// Ultra Fast Filter Module - Maximum performance filtering with advanced SIMD
// Targeting ultra-high-performance filtering to compete with Polars

use crate::dataframe::DataFrame;
use crate::types::Value;
use rayon::prelude::*;
use std::error::Error;

// Advanced SIMD imports
#[cfg(target_arch = "x86_64")]
use std::arch::x86_64::*;

/// Ultra-fast filtering engine with cache-optimized chunking and work-stealing
pub struct UltraFastFilter;

impl UltraFastFilter {
    /// Cache-optimized chunk size for maximum memory bandwidth utilization
    const OPTIMAL_CHUNK_SIZE: usize = 8192; // 32KB per chunk for L1 cache efficiency
    const _SIMD_ALIGNMENT: usize = 32; // AVX2 alignment requirement

    /// Ultra-fast parallel SIMD filtering with work-stealing and cache optimization
    pub fn ultra_filter_i32_gt(
        values: &[i32],
        threshold: i32,
    ) -> Result<Vec<bool>, Box<dyn Error>> {
        if values.is_empty() {
            return Ok(Vec::new());
        }

        #[cfg(target_arch = "x86_64")]
        {
            if is_x86_feature_detected!("avx2") {
                unsafe {
                    return Self::avx2_ultra_filter_i32_gt(values, threshold);
                }
            }
        }

        Self::portable_ultra_filter_i32_gt(values, threshold)
    }

    /// AVX2 ultra-optimized integer filtering with prefetching
    #[cfg(target_arch = "x86_64")]
    #[target_feature(enable = "avx2")]
    unsafe fn avx2_ultra_filter_i32_gt(
        values: &[i32],
        threshold: i32,
    ) -> Result<Vec<bool>, Box<dyn Error>> {
        let mut result = vec![false; values.len()];
        let threshold_vec = _mm256_set1_epi32(threshold);

        // Parallel processing with work-stealing
        values
            .par_chunks(Self::OPTIMAL_CHUNK_SIZE)
            .zip(result.par_chunks_mut(Self::OPTIMAL_CHUNK_SIZE))
            .for_each(|(chunk, result_chunk)| {
                Self::process_chunk_avx2_i32(chunk, result_chunk, threshold_vec, threshold);
            });

        Ok(result)
    }

    /// Process a single chunk with AVX2 optimization and prefetching
    #[cfg(target_arch = "x86_64")]
    #[target_feature(enable = "avx2")]
    unsafe fn process_chunk_avx2_i32(
        chunk: &[i32],
        result_chunk: &mut [bool],
        threshold_vec: __m256i,
        threshold: i32,
    ) {
        let mut i = 0;
        let len = chunk.len();

        // Process 8 integers at a time with AVX2
        while i + 8 <= len {
            // Prefetch next cache line for better memory access patterns
            if i + 16 < len {
                _mm_prefetch(chunk.as_ptr().add(i + 16) as *const i8, _MM_HINT_T0);
            }

            let data_vec = _mm256_loadu_si256(chunk.as_ptr().add(i) as *const __m256i);
            let cmp_result = _mm256_cmpgt_epi32(data_vec, threshold_vec);

            // Extract comparison results efficiently
            let mask = _mm256_movemask_ps(_mm256_castsi256_ps(cmp_result));

            // Unroll the loop for better performance
            result_chunk[i] = (mask & 1) != 0;
            result_chunk[i + 1] = (mask & 2) != 0;
            result_chunk[i + 2] = (mask & 4) != 0;
            result_chunk[i + 3] = (mask & 8) != 0;
            result_chunk[i + 4] = (mask & 16) != 0;
            result_chunk[i + 5] = (mask & 32) != 0;
            result_chunk[i + 6] = (mask & 64) != 0;
            result_chunk[i + 7] = (mask & 128) != 0;

            i += 8;
        }

        // Handle remaining elements with scalar operations
        while i < len {
            result_chunk[i] = chunk[i] > threshold;
            i += 1;
        }
    }

    /// Portable ultra-fast filtering with optimized chunking
    fn portable_ultra_filter_i32_gt(
        values: &[i32],
        threshold: i32,
    ) -> Result<Vec<bool>, Box<dyn Error>> {
        let result: Vec<bool> = values
            .par_chunks(Self::OPTIMAL_CHUNK_SIZE)
            .flat_map(|chunk| chunk.iter().map(|&val| val > threshold).collect::<Vec<_>>())
            .collect();

        Ok(result)
    }

    /// Ultra-fast floating point filtering with cache optimization
    pub fn ultra_filter_f64_gt(
        values: &[f64],
        threshold: f64,
    ) -> Result<Vec<bool>, Box<dyn Error>> {
        if values.is_empty() {
            return Ok(Vec::new());
        }

