//! Model tuning functionality for Ollama API client.

#[ cfg( feature = "model_tuning" ) ]
mod private
{
  use serde::{ Serialize, Deserialize };
  use core::time::Duration;
  use error_tools::untyped::Result;
  use std::collections::HashMap;

  // =====================================
  // Model Tuning Types
  // =====================================

  /// Status of a model tuning job
  #[ derive( Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize ) ]
  pub enum TuningJobStatus
  {
    /// Job is pending and waiting to start
    Pending,
    /// Job is currently running
    Running,
    /// Job is paused
    Paused,
    /// Job completed successfully
    Completed,
    /// Job was cancelled
    Cancelled,
    /// Job failed with an error
    Failed,
  }

  /// Fine-tuning methods available
  #[ derive( Debug, Clone, PartialEq, Eq, Serialize, Deserialize ) ]
  pub enum TuningMethod
  {
    /// Full fine-tuning of all parameters
    FullFineTuning,
    /// Low-Rank Adaptation (`LoRA`)
    LoRA,
    /// Adapter-based tuning
    Adapter,
    /// Prefix tuning
    PrefixTuning,
    /// Prompt tuning
    PromptTuning,
    /// P-tuning v2
    PTuningV2,
  }

  /// Training objectives for different tasks
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub enum TrainingObjective
  {
    /// Text generation objective
    TextGeneration
    {
      /// Maximum generation length
      max_length : usize,
      /// Temperature for sampling
      temperature : f32,
      /// Top-p nucleus sampling parameter
      top_p : f32,
    },
    /// Classification objective
    Classification
    {
      /// Number of classes
      num_classes : usize,
      /// Optional class weights for imbalanced data
      class_weights : Option< Vec< f32 > >,
    },
    /// Question answering objective
    QuestionAnswering
    {
      /// Maximum answer length
      max_answer_length : usize,
      /// Threshold for impossible answers
      impossible_answer_threshold : f32,
    },
    /// Named entity recognition
    NamedEntityRecognition
    {
      /// Entity types
      entity_types : Vec< String >,
      /// Use BIO tagging scheme
      use_bio_tagging : bool,
    },
  }

  /// Hyperparameters for model training
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct HyperParameters
  {
    /// Learning rate
    pub learning_rate : f32,
    /// Batch size
    pub batch_size : usize,
    /// Maximum number of training epochs
    pub max_epochs : usize,
    /// Warmup steps for learning rate scheduling
    pub warmup_steps : usize,
    /// Weight decay for regularization
    pub weight_decay : f32,
    /// Gradient clipping norm
    pub gradient_clip_norm : f32,
    /// `LoRA` rank (for `LoRA` tuning)
    pub lora_rank : Option< u32 >,
    /// `LoRA` alpha parameter
    pub lora_alpha : Option< f32 >,
    /// `LoRA` dropout rate
    pub lora_dropout : Option< f32 >,
    /// Target modules for `LoRA`
    pub target_modules : Vec< String >,
    /// Adapter size (for adapter tuning)
    pub adapter_size : Option< usize >,
    /// Adapter activation function
    pub adapter_activation : Option< String >,
  }

  /// Training progress information
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct TrainingProgress
  {
    /// Current epoch
    pub current_epoch : usize,
    /// Total epochs
    pub total_epochs : usize,
    /// Current step within epoch
    pub current_step : usize,
    /// Total steps per epoch
    pub steps_per_epoch : usize,
    /// Elapsed training time
    pub elapsed_time : Duration,
    /// Estimated remaining time
    pub estimated_remaining : Option< Duration >,
    /// Current training metrics
    pub metrics : HashMap<  String, f32  >,
    /// Validation metrics (if available)
    pub validation_metrics : Option< HashMap<  String, f32  > >,
  }

