"""
Lightning DiT — for flux_sr.
Based on VOSR/lightningdit.py, with timm dependency (matching VOSR).

Original credits:
  Built from DiT & SiT (https://github.com/facebookresearch/DiT; https://github.com/willisma/SiT)
  by Maple (Jingfeng Yao) from HUST-VL
"""

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from timm.models.vision_transformer import PatchEmbed, Mlp

from .swiglu_ffn import SwiGLUFFN
from .pos_embed import VisionRotaryEmbeddingFast
from .rmsnorm import RMSNorm


# ------------------------------------------------------------------
# MultiHeadCrossAttention
# ------------------------------------------------------------------
class MultiHeadCrossAttention(nn.Module):
    def __init__(self, d_model, num_heads, attn_drop=0.0, proj_drop=0.0, qk_norm=False, fused_attn: bool = True, **block_kwargs):
        super().__init__()
        assert d_model % num_heads == 0, "d_model must be divisible by num_heads"

        self.d_model = d_model
        self.num_heads = num_heads
        self.head_dim = d_model // num_heads
        self.scale = self.head_dim ** -0.5
        self.fused_attn = fused_attn

        self.q_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(d_model, d_model)
        self.proj_drop = nn.Dropout(proj_drop)

        if qk_norm:
            self.q_norm = RMSNorm(self.head_dim)
            self.k_norm = RMSNorm(self.head_dim)
        else:
            self.q_norm = nn.Identity()
            self.k_norm = nn.Identity()

    def forward(self, x, cond, mask=None):
        B, N, C = x.shape
        B_cond, N_cond, _ = cond.shape

        q = self.q_linear(x)
        k = self.k_linear(cond)
        v = self.v_linear(cond)

        q = q.view(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
        k = k.view(B_cond, N_cond, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
        v = v.view(B_cond, N_cond, self.num_heads, self.head_dim).permute(0, 2, 1, 3)

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

        if self.fused_attn:
            x = F.scaled_dot_product_attention(
                q, k, v,
                attn_mask=None,
                dropout_p=self.attn_drop.p if self.training else 0.0
            )
        else:
            q = q * self.scale
            attn = q @ k.transpose(-2, -1)
            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            x = attn @ v

        x = x.permute(0, 2, 1, 3).contiguous().view(B, N, C)

        x = self.proj(x)
        x = self.proj_drop(x)

        return x


# ------------------------------------------------------------------
# AdaLN modulation helpers
# ------------------------------------------------------------------
def modulate_adasin(x, shift, scale):
    if shift is None:
        return x * (1 + scale.unsqueeze(1))
    return x * (1 + scale) + shift


def modulate(x, shift, scale):
    if shift is None:
        return x * (1 + scale.unsqueeze(1))
    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)


# ------------------------------------------------------------------
# Attention
# ------------------------------------------------------------------
class Attention(nn.Module):
    """Attention module of LightningDiT."""

    def __init__(
        self,
        dim: int,
        num_heads: int = 8,
        qkv_bias: bool = False,
        qk_norm: bool = False,
        attn_drop: float = 0.,
        proj_drop: float = 0.,
        norm_layer: nn.Module = nn.LayerNorm,
        fused_attn: bool = True,
        use_rmsnorm: bool = False,
    ) -> None:
        super().__init__()
        assert dim % num_heads == 0, 'dim should be divisible by num_heads'

        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim ** -0.5
        self.fused_attn = fused_attn

        if use_rmsnorm:
            norm_layer = RMSNorm

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

    def forward(self, x: torch.Tensor, rope=None) -> torch.Tensor:
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)
        q, k = self.q_norm(q), self.k_norm(k)

        if rope is not None:
            q = rope(q)
            k = rope(k)

        if self.fused_attn:
            x = F.scaled_dot_product_attention(
                q, k, v,
                dropout_p=self.attn_drop.p if self.training else 0.,
            )
        else:
            q = q * self.scale
            attn = q @ k.transpose(-2, -1)
            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            x = attn @ v

        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


# ------------------------------------------------------------------
# TimestepEmbedder
# ------------------------------------------------------------------
class TimestepEmbedder(nn.Module):
    """Embeds scalar timesteps into vector representations."""

    def __init__(self, hidden_size: int, frequency_embedding_size: int = 256) -> None:
        super().__init__()
        self.frequency_embedding_size = frequency_embedding_size
        self.mlp = nn.Sequential(
            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True),
        )

    @staticmethod
    def timestep_embedding(t: torch.Tensor, dim: int, max_period: int = 10000) -> torch.Tensor:
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(device=t.device)

        args = t[:, None].float() * freqs[None]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)

        if dim % 2:
            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)

        return embedding

    def forward(self, t: torch.Tensor) -> torch.Tensor:
        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
        t_emb = self.mlp(t_freq)
        return t_emb


# ------------------------------------------------------------------
# LightningDiTBlock
# ------------------------------------------------------------------
class LightningDiTBlock(nn.Module):
    """Lightning DiT Block with RoPE, QKNorm, RMSNorm, SwiGLU, AdaLN."""

