// avatar/asr.js
//
// BROWSER-SIDE JAPANESE ASR. WebGPU when the machine has it, WASM when it does not.
//
// Every tier here costs ZERO visitor GPU quota, which is the whole point: SC-4 asks
// whether the turn loop still completes with the accelerated path disabled, and an ASR
// that reached for hosted acceleration would make that answer a lie about listening
// specifically. There is deliberately no remote transcription path in this file, and
// tests/test_transport_seam.py::test_no_remote_asr_path_exists keeps it that way.
//
// Like mic.js this module runs in the PARENT document under both transports and is
// imported by avatar/turn-loop.js, never by a transport file.
//
// Same esm.sh discipline as vrm-stage.js: one pinned URL, dynamically imported, no
// import map. A pinned URL is the difference between a reproducible Space rebuild and
// a model runtime that floats into a breaking release while nobody is watching.

const TRANSFORMERS_URL = 'https://esm.sh/@huggingface/transformers@4.2.0';

/** The two backends. Tier C - type instead of speak, VOIC-04 - is the universal floor
 *  and lives outside this module by construction. */
export const TIERS = {
  WEBGPU: 'webgpu',
  WASM: 'wasm',
};

/**
 * The measured choice. See docs/ASR-TIERS.md for the A/B table these came from and for
 * the caveat that the clips were synthesised, so the measured CER is a best case.
 *
 * THE DTYPE IS NOT NEGOTIABLE AND IS NOT q8. Measured on this stack, `q8`, `int8` and
 * `quantized` all fail to create a session on the WASM backend at all:
 *
 *   Can't create a session. ERROR_CODE: 1, ERROR_MESSAGE: qdq_actions.cc:137
 *   TransposeDQWeightsForMatMulNBits Missing required scale:
 *   model.decoder.embed_tokens.weight_merged_0_scale
 *
 * for whisper-base, whisper-small and whisper-large-v3-turbo alike. They load fine on
 * WebGPU, which is exactly how a q8 default would ship: green on the developer's machine
 * and dead on every browser that lacks an adapter - the tier the fallback exists for.
 * `q4` is the only quantisation measured working on BOTH tiers. 01-RESEARCH.md's
 * "whisper-base with dtype:'q8'" recommendation predates this measurement.
 */
export const MODELS = {
  default: { model: 'onnx-community/whisper-base', dtype: 'q4' },
  accurate: { model: 'onnx-community/whisper-large-v3-turbo', dtype: 'q4f16' },
};

/** Frozen at the facade in plan 01-03; repeated here so the shape is visible at source. */
export const TRANSCRIBE_OPTIONS = {
  language: 'ja',
  task: 'transcribe',
  chunk_length_s: 30,
  return_timestamps: false,
};

/** Whisper's feature extractor expects exactly this rate. */
export const REQUIRED_SAMPLE_RATE = 16000;

let modulePromise = null;

/** One dynamic import for the whole page, however many pipelines get built on top. */
function loadRuntime() {
  if (!modulePromise) {
    modulePromise = import(TRANSFORMERS_URL).then((mod) => {
      // Without this the runtime probes a same-origin /models/ path first and the
      // console fills with 404s that look like real failures.
      if (mod.env) {
        mod.env.allowLocalModels = false;
        mod.env.allowRemoteModels = true;
      }
      return mod;
    });
  }
  return modulePromise;
}

/** Does this browser expose the WebGPU API surface? Cheap, synchronous. NOT sufficient. */
export function hasWebGpu() {
  return typeof navigator !== 'undefined' && !!navigator.gpu;
}

/**
 * Whether WebGPU can ACTUALLY be used, which is a different question from whether
 * `navigator.gpu` exists.
 *
 * This probe is not defensive tidiness, it is load-bearing, and it is here because the
 * obvious design does not work. Measured on this stack: when `navigator.gpu` is present
 * but `requestAdapter()` resolves to null - the normal state of a headless browser, a
 * machine whose GPU is blocklisted, or a browser started with GPU access denied - calling
 * `pipeline(..., { device: 'webgpu' })` throws, AND POISONS THE ONNX RUNTIME BACKEND
 * REGISTRY FOR THE WHOLE PAGE. A subsequent `pipeline(..., { device: 'wasm' })` for the
 * same model then fails with the identical WebGPU error:
 *
 *   no available backend found. ERR: [webgpu] Error: Failed to get GPU adapter.
 *
 * So "feature-detect navigator.gpu, catch the failure, re-instantiate on WASM" - which is
 * what 01-RESEARCH.md prescribes - produces an app that is broken for exactly the users
 * the fallback exists to serve. Probing the adapter first means the doomed call is never
 * made and the registry is never poisoned. The try/catch below stays as a second line of
 * defence for failures a probe cannot predict, such as an out-of-memory adapter.
 *
 * @returns {Promise<{available: boolean, reason: string|null}>}
 */
export async function probeWebGpu() {
  if (!hasWebGpu()) return { available: false, reason: 'navigator.gpu is not present' };
  try {
    const adapter = await navigator.gpu.requestAdapter();
    if (!adapter) return { available: false, reason: 'requestAdapter() resolved to null' };
    return { available: true, reason: null };
  } catch (err) {
    return { available: false, reason: String(err?.message ?? err) };
  }
}

