from __future__ import annotations

import warnings
from contextlib import contextmanager
from typing import TYPE_CHECKING, Any, Hashable, Iterator, List, Mapping, Sequence

import numpy as np
import pandas as pd

from . import formatting
from .indexes import Index, Indexes, PandasMultiIndex, assert_no_index_corrupted
from .merge import merge_coordinates_without_align, merge_coords
from .utils import Frozen, ReprObject
from .variable import Variable, calculate_dimensions

if TYPE_CHECKING:
    from .common import DataWithCoords
    from .dataarray import DataArray
    from .dataset import Dataset
    from .types import T_DataArray

# Used as the key corresponding to a DataArray's variable when converting
# arbitrary DataArray objects to datasets
_THIS_ARRAY = ReprObject("<this-array>")

# TODO: Remove when min python version >= 3.9:
GenericAlias = type(List[int])


class Coordinates(Mapping[Hashable, "T_DataArray"]):
    _data: DataWithCoords
    __slots__ = ("_data",)

    # TODO: Remove when min python version >= 3.9:
    __class_getitem__ = classmethod(GenericAlias)

    def __getitem__(self, key: Hashable) -> T_DataArray:
        raise NotImplementedError()

    def __setitem__(self, key: Hashable, value: Any) -> None:
        self.update({key: value})

    @property
    def _names(self) -> set[Hashable]:
        raise NotImplementedError()

    @property
    def dims(self) -> Mapping[Hashable, int] | tuple[Hashable, ...]:
        raise NotImplementedError()

    @property
    def dtypes(self) -> Frozen[Hashable, np.dtype]:
        raise NotImplementedError()

    @property
    def indexes(self) -> Indexes[pd.Index]:
        return self._data.indexes

    @property
    def xindexes(self) -> Indexes[Index]:
        return self._data.xindexes

    @property
    def variables(self):
        raise NotImplementedError()

    def _update_coords(self, coords, indexes):
        raise NotImplementedError()

    def _maybe_drop_multiindex_coords(self, coords):
        raise NotImplementedError()

    def __iter__(self) -> Iterator[Hashable]:
        # needs to be in the same order as the dataset variables
        for k in self.variables:
            if k in self._names:
                yield k

    def __len__(self) -> int:
        return len(self._names)

    def __contains__(self, key: Hashable) -> bool:
        return key in self._names

    def __repr__(self) -> str:
        return formatting.coords_repr(self)

    def to_dataset(self) -> Dataset:
        raise NotImplementedError()

    def to_index(self, ordered_dims: Sequence[Hashable] | None = None) -> pd.Index:
        """Convert all index coordinates into a :py:class:`pandas.Index`.

        Parameters
        ----------
        ordered_dims : sequence of hashable, optional
            Possibly reordered version of this object's dimensions indicating
            the order in which dimensions should appear on the result.

        Returns
        -------
        pandas.Index
            Index subclass corresponding to the outer-product of all dimension
            coordinates. This will be a MultiIndex if this object is has more
            than more dimension.
        """
        if ordered_dims is None:
            ordered_dims = list(self.dims)
        elif set(ordered_dims) != set(self.dims):
            raise ValueError(
                "ordered_dims must match dims, but does not: "
                "{} vs {}".format(ordered_dims, self.dims)
            )

        if len(ordered_dims) == 0:
            raise ValueError("no valid index for a 0-dimensional object")
        elif len(ordered_dims) == 1:
            (dim,) = ordered_dims
            return self._data.get_index(dim)
        else:
            indexes = [self._data.get_index(k) for k in ordered_dims]

            # compute the sizes of the repeat and tile for the cartesian product
            # (taken from pandas.core.reshape.util)
            index_lengths = np.fromiter(
                (len(index) for index in indexes), dtype=np.intp
            )
            cumprod_lengths = np.cumproduct(index_lengths)

            if cumprod_lengths[-1] == 0:
                # if any factor is empty, the cartesian product is empty
                repeat_counts = np.zeros_like(cumprod_lengths)

            else:
                # sizes of the repeats
                repeat_counts = cumprod_lengths[-1] / cumprod_lengths
            # sizes of the tiles
            tile_counts = np.roll(cumprod_lengths, 1)
            tile_counts[0] = 1

            # loop over the indexes
            # for each MultiIndex or Index compute the cartesian product of the codes

            code_list = []
            level_list = []
            names = []

            for i, index in enumerate(indexes):
                if isinstance(index, pd.MultiIndex):
                    codes, levels = index.codes, index.levels
                else:
                    code, level = pd.factorize(index)
                    codes = [code]
                    levels = [level]

