{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2809ca28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu-ord01-03-658\r\n"
     ]
    }
   ],
   "source": [
    "!echo $HOSTNAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c6c00ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ef5151a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: verify stems and metas work\n",
    "# TODO: verify lyrics with chords from ultimate_guitar\n",
    "# TODO: verify aligned lyrics work\n",
    "# TODO: add hashed artists to tags\n",
    "# TODO: ditto encode: https://github.com/suno-ai/glockenspiel/blob/d341bfba429233aefab670f7b10a539507850ba1/suno_utils/suno_utils/tasks/ditto_v2.py#L198"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b3d719f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vocab: \n",
    "#   0-60_000 text\n",
    "#   1x0-3999   semantic\n",
    "\n",
    "#   4000 semantic pad token\n",
    "#   4001 semantic infer token\n",
    "\n",
    "# Memmaps:\n",
    "#   1d for semantic tokens\n",
    "# Metas (jsonl):\n",
    "#   N*Dict with meta keys \n",
    "#     'artists', 'bundle_id', 'dataset', 'duration_s', 'id', 'lang', 'n_tokens', \n",
    "#     'offset_idx', 'parent_id', 'playlist_ids', 'tags', 'task', 'text', 'type'\n",
    "# Infos (json):\n",
    "#   Dict with meta keys {\"dataset\": [\"idx_list\"]}\n",
    "\n",
    "# Bundles (mert_25_2x4k):\n",
    "# s3://suno-data/datasets/bundles/\n",
    "#  v4/discogs_subset\n",
    "#  v4/covers\n",
    "#  v4/genius\n",
    "#  v4/deezer\n",
    "#  v4/pond5\n",
    "#  v4/karaoke_stems\n",
    "#  v4/musdb_stems\n",
    "#  v4/imslp\n",
    "#  v4/discogs\n",
    "\n",
    "#  v4/youtube_music"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1535b931",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: update this to still keep the n_codebook dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ebfb8d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8692c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import tqdm\n",
    "import time\n",
    "import torch\n",
    "import funcy\n",
    "import json\n",
    "import gc\n",
    "import re\n",
    "import random\n",
    "import tempfile\n",
    "import collections\n",
    "import shutil\n",
    "from collections import defaultdict\n",
    "from joblib import Parallel, delayed\n",
    "from transformers import BertTokenizer\n",
    "\n",
    "from suno_utils.utils.text import write_jsonl, read_jsonl, write_json, read_json, normalize_whitespace\n",
    "from suno_utils.utils.s3 import read_from_s3, check_s3_file_exists\n",
    "\n",
    "TEXT_CODEBOOK_SIZE = 60_001\n",
    "TEXT_PAD_TOKEN = TEXT_CODEBOOK_SIZE\n",
    "TEXT_VOCAB_SIZE = 60_032\n",
    "\n",
    "SEMANTIC_CODEBOOK_SIZE = 4000\n",
    "SEMANTIC_N_CODEBOOKS = 1\n",
    "SEMANTIC_PAD_TOKEN = SEMANTIC_CODEBOOK_SIZE\n",
    "SEMANTIC_INFER_TOKEN = SEMANTIC_CODEBOOK_SIZE + 1\n",
    "SEMANTIC_VOCAB_SIZE = 4032\n",
    "SEMANTIC_RATE_HZ = 25\n",
    "SEMANTIC_SHIFT_FACTOR = 50\n",
    "assert(SEMANTIC_VOCAB_SIZE == (np.floor(SEMANTIC_CODEBOOK_SIZE // 64) + 1) * 64)\n",
    "\n",
    "SEMANTIC_EMBED_DIR = \"mert_25_2x4k\"\n",
    "\n",
    "METAS_DIR = \"/app/suno/tmp\"\n",
    "OUT_DATA_DIR = \"/app/suno/data/chirp_v5/v2\"\n",
    "os.makedirs(OUT_DATA_DIR, exist_ok=True)\n",
    "tokenizer_fp = os.path.join(OUT_DATA_DIR, \"tokenizer_60k.json\")\n",
    "if not os.path.exists(tokenizer_fp):\n",
    "    shutil.copy(\"/app/suno/data/chirp_v5/v1/tokenizer_60k.json\", tokenizer_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ed369b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load manifests of IDs and text and tags etc\n",
    "meta_info_map = {\n",
    "    \"discogs\": {m[\"id\"]: m for m in read_jsonl(os.path.join(METAS_DIR, \"clean_discogs_v0_metas.jsonl\"))},\n",
    "    \"discogs_subset\": {m[\"id\"]: m for m in read_jsonl(os.path.join(METAS_DIR, \"clean_discogs_subset_v0_metas.jsonl\"))},\n",
    "    \"covers\": {m[\"id\"]: m for m in read_jsonl(os.path.join(METAS_DIR, \"clean_covers_v0_metas.jsonl\"))},\n",
    "    \"genius\": {m[\"id\"]: m for m in read_jsonl(os.path.join(METAS_DIR, \"clean_genius_v0_metas.jsonl\"))},\n",
    "#     \"youtube_music\": {m[\"id\"]: m for m in read_jsonl(os.path.join(METAS_DIR, \"clean_youtube_music_v0_metas.jsonl\"))},\n",
    "    \"deezer\": {m[\"id\"]: m for m in read_jsonl(os.path.join(METAS_DIR, \"clean_deezer_v0_metas.jsonl\"))},\n",
    "    \"pond5\": {m[\"id\"]: m for m in read_jsonl(os.path.join(METAS_DIR, \"clean_pond5_v0_metas.jsonl\"))},\n",
    "    \"imslp\": {m[\"id\"]: m for m in read_jsonl(os.path.join(METAS_DIR, \"clean_imslp_v0_metas.jsonl\"))},\n",
    "    \"karaoke_stems\": {m[\"id\"]: m for m in read_jsonl(os.path.join(METAS_DIR, \"clean_karaoke_v0_metas.jsonl\"))},\n",
    "    \"musdb_stems\": {m[\"id\"]: m for m in read_jsonl(os.path.join(METAS_DIR, \"clean_musdb_v0_metas.jsonl\"))},\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bfa90044",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert SEMANTIC_N_CODEBOOKS == 1\n",
    "\n",
