//! Kernel fusion engine implementation
//!
//! This module contains the main KernelFusionEngine implementation with
//! pattern matching, constraint verification, and kernel generation logic.

#![allow(unused_variables)] // Kernel fusion engine

use crate::errors::{Result, TrustformersError};
use crate::kernel_fusion::graph::{ComputationGraph, Device, GraphNode, TensorInfo};
use crate::kernel_fusion::kernel::{FusedKernel, KernelImplementation};
use crate::kernel_fusion::operation_types::{FusionConstraint, FusionPattern, OperationType};
use crate::kernel_fusion::performance::{
    DeviceCharacteristics, FusionStatistics, OperationCost, PerformanceDatabase,
};
use anyhow::anyhow;
use std::collections::{HashMap, HashSet};
use std::sync::{Arc, RwLock};

/// Kernel fusion engine
pub struct KernelFusionEngine {
    pub patterns: Vec<FusionPattern>,
    pub constraints: Vec<FusionConstraint>,
    pub generated_kernels: Arc<RwLock<HashMap<String, FusedKernel>>>,
    pub performance_database: Arc<RwLock<PerformanceDatabase>>,
    pub fusion_statistics: Arc<RwLock<FusionStatistics>>,
}

pub struct FusionOpportunity {
    pub pattern: FusionPattern,
    pub node_ids: Vec<String>,
    pub estimated_benefit: f64,
    pub constraints_satisfied: bool,
}

impl KernelFusionEngine {
    pub fn new() -> Self {
        let mut engine = Self {
            patterns: Vec::new(),
            constraints: Vec::new(),
            generated_kernels: Arc::new(RwLock::new(HashMap::new())),
            performance_database: Arc::new(RwLock::new(PerformanceDatabase::default())),
            fusion_statistics: Arc::new(RwLock::new(FusionStatistics::default())),
        };

        engine.initialize_default_patterns();
        engine.initialize_performance_database();
        engine
    }

    pub fn analyze_graph(&self, graph: &ComputationGraph) -> Result<Vec<FusionOpportunity>> {
        let mut opportunities = Vec::new();

        for pattern in &self.patterns {
            let mut pattern_opportunities = self.find_pattern_matches(graph, pattern)?;
            opportunities.append(&mut pattern_opportunities);
        }

        // Sort by estimated benefit (descending)
        opportunities.sort_by(|a, b| {
            b.estimated_benefit
                .partial_cmp(&a.estimated_benefit)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(opportunities)
    }

    pub fn fuse_operations(
        &self,
        graph: &ComputationGraph,
        opportunity: &FusionOpportunity,
    ) -> Result<FusedKernel> {
        // Verify constraints one more time
        if !self.verify_fusion_constraints(&opportunity.node_ids, graph)? {
            return Err(TrustformersError::invalid_operation(
                "Fusion constraints not satisfied".to_string(),
            ));
        }

        // Generate fused kernel
        let kernel_name = self.generate_kernel_name(&opportunity.pattern);
        let implementation = self.generate_kernel_implementation(opportunity)?;

        let fused_kernel = FusedKernel::new(
            format!("fused_{}", uuid::Uuid::new_v4()),
            kernel_name,
            opportunity.pattern.clone(),
            opportunity.node_ids.clone(),
        )
        .with_implementation(implementation)
        .with_speedup(opportunity.estimated_benefit);

        // Store generated kernel
        self.generated_kernels
            .write()
            .unwrap()
            .insert(fused_kernel.id.clone(), fused_kernel.clone());

        // Calculate memory savings from eliminating intermediate tensors
        let memory_saved = self.calculate_memory_savings(graph, &opportunity.node_ids)?;

        // Update statistics
        let mut stats = self.fusion_statistics.write().unwrap();
        stats.record_successful_fusion(
            &self.pattern_name(&opportunity.pattern),
            opportunity.estimated_benefit,
            memory_saved,
        );

