import random

class TemporalForesight:
    def __init__(self):
        self.internal_simulations = {}
        self.environment_model = None
        self.actions_catalog = []
        self.timeline_count = 5
        self.certainty_threshold = 0.8
        self.risk_assessment = {}

    def initialize_simulation(self, environment_states, possible_actions):
        """Initialize the internal simulation layer with environment and actions."""
        self.internal_simulations = {
            'environment': environment_states,
            'actions': possible_actions,
            'predicted_outcomes': {}
        }

    def simulate_action(self, action, time_steps=3):
        """Simulate the outcome of an action over multiple time steps."""
        current_state = self.internal_simulations['environment']
        simulated_timeline = [current_state]

        for _ in range(time_steps):
            possible_next_states = self.predict_next_states(current_state)
            selected_outcome = self.select_optimal Outcome(possible_next_states)
            simulated_timeline.append(selected_outcome)
            current_state = selected_outcome

        return simulated_timeline

    def predict_next_states(self, current_state):
        """Predict all possible next states from the current state."""
        possible_transitions = self.internal_simulations['environment'].get_transitions(current_state)
        uncertain_outcomes = []
        
        for transition in possible_transitions:
            outcome_probability = transition.get('probability', 0.5)
            if random.random() < outcome_probability * self.certainty_threshold:
                uncertain_outcomes.append(transition)

        return uncertain_outcomes

    def select_optimal_outcome(self, outcomes):
        """Select the optimal outcome from a set of possible outcomes."""
        # Simple heuristic: prefer outcomes with higher success probability
        if not outcomes:
            return None
            
        best_outcome = max(outcomes, key=lambda x: x.get('success_probability', 0.5))
        return best_outcome

    def create_multiple_timelines(self, initial_state, action_sequence):
        """Create multiple possible timelines based on uncertain outcomes."""
        timelines = []
        
        for _ in range(self.timeline_count):
            current_timeline = [initial_state]
            current_action_index = 0
            
            while current_action_index < len(action_sequence):
                action = action_sequence[current_action_index]
                next_states = self.predict_next_states(current_timeline[-1])
                selected_outcome = self.select_optimal_outcome(next_states)
                
                if selected_outcome:
                    current_timeline.append(selected_outcome)
                else:
                    # No valid outcome, append failure state
                    current_timeline.append({'status': 'failure'})
                
                current_action_index += 1
                
            timelines.append(current_timeline)

        return timelines

    def assess_risk(self, timelines):
        """Assess risk across multiple timelines."""
        self.risk_assessment = {
            'timelines_count': len(timelines),
            'risk_level': 0,
            'potential_outcomes': []
        }

        adverse_events = 0
        total_timelines = len(timelines)

        for timeline in timelines:
            for state in timeline:
                if state.get('is_adverse', False):
                    adverse_events += 1

        self.risk_assessment['risk_level'] = (adverse_events / total_timelines) * 100
        self.risk_assessment['potential_outcomes'] = [
            state.get('description', '') for timeline in timelines 
            for state in timeline if state.get('is_adverse', False)
        ]

        return self.risk_assessment

    def model_consequences(self, outcomes):
        """Model and evaluate the consequences of different outcomes."""
        consequence_scores = []
        
        for outcome in outcomes:
            score = 0
            success_factors = outcome.get('success_factors', [])
            
            if 'high_success' in success_factors:
                score += 80
            elif 'moderate_success' in success_factors:
                score += 50
                
            negative_factors = outcome.get('negative_factors', [])
            if 'major_failure' in negative_factors:
                score -= 70
            elif 'minor_failure' in negative_factors:
                score -= 30
                
            consequence_scores.append(score)

        return consequence_scores

    def make_prediction(self, initial_state, action_sequence):
        """Make predictions based on temporal foresight."""
        # Create multiple simulation timelines
        timelines = self.create_multiple_timelines(initial_state, action_sequence)
        
        # Assess risks across timelines
        risk_assessment = self.assess_risk(timelines)
        
        # Model consequences of outcomes
        consequence_models = []
        for timeline in timelines:
            for state in timeline:
                if 'outcome' in state:
                    consequence_models.append(self.model_consequences([state]))
        
        # Return combined foresight analysis
        return {
            'timelines': timelines,
            'risk_assessment': risk_assessment,
            'consequence_models': consequence_models
        }

# Example usage:
def main():
    # Initialize the foresight system
    foresight = TemporalForesight()
    
    # Define environment states and possible actions
    environment_states = [
        {'state': 'current', 'transitions': [{'to': 'next1', 'probability': 0.7}, {'to': 'next2', 'probability': 0.3}]},
        {'state': 'next1', 'transitions': [...]},
        # Add more states as needed
    ]
    
    possible_actions = ['action1', 'action2', 'action3']
    foresight.initialize_simulation(environment_states, possible_actions)
    
    # Define initial state and action sequence
    initial_state = environment_states[0]
    action_sequence = ['action1', 'action2']
    
    # Make predictions
    prediction_results = foresight.make_prediction(initial_state, action_sequence)
    
    # Analyze results
    print("Risk Assessment:", prediction_results['risk_assessment'])
    print("\nConsequence Models:", prediction_results['consequence_models'])

if __name__ == "__main__":
    main()