use crate::distributed::ProcessGroup;
use anyhow::{anyhow, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};
use trustformers_core::tensor::Tensor;

/// Sequence Parallelism Configuration
///
/// Sequence parallelism distributes long sequences across multiple devices,
/// enabling the processing of sequences that are too long to fit on a single device.
/// This is particularly useful for very long document processing, DNA sequences,
/// or other sequential data that exceeds memory limits.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SequenceParallelismConfig {
    /// Number of devices for sequence parallelism
    pub sequence_parallel_size: usize,
    /// Maximum sequence length per device
    pub max_sequence_length_per_device: usize,
    /// Overlap size between adjacent sequence chunks
    pub overlap_size: usize,
    /// Whether to use attention communication optimization
    pub attention_communication_opt: bool,
    /// Communication pattern for sequence parallelism
    pub communication_pattern: SequenceCommunicationPattern,
    /// Sequence splitting strategy
    pub splitting_strategy: SequenceSplittingStrategy,
    /// Whether to use gradient synchronization across sequence chunks
    pub sync_gradients: bool,
    /// Memory optimization for long sequences
    pub memory_optimization: SequenceMemoryOptimization,
    /// Whether to use checkpointing for sequence chunks
    pub use_checkpointing: bool,
}

impl Default for SequenceParallelismConfig {
    fn default() -> Self {
        Self {
            sequence_parallel_size: 1,
            max_sequence_length_per_device: 2048,
            overlap_size: 128,
            attention_communication_opt: true,
            communication_pattern: SequenceCommunicationPattern::RingAllReduce,
            splitting_strategy: SequenceSplittingStrategy::EqualChunks,
            sync_gradients: true,
            memory_optimization: SequenceMemoryOptimization::Medium,
            use_checkpointing: true,
        }
    }
}

/// Communication patterns for sequence parallelism
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SequenceCommunicationPattern {
    /// Ring-based all-reduce for efficient communication
    RingAllReduce,
    /// Tree-based reduction
    TreeReduce,
    /// Point-to-point communication between adjacent chunks
    PointToPoint,
    /// All-to-all communication for global attention
    AllToAll,
    /// Hierarchical communication pattern
    Hierarchical,
}

/// Sequence splitting strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SequenceSplittingStrategy {
    /// Split into equal-sized chunks
    EqualChunks,
    /// Split based on attention patterns
    AttentionBased,
    /// Split at sentence/paragraph boundaries
    SemanticBoundaries,
    /// Dynamic splitting based on memory usage
    Dynamic,
    /// Split based on content complexity
    ComplexityBased,
}

/// Memory optimization strategies for sequence parallelism
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SequenceMemoryOptimization {
    None,
    Low,
    Medium,
    High,
    Extreme,
}

/// Sequence chunk information
#[derive(Debug, Clone)]
pub struct SequenceChunk {
    /// Chunk ID
    pub chunk_id: usize,
    /// Device rank where this chunk is processed
    pub device_rank: usize,
    /// Start position in the original sequence
    pub start_position: usize,
    /// End position in the original sequence (exclusive)
    pub end_position: usize,
    /// Effective length (excluding overlap)
    pub effective_length: usize,
    /// Overlap with previous chunk
    pub prev_overlap: usize,
    /// Overlap with next chunk
    pub next_overlap: usize,
    /// Whether this chunk needs attention communication
    pub needs_attention_comm: bool,
}

/// Attention communication info for cross-chunk attention
#[derive(Debug, Clone)]
pub struct AttentionCommunication {
    /// Source chunk ID
    pub source_chunk: usize,
    /// Target chunk ID
    pub target_chunk: usize,
    /// Attention scores that need to be communicated
    pub attention_positions: Vec<(usize, usize)>, // (query_pos, key_pos)
    /// Communication volume in bytes
    pub communication_size: usize,
}

/// Sequence parallelism coordinator
pub struct SequenceParallelism {
    config: SequenceParallelismConfig,
    global_rank: usize,
    #[allow(dead_code)]
    world_size: usize,

    // Sequence chunk assignments
    sequence_chunks: Vec<SequenceChunk>,
    local_chunks: Vec<usize>, // Chunk IDs local to this device

    // Process groups for sequence parallelism
    sequence_group: Arc<dyn ProcessGroup>,

    // Attention communication management
    attention_comm_manager: Arc<RwLock<AttentionCommManager>>,

    // Communication statistics
    communication_stats: Arc<Mutex<SequenceCommunicationStats>>,

    // Memory management for sequence chunks
    memory_manager: Arc<Mutex<SequenceMemoryManager>>,
}

/// Attention communication manager
#[derive(Debug, Default)]
struct AttentionCommManager {
    #[allow(dead_code)]
    communication_plan: Vec<AttentionCommunication>,
    attention_cache: HashMap<(usize, usize), Tensor>, // (chunk_pair, cached_attention)
    cache_hits: u64,
    cache_misses: u64,
}

/// Communication statistics for sequence parallelism
#[derive(Debug, Default)]
#[allow(dead_code)]
struct SequenceCommunicationStats {
    total_communication_time: Duration,
    attention_communication_time: Duration,
    gradient_sync_time: Duration,
    #[allow(dead_code)]
    total_bytes_communicated: u64,
    attention_cache_hit_rate: f32,
    communication_efficiency: f32,
}

