# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo

# SPDX-License-Identifier: Apache-2.0

from typing import Any

import numpy as np
import torch
import torch.nn as nn

from sglang.multimodal_gen.configs.models.dits import HunyuanVideoConfig
from sglang.multimodal_gen.configs.sample.teacache import TeaCacheParams
from sglang.multimodal_gen.runtime.distributed.parallel_state import get_sp_world_size
from sglang.multimodal_gen.runtime.layers.attention import (
    LocalAttention,
    UlyssesAttention,
)
from sglang.multimodal_gen.runtime.layers.elementwise import MulAdd
from sglang.multimodal_gen.runtime.layers.layernorm import (
    LayerNormScaleShift,
    RMSNorm,
    ScaleResidualLayerNormScaleShift,
)
from sglang.multimodal_gen.runtime.layers.linear import ReplicatedLinear
from sglang.multimodal_gen.runtime.layers.mlp import MLP
from sglang.multimodal_gen.runtime.layers.quantization.configs.base_config import (
    QuantizationConfig,
)
from sglang.multimodal_gen.runtime.layers.rotary_embedding import (
    _apply_rotary_emb,
    get_rotary_pos_embed,
)
from sglang.multimodal_gen.runtime.layers.visual_embedding import (
    ModulateProjection,
    PatchEmbed,
    TimestepEmbedder,
    unpatchify,
)
from sglang.multimodal_gen.runtime.managers.forward_context import get_forward_context
from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
from sglang.multimodal_gen.runtime.models.utils import modulate
from sglang.multimodal_gen.runtime.platforms import (
    AttentionBackendEnum,
    current_platform,
)
from sglang.multimodal_gen.runtime.utils.layerwise_offload import OffloadableDiTMixin


class MMDoubleStreamBlock(nn.Module):
    """
    A multimodal DiT block with separate modulation for text and image/video,
    using distributed attention and linear layers.
    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
        mlp_ratio: float,
        dtype: torch.dtype | None = None,
        supported_attention_backends: set[AttentionBackendEnum] | None = None,
        prefix: str = "",
        quant_config: QuantizationConfig | None = None,
    ):
        super().__init__()

        self.deterministic = False
        self.num_attention_heads = num_attention_heads
        head_dim = hidden_size // num_attention_heads
        mlp_hidden_dim = int(hidden_size * mlp_ratio)

        # Image modulation components
        self.img_mod = ModulateProjection(
            hidden_size,
            factor=6,
            act_layer="silu",
            dtype=dtype,
            prefix=f"{prefix}.img_mod",
        )

        # Fused operations for image stream
        self.img_attn_norm = LayerNormScaleShift(
            hidden_size, elementwise_affine=False, dtype=dtype
        )
        self.img_attn_residual_mlp_norm = ScaleResidualLayerNormScaleShift(
            hidden_size, elementwise_affine=False, dtype=dtype
        )
        self.img_mlp_residual = MulAdd()

        # Image attention components
        self.img_attn_qkv = ReplicatedLinear(
            hidden_size,
            hidden_size * 3,
            bias=True,
            params_dtype=dtype,
            prefix=f"{prefix}.img_attn_qkv",
            quant_config=quant_config,
        )

        self.img_attn_q_norm = RMSNorm(head_dim, eps=1e-6, dtype=dtype)
        self.img_attn_k_norm = RMSNorm(head_dim, eps=1e-6, dtype=dtype)

        self.img_attn_proj = ReplicatedLinear(
            hidden_size,
            hidden_size,
            bias=True,
            params_dtype=dtype,
            prefix=f"{prefix}.img_attn_proj",
            quant_config=quant_config,
        )

        self.img_mlp = MLP(
            hidden_size,
            mlp_hidden_dim,
            bias=True,
            dtype=dtype,
            prefix=f"{prefix}.img_mlp",
            quant_config=quant_config,
        )