                # compute the cartesian product
                code_list += [
                    np.tile(np.repeat(code, repeat_counts[i]), tile_counts[i])
                    for code in codes
                ]
                level_list += levels
                names += index.names

        return pd.MultiIndex(level_list, code_list, names=names)

    def update(self, other: Mapping[Any, Any]) -> None:
        other_vars = getattr(other, "variables", other)
        self._maybe_drop_multiindex_coords(set(other_vars))
        coords, indexes = merge_coords(
            [self.variables, other_vars], priority_arg=1, indexes=self.xindexes
        )
        self._update_coords(coords, indexes)

    def _merge_raw(self, other, reflexive):
        """For use with binary arithmetic."""
        if other is None:
            variables = dict(self.variables)
            indexes = dict(self.xindexes)
        else:
            coord_list = [self, other] if not reflexive else [other, self]
            variables, indexes = merge_coordinates_without_align(coord_list)
        return variables, indexes

    @contextmanager
    def _merge_inplace(self, other):
        """For use with in-place binary arithmetic."""
        if other is None:
            yield
        else:
            # don't include indexes in prioritized, because we didn't align
            # first and we want indexes to be checked
            prioritized = {
                k: (v, None)
                for k, v in self.variables.items()
                if k not in self.xindexes
            }
            variables, indexes = merge_coordinates_without_align(
                [self, other], prioritized
            )
            yield
            self._update_coords(variables, indexes)

    def merge(self, other: Coordinates | None) -> Dataset:
        """Merge two sets of coordinates to create a new Dataset

        The method implements the logic used for joining coordinates in the
        result of a binary operation performed on xarray objects:

        - If two index coordinates conflict (are not equal), an exception is
          raised. You must align your data before passing it to this method.
        - If an index coordinate and a non-index coordinate conflict, the non-
          index coordinate is dropped.
        - If two non-index coordinates conflict, both are dropped.

        Parameters
        ----------
        other : DatasetCoordinates or DataArrayCoordinates
            The coordinates from another dataset or data array.

        Returns
        -------
        merged : Dataset
            A new Dataset with merged coordinates.
        """
        from .dataset import Dataset

        if other is None:
            return self.to_dataset()

        if not isinstance(other, Coordinates):
            other = Dataset(coords=other).coords

        coords, indexes = merge_coordinates_without_align([self, other])
        coord_names = set(coords)
        return Dataset._construct_direct(
            variables=coords, coord_names=coord_names, indexes=indexes
        )


class DatasetCoordinates(Coordinates):
    """Dictionary like container for Dataset coordinates.

    Essentially an immutable dictionary with keys given by the array's
    dimensions and the values given by the corresponding xarray.Coordinate
    objects.
    """

    _data: Dataset

    __slots__ = ("_data",)

    def __init__(self, dataset: Dataset):
        self._data = dataset

    @property
    def _names(self) -> set[Hashable]:
        return self._data._coord_names

    @property
    def dims(self) -> Mapping[Hashable, int]:
        return self._data.dims

    @property
    def dtypes(self) -> Frozen[Hashable, np.dtype]:
        """Mapping from coordinate names to dtypes.

        Cannot be modified directly, but is updated when adding new variables.

        See Also
        --------
        Dataset.dtypes
        """
        return Frozen(
            {
                n: v.dtype
                for n, v in self._data._variables.items()
                if n in self._data._coord_names
            }
        )

    @property
    def variables(self) -> Mapping[Hashable, Variable]:
        return Frozen(
            {k: v for k, v in self._data.variables.items() if k in self._names}
        )

    def __getitem__(self, key: Hashable) -> DataArray:
        if key in self._data.data_vars:
            raise KeyError(key)
        return self._data[key]

    def to_dataset(self) -> Dataset:
        """Convert these coordinates into a new Dataset"""

        names = [name for name in self._data._variables if name in self._names]
        return self._data._copy_listed(names)

    def _update_coords(
        self, coords: dict[Hashable, Variable], indexes: Mapping[Any, Index]
    ) -> None:
        variables = self._data._variables.copy()
        variables.update(coords)

        # check for inconsistent state *before* modifying anything in-place
        dims = calculate_dimensions(variables)
        new_coord_names = set(coords)
        for dim, size in dims.items():
            if dim in variables:
                new_coord_names.add(dim)

        self._data._variables = variables
        self._data._coord_names.update(new_coord_names)
        self._data._dims = dims

        # TODO(shoyer): once ._indexes is always populated by a dict, modify
        # it to update inplace instead.
        original_indexes = dict(self._data.xindexes)
        original_indexes.update(indexes)
        self._data._indexes = original_indexes

    def _maybe_drop_multiindex_coords(self, coords: set[Hashable]) -> None:
        """Drops variables in coords, and any associated variables as well."""
        assert self._data.xindexes is not None
        variables, indexes = drop_coords(
            coords, self._data._variables, self._data.xindexes
        )
        self._data._coord_names.intersection_update(variables)
        self._data._variables = variables
        self._data._indexes = indexes

    def __delitem__(self, key: Hashable) -> None:
        if key in self:
            del self._data[key]
        else:
            raise KeyError(f"{key!r} is not a coordinate variable.")

    def _ipython_key_completions_(self):
        """Provide method for the key-autocompletions in IPython."""
        return [
            key
            for key in self._data._ipython_key_completions_()
            if key not in self._data.data_vars
        ]


class DataArrayCoordinates(Coordinates["T_DataArray"]):
    """Dictionary like container for DataArray coordinates.

