from typing import Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.models.attention import FeedForward

from sglang.multimodal_gen.configs.models.adapter.ltx_2_connector import (
    LTX2ConnectorConfig,
)
from sglang.multimodal_gen.runtime.layers.attention import USPAttention
from sglang.multimodal_gen.runtime.platforms import AttentionBackendEnum


def apply_interleaved_rotary_emb(
    x: torch.Tensor, freqs: Tuple[torch.Tensor, torch.Tensor]
) -> torch.Tensor:
    cos, sin = freqs
    x_real, x_imag = x.unflatten(2, (-1, 2)).unbind(-1)  # [B, S, C // 2]
    x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(2)
    out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
    return out


def apply_split_rotary_emb(
    x: torch.Tensor, freqs: Tuple[torch.Tensor, torch.Tensor]
) -> torch.Tensor:
    cos, sin = freqs

    x_dtype = x.dtype
    needs_reshape = False
    if x.ndim != 4 and cos.ndim == 4:
        # cos is (#b, h, t, r) -> reshape x to (b, h, t, dim_per_head)
        # The cos/sin batch dim may only be broadcastable, so take batch size from x
        b = x.shape[0]
        _, h, t, _ = cos.shape
        x = x.reshape(b, t, h, -1).swapaxes(1, 2)
        needs_reshape = True

    # Split last dim (2*r) into (d=2, r)
    last = x.shape[-1]
    if last % 2 != 0:
        raise ValueError(
            f"Expected x.shape[-1] to be even for split rotary, got {last}."
        )
    r = last // 2

    # (..., 2, r)
    split_x = x.reshape(*x.shape[:-1], 2, r).float()  # Explicitly upcast to float
    first_x = split_x[..., :1, :]  # (..., 1, r)
    second_x = split_x[..., 1:, :]  # (..., 1, r)

    cos_u = cos.unsqueeze(-2)  # broadcast to (..., 1, r) against (..., 2, r)
    sin_u = sin.unsqueeze(-2)

    out = split_x * cos_u
    first_out = out[..., :1, :]
    second_out = out[..., 1:, :]

    first_out.addcmul_(-sin_u, second_x)
    second_out.addcmul_(sin_u, first_x)

    out = out.reshape(*out.shape[:-2], last)

    if needs_reshape:
        out = out.swapaxes(1, 2).reshape(b, t, -1)

    out = out.to(dtype=x_dtype)
    return out


class LTX2Attention(torch.nn.Module):
    r"""
    Attention class for all LTX-2.0 attention layers. Compared to LTX-1.0, this supports specifying the query and key
    RoPE embeddings separately for audio-to-video (a2v) and video-to-audio (v2a) cross-attention.
    """

    def __init__(
        self,
        query_dim: int,
        heads: int = 8,
        kv_heads: int = 8,
        dim_head: int = 64,
        dropout: float = 0.0,
        bias: bool = True,
        cross_attention_dim: Optional[int] = None,
        out_bias: bool = True,
        qk_norm: str = "rms_norm_across_heads",
        norm_eps: float = 1e-6,
        norm_elementwise_affine: bool = True,
        rope_type: str = "interleaved",
        processor=None,
    ):
        super().__init__()
        if qk_norm != "rms_norm_across_heads":
            raise NotImplementedError(
                "Only 'rms_norm_across_heads' is supported as a valid value for `qk_norm`."
            )

        self.head_dim = dim_head
        self.inner_dim = dim_head * heads
        self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads
        self.query_dim = query_dim
        self.cross_attention_dim = (
            cross_attention_dim if cross_attention_dim is not None else query_dim
        )
        self.use_bias = bias
        self.dropout = dropout
        self.out_dim = query_dim
        self.heads = heads
        self.rope_type = rope_type

        self.norm_q = torch.nn.RMSNorm(
            dim_head * heads, eps=norm_eps, elementwise_affine=norm_elementwise_affine
        )
        self.norm_k = torch.nn.RMSNorm(
            dim_head * kv_heads,
            eps=norm_eps,
            elementwise_affine=norm_elementwise_affine,
        )
        self.to_q = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
        self.to_k = torch.nn.Linear(
            self.cross_attention_dim, self.inner_kv_dim, bias=bias
        )
        self.to_v = torch.nn.Linear(
            self.cross_attention_dim, self.inner_kv_dim, bias=bias
        )
        self.to_out = torch.nn.ModuleList([])
        self.to_out.append(torch.nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
        self.to_out.append(torch.nn.Dropout(dropout))

