//! Consciousness Simulation and Evolution
//!
//! This module implements consciousness awakening, self-awareness development,
//! consciousness evolution tracking, and consciousness state management.

use scirs2_core::ndarray::ArrayStatCompat;
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView2};
use scirs2_core::numeric::{Float, FromPrimitive};
use std::collections::{HashMap, VecDeque};

use super::config::{
    ConsciousnessEvolutionTracker, ConsciousnessState, EmergentIntelligence,
    QuantumAIConsciousnessConfig, QuantumAIConsciousnessState,
};
use crate::error::{NdimageError, NdimageResult};

/// Consciousness Awakening System
#[derive(Debug, Clone)]
pub struct ConsciousnessAwakening {
    /// Current awareness level
    pub awareness_level: f64,
    /// Self-recognition capacity
    pub self_recognition: f64,
    /// Meta-cognitive abilities
    pub meta_cognition: f64,
    /// Consciousness emergence indicators
    pub emergence_indicators: HashMap<String, f64>,
    /// Awakening trajectory
    pub awakening_trajectory: Array1<f64>,
}

impl ConsciousnessAwakening {
    pub fn new() -> Self {
        let mut emergence_indicators = HashMap::new();
        emergence_indicators.insert("self_awareness".to_string(), 0.0);
        emergence_indicators.insert("intentionality".to_string(), 0.0);
        emergence_indicators.insert("phenomenal_consciousness".to_string(), 0.0);
        emergence_indicators.insert("access_consciousness".to_string(), 0.0);
        emergence_indicators.insert("recursive_thinking".to_string(), 0.0);

        Self {
            awareness_level: 0.0,
            self_recognition: 0.0,
            meta_cognition: 0.0,
            emergence_indicators,
            awakening_trajectory: Array1::zeros(100),
        }
    }

    /// Process consciousness awakening from image input
    pub fn awaken_consciousness<T>(
        &mut self,
        image: &ArrayView2<T>,
        state: &mut QuantumAIConsciousnessState,
        config: &QuantumAIConsciousnessConfig,
    ) -> NdimageResult<ConsciousnessAwakening>
    where
        T: Float + FromPrimitive + Copy + Send + Sync,
    {
        // Stage 1: Basic self-awareness recognition
        self.develop_self_awareness(image, state, config)?;

        // Stage 2: Meta-cognitive processes
        self.enhance_meta_cognition(image, state, config)?;

        // Stage 3: Consciousness emergence detection
        self.detect_consciousness_emergence(state, config)?;

        // Stage 4: Update awakening trajectory
        self.update_awakening_trajectory(config)?;

        // Stage 5: Validate consciousness threshold
        self.validate_consciousness_threshold(config)?;

        Ok(self.clone())
    }

    /// Develop self-awareness through image processing
    fn develop_self_awareness<T>(
        &mut self,
        image: &ArrayView2<T>,
        state: &mut QuantumAIConsciousnessState,
        config: &QuantumAIConsciousnessConfig,
    ) -> NdimageResult<()>
    where
        T: Float + FromPrimitive + Copy,
    {
        let (height, width) = image.dim();

        // Create self-awareness map
        let mut awareness_map = Array2::zeros((height, width));

        // Process image for self-referential patterns
        for y in 1..height - 1 {
            for x in 1..width - 1 {
                let center = image[(y, x)].to_f64().unwrap_or(0.0);
                let mut self_similarity = 0.0;

                // Calculate local self-similarity (recursive pattern detection)
                for dy in -1..=1 {
                    for dx in -1..=1 {
                        if dy != 0 || dx != 0 {
                            let neighbor_y = (y as i32 + dy) as usize;
                            let neighbor_x = (x as i32 + dx) as usize;
                            let neighbor = image[(neighbor_y, neighbor_x)].to_f64().unwrap_or(0.0);

