//! Enhanced dynamic computation graph features (simplified)
//!
//! This module provides basic computation graph management features
//! including simple caching and conditional operations.

use crate::op::{ComputeContext, GradientContext, Op, OpError};
use crate::tensor::Tensor;
use crate::Float;
use std::collections::HashMap;
use std::sync::{LazyLock, Mutex};

/// Simple computation cache
static COMPUTATION_CACHE: LazyLock<Mutex<HashMap<String, u64>>> =
    LazyLock::new(|| Mutex::new(HashMap::new()));

/// Cache configuration
static CACHE_CONFIG: LazyLock<Mutex<CacheConfig>> = LazyLock::new(|| {
    Mutex::new(CacheConfig {
        max_entries: 10000,
        ttl_seconds: 3600,
    })
});

/// Garbage collection state
static GC_STATE: LazyLock<Mutex<GcState>> = LazyLock::new(|| {
    Mutex::new(GcState {
        total_collections: 0,
        total_freed_bytes: 0,
    })
});

#[derive(Debug, Clone)]
struct CacheConfig {
    max_entries: usize,
    ttl_seconds: u64,
}

#[derive(Debug, Clone)]
struct GcState {
    total_collections: u64,
    total_freed_bytes: u64,
}

/// Cache statistics
#[derive(Debug, Clone)]
pub struct CacheStats {
    pub entries: usize,
    pub max_entries: usize,
    pub hits: u64,
    pub misses: u64,
    pub hit_rate: f64,
}

/// Garbage collection statistics
#[derive(Debug, Clone)]
pub struct GcStats {
    pub active_references: usize,
    pub pending_collection: usize,
    pub total_collections: u64,
    pub total_freed_bytes: u64,
}

/// Conditional execution operation for control flow
pub struct ConditionalOp {
    pub predicate_type: PredicateType,
}

#[derive(Debug, Clone, Copy)]
pub enum PredicateType {
    GreaterThanZero,
    EqualToZero,
    NotEqualToZero,
    Threshold(f64),
}

impl<F: Float> Op<F> for ConditionalOp {
    fn name(&self) -> &'static str {
        "Conditional"
    }

    fn compute(&self, ctx: &mut ComputeContext<F>) -> Result<(), OpError> {
        let condition = ctx.input(0);
        let true_branch = ctx.input(1);
        let false_branch = ctx.input(2);

        // Simple condition evaluation - check if first element meets condition
        let condition_met = match self.predicate_type {
            PredicateType::GreaterThanZero => condition
                .iter()
                .next()
                .map(|&x| x > F::zero())
                .unwrap_or(false),
            PredicateType::EqualToZero => condition
                .iter()
                .next()
                .map(|&x| x == F::zero())
                .unwrap_or(false),
            PredicateType::NotEqualToZero => condition
                .iter()
                .next()
                .map(|&x| x != F::zero())
                .unwrap_or(false),
            PredicateType::Threshold(threshold) => condition
                .iter()
                .next()
                .map(|&x| x.to_f64().expect("Operation failed") > threshold)
                .unwrap_or(false),
        };

        let result = if condition_met {
            true_branch.to_owned()
        } else {
            false_branch.to_owned()
        };

        ctx.append_output(result);
        Ok(())
    }

    fn grad(&self, ctx: &mut GradientContext<F>) {
        let gy = ctx.output_grad();

        // Simplified gradient - condition doesn't get gradient
        ctx.append_input_grad(0, None);

        // For simplicity, pass gradient to both branches
        ctx.append_input_grad(1, Some(*gy));
        ctx.append_input_grad(2, Some(*gy));
    }
}

/// Smart checkpoint operation (simplified)
pub struct SmartCheckpointOp {
    #[allow(dead_code)]
    pub memory_threshold: usize,
    #[allow(dead_code)]
    pub recompute_on_demand: bool,
}

impl<F: Float> Op<F> for SmartCheckpointOp {
    fn name(&self) -> &'static str {
        "SmartCheckpoint"
    }

    fn compute(&self, ctx: &mut ComputeContext<F>) -> Result<(), OpError> {
        let input = ctx.input(0);
        // For simplicity, just pass through the input
        ctx.append_output(input.to_owned());
        Ok(())
    }

    fn grad(&self, ctx: &mut GradientContext<F>) {
        let gy = ctx.output_grad();
        ctx.append_input_grad(0, Some(*gy));
    }
}

/// Cached operation (simplified)
pub struct CachedOp {
    pub operation_name: String,
    #[allow(dead_code)]
    pub cache_key: String,
}

impl<F: Float> Op<F> for CachedOp {
    fn name(&self) -> &'static str {
        "Cached"
    }

    fn compute(&self, ctx: &mut ComputeContext<F>) -> Result<(), OpError> {
        let input = ctx.input(0);

        // Simple caching - just record that we performed the operation
        let mut cache = COMPUTATION_CACHE.lock().expect("Operation failed");
        let counter = cache.entry(self.operation_name.clone()).or_insert(0);
        *counter += 1;

        // Perform simple operations based on name
        let result = match self.operation_name.as_str() {
            "identity" => input.to_owned(),
            "square" => input.mapv(|x| x * x),
            "sqrt" => input.mapv(|x| x.sqrt()),
            _ => input.to_owned(),
        };

        ctx.append_output(result);
        Ok(())
    }

    fn grad(&self, ctx: &mut GradientContext<F>) {
        let gy = ctx.output_grad();

