"""
Evaluate word segmentation quality of UDD-1 treebank.

Analyses:
1. Syllable distribution per token (by UPOS)
2. Anomalous token detection (long tokens, cross-boundary merges, legal terms)
3. Inconsistent segmentation (bigram vs single token)
4. Comparison with underthesea word_tokenize() (optional)
5. Manual review samples
6. Dictionary-based validation (optional) — uses Viet74K/UTS_Dictionary
"""

import argparse
import random
import re
import sys
from collections import Counter, defaultdict
from os.path import dirname, join, exists


# Legal terms to check segmentation consistency
LEGAL_TERMS = [
    "vụ án", "hợp đồng", "tài sản", "pháp luật", "quy định",
    "nghị định", "cơ quan", "tổ chức", "cá nhân", "trách nhiệm",
    "quyền lợi", "nghĩa vụ", "xử phạt", "vi phạm", "bồi thường",
    "thẩm quyền", "giải quyết", "khiếu nại", "tố cáo", "hình sự",
]


def parse_conllu(filepath):
    """Parse CoNLL-U file and return sentences with full token info."""
    sentences = []
    current = {
        "sent_id": None,
        "text": None,
        "tokens": [],
        "upos": [],
        "xpos": [],
        "deprel": [],
        "head": [],
        "lemmas": [],
    }

    with open(filepath, "r", encoding="utf-8") as f:
        for line in f:
            line = line.rstrip("\n")
            if not line.strip():
                if current["tokens"]:
                    sentences.append(current)
                    current = {
                        "sent_id": None,
                        "text": None,
                        "tokens": [],
                        "upos": [],
                        "xpos": [],
                        "deprel": [],
                        "head": [],
                        "lemmas": [],
                    }
            elif line.startswith("#"):
                if line.startswith("# sent_id"):
                    current["sent_id"] = line.split("=", 1)[1].strip()
                elif line.startswith("# text"):
                    current["text"] = line.split("=", 1)[1].strip()
            else:
                parts = line.split("\t")
                if len(parts) >= 10:
                    if "-" in parts[0] or "." in parts[0]:
                        continue
                    current["tokens"].append(parts[1])
                    current["lemmas"].append(parts[2])
                    current["upos"].append(parts[3])
                    current["xpos"].append(parts[4])
                    current["head"].append(parts[6])
                    current["deprel"].append(parts[7])

    if current["tokens"]:
        sentences.append(current)

    return sentences


def count_syllables(token):
    """Count syllables in a Vietnamese token (space-separated)."""
    return len(token.split())


# ---- Analysis 1: Syllable distribution ----

def analysis_syllable_distribution(sentences):
    """Compute syllable count distribution per token, overall and by UPOS."""
    overall = Counter()
    by_upos = defaultdict(Counter)
    total_tokens = 0

    for sent in sentences:
        for token, upos in zip(sent["tokens"], sent["upos"]):
            n = count_syllables(token)
            overall[n] += 1
            by_upos[upos][n] += 1
            total_tokens += 1

    return overall, by_upos, total_tokens


def format_syllable_report(overall, by_upos, total_tokens):
    """Format syllable distribution as markdown."""
    lines = []
    lines.append("## 1. Syllable Distribution per Token")
    lines.append("")
    lines.append("### 1.1 Overall Distribution")
    lines.append("")
    lines.append("| Syllables | Count | Percentage |")
    lines.append("|---:|---:|---:|")

    for n in sorted(overall.keys()):
        label = f"{n}" if n < 4 else "4+"
        if n == 4:
            count = sum(overall[k] for k in overall if k >= 4)
            pct = count / total_tokens * 100
            lines.append(f"| {label} | {count:,} | {pct:.2f}% |")
            break
        else:
            count = overall[n]
            pct = count / total_tokens * 100
            lines.append(f"| {label} | {count:,} | {pct:.2f}% |")

    # Check if there are 4+ syllable tokens not yet printed
    if max(overall.keys()) >= 4 and 4 not in overall:
        count = sum(overall[k] for k in overall if k >= 4)
        pct = count / total_tokens * 100
        lines.append(f"| 4+ | {count:,} | {pct:.2f}% |")

    lines.append("")
    lines.append("### 1.2 Distribution by UPOS")
    lines.append("")

    # Top UPOS tags by frequency
    upos_totals = {upos: sum(counts.values()) for upos, counts in by_upos.items()}
    top_upos = sorted(upos_totals, key=upos_totals.get, reverse=True)[:10]

    lines.append("| UPOS | 1-syl | 2-syl | 3-syl | 4+-syl | Total | Avg syl |")
    lines.append("|:---|---:|---:|---:|---:|---:|---:|")

    for upos in top_upos:
        counts = by_upos[upos]
        total = upos_totals[upos]
        s1 = counts.get(1, 0)
        s2 = counts.get(2, 0)
        s3 = counts.get(3, 0)
        s4p = sum(counts[k] for k in counts if k >= 4)
        avg = sum(k * counts[k] for k in counts) / total if total else 0
        lines.append(
            f"| {upos} | {s1:,} | {s2:,} | {s3:,} | {s4p:,} | {total:,} | {avg:.2f} |"
        )

    lines.append("")
    return "\n".join(lines)


# ---- Analysis 2: Anomalous tokens ----

def analysis_anomalous_tokens(sentences):
    """Find anomalous tokens: long tokens, cross-boundary merges, legal term consistency."""

