""" miscellaneous sorting / groupby utilities """
from typing import Callable, Optional

import numpy as np

from pandas._libs import algos, hashtable, lib
from pandas._libs.hashtable import unique_label_indices

from pandas.core.dtypes.common import (
    ensure_int64,
    ensure_platform_int,
    is_categorical_dtype,
    is_extension_array_dtype,
)
from pandas.core.dtypes.generic import ABCMultiIndex
from pandas.core.dtypes.missing import isna

import pandas.core.algorithms as algorithms
from pandas.core.construction import extract_array

_INT64_MAX = np.iinfo(np.int64).max


def get_group_index(labels, shape, sort: bool, xnull: bool):
    """
    For the particular label_list, gets the offsets into the hypothetical list
    representing the totally ordered cartesian product of all possible label
    combinations, *as long as* this space fits within int64 bounds;
    otherwise, though group indices identify unique combinations of
    labels, they cannot be deconstructed.
    - If `sort`, rank of returned ids preserve lexical ranks of labels.
      i.e. returned id's can be used to do lexical sort on labels;
    - If `xnull` nulls (-1 labels) are passed through.

    Parameters
    ----------
    labels : sequence of arrays
        Integers identifying levels at each location
    shape : sequence of ints
        Number of unique levels at each location
    sort : bool
        If the ranks of returned ids should match lexical ranks of labels
    xnull : bool
        If true nulls are excluded. i.e. -1 values in the labels are
        passed through.

    Returns
    -------
    An array of type int64 where two elements are equal if their corresponding
    labels are equal at all location.

    Notes
    -----
    The length of `labels` and `shape` must be identical.
    """

    def _int64_cut_off(shape) -> int:
        acc = 1
        for i, mul in enumerate(shape):
            acc *= int(mul)
            if not acc < _INT64_MAX:
                return i
        return len(shape)

    def maybe_lift(lab, size):
        # promote nan values (assigned -1 label in lab array)
        # so that all output values are non-negative
        return (lab + 1, size + 1) if (lab == -1).any() else (lab, size)

    labels = map(ensure_int64, labels)
    if not xnull:
        labels, shape = map(list, zip(*map(maybe_lift, labels, shape)))

    labels = list(labels)
    shape = list(shape)

    # Iteratively process all the labels in chunks sized so less
    # than _INT64_MAX unique int ids will be required for each chunk
    while True:
        # how many levels can be done without overflow:
        nlev = _int64_cut_off(shape)

        # compute flat ids for the first `nlev` levels
        stride = np.prod(shape[1:nlev], dtype="i8")
        out = stride * labels[0].astype("i8", subok=False, copy=False)

        for i in range(1, nlev):
            if shape[i] == 0:
                stride = 0
            else:
                stride //= shape[i]
            out += labels[i] * stride

        if xnull:  # exclude nulls
            mask = labels[0] == -1
            for lab in labels[1:nlev]:
                mask |= lab == -1
            out[mask] = -1

        if nlev == len(shape):  # all levels done!
            break

        # compress what has been done so far in order to avoid overflow
        # to retain lexical ranks, obs_ids should be sorted
        comp_ids, obs_ids = compress_group_index(out, sort=sort)

        labels = [comp_ids] + labels[nlev:]
        shape = [len(obs_ids)] + shape[nlev:]

    return out


def get_compressed_ids(labels, sizes):
    """
    Group_index is offsets into cartesian product of all possible labels. This
    space can be huge, so this function compresses it, by computing offsets
    (comp_ids) into the list of unique labels (obs_group_ids).

    Parameters
    ----------
    labels : list of label arrays
    sizes : list of size of the levels

    Returns
    -------
    tuple of (comp_ids, obs_group_ids)
    """
    ids = get_group_index(labels, sizes, sort=True, xnull=False)
    return compress_group_index(ids, sort=True)


def is_int64_overflow_possible(shape) -> bool:
    the_prod = 1
    for x in shape:
        the_prod *= int(x)

    return the_prod >= _INT64_MAX


def decons_group_index(comp_labels, shape):
    # reconstruct labels
    if is_int64_overflow_possible(shape):
        # at some point group indices are factorized,
        # and may not be deconstructed here! wrong path!
        raise ValueError("cannot deconstruct factorized group indices!")

    label_list = []
    factor = 1
    y = 0
    x = comp_labels
    for i in reversed(range(len(shape))):
        labels = (x - y) % (factor * shape[i]) // factor
        np.putmask(labels, comp_labels < 0, -1)
        label_list.append(labels)
        y = labels * factor
        factor *= shape[i]
    return label_list[::-1]


def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull: bool):
    """
    Reconstruct labels from observed group ids.

