import re
from urllib.parse import urljoin

from bs4 import BeautifulSoup, NavigableString, Tag
from requests import Session

from app.parser.profile_url import normalize_profile_url, parse_profile_identity
from app.version import BACKEND_VERSION

_YEAR_PATTERN = re.compile(r"Начал[аи]?\s+работать.*?ВШЭ.*?(\d{4})", re.IGNORECASE)
_EMAIL_PATTERN = re.compile(r"([A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,})")
_PHONE_PATTERN = re.compile(r"(?:Телефон|Phone)\s*:\s*([+()\d\-\s]{8,})", re.IGNORECASE)


def normalize_ws(value: str | None) -> str:
    return re.sub(r"\s+", " ", value or "").strip()


def extract_person_tabs(soup: BeautifulSoup, source_url: str) -> list[dict[str, str | None]]:
    selectors = (
        "div.person-menu.is-desktop.small.person-menu-addition",
        ".person-menu",
    )
    for selector in selectors:
        menu = soup.select_one(selector)
        if not menu:
            continue
        tabs = []
        for anchor in menu.select("a[href]"):
            title = normalize_ws(anchor.get_text(" ", strip=True))
            href = anchor.get("href", "").strip()
            if title and href:
                tabs.append(
                    {
                        "data_index": anchor.get("data-index"),
                        "title": title,
                        "href": urljoin(source_url, href),
                    }
                )
        if tabs:
            return _dedupe_tabs(tabs)
    return []


def extract_person_header(soup: BeautifulSoup, source_url: str) -> dict:
    name_node = soup.select_one("h1.person-caption") or soup.find("h1")
    text = normalize_ws(soup.get_text(" ", strip=True))
    year_match = _YEAR_PATTERN.search(text)
    contacts = {"phones": [], "emails": [], "address": None, "items": []}

    for email in _EMAIL_PATTERN.findall(text):
        if email not in contacts["emails"]:
            contacts["emails"].append(email)
    for phone in _PHONE_PATTERN.findall(text):
        normalized_phone = normalize_ws(phone)
        if normalized_phone and normalized_phone not in contacts["phones"]:
            contacts["phones"].append(normalized_phone)

    address_match = re.search(
        r"(Адрес[:\s].{0,220}?)(?:\s+Время|\s+Расписание|\s+SPIN|\s+ORCID|$)",
        text,
        flags=re.IGNORECASE,
    )
    if address_match:
        contacts["address"] = normalize_ws(address_match.group(1)).rstrip(",")

    positions = []
    for li in soup.select("ul.g-ul.g-list.small.employment-add li, ul.employment-add li"):
        value = normalize_ws(li.get_text(" ", strip=True))
        if value:
            positions.append(value)

    external_ids = []
    id_domains = (
        ("ORCID", "orcid.org"),
        ("Scopus AuthorID", "scopus.com"),
        ("ResearcherID", "webofscience.com"),
        ("Google Scholar", "scholar.google."),
        ("SPIN РИНЦ", "elibrary.ru"),
    )
    for anchor in soup.select("a[href]"):
        href = anchor.get("href", "").strip()
        label = normalize_ws(anchor.get_text(" ", strip=True))
        for system, marker in id_domains:
            if marker in href:
                external_ids.append({"system": system, "value": label or system, "url": href})
                break

    return {
        "source_url": source_url,
        "full_name": normalize_ws(name_node.get_text(" ", strip=True)) if name_node else None,
        "positions": positions,
        "hse_start_year": int(year_match.group(1)) if year_match else None,
        "contacts": contacts,
        "external_ids": _dedupe_dicts(external_ids),
    }


def extract_sections(soup: BeautifulSoup, source_url: str) -> list[dict]:
    sections = []
    for h2 in soup.select("h2"):
        title = normalize_ws(h2.get_text(" ", strip=True))
        if not title or "расписание занятий" in title.lower():
            continue
        nodes = _collect_between_h2(h2)
        raw_text = _nodes_raw_text(nodes)
        paragraphs = _nodes_paragraphs(nodes)
        items = _nodes_list_items(nodes)
        links = []
        for node in nodes:
            if isinstance(node, Tag):
                links.extend(_extract_links(node, source_url))

        section_type = _infer_section_type(title, nodes)
        section = {
            "title": title,
            "slug": _slugify(title),
            "type": section_type,
            "raw_text": raw_text,
            "paragraphs": paragraphs,
            "items": items,
            "links": links,
        }