  /// Model checkpoint information
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct ModelCheckpoint
  {
    /// Checkpoint ID
    pub id : String,
    /// Step number at which checkpoint was created
    pub step : usize,
    /// Epoch number
    pub epoch : usize,
    /// Training loss at checkpoint
    pub training_loss : f32,
    /// Validation loss at checkpoint
    pub validation_loss : f32,
    /// Timestamp when checkpoint was created
    pub created_at : std::time::SystemTime,
    /// Checkpoint file size in bytes
    pub size_bytes : u64,
    /// Best checkpoint indicator
    pub is_best : bool,
  }

  /// Resource usage metrics during training
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct ResourceUsage
  {
    /// GPU memory used in MB
    pub gpu_memory_used_mb : u64,
    /// GPU utilization percentage
    pub gpu_utilization_percent : f32,
    /// CPU utilization percentage
    pub cpu_utilization_percent : f32,
    /// System memory used in MB
    pub memory_used_mb : u64,
    /// Disk I/O read in MB
    pub disk_io_read_mb : u64,
    /// Disk I/O write in MB
    pub disk_io_write_mb : u64,
    /// Network I/O read in MB
    pub network_io_read_mb : u64,
    /// Network I/O write in MB
    pub network_io_write_mb : u64,
  }

  /// Model evaluation metrics
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct ModelEvaluation
  {
    /// Perplexity score
    pub perplexity : f32,
    /// BLEU score for text generation
    pub bleu_score : f32,
    /// ROUGE scores
    pub rouge_scores : HashMap<  String, f32  >,
    /// Custom evaluation metrics
    pub custom_metrics : HashMap<  String, f32  >,
    /// Evaluation timestamp
    pub evaluated_at : std::time::SystemTime,
  }

  /// Model version information
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct ModelVersion
  {
    /// Version ID
    pub id : String,
    /// Version name
    pub name : String,
    /// Version description
    pub description : String,
    /// Base model name
    pub base_model : String,
    /// Tuning method used
    pub tuning_method : TuningMethod,
    /// Creation timestamp
    pub created_at : std::time::SystemTime,
    /// Model size in bytes
    pub size_bytes : u64,
    /// Performance metrics
    pub performance_metrics : HashMap<  String, f32  >,
  }

  /// Training data sample
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct TrainingSample
  {
    /// Input text
    pub input : String,
    /// Output text
    pub output : String,
    /// Optional metadata
    pub metadata : Option< HashMap<  String, serde_json::Value  > >,
  }

  /// Training data validation result
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct ValidationResult
  {
    /// Whether the data is valid
    pub is_valid : bool,
    /// Total number of samples
    pub total_samples : usize,
    /// Validation warnings
    pub warnings : Vec< String >,
    /// Validation errors
    pub errors : Vec< String >,
    /// Data statistics
    pub statistics : HashMap<  String, serde_json::Value  >,
  }

  /// Training data upload result
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct DataUploadResult
  {
    /// Data ID
    pub data_id : String,
    /// Size in bytes
    pub size_bytes : u64,
    /// Number of samples
    pub sample_count : usize,
    /// Upload timestamp
    pub uploaded_at : std::time::SystemTime,
  }

  /// Benchmark task result
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct BenchmarkTaskResult
  {
    /// Task accuracy score
    pub accuracy : f32,
    /// Average latency in milliseconds
    pub latency_ms : f32,
    /// Throughput in tokens per second
    pub throughput_tokens_per_sec : f32,
    /// Memory usage in MB
    pub memory_usage_mb : u64,
  }

  /// Benchmark results for multiple tasks
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct BenchmarkResults
  {
    /// Results for each benchmark task
    pub tasks : HashMap<  String, BenchmarkTaskResult  >,
    /// Overall benchmark score
    pub overall_score : f32,
    /// Benchmark timestamp
    pub benchmarked_at : std::time::SystemTime,
  }

