# Copyright 2025 The Wan Team and The HuggingFace Team. All rights reserved.
#
# 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.

import math
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
from ..attention import FeedForward
from ..cache_utils import CacheMixin
from ..modeling_outputs import Transformer2DModelOutput
from ..modeling_utils import ModelMixin
from ..normalization import FP32LayerNorm
from .transformer_wan import (
    WanAttention,
    WanAttnProcessor,
    WanRotaryPosEmbed,
    WanTimeTextImageEmbedding,
    WanTransformerBlock,
)


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


class WanVACETransformerBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        ffn_dim: int,
        num_heads: int,
        qk_norm: str = "rms_norm_across_heads",
        cross_attn_norm: bool = False,
        eps: float = 1e-6,
        added_kv_proj_dim: Optional[int] = None,
        apply_input_projection: bool = False,
        apply_output_projection: bool = False,
    ):
        super().__init__()

        # 1. Input projection
        self.proj_in = None
        if apply_input_projection:
            self.proj_in = nn.Linear(dim, dim)

        # 2. Self-attention
        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
        self.attn1 = WanAttention(
            dim=dim,
            heads=num_heads,
            dim_head=dim // num_heads,
            eps=eps,
            processor=WanAttnProcessor(),
        )

        # 3. Cross-attention
        self.attn2 = WanAttention(
            dim=dim,
            heads=num_heads,
            dim_head=dim // num_heads,
            eps=eps,
            added_kv_proj_dim=added_kv_proj_dim,
            processor=WanAttnProcessor(),
        )
        self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()

        # 4. Feed-forward
        self.ffn = FeedForward(dim, inner_dim=ffn_dim, activation_fn="gelu-approximate")
        self.norm3 = FP32LayerNorm(dim, eps, elementwise_affine=False)

        # 5. Output projection
        self.proj_out = None
        if apply_output_projection:
            self.proj_out = nn.Linear(dim, dim)

        self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        control_hidden_states: torch.Tensor,
        temb: torch.Tensor,
        rotary_emb: torch.Tensor,
    ) -> torch.Tensor:
        if self.proj_in is not None:
            control_hidden_states = self.proj_in(control_hidden_states)
            control_hidden_states = control_hidden_states + hidden_states

        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
            self.scale_shift_table + temb.float()
        ).chunk(6, dim=1)

        # 1. Self-attention
        norm_hidden_states = (self.norm1(control_hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(
            control_hidden_states
        )
        attn_output = self.attn1(norm_hidden_states, None, None, rotary_emb)
        control_hidden_states = (control_hidden_states.float() + attn_output * gate_msa).type_as(control_hidden_states)

        # 2. Cross-attention
        norm_hidden_states = self.norm2(control_hidden_states.float()).type_as(control_hidden_states)
        attn_output = self.attn2(norm_hidden_states, encoder_hidden_states, None, None)
        control_hidden_states = control_hidden_states + attn_output

        # 3. Feed-forward
        norm_hidden_states = (self.norm3(control_hidden_states.float()) * (1 + c_scale_msa) + c_shift_msa).type_as(
            control_hidden_states
        )
        ff_output = self.ffn(norm_hidden_states)
        control_hidden_states = (control_hidden_states.float() + ff_output.float() * c_gate_msa).type_as(
            control_hidden_states
        )

        conditioning_states = None
        if self.proj_out is not None:
            conditioning_states = self.proj_out(control_hidden_states)

        return conditioning_states, control_hidden_states


class WanVACETransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
    r"""
    A Transformer model for video-like data used in the Wan model.

