//! Framework Integration for TrustformeRS Training
//!
//! This module provides integrations with popular ML experiment tracking and monitoring
//! frameworks including WandB, MLflow, TensorBoard, Neptune.ai, and ClearML.

use anyhow::{anyhow, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::PathBuf;
use std::sync::{Arc, Mutex};
use std::time::SystemTime;

/// Main framework integration manager
pub struct FrameworkIntegrationManager {
    /// Active integrations
    integrations: Arc<Mutex<HashMap<String, Box<dyn ExperimentTracker>>>>,
    /// Configuration
    #[allow(dead_code)]
    config: IntegrationConfig,
    /// Experiment metadata
    experiment_metadata: Arc<Mutex<ExperimentMetadata>>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IntegrationConfig {
    /// Enabled integrations
    pub enabled_integrations: Vec<IntegrationType>,
    /// Default integration for logging
    pub default_integration: Option<IntegrationType>,
    /// Synchronization settings
    pub sync_config: SyncConfig,
    /// Data export settings
    pub export_config: ExportConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum IntegrationType {
    /// Weights & Biases
    WandB { config: WandBConfig },
    /// MLflow
    MLflow { config: MLflowConfig },
    /// TensorBoard
    TensorBoard { config: TensorBoardConfig },
    /// Neptune.ai
    Neptune { config: NeptuneConfig },
    /// ClearML
    ClearML { config: ClearMLConfig },
    /// Custom integration
    Custom {
        name: String,
        config: HashMap<String, String>,
    },
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SyncConfig {
    /// Synchronize metrics across all integrations
    pub sync_metrics: bool,
    /// Synchronize artifacts
    pub sync_artifacts: bool,
    /// Synchronization frequency
    pub sync_frequency: SyncFrequency,
    /// Conflict resolution strategy
    pub conflict_resolution: ConflictResolution,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SyncFrequency {
    /// Real-time synchronization
    RealTime,
    /// Batch synchronization
    Batch { interval_seconds: u64 },
    /// Manual synchronization
    Manual,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConflictResolution {
    /// Use first integration's value
    FirstWins,
    /// Use last integration's value
    LastWins,
    /// Merge values if possible
    Merge,
    /// Skip conflicting values
    Skip,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExportConfig {
    /// Export formats
    pub formats: Vec<ExportFormat>,
    /// Export frequency
    pub frequency: ExportFrequency,
    /// Output directory
    pub output_dir: PathBuf,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ExportFormat {
    JSON,
    CSV,
    Parquet,
    HDF5,
    SQLite,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ExportFrequency {
    /// Export at end of training
    EndOfTraining,
    /// Export at end of each epoch
    EndOfEpoch,
    /// Export at regular intervals
    Interval { seconds: u64 },
}

/// WandB integration configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WandBConfig {
    /// Project name
    pub project: String,
    /// Entity (team) name
    pub entity: Option<String>,
    /// Run name
    pub run_name: Option<String>,
    /// Run group
    pub group: Option<String>,
    /// Job type
    pub job_type: Option<String>,
    /// Tags
    pub tags: Vec<String>,
    /// API key
    pub api_key: Option<String>,
    /// Offline mode
    pub offline: bool,
    /// Resume configuration
    pub resume: ResumeConfig,
    /// Artifact configuration
    pub artifacts: ArtifactConfig,
    /// Advanced settings
    pub advanced: WandBAdvancedConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ResumeConfig {
    /// Never resume
    Never,
    /// Always resume if possible
    Always,
    /// Resume with specific run ID
    RunId { run_id: String },
    /// Auto resume
    Auto,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ArtifactConfig {
    /// Track model artifacts
    pub track_models: bool,
    /// Track dataset artifacts
    pub track_datasets: bool,
    /// Track code artifacts
    pub track_code: bool,
    /// Custom artifacts
    pub custom_artifacts: Vec<CustomArtifact>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomArtifact {
    /// Artifact name
    pub name: String,
    /// Artifact type
    pub artifact_type: String,
    /// Source path
    pub source_path: PathBuf,
    /// Metadata
    pub metadata: HashMap<String, String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WandBAdvancedConfig {
    /// Log system metrics
    pub log_system_metrics: bool,
    /// Log code changes
    pub log_code: bool,
    /// Save code
    pub save_code: bool,
    /// Watch model
    pub watch_model: WatchModelConfig,
    /// Custom metrics
    pub custom_metrics: Vec<CustomMetric>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WatchModelConfig {
    /// Enable model watching
    pub enabled: bool,
    /// Log frequency
    pub log_freq: usize,
    /// Log gradients
    pub log_gradients: bool,
    /// Log parameters
    pub log_parameters: bool,
    /// Log graph
    pub log_graph: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomMetric {
    /// Metric name
    pub name: String,
    /// Metric type
    pub metric_type: MetricType,
    /// Aggregation function
    pub aggregation: AggregationFunction,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MetricType {
    Scalar,
    Histogram,
    Image,
    Audio,
    Video,
    Table,
    Html,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AggregationFunction {
    Mean,
    Sum,
    Max,
    Min,
    Count,
    StdDev,
}