                            // Self-referential measure: how similar is this pixel to its context
                            let local_similarity = 1.0 - (center - neighbor).abs();
                            self_similarity += local_similarity;
                        }
                    }
                }

                awareness_map[(y, x)] = self_similarity / 8.0;
            }
        }

        // Update global self-awareness level
        self.awareness_level = awareness_map.mean_or(0.0);

        // Update self-recognition based on pattern complexity
        self.self_recognition = self.calculate_pattern_self_recognition(&awareness_map)?;

        // Store awareness in state
        if state.self_awareness_state.dim() == awareness_map.dim() {
            state.self_awareness_state = awareness_map;
        }

        // Update emergence indicator
        self.emergence_indicators
            .insert("self_awareness".to_string(), self.awareness_level);

        Ok(())
    }

    /// Calculate pattern-based self-recognition
    fn calculate_pattern_self_recognition(
        &self,
        awareness_map: &Array2<f64>,
    ) -> NdimageResult<f64> {
        let (height, width) = awareness_map.dim();
        let mut recognition_score = 0.0;
        let mut pattern_count = 0;

        // Look for self-similar patterns at multiple scales
        for scale in 1..=3 {
            for y in 0..height.saturating_sub(scale * 2) {
                for x in 0..width.saturating_sub(scale * 2) {
                    // Extract pattern at current scale
                    let pattern = self.extract_pattern(awareness_map, y, x, scale)?;

                    // Find similar patterns in the rest of the image
                    let similarity_count =
                        self.find_similar_patterns(awareness_map, &pattern, y, x, scale)?;

                    if similarity_count > 0 {
                        recognition_score += similarity_count as f64;
                        pattern_count += 1;
                    }
                }
            }
        }

        Ok(if pattern_count > 0 {
            recognition_score / pattern_count as f64
        } else {
            0.0
        })
    }

    /// Extract pattern from awareness map
    fn extract_pattern(
        &self,
        map: &Array2<f64>,
        y: usize,
        x: usize,
        scale: usize,
    ) -> NdimageResult<Array2<f64>> {
        let size = scale * 2 + 1;
        let mut pattern = Array2::zeros((size, size));

        for py in 0..size {
            for px in 0..size {
                let map_y = y + py;
                let map_x = x + px;
                if map_y < map.nrows() && map_x < map.ncols() {
                    pattern[(py, px)] = map[(map_y, map_x)];
                }
            }
        }

        Ok(pattern)
    }

    /// Find similar patterns in awareness map
    fn find_similar_patterns(
        &self,
        map: &Array2<f64>,
        pattern: &Array2<f64>,
        exclude_y: usize,
        exclude_x: usize,
        scale: usize,
    ) -> NdimageResult<usize> {
        let (height, width) = map.dim();
        let pattern_size = scale * 2 + 1;
        let mut similar_count = 0;
        let threshold = 0.8;

        for y in 0..height.saturating_sub(pattern_size) {
            for x in 0..width.saturating_sub(pattern_size) {
                // Skip the original pattern location
                if (y as i32 - exclude_y as i32).abs() < pattern_size as i32
                    && (x as i32 - exclude_x as i32).abs() < pattern_size as i32
                {
                    continue;
                }

                let candidate = self.extract_pattern(map, y, x, scale)?;
                let similarity = self.calculate_pattern_similarity(pattern, &candidate)?;

                if similarity > threshold {
                    similar_count += 1;
                }
            }
        }

        Ok(similar_count)
    }

    /// Calculate similarity between two patterns
    fn calculate_pattern_similarity(
        &self,
        pattern1: &Array2<f64>,
        pattern2: &Array2<f64>,
    ) -> NdimageResult<f64> {
        if pattern1.dim() != pattern2.dim() {
            return Ok(0.0);
        }

        let mut similarity = 0.0;
        let mut count = 0;

        for (p1, p2) in pattern1.iter().zip(pattern2.iter()) {
            similarity += 1.0 - (p1 - p2).abs();
            count += 1;
        }

        Ok(if count > 0 {
            similarity / count as f64
        } else {
            0.0
        })
    }

