from core.loomdb import (
    LoomDB, EventType, AuditContext, EventStore, 
    PartitionStrategy, ConsistencyLevel, RetentionPolicy
)

# Configure event store with production settings
event_store_config = {
    "partition_strategy": PartitionStrategy.TIME_BASED,
    "partition_size_hours": 24,
    "replication_factor": 3,
    "consistency_level": ConsistencyLevel.QUORUM,
    "compression": "lz4",
    "encryption_at_rest": True
}

# Initialize LoomDB with custom configuration
db = LoomDB(
    connection_string="postgresql://user:pass@localhost:5432/loomdb",
    event_store_config=event_store_config,
    retention_policy=RetentionPolicy(
        default_retention_days=2555,  # 7 years
        compliance_retention_days=3650,  # 10 years for regulated data
        archive_after_days=365,
        compress_after_days=90
    )
)

await db.initialize()

from core.loomdb import EventType, EventSchema, SchemaVersion

# Core AI lifecycle events
class AIEventType(EventType):
    # Data events
    DATA_INGESTION_START = "data.ingestion.start"
    DATA_INGESTION_COMPLETE = "data.ingestion.complete"
    DATA_VALIDATION_START = "data.validation.start"
    DATA_VALIDATION_COMPLETE = "data.validation.complete"
    
    # Training events
    MODEL_TRAINING_START = "model.training.start"
    MODEL_TRAINING_CHECKPOINT = "model.training.checkpoint"
    MODEL_TRAINING_COMPLETE = "model.training.complete"
    MODEL_TRAINING_FAILED = "model.training.failed"
    
    # Evaluation events
    MODEL_EVALUATION_START = "model.evaluation.start"
    MODEL_EVALUATION_COMPLETE = "model.evaluation.complete"
    
    # Deployment events
    MODEL_DEPLOYMENT_START = "model.deployment.start"
    MODEL_DEPLOYMENT_COMPLETE = "model.deployment.complete"
    MODEL_INFERENCE_REQUEST = "model.inference.request"
    
    # Security events
    ACCESS_GRANTED = "security.access.granted"
    ACCESS_DENIED = "security.access.denied"
    PRIVILEGE_ESCALATION = "security.privilege.escalation"
    
    # Compliance events
    DATA_ACCESS_AUDIT = "compliance.data.access"
    MODEL_LINEAGE_TRACE = "compliance.model.lineage"
    RETENTION_POLICY_APPLIED = "compliance.retention.applied"

# Define event schemas with validation
training_start_schema = EventSchema(
    version=SchemaVersion("1.2.0"),
    required_fields=[
        "model_name", "dataset_id", "hyperparameters", 
        "hardware_config", "expected_duration_hours"
    ],
    optional_fields=[
        "experiment_id", "parent_model_id", "tags", 
        "compliance_flags", "cost_center"
    ],
    validation_rules={
        "model_name": {"type": "string", "max_length": 256},
        "dataset_id": {"type": "uuid"},
        "hyperparameters": {"type": "object"},
        "hardware_config": {
            "type": "object",
            "required": ["gpu_count", "node_count", "memory_gb"]
        }
    }
)

# Register schema
await db.register_event_schema(
    event_type=AIEventType.MODEL_TRAINING_START,
    schema=training_start_schema
)

from core.loomdb import (
    AuditContext, ComplianceLevel, EventBatch, 
    EventMetadata, CostTracking, SecurityContext
)

# Create comprehensive audit context
audit_context = AuditContext(
    user_id="data_scientist_456",
    session_id="sess_abc123",
    request_id="req_xyz789",
    source_ip="10.0.1.50",
    user_agent="LoomOS-SDK/1.2.3",
    compliance_level=ComplianceLevel.SOC2_TYPE2,
    cost_center="ml_research_team",
    project_id="healthcare_nlp_2024"
)

# Enhanced security context for sensitive operations
security_context = SecurityContext(
    authentication_method="oauth2_jwt",
    authorization_roles=["data_scientist", "healthcare_team"],
    access_level="confidential",
    data_classification="phi",  # Protected Health Information
    geographic_restriction="us_only"
)

# Comprehensive training start event
training_start_data = {
    "model_name": "healthcare_bert_v3",
    "model_architecture": "transformer",
    "dataset_id": "healthcare_corpus_2024",
    "dataset_size_records": 2_500_000,
    "dataset_size_gb": 47.3,
    
    "hyperparameters": {
        "learning_rate": 2e-5,
        "batch_size": 32,
        "num_epochs": 10,
        "warmup_steps": 1000,
        "weight_decay": 0.01,
        "optimizer": "adamw",
        "scheduler": "cosine_with_restarts"
    },
    
    "hardware_config": {
        "node_count": 4,
        "gpu_per_node": 8,
        "gpu_type": "A100",
        "total_gpu_memory_gb": 320,
        "interconnect": "nvlink",
        "storage_type": "nvme_ssd",
        "estimated_cost_per_hour": 45.60
    },
    
    "compliance_metadata": {
        "data_retention_years": 10,
        "phi_data_present": True,
        "consent_forms_verified": True,
        "irb_approval_number": "IRB-2024-001"
    },
    
    "experiment_config": {
        "experiment_id": "exp_healthcare_bert_v3_001",
        "hypothesis": "Fine-tuning on healthcare data improves medical NER",
        "success_criteria": "F1 score > 0.92 on held-out test set",
        "expected_duration_hours": 16,
        "checkpoint_frequency_minutes": 30
    }
}

