//! 索引优化技术
//!
//! 包含各种高级索引优化技术，特别针对Windows平台的优化

use crate::indexing::algorithms::IndexError;
use crate::indexing::types::*;
use crate::memory::simd_processor::SIMDProcessor;
use rayon::prelude::*;
use std::time::Instant;

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

/// 索引优化管理器 - 统一管理各种索引优化技术
pub struct IndexOptimizationManager {
    config: IndexOptimizationConfig,
    simd_processor: SIMDProcessor,
    cache_optimizer: CacheOptimizer,
    windows_optimizer: WindowsIndexOptimizer,
    performance_tracker: OptimizationPerformanceTracker,
}

impl IndexOptimizationManager {
    pub fn new(config: IndexOptimizationConfig) -> Self {
        Self {
            simd_processor: SIMDProcessor::new(),
            cache_optimizer: CacheOptimizer::new(config.cache_size),
            windows_optimizer: WindowsIndexOptimizer::new(),
            performance_tracker: OptimizationPerformanceTracker::new(),
            config,
        }
    }

    /// 执行优化的索引操作
    pub fn execute_optimized_index(
        &mut self,
        strategy: AccessStrategy,
        indices: &[Vec<usize>],
        data: &[u8],
        shape: &[usize],
        itemsize: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        let start_time = Instant::now();

        // 根据策略选择优化技术
        let result = match strategy {
            AccessStrategy::DirectMemory => {
                self.direct_memory_optimized(indices, data, shape, itemsize)
            }
            AccessStrategy::BlockCopy => self.block_copy_optimized(indices, data, shape, itemsize),
            AccessStrategy::VectorizedGather => {
                self.vectorized_gather_optimized(indices, data, shape, itemsize)
            }
            AccessStrategy::ParallelPointAccess => {
                self.parallel_point_access_optimized(indices, data, shape, itemsize)
            }
            AccessStrategy::PrefetchOptimized => {
                self.prefetch_optimized_access(indices, data, shape, itemsize)
            }
            AccessStrategy::ZeroCopy => self.zero_copy_optimized(indices, data, shape, itemsize),
            AccessStrategy::Adaptive => self.adaptive_optimized(indices, data, shape, itemsize),
        };

        let latency_ns = start_time.elapsed().as_nanos() as u64;
        self.performance_tracker.record_optimization_performance(
            strategy,
            latency_ns,
            result.is_ok(),
        );

        result
    }

    /// 直接内存访问优化
    fn direct_memory_optimized(
        &mut self,
        indices: &[Vec<usize>],
        data: &[u8],
        shape: &[usize],
        itemsize: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        if indices.is_empty() {
            return Ok(Vec::new());
        }

        let row_size = shape[1..].iter().product::<usize>() * itemsize;
        let first_dim = &indices[0];

        // Windows特定优化：使用对齐的内存访问
        if cfg!(target_os = "windows") {
            self.windows_optimizer
                .direct_memory_access_windows_safe(first_dim, data, row_size)
        } else {
            // 标准直接内存访问
            self.standard_direct_memory_access(first_dim, data, row_size)
        }
    }

    /// 块复制优化
    fn block_copy_optimized(
        &mut self,
        indices: &[Vec<usize>],
        data: &[u8],
        shape: &[usize],
        itemsize: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        if indices.is_empty() {
            return Ok(Vec::new());
        }

        let row_size = shape[1..].iter().product::<usize>() * itemsize;
        let first_dim = &indices[0];

        // 检查是否可以使用连续块复制
        if self.is_continuous_block(first_dim) {
            self.continuous_block_copy(first_dim, data, row_size)
        } else {
            // 分组块复制
            self.grouped_block_copy(first_dim, data, row_size)
        }
    }

    /// 向量化聚集优化
    fn vectorized_gather_optimized(
        &mut self,
        indices: &[Vec<usize>],
        data: &[u8],
        shape: &[usize],
        itemsize: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        if indices.is_empty() {
            return Ok(Vec::new());
        }

        let row_size = shape[1..].iter().product::<usize>() * itemsize;
        let first_dim = &indices[0];

        if self.config.enable_simd {
            // SIMD优化的向量化聚集
            self.simd_vectorized_gather(first_dim, data, row_size)
        } else {
            // 标准向量化聚集
            self.standard_vectorized_gather(first_dim, data, row_size)
        }
    }

    /// 并行点访问优化
    fn parallel_point_access_optimized(
        &mut self,
        indices: &[Vec<usize>],
        data: &[u8],
        shape: &[usize],
        itemsize: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        if indices.is_empty() {
            return Ok(Vec::new());
        }

        let row_size = shape[1..].iter().product::<usize>() * itemsize;
        let first_dim = &indices[0];

        if self.config.enable_parallel && first_dim.len() > 100 {
            // 大规模并行访问
            self.massive_parallel_access(first_dim, data, row_size)
        } else {
            // 小规模并行访问
            self.small_scale_parallel_access(first_dim, data, row_size)
        }
    }