/// Memory management for sequence chunks
#[derive(Debug, Default)]
#[allow(dead_code)]
struct SequenceMemoryManager {
    #[allow(dead_code)]
    chunk_activations: HashMap<usize, Vec<Tensor>>,
    chunk_gradients: HashMap<usize, Vec<Tensor>>,
    checkpointed_chunks: HashMap<usize, Vec<Tensor>>,
    peak_memory_per_chunk: HashMap<usize, u64>,
    current_memory_usage: u64,
    memory_pressure: f32,
}

/// Comprehensive attention pattern analysis for intelligent sequence splitting
#[derive(Debug, Clone)]
pub struct AttentionPatternAnalysis {
    /// Total sequence length being analyzed
    pub total_length: usize,
    /// Positions identified as natural attention boundaries
    pub attention_boundaries: Vec<usize>,
    /// Attention intensity scores for different sequence regions
    pub attention_intensities: Vec<f32>,
    /// Cross-chunk attention strengths between adjacent chunks
    pub cross_chunk_attention: HashMap<(usize, usize), f32>,
    /// Token importance scores across the sequence
    pub token_importance: Vec<f32>,
    /// Attention head pattern analysis
    pub attention_head_patterns: Vec<AttentionHeadPattern>,
}

/// Attention pattern types identified by different attention heads
#[derive(Debug, Clone, PartialEq)]
pub enum AttentionPatternType {
    /// Local attention patterns (within small windows)
    Local,
    /// Global attention patterns (across entire sequence)
    Global,
    /// Syntactic attention patterns (grammatical structures)
    Syntactic,
    /// Semantic attention patterns (meaning-based dependencies)
    Semantic,
}

/// Analysis of individual attention head patterns
#[derive(Debug, Clone)]
pub struct AttentionHeadPattern {
    /// Attention head identifier
    pub head_id: usize,
    /// Type of attention pattern this head exhibits
    pub pattern_type: AttentionPatternType,
    /// Typical attention span for this head
    pub attention_span: usize,
    /// Strength of the attention pattern (0.0 to 1.0)
    pub pattern_strength: f32,
    /// Communication requirement for distributed processing
    pub communication_requirement: f32,
}

impl SequenceParallelism {
    /// Create a new sequence parallelism coordinator
    pub fn new(
        config: SequenceParallelismConfig,
        global_rank: usize,
        world_size: usize,
        sequence_group: Arc<dyn ProcessGroup>,
    ) -> Result<Self> {
        // Validate configuration
        if config.sequence_parallel_size > world_size {
            return Err(anyhow!(
                "Sequence parallel size ({}) cannot exceed world size ({})",
                config.sequence_parallel_size,
                world_size
            ));
        }

        if config.overlap_size >= config.max_sequence_length_per_device {
            return Err(anyhow!(
                "Overlap size ({}) must be smaller than max sequence length per device ({})",
                config.overlap_size,
                config.max_sequence_length_per_device
            ));
        }

        Ok(Self {
            config,
            global_rank,
            world_size,
            sequence_chunks: Vec::new(),
            local_chunks: Vec::new(),
            sequence_group,
            attention_comm_manager: Arc::new(RwLock::new(AttentionCommManager::default())),
            communication_stats: Arc::new(Mutex::new(SequenceCommunicationStats::default())),
            memory_manager: Arc::new(Mutex::new(SequenceMemoryManager::default())),
        })
    }

    /// Split a sequence across multiple devices
    pub fn split_sequence(&mut self, total_sequence_length: usize) -> Result<Vec<SequenceChunk>> {
        let chunks = match self.config.splitting_strategy {
            SequenceSplittingStrategy::EqualChunks => {
                self.split_equal_chunks(total_sequence_length)?
            },
            SequenceSplittingStrategy::AttentionBased => {
                self.split_attention_based(total_sequence_length)?
            },
            SequenceSplittingStrategy::SemanticBoundaries => {
                self.split_semantic_boundaries(total_sequence_length)?
            },
            SequenceSplittingStrategy::Dynamic => self.split_dynamic(total_sequence_length)?,
            SequenceSplittingStrategy::ComplexityBased => {
                self.split_complexity_based(total_sequence_length)?
            },
        };

        // Update local chunk assignments
        self.sequence_chunks = chunks.clone();
        self.local_chunks = chunks
            .iter()
            .enumerate()
            .filter(|(_, chunk)| chunk.device_rank == self.global_rank)
            .map(|(i, _)| i)
            .collect();

        Ok(chunks)
    }

    /// Split sequence into equal chunks
    fn split_equal_chunks(&self, total_length: usize) -> Result<Vec<SequenceChunk>> {
        let chunk_size = self.config.max_sequence_length_per_device;
        let overlap = self.config.overlap_size;
        let num_devices = self.config.sequence_parallel_size;

        let mut chunks = Vec::new();
        let mut current_pos = 0;
        let mut chunk_id = 0;

        while current_pos < total_length {
            let end_pos = std::cmp::min(current_pos + chunk_size, total_length);
            let device_rank = chunk_id % num_devices;

            let prev_overlap = if chunk_id > 0 { overlap } else { 0 };
            let next_overlap = if end_pos < total_length { overlap } else { 0 };

            let chunk = SequenceChunk {
                chunk_id,
                device_rank,
                start_position: current_pos,
                end_position: end_pos,
                effective_length: end_pos - current_pos - prev_overlap,
                prev_overlap,
                next_overlap,
                needs_attention_comm: true,
            };

            chunks.push(chunk);
            current_pos = end_pos - overlap;
            chunk_id += 1;
        }

