"""Validation sampling for LTX-2 training using ltx-core components.

This module provides a simplified validation pipeline for generating samples during training,
using the new ltx-core components (VideoLatentTools, AudioLatentTools, LatentState, etc.).
"""

from dataclasses import dataclass, replace
from typing import TYPE_CHECKING, Literal

import torch
from einops import rearrange
from torch import Tensor

from ltx_core.guidance.perturbations import (
    BatchedPerturbationConfig,
    Perturbation,
    PerturbationConfig,
    PerturbationType,
)
from ltx_core.model.transformer.modality import Modality
from ltx_core.model.transformer.model import X0Model
from ltx_core.pipeline.components.diffusion_steps import EulerDiffusionStep
from ltx_core.pipeline.components.guiders import CFGGuider, STGGuider
from ltx_core.pipeline.components.noisers import GaussianNoiser
from ltx_core.pipeline.components.patchifiers import (
    AudioLatentShape,
    AudioPatchifier,
    VideoLatentPatchifier,
    VideoLatentShape,
    get_pixel_coords,
)
from ltx_core.pipeline.components.protocols import VideoPixelShape
from ltx_core.pipeline.components.schedulers import LTX2Scheduler
from ltx_core.pipeline.conditioning.tools import AudioLatentTools, LatentState, VideoLatentTools
from ltx_core.tiling import SpatialTilingConfig, TemporalTilingConfig, TilingConfig
from ltx_trainer.progress import SamplingContext

if TYPE_CHECKING:
    from ltx_core.model.audio_vae.audio_vae import Decoder as AudioDecoder
    from ltx_core.model.audio_vae.vocoder import Vocoder
    from ltx_core.model.clip.gemma.encoders.av_encoder import AVGemmaTextEncoderModel
    from ltx_core.model.transformer.model import LTXModel
    from ltx_core.model.video_vae.video_vae import Decoder as VideoDecoder
    from ltx_core.model.video_vae.video_vae import Encoder as VideoEncoder

# Video VAE scale factors (temporal, height, width)
VIDEO_SCALE_FACTORS = (8, 32, 32)


@dataclass
class CachedPromptEmbeddings:
    """Pre-computed text embeddings for a validation prompt.

    These embeddings are computed once at training start and reused for all validation runs,
    avoiding the need to load the full Gemma text encoder during validation.
    """

    video_context_positive: Tensor  # [1, seq_len, hidden_dim]
    audio_context_positive: Tensor  # [1, seq_len, hidden_dim]
    video_context_negative: Tensor | None = None
    audio_context_negative: Tensor | None = None


@dataclass
class TiledDecodingConfig:
    """Configuration for tiled video decoding to reduce VRAM usage.

    Tiled decoding splits the latent tensor into overlapping tiles, decodes each
    tile individually, and blends them together. This significantly reduces peak
    VRAM usage at the cost of slightly slower decoding.

    Defaults match the recommended values from ltx-core tests.
    """

    enabled: bool = True  # Whether to use tiled decoding (enabled by default)
    tile_size_pixels: int = 192  # Spatial tile size in pixels (must be ≥64 and divisible by 32)
    tile_overlap_pixels: int = 64  # Spatial tile overlap in pixels (must be divisible by 32)
    tile_size_frames: int = 48  # Temporal tile size in frames (must be ≥16 and divisible by 8)
    tile_overlap_frames: int = 24  # Temporal tile overlap in frames (must be divisible by 8)


@dataclass
class GenerationConfig:
    """Configuration for video/audio generation."""

    prompt: str  # Text prompt for generation
    negative_prompt: str = ""  # Negative prompt to avoid unwanted artifacts
    height: int = 544  # Output video height in pixels
    width: int = 960  # Output video width in pixels
    num_frames: int = 97  # Number of frames to generate
    frame_rate: float = 25.0  # Frame rate for temporal position scaling
    num_inference_steps: int = 30  # Number of denoising steps
    guidance_scale: float = 3.0  # CFG guidance scale
    seed: int = 42  # Random seed for reproducibility
    condition_image: Tensor | None = None  # Optional first frame image for image-to-video
    reference_video: Tensor | None = None  # For IC-LoRA: [F, C, H, W] in [0, 1]
    generate_audio: bool = True  # Whether to generate audio alongside video
    include_reference_in_output: bool = False  # For IC-LoRA: concatenate original reference with generated output
    cached_embeddings: CachedPromptEmbeddings | None = None  # Pre-computed text embeddings (avoids loading Gemma)
    stg_scale: float = 0.0  # STG strength (0.0 = disabled, recommended: 1.0)
    stg_blocks: list[int] | None = None  # Transformer blocks to perturb (None = all, recommended: [29])
    stg_mode: Literal["stg_av", "stg_v"] = "stg_av"  # STG mode: "stg_av" (audio+video) or "stg_v" (video only)
    # Tiled decoding config: None = use defaults (enabled), False = disable, or TiledDecodingConfig for custom settings
    tiled_decoding: TiledDecodingConfig | Literal[False] | None = None

