from pathlib import Path

import dopt_basics.io

from dopt_sensor_anomalies import types as t
from dopt_sensor_anomalies.constants import LIB_ROOT_PATH, MODEL_FOLDER_NAME, STOP_FOLDER_NAME


def get_model_folder() -> Path:
    """retrieves the folder which contains the trained and needed models for anomaly detection

    Returns
    -------
    Path
        the path containing the models

    Raises
    ------
    FileNotFoundError
        raised if the application's root folder is not found or the model folder is not present
    """

    path_found = dopt_basics.io.search_folder_path(
        starting_path=LIB_ROOT_PATH, stop_folder_name=STOP_FOLDER_NAME
    )
    if path_found is None:
        raise FileNotFoundError("The application's root directory could not be determined.")

    model_folder = path_found / MODEL_FOLDER_NAME
    if not model_folder.exists():
        raise FileNotFoundError(
            "The model folder was not found in the application's root directory."
        )

    return model_folder


def get_detection_models(
    model_folder: Path,
) -> t.DetectionModels:
    """retrieve the model paths both for the left and the right side as a TypedDict

    Parameters
    ----------
    model_folder : Path
        the found path to the folder containing the models

    Returns
    -------
    t.DetectionModels
        TypedDict with key "left" and "right" with the corresponding paths to each model

    Raises
    ------
    ValueError
        raised if there are no or too many model files are found for one side
    """

    left_model_search = tuple(model_folder.glob("*left_hand_side*.pth"))
    if len(left_model_search) == 0:
        raise ValueError("No model for the left hand side found.")
    if len(left_model_search) > 1:
        raise ValueError("Too many models for the left hand side found.")

    right_model_search = tuple(model_folder.glob("*right_hand_side*.pth"))
    if len(right_model_search) == 0:
        raise ValueError("No model for the right hand side found.")
    elif len(right_model_search) > 1:
        raise ValueError("Too many models for the right hand side found.")

    left_model = left_model_search[0]
    right_model = right_model_search[0]

    return t.DetectionModels(left=left_model, right=right_model)