// ONNX Runtime inference backend for optimized model execution

#![allow(deprecated)] // Using rand legacy API, will migrate to scirs2_core

use crate::tensor::Tensor;
use anyhow::{anyhow, Result};
use scirs2_core::random::*; // SciRS2 Integration Policy
use std::collections::HashMap;
use std::path::Path;

/// ONNX Runtime session for model inference
#[derive(Debug)]
pub struct ONNXRuntimeSession {
    #[allow(dead_code)]
    session_config: ONNXRuntimeConfig,
    input_names: Vec<String>,
    output_names: Vec<String>,
    providers: Vec<ExecutionProvider>,
    model_path: String,
}

/// Configuration for ONNX Runtime
#[derive(Debug, Clone)]
pub struct ONNXRuntimeConfig {
    pub inter_op_num_threads: Option<usize>,
    pub intra_op_num_threads: Option<usize>,
    pub enable_cpu_mem_arena: bool,
    pub enable_mem_pattern: bool,
    pub execution_mode: ExecutionMode,
    pub graph_optimization_level: GraphOptimizationLevel,
    pub log_severity_level: LogLevel,
}

impl Default for ONNXRuntimeConfig {
    fn default() -> Self {
        Self {
            inter_op_num_threads: None,
            intra_op_num_threads: None,
            enable_cpu_mem_arena: true,
            enable_mem_pattern: true,
            execution_mode: ExecutionMode::Sequential,
            graph_optimization_level: GraphOptimizationLevel::All,
            log_severity_level: LogLevel::Warning,
        }
    }
}

/// Execution providers for different hardware backends
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub enum ExecutionProvider {
    CPU,
    CUDA { device_id: Option<i32> },
    TensorRT { device_id: Option<i32> },
    OpenVINO,
    DirectML,
    CoreML,
}

/// Execution modes
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub enum ExecutionMode {
    Sequential,
    Parallel,
}

/// Graph optimization levels
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub enum GraphOptimizationLevel {
    None,
    Basic,
    Extended,
    All,
}

/// Logging levels
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub enum LogLevel {
    Verbose,
    Info,
    Warning,
    Error,
    Fatal,
}

/// ONNX Runtime inference backend
pub struct ONNXRuntimeBackend {
    config: ONNXRuntimeConfig,
}

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

impl ONNXRuntimeBackend {
    /// Create a new ONNX Runtime backend
    pub fn new() -> Self {
        Self {
            config: ONNXRuntimeConfig::default(),
        }
    }

    /// Create with custom configuration
    pub fn with_config(config: ONNXRuntimeConfig) -> Self {
        Self { config }
    }

    /// Load a model from ONNX file
    pub fn load_model<P: AsRef<Path>>(&self, model_path: P) -> Result<ONNXRuntimeSession> {
        let model_path = model_path.as_ref();

        if !model_path.exists() {
            return Err(anyhow!("ONNX model file not found: {:?}", model_path));
        }

        // Validate file format
        if model_path.extension().and_then(|s| s.to_str()) != Some("onnx") {
            return Err(anyhow!("Invalid file format. Expected .onnx file"));
        }

        // Extract model metadata (in a real implementation, this would parse the ONNX protobuf)
        let (input_names, output_names) = self.extract_model_metadata(model_path)?;

        // Determine available execution providers
        let providers = self.get_available_providers();

        Ok(ONNXRuntimeSession {
            session_config: self.config.clone(),
            input_names,
            output_names,
            providers,
            model_path: model_path.to_string_lossy().to_string(),
        })
    }

    /// Extract input/output names from ONNX model with enhanced metadata parsing
    fn extract_model_metadata<P: AsRef<Path>>(
        &self,
        model_path: P,
    ) -> Result<(Vec<String>, Vec<String>)> {
        let model_path = model_path.as_ref();

        // Try to parse ONNX model file for actual metadata
        if model_path.exists() && model_path.extension().is_some_and(|ext| ext == "onnx") {
            // Read model file and attempt basic metadata extraction
            match std::fs::read(model_path) {
                Ok(model_bytes) => {
                    // Look for common ONNX metadata patterns in the binary data
                    let model_str = String::from_utf8_lossy(&model_bytes);
                    let mut input_names = Vec::new();
                    let mut output_names = Vec::new();

