//! Mixed-bit quantization implementation for TrustformeRS
//!
//! This module provides mixed-bit quantization where different layers/channels
//! can use different quantization bit widths based on their importance and
//! sensitivity to quantization errors.

#![allow(unused_variables)] // Mixed-bit quantization implementation

use crate::errors::Result;
use crate::quantization::base::QuantizationScheme;
use crate::tensor::Tensor;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Mixed-bit quantization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MixedBitConfig {
    /// Layer-specific quantization configurations
    pub layer_configs: HashMap<String, LayerQuantConfig>,
    /// Default configuration for layers not specified
    pub default_config: LayerQuantConfig,
    /// Sensitivity analysis configuration
    pub sensitivity_config: SensitivityConfig,
    /// Automatic bit allocation strategy
    pub auto_bit_allocation: Option<AutoBitAllocationStrategy>,
}

/// Layer-specific quantization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerQuantConfig {
    /// Bit width for weights
    pub weight_bits: u8,
    /// Bit width for activations
    pub activation_bits: u8,
    /// Quantization scheme to use
    pub scheme: QuantizationScheme,
    /// Whether to use symmetric quantization
    pub symmetric: bool,
    /// Group size for grouped quantization
    pub group_size: Option<usize>,
    /// Channel-specific bit allocation
    pub channel_bits: Option<Vec<u8>>,
}

/// Sensitivity analysis configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SensitivityConfig {
    /// Number of calibration samples
    pub calibration_samples: usize,
    /// Threshold for sensitivity (higher = more sensitive)
    pub sensitivity_threshold: f32,
    /// Metrics to consider for sensitivity analysis
    pub metrics: Vec<SensitivityMetric>,
}

/// Sensitivity metrics for determining quantization bit allocation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SensitivityMetric {
    /// Gradient magnitude
    GradientMagnitude,
    /// Hessian diagonal
    HessianDiagonal,
    /// Activation variance
    ActivationVariance,
    /// Weight variance
    WeightVariance,
    /// Output sensitivity
    OutputSensitivity,
}

/// Automatic bit allocation strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AutoBitAllocationStrategy {
    /// Sensitivity-based allocation
    SensitivityBased {
        /// Target model size compression ratio
        target_compression: f32,
        /// Minimum bits per layer
        min_bits: u8,
        /// Maximum bits per layer
        max_bits: u8,
    },
    /// Uniform allocation with adaptive adjustment
    AdaptiveUniform {
        /// Base bit width
        base_bits: u8,
        /// Adjustment range
        adjustment_range: u8,
    },
    /// Performance-driven allocation
    PerformanceDriven {
        /// Target inference latency (ms)
        target_latency: f32,
        /// Accuracy tolerance
        accuracy_tolerance: f32,
    },
}

/// Mixed-bit quantized tensor
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MixedBitQuantizedTensor {
    /// Layer name
    pub layer_name: String,
    /// Quantized data with different bit widths
    pub quantized_data: Vec<QuantizedBlock>,
    /// Original tensor shape
    pub shape: Vec<usize>,
    /// Quantization configuration used
    pub config: LayerQuantConfig,
    /// Sensitivity scores for each block
    pub sensitivity_scores: Vec<f32>,
}

/// A block of quantized data with specific bit width
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizedBlock {
    /// Quantized data
    pub data: Vec<u8>,
    /// Scale factor
    pub scale: f32,
    /// Zero point
    pub zero_point: i32,
    /// Bit width used for this block
    pub bit_width: u8,
    /// Block shape
    pub block_shape: Vec<usize>,
    /// Block offset in the original tensor
    pub block_offset: Vec<usize>,
}

/// Mixed-bit quantizer
pub struct MixedBitQuantizer {
    config: MixedBitConfig,
    sensitivity_analyzer: SensitivityAnalyzer,
}