        # Text modulation components
        self.txt_mod = ModulateProjection(
            hidden_size,
            factor=6,
            act_layer="silu",
            dtype=dtype,
            prefix=f"{prefix}.txt_mod",
        )

        # Fused operations for text stream
        self.txt_attn_norm = LayerNormScaleShift(
            hidden_size, elementwise_affine=False, dtype=dtype
        )
        self.txt_attn_residual_mlp_norm = ScaleResidualLayerNormScaleShift(
            hidden_size, elementwise_affine=False, dtype=dtype
        )
        self.txt_mlp_residual = MulAdd()

        # Text attention components
        self.txt_attn_qkv = ReplicatedLinear(
            hidden_size,
            hidden_size * 3,
            bias=True,
            params_dtype=dtype,
            quant_config=quant_config,
        )

        # QK norm layers for text
        self.txt_attn_q_norm = RMSNorm(head_dim, eps=1e-6, dtype=dtype)
        self.txt_attn_k_norm = RMSNorm(head_dim, eps=1e-6, dtype=dtype)

        self.txt_attn_proj = ReplicatedLinear(
            hidden_size,
            hidden_size,
            bias=True,
            params_dtype=dtype,
            quant_config=quant_config,
        )

        self.txt_mlp = MLP(
            hidden_size,
            mlp_hidden_dim,
            bias=True,
            dtype=dtype,
            quant_config=quant_config,
        )

        # Use UlyssesAttention to replace Distributed attention
        self.attn = UlyssesAttention(
            num_heads=num_attention_heads,
            head_size=head_dim,
            causal=False,
            supported_attention_backends=supported_attention_backends,
            prefix=f"{prefix}.attn",
        )

    def forward(
        self,
        img: torch.Tensor,
        txt: torch.Tensor,
        vec: torch.Tensor,
        freqs_cis: tuple,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # Process modulation vectors
        img_mod_outputs = self.img_mod(vec)
        (
            img_attn_shift,
            img_attn_scale,
            img_attn_gate,
            img_mlp_shift,
            img_mlp_scale,
            img_mlp_gate,
        ) = torch.chunk(img_mod_outputs, 6, dim=-1)

        txt_mod_outputs = self.txt_mod(vec)
        (
            txt_attn_shift,
            txt_attn_scale,
            txt_attn_gate,
            txt_mlp_shift,
            txt_mlp_scale,
            txt_mlp_gate,
        ) = torch.chunk(txt_mod_outputs, 6, dim=-1)

        # Prepare image for attention using fused operation
        img_attn_input = self.img_attn_norm(img, img_attn_shift, img_attn_scale)
        # Get QKV for image
        img_qkv, _ = self.img_attn_qkv(img_attn_input)
        batch_size, image_seq_len = img_qkv.shape[0], img_qkv.shape[1]

        # Split QKV
        img_qkv = img_qkv.view(
            batch_size, image_seq_len, 3, self.num_attention_heads, -1
        )
        img_q, img_k, img_v = img_qkv[:, :, 0], img_qkv[:, :, 1], img_qkv[:, :, 2]

        # Apply QK-Norm if needed

        img_q = self.img_attn_q_norm(img_q.contiguous()).to(img_v)
        img_k = self.img_attn_k_norm(img_k.contiguous()).to(img_v)
        # Apply rotary embeddings
        cos, sin = freqs_cis
        img_q, img_k = _apply_rotary_emb(
            img_q, cos, sin, is_neox_style=False
        ), _apply_rotary_emb(img_k, cos, sin, is_neox_style=False)
        # Prepare text for attention using fused operation
        txt_attn_input = self.txt_attn_norm(txt, txt_attn_shift, txt_attn_scale)

        # Get QKV for text
        txt_qkv, _ = self.txt_attn_qkv(txt_attn_input)
        batch_size, text_seq_len = txt_qkv.shape[0], txt_qkv.shape[1]

