"""Ideogram4 transformer backbone.

Ported from the reference ``modeling_ideogram4.py`` for ai-toolkit. The
transformer consumes Qwen3-VL hidden states and flow-matching noise tokens
(packed into a single sequence) and produces velocity predictions on the image
latent tokens.
"""

from __future__ import annotations

import math
from dataclasses import dataclass

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint

try:
    from flash_attn import flash_attn_varlen_func

    _FLASH_ATTN_AVAILABLE = True
except ImportError:
    flash_attn_varlen_func = None
    _FLASH_ATTN_AVAILABLE = False

# Supported attention backends. "native" -> SDPA, "flash" -> Flash Attention 2.
ATTENTION_BACKENDS = ("native", "flash")

# Per-token role indicators used inside the packed sequence.
SEQUENCE_PADDING_INDICATOR = -1
OUTPUT_IMAGE_INDICATOR = 2
LLM_TOKEN_INDICATOR = 3

# Image grid coordinates start at this offset so they never collide with text
# token indices (text positions start at 0 and never exceed max_text_tokens).
IMAGE_POSITION_OFFSET = 65536

# Layers of Qwen3-VL whose hidden states are concatenated and fed to the transformer.
QWEN3_VL_ACTIVATION_LAYERS = (0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 35)


@dataclass
class Ideogram4Config:
    emb_dim: int = 4608
    num_layers: int = 34
    num_heads: int = 18
    intermediate_size: int = 12288
    adanln_dim: int = 512

    # Latent dimension after patchification: ae_channels (32) * patch_size**2 (4) = 128.
    in_channels: int = 128

    # Hidden size of Qwen3-VL-8B-Instruct multiplied by the number of extracted layers.
    # Qwen3-VL hidden size = 4096
    llm_features_dim: int = 4096 * len(QWEN3_VL_ACTIVATION_LAYERS)

    rope_theta: int = 5_000_000
    mrope_section: tuple[int, ...] = (24, 20, 20)

    norm_eps: float = 1e-5


def _rotate_half(x: torch.Tensor) -> torch.Tensor:
    half = x.shape[-1] // 2
    x1 = x[..., :half]
    x2 = x[..., half:]
    return torch.cat((-x2, x1), dim=-1)


def _apply_rotary_pos_emb(
    q: torch.Tensor,
    k: torch.Tensor,
    cos: torch.Tensor,
    sin: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    # q, k: (B, num_heads, L, head_dim); cos/sin: (B, L, head_dim).
    cos = cos.unsqueeze(1)
    sin = sin.unsqueeze(1)
    q_embed = (q * cos) + (_rotate_half(q) * sin)
    k_embed = (k * cos) + (_rotate_half(k) * sin)
    return q_embed, k_embed


class Ideogram4MRoPE(nn.Module):
    inv_freq: torch.Tensor

    def __init__(
        self,
        head_dim: int,
        base: int,
        mrope_section: tuple[int, ...],
    ) -> None:
        super().__init__()
        inv_freq = 1.0 / (
            base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim)
        )
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.mrope_section = tuple(mrope_section)
        self.head_dim = head_dim

    @torch.no_grad()
    def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        # position_ids: (B, L, 3) of int.
        assert position_ids.ndim == 3 and position_ids.shape[-1] == 3
        batch_size, seq_len, _ = position_ids.shape
        
        if self.inv_freq.device == torch.device("cpu"):
            # sometimes it gets stuck on CPU
            self.inv_freq = self.inv_freq.to(position_ids.device)

        # (3, B, inv_freq_size, L)
        pos = position_ids.permute(2, 0, 1).to(dtype=torch.float32)
        inv_freq = self.inv_freq.to(dtype=torch.float32)[None, None, :, None].expand(
            3, batch_size, -1, 1
        )
        freqs = inv_freq @ pos.unsqueeze(2)
        freqs = freqs.transpose(2, 3)  # (3, B, L, inv_freq_size)

        # interleaved mrope: pull H freqs into idx 1 mod 3, W freqs into idx 2 mod 3.
        freqs_t = freqs[0].clone()
        for axis, offset in ((1, 1), (2, 2)):
            length = self.mrope_section[axis] * 3
            idx = torch.arange(offset, length, 3, device=freqs_t.device)
            freqs_t[..., idx] = freqs[axis][..., idx]

        emb = torch.cat((freqs_t, freqs_t), dim=-1)
        return emb.cos(), emb.sin()


class Ideogram4RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(dim))
        self.eps = eps

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return F.rms_norm(x, self.weight.shape, self.weight, self.eps)


def _build_flash_meta(
    segment_ids: torch.Tensor,
) -> tuple[torch.Tensor, int, torch.Tensor, torch.Tensor]:
    """Derive Flash Attention 2 packing metadata from segment ids.