    "def _parse_array(task_name, dset_name, meta_info, semantic_arr):\n",
    "    assert \"lyrics\" not in meta_info  # catch incorrect naming\n",
    "    if \"is_reliable\" in meta_info and not meta_info[\"is_reliable\"]:\n",
    "        # only used for imslp right now\n",
    "        return []\n",
    "    if task_name is None:\n",
    "        task_name = \"default\"\n",
    "    arr = semantic_arr[:, 0].copy()\n",
    "    new_meta = {\n",
    "        \"id\": meta_info[\"id\"],\n",
    "        \"duration_s\": round(len(semantic_arr) / SEMANTIC_RATE_HZ, 2),\n",
    "    }\n",
    "    if \"text_lines\" in meta_info:\n",
    "        new_meta[\"text_lines\"] = meta_info[\"text_lines\"]\n",
    "        new_meta[\"lang\"] = meta_info.get(\"lang\")\n",
    "        if task_name == \"default\":\n",
    "            new_meta[\"dset_suffix\"] = \"lyrics_aligned\"\n",
    "    elif \"text\" in meta_info:\n",
    "        new_meta[\"text\"] = meta_info[\"text\"]\n",
    "        new_meta[\"lang\"] = meta_info.get(\"lang\")\n",
    "        if task_name == \"default\":\n",
    "            new_meta[\"dset_suffix\"] = (\n",
    "                \"lyrics\" if meta_info.get(\"lang\") == \"en\" else \"lyrics_foreign\"\n",
    "            )\n",
    "    if \"tags\" in meta_info:\n",
    "        new_meta[\"tags\"] = meta_info[\"tags\"]\n",
    "    if \"parent_id\" in meta_info:\n",
    "        new_meta[\"parent_id\"] = meta_info[\"parent_id\"] \n",
    "    if \"playlist_ids\" in meta_info:\n",
    "        new_meta[\"playlist_ids\"] = meta_info[\"playlist_ids\"] \n",
    "    if \"artists\" in meta_info:\n",
    "        new_meta[\"artists\"] = meta_info[\"artists\"]\n",
    "    if task_name == \"stems\":\n",
    "        assert meta_info[\"type\"] in (\"instrumental\", \"vocals\", \"full\")\n",
    "        new_meta[\"bundle_id\"] = meta_info[\"bundle_id\"]\n",
    "        new_meta[\"type\"] = meta_info[\"type\"]\n",
    "        if meta_info[\"type\"] == \"vocals\":\n",
    "            new_meta[\"dset_suffix\"] = \"underpaint\"\n",
    "            return [(\"underpaint\", arr, new_meta)]\n",
    "        elif meta_info[\"type\"] == \"instrumental\":\n",
    "            new_meta[\"dset_suffix\"] = \"overpaint\"\n",
    "            return [(\"overpaint\", arr, new_meta)]\n",
    "        assert meta_info[\"type\"] == \"full\"\n",
    "        new_meta[\"dset_suffix\"] = \"overpaint\"\n",
    "        new_meta2 = {k: v for k, v in new_meta.items()}\n",
    "        new_meta2[\"dset_suffix\"] = \"underpaint\"\n",
    "        return [(\"overpaint\", arr, new_meta), (\"underpaint\", arr, new_meta2)]\n",
    "    return [(task_name, arr, new_meta)]\n",
    "\n",
    "\n",
    "def _process_archives(\n",
    "    task_name,\n",
    "    dset_name,\n",
    "    s3_semantic_archive_filepath,\n",
    "    relevant_metas,\n",
    "):\n",
    "#     print(len(relevant_metas))\n",
    "    semantic_archive = {}\n",
    "    if not check_s3_file_exists(s3_semantic_archive_filepath):\n",
    "        print(f\"missing {s3_semantic_archive_filepath}\")\n",
    "        return []    \n",
    "    try:\n",
    "        archive = {k: v for k, v in read_from_s3(s3_semantic_archive_filepath, read_f=np.load).items()}\n",
    "    except:\n",
    "        # corrupt archive\n",
    "        print(f\"corrupt {s3_semantic_archive_filepath}\")\n",
    "        return []\n",
    "    for k, v in archive.items():\n",
    "        semantic_archive[k] = v\n",
    "\n",
    "    semantic_uids = list(semantic_archive.keys())\n",
    "    arr_list = []\n",
    "    for uid in semantic_uids:\n",
    "        if uid not in relevant_metas:\n",
    "            continue\n",
    "        semantic_arr = semantic_archive[uid]\n",
    "        arr_list.extend(\n",
    "            _parse_array(task_name, dset_name, relevant_metas[uid], semantic_arr)\n",
    "        )\n",
    "    del semantic_archive\n",
    "    gc.collect()\n",
    "    return arr_list\n",
    "\n",
    "\n",
    "def _collect_uids(s3_semantic_metas_filepath):\n",
    "    try:\n",
    "        metas = read_from_s3(s3_semantic_metas_filepath, read_f=read_jsonl)\n",
    "    except:\n",
    "        print(f\"failed on metas for fp: {s3_semantic_metas_filepath}\")\n",
    "        return set([])\n",
    "    return set([m[\"id\"] for m in metas])\n",
    "\n",
    "\n",
    "def _prep_data(\n",
    "    dataset,\n",
    "    njobs=5,\n",
    "    chunksize=10,\n",
    "    is_val=False,\n",
    "    n_offs=0,\n",
    "):  \n",
    "    dset_name, dset_version, (start_idx, end_idx), task_name = dataset\n",
    "    dset_type = \"val\" if is_val else \"tr\"\n",
    "    out_mm_filepath = os.path.join(OUT_DATA_DIR, f\"data_{dset_type}.bin\")\n",
    "    out_metas_filepath = os.path.join(OUT_DATA_DIR, f\"metas_{dset_type}.jsonl\")\n",
    "    tot_duration_dict = defaultdict(int)\n",
    "    n_chunks = int(np.ceil((end_idx - start_idx) / chunksize))\n",
    "    for idx_chunk in tqdm.tqdm(\n",
    "        funcy.chunks(chunksize, list(range(start_idx, end_idx))), total=n_chunks\n",
    "    ):\n",
    "        n_jobs = np.min([njobs, chunksize, len(idx_chunk)])\n",
