#!/usr/bin/env python3
"""
Self-Learning Agent System - Comprehensive OOP Skeleton
This skeleton defines the complete architecture for agents that can modify their own behavior through learning.
"""

from abc import ABC, abstractmethod
from typing import Dict, Any, List, Optional, Tuple, Callable
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
import json
from pathlib import Path

from ..baseheavenagent import BaseHeavenAgent, HeavenAgentConfig
from ..registry.registry_service import RegistryService
from ..tool_utils.context_manager import ContextManager
from ..tool_utils.prompt_injection_system_vX1 import PromptInjectionSystemVX1


# ============================================================================
# CORE DATA STRUCTURES
# ============================================================================

class LearningStrategy(Enum):
    """Different strategies for learning and improvement"""
    REINFORCEMENT = "reinforcement"
    IMITATION = "imitation"
    EXPLORATION = "exploration"
    EXPLOITATION = "exploitation"
    META_LEARNING = "meta_learning"


@dataclass
class LearningMemory:
    """Stores learning experiences and patterns"""
    experience_id: str
    timestamp: datetime
    task: str
    action_taken: str
    outcome: Dict[str, Any]
    reward: float
    lesson_learned: Optional[str] = None
    pattern_detected: Optional[str] = None
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    def to_dict(self) -> Dict[str, Any]:
        """Serialize memory to dictionary"""
        # TODO: Implement serialization
        pass
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'LearningMemory':
        """Deserialize memory from dictionary"""
        # TODO: Implement deserialization
        pass


@dataclass
class PerformanceMetrics:
    """Tracks agent performance over time"""
    task_success_rate: float = 0.0
    average_reward: float = 0.0
    learning_rate: float = 0.0
    exploration_rate: float = 0.0
    total_experiences: int = 0
    successful_patterns: List[str] = field(default_factory=list)
    failed_patterns: List[str] = field(default_factory=list)
    
    def update(self, experience: LearningMemory) -> None:
        """Update metrics based on new experience"""
        # TODO: Implement metric updates
        pass
    
    def get_summary(self) -> Dict[str, Any]:
        """Get performance summary"""
        # TODO: Implement summary generation
        pass


@dataclass
class GameState:
    """Represents the current state of the learning game"""
    current_task: Optional[str] = None
    current_strategy: LearningStrategy = LearningStrategy.EXPLORATION
    cycle_count: int = 0
    agent_state: Dict[str, Any] = field(default_factory=dict)
    environment_state: Dict[str, Any] = field(default_factory=dict)
    active_hypotheses: List[str] = field(default_factory=list)
    
    def advance_cycle(self) -> None:
        """Move to next game cycle"""
        # TODO: Implement cycle advancement
        pass


# ============================================================================
# ABSTRACT BASE CLASSES
# ============================================================================

class LearningComponent(ABC):
    """Abstract base for all learning components"""
    
    @abstractmethod
    def process(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Process input and return output"""
        pass
    
    @abstractmethod
    def reset(self) -> None:
        """Reset component state"""
        pass
    
    @abstractmethod
    def get_state(self) -> Dict[str, Any]:
        """Get current component state"""
        pass


class Observer(ABC):
    """Abstract observer for the observer pattern"""
    
    @abstractmethod
    def update(self, event: str, data: Dict[str, Any]) -> None:
        """Receive update from observable"""
        pass


class Observable(ABC):
    """Abstract observable for the observer pattern"""
    
    def __init__(self):
        self._observers: List[Observer] = []
    
    def attach(self, observer: Observer) -> None:
        """Attach an observer"""
        if observer not in self._observers:
            self._observers.append(observer)
    
    def detach(self, observer: Observer) -> None:
        """Detach an observer"""
        if observer in self._observers:
            self._observers.remove(observer)
    
    def notify(self, event: str, data: Dict[str, Any]) -> None:
        """Notify all observers of an event"""
        for observer in self._observers:
            observer.update(event, data)


# ============================================================================
# SELF-LEARNING AGENT
# ============================================================================

class SelfLearningAgent(BaseHeavenAgent, Observable):
    """
    Agent that can modify its own behavior through learning.
    Inherits from BaseHeavenAgent and implements Observable pattern.
    """
    
    def __init__(self, config: HeavenAgentConfig, registry_name: str = "self_learning_prompts"):
        """
        Initialize self-learning agent.
        
