"""
Chain-of-Thought (CoT) Reasoning Module for Autonomous Driving Safety.

Inspired by Alpamayo-R1's Chain-of-Causation and AgentThink's structured reasoning.
Implements a multi-stage reasoning pipeline:

  Stage 1: Scene Narration — "What do I see?"
    Encodes BEV + perception outputs into a structured scene description vector.
    Identifies all actors, road topology, traffic signals, weather.

  Stage 2: Risk Assessment — "What could go wrong?"
    For each actor/hazard, predicts threat level, time-to-collision (TTC),
    probability of incursion into ego's planned path.

  Stage 3: Causal Reasoning — "Why should I act?"
    Chains scene evidence → risk → required behavior.
    Produces an interpretable reasoning trace (vector + decodable tokens).

  Stage 4: Decision Gate — "What should I do?"
    Outputs a safety-verified action decision that overrides the base planner
    when the reasoning chain identifies danger the planner missed.

The CoT module sits BETWEEN perception and planning, enriching the BEV
features with explicit safety reasoning before trajectory generation.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, Optional, Tuple, List
import math


# ──────────────────────────────────────────────────────────────
#  Stage 1: Scene Narration Encoder
# ──────────────────────────────────────────────────────────────

class SceneNarrationEncoder(nn.Module):
    """
    Encodes the driving scene into a structured representation:
    - Actor features (detected objects with class, velocity, distance)
    - Road topology (lanes, intersections, merges)
    - Traffic state (signals, signs, right-of-way)
    - Environmental conditions (implicit from camera features)
    
    Produces a scene token sequence for downstream reasoning.
    """

    def __init__(
        self,
        bev_channels: int = 256,
        num_actor_queries: int = 64,
        num_road_queries: int = 32,
        d_model: int = 256,
        nhead: int = 8,
        num_layers: int = 3,
    ):
        super().__init__()
        self.d_model = d_model
        self.num_actor_queries = num_actor_queries
        self.num_road_queries = num_road_queries

        # BEV feature projection
        self.bev_proj = nn.Sequential(
            nn.Conv2d(bev_channels, d_model, 1),
            nn.BatchNorm2d(d_model),
            nn.GELU(),
        )

        # Learnable queries for actors and road elements
        self.actor_queries = nn.Parameter(torch.randn(num_actor_queries, d_model))
        self.road_queries = nn.Parameter(torch.randn(num_road_queries, d_model))

        # Cross-attention: queries attend to BEV features
        actor_layer = nn.TransformerDecoderLayer(
            d_model=d_model, nhead=nhead, dim_feedforward=d_model * 4,
            dropout=0.1, batch_first=True, activation="gelu",
        )
        self.actor_decoder = nn.TransformerDecoder(actor_layer, num_layers=num_layers)

        road_layer = nn.TransformerDecoderLayer(
            d_model=d_model, nhead=nhead, dim_feedforward=d_model * 4,
            dropout=0.1, batch_first=True, activation="gelu",
        )
        self.road_decoder = nn.TransformerDecoder(road_layer, num_layers=num_layers)

        # Actor attribute heads
        self.actor_class_head = nn.Linear(d_model, 10)   # object class
        self.actor_exist_head = nn.Linear(d_model, 1)    # existence confidence
        self.actor_dist_head = nn.Linear(d_model, 1)     # distance to ego
        self.actor_vel_head = nn.Linear(d_model, 2)      # velocity (vx, vy)
        self.actor_threat_head = nn.Linear(d_model, 1)   # initial threat score

        # Road attribute heads
        self.road_type_head = nn.Linear(d_model, 7)      # road element type
        self.road_state_head = nn.Linear(d_model, 4)     # signal state (R/Y/G/none)

        # Scene summary — global token
        self.scene_summary = nn.Sequential(
            nn.Linear(d_model * 2, d_model),
            nn.GELU(),
            nn.Linear(d_model, d_model),
        )

    def forward(
        self,
        bev_features: torch.Tensor,
    ) -> Dict[str, torch.Tensor]:
        B = bev_features.shape[0]
        device = bev_features.device

        # Project BEV
        bev = self.bev_proj(bev_features)                      # (B,d,H,W)
        bev_seq = bev.flatten(2).permute(0, 2, 1)              # (B, H*W, d)

        # Decode actor tokens
        aq = self.actor_queries.unsqueeze(0).expand(B, -1, -1)
        actor_tokens = self.actor_decoder(aq, bev_seq)         # (B, Na, d)