        Ok(chunks)
    }

    /// Split sequence based on attention patterns using intelligent analysis
    fn split_attention_based(&self, total_length: usize) -> Result<Vec<SequenceChunk>> {
        // Analyze attention patterns to determine optimal split points
        let attention_analysis = self.analyze_attention_patterns(total_length)?;

        // Find optimal split points based on attention boundaries
        let split_points = self.find_optimal_split_points(&attention_analysis, total_length)?;

        // Create chunks based on attention-aware split points
        self.create_attention_aware_chunks(total_length, &split_points)
    }

    /// Analyze attention patterns in the sequence to identify natural boundaries
    fn analyze_attention_patterns(&self, total_length: usize) -> Result<AttentionPatternAnalysis> {
        // In a real implementation, this would:
        // 1. Run a lightweight forward pass to collect attention weights
        // 2. Analyze attention score distributions
        // 3. Identify positions with low cross-attention (natural break points)
        // 4. Consider attention head patterns and token importance

        let mut attention_boundaries = Vec::new();
        let mut attention_intensities = Vec::new();
        let mut cross_chunk_attention = HashMap::new();

        // Simulate attention pattern analysis
        let window_size = 512; // Analysis window size
        let num_windows = (total_length + window_size - 1) / window_size;

        for window_idx in 0..num_windows {
            let start_pos = window_idx * window_size;
            let end_pos = (start_pos + window_size).min(total_length);

            // Simulate attention intensity calculation
            let local_attention = self.calculate_local_attention_intensity(start_pos, end_pos)?;
            let cross_window_attention =
                self.calculate_cross_window_attention(window_idx, num_windows)?;

            attention_intensities.push(local_attention);

            // Identify potential boundary points (positions with low attention connectivity)
            if window_idx > 0 && cross_window_attention < 0.3 {
                attention_boundaries.push(start_pos);
            }

            // Store cross-chunk attention information
            if window_idx < num_windows - 1 {
                cross_chunk_attention.insert((window_idx, window_idx + 1), cross_window_attention);
            }
        }

        // Add sequence boundaries
        if !attention_boundaries.contains(&0) {
            attention_boundaries.insert(0, 0);
        }
        if !attention_boundaries.contains(&total_length) {
            attention_boundaries.push(total_length);
        }

        attention_boundaries.sort();

        Ok(AttentionPatternAnalysis {
            total_length,
            attention_boundaries,
            attention_intensities,
            cross_chunk_attention,
            token_importance: self.calculate_token_importance(total_length)?,
            attention_head_patterns: self.analyze_attention_head_patterns(total_length)?,
        })
    }

    /// Calculate local attention intensity within a window
    fn calculate_local_attention_intensity(&self, start_pos: usize, end_pos: usize) -> Result<f32> {
        let window_length = end_pos - start_pos;

        // Simulate attention intensity based on position and content patterns
        let position_factor = (start_pos as f32
            / self.config.max_sequence_length_per_device as f32)
            .sin()
            .abs();
        let length_factor = (window_length as f32 / 512.0).min(1.0);
        let content_complexity = fastrand::f32() * 0.3 + 0.5; // Simulate content complexity

        Ok(position_factor * length_factor * content_complexity)
    }

    /// Calculate cross-window attention connectivity
    fn calculate_cross_window_attention(
        &self,
        window_idx: usize,
        total_windows: usize,
    ) -> Result<f32> {
        // Simulate cross-window attention based on distance and content similarity
        let distance_decay = if window_idx == 0 || window_idx == total_windows - 1 {
            0.8 // Boundary windows have lower cross-attention
        } else {
            1.0 - (window_idx as f32 / total_windows as f32).abs()
        };

        let content_similarity = 0.6 + fastrand::f32() * 0.3; // Simulate content similarity
        let attention_spread = 0.4 + fastrand::f32() * 0.4; // Attention spread factor

        Ok(distance_decay * content_similarity * attention_spread)
    }

    /// Calculate token importance scores across the sequence
    fn calculate_token_importance(&self, total_length: usize) -> Result<Vec<f32>> {
        let mut importance_scores = Vec::with_capacity(total_length);

        for pos in 0..total_length {
            // Simulate token importance based on position and predicted content significance
            let position_bias = if pos < total_length / 4 || pos > 3 * total_length / 4 {
                1.2 // Higher importance for beginning and end tokens
            } else {
                1.0
            };

            let content_importance = 0.3 + fastrand::f32() * 0.7; // Simulate content importance
            let attention_centrality = self.calculate_attention_centrality(pos, total_length)?;

            importance_scores.push(position_bias * content_importance * attention_centrality);
        }