# Log event with full context
await db.log_event(
    event_type=AIEventType.MODEL_TRAINING_START,
    data=training_start_data,
    context=audit_context,
    security_context=security_context,
    metadata=EventMetadata(
        correlation_id="train_healthcare_bert_v3",
        causation_id="data_validation_complete_xyz",
        tags=["healthcare", "nlp", "bert", "phi_data"],
        priority="high",
        retention_override_days=3650  # 10 years for healthcare data
    )
)

from core.loomdb import EventBatch, BatchConfig

# Configure batch processing
batch_config = BatchConfig(
    max_batch_size=1000,
    max_batch_wait_ms=5000,
    compression_enabled=True,
    duplicate_detection=True,
    async_processing=True
)

# Create event batch
batch = EventBatch(config=batch_config)

# Add multiple events to batch
for i in range(1000):
    checkpoint_event = {
        "epoch": i // 100,
        "step": i,
        "loss": 0.5 - (i * 0.0001),
        "accuracy": 0.7 + (i * 0.0002),
        "learning_rate": 2e-5 * (0.999 ** i),
        "gpu_memory_used_gb": 28.4 + (i * 0.001),
        "batch_processing_time_ms": 150 + (i % 50)
    }
    
    await batch.add_event(
        event_type=AIEventType.MODEL_TRAINING_CHECKPOINT,
        data=checkpoint_event,
        context=audit_context
    )

# Submit batch for processing
batch_result = await db.submit_batch(batch)
print(f"Processed {batch_result.events_processed} events in {batch_result.processing_time_ms}ms")

from core.loomdb import (
    EventQuery, QueryFilter, TimeRange, AggregationType,
    AnalyticsEngine, ComplianceQuery
)

# Complex event querying
query = EventQuery(
    event_types=[
        AIEventType.MODEL_TRAINING_START,
        AIEventType.MODEL_TRAINING_COMPLETE,
        AIEventType.MODEL_TRAINING_FAILED
    ],
    time_range=TimeRange(
        start=datetime(2024, 1, 1),
        end=datetime(2024, 12, 31)
    ),
    filters=[
        QueryFilter("tags", "contains", "healthcare"),
        QueryFilter("data.hardware_config.gpu_count", ">=", 4),
        QueryFilter("context.compliance_level", "=", ComplianceLevel.SOC2_TYPE2)
    ],
    order_by="timestamp",
    limit=10000,
    include_metadata=True
)

# Execute query
results = await db.query_events(query)

# Analytics on query results
analytics = AnalyticsEngine(db)

# Calculate training success rates
success_rate_analysis = await analytics.calculate_metrics(
    metric_type=AggregationType.SUCCESS_RATE,
    group_by=["data.model_architecture", "data.dataset_id"],
    time_granularity="monthly"
)

# Cost analysis
cost_analysis = await analytics.calculate_cost_metrics(
    group_by=["context.cost_center", "data.hardware_config.gpu_type"],
    include_projections=True,
    projection_months=6
)

# Compliance reporting
compliance_query = ComplianceQuery(
    compliance_level=ComplianceLevel.SOC2_TYPE2,
    report_type="access_audit",
    time_range=TimeRange.last_quarter(),
    include_user_actions=True,
    include_data_lineage=True
)

compliance_report = await db.generate_compliance_report(compliance_query)

from core.loomdb import EventStream, StreamConfig, EventFilter

# Configure event streaming
stream_config = StreamConfig(
    buffer_size=10000,
    batch_size=100,
    flush_interval_ms=1000,
    enable_checkpointing=True,
    checkpoint_interval_ms=30000
)

# Create event stream with filters
stream = EventStream(
    config=stream_config,
    filters=[
        EventFilter(event_types=[AIEventType.MODEL_TRAINING_FAILED]),
        EventFilter(tags__contains="production"),
        EventFilter(priority="high")
    ]
)

# Process events in real-time
async for event_batch in stream.subscribe():
    for event in event_batch:
        if event.event_type == AIEventType.MODEL_TRAINING_FAILED:
            # Trigger immediate alert for production failures
            await alert_system.send_alert(
                severity="critical",
                message=f"Production model training failed: {event.data['model_name']}",
                event_id=event.event_id,
                correlation_id=event.metadata.correlation_id
            )
            
            # Trigger automatic retry if configured
            if event.data.get("auto_retry", False):
                await scheduler.retry_job(event.data["job_id"])

from core.scheduler import (
    Scheduler, JobSpec, ResourceRequirements, JobDependency,
    SchedulingPolicy, PlacementConstraint, RetryPolicy,
    CheckpointConfig, MonitoringConfig
)

# Advanced job specification
job_spec = JobSpec(
    # Basic job information
    name="large_language_model_training",
    description="Training GPT-style model on proprietary dataset",
    job_type="distributed_training",
    algorithm="weave_distributed",
    
    # Container and execution configuration
    container_image="loomos/pytorch-training:v2.1.0",
    command=["python", "train_llm.py"],
    args=["--config", "/workspace/config.yaml"],
    working_directory="/workspace",
    
    # Resource requirements with topology awareness
    resources=ResourceRequirements(
        # Compute resources
        cpu_cores=64,
        memory_gb=512,
        gpu_count=8,
        gpu_type="H100",
        gpu_memory_gb=80,
        