    Parameters
    ----------
    xnull : bool
        If nulls are excluded; i.e. -1 labels are passed through.
    """
    if not xnull:
        lift = np.fromiter(((a == -1).any() for a in labels), dtype="i8")
        shape = np.asarray(shape, dtype="i8") + lift

    if not is_int64_overflow_possible(shape):
        # obs ids are deconstructable! take the fast route!
        out = decons_group_index(obs_ids, shape)
        return out if xnull or not lift.any() else [x - y for x, y in zip(out, lift)]

    i = unique_label_indices(comp_ids)
    i8copy = lambda a: a.astype("i8", subok=False, copy=True)
    return [i8copy(lab[i]) for lab in labels]


def indexer_from_factorized(labels, shape, compress: bool = True):
    ids = get_group_index(labels, shape, sort=True, xnull=False)

    if not compress:
        ngroups = (ids.size and ids.max()) + 1
    else:
        ids, obs = compress_group_index(ids, sort=True)
        ngroups = len(obs)

    return get_group_index_sorter(ids, ngroups)


def lexsort_indexer(
    keys, orders=None, na_position: str = "last", key: Optional[Callable] = None
):
    """
    Performs lexical sorting on a set of keys

    Parameters
    ----------
    keys : sequence of arrays
        Sequence of ndarrays to be sorted by the indexer
    orders : boolean or list of booleans, optional
        Determines the sorting order for each element in keys. If a list,
        it must be the same length as keys. This determines whether the
        corresponding element in keys should be sorted in ascending
        (True) or descending (False) order. if bool, applied to all
        elements as above. if None, defaults to True.
    na_position : {'first', 'last'}, default 'last'
        Determines placement of NA elements in the sorted list ("last" or "first")
    key : Callable, optional
        Callable key function applied to every element in keys before sorting

        .. versionadded:: 1.0.0
    """
    from pandas.core.arrays import Categorical

    labels = []
    shape = []
    if isinstance(orders, bool):
        orders = [orders] * len(keys)
    elif orders is None:
        orders = [True] * len(keys)

    keys = [ensure_key_mapped(k, key) for k in keys]

    for k, order in zip(keys, orders):
        # we are already a Categorical
        if is_categorical_dtype(k):
            cat = k

        # create the Categorical
        else:
            cat = Categorical(k, ordered=True)

        if na_position not in ["last", "first"]:
            raise ValueError(f"invalid na_position: {na_position}")

        n = len(cat.categories)
        codes = cat.codes.copy()

        mask = cat.codes == -1
        if order:  # ascending
            if na_position == "last":
                codes = np.where(mask, n, codes)
            elif na_position == "first":
                codes += 1
        else:  # not order means descending
            if na_position == "last":
                codes = np.where(mask, n, n - codes - 1)
            elif na_position == "first":
                codes = np.where(mask, 0, n - codes)
        if mask.any():
            n += 1

        shape.append(n)
        labels.append(codes)

    return indexer_from_factorized(labels, shape)


def nargsort(
    items,
    kind: str = "quicksort",
    ascending: bool = True,
    na_position: str = "last",
    key: Optional[Callable] = None,
):
    """
    Intended to be a drop-in replacement for np.argsort which handles NaNs.

    Adds ascending, na_position, and key parameters.

