"""Local STT backends.

faster-whisper loading (CUDA->CPU fallback, Apple Silicon pinning), the
anti-hallucination transcribe kwargs and segment gate, and the local whisper CLI
(``local_command``) provider. The cached-model singleton and its idle-unload
watcher stay in ``transcription_tools`` (they own the module state).

Split out of ``tools/transcription_tools.py``, which re-imports every name (patch
surface) and is imported lazily here so origin patches still intercept.
"""

from __future__ import annotations

import logging
import os
import platform
import shlex
import subprocess
import tempfile
import importlib.util as _ilu
from pathlib import Path
from typing import Any, Dict, Optional

from tools.transcription_audio import _run_quiet
from tools.transcription_common import (
    DEFAULT_LOCAL_MODEL, DEFAULT_LOCAL_STT_LANGUAGE, GROQ_MODELS, LOCAL_STT_COMMAND_ENV,
    OPENAI_MODELS, _config_number, _error_result, _log_prompt_unsupported, _ok_result,
    _process_error_detail,
)

# Log-record parity with the origin module.
logger = logging.getLogger("tools.transcription_tools")


def _get_local_command_template() -> Optional[str]:
    from tools.transcription_tools import _find_whisper_binary
    configured = os.getenv(LOCAL_STT_COMMAND_ENV, "").strip()
    if configured:
        return configured
    whisper_binary = _find_whisper_binary()
    if whisper_binary:
        return (
            f"{shlex.quote(whisper_binary)} {{input_path}} --model {{model}} --output_format txt "
            "--output_dir {output_dir} --language {language}"
        )
    return None


def _has_local_command() -> bool:
    return _get_local_command_template() is not None


def _normalize_local_model(model_name: Optional[str]) -> str:
    """Return a valid faster-whisper size; cloud-only names (``whisper-1`` …) fall back to the default with a warning."""
    if not model_name:
        return DEFAULT_LOCAL_MODEL
    if model_name in OPENAI_MODELS or model_name in GROQ_MODELS:
        logger.warning(
            "STT model '%s' is a cloud-only name and cannot be used with the local "
            "provider. Falling back to '%s'. Set stt.local.model to a valid "
            "faster-whisper size (tiny, base, small, medium, large-v3).",
            model_name, DEFAULT_LOCAL_MODEL,
        )
        return DEFAULT_LOCAL_MODEL
    return model_name


def _try_lazy_install_stt() -> bool:
    """Lazy-install faster-whisper and re-check dynamically so it's usable without a restart."""
    try:
        from tools.lazy_deps import ensure
        # prompt=False: a bare input() deadlocks under the interactive CLI where
        # prompt_toolkit owns stdin; the install is already gated by
        # security.allow_lazy_installs, so reaching here is opt-in.
        ensure("stt.faster_whisper", prompt=False)
        if _ilu.find_spec("faster_whisper"):
            return True
        logger.warning("faster-whisper was installed but importlib still cannot find it (may require Python restart)")
    except Exception as exc:
        logger.warning(
            "Lazy install of faster-whisper failed: %s. "
            "This is often a permission issue: the Hermes process user cannot "
            "write to the virtual environment. Try running manually as the "
            "venv owner: `stat -c '%%u' '$(dirname $(dirname $(which python3)))'` "
            "then `su - <owner> -c 'VIRTUAL_ENV=/opt/hermes/.venv "
            "uv pip install faster-whisper==1.2.1'`",
            exc,
        )
    return False


# Substrings identifying a missing/unloadable CUDA runtime library: when
# ctranslate2 can't dlopen one of these the "auto" device picker has already
# committed to CUDA, so we fall back to CPU and reload. Deliberately narrow
# (library names + dlopen phrasing) so legitimate runtime failures like "CUDA
# out of memory" surface to the user instead of silently running on CPU.
_CUDA_LIB_ERROR_MARKERS = (
    "libcublas", "libcudnn", "libcudart", "cannot be loaded", "cannot open shared object",
    "no kernel image is available", "CUBLAS_STATUS_NOT_SUPPORTED", "no CUDA-capable device",
    "CUDA driver version is insufficient",
)


def _looks_like_cuda_lib_error(exc: BaseException) -> bool:
    """Heuristic: is this a missing/broken CUDA runtime library (not a legitimate runtime failure)?"""
    msg = str(exc)
    return any(marker in msg for marker in _CUDA_LIB_ERROR_MARKERS)


def _sysctl_value(name: str) -> str:
    """Return a sysctl value, or an empty string when unavailable."""
    try:
        return subprocess.check_output(
            ["/usr/sbin/sysctl", "-n", name],
            stderr=subprocess.DEVNULL,
            text=True,
            timeout=2,
        ).strip()
    except Exception:
        return ""


