# Adapted from Open-Sora-Plan

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# Open-Sora-Plan: https://github.com/PKU-YuanGroup/Open-Sora-Plan
# --------------------------------------------------------

from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from diffusers.configuration_utils import ConfigMixin, register_to_config


from diffusers.models.modeling_utils import ModelMixin

from diffusers.utils import BaseOutput, is_xformers_available
from einops import rearrange
from torch import nn
from diffusers.models.embeddings import PixArtAlphaTextProjection

from allegro.models.transformers.block import to_2tuple, BasicTransformerBlock, AdaLayerNormSingle
from allegro.models.transformers.embedding import PatchEmbed2D

from diffusers.utils import logging

logger = logging.get_logger(__name__)

@dataclass
class Transformer3DModelOutput(BaseOutput):
    """
    The output of [`Transformer2DModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
            distributions for the unnoised latent pixels.
    """

    sample: torch.FloatTensor


class AllegroTransformer3DModel(ModelMixin, ConfigMixin):
    _supports_gradient_checkpointing = True

    """
    A 2D Transformer model for image-like data.

    Parameters:
        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
        in_channels (`int`, *optional*):
            The number of channels in the input and output (specify if the input is **continuous**).
        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
            This is fixed during training since it is used to learn a number of position embeddings.
        num_vector_embeds (`int`, *optional*):
            The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
            Includes the class for the masked latent pixel.
        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
        num_embeds_ada_norm ( `int`, *optional*):
            The number of diffusion steps used during training. Pass if at least one of the norm_layers is
            `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
            added to the hidden states.

            During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
        attention_bias (`bool`, *optional*):
            Configure if the `TransformerBlocks` attention should contain a bias parameter.
    """

    @register_to_config
    def __init__(
        self,
        num_attention_heads: int = 16,
        attention_head_dim: int = 88,
        in_channels: Optional[int] = None,
        out_channels: Optional[int] = None,
        num_layers: int = 1,
        dropout: float = 0.0,
        cross_attention_dim: Optional[int] = None,
        attention_bias: bool = False,
        sample_size: Optional[int] = None,
        sample_size_t: Optional[int] = None,
        patch_size: Optional[int] = None,
        patch_size_t: Optional[int] = None,
        activation_fn: str = "geglu",
        num_embeds_ada_norm: Optional[int] = None,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        double_self_attention: bool = False,
        upcast_attention: bool = False,
        norm_type: str = "ada_norm",
        norm_elementwise_affine: bool = True,
        norm_eps: float = 1e-5,
        caption_channels: int = None,
        interpolation_scale_h: float = None,
        interpolation_scale_w: float = None,
        interpolation_scale_t: float = None,
        use_additional_conditions: Optional[bool] = None,
        sa_attention_mode: str = "flash", 
        ca_attention_mode: str = 'xformers', 
        downsampler: str = None, 
        use_rope: bool = False,
        model_max_length: int = 300,
    ):
        super().__init__()
        self.use_linear_projection = use_linear_projection
        self.interpolation_scale_t = interpolation_scale_t
        self.interpolation_scale_h = interpolation_scale_h
        self.interpolation_scale_w = interpolation_scale_w
        self.downsampler = downsampler
        self.caption_channels = caption_channels
        self.num_attention_heads = num_attention_heads
        self.attention_head_dim = attention_head_dim
        inner_dim = num_attention_heads * attention_head_dim
        self.inner_dim = inner_dim
        self.in_channels = in_channels
        self.out_channels = in_channels if out_channels is None else out_channels
        self.use_rope = use_rope
        self.model_max_length = model_max_length
        self.num_layers = num_layers
        self.config.hidden_size = inner_dim


        # 1. Transformer3DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
        # Define whether input is continuous or discrete depending on configuration
        assert in_channels is not None and patch_size is not None

        # 2. Initialize the right blocks.
        # Initialize the output blocks and other projection blocks when necessary.

        assert self.config.sample_size_t is not None, "AllegroTransformer3DModel over patched input must provide sample_size_t"
        assert self.config.sample_size is not None, "AllegroTransformer3DModel over patched input must provide sample_size"
        #assert not (self.config.sample_size_t == 1 and self.config.patch_size_t == 2), "Image do not need patchfy in t-dim"

