modeling_m3d_lamed.py

from __future__ import annotations
from typing import Union
from transformers import LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from .configuration_m3d_lamed import LamedConfig
from abc import ABC, abstractmethod
from torch import Tensor
import math
from typing import Any, Dict, List
import torch
import torch.nn as nn
from typing import Optional, Tuple, Type
from monai.networks.blocks import PatchEmbed
import numpy as np
import torch.nn.functional as F

from einops import rearrange
from einops.layers.torch import Rearrange
from collections.abc import Sequence
from monai.networks.blocks.patchembedding import PatchEmbeddingBlock
from monai.networks.blocks.transformerblock import TransformerBlock
from monai.networks.nets import ViT





class LayerNorm2d(nn.Module):
    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(num_channels))
        self.bias = nn.Parameter(torch.zeros(num_channels))
        self.eps = eps

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        u = x.mean(1, keepdim=True)
        s = (x - u).pow(2).mean(1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.eps)
        x = self.weight[:, None, None] * x + self.bias[:, None, None]
        return x


class MLPBlock(nn.Module):
    def __init__(
            self,
            embedding_dim: int,
            mlp_dim: int,
            act: Type[nn.Module] = nn.GELU,
    ) -> None:
        super().__init__()
        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
        self.act = act()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.lin2(self.act(self.lin1(x)))


class TwoWayTransformer(nn.Module):
    def __init__(
            self,
            depth: int,
            embedding_dim: int,
            num_heads: int,
            mlp_dim: int,
            activation: Type[nn.Module] = nn.ReLU,
            attention_downsample_rate: int = 2,
    ) -> None:
        """
        A transformer decoder that attends to an input image using
        queries whose positional embedding is supplied.

        Args:
          depth (int): number of layers in the transformer
          embedding_dim (int): the channel dimension for the input embeddings
          num_heads (int): the number of heads for multihead attention. Must
            divide embedding_dim
          mlp_dim (int): the channel dimension internal to the MLP block
          activation (nn.Module): the activation to use in the MLP block
        """
        super().__init__()
        self.depth = depth
        self.embedding_dim = embedding_dim
        self.num_heads = num_heads
        self.mlp_dim = mlp_dim
        self.layers = nn.ModuleList()

        for i in range(depth):
            self.layers.append(
                TwoWayAttentionBlock(
                    embedding_dim=embedding_dim,
                    num_heads=num_heads,
                    mlp_dim=mlp_dim,
                    activation=activation,
                    attention_downsample_rate=attention_downsample_rate,
                    skip_first_layer_pe=(i == 0),
                )
            )

        self.final_attn_token_to_image = Attention(
            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
        )
        self.norm_final_attn = nn.LayerNorm(embedding_dim)

    def forward(
            self,
            image_embedding: Tensor,
            image_pe: Tensor,
            point_embedding: Tensor,
    ) -> Tuple[Tensor, Tensor]:
        """
        Args:
          image_embedding (torch.Tensor): image to attend to. Should be shape
            B x embedding_dim x h x w for any h and w.
          image_pe (torch.Tensor): the positional encoding to add to the image. Must
            have the same shape as image_embedding.
          point_embedding (torch.Tensor): the embedding to add to the query points.
            Must have shape B x N_points x embedding_dim for any N_points.

        Returns:
          torch.Tensor: the processed point_embedding
          torch.Tensor: the processed image_embedding
        """
        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
        bs, c, h, w, d = image_embedding.shape
        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
        image_pe = image_pe.flatten(2).permute(0, 2, 1)

        # Prepare queries
        queries = point_embedding
        keys = image_embedding

        # Apply transformer blocks and final layernorm
        for layer in self.layers:
            queries, keys = layer(
                queries=queries,
                keys=keys,
                query_pe=point_embedding,
                key_pe=image_pe,
            )

        # Apply the final attention layer from the points to the image
        q = queries + point_embedding
        k = keys + image_pe
        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
        queries = queries + attn_out
        queries = self.norm_final_attn(queries)

        return queries, keys


class TwoWayAttentionBlock(nn.Module):
    def __init__(
            self,
            embedding_dim: int,
            num_heads: int,
            mlp_dim: int = 2048,
            activation: Type[nn.Module] = nn.ReLU,
            attention_downsample_rate: int = 2,
            skip_first_layer_pe: bool = False,
    ) -> None:
        """
        A transformer block with four layers: (1) self-attention of sparse
        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
        block on sparse inputs, and (4) cross attention of dense inputs to sparse
        inputs.