    /// 预取优化访问
    fn prefetch_optimized_access(
        &mut self,
        indices: &[Vec<usize>],
        data: &[u8],
        shape: &[usize],
        itemsize: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        if indices.is_empty() {
            return Ok(Vec::new());
        }

        let row_size = shape[1..].iter().product::<usize>() * itemsize;
        let first_dim = &indices[0];

        // 智能预取策略
        self.intelligent_prefetch_access(first_dim, data, row_size)
    }

    /// 零拷贝优化
    fn zero_copy_optimized(
        &mut self,
        indices: &[Vec<usize>],
        data: &[u8],
        shape: &[usize],
        itemsize: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        if indices.is_empty() {
            return Ok(Vec::new());
        }

        let row_size = shape[1..].iter().product::<usize>() * itemsize;
        let first_dim = &indices[0];

        // 尝试零拷贝访问
        if self.can_use_zero_copy(first_dim, row_size) {
            self.zero_copy_access(first_dim, data, row_size)
        } else {
            // 回退到直接内存访问
            self.direct_memory_optimized(indices, data, shape, itemsize)
        }
    }

    /// 自适应优化
    fn adaptive_optimized(
        &mut self,
        indices: &[Vec<usize>],
        data: &[u8],
        shape: &[usize],
        itemsize: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        if indices.is_empty() {
            return Ok(Vec::new());
        }

        let first_dim = &indices[0];
        let data_size = first_dim.len() * shape[1..].iter().product::<usize>() * itemsize;

        // 根据数据大小和特征自适应选择策略
        if data_size < 1024 {
            self.direct_memory_optimized(indices, data, shape, itemsize)
        } else if data_size < 1024 * 1024 {
            self.vectorized_gather_optimized(indices, data, shape, itemsize)
        } else {
            self.parallel_point_access_optimized(indices, data, shape, itemsize)
        }
    }

    // ===== 具体优化实现 =====

    /// 标准直接内存访问
    fn standard_direct_memory_access(
        &self,
        indices: &[usize],
        data: &[u8],
        row_size: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        let mut result = Vec::with_capacity(indices.len());

        for &idx in indices {
            let offset = idx * row_size;
            if offset + row_size <= data.len() {
                result.push(data[offset..offset + row_size].to_vec());
            } else {
                return Err(IndexError::IndexOutOfBounds);
            }
        }

        Ok(result)
    }

    /// 连续块复制
    fn continuous_block_copy(
        &self,
        indices: &[usize],
        data: &[u8],
        row_size: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        if indices.is_empty() {
            return Ok(Vec::new());
        }

        let start_offset = indices[0] * row_size;
        let total_size = indices.len() * row_size;

        if start_offset + total_size <= data.len() {
            let block_data = &data[start_offset..start_offset + total_size];
            let result = block_data
                .chunks_exact(row_size)
                .map(|chunk| chunk.to_vec())
                .collect();
            Ok(result)
        } else {
            Err(IndexError::IndexOutOfBounds)
        }
    }

    /// 分组块复制
    fn grouped_block_copy(
        &self,
        indices: &[usize],
        data: &[u8],
        row_size: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        // 将索引分组为连续块
        let groups = self.group_continuous_indices(indices);
        let mut result = Vec::with_capacity(indices.len());

        for group in groups {
            if group.len() == 1 {
                // 单个索引，直接访问
                let offset = group[0] * row_size;
                if offset + row_size <= data.len() {
                    result.push(data[offset..offset + row_size].to_vec());
                }
            } else {
                // 连续块，使用块复制
                let start_offset = group[0] * row_size;
                let total_size = group.len() * row_size;
                if start_offset + total_size <= data.len() {
                    let block_data = &data[start_offset..start_offset + total_size];
                    result.extend(
                        block_data
                            .chunks_exact(row_size)
                            .map(|chunk| chunk.to_vec()),
                    );
                }
            }
        }

        Ok(result)
    }

    /// SIMD向量化聚集
    fn simd_vectorized_gather(
        &mut self,
        indices: &[usize],
        data: &[u8],
        row_size: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        // 尝试使用SIMD处理器
        match self.simd_processor.gather_rows(indices, data, row_size) {
            Ok(result) => Ok(result),
            Err(_) => {
                // SIMD失败，回退到标准实现
                self.standard_vectorized_gather(indices, data, row_size)
            }
        }
    }

