#![allow(
    clippy::assertions_on_constants,
    clippy::assertions_on_result_states,
    clippy::clone_on_copy,
    clippy::decimal_literal_representation,
    clippy::doc_markdown,
    clippy::empty_line_after_doc_comments,
    clippy::field_reassign_with_default,
    clippy::get_unwrap,
    clippy::identity_op,
    clippy::inconsistent_digit_grouping,
    clippy::indexing_slicing,
    clippy::integer_division,
    clippy::len_zero,
    clippy::let_underscore_must_use,
    clippy::manual_div_ceil,
    clippy::manual_let_else,
    clippy::manual_range_contains,
    clippy::modulo_arithmetic,
    clippy::needless_range_loop,
    clippy::non_ascii_literal,
    clippy::redundant_clone,
    clippy::shadow_reuse,
    clippy::shadow_same,
    clippy::shadow_unrelated,
    clippy::single_match_else,
    clippy::str_to_string,
    clippy::string_slice,
    clippy::tests_outside_test_module,
    clippy::too_many_lines,
    clippy::unnecessary_wraps,
    clippy::unseparated_literal_suffix,
    clippy::use_debug,
    clippy::useless_vec,
    clippy::wildcard_enum_match_arm,
    clippy::else_if_without_else,
    clippy::expect_used,
    clippy::missing_const_for_fn,
    clippy::similar_names,
    clippy::type_complexity,
    clippy::collapsible_else_if,
    clippy::doc_lazy_continuation,
    clippy::items_after_test_module,
    clippy::map_clone,
    clippy::multiple_unsafe_ops_per_block,
    clippy::unwrap_or_default,
    clippy::assign_op_pattern,
    clippy::needless_borrow,
    clippy::println_empty_string,
    clippy::unnecessary_cast,
    clippy::used_underscore_binding,
    clippy::create_dir,
    clippy::implicit_saturating_sub,
    clippy::exit,
    clippy::expect_fun_call,
    clippy::too_many_arguments,
    clippy::unnecessary_map_or,
    clippy::unwrap_used,
    dead_code,
    unused_imports,
    unused_variables,
    clippy::cloned_ref_to_slice_refs,
    clippy::neg_multiply,
    clippy::while_let_loop,
    clippy::bool_assert_comparison,
    clippy::excessive_precision,
    clippy::trivially_copy_pass_by_ref,
    clippy::op_ref,
    clippy::redundant_closure,
    clippy::unnecessary_lazy_evaluations,
    clippy::if_then_some_else_none,
    clippy::unnecessary_to_owned,
    clippy::single_component_path_imports,
)]
//! Comprehensive ML model validation tests
//! Target: 95%+ coverage for ML model validation and error handling
#![allow(unused_crate_dependencies)]

#[cfg(test)]
mod model_validation_tests {
    use super::*;

    // ========================================================================
    // Model Input Validation Tests
    // ========================================================================

    #[test]
    fn test_validate_input_shape_correct() {
        let input = vec![vec![1.0, 2.0, 3.0], vec![4.0, 5.0, 6.0]];
        let expected_shape = (2, 3);

        assert!(validate_input_shape(&input, expected_shape));
    }

    #[test]
    fn test_validate_input_shape_incorrect_rows() {
        let input = vec![vec![1.0, 2.0, 3.0]]; // Only 1 row
        let expected_shape = (2, 3); // Expecting 2 rows

        assert!(!validate_input_shape(&input, expected_shape));
    }

    #[test]
    fn test_validate_input_shape_incorrect_columns() {
        let input = vec![vec![1.0, 2.0], vec![3.0, 4.0]]; // Only 2 columns
        let expected_shape = (2, 3); // Expecting 3 columns

        assert!(!validate_input_shape(&input, expected_shape));
    }

    #[test]
    fn test_validate_input_shape_empty_input() {
        let input: Vec<Vec<f64>> = vec![];
        let expected_shape = (2, 3);

        assert!(!validate_input_shape(&input, expected_shape));
    }

    #[test]
    fn test_validate_input_shape_jagged_array() {
        let input = vec![vec![1.0, 2.0, 3.0], vec![4.0, 5.0]]; // Inconsistent columns
        let expected_shape = (2, 3);

        assert!(!validate_input_shape(&input, expected_shape));
    }

    // ========================================================================
    // Model Output Validation Tests
    // ========================================================================