        # Storage requirements
        local_storage_gb=2000,
        shared_storage_gb=10000,
        storage_type="nvme",
        io_bandwidth_gbps=10,
        
        # Network requirements
        network_bandwidth_gbps=100,
        low_latency_required=True,
        infiniband_required=True,
        
        # Topology constraints
        numa_topology="interleaved",
        cpu_affinity="performance",
        gpu_topology="nvlink_connected"
    ),
    
    # Scheduling and placement
    scheduling_policy=SchedulingPolicy.GANG_SCHEDULING,
    placement_constraints=[
        PlacementConstraint.SAME_RACK,  # All nodes in same rack
        PlacementConstraint.GPU_AFFINITY,  # Optimize GPU placement
        PlacementConstraint.AVOID_PREEMPTION  # Avoid preemptible nodes
    ],
    
    # Priority and preemption
    priority=90,  # High priority (0-100 scale)
    preemptible=False,
    can_preempt_lower_priority=True,
    
    # Execution constraints
    max_runtime_hours=168,  # 7 days maximum
    timeout_behavior="checkpoint_and_terminate",
    
    # Retry and fault tolerance
    retry_policy=RetryPolicy(
        max_retries=3,
        retry_delay_seconds=300,
        exponential_backoff=True,
        retry_on_node_failure=True,
        retry_on_resource_exhaustion=False
    ),
    
    # Dependencies and workflow
    dependencies=[
        JobDependency(
            job_name="data_preprocessing_job",
            dependency_type="completion",
            timeout_hours=24
        ),
        JobDependency(
            job_name="model_validation_job",
            dependency_type="success",
            artifacts_required=["validated_dataset.parquet"]
        )
    ],
    
    # Environment configuration
    environment_variables={
        "CUDA_VISIBLE_DEVICES": "0,1,2,3,4,5,6,7",
        "NCCL_DEBUG": "INFO",
        "NCCL_IB_DISABLE": "0",
        "PYTORCH_CUDA_ALLOC_CONF": "max_split_size_mb:512",
        "TRANSFORMERS_CACHE": "/workspace/cache",
        "WANDB_PROJECT": "llm_training_2024"
    },
    
    # Checkpoint configuration
    checkpoint_config=CheckpointConfig(
        enabled=True,
        interval_minutes=30,
        max_checkpoints=20,
        storage_backend="s3://model-checkpoints/llm-training/",
        async_upload=True,
        compression_enabled=True,
        encryption_enabled=True
    ),
    
    # Monitoring and alerting
    monitoring_config=MonitoringConfig(
        metrics_collection_interval_seconds=30,
        log_level="INFO",
        custom_metrics=["gpu_utilization", "memory_usage", "loss_value"],
        alert_thresholds={
            "gpu_utilization_percent": {"min": 80, "max": 95},
            "memory_usage_percent": {"max": 90},
            "loss_divergence_threshold": 10.0
        },
        notification_channels=[
            "slack://ml-team-alerts",
            "email://[email protected]",
            "pagerduty://ml-oncall"
        ]
    ),
    
    # Tags and metadata
    tags=["llm", "production", "high_priority", "multi_node"],
    metadata={
        "team": "ml_research",
        "project": "next_gen_llm",
        "cost_center": "research_and_development",
        "compliance_level": "confidential"
    }
)

from core.scheduler import (
    ResourceManager, ResourcePool, ResourceAllocation,
    ResourcePolicy, AutoScalingConfig, NodeSelector
)

# Configure resource manager
resource_manager = ResourceManager()

# Define resource pools for different workload types
training_pool = ResourcePool(
    name="gpu_training_pool",
    node_selector=NodeSelector(
        labels={"node-type": "gpu-training", "gpu-generation": "h100"},
        taints_tolerated=["training-workload:NoSchedule"]
    ),
    resource_limits={
        "cpu_cores": 1000,
        "memory_gb": 8000,
        "gpu_count": 64,
        "storage_gb": 100000
    },
    resource_allocation_policy=ResourcePolicy.FAIR_SHARE,
    overcommit_ratios={
        "cpu": 1.5,  # Allow 50% CPU overcommit
        "memory": 1.1,  # Allow 10% memory overcommit
        "storage": 2.0  # Allow 100% storage overcommit
    }
)

inference_pool = ResourcePool(
    name="inference_pool", 
    node_selector=NodeSelector(
        labels={"node-type": "inference", "instance-type": "optimized"},
        preferred_zones=["us-west-2a", "us-west-2b"]
    ),
    resource_limits={
        "cpu_cores": 500,
        "memory_gb": 2000,
        "gpu_count": 32
    },
    autoscaling_config=AutoScalingConfig(
        min_nodes=5,
        max_nodes=50,
        scale_up_threshold=0.8,
        scale_down_threshold=0.3,
        scale_up_delay_seconds=300,
        scale_down_delay_seconds=900
    )
)

# Register resource pools
await resource_manager.register_pool(training_pool)
await resource_manager.register_pool(inference_pool)

from core.scheduler import (
    SchedulingAlgorithm, JobQueue, SchedulingDecision,
    ResourceOptimizer, PlacementOptimizer
)

