# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

# Copied and Adapted from:
# https://github.com/deepseek-ai/Janus


import collections
import math
import os
from dataclasses import field
from enum import Enum
from functools import partial
from itertools import repeat
from typing import (
    Callable,
    Final,
    Iterable,
    Literal,
    Optional,
    Sequence,
    Set,
    Tuple,
    Type,
    Union,
)

import torch
import torch.nn.functional as F
from einops import rearrange
from torch import Tensor, _assert, nn
from torch.nn.init import trunc_normal_
from transformers import AutoModel, PreTrainedModel

from sglang.srt.configs.janus_pro import *
from sglang.srt.layers.attention.vision import VisionAttention
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.quantization import QuantizationConfig
from sglang.srt.managers.mm_utils import (
    MultiModalityDataPaddingPatternTokenPairs,
    general_mm_embed_routine,
)
from sglang.srt.managers.schedule_batch import MultimodalDataItem, MultimodalInputs
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.models.llama import LlamaForCausalLM
from sglang.utils import logger

#################################################################################
#                              VQ Model Configs                                 #
#################################################################################


# Copied from:
# https://github.com/deepseek-ai/Janus/tree/main/janus/models/vq_model.py
@dataclass
class ModelArgs:
    codebook_size: int = 16384
    codebook_embed_dim: int = 8
    codebook_l2_norm: bool = True
    codebook_show_usage: bool = True
    commit_loss_beta: float = 0.25
    entropy_loss_ratio: float = 0.0

    encoder_ch_mult: List[int] = field(default_factory=lambda: [1, 1, 2, 2, 4])
    decoder_ch_mult: List[int] = field(default_factory=lambda: [1, 1, 2, 2, 4])
    z_channels: int = 256
    dropout_p: float = 0.0


def named_apply(
    fn: Callable,
    module: nn.Module,
    name="",
    depth_first: bool = True,
    include_root: bool = False,
) -> nn.Module:
    if not depth_first and include_root:
        fn(module=module, name=name)
    for child_name, child_module in module.named_children():
        child_name = ".".join((name, child_name)) if name else child_name
        named_apply(
            fn=fn,
            module=child_module,
            name=child_name,
            depth_first=depth_first,
            include_root=True,
        )
    if depth_first and include_root:
        fn(module=module, name=name)
    return module


def VQ_16(**kwargs):
    return VQModel(
        ModelArgs(
            encoder_ch_mult=[1, 1, 2, 2, 4], decoder_ch_mult=[1, 1, 2, 2, 4], **kwargs
        )
    )


VQ_models = {"VQ-16": VQ_16}

import collections.abc


# From PyTorch internals
def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
            return tuple(x)
        return tuple(repeat(x, n))

    return parse


def _trunc_normal_(tensor, mean, std, a, b):
    # Cut & paste from PyTorch official master until it's in a few official releases - RW
    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        logger.warn(
            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
            "The distribution of values may be incorrect.",
            stacklevel=2,
        )

    # Values are generated by using a truncated uniform distribution and
    # then using the inverse CDF for the normal distribution.
    # Get upper and lower cdf values
    l = norm_cdf((a - mean) / std)
    u = norm_cdf((b - mean) / std)

    # Uniformly fill tensor with values from [l, u], then translate to
    # [2l-1, 2u-1].
    tensor.uniform_(2 * l - 1, 2 * u - 1)

    # Use inverse cdf transform for normal distribution to get truncated
    # standard normal
    if tensor.dtype in [torch.float16, torch.bfloat16]:
        # The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu
        og_dtype = tensor.dtype
        tensor = tensor.to(torch.float32)
        tensor.erfinv_()
        tensor = tensor.to(og_dtype)
    else:
        tensor.erfinv_()

    # Transform to proper mean, std
    tensor.mul_(std * math.sqrt(2.0))
    tensor.add_(mean)

    # Clamp to ensure it's in the proper range
    if tensor.dtype == torch.float16:
        # The `clamp_` op is not (yet?) defined in float16+cpu
        tensor = tensor.to(torch.float32)
        tensor.clamp_(min=a, max=b)
    else:
        tensor.clamp_(min=a, max=b)


def trunc_normal_tf_(
    tensor: torch.Tensor,
    mean: float = 0.0,
    std: float = 1.0,
    a: float = -2.0,
    b: float = 2.0,
):
    """Fills the input Tensor with values drawn from a truncated
    normal distribution. The values are effectively drawn from the
    normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
    with values outside :math:`[a, b]` redrawn until they are within
    the bounds. The method used for generating the random values works
    best when :math:`a \\leq \text{mean} \\leq b`.
    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
    and the result is subsquently scaled and shifted by the mean and std args.
    Args:
        tensor: an n-dimensional `torch.Tensor`
        mean: the mean of the normal distribution
        std: the standard deviation of the normal distribution
        a: the minimum cutoff value
        b: the maximum cutoff value
    """
    with torch.no_grad():
        _trunc_normal_(tensor, 0, 1.0, a, b)
        tensor.mul_(std).add_(mean)


to_2tuple = _ntuple(2)


class Format(str, Enum):
    NCHW = "NCHW"
    NHWC = "NHWC"
    NCL = "NCL"
    NLC = "NLC"


def nchw_to(x: torch.Tensor, fmt: Format):
    if fmt == Format.NHWC:
        x = x.permute(0, 2, 3, 1)
    elif fmt == Format.NLC:
        x = x.flatten(2).transpose(1, 2)
    elif fmt == Format.NCL:
        x = x.flatten(2)
    return x


def resample_patch_embed(
    patch_embed,
    new_size: List[int],
    interpolation: str = "bicubic",
    antialias: bool = True,
    verbose: bool = False,
):
    """Resample the weights of the patch embedding kernel to target resolution.
    We resample the patch embedding kernel by approximately inverting the effect
    of patch resizing.

    Code based on:
      https://github.com/google-research/big_vision/blob/b00544b81f8694488d5f36295aeb7972f3755ffe/big_vision/models/proj/flexi/vit.py

    With this resizing, we can for example load a B/8 filter into a B/16 model
    and, on 2x larger input image, the result will match.

    Args:
        patch_embed: original parameter to be resized.
        new_size (tuple(int, int): target shape (height, width)-only.
        interpolation (str): interpolation for resize
        antialias (bool): use anti-aliasing filter in resize
        verbose (bool): log operation
    Returns:
        Resized patch embedding kernel.
    """
    import numpy as np

    try:
        from torch import vmap
    except ImportError:
        from torch.func import vmap

    assert len(patch_embed.shape) == 4, "Four dimensions expected"
    assert len(new_size) == 2, "New shape should only be hw"
    old_size = patch_embed.shape[-2:]
    if tuple(old_size) == tuple(new_size):
        return patch_embed

    if verbose:
        logger.info(
            f"Resize patch embedding {patch_embed.shape} to {new_size}, w/ {interpolation} interpolation."
        )

    def resize(x_np, _new_size):
        x_tf = torch.Tensor(x_np)[None, None, ...]
        x_upsampled = F.interpolate(
            x_tf, size=_new_size, mode=interpolation, antialias=antialias
        )[0, 0, ...].numpy()
        return x_upsampled

    def get_resize_mat(_old_size, _new_size):
        mat = []
        for i in range(np.prod(_old_size)):
            basis_vec = np.zeros(_old_size)
            basis_vec[np.unravel_index(i, _old_size)] = 1.0
            mat.append(resize(basis_vec, _new_size).reshape(-1))
        return np.stack(mat).T

    resize_mat = get_resize_mat(old_size, new_size)
    resize_mat_pinv = torch.tensor(
        np.linalg.pinv(resize_mat.T), device=patch_embed.device
    )

    def resample_kernel(kernel):
        resampled_kernel = resize_mat_pinv @ kernel.reshape(-1)
        return resampled_kernel.reshape(new_size)

    v_resample_kernel = vmap(vmap(resample_kernel, 0, 0), 1, 1)
    orig_dtype = patch_embed.dtype
    patch_embed = patch_embed.float()
    patch_embed = v_resample_kernel(patch_embed)
    patch_embed = patch_embed.to(orig_dtype)
    return patch_embed


