import textwrap
import warnings

from pandas._libs import NaT, lib

import pandas.core.common as com
from pandas.core.indexes.base import (
    Index,
    _new_Index,
    ensure_index,
    ensure_index_from_sequences,
)
from pandas.core.indexes.base import InvalidIndexError  # noqa:F401
from pandas.core.indexes.category import CategoricalIndex  # noqa:F401
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.indexes.interval import IntervalIndex  # noqa:F401
from pandas.core.indexes.multi import MultiIndex  # noqa:F401
from pandas.core.indexes.numeric import (  # noqa:F401
    Float64Index,
    Int64Index,
    NumericIndex,
    UInt64Index,
)
from pandas.core.indexes.period import PeriodIndex
from pandas.core.indexes.range import RangeIndex  # noqa:F401
from pandas.core.indexes.timedeltas import TimedeltaIndex

_sort_msg = textwrap.dedent(
    """\
Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.
"""
)


# TODO: there are many places that rely on these private methods existing in
# pandas.core.index
__all__ = [
    "Index",
    "MultiIndex",
    "NumericIndex",
    "Float64Index",
    "Int64Index",
    "CategoricalIndex",
    "IntervalIndex",
    "RangeIndex",
    "UInt64Index",
    "InvalidIndexError",
    "TimedeltaIndex",
    "PeriodIndex",
    "DatetimeIndex",
    "_new_Index",
    "NaT",
    "ensure_index",
    "ensure_index_from_sequences",
    "_get_combined_index",
    "_get_objs_combined_axis",
    "_union_indexes",
    "_get_consensus_names",
    "_all_indexes_same",
]


def _get_objs_combined_axis(objs, intersect=False, axis=0, sort=True):
    """
    Extract combined index: return intersection or union (depending on the
    value of "intersect") of indexes on given axis, or None if all objects
    lack indexes (e.g. they are numpy arrays).

    Parameters
    ----------
    objs : list of objects
        Each object will only be considered if it has a _get_axis
        attribute.
    intersect : bool, default False
        If True, calculate the intersection between indexes. Otherwise,
        calculate the union.
    axis : {0 or 'index', 1 or 'outer'}, default 0
        The axis to extract indexes from.
    sort : bool, default True
        Whether the result index should come out sorted or not.

    Returns
    -------
    Index
    """
    obs_idxes = [obj._get_axis(axis) for obj in objs if hasattr(obj, "_get_axis")]
    if obs_idxes:
        return _get_combined_index(obs_idxes, intersect=intersect, sort=sort)


def _get_distinct_objs(objs):
    """
    Return a list with distinct elements of "objs" (different ids).
    Preserves order.
    """
    ids = set()
    res = []
    for obj in objs:
        if not id(obj) in ids:
            ids.add(id(obj))
            res.append(obj)
    return res


def _get_combined_index(indexes, intersect=False, sort=False):
    """
    Return the union or intersection of indexes.

    Parameters
    ----------
    indexes : list of Index or list objects
        When intersect=True, do not accept list of lists.
    intersect : bool, default False
        If True, calculate the intersection between indexes. Otherwise,
        calculate the union.
    sort : bool, default False
        Whether the result index should come out sorted or not.

    Returns
    -------
    Index
    """

    # TODO: handle index names!
    indexes = _get_distinct_objs(indexes)
    if len(indexes) == 0:
        index = Index([])
    elif len(indexes) == 1:
        index = indexes[0]
    elif intersect:
        index = indexes[0]
        for other in indexes[1:]:
            index = index.intersection(other)
    else:
        index = _union_indexes(indexes, sort=sort)
        index = ensure_index(index)

    if sort:
        try:
            index = index.sort_values()
        except TypeError:
            pass
    return index


def _union_indexes(indexes, sort=True):
    """
    Return the union of indexes.

    The behavior of sort and names is not consistent.

    Parameters
    ----------
    indexes : list of Index or list objects
    sort : bool, default True
        Whether the result index should come out sorted or not.

    Returns
    -------
    Index
    """
    if len(indexes) == 0:
        raise AssertionError("Must have at least 1 Index to union")
    if len(indexes) == 1:
        result = indexes[0]
        if isinstance(result, list):
            result = Index(sorted(result))
        return result

    indexes, kind = _sanitize_and_check(indexes)

    def _unique_indices(inds):
        """
        Convert indexes to lists and concatenate them, removing duplicates.

        The final dtype is inferred.

        Parameters
        ----------
        inds : list of Index or list objects

        Returns
        -------
        Index
        """

        def conv(i):
            if isinstance(i, Index):
                i = i.tolist()
            return i

        return Index(lib.fast_unique_multiple_list([conv(i) for i in inds], sort=sort))

    if kind == "special":
        result = indexes[0]

        if hasattr(result, "union_many"):
            return result.union_many(indexes[1:])
        else:
            for other in indexes[1:]:
                result = result.union(other)
            return result
    elif kind == "array":
        index = indexes[0]
        for other in indexes[1:]:
            if not index.equals(other):

                if sort is None:
                    # TODO: remove once pd.concat sort default changes
                    warnings.warn(_sort_msg, FutureWarning, stacklevel=8)
                    sort = True

                return _unique_indices(indexes)

        name = _get_consensus_names(indexes)[0]
        if name != index.name:
            index = index._shallow_copy(name=name)
        return index
    else:  # kind='list'
        return _unique_indices(indexes)


def _sanitize_and_check(indexes):
    """
    Verify the type of indexes and convert lists to Index.

    Cases:

    - [list, list, ...]: Return ([list, list, ...], 'list')
    - [list, Index, ...]: Return _sanitize_and_check([Index, Index, ...])
        Lists are sorted and converted to Index.
    - [Index, Index, ...]: Return ([Index, Index, ...], TYPE)
        TYPE = 'special' if at least one special type, 'array' otherwise.

    Parameters
    ----------
    indexes : list of Index or list objects

    Returns
    -------
    sanitized_indexes : list of Index or list objects
    type : {'list', 'array', 'special'}
    """
    kinds = list({type(index) for index in indexes})

    if list in kinds:
        if len(kinds) > 1:
            indexes = [
                Index(com.try_sort(x)) if not isinstance(x, Index) else x
                for x in indexes
            ]
            kinds.remove(list)
        else:
            return indexes, "list"

    if len(kinds) > 1 or Index not in kinds:
        return indexes, "special"
    else:
        return indexes, "array"


def _get_consensus_names(indexes):
    """
    Give a consensus 'names' to indexes.

    If there's exactly one non-empty 'names', return this,
    otherwise, return empty.

    Parameters
    ----------
    indexes : list of Index objects

    Returns
    -------
    list
        A list representing the consensus 'names' found.
    """

    # find the non-none names, need to tupleify to make
    # the set hashable, then reverse on return
    consensus_names = {tuple(i.names) for i in indexes if com._any_not_none(*i.names)}
    if len(consensus_names) == 1:
        return list(list(consensus_names)[0])
    return [None] * indexes[0].nlevels


def _all_indexes_same(indexes):
    """
    Determine if all indexes contain the same elements.

    Parameters
    ----------
    indexes : list of Index objects

    Returns
    -------
    bool
        True if all indexes contain the same elements, False otherwise.
    """
    first = indexes[0]
    for index in indexes[1:]:
        if not first.equals(index):
            return False
    return True