// Copyright (c) 2024 TrustformeRS Contributors
// SPDX-License-Identifier: Apache-2.0

//! Hardware operation scheduling components
//!
//! This module provides scheduling algorithms and implementations for distributing
//! operations across available hardware devices efficiently.

#![allow(unused_variables)] // Hardware scheduling

use super::traits::{HardwareOperation, HardwareScheduler, SchedulerStatistics};
use super::{HardwareResult, OperationParameter};
use crate::errors::TrustformersError;
use crate::tensor::Tensor;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::{Duration, SystemTime};

/// Default scheduler implementation with priority-based scheduling
#[derive(Debug)]
pub struct DefaultScheduler {
    /// Scheduler statistics
    statistics: Arc<Mutex<SchedulerStatistics>>,
    /// Device priorities (higher is better)
    device_priorities: Arc<Mutex<HashMap<String, f64>>>,
    /// Operation queue
    operation_queue: Arc<Mutex<Vec<QueuedOperation>>>,
    /// Scheduling configuration
    config: SchedulerConfig,
}

/// Queued operation waiting for execution
#[derive(Debug, Clone)]
pub struct QueuedOperation {
    /// Operation identifier
    pub id: String,
    /// Operation to execute
    pub operation_type: String,
    /// Input tensors
    pub inputs: Vec<TensorInfo>,
    /// Operation parameters
    pub params: HashMap<String, OperationParameter>,
    /// Priority level
    pub priority: f64,
    /// Enqueue timestamp
    pub enqueued_at: SystemTime,
    /// Expected execution time
    pub estimated_duration: Duration,
}

/// Tensor information for scheduling
#[derive(Debug, Clone)]
pub struct TensorInfo {
    /// Tensor shape
    pub shape: Vec<usize>,
    /// Data type size in bytes
    pub dtype_size: usize,
    /// Memory layout
    pub layout: String,
}

/// Scheduler configuration
#[derive(Debug, Clone)]
pub struct SchedulerConfig {
    /// Maximum queue size
    pub max_queue_size: usize,
    /// Queue timeout
    pub queue_timeout: Duration,
    /// Enable priority scheduling
    pub enable_priority_scheduling: bool,
    /// Load balancing weight
    pub load_balancing_weight: f64,
    /// Performance weight
    pub performance_weight: f64,
    /// Availability weight
    pub availability_weight: f64,
}

/// Advanced scheduler with multiple scheduling algorithms
#[derive(Debug)]
pub struct AdvancedScheduler {
    /// Scheduling algorithm to use
    algorithm: SchedulingAlgorithm,
    /// Device load tracking
    device_loads: Arc<Mutex<HashMap<String, DeviceLoad>>>,
    /// Performance history
    performance_history: Arc<Mutex<HashMap<String, Vec<PerformanceRecord>>>>,
    /// Configuration
    config: AdvancedSchedulerConfig,
}

/// Available scheduling algorithms
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SchedulingAlgorithm {
    /// First Come First Served
    FCFS,
    /// Shortest Job First
    SJF,
    /// Priority-based scheduling
    Priority,
    /// Round Robin
    RoundRobin,
    /// Load-aware scheduling
    LoadAware,
    /// Performance-based scheduling
    PerformanceBased,
    /// Machine learning-based scheduling
    MLBased,
}

/// Device load information
#[derive(Debug, Clone)]
pub struct DeviceLoad {
    /// Current utilization (0.0 - 1.0)
    pub utilization: f64,
    /// Active operations count
    pub active_operations: u32,
    /// Queued operations count
    pub queued_operations: u32,
    /// Last update timestamp
    pub last_updated: SystemTime,
    /// Average response time
    pub avg_response_time: Duration,
}

/// Performance record for scheduling decisions
#[derive(Debug, Clone)]
pub struct PerformanceRecord {
    /// Operation type
    pub operation_type: String,
    /// Execution time
    pub execution_time: Duration,
    /// Throughput achieved
    pub throughput: f64,
    /// Resource utilization
    pub resource_utilization: HashMap<String, f64>,
    /// Timestamp
    pub timestamp: SystemTime,
}

/// Advanced scheduler configuration
#[derive(Debug, Clone)]
pub struct AdvancedSchedulerConfig {
    /// Learning rate for ML-based scheduling
    pub learning_rate: f64,
    /// History window size
    pub history_window: usize,
    /// Performance prediction accuracy threshold
    pub prediction_threshold: f64,
    /// Load balancing aggressiveness
    pub load_balancing_factor: f64,
}

impl DefaultScheduler {
    /// Create a new default scheduler
    pub fn new() -> Self {
        Self {
            statistics: Arc::new(Mutex::new(SchedulerStatistics::default())),
            device_priorities: Arc::new(Mutex::new(HashMap::new())),
            operation_queue: Arc::new(Mutex::new(Vec::new())),
            config: SchedulerConfig::default(),
        }
    }