# Copied from:
# https://github.com/deepseek-ai/Janus/tree/main/janus/models/siglip_vit.py
class PatchEmbed(nn.Module):
    """2D Image to Patch Embedding"""

    output_fmt: Format
    dynamic_img_pad: torch.jit.Final[bool]

    def __init__(
        self,
        img_size: Optional[int] = 224,
        patch_size: int = 16,
        in_chans: int = 3,
        embed_dim: int = 768,
        norm_layer: Optional[Callable] = None,
        flatten: bool = True,
        output_fmt: Optional[str] = None,
        bias: bool = True,
        strict_img_size: bool = True,
        dynamic_img_pad: bool = False,
    ):
        super().__init__()
        self.patch_size = tuple(to_2tuple(patch_size))
        self.img_size, self.grid_size, self.num_patches = self._init_img_size(img_size)

        if output_fmt is not None:
            self.flatten = False
            self.output_fmt = Format(output_fmt)
        else:
            # flatten spatial dim and transpose to channels last, kept for bwd compat
            self.flatten = flatten
            self.output_fmt = Format.NCHW
        self.strict_img_size = strict_img_size
        self.dynamic_img_pad = dynamic_img_pad

        self.proj = nn.Conv2d(
            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias
        )
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def _init_img_size(self, img_size: Union[int, Tuple[int, int]]):
        assert self.patch_size
        if img_size is None:
            return None, None, None
        img_size = to_2tuple(img_size)
        grid_size = tuple([s // p for s, p in zip(img_size, self.patch_size)])
        num_patches = grid_size[0] * grid_size[1]
        return img_size, grid_size, num_patches

    def set_input_size(
        self,
        img_size: Optional[Union[int, Tuple[int, int]]] = None,
        patch_size: Optional[Union[int, Tuple[int, int]]] = None,
    ):
        new_patch_size = None
        if patch_size is not None:
            new_patch_size = to_2tuple(patch_size)
        if new_patch_size is not None and new_patch_size != self.patch_size:
            with torch.no_grad():
                new_proj = nn.Conv2d(
                    self.proj.in_channels,
                    self.proj.out_channels,
                    kernel_size=new_patch_size,
                    stride=new_patch_size,
                    bias=self.proj.bias is not None,
                )
                new_proj.weight.copy_(
                    resample_patch_embed(self.proj.weight, new_patch_size, verbose=True)
                )
                if self.proj.bias is not None:
                    new_proj.bias.copy_(self.proj.bias)
                self.proj = new_proj
            self.patch_size = new_patch_size
        img_size = img_size or self.img_size
        if img_size != self.img_size or new_patch_size is not None:
            self.img_size, self.grid_size, self.num_patches = self._init_img_size(
                img_size
            )

    def feat_ratio(self, as_scalar=True) -> Union[Tuple[int, int], int]:
        if as_scalar:
            return max(self.patch_size)
        else:
            return self.patch_size

    def dynamic_feat_size(self, img_size: Tuple[int, int]) -> Tuple[int, int]:
        """Get grid (feature) size for given image size taking account of dynamic padding.
        NOTE: must be torchscript compatible so using fixed tuple indexing
        """
        if self.dynamic_img_pad:
            return math.ceil(img_size[0] / self.patch_size[0]), math.ceil(
                img_size[1] / self.patch_size[1]
            )
        else:
            return img_size[0] // self.patch_size[0], img_size[1] // self.patch_size[1]

    def forward(self, x):
        B, C, H, W = x.shape
        if self.img_size is not None:
            if self.strict_img_size:
                _assert(
                    H == self.img_size[0],
                    f"Input height ({H}) doesn't match model ({self.img_size[0]}).",
                )
                _assert(
                    W == self.img_size[1],
                    f"Input width ({W}) doesn't match model ({self.img_size[1]}).",
                )
            elif not self.dynamic_img_pad:
                _assert(
                    H % self.patch_size[0] == 0,
                    f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]}).",
                )
                _assert(
                    W % self.patch_size[1] == 0,
                    f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]}).",
                )
        if self.dynamic_img_pad:
            pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
            pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
            x = F.pad(x, (0, pad_w, 0, pad_h))
        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
        elif self.output_fmt != Format.NCHW:
            x = nchw_to(x, self.output_fmt)
        x = self.norm(x)
        return x


class Mlp(nn.Module):
    """MLP as used in Vision Transformer, MLP-Mixer and related networks

    NOTE: When use_conv=True, expects 2D NCHW tensors, otherwise N*C expected.
    """

    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        norm_layer=None,
        bias=True,
        drop=0.0,
        use_conv=False,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)
        drop_probs = to_2tuple(drop)
        linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear

        self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
        self.act = act_layer()
        self.drop1 = nn.Dropout(drop_probs[0])
        self.norm = (
            norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
        )
        self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
        self.drop2 = nn.Dropout(drop_probs[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop1(x)
        x = self.norm(x)
        x = self.fc2(x)
        x = self.drop2(x)
        return x


def drop_path(
    x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True
):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.

    """
    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (
        x.ndim - 1
    )  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
    if keep_prob > 0.0 and scale_by_keep:
        random_tensor.div_(keep_prob)
    return x * random_tensor


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob
        self.scale_by_keep = scale_by_keep

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)

    def extra_repr(self):
        return f"drop_prob={round(self.drop_prob, 3):0.3f}"


class VisionTransformerBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_ratio: float = 4.0,
        qkv_bias: bool = False,
        qk_norm: bool = False,
        proj_drop: float = 0.0,
        attn_drop: float = 0.0,
        init_values: Optional[float] = None,
        drop_path: float = 0.0,
        act_layer: nn.Module = nn.GELU,
        norm_layer: nn.Module = nn.LayerNorm,
        mlp_layer: nn.Module = Mlp,
    ) -> None:
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = VisionAttention(
            embed_dim=dim,
            num_heads=num_heads,
            projection_size=dim,
            use_qkv_parallel=True,
            qkv_backend="sdpa",
            softmax_in_single_precision=False,
            dropout=attn_drop,
        )

        self.ls1 = (
            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
        )
        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()

        self.norm2 = norm_layer(dim)
        self.mlp = mlp_layer(
            in_features=dim,
            hidden_features=int(dim * mlp_ratio),
            act_layer=act_layer,
            drop=proj_drop,
        )
        self.ls2 = (
            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
        )
        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
        x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
        return x


