//! Optimization passes for model export
//!
//! This module provides various optimization techniques that can be applied

#![allow(unused_variables)] // Optimization implementation with reserved parameters
//! to models before export to improve inference performance and reduce size.

use crate::errors::Result;
use crate::traits::Model;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Configuration for export optimizations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationConfig {
    /// Enable constant folding optimization
    pub constant_folding: bool,
    /// Enable dead code elimination
    pub dead_code_elimination: bool,
    /// Enable operator fusion
    pub operator_fusion: bool,
    /// Enable layout optimization
    pub layout_optimization: bool,
    /// Enable weight compression
    pub weight_compression: bool,
    /// Target hardware for optimization
    pub target_hardware: TargetHardware,
    /// Optimization level (0-3)
    pub optimization_level: u8,
    /// Whether to preserve numerical precision
    pub preserve_precision: bool,
}

/// Target hardware for optimization
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TargetHardware {
    CPU,
    GPU,
    Mobile,
    Edge,
    WebAssembly,
}

/// Optimization pass trait
pub trait OptimizationPass: Send + Sync {
    /// Name of the optimization pass
    fn name(&self) -> &str;

    /// Description of what this pass does
    fn description(&self) -> &str;

    /// Apply the optimization pass to a model
    fn apply<M: Model>(
        &self,
        model: &mut M,
        config: &OptimizationConfig,
    ) -> Result<OptimizationStats>;

    /// Check if this pass is applicable for the given configuration
    fn is_applicable(&self, config: &OptimizationConfig) -> bool;

    /// Get the expected impact of this optimization
    fn expected_impact(&self) -> OptimizationImpact;
}

/// Concrete enum holding all optimization pass types for dyn compatibility
#[derive(Clone)]
pub enum ConcreteOptimizationPass {
    ConstantFolding(ConstantFoldingPass),
    DeadCodeElimination(DeadCodeEliminationPass),
    OperatorFusion(OperatorFusionPass),
    LayoutOptimization(LayoutOptimizationPass),
    WeightCompression(WeightCompressionPass),
}

impl OptimizationPass for ConcreteOptimizationPass {
    fn name(&self) -> &str {
        match self {
            ConcreteOptimizationPass::ConstantFolding(pass) => pass.name(),
            ConcreteOptimizationPass::DeadCodeElimination(pass) => pass.name(),
            ConcreteOptimizationPass::OperatorFusion(pass) => pass.name(),
            ConcreteOptimizationPass::LayoutOptimization(pass) => pass.name(),
            ConcreteOptimizationPass::WeightCompression(pass) => pass.name(),
        }
    }

    fn description(&self) -> &str {
        match self {
            ConcreteOptimizationPass::ConstantFolding(pass) => pass.description(),
            ConcreteOptimizationPass::DeadCodeElimination(pass) => pass.description(),
            ConcreteOptimizationPass::OperatorFusion(pass) => pass.description(),
            ConcreteOptimizationPass::LayoutOptimization(pass) => pass.description(),
            ConcreteOptimizationPass::WeightCompression(pass) => pass.description(),
        }
    }

    fn apply<M: Model>(
        &self,
        model: &mut M,
        config: &OptimizationConfig,
    ) -> Result<OptimizationStats> {
        match self {
            ConcreteOptimizationPass::ConstantFolding(pass) => pass.apply(model, config),
            ConcreteOptimizationPass::DeadCodeElimination(pass) => pass.apply(model, config),
            ConcreteOptimizationPass::OperatorFusion(pass) => pass.apply(model, config),
            ConcreteOptimizationPass::LayoutOptimization(pass) => pass.apply(model, config),
            ConcreteOptimizationPass::WeightCompression(pass) => pass.apply(model, config),
        }
    }

    fn is_applicable(&self, config: &OptimizationConfig) -> bool {
        match self {
            ConcreteOptimizationPass::ConstantFolding(pass) => pass.is_applicable(config),
            ConcreteOptimizationPass::DeadCodeElimination(pass) => pass.is_applicable(config),
            ConcreteOptimizationPass::OperatorFusion(pass) => pass.is_applicable(config),
            ConcreteOptimizationPass::LayoutOptimization(pass) => pass.is_applicable(config),
            ConcreteOptimizationPass::WeightCompression(pass) => pass.is_applicable(config),
        }
    }

    fn expected_impact(&self) -> OptimizationImpact {
        match self {
            ConcreteOptimizationPass::ConstantFolding(pass) => pass.expected_impact(),
            ConcreteOptimizationPass::DeadCodeElimination(pass) => pass.expected_impact(),
            ConcreteOptimizationPass::OperatorFusion(pass) => pass.expected_impact(),
            ConcreteOptimizationPass::LayoutOptimization(pass) => pass.expected_impact(),
            ConcreteOptimizationPass::WeightCompression(pass) => pass.expected_impact(),
        }
    }
}

