"""
Section splitter for shared URLs in unified compliance checks.

When the same URL is used for multiple document types (e.g. /datenschutz
used for DSI + Cookie + DSB), this module splits the text at headings
and assigns the best-matching section to each doc_type.
"""

import logging
import re

logger = logging.getLogger(__name__)

# Heading keyword → doc_type mapping
_HEADING_TYPE_MAP = [
    ("cookie", "cookie"),
    ("datenschutzbeauftragte", "dsb"),
    ("widerruf", "widerruf"),
    ("impressum", "impressum"),
    ("agb", "agb"),
    ("nutzungsbedingung", "agb"),
    ("social media", "social_media"),
    ("soziale medien", "social_media"),
    ("soziale netzwerke", "social_media"),
    ("google analytics", "cookie"),
    ("tracking", "cookie"),
    ("verwendung von cookies", "cookie"),
    ("nutzung von google", "cookie"),
    ("webanalyse", "cookie"),
]


def split_shared_texts(
    doc_entries: list[dict],
    url_cache: dict[str, str],
) -> None:
    """When the same URL is used for multiple doc_types, split text into
    sections and assign the best-matching section to each doc_type.

    Mutates doc_entries in place.
    """
    # Group entries by normalized URL
    url_groups: dict[str, list[int]] = {}
    for i, entry in enumerate(doc_entries):
        if not entry.get("url"):
            continue
        key = entry["url"].strip().rstrip("/").lower()
        url_groups.setdefault(key, []).append(i)

    for url_key, indices in url_groups.items():
        if len(indices) < 2:
            continue

        full_text = doc_entries[indices[0]].get("text", "")
        if not full_text or len(full_text) < 200:
            continue

        sections = _split_at_headings(full_text)
        if not sections:
            continue

        for idx in indices:
            doc_type = doc_entries[idx]["doc_type"]
            best = _find_section_for_type(sections, doc_type)
            if best:
                doc_entries[idx]["text"] = best
                doc_entries[idx]["word_count"] = len(best.split())

        typed = [s for s in sections if s.get("type")]
        logger.info(
            "Split shared URL into %d typed sections for %d doc_types: %s",
            len(typed), len(indices),
            ", ".join(f"{s['type']}({len(s['text'].split())}w)" for s in typed),
        )


def _split_at_headings(text: str) -> list[dict]:
    """Split text at classified headings into typed sections."""
    lines = text.split("\n")
    sections: list[dict] = []
    current_type: str | None = None
    current_heading = ""
    current_lines: list[str] = []
    preamble_lines: list[str] = []

    for line in lines:
        stripped = line.strip()
        classified = _classify_heading(stripped)

        if classified:
            # Save previous section
            if current_type and current_lines:
                _add_section(sections, current_heading, current_type, current_lines)
            elif not current_type and current_lines:
                preamble_lines.extend(current_lines)

            current_type = classified
            current_heading = stripped
            current_lines = []
        else:
            current_lines.append(line)

    # Save last section
    if current_type and current_lines:
        _add_section(sections, current_heading, current_type, current_lines)
    elif current_lines:
        preamble_lines.extend(current_lines)

    # Add preamble as untyped section (main DSI text)
    if preamble_lines:
        preamble_text = "\n".join(preamble_lines)
        if len(preamble_text.split()) >= 30:
            sections.insert(0, {
                "heading": "(Haupttext)",
                "text": preamble_text,
                "type": "dse",
            })

    return sections


def _add_section(
    sections: list[dict], heading: str, sec_type: str, lines: list[str],
) -> None:
    """Add a section, merging with existing same-type sections."""
    text = "\n".join(lines)
    if len(text.split()) < 20:
        return
    # Merge if same type already exists
    for s in sections:
        if s["type"] == sec_type:
            s["text"] += "\n\n" + text
            return
    sections.append({"heading": heading, "text": text, "type": sec_type})


def _classify_heading(line: str) -> str | None:
    """Classify a line as a section heading. Returns doc_type or None."""
    if not line or len(line) < 5 or len(line) > 80:
        return None
    if line.endswith(".") or line.endswith(","):
        return None
    if len(line.split()) > 10:
        return None
    if not (line[0].isupper() or line[0].isdigit()):
        return None

    heading_lower = line.lower().strip()
    heading_lower = re.sub(r"^[\d\.\)\-]+\s*", "", heading_lower).strip()

    for keyword, doc_type in _HEADING_TYPE_MAP:
        if keyword in heading_lower:
            return doc_type
    return None


def _find_section_for_type(sections: list[dict], doc_type: str) -> str | None:
    """Find the best text section for a given doc_type.