/// MLflow integration configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MLflowConfig {
    /// Tracking URI
    pub tracking_uri: String,
    /// Experiment name
    pub experiment_name: String,
    /// Run name
    pub run_name: Option<String>,
    /// Registry URI
    pub registry_uri: Option<String>,
    /// Artifact location
    pub artifact_location: Option<PathBuf>,
    /// Authentication
    pub auth: MLflowAuth,
    /// Model registration
    pub model_registration: ModelRegistrationConfig,
    /// Advanced settings
    pub advanced: MLflowAdvancedConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MLflowAuth {
    /// Authentication type
    pub auth_type: MLflowAuthType,
    /// Authentication credentials
    pub credentials: HashMap<String, String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MLflowAuthType {
    None,
    BasicAuth,
    Token,
    OAuth,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelRegistrationConfig {
    /// Auto-register models
    pub auto_register: bool,
    /// Model name
    pub model_name: String,
    /// Model stage
    pub stage: ModelStage,
    /// Model description
    pub description: Option<String>,
    /// Model tags
    pub tags: HashMap<String, String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModelStage {
    Staging,
    Production,
    Archived,
    None,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MLflowAdvancedConfig {
    /// Log system metrics
    pub log_system_metrics: bool,
    /// Auto-log parameters
    pub autolog_parameters: bool,
    /// Auto-log metrics
    pub autolog_metrics: bool,
    /// Auto-log artifacts
    pub autolog_artifacts: bool,
    /// Nested runs
    pub nested_runs: bool,
}

/// TensorBoard integration configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorBoardConfig {
    /// Log directory
    pub log_dir: PathBuf,
    /// Experiment name
    pub experiment_name: String,
    /// Update frequency
    pub update_freq: UpdateFrequency,
    /// Histogram configuration
    pub histograms: HistogramConfig,
    /// Image logging
    pub images: ImageLoggingConfig,
    /// Audio logging
    pub audio: AudioLoggingConfig,
    /// Graph logging
    pub graph: GraphLoggingConfig,
    /// Advanced features
    pub advanced: TensorBoardAdvancedConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum UpdateFrequency {
    /// Update every N steps
    Steps(usize),
    /// Update every N epochs
    Epochs(usize),
    /// Update every N seconds
    Seconds(u64),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HistogramConfig {
    /// Enable histogram logging
    pub enabled: bool,
    /// Log weights
    pub log_weights: bool,
    /// Log gradients
    pub log_gradients: bool,
    /// Log activations
    pub log_activations: bool,
    /// Bucket count
    pub bucket_count: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImageLoggingConfig {
    /// Enable image logging
    pub enabled: bool,
    /// Maximum images per step
    pub max_images: usize,
    /// Image size
    pub image_size: (usize, usize),
    /// Color format
    pub color_format: ColorFormat,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ColorFormat {
    RGB,
    BGR,
    Grayscale,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AudioLoggingConfig {
    /// Enable audio logging
    pub enabled: bool,
    /// Sample rate
    pub sample_rate: usize,
    /// Maximum duration (seconds)
    pub max_duration: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GraphLoggingConfig {
    /// Enable graph logging
    pub enabled: bool,
    /// Profile execution
    pub profile_execution: bool,
    /// Log device placement
    pub log_device_placement: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorBoardAdvancedConfig {
    /// Enable profiling
    pub profiling: ProfilingConfig,
    /// Custom scalars
    pub custom_scalars: Vec<CustomScalar>,
    /// Mesh visualization
    pub mesh_visualization: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProfilingConfig {
    /// Enable profiling
    pub enabled: bool,
    /// Profile steps
    pub profile_steps: Vec<usize>,
    /// Profile memory
    pub profile_memory: bool,
    /// Profile operators
    pub profile_operators: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomScalar {
    /// Scalar name
    pub name: String,
    /// Layout configuration
    pub layout: ScalarLayout,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScalarLayout {
    /// Chart title
    pub title: String,
    /// Series names
    pub series: Vec<String>,
    /// Chart type
    pub chart_type: ChartType,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ChartType {
    Line,
    Scatter,
    Bar,
    Histogram,
}