        Arguments:
          embedding_dim (int): the channel dimension of the embeddings
          num_heads (int): the number of heads in the attention layers
          mlp_dim (int): the hidden dimension of the mlp block
          activation (nn.Module): the activation of the mlp block
          skip_first_layer_pe (bool): skip the PE on the first layer
        """
        super().__init__()
        self.self_attn = Attention(embedding_dim, num_heads)
        self.norm1 = nn.LayerNorm(embedding_dim)

        self.cross_attn_token_to_image = Attention(
            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
        )
        self.norm2 = nn.LayerNorm(embedding_dim)

        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
        self.norm3 = nn.LayerNorm(embedding_dim)

        self.norm4 = nn.LayerNorm(embedding_dim)
        self.cross_attn_image_to_token = Attention(
            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
        )

        self.skip_first_layer_pe = skip_first_layer_pe

    def forward(
            self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
    ) -> Tuple[Tensor, Tensor]:
        # Self attention block
        if self.skip_first_layer_pe:
            queries = self.self_attn(q=queries, k=queries, v=queries)
        else:
            q = queries + query_pe
            attn_out = self.self_attn(q=q, k=q, v=queries)
            queries = queries + attn_out
        queries = self.norm1(queries)

        # Cross attention block, tokens attending to image embedding
        q = queries + query_pe
        k = keys + key_pe
        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
        queries = queries + attn_out
        queries = self.norm2(queries)

        # MLP block
        mlp_out = self.mlp(queries)
        queries = queries + mlp_out
        queries = self.norm3(queries)

        # Cross attention block, image embedding attending to tokens
        q = queries + query_pe
        k = keys + key_pe
        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
        keys = keys + attn_out
        keys = self.norm4(keys)

        return queries, keys


class Attention(nn.Module):
    """
    An attention layer that allows for downscaling the size of the embedding
    after projection to queries, keys, and values.
    """

    def __init__(
            self,
            embedding_dim: int,
            num_heads: int,
            downsample_rate: int = 1,
    ) -> None:
        super().__init__()
        self.embedding_dim = embedding_dim
        self.internal_dim = embedding_dim // downsample_rate
        self.num_heads = num_heads
        assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."

        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
        self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
        self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)

    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
        b, n, c = x.shape
        x = x.reshape(b, n, num_heads, c // num_heads)
        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head

    def _recombine_heads(self, x: Tensor) -> Tensor:
        b, n_heads, n_tokens, c_per_head = x.shape
        x = x.transpose(1, 2)
        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C

    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
        # Input projections
        q = self.q_proj(q)
        k = self.k_proj(k)
        v = self.v_proj(v)

        # Separate into heads
        q = self._separate_heads(q, self.num_heads)
        k = self._separate_heads(k, self.num_heads)
        v = self._separate_heads(v, self.num_heads)

        # Attention
        _, _, _, c_per_head = q.shape
        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
        attn = attn / math.sqrt(c_per_head)
        attn = torch.softmax(attn, dim=-1)

        # Get output
        out = attn @ v
        out = self._recombine_heads(out)
        out = self.out_proj(out)

        return out



# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
class ImageEncoderViT(nn.Module):
    def __init__(
            self,
            img_size: int = 1024,
            patch_size: int = 16,
            in_chans: int = 1,
            embed_dim: int = 768,
            depth: int = 12,
            num_heads: int = 12,
            mlp_ratio: float = 4.0,
            out_chans: int = 256,
            qkv_bias: bool = True,
            norm_layer: Type[nn.Module] = nn.LayerNorm,
            act_layer: Type[nn.Module] = nn.GELU,
            use_abs_pos: bool = True,
            use_rel_pos: bool = False,
            rel_pos_zero_init: bool = True,
            window_size: int = 0,
            global_attn_indexes: Tuple[int, ...] = (),
    ) -> None:
        """
        Args:
            img_size (int): Input image size.
            patch_size (int): Patch size.
            in_chans (int): Number of input image channels.
            embed_dim (int): Patch embedding dimension.
            depth (int): Depth of ViT.
            num_heads (int): Number of attention heads in each ViT block.
            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
            qkv_bias (bool): If True, add a learnable bias to query, key, value.
            norm_layer (nn.Module): Normalization layer.
            act_layer (nn.Module): Activation layer.
            use_abs_pos (bool): If True, use absolute positional embeddings.
            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            window_size (int): Window size for window attention blocks.
            global_attn_indexes (list): Indexes for blocks using global attention.
        """
        super().__init__()
        self.img_size = img_size

