"""
Concat routines.
"""

from __future__ import annotations

from collections import abc
from itertools import pairwise
import types
from typing import (
    TYPE_CHECKING,
    Literal,
    cast,
    overload,
)
import warnings

import numpy as np

from pandas._libs import lib
from pandas.errors import Pandas4Warning
from pandas.util._decorators import set_module
from pandas.util._exceptions import find_stack_level

from pandas.core.dtypes.common import (
    is_bool,
    is_scalar,
)
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.generic import (
    ABCDataFrame,
    ABCSeries,
)
from pandas.core.dtypes.missing import isna

from pandas.core.arrays.categorical import (
    factorize_from_iterable,
    factorize_from_iterables,
)
import pandas.core.common as com
from pandas.core.indexes.api import (
    Index,
    MultiIndex,
    all_indexes_same,
    default_index,
    ensure_index,
    get_objs_combined_axis,
    get_unanimous_names,
    union_indexes,
)
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.internals import concatenate_managers

if TYPE_CHECKING:
    from collections.abc import (
        Callable,
        Hashable,
        Iterable,
        Mapping,
    )

    from pandas._typing import (
        Axis,
        AxisInt,
        HashableT,
    )

    from pandas import (
        DataFrame,
        Series,
    )

# ---------------------------------------------------------------------
# Concatenate DataFrame objects


@overload
def concat(
    objs: Iterable[DataFrame] | Mapping[HashableT, DataFrame],
    *,
    axis: Literal[0, "index"] = ...,
    join: str = ...,
    ignore_index: bool = ...,
    keys: Iterable[Hashable] | None = ...,
    levels=...,
    names: list[HashableT] | None = ...,
    verify_integrity: bool = ...,
    sort: bool = ...,
    copy: bool | lib.NoDefault = ...,
) -> DataFrame: ...


@overload
def concat(
    objs: Iterable[Series] | Mapping[HashableT, Series],
    *,
    axis: Literal[0, "index"] = ...,
    join: str = ...,
    ignore_index: bool = ...,
    keys: Iterable[Hashable] | None = ...,
    levels=...,
    names: list[HashableT] | None = ...,
    verify_integrity: bool = ...,
    sort: bool = ...,
    copy: bool | lib.NoDefault = ...,
) -> Series: ...


@overload
def concat(
    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
    *,
    axis: Literal[0, "index"] = ...,
    join: str = ...,
    ignore_index: bool = ...,
    keys: Iterable[Hashable] | None = ...,
    levels=...,
    names: list[HashableT] | None = ...,
    verify_integrity: bool = ...,
    sort: bool = ...,
    copy: bool | lib.NoDefault = ...,
) -> DataFrame | Series: ...


@overload
def concat(
    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
    *,
    axis: Literal[1, "columns"],
    join: str = ...,
    ignore_index: bool = ...,
    keys: Iterable[Hashable] | None = ...,
    levels=...,
    names: list[HashableT] | None = ...,
    verify_integrity: bool = ...,
    sort: bool = ...,
    copy: bool | lib.NoDefault = ...,
) -> DataFrame: ...


@overload
def concat(
    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
    *,
    axis: Axis = ...,
    join: str = ...,
    ignore_index: bool = ...,
    keys: Iterable[Hashable] | None = ...,
    levels=...,
    names: list[HashableT] | None = ...,
    verify_integrity: bool = ...,
    sort: bool = ...,
    copy: bool | lib.NoDefault = ...,
) -> DataFrame | Series: ...


@set_module("pandas")
def concat(
    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
    *,
    axis: Axis = 0,
    join: str = "outer",
    ignore_index: bool = False,
    keys: Iterable[Hashable] | None = None,
    levels=None,
    names: list[HashableT] | None = None,
    verify_integrity: bool = False,
    sort: bool | lib.NoDefault = lib.no_default,
    copy: bool | lib.NoDefault = lib.no_default,
) -> DataFrame | Series:
    """
    Concatenate pandas objects along a particular axis.

    Allows optional set logic along the other axes.