    def __init__(
        self,
        hidden_size,
        num_heads,
        mlp_ratio=4.0,
        use_qknorm=False,
        use_swiglu=False,
        use_rmsnorm=False,
        wo_shift=False,
        z_dims=None,
        num_fused_layers=1,
        encdim_ratio=2,
        **block_kwargs
    ):
        super().__init__()
        if not use_rmsnorm:
            self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
            self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        else:
            self.norm1 = RMSNorm(hidden_size)
            self.norm2 = RMSNorm(hidden_size)

        self.attn = Attention(
            hidden_size,
            num_heads=num_heads,
            qkv_bias=True,
            qk_norm=use_qknorm,
            use_rmsnorm=use_rmsnorm,
            **block_kwargs
        )

        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        if use_swiglu:
            self.mlp = SwiGLUFFN(hidden_size, int(2 / 3 * mlp_hidden_dim))
        else:
            self.mlp = Mlp(
                in_features=hidden_size,
                hidden_features=mlp_hidden_dim,
                act_layer=lambda: nn.GELU(approximate="tanh"),
                drop=0
            )

        self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)

        self.z_dims = z_dims
        if self.z_dims is not None:
            self.cross_attn = MultiHeadCrossAttention(d_model=hidden_size, num_heads=num_heads, qk_norm=use_qknorm)

    def forward(self, x, c, z=None, feat_rope=None):
        B, N, C = x.shape

        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
            self.scale_shift_table[None] + c.reshape(B, 6, -1)
        ).chunk(6, dim=1)

        x = x + gate_msa * self.attn(modulate_adasin(self.norm1(x), shift_msa, scale_msa), rope=feat_rope)
        if self.z_dims is not None:
            x = x + self.cross_attn(x, z)
        x = x + gate_mlp * self.mlp(modulate_adasin(self.norm2(x), shift_mlp, scale_mlp))

        return x


# ------------------------------------------------------------------
# FinalLayer
# ------------------------------------------------------------------
class FinalLayer(nn.Module):
    """The final layer of LightningDiT."""

    def __init__(self, hidden_size, patch_size, out_channels, use_rmsnorm=False):
        super().__init__()
        if not use_rmsnorm:
            self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        else:
            self.norm_final = RMSNorm(hidden_size)
        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
        )

    def forward(self, x, c):
        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
        x = modulate(self.norm_final(x), shift, scale)
        x = self.linear(x)
        return x


# ------------------------------------------------------------------
# LightningDiT (main model)
# ------------------------------------------------------------------
class LightningDiT(nn.Module):
    """Diffusion model with a Transformer backbone."""

    def __init__(
        self,
        input_size=32,
        patch_size=2,
        in_channels=32,
        out_channels=32,
        hidden_size=1152,
        depth=28,
        num_heads=16,
        mlp_ratio=4.0,
        learn_sigma=False,
        use_qknorm=False,
        use_swiglu=False,
        use_rope=False,
        use_rmsnorm=False,
        wo_shift=False,
        use_checkpoint=False,
        z_dims=None,
        encdim_ratio=2,
        num_fused_layers=1,
        auxiliary_time_cond=False,
    ):
        super().__init__()
        self.learn_sigma = learn_sigma
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.patch_size = patch_size
        self.num_heads = num_heads
        self.use_rope = use_rope
        self.use_rmsnorm = use_rmsnorm
        self.depth = depth
        self.hidden_size = hidden_size
        self.use_checkpoint = use_checkpoint
        self.x_embedder = PatchEmbed(input_size, patch_size, self.in_channels, hidden_size, bias=True)
        self.t_embedder = TimestepEmbedder(hidden_size)

        if self.use_rope:
            half_head_dim = hidden_size // num_heads // 2
            hw_seq_len = input_size // patch_size
            self.feat_rope = VisionRotaryEmbeddingFast(
                dim=half_head_dim,
                pt_seq_len=hw_seq_len,
            )
        else:
            self.feat_rope = None

        self.t_block = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
        )

        self.blocks = nn.ModuleList([
            LightningDiTBlock(hidden_size,
                              num_heads,
                              mlp_ratio=mlp_ratio,
                              use_qknorm=use_qknorm,
                              use_swiglu=use_swiglu,
                              use_rmsnorm=use_rmsnorm,
                              wo_shift=wo_shift,
                              z_dims=z_dims,
                              num_fused_layers=num_fused_layers,
                              encdim_ratio=encdim_ratio,
                              ) for _ in range(depth)
        ])
        self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels, use_rmsnorm=use_rmsnorm)

        self.z_dims = z_dims
        if self.z_dims is not None:
            self.num_fused_layers = num_fused_layers
            approx_gelu = lambda: nn.GELU(approximate="tanh")
            self.layer_norm = nn.LayerNorm(self.z_dims)
            self.mlp_ca = Mlp(
                in_features=self.z_dims,
                hidden_features=hidden_size * encdim_ratio,
                out_features=hidden_size,
                act_layer=approx_gelu,
                drop=0
            )

        self.auxiliary_time_cond = auxiliary_time_cond
        if auxiliary_time_cond:
            self.r_embedder = TimestepEmbedder(hidden_size)
        else:
            self.r_embedder = None

        self.initialize_weights()

    def initialize_weights(self):
        def _basic_init(module):
            if isinstance(module, nn.Linear):
                torch.nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)
        self.apply(_basic_init)

        w = self.x_embedder.proj.weight.data
        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
        nn.init.constant_(self.x_embedder.proj.bias, 0)