    "        # collect relevant parts of meta file to avoid copying all to subprocesses\n",
    "        tmp_uid_chunks = Parallel(n_jobs=n_jobs, prefer=\"threads\")(\n",
    "            delayed(_collect_uids)(\n",
    "                f\"s3://suno-data/datasets/bundles/{dset_version}/{dset_name}/{SEMANTIC_EMBED_DIR}/\"\n",
    "                + f\"metas/part_{idx}.jsonl\"\n",
    "            )\n",
    "            for idx in idx_chunk\n",
    "        )\n",
    "        ## PART A: takes ~40% of loop time\n",
    "        uids_per_part = {idx: tmp_uid_chunks[n] for n, idx in enumerate(idx_chunk)}\n",
    "        # print(len(uids_per_part))\n",
    "        # collect data\n",
    "        encoded_arrays_list = Parallel(n_jobs=n_jobs, prefer=\"processes\")(\n",
    "            delayed(_process_archives)(\n",
    "                task_name,\n",
    "                dset_name,\n",
    "                f\"s3://suno-data/datasets/bundles/{dset_version}/{dset_name}/{SEMANTIC_EMBED_DIR}/\"\n",
    "                + f\"part_{idx}.npz\",\n",
    "                {\n",
    "                    uid: meta_info_map[dset_name][uid]\n",
    "                    for uid in uids_per_part[idx]\n",
    "                    if uid in meta_info_map[dset_name]\n",
    "                },\n",
    "            )\n",
    "            for idx in idx_chunk\n",
    "        )\n",
    "        ## end Part A\n",
    "        ## PART B: takes ~40% of loop time\n",
    "        add_metas = []\n",
    "        for encoded_arrays in encoded_arrays_list:\n",
    "            to_write_len = np.sum([arr.size for _, arr, _ in encoded_arrays])\n",
    "            if to_write_len == 0:\n",
    "                continue\n",
    "            out_mm = np.memmap(\n",
    "                out_mm_filepath,\n",
    "                dtype=np.uint16,\n",
    "                mode=\"r+\",\n",
    "                shape=(n_offs + to_write_len,),\n",
    "            )\n",
    "            for assigned_task_name, arr, arr_meta in encoded_arrays:\n",
    "                out_mm[n_offs : n_offs + arr.size] = arr\n",
    "                dataset_str = dset_name\n",
    "                if \"dset_suffix\" in arr_meta:\n",
    "                    dataset_str += f\"_{arr_meta['dset_suffix']}\"\n",
    "                add_meta = {\n",
    "                    \"dataset\": dataset_str,\n",
    "                    \"task\": assigned_task_name,\n",
    "                    \"id\": arr_meta[\"id\"],\n",
    "                    \"duration_s\": arr_meta[\"duration_s\"],\n",
    "                    \"n_tokens\": arr.size,\n",
    "                    \"offset_idx\": n_offs,\n",
    "                }\n",
    "                for k in [\n",
    "                    \"original_id\",\n",
    "                    \"tags\",\n",
    "                    \"text\",\n",
    "                    \"lang\",\n",
    "                    \"text_lines\",\n",
    "                    \"artists\",\n",
    "                    \"parent_id\",\n",
    "                    \"bundle_id\",\n",
    "                    \"playlist_ids\",\n",
    "                    \"type\",\n",
    "                ]:\n",
    "                    if k in arr_meta:\n",
    "                        add_meta[k] = arr_meta[k]\n",
    "                n_offs += arr.size\n",
    "                tot_duration_dict[dataset_str] += arr_meta[\"duration_s\"]\n",
    "                add_metas.append(add_meta)\n",
    "            # write it once\n",
    "            out_mm.flush()\n",
    "            del out_mm\n",
    "        ## end Part B\n",
    "        write_jsonl(\n",
    "            add_metas,\n",
    "            os.path.join(out_metas_filepath),\n",
    "            do_append=bool(n_offs != 0),\n",
    "        )\n",
    "        del encoded_arrays_list\n",
    "        # TODO: this gc collect takes super long (>50% of loop if on) let's try to disable\n",
    "#         gc.collect()\n",
    "    for k, v in tot_duration_dict.items():\n",
    "        print(f\"{round(v / 60 / 60):,} hours of {k}\")\n",
    "    return n_offs\n",
    "\n",
    "\n",
    "def prep_data(\n",
    "    datasets,\n",
    "    is_val=False,\n",
    "    njobs=5,\n",
    "    chunksize=10,\n",
    "):\n",
    "    n_offs = 0\n",
    "    dset_type = \"val\" if is_val else \"tr\"\n",
    "    out_mm_filepath = os.path.join(OUT_DATA_DIR, f\"data_{dset_type}.bin\")\n",
    "    out_metas_filepath = os.path.join(OUT_DATA_DIR, f\"metas_{dset_type}.jsonl\")\n",
    "    out_mm = np.memmap(out_mm_filepath, dtype=np.uint16, mode=\"w+\", shape=(1,))\n",
    "    with open(out_metas_filepath, \"w\") as f:\n",
    "        f.write(\"\")\n",
    "    print(\"starting data prep...\")\n",
    "    for dataset in datasets:\n",
    "        n_offs = _prep_data(\n",
    "            dataset,\n",
    "            njobs=njobs,\n",
    "            chunksize=chunksize,\n",
    "            is_val=is_val,\n",
    "            n_offs=n_offs,\n",
    "        )\n",
    "    print(\"done.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4f2bf02",
   "metadata": {},
   "outputs": [],
