from __future__ import annotations

"""Custom Vision Transformer trained from scratch.

Supports ViT-Tiny (192-D, 5.7M), ViT-Small (384-D), ViT-Base (768-D), and
a DINOv2-compatible ViT-B/14 profile (``dinov2_vitb14_reg``).
Returns patch tokens, CLS token, and all per-layer attention maps for TRAM.

Unlike the timm-based ``vit_backbone.py`` (pretrained models), this module
is designed to be trained from scratch on fingerprint data with 1-channel
(grayscale) input — no 3-channel replication needed.
"""

import torch
import torch.nn as nn


# ─────────────────────────────────────────────  DropPath  ────
class DropPath(nn.Module):
    """Stochastic depth (per-sample drop of residual branches)."""

    def __init__(self, drop_prob: float = 0.0):
        super().__init__()
        self.drop_prob = drop_prob

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if self.drop_prob == 0.0 or not self.training:
            return x
        keep = 1.0 - self.drop_prob
        shape = (x.shape[0],) + (1,) * (x.ndim - 1)
        mask = torch.rand(shape, dtype=x.dtype, device=x.device).add_(keep).floor_()
        return x / keep * mask


# ─────────────────────────────────────────────  PatchEmbed  ──
class PatchEmbed(nn.Module):
    """Image → non-overlapping patch tokens via Conv2d."""

    def __init__(
        self,
        img_size: int = 224,
        patch_size: int = 16,
        in_chans: int = 1,
        embed_dim: int = 192,
    ):
        super().__init__()
        self.in_chans = in_chans
        self.grid_size = (img_size // patch_size, img_size // patch_size)
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.proj = nn.Conv2d(
            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if x.shape[1] != self.in_chans:
            # Allow transparent conversion between RGB and grayscale inputs.
            if self.in_chans == 1 and x.shape[1] == 3:
                x = x.mean(dim=1, keepdim=True)
            elif self.in_chans == 3 and x.shape[1] == 1:
                x = x.repeat(1, 3, 1, 1)
            else:
                raise RuntimeError(
                    f"PatchEmbed expected {self.in_chans} channels, got {x.shape[1]}"
                )
        return self.proj(x).flatten(2).transpose(1, 2)  # (B, N, D)


# ─────────────────────────────────────────────  Attention  ───
class Attention(nn.Module):
    """Multi-head self-attention — returns output AND attention weights."""

    def __init__(
        self,
        dim: int,
        num_heads: int = 12,
        attn_drop: float = 0.0,
        proj_drop: float = 0.0,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(
        self, x: torch.Tensor
    ) -> tuple[torch.Tensor, torch.Tensor]:
        B, N, C = x.shape
        H, d = self.num_heads, self.head_dim

        qkv = self.qkv(x).reshape(B, N, 3, H, d).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)  # each (B, H, N, d)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)          # (B, H, N, N)
        attn_weights = attn.detach()          # save for TRAM

        out = (self.attn_drop(attn) @ v).transpose(1, 2).reshape(B, N, C)
        out = self.proj_drop(self.proj(out))
        return out, attn_weights


# ─────────────────────────────────────────────  Block  ───────
class Block(nn.Module):
    """Pre-norm Transformer block with stochastic depth."""

    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_ratio: float = 4.0,
        drop: float = 0.0,
        attn_drop: float = 0.0,
        drop_path: float = 0.0,
    ):
        super().__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.attn = Attention(dim, num_heads, attn_drop, drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0 else nn.Identity()
        self.norm2 = nn.LayerNorm(dim)

        hidden = int(dim * mlp_ratio)
        self.mlp = nn.Sequential(
            nn.Linear(dim, hidden),
            nn.GELU(),
            nn.Dropout(drop),
            nn.Linear(hidden, dim),
            nn.Dropout(drop),
        )

    def forward(
        self, x: torch.Tensor
    ) -> tuple[torch.Tensor, torch.Tensor]:
        attn_out, attn_weights = self.attn(self.norm1(x))
        x = x + self.drop_path(attn_out)
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x, attn_weights


# ─────────────────────────────────────────────  ViT  ─────────
class ViT(nn.Module):
    """Vision Transformer with per-layer attention extraction.

