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

import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.attention import Attention, FeedForward
from diffusers.models.attention_processor import AttentionProcessor, FusedCogVideoXAttnProcessor2_0
from diffusers.models.embeddings import TimestepEmbedding, Timesteps, get_3d_sincos_pos_embed
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNorm
from diffusers.utils import is_torch_version, logging
from diffusers.utils.torch_utils import maybe_allow_in_graph

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


def apply_rotary_emb(
    x: torch.Tensor,
    freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
    use_real: bool = True,
    use_real_unbind_dim: int = -1,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
    to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
    reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
    tensors contain rotary embeddings and are returned as real tensors.

    Args:
        x (`torch.Tensor`):
            Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply
        freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)

    Returns:
        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
    """
    if use_real:
        cos, sin = freqs_cis  # [S, D]
        cos = cos[None, None]
        sin = sin[None, None]
        cos, sin = cos.to(x.device), sin.to(x.device)

        x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
        x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)

        out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)

        return out
    else:
        # used for lumina
        x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
        freqs_cis = freqs_cis.unsqueeze(2)
        x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)

        return x_out.type_as(x)


class CogVideoXLayerNormZero(nn.Module):
    def __init__(
        self,
        conditioning_dim: int,
        embedding_dim: int,
        elementwise_affine: bool = True,
        eps: float = 1e-5,
        bias: bool = True,
    ) -> None:
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(conditioning_dim, 6 * embedding_dim, bias=bias)
        self.norm = nn.LayerNorm(embedding_dim, eps=eps, elementwise_affine=elementwise_affine)

    def forward(
        self, hidden_states: torch.Tensor, temb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        shift, scale, gate, _, _, _ = self.linear(self.silu(temb)).chunk(6, dim=1)
        hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :]
        return hidden_states, gate[:, None, :]


class CogVideoXAttnProcessor1_0:
    r"""Processor for implementing scaled dot-product attention for the
    CogVideoX model.

    It applies a rotary embedding on query and key vectors, but does not include spatial normalization.
    """

    def __init__(self):
        if not hasattr(F, 'scaled_dot_product_attention'):
            raise ImportError('CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.')

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
        motion_rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:

        batch_size, sequence_length, _ = hidden_states.shape 

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

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

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)    # [batch_size, heads, seq_len, dim]
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # Apply RoPE if needed
        if image_rotary_emb is not None:
            query = apply_rotary_emb(query, image_rotary_emb)
            if motion_rotary_emb is not None:
                key = apply_rotary_emb(key, motion_rotary_emb)

        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        return hidden_states



class CogVideoXAttnProcessor2_0:
    r"""Processor for implementing scaled dot-product attention for the
    CogVideoX model.

    It applies a rotary embedding on query and key vectors, but does not include spatial normalization.
    """

    def __init__(self):
        if not hasattr(F, 'scaled_dot_product_attention'):
            raise ImportError('CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.')

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
        motion_rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:

        batch_size, sequence_length, _ = hidden_states.shape 

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        query = attn.to_q(hidden_states)
        key = attn.to_k(hidden_states)
        value = attn.to_v(hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)    # [batch_size, heads, seq_len, dim]
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # Apply RoPE if needed
        if image_rotary_emb is not None:
            image_seq_length = image_rotary_emb[0].shape[0]
            query[:, :, :image_seq_length] = apply_rotary_emb(query[:, :, :image_seq_length], image_rotary_emb)
            if motion_rotary_emb is not None:
                query[:, :, image_seq_length:] = apply_rotary_emb(query[:, :, image_seq_length:], motion_rotary_emb)
            if not attn.is_cross_attention:
                key[:, :, :image_seq_length] = apply_rotary_emb(key[:, :, :image_seq_length], image_rotary_emb)
                if motion_rotary_emb is not None:
                    key[:, :, image_seq_length:] = apply_rotary_emb(key[:, :, image_seq_length:], motion_rotary_emb)