        if section_type == "publications":
            section["publications_count"], section["publications"] = _parse_publications(title, nodes, source_url)
            section["items"] = [item["text"] for item in section["publications"] if item.get("text")]
        elif section_type == "courses_by_year":
            section["academic_year"], section["courses"] = _parse_courses(title, nodes, source_url)
            section.pop("items", None)
            section.pop("links", None)
        elif section_type == "table":
            section["table"] = _parse_table(nodes, source_url)
        elif "выпускные квалификационные работы студентов ниу вшэ" in title.lower():
            section["items"] = _parse_vkr_items(nodes)

        year_entries = _parse_year_entries(nodes, source_url)
        if year_entries:
            section["year_entries"] = year_entries
            if section_type in {"generic", "paragraphs"}:
                section["type"] = "year_blocks"
        sections.append(section)
    return sections


def parse_person_profile(
    session: Session,
    source_url: str,
    headers: dict[str, str],
    timeout: int,
    use_playwright: bool = False,
) -> dict | None:
    normalized_url = normalize_profile_url(source_url)
    if not normalized_url:
        return None
    response = session.get(normalized_url, headers=headers, timeout=timeout)
    response.raise_for_status()
    html = response.text
    if use_playwright:
        html = _render_with_playwright(normalized_url, html)

    soup = BeautifulSoup(html, "html.parser")
    profile_type, profile_id = parse_profile_identity(normalized_url)
    header = extract_person_header(soup, normalized_url)
    tabs = extract_person_tabs(soup, normalized_url)
    sections = extract_sections(soup, normalized_url)
    internal_links = [tab["href"] for tab in tabs if tab.get("href")]

    return {
        "source_url": normalized_url,
        "profile_type": profile_type,
        "profile_id": profile_id,
        "full_name": header.get("full_name"),
        "positions": header.get("positions") or [],
        "hse_start_year": header.get("hse_start_year"),
        "contacts": header.get("contacts") or {},
        "external_ids": header.get("external_ids") or [],
        "tabs": tabs,
        "sections": sections,
        "employee_internal_links": internal_links,
        "parser_version": BACKEND_VERSION,
        "_html": html,
    }


def _render_with_playwright(source_url: str, fallback_html: str) -> str:
    try:
        from playwright.sync_api import sync_playwright
    except Exception:
        return fallback_html
    try:
        with sync_playwright() as playwright:
            browser = playwright.chromium.launch(headless=True)
            page = browser.new_page()
            page.goto(source_url, wait_until="domcontentloaded", timeout=45000)
            for index in range(page.locator(".person-menu a").count()):
                try:
                    page.locator(".person-menu a").nth(index).click(timeout=2500, force=True)
                    page.wait_for_timeout(450)
                except Exception:
                    continue
            html = page.content()
            browser.close()
            return html
    except Exception:
        return fallback_html


def _collect_between_h2(start_h2: Tag) -> list[Tag | NavigableString | str]:
    nodes = []
    for sibling in start_h2.next_siblings:
        if isinstance(sibling, Tag) and sibling.name == "h2":
            break
        if isinstance(sibling, NavigableString) and not normalize_ws(str(sibling)):
            continue
        nodes.append(sibling)
    return nodes


def _extract_links(node: Tag, source_url: str) -> list[dict[str, str]]:
    links = []
    for anchor in node.select("a[href]"):
        text = normalize_ws(anchor.get_text(" ", strip=True))
        href = anchor.get("href", "").strip()
        if text and href and "timetable" not in href.lower() and "расписание" not in text.lower():
            links.append({"text": text, "url": urljoin(source_url, href)})
    return links


def _nodes_raw_text(nodes: list) -> str:
    chunks = []
    for node in nodes:
        text = normalize_ws(node.get_text(" ", strip=True) if isinstance(node, Tag) else str(node))
        if text:
            chunks.append(text)
    return "\n".join(chunks)


def _nodes_paragraphs(nodes: list) -> list[str]:
    paragraphs = []
    for node in nodes:
        if isinstance(node, Tag):
            paragraphs.extend(normalize_ws(p.get_text(" ", strip=True)) for p in node.select("p"))
    return [p for p in paragraphs if p]


def _nodes_list_items(nodes: list) -> list[str]:
    items = []
    for node in nodes:
        if isinstance(node, Tag):
            items.extend(normalize_ws(li.get_text(" ", strip=True)) for li in node.select("li"))
    return [item for item in items if item and "расписание" not in item.lower()]


