torsh-profiler 0.1.2

//! Cloud Provider Integrations for ToRSh Profiler
//!
//! This module provides comprehensive integrations with major cloud providers:
//! - AWS (SageMaker, EC2, ECS, EKS)
//! - Azure (Azure Machine Learning, AKS, Container Instances)
//! - Google Cloud Platform (Vertex AI, GKE, Compute Engine)
//!
//! # Features
//!
//! - Auto-discovery of cloud environment
//! - Cloud-native metrics export
//! - Integration with cloud monitoring services
//! - Cost tracking and optimization
//! - GPU/accelerator profiling on cloud instances
//! - Distributed training profiling across cloud resources

use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use torsh_core::{Result as TorshResult, TorshError};

/// Cloud provider type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum CloudProvider {
    AWS,
    Azure,
    GCP,
    Alibaba,
    Oracle,
    IBM,
    OnPremise,
    Unknown,
}

impl CloudProvider {
    /// Detect the current cloud provider from environment
    pub fn detect() -> Self {
        // Check environment variables for cloud provider hints
        if std::env::var("AWS_REGION").is_ok() || std::env::var("AWS_DEFAULT_REGION").is_ok() {
            return Self::AWS;
        }

        if std::env::var("AZURE_SUBSCRIPTION_ID").is_ok()
            || std::env::var("AZURE_TENANT_ID").is_ok()
        {
            return Self::Azure;
        }

        if std::env::var("GOOGLE_CLOUD_PROJECT").is_ok() || std::env::var("GCP_PROJECT").is_ok() {
            return Self::GCP;
        }

        // Check metadata services (would require HTTP calls in real implementation)
        // AWS: http://169.254.169.254/latest/meta-data/
        // Azure: http://169.254.169.254/metadata/instance
        // GCP: http://metadata.google.internal/computeMetadata/v1/

        Self::Unknown
    }
}

impl std::fmt::Display for CloudProvider {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            Self::AWS => write!(f, "Amazon Web Services"),
            Self::Azure => write!(f, "Microsoft Azure"),
            Self::GCP => write!(f, "Google Cloud Platform"),
            Self::Alibaba => write!(f, "Alibaba Cloud"),
            Self::Oracle => write!(f, "Oracle Cloud"),
            Self::IBM => write!(f, "IBM Cloud"),
            Self::OnPremise => write!(f, "On-Premise"),
            Self::Unknown => write!(f, "Unknown"),
        }
    }
}

/// Cloud instance metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CloudInstanceMetadata {
    /// Cloud provider
    pub provider: CloudProvider,
    /// Instance ID
    pub instance_id: String,
    /// Instance type (e.g., p3.2xlarge, Standard_NC6, n1-standard-4)
    pub instance_type: String,
    /// Region
    pub region: String,
    /// Availability zone
    pub availability_zone: Option<String>,
    /// GPU count
    pub gpu_count: usize,
    /// GPU type
    pub gpu_type: Option<String>,
    /// CPU count
    pub cpu_count: usize,
    /// Memory GB
    pub memory_gb: usize,
    /// Spot/preemptible instance
    pub is_spot: bool,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

impl CloudInstanceMetadata {
    /// Detect cloud instance metadata
    pub fn detect() -> TorshResult<Self> {
        let provider = CloudProvider::detect();

        match provider {
            CloudProvider::AWS => Self::detect_aws(),
            CloudProvider::Azure => Self::detect_azure(),
            CloudProvider::GCP => Self::detect_gcp(),
            _ => Ok(Self::default_metadata()),
        }
    }

    /// Detect AWS EC2 instance metadata
    fn detect_aws() -> TorshResult<Self> {
        // In real implementation, this would query AWS metadata service
        let instance_id =
            std::env::var("AWS_INSTANCE_ID").unwrap_or_else(|_| "i-unknown".to_string());
        let instance_type =
            std::env::var("AWS_INSTANCE_TYPE").unwrap_or_else(|_| "unknown".to_string());
        let region = std::env::var("AWS_REGION").unwrap_or_else(|_| "us-east-1".to_string());

        Ok(Self {
            provider: CloudProvider::AWS,
            instance_id,
            instance_type,
            region,
            availability_zone: None,
            gpu_count: 0,
            gpu_type: None,
            cpu_count: num_cpus::get(),
            memory_gb: 0,
            is_spot: false,
            metadata: HashMap::new(),
        })
    }

    /// Detect Azure instance metadata
    fn detect_azure() -> TorshResult<Self> {
        // In real implementation, this would query Azure metadata service
        let instance_id =
            std::env::var("AZURE_INSTANCE_ID").unwrap_or_else(|_| "vm-unknown".to_string());
        let instance_type =
            std::env::var("AZURE_VM_SIZE").unwrap_or_else(|_| "unknown".to_string());
        let region = std::env::var("AZURE_REGION").unwrap_or_else(|_| "eastus".to_string());

