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============================================================\n", "\n", "\n", "# ============================================================\n", "# 1. INSTALL\n", "# ============================================================\n", "\n", "!pip install -q -U huggingface_hub pyarrow pandas pyahocorasick tqdm\n", "\n", "\n", "# ============================================================\n", "# 2. IMPORTS AND AUTHENTICATION\n", "# ============================================================\n", "\n", "from huggingface_hub import (\n", " hf_hub_download,\n", " list_repo_files,\n", " HfApi,\n", " CommitOperationAdd,\n", " login\n", ")\n", "\n", "from google.colab import userdata\n", "\n", "import json\n", "import os\n", "import re\n", "import gc\n", "import shutil\n", "\n", "from collections import defaultdict\n", "from pathlib import Path\n", "\n", "import pandas as pd\n", "import ahocorasick\n", "\n", "from tqdm.auto import tqdm\n", "\n", "\n", "HF_TOKEN = userdata.get(\"HF_TOKEN\")\n", "\n", "if not HF_TOKEN:\n", " raise ValueError(\n", " \"HF_TOKEN not found in Google Colab secrets.\"\n", " )\n", "\n", "login(token=HF_TOKEN)\n", "\n", "api = HfApi(token=HF_TOKEN)\n", "\n", "print(\"Authenticated with Hugging Face.\")\n", "\n", "\n", "# ============================================================\n", "# 3. REPOSITORY CONFIGURATION & WORKSPACE PATHS\n", "# ============================================================\n", "\n", "SOURCE_REPO = \"lodestones/e621-captions\"\n", "REPO_TYPE = \"dataset\"\n", "\n", "# Workspace paths\n", "WORK_DIR = Path(\"/content/e621_character_pipeline\")\n", "DOWNLOAD_DIR = WORK_DIR / \"downloads\"\n", "TEMP_SECTIONS_DIR = WORK_DIR / \"temporary_sections\"\n", "CHECKPOINT_FILE = WORK_DIR / \"processed_files.json\"\n", "\n", "DOWNLOAD_DIR.mkdir(parents=True, exist_ok=True)\n", "TEMP_SECTIONS_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "# Load checkpoint progress of completed files to allow skipping\n", "processed_files = set()\n", "if CHECKPOINT_FILE.exists():\n", " try:\n", " with open(CHECKPOINT_FILE, \"r\", encoding=\"utf-8\") as cp_f:\n", " processed_files = set(json.load(cp_f))\n", " print(f\"Loaded progress checkpoint. Skipping {len(processed_files)} already processed file(s).\")\n", " except Exception as e:\n", " print(\"Warning: could not read checkpoint file, starting fresh.\", e)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r4jY2PCCw1yi", "outputId": "d1fc566c-ef10-4e23-d6c4-91452309b5fd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Authenticated with Hugging Face.\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wja4atRuwKG0", "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "50186d7c8fc24065a4494379e7e8cbf4", "0e88eac3ba8a4a00b47fd1644648177b", "89c796d67e5347e5845a7640166575ac", "0448c70d28b84abd9c7e5d57c6ac07ac", "b2f643f53dde4c17b511c22a518da816", "9752b35cf1e44ce98dd6d9bea0f82192", "4d874ef4946b427ca75990849dbb8c6a", "c2c1228c566540b38aeb51499ba7ce0e", "2886c01b50354b539666631925013811", "f81ce8ec9173488d9cd58b1f7b8768b7", 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"3fb6a23bf97c4dbbb77d7f1eacc048b9", "e55b15affd67418b8e2947983645b4b1", "4810b0428dd840e480a83cd5dab40bbe", "d26ac10ecb444c4884850bd3080b15d0", "07bcb140643d4871b263ac888988eee0", "f11a06ccf86e4427bbeb1115d0a82aab" ] }, "outputId": "6d2adda9-2250-4dc2-d6fb-8612e3e16de6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "google-colab 1.0.0 requires pandas==2.2.3, but you have pandas 3.1.1 which is incompatible.\n", "dask-cudf-cu13 26.6.0 requires pandas<2.4.0,>=2.0, but you have pandas 3.1.1 which is incompatible.\n", "cudf-cu13 26.6.0 requires pandas<2.4.0,>=2.0, but you have pandas 3.1.1 which is incompatible.\n", "cudf-cu13 26.6.0 requires pyarrow<24,>=19.0.0, but you have pyarrow 26.0.1 which is incompatible.\n", "datasets 4.8.5 requires huggingface-hub<2.0,>=0.25.0, but you have huggingface-hub 2.4.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0m" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.13/dist-packages/pandas/core/arrays/masked.py:63: UserWarning: Pandas requires version '1.5.0' or newer of 'bottleneck' (version '1.4.2' currently installed).\n", " from pandas.core import (\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Authenticated with Hugging Face.\n", "\n", "Character count: 165\n", "\n", "============================================\n", "SEARCH DIAGNOSTICS\n", "============================================\n", "Characters: 165\n", "Searchable terms: 304\n", "Generic terms excluded: 29\n", "Shared terms excluded: 9\n", "\n", "Generic exclusions:\n", "badger, bird, blue, bunny, cat, classic, echidna, fox, girl, in, jungle, lemur, master, mongoose, nurse, pokemon, princess, rabbit, raccoon, sheep, silver, squirrel, swallow, tenrec, the, tiger, tiny, trainer, wolf\n", "\n", "Shared exclusions:\n", "amelia, babs, blaze, carol, kong, may, mina, mrs., rover\n", "\n", "Example character terms:\n", "tikal_the_echidna => ['tikal', 'tikal the echidna']\n", "cream_the_rabbit => ['cream', 'cream the rabbit']\n", "vanilla_the_rabbit => ['vanilla', 'vanilla the rabbit']\n", "\n", "Automaton built: 304 terms\n", "\n", "============================================\n", "MATCHING TESTS\n", "============================================\n", "PASS | tikal_the_echidna | Expected: tikal_the_echidna | Found: ['tikal_the_echidna']\n", "CHECK | knuckles_the_echidna | Expected: tikal_the_echidna | Found: []\n", "CHECK | an_echidna_standing_in_a_field | Expected: tikal_the_echidna | Found: []\n", "PASS | tikal is standing in the forest | Expected: tikal_the_echidna | Found: ['tikal_the_echidna']\n", "PASS | cream_the_rabbit | Expected: cream_the_rabbit | Found: ['cream_the_rabbit']\n", "CHECK | vanilla_the_rabbit | Expected: cream_the_rabbit | Found: ['vanilla_the_rabbit']\n", "\n", "Fetching source repository file list...\n", "Found 215 source JSONL files.\n", "\n", "============================================\n", "PROCESSING SOURCE DATA\n", "============================================\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Source files: 0%| | 0/215 [00:00 MAX_COMPONENT_OWNERS:\n", "\n", " excluded_shared.add(word)\n", "\n", " continue\n", "\n", " character_terms[char].add(word)\n", "\n", " all_terms.add(word)\n", "\n", "\n", "# ============================================================\n", "# 10. SEARCH DIAGNOSTICS\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"SEARCH DIAGNOSTICS\")\n", "print(\"============================================\")\n", "\n", "print(\"Characters:\", len(character_search))\n", "\n", "print(\"Searchable terms:\", len(all_terms))\n", "\n", "print(\"Generic terms excluded:\", len(excluded_generic))\n", "\n", "print(\"Shared terms excluded:\", len(excluded_shared))\n", "\n", "print(\"\\nGeneric exclusions:\")\n", "\n", "print(\", \".join(sorted(excluded_generic)))\n", "\n", "print(\"\\nShared exclusions:\")\n", "\n", "print(\", \".join(sorted(excluded_shared)))\n", "\n", "print(\"\\nExample character terms:\")\n", "\n", "for example in [\n", " \"tikal_the_echidna\",\n", " \"cream_the_rabbit\",\n", " \"vanilla_the_rabbit\",\n", "]:\n", "\n", " print(example, \"=>\", sorted(character_terms[example]))\n", "\n", "\n", "# ============================================================\n", "# 11. BUILD AHO-CORASICK AUTOMATON\n", "# ============================================================\n", "\n", "term_to_chars = defaultdict(list)\n", "\n", "for char, terms in character_terms.items():\n", "\n", " for term in terms:\n", "\n", " term_to_chars[term].append(char)\n", "\n", "\n", "A = ahocorasick.Automaton()\n", "\n", "for term in all_terms:\n", "\n", " A.add_word(term, term)\n", "\n", "A.make_automaton()\n", "\n", "print(\n", " \"\\nAutomaton built:\",\n", " len(all_terms),\n", " \"terms\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# 12. WORD BOUNDARY CHECKING\n", "# ============================================================\n", "\n", "def is_word_boundary(text, start, end):\n", "\n", " left_ok = (\n", " start == 0 or\n", " not (\n", " text[start - 1].isalnum() or\n", " text[start - 1] == '_'\n", " )\n", " )\n", "\n", " right_ok = (\n", " end == len(text) or\n", " not (\n", " text[end].isalnum() or\n", " text[end] == '_'\n", " )\n", " )\n", "\n", " return left_ok and right_ok\n", "\n", "\n", "# ============================================================\n", "# 13. CHARACTER MATCHING\n", "# ============================================================\n", "\n", "def find_matched_chars(full_text):\n", "\n", " if not full_text:\n", "\n", " return []\n", "\n", " text = normalize_search_text(full_text)\n", "\n", " matched = set()\n", "\n", " for end_idx, term in A.iter(text):\n", "\n", " start = end_idx - len(term) + 1\n", "\n", " end = end_idx + 1\n", "\n", " if not is_word_boundary(text, start, end):\n", "\n", " continue\n", "\n", " matched.update(term_to_chars[term])\n", "\n", " return list(matched)\n", "\n", "\n", "# ============================================================\n", "# 14. TEST THE MATCHING SYSTEM\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"MATCHING TESTS\")\n", "print(\"============================================\")\n", "\n", "TEST_CASES = [\n", "\n", " (\"tikal_the_echidna\", \"tikal_the_echidna\"),\n", "\n", " (\"knuckles_the_echidna\", \"tikal_the_echidna\"),\n", "\n", " (\"an_echidna_standing_in_a_field\", \"tikal_the_echidna\"),\n", "\n", " (\"tikal is standing in the forest\", \"tikal_the_echidna\"),\n", "\n", " (\"cream_the_rabbit\", \"cream_the_rabbit\"),\n", "\n", " (\"vanilla_the_rabbit\", \"cream_the_rabbit\"),\n", "\n", "]\n", "\n", "for test_text, expected_char in TEST_CASES:\n", "\n", " result = find_matched_chars(test_text)\n", "\n", " success = expected_char in result\n", "\n", " print(\n", " f\"{'PASS' if success else 'CHECK'} | \"\n", " f\"{test_text} | \"\n", " f\"Expected: {expected_char} | \"\n", " f\"Found: {result}\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 15. FETCH SOURCE FILE LIST\n", "# ============================================================\n", "\n", "print(\"\\nFetching source repository file list...