scrape_analysis/scrape_hf.py

#!/usr/bin/env python3
"""
Scrape HuggingFace Hub for transformers-compatible models, fetch tokenizer.json
for each (streaming only pre_tokenizer + normalizer, not the vocab), dump to JSONL.
Uses cached HF token for higher rate limits.
"""
import json, os, sys, time, urllib.request, urllib.error, concurrent.futures, threading
from collections import Counter

BASE = "https://huggingface.co/api/models"
RESOLVE = "https://huggingface.co/{mid}/resolve/main/tokenizer.json"
OUT = os.path.join(os.path.dirname(__file__), "scrape_hf.jsonl")
MODELS_OUT = os.path.join(os.path.dirname(__file__), "model_list.json")

TARGET = 10000
MAX_WORKERS = 40
TIMEOUT = 15

# Load HF token from cache for auth  
def get_hf_token():
    tok_path = os.path.expanduser("~/.cache/huggingface/token")
    try:
        with open(tok_path) as f:
            return f.read().strip()
    except:
        return None

HF_TOKEN = get_hf_token()

def api_headers():
    h = {"User-Agent": "hf-scrape/1.0"}
    if HF_TOKEN:
        h["Authorization"] = f"Bearer {HF_TOKEN}"
    return h

# ── streaming pre_tokenizer extractor ──────────────────────────────────────────

def find_key_value(buf, key):
    needle = b'"' + key.encode() + b'"'
    idx = buf.find(needle)
    if idx == -1:
        return "NOT_FOUND", -1
    i = idx + len(needle)
    while i < len(buf) and buf[i:i+1] in (b' ', b'\t', b'\n', b'\r', b':'):
        i += 1
    if i >= len(buf):
        return "INCOMPLETE", idx
    start = i
    b0 = buf[i]
    if b0 in (0x6E, 0x74, 0x66):  # null/true/false
        j = i
        while j < len(buf) and buf[j] not in (b',', b'}', b']', 0x20, 0x09, 0x0A, 0x0D):
            j += 1
        if j < len(buf):
            return buf[start:j].decode('utf-8', errors='replace'), j
        return "INCOMPLETE", idx
    if b0 == 0x22:  # string
        j = i + 1
        esc = False
        while j < len(buf):
            if esc:
                esc = False
            elif buf[j] == 0x5C:
                esc = True
            elif buf[j] == 0x22:
                return buf[start:j+1].decode('utf-8', errors='replace'), j+1
            j += 1
        return "INCOMPLETE", idx
    if (0x30 <= b0 <= 0x39) or b0 == 0x2D:  # number
        j = i
        while j < len(buf) and buf[j] not in (b',', b'}', b']', 0x20, 0x09, 0x0A, 0x0D):
            j += 1
        if j < len(buf):
            return buf[start:j].decode('utf-8', errors='replace'), j
        return "INCOMPLETE", idx
    # object/array -- brace-match
    depth = 0
    in_str = False
    esc = False
    while i < len(buf):
        b = buf[i]
        if in_str:
            if esc:
                esc = False
            elif b == 0x5C:
                esc = True
            elif b == 0x22:
                in_str = False
        else:
            if b == 0x22:
                in_str = True
            elif b in (0x7B, 0x5B):
                depth += 1
            elif b in (0x7D, 0x5D):
                depth -= 1
                if depth == 0:
                    return buf[start:i+1].decode('utf-8', errors='replace'), i+1
        i += 1
    return "INCOMPLETE", idx

def stream_pre_tokenizer(url, timeout=TIMEOUT, max_bytes=3_000_000):
    req = urllib.request.Request(url, headers={"User-Agent": "hf-scrape/1.0"})
    out = {}
    for attempt in range(3):
        try:
            resp = urllib.request.urlopen(req, timeout=timeout)
            buf = b""
            have = set()
            wanted = {"pre_tokenizer", "normalizer"}
            try:
                while True:
                    chunk = resp.read(65536)
                    if not chunk:
                        break
                    buf += chunk
                    for key in wanted:
                        if key not in have:
                            val, _ = find_key_value(buf, key)
                            if val == "NOT_FOUND":
                                continue
                            if val == "INCOMPLETE":
                                continue
                            try:
                                out[key] = json.loads(val) if val != "null" else None
                                have.add(key)
                            except:
                                pass
                    if have == wanted:
                        break
                    if b'"model"' in buf and have:
                        for key in wanted:
                            if key not in have:
                                out[key] = None
                                have.add(key)
                        break
                    if len(buf) > max_bytes:
                        break
            finally:
                resp.close()
            break
        except urllib.error.HTTPError as e:
            if e.code == 429 and attempt < 2:
                time.sleep(3 * (attempt+1))
                continue
            out["error"] = f"HTTP {e.code}"
            break
        except Exception as e:
            out["error"] = str(e)[:200]
            break
    return out