    def __post_init__(self) -> None:
        """Apply default tiled decoding config if not provided."""
        if self.tiled_decoding is None:
            # Use default config with tiling enabled
            object.__setattr__(self, "tiled_decoding", TiledDecodingConfig())
        elif self.tiled_decoding is False:
            # Explicitly disabled - use config with enabled=False
            object.__setattr__(self, "tiled_decoding", TiledDecodingConfig(enabled=False))


class ValidationSampler:
    """Generates validation samples during training using ltx-core components.

    This class provides a simplified interface for generating video (and optionally audio)
    samples during training validation. It supports:
    - Text-to-video generation
    - Image-to-video generation (first frame conditioning)
    - Video-to-video generation (IC-LoRA reference video conditioning)
    - Optional audio generation

    The implementation follows the patterns from ltx_pipelines.single_stage.

    Text embeddings can be provided either via:
    - A full text_encoder (encodes prompts on-the-fly)
    - Pre-computed cached_embeddings (avoids loading Gemma during validation)
    """

    def __init__(
        self,
        transformer: "LTXModel",
        vae_decoder: "VideoDecoder",
        vae_encoder: "VideoEncoder | None",
        text_encoder: "AVGemmaTextEncoderModel | None" = None,
        audio_decoder: "AudioDecoder | None" = None,
        vocoder: "Vocoder | None" = None,
        sampling_context: SamplingContext | None = None,
    ):
        """Initialize the validation sampler.

        Args:
            transformer: LTX-2 transformer model
            vae_decoder: Video VAE decoder
            vae_encoder: Video VAE encoder (for image/video conditioning), can be None if not needed
            text_encoder: Gemma text encoder with embeddings connector (optional if cached_embeddings in config)
            audio_decoder: Optional audio VAE decoder (for audio generation)
            vocoder: Optional vocoder (for audio generation)
            sampling_context: Optional SamplingContext for progress display during denoising
        """
        self._transformer = transformer
        self._vae_decoder = vae_decoder
        self._vae_encoder = vae_encoder
        self._text_encoder = text_encoder
        self._audio_decoder = audio_decoder
        self._vocoder = vocoder
        self._sampling_context = sampling_context

        # Patchifiers
        self._video_patchifier = VideoLatentPatchifier(patch_size=1)
        self._audio_patchifier = AudioPatchifier(patch_size=1)

    # Note: Use @torch.no_grad() instead of @torch.inference_mode() to avoid FSDP inplace update errors after validation
    @torch.no_grad()
    def generate(
        self,
        config: GenerationConfig,
        device: torch.device | str = "cuda",
    ) -> tuple[Tensor, Tensor | None]:
        """Generate a video (and optionally audio) sample.

        Args:
            config: Generation configuration
            device: Device to run generation on

        Returns:
            Tuple of:
                - video: Video tensor [C, F, H, W] in [0, 1] (float32)
                - audio: Audio waveform tensor [C, samples] or None
        """
        device = torch.device(device) if isinstance(device, str) else device
        self._validate_config(config)

        # Route to appropriate generation method
        if config.reference_video is not None:
            return self._generate_with_reference(config, device)
        return self._generate_standard(config, device)

    def _generate_standard(self, config: GenerationConfig, device: torch.device) -> tuple[Tensor, Tensor | None]:
        """Standard generation (text-to-video or image-to-video)."""
        # Get prompt embeddings (from cache or encode on-the-fly)
        v_ctx_pos, a_ctx_pos, v_ctx_neg, a_ctx_neg = self._get_prompt_embeddings(config, device)

        # Setup generator
        generator = torch.Generator(device=device).manual_seed(config.seed)

        # Create latent tools
        video_tools = self._create_video_latent_tools(config)
        audio_tools = self._create_audio_latent_tools(config) if config.generate_audio else None

