from __future__ import annotations

import copy
import itertools
from collections.abc import Iterable
from typing import Any, Optional, Tuple, Type

import numpy as np
import pandas as pd
from pandas.core.internals import ArrayManager, BlockManager

ITEMID = "item_id"
TIMESTAMP = "timestamp"

IRREGULAR_TIME_INDEX_FREQSTR = "IRREG"


class TimeSeriesDataFrame(pd.DataFrame):
    """``TimeSeriesDataFrame`` s represent a collection of time series, where each row
    identifies the values of an (``item_id``, ``timestamp``) pair.

    For example, a time series data frame could represent the daily sales of a collection
    of products, where each ``item_id`` identifies a product and ``timestamp`` s correspond to
    the days.

    Parameters
    ----------
    data: Any
        Time-series data to construct a ``TimeSeriesDataFrame``. The class currently supports three
        input formats.

        1. Time-series data in Iterable format. For example::

                iterable_dataset = [
                    {"target": [0, 1, 2], "start": pd.Timestamp("01-01-2019", freq='D')},
                    {"target": [3, 4, 5], "start": pd.Timestamp("01-01-2019", freq='D')},
                    {"target": [6, 7, 8], "start": pd.Timestamp("01-01-2019", freq='D')}
                ]

        2. Time-series data in a pandas DataFrame format without multi-index. For example::

                   item_id  timestamp  target
                0        0 2019-01-01       0
                1        0 2019-01-02       1
                2        0 2019-01-03       2
                3        1 2019-01-01       3
                4        1 2019-01-02       4
                5        1 2019-01-03       5
                6        2 2019-01-01       6
                7        2 2019-01-02       7
                8        2 2019-01-03       8

        3. Time-series data in pandas DataFrame format with multi-index on item_id and timestamp. For example::

                                        target
                item_id timestamp
                0       2019-01-01       0
                        2019-01-02       1
                        2019-01-03       2
                1       2019-01-01       3
                        2019-01-02       4
                        2019-01-03       5
                2       2019-01-01       6
                        2019-01-02       7
                        2019-01-03       8

    static_features: Optional[pd.DataFrame]
        An optional data frame describing the metadata attributes of individual items in the item index. These
        may be categorical or real valued attributes for each item. For example, if the item index refers to
        time series data of individual households, static features may refer to time-independent demographic
        features. When provided during ``fit``, the ``TimeSeriesPredictor`` expects the same metadata to be available
        during prediction time. When provided, the index of the ``static_features`` index must match the item index
        of the ``TimeSeriesDataFrame``.

        ``TimeSeriesDataFrame`` will ensure consistency of static features during serialization/deserialization,
        copy and slice operations although these features should be considered experimental.

    Attributes
    ----------
    freq: str
        A pandas and gluon-ts compatible string describing the frequency of the time series. For example
        "D" is daily data, etc. Also see,
        https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
    num_items: int
        Number of items (time series) in the data set.
    """

    DUMMY_INDEX_START_TIME = pd.Timestamp("1900-01-01 00:00:00")
    index: pd.MultiIndex
    _metadata = ["_static_features", "_cached_freq"]

    def __init__(self, data: Any, static_features: Optional[pd.DataFrame] = None, *args, **kwargs):
        if isinstance(data, (BlockManager, ArrayManager)):
            # necessary for copy constructor to work. see _constructor
            # and pandas.DataFrame
            pass
        elif isinstance(data, pd.DataFrame):
            if isinstance(data.index, pd.MultiIndex):
                self._validate_multi_index_data_frame(data)
            else:
                data = self._construct_pandas_frame_from_data_frame(data)
        elif isinstance(data, Iterable):
            data = self._construct_pandas_frame_from_iterable_dataset(data)
        else:
            raise ValueError("Data input type not recognized, must be DataFrame or iterable.")
        super().__init__(data=data, *args, **kwargs)
        self._static_features: Optional[pd.DataFrame] = None
        if static_features is not None:
            self.static_features = static_features

        # internal value for cached frequency values that are inferred. corresponds to either a
        # pandas-compatible frequency string, the value IRREGULAR_TIME_INDEX_FREQSTR that signals
        # the time series have irregular timestamps (in which case tsdf.freq returns None), or None
        # if inference was not yet performed.
        self._cached_freq: Optional[str] = None