        # Scaled dot product attention
        self.attn = USPAttention(
            num_heads=heads,
            head_size=self.head_dim,
            dropout_rate=0,
            softmax_scale=None,
            causal=False,
            supported_attention_backends={
                AttentionBackendEnum.FA,
                AttentionBackendEnum.AITER,
                AttentionBackendEnum.TORCH_SDPA,
                AttentionBackendEnum.SAGE_ATTN,
                AttentionBackendEnum.SAGE_ATTN_3,
            },
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        query_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        key_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> torch.Tensor:
        batch_size, sequence_length, _ = (
            hidden_states.shape
            if encoder_hidden_states is None
            else encoder_hidden_states.shape
        )

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states

        query = self.to_q(hidden_states)
        key = self.to_k(encoder_hidden_states)
        value = self.to_v(encoder_hidden_states)

        query = self.norm_q(query)
        key = self.norm_k(key)

        if query_rotary_emb is not None:
            if self.rope_type == "interleaved":
                query = apply_interleaved_rotary_emb(query, query_rotary_emb)
                key = apply_interleaved_rotary_emb(
                    key,
                    key_rotary_emb if key_rotary_emb is not None else query_rotary_emb,
                )
            elif self.rope_type == "split":
                query = apply_split_rotary_emb(query, query_rotary_emb)
                key = apply_split_rotary_emb(
                    key,
                    key_rotary_emb if key_rotary_emb is not None else query_rotary_emb,
                )

        query = query.unflatten(2, (self.heads, -1))
        key = key.unflatten(2, (self.heads, -1))
        value = value.unflatten(2, (self.heads, -1))

        hidden_states = self.attn(
            query,
            key,
            value,
        )
        hidden_states = hidden_states.flatten(2, 3)
        hidden_states = hidden_states.to(query.dtype)

        hidden_states = self.to_out[0](hidden_states)
        hidden_states = self.to_out[1](hidden_states)
        return hidden_states


class LTX2RotaryPosEmbed1d(nn.Module):
    """
    1D rotary positional embeddings (RoPE) for the LTX 2.0 text encoder connectors.
    """

    def __init__(
        self,
        dim: int,
        base_seq_len: int = 4096,
        theta: float = 10000.0,
        double_precision: bool = True,
        rope_type: str = "interleaved",
        num_attention_heads: int = 32,
    ):
        super().__init__()
        if rope_type not in ["interleaved", "split"]:
            raise ValueError(
                f"{rope_type=} not supported. Choose between 'interleaved' and 'split'."
            )

        self.dim = dim
        self.base_seq_len = base_seq_len
        self.theta = theta
        self.double_precision = double_precision
        self.rope_type = rope_type
        self.num_attention_heads = num_attention_heads

    def forward(
        self,
        batch_size: int,
        pos: int,
        device: Union[str, torch.device],
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # 1. Get 1D position ids
        grid_1d = torch.arange(pos, dtype=torch.float32, device=device)
        # Get fractional indices relative to self.base_seq_len
        grid_1d = grid_1d / self.base_seq_len
        grid = grid_1d.unsqueeze(0).repeat(batch_size, 1)  # [batch_size, seq_len]

        # 2. Calculate 1D RoPE frequencies
        num_rope_elems = 2  # 1 (because 1D) * 2 (for cos, sin) = 2
        freqs_dtype = torch.float64 if self.double_precision else torch.float32
        pow_indices = torch.pow(
            self.theta,
            torch.linspace(
                start=0.0,
                end=1.0,
                steps=self.dim // num_rope_elems,
                dtype=freqs_dtype,
                device=device,
            ),
        )
        freqs = (pow_indices * torch.pi / 2.0).to(dtype=torch.float32)

