"""
TabICL Wrapper
==============

Sklearn-compatible wrapper for TabICL (Tabular In-Context Learning).

TabICL uses language models for tabular prediction via in-context learning.

Author: UW MSIM Team
Date: November 2025
"""

import time
import logging
from typing import Optional, Union
import numpy as np
import pandas as pd

from .base_wrapper import BaseModelWrapper

logger = logging.getLogger(__name__)


class TabICLWrapper(BaseModelWrapper):
    """
    TabICL (Tabular In-Context Learning) wrapper.

    Parameters
    ----------
    task_type : str, default='classification'
        Task type: 'classification' or 'regression'
    model_name : str, default='gpt2'
        Base language model to use
    max_samples : int, default=100
        Maximum number of in-context examples
    device : str, default='auto'
        Device: 'cpu', 'cuda', or 'auto'
    random_state : int, default=42
        Random seed
    """

    def __init__(
        self,
        task_type: str = 'classification',
        model_name: str = 'gpt2',
        max_samples: int = 100,
        device: str = 'auto',
        random_state: int = 42
    ):
        super().__init__(task_type=task_type, random_state=random_state)
        self.model_name = model_name
        self.max_samples = max_samples
        self.device = device

    def fit(self, X: Union[pd.DataFrame, np.ndarray], y: Union[pd.Series, np.ndarray]) -> 'TabICLWrapper':
        """
        Fit TabICL (stores training data for in-context learning).

        Parameters
        ----------
        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
            Training features
        y : pd.Series or np.ndarray, shape (n_samples,)
            Training target

        Returns
        -------
        self : TabICLWrapper
            Fitted model
        """
        self._validate_input(X, y)

        logger.info(f"Fitting TabICL with {self.model_name} on {X.shape[0]} samples...")
        start_time = time.time()

        try:
            # Note: Actual TabICL implementation may vary
            # This is a template; adjust imports based on actual TabICL package

            # Store training data for in-context learning
            if isinstance(X, pd.DataFrame):
                self.X_train_ = X.copy()
            else:
                self.X_train_ = pd.DataFrame(X)

            if isinstance(y, pd.Series):
                self.y_train_ = y.copy()
            else:
                self.y_train_ = pd.Series(y)

            # Limit to max_samples for efficiency
            if len(self.X_train_) > self.max_samples:
                logger.info(f"Sampling {self.max_samples} from {len(self.X_train_)} training samples")
                sample_idx = np.random.RandomState(self.random_state).choice(
                    len(self.X_train_), self.max_samples, replace=False
                )
                self.X_train_ = self.X_train_.iloc[sample_idx]
                self.y_train_ = self.y_train_.iloc[sample_idx]

            # Initialize TabICL model (placeholder - adjust for actual implementation)
            # from tabicl import TabICLModel
            # self.model = TabICLModel(model_name=self.model_name, device=self.device)

            self.is_fitted = True
            self.fit_time = time.time() - start_time

            logger.info(f"TabICL fitted in {self.fit_time:.2f} seconds")
            logger.warning("TabICL wrapper is a template. Adjust for actual TabICL implementation.")

        except Exception as e:
            logger.error(f"Error fitting TabICL: {e}")
            raise

        return self

    def predict(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
        """
        Make predictions with TabICL.

        Parameters
        ----------
        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
            Test features

        Returns
        -------
        predictions : np.ndarray, shape (n_samples,)
            Predicted values or class labels
        """
        if not self.is_fitted:
            raise ValueError("Model not fitted. Call fit() first.")

        self._validate_input(X)

        logger.info(f"Predicting on {X.shape[0]} samples with TabICL...")
        start_time = time.time()

        try:
            # Placeholder implementation
            # In actual TabICL, this would use the language model with in-context examples
            logger.warning("Using placeholder predictions. Integrate actual TabICL model.")

            # Fallback: predict the majority class for classification to ensure valid type
            if self.task_type == 'classification':
                majority_class = self.y_train_.mode()[0]
                predictions = np.full(len(X), majority_class)
            else:
                predictions = np.zeros(len(X))

            self.predict_time = time.time() - start_time

            logger.info(f"Predictions complete in {self.predict_time:.2f} seconds")

            return predictions

        except Exception as e:
            logger.error(f"Error during prediction: {e}")
            raise

    def _predict_proba_impl(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
        """
        Predict class probabilities with TabICL.

        Parameters
        ----------
        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
            Test features

        Returns
        -------
        probabilities : np.ndarray, shape (n_samples, n_classes)
            Class probabilities
        """
        # Placeholder implementation
        n_samples = len(X)
        n_classes = len(np.unique(self.y_train_))
        proba = np.ones((n_samples, n_classes)) / n_classes

        logger.warning("Using uniform probability distribution. Integrate actual TabICL model.")

        return proba

    def get_params(self, deep: bool = True) -> dict:
        """Get parameters for this estimator."""
        params = super().get_params(deep)
        params.update({
            'model_name': self.model_name,
            'max_samples': self.max_samples,
            'device': self.device
        })
        return params