                    // Enhanced pattern matching for ONNX model inputs/outputs
                    let common_input_patterns = [
                        "input_ids",
                        "attention_mask",
                        "token_type_ids",
                        "position_ids",
                        "inputs",
                        "input",
                        "x",
                        "data",
                        "image",
                        "pixel_values",
                        "input_features",
                        "encoder_input",
                        "decoder_input",
                    ];

                    let common_output_patterns = [
                        "logits",
                        "output",
                        "outputs",
                        "predictions",
                        "scores",
                        "last_hidden_state",
                        "hidden_states",
                        "pooler_output",
                        "encoder_output",
                        "decoder_output",
                        "classification_head",
                    ];

                    // Search for input patterns
                    for pattern in &common_input_patterns {
                        if model_str.contains(pattern) {
                            input_names.push(pattern.to_string());
                        }
                    }

                    // Search for output patterns
                    for pattern in &common_output_patterns {
                        if model_str.contains(pattern) {
                            output_names.push(pattern.to_string());
                        }
                    }

                    // If we found patterns, return them
                    if !input_names.is_empty() && !output_names.is_empty() {
                        // Remove duplicates and limit to most common ones
                        input_names.truncate(4);
                        output_names.truncate(3);
                        return Ok((input_names, output_names));
                    }
                },
                Err(_) => {
                    log::warn!("Failed to read ONNX model file for metadata extraction");
                },
            }
        }

        // Enhanced fallback based on model filename heuristics
        let model_name =
            model_path.file_stem().and_then(|s| s.to_str()).unwrap_or("").to_lowercase();

        let (input_names, output_names) =
            if model_name.contains("bert") || model_name.contains("transformer") {
                // BERT-like models
                (
                    vec![
                        "input_ids".to_string(),
                        "attention_mask".to_string(),
                        "token_type_ids".to_string(),
                    ],
                    vec!["last_hidden_state".to_string(), "pooler_output".to_string()],
                )
            } else if model_name.contains("gpt") || model_name.contains("llama") {
                // Generative models
                (
                    vec!["input_ids".to_string(), "attention_mask".to_string()],
                    vec!["logits".to_string()],
                )
            } else if model_name.contains("vision") || model_name.contains("vit") {
                // Vision models
                (
                    vec!["pixel_values".to_string()],
                    vec!["logits".to_string(), "features".to_string()],
                )
            } else if model_name.contains("audio") || model_name.contains("wav2vec") {
                // Audio models
                (
                    vec!["input_features".to_string(), "attention_mask".to_string()],
                    vec!["logits".to_string(), "hidden_states".to_string()],
                )
            } else {
                // Default transformer-like model
                (
                    vec!["input_ids".to_string(), "attention_mask".to_string()],
                    vec!["logits".to_string()],
                )
            };

        Ok((input_names, output_names))
    }

    /// Get available execution providers on this system
    fn get_available_providers(&self) -> Vec<ExecutionProvider> {
        let mut providers = vec![ExecutionProvider::CPU];

        // Check for CUDA availability (simplified check)
        if std::env::var("CUDA_VISIBLE_DEVICES").is_ok()
            || std::path::Path::new("/usr/local/cuda").exists()
        {
            providers.push(ExecutionProvider::CUDA { device_id: Some(0) });
        }

        // Check for other providers based on system capabilities
        if cfg!(target_os = "windows") {
            providers.push(ExecutionProvider::DirectML);
        }

        if cfg!(target_os = "macos") {
            providers.push(ExecutionProvider::CoreML);
        }

        providers
    }

    /// Create optimized session configuration
    pub fn create_session_options(&self) -> ONNXSessionOptions {
        ONNXSessionOptions {
            execution_providers: self.get_available_providers(),
            inter_op_num_threads: self.config.inter_op_num_threads,
            intra_op_num_threads: self.config.intra_op_num_threads,
            enable_cpu_mem_arena: self.config.enable_cpu_mem_arena,
            enable_mem_pattern: self.config.enable_mem_pattern,
            execution_mode: self.config.execution_mode.clone(),
            graph_optimization_level: self.config.graph_optimization_level.clone(),
            log_severity_level: self.config.log_severity_level.clone(),
        }
    }
}