        Ok(fused_kernel)
    }

    fn initialize_default_patterns(&mut self) {
        // Element-wise operation chains
        self.patterns.push(FusionPattern::ElementWiseChain(vec![
            OperationType::Add,
            OperationType::ReLU,
        ]));

        self.patterns.push(FusionPattern::ElementWiseChain(vec![
            OperationType::Multiply,
            OperationType::Add,
            OperationType::GELU,
        ]));

        // Linear + activation patterns
        self.patterns.push(FusionPattern::LinearActivation {
            matmul: OperationType::MatMul,
            bias_add: true,
            activation: Some(OperationType::ReLU),
        });

        self.patterns.push(FusionPattern::LinearActivation {
            matmul: OperationType::MatMul,
            bias_add: true,
            activation: Some(OperationType::GELU),
        });

        // Layer normalization patterns
        self.patterns.push(FusionPattern::BatchNorm {
            normalize: true,
            scale: true,
            shift: true,
            activation: None,
        });

        // Attention fusion
        self.patterns.push(FusionPattern::AttentionFusion {
            query_key_matmul: true,
            softmax: true,
            value_matmul: true,
            dropout: false,
        });

        // Reduce-broadcast patterns
        self.patterns.push(FusionPattern::ReduceBroadcast {
            reduction: OperationType::Mean,
            broadcast: OperationType::Broadcast,
        });

        // Modern transformer fusion patterns

        // RoPE fusion for rotary position embedding
        self.patterns.push(FusionPattern::RoPEFusion {
            apply_rope: true,
            cos_sin_cached: true,
            dimensions: 128, // Common dimension for RoPE
        });

        // SwiGLU activation fusion (used in LLaMA, PaLM, etc.)
        self.patterns.push(FusionPattern::SwiGLU {
            gate_projection: true,
            up_projection: true,
            swish_activation: true,
            element_wise_multiply: true,
        });

        // Group normalization fusion
        self.patterns.push(FusionPattern::GroupNorm {
            groups: 32,
            normalize: true,
            scale: true,
            shift: true,
            activation: None,
        });

        // Optimized flash attention with memory-efficient blocking
        self.patterns.push(FusionPattern::FlashAttentionOptimized {
            query_key_matmul: true,
            scaled_softmax: true,
            value_matmul: true,
            causal_mask: true,
            dropout: false,
            block_size: 128, // Optimal block size for most hardware
        });

        // RMSNorm fusion (used in LLaMA and other models)
        self.patterns.push(FusionPattern::Custom {
            name: "RMSNorm".to_string(),
            operations: vec![
                OperationType::Power,    // x^2
                OperationType::Mean,     // mean(x^2)
                OperationType::Add,      // + eps
                OperationType::Power,    // sqrt (power 0.5)
                OperationType::Divide,   // x / rms
                OperationType::Multiply, // * weight
            ],
            constraints: vec![
                FusionConstraint::ShapeCompatible,
                FusionConstraint::DataTypeCompatible,
                FusionConstraint::Contiguous,
            ],
        });

        // Initialize default constraints
        self.constraints.extend(vec![
            FusionConstraint::ShapeCompatible,
            FusionConstraint::DataTypeCompatible,
            FusionConstraint::DeviceCompatible,
            FusionConstraint::MaxOperations(8),
            FusionConstraint::MaxMemoryUsage(1024 * 1024 * 1024), // 1GB
            FusionConstraint::Contiguous,
        ]);
    }

    fn initialize_performance_database(&mut self) {
        let mut db = self.performance_database.write().unwrap();

        // Add operation costs for common operations
        db.add_operation_cost(
            OperationType::Add,
            OperationCost::new(1.0, 0.1).with_launch_overhead(500),
        );

        db.add_operation_cost(
            OperationType::Multiply,
            OperationCost::new(1.0, 0.1).with_launch_overhead(500),
        );

        db.add_operation_cost(
            OperationType::MatMul,
            OperationCost::new(100.0, 1.0).with_launch_overhead(2000),
        );

        db.add_operation_cost(
            OperationType::ReLU,
            OperationCost::new(1.0, 0.05).with_launch_overhead(300),
        );

        db.add_operation_cost(
            OperationType::GELU,
            OperationCost::new(10.0, 0.1).with_launch_overhead(800),
        );

