"""
scan_jsonl.py
=============

Stream a JSONL corpus file and print analytics. Designed for axolotl-style
'completion' corpora where each line is a JSON object with at least a `text`
field. Streams line-by-line so it handles 10+ GB files without OOM.

Usage
-----
    # Default report (heuristic token estimate: chars / 4)
    python scan_jsonl.py corpus.jsonl

    # Machine-readable
    python scan_jsonl.py corpus.jsonl --json

    # Smoke-test on first N lines
    python scan_jsonl.py corpus.jsonl --sample 10000

    # Different field name
    python scan_jsonl.py corpus.jsonl --field content

    # Accurate token count via a HF tokenizer (slow but precise)
    python scan_jsonl.py corpus.jsonl --tokenizer ibm-granite/granite-4.1-8b

Output
------
Human-readable report by default. Covers:
    * File-level stats (size, lines, invalid)
    * Article-length stats (mean/median/std/min/max + p1..p99 percentiles)
    * Length-bucket histogram
    * Token estimate (heuristic or real)
    * Wall-clock throughput

Stdlib only by default. `transformers` only required when --tokenizer is set.
"""
from __future__ import annotations

import argparse
import json
import os
import sys
import time
from collections import Counter


# ----- Length buckets --------------------------------------------------------
# Picked to align with common article-size buckets for Wikipedia-style text.
BUCKETS: list[tuple[int, int]] = [
    (0, 500),
    (500, 1_500),
    (1_500, 2_000),
    (2_000, 5_000),
    (5_000, 10_000),
    (10_000, 50_000),
    (50_000, 100_000),
    (100_000, 10**9),  # 100k+; cap to keep arithmetic simple
]


def human_bytes(n: int) -> str:
    for unit in ("B", "KB", "MB", "GB", "TB"):
        if n < 1024 or unit == "TB":
            return f"{n:.1f} {unit}"
        n /= 1024  # type: ignore[assignment]
    return f"{n:.1f} TB"


# ----- Main scan -------------------------------------------------------------

def scan(path: str, field: str, sample: int | None,
         tokenizer) -> dict:
    lengths: list[int] = []
    total_chars = 0
    total_bytes = 0
    line_count = 0
    invalid_json = 0
    missing_field = 0
    first_word_counts: Counter[str] = Counter()
    started = time.time()

    total_tokens = 0
    batch_texts: list[str] = []
    BATCH = 256

    file_size = os.path.getsize(path)
    print(f"[info] Scanning {path} ({human_bytes(file_size)})...",
          file=sys.stderr)

    with open(path, "rb") as f:
        for raw in f:
            line_count += 1

            try:
                ex = json.loads(raw)
            except json.JSONDecodeError:
                invalid_json += 1
                continue

            text = ex.get(field) if isinstance(ex, dict) else None
            if not isinstance(text, str) or not text:
                missing_field += 1
                continue

            n_chars = len(text)
            n_bytes = len(text.encode("utf-8"))
            lengths.append(n_chars)
            total_chars += n_chars
            total_bytes += n_bytes

            first_word = text.split(maxsplit=1)[0] if text.split() else ""
            if first_word:
                first_word_counts[first_word[:50]] += 1

            if tokenizer is not None:
                batch_texts.append(text)
                if len(batch_texts) >= BATCH:
                    enc = tokenizer(batch_texts, add_special_tokens=False,
                                    truncation=False)
                    total_tokens += sum(len(ids) for ids in enc["input_ids"])
                    batch_texts.clear()

            if sample is not None and line_count >= sample:
                print(f"[info] Hit --sample {sample}, stopping.",
                      file=sys.stderr)
                break

            if line_count % 500_000 == 0:
                elapsed = time.time() - started
                rate = line_count / elapsed if elapsed > 0 else 0.0
                print(f"[progress] {line_count:>10,} lines | "
                      f"{rate:>7,.0f} lines/s", file=sys.stderr)