/// Sensitivity analyzer for determining optimal bit allocations
struct SensitivityAnalyzer {
    config: SensitivityConfig,
    sensitivity_cache: HashMap<String, Vec<f32>>,
}

impl Default for MixedBitConfig {
    fn default() -> Self {
        Self {
            layer_configs: HashMap::new(),
            default_config: LayerQuantConfig::default(),
            sensitivity_config: SensitivityConfig::default(),
            auto_bit_allocation: Some(AutoBitAllocationStrategy::SensitivityBased {
                target_compression: 0.25, // 4x compression
                min_bits: 2,
                max_bits: 8,
            }),
        }
    }
}

impl Default for LayerQuantConfig {
    fn default() -> Self {
        Self {
            weight_bits: 4,
            activation_bits: 8,
            scheme: QuantizationScheme::Int4,
            symmetric: true,
            group_size: Some(128),
            channel_bits: None,
        }
    }
}

impl Default for SensitivityConfig {
    fn default() -> Self {
        Self {
            calibration_samples: 128,
            sensitivity_threshold: 0.01,
            metrics: vec![
                SensitivityMetric::GradientMagnitude,
                SensitivityMetric::ActivationVariance,
                SensitivityMetric::WeightVariance,
            ],
        }
    }
}

impl MixedBitQuantizer {
    /// Create a new mixed-bit quantizer
    pub fn new(config: MixedBitConfig) -> Self {
        let sensitivity_analyzer = SensitivityAnalyzer::new(config.sensitivity_config.clone());
        Self {
            config,
            sensitivity_analyzer,
        }
    }

    /// Quantize a tensor using mixed-bit quantization
    pub fn quantize(
        &mut self,
        tensor: &Tensor,
        layer_name: &str,
    ) -> Result<MixedBitQuantizedTensor> {
        // Get or create layer configuration
        let layer_config = self
            .config
            .layer_configs
            .get(layer_name)
            .cloned()
            .unwrap_or_else(|| self.config.default_config.clone());

        // Analyze sensitivity if needed
        let sensitivity_scores = if let Some(ref auto_strategy) = self.config.auto_bit_allocation {
            self.sensitivity_analyzer
                .analyze_sensitivity(tensor, layer_name, &layer_config)?
        } else {
            vec![1.0; tensor.shape().iter().product()]
        };

        // Allocate bits based on sensitivity
        let bit_allocation = self.allocate_bits(&sensitivity_scores, &layer_config)?;

        // Quantize tensor into blocks
        let quantized_blocks = self.quantize_blocks(tensor, &bit_allocation, &layer_config)?;

        Ok(MixedBitQuantizedTensor {
            layer_name: layer_name.to_string(),
            quantized_data: quantized_blocks,
            shape: tensor.shape(),
            config: layer_config,
            sensitivity_scores,
        })
    }

    /// Allocate bits based on sensitivity scores
    fn allocate_bits(
        &self,
        sensitivity_scores: &[f32],
        config: &LayerQuantConfig,
    ) -> Result<Vec<u8>> {
        let mut bit_allocation = vec![config.weight_bits; sensitivity_scores.len()];

        if let Some(ref strategy) = self.config.auto_bit_allocation {
            match strategy {
                AutoBitAllocationStrategy::SensitivityBased {
                    target_compression,
                    min_bits,
                    max_bits,
                } => {
                    // Sort indices by sensitivity
                    let mut indexed_scores: Vec<(usize, f32)> = sensitivity_scores
                        .iter()
                        .enumerate()
                        .map(|(i, &score)| (i, score))
                        .collect();
                    indexed_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

                    // Calculate target total bits
                    let total_elements = sensitivity_scores.len();
                    let target_total_bits = (total_elements as f32
                        * config.weight_bits as f32
                        * target_compression) as usize;
                    let mut allocated_bits = 0;

                    // Allocate bits starting from most sensitive
                    for (idx, _) in indexed_scores {
                        let remaining_elements =
                            total_elements - allocated_bits / (*max_bits as usize);
                        let remaining_budget = target_total_bits.saturating_sub(allocated_bits);