        // Add device characteristics
        db.add_device_characteristics(Device::CPU, DeviceCharacteristics::cpu_characteristics());
        db.add_device_characteristics(Device::GPU(0), DeviceCharacteristics::gpu_characteristics());
    }

    fn find_pattern_matches(
        &self,
        graph: &ComputationGraph,
        pattern: &FusionPattern,
    ) -> Result<Vec<FusionOpportunity>> {
        match pattern {
            FusionPattern::ElementWiseChain(ops) => self.find_elementwise_chains(graph, ops),
            FusionPattern::LinearActivation { .. } => {
                self.find_linear_activation_patterns(graph, pattern)
            },
            FusionPattern::AttentionFusion { .. } => self.find_attention_patterns(graph),
            // Add more pattern matching logic for other patterns
            _ => Ok(Vec::new()), // Placeholder for unimplemented patterns
        }
    }

    fn find_elementwise_chains(
        &self,
        graph: &ComputationGraph,
        target_ops: &[OperationType],
    ) -> Result<Vec<FusionOpportunity>> {
        let mut opportunities = Vec::new();

        // Look for sequences of element-wise operations that match the target pattern
        for node_id in &graph.execution_order {
            if let Some(node) = graph.get_node(node_id) {
                if node.operation == target_ops[0] {
                    // Try to match the complete chain starting from this node
                    let mut chain = vec![node_id.clone()];
                    let mut current_id = node_id.clone();

                    for target_op in target_ops.iter().skip(1) {
                        // Find the next node in the chain
                        if let Some(next_id) =
                            self.find_next_operation(&current_id, target_op.clone(), graph)
                        {
                            chain.push(next_id.clone());
                            current_id = next_id;
                        } else {
                            break;
                        }
                    }

                    if chain.len() == target_ops.len() {
                        let benefit = self.estimate_fusion_benefit(&chain, graph)?;
                        let constraints_satisfied =
                            self.verify_fusion_constraints(&chain, graph)?;

                        opportunities.push(FusionOpportunity {
                            pattern: FusionPattern::ElementWiseChain(target_ops.to_vec()),
                            node_ids: chain,
                            estimated_benefit: benefit,
                            constraints_satisfied,
                        });
                    }
                }
            }
        }

        Ok(opportunities)
    }

    fn find_linear_activation_patterns(
        &self,
        graph: &ComputationGraph,
        pattern: &FusionPattern,
    ) -> Result<Vec<FusionOpportunity>> {
        let mut opportunities = Vec::new();

        // Look for MatMul -> Add -> Activation patterns
        for node_id in &graph.execution_order {
            if let Some(node) = graph.get_node(node_id) {
                if node.operation == OperationType::MatMul {
                    let mut chain = vec![node_id.clone()];

                    // Look for bias add
                    if let Some(add_id) =
                        self.find_next_operation(node_id, OperationType::Add, graph)
                    {
                        chain.push(add_id.clone());

                        // Look for activation
                        if let FusionPattern::LinearActivation {
                            activation: Some(act_type),
                            ..
                        } = pattern
                        {
                            if let Some(act_id) =
                                self.find_next_operation(&add_id, act_type.clone(), graph)
                            {
                                chain.push(act_id);
                            }
                        }
                    }

                    if chain.len() >= 2 {
                        // At least MatMul + Add
                        let benefit = self.estimate_fusion_benefit(&chain, graph)?;
                        let constraints_satisfied =
                            self.verify_fusion_constraints(&chain, graph)?;

                        opportunities.push(FusionOpportunity {
                            pattern: pattern.clone(),
                            node_ids: chain,
                            estimated_benefit: benefit,
                            constraints_satisfied,
                        });
                    }
                }
            }
        }

