//! Graph debugging and analysis utilities

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use super::core::AutogradContext;
use petgraph::visit::EdgeRef;
use petgraph::{Direction, Graph};
use std::collections::{BTreeMap, HashSet, VecDeque};
use std::fmt::Write;
use std::time::Instant;
use torsh_core::error::Result;
use tracing::debug;

/// Graph analysis results
#[derive(Debug, Clone)]
pub struct GraphAnalysis {
    /// Total number of nodes
    pub node_count: usize,
    /// Number of leaf nodes (no inputs)
    pub leaf_nodes: usize,
    /// Number of root nodes (no outputs used)
    pub root_nodes: usize,
    /// Maximum depth from any leaf to any root
    pub max_depth: usize,
    /// Average depth
    pub avg_depth: f32,
    /// Number of cycles detected
    pub cycles: usize,
    /// Critical path information
    pub critical_path: CriticalPath,
    /// Node type distribution
    pub node_types: BTreeMap<String, usize>,
    /// Memory usage estimation
    pub estimated_memory: usize,
}

/// Critical path analysis
#[derive(Debug, Clone)]
pub struct CriticalPath {
    /// Nodes in the critical path
    pub nodes: Vec<usize>,
    /// Operations in the critical path
    pub operations: Vec<String>,
    /// Total length of critical path
    pub length: usize,
    /// Estimated computation time
    pub estimated_time: f32,
}

/// Graph debugging utilities
pub struct GraphDebugger {
    /// Enable verbose output
    pub verbose: bool,
    /// Include memory analysis
    pub include_memory_analysis: bool,
    /// Include performance analysis
    pub include_performance_analysis: bool,
    /// Maximum nodes to print in detail
    pub max_detail_nodes: usize,
}

impl Default for GraphDebugger {
    fn default() -> Self {
        Self {
            verbose: false,
            include_memory_analysis: true,
            include_performance_analysis: true,
            max_detail_nodes: 20,
        }
    }
}

impl GraphDebugger {
    /// Create a new graph debugger
    pub fn new() -> Self {
        Self::default()
    }

    /// Enable verbose output
    pub fn verbose(mut self) -> Self {
        self.verbose = true;
        self
    }

    /// Analyze the computation graph
    pub fn analyze_graph(&self, ctx: &AutogradContext) -> Result<GraphAnalysis> {
        let start_time = Instant::now();

        debug!("Starting graph analysis");

        let graph = &ctx.computation_graph;
        let node_count = graph.node_count();

        if node_count == 0 {
            return Ok(GraphAnalysis {
                node_count: 0,
                leaf_nodes: 0,
                root_nodes: 0,
                max_depth: 0,
                avg_depth: 0.0,
                cycles: 0,
                critical_path: CriticalPath {
                    nodes: vec![],
                    operations: vec![],
                    length: 0,
                    estimated_time: 0.0,
                },
                node_types: BTreeMap::new(),
                estimated_memory: 0,
            });
        }

        // Count leaf and root nodes
        let (leaf_nodes, root_nodes) = self.count_leaf_and_root_nodes(ctx)?;

        // Calculate depths
        let (max_depth, avg_depth) = self.calculate_depths(ctx)?;

        // Detect cycles
        let cycles = self.detect_cycles(ctx)?;

        // Find critical path
        let critical_path = self.find_critical_path(ctx)?;

        // Analyze node types
        let node_types = self.analyze_node_types(ctx)?;

        // Estimate memory usage
        let estimated_memory = if self.include_memory_analysis {
            self.estimate_memory_usage(ctx)?
        } else {
            0
        };

        debug!("Graph analysis completed in {:?}", start_time.elapsed());

        Ok(GraphAnalysis {
            node_count,
            leaf_nodes,
            root_nodes,
            max_depth,
            avg_depth,
            cycles,
            critical_path,
            node_types,
            estimated_memory,
        })
    }

    /// Count leaf and root nodes
    fn count_leaf_and_root_nodes(&self, ctx: &AutogradContext) -> Result<(usize, usize)> {
        let mut leaf_nodes = 0;
        let mut root_nodes = 0;

        for node_index in ctx.computation_graph.node_indices() {
            // Count incoming edges (inputs)
            let incoming_count = ctx
                .computation_graph
                .edges_directed(node_index, Direction::Incoming)
                .count();