                        if remaining_elements > 0 {
                            let avg_bits_remaining = remaining_budget / remaining_elements;
                            let bits = (avg_bits_remaining as u8).clamp(*min_bits, *max_bits);
                            bit_allocation[idx] = bits;
                            allocated_bits += bits as usize;
                        }
                    }
                },
                AutoBitAllocationStrategy::AdaptiveUniform {
                    base_bits,
                    adjustment_range,
                } => {
                    // Calculate mean sensitivity
                    let mean_sensitivity =
                        sensitivity_scores.iter().sum::<f32>() / sensitivity_scores.len() as f32;

                    for (i, &score) in sensitivity_scores.iter().enumerate() {
                        let normalized_score = score / mean_sensitivity;
                        let adjustment = (normalized_score * *adjustment_range as f32) as i8;
                        let bits = (*base_bits as i8 + adjustment).clamp(1, 8) as u8;
                        bit_allocation[i] = bits;
                    }
                },
                AutoBitAllocationStrategy::PerformanceDriven {
                    target_latency,
                    accuracy_tolerance,
                } => {
                    // Performance-driven allocation optimizes for latency while maintaining accuracy
                    return self.allocate_bits_performance_driven(
                        sensitivity_scores,
                        config,
                        *target_latency,
                        *accuracy_tolerance,
                    );
                },
            }
        }

        Ok(bit_allocation)
    }

    /// Allocate bits based on sensitivity scores (fallback implementation)
    #[allow(dead_code)]
    fn allocate_bits_sensitivity_based(
        &self,
        sensitivity_scores: &[f32],
        config: &LayerQuantConfig,
    ) -> Result<Vec<u8>> {
        let mut bit_allocation = vec![config.weight_bits; sensitivity_scores.len()];

        // Find sensitivity percentiles
        let mut sorted_scores = sensitivity_scores.to_vec();
        sorted_scores.sort_by(|a, b| a.partial_cmp(b).unwrap());

        let high_sensitivity_threshold =
            sorted_scores[(sorted_scores.len() * 90 / 100).min(sorted_scores.len() - 1)];
        let low_sensitivity_threshold = sorted_scores[sorted_scores.len() * 10 / 100];

        for (i, &score) in sensitivity_scores.iter().enumerate() {
            if score >= high_sensitivity_threshold {
                bit_allocation[i] = 8; // High precision for sensitive parts
            } else if score <= low_sensitivity_threshold {
                bit_allocation[i] = 2; // Low precision for insensitive parts
            } else {
                bit_allocation[i] = 4; // Medium precision
            }
        }

        Ok(bit_allocation)
    }

    /// Performance-driven bit allocation optimizing for latency while maintaining accuracy
    fn allocate_bits_performance_driven(
        &self,
        sensitivity_scores: &[f32],
        config: &LayerQuantConfig,
        target_latency: f32,
        accuracy_tolerance: f32,
    ) -> Result<Vec<u8>> {
        let total_elements = sensitivity_scores.len();

        // Model performance characteristics (simplified model)
        // Lower bits = faster computation but potentially lower accuracy
        let performance_factor = |bits: u8| -> f32 {
            match bits {
                1 => 0.1,  // Very fast, very low accuracy impact
                2 => 0.25, // Fast, low accuracy impact
                3 => 0.4,  // Medium-fast, medium accuracy impact
                4 => 0.6,  // Medium, medium accuracy impact
                5 => 0.75, // Medium-slow, higher accuracy
                6 => 0.85, // Slow, high accuracy
                7 => 0.92, // Very slow, very high accuracy
                8 => 1.0,  // Slowest, highest accuracy
                _ => 1.0,
            }
        };

