"""
ATS scoring v2 (spec items #1, #6).

Two independent scores instead of one vague number:

  A. ATS Readability — is the EXPORTED resume machine-parseable?
     (standard headings, no tables/columns, dates present, contact readable,
      skills/experience/education detected, not too short/long)

  B. JD Match — weighted by requirement category, not raw keyword count:
       must-have hard skills 35% | responsibilities 20% | tools 15% |
       title+domain 10% | seniority 10% | certs/education 5% | soft 5%
     Plus penalties (unsupported skill injected, must-have missing, skill listed
     but never evidenced in experience, stuffing, parse failure).

Matching reuses the 3-layer evidence logic: a requirement is "covered" if it
matches the resume by exact/alias/semantic.
"""

from __future__ import annotations

import re
from dataclasses import dataclass, field
from typing import List, Dict

from .ats_scorer import _kw_in_text, _detect_sections
from .jd_analyzer import JDRequirements, Requirement

# Category weights for JD Match (must sum to 1.0)
_WEIGHTS = {
    "must_have_hard": 0.35,
    "responsibilities": 0.20,
    "tools": 0.15,
    "title_domain": 0.10,
    "seniority": 0.10,
    "certs_education": 0.05,
    "soft": 0.05,
}


def _covered(term: str, aliases: List[str], resume_low: str) -> bool:
    if _kw_in_text(term, resume_low):
        return True
    return any(_kw_in_text(a, resume_low) for a in (aliases or []))


def _ratio(reqs: List[Requirement], resume_low: str) -> tuple:
    """(#covered, #total) for a requirement bucket."""
    if not reqs:
        return (0, 0)
    covered = sum(1 for r in reqs if _covered(r.term, r.aliases, resume_low))
    return (covered, len(reqs))


# ── ATS Readability ──────────────────────────────────────────────────────────

@dataclass
class ReadabilityResult:
    score: int
    checks: List[dict] = field(default_factory=list)  # {check, passed, detail}

    def to_dict(self):
        return {"score": self.score, "checks": self.checks}


_STD_HEADINGS = ("professional summary", "summary", "experience",
                 "professional experience", "work experience", "skills",
                 "education", "key achievements", "certifications")
_CONTACT_PAT = re.compile(r"[\w.+-]+@[\w-]+\.[\w.-]+|\+?\d[\d\s\-()]{7,}")
_DATE_PAT = re.compile(r"\b(20\d{2}|19\d{2}|present|jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)\b", re.I)


def score_ats_readability(resume_text: str, has_tables: bool = False) -> ReadabilityResult:
    """Score how cleanly an ATS can parse the exported resume text."""
    checks: List[dict] = []

    def add(name, passed, detail=""):
        checks.append({"check": name, "passed": bool(passed), "detail": detail})

    low = resume_text.lower()
    sections = _detect_sections(resume_text)

    has_summary = any(h in low for h in ("professional summary", "summary"))
    has_exp = any(h in low for h in ("professional experience", "experience", "work experience"))
    has_skills = "skills" in low
    has_edu = "education" in low
    has_contact = bool(_CONTACT_PAT.search(resume_text))
    has_dates = bool(_DATE_PAT.search(resume_text))
    # Renderer uses Word "List Bullet" style → re-parsed lines have no glyph.
    # Count substantive content lines (not headings/short meta) as bullet proxy.
    _HEADINGS = {"professional summary", "professional experience", "experience",
                 "key achievements", "skills", "education", "certifications", "summary"}
    bullet_lines = sum(
        1 for ln in resume_text.splitlines()
        if ln.strip() and ln.strip().lower() not in _HEADINGS and len(ln.split()) >= 6)
    word_count = len(resume_text.split())

    add("standard_section_headings", has_summary and has_exp and has_edu,
        "summary/experience/education present")
    add("skills_section_present", has_skills)
    add("contact_info_readable", has_contact)
    add("dates_present", has_dates)
    add("bullet_structure", bullet_lines >= 3, f"{bullet_lines} bullet lines")
    add("no_tables_or_columns", not has_tables,
        "tables/columns break ATS parsing" if has_tables else "single-column text")
    add("reasonable_length", 250 <= word_count <= 1200, f"{word_count} words")
    add("experience_parsed", bool(sections.get("experience") or has_exp))

    passed = sum(1 for c in checks if c["passed"])
    score = round(100 * passed / len(checks))
    return ReadabilityResult(score=score, checks=checks)


# ── JD Match (weighted) ──────────────────────────────────────────────────────

@dataclass
class JDMatchResult:
    score: int
    breakdown: Dict[str, dict] = field(default_factory=dict)  # bucket → {covered,total,weight,contribution}
    penalties: List[dict] = field(default_factory=list)
    covered_terms: List[str] = field(default_factory=list)
    missing_terms: List[str] = field(default_factory=list)

    def to_dict(self):
        return {
            "score": self.score, "breakdown": self.breakdown,
            "penalties": self.penalties,
            "covered_terms": self.covered_terms, "missing_terms": self.missing_terms,
        }


def score_jd_match(resume_text: str, req: JDRequirements,
                   experience_text: str = None,
                   injected_unsupported: List[str] = None) -> JDMatchResult:
    """Weighted JD-match score.

