import numpy as np
from datasketch import MinHash, MinHashLSH
from annime.interface_ann import ANNInterface
import logging
import pickle
from sklearn.base import BaseEstimator, TransformerMixin


class DatasketchANN(ANNInterface, BaseEstimator, TransformerMixin):
    """
    An implementation of ANNInterface using the datasketch library.
    """

    def __init__(self, num_perm=128, threshold=0.5):
        self.num_perm = num_perm
        self.threshold = threshold
        self.lsh = MinHashLSH(threshold=self.threshold, num_perm=self.num_perm)
        self.id_map = {}
        self.built = False
        self.logger = logging.getLogger(__name__)
        logging.basicConfig(level=logging.INFO)
        self.logger.info("DatasketchANN instance created.")

    def build_index(self, data_points, **kwargs):
        for idx, point in enumerate(data_points):
            m = MinHash(num_perm=self.num_perm)
            for d in point:
                m.update(str(d).encode('utf8'))
            point_id = f"point_{idx}"
            self.lsh.insert(point_id, m)
            self.id_map[point_id] = idx
        self.built = True
        self.logger.info("Index built with %d data points.", len(data_points))

    def query(self, query_point, k=1, **kwargs):
        m = MinHash(num_perm=self.num_perm)
        for d in query_point:
            m.update(str(d).encode('utf8'))
        result = self.lsh.query(m)
        self.logger.debug("Query result: %s", result)
        if result:
            return [self.id_map[r] for r in result[:k]]
        else:
            return [-1]

    def save_index(self, filepath):
        """
        Save the MinHash LSH index to a file.

        Args:
            filepath (str): The path to the file where the index is to be saved.

        Raises:
            ValueError: If the index is not built.
        """
        if not self.built:
            raise ValueError("Index not built. Call build_index first.")
        with open(filepath, 'wb') as f:
            pickle.dump(self.lsh, f)
        self.logger.info("Index saved to %s.", filepath)

    def load_index(self, filepath):
        """
        Load the MinHash LSH index from a file.

        Args:
            filepath (str): The path to the file from which the index is to be loaded.

        Raises:
            FileNotFoundError: If the file does not exist.
        """
        with open(filepath, 'rb') as f:
            self.lsh = pickle.load(f)
        self.built = True
        self.logger.info("Index loaded from %s.", filepath)

    def add_items(self, data_points, ids=None):
        """
        Add items to the MinHash LSH index.

        Args:
            data_points (np.ndarray): A numpy array of data points to add to the index.
            ids (list): Optional list of ids corresponding to each data point.

        Raises:
            ValueError: If the index is not built.
        """
        if not self.built:
            raise ValueError("Index not built. Call build_index first.")
        for idx, point in enumerate(data_points):
            m = MinHash(num_perm=self.num_perm)
            for d in point:
                m.update(str(d).encode('utf8'))
            item_id = f"new_point_{len(self.data_points) + idx}" if ids is None else ids[idx]
            self.lsh.insert(item_id, m)
        self.data_points = np.vstack([self.data_points, data_points])
        self.logger.info("Added %d items to the index.", len(data_points))

    def get_index_size(self):
        """
        Return the current size of the MinHash LSH index in terms of the number of items.

        Returns:
            int: The number of items in the index.
        """
        return len(self.data_points) if self.data_points is not None else 0

    def set_distance_metric(self, metric):
        """
        Set the distance metric for the index. (Not applicable for MinHash LSH)
        """
        raise NotImplementedError("DatasketchANN does not support setting a distance metric.")

    def set_index_parameters(self, **params):
        """
        Set parameters for the index. (Not applicable for MinHash LSH)
        """
        raise NotImplementedError("DatasketchANN does not support setting index parameters.")

    def delete_item(self, item_id):
        """
        Delete an item from the index by id.

        Args:
            item_id (str): The id of the item to be deleted.
        """
        self.lsh.remove(item_id)
        self.logger.info("Item %s removed from the index.", item_id)

    def clear_index(self):
        """
        Clear all items from the index.
        """
        self.lsh = MinHashLSH(threshold=self.threshold, num_perm=self.num_perm)
        self.data_points = []
        self.built = False
        self.logger.info("Index cleared.")

    def get_item_vector(self, item_id):
        """
        Retrieve the vector of an item by id. (Not applicable for MinHash LSH)
        """
        raise NotImplementedError("DatasketchANN does not support retrieving item vectors.")

    def optimize_index(self):
        """
        Optimize the index for better performance during queries. (Not applicable for MinHash LSH)
        """
        raise NotImplementedError("DatasketchANN does not support optimizing the index.")

    def get_index_memory_usage(self):
        """
        Return the amount of memory used by the index. (Not applicable for MinHash LSH)
        """
        raise NotImplementedError("DatasketchANN does not support getting memory usage.")

    def batch_query(self, query_points, k=5, include_distances=False):
        """
        Perform a batch query for multiple points, returning their k nearest neighbors.

