#![allow(clippy::approx_constant)]
use approx::assert_abs_diff_eq;
use rtx_preprocessing::{InvertibleTransformer, MinMaxScaler, PreprocessingError, Transformer};
use rtx_tensor::{Device, Tensor};

/// Test fixture for MinMaxScaler tests
struct MinMaxScalerTestFixture {
    simple_data: Tensor,
    multi_feature_data: Tensor,
    single_value_data: Tensor,
    constant_data: Tensor,
    negative_data: Tensor,
    large_range_data: Tensor,
}

impl MinMaxScalerTestFixture {
    fn new() -> Self {
        // Simple data: [1, 2, 3, 4, 5] -> should scale to [0, 0.25, 0.5, 0.75, 1.0]
        let simple_data =
            Tensor::from_slice(&[1.0, 2.0, 3.0, 4.0, 5.0], &[5, 1], &Device::cpu()).unwrap();

        // Multi-feature data
        let multi_feature_data = Tensor::from_slice(
            &[1.0, 10.0, 2.0, 20.0, 3.0, 30.0, 4.0, 40.0],
            &[4, 2],
            &Device::cpu(),
        )
        .unwrap();

        // Single value
        let single_value_data = Tensor::from_slice(&[5.0], &[1, 1], &Device::cpu()).unwrap();

        // Constant data (all same values)
        let constant_data =
            Tensor::from_slice(&[3.0, 3.0, 3.0, 3.0], &[4, 1], &Device::cpu()).unwrap();

        // Data with negative values: [-2, -1, 0, 1, 2]
        let negative_data =
            Tensor::from_slice(&[-2.0, -1.0, 0.0, 1.0, 2.0], &[5, 1], &Device::cpu()).unwrap();

        // Large range data: [0, 1000]
        let large_range_data =
            Tensor::from_slice(&[0.0, 250.0, 500.0, 750.0, 1000.0], &[5, 1], &Device::cpu())
                .unwrap();

        Self {
            simple_data,
            multi_feature_data,
            single_value_data,
            constant_data,
            negative_data,
            large_range_data,
        }
    }
}

#[test]
fn test_minmax_scaler_creation() {
    // Default range [0, 1]
    let scaler = MinMaxScaler::new();
    assert!(!scaler.is_fitted());
    assert_eq!(scaler.feature_range(), (0.0, 1.0));

    // Custom range [-1, 1]
    let scaler = MinMaxScaler::with_range(-1.0, 1.0);
    assert!(!scaler.is_fitted());
    assert_eq!(scaler.feature_range(), (-1.0, 1.0));

    // Custom range [0, 10]
    let scaler = MinMaxScaler::with_range(0.0, 10.0);
    assert!(!scaler.is_fitted());
    assert_eq!(scaler.feature_range(), (0.0, 10.0));
}

#[test]
fn test_minmax_scaler_invalid_range() {
    // Should fail with invalid range (min >= max)
    let result = std::panic::catch_unwind(|| MinMaxScaler::with_range(1.0, 1.0));
    assert!(result.is_err());

    let result = std::panic::catch_unwind(|| MinMaxScaler::with_range(2.0, 1.0));
    assert!(result.is_err());
}

#[test]
fn test_minmax_scaler_fit_simple() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Should fit successfully
    let result = scaler.fit(&fixture.simple_data);
    assert!(result.is_ok());
    assert!(scaler.is_fitted());

    // Should have computed correct min and max
    assert_abs_diff_eq!(scaler.data_min()[0], 1.0, epsilon = 1e-5);
    assert_abs_diff_eq!(scaler.data_max()[0], 5.0, epsilon = 1e-5);
    assert_abs_diff_eq!(scaler.data_range()[0], 4.0, epsilon = 1e-5);
}

#[test]
fn test_minmax_scaler_fit_multi_feature() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Should fit successfully on multi-feature data
    let result = scaler.fit(&fixture.multi_feature_data);
    assert!(result.is_ok());
    assert!(scaler.is_fitted());

    // Should have computed correct min/max for each feature
    assert_eq!(scaler.data_min().len(), 2);
    assert_eq!(scaler.data_max().len(), 2);
    assert_eq!(scaler.data_range().len(), 2);

    // Feature 0: [1, 2, 3, 4]
    assert_abs_diff_eq!(scaler.data_min()[0], 1.0, epsilon = 1e-5);
    assert_abs_diff_eq!(scaler.data_max()[0], 4.0, epsilon = 1e-5);
    assert_abs_diff_eq!(scaler.data_range()[0], 3.0, epsilon = 1e-5);