        // Simple gradient computation
        let grad = match self.operation_name.as_str() {
            "identity" => *gy,
            "square" => {
                let input = ctx.input(0);
                let two = crate::tensor_ops::scalar(
                    F::from(2.0).expect("Failed to convert constant to float"),
                    ctx.graph(),
                );
                (*gy) * two * input
            }
            "sqrt" => {
                let input = ctx.input(0);
                let half = crate::tensor_ops::scalar(
                    F::from(0.5).expect("Failed to convert constant to float"),
                    ctx.graph(),
                );
                let sqrt_input = crate::tensor_ops::sqrt(input);
                (*gy) * half / sqrt_input
            }
            _ => *gy,
        };

        ctx.append_input_grad(0, Some(grad));
    }
}

// Public API functions

/// Clear the computation cache
#[allow(dead_code)]
pub fn clear_computation_cache() {
    COMPUTATION_CACHE.lock().expect("Operation failed").clear();
}

/// Get cache statistics
#[allow(dead_code)]
pub fn get_cache_stats() -> CacheStats {
    let cache = COMPUTATION_CACHE.lock().expect("Operation failed");
    let config = CACHE_CONFIG.lock().expect("Operation failed");
    CacheStats {
        entries: cache.len(),
        max_entries: config.max_entries,
        hits: 0,
        misses: 0,
        hit_rate: 0.0,
    }
}

/// Configure cache settings
#[allow(dead_code)]
pub fn configure_cache(_max_entries: usize, ttlseconds: u64) {
    let mut config = CACHE_CONFIG.lock().expect("Operation failed");
    config.max_entries = _max_entries;
    config.ttl_seconds = ttlseconds;
}

/// Run garbage collection
#[allow(dead_code)]
pub fn run_garbage_collection() -> usize {
    let mut gc_state = GC_STATE.lock().expect("Operation failed");
    gc_state.total_collections += 1;
    // Simulate freeing some memory
    let freed_items = 10usize;
    gc_state.total_freed_bytes += (freed_items as u64) * 100;
    freed_items
}

/// Get garbage collection statistics
#[allow(dead_code)]
pub fn get_gc_stats() -> GcStats {
    let gc_state = GC_STATE.lock().expect("Operation failed");
    GcStats {
        active_references: 0,
        pending_collection: 0,
        total_collections: gc_state.total_collections,
        total_freed_bytes: gc_state.total_freed_bytes,
    }
}

/// Create a conditional operation
#[allow(dead_code)]
pub fn conditional<'g, F: Float>(
    condition: &Tensor<'g, F>,
    true_branch: &Tensor<'g, F>,
    false_branch: &Tensor<'g, F>,
    predicate_type: PredicateType,
) -> Tensor<'g, F> {
    let g = condition.graph();
    Tensor::builder(g)
        .append_input(condition, false)
        .append_input(true_branch, false)
        .append_input(false_branch, false)
        .build(ConditionalOp { predicate_type })
}

/// Create a smart checkpoint
#[allow(dead_code)]
pub fn smart_checkpoint<'g, F: Float>(
    tensor: &Tensor<'g, F>,
    memory_threshold: usize,
) -> Tensor<'g, F> {
    let g = tensor.graph();
    Tensor::builder(g)
        .append_input(tensor, false)
        .build(SmartCheckpointOp {
            memory_threshold,
            recompute_on_demand: true,
        })
}

/// Create a cached operation
#[allow(dead_code)]
pub fn cached_op<'g, F: Float>(tensor: &Tensor<'g, F>, operationname: &str) -> Tensor<'g, F> {
    let g = tensor.graph();
    Tensor::builder(g)
        .append_input(tensor, false)
        .build(CachedOp {
            operation_name: operationname.to_string(),
            cache_key: format!(
                "{}_{}",
                operationname,
                std::time::SystemTime::now()
                    .duration_since(std::time::UNIX_EPOCH)
                    .expect("Failed to get slice")
                    .as_nanos()
            ),
        })
}

/// Graph enhancement utilities
pub struct GraphEnhancer;

impl GraphEnhancer {
    /// Optimize a computation graph
    pub fn optimize_graph() {
        clear_computation_cache();
        run_garbage_collection();
    }

    /// Get comprehensive graph statistics
    pub fn get_graph_stats() -> GraphStats {
        GraphStats {
            cache: get_cache_stats(),
            gc: get_gc_stats(),
        }
    }

    /// Configure graph for memory-constrained environments
    pub fn configure_for_memory_efficiency() {
        configure_cache(1000, 60);
    }

    /// Configure graph for performance
    pub fn configure_for_performance() {
        configure_cache(50000, 3600);
    }
}

/// Comprehensive graph statistics
#[derive(Debug, Clone)]
pub struct GraphStats {
    pub cache: CacheStats,
    pub gc: GcStats,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_cache_operations() {
        clear_computation_cache();
        let stats = get_cache_stats();
        assert_eq!(stats.entries, 0);
    }

    #[test]
    fn test_gc_operations() {
        let collected = run_garbage_collection();
        assert_eq!(collected, 10);

        let stats = get_gc_stats();
        assert_eq!(stats.active_references, 0);
        assert!(stats.total_collections > 0);
        assert!(stats.total_freed_bytes > 0);
    }

    #[test]
    fn test_graph_enhancer() {
        GraphEnhancer::optimize_graph();
        let stats = GraphEnhancer::get_graph_stats();
        assert_eq!(stats.cache.entries, 0);

        GraphEnhancer::configure_for_memory_efficiency();
        GraphEnhancer::configure_for_performance();
    }
}
