//! Compression Metrics and Evaluation

#![allow(unused_variables)] // Compression metrics

use crate::tensor::Tensor;
use anyhow::Result;
use std::collections::HashMap;

/// Compression metrics for evaluation
#[derive(Debug, Clone)]
pub struct CompressionMetrics {
    /// Model size reduction
    pub size_reduction: ModelSizeReduction,
    /// Sparsity metrics
    pub sparsity: SparsityMetric,
    /// Accuracy retention
    pub accuracy_retention: AccuracyRetention,
    /// Inference speedup
    pub inference_speedup: InferenceSpeedup,
    /// Compression ratio
    pub compression_ratio: CompressionRatio,
    /// Memory usage
    pub memory_usage: MemoryUsage,
    /// Energy efficiency
    pub energy_efficiency: Option<EnergyEfficiency>,
}

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

impl CompressionMetrics {
    pub fn new() -> Self {
        Self {
            size_reduction: ModelSizeReduction::default(),
            sparsity: SparsityMetric::default(),
            accuracy_retention: AccuracyRetention::default(),
            inference_speedup: InferenceSpeedup::default(),
            compression_ratio: CompressionRatio::default(),
            memory_usage: MemoryUsage::default(),
            energy_efficiency: None,
        }
    }

    /// Generate summary report
    pub fn summary(&self) -> String {
        format!(
            "Compression Metrics Summary:\n\
             ├─ Size Reduction: {:.1}%\n\
             ├─ Sparsity: {:.1}%\n\
             ├─ Accuracy Retention: {:.1}%\n\
             ├─ Inference Speedup: {:.2}x\n\
             ├─ Compression Ratio: {:.2}x\n\
             └─ Memory Reduction: {:.1}%",
            self.size_reduction.percentage * 100.0,
            self.sparsity.overall * 100.0,
            self.accuracy_retention.percentage * 100.0,
            self.inference_speedup.speedup_factor,
            self.compression_ratio.ratio,
            self.memory_usage.reduction_percentage * 100.0
        )
    }

    /// Check if compression meets targets
    pub fn meets_targets(&self, targets: &CompressionTargets) -> bool {
        self.size_reduction.percentage >= targets.min_size_reduction
            && self.accuracy_retention.percentage >= targets.min_accuracy
            && self.inference_speedup.speedup_factor >= targets.min_speedup
    }
}

/// Compression targets to achieve
#[derive(Debug, Clone)]
pub struct CompressionTargets {
    pub min_size_reduction: f32,
    pub min_accuracy: f32,
    pub min_speedup: f32,
}

impl Default for CompressionTargets {
    fn default() -> Self {
        Self {
            min_size_reduction: 0.5, // 50% reduction
            min_accuracy: 0.95,      // 95% accuracy retention
            min_speedup: 2.0,        // 2x speedup
        }
    }
}

/// Model size reduction metrics
#[derive(Debug, Clone, Default)]
pub struct ModelSizeReduction {
    pub original_size_bytes: usize,
    pub compressed_size_bytes: usize,
    pub percentage: f32,
    pub size_breakdown: HashMap<String, usize>,
}

impl ModelSizeReduction {
    pub fn calculate<O, C>(original: &O, compressed: &C) -> Self
    where
        O: crate::traits::Model,
        C: crate::traits::Model,
    {
        // Calculate actual sizes using num_parameters method
        let original_params = original.num_parameters();
        let compressed_params = compressed.num_parameters();

        let original_size = original_params * std::mem::size_of::<f32>();
        let compressed_size = compressed_params * std::mem::size_of::<f32>();
        let percentage = 1.0 - (compressed_size as f32 / original_size as f32);

        Self {
            original_size_bytes: original_size,
            compressed_size_bytes: compressed_size,
            percentage,
            size_breakdown: HashMap::new(),
        }
    }
}

/// Sparsity metrics
#[derive(Debug, Clone, Default)]
pub struct SparsityMetric {
    pub overall: f32,
    pub layer_sparsity: HashMap<String, f32>,
    pub structured_sparsity: f32,
    pub unstructured_sparsity: f32,
}

impl SparsityMetric {
    pub fn calculate_from_tensor(tensor: &Tensor) -> Result<f32> {
        let data = tensor.data()?;
        let zero_count = data.iter().filter(|&&x| x.abs() < 1e-8).count();
        Ok(zero_count as f32 / data.len() as f32)
    }

    pub fn calculate_layer_sparsity(
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, f32>> {
        let mut result = HashMap::new();
        for (name, tensor) in weights {
            result.insert(name.clone(), Self::calculate_from_tensor(tensor)?);
        }
        Ok(result)
    }
}