    /// 标准向量化聚集
    fn standard_vectorized_gather(
        &self,
        indices: &[usize],
        data: &[u8],
        row_size: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        if self.config.enable_parallel && indices.len() > 50 {
            // 并行向量化聚集
            let result: Result<Vec<_>, _> = indices
                .par_iter()
                .map(|&idx| {
                    let offset = idx * row_size;
                    if offset + row_size <= data.len() {
                        Ok(data[offset..offset + row_size].to_vec())
                    } else {
                        Err(IndexError::IndexOutOfBounds)
                    }
                })
                .collect();
            result
        } else {
            // 串行向量化聚集
            self.standard_direct_memory_access(indices, data, row_size)
        }
    }

    /// 大规模并行访问
    fn massive_parallel_access(
        &self,
        indices: &[usize],
        data: &[u8],
        row_size: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        let chunk_size = (indices.len() / self.config.thread_pool_size).max(1);

        let result: Result<Vec<_>, _> = indices
            .par_chunks(chunk_size)
            .map(|chunk| {
                let mut chunk_result = Vec::with_capacity(chunk.len());
                for &idx in chunk {
                    let offset = idx * row_size;
                    if offset + row_size <= data.len() {
                        chunk_result.push(data[offset..offset + row_size].to_vec());
                    } else {
                        return Err(IndexError::IndexOutOfBounds);
                    }
                }
                Ok(chunk_result)
            })
            .collect::<Result<Vec<_>, _>>();

        match result {
            Ok(chunks) => Ok(chunks.into_iter().flatten().collect()),
            Err(e) => Err(e),
        }
    }

    /// 小规模并行访问
    fn small_scale_parallel_access(
        &self,
        indices: &[usize],
        data: &[u8],
        row_size: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        if indices.len() > 10 {
            // 使用rayon的并行迭代器
            let result: Result<Vec<_>, _> = indices
                .par_iter()
                .map(|&idx| {
                    let offset = idx * row_size;
                    if offset + row_size <= data.len() {
                        Ok(data[offset..offset + row_size].to_vec())
                    } else {
                        Err(IndexError::IndexOutOfBounds)
                    }
                })
                .collect();
            result
        } else {
            // 太少的数据，使用串行处理
            self.standard_direct_memory_access(indices, data, row_size)
        }
    }

    /// 智能预取访问
    fn intelligent_prefetch_access(
        &mut self,
        indices: &[usize],
        data: &[u8],
        row_size: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        // 分析访问模式并预取
        let prefetch_groups = self.analyze_and_group_for_prefetch(indices);
        let mut result = Vec::with_capacity(indices.len());

        for group in prefetch_groups {
            // 预取整个组的数据
            self.prefetch_memory_group(&group, data, row_size);

            // 访问预取的数据
            for &idx in &group {
                let offset = idx * row_size;
                if offset + row_size <= data.len() {
                    result.push(data[offset..offset + row_size].to_vec());
                } else {
                    return Err(IndexError::IndexOutOfBounds);
                }
            }
        }

        Ok(result)
    }

    /// 零拷贝访问
    fn zero_copy_access(
        &self,
        indices: &[usize],
        data: &[u8],
        row_size: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        // 注意：这里仍然需要复制数据，因为返回类型是Vec<Vec<u8>>
        // 真正的零拷贝需要返回引用或视图类型
        self.standard_direct_memory_access(indices, data, row_size)
    }

    // ===== 辅助方法 =====

    /// 检查是否为连续块
    fn is_continuous_block(&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
    }

    /// 将索引分组为连续块
    fn group_continuous_indices(&self, indices: &[usize]) -> Vec<Vec<usize>> {
        if indices.is_empty() {
            return Vec::new();
        }

        let mut groups = Vec::new();
        let mut current_group = vec![indices[0]];

        for i in 1..indices.len() {
            if indices[i] == indices[i - 1] + 1 {
                current_group.push(indices[i]);
            } else {
                groups.push(current_group);
                current_group = vec![indices[i]];
            }
        }
        groups.push(current_group);

        groups
    }

    /// 分析并分组以便预取
    fn analyze_and_group_for_prefetch(&self, indices: &[usize]) -> Vec<Vec<usize>> {
        const PREFETCH_GROUP_SIZE: usize = 8; // 每组预取8个元素

        indices
            .chunks(PREFETCH_GROUP_SIZE)
            .map(|chunk| chunk.to_vec())
            .collect()
    }

    /// 预取内存组
    fn prefetch_memory_group(&self, group: &[usize], data: &[u8], row_size: usize) {
        // 在支持的平台上使用内存预取指令
        #[cfg(target_arch = "x86_64")]
        {
            for &idx in group {
                let offset = idx * row_size;
                if offset < data.len() {
                    unsafe {
                        // 预取数据到L1缓存
                        _mm_prefetch(data.as_ptr().add(offset) as *const i8, _MM_HINT_T0);
                    }
                }
            }
        }
    }