  /// Model tuning configuration
  #[ derive( Debug, Clone, Serialize, Deserialize ) ]
  pub struct ModelTuningConfig
  {
    /// Base model name
    pub model_name : String,
    /// Tuning method
    pub tuning_method : TuningMethod,
    /// Training objective
    pub training_objective : TrainingObjective,
    /// Hyperparameters
    pub hyperparameters : HyperParameters,
    /// Validation split ratio
    pub validation_split : f32,
    /// Checkpoint frequency (steps)
    pub checkpoint_frequency : usize,
    /// Number of best checkpoints to keep
    pub keep_best_checkpoints : usize,
    /// Enable early stopping
    pub early_stopping : bool,
    /// Early stopping patience (epochs)
    pub early_stopping_patience : usize,
    /// Memory optimization settings
    pub memory_optimization : bool,
    /// Gradient checkpointing
    pub gradient_checkpointing : bool,
    /// Mixed precision training
    pub mixed_precision : bool,
    /// Version name for the resulting model
    pub version_name : Option< String >,
    /// Version description
    pub version_description : Option< String >,
  }

  /// Training data container
  #[ derive( Debug, Clone ) ]
  pub struct TrainingData
  {
    /// Training samples
    pub samples : Vec< TrainingSample >,
    /// Data format metadata
    pub metadata : HashMap<  String, serde_json::Value  >,
  }

  /// Data preprocessing utilities
  #[ derive( Debug ) ]
  pub struct DataPreprocessor
  {
    /// Tokenizer settings
    pub tokenizer_config : HashMap<  String, serde_json::Value  >,
    /// Normalization settings
    pub normalization_config : HashMap<  String, bool  >,
  }

  /// Model benchmark configuration
  #[ derive( Debug, Clone ) ]
  pub struct ModelBenchmark
  {
    /// Benchmark tasks to run
    pub tasks : Vec< String >,
    /// Test data size for each task
    pub test_data_size : usize,
    /// Benchmark configuration
    pub config : HashMap<  String, serde_json::Value  >,
  }

  /// Model tuning job
  #[ derive( Debug ) ]
  pub struct ModelTuningJob
  {
    /// Job ID
    pub id : String,
    /// Job configuration
    pub config : ModelTuningConfig,
    /// Training data
    pub training_data : TrainingData,
    /// Validation data
    pub validation_data : Option< TrainingData >,
    /// Current job status
    pub status : TuningJobStatus,
    /// Job creation timestamp
    pub created_at : std::time::SystemTime,
    /// Job start timestamp
    pub started_at : Option< std::time::SystemTime >,
    /// Job completion timestamp
    pub completed_at : Option< std::time::SystemTime >,
    /// Latest checkpoint ID
    pub latest_checkpoint_id : Option< String >,
    /// Current training progress
    pub progress : Option< TrainingProgress >,
    /// Resource usage metrics
    pub resource_usage : Option< ResourceUsage >,
  }

  /// Model tuning configuration builder
  #[ derive( Debug, Default ) ]
  pub struct ModelTuningConfigBuilder
  {
    model_name : Option< String >,
    tuning_method : Option< TuningMethod >,
  }

  // =====================================
  // Implementation blocks
  // =====================================

  impl ModelTuningConfig
  {
    /// Create a new configuration builder
    #[ inline ]
    pub fn builder() -> ModelTuningConfigBuilder
    {
      ModelTuningConfigBuilder::new()
    }

    /// Create with default values
    #[ inline ]
    #[ must_use ]
    pub fn default() -> Self
    {
      Self
      {
        model_name : "llama2".to_string(),
        tuning_method : TuningMethod::FullFineTuning,
        training_objective : TrainingObjective::TextGeneration
        {
          max_length : 512,
          temperature : 0.7,
          top_p : 0.9,
        },
        hyperparameters : HyperParameters::new(),
        validation_split : 0.1,
        checkpoint_frequency : 1000,
        keep_best_checkpoints : 3,
        early_stopping : true,
        early_stopping_patience : 5,
        memory_optimization : true,
        gradient_checkpointing : false,
        mixed_precision : false,
        version_name : None,
        version_description : None,
      }
    }