        Ok(importance_scores)
    }

    /// Calculate attention centrality for a token position
    fn calculate_attention_centrality(&self, pos: usize, total_length: usize) -> Result<f32> {
        // Simulate how much attention this position receives/gives
        let relative_pos = pos as f32 / total_length as f32;

        // Tokens in the middle tend to have higher centrality
        let position_centrality = 1.0 - (2.0 * relative_pos - 1.0).abs();

        // Add some randomness to simulate content-dependent centrality
        let content_centrality = 0.5 + fastrand::f32() * 0.5;

        Ok(position_centrality * content_centrality)
    }

    /// Analyze attention head patterns to understand different types of attention
    fn analyze_attention_head_patterns(
        &self,
        total_length: usize,
    ) -> Result<Vec<AttentionHeadPattern>> {
        let num_heads = 12; // Typical number of attention heads
        let mut head_patterns = Vec::with_capacity(num_heads);

        for head_idx in 0..num_heads {
            let pattern_type = match head_idx % 4 {
                0 => AttentionPatternType::Local,     // Local attention patterns
                1 => AttentionPatternType::Global,    // Global attention patterns
                2 => AttentionPatternType::Syntactic, // Syntactic attention patterns
                3 => AttentionPatternType::Semantic,  // Semantic attention patterns
                _ => AttentionPatternType::Local,
            };

            let attention_span = match pattern_type {
                AttentionPatternType::Local => total_length / 8,
                AttentionPatternType::Global => total_length,
                AttentionPatternType::Syntactic => total_length / 4,
                AttentionPatternType::Semantic => total_length / 2,
            };

            let pattern_strength = 0.4 + fastrand::f32() * 0.6;
            let communication_requirement =
                self.calculate_communication_requirement(&pattern_type, total_length)?;

            head_patterns.push(AttentionHeadPattern {
                head_id: head_idx,
                pattern_type,
                attention_span,
                pattern_strength,
                communication_requirement,
            });
        }

        Ok(head_patterns)
    }

    /// Calculate communication requirement for an attention pattern type
    fn calculate_communication_requirement(
        &self,
        pattern_type: &AttentionPatternType,
        _total_length: usize,
    ) -> Result<f32> {
        match pattern_type {
            AttentionPatternType::Local => Ok(0.1), // Low communication for local patterns
            AttentionPatternType::Global => Ok(0.9), // High communication for global patterns
            AttentionPatternType::Syntactic => Ok(0.4), // Medium communication for syntactic patterns
            AttentionPatternType::Semantic => Ok(0.6), // Medium-high communication for semantic patterns
        }
    }

    /// Find optimal split points based on attention analysis
    fn find_optimal_split_points(
        &self,
        analysis: &AttentionPatternAnalysis,
        total_length: usize,
    ) -> Result<Vec<usize>> {
        let target_chunks = self.config.sequence_parallel_size;
        let min_chunk_size = self.config.max_sequence_length_per_device / 2;
        let max_chunk_size = self.config.max_sequence_length_per_device;

        if target_chunks == 1 {
            return Ok(vec![0, total_length]);
        }

        // Use dynamic programming to find optimal split points
        let mut split_points = vec![0];
        let mut remaining_length = total_length;
        let mut remaining_chunks = target_chunks;

        for chunk_idx in 0..target_chunks - 1 {
            let avg_remaining_chunk_size = remaining_length / remaining_chunks;
            let target_split_pos = split_points[chunk_idx] + avg_remaining_chunk_size;

            // Find the best boundary near the target position
            let best_boundary = self.find_best_boundary_near_position(
                analysis,
                target_split_pos,
                min_chunk_size,
                max_chunk_size,
            )?;

            split_points.push(best_boundary);
            remaining_length = total_length - best_boundary;
            remaining_chunks -= 1;
        }

        split_points.push(total_length);
        Ok(split_points)
    }

    /// Find the best attention boundary near a target position
    fn find_best_boundary_near_position(
        &self,
        analysis: &AttentionPatternAnalysis,
        target_pos: usize,
        min_chunk_size: usize,
        _max_chunk_size: usize,
    ) -> Result<usize> {
        let search_radius = min_chunk_size / 4;
        let start_search = target_pos.saturating_sub(search_radius);
        let end_search = (target_pos + search_radius).min(analysis.total_length);

        let mut best_pos = target_pos;
        let mut best_score = f32::NEG_INFINITY;

        // Evaluate each potential boundary position
        for candidate_pos in (start_search..=end_search).step_by(16) {
            if candidate_pos < min_chunk_size
                || candidate_pos > analysis.total_length - min_chunk_size
            {
                continue;
            }

            let boundary_score =
                self.calculate_boundary_score(analysis, candidate_pos, target_pos)?;

            if boundary_score > best_score {
                best_score = boundary_score;
                best_pos = candidate_pos;
            }
        }

        Ok(best_pos)
    }

    /// Calculate boundary score for a potential split position
    fn calculate_boundary_score(
        &self,
        analysis: &AttentionPatternAnalysis,
        pos: usize,
        target_pos: usize,
    ) -> Result<f32> {
        // Distance penalty (prefer positions close to target)
        let distance_penalty =
            1.0 - (pos as f32 - target_pos as f32).abs() / (target_pos as f32 + 1.0);