/// Neptune.ai integration configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeptuneConfig {
    /// Project name
    pub project: String,
    /// API token
    pub api_token: String,
    /// Run name
    pub run_name: Option<String>,
    /// Tags
    pub tags: Vec<String>,
    /// Source files
    pub source_files: Vec<PathBuf>,
    /// Monitoring
    pub monitoring: NeptuneMonitoringConfig,
    /// Experiment tracking
    pub experiment_tracking: NeptuneExperimentConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeptuneMonitoringConfig {
    /// Monitor system metrics
    pub system_metrics: bool,
    /// Monitor GPU metrics
    pub gpu_metrics: bool,
    /// Custom monitoring
    pub custom_monitoring: Vec<CustomMonitoring>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomMonitoring {
    /// Metric name
    pub name: String,
    /// Monitoring function
    pub function: String,
    /// Update frequency
    pub frequency: u64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeptuneExperimentConfig {
    /// Log hyperparameters
    pub log_hyperparameters: bool,
    /// Log model summary
    pub log_model_summary: bool,
    /// Log datasets
    pub log_datasets: bool,
    /// Log artifacts
    pub log_artifacts: bool,
}

/// ClearML integration configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClearMLConfig {
    /// Project name
    pub project_name: String,
    /// Task name
    pub task_name: String,
    /// Task type
    pub task_type: ClearMLTaskType,
    /// Auto-connect frameworks
    pub auto_connect: AutoConnectConfig,
    /// Output URI
    pub output_uri: Option<String>,
    /// Artifacts
    pub artifacts: ClearMLArtifactConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ClearMLTaskType {
    Training,
    Testing,
    Inference,
    DataProcessing,
    Application,
    Monitor,
    Controller,
    Optimizer,
    Service,
    Custom,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AutoConnectConfig {
    /// Auto-connect frameworks
    pub frameworks: bool,
    /// Auto-connect arguments
    pub arguments: bool,
    /// Auto-connect models
    pub models: bool,
    /// Auto-connect artifacts
    pub artifacts: bool,
    /// Auto-connect datasets
    pub datasets: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClearMLArtifactConfig {
    /// Upload artifacts
    pub upload_artifacts: bool,
    /// Artifact types to track
    pub tracked_types: Vec<String>,
    /// Compression
    pub compression: Option<String>,
}

/// Experiment metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExperimentMetadata {
    /// Experiment ID
    pub experiment_id: String,
    /// Experiment name
    pub name: String,
    /// Description
    pub description: Option<String>,
    /// Start time
    pub start_time: SystemTime,
    /// End time
    pub end_time: Option<SystemTime>,
    /// Status
    pub status: ExperimentStatus,
    /// Tags
    pub tags: Vec<String>,
    /// Hyperparameters
    pub hyperparameters: HashMap<String, ParameterValue>,
    /// Metrics
    pub metrics: HashMap<String, Vec<MetricValue>>,
    /// Artifacts
    pub artifacts: Vec<ArtifactInfo>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ExperimentStatus {
    Running,
    Completed,
    Failed,
    Cancelled,
    Paused,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ParameterValue {
    Float(f64),
    Int(i64),
    String(String),
    Bool(bool),
    List(Vec<ParameterValue>),
    Dict(HashMap<String, ParameterValue>),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MetricValue {
    /// Metric value
    pub value: f64,
    /// Step/epoch
    pub step: usize,
    /// Timestamp
    pub timestamp: SystemTime,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ArtifactInfo {
    /// Artifact name
    pub name: String,
    /// Artifact type
    pub artifact_type: String,
    /// File path
    pub path: PathBuf,
    /// Size in bytes
    pub size: u64,
    /// Checksum
    pub checksum: String,
    /// Upload time
    pub upload_time: SystemTime,
    /// Metadata
    pub metadata: HashMap<String, String>,
}

/// Trait for experiment tracking integrations
pub trait ExperimentTracker: Send + Sync {
    /// Initialize the tracker
    fn initialize(&mut self) -> Result<()>;

    /// Start a new experiment/run
    fn start_experiment(&mut self, metadata: &ExperimentMetadata) -> Result<String>;

    /// Log a parameter
    fn log_parameter(&mut self, name: &str, value: ParameterValue) -> Result<()>;

    /// Log a metric
    fn log_metric(&mut self, name: &str, value: f64, step: Option<usize>) -> Result<()>;

    /// Log multiple metrics
    fn log_metrics(&mut self, metrics: HashMap<String, f64>, step: Option<usize>) -> Result<()>;

    /// Log an artifact
    fn log_artifact(&mut self, artifact: &ArtifactInfo) -> Result<()>;

    /// Log a model
    fn log_model(&mut self, model_path: &PathBuf, metadata: HashMap<String, String>) -> Result<()>;

    /// Log system information
    fn log_system_info(&mut self, info: HashMap<String, String>) -> Result<()>;

    /// Update experiment status
    fn update_status(&mut self, status: ExperimentStatus) -> Result<()>;

    /// End the experiment
    fn end_experiment(&mut self) -> Result<()>;

    /// Get the integration name
    fn name(&self) -> &str;