LayerType = Union[str, Callable, Type[torch.nn.Module]]


class PatchDropout(nn.Module):
    """
    https://arxiv.org/abs/2212.00794 and https://arxiv.org/pdf/2208.07220
    """

    return_indices: torch.jit.Final[bool]

    def __init__(
        self,
        prob: float = 0.5,
        num_prefix_tokens: int = 1,
        ordered: bool = False,
        return_indices: bool = False,
    ):
        super().__init__()
        assert 0 <= prob < 1.0
        self.prob = prob
        self.num_prefix_tokens = (
            num_prefix_tokens  # exclude CLS token (or other prefix tokens)
        )
        self.ordered = ordered
        self.return_indices = return_indices

    def forward(
        self, x
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]:
        if not self.training or self.prob == 0.0:
            if self.return_indices:
                return x, None
            return x

        if self.num_prefix_tokens:
            prefix_tokens, x = (
                x[:, : self.num_prefix_tokens],
                x[:, self.num_prefix_tokens :],
            )
        else:
            prefix_tokens = None

        B = x.shape[0]
        L = x.shape[1]
        num_keep = max(1, int(L * (1.0 - self.prob)))
        keep_indices = torch.argsort(torch.randn(B, L, device=x.device), dim=-1)[
            :, :num_keep
        ]
        if self.ordered:
            # NOTE does not need to maintain patch order in typical transformer use,
            # but possibly useful for debug / visualization
            keep_indices = keep_indices.sort(dim=-1)[0]
        x = x.gather(1, keep_indices.unsqueeze(-1).expand((-1, -1) + x.shape[2:]))

        if prefix_tokens is not None:
            x = torch.cat((prefix_tokens, x), dim=1)

        if self.return_indices:
            return x, keep_indices
        return x


def resample_abs_pos_embed(
    posemb: torch.Tensor,
    new_size: List[int],
    old_size: Optional[List[int]] = None,
    num_prefix_tokens: int = 1,
    interpolation: str = "bicubic",
    antialias: bool = True,
    verbose: bool = False,
):
    # sort out sizes, assume square if old size not provided
    num_pos_tokens = posemb.shape[1]
    num_new_tokens = new_size[0] * new_size[1] + num_prefix_tokens
    if num_new_tokens == num_pos_tokens and new_size[0] == new_size[1]:
        return posemb

    if old_size is None:
        hw = int(math.sqrt(num_pos_tokens - num_prefix_tokens))
        old_size = hw, hw

    if num_prefix_tokens:
        posemb_prefix, posemb = (
            posemb[:, :num_prefix_tokens],
            posemb[:, num_prefix_tokens:],
        )
    else:
        posemb_prefix, posemb = None, posemb

    # do the interpolation
    embed_dim = posemb.shape[-1]
    orig_dtype = posemb.dtype
    posemb = posemb.float()  # interpolate needs float32
    posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2)
    posemb = F.interpolate(
        posemb, size=new_size, mode=interpolation, antialias=antialias
    )
    posemb = posemb.permute(0, 2, 3, 1).reshape(1, -1, embed_dim)
    posemb = posemb.to(orig_dtype)

    # add back extra (class, etc) prefix tokens
    if posemb_prefix is not None:
        posemb = torch.cat([posemb_prefix, posemb], dim=1)

    if not torch.jit.is_scripting() and verbose:
        logger.info(f"Resized position embedding: {old_size} to {new_size}.")

    return posemb


def init_weights(self):
    if self.pos_embed is not None:
        trunc_normal_(self.pos_embed, std=self.pos_embed.shape[1] ** -0.5)
    trunc_normal_(self.latent, std=self.latent_dim**-0.5)


def init_weights_vit_timm(module: nn.Module, name: str = "") -> None:
    """ViT weight initialization, original timm impl (for reproducibility)"""
    if isinstance(module, nn.Linear):
        trunc_normal_(module.weight, std=0.02)
        if module.bias is not None:
            nn.init.zeros_(module.bias)
    elif hasattr(module, "init_weights"):
        module.init_weights()


class VisionTransformer(nn.Module):
    """Vision Transformer

    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
        - https://arxiv.org/abs/2010.11929
    """

    dynamic_img_size: Final[bool]

    def __init__(
        self,
        img_size: Union[int, Tuple[int, int]] = 224,
        patch_size: Union[int, Tuple[int, int]] = 16,
        in_chans: int = 3,
        num_classes: int = 1000,
        global_pool: Literal["", "avg", "token", "map"] = "token",
        embed_dim: int = 768,
        depth: int = 12,
        num_heads: int = 12,
        mlp_ratio: float = 4.0,
        qkv_bias: bool = True,
        qk_norm: bool = False,
        init_values: Optional[float] = None,
        class_token: bool = True,
        no_embed_class: bool = False,
        reg_tokens: int = 0,
        pre_norm: bool = False,
        fc_norm: Optional[bool] = None,
        dynamic_img_size: bool = False,
        dynamic_img_pad: bool = False,
        drop_rate: float = 0.0,
        pos_drop_rate: float = 0.0,
        patch_drop_rate: float = 0.0,
        proj_drop_rate: float = 0.0,
        attn_drop_rate: float = 0.0,
        drop_path_rate: float = 0.0,
        weight_init: Literal["skip", "jax", "jax_nlhb", "moco", ""] = "",
        embed_layer: Callable = PatchEmbed,
        _norm_layer: Optional[LayerType] = None,
        _act_layer: Optional[LayerType] = None,
        block_fn: Type[nn.Module] = VisionTransformerBlock,
        mlp_layer: Type[nn.Module] = Mlp,
        ignore_head: bool = False,
    ) -> None:
        """
        Args:
            img_size: Input image size.
            patch_size: Patch size.
            in_chans: Number of image input channels.
            num_classes: Mumber of classes for classification head.
            global_pool: Type of global pooling for final sequence (default: 'token').
            embed_dim: Transformer embedding dimension.
            depth: Depth of transformer.
            num_heads: Number of attention heads.
            mlp_ratio: Ratio of mlp hidden dim to embedding dim.
            qkv_bias: Enable bias for qkv projections if True.
            init_values: Layer-scale init values (layer-scale enabled if not None).
            class_token: Use class token.
            no_embed_class: Don't include position embeddings for class (or reg) tokens.
            reg_tokens: Number of register tokens.
            fc_norm: Pre head norm after pool (instead of before), if None, enabled when global_pool == 'avg'.
            drop_rate: Head dropout rate.
            pos_drop_rate: Position embedding dropout rate.
            attn_drop_rate: Attention dropout rate.
            drop_path_rate: Stochastic depth rate.
            weight_init: Weight initialization scheme.
            embed_layer: Patch embedding layer.
            _norm_layer: Normalization layer.
            _act_layer: MLP activation layer.
            block_fn: Transformer block layer.
        """
        super().__init__()
        assert global_pool in ("", "avg", "token", "map")
        assert class_token or global_pool != "token"
        use_fc_norm = global_pool == "avg" if fc_norm is None else fc_norm
        # norm_layer = get_norm_layer(norm_layer) or partial(nn.LayerNorm, eps=1e-6)
        # act_layer = get_act_layer(act_layer) or nn.GELU
        norm_layer = partial(nn.LayerNorm, eps=1e-6)
        act_layer = nn.GELU