        Args:
            config: Base agent configuration
            registry_name: Name of registry for storing learned prompts
        """
        super().__init__(config)
        Observable.__init__(self)
        
        self.registry_name = registry_name
        self.registry_service = RegistryService()
        self.context_manager = ContextManager()
        
        # Learning components
        self.memory_bank: List[LearningMemory] = []
        self.performance_metrics = PerformanceMetrics()
        self.current_strategy = LearningStrategy.EXPLORATION
        
        # Prompt management
        self.prompt_versions: Dict[str, List[str]] = {}  # Track prompt evolution
        self.active_prompts: Dict[str, str] = {}  # Current active prompts
        
        self._initialize_registry()
    
    def _initialize_registry(self) -> None:
        """Initialize the prompt registry"""
        # TODO: Set up initial registry structure
        pass
    
    def learn_from_experience(self, experience: LearningMemory) -> None:
        """
        Learn from a single experience and update behavior.
        
        Args:
            experience: The learning experience to process
        """
        # Store experience
        self.memory_bank.append(experience)
        
        # Update metrics
        self.performance_metrics.update(experience)
        
        # Detect patterns
        pattern = self._detect_pattern(experience)
        if pattern:
            experience.pattern_detected = pattern
        
        # Generate lesson
        lesson = self._generate_lesson(experience)
        if lesson:
            experience.lesson_learned = lesson
            self._update_prompts(lesson)
        
        # Notify observers
        self.notify("experience_processed", experience.to_dict())
    
    def _detect_pattern(self, experience: LearningMemory) -> Optional[str]:
        """
        Detect patterns from experience and memory bank.
        
        Args:
            experience: Current experience to analyze
            
        Returns:
            Detected pattern or None
        """
        # TODO: Implement pattern detection algorithm
        pass
    
    def _generate_lesson(self, experience: LearningMemory) -> Optional[str]:
        """
        Generate a lesson from the experience.
        
        Args:
            experience: Experience to learn from
            
        Returns:
            Lesson learned or None
        """
        # TODO: Implement lesson generation
        pass
    
    def _update_prompts(self, lesson: str) -> None:
        """
        Update agent prompts based on learned lesson.
        
        Args:
            lesson: The lesson to incorporate into prompts
        """
        # TODO: Implement prompt updating logic
        pass
    
    def update_strategy(self, new_strategy: LearningStrategy) -> None:
        """
        Update the learning strategy.
        
        Args:
            new_strategy: New strategy to adopt
        """
        old_strategy = self.current_strategy
        self.current_strategy = new_strategy
        
        # Notify observers
        self.notify("strategy_changed", {
            "old": old_strategy.value,
            "new": new_strategy.value
        })
    
    def get_performance_summary(self) -> Dict[str, Any]:
        """Get summary of agent performance"""
        return self.performance_metrics.get_summary()
    
    def save_state(self, filepath: Path) -> None:
        """
        Save agent state to file.
        
        Args:
            filepath: Path to save state
        """
        # TODO: Implement state saving
        pass
    
    def load_state(self, filepath: Path) -> None:
        """
        Load agent state from file.
        
        Args:
            filepath: Path to load state from
        """
        # TODO: Implement state loading
        pass
    
    def reset(self) -> None:
        """Reset agent to initial state"""
        self.memory_bank.clear()
        self.performance_metrics = PerformanceMetrics()
        self.current_strategy = LearningStrategy.EXPLORATION
        self._initialize_registry()
        
        # Notify observers
        self.notify("agent_reset", {})


# ============================================================================
# PLAYER AGENT - ML RESEARCH CONSOLE
# ============================================================================

class MLResearchConsole:
    """Console for monitoring and controlling ML experiments"""
    
    def __init__(self):
        self.display_buffer: List[str] = []
        self.command_history: List[str] = []
        self.metrics_display: Dict[str, Any] = {}
    
    def display(self, message: str, level: str = "INFO") -> None:
        """
        Display message on console.
        
        Args:
            message: Message to display
            level: Log level (INFO, DEBUG, ERROR, etc.)
        """
        # TODO: Implement display logic
        pass
    
    def update_metrics(self, metrics: Dict[str, Any]) -> None:
        """
        Update metrics display.
        
        Args:
            metrics: Metrics to display
        """
        # TODO: Implement metrics update
        pass
    
    def execute_command(self, command: str) -> Any:
        """
        Execute a console command.
        
