import cfig

config = cfig.Configuration()


@config.optional()
def MONGO_HOST(val: str | None) -> str:
    """
    The hostname of the MongoDB database to connect to.
    Defaults to `"127.0.0.1"`.
    """
    return val or "127.0.0.1"


@config.optional()
def MONGO_PORT(val: str | None) -> int:
    """
    The port of the MongoDB database to connect to.
    Defaults to `27017`.
    """
    if val is None:
        return 27017
    try:
        return int(val)
    except ValueError:
        raise cfig.InvalidValueError("Not an int.")


@config.optional()
def WORKING_SET_SIZE(val: str | None) -> int:
    """
    The number of reviews to consider from the database.
    Set this to a low number to prevent slowness due to the dataset's huge size.
    Defaults to `1000000`.
    """
    if val is None:
        return 1000000
    try:
        return int(val)
    except ValueError:
        raise cfig.InvalidValueError("Not an int.")


@config.optional()
def TRAINING_SET_SIZE(val: str | None) -> int:
    """
    The number of reviews from each category to fetch for the training dataset.
    Defaults to `4000`.
    """
    if val is None:
        return 4000
    try:
        return int(val)
    except ValueError:
        raise cfig.InvalidValueError("Not an int.")


@config.optional()
def VALIDATION_SET_SIZE(val: str | None) -> int:
    """
    The number of reviews from each category to fetch for the training dataset.
    Defaults to `400`.
    """
    if val is None:
        return 400
    try:
        return int(val)
    except ValueError:
        raise cfig.InvalidValueError("Not an int.")


@config.optional()
def EVALUATION_SET_SIZE(val: str | None) -> int:
    """
    The number of reviews from each category to fetch for the evaluation dataset.
    Defaults to `1000`.
    """
    if val is None:
        return 1000
    try:
        return int(val)
    except ValueError:
        raise cfig.InvalidValueError("Not an int.")


@config.optional()
def TENSORFLOW_MAX_FEATURES(val: str | None) -> int:
    """
    The maximum number of features to use in Tensorflow models.
    Defaults to `300000`.
    """
    if val is None:
        return 300000
    try:
        return int(val)
    except ValueError:
        raise cfig.InvalidValueError("Not an int.")


@config.optional()
def TENSORFLOW_EMBEDDING_SIZE(val: str | None) -> int:
    """
    The size of the embeddings tensor to use in Tensorflow models.
    Defaults to `12`.
    """
    if val is None:
        return 12
    try:
        return int(val)
    except ValueError:
        raise cfig.InvalidValueError("Not an int.")


@config.optional()
def TENSORFLOW_EPOCHS(val: str | None) -> int:
    """
    The number of epochs to train Tensorflow models for.
    Defaults to `3`.
    """
    if val is None:
        return 3
    try:
        return int(val)
    except ValueError:
        raise cfig.InvalidValueError("Not an int.")


@config.optional()
def TARGET_RUNS(val: str | None) -> int:
    """
    The amount of successful runs to perform on a sample-model-tokenizer combination.
    Defaults to `1`.
    """
    if val is None:
        return 1
    try:
        return int(val)
    except ValueError:
        raise cfig.InvalidValueError("Not an int.")


@config.optional()
def MAXIMUM_RUNS(val: str | None) -> int:
    """
    The maximum amount of runs to perform on a sample-model-tokenizer combination before skipping it.
    Defaults to `25`.
    """
    if val is None:
        return 25
    try:
        return int(val)
    except ValueError:
        raise cfig.InvalidValueError("Not an int.")



__all__ = (
    "config",
    "MONGO_HOST",
    "MONGO_PORT",
    "WORKING_SET_SIZE",
    "TRAINING_SET_SIZE",
    "VALIDATION_SET_SIZE",
    "EVALUATION_SET_SIZE",
    "TENSORFLOW_MAX_FEATURES",
    "TENSORFLOW_EMBEDDING_SIZE",
    "TENSORFLOW_EPOCHS",
)


if __name__ == "__main__":
    config.cli()