"""Quotation detection (Subtask 1A): finds Quran / Hadith quotations in a text and their boundaries.

The detector is rule-based: quotation marks and brackets mark candidate segments and a corpus lookup decides the type
(Ayah / Hadith). Introductory phrases such as "قال الله تعالى" are NOT required; they are only a soft fallback for
altered quotations the corpus cannot confirm. No model weights are loaded."""
from __future__ import annotations

import re
from dataclasses import dataclass
from typing import List, Optional

from normalization import normalize_for_matching, normalize_lenient, normalize_strict, tokenize


# --------------------------------------------------------------------------------------------------------------
@dataclass
class DetectedSpan:
    start: int
    end: int                        # exclusive
    label: str                      # 'Ayah' | 'Hadith'
    confidence: Optional[float]     # None for the rule backend
    source: str                     # 'rules' | 'given'
    text: str = ""


QUOTE_CHARS = " \t\r\n\"“”«»﴿﴾{}()[]"


def trim_span(text: str, start: int, end: int):
    """Drop whitespace and quotation marks at both edges (gold spans exclude the quote marks)."""
    while start < end and text[start] in QUOTE_CHARS:
        start += 1
    while end > start and text[end - 1] in QUOTE_CHARS + ".،,؛:":
        end -= 1
    return start, end


AYAH_TRIGGERS = [
    "قال الله", "قوله تعالى", "قال تعالى", "يقول الله", "يقول تعالى", "قال سبحانه", "قوله سبحانه", "في كتابه",
    "سورة", "الآية", "الاية", "الآيات", "القرآن", "القران", "كتاب الله", "عز وجل", "جل جلاله", "فقال تعالى",
    "ذكر الله", "﴿",
]
HADITH_TRIGGERS = [
    "رسول الله", "النبي", "صلى الله عليه وسلم", "ﷺ", "عليه الصلاة والسلام", "حديث", "رواه", "روى", "متفق عليه",
    "الحديث", "فقال", "قال ص", "صلى الله عليه", "وسلم",
]
_FORMULA_WORDS = {
    normalize_for_matching(w)
    for w in "قال قالت رسول الله صلى عليه وسلم النبي تعالى سبحانه عز وجل فقال يقول الكريم الشريف الحديث الآية روى رواه عن أن أنه البخاري ومسلم".split()
}
_BRACKET_PAIRS = [("“", "”"), ("«", "»"), ("﴿", "﴾"), ("{", "}"), ("(", ")"), ("[", "]")]


class RuleDetector:
    """Quotation-mark and trigger-phrase detector with an optional corpus lookup (no training, no GPU)."""

    def __init__(self, retriever=None, min_words: int = 3, context_chars: int = 110,
                 min_corpus_cov: float = 0.6) -> None:
        self.kb, self.min_words, self.context_chars, self.min_corpus_cov = retriever, min_words, context_chars, min_corpus_cov

    @staticmethod
    def _segments(text: str):
        segments = []
        quote_positions = [m.start() for m in re.finditer('"', text)]
        if len(quote_positions) % 2 == 0:
            pairs = zip(quote_positions[0::2], quote_positions[1::2])   # opening/closing pairs
        else:   # a stray quote: fall back to every consecutive pair
            pairs = zip(quote_positions, quote_positions[1:])
        for a, b in pairs:
            segments.append((a + 1, b))
        for opener, closer in _BRACKET_PAIRS:
            for m in re.finditer(re.escape(opener) + r"(.*?)" + re.escape(closer), text, re.S):
                segments.append((m.start(1), m.end(1)))
        return segments

    @staticmethod
    def _trigger_type(context: str) -> Optional[str]:
        best_end, best_label = -1, None
        for label, triggers in (("Ayah", AYAH_TRIGGERS), ("Hadith", HADITH_TRIGGERS)):
            for trigger in triggers:
                pos = context.rfind(trigger)
                if pos >= 0 and pos + len(trigger) > best_end:
                    best_end, best_label = pos + len(trigger), label
        return best_label

    def _corpus_coverage(self, span: str):
        """Highest word coverage of the span by any top Quran ayah / Hadith candidate."""
        if self.kb is None:
            return 0.0, 0.0
        quran_words = set(tokenize(normalize_strict(span)))
        if not quran_words:
            return 0.0, 0.0
        quran_cov = max(
            (len(quran_words & set(tokenize(normalize_strict(c["text"])))) / len(quran_words)
             for c in self.kb.search_quran_ayahs(span, top_k=5)),
            default=0.0,
        )
        hadith_words = set(tokenize(normalize_lenient(span)))
        hadith_cov = max(
            (len(hadith_words & set(tokenize(normalize_lenient(c["text"])))) / len(hadith_words)
             for c in self.kb.search_hadith(span, top_k=5)),
            default=0.0,
        ) if hadith_words else 0.0
        return quran_cov, hadith_cov

    def detect(self, text: str) -> List[DetectedSpan]:
        candidates = []
        for start, end in self._segments(text):
            start, end = trim_span(text, start, end)
            if end <= start:
                continue
            inner = text[start:end]
            words = [w for w in normalize_for_matching(inner).split() if w]
            if len(words) < self.min_words or len(inner) > 3000:
                continue
            if sum(w in _FORMULA_WORDS for w in words) / len(words) >= 0.6:
                continue
            trigger = self._trigger_type(text[max(0, start - self.context_chars):start])
            quran_cov, hadith_cov = self._corpus_coverage(inner)
            label = None
            # 1) The corpus decides: a quoted segment that matches the Quran / Hadith needs no introductory phrase.
            if max(quran_cov, hadith_cov) >= self.min_corpus_cov and len(words) >= 4:
                label = "Ayah" if quran_cov >= hadith_cov else "Hadith"
            # 2) Introductory phrases are only a soft fallback for altered quotations that the corpus cannot confirm.
            elif trigger:
                label = trigger
                other, mine = (hadith_cov, quran_cov) if trigger == "Ayah" else (quran_cov, hadith_cov)
                if other >= 0.8 and mine < 0.5:
                    label = "Hadith" if trigger == "Ayah" else "Ayah"
            if label is None:
                continue
            candidates.append((bool(trigger), max(quran_cov, hadith_cov), end - start, start, end, label))

        candidates.sort(key=lambda c: (c[0], c[1], c[2]), reverse=True)   # trigger first, then corpus match, then length
        taken = []
        for _, _, _, start, end, label in candidates:
            if all(end <= t_start or start >= t_end for t_start, t_end, _ in taken):
                taken.append((start, end, label))
        taken.sort()
        return [DetectedSpan(s, e, label, None, "rules", text[s:e]) for s, e, label in taken]