        #[cfg(target_arch = "x86_64")]
        {
            if is_x86_feature_detected!("avx2") {
                unsafe {
                    return Self::avx2_ultra_filter_f64_gt(values, threshold);
                }
            }
        }

        Self::portable_ultra_filter_f64_gt(values, threshold)
    }

    /// AVX2 ultra-optimized float filtering
    #[cfg(target_arch = "x86_64")]
    #[target_feature(enable = "avx2")]
    unsafe fn avx2_ultra_filter_f64_gt(
        values: &[f64],
        threshold: f64,
    ) -> Result<Vec<bool>, Box<dyn Error>> {
        let mut result = vec![false; values.len()];
        let threshold_vec = _mm256_set1_pd(threshold);

        values
            .par_chunks(Self::OPTIMAL_CHUNK_SIZE)
            .zip(result.par_chunks_mut(Self::OPTIMAL_CHUNK_SIZE))
            .for_each(|(chunk, result_chunk)| {
                Self::process_chunk_avx2_f64(chunk, result_chunk, threshold_vec, threshold);
            });

        Ok(result)
    }

    /// Process float chunk with AVX2 optimization
    #[cfg(target_arch = "x86_64")]
    #[target_feature(enable = "avx2")]
    unsafe fn process_chunk_avx2_f64(
        chunk: &[f64],
        result_chunk: &mut [bool],
        threshold_vec: __m256d,
        threshold: f64,
    ) {
        let mut i = 0;
        let len = chunk.len();

        // Process 4 doubles at a time with AVX2
        while i + 4 <= len {
            // Prefetch for better cache utilization
            if i + 8 < len {
                _mm_prefetch(chunk.as_ptr().add(i + 8) as *const i8, _MM_HINT_T0);
            }

            let data_vec = _mm256_loadu_pd(chunk.as_ptr().add(i));
            let cmp_result = _mm256_cmp_pd(data_vec, threshold_vec, _CMP_GT_OQ);

            let mask = _mm256_movemask_pd(cmp_result);

            // Unrolled extraction for performance
            result_chunk[i] = (mask & 1) != 0;
            result_chunk[i + 1] = (mask & 2) != 0;
            result_chunk[i + 2] = (mask & 4) != 0;
            result_chunk[i + 3] = (mask & 8) != 0;

            i += 4;
        }

        // Handle remaining elements
        while i < len {
            result_chunk[i] = chunk[i] > threshold;
            i += 1;
        }
    }

    /// Portable ultra-fast float filtering
    fn portable_ultra_filter_f64_gt(
        values: &[f64],
        threshold: f64,
    ) -> Result<Vec<bool>, Box<dyn Error>> {
        let result: Vec<bool> = values
            .par_chunks(Self::OPTIMAL_CHUNK_SIZE)
            .flat_map(|chunk| chunk.iter().map(|&val| val > threshold).collect::<Vec<_>>())
            .collect();

        Ok(result)
    }

    /// Ultra-fast range filtering with dual-threshold SIMD optimization
    pub fn ultra_filter_range_f64(
        values: &[f64],
        min_threshold: f64,
        max_threshold: f64,
    ) -> Result<Vec<bool>, Box<dyn Error>> {
        #[cfg(target_arch = "x86_64")]
        {
            if is_x86_feature_detected!("avx2") {
                unsafe {
                    return Self::avx2_ultra_filter_range_f64(values, min_threshold, max_threshold);
                }
            }
        }

        Self::portable_ultra_filter_range_f64(values, min_threshold, max_threshold)
    }

    /// AVX2 range filtering with dual comparisons
    #[cfg(target_arch = "x86_64")]
    #[target_feature(enable = "avx2")]
    unsafe fn avx2_ultra_filter_range_f64(
        values: &[f64],
        min_threshold: f64,
        max_threshold: f64,
    ) -> Result<Vec<bool>, Box<dyn Error>> {
        let mut result = vec![false; values.len()];
        let min_vec = _mm256_set1_pd(min_threshold);
        let max_vec = _mm256_set1_pd(max_threshold);

        values
            .par_chunks(Self::OPTIMAL_CHUNK_SIZE)
            .zip(result.par_chunks_mut(Self::OPTIMAL_CHUNK_SIZE))
            .for_each(|(chunk, result_chunk)| {
                let mut i = 0;
                let len = chunk.len();

                while i + 4 <= len {
                    let data_vec = _mm256_loadu_pd(chunk.as_ptr().add(i));