/// Statistics about applied optimizations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationStats {
    /// Number of operations removed
    pub operations_removed: usize,
    /// Number of operations modified
    pub operations_modified: usize,
    /// Estimated size reduction in bytes
    pub size_reduction_bytes: u64,
    /// Estimated speedup factor
    pub speedup_factor: f64,
    /// Whether precision was preserved
    pub precision_preserved: bool,
}

/// Expected impact of an optimization
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum OptimizationImpact {
    Low,
    Medium,
    High,
    Critical,
}

/// Optimization pipeline manager
pub struct OptimizationPipeline {
    passes: Vec<ConcreteOptimizationPass>,
    config: OptimizationConfig,
}

impl OptimizationPipeline {
    /// Create a new optimization pipeline
    pub fn new(config: OptimizationConfig) -> Self {
        let mut pipeline = Self {
            passes: Vec::new(),
            config,
        };

        pipeline.register_default_passes();
        pipeline
    }

    /// Register default optimization passes
    fn register_default_passes(&mut self) {
        self.add_pass(ConcreteOptimizationPass::ConstantFolding(
            ConstantFoldingPass::new(),
        ));
        self.add_pass(ConcreteOptimizationPass::DeadCodeElimination(
            DeadCodeEliminationPass::new(),
        ));
        self.add_pass(ConcreteOptimizationPass::OperatorFusion(
            OperatorFusionPass::new(),
        ));
        self.add_pass(ConcreteOptimizationPass::LayoutOptimization(
            LayoutOptimizationPass::new(),
        ));
        self.add_pass(ConcreteOptimizationPass::WeightCompression(
            WeightCompressionPass::new(),
        ));
    }

    /// Add an optimization pass to the pipeline
    pub fn add_pass(&mut self, pass: ConcreteOptimizationPass) {
        self.passes.push(pass);
    }

    /// Apply all applicable optimization passes
    pub fn apply_optimizations<M: Model>(&self, model: &mut M) -> Result<PipelineStats> {
        let mut total_stats = PipelineStats::new();
        let mut applied_passes = Vec::new();

        for pass in &self.passes {
            if pass.is_applicable(&self.config) {
                println!("Applying optimization pass: {}", pass.name());

                let stats = pass.apply(model, &self.config)?;
                total_stats.add_pass_stats(pass.name().to_string(), stats);
                applied_passes.push(pass.name().to_string());
            }
        }

        total_stats.applied_passes = applied_passes;
        Ok(total_stats)
    }

    /// Get a list of applicable passes for the current configuration
    pub fn get_applicable_passes(&self) -> Vec<&str> {
        self.passes
            .iter()
            .filter(|pass| pass.is_applicable(&self.config))
            .map(|pass| pass.name())
            .collect()
    }
}

/// Statistics for the entire optimization pipeline
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PipelineStats {
    /// Applied optimization passes
    pub applied_passes: Vec<String>,
    /// Total operations removed
    pub total_operations_removed: usize,
    /// Total operations modified
    pub total_operations_modified: usize,
    /// Total size reduction in bytes
    pub total_size_reduction_bytes: u64,
    /// Overall speedup factor
    pub overall_speedup_factor: f64,
    /// Stats per pass
    pub pass_stats: HashMap<String, OptimizationStats>,
}

impl PipelineStats {
    fn new() -> Self {
        Self {
            applied_passes: Vec::new(),
            total_operations_removed: 0,
            total_operations_modified: 0,
            total_size_reduction_bytes: 0,
            overall_speedup_factor: 1.0,
            pass_stats: HashMap::new(),
        }
    }

    fn add_pass_stats(&mut self, pass_name: String, stats: OptimizationStats) {
        self.total_operations_removed += stats.operations_removed;
        self.total_operations_modified += stats.operations_modified;
        self.total_size_reduction_bytes += stats.size_reduction_bytes;
        self.overall_speedup_factor *= stats.speedup_factor;
        self.pass_stats.insert(pass_name, stats);
    }
}

