# Adapted from PixArt
#
# Copyright (C) 2023  PixArt-alpha/PixArt-alpha
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.
#
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# PixArt: https://github.com/PixArt-alpha/PixArt-alpha
# DiT:    https://github.com/facebookresearch/DiT/tree/main
# --------------------------------------------------------

import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from timm.models.layers import DropPath
from timm.models.vision_transformer import Mlp

# from .builder import MODELS
from opensora.acceleration.checkpoint import auto_grad_checkpoint
from opensora.models.layers.blocks import (
    Attention,
    CaptionEmbedder,
    MultiHeadCrossAttention,
    PatchEmbed3D,
    SeqParallelAttention,
    SeqParallelMultiHeadCrossAttention,
    SizeEmbedder,
    T2IFinalLayer,
    TimestepEmbedder,
    approx_gelu,
    get_1d_sincos_pos_embed,
    get_2d_sincos_pos_embed,
    get_layernorm,
    t2i_modulate,
)
from opensora.registry import MODELS
from opensora.utils.ckpt_utils import load_checkpoint


class PixArtBlock(nn.Module):
    """
    A PixArt block with adaptive layer norm (adaLN-single) conditioning.
    """

    def __init__(
        self,
        hidden_size,
        num_heads,
        mlp_ratio=4.0,
        drop_path=0.0,
        enable_flashattn=False,
        enable_layernorm_kernel=False,
        enable_sequence_parallelism=False,
    ):
        super().__init__()
        self.hidden_size = hidden_size
        self.enable_flashattn = enable_flashattn
        self._enable_sequence_parallelism = enable_sequence_parallelism

        if enable_sequence_parallelism:
            self.attn_cls = SeqParallelAttention
            self.mha_cls = SeqParallelMultiHeadCrossAttention
        else:
            self.attn_cls = Attention
            self.mha_cls = MultiHeadCrossAttention

        self.norm1 = get_layernorm(hidden_size, eps=1e-6, affine=False, use_kernel=enable_layernorm_kernel)
        self.attn = self.attn_cls(
            hidden_size,
            num_heads=num_heads,
            qkv_bias=True,
            enable_flashattn=enable_flashattn,
        )
        self.cross_attn = self.mha_cls(hidden_size, num_heads)
        self.norm2 = get_layernorm(hidden_size, eps=1e-6, affine=False, use_kernel=enable_layernorm_kernel)
        self.mlp = Mlp(
            in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)

    def forward(self, x, y, t, mask=None):
        B, N, C = x.shape

        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
            self.scale_shift_table[None] + t.reshape(B, 6, -1)
        ).chunk(6, dim=1)
        x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)).reshape(B, N, C))
        x = x + self.cross_attn(x, y, mask)
        x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))

        return x


@MODELS.register_module()
class PixArt(nn.Module):
    """
    Diffusion model with a Transformer backbone.
    """

    def __init__(
        self,
        input_size=(1, 32, 32),
        in_channels=4,
        patch_size=(1, 2, 2),
        hidden_size=1152,
        depth=28,
        num_heads=16,
        mlp_ratio=4.0,
        class_dropout_prob=0.1,
        pred_sigma=True,
        drop_path: float = 0.0,
        no_temporal_pos_emb=False,
        caption_channels=4096,
        model_max_length=120,
        dtype=torch.float32,
        freeze=None,
        space_scale=1.0,
        time_scale=1.0,
        enable_flashattn=False,
        enable_layernorm_kernel=False,
    ):
        super().__init__()
        self.pred_sigma = pred_sigma
        self.in_channels = in_channels
        self.out_channels = in_channels * 2 if pred_sigma else in_channels
        self.hidden_size = hidden_size
        self.patch_size = patch_size
        self.input_size = input_size
        num_patches = np.prod([input_size[i] // patch_size[i] for i in range(3)])
        self.num_patches = num_patches
        self.num_temporal = input_size[0] // patch_size[0]
        self.num_spatial = num_patches // self.num_temporal
        self.base_size = int(np.sqrt(self.num_spatial))
        self.num_heads = num_heads
        self.dtype = dtype
        self.no_temporal_pos_emb = no_temporal_pos_emb
        self.depth = depth
        self.mlp_ratio = mlp_ratio
        self.enable_flashattn = enable_flashattn
        self.enable_layernorm_kernel = enable_layernorm_kernel
        self.space_scale = space_scale
        self.time_scale = time_scale

