"""Evaluate CRF word segmentation against gold annotations.

Compares silver (CRF) predictions from udd-ws-v1.1-{dev,test}.txt against
gold corrections in gold_ws_cycle1.txt. Reports Word F1/Precision/Recall,
per-domain breakdown, and detailed error analysis.

With --model, uses the CRF model to predict directly on gold sentences
(instead of reading from silver files). This is needed when gold has been
merged into silver files.

Usage:
    python src/eval_ws_gold.py
    python src/eval_ws_gold.py --model path/to/model.crfsuite
"""

import argparse
import sys
from collections import Counter, defaultdict
from pathlib import Path


def parse_bio_file(path):
    """Parse BIO file into dict of {sent_id: [(syllable, tag), ...]}."""
    sentences = {}
    current_id = None
    current_tokens = []

    with open(path, "r", encoding="utf-8") as f:
        for line in f:
            line = line.rstrip("\n")
            if line.startswith("# sent_id = "):
                if current_id and current_tokens:
                    sentences[current_id] = current_tokens
                current_id = line.split("= ", 1)[1]
                current_tokens = []
            elif line.startswith("# text = "):
                continue
            elif line.strip() == "":
                if current_id and current_tokens:
                    sentences[current_id] = current_tokens
                    current_id = None
                    current_tokens = []
            elif "\t" in line:
                parts = line.split("\t")
                if len(parts) >= 2:
                    current_tokens.append((parts[0], parts[1]))

    if current_id and current_tokens:
        sentences[current_id] = current_tokens

    return sentences


def bio_to_words(tokens):
    """Convert BIO token list to list of word strings."""
    words = []
    current = []
    for syl, tag in tokens:
        if tag == "B-W":
            if current:
                words.append("_".join(current))
            current = [syl]
        elif tag == "I-W":
            current.append(syl)
    if current:
        words.append("_".join(current))
    return words


def bio_to_word_spans(tokens):
    """Convert BIO tokens to word spans as (start_idx, end_idx) tuples."""
    spans = []
    start = 0
    for i, (syl, tag) in enumerate(tokens):
        if tag == "B-W" and i > 0:
            spans.append((start, i))
            start = i
    spans.append((start, len(tokens)))
    return spans


def get_domain(sent_id):
    """Extract domain from sent_id prefix."""
    if sent_id.startswith("vlc-"):
        return "legal"
    elif sent_id.startswith("uvn-"):
        return "news"
    elif sent_id.startswith("uvw-"):
        return "wikipedia"
    elif sent_id.startswith("uvb-f-"):
        return "fiction"
    elif sent_id.startswith("uvb-n-"):
        return "non-fiction"
    return "unknown"


def compute_word_metrics(silver_words, gold_words):
    """Compute word-level precision, recall, F1.

    Uses multiset intersection (same as CoNLL WS eval).
    """
    silver_counter = Counter(silver_words)
    gold_counter = Counter(gold_words)

    # Multiset intersection
    tp = sum((silver_counter & gold_counter).values())
    pred_total = len(silver_words)
    gold_total = len(gold_words)

    precision = tp / pred_total if pred_total > 0 else 0
    recall = tp / gold_total if gold_total > 0 else 0
    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0

    return precision, recall, f1, tp, pred_total, gold_total


def compute_boundary_metrics(silver_tokens, gold_tokens):
    """Compute boundary-level (syllable tag) accuracy."""
    assert len(silver_tokens) == len(gold_tokens), \
        f"Length mismatch: {len(silver_tokens)} vs {len(gold_tokens)}"

    correct = 0
    total = len(silver_tokens)
    changes = {"B→I": 0, "I→B": 0}

    for (s_syl, s_tag), (g_syl, g_tag) in zip(silver_tokens, gold_tokens):
        if s_tag == g_tag:
            correct += 1
        else:
            key = f"{s_tag[0]}→{g_tag[0]}"
            changes[key] = changes.get(key, 0) + 1

    accuracy = correct / total if total > 0 else 0
    return accuracy, correct, total, changes


def find_differences(sent_id, silver_tokens, gold_tokens):
    """Find specific word segmentation differences between silver and gold."""
    diffs = []
    silver_words = bio_to_words(silver_tokens)
    gold_words = bio_to_words(gold_tokens)

    # Build position-based word mapping
    silver_spans = bio_to_word_spans(silver_tokens)
    gold_spans = bio_to_word_spans(gold_tokens)

    if silver_spans == gold_spans:
        return []

    # Find differing positions
    silver_set = set(silver_spans)
    gold_set = set(gold_spans)

    only_silver = silver_set - gold_set
    only_gold = gold_set - silver_set

    # Map spans to words
    def span_to_word(tokens, start, end):
        return "_".join(t[0] for t in tokens[start:end])

    for s in sorted(only_silver):
        word = span_to_word(silver_tokens, s[0], s[1])
        diffs.append(("silver", s, word))

    for g in sorted(only_gold):
        word = span_to_word(gold_tokens, g[0], g[1])
        diffs.append(("gold", g, word))

    return diffs


def classify_error(silver_word, gold_words_at_pos):
    """Classify error type: over-merge, over-split, boundary-shift."""
    s_parts = silver_word.split("_")
    if len(s_parts) > 1 and all(len(g.split("_")) < len(s_parts) for g in gold_words_at_pos):
        return "over-merge"
    if len(s_parts) < max(len(g.split("_")) for g in gold_words_at_pos):
        return "over-split"
    return "boundary-shift"


def predict_with_model(model_path, gold):
    """Use CRF model to predict on gold sentence syllables.