   "source": [
    "NJOBS = 32\n",
    "CHUNKSIZE = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3de7e3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting data prep...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 1/1 [02:12<00:00, 132.63s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 hours of discogs_subset_lyrics_foreign\n",
      "9 hours of discogs_subset\n",
      "12 hours of discogs_subset_lyrics\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 1/1 [02:12<00:00, 132.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 hours of covers\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 1/1 [02:12<00:00, 132.50s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 hours of karaoke_stems_overpaint\n",
      "4 hours of karaoke_stems_underpaint\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 1/1 [02:13<00:00, 133.93s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 hours of musdb_stems_overpaint\n",
      "2 hours of musdb_stems_underpaint\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 1/1 [02:14<00:00, 134.16s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 hours of genius_lyrics\n",
      "11 hours of genius_lyrics_foreign\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 1/1 [02:14<00:00, 134.10s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 hours of deezer_lyrics_aligned\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 1/1 [02:14<00:00, 134.14s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 hours of pond5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 1/1 [02:14<00:00, 134.17s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 hours of imslp\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 1/1 [02:14<00:00, 134.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27 hours of discogs\n",
      "3 hours of discogs_lyrics\n",
      "2 hours of discogs_lyrics_foreign\n",
      "done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "datasets = [\n",
    "    (\"discogs_subset\", \"v4\", (0, 1), \"default\"),\n",
    "    (\"covers\", \"v4\", (0, 1), \"covers\"),\n",
    "    (\"karaoke_stems\", \"v4\", (0, 1), \"stems\"),\n",
    "    (\"musdb_stems\", \"v4\", (0, 1), \"stems\"),\n",
    "    (\"genius\", \"v4\", (0, 1), \"default\"),\n",
    "    (\"deezer\", \"v4\", (0, 1), \"default\"),\n",
    "    (\"pond5\", \"v4\", (0, 1), \"default\"),\n",
    "#     (\"youtube_music\", \"v4\", (0, 1), \"default\"),\n",
    "    (\"imslp\", \"v4\", (0, 1), \"default\"),\n",
    "    (\"discogs\", \"v4\", (0, 1), \"default\"),\n",
    "]\n",
    "prep_data(\n",
    "    datasets,\n",
    "    is_val=True,\n",
    "    njobs=NJOBS,\n",
    "    chunksize=CHUNKSIZE,\n",
    ")\n",
    "#  9 hours of discogs_subset\n",
    "# 12 hours of discogs_subset_lyrics\n",
    "# 12 hours of discogs_subset_lyrics_foreign\n",
    "# 18 hours of covers\n",
    "#  4 hours of karaoke_stems_overpaint\n",
    "#  4 hours of karaoke_stems_underpaint\n",
    "#  2 hours of musdb_stems_overpaint\n",
    "#  2 hours of musdb_stems_underpaint\n",
    "# 11 hours of genius_lyrics_foreign\n",
    "# 19 hours of genius_lyrics\n",
    "# 28 hours of deezer_lyrics_aligned\n",
    "# 15 hours of pond5\n",
    "#  3 hours of imslp\n",
    "# 27 hours of discogs\n",
    "# 3 hours of discogs_lyrics\n",
    "# 2 hours of discogs_lyrics_foreign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d4b7f18f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting data prep...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████| 183/183 [16:28<00:00,  5.40s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71,026 hours of discogs_subset_lyrics_foreign\n",
      "69,964 hours of discogs_subset_lyrics\n",
      "48,850 hours of discogs_subset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████| 117/117 [08:50<00:00,  4.54s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65,475 hours of covers\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████| 131/131 [12:04<00:00,  5.53s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75,758 hours of genius_lyrics\n",
      "45,223 hours of genius_lyrics_foreign\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 49/49 [06:53<00:00,  8.43s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42,892 hours of deezer_lyrics_aligned\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████| 130/130 [07:39<00:00,  3.53s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63,947 hours of pond5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████| 18/18 [02:43<00:00,  9.10s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,181 hours of imslp\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 3264/3264 [4:19:51<00:00,  4.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,812,865 hours of discogs\n",
      "190,154 hours of discogs_lyrics_foreign\n",
      "226,158 hours of discogs_lyrics\n",
      "done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "datasets = [\n",
    "    (\"discogs_subset\", \"v4\", (1, 5834), \"default\"),\n",
    "    (\"covers\", \"v4\", (1, 3739), \"covers\"),\n",
    "    (\"karaoke_stems\", \"v4\", (1, 387), \"stems\"),\n",