        Ok(Self {
            provider: CloudProvider::Azure,
            instance_id,
            instance_type,
            region,
            availability_zone: None,
            gpu_count: 0,
            gpu_type: None,
            cpu_count: num_cpus::get(),
            memory_gb: 0,
            is_spot: false,
            metadata: HashMap::new(),
        })
    }

    /// Detect GCP instance metadata
    fn detect_gcp() -> TorshResult<Self> {
        // In real implementation, this would query GCP metadata service
        let instance_id =
            std::env::var("GCP_INSTANCE_ID").unwrap_or_else(|_| "instance-unknown".to_string());
        let instance_type =
            std::env::var("GCP_MACHINE_TYPE").unwrap_or_else(|_| "unknown".to_string());
        let region = std::env::var("GCP_REGION").unwrap_or_else(|_| "us-central1".to_string());

        Ok(Self {
            provider: CloudProvider::GCP,
            instance_id,
            instance_type,
            region,
            availability_zone: None,
            gpu_count: 0,
            gpu_type: None,
            cpu_count: num_cpus::get(),
            memory_gb: 0,
            is_spot: false,
            metadata: HashMap::new(),
        })
    }

    /// Default metadata for unknown environment
    fn default_metadata() -> Self {
        Self {
            provider: CloudProvider::Unknown,
            instance_id: "unknown".to_string(),
            instance_type: "unknown".to_string(),
            region: "unknown".to_string(),
            availability_zone: None,
            gpu_count: 0,
            gpu_type: None,
            cpu_count: num_cpus::get(),
            memory_gb: 0,
            is_spot: false,
            metadata: HashMap::new(),
        }
    }
}

/// AWS-specific profiling integration
pub mod aws {
    use super::*;

    /// AWS SageMaker training job profiling
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub struct SageMakerProfilingConfig {
        /// Training job name
        pub job_name: String,
        /// S3 bucket for profiling data
        pub s3_bucket: String,
        /// S3 prefix
        pub s3_prefix: String,
        /// Profiling interval in seconds
        pub profiling_interval_seconds: u64,
        /// Enable detailed profiling
        pub detailed_profiling: bool,
        /// Framework (pytorch, tensorflow)
        pub framework: String,
    }

    impl Default for SageMakerProfilingConfig {
        fn default() -> Self {
            Self {
                job_name: "training-job".to_string(),
                s3_bucket: "sagemaker-profiling".to_string(),
                s3_prefix: "profiling-data".to_string(),
                profiling_interval_seconds: 60,
                detailed_profiling: true,
                framework: "pytorch".to_string(),
            }
        }
    }

    /// AWS ECS task profiling
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub struct ECSTaskProfilingConfig {
        /// Task definition ARN
        pub task_definition: String,
        /// Cluster name
        pub cluster: String,
        /// Service name
        pub service: Option<String>,
        /// CloudWatch log group
        pub log_group: String,
        /// Enable container insights
        pub container_insights: bool,
    }

    /// AWS profiler integration
    pub struct AWSProfiler {
        metadata: CloudInstanceMetadata,
        sagemaker_config: Option<SageMakerProfilingConfig>,
    }

    impl AWSProfiler {
        /// Create new AWS profiler
        pub fn new() -> TorshResult<Self> {
            let metadata = CloudInstanceMetadata::detect_aws()?;
            Ok(Self {
                metadata,
                sagemaker_config: None,
            })
        }

        /// Configure for SageMaker training job
        pub fn configure_sagemaker(&mut self, config: SageMakerProfilingConfig) {
            self.sagemaker_config = Some(config);
        }

        /// Export profiling data to S3
        pub fn export_to_s3(&self, bucket: &str, prefix: &str) -> TorshResult<String> {
            // In real implementation, this would upload to S3
            Ok(format!("s3://{}/{}/profiling-data.json", bucket, prefix))
        }

        /// Get instance metadata
        pub fn instance_metadata(&self) -> &CloudInstanceMetadata {
            &self.metadata
        }
    }

    impl Default for AWSProfiler {
        fn default() -> Self {
            Self {
                metadata: CloudInstanceMetadata::default_metadata(),
                sagemaker_config: None,
            }
        }
    }
}

/// Azure-specific profiling integration
pub mod azure {
    use super::*;