    Args:
        patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`):
            3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
        num_attention_heads (`int`, defaults to `40`):
            Fixed length for text embeddings.
        attention_head_dim (`int`, defaults to `128`):
            The number of channels in each head.
        in_channels (`int`, defaults to `16`):
            The number of channels in the input.
        out_channels (`int`, defaults to `16`):
            The number of channels in the output.
        text_dim (`int`, defaults to `512`):
            Input dimension for text embeddings.
        freq_dim (`int`, defaults to `256`):
            Dimension for sinusoidal time embeddings.
        ffn_dim (`int`, defaults to `13824`):
            Intermediate dimension in feed-forward network.
        num_layers (`int`, defaults to `40`):
            The number of layers of transformer blocks to use.
        window_size (`Tuple[int]`, defaults to `(-1, -1)`):
            Window size for local attention (-1 indicates global attention).
        cross_attn_norm (`bool`, defaults to `True`):
            Enable cross-attention normalization.
        qk_norm (`bool`, defaults to `True`):
            Enable query/key normalization.
        eps (`float`, defaults to `1e-6`):
            Epsilon value for normalization layers.
        add_img_emb (`bool`, defaults to `False`):
            Whether to use img_emb.
        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
            The number of channels to use for the added key and value projections. If `None`, no projection is used.
    """

    _supports_gradient_checkpointing = True
    _skip_layerwise_casting_patterns = ["patch_embedding", "vace_patch_embedding", "condition_embedder", "norm"]
    _no_split_modules = ["WanTransformerBlock", "WanVACETransformerBlock"]
    _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
    _keys_to_ignore_on_load_unexpected = ["norm_added_q"]

    @register_to_config
    def __init__(
        self,
        patch_size: Tuple[int] = (1, 2, 2),
        num_attention_heads: int = 40,
        attention_head_dim: int = 128,
        in_channels: int = 16,
        out_channels: int = 16,
        text_dim: int = 4096,
        freq_dim: int = 256,
        ffn_dim: int = 13824,
        num_layers: int = 40,
        cross_attn_norm: bool = True,
        qk_norm: Optional[str] = "rms_norm_across_heads",
        eps: float = 1e-6,
        image_dim: Optional[int] = None,
        added_kv_proj_dim: Optional[int] = None,
        rope_max_seq_len: int = 1024,
        pos_embed_seq_len: Optional[int] = None,
        vace_layers: List[int] = [0, 5, 10, 15, 20, 25, 30, 35],
        vace_in_channels: int = 96,
    ) -> None:
        super().__init__()

        inner_dim = num_attention_heads * attention_head_dim
        out_channels = out_channels or in_channels

        if max(vace_layers) >= num_layers:
            raise ValueError(f"VACE layers {vace_layers} exceed the number of transformer layers {num_layers}.")
        if 0 not in vace_layers:
            raise ValueError("VACE layers must include layer 0.")

        # 1. Patch & position embedding
        self.rope = WanRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len)
        self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)
        self.vace_patch_embedding = nn.Conv3d(vace_in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)

        # 2. Condition embeddings
        # image_embedding_dim=1280 for I2V model
        self.condition_embedder = WanTimeTextImageEmbedding(
            dim=inner_dim,
            time_freq_dim=freq_dim,
            time_proj_dim=inner_dim * 6,
            text_embed_dim=text_dim,
            image_embed_dim=image_dim,
            pos_embed_seq_len=pos_embed_seq_len,
        )

        # 3. Transformer blocks
        self.blocks = nn.ModuleList(
            [
                WanTransformerBlock(
                    inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim
                )
                for _ in range(num_layers)
            ]
        )

        self.vace_blocks = nn.ModuleList(
            [
                WanVACETransformerBlock(
                    inner_dim,
                    ffn_dim,
                    num_attention_heads,
                    qk_norm,
                    cross_attn_norm,
                    eps,
                    added_kv_proj_dim,
                    apply_input_projection=i == 0,  # Layer 0 always has input projection and is in vace_layers
                    apply_output_projection=True,
                )
                for i in range(len(vace_layers))
            ]
        )

