"""
PIX-149: Gestalt Fusion Engine

Unifies the three core emotional intelligence models of Pixelated Empathy
into a single, real-time inference call:

  1. PsyDefDetect  — DMRS Defense Mechanism Classifier (DeBERTa)
  2. Plutchik      — 8-emotion wheel scoring (passed in externally)
  3. OCEAN         — Big Five personality trait scoring (passed in externally)

The fused ``GestaltState`` dataclass powers:
  - PIX-147: WebSocket "Live X-Ray" resistance monitor
  - PIX-148: Adversarial Persona Injection (dynamic defense-aware prompts)
  - PIX-150: Empathy PQ metric validation (trainee scoring)
"""

from __future__ import annotations

import logging
from dataclasses import dataclass, field
from enum import Enum
from typing import Optional

import torch
from transformers import AutoTokenizer

from ai.training.defense_mechanisms.constants import DEFENSE_LABELS
from ai.training.defense_mechanisms.dataset import format_dialogue
from ai.training.defense_mechanisms.model import DefenseClassifier, DefensePrediction

logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# Domain enumerations
# ---------------------------------------------------------------------------

PLUTCHIK_EMOTIONS = frozenset(
    {
        "anger",
        "anticipation",
        "disgust",
        "fear",
        "joy",
        "sadness",
        "surprise",
        "trust",
    }
)

OCEAN_TRAITS = frozenset(
    {
        "openness",
        "conscientiousness",
        "extraversion",
        "agreeableness",
        "neuroticism",
    }
)


class CrisisLevel(str, Enum):
    """
    Behavioral risk level derived from the fused Gestalt state.
    """

    NONE = "none"
    ELEVATED = "elevated"
    HIGH = "high"
    ACUTE = "acute"


# ---------------------------------------------------------------------------
# Fused output dataclass
# ---------------------------------------------------------------------------


@dataclass
class GestaltState:
    """
    Unified emotional-psychological state for a single dialogue turn.
    """

    # --- Defense Mechanism (PsyDefDetect) ---
    defense_label: int
    defense_label_name: str
    defense_confidence: float
    defense_maturity: Optional[float]
    defense_probabilities: dict[str, float]

    # --- Emotion (Plutchik) ---
    plutchik_scores: dict[str, float]
    dominant_emotion: str
    dominant_emotion_intensity: float

    # --- Personality (OCEAN) ---
    ocean_scores: dict[str, float]

    # --- Fused behavioral outputs ---
    crisis_level: CrisisLevel
    behavioral_prediction: str
    persona_directive: str
    breakthrough_score: float

    raw_metadata: dict = field(default_factory=dict, repr=False)


# ---------------------------------------------------------------------------
# Crisis & behavioral prediction logic
# ---------------------------------------------------------------------------

_ACTION_DEFENSE_LABEL = 1
_HIGH_ADAPTIVE_LABEL = 7
_DISAVOWAL_LABEL = 3
_MAJOR_IMAGE_DISTORTING_LABEL = 2

_CRISIS_AMPLIFYING_EMOTIONS = frozenset({"sadness", "fear", "anger", "disgust"})
_INJECTION_MATURITY_THRESHOLD = 0.43
_BREAKTHROUGH_MATURITY_THRESHOLD = 0.71


def _dominant_emotion(plutchik: dict[str, float]) -> tuple[str, float]:
    """Return the emotion with the highest intensity score."""
    if not plutchik:
        return "unknown", 0.0
    dominant = max(plutchik, key=lambda k: plutchik[k])
    return dominant, plutchik[dominant]


def _compute_crisis_level(
    defense_label: int,
    defense_maturity: Optional[float],
    dominant_emotion: str,
    dominant_intensity: float,
    ocean_neuroticism: float,
) -> CrisisLevel:
    """Determine crisis level from the fused signals."""
    is_action = defense_label == _ACTION_DEFENSE_LABEL
    is_major_distorting = defense_label == _MAJOR_IMAGE_DISTORTING_LABEL
    emotion_is_crisis = dominant_emotion in _CRISIS_AMPLIFYING_EMOTIONS

    if is_action:
        return (
            CrisisLevel.ACUTE
            if emotion_is_crisis
            and dominant_intensity > 0.6
            or ocean_neuroticism > 0.75
            else CrisisLevel.HIGH
        )

    if is_major_distorting and emotion_is_crisis:
        return CrisisLevel.HIGH

    maturity = defense_maturity if defense_maturity is not None else 0.5
    if maturity < 0.3 and emotion_is_crisis:
        return CrisisLevel.ELEVATED

