#![allow(missing_docs)] // Internal implementation details
// ML training service domain lints: generated protobuf code and training orchestration
#![allow(clippy::mixed_attributes_style)] // Generated protobuf code uses mixed attributes
#![allow(clippy::manual_clamp)] // Explicit min/max preferred in training parameters
#![allow(clippy::too_many_arguments)] // Training functions need many hyperparameters
#![allow(clippy::type_complexity)] // Complex types in async service handlers
#![allow(clippy::wildcard_in_or_patterns)] // Wildcard patterns in job queue matching
#![allow(clippy::redundant_pattern_matching)] // Explicit pattern matching preferred
#![allow(clippy::while_let_loop)] // Explicit loop with break for service processing
#![allow(clippy::doc_lazy_continuation)] // Doc formatting acceptable
//! ML Training Service Library
//!
//! This library provides the core functionality for the ML Training Service,
//! including training orchestration, job management, and gRPC API implementation.
#![deny(unsafe_code)]
#![deny(clippy::unwrap_used, clippy::expect_used)]

pub mod asset_parser;
pub mod batch_tuning_manager;
pub mod checkpoint_manager;
pub mod data_config;
pub mod data_loader;
pub mod data_file_discovery;
pub mod database;
pub mod dbn_data_loader;
pub mod deployment_pipeline;
pub mod encryption;
pub mod ensemble_training_coordinator;
pub mod gpu_config;
pub mod gpu_resource_manager;
pub mod grpc;
pub mod grpc_tuning_handlers;
pub mod job_tracker;
pub mod k8s_dispatcher;
pub mod job_queue;
pub mod job_spawner;
pub mod monitoring;
pub mod optuna_persistence;
pub mod orchestrator;
pub mod promotion_manager;
pub mod queue_consumer;
pub mod schema_types;
pub mod service;
pub mod simple_metrics;
pub mod storage;
pub mod technical_indicators;
pub mod training_metrics;
pub mod trial_executor;
pub mod tuning_manager;
pub mod validation_pipeline;

// TLI BacktestingService proto (client only -- for validation pipeline)
pub mod backtesting_proto {
    tonic::include_proto!("foxhunt.tli");
}

// Re-export proto module for test access
pub use service::proto;

/// Error types for the ML training service
pub mod errors {
    use thiserror::Error;

    /// Training service errors
    #[derive(Error, Debug)]
    pub enum TrainingServiceError {
        /// Configuration error
        #[error("Configuration error: {message}")]
        Configuration { message: String },

        /// Database error
        #[error("Database error: {message}")]
        Database { message: String },

        /// Storage error
        #[error("Storage error: {message}")]
        Storage { message: String },

        /// Training error
        #[error("Training error: {message}")]
        Training { message: String },

        /// Resource allocation error
        #[error("Resource allocation error: {message}")]
        Resource { message: String },

        /// Invalid request error
        #[error("Invalid request: {message}")]
        InvalidRequest { message: String },

        /// Job not found error
        #[error("Job not found: {job_id}")]
        JobNotFound { job_id: String },

        /// Internal service error
        #[error("Internal error: {message}")]
        Internal { message: String },
    }

    /// Result type for training service operations
    pub type Result<T> = std::result::Result<T, TrainingServiceError>;
}

/// Version information
pub const VERSION: &str = env!("CARGO_PKG_VERSION");

/// Service metadata
pub const SERVICE_NAME: &str = "ml_training_service";

#[cfg(test)]
#[allow(clippy::unwrap_used, clippy::expect_used)]
mod tests {
    use super::*;

    #[test]
    fn test_version() {
        assert_ne!(VERSION, "");
    }

    #[test]
    fn test_service_name() {
        assert_eq!(SERVICE_NAME, "ml_training_service");
    }
}