# ── model list scraping ────────────────────────────────────────────────────────

def fetch_model_list(target):
    models = []
    seen = set()
    offset = 0
    limit = 500  # larger pages with auth
    url_base = f"{BASE}?library=transformers&sort=downloads&direction=-1&limit={limit}"
    print(f"Fetching model list with AUTH (target={target})...", flush=True)
    consecutive_fails = 0
    while len(models) < target:
        page_url = f"{url_base}&offset={offset}"
        batch = []
        got = False
        for attempt in range(5):
            try:
                req = urllib.request.Request(page_url, headers=api_headers())
                with urllib.request.urlopen(req, timeout=30) as resp:
                    batch = json.loads(resp.read())
                got = True
                break
            except urllib.error.HTTPError as e:
                if e.code == 429:
                    wait = min(60, 5 * (attempt+1))
                    print(f"  429 at offset={offset}, waiting {wait}s", flush=True)
                    time.sleep(wait)
                    continue
                print(f"  HTTP {e.code} at offset={offset}", flush=True)
                break
            except Exception as e:
                if attempt < 4:
                    time.sleep(2 * (attempt+1))
                    continue
                print(f"  FAILED offset={offset}: {e}", flush=True)
                break
        if not got or not batch:
            consecutive_fails += 1
            if consecutive_fails >= 3:
                print(f"  3 consecutive fails, stopping", flush=True)
                break
            offset += limit
            continue
        consecutive_fails = 0
        for m in batch:
            mid = m["id"] if isinstance(m, dict) else m
            if mid in seen:
                continue
            seen.add(mid)
            models.append({
                "id": mid,
                "downloads": m.get("downloads", 0) if isinstance(m, dict) else 0,
                "likes": m.get("likes", 0) if isinstance(m, dict) else 0,
            })
            if len(models) >= target:
                break
        offset += limit
        if len(models) >= target or offset % 5000 == 0:
            print(f"  fetched {len(models)} models (offset={offset})", flush=True)
        time.sleep(0.05)
    return models[:target]

# ── main ───────────────────────────────────────────────────────────────────────

def main():
    t0 = time.time()
    models = fetch_model_list(TARGET)
    print(f"\nGot {len(models)} model IDs in {time.time()-t0:.1f}s", flush=True)
    with open(MODELS_OUT, "w") as f:
        json.dump(models, f)

    total = len(models)
    results = []
    done = [0]
    lock = threading.Lock()

    def fetch_one(m):
        url = RESOLVE.format(mid=m["id"])
        out = stream_pre_tokenizer(url)
        rec = {
            "id": m["id"],
            "downloads": m.get("downloads", 0),
            "likes": m.get("likes", 0),
            "pre_tokenizer": out.get("pre_tokenizer"),
            "normalizer": out.get("normalizer"),
            "error": out.get("error"),
        }
        with lock:
            done[0] += 1
            if done[0] % 500 == 0:
                print(f"  progress: {done[0]}/{total}", flush=True)
        return rec

    print(f"\nFetching tokenizer.json for {total} models with {MAX_WORKERS} workers...", flush=True)
    with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as ex:
        futures = {ex.submit(fetch_one, m): m for m in models}
        for f in concurrent.futures.as_completed(futures):
            try:
                results.append(f.result())
            except Exception as e:
                m = futures[f]
                results.append({"id": m["id"], "error": str(e)[:200]})

    # Sort by downloads desc
    results.sort(key=lambda r: r.get("downloads", 0), reverse=True)
    with open(OUT, "w") as f:
        for r in results:
            f.write(json.dumps(r, ensure_ascii=False) + "\n")

    elapsed = time.time() - t0
    ok = sum(1 for r in results if r.get("pre_tokenizer") is not None)
    null_pt = sum(1 for r in results if r.get("pre_tokenizer") is None and not r.get("error"))
    err = sum(1 for r in results if r.get("error"))
    err404 = sum(1 for r in results if r.get("error") == "HTTP 404")
    err401 = sum(1 for r in results if r.get("error") == "HTTP 401")
    print(f"\n=== DONE in {elapsed:.1f}s ===", flush=True)
    print(f"  models listed: {len(models)}", flush=True)
    print(f"  had tokenizer.json (no 404): {len(results) - err404}", flush=True)
    print(f"  404 (no tokenizer.json): {err404}", flush=True)
    print(f"  401 (gated): {err401}", flush=True)
    print(f"  pre_tokenizer != None: {ok}", flush=True)
    print(f"  pre_tokenizer == null: {null_pt}", flush=True)
    print(f"  output: {OUT}", flush=True)

if __name__ == "__main__":
    main()