# 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 math
import torch
import torch.nn as nn
from timm.models.vision_transformer import Mlp, Attention as Attention_
from einops import rearrange, repeat
import torch.nn.functional as F

from .utils import add_decomposed_rel_pos


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


def t2i_modulate(x, shift, scale):
    return x * (1 + scale) + shift


class MultiHeadCrossAttention_org(nn.Module):

    def __init__(self, n_feat, n_head, attn_drop=0.0, proj_drop=0):
        """Construct an MultiHeadedAttention object."""
        super(MultiHeadCrossAttention, self).__init__()
        assert n_feat % n_head == 0
        # We assume d_v always equals d_k
        self.d_k = n_feat // n_head
        self.h = n_head
        self.linear_q = nn.Linear(n_feat, n_feat)
        self.linear_k = nn.Linear(n_feat, n_feat)
        self.linear_v = nn.Linear(n_feat, n_feat)
        self.proj = nn.Linear(n_feat, n_feat)
        self.attn = None
        self.dropout = nn.Dropout(p=attn_drop)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward_qkv(self, query, key, value):
        """Transform query, key and value.

        Args:
            query (torch.Tensor): Query tensor (#batch, time1, size).
            key (torch.Tensor): Key tensor (#batch, time2, size).
            value (torch.Tensor): Value tensor (#batch, time2, size).

        Returns:
            torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
            torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
            torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).

        """
        n_batch = query.size(0)
        q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
        k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
        v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)

        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)

        return q, k, v

    def forward_attention(self, value, scores, mask):
        """Compute attention context vector.

        Args:
            value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
            scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
            mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).

        Returns:
            torch.Tensor: Transformed value (#batch, time1, d_model)
                weighted by the attention score (#batch, time1, time2).

        """
        n_batch = value.size(0)
        if mask is not None:
            mask = mask.unsqueeze(1).eq(0)
            min_value = torch.finfo(scores.dtype).min
            scores = scores.masked_fill(mask, min_value)
            self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)
        else:
            self.attn = torch.softmax(scores, dim=-1)

        p_attn = self.dropout(self.attn)
        x = torch.matmul(p_attn, value)
        x = x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)

        return self.proj_drop(self.proj(x))

    def forward(self, x, cond, mask):
        """Compute scaled dot product attention.

        Args:
            query (torch.Tensor): Query tensor (#batch, time1, size).
            key (torch.Tensor): Key tensor (#batch, time2, size).
            value (torch.Tensor): Value tensor (#batch, time2, size).
            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
                (#batch, time1, time2).

        Returns:
            torch.Tensor: Output tensor (#batch, time1, d_model).

        """
        q, k, v = self.forward_qkv(x, cond, cond)
        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
        return self.forward_attention(v, scores, mask)


class MultiHeadCrossAttention(nn.Module):
    def __init__(
        self, d_model, num_heads, attn_drop=0.0, proj_drop=0.0, **block_kwargs
    ):
        super(MultiHeadCrossAttention, self).__init__()
        assert d_model % num_heads == 0, "d_model must be divisible by num_heads"

        self.d_model = d_model
        self.num_heads = num_heads
        self.head_dim = d_model // num_heads

        self.q_linear = nn.Linear(d_model, d_model)
        self.kv_linear = nn.Linear(d_model, d_model * 2)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(d_model, d_model)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x, cond, mask=None):
        # query/value: img tokens; key: condition; mask: if padding tokens
        B, N, C = x.shape
        assert mask is not None

        q = self.q_linear(x).view(B, N, self.num_heads, self.head_dim)

        if isinstance(mask, list):
            # mask = y_lens: list of kv sequence lengths per batch item.
            # cond is (1, total_valid_tokens, C) — batch flattened, padding removed.
            kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim)
            k, v = kv.unbind(2)
            total_kv = k.shape[1]

            # (B, N, heads, dim) → (B, heads, N, dim)
            q = q.permute(0, 2, 1, 3)
            # (1, total_kv, heads, dim) → (B, heads, total_kv, dim)
            k = k.permute(0, 2, 1, 3).expand(B, -1, -1, -1)
            v = v.permute(0, 2, 1, 3).expand(B, -1, -1, -1)

            # Build block-diagonal attention mask from sequence lengths
            attn_mask = torch.full(
                (B, 1, N, total_kv), float("-inf"), dtype=q.dtype, device=q.device
            )
            offset = 0
            for i, kv_len in enumerate(mask):
                attn_mask[i, :, :, offset:offset + kv_len] = 0
                offset += kv_len
        else:
            # mask is a padding mask tensor (B, 1, N_kv)
            kv = self.kv_linear(cond).view(B, -1, 2, self.num_heads, self.head_dim)
            k, v = kv.unbind(2)