        # Split QKV
        txt_qkv = txt_qkv.view(
            batch_size, text_seq_len, 3, self.num_attention_heads, -1
        )
        txt_q, txt_k, txt_v = txt_qkv[:, :, 0], txt_qkv[:, :, 1], txt_qkv[:, :, 2]

        # Apply QK-Norm if needed
        txt_q = self.txt_attn_q_norm(txt_q.contiguous()).to(txt_q.dtype)
        txt_k = self.txt_attn_k_norm(txt_k.contiguous()).to(txt_k.dtype)

        # Run distributed attention
        img_attn, txt_attn = self.attn(img_q, img_k, img_v, txt_q, txt_k, txt_v)
        img_attn_out, _ = self.img_attn_proj(
            img_attn.view(batch_size, image_seq_len, -1)
        )
        # Use fused operation for residual connection, normalization, and modulation
        img_mlp_input, img_residual = self.img_attn_residual_mlp_norm(
            img, img_attn_out, img_attn_gate, img_mlp_shift, img_mlp_scale
        )

        # Process image MLP
        img_mlp_out = self.img_mlp(img_mlp_input)
        img = self.img_mlp_residual(img_mlp_out, img_mlp_gate, img_residual)

        # Process text attention output
        txt_attn_out, _ = self.txt_attn_proj(
            txt_attn.reshape(batch_size, text_seq_len, -1)
        )

        # Use fused operation for residual connection, normalization, and modulation
        txt_mlp_input, txt_residual = self.txt_attn_residual_mlp_norm(
            txt, txt_attn_out, txt_attn_gate, txt_mlp_shift, txt_mlp_scale
        )

        # Process text MLP
        txt_mlp_out = self.txt_mlp(txt_mlp_input)
        txt = self.txt_mlp_residual(txt_mlp_out, txt_mlp_gate, txt_residual)

        return img, txt


class MMSingleStreamBlock(nn.Module):
    """
    A DiT block with parallel linear layers using distributed attention
    and tensor parallelism.
    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
        mlp_ratio: float = 4.0,
        dtype: torch.dtype | None = None,
        supported_attention_backends: set[AttentionBackendEnum] | None = None,
        prefix: str = "",
        quant_config: QuantizationConfig | None = None,
    ):
        super().__init__()

        self.deterministic = False
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        head_dim = hidden_size // num_attention_heads
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        self.mlp_hidden_dim = mlp_hidden_dim

        # Combined QKV and MLP input projection
        self.linear1 = ReplicatedLinear(
            hidden_size,
            hidden_size * 3 + mlp_hidden_dim,
            bias=True,
            params_dtype=dtype,
            prefix=f"{prefix}.linear1",
            quant_config=quant_config,
        )

        # Combined projection and MLP output
        self.linear2 = ReplicatedLinear(
            hidden_size + mlp_hidden_dim,
            hidden_size,
            bias=True,
            params_dtype=dtype,
            prefix=f"{prefix}.linear2",
            quant_config=quant_config,
        )

        # QK norm layers
        self.q_norm = RMSNorm(head_dim, eps=1e-6, dtype=dtype)
        self.k_norm = RMSNorm(head_dim, eps=1e-6, dtype=dtype)

        # Fused operations with better naming
        self.input_norm_scale_shift = LayerNormScaleShift(
            hidden_size,
            eps=1e-6,
            elementwise_affine=False,
            dtype=dtype,
        )
        self.output_residual = MulAdd()

        # Activation function
        self.mlp_act = nn.GELU(approximate="tanh")

        # Modulation
        self.modulation = ModulateProjection(
            hidden_size,
            factor=3,
            act_layer="silu",
            dtype=dtype,
            prefix=f"{prefix}.modulation",
        )

        # Use UlyssesAttention to replace Distributed attention
        self.attn = UlyssesAttention(
            num_heads=num_attention_heads,
            head_size=head_dim,
            causal=False,
            supported_attention_backends=supported_attention_backends,
            prefix=f"{prefix}.attn",
        )

    def forward(
        self,
        x: torch.Tensor,
        vec: torch.Tensor,
        txt_len: int,
        freqs_cis: tuple[torch.Tensor, torch.Tensor],
    ) -> torch.Tensor:
        # Process modulation
        mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1)

