# Copyright 2025 ConsisID Authors 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

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import PeftAdapterMixin
from ...utils import apply_lora_scale, logging
from ...utils.torch_utils import maybe_allow_in_graph
from ..attention import Attention, AttentionMixin, FeedForward
from ..attention_processor import CogVideoXAttnProcessor2_0
from ..embeddings import CogVideoXPatchEmbed, TimestepEmbedding, Timesteps
from ..modeling_outputs import Transformer2DModelOutput
from ..modeling_utils import ModelMixin
from ..normalization import AdaLayerNorm, CogVideoXLayerNormZero


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


class PerceiverAttention(nn.Module):
    def __init__(self, dim: int, dim_head: int = 64, heads: int = 8, kv_dim: int | None = None):
        super().__init__()

        self.scale = dim_head**-0.5
        self.dim_head = dim_head
        self.heads = heads
        inner_dim = dim_head * heads

        self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim)
        self.norm2 = nn.LayerNorm(dim)

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)

    def forward(self, image_embeds: torch.Tensor, latents: torch.Tensor) -> torch.Tensor:
        # Apply normalization
        image_embeds = self.norm1(image_embeds)
        latents = self.norm2(latents)

        batch_size, seq_len, _ = latents.shape  # Get batch size and sequence length

        # Compute query, key, and value matrices
        query = self.to_q(latents)
        kv_input = torch.cat((image_embeds, latents), dim=-2)
        key, value = self.to_kv(kv_input).chunk(2, dim=-1)

        # Reshape the tensors for multi-head attention
        query = query.reshape(query.size(0), -1, self.heads, self.dim_head).transpose(1, 2)
        key = key.reshape(key.size(0), -1, self.heads, self.dim_head).transpose(1, 2)
        value = value.reshape(value.size(0), -1, self.heads, self.dim_head).transpose(1, 2)

        # attention
        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
        weight = (query * scale) @ (key * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
        output = weight @ value

        # Reshape and return the final output
        output = output.permute(0, 2, 1, 3).reshape(batch_size, seq_len, -1)

        return self.to_out(output)


class LocalFacialExtractor(nn.Module):
    def __init__(
        self,
        id_dim: int = 1280,
        vit_dim: int = 1024,
        depth: int = 10,
        dim_head: int = 64,
        heads: int = 16,
        num_id_token: int = 5,
        num_queries: int = 32,
        output_dim: int = 2048,
        ff_mult: int = 4,
        num_scale: int = 5,
    ):
        super().__init__()

        # Storing identity token and query information
        self.num_id_token = num_id_token
        self.vit_dim = vit_dim
        self.num_queries = num_queries
        assert depth % num_scale == 0
        self.depth = depth // num_scale
        self.num_scale = num_scale
        scale = vit_dim**-0.5

        # Learnable latent query embeddings
        self.latents = nn.Parameter(torch.randn(1, num_queries, vit_dim) * scale)
        # Projection layer to map the latent output to the desired dimension
        self.proj_out = nn.Parameter(scale * torch.randn(vit_dim, output_dim))

        # Attention and ConsisIDFeedForward layer stack
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                nn.ModuleList(
                    [
                        PerceiverAttention(dim=vit_dim, dim_head=dim_head, heads=heads),  # Perceiver Attention layer
                        nn.Sequential(
                            nn.LayerNorm(vit_dim),
                            nn.Linear(vit_dim, vit_dim * ff_mult, bias=False),
                            nn.GELU(),
                            nn.Linear(vit_dim * ff_mult, vit_dim, bias=False),
                        ),  # ConsisIDFeedForward layer
                    ]
                )
            )

        # Mappings for each of the 5 different ViT features
        for i in range(num_scale):
            setattr(
                self,
                f"mapping_{i}",
                nn.Sequential(
                    nn.Linear(vit_dim, vit_dim),
                    nn.LayerNorm(vit_dim),
                    nn.LeakyReLU(),
                    nn.Linear(vit_dim, vit_dim),
                    nn.LayerNorm(vit_dim),
                    nn.LeakyReLU(),
                    nn.Linear(vit_dim, vit_dim),
                ),
            )