\")\n", "\n", "all_files = list_repo_files(\n", " SOURCE_REPO,\n", " repo_type=REPO_TYPE,\n", " token=HF_TOKEN\n", ")\n", "\n", "jsonl_files = sorted([\n", " f for f in all_files\n", " if f.endswith(\"_grouped.jsonl\")\n", "])\n", "\n", "if not jsonl_files:\n", "\n", " raise RuntimeError(\n", " \"No *_grouped.jsonl files found.\"\n", " )\n", "\n", "print(\n", " f\"Found {len(jsonl_files)} source JSONL files.\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# 16. TAG FORMATTING\n", "# ============================================================\n", "\n", "def format_tags(tag_string):\n", "\n", " if not tag_string:\n", "\n", " return \"\"\n", "\n", " tags = re.split(\n", " r'[,\\s]+',\n", " tag_string.strip()\n", " )\n", "\n", " tags = [\n", " t.strip()\n", " for t in tags\n", " if t.strip()\n", " ]\n", "\n", " return \" , \".join(tags)\n", "\n", "\n", "# ============================================================\n", "# 17. SAVE TEMPORARY PARQUET SECTIONS\n", "# ============================================================\n", "\n", "def save_section(rows, section_idx):\n", "\n", " if not rows:\n", "\n", " return None\n", "\n", " df = pd.DataFrame(rows)\n", "\n", " path = (\n", " TEMP_SECTIONS_DIR /\n", " f\"section_{section_idx:04d}.parquet\"\n", " )\n", "\n", " df.to_parquet(\n", " path,\n", " index=False,\n", " engine=\"pyarrow\"\n", " )\n", "\n", " print(\n", " f\"Saved temporary section {section_idx:04d}: \"\n", " f\"{len(rows):,} rows\"\n", " )\n", "\n", " del df\n", "\n", " return path\n", "\n", "\n", "# ============================================================\n", "# 18. PROCESS ALL SOURCE JSONL FILES\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"PROCESSING SOURCE DATA\")\n", "print(\"============================================\")\n", "\n", "collected_rows = []\n", "\n", "section_idx = 0\n", "\n", "total_matches = 0\n", "\n", "total_items = 0\n", "\n", "\n", "for idx, filename in enumerate(\n", " tqdm(jsonl_files, desc=\"Source files\"),\n", " 1\n", "):\n", "\n", " print(\n", " f\"\\nDownloading {idx}/{len(jsonl_files)}: \"\n", " f\"{filename}\"\n", " )\n", "\n", " local_path = hf_hub_download(\n", " repo_id=SOURCE_REPO,\n", " filename=filename,\n", " repo_type=REPO_TYPE,\n", " token=HF_TOKEN,\n", " local_dir=str(DOWNLOAD_DIR)\n", " )\n", "\n", " file_matches = 0\n", "\n", " file_items = 0\n", "\n", " with open(\n", " local_path,\n", " \"r\",\n", " encoding=\"utf-8\"\n", " ) as f:\n", "\n", " for line in f:\n", "\n", " line = line.strip()\n", "\n", " if not line:\n", "\n", " continue\n", "\n", " try:\n", "\n", " item = json.loads(line)\n", "\n", " except json.JSONDecodeError:\n", "\n", " continue\n", "\n", " total_items += 1\n", "\n", " file_items += 1\n", "\n", " # ------------------------------------------------\n", " # Extract searchable text\n", " # ------------------------------------------------\n", "\n", " raw_tags = (\n", " item.get(\"tags\") or\n", " item.get(\"tag_string\") or\n", " \"\"\n", " )\n", "\n", " parts = [raw_tags]\n", "\n", " for field in NL_FIELDS:\n", "\n", " val = item.get(field)\n", "\n", " if (\n", " isinstance(val, str) and\n", " val.strip()\n", " ):\n", "\n", " parts.append(val)\n", "\n", " full_text = \" \".join(parts)\n", "\n", " # ------------------------------------------------\n", " # Match characters\n", " # ------------------------------------------------\n", "\n", " matched_chars = find_matched_chars(\n", " full_text\n", " )\n", "\n", " if not matched_chars:\n", "\n", " continue\n", "\n", " # ------------------------------------------------\n", " # Rating\n", " # ------------------------------------------------\n", "\n", " rating = str(\n", " item.get(\"rating\", \"unknown\")\n", " ).lower()\n", "\n", " formatted_tags = format_tags(\n", " raw_tags\n", " )\n", "\n", " # ------------------------------------------------\n", " # Description\n", " # ------------------------------------------------\n", "\n", " naturals = []\n", "\n", " for field in NL_FIELDS:\n", "\n", " value = item.get(field)\n", "\n", " if (\n", " isinstance(value, str) and\n", " value.strip()\n", " ):\n", "\n", " naturals.append(\n", " value.strip()\n", " )\n", "\n", " if naturals:\n", "\n", " desc_parts = [\n", "\n", " f\"{n} + {formatted_tags}\"\n", "\n", " for n in naturals\n", "\n", " ]\n", "\n", " description = \"|\".join(\n", " desc_parts\n", " )\n", "\n", " else:\n", "\n", " description = formatted_tags\n", "\n", " # ------------------------------------------------\n", " # Add one row per character\n", " # ------------------------------------------------\n", "\n", " for char in matched_chars:\n", "\n", " collected_rows.append({\n", "\n", " \"character\": char,\n", "\n", " \"rating\": rating,\n", "\n", " \"description\": description\n", "\n", " })\n", "\n", " file_matches += 1\n", "\n", " total_matches += 1\n", "\n", " # ------------------------------------------------\n", " # Flush temporary section\n", " # ------------------------------------------------\n", "\n", " if len(collected_rows) >= SECTION_SIZE:\n", "\n", " save_section(\n", " collected_rows,\n", " section_idx\n", " )\n", "\n", " section_idx += 1\n", "\n", " collected_rows = []\n", "\n", " gc.collect()\n", "\n", " # --------------------------------------------------------\n", " # Delete local downloaded source file.\n", " # --------------------------------------------------------\n", "\n", " try:\n", "\n", " os.remove(local_path)\n", "\n", " except Exception as e:\n", "\n", " print(\n", " \"Download cleanup warning:\",\n", " e\n", " )\n", "\n", " print(\n", " f\"Finished {filename}: \"\n", " f\"{file_items:,} items, \"\n", " f\"{file_matches:,} matches\"\n", " )\n", "\n", " gc.collect()\n", "\n", "\n", "# ============================================================\n", "# 19. FLUSH FINAL TEMPORARY SECTION\n", "# ============================================================\n", "\n", "if collected_rows:\n", "\n", " save_section(\n", " collected_rows,\n", " section_idx\n", " )\n", "\n", " section_idx += 1\n", "\n", " collected_rows = []\n", "\n", " gc.collect()\n", "\n", "\n", "print(\"\\n============================================\")\n", "print(\"SOURCE PROCESSING COMPLETE\")\n", "print(\"============================================\")\n", "\n", "print(\"Items processed:\", f\"{total_items:,}\")\n", "\n", "print(\"Matching rows:\", f\"{total_matches:,}\")\n", "\n", "print(\"Temporary sections:\", section_idx)\n", "\n", "\n" ] }, { "cell_type": "code", "source": [ "\n", "# ============================================================\n", "# 20. DISK-BASED MERGING AND SPLITTING WITH DUCKDB\n", "# ============================================================\n", "\n", "!pip install -q duckdb\n", "\n", "import duckdb\n", "import shutil\n", "import gc\n", "import os\n", "import re\n", "from pathlib import Path\n", "\n", "from huggingface_hub import (\n", " HfApi,\n", " CommitOperationAdd\n", ")\n", "\n", "print(\"\\n============================================\")\n", "print(\"DISK-BASED MERGING WITH DUCKDB\")\n", "print(\"============================================\")\n", "\n", "\n", "# ============================================================\n", "# CONFIGURATION\n", "# ============================================================\n", "\n", "# DuckDB RAM limit adjusted to utilize more Colab system RAM.\n", "DUCKDB_MEMORY_LIMIT = \"10GB\"\n", "\n", "# Temporary disk directory for DuckDB.\n", "DUCKDB_TEMP_DIR = WORK_DIR / \"duckdb_temp\"\n", "\n", "# Intermediate partition directory.\n", "PARTITIONED_DIR = WORK_DIR / \"partitioned\"\n", "\n", "# Final upload directory.\n", "UPLOAD_DIR = WORK_DIR / \"upload\"\n", "\n", "FINAL_SECTIONS_DIR = UPLOAD_DIR / \"sections\"\n", "\n", "DUCKDB_TEMP_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "# Remove old partitioned output.\n", "if PARTITIONED_DIR.exists():\n", " shutil.rmtree(PARTITIONED_DIR)\n", "\n", "PARTITIONED_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "# Clear previous final outputs to avoid accidentally\n", "# uploading stale files from an earlier run.