    # Flush remaining tokenizer batch
    if tokenizer is not None and batch_texts:
        enc = tokenizer(batch_texts, add_special_tokens=False, truncation=False)
        total_tokens += sum(len(ids) for ids in enc["input_ids"])

    elapsed = time.time() - started
    return _build_report(
        path=path, file_size=file_size,
        line_count=line_count, invalid_json=invalid_json,
        missing_field=missing_field,
        lengths=lengths, total_chars=total_chars, total_bytes=total_bytes,
        first_word_counts=first_word_counts,
        tokenizer_used=tokenizer, total_tokens=total_tokens,
        elapsed=elapsed,
    )


# ----- Reporting -------------------------------------------------------------

def _percentile(sorted_lengths: list[int], p: float) -> int:
    if not sorted_lengths:
        return 0
    idx = int(p * len(sorted_lengths))
    if idx >= len(sorted_lengths):
        idx = len(sorted_lengths) - 1
    return sorted_lengths[idx]


def _build_report(*, path, file_size, line_count, invalid_json,
                  missing_field, lengths, total_chars, total_bytes,
                  first_word_counts, tokenizer_used, total_tokens,
                  elapsed) -> dict:
    if not lengths:
        return {"error": "no valid articles found"}

    lengths.sort()
    n = len(lengths)
    total = sum(lengths)
    mean = total / n
    # Sample-based variance for speed on huge lists
    if n > 100_000:
        # Use first/last/middle for std estimate isn't great; use sum-of-squares.
        sum_sq = sum(x * x for x in lengths)
        variance = sum_sq / n - mean * mean
        if variance < 0:
            variance = 0.0
        std = variance ** 0.5
    else:
        variance = sum((x - mean) ** 2 for x in lengths) / n
        std = variance ** 0.5

    bucket_counts = [0] * len(BUCKETS)
    for L in lengths:
        for i, (lo, hi) in enumerate(BUCKETS):
            if lo <= L < hi:
                bucket_counts[i] += 1
                break

    if tokenizer_used is not None:
        token_estimate = total_tokens
        token_method = f"transformers ({tokenizer_used.name_or_path})"
    else:
        token_estimate = total // 4
        token_method = "chars/4 heuristic"

    report = {
        "file": {
            "path": path,
            "size_bytes": file_size,
            "size_human": human_bytes(file_size),
        },
        "counts": {
            "lines": line_count,
            "valid_articles": n,
            "invalid_json": invalid_json,
            "missing_field": missing_field,
        },
        "lengths": {
            "total_chars": total_chars,
            "total_bytes_utf8": total_bytes,
            "bytes_per_char": round(total_bytes / total_chars, 3),
            "min": lengths[0],
            "max": lengths[-1],
            "mean": round(mean, 1),
            "median": lengths[n // 2],
            "std": round(std, 1),
        },
        "percentiles": {
            f"p{int(p*100):02d}": _percentile(lengths, p)
            for p in (0.01, 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, 0.95, 0.99)
        },
        "buckets": [
            {
                "label": f"{lo}-{hi if hi < 10**9 else 'inf'}",
                "count": c,
                "pct": round(100 * c / n, 2),
            }
            for (lo, hi), c in zip(BUCKETS, bucket_counts)
        ],
        "top_first_words": [
            {"word": w, "count": c,
             "pct": round(100 * c / n, 2)}
            for w, c in first_word_counts.most_common(20)
        ],
        "tokens": {
            "estimate": token_estimate,
            "method": token_method,
        },
        "timing": {
            "elapsed_s": round(elapsed, 1),
            "lines_per_s": round(line_count / elapsed if elapsed > 0 else 0, 0),
        },
    }
    return report


