oxirs-vec 0.2.4

//! TensorFlow integration for embedding generation and model serving

use crate::real_time_embedding_pipeline::traits::{
    ContentItem, EmbeddingGenerator, GeneratorStatistics, ProcessingResult, ProcessingStatus,
};
use crate::Vector;
use anyhow::{anyhow, Result};
use scirs2_core::random::Random;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::PathBuf;
use std::time::{Duration, Instant};

/// TensorFlow model configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorFlowConfig {
    pub model_path: PathBuf,
    pub input_name: String,
    pub output_name: String,
    pub device: TensorFlowDevice,
    pub batch_size: usize,
    pub max_sequence_length: usize,
    pub optimization_level: OptimizationLevel,
    pub use_mixed_precision: bool,
    pub session_config: SessionConfig,
}

/// TensorFlow device configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TensorFlowDevice {
    Cpu { num_threads: Option<usize> },
    Gpu { device_id: i32, memory_growth: bool },
    Tpu { worker: String },
}

/// TensorFlow optimization levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationLevel {
    None,
    Basic,
    Extended,
    Aggressive,
}

/// TensorFlow session configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SessionConfig {
    pub inter_op_parallelism_threads: Option<usize>,
    pub intra_op_parallelism_threads: Option<usize>,
    pub allow_soft_placement: bool,
    pub log_device_placement: bool,
}

impl Default for TensorFlowConfig {
    fn default() -> Self {
        Self {
            model_path: PathBuf::from("./models/universal-sentence-encoder"),
            input_name: "inputs".to_string(),
            output_name: "outputs".to_string(),
            device: TensorFlowDevice::Cpu { num_threads: None },
            batch_size: 32,
            max_sequence_length: 512,
            optimization_level: OptimizationLevel::Basic,
            use_mixed_precision: false,
            session_config: SessionConfig::default(),
        }
    }
}

impl Default for SessionConfig {
    fn default() -> Self {
        Self {
            inter_op_parallelism_threads: None,
            intra_op_parallelism_threads: None,
            allow_soft_placement: true,
            log_device_placement: false,
        }
    }
}

/// TensorFlow model metadata
#[derive(Debug, Clone)]
pub struct TensorFlowModelInfo {
    pub model_path: PathBuf,
    pub input_signature: Vec<TensorSpec>,
    pub output_signature: Vec<TensorSpec>,
    pub model_version: String,
    pub dimensions: usize,
    pub preprocessing_required: bool,
}

/// TensorFlow tensor specification
#[derive(Debug, Clone)]
pub struct TensorSpec {
    pub name: String,
    pub dtype: TensorDataType,
    pub shape: Vec<Option<i64>>,
}

/// TensorFlow data types
#[derive(Debug, Clone)]
pub enum TensorDataType {
    Float32,
    Float64,
    Int32,
    Int64,
    String,
    Bool,
}

/// TensorFlow embedder for generating embeddings
#[derive(Debug)]
pub struct TensorFlowEmbedder {
    config: TensorFlowConfig,
    model_info: Option<TensorFlowModelInfo>,
    session_initialized: bool,
    preprocessing_pipeline: PreprocessingPipeline,
}

/// Text preprocessing pipeline for TensorFlow models
#[derive(Debug)]
pub struct PreprocessingPipeline {
    pub lowercase: bool,
    pub remove_punctuation: bool,
    pub tokenizer: Option<String>,
    pub vocabulary: Option<HashMap<String, i32>>,
}

impl Default for PreprocessingPipeline {
    fn default() -> Self {
        Self {
            lowercase: true,
            remove_punctuation: false,
            tokenizer: None,
            vocabulary: None,
        }
    }
}

impl TensorFlowEmbedder {
    /// Create a new TensorFlow embedder
    pub fn new(config: TensorFlowConfig) -> Result<Self> {
        Ok(Self {
            config,
            model_info: None,
            session_initialized: false,
            preprocessing_pipeline: PreprocessingPipeline::default(),
        })
    }

    /// Load and initialize the TensorFlow model
    pub fn load_model(&mut self) -> Result<()> {
        if !self.config.model_path.exists() {
            return Err(anyhow!(
                "Model path does not exist: {:?}",
                self.config.model_path
            ));
        }

        // Mock model loading - in a real implementation, this would use tensorflow-rust
        let model_info = TensorFlowModelInfo {
            model_path: self.config.model_path.clone(),
            input_signature: vec![TensorSpec {
                name: self.config.input_name.clone(),
                dtype: TensorDataType::String,
                shape: vec![None, None], // batch_size, sequence_length
            }],
            output_signature: vec![TensorSpec {
                name: self.config.output_name.clone(),
                dtype: TensorDataType::Float32,
                shape: vec![None, Some(512)], // batch_size, embedding_dim
            }],
            model_version: "1.0.0".to_string(),
            dimensions: 512,
            preprocessing_required: true,
        };

        self.model_info = Some(model_info);
        self.session_initialized = true;
        Ok(())
    }

