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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"context:\n",
"- All images from sakugabooru has been downloaded, with schema:\n",
" - `./data/post_{id}/post_{id}.{ext}`\n",
" - `./data/post_{id}/post_{id}.json`\n",
"- Now we need to upload them to huggingface by tarring it.\n",
"\n",
"To install the libraries:\n",
"\n",
"```bash\n",
"pip install unibox hfutils\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import tarfile\n",
"from pathlib import Path\n",
"from typing import List\n",
"import unibox as ub"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"get available files:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
}
],
"source": [
"# Example usage:\n",
"data_dir = \"/rmt/yada/dev/sakuga-scraper/data\"\n",
"output_dir = \"/rmt/yada/dev/sakuga-scraper/tars\"\n",
"\n",
"# Collect all valid media files\n",
"all_files = ub.traverses(\"/rmt/yada/dev/sakuga-scraper/data\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"all files: 404160\n",
"image files: 8680, video files: 155238, json files: 240242\n",
"total unique files: 404160\n"
]
}
],
"source": [
"# Convert extensions to sets for validation\n",
"image_extensions = set(ub.IMG_FILES) - {'.gif'}\n",
"video_extensions = {'.webm', '.mp4', '.gif'}\n",
"\n",
"# Categorize files\n",
"image_files = [f for f in all_files if Path(f).suffix in image_extensions]\n",
"video_files = [f for f in all_files if Path(f).suffix in video_extensions]\n",
"json_files = [f for f in all_files if Path(f).suffix == '.json']\n",
"\n",
"# Recompute lengths\n",
"total_unique_files = len(set(image_files + video_files + json_files))\n",
"print(f\"all files: {len(all_files)}\\nimage files: {len(image_files)}, video files: {len(video_files)}, json files: {len(json_files)}\\ntotal unique files: {total_unique_files}\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(['/rmt/yada/dev/sakuga-scraper/data/post_100058/sankaku_100058.json'],\n",
" ['/rmt/yada/dev/sakuga-scraper/data/post_105581/sankaku_105581.jpg'],\n",
" ['/rmt/yada/dev/sakuga-scraper/data/post_102570/sankaku_102570.mp4'])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"json_files[:1], image_files[:1], video_files[:1]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((27, 272758), (27, 273264))"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def determine_tar_info(files: List[str], modulo: int = 10000):\n",
" \"\"\"\n",
" Determine how many full tars can be created and the current last post number.\n",
"\n",
" Args:\n",
" files (List[str]): List of file paths (images/videos).\n",
" modulo (int): Constant range of IDs per tar file (default: 10,000).\n",
"\n",
" Returns:\n",
" tuple: (number_of_full_tars, last_post_id)\n",
" \"\"\"\n",
" post_ids = []\n",
" for file_path in files:\n",
" filename = os.path.basename(file_path)\n",
" try:\n",
" post_id = int(filename.split('_')[1].split('.')[0])\n",
" post_ids.append(post_id)\n",
" except (IndexError, ValueError):\n",
" print(f\"Skipping file with invalid format: {filename}\")\n",
" continue\n",
"\n",
" if not post_ids:\n",
" return 0, None\n",
"\n",
" last_post_id = max(post_ids)\n",
" number_of_full_tars = (last_post_id + 1) // modulo\n",
"\n",
" return number_of_full_tars, last_post_id\n",
"\n",
"\n",
"# full tar count, last post id\n",
"determine_tar_info(image_files), determine_tar_info(video_files)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"do the tarring:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We can make 27 full tars, last post id is 273264\n"
]
}
],
"source": [
"import os\n",
"import tarfile\n",
"from pathlib import Path\n",
"from typing import List\n",
"\n",
"def generate_tar_from_files(\n",
" files: List[str], \n",
" output_dir: str, \n",
" id: int, \n",
" modulo: int = 10000\n",
") -> List[str]:\n",
" \"\"\"\n",
" Create tar files containing files (images/videos) grouped by post ID ranges.\n",
"\n",
" Args:\n",
" files (List[str]): List of file paths (images/videos).\n",
" output_dir (str): Directory to store the tar files.\n",
" id (int): The ID used to determine the range of files to include in the tar.\n",
" modulo (int): Constant range of IDs per tar file (default: 10,000).\n",
"\n",
" Returns:\n",
" List[str]: List of created tar file paths.\n",
" \"\"\"\n",
" output_dir = Path(output_dir)\n",
" output_dir.mkdir(parents=True, exist_ok=True)\n",
"\n",
" # Determine ID range\n",
" range_start = id * modulo\n",
" range_end = (id + 1) * modulo\n",
"\n",
" # Filter files by ID range\n",
" files_in_range = []\n",
" for file_path in files:\n",
" filename = os.path.basename(file_path)\n",
" try:\n",
" post_id = int(filename.split('_')[1].split('.')[0])\n",
" except (IndexError, ValueError):\n",
" print(f\"Skipping file with invalid format: {filename}\")\n",
" continue\n",
"\n",
" if range_start <= post_id < range_end:\n",
" files_in_range.append(file_path)\n",
"\n",
" if not files_in_range:\n",
" print(f\"No files found in range {range_start}-{range_end}.\")\n",
" return []\n",
"\n",
" tar_file_path = output_dir / f\"{id}.tar\"\n",
