home_5813761_10.mp4</td>\n",
              "      <td>30</td>\n",
              "      <td>384x384</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.684170</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>living_room_home_5813761_10.mp4</td>\n",
              "      <td>30</td>\n",
              "      <td>384x384</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.687432</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>living_room_home_5813761_10.mp4</td>\n",
              "      <td>30</td>\n",
              "      <td>384x384</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.685301</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>restaurant_cafe_5498709_12.mp4</td>\n",
              "      <td>43</td>\n",
              "      <td>384x384</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.277881</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>restaurant_cafe_5498709_12.mp4</td>\n",
              "      <td>43</td>\n",
              "      <td>384x384</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.564297</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>restaurant_cafe_5498709_12.mp4</td>\n",
              "      <td>43</td>\n",
              "      <td>384x384</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.196003</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>restaurant_cafe_5498709_12.mp4</td>\n",
              "      <td>43</td>\n",
              "      <td>384x384</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.749658</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>restaurant_cafe_5498709_12.mp4</td>\n",
              "      <td>43</td>\n",
              "      <td>384x384</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.777313</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>living_room_home_6158703_11.mp4</td>\n",
              "      <td>61</td>\n",
              "      <td>384x384</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.962868</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>living_room_home_6158703_11.mp4</td>\n",
              "      <td>61</td>\n",
              "      <td>384x384</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.955896</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>living_room_home_6158703_11.mp4</td>\n",
              "      <td>61</td>\n",
              "      <td>384x384</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.192681</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>living_room_home_6158703_11.mp4</td>\n",
              "      <td>61</td>\n",
              "      <td>384x384</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.989384</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>living_room_home_6158703_11.mp4</td>\n",
              "      <td>61</td>\n",
              "      <td>384x384</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.899559</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             filename  video_duration video_resolution  \\\n",
              "0   classroom_students_6209572_19.mp4               4          384x384   \n",
              "1       office_meeting_3255275_03.mp4              10          384x384   \n",
              "2       office_meeting_3255275_03.mp4              10          384x384   \n",
              "3       office_meeting_3255275_03.mp4              10          384x384   \n",
              "4   classroom_students_1580507_18.mp4              22          384x384   \n",
              "5   classroom_students_1580507_18.mp4              22          384x384   \n",
              "6   classroom_students_1580507_18.mp4              22          384x384   \n",
              "7   classroom_students_1580507_18.mp4              22          384x384   \n",
              "8   classroom_students_1580507_18.mp4              22          384x384   \n",
              "9     living_room_home_5813761_10.mp4              30          384x384   \n",
              "10    living_room_home_5813761_10.mp4              30          384x384   \n",
              "11    living_room_home_5813761_10.mp4              30          384x384   \n",
              "12    living_room_home_5813761_10.mp4              30          384x384   \n",
              "13    living_room_home_5813761_10.mp4              30          384x384   \n",
              "14     restaurant_cafe_5498709_12.mp4              43          384x384   \n",
              "15     restaurant_cafe_5498709_12.mp4              43          384x384   \n",
              "16     restaurant_cafe_5498709_12.mp4              43          384x384   \n",
              "17     restaurant_cafe_5498709_12.mp4              43          384x384   \n",
              "18     restaurant_cafe_5498709_12.mp4              43          384x384   \n",
              "19    living_room_home_6158703_11.mp4              61          384x384   \n",
              "20    living_room_home_6158703_11.mp4              61          384x384   \n",
              "21    living_room_home_6158703_11.mp4              61          384x384   \n",