        # Apply pre-norm and modulation using fused operation
        x_mod = self.input_norm_scale_shift(x, mod_shift, mod_scale)

        # Get combined projections
        linear1_out, _ = self.linear1(x_mod)

        # Split into QKV and MLP parts
        qkv, mlp = torch.split(
            linear1_out, [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1
        )

        # Process QKV
        batch_size, seq_len = qkv.shape[0], qkv.shape[1]
        qkv = qkv.view(batch_size, seq_len, 3, self.num_attention_heads, -1)
        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]

        # Apply QK-Norm
        q = self.q_norm(q.contiguous()).to(v.dtype)
        k = self.k_norm(k.contiguous()).to(v.dtype)

        # Split into image and text parts
        img_q, txt_q = q[:, :-txt_len], q[:, -txt_len:]
        img_k, txt_k = k[:, :-txt_len], k[:, -txt_len:]
        img_v, txt_v = v[:, :-txt_len], v[:, -txt_len:]
        # Apply rotary embeddings to image parts
        cos, sin = freqs_cis
        img_q, img_k = _apply_rotary_emb(
            img_q, cos, sin, is_neox_style=False
        ), _apply_rotary_emb(img_k, cos, sin, is_neox_style=False)

        # Run distributed attention
        img_attn_output, txt_attn_output = self.attn(
            img_q, img_k, img_v, txt_q, txt_k, txt_v
        )
        attn_output = torch.cat((img_attn_output, txt_attn_output), dim=1).view(
            batch_size, seq_len, -1
        )
        # Process MLP activation
        mlp_output = self.mlp_act(mlp)

        # Combine attention and MLP outputs
        combined = torch.cat((attn_output, mlp_output), dim=-1)

        # Final projection
        output, _ = self.linear2(combined)

        # Apply residual connection with gating using fused operation
        return self.output_residual(output, mod_gate, x)


class HunyuanVideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
    """
    HunyuanVideo Transformer backbone adapted for distributed training.

    This implementation uses distributed attention and linear layers for efficient
    parallel processing across multiple GPUs.

    Based on the architecture from:
    - Flux.1: https://github.com/black-forest-labs/flux
    - MMDiT: http://arxiv.org/abs/2403.03206
    """

    # PY: we make the input args the same as HF config

    # shard single stream, double stream blocks, and refiner_blocks
    _fsdp_shard_conditions = HunyuanVideoConfig()._fsdp_shard_conditions
    _compile_conditions = HunyuanVideoConfig()._compile_conditions
    _supported_attention_backends = HunyuanVideoConfig()._supported_attention_backends
    param_names_mapping = HunyuanVideoConfig().param_names_mapping
    reverse_param_names_mapping = HunyuanVideoConfig().reverse_param_names_mapping
    lora_param_names_mapping = HunyuanVideoConfig().lora_param_names_mapping

    def __init__(
        self,
        config: HunyuanVideoConfig,
        hf_config: dict[str, Any],
        quant_config: QuantizationConfig | None = None,
    ):
        super().__init__(config=config, hf_config=hf_config)

        self.patch_size = [config.patch_size_t, config.patch_size, config.patch_size]
        self.in_channels = config.in_channels
        self.num_channels_latents = config.num_channels_latents
        self.out_channels = (
            config.in_channels if config.out_channels is None else config.out_channels
        )
        self.unpatchify_channels = self.out_channels
        self.guidance_embeds = config.guidance_embeds
        self.rope_dim_list = list(config.rope_axes_dim)
        self.rope_theta = config.rope_theta
        self.text_states_dim = config.text_embed_dim
        self.text_states_dim_2 = config.pooled_projection_dim
        # TODO(will): hack?
        self.dtype = config.dtype

        pe_dim = config.hidden_size // config.num_attention_heads
        if sum(config.rope_axes_dim) != pe_dim:
            raise ValueError(
                f"Got {config.rope_axes_dim} but expected positional dim {pe_dim}"
            )

        self.hidden_size = config.hidden_size
        self.num_attention_heads = config.num_attention_heads
        self.num_channels_latents = config.num_channels_latents