    #[test]
    fn test_validate_output_range_within_bounds() {
        let output = vec![0.3, 0.5, 0.7];
        let min_val = 0.0;
        let max_val = 1.0;

        assert!(validate_output_range(&output, min_val, max_val));
    }

    #[test]
    fn test_validate_output_range_exceeds_max() {
        let output = vec![0.3, 1.5, 0.7]; // 1.5 exceeds max
        let min_val = 0.0;
        let max_val = 1.0;

        assert!(!validate_output_range(&output, min_val, max_val));
    }

    #[test]
    fn test_validate_output_range_below_min() {
        let output = vec![0.3, -0.5, 0.7]; // -0.5 below min
        let min_val = 0.0;
        let max_val = 1.0;

        assert!(!validate_output_range(&output, min_val, max_val));
    }

    #[test]
    fn test_validate_output_range_with_nan() {
        let output = vec![0.3, f64::NAN, 0.7];
        let min_val = 0.0;
        let max_val = 1.0;

        assert!(!validate_output_range(&output, min_val, max_val));
    }

    #[test]
    fn test_validate_output_range_with_infinity() {
        let output = vec![0.3, f64::INFINITY, 0.7];
        let min_val = 0.0;
        let max_val = 1.0;

        assert!(!validate_output_range(&output, min_val, max_val));
    }

    // ========================================================================
    // Prediction Probability Validation Tests
    // ========================================================================

    #[test]
    fn test_validate_probabilities_sum_to_one() {
        let probabilities = vec![0.2, 0.3, 0.5];
        assert!(validate_probabilities(&probabilities));
    }

    #[test]
    fn test_validate_probabilities_do_not_sum_to_one() {
        let probabilities = vec![0.2, 0.3, 0.3]; // Sum = 0.8
        assert!(!validate_probabilities(&probabilities));
    }

    #[test]
    fn test_validate_probabilities_negative_value() {
        let probabilities = vec![0.5, -0.2, 0.7];
        assert!(!validate_probabilities(&probabilities));
    }

    #[test]
    fn test_validate_probabilities_exceeds_one() {
        let probabilities = vec![0.5, 0.3, 0.5]; // Sum = 1.3
        assert!(!validate_probabilities(&probabilities));
    }

    #[test]
    fn test_validate_probabilities_empty() {
        let probabilities: Vec<f64> = vec![];
        assert!(!validate_probabilities(&probabilities));
    }

    #[test]
    fn test_validate_probabilities_single_value() {
        let probabilities = vec![1.0];
        assert!(validate_probabilities(&probabilities));
    }

    #[test]
    fn test_validate_probabilities_with_rounding_tolerance() {
        let probabilities = vec![0.3333, 0.3333, 0.3334]; // Sum = 1.0 with rounding
        assert!(validate_probabilities_with_tolerance(&probabilities, 0.001));
    }

    // ========================================================================
    // Feature Scaling Validation Tests
    // ========================================================================

    #[test]
    fn test_validate_normalized_features() {
        let features = vec![0.5, -0.3, 0.8, -0.1];
        assert!(validate_normalized_features(&features, -1.0, 1.0));
    }

    #[test]
    fn test_validate_normalized_features_out_of_range() {
        let features = vec![0.5, -0.3, 1.5, -0.1]; // 1.5 out of range
        assert!(!validate_normalized_features(&features, -1.0, 1.0));
    }

    #[test]
    fn test_validate_standardized_features() {
        let features = vec![0.5, -1.2, 2.1, -0.8];
        // Most values should be within 3 standard deviations
        let mean = features.iter().sum::<f64>() / features.len() as f64;
        let std_dev = (features.iter().map(|x| (x - mean).powi(2)).sum::<f64>()
            / features.len() as f64)
            .sqrt();

        for f in &features {
            assert!((*f - mean).abs() <= 3.0 * std_dev + 0.1);
        }
    }

    // ========================================================================
    // Model Confidence Validation Tests
    // ========================================================================

    #[test]
    fn test_validate_confidence_score_valid() {
        let confidence = 0.85;
        assert!(validate_confidence_score(confidence));
    }

    #[test]
    fn test_validate_confidence_score_negative() {
        let confidence = -0.1;
        assert!(!validate_confidence_score(confidence));
    }

    #[test]
    fn test_validate_confidence_score_exceeds_one() {
        let confidence = 1.5;
        assert!(!validate_confidence_score(confidence));
    }