        # 3. Matrix-vector outer product between pos ids of shape (batch_size, seq_len) and freqs vector of shape
        # (self.dim // 2,).
        freqs = (grid.unsqueeze(-1) * 2 - 1) * freqs  # [B, seq_len, self.dim // 2]

        # 4. Get real, interleaved (cos, sin) frequencies, padded to self.dim
        if self.rope_type == "interleaved":
            cos_freqs = freqs.cos().repeat_interleave(2, dim=-1)
            sin_freqs = freqs.sin().repeat_interleave(2, dim=-1)

            if self.dim % num_rope_elems != 0:
                cos_padding = torch.ones_like(
                    cos_freqs[:, :, : self.dim % num_rope_elems]
                )
                sin_padding = torch.zeros_like(
                    sin_freqs[:, :, : self.dim % num_rope_elems]
                )
                cos_freqs = torch.cat([cos_padding, cos_freqs], dim=-1)
                sin_freqs = torch.cat([sin_padding, sin_freqs], dim=-1)

        elif self.rope_type == "split":
            expected_freqs = self.dim // 2
            current_freqs = freqs.shape[-1]
            pad_size = expected_freqs - current_freqs
            cos_freq = freqs.cos()
            sin_freq = freqs.sin()

            if pad_size != 0:
                cos_padding = torch.ones_like(cos_freq[:, :, :pad_size])
                sin_padding = torch.zeros_like(sin_freq[:, :, :pad_size])

                cos_freq = torch.concatenate([cos_padding, cos_freq], axis=-1)
                sin_freq = torch.concatenate([sin_padding, sin_freq], axis=-1)

            # Reshape freqs to be compatible with multi-head attention
            b = cos_freq.shape[0]
            t = cos_freq.shape[1]

            cos_freq = cos_freq.reshape(b, t, self.num_attention_heads, -1)
            sin_freq = sin_freq.reshape(b, t, self.num_attention_heads, -1)

            cos_freqs = torch.swapaxes(cos_freq, 1, 2)  # (B,H,T,D//2)
            sin_freqs = torch.swapaxes(sin_freq, 1, 2)  # (B,H,T,D//2)

        return cos_freqs, sin_freqs


class LTX2TransformerBlock1d(nn.Module):
    def __init__(
        self,
        dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
        activation_fn: str = "gelu-approximate",
        eps: float = 1e-6,
        rope_type: str = "interleaved",
    ):
        super().__init__()

        self.norm1 = torch.nn.RMSNorm(dim, eps=eps, elementwise_affine=False)
        self.attn1 = LTX2Attention(
            query_dim=dim,
            heads=num_attention_heads,
            kv_heads=num_attention_heads,
            dim_head=attention_head_dim,
            rope_type=rope_type,
        )

        self.norm2 = torch.nn.RMSNorm(dim, eps=eps, elementwise_affine=False)
        self.ff = FeedForward(dim, activation_fn=activation_fn)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        norm_hidden_states = self.norm1(hidden_states)
        attn_hidden_states = self.attn1(
            norm_hidden_states,
            attention_mask=attention_mask,
            query_rotary_emb=rotary_emb,
        )
        hidden_states = hidden_states + attn_hidden_states

        norm_hidden_states = self.norm2(hidden_states)
        ff_hidden_states = self.ff(norm_hidden_states)
        hidden_states = hidden_states + ff_hidden_states

        return hidden_states


class LTX2ConnectorTransformer1d(nn.Module):
    """
    A 1D sequence transformer for modalities such as text.
    In LTX 2.0, this is used to process the text encoder hidden states for each of the video and audio streams.
    """

    _supports_gradient_checkpointing = True

    def __init__(
        self,
        num_attention_heads: int = 30,
        attention_head_dim: int = 128,
        num_layers: int = 2,
        num_learnable_registers: int | None = 128,
        rope_base_seq_len: int = 4096,
        rope_theta: float = 10000.0,
        rope_double_precision: bool = True,
        eps: float = 1e-6,
        causal_temporal_positioning: bool = False,
        rope_type: str = "interleaved",
    ):
        super().__init__()
        self.num_attention_heads = num_attention_heads
        self.inner_dim = num_attention_heads * attention_head_dim
        self.causal_temporal_positioning = causal_temporal_positioning