    Can also add a layer of hierarchical indexing on the concatenation axis,
    which may be useful if the labels are the same (or overlapping) on
    the passed axis number.

    Parameters
    ----------
    objs : an iterable or mapping of Series or DataFrame objects
        If a mapping is passed, the keys will be used as the `keys`
        argument, unless it is passed, in which case the values will be
        selected (see below). Any None objects will be dropped silently unless
        they are all None in which case a ValueError will be raised.
    axis : {0/'index', 1/'columns'}, default 0
        The axis to concatenate along.
    join : {'inner', 'outer'}, default 'outer'
        How to handle indexes on other axis (or axes).
    ignore_index : bool, default False
        If True, do not use the index values along the concatenation axis. The
        resulting axis will be labeled 0, ..., n - 1. This is useful if you are
        concatenating objects where the concatenation axis does not have
        meaningful indexing information. Note the index values on the other
        axes are still respected in the join.
    keys : sequence, default None
        If multiple levels passed, should contain tuples. Construct
        hierarchical index using the passed keys as the outermost level.
    levels : list of sequences, default None
        Specific levels (unique values) to use for constructing a
        MultiIndex. Otherwise they will be inferred from the keys.
    names : list, default None
        Names for the levels in the resulting hierarchical index.
    verify_integrity : bool, default False
        Check whether the new concatenated axis contains duplicates. This can
        be very expensive relative to the actual data concatenation.
    sort : bool, default False
        Sort non-concatenation axis. One exception to this is when the
        non-concatenation axis is a DatetimeIndex and join='outer' and the axis is
        not already aligned. In that case, the non-concatenation axis is always
        sorted lexicographically.
    copy : bool, default False
        This keyword is now ignored; changing its value will have no
        impact on the method.

        .. deprecated:: 3.0.0

            This keyword is ignored and will be removed in pandas 4.0. Since
            pandas 3.0, this method always returns a new object using a lazy
            copy mechanism that defers copies until necessary
            (Copy-on-Write). See the `user guide on Copy-on-Write
            <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
            for more details.

    Returns
    -------
    object, type of objs
        When concatenating all ``Series`` along the index (axis=0), a
        ``Series`` is returned. When ``objs`` contains at least one
        ``DataFrame``, a ``DataFrame`` is returned. When concatenating along
        the columns (axis=1), a ``DataFrame`` is returned.

    See Also
    --------
    DataFrame.join : Join DataFrames using indexes.
    DataFrame.merge : Merge DataFrames by indexes or columns.

    Notes
    -----
    The keys, levels, and names arguments are all optional.

    A walkthrough of how this method fits in with other tools for combining
    pandas objects can be found `here
    <https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html>`__.

    It is not recommended to build DataFrames by adding single rows in a
    for loop. Build a list of rows and make a DataFrame in a single concat.

    Examples
    --------
    Combine two ``Series``.

    >>> s1 = pd.Series(["a", "b"])
    >>> s2 = pd.Series(["c", "d"])
    >>> pd.concat([s1, s2])
    0    a
    1    b
    0    c
    1    d
    dtype: str

    Clear the existing index and reset it in the result
    by setting the ``ignore_index`` option to ``True``.

    >>> pd.concat([s1, s2], ignore_index=True)
    0    a
    1    b
    2    c
    3    d
    dtype: str

    Add a hierarchical index at the outermost level of
    the data with the ``keys`` option.

    >>> pd.concat([s1, s2], keys=["s1", "s2"])
    s1  0    a
        1    b
    s2  0    c
        1    d
    dtype: str

    Label the index keys you create with the ``names`` option.

    >>> pd.concat([s1, s2], keys=["s1", "s2"], names=["Series name", "Row ID"])
    Series name  Row ID
    s1           0         a
                 1         b
    s2           0         c
                 1         d
    dtype: str

    Combine two ``DataFrame`` objects with identical columns.