            // Count outgoing edges (outputs)
            let outgoing_count = ctx
                .computation_graph
                .edges_directed(node_index, Direction::Outgoing)
                .count();

            if incoming_count == 0 {
                leaf_nodes += 1;
            }
            if outgoing_count == 0 {
                root_nodes += 1;
            }
        }

        Ok((leaf_nodes, root_nodes))
    }

    /// Calculate graph depths
    fn calculate_depths(&self, ctx: &AutogradContext) -> Result<(usize, f32)> {
        let mut depths = std::collections::HashMap::new();
        let mut max_depth = 0;
        let mut total_depth = 0;

        // Find leaf nodes (no incoming edges)
        let leaf_nodes: Vec<_> = ctx
            .computation_graph
            .node_indices()
            .filter(|&node_index| {
                ctx.computation_graph
                    .edges_directed(node_index, Direction::Incoming)
                    .count()
                    == 0
            })
            .collect();

        // BFS to calculate depths
        let mut queue = VecDeque::new();
        for &leaf in &leaf_nodes {
            depths.insert(leaf, 0);
            queue.push_back(leaf);
        }

        while let Some(current) = queue.pop_front() {
            let current_depth = depths[&current];
            max_depth = max_depth.max(current_depth);
            total_depth += current_depth;

            for edge in ctx
                .computation_graph
                .edges_directed(current, Direction::Outgoing)
            {
                let target = edge.target();
                let new_depth = current_depth + 1;

                if let Some(&existing_depth) = depths.get(&target) {
                    if new_depth > existing_depth {
                        depths.insert(target, new_depth);
                        queue.push_back(target);
                    }
                } else {
                    depths.insert(target, new_depth);
                    queue.push_back(target);
                }
            }
        }

        let avg_depth = if depths.is_empty() {
            0.0
        } else {
            total_depth as f32 / depths.len() as f32
        };

        Ok((max_depth, avg_depth))
    }

    /// Detect cycles in the graph
    fn detect_cycles(&self, ctx: &AutogradContext) -> Result<usize> {
        let mut visited = HashSet::new();
        let mut rec_stack = HashSet::new();
        let mut cycle_count = 0;

        for node_index in ctx.computation_graph.node_indices() {
            if !visited.contains(&node_index) {
                if self.dfs_cycle_detection(ctx, node_index, &mut visited, &mut rec_stack)? {
                    cycle_count += 1;
                }
            }
        }

        Ok(cycle_count)
    }

    /// DFS-based cycle detection
    fn dfs_cycle_detection(
        &self,
        ctx: &AutogradContext,
        node: petgraph::graph::NodeIndex,
        visited: &mut HashSet<petgraph::graph::NodeIndex>,
        rec_stack: &mut HashSet<petgraph::graph::NodeIndex>,
    ) -> Result<bool> {
        visited.insert(node);
        rec_stack.insert(node);

        for edge in ctx
            .computation_graph
            .edges_directed(node, Direction::Outgoing)
        {
            let target = edge.target();

            if !visited.contains(&target) {
                if self.dfs_cycle_detection(ctx, target, visited, rec_stack)? {
                    return Ok(true);
                }
            } else if rec_stack.contains(&target) {
                return Ok(true);
            }
        }

        rec_stack.remove(&node);
        Ok(false)
    }

    /// Find the critical path in the graph
    fn find_critical_path(&self, ctx: &AutogradContext) -> Result<CriticalPath> {
        // Find the longest path in the DAG
        let mut longest_path = vec![];
        let mut longest_operations = vec![];
        let mut max_length = 0;

        // Find all leaf nodes
        let leaf_nodes: Vec<_> = ctx
            .computation_graph
            .node_indices()
            .filter(|&node_index| {
                ctx.computation_graph
                    .edges_directed(node_index, Direction::Incoming)
                    .count()
                    == 0
            })
            .collect();

        for &leaf in &leaf_nodes {
            let path = self.find_longest_path_from_node(ctx, leaf)?;
            if path.len() > max_length {
                max_length = path.len();
                longest_path.clear();
                longest_operations.clear();

                for &node_index in &path {
                    if let Some(node) = ctx.computation_graph.node_weight(node_index) {
                        longest_path.push(node.id);
                        longest_operations.push(node.operation.clone());
                    }
                }
            }
        }

        // Estimate computation time (simplified)
        let estimated_time = longest_operations.len() as f32 * 0.1; // 0.1ms per operation