    @property
    def _constructor(self) -> Type[TimeSeriesDataFrame]:
        return TimeSeriesDataFrame

    @property
    def _item_index(self) -> pd.Index:
        return self.index.unique(level=ITEMID)

    @property
    def static_features(self):
        return self._static_features

    @static_features.setter
    def static_features(self, value: Optional[pd.DataFrame]):
        # if the current item index is not a multiindex, then we are dealing with a single
        # item slice. this should only happen when the user explicitly requests only a
        # single item or during `slice_by_timestep`. In this case we do not set static features
        if not isinstance(self.index, pd.MultiIndex):
            return

        if value is not None and not set(value.index).issuperset(set(self._item_index)):
            raise ValueError("Static features index should match item index")

        # if static features being set are a strict superset of the item index, we take a
        # subset to ensure consistency
        if value is not None and len(set(value.index) - set(self._item_index)) > 0:
            value = value.loc[self._item_index].copy()

        self._static_features = value

    @property
    def freq(self):
        if self._cached_freq is not None and self._cached_freq == IRREGULAR_TIME_INDEX_FREQSTR:
            return None  # irregularly sampled time series
        elif self._cached_freq:
            return self._cached_freq

        def get_freq(series):
            return series.index.freq or series.index.inferred_freq

        # check the frequencies of the first 100 items to see if frequencies are consistent and
        # can be inferred
        freq_for_each_series = [get_freq(self.loc[idx]) for idx in self._item_index[:100]]
        freq = freq_for_each_series[0]
        if len(set(freq_for_each_series)) > 1 or freq is None:
            self._cached_freq = IRREGULAR_TIME_INDEX_FREQSTR
            return None

        freq = freq.freqstr if isinstance(freq, pd._libs.tslibs.BaseOffset) else freq
        self._cached_freq = freq
        return freq

    def iter_items(self) -> Iterable[Any]:
        return iter(self._item_index)

    @property
    def num_items(self):
        return len(self._item_index)

    def num_timesteps_per_item(self) -> pd.Series:
        return self.groupby(level=ITEMID).size()

    @classmethod
    def _validate_iterable(cls, data: Iterable):
        if not isinstance(data, Iterable):
            raise ValueError("data must be of type Iterable.")

        first = next(iter(data), None)
        if first is None:
            raise ValueError("data has no time-series.")

        for i, ts in enumerate(itertools.chain([first], data)):
            if not isinstance(ts, dict):
                raise ValueError(f"{i}'th time-series in data must be a dict, got{type(ts)}")
            if not ("target" in ts and "start" in ts):
                raise ValueError(f"{i}'th time-series in data must have 'target' and 'start', got{ts.keys()}")
            if not isinstance(ts["start"], pd.Timestamp) or ts["start"].freq is None:
                raise ValueError(
                    f"{i}'th time-series must have timestamp as 'start' with freq specified, got {ts['start']}"
                )

    @classmethod
    def _validate_data_frame(cls, df: pd.DataFrame):
        if not isinstance(df, pd.DataFrame):
            raise ValueError(f"data must be a pd.DataFrame, got {type(df)}")
        if ITEMID not in df.columns:
            raise ValueError(f"data must have a `{ITEMID}` column")
        if TIMESTAMP not in df.columns:
            raise ValueError(f"data must have a `{TIMESTAMP}` column")
        if df[ITEMID].isnull().any():
            raise ValueError(f"`{ITEMID}` column can not have nan")
        if df[TIMESTAMP].isnull().any():
            raise ValueError(f"`{TIMESTAMP}` column can not have nan")
        if not df[TIMESTAMP].dtype == "datetime64[ns]":
            raise ValueError(f"for {TIMESTAMP}, the only pandas dtype allowed is `datetime64[ns]`.")
        item_id_column = df[ITEMID]
        # workaround for pd.api.types.is_string_dtype issue https://github.com/pandas-dev/pandas/issues/15585
        item_id_is_string = (item_id_column == item_id_column.astype(str)).all()
        item_id_is_int = pd.api.types.is_integer_dtype(item_id_column)
        if not (item_id_is_string or item_id_is_int):
            raise ValueError(f"all entries in column `{ITEMID}` must be of integer or string dtype")