    /// Create scheduler with custom configuration
    pub fn with_config(config: SchedulerConfig) -> Self {
        Self {
            statistics: Arc::new(Mutex::new(SchedulerStatistics::default())),
            device_priorities: Arc::new(Mutex::new(HashMap::new())),
            operation_queue: Arc::new(Mutex::new(Vec::new())),
            config,
        }
    }

    /// Enqueue an operation for scheduling
    pub fn enqueue_operation(&self, operation: QueuedOperation) -> HardwareResult<()> {
        let mut queue = self.operation_queue.lock().map_err(|_| {
            TrustformersError::model_error("Failed to lock operation queue".to_string())
        })?;

        if queue.len() >= self.config.max_queue_size {
            return Err(TrustformersError::model_error(
                "Operation queue is full".to_string(),
            ));
        }

        // Insert operation maintaining priority order
        let insert_pos = queue
            .iter()
            .position(|op| op.priority < operation.priority)
            .unwrap_or(queue.len());

        queue.insert(insert_pos, operation);

        // Update statistics
        if let Ok(mut stats) = self.statistics.lock() {
            stats.total_operations += 1;
        }

        Ok(())
    }

    /// Get next operation from queue
    pub fn dequeue_operation(&self) -> Option<QueuedOperation> {
        if let Ok(mut queue) = self.operation_queue.lock() {
            if !queue.is_empty() {
                return Some(queue.remove(0));
            }
        }
        None
    }

    /// Find best device for operation based on current scheduling strategy
    fn find_best_device(&self, operation: &QueuedOperation) -> HardwareResult<String> {
        let priorities = self.device_priorities.lock().map_err(|_| {
            TrustformersError::model_error("Failed to lock device priorities".to_string())
        })?;

        if priorities.is_empty() {
            return Err(TrustformersError::model_error(
                "No devices available".to_string(),
            ));
        }

        // Find device with highest priority that can handle the operation
        let best_device = priorities
            .iter()
            .max_by(|a, b| a.1.partial_cmp(b.1).unwrap())
            .map(|(device_id, _)| device_id.clone())
            .ok_or_else(|| {
                TrustformersError::model_error("No suitable device found".to_string())
            })?;

        Ok(best_device)
    }

    /// Update device performance metrics
    pub fn update_device_metrics(&self, device_id: &str, performance: &PerformanceRecord) {
        // Update internal performance tracking for better scheduling decisions
        if let Ok(mut priorities) = self.device_priorities.lock() {
            let current_priority = priorities.get(device_id).cloned().unwrap_or(1.0);

            // Adjust priority based on performance
            let performance_factor = match performance.execution_time.as_millis() {
                0..=100 => 1.2,    // Very fast
                101..=500 => 1.0,  // Normal
                501..=1000 => 0.8, // Slow
                _ => 0.5,          // Very slow
            };

            let new_priority = current_priority * performance_factor;
            priorities.insert(device_id.to_string(), new_priority);
        }
    }
}

impl HardwareScheduler for DefaultScheduler {
    fn schedule_operation(
        &self,
        operation: &dyn HardwareOperation,
        inputs: &[Tensor],
        params: &HashMap<String, OperationParameter>,
    ) -> HardwareResult<String> {
        // Convert to queued operation
        let queued_op = QueuedOperation {
            id: format!(
                "op_{}",
                SystemTime::now()
                    .duration_since(SystemTime::UNIX_EPOCH)
                    .unwrap_or_default()
                    .as_nanos()
            ),
            operation_type: operation.name().to_string(),
            inputs: inputs
                .iter()
                .map(|t| TensorInfo {
                    shape: t.shape(),
                    dtype_size: 4, // Assume f32 for now
                    layout: "contiguous".to_string(),
                })
                .collect(),
            params: params.clone(),
            priority: 1.0, // Default priority since HardwareOperation doesn't have priority()
            enqueued_at: SystemTime::now(),
            estimated_duration: Duration::from_millis(100), // Default estimate since no estimated_duration()
        };

        // Find best device for this operation
        let device_id = self.find_best_device(&queued_op)?;

        // Update statistics
        if let Ok(mut stats) = self.statistics.lock() {
            stats.total_operations += 1;
        }