    # 2a: Long tokens (4+ syllables)
    long_tokens = []
    for sent in sentences:
        for i, (token, upos) in enumerate(zip(sent["tokens"], sent["upos"])):
            n = count_syllables(token)
            if n >= 4:
                long_tokens.append({
                    "sent_id": sent["sent_id"],
                    "token": token,
                    "upos": upos,
                    "syllables": n,
                })

    long_token_counter = Counter(t["token"] for t in long_tokens)

    # 2b: Cross-boundary merges (uppercase letter after space inside token)
    # Indicates possible incorrect merging of adjacent words
    cross_boundary = []
    for sent in sentences:
        for i, (token, upos) in enumerate(zip(sent["tokens"], sent["upos"])):
            if upos == "PROPN":
                continue  # Proper nouns naturally have capitals
            if " " not in token:
                continue
            # Check if any syllable after the first starts with uppercase
            syllables = token.split()
            has_mid_upper = any(s[0].isupper() for s in syllables[1:] if s)
            if has_mid_upper:
                cross_boundary.append({
                    "sent_id": sent["sent_id"],
                    "token": token,
                    "upos": upos,
                })

    cross_boundary_counter = Counter(t["token"] for t in cross_boundary)

    # 2c: Legal term segmentation consistency
    legal_term_stats = {}
    for term in LEGAL_TERMS:
        parts = term.split()
        as_single = 0  # Found as single token
        as_split = 0  # Found as adjacent tokens (split)

        for sent in sentences:
            tokens = sent["tokens"]
            # Check as single token
            for token in tokens:
                if token.lower() == term:
                    as_single += 1
            # Check as split (adjacent tokens matching)
            if len(parts) == 2:
                for j in range(len(tokens) - 1):
                    if tokens[j].lower() == parts[0] and tokens[j + 1].lower() == parts[1]:
                        as_split += 1

        if as_single > 0 or as_split > 0:
            legal_term_stats[term] = {
                "as_single": as_single,
                "as_split": as_split,
                "total": as_single + as_split,
                "consistency": max(as_single, as_split) / (as_single + as_split) * 100
                if (as_single + as_split) > 0 else 0,
            }

    return long_tokens, long_token_counter, cross_boundary, cross_boundary_counter, legal_term_stats


def format_anomalous_report(long_tokens, long_token_counter, cross_boundary,
                            cross_boundary_counter, legal_term_stats):
    """Format anomalous token report as markdown."""
    lines = []
    lines.append("## 2. Anomalous Token Detection")
    lines.append("")

    # 2a: Long tokens
    lines.append("### 2a. Long Tokens (4+ syllables)")
    lines.append("")
    lines.append(f"Total occurrences: {len(long_tokens):,}")
    lines.append(f"Unique tokens: {len(long_token_counter):,}")
    lines.append("")
    lines.append("**Top 30 by frequency:**")
    lines.append("")
    lines.append("| Token | Count | UPOS | Syllables |")
    lines.append("|:---|---:|:---|---:|")
    for token, count in long_token_counter.most_common(30):
        # Find first occurrence for UPOS
        upos = next(t["upos"] for t in long_tokens if t["token"] == token)
        n_syl = count_syllables(token)
        lines.append(f"| {token} | {count} | {upos} | {n_syl} |")
    lines.append("")

    # 2b: Cross-boundary merges
    lines.append("### 2b. Possible Cross-Boundary Merges")
    lines.append("")
    lines.append("Tokens (non-PROPN) with uppercase letters after spaces, suggesting")
    lines.append("incorrect merging of adjacent words.")
    lines.append("")
    lines.append(f"Total occurrences: {len(cross_boundary):,}")
    lines.append(f"Unique tokens: {len(cross_boundary_counter):,}")
    lines.append("")
    if cross_boundary_counter:
        lines.append("| Token | Count | UPOS | Example sent_id |")
        lines.append("|:---|---:|:---|:---|")
        for token, count in cross_boundary_counter.most_common(30):
            example = next(t for t in cross_boundary if t["token"] == token)
            lines.append(
                f"| {token} | {count} | {example['upos']} | {example['sent_id']} |"
            )
    else:
        lines.append("No cross-boundary merges detected.")
    lines.append("")

