api_openai 0.3.0

//! Model Tuning Module
//!
//! This module provides stateless model tuning and fine-tuning utilities for `OpenAI` API models.
//! Following the "Thin Client, Rich API" principle, this module offers training management
//! patterns and optimization tools without automatic behaviors or persistent state management.

#![ allow( clippy::missing_inline_in_public_items, clippy::unused_async ) ]

mod private
{
  use std::
  {
    collections ::HashMap,
    time ::SystemTime,
  };
  use core::time::Duration;
  use serde::{ Deserialize, Serialize };
  use tokio::sync::mpsc;

  /// Fine-tuning job status
  #[ derive( Debug, Clone, PartialEq, Serialize, Deserialize ) ]
  pub enum TuningStatus
  {
    /// Job is being prepared and validated
    Validating,
    /// Job is queued for execution
    Queued,
    /// Training is in progress
    Running,
    /// Job completed successfully
    Succeeded,
    /// Job failed due to error
    Failed( String ),
    /// Job was cancelled by user
    Cancelled,
  }

  /// Training objective type
  #[ derive( Debug, Clone, PartialEq, Serialize, Deserialize ) ]
  pub enum TrainingObjective
  {
    /// Standard language modeling objective
    LanguageModeling,
    /// Supervised fine-tuning with custom objectives
    SupervisedFineTuning,
    /// Reinforcement learning from human feedback
    RLHF,
    /// Custom objective with specific parameters
    Custom
    {
      /// Objective name
      name : String,
      /// Objective-specific parameters
      parameters : HashMap<  String, String  >,
    },
  }

  /// Parameter-efficient fine-tuning method
  #[ derive( Debug, Clone, PartialEq, Serialize, Deserialize ) ]
  pub enum FineTuningMethod
  {
    /// Full model fine-tuning
    Full,
    /// Low-Rank Adaptation (`LoRA`)
    LoRA
    {
      /// Rank of adaptation matrices
      rank : u32,
      /// Alpha scaling parameter
      alpha : f64,
      /// Dropout rate for `LoRA` layers
      dropout : f64,
    },
    /// Adapter-based fine-tuning
    Adapter
    {
      /// Adapter hidden dimension
      hidden_dim : u32,
      /// Number of adapter layers
      num_layers : u32,
    },
    /// Prefix tuning
    PrefixTuning
    {
      /// Number of prefix tokens
      prefix_length : u32,
      /// Prefix embedding dimension
      embedding_dim : u32,
    },
  }

  /// Training hyperparameters
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct HyperParameters
  {
    /// Learning rate for optimization
    pub learning_rate : f64,
    /// Batch size for training
    pub batch_size : u32,
    /// Number of training epochs
    pub epochs : u32,
    /// Warmup steps for learning rate schedule
    pub warmup_steps : u32,
    /// Weight decay for regularization
    pub weight_decay : f64,
    /// Gradient clipping threshold
    pub gradient_clip_norm : f64,
    /// Learning rate schedule type
    pub lr_schedule : String,
    /// Custom hyperparameters
    pub custom_params : HashMap<  String, String  >,
  }

  impl Default for HyperParameters
  {
    fn default() -> Self
    {
      Self
      {
        learning_rate : 1e-4,
        batch_size : 32,
        epochs : 3,
        warmup_steps : 100,
        weight_decay : 0.01,
        gradient_clip_norm : 1.0,
        lr_schedule : "linear".to_string(),
        custom_params : HashMap::new(),
      }
    }
  }

  /// Training data configuration
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct TrainingDataConfig
  {
    /// Training dataset file path or identifier
    pub training_file : String,
    /// Validation dataset file path or identifier
    pub validation_file : Option< String >,
    /// Data format specification
    pub data_format : String,
    /// Maximum sequence length
    pub max_sequence_length : u32,
    /// Data preprocessing options
    pub preprocessing : HashMap<  String, String  >,
  }

