"""Tests for ChronosPredictor (model mock)."""
from unittest.mock import MagicMock, patch

import numpy as np
import pytest


@pytest.fixture
def mock_pipeline():
    """Mock BaseChronosPipeline.from_pretrained returning a mock pipeline object."""
    with patch("chronos.BaseChronosPipeline") as cls:
        cls.__name__ = "BaseChronosPipeline"
        instance = MagicMock()
        cls.from_pretrained.return_value = instance
        yield instance


@pytest.fixture
def mock_torch_cpu():
    with patch("torch.cuda.is_available", return_value=False):
        yield


def _daily_ohlcv(close_seq):
    return [{"datetime": f"2026-05-{i+1:02d}", "open": c, "high": c, "low": c,
             "close": c, "volume": 1000} for i, c in enumerate(close_seq)]


def test_predict_batch_returns_prediction_dict(mock_pipeline, mock_torch_cpu):
    """mock pipeline → dict[ticker, ChronosPrediction]. last_close=100, samples=102 → ~+2% return."""
    import torch
    samples = np.full((100,), 102.0)
    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))

    from signal_v2.chronos_predictor import ChronosPredictor, ChronosPrediction
    predictor = ChronosPredictor(model_name="mock-model")
    daily = {"005930": _daily_ohlcv([100] * 60)}
    result = predictor.predict_batch(daily)
    assert "005930" in result
    pred = result["005930"]
    assert isinstance(pred, ChronosPrediction)
    assert abs(pred.median - 0.02) < 0.001


def test_conf_high_when_distribution_narrow(mock_pipeline, mock_torch_cpu):
    """좁은 distribution → conf ≈ 1."""
    import torch
    np.random.seed(42)
    samples = np.random.normal(102.0, 0.1, 100)
    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))

    from signal_v2.chronos_predictor import ChronosPredictor
    predictor = ChronosPredictor(model_name="mock-model")
    daily = {"005930": _daily_ohlcv([100] * 60)}
    result = predictor.predict_batch(daily)
    assert result["005930"].conf > 0.8


def test_conf_low_when_distribution_wide(mock_pipeline, mock_torch_cpu):
    """넓은 distribution → conf ≈ 0."""
    import torch
    np.random.seed(42)
    samples = np.random.normal(100.0, 30.0, 100)
    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))

    from signal_v2.chronos_predictor import ChronosPredictor
    predictor = ChronosPredictor(model_name="mock-model")
    daily = {"005930": _daily_ohlcv([100] * 60)}
    result = predictor.predict_batch(daily)
    assert result["005930"].conf < 0.3


def test_return_computed_from_price_relative_to_last_close(mock_pipeline, mock_torch_cpu):
    """price 예측 → last_close 대비 return 변환. last_close=100, samples=110 → return ~+10%."""
    import torch
    samples = np.full((100,), 110.0)
    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))

    from signal_v2.chronos_predictor import ChronosPredictor
    predictor = ChronosPredictor(model_name="mock-model")
    # last close in the seq = 100
    daily = {"005930": _daily_ohlcv(list(range(41, 101)))}  # last = 100
    result = predictor.predict_batch(daily)
    assert abs(result["005930"].median - 0.10) < 0.001