from __future__ import annotations

import os
import shlex
import subprocess
from pathlib import Path
from typing import Dict, Any, List, Tuple, Optional

# Minimal, robust MVP audio-only pipeline
# - Extract audio with ffmpeg
# - Diarize with pyannote (if HF token available); otherwise, fallback: single segment over full duration
# - ASR with Whisper (AINA if available optional). To keep footprint reasonable and robust,
#   we'll default to a lightweight faster-whisper if present; otherwise, return empty text.
# - Generate basic SRT from segments and ASR texts.


def extract_audio_ffmpeg(video_path: str, audio_out: Path, sr: int = 16000, mono: bool = True) -> str:
    audio_out.parent.mkdir(parents=True, exist_ok=True)
    cmd = f'ffmpeg -y -i "{video_path}" -vn {"-ac 1" if mono else ""} -ar {sr} -f wav "{audio_out}"'
    subprocess.run(shlex.split(cmd), check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
    return str(audio_out)


def _get_video_duration_seconds(video_path: str) -> float:
    try:
        # Use ffprobe to get duration
        cmd = f'ffprobe -v error -select_streams v:0 -show_entries stream=duration -of default=nw=1 "{video_path}"'
        out = subprocess.check_output(shlex.split(cmd), stderr=subprocess.DEVNULL).decode("utf-8", errors="ignore")
        for line in out.splitlines():
            if line.startswith("duration="):
                try:
                    return float(line.split("=", 1)[1])
                except Exception:
                    pass
    except Exception:
        pass
    return 0.0


def diarize_audio(wav_path: str, base_dir: Path, hf_token_env: str | None = None) -> Tuple[List[Dict[str, Any]], List[str]]:
    """Returns segments [{'start','end','speaker'}] and dummy clip_paths (not used in MVP)."""
    segments: List[Dict[str, Any]] = []
    clip_paths: List[str] = []
    # Prefer PYANNOTE_TOKEN if provided; fallback to explicit env name, then HF_TOKEN
    token = os.getenv("PYANNOTE_TOKEN") or (os.getenv(hf_token_env) if hf_token_env else os.getenv("HF_TOKEN"))
    try:
        if token:
            from pyannote.audio import Pipeline  # type: ignore
            pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", use_auth_token=token)
            diarization = pipeline(wav_path)
            # Collect segments
            # We don't export individual clips in MVP; just timestamps.
            for i, (turn, _, speaker) in enumerate(diarization.itertracks(yield_label=True)):
                segments.append({
                    "start": float(getattr(turn, "start", 0.0) or 0.0),
                    "end": float(getattr(turn, "end", 0.0) or 0.0),
                    "speaker": str(speaker) if speaker is not None else f"SPEAKER_{i:02d}",
                })
        else:
            # Fallback: single segment using full duration
            # Caller must provide video path to compute exact duration; as we only have wav, skip precise duration
            # and fallback to 0..0 (UI tolerates).
            segments.append({"start": 0.0, "end": 0.0, "speaker": "SPEAKER_00"})
    except Exception:
        # Robust fallback
        segments.append({"start": 0.0, "end": 0.0, "speaker": "SPEAKER_00"})
    # Sort by start
    segments = sorted(segments, key=lambda s: s.get("start", 0.0))
    return segments, clip_paths


def _fmt_srt_time(seconds: float) -> str:
    h = int(seconds // 3600)
    m = int((seconds % 3600) // 60)
    s = int(seconds % 60)
    ms = int(round((seconds - int(seconds)) * 1000))
    return f"{h:02}:{m:02}:{s:02},{ms:03}"


def _generate_srt(segments: List[Dict[str, Any]], texts: List[str]) -> str:
    n = min(len(segments), len(texts))
    lines: List[str] = []
    for i in range(n):
        seg = segments[i]
        text = (texts[i] or "").strip()
        start = float(seg.get("start", 0.0))
        end = float(seg.get("end", max(start + 2.0, start)))
        speaker = seg.get("speaker")
        if speaker:
            text = f"[{speaker}]: {text}" if text else f"[{speaker}]"
        lines.append(str(i + 1))
        lines.append(f"{_fmt_srt_time(start)} --> {_fmt_srt_time(end)}")
        lines.append(text)
        lines.append("")
    return "\n".join(lines).strip() + "\n"


def asr_transcribe_wav_simple(wav_path: str) -> str:
    """Very robust ASR stub: try faster-whisper small if present; otherwise return empty text.
    Intended for MVP in Spaces without heavy GPU. """
    try:
        from faster_whisper import WhisperModel  # type: ignore
        model = WhisperModel("Systran/faster-whisper-small", device="cpu")
        # Short transcript without timestamps
        segments, info = model.transcribe(wav_path, vad_filter=True, without_timestamps=True, language=None)
        text = " ".join(seg.text.strip() for seg in segments if getattr(seg, "text", None))
        return text.strip()
    except Exception:
        # As last resort, empty text
        return ""


def generate(video_path: str, out_dir: Path) -> Dict[str, Any]:
    """End-to-end MVP that returns {'une_srt','free_text','artifacts':{...}}."""
    out_dir.mkdir(parents=True, exist_ok=True)
    wav_path = extract_audio_ffmpeg(video_path, out_dir / f"{Path(video_path).stem}.wav")

    # Diarization (robust)
    segments, _ = diarize_audio(wav_path, out_dir, hf_token_env="HF_TOKEN")

    # ASR (for MVP: single transcript of full audio to use as 'free_text')
    free_text = asr_transcribe_wav_simple(wav_path)

    # Build per-segment 'texts' using a simple split of free_text if we have multiple segments
    if not segments:
        segments = [{"start": 0.0, "end": 0.0, "speaker": "SPEAKER_00"}]
    texts: List[str] = []
    if len(segments) <= 1:
        texts = [free_text]
    else:
        # Naive split into N parts by words
        words = free_text.split()
        chunk = max(1, len(words) // len(segments))
        for i in range(len(segments)):
            start_idx = i * chunk
            end_idx = (i + 1) * chunk if i < len(segments) - 1 else len(words)
            texts.append(" ".join(words[start_idx:end_idx]))

    une_srt = _generate_srt(segments, texts)

    return {
        "une_srt": une_srt,
        "free_text": free_text,
        "artifacts": {
            "wav_path": str(wav_path),
        },
    }