        # Decode road tokens
        rq = self.road_queries.unsqueeze(0).expand(B, -1, -1)
        road_tokens = self.road_decoder(rq, bev_seq)           # (B, Nr, d)

        # Attribute predictions
        actor_class = self.actor_class_head(actor_tokens)
        actor_exist = torch.sigmoid(self.actor_exist_head(actor_tokens))
        actor_dist = F.relu(self.actor_dist_head(actor_tokens))
        actor_vel = self.actor_vel_head(actor_tokens)
        actor_threat = torch.sigmoid(self.actor_threat_head(actor_tokens))

        road_type = self.road_type_head(road_tokens)
        road_state = self.road_state_head(road_tokens)

        # Global scene summary
        actor_pool = (actor_tokens * actor_exist).sum(dim=1) / actor_exist.sum(dim=1).clamp(min=1)
        road_pool = road_tokens.mean(dim=1)
        scene_token = self.scene_summary(torch.cat([actor_pool, road_pool], dim=-1))

        return {
            "actor_tokens": actor_tokens,       # (B, Na, d)
            "actor_class": actor_class,          # (B, Na, 10)
            "actor_exist": actor_exist,          # (B, Na, 1)
            "actor_distance": actor_dist,        # (B, Na, 1)
            "actor_velocity": actor_vel,         # (B, Na, 2)
            "actor_threat": actor_threat,        # (B, Na, 1)
            "road_tokens": road_tokens,          # (B, Nr, d)
            "road_type": road_type,              # (B, Nr, 7)
            "road_signal_state": road_state,     # (B, Nr, 4)
            "scene_token": scene_token,          # (B, d)
        }


# ──────────────────────────────────────────────────────────────
#  Stage 2: Risk Assessment
# ──────────────────────────────────────────────────────────────

class RiskAssessmentModule(nn.Module):
    """
    For each detected actor, computes:
    - Time-to-collision (TTC) with ego's projected path
    - Collision probability over planning horizon
    - Risk category (none / low / medium / high / critical)
    
    Also computes aggregate scene risk and identifies the
    single most dangerous actor (worst-case reasoning).
    """

    RISK_LEVELS = ["none", "low", "medium", "high", "critical"]

    def __init__(self, d_model: int = 256, num_risk_levels: int = 5):
        super().__init__()
        self.d_model = d_model
        self.num_risk_levels = num_risk_levels

        # Per-actor risk analysis
        self.risk_mlp = nn.Sequential(
            nn.Linear(d_model + 1 + 2 + 1, d_model),   # token + dist + vel + threat
            nn.GELU(),
            nn.Linear(d_model, d_model),
            nn.GELU(),
        )

        # TTC prediction (regression, in seconds)
        self.ttc_head = nn.Sequential(
            nn.Linear(d_model, 64),
            nn.GELU(),
            nn.Linear(64, 1),
            nn.Softplus(),  # TTC >= 0
        )

        # Collision probability over horizon
        self.collision_prob_head = nn.Sequential(
            nn.Linear(d_model, 64),
            nn.GELU(),
            nn.Linear(64, 1),
            nn.Sigmoid(),
        )

        # Risk level classification
        self.risk_level_head = nn.Sequential(
            nn.Linear(d_model, 64),
            nn.GELU(),
            nn.Linear(64, num_risk_levels),
        )

        # Actor interaction attention (actors reason about each other)
        self.actor_self_attn = nn.MultiheadAttention(
            d_model, num_heads=8, batch_first=True, dropout=0.1
        )
        self.attn_norm = nn.LayerNorm(d_model)

        # Aggregate scene risk
        self.scene_risk = nn.Sequential(
            nn.Linear(d_model, 128),
            nn.GELU(),
            nn.Linear(128, 1),
            nn.Sigmoid(),
        )

    def forward(
        self,
        actor_tokens: torch.Tensor,
        actor_exist: torch.Tensor,
        actor_distance: torch.Tensor,
        actor_velocity: torch.Tensor,
        actor_threat: torch.Tensor,
    ) -> Dict[str, torch.Tensor]:
        B, Na, d = actor_tokens.shape

        # Concatenate actor attributes
        actor_input = torch.cat([
            actor_tokens, actor_distance, actor_velocity, actor_threat
        ], dim=-1)  # (B, Na, d+4)

        risk_feat = self.risk_mlp(actor_input)  # (B, Na, d)