  /// Model checkpoint information
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct ModelCheckpoint
  {
    /// Checkpoint identifier
    pub checkpoint_id : String,
    /// Training step at checkpoint
    pub step : u64,
    /// Training loss at checkpoint
    pub loss : f64,
    /// Validation metrics at checkpoint
    pub validation_metrics : HashMap<  String, f64  >,
    /// Checkpoint creation timestamp
    pub created_at : SystemTime,
    /// Checkpoint file path
    pub file_path : String,
  }

  /// Training progress metrics
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct TrainingMetrics
  {
    /// Current training step
    pub step : u64,
    /// Current epoch
    pub epoch : u32,
    /// Training loss
    pub training_loss : f64,
    /// Validation loss
    pub validation_loss : Option< f64 >,
    /// Learning rate
    pub learning_rate : f64,
    /// Throughput (tokens per second)
    pub throughput : f64,
    /// Estimated time remaining
    pub eta_seconds : Option< u64 >,
    /// Custom metrics
    pub custom_metrics : HashMap<  String, f64  >,
  }

  /// Model tuning job configuration
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct TuningJobConfig
  {
    /// Job name/identifier
    pub job_name : String,
    /// Base model to fine-tune
    pub base_model : String,
    /// Training data configuration
    pub training_data : TrainingDataConfig,
    /// Hyperparameters for training
    pub hyperparameters : HyperParameters,
    /// Fine-tuning method
    pub method : FineTuningMethod,
    /// Training objective
    pub objective : TrainingObjective,
    /// Resource requirements
    pub resource_requirements : TuningResourceRequirements,
    /// Checkpointing configuration
    pub checkpointing : CheckpointConfig,
    /// Environment variables for training
    pub env_vars : HashMap<  String, String  >,
  }

  /// Resource requirements for training
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct TuningResourceRequirements
  {
    /// Number of GPUs required
    pub gpu_count : u32,
    /// GPU type preference
    pub gpu_type : Option< String >,
    /// Memory requirements in GB
    pub memory_gb : u64,
    /// CPU cores required
    pub cpu_cores : u32,
    /// Storage requirements in GB
    pub storage_gb : u64,
  }

  impl Default for TuningResourceRequirements
  {
    fn default() -> Self
    {
      Self
      {
        gpu_count : 1,
        gpu_type : None,
        memory_gb : 16,
        cpu_cores : 4,
        storage_gb : 100,
      }
    }
  }

  /// Checkpointing configuration
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct CheckpointConfig
  {
    /// Enable automatic checkpointing
    pub enabled : bool,
    /// Checkpoint save interval (in steps)
    pub save_interval : u64,
    /// Maximum number of checkpoints to keep
    pub max_checkpoints : u32,
    /// Save directory for checkpoints
    pub save_directory : String,
  }

  impl Default for CheckpointConfig
  {
    fn default() -> Self
    {
      Self
      {
        enabled : true,
        save_interval : 1000,
        max_checkpoints : 5,
        save_directory : "./checkpoints".to_string(),
      }
    }
  }

  /// Model tuning job instance
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct TuningJob
  {
    /// Job configuration
    pub config : TuningJobConfig,
    /// Current job status
    pub status : TuningStatus,
    /// Job creation timestamp
    pub created_at : SystemTime,
    /// Last update timestamp
    pub updated_at : SystemTime,
    /// Current training metrics
    pub current_metrics : Option< TrainingMetrics >,
    /// Saved checkpoints
    pub checkpoints : Vec< ModelCheckpoint >,
    /// Job execution history
    pub execution_log : Vec< TuningEvent >,
  }

  impl TuningJob
  {
    /// Create a new tuning job
    #[ must_use ]
    pub fn new( config : TuningJobConfig ) -> Self
    {
      let now = SystemTime::now();
      Self
      {
        config,
        status : TuningStatus::Validating,
        created_at : now,
        updated_at : now,
        current_metrics : None,
        checkpoints : Vec::new(),
        execution_log : Vec::new(),
      }
    }

