//! Graph optimization, pruning, and compression functionality

use super::core::AutogradContext;
use crate::gradient_storage::GradientStorage;
use petgraph::{visit::EdgeRef, Direction};
use std::collections::{HashSet, VecDeque};
use torsh_core::error::Result;

/// Statistics about the computation graph
#[derive(Debug, Clone)]
pub struct GraphStats {
    pub node_count: usize,
    pub edge_count: usize,
    pub cache_size: usize,
    pub memory_usage: usize,
}

/// Checkpoint of graph state for performance analysis
#[derive(Debug, Clone)]
pub struct GraphCheckpoint {
    pub node_count: usize,
    pub edge_count: usize,
    pub cache_size: usize,
    pub timestamp: std::time::Instant,
}

/// Difference between graph states
#[derive(Debug, Clone)]
pub struct GraphDiff {
    pub nodes_added: usize,
    pub edges_added: usize,
    pub cache_entries_added: usize,
    pub elapsed: std::time::Duration,
}

impl AutogradContext {
    /// Get computation graph statistics
    pub fn graph_stats(&self) -> GraphStats {
        GraphStats {
            node_count: self.computation_graph.node_count(),
            edge_count: self.computation_graph.edge_count(),
            cache_size: self.gradient_storage.gradient_tensor_ids().len(),
            memory_usage: self.estimate_memory_usage(),
        }
    }

    /// Estimate memory usage of the computation graph
    pub(crate) fn estimate_memory_usage(&self) -> usize {
        // Rough estimate: nodes + edges + gradient cache
        let node_size = std::mem::size_of::<super::core::GraphNode>();
        let edge_size = std::mem::size_of::<()>(); // Edge weight is ()
        let gradient_entry_size = std::mem::size_of::<(usize, Vec<f32>)>();

        self.computation_graph.node_count() * node_size
            + self.computation_graph.edge_count() * edge_size
            + self.gradient_cache.len() * gradient_entry_size
    }

    /// Prune unused nodes from the computation graph
    pub fn prune_graph(&mut self) -> Result<usize> {
        let mut nodes_removed = 0;
        let mut nodes_to_remove = Vec::new();

        // Find nodes that are not reachable from any output that requires grad
        for node_index in self.computation_graph.node_indices() {
            if let Some(node) = self.computation_graph.node_weight(node_index) {
                if !node.requires_grad && !self.is_node_reachable_from_output(node_index) {
                    nodes_to_remove.push(node_index);
                }
            }
        }

        // Remove unreachable nodes
        for node_index in nodes_to_remove {
            if let Some(node) = self.computation_graph.remove_node(node_index) {
                self.tensor_to_node.remove(&node.id);
                self.gradient_cache.remove(&node.id);
                nodes_removed += 1;
            }
        }

        tracing::debug!(
            "Pruned {} unused nodes from computation graph",
            nodes_removed
        );
        Ok(nodes_removed)
    }

    /// Check if a node is reachable from any output that requires gradients
    fn is_node_reachable_from_output(&self, target_node: petgraph::graph::NodeIndex) -> bool {
        // Simple BFS to check reachability
        let mut visited = HashSet::new();
        let mut queue = VecDeque::new();

        // Start from all output nodes that require gradients
        for node_index in self.computation_graph.node_indices() {
            if let Some(node) = self.computation_graph.node_weight(node_index) {
                if node.requires_grad
                    && self
                        .computation_graph
                        .edges_directed(node_index, Direction::Outgoing)
                        .count()
                        == 0
                {
                    queue.push_back(node_index);
                    visited.insert(node_index);
                }
            }
        }

        while let Some(current) = queue.pop_front() {
            if current == target_node {
                return true;
            }

            for edge in self
                .computation_graph
                .edges_directed(current, Direction::Incoming)
            {
                let source = edge.source();
                if !visited.contains(&source) {
                    visited.insert(source);
                    queue.push_back(source);
                }
            }
        }

        false
    }

    /// Compress the computation graph by removing redundant operations
    pub fn compress_graph(&mut self) -> Result<usize> {
        let mut operations_removed = 0;

