from __future__ import annotations

import torch
import torch.nn as nn


class SIFQ(nn.Module):
    """Full SIFQ model wrapper."""

    def __init__(
        self,
        backbone: nn.Module,
        concept_head: nn.Module,
        aggregator: nn.Module,
        sensor_disc: nn.Module,
    ):
        super().__init__()
        self.backbone = backbone
        self.concept_head = concept_head
        self.aggregator = aggregator
        self.sensor_disc = sensor_disc

    def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
        if getattr(self.concept_head, "uses_spatial", False):
            # SpatialConceptHead: pass full 14×14 token map [B, N, D]
            spatial = self.backbone.forward_spatial(x)  # [B, N, D]
            features = spatial.mean(dim=1)              # [B, D] — for sensor_disc & L_mat
            concepts = self.concept_head(spatial)       # [B, k]
        else:
            # ConceptHead (legacy): pass globally-pooled vector [B, D]
            features = self.backbone(x)                 # [B, D]
            concepts = self.concept_head(features)      # [B, k]

        score = self.aggregator(concepts)
        sensor_logits = self.sensor_disc(features)
        return {
            "score": score,
            "concepts": concepts,
            "sensor_logits": sensor_logits,
            "features": features,
        }