\n", "if FINAL_SECTIONS_DIR.exists():\n", " shutil.rmtree(FINAL_SECTIONS_DIR)\n", "\n", "FINAL_SECTIONS_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "\n", "# ============================================================\n", "# 21. FIND TEMPORARY PARQUETS\n", "# ============================================================\n", "\n", "section_files = sorted(\n", " TEMP_SECTIONS_DIR.glob(\"section_*.parquet\")\n", ")\n", "\n", "if not section_files:\n", " raise RuntimeError(\n", " \"No temporary Parquets found!\"\n", " )\n", "\n", "print(\n", " f\"Found {len(section_files)} temporary Parquets.\"\n", ")\n", "\n", "print(\n", " \"Total temporary size:\",\n", " f\"{sum(p.stat().st_size for p in section_files) / 1024**3:.2f} GB\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# 22. START DUCKDB\n", "# ============================================================\n", "\n", "db = duckdb.connect(\n", " database=str(WORK_DIR / \"processing.duckdb\")\n", ")\n", "\n", "db.execute(\n", " f\"SET memory_limit = '{DUCKDB_MEMORY_LIMIT}'\"\n", ")\n", "\n", "db.execute(\n", " f\"SET temp_directory = '{DUCKDB_TEMP_DIR.as_posix()}'\"\n", ")\n", "\n", "db.execute(\n", " \"SET preserve_insertion_order = false\"\n", ")\n", "\n", "# Reduced threads from 4 to 2 to minimize active memory overhead during concurrent writes\n", "db.execute(\n", " \"SET threads = 2\"\n", ")\n", "\n", "print(\"\\nDuckDB initialized.\")\n", "\n", "print(\"RAM limit:\", DUCKDB_MEMORY_LIMIT)\n", "\n", "print(\"Temporary disk:\", DUCKDB_TEMP_DIR)\n", "\n", "\n", "# ============================================================\n", "# 23. SQL PATH HELPERS\n", "# ============================================================\n", "\n", "def sql_string(value):\n", " \"\"\"\n", " Safely quote a string as a SQL literal.\n", " \"\"\"\n", "\n", " return \"'\" + str(value).replace(\"'\", \"''\") + \"'\"\n", "\n", "\n", "def safe_name(s):\n", "\n", " return re.sub(\n", " r'[^\\w\\-.]',\n", " '_',\n", " str(s)\n", " )\n", "\n", "\n", "# ============================================================\n", "# 24. PARTITION ALL DATA ON DISK\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"PARTITIONING BY CHARACTER AND RATING\")\n", "print(\"============================================\")\n", "\n", "# DuckDB reads all section files directly.\n", "# It does not concatenate them into Pandas.\n", "\n", "input_glob = (\n", " TEMP_SECTIONS_DIR / \"section_*.parquet\"\n", ").as_posix()\n", "\n", "partition_output = PARTITIONED_DIR.as_posix()\n", "\n", "partition_query = f\"\"\"\n", "COPY (\n", " SELECT\n", " character,\n", " rating,\n", " description\n", " FROM read_parquet({sql_string(input_glob)})\n", ")\n", "TO {sql_string(partition_output)}\n", "(\n", " FORMAT PARQUET,\n", " PARTITION_BY (character, rating),\n", " COMPRESSION ZSTD\n", ")\n", "\"\"\"\n", "\n", "print(\"Starting disk-based partitioning...\")\n", "\n", "db.execute(partition_query)\n", "\n", "print(\"Partitioning completed.\")\n", "\n", "gc.collect()\n", "\n", "\n", "# ============================================================\n", "# 25. FIND GENERATED PARTITIONS\n", "# ============================================================\n", "\n", "partition_files = sorted(\n", " PARTITIONED_DIR.rglob(\"*.parquet\")\n", ")\n", "\n", "if not partition_files:\n", "\n", " raise RuntimeError(\n", " \"DuckDB did not produce any partition files!\"\n", " )\n", "\n", "print(\n", " f\"\\nGenerated {len(partition_files)} partition files.\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# 26. CONVERT EACH PARTITION INTO FINAL PARQUET\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"CREATING FINAL CHARACTER PARQUETS\")\n", "print(\"============================================\")\n", "\n", "completed_files = 0\n", "\n", "total_final_bytes = 0\n", "\n", "for partition_file in tqdm(\n", " partition_files,\n", " desc=\"Creating character files\"\n", "):\n", "\n", " partition_path = partition_file.as_posix()\n", "\n", " # --------------------------------------------------------\n", " # Read character and rating from Hive partition metadata.\n", " # Only one row is read.\n", " # --------------------------------------------------------\n", "\n", " metadata_query = f\"\"\"\n", " SELECT\n", " character,\n", " rating\n", " FROM read_parquet(\n", " {sql_string(partition_path)},\n", " hive_partitioning = true\n", " )\n", " LIMIT 1\n", " \"\"\"\n", "\n", " result = db.execute(\n", " metadata_query\n", " ).fetchone()\n", "\n", " if result is None:\n", " continue\n", "\n", " char, rating = result\n", "\n", " # --------------------------------------------------------\n", " # Construct final filename.\n", " # --------------------------------------------------------\n", "\n", " char_safe = safe_name(char)\n", "\n", " rating_safe = safe_name(rating)\n", "\n", " filename = (\n", " f\"{char_safe}__{rating_safe}.parquet\"\n", " )\n", "\n", " output_path = (\n", " FINAL_SECTIONS_DIR / filename\n", " )\n", "\n", " # --------------------------------------------------------\n", " # Enumerate descriptions directly in DuckDB.\n", " #\n", " # No Pandas DataFrame is created.\n", " # DuckDB streams the result to disk.\n", " # --------------------------------------------------------\n", "\n", " conversion_query = f\"\"\"\n", " COPY (\n", " SELECT\n", " ROW_NUMBER() OVER () AS idx,\n", " description\n", " FROM read_parquet(\n", " {sql_string(partition_path)}\n", " )\n", " )\n", " TO {sql_string(output_path.as_posix())}\n", " (\n", " FORMAT PARQUET,\n", " COMPRESSION ZSTD\n", " )\n", " \"\"\"\n", "\n", " db.execute(conversion_query)\n", "\n", " completed_files += 1\n", "\n", " total_final_bytes += output_path.stat().st_size\n", "\n", " # --------------------------------------------------------\n", " # Progress\n", " # --------------------------------------------------------\n", "\n", " if completed_files % 25 == 0:\n", "\n", " print(\n", " f\"\\nCompleted {completed_files}/\"\n", " f\"{len(partition_files)} files.\"\n", " )\n", "\n", " print(\n", " \"Final output size:\",\n", " f\"{total_final_bytes / 1024**3:.2f} GB\"\n", " )\n", "\n", "\n", "print(\"\\n============================================\")\n", "print(\"FINAL PARQUETS COMPLETE\")\n", "print(\"============================================\")\n", "\n", "print(\"Files created:\", completed_files)\n", "\n", "print(\n", " \"Total output size:\",\n", " f\"{total_final_bytes / 1024**3:.2f} GB\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# 27. CLOSE DUCKDB\n", "# ============================================================\n", "\n", "db.close()\n", "\n", "gc.collect()\n", "\n", "print(\"\\nDuckDB connection closed.\")\n", "\n", "\n", "# ============================================================\n", "# 28. REMOVE INTERMEDIATE PARQUETS\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"CLEANING INTERMEDIATE FILES\")\n", "print(\"============================================\")\n", "\n", "# All final character/rating Parquets are now stored\n", "# separately in FINAL_SECTIONS_DIR.\n", "\n", "if TEMP_SECTIONS_DIR.exists():\n", "\n", " shutil.rmtree(TEMP_SECTIONS_DIR)\n", "\n", " print(\"Temporary sections removed.\")\n", "\n", "if PARTITIONED_DIR.exists():\n", "\n", " shutil.rmtree(PARTITIONED_DIR)\n", "\n", " print(\"Partitioned intermediate files removed.\")\n", "\n", "if MERGED_PATH.exists():\n", "\n", " MERGED_PATH.unlink()\n", "\n", " print(\"Merged intermediate file removed.\")\n", "\n", "if DUCKDB_TEMP_DIR.exists():\n", "\n", " shutil.rmtree(DUCKDB_TEMP_DIR)\n", "\n", " print(\"DuckDB temporary files removed.\")\n", "\n", "gc.collect()\n", "\n", "\n", "# ============================================================\n", "# 29. VERIFY FINAL OUTPUT\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"VERIFYING FINAL OUTPUT\")\n", "print(\"============================================\")\n", "\n", "upload_files = sorted(\n", " FINAL_SECTIONS_DIR.glob(\"*.parquet\")\n", ")\n", "\n", "if not upload_files:\n", "\n", " raise RuntimeError(\n", " \"No final Parquet files found.\"\n", " )\n", "\n", "total_bytes = sum(\n", " p.stat().st_size\n", " for p in upload_files\n", ")\n", "\n", "print(\"Files prepared:\", len(upload_files))\n", "\n", "print(\n", " \"Total size:\",\n", " f\"{total_bytes / 1024**3:.3f} GB\"\n", ")\n", "\n", "print(\"\\nExample files:\")\n", "\n", "for p in upload_files[:20]:\n", "\n", " print(\n", " f\"{p.name} \"\n", " f\"({p.stat().st_size / 1024**2:.2f} MB)\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 30. VERIFY DESTINATION REPOSITORY\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"CHECKING HUGGING FACE DESTINATION\")\n", "print(\"============================================\")\n", "\n", "DEST_REPO = \"codeShare/e621-captions-character-sorted\"\n", "\n", "REPO_TYPE = \"dataset\"\n", "\n", "HF_TOKEN = userdata.get(\"HF_TOKEN\")\n", "\n", "if not HF_TOKEN:\n", "\n", " raise ValueError(\n", " \"HF_TOKEN not found in Colab secrets.