    DSI always gets the full text (main document).
    Other types get their matching section if found.
    """
    if doc_type in ("dse", "datenschutz", "privacy"):
        return None  # Keep full text for DSI

    for section in sections:
        if section.get("type") == doc_type and section.get("text"):
            return section["text"]

    return None  # No match → keep full text


def auto_fill_from_dsi(doc_entries: list[dict]) -> None:
    """Auto-fill empty document rows from sections found in the DSI text.

    If the user only entered the DSI URL but left Cookie/Social-Media empty,
    and the DSI text contains those sections, auto-fill them.
    """
    # Find the DSI entry
    dsi_entry = None
    for entry in doc_entries:
        if entry["doc_type"] in ("dse", "datenschutz", "privacy") and entry.get("text"):
            dsi_entry = entry
            break
    if not dsi_entry:
        return

    dsi_text = dsi_entry["text"]
    if len(dsi_text) < 300:
        return

    # Split DSI into sections
    sections = _split_at_headings(dsi_text)
    if not sections:
        return

    # Find empty entries that could be filled from DSI sections
    filled = []
    for entry in doc_entries:
        if entry.get("text") or entry.get("url"):
            continue  # Already has content
        # Auto-discovery already tried + decided: skip. Don't override its
        # 'NICHT GEFUNDEN' verdict with a pseudo-match from DSI sections
        # (which produces false MANGELHAFT findings for genuinely missing
        # docs like BMW's AGB).
        if entry.get("discovery_attempted") and not entry.get("auto_discovered"):
            continue

        doc_type = entry["doc_type"]
        section_text = _find_section_for_type(sections, doc_type)
        if section_text and len(section_text.split()) >= 30:
            entry["text"] = section_text
            entry["word_count"] = len(section_text.split())
            entry["url"] = f"{dsi_entry.get('url', '')} (Abschnitt)"
            filled.append(doc_type)

    if filled:
        logger.info(
            "Auto-filled %d empty rows from DSI sections: %s",
            len(filled), ", ".join(filled),
        )


# ── Cross-Document Search ────────────────────────────────────────────

# Keywords that indicate a doc_type is present in text (case-insensitive)
_DOC_TYPE_KEYWORDS = {
    "widerruf": [
        "widerrufsrecht", "widerrufsbelehrung", "widerrufsfrist",
        "binnen 14 tagen", "widerruf erklaeren", "muster-widerrufsformular",
    ],
    "cookie": [
        "cookie-richtlinie", "cookie-tabelle", "cookiebot", "consent-tool",
        "arten der cookies", "session-cookie", "tracking-cookie",
    ],
    "social_media": [
        "gemeinsam verantwortlich", "art. 26 dsgvo", "fanpage",
        "social media plugin", "facebook-seite", "instagram-profil",
    ],
    "impressum": [
        "angaben gemaess", "angaben gemäß", "§ 5 tmg", "§5 tmg",
        "telemediengesetz", "impressum",
    ],
    "agb": [
        "allgemeine geschaeftsbedingungen", "allgemeine geschäftsbedingungen",
        "geltungsbereich", "vertragsschluss", "§305 bgb",
    ],
    "dsb": [
        "datenschutzbeauftragte", "dsb@", "dpo@",
        "datenschutzbeauftragten",
    ],
}


def cross_search_documents(doc_entries: list[dict]) -> list[dict]:
    """Search ALL texts for ALL doc_types and fill missing entries.

    For each empty doc_type row, search through all other documents'
    texts to find the content. If found in the wrong document, extract
    it, assign it, and create a finding about incorrect placement.

    Returns list of findings (misplacement warnings).
    """
    findings: list[dict] = []

    # Collect all available texts with their source doc_type
    all_texts: list[tuple[str, str, str]] = []  # (doc_type, url, text)
    for entry in doc_entries:
        if entry.get("text") and len(entry["text"]) > 100:
            all_texts.append((entry["doc_type"], entry.get("url", ""), entry["text"]))

    if not all_texts:
        return findings

    # For each entry, check if:
    # a) It has text but the text doesn't match the doc_type → search other texts
    # (Empty entries from auto-discovery 'not found' are NOT pseudo-filled
    # from other docs — that would silently revive a 'NICHT GEFUNDEN' verdict
    # as a misleading MANGELHAFT row.)
    for entry in doc_entries:
        target_type = entry["doc_type"]
        keywords = _DOC_TYPE_KEYWORDS.get(target_type, [])
        if not keywords:
            continue

        has_text = entry.get("text") and len(entry["text"].split()) > 50
        text_matches = False
        if has_text:
            entry_lower = entry["text"].lower()
            match_score = sum(1 for kw in keywords if kw in entry_lower)
            text_matches = match_score >= 2

        if has_text and text_matches:
            continue  # Text present AND matches doc_type → skip
        # Skip empty entries the auto-discovery has already ruled on.
        if not has_text and entry.get("discovery_attempted") and not entry.get("auto_discovered"):
            continue

