"""
Misc tools for implementing data structures

Note: pandas.core.common is *not* part of the public API.
"""

from collections import abc, defaultdict
import contextlib
from datetime import datetime, timedelta
from functools import partial
import inspect
from typing import Any, Collection, Iterable, Iterator, List, Union
import warnings

import numpy as np

from pandas._libs import lib, tslibs
from pandas._typing import AnyArrayLike, Scalar, T
from pandas.compat.numpy import _np_version_under1p18

from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
from pandas.core.dtypes.common import (
    is_array_like,
    is_bool_dtype,
    is_extension_array_dtype,
    is_integer,
)
from pandas.core.dtypes.generic import (
    ABCExtensionArray,
    ABCIndex,
    ABCIndexClass,
    ABCSeries,
)
from pandas.core.dtypes.inference import _iterable_not_string
from pandas.core.dtypes.missing import isna, isnull, notnull  # noqa


class SettingWithCopyError(ValueError):
    pass


class SettingWithCopyWarning(Warning):
    pass


def flatten(l):
    """
    Flatten an arbitrarily nested sequence.

    Parameters
    ----------
    l : sequence
        The non string sequence to flatten

    Notes
    -----
    This doesn't consider strings sequences.

    Returns
    -------
    flattened : generator
    """
    for el in l:
        if _iterable_not_string(el):
            for s in flatten(el):
                yield s
        else:
            yield el


def consensus_name_attr(objs):
    name = objs[0].name
    for obj in objs[1:]:
        try:
            if obj.name != name:
                name = None
        except ValueError:
            name = None
    return name


def maybe_box_datetimelike(value, dtype=None):
    # turn a datetime like into a Timestamp/timedelta as needed
    if dtype == object:
        # If we dont have datetime64/timedelta64 dtype, we dont want to
        #  box datetimelike scalars
        return value

    if isinstance(value, (np.datetime64, datetime)):
        value = tslibs.Timestamp(value)
    elif isinstance(value, (np.timedelta64, timedelta)):
        value = tslibs.Timedelta(value)

    return value


def is_bool_indexer(key: Any) -> bool:
    """
    Check whether `key` is a valid boolean indexer.

    Parameters
    ----------
    key : Any
        Only list-likes may be considered boolean indexers.
        All other types are not considered a boolean indexer.
        For array-like input, boolean ndarrays or ExtensionArrays
        with ``_is_boolean`` set are considered boolean indexers.

    Returns
    -------
    bool
        Whether `key` is a valid boolean indexer.

    Raises
    ------
    ValueError
        When the array is an object-dtype ndarray or ExtensionArray
        and contains missing values.

    See Also
    --------
    check_array_indexer : Check that `key` is a valid array to index,
        and convert to an ndarray.
    """
    if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or (
        is_array_like(key) and is_extension_array_dtype(key.dtype)
    ):
        if key.dtype == np.object_:
            key = np.asarray(key)

            if not lib.is_bool_array(key):
                na_msg = "Cannot mask with non-boolean array containing NA / NaN values"
                if isna(key).any():
                    raise ValueError(na_msg)
                return False
            return True
        elif is_bool_dtype(key.dtype):
            return True
    elif isinstance(key, list):
        try:
            arr = np.asarray(key)
            return arr.dtype == np.bool_ and len(arr) == len(key)
        except TypeError:  # pragma: no cover
            return False

    return False


def cast_scalar_indexer(val, warn_float=False):
    """
    To avoid numpy DeprecationWarnings, cast float to integer where valid.

    Parameters
    ----------
    val : scalar
    warn_float : bool, default False
        If True, issue deprecation warning for a float indexer.