\"\n", " )\n", "\n", "api = HfApi(token=HF_TOKEN)\n", "\n", "repo_info = api.repo_info(\n", " repo_id=DEST_REPO,\n", " repo_type=REPO_TYPE\n", ")\n", "\n", "print(\n", " \"Destination repository:\",\n", " repo_info.id\n", ")\n", "\n", "\n", "# ============================================================\n", "# 31. PREPARE ONE COMMIT\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"PREPARING SINGLE COMMIT\")\n", "print(\"============================================\")\n", "\n", "COMMIT_MESSAGE = (\n", " \"Add rarity-filtered character caption Parquets\"\n", ")\n", "\n", "operations = []\n", "\n", "for local_file in upload_files:\n", "\n", " remote_path = (\n", " f\"sections/{local_file.name}\"\n", " )\n", "\n", " operations.append(\n", " CommitOperationAdd(\n", " path_in_repo=remote_path,\n", " path_or_fileobj=str(local_file)\n", " )\n", " )\n", "\n", "\n", "print(\"Files to upload:\", len(operations))\n", "\n", "print(\"Destination folder: sections/\")\n", "\n", "print(\"Commit message:\", COMMIT_MESSAGE)\n", "\n", "print(\n", " \"Total upload size:\",\n", " f\"{total_bytes / 1024**3:.3f} GB\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# 32. SINGLE HUGGING FACE COMMIT\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"UPLOADING ALL FILES IN ONE COMMIT\")\n", "print(\"============================================\")\n", "\n", "commit_result = api.create_commit(\n", "\n", " repo_id=DEST_REPO,\n", "\n", " repo_type=REPO_TYPE,\n", "\n", " operations=operations,\n", "\n", " commit_message=COMMIT_MESSAGE\n", "\n", ")\n", "\n", "\n", "# ============================================================\n", "# 33. COMMIT RESULT\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"COMMIT SUCCESSFUL\")\n", "print(\"============================================\")\n", "\n", "print(\"Commit URL:\")\n", "\n", "print(commit_result.commit_url)\n", "\n", "print(\"\\nCommit OID:\")\n", "\n", "print(commit_result.oid)\n", "\n", "print(\"\\nRepository:\")\n", "\n", "print(\n", " f\"https://huggingface.co/datasets/{DEST_REPO}\"\n", ")\n", "\n", "print(\"\\nOutput folder:\")\n", "\n", "print(\n", " f\"https://huggingface.co/datasets/{DEST_REPO}/tree/main/sections\"\n", ")\n", "\n", "print(\"\\nFiles committed:\", len(upload_files))\n", "\n", "print(\n", " \"Committed size:\",\n", " f\"{total_bytes / 1024**3:.3f} GB\"\n", ")\n", "\n", "\n", "# ============================================================\n", "# 34. LOCAL CLEANUP AFTER SUCCESSFUL COMMIT\n", "# ============================================================\n", "\n", "print(\"\\n============================================\")\n", "print(\"FINAL CLEANUP\")\n", "print(\"============================================\")\n", "\n", "shutil.rmtree(\n", " WORK_DIR,\n", " ignore_errors=True\n", ")\n", "\n", "print(\"Local workspace removed.\")\n", "\n", "print(\"\\n============================================\")\n", "print(\"ALL OPERATIONS COMPLETE\")\n", "print(\"============================================\")\n", "\n", "print(\"Disk-based DuckDB processing: SUCCESS\")\n", "\n", "print(\"Google Drive usage: NONE\")\n", "\n", "print(\"Hugging Face commits: 1\")\n", "\n", "print(\"Destination folder: sections/\")\n", "\n", "print(\"All final Parquets uploaded successfully.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "0ed8acfe72764f609820dfeef3399278", "fb0cf9ba6ca84ba3b87b1443f848ed3c", "e2c9f198aace4aa58836486bb0bc7dd2", "9fc97a9d4ee5475aa8cd31aa491e0823", "d0748a61b3624786820b83c45d7f4aae", "dd900d5b0fb547f89cca7a77b1cb1842", "a481ee9b33ec4681a422b2666a4c7e0e", "3b5ebff4c0c142aca75e3abe5ebf7195", "f6f90b48d3ae4c3e9a248c4679d14615", "7bfebd3684b74bdd93b086b0bd8c208b", "45339697920d4e15a72820fbdb605156", "65d6aad52b204ad791223a4023bce702", "2f9923261d004e85a932a4e69d0bc3c9", "480568b2ab9b4c39b3e3827de64d53f8", "34dd3b17e73a4238b7850d4f40f5a0fe", "28e9c76e338e4c2894fb66cdc168ef1f", "601edb4d259e4a13b30fc4c88b249479", "4fb11e94061e4773b736bdcedc957e47", "a1e83e0d174e4855949b830625e8e8ee", "07f160aae409430990da7deaa36b7d47", "94790b71563b4116becdcce41005ef7a", "8edb8afd353240bb836a799cfc89d583", "d83f9de114524840b7e2d3c951ac575d", "19e21048a9864fa09514262b3e48ab60", "d702266e51ec4c0a8aedc84283562d76", "a2b60bf7150644d3822283e62a6f3dcf", "b3c9ac296c5f4d728d3fd860e721e941", "5a14ca633b9f4b26a02fde2e04007c3a", "7c6a740155a24c218e47457707c1c77d", "b267b399db424510b0ebfab21df4ae79", "aed65fd25cbd43cea34de04bef630b76", "38414f823db74352877f2208e448d7e4", "bf31ea528ce0411f801929ba227904ec", "68aeff416a6e4d639b4abcb6263feffa", "ed342ab8bdc64e53a5f2910c1980bceb", "63661b99acb54ee4935b15522f00115d", "67afe880772647fd9382b3ab33d2fa2a", "3e7800686b934fa3aa5918e89e26d1f4", "f5791fb1c7254e0a9b90aa899e0676bd", "5c8412e7f96c4833807140f46ce40b1f", "1032629e26204d3fb616ede1d63b9815", "b46e9b86ed174aa08f2c9a3184b3c40b", "b2b4083c8306448e9e8ee85787284306", "1e0ce873999f45a08a85382a462684aa", "5d76e1be7dd24a43b3362fc77178db6d", "b37a97c8e6d64c34908daa9bc24906cc", "1353db6c56ce47c190d9896f25f5c853", "2a4b034ec7a64c1b8f2f53a73e9f91fa", "603115db983d484bad1652dd146f5a57", "87f5df424dd24ca6bf2f7a8d08930cf3", "befacc7843ad4bd0b5b6a3804f63a9b6", "0436bd1122454123bd4a4eea3fb7e0c7", "96ad20194de34db18b2cacee198ee24d", "2829221e279d49c8828a31cb3091bbf3", "f8037602bad04c1cb650aecddb86700e", "15305c735a234efcac1cc7bd84f49fb0", "e6b215d622f148afb73531401d0a58cb", "31dba6a930994db08eefbb7d0367b043", "66b803b3cd4c4bb096b6a9069d33a737", "8bd928bcebbd48b6afd95edd6b1247f6", "a7f702a45a20494dbd39e5ea3f203c4c", "4b5f77fc6ff541ef95783d1b3eb79777", "2eee4b962491429cafa9a21e3d07e624", "9f9c0021484a4201a4c39577a0d6bc76", "5d931afdb20340aeb6b4f41fb7f4695d", "32e32e3dcea9452a83e30c18629630a3", "41fe13365d1d44d1af62fea99c0c776f", "fe4daadbd70944d193f354b7748879b0", "15288e2537cf42f5a4424aa52be67457", "89ce60bf2130425198e275263a2bd29d", "c7185000d34a4caea6faa85208747e7e", "91cbe90eddaf4b3db31572edd9045c8c", "b6cc68b281fa463ea91dbd3a10185f56", "b927e832257d4ed1903cf2614d00b95b", "96cfeeff3f42415592c38d972227c108", "f3669794af384cb19a2d203a32f80ed2", "dbe476b3a7864fe6824285d0fbbac224", "51756a8a69704505b73dbab066a3f842", "48a73876b4f8483e886210a16e4741bc", "1ae30524aef54d2ca5bccdf8d824410d", "b43e3a5e515e455c94443a982965df86", "31377a07d04d49f18ba89822c502ca1a", "344103edd461406e9294191181e38bf3", "bcd5fcc3026f456ab9551a638ebae051", "1219277d26864d08b0ae3b76cf36cbae", "d8bef967b9894dbd922e96a87f4f8410", "23c186fca8584d848d3347e44c713098", "a3f30c534cd945a0bb6ce2aab0e38e27", "1d5a00e3a3d148a68c8bcff44bf06d48", "d6242925a646486da19d80217a83e644", "742ba267bb634733ae7cf7c09f779813", "1073b0a9730c4d908b48ca3f0bd417ca", "a7c4a132038c4725b2786bcb234925bf", "2ea35fa061604e8d99986559177fd00e", "4c64900095dd4716a9533e43258d652e", "e0e686edba7e43d4b7049439b0f8b4d8", "5573d31100c44ab6a8c0692699cca1bc", "36a85f40fc5d45609550ca50b5e38002", "a15ce15204f44734b04b8bd586a61522", "ee7281a991a24832b983c5694e8d7f9a", "de2214d549304d2d83e28d688d0174a7", "2fd9aa0311774aa6b4e13554bcfcc7e9", "12fbd856c2924df6b7ad592104dadb07", "2bb8a652eb524bbe971457254c703c36", "c44a275a839247bbbb6c0f22ac0caec3", "b80f52d7946340ed84cf1b532ed007e9", "de91a5753c9344baa208f31f0b7c20dc", "e90f161a9c6c4041b6179e580d080b41", "8c8048aa1586411c85067fe19f26634e", "0589f3db19b343dea4a028013ac48e22", "110030f2c77d496ebbf7da337d4790fd", "2b9c83de16774e8fa0a569ebb3ad20e2", "ca914965d4c747f291a7403b06c4a779", "81c5e5d20a4446f199335e8fdb53f8a9", "6e13a6d05e324b919e9d0cdd3aaffe84", "0d545f1e8432433e8db3a4eee926e4eb", "5a549d7b43d04056b611674323eb03f3", "66816de51b534a7c80d826b63c9fc24d", "dc2325ea74a4425b90b2b42599d80d97", "53bcd6e973554888ad20b666108e5716", "33ee338201514a03aee3ba39f61f9cd0", "b7823b44b0954d45bf234fb7e2960b5a", "f56d6d28bce048a480bf0b8a75ade835", "3bce78e825654399aa9dfb73b78f72d4", "ceff22594932499a80e73137bd15fac4", "6b361b382ab3464894486d75b2166aff", "a129274c808d4cafb94ad23b7c7b3942", "5aa575576e004d3ca9c15439e59507b1", "d6d2f61d20284ede8dec11f3d4ede100", "bbae4c2776094b2bbb0eb651d4e30b7c", "bf2a23895efb46fa9e4e94c5742ad072", "35f033942e2a4db484ff4de2aeda65a8", "753484398a514f47890468e5ce0ffc75", "f3f870fcb0d54621b10b040ac72ac769", "b18ce7becd26433493b563dc54240937", "54ac453945b9444aacc233119b349d01", "1a1b9fd6a1e449daa3470b543859f966", "5e295f10b2f14b43a75c932a93aaf1de", "6f96004adb1044feae9369493b75affb", "863538b0e7854f298a4f85c49b8aab97", "e11d5ec6868e49898f4e8b52f92b4755", "23a4801c14544204bf2ed86b70aacd3a", "c3dc697683cd454d857d2b73800b37f4", "fee054767eb6452fac8373f9a52864a2", "dbdcaaae6d0d4515a647718332ce5e3f", "336afa162a74448ca5e3ba19f4903d1e", "78424a223c6944a195369f6b3149096c", "762f780788c44dd38edd68e9546950c2", "036c59f63ee04ecdbca7f45a704b3a4c", "d809f8200f244d6c9314642765449528", "fc966b046e784075a0f9df99e6c1dbd0", "53e19952e8ab4a4e8799f7aede8189ab", "f1162774a98c4b6c969f84b858bf9187", "ec678a9123e048a6bec7f0f7418f7b10", "c921a8124f78421ab357e75159c01e84", "3b3e86066aaf40c09c512b6a035409d4", "487084c0ba104cb4bfff38dcbf44e01e", "d28051df53744dd1805666e73840a226", "bc622832cc6d4417aafefacafe7e0480", "efea01e9389847e5b111876159fb0b12", "fc45ad9c97a64b07890fc5eba056f202", "f101e5b63672474f9aab181315670791", "e6977edc8f814668ba1b3eed4841bfc7", 