        self.num_classes = num_classes
        self.global_pool = global_pool
        self.num_features = self.embed_dim = (
            embed_dim  # num_features for consistency with other models
        )
        self.num_prefix_tokens = 1 if class_token else 0
        self.num_prefix_tokens += reg_tokens
        self.num_reg_tokens = reg_tokens
        self.has_class_token = class_token
        self.no_embed_class = (
            no_embed_class  # don't embed prefix positions (includes reg)
        )
        self.dynamic_img_size = dynamic_img_size
        self.grad_checkpointing = False
        self.ignore_head = ignore_head

        embed_args = {}
        if dynamic_img_size:
            # flatten deferred until after pos embed
            embed_args.update(dict(strict_img_size=False, output_fmt="NHWC"))
        self.patch_embed = embed_layer(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            bias=not pre_norm,  # disable bias if pre-norm is used (e.g. CLIP)
            dynamic_img_pad=dynamic_img_pad,
            **embed_args,
        )
        num_patches = self.patch_embed.num_patches

        self.cls_token = (
            nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
        )
        self.reg_token = (
            nn.Parameter(torch.zeros(1, reg_tokens, embed_dim)) if reg_tokens else None
        )
        embed_len = (
            num_patches if no_embed_class else num_patches + self.num_prefix_tokens
        )
        self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * 0.02)
        self.pos_drop = nn.Dropout(p=pos_drop_rate)
        if patch_drop_rate > 0:
            self.patch_drop = PatchDropout(
                patch_drop_rate,
                num_prefix_tokens=self.num_prefix_tokens,
            )
        else:
            self.patch_drop = nn.Identity()
        self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()

        dpr = [
            x.item() for x in torch.linspace(0, drop_path_rate, depth)
        ]  # stochastic depth decay rule
        self.blocks = nn.Sequential(
            *[
                block_fn(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_norm=qk_norm,
                    init_values=init_values,
                    proj_drop=proj_drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                    act_layer=act_layer,
                    mlp_layer=mlp_layer,
                )
                for i in range(depth)
            ]
        )
        self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()

        # Classifier Head
        if global_pool == "map":
            AttentionPoolLatent.init_weights = init_weights
            self.attn_pool = AttentionPoolLatent(
                self.embed_dim,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
                norm_layer=norm_layer,
            )
        else:
            self.attn_pool = None
        self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
        self.head_drop = nn.Dropout(drop_rate)
        self.head = (
            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
        )

        if weight_init != "skip":
            self.init_weights(weight_init)

    def init_weights(self, mode: Literal["jax", "jax_nlhb", "moco", ""] = "") -> None:
        assert mode in ("jax", "jax_nlhb", "moco", "")
        # head_bias = -math.log(self.num_classes) if "nlhb" in mode else 0.0
        trunc_normal_(self.pos_embed, std=0.02)
        if self.cls_token is not None:
            nn.init.normal_(self.cls_token, std=1e-6)
        named_apply(init_weights_vit_timm, self)

    @torch.jit.ignore
    def no_weight_decay(self) -> Set:
        return {"pos_embed", "cls_token", "dist_token"}

    @torch.jit.ignore
    def group_matcher(self, coarse: bool = False) -> Dict:
        return dict(
            stem=r"^cls_token|pos_embed|patch_embed",  # stem and embed
            blocks=[(r"^blocks\.(\d+)", None), (r"^norm", (99999,))],
        )

    @torch.jit.ignore
    def get_classifier(self) -> nn.Module:
        return self.head

    def reset_classifier(self, num_classes: int, global_pool=None) -> None:
        self.num_classes = num_classes
        if global_pool is not None:
            assert global_pool in ("", "avg", "token", "map")
            if global_pool == "map" and self.attn_pool is None:
                assert (
                    False
                ), "Cannot currently add attention pooling in reset_classifier()."
            elif global_pool != "map " and self.attn_pool is not None:
                self.attn_pool = None  # remove attention pooling
            self.global_pool = global_pool
        self.head = (
            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
        )

    def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
        if self.dynamic_img_size:
            B, H, W, C = x.shape
            pos_embed = resample_abs_pos_embed(
                self.pos_embed,
                [H, W],
                num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens,
            )
            x = x.view(B, -1, C)
        else:
            pos_embed = self.pos_embed

        to_cat = []
        if self.cls_token is not None:
            to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
        if self.reg_token is not None:
            to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))

        if self.no_embed_class:
            # deit-3, updated JAX (big vision)
            # position embedding does not overlap with class token, add then concat
            x = x + pos_embed
            if to_cat:
                x = torch.cat(to_cat + [x], dim=1)
        else:
            # original timm, JAX, and deit vit impl
            # pos_embed has entry for class token, concat then add
            if to_cat:
                x = torch.cat(to_cat + [x], dim=1)
            x = x + pos_embed

        return self.pos_drop(x)

    def _intermediate_layers(
        self,
        x: torch.Tensor,
        n: Union[int, Sequence] = 1,
    ) -> List[torch.Tensor]:
        outputs, num_blocks = [], len(self.blocks)
        take_indices = set(
            range(num_blocks - n, num_blocks) if isinstance(n, int) else n
        )

        # forward pass
        x = self.patch_embed(x)
        x = self._pos_embed(x)
        x = self.patch_drop(x)
        x = self.norm_pre(x)
        for i, blk in enumerate(self.blocks):
            x = blk(x)
            if i in take_indices:
                outputs.append(x)

        return outputs

    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
        x = self.patch_embed(x)
        x = self._pos_embed(x)
        x = self.patch_drop(x)
        x = self.norm_pre(x)
        x = self.blocks(x)
        x = self.norm(x)
        return x

    def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
        if self.attn_pool is not None:
            x = self.attn_pool(x)
        elif self.global_pool == "avg":
            x = x[:, self.num_prefix_tokens :].mean(dim=1)
        elif self.global_pool:
            x = x[:, 0]  # class token
        x = self.fc_norm(x)
        x = self.head_drop(x)
        return x if pre_logits else self.head(x)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.forward_features(x)
        if not self.ignore_head:
            x = self.forward_head(x)
        return x


def model_name_to_cls(cls_name):
    if "MlpProjector" in cls_name:
        cls = MlpProjector

    elif "CLIPVisionTower" in cls_name:
        cls = CLIPVisionTower

    elif "VQ" in cls_name:

        cls = VQ_models[cls_name]
    elif "vision_head" in cls_name:
        cls = vision_head
    else:
        raise ValueError(f"class_name {cls_name} is invalid.")

    return cls


class vision_head(torch.nn.Module):
    def __init__(self, params):
        super().__init__()
        self.output_mlp_projector = torch.nn.Linear(
            params["n_embed"], params["image_token_embed"]
        )
        self.vision_activation = torch.nn.GELU()
        self.vision_head = torch.nn.Linear(
            params["image_token_embed"], params["image_token_size"]
        )

    def forward(self, x):
        x = self.output_mlp_projector(x)
        x = self.vision_activation(x)
        x = self.vision_head(x)
        return x