        // Attention boundary score (prefer positions with low cross-attention)
        let attention_score = if analysis.attention_boundaries.contains(&pos) {
            1.0
        } else {
            // Calculate interpolated attention score
            0.5 + 0.3 * (1.0 - self.get_cross_attention_at_position(analysis, pos)?)
        };

        // Token importance penalty (avoid splitting at important tokens)
        let importance_penalty = if pos < analysis.token_importance.len() {
            1.0 - analysis.token_importance[pos] * 0.3
        } else {
            1.0
        };

        // Communication cost consideration
        let communication_score =
            1.0 - self.estimate_communication_cost_at_boundary(analysis, pos)?;

        Ok(distance_penalty * 0.3
            + attention_score * 0.4
            + importance_penalty * 0.15
            + communication_score * 0.15)
    }

    /// Get cross-attention strength at a specific position
    fn get_cross_attention_at_position(
        &self,
        analysis: &AttentionPatternAnalysis,
        pos: usize,
    ) -> Result<f32> {
        let window_size = 512;
        let window_idx = pos / window_size;
        let next_window_idx = window_idx + 1;

        // Get cross-chunk attention for this window boundary
        if let Some(&cross_attention) =
            analysis.cross_chunk_attention.get(&(window_idx, next_window_idx))
        {
            Ok(cross_attention)
        } else {
            Ok(0.5) // Default moderate cross-attention
        }
    }

    /// Estimate communication cost if boundary is placed at this position
    fn estimate_communication_cost_at_boundary(
        &self,
        analysis: &AttentionPatternAnalysis,
        pos: usize,
    ) -> Result<f32> {
        let mut total_cost = 0.0;

        // Calculate cost based on attention head patterns
        for head_pattern in &analysis.attention_head_patterns {
            if head_pattern.attention_span > pos && pos > 0 {
                // This boundary would require communication for this attention head
                total_cost +=
                    head_pattern.communication_requirement * head_pattern.pattern_strength;
            }
        }

        // Normalize by number of heads
        Ok(total_cost / analysis.attention_head_patterns.len() as f32)
    }

    /// Create attention-aware chunks based on split points
    fn create_attention_aware_chunks(
        &self,
        _total_length: usize,
        split_points: &[usize],
    ) -> Result<Vec<SequenceChunk>> {
        let mut chunks = Vec::new();

        for i in 0..split_points.len() - 1 {
            let start_pos = split_points[i];
            let end_pos = split_points[i + 1];
            let chunk_length = end_pos - start_pos;

            // Calculate overlaps for attention communication
            let prev_overlap =
                if i > 0 { self.config.overlap_size.min(chunk_length / 4) } else { 0 };

            let next_overlap = if i < split_points.len() - 2 {
                self.config.overlap_size.min(chunk_length / 4)
            } else {
                0
            };

            // Determine if this chunk needs attention communication
            let needs_attention_comm =
                self.chunk_needs_attention_communication(i, split_points.len() - 1)?;

            chunks.push(SequenceChunk {
                chunk_id: i,
                device_rank: i % self.config.sequence_parallel_size,
                start_position: start_pos,
                end_position: end_pos,
                effective_length: chunk_length - prev_overlap - next_overlap,
                prev_overlap,
                next_overlap,
                needs_attention_comm,
            });
        }

        Ok(chunks)
    }

    /// Determine if a chunk needs attention communication with other chunks
    fn chunk_needs_attention_communication(
        &self,
        chunk_idx: usize,
        total_chunks: usize,
    ) -> Result<bool> {
        if total_chunks == 1 {
            return Ok(false);
        }

        // Chunks need attention communication if they have cross-chunk dependencies
        let has_prev_dependency = chunk_idx > 0;
        let has_next_dependency = chunk_idx < total_chunks - 1;

        // Enable attention communication optimization if configured
        if self.config.attention_communication_opt {
            Ok(has_prev_dependency || has_next_dependency)
        } else {
            Ok(false)
        }
    }

    /// Split sequence at semantic boundaries (simplified)
    fn split_semantic_boundaries(&self, total_length: usize) -> Result<Vec<SequenceChunk>> {
        // For now, fallback to equal chunks
        // In practice, would use NLP techniques to find sentence/paragraph boundaries
        self.split_equal_chunks(total_length)
    }

    /// Dynamic sequence splitting based on memory usage
    fn split_dynamic(&self, total_length: usize) -> Result<Vec<SequenceChunk>> {
        let memory_manager = self.memory_manager.lock().unwrap();
        let pressure = memory_manager.memory_pressure;

        // Adjust chunk size based on memory pressure
        let base_chunk_size = self.config.max_sequence_length_per_device;
        let adjusted_chunk_size = if pressure > 0.8 {
            base_chunk_size / 2
        } else if pressure > 0.6 {
            (base_chunk_size * 3) / 4
        } else {
            base_chunk_size
        };

        // Create config with adjusted chunk size
        let mut adjusted_config = self.config.clone();
        adjusted_config.max_sequence_length_per_device = adjusted_chunk_size;

        // Use equal chunks with adjusted size
        self.split_equal_chunks(total_length)
    }

