# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
#
# Modifications Copyright (c) Ensemble AI, 2025.
# Description of modifications: Using NdLinear in the model to
# make the model more compact yet with similar performance.
import torch
import torch.nn as nn
import numpy as np
import math
from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
from mlp import NdMlp
from ndlinear import NdLinear
from transformers import PreTrainedModel, PretrainedConfig

def modulate(x, shift, scale):
    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)

class TimestepEmbedder(nn.Module):
    def __init__(self, hidden_size, frequency_embedding_size=256):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True),
        )
        self.frequency_embedding_size = frequency_embedding_size

    @staticmethod
    def timestep_embedding(t, dim, max_period=10000):
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(device=t.device)
        args = t[:, None].float() * freqs[None]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        if dim % 2:
            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
        return embedding

    def forward(self, t):
        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
        t_emb = self.mlp(t_freq)
        return t_emb

class NdTimestepEmbedder(nn.Module):
    def __init__(self, hidden_size, frequency_embedding_size=256, use_num_transforms=2, tse_scale_factor=1, knowledge_transfer=False, src_layers=None):
        super().__init__()
        self.activation = nn.SiLU()
        self.frequency_embedding_size = frequency_embedding_size
        self.use_num_transforms = use_num_transforms

        if knowledge_transfer and not src_layers:
            raise ValueError("Source layers must be provided for knowledge transfer.")

        if use_num_transforms == 2:
            self.ndlinear_1 = NdLinear((frequency_embedding_size // 16, 16), (int(hidden_size // tse_scale_factor // 2), 2))
            self.ndlinear_2 = NdLinear((int(hidden_size // tse_scale_factor // 2), 2), (hidden_size, 1))

        if use_num_transforms == 20:
            self.ndlinear_1 = NdLinear((frequency_embedding_size, 1), (int(hidden_size // tse_scale_factor), 1))
            self.ndlinear_2 = NdLinear((int(hidden_size // tse_scale_factor), 1), (hidden_size, 1))

    @staticmethod
    def timestep_embedding(t, dim, max_period=10000):
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(device=t.device)
        args = t[:, None].float() * freqs[None]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        if dim % 2:
            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
        return embedding

    def forward(self, t):
        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
        if self.use_num_transforms == 2:
            t_freq = t_freq.reshape(*t_freq.shape, 1)
        elif self.use_num_transforms == 21:
            t_freq = t_freq.reshape(t_freq.shape[0], 16, 16)
        elif self.use_num_transforms == 3:
            t_freq = t_freq.reshape(t_freq.shape[0], t_freq.shape[1] // 16, 16, 1)
        elif self.use_num_transforms == 4:
            t_freq = t_freq.reshape(t_freq.shape[0], t_freq.shape[1] // 16, 4, 4, 1)
        t_emb = self.ndlinear_1(t_freq)
        t_emb = self.activation(t_emb)
        t_emb = self.ndlinear_2(t_emb)
        t_emb = t_emb.squeeze()
        return t_emb

class LabelEmbedder(nn.Module):
    def __init__(self, num_classes, hidden_size, dropout_prob):
        super().__init__()
        use_cfg_embedding = dropout_prob > 0
        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
        self.num_classes = num_classes
        self.dropout_prob = dropout_prob

    def token_drop(self, labels, force_drop_ids=None):
        if force_drop_ids is None:
            drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
        else:
            drop_ids = force_drop_ids == 1
        labels = torch.where(drop_ids, self.num_classes, labels)
        return labels

    def forward(self, labels, train, force_drop_ids=None):
        use_dropout = self.dropout_prob > 0
        if (train and use_dropout) or (force_drop_ids is not None):
            labels = self.token_drop(labels, force_drop_ids)
        embeddings = self.embedding_table(labels)
        return embeddings

class DiTBlock(nn.Module):
    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, use_ndmlp=False, use_variant=4, use_ndadaln=False, **block_kwargs):
        super().__init__()
        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        approx_gelu = lambda: nn.GELU(approximate="tanh")
        if use_ndmlp:
            self.mlp = NdMlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0, use_variant=use_variant)
        else:
            self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)

    def forward(self, x, c):
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
        x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
        modulated_x = modulate(self.norm2(x), shift_mlp, scale_mlp)
        mlp_output = self.mlp(modulated_x)
        gated_mlp_output = gate_mlp.unsqueeze(1) * mlp_output
        x = x + gated_mlp_output
        return x