              "22    living_room_home_6158703_11.mp4              61          384x384   \n",
              "23    living_room_home_6158703_11.mp4              61          384x384   \n",
              "\n",
              "    chunk_duration  elapsed_seconds  num_chunks  \n",
              "0              2.0         0.478574           2  \n",
              "1              2.0         0.923646           5  \n",
              "2              5.0         0.621022           2  \n",
              "3             10.0         0.642984           1  \n",
              "4              2.0         1.746246          11  \n",
              "5              5.0         1.104896           5  \n",
              "6             10.0         0.675051           3  \n",
              "7             15.0         0.679118           2  \n",
              "8             20.0         0.732817           2  \n",
              "9              2.0         2.032840          15  \n",
              "10             5.0         0.991365           6  \n",
              "11            10.0         0.684170           3  \n",
              "12            15.0         0.687432           2  \n",
              "13            20.0         0.685301           2  \n",
              "14             2.0         3.277881          22  \n",
              "15             5.0         1.564297           9  \n",
              "16            10.0         1.196003           5  \n",
              "17            15.0         0.749658           3  \n",
              "18            20.0         0.777313           3  \n",
              "19             2.0         3.962868          31  \n",
              "20             5.0         1.955896          13  \n",
              "21            10.0         1.192681           7  \n",
              "22            15.0         0.989384           5  \n",
              "23            20.0         0.899559           4  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create results DataFrame\n",
        "results_df = pd.DataFrame(results)\n",
        "results_df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create heatmap: Video Duration vs Chunk Duration -> Execution Time\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "# Pivot the data for heatmap\n",
        "heatmap_data = results_df.pivot_table(\n",
        "    index=\"video_duration\",\n",
        "    columns=\"chunk_duration\",\n",
        "    values=\"elapsed_seconds\",\n",
        "    aggfunc=\"mean\"\n",
        ")\n",
        "\n",
        "# Sort index for better visualization\n",
        "heatmap_data = heatmap_data.sort_index(ascending=True)\n",
        "\n",
        "# Create heatmap\n",
        "sns.heatmap(\n",
        "    heatmap_data,\n",
        "    annot=True,\n",
        "    fmt=\".2f\",\n",
        "    cmap=\"YlOrRd\",\n",
        "    cbar_kws={\"label\": \"Execution Time (seconds)\"},\n",
        "    linewidths=0.5,\n",
        ")\n",
        "\n",
        "plt.title(\"chunk_video_async Execution Time\\nVideo Duration vs Chunk Duration (384x384 videos)\", fontsize=14, fontweight=\"bold\")\n",
        "plt.xlabel(\"Chunk Duration (seconds)\", fontsize=12)\n",
        "plt.ylabel(\"Video Duration (seconds)\", fontsize=12)\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"assets/chunk_video_heatmap.png\", dpi=150, bbox_inches=\"tight\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Additional analysis: Execution time per chunk\n",
        "results_df[\"time_per_chunk\"] = results_df[\"elapsed_seconds\"] / results_df[\"num_chunks\"]\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Plot 1: Total time by chunk duration\n",
        "plt.subplot(1, 2, 1)\n",
        "for duration in sorted(results_df[\"video_duration\"].unique()):\n",
        "    subset = results_df[results_df[\"video_duration\"] == duration]\n",
        "    plt.plot(\n",
        "        subset[\"chunk_duration\"],\n",
        "        subset[\"elapsed_seconds\"],\n",
        "        marker=\"o\",\n",
        "        label=f\"{duration}s video\"\n",
        "    )\n",
        "\n",
        "plt.xlabel(\"Chunk Duration (seconds)\")\n",
        "plt.ylabel(\"Total Execution Time (seconds)\")\n",
        "plt.title(\"Total Execution Time vs Chunk Duration\")\n",
        "plt.legend(loc=\"best\", fontsize=8)\n",
        "plt.grid(True, alpha=0.3)\n",
        "\n",
        "# Plot 2: Time per chunk by chunk duration\n",
        "plt.subplot(1, 2, 2)\n",
        "for duration in sorted(results_df[\"video_duration\"].unique()):\n",
        "    subset = results_df[results_df[\"video_duration\"] == duration]\n",
        "    plt.plot(\n",
        "        subset[\"chunk_duration\"],\n",
        "        subset[\"time_per_chunk\"],\n",
        "        marker=\"s\",\n",
        "        label=f\"{duration}s video\"\n",
        "    )\n",
        "\n",
        "plt.xlabel(\"Chunk Duration (seconds)\")\n",
        "plt.ylabel(\"Time per Chunk (seconds)\")\n",
        "plt.title(\"Time per Chunk vs Chunk Duration\")\n",
        "plt.legend(loc=\"best\", fontsize=8)\n",