    /// Split sequence based on complexity
    fn split_complexity_based(&self, total_length: usize) -> Result<Vec<SequenceChunk>> {
        // For now, fallback to equal chunks
        // In practice, would analyze content complexity to balance computational load
        self.split_equal_chunks(total_length)
    }

    /// Process forward pass for a sequence chunk
    pub fn forward_chunk(
        &self,
        chunk_id: usize,
        input: &Tensor,
        _attention_mask: Option<&Tensor>,
    ) -> Result<Tensor> {
        let start_time = Instant::now();

        if !self.local_chunks.contains(&chunk_id) {
            return Err(anyhow!("Chunk {} is not local to this device", chunk_id));
        }

        let chunk = &self.sequence_chunks[chunk_id];

        // Process the chunk locally
        let output = self.process_local_chunk(input, chunk)?;

        // Handle attention communication if needed
        let final_output = if chunk.needs_attention_comm {
            self.handle_attention_communication(chunk_id, &output)?
        } else {
            output
        };

        // Update statistics
        {
            let mut stats = self.communication_stats.lock().unwrap();
            stats.total_communication_time += start_time.elapsed();
        }

        Ok(final_output)
    }

    /// Process a local chunk
    fn process_local_chunk(&self, input: &Tensor, _chunk: &SequenceChunk) -> Result<Tensor> {
        // Simplified local processing
        // In practice, would apply transformer layers to the chunk
        Ok(input.clone())
    }

    /// Handle attention communication between chunks
    fn handle_attention_communication(
        &self,
        chunk_id: usize,
        chunk_output: &Tensor,
    ) -> Result<Tensor> {
        let start_time = Instant::now();

        match self.config.communication_pattern {
            SequenceCommunicationPattern::RingAllReduce => {
                self.ring_attention_communication(chunk_id, chunk_output)
            },
            SequenceCommunicationPattern::TreeReduce => {
                self.tree_attention_communication(chunk_id, chunk_output)
            },
            SequenceCommunicationPattern::PointToPoint => {
                self.point_to_point_attention(chunk_id, chunk_output)
            },
            SequenceCommunicationPattern::AllToAll => {
                self.all_to_all_attention(chunk_id, chunk_output)
            },
            SequenceCommunicationPattern::Hierarchical => {
                self.hierarchical_attention_communication(chunk_id, chunk_output)
            },
        }
        .map(|result| {
            // Update attention communication statistics
            let mut stats = self.communication_stats.lock().unwrap();
            stats.attention_communication_time += start_time.elapsed();
            result
        })
    }

    /// Ring-based attention communication
    fn ring_attention_communication(
        &self,
        chunk_id: usize,
        chunk_output: &Tensor,
    ) -> Result<Tensor> {
        // Simplified ring communication
        // In practice, would implement efficient ring-based attention sharing

        let chunk = &self.sequence_chunks[chunk_id];
        let num_chunks = self.sequence_chunks.len();

        // Get attention from adjacent chunks
        let combined_attention = chunk_output.clone();

        // Communicate with previous chunk
        if chunk_id > 0 && chunk.prev_overlap > 0 {
            // In practice, would get attention from previous chunk
            let _prev_attention = self.get_cached_attention(chunk_id - 1, chunk_id)?;
        }

        // Communicate with next chunk
        if chunk_id < num_chunks - 1 && chunk.next_overlap > 0 {
            // In practice, would get attention from next chunk
            let _next_attention = self.get_cached_attention(chunk_id + 1, chunk_id)?;
        }

        Ok(combined_attention)
    }

    /// Tree-based attention communication
    fn tree_attention_communication(
        &self,
        _chunk_id: usize,
        chunk_output: &Tensor,
    ) -> Result<Tensor> {
        // Simplified tree communication
        // In practice, would implement tree-based attention aggregation
        Ok(chunk_output.clone())
    }

    /// Point-to-point attention communication
    fn point_to_point_attention(&self, _chunk_id: usize, chunk_output: &Tensor) -> Result<Tensor> {
        // Simplified point-to-point communication
        // In practice, would exchange attention with adjacent chunks only
        Ok(chunk_output.clone())
    }

    /// All-to-all attention communication
    fn all_to_all_attention(&self, _chunk_id: usize, chunk_output: &Tensor) -> Result<Tensor> {
        // Simplified all-to-all communication
        // In practice, would gather attention from all chunks
        Ok(chunk_output.clone())
    }

    /// Hierarchical attention communication
    fn hierarchical_attention_communication(
        &self,
        _chunk_id: usize,
        chunk_output: &Tensor,
    ) -> Result<Tensor> {
        // Simplified hierarchical communication
        // In practice, would use a hierarchy of attention aggregation
        Ok(chunk_output.clone())
    }

    /// Get cached attention between chunks
    fn get_cached_attention(&self, source_chunk: usize, target_chunk: usize) -> Result<Tensor> {
        let mut comm_manager = self.attention_comm_manager.write().unwrap();

        let cache_key = (source_chunk, target_chunk);
        if let Some(cached_attention) = comm_manager.attention_cache.get(&cache_key).cloned() {
            comm_manager.cache_hits += 1;
            Ok(cached_attention)
        } else {
            comm_manager.cache_misses += 1;
            // Create dummy attention tensor
            let attention = Tensor::zeros(&[64, 64])?;
            comm_manager.attention_cache.insert(cache_key, attention.clone());
            Ok(attention)
        }
    }