        # Search all other texts for this doc_type's keywords
        best_match: dict | None = None
        best_score = 0

        for source_type, source_url, source_text in all_texts:
            if source_type == target_type:
                continue

            text_lower = source_text.lower()
            score = sum(1 for kw in keywords if kw in text_lower)

            if score >= 2 and score > best_score:
                best_score = score
                # Extract the relevant section
                section = _extract_section_by_keywords(source_text, keywords)
                if section and len(section.split()) >= 30:
                    best_match = {
                        "source_type": source_type,
                        "source_url": source_url,
                        "section_text": section,
                        "keyword_hits": score,
                    }

        if best_match:
            entry["text"] = best_match["section_text"]
            entry["word_count"] = len(best_match["section_text"].split())
            source_label = best_match["source_type"].upper()
            entry["url"] = f"(gefunden in {source_label})"

            findings.append({
                "id": f"placement-{target_type}",
                "label": f"{_type_label(target_type)} in falschem Dokument",
                "passed": False,
                "severity": "MEDIUM",
                "level": 1,
                "parent": None,
                "skipped": False,
                "matched_text": "",
                "hint": (
                    f"Die {_type_label(target_type)} wurde nicht als eigenes "
                    f"Dokument gefunden, sondern in der/den {source_label} "
                    f"({best_match['source_url']}). Gemaess Art. 246a EGBGB / "
                    f"§312d BGB muss die {_type_label(target_type)} leicht "
                    f"auffindbar und klar erkennbar sein. Empfehlung: Als "
                    f"eigenen Link im Footer oder als separates Dokument "
                    f"bereitstellen."
                ),
                "source": "cross_document_search",
                "doc_type": target_type,
            })

            logger.info(
                "Cross-doc: Found %s in %s (%d keywords, %d words)",
                target_type, best_match["source_type"],
                best_match["keyword_hits"],
                entry["word_count"],
            )
        elif has_text and not text_matches:
            # Text present but doesn't match — wrong text assigned
            findings.append({
                "id": f"wrong-text-{target_type}",
                "label": f"{_type_label(target_type)} nicht im eingereichten Text",
                "passed": False,
                "severity": "HIGH",
                "level": 1,
                "parent": None,
                "skipped": False,
                "matched_text": "",
                "hint": (
                    f"Der eingereichte Text enthaelt keine "
                    f"{_type_label(target_type)}. Moeglicherweise wurde "
                    f"die falsche URL eingegeben. Das System konnte die "
                    f"{_type_label(target_type)} auch in keinem anderen "
                    f"eingereichten Dokument finden."
                ),
                "source": "cross_document_search",
                "doc_type": target_type,
            })
            logger.info("Cross-doc: %s text doesn't match doc_type, not found elsewhere", target_type)

    return findings


def _extract_section_by_keywords(
    text: str, keywords: list[str],
) -> str | None:
    """Extract the section of text around the keyword matches."""
    text_lower = text.lower()
    lines = text.split("\n")

    # Find first and last line containing any keyword
    first_line = len(lines)
    last_line = 0
    for i, line in enumerate(lines):
        line_lower = line.lower()
        if any(kw in line_lower for kw in keywords):
            first_line = min(first_line, i)
            last_line = max(last_line, i)

    if first_line >= last_line:
        return None

    # Expand to include context (5 lines before first, 10 after last)
    start = max(0, first_line - 5)
    end = min(len(lines), last_line + 10)

    section = "\n".join(lines[start:end])
    return section if len(section.split()) >= 30 else None


def _type_label(doc_type: str) -> str:
    labels = {
        "widerruf": "Widerrufsbelehrung",
        "cookie": "Cookie-Richtlinie",
        "social_media": "Social-Media-Datenschutz",
        "impressum": "Impressum",
        "agb": "AGB",
        "dsb": "DSB-Kontakt",
        "dse": "Datenschutzerklaerung",
    }
    return labels.get(doc_type, doc_type)