        # Create initial states
        video_clean_state = video_tools.create_initial_state(device=device, dtype=torch.bfloat16)
        audio_clean_state = (
            audio_tools.create_initial_state(device=device, dtype=torch.bfloat16) if audio_tools else None
        )

        # Apply image conditioning if provided
        if config.condition_image is not None:
            video_clean_state = self._apply_image_conditioning(
                video_clean_state, config.condition_image, config, device
            )

        # Add noise
        noiser = GaussianNoiser(generator=generator)
        video_state = noiser(latent_state=video_clean_state, noise_scale=1.0)
        audio_state = noiser(latent_state=audio_clean_state, noise_scale=1.0) if audio_clean_state else None

        # Run denoising loop
        video_state, audio_state = self._run_denoising(
            config=config,
            video_state=video_state,
            audio_state=audio_state,
            video_clean_state=video_clean_state,
            audio_clean_state=audio_clean_state,
            v_ctx_pos=v_ctx_pos,
            a_ctx_pos=a_ctx_pos,
            v_ctx_neg=v_ctx_neg,
            a_ctx_neg=a_ctx_neg,
            device=device,
        )

        # Decode outputs
        video_state = video_tools.clear_conditioning(video_state)
        video_state = video_tools.unpatchify(video_state)
        video_output = self._decode_video(video_state, device, config.tiled_decoding)

        audio_output = None
        if audio_state is not None and audio_tools is not None:
            audio_state = audio_tools.clear_conditioning(audio_state)
            audio_state = audio_tools.unpatchify(audio_state)
            audio_output = self._decode_audio(audio_state, device)

        return video_output, audio_output

    def _generate_with_reference(self, config: GenerationConfig, device: torch.device) -> tuple[Tensor, Tensor | None]:
        """Generate with reference video conditioning (IC-LoRA style).

        For IC-LoRA:
        - Reference video latents are concatenated with target latents
        - Reference latents have timestep=0 (clean, not denoised)
        - Target latents are denoised normally
        - If condition_image is also provided, the first frame of the target is conditioned
        - If include_reference_in_output is True, the preprocessed reference video
          is concatenated side-by-side with the generated video
        """
        # Get prompt embeddings (from cache or encode on-the-fly)
        v_ctx_pos, a_ctx_pos, v_ctx_neg, a_ctx_neg = self._get_prompt_embeddings(config, device)

        # Setup generator
        generator = torch.Generator(device=device).manual_seed(config.seed)

        # Preprocess and encode reference video
        ref_video_preprocessed = self._preprocess_reference_video(config)
        ref_latent, ref_positions = self._encode_video(ref_video_preprocessed, config.frame_rate, device)
        ref_seq_len = ref_latent.shape[1]

        # Create target video state
        video_tools = self._create_video_latent_tools(config)
        target_clean_state = video_tools.create_initial_state(device=device, dtype=torch.bfloat16)

        # Apply first-frame image conditioning to target if provided
        if config.condition_image is not None:
            target_clean_state = self._apply_image_conditioning(
                target_clean_state, config.condition_image, config, device
            )

        # Create combined state (reference + target)
        # denoise_mask shape is [B, seq_len, 1] after patchification
        ref_denoise_mask = torch.zeros(1, ref_seq_len, 1, device=device, dtype=torch.float32)
        combined_clean_state = LatentState(
            latent=torch.cat([ref_latent, target_clean_state.latent], dim=1),
            denoise_mask=torch.cat([ref_denoise_mask, target_clean_state.denoise_mask], dim=1),
            positions=torch.cat([ref_positions, target_clean_state.positions], dim=2),
            clean_latent=torch.cat([ref_latent, target_clean_state.clean_latent], dim=1),
        )

        # Add noise (only to the target portion via denoise_mask)
        noiser = GaussianNoiser(generator=generator)
        combined_state = noiser(latent_state=combined_clean_state, noise_scale=1.0)

        # Create audio state if needed
        audio_tools = self._create_audio_latent_tools(config) if config.generate_audio else None
        audio_clean_state = (
            audio_tools.create_initial_state(device=device, dtype=torch.bfloat16) if audio_tools else None
        )
        audio_state = noiser(latent_state=audio_clean_state, noise_scale=1.0) if audio_clean_state else None

