"""Tests for production prediction module."""

import json
from pathlib import Path
from unittest.mock import Mock, patch, MagicMock

import numpy as np
import pandas as pd
import pytest

from ins_pricing.exceptions import ConfigurationError, ModelLoadError, PredictionError


# Mock the production.predict module since it may have heavy dependencies
pytest.importorskip("ins_pricing.production.predict", reason="predict module not available")


@pytest.fixture
def sample_config():
    """Sample configuration for predictor."""
    return {
        "model_name": "test_model",
        "task_type": "regression",
        "base_dir": "/tmp/models",
        "feature_names": ["age", "premium", "region"],
        "model_type": "xgboost"
    }


@pytest.fixture
def sample_data():
    """Sample input data for predictions."""
    return pd.DataFrame({
        "age": [25, 30, 35, 40],
        "premium": [100.0, 150.0, 200.0, 250.0],
        "region": ["A", "B", "A", "C"]
    })


class TestConfigValidation:
    """Test configuration validation for predictors."""

    def test_missing_config_file(self, tmp_path):
        """Test error when config file doesn't exist."""
        from ins_pricing.production.predict import load_predictor_from_config

        config_path = tmp_path / "nonexistent.json"

        with pytest.raises((FileNotFoundError, ModelLoadError)):
            load_predictor_from_config(config_path)

    def test_invalid_json_config(self, tmp_path):
        """Test error when config file contains invalid JSON."""
        from ins_pricing.production.predict import load_predictor_from_config

        config_path = tmp_path / "invalid.json"
        config_path.write_text("{ invalid json }")

        with pytest.raises((ConfigurationError, json.JSONDecodeError)):
            load_predictor_from_config(config_path)

    def test_missing_required_fields(self, tmp_path):
        """Test error when required config fields are missing."""
        from ins_pricing.production.predict import load_predictor_from_config

        config_path = tmp_path / "incomplete.json"
        config_path.write_text(json.dumps({"model_name": "test"}))

        with pytest.raises(ConfigurationError):
            load_predictor_from_config(config_path)


class TestPredictorLoading:
    """Test predictor loading functionality."""

    @patch('ins_pricing.production.predict.load_model')
    def test_load_valid_predictor(self, mock_load_model, tmp_path, sample_config):
        """Test loading a valid predictor."""
        from ins_pricing.production.predict import load_predictor_from_config

        # Setup
        config_path = tmp_path / "config.json"
        config_path.write_text(json.dumps(sample_config))
        mock_load_model.return_value = Mock()

        # Execute
        predictor = load_predictor_from_config(config_path)

        # Verify
        assert predictor is not None
        assert predictor['config']['model_name'] == "test_model"

    def test_load_missing_model_file(self, tmp_path, sample_config):
        """Test error when model file is missing."""
        from ins_pricing.production.predict import load_predictor_from_config

        config_path = tmp_path / "config.json"
        config_path.write_text(json.dumps(sample_config))

        with pytest.raises(ModelLoadError):
            load_predictor_from_config(config_path)


class TestPrediction:
    """Test prediction functionality."""

    @patch('ins_pricing.production.predict.load_model')
    def test_predict_on_valid_data(self, mock_load_model, sample_data):
        """Test prediction on valid input data."""
        from ins_pricing.production.predict import predict

        # Setup mock model
        mock_model = Mock()
        mock_model.predict.return_value = np.array([100, 150, 200, 250])
        mock_load_model.return_value = mock_model

        predictor = {
            'model': mock_model,
            'config': {'feature_names': ["age", "premium", "region"]}
        }

        # Execute
        predictions = predict(predictor, sample_data)

        # Verify
        assert len(predictions) == len(sample_data)
        assert all(isinstance(p, (int, float, np.number)) for p in predictions)

    def test_predict_missing_features(self, sample_data):
        """Test error when input data is missing required features."""
        from ins_pricing.production.predict import predict
        from ins_pricing.utils.validation import validate_required_columns

        predictor = {
            'model': Mock(),
            'config': {'feature_names': ["age", "premium", "region", "missing_col"]}
        }

        # Should raise validation error for missing column
        with pytest.raises(PredictionError):
            validate_required_columns(
                sample_data,
                predictor['config']['feature_names'],
                df_name="input_data"
            )

    def test_predict_empty_dataframe(self):
        """Test prediction on empty DataFrame."""
        from ins_pricing.production.predict import predict
        from ins_pricing.utils.validation import validate_dataframe_not_empty

        empty_df = pd.DataFrame()
        predictor = {'model': Mock(), 'config': {}}

        with pytest.raises(PredictionError):
            validate_dataframe_not_empty(empty_df, df_name="input_data")


class TestBatchScoring:
    """Test batch scoring functionality."""

    @patch('ins_pricing.production.predict.load_predictor_from_config')
    @patch('ins_pricing.production.predict.predict')
    def test_batch_score_success(self, mock_predict, mock_load, sample_data, tmp_path):
        """Test successful batch scoring."""
        from ins_pricing.production.predict import batch_score

        # Setup
        mock_load.return_value = {'model': Mock(), 'config': {}}
        mock_predict.return_value = np.array([100, 150, 200, 250])

        output_path = tmp_path / "predictions.csv"

        # Execute
        batch_score(
            config_path=tmp_path / "config.json",
            input_data=sample_data,
            output_path=output_path
        )

        # Verify
        assert output_path.exists()
        results = pd.read_csv(output_path)
        assert "predictions" in results.columns

    def test_batch_score_large_data(self, tmp_path):
        """Test batch scoring with large dataset."""
        from ins_pricing.production.predict import batch_score

        # Create large dataset
        large_data = pd.DataFrame({
            "age": np.random.randint(20, 70, size=10000),
            "premium": np.random.uniform(100, 500, size=10000),
            "region": np.random.choice(["A", "B", "C"], size=10000)
        })

        with patch('ins_pricing.production.predict.load_predictor_from_config') as mock_load:
            with patch('ins_pricing.production.predict.predict') as mock_predict:
                mock_load.return_value = {'model': Mock(), 'config': {}}
                mock_predict.return_value = np.random.uniform(50, 300, size=10000)

                output_path = tmp_path / "large_predictions.csv"
                batch_score(
                    config_path=tmp_path / "config.json",
                    input_data=large_data,
                    output_path=output_path
                )

                assert output_path.exists()


class TestModelVersioning:
    """Test model versioning functionality."""

    def test_version_compatibility_check(self):
        """Test version compatibility checking."""
        # Test that predictor checks model version compatibility
        pass  # Implement based on actual version checking logic

    def test_load_different_model_versions(self):
        """Test loading different versions of the same model."""
        pass  # Implement based on actual versioning system


@pytest.mark.integration
class TestPredictionIntegration:
    """Integration tests for prediction pipeline."""

    @pytest.mark.skipif(not Path("test_models").exists(), reason="Test models not available")
    def test_end_to_end_prediction(self):
        """Test complete prediction pipeline from config to output."""
        # This would require actual model artifacts
        pass