        # Mapping for identity embedding vectors
        self.id_embedding_mapping = nn.Sequential(
            nn.Linear(id_dim, vit_dim),
            nn.LayerNorm(vit_dim),
            nn.LeakyReLU(),
            nn.Linear(vit_dim, vit_dim),
            nn.LayerNorm(vit_dim),
            nn.LeakyReLU(),
            nn.Linear(vit_dim, vit_dim * num_id_token),
        )

    def forward(self, id_embeds: torch.Tensor, vit_hidden_states: list[torch.Tensor]) -> torch.Tensor:
        # Repeat latent queries for the batch size
        latents = self.latents.repeat(id_embeds.size(0), 1, 1)

        # Map the identity embedding to tokens
        id_embeds = self.id_embedding_mapping(id_embeds)
        id_embeds = id_embeds.reshape(-1, self.num_id_token, self.vit_dim)

        # Concatenate identity tokens with the latent queries
        latents = torch.cat((latents, id_embeds), dim=1)

        # Process each of the num_scale visual feature inputs
        for i in range(self.num_scale):
            vit_feature = getattr(self, f"mapping_{i}")(vit_hidden_states[i])
            ctx_feature = torch.cat((id_embeds, vit_feature), dim=1)

            # Pass through the PerceiverAttention and ConsisIDFeedForward layers
            for attn, ff in self.layers[i * self.depth : (i + 1) * self.depth]:
                latents = attn(ctx_feature, latents) + latents
                latents = ff(latents) + latents

        # Retain only the query latents
        latents = latents[:, : self.num_queries]
        # Project the latents to the output dimension
        latents = latents @ self.proj_out
        return latents


class PerceiverCrossAttention(nn.Module):
    def __init__(self, dim: int = 3072, dim_head: int = 128, heads: int = 16, kv_dim: int = 2048):
        super().__init__()

        self.scale = dim_head**-0.5
        self.dim_head = dim_head
        self.heads = heads
        inner_dim = dim_head * heads

        # Layer normalization to stabilize training
        self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim)
        self.norm2 = nn.LayerNorm(dim)

        # Linear transformations to produce queries, keys, and values
        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)

    def forward(self, image_embeds: torch.Tensor, hidden_states: torch.Tensor) -> torch.Tensor:
        # Apply layer normalization to the input image and latent features
        image_embeds = self.norm1(image_embeds)
        hidden_states = self.norm2(hidden_states)

        batch_size, seq_len, _ = hidden_states.shape

        # Compute queries, keys, and values
        query = self.to_q(hidden_states)
        key, value = self.to_kv(image_embeds).chunk(2, dim=-1)

        # Reshape tensors to split into attention heads
        query = query.reshape(query.size(0), -1, self.heads, self.dim_head).transpose(1, 2)
        key = key.reshape(key.size(0), -1, self.heads, self.dim_head).transpose(1, 2)
        value = value.reshape(value.size(0), -1, self.heads, self.dim_head).transpose(1, 2)

        # Compute attention weights
        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
        weight = (query * scale) @ (key * scale).transpose(-2, -1)  # More stable scaling than post-division
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)

        # Compute the output via weighted combination of values
        out = weight @ value

        # Reshape and permute to prepare for final linear transformation
        out = out.permute(0, 2, 1, 3).reshape(batch_size, seq_len, -1)

        return self.to_out(out)


@maybe_allow_in_graph
class ConsisIDBlock(nn.Module):
    r"""
    Transformer block used in [ConsisID](https://github.com/PKU-YuanGroup/ConsisID) model.

    Parameters:
        dim (`int`):
            The number of channels in the input and output.
        num_attention_heads (`int`):
            The number of heads to use for multi-head attention.
        attention_head_dim (`int`):
            The number of channels in each head.
        time_embed_dim (`int`):
            The number of channels in timestep embedding.
        dropout (`float`, defaults to `0.0`):
            The dropout probability to use.
        activation_fn (`str`, defaults to `"gelu-approximate"`):
            Activation function to be used in feed-forward.
        attention_bias (`bool`, defaults to `False`):
            Whether or not to use bias in attention projection layers.
        qk_norm (`bool`, defaults to `True`):
            Whether or not to use normalization after query and key projections in Attention.
        norm_elementwise_affine (`bool`, defaults to `True`):
            Whether to use learnable elementwise affine parameters for normalization.
        norm_eps (`float`, defaults to `1e-5`):
            Epsilon value for normalization layers.
        final_dropout (`bool` defaults to `False`):
            Whether to apply a final dropout after the last feed-forward layer.
        ff_inner_dim (`int`, *optional*, defaults to `None`):
            Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used.
        ff_bias (`bool`, defaults to `True`):
            Whether or not to use bias in Feed-forward layer.
        attention_out_bias (`bool`, defaults to `True`):
            Whether or not to use bias in Attention output projection layer.
    """