    "    (\"musdb_stems\", \"v4\", (1, 9), \"stems\"),\n",
    "    (\"genius\", \"v4\", (1, 4181), \"default\"),\n",
    "    (\"deezer\", \"v4\", (1, 1538), \"default\"),\n",
    "    (\"pond5\", \"v4\", (1, 4138), \"default\"),\n",
    "#     (\"youtube_music\", \"v4\", (1, 4028), \"default\"),\n",
    "    (\"imslp\", \"v4\", (1, 549), \"default\"),\n",
    "    (\"discogs\", \"v4\", (1, 104442), \"default\"),\n",
    "]\n",
    "prep_data(\n",
    "    datasets,\n",
    "    is_val=False,\n",
    "    njobs=NJOBS,\n",
    "    chunksize=CHUNKSIZE,\n",
    ")\n",
    "#  48,850 hours of discogs_subset\n",
    "#  69,964 hours of discogs_subset_lyrics\n",
    "#  71,026 hours of discogs_subset_lyrics_foreign\n",
    "# 112,044 hours of covers\n",
    "#   1,480 hours of karaoke_stems_overpaint\n",
    "#   1,480 hours of karaoke_stems_underpaint\n",
    "#      17 hours of musdb_stems_overpaint\n",
    "#      17 hours of musdb_stems_underpaint\n",
    "#  75,758 hours of genius_lyrics\n",
    "#  45,223 hours of genius_lyrics_foreign\n",
    "#  42,892 hours of deezer_lyrics_aligned\n",
    "#  63,947 hours of pond5\n",
    "#   2,181 hours of imslp\n",
    "# XXX hours of discogs\n",
    "# XXX hours of discogs_lyrics\n",
    "# XXX hours of discogs_lyrics_foreign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8ef37feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST: check memmaps in diffusion infer notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "18645d1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# verify a few basics\n",
    "mm = np.memmap(os.path.join(OUT_DATA_DIR, \"data_val.bin\"), dtype=np.uint16, mode=\"r\")\n",
    "metas = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_val.jsonl\"))\n",
    "assert len(mm.shape) == 1\n",
    "assert len(mm) == sum([m[\"n_tokens\"] for m in metas])\n",
    "assert len(mm) == metas[-1][\"offset_idx\"] + metas[-1][\"n_tokens\"]\n",
    "assert mm[:1000].min() >= 0\n",
    "assert mm[:1000].max() <= 4000-1\n",
    "assert(\n",
    "    set(funcy.flatten([list(m.keys()) for m in metas])) == \n",
    "    set([\n",
    "        'id', 'duration_s', 'dataset', 'task', 'offset_idx', 'n_tokens', \n",
    "        'tags', 'text', 'text_lines', 'lang', 'artists', 'parent_id', 'bundle_id', 'playlist_ids', 'type'\n",
    "    ])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b8cc49b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "val:\n",
      "3,578 metas with 170 hours total\n",
      "2.3 of tracks with cover\n",
      "6.2 of tracks with cover input\n",
      "5.1 of tracks with overpaint\n",
      "5.1 of tracks with underpaint\n",
      "27.8% of tracks with lyrics\n",
      "13.9% of tracks with aligned lyrics\n",
      "44.1% of tracks with tags\n",
      "0.5% of tracks with artists\n",
      "0.0% of tracks with playlists\n",
      "\n",
      "tr:\n",
      "61,104,892 metas with 3,717,487 hours total\n",
      "0.5 of tracks with cover\n",
      "1.3 of tracks with cover input\n",
      "0.1 of tracks with overpaint\n",
      "0.1 of tracks with underpaint\n",
      "18.3% of tracks with lyrics\n",
      "1.3% of tracks with aligned lyrics\n",
      "26.9% of tracks with tags\n",
      "84.5% of tracks with artists\n",
      "13.9% of tracks with playlists\n"
     ]
    }
   ],
   "source": [
    "# write info files (includes task index mapping)\n",
    "from collections import defaultdict\n",
    "from suno_utils.utils.text import _fast_count_lines\n",
    "for dset_type in [\"val\", \"tr\"]:\n",
    "    metas = read_jsonl(os.path.join(OUT_DATA_DIR, f\"metas_{dset_type}.jsonl\"))\n",
    "    out_info_filepath = os.path.join(OUT_DATA_DIR, f\"info_{dset_type}.json\")\n",
    "\n",
    "    id2idx_map = {f\"{m['dataset']}__{m['id']}\": idx for idx, m in enumerate(metas)}\n",
    "\n",
    "    tmp_covers_map = {}\n",
    "    tmp_overpaint_map = {}\n",
    "    tmp_overpaint_bundle_map = {}\n",
    "    tmp_underpaint_map = {}\n",
    "    tmp_underpaint_bundle_map = {}\n",
    "    datasets_info = {}\n",
    "    for idx, m in enumerate(metas):\n",
    "        if m[\"dataset\"] not in datasets_info:\n",
    "            datasets_info[m[\"dataset\"]] = {\n",
    "                \"idx_list\": [],\n",
    "                \"task\": m[\"task\"],\n",
    "            }\n",
    "        if m[\"task\"] == \"default\":\n",
    "            datasets_info[m[\"dataset\"]][\"idx_list\"].append(idx)\n",
    "        elif m[\"task\"] == \"covers\":\n",
    "            if \"parent_id\" not in m:\n",
    "                tmp_covers_map[idx] = []\n",
    "                datasets_info[m[\"dataset\"]][\"idx_list\"].append(idx)\n",
    "            else:\n",
    "                if f\"{m['dataset']}__{m['parent_id']}\" not in id2idx_map:\n",
    "                    continue\n",
    "                parent_idx = id2idx_map[f\"{m['dataset']}__{m['parent_id']}\"]\n",
    "                if parent_idx not in tmp_covers_map:\n",
    "                    tmp_covers_map[parent_idx] = []\n",
    "                tmp_covers_map[parent_idx].append(idx) \n",