"\n",
" # Create the tar\n",
" with tarfile.open(tar_file_path, \"w\") as tar:\n",
" for file_path in files_in_range:\n",
" tar.add(file_path, arcname=os.path.basename(file_path))\n",
"\n",
" print(f\"Created tar: {tar_file_path} with {len(files_in_range)} files.\")\n",
"\n",
" return [str(tar_file_path)]\n",
"\n",
"# Example usage:\n",
"# Suppose we have a collection of images & videos:\n",
"# files = [\n",
"# \"/path/to/sankaku_16465.jpg\",\n",
"# \"/path/to/sankaku_20000.mp4\",\n",
"# ...\n",
"# ]\n",
"\n",
"# See how many tars and the highest post ID:\n",
"media_files = video_files + image_files\n",
"tar_count, last_id = determine_tar_info(media_files)\n",
"print(\"We can make\", tar_count, \"full tars, last post id is\", last_id)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created tar: output/0.tar with 1827 files.\n",
"['output/0.tar']\n"
]
}
],
"source": [
"# Create the tar for the first batch (id=0):\n",
"tars_created = generate_tar_from_files(media_files, output_dir=\"output\", id=0)\n",
"print(tars_created)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"make indexes:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "525bf979329a498b90ce44405f13238a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Indexing tar file '/rmt/yada/dev/sakuga-scraper/tars/0.tar' ...: 0it [00:00, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'/rmt/yada/dev/sakuga-scraper/tars/0.json'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from hfutils.index.make import tar_create_index\n",
"\n",
"# Create index for the generated tars\n",
"tar_create_index(src_tar_file=\"/rmt/yada/dev/sakuga-scraper/tars/0.tar\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"testing partial tar read:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All files verified successfully via local partial-read checks.\n"
]
}
],
"source": [
"import hashlib\n",
"import json\n",
"import os\n",
"\n",
"def test_local_partial_read(tar_path: str, index_json_path: str):\n",
" \"\"\"\n",
" Perform local partial reads of an uncompressed tar file to verify offsets and sizes\n",
" match the metadata in the JSON index.\n",
"\n",
" :param tar_path: Path to the tar file.\n",
" :param index_json_path: Path to the JSON index (the .tar.json file).\n",
" \"\"\"\n",
" with open(index_json_path, 'r', encoding='utf-8') as jf:\n",
" index_data = json.load(jf)\n",
"\n",
" # Sanity check: Compare tar size\n",
" reported_size = index_data.get(\"filesize\", None)\n",
" actual_size = os.path.getsize(tar_path)\n",
" if reported_size and reported_size != actual_size:\n",
" raise ValueError(f\"Tar size mismatch! JSON says {reported_size} bytes, actual is {actual_size} bytes.\")\n",
"\n",
" with open(tar_path, \"rb\") as tar_f:\n",
" for fname, info in index_data[\"files\"].items():\n",
" offset = info[\"offset\"]\n",
" size = info[\"size\"]\n",
" expected_sha256 = info[\"sha256\"]\n",
"\n",
" # Seek to offset and read exactly `size` bytes\n",
" tar_f.seek(offset, 0)\n",
" data = tar_f.read(size)\n",
" if len(data) != size:\n",
" raise ValueError(f\"Read {len(data)} bytes instead of {size} for '{fname}'.\")\n",
"\n",
" # Compute SHA-256 and compare\n",
" actual_sha256 = hashlib.sha256(data).hexdigest()\n",
" if actual_sha256 != expected_sha256:\n",
" raise ValueError(f\"SHA mismatch for '{fname}'. Expected {expected_sha256}, got {actual_sha256}.\")\n",
"\n",
" print(\"All files verified successfully via local partial-read checks.\")\n",
"\n",
"\n",
"test_local_partial_read(\"../tars/0.tar\", \"../tars/0.json\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"tarring the entire set:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0557e54d0bce48f3aba219ebf8b470c5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/26 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created tar: ../tars/1.tar with 3011 files.\n",
"['../tars/1.tar']\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "54217658da474bd8b03606cf6a56f1de",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Indexing tar file '../tars/1.tar' ...: 0it [00:00, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"All files verified successfully via local partial-read checks.\n",
"Created tar: ../tars/2.tar with 4460 files.\n",
"['../tars/2.tar']\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d81ec72a9eb04d6881ffa06370c41778",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Indexing tar file '../tars/2.tar' ...: 0it [00:00, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"All files verified successfully via local partial-read checks.\n"
]
}
],
"source": [
"from tqdm.auto import tqdm\n",
"\n",
"# since we already created the first tar, we can start from 1\n",
"for i in tqdm(range(1, tar_count)):\n",
" tars_created = generate_tar_from_files(media_files, output_dir=\"../tars\", id=i)\n",
" print(tars_created)\n",
" tar_create_index(src_tar_file=tars_created[0])\n",
"\n",
" test_local_partial_read(tars_created[0], f\"../tars/{i}.json\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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