/// Session options for ONNX Runtime
#[derive(Debug, Clone)]
pub struct ONNXSessionOptions {
    pub execution_providers: Vec<ExecutionProvider>,
    pub inter_op_num_threads: Option<usize>,
    pub intra_op_num_threads: Option<usize>,
    pub enable_cpu_mem_arena: bool,
    pub enable_mem_pattern: bool,
    pub execution_mode: ExecutionMode,
    pub graph_optimization_level: GraphOptimizationLevel,
    pub log_severity_level: LogLevel,
}

impl ONNXRuntimeSession {
    /// Run inference on the loaded model
    pub fn run(&self, inputs: HashMap<String, Tensor>) -> Result<HashMap<String, Tensor>> {
        // Validate inputs
        for input_name in &self.input_names {
            if !inputs.contains_key(input_name) {
                return Err(anyhow!("Missing required input: {}", input_name));
            }
        }

        // Check for extra inputs
        for input_name in inputs.keys() {
            if !self.input_names.contains(input_name) {
                return Err(anyhow!("Unknown input: {}", input_name));
            }
        }

        // In a real implementation, this would call into ONNX Runtime C++ API
        // For now, simulate inference with placeholder results
        self.simulate_inference(inputs)
    }

    /// Simulate inference with realistic outputs based on model type and input characteristics
    fn simulate_inference(
        &self,
        inputs: HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        let mut outputs = HashMap::new();
        use rand::Rng;
        let mut rng = thread_rng();

        // Determine model type from output names and generate appropriate simulated outputs
        if self.output_names.contains(&"logits".to_string()) {
            // Language model or classification model
            if let Some(input_ids) = inputs.get("input_ids") {
                let input_shape = input_ids.shape();
                let batch_size = input_shape[0];
                let seq_length = if input_shape.len() > 1 { input_shape[1] } else { 1 };

                // Determine vocab size based on model type heuristics
                let vocab_size = if self.model_path.to_lowercase().contains("gpt2") {
                    50257 // GPT-2 vocab size
                } else if self.model_path.to_lowercase().contains("bert") {
                    30522 // BERT vocab size
                } else if self.model_path.to_lowercase().contains("llama") {
                    32000 // LLaMA vocab size (approximate)
                } else {
                    50000 // Default vocab size
                };

                // Generate realistic logits with some variation
                let logits_shape = vec![batch_size, seq_length, vocab_size];
                let mut logits_data = Vec::with_capacity(batch_size * seq_length * vocab_size);

                for _ in 0..(batch_size * seq_length * vocab_size) {
                    // Generate logits with realistic distribution (mean around 0, std around 2)
                    let logit: f32 = rng.gen_range(-6.0..6.0) * (rng.gen::<f32>().powf(0.5));
                    logits_data.push(logit);
                }

                let logits = Tensor::from_vec(logits_data, &logits_shape)?;
                outputs.insert("logits".to_string(), logits);
            }
        }

        if self.output_names.contains(&"last_hidden_state".to_string()) {
            // BERT-like encoder model
            if let Some(input_ids) = inputs.get("input_ids") {
                let input_shape = input_ids.shape();
                let batch_size = input_shape[0];
                let seq_length = if input_shape.len() > 1 { input_shape[1] } else { 1 };
                let hidden_size = 768; // Common BERT hidden size

                let hidden_shape = vec![batch_size, seq_length, hidden_size];
                let mut hidden_data = Vec::with_capacity(batch_size * seq_length * hidden_size);

                for _ in 0..(batch_size * seq_length * hidden_size) {
                    // Generate hidden states with realistic activation patterns
                    let activation: f32 = rng.gen_range(-2.0..2.0) * rng.gen::<f32>().sqrt();
                    hidden_data.push(activation);
                }

                let hidden_states = Tensor::from_vec(hidden_data, &hidden_shape)?;
                outputs.insert("last_hidden_state".to_string(), hidden_states);
            }
        }

