"""The denoising half of a **split** MiniMax-H3 deployment.

MiniMax-H3 is ~196 GiB in bfloat16 and a ZeroGPU Space is evicted at 150 GB of storage, so `MiniMaxH3Blocks` is cut at
its `text_encoder` step: the 62.14 GiB Qwen3-VL conditioner runs in its own Space and everything else — the distilled
transformer and the two autoencoders — runs here. `prompt_embeds` + `text_token_tags` is the whole wire format between
the two halves.

For a text-only (`t2va`) request the full `MiniMaxH3Blocks` sequence is `before_encode -> text_encoder -> vae_encoder
-> denoise -> decode`, and the two `Auto*` encoder steps select nothing without a keyframe or a reference. What is
left once the text encoder goes is exactly `MiniMaxH3CoreDenoiseStep -> MiniMaxH3DecodeStep`, and those two declare
only `transformer`, the two schedulers, both autoencoders and `video_processor` — so `load_components` resolves those
out of the checkpoint's `modular_model_index.json` and never fetches the conditioner or the `ref2va` partition.

Adapted from `multimodalart/minimax-h3`, trimmed to the `t2va` (text -> video + audio) branch this Space serves.
"""

import torch
from diffusers.modular_pipelines.minimax_h3.modular_blocks_minimax_h3 import (
    MiniMaxH3CoreDenoiseStep,
    MiniMaxH3DecodeStep,
)
from diffusers.modular_pipelines.modular_pipeline import SequentialPipelineBlocks
from diffusers.modular_pipelines.modular_pipeline_utils import OutputParam


class MiniMaxH3GeneratorBlocks(SequentialPipelineBlocks):
    """The denoising half of a split MiniMax-H3: the `t2va` branch of `MiniMaxH3Blocks` without its text encoder."""

    model_name = "minimax-h3"
    block_classes = [MiniMaxH3CoreDenoiseStep, MiniMaxH3DecodeStep]
    block_names = ["denoise", "decode"]

    @property
    def description(self):
        return (
            "The denoising half of a split MiniMax-H3 deployment: the `t2va` branch of `MiniMaxH3Blocks` without its "
            "text-encoder step, so `prompt_embeds` and `text_token_tags` come in as inputs and the 62.14 GiB Qwen3-VL "
            "conditioner is never loaded here."
        )

    @property
    def outputs(self):
        return [
            OutputParam.template("videos", description="The generated video."),
            OutputParam(
                "audio",
                type_hint=torch.Tensor,
                description="The generated soundtrack, of shape `(1, 2, num_samples)`.",
            ),
            OutputParam("sampling_rate", type_hint=int, description="Sample rate of the generated soundtrack in Hz."),
        ]