        // Calculate accuracy impact based on sensitivity
        let accuracy_impact = |sensitivity: f32, bits: u8| -> f32 {
            let base_impact = sensitivity / 100.0; // Normalize sensitivity
            let bit_factor = (8.0 - bits as f32) / 7.0; // Higher impact with fewer bits
            base_impact * bit_factor
        };

        // Sort elements by sensitivity to prioritize important layers
        let mut indexed_scores: Vec<(usize, f32)> =
            sensitivity_scores.iter().enumerate().map(|(i, &score)| (i, score)).collect();
        indexed_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

        // Start with lowest bits for maximum performance
        let mut current_bits = vec![2u8; total_elements];
        let mut current_latency = 0.0;
        let mut current_accuracy_loss = 0.0;

        // Calculate initial latency and accuracy
        for (i, &score) in sensitivity_scores.iter().enumerate() {
            current_latency += performance_factor(2);
            current_accuracy_loss += accuracy_impact(score, 2);
        }

        // Iteratively increase bits for most sensitive elements until we hit constraints
        for (idx, sensitivity) in indexed_scores {
            let current_element_bits = current_bits[idx];

            // Try increasing bits for this element
            for new_bits in (current_element_bits + 1)..=8 {
                let latency_change =
                    performance_factor(new_bits) - performance_factor(current_element_bits);
                let accuracy_change = accuracy_impact(sensitivity, current_element_bits)
                    - accuracy_impact(sensitivity, new_bits);

                let new_latency = current_latency + latency_change;
                let new_accuracy_loss = current_accuracy_loss - accuracy_change;

                // Check if this change fits within our constraints
                let normalized_latency = new_latency / total_elements as f32;
                if normalized_latency <= target_latency && new_accuracy_loss <= accuracy_tolerance {
                    // Apply the change
                    current_bits[idx] = new_bits;
                    current_latency = new_latency;
                    current_accuracy_loss = new_accuracy_loss;
                } else {
                    // Can't improve this element further, move to next
                    break;
                }
            }
        }

        // Apply final allocation
        let bit_allocation = current_bits;

        Ok(bit_allocation)
    }

    /// Quantize tensor into blocks with different bit widths
    fn quantize_blocks(
        &self,
        tensor: &Tensor,
        bit_allocation: &[u8],
        config: &LayerQuantConfig,
    ) -> Result<Vec<QuantizedBlock>> {
        let data = tensor.data()?;
        let shape = tensor.shape();
        let mut blocks = Vec::new();

        // Group elements by bit width
        let mut bit_groups: HashMap<u8, Vec<(usize, f32)>> = HashMap::new();
        for (i, (&bits, &value)) in bit_allocation.iter().zip(data.iter()).enumerate() {
            bit_groups.entry(bits).or_default().push((i, value));
        }

        // Quantize each group
        for (bit_width, elements) in bit_groups {
            let values: Vec<f32> = elements.iter().map(|(_, v)| *v).collect();
            let indices: Vec<usize> = elements.iter().map(|(i, _)| *i).collect();

            let (quantized_data, scale, zero_point) =
                self.quantize_group(&values, bit_width, config)?;

            blocks.push(QuantizedBlock {
                data: quantized_data,
                scale,
                zero_point,
                bit_width,
                block_shape: vec![values.len()],
                block_offset: vec![indices[0]], // Simplified for now
            });
        }

        Ok(blocks)
    }

    /// Quantize a group of values with specified bit width
    fn quantize_group(
        &self,
        values: &[f32],
        bit_width: u8,
        config: &LayerQuantConfig,
    ) -> Result<(Vec<u8>, f32, i32)> {
        if values.is_empty() {
            return Ok((Vec::new(), 1.0, 0));
        }

        let min_val = values.iter().fold(f32::INFINITY, |a, &b| a.min(b));
        let max_val = values.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));

        let qmin = 0;
        let qmax = (1 << bit_width) - 1;

        let (scale, zero_point) = if config.symmetric {
            let max_abs = max_val.abs().max(min_val.abs());
            let scale = max_abs / (qmax as f32 / 2.0);
            (scale, qmax / 2)
        } else {
            let scale = (max_val - min_val) / (qmax - qmin) as f32;
            let zero_point = qmin as f32 - min_val / scale;
            (scale, zero_point.round() as i32)
        };

        let mut quantized = Vec::with_capacity(values.len());
        for &value in values {
            let q_val = (value / scale + zero_point as f32).round() as i32;
            let clamped = q_val.clamp(qmin, qmax) as u8;
            quantized.push(clamped);
        }