        if self.output_names.contains(&"pooler_output".to_string()) {
            // BERT pooler output for classification
            if let Some(input_ids) = inputs.get("input_ids") {
                let input_shape = input_ids.shape();
                let batch_size = input_shape[0];
                let hidden_size = 768;

                let pooler_shape = vec![batch_size, hidden_size];
                let mut pooler_data = Vec::with_capacity(batch_size * hidden_size);

                for _ in 0..(batch_size * hidden_size) {
                    // Pooler outputs are typically post-tanh, so range [-1, 1]
                    let pooled: f32 = (rng.gen_range(-3.0f32..3.0f32)).tanh();
                    pooler_data.push(pooled);
                }

                let pooler_output = Tensor::from_vec(pooler_data, &pooler_shape)?;
                outputs.insert("pooler_output".to_string(), pooler_output);
            }
        }

        if self.output_names.contains(&"features".to_string())
            || inputs.contains_key("pixel_values")
        {
            // Vision model
            if let Some(pixel_values) = inputs.get("pixel_values") {
                let input_shape = pixel_values.shape();
                let batch_size = input_shape[0];
                let feature_dim = 2048; // Common vision model feature dimension

                let features_shape = vec![batch_size, feature_dim];
                let mut features_data = Vec::with_capacity(batch_size * feature_dim);

                for _ in 0..(batch_size * feature_dim) {
                    // Vision features are often ReLU-activated, so mostly positive
                    let feature: f32 = rng.gen_range(0.0..5.0) * rng.gen::<f32>().sqrt();
                    features_data.push(feature.max(0.0));
                }

                let features = Tensor::from_vec(features_data, &features_shape)?;
                outputs.insert("features".to_string(), features);
            }
        }

        // If no specific outputs were generated, create a generic output
        if outputs.is_empty() {
            let first_input = inputs.values().next().unwrap();
            let input_shape = first_input.shape();
            let batch_size = input_shape[0];

            // Create a generic classification output
            let num_classes = 1000; // ImageNet-style classification
            let output_shape = vec![batch_size, num_classes];
            let mut output_data = Vec::with_capacity(batch_size * num_classes);

            for _ in 0..(batch_size * num_classes) {
                let score: f32 = rng.gen_range(-10.0..10.0) * rng.gen::<f32>().powf(2.0);
                output_data.push(score);
            }

            let generic_output = Tensor::from_vec(output_data, &output_shape)?;
            outputs.insert("output".to_string(), generic_output);
        }

        Ok(outputs)
    }

    /// Get input names
    pub fn input_names(&self) -> &[String] {
        &self.input_names
    }

    /// Get output names
    pub fn output_names(&self) -> &[String] {
        &self.output_names
    }

    /// Get model path
    pub fn model_path(&self) -> &str {
        &self.model_path
    }

    /// Get available execution providers
    pub fn execution_providers(&self) -> &[ExecutionProvider] {
        &self.providers
    }

    /// Run inference with specific execution provider
    pub fn run_with_provider(
        &self,
        inputs: HashMap<String, Tensor>,
        provider: ExecutionProvider,
    ) -> Result<HashMap<String, Tensor>> {
        // Check if the requested provider is available
        if !self
            .providers
            .iter()
            .any(|p| std::mem::discriminant(p) == std::mem::discriminant(&provider))
        {
            return Err(anyhow!("Execution provider not available: {:?}", provider));
        }

        // For now, delegate to regular run method
        // In a real implementation, this would set the execution provider specifically
        self.run(inputs)
    }

    /// Benchmark inference performance
    pub fn benchmark(
        &self,
        inputs: HashMap<String, Tensor>,
        num_runs: usize,
    ) -> Result<BenchmarkResults> {
        let mut latencies = Vec::with_capacity(num_runs);

        for _ in 0..num_runs {
            let start = std::time::Instant::now();
            let _outputs = self.run(inputs.clone())?;
            let duration = start.elapsed();
            latencies.push(duration.as_secs_f64() * 1000.0); // Convert to milliseconds
        }