    Returns dict of {sent_id: [(syllable, tag), ...]}.
    """
    import pycrfsuite
    # Reuse feature extraction from al_score_ws
    sys.path.insert(0, str(Path(__file__).parent))
    from al_score_ws import extract_syllable_features, load_dictionary

    model_dir = model_path.parent
    dict_path = model_dir / "dictionary.txt"

    tagger = pycrfsuite.Tagger()
    tagger.open(str(model_path))
    print(f"Model loaded: {model_path}")

    dictionary = None
    if dict_path.exists():
        dictionary = load_dictionary(dict_path)
        print(f"Dictionary loaded: {len(dictionary)} entries")

    tag_map = {"B": "B-W", "I": "I-W"}
    predictions = {}
    for sid, tokens in gold.items():
        syllables = [t[0] for t in tokens]
        # Extract features
        xseq = [
            [f"{k}={v}" for k, v in extract_syllable_features(syllables, i, dictionary).items()]
            for i in range(len(syllables))
        ]
        pred_tags = tagger.tag(xseq)
        predictions[sid] = [
            (syl, tag_map.get(tag, tag)) for syl, tag in zip(syllables, pred_tags)
        ]

    return predictions


def main():
    parser = argparse.ArgumentParser(description="Evaluate WS against gold")
    parser.add_argument("--model", type=str, default=None,
                        help="CRF model path for direct prediction")
    args = parser.parse_args()

    base = Path("/home/claude-code/projects/workspace_underthesea/UDD-1")
    gold_path = base / "gold_ws_cycle1.txt"

    # Parse gold
    gold = parse_bio_file(gold_path)
    print(f"Gold sentences: {len(gold)}")

    if args.model:
        # Predict using CRF model directly
        model_path = Path(args.model)
        if not model_path.exists():
            # Auto-detect latest model
            tree1_models = base.parent / "tree-1" / "models" / "word_segmentation"
            model_dirs = sorted(tree1_models.glob("udd_ws_v1_1-*"))
            if model_dirs:
                model_path = model_dirs[-1] / "model.crfsuite"
        silver = predict_with_model(model_path, gold)
        print(f"CRF predictions: {len(silver)} sentences")
    else:
        # Parse silver (dev + test) from files
        silver_dev = parse_bio_file(base / "udd-ws-v1.1-dev.txt")
        silver_test = parse_bio_file(base / "udd-ws-v1.1-test.txt")
        silver = {**silver_dev, **silver_test}
        print(f"Silver sentences loaded: {len(silver_dev)} dev + {len(silver_test)} test")

    # Match gold to silver
    matched = []
    missing = []
    for sid in gold:
        if sid in silver:
            matched.append(sid)
        else:
            missing.append(sid)

    print(f"Matched: {len(matched)}, Missing in silver: {len(missing)}")
    if missing:
        print(f"  Missing: {missing}")

    # === Overall metrics ===
    total_tp = total_pred = total_gold = 0
    total_syl_correct = total_syl = 0
    all_changes = Counter()
    domain_stats = defaultdict(lambda: {"tp": 0, "pred": 0, "gold": 0, "syl_correct": 0, "syl_total": 0, "n": 0})
    all_diffs = []
    error_types = Counter()

    for sid in matched:
        s_tokens = silver[sid]
        g_tokens = gold[sid]

        # Check syllable alignment
        s_syls = [t[0] for t in s_tokens]
        g_syls = [t[0] for t in g_tokens]
        if s_syls != g_syls:
            print(f"  WARNING: syllable mismatch in {sid}")
            print(f"    Silver: {' '.join(s_syls[:10])}...")
            print(f"    Gold:   {' '.join(g_syls[:10])}...")
            continue

        # Word metrics
        s_words = bio_to_words(s_tokens)
        g_words = bio_to_words(g_tokens)
        p, r, f1, tp, pred, gtotal = compute_word_metrics(s_words, g_words)
        total_tp += tp
        total_pred += pred
        total_gold += gtotal

        # Boundary metrics
        acc, correct, total, changes = compute_boundary_metrics(s_tokens, g_tokens)
        total_syl_correct += correct
        total_syl += total
        all_changes.update(changes)

        # Domain stats
        domain = get_domain(sid)
        ds = domain_stats[domain]
        ds["tp"] += tp
        ds["pred"] += pred
        ds["gold"] += gtotal
        ds["syl_correct"] += correct
        ds["syl_total"] += total
        ds["n"] += 1

