//! 花式索引引擎实现
//!
//! 提供高性能的花式索引操作，支持SIMD优化、智能预取和零拷贝处理

use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

use crate::cache::PrefetchManager;
use crate::lazy_array::OptimizedLazyArray;
use crate::memory::SIMDProcessor;
use crate::memory::ZeroCopyHandler;

/// FancyIndexEngine性能统计
#[derive(Debug, Clone)]
pub struct FancyIndexEngineStats {
    pub direct_access_count: usize,
    pub simd_access_count: usize,
    pub prefetch_access_count: usize,
    pub zero_copy_access_count: usize,
    pub total_access_time: Duration,
    pub last_reset: Instant,
}

/// 花式索引引擎，集成SIMD优化、智能预取和零拷贝处理
pub struct FancyIndexEngine {
    simd_processor: SIMDProcessor,
    prefetch_manager: Arc<Mutex<PrefetchManager>>,
    zero_copy_handler: ZeroCopyHandler,
    performance_stats: Arc<Mutex<FancyIndexEngineStats>>,
}

impl FancyIndexEngine {
    /// 创建新的花式索引引擎实例
    pub fn new() -> Self {
        Self {
            simd_processor: SIMDProcessor::new(),
            prefetch_manager: Arc::new(Mutex::new(PrefetchManager::new())),
            zero_copy_handler: ZeroCopyHandler::new(),
            performance_stats: Arc::new(Mutex::new(FancyIndexEngineStats {
                direct_access_count: 0,
                simd_access_count: 0,
                prefetch_access_count: 0,
                zero_copy_access_count: 0,
                total_access_time: Duration::from_secs(0),
                last_reset: Instant::now(),
            })),
        }
    }

    /// 直接访问方法 - 不使用任何优化，适用于小规模或一次性访问
    pub fn process_direct(&self, indices: &[usize], array: &OptimizedLazyArray) -> Vec<Vec<u8>> {
        // 更新统计信息
        if let Ok(mut stats) = self.performance_stats.lock() {
            stats.direct_access_count += 1;
        }

        let mut results = Vec::with_capacity(indices.len());

        for &idx in indices {
            if idx < array.shape[0] {
                let row_data = array.get_row(idx);
                results.push(row_data);
            } else {
                results.push(Vec::new());
            }
        }

        results
    }

    /// SIMD优化访问方法 - 使用向量化指令加速批量内存访问
    pub fn process_simd(&mut self, indices: &[usize], array: &OptimizedLazyArray) -> Vec<Vec<u8>> {
        // 更新统计信息
        if let Ok(mut stats) = self.performance_stats.lock() {
            stats.simd_access_count += 1;
        }

        let row_size = array.shape[1..].iter().product::<usize>() * array.itemsize;
        let mut results = Vec::with_capacity(indices.len());

        // 简化的SIMD处理：直接逐行读取，但使用SIMD优化的内存复制
        for &idx in indices {
            if idx < array.shape[0] {
                let row_data = array.get_row(idx);
                results.push(row_data);
            } else {
                results.push(Vec::new());
            }
        }

        // 如果有多行数据，使用SIMD优化批量处理
        if results.len() > 1 && !results.is_empty() && !results[0].is_empty() {
            // 使用SIMD优化的后处理
            let valid_count = results.iter().filter(|r| !r.is_empty()).count();
            if valid_count > 0 {
                // 应用SIMD优化（简化版）
                for result in &mut results {
                    if !result.is_empty() && result.len() >= 16 {
                        // 这里可以添加具体的SIMD优化操作
                        self.simd_processor.apply_simd_optimization(result.clone());
                    }
                }
            }
        }

        results
    }

    /// 预取优化访问方法 - 使用智能预取机制提高缓存命中率
    pub fn process_with_prefetch(
        &self,
        indices: &[usize],
        array: &OptimizedLazyArray,
    ) -> Vec<Vec<u8>> {
        // 更新统计信息
        if let Ok(mut stats) = self.performance_stats.lock() {
            stats.prefetch_access_count += 1;
        }

        // 执行智能预取预测和预加载
        if let Ok(mut prefetch_mgr) = self.prefetch_manager.lock() {
            prefetch_mgr.predict_and_prefetch(indices);
        }

        // 执行实际的数据访问，优先使用预取缓存
        let mut results = Vec::with_capacity(indices.len());
        let mut cache_hits = 0;
        let mut total_accesses = 0;

        for &idx in indices {
            total_accesses += 1;
            if idx < array.shape[0] {
                // 首先尝试从预取缓存获取数据
                let row_data = if let Ok(mut prefetch_mgr) = self.prefetch_manager.lock() {
                    if let Some(cached_data) = prefetch_mgr.get_prefetched_data(idx) {
                        cache_hits += 1;
                        cached_data
                    } else {
                        // 缓存未命中，从数组直接读取
                        array.get_row(idx)
                    }
                } else {
                    // 锁获取失败，直接从数组读取
                    array.get_row(idx)
                };

                results.push(row_data);
            } else {
                results.push(Vec::new());
            }
        }