    /// Synchronize gradients across sequence chunks
    pub fn synchronize_gradients(&self, gradients: &mut HashMap<String, Tensor>) -> Result<()> {
        if !self.config.sync_gradients {
            return Ok(());
        }

        let start_time = Instant::now();

        // Convert gradients to vector for all-reduce
        let mut gradient_tensors: Vec<Tensor> = gradients.values().cloned().collect();

        // Perform all-reduce to synchronize gradients across sequence chunks
        self.sequence_group.all_reduce(&mut gradient_tensors)?;

        // Average the gradients
        let world_size = self.sequence_group.world_size() as f32;
        for tensor in &mut gradient_tensors {
            *tensor = tensor.scalar_mul(1.0 / world_size)?;
        }

        // Update the gradients map
        for (i, (_, gradient)) in gradients.iter_mut().enumerate() {
            if i < gradient_tensors.len() {
                *gradient = gradient_tensors[i].clone();
            }
        }

        // Update statistics
        {
            let mut stats = self.communication_stats.lock().unwrap();
            stats.gradient_sync_time += start_time.elapsed();
        }

        Ok(())
    }

    /// Update memory usage statistics
    pub fn update_memory_usage(&self, chunk_id: usize, memory_usage: u64) -> Result<()> {
        let mut memory_manager = self.memory_manager.lock().unwrap();

        memory_manager.peak_memory_per_chunk.insert(chunk_id, memory_usage);
        memory_manager.current_memory_usage = memory_usage;

        // Calculate memory pressure (simplified)
        let max_memory = 16u64 * 1024 * 1024 * 1024; // 16GB assumed max
        memory_manager.memory_pressure = memory_usage as f32 / max_memory as f32;

        Ok(())
    }

    /// Get sequence parallelism statistics
    pub fn get_statistics(&self) -> SequenceParallelismStats {
        let comm_stats = self.communication_stats.lock().unwrap();
        let comm_manager = self.attention_comm_manager.read().unwrap();
        let memory_manager = self.memory_manager.lock().unwrap();

        let cache_hit_rate = if comm_manager.cache_hits + comm_manager.cache_misses > 0 {
            comm_manager.cache_hits as f32
                / (comm_manager.cache_hits + comm_manager.cache_misses) as f32
        } else {
            0.0
        };

        SequenceParallelismStats {
            total_chunks: self.sequence_chunks.len(),
            local_chunks: self.local_chunks.len(),
            communication_time: comm_stats.total_communication_time,
            attention_communication_time: comm_stats.attention_communication_time,
            gradient_sync_time: comm_stats.gradient_sync_time,
            attention_cache_hit_rate: cache_hit_rate,
            memory_pressure: memory_manager.memory_pressure,
            peak_memory_usage: memory_manager.current_memory_usage,
        }
    }

    /// Get local chunk IDs
    pub fn local_chunks(&self) -> &[usize] {
        &self.local_chunks
    }

    /// Get chunk information
    pub fn get_chunk(&self, chunk_id: usize) -> Option<&SequenceChunk> {
        self.sequence_chunks.get(chunk_id)
    }

    /// Get configuration
    pub fn config(&self) -> &SequenceParallelismConfig {
        &self.config
    }
}

/// Sequence parallelism statistics
#[derive(Debug, Clone)]
pub struct SequenceParallelismStats {
    pub total_chunks: usize,
    pub local_chunks: usize,
    pub communication_time: Duration,
    pub attention_communication_time: Duration,
    pub gradient_sync_time: Duration,
    pub attention_cache_hit_rate: f32,
    pub memory_pressure: f32,
    pub peak_memory_usage: u64,
}

/// Sequence parallelism utilities
pub mod utils {
    use super::*;

    /// Calculate optimal sequence parallelism configuration
    pub fn calculate_optimal_sequence_config(
        total_sequence_length: usize,
        max_memory_per_device: usize,
        memory_per_token: usize,
        world_size: usize,
    ) -> Result<SequenceParallelismConfig> {
        let max_tokens_per_device = max_memory_per_device / memory_per_token;

        if max_tokens_per_device == 0 {
            return Err(anyhow!("Insufficient memory for sequence parallelism"));
        }

        let required_devices =
            (total_sequence_length + max_tokens_per_device - 1) / max_tokens_per_device;
        let sequence_parallel_size = std::cmp::min(required_devices, world_size);

        let tokens_per_device =
            (total_sequence_length + sequence_parallel_size - 1) / sequence_parallel_size;
        let overlap_size = std::cmp::min(128, tokens_per_device / 10); // 10% overlap

        Ok(SequenceParallelismConfig {
            sequence_parallel_size,
            max_sequence_length_per_device: tokens_per_device,
            overlap_size,
            ..Default::default()
        })
    }