        # self.patch_embed = PatchEmbed(
        #     kernel_size=(patch_size, patch_size),
        #     stride=(patch_size, patch_size),
        #     in_chans=in_chans,
        #     embed_dim=embed_dim,
        # )

        self.patch_embed = PatchEmbed(
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            spatial_dims=3,
        )

        self.pos_embed: Optional[nn.Parameter] = None
        if use_abs_pos:
            # Initialize absolute positional embedding with pretrain image size.
            self.pos_embed = nn.Parameter(
                torch.zeros(1, img_size // patch_size, img_size // patch_size, img_size // patch_size, embed_dim)
            )

        self.blocks = nn.ModuleList()
        for i in range(depth):
            block = Block(
                dim=embed_dim,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                norm_layer=norm_layer,
                act_layer=act_layer,
                use_rel_pos=use_rel_pos,
                rel_pos_zero_init=rel_pos_zero_init,
                window_size=window_size if i not in global_attn_indexes else 0,
                input_size=(img_size // patch_size, img_size // patch_size),
            )
            self.blocks.append(block)

        self.neck = nn.Sequential(
            nn.Conv2d(
                embed_dim,
                out_chans,
                kernel_size=1,
                bias=False,
            ),
            LayerNorm2d(out_chans),
            nn.Conv2d(
                out_chans,
                out_chans,
                kernel_size=3,
                padding=1,
                bias=False,
            ),
            LayerNorm2d(out_chans),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.patch_embed(x)
        print('patch embedded shape: ', x.shape)  # embedded: [8, 768, 6, 6, 6]
        if self.pos_embed is not None:
            x = x + self.pos_embed

        for blk in self.blocks:
            x = blk(x)

        x = self.neck(x.permute(0, 3, 1, 2))

        return x


class Block(nn.Module):
    """Transformer blocks with support of window attention and residual propagation blocks"""

    def __init__(
            self,
            dim: int,
            num_heads: int,
            mlp_ratio: float = 4.0,
            qkv_bias: bool = True,
            norm_layer: Type[nn.Module] = nn.LayerNorm,
            act_layer: Type[nn.Module] = nn.GELU,
            use_rel_pos: bool = False,
            rel_pos_zero_init: bool = True,
            window_size: int = 0,
            input_size: Optional[Tuple[int, int]] = None,
    ) -> None:
        """
        Args:
            dim (int): Number of input channels.
            num_heads (int): Number of attention heads in each ViT block.
            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
            qkv_bias (bool): If True, add a learnable bias to query, key, value.
            norm_layer (nn.Module): Normalization layer.
            act_layer (nn.Module): Activation layer.
            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            window_size (int): Window size for window attention blocks. If it equals 0, then
                use global attention.
            input_size (tuple(int, int) or None): Input resolution for calculating the relative
                positional parameter size.
        """
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention2(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            use_rel_pos=use_rel_pos,
            rel_pos_zero_init=rel_pos_zero_init,
            input_size=input_size if window_size == 0 else (window_size, window_size),
        )

        self.norm2 = norm_layer(dim)
        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)

        self.window_size = window_size

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        shortcut = x
        x = self.norm1(x)
        # Window partition
        if self.window_size > 0:
            H, W = x.shape[1], x.shape[2]
            x, pad_hw = window_partition(x, self.window_size)

        x = self.attn(x)
        # Reverse window partition
        if self.window_size > 0:
            x = window_unpartition(x, self.window_size, pad_hw, (H, W))

        x = shortcut + x
        x = x + self.mlp(self.norm2(x))

        return x


class Attention2(nn.Module):
    """Multi-head Attention block with relative position embeddings."""