        # 4. Output norm & projection
        self.norm_out = FP32LayerNorm(inner_dim, eps, elementwise_affine=False)
        self.proj_out = nn.Linear(inner_dim, out_channels * math.prod(patch_size))
        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        timestep: torch.LongTensor,
        encoder_hidden_states: torch.Tensor,
        encoder_hidden_states_image: Optional[torch.Tensor] = None,
        control_hidden_states: torch.Tensor = None,
        control_hidden_states_scale: torch.Tensor = None,
        return_dict: bool = True,
        attention_kwargs: Optional[Dict[str, Any]] = None,
    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
        if attention_kwargs is not None:
            attention_kwargs = attention_kwargs.copy()
            lora_scale = attention_kwargs.pop("scale", 1.0)
        else:
            lora_scale = 1.0

        if USE_PEFT_BACKEND:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self, lora_scale)
        else:
            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
                logger.warning(
                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
                )

        batch_size, num_channels, num_frames, height, width = hidden_states.shape
        p_t, p_h, p_w = self.config.patch_size
        post_patch_num_frames = num_frames // p_t
        post_patch_height = height // p_h
        post_patch_width = width // p_w

        if control_hidden_states_scale is None:
            control_hidden_states_scale = control_hidden_states.new_ones(len(self.config.vace_layers))
        control_hidden_states_scale = torch.unbind(control_hidden_states_scale)
        if len(control_hidden_states_scale) != len(self.config.vace_layers):
            raise ValueError(
                f"Length of `control_hidden_states_scale` {len(control_hidden_states_scale)} should be "
                f"equal to {len(self.config.vace_layers)}."
            )

        # 1. Rotary position embedding
        rotary_emb = self.rope(hidden_states)

        # 2. Patch embedding
        hidden_states = self.patch_embedding(hidden_states)
        hidden_states = hidden_states.flatten(2).transpose(1, 2)

        control_hidden_states = self.vace_patch_embedding(control_hidden_states)
        control_hidden_states = control_hidden_states.flatten(2).transpose(1, 2)
        control_hidden_states_padding = control_hidden_states.new_zeros(
            batch_size, hidden_states.size(1) - control_hidden_states.size(1), control_hidden_states.size(2)
        )
        control_hidden_states = torch.cat([control_hidden_states, control_hidden_states_padding], dim=1)

        # 3. Time embedding
        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
            timestep, encoder_hidden_states, encoder_hidden_states_image
        )
        timestep_proj = timestep_proj.unflatten(1, (6, -1))

        # 4. Image embedding
        if encoder_hidden_states_image is not None:
            encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)

        # 5. Transformer blocks
        if torch.is_grad_enabled() and self.gradient_checkpointing:
            # Prepare VACE hints
            control_hidden_states_list = []
            for i, block in enumerate(self.vace_blocks):
                conditioning_states, control_hidden_states = self._gradient_checkpointing_func(
                    block, hidden_states, encoder_hidden_states, control_hidden_states, timestep_proj, rotary_emb
                )
                control_hidden_states_list.append((conditioning_states, control_hidden_states_scale[i]))
            control_hidden_states_list = control_hidden_states_list[::-1]

            for i, block in enumerate(self.blocks):
                hidden_states = self._gradient_checkpointing_func(
                    block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb
                )
                if i in self.config.vace_layers:
                    control_hint, scale = control_hidden_states_list.pop()
                    hidden_states = hidden_states + control_hint * scale
        else:
            # Prepare VACE hints
            control_hidden_states_list = []
            for i, block in enumerate(self.vace_blocks):
                conditioning_states, control_hidden_states = block(
                    hidden_states, encoder_hidden_states, control_hidden_states, timestep_proj, rotary_emb
                )
                control_hidden_states_list.append((conditioning_states, control_hidden_states_scale[i]))
            control_hidden_states_list = control_hidden_states_list[::-1]

            for i, block in enumerate(self.blocks):
                hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb)
                if i in self.config.vace_layers:
                    control_hint, scale = control_hidden_states_list.pop()
                    hidden_states = hidden_states + control_hint * scale

        # 6. Output norm, projection & unpatchify
        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)

        # Move the shift and scale tensors to the same device as hidden_states.
        # When using multi-GPU inference via accelerate these will be on the
        # first device rather than the last device, which hidden_states ends up
        # on.
        shift = shift.to(hidden_states.device)
        scale = scale.to(hidden_states.device)

        hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)
        hidden_states = self.proj_out(hidden_states)

        hidden_states = hidden_states.reshape(
            batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1
        )
        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

        if USE_PEFT_BACKEND:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self, lora_scale)

        if not return_dict:
            return (output,)

        return Transformer2DModelOutput(sample=output)