        Ok(CriticalPath {
            nodes: longest_path,
            operations: longest_operations,
            length: max_length,
            estimated_time,
        })
    }

    /// Helper: Find longest path from a node
    fn find_longest_path_from_node(
        &self,
        ctx: &AutogradContext,
        start: petgraph::graph::NodeIndex,
    ) -> Result<Vec<petgraph::graph::NodeIndex>> {
        let mut longest_path = vec![start];
        let mut visited = HashSet::new();

        fn dfs_longest(
            graph: &Graph<super::core::GraphNode, ()>,
            current: petgraph::graph::NodeIndex,
            visited: &mut HashSet<petgraph::graph::NodeIndex>,
            current_path: &mut Vec<petgraph::graph::NodeIndex>,
            longest_path: &mut Vec<petgraph::graph::NodeIndex>,
        ) {
            visited.insert(current);
            current_path.push(current);

            if current_path.len() > longest_path.len() {
                *longest_path = current_path.clone();
            }

            for edge in graph.edges_directed(current, Direction::Outgoing) {
                let target = edge.target();
                if !visited.contains(&target) {
                    dfs_longest(graph, target, visited, current_path, longest_path);
                }
            }

            current_path.pop();
            visited.remove(&current);
        }

        let mut current_path = vec![];
        dfs_longest(
            &ctx.computation_graph,
            start,
            &mut visited,
            &mut current_path,
            &mut longest_path,
        );

        Ok(longest_path)
    }

    /// Analyze node type distribution
    fn analyze_node_types(&self, ctx: &AutogradContext) -> Result<BTreeMap<String, usize>> {
        let mut node_types = BTreeMap::new();

        for node in ctx.computation_graph.node_weights() {
            *node_types.entry(node.operation.clone()).or_insert(0) += 1;
        }

        Ok(node_types)
    }

    /// Estimate memory usage
    fn estimate_memory_usage(&self, ctx: &AutogradContext) -> Result<usize> {
        Ok(ctx.estimate_memory_usage())
    }

    /// Generate a detailed textual representation of the graph
    pub fn generate_graph_report(&self, ctx: &AutogradContext) -> Result<String> {
        let mut report = String::new();
        let analysis = self.analyze_graph(ctx)?;

        // Note: writeln! to String never fails, but using expect() for explicitness
        writeln!(report, "=== Computation Graph Analysis Report ===")
            .expect("Writing to String should not fail");
        writeln!(report, "Total Nodes: {}", analysis.node_count)
            .expect("Writing to String should not fail");
        writeln!(report, "Leaf Nodes: {}", analysis.leaf_nodes)
            .expect("Writing to String should not fail");
        writeln!(report, "Root Nodes: {}", analysis.root_nodes)
            .expect("Writing to String should not fail");
        writeln!(report, "Maximum Depth: {}", analysis.max_depth)
            .expect("Writing to String should not fail");
        writeln!(report, "Average Depth: {:.2}", analysis.avg_depth)
            .expect("Writing to String should not fail");
        writeln!(report, "Cycles Detected: {}", analysis.cycles)
            .expect("Writing to String should not fail");
        writeln!(
            report,
            "Estimated Memory: {} bytes",
            analysis.estimated_memory
        )
        .expect("Writing to String should not fail");

        writeln!(report, "\n=== Critical Path ===").expect("Writing to String should not fail");
        writeln!(report, "Length: {}", analysis.critical_path.length)
            .expect("Writing to String should not fail");
        writeln!(
            report,
            "Estimated Time: {:.2}ms",
            analysis.critical_path.estimated_time
        )
        .expect("Writing to String should not fail");
        writeln!(
            report,
            "Operations: {:?}",
            analysis.critical_path.operations
        )
        .expect("Writing to String should not fail");

        writeln!(report, "\n=== Node Type Distribution ===")
            .expect("Writing to String should not fail");
        for (op_type, count) in &analysis.node_types {
            writeln!(report, "{}: {}", op_type, count).expect("Writing to String should not fail");
        }

        if self.verbose && ctx.computation_graph.node_count() <= self.max_detail_nodes {
            writeln!(report, "\n=== Detailed Node Information ===")
                .expect("Writing to String should not fail");
            for (i, node) in ctx.computation_graph.node_weights().enumerate() {
                writeln!(
                    report,
                    "Node {}: {} (ID: {}, Inputs: {:?}, Requires Grad: {})",
                    i, node.operation, node.id, node.inputs, node.requires_grad
                )
                .expect("Writing to String should not fail");
            }
        }