        self.x_embedder = PatchEmbed3D(patch_size, in_channels, hidden_size)
        self.t_embedder = TimestepEmbedder(hidden_size)
        self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
        self.y_embedder = CaptionEmbedder(
            in_channels=caption_channels,
            hidden_size=hidden_size,
            uncond_prob=class_dropout_prob,
            act_layer=approx_gelu,
            token_num=model_max_length,
        )

        self.register_buffer("pos_embed", self.get_spatial_pos_embed())
        self.register_buffer("pos_embed_temporal", self.get_temporal_pos_embed())

        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
        self.blocks = nn.ModuleList(
            [
                PixArtBlock(
                    hidden_size,
                    num_heads,
                    mlp_ratio=mlp_ratio,
                    drop_path=drop_path[i],
                    enable_flashattn=enable_flashattn,
                    enable_layernorm_kernel=enable_layernorm_kernel,
                )
                for i in range(depth)
            ]
        )
        self.final_layer = T2IFinalLayer(hidden_size, np.prod(self.patch_size), self.out_channels)

        self.initialize_weights()
        if freeze is not None:
            assert freeze in ["text"]
            if freeze == "text":
                self.freeze_text()

    def forward(self, x, timestep, y, mask=None):
        """
        Forward pass of PixArt.
        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        t: (N,) tensor of diffusion timesteps
        y: (N, 1, 120, C) tensor of class labels
        """
        x = x.to(self.dtype)
        timestep = timestep.to(self.dtype)
        y = y.to(self.dtype)

        # embedding
        x = self.x_embedder(x)  # (B, N, D)
        x = rearrange(x, "b (t s) d -> b t s d", t=self.num_temporal, s=self.num_spatial)
        x = x + self.pos_embed
        if not self.no_temporal_pos_emb:
            x = rearrange(x, "b t s d -> b s t d")
            x = x + self.pos_embed_temporal
            x = rearrange(x, "b s t d -> b (t s) d")
        else:
            x = rearrange(x, "b t s d -> b (t s) d")

        t = self.t_embedder(timestep, dtype=x.dtype)  # (N, D)
        t0 = self.t_block(t)
        y = self.y_embedder(y, self.training)  # (N, 1, L, D)
        if mask is not None:
            if mask.shape[0] != y.shape[0]:
                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
            mask = mask.squeeze(1).squeeze(1)
            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
            y_lens = mask.sum(dim=1).tolist()
        else:
            y_lens = [y.shape[2]] * y.shape[0]
            y = y.squeeze(1).view(1, -1, x.shape[-1])

        # blocks
        for block in self.blocks:
            x = auto_grad_checkpoint(block, x, y, t0, y_lens)

        # final process
        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
        x = self.unpatchify(x)  # (N, out_channels, H, W)

        # cast to float32 for better accuracy
        x = x.to(torch.float32)
        return x

    def unpatchify(self, x):
        c = self.out_channels
        t, h, w = [self.input_size[i] // self.patch_size[i] for i in range(3)]
        pt, ph, pw = self.patch_size

        x = x.reshape(shape=(x.shape[0], t, h, w, pt, ph, pw, c))
        x = rearrange(x, "n t h w r p q c -> n c t r h p w q")
        imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw))
        return imgs

    def get_spatial_pos_embed(self, grid_size=None):
        if grid_size is None:
            grid_size = self.input_size[1:]
        pos_embed = get_2d_sincos_pos_embed(
            self.hidden_size,
            (grid_size[0] // self.patch_size[1], grid_size[1] // self.patch_size[2]),
            scale=self.space_scale,
            base_size=self.base_size,
        )
        pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).requires_grad_(False)
        return pos_embed

    def get_temporal_pos_embed(self):
        pos_embed = get_1d_sincos_pos_embed(
            self.hidden_size,
            self.input_size[0] // self.patch_size[0],
            scale=self.time_scale,
        )
        pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).requires_grad_(False)
        return pos_embed

    def freeze_text(self):
        for n, p in self.named_parameters():
            if "cross_attn" in n:
                p.requires_grad = False

    def initialize_weights(self):
        # Initialize transformer layers:
        def _basic_init(module):
            if isinstance(module, nn.Linear):
                torch.nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)

        self.apply(_basic_init)