    # 2c: Legal terms
    lines.append("### 2c. Legal Term Segmentation Consistency")
    lines.append("")
    lines.append("| Term | As Single Token | As Split Tokens | Total | Consistency |")
    lines.append("|:---|---:|---:|---:|---:|")
    for term in sorted(legal_term_stats, key=lambda t: legal_term_stats[t]["total"], reverse=True):
        s = legal_term_stats[term]
        dominant = "single" if s["as_single"] >= s["as_split"] else "split"
        lines.append(
            f"| {term} | {s['as_single']:,} | {s['as_split']:,} | {s['total']:,} | {s['consistency']:.1f}% ({dominant}) |"
        )
    lines.append("")
    return "\n".join(lines)


# ---- Analysis 3: Inconsistent segmentation ----

def analysis_inconsistency(sentences):
    """Find bigrams that also appear as single tokens elsewhere."""
    # Build token vocabulary
    token_set = set()
    for sent in sentences:
        for token in sent["tokens"]:
            token_set.add(token.lower())

    # Find bigrams that exist as single tokens
    bigram_as_single = Counter()
    single_as_bigram = Counter()

    for sent in sentences:
        tokens = sent["tokens"]
        for i in range(len(tokens) - 1):
            bigram = tokens[i].lower() + " " + tokens[i + 1].lower()
            if bigram in token_set:
                bigram_as_single[bigram] += 1

    # Count how often each of those appears as a single token
    for sent in sentences:
        for token in sent["tokens"]:
            t = token.lower()
            if t in bigram_as_single:
                single_as_bigram[t] += 1

    # Combine
    inconsistencies = {}
    for bigram in bigram_as_single:
        inconsistencies[bigram] = {
            "as_split": bigram_as_single[bigram],
            "as_single": single_as_bigram.get(bigram, 0),
        }

    return inconsistencies


def format_inconsistency_report(inconsistencies):
    """Format inconsistency report as markdown."""
    lines = []
    lines.append("## 3. Inconsistent Segmentation")
    lines.append("")
    lines.append("Cases where two adjacent tokens appear elsewhere as a single token,")
    lines.append("or vice versa. Sorted by total occurrences.")
    lines.append("")

    if not inconsistencies:
        lines.append("No inconsistencies found.")
        lines.append("")
        return "\n".join(lines)

    lines.append(f"Total inconsistent forms: {len(inconsistencies):,}")
    lines.append("")
    lines.append("| Token | As Single | As Split (bigram) | Total |")
    lines.append("|:---|---:|---:|---:|")

    sorted_items = sorted(
        inconsistencies.items(),
        key=lambda x: x[1]["as_single"] + x[1]["as_split"],
        reverse=True,
    )
    for token, stats in sorted_items[:50]:
        total = stats["as_single"] + stats["as_split"]
        lines.append(
            f"| {token} | {stats['as_single']:,} | {stats['as_split']:,} | {total:,} |"
        )
    lines.append("")
    return "\n".join(lines)


# ---- Analysis 4: Comparison with word_tokenize ----

def analysis_compare_tokenize(sentences, sample_size=300):
    """Compare parser tokenization with underthesea word_tokenize()."""
    try:
        from underthesea import word_tokenize
    except ImportError:
        return None, None

    # Filter sentences with text
    with_text = [s for s in sentences if s["text"]]
    if not with_text:
        return None, None

    random.seed(42)
    sample = random.sample(with_text, min(sample_size, len(with_text)))

    results = []
    match_count = 0
    mismatch_count = 0
    diff_categories = Counter()

    for sent in sample:
        text = sent["text"]
        parser_tokens = sent["tokens"]

        # word_tokenize returns list of tokens (underscore-joined for multi-syllable)
        wt_raw = word_tokenize(text)
        # Convert underscore-joined tokens to space-separated
        wt_tokens = [t.replace("_", " ") for t in wt_raw]

        # Compare
        if parser_tokens == wt_tokens:
            match_count += 1
            results.append({
                "sent_id": sent["sent_id"],
                "match": True,
                "parser_tokens": parser_tokens,
                "wt_tokens": wt_tokens,
                "diffs": [],
            })
        else:
            mismatch_count += 1
            diffs = find_token_diffs(parser_tokens, wt_tokens)
            results.append({
                "sent_id": sent["sent_id"],
                "match": False,
                "parser_tokens": parser_tokens,
                "wt_tokens": wt_tokens,
                "diffs": diffs,
            })
            for d in diffs:
                diff_categories[d["type"]] += 1

    return results, {
        "sample_size": len(sample),
        "match_count": match_count,
        "mismatch_count": mismatch_count,
        "match_rate": match_count / len(sample) * 100 if sample else 0,
        "diff_categories": diff_categories,
    }


def find_token_diffs(parser_tokens, wt_tokens):
    """Find differences between two tokenizations using alignment."""
    diffs = []

