# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
# All rights reserved.
# This file contains code that is adapted from
# timm: https://github.com/huggingface/pytorch-image-models
# pixart: https://github.com/PixArt-alpha/PixArt-alpha
import math

import torch
import torch.nn as nn
from einops import rearrange

from scepter.modules.model.backbone.transformer.attention import drop_path


def modulate(x, shift, scale, unsqueeze=False):
    if unsqueeze:
        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
    else:
        return x * (1 + scale) + shift


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """
    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class MaskFinalLayer(nn.Module):
    """
    The final layer of PixArt.
    """
    def __init__(self, final_hidden_size, c_emb_size, patch_size,
                 out_channels):
        super().__init__()
        self.norm_final = nn.LayerNorm(final_hidden_size,
                                       elementwise_affine=False,
                                       eps=1e-6)
        self.linear = nn.Linear(final_hidden_size,
                                patch_size * patch_size * out_channels,
                                bias=True)
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(), nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True))

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


class DecoderLayer(nn.Module):
    """
    The final layer of PixArt.
    """
    def __init__(self, hidden_size, decoder_hidden_size):
        super().__init__()
        self.norm_decoder = nn.LayerNorm(hidden_size,
                                         elementwise_affine=False,
                                         eps=1e-6)
        self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True)
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))

    def forward(self, x, t):
        shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
        x = modulate(self.norm_decoder(x), shift, scale, unsqueeze=True)
        x = self.linear(x)
        return x


class TimestepEmbedder(nn.Module):
    """
    Embeds scalar timesteps into vector representations.
    """
    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):
        """
        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.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 SizeEmbedder(TimestepEmbedder):
    """
    Embeds scalar timesteps into vector representations.
    """
    def __init__(self, hidden_size, frequency_embedding_size=256):
        super().__init__(hidden_size=hidden_size,
                         frequency_embedding_size=frequency_embedding_size)
        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.outdim = hidden_size

    def forward(self, s, bs):
        if s.ndim == 1:
            s = s[:, None]
        assert s.ndim == 2
        if s.shape[0] != bs:
            s = s.repeat(bs // s.shape[0], 1)
            assert s.shape[0] == bs
        b, dims = s.shape[0], s.shape[1]
        s = rearrange(s, 'b d -> (b d)')
        s_freq = self.timestep_embedding(s, self.frequency_embedding_size).to(
            self.dtype)
        s_emb = self.mlp(s_freq)
        s_emb = rearrange(s_emb,
                          '(b d) d2 -> b (d d2)',
                          b=b,
                          d=dims,
                          d2=self.outdim)
        return s_emb

    @property
    def dtype(self):
        return next(self.parameters()).dtype


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]).cuda() < 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)
        return self.embedding_table(labels)


class CaptionEmbedder(nn.Module):
    """
    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
    """
    def __init__(self,
                 in_channels,
                 hidden_size,
                 uncond_prob,
                 act_layer=nn.GELU(approximate='tanh'),
                 token_num=120):
        super().__init__()
        self.y_proj = Mlp(in_features=in_channels,
                          hidden_features=hidden_size,
                          out_features=hidden_size,
                          act_layer=act_layer,
                          drop=0)
        self.register_buffer(
            'y_embedding',
            nn.Parameter(
                torch.randn(token_num, in_channels) / in_channels**0.5))
        self.uncond_prob = uncond_prob

    def token_drop(self, caption, force_drop_ids=None):
        """
        Drops labels to enable classifier-free guidance.
        """
        if force_drop_ids is None:
            drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob
        else:
            drop_ids = force_drop_ids == 1
        caption = torch.where(drop_ids[:, None, None], self.y_embedding,
                              caption)
        return caption

    def forward(self, caption, train, force_drop_ids=None):
        if train:
            assert caption.shape[1:] == self.y_embedding.shape
        use_dropout = self.uncond_prob > 0
        if (train and use_dropout) or (force_drop_ids is not None):
            caption = self.token_drop(caption, force_drop_ids)
        caption = self.y_proj(caption)
        return caption


class CaptionEmbedderDoubleBr(nn.Module):
    """
    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
    """
    def __init__(self,
                 in_channels,
                 hidden_size,
                 uncond_prob,
                 act_layer=nn.GELU(approximate='tanh'),
                 token_num=120):
        super().__init__()
        self.proj = Mlp(in_features=in_channels,
                        hidden_features=hidden_size,
                        out_features=hidden_size,
                        act_layer=act_layer,
                        drop=0)
        self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10**0.5)
        self.y_embedding = nn.Parameter(
            torch.randn(token_num, in_channels) / 10**0.5)
        self.uncond_prob = uncond_prob

    def token_drop(self, global_caption, caption, force_drop_ids=None):
        """
        Drops labels to enable classifier-free guidance.
        """
        if force_drop_ids is None:
            drop_ids = torch.rand(
                global_caption.shape[0]).cuda() < self.uncond_prob
        else:
            drop_ids = force_drop_ids == 1
        global_caption = torch.where(drop_ids[:, None], self.embedding,
                                     global_caption)
        caption = torch.where(drop_ids[:, None, None, None], self.y_embedding,
                              caption)
        return global_caption, caption

    def forward(self, caption, train, force_drop_ids=None):
        assert caption.shape[2:] == self.y_embedding.shape
        global_caption = caption.mean(dim=2).squeeze()
        use_dropout = self.uncond_prob > 0
        if (train and use_dropout) or (force_drop_ids is not None):
            global_caption, caption = self.token_drop(global_caption, caption,
                                                      force_drop_ids)
        y_embed = self.proj(global_caption)
        return y_embed, caption


class Mlp(nn.Module):
    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
    """
    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class T2IFinalLayer(nn.Module):
    """
    The final layer of PixArt.
    """
    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.scale_shift_table = nn.Parameter(
            torch.randn(2, hidden_size) / hidden_size**0.5)
        self.out_channels = out_channels

    def forward(self, x, t):
        shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2,
                                                                         dim=1)
        shift, scale = shift.squeeze(1), scale.squeeze(1)
        x = modulate(self.norm_final(x), shift, scale)
        x = self.linear(x)
        return x