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"7dfc2170434e4c84a90818faccb67c54", "4dd6347c81f54139a81c95fa28328416", "0b422ee34d7b48dfbc6b09b7180f08fe", "1d89f50b8da248b49ab3979c5bf1c002", "b7bf25f4bddb43fb9a5343f61b39bc39", "1016ba9b78c143d6b328a2c629a3d721", "1c1aed1001fd48f893f496819f3c9411", "2804e3cdb4124e688bba8c024da4511e", "e5b788b0f2da4c4aaad0f6ed1874b6f0", "b254a0fb169d4f36b26fddf8d1d1f587", "7ac369b9fafe48c18e065bab572a64ff", "cea0c48b24854c0e82c7b07e05fd3a25", "f797d1f707d24651ad80a52a4fbb2758", "a53174c3e27245d6a4269137930311fc", "4d94903eb51e4d95b77334841721a1c9", "6d8e092fa439461f9abf4ad6477c5c09", "d4a277d7810c427d9e2901f2bb8b4a5f", "c21d31f157664aaa9212cdf998396be7", "fa54ef8f5d2e4bb1b1e00835267301b4", "b03ddf57f7394f92a158cd8e34186321", "7d29fe07f52f430ca5fbba5db40907b7", "e0e3a27441e241eab181cdaa2266fa8d", "c5146c828f3f4a41ae8f8cc45044c548", "b5d0bb492c87425b84d678a9580a164b", "31e0d34ce06b478f8d6312150f0e4bf2", "16762f5b65ab427a990d368cf659fa62", "190215bda8314af6a68ea6ef6e61e875", "ade1559352884a55ab4f36e61258671a", "ba0555ca81254f7da3c36aa4d2df2975", "22641e5bf3744bf4af8a60086a27db15", "fd123df515054b3b992cb8e5aeccc5c6", "24fe35e2c9c5447387a4fa7bd7c6a457", "9a2da6d612754f008ccc27a0be6fe5e9", "d65267b8b9c045a89aef49cbea736e6a", "7562bf69386c4d7095a935b484f44310", "9f0d1adfa0f54d0396119de006da12a6", "62533a707b504d58acd0e86ca648d803", "03a64db89be5452e85d000daf2f472e7", "c3ab03c861e04b41aa64f78212ea5cce", "1754b77cf4f445b8add6ed1a374e6a81", "e47a0c9eb6044d02a48377c30b461847", "7cde81a3408748de9fca1f6b8f5fb8f0", "ba971d5959114f1d8e485c668f3a0fb0", "5d0711fbbee446fb9403efcdd3d327bb", "4ef53b6885d5405f8859aa4bae8819da", "bbeb0f63400445b1a1fb7409ffd2f15c", "007bbcfcab9142a2834b48ba6c19d4b1", "671c034b7f3a4f2880782a1ac9b320ab", "1baad027b709401a92923865dc1611ed", "93bb2584318543899e529f94994db9a8", "8d0cfe9f784e4a05bc33cd659432d585", "d65c4155caa84f5e895cb40ac11bce2b", "58cacd3dec1946bebf31b53fb8f2fbdc", "e653dc72f5f64d588f0ff40a55626708", "a0aa5b8db0a04112a6cf3082ae450031", "9f9bc496d47c4d55a810e3d69eae13fd", "a20dc960f39f44009a201cbe859fabb6", "9cbfbf8c7de64c1ea54fcb8f5725df4d", "a1b8424fc72846c3b45092ca8ad5a88e", "8e99dfb3d0da41b182a2a57c4a1d33f2", "9fff40ff17374bb892a8542b32814f75", "f4f6111b81ce40e3a1e3185b249bfb3e", "6632beb98d4c42c88070b21ffe4587a0", "957f06c703df4a70aff43403c83a8c25", "24170140bb8447a98e00890c97390f9e", "7c1e8c142a5e4d3c96c7a6689b3022be", "478d064477bc4e0db8216d82b66be522", "1ea4eac0dce54c45a8ced119717ef620", "23773a2366b948419d96dec416c21f86", "1890ac9177cd4fadbb0a61af6bf3ac7f", "982124b533c24e2fb5c5fb027155c696", "33119ef4c90143eaa80b3256e9a9f47c", "2d40011a50864eddbed4c47b913f7766", "8d8e36c4d5ed4d11885fc3b5b3262b91" ] }, "id": "QoEregTPwsus", "outputId": "153d12f6-7385-4401-e102-91928839c246" }, "execution_count": 3, "outputs": [ { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================\n", "DISK-BASED MERGING WITH DUCKDB\n", "============================================\n", "Found 74 temporary Parquets.\n", "Total temporary size: 14.04 GB\n", "\n", "DuckDB initialized.\n", "RAM limit: 10GB\n", "Temporary disk: /content/e621_character_pipeline/duckdb_temp\n", "\n", "============================================\n", "PARTITIONING BY CHARACTER AND RATING\n", "============================================\n", "Starting disk-based partitioning...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0ed8acfe72764f609820dfeef3399278", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))" ] }, "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "name": "stdout", "text": [ "Partitioning completed.\n", "\n", "Generated 24752 partition files.\n", "\n", "============================================\n", "CREATING FINAL CHARACTER PARQUETS\n", "============================================\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Creating character files: 0%| | 0/24752 [00:00\u001b[0;34m()\u001b[0m\n\u001b[1;32m 345\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Partitioned intermediate files removed.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mMERGED_PATH\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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 348\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 349\u001b[0m \u001b[0mMERGED_PATH\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'MERGED_PATH' is not defined" ] } ] }, { "cell_type": "code", "metadata": { "id": "659b96b1" }, "source": [ "import duckdb\n", "import shutil\n", "import gc\n", "import os\n", "import re\n", "from pathlib import Path\n", "from tqdm.auto import tqdm\n", "\n", "# ============================================================\n", "# RESUME CONFIGURATION\n", "# ============================================================\n", "WORK_DIR = Path(\"/content/e621_character_pipeline\")\n", "TEMP_SECTIONS_DIR = WORK_DIR / \"temporary_sections\"\n", "DUCKDB_TEMP_DIR = WORK_DIR / \"duckdb_temp\"\n", "PARTITIONED_DIR = WORK_DIR / \"partitioned\"\n", "UPLOAD_DIR = WORK_DIR / \"upload\"\n", "FINAL_SECTIONS_DIR = UPLOAD_DIR / \"sections\"\n", "\n", "# Ensure output directories exist\n", "DUCKDB_TEMP_DIR.mkdir(parents=True, exist_ok=True)\n", "if PARTITIONED_DIR.exists():\n", " shutil.rmtree(PARTITIONED_DIR)\n", "PARTITIONED_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "if not FINAL_SECTIONS_DIR.exists():\n", " FINAL_SECTIONS_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "# Find existing temporary parquet files\n", "section_files = sorted(TEMP_SECTIONS_DIR.glob(\"section_*.parquet\"))\n", "if not section_files:\n", " raise RuntimeError(\"No temporary Parquets found in temporary_sections. Cannot resume partition.\")\n", "\n", "print(f\"Found {len(section_files)} temporary Parquets to partition.\")\n", "print(f\"Total size to process: {sum(p.stat().st_size for p in section_files) / 1024**3:.2f} GB\")\n", "\n", "# ============================================================\n", "# CONNECT TO AN IN-MEMORY DUCKDB WITH OPTIMIZED SETTINGS\n", "# ============================================================\n", "# Using an in-memory connection avoids the 'Cannot switch temporary directory' error\n", "db = duckdb.connect()\n", "db.execute(\"SET memory_limit = '10GB'\")\n", "db.execute(f\"SET temp_directory = '{DUCKDB_TEMP_DIR.as_posix()}'\")\n", "db.execute(\"SET preserve_insertion_order = false\")\n", "db.execute(\"SET threads = 2\")\n", "print(\"\\nDuckDB initialized with memory limit 10GB and threads capped at 2.\\n\")\n", "\n", "# ============================================================\n", "# PARTITION DATA ON DISK\n", "# ============================================================\n", "input_glob = (TEMP_SECTIONS_DIR / \"section_*.parquet\").as_posix()\n", "partition_output = PARTITIONED_DIR.as_posix()\n", "\n", "def sql_string(value):\n", " return \"'\" + str(value).replace(\"'\", \"''\") + \"'\"\n", "\n", "def safe_name(s):\n", " return re.sub(r'[^\\w\\-.]', '_', str(s))\n", "\n", "partition_query = f\"\"\"\n", "COPY (\n", " SELECT\n", " character,\n", " rating,\n", " description\n", " FROM read_parquet({sql_string(input_glob)})\n", ")\n", "TO {sql_string(partition_output)}\n", "(\n", " FORMAT PARQUET,\n", " PARTITION_BY (character, rating),\n", " COMPRESSION ZSTD\n", ")\n", "\"\"\"\n", "\n", "print(\"Step 1: Partitioning temporary Parquet files by character and rating on disk...\")\n", "db.execute(partition_query)\n", "print(\"Partitioning complete.\")\n", "gc.collect()\n", "\n", "# ============================================================\n", "# CREATE FINAL SORTED PARQUETS\n", "# ============================================================\n", "partition_files = sorted(PARTITIONED_DIR.rglob(\"*.parquet\"))\n", "print(f\"\\nStep 2: Found {len(partition_files)} partitioned file sets. Writing final datasets...\")\n", "\n", "completed_files = 0\n", "total_final_bytes = 0\n", "\n", "for partition_file in tqdm(partition_files, desc=\"Creating character datasets\"):\n", " partition_path = partition_file.as_posix()\n", "\n", " metadata_query = f\"\"\"\n", " SELECT character, rating\n", " FROM read_parquet({sql_string(partition_path)}, hive_partitioning = true)\n", " LIMIT 1\n", " \"\"\"\n", " result = db.execute(metadata_query).fetchone()\n", " if result is None:\n", " continue\n", "\n", " char, rating = result\n", " char_safe = safe_name(char)\n", " rating_safe = safe_name(rating)\n", " filename = f\"{char_safe}__{rating_safe}.parquet\"\n", " output_path = FINAL_SECTIONS_DIR / filename\n", "\n", " conversion_query = f\"\"\"\n", " COPY (\n", " SELECT\n", " ROW_NUMBER() OVER () AS idx,\n", " description\n", " FROM read_parquet({sql_string(partition_path)})\n", " )\n", " TO {sql_string(output_path.as_posix())}\n", " (\n", " FORMAT PARQUET,\n", " COMPRESSION ZSTD\n", " )\n", " \"\"\"\n", " db.execute(conversion_query)\n", " completed_files += 1\n", " total_final_bytes += output_path.stat().st_size\n", "\n", "print(f\"\\nCompleted! Created {completed_files} final datasets under {FINAL_SECTIONS_DIR}.