        "plt.grid(True, alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"assets/chunk_video_analysis.png\", dpi=150, bbox_inches=\"tight\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "SUMMARY STATISTICS\n",
            "============================================================\n",
            "\n",
            "By Chunk Duration:\n",
            "               elapsed_seconds                      num_chunks time_per_chunk\n",
            "                          mean    std    min    max       mean           mean\n",
            "chunk_duration                                                               \n",
            "2.0                      2.070  1.341  0.479  3.963     14.333          0.166\n",
            "5.0                      1.247  0.520  0.621  1.956      7.000          0.204\n",
            "10.0                     0.878  0.289  0.643  1.196      3.800          0.301\n",
            "15.0                     0.776  0.145  0.679  0.989      3.000          0.283\n",
            "20.0                     0.774  0.092  0.685  0.900      2.750          0.298\n",
            "\n",
            "By Video Duration:\n",
            "               elapsed_seconds                      num_chunks\n",
            "                          mean    std    min    max       mean\n",
            "video_duration                                                \n",
            "4                        0.479    NaN  0.479  0.479      2.000\n",
            "10                       0.729  0.169  0.621  0.924      2.667\n",
            "22                       0.988  0.460  0.675  1.746      4.600\n",
            "30                       1.016  0.584  0.684  2.033      5.600\n",
            "43                       1.513  1.042  0.750  3.278      8.400\n",
            "61                       1.800  1.279  0.900  3.963     12.000\n"
          ]
        }
      ],
      "source": [
        "# Summary statistics\n",
        "print(\"=\" * 60)\n",
        "print(\"SUMMARY STATISTICS\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "print(\"\\nBy Chunk Duration:\")\n",
        "chunk_summary = results_df.groupby(\"chunk_duration\").agg({\n",
        "    \"elapsed_seconds\": [\"mean\", \"std\", \"min\", \"max\"],\n",
        "    \"num_chunks\": \"mean\",\n",
        "    \"time_per_chunk\": \"mean\"\n",
        "}).round(3)\n",
        "print(chunk_summary)\n",
        "\n",
        "print(\"\\nBy Video Duration:\")\n",
        "video_summary = results_df.groupby(\"video_duration\").agg({\n",
        "    \"elapsed_seconds\": [\"mean\", \"std\", \"min\", \"max\"],\n",
        "    \"num_chunks\": \"mean\",\n",
        "}).round(3)\n",
        "print(video_summary)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Sync vs Async Comparison\n",
        "\n",
        "Compare `chunk_video` (sequential) vs `chunk_video_async` (parallel) using the longest video.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Longest video: living_room_home_6158703_11.mp4\n",
            "Duration: 61s\n",
            "Resolution: 384x384\n"
          ]
        }
      ],
      "source": [
        "# Import sync version and download utility\n",
        "from utils.video import chunk_video, chunk_video_async\n",
        "import tempfile\n",
        "\n",
        "# Find the longest video\n",
        "longest_video = max(manifest[\"videos\"], key=lambda v: v[\"duration\"])\n",
        "print(f\"Longest video: {longest_video['filename']}\")\n",
        "print(f\"Duration: {longest_video['duration']}s\")\n",
        "print(f\"Resolution: {longest_video['width']}x{longest_video['height']}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading s3://abrar-test-bucket-123/stock-videos/living_room_home_6158703_11.mp4 using aioboto3...\n",
            "Downloaded to: /tmp/living_room_home_6158703_11.mp4\n"
          ]
        }
      ],
      "source": [
        "# Download video locally for fair comparison (removes S3 network variability)\n",
        "local_video_path = f\"/tmp/{longest_video['filename']}\"\n",
        "\n",
        "# Download from S3 using aioboto3\n",
        "print(f\"Downloading {longest_video['s3_uri']} using aioboto3...\")\n",
        "await download_s3_to_path(\n",
        "    manifest[\"s3_bucket\"],\n",
        "    longest_video[\"s3_key\"],\n",
        "    local_video_path\n",
        ")\n",
        "\n",
        "print(f\"Downloaded to: {local_video_path}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Benchmarking with chunk_duration=10.0s, 3 runs each\n",
            "Video: living_room_home_6158703_11.mp4 (61s)\n",
            "============================================================\n",
            "  chunk_video (sync) run 1: 0.410s (7 chunks)\n",
            "  chunk_video (sync) run 2: 0.411s (7 chunks)\n",
            "  chunk_video (sync) run 3: 0.421s (7 chunks)\n",
            "  chunk_video_async run 1: 0.452s (7 chunks)\n",
            "  chunk_video_async run 2: 0.460s (7 chunks)\n",