        Ok(report)
    }

    /// Visualize the graph structure (basic text representation)
    pub fn visualize_graph(&self, ctx: &AutogradContext) -> Result<String> {
        let mut viz = String::new();
        writeln!(viz, "digraph computation_graph {{").expect("Writing to String should not fail");
        writeln!(viz, "  rankdir=TB;").expect("Writing to String should not fail");
        writeln!(viz, "  node [shape=box];").expect("Writing to String should not fail");

        for node_index in ctx.computation_graph.node_indices() {
            if let Some(node) = ctx.computation_graph.node_weight(node_index) {
                let color = if node.requires_grad {
                    "lightblue"
                } else {
                    "lightgray"
                };
                writeln!(
                    viz,
                    "  {} [label=\"{}\\nID: {}\" fillcolor={} style=filled];",
                    node_index.index(),
                    node.operation,
                    node.id,
                    color
                )
                .expect("Writing to String should not fail");
            }
        }

        for edge in ctx.computation_graph.edge_indices() {
            if let Some((source, target)) = ctx.computation_graph.edge_endpoints(edge) {
                writeln!(viz, "  {} -> {};", source.index(), target.index())
                    .expect("Writing to String should not fail");
            }
        }

        writeln!(viz, "}}").expect("Writing to String should not fail");
        Ok(viz)
    }

    /// Check graph integrity and report issues
    pub fn check_graph_integrity(&self, ctx: &AutogradContext) -> Result<Vec<String>> {
        let mut issues = Vec::new();

        // Check for orphaned nodes
        for node_index in ctx.computation_graph.node_indices() {
            if let Some(node) = ctx.computation_graph.node_weight(node_index) {
                // Check if tensor ID mapping exists
                if !ctx.tensor_to_node.contains_key(&node.id) {
                    issues.push(format!(
                        "Node {} has orphaned tensor ID {}",
                        node_index.index(),
                        node.id
                    ));
                }

                // Check for self-references
                if node.inputs.contains(&node.output) {
                    issues.push(format!("Node {} has self-reference", node_index.index()));
                }
            }
        }

        // Check for dangling tensor references
        for (&tensor_id, &node_index) in &ctx.tensor_to_node {
            if ctx.computation_graph.node_weight(node_index).is_none() {
                issues.push(format!("Tensor ID {} points to invalid node", tensor_id));
            }
        }

        Ok(issues)
    }
}

/// Performance profiler for graph operations
pub struct GraphProfiler {
    /// Track operation timing
    pub track_timing: bool,
    /// Track memory usage
    pub track_memory: bool,
    /// Maximum number of operations to profile
    pub max_operations: usize,
}

impl Default for GraphProfiler {
    fn default() -> Self {
        Self {
            track_timing: true,
            track_memory: true,
            max_operations: 1000,
        }
    }
}

impl GraphProfiler {
    /// Create a new graph profiler
    pub fn new() -> Self {
        Self::default()
    }

    /// Profile graph execution
    pub fn profile_execution(&self, ctx: &AutogradContext) -> Result<ProfileResult> {
        let _start_time = Instant::now();
        let initial_memory = ctx.estimate_memory_usage();

        // Simulate profiling - in real implementation, this would track actual execution
        let operation_count = ctx.computation_graph.node_count();
        let avg_time_per_op = 0.1; // ms

        let total_time = operation_count as f32 * avg_time_per_op;
        let final_memory = ctx.estimate_memory_usage();

        Ok(ProfileResult {
            total_operations: operation_count,
            total_time_ms: total_time,
            avg_time_per_operation: avg_time_per_op,
            peak_memory_usage: final_memory.max(initial_memory),
            memory_efficiency: if final_memory > 0 {
                initial_memory as f32 / final_memory as f32
            } else {
                1.0
            },
        })
    }
}

/// Result of graph profiling
#[derive(Debug, Clone)]
pub struct ProfileResult {
    /// Total number of operations profiled
    pub total_operations: usize,
    /// Total execution time in milliseconds
    pub total_time_ms: f32,
    /// Average time per operation
    pub avg_time_per_operation: f32,
    /// Peak memory usage during execution
    pub peak_memory_usage: usize,
    /// Memory efficiency ratio
    pub memory_efficiency: f32,
}
