初始提交：3 款纯音频游戏 + 共享引擎 + 完整文档与资产管线

- 共享引擎（app/src/main）：GestureEngine 6 手势识别、AudioEngine 三层音频（ExoPlayer BGM 交叉淡入 / 对白 ducking / SoundPool 低延迟）、Game/GameEngine 状态机接口、MainActivity 全屏封面 + TalkBack - 《声命线》storyline：末世互动广播剧，3 幕 8 抉择点 4 结局 - 《声东击西》reflex：听声辨位反应，12 波战斗 + BGM 三档 BPM 变速 - 《暗室》escape：3x3 网格密室逃脱，钥匙→铁柜→保险丝→电箱→大门谜题链 - 文档：setup（从零搭建）/ architecture（架构）/ engine-api（引擎 API）/ asset-pipeline（资产流水线）/ games/*（三款游戏设计文档） - 资产管线：Qwen3-TTS 对白 + 程序合成音效/BGM + Mureka BGM 的 gen 脚本与台账，全部 ogg 资产入库可复现 - env.d 环境脚本：android/tts 入库；sfx/mureka 含密钥不入库，仅提交 .example.sh 模板
2026-08-29 13:00:34 +08:00
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+"""
+Runtime support: tokenizer + distribution-shift constructors.
+
+Everything model-dependent (tokenizer, T5Gemma engine, DiT engines, decoder
+engines) ships from HuggingFace under models/. The DiT engines bundle their
+per-DiT conditioner tensors (padding_embedding + seconds_total Linear) as
+graph Constants, and DistributionShift's schedule constants are SA3-canonical
+and live as defaults on the class.
+"""
+
+import math
+import os
+import torch
+
+# runtime.py lives in scripts/; models/ lives one level up at the repo root.
+_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+MODELS_DIR = os.path.join(_REPO, "models")
+# ARCH_DIR is set by sa3_trt.py before calling load() (it knows the GPU arch).
+ARCH_DIR = MODELS_DIR  # placeholder; overwritten by sa3_trt.py
+IO_CHANNELS = 256
+SAMPLE_RATE = 44100
+DOWNSAMPLING_RATIO = 4096
+
+
+class FastTokenizer:
+    """Tokenizer using HuggingFace tokenizers lib directly (no transformers import)."""
+    def __init__(self, tokenizer_json_path, max_length=256, pad_token_id=0):
+        from tokenizers import Tokenizer
+        self.tokenizer = Tokenizer.from_file(tokenizer_json_path)
+        self.max_length = max_length
+        self.pad_token_id = pad_token_id
+
+    def __call__(self, text, return_tensors="pt", max_length=None, padding="max_length", truncation=True):
+        max_len = max_length or self.max_length
+        enc = self.tokenizer.encode(text)
+        ids = enc.ids[:max_len]
+        mask = [1] * len(ids)
+        if padding == "max_length":
+            pad_len = max_len - len(ids)
+            ids = ids + [self.pad_token_id] * pad_len
+            mask = mask + [0] * pad_len
+        if return_tensors == "pt":
+            return {
+                "input_ids": torch.tensor([ids], dtype=torch.long),
+                "attention_mask": torch.tensor([mask], dtype=torch.long),
+            }
+        return {"input_ids": ids, "attention_mask": mask}
+
+
+def load_tokenizer():
+    """Load tokenizer using fast tokenizers lib (no HF transformers import).
+
+    tokenizer.json is bundled in the repo next to this module (it's arch-
+    agnostic — same T5Gemma vocab whether you're on sm_90, sm_100, sm_120
+    etc.). Falls back to the legacy per-arch location if the bundled file
+    is somehow missing (e.g. older checkouts).
+    """
+    bundled = os.path.join(os.path.dirname(__file__), "tokenizer.json")
+    legacy  = os.path.join(ARCH_DIR, "t5gemma", "tokenizer.json")
+    path = bundled if os.path.exists(bundled) else legacy
+    return FastTokenizer(path)
+
+
+class DistributionShift:
+    """FluxDistributionShift — schedule warp used by SA3 models (rf_denoiser).
+
+    Without this warp the model is conditioned on the wrong t at each step and
+    final latent magnitudes blow up ~60×. Defaults match HF SA3 model configs
+    (all variants share base_shift=0.5, max_shift=1.15, min/max_length=256/4096).
+    """
+    def __init__(self, base_shift=0.5, max_shift=1.15, min_length=256, max_length=4096):
+        self.base_shift = base_shift
+        self.max_shift = max_shift
+        self.min_length = min_length
+        self.max_length = max_length
+
+    def shift(self, t, seq_len):
+        sl = min(max(int(seq_len), self.min_length), self.max_length)
+        mu = -(self.base_shift + (self.max_shift - self.base_shift) *
+               (sl - self.min_length) / (self.max_length - self.min_length))
+        return 1 - math.exp(mu) / (math.exp(mu) + (1.0 / (1.0 - t) - 1.0))
+
+
+def load():
+    """Load the runtime pieces needed for TRT inference: tokenizer and distribution
+    shift. Neither requires the full PyTorch stable_audio_tools model.
+    """
+    return {"tokenizer": load_tokenizer(), "dist_shift": DistributionShift()}
+
+
+if __name__ == "__main__":
+    state = load()
+    print(f"Loaded: {list(state.keys())}")