            "  chunk_video_async run 3: 0.440s (7 chunks)\n",
            "============================================================\n"
          ]
        }
      ],
      "source": [
        "# Benchmark: chunk_video (sync/sequential) vs chunk_video_async (parallel)\n",
        "CHUNK_DURATION = 10.0\n",
        "NUM_RUNS = 3\n",
        "\n",
        "print(f\"Benchmarking with chunk_duration={CHUNK_DURATION}s, {NUM_RUNS} runs each\")\n",
        "print(f\"Video: {longest_video['filename']} ({longest_video['duration']}s)\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "# Benchmark sync version\n",
        "sync_times = []\n",
        "for i in range(NUM_RUNS):\n",
        "    start = time.perf_counter()\n",
        "    chunks_sync = chunk_video(local_video_path, chunk_duration=CHUNK_DURATION)\n",
        "    elapsed = time.perf_counter() - start\n",
        "    sync_times.append(elapsed)\n",
        "    print(f\"  chunk_video (sync) run {i+1}: {elapsed:.3f}s ({len(chunks_sync)} chunks)\")\n",
        "\n",
        "# Benchmark async version\n",
        "async_times = []\n",
        "for i in range(NUM_RUNS):\n",
        "    start = time.perf_counter()\n",
        "    chunks_async = await chunk_video_async(local_video_path, chunk_duration=CHUNK_DURATION)\n",
        "    elapsed = time.perf_counter() - start\n",
        "    async_times.append(elapsed)\n",
        "    print(f\"  chunk_video_async run {i+1}: {elapsed:.3f}s ({len(chunks_async)} chunks)\")\n",
        "\n",
        "print(\"=\" * 60)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RESULTS SUMMARY\n",
            "============================================================\n",
            "chunk_video (sync):       0.414s ± 0.005s\n",
            "chunk_video_async:        0.451s ± 0.008s\n",
            "Speedup (async vs sync):  0.92x\n",
            "============================================================\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Results comparison\n",
        "sync_mean = np.mean(sync_times)\n",
        "async_mean = np.mean(async_times)\n",
        "speedup = sync_mean / async_mean\n",
        "\n",
        "print(\"RESULTS SUMMARY\")\n",
        "print(\"=\" * 60)\n",
        "print(f\"chunk_video (sync):       {sync_mean:.3f}s ± {np.std(sync_times):.3f}s\")\n",
        "print(f\"chunk_video_async:        {async_mean:.3f}s ± {np.std(async_times):.3f}s\")\n",
        "print(f\"Speedup (async vs sync):  {speedup:.2f}x\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "# Bar chart comparison\n",
        "fig, ax = plt.subplots(figsize=(8, 5))\n",
        "\n",
        "methods = [\"chunk_video\\n(sequential)\", \"chunk_video_async\\n(parallel)\"]\n",
        "means = [sync_mean, async_mean]\n",
        "stds = [np.std(sync_times), np.std(async_times)]\n",
        "colors = [\"#e74c3c\", \"#27ae60\"]\n",
        "\n",
        "bars = ax.bar(methods, means, yerr=stds, capsize=5, color=colors, edgecolor=\"black\", linewidth=1.2)\n",
        "\n",
        "ax.set_ylabel(\"Execution Time (seconds)\", fontsize=12)\n",
        "ax.set_title(f\"Sync vs Async Chunking Performance\\n{longest_video['filename']} ({longest_video['duration']}s, chunk_duration={CHUNK_DURATION}s)\", \n",
        "             fontsize=12, fontweight=\"bold\")\n",
        "ax.grid(axis=\"y\", alpha=0.3)\n",
        "\n",
        "# Add value labels on bars\n",
        "for bar, mean in zip(bars, means):\n",
        "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1, \n",
        "            f\"{mean:.2f}s\", ha=\"center\", va=\"bottom\", fontsize=11, fontweight=\"bold\")\n",
        "\n",
        "# Add speedup annotation\n",
        "ax.annotate(f\"{speedup:.1f}x faster\", \n",
        "            xy=(1, async_mean), xytext=(0.5, (sync_mean + async_mean)/2),\n",
        "            fontsize=12, fontweight=\"bold\", color=\"#27ae60\",\n",
        "            arrowprops=dict(arrowstyle=\"->\", color=\"#27ae60\", lw=2))\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"assets/sync_vs_async_comparison.png\", dpi=150, bbox_inches=\"tight\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Benchmarking Single FFmpeg vs Multi-FFmpeg\n",
            "Video: living_room_home_6158703_11.mp4 (61s)\n",
            "======================================================================\n",
            "\n",
            "Chunk duration: 5.0s\n",
            "--------------------------------------------------\n",
            "  Multi-FFmpeg  run 1: 0.643s (13 chunks)\n",
            "  Multi-FFmpeg  run 2: 0.594s (13 chunks)\n",
            "  Multi-FFmpeg  run 3: 0.602s (13 chunks)\n",
            "  Single-FFmpeg run 1: 0.516s (13 chunks)\n",
            "  Single-FFmpeg run 2: 0.518s (13 chunks)\n",
            "  Single-FFmpeg run 3: 0.490s (13 chunks)\n",
            "\n",
            "Chunk duration: 10.0s\n",
            "--------------------------------------------------\n",
            "  Multi-FFmpeg  run 1: 0.487s (7 chunks)\n",
            "  Multi-FFmpeg  run 2: 0.477s (7 chunks)\n",