    # Reconstruct syllable sequences
    p_syls = []
    for i, tok in enumerate(parser_tokens):
        for syl in tok.split():
            p_syls.append((syl, i))

    w_syls = []
    for i, tok in enumerate(wt_tokens):
        for syl in tok.split():
            w_syls.append((syl, i))

    # Align by syllable
    pi, wi = 0, 0
    while pi < len(p_syls) and wi < len(w_syls):
        if p_syls[pi][0] == w_syls[wi][0]:
            if p_syls[pi][1] != w_syls[wi][1]:
                # Same syllable, different token boundary
                p_tok = parser_tokens[p_syls[pi][1]]
                w_tok = wt_tokens[w_syls[wi][1]]
                if count_syllables(p_tok) > count_syllables(w_tok):
                    diff_type = "parser_merges"
                elif count_syllables(p_tok) < count_syllables(w_tok):
                    diff_type = "parser_splits"
                else:
                    diff_type = "boundary_shift"
                diffs.append({
                    "type": diff_type,
                    "parser": p_tok,
                    "wt": w_tok,
                })
            pi += 1
            wi += 1
        else:
            # Syllable mismatch - skip ahead
            pi += 1
            wi += 1

    return diffs


def format_compare_report(results, stats):
    """Format comparison report as markdown."""
    lines = []
    lines.append("## 4. Comparison with `word_tokenize()`")
    lines.append("")

    if results is None:
        lines.append("**Skipped**: `underthesea` not available or not requested. "
                      "Use `--compare-tokenize` to enable.")
        lines.append("")
        return "\n".join(lines)

    lines.append(f"Sample size: {stats['sample_size']} sentences")
    lines.append(f"- Exact match: {stats['match_count']} ({stats['match_rate']:.1f}%)")
    lines.append(f"- Mismatch: {stats['mismatch_count']} ({100 - stats['match_rate']:.1f}%)")
    lines.append("")

    if stats["match_rate"] > 99.0:
        lines.append("### Finding: Shared Tokenizer")
        lines.append("")
        lines.append("The near-100% match rate confirms that Underthesea's `dependency_parse()` "
                      "internally uses the same `word_tokenize()` model for segmentation. "
                      "This means segmentation errors in UDD-1 are inherent to the Underthesea "
                      "tokenizer and cannot be detected by comparing against `word_tokenize()`. "
                      "A meaningful comparison would require an independent segmentation tool "
                      "(e.g., VnCoreNLP, pyvi) or gold-standard segmented data.")
        lines.append("")

    if stats["diff_categories"]:
        lines.append("### Difference Categories")
        lines.append("")
        lines.append("| Category | Count | Description |")
        lines.append("|:---|---:|:---|")
        descs = {
            "parser_merges": "Parser joins tokens that word_tokenize keeps separate",
            "parser_splits": "Parser splits tokens that word_tokenize joins",
            "boundary_shift": "Different token boundary placement",
        }
        for cat, count in stats["diff_categories"].most_common():
            desc = descs.get(cat, cat)
            lines.append(f"| {cat} | {count} | {desc} |")
        lines.append("")

    # Show sample mismatches
    mismatches = [r for r in results if not r["match"]]
    if mismatches:
        lines.append("### Sample Mismatches (first 20)")
        lines.append("")
        for r in mismatches[:20]:
            lines.append(f"**{r['sent_id']}**")
            lines.append(f"- Parser: `{'` `'.join(r['parser_tokens'])}`")
            lines.append(f"- word_tokenize: `{'` `'.join(r['wt_tokens'])}`")
            if r["diffs"]:
                diff_strs = [f"{d['type']}: \"{d['parser']}\" vs \"{d['wt']}\"" for d in r["diffs"][:5]]
                lines.append(f"- Diffs: {'; '.join(diff_strs)}")
            lines.append("")

    lines.append("")
    return "\n".join(lines)


# ---- Analysis 5: Manual review samples ----

def analysis_manual_samples(sentences, long_tokens, cross_boundary, inconsistencies,
                            compare_results=None, n_samples=100):
    """Generate samples for manual review."""
    suspicious_ids = set()

    # Collect suspicious sentence IDs
    for t in long_tokens:
        suspicious_ids.add(t["sent_id"])
    for t in cross_boundary:
        suspicious_ids.add(t["sent_id"])

    # Sentences with inconsistent segmentation
    inconsistent_tokens = set(inconsistencies.keys()) if inconsistencies else set()
    for sent in sentences:
        for token in sent["tokens"]:
            if token.lower() in inconsistent_tokens:
                suspicious_ids.add(sent["sent_id"])
                break