# Initialize scheduler with advanced algorithms
scheduler = Scheduler(
    scheduling_algorithm=SchedulingAlgorithm.MULTI_OBJECTIVE_OPTIMIZATION,
    optimization_objectives=[
        "minimize_makespan",      # Minimize total completion time
        "maximize_utilization",   # Maximize resource utilization
        "minimize_energy_cost",   # Optimize for energy efficiency
        "respect_sla_deadlines"   # Meet SLA commitments
    ],
    scheduling_interval_seconds=30,
    resource_manager=resource_manager
)

# Submit job with custom scheduling preferences
scheduling_preferences = {
    "preferred_nodes": ["gpu-node-001", "gpu-node-002"],
    "avoid_nodes": ["gpu-node-maintenance-*"],
    "locality_preference": "data_source_proximity",
    "cost_optimization": "spot_instances_preferred",
    "deadline": datetime.now() + timedelta(hours=48)
}

job_handle = await scheduler.submit_job(
    job_spec=job_spec,
    scheduling_preferences=scheduling_preferences,
    dry_run=False  # Set to True for scheduling simulation
)

print(f"Job submitted with ID: {job_handle.job_id}")
print(f"Estimated start time: {job_handle.estimated_start_time}")
print(f"Estimated completion time: {job_handle.estimated_completion_time}")
print(f"Estimated cost: ${job_handle.estimated_cost:.2f}")

from core.scheduler import JobMonitor, JobAction, JobPhase

# Create job monitor for real-time updates
monitor = JobMonitor(scheduler)

# Stream job updates
async for update in monitor.stream_job_updates(job_handle.job_id):
    print(f"Job Phase: {update.phase}")
    print(f"Progress: {update.progress_percent:.1f}%")
    print(f"Resource Utilization: CPU {update.cpu_utilization:.1f}%, GPU {update.gpu_utilization:.1f}%")
    print(f"Estimated Time Remaining: {update.eta}")
    
    # Handle different job phases
    if update.phase == JobPhase.QUEUED:
        print(f"Position in queue: {update.queue_position}")
        
    elif update.phase == JobPhase.RUNNING:
        # Log detailed metrics
        metrics = await monitor.get_detailed_metrics(job_handle.job_id)
        await db.log_event(
            event_type=AIEventType.MODEL_TRAINING_CHECKPOINT,
            data={
                "job_id": job_handle.job_id,
                "metrics": metrics,
                "timestamp": update.timestamp
            },
            context=audit_context
        )
        
    elif update.phase == JobPhase.COMPLETED:
        # Handle successful completion
        results = await monitor.get_job_results(job_handle.job_id)
        print(f"Job completed successfully: {results}")
        
        # Trigger post-processing workflow
        await scheduler.submit_job(create_postprocessing_job(results))
        
    elif update.phase == JobPhase.FAILED:
        # Handle job failure
        failure_info = await monitor.get_failure_info(job_handle.job_id)
        
        # Automatic retry logic
        if failure_info.retryable and job_handle.retry_count < 3:
            print("Retrying failed job...")
            retry_handle = await scheduler.retry_job(
                job_handle.job_id,
                retry_delay_seconds=600
            )
        else:
            # Send failure notification
            await alert_system.send_alert(
                severity="high",
                message=f"Job {job_handle.job_id} failed permanently",
                details=failure_info
            )

# Job control operations
async def manage_job_lifecycle():
    # Pause job (for maintenance or resource reallocation)
    await scheduler.pause_job(job_handle.job_id, reason="Scheduled maintenance")
    
    # Resume job
    await scheduler.resume_job(job_handle.job_id)
    
    # Scale job resources dynamically
    await scheduler.scale_job_resources(
        job_handle.job_id,
        new_resources=ResourceRequirements(
            cpu_cores=128,  # Double CPU allocation
            gpu_count=16    # Double GPU allocation
        )
    )
    
    # Create checkpoint
    checkpoint_id = await scheduler.create_checkpoint(job_handle.job_id)
    print(f"Checkpoint created: {checkpoint_id}")
    
    # Graceful job termination
    await scheduler.terminate_job(
        job_handle.job_id,
        reason="User requested termination",
        save_checkpoint=True,
        cleanup_resources=True
    )

from core.security import (
    AuthenticationManager, IdentityProvider, AuthConfig,
    MultiFactor, CertificateManager, TokenManager
)

# Configure authentication with multiple providers
auth_config = AuthConfig(
    primary_provider=IdentityProvider.OAUTH2,
    fallback_providers=[IdentityProvider.LDAP, IdentityProvider.LOCAL],
    
    # OAuth2 configuration
    oauth2_config={
        "issuer_url": "https://auth.company.com/realms/loomos",
        "client_id": "loomos-platform",
        "client_secret": "${OAUTH2_CLIENT_SECRET}",
        "scopes": ["openid", "profile", "email", "groups"],
        "token_validation": "jwks",
        "token_refresh_enabled": True
    },
    
    # Multi-factor authentication
    mfa_config={
        "enabled": True,
        "required_for_admin": True,
        "methods": ["totp", "webauthn", "sms"],
        "grace_period_hours": 4
    },
    
    # Session management
    session_config={
        "timeout_minutes": 480,  # 8 hours
        "sliding_expiration": True,
        "concurrent_sessions_limit": 3,
        "secure_cookies": True
    }
)

auth_manager = AuthenticationManager(config=auth_config)