    Tokens attend to each other iff they share a ``(batch_row, segment_id)``
    group, exactly matching the SDPA block-diagonal mask. The groups are NOT
    contiguous in the packed layout -- e.g. ``[real_text | text_pad | image]``
    gives real-text and image the same segment id but splits them with the pad
    run -- so flash (which only attends over contiguous ``cu_seqlens`` ranges)
    can't consume the sequence as-is. We sort tokens into contiguous groups,
    build ``cu_seqlens`` over the sorted order, and return the permutation plus
    its inverse so the attention output can be scattered back to the original
    token order.

    Returns ``(cu_seqlens, max_seqlen, order, inv_order)`` where ``order`` and
    ``inv_order`` index the flattened ``(B * L,)`` token axis.
    """
    batch_size, _ = segment_ids.shape
    device = segment_ids.device

    # Unique group id per (row, segment). Shift so the -1 pad segment is >= 0.
    seg = segment_ids.to(torch.long)
    seg_shifted = seg - int(seg.min())
    num_seg = int(seg_shifted.max()) + 1
    row = torch.arange(batch_size, device=device).unsqueeze(1)
    group = (row * num_seg + seg_shifted).reshape(-1)

    order = torch.argsort(group, stable=True)
    inv_order = torch.argsort(order, stable=True)
    sorted_group = group[order]

    change = torch.ones_like(sorted_group, dtype=torch.bool)
    change[1:] = sorted_group[1:] != sorted_group[:-1]
    boundaries = torch.nonzero(change, as_tuple=False).flatten()

    total = torch.tensor([sorted_group.numel()], device=device, dtype=boundaries.dtype)
    cu_seqlens = torch.cat([boundaries, total]).to(torch.int32)
    max_seqlen = int((cu_seqlens[1:] - cu_seqlens[:-1]).max())
    return cu_seqlens, max_seqlen, order, inv_order


class Ideogram4Attention(nn.Module):
    def __init__(self, hidden_size: int, num_heads: int, eps: float = 1e-5) -> None:
        super().__init__()
        assert hidden_size % num_heads == 0
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads
        self.attention_backend = "native"

        self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=False)
        self.norm_q = Ideogram4RMSNorm(self.head_dim, eps=eps)
        self.norm_k = Ideogram4RMSNorm(self.head_dim, eps=eps)
        self.o = nn.Linear(hidden_size, hidden_size, bias=False)

    def forward(
        self,
        x: torch.Tensor,
        attn_mask: torch.Tensor,
        cos: torch.Tensor,
        sin: torch.Tensor,
        flash_meta: tuple[torch.Tensor, int, torch.Tensor, torch.Tensor] | None = None,
    ) -> torch.Tensor:
        batch_size, seq_len, _ = x.shape

        qkv = self.qkv(x)
        qkv = qkv.view(batch_size, seq_len, 3, self.num_heads, self.head_dim)
        q, k, v = qkv.unbind(dim=2)

        q = self.norm_q(q)
        k = self.norm_k(k)

        # SDPA / rope expect (B, num_heads, L, head_dim).
        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)

        q, k = _apply_rotary_pos_emb(q, k, cos, sin)

        if self.attention_backend == "flash":
            # Flash Attention 2 takes packed (total_tokens, num_heads, head_dim)
            # tensors and expresses the block-diagonal structure via cu_seqlens
            # over contiguous ranges. The attention groups aren't contiguous in
            # the packed layout, so we reorder tokens into their groups, run
            # flash, then scatter the result back to the original order.
            cu_seqlens, max_seqlen, order, inv_order = flash_meta
            qf = q.transpose(1, 2).reshape(-1, self.num_heads, self.head_dim)
            kf = k.transpose(1, 2).reshape(-1, self.num_heads, self.head_dim)
            vf = v.transpose(1, 2).reshape(-1, self.num_heads, self.head_dim)
            qf = qf.index_select(0, order)
            kf = kf.index_select(0, order)
            vf = vf.index_select(0, order)
            out = flash_attn_varlen_func(
                qf,
                kf,
                vf,
                cu_seqlens_q=cu_seqlens,
                cu_seqlens_k=cu_seqlens,
                max_seqlen_q=max_seqlen,
                max_seqlen_k=max_seqlen,
                causal=False,
            )
            out = out.index_select(0, inv_order)
            out = out.reshape(batch_size, seq_len, self.hidden_size)
        else:
            out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
            out = out.transpose(1, 2).reshape(batch_size, seq_len, self.hidden_size)
        return self.o(out)


class Ideogram4MLP(nn.Module):
    def __init__(self, dim: int, hidden_dim: int) -> None:
        super().__init__()
        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
        self.w3 = nn.Linear(dim, hidden_dim, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w2(F.silu(self.w1(x)) * self.w3(x))