        Args:
            query_points (list): A list of query points.
            k (int): The number of nearest neighbors to return.
            include_distances (bool): Whether to include distances in the results.

        Returns:
            list: A list of results for each query point.
        """
        results = []
        for query_point in query_points:
            results.append(self.query(query_point, k))
        return results

    def parallel_query(self, query_points, k=5, num_threads=4):
        """
        Perform multiple queries in parallel, using a specified number of threads.

        Args:
            query_points (list): A list of query points.
            k (int): The number of nearest neighbors to return.
            num_threads (int): The number of threads to use.

        Returns:
            list: A list of results for each query point.
        """
        from multiprocessing.pool import ThreadPool
        pool = ThreadPool(num_threads)
        results = pool.starmap(self.query, [(query_point, k) for query_point in query_points])
        pool.close()
        pool.join()
        return results

    def benchmark_performance(self, queries, k=5, rounds=10):
        """
        Benchmark the query performance of the index with a set of queries repeated over several rounds.

        Args:
            queries (list): A list of query points.
            k (int): The number of nearest neighbors to return.
            rounds (int): The number of rounds to repeat the benchmark.

        Returns:
            str: The average query time.
        """
        import time
        total_time = 0
        for _ in range(rounds):
            start_time = time.time()
            self.batch_query(queries, k)
            total_time += time.time() - start_time
        average_time = total_time / rounds
        return f"Average query time: {average_time:.4f} seconds"

    def export_to_dot(self, filepath):
        """
        Export the structure of the index to a DOT file for visualization, if applicable. (Not applicable for MinHash
        LSH)
        """
        raise NotImplementedError("DatasketchANN does not support exporting to DOT format.")

    def enable_logging(self, level='INFO'):
        """
        Enable detailed logging of operations within the index.

        Args:
            level (str): The logging level (e.g., 'INFO', 'DEBUG').
        """
        logging.basicConfig(level=level)
        self.logger.setLevel(level)
        self.logger.info("Logging enabled at %s level.", level)

    def rebuild_index(self, **kwargs):
        """
        Explicitly rebuilds the entire index according to the current configuration and data points. Useful for
        optimizing index performance periodically.
        """
        self.clear_index()
        self.build_index(self.data_points)
        self.logger.info("Index rebuilt.")

    def refresh_index(self):
        """
        Refreshes the index by optimizing internal structures without full rebuilding. Less resource-intensive than
        rebuild_index.
        """
        raise NotImplementedError("DatasketchANN does not support refreshing the index.")

    def serialize_index(self, output_format='binary'):
        """
        Serialize the index into a specified format (e.g., binary, JSON) to enable easy transmission or storage.

        Args:
            output_format (str): The format for serialization (default is 'binary').

        Returns:
            bytes: The serialized index data.
        """
        raise NotImplementedError("DatasketchANN does not support serializing the index.")

    def deserialize_index(self, data, input_format='binary'):
        """
        Deserialize the index from a given format, restoring it to an operational state.

        Args:
            data (bytes): The serialized index data.
            input_format (str): The format of the serialized data (default is 'binary').

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        raise NotImplementedError("DatasketchANN does not support deserializing the index.")

    def query_radius(self, query_point, radius, sort_results=True):
        """
        Query all points within a specified distance (radius) from the query point. (Not applicable for MinHash LSH)
        """
        raise NotImplementedError("DatasketchANN does not support radius queries.")

    def nearest_centroid(self, centroids, k=1):
        """
        For each centroid provided, find the nearest k data points. (Not applicable for MinHash LSH)
        """
        raise NotImplementedError("DatasketchANN does not support querying with centroids.")

    def incremental_update(self, new_data_points, removal_ids=None):
        """
        Update the index incrementally with new data points and optionally remove some existing points by IDs.

        Args:
            new_data_points (ndarray): A list of new data points to add to the index.
            removal_ids (list): A list of IDs of points to remove from the index (default is None).

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        if removal_ids is not None:
            for item_id in removal_ids:
                self.delete_item(item_id)
        self.add_items(new_data_points)
        self.logger.info("Index incrementally updated.")

    def backup_index(self, backup_location):
        """
        Create a backup of the current index state to a specified location.

