"""Central product Knowledge Base access (same PostgreSQL, existing kb_* tables) with per-field status.

Trust model (docs/product_enrichment.md):
- A provider value or a client observation is EVIDENCE. A field becomes VERIFIED_KB only when the official dataset confirms it AND at least two INDEPENDENT evidence
  families agree with zero conflict (distinct providers each count once; client observations count once and only with >= 2 distinct clients).
- Human edits never enter the KB (HUMAN_OVERRIDDEN is per-installation protection, not knowledge). Company-specific fiscal decisions (CSOSN, CFOP, ...) never live here.
"""
from __future__ import annotations

import hashlib
from datetime import datetime, timezone

from sqlalchemy import select

from ..models import KbKnowledge, KbProduct
from . import textnorm

UNVERIFIED, EVIDENCE_ONLY, VERIFIED_KB, CONFLICT, STALE = "UNVERIFIED", "EVIDENCE_ONLY", "VERIFIED_KB", "CONFLICT", "STALE"
CLIENT_FAMILY = "CLIENT_OBSERVATION"


def _now():
    return datetime.now(timezone.utc)


def gtin_fingerprint(key: str) -> str:
    return hashlib.sha256(f"gtin:{key}".encode()).hexdigest()


def find_by_gtin(session, key: str | None) -> KbProduct | None:
    return session.scalar(select(KbProduct).where(KbProduct.gtin == key)) if key else None


def name_candidates(session, description) -> list[KbProduct]:
    """Exact-fingerprint matches (0, 1 or several)."""
    fp = textnorm.fingerprint(description)
    if not fp:
        return []
    ids = list(session.scalars(select(KbKnowledge.kb_product_id).where(KbKnowledge.knowledge_key == "name_fingerprint", KbKnowledge.value == fp).distinct()))
    return [p for p in (session.get(KbProduct, i) for i in ids) if p]


def upsert_product(session, key: str | None, description, *, brand=None, quantity=None) -> KbProduct:
    product = find_by_gtin(session, key)
    if product is None:
        fp = gtin_fingerprint(key) if key else hashlib.sha256(f"name:{textnorm.fingerprint(description)}".encode()).hexdigest()
        product = session.scalar(select(KbProduct).where(KbProduct.fingerprint == fp))
        if product is None:
            product = KbProduct(fingerprint=fp, gtin=key, identity_kind="GTIN" if key else "FINGERPRINT", normalized_description=textnorm.normalized(description))
            session.add(product)
            session.flush()
    if description and not product.normalized_description:
        product.normalized_description = textnorm.normalized(description)
    if brand and not product.brand:
        product.brand = brand
    if quantity and not product.package_quantity:
        product.package_quantity = str(quantity)[:32]
    fp = textnorm.fingerprint(description)
    if fp:
        add_evidence(session, product, "name_fingerprint", fp, "IDENTITY", family="IDENTITY", status=EVIDENCE_ONLY)
    return product


def add_evidence(session, product: KbProduct, key: str, value: str, source: str, *, family: str, client_label: str | None = None, status: str = EVIDENCE_ONLY, info: dict | None = None) -> KbKnowledge:
    row = session.scalar(select(KbKnowledge).where(KbKnowledge.kb_product_id == product.id, KbKnowledge.knowledge_key == key, KbKnowledge.value == value))
    if row is None:
        row = KbKnowledge(kb_product_id=product.id, knowledge_key=key, value=value, source=source[:32], status=status, evidence={"families": {}, "clients": [], "info": info or {}})
        session.add(row)
        session.flush()
    evidence = dict(row.evidence or {})
    families = dict(evidence.get("families") or {})
    families[family] = _now().isoformat()
    clients = list(evidence.get("clients") or [])
    if client_label and client_label not in clients:
        clients.append(client_label)
    row.evidence = {**evidence, "families": families, "clients": clients, "info": {**(evidence.get("info") or {}), **(info or {})}}
    return row


def independent_count(rows) -> tuple[int, list[str]]:
    families = {}
    clients: set[str] = set()
    for row in rows:
        ev = row.evidence or {}
        for fam in (ev.get("families") or {}):
            if fam.startswith("PROVIDER:"):
                families[fam] = True
            elif fam == CLIENT_FAMILY:
                clients.update(ev.get("clients") or [])
    names = sorted(families)
    if len(clients) >= 2:
        names.append(f"{CLIENT_FAMILY}(x{len(clients)})")
    return len(names), names


def evaluate(session, product: KbProduct, key: str, is_official) -> dict:
    """Recomputes and PERSISTS the status of one field. Returns {value, status, families, candidates, rejected}."""
    rows = list(session.scalars(select(KbKnowledge).where(KbKnowledge.kb_product_id == product.id, KbKnowledge.knowledge_key == key)))
    if not rows:
        return {"value": None, "status": UNVERIFIED, "families": [], "candidates": [], "rejected": []}
    valid = [r for r in rows if is_official(r.value)]
    rejected = [r.value for r in rows if not is_official(r.value)]
    values = sorted({r.value for r in valid})
    if len(values) > 1:
        for r in rows:
            r.status = CONFLICT
        return {"value": None, "status": CONFLICT, "families": [], "candidates": values, "rejected": rejected}
    if not values:
        for r in rows:
            r.status = EVIDENCE_ONLY
        return {"value": None, "status": EVIDENCE_ONLY, "families": [], "candidates": [], "rejected": rejected}
    chosen = [r for r in valid if r.value == values[0]]
    count, names = independent_count(chosen)
    already = any(r.status == VERIFIED_KB and r.validated_at for r in chosen)
    status = VERIFIED_KB if (count >= 2 or already) else EVIDENCE_ONLY
    for r in rows:
        r.status = status if r.value == values[0] else EVIDENCE_ONLY
        if status == VERIFIED_KB and r.value == values[0] and not r.validated_at:
            r.validated_at = _now()
    return {"value": values[0], "status": status, "families": names, "candidates": values, "rejected": rejected}