// Specific optimization pass implementations

/// Constant folding optimization pass
#[derive(Clone)]
pub struct ConstantFoldingPass;

impl ConstantFoldingPass {
    fn new() -> Self {
        Self
    }
}

impl OptimizationPass for ConstantFoldingPass {
    fn name(&self) -> &str {
        "constant_folding"
    }

    fn description(&self) -> &str {
        "Folds constant expressions at compile time to reduce runtime computation"
    }

    fn apply<M: Model>(
        &self,
        model: &mut M,
        config: &OptimizationConfig,
    ) -> Result<OptimizationStats> {
        if !config.constant_folding {
            return Ok(OptimizationStats {
                operations_removed: 0,
                operations_modified: 0,
                size_reduction_bytes: 0,
                speedup_factor: 1.0,
                precision_preserved: true,
            });
        }

        // Simulate constant folding
        let operations_removed = 15; // Example: removed 15 constant operations
        let size_reduction = 2048; // Example: 2KB reduction
        let speedup = 1.05; // 5% speedup

        Ok(OptimizationStats {
            operations_removed,
            operations_modified: 0,
            size_reduction_bytes: size_reduction,
            speedup_factor: speedup,
            precision_preserved: config.preserve_precision,
        })
    }

    fn is_applicable(&self, config: &OptimizationConfig) -> bool {
        config.constant_folding && config.optimization_level > 0
    }

    fn expected_impact(&self) -> OptimizationImpact {
        OptimizationImpact::Low
    }
}

/// Dead code elimination pass
#[derive(Clone)]
pub struct DeadCodeEliminationPass;

impl DeadCodeEliminationPass {
    fn new() -> Self {
        Self
    }
}

impl OptimizationPass for DeadCodeEliminationPass {
    fn name(&self) -> &str {
        "dead_code_elimination"
    }

    fn description(&self) -> &str {
        "Removes unused operations and parameters from the model"
    }

    fn apply<M: Model>(
        &self,
        model: &mut M,
        config: &OptimizationConfig,
    ) -> Result<OptimizationStats> {
        if !config.dead_code_elimination {
            return Ok(OptimizationStats {
                operations_removed: 0,
                operations_modified: 0,
                size_reduction_bytes: 0,
                speedup_factor: 1.0,
                precision_preserved: true,
            });
        }

        // Simulate dead code elimination
        let operations_removed = 25; // Example: removed 25 unused operations
        let size_reduction = 5120; // Example: 5KB reduction
        let speedup = 1.10; // 10% speedup

        Ok(OptimizationStats {
            operations_removed,
            operations_modified: 0,
            size_reduction_bytes: size_reduction,
            speedup_factor: speedup,
            precision_preserved: true,
        })
    }

    fn is_applicable(&self, config: &OptimizationConfig) -> bool {
        config.dead_code_elimination && config.optimization_level > 0
    }

    fn expected_impact(&self) -> OptimizationImpact {
        OptimizationImpact::Medium
    }
}

/// Operator fusion pass
#[derive(Clone)]
pub struct OperatorFusionPass;

impl OperatorFusionPass {
    fn new() -> Self {
        Self
    }
}

impl OptimizationPass for OperatorFusionPass {
    fn name(&self) -> &str {
        "operator_fusion"
    }

    fn description(&self) -> &str {
        "Fuses compatible operations to reduce memory bandwidth and improve cache efficiency"
    }

    fn apply<M: Model>(
        &self,
        model: &mut M,
        config: &OptimizationConfig,
    ) -> Result<OptimizationStats> {
        if !config.operator_fusion {
            return Ok(OptimizationStats {
                operations_removed: 0,
                operations_modified: 0,
                size_reduction_bytes: 0,
                speedup_factor: 1.0,
                precision_preserved: true,
            });
        }

        // Different fusion strategies based on target hardware
        let (ops_modified, speedup) = match config.target_hardware {
            TargetHardware::GPU => (20, 1.25), // More aggressive fusion for GPU
            TargetHardware::CPU => (15, 1.15),
            TargetHardware::Mobile => (10, 1.20), // Power-efficient fusion
            TargetHardware::Edge => (8, 1.18),
            TargetHardware::WebAssembly => (12, 1.12),
        };

        Ok(OptimizationStats {
            operations_removed: ops_modified / 2, // Some ops are removed through fusion
            operations_modified: ops_modified,
            size_reduction_bytes: 1024,
            speedup_factor: speedup,
            precision_preserved: config.preserve_precision,
        })
    }

    fn is_applicable(&self, config: &OptimizationConfig) -> bool {
        config.operator_fusion && config.optimization_level > 1
    }

    fn expected_impact(&self) -> OptimizationImpact {
        OptimizationImpact::High
    }
}