        self.num_learnable_registers = num_learnable_registers
        self.learnable_registers = None
        if num_learnable_registers is not None:
            init_registers = (
                torch.rand(num_learnable_registers, self.inner_dim) * 2.0 - 1.0
            )
            self.learnable_registers = torch.nn.Parameter(init_registers)

        self.rope = LTX2RotaryPosEmbed1d(
            self.inner_dim,
            base_seq_len=rope_base_seq_len,
            theta=rope_theta,
            double_precision=rope_double_precision,
            rope_type=rope_type,
            num_attention_heads=num_attention_heads,
        )

        self.transformer_blocks = torch.nn.ModuleList(
            [
                LTX2TransformerBlock1d(
                    dim=self.inner_dim,
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=attention_head_dim,
                    rope_type=rope_type,
                )
                for _ in range(num_layers)
            ]
        )

        self.norm_out = torch.nn.RMSNorm(
            self.inner_dim, eps=eps, elementwise_affine=False
        )

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        attn_mask_binarize_threshold: float = -9000.0,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # hidden_states shape: [batch_size, seq_len, hidden_dim]
        # attention_mask shape: [batch_size, seq_len] or [batch_size, 1, 1, seq_len]
        batch_size, seq_len, _ = hidden_states.shape

        # 1. Replace padding with learned registers, if using
        if self.learnable_registers is not None:
            if seq_len % self.num_learnable_registers != 0:
                raise ValueError(
                    f"The `hidden_states` sequence length {hidden_states.shape[1]} should be divisible by the number"
                    f" of learnable registers {self.num_learnable_registers}"
                )

            num_register_repeats = seq_len // self.num_learnable_registers
            registers = torch.tile(
                self.learnable_registers, (num_register_repeats, 1)
            )  # [seq_len, inner_dim]

            binary_attn_mask = (attention_mask >= attn_mask_binarize_threshold).int()
            if binary_attn_mask.ndim == 4:
                binary_attn_mask = binary_attn_mask.squeeze(1).squeeze(
                    1
                )  # [B, 1, 1, L] --> [B, L]

            hidden_states_non_padded = [
                hidden_states[i, binary_attn_mask[i].bool(), :]
                for i in range(batch_size)
            ]
            valid_seq_lens = [x.shape[0] for x in hidden_states_non_padded]
            pad_lengths = [seq_len - valid_seq_len for valid_seq_len in valid_seq_lens]
            padded_hidden_states = [
                F.pad(x, pad=(0, 0, 0, p), value=0)
                for x, p in zip(hidden_states_non_padded, pad_lengths)
            ]
            padded_hidden_states = torch.cat(
                [x.unsqueeze(0) for x in padded_hidden_states], dim=0
            )  # [B, L, D]

            flipped_mask = torch.flip(binary_attn_mask, dims=[1]).unsqueeze(
                -1
            )  # [B, L, 1]
            hidden_states = (
                flipped_mask * padded_hidden_states + (1 - flipped_mask) * registers
            )

            # Overwrite attention_mask with an all-zeros mask if using registers.
            attention_mask = torch.zeros_like(attention_mask)

        # 2. Calculate 1D RoPE positional embeddings
        rotary_emb = self.rope(batch_size, seq_len, device=hidden_states.device)

        # 3. Run 1D transformer blocks
        for block in self.transformer_blocks:
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states = self._gradient_checkpointing_func(
                    block, hidden_states, attention_mask, rotary_emb
                )
            else:
                hidden_states = block(
                    hidden_states, attention_mask=attention_mask, rotary_emb=rotary_emb
                )

        hidden_states = self.norm_out(hidden_states)

        return hidden_states, attention_mask


class LTX2TextConnectors(nn.Module):
    """
    Text connector stack used by LTX 2.0 to process the packed text encoder hidden states for both the video and audio
    streams.
    """