    >>> df1 = pd.DataFrame([["a", 1], ["b", 2]], columns=["letter", "number"])
    >>> df1
      letter  number
    0      a       1
    1      b       2
    >>> df2 = pd.DataFrame([["c", 3], ["d", 4]], columns=["letter", "number"])
    >>> df2
      letter  number
    0      c       3
    1      d       4
    >>> pd.concat([df1, df2])
      letter  number
    0      a       1
    1      b       2
    0      c       3
    1      d       4

    Combine ``DataFrame`` objects with overlapping columns
    and return everything. Columns outside the intersection will
    be filled with ``NaN`` values.

    >>> df3 = pd.DataFrame(
    ...     [["c", 3, "cat"], ["d", 4, "dog"]], columns=["letter", "number", "animal"]
    ... )
    >>> df3
      letter  number animal
    0      c       3    cat
    1      d       4    dog
    >>> pd.concat([df1, df3], sort=False)
      letter  number animal
    0      a       1    NaN
    1      b       2    NaN
    0      c       3    cat
    1      d       4    dog

    Combine ``DataFrame`` objects with overlapping columns
    and return only those that are shared by passing ``inner`` to
    the ``join`` keyword argument.

    >>> pd.concat([df1, df3], join="inner")
      letter  number
    0      a       1
    1      b       2
    0      c       3
    1      d       4

    Combine ``DataFrame`` objects horizontally along the x axis by
    passing in ``axis=1``.

    >>> df4 = pd.DataFrame(
    ...     [["bird", "polly"], ["monkey", "george"]], columns=["animal", "name"]
    ... )
    >>> pd.concat([df1, df4], axis=1)
      letter  number  animal    name
    0      a       1    bird   polly
    1      b       2  monkey  george

    Prevent the result from including duplicate index values with the
    ``verify_integrity`` option.

    >>> df5 = pd.DataFrame([1], index=["a"])
    >>> df5
       0
    a  1
    >>> df6 = pd.DataFrame([2], index=["a"])
    >>> df6
       0
    a  2
    >>> pd.concat([df5, df6], verify_integrity=True)
    Traceback (most recent call last):
        ...
    ValueError: Indexes have overlapping values: ['a']

    Append a single row to the end of a ``DataFrame`` object.

    >>> df7 = pd.DataFrame({"a": 1, "b": 2}, index=[0])
    >>> df7
        a   b
    0   1   2
    >>> new_row = pd.Series({"a": 3, "b": 4})
    >>> new_row
    a    3
    b    4
    dtype: int64
    >>> pd.concat([df7, new_row.to_frame().T], ignore_index=True)
        a   b
    0   1   2
    1   3   4
    """
    if ignore_index and keys is not None:
        raise ValueError(
            f"Cannot set {ignore_index=} and specify keys. Either should be used."
        )

    if copy is not lib.no_default:
        warnings.warn(
            "The copy keyword is deprecated and will be removed in a future "
            "version. Copy-on-Write is active in pandas since 3.0 which utilizes "
            "a lazy copy mechanism that defers copies until necessary. Use "
            ".copy() to make an eager copy if necessary.",
            Pandas4Warning,
            stacklevel=find_stack_level(),
        )
    if join == "outer":
        intersect = False
    elif join == "inner":
        intersect = True
    else:  # pragma: no cover
        raise ValueError(
            "Only can inner (intersect) or outer (union) join the other axis"
        )

    objs, keys, ndims = _clean_keys_and_objs(objs, keys)

    if sort is lib.no_default:
        if axis == 0:
            non_concat_axis = [
                obj.columns if isinstance(obj, ABCDataFrame) else Index([obj.name])
                for obj in objs
            ]
        else:
            non_concat_axis = [obj.index for obj in objs]

        if (
            intersect
            or any(not isinstance(index, DatetimeIndex) for index in non_concat_axis)
            or all(prev is curr for prev, curr in pairwise(non_concat_axis))
            or (
                all(
                    prev[-1] <= curr[0] and prev.is_monotonic_increasing
                    for prev, curr in pairwise(non_concat_axis)
                    if not prev.empty and not curr.empty
                )
                and non_concat_axis[-1].is_monotonic_increasing
            )
        ):
            # Sorting or not will not impact the result.
            sort = False
    elif not is_bool(sort):
        raise ValueError(
            f"The 'sort' keyword only accepts boolean values; {sort} was passed."
        )
    else:
        sort = bool(sort)

