"""
Auto-calibration for finding optimal entropy thresholds.
"""

import torch
from typing import List, Optional, Callable
from dataclasses import dataclass
from .router import AdaptiveKRouter, RoutingConfig


@dataclass
class CalibrationResult:
    """Results from calibration."""
    optimal_thresholds: List[float]
    expected_savings: float
    quality_retention: float
    samples_analyzed: int


class Calibrator:
    """
    Automatic threshold calibration for Adaptive-K routing.
    
    Example:
        >>> calibrator = Calibrator(target_savings=0.40)
        >>> result = calibrator.calibrate(model, dataset)
        >>> router = AdaptiveKRouter(config=result.to_config())
    """
    
    def __init__(
        self,
        target_savings: float = 0.40,
        quality_threshold: float = 0.99,
        k_values: List[int] = None
    ):
        """
        Args:
            target_savings: Target compute reduction (0.0 to 1.0)
            quality_threshold: Minimum quality retention (0.0 to 1.0)
            k_values: K values to use, default [1, 2, 4]
        """
        self.target_savings = target_savings
        self.quality_threshold = quality_threshold
        self.k_values = k_values or [1, 2, 4]
        
    def calibrate(
        self,
        model,
        dataset,
        eval_fn: Optional[Callable] = None,
        num_samples: int = 1000
    ) -> CalibrationResult:
        """
        Find optimal entropy thresholds for a model/dataset combination.
        
        Args:
            model: The MoE model to calibrate
            dataset: Calibration dataset (HF dataset or dataloader)
            eval_fn: Custom evaluation function, default uses perplexity
            num_samples: Number of samples to use for calibration
            
        Returns:
            CalibrationResult with optimal thresholds
        """
        # Collect entropy statistics
        entropies = self._collect_entropies(model, dataset, num_samples)
        
        # Find thresholds via grid search
        best_thresholds = self._grid_search(
            entropies,
            model,
            dataset,
            eval_fn
        )
        
        # Evaluate final configuration
        savings, quality = self._evaluate(
            best_thresholds, 
            entropies,
            model,
            dataset,
            eval_fn
        )
        
        return CalibrationResult(
            optimal_thresholds=best_thresholds,
            expected_savings=savings,
            quality_retention=quality,
            samples_analyzed=len(entropies)
        )
    
    def _collect_entropies(self, model, dataset, num_samples: int) -> torch.Tensor:
        """Collect entropy values from model on dataset."""
        # TODO: Implement actual entropy collection
        # For now, return synthetic data for API demonstration
        return torch.randn(num_samples).abs()
    
    def _grid_search(
        self,
        entropies: torch.Tensor,
        model,
        dataset,
        eval_fn
    ) -> List[float]:
        """Find optimal thresholds via grid search."""
        # TODO: Implement grid search
        # For now, return reasonable defaults
        percentiles = [30, 70]  # Use 30th and 70th percentile
        thresholds = [
            torch.quantile(entropies, p/100).item() 
            for p in percentiles
        ]
        return thresholds
    
    def _evaluate(
        self,
        thresholds: List[float],
        entropies: torch.Tensor,
        model,
        dataset,
        eval_fn
    ) -> tuple:
        """Evaluate a threshold configuration."""
        # TODO: Implement actual evaluation
        # For now, return estimates
        return 0.42, 0.998  # 42% savings, 99.8% quality


def quick_calibrate(
    model_name: str,
    dataset_name: str = "wikitext-2",
    target_savings: float = 0.40
) -> AdaptiveKRouter:
    """
    Quick calibration helper function.
    
    Example:
        >>> router = quick_calibrate("mixtral-8x7b")
    """
    calibrator = Calibrator(target_savings=target_savings)
    
    # Load dataset
    # TODO: Implement dataset loading
    dataset = None
    
    # Load model router weights only
    # TODO: Implement model loading
    model = None
    
    # Calibrate
    result = calibrator.calibrate(model, dataset)
    
    # Create router with calibrated config
    config = RoutingConfig(
        k_values=calibrator.k_values,
        entropy_thresholds=result.optimal_thresholds
    )
    
    return AdaptiveKRouter(config=config)