    def __init__(
            self,
            dim: int,
            num_heads: int = 8,
            qkv_bias: bool = True,
            use_rel_pos: bool = False,
            rel_pos_zero_init: bool = True,
            input_size: Optional[Tuple[int, int]] = None,
    ) -> None:
        """
        Args:
            dim (int): Number of input channels.
            num_heads (int): Number of attention heads.
            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
            rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            input_size (tuple(int, int) or None): Input resolution for calculating the relative
                positional parameter size.
        """
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.proj = nn.Linear(dim, dim)

        self.use_rel_pos = use_rel_pos
        if self.use_rel_pos:
            assert (
                    input_size is not None
            ), "Input size must be provided if using relative positional encoding."
            # initialize relative positional embeddings
            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, H, W, _ = x.shape
        # qkv with shape (3, B, nHead, H * W, C)
        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        # q, k, v with shape (B * nHead, H * W, C)
        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)

        attn = (q * self.scale) @ k.transpose(-2, -1)

        if self.use_rel_pos:
            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))

        attn = attn.softmax(dim=-1)
        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
        x = self.proj(x)

        return x


def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
    """
    Partition into non-overlapping windows with padding if needed.
    Args:
        x (tensor): input tokens with [B, H, W, C].
        window_size (int): window size.

    Returns:
        windows: windows after partition with [B * num_windows, window_size, window_size, C].
        (Hp, Wp): padded height and width before partition
    """
    B, H, W, C = x.shape

    pad_h = (window_size - H % window_size) % window_size
    pad_w = (window_size - W % window_size) % window_size
    if pad_h > 0 or pad_w > 0:
        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
    Hp, Wp = H + pad_h, W + pad_w

    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows, (Hp, Wp)


def window_unpartition(windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]) -> torch.Tensor:
    """
    Window unpartition into original sequences and removing padding.
    Args:
        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
        window_size (int): window size.
        pad_hw (Tuple): padded height and width (Hp, Wp).
        hw (Tuple): original height and width (H, W) before padding.

    Returns:
        x: unpartitioned sequences with [B, H, W, C].
    """
    Hp, Wp = pad_hw
    H, W = hw
    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)

    if Hp > H or Wp > W:
        x = x[:, :H, :W, :].contiguous()
    return x


def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
    """
    Get relative positional embeddings according to the relative positions of
        query and key sizes.
    Args:
        q_size (int): size of query q.
        k_size (int): size of key k.
        rel_pos (Tensor): relative position embeddings (L, C).

    Returns:
        Extracted positional embeddings according to relative positions.
    """
    max_rel_dist = int(2 * max(q_size, k_size) - 1)
    # Interpolate rel pos if needed.
    if rel_pos.shape[0] != max_rel_dist:
        # Interpolate rel pos.
        rel_pos_resized = F.interpolate(
            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
            size=max_rel_dist,
            mode="linear",
        )
        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
    else:
        rel_pos_resized = rel_pos

    # Scale the coords with short length if shapes for q and k are different.
    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)

    return rel_pos_resized[relative_coords.long()]


def add_decomposed_rel_pos(
        attn: torch.Tensor,
        q: torch.Tensor,
        rel_pos_h: torch.Tensor,
        rel_pos_w: torch.Tensor,
        q_size: Tuple[int, int],
        k_size: Tuple[int, int],
) -> torch.Tensor:
    """
    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
    Args:
        attn (Tensor): attention map.
        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).

    Returns:
        attn (Tensor): attention map with added relative positional embeddings.
    """
    q_h, q_w = q_size
    k_h, k_w = k_size
    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
    Rw = get_rel_pos(q_w, k_w, rel_pos_w)

    B, _, dim = q.shape
    r_q = q.reshape(B, q_h, q_w, dim)
    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)

    attn = (
            attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
    ).view(B, q_h * q_w, k_h * k_w)

    return attn











class IdentityMap(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x, *args, **kwargs):
        return x

