# 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
# --------------------------------------------------------

import torch
import torch.nn as nn
import numpy as np
import math
import functools
from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
 
from torch_utils import persistence
from einops import repeat

def modulate(x, shift, scale ): 
    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
 
#################################################################################
#               Embedding Layers for Timesteps and Class Labels                 #
#################################################################################
 

@persistence.persistent_class
class FourierEmbedding(torch.nn.Module):
    def __init__(self, num_channels, scale=16,  **kwargs):
        super().__init__()
        print("FourierEmbedding scale:", scale) 
        self.register_buffer("freqs", torch.randn(num_channels // 2) * scale)

    def forward(self, x):
        dtype = x.dtype
        x = x.to(torch.float64).ger((2 * np.pi * self.freqs.to(torch.float64)) )
        x = torch.cat([x.cos(), x.sin()], dim=1).to(dtype)
        return x

@persistence.persistent_class
class TimestepEmbedder(nn.Module):
    """
    Embeds scalar timesteps into vector representations.
    """

    def __init__(self, hidden_size, frequency_embedding_size=256,   embedding_type='positional', use_mlp=True, scale=1):
        super().__init__()
        self.use_mlp = use_mlp
        self.hidden_size = hidden_size
        if use_mlp:
            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
        
        self.embedding_type = embedding_type
        
        if self.embedding_type == 'fourier':
            self.register_buffer("freqs", torch.randn(frequency_embedding_size // 2) * scale)


    @staticmethod
    def positional_timestep_embedding(t, dim, max_period=10000 ):
        """
        Create sinusoidal timestep embeddings.
        :param t: a 1-D Tensor of N indices, one per batch element.
                          These may be fractional.
        :param dim: the dimension of the output.
        :param max_period: controls the minimum frequency of the embeddings.
        :return: an (N, D) Tensor of positional embeddings.
        """
        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period)
            * torch.arange(start=0, end=half, dtype=torch.float64)
            / half
        ).to(device=t.device)
        args = t[:, None].to(torch.float64) * 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 fourier_timestep_embedding(self, t,  ): 
        x = t.to(torch.float64).ger((2 * np.pi * self.freqs.to(torch.float64)) )
        x = torch.cat([x.cos(), x.sin()], dim=1) 
        
        return x

    def forward(self, t ):
        if self.embedding_type == 'positional':
            t_freq = self.positional_timestep_embedding(t, self.frequency_embedding_size) 
        elif self.embedding_type == 'fourier':
            t_freq = self.fourier_timestep_embedding(t)
            
        if self.use_mlp:
            t_emb = self.mlp(t_freq.to(dtype=t.dtype)  )
        else:
            t_emb = t_freq.to(dtype=t.dtype)
        return t_emb


@persistence.persistent_class
class LabelEmbedder(nn.Module):
    """
    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
    """

    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):
        """
        Drops labels to enable classifier-free guidance.
        """
        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


#################################################################################
#                                 Core DiT Model                                #
#################################################################################

 
 

@persistence.persistent_class
class DiTBlock(nn.Module):
    """
    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
    """

    def __init__(self, hidden_size, num_heads, temb_size, mlp_ratio=4.0,  skip=False,  dropout=0, **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") 
        self.mlp = Mlp(
            in_features=hidden_size,
            hidden_features=mlp_hidden_dim,
            act_layer=approx_gelu,
            drop=dropout,
        ) 
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(), nn.Linear(temb_size, 6 * hidden_size, bias=True)
        )  
        self.skip_linear = nn.Linear(2 * hidden_size, hidden_size) if skip else None
         
    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 ) 
        )  
        x = x + gate_mlp.unsqueeze(1) * self.mlp(
            modulate(self.norm2(x), shift_mlp, scale_mlp )
        )
         
        return x


@persistence.persistent_class
class FinalLayer(nn.Module):
    """
    The final layer of DiT.
    """

    def __init__(self, hidden_size, patch_size, out_channels):
        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.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


@persistence.persistent_class
class DiT(nn.Module):
    """
    Diffusion model with a Transformer backbone.
    """

    def __init__(
        self,
        img_resolution,
        patch_size=2,
        in_channels=4,
        hidden_size=1152,
        depth=28,
        num_heads=16,
        mlp_ratio=4.0,
        class_dropout_prob=0.,
        num_classes=1000,  
        s_embed=True,
        qk_norm=False, 
        skip=False,
        embedding_kwargs={},
        temb_mult=1, 
        dropout=0,
        **kwargs
    ):
        super().__init__() 
        self.in_channels = in_channels
        self.out_channels = in_channels  
        self.patch_size = patch_size
        self.num_heads = num_heads
        self.skip = skip 
        temb_size = hidden_size * temb_mult
        
        self.s_embed = s_embed
        if s_embed:  
            self.s_embedder = TimestepEmbedder(temb_size, **embedding_kwargs )
  
        self.x_embedder = PatchEmbed(
            img_resolution, patch_size, in_channels, hidden_size, bias=True, 
        ) 
        self.t_embedder = TimestepEmbedder(temb_size, **embedding_kwargs )
        self.y_embedder = LabelEmbedder(num_classes + 1, temb_size, class_dropout_prob)
        num_patches = self.x_embedder.num_patches
        # Will use fixed sin-cos embedding:
        self.pos_embed = nn.Parameter(
            torch.zeros(1, num_patches, hidden_size), requires_grad=False
        )
        self.blocks = nn.ModuleList(
            [
                DiTBlock(hidden_size, num_heads,temb_size, mlp_ratio=mlp_ratio, qk_norm=qk_norm, dropout=dropout, ) 
                for _ in range(depth)
            ]
        )
        self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
        self.initialize_weights()