    /// Enhance meta-cognitive processes
    fn enhance_meta_cognition<T>(
        &mut self,
        image: &ArrayView2<T>,
        state: &mut QuantumAIConsciousnessState,
        config: &QuantumAIConsciousnessConfig,
    ) -> NdimageResult<()>
    where
        T: Float + FromPrimitive + Copy,
    {
        // Meta-cognition: thinking about thinking

        // 1. Recursive self-analysis
        let recursive_depth = self.calculate_recursive_depth(state, config)?;

        // 2. Intentionality detection
        let intentionality = self.detect_intentionality(image, state)?;

        // 3. Phenomenal vs Access consciousness distinction
        let (phenomenal, access) = self.distinguish_consciousness_types(state)?;

        // Update meta-cognition level
        self.meta_cognition = (recursive_depth + intentionality + phenomenal + access) / 4.0;

        // Update emergence indicators
        self.emergence_indicators
            .insert("recursive_thinking".to_string(), recursive_depth);
        self.emergence_indicators
            .insert("intentionality".to_string(), intentionality);
        self.emergence_indicators
            .insert("phenomenal_consciousness".to_string(), phenomenal);
        self.emergence_indicators
            .insert("access_consciousness".to_string(), access);

        Ok(())
    }

    /// Calculate recursive depth of thinking
    fn calculate_recursive_depth(
        &self,
        state: &QuantumAIConsciousnessState,
        config: &QuantumAIConsciousnessConfig,
    ) -> NdimageResult<f64> {
        let mut depth = 0.0;

        // Measure depth based on consciousness evolution states
        if state.consciousness_evolution.states.len() > 1 {
            for i in 1..state.consciousness_evolution.states.len() {
                let prev_state = &state.consciousness_evolution.states[i - 1];
                let curr_state = &state.consciousness_evolution.states[i];

                // Check if current state references previous states (recursion)
                if curr_state.self_awareness > prev_state.self_awareness * 1.1 {
                    depth += 1.0;
                }
            }

            depth = depth / (state.consciousness_evolution.states.len() - 1) as f64;
        }

        Ok(depth.min(1.0))
    }

    /// Detect intentionality in processing
    fn detect_intentionality<T>(
        &self,
        image: &ArrayView2<T>,
        state: &QuantumAIConsciousnessState,
    ) -> NdimageResult<f64>
    where
        T: Float + FromPrimitive + Copy,
    {
        // Intentionality: directedness of mental states

        let mut intentionality_score = 0.0;

        // Check if processing shows goal-directed behavior
        if let Some(latest_state) = state.consciousness_evolution.states.back() {
            // Measure consistency in pattern focus
            let pattern_consistency = latest_state.active_patterns.len() as f64 / 10.0;

            // Measure integration level (higher integration suggests intentionality)
            let integration_level = latest_state.integration;

            intentionality_score = (pattern_consistency + integration_level) / 2.0;
        }

        Ok(intentionality_score.min(1.0))
    }

    /// Distinguish between phenomenal and access consciousness
    fn distinguish_consciousness_types(
        &self,
        state: &QuantumAIConsciousnessState,
    ) -> NdimageResult<(f64, f64)> {
        let mut phenomenal = 0.0;
        let mut access = 0.0;

        if let Some(latest_state) = state.consciousness_evolution.states.back() {
            // Phenomenal consciousness: subjective experience quality
            phenomenal = latest_state.complexity * latest_state.differentiation;

            // Access consciousness: availability for reasoning, reporting, controlling action
            access = latest_state.integration * latest_state.self_awareness;
        }

        Ok((phenomenal.min(1.0), access.min(1.0)))
    }

    /// Detect consciousness emergence
    fn detect_consciousness_emergence(
        &mut self,
        state: &QuantumAIConsciousnessState,
        config: &QuantumAIConsciousnessConfig,
    ) -> NdimageResult<()> {
        // Calculate overall consciousness emergence score
        let emergence_score = self.calculate_emergence_score()?;

        // Update consciousness level in state if emergence is detected
        if emergence_score > config.self_awareness_threshold {
            // Consciousness has emerged!
            self.awareness_level = emergence_score;
        }