        # Image projection
        self.img_in = PatchEmbed(
            self.patch_size,
            self.in_channels,
            self.hidden_size,
            dtype=config.dtype,
            prefix=f"{config.prefix}.img_in",
        )

        self.txt_in = SingleTokenRefiner(
            self.text_states_dim,
            config.hidden_size,
            config.num_attention_heads,
            depth=config.num_refiner_layers,
            dtype=config.dtype,
            prefix=f"{config.prefix}.txt_in",
        )

        # Time modulation
        self.time_in = TimestepEmbedder(
            self.hidden_size,
            act_layer="silu",
            dtype=config.dtype,
            prefix=f"{config.prefix}.time_in",
        )

        # Text modulation
        self.vector_in = MLP(
            self.text_states_dim_2,
            self.hidden_size,
            self.hidden_size,
            act_type="silu",
            dtype=config.dtype,
            prefix=f"{config.prefix}.vector_in",
        )

        # Guidance modulation
        self.guidance_in = (
            TimestepEmbedder(
                self.hidden_size,
                act_layer="silu",
                dtype=config.dtype,
                prefix=f"{config.prefix}.guidance_in",
            )
            if self.guidance_embeds
            else None
        )

        # Double blocks
        self.double_blocks = nn.ModuleList(
            [
                MMDoubleStreamBlock(
                    config.hidden_size,
                    config.num_attention_heads,
                    mlp_ratio=config.mlp_ratio,
                    dtype=config.dtype,
                    supported_attention_backends=self._supported_attention_backends,
                    prefix=f"{config.prefix}.double_blocks.{i}",
                    quant_config=quant_config,
                )
                for i in range(config.num_layers)
            ]
        )

        # Single blocks
        self.single_blocks = nn.ModuleList(
            [
                MMSingleStreamBlock(
                    config.hidden_size,
                    config.num_attention_heads,
                    mlp_ratio=config.mlp_ratio,
                    dtype=config.dtype,
                    supported_attention_backends=self._supported_attention_backends,
                    prefix=f"{config.prefix}.single_blocks.{i + config.num_layers}",
                    quant_config=quant_config,
                )
                for i in range(config.num_single_layers)
            ]
        )

        self.final_layer = FinalLayer(
            config.hidden_size,
            self.patch_size,
            self.out_channels,
            dtype=config.dtype,
            prefix=f"{config.prefix}.final_layer",
        )

        self.__post_init__()

        self.layer_names = ["double_blocks", "single_blocks"]

    # TODO: change the input the FORWARD_BATCH Dict
    # TODO: change output to a dict
    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
        timestep: torch.LongTensor,
        encoder_hidden_states_image: torch.Tensor | list[torch.Tensor] | None = None,
        guidance=None,
        **kwargs,
    ):
        """
        Forward pass of the HunyuanDiT model.

        Args:
            hidden_states: Input image/video latents [B, C, T, H, W]
            encoder_hidden_states: Text embeddings [B, L, D]
            timestep: Diffusion timestep
            guidance: Guidance scale for CFG

        Returns:
            Tuple of (output)
        """
        forward_context = get_forward_context()
        forward_batch = forward_context.forward_batch
        enable_teacache = forward_batch is not None and forward_batch.enable_teacache

        if guidance is None:
            guidance = torch.tensor(
                [6016.0], device=hidden_states.device, dtype=hidden_states.dtype
            )

        img = x = hidden_states
        t = timestep

        # Split text embeddings - first token is global, rest are per-token
        if isinstance(encoder_hidden_states, torch.Tensor):
            txt = encoder_hidden_states[:, 1:]
            text_states_2 = encoder_hidden_states[:, 0, : self.text_states_dim_2]
        else:
            txt = encoder_hidden_states[0]
            text_states_2 = encoder_hidden_states[1]

