# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.
"""

from typing import Optional, Tuple

import torch
from einops import rearrange
from torch import nn
from torchvision import transforms

from enum import Enum
import logging

from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm

from .blocks import (
    FinalLayer,
    GeneralDITTransformerBlock,
    PatchEmbed,
    TimestepEmbedding,
    Timesteps,
)

from .position_embedding import LearnablePosEmbAxis, VideoRopePosition3DEmb


class DataType(Enum):
    IMAGE = "image"
    VIDEO = "video"


class GeneralDIT(nn.Module):
    """
    A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.

    Args:
        max_img_h (int): Maximum height of the input images.
        max_img_w (int): Maximum width of the input images.
        max_frames (int): Maximum number of frames in the video sequence.
        in_channels (int): Number of input channels (e.g., RGB channels for color images).
        out_channels (int): Number of output channels.
        patch_spatial (tuple): Spatial resolution of patches for input processing.
        patch_temporal (int): Temporal resolution of patches for input processing.
        concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding.
        block_config (str): Configuration of the transformer block. See Notes for supported block types.
        model_channels (int): Base number of channels used throughout the model.
        num_blocks (int): Number of transformer blocks.
        num_heads (int): Number of heads in the multi-head attention layers.
        mlp_ratio (float): Expansion ratio for MLP blocks.
        block_x_format (str): Format of input tensor for transformer blocks ('BTHWD' or 'THWBD').
        crossattn_emb_channels (int): Number of embedding channels for cross-attention.
        use_cross_attn_mask (bool): Whether to use mask in cross-attention.
        pos_emb_cls (str): Type of positional embeddings.
        pos_emb_learnable (bool): Whether positional embeddings are learnable.
        pos_emb_interpolation (str): Method for interpolating positional embeddings.
        affline_emb_norm (bool): Whether to normalize affine embeddings.
        use_adaln_lora (bool): Whether to use AdaLN-LoRA.
        adaln_lora_dim (int): Dimension for AdaLN-LoRA.
        rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE.
        rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE.
        rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE.
        extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings.
        extra_per_block_abs_pos_emb_type (str): Type of extra per-block positional embeddings.
        extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings.
        extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings.
        extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings.

    Notes:
        Supported block types in block_config:
        * cross_attn, ca: Cross attention
        * full_attn: Full attention on all flattened tokens
        * mlp, ff: Feed forward block
    """

    def __init__(
        self,
        max_img_h: int,
        max_img_w: int,
        max_frames: int,
        in_channels: int,
        out_channels: int,
        patch_spatial: tuple,
        patch_temporal: int,
        concat_padding_mask: bool = True,
        # attention settings
        block_config: str = "FA-CA-MLP",
        model_channels: int = 768,
        num_blocks: int = 10,
        num_heads: int = 16,
        mlp_ratio: float = 4.0,
        block_x_format: str = "BTHWD",
        # cross attention settings
        crossattn_emb_channels: int = 1024,
        use_cross_attn_mask: bool = False,
        # positional embedding settings
        pos_emb_cls: str = "sincos",
        pos_emb_learnable: bool = False,
        pos_emb_interpolation: str = "crop",
        affline_emb_norm: bool = False,  # whether or not to normalize the affine embedding
        use_adaln_lora: bool = False,
        adaln_lora_dim: int = 256,
        rope_h_extrapolation_ratio: float = 1.0,
        rope_w_extrapolation_ratio: float = 1.0,
        rope_t_extrapolation_ratio: float = 1.0,
        extra_per_block_abs_pos_emb: bool = False,
        extra_per_block_abs_pos_emb_type: str = "sincos",
        extra_h_extrapolation_ratio: float = 1.0,
        extra_w_extrapolation_ratio: float = 1.0,
        extra_t_extrapolation_ratio: float = 1.0,
        image_model=None,
        device=None,
        dtype=None,
        operations=None,
    ) -> None:
        super().__init__()
        self.max_img_h = max_img_h
        self.max_img_w = max_img_w
        self.max_frames = max_frames
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.patch_spatial = patch_spatial
        self.patch_temporal = patch_temporal
        self.num_heads = num_heads
        self.num_blocks = num_blocks
        self.model_channels = model_channels
        self.use_cross_attn_mask = use_cross_attn_mask
        self.concat_padding_mask = concat_padding_mask
        # positional embedding settings
        self.pos_emb_cls = pos_emb_cls
        self.pos_emb_learnable = pos_emb_learnable
        self.pos_emb_interpolation = pos_emb_interpolation
        self.affline_emb_norm = affline_emb_norm
        self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio
        self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio
        self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio
        self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb
        self.extra_per_block_abs_pos_emb_type = extra_per_block_abs_pos_emb_type.lower()
        self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio
        self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio
        self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio
        self.dtype = dtype
        weight_args = {"device": device, "dtype": dtype}