    Returns
    -------
    outval : scalar
    """
    # assumes lib.is_scalar(val)
    if lib.is_float(val) and val.is_integer():
        if warn_float:
            warnings.warn(
                "Indexing with a float is deprecated, and will raise an IndexError "
                "in pandas 2.0. You can manually convert to an integer key instead.",
                FutureWarning,
                stacklevel=3,
            )
        return int(val)
    return val


def not_none(*args):
    """
    Returns a generator consisting of the arguments that are not None.
    """
    return (arg for arg in args if arg is not None)


def any_none(*args) -> bool:
    """
    Returns a boolean indicating if any argument is None.
    """
    return any(arg is None for arg in args)


def all_none(*args) -> bool:
    """
    Returns a boolean indicating if all arguments are None.
    """
    return all(arg is None for arg in args)


def any_not_none(*args) -> bool:
    """
    Returns a boolean indicating if any argument is not None.
    """
    return any(arg is not None for arg in args)


def all_not_none(*args) -> bool:
    """
    Returns a boolean indicating if all arguments are not None.
    """
    return all(arg is not None for arg in args)


def count_not_none(*args) -> int:
    """
    Returns the count of arguments that are not None.
    """
    return sum(x is not None for x in args)


def asarray_tuplesafe(values, dtype=None):

    if not (isinstance(values, (list, tuple)) or hasattr(values, "__array__")):
        values = list(values)
    elif isinstance(values, ABCIndexClass):
        return values._values

    if isinstance(values, list) and dtype in [np.object_, object]:
        return construct_1d_object_array_from_listlike(values)

    result = np.asarray(values, dtype=dtype)

    if issubclass(result.dtype.type, str):
        result = np.asarray(values, dtype=object)

    if result.ndim == 2:
        # Avoid building an array of arrays:
        values = [tuple(x) for x in values]
        result = construct_1d_object_array_from_listlike(values)

    return result


def index_labels_to_array(labels, dtype=None):
    """
    Transform label or iterable of labels to array, for use in Index.

    Parameters
    ----------
    dtype : dtype
        If specified, use as dtype of the resulting array, otherwise infer.

    Returns
    -------
    array
    """
    if isinstance(labels, (str, tuple)):
        labels = [labels]

    if not isinstance(labels, (list, np.ndarray)):
        try:
            labels = list(labels)
        except TypeError:  # non-iterable
            labels = [labels]

    labels = asarray_tuplesafe(labels, dtype=dtype)

    return labels


def maybe_make_list(obj):
    if obj is not None and not isinstance(obj, (tuple, list)):
        return [obj]
    return obj


def maybe_iterable_to_list(obj: Union[Iterable[T], T]) -> Union[Collection[T], T]:
    """
    If obj is Iterable but not list-like, consume into list.
    """
    if isinstance(obj, abc.Iterable) and not isinstance(obj, abc.Sized):
        return list(obj)
    return obj


def is_null_slice(obj) -> bool:
    """
    We have a null slice.
    """
    return (
        isinstance(obj, slice)
        and obj.start is None
        and obj.stop is None
        and obj.step is None
    )


def is_true_slices(l):
    """
    Find non-trivial slices in "l": return a list of booleans with same length.
    """
    return [isinstance(k, slice) and not is_null_slice(k) for k in l]


# TODO: used only once in indexing; belongs elsewhere?
def is_full_slice(obj, l) -> bool:
    """
    We have a full length slice.
    """
    return (
        isinstance(obj, slice) and obj.start == 0 and obj.stop == l and obj.step is None
    )


def get_callable_name(obj):
    # typical case has name
    if hasattr(obj, "__name__"):
        return getattr(obj, "__name__")
    # some objects don't; could recurse
    if isinstance(obj, partial):
        return get_callable_name(obj.func)
    # fall back to class name
    if hasattr(obj, "__call__"):
        return type(obj).__name__
    # everything failed (probably because the argument
    # wasn't actually callable); we return None
    # instead of the empty string in this case to allow
    # distinguishing between no name and a name of ''
    return None


def apply_if_callable(maybe_callable, obj, **kwargs):
    """
    Evaluate possibly callable input using obj and kwargs if it is callable,
    otherwise return as it is.

    Parameters
    ----------
    maybe_callable : possibly a callable
    obj : NDFrame
    **kwargs
    """
    if callable(maybe_callable):
        return maybe_callable(obj, **kwargs)

    return maybe_callable


def dict_compat(d):
    """
    Helper function to convert datetimelike-keyed dicts
    to Timestamp-keyed dict.

    Parameters
    ----------
    d: dict like object

    Returns
    -------
    dict

    """
    return {maybe_box_datetimelike(key): value for key, value in d.items()}


def standardize_mapping(into):
    """
    Helper function to standardize a supplied mapping.

