//! Real LLM Inference with CPU SIMD Optimization
//!
//! Uses candle for native Rust tensor operations with SIMD support (AVX2/AVX512).
//! Optimized for CPU sandbox environments with small, efficient models.

#[cfg(feature = "real-inference")]
mod real {
    use candle_core::{DType, Device, Tensor, D};
    use candle_nn::{linear, Linear, Module, VarBuilder};
    use candle_transformers::models::quantized_llama as llama;
    use hf_hub::{api::tokio::Api, Repo, RepoType};
    use tokenizers::Tokenizer;

    use crate::config::InferenceConfig;
    use crate::error::{Error, InferenceError, Result};
    use crate::types::ModelSize;

    use dashmap::DashMap;
    use parking_lot::RwLock;
    use std::path::PathBuf;
    use std::sync::Arc;
    use std::time::Instant;

    /// Supported small models optimized for CPU
    #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
    pub enum SmallModel {
        /// SmolLM 135M - Smallest viable model
        SmolLM135M,
        /// SmolLM 360M - Better quality, still fast
        SmolLM360M,
        /// Qwen2 0.5B - Good balance
        Qwen2_500M,
        /// TinyLlama 1.1B - Best quality for small
        TinyLlama1B,
    }

    impl SmallModel {
        pub fn repo_id(&self) -> &'static str {
            match self {
                SmallModel::SmolLM135M => "HuggingFaceTB/SmolLM-135M",
                SmallModel::SmolLM360M => "HuggingFaceTB/SmolLM-360M",
                SmallModel::Qwen2_500M => "Qwen/Qwen2-0.5B",
                SmallModel::TinyLlama1B => "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
            }
        }

        pub fn quantized_repo(&self) -> &'static str {
            match self {
                SmallModel::SmolLM135M => "HuggingFaceTB/SmolLM-135M-GGUF",
                SmallModel::SmolLM360M => "HuggingFaceTB/SmolLM-360M-GGUF",
                SmallModel::Qwen2_500M => "Qwen/Qwen2-0.5B-GGUF",
                SmallModel::TinyLlama1B => "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF",
            }
        }

        pub fn gguf_file(&self) -> &'static str {
            match self {
                SmallModel::SmolLM135M => "smollm-135m-q4_k_m.gguf",
                SmallModel::SmolLM360M => "smollm-360m-q4_k_m.gguf",
                SmallModel::Qwen2_500M => "qwen2-0_5b-instruct-q4_k_m.gguf",
                SmallModel::TinyLlama1B => "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf",
            }
        }

        pub fn context_size(&self) -> usize {
            match self {
                SmallModel::SmolLM135M => 2048,
                SmallModel::SmolLM360M => 2048,
                SmallModel::Qwen2_500M => 4096,
                SmallModel::TinyLlama1B => 2048,
            }
        }

        pub fn from_model_size(size: ModelSize) -> Self {
            match size {
                ModelSize::M350 => SmallModel::SmolLM135M,
                ModelSize::M700 => SmallModel::SmolLM360M,
                ModelSize::B1_2 => SmallModel::Qwen2_500M,
                ModelSize::B2_6 => SmallModel::TinyLlama1B,
            }
        }
    }

    /// Generation configuration
    #[derive(Debug, Clone)]
    pub struct GenerationConfig {
        pub max_tokens: usize,
        pub temperature: f32,
        pub top_p: f32,
        pub top_k: usize,
        pub repeat_penalty: f32,
        pub seed: u64,
    }

    impl Default for GenerationConfig {
        fn default() -> Self {
            Self {
                max_tokens: 256,
                temperature: 0.7,
                top_p: 0.9,
                top_k: 40,
                repeat_penalty: 1.1,
                seed: 42,
            }
        }
    }

    /// Generation result
    #[derive(Debug, Clone)]
    pub struct GenerationResult {
        pub text: String,
        pub tokens_generated: usize,
        pub model_used: ModelSize,
        pub cache_hit: bool,
        pub inference_time_ms: f64,
        pub tokens_per_second: f64,
    }

    /// KV Cache for efficient generation
    struct KvCache {
        key: Option<Tensor>,
        value: Option<Tensor>,
        seq_len: usize,
    }

    impl KvCache {
        fn new() -> Self {
            Self {
                key: None,
                value: None,
                seq_len: 0,
            }
        }

        fn append(&mut self, key: Tensor, value: Tensor) -> Result<(Tensor, Tensor)> {
            let (key, value) = match (&self.key, &self.value) {
                (Some(k), Some(v)) => {
                    let key = Tensor::cat(&[k, &key], 2)?;
                    let value = Tensor::cat(&[v, &value], 2)?;
                    (key, value)
                }
                _ => (key, value),
            };
            self.seq_len = key.dims()[2];
            self.key = Some(key.clone());
            self.value = Some(value.clone());
            Ok((key, value))
        }

        fn reset(&mut self) {
            self.key = None;
            self.value = None;
            self.seq_len = 0;
        }
    }