    /// Generate embeddings for text content
    pub fn embed_text(&self, text: &str) -> Result<Vector> {
        if !self.session_initialized {
            return Err(anyhow!("Model not loaded. Call load_model() first."));
        }

        let preprocessed_text = self.preprocess_text(text)?;
        let embedding = self.run_inference(&preprocessed_text)?;
        Ok(Vector::new(embedding))
    }

    /// Generate embeddings for multiple texts
    pub fn embed_batch(&self, texts: &[String]) -> Result<Vec<Vector>> {
        if !self.session_initialized {
            return Err(anyhow!("Model not loaded. Call load_model() first."));
        }

        let mut results = Vec::new();
        for text in texts {
            let embedding = self.embed_text(text)?;
            results.push(embedding);
        }
        Ok(results)
    }

    /// Preprocess text according to model requirements
    fn preprocess_text(&self, text: &str) -> Result<String> {
        let mut processed = text.to_string();

        if self.preprocessing_pipeline.lowercase {
            processed = processed.to_lowercase();
        }

        if self.preprocessing_pipeline.remove_punctuation {
            processed = processed
                .chars()
                .filter(|c| c.is_alphanumeric() || c.is_whitespace())
                .collect();
        }

        // Truncate to max sequence length
        if processed.len() > self.config.max_sequence_length {
            processed.truncate(self.config.max_sequence_length);
        }

        Ok(processed)
    }

    /// Run TensorFlow inference (mock implementation)
    fn run_inference(&self, text: &str) -> Result<Vec<f32>> {
        let model_info = self
            .model_info
            .as_ref()
            .ok_or_else(|| anyhow!("Model info not available"))?;

        // Mock inference - generate random embeddings
        let mut rng = Random::seed(text.len() as u64);

        let mut embedding = vec![0.0f32; model_info.dimensions];
        for value in &mut embedding {
            *value = rng.gen_range(-1.0..1.0);
        }

        // Normalize embedding
        let norm = embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
        if norm > 0.0 {
            for x in &mut embedding {
                *x /= norm;
            }
        }

        Ok(embedding)
    }

    /// Get model information
    pub fn get_model_info(&self) -> Option<&TensorFlowModelInfo> {
        self.model_info.as_ref()
    }

    /// Get output dimensions
    pub fn get_dimensions(&self) -> Option<usize> {
        self.model_info.as_ref().map(|info| info.dimensions)
    }

    /// Update preprocessing pipeline
    pub fn set_preprocessing_pipeline(&mut self, pipeline: PreprocessingPipeline) {
        self.preprocessing_pipeline = pipeline;
    }
}

/// TensorFlow model server for serving multiple models
#[derive(Debug)]
pub struct TensorFlowModelServer {
    models: HashMap<String, TensorFlowEmbedder>,
    default_model: String,
    server_config: ServerConfig,
}

/// Server configuration for TensorFlow model serving
#[derive(Debug, Clone)]
pub struct ServerConfig {
    pub model_warming: bool,
    pub request_batching: bool,
    pub max_batch_size: usize,
    pub batch_timeout_ms: u64,
    pub model_versions: HashMap<String, String>,
}

impl Default for ServerConfig {
    fn default() -> Self {
        Self {
            model_warming: true,
            request_batching: true,
            max_batch_size: 64,
            batch_timeout_ms: 10,
            model_versions: HashMap::new(),
        }
    }
}

impl TensorFlowModelServer {
    /// Create a new TensorFlow model server
    pub fn new(default_model: String, config: ServerConfig) -> Self {
        Self {
            models: HashMap::new(),
            default_model,
            server_config: config,
        }
    }

    /// Register a model with the server
    pub fn register_model(&mut self, name: String, embedder: TensorFlowEmbedder) -> Result<()> {
        self.models.insert(name.clone(), embedder);

        if self.server_config.model_warming {
            if let Some(model) = self.models.get(&name) {
                // Warm up the model with a test embedding
                let _ = model.embed_text("warmup text");
            }
        }

        Ok(())
    }

    /// Get available models
    pub fn list_models(&self) -> Vec<String> {
        self.models.keys().cloned().collect()
    }

    /// Generate embeddings using a specific model
    pub fn embed_with_model(&self, model_name: &str, texts: &[String]) -> Result<Vec<Vector>> {
        let model = self
            .models
            .get(model_name)
            .ok_or_else(|| anyhow!("Model not found: {}", model_name))?;

        if self.server_config.request_batching && texts.len() > 1 {
            model.embed_batch(texts)
        } else {
            let mut results = Vec::new();
            for text in texts {
                results.push(model.embed_text(text)?);
            }
            Ok(results)
        }
    }

    /// Generate embeddings using the default model
    pub fn embed(&self, texts: &[String]) -> Result<Vec<Vector>> {
        self.embed_with_model(&self.default_model, texts)
    }

    /// Get model info for a specific model
    pub fn get_model_info(&self, model_name: &str) -> Option<&TensorFlowModelInfo> {
        self.models.get(model_name)?.get_model_info()
    }