        in_channels = in_channels + 1 if concat_padding_mask else in_channels
        self.x_embedder = PatchEmbed(
            spatial_patch_size=patch_spatial,
            temporal_patch_size=patch_temporal,
            in_channels=in_channels,
            out_channels=model_channels,
            bias=False,
            weight_args=weight_args,
            operations=operations,
        )

        self.build_pos_embed(device=device)
        self.block_x_format = block_x_format
        self.use_adaln_lora = use_adaln_lora
        self.adaln_lora_dim = adaln_lora_dim
        self.t_embedder = nn.ModuleList(
            [Timesteps(model_channels),
             TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, weight_args=weight_args, operations=operations),]
        )

        self.blocks = nn.ModuleDict()

        for idx in range(num_blocks):
            self.blocks[f"block{idx}"] = GeneralDITTransformerBlock(
                x_dim=model_channels,
                context_dim=crossattn_emb_channels,
                num_heads=num_heads,
                block_config=block_config,
                mlp_ratio=mlp_ratio,
                x_format=self.block_x_format,
                use_adaln_lora=use_adaln_lora,
                adaln_lora_dim=adaln_lora_dim,
                weight_args=weight_args,
                operations=operations,
            )

        if self.affline_emb_norm:
            logging.debug("Building affine embedding normalization layer")
            self.affline_norm = RMSNorm(model_channels, elementwise_affine=True, eps=1e-6)
        else:
            self.affline_norm = nn.Identity()

        self.final_layer = FinalLayer(
            hidden_size=self.model_channels,
            spatial_patch_size=self.patch_spatial,
            temporal_patch_size=self.patch_temporal,
            out_channels=self.out_channels,
            use_adaln_lora=self.use_adaln_lora,
            adaln_lora_dim=self.adaln_lora_dim,
            weight_args=weight_args,
            operations=operations,
        )

    def build_pos_embed(self, device=None):
        if self.pos_emb_cls == "rope3d":
            cls_type = VideoRopePosition3DEmb
        else:
            raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}")

        logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}")
        kwargs = dict(
            model_channels=self.model_channels,
            len_h=self.max_img_h // self.patch_spatial,
            len_w=self.max_img_w // self.patch_spatial,
            len_t=self.max_frames // self.patch_temporal,
            is_learnable=self.pos_emb_learnable,
            interpolation=self.pos_emb_interpolation,
            head_dim=self.model_channels // self.num_heads,
            h_extrapolation_ratio=self.rope_h_extrapolation_ratio,
            w_extrapolation_ratio=self.rope_w_extrapolation_ratio,
            t_extrapolation_ratio=self.rope_t_extrapolation_ratio,
            device=device,
        )
        self.pos_embedder = cls_type(
            **kwargs,
        )

        if self.extra_per_block_abs_pos_emb:
            assert self.extra_per_block_abs_pos_emb_type in [
                "learnable",
            ], f"Unknown extra_per_block_abs_pos_emb_type {self.extra_per_block_abs_pos_emb_type}"
            kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio
            kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio
            kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio
            kwargs["device"] = device
            self.extra_pos_embedder = LearnablePosEmbAxis(
                **kwargs,
            )

    def prepare_embedded_sequence(
        self,
        x_B_C_T_H_W: torch.Tensor,
        fps: Optional[torch.Tensor] = None,
        padding_mask: Optional[torch.Tensor] = None,
        latent_condition: Optional[torch.Tensor] = None,
        latent_condition_sigma: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        """
        Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks.

        Args:
            x_B_C_T_H_W (torch.Tensor): video
            fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required.
                                    If None, a default value (`self.base_fps`) will be used.
            padding_mask (Optional[torch.Tensor]): current it is not used

        Returns:
            Tuple[torch.Tensor, Optional[torch.Tensor]]:
                - A tensor of shape (B, T, H, W, D) with the embedded sequence.
                - An optional positional embedding tensor, returned only if the positional embedding class
                (`self.pos_emb_cls`) includes 'rope'. Otherwise, None.

