"""
Layer 6: Geopolitical & Social Response
======================================

Models geopolitical and social responses using transformer-based NLP and scenario analysis.
"""

from typing import Dict, Any, Optional
from dataclasses import dataclass

from ..config.layer_configs import GeopoliticalConfig


@dataclass
class GeopoliticalImpact:
    """Geopolitical impact prediction"""

    social_sentiment: Dict[str, float]
    protest_risk: Dict[str, float]
    diplomatic_actions: Dict[str, str]
    media_coverage: Dict[str, float]


class GeopoliticalLayer:
    """Layer 6: Geopolitical & Social Response"""

    def __init__(self, config: GeopoliticalConfig):
        self.config = config
        self.is_fitted = False

    def fit(self, geopolitical_data: Optional[Any]) -> "GeopoliticalLayer":
        """Train geopolitical response models"""
        self.is_fitted = True
        return self

    def predict(self, consumer_impact) -> GeopoliticalImpact:
        """Predict geopolitical and social responses"""
        if not self.is_fitted:
            raise ValueError("Layer must be fitted before prediction")
        # Simulate geopolitical impacts
        social_sentiment = {}
        protest_risk = {}
        diplomatic_actions = {}
        media_coverage = {}
        # Placeholder logic
        for sector in getattr(consumer_impact, "welfare_effects", {}):
            welfare_loss = consumer_impact.welfare_effects[sector]
            social_sentiment[sector] = max(-1.0, min(1.0, -welfare_loss * 0.1))
            protest_risk[sector] = max(0.0, min(1.0, welfare_loss * 0.05))
            diplomatic_actions[sector] = (
                "monitor" if protest_risk[sector] > 0.2 else "none"
            )
            media_coverage[sector] = abs(welfare_loss) * 0.2
        return GeopoliticalImpact(
            social_sentiment=social_sentiment,
            protest_risk=protest_risk,
            diplomatic_actions=diplomatic_actions,
            media_coverage=media_coverage,
        )