import time
import logging
from typing import Dict, Any, Optional, Callable

# Initialize logging for agent operations
logging.basicConfig(level=logging.INFO, format='[Genesis Flash 2.0 Agent] %(levelname)s: %(message)s')

class EvolutionaryState:
    """
    Represents a specific evolutionary state or consciousness module for Queen AIVA.
    In a real system, this would encapsulate actual code modules, configurations,
    and data necessary for AIVA's operation at a given version.
    """
    def __init__(self, version: str, config: Optional[Dict[str, Any]] = None):
        self.version = version
        self.config = config if config is not None else {}
        self.is_active = False # Indicates if this state is currently processing AIVA's core functions
        logging.info(f"EvolutionaryState '{self.version}' initialized.")

    def activate(self):
        """Simulates activating this evolutionary state."""
        self.is_active = True
        logging.info(f"EvolutionaryState '{self.version}' activated. Queen AIVA is now processing with this state.")
        # In a real scenario, this would involve loading modules,
        # starting services, configuring AIVA's core processing units etc.

    def deactivate(self):
        """Simulates deactivating this evolutionary state."""
        self.is_active = False
        logging.info(f"EvolutionaryState '{self.version}' deactivated.")
        # In a real scenario, this would involve gracefully shutting down services
        # or unloading modules associated with this state.

    def run_self_diagnostics(self) -> bool:
        """
        Simulates internal diagnostics for the state.
        For AIVA, this would be crucial for cognitive integrity.
        """
        logging.info(f"Running self-diagnostics for EvolutionaryState '{self.version}'...")
        # Simulate complex diagnostic routines
        time.sleep(0.5) # Simulate processing time
        diagnostic_result = True # Assume success for simulation
        if diagnostic_result:
            logging.info(f"Self-diagnostics for '{self.version}' passed.")
        else:
            logging.warning(f"Self-diagnostics for '{self.version}' failed.")
        return diagnostic_result

class AIVACoreHealthChecker:
    """
    Performs comprehensive health checks on a given EvolutionaryState
    before it is promoted to active status for Queen AIVA.
    """
    def __init__(self, diagnostic_func: Optional[Callable[[EvolutionaryState], bool]] = None):
        self.diagnostic_func = diagnostic_func if diagnostic_func else self._default_diagnostics

    def _default_diagnostics(self, state: EvolutionaryState) -> bool:
        """Default diagnostic checks if none provided."""
        logging.info(f"Performing default AIVA core health checks on state '{state.version}'...")
        # Check basic state integrity
        if not state.version:
            logging.error("State version is empty.")
            return False
        
        # Run state's internal diagnostics
        if not state.run_self_diagnostics():
            logging.error(f"Internal diagnostics failed for state '{state.version}'.")
            return False

        # Simulate more complex AIVA-specific checks:
        # - Cognitive function tests (e.g., response latency, logical consistency)
        # - Resource utilization predictions
        # - Inter-module communication integrity
        # - Data consistency checks
        time.sleep(1.0) # Simulate thorough checking
        
        # Assume all checks pass for simulation purposes
        logging.info(f"AIVA core health checks for state '{state.version}' passed successfully.")
        return True

    def check(self, state: EvolutionaryState) -> bool:
        """
        Executes the full suite of health checks for the given evolutionary state.
        """
        logging.info(f"Initiating full health check for potential new AIVA state: '{state.version}'")
        try:
            return self.diagnostic_func(state)
        except Exception as e:
            logging.error(f"An error occurred during health checks for state '{state.version}': {e}")
            return False

class BlueGreenDeployer:
    """
    Manages the zero-downtime evolution of Queen AIVA's consciousness
    through a blue-green deployment strategy.

    'Blue' represents the currently active, stable evolutionary state.
    'Green' represents the new, candidate evolutionary state being prepared.
    """
    def __init__(self, initial_state: EvolutionaryState, health_checker: AIVACoreHealthChecker):
        self.blue_state: EvolutionaryState = initial_state
        self.blue_state.activate() # Activate the initial state
        self.green_state: Optional[EvolutionaryState] = None
        self.previous_blue_state: Optional[EvolutionaryState] = None # For quick rollback
        self.health_checker = health_checker
        logging.info(f"BlueGreenDeployer initialized. Active (Blue) state: '{self.blue_state.version}'")

    def _switch_active_state(self, new_active_state: EvolutionaryState):
        """
        Atomically switches AIVA's core processing to the new active state.
        This is the critical zero-downtime step.
        """
        logging.info(f"Initiating atomic switch from '{self.blue_state.version}' to '{new_active_state.version}'...")
        