SigLIP_MODEL_CONFIG = {
    "siglip_so400m_patch14_384": {
        "image_size": 336,
        "patch_size": 14,
        "width": 1152,
        "layers": 27,
        "heads": 16,
        "mlp_ratio": 3.7362,
        "global_pool": "map",
        "use_checkpoint": False,
    },
    "siglip_so400m_patch14_224": {
        "image_size": 224,
        "patch_size": 14,
        "width": 1152,
        "layers": 27,
        "heads": 16,
        "mlp_ratio": 3.7362,
        "global_pool": "map",
        "use_checkpoint": False,
    },
    "siglip_large_patch16_384": {
        "image_size": 384,
        "patch_size": 16,
        "width": 1024,
        "layers": 24,
        "heads": 16,
        "mlp_ratio": 4,
        "global_pool": "map",
        "use_checkpoint": False,
    },
}


def create_siglip_vit(
    model_name: str = "siglip_so400m_patch14_384",
    image_size: int = 384,
    select_layer: int = -1,
    ckpt_path: str = "",
    **kwargs,
):
    assert (
        model_name in SigLIP_MODEL_CONFIG.keys()
    ), f"model name should be in {SigLIP_MODEL_CONFIG.keys()}"

    vision_cfg = SigLIPVisionCfg(**SigLIP_MODEL_CONFIG[model_name])

    if select_layer <= 0:
        layers = min(vision_cfg.layers, vision_cfg.layers + select_layer + 1)
    else:
        layers = min(vision_cfg.layers, select_layer)

    model = VisionTransformer(
        img_size=image_size,
        patch_size=vision_cfg.patch_size,
        embed_dim=vision_cfg.width,
        depth=layers,
        num_heads=vision_cfg.heads,
        mlp_ratio=vision_cfg.mlp_ratio,
        class_token=vision_cfg.class_token,
        global_pool=vision_cfg.global_pool,
        ignore_head=kwargs.get("ignore_head", True),
        weight_init=kwargs.get("weight_init", "skip"),
        num_classes=0,
    )

    if ckpt_path:
        state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)

        incompatible_keys = model.load_state_dict(state_dict, strict=False)
        print(
            f"SigLIP-ViT restores from {ckpt_path},\n"
            f"\tincompatible_keys:', {incompatible_keys}."
        )

    return model


class Normalize(torch.nn.Module):
    """Normalize a tensor image with mean and standard deviation.
    This transform does not support PIL Image.
    Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n``
    channels, this transform will normalize each channel of the input
    ``torch.*Tensor`` i.e.,
    ``output[channel] = (input[channel] - mean[channel]) / std[channel]``

    .. note::
        This transform acts out of place, i.e., it does not mutate the input tensor.

    Args:
        mean (sequence): Sequence of means for each channel.
        std (sequence): Sequence of standard deviations for each channel.
        inplace(bool,optional): Bool to make this operation in-place.

    """

    def __init__(self, mean, std, inplace=False):
        super().__init__()
        # _log_api_usage_once(self)
        self.mean = mean
        self.std = std
        self.inplace = inplace

    def forward(self, tensor: Tensor) -> Tensor:
        """
        Args:
            tensor (Tensor): Tensor image to be normalized.

        Returns:
            Tensor: Normalized Tensor image.
        """
        return F.normalize(tensor, self.mean, self.std, self.inplace)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(mean={self.mean}, std={self.std})"


class CLIPVisionTower(nn.Module):
    def __init__(
        self,
        model_name: str = "siglip_large_patch16_384",
        image_size: Union[Tuple[int, int], int] = 336,
        select_feature: str = "patch",
        select_layer: int = -2,
        select_layers: list = None,
        ckpt_path: str = "",
        pixel_mean: Optional[List[float]] = None,
        pixel_std: Optional[List[float]] = None,
        **kwargs,
    ):
        super().__init__()

        self.model_name = model_name
        self.select_feature = select_feature
        self.select_layer = select_layer
        self.select_layers = select_layers

        vision_tower_params = {
            "model_name": model_name,
            "image_size": image_size,
            "ckpt_path": ckpt_path,
            "select_layer": select_layer,
        }
        vision_tower_params.update(kwargs)
        self.vision_tower, self.forward_kwargs = self.build_vision_tower(
            vision_tower_params
        )

        if pixel_mean is not None and pixel_std is not None:
            image_norm = Normalize(mean=pixel_mean, std=pixel_std)
        else:
            image_norm = None

        self.image_norm = image_norm

    @property
    def device(self) -> torch.device:
        return next(self.vision_tower.parameters()).device

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

    def build_vision_tower(self, vision_tower_params):
        if self.model_name.startswith("siglip"):
            self.select_feature = "same"
            vision_tower = create_siglip_vit(**vision_tower_params)
            forward_kwargs = dict()

        elif self.model_name.startswith("sam"):
            # vision_tower = create_sam_vit(**vision_tower_params)
            forward_kwargs = dict()

        else:  # huggingface
            from transformers import CLIPVisionModel

            vision_tower = CLIPVisionModel.from_pretrained(**vision_tower_params)
            forward_kwargs = dict(output_hidden_states=True)

        return vision_tower, forward_kwargs

    def feature_select(self, image_forward_outs):
        if isinstance(image_forward_outs, torch.Tensor):
            # the output has been the self.select_layer"s features
            image_features = image_forward_outs
        else:
            image_features = image_forward_outs.hidden_states[self.select_layer]

        if self.select_feature == "patch":
            # if the output has cls_token
            image_features = image_features[:, 1:]
        elif self.select_feature == "cls_patch":
            image_features = image_features
        elif self.select_feature == "same":
            image_features = image_features

        else:
            raise ValueError(f"Unexpected select feature: {self.select_feature}")
        return image_features

    def forward(self, images):
        """

        Args:
            images (torch.Tensor): [b, 3, H, W]

        Returns:
            image_features (torch.Tensor): [b, n_patch, d]
        """

        if self.image_norm is not None:
            images = self.image_norm(images)

        image_forward_outs = self.vision_tower(images, **self.forward_kwargs)
        image_features = self.feature_select(image_forward_outs)
        return image_features


class MlpProjector(nn.Module):
    def __init__(self, cfg):
        super().__init__()

        self.cfg = cfg

        if cfg["projector_type"] == "identity":
            modules = nn.Identity()

        elif cfg["projector_type"] == "linear":
            modules = nn.Linear(cfg["input_dim"], cfg["n_embed"])

        elif cfg["projector_type"] == "mlp_gelu":
            mlp_depth = cfg.get("depth", 1)
            modules = [nn.Linear(cfg["input_dim"], cfg["n_embed"])]
            for _ in range(1, mlp_depth):
                modules.append(nn.GELU())
                modules.append(nn.Linear(cfg["n_embed"], cfg["n_embed"]))
            modules = nn.Sequential(*modules)

        elif cfg["projector_type"] == "low_high_hybrid_split_mlp_gelu":
            mlp_depth = cfg.get("depth", 1)
            self.high_up_proj = nn.Linear(cfg["input_dim"], cfg["n_embed"] // 2)
            self.low_up_proj = nn.Linear(cfg["input_dim"], cfg["n_embed"] // 2)

            modules = []
            for _ in range(1, mlp_depth):
                modules.append(nn.GELU())
                modules.append(nn.Linear(cfg["n_embed"], cfg["n_embed"]))
            modules = nn.Sequential(*modules)

        else:
            raise ValueError(f"Unknown projector type: {cfg['projector_type']}")

        self.layers = modules

    def forward(
        self, x_or_tuple: Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]
    ):
        """

        Args:
            x_or_tuple (Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]:  if it is a tuple of torch.Tensor,
                then it comes from the hybrid vision encoder, and x = high_res_x, low_res_x);
                otherwise it is the feature from the single vision encoder.