    /// Sync with remote
    fn sync(&mut self) -> Result<()>;
}

/// WandB integration implementation
pub struct WandBTracker {
    #[allow(dead_code)]
    config: WandBConfig,
    run_id: Option<String>,
    initialized: bool,
}

impl WandBTracker {
    pub fn new(config: WandBConfig) -> Self {
        Self {
            config,
            run_id: None,
            initialized: false,
        }
    }
}

impl ExperimentTracker for WandBTracker {
    fn initialize(&mut self) -> Result<()> {
        // Initialize WandB connection
        // In real implementation, this would call wandb.init()
        self.initialized = true;
        Ok(())
    }

    fn start_experiment(&mut self, metadata: &ExperimentMetadata) -> Result<String> {
        if !self.initialized {
            self.initialize()?;
        }

        // Start a new WandB run
        let run_id = format!("wandb_run_{}", metadata.experiment_id);
        self.run_id = Some(run_id.clone());

        // Log initial metadata
        for (key, value) in &metadata.hyperparameters {
            self.log_parameter(key, value.clone())?;
        }

        Ok(run_id)
    }

    fn log_parameter(&mut self, name: &str, value: ParameterValue) -> Result<()> {
        // Log parameter to WandB
        println!("WandB: Logging parameter {} = {:?}", name, value);
        Ok(())
    }

    fn log_metric(&mut self, name: &str, value: f64, step: Option<usize>) -> Result<()> {
        // Log metric to WandB
        println!(
            "WandB: Logging metric {} = {} at step {:?}",
            name, value, step
        );
        Ok(())
    }

    fn log_metrics(&mut self, metrics: HashMap<String, f64>, step: Option<usize>) -> Result<()> {
        for (name, value) in metrics {
            self.log_metric(&name, value, step)?;
        }
        Ok(())
    }

    fn log_artifact(&mut self, artifact: &ArtifactInfo) -> Result<()> {
        // Log artifact to WandB
        println!("WandB: Logging artifact {}", artifact.name);
        Ok(())
    }

    fn log_model(&mut self, model_path: &PathBuf, metadata: HashMap<String, String>) -> Result<()> {
        // Log model to WandB
        println!(
            "WandB: Logging model at {:?} with metadata {:?}",
            model_path, metadata
        );
        Ok(())
    }

    fn log_system_info(&mut self, info: HashMap<String, String>) -> Result<()> {
        // Log system info to WandB
        println!("WandB: Logging system info {:?}", info);
        Ok(())
    }

    fn update_status(&mut self, status: ExperimentStatus) -> Result<()> {
        // Update run status
        println!("WandB: Updating status to {:?}", status);
        Ok(())
    }

    fn end_experiment(&mut self) -> Result<()> {
        // Finish WandB run
        println!("WandB: Ending experiment");
        self.run_id = None;
        Ok(())
    }

    fn name(&self) -> &str {
        "WandB"
    }

    fn sync(&mut self) -> Result<()> {
        // Sync with WandB servers
        println!("WandB: Syncing with servers");
        Ok(())
    }
}

/// MLflow integration implementation
pub struct MLflowTracker {
    #[allow(dead_code)]
    config: MLflowConfig,
    run_id: Option<String>,
    initialized: bool,
}

impl MLflowTracker {
    pub fn new(config: MLflowConfig) -> Self {
        Self {
            config,
            run_id: None,
            initialized: false,
        }
    }
}

impl ExperimentTracker for MLflowTracker {
    fn initialize(&mut self) -> Result<()> {
        // Initialize MLflow connection
        self.initialized = true;
        Ok(())
    }

    fn start_experiment(&mut self, metadata: &ExperimentMetadata) -> Result<String> {
        if !self.initialized {
            self.initialize()?;
        }

        let run_id = format!("mlflow_run_{}", metadata.experiment_id);
        self.run_id = Some(run_id.clone());

        Ok(run_id)
    }

    fn log_parameter(&mut self, name: &str, value: ParameterValue) -> Result<()> {
        println!("MLflow: Logging parameter {} = {:?}", name, value);
        Ok(())
    }

    fn log_metric(&mut self, name: &str, value: f64, step: Option<usize>) -> Result<()> {
        println!(
            "MLflow: Logging metric {} = {} at step {:?}",
            name, value, step
        );
        Ok(())
    }

    fn log_metrics(&mut self, metrics: HashMap<String, f64>, step: Option<usize>) -> Result<()> {
        for (name, value) in metrics {
            self.log_metric(&name, value, step)?;
        }
        Ok(())
    }

    fn log_artifact(&mut self, artifact: &ArtifactInfo) -> Result<()> {
        println!("MLflow: Logging artifact {}", artifact.name);
        Ok(())
    }

    fn log_model(&mut self, model_path: &PathBuf, metadata: HashMap<String, String>) -> Result<()> {
        println!(
            "MLflow: Logging model at {:?} with metadata {:?}",
            model_path, metadata
        );
        Ok(())
    }

    fn log_system_info(&mut self, info: HashMap<String, String>) -> Result<()> {
        println!("MLflow: Logging system info {:?}", info);
        Ok(())
    }

    fn update_status(&mut self, status: ExperimentStatus) -> Result<()> {
        println!("MLflow: Updating status to {:?}", status);
        Ok(())
    }

    fn end_experiment(&mut self) -> Result<()> {
        println!("MLflow: Ending experiment");
        self.run_id = None;
        Ok(())
    }

    fn name(&self) -> &str {
        "MLflow"
    }

    fn sync(&mut self) -> Result<()> {
        println!("MLflow: Syncing with tracking server");
        Ok(())
    }
}