    "        elif m[\"task\"] == \"overpaint\":\n",
    "            if m[\"type\"] == \"instrumental\":\n",
    "                datasets_info[m[\"dataset\"]][\"idx_list\"].append(idx)\n",
    "            else:\n",
    "                assert m[\"type\"] == \"full\"\n",
    "            if m[\"bundle_id\"] not in tmp_overpaint_map:\n",
    "                tmp_overpaint_map[m[\"bundle_id\"]] = {}\n",
    "            tmp_overpaint_map[m[\"bundle_id\"]][m[\"type\"]] = idx\n",
    "            tmp_overpaint_bundle_map[idx] = m[\"bundle_id\"]\n",
    "        elif m[\"task\"] == \"underpaint\":\n",
    "            if m[\"type\"] == \"vocals\":\n",
    "                datasets_info[m[\"dataset\"]][\"idx_list\"].append(idx)\n",
    "            else:\n",
    "                assert m[\"type\"] == \"full\"\n",
    "            if m[\"bundle_id\"] not in tmp_underpaint_map:\n",
    "                tmp_underpaint_map[m[\"bundle_id\"]] = {}\n",
    "            tmp_underpaint_map[m[\"bundle_id\"]][m[\"type\"]] = idx\n",
    "            tmp_underpaint_bundle_map[idx] = m[\"bundle_id\"]\n",
    "        else:\n",
    "            raise NotImplementedError()\n",
    "\n",
    "    # translate idx_list for tasks\n",
    "    for k in datasets_info.keys():\n",
    "        if datasets_info[k][\"task\"] == \"covers\":\n",
    "            if \"idx_map\" not in datasets_info[k]:\n",
    "                datasets_info[k][\"idx_map\"] = {}\n",
    "            for idx in datasets_info[k][\"idx_list\"]:\n",
    "                if len(tmp_covers_map[idx]) > 0:\n",
    "                    datasets_info[k][\"idx_map\"][idx] = tmp_covers_map[idx]\n",
    "            del datasets_info[k][\"idx_list\"]\n",
    "        elif datasets_info[k][\"task\"] == \"overpaint\":\n",
    "            if \"idx_map\" not in datasets_info[k]:\n",
    "                datasets_info[k][\"idx_map\"] = {}\n",
    "            for idx in datasets_info[k][\"idx_list\"]:\n",
    "                if len(tmp_overpaint_map[tmp_overpaint_bundle_map[idx]]) == 2:\n",
    "                    assert idx == tmp_overpaint_map[tmp_overpaint_bundle_map[idx]][\"instrumental\"]\n",
    "                    full_idx = tmp_overpaint_map[tmp_overpaint_bundle_map[idx]][\"full\"]\n",
    "                    datasets_info[k][\"idx_map\"][full_idx] = idx\n",
    "            del datasets_info[k][\"idx_list\"]\n",
    "        elif datasets_info[k][\"task\"] == \"underpaint\":\n",
    "            if \"idx_map\" not in datasets_info[k]:\n",
    "                datasets_info[k][\"idx_map\"] = {}\n",
    "            for idx in datasets_info[k][\"idx_list\"]:\n",
    "                if len(tmp_underpaint_map[tmp_underpaint_bundle_map[idx]]) == 2:\n",
    "                    assert idx == tmp_underpaint_map[tmp_underpaint_bundle_map[idx]][\"vocals\"]\n",
    "                    full_idx = tmp_underpaint_map[tmp_underpaint_bundle_map[idx]][\"full\"]\n",
    "                    datasets_info[k][\"idx_map\"][full_idx] = idx\n",
    "            del datasets_info[k][\"idx_list\"]\n",
    "        elif datasets_info[k][\"task\"] == \"default\":\n",
    "            pass\n",
    "        else:\n",
    "            raise NotImplementedError()\n",
    "            \n",
    "    write_json(datasets_info, out_info_filepath)\n",
    "    \n",
    "    print(f\"\\n{dset_type}:\")\n",
    "    datasets_info = read_json(os.path.join(OUT_DATA_DIR, f\"info_{dset_type}.json\"))\n",
    "#     n_metas = _fast_count_lines(os.path.join(OUT_DATA_DIR, f\"metas_{dset_type}.jsonl\"))\n",
    "    n_default = 0\n",
    "    n_covers = 0\n",
    "    n_covers_inputs = 0\n",
    "    n_overpaint = 0\n",
    "    n_underpaint = 0\n",
    "    for k in datasets_info.keys():\n",
    "        if datasets_info[k][\"task\"] == \"covers\":\n",
    "            n_covers += len(datasets_info[k][\"idx_map\"])\n",
    "            for _, e in datasets_info[k][\"idx_map\"].items():\n",
    "                n_covers_inputs += len(e)\n",
    "        elif datasets_info[k][\"task\"] == \"overpaint\":\n",
    "            n_overpaint += len(datasets_info[k][\"idx_map\"])\n",
    "        elif datasets_info[k][\"task\"] == \"underpaint\":\n",
    "            n_underpaint += len(datasets_info[k][\"idx_map\"])\n",
    "        else:\n",
    "            n_default += len(datasets_info[k][\"idx_list\"])\n",
    "    print(f\"{len(metas):,} metas with {round(sum([m['duration_s'] for m in metas])/60/60):,} hours total\")\n",
    "#     print(f\"{round(n_default/len(metas)*100, 1):,} tracks\")\n",
    "    print(f\"{round(n_covers/len(metas)*100, 1):,} of tracks with cover\")\n",
    "    print(f\"{round(n_covers_inputs/len(metas)*100, 1):,} of tracks with cover input\")\n",
    "    print(f\"{round(n_overpaint/len(metas)*100, 1):,} of tracks with overpaint\")\n",
    "    print(f\"{round(n_underpaint/len(metas)*100, 1):,} of tracks with underpaint\")\n",
    "    \n",
    "    n_lyrics = 0\n",
    "    n_lyrics_aligned = 0\n",
    "    n_tags = 0\n",
    "    d_artists = defaultdict(set)\n",
    "    d_playlists = defaultdict(set)\n",
    "    for m in metas:\n",
    "        if \"text\" in m:\n",