    @classmethod
    def _validate_multi_index_data_frame(cls, data: pd.DataFrame):
        """Validate a multi-index pd.DataFrame can be converted to TimeSeriesDataFrame

        Parameters
        ----------
        data: pd.DataFrame
            a data frame in pd.DataFrame format.
        """

        if not isinstance(data, pd.DataFrame):
            raise ValueError(f"data must be a pd.DataFrame, got {type(data)}")
        if not isinstance(data.index, pd.MultiIndex):
            raise ValueError(f"data must have pd.MultiIndex, got {type(data.index)}")
        if not data.index.dtypes.array[1] == "datetime64[ns]":
            raise ValueError(f"for {TIMESTAMP}, the only pandas dtype allowed is `datetime64[ns]`.")
        if not data.index.names == (f"{ITEMID}", f"{TIMESTAMP}"):
            raise ValueError(f"data must have index names as ('{ITEMID}', '{TIMESTAMP}'), got {data.index.names}")
        item_id_index = data.index.get_level_values(level=ITEMID)
        # workaround for pd.api.types.is_string_dtype issue https://github.com/pandas-dev/pandas/issues/15585
        item_id_is_string = (item_id_index == item_id_index.astype(str)).all()
        item_id_is_int = pd.api.types.is_integer_dtype(item_id_index)
        if not (item_id_is_string or item_id_is_int):
            raise ValueError(f"all entries in index `{ITEMID}` must be of integer or string dtype")

    @classmethod
    def _construct_pandas_frame_from_iterable_dataset(cls, iterable_dataset: Iterable) -> pd.DataFrame:
        cls._validate_iterable(iterable_dataset)

        all_ts = []
        for i, ts in enumerate(iterable_dataset):
            start_timestamp = ts["start"]
            target = ts["target"]
            datetime_index = tuple(pd.date_range(start_timestamp, periods=len(target), freq=start_timestamp.freq))
            idx = pd.MultiIndex.from_product([(i,), datetime_index], names=[ITEMID, TIMESTAMP])
            ts_df = pd.Series(target, name="target", index=idx).to_frame()
            all_ts.append(ts_df)
        return pd.concat(all_ts)

    @classmethod
    def from_iterable_dataset(cls, iterable_dataset: Iterable) -> pd.DataFrame:
        """Construct a ``TimeSeriesDataFrame`` from an Iterable of dictionaries each of which
        represent a single time series.

        This function also offers compatibility with GluonTS data sets, see
        https://ts.gluon.ai/_modules/gluonts/dataset/common.html#ListDataset.

        Parameters
        ----------
        iterable_dataset: Iterable
            An iterator over dictionaries, each with a ``target`` field specifying the value of the
            (univariate) time series, and a ``start`` field that features a pandas Timestamp with features.
            Example::

                iterable_dataset = [
                    {"target": [0, 1, 2], "start": pd.Timestamp("01-01-2019", freq='D')},
                    {"target": [3, 4, 5], "start": pd.Timestamp("01-01-2019", freq='D')},
                    {"target": [6, 7, 8], "start": pd.Timestamp("01-01-2019", freq='D')}
                ]

        Returns
        -------
        ts_df: TimeSeriesDataFrame
            A data frame in TimeSeriesDataFrame format.
        """
        return cls(cls._construct_pandas_frame_from_iterable_dataset(iterable_dataset))

    @classmethod
    def _construct_pandas_frame_from_data_frame(
        cls,
        df: pd.DataFrame,
        id_column: Optional[str] = None,
        timestamp_column: Optional[str] = None,
    ) -> pd.DataFrame:

        df = df.copy()
        if id_column is not None:
            assert id_column in df.columns, f"Column {id_column} not found!"
            df.rename(columns={id_column: ITEMID}, inplace=True)

        if timestamp_column is not None:
            assert timestamp_column in df.columns, f"Column {timestamp_column} not found!"
            df.rename(columns={timestamp_column: TIMESTAMP}, inplace=True)

        cls._validate_data_frame(df)
        return df.set_index([ITEMID, TIMESTAMP])

    @classmethod
    def from_data_frame(
        cls,
        df: pd.DataFrame,
        id_column: Optional[str] = None,
        timestamp_column: Optional[str] = None,
    ) -> TimeSeriesDataFrame:
        """Construct a ``TimeSeriesDataFrame`` from a pandas DataFrame.