    resume_text     — full parsed resume text (skills + everything)
    experience_text — JUST the experience section (to check skills are evidenced,
                      not only listed). Defaults to resume_text.
    injected_unsupported — terms we know were injected without evidence (penalty).
    """
    resume_low = resume_text.lower()
    exp_low = (experience_text or resume_text).lower()

    # Bucket the requirements
    buckets = {
        "must_have_hard": req.required_hard_skills,
        "responsibilities": req.responsibilities,
        "tools": req.tools_platforms,
        "title_domain": req.domain_terms,  # title handled separately below
        "seniority": req.seniority_signals,
        "certs_education": req.certifications + req.education_requirements,
        "soft": req.soft_skills,
    }

    breakdown: Dict[str, dict] = {}
    weighted_sum = 0.0
    covered_terms, missing_terms = [], []

    # Resume-level signals for fuzzy bucket credit (legitimate — the candidate
    # genuinely has years of experience and a degree; exact-phrase matching like
    # "7 years"/"degree in" would wrongly miss them).
    has_seniority_signal = bool(re.search(
        r"\b(\d{1,2}\+?\s*years?|senior|lead|principal|head|director|manager)\b",
        resume_low))
    has_degree_signal = bool(re.search(
        r"\b(bachelor|master|mba|b\.?tech|m\.?tech|b\.?e\.?|degree|engineering|"
        r"university|institute|college)\b", resume_low))

    for key, reqs in buckets.items():
        cov, tot = _ratio(reqs, resume_low)
        # Title alignment folded into title_domain: +1 covered if any target
        # title appears in the resume.
        if key == "title_domain":
            title_cov = sum(1 for t in req.target_role_titles if _kw_in_text(t, resume_low))
            cov += title_cov
            tot += len(req.target_role_titles)
        # Seniority: any years/seniority signal in the resume satisfies the
        # JD's seniority requirements (candidate has the experience level).
        if key == "seniority" and tot and has_seniority_signal:
            cov = tot
        # Education: a degree requirement is satisfied if the resume shows ANY
        # degree (we never fake a specific degree, but a held degree counts).
        if key == "certs_education" and tot and has_degree_signal:
            edu_reqs = [r for r in reqs if r.category == "education"]
            cov = max(cov, len(edu_reqs))
        frac = (cov / tot) if tot else 1.0   # empty bucket = full credit (N/A)
        w = _WEIGHTS[key]
        contribution = frac * w
        weighted_sum += contribution
        breakdown[key] = {
            "covered": cov, "total": tot, "weight": w,
            "contribution": round(contribution * 100, 1),
        }
        for r in reqs:
            (covered_terms if _covered(r.term, r.aliases, resume_low)
             else missing_terms).append(r.term)

    score = weighted_sum * 100
    penalties: List[dict] = []

    def penalize(pts, reason):
        penalties.append({"points": pts, "reason": reason})

    # Penalty: must-have hard skill missing (extra sting beyond the 35% weight)
    mh_cov, mh_tot = _ratio(req.required_hard_skills, resume_low)
    if mh_tot and mh_cov < mh_tot:
        penalize(-min(8, (mh_tot - mh_cov) * 2),
                 f"{mh_tot - mh_cov} must-have hard skill(s) missing")

    # Penalty: skill listed in Skills but NOT evidenced in Experience
    listed_not_evidenced = 0
    for r in (req.required_hard_skills + req.tools_platforms):
        if _covered(r.term, r.aliases, resume_low) and "experience" in (r.recommended_placement or []):
            if not _covered(r.term, r.aliases, exp_low):
                listed_not_evidenced += 1
    if listed_not_evidenced:
        penalize(-min(6, listed_not_evidenced),
                 f"{listed_not_evidenced} skill(s) listed but not evidenced in experience")

    # Penalty: unsupported skills injected (honesty violation)
    if injected_unsupported:
        penalize(-min(10, len(injected_unsupported) * 3),
                 f"{len(injected_unsupported)} unsupported skill(s) injected")

    score = max(0, min(100, score + sum(p["points"] for p in penalties)))

    return JDMatchResult(
        score=round(score), breakdown=breakdown, penalties=penalties,
        covered_terms=covered_terms, missing_terms=missing_terms)


def combined_range(jd_match: int, readability: int) -> str:
    """Display a small honest range around the JD match (checkers vary ±)."""
    lo = max(0, jd_match - 4)
    hi = min(100, jd_match + 4)
    return f"{lo}-{hi}%"