        self.num_frames = self.config.sample_size_t
        self.config.sample_size = to_2tuple(self.config.sample_size)
        self.height = self.config.sample_size[0]
        self.width = self.config.sample_size[1]
        self.patch_size_t = self.config.patch_size_t
        self.patch_size = self.config.patch_size
        interpolation_scale_t = ((self.config.sample_size_t - 1) // 16 + 1) if self.config.sample_size_t % 2 == 1 else self.config.sample_size_t / 16
        interpolation_scale_t = (
            self.config.interpolation_scale_t if self.config.interpolation_scale_t is not None else interpolation_scale_t
        )
        interpolation_scale = (
            self.config.interpolation_scale_h if self.config.interpolation_scale_h is not None else self.config.sample_size[0] / 30, 
            self.config.interpolation_scale_w if self.config.interpolation_scale_w is not None else self.config.sample_size[1] / 40, 
        )
        self.pos_embed = PatchEmbed2D(
            num_frames=self.config.sample_size_t,
            height=self.config.sample_size[0],
            width=self.config.sample_size[1],
            patch_size_t=self.config.patch_size_t,
            patch_size=self.config.patch_size,
            in_channels=self.in_channels,
            embed_dim=self.inner_dim,
            interpolation_scale=interpolation_scale, 
            interpolation_scale_t=interpolation_scale_t,
            use_abs_pos=not self.config.use_rope, 
        )
        interpolation_scale_thw = (interpolation_scale_t, *interpolation_scale)

        # 3. Define transformers blocks, spatial attention
        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim,
                    num_attention_heads,
                    attention_head_dim,
                    dropout=dropout,
                    cross_attention_dim=cross_attention_dim,
                    activation_fn=activation_fn,
                    num_embeds_ada_norm=num_embeds_ada_norm,
                    attention_bias=attention_bias,
                    only_cross_attention=only_cross_attention,
                    double_self_attention=double_self_attention,
                    upcast_attention=upcast_attention,
                    norm_type=norm_type,
                    norm_elementwise_affine=norm_elementwise_affine,
                    norm_eps=norm_eps,
                    sa_attention_mode=sa_attention_mode,
                    ca_attention_mode=ca_attention_mode, 
                    use_rope=use_rope,
                    interpolation_scale_thw=interpolation_scale_thw, 
                    block_idx=d,
                )
                for d in range(num_layers)
            ]
        )

        # 4. Define output layers

        if norm_type != "ada_norm_single":
            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
            self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
            self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
        elif norm_type == "ada_norm_single":
            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
            self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
            self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)

        # 5. PixArt-Alpha blocks.
        self.adaln_single = None
        self.use_additional_conditions = False
        if norm_type == "ada_norm_single":
            # self.use_additional_conditions = self.config.sample_size[0] == 128  # False, 128 -> 1024
            # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
            # additional conditions until we find better name
            self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions)

        self.caption_projection = None
        if caption_channels is not None:
            self.caption_projection = PixArtAlphaTextProjection(
                in_features=caption_channels, hidden_size=inner_dim
            )
        
        self.gradient_checkpointing = False

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


    def forward(
        self,
        hidden_states: torch.Tensor,
        timestep: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        added_cond_kwargs: Dict[str, torch.Tensor] = None,
        class_labels: Optional[torch.LongTensor] = None,
        cross_attention_kwargs: Dict[str, Any] = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ):
        """
        The [`Transformer2DModel`] forward method.

        Args:
            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, frame, channel, height, width)` if continuous):
                Input `hidden_states`.
            encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            timestep ( `torch.LongTensor`, *optional*):
                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
                `AdaLayerZeroNorm`.
            added_cond_kwargs ( `Dict[str, Any]`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AdaLayerNormSingle`
            cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            attention_mask ( `torch.Tensor`, *optional*):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            encoder_attention_mask ( `torch.Tensor`, *optional*):
                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:

                    * Mask `(batch, sequence_length)` True = keep, False = discard.
                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.

                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
                above. This bias will be added to the cross-attention scores.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.

        Returns:
            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
            `tuple` where the first element is the sample tensor.
        """
        batch_size, c, frame, h, w = hidden_states.shape

        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
        # expects mask of shape:
        #   [batch, key_tokens]
        # adds singleton query_tokens dimension:
        #   [batch,                    1, key_tokens]
        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)        attention_mask_vid, attention_mask_img = None, None
        if attention_mask is not None and attention_mask.ndim == 4:
            # assume that mask is expressed as:
            #   (1 = keep,      0 = discard)
            # convert mask into a bias that can be added to attention scores:
            #   (keep = +0,     discard = -10000.0)
            # b, frame+use_image_num, h, w -> a video with images
            # b, 1, h, w -> only images
            attention_mask = attention_mask.to(self.dtype)
            attention_mask_vid = attention_mask[:, :frame]  # b, frame, h, w

            if attention_mask_vid.numel() > 0:
                attention_mask_vid = attention_mask_vid.unsqueeze(1)  # b 1 t h w
                attention_mask_vid = F.max_pool3d(attention_mask_vid, kernel_size=(self.patch_size_t, self.patch_size, self.patch_size), 
                                                  stride=(self.patch_size_t, self.patch_size, self.patch_size))
                attention_mask_vid = rearrange(attention_mask_vid, 'b 1 t h w -> (b 1) 1 (t h w)') 

            attention_mask_vid = (1 - attention_mask_vid.bool().to(self.dtype)) * -10000.0 if attention_mask_vid.numel() > 0 else None