        Ok(opportunities)
    }

    fn find_attention_patterns(&self, graph: &ComputationGraph) -> Result<Vec<FusionOpportunity>> {
        // Placeholder implementation for attention pattern detection
        // In a full implementation, this would look for Q*K^T -> Softmax -> *V patterns
        Ok(Vec::new())
    }

    fn find_next_operation(
        &self,
        current_id: &str,
        target_op: OperationType,
        graph: &ComputationGraph,
    ) -> Option<String> {
        // Find consumers of the current node
        for (node_id, dependencies) in &graph.edges {
            if dependencies.contains(&current_id.to_string()) {
                if let Some(node) = graph.get_node(node_id) {
                    if node.operation == target_op {
                        return Some(node_id.clone());
                    }
                }
            }
        }
        None
    }

    fn verify_fusion_constraints(
        &self,
        node_ids: &[String],
        graph: &ComputationGraph,
    ) -> Result<bool> {
        let nodes: Vec<&GraphNode> = node_ids.iter().filter_map(|id| graph.get_node(id)).collect();

        if nodes.len() != node_ids.len() {
            return Ok(false); // Some nodes not found
        }

        for constraint in &self.constraints {
            match constraint {
                FusionConstraint::ShapeCompatible => {
                    if !self.check_shape_compatibility(&nodes)? {
                        return Ok(false);
                    }
                },
                FusionConstraint::DataTypeCompatible => {
                    if !self.check_data_type_compatibility(&nodes)? {
                        return Ok(false);
                    }
                },
                FusionConstraint::DeviceCompatible => {
                    if !self.check_device_compatibility(&nodes)? {
                        return Ok(false);
                    }
                },
                FusionConstraint::MaxOperations(max_ops) => {
                    if nodes.len() > *max_ops {
                        return Ok(false);
                    }
                },
                FusionConstraint::Contiguous => {
                    if !self.check_contiguity(node_ids, graph)? {
                        return Ok(false);
                    }
                },
                // Add more constraint checks as needed
                _ => {}, // Placeholder for other constraints
            }
        }

        Ok(true)
    }

    fn check_shape_compatibility(&self, nodes: &[&GraphNode]) -> Result<bool> {
        if nodes.is_empty() {
            return Ok(true);
        }

        // Check if all output shapes are compatible (can be broadcasted or are identical)
        let first_output_shape =
            &nodes[0].outputs.first().ok_or_else(|| anyhow!("Node has no outputs"))?.shape;

        for node in nodes.iter().skip(1) {
            let output_shape =
                &node.outputs.first().ok_or_else(|| anyhow!("Node has no outputs"))?.shape;

            if !self.shapes_broadcastable(first_output_shape, output_shape) {
                return Ok(false);
            }
        }

        Ok(true)
    }

    pub fn shapes_broadcastable(&self, shape1: &[usize], shape2: &[usize]) -> bool {
        let max_len = shape1.len().max(shape2.len());

        for i in 0..max_len {
            let dim1 = shape1.get(shape1.len().saturating_sub(max_len - i)).copied().unwrap_or(1);
            let dim2 = shape2.get(shape2.len().saturating_sub(max_len - i)).copied().unwrap_or(1);

            if dim1 != dim2 && dim1 != 1 && dim2 != 1 {
                return false;
            }
        }

        true
    }

    fn check_data_type_compatibility(&self, nodes: &[&GraphNode]) -> Result<bool> {
        if nodes.is_empty() {
            return Ok(true);
        }

        let first_dtype =
            &nodes[0].outputs.first().ok_or_else(|| anyhow!("Node has no outputs"))?.dtype;

        for node in nodes.iter().skip(1) {
            let dtype = &node.outputs.first().ok_or_else(|| anyhow!("Node has no outputs"))?.dtype;

            if dtype != first_dtype {
                return Ok(false);
            }
        }

        Ok(true)
    }

    fn check_device_compatibility(&self, nodes: &[&GraphNode]) -> Result<bool> {
        if nodes.is_empty() {
            return Ok(true);
        }

        let first_device =
            &nodes[0].outputs.first().ok_or_else(|| anyhow!("Node has no outputs"))?.device;

        for node in nodes.iter().skip(1) {
            let device =
                &node.outputs.first().ok_or_else(|| anyhow!("Node has no outputs"))?.device;

            if device != first_device {
                return Ok(false);
            }
        }

