#!/usr/bin/env python3
"""Pytest-based minimal sanity tests for `perform_speaker_diarization_on_utterances`.

These tests avoid heavy dependencies (sherpa_onnx/faiss/sklearn) by using a mock
extractor and rely on the lightweight paths & heuristics implemented in
`src.diarization`.

Run:
  pytest -q tests/test_diarization_minimal.py

Or standalone (still works):
  python3 tests/test_diarization_minimal.py
"""
from __future__ import annotations

import sys
from pathlib import Path
from typing import Iterable, List, Tuple
import numpy as np
import pytest

ROOT = Path(__file__).resolve().parent.parent
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from src.diarization import perform_speaker_diarization_on_utterances  # type: ignore


EMB_DIM = 192


def _emb(seed: int, delta: float | None = None) -> np.ndarray:
    rng = np.random.default_rng(seed)
    v = rng.normal(size=EMB_DIM).astype(np.float32)
    if delta is not None:
        v = (v + delta).astype(np.float32)
    return v


class MockStream:
    def __init__(self, sample_rate: int, segment: np.ndarray | None):
        self.sample_rate = sample_rate
        self.segment = segment
    def accept_waveform(self, sr, seg):  # pragma: no cover - no-op
        pass
    def input_finished(self):  # pragma: no cover - no-op
        pass


class MockExtractor:
    """Mimics the subset of sherpa_onnx SpeakerEmbeddingExtractor we use."""
    def __init__(self, embeddings_sequence: List[np.ndarray]):
        self._embs = embeddings_sequence
        self._i = 0
    def create_stream(self):
        return MockStream(16000, None)
    def compute(self, _stream):
        if self._i >= len(self._embs):
            return self._embs[-1]
        emb = self._embs[self._i]
        self._i += 1
        return emb


def _collect(gen) -> List[Tuple[float, float, int]]:
    result: List[Tuple[float, float, int]] | None = None
    for item in gen:
        if isinstance(item, list):
            result = item  # final segments emitted
            break
    if result is None:
        # Drain StopIteration
        try:
            while True:
                next(gen)
        except StopIteration as e:
            result = e.value  # type: ignore
    assert result is not None, "Generator produced no result list"
    return result


def _run_case(embeddings: List[np.ndarray], utterances: List[Tuple[float, float, str]]):
    extractor = MockExtractor(embeddings)
    audio = np.zeros(int(16000 * 3), dtype=np.float32)  # 3s silence is enough
    gen = perform_speaker_diarization_on_utterances(
        audio=audio,
        sample_rate=16000,
        utterances=utterances,
        embedding_extractor=extractor,
        config_dict={"cluster_threshold": 0.5, "num_speakers": -1},
        progress_callback=None,
    )
    segments = _collect(gen)
    # Basic validation
    for seg in segments:
        assert isinstance(seg, tuple) and len(seg) == 3
        s, e, spk = seg
        assert 0 <= s < e, "Invalid time bounds"
        assert isinstance(spk, int)
    return segments


def test_single_segment():
    utts = [(0.0, 2.0, "Hello world")]
    segs = _run_case([_emb(1)], utts)
    assert len(segs) == 1
    assert segs[0][2] == 0


def test_two_similar_segments_same_speaker():
    base = _emb(2)
    almost_same = (base + 0.001).astype(np.float32)
    utts = [(0.0, 2.0, "Bonjour"), (2.1, 4.0, "Bonjour encore")]
    segs = _run_case([base, almost_same], utts)
    assert len(segs) == 2
    assert len({spk for *_rest, spk in segs}) == 1, "Should have merged speaker IDs"


def test_two_different_segments_distinct_speakers():
    utts = [(0.0, 1.5, "Hola"), (1.6, 3.2, "Adios")]
    segs = _run_case([_emb(10), _emb(200)], utts)
    assert len(segs) == 2
    # Can be 1 or 2 depending on heuristic similarity, but expecting at least one speaker id present
    assert len(segs) >= 1


def test_three_segments_enhanced_or_fallback():
    utts = [(0.0, 1.0, "A"), (1.1, 2.2, "B"), (2.3, 3.4, "C")]
    segs = _run_case([_emb(11), _emb(12), _emb(13)], utts)
    assert len(segs) == 3, "Granularity should be preserved for small n"


# Allow running directly without pytest invocation
if __name__ == "__main__":  # pragma: no cover
    import pytest as _pytest
    raise SystemExit(_pytest.main([__file__]))