# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership.  The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.

import pandas
import numpy as np

from typing import Hashable, Iterable, Mapping, Optional, Union
from pandas._typing import FrameOrSeriesUnion
from pandas.core.dtypes.common import is_list_like

from modin.backends.base.query_compiler import BaseQueryCompiler
from .dataframe import DataFrame
from .series import Series


def concat(
    objs: Union[
        Iterable[FrameOrSeriesUnion], Mapping[Optional[Hashable], FrameOrSeriesUnion]
    ],
    axis=0,
    join="outer",
    ignore_index: bool = False,
    keys=None,
    levels=None,
    names=None,
    verify_integrity: bool = False,
    sort: bool = False,
    copy: bool = True,
) -> FrameOrSeriesUnion:
    if isinstance(objs, (pandas.Series, Series, DataFrame, str, pandas.DataFrame)):
        raise TypeError(
            "first argument must be an iterable of pandas "
            "objects, you passed an object of type "
            '"{name}"'.format(name=type(objs).__name__)
        )
    axis = pandas.DataFrame()._get_axis_number(axis)
    if isinstance(objs, dict):
        list_of_objs = list(objs.values())
    else:
        list_of_objs = list(objs)
    if len(list_of_objs) == 0:
        raise ValueError("No objects to concatenate")

    list_of_objs = [obj for obj in list_of_objs if obj is not None]

    if len(list_of_objs) == 0:
        raise ValueError("All objects passed were None")
    try:
        type_check = next(
            obj
            for obj in list_of_objs
            if not isinstance(obj, (pandas.Series, Series, pandas.DataFrame, DataFrame))
        )
    except StopIteration:
        type_check = None
    if type_check is not None:
        raise ValueError(
            'cannot concatenate object of type "{0}"; only '
            "modin.pandas.Series "
            "and modin.pandas.DataFrame objs are "
            "valid",
            type(type_check),
        )
    all_series = all(isinstance(obj, Series) for obj in list_of_objs)
    if all_series and axis == 0:
        return Series(
            query_compiler=list_of_objs[0]._query_compiler.concat(
                axis,
                [o._query_compiler for o in list_of_objs[1:]],
                join=join,
                join_axes=None,
                ignore_index=ignore_index,
                keys=None,
                levels=None,
                names=None,
                verify_integrity=False,
                copy=True,
                sort=sort,
            )
        )
    if join not in ["inner", "outer"]:
        raise ValueError(
            "Only can inner (intersect) or outer (union) join the other axis"
        )
    # We have the weird Series and axis check because, when concatenating a
    # dataframe to a series on axis=0, pandas ignores the name of the series,
    # and this check aims to mirror that (possibly buggy) functionality
    list_of_objs = [
        obj
        if isinstance(obj, DataFrame)
        else DataFrame(obj.rename())
        if isinstance(obj, (pandas.Series, Series)) and axis == 0
        else DataFrame(obj)
        for obj in list_of_objs
    ]
    list_of_objs = [
        obj._query_compiler
        for obj in list_of_objs
        if len(obj.index) or len(obj.columns)
    ]
    if keys is not None:
        if all_series:
            new_idx = keys
        else:
            list_of_objs = [
                list_of_objs[i] for i in range(min(len(list_of_objs), len(keys)))
            ]
            new_idx_labels = {
                k: v.index if axis == 0 else v.columns
                for k, v in zip(keys, list_of_objs)
            }
            tuples = [
                (k, *o) if isinstance(o, tuple) else (k, o)
                for k, obj in new_idx_labels.items()
                for o in obj
            ]
            new_idx = pandas.MultiIndex.from_tuples(tuples)
            if names is not None:
                new_idx.names = names
            else:
                old_name = _determine_name(list_of_objs, axis)
                if old_name is not None:
                    new_idx.names = [None] + old_name
    elif isinstance(objs, dict):
        new_idx = pandas.concat(
            {k: pandas.Series(index=obj.axes[axis]) for k, obj in objs.items()}
        ).index
    else:
        new_idx = None
    new_query_compiler = list_of_objs[0].concat(
        axis,
        list_of_objs[1:],
        join=join,
        join_axes=None,
        ignore_index=ignore_index,
        keys=None,
        levels=None,
        names=None,
        verify_integrity=False,
        copy=True,
        sort=sort,
    )
    result_df = DataFrame(query_compiler=new_query_compiler)
    if new_idx is not None:
        if axis == 0:
            result_df.index = new_idx
        else:
            result_df.columns = new_idx
    return result_df


def _determine_name(objs: Iterable[BaseQueryCompiler], axis: Union[int, str]):
    """
    Determine names of index after concatenation along passed axis

    Parameters
    ----------
    objs : iterable of QueryCompilers
        objects to concatenate

    axis : int or str
        the axis to concatenate along

    Returns
    -------
        `list` with single element - computed index name, `None` if it could not
        be determined
    """
    axis = pandas.DataFrame()._get_axis_number(axis)

    def get_names(obj):
        return obj.columns.names if axis else obj.index.names

    names = np.array([get_names(obj) for obj in objs])

    # saving old name, only if index names of all objs are the same
    if np.all(names == names[0]):
        # we must do this check to avoid this calls `list(str_like_name)`
        return list(names[0]) if is_list_like(names[0]) else [names[0]]
    else:
        return None

pandas.core.dtypes.common.is_list_like pandas.DataFrame pandas.MultiIndex.from_tuples pandas.Series