    Preset configurations::

        tiny:              192-D, 12 layers, 12 heads  (~5.7M params, 1ch input)
        small:             384-D, 12 layers, 12 heads  (~22M params)
        base:              768-D, 12 layers, 12 heads  (~86M params)
        dinov2_vitb14_reg: 768-D, 12 layers, 12 heads  (profile-compatible)

    Parameters
    ----------
    variant : str
        ``"tiny"`` | ``"small"`` | ``"base"`` | ``"dinov2_vitb14_reg"``.
    img_size : int
        Input image resolution (square).
    patch_size : int
        Patch side length in pixels.
    in_chans : int
        Input channels (1 for grayscale, 3 for RGB).
    drop_rate : float
        Dropout for embeddings and MLP.
    attn_drop_rate : float
        Dropout on attention weights.
    drop_path_rate : float
        Stochastic depth max rate (linearly increased per layer).
    """

    PRESETS: dict[str, dict] = {
        "tiny":  dict(embed_dim=192,  depth=12, num_heads=12),
        "small": dict(embed_dim=384,  depth=12, num_heads=12),
        "base":  dict(embed_dim=768,  depth=12, num_heads=12),
        "dinov2_vitb14_reg": dict(embed_dim=768, depth=12, num_heads=12),
    }

    def __init__(
        self,
        variant: str = "tiny",
        img_size: int = 224,
        patch_size: int = 16,
        in_chans: int = 1,
        drop_rate: float = 0.0,
        attn_drop_rate: float = 0.0,
        drop_path_rate: float = 0.1,
    ):
        super().__init__()
        preset = self.PRESETS[variant]
        self.embed_dim: int = preset["embed_dim"]
        depth: int = preset["depth"]
        num_heads: int = preset["num_heads"]

        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, self.embed_dim)
        num_patches = self.patch_embed.num_patches
        self.grid_size = self.patch_embed.grid_size

        self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, self.embed_dim))
        self.pos_drop = nn.Dropout(drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
        self.blocks = nn.ModuleList([
            Block(self.embed_dim, num_heads, mlp_ratio=4.0,
                  drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i])
            for i in range(depth)
        ])
        self.norm = nn.LayerNorm(self.embed_dim)

        self._init_weights()

    # ------------------------------------------------------------------
    def _init_weights(self):
        nn.init.trunc_normal_(self.pos_embed, std=0.02)
        nn.init.trunc_normal_(self.cls_token, std=0.02)
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.trunc_normal_(m.weight, std=0.02)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.LayerNorm):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out")
                if m.bias is not None:
                    nn.init.zeros_(m.bias)

    # ------------------------------------------------------------------
    def forward(
        self, images: torch.Tensor
    ) -> tuple[torch.Tensor, torch.Tensor, list[torch.Tensor]]:
        """
        Args:
            images: ``(B, C, H, W)`` fingerprint images.

        Returns:
            patch_tokens: ``(B, N, D)`` — patch features (196 tokens for 224px).
            cls_token:    ``(B, D)`` — CLS token (for auxiliary classification).
            attn_maps:    list of L tensors ``(B, H, N+1, N+1)`` attention weights.
        """
        x = self.patch_embed(images)                         # (B, N, D)
        cls = self.cls_token.expand(x.shape[0], -1, -1)      # (B, 1, D)
        x = torch.cat([cls, x], dim=1)                       # (B, N+1, D)
        x = self.pos_drop(x + self.pos_embed)

        attn_maps: list[torch.Tensor] = []
        for blk in self.blocks:
            x, attn = blk(x)
            attn_maps.append(attn)

        x = self.norm(x)
        return x[:, 1:], x[:, 0], attn_maps