    "            n_lyrics += 1\n",
    "        if \"text_lines\" in m:\n",
    "            n_lyrics_aligned += 1\n",
    "        if \"tags\" in m:\n",
    "            n_tags += 1\n",
    "        for _id in m.get(\"artists\", []):\n",
    "            d_artists[_id].add(m[\"id\"])\n",
    "        for _id in m.get(\"playlist_ids\", []):\n",
    "            d_playlists[_id].add(m[\"id\"])\n",
    "    s_artists = set()\n",
    "    s_playlists = set()\n",
    "    for k, v in d_artists.items():\n",
    "        if len(v) >= 2:\n",
    "            s_artists |= v\n",
    "    for k, v in d_playlists.items():\n",
    "        if len(v) >= 2:\n",
    "            s_playlists |= v\n",
    "    n_artists = len(s_artists)\n",
    "    n_playlists = len(s_playlists)\n",
    "    print(f\"{round(n_lyrics/len(metas)*100, 1):,}% of tracks with lyrics\")\n",
    "    print(f\"{round(n_lyrics_aligned/len(metas)*100, 1):,}% of tracks with aligned lyrics\")\n",
    "    print(f\"{round(n_tags/len(metas)*100, 1):,}% of tracks with tags\")\n",
    "    print(f\"{round(n_artists/len(metas)*100, 1):,}% of tracks with artists\")\n",
    "    print(f\"{round(n_playlists/len(metas)*100, 1):,}% of tracks with playlists\")\n",
    "#     break\n",
    "    \n",
    "# val:\n",
    "# 3,578 metas with 170 hours total\n",
    "# 2.3 of tracks with cover\n",
    "# 6.2 of tracks with cover input\n",
    "# 5.1 of tracks with overpaint\n",
    "# 5.1 of tracks with underpaint\n",
    "# 27.8% of tracks with lyrics\n",
    "# 13.9% of tracks with aligned lyrics\n",
    "# 44.1% of tracks with tags\n",
    "# 0.5% of tracks with artists\n",
    "# 0.0% of tracks with playlists\n",
    "\n",
    "# tr:\n",
    "# 61,104,892 metas with 3,717,487 hours total\n",
    "# 0.5 of tracks with cover\n",
    "# 1.3 of tracks with cover input\n",
    "# 0.1 of tracks with overpaint\n",
    "# 0.1 of tracks with underpaint\n",
    "# 18.3% of tracks with lyrics\n",
    "# 1.3% of tracks with aligned lyrics\n",
    "# 26.9% of tracks with tags\n",
    "# 84.5% of tracks with artists\n",
    "# 13.9% of tracks with playlists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fb5dfee7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/app/suno/data/chirp_v5/v2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['data_tr.bin',\n",
       " 'data_val.bin',\n",
       " 'info_tr.json',\n",
       " 'info_val.json',\n",
       " 'metas_tr.jsonl',\n",
       " 'metas_val.jsonl',\n",
       " 'tokenizer_60k.json']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(OUT_DATA_DIR)\n",
    "sorted(os.listdir(OUT_DATA_DIR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55a2fce3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e4d504f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "174cf902",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a8dd16d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e67c61d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8c7ebf8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb70b1a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "630416c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0a6a2263",
   "metadata": {},
   "source": [
    "## Test dataload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "025c79b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a976cb64",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, \"/home/georg/code/neon/sunoGPT/\")\n",
    "from data_utils import get_batch, tokenize_batch, _load_tokenizer, get_sample, load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3a65e34f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-01-25_16:18:03]: number of parameters: 3056M\n"
     ]
    }
   ],
   "source": [
    "from modules.gpt import GPT, GPTConfig, GPTTrainConfig\n",
    "model_cfg = GPTConfig(\n",
    "#     n_layer=28,\n",
    "#     n_head=28,\n",
    "#     d_head=112,\n",
    "#     n_kv_head=4,\n",
    "    block_size=14_080,\n",
    "    t_text=2048,\n",
    "    t_audio=12_000,\n",
    "    coarse_n_codebooks=0,\n",
    ")\n",
    "train_cfg = GPTTrainConfig()\n",
    "model = GPT(model_cfg, train_cfg)\n",
    "cfg = model.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "763928d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "import json\n",
    "from data_utils import read_jsonl\n",
    "from suno_utils.utils.text import read_json\n",
    "\n",
    "data_dir = \"/app/suno/data/chirp_v5/v0\"\n",
    "\n",
    "val_filename = \"data_val.bin\"\n",
    "val_info_filename = \"info_val.json\"\n",
    "val_metas_filename = \"metas_val.jsonl\"\n",
    "\n",
    "infill_augment = True\n",
    "artist_condition = True\n",
    "cover_condition = True\n",
    "overpaint_condition = True\n",
    "underpaint_condition = True\n",
    "playlist_condition = False\n",
    "pack = True\n",
    "batch_size = 2\n",
    "batch_size_tokens = cfg.block_size * batch_size\n",
    "local_data_shard_dir = None\n",
    "weights_multiplier_map = {}\n",
    "is_finetune = False\n",
    "\n",
    "data_load_kwargs = dict(\n",
    "    t_text=cfg.t_text,\n",
    "    semantic_n_codebooks=cfg.semantic_n_codebooks,\n",