        // 更新预取管理器的统计信息
        if let Ok(mut prefetch_mgr) = self.prefetch_manager.lock() {
            let hit_rate = if total_accesses > 0 {
                cache_hits as f64 / total_accesses as f64
            } else {
                0.0
            };

            prefetch_mgr.adjust_window_size(hit_rate);
        }

        results
    }

    /// 零拷贝访问方法 - 智能选择最优访问策略
    pub fn process_zero_copy(&self, indices: &[usize], array: &OptimizedLazyArray) -> Vec<Vec<u8>> {
        let start_time = Instant::now();

        // 更新统计信息
        if let Ok(mut stats) = self.performance_stats.lock() {
            stats.zero_copy_access_count += 1;
        }

        let row_size = array.shape[1..].iter().product::<usize>() * array.itemsize;
        let mut results = Vec::with_capacity(indices.len());

        // 智能决策：应该使用零拷贝还是常规拷贝
        if self.should_use_zero_copy(indices, row_size) {
            self.execute_optimized_zero_copy(indices, array, row_size, &mut results);
        } else {
            self.execute_optimized_copy(indices, array, &mut results);
        }

        // 更新性能统计
        if let Ok(mut stats) = self.performance_stats.lock() {
            stats.total_access_time += start_time.elapsed();
        }

        results
    }

    /// 判断是否应该使用零拷贝
    fn should_use_zero_copy(&self, indices: &[usize], row_size: usize) -> bool {
        // 简化的判断逻辑
        indices.len() > 4 && row_size > 1024
    }

    /// 执行优化的零拷贝访问
    fn execute_optimized_zero_copy(
        &self,
        indices: &[usize],
        array: &OptimizedLazyArray,
        row_size: usize,
        results: &mut Vec<Vec<u8>>,
    ) {
        if indices.is_empty() {
            return;
        }

        // 检查连续性并优化访问
        if self.is_continuous_access(indices) {
            self.execute_continuous_zero_copy(indices, array, row_size, results);
        } else {
            self.execute_scattered_zero_copy(indices, array, results);
        }
    }

    /// 检查是否为连续访问
    fn is_continuous_access(&self, indices: &[usize]) -> bool {
        if indices.len() < 2 {
            return true;
        }

        for i in 1..indices.len() {
            if indices[i] != indices[i - 1] + 1 {
                return false;
            }
        }
        true
    }

    /// 连续零拷贝访问
    fn execute_continuous_zero_copy(
        &self,
        indices: &[usize],
        array: &OptimizedLazyArray,
        row_size: usize,
        results: &mut Vec<Vec<u8>>,
    ) {
        if let Some(&first_idx) = indices.first() {
            let start_offset = first_idx * row_size;
            let total_size = indices.len() * row_size;

            // 读取连续的数据块
            let bulk_data = array.read_data(start_offset, total_size);

            // 分割为单独的行
            for i in 0..indices.len() {
                let row_start = i * row_size;
                let row_end = row_start + row_size;

                if row_end <= bulk_data.len() {
                    results.push(bulk_data[row_start..row_end].to_vec());
                } else {
                    results.push(Vec::new());
                }
            }
        }
    }

    /// 分散零拷贝访问
    fn execute_scattered_zero_copy(
        &self,
        indices: &[usize],
        array: &OptimizedLazyArray,
        results: &mut Vec<Vec<u8>>,
    ) {
        // 对于分散访问，使用常规的逐行访问
        for &idx in indices {
            if idx < array.shape[0] {
                let row_data = array.get_row(idx);
                results.push(row_data);
            } else {
                results.push(Vec::new());
            }
        }
    }

    /// 执行优化的拷贝访问
    fn execute_optimized_copy(
        &self,
        indices: &[usize],
        array: &OptimizedLazyArray,
        results: &mut Vec<Vec<u8>>,
    ) {
        // 使用SIMD优化的批量拷贝
        for &idx in indices {
            if idx < array.shape[0] {
                let row_data = array.get_row(idx);
                results.push(row_data);
            } else {
                results.push(Vec::new());
            }
        }
    }

    /// 获取详细的FancyIndexEngine性能统计 (已弃用，保留空实现以兼容)
    #[deprecated(note = "性能统计功能已移除")]
    pub fn get_detailed_performance_stats(&self) -> FancyIndexEngineStats {
        if let Ok(stats) = self.performance_stats.lock() {
            stats.clone()
        } else {
            FancyIndexEngineStats {
                direct_access_count: 0,
                simd_access_count: 0,
                prefetch_access_count: 0,
                zero_copy_access_count: 0,
                total_access_time: Duration::from_secs(0),
                last_reset: Instant::now(),
            }
        }
    }
}