            "  Multi-FFmpeg  run 3: 0.495s (7 chunks)\n",
            "  Single-FFmpeg run 1: 0.437s (7 chunks)\n",
            "  Single-FFmpeg run 2: 0.431s (7 chunks)\n",
            "  Single-FFmpeg run 3: 0.436s (7 chunks)\n",
            "\n",
            "Chunk duration: 20.0s\n",
            "--------------------------------------------------\n",
            "  Multi-FFmpeg  run 1: 0.381s (4 chunks)\n",
            "  Multi-FFmpeg  run 2: 0.379s (4 chunks)\n",
            "  Multi-FFmpeg  run 3: 0.384s (4 chunks)\n",
            "  Single-FFmpeg run 1: 0.405s (4 chunks)\n",
            "  Single-FFmpeg run 2: 0.386s (4 chunks)\n",
            "  Single-FFmpeg run 3: 0.420s (4 chunks)\n",
            "\n",
            "======================================================================\n"
          ]
        }
      ],
      "source": [
        "# Benchmark: Single FFmpeg vs Multi-FFmpeg\n",
        "# Uses the local video downloaded earlier\n",
        "\n",
        "NUM_RUNS = 3\n",
        "CHUNK_DURATIONS_TEST = [5.0, 10.0, 20.0]\n",
        "\n",
        "print(f\"Benchmarking Single FFmpeg vs Multi-FFmpeg\")\n",
        "print(f\"Video: {longest_video['filename']} ({longest_video['duration']}s)\")\n",
        "print(\"=\" * 70)\n",
        "\n",
        "ffmpeg_comparison_results = []\n",
        "\n",
        "for chunk_dur in CHUNK_DURATIONS_TEST:\n",
        "    print(f\"\\nChunk duration: {chunk_dur}s\")\n",
        "    print(\"-\" * 50)\n",
        "    \n",
        "    # Benchmark Multi-FFmpeg (parallel processes)\n",
        "    multi_times = []\n",
        "    for i in range(NUM_RUNS):\n",
        "        start = time.perf_counter()\n",
        "        chunks = await chunk_video_async(local_video_path, chunk_duration=chunk_dur, use_single_ffmpeg=False, ffmpeg_threads=2)\n",
        "        elapsed = time.perf_counter() - start\n",
        "        multi_times.append(elapsed)\n",
        "        print(f\"  Multi-FFmpeg  run {i+1}: {elapsed:.3f}s ({len(chunks)} chunks)\")\n",
        "    \n",
        "    # Benchmark Single-FFmpeg (select filter)\n",
        "    single_times = []\n",
        "    for i in range(NUM_RUNS):\n",
        "        start = time.perf_counter()\n",
        "        chunks = await chunk_video_async(local_video_path, chunk_duration=chunk_dur, use_single_ffmpeg=True, ffmpeg_threads=6)\n",
        "        elapsed = time.perf_counter() - start\n",
        "        single_times.append(elapsed)\n",
        "        print(f\"  Single-FFmpeg run {i+1}: {elapsed:.3f}s ({len(chunks)} chunks)\")\n",
        "    \n",
        "    ffmpeg_comparison_results.append({\n",
        "        \"chunk_duration\": chunk_dur,\n",
        "        \"num_chunks\": len(chunks),\n",
        "        \"multi_ffmpeg_mean\": np.mean(multi_times),\n",
        "        \"multi_ffmpeg_std\": np.std(multi_times),\n",
        "        \"single_ffmpeg_mean\": np.mean(single_times),\n",
        "        \"single_ffmpeg_std\": np.std(single_times),\n",
        "    })\n",
        "\n",
        "print(\"\\n\" + \"=\" * 70)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RESULTS: Single FFmpeg vs Multi-FFmpeg\n",
            "======================================================================\n",
            " Chunk Dur   Chunks    Multi-FFmpeg   Single-FFmpeg    Speedup\n",
            "----------------------------------------------------------------------\n",
            "       5.0s     13.0        0.613s ± 0.021        0.508s ± 0.013      1.21x\n",
            "      10.0s      7.0        0.486s ± 0.008        0.435s ± 0.003      1.12x\n",
            "      20.0s      4.0        0.381s ± 0.002        0.404s ± 0.014      0.94x\n",
            "======================================================================\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Results visualization: Single FFmpeg vs Multi-FFmpeg\n",
        "ffmpeg_comparison_df = pd.DataFrame(ffmpeg_comparison_results)\n",
        "ffmpeg_comparison_df[\"speedup\"] = ffmpeg_comparison_df[\"multi_ffmpeg_mean\"] / ffmpeg_comparison_df[\"single_ffmpeg_mean\"]\n",
        "\n",
        "print(\"RESULTS: Single FFmpeg vs Multi-FFmpeg\")\n",
        "print(\"=\" * 70)\n",
        "print(f\"{'Chunk Dur':>10} {'Chunks':>8} {'Multi-FFmpeg':>15} {'Single-FFmpeg':>15} {'Speedup':>10}\")\n",
        "print(\"-\" * 70)\n",
        "\n",
        "for _, row in ffmpeg_comparison_df.iterrows():\n",
        "    print(f\"{row['chunk_duration']:>10.1f}s {row['num_chunks']:>8} \"\n",
        "          f\"{row['multi_ffmpeg_mean']:>12.3f}s ± {row['multi_ffmpeg_std']:.3f} \"\n",
        "          f\"{row['single_ffmpeg_mean']:>12.3f}s ± {row['single_ffmpeg_std']:.3f} \"\n",
        "          f\"{row['speedup']:>9.2f}x\")\n",
        "\n",
        "print(\"=\" * 70)\n",
        "\n",
        "# Bar chart\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "x = np.arange(len(ffmpeg_comparison_df))\n",
        "width = 0.35\n",
        "\n",
        "bars1 = ax.bar(x - width/2, ffmpeg_comparison_df[\"multi_ffmpeg_mean\"], width,\n",