        Ok((quantized, scale, zero_point))
    }

    /// Get compression ratio achieved
    pub fn compression_ratio(
        &self,
        original_size: usize,
        quantized_tensor: &MixedBitQuantizedTensor,
    ) -> f32 {
        let compressed_size: usize =
            quantized_tensor.quantized_data.iter().map(|block| block.data.len()).sum();

        original_size as f32 / compressed_size as f32
    }

    /// Estimate memory savings
    pub fn memory_savings(
        &self,
        original_tensor: &Tensor,
        quantized_tensor: &MixedBitQuantizedTensor,
    ) -> f32 {
        let original_bytes = original_tensor.size() * std::mem::size_of::<f32>();
        let quantized_bytes: usize =
            quantized_tensor.quantized_data.iter().map(|block| block.data.len()).sum();

        1.0 - (quantized_bytes as f32 / original_bytes as f32)
    }
}

impl MixedBitQuantizedTensor {
    /// Dequantize back to original tensor
    pub fn dequantize(&self) -> Result<Tensor> {
        let total_elements: usize = self.shape.iter().product();
        let mut result = vec![0.0f32; total_elements];

        for block in &self.quantized_data {
            for (i, &quantized_val) in block.data.iter().enumerate() {
                let dequantized = (quantized_val as i32 - block.zero_point) as f32 * block.scale;
                // Simplified mapping - in practice, would need proper index mapping
                if i < result.len() {
                    result[i] = dequantized;
                }
            }
        }

        Tensor::from_vec(result, &self.shape)
    }

    /// Get average bit width used
    pub fn average_bit_width(&self) -> f32 {
        let total_elements: usize = self.quantized_data.iter().map(|b| b.data.len()).sum();
        if total_elements == 0 {
            return 0.0;
        }

        let total_bits: f32 = self
            .quantized_data
            .iter()
            .map(|block| block.data.len() as f32 * block.bit_width as f32)
            .sum();

        total_bits / total_elements as f32
    }

    /// Get memory footprint in bytes
    pub fn memory_footprint(&self) -> usize {
        self.quantized_data.iter().map(|block| block.data.len()).sum()
    }
}

impl SensitivityAnalyzer {
    fn new(config: SensitivityConfig) -> Self {
        Self {
            config,
            sensitivity_cache: HashMap::new(),
        }
    }

    /// Analyze sensitivity of tensor elements
    fn analyze_sensitivity(
        &mut self,
        tensor: &Tensor,
        layer_name: &str,
        _config: &LayerQuantConfig,
    ) -> Result<Vec<f32>> {
        // Check cache first
        if let Some(cached_scores) = self.sensitivity_cache.get(layer_name) {
            return Ok(cached_scores.clone());
        }

        let data = tensor.data()?;
        let mut sensitivity_scores = vec![0.0; data.len()];

        // Analyze each configured metric
        for metric in &self.config.metrics {
            let metric_scores = self.compute_metric_scores(tensor, metric)?;

            // Combine metrics (simple averaging for now)
            for (i, score) in metric_scores.iter().enumerate() {
                sensitivity_scores[i] += score / self.config.metrics.len() as f32;
            }
        }

        // Cache the results
        self.sensitivity_cache
            .insert(layer_name.to_string(), sensitivity_scores.clone());

