"""
Layer 3: Industry-Level Economic Response
=======================================

Predicts how industries respond to trade flow disruptions
using multi-output regression and time series models.
"""

from dataclasses import dataclass
from typing import Any, Dict

from tipm.config.layer_configs import IndustryConfig


@dataclass
class IndustryResponse:
    """Industry response prediction"""

    sector_impacts: Dict[str, float]
    substitution_effects: Dict[str, float]
    cost_adjustments: Dict[str, float]
    capacity_utilization: Dict[str, float]
    employment_effects: Dict[str, float]


class IndustryResponseLayer:
    """Layer 3: Industry-Level Economic Response"""

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

        # Industry mappings
        self.industry_sectors = [
            "agriculture",
            "mining",
            "manufacturing",
            "electronics",
            "automotive",
            "textiles",
            "chemicals",
            "food_processing",
            "energy",
            "services",
            "construction",
            "transportation",
        ]

    def fit(self, industry_data: Any = None) -> "IndustryResponseLayer":
        """Train industry response models"""
        # Placeholder - would train on historical industry response data
        self.is_fitted = True
        return self

    def predict(self, trade_impact: Any) -> IndustryResponse:
        """Predict industry-level responses to trade disruptions"""
        if not self.is_fitted:
            raise ValueError("Layer must be fitted before prediction")

        # Extract supply chain disruption info
        disruption_scores = getattr(trade_impact, "supply_chain_disruption", {})

        # Simulate industry responses
        sector_impacts = {}
        substitution_effects = {}
        cost_adjustments = {}
        capacity_utilization = {}
        employment_effects = {}

        for sector in self.industry_sectors:
            # Base impact from trade disruption
            base_impact = disruption_scores.get(sector, 0.1)

            sector_impacts[sector] = base_impact
            substitution_effects[sector] = base_impact * 0.6  # 60% substitution effect
            cost_adjustments[sector] = base_impact * 1.2  # 120% cost pass-through
            capacity_utilization[sector] = 1 - (base_impact * 0.3)  # Capacity reduction
            employment_effects[sector] = -base_impact * 0.4  # Employment reduction

        return IndustryResponse(
            sector_impacts=sector_impacts,
            substitution_effects=substitution_effects,
            cost_adjustments=cost_adjustments,
            capacity_utilization=capacity_utilization,
            employment_effects=employment_effects,
        )

    def fit(self, X, y=None):
        """Fit the industry response model."""
        pass

    def predict(self, X):
        """Predict industry response impacts."""
        return None