    Essentially a dict with keys given by the array's
    dimensions and the values given by corresponding DataArray objects.
    """

    _data: T_DataArray

    __slots__ = ("_data",)

    def __init__(self, dataarray: T_DataArray) -> None:
        self._data = dataarray

    @property
    def dims(self) -> tuple[Hashable, ...]:
        return self._data.dims

    @property
    def dtypes(self) -> Frozen[Hashable, np.dtype]:
        """Mapping from coordinate names to dtypes.

        Cannot be modified directly, but is updated when adding new variables.

        See Also
        --------
        DataArray.dtype
        """
        return Frozen({n: v.dtype for n, v in self._data._coords.items()})

    @property
    def _names(self) -> set[Hashable]:
        return set(self._data._coords)

    def __getitem__(self, key: Hashable) -> T_DataArray:
        return self._data._getitem_coord(key)

    def _update_coords(
        self, coords: dict[Hashable, Variable], indexes: Mapping[Any, Index]
    ) -> None:
        coords_plus_data = coords.copy()
        coords_plus_data[_THIS_ARRAY] = self._data.variable
        dims = calculate_dimensions(coords_plus_data)
        if not set(dims) <= set(self.dims):
            raise ValueError(
                "cannot add coordinates with new dimensions to a DataArray"
            )
        self._data._coords = coords

        # TODO(shoyer): once ._indexes is always populated by a dict, modify
        # it to update inplace instead.
        original_indexes = dict(self._data.xindexes)
        original_indexes.update(indexes)
        self._data._indexes = original_indexes

    def _maybe_drop_multiindex_coords(self, coords: set[Hashable]) -> None:
        """Drops variables in coords, and any associated variables as well."""
        variables, indexes = drop_coords(
            coords, self._data._coords, self._data.xindexes
        )
        self._data._coords = variables
        self._data._indexes = indexes

    @property
    def variables(self):
        return Frozen(self._data._coords)

    def to_dataset(self) -> Dataset:
        from .dataset import Dataset

        coords = {k: v.copy(deep=False) for k, v in self._data._coords.items()}
        indexes = dict(self._data.xindexes)
        return Dataset._construct_direct(coords, set(coords), indexes=indexes)

    def __delitem__(self, key: Hashable) -> None:
        if key not in self:
            raise KeyError(f"{key!r} is not a coordinate variable.")
        assert_no_index_corrupted(self._data.xindexes, {key})

        del self._data._coords[key]
        if self._data._indexes is not None and key in self._data._indexes:
            del self._data._indexes[key]

    def _ipython_key_completions_(self):
        """Provide method for the key-autocompletions in IPython."""
        return self._data._ipython_key_completions_()


def drop_coords(
    coords_to_drop: set[Hashable], variables, indexes: Indexes
) -> tuple[dict, dict]:
    """Drop index variables associated with variables in coords_to_drop."""
    # Only warn when we're dropping the dimension with the multi-indexed coordinate
    # If asked to drop a subset of the levels in a multi-index, we raise an error
    # later but skip the warning here.
    new_variables = dict(variables.copy())
    new_indexes = dict(indexes.copy())
    for key in coords_to_drop & set(indexes):
        maybe_midx = indexes[key]
        idx_coord_names = set(indexes.get_all_coords(key))
        if (
            isinstance(maybe_midx, PandasMultiIndex)
            and key == maybe_midx.dim
            and (idx_coord_names - coords_to_drop)
        ):
            warnings.warn(
                f"Updating MultiIndexed coordinate {key!r} would corrupt indices for "
                f"other variables: {list(maybe_midx.index.names)!r}. "
                f"This will raise an error in the future. Use `.drop_vars({idx_coord_names!r})` before "
                "assigning new coordinate values.",
                FutureWarning,
                stacklevel=4,
            )
            for k in idx_coord_names:
                del new_variables[k]
                del new_indexes[k]
    return new_variables, new_indexes


def assert_coordinate_consistent(
    obj: T_DataArray | Dataset, coords: Mapping[Any, Variable]
) -> None:
    """Make sure the dimension coordinate of obj is consistent with coords.

    obj: DataArray or Dataset
    coords: Dict-like of variables
    """
    for k in obj.dims:
        # make sure there are no conflict in dimension coordinates
        if k in coords and k in obj.coords and not coords[k].equals(obj[k].variable):
            raise IndexError(
                f"dimension coordinate {k!r} conflicts between "
                f"indexed and indexing objects:\n{obj[k]}\nvs.\n{coords[k]}"
            )