        Returns:
            x (torch.Tensor): [b, s, c]
        """

        if isinstance(x_or_tuple, tuple):
            # self.cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
            high_x, low_x = x_or_tuple
            high_x = self.high_up_proj(high_x)
            low_x = self.low_up_proj(low_x)
            x = torch.cat([high_x, low_x], dim=-1)
        else:
            x = x_or_tuple

        return self.layers(x)


class LayerScale(nn.Module):
    def __init__(
        self,
        dim: int,
        init_values: float = 1e-5,
        inplace: bool = False,
    ) -> None:
        super().__init__()
        self.inplace = inplace
        self.gamma = nn.Parameter(init_values * torch.ones(dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x.mul_(self.gamma) if self.inplace else x * self.gamma


# use torch.scaled_dot_product_attention where possible
_HAS_FUSED_ATTN = hasattr(torch.nn.functional, "scaled_dot_product_attention")
if "TIMM_FUSED_ATTN" in os.environ:
    _USE_FUSED_ATTN = int(os.environ["TIMM_FUSED_ATTN"])
else:
    _USE_FUSED_ATTN = (
        1  # 0 == off, 1 == on (for tested use), 2 == on (for experimental use)
    )

# Set to True if exporting a model with Same padding via ONNX
_EXPORTABLE = False


def use_fused_attn(experimental: bool = False) -> bool:
    # NOTE: ONNX export cannot handle F.scaled_dot_product_attention as of pytorch 2.0
    if not _HAS_FUSED_ATTN or _EXPORTABLE:
        return False
    if experimental:
        return _USE_FUSED_ATTN > 1
    return _USE_FUSED_ATTN > 0


class AttentionPoolLatent(nn.Module):
    """Attention pooling w/ latent query"""

    fused_attn: torch.jit.Final[bool]

    def __init__(
        self,
        in_features: int,
        out_features: int = None,
        embed_dim: int = None,
        num_heads: int = 8,
        feat_size: Optional[int] = None,
        mlp_ratio: float = 4.0,
        qkv_bias: bool = True,
        qk_norm: bool = False,
        latent_len: int = 1,
        latent_dim: int = None,
        pos_embed: str = "",
        pool_type: str = "token",
        norm_layer: Optional[nn.Module] = None,
        drop: float = 0.0,
    ):
        super().__init__()
        embed_dim = embed_dim or in_features
        out_features = out_features or in_features
        assert embed_dim % num_heads == 0
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads
        self.feat_size = feat_size
        self.scale = self.head_dim**-0.5
        self.pool = pool_type
        self.fused_attn = use_fused_attn()

        if pos_embed == "abs":
            assert feat_size is not None
            self.pos_embed = nn.Parameter(torch.zeros(feat_size, in_features))
        else:
            self.pos_embed = None

        self.latent_dim = latent_dim or embed_dim
        self.latent_len = latent_len
        self.latent = nn.Parameter(torch.zeros(1, self.latent_len, embed_dim))

        self.q = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
        self.kv = nn.Linear(embed_dim, embed_dim * 2, bias=qkv_bias)
        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.proj = nn.Linear(embed_dim, embed_dim)
        self.proj_drop = nn.Dropout(drop)

        self.norm = (
            norm_layer(out_features) if norm_layer is not None else nn.Identity()
        )
        self.mlp = Mlp(embed_dim, int(embed_dim * mlp_ratio))

        self.init_weights()

    def init_weights(self):
        if self.pos_embed is not None:
            trunc_normal_tf_(self.pos_embed, std=self.pos_embed.shape[1] ** -0.5)
        trunc_normal_tf_(self.latent, std=self.latent_dim**-0.5)

    def forward(self, x):
        B, N, C = x.shape

        if self.pos_embed is not None:
            # FIXME interpolate
            x = x + self.pos_embed.unsqueeze(0).to(x.dtype)

        q_latent = self.latent.expand(B, -1, -1)
        q = (
            self.q(q_latent)
            .reshape(B, self.latent_len, self.num_heads, self.head_dim)
            .transpose(1, 2)
        )

        kv = (
            self.kv(x)
            .reshape(B, N, 2, self.num_heads, self.head_dim)
            .permute(2, 0, 3, 1, 4)
        )
        k, v = kv.unbind(0)

        q, k = self.q_norm(q), self.k_norm(k)

        if self.fused_attn:
            x = F.scaled_dot_product_attention(q, k, v)
        else:
            q = q * self.scale
            attn = q @ k.transpose(-2, -1)
            attn = attn.softmax(dim=-1)
            x = attn @ v
        x = x.transpose(1, 2).reshape(B, self.latent_len, C)
        x = self.proj(x)
        x = self.proj_drop(x)

        x = x + self.mlp(self.norm(x))

        # optional pool if latent seq_len > 1 and pooled output is desired
        if self.pool == "token":
            x = x[:, 0]
        elif self.pool == "avg":
            x = x.mean(1)


class Encoder(nn.Module):
    def __init__(
        self,
        in_channels=3,
        ch=128,
        ch_mult=(1, 1, 2, 2, 4),
        num_res_blocks=2,
        norm_type="group",
        dropout=0.0,
        resamp_with_conv=True,
        z_channels=256,
    ):
        super().__init__()
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.conv_in = nn.Conv2d(in_channels, ch, kernel_size=3, stride=1, padding=1)

        # downsampling
        in_ch_mult = (1,) + tuple(ch_mult)
        self.conv_blocks = nn.ModuleList()
        for i_level in range(self.num_resolutions):
            conv_block = nn.Module()
            # res & attn
            res_block = nn.ModuleList()
            attn_block = nn.ModuleList()
            block_in = ch * in_ch_mult[i_level]
            block_out = ch * ch_mult[i_level]
            for _ in range(self.num_res_blocks):
                res_block.append(
                    ResnetBlock(
                        block_in, block_out, dropout=dropout, norm_type=norm_type
                    )
                )
                block_in = block_out
                if i_level == self.num_resolutions - 1:
                    attn_block.append(AttnBlock(block_in, norm_type))
            conv_block.res = res_block
            conv_block.attn = attn_block
            # downsample
            if i_level != self.num_resolutions - 1:
                conv_block.downsample = Downsample(block_in, resamp_with_conv)
            self.conv_blocks.append(conv_block)

        # middle
        self.mid = nn.ModuleList()
        self.mid.append(
            ResnetBlock(block_in, block_in, dropout=dropout, norm_type=norm_type)
        )
        self.mid.append(AttnBlock(block_in, norm_type=norm_type))
        self.mid.append(
            ResnetBlock(block_in, block_in, dropout=dropout, norm_type=norm_type)
        )

        # end
        self.norm_out = Normalize(block_in, norm_type)
        self.conv_out = nn.Conv2d(
            block_in, z_channels, kernel_size=3, stride=1, padding=1
        )

    def forward(self, x):
        h = self.conv_in(x)
        # downsampling
        for i_level, block in enumerate(self.conv_blocks):
            for i_block in range(self.num_res_blocks):
                h = block.res[i_block](h)
                if len(block.attn) > 0:
                    h = block.attn[i_block](h)
            if i_level != self.num_resolutions - 1:
                h = block.downsample(h)

        # middle
        for mid_block in self.mid:
            h = mid_block(h)

        # end
        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h


class Decoder(nn.Module):
    def __init__(
        self,
        z_channels=256,
        ch=128,
        ch_mult=(1, 1, 2, 2, 4),
        num_res_blocks=2,
        norm_type="group",
        dropout=0.0,
        resamp_with_conv=True,
        out_channels=3,
    ):
        super().__init__()
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks

        block_in = ch * ch_mult[self.num_resolutions - 1]
        # z to block_in
        self.conv_in = nn.Conv2d(
            z_channels, block_in, kernel_size=3, stride=1, padding=1
        )