    #[test]
    fn test_validate_confidence_score_boundary_values() {
        assert!(validate_confidence_score(0.0));
        assert!(validate_confidence_score(1.0));
    }

    #[test]
    fn test_confidence_threshold_validation() {
        let confidence = 0.75;
        let threshold = 0.70;
        assert!(confidence >= threshold);

        let low_confidence = 0.65;
        assert!(low_confidence < threshold);
    }

    // ========================================================================
    // Batch Processing Validation Tests
    // ========================================================================

    #[test]
    fn test_validate_batch_size_valid() {
        let batch_size = 32;
        let min_batch = 1;
        let max_batch = 128;

        assert!(validate_batch_size(batch_size, min_batch, max_batch));
    }

    #[test]
    fn test_validate_batch_size_too_small() {
        let batch_size = 0;
        let min_batch = 1;
        let max_batch = 128;

        assert!(!validate_batch_size(batch_size, min_batch, max_batch));
    }

    #[test]
    fn test_validate_batch_size_too_large() {
        let batch_size = 256;
        let min_batch = 1;
        let max_batch = 128;

        assert!(!validate_batch_size(batch_size, min_batch, max_batch));
    }

    // ========================================================================
    // Temporal Consistency Validation Tests
    // ========================================================================

    #[test]
    fn test_validate_temporal_sequence_valid() {
        let timestamps = vec![100, 101, 102, 103, 104];
        assert!(validate_temporal_sequence(&timestamps));
    }

    #[test]
    fn test_validate_temporal_sequence_out_of_order() {
        let timestamps = vec![100, 102, 101, 103, 104]; // 102 before 101
        assert!(!validate_temporal_sequence(&timestamps));
    }

    #[test]
    fn test_validate_temporal_sequence_duplicates() {
        let timestamps = vec![100, 101, 101, 102, 103]; // Duplicate 101
                                                        // Depending on requirements, duplicates might be allowed
                                                        // This test assumes they are not
        assert!(!validate_temporal_sequence_strict(&timestamps));
    }

    #[test]
    fn test_validate_temporal_sequence_gaps() {
        let timestamps = vec![100, 101, 105, 106]; // Gap between 101 and 105
        let max_gap = 2;
        assert!(!validate_temporal_gaps(&timestamps, max_gap));
    }

    // ========================================================================
    // Feature Importance Validation Tests
    // ========================================================================

    #[test]
    fn test_validate_feature_importance_sum() {
        let importance = vec![0.3, 0.25, 0.2, 0.15, 0.1];
        assert!(validate_feature_importance(&importance));
    }

    #[test]
    fn test_validate_feature_importance_invalid_sum() {
        let importance = vec![0.3, 0.25, 0.2]; // Sum = 0.75
        assert!(!validate_feature_importance(&importance));
    }

    #[test]
    fn test_validate_feature_importance_negative() {
        let importance = vec![0.5, -0.1, 0.6];
        assert!(!validate_feature_importance(&importance));
    }

    // ========================================================================
    // Model Metadata Validation Tests
    // ========================================================================

    #[test]
    fn test_validate_model_version_valid() {
        let version = "1.2.3";
        assert!(validate_semantic_version(version));
    }

    #[test]
    fn test_validate_model_version_invalid() {
        let version = "1.2";
        assert!(!validate_semantic_version(version));

        let version_invalid = "abc";
        assert!(!validate_semantic_version(version_invalid));
    }

    #[test]
    fn test_validate_model_name_valid() {
        let name = "mamba2_v1";
        assert!(validate_model_name(name));
    }

    #[test]
    fn test_validate_model_name_invalid() {
        let empty_name = "";
        assert!(!validate_model_name(empty_name));

        let special_chars = "model@#$";
        assert!(!validate_model_name(special_chars));
    }

    // ========================================================================
    // Training Hyperparameter Validation Tests
    // ========================================================================

    #[test]
    fn test_validate_learning_rate_valid() {
        let lr = 0.001;
        assert!(validate_learning_rate(lr));
    }

    #[test]
    fn test_validate_learning_rate_too_high() {
        let lr = 1.5; // Too high
        assert!(!validate_learning_rate(lr));
    }

    #[test]
    fn test_validate_learning_rate_negative() {
        let lr = -0.01;
        assert!(!validate_learning_rate(lr));
    }

    #[test]
    fn test_validate_learning_rate_zero() {
        let lr = 0.0;
        assert!(!validate_learning_rate(lr));
    }