    Parameters
    ----------
    into : instance or subclass of collections.abc.Mapping
        Must be a class, an initialized collections.defaultdict,
        or an instance of a collections.abc.Mapping subclass.

    Returns
    -------
    mapping : a collections.abc.Mapping subclass or other constructor
        a callable object that can accept an iterator to create
        the desired Mapping.

    See Also
    --------
    DataFrame.to_dict
    Series.to_dict
    """
    if not inspect.isclass(into):
        if isinstance(into, defaultdict):
            return partial(defaultdict, into.default_factory)
        into = type(into)
    if not issubclass(into, abc.Mapping):
        raise TypeError(f"unsupported type: {into}")
    elif into == defaultdict:
        raise TypeError("to_dict() only accepts initialized defaultdicts")
    return into


def random_state(state=None):
    """
    Helper function for processing random_state arguments.

    Parameters
    ----------
    state : int, array-like, BitGenerator (NumPy>=1.17), np.random.RandomState, None.
        If receives an int, array-like, or BitGenerator, passes to
        np.random.RandomState() as seed.
        If receives an np.random.RandomState object, just returns object.
        If receives `None`, returns np.random.
        If receives anything else, raises an informative ValueError.

        .. versionchanged:: 1.1.0

            array-like and BitGenerator (for NumPy>=1.18) object now passed to
            np.random.RandomState() as seed

        Default None.

    Returns
    -------
    np.random.RandomState

    """
    if (
        is_integer(state)
        or is_array_like(state)
        or (not _np_version_under1p18 and isinstance(state, np.random.BitGenerator))
    ):
        return np.random.RandomState(state)
    elif isinstance(state, np.random.RandomState):
        return state
    elif state is None:
        return np.random
    else:
        raise ValueError(
            (
                "random_state must be an integer, array-like, a BitGenerator, "
                "a numpy RandomState, or None"
            )
        )


def pipe(obj, func, *args, **kwargs):
    """
    Apply a function ``func`` to object ``obj`` either by passing obj as the
    first argument to the function or, in the case that the func is a tuple,
    interpret the first element of the tuple as a function and pass the obj to
    that function as a keyword argument whose key is the value of the second
    element of the tuple.

    Parameters
    ----------
    func : callable or tuple of (callable, str)
        Function to apply to this object or, alternatively, a
        ``(callable, data_keyword)`` tuple where ``data_keyword`` is a
        string indicating the keyword of `callable`` that expects the
        object.
    *args : iterable, optional
        Positional arguments passed into ``func``.
    **kwargs : dict, optional
        A dictionary of keyword arguments passed into ``func``.

    Returns
    -------
    object : the return type of ``func``.
    """
    if isinstance(func, tuple):
        func, target = func
        if target in kwargs:
            msg = f"{target} is both the pipe target and a keyword argument"
            raise ValueError(msg)
        kwargs[target] = obj
        return func(*args, **kwargs)
    else:
        return func(obj, *args, **kwargs)


def get_rename_function(mapper):
    """
    Returns a function that will map names/labels, dependent if mapper
    is a dict, Series or just a function.
    """
    if isinstance(mapper, (abc.Mapping, ABCSeries)):

        def f(x):
            if x in mapper:
                return mapper[x]
            else:
                return x

    else:
        f = mapper

    return f


def convert_to_list_like(
    values: Union[Scalar, Iterable, AnyArrayLike]
) -> Union[List, AnyArrayLike]:
    """
    Convert list-like or scalar input to list-like. List, numpy and pandas array-like
    inputs are returned unmodified whereas others are converted to list.
    """
    if isinstance(values, (list, np.ndarray, ABCIndex, ABCSeries, ABCExtensionArray)):
        return values
    elif isinstance(values, abc.Iterable) and not isinstance(values, str):
        return list(values)

    return [values]


@contextlib.contextmanager
def temp_setattr(obj, attr: str, value) -> Iterator[None]:
    """Temporarily set attribute on an object.

    Args:
        obj: Object whose attribute will be modified.
        attr: Attribute to modify.
        value: Value to temporarily set attribute to.

    Yields:
        obj with modified attribute.
    """
    old_value = getattr(obj, attr)
    setattr(obj, attr, value)
    yield obj
    setattr(obj, attr, old_value)