        // Calculate statistics
        latencies.sort_by(|a, b| a.partial_cmp(b).unwrap());
        let mean = latencies.iter().sum::<f64>() / latencies.len() as f64;
        let median = latencies[latencies.len() / 2];
        let p90 = latencies[(latencies.len() as f64 * 0.9) as usize];
        let p95 = latencies[(latencies.len() as f64 * 0.95) as usize];
        let p99 = latencies[(latencies.len() as f64 * 0.99) as usize];

        Ok(BenchmarkResults {
            num_runs,
            mean_latency_ms: mean,
            median_latency_ms: median,
            p90_latency_ms: p90,
            p95_latency_ms: p95,
            p99_latency_ms: p99,
            min_latency_ms: latencies[0],
            max_latency_ms: latencies[latencies.len() - 1],
        })
    }

    /// Get memory usage information
    pub fn get_memory_info(&self) -> Result<MemoryInfo> {
        // In a real implementation, this would query ONNX Runtime for memory usage
        Ok(MemoryInfo {
            total_memory_bytes: 0,
            available_memory_bytes: 0,
            model_memory_bytes: 0,
        })
    }
}

/// Benchmark results for inference performance
#[derive(Debug, Clone)]
pub struct BenchmarkResults {
    pub num_runs: usize,
    pub mean_latency_ms: f64,
    pub median_latency_ms: f64,
    pub p90_latency_ms: f64,
    pub p95_latency_ms: f64,
    pub p99_latency_ms: f64,
    pub min_latency_ms: f64,
    pub max_latency_ms: f64,
}

impl BenchmarkResults {
    /// Print benchmark summary
    pub fn print_summary(&self) {
        println!("ONNX Runtime Benchmark Results");
        println!("==============================");
        println!("Number of runs: {}", self.num_runs);
        println!("Mean latency: {:.2} ms", self.mean_latency_ms);
        println!("Median latency: {:.2} ms", self.median_latency_ms);
        println!("P90 latency: {:.2} ms", self.p90_latency_ms);
        println!("P95 latency: {:.2} ms", self.p95_latency_ms);
        println!("P99 latency: {:.2} ms", self.p99_latency_ms);
        println!("Min latency: {:.2} ms", self.min_latency_ms);
        println!("Max latency: {:.2} ms", self.max_latency_ms);
    }
}

/// Memory usage information
#[derive(Debug, Clone)]
pub struct MemoryInfo {
    pub total_memory_bytes: usize,
    pub available_memory_bytes: usize,
    pub model_memory_bytes: usize,
}

/// ONNX model optimization utilities
pub struct ONNXOptimizer;

impl ONNXOptimizer {
    /// Optimize ONNX model for inference
    pub fn optimize_model<P: AsRef<Path>>(
        input_path: P,
        output_path: P,
        optimization_level: GraphOptimizationLevel,
    ) -> Result<()> {
        let input_path = input_path.as_ref();
        let output_path = output_path.as_ref();

        if !input_path.exists() {
            return Err(anyhow!("Input ONNX model not found: {:?}", input_path));
        }

        // In a real implementation, this would:
        // 1. Load the ONNX model
        // 2. Apply graph optimizations based on optimization_level
        // 3. Save the optimized model

        println!("Optimizing ONNX model with level: {:?}", optimization_level);
        println!("Input: {:?}", input_path);
        println!("Output: {:?}", output_path);

        // Copy file for now (placeholder)
        std::fs::copy(input_path, output_path)?;

        Ok(())
    }

    /// Quantize ONNX model for smaller size and faster inference
    pub fn quantize_model<P: AsRef<Path>>(
        input_path: P,
        output_path: P,
        quantization_mode: QuantizationMode,
    ) -> Result<()> {
        let input_path = input_path.as_ref();
        let output_path = output_path.as_ref();

        if !input_path.exists() {
            return Err(anyhow!("Input ONNX model not found: {:?}", input_path));
        }

        println!("Quantizing ONNX model with mode: {:?}", quantization_mode);
        println!("Input: {:?}", input_path);
        println!("Output: {:?}", output_path);

        // Placeholder implementation
        std::fs::copy(input_path, output_path)?;

        Ok(())
    }
}

/// Quantization modes for ONNX models
#[derive(Debug, Clone)]
pub enum QuantizationMode {
    Static,
    Dynamic,
}

