from annoy import AnnoyIndex
from annime.interface_ann import ANNInterface
from sklearn.base import BaseEstimator, TransformerMixin
import logging
import numpy as np


class AnnoyANN(ANNInterface, BaseEstimator, TransformerMixin):
    """
    An implementation of ANNInterface for the Annoy library.
    """

    def __init__(self, dim, metric="euclidean", num_trees=10):
        super().__init__()
        self.index = AnnoyIndex(dim, metric)
        self.num_trees = num_trees
        self.dim = dim
        self.metric = metric
        self.built = False
        self.logger = logging.getLogger(__name__)

    def build_index(self, data_points, **kwargs):
        """
        Build the Annoy index from the provided data points.

        Args:
            data_points (ndarray): A list of data points to index.
            **kwargs: Arbitrary keyword arguments for index configuration.
        """
        num_trees = kwargs.get('num_trees', self.num_trees)
        for i, vector in enumerate(data_points):
            self.index.add_item(i, vector)
        self.index.build(num_trees)
        self.built = True
        self.logger.info("Index built with %d trees and %d data points.", num_trees, len(data_points))

    def query(self, query_point, k=5, **kwargs):
        """
        Query the Annoy index for the k nearest neighbors of the provided point.

        Args:
            query_point (ndarray): The query point.
            k (int): The number of nearest neighbors to return.

        Returns:
            ndarray: The k nearest neighbors.
        """
        if not self.built:
            raise Exception("Index must be built before querying")
        return self.index.get_nns_by_vector(query_point, k, include_distances=True)

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

        Args:
            filepath (str): The path to the file where the index is to be saved.
        """
        if not self.built:
            raise Exception("Index must be built before it can be saved")
        self.index.save(filepath)
        self.logger.info("Index saved to %s.", filepath)

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

        Args:
            filepath (str): The path to the file from which the index is to be loaded.
        """
        self.index.load(filepath)
        self.built = True
        self.logger.info("Index loaded from %s.", filepath)

    def add_items(self, data_points, ids=None):
        """
        Add items to the Annoy index, optionally with specific ids.

        Args:
            data_points (ndarray): A numpy list of data points to add to the index.
            ids (list): Optional list of ids corresponding to each data point.
        """
        if ids and len(data_points) != len(ids):
            raise ValueError("Length of data_points and ids must match")
        for i, vector in enumerate(data_points):
            idx = ids[i] if ids else i
            self.index.add_item(idx, vector)
        self.logger.info("Added %d items to the index.", len(data_points))

    def delete_item(self, item_id):
        """
        Annoy does not support removing items directly.

        Args:
            item_id (int): The id of the item to be deleted.

        Raises:
            NotImplementedError: Annoy does not support removing items.
        """
        raise NotImplementedError("Annoy does not support removing items directly")

    def clear_index(self):
        """
        Clear all items from the Annoy index.
        """
        self.index = AnnoyIndex(self.dim, self.metric)
        self.built = False
        self.logger.info("Index cleared.")

    def get_item_vector(self, item_id):
        """
        Retrieve the vector of an item by id from the Annoy index.

        Args:
            item_id (int): The id of the item.

        Returns:
            ndarray: The vector of the item.
        """
        return self.index.get_item_vector(item_id)

    def optimize_index(self):
        """
        Optimize the Annoy index for better performance during queries.

        Note: Annoy does not have a direct optimize method; rebuild the index for optimization.
        """
        if self.built:
            self.index.unbuild()
            self.index.build(self.num_trees)
            self.logger.info("Index optimized by rebuilding.")

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

        Returns:
            int: The number of items in the index.
        """
        return self.index.get_n_items()

    def get_index_memory_usage(self):
        """
        Return the amount of memory used by the Annoy index.

        Note: Annoy does not directly provide memory usage stats; this is a placeholder.

        Returns:
            str: Placeholder text indicating memory usage functionality is not supported by Annoy.
        """
        return "Memory usage functionality not supported by Annoy."

    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 (ndarray): 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:
            ndarray: A list of results for each query point.
        """
        results = []
        for query_point in query_points:
            result = self.query(query_point, k=k)
            if include_distances:
                results.append(result)
            else:
                results.append([x[0] for x in result])
        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 (ndarray): A list of query points.
            k (int): The number of nearest neighbors to return.
            num_threads (int): The number of threads to use.

        Returns:
            ndarray: A list of results for each query point.
        """
        return self.batch_query(query_points, k=k)  # Annoy does not support true parallel queries

    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 (ndarray): 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
        start_time = time.time()
        for _ in range(rounds):
            for query in queries:
                self.query(query, k=k)
        end_time = time.time()
        avg_query_time = (end_time - start_time) / (len(queries) * rounds)
        self.logger.info("Benchmark completed: Avg query time = %.5f seconds", avg_query_time)
        return f"Average query time: {avg_query_time:.5f} seconds"

    def export_to_dot(self, filepath):
        """
        Export the structure of the Annoy index to a DOT file for visualization.

