{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Analyze Benchmarks\n",
    "\n",
    "This notebook analyzes generated benchmarks:\n",
    "- Load and inspect benchmark CSV and ground truth JSON\n",
    "- Visualize statistics\n",
    "- Validate ground truth consistency\n",
    "\n",
    "**Prerequisites**: Benchmarks created from notebook 02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "STRATEGY = 'poly_seq'  # mono_seq, mono_rand, poly_seq, poly_int, poly_rand\n",
    "SIZE = 'small'  # small or large\n",
    "SPLIT = 'train'  # train, test, or validation\n",
    "\n",
    "BENCHMARK_PATH = f'../data/benchmarks/{STRATEGY}/{SIZE}'\n",
    "CSV_FILE = f'{BENCHMARK_PATH}/{SPLIT}.csv'\n",
    "JSON_DIR = f'{BENCHMARK_PATH}/ground_truth_json/{SPLIT}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load CSV Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset using HuggingFace datasets library\n",
    "dataset = load_dataset('csv', data_files=CSV_FILE)['train']\n",
    "\n",
    "print(f\"Loaded {len(dataset)} rows from {CSV_FILE}\")\n",
    "print(f\"\\nColumns: {dataset.column_names}\")\n",
    "print(\"\\nFirst 5 rows:\")\n",
    "# Display first 5 rows\n",
    "for i in range(min(5, len(dataset))):\n",
    "    print(dataset[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count unique parent documents\n",
    "unique_parents = set(dataset['parent_doc_name'])\n",
    "num_parent_docs = len(unique_parents)\n",
    "total_pages = len(dataset)\n",
    "unique_doc_types = set(dataset['doc_type'])\n",
    "num_doc_types = len(unique_doc_types)\n",
    "\n",
    "print(\"📊 Benchmark Statistics\")\n",
    "print(\"=\"*50)\n",
    "print(f\"Strategy: {STRATEGY}\")\n",
    "print(f\"Size: {SIZE}\")\n",
    "print(f\"Split: {SPLIT}\")\n",
    "print(f\"\\nTotal parent documents: {num_parent_docs}\")\n",
    "print(f\"Total pages: {total_pages}\")\n",
    "print(f\"Unique document types: {num_doc_types}\")\n",
    "print(f\"Average pages per document: {total_pages / num_parent_docs:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pages per Parent Document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by parent_doc_name and count pages\n",
    "\n",
    "pages_per_doc = Counter(dataset['parent_doc_name'])\n",
    "pages_counts = list(pages_per_doc.values())\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(pages_counts, bins=30, edgecolor='black')\n",
    "plt.xlabel('Total Pages')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title(f'Distribution of Pages per Parent Document ({STRATEGY}/{SIZE}/{SPLIT})')\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nPages per document statistics:\")\n",
    "\n",
    "print(f\"count    {len(pages_counts)}\")\n",
    "print(f\"mean     {np.mean(pages_counts):.6f}\")\n",
    "print(f\"std      {np.std(pages_counts):.6f}\")\n",
    "print(f\"min      {np.min(pages_counts):.6f}\")\n",
    "print(f\"25%      {np.percentile(pages_counts, 25):.6f}\")\n",
    "print(f\"50%      {np.percentile(pages_counts, 50):.6f}\")\n",
    "print(f\"75%      {np.percentile(pages_counts, 75):.6f}\")\n",
    "print(f\"max      {np.max(pages_counts):.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Document Type Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count document types\n",
    "type_counts = Counter(dataset['doc_type'])\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(type_counts.keys(), type_counts.values(), edgecolor='black')\n",
    "plt.xlabel('Document Type')\n",
    "plt.ylabel('Number of Pages')\n",
    "plt.title(f'Document Type Distribution ({STRATEGY}/{SIZE}/{SPLIT})')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nDocument type page counts:\")\n",
    "for doc_type, count in sorted(type_counts.items(), key=lambda x: x[1], reverse=True):\n",
    "    print(f\"  {doc_type}: {count} pages\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect Sample Parent Document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get first parent document\n",
    "sample_parent = dataset['parent_doc_name'][0]\n",
    "# Filter dataset for this parent\n",
    "sample_indices = [i for i, parent in enumerate(dataset['parent_doc_name']) if parent == sample_parent]\n",
    "sample_data = dataset.select(sample_indices)\n",
    "\n",
    "print(f\"Parent Document: {sample_parent}\")\n",
    "print(f\"Total Pages: {len(sample_data)}\")\n",
    "print(\"\\nSubdocuments (by group_id):\")\n",
    "unique_groups = set(sample_data['group_id'])\n",
    "for group_id in sorted(unique_groups):\n",
    "    group_indices = [i for i, g in enumerate(sample_data['group_id']) if g == group_id]\n",
    "    group_data = sample_data.select(group_indices)\n",
    "    doc_type = group_data['doc_type'][0]\n",
    "    original_doc = group_data['original_doc_name'][0]\n",
    "    pages = group_data['page']\n",
    "    print(f\"  {group_id}: {doc_type}/{original_doc} - Pages: {pages[:5]}{'...' if len(pages) > 5 else ''}\")\n",
    "\n",
    "print(\"\\nFirst 10 pages:\")\n",
    "for i in range(min(10, len(sample_data))):\n",
    "    row = sample_data[i]\n",
    "    print(f\"  Page {row['page']}: {row['doc_type']}/{row['original_doc_name']} (group: {row['group_id']}, ordinal: {row['local_doc_id_page_ordinal']})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View Sample Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display all pages vertically (scrollable)\n",
    "for i in range(len(sample_data)):\n",
    "    row = sample_data[i]\n",
    "    img_path = f\"../{row['image_path']}\"\n",
    "    \n",
    "    if os.path.exists(img_path):\n",
    "        img = Image.open(img_path)\n",
    "        \n",
    "        plt.figure(figsize=(8, 10))\n",
    "        plt.imshow(img)\n",
    "        plt.title(f\"Page {row['page']} | {row['doc_type']} | Group: {row['group_id']}\", fontsize=12)\n",
    "        plt.axis('off')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        \n",
    "        print(f\"Page {row['page']}: {row['original_doc_name']}\")\n",
    "        print(f\"Group: {row['group_id']} | Ordinal: {row['local_doc_id_page_ordinal']}\")\n",
    "        print(\"-\" * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Inspect Ground Truth JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load ground truth JSON for sample parent document\n",
    "json_file = f'{JSON_DIR}/{sample_parent}.json'\n",
    "\n",
    "with open(json_file, 'r') as f:\n",
    "    gt_json = json.load(f)\n",
    "\n",
    "print(f\"Ground Truth JSON for: {gt_json['doc_id']}\")\n",
    "print(f\"Total Pages: {gt_json['total_pages']}\")\n",
    "print(f\"Number of Subdocuments: {len(gt_json['subdocuments'])}\")\n",
    "print(f\"\\nFull JSON structure:\")\n",
    "print(json.dumps(gt_json, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\n📊 Final Summary\")\n",
    "print(\"=\"*50)\n",
    "print(f\"Strategy: {STRATEGY}\")\n",
    "print(f\"Size: {SIZE}\")\n",
    "print(f\"Split: {SPLIT}\")\n",
    "print(f\"\\nParent documents: {num_parent_docs}\")\n",
    "print(f\"Total pages: {total_pages}\")\n",
    "print(f\"Unique document types: {num_doc_types}\")\n",
    "print(f\"\\nAverage pages per document: {total_pages / num_parent_docs:.2f}\")"
   ]
  }
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