    /// Update job status
    pub fn update_status( &mut self, status : TuningStatus )
    {
      let event = TuningEvent
      {
        event_type : TuningEventType::StatusChanged
        {
          from : self.status.clone(),
          to : status.clone(),
        },
        message : format!( "Status changed from {:?} to {:?}", self.status, status ),
        timestamp : SystemTime::now(),
      };

      self.status = status;
      self.updated_at = SystemTime::now();
      self.execution_log.push( event );
    }

    /// Update training metrics
    pub fn update_metrics( &mut self, metrics : TrainingMetrics )
    {
      let event = TuningEvent
      {
        event_type : TuningEventType::MetricsUpdated
        {
          step : metrics.step,
          loss : metrics.training_loss,
        },
        message : format!( "Metrics updated at step {}", metrics.step ),
        timestamp : SystemTime::now(),
      };

      self.current_metrics = Some( metrics );
      self.updated_at = SystemTime::now();
      self.execution_log.push( event );
    }

    /// Add a checkpoint
    pub fn add_checkpoint( &mut self, checkpoint : ModelCheckpoint )
    {
      let event = TuningEvent
      {
        event_type : TuningEventType::CheckpointSaved
        {
          checkpoint_id : checkpoint.checkpoint_id.clone(),
          step : checkpoint.step,
        },
        message : format!( "Checkpoint saved at step {}", checkpoint.step ),
        timestamp : SystemTime::now(),
      };

      self.checkpoints.push( checkpoint );
      self.updated_at = SystemTime::now();
      self.execution_log.push( event );
    }

    /// Calculate job duration
    #[ must_use ]
    pub fn duration( &self ) -> Duration
    {
      self.updated_at.duration_since( self.created_at ).unwrap_or( Duration::from_secs( 0 ) )
    }
  }

  /// Tuning event for logging
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct TuningEvent
  {
    /// Event type
    pub event_type : TuningEventType,
    /// Event message
    pub message : String,
    /// Event timestamp
    pub timestamp : SystemTime,
  }

  /// Types of tuning events
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub enum TuningEventType
  {
    /// Status change event
    StatusChanged
    {
      /// Previous status
      from : TuningStatus,
      /// New status
      to : TuningStatus,
    },
    /// Metrics update event
    MetricsUpdated
    {
      /// Training step
      step : u64,
      /// Current loss
      loss : f64,
    },
    /// Checkpoint saved event
    CheckpointSaved
    {
      /// Checkpoint identifier
      checkpoint_id : String,
      /// Training step
      step : u64,
    },
  }

  /// Model tuning manager
  #[ derive( Debug ) ]
  pub struct TuningManager
  {
    /// Active tuning jobs
    jobs : HashMap<  String, TuningJob  >,
  }

  impl TuningManager
  {
    /// Create a new tuning manager
    #[ must_use ]
    pub fn new() -> Self
    {
      Self
      {
        jobs : HashMap::new(),
      }
    }

    /// Create a new tuning job
    ///
    /// # Errors
    /// Returns an error if a job with the same name already exists.
    pub async fn create_job( &mut self, config : TuningJobConfig ) -> Result< String, String >
    {
      let job_name = config.job_name.clone();

      if self.jobs.contains_key( &job_name )
      {
        return Err( format!( "Job with name '{job_name}' already exists" ) );
      }

      let job = TuningJob::new( config );
      self.jobs.insert( job_name.clone(), job );

      Ok( job_name )
    }

    /// Get a tuning job by name
    pub async fn get_job( &self, job_name : &str ) -> Option< &TuningJob >
    {
      self.jobs.get( job_name )
    }

    /// Get a mutable reference to a tuning job
    pub async fn get_job_mut( &mut self, job_name : &str ) -> Option< &mut TuningJob >
    {
      self.jobs.get_mut( job_name )
    }