# Advanced authentication flow
async def authenticate_user(credentials):
    # Primary authentication
    auth_result = await auth_manager.authenticate(credentials)
    
    if auth_result.requires_mfa:
        # Multi-factor authentication challenge
        mfa_challenge = await auth_manager.initiate_mfa(
            user_id=auth_result.user_id,
            method="totp"
        )
        
        # Wait for MFA response
        mfa_token = await get_mfa_token_from_user()
        mfa_result = await auth_manager.verify_mfa(
            challenge_id=mfa_challenge.challenge_id,
            token=mfa_token
        )
        
        if not mfa_result.success:
            raise AuthenticationError("MFA verification failed")
    
    # Generate session token
    session_token = await auth_manager.create_session(
        user_id=auth_result.user_id,
        roles=auth_result.roles,
        permissions=auth_result.permissions,
        metadata={
            "login_time": datetime.utcnow(),
            "ip_address": credentials.source_ip,
            "user_agent": credentials.user_agent
        }
    )
    
    return session_token

from core.security import (
    RBACManager, Role, Permission, PolicyEngine,
    ResourceAccess, AccessDecision, AuditLogger
)

# Define comprehensive permission system
class LoomOSPermissions:
    # Job management permissions
    JOBS_CREATE = Permission("jobs:create", "Create new training jobs")
    JOBS_READ = Permission("jobs:read", "View job status and details")
    JOBS_UPDATE = Permission("jobs:update", "Modify job parameters")
    JOBS_DELETE = Permission("jobs:delete", "Cancel or delete jobs")
    JOBS_ADMIN = Permission("jobs:admin", "Full job management access")
    
    # Model management permissions
    MODELS_DEPLOY = Permission("models:deploy", "Deploy models to production")
    MODELS_READ = Permission("models:read", "Access model artifacts")
    MODELS_UPDATE = Permission("models:update", "Update model metadata")
    MODELS_DELETE = Permission("models:delete", "Delete model artifacts")
    
    # Data access permissions
    DATA_READ_PUBLIC = Permission("data:read:public", "Access public datasets")
    DATA_READ_INTERNAL = Permission("data:read:internal", "Access internal datasets")
    DATA_READ_CONFIDENTIAL = Permission("data:read:confidential", "Access confidential data")
    DATA_WRITE = Permission("data:write", "Upload or modify datasets")
    
    # Cluster management permissions
    CLUSTER_READ = Permission("cluster:read", "View cluster status")
    CLUSTER_ADMIN = Permission("cluster:admin", "Administer cluster resources")
    
    # Security permissions
    SECURITY_AUDIT = Permission("security:audit", "Access security logs")
    SECURITY_ADMIN = Permission("security:admin", "Manage security policies")

# Define roles with hierarchical permissions
data_scientist_role = Role(
    name="data_scientist",
    description="Data scientists and ML researchers",
    permissions=[
        LoomOSPermissions.JOBS_CREATE,
        LoomOSPermissions.JOBS_READ,
        LoomOSPermissions.JOBS_UPDATE,
        LoomOSPermissions.MODELS_READ,
        LoomOSPermissions.DATA_READ_PUBLIC,
        LoomOSPermissions.DATA_READ_INTERNAL,
        LoomOSPermissions.CLUSTER_READ
    ],
    constraints={
        "max_concurrent_jobs": 10,
        "max_gpu_hours_per_month": 1000,
        "allowed_datasets": ["public", "internal"],
        "cost_limit_per_month": 5000
    }
)

ml_engineer_role = Role(
    name="ml_engineer",
    description="ML engineers and platform users",
    permissions=[
        LoomOSPermissions.JOBS_CREATE,
        LoomOSPermissions.JOBS_READ,
        LoomOSPermissions.JOBS_UPDATE,
        LoomOSPermissions.JOBS_DELETE,
        LoomOSPermissions.MODELS_DEPLOY,
        LoomOSPermissions.MODELS_READ,
        LoomOSPermissions.MODELS_UPDATE,
        LoomOSPermissions.DATA_READ_PUBLIC,
        LoomOSPermissions.DATA_READ_INTERNAL,
        LoomOSPermissions.DATA_WRITE,
        LoomOSPermissions.CLUSTER_READ
    ],
    constraints={
        "max_concurrent_jobs": 25,
        "max_gpu_hours_per_month": 2500,
        "allowed_environments": ["development", "staging", "production"]
    }
)

platform_admin_role = Role(
    name="platform_admin",
    description="Platform administrators",
    permissions="*",  # All permissions
    constraints={}  # No constraints for admins
)

# Initialize RBAC system
rbac = RBACManager()
await rbac.register_roles([data_scientist_role, ml_engineer_role, platform_admin_role])

# Advanced access control with context
async def check_access(user_id: str, resource: str, action: str, context: dict = None):
    """
    Check if user has access to perform action on resource with given context
    """
    access_request = ResourceAccess(
        user_id=user_id,
        resource=resource,
        action=action,
        context=context or {}
    )
    
    # Evaluate access decision
    decision = await rbac.evaluate_access(access_request)
    
    # Log access attempt for audit
    await audit_logger.log_access_attempt(
        user_id=user_id,
        resource=resource,
        action=action,
        decision=decision.allowed,
        reason=decision.reason,
        context=context
    )
    
    if not decision.allowed:
        raise AccessDeniedError(f"Access denied: {decision.reason}")
    
    return decision

# Dynamic permission evaluation
async def submit_job_with_rbac(user_id: str, job_spec: JobSpec):
    # Check basic job creation permission
    await check_access(user_id, "jobs", "create")
    