    /// Estimate communication cost for sequence parallelism
    pub fn estimate_communication_cost(
        config: &SequenceParallelismConfig,
        hidden_size: usize,
        num_attention_heads: usize,
    ) -> f32 {
        let overlap_tokens = config.overlap_size;
        let communication_per_overlap = overlap_tokens * hidden_size * 4; // 4 bytes per float
        let attention_communication = overlap_tokens * overlap_tokens * num_attention_heads * 4;

        (communication_per_overlap + attention_communication) as f32 / (1024.0 * 1024.0)
        // Convert to MB
    }

    /// Calculate memory savings from sequence parallelism
    pub fn calculate_memory_savings(
        total_sequence_length: usize,
        sequence_parallel_size: usize,
        hidden_size: usize,
    ) -> f32 {
        let tokens_per_device = total_sequence_length / sequence_parallel_size;
        let memory_per_device = tokens_per_device * hidden_size * 4; // 4 bytes per float
        let total_memory_without_sp = total_sequence_length * hidden_size * 4;

        1.0 - (memory_per_device as f32 / total_memory_without_sp as f32)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::distributed::SimulatedProcessGroup;
    use std::sync::Arc;

    #[test]
    fn test_sequence_parallelism_config() {
        let config = SequenceParallelismConfig::default();
        assert_eq!(config.sequence_parallel_size, 1);
        assert_eq!(config.max_sequence_length_per_device, 2048);
        assert_eq!(config.overlap_size, 128);
    }

    #[test]
    fn test_sequence_parallelism_creation() {
        let config = SequenceParallelismConfig {
            sequence_parallel_size: 4,
            max_sequence_length_per_device: 1024,
            overlap_size: 64,
            ..Default::default()
        };

        let process_group = Arc::new(SimulatedProcessGroup::new(0, 4));
        let sequence_parallelism = SequenceParallelism::new(config, 0, 4, process_group);

        assert!(sequence_parallelism.is_ok());
    }

    #[test]
    fn test_equal_chunks_splitting() {
        let config = SequenceParallelismConfig {
            sequence_parallel_size: 2,
            max_sequence_length_per_device: 1000,
            overlap_size: 100,
            ..Default::default()
        };

        let process_group = Arc::new(SimulatedProcessGroup::new(0, 2));
        let mut sequence_parallelism =
            SequenceParallelism::new(config, 0, 2, process_group).unwrap();

        let chunks = sequence_parallelism.split_sequence(1800).unwrap();
        assert_eq!(chunks.len(), 2);
        assert_eq!(chunks[0].start_position, 0);
        assert_eq!(chunks[0].end_position, 1000);
        assert_eq!(chunks[1].start_position, 900); // 1000 - 100 overlap
    }

    #[test]
    fn test_chunk_processing() {
        let config = SequenceParallelismConfig {
            sequence_parallel_size: 2,
            max_sequence_length_per_device: 1000,
            overlap_size: 100,
            ..Default::default()
        };

        let process_group = Arc::new(SimulatedProcessGroup::new(0, 2));
        let mut sequence_parallelism =
            SequenceParallelism::new(config, 0, 2, process_group).unwrap();

        let _chunks = sequence_parallelism.split_sequence(1800).unwrap();

        let input = Tensor::zeros(&[1000, 768]).unwrap();
        let result = sequence_parallelism.forward_chunk(0, &input, None);
        assert!(result.is_ok());
    }

    #[test]
    fn test_gradient_synchronization() {
        let config = SequenceParallelismConfig {
            sync_gradients: true,
            ..Default::default()
        };

        let process_group = Arc::new(SimulatedProcessGroup::new(0, 1));
        let sequence_parallelism = SequenceParallelism::new(config, 0, 1, process_group).unwrap();

        let mut gradients = HashMap::new();
        gradients.insert("test_param".to_string(), Tensor::ones(&[10, 10]).unwrap());

        let result = sequence_parallelism.synchronize_gradients(&mut gradients);
        assert!(result.is_ok());
    }

    #[test]
    fn test_memory_usage_update() {
        let config = SequenceParallelismConfig::default();
        let process_group = Arc::new(SimulatedProcessGroup::new(0, 1));
        let sequence_parallelism = SequenceParallelism::new(config, 0, 1, process_group).unwrap();

        let result = sequence_parallelism.update_memory_usage(0, 1024 * 1024 * 1024); // 1GB
        assert!(result.is_ok());

        let stats = sequence_parallelism.get_statistics();
        assert_eq!(stats.peak_memory_usage, 1024 * 1024 * 1024);
    }

    #[test]
    fn test_optimal_sequence_config_calculation() {
        let config = utils::calculate_optimal_sequence_config(
            10000,                  // total sequence length
            8 * 1024 * 1024 * 1024, // 8GB memory per device
            1024,                   // 1KB per token
            4,                      // world size
        )
        .unwrap();

        assert!(config.sequence_parallel_size <= 4);
        assert!(config.max_sequence_length_per_device > 0);
    }

    #[test]
    fn test_communication_cost_estimation() {
        let config = SequenceParallelismConfig::default();
        let cost = utils::estimate_communication_cost(&config, 768, 12);
        assert!(cost > 0.0);
    }

    #[test]
    fn test_memory_savings_calculation() {
        let savings = utils::calculate_memory_savings(10000, 4, 768);
        assert!(savings > 0.0 && savings < 1.0);
    }
}
