"""
Pixel Decoder: ViT-MAE style decoder following RAE architecture.
Takes 576×embed_dim ViT features and reconstructs 384×384×3 images.
Architecture: ViT-L decoder (24 layers, hidden=1024, heads=16, intermediate=4096).
"""

import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F


# ─── Sincos Positional Embeddings ───────────────────────────────────────────

def get_2d_sincos_pos_embed(embed_dim, grid_size, add_cls_token=False):
    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).reshape([2, 1, grid_size, grid_size])

    emb_h = get_1d_sincos_pos_embed(embed_dim // 2, grid[0].reshape(-1))
    emb_w = get_1d_sincos_pos_embed(embed_dim // 2, grid[1].reshape(-1))
    emb = np.concatenate([emb_h, emb_w], axis=1)

    if add_cls_token:
        emb = np.concatenate([np.zeros([1, embed_dim]), emb], axis=0)
    return emb


def get_1d_sincos_pos_embed(embed_dim, pos):
    omega = np.arange(embed_dim // 2, dtype=float)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega

    pos = pos.reshape(-1)
    out = np.einsum("m,d->md", pos, omega)
    return np.concatenate([np.sin(out), np.cos(out)], axis=1)


# ─── Transformer Components ────────────────────────────────────────────────

class MAESelfAttention(nn.Module):
    def __init__(self, hidden_size, num_heads, qkv_bias=True, attn_drop=0.0, proj_drop=0.0):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads

        self.query = nn.Linear(hidden_size, hidden_size, bias=qkv_bias)
        self.key = nn.Linear(hidden_size, hidden_size, bias=qkv_bias)
        self.value = nn.Linear(hidden_size, hidden_size, bias=qkv_bias)
        self.out_proj = nn.Linear(hidden_size, hidden_size)
        self.attn_drop = attn_drop

    def forward(self, x):
        B, N, C = x.shape
        q = self.query(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
        k = self.key(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
        v = self.value(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)

        x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop if self.training else 0.0)
        x = x.permute(0, 2, 1, 3).reshape(B, N, C)
        return self.out_proj(x)


class MAEBlock(nn.Module):
    """Standard ViT block: pre-norm self-attention + pre-norm FFN."""
    def __init__(self, hidden_size, num_heads, intermediate_size, hidden_act="gelu",
                 qkv_bias=True, layer_norm_eps=1e-6):
        super().__init__()
        self.layernorm_before = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
        self.attention = MAESelfAttention(hidden_size, num_heads, qkv_bias=qkv_bias)
        self.layernorm_after = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
        self.intermediate = nn.Linear(hidden_size, intermediate_size)
        self.output_proj = nn.Linear(intermediate_size, hidden_size)
        self.act_fn = nn.GELU()

    def forward(self, x):
        # Self-attention with residual
        x = x + self.attention(self.layernorm_before(x))
        # FFN with residual
        h = self.layernorm_after(x)
        h = self.act_fn(self.intermediate(h))
        x = x + self.output_proj(h)
        return x


# ─── Main Pixel Decoder ────────────────────────────────────────────────────

class PixelDecoderMAE(nn.Module):
    """
    ViT-MAE style pixel decoder following RAE.

    Input: [B, 576, input_dim] ViT features (or FAE-reconstructed features)
    Output: [B, 3, 384, 384] reconstructed images

    Architecture (ViT-L):
      - Linear projection: input_dim → decoder_hidden_size
      - Trainable CLS token + sincos positional embeddings
      - 24 Transformer blocks
      - LayerNorm + linear head → patch_size² × 3 per token
      - Unpatchify → full image
    """

    def __init__(self, input_dim=1152, decoder_hidden_size=1024,
                 decoder_num_layers=24, decoder_num_heads=16,
                 decoder_intermediate_size=4096, patch_size=16,
                 img_size=384, num_channels=3, layer_norm_eps=1e-6):
        super().__init__()
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.grid_size = img_size // patch_size  # 24
        self.num_patches = self.grid_size ** 2   # 576

        # Project encoder features to decoder dimension + normalize
        self.decoder_embed = nn.Linear(input_dim, decoder_hidden_size)
        self.embed_norm = nn.LayerNorm(decoder_hidden_size, eps=layer_norm_eps)

