//! # RTX Natural Language Generation
//!
//! Advanced Natural Language Generation capabilities for RTX with production-ready
//! text generation algorithms and optimization techniques.
//!
//! ## Features
//!
//! - **Advanced Generation**: Beam search, nucleus sampling, top-k sampling with optimizations
//! - **Constrained Generation**: Lexical and syntactic constraints with controllable attributes
//! - **NLP Tasks**: Translation, summarization, QA, dialogue, completion
//! - **Model Serving**: Streaming generation, batch optimization, multi-turn conversations
//! - **Quality Features**: Repetition penalty, diversity promotion, factuality enhancement
//! - **Production Optimizations**: KV cache, speculative decoding, model parallelism
//!
//! ## Quick Start
//!
//! ```rust
//! use rtx_nlg::{GenerationConfig, TextGenerator};
//!
//! // Create a text generator with beam search
//! let config = GenerationConfig::beam_search()
//!     .num_beams(4)
//!     .max_length(100)
//!     .temperature(0.7);
//!
//! let generator = TextGenerator::new(config)?;
//! let output = generator.generate("The future of AI is")?;
//! ```

pub mod dialogue;
pub mod error;
pub mod generation;
pub mod optimization;
pub mod quality;
pub mod serving;
pub mod summarization;
pub mod tensor_helpers;
pub mod translation;

// Re-export core types and traits
pub use error::{NlgError, Result};
pub use generation::{
    BeamSearchConfig, GenerationConfig, GenerationStrategy, NucleusSamplingConfig, TextGenerator,
    TopKSamplingConfig,
};
pub use quality::{
    DiversityPromoter, QualityChecker, QualityFilter, RepetitionPenalty, ToxicityFilter,
};
pub use serving::{
    BatchGenerator, ConversationManager, ModelServer, PromptTemplate, StreamingGenerator,
};

use dashmap::DashMap;
use rtx_tensor::Tensor;
use std::sync::Arc;
use tokio::sync::RwLock;

/// Global configuration for RTX-NLG
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct NlgConfig {
    /// Default device for computations
    pub device: Device,
    /// Maximum sequence length
    pub max_sequence_length: usize,
    /// Batch size for generation
    pub batch_size: usize,
    /// Enable memory optimizations
    pub memory_optimization: bool,
    /// Number of worker threads
    pub num_workers: usize,
    /// Cache size in MB
    pub cache_size_mb: usize,
}

impl Default for NlgConfig {
    fn default() -> Self {
        Self {
            device: Device::default(),
            max_sequence_length: 2048,
            batch_size: 8,
            memory_optimization: true,
            num_workers: num_cpus::get(),
            cache_size_mb: 1024,
        }
    }
}

/// Main RTX-NLG runtime providing centralized model and resource management
pub struct NlgRuntime {
    config: NlgConfig,
    model_cache: Arc<DashMap<String, Arc<dyn ModelInterface>>>,
    generation_cache: Arc<RwLock<lru::LruCache<String, GeneratedOutput>>>,
    metrics: Arc<MetricsCollector>,
}

impl NlgRuntime {
    /// Create a new NLG runtime with default configuration
    pub fn new() -> Result<Self> {
        Self::with_config(NlgConfig::default())
    }

    /// Create a new NLG runtime with custom configuration
    pub fn with_config(config: NlgConfig) -> Result<Self> {
        let cache_entries = config.cache_size_mb * 1024 * 1024 / 4096; // Estimate cache entries

        Ok(Self {
            config,
            model_cache: Arc::new(DashMap::new()),
            generation_cache: Arc::new(RwLock::new(lru::LruCache::new(
                std::num::NonZeroUsize::new(cache_entries).unwrap(),
            ))),
            metrics: Arc::new(MetricsCollector::new()),
        })
    }