/// TensorBoard integration implementation
pub struct TensorBoardTracker {
    #[allow(dead_code)]
    config: TensorBoardConfig,
    log_dir: PathBuf,
    initialized: bool,
}

impl TensorBoardTracker {
    pub fn new(config: TensorBoardConfig) -> Self {
        let log_dir = config.log_dir.clone();
        Self {
            config,
            log_dir,
            initialized: false,
        }
    }
}

impl ExperimentTracker for TensorBoardTracker {
    fn initialize(&mut self) -> Result<()> {
        // Initialize TensorBoard writer
        std::fs::create_dir_all(&self.log_dir)?;
        self.initialized = true;
        Ok(())
    }

    fn start_experiment(&mut self, metadata: &ExperimentMetadata) -> Result<String> {
        if !self.initialized {
            self.initialize()?;
        }

        let run_id = format!("tensorboard_run_{}", metadata.experiment_id);
        Ok(run_id)
    }

    fn log_parameter(&mut self, name: &str, value: ParameterValue) -> Result<()> {
        println!("TensorBoard: Logging parameter {} = {:?}", name, value);
        Ok(())
    }

    fn log_metric(&mut self, name: &str, value: f64, step: Option<usize>) -> Result<()> {
        println!(
            "TensorBoard: Logging metric {} = {} at step {:?}",
            name, value, step
        );
        Ok(())
    }

    fn log_metrics(&mut self, metrics: HashMap<String, f64>, step: Option<usize>) -> Result<()> {
        for (name, value) in metrics {
            self.log_metric(&name, value, step)?;
        }
        Ok(())
    }

    fn log_artifact(&mut self, artifact: &ArtifactInfo) -> Result<()> {
        println!("TensorBoard: Logging artifact {}", artifact.name);
        Ok(())
    }

    fn log_model(&mut self, model_path: &PathBuf, metadata: HashMap<String, String>) -> Result<()> {
        println!(
            "TensorBoard: Logging model at {:?} with metadata {:?}",
            model_path, metadata
        );
        Ok(())
    }

    fn log_system_info(&mut self, info: HashMap<String, String>) -> Result<()> {
        println!("TensorBoard: Logging system info {:?}", info);
        Ok(())
    }

    fn update_status(&mut self, status: ExperimentStatus) -> Result<()> {
        println!("TensorBoard: Updating status to {:?}", status);
        Ok(())
    }

    fn end_experiment(&mut self) -> Result<()> {
        println!("TensorBoard: Ending experiment");
        Ok(())
    }

    fn name(&self) -> &str {
        "TensorBoard"
    }

    fn sync(&mut self) -> Result<()> {
        println!("TensorBoard: Flushing logs to disk");
        Ok(())
    }
}

/// Neptune.ai experiment tracker
pub struct NeptuneTracker {
    config: NeptuneConfig,
    run_id: Option<String>,
    initialized: bool,
}

impl NeptuneTracker {
    pub fn new(config: NeptuneConfig) -> Self {
        Self {
            config,
            run_id: None,
            initialized: false,
        }
    }
}

impl ExperimentTracker for NeptuneTracker {
    fn initialize(&mut self) -> Result<()> {
        println!(
            "Neptune: Initializing connection to project: {}",
            self.config.project
        );
        self.initialized = true;
        Ok(())
    }

    fn start_experiment(&mut self, metadata: &ExperimentMetadata) -> Result<String> {
        if !self.initialized {
            self.initialize()?;
        }

        let run_id = format!("neptune_run_{}", metadata.experiment_id);
        self.run_id = Some(run_id.clone());

        println!(
            "Neptune: Starting experiment {} with run ID: {}",
            metadata.experiment_id, run_id
        );

        // Log initial metadata
        for (key, value) in &metadata.hyperparameters {
            self.log_parameter(key, value.clone())?;
        }

        // Log tags
        for tag in &self.config.tags {
            println!("Neptune: Adding tag: {}", tag);
        }