    (GH #6399, #5231, #27237)

    Parameters
    ----------
    kind : str, default 'quicksort'
    ascending : bool, default True
    na_position : {'first', 'last'}, default 'last'
    key : Optional[Callable], default None
    """

    if key is not None:
        items = ensure_key_mapped(items, key)
        return nargsort(
            items, kind=kind, ascending=ascending, na_position=na_position, key=None
        )

    items = extract_array(items)
    mask = np.asarray(isna(items))

    if is_extension_array_dtype(items):
        items = items._values_for_argsort()
    else:
        items = np.asanyarray(items)

    idx = np.arange(len(items))
    non_nans = items[~mask]
    non_nan_idx = idx[~mask]

    nan_idx = np.nonzero(mask)[0]
    if not ascending:
        non_nans = non_nans[::-1]
        non_nan_idx = non_nan_idx[::-1]
    indexer = non_nan_idx[non_nans.argsort(kind=kind)]
    if not ascending:
        indexer = indexer[::-1]
    # Finally, place the NaNs at the end or the beginning according to
    # na_position
    if na_position == "last":
        indexer = np.concatenate([indexer, nan_idx])
    elif na_position == "first":
        indexer = np.concatenate([nan_idx, indexer])
    else:
        raise ValueError(f"invalid na_position: {na_position}")
    return indexer


def nargminmax(values, method: str):
    """
    Implementation of np.argmin/argmax but for ExtensionArray and which
    handles missing values.

    Parameters
    ----------
    values : ExtensionArray
    method : {"argmax", "argmin"}

    Returns
    -------
    int
    """
    assert method in {"argmax", "argmin"}
    func = np.argmax if method == "argmax" else np.argmin

    mask = np.asarray(isna(values))
    values = values._values_for_argsort()

    idx = np.arange(len(values))
    non_nans = values[~mask]
    non_nan_idx = idx[~mask]

    return non_nan_idx[func(non_nans)]


def ensure_key_mapped_multiindex(index, key: Callable, level=None):
    """
    Returns a new MultiIndex in which key has been applied
    to all levels specified in level (or all levels if level
    is None). Used for key sorting for MultiIndex.

    Parameters
    ----------
    index : MultiIndex
        Index to which to apply the key function on the
        specified levels.
    key : Callable
        Function that takes an Index and returns an Index of
        the same shape. This key is applied to each level
        separately. The name of the level can be used to
        distinguish different levels for application.
    level : list-like, int or str, default None
        Level or list of levels to apply the key function to.
        If None, key function is applied to all levels. Other
        levels are left unchanged.

    Returns
    -------
    labels : MultiIndex
        Resulting MultiIndex with modified levels.
    """
    from pandas.core.indexes.api import MultiIndex

    if level is not None:
        if isinstance(level, (str, int)):
            sort_levels = [level]
        else:
            sort_levels = level

        sort_levels = [index._get_level_number(lev) for lev in sort_levels]
    else:
        sort_levels = list(range(index.nlevels))  # satisfies mypy

    mapped = [
        ensure_key_mapped(index._get_level_values(level), key)
        if level in sort_levels
        else index._get_level_values(level)
        for level in range(index.nlevels)
    ]

    labels = MultiIndex.from_arrays(mapped)

    return labels


def ensure_key_mapped(values, key: Optional[Callable], levels=None):
    """
    Applies a callable key function to the values function and checks
    that the resulting value has the same shape. Can be called on Index
    subclasses, Series, DataFrames, or ndarrays.

    Parameters
    ----------
    values : Series, DataFrame, Index subclass, or ndarray
    key : Optional[Callable], key to be called on the values array
    levels : Optional[List], if values is a MultiIndex, list of levels to
    apply the key to.
    """
    from pandas.core.indexes.api import Index

    if not key:
        return values

    if isinstance(values, ABCMultiIndex):
        return ensure_key_mapped_multiindex(values, key, level=levels)

    result = key(values.copy())
    if len(result) != len(values):
        raise ValueError(
            "User-provided `key` function must not change the shape of the array."
        )

    try:
        if isinstance(
            values, Index
        ):  # convert to a new Index subclass, not necessarily the same
            result = Index(result)
        else:
            type_of_values = type(values)
            result = type_of_values(result)  # try to revert to original type otherwise
    except TypeError:
        raise TypeError(
            f"User-provided `key` function returned an invalid type {type(result)} \
            which could not be converted to {type(values)}."
        )

    return result


class _KeyMapper:
    """
    Map compressed group id -> key tuple.
    """