        Args:
            command: Command to execute
            
        Returns:
            Command result
        """
        # TODO: Implement command execution
        pass
    
    def get_display_state(self) -> Dict[str, Any]:
        """Get current display state"""
        # TODO: Implement state retrieval
        pass


class PlayerAgent(BaseHeavenAgent, Observer):
    """
    Agent that controls and observes the self-learning agent.
    Acts as the ML researcher conducting experiments.
    """
    
    def __init__(self, config: HeavenAgentConfig):
        """
        Initialize player agent.
        
        Args:
            config: Agent configuration
        """
        super().__init__(config)
        
        self.console = MLResearchConsole()
        self.controlled_agent: Optional[SelfLearningAgent] = None
        self.game_state = GameState()
        self.experiment_log: List[Dict[str, Any]] = []
        
        # Control parameters
        self.control_policy: Dict[str, Any] = {}
        self.intervention_threshold: float = 0.3
        self.observation_window: int = 10
    
    def attach_agent(self, agent: SelfLearningAgent) -> None:
        """
        Attach a self-learning agent to control.
        
        Args:
            agent: Agent to control
        """
        if self.controlled_agent:
            self.controlled_agent.detach(self)
        
        self.controlled_agent = agent
        agent.attach(self)
        
        self.console.display(f"Attached agent: {agent.__class__.__name__}")
    
    def update(self, event: str, data: Dict[str, Any]) -> None:
        """
        Receive updates from observed agent.
        
        Args:
            event: Event name
            data: Event data
        """
        # Log event
        self.experiment_log.append({
            "timestamp": datetime.now(),
            "event": event,
            "data": data
        })
        
        # Update console
        self.console.display(f"Event: {event}", "DEBUG")
        
        # Check if intervention needed
        if self._should_intervene(event, data):
            self._intervene()
    
    def _should_intervene(self, event: str, data: Dict[str, Any]) -> bool:
        """
        Determine if intervention is needed.
        
        Args:
            event: Current event
            data: Event data
            
        Returns:
            True if intervention needed
        """
        # TODO: Implement intervention logic
        pass
    
    def _intervene(self) -> None:
        """Intervene in agent's learning process"""
        # TODO: Implement intervention
        pass
    
    def design_experiment(self, hypothesis: str, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """
        Design an ML experiment.
        
        Args:
            hypothesis: Hypothesis to test
            parameters: Experiment parameters
            
        Returns:
            Experiment design
        """
        # TODO: Implement experiment design
        pass
    
    def run_experiment(self, experiment: Dict[str, Any]) -> Dict[str, Any]:
        """
        Run an ML experiment.
        
        Args:
            experiment: Experiment to run
            
        Returns:
            Experiment results
        """
        # TODO: Implement experiment execution
        pass
    
    def analyze_results(self, results: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze experiment results.
        
        Args:
            results: Results to analyze
            
        Returns:
            Analysis
        """
        # TODO: Implement result analysis
        pass
    
    def steer_agent(self, direction: Dict[str, Any]) -> None:
        """
        Steer the controlled agent in a direction.
        
        Args:
            direction: Steering parameters
        """
        if not self.controlled_agent:
            raise ValueError("No agent attached")
        
        # TODO: Implement steering logic
        pass


# ============================================================================
# SELF-TEACHER / STRATEGIZER AGENT
# ============================================================================

class StrategizerAgent(BaseHeavenAgent):
    """
    Agent that analyzes learning cycles and generates improvement strategies.
    Acts as a meta-learner observing the player-agent interaction.
    """
    
    def __init__(self, config: HeavenAgentConfig):
        """
        Initialize strategizer agent.
        
        Args:
            config: Agent configuration
        """
        super().__init__(config)
        
        self.hypothesis_bank: List[Dict[str, Any]] = []
        self.strategy_history: List[Tuple[LearningStrategy, float]] = []
        self.meta_patterns: Dict[str, List[str]] = {}
        
        # Analysis windows
        self.short_term_window = 10
        self.long_term_window = 100
    
    def observe_cycle(self, cycle_data: Dict[str, Any]) -> None:
        """
        Observe a player-agent learning cycle.
        
        Args:
            cycle_data: Data from the cycle
        """
        # TODO: Implement cycle observation
        pass
    
    def generate_hypothesis(self, observations: List[Dict[str, Any]]) -> str:
        """
        Generate hypothesis about improvements.
        