/**
 * @param {object}   opts
 * @param {Function} [opts.emit]    the facade event bus; 'asr-tier' is announced through it
 * @param {string}   [opts.model]   defaults to the measured default model
 * @param {string}   [opts.dtype]   dtype used on the WASM tier
 * @param {string}   [opts.webgpuDtype] dtype used on the WebGPU tier
 * @param {string}   [opts.device]  'auto' | 'webgpu' | 'wasm'; 'auto' feature-detects
 */
export function createAsr({
  emit = () => {},
  model = MODELS.default.model,
  dtype = MODELS.default.dtype,
  webgpuDtype = null,
  device = 'auto',
} = {}) {
  const debug = {
    tier: null,
    model,
    dtype: null,
    loadMs: 0,
    inferMs: 0,
    transcribeCount: 0,
    /** the API surface exists */
    webgpuPresent: hasWebGpu(),
    /** an adapter was actually obtained; null until init() has probed */
    webgpuAvailable: null,
    webgpuError: null,
  };

  let pipe = null;
  let initPromise = null;

  async function build(requestedTier) {
    const { pipeline } = await loadRuntime();
    if (requestedTier === TIERS.WEBGPU) {
      const gpuDtype = webgpuDtype || dtype;
      const built = await pipeline('automatic-speech-recognition', model, {
        device: 'webgpu',
        dtype: gpuDtype,
      });
      return { built, tier: TIERS.WEBGPU, dtype: gpuDtype };
    }
    // Ask for WASM by name rather than leaving `device` unset: the runtime's own
    // auto-selection reaches for WebGPU whenever navigator.gpu exists, which is the
    // same trap probeWebGpu() exists to avoid.
    const built = await pipeline('automatic-speech-recognition', model, {
      device: 'wasm',
      dtype,
    });
    return { built, tier: TIERS.WASM, dtype };
  }

  /**
   * Tier selection. WebGPU is opt-in and still labelled experimental upstream, so a
   * failure to initialise it is an expected branch rather than an incident: catch it,
   * record why, and re-instantiate on WASM. A learner must never see a broken app
   * because their GPU adapter was busy.
   */
  async function init() {
    if (initPromise) return initPromise;
    initPromise = (async () => {
      const started = performance.now();
      let outcome = null;

      // 'wasm' is the only value that skips the probe entirely; both 'auto' and an
      // explicit 'webgpu' request are subject to it, because an explicit request that
      // cannot be honoured must degrade rather than break the page.
      if (device !== TIERS.WASM) {
        const probe = await probeWebGpu();
        debug.webgpuAvailable = probe.available;
        if (!probe.available) debug.webgpuError = probe.reason;
        if (probe.available) {
          try {
            outcome = await build(TIERS.WEBGPU);
          } catch (err) {
            debug.webgpuError = String(err?.message ?? err);
            console.warn('WebGPU ASR init failed, falling back to WASM:', debug.webgpuError);
            outcome = null;
          }
        }
      } else {
        debug.webgpuAvailable = false;
      }
      if (!outcome) outcome = await build(TIERS.WASM);

      pipe = outcome.built;
      debug.tier = outcome.tier;
      debug.dtype = outcome.dtype;
      debug.loadMs = Math.round(performance.now() - started);
      emit('asr-tier', {
        tier: debug.tier,
        model: debug.model,
        dtype: debug.dtype,
        loadMs: debug.loadMs,
      });
      return debug.tier;
    })();
    return initPromise;
  }

  /**
   * @param {Float32Array} samples mono
   * @param {number} sampleRate must be 16000; mic.js already resamples
   * @returns {Promise<{text:string, tier:string, model:string, inferMs:number}>}
   */
  async function transcribe(samples, sampleRate = REQUIRED_SAMPLE_RATE) {
    if (sampleRate !== REQUIRED_SAMPLE_RATE) {
      throw new Error(
        `asr.transcribe expects ${REQUIRED_SAMPLE_RATE} Hz mono, got ${sampleRate} Hz`
      );
    }
    await init();
    const started = performance.now();
    const out = await pipe(samples, { ...TRANSCRIBE_OPTIONS });
    debug.inferMs = Math.round(performance.now() - started);
    debug.transcribeCount += 1;
    const text = String((Array.isArray(out) ? out[0]?.text : out?.text) ?? '').trim();
    return { text, tier: debug.tier, model: debug.model, inferMs: debug.inferMs };
  }

  return {
    __debug: debug,
    init,
    transcribe,
    getTier: () => debug.tier,
    getModel: () => debug.model,
    isReady: () => pipe !== null,
  };
}