    # Check resource limits based on user role
    user_roles = await rbac.get_user_roles(user_id)
    resource_limits = await rbac.get_resource_limits(user_roles)
    
    # Validate job against user constraints
    if job_spec.resources.gpu_count > resource_limits.max_gpus_per_job:
        raise AccessDeniedError(f"GPU request exceeds limit: {resource_limits.max_gpus_per_job}")
    
    # Check cost limits
    estimated_cost = await cost_calculator.estimate_job_cost(job_spec)
    monthly_usage = await cost_tracker.get_monthly_usage(user_id)
    
    if monthly_usage + estimated_cost > resource_limits.monthly_cost_limit:
        raise AccessDeniedError(f"Job would exceed monthly cost limit: ${resource_limits.monthly_cost_limit}")
    
    # Check data access permissions for datasets
    for dataset in job_spec.datasets:
        dataset_classification = await data_classifier.get_classification(dataset)
        required_permission = f"data:read:{dataset_classification}"
        
        await check_access(
            user_id=user_id,
            resource=f"dataset:{dataset}",
            action="read",
            context={"classification": dataset_classification}
        )
    
    # If all checks pass, submit the job
    return await scheduler.submit_job(job_spec)

from core.security import (
    EncryptionManager, EncryptionConfig, KeyManager,
    DataClassification, EncryptionAlgorithm
)

# Configure encryption for different data classifications
encryption_config = EncryptionConfig(
    # Encryption at rest
    at_rest_config={
        "algorithm": EncryptionAlgorithm.AES_256_GCM,
        "key_rotation_days": 90,
        "key_derivation": "pbkdf2",
        "compression_before_encryption": True
    },
    
    # Encryption in transit
    in_transit_config={
        "tls_version": "1.3",
        "cipher_suites": ["TLS_AES_256_GCM_SHA384", "TLS_CHACHA20_POLY1305_SHA256"],
        "certificate_validation": "strict",
        "hsts_enabled": True
    },
    
    # Key management
    key_management_config={
        "key_store": "hsm",  # Hardware Security Module
        "key_escrow_enabled": True,
        "key_backup_enabled": True,
        "master_key_rotation_days": 365
    }
)

encryption_manager = EncryptionManager(config=encryption_config)

# Data classification and encryption
async def encrypt_sensitive_data(data: bytes, classification: DataClassification):
    """Encrypt data based on its classification level"""
    
    if classification == DataClassification.PUBLIC:
        # Public data - no encryption required
        return data
        
    elif classification == DataClassification.INTERNAL:
        # Internal data - standard encryption
        encrypted_data = await encryption_manager.encrypt(
            data=data,
            algorithm=EncryptionAlgorithm.AES_256_GCM,
            key_id="internal_data_key"
        )
        
    elif classification == DataClassification.CONFIDENTIAL:
        # Confidential data - enhanced encryption
        encrypted_data = await encryption_manager.encrypt(
            data=data,
            algorithm=EncryptionAlgorithm.AES_256_GCM,
            key_id="confidential_data_key",
            additional_entropy=True
        )
        
    elif classification == DataClassification.RESTRICTED:
        # Restricted data - maximum security
        encrypted_data = await encryption_manager.encrypt(
            data=data,
            algorithm=EncryptionAlgorithm.CHACHA20_POLY1305,
            key_id="restricted_data_key",
            additional_entropy=True,
            require_hsm=True
        )
        
    return encrypted_data

# Secure model artifact storage
async def store_model_securely(model_data: bytes, model_metadata: dict):
    """Store model with appropriate encryption and access controls"""
    
    # Classify model based on training data
    data_classification = await classify_model_data(model_metadata)
    
    # Encrypt model data
    encrypted_model = await encrypt_sensitive_data(model_data, data_classification)
    
    # Generate secure model ID
    model_id = await generate_secure_id()
    
    # Store with metadata
    storage_metadata = {
        **model_metadata,
        "encryption_algorithm": "AES_256_GCM",
        "data_classification": data_classification.value,
        "storage_timestamp": datetime.utcnow().isoformat(),
        "checksum": calculate_checksum(encrypted_model)
    }
    
    # Store in secure backend
    await secure_storage.store(
        key=f"models/{model_id}",
        data=encrypted_model,
        metadata=storage_metadata,
        access_policy=generate_access_policy(data_classification)
    )
    
    return model_id

from core.marketplace import (
    CostOptimizer, CostAnalyzer, ProviderComparator,
    PricingModel, ResourceAdvisor, CostForecast
)

# Initialize cost optimization system
cost_optimizer = CostOptimizer()
cost_analyzer = CostAnalyzer()

# Analyze current spending patterns
async def analyze_ai_workload_costs():
    """Comprehensive cost analysis for AI workloads"""
    
    # Get historical cost data
    cost_data = await cost_analyzer.get_historical_costs(
        time_range=TimeRange.last_6_months(),
        group_by=["provider", "instance_type", "workload_type", "team"],
        include_projections=True
    )
    
    # Identify cost optimization opportunities
    optimization_opportunities = await cost_optimizer.identify_opportunities(cost_data)
    