        // Find patterns that can be compressed
        // For example: x -> op1 -> temp -> op2 -> y can sometimes be x -> fused_op -> y
        let mut nodes_to_fuse = Vec::new();

        for node_index in self.computation_graph.node_indices() {
            if let Some(node) = self.computation_graph.node_weight(node_index) {
                // Look for fusable patterns (simple example: consecutive additions)
                if node.operation == "add" {
                    if let Some(fusable) = self.find_fusable_operations(node_index) {
                        nodes_to_fuse.push(fusable);
                    }
                }
            }
        }

        // Apply fusion optimizations
        for fusion_group in nodes_to_fuse {
            if self.fuse_operations(fusion_group).is_ok() {
                operations_removed += 1;
            }
        }

        tracing::debug!(
            "Compressed {} operations in computation graph",
            operations_removed
        );
        Ok(operations_removed)
    }

    /// Find operations that can be fused together
    fn find_fusable_operations(
        &self,
        _node_index: petgraph::graph::NodeIndex,
    ) -> Option<Vec<petgraph::graph::NodeIndex>> {
        // Placeholder for more sophisticated fusion logic
        // In a real implementation, this would identify patterns like:
        // - Consecutive element-wise operations
        // - Matrix operations that can be combined
        // - Activation functions that can be fused with previous operations
        None
    }

    /// Fuse a group of operations into a single operation
    fn fuse_operations(&mut self, _fusion_group: Vec<petgraph::graph::NodeIndex>) -> Result<()> {
        // Placeholder for operation fusion logic
        // In a real implementation, this would:
        // 1. Create a new fused operation node
        // 2. Update the graph connectivity
        // 3. Remove the original nodes
        // 4. Update gradient functions to handle the fused operation
        Ok(())
    }

    /// Set memory pressure threshold for automatic graph management
    pub fn set_memory_threshold(&mut self, threshold_bytes: usize) {
        self.memory_threshold = Some(threshold_bytes);
    }

    /// Check if memory pressure is high and trigger cleanup if needed
    pub fn check_memory_pressure(&mut self) -> Result<bool> {
        if let Some(threshold) = self.memory_threshold {
            let current_usage = self.estimate_memory_usage();
            if current_usage > threshold {
                tracing::warn!(
                    "Memory pressure detected ({} > {} bytes), triggering cleanup",
                    current_usage,
                    threshold
                );
                self.prune_graph()?;
                self.compress_graph()?;

                // Clear old gradients if still over threshold
                let new_usage = self.estimate_memory_usage();
                if new_usage > threshold {
                    self.clear_old_gradients();
                }
                return Ok(true);
            }
        }
        Ok(false)
    }

    /// Clear gradients for tensors that are no longer needed
    pub(crate) fn clear_old_gradients(&mut self) {
        let mut gradients_to_remove = Vec::new();

        for &tensor_id in self.gradient_cache.keys() {
            // Remove gradients for tensors not in the current graph
            if !self.tensor_to_node.contains_key(&tensor_id) {
                gradients_to_remove.push(tensor_id);
            }
        }

        for tensor_id in gradients_to_remove {
            self.gradient_cache.remove(&tensor_id);
        }
    }

    /// Create a checkpoint of the current graph state
    pub fn checkpoint(&self) -> GraphCheckpoint {
        GraphCheckpoint {
            node_count: self.computation_graph.node_count(),
            edge_count: self.computation_graph.edge_count(),
            cache_size: self.gradient_cache.len(),
            timestamp: std::time::Instant::now(),
        }
    }

    /// Get the difference between current state and a checkpoint
    pub fn diff_from_checkpoint(&self, checkpoint: &GraphCheckpoint) -> GraphDiff {
        GraphDiff {
            nodes_added: self
                .computation_graph
                .node_count()
                .saturating_sub(checkpoint.node_count),
            edges_added: self
                .computation_graph
                .edge_count()
                .saturating_sub(checkpoint.edge_count),
            cache_entries_added: self
                .gradient_cache
                .len()
                .saturating_sub(checkpoint.cache_size),
            elapsed: checkpoint.timestamp.elapsed(),
        }
    }

