"""Backfill style_patterns.frequency with real occurrence counts.

The analyzer currently stores frequency=1 for every pattern (it only extracts
unique patterns, doesn't count occurrences). This script scans the full_text
of every decision in style_corpus and updates each pattern's frequency to
the true count of decisions containing the pattern_text as a substring.

Run once after analysis, and again whenever new decisions are added.
"""

from __future__ import annotations

import asyncio
import os
import re
import sys
import unicodedata
from pathlib import Path

# Load env
for line in (Path.home() / ".env").read_text().splitlines():
    if "=" in line and not line.startswith("#"):
        k, v = line.split("=", 1)
        os.environ.setdefault(k.strip(), v.strip().strip('"').strip("'"))

sys.path.insert(0, "/home/chaim/legal-ai/mcp-server/src")

from legal_mcp.services import db as db_mod  # noqa: E402


def _strip_nikud(text: str) -> str:
    """Remove Hebrew combining marks (nikud) for robust matching."""
    return "".join(
        c for c in unicodedata.normalize("NFD", text)
        if not unicodedata.combining(c)
    )


def _extract_searchable_variants(pattern_text: str) -> list[str]:
    """Extract searchable substrings from a pattern template.

    The analyzer stores patterns as templates with:
      - Placeholders in [brackets]:  "בפנינו ערר על החלטת [הגוף] מיום [תאריך]"
      - Alternatives separated by / :  "נפנה ל... / ראה והשווה / נפנה להחלטה"
      - Ellipsis ... for variable parts

    This function returns a list of concrete substrings to search for.
    We pick the longest fixed segment from each alternative (>= 4 chars)
    so that matching is specific enough to be meaningful but still flexible.
    """
    # Split on " / " or " או " to get alternatives
    alternatives = re.split(r"\s*/\s*|\s+או\s+", pattern_text)

    variants: list[str] = []
    for alt in alternatives:
        alt = alt.strip()
        if not alt:
            continue

        # Remove bracket placeholders [X]
        alt = re.sub(r"\[[^\]]*\]", "|", alt)
        # Replace ellipsis with separator
        alt = re.sub(r"\.{2,}", "|", alt)
        # Remove ellipsis unicode
        alt = alt.replace("…", "|")

        # Split on the | separator and take fixed segments
        segments = [s.strip(" ,.:;\"'") for s in alt.split("|")]
        # Keep segments long enough to be meaningful (>= 4 chars, not just common words)
        good = [s for s in segments if len(s) >= 4]

        if good:
            # Use the longest segment as the key variant for this alternative
            variants.append(max(good, key=len))
        elif alt.strip():
            # Fallback: use the whole cleaned alternative
            stripped = alt.replace("|", " ").strip()
            if len(stripped) >= 4:
                variants.append(stripped)

    # Deduplicate while preserving order
    seen = set()
    unique = []
    for v in variants:
        if v not in seen:
            seen.add(v)
            unique.append(v)
    return unique


def _count_decisions_containing(variants: list[str], normalized_decisions: list) -> int:
    """Count how many decisions contain ANY of the variants."""
    count = 0
    for _, _, text in normalized_decisions:
        if any(v in text for v in variants):
            count += 1
    return count


async def main() -> int:
    pool = await db_mod.get_pool()

    async with pool.acquire() as conn:
        decisions = await conn.fetch(
            "SELECT id, decision_number, full_text FROM style_corpus "
            "WHERE full_text IS NOT NULL AND length(full_text) > 0"
        )
        patterns = await conn.fetch(
            "SELECT id, pattern_text, pattern_type FROM style_patterns"
        )

        print(f"Scanning {len(patterns)} patterns across {len(decisions)} decisions...")

        # Normalize decisions once
        normalized_decisions = [
            (d["id"], d["decision_number"], _strip_nikud(d["full_text"]))
            for d in decisions
        ]

        updates = []
        for p in patterns:
            pattern_text = p["pattern_text"]
            if not pattern_text or len(pattern_text) < 3:
                updates.append((0, p["id"]))
                continue

            variants = _extract_searchable_variants(_strip_nikud(pattern_text))
            if not variants:
                updates.append((0, p["id"]))
                continue

            count = _count_decisions_containing(variants, normalized_decisions)
            updates.append((count, p["id"]))

        await conn.executemany(
            "UPDATE style_patterns SET frequency = $1 WHERE id = $2",
            updates,
        )

        # Show distribution
        rows = await conn.fetch(
            "SELECT pattern_type, pattern_text, frequency "
            "FROM style_patterns "
            "ORDER BY frequency DESC "
            "LIMIT 15"
        )
        print(f"\nTop 15 patterns by real frequency:")
        for r in rows:
            print(f"  {r['frequency']:>3}  [{r['pattern_type']:<22}] {r['pattern_text'][:90]}")

        dist = await conn.fetch(
            "SELECT frequency, count(*) FROM style_patterns "
            "GROUP BY frequency ORDER BY frequency DESC"
        )
        print(f"\nFrequency distribution:")
        for r in dist:
            print(f"  frequency={r['frequency']:>3} → {r['count']} patterns")

    return 0


if __name__ == "__main__":
    sys.exit(asyncio.run(main()))