from __future__ import annotations

from collections.abc import Callable
from time import monotonic, sleep
from typing import TYPE_CHECKING, Any


if TYPE_CHECKING:
    from pymongo.collection import Collection as MongoCollection


def _vector_search_index_definition(
    dimensions: int,
    path: str,
    similarity: str,
    filters: list[str] | None = 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})  # noqa: PERF401
    definition = {"fields": fields}
    definition.update(kwargs)
    return definition


def create_vector_search_index(
    collection: MongoCollection[Any],
    index_name: str,
    dimensions: int,
    path: str,
    similarity: str,
    filters: list[str] | None = None,
    *,
    wait_until_complete: float | None = 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.
    """
    from pymongo.operations import SearchIndexModel

    if collection.name not in collection.database.list_collection_names():
        collection.database.create_collection(collection.name)

    collection.create_search_index(
        SearchIndexModel(
            definition=_vector_search_index_definition(
                dimensions=dimensions,
                path=path,
                similarity=similarity,
                filters=filters,
                **kwargs,
            ),
            name=index_name,
            type="vectorSearch",
        )
    )

    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,
        )


def _is_index_ready(collection: MongoCollection[Any], 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
    """
    for index in collection.list_search_indexes(index_name):
        if index["status"] == "READY":
            return True
    return False


def _wait_for_predicate(
    predicate: Callable[[], bool], 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)