    # Build id -> sentence map
    id_map = {s["sent_id"]: s for s in sentences}

    # Select samples: 30% suspicious, 70% random
    n_suspicious = min(int(n_samples * 0.3), len(suspicious_ids))
    n_random = n_samples - n_suspicious

    random.seed(42)
    suspicious_sample = random.sample(sorted(suspicious_ids), min(n_suspicious, len(suspicious_ids)))

    remaining_ids = [s["sent_id"] for s in sentences if s["sent_id"] not in suspicious_ids]
    random_sample = random.sample(remaining_ids, min(n_random, len(remaining_ids)))

    samples = []

    # Build compare lookup
    compare_lookup = {}
    if compare_results:
        for r in compare_results:
            compare_lookup[r["sent_id"]] = r

    for sid in suspicious_sample + random_sample:
        sent = id_map.get(sid)
        if not sent:
            continue

        flags = []
        # Check for long tokens
        for token in sent["tokens"]:
            if count_syllables(token) >= 4:
                flags.append(f"long_token: \"{token}\"")
        # Check for cross-boundary
        for token, upos in zip(sent["tokens"], sent["upos"]):
            if upos != "PROPN" and " " in token:
                syllables = token.split()
                if any(s[0].isupper() for s in syllables[1:] if s):
                    flags.append(f"cross_boundary: \"{token}\"")
        # Check for inconsistency
        for i in range(len(sent["tokens"]) - 1):
            bigram = sent["tokens"][i].lower() + " " + sent["tokens"][i + 1].lower()
            if bigram in inconsistent_tokens:
                flags.append(f"inconsistent: \"{sent['tokens'][i]}\" + \"{sent['tokens'][i+1]}\" (also as \"{bigram}\")")

        wt_output = None
        if sid in compare_lookup:
            cr = compare_lookup[sid]
            wt_output = cr["wt_tokens"]

        samples.append({
            "sent_id": sid,
            "text": sent["text"],
            "tokens": sent["tokens"],
            "upos": sent["upos"],
            "flags": flags,
            "wt_tokens": wt_output,
            "is_suspicious": sid in suspicious_ids,
        })

    return samples


def format_samples_report(samples):
    """Format manual review samples as markdown."""
    lines = []
    lines.append("## 5. Manual Review Samples")
    lines.append("")

    n_suspicious = sum(1 for s in samples if s["is_suspicious"])
    n_random = len(samples) - n_suspicious
    lines.append(f"Total samples: {len(samples)} ({n_suspicious} suspicious, {n_random} random)")
    lines.append("")

    for i, s in enumerate(samples, 1):
        tag = "SUSPICIOUS" if s["is_suspicious"] else "RANDOM"
        lines.append(f"### Sample {i} [{tag}] — {s['sent_id']}")
        lines.append("")
        if s["text"]:
            lines.append(f"**Text:** {s['text']}")
        lines.append(f"**Tokens:** `{'` `'.join(s['tokens'])}`")
        lines.append(f"**UPOS:** {' '.join(s['upos'])}")
        if s["wt_tokens"]:
            lines.append(f"**word_tokenize:** `{'` `'.join(s['wt_tokens'])}`")
        if s["flags"]:
            lines.append(f"**Flags:** {'; '.join(s['flags'])}")
        lines.append("")

    return "\n".join(lines)


# ---- Analysis 6: Dictionary-based validation ----

def load_dictionary():
    """Load Vietnamese dictionary from underthesea (Viet74K / UTS_Dictionary)."""
    try:
        from underthesea.corpus import viet_dict_74K
        words = viet_dict_74K.words
        word_set = set(w.lower().strip() for w in words if w.strip())
        return word_set, "Viet74K"
    except Exception:
        pass
    try:
        from underthesea.datasets.uts_dictionary import UTSDictionary
        d = UTSDictionary()
        word_set = set(w.lower().strip() for w in d.words if w.strip())
        return word_set, "UTS_Dictionary"
    except Exception:
        pass
    return None, None


def analysis_dictionary_validation(sentences, dict_set):
    """Validate word segmentation against a dictionary.