    /// Real inference pool with CPU SIMD optimization
    pub struct RealInferencePool {
        /// Device (CPU with SIMD)
        device: Device,
        /// Loaded GGUF models
        models: DashMap<SmallModel, Arc<llama::ModelWeights>>,
        /// Tokenizers
        tokenizers: DashMap<SmallModel, Arc<Tokenizer>>,
        /// KV caches per session
        kv_caches: DashMap<String, Vec<KvCache>>,
        /// Configuration
        config: InferenceConfig,
        /// Model cache directory
        cache_dir: PathBuf,
    }

    impl RealInferencePool {
        /// Create new inference pool
        pub async fn new(config: &InferenceConfig) -> Result<Self> {
            // Use CPU device - candle will auto-detect SIMD capabilities
            let device = Device::Cpu;

            // Setup cache directory
            let cache_dir = dirs::cache_dir()
                .unwrap_or_else(|| PathBuf::from("."))
                .join("ruvllm")
                .join("models");

            tokio::fs::create_dir_all(&cache_dir).await.map_err(|e| {
                Error::Inference(InferenceError::InitFailed(format!(
                    "Failed to create cache dir: {}",
                    e
                )))
            })?;

            Ok(Self {
                device,
                models: DashMap::new(),
                tokenizers: DashMap::new(),
                kv_caches: DashMap::new(),
                config: config.clone(),
                cache_dir,
            })
        }

        /// Download and load a model
        async fn load_model(&self, model: SmallModel) -> Result<Arc<llama::ModelWeights>> {
            // Check if already loaded
            if let Some(m) = self.models.get(&model) {
                return Ok(m.clone());
            }

            tracing::info!("Downloading model: {:?}", model);

            // Download from HuggingFace Hub
            let api = Api::new().map_err(|e| {
                Error::Inference(InferenceError::InitFailed(format!("HF API error: {}", e)))
            })?;

            let repo = api.repo(Repo::with_revision(
                model.quantized_repo().to_string(),
                RepoType::Model,
                "main".to_string(),
            ));

            let model_path = repo.get(model.gguf_file()).await.map_err(|e| {
                Error::Inference(InferenceError::InitFailed(format!(
                    "Failed to download model: {}",
                    e
                )))
            })?;

            tracing::info!("Loading GGUF model from: {:?}", model_path);

            // Load GGUF model with memory mapping for efficiency
            let mut file = std::fs::File::open(&model_path).map_err(|e| {
                Error::Inference(InferenceError::InitFailed(format!(
                    "Failed to open model: {}",
                    e
                )))
            })?;

            let model_weights = llama::ModelWeights::from_gguf(file, &mut file, &self.device)
                .map_err(|e| {
                    Error::Inference(InferenceError::InitFailed(format!(
                        "Failed to load GGUF: {}",
                        e
                    )))
                })?;

            let model_arc = Arc::new(model_weights);
            self.models.insert(model, model_arc.clone());

            Ok(model_arc)
        }

        /// Download and load tokenizer
        async fn load_tokenizer(&self, model: SmallModel) -> Result<Arc<Tokenizer>> {
            if let Some(t) = self.tokenizers.get(&model) {
                return Ok(t.clone());
            }

            let api = Api::new().map_err(|e| {
                Error::Inference(InferenceError::InitFailed(format!("HF API error: {}", e)))
            })?;

            let repo = api.repo(Repo::new(model.repo_id().to_string(), RepoType::Model));

            let tokenizer_path = repo.get("tokenizer.json").await.map_err(|e| {
                Error::Inference(InferenceError::InitFailed(format!(
                    "Failed to download tokenizer: {}",
                    e
                )))
            })?;

            let tokenizer = Tokenizer::from_file(tokenizer_path).map_err(|e| {
                Error::Inference(InferenceError::InitFailed(format!(
                    "Failed to load tokenizer: {}",
                    e
                )))
            })?;

            let tokenizer_arc = Arc::new(tokenizer);
            self.tokenizers.insert(model, tokenizer_arc.clone());

            Ok(tokenizer_arc)
        }

        /// Sample next token with temperature and top-p
        fn sample_token(
            &self,
            logits: &Tensor,
            config: &GenerationConfig,
            generated_tokens: &[u32],
        ) -> Result<u32> {
            let logits = logits.squeeze(0)?.squeeze(0)?;
            let mut logits_vec: Vec<f32> = logits.to_vec1()?;

            // Apply repeat penalty
            for &token in generated_tokens {
                if (token as usize) < logits_vec.len() {
                    logits_vec[token as usize] /= config.repeat_penalty;
                }
            }

            // Apply temperature
            if config.temperature > 0.0 {
                for l in &mut logits_vec {
                    *l /= config.temperature;
                }
            }

            // Softmax
            let max_logit = logits_vec.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
            let mut probs: Vec<f32> = logits_vec.iter().map(|l| (l - max_logit).exp()).collect();
            let sum: f32 = probs.iter().sum();
            for p in &mut probs {
                *p /= sum;
            }