    # select an object to be our result reference
    sample, objs = _get_sample_object(objs, ndims, keys, names, levels, intersect)

    # Standardize axis parameter to int
    if sample.ndim == 1:
        from pandas import DataFrame

        bm_axis = DataFrame._get_axis_number(axis)
        is_frame = False
        is_series = True
    else:
        bm_axis = sample._get_axis_number(axis)
        is_frame = True
        is_series = False

        # Need to flip BlockManager axis in the DataFrame special case
        bm_axis = sample._get_block_manager_axis(bm_axis)

    # if we have mixed ndims, then convert to highest ndim
    # creating column numbers as needed
    if len(ndims) > 1:
        objs = _sanitize_mixed_ndim(objs, sample, ignore_index, bm_axis)

    orig_axis = axis
    axis = 1 - bm_axis if is_frame else 0
    names = names or getattr(keys, "names", None)
    result = _get_result(
        objs,
        is_series,
        bm_axis,
        ignore_index,
        intersect,
        sort,
        keys,
        levels,
        verify_integrity,
        names,
        axis,
    )

    if sort is lib.no_default:
        if orig_axis == 0:
            non_concat_axis = [
                obj.columns if isinstance(obj, ABCDataFrame) else Index([obj.name])
                for obj in objs
            ]
        else:
            non_concat_axis = [obj.index for obj in objs]
        no_sort_result_index = union_indexes(non_concat_axis, sort=False)
        orig = result.index if orig_axis == 1 else result.columns
        if not no_sort_result_index.equals(orig):
            msg = (
                "Sorting by default when concatenating all DatetimeIndex is "
                "deprecated.  In the future, pandas will respect the default "
                "of `sort=False`. Specify `sort=True` or `sort=False` to "
                "silence this message. If you see this warnings when not "
                "directly calling concat, report a bug to pandas."
            )
            warnings.warn(msg, Pandas4Warning, stacklevel=find_stack_level())

    return result


def _sanitize_mixed_ndim(
    objs: list[Series | DataFrame],
    sample: Series | DataFrame,
    ignore_index: bool,
    axis: AxisInt,
) -> list[Series | DataFrame]:
    # if we have mixed ndims, then convert to highest ndim
    # creating column numbers as needed

    new_objs = []

    current_column = 0
    max_ndim = sample.ndim
    for obj in objs:
        ndim = obj.ndim
        if ndim == max_ndim:
            pass

        elif ndim != max_ndim - 1:
            raise ValueError(
                "cannot concatenate unaligned mixed dimensional NDFrame objects"
            )

        else:
            name = getattr(obj, "name", None)
            rename_columns = False
            if ignore_index or name is None:
                if axis == 1:
                    # doing a row-wise concatenation so need everything
                    # to line up
                    if name is None:
                        name = 0
                        rename_columns = True
                # doing a column-wise concatenation so need series
                # to have unique names
                elif name is None:
                    rename_columns = True
                    name = current_column
                    current_column += 1
                obj = sample._constructor(obj, copy=False)
                if isinstance(obj, ABCDataFrame) and rename_columns:
                    obj.columns = range(name, name + 1, 1)
            else:
                obj = sample._constructor({name: obj}, copy=False)

        new_objs.append(obj)

    return new_objs


def _get_result(
    objs: list[Series | DataFrame],
    is_series: bool,
    bm_axis: AxisInt,
    ignore_index: bool,
    intersect: bool,
    sort: bool | lib.NoDefault,
    keys: Iterable[Hashable] | None,
    levels,
    verify_integrity: bool,
    names: list[HashableT] | None,
    axis: AxisInt,
):
    cons: Callable[..., DataFrame | Series]
    sample: DataFrame | Series