/// Accuracy retention metrics
#[derive(Debug, Clone, Default)]
pub struct AccuracyRetention {
    pub original_accuracy: f32,
    pub compressed_accuracy: f32,
    pub percentage: f32,
    pub task_specific_retention: HashMap<String, f32>,
}

impl AccuracyRetention {
    pub fn new(original: f32, compressed: f32) -> Self {
        Self {
            original_accuracy: original,
            compressed_accuracy: compressed,
            percentage: compressed / original,
            task_specific_retention: HashMap::new(),
        }
    }

    pub fn add_task_metric(&mut self, task: String, retention: f32) {
        self.task_specific_retention.insert(task, retention);
    }
}

/// Inference speedup metrics
#[derive(Debug, Clone, Default)]
pub struct InferenceSpeedup {
    pub original_latency_ms: f32,
    pub compressed_latency_ms: f32,
    pub speedup_factor: f32,
    pub throughput_improvement: f32,
    pub batch_size_scaling: HashMap<usize, f32>,
}

impl InferenceSpeedup {
    pub fn calculate(original_ms: f32, compressed_ms: f32) -> Self {
        let speedup = original_ms / compressed_ms;
        let throughput_improvement = speedup;

        Self {
            original_latency_ms: original_ms,
            compressed_latency_ms: compressed_ms,
            speedup_factor: speedup,
            throughput_improvement,
            batch_size_scaling: HashMap::new(),
        }
    }

    pub fn add_batch_scaling(&mut self, batch_size: usize, speedup: f32) {
        self.batch_size_scaling.insert(batch_size, speedup);
    }
}

/// Compression ratio metrics
#[derive(Debug, Clone, Default)]
pub struct CompressionRatio {
    pub ratio: f32,
    pub bits_per_weight: f32,
    pub effective_compression: f32,
}

impl CompressionRatio {
    pub fn calculate(original_bits: usize, compressed_bits: usize) -> Self {
        let ratio = original_bits as f32 / compressed_bits as f32;

        Self {
            ratio,
            bits_per_weight: compressed_bits as f32 / original_bits as f32 * 32.0,
            effective_compression: ratio,
        }
    }
}

/// Memory usage metrics
#[derive(Debug, Clone, Default)]
pub struct MemoryUsage {
    pub peak_memory_mb: f32,
    pub average_memory_mb: f32,
    pub reduction_percentage: f32,
}

/// Energy efficiency metrics
#[derive(Debug, Clone)]
pub struct EnergyEfficiency {
    pub original_energy_per_inference: f32,
    pub compressed_energy_per_inference: f32,
    pub energy_savings_percentage: f32,
}

/// Compression evaluator
pub struct CompressionEvaluator {
    validation_data: Option<Vec<(Tensor, Tensor)>>,
}

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

impl CompressionEvaluator {
    pub fn new() -> Self {
        Self {
            validation_data: None,
        }
    }

    pub fn with_validation_data(mut self, data: Vec<(Tensor, Tensor)>) -> Self {
        self.validation_data = Some(data);
        self
    }

    /// Evaluate compression quality
    pub fn evaluate<O, C>(&self, original: &O, compressed: &C) -> Result<CompressionMetrics>
    where
        O: crate::traits::Model<Input = Tensor, Output = Tensor>,
        C: crate::traits::Model<Input = Tensor, Output = Tensor>,
    {
        let mut metrics = CompressionMetrics::new();

        // Calculate size reduction
        metrics.size_reduction = ModelSizeReduction::calculate(original, compressed);

        // Calculate sparsity (would need access to model weights)
        // metrics.sparsity = self.calculate_sparsity(compressed)?;

        // Calculate accuracy retention if validation data available
        if let Some(ref data) = self.validation_data {
            metrics.accuracy_retention =
                self.evaluate_accuracy_retention(original, compressed, data)?;
        }

        // Measure inference speedup
        metrics.inference_speedup = self.measure_inference_speedup(original, compressed)?;