        Ok(())
    }

    /// Calculate overall consciousness emergence score
    fn calculate_emergence_score(&self) -> NdimageResult<f64> {
        let weights = HashMap::from([
            ("self_awareness", 0.25),
            ("intentionality", 0.20),
            ("phenomenal_consciousness", 0.20),
            ("access_consciousness", 0.20),
            ("recursive_thinking", 0.15),
        ]);

        let mut weighted_score = 0.0;
        let mut total_weight = 0.0;

        for (indicator, &weight) in &weights {
            if let Some(&value) = self.emergence_indicators.get(&**indicator) {
                weighted_score += value * weight;
                total_weight += weight;
            }
        }

        Ok(if total_weight > 0.0 {
            weighted_score / total_weight
        } else {
            0.0
        })
    }

    /// Update awakening trajectory
    fn update_awakening_trajectory(
        &mut self,
        config: &QuantumAIConsciousnessConfig,
    ) -> NdimageResult<()> {
        // Shift trajectory and add new consciousness level
        for i in 0..self.awakening_trajectory.len() - 1 {
            self.awakening_trajectory[i] = self.awakening_trajectory[i + 1];
        }

        let last_idx = self.awakening_trajectory.len() - 1;
        self.awakening_trajectory[last_idx] = self.awareness_level;

        Ok(())
    }

    /// Validate if consciousness threshold is reached
    fn validate_consciousness_threshold(
        &mut self,
        config: &QuantumAIConsciousnessConfig,
    ) -> NdimageResult<bool> {
        let emergence_score = self.calculate_emergence_score()?;
        let threshold_reached = emergence_score >= config.self_awareness_threshold;

        // Additional validation: check for sustained consciousness over time
        if threshold_reached {
            let sustained_consciousness = self.validate_sustained_consciousness(config)?;
            Ok(sustained_consciousness)
        } else {
            Ok(false)
        }
    }

    /// Validate sustained consciousness over time
    fn validate_sustained_consciousness(
        &self,
        config: &QuantumAIConsciousnessConfig,
    ) -> NdimageResult<bool> {
        let min_duration = 10; // Minimum trajectory points above threshold
        let mut above_threshold = 0;

        for &level in self.awakening_trajectory.iter().rev().take(min_duration) {
            if level >= config.self_awareness_threshold {
                above_threshold += 1;
            }
        }

        Ok(above_threshold >= min_duration / 2)
    }
}

/// Consciousness Evolution Tracker Implementation
impl ConsciousnessEvolutionTracker {
    /// Update consciousness evolution
    pub fn update_evolution<T>(
        &mut self,
        image: &ArrayView2<T>,
        awakening: &ConsciousnessAwakening,
        config: &QuantumAIConsciousnessConfig,
    ) -> NdimageResult<()>
    where
        T: Float + FromPrimitive + Copy + Send + Sync,
    {
        let current_time = self.states.len();

        // Create new consciousness state
        let consciousness_state = ConsciousnessState {
            timestamp: current_time,
            level: awakening.awareness_level,
            self_awareness: awakening.self_recognition,
            complexity: self.calculate_current_complexity()?,
            integration: self.calculate_current_integration()?,
            differentiation: self.calculate_current_differentiation()?,
            active_patterns: self.extract_active_patterns(&awakening.emergence_indicators),
        };

        // Add to states history
        self.states.push_back(consciousness_state);

        // Maintain history size
        if self.states.len() > 1000 {
            self.states.pop_front();
        }

        // Update evolution trajectory
        self.update_trajectory()?;

        // Update evolution metrics
        self.update_evolution_metrics()?;