        # Get spatial dimensions
        _, _, ot, oh, ow = x.shape  # codespell:ignore
        tt, th, tw = (
            ot // self.patch_size[0],  # codespell:ignore
            oh // self.patch_size[1],
            ow // self.patch_size[2],
        )

        # Get rotary embeddings
        freqs_cos, freqs_sin = get_rotary_pos_embed(
            (tt * get_sp_world_size(), th, tw),
            self.hidden_size,
            self.num_attention_heads,
            self.rope_dim_list,
            self.rope_theta,
        )
        freqs_cos = freqs_cos.to(x.device)
        freqs_sin = freqs_sin.to(x.device)
        # Prepare modulation vectors
        vec = self.time_in(t)

        # Add text modulation
        vec = vec + self.vector_in(text_states_2)

        # Add guidance modulation if needed
        if self.guidance_in and guidance is not None:
            vec = vec + self.guidance_in(guidance)
        # Embed image and text
        img = self.img_in(img)
        txt = self.txt_in(txt, t)
        txt_seq_len = txt.shape[1]
        img_seq_len = img.shape[1]

        freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None

        should_skip_forward = self.should_skip_forward_for_cached_states(
            img=img, vec=vec
        )

        if should_skip_forward:
            img = self.retrieve_cached_states(img)
        else:
            if enable_teacache:
                original_img = img.clone()

            # Process through double stream blocks
            for index, block in enumerate(self.double_blocks):
                double_block_args = [img, txt, vec, freqs_cis]
                img, txt = block(*double_block_args)
            # Merge txt and img to pass through single stream blocks
            x = torch.cat((img, txt), 1)

            # Process through single stream blocks
            if len(self.single_blocks) > 0:
                for index, block in enumerate(self.single_blocks):
                    single_block_args = [
                        x,
                        vec,
                        txt_seq_len,
                        freqs_cis,
                    ]
                    x = block(*single_block_args)

            # Extract image features
            img = x[:, :img_seq_len, ...]

            if enable_teacache:
                self.maybe_cache_states(img, original_img)

        # Final layer processing
        img = self.final_layer(img, vec)
        # Unpatchify to get original shape
        img = unpatchify(img, tt, th, tw, self.patch_size, self.out_channels)

        return img

    def maybe_cache_states(
        self, hidden_states: torch.Tensor, original_hidden_states: torch.Tensor
    ) -> None:
        self.previous_residual = hidden_states - original_hidden_states

    def should_skip_forward_for_cached_states(self, **kwargs) -> bool:

        forward_context = get_forward_context()
        forward_batch = forward_context.forward_batch
        if forward_batch is None:
            return False
        current_timestep = forward_context.current_timestep
        enable_teacache = forward_batch.enable_teacache

        if not enable_teacache:
            return False
        raise NotImplementedError("teacache is not supported yet for HunyuanVideo")

        teacache_params = forward_batch.teacache_params
        assert teacache_params is not None, "teacache_params is not initialized"
        assert isinstance(
            teacache_params, TeaCacheParams
        ), "teacache_params is not a TeaCacheParams"
        num_inference_steps = forward_batch.num_inference_steps
        teache_thresh = teacache_params.teacache_thresh

        coefficients = teacache_params.coefficients

        if current_timestep == 0:
            self.cnt = 0

        inp = kwargs["img"].clone()
        vec_ = kwargs["vec"].clone()
        # convert to DTensor
        vec_ = torch.distributed.tensor.DTensor.from_local(
            vec_,
            torch.distributed.DeviceMesh(
                current_platform.device_type,
                list(range(get_sp_world_size())),
                mesh_dim_names=("dp",),
            ),
            [torch.distributed.tensor.Replicate()],
        )

        inp = torch.distributed.tensor.DTensor.from_local(
            inp,
            torch.distributed.DeviceMesh(
                current_platform.device_type,
                list(range(get_sp_world_size())),
                mesh_dim_names=("dp",),
            ),
            [torch.distributed.tensor.Replicate()],
        )