class FinalLayer(nn.Module):
    def __init__(self, hidden_size, patch_size, out_channels, use_ndadaln=False):
        super().__init__()
        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
        self.use_ndadaln = use_ndadaln
        if self.use_ndadaln:
            self.adaLN_modulation = nn.Sequential(
                nn.SiLU(),
                NdLinear((hidden_size, 1), (2 * hidden_size, 1))
            )
        else:
            self.adaLN_modulation = nn.Sequential(
                nn.SiLU(),
                nn.Linear(hidden_size, 2 * hidden_size, bias=True)
            )

    def forward(self, x, c):
        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
        x = modulate(self.norm_final(x), shift, scale)
        x = self.linear(x)
        return x

class DiTConfig(PretrainedConfig):
    model_type = "ndlinear_dit"

    def __init__(self, input_size=32, patch_size=2, in_channels=4, hidden_size=1152, depth=28, num_heads=16, mlp_ratio=4.0, class_dropout_prob=0.1, num_classes=1000, learn_sigma=True, use_ndmlp=False, use_ndtse=False, use_variant=4, tse_scale_factor=2, use_num_transforms=2, **kwargs):
        super().__init__(**kwargs)
        self.input_size = input_size
        self.patch_size = patch_size
        self.in_channels = in_channels
        self.out_channels = in_channels * 2 if learn_sigma else in_channels
        self.hidden_size = hidden_size
        self.depth = depth
        self.num_heads = num_heads
        self.mlp_ratio = mlp_ratio
        self.class_dropout_prob = class_dropout_prob
        self.num_classes = num_classes
        self.learn_sigma = learn_sigma
        self.use_ndmlp = use_ndmlp
        self.use_ndtse = use_ndtse
        self.use_variant = use_variant
        self.tse_scale_factor = tse_scale_factor
        self.use_num_transforms = use_num_transforms

class DiT(PreTrainedModel):
    config_class = DiTConfig

    def __init__(self, config):
        super().__init__(config)
        self.input_size = config.input_size
        self.patch_size = config.patch_size
        self.in_channels = config.in_channels
        self.hidden_size = config.hidden_size
        self.depth = config.depth
        self.num_heads = config.num_heads
        self.mlp_ratio = config.mlp_ratio
        self.class_dropout_prob = config.class_dropout_prob
        self.num_classes = config.num_classes
        self.learn_sigma = config.learn_sigma
        self.use_ndmlp = config.use_ndmlp
        self.use_ndtse = config.use_ndtse
        self.use_variant = config.use_variant
        self.tse_scale_factor = config.tse_scale_factor
        self.use_num_transforms = config.use_num_transforms
        self.out_channels = config.out_channels
        self.ndadaln = getattr(config, "ndadaln", False)

        self.x_embedder = PatchEmbed(self.input_size, self.patch_size, self.in_channels, self.hidden_size, bias=True)

        if self.use_ndtse:
            self.t_embedder = NdTimestepEmbedder(
                hidden_size=self.hidden_size,
                frequency_embedding_size=256,
                use_num_transforms=self.use_num_transforms,
                tse_scale_factor=1,
                knowledge_transfer=False,
                src_layers=None
            )
        else:
            self.t_embedder = TimestepEmbedder(self.hidden_size)

        self.y_embedder = LabelEmbedder(self.num_classes, self.hidden_size, self.class_dropout_prob)
        num_patches = self.x_embedder.num_patches
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, self.hidden_size), requires_grad=False)

        self.blocks = nn.ModuleList([
            DiTBlock(self.hidden_size, self.num_heads, mlp_ratio=self.mlp_ratio,
                     use_ndmlp=self.use_ndmlp, use_variant=self.use_variant)
            for _ in range(self.depth)
        ])
        if self.use_ndmlp:
            approx_gelu = lambda: nn.GELU(approximate="tanh")
            for idx, layer in enumerate(self.blocks):
                if idx % 2 == 0:
                    layer.mlp = NdMlp(
                        in_features=self.hidden_size,
                        hidden_features=self.hidden_size * 4,
                        act_layer=approx_gelu,
                        drop=0,
                        use_variant=self.use_variant
                    )
        self.final_layer = FinalLayer(self.hidden_size, self.patch_size, self.out_channels, use_ndadaln=self.ndadaln)
        self.initialize_weights()

    def initialize_weights(self):
        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)

        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5))
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        w = self.x_embedder.proj.weight.data
        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
        nn.init.constant_(self.x_embedder.proj.bias, 0)

        nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)

        if not self.use_ndtse:
            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.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
        nn.init.constant_(self.final_layer.linear.weight, 0)
        nn.init.constant_(self.final_layer.linear.bias, 0)

    def unpatchify(self, x):
        c = self.out_channels
        p = self.x_embedder.patch_size[0]
        h = w = int(x.shape[1] ** 0.5)
        assert h * w == x.shape[1]