\")\n", "print(f\"Total processed size: {total_final_bytes / 1024**3:.2f} GB\")\n", "db.close()\n", "gc.collect()\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "0dc83ae9" }, "source": [ "import re\n", "import pandas as pd\n", "from pathlib import Path\n", "from tqdm.auto import tqdm\n", "\n", "# Define paths\n", "SECTIONS_DIR = Path(\"/content/e621_character_pipeline/upload/sections\")\n", "OUTPUT_DIR = Path(\"/content/character_parquets\")\n", "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "# Find all individual rating parquets\n", "section_files = list(SECTIONS_DIR.glob(\"*.parquet\"))\n", "print(f\"Found {len(section_files)} rating-specific Parquet files.\")\n", "\n", "# Group the files by character name\n", "# Filename format: __.parquet\n", "char_groups = {}\n", "for file_path in section_files:\n", " name = file_path.stem\n", " if \"__\" in name:\n", " parts = name.rsplit(\"__\", 1)\n", " char_name = parts[0]\n", " rating = parts[1]\n", "\n", " # Clean up double underscores or ending underscores from earlier parsing\n", " # e.g., nurse_redheart__mlp -> nurse_redheart_mlp\n", " char_clean = re.sub(r'_+', '_', char_name).strip('_')\n", "\n", " if char_clean not in char_groups:\n", " char_groups[char_clean] = {}\n", " char_groups[char_clean][rating] = file_path\n", "\n", "print(f\"Grouped into {len(char_groups)} unique characters.\")\n", "\n", "# Process and merge for each character\n", "for char_name, ratings_dict in tqdm(char_groups.items(), desc=\"Merging character ratings\"):\n", " # Read description lists for each rating\n", " data = {\n", " \"safe\": [],\n", " \"questionable\": [],\n", " \"explicit\": []\n", " }\n", "\n", " # Map the suffix shortcodes to full column names\n", " rating_map = {\n", " \"s\": \"safe\",\n", " \"q\": \"questionable\",\n", " \"e\": \"explicit\"\n", " }\n", "\n", " for r_short, col_name in rating_map.items():\n", " if r_short in ratings_dict:\n", " try:\n", " df = pd.read_parquet(ratings_dict[r_short])\n", " if \"description\" in df.columns:\n", " data[col_name] = df[\"description\"].tolist()\n", " except Exception as e:\n", " print(f\"Warning reading {ratings_dict[r_short].name}: {e}\")\n", "\n", " # Find the maximum list length to build a padded DataFrame\n", " max_len = max(len(data[\"safe\"]), len(data[\"questionable\"]), len(data[\"explicit\"]))\n", "\n", " if max_len == 0:\n", " continue\n", "\n", " # Pad lists with None so they can align into a tabular DataFrame\n", " padded_data = {\n", " \"safe\": data[\"safe\"] + [None] * (max_len - len(data[\"safe\"])),\n", " \"questionable\": data[\"questionable\"] + [None] * (max_len - len(data[\"questionable\"])),\n", " \"explicit\": data[\"explicit\"] + [None] * (max_len - len(data[\"explicit\"]))\n", " }\n", "\n", " # Write out consolidated Parquet\n", " char_df = pd.DataFrame(padded_data)\n", " char_df.to_parquet(OUTPUT_DIR / f\"{char_name}.parquet\", index=False, compression=\"zstd\")\n", "\n", "print(f\"\\nSuccessfully merged and saved character parquets to: {OUTPUT_DIR}\")\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 592, "referenced_widgets": [ "d1c9dd1aa17c47f9ae1a0a658f62086c", "5ed73982a4f94e939d5ba8810409fb17", "65bd788dcf4245fbad87a6f5c5b4f219", "d8288504905b48589402b0566890ab31", "788d2aa3b26d48278538455a583c1f71", "ddfcecf3fd284544a07f3cbb2197664a", "9cd37fbd61814d1e9932b79b78240cc4", "c31eee91d89b43ac8ed4c04bf5d72565", "ae1c5201bdb045cbaff47b7456b0abe4", "f9d47ee386b0481ba3831bb14d210a0c", "0ec6268e2fd54f44a4b788403f12e6ba", "6b4982bd14d341e08c61f669777d6f43", "bfe15b449f6d4385a0169195f9c68870", "b3d8df350aba45cc8904f5fa89099eae", "b0b59459517c44a68ca18a34ba792b53", "1dcf051302de46dc8abfe98db00c7dd9", "d3ae3800139747169c7ce82f70692d6c", "57d0f4c0c2674ed18cfe2433cdc54415", "eebb98f7d22a4d57bb74a4515d41045e", "15995c3ec9c8416fb4b6c80c92f8e50a", "02e668295cd34228b938d1d58b619f5d", "323950d12e814067b929e2365e1d05a9", "3aabd4981fce41e288851ae8f6cf95d0", "8c85210cfb03494680bf9dbf85b05414", "eab17ebe765446208f7274cb328122e6", "370eaf9b27e84e91866f9451a097ce82", "e977e2d261de4566a5865b91c5d0a153", "c25e861cc05a47c0be25d19c4ccec70c", "c1852bb3477845f8acc40b2230a1f6c5", "96ade9a1c19d4309a7e7713339571b24", "ad8e4cf4f5c74c7ca47b36a93b565d8d", "a2ee137f333f4ac8b9a45d7e4b74d5b4", "dcabf219660e464d8bd32722a52febbe", "91744cd969f64595baed8a0738aa8abb", "6f223fe0a7d64a07a7eba9e5bab5f5ee", "31eb317a232f466f8627ef36355a7777", "1748a3936e23492dae5e6bf165bcf11a", "48d723ec609c40148ab299365e14281f", "8fdf567c180045209ff047d2e6cc3d2b", "7a74f377f11646769f5149118339dfb9", "11aac8ba6d264c64bce4599b083f4f83", "0b58df21cf4c4b9bbea007c6dff500d6", "cb02cad3bebd435fb1be957822498629", "0ae4723c13074a4f82be9773486b2b4b", "253f5d602b2e4e419faac0637529e9b2", "4608a1ec8d6147e8a80db3f6ecc8953c", "663a669708e84ee4a71ae2ff181f26fa", "647e252c744846aaa22e8282a117f7fb", "9a01db371f8e40c5bb317def122de3e8", "7927681847074c19ad5fb086f5d9f118", "549cf0545daa4a54bd4e5a19714e249e", "719a9dee807d4f5184d57cbf55e58dd5", "189056cbe87748458fb4cb698075b4d1", "8b5a70bf2267412898550fc426382ba5", "f6e3f189f9064769a65a0c0588e2a5f2", "288337bac9284db8821a6ac89e11e63e", "79906e1c26ec45b0be164fd5cf526767", "15e3d83589ef4a018a8d6d51724a41c4", "85fb94725cc34a76b9f635eedc57f75e", "9ba5233708954f54af3c6060fe2479cf", "815bc1c6bc034971aaae06ceb8fdbd36", "fe003e8a0bc4449a8de37356b8aad7d6", "c90482b6761b4914a1c0e6dfa7eefbdd", "a9f9901207fa4a78b88655efa23b225e", "b13c99b44b4c42909bf536bb744087d9", "767ece98bdb6405f83467b48ade7707e", "7ce999ee601b4cf1982c6e483cc0ade6", "b063c8d5e3fe4f43a8159c4d865b665a", "a6c5abc9d3974aa5bc56c6f53b3eef1e", "d355ab59292f4caaa1edcda1f40f5f46", "2b4f0d1aa7dc46c1b58cbe02f3569f6d", "af7491cbc1e748c1993a5b3b54e91c33", "6ab63a04a617460e9468f68374118bf3", "fb2b9dcdb71f4eb384025ba74b93a621", "1a1468bb7d634644ac6b483056c39266", "a07dfd481c224e70ac902e3d9459ea82", "0042eca14f61415f8d90d076201c72e5", "50722b6020dd4705b2f46164205a3f4f", "07cb8fafbaf04aa591aba8f496c61b08", "44f2949c9eef42df8ac6734fd4ae55a6", "0f2cf457060946c899b5d17555da22f4", "1190885587a94c29af80434a9a2eb320", "6c27874e34be4ea6bc69e1d682ecbb28", "60aba01caa904ac9bc7b785470e71c4b", "8575ec34537b443ab5565b75ae72cfe1", "f28a3b6160b841ecba92094c52af15c9", "7f48cb35b54e4ce3adafbd97e8b917b2", "44693de77fc74be79ce407fa4f57c044", "d01adfbd20d749788dacc57cdf116399", "0d92eaa470ac44f28640adf2368b79bc", "f0f531d7336548738baeada5ba93135f", "ae531819959440fda374406786ff8f44", "8792c13fd6d0403db87cf46e10cb52c0", "e87c53f7695844cb919c9623c9dc9266", "bad9e65b42134d28a854d83ceaeac22b", "3e6cbce7c3db4fb6987ba4879d22ca01", "851f7a186ced46d18a2ca58a69c5aef3", "e3533e1e7d7c4f8c941af53850735244", "ed1c4484bee24c5498363fa4ffbd621e", "be10035ef67f4b18a6f412028bd0b1b1", "8788d3eb35914a768d19531ac0d6dec5", "bfb40d74468a4418957400d8ba89cc05", "7ac92de70daa426583188fa44ef0f767", "9d0f8f44dc104a98b847ea03c4d3be1b", "eb6a81a9ed7449d480f539f811e2bb3d", "238d0ccb2a134b0da913dd470d827077", "d0dfe62383d9418ab675804fe5f6792e", "4b71ab5b2f0e47ac8f3d618a1e91175a", "855a7218cbf04c42807f99ff9b4897a7", "3ba8048e59ed427a925097d1ba8968c3", "978c6f1e5094441c81f94c2735ad7cac", "d7aed144f9d04fdfa67cf1a0bcbd76ee", "d4d9fb0abdad4efa89b828dad033252a", "9609c091a04f4f70940898122083f144", "f6efa1d256e94831bc7c69612a0d5cb3", "dc97eb7975e94a34add54afa492ccb6f", "43af8f21204d431fb8d8416e15f476bf", "093ee536d7ed4980b3bbbb748d3f64d8", "382d64fbcbad4c4895864232c0c60ef9", "f543d30c171f4a5ba92183671a46fe34", "06a912bdc5314ff3b520e189971009c5", "d949adbe53d347b0aefcc1aba87659f4", "f952f28a25f048beb0457b73c76b822f", "d32e28e8c5c54c35a2d0106567b7abe9", "0d6045400de64689adcac6662d150eba", "d47c794c81f34a8a90dee8fab876f555", "4543a30b1ec747be902827d32d580b46", "5d2f0ab15d784ed29d46d1ac449513da", "ff8fb953048f4b1684e91387ded0b7b3", "466b285a9d9f459682082797164cb65d", "4a035485937f4b1993d2cf97455e4896", "22bd76a86de24656b6747fdf3c0ed465", "0da4461aa8454b2bbb0dac58a6c3aa92", "72c341d90b46496e974459170e4e7b4f", "1cae6b6c0c95486a9e3cbfac8f6b4078", "3cc69bd548664b59859850b4ee35a2cd", "fd8325d03bec4c319f3e91e90f847d81", "66b3257b27534bfa8bcf2ab67fbbdd74", "4e751e3cc0eb4771b47cf336fffc20d4", "c7b1952beb4647ce9af03a3cac274cb2", "52c52d9146b9467dbcae2af7172660a5", "2d6abd70703b493b8a08ae39c1c40da9", "a7b569eb93b3463c8687cdf3eca2b2ee" ] }, "id": "ebad9039", "outputId": "60e22aab-c1a2-4735-e147-b8b64988aac7" }, "source": [ "from huggingface_hub import HfApi, CommitOperationAdd\n", "from google.colab import userdata\n", "from pathlib import Path\n", "from tqdm.auto import tqdm\n", "\n", "# Configure repository details\n", "DEST_REPO = \"codeShare/e621-captions-character-sorted\"\n", "REPO_TYPE = \"dataset\"\n", "LOCAL_DIR = Path(\"/content/character_parquets\")\n", "\n", "# Retrieve credentials\n", "HF_TOKEN = userdata.get(\"HF_TOKEN\")\n", "if not HF_TOKEN:\n", " raise ValueError(\"HF_TOKEN not found in Google Colab secrets.\")\n", "\n", "api = HfApi(token=HF_TOKEN)\n", "\n", "# Gather all .parquet files to be committed\n", "upload_files = sorted(LOCAL_DIR.glob(\"*.parquet\"))\n", "print(f\"Found {len(upload_files)} consolidated character files for upload.