        Ok(true)
    }

    fn check_contiguity(&self, node_ids: &[String], graph: &ComputationGraph) -> Result<bool> {
        // Check if nodes are contiguous in the execution order
        let execution_positions: HashMap<String, usize> = graph
            .execution_order
            .iter()
            .enumerate()
            .map(|(i, id)| (id.clone(), i))
            .collect();

        let mut positions: Vec<usize> =
            node_ids.iter().filter_map(|id| execution_positions.get(id)).copied().collect();

        if positions.len() != node_ids.len() {
            return Ok(false); // Some nodes not in execution order
        }

        positions.sort();

        // Check if positions are consecutive
        for i in 1..positions.len() {
            if positions[i] != positions[i - 1] + 1 {
                return Ok(false);
            }
        }

        Ok(true)
    }

    fn estimate_fusion_benefit(
        &self,
        node_ids: &[String],
        graph: &ComputationGraph,
    ) -> Result<f64> {
        let db = self.performance_database.read().unwrap();

        let mut total_individual_cost = 0.0;
        let mut total_ops = 0u64;

        for node_id in node_ids {
            if let Some(node) = graph.get_node(node_id) {
                if let Some(cost) = db.get_operation_cost(&node.operation) {
                    let elements = node.outputs.first().map(|t| t.element_count()).unwrap_or(1);

                    total_individual_cost +=
                        cost.ops_per_element * elements as f64 + cost.launch_overhead_ns as f64;
                    total_ops += node.metadata.estimated_ops;
                }
            }
        }

        // Estimate fused cost (reduced launch overhead, better cache utilization)
        let launch_overhead_reduction = (node_ids.len() - 1) as f64 * 1000.0; // Save 1µs per avoided launch
        let cache_efficiency_gain = 1.2; // 20% improvement from better cache utilization

        let fused_cost =
            (total_individual_cost - launch_overhead_reduction) / cache_efficiency_gain;

        let speedup = if fused_cost > 0.0 { total_individual_cost / fused_cost } else { 1.0 };

        Ok(speedup)
    }

    fn generate_kernel_name(&self, pattern: &FusionPattern) -> String {
        match pattern {
            FusionPattern::ElementWiseChain(ops) => {
                let op_names: Vec<String> =
                    ops.iter().map(|op| format!("{:?}", op).to_lowercase()).collect();
                format!("elementwise_{}", op_names.join("_"))
            },
            FusionPattern::LinearActivation { activation, .. } => match activation {
                Some(act) => format!("linear_{:?}", act).to_lowercase(),
                None => "linear".to_string(),
            },
            FusionPattern::AttentionFusion { .. } => "attention_fusion".to_string(),
            FusionPattern::BatchNorm { .. } => "batch_norm".to_string(),
            FusionPattern::Custom { name, .. } => name.to_lowercase(),
            _ => "custom_fusion".to_string(),
        }
    }

    fn generate_kernel_implementation(
        &self,
        opportunity: &FusionOpportunity,
    ) -> Result<KernelImplementation> {
        // For simplicity, generate CPU implementation
        // In a full implementation, this would choose based on device capabilities
        self.generate_cpu_kernel(opportunity)
    }

    fn generate_cpu_kernel(&self, opportunity: &FusionOpportunity) -> Result<KernelImplementation> {
        let kernel_code = match &opportunity.pattern {
            FusionPattern::ElementWiseChain(ops) => self.generate_elementwise_cpu_code(ops),
            FusionPattern::LinearActivation { .. } => self.generate_linear_activation_cpu_code(),
            _ => "// Generic fused kernel implementation".to_string(),
        };