    # series only
    if is_series:
        sample = cast("Series", objs[0])

        # stack blocks
        if bm_axis == 0:
            name = com.consensus_name_attr(objs)
            cons = sample._constructor

            arrs = [ser._values for ser in objs]

            res = concat_compat(arrs, axis=0)

            if ignore_index:
                new_index: Index = default_index(len(res))
            else:
                new_index = _get_concat_axis_series(
                    objs,
                    ignore_index,
                    bm_axis,
                    keys,
                    levels,
                    verify_integrity,
                    names,
                )

            mgr = type(sample._mgr).from_array(res, index=new_index)

            result = sample._constructor_from_mgr(mgr, axes=mgr.axes)
            result._name = name
            return result.__finalize__(
                types.SimpleNamespace(input_objs=objs, objs=objs), method="concat"
            )

        # combine as columns in a frame
        else:
            data = dict(enumerate(objs))

            # GH28330 Preserves subclassed objects through concat
            cons = sample._constructor_expanddim

            index = get_objs_combined_axis(
                objs,
                axis=objs[0]._get_block_manager_axis(0),
                intersect=intersect,
                sort=sort,
            )
            columns = _get_concat_axis_series(
                objs, ignore_index, bm_axis, keys, levels, verify_integrity, names
            )
            df = cons(data, index=index, copy=False)
            df.columns = columns
            return df.__finalize__(
                types.SimpleNamespace(input_objs=objs, objs=objs), method="concat"
            )

    # combine block managers
    else:
        sample = cast("DataFrame", objs[0])

        mgrs_indexers = []
        result_axes = new_axes(
            objs,
            bm_axis,
            intersect,
            sort,
            keys,
            names,
            axis,
            levels,
            verify_integrity,
            ignore_index,
        )
        for obj in objs:
            indexers = {}
            for ax, new_labels in enumerate(result_axes):
                # ::-1 to convert BlockManager ax to DataFrame ax
                if ax == bm_axis:
                    # Suppress reindexing on concat axis
                    continue

                # 1-ax to convert BlockManager axis to DataFrame axis
                obj_labels = obj.axes[1 - ax]
                if not new_labels.equals(obj_labels):
                    indexers[ax] = obj_labels.get_indexer(new_labels)

            mgrs_indexers.append((obj._mgr, indexers))

        new_data = concatenate_managers(
            mgrs_indexers, result_axes, concat_axis=bm_axis, copy=False
        )

        out = sample._constructor_from_mgr(new_data, axes=new_data.axes)
        return out.__finalize__(
            types.SimpleNamespace(input_objs=objs, objs=objs), method="concat"
        )


def new_axes(
    objs: list[Series | DataFrame],
    bm_axis: AxisInt,
    intersect: bool,
    sort: bool | lib.NoDefault,
    keys: Iterable[Hashable] | None,
    names: list[HashableT] | None,
    axis: AxisInt,
    levels,
    verify_integrity: bool,
    ignore_index: bool,
) -> list[Index]:
    """Return the new [index, column] result for concat."""
    return [
        _get_concat_axis_dataframe(
            objs,
            axis,
            ignore_index,
            keys,
            names,
            levels,
            verify_integrity,
        )
        if i == bm_axis
        else get_objs_combined_axis(
            objs,
            axis=objs[0]._get_block_manager_axis(i),
            intersect=intersect,
            sort=sort,
        )
        for i in range(2)
    ]


def _get_concat_axis_series(
    objs: list[Series | DataFrame],
    ignore_index: bool,
    bm_axis: AxisInt,
    keys: Iterable[Hashable] | None,
    levels,
    verify_integrity: bool,
    names: list[HashableT] | None,
) -> Index:
    """Return result concat axis when concatenating Series objects."""
    if ignore_index:
        return default_index(len(objs))
    elif bm_axis == 0:
        indexes = [x.index for x in objs]
        if keys is None:
            if levels is not None:
                raise ValueError("levels supported only when keys is not None")
            concat_axis = _concat_indexes(indexes)
        else:
            concat_axis = _make_concat_multiindex(indexes, keys, levels, names)
        if verify_integrity and not concat_axis.is_unique:
            overlap = concat_axis[concat_axis.duplicated()].unique()
            raise ValueError(f"Indexes have overlapping values: {overlap}")
        return concat_axis
    elif keys is None:
        result_names: list[Hashable] = [None] * len(objs)
        num = 0
        has_names = False
        for i, x in enumerate(objs):
            if x.ndim != 1:
                raise TypeError(
                    f"Cannot concatenate type 'Series' with "
                    f"object of type '{type(x).__name__}'"
                )
            if x.name is not None:
                result_names[i] = x.name
                has_names = True
            else:
                result_names[i] = num
                num += 1
        if has_names:
            return Index(result_names)
        else:
            return default_index(len(objs))
    else:
        return ensure_index(keys).set_names(names)  # type: ignore[arg-type]


