# Copyright (C) 2025 Hugging Face Team and Overworld
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.

"""VAE model for WorldEngine frame encoding/decoding."""

from dataclasses import dataclass
from typing import List, Tuple

import torch
from torch import Tensor

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
from .dcae import Encoder, Decoder, bake_weight_norm


@dataclass
class EncoderDecoderConfig:
    """Config object for Encoder/Decoder initialization."""

    channels: int
    latent_channels: int
    ch_0: int
    ch_max: int
    encoder_blocks_per_stage: List[int]
    decoder_blocks_per_stage: List[int]
    skip_logvar: bool = False


class WorldEngineVAE(ModelMixin, ConfigMixin):
    """
    VAE for encoding/decoding video frames using DCAE architecture.

    Encodes RGB uint8 images to latent space and decodes latents back to RGB.
    """

    _supports_gradient_checkpointing = False

    @register_to_config
    def __init__(
        self,
        # Common parameters
        sample_size: Tuple[int, int] = (360, 640),
        channels: int = 3,
        latent_channels: int = 16,
        # Encoder parameters
        encoder_ch_0: int = 64,
        encoder_ch_max: int = 256,
        encoder_blocks_per_stage: List[int] = None,
        # Decoder parameters
        decoder_ch_0: int = 128,
        decoder_ch_max: int = 1024,
        decoder_blocks_per_stage: List[int] = None,
        # Shared parameters
        skip_logvar: bool = False,
        # Scaling factors
        scale_factor: float = 1.0,
        shift_factor: float = 0.0,
    ):
        super().__init__()

        # Default blocks per stage
        if encoder_blocks_per_stage is None:
            encoder_blocks_per_stage = [1, 1, 1, 1]
        if decoder_blocks_per_stage is None:
            decoder_blocks_per_stage = [1, 1, 1, 1]

        # Create encoder config
        encoder_config = EncoderDecoderConfig(
            channels=channels,
            latent_channels=latent_channels,
            ch_0=encoder_ch_0,
            ch_max=encoder_ch_max,
            encoder_blocks_per_stage=list(encoder_blocks_per_stage),
            decoder_blocks_per_stage=list(decoder_blocks_per_stage),
            skip_logvar=skip_logvar,
        )

        # Create decoder config
        decoder_config = EncoderDecoderConfig(
            channels=channels,
            latent_channels=latent_channels,
            ch_0=decoder_ch_0,
            ch_max=decoder_ch_max,
            encoder_blocks_per_stage=list(encoder_blocks_per_stage),
            decoder_blocks_per_stage=list(decoder_blocks_per_stage),
            skip_logvar=skip_logvar,
        )

        self.encoder = Encoder(encoder_config)
        self.decoder = Decoder(decoder_config)

    def encode(self, img: Tensor):
        """RGB -> RGB+D -> latent"""
        assert img.dim() == 3, "Expected [H, W, C] image tensor"
        img = img.unsqueeze(0).to(device=self.device, dtype=self.dtype)
        rgb = img.permute(0, 3, 1, 2).contiguous().div(255).mul(2).sub(1)
        return self.encoder(rgb)

    def decode(self, latent: Tensor):
        decoded = self.decoder(latent)
        decoded = (decoded / 2 + 0.5).clamp(0, 1)
        decoded = (decoded * 255).round().to(torch.uint8)
        return decoded.squeeze(0).permute(1, 2, 0)[..., :3]

    def forward(self, x: Tensor, encode: bool = True) -> Tensor:
        """
        Forward pass - encode or decode based on flag.

        Args:
            x: Input tensor (image for encode, latent for decode)
            encode: If True, encode; if False, decode

        Returns:
            Encoded latent or decoded image
        """
        if encode:
            return self.encode(x)
        else:
            return self.decode(x)

    def bake_weight_norm(self):
        """Remove weight_norm parametrizations, baking normalized weights into regular tensors.

        Call this after loading weights and before torch.compile to avoid
        CUDA graph capture errors from in-place weight updates.
        """
        bake_weight_norm(self)
        return self