        # Run denoising loop
        combined_state, audio_state = self._run_denoising(
            config=config,
            video_state=combined_state,
            audio_state=audio_state,
            video_clean_state=combined_clean_state,
            audio_clean_state=audio_clean_state,
            v_ctx_pos=v_ctx_pos,
            a_ctx_pos=a_ctx_pos,
            v_ctx_neg=v_ctx_neg,
            a_ctx_neg=a_ctx_neg,
            device=device,
        )

        # Extract target portion and decode
        target_latent = combined_state.latent[:, ref_seq_len:]
        video_output = self._decode_video_latent(target_latent, config, device)

        # Optionally concatenate original reference video side-by-side
        if config.include_reference_in_output:
            # Use preprocessed reference (already resized/cropped, in pixel space)
            # Convert from [B, C, F, H, W] to [C, F, H, W]
            ref_video_pixels = ref_video_preprocessed[0].cpu()
            # Normalize from [-1, 1] to [0, 1]
            ref_video_pixels = ((ref_video_pixels + 1.0) / 2.0).clamp(0.0, 1.0)
            video_output = self._concatenate_videos_side_by_side(ref_video_pixels, video_output)

        # Decode audio
        audio_output = None
        if audio_state is not None and audio_tools is not None:
            audio_state = audio_tools.clear_conditioning(audio_state)
            audio_state = audio_tools.unpatchify(audio_state)
            audio_output = self._decode_audio(audio_state, device)

        return video_output, audio_output

    def _create_video_latent_tools(self, config: GenerationConfig) -> VideoLatentTools:
        """Create video latent tools for the given configuration."""
        pixel_shape = VideoPixelShape(
            batch=1,
            frames=config.num_frames,
            height=config.height,
            width=config.width,
            fps=config.frame_rate,
        )
        return VideoLatentTools(
            patchifier=self._video_patchifier,
            target_shape=VideoLatentShape.from_pixel_shape(shape=pixel_shape),
            fps=config.frame_rate,
            scale_factors=VIDEO_SCALE_FACTORS,
            causal_fix=True,
        )

    def _create_audio_latent_tools(self, config: GenerationConfig) -> AudioLatentTools:
        """Create audio latent tools for the given configuration."""
        return AudioLatentTools(
            patchifier=self._audio_patchifier,
            target_shape=AudioLatentShape.from_duration(batch=1, duration=config.num_frames / config.frame_rate),
        )

    def _apply_image_conditioning(
        self, video_state: LatentState, image: Tensor, config: GenerationConfig, device: torch.device
    ) -> LatentState:
        """Apply first-frame image conditioning to the video state."""
        # Encode the image
        encoded_image = self._encode_conditioning_image(image, config.height, config.width, device)

        # Patchify the encoded image (single frame)
        patchified_image = self._video_patchifier.patchify(encoded_image)  # [1, 1, C] -> [1, num_patches, C]
        num_image_tokens = patchified_image.shape[1]

        # Update the first frame tokens in the latent
        new_latent = video_state.latent.clone()
        new_latent[:, :num_image_tokens] = patchified_image.to(new_latent.dtype)

        # Update clean_latent as well (conditioning image is clean)
        new_clean_latent = video_state.clean_latent.clone()
        new_clean_latent[:, :num_image_tokens] = patchified_image.to(new_clean_latent.dtype)

        # Set denoise_mask to 0 for conditioned tokens (don't denoise them)
        new_denoise_mask = video_state.denoise_mask.clone()
        new_denoise_mask[:, :num_image_tokens] = 0.0

        return LatentState(
            latent=new_latent,
            denoise_mask=new_denoise_mask,
            positions=video_state.positions,
            clean_latent=new_clean_latent,
        )

    @staticmethod
    def _preprocess_reference_video(config: GenerationConfig) -> Tensor:
        """Preprocess reference video: resize, crop, and convert to model input format.

        Args:
            config: Generation configuration with reference_video

        Returns:
            Preprocessed video tensor [B, C, F, H, W] in [-1, 1] range
        """
        ref_video = config.reference_video  # [F, C, H, W] in [0, 1]
        target_height, target_width = config.height, config.width
        current_height, current_width = ref_video.shape[2:]

        # Resize maintaining aspect ratio and center crop if needed
        if current_height != target_height or current_width != target_width:
            aspect_ratio = current_width / current_height
            target_aspect_ratio = target_width / target_height

            if aspect_ratio > target_aspect_ratio:
                resize_height, resize_width = target_height, int(target_height * aspect_ratio)
            else:
                resize_height, resize_width = int(target_width / aspect_ratio), target_width

            ref_video = torch.nn.functional.interpolate(
                ref_video, size=(resize_height, resize_width), mode="bilinear", align_corners=False
            )