        Ok(run_id)
    }

    fn log_parameter(&mut self, name: &str, value: ParameterValue) -> Result<()> {
        println!("Neptune: Logging parameter {} = {:?}", name, value);
        Ok(())
    }

    fn log_metric(&mut self, name: &str, value: f64, step: Option<usize>) -> Result<()> {
        println!(
            "Neptune: Logging metric {} = {} at step {:?}",
            name, value, step
        );
        Ok(())
    }

    fn log_metrics(&mut self, metrics: HashMap<String, f64>, step: Option<usize>) -> Result<()> {
        for (name, value) in metrics {
            self.log_metric(&name, value, step)?;
        }
        Ok(())
    }

    fn log_artifact(&mut self, artifact: &ArtifactInfo) -> Result<()> {
        println!(
            "Neptune: Logging artifact: {} ({})",
            artifact.name, artifact.artifact_type
        );
        Ok(())
    }

    fn log_model(&mut self, model_path: &PathBuf, metadata: HashMap<String, String>) -> Result<()> {
        println!("Neptune: Logging model from path: {:?}", model_path);
        for (key, value) in metadata {
            println!("Neptune: Model metadata - {}: {}", key, value);
        }
        Ok(())
    }

    fn log_system_info(&mut self, info: HashMap<String, String>) -> Result<()> {
        println!("Neptune: Logging system information");
        for (key, value) in info {
            println!("Neptune: System info - {}: {}", key, value);
        }
        Ok(())
    }

    fn update_status(&mut self, status: ExperimentStatus) -> Result<()> {
        println!("Neptune: Updating experiment status to {:?}", status);
        Ok(())
    }

    fn end_experiment(&mut self) -> Result<()> {
        if let Some(run_id) = &self.run_id {
            println!("Neptune: Ending experiment with run ID: {}", run_id);
        } else {
            println!("Neptune: Ending experiment (no active run)");
        }
        self.run_id = None;
        Ok(())
    }

    fn name(&self) -> &str {
        "Neptune"
    }

    fn sync(&mut self) -> Result<()> {
        println!("Neptune: Syncing with Neptune.ai servers");
        Ok(())
    }
}

/// ClearML experiment tracker
pub struct ClearMLTracker {
    config: ClearMLConfig,
    task_id: Option<String>,
    initialized: bool,
}

impl ClearMLTracker {
    pub fn new(config: ClearMLConfig) -> Self {
        Self {
            config,
            task_id: None,
            initialized: false,
        }
    }
}

impl ExperimentTracker for ClearMLTracker {
    fn initialize(&mut self) -> Result<()> {
        println!(
            "ClearML: Initializing connection to project: {}",
            self.config.project_name
        );
        self.initialized = true;
        Ok(())
    }

    fn start_experiment(&mut self, metadata: &ExperimentMetadata) -> Result<String> {
        if !self.initialized {
            self.initialize()?;
        }

        let task_id = format!("clearml_task_{}", metadata.experiment_id);
        self.task_id = Some(task_id.clone());

        println!(
            "ClearML: Starting task {} of type {:?} with task ID: {}",
            self.config.task_name, self.config.task_type, task_id
        );

        // Log initial metadata
        for (key, value) in &metadata.hyperparameters {
            self.log_parameter(key, value.clone())?;
        }

        Ok(task_id)
    }

    fn log_parameter(&mut self, name: &str, value: ParameterValue) -> Result<()> {
        println!("ClearML: Logging parameter {} = {:?}", name, value);
        Ok(())
    }

    fn log_metric(&mut self, name: &str, value: f64, step: Option<usize>) -> Result<()> {
        println!(
            "ClearML: Logging metric {} = {} at step {:?}",
            name, value, step
        );
        Ok(())
    }

    fn log_metrics(&mut self, metrics: HashMap<String, f64>, step: Option<usize>) -> Result<()> {
        for (name, value) in metrics {
            self.log_metric(&name, value, step)?;
        }
        Ok(())
    }

    fn log_artifact(&mut self, artifact: &ArtifactInfo) -> Result<()> {
        println!(
            "ClearML: Logging artifact: {} ({})",
            artifact.name, artifact.artifact_type
        );
        if self
            .config
            .artifacts
            .tracked_types
            .contains(&artifact.artifact_type.to_string())
        {
            println!("ClearML: Auto-tracking {} artifact", artifact.artifact_type);
        }
        Ok(())
    }

    fn log_model(&mut self, model_path: &PathBuf, metadata: HashMap<String, String>) -> Result<()> {
        println!("ClearML: Logging model from path: {:?}", model_path);
        for (key, value) in metadata {
            println!("ClearML: Model metadata - {}: {}", key, value);
        }
        Ok(())
    }

    fn log_system_info(&mut self, info: HashMap<String, String>) -> Result<()> {
        println!("ClearML: Logging system information");
        for (key, value) in info {
            println!("ClearML: System info - {}: {}", key, value);
        }
        Ok(())
    }

    fn update_status(&mut self, status: ExperimentStatus) -> Result<()> {
        println!("ClearML: Updating task status to {:?}", status);
        Ok(())
    }