        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


class CogVideoXPatchEmbed(nn.Module):
    def __init__(
        self,
        patch_size: int = 2,
        in_channels: int = 16,
        embed_dim: int = 1920,
        text_embed_dim: int = 4096,
        bias: bool = True,
        sample_width: int = 90,
        sample_height: int = 60,
        sample_frames: int = 49,
        temporal_compression_ratio: int = 4,
        max_text_seq_length: int = 226,
        spatial_interpolation_scale: float = 1.875,
        temporal_interpolation_scale: float = 1.0,
        use_positional_embeddings: bool = True,
    ) -> None:
        super().__init__()

        self.patch_size = patch_size
        self.embed_dim = embed_dim
        self.sample_height = sample_height
        self.sample_width = sample_width
        self.sample_frames = sample_frames
        self.temporal_compression_ratio = temporal_compression_ratio
        self.max_text_seq_length = max_text_seq_length
        self.spatial_interpolation_scale = spatial_interpolation_scale
        self.temporal_interpolation_scale = temporal_interpolation_scale
        self.use_positional_embeddings = use_positional_embeddings

        self.proj = nn.Conv2d(
            in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
        )
        self.text_proj = nn.Linear(text_embed_dim, embed_dim)

        if use_positional_embeddings:
            pos_embedding = self._get_positional_embeddings(sample_height, sample_width, sample_frames)
            self.register_buffer('pos_embedding', pos_embedding, persistent=False)

    def _get_positional_embeddings(self, sample_height: int, sample_width: int, sample_frames: int) -> torch.Tensor:
        post_patch_height = sample_height // self.patch_size
        post_patch_width = sample_width // self.patch_size
        post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1
        num_patches = post_patch_height * post_patch_width * post_time_compression_frames

        pos_embedding = get_3d_sincos_pos_embed(
            self.embed_dim,
            (post_patch_width, post_patch_height),
            post_time_compression_frames,
            self.spatial_interpolation_scale,
            self.temporal_interpolation_scale,
        )
        pos_embedding = torch.from_numpy(pos_embedding).flatten(0, 1)
        joint_pos_embedding = torch.zeros(
            1, self.max_text_seq_length + num_patches, self.embed_dim, requires_grad=False
        )
        joint_pos_embedding.data[:, self.max_text_seq_length :].copy_(pos_embedding)

        return joint_pos_embedding

    def forward(self, image_embeds: torch.Tensor):
        r"""
        Args:
            text_embeds (`torch.Tensor`):
                Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
            image_embeds (`torch.Tensor`):
                Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
        """
        batch, num_frames, channels, height, width = image_embeds.shape
        image_embeds = image_embeds.reshape(-1, channels, height, width)
        image_embeds = self.proj(image_embeds)      # [2*7, 3072, h/8/2, w/8/2]
        image_embeds = image_embeds.view(batch, num_frames, *image_embeds.shape[1:])
        image_embeds = image_embeds.flatten(3).transpose(2, 3)  # [batch, num_frames, height x width, channels]
        image_embeds = image_embeds.flatten(1, 2).contiguous()  # [batch, num_frames x height x width, channels]

        if self.use_positional_embeddings:
            pre_time_compression_frames = (num_frames - 1) * self.temporal_compression_ratio + 1
            if (
                self.sample_height != height
                or self.sample_width != width
                or self.sample_frames != pre_time_compression_frames
            ):
                pos_embedding = self._get_positional_embeddings(height, width, pre_time_compression_frames)
                pos_embedding = pos_embedding.to(embeds.device, dtype=embeds.dtype)
            else:
                pos_embedding = self.pos_embedding

            embeds = embeds + pos_embedding

        return image_embeds


@maybe_allow_in_graph
class CogVideoXBlock(nn.Module):
    r"""
    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,
        motion_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: Optional[int] = None,
        ff_bias: bool = True,
        attention_out_bias: bool = True,
        cross_attention: bool = False,
    ):
        super().__init__()

        self.is_cross_attention = cross_attention
        
        if self.is_cross_attention:
            self.attn0 = Attention(
                query_dim=dim,
                cross_attention_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=CogVideoXAttnProcessor1_0(),
            )

        # 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: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        motion_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> torch.Tensor:

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

        # self attention
        attn_hidden_states = self.attn1(
            hidden_states=norm_hidden_states,
            image_rotary_emb=image_rotary_emb,
        )
        hidden_states = hidden_states + gate_msa * attn_hidden_states

        if self.is_cross_attention:
            cross_attn_hidden_states = self.attn0(
                hidden_states=hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                image_rotary_emb=image_rotary_emb,
                motion_rotary_emb=motion_rotary_emb,
            )
            hidden_states = hidden_states + cross_attn_hidden_states