            # Center crop
            h_start = (resize_height - target_height) // 2
            w_start = (resize_width - target_width) // 2
            ref_video = ref_video[:, :, h_start : h_start + target_height, w_start : w_start + target_width]

        # Convert to [B, C, F, H, W] and trim to valid frame count (k*8 + 1)
        ref_video = rearrange(ref_video, "f c h w -> 1 c f h w")
        valid_frames = (ref_video.shape[2] - 1) // 8 * 8 + 1
        ref_video = ref_video[:, :, :valid_frames]

        # Convert to [-1, 1] range
        return ref_video * 2.0 - 1.0

    def _encode_video(self, video: Tensor, fps: float, device: torch.device) -> tuple[Tensor, Tensor]:
        """Encode video to patchified latents and compute positions.

        Args:
            video: Video tensor [B, C, F, H, W] in [-1, 1] range
            fps: Frame rate for temporal position scaling
            device: Device to run encoding on

        Returns:
            Tuple of (patchified_latents, positions)
        """
        video = video.to(device=device, dtype=torch.float32)

        # Encode with VAE
        self._vae_encoder.to(device)
        with torch.autocast(device_type=str(device).split(":")[0], dtype=torch.bfloat16):
            latents = self._vae_encoder(video)
        self._vae_encoder.to("cpu")

        latents = latents.to(torch.bfloat16)
        patchified = self._video_patchifier.patchify(latents)

        # Compute positions
        latent_shape = VideoLatentShape(
            batch=1,
            channels=latents.shape[1],
            frames=latents.shape[2],
            height=latents.shape[3],
            width=latents.shape[4],
        )
        latent_coords = self._video_patchifier.get_patch_grid_bounds(output_shape=latent_shape, device=device)
        positions = get_pixel_coords(latent_coords, scale_factors=VIDEO_SCALE_FACTORS, causal_fix=True)
        positions = positions.to(torch.bfloat16)
        positions[:, 0, ...] = positions[:, 0, ...] / fps

        return patchified, positions

    def _run_denoising(
        self,
        config: GenerationConfig,
        video_state: LatentState,
        audio_state: LatentState | None,
        video_clean_state: LatentState,
        audio_clean_state: LatentState | None,
        v_ctx_pos: Tensor,
        a_ctx_pos: Tensor,
        v_ctx_neg: Tensor | None,
        a_ctx_neg: Tensor | None,
        device: torch.device,
    ) -> tuple[LatentState, LatentState | None]:
        """Run the denoising loop using X0 prediction with CFG and optional STG."""
        scheduler = LTX2Scheduler()
        sigmas = scheduler.execute(steps=config.num_inference_steps).to(device).float()
        stepper = EulerDiffusionStep()
        cfg_guider = CFGGuider(config.guidance_scale)
        stg_guider = STGGuider(config.stg_scale)

        # Build STG perturbation config if STG is enabled
        stg_perturbation_config = self._build_stg_perturbation_config(config) if stg_guider.enabled() else None

        # Create initial modalities (will be updated each step via replace())
        video = Modality(
            enabled=True,
            latent=video_state.latent,
            timesteps=video_state.denoise_mask,
            positions=video_state.positions,
            context=v_ctx_pos,
            context_mask=None,
        )

        # Audio modality is None when not generating audio
        audio: Modality | None = None
        if audio_state is not None:
            audio = Modality(
                enabled=True,
                latent=audio_state.latent,
                timesteps=audio_state.denoise_mask,
                positions=audio_state.positions,
                context=a_ctx_pos,
                context_mask=None,
            )

        # Wrap transformer with X0Model to convert velocity predictions to denoised outputs
        self._transformer.to(device)
        x0_model = X0Model(self._transformer)

        with torch.autocast(device_type=str(device).split(":")[0], dtype=torch.bfloat16):
            for step_idx, sigma in enumerate(sigmas[:-1]):
                # Update modalities with current state and timesteps
                video = replace(
                    video,
                    latent=video_state.latent,
                    timesteps=sigma * video_state.denoise_mask,
                    positions=video_state.positions,
                )

                if audio is not None and audio_state is not None:
                    audio = replace(
                        audio,
                        latent=audio_state.latent,
                        timesteps=sigma * audio_state.denoise_mask,
                        positions=audio_state.positions,
                    )

