"""Link a listing to its canonical product (one real-world variant).

From most to least certain:
  1. GTIN        - same barcode                                 -> auto
  2. MPN         - same manufacturer part number (laptops)       -> auto
  3. variant key - same brand, model, RAM and storage            -> auto
  4. fuzzy       - model names ≥ AUTO_RATIO similar AND every
                   hard attribute (RAM, storage, processor) equal -> auto
                   ≥ REVIEW_RATIO                                 -> pending (review queue)
  5. otherwise a new product is created for the variant.

A listing that states too little to identify a variant (no storage on a
phone title, for example) is stored but not linked to any product.
"""
from __future__ import annotations

from dataclasses import dataclass
from decimal import Decimal
from typing import List, Optional

from rapidfuzz import fuzz

from app.electronics.normalise.title_parser import laptop_line, processor_is_specific

AUTO_RATIO = 92
REVIEW_RATIO = 85


@dataclass
class MatchDecision:
    product_id: Optional[int]   # None -> create a new product
    method: str
    confidence: float
    review_status: str


def _eq(a: Optional[Decimal], b: Optional[Decimal]) -> bool:
    if a is None or b is None:
        return a is None and b is None
    return Decimal(a) == Decimal(b)


def _number_tokens(model_norm: str) -> set:
    """Tokens that carry a digit ("s25", "a37", "15", "2a", "8a"). Two models
    whose number tokens differ are different products, however similar the
    rest of the name is: Galaxy S25 vs S26, A27 vs A37, iPhone 15 vs 16."""
    return {t for t in (model_norm or "").split() if any(ch.isdigit() for ch in t)}


def _lines_compatible(a: str, b: str) -> bool:
    """One model line is the other plus/minus extra words, and they agree on
    every number token they both carry ("15" is not "15s", "slim 3" is not
    "slim 5")."""
    ta, tb = set(a.split()), set(b.split())
    return bool(ta and tb) and (ta <= tb or tb <= ta)


def decide(listing, candidates: List[dict]) -> Optional[MatchDecision]:
    """`listing` is a models.Listing with variant_key/model_norm set;
    `candidates` are product rows of the same brand and category."""
    if not listing.variant_key:
        return None
    if listing.gtin:
        for c in candidates:
            if c.get("gtin") and c["gtin"] == listing.gtin:
                return MatchDecision(c["id"], "gtin", 0.99, "auto")
    if listing.model_number:
        for c in candidates:
            if c.get("mpn") and c["mpn"].lower() == listing.model_number.lower():
                return MatchDecision(c["id"], "mpn", 0.97, "auto")
    for c in candidates:
        if c["variant_key"] == listing.variant_key:
            return MatchDecision(c["id"], "variant_key", 0.95, "auto")

    # Laptops: the same configuration (exact CPU model, RAM, storage) within a
    # compatible model line - "ideapad slim 3" and "ideapad slim 3 15amn8" -
    # is the same product. Two compatible candidates means the title is too
    # vague to choose ("pavilion" vs "pavilion 14" and "pavilion 15"): review.
    if listing.category == "laptops" and processor_is_specific(listing.processor) \
            and listing.ram_gb is not None and listing.storage_gb is not None:
        line = laptop_line(listing.model_norm)
        same_config = [
            c for c in candidates
            if c.get("processor") == listing.processor
            and _eq(c.get("ram_gb"), listing.ram_gb) and _eq(c.get("storage_gb"), listing.storage_gb)
            and _lines_compatible(line, laptop_line(c["model_norm"]))
        ]
        if len(same_config) == 1:
            return MatchDecision(same_config[0]["id"], "variant_key", 0.85, "auto")
        if len(same_config) > 1:
            return MatchDecision(same_config[0]["id"], "variant_key", 0.6, "pending")

    # One side does not state the RAM ("Apple iPhone 15 (128 GB)"). Same model
    # and storage with exactly one candidate is the same variant; with several
    # candidates it is ambiguous and goes to review.
    same_model = [c for c in candidates
                  if c["model_norm"] == listing.model_norm and _eq(c.get("storage_gb"), listing.storage_gb)
                  and (c.get("ram_gb") is None) != (listing.ram_gb is None)]
    if len(same_model) == 1:
        return MatchDecision(same_model[0]["id"], "variant_key", 0.85, "auto")
    if len(same_model) > 1:
        return MatchDecision(same_model[0]["id"], "variant_key", 0.6, "pending")

    best, best_score = None, 0.0
    for c in candidates:
        if not (_eq(c.get("storage_gb"), listing.storage_gb) and _eq(c.get("ram_gb"), listing.ram_gb)):
            continue
        if listing.category == "laptops" and (c.get("processor") or listing.processor) and c.get("processor") != listing.processor:
            continue
        if _number_tokens(c["model_norm"]) != _number_tokens(listing.model_norm):
            continue
        score = fuzz.token_set_ratio(c["model_norm"], listing.model_norm or "")
        # token_set_ratio treats "galaxy s24" and "galaxy s24 ultra" as a
        # subset match (100). Different words mean different models.
        extra = set((listing.model_norm or "").split()) ^ set(c["model_norm"].split())
        if extra:
            score = min(score, fuzz.ratio(c["model_norm"], listing.model_norm or ""))
        if score > best_score:
            best, best_score = c, score
    if best is not None and best_score >= AUTO_RATIO:
        return MatchDecision(best["id"], "fuzzy", round(best_score / 100 * 0.9, 2), "auto")
    if best is not None and best_score >= REVIEW_RATIO:
        return MatchDecision(best["id"], "fuzzy", round(best_score / 100 * 0.8, 2), "pending")
    return MatchDecision(None, "variant_key", 0.9, "auto")