    /// List all tuning jobs
    pub async fn list_jobs( &self ) -> Vec< &TuningJob >
    {
      self.jobs.values().collect()
    }

    /// Update job status
    ///
    /// # Errors
    /// Returns an error if the job is not found.
    pub async fn update_job_status( &mut self, job_name : &str, status : TuningStatus ) -> Result< (), String >
    {
      match self.jobs.get_mut( job_name )
      {
        Some( job ) =>
        {
          job.update_status( status );
          Ok( () )
        }
        None => Err( format!( "Job '{job_name}' not found" ) ),
      }
    }

    /// Cancel a tuning job
    ///
    /// # Errors
    /// Returns an error if the job is not found.
    pub async fn cancel_job( &mut self, job_name : &str ) -> Result< (), String >
    {
      self.update_job_status( job_name, TuningStatus::Cancelled ).await
    }

    /// Delete a tuning job
    ///
    /// # Errors
    /// Returns an error if the job is not found.
    pub async fn delete_job( &mut self, job_name : &str ) -> Result< (), String >
    {
      match self.jobs.remove( job_name )
      {
        Some( _ ) => Ok( () ),
        None => Err( format!( "Job '{job_name}' not found" ) ),
      }
    }

    /// Get tuning statistics
    #[ must_use ]
    pub fn tuning_stats( &self ) -> TuningStats
    {
      let mut stats = TuningStats
      {
        total : self.jobs.len(),
        validating : 0,
        queued : 0,
        running : 0,
        succeeded : 0,
        failed : 0,
        cancelled : 0,
      };

      for job in self.jobs.values()
      {
        match job.status
        {
          TuningStatus::Validating => stats.validating += 1,
          TuningStatus::Queued => stats.queued += 1,
          TuningStatus::Running => stats.running += 1,
          TuningStatus::Succeeded => stats.succeeded += 1,
          TuningStatus::Failed( _ ) => stats.failed += 1,
          TuningStatus::Cancelled => stats.cancelled += 1,
        }
      }

      stats
    }
  }

  impl Default for TuningManager
  {
    #[ inline ]
    fn default() -> Self
    {
      Self::new()
    }
  }

  /// Tuning statistics
  #[ derive( Debug, Clone ) ]
  pub struct TuningStats
  {
    /// Total number of jobs
    pub total : usize,
    /// Number of validating jobs
    pub validating : usize,
    /// Number of queued jobs
    pub queued : usize,
    /// Number of running jobs
    pub running : usize,
    /// Number of succeeded jobs
    pub succeeded : usize,
    /// Number of failed jobs
    pub failed : usize,
    /// Number of cancelled jobs
    pub cancelled : usize,
  }

  /// Tuning event notification
  #[ derive( Debug, Clone ) ]
  pub struct TuningNotification
  {
    /// Job name
    pub job_name : String,
    /// Current status
    pub status : TuningStatus,
    /// Current metrics (if available)
    pub metrics : Option< TrainingMetrics >,
    /// Notification timestamp
    pub timestamp : SystemTime,
  }

  /// Tuning event sender
  #[ derive( Debug ) ]
  pub struct TuningEventSender
  {
    /// Internal sender
    pub sender : mpsc::UnboundedSender< TuningNotification >,
  }

  /// Tuning event receiver
  #[ derive( Debug ) ]
  pub struct TuningEventReceiver
  {
    /// Internal receiver
    pub receiver : mpsc::UnboundedReceiver< TuningNotification >,
  }

  /// Model tuning utilities
  #[ derive( Debug ) ]
  pub struct ModelTuningUtils;

  impl ModelTuningUtils
  {
    /// Create a tuning event notifier
    #[ must_use ]
    pub fn create_event_notifier() -> ( TuningEventSender, TuningEventReceiver )
    {
      let ( tx, rx ) = mpsc::unbounded_channel();
      ( TuningEventSender { sender : tx }, TuningEventReceiver { receiver : rx } )
    }