                # Run model (positive pass) - X0Model returns denoised outputs
                pos_video, pos_audio = x0_model(video=video, audio=audio, perturbations=None)
                denoised_video, denoised_audio = pos_video, pos_audio

                # Apply CFG if guidance_scale != 1.0
                if cfg_guider.enabled() and v_ctx_neg is not None:
                    video_neg = replace(video, context=v_ctx_neg)
                    audio_neg = replace(audio, context=a_ctx_neg) if audio is not None else None
                    neg_video, neg_audio = x0_model(video=video_neg, audio=audio_neg, perturbations=None)

                    denoised_video = denoised_video + cfg_guider.delta(pos_video, neg_video)
                    if audio is not None and denoised_audio is not None:
                        denoised_audio = denoised_audio + cfg_guider.delta(pos_audio, neg_audio)

                # Apply STG if stg_scale != 0.0
                if stg_guider.enabled() and stg_perturbation_config is not None:
                    perturbed_video, perturbed_audio = x0_model(
                        video=video, audio=audio, perturbations=stg_perturbation_config
                    )
                    denoised_video = denoised_video + stg_guider.delta(pos_video, perturbed_video)
                    if audio is not None and denoised_audio is not None and perturbed_audio is not None:
                        denoised_audio = denoised_audio + stg_guider.delta(pos_audio, perturbed_audio)

                # Apply conditioning mask (keep conditioned tokens clean)
                denoised_video = denoised_video * video_state.denoise_mask + video_clean_state.latent.float() * (
                    1 - video_state.denoise_mask
                )
                if audio is not None and audio_state is not None and audio_clean_state is not None:
                    denoised_audio = denoised_audio * audio_state.denoise_mask + audio_clean_state.latent.float() * (
                        1 - audio_state.denoise_mask
                    )

                # Euler step
                video_state = replace(
                    video_state,
                    latent=stepper.step(
                        sample=video.latent, denoised_sample=denoised_video, sigmas=sigmas, step_index=step_idx
                    ),
                )
                if audio is not None and audio_state is not None:
                    audio_state = replace(
                        audio_state,
                        latent=stepper.step(
                            sample=audio.latent, denoised_sample=denoised_audio, sigmas=sigmas, step_index=step_idx
                        ),
                    )

                # Update progress
                if self._sampling_context is not None:
                    self._sampling_context.advance_step()

        return video_state, audio_state

    @staticmethod
    def _build_stg_perturbation_config(config: GenerationConfig) -> BatchedPerturbationConfig:
        """Build the perturbation config for STG based on the stg_mode."""
        # Always skip video self-attention for STG
        perturbations: list[Perturbation] = [
            Perturbation(type=PerturbationType.SKIP_VIDEO_SELF_ATTN, blocks=config.stg_blocks)
        ]

        # Optionally also skip audio self-attention (stg_av mode)
        if config.stg_mode == "stg_av":
            perturbations.append(Perturbation(type=PerturbationType.SKIP_AUDIO_SELF_ATTN, blocks=config.stg_blocks))

        perturbation_config = PerturbationConfig(perturbations=perturbations)
        # Batch size is 1 for validation
        return BatchedPerturbationConfig(perturbations=[perturbation_config])

    def _decode_video_latent(self, latent: Tensor, config: GenerationConfig, device: torch.device) -> Tensor:
        """Decode patchified video latent to pixel space."""
        # Unpatchify
        latent_frames = config.num_frames // VIDEO_SCALE_FACTORS[0] + 1
        latent_height = config.height // VIDEO_SCALE_FACTORS[1]
        latent_width = config.width // VIDEO_SCALE_FACTORS[2]

        unpatchified = self._video_patchifier.unpatchify(
            latent,
            output_shape=VideoLatentShape(
                height=latent_height,
                width=latent_width,
                frames=latent_frames,
                batch=1,
                channels=128,
            ),
        )