    @property
    def config(self):
        return {"mm_projector_type": 'identity'}


class SpatialPoolingProjector(nn.Module):
    def __init__(self, image_size, patch_size, in_dim, out_dim, layer_type, layer_num, pooling_type='spatial', pooling_size=2):
        super().__init__()
        self.in_dim = in_dim
        self.pooling_size = pooling_size

        self.num_patches_pre = [img // pch for img, pch in zip(image_size, patch_size)]
        self.num_patches_post = [num // pooling_size for num in self.num_patches_pre]

        if layer_type == 'linear':
            depth = int(layer_num)
            modules = [nn.Linear(in_dim, out_dim)]
            for _ in range(1, depth):
                modules.append(nn.Linear(out_dim, out_dim))
            self.projector = nn.Sequential(*modules)
        elif layer_type == 'mlp':
            depth = int(layer_num)
            modules = [nn.Linear(in_dim, out_dim)]
            for _ in range(1, depth):
                modules.append(nn.GELU())
                modules.append(nn.Linear(out_dim, out_dim))
            self.projector = nn.Sequential(*modules)
        else:
            print("Projector error!")

        self.pooling_type = pooling_type

    def forward(self, x):
        B = x.shape[0] # B*N*D

        if self.pooling_type == 'spatial':
            to_3d = Rearrange("b (p1 p2 p3) d -> b d p1 p2 p3", b=B, d=self.in_dim, p1=self.num_patches_pre[0], p2=self.num_patches_pre[1], p3=self.num_patches_pre[2])
            x = to_3d(x)
            x = F.avg_pool3d(x, kernel_size=self.pooling_size, stride=self.pooling_size)
            to_seq = Rearrange("b d p1 p2 p3 -> b (p1 p2 p3) d", b=B, d=self.in_dim, p1=self.num_patches_post[0], p2=self.num_patches_post[1], p3=self.num_patches_post[2])
            x = to_seq(x)
        elif self.pooling_type == 'sequence':
            x = x.permute(0, 2, 1) #b d n
            x = F.avg_pool1d(x, kernel_size=self.pooling_size**3, stride=self.pooling_size**3)
            x = x.permute(0, 2, 1) #b n d

        x = rearrange(x, "b n d -> (b n) d")
        x = self.projector(x)
        x = rearrange(x, "(b n) d -> b n d", b=B)

        return x

    @property
    def proj_out_num(self):
        num = 1
        for n in self.num_patches_post:
            num *= n
        return num


class Minigpt(nn.Module):
    def __init__(self, config=None):
        super(Minigpt, self).__init__()
        # c*4 is the input size, and c is the output size for the linear layer
        inc, ouc = config.mm_hidden_size, config.hidden_size
        self.linear = nn.Linear(inc * 4, ouc)

    def forward(self, x):
        # x is the input tensor with shape [b, num_tokens, c]
        b, num_tokens, c = x.shape

        # Check if num_tokens is divisible by 4
        if num_tokens % 4 != 0:
            raise ValueError("num_tokens must be divisible by 4")

        # Reshape x to [b, num_tokens/4, c*4]
        x = x.view(b, num_tokens // 4, c * 4)

        # Apply the linear transformation
        x = self.linear(x)
        return x


class Vanilla(nn.Module):
    def __init__(self, config=None):
        super(Vanilla, self).__init__()
        # c*4 is the input size, and c is the output size for the linear layer
        inc, ouc = config.mm_hidden_size, config.hidden_size
        self.linear = nn.Linear(inc * 4, ouc)

    def forward(self, x):
        b, num_tokens, c = x.shape

        # Check if num_tokens is divisible by 4
        if num_tokens % 4 != 0:
            raise ValueError("num_tokens must be divisible by 4")

        # First, reshape to [b, num_tokens//4, 4, c]
        x = x.view(b, num_tokens // 4, 4, c)

        # Then, permute to interleave the tokens
        x = x.permute(0, 1, 3, 2).contiguous()

        # Finally, reshape to [b, num_tokens//4, c*4] to interleave features of 4 tokens
        x = x.view(b, num_tokens // 4, c * 4)

