"""Search Index Commands"""

import logging
from time import monotonic, sleep
from typing import Any, Callable, Dict, List, Optional

from pymongo.collection import Collection
from pymongo.errors import OperationFailure
from pymongo.operations import SearchIndexModel

logger = logging.getLogger(__file__)


def _search_index_error_message() -> str:
    return (
        "Search index operations are not currently available on shared clusters, "
        "such as MO. They require dedicated clusters >= M10. "
        "You may still perform vector search. "
        "You simply must set up indexes manually. Follow the instructions here: "
        "https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/"
    )


def _vector_search_index_definition(
    dimensions: int,
    path: str,
    similarity: str,
    filters: Optional[List[str]] = None,
    **kwargs: Any,
) -> Dict[str, Any]:
    # https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/
    fields = [
        {
            "numDimensions": dimensions,
            "path": path,
            "similarity": similarity,
            "type": "vector",
        },
    ]
    if filters:
        for field in filters:
            fields.append({"type": "filter", "path": field})
    definition = {"fields": fields}
    definition.update(kwargs)
    return definition


def create_vector_search_index(
    collection: Collection,
    index_name: str,
    dimensions: int,
    path: str,
    similarity: str,
    filters: Optional[List[str]] = None,
    *,
    wait_until_complete: Optional[float] = None,
    **kwargs: Any,
) -> None:
    """Experimental Utility function to create a vector search index

    Args:
        collection (Collection): MongoDB Collection
        index_name (str): Name of Index
        dimensions (int): Number of dimensions in embedding
        path (str): field with vector embedding
        similarity (str): The similarity score used for the index
        filters (List[str]): Fields/paths to index to allow filtering in $vectorSearch
        wait_until_complete (Optional[float]): If provided, number of seconds to wait
            until search index is ready.
        kwargs: Keyword arguments supplying any additional options to SearchIndexModel.
    """
    logger.info("Creating Search Index %s on %s", index_name, collection.name)

    try:
        result = collection.create_search_index(
            SearchIndexModel(
                definition=_vector_search_index_definition(
                    dimensions=dimensions,
                    path=path,
                    similarity=similarity,
                    filters=filters,
                    **kwargs,
                ),
                name=index_name,
                type="vectorSearch",
            )
        )
    except OperationFailure as e:
        raise OperationFailure(_search_index_error_message()) from e

    if wait_until_complete:
        _wait_for_predicate(
            predicate=lambda: _is_index_ready(collection, index_name),
            err=f"{index_name=} did not complete in {wait_until_complete}!",
            timeout=wait_until_complete,
        )
    logger.info(result)


def drop_vector_search_index(
    collection: Collection,
    index_name: str,
    *,
    wait_until_complete: Optional[float] = None,
) -> None:
    """Drop a created vector search index

    Args:
        collection (Collection): MongoDB Collection with index to be dropped
        index_name (str): Name of the MongoDB index
        wait_until_complete (Optional[float]): If provided, number of seconds to wait
            until search index is ready.
    """
    logger.info(
        "Dropping Search Index %s from Collection: %s", index_name, collection.name
    )
    try:
        collection.drop_search_index(index_name)
    except OperationFailure as e:
        if "CommandNotSupported" in str(e):
            raise OperationFailure(_search_index_error_message()) from e
        # else this most likely means an ongoing drop request was made so skip
    if wait_until_complete:
        _wait_for_predicate(
            predicate=lambda: len(list(collection.list_search_indexes())) == 0,
            err=f"Index {index_name} did not drop in {wait_until_complete}!",
            timeout=wait_until_complete,
        )
    logger.info("Vector Search index %s.%s dropped", collection.name, index_name)


def update_vector_search_index(
    collection: Collection,
    index_name: str,
    dimensions: int,
    path: str,
    similarity: str,
    filters: Optional[List[str]] = None,
    *,
    wait_until_complete: Optional[float] = None,
    **kwargs: Any,
) -> None:
    """Update a search index.

    Replace the existing index definition with the provided definition.

    Args:
        collection (Collection): MongoDB Collection
        index_name (str): Name of Index
        dimensions (int): Number of dimensions in embedding
        path (str): field with vector embedding
        similarity (str): The similarity score used for the index.
        filters (List[str]): Fields/paths to index to allow filtering in $vectorSearch
        wait_until_complete (Optional[float]): If provided, number of seconds to wait
            until search index is ready.
        kwargs: Keyword arguments supplying any additional options to SearchIndexModel.
    """

    logger.info(
        "Updating Search Index %s from Collection: %s", index_name, collection.name
    )
    try:
        collection.update_search_index(
            name=index_name,
            definition=_vector_search_index_definition(
                dimensions=dimensions,
                path=path,
                similarity=similarity,
                filters=filters,
                **kwargs,
            ),
        )
    except OperationFailure as e:
        raise OperationFailure(_search_index_error_message()) from e

    if wait_until_complete:
        _wait_for_predicate(
            predicate=lambda: _is_index_ready(collection, index_name),
            err=f"Index {index_name} update did not complete in {wait_until_complete}!",
            timeout=wait_until_complete,
        )
    logger.info("Update succeeded")


def _is_index_ready(collection: Collection, index_name: str) -> bool:
    """Check for the index name in the list of available search indexes to see if the
    specified index is of status READY

    Args:
        collection (Collection): MongoDB Collection to for the search indexes
        index_name (str): Vector Search Index name

    Returns:
        bool : True if the index is present and READY false otherwise
    """
    try:
        search_indexes = collection.list_search_indexes(index_name)
    except OperationFailure as e:
        raise OperationFailure(_search_index_error_message()) from e

    for index in search_indexes:
        if index["type"] == "vectorSearch" and index["status"] == "READY":
            return True
    return False


def _wait_for_predicate(
    predicate: Callable, err: str, timeout: float = 120, interval: float = 0.5
) -> None:
    """Generic to block until the predicate returns true

    Args:
        predicate (Callable[, bool]): A function that returns a boolean value
        err (str): Error message to raise if nothing occurs
        timeout (float, optional): Wait time for predicate. Defaults to TIMEOUT.
        interval (float, optional): Interval to check predicate. Defaults to DELAY.

    Raises:
        TimeoutError: _description_
    """
    start = monotonic()
    while not predicate():
        if monotonic() - start > timeout:
            raise TimeoutError(err)
        sleep(interval)


def create_fulltext_search_index(
    collection: Collection,
    index_name: str,
    field: str,
    *,
    wait_until_complete: Optional[float] = None,
    **kwargs: Any,
) -> None:
    """Experimental Utility function to create an Atlas Search index

    Args:
        collection (Collection): MongoDB Collection
        index_name (str): Name of Index
        field (str): Field to index
        wait_until_complete (Optional[float]): If provided, number of seconds to wait
            until search index is ready
        kwargs: Keyword arguments supplying any additional options to SearchIndexModel.
    """
    logger.info("Creating Search Index %s on %s", index_name, collection.name)

    definition = {
        "mappings": {"dynamic": False, "fields": {field: [{"type": "string"}]}}
    }

    try:
        result = collection.create_search_index(
            SearchIndexModel(
                definition=definition,
                name=index_name,
                type="search",
                **kwargs,
            )
        )
    except OperationFailure as e:
        raise OperationFailure(_search_index_error_message()) from e

    if wait_until_complete:
        _wait_for_predicate(
            predicate=lambda: _is_index_ready(collection, index_name),
            err=f"{index_name=} did not complete in {wait_until_complete}!",
            timeout=wait_until_complete,
        )
    logger.info(result)