    fn end_experiment(&mut self) -> Result<()> {
        if let Some(task_id) = &self.task_id {
            println!("ClearML: Completing task with ID: {}", task_id);
        } else {
            println!("ClearML: Completing task (no active task)");
        }
        self.task_id = None;
        Ok(())
    }

    fn name(&self) -> &str {
        "ClearML"
    }

    fn sync(&mut self) -> Result<()> {
        println!("ClearML: Syncing with ClearML servers");
        Ok(())
    }
}

impl FrameworkIntegrationManager {
    pub fn new(config: IntegrationConfig) -> Self {
        Self {
            integrations: Arc::new(Mutex::new(HashMap::new())),
            config,
            experiment_metadata: Arc::new(Mutex::new(ExperimentMetadata::default())),
        }
    }

    pub fn add_integration(&self, integration_type: IntegrationType) -> Result<()> {
        let mut integrations = self.integrations.lock().unwrap();

        let tracker: Box<dyn ExperimentTracker> = match integration_type {
            IntegrationType::WandB { config } => Box::new(WandBTracker::new(config)),
            IntegrationType::MLflow { config } => Box::new(MLflowTracker::new(config)),
            IntegrationType::TensorBoard { config } => Box::new(TensorBoardTracker::new(config)),
            IntegrationType::Neptune { config } => Box::new(NeptuneTracker::new(config)),
            IntegrationType::ClearML { config } => Box::new(ClearMLTracker::new(config)),
            IntegrationType::Custom { name, config: _ } => {
                return Err(anyhow!("Custom integration '{}' not implemented", name));
            },
        };

        let integration_name = tracker.name().to_string();
        integrations.insert(integration_name, tracker);

        Ok(())
    }

    pub fn start_experiment(&self, name: &str, description: Option<String>) -> Result<String> {
        let experiment_id = uuid::Uuid::new_v4().to_string();

        let metadata = ExperimentMetadata {
            experiment_id: experiment_id.clone(),
            name: name.to_string(),
            description,
            start_time: SystemTime::now(),
            end_time: None,
            status: ExperimentStatus::Running,
            tags: vec![],
            hyperparameters: HashMap::new(),
            metrics: HashMap::new(),
            artifacts: vec![],
        };

        // Update stored metadata
        {
            let mut stored_metadata = self.experiment_metadata.lock().unwrap();
            *stored_metadata = metadata.clone();
        }

        // Start experiment in all integrations
        let mut integrations = self.integrations.lock().unwrap();
        for (_, tracker) in integrations.iter_mut() {
            tracker.start_experiment(&metadata)?;
        }

        Ok(experiment_id)
    }

    pub fn log_hyperparameters(&self, parameters: HashMap<String, ParameterValue>) -> Result<()> {
        // Update metadata
        {
            let mut metadata = self.experiment_metadata.lock().unwrap();
            metadata.hyperparameters.extend(parameters.clone());
        }

        // Log to all integrations
        let mut integrations = self.integrations.lock().unwrap();
        for (_, tracker) in integrations.iter_mut() {
            for (name, value) in &parameters {
                tracker.log_parameter(name, value.clone())?;
            }
        }

        Ok(())
    }

    pub fn log_metrics(&self, metrics: HashMap<String, f64>, step: Option<usize>) -> Result<()> {
        // Update metadata
        {
            let mut metadata = self.experiment_metadata.lock().unwrap();
            for (name, value) in &metrics {
                let metric_value = MetricValue {
                    value: *value,
                    step: step.unwrap_or(0),
                    timestamp: SystemTime::now(),
                    metadata: HashMap::new(),
                };
                metadata.metrics.entry(name.clone()).or_default().push(metric_value);
            }
        }

        // Log to all integrations
        let mut integrations = self.integrations.lock().unwrap();
        for (_, tracker) in integrations.iter_mut() {
            tracker.log_metrics(metrics.clone(), step)?;
        }

        Ok(())
    }

    pub fn log_artifact(&self, name: &str, path: &PathBuf, artifact_type: &str) -> Result<()> {
        let artifact = ArtifactInfo {
            name: name.to_string(),
            artifact_type: artifact_type.to_string(),
            path: path.clone(),
            size: std::fs::metadata(path)?.len(),
            checksum: "".to_string(), // Would compute actual checksum
            upload_time: SystemTime::now(),
            metadata: HashMap::new(),
        };

        // Update metadata
        {
            let mut metadata = self.experiment_metadata.lock().unwrap();
            metadata.artifacts.push(artifact.clone());
        }

        // Log to all integrations
        let mut integrations = self.integrations.lock().unwrap();
        for (_, tracker) in integrations.iter_mut() {
            tracker.log_artifact(&artifact)?;
        }

        Ok(())
    }

    pub fn end_experiment(&self) -> Result<()> {
        // Update metadata
        {
            let mut metadata = self.experiment_metadata.lock().unwrap();
            metadata.end_time = Some(SystemTime::now());
            metadata.status = ExperimentStatus::Completed;
        }