        Args:
            backup_location (str): The path to the backup location.

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        raise NotImplementedError("DatasketchANN does not support backing up the index.")

    def restore_index_from_backup(self, backup_location):
        """
        Restore the index state from a backup located at the specified location.

        Args:
            backup_location (str): The path to the backup location.

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        raise NotImplementedError("DatasketchANN does not support restoring from backup.")

    def apply_filter(self, filter_function):
        """
        Apply a custom filter function to all data points in the index, possibly modifying or flagging
        them based on user-defined criteria.

        Args:
            filter_function (function): A function to apply to each data point.

        Returns:
            dict: A dictionary of filtered data points.
        """
        filtered_data = {idx: point for idx, point in enumerate(self.data_points) if filter_function(point)}
        self.logger.info("Applied filter to index.")
        return filtered_data

    def get_statistics(self):
        """
        Gather and return statistical data about the index, such as point distribution, space utilization, etc.

        Returns:
            dict: A dictionary of statistical data.
        """
        stats = {
            'num_points': len(self.data_points),
            'num_permutations': self.num_perm,
            'threshold': self.threshold
        }
        self.logger.info("Index statistics gathered.")
        return stats

    def register_callback(self, event, callback_function):
        """
        Register a callback function to be called on specific events (e.g., after rebuilding the index,
        before saving, etc.).

        Args:
            event (str): The event to register the callback for.
            callback_function (function): The function to call when the event occurs.

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        raise NotImplementedError("DatasketchANN does not support registering callbacks.")

    def unregister_callback(self, event):
        """
        Unregister a previously registered callback for a specific event.

        Args:
            event (str): The event to unregister the callback for.

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        raise NotImplementedError("DatasketchANN does not support unregistering callbacks.")

    def list_registered_callbacks(self):
        """
        List all currently registered callbacks and the events they are associated with.

        Returns:
            list: A list of registered callbacks.

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        raise NotImplementedError("DatasketchANN does not support listing callbacks.")

    def perform_maintenance(self):
        """
        Perform routine maintenance on the index to ensure optimal performance and stability.

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        raise NotImplementedError("DatasketchANN does not support performing maintenance.")

    def export_statistics(self, format='csv'):
        """
        Export collected statistical data in a specified format for analysis and reporting purposes.

        Args:
            format (str): The format for exporting statistics (default is 'csv').

        Returns:
            str: The exported statistics.

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        raise NotImplementedError("DatasketchANN does not support exporting statistics.")

    def adjust_algorithm_parameters(self, **params):
        """
        Dynamically adjust the algorithmic parameters of the underlying ANN algorithm,
        facilitating on-the-fly optimization based on operational feedback.

        Args:
            **params: Arbitrary keyword arguments for adjusting algorithm parameters.

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        raise NotImplementedError("DatasketchANN does not support adjusting algorithm parameters.")

    def query_with_constraints(self, query_point, constraints, k=5):
        """
        Perform a query for nearest neighbors that meet certain user-defined constraints.

        Args:
            query_point (list): The query point.
            constraints (function): A function to apply constraints to the results.
            k (int): The number of nearest neighbors to return.

        Returns:
            list: The constrained nearest neighbors.

        Raises:
            NotImplementedError: If the method is not implemented by the subclass.
        """
        raise NotImplementedError("DatasketchANN does not support querying with constraints.")

    def fit(self, X, y=None, **kwargs):
        """
        Fit the Annoy index with the provided data.

        Args:
            X (ndarray): Training data.
            y (ndarray): Training labels (optional).
            **kwargs: Additional parameters for building the index.

        Returns:
            self
        """
        self.build_index(X, **kwargs)
        return self

    def transform(self, X, k=1, **kwargs):
        """
        Transform the data using the Annoy index by querying the nearest neighbors.

        Args:
            X (ndarray): Data to transform.
            k (int): Number of nearest neighbors to query.
            **kwargs: Additional parameters for querying the index.

        Returns:
            ndarray: Indices of the nearest neighbors.
        """
        results = np.array([self.query(x, k=k, **kwargs)[0] for x in X], dtype=int)
        return results

    def fit_transform(self, X, y=None, **kwargs):
        """
        Fit the Annoy index with the provided data and transform it.

        Args:
            X (ndarray): Training data.
            y (ndarray): Training labels (optional).
            **kwargs: Additional parameters for building and querying the index.

        Returns:
            ndarray: Indices of the nearest neighbors.
        """
        return self.fit(X, y, **kwargs).transform(X, **kwargs)