        Args:
            observations: Recent observations
            
        Returns:
            Generated hypothesis
        """
        # TODO: Implement hypothesis generation
        pass
    
    def test_hypothesis(self, hypothesis: str) -> Dict[str, Any]:
        """
        Test a hypothesis.
        
        Args:
            hypothesis: Hypothesis to test
            
        Returns:
            Test results
        """
        # TODO: Implement hypothesis testing
        pass
    
    def recommend_strategy(self, context: Dict[str, Any]) -> LearningStrategy:
        """
        Recommend a learning strategy.
        
        Args:
            context: Current context
            
        Returns:
            Recommended strategy
        """
        # TODO: Implement strategy recommendation
        pass
    
    def identify_meta_pattern(self, patterns: List[str]) -> Optional[str]:
        """
        Identify meta-patterns across multiple patterns.
        
        Args:
            patterns: List of patterns to analyze
            
        Returns:
            Meta-pattern or None
        """
        # TODO: Implement meta-pattern identification
        pass
    
    def generate_curriculum(self, target_skill: str) -> List[Dict[str, Any]]:
        """
        Generate a learning curriculum for a skill.
        
        Args:
            target_skill: Skill to learn
            
        Returns:
            Curriculum as list of lessons
        """
        # TODO: Implement curriculum generation
        pass


# ============================================================================
# GAME LOOP ORCHESTRATOR
# ============================================================================

class LearningGameOrchestrator:
    """
    Orchestrates the complete self-learning game loop.
    Manages interaction between all agents.
    """
    
    def __init__(self):
        self.self_learning_agent: Optional[SelfLearningAgent] = None
        self.player_agent: Optional[PlayerAgent] = None
        self.strategizer_agent: Optional[StrategizerAgent] = None
        self.game_state = GameState()
        
        # Game configuration
        self.max_cycles: int = 1000
        self.checkpoint_frequency: int = 100
        self.auto_save: bool = True
    
    def setup_game(self, 
                   self_learning_config: HeavenAgentConfig,
                   player_config: HeavenAgentConfig,
                   strategizer_config: HeavenAgentConfig) -> None:
        """
        Set up the game with all agents.
        
        Args:
            self_learning_config: Config for self-learning agent
            player_config: Config for player agent
            strategizer_config: Config for strategizer agent
        """
        # Initialize agents
        self.self_learning_agent = SelfLearningAgent(self_learning_config)
        self.player_agent = PlayerAgent(player_config)
        self.strategizer_agent = StrategizerAgent(strategizer_config)
        
        # Connect agents
        self.player_agent.attach_agent(self.self_learning_agent)
    
    def run_cycle(self) -> Dict[str, Any]:
        """
        Run a single game cycle.
        
        Returns:
            Cycle results
        """
        # TODO: Implement game cycle
        pass
    
    def run_game(self, num_cycles: Optional[int] = None) -> Dict[str, Any]:
        """
        Run the complete game loop.
        
        Args:
            num_cycles: Number of cycles to run (None for max_cycles)
            
        Returns:
            Game results
        """
        # TODO: Implement game loop
        pass
    
    def checkpoint(self) -> None:
        """Save game checkpoint"""
        # TODO: Implement checkpointing
        pass
    
    def restore_checkpoint(self, checkpoint_path: Path) -> None:
        """
        Restore from checkpoint.
        
        Args:
            checkpoint_path: Path to checkpoint
        """
        # TODO: Implement restoration
        pass
    
    def get_game_summary(self) -> Dict[str, Any]:
        """Get summary of game progress"""
        # TODO: Implement summary generation
        pass


# ============================================================================
# LEARNING STRATEGIES IMPLEMENTATIONS
# ============================================================================

class ReinforcementLearningStrategy(LearningComponent):
    """Reinforcement learning strategy implementation"""
    
    def __init__(self, learning_rate: float = 0.1, discount_factor: float = 0.9):
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor
        self.q_table: Dict[str, Dict[str, float]] = {}
    
    def process(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Process reinforcement learning update"""
        # TODO: Implement Q-learning or similar
        pass
    
    def reset(self) -> None:
        """Reset strategy state"""
        self.q_table.clear()
    
    def get_state(self) -> Dict[str, Any]:
        """Get strategy state"""
        return {
            "learning_rate": self.learning_rate,
            "discount_factor": self.discount_factor,
            "q_table_size": len(self.q_table)
        }


class ImitationLearningStrategy(LearningComponent):
    """Imitation learning strategy implementation"""
    