    def __init__(self, comp_ids, ngroups: int, levels, labels):
        self.levels = levels
        self.labels = labels
        self.comp_ids = comp_ids.astype(np.int64)

        self.k = len(labels)
        self.tables = [hashtable.Int64HashTable(ngroups) for _ in range(self.k)]

        self._populate_tables()

    def _populate_tables(self):
        for labs, table in zip(self.labels, self.tables):
            table.map(self.comp_ids, labs.astype(np.int64))

    def get_key(self, comp_id):
        return tuple(
            level[table.get_item(comp_id)]
            for table, level in zip(self.tables, self.levels)
        )


def get_flattened_iterator(comp_ids, ngroups, levels, labels):
    # provide "flattened" iterator for multi-group setting
    mapper = _KeyMapper(comp_ids, ngroups, levels, labels)
    return [mapper.get_key(i) for i in range(ngroups)]


def get_indexer_dict(label_list, keys):
    """
    Returns
    -------
    dict
        Labels mapped to indexers.
    """
    shape = [len(x) for x in keys]

    group_index = get_group_index(label_list, shape, sort=True, xnull=True)
    ngroups = (
        ((group_index.size and group_index.max()) + 1)
        if is_int64_overflow_possible(shape)
        else np.prod(shape, dtype="i8")
    )

    sorter = get_group_index_sorter(group_index, ngroups)

    sorted_labels = [lab.take(sorter) for lab in label_list]
    group_index = group_index.take(sorter)

    return lib.indices_fast(sorter, group_index, keys, sorted_labels)


# ----------------------------------------------------------------------
# sorting levels...cleverly?


def get_group_index_sorter(group_index, ngroups: int):
    """
    algos.groupsort_indexer implements `counting sort` and it is at least
    O(ngroups), where
        ngroups = prod(shape)
        shape = map(len, keys)
    that is, linear in the number of combinations (cartesian product) of unique
    values of groupby keys. This can be huge when doing multi-key groupby.
    np.argsort(kind='mergesort') is O(count x log(count)) where count is the
    length of the data-frame;
    Both algorithms are `stable` sort and that is necessary for correctness of
    groupby operations. e.g. consider:
        df.groupby(key)[col].transform('first')
    """
    count = len(group_index)
    alpha = 0.0  # taking complexities literally; there may be
    beta = 1.0  # some room for fine-tuning these parameters
    do_groupsort = count > 0 and ((alpha + beta * ngroups) < (count * np.log(count)))
    if do_groupsort:
        sorter, _ = algos.groupsort_indexer(ensure_int64(group_index), ngroups)
        return ensure_platform_int(sorter)
    else:
        return group_index.argsort(kind="mergesort")


def compress_group_index(group_index, sort: bool = True):
    """
    Group_index is offsets into cartesian product of all possible labels. This
    space can be huge, so this function compresses it, by computing offsets
    (comp_ids) into the list of unique labels (obs_group_ids).
    """
    size_hint = min(len(group_index), hashtable._SIZE_HINT_LIMIT)
    table = hashtable.Int64HashTable(size_hint)

    group_index = ensure_int64(group_index)

    # note, group labels come out ascending (ie, 1,2,3 etc)
    comp_ids, obs_group_ids = table.get_labels_groupby(group_index)

    if sort and len(obs_group_ids) > 0:
        obs_group_ids, comp_ids = _reorder_by_uniques(obs_group_ids, comp_ids)

    return comp_ids, obs_group_ids


def _reorder_by_uniques(uniques, labels):
    # sorter is index where elements ought to go
    sorter = uniques.argsort()

    # reverse_indexer is where elements came from
    reverse_indexer = np.empty(len(sorter), dtype=np.int64)
    reverse_indexer.put(sorter, np.arange(len(sorter)))

    mask = labels < 0

    # move labels to right locations (ie, unsort ascending labels)
    labels = algorithms.take_nd(reverse_indexer, labels, allow_fill=False)
    np.putmask(labels, mask, -1)

    # sort observed ids
    uniques = algorithms.take_nd(uniques, sorter, allow_fill=False)

    return uniques, labels