    /// Update server configuration
    pub fn update_config(&mut self, config: ServerConfig) {
        self.server_config = config;
    }
}

impl EmbeddingGenerator for TensorFlowEmbedder {
    fn generate_embedding(&self, content: &ContentItem) -> Result<Vector> {
        self.embed_text(&content.content)
    }

    fn generate_batch_embeddings(&self, content: &[ContentItem]) -> Result<Vec<ProcessingResult>> {
        let mut results = Vec::new();

        for item in content {
            let start_time = Instant::now();
            let vector_result = self.generate_embedding(item);
            let duration = start_time.elapsed();

            let result = match vector_result {
                Ok(vector) => ProcessingResult {
                    item: item.clone(),
                    vector: Some(vector),
                    status: ProcessingStatus::Completed,
                    duration,
                    error: None,
                    metadata: HashMap::new(),
                },
                Err(e) => ProcessingResult {
                    item: item.clone(),
                    vector: None,
                    status: ProcessingStatus::Failed {
                        reason: e.to_string(),
                    },
                    duration,
                    error: Some(e.to_string()),
                    metadata: HashMap::new(),
                },
            };

            results.push(result);
        }

        Ok(results)
    }

    fn embedding_dimensions(&self) -> usize {
        self.get_dimensions().unwrap_or(512)
    }

    fn get_config(&self) -> serde_json::Value {
        serde_json::to_value(&self.config).unwrap_or_default()
    }

    fn is_ready(&self) -> bool {
        self.session_initialized
    }

    fn get_statistics(&self) -> GeneratorStatistics {
        GeneratorStatistics {
            total_embeddings: 0,
            total_processing_time: Duration::from_millis(0),
            average_processing_time: Duration::from_millis(0),
            error_count: 0,
            last_error: None,
        }
    }
}

#[cfg(test)]
#[allow(unused_imports, clippy::useless_vec)]
mod tests {
    use super::*;
    use anyhow::Result;
    use std::path::PathBuf;

    #[test]
    fn test_tensorflow_config_creation() {
        let config = TensorFlowConfig::default();
        assert_eq!(config.batch_size, 32);
        assert_eq!(config.max_sequence_length, 512);
        assert!(matches!(config.device, TensorFlowDevice::Cpu { .. }));
    }

    #[test]
    fn test_tensorflow_embedder_creation() {
        let config = TensorFlowConfig::default();
        let embedder = TensorFlowEmbedder::new(config);
        assert!(embedder.is_ok());
    }

    #[test]
    fn test_preprocessing_pipeline() -> Result<()> {
        let mut embedder = TensorFlowEmbedder::new(TensorFlowConfig::default())?;
        let pipeline = PreprocessingPipeline {
            lowercase: true,
            remove_punctuation: true,
            ..Default::default()
        };
        embedder.set_preprocessing_pipeline(pipeline);

        let processed = embedder.preprocess_text("Hello, World!")?;
        assert_eq!(processed, "hello world");
        Ok(())
    }

    #[test]
    fn test_model_server_creation() {
        let server = TensorFlowModelServer::new("default".to_string(), ServerConfig::default());
        assert_eq!(server.default_model, "default");
        assert!(server.list_models().is_empty());
    }

    #[test]
    fn test_model_registration() -> Result<()> {
        let mut server =
            TensorFlowModelServer::new("test_model".to_string(), ServerConfig::default());

        let config = TensorFlowConfig::default();
        let embedder = TensorFlowEmbedder::new(config)?;

        let result = server.register_model("test_model".to_string(), embedder);
        assert!(result.is_ok());
        assert_eq!(server.list_models().len(), 1);
        Ok(())
    }

    #[test]
    fn test_tensor_spec_creation() {
        let spec = TensorSpec {
            name: "input".to_string(),
            dtype: TensorDataType::Float32,
            shape: vec![None, Some(512)],
        };
        assert_eq!(spec.name, "input");
        assert!(matches!(spec.dtype, TensorDataType::Float32));
    }

    #[test]
    fn test_session_config_default() {
        let config = SessionConfig::default();
        assert!(config.allow_soft_placement);
        assert!(!config.log_device_placement);
        assert!(config.inter_op_parallelism_threads.is_none());
    }

    #[test]
    fn test_device_configuration() {
        let cpu_device = TensorFlowDevice::Cpu {
            num_threads: Some(4),
        };
        let gpu_device = TensorFlowDevice::Gpu {
            device_id: 0,
            memory_growth: true,
        };

        assert!(matches!(cpu_device, TensorFlowDevice::Cpu { .. }));
        assert!(matches!(gpu_device, TensorFlowDevice::Gpu { .. }));
    }

    #[test]
    fn test_optimization_levels() {
        let levels = vec![
            OptimizationLevel::None,
            OptimizationLevel::Basic,
            OptimizationLevel::Extended,
            OptimizationLevel::Aggressive,
        ];
        assert_eq!(levels.len(), 4);
    }
}