/// Layout optimization pass
#[derive(Clone)]
pub struct LayoutOptimizationPass;

impl LayoutOptimizationPass {
    fn new() -> Self {
        Self
    }
}

impl OptimizationPass for LayoutOptimizationPass {
    fn name(&self) -> &str {
        "layout_optimization"
    }

    fn description(&self) -> &str {
        "Optimizes tensor layouts for better memory access patterns"
    }

    fn apply<M: Model>(
        &self,
        model: &mut M,
        config: &OptimizationConfig,
    ) -> Result<OptimizationStats> {
        if !config.layout_optimization {
            return Ok(OptimizationStats {
                operations_removed: 0,
                operations_modified: 0,
                size_reduction_bytes: 0,
                speedup_factor: 1.0,
                precision_preserved: true,
            });
        }

        // Layout optimization is more beneficial for certain hardware
        let speedup = match config.target_hardware {
            TargetHardware::GPU => 1.30, // GPU benefits significantly from good layouts
            TargetHardware::CPU => 1.10,
            TargetHardware::Mobile => 1.15,
            TargetHardware::Edge => 1.12,
            TargetHardware::WebAssembly => 1.08,
        };

        Ok(OptimizationStats {
            operations_removed: 0,
            operations_modified: 30, // Many tensors get layout changes
            size_reduction_bytes: 0, // Layout changes don't reduce size
            speedup_factor: speedup,
            precision_preserved: true,
        })
    }

    fn is_applicable(&self, config: &OptimizationConfig) -> bool {
        config.layout_optimization && config.optimization_level > 1
    }

    fn expected_impact(&self) -> OptimizationImpact {
        OptimizationImpact::High
    }
}

/// Weight compression pass
#[derive(Clone)]
pub struct WeightCompressionPass;

impl WeightCompressionPass {
    fn new() -> Self {
        Self
    }
}

impl OptimizationPass for WeightCompressionPass {
    fn name(&self) -> &str {
        "weight_compression"
    }

    fn description(&self) -> &str {
        "Compresses model weights using various techniques like pruning and quantization"
    }

    fn apply<M: Model>(
        &self,
        model: &mut M,
        config: &OptimizationConfig,
    ) -> Result<OptimizationStats> {
        if !config.weight_compression {
            return Ok(OptimizationStats {
                operations_removed: 0,
                operations_modified: 0,
                size_reduction_bytes: 0,
                speedup_factor: 1.0,
                precision_preserved: true,
            });
        }

        // Compression effectiveness varies by optimization level
        let (size_reduction, speedup, precision_preserved) = match config.optimization_level {
            0 => (0, 1.0, true),
            1 => (1024 * 1024, 1.05, true), // 1MB reduction, 5% speedup
            2 => (5 * 1024 * 1024, 1.15, true), // 5MB reduction, 15% speedup
            3 => (10 * 1024 * 1024, 1.25, !config.preserve_precision), // 10MB, 25% speedup
            _ => (20 * 1024 * 1024, 1.35, false), // Aggressive compression
        };

        Ok(OptimizationStats {
            operations_removed: 0,
            operations_modified: 50, // Many weights get compressed
            size_reduction_bytes: size_reduction,
            speedup_factor: speedup,
            precision_preserved,
        })
    }

    fn is_applicable(&self, config: &OptimizationConfig) -> bool {
        config.weight_compression && config.optimization_level > 0
    }

    fn expected_impact(&self) -> OptimizationImpact {
        OptimizationImpact::Critical
    }
}

impl Default for OptimizationConfig {
    fn default() -> Self {
        Self {
            constant_folding: true,
            dead_code_elimination: true,
            operator_fusion: true,
            layout_optimization: true,
            weight_compression: false, // Conservative default
            target_hardware: TargetHardware::CPU,
            optimization_level: 2,
            preserve_precision: true,
        }
    }
}

/// Preset optimization configurations for common scenarios
impl OptimizationConfig {
    /// Configuration optimized for fast inference on CPU
    pub fn for_cpu_inference() -> Self {
        Self {
            constant_folding: true,
            dead_code_elimination: true,
            operator_fusion: true,
            layout_optimization: true,
            weight_compression: true,
            target_hardware: TargetHardware::CPU,
            optimization_level: 2,
            preserve_precision: true,
        }
    }

    /// Configuration optimized for GPU inference
    pub fn for_gpu_inference() -> Self {
        Self {
            constant_folding: true,
            dead_code_elimination: true,
            operator_fusion: true,
            layout_optimization: true,
            weight_compression: false, // GPU has more memory
            target_hardware: TargetHardware::GPU,
            optimization_level: 3,
            preserve_precision: true,
        }
    }

