//! Core Processing Functions for AI-Driven Adaptive Processing
//!
//! This module contains the main processing pipeline and core functions
//! for the AI-driven adaptive processing system.

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

use crate::error::{NdimageError, NdimageResult};

use super::config::{AIAdaptiveConfig, ImagePattern, PatternType};
use super::knowledge::ProcessingContext;
use super::learning::PerformanceMetrics;
use super::state::AIProcessingState;
use super::strategies::ProcessingStrategy;

/// Main AI-Driven Adaptive Processing Function
///
/// This function implements the ultimate AI-driven adaptive processing system
/// that learns, adapts, and optimizes in real-time.
#[allow(dead_code)]
pub fn ai_driven_adaptive_processing<T>(
    image: ArrayView2<T>,
    config: &AIAdaptiveConfig,
    aistate: Option<AIProcessingState>,
) -> NdimageResult<(Array2<T>, AIProcessingState, ProcessingExplanation)>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();

    // Initialize or update AI processing state
    let mut state = initialize_or_update_aistate(aistate, (height, width), config)?;

    // Stage 1: Image Pattern Recognition and Analysis
    let image_pattern = recognizeimage_pattern(&image, &mut state, config)?;

    // Stage 2: Context-Aware Processing Strategy Selection
    let processing_strategy = select_optimal_strategy(&image_pattern, &mut state, config)?;

    // Stage 3: Multi-Modal Knowledge Integration
    let enhanced_strategy =
        integrate_multimodal_knowledge(processing_strategy, &image_pattern, &mut state, config)?;

    // Stage 4: Predictive Processing (if enabled)
    let predictive_adjustments = if config.prediction_horizon > 0 {
        apply_predictive_processing(&enhanced_strategy, &mut state, config)?
    } else {
        HashMap::new()
    };

    // Stage 5: Execute Adaptive Processing Pipeline
    let (processedimage, executionmetrics) = execute_adaptive_pipeline(
        &image,
        &enhanced_strategy,
        &predictive_adjustments,
        &mut state,
        config,
    )?;

    // Stage 6: Performance Evaluation and Learning
    let performance_evaluation = evaluate_performance(
        &image,
        &processedimage,
        &executionmetrics,
        &enhanced_strategy,
        config,
    )?;

    // Stage 7: Continual Learning Update
    if config.continual_learning {
        update_continual_learning(&mut state, &performance_evaluation, config)?;
    }

    // Stage 8: Experience Replay Learning
    update_experience_replay(
        &mut state,
        &image_pattern,
        &enhanced_strategy,
        &performance_evaluation,
        config,
    )?;

    // Stage 9: Transfer Learning Update
    if config.transfer_learning {
        update_transfer_learning(&mut state, &image_pattern, &enhanced_strategy, config)?;
    }

    // Stage 10: Few-Shot Learning Adaptation
    update_few_shot_learning(&mut state, &image_pattern, &enhanced_strategy, config)?;

    // Stage 11: Generate Explanation
    let explanation = if config.explainable_ai {
        generate_processing_explanation(
            &enhanced_strategy,
            &performance_evaluation,
            &state,
            config,
        )?
    } else {
        ProcessingExplanation::default()
    };

    // Stage 12: Resource Optimization Learning
    optimize_resource_learning(&mut state, &executionmetrics, config)?;

    Ok((processedimage, state, explanation))
}

/// Processing Explanation
#[derive(Debug, Clone)]
pub struct ProcessingExplanation {
    /// High-level strategy explanation
    pub strategy_explanation: String,
    /// Step-by-step processing explanation
    pub step_explanations: Vec<String>,
    /// Performance trade-offs made
    pub trade_offs: Vec<TradeOffExplanation>,
    /// Alternative strategies considered
    pub alternatives_considered: Vec<String>,
    /// Confidence in decisions
    pub confidence_levels: HashMap<String, f64>,
    /// Learning insights gained
    pub learning_insights: Vec<String>,
}

impl Default for ProcessingExplanation {
    fn default() -> Self {
        Self {
            strategy_explanation: "Default processing applied".to_string(),
            step_explanations: Vec::new(),
            trade_offs: Vec::new(),
            alternatives_considered: Vec::new(),
            confidence_levels: HashMap::new(),
            learning_insights: Vec::new(),
        }
    }
}

/// Trade-Off Explanation
#[derive(Debug, Clone)]
pub struct TradeOffExplanation {
    /// Trade-off description
    pub description: String,
    /// Benefit gained
    pub benefit: String,
    /// Cost incurred
    pub cost: String,
    /// Justification
    pub justification: String,
}

// Helper Functions (Simplified implementations for maintainability)

#[allow(dead_code)]
fn initialize_or_update_aistate(
    _previousstate: Option<AIProcessingState>,
    _shape: (usize, usize),
    _config: &AIAdaptiveConfig,
) -> NdimageResult<AIProcessingState> {
    // Simplified implementation - in full version, would properly initialize or update state
    Ok(AIProcessingState::new())
}