                    // Dual comparison: val >= min AND val <= max
                    let min_cmp = _mm256_cmp_pd(data_vec, min_vec, _CMP_GE_OQ);
                    let max_cmp = _mm256_cmp_pd(data_vec, max_vec, _CMP_LE_OQ);
                    let combined = _mm256_and_pd(min_cmp, max_cmp);

                    let mask = _mm256_movemask_pd(combined);

                    result_chunk[i] = (mask & 1) != 0;
                    result_chunk[i + 1] = (mask & 2) != 0;
                    result_chunk[i + 2] = (mask & 4) != 0;
                    result_chunk[i + 3] = (mask & 8) != 0;

                    i += 4;
                }

                while i < len {
                    result_chunk[i] = chunk[i] >= min_threshold && chunk[i] <= max_threshold;
                    i += 1;
                }
            });

        Ok(result)
    }

    /// Portable range filtering
    fn portable_ultra_filter_range_f64(
        values: &[f64],
        min_threshold: f64,
        max_threshold: f64,
    ) -> Result<Vec<bool>, Box<dyn Error>> {
        let result: Vec<bool> = values
            .par_chunks(Self::OPTIMAL_CHUNK_SIZE)
            .flat_map(|chunk| {
                chunk
                    .iter()
                    .map(|&val| val >= min_threshold && val <= max_threshold)
                    .collect::<Vec<_>>()
            })
            .collect();

        Ok(result)
    }

    /// Ultra-fast DataFrame filtering with column optimization
    pub fn ultra_filter_dataframe(
        _df: &DataFrame,
        _column_name: &str,
        _condition: FilterCondition,
    ) -> Result<DataFrame, Box<dyn Error>> {
        // Implementation would integrate with DataFrame structure
        // For now, return error indicating this needs DataFrame integration
        Err("DataFrame integration pending".into())
    }
}

/// Filter condition enumeration for ultra-fast processing
#[derive(Debug, Clone)]
pub enum FilterCondition {
    GreaterThan(Value),
    LessThan(Value),
    Equal(Value),
    Range(Value, Value),
    Contains(String),
}

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

    #[test]
    fn test_ultra_filter_i32_basic() {
        let values = vec![1, 5, 3, 8, 2, 9, 4, 7, 6];
        let threshold = 5;

        let result = UltraFastFilter::ultra_filter_i32_gt(&values, threshold).unwrap();
        let expected = vec![false, false, false, true, false, true, false, true, true];

        assert_eq!(result, expected);
    }

    #[test]
    fn test_ultra_filter_f64_basic() {
        let values = vec![1.1, 5.5, 3.3, 8.8, 2.2];
        let threshold = 5.0;

        let result = UltraFastFilter::ultra_filter_f64_gt(&values, threshold).unwrap();
        let expected = vec![false, true, false, true, false];

        assert_eq!(result, expected);
    }

    #[test]
    fn test_ultra_filter_range() {
        let values = vec![1.0, 3.0, 5.0, 7.0, 9.0];
        let min_threshold = 3.0;
        let max_threshold = 7.0;

        let result =
            UltraFastFilter::ultra_filter_range_f64(&values, min_threshold, max_threshold).unwrap();
        let expected = vec![false, true, true, true, false];

        assert_eq!(result, expected);
    }

    #[test]
    fn test_ultra_filter_empty() {
        let values: Vec<i32> = vec![];
        let threshold = 5;

        let result = UltraFastFilter::ultra_filter_i32_gt(&values, threshold).unwrap();
        assert!(result.is_empty());
    }

    #[test]
    fn test_ultra_filter_large_dataset() {
        let size = 1_000_000;
        let values: Vec<i32> = (0..size as i32).collect();
        let threshold = size as i32 / 2;

        let result = UltraFastFilter::ultra_filter_i32_gt(&values, threshold).unwrap();

        assert_eq!(result.len(), size);

        // Verify correctness on sample points
        assert!(!result[size / 4]); // Should be false
        assert!(result[3 * size / 4]); // Should be true

        // Count true values - should be approximately half
        let true_count = result.iter().filter(|&&x| x).count();
        let expected_count = size - threshold as usize - 1; // Elements greater than threshold
        assert_eq!(true_count, expected_count);
    }

    #[test]
    fn test_ultra_filter_performance_benchmark() {
        // This test is for performance validation
        let size = 100_000;
        let values: Vec<f64> = (0..size).map(|i| i as f64).collect();
        let threshold = (size / 2) as f64;

        let start = std::time::Instant::now();
        let result = UltraFastFilter::ultra_filter_f64_gt(&values, threshold).unwrap();
        let duration = start.elapsed();

        assert_eq!(result.len(), size);

        // Performance check: should complete in reasonable time
        // This is a rough check - actual performance depends on hardware
        println!("Ultra filter processed {} elements in {:?}", size, duration);
        assert!(duration.as_millis() < 100); // Should be very fast
    }
}