        // End experiment in all integrations
        let mut integrations = self.integrations.lock().unwrap();
        for (_, tracker) in integrations.iter_mut() {
            tracker.end_experiment()?;
        }

        Ok(())
    }

    pub fn sync_all(&self) -> Result<()> {
        let mut integrations = self.integrations.lock().unwrap();
        for (_, tracker) in integrations.iter_mut() {
            tracker.sync()?;
        }
        Ok(())
    }
}

impl Default for ExperimentMetadata {
    fn default() -> Self {
        Self {
            experiment_id: "".to_string(),
            name: "".to_string(),
            description: None,
            start_time: SystemTime::now(),
            end_time: None,
            status: ExperimentStatus::Running,
            tags: vec![],
            hyperparameters: HashMap::new(),
            metrics: HashMap::new(),
            artifacts: vec![],
        }
    }
}

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

    #[test]
    fn test_framework_integration_manager() {
        let config = IntegrationConfig {
            enabled_integrations: vec![],
            default_integration: None,
            sync_config: SyncConfig {
                sync_metrics: true,
                sync_artifacts: true,
                sync_frequency: SyncFrequency::RealTime,
                conflict_resolution: ConflictResolution::LastWins,
            },
            export_config: ExportConfig {
                formats: vec![ExportFormat::JSON],
                frequency: ExportFrequency::EndOfTraining,
                output_dir: PathBuf::from("/tmp/exports"),
            },
        };

        let manager = FrameworkIntegrationManager::new(config);
        assert!(manager.integrations.lock().unwrap().is_empty());
    }

    #[test]
    fn test_wandb_tracker() {
        let config = WandBConfig {
            project: "test-project".to_string(),
            entity: None,
            run_name: None,
            group: None,
            job_type: None,
            tags: vec![],
            api_key: None,
            offline: true,
            resume: ResumeConfig::Never,
            artifacts: ArtifactConfig {
                track_models: true,
                track_datasets: true,
                track_code: true,
                custom_artifacts: vec![],
            },
            advanced: WandBAdvancedConfig {
                log_system_metrics: true,
                log_code: true,
                save_code: true,
                watch_model: WatchModelConfig {
                    enabled: false,
                    log_freq: 100,
                    log_gradients: false,
                    log_parameters: false,
                    log_graph: false,
                },
                custom_metrics: vec![],
            },
        };

        let tracker = WandBTracker::new(config);
        assert_eq!(tracker.name(), "WandB");
        assert!(!tracker.initialized);
    }

    #[test]
    fn test_mlflow_tracker() {
        let config = MLflowConfig {
            tracking_uri: "http://localhost:5000".to_string(),
            experiment_name: "test-experiment".to_string(),
            run_name: None,
            registry_uri: None,
            artifact_location: None,
            auth: MLflowAuth {
                auth_type: MLflowAuthType::None,
                credentials: HashMap::new(),
            },
            model_registration: ModelRegistrationConfig {
                auto_register: false,
                model_name: "test-model".to_string(),
                stage: ModelStage::None,
                description: None,
                tags: HashMap::new(),
            },
            advanced: MLflowAdvancedConfig {
                log_system_metrics: true,
                autolog_parameters: true,
                autolog_metrics: true,
                autolog_artifacts: true,
                nested_runs: false,
            },
        };

        let tracker = MLflowTracker::new(config);
        assert_eq!(tracker.name(), "MLflow");
        assert!(!tracker.initialized);
    }

    #[test]
    fn test_tensorboard_tracker() {
        let config = TensorBoardConfig {
            log_dir: PathBuf::from("/tmp/tensorboard"),
            experiment_name: "test-experiment".to_string(),
            update_freq: UpdateFrequency::Steps(100),
            histograms: HistogramConfig {
                enabled: true,
                log_weights: true,
                log_gradients: true,
                log_activations: false,
                bucket_count: 50,
            },
            images: ImageLoggingConfig {
                enabled: false,
                max_images: 10,
                image_size: (224, 224),
                color_format: ColorFormat::RGB,
            },
            audio: AudioLoggingConfig {
                enabled: false,
                sample_rate: 22050,
                max_duration: 10.0,
            },
            graph: GraphLoggingConfig {
                enabled: true,
                profile_execution: false,
                log_device_placement: false,
            },
            advanced: TensorBoardAdvancedConfig {
                profiling: ProfilingConfig {
                    enabled: false,
                    profile_steps: vec![],
                    profile_memory: false,
                    profile_operators: false,
                },
                custom_scalars: vec![],
                mesh_visualization: false,
            },
        };

        let tracker = TensorBoardTracker::new(config);
        assert_eq!(tracker.name(), "TensorBoard");
        assert!(!tracker.initialized);
    }
}