        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
        if self.auxiliary_time_cond:
            nn.init.normal_(self.r_embedder.mlp[0].weight, std=0.02)
            nn.init.normal_(self.r_embedder.mlp[2].weight, std=0.02)

        nn.init.normal_(self.t_block[1].weight, std=0.02)

        # Zero-out output layers:
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
        nn.init.constant_(self.final_layer.linear.weight, 0)
        nn.init.constant_(self.final_layer.linear.bias, 0)

    def unpatchify(self, x):
        """x: (N, T, patch_size**2 * C) → imgs: (N, C, H, W)"""
        c = self.out_channels
        p = self.x_embedder.patch_size[0]
        h = w = int(x.shape[1] ** 0.5)
        assert h * w == x.shape[1]

        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
        x = torch.einsum('nhwpqc->nchpwq', x)
        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
        return imgs

    def enable_fused_attn(self):
        for block in self.blocks:
            block.attn.fused_attn = True
            if block.z_dims is not None:
                block.cross_attn.fused_attn = True

    def disable_fused_attn(self):
        for block in self.blocks:
            block.attn.fused_attn = False
            if block.z_dims is not None:
                block.cross_attn.fused_attn = False

    def forward(self, x, t, r=None, z=None):
        """Forward pass of LightningDiT.
        x: (N, C, H, W) tensor of spatial inputs
        t: (N,) tensor of diffusion timesteps
        r: (N,) tensor of auxiliary timesteps (optional)
        z: DinoV2 features (optional, skipped when z_dims=None)
        """
        use_checkpoint = self.use_checkpoint
        x = self.x_embedder(x)
        t_raw = t
        t = self.t_embedder(t)
        r_val = self.r_embedder(r) * (t_raw - r).unsqueeze(-1) if self.r_embedder is not None else 0
        c = t + r_val

        c0 = self.t_block(c)

        if self.z_dims is not None:
            z = z[0]
            z = self.layer_norm(z)
            z = self.mlp_ca(z)

        for block in self.blocks:
            if use_checkpoint:
                x = checkpoint(block, x, c0, z, self.feat_rope, use_reentrant=True)
            else:
                x = block(x, c0, z, self.feat_rope)

        x = self.final_layer(x, c)
        x = self.unpatchify(x)

        return x

    def _get_dynamic_rope(self, hw_seq_len, device, dtype):
        """Dynamically generate RoPE for variable-size inputs."""
        if not self.use_rope:
            return None

        half_head_dim = self.hidden_size // self.num_heads // 2
        pt_seq_len = self.x_embedder.img_size[0] // self.patch_size

        from einops import repeat
        theta = 10000
        freqs = 1. / (theta ** (torch.arange(0, half_head_dim, 2, device=device)[:(half_head_dim // 2)].float() / half_head_dim))

        t = torch.arange(hw_seq_len, device=device).float() / hw_seq_len * pt_seq_len

        freqs = torch.einsum('..., f -> ... f', t, freqs)
        freqs = repeat(freqs, '... n -> ... (n r)', r=2)

        from .pos_embed import broadcat
        freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim=-1)

        freqs_cos = freqs.cos().view(-1, freqs.shape[-1]).to(dtype)
        freqs_sin = freqs.sin().view(-1, freqs.shape[-1]).to(dtype)

        def dynamic_rope_fn(t_input):
            from .pos_embed import rotate_half
            return t_input * freqs_cos + rotate_half(t_input) * freqs_sin

        return dynamic_rope_fn

    def forward_flexible(self, x, t, r=None, z=None):
        """Forward pass that supports variable input sizes."""
        use_checkpoint = self.use_checkpoint

        N, C, H, W = x.shape
        assert H == W, "forward_flexible currently only supports square inputs"

        current_hw_seq_len = H // self.patch_size

        x = self.x_embedder(x)
        t_raw = t
        t = self.t_embedder(t)
        r_val = self.r_embedder(r) * (t_raw - r).unsqueeze(-1) if self.r_embedder is not None else 0
        c = t + r_val

        c0 = self.t_block(c)

        if self.z_dims is not None and z is not None:
            z = z[0]
            z = self.layer_norm(z)
            z = self.mlp_ca(z)

        if self.use_rope:
            train_hw_seq_len = self.x_embedder.img_size[0] // self.patch_size
            if current_hw_seq_len != train_hw_seq_len:
                feat_rope = self._get_dynamic_rope(current_hw_seq_len, x.device, x.dtype)
            else:
                feat_rope = self.feat_rope
        else:
            feat_rope = None

        for block in self.blocks:
            if use_checkpoint:
                x = checkpoint(block, x, c0, z, feat_rope, use_reentrant=True)
            else:
                x = block(x, c0, z, feat_rope)

        x = self.final_layer(x, c)
        x = self.unpatchify(x)

        return x