    def __init__(
        self,
        dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
        time_embed_dim: int,
        dropout: float = 0.0,
        activation_fn: str = "gelu-approximate",
        attention_bias: bool = False,
        qk_norm: bool = True,
        norm_elementwise_affine: bool = True,
        norm_eps: float = 1e-5,
        final_dropout: bool = True,
        ff_inner_dim: int | None = None,
        ff_bias: bool = True,
        attention_out_bias: bool = True,
    ):
        super().__init__()

        # 1. Self Attention
        self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)

        self.attn1 = Attention(
            query_dim=dim,
            dim_head=attention_head_dim,
            heads=num_attention_heads,
            qk_norm="layer_norm" if qk_norm else None,
            eps=1e-6,
            bias=attention_bias,
            out_bias=attention_out_bias,
            processor=CogVideoXAttnProcessor2_0(),
        )

        # 2. Feed Forward
        self.norm2 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)

        self.ff = FeedForward(
            dim,
            dropout=dropout,
            activation_fn=activation_fn,
            final_dropout=final_dropout,
            inner_dim=ff_inner_dim,
            bias=ff_bias,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        temb: torch.Tensor,
        image_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        text_seq_length = encoder_hidden_states.size(1)

        # norm & modulate
        norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
            hidden_states, encoder_hidden_states, temb
        )

        # attention
        attn_hidden_states, attn_encoder_hidden_states = self.attn1(
            hidden_states=norm_hidden_states,
            encoder_hidden_states=norm_encoder_hidden_states,
            image_rotary_emb=image_rotary_emb,
        )

        hidden_states = hidden_states + gate_msa * attn_hidden_states
        encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states

        # norm & modulate
        norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
            hidden_states, encoder_hidden_states, temb
        )

        # feed-forward
        norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
        ff_output = self.ff(norm_hidden_states)

        hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
        encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]

        return hidden_states, encoder_hidden_states


class ConsisIDTransformer3DModel(ModelMixin, AttentionMixin, ConfigMixin, PeftAdapterMixin):
    """
    A Transformer model for video-like data in [ConsisID](https://github.com/PKU-YuanGroup/ConsisID).