    Checks:
    A) Token coverage: is each token in the dictionary?
    B) Under-segmentation: multi-syllable tokens NOT in dictionary (possible over-merge)
    C) Over-segmentation: adjacent token pairs that form a dictionary word (possible under-merge)
    """
    # A) Token coverage
    total_tokens = 0
    in_dict = 0
    not_in_dict = 0
    oov_by_upos = defaultdict(Counter)  # upos -> {token: count}
    oov_counter = Counter()
    in_dict_by_upos = Counter()
    total_by_upos = Counter()

    # B) Under-segmentation: multi-syllable OOV
    under_seg_candidates = []  # tokens not in dict, but sub-parts are
    multi_oov_counter = Counter()

    # C) Over-segmentation: bigrams that form a dictionary word
    over_seg_counter = Counter()
    over_seg_examples = {}  # bigram -> first sent_id

    for sent in sentences:
        tokens = sent["tokens"]
        upos_list = sent["upos"]

        for i, (token, upos) in enumerate(zip(tokens, upos_list)):
            t_lower = token.lower().strip()
            total_tokens += 1
            total_by_upos[upos] += 1

            if upos == "PUNCT" or upos == "NUM" or upos == "SYM":
                # Skip punctuation, numbers, symbols — not meaningful for dict lookup
                in_dict += 1
                in_dict_by_upos[upos] += 1
                continue

            if t_lower in dict_set:
                in_dict += 1
                in_dict_by_upos[upos] += 1
            else:
                not_in_dict += 1
                oov_counter[t_lower] += 1
                oov_by_upos[upos][t_lower] += 1

                # B) Check under-segmentation for multi-syllable OOV
                syllables = t_lower.split()
                if len(syllables) >= 2:
                    # Check if all individual syllables or sub-parts are in dict
                    all_parts_known = all(s in dict_set for s in syllables)
                    if all_parts_known:
                        multi_oov_counter[t_lower] += 1
                        under_seg_candidates.append({
                            "sent_id": sent["sent_id"],
                            "token": token,
                            "upos": upos,
                            "syllables": syllables,
                        })

        # C) Check over-segmentation: adjacent pairs forming a dict word
        # Filter: skip if both tokens are function words (likely false positive)
        func_upos = {"ADP", "AUX", "CCONJ", "SCONJ", "DET", "PART", "PUNCT"}
        for i in range(len(tokens) - 1):
            # Skip if both tokens are function words or punctuation
            if upos_list[i] in func_upos and upos_list[i + 1] in func_upos:
                continue
            # Skip if either token is punctuation
            if upos_list[i] == "PUNCT" or upos_list[i + 1] == "PUNCT":
                continue
            bigram = tokens[i].lower().strip() + " " + tokens[i + 1].lower().strip()
            if bigram in dict_set:
                over_seg_counter[bigram] += 1
                if bigram not in over_seg_examples:
                    over_seg_examples[bigram] = sent["sent_id"]

        # Also check trigrams (only content-word sequences)
        for i in range(len(tokens) - 2):
            if upos_list[i] == "PUNCT" or upos_list[i + 2] == "PUNCT":
                continue
            trigram = (tokens[i].lower().strip() + " " +
                       tokens[i + 1].lower().strip() + " " +
                       tokens[i + 2].lower().strip())
            if trigram in dict_set:
                over_seg_counter[trigram] += 1
                if trigram not in over_seg_examples:
                    over_seg_examples[trigram] = sent["sent_id"]

    return {
        "total_tokens": total_tokens,
        "in_dict": in_dict,
        "not_in_dict": not_in_dict,
        "coverage": in_dict / total_tokens * 100 if total_tokens else 0,
        "oov_counter": oov_counter,
        "oov_by_upos": oov_by_upos,
        "in_dict_by_upos": in_dict_by_upos,
        "total_by_upos": total_by_upos,
        "multi_oov_counter": multi_oov_counter,
        "under_seg_candidates": under_seg_candidates,
        "over_seg_counter": over_seg_counter,
        "over_seg_examples": over_seg_examples,
    }


def format_dictionary_report(stats, dict_name, dict_size=0):
    """Format dictionary validation report as markdown."""
    lines = []
    lines.append("## 6. Dictionary-Based Validation")
    lines.append("")

    if stats is None:
        lines.append("**Skipped**: Dictionary not available or not requested. "
                      "Use `--dict-validate` to enable.")
        lines.append("")
        return "\n".join(lines)

    lines.append(f"**Dictionary:** {dict_name}")
    lines.append(f"**Dictionary size:** {dict_size:,} entries")
    lines.append("")

    # A) Coverage
    lines.append("### 6a. Token Coverage")
    lines.append("")
    lines.append(f"| Metric | Count | Percentage |")
    lines.append(f"|:---|---:|---:|")
    lines.append(f"| In dictionary | {stats['in_dict']:,} | {stats['coverage']:.1f}% |")
    oov_pct = stats['not_in_dict'] / stats['total_tokens'] * 100
    lines.append(f"| Out-of-vocabulary (OOV) | {stats['not_in_dict']:,} | {oov_pct:.1f}% |")
    lines.append(f"| Total (excl. PUNCT/NUM/SYM) | {stats['total_tokens']:,} | 100% |")
    lines.append("")

