"""AutoencoderKL Decoder — pure MLX implementation.

Decodes latent representations to RGB images without PyTorch/diffusers
dependency.  Architecture matches diffusers AutoencoderKL with the
Z-Image-Turbo VAE config:

    latent_channels  = 16
    block_out_channels = [128, 256, 512, 512]
    layers_per_block = 2       (decoder uses layers_per_block + 1 = 3)
    norm_num_groups  = 32
    mid_block_add_attention = true
    force_upcast     = true    (all ops in float32)
    scaling_factor   = 0.3611
    shift_factor     = 0.1159

Data format: NHWC throughout (MLX convention).
"""

from __future__ import annotations

import math

import mlx.core as mx
import mlx.nn as nn

# Match diffusers VAE GroupNorm: eps=1e-6, pytorch_compatible=True
_GN_EPS = 1e-6


def _gn(groups: int, channels: int) -> nn.GroupNorm:
    return nn.GroupNorm(groups, channels, eps=_GN_EPS, pytorch_compatible=True)


# ── Building blocks ──────────────────────────────────────────────


class ResnetBlock2D(nn.Module):
    """Residual block: GroupNorm → SiLU → Conv → GroupNorm → SiLU → Conv + skip."""

    def __init__(self, in_channels: int, out_channels: int, groups: int = 32):
        super().__init__()
        self.norm1 = _gn(groups, in_channels)
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.norm2 = _gn(groups, out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)

        self.conv_shortcut = None
        if in_channels != out_channels:
            self.conv_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def __call__(self, x: mx.array) -> mx.array:
        residual = x
        x = nn.silu(self.norm1(x))
        x = self.conv1(x)
        x = nn.silu(self.norm2(x))
        x = self.conv2(x)
        if self.conv_shortcut is not None:
            residual = self.conv_shortcut(residual)
        return x + residual


class AttentionBlock(nn.Module):
    """Single-head self-attention over spatial positions (NHWC)."""

    def __init__(self, channels: int, groups: int = 32):
        super().__init__()
        self.group_norm = _gn(groups, channels)
        self.to_q = nn.Linear(channels, channels)
        self.to_k = nn.Linear(channels, channels)
        self.to_v = nn.Linear(channels, channels)
        # diffusers wraps out-proj in a list (Sequential): to_out.0
        self.to_out = [nn.Linear(channels, channels)]

    def __call__(self, x: mx.array) -> mx.array:
        residual = x
        B, H, W, C = x.shape
        x = self.group_norm(x)
        x = x.reshape(B, H * W, C)

        q = self.to_q(x)
        k = self.to_k(x)
        v = self.to_v(x)

        scale = 1.0 / math.sqrt(C)
        attn = (q @ k.transpose(0, 2, 1)) * scale
        attn = mx.softmax(attn, axis=-1)
        x = attn @ v

        x = self.to_out[0](x)
        x = x.reshape(B, H, W, C)
        return x + residual


class Upsample2D(nn.Module):
    """2× nearest-neighbour upsample followed by a 3×3 conv."""

    def __init__(self, channels: int):
        super().__init__()
        self.conv = nn.Conv2d(channels, channels, kernel_size=3, padding=1)

    def __call__(self, x: mx.array) -> mx.array:
        # Nearest-neighbour 2× in NHWC
        B, H, W, C = x.shape
        x = mx.repeat(x, 2, axis=1)
        x = mx.repeat(x, 2, axis=2)
        x = self.conv(x)
        return x


class UpDecoderBlock2D(nn.Module):
    """Decoder up-block: N resnet blocks + optional 2× upsample."""

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        num_layers: int = 3,
        add_upsample: bool = True,
        groups: int = 32,
    ):
        super().__init__()
        self.resnets = []
        for i in range(num_layers):
            res_in = in_channels if i == 0 else out_channels
            self.resnets.append(ResnetBlock2D(res_in, out_channels, groups))

        self.upsamplers = []
        if add_upsample:
            self.upsamplers.append(Upsample2D(out_channels))

    def __call__(self, x: mx.array) -> mx.array:
        for resnet in self.resnets:
            x = resnet(x)
        for up in self.upsamplers:
            x = up(x)
        return x


class MidBlock2D(nn.Module):
    """Mid block: resnet → self-attention → resnet."""

    def __init__(self, channels: int, groups: int = 32):
        super().__init__()
        self.resnets = [
            ResnetBlock2D(channels, channels, groups),
            ResnetBlock2D(channels, channels, groups),
        ]
        self.attentions = [AttentionBlock(channels, groups)]

    def __call__(self, x: mx.array) -> mx.array:
        x = self.resnets[0](x)
        x = self.attentions[0](x)
        x = self.resnets[1](x)
        return x


# ── Decoder ──────────────────────────────────────────────────────


class Decoder(nn.Module):
    """AutoencoderKL Decoder (NHWC, pure MLX).

    Module hierarchy matches diffusers weight-key paths after stripping
    the ``decoder.`` prefix, so weights can be loaded directly.
    """

    def __init__(
        self,
        latent_channels: int = 16,
        block_out_channels: tuple[int, ...] = (128, 256, 512, 512),
        layers_per_block: int = 2,
        norm_num_groups: int = 32,
    ):
        super().__init__()
        reversed_ch = list(reversed(block_out_channels))  # [512, 512, 256, 128]

        # Input projection
        self.conv_in = nn.Conv2d(latent_channels, reversed_ch[0], kernel_size=3, padding=1)

        # Mid block
        self.mid_block = MidBlock2D(reversed_ch[0], norm_num_groups)

        # Up blocks (3 upsamples → total 8× spatial increase)
        self.up_blocks = []
        for i, out_ch in enumerate(reversed_ch):
            in_ch = reversed_ch[i - 1] if i > 0 else reversed_ch[0]
            add_upsample = i < len(reversed_ch) - 1
            self.up_blocks.append(
                UpDecoderBlock2D(
                    in_channels=in_ch,
                    out_channels=out_ch,
                    num_layers=layers_per_block + 1,
                    add_upsample=add_upsample,
                    groups=norm_num_groups,
                )
            )

        # Output
        self.conv_norm_out = _gn(norm_num_groups, reversed_ch[-1])
        self.conv_out = nn.Conv2d(reversed_ch[-1], 3, kernel_size=3, padding=1)

    def __call__(self, z: mx.array) -> mx.array:
        """Decode latents → image.

        Args:
            z: (B, H, W, C) latent tensor in NHWC, **already scaled**.

        Returns:
            (B, 8H, 8W, 3) decoded image.
        """
        x = self.conv_in(z)
        x = self.mid_block(x)
        for block in self.up_blocks:
            x = block(x)
        x = nn.silu(self.conv_norm_out(x))
        x = self.conv_out(x)
        return x