    /// Set model name
    #[ inline ]
    #[ must_use ]
    pub fn with_model_name( mut self, name : &str ) -> Self
    {
      self.model_name = name.to_string();
      self
    }
  }

  impl ModelTuningConfigBuilder
  {
    /// Create new builder
    #[ inline ]
    #[ must_use ]
    pub fn new() -> Self
    {
      Self::default()
    }

    /// Set model name
    #[ inline ]
    #[ must_use ]
    pub fn model_name( mut self, name : &str ) -> Self
    {
      self.model_name = Some( name.to_string() );
      self
    }

    /// Set tuning method
    #[ inline ]
    #[ must_use ]
    pub fn tuning_method( mut self, method : TuningMethod ) -> Self
    {
      self.tuning_method = Some( method );
      self
    }

    /// Build the configuration
    #[ inline ]
    pub fn build( self ) -> Result< ModelTuningConfig >
    {
      Ok( ModelTuningConfig
      {
        model_name : self.model_name.unwrap_or( "llama2".to_string() ),
        tuning_method : self.tuning_method.unwrap_or( TuningMethod::FullFineTuning ),
        training_objective : TrainingObjective::TextGeneration
        {
          max_length : 512,
          temperature : 0.7,
          top_p : 0.9,
        },
        hyperparameters : HyperParameters::new(),
        validation_split : 0.1,
        checkpoint_frequency : 1000,
        keep_best_checkpoints : 3,
        early_stopping : true,
        early_stopping_patience : 5,
        memory_optimization : true,
        gradient_checkpointing : false,
        mixed_precision : false,
        version_name : None,
        version_description : None,
      } )
    }
  }

  impl TrainingData
  {
    /// Create new training data
    #[ inline ]
    #[ must_use ]
    pub fn new() -> Self
    {
      Self
      {
        samples : Vec::new(),
        metadata : HashMap::new(),
      }
    }

    /// Load from JSONL file
    #[ inline ]
    pub fn from_jsonl_file( _path : &str ) -> Result< Self >
    {
      // Placeholder implementation
      Ok( Self::new() )
    }

    /// Create from samples
    #[ inline ]
    pub fn from_samples( samples : Vec< TrainingSample > ) -> Result< Self >
    {
      Ok( Self
      {
        samples,
        metadata : HashMap::new(),
      } )
    }

    /// Load from text file
    #[ inline ]
    pub fn from_text_file( _path : &str ) -> Result< Self >
    {
      Ok( Self::new() )
    }

    /// Create from text
    #[ inline ]
    pub fn from_text( _text : &str ) -> Result< Self >
    {
      Ok( Self::new() )
    }

    /// Add sample to the training data
    #[ inline ]
    pub fn add_sample( &mut self, _prompt : &str, _completion : &str )
    {
      // Placeholder implementation
    }

    /// Add validation sample
    #[ inline ]
    pub fn add_validation_sample( &mut self, _prompt : &str, _completion : &str )
    {
      // Placeholder implementation
    }
  }

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

  impl DataPreprocessor
  {
    /// Create new preprocessor
    #[ inline ]
    #[ must_use ]
    pub fn new() -> Self
    {
      Self
      {
        tokenizer_config : HashMap::new(),
        normalization_config : HashMap::new(),
      }
    }

    /// Configure tokenization
    #[ inline ]
    pub fn configure_tokenization( &mut self, _settings : HashMap<  String, serde_json::Value  > )
    {
      // Placeholder implementation
    }

    /// Set normalization
    #[ inline ]
    pub fn set_normalization( &mut self, _enabled : bool )
    {
      // Placeholder implementation
    }

    /// Preprocess data
    #[ inline ]
    pub fn preprocess( &self, _data : &mut TrainingData ) -> Result< () >
    {
      Ok( () )
    }
  }

  impl ModelBenchmark
  {
    /// Create new benchmark
    #[ inline ]
    #[ must_use ]
    pub fn new() -> Self
    {
      Self
      {
        tasks : Vec::new(),
        test_data_size : 1000,
        config : HashMap::new(),
      }
    }