        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
        w = self.x_embedder.proj.weight.data
        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # Initialize timestep embedding MLP:
        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
        nn.init.normal_(self.t_block[1].weight, std=0.02)

        # Initialize caption embedding MLP:
        nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02)
        nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02)

        # Zero-out adaLN modulation layers in PixArt blocks:
        for block in self.blocks:
            nn.init.constant_(block.cross_attn.proj.weight, 0)
            nn.init.constant_(block.cross_attn.proj.bias, 0)

        # Zero-out output layers:
        nn.init.constant_(self.final_layer.linear.weight, 0)
        nn.init.constant_(self.final_layer.linear.bias, 0)


@MODELS.register_module()
class PixArtMS(PixArt):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        assert self.hidden_size % 3 == 0, "hidden_size must be divisible by 3"
        self.csize_embedder = SizeEmbedder(self.hidden_size // 3)
        self.ar_embedder = SizeEmbedder(self.hidden_size // 3)

    def forward(self, x, timestep, y, mask=None, data_info=None):
        """
        Forward pass of PixArt.
        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        t: (N,) tensor of diffusion timesteps
        y: (N, 1, 120, C) tensor of class labels
        """
        x = x.to(self.dtype)
        timestep = timestep.to(self.dtype)
        y = y.to(self.dtype)

        c_size = data_info["hw"]
        ar = data_info["ar"]
        pos_embed = self.get_spatial_pos_embed((x.shape[-2], x.shape[-1])).to(x.dtype)

        # embedding
        x = self.x_embedder(x)  # (B, N, D)
        x = rearrange(x, "b (t s) d -> b t s d", t=self.num_temporal, s=self.num_spatial)
        x = x + pos_embed.to(x.device)
        if not self.no_temporal_pos_emb:
            x = rearrange(x, "b t s d -> b s t d")
            x = x + self.pos_embed_temporal
            x = rearrange(x, "b s t d -> b (t s) d")
        else:
            x = rearrange(x, "b t s d -> b (t s) d")

        t = self.t_embedder(timestep, dtype=x.dtype)  # (N, D)
        B = x.shape[0]
        csize = self.csize_embedder(c_size, B)
        ar = self.ar_embedder(ar, B)
        t = t + torch.cat([csize, ar], dim=1)

        t0 = self.t_block(t)
        y = self.y_embedder(y, self.training)  # (N, 1, L, D)
        if mask is not None:
            if mask.shape[0] != y.shape[0]:
                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
            mask = mask.squeeze(1).squeeze(1)
            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
            y_lens = mask.sum(dim=1).tolist()
        else:
            y_lens = [y.shape[2]] * y.shape[0]
            y = y.squeeze(1).view(1, -1, x.shape[-1])

        # blocks
        for block in self.blocks:
            x = block(x, y, t0, y_lens)

        # final process
        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
        x = self.unpatchify(x)  # (N, out_channels, H, W)

        # cast to float32 for better accuracy
        x = x.to(torch.float32)
        return x


@MODELS.register_module("PixArt-XL/2")
def PixArt_XL_2(from_pretrained=None, **kwargs):
    model = PixArt(depth=28, hidden_size=1152, patch_size=(1, 2, 2), num_heads=16, **kwargs)
    if from_pretrained is not None:
        load_checkpoint(model, from_pretrained)
    return model


@MODELS.register_module("PixArtMS-XL/2")
def PixArtMS_XL_2(from_pretrained=None, **kwargs):
    model = PixArtMS(depth=28, hidden_size=1152, patch_size=(1, 2, 2), num_heads=16, **kwargs)
    if from_pretrained is not None:
        load_checkpoint(model, from_pretrained)
    return model
