from __future__ import annotations

"""TRAM — Token Reduction via Attention-based Multilayer network centrality.

Original paper implementation adapted for post-hoc token selection:
    1. For each ViT layer, extract patch-to-patch attention (max across heads).
    2. Compute weighted in-degree centrality:
       c_l[j] = Σ_i A[i,j] * d_in[i]
       where d_in[i] = Σ_j A[i,j] is the in-degree of the source token.
    3. Accumulate across layers with linear weighting:
       centrality = c_l * (l/L) + centrality_prev
    4. Select top-K tokens by centrality score.

Reference:
    Marchetti et al. — TRAM: Token Reduction via Attention-based
    Multilayer network centrality (Neural Networks, 2024).
"""

import torch
import torch.nn as nn


class TRAMSelector(nn.Module):
    """Select K most important tokens using TRAM centrality.

    Parameters
    ----------
    num_tokens : int
        Number of tokens to keep (K).
    method : str
        Centrality method:
        - ``"tram"``: original paper weighted in-degree centrality (default).
        - ``"incoming_sum"``: simple incoming attention sum across layers.
    """

    def __init__(
        self,
        num_tokens: int = 30,
        method: str = "tram",
    ):
        super().__init__()
        self.num_tokens = num_tokens
        self.method = method

    # ------------------------------------------------------------------
    def forward(
        self,
        patch_tokens: torch.Tensor,
        attn_maps: list[torch.Tensor],
        num_prefix_tokens: int = 1,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Args:
            patch_tokens:      ``(B, P, D)`` patch features from ViT.
            attn_maps:         list of L tensors, each ``(B, H, N, N)``
                               where ``N = P + num_prefix_tokens``.
            num_prefix_tokens: how many prefix tokens (CLS + registers)
                               to skip when extracting patch-to-patch
                               attention.

        Returns:
            selected_tokens:   ``(B, K, D)`` features of selected tokens.
            selected_indices:  ``(B, K)`` indices into the P patch tokens
                               (sorted to preserve spatial order).
            centrality_scores: ``(B, P)`` centrality score for every patch.
        """
        B, P, D = patch_tokens.shape
        K = min(self.num_tokens, P)

        # ---- Compute centrality (no grad — discrete selection) ----
        if self.method == "incoming_sum":
            indices, centrality = self._compute_incoming_sum(
                attn_maps, num_prefix_tokens, B, P, patch_tokens.device,
            )
        else:
            indices, centrality = self._compute_tram_centrality(
                attn_maps, num_prefix_tokens, B, P, patch_tokens.device,
            )

        # ---- Gather selected tokens (WITH grad for end-to-end ViT finetune) ----
        selected = torch.gather(
            patch_tokens,
            dim=1,
            index=indices.unsqueeze(-1).expand(-1, -1, D),
        )  # (B, K, D)

        return selected, indices, centrality

    # ------------------------------------------------------------------
    @torch.no_grad()
    def _compute_tram_centrality(
        self,
        attn_maps: list[torch.Tensor],
        num_prefix_tokens: int,
        B: int,
        P: int,
        device: torch.device,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """Original TRAM paper centrality (Marchetti et al.).

        For each layer l (0-indexed):
            1. Extract patch attention: max across heads, drop CLS/prefix.
            2. In-degree:  d_in[j] = Σ_i A[i,j]
            3. Rescale:    A'[i,j] = A[i,j] * d_in[i]
               (weight attention by source token importance)
            4. Weighted centrality:  c_l[j] = Σ_i A'[i,j]
            5. Accumulate: centrality = c_l * ((l+1)/L) + centrality_prev

        Deeper layers get higher weight via linear scaling ``(l+1)/L``.
        """
        K = min(self.num_tokens, P)
        L = len(attn_maps)
        centrality = torch.zeros(B, P, device=device)

        for idx, attn in enumerate(attn_maps):
            # Max across heads (paper's create_matrices)
            a = attn.max(dim=1).values                         # (B, N, N)
            a_pp = a[:, num_prefix_tokens:, num_prefix_tokens:]  # (B, P, P)

            # In-degree: how much each token is attended to
            # d_in[j] = Σ_i A[i,j] — sum over source dim
            d_in = a_pp.sum(dim=1)                              # (B, P)

            # Rescale matrix: weight rows by source's in-degree
            # A'[i,j] = A[i,j] * d_in[i]
            a_rescaled = a_pp * d_in.unsqueeze(-1)              # (B, P, P)

            # Weighted in-degree for this layer
            # c_l[j] = Σ_i A'[i,j] = Σ_i A[i,j] * d_in[i]
            centrality_l = a_rescaled.sum(dim=1)                # (B, P)

            # Accumulate with linear layer weighting: (l+1)/L
            layer_weight = (idx + 1) / L
            centrality = centrality_l * layer_weight + centrality

        _, top_indices = centrality.topk(K, dim=-1)
        return top_indices.sort(dim=-1).values, centrality

    # ------------------------------------------------------------------
    @torch.no_grad()
    def _compute_incoming_sum(
        self,
        attn_maps: list[torch.Tensor],
        num_prefix_tokens: int,
        B: int,
        P: int,
        device: torch.device,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """Simple incoming attention sum across layers (fallback method).

        For each layer, max across heads then sum incoming attention.
        """
        K = min(self.num_tokens, P)
        centrality = torch.zeros(B, P, device=device)

        for attn in attn_maps:
            a = attn.max(dim=1).values                         # (B, N, N)
            a_pp = a[:, num_prefix_tokens:, num_prefix_tokens:]  # (B, P, P)
            centrality = centrality + a_pp.sum(dim=1)           # (B, P)

        _, top_indices = centrality.topk(K, dim=-1)
        return top_indices.sort(dim=-1).values, centrality