        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
        x = torch.einsum('nhwpqc->nchpwq', x)
        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
        return imgs

    def ckpt_wrapper(self, module):
        def ckpt_forward(*inputs):
            outputs = module(*inputs)
            return outputs

        return ckpt_forward

    def forward(self, x, t, y):
        x = self.x_embedder(x) + self.pos_embed
        t = self.t_embedder(t)
        y = self.y_embedder(y, self.training)
        c = t + y

        for block in self.blocks:
            x = torch.utils.checkpoint.checkpoint(self.ckpt_wrapper(block), x, c)
        x = self.final_layer(x, c)
        x = self.unpatchify(x)
        return x

    def forward_with_cfg(self, x, t, y, cfg_scale):
        half = x[: len(x) // 2]
        combined = torch.cat([half, half], dim=0)
        model_out = self.forward(combined, t, y)
        eps, rest = model_out[:, :3], model_out[:, 3:]
        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
        eps = torch.cat([half_eps, half_eps], dim=0)
        return torch.cat([eps, rest], dim=1)

def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
    grid_h = np.arange(grid_size, dtype=np.float32)
    grid_w = np.arange(grid_size, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size, grid_size])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token and extra_tokens > 0:
        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
    return pos_embed

def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    assert embed_dim % 2 == 0

    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])

    emb = np.concatenate([emb_h, emb_w], axis=1)
    return emb

def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    assert embed_dim % 2 == 0
    omega = np.arange(embed_dim // 2, dtype=np.float64)
    omega /= embed_dim / 2.
    omega = 1. / 10000 ** omega

    pos = pos.reshape(-1)
    out = np.einsum('m,d->md', pos, omega)

    emb_sin = np.sin(out)
    emb_cos = np.cos(out)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)
    return emb

def DiT_XL_2(**kwargs):
    config = DiTConfig(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs)
    return DiT(config)

def DiT_XL_4(**kwargs):
    config = DiTConfig(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs)
    return DiT(config)

def DiT_XL_8(**kwargs):
    config = DiTConfig(depth=28, hidden_size=1152, patch_size=8, num_heads=16, **kwargs)
    return DiT(config)

def DiT_L_2(**kwargs):
    config = DiTConfig(depth=24, hidden_size=1024, patch_size=2, num_heads=16, **kwargs)
    return DiT(config)

def DiT_L_4(**kwargs):
    config = DiTConfig(depth=24, hidden_size=1024, patch_size=4, num_heads=16, **kwargs)
    return DiT(config)

def DiT_L_8(**kwargs):
    config = DiTConfig(depth=24, hidden_size=1024, patch_size=8, num_heads=16, **kwargs)
    return DiT(config)

def DiT_B_2(**kwargs):
    config = DiTConfig(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)
    return DiT(config)

def DiT_B_4(**kwargs):
    config = DiTConfig(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)
    return DiT(config)

def DiT_B_8(**kwargs):
    config = DiTConfig(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
    return DiT(config)

def DiT_S_2(**kwargs):
    config = DiTConfig(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
    return DiT(config)

def DiT_S_4(**kwargs):
    config = DiTConfig(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
    return DiT(config)

def DiT_S_8(**kwargs):
    config = DiTConfig(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
    return DiT(config)

def DiT_MS_2(**kwargs):
    config = DiTConfig(depth=6, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
    return DiT(config)

def DiT_MS_4(**kwargs):
    config = DiTConfig(depth=6, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
    return DiT(config)

def DiT_MS_8(**kwargs):
    config = DiTConfig(depth=6, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
    return DiT(config)

def DiT_XS_2(**kwargs):
    config = DiTConfig(depth=1, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
    return DiT(config)

def DiT_XS_4(**kwargs):
    config = DiTConfig(depth=1, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
    return DiT(config)

def DiT_XS_8(**kwargs):
    config = DiTConfig(depth=1, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
    return DiT(config)

DiT_models = {
    'DiT-XL/2': DiT_XL_2, 'DiT-XL/4': DiT_XL_4, 'DiT-XL/8': DiT_XL_8,
    'DiT-L/2': DiT_L_2, 'DiT-L/4': DiT_L_4, 'DiT-L/8': DiT_L_8,
    'DiT-B/2': DiT_B_2, 'DiT-B/4': DiT_B_4, 'DiT-B/8': DiT_B_8,
    'DiT-S/2': DiT_S_2, 'DiT-S/4': DiT_S_4, 'DiT-S/8': DiT_S_8,
    'DiT-XS/2': DiT_XS_2, 'DiT-XS/4': DiT_XS_4, 'DiT-XS/8': DiT_XS_8,
    'DiT-MS/2': DiT_MS_2, 'DiT-MS/4': DiT_MS_4, 'DiT-MS/8': DiT_MS_8
}