from typing import Mapping, List, Union, Tuple

import numpy as np
from PIL.Image import Image

from image_prediction.estimator.adapter.adapter import EstimatorAdapter
from image_prediction.exceptions import UnexpectedPredictionFormat
from image_prediction.utils import get_logger

logger = get_logger()


class Classifier:
    def __init__(self, estimator_adapter: EstimatorAdapter, classes: Mapping[int, str]):
        """Abstraction layer over different estimator backends (e.g. keras or scikit-learn). For each backend to be used
        an EstimatorAdapter must be implemented.

        Args:
            estimator_adapter: adapter for a given estimator backend; expected to be a classifier that returns mappings
                from numeric labels to probabilities as predictions or numeric labels
            classes: mapping from a numerical label to a human-readable label for classes
        """
        self.__estimator_adapter = estimator_adapter
        self._classes = classes

    def __validate_dict_prediction_format(self, prediction):
        if not max(prediction.keys) <= len(self._classes):
            raise UnexpectedPredictionFormat(f"Received prediction in an unexpected format: {prediction}")

    def __validate_array_prediction_format(self, prediction):
        if not len(prediction) == len(self._classes):
            raise UnexpectedPredictionFormat(
                f"Received fewer probabilities ({len(prediction)}) than classes were specified ({len(self._classes)}."
            )

    def __format_prediction(self, prediction):
        if isinstance(prediction, int):
            return self._classes[prediction]

        elif isinstance(prediction, dict):
            self.__validate_dict_prediction_format(prediction)
            return {self._classes[cls_idx] for cls_idx, prob in prediction.items()}

        elif isinstance(prediction, np.ndarray):
            self.__validate_array_prediction_format(prediction)
            return dict(zip(self._classes, prediction))

        else:
            return prediction

    def predict(self, batch: Union[np.array, Tuple[Image]]) -> List[str]:

        if not isinstance(batch, tuple) and batch.shape[0] == 0:
            return []

        return list(map(self.__format_prediction, self.__estimator_adapter.predict(batch)))

    def __call__(self, batch: np.array) -> List[str]:
        return self.predict(batch)