        # convert encoder_attention_mask to a bias the same way we do for attention_mask
        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 3:  
            # b, 1+use_image_num, l -> a video with images
            # b, 1, l -> only images
            encoder_attention_mask = (1 - encoder_attention_mask.to(self.dtype)) * -10000.0
            encoder_attention_mask_vid = rearrange(encoder_attention_mask, 'b 1 l -> (b 1) 1 l') if encoder_attention_mask.numel() > 0 else None

        # 1. Input
        frame = frame // self.patch_size_t  # patchfy
        # print('frame', frame)
        height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size

        added_cond_kwargs = {"resolution": None, "aspect_ratio": None} if added_cond_kwargs is None else added_cond_kwargs
        hidden_states, encoder_hidden_states_vid, \
        timestep_vid, embedded_timestep_vid = self._operate_on_patched_inputs(
            hidden_states, encoder_hidden_states, timestep, added_cond_kwargs, batch_size,
        )


        for _, block in enumerate(self.transformer_blocks):
            hidden_states = block(
                hidden_states,
                attention_mask_vid,
                encoder_hidden_states_vid,
                encoder_attention_mask_vid,
                timestep_vid,
                cross_attention_kwargs,
                class_labels,
                frame=frame, 
                height=height, 
                width=width, 
            )

         # 3. Output
        output = None 
        if hidden_states is not None:
            output = self._get_output_for_patched_inputs(
                hidden_states=hidden_states,
                timestep=timestep_vid,
                class_labels=class_labels,
                embedded_timestep=embedded_timestep_vid,
                num_frames=frame, 
                height=height,
                width=width,
            )  # b c t h w

        if not return_dict:
            return (output,)

        return Transformer3DModelOutput(sample=output)

    def _operate_on_patched_inputs(self, hidden_states, encoder_hidden_states, timestep, added_cond_kwargs, batch_size):
            # batch_size = hidden_states.shape[0]
            hidden_states_vid = self.pos_embed(hidden_states.to(self.dtype))
            timestep_vid = None
            embedded_timestep_vid = None
            encoder_hidden_states_vid = None

            if self.adaln_single is not None:
                if self.use_additional_conditions and added_cond_kwargs is None:
                    raise ValueError(
                        "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`."
                    )
                timestep, embedded_timestep = self.adaln_single(
                    timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=self.dtype
                )  # b 6d, b d

                timestep_vid = timestep
                embedded_timestep_vid = embedded_timestep

            if self.caption_projection is not None:
                encoder_hidden_states = self.caption_projection(encoder_hidden_states)  # b, 1+use_image_num, l, d or b, 1, l, d
                encoder_hidden_states_vid = rearrange(encoder_hidden_states[:, :1], 'b 1 l d -> (b 1) l d')

            return hidden_states_vid, encoder_hidden_states_vid, timestep_vid, embedded_timestep_vid

    def _get_output_for_patched_inputs(
        self, hidden_states, timestep, class_labels, embedded_timestep, num_frames, height=None, width=None
    ):  
        # import ipdb;ipdb.set_trace()
        if self.config.norm_type != "ada_norm_single":
            conditioning = self.transformer_blocks[0].norm1.emb(
                timestep, class_labels, hidden_dtype=self.dtype
            )
            shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
            hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
            hidden_states = self.proj_out_2(hidden_states)
        elif self.config.norm_type == "ada_norm_single":
            shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)
            hidden_states = self.norm_out(hidden_states)
            # Modulation
            hidden_states = hidden_states * (1 + scale) + shift
            hidden_states = self.proj_out(hidden_states)
            hidden_states = hidden_states.squeeze(1)

        # unpatchify
        if self.adaln_single is None:
            height = width = int(hidden_states.shape[1] ** 0.5)
        hidden_states = hidden_states.reshape(
            shape=(-1, num_frames, height, width, self.patch_size_t, self.patch_size, self.patch_size, self.out_channels)
        )
        hidden_states = torch.einsum("nthwopqc->nctohpwq", hidden_states)
        output = hidden_states.reshape(
            shape=(-1, self.out_channels, num_frames * self.patch_size_t, height * self.patch_size, width * self.patch_size)
        )
        return output