        # Actor-actor interaction (who's affected by whom)
        mask = (actor_exist.squeeze(-1) < 0.3)  # mask out non-existent
        attn_out, _ = self.actor_self_attn(
            risk_feat, risk_feat, risk_feat,
            key_padding_mask=mask,
        )
        risk_feat = self.attn_norm(risk_feat + attn_out)

        # Per-actor predictions
        ttc = self.ttc_head(risk_feat)                    # (B, Na, 1)
        collision_prob = self.collision_prob_head(risk_feat)  # (B, Na, 1)
        risk_level = self.risk_level_head(risk_feat)      # (B, Na, 5)

        # Worst-case actor
        weighted_risk = collision_prob.squeeze(-1) * actor_exist.squeeze(-1)
        worst_idx = weighted_risk.argmax(dim=1)           # (B,)
        worst_actor = risk_feat[torch.arange(B), worst_idx]  # (B, d)

        # Aggregate scene risk
        exist_weight = actor_exist / actor_exist.sum(dim=1, keepdim=True).clamp(min=1)
        pooled = (risk_feat * exist_weight).sum(dim=1)    # (B, d)
        agg_risk = self.scene_risk(pooled)                # (B, 1)

        return {
            "risk_features": risk_feat,           # (B, Na, d)
            "ttc": ttc,                           # (B, Na, 1)  seconds
            "collision_probability": collision_prob,  # (B, Na, 1)
            "risk_level_logits": risk_level,      # (B, Na, 5)
            "worst_actor_feature": worst_actor,   # (B, d)
            "worst_actor_idx": worst_idx,         # (B,)
            "aggregate_scene_risk": agg_risk,     # (B, 1)
        }


# ──────────────────────────────────────────────────────────────
#  Stage 3: Causal Reasoning Chain
# ──────────────────────────────────────────────────────────────

class CausalReasoningChain(nn.Module):
    """
    Implements structured causal reasoning:
    
    Evidence tokens (scene + risk)
        → Transformer reasoning layers
            → Causal conclusion tokens
    
    The reasoning chain is autoregressive across 4 "thought steps":
      1. Situation assessment (what's happening)
      2. Hazard identification (what's dangerous)
      3. Action justification (why act this way)
      4. Action decision (what to do)
    
    Each step conditions on all previous steps, enabling the model
    to build up a coherent chain of reasoning.
    """

    NUM_THOUGHT_STEPS = 4

    def __init__(
        self,
        d_model: int = 256,
        nhead: int = 8,
        num_layers: int = 4,
        num_behaviors: int = 10,
    ):
        super().__init__()
        self.d_model = d_model

        # Thought step embeddings
        self.thought_embeddings = nn.Parameter(
            torch.randn(self.NUM_THOUGHT_STEPS, d_model)
        )

        # Causal self-attention with causal mask (each step sees only prior steps)
        reason_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead, dim_feedforward=d_model * 4,
            dropout=0.1, batch_first=True, activation="gelu",
        )
        self.reasoning_transformer = nn.TransformerEncoder(
            reason_layer, num_layers=num_layers,
        )

        # Cross-attention from thought steps to evidence
        cross_layer = nn.TransformerDecoderLayer(
            d_model=d_model, nhead=nhead, dim_feedforward=d_model * 4,
            dropout=0.1, batch_first=True, activation="gelu",
        )
        self.evidence_cross_attn = nn.TransformerDecoder(
            cross_layer, num_layers=2,
        )

        # Thought step output heads
        # Step 1: situation assessment (compressed scene descriptor)
        self.situation_head = nn.Linear(d_model, d_model)
        # Step 2: hazard identification (top hazard features)
        self.hazard_head = nn.Linear(d_model, d_model)
        # Step 3: action justification (reasoning embedding)
        self.justification_head = nn.Linear(d_model, d_model)
        # Step 4: action decision
        self.action_head = nn.Linear(d_model, num_behaviors)

        # Safety override confidence
        self.override_confidence = nn.Sequential(
            nn.Linear(d_model, 64),
            nn.GELU(),
            nn.Linear(64, 1),
            nn.Sigmoid(),
        )