def _get_concat_axis_dataframe(
    objs: list[Series | DataFrame],
    axis: AxisInt,
    ignore_index: bool,
    keys: Iterable[Hashable] | None,
    names: list[HashableT] | None,
    levels,
    verify_integrity: bool,
) -> Index:
    """Return result concat axis when concatenating DataFrame objects."""
    indexes_gen = (x.axes[axis] for x in objs)

    if ignore_index:
        return default_index(sum(len(i) for i in indexes_gen))
    else:
        indexes = list(indexes_gen)

    if keys is None:
        if levels is not None:
            raise ValueError("levels supported only when keys is not None")
        concat_axis = _concat_indexes(indexes)
    else:
        concat_axis = _make_concat_multiindex(indexes, keys, levels, names)

    if verify_integrity and not concat_axis.is_unique:
        overlap = concat_axis[concat_axis.duplicated()].unique()
        raise ValueError(f"Indexes have overlapping values: {overlap}")

    return concat_axis


def _clean_keys_and_objs(
    objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
    keys,
) -> tuple[list[Series | DataFrame], Index | None, set[int]]:
    """
    Returns
    -------
    clean_objs : list[Series | DataFrame]
        List of DataFrame and Series with Nones removed.
    keys : Index | None
        None if keys was None
        Index if objs was a Mapping or keys was not None. Filtered where objs was None.
    ndim : set[int]
        Unique .ndim attribute of obj encountered.
    """
    if isinstance(objs, abc.Mapping):
        if keys is None:
            keys = objs.keys()
        objs = [objs[k] for k in keys]
    elif isinstance(objs, (ABCSeries, ABCDataFrame)) or is_scalar(objs):
        raise TypeError(
            "first argument must be an iterable of pandas "
            f'objects, you passed an object of type "{type(objs).__name__}"'
        )
    elif not isinstance(objs, abc.Sized):
        objs = list(objs)

    if len(objs) == 0:
        raise ValueError("No objects to concatenate")

    if keys is not None:
        if not isinstance(keys, Index):
            keys = Index(keys)
        if len(keys) != len(objs):
            # GH#43485
            raise ValueError(
                f"The length of the keys ({len(keys)}) must match "
                f"the length of the objects to concatenate ({len(objs)})"
            )

    # GH#1649
    key_indices = []
    clean_objs = []
    ndims = set()
    for i, obj in enumerate(objs):
        if obj is None:
            continue
        elif isinstance(obj, (ABCSeries, ABCDataFrame)):
            key_indices.append(i)
            clean_objs.append(obj)
            ndims.add(obj.ndim)
        else:
            msg = (
                f"cannot concatenate object of type '{type(obj)}'; "
                "only Series and DataFrame objs are valid"
            )
            raise TypeError(msg)

    if keys is not None and len(key_indices) < len(keys):
        keys = keys.take(key_indices)

    if len(clean_objs) == 0:
        raise ValueError("All objects passed were None")

    return clean_objs, keys, ndims


def _get_sample_object(
    objs: list[Series | DataFrame],
    ndims: set[int],
    keys,
    names,
    levels,
    intersect: bool,
) -> tuple[Series | DataFrame, list[Series | DataFrame]]:
    # get the sample
    # want the highest ndim that we have, and must be non-empty
    # unless all objs are empty
    if len(ndims) > 1:
        max_ndim = max(ndims)
        for obj in objs:
            if obj.ndim == max_ndim and sum(obj.shape):  # type: ignore[arg-type]
                return obj, objs
    elif keys is None and names is None and levels is None and not intersect:
        # filter out the empties if we have not multi-index possibilities
        # note to keep empty Series as it affect to result columns / name
        if ndims.pop() == 2:
            non_empties = [obj for obj in objs if sum(obj.shape)]
        else:
            non_empties = objs