#[cfg(test)]
mod tests {
    use super::*;
    use tempfile::tempdir;

    #[test]
    fn test_onnx_runtime_backend_creation() {
        let backend = ONNXRuntimeBackend::new();
        assert!(backend.config.enable_cpu_mem_arena);
    }

    #[test]
    fn test_onnx_runtime_config() {
        let config = ONNXRuntimeConfig {
            inter_op_num_threads: Some(4),
            intra_op_num_threads: Some(2),
            enable_cpu_mem_arena: false,
            enable_mem_pattern: false,
            execution_mode: ExecutionMode::Parallel,
            graph_optimization_level: GraphOptimizationLevel::Basic,
            log_severity_level: LogLevel::Error,
        };

        let backend = ONNXRuntimeBackend::with_config(config.clone());
        assert_eq!(backend.config.inter_op_num_threads, Some(4));
        assert_eq!(backend.config.intra_op_num_threads, Some(2));
        assert!(!backend.config.enable_cpu_mem_arena);
    }

    #[test]
    fn test_execution_providers() {
        let backend = ONNXRuntimeBackend::new();
        let providers = backend.get_available_providers();

        // CPU provider should always be available
        assert!(providers.iter().any(|p| matches!(p, ExecutionProvider::CPU)));
    }

    #[test]
    fn test_session_options() {
        let backend = ONNXRuntimeBackend::new();
        let options = backend.create_session_options();

        assert!(!options.execution_providers.is_empty());
        assert!(options.enable_cpu_mem_arena);
    }

    #[test]
    fn test_load_nonexistent_model() {
        let backend = ONNXRuntimeBackend::new();
        let result = backend.load_model("nonexistent.onnx");

        assert!(result.is_err());
        assert!(result.unwrap_err().to_string().contains("not found"));
    }

    #[test]
    fn test_benchmark_results() {
        let results = BenchmarkResults {
            num_runs: 100,
            mean_latency_ms: 15.5,
            median_latency_ms: 14.2,
            p90_latency_ms: 18.7,
            p95_latency_ms: 20.1,
            p99_latency_ms: 25.3,
            min_latency_ms: 12.1,
            max_latency_ms: 28.9,
        };

        assert_eq!(results.num_runs, 100);
        assert!((results.mean_latency_ms - 15.5).abs() < 1e-6);
    }

    #[test]
    fn test_memory_info() {
        let info = MemoryInfo {
            total_memory_bytes: 1024 * 1024 * 1024,    // 1GB
            available_memory_bytes: 512 * 1024 * 1024, // 512MB
            model_memory_bytes: 100 * 1024 * 1024,     // 100MB
        };

        assert_eq!(info.total_memory_bytes, 1024 * 1024 * 1024);
        assert_eq!(info.available_memory_bytes, 512 * 1024 * 1024);
        assert_eq!(info.model_memory_bytes, 100 * 1024 * 1024);
    }

    #[test]
    fn test_quantization_modes() {
        // Test quantization mode creation
        let static_mode = QuantizationMode::Static;
        let dynamic_mode = QuantizationMode::Dynamic;

        match static_mode {
            QuantizationMode::Static => assert!(true),
            _ => panic!("Expected Static mode"),
        }

        match dynamic_mode {
            QuantizationMode::Dynamic => assert!(true),
            _ => panic!("Expected Dynamic mode"),
        }
    }

    #[test]
    fn test_optimizer_operations() -> Result<()> {
        let temp_dir = tempdir()?;
        let input_path = temp_dir.path().join("input.onnx");
        let output_path = temp_dir.path().join("output.onnx");

        // Create a dummy input file
        std::fs::write(&input_path, "dummy onnx content")?;

        // Test optimization
        ONNXOptimizer::optimize_model(&input_path, &output_path, GraphOptimizationLevel::All)?;
        assert!(output_path.exists());

        // Test quantization
        let quantized_path = temp_dir.path().join("quantized.onnx");
        ONNXOptimizer::quantize_model(&output_path, &quantized_path, QuantizationMode::Dynamic)?;
        assert!(quantized_path.exists());

        Ok(())
    }
}