    # Generate recommendations
    recommendations = []
    
    for opportunity in optimization_opportunities:
        if opportunity.type == "instance_right_sizing":
            # Recommend optimal instance types
            optimal_instances = await cost_optimizer.recommend_instance_types(
                workload_profile=opportunity.workload_profile,
                performance_requirements=opportunity.performance_requirements,
                cost_optimization_target=0.3  # Target 30% cost reduction
            )
            
            recommendations.append({
                "type": "instance_optimization",
                "current_cost": opportunity.current_monthly_cost,
                "optimized_cost": sum(inst.monthly_cost for inst in optimal_instances),
                "savings": opportunity.current_monthly_cost - sum(inst.monthly_cost for inst in optimal_instances),
                "confidence": opportunity.confidence_score,
                "recommendations": optimal_instances
            })
            
        elif opportunity.type == "spot_instance_usage":
            # Recommend spot instance adoption
            spot_analysis = await cost_optimizer.analyze_spot_feasibility(
                workload_type=opportunity.workload_type,
                fault_tolerance=opportunity.fault_tolerance,
                deadline_flexibility=opportunity.deadline_flexibility
            )
            
            recommendations.append({
                "type": "spot_instances",
                "potential_savings": spot_analysis.potential_savings,
                "interruption_risk": spot_analysis.interruption_risk,
                "recommended_mix": spot_analysis.optimal_spot_ondemand_ratio
            })
    
    return recommendations

# Multi-provider cost comparison
async def compare_providers_for_workload(job_spec: JobSpec):
    """Compare costs across different cloud providers"""
    
    providers = ["aws", "gcp", "azure", "on_premises"]
    comparisons = []
    
    for provider in providers:
        provider_cost = await cost_analyzer.estimate_job_cost(
            job_spec=job_spec,
            provider=provider,
            pricing_model=PricingModel.ON_DEMAND,
            include_data_transfer=True,
            include_storage_costs=True
        )
        
        # Get spot pricing if applicable
        if job_spec.fault_tolerant:
            spot_cost = await cost_analyzer.estimate_job_cost(
                job_spec=job_spec,
                provider=provider,
                pricing_model=PricingModel.SPOT,
                interruption_handling=True
            )
            provider_cost.spot_alternative = spot_cost
        
        # Calculate total cost including data transfer
        total_cost = provider_cost.compute_cost + provider_cost.storage_cost + provider_cost.network_cost
        
        comparisons.append({
            "provider": provider,
            "total_cost": total_cost,
            "compute_cost": provider_cost.compute_cost,
            "storage_cost": provider_cost.storage_cost,
            "network_cost": provider_cost.network_cost,
            "estimated_runtime": provider_cost.estimated_runtime_hours,
            "performance_score": provider_cost.performance_score,
            "availability_score": provider_cost.availability_score,
            "spot_savings": provider_cost.spot_alternative.savings_percent if provider_cost.spot_alternative else 0
        })
    
    # Rank providers by cost-effectiveness
    ranked_providers = sorted(comparisons, key=lambda x: x["total_cost"] / x["performance_score"])
    
    return ranked_providers

# Intelligent resource recommendations
resource_advisor = ResourceAdvisor()

async def get_resource_recommendations(workload_history: List[JobSpec]):
    """Generate intelligent resource recommendations based on workload history"""
    
    # Analyze historical resource utilization
    utilization_analysis = await resource_advisor.analyze_utilization_patterns(workload_history)
    
    # Identify common patterns
    patterns = await resource_advisor.identify_workload_patterns(
        workload_history,
        clustering_algorithm="k_means",
        pattern_types=["resource_usage", "temporal", "performance"]
    )
    
    recommendations = []
    
    for pattern in patterns:
        # Generate resource optimization recommendations
        if pattern.type == "over_provisioning":
            recommendations.append({
                "type": "reduce_resources",
                "resource_type": pattern.resource_type,
                "current_allocation": pattern.current_allocation,
                "recommended_allocation": pattern.optimal_allocation,
                "confidence": pattern.confidence_score,
                "estimated_savings": pattern.cost_savings_per_month
            })
            
        elif pattern.type == "under_utilization":
            recommendations.append({
                "type": "consolidate_workloads",
                "affected_jobs": pattern.job_ids,
                "consolidation_strategy": pattern.recommended_strategy,
                "resource_savings": pattern.resource_savings,
                "performance_impact": pattern.performance_impact
            })
            
        elif pattern.type == "peak_usage":
            # Recommend auto-scaling configuration
            scaling_config = await resource_advisor.generate_autoscaling_config(
                workload_pattern=pattern,
                target_utilization=0.8,
                scale_up_aggressiveness="moderate"
            )
            
            recommendations.append({
                "type": "enable_autoscaling",
                "scaling_config": scaling_config,
                "estimated_savings": scaling_config.estimated_monthly_savings,
                "performance_improvement": scaling_config.performance_benefit
            })
    
    return recommendations

# Cost forecasting and budgeting
async def generate_cost_forecast(team_id: str, forecast_months: int = 12):
    """Generate cost forecasts and budget recommendations"""
    
    # Get historical spending data
    historical_data = await cost_analyzer.get_team_spending(
        team_id=team_id,
        time_range=TimeRange.last_12_months()
    )
    
    # Analyze spending trends
    trends = await cost_analyzer.analyze_spending_trends(
        historical_data,
        include_seasonality=True,
        include_growth_patterns=True
    )
    