class Ideogram4TransformerBlock(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        num_heads: int,
        norm_eps: float,
        adanln_dim: int,
    ) -> None:
        super().__init__()
        self.attention = Ideogram4Attention(hidden_size, num_heads, eps=1e-5)
        self.feed_forward = Ideogram4MLP(hidden_size, intermediate_size)

        self.attention_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
        self.ffn_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
        self.attention_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
        self.ffn_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)

        self.adaln_modulation = nn.Linear(adanln_dim, 4 * hidden_size, bias=True)

    def forward(
        self,
        x: torch.Tensor,
        attn_mask: torch.Tensor,
        cos: torch.Tensor,
        sin: torch.Tensor,
        adaln_input: torch.Tensor,
        flash_meta: tuple[torch.Tensor, int, torch.Tensor, torch.Tensor] | None = None,
    ) -> torch.Tensor:
        mod = self.adaln_modulation(adaln_input)
        scale_msa, gate_msa, scale_mlp, gate_mlp = mod.chunk(4, dim=-1)
        gate_msa = torch.tanh(gate_msa)
        gate_mlp = torch.tanh(gate_mlp)
        scale_msa = 1.0 + scale_msa
        scale_mlp = 1.0 + scale_mlp

        attn_out = self.attention(
            self.attention_norm1(x) * scale_msa,
            attn_mask=attn_mask,
            cos=cos,
            sin=sin,
            flash_meta=flash_meta,
        )
        x = x + gate_msa * self.attention_norm2(attn_out)
        x = x + gate_mlp * self.ffn_norm2(
            self.feed_forward(self.ffn_norm1(x) * scale_mlp)
        )
        return x


def _sinusoidal_embedding(
    t: torch.Tensor, dim: int, scale: float = 1e4
) -> torch.Tensor:
    t = t.to(torch.float32)
    half = dim // 2
    freq = math.log(scale) / (half - 1)
    freq = torch.exp(torch.arange(half, dtype=torch.float32, device=t.device) * -freq)
    emb = t.unsqueeze(-1) * freq
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
    if dim % 2 == 1:
        emb = F.pad(emb, (0, 1))
    return emb


class Ideogram4EmbedScalar(nn.Module):
    def __init__(self, dim: int, input_range: tuple[float, float]) -> None:
        super().__init__()
        self.dim = dim
        self.range_min, self.range_max = input_range
        assert self.range_max > self.range_min
        self.mlp_in = nn.Linear(dim, dim, bias=True)
        self.mlp_out = nn.Linear(dim, dim, bias=True)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # x is shape (..., 1) or (...,) holding a scalar per token.
        x = x.to(torch.float32)
        scaled = 1e4 * (x - self.range_min) / (self.range_max - self.range_min)
        emb = _sinusoidal_embedding(scaled, self.dim)
        emb = emb.to(self.mlp_in.weight.dtype)
        emb = F.silu(self.mlp_in(emb))
        return self.mlp_out(emb)


class Ideogram4FinalLayer(nn.Module):
    def __init__(self, hidden_size: int, out_channels: int, adanln_dim: int) -> None:
        super().__init__()
        self.norm_final = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False)
        self.linear = nn.Linear(hidden_size, out_channels, bias=True)
        self.adaln_modulation = nn.Linear(adanln_dim, hidden_size, bias=True)

    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
        scale = 1.0 + self.adaln_modulation(F.silu(c))
        return self.linear(self.norm_final(x) * scale)


class Ideogram4Transformer2DModel(nn.Module):
    """Ideogram 4 flow-matching transformer."""

    def __init__(self, config: Ideogram4Config) -> None:
        super().__init__()
        self.config = config
        self.gradient_checkpointing = False
        self.attention_backend = "native"

        head_dim = config.emb_dim // config.num_heads

        self.input_proj = nn.Linear(config.in_channels, config.emb_dim, bias=True)
        self.llm_cond_norm = Ideogram4RMSNorm(config.llm_features_dim, eps=1e-6)
        self.llm_cond_proj = nn.Linear(
            config.llm_features_dim, config.emb_dim, bias=True
        )
        self.t_embedding = Ideogram4EmbedScalar(config.emb_dim, input_range=(0.0, 1.0))
        self.adaln_proj = nn.Linear(config.emb_dim, config.adanln_dim, bias=True)

        self.embed_image_indicator = nn.Embedding(2, config.emb_dim)

        self.rotary_emb = Ideogram4MRoPE(
            head_dim=head_dim,
            base=config.rope_theta,
            mrope_section=config.mrope_section,
        )

        self.layers = nn.ModuleList(
            [
                Ideogram4TransformerBlock(
                    hidden_size=config.emb_dim,
                    intermediate_size=config.intermediate_size,
                    num_heads=config.num_heads,
                    norm_eps=config.norm_eps,
                    adanln_dim=config.adanln_dim,
                )
                for _ in range(config.num_layers)
            ]
        )

        self.final_layer = Ideogram4FinalLayer(
            hidden_size=config.emb_dim,
            out_channels=config.in_channels,
            adanln_dim=config.adanln_dim,
        )

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

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

    def enable_gradient_checkpointing(self):
        self.gradient_checkpointing = True

    def disable_gradient_checkpointing(self):
        self.gradient_checkpointing = False

    def set_attention_backend(self, backend: str) -> None:
        """Select the attention implementation.