def _should_force_faster_whisper_cpu() -> bool:
    """Force CPU on Apple Silicon (incl. x86_64 under Rosetta), where ctranslate2's
    ``device="auto"`` can abort inside native code before Python can catch it."""
    if platform.system() != "Darwin":
        return False
    if platform.machine().lower() in {"arm64", "aarch64"}:
        return True
    # Under Rosetta platform.machine() reports x86_64; sysctl.proc_translated
    # flags translation and hw.optional.arm64 distinguishes Apple Silicon hosts.
    return _sysctl_value("sysctl.proc_translated") == "1" or _sysctl_value("hw.optional.arm64") == "1"


def _get_idle_unload_seconds(local_cfg: Dict[str, Any]) -> int:
    """Resolve the idle unload timeout from config; 0 = never (default), negatives clamp to 0."""
    return max(_config_number(local_cfg, "unload_after_idle_seconds", 0, int), 0)


def _load_local_whisper_model(model_name: str, device: str = "auto", compute_type: str = "auto"):
    """Load faster-whisper with graceful CUDA → CPU fallback.

    ``device="auto"`` picks CUDA whenever the ctranslate2 wheel ships CUDA libs,
    even on hosts without the NVIDIA runtime (WSL2, headless servers, CPU-only
    dev boxes). Try the requested config first; on a CUDA library load failure
    fall back to CPU + int8. Pass ``stt.local.device`` / ``compute_type`` to pin.
    """
    force_cpu = _should_force_faster_whisper_cpu()
    if force_cpu:
        # Importing ctranslate2 can itself abort on Apple Silicon/Rosetta when
        # multiple Intel OpenMP runtimes are loaded — set before the import.
        os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "TRUE")

    from faster_whisper import WhisperModel
    if force_cpu:
        logger.info(
            "Apple Silicon/Rosetta detected — loading faster-whisper on CPU "
            "(int8) to avoid native device autodetection crashes"
        )
        return WhisperModel(model_name, device="cpu", compute_type="int8")

    try:
        return WhisperModel(model_name, device=device, compute_type=compute_type)
    except Exception as exc:
        if not _looks_like_cuda_lib_error(exc):
            raise
        logger.warning(
            "faster-whisper CUDA load failed (%s) — falling back to CPU (int8). "
            "Install the NVIDIA CUDA runtime (libcublas/libcudnn) to use GPU.",
            exc,
        )
        return WhisperModel(model_name, device="cpu", compute_type="int8")


# Silence-hallucination hardening for local faster-whisper (whisper decodes
# junk like "You"/"Thank you." from pure silence). Three layers, all tunable
# under ``stt.local``: Silero VAD so silence never reaches the model
# (``vad: false`` restores raw behaviour for music/ambient audio);
# condition_on_previous_text=False so one hallucinated token can't seed a run;
# and the segment confidence gate in _is_hallucinated_segment.
_VAD_MIN_SILENCE_MS_DEFAULT = 500
_NO_SPEECH_PROB_THRESHOLD_DEFAULT = 0.6
_LOGPROB_THRESHOLD_DEFAULT = -1.0


def build_local_transcribe_kwargs(stt_config: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
    """Build the kwargs for EVERY local faster-whisper ``model.transcribe`` call.

    Single owner for the anti-hallucination hardening — new local-whisper call
    sites must go through here instead of hand-rolling kwargs.
    """
    from tools.transcription_tools import _load_stt_config, _resolve_stt_language
    stt_config = stt_config if isinstance(stt_config, dict) else _load_stt_config()
    local_cfg = stt_config.get("local") or {}

    # ``vad: null`` in YAML means "default on".
    vad_enabled = local_cfg.get("vad", True)
    kwargs: Dict[str, Any] = {
        "beam_size": 5,
        "condition_on_previous_text": False,
        "vad_filter": vad_enabled is None or bool(vad_enabled),
    }
    if kwargs["vad_filter"]:
        kwargs["vad_parameters"] = {
            "min_silence_duration_ms": _config_number(local_cfg, "vad_min_silence_ms", _VAD_MIN_SILENCE_MS_DEFAULT, int)
        }