\")\n", "\n", "# Build the single commit operations list\n", "operations = []\n", "for local_file in upload_files:\n", " # Target path must be directly inside 'characters/' folder\n", " remote_path = f\"characters/{local_file.name}\"\n", " operations.append(\n", " CommitOperationAdd(\n", " path_in_repo=remote_path,\n", " path_or_fileobj=str(local_file)\n", " )\n", " )\n", "\n", "print(f\"Staged {len(operations)} operations. Uploading to dataset: {DEST_REPO}...\")\n", "\n", "# Perform a single atomic commit\n", "commit_result = api.create_commit(\n", " repo_id=DEST_REPO,\n", " repo_type=REPO_TYPE,\n", " operations=operations,\n", " commit_message=\"Add consolidated character-specific rating parquets\"\n", ")\n", "\n", "print(\"\\n============================================\")\n", "print(\"UPLOAD COMPLETED SUCCESSFUL\")\n", "print(\"============================================\")\n", "print(f\"Commit URL: {commit_result.commit_url}\")\n", "print(f\"Dataset Path: https://huggingface.co/datasets/{DEST_REPO}/tree/main/characters\")\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 165 consolidated character files for upload.\n", "Staged 165 operations. Uploading to dataset: codeShare/e621-captions-character-sorted...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Processing Files (0 / 0) : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "d1c9dd1aa17c47f9ae1a0a658f62086c" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "New Data Upload : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "6b4982bd14d341e08c61f669777d6f43" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...na_trials_of_mana.parquet: 1%| | 3.82kB / 478kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "3aabd4981fce41e288851ae8f6cf95d0" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " .../averi_fiddleafox.parquet: 1%| | 2.34kB / 292kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "91744cd969f64595baed8a0738aa8abb" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...iku_cryptiacurves.parquet: 1%| | 191B / 24.0kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "253f5d602b2e4e419faac0637529e9b2" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...ts/jessica_rabbit.parquet: 1%| | 851B / 106kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "288337bac9284db8821a6ac89e11e63e" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...rquets/clawroline.parquet: 1%| | 236B / 29.6kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "7ce999ee601b4cf1982c6e483cc0ade6" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...arquets/fiona_fox.parquet: 1%| | 408B / 51.0kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "50722b6020dd4705b2f46164205a3f4f" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...rquets/busty_bird.parquet: 1%| | 133kB / 16.6MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "d01adfbd20d749788dacc57cdf116399" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...ets/akari_pokemon.parquet: 1%| | 2.37kB / 296kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "be10035ef67f4b18a6f412028bd0b1b1" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...lo_spice_and_wolf.parquet: 1%| | 153B / 19.2kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "978c6f1e5094441c81f94c2735ad7cac" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " ...ets/honey_the_cat.parquet: 1%| | 3.73kB / 466kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "d949adbe53d347b0aefcc1aba87659f4" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Validating : | 0% " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "0da4461aa8454b2bbb0dac58a6c3aa92" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "============================================\n", "UPLOAD COMPLETED SUCCESSFUL\n", "============================================\n", "Commit URL: https://huggingface.co/datasets/codeShare/e621-captions-character-sorted/commit/cf743cfa1b0992e2c1a576352a1cbc096ab33280\n", "Dataset Path: https://huggingface.co/datasets/codeShare/e621-captions-character-sorted/tree/main/characters\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "958d1239" }, "source": [ "from huggingface_hub import hf_hub_download, HfApi, CommitOperationAdd\n", "from google.colab import userdata\n", "from pathlib import Path\n", "\n", "# Configure repository details\n", "DEST_REPO = \"codeShare/e621-captions-character-sorted\"\n", "REPO_TYPE = \"dataset\"\n", "LOCAL_DIR = Path(\"/content/character_parquets\")\n", "TARGET_FILE = \"processed_characters.txt\"\n", "TEMP_FILE_PATH = Path(\"/content\") / TARGET_FILE\n", "\n", "# Fetch authentication credentials\n", "HF_TOKEN = userdata.get(\"HF_TOKEN\")\n", "if not HF_TOKEN:\n", " raise ValueError(\"HF_TOKEN not found in Google Colab secrets.\")\n", "\n", "api = HfApi(token=HF_TOKEN)\n", "\n", "# Step 1: Download existing file if it exists, or start a new set to ensure we do not overwrite previous progress\n", "existing_characters = set()\n", "try:\n", " downloaded_path = hf_hub_download(\n", " repo_id=DEST_REPO,\n", " filename=TARGET_FILE,\n", " repo_type=REPO_TYPE,\n", " token=HF_TOKEN\n", " )\n", " with open(downloaded_path, \"r\", encoding=\"utf-8\") as f:\n", " for line in f:\n", " char = line.strip()\n", " if char:\n", " existing_characters.add(char)\n", " print(f\"Downloaded existing list containing {len(existing_characters)} processed characters.\")\n", "except Exception as e:\n", " print(\"No existing processed_characters.txt found in the repository or failed to read. Starting a fresh list.\")\n", "\n", "# Step 2: Identify the newly processed character names (stems of the merged parquet files)\n", "newly_uploaded = {p.stem for p in LOCAL_DIR.glob(\"*.parquet\")}\n", "print(f\"Found {len(newly_uploaded)} newly processed character files locally.\")\n", "\n", "# Step 3: Combine both sets and sort alphabetically to prevent duplicates and keep formatting neat\n", "all_characters = sorted(existing_characters.union(newly_uploaded))\n", "with open(TEMP_FILE_PATH, \"w\", encoding=\"utf-8\") as f:\n", " for char in all_characters:\n", " f.write(f\"{char}\\n\")\n", "\n", "print(f\"Updated character list compiled successfully with {len(all_characters)} total unique characters.\")\n", "\n", "# Step 4: Commit and upload the updated file back to Hugging Face\n", "commit_result = api.create_commit(\n", " repo_id=DEST_REPO,\n", " repo_type=REPO_TYPE,\n", " operations=[\n", " CommitOperationAdd(\n", " path_in_repo=TARGET_FILE,\n", " path_or_fileobj=str(TEMP_FILE_PATH)\n", " )\n", " ],\n", " commit_message=\"Update processed_characters.txt index with new character parquets\"\n", ")\n", "\n", "print(\"\\n============================================\")\n", "print(\"PROCESSED CHARACTERS INDEX COMMITTED\")\n", "print(\"============================================\")\n", "print(f\"Commit URL: {commit_result.commit_url}\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 208, "referenced_widgets": [ "3abe6d5d94c24efbbc7ee7930c895bd9", "9140c3c654144fd692c0b07aec61e081", "a5b0c58d7d424ad2adcd70e559a8a74c", "9fd08048b49e4c44848473207e2dc312", "e1fc6ed3eaf6429f9505dd94ed11b172", "45e6d12a9e7240b0b7ad455a718ba905", "0c60572ad6724c2283ae8d1e24e07218", "9b8a39d4406545e78128de381f06c680", "c705bb194aad4156b7bfe5ae166bad5d", "50f4a5e0f8f7403094d96d5adab711db", "a7fe4d84cad445ff9ff55478fcda6687" ] }, "id": "6eddf44e", "outputId": "30d5f0f6-4ed0-4385-d7b3-ee6d0be6332e" }, "source": [ "from huggingface_hub import hf_hub_download, HfApi, CommitOperationAdd\n", "from google.colab import userdata\n", "from pathlib import Path\n", "\n", "# Configure details\n", "DEST_REPO = \"codeShare/e621-captions-character-sorted\"\n", "REPO_TYPE = \"dataset\"\n", "LOCAL_DIR = Path(\"/content/character_parquets\")\n", "TARGET_FILE = \"processed_characters.txt\"\n", "TEMP_FILE_PATH = Path(\"/content\") / TARGET_FILE\n", "\n", "# Get HF Token\n", "HF_TOKEN = userdata.get(\"HF_TOKEN\")\n", "if not HF_TOKEN:\n", " raise ValueError(\"HF_TOKEN not found in Google Colab secrets.\")\n", "\n", "api = HfApi(token=HF_TOKEN)\n", "\n", "# Step 1: Download existing file if it exists, or start a new set\n", "existing_characters = set()\n", "try:\n", " downloaded_path = hf_hub_download(\n", " repo_id=DEST_REPO,\n", " filename=TARGET_FILE,\n", " repo_type=REPO_TYPE,\n", " token=HF_TOKEN\n", " )\n", " with open(downloaded_path, \"r\", encoding=\"utf-8\") as f:\n", " for line in f:\n", " char = line.strip()\n", " if char:\n", " existing_characters.add(char)\n", " print(f\"Downloaded existing list with {len(existing_characters)} characters.\")\n", "except Exception as e:\n", " print(\"No existing processed_characters.txt found or failed to read. Starting a new list.\")\n", "\n", "# Step 2: Get names of characters we uploaded (stem names of parquet files)\n", "newly_uploaded = {p.stem for p in LOCAL_DIR.glob(\"*.parquet\")}\n", "print(f\"Found {len(newly_uploaded)} newly processed characters from local directory.\")\n", "\n", "# Step 3: Union both sets and write to local file sorted alphabetically\n", "all_characters = sorted(existing_characters.union(newly_uploaded))\n", "with open(TEMP_FILE_PATH, \"w\", encoding=\"utf-8\") as f:\n", " for char in all_characters:\n", " f.write(f\"{char}\\n\")\n", "\n", "print(f\"Updated list contains {len(all_characters)} characters. Saved locally to {TEMP_FILE_PATH}.