    // Feature 1: [10, 20, 30, 40]
    assert_abs_diff_eq!(scaler.data_min()[1], 10.0, epsilon = 1e-5);
    assert_abs_diff_eq!(scaler.data_max()[1], 40.0, epsilon = 1e-5);
    assert_abs_diff_eq!(scaler.data_range()[1], 30.0, epsilon = 1e-5);
}

#[test]
fn test_minmax_scaler_fit_empty_data() {
    let empty_data = Tensor::zeros(&[0, 1], &Device::cpu()).unwrap();
    let mut scaler = MinMaxScaler::new();

    // Should fail on empty data
    let result = scaler.fit(&empty_data);
    assert!(result.is_err());
    assert!(matches!(
        result.unwrap_err(),
        PreprocessingError::EmptyDataset
    ));
    assert!(!scaler.is_fitted());
}

#[test]
fn test_minmax_scaler_fit_single_value() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Should handle single value
    let result = scaler.fit(&fixture.single_value_data);
    assert!(result.is_ok());
    assert!(scaler.is_fitted());

    // Min and max should be the same value
    assert_abs_diff_eq!(scaler.data_min()[0], 5.0, epsilon = 1e-5);
    assert_abs_diff_eq!(scaler.data_max()[0], 5.0, epsilon = 1e-5);
    assert_abs_diff_eq!(scaler.data_range()[0], 0.0, epsilon = 1e-5);
}

#[test]
fn test_minmax_scaler_fit_constant_data() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Should handle constant data
    let result = scaler.fit(&fixture.constant_data);
    assert!(result.is_ok());
    assert!(scaler.is_fitted());

    // Range should be zero
    assert_abs_diff_eq!(scaler.data_range()[0], 0.0, epsilon = 1e-5);
}

#[test]
fn test_minmax_scaler_transform_not_fitted() {
    let fixture = MinMaxScalerTestFixture::new();
    let scaler = MinMaxScaler::new();

    // Should fail when not fitted
    let result = scaler.transform(&fixture.simple_data);
    assert!(result.is_err());
    assert!(matches!(result.unwrap_err(), PreprocessingError::NotFitted));
}

#[test]
fn test_minmax_scaler_transform_simple() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Fit first
    scaler.fit(&fixture.simple_data).unwrap();

    // Transform should scale to [0, 1] range
    let transformed = scaler.transform(&fixture.simple_data).unwrap();
    assert_eq!(transformed.shape(), &[5, 1]);

    let values = transformed
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();
    let expected = vec![0.0, 0.25, 0.5, 0.75, 1.0];

    for (actual, expected) in values.iter().zip(expected.iter()) {
        assert_abs_diff_eq!(actual, expected, epsilon = 1e-5);
    }
}

#[test]
fn test_minmax_scaler_transform_custom_range() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::with_range(-1.0, 1.0);

    // Fit and transform
    scaler.fit(&fixture.simple_data).unwrap();
    let transformed = scaler.transform(&fixture.simple_data).unwrap();

    let values = transformed
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();
    let expected = vec![-1.0, -0.5, 0.0, 0.5, 1.0];

    for (actual, expected) in values.iter().zip(expected.iter()) {
        assert_abs_diff_eq!(actual, expected, epsilon = 1e-5);
    }
}

#[test]
fn test_minmax_scaler_transform_negative_data() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Fit and transform negative data
    scaler.fit(&fixture.negative_data).unwrap();
    let transformed = scaler.transform(&fixture.negative_data).unwrap();

    let values = transformed
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();
    let expected = vec![0.0, 0.25, 0.5, 0.75, 1.0];

    for (actual, expected) in values.iter().zip(expected.iter()) {
        assert_abs_diff_eq!(actual, expected, epsilon = 1e-5);
    }
}

#[test]
fn test_minmax_scaler_transform_constant_data() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Fit and transform constant data
    scaler.fit(&fixture.constant_data).unwrap();
    let transformed = scaler.transform(&fixture.constant_data).unwrap();

    let values = transformed
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    // All values should be 0.0 (min of range) when data has no variance
    for value in values {
        assert_abs_diff_eq!(value, 0.0, epsilon = 1e-5);
    }
}

#[test]
fn test_minmax_scaler_transform_dimension_mismatch() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Fit on simple data (1 feature)
    scaler.fit(&fixture.simple_data).unwrap();

    // Try to transform multi-feature data (2 features)
    let result = scaler.transform(&fixture.multi_feature_data);
    assert!(result.is_err());
    assert!(matches!(
        result.unwrap_err(),
        PreprocessingError::DimensionMismatch {
            expected: 1,
            actual: 2
        }
    ));
}

#[test]
fn test_minmax_scaler_inverse_transform() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Fit and transform
    scaler.fit(&fixture.simple_data).unwrap();
    let transformed = scaler.transform(&fixture.simple_data).unwrap();

