from __future__ import annotations

import re
from typing import Dict, List, Optional, Tuple


_SKILL_SYNONYMS: Dict[str, List[str]] = {
    "python": ["py"],
    "javascript": ["js", "node", "nodejs", "java script", "java-script"],
    "typescript": ["ts", "type script", "type-script"],
    "postgresql": ["postgres", "psql"],
    "kubernetes": ["k8s"],
    "docker": [],
    "fastapi": [],
    "django": ["drf", "django rest framework"],
    "flask": [],
    "golang": ["go"],
    "c++": ["cpp"],
    "c#": ["csharp"],
    "redis": [],
    "kafka": [],
    "rabbitmq": [],
    "grpc": [],
    "rest": [],
}

_SKILL_STOP = {"rest", "http", "json", "xml", "oop"}

_ROLE_SYNONYMS: Dict[str, List[str]] = {
    "backend": ["backend developer", "backend engineer", "бэкенд", "бекенд", "серверный разработчик"],
    "frontend": ["frontend developer", "frontend engineer", "фронтенд", "фронт"],
    "fullstack": ["full stack", "full-stack", "фулстек", "fullstack developer"],
    "devops": ["sre", "site reliability"],
    "qa": ["tester", "тестировщик"],
    "data": ["data engineer", "data scientist", "ml engineer", "машинное обучение"],
    "mobile": ["android", "ios", "mobile developer", "мобильный разработчик"],
}


def _build_alias_map(src: Dict[str, List[str]]) -> Dict[str, str]:
    alias = {}
    for canonical, al in src.items():
        alias[canonical] = canonical
        for a in al:
            alias[a] = canonical
    return {k.lower(): v for k, v in alias.items()}


_SKILL_ALIAS = _build_alias_map(_SKILL_SYNONYMS)
_ROLE_ALIAS = _build_alias_map(_ROLE_SYNONYMS)


def _normalize_skill_surface(token: str) -> str:
    t = (token or "").strip().lower()
    if not t:
        return ""
    t = t.replace("/", " ")
    t = re.sub(r"[_\-]+", " ", t)
    t = re.sub(r"\s+", " ", t).strip()

    # "java script", "type script", "postgre sql", "graph ql", "g rpc"
    t = re.sub(r"\bjava\s+script\b", "javascript", t)
    t = re.sub(r"\btype\s+script\b", "typescript", t)
    t = re.sub(r"\bpostgre\s+sql\b", "postgresql", t)
    t = re.sub(r"\bgraph\s+ql\b", "graphql", t)
    t = re.sub(r"\bg\s+rpc\b", "grpc", t)
    t = re.sub(r"\bdocker\s+compose\b", "docker compose", t)
    return t


def normalize_skill(token: str) -> Optional[str]:
    t = _normalize_skill_surface(token)
    if not t:
        return None

    # Avoid false-positive java from "javascript"
    if t == "java" and re.search(r"\bjava\s*script\b", _normalize_skill_surface(token)):
        return "javascript"

    return _SKILL_ALIAS.get(t, t)


def normalize_skills(skills: List[str]) -> List[str]:
    out: List[str] = []
    seen = set()
    for s in skills or []:
        canon = normalize_skill(s)
        if not canon or canon in seen:
            continue
        seen.add(canon)
        out.append(canon)
    return out


def normalize_role(token: str) -> Optional[str]:
    t = (token or "").strip().lower()
    if not t:
        return None
    return _ROLE_ALIAS.get(t, t)


def normalize_roles(roles: List[str]) -> List[str]:
    out: List[str] = []
    seen = set()
    for r in roles or []:
        canon = normalize_role(r)
        if not canon or canon in seen:
            continue
        seen.add(canon)
        out.append(canon)
    return out


def split_skills_primary_secondary(
    skills: List[str],
    *,
    clean_text: str,
    sections: Dict[str, str] | None = None,
    primary_limit: int = 25,
) -> Tuple[List[str], List[str]]:
    if not skills:
        return [], []

    text = (clean_text or "").lower()
    skills_section = (sections or {}).get("skills", "").lower()
    experience_section = (sections or {}).get("experience", "").lower()

    scores: Dict[str, float] = {}
    for sk in skills:
        s = sk.lower()
        score = 1.0
        if s in skills_section:
            score += 2.2
        if s in experience_section:
            score += 1.2
        count = len(re.findall(r"\b" + re.escape(s) + r"\b", text))
        score += min(2.5, count * 0.5)
        if s in _SKILL_STOP:
            score -= 1.5
        scores[sk] = score

    ranked = sorted(skills, key=lambda x: scores.get(x, 0.0), reverse=True)
    primary = [s for s in ranked if scores.get(s, 0.0) >= 2.0][:primary_limit]
    secondary = [s for s in ranked if s not in primary]
    return primary, secondary


def normalize_location(raw: Optional[str]) -> Optional[str]:
    if not raw:
        return None
    t = raw.strip()
    low = t.lower()
    if low in ("москва", "moscow", "moscow, russia"):
        return "Moscow, Russia"
    if low in ("санкт-петербург", "спб", "питер", "saint petersburg"):
        return "Saint Petersburg, Russia"
    return t


def find_skills_in_text(text: str) -> List[str]:
    if not text:
        return []
    found: List[str] = []
    seen = set()
    low = _normalize_skill_surface(text)
    for alias, canon in _SKILL_ALIAS.items():
        key = _normalize_skill_surface(alias)
        if key in seen:
            continue
        if re.search(r"\b" + re.escape(key) + r"\b", low):
            if canon not in seen:
                found.append(canon)
                seen.add(canon)
    return found