        "               yerr=ffmpeg_comparison_df[\"multi_ffmpeg_std\"], capsize=5,\n",
        "               label=\"Multi-FFmpeg (parallel)\", color=\"#e74c3c\", edgecolor=\"black\")\n",
        "bars2 = ax.bar(x + width/2, ffmpeg_comparison_df[\"single_ffmpeg_mean\"], width,\n",
        "               yerr=ffmpeg_comparison_df[\"single_ffmpeg_std\"], capsize=5,\n",
        "               label=\"Single-FFmpeg (select filter)\", color=\"#27ae60\", edgecolor=\"black\")\n",
        "\n",
        "ax.set_ylabel(\"Execution Time (seconds)\")\n",
        "ax.set_xlabel(\"Chunk Duration\")\n",
        "ax.set_title(f\"Single vs Multi-FFmpeg Performance\\n{longest_video['filename']} ({longest_video['duration']}s)\", fontweight=\"bold\")\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels([f\"{int(d)}s\\n({n} chunks)\" for d, n in \n",
        "                   zip(ffmpeg_comparison_df[\"chunk_duration\"], ffmpeg_comparison_df[\"num_chunks\"])])\n",
        "ax.legend()\n",
        "ax.grid(axis=\"y\", alpha=0.3)\n",
        "\n",
        "# Add speedup annotations\n",
        "for i, (bar1, bar2) in enumerate(zip(bars1, bars2)):\n",
        "    speedup = ffmpeg_comparison_df.iloc[i][\"speedup\"]\n",
        "    ax.annotate(f\"{speedup:.2f}x\", \n",
        "               xy=(bar2.get_x() + bar2.get_width()/2, bar2.get_height()),\n",
        "               xytext=(0, 8), textcoords=\"offset points\",\n",
        "               ha=\"center\", fontsize=10, fontweight=\"bold\", color=\"#27ae60\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"assets/single_vs_multi_ffmpeg.png\", dpi=150, bbox_inches=\"tight\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Testing with S3 presigned URL...\n",
            "======================================================================\n",
            "\n",
            "Multi-FFmpeg (multiple HTTP connections):\n",
            "  Run 1: 1.289s (7 chunks)\n",
            "  Run 2: 1.200s (7 chunks)\n",
            "  Run 3: 1.237s (7 chunks)\n",
            "\n",
            "Single-FFmpeg (single HTTP stream):\n",
            "  Run 1: 0.737s (7 chunks)\n",
            "  Run 2: 0.727s (7 chunks)\n",
            "  Run 3: 0.734s (7 chunks)\n",
            "\n",
            "======================================================================\n",
            "RESULTS: S3 Presigned URL\n",
            "======================================================================\n",
            "Multi-FFmpeg:  1.242s ± 0.036s\n",
            "Single-FFmpeg: 0.732s ± 0.004s\n",
            "Speedup:       1.70x\n",
            "======================================================================\n"
          ]
        }
      ],
      "source": [
        "# Test with S3 presigned URL (where single-FFmpeg should benefit most)\n",
        "print(\"Testing with S3 presigned URL...\")\n",
        "print(\"=\" * 70)\n",
        "\n",
        "test_url = await get_presigned_url(longest_video[\"s3_key\"])\n",
        "CHUNK_DUR = 10.0\n",
        "\n",
        "# Multi-FFmpeg with URL\n",
        "print(f\"\\nMulti-FFmpeg (multiple HTTP connections):\")\n",
        "multi_url_times = []\n",
        "for i in range(NUM_RUNS):\n",
        "    start = time.perf_counter()\n",
        "    chunks = await chunk_video_async(test_url, chunk_duration=CHUNK_DUR, use_single_ffmpeg=False)\n",
        "    elapsed = time.perf_counter() - start\n",
        "    multi_url_times.append(elapsed)\n",
        "    print(f\"  Run {i+1}: {elapsed:.3f}s ({len(chunks)} chunks)\")\n",
        "\n",
        "# Single-FFmpeg with URL\n",
        "print(f\"\\nSingle-FFmpeg (single HTTP stream):\")\n",
        "single_url_times = []\n",
        "for i in range(NUM_RUNS):\n",
        "    start = time.perf_counter()\n",
        "    chunks = await chunk_video_async(test_url, chunk_duration=CHUNK_DUR, use_single_ffmpeg=True)\n",
        "    elapsed = time.perf_counter() - start\n",
        "    single_url_times.append(elapsed)\n",
        "    print(f\"  Run {i+1}: {elapsed:.3f}s ({len(chunks)} chunks)\")\n",
        "\n",
        "multi_mean = np.mean(multi_url_times)\n",
        "single_mean = np.mean(single_url_times)\n",
        "speedup = multi_mean / single_mean\n",
        "\n",
        "print(\"\\n\" + \"=\" * 70)\n",
        "print(\"RESULTS: S3 Presigned URL\")\n",
        "print(\"=\" * 70)\n",
        "print(f\"Multi-FFmpeg:  {multi_mean:.3f}s ± {np.std(multi_url_times):.3f}s\")\n",
        "print(f\"Single-FFmpeg: {single_mean:.3f}s ± {np.std(single_url_times):.3f}s\")\n",
        "print(f\"Speedup:       {speedup:.2f}x\")\n",
        "print(\"=\" * 70)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Presigned URL vs Direct S3 Download Performance\n",
        "\n",
        "Compare video access methods:\n",
        "1. **Presigned URL**: Generate URL with boto3, pass to ffmpeg (current approach)\n",