        Parameters
        ----------
        df: pd.DataFrame
            A pd.DataFrame with 'item_id' and 'timestamp' as columns. For example:

            .. code-block::

                   item_id  timestamp  target
                0        0 2019-01-01       0
                1        0 2019-01-02       1
                2        0 2019-01-03       2
                3        1 2019-01-01       3
                4        1 2019-01-02       4
                5        1 2019-01-03       5
                6        2 2019-01-01       6
                7        2 2019-01-02       7
                8        2 2019-01-03       8
        id_column: str
            Name of the 'item_id' column if column name is different
        timestamp_column: str
            Name of the 'timestamp' column if column name is different

        Returns
        -------
        ts_df: TimeSeriesDataFrame
            A data frame in TimeSeriesDataFrame format.
        """
        return cls(
            cls._construct_pandas_frame_from_data_frame(df, id_column=id_column, timestamp_column=timestamp_column)
        )

    def copy(self: TimeSeriesDataFrame, deep: bool = True) -> pd.DataFrame:  # noqa
        obj = super().copy(deep=deep)

        # also perform a deep copy for static features
        if deep:
            for k in obj._metadata:
                setattr(obj, k, copy.deepcopy(getattr(obj, k)))
        return obj

    def __finalize__(  # noqa
        self: TimeSeriesDataFrame, other, method: Optional[str] = None, **kwargs
    ) -> TimeSeriesDataFrame:
        super().__finalize__(other=other, method=method, **kwargs)
        # when finalizing the copy/slice operation, we use the property setter to stay consistent
        # with the item index
        if hasattr(other, "_static_features"):
            self.static_features = other._static_features
        if hasattr(other, "_cached_freq"):
            self._cached_freq = other._cached_freq
        return self

    def split_by_time(self, cutoff_time: pd.Timestamp) -> Tuple[TimeSeriesDataFrame, TimeSeriesDataFrame]:
        """Split dataframe to two different ``TimeSeriesDataFrame`` s before and after a certain
        ``cutoff_time``.

        Parameters
        ----------
        cutoff_time: pd.Timestamp
            The time to split the current data frame into two data frames.

        Returns
        -------
        data_before: TimeSeriesDataFrame
            Data frame containing time series before the ``cutoff_time`` (exclude ``cutoff_time``).
        data_after: TimeSeriesDataFrame
            Data frame containing time series after the ``cutoff_time`` (include ``cutoff_time``).
        """

        nanosecond_before_cutoff = cutoff_time - pd.Timedelta(nanoseconds=1)
        data_before = self.loc[(slice(None), slice(None, nanosecond_before_cutoff)), :]
        data_after = self.loc[(slice(None), slice(cutoff_time, None)), :]
        before, after = TimeSeriesDataFrame(data_before, static_features=self.static_features), TimeSeriesDataFrame(
            data_after, static_features=self.static_features
        )
        before._cached_freq = self._cached_freq
        after._cached_freq = self._cached_freq
        return before, after

    def slice_by_timestep(self, time_step_slice: slice) -> TimeSeriesDataFrame:
        """Return a slice of time steps (with no regards to the actual timestamp) from within
        each item in a time series data frame. For example, if a data frame is constructed as::

            item_id  timestamp  target
                  0 2019-01-01       0
                  0 2019-01-02       1
                  0 2019-01-03       2
                  1 2019-01-02       3
                  1 2019-01-03       4
                  1 2019-01-04       5
                  2 2019-01-03       6
                  2 2019-01-04       7
                  2 2019-01-05       8

        then :code:`df.slice_by_timestep(time_step_slice=slice(-2, None))` would return the last two
        time steps from each item::

            item_id  timestamp  target
                  0 2019-01-02       1
                  0 2019-01-03       2
                  1 2019-01-03       4
                  1 2019-01-04       5
                  2 2019-01-04       7
                  2 2019-01-05       8

        Note that this function returns a copy of the original data. This function is useful for
        constructing holdout sets for validation.