    def __init__(self):
        self.demonstrations: List[Dict[str, Any]] = []
        self.learned_behaviors: Dict[str, Any] = {}
    
    def process(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Process imitation learning update"""
        # TODO: Implement behavior cloning or similar
        pass
    
    def reset(self) -> None:
        """Reset strategy state"""
        self.demonstrations.clear()
        self.learned_behaviors.clear()
    
    def get_state(self) -> Dict[str, Any]:
        """Get strategy state"""
        return {
            "num_demonstrations": len(self.demonstrations),
            "num_behaviors": len(self.learned_behaviors)
        }


class MetaLearningStrategy(LearningComponent):
    """Meta-learning strategy implementation"""
    
    def __init__(self):
        self.meta_parameters: Dict[str, Any] = {}
        self.task_embeddings: Dict[str, List[float]] = {}
        self.adaptation_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Process meta-learning update"""
        # TODO: Implement MAML or similar
        pass
    
    def reset(self) -> None:
        """Reset strategy state"""
        self.meta_parameters.clear()
        self.task_embeddings.clear()
        self.adaptation_history.clear()
    
    def get_state(self) -> Dict[str, Any]:
        """Get strategy state"""
        return {
            "num_meta_params": len(self.meta_parameters),
            "num_tasks": len(self.task_embeddings),
            "adaptation_count": len(self.adaptation_history)
        }


# ============================================================================
# FACTORY AND BUILDER PATTERNS
# ============================================================================

class AgentFactory:
    """Factory for creating different types of agents"""
    
    @staticmethod
    def create_self_learning_agent(config_dict: Dict[str, Any]) -> SelfLearningAgent:
        """Create a self-learning agent from config dict"""
        config = HeavenAgentConfig(**config_dict)
        return SelfLearningAgent(config)
    
    @staticmethod
    def create_player_agent(config_dict: Dict[str, Any]) -> PlayerAgent:
        """Create a player agent from config dict"""
        config = HeavenAgentConfig(**config_dict)
        return PlayerAgent(config)
    
    @staticmethod
    def create_strategizer_agent(config_dict: Dict[str, Any]) -> StrategizerAgent:
        """Create a strategizer agent from config dict"""
        config = HeavenAgentConfig(**config_dict)
        return StrategizerAgent(config)


class GameBuilder:
    """Builder for setting up learning games"""
    
    def __init__(self):
        self.orchestrator = LearningGameOrchestrator()
        self._configs: Dict[str, Dict[str, Any]] = {}
    
    def with_self_learning_agent(self, config: Dict[str, Any]) -> 'GameBuilder':
        """Add self-learning agent config"""
        self._configs['self_learning'] = config
        return self
    
    def with_player_agent(self, config: Dict[str, Any]) -> 'GameBuilder':
        """Add player agent config"""
        self._configs['player'] = config
        return self
    
    def with_strategizer_agent(self, config: Dict[str, Any]) -> 'GameBuilder':
        """Add strategizer agent config"""
        self._configs['strategizer'] = config
        return self
    
    def with_max_cycles(self, max_cycles: int) -> 'GameBuilder':
        """Set maximum cycles"""
        self.orchestrator.max_cycles = max_cycles
        return self
    
    def build(self) -> LearningGameOrchestrator:
        """Build the game"""
        # Create configs
        configs = {}
        for agent_type, config_dict in self._configs.items():
            configs[f'{agent_type}_config'] = HeavenAgentConfig(**config_dict)
        
        # Setup game
        self.orchestrator.setup_game(**configs)
        
        return self.orchestrator


# ============================================================================
# USAGE EXAMPLE (for reference)
# ============================================================================

def example_usage():
    """Example of how to use the self-learning system"""
    
    # Build a learning game
    game = GameBuilder() \
        .with_self_learning_agent({
            "name": "learner",
            "model": "gpt-5-mini",
            "provider": "OPENAI"
        }) \
        .with_player_agent({
            "name": "researcher",
            "model": "gpt-5-mini", 
            "provider": "OPENAI"
        }) \
        .with_strategizer_agent({
            "name": "strategist",
            "model": "gpt-5-mini",
            "provider": "OPENAI"
        }) \
        .with_max_cycles(100) \
        .build()
    
    # Run the game
    results = game.run_game()
    
    # Get summary
    summary = game.get_game_summary()
    
    return results, summary


if __name__ == "__main__":
    # This would be filled in by the coder agent
    print("Self-Learning Agent System Skeleton Ready for Implementation")