        # Decode - ensure bfloat16 to match decoder weights
        self._vae_decoder.to(device)
        unpatchified = unpatchified.to(dtype=torch.bfloat16)
        tiled_config = config.tiled_decoding

        if tiled_config is not None and tiled_config.enabled:
            # Use tiled decoding for reduced VRAM
            tiling_config = TilingConfig(
                spatial_config=SpatialTilingConfig(
                    tile_size_in_pixels=tiled_config.tile_size_pixels,
                    tile_overlap_in_pixels=tiled_config.tile_overlap_pixels,
                ),
                temporal_config=TemporalTilingConfig(
                    tile_size_in_frames=tiled_config.tile_size_frames,
                    tile_overlap_in_frames=tiled_config.tile_overlap_frames,
                ),
            )
            chunks = []
            for video_chunk, _ in self._vae_decoder.tiled_decode(
                unpatchified,
                tiling_config=tiling_config,
            ):
                chunks.append(video_chunk)
            decoded_video = torch.cat(chunks, dim=2)
        else:
            # Standard full decoding
            decoded_video = self._vae_decoder(unpatchified)

        decoded_video = ((decoded_video + 1.0) / 2.0).clamp(0.0, 1.0)
        self._vae_decoder.to("cpu")

        return decoded_video[0].float().cpu()

    def _validate_config(self, config: GenerationConfig) -> None:
        """Validate generation configuration."""
        if config.height % 32 != 0 or config.width % 32 != 0:
            raise ValueError(f"height and width must be divisible by 32, got {config.height}x{config.width}")
        if config.num_frames % 8 != 1:
            raise ValueError(f"num_frames must satisfy num_frames % 8 == 1, got {config.num_frames}")
        if config.generate_audio and (self._audio_decoder is None or self._vocoder is None):
            raise ValueError("Audio generation requires audio_decoder and vocoder")
        if config.condition_image is not None and self._vae_encoder is None:
            raise ValueError("Image conditioning requires vae_encoder")
        if config.reference_video is not None and self._vae_encoder is None:
            raise ValueError("Reference video conditioning requires vae_encoder")

        # Validate prompt embedding source
        if config.cached_embeddings is None and self._text_encoder is None:
            raise ValueError("Either text_encoder or config.cached_embeddings must be provided")

    def _get_prompt_embeddings(
        self, config: GenerationConfig, device: torch.device
    ) -> tuple[Tensor, Tensor, Tensor | None, Tensor | None]:
        """Get prompt embeddings from config cache or encode on-the-fly."""
        if config.cached_embeddings is not None:
            # Use pre-computed embeddings from config
            cached = config.cached_embeddings
            v_ctx_pos = cached.video_context_positive.to(device)
            a_ctx_pos = cached.audio_context_positive.to(device)
            v_ctx_neg = cached.video_context_negative.to(device) if cached.video_context_negative is not None else None
            a_ctx_neg = cached.audio_context_negative.to(device) if cached.audio_context_negative is not None else None
            return v_ctx_pos, a_ctx_pos, v_ctx_neg, a_ctx_neg

        # Fall back to encoding on-the-fly
        return self._encode_prompts(config, device)

    def _encode_prompts(
        self, config: GenerationConfig, device: torch.device
    ) -> tuple[Tensor, Tensor, Tensor | None, Tensor | None]:
        """Encode positive and negative prompts using the text encoder."""
        self._text_encoder.to(device)
        v_ctx_pos, a_ctx_pos, _ = self._text_encoder(config.prompt)
        v_ctx_neg, a_ctx_neg = None, None
        if config.guidance_scale != 1.0:
            v_ctx_neg, a_ctx_neg, _ = self._text_encoder(config.negative_prompt)

        # Move the base Gemma model to CPU but keep embeddings connectors on GPU
        # as this module is also used during training
        self._text_encoder.model.to("cpu")
        self._text_encoder.feature_extractor_linear.to("cpu")

        return v_ctx_pos, a_ctx_pos, v_ctx_neg, a_ctx_neg

    def _decode_video(
        self, video_state: LatentState, device: torch.device, tiled_config: TiledDecodingConfig | None = None
    ) -> Tensor:
        """Decode video latents to pixel space.

        Args:
            video_state: Video latent state to decode
            device: Device to run decoding on
            tiled_config: Optional tiled decoding configuration for reduced VRAM usage