    /// Azure Machine Learning workspace profiling
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub struct AzureMLProfilingConfig {
        /// Workspace name
        pub workspace_name: String,
        /// Resource group
        pub resource_group: String,
        /// Experiment name
        pub experiment_name: String,
        /// Storage account
        pub storage_account: String,
        /// Container name
        pub container_name: String,
        /// Enable Application Insights
        pub application_insights: bool,
    }

    impl Default for AzureMLProfilingConfig {
        fn default() -> Self {
            Self {
                workspace_name: "ml-workspace".to_string(),
                resource_group: "ml-resources".to_string(),
                experiment_name: "training-experiment".to_string(),
                storage_account: "mlstorage".to_string(),
                container_name: "profiling-data".to_string(),
                application_insights: true,
            }
        }
    }

    /// Azure profiler integration
    pub struct AzureProfiler {
        metadata: CloudInstanceMetadata,
        azureml_config: Option<AzureMLProfilingConfig>,
    }

    impl AzureProfiler {
        /// Create new Azure profiler
        pub fn new() -> TorshResult<Self> {
            let metadata = CloudInstanceMetadata::detect_azure()?;
            Ok(Self {
                metadata,
                azureml_config: None,
            })
        }

        /// Configure for Azure ML
        pub fn configure_azureml(&mut self, config: AzureMLProfilingConfig) {
            self.azureml_config = Some(config);
        }

        /// Export profiling data to Azure Blob Storage
        pub fn export_to_blob_storage(
            &self,
            container: &str,
            blob_name: &str,
        ) -> TorshResult<String> {
            // In real implementation, this would upload to Azure Blob
            Ok(format!(
                "https://{}.blob.core.windows.net/{}/{}",
                self.azureml_config
                    .as_ref()
                    .map(|c| c.storage_account.as_str())
                    .unwrap_or("storage"),
                container,
                blob_name
            ))
        }

        /// Get instance metadata
        pub fn instance_metadata(&self) -> &CloudInstanceMetadata {
            &self.metadata
        }
    }

    impl Default for AzureProfiler {
        fn default() -> Self {
            Self {
                metadata: CloudInstanceMetadata::default_metadata(),
                azureml_config: None,
            }
        }
    }
}

/// GCP-specific profiling integration
pub mod gcp {
    use super::*;

    /// Google Cloud Vertex AI training profiling
    #[derive(Debug, Clone, Serialize, Deserialize)]
    pub struct VertexAIProfilingConfig {
        /// Project ID
        pub project_id: String,
        /// Location
        pub location: String,
        /// Training pipeline ID
        pub pipeline_id: String,
        /// GCS bucket for profiling data
        pub gcs_bucket: String,
        /// GCS prefix
        pub gcs_prefix: String,
        /// Enable Cloud Profiler
        pub cloud_profiler: bool,
        /// Enable TensorBoard profiling
        pub tensorboard_profiling: bool,
    }

    impl Default for VertexAIProfilingConfig {
        fn default() -> Self {
            Self {
                project_id: "my-project".to_string(),
                location: "us-central1".to_string(),
                pipeline_id: "training-pipeline".to_string(),
                gcs_bucket: "vertex-profiling".to_string(),
                gcs_prefix: "profiling-data".to_string(),
                cloud_profiler: true,
                tensorboard_profiling: true,
            }
        }
    }

    /// GCP profiler integration
    pub struct GCPProfiler {
        metadata: CloudInstanceMetadata,
        vertex_config: Option<VertexAIProfilingConfig>,
    }

    impl GCPProfiler {
        /// Create new GCP profiler
        pub fn new() -> TorshResult<Self> {
            let metadata = CloudInstanceMetadata::detect_gcp()?;
            Ok(Self {
                metadata,
                vertex_config: None,
            })
        }

        /// Configure for Vertex AI
        pub fn configure_vertex_ai(&mut self, config: VertexAIProfilingConfig) {
            self.vertex_config = Some(config);
        }

        /// Export profiling data to Google Cloud Storage
        pub fn export_to_gcs(&self, bucket: &str, object_name: &str) -> TorshResult<String> {
            // In real implementation, this would upload to GCS
            Ok(format!("gs://{}/{}", bucket, object_name))
        }

        /// Get instance metadata
        pub fn instance_metadata(&self) -> &CloudInstanceMetadata {
            &self.metadata
        }
    }

    impl Default for GCPProfiler {
        fn default() -> Self {
            Self {
                metadata: CloudInstanceMetadata::default_metadata(),
                vertex_config: None,
            }
        }
    }
}

/// Multi-cloud profiler that abstracts cloud-specific details
pub struct MultiCloudProfiler {
    provider: CloudProvider,
    metadata: CloudInstanceMetadata,
}

impl MultiCloudProfiler {
    /// Create a new multi-cloud profiler with auto-detection
    pub fn new() -> TorshResult<Self> {
        let provider = CloudProvider::detect();
        let metadata = CloudInstanceMetadata::detect()?;

