import json
import os
from functools import lru_cache
from pathlib import Path
from typing import Any, Callable, Optional

from faster_whisper import WhisperModel

from . import config


def cleanup_outputs() -> None:
    for child in config.OUTPUT_DIR.iterdir():
        if child.is_file():
            child.unlink()


def _detect_device() -> tuple[str, str]:
    """Return (device, compute_type). Prefers CUDA when available."""
    try:
        import ctranslate2
        if ctranslate2.get_cuda_device_count() > 0:
            return "cuda", "float16"
    except Exception:
        pass
    return "cpu", "int8"


# Detect once at import time so every model load uses the same device
DEVICE, COMPUTE_TYPE = _detect_device()
CPU_THREADS = max(1, min(os.cpu_count() or 4, 8))


@lru_cache(maxsize=8)
def get_model(model_size: str) -> WhisperModel:
    kwargs: dict[str, Any] = {"device": DEVICE, "compute_type": COMPUTE_TYPE}
    if DEVICE == "cpu":
        kwargs["cpu_threads"] = CPU_THREADS
        kwargs["num_workers"] = 2
    return WhisperModel(model_size, **kwargs)


def device_info() -> dict[str, str]:
    return {"device": DEVICE, "compute_type": COMPUTE_TYPE}


def _srt_time(s: float) -> str:
    h, rem = divmod(int(s), 3600)
    m, sec = divmod(rem, 60)
    ms = int((s % 1) * 1000)
    return f"{h:02d}:{m:02d}:{sec:02d},{ms:03d}"


def _vtt_time(s: float) -> str:
    h, rem = divmod(int(s), 3600)
    m, sec = divmod(rem, 60)
    ms = int((s % 1) * 1000)
    return f"{h:02d}:{m:02d}:{sec:02d}.{ms:03d}"


def format_segments(segments: list[dict], fmt: str) -> str:
    if fmt == "txt":
        return "\n".join(s["text"] for s in segments)

    if fmt == "srt":
        lines: list[str] = []
        for i, s in enumerate(segments, 1):
            lines += [str(i), f"{_srt_time(s['start'])} --> {_srt_time(s['end'])}", s["text"], ""]
        return "\n".join(lines)

    if fmt == "vtt":
        lines = ["WEBVTT", ""]
        for s in segments:
            lines += [f"{_vtt_time(s['start'])} --> {_vtt_time(s['end'])}", s["text"], ""]
        return "\n".join(lines)

    if fmt == "json":
        return json.dumps({"segments": segments}, indent=2, ensure_ascii=False)

    if fmt == "tsv":
        lines = ["start\tend\ttext"]
        lines += [f"{int(s['start'] * 1000)}\t{int(s['end'] * 1000)}\t{s['text']}" for s in segments]
        return "\n".join(lines)

    return "\n".join(s["text"] for s in segments)


def transcribe_file(
    audio_path: Path,
    model_size: str,
    language: Optional[str] = None,
    task: str = "transcribe",
    word_timestamps: bool = False,
    no_condition: bool = False,
    fmt: str = "srt",
    progress_callback: Optional[Callable[[int, str], None]] = None,
) -> dict[str, Any]:
    if progress_callback:
        progress_callback(5, f"Loading model ({DEVICE.upper()})…")

    model = get_model(model_size)

    if progress_callback:
        progress_callback(12, "Analysing audio…")

    segments_iter, info = model.transcribe(
        str(audio_path),
        language=language,
        task=task,
        word_timestamps=word_timestamps,
        condition_on_previous_text=not no_condition,
        vad_filter=True,
        # Greedy decoding — 3-5× faster than beam_size=5 with minimal quality loss
        beam_size=1,
        best_of=1,
        temperature=0.0,
    )

    result_segments: list[dict] = []
    total = max(info.duration or 1.0, 1.0)

    for segment in segments_iter:
        seg: dict[str, Any] = {
            "start": round(segment.start, 3),
            "end":   round(segment.end, 3),
            "text":  segment.text.strip(),
        }
        if word_timestamps and segment.words:
            seg["words"] = [
                {"start": round(w.start, 3), "end": round(w.end, 3), "word": w.word}
                for w in segment.words
            ]
        result_segments.append(seg)

        if progress_callback:
            pct = min(95, 15 + int((segment.end / total) * 80))
            progress_callback(pct, f"Transcribing… {int(segment.end)}s / {int(total)}s")

    formatted = format_segments(result_segments, fmt)
    full_text = " ".join(s["text"] for s in result_segments).strip()

    return {
        "text":                 full_text,
        "segments":             result_segments,
        "language":             info.language,
        "language_probability": info.language_probability,
        "duration":             round(info.duration, 2),
        "format":               fmt,
        "model_size":           model_size,
        "task":                 task,
        "formatted":            formatted,
    }