        # Apply the linear transformation
        x = self.linear(x)
        return x


def build_mm_projector(config, delay_load=False, **kwargs):
    projector_type = getattr(config, 'mm_projector_type')

    if projector_type == 'linear':
        return nn.Linear(config.mm_hidden_size, config.hidden_size)


    elif projector_type == 'spp':
        return SpatialPoolingProjector(image_size=config.image_size,
                                        patch_size=config.patch_size,
                                        in_dim=config.mm_hidden_size,
                                        out_dim=config.hidden_size,
                                        layer_type=config.proj_layer_type,
                                        layer_num=config.proj_layer_num,
                                        pooling_type=config.proj_pooling_type,
                                        pooling_size=config.proj_pooling_size)


    elif projector_type == 'identity':
        return IdentityMap()
    else:
        raise ValueError(f'Unknown projector type: {projector_type}')


class myViT(nn.Module):
    """
    Vision Transformer (ViT), based on: "Dosovitskiy et al.,
    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"

    ViT supports Torchscript but only works for Pytorch after 1.8.
    """

    def __init__(
        self,
        in_channels: int,
        img_size: Sequence[int] | int,
        patch_size: Sequence[int] | int,
        hidden_size: int = 768,
        mlp_dim: int = 3072,
        num_layers: int = 12,
        num_heads: int = 12,
        pos_embed: str = "conv",
        classification: bool = False,
        num_classes: int = 2,
        dropout_rate: float = 0.0,
        spatial_dims: int = 3,
        post_activation="Tanh",
        qkv_bias: bool = False,
        save_attn: bool = False,
    ) -> None:
        """
        Args:
            in_channels (int): dimension of input channels.
            img_size (Union[Sequence[int], int]): dimension of input image.
            patch_size (Union[Sequence[int], int]): dimension of patch size.
            hidden_size (int, optional): dimension of hidden layer. Defaults to 768.
            mlp_dim (int, optional): dimension of feedforward layer. Defaults to 3072.
            num_layers (int, optional): number of transformer blocks. Defaults to 12.
            num_heads (int, optional): number of attention heads. Defaults to 12.
            pos_embed (str, optional): position embedding layer type. Defaults to "conv".
            classification (bool, optional): bool argument to determine if classification is used. Defaults to False.
            num_classes (int, optional): number of classes if classification is used. Defaults to 2.
            dropout_rate (float, optional): faction of the input units to drop. Defaults to 0.0.
            spatial_dims (int, optional): number of spatial dimensions. Defaults to 3.
            post_activation (str, optional): add a final acivation function to the classification head
                when `classification` is True. Default to "Tanh" for `nn.Tanh()`.
                Set to other values to remove this function.
            qkv_bias (bool, optional): apply bias to the qkv linear layer in self attention block. Defaults to False.
            save_attn (bool, optional): to make accessible the attention in self attention block. Defaults to False.

        Examples::

            # for single channel input with image size of (96,96,96), conv position embedding and segmentation backbone
            >>> net = ViT(in_channels=1, img_size=(96,96,96), pos_embed='conv')

            # for 3-channel with image size of (128,128,128), 24 layers and classification backbone
            >>> net = ViT(in_channels=3, img_size=(128,128,128), pos_embed='conv', classification=True)

            # for 3-channel with image size of (224,224), 12 layers and classification backbone
            >>> net = ViT(in_channels=3, img_size=(224,224), pos_embed='conv', classification=True, spatial_dims=2)

        """

        super().__init__()

        if not (0 <= dropout_rate <= 1):
            raise ValueError("dropout_rate should be between 0 and 1.")

        if hidden_size % num_heads != 0:
            raise ValueError("hidden_size should be divisible by num_heads.")
        self.hidden_size = hidden_size
        self.classification = classification
        self.patch_embedding = PatchEmbeddingBlock(
            in_channels=in_channels,
            img_size=img_size,
            patch_size=patch_size,
            hidden_size=hidden_size,
            num_heads=num_heads,
            pos_embed=pos_embed,
            dropout_rate=dropout_rate,
            spatial_dims=spatial_dims,
        )
        self.blocks = nn.ModuleList(
            [
                TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate, qkv_bias, save_attn)
                for i in range(num_layers)
            ]
        )
        self.norm = nn.LayerNorm(hidden_size)
        if self.classification:
            self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size))
            # if post_activation == "Tanh":
            #     self.classification_head = nn.Sequential(nn.Linear(hidden_size, num_classes), nn.Tanh())
            # else:
            #     self.classification_head = nn.Linear(hidden_size, num_classes)  # type: ignore

