"""
Missing Values Filler
---------------------
"""
from typing import Union, Sequence, List
from my_code.github_repos.darts.darts.darts.timeseries import TimeSeries
from my_code.github_repos.darts.darts.darts.dataprocessing.transformers import BaseDataTransformer
from my_code.github_repos.darts.darts.darts.utils.missing_values import fill_missing_values
from my_code.github_repos.darts.darts.darts.logging import get_logger, raise_if, raise_if_not

logger = get_logger(__name__)


class MissingValuesFiller(BaseDataTransformer):
    def __init__(self,
                 fill: Union[str, float] = 'auto',
                 name: str = "MissingValuesFiller",
                 n_jobs: int = 1,
                 verbose: bool = False):
        """
        Data transformer to fill missing values from a (sequence of) TimeSeries

        Parameters
        ----------
        fill
            The value used to replace the missing values.
            If set to 'auto', will auto-fill missing values using the `pandas.Dataframe.interpolate()` method.
        name
            A specific name for the transformer
        n_jobs
            The number of jobs to run in parallel. Parallel jobs are created only when a `Sequence[TimeSeries]` is
            passed as input to a method, parallelising operations regarding different `TimeSeries`. Defaults to `1`
            (sequential). Setting the parameter to `-1` means using all the available processors.
            Note: for a small amount of data, the parallelisation overhead could end up increasing the total
            required amount of time.
        verbose
            Optionally, whether to print operations progress
        """
        raise_if_not(isinstance(fill, str) or isinstance(fill, float),
                     "`fill` should either be a string or a float",
                     logger)
        raise_if(isinstance(fill, str) and fill != 'auto',
                 "invalid string for `fill`: can only be set to 'auto'",
                 logger)

        super().__init__(name=name, n_jobs=n_jobs, verbose=verbose)
        self._fill = fill

    @staticmethod
    def ts_transform(series: TimeSeries, fill: Union[str, float], **kwargs) -> TimeSeries:
        return fill_missing_values(series, fill, **kwargs)

    def transform(self,
                  series: Union[TimeSeries, Sequence[TimeSeries]],
                  *args, **kwargs) -> Union[TimeSeries, List[TimeSeries]]:
        # adding the fill param
        return super().transform(series, self._fill, *args, **kwargs)