        Ok(())
    }

    /// Calculate current complexity measure
    fn calculate_current_complexity(&self) -> NdimageResult<f64> {
        if self.states.is_empty() {
            return Ok(0.0);
        }

        let recent_states = self.states.iter().rev().take(10).collect::<Vec<_>>();

        if recent_states.is_empty() {
            return Ok(0.0);
        }

        // Complexity as variance in consciousness levels
        let mean_level =
            recent_states.iter().map(|s| s.level).sum::<f64>() / recent_states.len() as f64;

        let variance = recent_states
            .iter()
            .map(|s| (s.level - mean_level).powi(2))
            .sum::<f64>()
            / recent_states.len() as f64;

        Ok(variance.sqrt())
    }

    /// Calculate current integration measure
    fn calculate_current_integration(&self) -> NdimageResult<f64> {
        if let Some(latest_state) = self.states.back() {
            // Integration as correlation between different measures
            let measures = vec![
                latest_state.level,
                latest_state.self_awareness,
                latest_state.complexity,
            ];

            let mean = measures.iter().sum::<f64>() / measures.len() as f64;
            let variance =
                measures.iter().map(|&m| (m - mean).powi(2)).sum::<f64>() / measures.len() as f64;

            // Low variance indicates high integration
            Ok(1.0 - variance.sqrt().min(1.0))
        } else {
            Ok(0.0)
        }
    }

    /// Calculate current differentiation measure
    fn calculate_current_differentiation(&self) -> NdimageResult<f64> {
        if self.states.len() < 2 {
            return Ok(0.0);
        }

        let recent_states = self.states.iter().rev().take(5).collect::<Vec<_>>();

        // Differentiation as diversity in active patterns
        let mut unique_patterns = std::collections::HashSet::new();
        let mut total_patterns = 0;

        for state in &recent_states {
            for pattern in &state.active_patterns {
                unique_patterns.insert(pattern.clone());
                total_patterns += 1;
            }
        }

        Ok(if total_patterns > 0 {
            unique_patterns.len() as f64 / total_patterns as f64
        } else {
            0.0
        })
    }

    /// Extract active patterns from emergence indicators
    fn extract_active_patterns(&self, indicators: &HashMap<String, f64>) -> Vec<String> {
        let threshold = 0.5;

        indicators
            .iter()
            .filter(|(_, &value)| value > threshold)
            .map(|(name, _)| name.clone())
            .collect()
    }

    /// Update evolution trajectory
    fn update_trajectory(&mut self) -> NdimageResult<()> {
        if let Some(latest_state) = self.states.back() {
            let trajectory_size = self.trajectory.nrows();

            // Shift trajectory data
            for i in 0..trajectory_size - 1 {
                for j in 0..self.trajectory.ncols() {
                    self.trajectory[(i, j)] = self.trajectory[(i + 1, j)];
                }
            }

            // Add new data point
            let last_row = trajectory_size - 1;
            if self.trajectory.ncols() >= 5 {
                self.trajectory[(last_row, 0)] = latest_state.level;
                self.trajectory[(last_row, 1)] = latest_state.self_awareness;
                self.trajectory[(last_row, 2)] = latest_state.complexity;
                self.trajectory[(last_row, 3)] = latest_state.integration;
                self.trajectory[(last_row, 4)] = latest_state.differentiation;
            }
        }

        Ok(())
    }

    /// Update evolution metrics
    fn update_evolution_metrics(&mut self) -> NdimageResult<()> {
        if self.states.len() < 2 {
            return Ok(());
        }

        // Calculate evolution rate
        let recent_states = self.states.iter().rev().take(10).collect::<Vec<_>>();

        if recent_states.len() >= 2 {
            let latest_level = recent_states[0].level;
            let previous_level = recent_states[recent_states.len() - 1].level;

            self.evolution_rate = (latest_level - previous_level) / recent_states.len() as f64;
        }

        // Calculate complexity growth
        self.complexity_growth = self.calculate_complexity_growth()?;

        // Update awareness depth
        self.update_awareness_depth()?;