        # Trainable CLS token
        self.cls_token = nn.Parameter(torch.zeros(1, 1, decoder_hidden_size))

        # Fixed sincos positional embeddings (576 patches + 1 CLS)
        pos_embed = get_2d_sincos_pos_embed(decoder_hidden_size, self.grid_size, add_cls_token=True)
        self.decoder_pos_embed = nn.Parameter(
            torch.from_numpy(pos_embed).float().unsqueeze(0),
            requires_grad=False
        )

        # Transformer decoder blocks
        self.decoder_layers = nn.ModuleList([
            MAEBlock(
                hidden_size=decoder_hidden_size,
                num_heads=decoder_num_heads,
                intermediate_size=decoder_intermediate_size,
                layer_norm_eps=layer_norm_eps,
            )
            for _ in range(decoder_num_layers)
        ])

        self.decoder_norm = nn.LayerNorm(decoder_hidden_size, eps=layer_norm_eps)

        # Prediction head: project to pixel patches
        self.decoder_pred = nn.Linear(
            decoder_hidden_size, patch_size ** 2 * num_channels
        )

        self._init_weights()

    def _init_weights(self):
        nn.init.normal_(self.cls_token, std=0.02)
        # Initialize decoder_embed like a linear layer
        nn.init.xavier_uniform_(self.decoder_embed.weight)
        if self.decoder_embed.bias is not None:
            nn.init.zeros_(self.decoder_embed.bias)
        # Initialize decoder_pred
        nn.init.xavier_uniform_(self.decoder_pred.weight)
        if self.decoder_pred.bias is not None:
            nn.init.zeros_(self.decoder_pred.bias)

    def unpatchify(self, x):
        """
        x: [B, num_patches, patch_size²×3]
        Returns: [B, 3, H, W]
        """
        p = self.patch_size
        h = w = self.grid_size
        c = self.num_channels

        x = x.reshape(-1, h, w, p, p, c)
        x = torch.einsum("nhwpqc->nchpwq", x)
        return x.reshape(-1, c, h * p, w * p)

    def forward(self, features, noise_tau=0.0):
        """
        Args:
            features: [B, 576, input_dim] ViT features
            noise_tau: max noise level applied AFTER normalization (where std≈1)
        Returns:
            images: [B, 3, 384, 384] reconstructed images in [-1, 1]
        """
        # Project to decoder dimension and normalize
        x = self.embed_norm(self.decoder_embed(features))  # [B, 576, decoder_hidden]

        # Add noise after normalization (features now have std≈1, so tau=0.8 is meaningful)
        if noise_tau > 0 and self.training:
            noise_sigma = noise_tau * torch.rand(
                (x.size(0),) + (1,) * (len(x.shape) - 1), device=x.device
            )
            x = x + noise_sigma * torch.randn_like(x)

        # Prepend CLS token
        cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat([cls_tokens, x], dim=1)  # [B, 577, decoder_hidden]

        # Add positional embeddings
        x = x + self.decoder_pos_embed

        # Transformer blocks
        for layer in self.decoder_layers:
            x = layer(x)

        x = self.decoder_norm(x)

        # Predict pixel patches (remove CLS token)
        x = self.decoder_pred(x[:, 1:, :])  # [B, 576, patch_size²×3]

        # Unpatchify to full image
        img = self.unpatchify(x)  # [B, 3, 384, 384]

        return img


class PatchGANDiscriminator(nn.Module):
    """PatchGAN discriminator for adversarial loss."""

    def __init__(self, in_channels=3, ndf=64):
        super().__init__()
        self.model = nn.Sequential(
            nn.Conv2d(in_channels, ndf, 4, stride=2, padding=1),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf, ndf * 2, 4, stride=2, padding=1),
            nn.InstanceNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 2, ndf * 4, 4, stride=2, padding=1),
            nn.InstanceNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 4, ndf * 8, 4, stride=1, padding=1),
            nn.InstanceNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 8, 1, 4, stride=1, padding=1),
        )

    def forward(self, x):
        return self.model(x)