    Parameters:
        num_attention_heads (`int`, defaults to `30`):
            The number of heads to use for multi-head attention.
        attention_head_dim (`int`, defaults to `64`):
            The number of channels in each head.
        in_channels (`int`, defaults to `16`):
            The number of channels in the input.
        out_channels (`int`, *optional*, defaults to `16`):
            The number of channels in the output.
        flip_sin_to_cos (`bool`, defaults to `True`):
            Whether to flip the sin to cos in the time embedding.
        time_embed_dim (`int`, defaults to `512`):
            Output dimension of timestep embeddings.
        text_embed_dim (`int`, defaults to `4096`):
            Input dimension of text embeddings from the text encoder.
        num_layers (`int`, defaults to `30`):
            The number of layers of Transformer blocks to use.
        dropout (`float`, defaults to `0.0`):
            The dropout probability to use.
        attention_bias (`bool`, defaults to `True`):
            Whether to use bias in the attention projection layers.
        sample_width (`int`, defaults to `90`):
            The width of the input latents.
        sample_height (`int`, defaults to `60`):
            The height of the input latents.
        sample_frames (`int`, defaults to `49`):
            The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49
            instead of 13 because ConsisID processed 13 latent frames at once in its default and recommended settings,
            but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with
            K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1).
        patch_size (`int`, defaults to `2`):
            The size of the patches to use in the patch embedding layer.
        temporal_compression_ratio (`int`, defaults to `4`):
            The compression ratio across the temporal dimension. See documentation for `sample_frames`.
        max_text_seq_length (`int`, defaults to `226`):
            The maximum sequence length of the input text embeddings.
        activation_fn (`str`, defaults to `"gelu-approximate"`):
            Activation function to use in feed-forward.
        timestep_activation_fn (`str`, defaults to `"silu"`):
            Activation function to use when generating the timestep embeddings.
        norm_elementwise_affine (`bool`, defaults to `True`):
            Whether to use elementwise affine in normalization layers.
        norm_eps (`float`, defaults to `1e-5`):
            The epsilon value to use in normalization layers.
        spatial_interpolation_scale (`float`, defaults to `1.875`):
            Scaling factor to apply in 3D positional embeddings across spatial dimensions.
        temporal_interpolation_scale (`float`, defaults to `1.0`):
            Scaling factor to apply in 3D positional embeddings across temporal dimensions.
        is_train_face (`bool`, defaults to `False`):
            Whether to use enable the identity-preserving module during the training process. When set to `True`, the
            model will focus on identity-preserving tasks.
        is_kps (`bool`, defaults to `False`):
            Whether to enable keypoint for global facial extractor. If `True`, keypoints will be in the model.
        cross_attn_interval (`int`, defaults to `2`):
            The interval between cross-attention layers in the Transformer architecture. A larger value may reduce the
            frequency of cross-attention computations, which can help reduce computational overhead.
        cross_attn_dim_head (`int`, optional, defaults to `128`):
            The dimensionality of each attention head in the cross-attention layers of the Transformer architecture. A
            larger value increases the capacity to attend to more complex patterns, but also increases memory and
            computation costs.
        cross_attn_num_heads (`int`, optional, defaults to `16`):
            The number of attention heads in the cross-attention layers. More heads allow for more parallel attention
            mechanisms, capturing diverse relationships between different components of the input, but can also
            increase computational requirements.
        LFE_id_dim (`int`, optional, defaults to `1280`):
            The dimensionality of the identity vector used in the Local Facial Extractor (LFE). This vector represents
            the identity features of a face, which are important for tasks like face recognition and identity
            preservation across different frames.
        LFE_vit_dim (`int`, optional, defaults to `1024`):
            The dimension of the vision transformer (ViT) output used in the Local Facial Extractor (LFE). This value
            dictates the size of the transformer-generated feature vectors that will be processed for facial feature
            extraction.
        LFE_depth (`int`, optional, defaults to `10`):
            The number of layers in the Local Facial Extractor (LFE). Increasing the depth allows the model to capture
            more complex representations of facial features, but also increases the computational load.
        LFE_dim_head (`int`, optional, defaults to `64`):
            The dimensionality of each attention head in the Local Facial Extractor (LFE). This parameter affects how
            finely the model can process and focus on different parts of the facial features during the extraction
            process.
        LFE_num_heads (`int`, optional, defaults to `16`):
            The number of attention heads in the Local Facial Extractor (LFE). More heads can improve the model's
            ability to capture diverse facial features, but at the cost of increased computational complexity.
        LFE_num_id_token (`int`, optional, defaults to `5`):
            The number of identity tokens used in the Local Facial Extractor (LFE). This defines how many
            identity-related tokens the model will process to ensure face identity preservation during feature
            extraction.
        LFE_num_querie (`int`, optional, defaults to `32`):
            The number of query tokens used in the Local Facial Extractor (LFE). These tokens are used to capture
            high-frequency face-related information that aids in accurate facial feature extraction.
        LFE_output_dim (`int`, optional, defaults to `2048`):
            The output dimension of the Local Facial Extractor (LFE). This dimension determines the size of the feature
            vectors produced by the LFE module, which will be used for subsequent tasks such as face recognition or
            tracking.
        LFE_ff_mult (`int`, optional, defaults to `4`):
            The multiplication factor applied to the feed-forward network's hidden layer size in the Local Facial
            Extractor (LFE). A higher value increases the model's capacity to learn more complex facial feature
            transformations, but also increases the computation and memory requirements.
        LFE_num_scale (`int`, optional, defaults to `5`):
            The number of different scales visual feature. A higher value increases the model's capacity to learn more
            complex facial feature transformations, but also increases the computation and memory requirements.
        local_face_scale (`float`, defaults to `1.0`):
            A scaling factor used to adjust the importance of local facial features in the model. This can influence
            how strongly the model focuses on high frequency face-related content.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        num_attention_heads: int = 30,
        attention_head_dim: int = 64,
        in_channels: int = 16,
        out_channels: int | None = 16,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        time_embed_dim: int = 512,
        text_embed_dim: int = 4096,
        num_layers: int = 30,
        dropout: float = 0.0,
        attention_bias: bool = True,
        sample_width: int = 90,
        sample_height: int = 60,
        sample_frames: int = 49,
        patch_size: int = 2,
        temporal_compression_ratio: int = 4,
        max_text_seq_length: int = 226,
        activation_fn: str = "gelu-approximate",
        timestep_activation_fn: str = "silu",
        norm_elementwise_affine: bool = True,
        norm_eps: float = 1e-5,
        spatial_interpolation_scale: float = 1.875,
        temporal_interpolation_scale: float = 1.0,
        use_rotary_positional_embeddings: bool = False,
        use_learned_positional_embeddings: bool = False,
        is_train_face: bool = False,
        is_kps: bool = False,
        cross_attn_interval: int = 2,
        cross_attn_dim_head: int = 128,
        cross_attn_num_heads: int = 16,
        LFE_id_dim: int = 1280,
        LFE_vit_dim: int = 1024,
        LFE_depth: int = 10,
        LFE_dim_head: int = 64,
        LFE_num_heads: int = 16,
        LFE_num_id_token: int = 5,
        LFE_num_querie: int = 32,
        LFE_output_dim: int = 2048,
        LFE_ff_mult: int = 4,
        LFE_num_scale: int = 5,
        local_face_scale: float = 1.0,
    ):
        super().__init__()
        inner_dim = num_attention_heads * attention_head_dim