    # Coverage by UPOS
    lines.append("**Coverage by UPOS** (top tags):")
    lines.append("")
    lines.append("| UPOS | In Dict | Total | Coverage |")
    lines.append("|:---|---:|---:|---:|")
    for upos in sorted(stats["total_by_upos"], key=stats["total_by_upos"].get, reverse=True)[:12]:
        total = stats["total_by_upos"][upos]
        in_d = stats["in_dict_by_upos"].get(upos, 0)
        cov = in_d / total * 100 if total else 0
        lines.append(f"| {upos} | {in_d:,} | {total:,} | {cov:.1f}% |")
    lines.append("")

    # Top OOV tokens
    lines.append("**Top 30 OOV tokens:**")
    lines.append("")
    lines.append("| Token | Count | UPOS |")
    lines.append("|:---|---:|:---|")
    for token, count in stats["oov_counter"].most_common(30):
        # Find primary UPOS for this token
        upos_for_token = "?"
        for upos, tokens in stats["oov_by_upos"].items():
            if token in tokens:
                upos_for_token = upos
                break
        lines.append(f"| {token} | {count} | {upos_for_token} |")
    lines.append("")

    # B) Under-segmentation candidates
    lines.append("### 6b. Possible Under-Segmentation (Over-Merged Tokens)")
    lines.append("")
    lines.append("Multi-syllable tokens NOT in dictionary, but all individual syllables ARE")
    lines.append("in dictionary. These may be incorrectly merged by the tokenizer.")
    lines.append("")
    n_under = sum(stats["multi_oov_counter"].values())
    lines.append(f"Total occurrences: {n_under:,}")
    lines.append(f"Unique forms: {len(stats['multi_oov_counter']):,}")
    lines.append("")
    if stats["multi_oov_counter"]:
        lines.append("| Token | Count | Sub-parts |")
        lines.append("|:---|---:|:---|")
        for token, count in stats["multi_oov_counter"].most_common(40):
            parts = " + ".join(token.split())
            lines.append(f"| {token} | {count} | {parts} |")
    lines.append("")

    # C) Over-segmentation candidates
    lines.append("### 6c. Possible Over-Segmentation (Under-Merged Tokens)")
    lines.append("")
    lines.append("Adjacent tokens that together form a word found in the dictionary.")
    lines.append("These may be incorrectly split by the tokenizer.")
    lines.append("")
    n_over = sum(stats["over_seg_counter"].values())
    lines.append(f"Total occurrences: {n_over:,}")
    lines.append(f"Unique dictionary words split: {len(stats['over_seg_counter']):,}")
    lines.append("")
    if stats["over_seg_counter"]:
        lines.append("| Dictionary Word | Times Split | Example sent_id |")
        lines.append("|:---|---:|:---|")
        for word, count in stats["over_seg_counter"].most_common(50):
            example_id = stats["over_seg_examples"].get(word, "?")
            lines.append(f"| {word} | {count} | {example_id} |")
    lines.append("")

    # Summary
    lines.append("### 6d. Summary")
    lines.append("")
    lines.append(f"- **Dictionary coverage**: {stats['coverage']:.1f}% of tokens are known words")
    lines.append(f"- **Possible over-merges**: {len(stats['multi_oov_counter']):,} unique multi-syllable "
                 f"OOV forms ({n_under:,} occurrences)")
    lines.append(f"- **Possible under-merges**: {len(stats['over_seg_counter']):,} unique dictionary words "
                 f"found split ({n_over:,} occurrences)")
    lines.append("")

    return "\n".join(lines)


# ---- Main ----

def main():
    parser = argparse.ArgumentParser(description="Evaluate UDD-1 word segmentation quality")
    parser.add_argument(
        "-i", "--input", nargs="+",
        help="Input CoNLL-U files. If not specified, uses default UDD-1 files.",
    )
    parser.add_argument(
        "--all-files", action="store_true",
        help="Use all UDD-1 files (train, dev, test)",
    )
    parser.add_argument(
        "-o", "--output", default="SEGMENTATION_EVAL.md",
        help="Output markdown report file (default: SEGMENTATION_EVAL.md)",
    )
    parser.add_argument(
        "--compare-tokenize", action="store_true",
        help="Compare with underthesea word_tokenize() (requires underthesea)",
    )
    parser.add_argument(
        "--sample-size", type=int, default=300,
        help="Number of sentences to sample for word_tokenize comparison (default: 300)",
    )
    parser.add_argument(
        "--review-samples", type=int, default=100,
        help="Number of manual review samples (default: 100)",
    )
    parser.add_argument(
        "--dict-validate", action="store_true",
        help="Validate segmentation against Vietnamese dictionary (requires underthesea)",
    )
    args = parser.parse_args()

    base_dir = dirname(dirname(__file__))