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

        # feed-forward
        ff_output = self.ff(norm_hidden_states)

        hidden_states = hidden_states + gate_ff * ff_output

        return hidden_states


class Transformer7B(ModelMixin, ConfigMixin):
    """
    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 or not 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 CogVideoX 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 or not 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.
    """

    _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: Optional[int] = 16,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        time_embed_dim: int = 512,
        text_embed_dim: int = 4096,
        motion_dim: int = 168,
        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,
    ):
        super().__init__()
        inner_dim = num_attention_heads * attention_head_dim    # 48 * 64 = 3072

        self.unconditional_motion_token = torch.nn.Parameter(torch.randn(312, 3072))
        print(self.unconditional_motion_token[0])

        # 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,
        )
        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)      # 3072 --> 512

        self.transformer_blocks = nn.ModuleList(
            [
                CogVideoXBlock(
                    dim=inner_dim,
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=attention_head_dim,
                    time_embed_dim=time_embed_dim,
                    motion_dim=motion_dim,
                    dropout=dropout,
                    activation_fn=activation_fn,
                    attention_bias=attention_bias,
                    norm_elementwise_affine=norm_elementwise_affine,
                    norm_eps=norm_eps,
                    cross_attention=True,
                )
                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.gradient_checkpointing = False

    def _set_gradient_checkpointing(self, module, value=False):
        self.gradient_checkpointing = value

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, 'get_processor'):
                processors[f'{name}.processor'] = module.get_processor()

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f'{name}.{sub_name}', child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.
        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f'A dict of processors was passed, but the number of processors {len(processor)} does not match the'
                f' number of attention layers: {count}. Please make sure to pass {count} processor classes.'
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, 'set_processor'):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f'{name}.processor'))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f'{name}.{sub_name}', child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with
    # FusedAttnProcessor2_0->FusedCogVideoXAttnProcessor2_0
    def fuse_qkv_projections(self):
        """Enables fused QKV projections. For self-attention modules, all
        projection matrices (i.e., query, key, value) are fused. For cross-
        attention modules, key and value projection matrices are fused.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>
        """
        self.original_attn_processors = None

        for _, attn_processor in self.attn_processors.items():
            if 'Added' in str(attn_processor.__class__.__name__):
                raise ValueError('`fuse_qkv_projections()` is not supported for models having added KV projections.')

        self.original_attn_processors = self.attn_processors

        for module in self.modules():
            if isinstance(module, Attention):
                module.fuse_projections(fuse=True)

        self.set_attn_processor(FusedCogVideoXAttnProcessor2_0())

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
    def unfuse_qkv_projections(self):
        """Disables the fused QKV projection if enabled.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>
        """
        if self.original_attn_processors is not None:
            self.set_attn_processor(self.original_attn_processors)

    def forward(
        self,
        hidden_states: torch.Tensor,
        timestep: Union[int, float, torch.LongTensor],
        timestep_cond: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        motion_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        motion_emb: Optional[torch.Tensor] = None,
        camera_emb: Optional[torch.Tensor] = None,
        need_broadcast: bool = True,
        return_dict: bool = True,
    ):
        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)      # (2, 3072)
        emb = self.time_embedding(t_emb, timestep_cond)  # (2, 3072) --> (2, 512)

        # 2. Patch embedding
        hidden_states = self.patch_embed(hidden_states)      # (2, 226+9450, dim=3072) 
        hidden_states = self.embedding_dropout(hidden_states)
        image_seq_length = image_rotary_emb[0].shape[0]
        motion_seq_length = motion_emb.shape[1]   # 168
        # hidden_states = hidden_states[:, motion_seq_length:]
        encoder_hidden_states = motion_emb
        # encoder_hidden_states = self.motion_proj(motion_emb)

        # 3. Transformer blocks
        for i, block in enumerate(self.transformer_blocks):
            if self.training and self.gradient_checkpointing:    # train with gradient checkpointing to save memory

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {'use_reentrant': False} if is_torch_version('>=', '1.11.0') else {}
                hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    encoder_hidden_states,
                    emb,
                    image_rotary_emb,
                    motion_rotary_emb,
                    **ckpt_kwargs,
                )
            else:
                hidden_states = block(
                    hidden_states=hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    temb=emb,
                    image_rotary_emb=image_rotary_emb,
                    motion_rotary_emb=motion_rotary_emb,
                )

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

        # 5. Unpatchify
        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)