    /// Add benchmark task
    #[ inline ]
    #[ must_use ]
    pub fn add_task( mut self, task : &str ) -> Self
    {
      self.tasks.push( task.to_string() );
      self
    }

    /// Set test data size
    #[ inline ]
    #[ must_use ]
    pub fn test_data_size( mut self, size : usize ) -> Self
    {
      self.test_data_size = size;
      self
    }
  }

  impl HyperParameters
  {
    /// Create new hyperparameters
    #[ inline ]
    #[ must_use ]
    pub fn new() -> Self
    {
      Self
      {
        learning_rate : 0.001,
        batch_size : 32,
        max_epochs : 10,
        warmup_steps : 1000,
        weight_decay : 0.01,
        gradient_clip_norm : 1.0,
        lora_rank : None,
        lora_alpha : None,
        lora_dropout : None,
        target_modules : vec![ "query".to_string(), "value".to_string() ],
        adapter_size : None,
        adapter_activation : None,
      }
    }

    /// Set learning rate
    #[ inline ]
    pub fn set_learning_rate( &mut self, lr : f32 )
    {
      self.learning_rate = lr;
    }

    /// Set batch size
    #[ inline ]
    pub fn set_batch_size( &mut self, size : usize )
    {
      self.batch_size = size;
    }

    /// Set epochs
    #[ inline ]
    pub fn set_epochs( &mut self, epochs : usize )
    {
      self.max_epochs = epochs;
    }
  }

  impl ModelTuningJob
  {
    /// Get the model ID from this tuning job
    #[ inline ]
    pub fn model_id( &self ) -> &str
    {
      &self.config.model_name
    }

    /// Check if the job is completed
    #[ inline ]
    #[ must_use ]
    pub fn is_completed( &self ) -> bool
    {
      matches!( self.status, TuningJobStatus::Completed )
    }

    /// Get progress percentage (0-100)
    #[ inline ]
    #[ must_use ]
    pub fn progress_percentage( &self ) -> f32
    {
      self.progress.as_ref().map( | p |
        if p.total_epochs > 0
        {
          ( p.current_epoch as f32 / p.total_epochs as f32 ) * 100.0
        }
        else
        {
          0.0
        }
      ).unwrap_or( 0.0 )
    }

    /// Wait for job completion with monitoring
    #[ inline ]
    pub async fn wait_for_completion( &mut self ) -> Result< () >
    {
      // Placeholder implementation - simulate completion
      tokio ::time::sleep( core::time::Duration::from_millis( 100 ) ).await;
      self.status = TuningJobStatus::Completed;
      self.completed_at = Some( std::time::SystemTime::now() );
      Ok( () )
    }

    /// Get current resource usage
    #[ inline ]
    #[ must_use ]
    pub fn get_resource_usage( &self ) -> Option< &ResourceUsage >
    {
      self.resource_usage.as_ref()
    }
  }
}

#[ cfg( feature = "model_tuning" ) ]
crate ::mod_interface!
{
  exposed use private::TuningJobStatus;
  exposed use private::TuningMethod;
  exposed use private::TrainingObjective;
  exposed use private::HyperParameters;
  exposed use private::TrainingProgress;
  exposed use private::ModelCheckpoint;
  exposed use private::ResourceUsage;
  exposed use private::ModelEvaluation;
  exposed use private::ModelVersion;
  exposed use private::TrainingSample;
  exposed use private::ValidationResult;
  exposed use private::DataUploadResult;
  exposed use private::BenchmarkTaskResult;
  exposed use private::BenchmarkResults;
  exposed use private::ModelTuningConfig;
  exposed use private::TrainingData;
  exposed use private::DataPreprocessor;
  exposed use private::ModelBenchmark;
  exposed use private::ModelTuningJob;
  exposed use private::ModelTuningConfigBuilder;
}