    def forward(self, x):
        x = self.patch_embedding(x)
        if hasattr(self, "cls_token"):
            cls_token = self.cls_token.expand(x.shape[0], -1, -1)
            x = torch.cat((cls_token, x), dim=1)
        hidden_states_out = []
        for blk in self.blocks:
            x = blk(x)
            hidden_states_out.append(x)
        x = self.norm(x)
        # if hasattr(self, "classification_head"):
        #     x = self.classification_head(x[:, 0])
        return x, hidden_states_out


class ViT3DTower(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.select_layer = config.vision_select_layer
        self.select_feature = config.vision_select_feature

        self.vision_tower = myViT(
            in_channels=self.config.image_channel,
            img_size=self.config.image_size,
            patch_size=self.config.patch_size,
            pos_embed="perceptron",
            spatial_dims=len(self.config.patch_size),
            classification=True,
        )

    def forward(self, images):
        last_feature, hidden_states = self.vision_tower(images)
        if self.select_layer == -1:
            image_features = last_feature
        elif self.select_layer < -1:
            image_features = hidden_states[self.select_feature]
        else:
            raise ValueError(f'Unexpected select layer: {self.select_layer}')

        if self.select_feature == 'patch':
            image_features = image_features[:, 1:]
        elif self.select_feature == 'cls_patch':
            image_features = image_features
        else:
            raise ValueError(f'Unexpected select feature: {self.select_feature}')

        return image_features

    @property
    def dtype(self):
        return self.vision_tower.dtype

    @property
    def device(self):
        return self.vision_tower.device

    @property
    def hidden_size(self):
        return self.vision_tower.hidden_size


def build_vision_tower(config, **kwargs):
    vision_tower = getattr(config, 'vision_tower', None)
    if 'vit3d' in vision_tower.lower():
        return ViT3DTower(config, **kwargs)
    else:
        raise ValueError(f'Unknown vision tower: {vision_tower}')


class LamedMetaModel:
    def __init__(self, config):
        super(LamedMetaModel, self).__init__(config)

        self.config = config

        if hasattr(config, "vision_tower"):
            self.vision_tower = build_vision_tower(config)
            self.mm_projector = build_mm_projector(config)

    def get_vision_tower(self):
        vision_tower = getattr(self, 'vision_tower', None)
        return vision_tower

    def initialize_vision_modules(self, model_args):
        self.config.image_channel = model_args.image_channel
        self.config.image_size = model_args.image_size
        self.config.patch_size = model_args.patch_size

        self.config.vision_tower = model_args.vision_tower
        self.config.vision_select_layer = model_args.vision_select_layer
        self.config.vision_select_feature = model_args.vision_select_feature

        self.config.mm_projector_type = model_args.mm_projector_type
        self.config.proj_layer_type = model_args.proj_layer_type
        self.config.proj_layer_num = model_args.proj_layer_num
        self.config.proj_pooling_type = model_args.proj_pooling_type
        self.config.proj_pooling_size = model_args.proj_pooling_size

        # vision tower
        if self.get_vision_tower() is None:
            self.vision_tower = build_vision_tower(self.config)
            # If you have a more robust vision encoder, try freezing the vision tower by requires_grad_(False)


        if model_args.pretrain_vision_model is not None:
            vision_model_weights = torch.load(model_args.pretrain_vision_model, map_location='cpu')
            self.vision_tower.vision_tower.load_state_dict(vision_model_weights, strict=True)

        self.config.mm_hidden_size = self.vision_tower.hidden_size

        # mm_projector
        if getattr(self, 'mm_projector', None) is None:
            self.mm_projector = build_mm_projector(self.config)

        if model_args.pretrain_mm_mlp_adapter is not None:
            mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
            def get_w(weights, keyword):
                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
            self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'), strict=True)


class LamedMetaForCausalLM(ABC):
    @abstractmethod
    def get_model(self):
        pass

    def get_vision_tower(self):
        return self.get_model().get_vision_tower()

    def encode_images(self, images):
        image_features = self.get_model().get_vision_tower()(images)
        image_features = self.get_model().mm_projector(image_features)
        return image_features