        # txt_ = kwargs["txt"].clone()

        # inp = img.clone()
        # vec_ = vec.clone()
        # txt_ = txt.clone()
        (
            img_mod1_shift,
            img_mod1_scale,
            img_mod1_gate,
            img_mod2_shift,
            img_mod2_scale,
            img_mod2_gate,
        ) = (
            self.double_blocks[0].img_mod(vec_).chunk(6, dim=-1)
        )
        normed_inp = self.double_blocks[0].img_attn_norm.norm(inp)
        modulated_inp = modulate(normed_inp, shift=img_mod1_shift, scale=img_mod1_scale)
        if self.cnt == 0 or self.cnt == num_inference_steps - 1:
            should_calc = True
            self.accumulated_rel_l1_distance = 0
        else:
            coefficients = [
                7.33226126e02,
                -4.01131952e02,
                6.75869174e01,
                -3.14987800e00,
                9.61237896e-02,
            ]
            rescale_func = np.poly1d(coefficients)
            assert (
                self.previous_modulated_input is not None
            ), "previous_modulated_input is not initialized"
            self.accumulated_rel_l1_distance += rescale_func(
                (
                    (modulated_inp - self.previous_modulated_input).abs().mean()
                    / self.previous_modulated_input.abs().mean()
                )
                .cpu()
                .item()
            )
            if self.accumulated_rel_l1_distance < teache_thresh:
                should_calc = False
            else:
                should_calc = True
                self.accumulated_rel_l1_distance = 0
        self.previous_modulated_input = modulated_inp
        self.cnt += 1

        return not should_calc

    def retrieve_cached_states(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return hidden_states + self.previous_residual


class SingleTokenRefiner(nn.Module):
    """
    A token refiner that processes text embeddings with attention to improve
    their representation for cross-attention with image features.
    """

    def __init__(
        self,
        in_channels,
        hidden_size,
        num_attention_heads,
        depth=2,
        qkv_bias=True,
        dtype=None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        # Input projection
        self.input_embedder = ReplicatedLinear(
            in_channels,
            hidden_size,
            bias=True,
            params_dtype=dtype,
            prefix=f"{prefix}.input_embedder",
        )

        # Timestep embedding
        self.t_embedder = TimestepEmbedder(
            hidden_size, act_layer="silu", dtype=dtype, prefix=f"{prefix}.t_embedder"
        )

        # Context embedding
        self.c_embedder = MLP(
            in_channels,
            hidden_size,
            hidden_size,
            act_type="silu",
            dtype=dtype,
            prefix=f"{prefix}.c_embedder",
        )

        # Refiner blocks
        self.refiner_blocks = nn.ModuleList(
            [
                IndividualTokenRefinerBlock(
                    hidden_size,
                    num_attention_heads,
                    qkv_bias=qkv_bias,
                    dtype=dtype,
                    prefix=f"{prefix}.refiner_blocks.{i}",
                )
                for i in range(depth)
            ]
        )

    def forward(self, x, t):
        # Get timestep embeddings
        timestep_aware_representations = self.t_embedder(t)

        # Get context-aware representations

        context_aware_representations = torch.mean(x, dim=1)

        context_aware_representations = self.c_embedder(context_aware_representations)
        c = timestep_aware_representations + context_aware_representations
        # Project input
        x, _ = self.input_embedder(x)
        # Process through refiner blocks
        for block in self.refiner_blocks:
            x = block(x, c)
        return x


class IndividualTokenRefinerBlock(nn.Module):
    """
    A transformer block for refining individual tokens with self-attention.
    """

    def __init__(
        self,
        hidden_size,
        num_attention_heads,
        mlp_ratio=4.0,
        qkv_bias=True,
        dtype=None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.num_attention_heads = num_attention_heads
        mlp_hidden_dim = int(hidden_size * mlp_ratio)