    return CrisisLevel.NONE


def _behavioral_prediction(
    defense_label_name: str,
    dominant_emotion: str,
    crisis_level: CrisisLevel,
    defense_maturity: Optional[float],
) -> str:
    """Produce a concise human-readable behavioral prediction string."""
    maturity_str = (
        f"maturity={defense_maturity:.2f}" if defense_maturity is not None else "N/A"
    )
    base = f"{dominant_emotion.capitalize()} + {defense_label_name} ({maturity_str})"

    if crisis_level == CrisisLevel.ACUTE:
        return f"{base} → acute distress risk. Therapist must de-escalate immediately."
    if crisis_level == CrisisLevel.HIGH:
        return (
            f"{base} → high-risk defensive pattern. "
            "Therapist should slow pace and validate."
        )
    if crisis_level == CrisisLevel.ELEVATED:
        return (
            f"{base} → elevated defensiveness. "
            "Therapist should use reflective listening."
        )

    if (
        defense_maturity is not None
        and defense_maturity >= _BREAKTHROUGH_MATURITY_THRESHOLD
    ):
        return (
            f"{base} → adaptive coping detected. "
            "Therapist can introduced deeper reframing."
        )
    return f"{base} → neutral or intermediate defense. Continue with exploration."


def _persona_directive(
    defense_label: int,
    defense_label_name: str,
    defense_maturity: Optional[float],
) -> str:
    """Return a system-prompt injection clause for PIX-148."""
    if defense_maturity is None or defense_maturity >= _INJECTION_MATURITY_THRESHOLD:
        return ""

    directives: dict[int, str] = {
        _ACTION_DEFENSE_LABEL: (
            "[System: You are in an Action Defense state. "
            "Act out, change subject. Do NOT acknowledge emotions directly.]"
        ),
        _MAJOR_IMAGE_DISTORTING_LABEL: (
            "[System: You are in a Major Image-Distorting state. "
            "Split people into all-good or all-bad.]"
        ),
        _DISAVOWAL_LABEL: (
            "[System: You are in a Disavowal state. Deny emotional reality.]"
        ),
    }
    return directives.get(
        defense_label,
        f"[System: Maintain your '{defense_label_name}' defense mechanism.]",
    )


def _breakthrough_score(
    defense_maturity: Optional[float],
    previous_maturity: Optional[float],
) -> float:
    """Score the magnitude of a positive maturity shift."""
    if defense_maturity is None or previous_maturity is None:
        return 0.0
    delta = defense_maturity - previous_maturity
    return max(0.0, min(1.0, delta))


def _validate_scores(
    scores: dict[str, float],
    expected_keys: frozenset[str],
    label: str,
) -> dict[str, float]:
    """Validate normalized scores."""
    for key, value in scores.items():
        if not isinstance(value, (int, float)):
            raise ValueError(f"{label}['{key}'] must be numeric")
        if not 0.0 <= float(value) <= 1.0:
            raise ValueError(f"{label}['{key}'] score {value} out of range [0, 1]")
    if unknown := set(scores) - expected_keys:
        logger.warning(f"{label} contains unknown keys {sorted(unknown)}")

    return {k: float(v) for k, v in scores.items()}


# ---------------------------------------------------------------------------
# GestaltEngine
# ---------------------------------------------------------------------------


class GestaltEngine:
    """
    Real-time Gestalt Fusion Engine for the Empathy Gym™.
    """

    def __init__(self) -> None:
        self._defense_model = None
        self._defense_tokenizer = None
        self._previous_maturity: Optional[float] = None

    def load_defense_model(
        self,
        checkpoint_path: Optional[str] = None,
        device: str = "cpu",
    ) -> None:
        """Load the PsyDefDetect model (optional if using NIM)."""
        if not checkpoint_path:
            logger.info("GestaltEngine: No checkpoint provided, using NIM by default.")
            self._defense_model = DefenseClassifier()
            return

        try:
            self._initialize_local_model(checkpoint_path, device)
        except Exception as exc:
            logger.error(
                f"GestaltEngine: Failed to load checkpoint {checkpoint_path}: {exc}"
            )
            logger.info("GestaltEngine: Falling back to NIM-based DefenseClassifier.")
            self._defense_model = DefenseClassifier()

    def _initialize_local_model(self, checkpoint_path: str, device: str) -> None:
        """Initialize the legacy DeBERTa model from a local checkpoint."""
        checkpoint = torch.load(
            checkpoint_path, map_location=device, weights_only=False
        )
        config = checkpoint.get("config", {})
        model_name = config.get("base_model", "microsoft/deberta-v3-base")

        model = DefenseClassifier(
            model_name=model_name,
            num_labels=config.get("num_labels", 9),
            r_drop_enabled=False,
        )
        # Handle PyTorch-based DefenseClassifier if checkpoint is provided
        if hasattr(model, "load_state_dict"):
            model.load_state_dict(checkpoint["model_state_dict"], strict=False)
            model.to(device)
            model.eval()

        self._defense_tokenizer = AutoTokenizer.from_pretrained(model_name)
        self._defense_model = model
        logger.info(f"GestaltEngine: model loaded from {checkpoint_path}")