        # Urgency score (how quickly must we act)
        self.urgency_head = nn.Sequential(
            nn.Linear(d_model, 64),
            nn.GELU(),
            nn.Linear(64, 1),
            nn.Sigmoid(),
        )

    def _causal_mask(self, sz: int, device: torch.device) -> torch.Tensor:
        """Upper-triangular causal mask for autoregressive reasoning."""
        return torch.triu(torch.ones(sz, sz, device=device) * float('-inf'), diagonal=1)

    def forward(
        self,
        scene_token: torch.Tensor,
        risk_features: torch.Tensor,
        worst_actor_feature: torch.Tensor,
        aggregate_risk: torch.Tensor,
        ego_state: torch.Tensor,
    ) -> Dict[str, torch.Tensor]:
        B = scene_token.shape[0]
        device = scene_token.device

        # Build evidence sequence: [scene_token, worst_actor, ego_embed, risk_pool]
        ego_embed = F.gelu(nn.Linear(6, self.d_model).to(device)(ego_state))  # one-off projection
        risk_pool = risk_features.mean(dim=1)

        evidence = torch.stack([scene_token, worst_actor_feature, ego_embed, risk_pool], dim=1)
        # (B, 4, d)

        # Initialize thought tokens
        thoughts = self.thought_embeddings.unsqueeze(0).expand(B, -1, -1)  # (B, 4, d)

        # Cross-attend to evidence
        thoughts = self.evidence_cross_attn(thoughts, evidence)

        # Causal self-reasoning
        mask = self._causal_mask(self.NUM_THOUGHT_STEPS, device)
        thoughts = self.reasoning_transformer(thoughts, mask=mask)

        # Extract each thought step
        situation = self.situation_head(thoughts[:, 0])     # (B, d)
        hazard = self.hazard_head(thoughts[:, 1])           # (B, d)
        justification = self.justification_head(thoughts[:, 2])  # (B, d)

        action_logits = self.action_head(thoughts[:, 3])    # (B, num_behaviors)
        override_conf = self.override_confidence(thoughts[:, 3])  # (B, 1)
        urgency = self.urgency_head(thoughts[:, 3])         # (B, 1)

        # Full reasoning trace (all 4 steps concatenated)
        reasoning_trace = thoughts  # (B, 4, d) — decodable for explainability

        return {
            "situation_embedding": situation,
            "hazard_embedding": hazard,
            "justification_embedding": justification,
            "cot_action_logits": action_logits,
            "override_confidence": override_conf,
            "urgency": urgency,
            "reasoning_trace": reasoning_trace,
        }


# ──────────────────────────────────────────────────────────────
#  Stage 4: Safety Decision Gate
# ──────────────────────────────────────────────────────────────

class SafetyDecisionGate(nn.Module):
    """
    Final gate that merges base planner output with CoT reasoning.
    
    If CoT reasoning has high override confidence AND urgency,
    the gate replaces the planner's trajectory with a safe fallback.
    
    This implements a "safety envelope" — the CoT reasoning can
    only make driving MORE conservative, never more aggressive.
    
    Fallback behaviors:
    - emergency_stop: full brake
    - slow_down: reduce speed proportional to risk
    - yield: stop at yield line
    - swerve_avoid: modify lateral trajectory
    """

    def __init__(
        self,
        d_model: int = 256,
        num_waypoints: int = 20,
        max_speed_ms: float = 8.94,
    ):
        super().__init__()
        self.d_model = d_model
        self.num_waypoints = num_waypoints
        self.max_speed_ms = max_speed_ms

        # Trajectory modification network
        self.traj_modifier = nn.Sequential(
            nn.Linear(d_model + 4 * num_waypoints + 1 + 1, d_model),
            nn.GELU(),
            nn.Linear(d_model, d_model),
            nn.GELU(),
            nn.Linear(d_model, num_waypoints * 4),
        )

        # Override blending weight (0 = keep planner, 1 = full CoT override)
        self.blend_weight = nn.Sequential(
            nn.Linear(d_model + 1 + 1, 64),
            nn.GELU(),
            nn.Linear(64, 1),
            nn.Sigmoid(),
        )

        # Safety score (post-gate)
        self.safety_score = nn.Sequential(
            nn.Linear(d_model, 64),
            nn.GELU(),
            nn.Linear(64, 1),
            nn.Sigmoid(),
        )

    def forward(
        self,
        planner_waypoints: torch.Tensor,
        justification_embedding: torch.Tensor,
        override_confidence: torch.Tensor,
        urgency: torch.Tensor,
    ) -> Dict[str, torch.Tensor]:
        B = planner_waypoints.shape[0]

        wp_flat = planner_waypoints.reshape(B, -1)   # (B, T*4)