    "    semantic_vocab_size=cfg.semantic_vocab_size,\n",
    "    coarse_n_codebooks=cfg.coarse_n_codebooks,\n",
    "    coarse_vocab_size=cfg.coarse_vocab_size,\n",
    "    pack=pack,\n",
    "    batch_size_tokens=batch_size_tokens,\n",
    "    artist_condition=artist_condition,\n",
    "    cover_condition=cover_condition,\n",
    "    overpaint_condition=overpaint_condition,\n",
    "    underpaint_condition=underpaint_condition,\n",
    "    playlist_condition=playlist_condition,\n",
    "    local_data_shard_dir=local_data_shard_dir,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ea6c3c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-01-24_23:56:14]: found 2 samples with matching artists found\n",
      "[2025-01-24_23:56:14]: found 105 samples with 395 total covers\n",
      "[2025-01-24_23:56:14]: found 166 total overpaints\n",
      "[2025-01-24_23:56:14]: found 167 total underpaint\n",
      "[2025-01-24_23:56:14]: found 16 total overpaints\n",
      "[2025-01-24_23:56:14]: found 17 total underpaint\n",
      "[2025-01-24_23:56:14]: indexed 99.9% of data\n",
      "[2025-01-24_23:56:14]: 3,225 lines of metas_val.jsonl loaded.\n"
     ]
    }
   ],
   "source": [
    "(\n",
    "    val_dataset_names,\n",
    "    val_data_idx_lists,\n",
    "    val_data_weights,\n",
    "    val_data,\n",
    "    val_metas,\n",
    "    val_info,\n",
    "    val_artist_to_songs,\n",
    "    val_playlist_to_songs,\n",
    ") = load_dataset(\n",
    "    data_load_kwargs,\n",
    "    data_dir,\n",
    "    val_filename,\n",
    "    val_info_filename,\n",
    "    val_metas_filename,\n",
    "    weights_multiplier_map,\n",
    "    is_finetune,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f800675c",
   "metadata": {},
   "source": [
    "### get_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b390762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WILL USE FLASH ATTN: True\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0, \"/home/georg/code/glockenspiel/suno_utils/\")\n",
    "from suno_utils.gpt.prompt import Prompt\n",
    "prompt = Prompt(\"\", cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c3039407",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = next(model.parameters()).device\n",
    "use_val_data_weights = val_data_weights\n",
    "\n",
    "# override weights\n",
    "use_val_data_weights = [0]*len(val_data_weights)\n",
    "use_val_data_weights[val_dataset_names.index(\"covers\")] = 1\n",
    "\n",
    "data_sampling_info = {\n",
    "    \"cfg\": cfg,\n",
    "    \"train_cfg\": train_cfg,\n",
    "    \"batch_size\": batch_size,\n",
    "    \"batch_size_tokens\": batch_size_tokens,\n",
    "    \"tokenizer_fp\": \"/app/suno/data/chirp_v4/base/tokenizer_60k.json\",\n",
    "    \"device\": device,\n",
    "    \"device_type\": \"cuda\" if \"cuda\" in str(device) else \"cpu\",\n",
    "    \"val\": {\n",
    "        \"data\": val_data,\n",
    "        \"metas\": val_metas,\n",
    "        \"infos\": val_info,\n",
    "        \"artist_to_songs\": val_artist_to_songs,\n",
    "        \"playlist_to_songs\": val_playlist_to_songs,\n",
    "        \"names\": val_dataset_names,\n",
    "        \"weights\": use_val_data_weights,\n",
    "        \"idx_lists\": val_data_idx_lists,\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6c42add6",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_idx, text, _, x_arr = get_sample(\n",
    "    data_sampling_info,\n",
    "    \"val\",\n",
    "    dataset_idx=None,\n",
    "    row_idx=None,  # absolute, overrides dataset_idx\n",
    "    inference=False,\n",
    "    suppress_text=False,\n",
    "    dummy_data=False,\n",
    "    min_text_offs=None,\n",
    "    infill_augment=infill_augment,\n",
    "    dropout_semantic=False,\n",
    "    allow_artist_condition=artist_condition,\n",
    "    allow_cover=cover_condition,\n",
    "    allow_overpaint=overpaint_condition,\n",
    "    allow_underpaint=underpaint_condition,\n",
    "    allow_playlist_condition=playlist_condition,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "02d483d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(len(val_metas))\n",
    "# for dset in val_dataset_names:\n",
    "#     l = [m for m in val_metas if dset in m[\"dataset\"]]\n",
    "#     print(dset, \"--\", len([m for m in l if \"tags\" in m]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8fa5a75b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# val_metas[row_idx].get(\"tags\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4f5fc21c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 10000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 10000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prompt.visualize(x_arr, compress=True)\n",
    "prompt.visualize(x_arr, compress=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b20c419",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8307a211",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b85d8d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f6c1359",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}