    // Inverse transform should recover original data
    let recovered = scaler.inverse_transform(&transformed).unwrap();
    assert_eq!(recovered.shape(), fixture.simple_data.shape());

    let original_values = fixture
        .simple_data
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();
    let recovered_values = recovered
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    for (orig, rec) in original_values.iter().zip(recovered_values.iter()) {
        assert_abs_diff_eq!(orig, rec, epsilon = 1e-5);
    }
}

#[test]
fn test_minmax_scaler_inverse_transform_custom_range() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::with_range(-5.0, 5.0);

    // Fit and transform
    scaler.fit(&fixture.simple_data).unwrap();
    let transformed = scaler.transform(&fixture.simple_data).unwrap();

    // Inverse transform should recover original data
    let recovered = scaler.inverse_transform(&transformed).unwrap();

    let original_values = fixture
        .simple_data
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();
    let recovered_values = recovered
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    for (orig, rec) in original_values.iter().zip(recovered_values.iter()) {
        assert_abs_diff_eq!(orig, rec, epsilon = 1e-5);
    }
}

#[test]
fn test_minmax_scaler_fit_transform() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // fit_transform should be equivalent to fit + transform
    let result1 = scaler.fit_transform(&fixture.simple_data).unwrap();

    let mut scaler2 = MinMaxScaler::new();
    scaler2.fit(&fixture.simple_data).unwrap();
    let result2 = scaler2.transform(&fixture.simple_data).unwrap();

    assert_eq!(result1.shape(), result2.shape());
    let values1 = result1
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();
    let values2 = result2
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    for (v1, v2) in values1.iter().zip(values2.iter()) {
        assert_abs_diff_eq!(v1, v2, epsilon = 1e-5);
    }
}

#[test]
fn test_minmax_scaler_reset() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Fit scaler
    scaler.fit(&fixture.simple_data).unwrap();
    assert!(scaler.is_fitted());

    // Reset should make it unfitted
    scaler.reset();
    assert!(!scaler.is_fitted());

    // Should fail to transform after reset
    let result = scaler.transform(&fixture.simple_data);
    assert!(result.is_err());
    assert!(matches!(result.unwrap_err(), PreprocessingError::NotFitted));
}

#[test]
fn test_minmax_scaler_out_of_range_data() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Fit on simple data [1, 2, 3, 4, 5]
    scaler.fit(&fixture.simple_data).unwrap();

    // Transform data outside the fitted range
    let out_of_range_data = Tensor::from_slice(&[0.0, 6.0], &[2, 1], &Device::cpu()).unwrap();
    let transformed = scaler.transform(&out_of_range_data).unwrap();

    let values = transformed
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    // Should extrapolate: 0 -> -0.25, 6 -> 1.25
    assert_abs_diff_eq!(values[0], -0.25, epsilon = 1e-5);
    assert_abs_diff_eq!(values[1], 1.25, epsilon = 1e-5);
}

#[test]
fn test_minmax_scaler_large_range_data() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::new();

    // Should handle large range data efficiently
    scaler.fit(&fixture.large_range_data).unwrap();
    let transformed = scaler.transform(&fixture.large_range_data).unwrap();

    let values = transformed
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();
    let expected = vec![0.0, 0.25, 0.5, 0.75, 1.0];

    for (actual, expected) in values.iter().zip(expected.iter()) {
        assert_abs_diff_eq!(actual, expected, epsilon = 1e-5);
    }
}

#[test]
fn test_minmax_scaler_serialization() {
    let fixture = MinMaxScalerTestFixture::new();
    let mut scaler = MinMaxScaler::with_range(-2.0, 2.0);

    // Fit scaler
    scaler.fit(&fixture.simple_data).unwrap();

    // Should be serializable and deserializable
    let serialized = bincode::serialize(&scaler).unwrap();
    let deserialized: MinMaxScaler = bincode::deserialize(&serialized).unwrap();

    // Should maintain fitted state and parameters
    assert!(deserialized.is_fitted());
    assert_eq!(scaler.feature_range(), deserialized.feature_range());
    assert_eq!(scaler.data_min(), deserialized.data_min());
    assert_eq!(scaler.data_max(), deserialized.data_max());
    assert_eq!(scaler.data_range(), deserialized.data_range());

    // Should produce same transform results
    let original_result = scaler.transform(&fixture.simple_data).unwrap();
    let deserialized_result = deserialized.transform(&fixture.simple_data).unwrap();

    let orig_values = original_result
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();
    let deser_values = deserialized_result
        .to_cpu()
        .unwrap()
        .iter()
        .copied()
        .collect::<Vec<f32>>();

    for (orig, deser) in orig_values.iter().zip(deser_values.iter()) {
        assert_abs_diff_eq!(orig, deser, epsilon = 1e-5);
    }
}