{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Create Benchmarks from Assets\n",
    "\n",
    "This notebook generates document splitting benchmarks using all strategies.\n",
    "\n",
    "**Prerequisites**: Assets created from notebook 01\n",
    "\n",
    "**Output**: Benchmark datasets with ground truth for train/test/validation splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../src/benchmarks')\n",
    "\n",
    "from services.asset_loader import AssetLoader\n",
    "from services.split_manager import SplitManager\n",
    "from services.benchmark_generator import BenchmarkGenerator\n",
    "from services.benchmark_writer import BenchmarkWriter\n",
    "from services.shuffle_strategies import get_strategy, STRATEGIES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ASSETS_PATH = '../data/assets'\n",
    "OUTPUT_PATH = '../data/benchmarks'\n",
    "SPLIT_MAPPING_PATH = '../data/metadata/split_mapping.json'\n",
    "\n",
    "# Number of spliced documents per split\n",
    "NUM_TRAIN = 800\n",
    "NUM_TEST = 200\n",
    "NUM_VAL = 500\n",
    "\n",
    "# Size: small (5-20 pages) or large (20-500 pages)\n",
    "SIZE = 'small'\n",
    "MIN_PAGES = 5 if SIZE == 'small' else 20\n",
    "MAX_PAGES = 20 if SIZE == 'small' else 500\n",
    "\n",
    "RANDOM_SEED = 42\n",
    "\n",
    "print(f\"Configuration:\")\n",
    "print(f\"  Size: {SIZE} ({MIN_PAGES}-{MAX_PAGES} pages)\")\n",
    "print(f\"  Train: {NUM_TRAIN}, Test: {NUM_TEST}, Val: {NUM_VAL}\")\n",
    "print(f\"  Strategies: {list(STRATEGIES.keys())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = AssetLoader(assets_path=ASSETS_PATH)\n",
    "documents_by_type = loader.load_all_documents()\n",
    "\n",
    "print(f\"Loaded {loader.total_documents} documents across {len(documents_by_type)} types\")\n",
    "for doc_type, docs in documents_by_type.items():\n",
    "    print(f\"  {doc_type}: {len(docs)} documents\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Train/Test/Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_manager = SplitManager(random_seed=RANDOM_SEED)\n",
    "splits = split_manager.create_split(documents_by_type)\n",
    "\n",
    "# Save split mapping\n",
    "split_manager.save_split(splits, SPLIT_MAPPING_PATH)\n",
    "\n",
    "print(f\"\\nSplit statistics:\")\n",
    "for split_name in ['train', 'test', 'validation']:\n",
    "    total = sum(len(docs) for docs in splits[split_name].values())\n",
    "    print(f\"  {split_name}: {total} documents\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate All Benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_strategies = ['mono_seq', 'mono_rand', 'poly_seq', 'poly_int', 'poly_rand']\n",
    "\n",
    "for strategy_name in all_strategies:\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(f\"Processing strategy: {strategy_name}\")\n",
    "    print(f\"{'='*60}\")\n",
    "    \n",
    "    # Initialize strategy\n",
    "    strategy = get_strategy(\n",
    "        strategy_name,\n",
    "        min_pages=MIN_PAGES,\n",
    "        max_pages=MAX_PAGES,\n",
    "        random_seed=RANDOM_SEED\n",
    "    )\n",
    "    \n",
    "    generator = BenchmarkGenerator(strategy=strategy)\n",
    "    output_path = f'{OUTPUT_PATH}/{strategy_name}/{SIZE}'\n",
    "    writer = BenchmarkWriter(output_base_path=output_path, assets_path=ASSETS_PATH)\n",
    "    \n",
    "    # Generate train\n",
    "    print(f\"Generating train...\")\n",
    "    train_benchmark = generator.generate_for_split(\n",
    "        documents_by_type=documents_by_type,\n",
    "        doc_names_for_split=splits['train'],\n",
    "        num_spliced_docs=NUM_TRAIN,\n",
    "        split_name='train',\n",
    "        benchmark_name=strategy_name\n",
    "    )\n",
    "    writer.save_benchmark_set(train_benchmark, 'train')\n",
    "    \n",
    "    # Generate test\n",
    "    print(f\"Generating test...\")\n",
    "    test_benchmark = generator.generate_for_split(\n",
    "        documents_by_type=documents_by_type,\n",
    "        doc_names_for_split=splits['test'],\n",
    "        num_spliced_docs=NUM_TEST,\n",
    "        split_name='test',\n",
    "        benchmark_name=strategy_name\n",
    "    )\n",
    "    writer.save_benchmark_set(test_benchmark, 'test')\n",
    "    \n",
    "    # Generate validation\n",
    "    print(f\"Generating validation...\")\n",
    "    val_benchmark = generator.generate_for_split(\n",
    "        documents_by_type=documents_by_type,\n",
    "        doc_names_for_split=splits['validation'],\n",
    "        num_spliced_docs=NUM_VAL,\n",
    "        split_name='validation',\n",
    "        benchmark_name=strategy_name\n",
    "    )\n",
    "    writer.save_benchmark_set(val_benchmark, 'validation')\n",
    "    \n",
    "    print(f\"✅ Completed {strategy_name}\")\n",
    "\n",
    "print(f\"\\n{'='*60}\")\n",
    "print(f\"✅ All benchmarks generated!\")\n",
    "print(f\"{'='*60}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"\\n✅ Benchmark creation complete!\")\n",
    "print(f\"\\nOutput structure:\")\n",
    "for strategy_name in all_strategies:\n",
    "    print(f\"\\n{OUTPUT_PATH}/{strategy_name}/{SIZE}/\")\n",
    "    print(f\"  ├── train.csv ({NUM_TRAIN} documents)\")\n",
    "    print(f\"  ├── test.csv ({NUM_TEST} documents)\")\n",
    "    print(f\"  ├── validation.csv ({NUM_VAL} documents)\")\n",
    "    print(f\"  └── ground_truth_json/\")\n",
    "    print(f\"      ├── train/ (JSON per document)\")\n",
    "    print(f\"      ├── test/ (JSON per document)\")\n",
    "    print(f\"      └── validation/ (JSON per document)\")\n",
    "print(f\"\\nSplit mapping: {SPLIT_MAPPING_PATH}\")"
   ]
  }
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