    /// Validate tuning job configuration
    ///
    /// # Errors
    /// Returns a vector of validation error messages if the configuration is invalid.
    pub fn validate_config( config : &TuningJobConfig ) -> Result< (), Vec< String > >
    {
      let mut errors = Vec::new();

      if config.job_name.is_empty()
      {
        errors.push( "Job name cannot be empty".to_string() );
      }

      if config.base_model.is_empty()
      {
        errors.push( "Base model cannot be empty".to_string() );
      }

      if config.training_data.training_file.is_empty()
      {
        errors.push( "Training file cannot be empty".to_string() );
      }

      if config.hyperparameters.learning_rate <= 0.0
      {
        errors.push( "Learning rate must be positive".to_string() );
      }

      if config.hyperparameters.batch_size == 0
      {
        errors.push( "Batch size must be positive".to_string() );
      }

      if config.hyperparameters.epochs == 0
      {
        errors.push( "Number of epochs must be positive".to_string() );
      }

      if errors.is_empty()
      {
        Ok( () )
      }
      else
      {
        Err( errors )
      }
    }

    /// Estimate training time
    #[ must_use ]
    pub fn estimate_training_time( config : &TuningJobConfig, dataset_size : u64 ) -> Duration
    {
      let tokens_per_epoch = dataset_size;
      let total_tokens = tokens_per_epoch * u64::from( config.hyperparameters.epochs );
      let tokens_per_second = 1000; // Estimated throughput

      let total_seconds = total_tokens / tokens_per_second;
      Duration::from_secs( total_seconds )
    }

    /// Estimate resource cost
    #[ must_use ]
    pub fn estimate_training_cost( config : &TuningJobConfig, duration : Duration ) -> f64
    {
      let hours = duration.as_secs_f64() / 3600.0;
      let gpu_cost_per_hour = match config.resource_requirements.gpu_type.as_deref()
      {
        Some( "A100" ) => 4.0,
        Some( "V100" ) => 2.0,
        Some( "T4" ) => 0.5,
        _ => 1.0,
      };

      hours * gpu_cost_per_hour * f64::from( config.resource_requirements.gpu_count )
    }

    /// Optimize hyperparameters
    #[ must_use ]
    pub fn suggest_hyperparameters( method : &FineTuningMethod, dataset_size : u64 ) -> HyperParameters
    {
      let mut params = HyperParameters::default();

      match method
      {
        FineTuningMethod::Full =>
        {
          params.learning_rate = if dataset_size > 100_000 { 1e-5 } else { 5e-5 };
          params.batch_size = if dataset_size > 50000 { 16 } else { 32 };
        }
        FineTuningMethod::LoRA { .. } =>
        {
          params.learning_rate = 1e-4;
          params.batch_size = 64;
          params.epochs = 5;
        }
        FineTuningMethod::Adapter { .. } =>
        {
          params.learning_rate = 5e-4;
          params.batch_size = 32;
          params.epochs = 10;
        }
        FineTuningMethod::PrefixTuning { .. } =>
        {
          params.learning_rate = 1e-3;
          params.batch_size = 16;
          params.epochs = 20;
        }
      }

      params
    }
  }

}

crate ::mod_interface!
{
  exposed use private::TuningStatus;
  exposed use private::TrainingObjective;
  exposed use private::FineTuningMethod;
  exposed use private::HyperParameters;
  exposed use private::TrainingDataConfig;
  exposed use private::ModelCheckpoint;
  exposed use private::TrainingMetrics;
  exposed use private::TuningJobConfig;
  exposed use private::TuningResourceRequirements;
  exposed use private::CheckpointConfig;
  exposed use private::TuningJob;
  exposed use private::TuningEvent;
  exposed use private::TuningEventType;
  exposed use private::TuningManager;
  exposed use private::TuningStats;
  exposed use private::TuningNotification;
  exposed use private::TuningEventSender;
  exposed use private::TuningEventReceiver;
  exposed use private::ModelTuningUtils;
}