        Notes:
            - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor.
            - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`.
            - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using
                the `self.pos_embedder` with the shape [T, H, W].
            - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the
            `self.pos_embedder` with the fps tensor.
            - Otherwise, the positional embeddings are generated without considering fps.
        """
        if self.concat_padding_mask:
            if padding_mask is not None:
                padding_mask = transforms.functional.resize(
                    padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST
                )
            else:
                padding_mask = torch.zeros((x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[-2], x_B_C_T_H_W.shape[-1]), dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device)

            x_B_C_T_H_W = torch.cat(
                [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1
            )
        x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)

        if self.extra_per_block_abs_pos_emb:
            extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device)
        else:
            extra_pos_emb = None

        if "rope" in self.pos_emb_cls.lower():
            return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb

        if "fps_aware" in self.pos_emb_cls:
            x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device)  # [B, T, H, W, D]
        else:
            x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device)  # [B, T, H, W, D]

        return x_B_T_H_W_D, None, extra_pos_emb

    def decoder_head(
        self,
        x_B_T_H_W_D: torch.Tensor,
        emb_B_D: torch.Tensor,
        crossattn_emb: torch.Tensor,
        origin_shape: Tuple[int, int, int, int, int],  # [B, C, T, H, W]
        crossattn_mask: Optional[torch.Tensor] = None,
        adaln_lora_B_3D: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        del crossattn_emb, crossattn_mask
        B, C, T_before_patchify, H_before_patchify, W_before_patchify = origin_shape
        x_BT_HW_D = rearrange(x_B_T_H_W_D, "B T H W D -> (B T) (H W) D")
        x_BT_HW_D = self.final_layer(x_BT_HW_D, emb_B_D, adaln_lora_B_3D=adaln_lora_B_3D)
        # This is to ensure x_BT_HW_D has the correct shape because
        # when we merge T, H, W into one dimension, x_BT_HW_D has shape (B * T * H * W, 1*1, D).
        x_BT_HW_D = x_BT_HW_D.view(
            B * T_before_patchify // self.patch_temporal,
            H_before_patchify // self.patch_spatial * W_before_patchify // self.patch_spatial,
            -1,
        )
        x_B_D_T_H_W = rearrange(
            x_BT_HW_D,
            "(B T) (H W) (p1 p2 t C) -> B C (T t) (H p1) (W p2)",
            p1=self.patch_spatial,
            p2=self.patch_spatial,
            H=H_before_patchify // self.patch_spatial,
            W=W_before_patchify // self.patch_spatial,
            t=self.patch_temporal,
            B=B,
        )
        return x_B_D_T_H_W

    def forward_before_blocks(
        self,
        x: torch.Tensor,
        timesteps: torch.Tensor,
        crossattn_emb: torch.Tensor,
        crossattn_mask: Optional[torch.Tensor] = None,
        fps: Optional[torch.Tensor] = None,
        image_size: Optional[torch.Tensor] = None,
        padding_mask: Optional[torch.Tensor] = None,
        scalar_feature: Optional[torch.Tensor] = None,
        data_type: Optional[DataType] = DataType.VIDEO,
        latent_condition: Optional[torch.Tensor] = None,
        latent_condition_sigma: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> torch.Tensor:
        """
        Args:
            x: (B, C, T, H, W) tensor of spatial-temp inputs
            timesteps: (B, ) tensor of timesteps
            crossattn_emb: (B, N, D) tensor of cross-attention embeddings
            crossattn_mask: (B, N) tensor of cross-attention masks
        """
        del kwargs
        assert isinstance(
            data_type, DataType
        ), f"Expected DataType, got {type(data_type)}. We need discuss this flag later."
        original_shape = x.shape
        x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence(
            x,
            fps=fps,
            padding_mask=padding_mask,
            latent_condition=latent_condition,
            latent_condition_sigma=latent_condition_sigma,
        )
        # logging affline scale information
        affline_scale_log_info = {}

        timesteps_B_D, adaln_lora_B_3D = self.t_embedder[1](self.t_embedder[0](timesteps.flatten()).to(x.dtype))
        affline_emb_B_D = timesteps_B_D
        affline_scale_log_info["timesteps_B_D"] = timesteps_B_D.detach()

        if scalar_feature is not None:
            raise NotImplementedError("Scalar feature is not implemented yet.")