        Ok(device_id)
    }

    fn statistics(&self) -> SchedulerStatistics {
        self.statistics.lock().unwrap().clone()
    }

    fn update_priorities(&mut self, priorities: HashMap<String, f64>) {
        if let Ok(mut device_priorities) = self.device_priorities.lock() {
            *device_priorities = priorities;
        }
    }
}

impl AdvancedScheduler {
    /// Create a new advanced scheduler
    pub fn new(algorithm: SchedulingAlgorithm) -> Self {
        Self {
            algorithm,
            device_loads: Arc::new(Mutex::new(HashMap::new())),
            performance_history: Arc::new(Mutex::new(HashMap::new())),
            config: AdvancedSchedulerConfig::default(),
        }
    }

    /// Update device load information
    pub fn update_device_load(&self, device_id: &str, load: DeviceLoad) {
        if let Ok(mut loads) = self.device_loads.lock() {
            loads.insert(device_id.to_string(), load);
        }
    }

    /// Record performance for learning-based scheduling
    pub fn record_performance(&self, device_id: &str, record: PerformanceRecord) {
        if let Ok(mut history) = self.performance_history.lock() {
            let device_history = history.entry(device_id.to_string()).or_default();
            device_history.push(record);

            // Keep only recent history within window
            if device_history.len() > self.config.history_window {
                device_history.drain(..device_history.len() - self.config.history_window);
            }
        }
    }

    /// Predict performance for a given operation on a device
    pub fn predict_performance(&self, device_id: &str, operation_type: &str) -> Option<Duration> {
        if let Ok(history) = self.performance_history.lock() {
            if let Some(device_history) = history.get(device_id) {
                let matching_ops: Vec<_> = device_history
                    .iter()
                    .filter(|record| record.operation_type == operation_type)
                    .collect();

                if !matching_ops.is_empty() {
                    let avg_duration = matching_ops
                        .iter()
                        .map(|record| record.execution_time.as_millis())
                        .sum::<u128>()
                        / matching_ops.len() as u128;

                    return Some(Duration::from_millis(avg_duration as u64));
                }
            }
        }
        None
    }

    /// Schedule using the configured algorithm
    pub fn schedule_advanced(
        &self,
        operation: &QueuedOperation,
        available_devices: &[String],
    ) -> HardwareResult<String> {
        match self.algorithm {
            SchedulingAlgorithm::FCFS => self.schedule_fcfs(available_devices),
            SchedulingAlgorithm::SJF => self.schedule_sjf(operation, available_devices),
            SchedulingAlgorithm::Priority => self.schedule_priority(operation, available_devices),
            SchedulingAlgorithm::RoundRobin => self.schedule_round_robin(available_devices),
            SchedulingAlgorithm::LoadAware => self.schedule_load_aware(available_devices),
            SchedulingAlgorithm::PerformanceBased => {
                self.schedule_performance_based(operation, available_devices)
            },
            SchedulingAlgorithm::MLBased => self.schedule_ml_based(operation, available_devices),
        }
    }

    fn schedule_fcfs(&self, available_devices: &[String]) -> HardwareResult<String> {
        available_devices
            .first()
            .ok_or_else(|| TrustformersError::model_error("No devices available".to_string()))
            .cloned()
    }

    fn schedule_sjf(
        &self,
        operation: &QueuedOperation,
        available_devices: &[String],
    ) -> HardwareResult<String> {
        // Select device with shortest predicted execution time
        let mut best_device = None;
        let mut best_time = Duration::from_secs(u64::MAX);

        for device_id in available_devices {
            if let Some(predicted_time) =
                self.predict_performance(device_id, &operation.operation_type)
            {
                if predicted_time < best_time {
                    best_time = predicted_time;
                    best_device = Some(device_id.clone());
                }
            }
        }

        best_device
            .or_else(|| available_devices.first().cloned())
            .ok_or_else(|| TrustformersError::model_error("No devices available".to_string()))
    }

    fn schedule_priority(
        &self,
        operation: &QueuedOperation,
        available_devices: &[String],
    ) -> HardwareResult<String> {
        // Select device based on operation priority and device capability
        // For now, return first available device
        available_devices
            .first()
            .ok_or_else(|| TrustformersError::model_error("No devices available".to_string()))
            .cloned()
    }

    fn schedule_round_robin(&self, available_devices: &[String]) -> HardwareResult<String> {
        // Implement round-robin selection
        // This would maintain state for the next device index
        available_devices
            .first()
            .ok_or_else(|| TrustformersError::model_error("No devices available".to_string()))
            .cloned()
    }

    fn schedule_load_aware(&self, available_devices: &[String]) -> HardwareResult<String> {
        if let Ok(loads) = self.device_loads.lock() {
            let mut best_device = None;
            let mut lowest_load = f64::MAX;

            for device_id in available_devices {
                if let Some(load) = loads.get(device_id) {
                    if load.utilization < lowest_load {
                        lowest_load = load.utilization;
                        best_device = Some(device_id.clone());
                    }
                }
            }