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

        Raises:
            NotImplementedError: Annoy does not support exporting to DOT format.
        """
        raise NotImplementedError("Export to DOT format is not supported by Annoy.")

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

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

    def rebuild_index(self, **kwargs):
        """
        Explicitly rebuilds the entire Annoy index according to the current configuration and data points.

        Args:
            **kwargs: Arbitrary keyword arguments for index configuration.
        """
        if not self.built:
            raise Exception("Index must exist to rebuild")
        self.index.unbuild()
        self.index.build(self.num_trees)
        self.logger.info("Index rebuilt with %d trees.", self.num_trees)

    def refresh_index(self):
        """
        Refreshes the Annoy index by optimizing internal structures without full rebuilding.

        Note: Annoy does not have a native refresh method; simulate by rebuilding.
        """
        self.rebuild_index()

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

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

        Returns:
            bytes: The serialized index data.

        Raises:
            ValueError: If the output format is not supported.
            Exception: If the index is not built before serialization.
        """
        if output_format != 'binary':
            raise ValueError("Annoy currently supports only binary serialization format.")
        if not self.built:
            raise Exception("Index must be built before serialization")
        temp_path = "temp_annoy_index.ann"
        self.index.save(temp_path)
        with open(temp_path, 'rb') as file:
            serialized_data = file.read()
        return serialized_data

    def deserialize_index(self, data, input_format='binary'):
        """
        Deserialize the Annoy 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:
            ValueError: If the input format is not supported.
        """
        if input_format != 'binary':
            raise ValueError("Annoy currently supports only binary deserialization format.")
        temp_path = "temp_load_annoy_index.ann"
        with open(temp_path, 'wb') as file:
            file.write(data)
        self.load_index(temp_path)

    def query_radius(self, query_point, radius, sort_results=True):
        """
        Query all points within a specified distance (radius) from the query point.

        Args:
            query_point (ndarray): The query point.
            radius (float): The radius within which to search.
            sort_results (bool): Whether to sort the results by distance (default is True).

        Returns:
            ndarray: A list of points within the specified radius.
        """
        if not self.built:
            raise Exception("Index must be built before querying.")
        all_neighbors = self.index.get_nns_by_vector(query_point, n=10000, include_distances=True)
        result = [(i, dist) for i, dist in zip(*all_neighbors) if dist < radius]
        if sort_results:
            result.sort(key=lambda x: x[1])
        return result

    def nearest_centroid(self, centroids, k=1):
        """
        For each centroid provided, find the nearest k data points.

        Args:
            centroids (list): A list of centroids.
            k (int): The number of nearest neighbors to return.

        Returns:
            list: A list of results for each centroid.
        """
        results = []
        for centroid in centroids:
            neighbors = self.index.get_nns_by_vector(centroid, k, include_distances=True)
            results.append(neighbors)
        return results

    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: Annoy does not support removing items.
        """
        if removal_ids is not None:
            raise NotImplementedError("Annoy does not support removing items. Rebuild the index without the items "
                                      "instead.")
        existing_data = [self.index.get_item_vector(i) for i in range(self.index.get_n_items())]
        self.clear_index()
        all_data = existing_data + list(new_data_points)
        for i, vector in enumerate(all_data):
            self.index.add_item(i, vector)
        self.index.build(self.num_trees)
        self.logger.info("Index incrementally updated.")

    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.
        """
        results = {}
        for i in range(self.index.get_n_items()):
            vector = self.index.get_item_vector(i)
            if filter_function(vector):
                results[i] = vector
        return results

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

        Returns:
            dict: A dictionary of statistical data.
        """
        if not self.built:
            raise Exception("Index must be built before statistics can be retrieved.")
        stats = {
            'num_items': self.index.get_n_items(),
            'num_trees': self.num_trees
        }
        self.logger.info("Statistics retrieved: %s", stats)
        return stats

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

        Args:
            **params: Arbitrary keyword arguments for adjusting algorithm parameters.
        """
        if self.built:
            raise Exception("Parameters must be adjusted before the index is built.")
        self.num_trees = params.get('num_trees', self.num_trees)
        self.logger.info("Algorithm parameters adjusted: %s", params)

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

        Returns:
            ndarray: The constrained nearest neighbors.
        """
        all_neighbors = self.index.get_nns_by_vector(query_point, n=k * 10, include_distances=True)  # Get more
        # results initially
        filtered_neighbors = [n for n in all_neighbors if constraints(n)]
        return filtered_neighbors[:k]  # Return only k results after filtering

    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)