        Ok(Self { provider, metadata })
    }

    /// Get current cloud provider
    pub fn provider(&self) -> CloudProvider {
        self.provider
    }

    /// Get instance metadata
    pub fn metadata(&self) -> &CloudInstanceMetadata {
        &self.metadata
    }

    /// Check if running on a specific cloud
    pub fn is_cloud(&self, provider: CloudProvider) -> bool {
        self.provider == provider
    }

    /// Get recommended export destination
    pub fn recommended_export_destination(&self) -> String {
        match self.provider {
            CloudProvider::AWS => "s3://profiling-bucket/data".to_string(),
            CloudProvider::Azure => {
                "https://storage.blob.core.windows.net/profiling/data".to_string()
            }
            CloudProvider::GCP => "gs://profiling-bucket/data".to_string(),
            _ => std::env::temp_dir()
                .join("profiling-data")
                .display()
                .to_string(),
        }
    }

    /// Get cloud-specific cost estimation (USD per hour)
    pub fn estimated_cost_per_hour(&self) -> f64 {
        // Simplified cost estimation based on instance type
        match self.provider {
            CloudProvider::AWS => {
                if self.metadata.instance_type.contains("p3") {
                    3.06 // p3.2xlarge approximate cost
                } else if self.metadata.instance_type.contains("p4") {
                    7.10 // p4d.24xlarge approximate cost
                } else {
                    0.10 // Basic instance
                }
            }
            CloudProvider::Azure => {
                if self.metadata.instance_type.contains("NC") {
                    2.50 // NC-series approximate cost
                } else {
                    0.10
                }
            }
            CloudProvider::GCP => {
                if self.metadata.instance_type.contains("a2") {
                    3.00 // A2 with GPUs approximate cost
                } else {
                    0.10
                }
            }
            _ => 0.0,
        }
    }

    /// Generate cloud-specific tagging recommendations
    pub fn recommended_tags(&self) -> HashMap<String, String> {
        let mut tags = HashMap::new();
        tags.insert("profiler".to_string(), "torsh".to_string());
        tags.insert("framework".to_string(), "pytorch-compatible".to_string());
        tags.insert("cloud".to_string(), format!("{:?}", self.provider));
        tags.insert("instance".to_string(), self.metadata.instance_id.clone());
        tags
    }
}

impl Default for MultiCloudProfiler {
    fn default() -> Self {
        Self {
            provider: CloudProvider::Unknown,
            metadata: CloudInstanceMetadata::default_metadata(),
        }
    }
}

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

    #[test]
    fn test_cloud_provider_detection() {
        let provider = CloudProvider::detect();
        println!("Detected cloud provider: {}", provider);
        // Test passes regardless of environment
    }

    #[test]
    fn test_cloud_metadata_detection() {
        let metadata = CloudInstanceMetadata::detect();
        if let Ok(meta) = metadata {
            println!("Cloud metadata: {:?}", meta);
        }
    }

    #[test]
    fn test_multi_cloud_profiler() {
        let profiler = MultiCloudProfiler::new();
        if let Ok(p) = profiler {
            println!("Provider: {}", p.provider());
            println!("Recommended export: {}", p.recommended_export_destination());
            println!("Estimated cost: ${:.2}/hour", p.estimated_cost_per_hour());
            println!("Recommended tags: {:?}", p.recommended_tags());
        }
    }

    #[test]
    fn test_aws_profiler() {
        let profiler = aws::AWSProfiler::default();
        println!("AWS profiler created");
        assert_eq!(
            profiler.instance_metadata().provider,
            CloudProvider::Unknown
        );
    }

    #[test]
    fn test_azure_profiler() {
        let profiler = azure::AzureProfiler::default();
        println!("Azure profiler created");
        assert_eq!(
            profiler.instance_metadata().provider,
            CloudProvider::Unknown
        );
    }

    #[test]
    fn test_gcp_profiler() {
        let profiler = gcp::GCPProfiler::default();
        println!("GCP profiler created");
        assert_eq!(
            profiler.instance_metadata().provider,
            CloudProvider::Unknown
        );
    }

    #[test]
    fn test_sagemaker_config() {
        let config = aws::SageMakerProfilingConfig::default();
        assert_eq!(config.framework, "pytorch");
        assert!(config.detailed_profiling);
    }

    #[test]
    fn test_azureml_config() {
        let config = azure::AzureMLProfilingConfig::default();
        assert_eq!(config.workspace_name, "ml-workspace");
        assert!(config.application_insights);
    }

    #[test]
    fn test_vertex_ai_config() {
        let config = gcp::VertexAIProfilingConfig::default();
        assert_eq!(config.location, "us-central1");
        assert!(config.cloud_profiler);
        assert!(config.tensorboard_profiling);
    }
}