"""Diarization service — pyannote.audio speaker identification."""

from __future__ import annotations

import sys
import time
from dataclasses import dataclass, field
from typing import Any

from voice_to_notes.ipc.messages import progress_message
from voice_to_notes.ipc.protocol import write_message


@dataclass
class SpeakerSegment:
    """A time span assigned to a speaker."""

    speaker: str
    start_ms: int
    end_ms: int


@dataclass
class DiarizationResult:
    """Full diarization output."""

    speaker_segments: list[SpeakerSegment] = field(default_factory=list)
    num_speakers: int = 0
    speakers: list[str] = field(default_factory=list)


class DiarizeService:
    """Handles speaker diarization via pyannote.audio."""

    def __init__(self) -> None:
        self._pipeline: Any = None

    def _ensure_pipeline(self) -> Any:
        """Load the pyannote diarization pipeline (lazy)."""
        if self._pipeline is not None:
            return self._pipeline

        print("[sidecar] Loading pyannote diarization pipeline...", file=sys.stderr, flush=True)

        try:
            from pyannote.audio import Pipeline

            self._pipeline = Pipeline.from_pretrained(
                "pyannote/speaker-diarization-3.1",
                use_auth_token=False,
            )
        except Exception:
            # Fall back to a simpler approach if the model isn't available
            # pyannote requires HuggingFace token for some models
            # Try the community model first
            try:
                from pyannote.audio import Pipeline

                self._pipeline = Pipeline.from_pretrained(
                    "pyannote/speaker-diarization",
                    use_auth_token=False,
                )
            except Exception as e:
                print(
                    f"[sidecar] Warning: Could not load pyannote pipeline: {e}",
                    file=sys.stderr,
                    flush=True,
                )
                raise RuntimeError(
                    "pyannote.audio pipeline not available. "
                    "You may need to accept the model license at "
                    "https://huggingface.co/pyannote/speaker-diarization-3.1 "
                    "and set a HF_TOKEN environment variable."
                ) from e

        return self._pipeline

    def diarize(
        self,
        request_id: str,
        file_path: str,
        num_speakers: int | None = None,
        min_speakers: int | None = None,
        max_speakers: int | None = None,
    ) -> DiarizationResult:
        """Run speaker diarization on an audio file.

        Args:
            request_id: IPC request ID for progress messages.
            file_path: Path to audio file.
            num_speakers: Exact number of speakers (if known).
            min_speakers: Minimum expected speakers.
            max_speakers: Maximum expected speakers.

        Returns:
            DiarizationResult with speaker segments.
        """
        write_message(
            progress_message(request_id, 0, "loading_diarization", "Loading diarization model...")
        )

        pipeline = self._ensure_pipeline()

        write_message(
            progress_message(request_id, 20, "diarizing", "Running speaker diarization...")
        )

        start_time = time.time()

        # Build kwargs for speaker constraints
        kwargs: dict[str, Any] = {}
        if num_speakers is not None:
            kwargs["num_speakers"] = num_speakers
        if min_speakers is not None:
            kwargs["min_speakers"] = min_speakers
        if max_speakers is not None:
            kwargs["max_speakers"] = max_speakers

        # Run diarization
        diarization = pipeline(file_path, **kwargs)

        # Convert pyannote output to our format
        result = DiarizationResult()
        seen_speakers: set[str] = set()

        for turn, _, speaker in diarization.itertracks(yield_label=True):
            result.speaker_segments.append(
                SpeakerSegment(
                    speaker=speaker,
                    start_ms=int(turn.start * 1000),
                    end_ms=int(turn.end * 1000),
                )
            )
            seen_speakers.add(speaker)

        result.speakers = sorted(seen_speakers)
        result.num_speakers = len(seen_speakers)

        elapsed = time.time() - start_time
        print(
            f"[sidecar] Diarization complete: {result.num_speakers} speakers, "
            f"{len(result.speaker_segments)} segments in {elapsed:.1f}s",
            file=sys.stderr,
            flush=True,
        )

        write_message(
            progress_message(request_id, 100, "done", "Diarization complete")
        )

        return result


def diarization_to_payload(result: DiarizationResult) -> dict[str, Any]:
    """Convert DiarizationResult to IPC payload dict."""
    return {
        "speaker_segments": [
            {
                "speaker": seg.speaker,
                "start_ms": seg.start_ms,
                "end_ms": seg.end_ms,
            }
            for seg in result.speaker_segments
        ],
        "num_speakers": result.num_speakers,
        "speakers": result.speakers,
    }