        # middle
        self.mid = nn.ModuleList()
        self.mid.append(
            ResnetBlock(block_in, block_in, dropout=dropout, norm_type=norm_type)
        )
        self.mid.append(AttnBlock(block_in, norm_type=norm_type))
        self.mid.append(
            ResnetBlock(block_in, block_in, dropout=dropout, norm_type=norm_type)
        )

        # upsampling
        self.conv_blocks = nn.ModuleList()
        for i_level in reversed(range(self.num_resolutions)):
            conv_block = nn.Module()
            # res & attn
            res_block = nn.ModuleList()
            attn_block = nn.ModuleList()
            block_out = ch * ch_mult[i_level]
            for _ in range(self.num_res_blocks + 1):
                res_block.append(
                    ResnetBlock(
                        block_in, block_out, dropout=dropout, norm_type=norm_type
                    )
                )
                block_in = block_out
                if i_level == self.num_resolutions - 1:
                    attn_block.append(AttnBlock(block_in, norm_type))
            conv_block.res = res_block
            conv_block.attn = attn_block
            # downsample
            if i_level != 0:
                conv_block.upsample = Upsample(block_in, resamp_with_conv)
            self.conv_blocks.append(conv_block)

        # end
        self.norm_out = Normalize(block_in, norm_type)
        self.conv_out = nn.Conv2d(
            block_in, out_channels, kernel_size=3, stride=1, padding=1
        )

    @property
    def last_layer(self):
        return self.conv_out.weight

    def forward(self, z):
        # z to block_in
        h = self.conv_in(z)

        # middle
        for mid_block in self.mid:
            h = mid_block(h)

        # upsampling
        for i_level, block in enumerate(self.conv_blocks):
            for i_block in range(self.num_res_blocks + 1):
                h = block.res[i_block](h)
                if len(block.attn) > 0:
                    h = block.attn[i_block](h)
            if i_level != self.num_resolutions - 1:
                h = block.upsample(h)

        # end
        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h


class VectorQuantizer(nn.Module):
    def __init__(self, n_e, e_dim, beta, entropy_loss_ratio, l2_norm, show_usage):
        super().__init__()
        self.n_e = n_e
        self.e_dim = e_dim
        self.beta = beta
        self.entropy_loss_ratio = entropy_loss_ratio
        self.l2_norm = l2_norm
        self.show_usage = show_usage

        self.embedding = nn.Embedding(self.n_e, self.e_dim)
        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
        if self.l2_norm:
            self.embedding.weight.data = F.normalize(
                self.embedding.weight.data, p=2, dim=-1
            )
        if self.show_usage:
            # self.register_buffer("codebook_used", nn.Parameter(torch.zeros(65536)))
            self.codebook_used = nn.Parameter(torch.zeros(65536))

    def forward(self, z):
        # reshape z -> (batch, height, width, channel) and flatten
        z = torch.einsum("b c h w -> b h w c", z).contiguous()
        z_flattened = z.view(-1, self.e_dim)
        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z

        if self.l2_norm:
            z = F.normalize(z, p=2, dim=-1)
            z_flattened = F.normalize(z_flattened, p=2, dim=-1)
            embedding = F.normalize(self.embedding.weight, p=2, dim=-1)
        else:
            embedding = self.embedding.weight

        d = (
            torch.sum(z_flattened**2, dim=1, keepdim=True)
            + torch.sum(embedding**2, dim=1)
            - 2
            * torch.einsum(
                "bd,dn->bn", z_flattened, torch.einsum("n d -> d n", embedding)
            )
        )

        min_encoding_indices = torch.argmin(d, dim=1)
        z_q = embedding[min_encoding_indices].view(z.shape)
        perplexity = None
        min_encodings = None
        vq_loss = None
        commit_loss = None
        entropy_loss = None

        # compute loss for embedding
        if self.training:
            vq_loss = torch.mean((z_q - z.detach()) ** 2)
            commit_loss = self.beta * torch.mean((z_q.detach() - z) ** 2)
            entropy_loss = self.entropy_loss_ratio * compute_entropy_loss(-d)

        # preserve gradients
        z_q = z + (z_q - z).detach()

        # reshape back to match original input shape
        z_q = torch.einsum("b h w c -> b c h w", z_q)

        return (
            z_q,
            (vq_loss, commit_loss, entropy_loss),
            (perplexity, min_encodings, min_encoding_indices),
        )

    def get_codebook_entry(self, indices, shape=None, channel_first=True):
        # shape = (batch, channel, height, width) if channel_first else (batch, height, width, channel)
        if self.l2_norm:
            embedding = F.normalize(self.embedding.weight, p=2, dim=-1)
        else:
            embedding = self.embedding.weight
        z_q = embedding[indices]  # (b*h*w, c)

        if shape is not None:
            if channel_first:
                z_q = z_q.reshape(shape[0], shape[2], shape[3], shape[1])
                # reshape back to match original input shape
                z_q = z_q.permute(0, 3, 1, 2).contiguous()
            else:
                z_q = z_q.view(shape)
        return z_q


class ResnetBlock(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels=None,
        conv_shortcut=False,
        dropout=0.0,
        norm_type="group",
    ):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.use_conv_shortcut = conv_shortcut

        self.norm1 = Normalize(in_channels, norm_type)
        self.conv1 = nn.Conv2d(
            in_channels, out_channels, kernel_size=3, stride=1, padding=1
        )
        self.norm2 = Normalize(out_channels, norm_type)
        self.dropout = nn.Dropout(dropout)
        self.conv2 = nn.Conv2d(
            out_channels, out_channels, kernel_size=3, stride=1, padding=1
        )

        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                self.conv_shortcut = nn.Conv2d(
                    in_channels, out_channels, kernel_size=3, stride=1, padding=1
                )
            else:
                self.nin_shortcut = nn.Conv2d(
                    in_channels, out_channels, kernel_size=1, stride=1, padding=0
                )

    def forward(self, x):
        h = x
        h = self.norm1(h)
        h = nonlinearity(h)
        h = self.conv1(h)
        h = self.norm2(h)
        h = nonlinearity(h)
        h = self.dropout(h)
        h = self.conv2(h)

        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                x = self.conv_shortcut(x)
            else:
                x = self.nin_shortcut(x)
        return x + h


class AttnBlock(nn.Module):
    def __init__(self, in_channels, norm_type="group"):
        super().__init__()
        self.norm = Normalize(in_channels, norm_type)
        self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.proj_out = nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q.shape
        q = q.reshape(b, c, h * w)
        q = q.permute(0, 2, 1)  # b,hw,c
        k = k.reshape(b, c, h * w)  # b,c,hw
        w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
        w_ = w_ * (int(c) ** (-0.5))
        w_ = F.softmax(w_, dim=2)