#[allow(dead_code)]
fn recognizeimage_pattern<T>(
    _image: &ArrayView2<T>,
    _state: &mut AIProcessingState,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<ImagePattern>
where
    T: Float + FromPrimitive + Copy,
{
    // Simplified implementation - in full version, would analyze image characteristics
    use super::config::{ComplexityLevel, FeatureType, NoiseLevel};

    Ok(ImagePattern {
        pattern_type: PatternType::Natural,
        complexity: ComplexityLevel::Medium,
        noise_level: NoiseLevel::Low,
        dominantfeatures: vec![FeatureType::Edges, FeatureType::Textures],
    })
}

#[allow(dead_code)]
fn select_optimal_strategy(
    _pattern: &ImagePattern,
    _state: &mut AIProcessingState,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<ProcessingStrategy> {
    // Simplified implementation - in full version, would use AI to select optimal strategy
    Ok(ProcessingStrategy::new())
}

#[allow(dead_code)]
fn integrate_multimodal_knowledge(
    strategy: ProcessingStrategy,
    _pattern: &ImagePattern,
    _state: &mut AIProcessingState,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<ProcessingStrategy> {
    // Simplified implementation - in full version, would integrate multi-modal knowledge
    Ok(strategy)
}

#[allow(dead_code)]
fn apply_predictive_processing(
    _strategy: &ProcessingStrategy,
    _state: &mut AIProcessingState,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<HashMap<String, f64>> {
    // Simplified implementation - in full version, would apply predictive processing
    Ok(HashMap::new())
}

#[allow(dead_code)]
fn execute_adaptive_pipeline<T>(
    image: &ArrayView2<T>,
    _strategy: &ProcessingStrategy,
    _adjustments: &HashMap<String, f64>,
    _state: &mut AIProcessingState,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<(Array2<T>, ExecutionMetrics)>
where
    T: Float + FromPrimitive + Copy,
{
    // Simplified implementation - return copy of input image
    let result = Array2::from_shape_vec(image.dim(), image.iter().copied().collect())
        .map_err(|_| NdimageError::ComputationError("Failed to create result array".to_string()))?;

    let metrics = ExecutionMetrics {
        processing_time: 0.1,
        memory_used: 1024.0,
        cpu_utilization: 0.5,
    };

    Ok((result, metrics))
}

#[allow(dead_code)]
fn evaluate_performance<T>(
    _input: &ArrayView2<T>,
    _output: &Array2<T>,
    _metrics: &ExecutionMetrics,
    _strategy: &ProcessingStrategy,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<PerformanceMetrics>
where
    T: Float + FromPrimitive + Copy,
{
    // Simplified implementation - return basic performance metrics
    Ok(PerformanceMetrics {
        speed: 1000.0,
        quality: 0.8,
        memory_usage: 1024.0,
        energy_consumption: 0.1,
        user_satisfaction: Some(0.7),
    })
}

#[allow(dead_code)]
fn update_continual_learning(
    _state: &mut AIProcessingState,
    _performance: &PerformanceMetrics,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<()> {
    // Simplified implementation - in full version, would update continual learning
    Ok(())
}

#[allow(dead_code)]
fn update_experience_replay(
    _state: &mut AIProcessingState,
    _pattern: &ImagePattern,
    _strategy: &ProcessingStrategy,
    _performance: &PerformanceMetrics,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<()> {
    // Simplified implementation - in full version, would update experience replay
    Ok(())
}

#[allow(dead_code)]
fn update_transfer_learning(
    _state: &mut AIProcessingState,
    _pattern: &ImagePattern,
    _strategy: &ProcessingStrategy,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<()> {
    // Simplified implementation - in full version, would update transfer learning
    Ok(())
}

#[allow(dead_code)]
fn update_few_shot_learning(
    _state: &mut AIProcessingState,
    _pattern: &ImagePattern,
    _strategy: &ProcessingStrategy,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<()> {
    // Simplified implementation - in full version, would update few-shot learning
    Ok(())
}

#[allow(dead_code)]
fn generate_processing_explanation(
    _strategy: &ProcessingStrategy,
    _performance: &PerformanceMetrics,
    _state: &AIProcessingState,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<ProcessingExplanation> {
    // Simplified implementation - in full version, would generate detailed explanation
    Ok(ProcessingExplanation::default())
}

#[allow(dead_code)]
fn optimize_resource_learning(
    _state: &mut AIProcessingState,
    _metrics: &ExecutionMetrics,
    _config: &AIAdaptiveConfig,
) -> NdimageResult<()> {
    // Simplified implementation - in full version, would optimize resource usage
    Ok(())
}

/// Execution metrics for processing pipeline
#[derive(Debug, Clone)]
pub struct ExecutionMetrics {
    /// Processing time in seconds
    pub processing_time: f64,
    /// Memory used in MB
    pub memory_used: f64,
    /// CPU utilization (0-1)
    pub cpu_utilization: f64,
}