    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)
            elif isinstance(module, nn.LayerNorm):
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)
                if module.weight is not None:
                    nn.init.constant_(module.weight, 1.0)
                

        self.apply(_basic_init)

        # Initialize (and freeze) pos_embed by sin-cos embedding:
        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))

        # 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]))
        nn.init.constant_(self.x_embedder.proj.bias, 0)

        # Initialize label embedding table:
        nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)

        # Initialize timestep embedding MLP:
        if self.t_embedder.use_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)
        
        if self.s_embed  and self.s_embedder.use_mlp:
            # Initialize timestep embedding MLP:
            nn.init.normal_(self.s_embedder.mlp[0].weight, std=0.02)
            nn.init.normal_(self.s_embedder.mlp[2].weight, std=0.02)

        # Zero-out adaLN modulation layers in DiT blocks:
        for block in self.blocks:
            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
        # Zero-out output layers:
        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):
        """
        x: (N, T, patch_size**2 * C)
        imgs: (N, H, W, C)
        """
        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 forward(self, x, 
        noise_labels_t,
        noise_labels_s=None, 
        class_labels=None,
        **kwargs):
        """
        Forward pass of DiT.
        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        t: (N,) tensor of diffusion timesteps
        y: (N, num_classes) tensor of one-hot vector labels, all zeros denote unconditional
        """
        is_uncond = 1 - class_labels.sum(dim=1, keepdims=True) # 1 if unconditional 0 otherwise
        y = torch.cat([ class_labels, is_uncond], dim=1) 
        y = y.argmax(dim=1) # to (N,) tensor of class labels
         
        x = (
            self.x_embedder(x)  + self.pos_embed
        )  # (N, T, D), where T = H * W / patch_size ** 2
        if noise_labels_t.shape[0] == 1:
            noise_labels_t = repeat(noise_labels_t, '1 ... -> B ...', B=x.shape[0])
              
        t = self.t_embedder(noise_labels_t )  # (N, D)  
        if noise_labels_s is not None and self.s_embed:

            if noise_labels_s.shape[0] == 1:
                noise_labels_s = repeat(noise_labels_s, '1 ... -> B ...', B=x.shape[0])
            
            s = self.s_embedder(noise_labels_s )
             
            t = t + s  
              
            
        y = self.y_embedder(y, self.training)  # (N, D)  
        c = t + y  # (N, D)  
          
        for block in self.blocks:
            x = block(x, c) # (N, T, D) 
         
            
        x = self.final_layer(x, c)  # (N, T, patch_size ** 2 * out_channels)  
        x = self.unpatchify(x)  # (N, out_channels, H, W)
         
        return x
 

#################################################################################
#                   Sine/Cosine Positional Embedding Functions                  #
#################################################################################
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py


def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
    """
    grid_size: int of the grid height and width
    return:
    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    grid_h = np.arange(grid_size, dtype=np.float64)
    grid_w = np.arange(grid_size, dtype=np.float64)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    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

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
    return emb


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position
    pos: a list of positions to be encoded: size (M,)
    out: (M, D)
    """
    assert embed_dim % 2 == 0
    omega = np.arange(embed_dim // 2, dtype=np.float64)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out)  # (M, D/2)
    emb_cos = np.cos(out)  # (M, D/2)

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


#################################################################################
#                                   DiT Configs                                  #
#################################################################################


@persistence.persistent_class
class DiT_XL_2(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=2, hidden_size=1152, depth=28, num_heads=16, **kwargs)

@persistence.persistent_class
class DiT_XL_4(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=4, hidden_size=1152, depth=28, num_heads=16, **kwargs)
        
@persistence.persistent_class
class DiT_XL_8(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=8, hidden_size=1152, depth=28, num_heads=16, **kwargs)
        
@persistence.persistent_class
class DiT_L_2(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=2, hidden_size=1024, depth=24, num_heads=16, **kwargs)
        
@persistence.persistent_class
class DiT_L_4(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=4, hidden_size=1024, depth=24, num_heads=16, **kwargs)
        
@persistence.persistent_class
class DiT_L_8(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=8, hidden_size=1024, depth=24, num_heads=16, **kwargs)
        
@persistence.persistent_class
class DiT_B_2(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=2, hidden_size=768, depth=12, num_heads=12, **kwargs)
        
@persistence.persistent_class
class DiT_B_4(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=4, hidden_size=768, depth=12, num_heads=12, **kwargs)
        
@persistence.persistent_class
class DiT_B_8(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=8, hidden_size=768, depth=12, num_heads=12, **kwargs)
        
@persistence.persistent_class
class DiT_S_2(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=2, hidden_size=384, depth=12, num_heads=6, **kwargs)
         
@persistence.persistent_class
class DiT_S_4(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=4, hidden_size=384, depth=12, num_heads=6, **kwargs)
        
@persistence.persistent_class
class DiT_S_8(DiT):
    def __init__(self, **kwargs):
        super().__init__(patch_size=8, hidden_size=384, depth=12, num_heads=6, **kwargs)

 