            // Top-p sampling
            let mut sorted_indices: Vec<usize> = (0..probs.len()).collect();
            sorted_indices.sort_by(|&a, &b| probs[b].partial_cmp(&probs[a]).unwrap());

            let mut cumsum = 0.0;
            let mut cutoff_idx = sorted_indices.len();
            for (i, &idx) in sorted_indices.iter().enumerate() {
                cumsum += probs[idx];
                if cumsum > config.top_p {
                    cutoff_idx = i + 1;
                    break;
                }
            }

            // Top-k limiting
            cutoff_idx = cutoff_idx.min(config.top_k);

            // Renormalize
            let valid_indices: Vec<usize> = sorted_indices[..cutoff_idx].to_vec();
            let mut valid_probs: Vec<f32> = valid_indices.iter().map(|&i| probs[i]).collect();
            let sum: f32 = valid_probs.iter().sum();
            for p in &mut valid_probs {
                *p /= sum;
            }

            // Sample
            use rand::Rng;
            let mut rng = rand::thread_rng();
            let r: f32 = rng.gen();
            let mut cumsum = 0.0;
            for (i, &p) in valid_probs.iter().enumerate() {
                cumsum += p;
                if r < cumsum {
                    return Ok(valid_indices[i] as u32);
                }
            }

            Ok(valid_indices[0] as u32)
        }

        /// Generate text with real inference
        pub async fn generate(
            &self,
            model_size: ModelSize,
            prompt: &str,
            config: GenerationConfig,
            session_key: Option<&str>,
        ) -> Result<GenerationResult> {
            let start = Instant::now();
            let small_model = SmallModel::from_model_size(model_size);

            // Load model and tokenizer
            let model = self.load_model(small_model).await?;
            let tokenizer = self.load_tokenizer(small_model).await?;

            // Tokenize input
            let encoding = tokenizer.encode(prompt, true).map_err(|e| {
                Error::Inference(InferenceError::GenerationFailed(format!(
                    "Tokenization failed: {}",
                    e
                )))
            })?;

            let mut tokens: Vec<u32> = encoding.get_ids().to_vec();
            let input_len = tokens.len();

            // Initialize or get KV cache
            let cache_key = session_key
                .map(|s| s.to_string())
                .unwrap_or_else(|| uuid::Uuid::new_v4().to_string());

            let num_layers = 12; // Typical for small models
            if !self.kv_caches.contains_key(&cache_key) {
                let caches: Vec<KvCache> = (0..num_layers).map(|_| KvCache::new()).collect();
                self.kv_caches.insert(cache_key.clone(), caches);
            }

            // Generate tokens
            let mut generated = Vec::new();
            let eos_token = tokenizer
                .token_to_id("</s>")
                .or_else(|| tokenizer.token_to_id("<|endoftext|>"))
                .unwrap_or(2);

            for _ in 0..config.max_tokens {
                // Create input tensor
                let input = Tensor::new(&tokens[tokens.len() - 1..], &self.device)?;
                let input = input.unsqueeze(0)?;

                // Forward pass with SIMD-optimized operations
                let logits = model.forward(&input, tokens.len() - 1)?;

                // Sample next token
                let next_token = self.sample_token(&logits, &config, &generated)?;

                if next_token == eos_token {
                    break;
                }

                tokens.push(next_token);
                generated.push(next_token);
            }

            // Decode output
            let output_text = tokenizer.decode(&generated, true).map_err(|e| {
                Error::Inference(InferenceError::GenerationFailed(format!(
                    "Decoding failed: {}",
                    e
                )))
            })?;

            let elapsed = start.elapsed().as_secs_f64() * 1000.0;
            let tokens_per_second = if elapsed > 0.0 {
                (generated.len() as f64 / elapsed) * 1000.0
            } else {
                0.0
            };

            Ok(GenerationResult {
                text: output_text,
                tokens_generated: generated.len(),
                model_used: model_size,
                cache_hit: session_key.is_some(),
                inference_time_ms: elapsed,
                tokens_per_second,
            })
        }

        /// Get pool health info
        pub async fn health_check(&self) -> Result<HealthInfo> {
            Ok(HealthInfo {
                loaded_models: self.models.len(),
                loaded_tokenizers: self.tokenizers.len(),
                active_sessions: self.kv_caches.len(),
                device: "CPU (SIMD)".to_string(),
            })
        }
    }

    /// Health information
    #[derive(Debug, Clone)]
    pub struct HealthInfo {
        pub loaded_models: usize,
        pub loaded_tokenizers: usize,
        pub active_sessions: usize,
        pub device: String,
    }
}

#[cfg(feature = "real-inference")]
pub use real::*;

// Re-export types for non-real-inference builds
#[cfg(not(feature = "real-inference"))]
pub use crate::inference::{GenerationConfig, GenerationResult, HealthInfo, InferencePool};