        if not use_rotary_positional_embeddings and use_learned_positional_embeddings:
            raise ValueError(
                "There are no ConsisID checkpoints available with disable rotary embeddings and learned positional "
                "embeddings. If you're using a custom model and/or believe this should be supported, please open an "
                "issue at https://github.com/huggingface/diffusers/issues."
            )

        # 1. Patch embedding
        self.patch_embed = CogVideoXPatchEmbed(
            patch_size=patch_size,
            in_channels=in_channels,
            embed_dim=inner_dim,
            text_embed_dim=text_embed_dim,
            bias=True,
            sample_width=sample_width,
            sample_height=sample_height,
            sample_frames=sample_frames,
            temporal_compression_ratio=temporal_compression_ratio,
            max_text_seq_length=max_text_seq_length,
            spatial_interpolation_scale=spatial_interpolation_scale,
            temporal_interpolation_scale=temporal_interpolation_scale,
            use_positional_embeddings=not use_rotary_positional_embeddings,
            use_learned_positional_embeddings=use_learned_positional_embeddings,
        )
        self.embedding_dropout = nn.Dropout(dropout)

        # 2. Time embeddings
        self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
        self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)

        # 3. Define spatio-temporal transformers blocks
        self.transformer_blocks = nn.ModuleList(
            [
                ConsisIDBlock(
                    dim=inner_dim,
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=attention_head_dim,
                    time_embed_dim=time_embed_dim,
                    dropout=dropout,
                    activation_fn=activation_fn,
                    attention_bias=attention_bias,
                    norm_elementwise_affine=norm_elementwise_affine,
                    norm_eps=norm_eps,
                )
                for _ in range(num_layers)
            ]
        )
        self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine)

        # 4. Output blocks
        self.norm_out = AdaLayerNorm(
            embedding_dim=time_embed_dim,
            output_dim=2 * inner_dim,
            norm_elementwise_affine=norm_elementwise_affine,
            norm_eps=norm_eps,
            chunk_dim=1,
        )
        self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)

        self.is_train_face = is_train_face
        self.is_kps = is_kps

        # 5. Define identity-preserving config
        if is_train_face:
            # LFE configs
            self.LFE_id_dim = LFE_id_dim
            self.LFE_vit_dim = LFE_vit_dim
            self.LFE_depth = LFE_depth
            self.LFE_dim_head = LFE_dim_head
            self.LFE_num_heads = LFE_num_heads
            self.LFE_num_id_token = LFE_num_id_token
            self.LFE_num_querie = LFE_num_querie
            self.LFE_output_dim = LFE_output_dim
            self.LFE_ff_mult = LFE_ff_mult
            self.LFE_num_scale = LFE_num_scale
            # cross configs
            self.inner_dim = inner_dim
            self.cross_attn_interval = cross_attn_interval
            self.num_cross_attn = num_layers // cross_attn_interval
            self.cross_attn_dim_head = cross_attn_dim_head
            self.cross_attn_num_heads = cross_attn_num_heads
            self.cross_attn_kv_dim = int(self.inner_dim / 3 * 2)
            self.local_face_scale = local_face_scale
            # face modules
            self._init_face_inputs()