        Args:
          backend: "native" for ``F.scaled_dot_product_attention`` or "flash"
            for Flash Attention 2 (``flash_attn_varlen_func``). Selecting "flash"
            requires the ``flash_attn`` package to be installed.
        """
        backend = backend.lower()
        if backend not in ATTENTION_BACKENDS:
            raise ValueError(
                f"Unknown attention backend {backend!r}. "
                f"Expected one of {ATTENTION_BACKENDS}."
            )
        if backend == "flash" and not _FLASH_ATTN_AVAILABLE:
            raise RuntimeError(
                "Flash attention 2 backend requested but the `flash_attn` package "
                "is not installed. Install it with `pip install flash-attn` or use "
                "the 'native' backend."
            )
        self.attention_backend = backend
        for layer in self.layers:
            layer.attention.attention_backend = backend

    def forward(
        self,
        *,
        llm_features: torch.Tensor,
        x: torch.Tensor,
        t: torch.Tensor,
        position_ids: torch.Tensor,
        segment_ids: torch.Tensor,
        indicator: torch.Tensor,
    ) -> torch.Tensor:
        """Velocity prediction.

        Args:
          llm_features: (B, L, llm_features_dim) Qwen3-VL conditioning features.
          x: (B, L, in_channels) noise tokens.
          t: (B,) or (B, L) flow-matching time in [0, 1].
          position_ids: (B, L, 3) (t, h, w) positions for MRoPE.
          segment_ids: (B, L) sample id within a packed batch.
          indicator: (B, L) per-token role: LLM_TOKEN_INDICATOR or OUTPUT_IMAGE_INDICATOR.

        Returns:
          (B, L, in_channels) velocity prediction in float32. Only the positions
          with ``indicator == OUTPUT_IMAGE_INDICATOR`` are meaningful.
        """
        batch_size, seq_len, in_channels = x.shape
        assert in_channels == self.config.in_channels

        param_dtype = self.input_proj.weight.dtype
        x = x.to(param_dtype)
        t = t.to(param_dtype)
        llm_features = llm_features.to(param_dtype)

        indicator = indicator.to(torch.long)
        llm_token_mask = (indicator == LLM_TOKEN_INDICATOR).to(x.dtype).unsqueeze(-1)
        output_image_mask = (
            (indicator == OUTPUT_IMAGE_INDICATOR).to(x.dtype).unsqueeze(-1)
        )

        llm_features = llm_features * llm_token_mask
        x = x * output_image_mask

        x = self.input_proj(x) * output_image_mask

        # Keep shape (B, 1, ...) when t is per-sample so downstream adaln_modulation
        # projections don't pay for L identical copies.
        t_cond = self.t_embedding(t)
        if t.dim() == 1:
            t_cond = t_cond.unsqueeze(1)
        adaln_input = F.silu(self.adaln_proj(t_cond))

        llm_features = self.llm_cond_norm(llm_features)
        llm_features = self.llm_cond_proj(llm_features) * llm_token_mask

        h = x + llm_features

        image_indicator_embedding = self.embed_image_indicator(
            (indicator == OUTPUT_IMAGE_INDICATOR).to(torch.long)
        )
        h = h + image_indicator_embedding

        cos, sin = self.rotary_emb(position_ids)
        cos = cos.to(h.dtype)
        sin = sin.to(h.dtype)

        # Block-diagonal mask from segment ids: (B, 1, L, L), True = attend.
        # Only built for the native (SDPA) backend; flash expresses the same
        # block structure through cu_seqlens instead.
        if self.attention_backend == "flash":
            attn_mask = None
            flash_meta = _build_flash_meta(segment_ids)
        else:
            attn_mask = (
                segment_ids.unsqueeze(2) == segment_ids.unsqueeze(1)
            ).unsqueeze(1)
            flash_meta = None

        for layer in self.layers:
            if self.gradient_checkpointing and torch.is_grad_enabled():
                h = checkpoint(
                    layer,
                    h,
                    attn_mask,
                    cos,
                    sin,
                    adaln_input,
                    flash_meta,
                    use_reentrant=False,
                )
            else:
                h = layer(h, attn_mask, cos, sin, adaln_input, flash_meta)

        out = self.final_layer(h, c=adaln_input)
        return out.to(torch.float32)