        if len(non_empties):
            return non_empties[0], non_empties

    return objs[0], objs


def _concat_indexes(indexes) -> Index:
    return indexes[0].append(indexes[1:])


def validate_unique_levels(levels: list[Index]) -> None:
    for level in levels:
        if not level.is_unique:
            raise ValueError(f"Level values not unique: {level.tolist()}")


def _make_concat_multiindex(indexes, keys, levels=None, names=None) -> MultiIndex:
    if (levels is None and isinstance(keys[0], tuple)) or (
        levels is not None and len(levels) > 1
    ):
        zipped = list(zip(*keys, strict=True))
        if names is None:
            names = [None] * len(zipped)

        if levels is None:
            _, levels = factorize_from_iterables(zipped)
        else:
            levels = [ensure_index(x) for x in levels]
            validate_unique_levels(levels)
    else:
        zipped = [keys]
        if names is None:
            names = [None]

        if levels is None:
            levels = [ensure_index(keys).unique()]
        else:
            levels = [ensure_index(x) for x in levels]
            validate_unique_levels(levels)

    if not all_indexes_same(indexes):
        codes_list = []

        # things are potentially different sizes, so compute the exact codes
        # for each level and pass those to MultiIndex.from_arrays

        for hlevel, level in zip(zipped, levels, strict=True):
            to_concat = []
            if isinstance(hlevel, Index) and hlevel.equals(level):
                lens = [len(idx) for idx in indexes]
                codes_list.append(np.repeat(np.arange(len(hlevel)), lens))
            else:
                for key, index in zip(hlevel, indexes, strict=True):
                    # Find matching codes, include matching nan values as equal.
                    mask = (isna(level) & isna(key)) | (level == key)
                    if not mask.any():
                        raise ValueError(f"Key {key} not in level {level}")
                    i = np.nonzero(mask)[0][0]

                    to_concat.append(np.repeat(i, len(index)))
                codes_list.append(np.concatenate(to_concat))

        concat_index = _concat_indexes(indexes)

        # these go at the end
        if isinstance(concat_index, MultiIndex):
            levels.extend(concat_index.levels)
            codes_list.extend(concat_index.codes)
        else:
            codes, categories = factorize_from_iterable(concat_index)
            levels.append(categories)
            codes_list.append(codes)

        if len(names) == len(levels):
            names = list(names)
        else:
            # make sure that all of the passed indices have the same nlevels
            if not len({idx.nlevels for idx in indexes}) == 1:
                raise AssertionError(
                    "Cannot concat indices that do not have the same number of levels"
                )

            # also copies
            names = list(names) + list(get_unanimous_names(*indexes))

        return MultiIndex(
            levels=levels, codes=codes_list, names=names, verify_integrity=False
        )

    new_index = indexes[0]
    n = len(new_index)
    kpieces = len(indexes)

    # also copies
    new_names = list(names)
    new_levels = list(levels)

    # construct codes
    new_codes = []

    # do something a bit more speedy

    for hlevel, level in zip(zipped, levels, strict=True):
        hlevel_index = ensure_index(hlevel)
        mapped = level.get_indexer(hlevel_index)

        mask = mapped == -1
        if mask.any():
            raise ValueError(
                f"Values not found in passed level: {hlevel_index[mask]!s}"
            )

        new_codes.append(np.repeat(mapped, n))

    if isinstance(new_index, MultiIndex):
        new_levels.extend(new_index.levels)
        new_codes.extend(np.tile(lab, kpieces) for lab in new_index.codes)
    else:
        new_levels.append(new_index.unique())
        single_codes = new_index.unique().get_indexer(new_index)
        new_codes.append(np.tile(single_codes, kpieces))

    if len(new_names) < len(new_levels):
        new_names.extend(new_index.names)

    return MultiIndex(
        levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
    )