    def prepare_inputs_for_multimodal(self, input_ids, position_ids, attention_mask, past_key_values, labels, images):
        vision_tower = self.get_vision_tower()
        if vision_tower is None or images is None or input_ids.shape[1] == 1:
            return input_ids, position_ids, attention_mask, past_key_values, None, labels
        else:
            image_features = self.encode_images(images)
            inputs_embeds = self.get_model().embed_tokens(input_ids)
            inputs_embeds = torch.cat((inputs_embeds[:, :1, :], image_features, inputs_embeds[:, (image_features.shape[1] + 1):, :]), dim=1)
        return None, position_ids, attention_mask, past_key_values, inputs_embeds, labels

    def initialize_vision_tokenizer(self, model_args, tokenizer):
        num_new_tokens = model_args.num_new_tokens

        self.resize_token_embeddings(len(tokenizer))

        if num_new_tokens > 0:
            input_embeddings = self.get_input_embeddings().weight.data
            output_embeddings = self.get_output_embeddings().weight.data

            input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
                dim=0, keepdim=True)
            output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
                dim=0, keepdim=True)

            input_embeddings[-num_new_tokens:] = input_embeddings_avg
            output_embeddings[-num_new_tokens:] = output_embeddings_avg

            if model_args.tune_mm_mlp_adapter:
                for p in self.get_input_embeddings().parameters():
                    p.requires_grad = True
                for p in self.get_output_embeddings().parameters():
                    p.requires_grad = False
            else:
                # we add 4 new tokens
                # if new tokens need input, please train input_embeddings
                for p in self.get_input_embeddings().parameters():
                    p.requires_grad = True
                # if new tokens need predict, please train output_embeddings
                for p in self.get_output_embeddings().parameters():
                    p.requires_grad = True

        if model_args.pretrain_mm_mlp_adapter:
            mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
            embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']

            if input_embeddings.shape == embed_tokens_weight.shape:
                input_embeddings = embed_tokens_weight
            elif embed_tokens_weight.shape[0] == num_new_tokens:
                input_embeddings[-num_new_tokens:] = embed_tokens_weight
            else:
                raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")


class LamedLlamaModel(LamedMetaModel, LlamaModel):
    config_class = LamedConfig
    def __init__(self, config: LlamaConfig):
        super(LamedLlamaModel, self).__init__(config)


class LamedLlamaForCausalLM(LamedMetaForCausalLM, LlamaForCausalLM):
    config_class = LamedConfig

    def __init__(self, config):
        super(LlamaForCausalLM, self).__init__(config)
        self.model = LamedLlamaModel(config)
        self.pretraining_tp = config.pretraining_tp
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_model(self):
        return self.model

    def forward(
            self,
            images: Optional[torch.FloatTensor] = None,
            input_ids: torch.LongTensor = None,
            labels: Optional[torch.LongTensor] = None,
            attention_mask: Optional[torch.Tensor] = None,

            position_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[List[torch.FloatTensor]] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:

        input_ids_pre = input_ids

        if inputs_embeds is None:
            (
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                inputs_embeds,
                labels
            ) = self.prepare_inputs_for_multimodal(
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                labels,
                images,
            )

        return super().forward(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            labels=labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict
        )

    @torch.no_grad()
    def generate(
        self,
        images: Optional[torch.Tensor] = None,
        inputs: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor, Any]:
        position_ids = kwargs.pop("position_ids", None)
        attention_mask = kwargs.pop("attention_mask", None)
        if "inputs_embeds" in kwargs:
            raise NotImplementedError("`inputs_embeds` is not supported")

        if images is not None:
            (
                inputs,
                position_ids,
                attention_mask,
                _,
                inputs_embeds,
                _
            ) = self.prepare_inputs_for_multimodal(
                inputs,
                position_ids,
                attention_mask,
                None,
                None,
                images,
            )
        else:
            inputs_embeds = self.get_model().embed_tokens(inputs)

        return super().generate(
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            **kwargs
        )


    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
        images = kwargs.pop("images", None)
        inputs = super().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs)
        if images is not None:
            inputs['images'] = images
        return inputs