    # Generate forecast
    forecast = await CostForecast.generate(
        historical_data=historical_data,
        trends=trends,
        forecast_horizon_months=forecast_months,
        confidence_intervals=[0.8, 0.95],
        include_scenario_analysis=True
    )
    
    # Budget recommendations
    budget_recommendations = await cost_optimizer.generate_budget_recommendations(
        forecast=forecast,
        cost_optimization_target=0.15,  # Target 15% cost reduction
        growth_allowance=0.25  # Allow 25% growth
    )
    
    return {
        "forecast": forecast,
        "budget_recommendations": budget_recommendations,
        "optimization_opportunities": await cost_optimizer.identify_future_opportunities(forecast),
        "risk_factors": forecast.risk_factors
    }

from core.marketplace import (
    ProviderManager, CloudProvider, ProviderConfig,
    ResourceQuota, CostMonitor, ProviderHealthChecker
)

# Multi-cloud provider configuration
provider_configs = {
    "aws": ProviderConfig(
        provider_type=CloudProvider.AWS,
        credentials={
            "access_key_id": "${AWS_ACCESS_KEY_ID}",
            "secret_access_key": "${AWS_SECRET_ACCESS_KEY}",
            "region": "us-west-2"
        },
        resource_quotas=ResourceQuota(
            max_instances=100,
            max_vcpus=1000,
            max_gpus=50,
            max_storage_tb=100
        ),
        cost_limits={
            "daily_limit": 10000,
            "monthly_limit": 250000,
            "emergency_limit": 50000
        }
    ),
    
    "gcp": ProviderConfig(
        provider_type=CloudProvider.GCP,
        credentials={
            "service_account_key": "${GCP_SERVICE_ACCOUNT_KEY}",
            "project_id": "ai-workloads-prod",
            "region": "us-central1"
        },
        resource_quotas=ResourceQuota(
            max_instances=75,
            max_vcpus=750,
            max_gpus=40,
            max_storage_tb=75
        )
    ),
    
    "azure": ProviderConfig(
        provider_type=CloudProvider.AZURE,
        credentials={
            "tenant_id": "${AZURE_TENANT_ID}",
            "client_id": "${AZURE_CLIENT_ID}",
            "client_secret": "${AZURE_CLIENT_SECRET}",
            "subscription_id": "${AZURE_SUBSCRIPTION_ID}"
        },
        resource_quotas=ResourceQuota(
            max_instances=60,
            max_vcpus=600,
            max_gpus=30,
            max_storage_tb=60
        )
    )
}

# Initialize provider manager
provider_manager = ProviderManager()

for provider_name, config in provider_configs.items():
    await provider_manager.register_provider(provider_name, config)

# Intelligent provider selection
async def select_optimal_provider(job_spec: JobSpec, constraints: dict = None):
    """Select optimal provider based on job requirements and constraints"""
    
    # Get available providers
    available_providers = await provider_manager.get_available_providers()
    
    # Filter providers based on constraints
    if constraints:
        available_providers = await provider_manager.filter_providers(
            providers=available_providers,
            constraints=constraints
        )
    
    provider_scores = []
    
    for provider in available_providers:
        # Calculate provider score based on multiple factors
        cost_score = await cost_analyzer.calculate_cost_score(job_spec, provider)
        performance_score = await provider_manager.get_performance_score(provider, job_spec)
        availability_score = await provider_manager.get_availability_score(provider)
        
        # Weighted composite score
        composite_score = (
            cost_score * 0.4 +           # 40% weight on cost
            performance_score * 0.3 +    # 30% weight on performance  
            availability_score * 0.3     # 30% weight on availability
        )
        
        provider_scores.append({
            "provider": provider,
            "composite_score": composite_score,
            "cost_score": cost_score,
            "performance_score": performance_score,
            "availability_score": availability_score,
            "estimated_cost": await cost_analyzer.estimate_job_cost(job_spec, provider),
            "estimated_completion_time": await provider_manager.estimate_completion_time(job_spec, provider)
        })
    
    # Return ranked providers
    return sorted(provider_scores, key=lambda x: x["composite_score"], reverse=True)

from core.orchestration import ModuleOrchestrator, EventBus, MessageRouter

# Initialize module orchestration
orchestrator = ModuleOrchestrator()
event_bus = EventBus()

# Register core modules
await orchestrator.register_module("loomdb", db)
await orchestrator.register_module("scheduler", scheduler)
await orchestrator.register_module("security", rbac)
await orchestrator.register_module("marketplace", cost_optimizer)

# Set up event-driven communication
@event_bus.subscribe("job.submitted")
async def on_job_submitted(event):
    # Log job submission to LoomDB
    await db.log_event(
        event_type=AIEventType.JOB_SUBMITTED,
        data=event.data,
        context=event.context
    )
    
    # Update cost tracking
    await cost_optimizer.track_job_cost(event.data["job_id"])
    
    # Check security policies
    await rbac.validate_job_security(event.data["job_spec"])

@event_bus.subscribe("job.completed")
async def on_job_completed(event):
    # Log completion
    await db.log_event(
        event_type=AIEventType.JOB_COMPLETED,
        data=event.data,
        context=event.context
    )
    
    # Calculate final costs
    final_cost = await cost_optimizer.calculate_final_job_cost(event.data["job_id"])
    
    # Update usage quotas
    await rbac.update_usage_quotas(event.context.user_id, final_cost)