def _infer_section_type(title: str, nodes: list) -> str:
    lowered = title.lower()
    if _has_table(nodes):
        return "table"
    if "публикац" in lowered:
        return "publications"
    if "учебные курсы" in lowered:
        return "courses_by_year"
    if _nodes_list_items(nodes):
        return "list"
    if _nodes_paragraphs(nodes):
        return "paragraphs"
    return "generic"


def _has_table(nodes: list) -> bool:
    return any(isinstance(node, Tag) and (node.name == "table" or node.find("table")) for node in nodes)


def _parse_table(nodes: list, source_url: str) -> dict:
    for node in nodes:
        if not isinstance(node, Tag):
            continue
        table = node if node.name == "table" else node.find("table")
        if not table:
            continue
        headers = [normalize_ws(th.get_text(" ", strip=True)) for th in table.select("th")]
        rows = []
        for tr in table.select("tr"):
            cells = [normalize_ws(td.get_text(" ", strip=True)) for td in tr.select("td")]
            if cells:
                link = tr.find("a", href=True)
                rows.append({"cells": cells, "link_url": urljoin(source_url, link["href"]) if link else None})
        return {"headers": headers, "rows": rows}
    return {"headers": [], "rows": []}


def _parse_publications(title: str, nodes: list, source_url: str) -> tuple[int | None, list[dict]]:
    count_match = re.search(r"(\d+)\s*$", title)
    publications = []
    for node in nodes:
        if not isinstance(node, Tag):
            continue
        for li in node.select("li"):
            text = normalize_ws(li.get_text(" ", strip=True))
            anchor = li.find("a", href=True)
            if text:
                publications.append(
                    {
                        "title": normalize_ws(anchor.get_text(" ", strip=True)) if anchor else text,
                        "url": urljoin(source_url, anchor["href"]) if anchor else None,
                        "text": text,
                    }
                )
        if publications:
            break
    if not publications:
        publications = [{"title": line, "url": None, "text": line} for line in _nodes_raw_text(nodes).split("\n") if line]
    return int(count_match.group(1)) if count_match else None, publications


def _parse_courses(title: str, nodes: list, source_url: str) -> tuple[str | None, list[dict]]:
    year_match = re.search(r"(\d{4}/\d{4})", title)
    courses = []
    for node in nodes:
        if isinstance(node, Tag):
            for li in node.select("li"):
                anchor = li.find("a", href=True)
                course_title = normalize_ws(anchor.get_text(" ", strip=True) if anchor else li.get_text(" ", strip=True))
                if course_title:
                    courses.append({"title": course_title, "url": urljoin(source_url, anchor["href"]) if anchor else None})
    return year_match.group(1) if year_match else None, _dedupe_dicts(courses)


def _parse_year_entries(nodes: list, source_url: str) -> list[dict]:
    entries = []
    for node in nodes:
        if not isinstance(node, Tag):
            continue
        for year_node in node.select(".person-list-hangover"):
            year_match = re.search(r"(19\d{2}|20\d{2})", year_node.get_text(" ", strip=True))
            parent = year_node.parent
            if parent:
                entries.append(
                    {
                        "year": int(year_match.group(1)) if year_match else None,
                        "text": normalize_ws(parent.get_text(" ", strip=True)),
                        "links": _extract_links(parent, source_url),
                    }
                )
    return entries


def _parse_vkr_items(nodes: list) -> list[str]:
    items = []
    for node in nodes:
        if isinstance(node, Tag):
            items.extend(normalize_ws(li.get_text(" ", strip=True)) for li in node.select("li"))
    return [item for item in dict.fromkeys(items) if item]


def _slugify(value: str) -> str:
    cleaned = re.sub(r"[^\w\s-]", "", value.lower(), flags=re.UNICODE)
    return re.sub(r"[-\s]+", "_", cleaned).strip("_") or "section"


def _dedupe_tabs(items: list[dict]) -> list[dict]:
    seen = set()
    unique = []
    for item in items:
        key = (item.get("title"), item.get("href"))
        if key not in seen:
            seen.add(key)
            unique.append(item)
    return unique


def _dedupe_dicts(items: list[dict]) -> list[dict]:
    seen = set()
    unique = []
    for item in items:
        key = tuple(sorted(item.items()))
        if key not in seen:
            seen.add(key)
            unique.append(item)
    return unique