    # Push the confidence gate into faster-whisper itself: its internal
    # defaults drop low-confidence segments BEFORE our post-filter sees them,
    # so without this the ``stt.local`` threshold knobs were dead for that
    # first gate (non-English speech decodes at lower avg_logprob and was
    # silently discarded). Same values feed both gates; defaults unchanged.
    kwargs["no_speech_threshold"], kwargs["log_prob_threshold"] = _confidence_thresholds(local_cfg)

    forced_lang = _resolve_stt_language("local", stt_config)
    if forced_lang:
        kwargs["language"] = forced_lang

    initial_prompt = local_cfg.get("initial_prompt")
    if isinstance(initial_prompt, str) and initial_prompt.strip():
        kwargs["initial_prompt"] = initial_prompt

    return kwargs


def _confidence_thresholds(local_cfg: Dict[str, Any]) -> tuple[float, float]:
    """Resolve (no_speech_prob, avg_logprob) gate thresholds from config."""
    return (
        _config_number(local_cfg, "no_speech_prob_threshold", _NO_SPEECH_PROB_THRESHOLD_DEFAULT),
        _config_number(local_cfg, "logprob_threshold", _LOGPROB_THRESHOLD_DEFAULT),
    )


def _is_hallucinated_segment(segment: Any, no_speech_threshold: float, logprob_threshold: float) -> bool:
    """True when a segment is very likely a silence hallucination.

    Conservative AND gate (openai-whisper's own heuristic): the model must BOTH
    think the window is non-speech AND have decoded it with low confidence, so
    quiet-but-real speech survives. Unknown segment shapes are never dropped.
    """
    try:
        no_speech_prob = float(getattr(segment, "no_speech_prob"))
        avg_logprob = float(getattr(segment, "avg_logprob"))
    except (AttributeError, TypeError, ValueError):
        return False
    return no_speech_prob > no_speech_threshold and avg_logprob < logprob_threshold


def _join_confident_segments(segments: Any, local_cfg: Dict[str, Any]) -> str:
    """Join segment texts, dropping probable silence hallucinations."""
    no_speech_threshold, logprob_threshold = _confidence_thresholds(local_cfg)
    kept: list[str] = []
    for segment in segments:
        if _is_hallucinated_segment(segment, no_speech_threshold, logprob_threshold):
            logger.debug(
                "Dropping probable hallucinated segment %r (no_speech_prob=%.3f, avg_logprob=%.3f)",
                getattr(segment, "text", ""),
                getattr(segment, "no_speech_prob", float("nan")),
                getattr(segment, "avg_logprob", float("nan")),
            )
            continue
        kept.append(segment.text.strip())
    return " ".join(kept).strip()


def _transcribe_local_command(
    file_path: str,
    model_name: str,
    *,
    language: Optional[str] = None,
    prompt: Optional[str] = None,
) -> Dict[str, Any]:
    """Run the configured local STT command template and read back a .txt transcript."""
    from tools.transcription_tools import _prepare_local_audio, _resolve_stt_language
    if prompt:
        _log_prompt_unsupported("STT provider 'local_command'")

    command_template = _get_local_command_template()
    if not command_template:
        return _error_result(f"{LOCAL_STT_COMMAND_ENV} not configured and no local whisper binary was found")

    # Language: hook override > stt.local.language > stt.language > env > "en".
    language = language or _resolve_stt_language("local") or DEFAULT_LOCAL_STT_LANGUAGE
    normalized_model = _normalize_local_model(model_name)

    try:
        with tempfile.TemporaryDirectory(prefix="hermes-local-stt-") as output_dir:
            prepared_input, prep_error = _prepare_local_audio(file_path, output_dir)
            if prep_error:
                return _error_result(prep_error)

            command = command_template.format(
                input_path=shlex.quote(prepared_input),
                output_dir=shlex.quote(output_dir),
                language=shlex.quote(language),
                model=shlex.quote(normalized_model),
            )
            # Scrub Hermes secrets from the child env (same policy as _run_command_stt).
            from tools.environments.local import hermes_subprocess_env

            _run_quiet(shlex.split(command), timeout=300, env=hermes_subprocess_env(inherit_credentials=False))

            txt_files = sorted(Path(output_dir).glob("*.txt"))
            if not txt_files:
                return _error_result("Local STT command completed but did not produce a .txt transcript")

            transcript_text = txt_files[0].read_text(encoding="utf-8").strip()
            logger.info(
                "Transcribed %s via local STT command (%s, %d chars)",
                Path(file_path).name, normalized_model, len(transcript_text),
            )
            return _ok_result(transcript_text, "local_command")

    except KeyError as e:
        return _error_result(f"Invalid {LOCAL_STT_COMMAND_ENV} template, missing placeholder: {e}")
    except subprocess.CalledProcessError as e:
        details = _process_error_detail(e)
        logger.error("Local STT command failed for %s: %s", file_path, details)
        return _error_result(f"Local STT failed: {details}")
    except Exception as e:
        logger.error("Unexpected error during local command transcription: %s", e, exc_info=True)
        return _error_result(f"Local transcription failed: {e}")