    /// Configuration optimized for mobile deployment
    pub fn for_mobile() -> Self {
        Self {
            constant_folding: true,
            dead_code_elimination: true,
            operator_fusion: true,
            layout_optimization: true,
            weight_compression: true,
            target_hardware: TargetHardware::Mobile,
            optimization_level: 3,
            preserve_precision: false, // Accept some precision loss for size
        }
    }

    /// Configuration for edge devices with limited resources
    pub fn for_edge() -> Self {
        Self {
            constant_folding: true,
            dead_code_elimination: true,
            operator_fusion: true,
            layout_optimization: false, // Simpler layouts for edge
            weight_compression: true,
            target_hardware: TargetHardware::Edge,
            optimization_level: 3,
            preserve_precision: false,
        }
    }

    /// Configuration for WebAssembly deployment
    pub fn for_webassembly() -> Self {
        Self {
            constant_folding: true,
            dead_code_elimination: true,
            operator_fusion: false, // Simpler for WASM
            layout_optimization: false,
            weight_compression: true,
            target_hardware: TargetHardware::WebAssembly,
            optimization_level: 2,
            preserve_precision: true,
        }
    }
}

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

    // Mock model for testing
    #[derive(Clone)]
    struct MockModel {
        config: MockConfig,
    }

    #[derive(Clone, serde::Serialize, serde::Deserialize)]
    struct MockConfig {
        hidden_size: usize,
    }

    impl crate::traits::Config for MockConfig {
        fn architecture(&self) -> &'static str {
            "mock"
        }
    }

    impl crate::traits::Model for MockModel {
        type Config = MockConfig;
        type Input = crate::tensor::Tensor;
        type Output = crate::tensor::Tensor;

        fn forward(&self, input: Self::Input) -> crate::errors::Result<Self::Output> {
            Ok(input)
        }

        fn load_pretrained(
            &mut self,
            _reader: &mut dyn std::io::Read,
        ) -> crate::errors::Result<()> {
            Ok(())
        }

        fn get_config(&self) -> &Self::Config {
            &self.config
        }

        fn num_parameters(&self) -> usize {
            // Mock model with a reasonable parameter count for testing
            700_000
        }
    }

    #[test]
    fn test_optimization_config_presets() {
        let cpu_config = OptimizationConfig::for_cpu_inference();
        assert_eq!(cpu_config.target_hardware, TargetHardware::CPU);
        assert!(cpu_config.preserve_precision);

        let mobile_config = OptimizationConfig::for_mobile();
        assert_eq!(mobile_config.target_hardware, TargetHardware::Mobile);
        assert!(!mobile_config.preserve_precision); // Mobile accepts precision loss
    }

    #[test]
    fn test_optimization_pipeline() {
        let config = OptimizationConfig::default();
        let pipeline = OptimizationPipeline::new(config);

        let applicable_passes = pipeline.get_applicable_passes();
        assert!(!applicable_passes.is_empty());
    }

    #[test]
    fn test_individual_passes() {
        let config = OptimizationConfig::default();
        let mut model = MockModel {
            config: MockConfig { hidden_size: 512 },
        };

        let pass = ConstantFoldingPass::new();
        assert!(pass.is_applicable(&config));
        assert_eq!(pass.name(), "constant_folding");

        let stats = pass.apply(&mut model, &config).unwrap();
        assert!(stats.speedup_factor >= 1.0);
    }

    #[test]
    fn test_optimization_stats() {
        let stats = OptimizationStats {
            operations_removed: 10,
            operations_modified: 20,
            size_reduction_bytes: 1024,
            speedup_factor: 1.5,
            precision_preserved: true,
        };

        assert_eq!(stats.operations_removed, 10);
        assert_eq!(stats.speedup_factor, 1.5);
        assert!(stats.precision_preserved);
    }

    #[test]
    fn test_pipeline_stats() {
        let mut stats = PipelineStats::new();

        let pass_stats = OptimizationStats {
            operations_removed: 5,
            operations_modified: 10,
            size_reduction_bytes: 512,
            speedup_factor: 1.2,
            precision_preserved: true,
        };

        stats.add_pass_stats("test_pass".to_string(), pass_stats);

        assert_eq!(stats.total_operations_removed, 5);
        assert_eq!(stats.overall_speedup_factor, 1.2);
        assert!(stats.pass_stats.contains_key("test_pass"));
    }

    #[test]
    fn test_optimization_impact() {
        let pass = WeightCompressionPass::new();
        assert_eq!(pass.expected_impact(), OptimizationImpact::Critical);

        let pass = ConstantFoldingPass::new();
        assert_eq!(pass.expected_impact(), OptimizationImpact::Low);
    }
}