        "2. **Direct S3 Download**: Download video bytes with aioboto3, save to temp file, then process\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Benchmark functions for presigned URL vs direct S3 comparison\n",
        "# (aioboto3 session and download_s3_to_temp are defined in cell 3)\n",
        "\n",
        "async def benchmark_presigned_url(video: dict, chunk_duration: float) -> dict:\n",
        "    \"\"\"Benchmark using presigned URL (ffmpeg streams directly from URL).\"\"\"\n",
        "    start = time.perf_counter()\n",
        "    \n",
        "    url = await get_presigned_url(video[\"s3_key\"])\n",
        "    url_gen_time = time.perf_counter() - start\n",
        "    \n",
        "    chunks = await chunk_video_async(url, chunk_duration=chunk_duration, ffmpeg_threads=6, use_single_ffmpeg=True)\n",
        "    total_time = time.perf_counter() - start\n",
        "    \n",
        "    return {\n",
        "        \"method\": \"presigned_url\",\n",
        "        \"url_gen_time\": url_gen_time,\n",
        "        \"total_time\": total_time,\n",
        "        \"num_chunks\": len(chunks),\n",
        "    }\n",
        "\n",
        "async def benchmark_direct_s3(video: dict, chunk_duration: float) -> dict:\n",
        "    \"\"\"Benchmark using direct S3 download with aioboto3.\"\"\"\n",
        "    start = time.perf_counter()\n",
        "    \n",
        "    # Download to temp file\n",
        "    temp_path = await download_s3_to_temp(manifest[\"s3_bucket\"], video[\"s3_key\"])\n",
        "    download_time = time.perf_counter() - start\n",
        "    \n",
        "    try:\n",
        "        chunks = await chunk_video_async(temp_path, chunk_duration=chunk_duration, ffmpeg_threads=6, use_single_ffmpeg=True)\n",
        "        total_time = time.perf_counter() - start\n",
        "    finally:\n",
        "        # Cleanup temp file\n",
        "        os.unlink(temp_path)\n",
        "    \n",
        "    return {\n",
        "        \"method\": \"direct_s3_aioboto3\",\n",
        "        \"download_time\": download_time,\n",
        "        \"total_time\": total_time,\n",
        "        \"num_chunks\": len(chunks),\n",
        "    }\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "============================================================\n",
            "Video: kitchen_cooking_35395675_00.mp4 (8s, 384x384)\n",
            "============================================================\n",
            "  Run 1: Presigned=0.44s, Direct S3=0.40s (download=0.24s)\n",
            "  Run 2: Presigned=0.45s, Direct S3=0.40s (download=0.24s)\n",
            "  Run 3: Presigned=0.46s, Direct S3=0.41s (download=0.25s)\n",
            "\n",
            "============================================================\n",
            "Video: kitchen_cooking_3992465_01.mp4 (24s, 384x384)\n",
            "============================================================\n",
            "  Run 1: Presigned=0.54s, Direct S3=0.52s (download=0.27s)\n",
            "  Run 2: Presigned=0.52s, Direct S3=0.52s (download=0.27s)\n",
            "  Run 3: Presigned=0.54s, Direct S3=0.50s (download=0.25s)\n",
            "\n",
            "============================================================\n",
            "Video: kitchen_cooking_5036096_02.mp4 (49s, 384x384)\n",
            "============================================================\n",
            "  Run 1: Presigned=0.77s, Direct S3=0.70s (download=0.29s)\n",
            "  Run 2: Presigned=0.80s, Direct S3=0.70s (download=0.29s)\n",
            "  Run 3: Presigned=0.76s, Direct S3=0.71s (download=0.29s)\n",
            "\n",
            "✓ Completed comparison benchmarks\n"
          ]
        }
      ],
      "source": [
        "# Run comparison benchmark\n",
        "CHUNK_DURATION = 10.0\n",
        "NUM_RUNS = 3\n",
        "\n",
        "# Use a few videos of different sizes\n",
        "test_videos = [\n",
        "    next(v for v in manifest[\"videos\"] if v[\"duration\"] <= 10),   # Short\n",
        "    next(v for v in manifest[\"videos\"] if 20 <= v[\"duration\"] <= 30),  # Medium\n",
        "    next(v for v in manifest[\"videos\"] if v[\"duration\"] >= 40),   # Long\n",
        "]\n",
        "\n",
        "comparison_results = []\n",
        "\n",
        "for video in test_videos:\n",
        "    print(f\"\\n{'='*60}\")\n",
        "    print(f\"Video: {video['filename']} ({video['duration']}s, {video['width']}x{video['height']})\")\n",
        "    print(f\"{'='*60}\")\n",
        "    \n",
        "    presigned_times = []\n",
        "    direct_times = []\n",
        "    \n",
        "    for run in range(NUM_RUNS):\n",
        "        # Benchmark presigned URL\n",
        "        result_presigned = await benchmark_presigned_url(video, CHUNK_DURATION)\n",
        "        presigned_times.append(result_presigned[\"total_time\"])\n",
        "        \n",
        "        # Benchmark direct S3 download\n",
        "        result_direct = await benchmark_direct_s3(video, CHUNK_DURATION)\n",
        "        direct_times.append(result_direct[\"total_time\"])\n",