        Ok(KernelImplementation::CPU(kernel_code))
    }

    fn generate_elementwise_cpu_code(&self, ops: &[OperationType]) -> String {
        let mut code = String::new();
        code.push_str("void fused_elementwise_kernel(float* input, float* output, int size) {\n");
        code.push_str("    #pragma omp parallel for\n");
        code.push_str("    for (int i = 0; i < size; i++) {\n");
        code.push_str("        float value = input[i];\n");

        for op in ops {
            match op {
                OperationType::Add => code.push_str("        value = value + 1.0f; // Simplified\n"),
                OperationType::ReLU => code.push_str("        value = fmaxf(0.0f, value);\n"),
                OperationType::GELU => code.push_str("        value = 0.5f * value * (1.0f + tanhf(0.797885f * (value + 0.044715f * value * value * value)));\n"),
                _ => code.push_str("        // Other operation\n"),
            }
        }

        code.push_str("        output[i] = value;\n");
        code.push_str("    }\n");
        code.push_str("}\n");

        code
    }

    fn generate_linear_activation_cpu_code(&self) -> String {
        r#"
void fused_linear_activation_kernel(
    float* input, float* weight, float* bias, float* output,
    int batch_size, int input_dim, int output_dim
) {
    #pragma omp parallel for
    for (int b = 0; b < batch_size; b++) {
        for (int o = 0; o < output_dim; o++) {
            float sum = bias[o];
            for (int i = 0; i < input_dim; i++) {
                sum += input[b * input_dim + i] * weight[o * input_dim + i];
            }
            // Apply ReLU activation
            output[b * output_dim + o] = fmaxf(0.0f, sum);
        }
    }
}
        "#
        .to_string()
    }

    /// Calculate memory savings from fusing operations by eliminating intermediate tensors
    fn calculate_memory_savings(
        &self,
        graph: &ComputationGraph,
        node_ids: &[String],
    ) -> Result<u64> {
        let mut total_memory_saved = 0u64;

        // For each node in the fusion (except the last one), calculate memory of intermediate outputs
        // that will be eliminated by fusion
        for (i, node_id) in node_ids.iter().enumerate() {
            // Skip the last node as its output is still needed
            if i == node_ids.len() - 1 {
                continue;
            }

            let node = graph
                .nodes
                .get(node_id)
                .ok_or_else(|| anyhow!("Node {} not found in graph", node_id))?;

            // Calculate memory used by this node's output tensors that will be eliminated
            for output in &node.outputs {
                // Only count intermediate tensors that are consumed only by nodes within the fusion
                if self.is_intermediate_tensor_in_fusion(node_id, output, graph, node_ids)? {
                    total_memory_saved += output.memory_size() as u64;
                }
            }
        }

        Ok(total_memory_saved)
    }

    /// Check if a tensor is intermediate (only consumed within the fusion group)
    fn is_intermediate_tensor_in_fusion(
        &self,
        producer_id: &str,
        _tensor: &TensorInfo,
        graph: &ComputationGraph,
        fusion_node_ids: &[String],
    ) -> Result<bool> {
        let fusion_set: HashSet<String> = fusion_node_ids.iter().cloned().collect();

        // Find all consumers of this producer node
        let mut consumers = Vec::new();
        for (node_id, dependencies) in &graph.edges {
            if dependencies.contains(&producer_id.to_string()) {
                consumers.push(node_id);
            }
        }

        // If all consumers are within the fusion group, then this is an intermediate tensor
        Ok(
            !consumers.is_empty()
                && consumers.iter().all(|consumer| fusion_set.contains(*consumer)),
        )
    }

    fn pattern_name(&self, pattern: &FusionPattern) -> String {
        match pattern {
            FusionPattern::ElementWiseChain(_) => "ElementWiseChain".to_string(),
            FusionPattern::LinearActivation { .. } => "LinearActivation".to_string(),
            FusionPattern::AttentionFusion { .. } => "AttentionFusion".to_string(),
            FusionPattern::BatchNorm { .. } => "BatchNorm".to_string(),
            FusionPattern::Custom { name, .. } => name.clone(),
            _ => "Unknown".to_string(),
        }
    }
}

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