        // Calculate compression ratio using actual parameter counts
        let original_params = original.num_parameters();
        let compressed_params = compressed.num_parameters();
        let original_bits = original_params * 32; // Assuming f32 parameters (32 bits each)
        let compressed_bits = compressed_params * 32; // Assuming f32 parameters (32 bits each)
        metrics.compression_ratio = CompressionRatio::calculate(original_bits, compressed_bits);

        Ok(metrics)
    }

    fn evaluate_accuracy_retention<O, C>(
        &self,
        original: &O,
        compressed: &C,
        data: &[(Tensor, Tensor)],
    ) -> Result<AccuracyRetention>
    where
        O: crate::traits::Model<Input = Tensor, Output = Tensor>,
        C: crate::traits::Model<Input = Tensor, Output = Tensor>,
    {
        let mut original_correct = 0;
        let mut compressed_correct = 0;

        for (input, target) in data {
            let original_output = original.forward(input.clone())?;
            let compressed_output = compressed.forward(input.clone())?;

            // Simplified accuracy calculation
            if self.is_correct(&original_output, target)? {
                original_correct += 1;
            }
            if self.is_correct(&compressed_output, target)? {
                compressed_correct += 1;
            }
        }

        let original_acc = original_correct as f32 / data.len() as f32;
        let compressed_acc = compressed_correct as f32 / data.len() as f32;

        Ok(AccuracyRetention::new(original_acc, compressed_acc))
    }

    fn measure_inference_speedup<O, C>(
        &self,
        original: &O,
        compressed: &C,
    ) -> Result<InferenceSpeedup>
    where
        O: crate::traits::Model<Input = Tensor, Output = Tensor>,
        C: crate::traits::Model<Input = Tensor, Output = Tensor>,
    {
        use std::time::Instant;

        // Create dummy input
        let input = Tensor::zeros(&[1, 512])?;

        // Measure original model
        let start = Instant::now();
        for _ in 0..100 {
            original.forward(input.clone())?;
        }
        let original_ms = start.elapsed().as_millis() as f32 / 100.0;

        // Measure compressed model
        let start = Instant::now();
        for _ in 0..100 {
            compressed.forward(input.clone())?;
        }
        let compressed_ms = start.elapsed().as_millis() as f32 / 100.0;

        Ok(InferenceSpeedup::calculate(original_ms, compressed_ms))
    }

    fn is_correct(&self, output: &Tensor, target: &Tensor) -> Result<bool> {
        // Simplified correctness check
        let output_data = output.data()?;
        let target_data = target.data()?;

        if output_data.is_empty() || target_data.is_empty() {
            return Ok(false);
        }

        // Find argmax
        let pred_idx = output_data
            .iter()
            .enumerate()
            .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())
            .map(|(i, _)| i)
            .unwrap_or(0);

        let target_idx = target_data
            .iter()
            .enumerate()
            .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())
            .map(|(i, _)| i)
            .unwrap_or(0);

        Ok(pred_idx == target_idx)
    }
}

/// Benchmark different compression techniques
pub struct CompressionBenchmark {
    techniques: Vec<String>,
    results: HashMap<String, CompressionMetrics>,
}

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

impl CompressionBenchmark {
    pub fn new() -> Self {
        Self {
            techniques: vec![],
            results: HashMap::new(),
        }
    }

    pub fn add_technique(&mut self, name: String) {
        self.techniques.push(name);
    }

    pub fn run_benchmark<M>(&mut self, model: &M) -> Result<()>
    where
        M: crate::traits::Model,
    {
        // Would benchmark each technique
        for technique in &self.techniques {
            println!("Benchmarking {}", technique);
            // Run compression and evaluation
            // Store results
        }
        Ok(())
    }

    pub fn get_best_technique(&self) -> Option<&String> {
        self.results
            .iter()
            .max_by(|(_, a), (_, b)| {
                // Compare by a composite score
                let score_a = a.accuracy_retention.percentage * a.inference_speedup.speedup_factor;
                let score_b = b.accuracy_retention.percentage * b.inference_speedup.speedup_factor;
                score_a.partial_cmp(&score_b).unwrap()
            })
            .map(|(name, _)| name)
    }

    pub fn generate_comparison_report(&self) -> String {
        let mut report = String::from("Compression Technique Comparison:\n\n");

        for (name, metrics) in &self.results {
            report.push_str(&format!("{}\n{}\n\n", name, metrics.summary()));
        }

        if let Some(best) = self.get_best_technique() {
            report.push_str(&format!("Best technique: {}\n", best));
        }

        report
    }
}