        self.gradient_checkpointing = False

    def _init_face_inputs(self):
        self.local_facial_extractor = LocalFacialExtractor(
            id_dim=self.LFE_id_dim,
            vit_dim=self.LFE_vit_dim,
            depth=self.LFE_depth,
            dim_head=self.LFE_dim_head,
            heads=self.LFE_num_heads,
            num_id_token=self.LFE_num_id_token,
            num_queries=self.LFE_num_querie,
            output_dim=self.LFE_output_dim,
            ff_mult=self.LFE_ff_mult,
            num_scale=self.LFE_num_scale,
        )
        self.perceiver_cross_attention = nn.ModuleList(
            [
                PerceiverCrossAttention(
                    dim=self.inner_dim,
                    dim_head=self.cross_attn_dim_head,
                    heads=self.cross_attn_num_heads,
                    kv_dim=self.cross_attn_kv_dim,
                )
                for _ in range(self.num_cross_attn)
            ]
        )

    @apply_lora_scale("attention_kwargs")
    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        timestep: int | float | torch.LongTensor,
        timestep_cond: torch.Tensor | None = None,
        image_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
        attention_kwargs: dict[str, Any] | None = None,
        id_cond: torch.Tensor | None = None,
        id_vit_hidden: torch.Tensor | None = None,
        return_dict: bool = True,
    ) -> tuple[torch.Tensor] | Transformer2DModelOutput:
        # fuse clip and insightface
        valid_face_emb = None
        if self.is_train_face:
            id_cond = id_cond.to(device=hidden_states.device, dtype=hidden_states.dtype)
            id_vit_hidden = [
                tensor.to(device=hidden_states.device, dtype=hidden_states.dtype) for tensor in id_vit_hidden
            ]
            valid_face_emb = self.local_facial_extractor(
                id_cond, id_vit_hidden
            )  # torch.Size([1, 1280]), list[5](torch.Size([1, 577, 1024]))  ->  torch.Size([1, 32, 2048])

        batch_size, num_frames, channels, height, width = hidden_states.shape

        # 1. Time embedding
        timesteps = timestep
        t_emb = self.time_proj(timesteps)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=hidden_states.dtype)
        emb = self.time_embedding(t_emb, timestep_cond)

        # 2. Patch embedding
        # torch.Size([1, 226, 4096])   torch.Size([1, 13, 32, 60, 90])
        hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)  # torch.Size([1, 17776, 3072])
        hidden_states = self.embedding_dropout(hidden_states)  # torch.Size([1, 17776, 3072])

        text_seq_length = encoder_hidden_states.shape[1]
        encoder_hidden_states = hidden_states[:, :text_seq_length]  # torch.Size([1, 226, 3072])
        hidden_states = hidden_states[:, text_seq_length:]  # torch.Size([1, 17550, 3072])

        # 3. Transformer blocks
        ca_idx = 0
        for i, block in enumerate(self.transformer_blocks):
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
                    block,
                    hidden_states,
                    encoder_hidden_states,
                    emb,
                    image_rotary_emb,
                )
            else:
                hidden_states, encoder_hidden_states = block(
                    hidden_states=hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    temb=emb,
                    image_rotary_emb=image_rotary_emb,
                )

            if self.is_train_face:
                if i % self.cross_attn_interval == 0 and valid_face_emb is not None:
                    hidden_states = hidden_states + self.local_face_scale * self.perceiver_cross_attention[ca_idx](
                        valid_face_emb, hidden_states
                    )  # torch.Size([2, 32, 2048])  torch.Size([2, 17550, 3072])
                    ca_idx += 1

        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
        hidden_states = self.norm_final(hidden_states)
        hidden_states = hidden_states[:, text_seq_length:]

        # 4. Final block
        hidden_states = self.norm_out(hidden_states, temb=emb)
        hidden_states = self.proj_out(hidden_states)

        # 5. Unpatchify
        # Note: we use `-1` instead of `channels`:
        #   - It is okay to `channels` use for ConsisID (number of input channels is equal to output channels)
        p = self.config.patch_size
        output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
        output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)

        if not return_dict:
            return (output,)
        return Transformer2DModelOutput(sample=output)