    # Determine input files
    if args.all_files:
        input_files = [
            join(base_dir, "vi_udd-ud-train.conllu"),
            join(base_dir, "vi_udd-ud-dev.conllu"),
            join(base_dir, "vi_udd-ud-test.conllu"),
        ]
    elif args.input:
        input_files = args.input
    else:
        input_files = [join(base_dir, "vi_udd-ud-train.conllu")]

    # Parse all files
    print(f"Parsing {len(input_files)} file(s)...")
    all_sentences = []
    for filepath in input_files:
        if not exists(filepath):
            print(f"  WARNING: {filepath} not found, skipping")
            continue
        sents = parse_conllu(filepath)
        print(f"  {filepath}: {len(sents):,} sentences")
        all_sentences.extend(sents)

    print(f"Total: {len(all_sentences):,} sentences, "
          f"{sum(len(s['tokens']) for s in all_sentences):,} tokens")
    print()

    # Run analyses
    report_parts = []
    report_parts.append("# UDD-1 Word Segmentation Evaluation")
    report_parts.append("")
    report_parts.append(f"**Files analyzed:** {', '.join(f.split('/')[-1] for f in input_files)}")
    report_parts.append(f"**Total sentences:** {len(all_sentences):,}")
    report_parts.append(f"**Total tokens:** {sum(len(s['tokens']) for s in all_sentences):,}")
    report_parts.append("")

    # Analysis 1
    print("Analysis 1: Syllable distribution...")
    overall, by_upos, total_tokens = analysis_syllable_distribution(all_sentences)
    report_parts.append(format_syllable_report(overall, by_upos, total_tokens))

    # Analysis 2
    print("Analysis 2: Anomalous tokens...")
    long_tokens, long_counter, cross_boundary, cross_counter, legal_stats = \
        analysis_anomalous_tokens(all_sentences)
    report_parts.append(format_anomalous_report(
        long_tokens, long_counter, cross_boundary, cross_counter, legal_stats))

    # Analysis 3
    print("Analysis 3: Inconsistent segmentation...")
    inconsistencies = analysis_inconsistency(all_sentences)
    report_parts.append(format_inconsistency_report(inconsistencies))

    # Analysis 4
    compare_results = None
    compare_stats = None
    if args.compare_tokenize:
        print(f"Analysis 4: Comparing with word_tokenize() (sample={args.sample_size})...")
        compare_results, compare_stats = analysis_compare_tokenize(
            all_sentences, sample_size=args.sample_size)
    else:
        print("Analysis 4: Skipped (use --compare-tokenize to enable)")
    report_parts.append(format_compare_report(compare_results, compare_stats))

    # Analysis 5
    print(f"Analysis 5: Manual review samples (n={args.review_samples})...")
    samples = analysis_manual_samples(
        all_sentences, long_tokens, cross_boundary, inconsistencies,
        compare_results=compare_results, n_samples=args.review_samples,
    )
    report_parts.append(format_samples_report(samples))

    # Analysis 6
    dict_stats = None
    dict_name = None
    if args.dict_validate:
        print("Analysis 6: Dictionary-based validation...")
        dict_set, dict_name = load_dictionary()
        if dict_set:
            print(f"  Dictionary: {dict_name} ({len(dict_set):,} entries)")
            dict_stats = analysis_dictionary_validation(all_sentences, dict_set)
        else:
            print("  WARNING: No dictionary available, skipping")
    else:
        print("Analysis 6: Skipped (use --dict-validate to enable)")
    dict_size = len(dict_set) if dict_set else 0
    report_parts.append(format_dictionary_report(dict_stats, dict_name, dict_size))

    # Write report
    output_path = args.output
    if not output_path.startswith("/"):
        output_path = join(base_dir, output_path)

    report = "\n".join(report_parts)
    with open(output_path, "w", encoding="utf-8") as f:
        f.write(report)

    print(f"\nReport written to: {output_path}")
    print(f"  Total inconsistent forms: {len(inconsistencies):,}")
    print(f"  Long tokens (4+ syl): {len(long_tokens):,} occurrences")
    print(f"  Cross-boundary candidates: {len(cross_boundary):,} occurrences")
    if compare_stats:
        print(f"  word_tokenize match rate: {compare_stats['match_rate']:.1f}%")
    if dict_stats:
        print(f"  Dictionary coverage: {dict_stats['coverage']:.1f}%")
        n_under = sum(dict_stats["multi_oov_counter"].values())
        n_over = sum(dict_stats["over_seg_counter"].values())
        print(f"  Possible over-merges: {len(dict_stats['multi_oov_counter']):,} forms ({n_under:,} occ)")
        print(f"  Possible under-merges: {len(dict_stats['over_seg_counter']):,} forms ({n_over:,} occ)")


if __name__ == "__main__":
    main()