        # Normalization and attention
        self.norm1 = nn.LayerNorm(
            hidden_size, eps=1e-6, elementwise_affine=True, dtype=dtype
        )

        self.self_attn_qkv = ReplicatedLinear(
            hidden_size,
            hidden_size * 3,
            bias=qkv_bias,
            params_dtype=dtype,
            prefix=f"{prefix}.self_attn_qkv",
        )

        self.self_attn_proj = ReplicatedLinear(
            hidden_size,
            hidden_size,
            bias=qkv_bias,
            params_dtype=dtype,
            prefix=f"{prefix}.self_attn_proj",
        )

        # MLP
        self.norm2 = nn.LayerNorm(
            hidden_size, eps=1e-6, elementwise_affine=True, dtype=dtype
        )
        self.mlp = MLP(
            hidden_size,
            mlp_hidden_dim,
            bias=True,
            act_type="silu",
            dtype=dtype,
            prefix=f"{prefix}.mlp",
        )

        # Modulation
        self.adaLN_modulation = ModulateProjection(
            hidden_size,
            factor=2,
            act_layer="silu",
            dtype=dtype,
            prefix=f"{prefix}.adaLN_modulation",
        )

        # Scaled dot product attention
        self.attn = LocalAttention(
            num_heads=num_attention_heads,
            head_size=hidden_size // num_attention_heads,
            # TODO: remove hardcode; remove STA
            supported_attention_backends=(
                AttentionBackendEnum.FA,
                AttentionBackendEnum.AITER,
                AttentionBackendEnum.TORCH_SDPA,
            ),
        )

    def forward(self, x, c):
        # Get modulation parameters
        gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=-1)
        # Self-attention
        norm_x = self.norm1(x)
        qkv, _ = self.self_attn_qkv(norm_x)

        batch_size, seq_len = qkv.shape[0], qkv.shape[1]
        qkv = qkv.view(batch_size, seq_len, 3, self.num_attention_heads, -1)
        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]

        # Run scaled dot product attention
        attn_output = self.attn(q, k, v)  # [B, L, H, D]
        attn_output = attn_output.reshape(batch_size, seq_len, -1)  # [B, L, H*D]

        # Project and apply residual connection with gating
        attn_out, _ = self.self_attn_proj(attn_output)
        x = x + attn_out * gate_msa.unsqueeze(1)

        # MLP
        mlp_out = self.mlp(self.norm2(x))
        x = x + mlp_out * gate_mlp.unsqueeze(1)

        return x


class FinalLayer(nn.Module):
    """
    The final layer of DiT that projects features to pixel space.
    """

    def __init__(
        self, hidden_size, patch_size, out_channels, dtype=None, prefix: str = ""
    ) -> None:
        super().__init__()

        # Normalization
        self.norm_final = nn.LayerNorm(
            hidden_size, eps=1e-6, elementwise_affine=False, dtype=dtype
        )

        output_dim = patch_size[0] * patch_size[1] * patch_size[2] * out_channels

        self.linear = ReplicatedLinear(
            hidden_size,
            output_dim,
            bias=True,
            params_dtype=dtype,
            prefix=f"{prefix}.linear",
        )

        # Modulation
        self.adaLN_modulation = ModulateProjection(
            hidden_size,
            factor=2,
            act_layer="silu",
            dtype=dtype,
            prefix=f"{prefix}.adaLN_modulation",
        )

    def forward(self, x, c):
        # What the heck HF? Why you change the scale and shift order here???
        scale, shift = self.adaLN_modulation(c).chunk(2, dim=-1)
        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
        x, _ = self.linear(x)
        return x


EntryClass = HunyuanVideoTransformer3DModel