    /// Compact the graph by reorganizing node indices
    pub(crate) fn compact_graph(&mut self) -> Result<()> {
        // This is a placeholder for graph compaction logic
        // In a real implementation, this would:
        // 1. Create a new graph with consecutive node indices
        // 2. Copy nodes and edges with new indices
        // 3. Update the tensor_to_node mapping
        // 4. Update any other internal data structures
        tracing::debug!("Graph compaction placeholder executed");
        Ok(())
    }

    /// Defragment the graph for better memory layout
    pub(crate) fn defragment_graph(&mut self) -> Result<()> {
        // This is a placeholder for graph defragmentation logic
        // In a real implementation, this would:
        // 1. Analyze memory fragmentation in the graph
        // 2. Reorganize nodes for better cache locality
        // 3. Optimize edge storage layout
        // 4. Compress gradient storage
        tracing::debug!("Graph defragmentation placeholder executed");
        Ok(())
    }

    /// Clear weak references that might cause memory leaks
    pub(crate) fn clear_weak_references(&mut self) {
        // This is a placeholder for clearing weak references
        // In a real implementation, this would clear any weak references
        // that might be holding onto released memory
        tracing::debug!("Weak references cleared");
    }
}

/// Advanced graph optimization strategies
pub struct GraphOptimizer {
    /// Enable dead code elimination
    pub dead_code_elimination: bool,
    /// Enable common subexpression elimination
    pub common_subexpression_elimination: bool,
    /// Enable operation fusion
    pub operation_fusion: bool,
    /// Enable constant folding
    pub constant_folding: bool,
}

impl Default for GraphOptimizer {
    fn default() -> Self {
        Self {
            dead_code_elimination: true,
            common_subexpression_elimination: false,
            operation_fusion: false,
            constant_folding: false,
        }
    }
}

impl GraphOptimizer {
    /// Create a new graph optimizer with default settings
    pub fn new() -> Self {
        Self::default()
    }

    /// Apply all enabled optimizations to the graph
    pub fn optimize(&self, ctx: &mut AutogradContext) -> Result<OptimizationResult> {
        let mut result = OptimizationResult::default();
        let initial_nodes = ctx.computation_graph.node_count();

        // Apply dead code elimination
        if self.dead_code_elimination {
            result.nodes_removed += ctx.prune_graph()?;
        }

        // Apply common subexpression elimination
        if self.common_subexpression_elimination {
            result.cse_eliminations += self.eliminate_common_subexpressions(ctx)?;
        }

        // Apply operation fusion
        if self.operation_fusion {
            result.operations_fused += ctx.compress_graph()?;
        }

        // Apply constant folding
        if self.constant_folding {
            result.constants_folded += self.fold_constants(ctx)?;
        }

        let final_nodes = ctx.computation_graph.node_count();
        result.total_nodes_reduced = initial_nodes.saturating_sub(final_nodes);

        Ok(result)
    }

    /// Eliminate common subexpressions in the graph
    fn eliminate_common_subexpressions(&self, _ctx: &mut AutogradContext) -> Result<usize> {
        // Placeholder for CSE implementation
        Ok(0)
    }

    /// Fold constant expressions in the graph
    fn fold_constants(&self, _ctx: &mut AutogradContext) -> Result<usize> {
        // Placeholder for constant folding implementation
        Ok(0)
    }
}

/// Result of graph optimization
#[derive(Debug, Default, Clone)]
pub struct OptimizationResult {
    /// Number of nodes removed through pruning
    pub nodes_removed: usize,
    /// Number of common subexpressions eliminated
    pub cse_eliminations: usize,
    /// Number of operations fused
    pub operations_fused: usize,
    /// Number of constants folded
    pub constants_folded: usize,
    /// Total number of nodes reduced
    pub total_nodes_reduced: usize,
}