            return best_device
                .or_else(|| available_devices.first().cloned())
                .ok_or_else(|| TrustformersError::model_error("No devices available".to_string()));
        }

        self.schedule_fcfs(available_devices)
    }

    fn schedule_performance_based(
        &self,
        operation: &QueuedOperation,
        available_devices: &[String],
    ) -> HardwareResult<String> {
        // Select device with best historical performance for this operation type
        if let Ok(history) = self.performance_history.lock() {
            let mut best_device = None;
            let mut best_throughput = 0.0;

            for device_id in available_devices {
                if let Some(device_history) = history.get(device_id) {
                    let avg_throughput = device_history
                        .iter()
                        .filter(|record| record.operation_type == operation.operation_type)
                        .map(|record| record.throughput)
                        .sum::<f64>()
                        / device_history.len() as f64;

                    if avg_throughput > best_throughput {
                        best_throughput = avg_throughput;
                        best_device = Some(device_id.clone());
                    }
                }
            }

            return best_device
                .or_else(|| available_devices.first().cloned())
                .ok_or_else(|| TrustformersError::model_error("No devices available".to_string()));
        }

        self.schedule_fcfs(available_devices)
    }

    fn schedule_ml_based(
        &self,
        _operation: &QueuedOperation,
        available_devices: &[String],
    ) -> HardwareResult<String> {
        // Placeholder for ML-based scheduling
        // Would implement neural network or other ML model for scheduling decisions
        self.schedule_load_aware(available_devices)
    }
}

impl Default for SchedulerConfig {
    fn default() -> Self {
        Self {
            max_queue_size: 1000,
            queue_timeout: Duration::from_secs(30),
            enable_priority_scheduling: true,
            load_balancing_weight: 0.3,
            performance_weight: 0.4,
            availability_weight: 0.3,
        }
    }
}

impl Default for AdvancedSchedulerConfig {
    fn default() -> Self {
        Self {
            learning_rate: 0.001,
            history_window: 1000,
            prediction_threshold: 0.8,
            load_balancing_factor: 1.0,
        }
    }
}

impl Default for DeviceLoad {
    fn default() -> Self {
        Self {
            utilization: 0.0,
            active_operations: 0,
            queued_operations: 0,
            last_updated: SystemTime::now(),
            avg_response_time: Duration::from_millis(100),
        }
    }
}

impl Default for DefaultScheduler {
    fn default() -> Self {
        Self::new()
    }
}

impl HardwareScheduler for AdvancedScheduler {
    fn schedule_operation(
        &self,
        operation: &dyn HardwareOperation,
        inputs: &[Tensor],
        params: &HashMap<String, OperationParameter>,
    ) -> crate::hardware::HardwareResult<String> {
        // Convert to queued operation for advanced scheduling
        let queued_op = QueuedOperation {
            id: format!(
                "op_{}",
                SystemTime::now()
                    .duration_since(SystemTime::UNIX_EPOCH)
                    .unwrap_or_default()
                    .as_nanos()
            ),
            operation_type: operation.name().to_string(),
            inputs: inputs
                .iter()
                .map(|t| TensorInfo {
                    shape: t.shape(),
                    dtype_size: 4, // Assume f32 for now
                    layout: "contiguous".to_string(),
                })
                .collect(),
            params: params.clone(),
            priority: 1.0, // Default priority
            enqueued_at: SystemTime::now(),
            estimated_duration: Duration::from_millis(100), // Default estimate
        };

        // Get available devices (simplified implementation)
        let available_devices = vec!["cpu".to_string(), "gpu".to_string()];

        match self.algorithm {
            SchedulingAlgorithm::FCFS => self.schedule_fcfs(&available_devices),
            SchedulingAlgorithm::SJF => self.schedule_sjf(&queued_op, &available_devices),
            SchedulingAlgorithm::Priority => self.schedule_priority(&queued_op, &available_devices),
            SchedulingAlgorithm::RoundRobin => self.schedule_round_robin(&available_devices),
            SchedulingAlgorithm::LoadAware => self.schedule_load_aware(&available_devices),
            SchedulingAlgorithm::PerformanceBased => {
                self.schedule_performance_based(&queued_op, &available_devices)
            },
            SchedulingAlgorithm::MLBased => self.schedule_ml_based(&queued_op, &available_devices),
        }
    }

    fn statistics(&self) -> SchedulerStatistics {
        SchedulerStatistics {
            total_operations: 0,
            operations_per_device: HashMap::new(),
            avg_scheduling_time: 10.0,
            device_utilization: HashMap::new(),
            failed_operations: 0,
        }
    }

    fn update_priorities(&mut self, _priorities: HashMap<String, f64>) {
        // Implementation would update internal priority tracking
        // For now this is a no-op as AdvancedScheduler uses different mechanisms
    }
}