        # Differences
        diffs = find_differences(sid, s_tokens, g_tokens)
        if diffs:
            all_diffs.append((sid, domain, diffs, s_tokens, g_tokens))

    # === Print Results ===
    print("\n" + "=" * 60)
    print("EVALUATION: CRF Silver vs Gold (Cycle 1)")
    print("=" * 60)

    # Overall
    overall_p = total_tp / total_pred if total_pred else 0
    overall_r = total_tp / total_gold if total_gold else 0
    overall_f1 = 2 * overall_p * overall_r / (overall_p + overall_r) if (overall_p + overall_r) else 0
    syl_acc = total_syl_correct / total_syl if total_syl else 0

    print(f"\n## Overall ({len(matched)} sentences)")
    print(f"  Syllable Accuracy: {syl_acc:.4f} ({total_syl_correct}/{total_syl})")
    print(f"  Word Precision:    {overall_p:.4f}")
    print(f"  Word Recall:       {overall_r:.4f}")
    print(f"  Word F1:           {overall_f1:.4f}")
    print(f"  Boundary changes:  {dict(all_changes)}")
    print(f"    B→I (over-merge in silver): {all_changes.get('B→I', 0)}")
    print(f"    I→B (over-split in silver): {all_changes.get('I→B', 0)}")

    # Per-domain
    print(f"\n## Per-Domain Breakdown")
    print(f"  {'Domain':<14} {'N':>4} {'Syl Acc':>8} {'P':>7} {'R':>7} {'F1':>7}")
    print(f"  {'-'*14} {'-'*4} {'-'*8} {'-'*7} {'-'*7} {'-'*7}")
    for domain in ["legal", "news", "wikipedia", "fiction", "non-fiction"]:
        ds = domain_stats[domain]
        if ds["n"] == 0:
            continue
        dp = ds["tp"] / ds["pred"] if ds["pred"] else 0
        dr = ds["tp"] / ds["gold"] if ds["gold"] else 0
        df1 = 2 * dp * dr / (dp + dr) if (dp + dr) else 0
        dacc = ds["syl_correct"] / ds["syl_total"] if ds["syl_total"] else 0
        print(f"  {domain:<14} {ds['n']:>4} {dacc:>8.4f} {dp:>7.4f} {dr:>7.4f} {df1:>7.4f}")

    # Error analysis
    print(f"\n## Error Analysis ({len(all_diffs)} sentences with differences)")

    merge_errors = []  # silver merged, gold split
    split_errors = []  # silver split, gold merged

    for sid, domain, diffs, s_tokens, g_tokens in all_diffs:
        s_spans = set(bio_to_word_spans(s_tokens))
        g_spans = set(bio_to_word_spans(g_tokens))

        only_silver = s_spans - g_spans
        only_gold = g_spans - s_spans

        def span_word(tokens, s, e):
            return "_".join(t[0] for t in tokens[s:e])

        for span in only_silver:
            word = span_word(s_tokens, span[0], span[1])
            n_syls = span[1] - span[0]
            # Check if this span overlaps with multiple gold spans (over-merge)
            overlapping_gold = [g for g in only_gold if g[0] < span[1] and g[1] > span[0]]
            if overlapping_gold and n_syls > 1:
                gold_words = [span_word(g_tokens, g[0], g[1]) for g in overlapping_gold]
                merge_errors.append((sid, domain, word, gold_words))

        for span in only_gold:
            word = span_word(g_tokens, span[0], span[1])
            n_syls = span[1] - span[0]
            overlapping_silver = [s for s in only_silver if s[0] < span[1] and s[1] > span[0]]
            if overlapping_silver and n_syls > 1:
                silver_words = [span_word(s_tokens, s[0], s[1]) for s in overlapping_silver]
                split_errors.append((sid, domain, word, silver_words))

    print(f"\n### Over-merge errors (silver merged what gold splits): {len(merge_errors)}")
    for sid, domain, silver_word, gold_words in sorted(merge_errors, key=lambda x: x[1]):
        print(f"  [{domain:>12}] {sid}: {silver_word} → {' | '.join(gold_words)}")

    print(f"\n### Over-split errors (silver split what gold merges): {len(split_errors)}")
    for sid, domain, gold_word, silver_words in sorted(split_errors, key=lambda x: x[1]):
        print(f"  [{domain:>12}] {sid}: {' | '.join(silver_words)} → {gold_word}")

    # Summary of all differences per sentence
    print(f"\n## All Differences (sentence-level)")
    for sid, domain, diffs, s_tokens, g_tokens in sorted(all_diffs, key=lambda x: x[1]):
        s_words = bio_to_words(s_tokens)
        g_words = bio_to_words(g_tokens)
        s_set = set(s_words)
        g_set = set(g_words)

        s_only = Counter(s_words) - Counter(g_words)
        g_only = Counter(g_words) - Counter(s_words)
        if s_only or g_only:
            print(f"\n  [{domain}] {sid}")
            if s_only:
                print(f"    Silver only: {dict(s_only)}")
            if g_only:
                print(f"    Gold only:   {dict(g_only)}")


if __name__ == "__main__":
    main()