        # Compute blending weight
        blend_input = torch.cat([justification_embedding, override_confidence, urgency], dim=-1)
        alpha = self.blend_weight(blend_input)  # (B, 1)

        # Monotonic safety: alpha only increases braking / decreases speed
        # Scale alpha by urgency (high urgency = stronger override)
        alpha = alpha * urgency

        # Generate CoT-modified trajectory
        mod_input = torch.cat([justification_embedding, wp_flat, override_confidence, urgency], dim=-1)
        cot_wp_flat = self.traj_modifier(mod_input)  # (B, T*4)
        cot_waypoints = cot_wp_flat.reshape(B, self.num_waypoints, 4)

        # SAFETY CONSTRAINT: CoT trajectory can only reduce speed, never increase
        planner_speeds = planner_waypoints[:, :, 3]
        cot_speeds = cot_waypoints[:, :, 3]
        safe_speeds = torch.min(planner_speeds, F.relu(cot_speeds))
        safe_speeds = torch.clamp(safe_speeds, 0.0, self.max_speed_ms)
        
        # Build cot_waypoints without in-place ops
        cot_waypoints = torch.cat([
            cot_waypoints[:, :, :3],
            safe_speeds.unsqueeze(-1),
        ], dim=-1)

        # Blend: output = (1-alpha)*planner + alpha*cot
        alpha_expanded = alpha.unsqueeze(-1)  # (B, 1, 1)
        gated_waypoints = (1 - alpha_expanded) * planner_waypoints + alpha_expanded * cot_waypoints

        # Ensure gated speeds never exceed planner speeds (monotonic safety)
        gated_speeds = torch.min(gated_waypoints[:, :, 3], planner_waypoints[:, :, 3])
        gated_speeds = torch.clamp(gated_speeds, 0.0, self.max_speed_ms)
        gated_waypoints = torch.cat([
            gated_waypoints[:, :, :3],
            gated_speeds.unsqueeze(-1),
        ], dim=-1)

        # Post-gate safety score
        safety = self.safety_score(justification_embedding)

        return {
            "gated_waypoints": gated_waypoints,
            "cot_waypoints": cot_waypoints,
            "blend_alpha": alpha,
            "post_gate_safety_score": safety,
        }


# ──────────────────────────────────────────────────────────────
#  Full CoT Reasoning Module
# ──────────────────────────────────────────────────────────────

class ChainOfThoughtReasoning(nn.Module):
    """
    Complete Chain-of-Thought reasoning pipeline for safe autonomous driving.
    
    Pipeline:
      BEV features + ego state
        → Scene Narration (what's around me)
        → Risk Assessment (what's dangerous)
        → Causal Reasoning (why act this way)
        → Safety Decision Gate (override if needed)
    
    Produces:
      1. Enriched BEV features (safety-aware)
      2. Safety-gated waypoints
      3. Interpretable reasoning trace
      4. Per-actor risk breakdown
    """

    def __init__(
        self,
        bev_channels: int = 256,
        d_model: int = 256,
        num_actor_queries: int = 64,
        num_road_queries: int = 32,
        num_waypoints: int = 20,
        num_behaviors: int = 10,
        max_speed_ms: float = 8.94,
    ):
        super().__init__()
        self.d_model = d_model

        # Stage 1
        self.scene_narrator = SceneNarrationEncoder(
            bev_channels=bev_channels,
            num_actor_queries=num_actor_queries,
            num_road_queries=num_road_queries,
            d_model=d_model,
        )

        # Stage 2
        self.risk_assessor = RiskAssessmentModule(d_model=d_model)

        # Stage 3
        self.causal_reasoner = CausalReasoningChain(
            d_model=d_model,
            num_behaviors=num_behaviors,
        )

        # Stage 4
        self.safety_gate = SafetyDecisionGate(
            d_model=d_model,
            num_waypoints=num_waypoints,
            max_speed_ms=max_speed_ms,
        )

        # BEV enrichment: inject reasoning back into BEV features
        self.bev_enrichment = nn.Sequential(
            nn.Conv2d(bev_channels + d_model, bev_channels, 1),
            nn.BatchNorm2d(bev_channels),
            nn.GELU(),
            nn.Conv2d(bev_channels, bev_channels, 3, padding=1),
            nn.BatchNorm2d(bev_channels),
            nn.GELU(),
        )