        Parameters
        ----------
        time_step_slice: slice
            A python slice object representing the slices to return from each item

        Returns
        -------
        ts_df: TimeSeriesDataFrame
            Data frame containing only the time steps of each ``item_id`` sliced according to the
            input ``time_step_slice``.
        """
        if time_step_slice.step is not None and time_step_slice != 1:
            raise ValueError("Upsampling via slicing with step sizes is not supported with `slice_by_timestep`.")

        num_timesteps_per_item = self.num_timesteps_per_item()
        # Create a boolean index that selects the correct slice in each timeseries
        boolean_indicators = []
        for length in num_timesteps_per_item:
            indicator = np.zeros(length, dtype=bool)
            indicator[time_step_slice] = True
            boolean_indicators.append(indicator)
        index = np.concatenate(boolean_indicators)
        slice_df = self.__class__(self[index].copy(), static_features=self.static_features)
        slice_df._cached_freq = self._cached_freq
        return slice_df

    def subsequence(self, start: pd.Timestamp, end: pd.Timestamp) -> TimeSeriesDataFrame:
        """Extract time-series between start (inclusive) and end (exclusive) time.

        Parameters
        ----------
        start: pd.Timestamp
            The start time (inclusive) of a time range that will be used for subsequence.
        end: pd.Timestamp
            The end time (exclusive) of a time range that will be used for subsequence.

        Returns
        -------
        ts_df: TimeSeriesDataFrame
            A new data frame in ``TimeSeriesDataFrame`` format contains time-series in a time range
            defined between start and end time.
        """

        if end < start:
            raise ValueError(f"end time {end} is earlier than stat time {start}")

        nanosecond_before_end = end - pd.Timedelta(nanoseconds=1)
        return TimeSeriesDataFrame(
            self.loc[(slice(None), slice(start, nanosecond_before_end)), :],
            static_features=self.static_features,
        )

    @classmethod
    def from_pickle(cls, filepath_or_buffer: Any) -> "TimeSeriesDataFrame":
        """Convenience method to read pickled time series data frames. If the read pickle
        file refers to a plain pandas DataFrame, it will be cast to a TimeSeriesDataFrame.

        Parameters
        ----------
        filepath_or_buffer: Any
            Filename provided as a string or an ``IOBuffer`` containing the pickled object.

        Returns
        -------
        ts_df: TimeSeriesDataFrame
            The pickled time series data frame.
        """
        try:
            data = pd.read_pickle(filepath_or_buffer)
            return data if isinstance(data, cls) else cls(data)
        except Exception as err:  # noqa
            raise IOError(f"Could not load pickled data set due to error: {str(err)}")

    def get_reindexed_view(self, freq: str = "S") -> TimeSeriesDataFrame:
        """Returns a new TimeSeriesDataFrame object with the same underlying data and
        static features as the current data frame, except the time index is replaced by
        a new "dummy" time series index with the given frequency. This is useful when
        suggesting AutoGluon-TimeSeries to "ignore" the time information, for example when
        dealing with irregularly sampled time series or sequences (e.g., financial time
        series).

        Parameters
        ----------
        freq: str
            Frequency string of the new time series data index.

        Returns
            TimeSeriesDataFrame: the new view object with replaced index, but the same underlying
            data. Note that the underlying data is not copied.
        """
        df_view = self.iloc[:]  # return a view without copying data

        # build the surrogate index
        indexes = []
        for i in self._item_index:
            idx = pd.MultiIndex.from_product(
                [(i,), pd.date_range(self.DUMMY_INDEX_START_TIME, periods=len(self.loc[i]), freq=freq)]
            )
            indexes.append(idx)

        new_index = pd.MultiIndex.from_tuples(np.concatenate(indexes), names=[ITEMID, TIMESTAMP])
        df_view.set_index(new_index, inplace=True)
        df_view._cached_freq = freq

        return df_view