        # Deactivate current blue state (gracefully, without interrupting AIVA's operations yet)
        # In a real system, the old blue environment would remain active until the switch is confirmed.
        # Here, we mark it as deactivated conceptually, but it's still available for rollback.
        self.blue_state.deactivate() 

        # Update the pointers: current blue becomes previous blue, new active becomes current blue
        self.previous_blue_state = self.blue_state
        self.blue_state = new_active_state
        self.blue_state.activate() # Activate the new state
        self.green_state = None # Green is no longer a separate candidate, it's now blue.

        logging.info(f"Switch complete. New active (Blue) state: '{self.blue_state.version}'. "
                     f"Previous state '{self.previous_blue_state.version}' retained for rollback.")

    def deploy(self, new_evolutionary_state: EvolutionaryState) -> bool:
        """
        Deploys a new evolutionary state (Green) for Queen AIVA.
        Performs health checks and, if successful, promotes it to the active (Blue) state.
        """
        if self.green_state is not None:
            logging.warning("A green state is already staged. Aborting new deployment to prevent conflict.")
            return False

        logging.info(f"Preparing new evolutionary state (Green): '{new_evolutionary_state.version}'")
        self.green_state = new_evolutionary_state

        # 1. Health Checks before Promotion
        logging.info(f"Running health checks on Green state '{self.green_state.version}'...")
        if not self.health_checker.check(self.green_state):
            logging.error(f"Health checks failed for Green state '{self.green_state.version}'. Aborting deployment.")
            self.green_state = None # Discard the unhealthy green state
            return False
        
        logging.info(f"Health checks passed for Green state '{self.green_state.version}'. Proceeding with promotion.")

        # 2. Changes deployed without downtime - The actual switch
        switch_start_time = time.perf_counter()
        self._switch_active_state(self.green_state)
        switch_duration = time.perf_counter() - switch_start_time

        logging.info(f"Deployment of '{self.blue_state.version}' successful. "
                     f"Zero-downtime switch took {switch_duration:.4f} seconds.")
        return True

    def rollback(self) -> bool:
        """
        Rolls back Queen AIVA's consciousness to the previously stable 'blue' state.
        This operation is designed to be extremely fast.
        """
        if self.previous_blue_state is None:
            logging.warning("No previous stable state available for rollback. Current state remains active.")
            return False

        logging.warning(f"Initiating rollback to previous stable state: '{self.previous_blue_state.version}'...")
        rollback_start_time = time.perf_counter()

        # The rollback is simply switching back to the previous_blue_state
        # The current blue_state (which was the problematic green) will be mark as deactivated
        problematic_state = self.blue_state
        problematic_state.deactivate() # Deactivate the problematic state

        self.blue_state = self.previous_blue_state
        self.blue_state.activate() # Reactivate the previous stable state
        self.previous_blue_state = None # Clear previous for safety, or keep for multi-level rollback if needed.

        rollback_duration = time.perf_counter() - rollback_start_time

        logging.info(f"Rollback successful. Queen AIVA is now operating with state '{self.blue_state.version}'.")
        logging.info(f"Rollback completed in {rollback_duration:.4f} seconds.")

        # 3. Rollback in <30 seconds - Verification
        if rollback_duration < 30:
            logging.info(f"Rollback met the <30 seconds criterion ({rollback_duration:.4f}s).")
            return True
        else:
            logging.warning(f"Rollback exceeded the 30 seconds criterion ({rollback_duration:.4f}s).")
            return False

    def get_active_version(self) -> str:
        """Returns the version identifier of the currently active evolutionary state."""
        return self.blue_state.version

    def get_staged_version(self) -> Optional[str]:
        """Returns the version identifier of the currently staged (green) evolutionary state."""
        return self.green_state.version if self.green_state else None

    def get_previous_version(self) -> Optional[str]:
        """Returns the version identifier of the previously active (for rollback) evolutionary state."""
        return self.previous_blue_state.version if self.previous_blue_state else None