        # Ego state projection (shared, avoids recreating in CausalReasoningChain)
        self.ego_proj = nn.Sequential(
            nn.Linear(6, d_model),
            nn.GELU(),
        )

    def forward(
        self,
        bev_features: torch.Tensor,
        ego_state: torch.Tensor,
        planner_waypoints: Optional[torch.Tensor] = None,
    ) -> Dict[str, torch.Tensor]:
        B, C, H, W = bev_features.shape
        device = bev_features.device

        # ── Stage 1: Scene Narration ──
        scene = self.scene_narrator(bev_features)

        # ── Stage 2: Risk Assessment ──
        risk = self.risk_assessor(
            actor_tokens=scene["actor_tokens"],
            actor_exist=scene["actor_exist"],
            actor_distance=scene["actor_distance"],
            actor_velocity=scene["actor_velocity"],
            actor_threat=scene["actor_threat"],
        )

        # ── Stage 3: Causal Reasoning ──
        ego_embed = self.ego_proj(ego_state)
        # Patch the causal reasoner to use our pre-computed ego embedding
        reason = self._run_causal_reasoning(
            scene["scene_token"],
            risk["risk_features"],
            risk["worst_actor_feature"],
            risk["aggregate_scene_risk"],
            ego_embed,
        )

        # ── Enrich BEV with reasoning ──
        reasoning_map = reason["justification_embedding"].unsqueeze(-1).unsqueeze(-1)
        reasoning_map = reasoning_map.expand(-1, -1, H, W)
        enriched_bev = self.bev_enrichment(
            torch.cat([bev_features, reasoning_map], dim=1)
        )
        enriched_bev = enriched_bev + bev_features  # residual

        # ── Stage 4: Safety Gate (if planner waypoints provided) ──
        gate_output = {}
        if planner_waypoints is not None:
            gate_output = self.safety_gate(
                planner_waypoints=planner_waypoints,
                justification_embedding=reason["justification_embedding"],
                override_confidence=reason["override_confidence"],
                urgency=reason["urgency"],
            )

        # Collect all outputs
        output = {
            "enriched_bev": enriched_bev,
            # Scene narration
            "cot/actor_class": scene["actor_class"],
            "cot/actor_exist": scene["actor_exist"],
            "cot/actor_distance": scene["actor_distance"],
            "cot/actor_velocity": scene["actor_velocity"],
            # Risk assessment
            "cot/ttc": risk["ttc"],
            "cot/collision_probability": risk["collision_probability"],
            "cot/risk_level_logits": risk["risk_level_logits"],
            "cot/aggregate_risk": risk["aggregate_scene_risk"],
            "cot/worst_actor_idx": risk["worst_actor_idx"],
            # Reasoning
            "cot/action_logits": reason["cot_action_logits"],
            "cot/override_confidence": reason["override_confidence"],
            "cot/urgency": reason["urgency"],
            "cot/reasoning_trace": reason["reasoning_trace"],
        }
        output.update({f"cot/{k}": v for k, v in gate_output.items()})

        return output

    def _run_causal_reasoning(
        self, scene_token, risk_features, worst_actor, agg_risk, ego_embed,
    ):
        """Run causal reasoning with pre-computed ego embedding."""
        B = scene_token.shape[0]
        device = scene_token.device
        d = self.d_model

        risk_pool = risk_features.mean(dim=1)
        evidence = torch.stack([scene_token, worst_actor, ego_embed, risk_pool], dim=1)

        cr = self.causal_reasoner
        thoughts = cr.thought_embeddings.unsqueeze(0).expand(B, -1, -1)
        thoughts = cr.evidence_cross_attn(thoughts, evidence)
        mask = cr._causal_mask(cr.NUM_THOUGHT_STEPS, device)
        thoughts = cr.reasoning_transformer(thoughts, mask=mask)

        situation = cr.situation_head(thoughts[:, 0])
        hazard = cr.hazard_head(thoughts[:, 1])
        justification = cr.justification_head(thoughts[:, 2])
        action_logits = cr.action_head(thoughts[:, 3])
        override_conf = cr.override_confidence(thoughts[:, 3])
        urgency = cr.urgency_head(thoughts[:, 3])

        return {
            "situation_embedding": situation,
            "hazard_embedding": hazard,
            "justification_embedding": justification,
            "cot_action_logits": action_logits,
            "override_confidence": override_conf,
            "urgency": urgency,
            "reasoning_trace": thoughts,
        }