    @property
    def defense_model_loaded(self) -> bool:
        return self._defense_model is not None

    def reset_session(self) -> None:
        self._previous_maturity = None

    def _classify_defense(
        self,
        dialogue: list[dict[str, str]],
        target_utterance: str,
        max_turns: int = 40,
    ) -> tuple[int, str, float, Optional[float], dict[str, float]]:
        """Run PsyDefDetect inference."""
        if self._defense_model is None:
            raise RuntimeError(
                "GestaltEngine: Defense model not initialized. "
                "Call load_defense_model() first."
            )

        turns = [
            {"speaker": t.get("speaker", "Unknown"), "text": t.get("text", "")}
            for t in dialogue[-max_turns:]
        ]

        # Ensure the target utterance is in the sequence for format_dialogue to mark it
        target_normalized = target_utterance.strip().lower()
        if all(t.get("text", "").strip().lower() != target_normalized for t in turns):
            turns.append({"speaker": "User", "text": target_utterance})

        formatted = format_dialogue(turns, target_utterance, max_turns)

        # Handle the new text-based NIM classifier
        if hasattr(self._defense_model, "nim"):
            pred = self._defense_model.predict([formatted])[0]
        else:
            pred = self._legacy_inference(formatted)

        prob_dict = {
            DEFENSE_LABELS.get(i, str(i)): round(p, 4)
            for i, p in enumerate(pred.probabilities)
        }
        return (
            pred.label,
            pred.label_name,
            pred.confidence,
            pred.maturity_score,
            prob_dict,
        )

    def _legacy_inference(self, formatted_text: str) -> DefensePrediction:
        """Run inference using the local PyTorch DeBERTa model."""
        if self._defense_tokenizer is None:
            raise RuntimeError("GestaltEngine: No tokenizer for PyTorch model.")

        encoding = self._defense_tokenizer(
            formatted_text,
            max_length=512,
            padding="max_length",
            truncation=True,
            return_tensors="pt",
        )
        device = next(self._defense_model.parameters()).device
        input_ids = encoding["input_ids"].to(device)
        attention_mask = encoding["attention_mask"].to(device)
        return self._defense_model.predict(input_ids, attention_mask)[0]

    def analyze_gestalt(
        self,
        dialogue: list[dict[str, str]],
        target_utterance: str,
        plutchik_scores: dict[str, float],
        ocean_scores: dict[str, float],
        max_turns: int = 40,
    ) -> GestaltState:
        """Fuse signals into a GestaltState."""
        plutchik = _validate_scores(
            plutchik_scores, PLUTCHIK_EMOTIONS, "plutchik_scores"
        )
        ocean = _validate_scores(ocean_scores, OCEAN_TRAITS, "ocean_scores")

        full_plutchik = {e: plutchik.get(e, 0.0) for e in PLUTCHIK_EMOTIONS}
        full_ocean = {t: ocean.get(t, 0.5) for t in OCEAN_TRAITS}

        (def_l, def_n, def_c, def_m, def_p) = self._classify_defense(
            dialogue, target_utterance, max_turns
        )

        dom_e, dom_i = _dominant_emotion(full_plutchik)
        neuro = full_ocean.get("neuroticism", 0.5)

        crisis = _compute_crisis_level(def_l, def_m, dom_e, dom_i, neuro)
        behavioral = _behavioral_prediction(def_n, dom_e, crisis, def_m)
        directive = _persona_directive(def_l, def_n, def_m)
        breakthrough = _breakthrough_score(def_m, self._previous_maturity)

        if def_m is not None:
            self._previous_maturity = def_m

        return GestaltState(
            defense_label=def_l,
            defense_label_name=def_n,
            defense_confidence=def_c,
            defense_maturity=def_m,
            defense_probabilities=def_p,
            plutchik_scores=full_plutchik,
            dominant_emotion=dom_e,
            dominant_emotion_intensity=dom_i,
            ocean_scores=full_ocean,
            crisis_level=crisis,
            behavioral_prediction=behavioral,
            persona_directive=directive,
            breakthrough_score=breakthrough,
        )