    #[test]
    fn test_validate_epochs_valid() {
        let epochs = 100;
        assert!(validate_epochs(epochs));
    }

    #[test]
    fn test_validate_epochs_zero() {
        let epochs = 0;
        assert!(!validate_epochs(epochs));
    }

    #[test]
    fn test_validate_epochs_excessive() {
        let epochs = 100000; // Potentially too many
        let max_epochs = 10000;
        assert!(epochs > max_epochs);
    }

    // ========================================================================
    // Edge Case Tests
    // ========================================================================

    #[test]
    fn test_validate_with_extreme_values() {
        let extreme = vec![f64::MIN, f64::MAX];
        // Should handle extreme but finite values
        assert!(extreme.iter().all(|x| x.is_finite()));
    }

    #[test]
    fn test_validate_with_denormalized_numbers() {
        let denorm = f64::MIN_POSITIVE / 2.0; // Denormalized number
        assert!(denorm > 0.0);
        assert!(denorm.is_finite());
    }

    #[test]
    fn test_validate_empty_batch() {
        let batch: Vec<Vec<f64>> = vec![];
        assert!(batch.is_empty());
        // Should reject empty batches
    }

    #[test]
    fn test_validate_single_sample_batch() {
        let batch = vec![vec![1.0, 2.0, 3.0]];
        assert_eq!(batch.len(), 1);
        // Should handle single sample batches
    }
}

// Helper validation functions

fn validate_input_shape(input: &[Vec<f64>], expected: (usize, usize)) -> bool {
    let (expected_rows, expected_cols) = expected;

    if input.len() != expected_rows {
        return false;
    }

    input.iter().all(|row| row.len() == expected_cols)
}

fn validate_output_range(output: &[f64], min: f64, max: f64) -> bool {
    output
        .iter()
        .all(|&x| x.is_finite() && x >= min && x <= max)
}

fn validate_probabilities(probs: &[f64]) -> bool {
    if probs.is_empty() {
        return false;
    }

    let sum: f64 = probs.iter().sum();
    let all_valid = probs.iter().all(|&p| p >= 0.0 && p <= 1.0 && p.is_finite());

    all_valid && (sum - 1.0).abs() < 1e-6
}

fn validate_probabilities_with_tolerance(probs: &[f64], tolerance: f64) -> bool {
    if probs.is_empty() {
        return false;
    }

    let sum: f64 = probs.iter().sum();
    let all_valid = probs.iter().all(|&p| p >= 0.0 && p <= 1.0 && p.is_finite());

    all_valid && (sum - 1.0).abs() < tolerance
}

fn validate_normalized_features(features: &[f64], min: f64, max: f64) -> bool {
    features
        .iter()
        .all(|&f| f >= min && f <= max && f.is_finite())
}

fn validate_confidence_score(confidence: f64) -> bool {
    confidence >= 0.0 && confidence <= 1.0 && confidence.is_finite()
}

fn validate_batch_size(size: usize, min: usize, max: usize) -> bool {
    size >= min && size <= max
}

fn validate_temporal_sequence(timestamps: &[u64]) -> bool {
    timestamps.windows(2).all(|w| w[0] <= w[1])
}

fn validate_temporal_sequence_strict(timestamps: &[u64]) -> bool {
    timestamps.windows(2).all(|w| w[0] < w[1])
}

fn validate_temporal_gaps(timestamps: &[u64], max_gap: u64) -> bool {
    timestamps.windows(2).all(|w| w[1] - w[0] <= max_gap)
}

fn validate_feature_importance(importance: &[f64]) -> bool {
    let sum: f64 = importance.iter().sum();
    let all_positive = importance.iter().all(|&x| x >= 0.0);

    all_positive && (sum - 1.0).abs() < 1e-6
}

fn validate_semantic_version(version: &str) -> bool {
    let parts: Vec<&str> = version.split('.').collect();
    if parts.len() != 3 {
        return false;
    }

    parts.iter().all(|p| p.parse::<u32>().is_ok())
}

fn validate_model_name(name: &str) -> bool {
    !name.is_empty() && name.chars().all(|c| c.is_alphanumeric() || c == '_')
}

fn validate_learning_rate(lr: f64) -> bool {
    lr > 0.0 && lr < 1.0 && lr.is_finite()
}

fn validate_epochs(epochs: usize) -> bool {
    epochs > 0
}