        Ok(sensitivity_scores)
    }

    /// Compute sensitivity scores for a specific metric
    fn compute_metric_scores(
        &self,
        tensor: &Tensor,
        metric: &SensitivityMetric,
    ) -> Result<Vec<f32>> {
        let data = tensor.data()?;

        match metric {
            SensitivityMetric::WeightVariance => {
                // Compute local variance as a sensitivity measure
                let mean = data.iter().sum::<f32>() / data.len() as f32;
                let variance: Vec<f32> = data.iter().map(|&x| (x - mean).powi(2)).collect();
                Ok(variance)
            },
            SensitivityMetric::GradientMagnitude => {
                // Approximate gradient magnitude using weight magnitude
                Ok(data.iter().map(|&x| x.abs()).collect())
            },
            SensitivityMetric::ActivationVariance => {
                // For weights, use magnitude as proxy for activation impact
                Ok(data.iter().map(|&x| x.abs()).collect())
            },
            SensitivityMetric::HessianDiagonal => {
                // Simplified hessian approximation
                let hessian_approx: Vec<f32> = data.iter().map(|&x| x.powi(2)).collect();
                Ok(hessian_approx)
            },
            SensitivityMetric::OutputSensitivity => {
                // Use weight magnitude as proxy for output sensitivity
                Ok(data.iter().map(|&x| x.abs()).collect())
            },
        }
    }
}

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

    #[test]
    fn test_mixed_bit_quantizer_creation() {
        let config = MixedBitConfig::default();
        let quantizer = MixedBitQuantizer::new(config);
        assert!(quantizer.config.auto_bit_allocation.is_some());
    }

    #[test]
    fn test_mixed_bit_quantization() -> Result<()> {
        let mut quantizer = MixedBitQuantizer::new(MixedBitConfig::default());
        let tensor = Tensor::randn(&[4, 4])?;

        let quantized = quantizer.quantize(&tensor, "test_layer")?;
        assert_eq!(quantized.shape, vec![4, 4]);
        assert!(!quantized.quantized_data.is_empty());

        Ok(())
    }

    #[test]
    fn test_mixed_bit_dequantization() -> Result<()> {
        let mut quantizer = MixedBitQuantizer::new(MixedBitConfig::default());
        let tensor = Tensor::randn(&[2, 2])?;

        let quantized = quantizer.quantize(&tensor, "test_layer")?;
        let dequantized = quantized.dequantize()?;

        assert_eq!(dequantized.shape(), tensor.shape());
        Ok(())
    }

    #[test]
    fn test_average_bit_width() -> Result<()> {
        let mut quantizer = MixedBitQuantizer::new(MixedBitConfig::default());
        let tensor = Tensor::randn(&[8])?;

        let quantized = quantizer.quantize(&tensor, "test_layer")?;
        let avg_bits = quantized.average_bit_width();

        assert!(avg_bits > 0.0);
        assert!(avg_bits <= 8.0);
        Ok(())
    }

    #[test]
    fn test_compression_ratio() -> Result<()> {
        let mut quantizer = MixedBitQuantizer::new(MixedBitConfig::default());
        let tensor = Tensor::randn(&[1024])?; // Use larger tensor to overcome metadata overhead

        let quantized = quantizer.quantize(&tensor, "test_layer")?;
        let ratio = quantizer.compression_ratio(tensor.size(), &quantized);

        assert!(ratio >= 1.0); // Current implementation stores as bytes, so ratio may be 1.0
        Ok(())
    }

    #[test]
    fn test_sensitivity_analysis() -> Result<()> {
        let config = SensitivityConfig::default();
        let mut analyzer = SensitivityAnalyzer::new(config);
        let tensor = Tensor::randn(&[4, 4])?;

        let layer_config = LayerQuantConfig::default();
        let scores = analyzer.analyze_sensitivity(&tensor, "test_layer", &layer_config)?;

        assert_eq!(scores.len(), 16);
        assert!(scores.iter().all(|&score| score >= 0.0));
        Ok(())
    }
}