        affline_scale_log_info["affline_emb_B_D"] = affline_emb_B_D.detach()
        affline_emb_B_D = self.affline_norm(affline_emb_B_D)

        if self.use_cross_attn_mask:
            if crossattn_mask is not None and not torch.is_floating_point(crossattn_mask):
                crossattn_mask = (crossattn_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max
            crossattn_mask = crossattn_mask[:, None, None, :]  # .to(dtype=torch.bool)  # [B, 1, 1, length]
        else:
            crossattn_mask = None

        if self.blocks["block0"].x_format == "THWBD":
            x = rearrange(x_B_T_H_W_D, "B T H W D -> T H W B D")
            if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
                extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = rearrange(
                    extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, "B T H W D -> T H W B D"
                )
            crossattn_emb = rearrange(crossattn_emb, "B M D -> M B D")

            if crossattn_mask:
                crossattn_mask = rearrange(crossattn_mask, "B M -> M B")

        elif self.blocks["block0"].x_format == "BTHWD":
            x = x_B_T_H_W_D
        else:
            raise ValueError(f"Unknown x_format {self.blocks[0].x_format}")
        output = {
            "x": x,
            "affline_emb_B_D": affline_emb_B_D,
            "crossattn_emb": crossattn_emb,
            "crossattn_mask": crossattn_mask,
            "rope_emb_L_1_1_D": rope_emb_L_1_1_D,
            "adaln_lora_B_3D": adaln_lora_B_3D,
            "original_shape": original_shape,
            "extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
        }
        return output

    def forward(
        self,
        x: torch.Tensor,
        timesteps: torch.Tensor,
        context: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        # crossattn_emb: torch.Tensor,
        # crossattn_mask: Optional[torch.Tensor] = None,
        fps: Optional[torch.Tensor] = None,
        image_size: Optional[torch.Tensor] = None,
        padding_mask: Optional[torch.Tensor] = None,
        scalar_feature: Optional[torch.Tensor] = None,
        data_type: Optional[DataType] = DataType.VIDEO,
        latent_condition: Optional[torch.Tensor] = None,
        latent_condition_sigma: Optional[torch.Tensor] = None,
        condition_video_augment_sigma: Optional[torch.Tensor] = None,
        **kwargs,
    ):
        """
        Args:
            x: (B, C, T, H, W) tensor of spatial-temp inputs
            timesteps: (B, ) tensor of timesteps
            crossattn_emb: (B, N, D) tensor of cross-attention embeddings
            crossattn_mask: (B, N) tensor of cross-attention masks
            condition_video_augment_sigma: (B,) used in lvg(long video generation), we add noise with this sigma to
                augment condition input, the lvg model will condition on the condition_video_augment_sigma value;
                we need forward_before_blocks pass to the forward_before_blocks function.
        """

        crossattn_emb = context
        crossattn_mask = attention_mask

        inputs = self.forward_before_blocks(
            x=x,
            timesteps=timesteps,
            crossattn_emb=crossattn_emb,
            crossattn_mask=crossattn_mask,
            fps=fps,
            image_size=image_size,
            padding_mask=padding_mask,
            scalar_feature=scalar_feature,
            data_type=data_type,
            latent_condition=latent_condition,
            latent_condition_sigma=latent_condition_sigma,
            condition_video_augment_sigma=condition_video_augment_sigma,
            **kwargs,
        )
        x, affline_emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D, adaln_lora_B_3D, original_shape = (
            inputs["x"],
            inputs["affline_emb_B_D"],
            inputs["crossattn_emb"],
            inputs["crossattn_mask"],
            inputs["rope_emb_L_1_1_D"],
            inputs["adaln_lora_B_3D"],
            inputs["original_shape"],
        )
        extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = inputs["extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D"].to(x.dtype)
        if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
            assert (
                x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
            ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}"

        for _, block in self.blocks.items():
            assert (
                self.blocks["block0"].x_format == block.x_format
            ), f"First block has x_format {self.blocks[0].x_format}, got {block.x_format}"

            x = block(
                x,
                affline_emb_B_D,
                crossattn_emb,
                crossattn_mask,
                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
                adaln_lora_B_3D=adaln_lora_B_3D,
                extra_per_block_pos_emb=extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
            )

        x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D")

        x_B_D_T_H_W = self.decoder_head(
            x_B_T_H_W_D=x_B_T_H_W_D,
            emb_B_D=affline_emb_B_D,
            crossattn_emb=None,
            origin_shape=original_shape,
            crossattn_mask=None,
            adaln_lora_B_3D=adaln_lora_B_3D,
        )

        return x_B_D_T_H_W