"""
core/confidence_scorer.py
Computes composite authenticity confidence scores (0.0 - 1.0)
across YouTube channel titles, MusicBrainz, and Wikidata with
noise-cleaning and cross-referencing.
"""

import re
from typing import Dict, Any, Optional


class ConfidenceScorer:
    """
    Evaluates multi-source identity alignment across YouTube, MusicBrainz, and Wikidata.
    """

    def __init__(
        self,
        yt_weight: float = 0.40,
        mb_weight: float = 0.35,
        wd_weight: float = 0.25,
    ):
        self.yt_weight = yt_weight
        self.mb_weight = mb_weight
        self.wd_weight = wd_weight

    @staticmethod
    def _clean_string(text: Optional[str]) -> str:
        """Strips common artist channel noise tokens and punctuation."""
        if not text:
            return ""
        s = text.lower()
        # Remove suffixes like VEVO, Official, Records, Music, Topic
        noise_tokens = [
            r"\bvevo\b",
            r"\bofficial\b",
            r"\btopic\b",
            r"\bmusic\b",
            r"\brecords\b",
            r"\btv\b",
            r"\bchannel\b",
        ]
        for token in noise_tokens:
            s = re.sub(token, "", s)
        # Remove all non-alphanumeric characters
        return re.sub(r"[^a-z0-9]", "", s)

    def _match_youtube(self, artist_name: str, yt_title: Optional[str]) -> float:
        """Evaluates whether the YouTube channel title matches the artist."""
        if not yt_title or not artist_name:
            return 0.0

        clean_artist = self._clean_string(artist_name)
        clean_title = self._clean_string(yt_title)

        if not clean_artist or not clean_title:
            return 0.0

        # Exact match after cleaning (e.g. 'KendrickLamarVEVO' -> 'kendricklamar' == 'kendricklamar')
        if clean_artist == clean_title:
            return 1.0

        # Substring containment
        if clean_artist in clean_title or clean_title in clean_artist:
            return 0.85

        return 0.0

    def calculate_score(
        self,
        yt_metrics: Dict[str, Any],
        mb_result: Dict[str, Any],
        wd_result: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Computes composite confidence based on:
        - YouTube channel name alignment (40%)
        - MusicBrainz match verification (35%)
        - Wikidata entity validation (25%)
        - Cross-referencing bonus: MBID linked directly inside Wikidata
        """
        artist_name = yt_metrics.get("channel_title") or ""
        # If artist name was provided separately in the identifier dict, we compare against channel_title
        yt_title = yt_metrics.get("channel_title")
        raw_name = yt_metrics.get("name") or yt_title

        # 1. YouTube Title Alignment Score
        yt_score = self._match_youtube(raw_name, yt_title)

        # 2. MusicBrainz Score
        mb_match = mb_result.get("verified", False) if isinstance(mb_result, dict) else bool(mb_result)
        mb_score = 1.0 if mb_match else 0.0

        # 3. Wikidata Score
        wd_match = wd_result.get("verified", False) if isinstance(wd_result, dict) else bool(wd_result)
        wd_score = 1.0 if wd_match else 0.0

        # 4. Cross-Referencing Bonus: Wikidata linked to MusicBrainz ID
        cross_ref_bonus = 0.0
        if isinstance(wd_result, dict) and isinstance(mb_result, dict):
            wd_mbid = wd_result.get("musicbrainz_id")
            actual_mbid = mb_result.get("mbid")
            if wd_mbid and actual_mbid and wd_mbid == actual_mbid:
                cross_ref_bonus = 0.05  # Extra 5% confidence for direct KG linkage

        # Composite calculation
        composite = (
            self.yt_weight * yt_score
            + self.mb_weight * mb_score
            + self.wd_weight * wd_score
            + cross_ref_bonus
        )
        composite = round(min(composite, 1.0), 2)

        # Tier classification
        if composite >= 0.80:
            tier = "High Confidence"
        elif composite >= 0.50:
            tier = "Medium Confidence"
        else:
            tier = "Low / Emerging"

        return {
            "composite_confidence": composite,
            "confidence_tier": tier,
            "sub_scores": {
                "youtube": yt_score,
                "musicbrainz": mb_score,
                "wikidata": wd_score,
                "cross_referenced": cross_ref_bonus > 0.0,
            },
        }