    /// Load a model for text generation
    pub async fn load_model(&self, model_id: &str, model_path: &str) -> Result<()> {
        let model = self.create_model_interface(model_path).await?;
        self.model_cache
            .insert(model_id.to_string(), Arc::new(model));
        Ok(())
    }

    /// Get a model interface by ID
    pub fn get_model(&self, model_id: &str) -> Option<Arc<dyn ModelInterface>> {
        self.model_cache
            .get(model_id)
            .map(|entry| entry.value().clone())
    }

    /// Create a text generator for the specified model
    pub fn create_generator(
        &self,
        model_id: &str,
        config: GenerationConfig,
    ) -> Result<TextGenerator> {
        let model = self
            .get_model(model_id)
            .ok_or_else(|| NlgError::ModelNotFound(model_id.to_string()))?;

        TextGenerator::with_model(model, config)
    }

    /// Create a streaming generator
    pub fn create_streaming_generator(
        &self,
        model_id: &str,
        config: GenerationConfig,
    ) -> Result<StreamingGenerator> {
        let model = self
            .get_model(model_id)
            .ok_or_else(|| NlgError::ModelNotFound(model_id.to_string()))?;

        StreamingGenerator::with_model(model, config)
    }

    /// Create a batch generator for high throughput
    pub fn create_batch_generator(
        &self,
        model_id: &str,
        config: GenerationConfig,
    ) -> Result<BatchGenerator> {
        let model = self
            .get_model(model_id)
            .ok_or_else(|| NlgError::ModelNotFound(model_id.to_string()))?;

        BatchGenerator::with_model(model, config, self.config.batch_size)
    }

    /// Get runtime metrics
    pub fn metrics(&self) -> Arc<MetricsCollector> {
        self.metrics.clone()
    }

    async fn create_model_interface(&self, model_path: &str) -> Result<MockModelInterface> {
        // Implementation would load the actual model
        // For now, return a mock implementation
        MockModelInterface::new(model_path)
    }
}

/// Common interface for all language models
pub trait ModelInterface: Send + Sync {
    /// Generate text tokens given input context
    fn generate_tokens(
        &self,
        input_ids: &Tensor,
        attention_mask: Option<&Tensor>,
        generation_config: &GenerationConfig,
    ) -> Result<GenerationOutput>;

    /// Get model configuration
    fn config(&self) -> &ModelConfig;

    /// Get model vocabulary size
    fn vocab_size(&self) -> usize;

    /// Get tokenizer
    fn tokenizer(&self) -> Arc<dyn TokenizerInterface>;

    /// Check if model supports streaming generation
    fn supports_streaming(&self) -> bool {
        true
    }

    /// Check if model supports batch generation
    fn supports_batch(&self) -> bool {
        true
    }
}

/// Tokenizer interface for text processing
pub trait TokenizerInterface: Send + Sync {
    /// Encode text to token IDs
    fn encode(&self, text: &str) -> Result<Vec<u32>>;

    /// Decode token IDs to text
    fn decode(&self, token_ids: &[u32]) -> Result<String>;

    /// Get vocabulary size
    fn vocab_size(&self) -> usize;

    /// Get special token IDs
    fn special_tokens(&self) -> &SpecialTokens;
}

/// Special token IDs for various model operations
#[derive(Debug, Clone)]
pub struct SpecialTokens {
    pub pad_token: u32,
    pub eos_token: u32,
    pub bos_token: u32,
    pub unk_token: u32,
    pub sep_token: Option<u32>,
    pub cls_token: Option<u32>,
    pub mask_token: Option<u32>,
}

/// Model configuration containing architecture details
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct ModelConfig {
    pub vocab_size: usize,
    pub hidden_size: usize,
    pub num_layers: usize,
    pub num_heads: usize,
    pub intermediate_size: usize,
    pub max_position_embeddings: usize,
    pub layer_norm_eps: f64,
    pub dropout: f64,
    pub attention_dropout: f64,
    pub activation_function: std::borrow::Cow<'static, str>,
}