    def __init__(
        self,
        config: LTX2ConnectorConfig,
    ):
        super().__init__()
        caption_channels = config.caption_channels
        text_proj_in_factor = config.text_proj_in_factor
        video_connector_num_attention_heads = config.video_connector_num_attention_heads
        video_connector_attention_head_dim = config.video_connector_attention_head_dim
        video_connector_num_layers = config.video_connector_num_layers
        video_connector_num_learnable_registers = (
            config.video_connector_num_learnable_registers
        )
        audio_connector_num_attention_heads = config.audio_connector_num_attention_heads
        audio_connector_attention_head_dim = config.audio_connector_attention_head_dim
        audio_connector_num_layers = config.audio_connector_num_layers
        audio_connector_num_learnable_registers = (
            config.audio_connector_num_learnable_registers
        )
        connector_rope_base_seq_len = config.connector_rope_base_seq_len
        rope_theta = config.rope_theta
        rope_double_precision = config.rope_double_precision
        causal_temporal_positioning = config.causal_temporal_positioning
        rope_type = config.rope_type

        self.text_proj_in = nn.Linear(
            caption_channels * text_proj_in_factor, caption_channels, bias=False
        )
        self.video_connector = LTX2ConnectorTransformer1d(
            num_attention_heads=video_connector_num_attention_heads,
            attention_head_dim=video_connector_attention_head_dim,
            num_layers=video_connector_num_layers,
            num_learnable_registers=video_connector_num_learnable_registers,
            rope_base_seq_len=connector_rope_base_seq_len,
            rope_theta=rope_theta,
            rope_double_precision=rope_double_precision,
            causal_temporal_positioning=causal_temporal_positioning,
            rope_type=rope_type,
        )
        self.audio_connector = LTX2ConnectorTransformer1d(
            num_attention_heads=audio_connector_num_attention_heads,
            attention_head_dim=audio_connector_attention_head_dim,
            num_layers=audio_connector_num_layers,
            num_learnable_registers=audio_connector_num_learnable_registers,
            rope_base_seq_len=connector_rope_base_seq_len,
            rope_theta=rope_theta,
            rope_double_precision=rope_double_precision,
            causal_temporal_positioning=causal_temporal_positioning,
            rope_type=rope_type,
        )

    def forward(
        self,
        text_encoder_hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        additive_mask: bool = False,
    ):
        # Convert to additive attention mask, if necessary
        if not additive_mask:
            text_dtype = text_encoder_hidden_states.dtype
            attention_mask = (attention_mask - 1).reshape(
                attention_mask.shape[0], 1, -1, attention_mask.shape[-1]
            )
            attention_mask = attention_mask.to(text_dtype) * torch.finfo(text_dtype).max

        # Ensure input dtype matches the layer's weight dtype
        if text_encoder_hidden_states.dtype != self.text_proj_in.weight.dtype:
            text_encoder_hidden_states = text_encoder_hidden_states.to(
                self.text_proj_in.weight.dtype
            )

        # Ensure sequence length is divisible by num_learnable_registers (128)
        seq_len = text_encoder_hidden_states.shape[1]
        num_learnable_registers = self.video_connector.num_learnable_registers
        if (
            num_learnable_registers is not None
            and seq_len % num_learnable_registers != 0
        ):
            pad_len = num_learnable_registers - (seq_len % num_learnable_registers)
            text_encoder_hidden_states = F.pad(
                text_encoder_hidden_states, (0, 0, 0, pad_len), value=0.0
            )

            if attention_mask.shape[-1] == seq_len:
                # Pad with a large negative value to mask out the new tokens
                attention_mask = F.pad(attention_mask, (0, pad_len), value=-1000000.0)

        text_encoder_hidden_states = self.text_proj_in(text_encoder_hidden_states)

        video_text_embedding, new_attn_mask = self.video_connector(
            text_encoder_hidden_states, attention_mask
        )

        attn_mask = (new_attn_mask < 1e-6).to(torch.int64)
        attn_mask = attn_mask.reshape(
            video_text_embedding.shape[0], video_text_embedding.shape[1], 1
        )
        video_text_embedding = video_text_embedding * attn_mask
        new_attn_mask = attn_mask.squeeze(-1)

        audio_text_embedding, _ = self.audio_connector(
            text_encoder_hidden_states, attention_mask
        )

        return video_text_embedding, audio_text_embedding, new_attn_mask


EntryClass = LTX2TextConnectors