\")\n", "\n", "# Step 4: Upload the updated file to HF repository\n", "commit_result = api.create_commit(\n", " repo_id=DEST_REPO,\n", " repo_type=REPO_TYPE,\n", " operations=[\n", " CommitOperationAdd(\n", " path_in_repo=TARGET_FILE,\n", " path_or_fileobj=str(TEMP_FILE_PATH)\n", " )\n", " ],\n", " commit_message=\"Update processed_characters.txt list with uploaded characters\"\n", ")\n", "\n", "print(\"\\n============================================\")\n", "print(\"PROCESSED CHARACTERS LIST UPDATED AND COMMITTED\")\n", "print(\"============================================\")\n", "print(f\"Commit URL: {commit_result.commit_url}\")" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "processed_characters.txt: 0%| | 0.00/3.06k [00:00 MAX_COMPONENT_OWNERS: continue\n", " character_terms[char].add(word)\n", " all_terms.add(word)\n", "\n", "term_to_chars = defaultdict(list)\n", "for char, terms in character_terms.items():\n", " for term in terms:\n", " term_to_chars[term].append(char)\n", "\n", "A = ahocorasick.Automaton()\n", "for term in all_terms:\n", " A.add_word(term, term)\n", "A.make_automaton()\n", "\n", "def is_word_boundary(text, start, end):\n", " left_ok = start == 0 or not (text[start - 1].isalnum() or text[start - 1] == '_')\n", " right_ok = end == len(text) or not (text[end].isalnum() or text[end] == '_')\n", " return left_ok and right_ok\n", "\n", "def find_matched_chars(full_text):\n", " if not full_text: return []\n", " text = normalize_search_text(full_text)\n", " matched = set()\n", " for end_idx, term in A.iter(text):\n", " start = end_idx - len(term) + 1\n", " if is_word_boundary(text, start, end_idx + 1):\n", " matched.update(term_to_chars[term])\n", " return list(matched)\n", "\n", "# -----------------------------------------------------------------------------\n", "# 4. Stream Source Files & Parse Matches\n", "# -----------------------------------------------------------------------------\n", "print(\"Downloading files from lodestones/e621-captions...\")\n", "all_files = list_repo_files(SOURCE_REPO, repo_type=REPO_TYPE, token=HF_TOKEN)\n", "jsonl_files = sorted([f for f in all_files if f.endswith(\"_grouped.jsonl\")])\n", "\n", "collected_rows = []\n", "section_idx = 0\n", "\n", "def save_section(rows, idx):\n", " df = pd.DataFrame(rows)\n", " path = TEMP_SECTIONS_DIR / f\"section_{idx:04d}.parquet\"\n", " df.to_parquet(path, index=False, engine=\"pyarrow\")\n", " return path\n", "\n", "for idx, filename in enumerate(tqdm(jsonl_files, desc=\"Streaming Source Logs\")):\n", " local_path = hf_hub_download(\n", " repo_id=SOURCE_REPO, filename=filename, repo_type=REPO_TYPE, token=HF_TOKEN, local_dir=str(DOWNLOAD_DIR)\n", " )\n", " with open(local_path, \"r\", encoding=\"utf-8\") as f:\n", " for line in f:\n", " try:\n", " item = json.loads(line)\n", " except:\n", " continue\n", " raw_tags = item.get(\"tags\") or item.get(\"tag_string\") or \"\"\n", " parts = [raw_tags] + [item.get(field) for field in NL_FIELDS if isinstance(item.get(field), str)]\n", " full_text = \" \".join(parts)\n", "\n", " matched_chars = find_matched_chars(full_text)\n", " if not matched_chars: continue\n", "\n", " rating = str(item.get(\"rating\", \"unknown\")).lower()\n", " formatted_tags = \" , \".join([t.strip() for t in re.split(r'[,\\s]+', raw_tags.strip()) if t.strip()])\n", "\n", " naturals = [item.get(field).strip() for field in NL_FIELDS if isinstance(item.get(field), str) and item.get(field).strip()]\n", " description = \"|\".join([f\"{n} + {formatted_tags}\" for n in naturals]) if naturals else formatted_tags\n", "\n", " for char in matched_chars:\n", " collected_rows.append({\"character\": char, \"rating\": rating, \"description\": description})\n", " if len(collected_rows) >= SECTION_SIZE:\n", " save_section(collected_rows, section_idx)\n", " section_idx += 1\n", " collected_rows = []\n", " gc.collect()\n", " try:\n", " os.remove(local_path)\n", " except:\n", " pass\n", "\n", "if collected_rows:\n", " save_section(collected_rows, section_idx)\n", " collected_rows = []\n", " gc.collect()\n", "\n", "# -----------------------------------------------------------------------------\n", "# 5. DuckDB RAM-Optimized Disk Merge\n", "# -----------------------------------------------------------------------------\n", "print(\"Beginning memory-safe partitioning with DuckDB...\")\n", "db = duckdb.connect()\n", "db.execute(\"SET memory_limit = '10GB'\")\n", "db.execute(f\"SET temp_directory = '{DUCKDB_TEMP_DIR.as_posix()}'\")\n", "db.execute(\"SET preserve_insertion_order = false\")\n", "db.execute(\"SET threads = 2\")\n", "\n", "input_glob = (TEMP_SECTIONS_DIR / \"section_*.parquet\").as_posix()\n", "partition_output = PARTITIONED_DIR.as_posix()\n", "\n", "partition_query = f\"\"\"\n", "COPY (\n", " SELECT character, rating, description\n", " FROM read_parquet('{input_glob}')\n", ")\n", "TO '{partition_output}'\n", "(FORMAT PARQUET, PARTITION_BY (character, rating), COMPRESSION ZSTD)\n", "\"\"\"\n", "db.execute(partition_query)\n", "\n", "partition_files = sorted(PARTITIONED_DIR.rglob(\"*.parquet\"))\n", "for partition_file in tqdm(partition_files, desc=\"Creating character datasets\"):\n", " partition_path = partition_file.as_posix()\n", " meta = db.execute(f\"SELECT character, rating FROM read_parquet('{partition_path}', hive_partitioning=true) LIMIT 1\").fetchone()\n", " if not meta: continue\n", " char, r_val = meta\n", "\n", " char_safe = re.sub(r'[^\\w\\-.]', '_', str(char))\n", " rating_safe = re.sub(r'[^\\w\\-.]', '_', str(r_val))\n", " out_filename = f\"{char_safe}__{rating_safe}.parquet\"\n", "\n", " db.execute(f\"\"\"\n", " COPY (\n", " SELECT ROW_NUMBER() OVER () as idx, description\n", " FROM read_parquet('{partition_path}')\n", " )\n", " TO '{(FINAL_SECTIONS_DIR / out_filename).as_posix()}'\n", " (FORMAT PARQUET, COMPRESSION ZSTD)\n", " \"\"\")\n", "\n", "db.close()\n", "gc.collect()\n", "\n", "# -----------------------------------------------------------------------------\n", "# 6. Rating Consolidation & Padding\n", "# -----------------------------------------------------------------------------\n", "print(\"Aggregating rating columns into unified character Parquets...\")\n", "char_groups = {}\n", "for file_path in FINAL_SECTIONS_DIR.glob(\"*.parquet\"):\n", " parts = file_path.stem.rsplit(\"__\", 1)\n", " if len(parts) == 2:\n", " char_clean = re.sub(r'_+', '_', parts[0]).strip('_')\n", " if char_clean not in char_groups: char_groups[char_clean] = {}\n", " char_groups[char_clean][parts[1]] = file_path\n", "\n", "for char_name, ratings_dict in tqdm(char_groups.items(), desc=\"Merging Ratings\"):\n", " data = {\"safe\": [], \"questionable\": [], \"explicit\": []}\n", " rating_map = {\"s\": \"safe\", \"q\": \"questionable\", \"e\": \"explicit\"}\n", " for r_short, col_name in rating_map.items():\n", " if r_short in ratings_dict:\n", " try:\n", " df = pd.read_parquet(ratings_dict[r_short])\n", " if \"description\" in df.columns:\n", " data[col_name] = df[\"description\"].tolist()\n", " except:\n", " pass\n", " max_len = max(len(data[\"safe\"]), len(data[\"questionable\"]), len(data[\"explicit\"]))\n", " if max_len == 0: continue\n", "\n", " padded_data = {col: data[col] + [None] * (max_len - len(data[col])) for col in [\"safe\", \"questionable\", \"explicit\"]}\n", " pd.DataFrame(padded_data).to_parquet(OUTPUT_DIR / f\"{char_name}.parquet\", index=False, compression=\"zstd\")\n", "\n", "# -----------------------------------------------------------------------------\n", "# 7. Atomic Upload and Index Commits\n", "# -----------------------------------------------------------------------------\n", "print(\"Uploading everything in a single atomic commit...\")\n", "upload_files = sorted(OUTPUT_DIR.glob(\"*.parquet\"))\n", "operations = [\n", " CommitOperationAdd(path_in_repo=f\"characters/{f.name}\", path_or_fileobj=str(f)) for f in upload_files\n", "]\n", "\n", "# Read current indices to prevent overwrites\n", "existing_chars = set()\n", "try:\n", " dl_path = hf_hub_download(repo_id=DEST_REPO, filename=TARGET_FILE, repo_type=REPO_TYPE, token=HF_TOKEN)\n", " with open(dl_path, \"r\", encoding=\"utf-8\") as idx_f:\n", " existing_chars = {line.strip() for line in idx_f if line.strip()}\n", "except:\n", " pass\n", "\n", "all_chars = sorted(existing_chars.union({f.stem for f in upload_files}))\n", "with open(TEMP_FILE_PATH, \"w\", encoding=\"utf-8\") as idx_f:\n", " for c in all_chars:\n", " idx_f.write(f\"{c}\\n\")\n", "\n", "operations.append(CommitOperationAdd(path_in_repo=TARGET_FILE, path_or_fileobj=str(TEMP_FILE_PATH)))\n", "\n", "commit_res = api.create_commit(\n", " repo_id=DEST_REPO,\n", " repo_type=REPO_TYPE,\n", " operations=operations,\n", " commit_message=\"Consolidated character captions pipeline: Parquets & Index list update\"\n", ")\n", "print(f\"All processes successfully completed. Commit URL: {commit_res.commit_url}\")\n" ], "execution_count": null, "outputs": [] } ] }