/// Output from text generation
#[derive(Debug, Clone)]
pub struct GenerationOutput {
    pub sequences: Vec<Vec<u32>>,
    pub scores: Option<Vec<f32>>,
    pub attention_weights: Option<Vec<Tensor>>,
    pub past_key_values: Option<Vec<Tensor>>,
    pub metadata: GenerationMetadata,
}

/// Generated text output with metadata
#[derive(Debug, Clone)]
pub struct GeneratedOutput {
    pub text: String,
    pub tokens: Vec<u32>,
    pub score: f32,
    pub metadata: GenerationMetadata,
}

/// Metadata associated with generated text
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct GenerationMetadata {
    pub generation_time_ms: f64,
    pub tokens_per_second: f64,
    pub num_generated_tokens: usize,
    pub finish_reason: FinishReason,
    pub quality_scores: QualityScores,
}

/// Reason why generation finished
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub enum FinishReason {
    MaxLength,
    EosToken,
    StopSequence(String),
    QualityFilter(String),
    Error(String),
}

/// Quality assessment scores for generated text
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct QualityScores {
    pub fluency: f32,
    pub coherence: f32,
    pub relevance: f32,
    pub diversity: f32,
    pub factuality: f32,
    pub safety: f32,
}

/// Metrics collection for monitoring NLG performance
pub struct MetricsCollector {
    generation_latency: metrics::Histogram,
    throughput: metrics::Gauge,
    cache_hit_rate: metrics::Gauge,
    quality_scores: metrics::Histogram,
    error_count: metrics::Counter,
}

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

impl MetricsCollector {
    pub fn new() -> Self {
        Self {
            generation_latency: metrics::histogram!("nlg_generation_latency_ms"),
            throughput: metrics::gauge!("nlg_throughput_tokens_per_second"),
            cache_hit_rate: metrics::gauge!("nlg_cache_hit_rate"),
            quality_scores: metrics::histogram!("nlg_quality_scores"),
            error_count: metrics::counter!("nlg_error_count"),
        }
    }

    pub fn record_generation(&self, latency_ms: f64, tokens_generated: usize) {
        self.generation_latency.record(latency_ms);
        let throughput = tokens_generated as f64 / (latency_ms / 1000.0);
        self.throughput.set(throughput);
    }

    pub fn record_cache_hit(&self, hit_rate: f64) {
        self.cache_hit_rate.set(hit_rate);
    }

    pub fn record_quality(&self, scores: &QualityScores) {
        self.quality_scores.record(scores.fluency as f64);
        self.quality_scores.record(scores.coherence as f64);
        self.quality_scores.record(scores.relevance as f64);
    }

    pub fn record_error(&self) {
        self.error_count.increment(1);
    }
}

/// Mock model interface for testing and development
pub struct MockModelInterface {
    config: ModelConfig,
    tokenizer: Arc<dyn TokenizerInterface>,
}

impl MockModelInterface {
    pub fn new(_model_path: &str) -> Result<Self> {
        let config = ModelConfig {
            vocab_size: 50000,
            hidden_size: 768,
            num_layers: 12,
            num_heads: 12,
            intermediate_size: 3072,
            max_position_embeddings: 1024,
            layer_norm_eps: 1e-12,
            dropout: 0.1,
            attention_dropout: 0.1,
            activation_function: std::borrow::Cow::Borrowed("gelu"),
        };

        let tokenizer = Arc::new(MockTokenizer::new());

        Ok(Self { config, tokenizer })
    }
}

impl ModelInterface for MockModelInterface {
    fn generate_tokens(
        &self,
        input_ids: &Tensor,
        _attention_mask: Option<&Tensor>,
        generation_config: &GenerationConfig,
    ) -> Result<GenerationOutput> {
        // Mock generation - in real implementation would run inference
        let batch_size = input_ids.shape()[0];
        let input_len = input_ids.shape().dims().get(1).copied().unwrap_or(1);
        let max_length = generation_config.max_length.unwrap_or(50);

        let mut sequences = Vec::new();
        let mut scores = Vec::new();

        for _ in 0..batch_size {
            // Start with some mock input tokens
            let mut sequence: Vec<u32> = (0..input_len).map(|i| i as u32).collect();
            let mut score = 0.0f32;