    /// 检查是否可以使用零拷贝
    fn can_use_zero_copy(&self, indices: &[usize], row_size: usize) -> bool {
        // 简化检查：如果是小数据量且连续，可以考虑零拷贝
        indices.len() < 10 && self.is_continuous_block(indices) && row_size < 1024
    }

    /// 获取性能报告
    pub fn get_optimization_report(&self) -> OptimizationReport {
        self.performance_tracker.get_report()
    }
}

/// 缓存优化器
struct CacheOptimizer {
    cache_size: usize,
    hit_count: u64,
    miss_count: u64,
}

impl CacheOptimizer {
    fn new(cache_size: usize) -> Self {
        Self {
            cache_size,
            hit_count: 0,
            miss_count: 0,
        }
    }
}

/// Windows索引优化器
struct WindowsIndexOptimizer {
    page_size: usize,
}

impl WindowsIndexOptimizer {
    fn new() -> Self {
        Self {
            page_size: 4096, // 4KB页面大小
        }
    }

    /// Windows安全的直接内存访问
    fn direct_memory_access_windows_safe(
        &self,
        indices: &[usize],
        data: &[u8],
        row_size: usize,
    ) -> Result<Vec<Vec<u8>>, IndexError> {
        let mut result = Vec::with_capacity(indices.len());

        for &idx in indices {
            let offset = idx * row_size;

            // Windows特定：检查页面边界
            if self.check_page_boundary_safe(offset, row_size, data.len()) {
                if offset + row_size <= data.len() {
                    result.push(data[offset..offset + row_size].to_vec());
                } else {
                    return Err(IndexError::IndexOutOfBounds);
                }
            } else {
                // 使用安全的跨页面访问
                let safe_data = self.safe_cross_page_access(offset, row_size, data)?;
                result.push(safe_data);
            }
        }

        Ok(result)
    }

    /// 检查页面边界安全性
    fn check_page_boundary_safe(&self, offset: usize, size: usize, data_len: usize) -> bool {
        let start_page = offset / self.page_size;
        let end_page = (offset + size - 1) / self.page_size;

        // 确保不跨页面且在数据范围内
        start_page == end_page && offset + size <= data_len
    }

    /// 安全的跨页面访问
    fn safe_cross_page_access(
        &self,
        offset: usize,
        size: usize,
        data: &[u8],
    ) -> Result<Vec<u8>, IndexError> {
        if offset + size > data.len() {
            return Err(IndexError::IndexOutOfBounds);
        }

        // 分块读取，避免跨页面问题
        let mut result = Vec::with_capacity(size);
        let mut current_offset = offset;
        let mut remaining = size;

        while remaining > 0 {
            let current_page = current_offset / self.page_size;
            let page_boundary = (current_page + 1) * self.page_size;
            let chunk_size = remaining.min(page_boundary - current_offset);

            if current_offset + chunk_size <= data.len() {
                result.extend_from_slice(&data[current_offset..current_offset + chunk_size]);
            } else {
                return Err(IndexError::MemoryError);
            }

            current_offset += chunk_size;
            remaining -= chunk_size;
        }

        Ok(result)
    }
}

/// 优化性能跟踪器
struct OptimizationPerformanceTracker {
    strategy_stats: std::collections::HashMap<AccessStrategy, OptimizationStats>,
}

#[derive(Debug, Clone)]
struct OptimizationStats {
    total_operations: u64,
    success_count: u64,
    total_latency_ns: u64,
    last_used: Instant,
}

impl OptimizationPerformanceTracker {
    fn new() -> Self {
        Self {
            strategy_stats: std::collections::HashMap::new(),
        }
    }

    fn record_optimization_performance(
        &mut self,
        strategy: AccessStrategy,
        latency_ns: u64,
        success: bool,
    ) {
        let stats = self
            .strategy_stats
            .entry(strategy)
            .or_insert_with(|| OptimizationStats {
                total_operations: 0,
                success_count: 0,
                total_latency_ns: 0,
                last_used: Instant::now(),
            });

        stats.total_operations += 1;
        stats.total_latency_ns += latency_ns;
        stats.last_used = Instant::now();

        if success {
            stats.success_count += 1;
        }
    }

    fn get_report(&self) -> OptimizationReport {
        OptimizationReport {
            strategy_stats: self.strategy_stats.clone(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct OptimizationReport {
    pub strategy_stats: std::collections::HashMap<AccessStrategy, OptimizationStats>,
}