        "        \n",
        "        print(f\"  Run {run+1}: Presigned={result_presigned['total_time']:.2f}s, Direct S3={result_direct['total_time']:.2f}s (download={result_direct['download_time']:.2f}s)\")\n",
        "    \n",
        "    comparison_results.append({\n",
        "        \"filename\": video[\"filename\"],\n",
        "        \"duration\": video[\"duration\"],\n",
        "        \"resolution\": f\"{video['width']}x{video['height']}\",\n",
        "        \"presigned_mean\": np.mean(presigned_times),\n",
        "        \"presigned_std\": np.std(presigned_times),\n",
        "        \"direct_s3_mean\": np.mean(direct_times),\n",
        "        \"direct_s3_std\": np.std(direct_times),\n",
        "    })\n",
        "\n",
        "print(f\"\\n✓ Completed comparison benchmarks\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Comparison Results:\n",
            "                       filename  duration resolution  presigned_mean  presigned_std  direct_s3_mean  direct_s3_std\n",
            "kitchen_cooking_35395675_00.mp4         8    384x384        0.449107       0.005435        0.401356       0.007048\n",
            " kitchen_cooking_3992465_01.mp4        24    384x384        0.530370       0.009030        0.512359       0.009844\n",
            " kitchen_cooking_5036096_02.mp4        49    384x384        0.773435       0.016460        0.703364       0.005439\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "============================================================\n",
            "SUMMARY: Presigned URL vs Direct S3 Download\n",
            "============================================================\n",
            "kitchen_cooking_35395675_00.mp: Direct S3 wins by 0.05s\n",
            "kitchen_cooking_3992465_01.mp4: Direct S3 wins by 0.02s\n",
            "kitchen_cooking_5036096_02.mp4: Direct S3 wins by 0.07s\n"
          ]
        }
      ],
      "source": [
        "# Visualize comparison results\n",
        "comparison_df = pd.DataFrame(comparison_results)\n",
        "print(\"Comparison Results:\")\n",
        "print(comparison_df.to_string(index=False))\n",
        "\n",
        "# Calculate speedup\n",
        "comparison_df[\"speedup\"] = comparison_df[\"direct_s3_mean\"] / comparison_df[\"presigned_mean\"]\n",
        "\n",
        "# Bar chart comparison\n",
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "\n",
        "x = np.arange(len(comparison_df))\n",
        "width = 0.35\n",
        "\n",
        "bars1 = ax.bar(x - width/2, comparison_df[\"presigned_mean\"], width, \n",
        "               yerr=comparison_df[\"presigned_std\"], capsize=5,\n",
        "               label=\"Presigned URL\", color=\"#3498db\", edgecolor=\"black\")\n",
        "bars2 = ax.bar(x + width/2, comparison_df[\"direct_s3_mean\"], width,\n",
        "               yerr=comparison_df[\"direct_s3_std\"], capsize=5,\n",
        "               label=\"Direct S3 (aioboto3)\", color=\"#e74c3c\", edgecolor=\"black\")\n",
        "\n",
        "ax.set_ylabel(\"Total Execution Time (seconds)\", fontsize=12)\n",
        "ax.set_xlabel(\"Video\", fontsize=12)\n",
        "ax.set_title(\"Presigned URL vs Direct S3 Download Performance\\n(chunk_duration=10s)\", fontsize=14, fontweight=\"bold\")\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels([f\"{r['filename'][:20]}...\\n({r['duration']}s)\" for _, r in comparison_df.iterrows()], fontsize=9)\n",
        "ax.legend(loc=\"upper left\")\n",
        "ax.grid(axis=\"y\", alpha=0.3)\n",
        "\n",
        "# Add value labels\n",
        "for bar in bars1:\n",
        "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1,\n",
        "            f\"{bar.get_height():.2f}s\", ha=\"center\", va=\"bottom\", fontsize=9)\n",
        "for bar in bars2:\n",
        "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1,\n",
        "            f\"{bar.get_height():.2f}s\", ha=\"center\", va=\"bottom\", fontsize=9)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"assets/presigned_vs_direct_s3.png\", dpi=150, bbox_inches=\"tight\")\n",
        "plt.show()\n",
        "\n",
        "# Summary\n",
        "print(\"\\n\" + \"=\" * 60)\n",
        "print(\"SUMMARY: Presigned URL vs Direct S3 Download\")\n",
        "print(\"=\" * 60)\n",
        "for _, row in comparison_df.iterrows():\n",
        "    winner = \"Presigned URL\" if row[\"presigned_mean\"] < row[\"direct_s3_mean\"] else \"Direct S3\"\n",
        "    diff = abs(row[\"presigned_mean\"] - row[\"direct_s3_mean\"])\n",
        "    print(f\"{row['filename'][:30]}: {winner} wins by {diff:.2f}s\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "✓ Cleaned up /tmp/living_room_home_6158703_11.mp4\n"
          ]
        }
      ],
      "source": [
        "# Cleanup\n",
        "if os.path.exists(local_video_path):\n",
        "    os.unlink(local_video_path)\n",
        "    print(f\"✓ Cleaned up {local_video_path}\")\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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