            // Generate tokens up to max length
            for _ in sequence.len()..max_length {
                let next_token = rand::random::<u32>() % self.config.vocab_size as u32;
                sequence.push(next_token);
                score += rand::random::<f32>();

                if next_token == 2 {
                    // Mock EOS token
                    break;
                }
            }

            sequences.push(sequence);
            scores.push(score);
        }

        Ok(GenerationOutput {
            sequences,
            scores: Some(scores),
            attention_weights: None,
            past_key_values: None,
            metadata: GenerationMetadata {
                generation_time_ms: 100.0,
                tokens_per_second: 50.0,
                num_generated_tokens: 25,
                finish_reason: FinishReason::MaxLength,
                quality_scores: QualityScores {
                    fluency: 0.8,
                    coherence: 0.7,
                    relevance: 0.9,
                    diversity: 0.6,
                    factuality: 0.8,
                    safety: 0.95,
                },
            },
        })
    }

    fn config(&self) -> &ModelConfig {
        &self.config
    }

    fn vocab_size(&self) -> usize {
        self.config.vocab_size
    }

    fn tokenizer(&self) -> Arc<dyn TokenizerInterface> {
        self.tokenizer.clone()
    }
}

/// Mock tokenizer for testing
pub struct MockTokenizer {
    special_tokens: SpecialTokens,
}

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

impl MockTokenizer {
    pub fn new() -> Self {
        Self {
            special_tokens: SpecialTokens {
                pad_token: 0,
                eos_token: 2,
                bos_token: 1,
                unk_token: 3,
                sep_token: Some(4),
                cls_token: Some(5),
                mask_token: Some(6),
            },
        }
    }
}

impl TokenizerInterface for MockTokenizer {
    fn encode(&self, text: &str) -> Result<Vec<u32>> {
        // Mock encoding - split by whitespace and hash
        let tokens: Vec<u32> = text
            .split_whitespace()
            .map(|word| {
                let mut hash = 0u32;
                for byte in word.bytes() {
                    hash = hash.wrapping_mul(31).wrapping_add(byte as u32);
                }
                hash % 50000 // Mock vocab size
            })
            .collect();
        Ok(tokens)
    }

    fn decode(&self, token_ids: &[u32]) -> Result<String> {
        // Mock decoding - convert IDs back to placeholder words
        let words: Vec<String> = token_ids.iter().map(|&id| format!("token_{id}")).collect();
        Ok(words.join(" "))
    }

    fn vocab_size(&self) -> usize {
        50000
    }

    fn special_tokens(&self) -> &SpecialTokens {
        &self.special_tokens
    }
}

// Re-export commonly used external types
pub use rtx_tensor::{DType, Device, Shape};

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

    #[tokio::test]
    async fn test_nlg_runtime_creation() -> Result<()> {
        let runtime = NlgRuntime::new()?;
        assert_eq!(runtime.config.max_sequence_length, 2048);
        Ok(())
    }

    #[tokio::test]
    async fn test_model_loading() -> Result<()> {
        let runtime = NlgRuntime::new()?;
        runtime.load_model("test_model", "/path/to/model").await?;

        let model = runtime.get_model("test_model");
        assert!(model.is_some());
        Ok(())
    }

    #[test]
    fn test_mock_tokenizer() -> Result<()> {
        let tokenizer = MockTokenizer::new();
        let text = "Hello world";
        let tokens = tokenizer.encode(text)?;
        let decoded = tokenizer.decode(&tokens)?;

        assert!(!tokens.is_empty());
        assert!(!decoded.is_empty());
        Ok(())
    }
}