        Returns:
            Decoded video tensor [C, F, H, W] in [0, 1] range
        """
        self._vae_decoder.to(device)
        # Ensure latent is bfloat16 to match decoder weights
        latent = video_state.latent.to(dtype=torch.bfloat16)

        if tiled_config is not None and tiled_config.enabled:
            # Use tiled decoding for reduced VRAM
            tiling_config = TilingConfig(
                spatial_config=SpatialTilingConfig(
                    tile_size_in_pixels=tiled_config.tile_size_pixels,
                    tile_overlap_in_pixels=tiled_config.tile_overlap_pixels,
                ),
                temporal_config=TemporalTilingConfig(
                    tile_size_in_frames=tiled_config.tile_size_frames,
                    tile_overlap_in_frames=tiled_config.tile_overlap_frames,
                ),
            )
            chunks = []
            for video_chunk, _ in self._vae_decoder.tiled_decode(
                latent,
                tiling_config=tiling_config,
            ):
                chunks.append(video_chunk)
            decoded_video = torch.cat(chunks, dim=2)
        else:
            # Standard full decoding
            decoded_video = self._vae_decoder(latent)

        decoded_video = ((decoded_video + 1.0) / 2.0).clamp(0.0, 1.0)
        self._vae_decoder.to("cpu")
        return decoded_video[0].float().cpu()

    def _decode_audio(self, audio_state: LatentState, device: torch.device) -> Tensor:
        """Decode audio latents to waveform."""
        self._audio_decoder.to(device)
        # Ensure latent is bfloat16 to match decoder weights
        latent = audio_state.latent.to(dtype=torch.bfloat16)
        decoded_audio = self._audio_decoder(latent)
        self._audio_decoder.to("cpu")

        self._vocoder.to(device)
        audio_waveform = self._vocoder(decoded_audio)
        self._vocoder.to("cpu")

        return audio_waveform.squeeze(0).float().cpu()

    @staticmethod
    def _concatenate_videos_side_by_side(left_video: Tensor, right_video: Tensor) -> Tensor:
        """Concatenate two videos side-by-side (horizontally).

        If the videos have different frame counts, the shorter one is padded with
        its last frame repeated.

        Args:
            left_video: Left video tensor [C, F1, H, W] in [0, 1]
            right_video: Right video tensor [C, F2, H, W] in [0, 1]

        Returns:
            Concatenated video tensor [C, max(F1,F2), H, W*2] in [0, 1]
        """
        left_frames = left_video.shape[1]
        right_frames = right_video.shape[1]

        # Pad shorter video by repeating last frame
        if left_frames < right_frames:
            padding = left_video[:, -1:, :, :].expand(-1, right_frames - left_frames, -1, -1)
            left_video = torch.cat([left_video, padding], dim=1)
        elif right_frames < left_frames:
            padding = right_video[:, -1:, :, :].expand(-1, left_frames - right_frames, -1, -1)
            right_video = torch.cat([right_video, padding], dim=1)

        # Concatenate along width dimension
        return torch.cat([left_video, right_video], dim=3)

    def _encode_conditioning_image(
        self,
        image: Tensor,
        target_height: int,
        target_width: int,
        device: torch.device,
    ) -> Tensor:
        """Encode a conditioning image to latent space.

        The image is resized to cover the target dimensions while preserving aspect ratio,
        then center-cropped to exactly match the target size.
        """
        # image is [C, H, W] in [0, 1]  # noqa: ERA001
        current_height, current_width = image.shape[1:]

        # Resize maintaining aspect ratio (cover target, then center crop)
        if current_height != target_height or current_width != target_width:
            aspect_ratio = current_width / current_height
            target_aspect_ratio = target_width / target_height

            if aspect_ratio > target_aspect_ratio:
                # Image is wider than target - resize to match height, crop width
                resize_height = target_height
                resize_width = int(target_height * aspect_ratio)
            else:
                # Image is taller than target - resize to match width, crop height
                resize_height = int(target_width / aspect_ratio)
                resize_width = target_width

            image = rearrange(image, "c h w -> 1 c h w")
            image = torch.nn.functional.interpolate(
                image, size=(resize_height, resize_width), mode="bilinear", align_corners=False
            )

            # Center crop to target dimensions
            h_start = (resize_height - target_height) // 2
            w_start = (resize_width - target_width) // 2
            image = image[:, :, h_start : h_start + target_height, w_start : w_start + target_width]
        else:
            image = rearrange(image, "c h w -> 1 c h w")

        # Add frame dimension and convert to [-1, 1]
        image = rearrange(image, "b c h w -> b c 1 h w")
        image = (image * 2.0 - 1.0).to(device=device, dtype=torch.float32)

        # Encode
        self._vae_encoder.to(device)
        with torch.autocast(device_type=str(device).split(":")[0], dtype=torch.bfloat16):
            encoded = self._vae_encoder(image)
        self._vae_encoder.to("cpu")

        return encoded