            # (B, N, heads, dim) → (B, heads, N, dim)
            q = q.permute(0, 2, 1, 3)
            k = k.permute(0, 2, 1, 3)
            v = v.permute(0, 2, 1, 3)

            attn_mask = torch.zeros_like(mask, dtype=q.dtype)
            attn_mask.masked_fill_(mask == 0, float("-inf"))
            attn_mask = attn_mask.unsqueeze(1)

        x = F.scaled_dot_product_attention(
            q, k, v, dropout_p=self.attn_drop.p, attn_mask=attn_mask
        )

        # (B, heads, N, dim) → (B, N, C)
        x = x.permute(0, 2, 1, 3).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class WindowAttention(Attention_):
    """Multi-head Attention block with relative position embeddings."""

    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=True,
        use_rel_pos=False,
        rel_pos_zero_init=True,
        input_size=None,
        **block_kwargs,
    ):
        """
        Args:
            dim (int): Number of input channels.
            num_heads (int): Number of attention heads.
            qkv_bias (bool:  If True, add a learnable bias to query, key, value.
            rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            input_size (int or None): Input resolution for calculating the relative positional
                parameter size.
        """
        super().__init__(dim, num_heads=num_heads, qkv_bias=qkv_bias, **block_kwargs)

        self.use_rel_pos = use_rel_pos
        if self.use_rel_pos:
            # initialize relative positional embeddings
            self.rel_pos_h = nn.Parameter(
                torch.zeros(2 * input_size[0] - 1, self.head_dim)
            )
            self.rel_pos_w = nn.Parameter(
                torch.zeros(2 * input_size[1] - 1, self.head_dim)
            )

            if not rel_pos_zero_init:
                nn.init.trunc_normal_(self.rel_pos_h, std=0.02)
                nn.init.trunc_normal_(self.rel_pos_w, std=0.02)

    def forward(self, x, mask=None):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
        q, k, v = qkv.unbind(2)
        if use_fp32_attention := getattr(self, "fp32_attention", False):
            q, k, v = q.float(), k.float(), v.float()

        # (B, N, heads, dim) → (B, heads, N, dim) for SDPA
        q = q.permute(0, 2, 1, 3)
        k = k.permute(0, 2, 1, 3)
        v = v.permute(0, 2, 1, 3)

        attn_mask = None
        if mask is not None:
            attn_mask = torch.zeros(
                B * self.num_heads, q.shape[2], k.shape[2],
                dtype=q.dtype, device=q.device,
            )
            attn_mask.masked_fill_(
                mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float("-inf")
            )
            attn_mask = attn_mask.view(B, self.num_heads, q.shape[2], k.shape[2])

        x = F.scaled_dot_product_attention(
            q, k, v, dropout_p=self.attn_drop.p, attn_mask=attn_mask
        )

        # (B, heads, N, dim) → (B, N, C)
        x = x.permute(0, 2, 1, 3).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


#################################################################################
#   AMP attention with fp32 softmax to fix loss NaN problem during training     #
#################################################################################
class Attention(Attention_):
    def forward(self, x):
        B, N, C = x.shape
        qkv = (
            self.qkv(x)
            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
            .permute(2, 0, 3, 1, 4)
        )
        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
        use_fp32_attention = getattr(self, "fp32_attention", False)
        if use_fp32_attention:
            q, k = q.float(), k.float()
        with torch.cuda.amp.autocast(enabled=not use_fp32_attention):
            attn = (q @ k.transpose(-2, -1)) * self.scale
            attn = attn.softmax(dim=-1)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class FinalLayer(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.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 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[0] * patch_size[1] * out_channels, bias=True
        )
        self.scale_shift_table = nn.Parameter(
            torch.randn(2, hidden_size) / hidden_size**0.5
        )
        self.out_channels = out_channels
        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)

    def forward(self, x, t):
        shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1)
        x = t2i_modulate(self.norm_final(x), shift, scale)
        x = self.linear(x)
        return x


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)
        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)
        x = self.linear(x)
        return x


#################################################################################
#               Embedding Layers for Timesteps and Class Labels                 #
#################################################################################
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, device=t.device)
            / half
        )
        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).to(
            self.dtype
        )
        return self.mlp(t_freq)

    @property
    def dtype(self):

        return next(self.parameters()).dtype


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, None], self.y_embedding, caption)
        return caption

    def forward(self, caption, train, force_drop_ids=None):
        if train:
            assert caption.shape[2:] == 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