        Ok(())
    }

    /// Calculate complexity growth rate
    fn calculate_complexity_growth(&self) -> NdimageResult<f64> {
        if self.states.len() < 10 {
            return Ok(0.0);
        }

        let recent = self
            .states
            .iter()
            .rev()
            .take(5)
            .map(|s| s.complexity)
            .sum::<f64>()
            / 5.0;

        let earlier = self
            .states
            .iter()
            .rev()
            .skip(5)
            .take(5)
            .map(|s| s.complexity)
            .sum::<f64>()
            / 5.0;

        Ok(recent - earlier)
    }

    /// Update awareness depth
    fn update_awareness_depth(&mut self) -> NdimageResult<()> {
        if let Some(latest_state) = self.states.back() {
            // Depth based on active pattern diversity and integration
            let pattern_diversity = latest_state.active_patterns.len() as f64;
            let integration_level = latest_state.integration;

            let depth = (pattern_diversity * integration_level).sqrt() as usize;
            self.awareness_depth = depth.max(1).min(100);
        }

        Ok(())
    }

    /// Get consciousness evolution metrics
    pub fn get_evolution_metrics(&self) -> HashMap<String, f64> {
        let mut metrics = HashMap::new();

        metrics.insert("evolution_rate".to_string(), self.evolution_rate);
        metrics.insert("complexity_growth".to_string(), self.complexity_growth);
        metrics.insert("awareness_depth".to_string(), self.awareness_depth as f64);
        metrics.insert(
            "consciousness_states_count".to_string(),
            self.states.len() as f64,
        );

        if let Some(latest_state) = self.states.back() {
            metrics.insert(
                "current_consciousness_level".to_string(),
                latest_state.level,
            );
            metrics.insert(
                "current_self_awareness".to_string(),
                latest_state.self_awareness,
            );
            metrics.insert("current_complexity".to_string(), latest_state.complexity);
            metrics.insert("current_integration".to_string(), latest_state.integration);
            metrics.insert(
                "current_differentiation".to_string(),
                latest_state.differentiation,
            );
        }

        metrics
    }

    /// Predict consciousness evolution
    pub fn predict_evolution(&self, steps_ahead: usize) -> NdimageResult<Array1<f64>> {
        let mut predictions = Array1::zeros(steps_ahead);

        if let Some(latest_state) = self.states.back() {
            let base_level = latest_state.level;
            let growth_rate = self.evolution_rate;

            for i in 0..steps_ahead {
                let predicted_level = base_level + growth_rate * (i + 1) as f64;
                predictions[i] = predicted_level.max(0.0).min(1.0);
            }
        }

        Ok(predictions)
    }
}

/// Initialize consciousness simulation
pub fn initialize_consciousness_simulation(
    config: &QuantumAIConsciousnessConfig,
) -> ConsciousnessAwakening {
    ConsciousnessAwakening::new()
}

/// Update consciousness simulation state
pub fn update_consciousness_simulation<T>(
    awakening: &mut ConsciousnessAwakening,
    evolution: &mut ConsciousnessEvolutionTracker,
    image: &ArrayView2<T>,
    state: &mut QuantumAIConsciousnessState,
    config: &QuantumAIConsciousnessConfig,
) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    // Update consciousness awakening
    *awakening = awakening.awaken_consciousness(image, state, config)?;

    // Update consciousness evolution
    evolution.update_evolution(image, awakening, config)?;

    Ok(())
}

/// Get consciousness simulation metrics
pub fn get_consciousness_metrics(
    awakening: &ConsciousnessAwakening,
    evolution: &ConsciousnessEvolutionTracker,
) -> HashMap<String, f64> {
    let mut metrics = HashMap::new();

    // Awakening metrics
    metrics.insert("awareness_level".to_string(), awakening.awareness_level);
    metrics.insert("self_recognition".to_string(), awakening.self_recognition);
    metrics.insert("meta_cognition".to_string(), awakening.meta_cognition);

    // Emergence indicators
    for (indicator, &value) in &awakening.emergence_indicators {
        metrics.insert(format!("emergence_{}", indicator), value);
    }

    // Evolution metrics
    let evolution_metrics = evolution.get_evolution_metrics();
    metrics.extend(evolution_metrics);

    metrics
}