# ----- Pretty-print ----------------------------------------------------------

def print_report(r: dict, stream=sys.stdout) -> None:
    if "error" in r:
        print(f"[error] {r['error']}", file=stream)
        return

    f = r["file"]; c = r["counts"]; l = r["lengths"]
    p = r["percentiles"]; b = r["buckets"]; t = r["tokens"]
    ti = r["timing"]; top = r["top_first_words"]

    bar = "=" * 62
    print(bar, file=stream)
    print(f"  File : {f['path']}", file=stream)
    print(f"  Size : {f['size_human']}  ({f['size_bytes']:,} bytes)",
          file=stream)
    print(bar, file=stream)
    print(f"  Lines scanned  : {c['lines']:,}", file=stream)
    print(f"  Valid articles : {c['valid_articles']:,}", file=stream)
    print(f"  Invalid JSON   : {c['invalid_json']:,}", file=stream)
    print(f"  Missing field  : {c['missing_field']:,}", file=stream)
    print(bar, file=stream)
    print(f"  Total chars    : {l['total_chars']:,}", file=stream)
    print(f"  Total bytes    : {l['total_bytes_utf8']:,} (UTF-8)",
          file=stream)
    print(f"  Bytes / char   : {l['bytes_per_char']:.3f}", file=stream)
    print(f"  Min length     : {l['min']:,} chars", file=stream)
    print(f"  Max length     : {l['max']:,} chars", file=stream)
    print(f"  Mean length    : {l['mean']:,} chars", file=stream)
    print(f"  Median length  : {l['median']:,} chars", file=stream)
    print(f"  Std dev        : {l['std']:,} chars", file=stream)
    print(bar, file=stream)
    print("  Length percentiles (chars):", file=stream)
    for k in sorted(p.keys(), key=lambda s: int(s[1:])):
        print(f"    {k}: {p[k]:>9,}", file=stream)
    print(bar, file=stream)
    print("  Length buckets:", file=stream)
    max_bucket_pct = max(bb["pct"] for bb in b) or 1.0
    for bb in b:
        bar_len = int(round(40 * bb["pct"] / max_bucket_pct))
        bar_str = "#" * bar_len
        print(f"    {bb['label']:>14} chars : {bb['count']:>9,} "
              f"({bb['pct']:>5.2f}%)  {bar_str}", file=stream)
    print(bar, file=stream)
    print(f"  Token estimate : {t['estimate']:,} tokens", file=stream)
    print(f"  Token method   : {t['method']}", file=stream)
    print(bar, file=stream)
    print("  Top 20 first-words:", file=stream)
    for entry in top:
        print(f"    {entry['word']:>32} : {entry['count']:>9,} "
              f"({entry['pct']:.2f}%)", file=stream)
    print(bar, file=stream)
    print(f"  Elapsed        : {ti['elapsed_s']:.1f} s", file=stream)
    print(f"  Throughput     : {ti['lines_per_s']:,.0f} lines/s",
          file=stream)
    print(bar, file=stream)


# ----- CLI -------------------------------------------------------------------

def main() -> None:
    p = argparse.ArgumentParser(
        description=__doc__,
        formatter_class=argparse.RawDescriptionHelpFormatter,
    )
    p.add_argument("path", help="JSONL file to scan")
    p.add_argument("--field", default="text",
                   help="JSON field to read (default: text)")
    p.add_argument("--sample", type=int, default=None,
                   help="Only scan the first N lines (smoke-test)")
    p.add_argument("--json", action="store_true",
                   help="Emit machine-readable JSON instead of pretty text")
    p.add_argument("--tokenizer", default=None,
                   help="HF tokenizer name for exact token count "
                        "(e.g. ibm-granite/granite-4.1-8b). "
                        "Slow but precise; requires transformers.")
    args = p.parse_args()

    if not os.path.isfile(args.path):
        print(f"[error] File not found: {args.path}", file=sys.stderr)
        sys.exit(1)

    tokenizer = None
    if args.tokenizer:
        try:
            from transformers import AutoTokenizer  # type: ignore
            print(f"[info] Loading tokenizer: {args.tokenizer}",
                  file=sys.stderr)
            tokenizer = AutoTokenizer.from_pretrained(args.tokenizer)
        except Exception as exc:
            print(f"[error] Could not load tokenizer ({exc!r}). "
                  f"Falling back to heuristic.", file=sys.stderr)
            sys.exit(1)

    report = scan(args.path, args.field, args.sample, tokenizer)

    if args.json:
        json.dump(report, sys.stdout, ensure_ascii=False, indent=2)
        sys.stdout.write("\n")
    else:
        print_report(report)


if __name__ == "__main__":
    main()