        # attend to values
        v = v.reshape(b, c, h * w)
        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
        h_ = h_.reshape(b, c, h, w)

        h_ = self.proj_out(h_)

        return x + h_


def nonlinearity(x):
    # swish
    return x * torch.sigmoid(x)


def Normalize(in_channels, norm_type="group"):
    assert norm_type in ["group", "batch"]
    if norm_type == "group":
        return nn.GroupNorm(
            num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
        )
    elif norm_type == "batch":
        return nn.SyncBatchNorm(in_channels)


class Upsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = nn.Conv2d(
                in_channels, in_channels, kernel_size=3, stride=1, padding=1
            )

    def forward(self, x):
        if x.dtype != torch.float32:
            x = F.interpolate(x.to(torch.float), scale_factor=2.0, mode="nearest").to(
                torch.bfloat16
            )
        else:
            x = F.interpolate(x, scale_factor=2.0, mode="nearest")

        if self.with_conv:
            x = self.conv(x)
        return x


class Downsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            # no asymmetric padding in torch conv, must do it ourselves
            self.conv = nn.Conv2d(
                in_channels, in_channels, kernel_size=3, stride=2, padding=0
            )

    def forward(self, x):
        if self.with_conv:
            pad = (0, 1, 0, 1)
            x = F.pad(x, pad, mode="constant", value=0)
            x = self.conv(x)
        else:
            x = F.avg_pool2d(x, kernel_size=2, stride=2)
        return x


def compute_entropy_loss(affinity, loss_type="softmax", temperature=0.01):
    flat_affinity = affinity.reshape(-1, affinity.shape[-1])
    flat_affinity /= temperature
    probs = F.softmax(flat_affinity, dim=-1)
    log_probs = F.log_softmax(flat_affinity + 1e-5, dim=-1)
    if loss_type == "softmax":
        target_probs = probs
    else:
        raise ValueError("Entropy loss {} not supported".format(loss_type))
    avg_probs = torch.mean(target_probs, dim=0)
    avg_entropy = -torch.sum(avg_probs * torch.log(avg_probs + 1e-5))
    sample_entropy = -torch.mean(torch.sum(target_probs * log_probs, dim=-1))
    loss = sample_entropy - avg_entropy
    return loss


class VQModel(nn.Module):
    def __init__(self, config: ModelArgs):
        super().__init__()
        self.config = config
        self.encoder = Encoder(
            ch_mult=config.encoder_ch_mult,
            z_channels=config.z_channels,
            dropout=config.dropout_p,
        )
        self.decoder = Decoder(
            ch_mult=config.decoder_ch_mult,
            z_channels=config.z_channels,
            dropout=config.dropout_p,
        )

        self.quantize = VectorQuantizer(
            config.codebook_size,
            config.codebook_embed_dim,
            config.commit_loss_beta,
            config.entropy_loss_ratio,
            config.codebook_l2_norm,
            config.codebook_show_usage,
        )
        self.quant_conv = nn.Conv2d(config.z_channels, config.codebook_embed_dim, 1)
        self.post_quant_conv = nn.Conv2d(
            config.codebook_embed_dim, config.z_channels, 1
        )

    def encode(self, x):
        h = self.encoder(x)
        h = self.quant_conv(h)
        quant, emb_loss, info = self.quantize(h)
        return quant, emb_loss, info

    def decode(self, quant):
        quant = self.post_quant_conv(quant)
        dec = self.decoder(quant)
        return dec

    def decode_code(self, code_b, shape=None, channel_first=True):
        quant_b = self.quantize.get_codebook_entry(code_b, shape, channel_first)
        dec = self.decode(quant_b)
        return dec

    def forward(self, input):
        quant, diff, _ = self.encode(input)
        dec = self.decode(quant)
        return dec, diff


class MultiModalityPreTrainedModel(PreTrainedModel):
    config_class = MultiModalityConfig
    base_model_prefix = "multi_modality"
    _no_split_modules = []
    _skip_keys_device_placement = "past_key_values"


# Copied and adapted from:
# https://github.com/deepseek-ai/Janus/tree/main/janus/models/modeling_vlm.py
class MultiModalityCausalLM(MultiModalityPreTrainedModel):

    def __init__(
        self,
        config: MultiModalityConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__(config)

        vision_config = config.vision_config
        vision_cls = model_name_to_cls(vision_config.cls)
        self.vision_model = vision_cls(**vision_config.params)

        aligner_config = config.aligner_config
        aligner_cls = model_name_to_cls(aligner_config.cls)
        self.aligner = aligner_cls(aligner_config.params)

        gen_vision_config = config.gen_vision_config
        gen_vision_cls = model_name_to_cls(gen_vision_config.cls)
        self.gen_vision_model = gen_vision_cls()

        gen_aligner_config = config.gen_aligner_config
        gen_aligner_cls = model_name_to_cls(gen_aligner_config.cls)
        self.gen_aligner = gen_aligner_cls(gen_aligner_config.params)

        gen_head_config = config.gen_head_config
        gen_head_cls = model_name_to_cls(gen_head_config.cls)
        self.gen_head = gen_head_cls(gen_head_config.params)

        self.gen_embed = torch.nn.Embedding(
            gen_vision_config.params["image_token_size"],
            gen_vision_config.params["n_embed"],
        )

        language_config = config.language_config
        self.language_model = LlamaForCausalLM(
            language_config, quant_config=quant_config
        )
        self.logits_processor = LogitsProcessor(config)

    def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
        pixel_values = torch.concat([item.pixel_values for item in items], dim=0)
        bs, n = pixel_values.shape[0:2]
        pixel_values = pixel_values.to(
            device=self.vision_model.device, dtype=self.vision_model.dtype
        )
        images = rearrange(pixel_values, "b n c h w -> (b n) c h w")

        # [b x n, T2, D]
        images_embeds = self.aligner(self.vision_model(images))

        # [b x n, T2, D] -> [b, n x T2, D]
        images_embeds = rearrange(images_embeds, "(b n) t d -> b (n t) d", b=bs, n=n)

        return images_embeds

    def get_input_embeddings(self) -> nn.Embedding:
        return self.language_model.get_input_embeddings()

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.LongTensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        get_embedding: bool = False,
    ) -> torch.Tensor:
        hidden_states = general_mm_embed_routine(
            input_ids=input_ids,
            forward_batch=forward_batch,
            multimodal_model=self,
            language_model=self.language_model,
            positions=positions,
        )

        return hidden_states

    def prepare_gen_img_embeds(self, image_ids: torch.LongTensor):
        return self.gen_aligner(self.gen_embed(image_ids))

    def pad_input_ids(self, input_ids: List[int], image_inputs: MultimodalInputs):
        im_start_id = image_inputs.im_start_id
        im_end_id = image_inputs.im_end_id
        media_token_pairs = [(im_start_id, im_end_id)]

        helper = MultiModalityDataPaddingPatternTokenPairs(media_token_pairs)

        return helper.pad_input_tokens(input_ids, image_inputs)

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq~" in name or "projector" in name:
                continue
            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
                # Models trained using ColossalAI may include these tensors in
                # the checkpoint. Skip them.
                continue
            if name.startswith("model.vision_tower") and name not in params_dict:
                continue

            # skip generation sub model
            if "gen" in name:
                continue

            # adapt to VisionAttention
            name = name.replace(r"self_attn.out_proj", r"self_attn.proj")
            if "vision_model.vision_tower" in name:
                name = name.replace("attn.qkv", "attn.qkv_proj")

            for param_name, weight_name, shard_id in stacked_params_mapping:
                # replace the name and load with customized loader
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)

                # # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", None)
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue

                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)


AutoModel.register(config_class=MultiModalityConfig, model_class=MultiModalityCausalLM)
EntryClass = [MultiModalityCausalLM]
