"""
For compatibility with numpy libraries, pandas functions or
methods have to accept '*args' and '**kwargs' parameters to
accommodate numpy arguments that are not actually used or
respected in the pandas implementation.

To ensure that users do not abuse these parameters, validation
is performed in 'validators.py' to make sure that any extra
parameters passed correspond ONLY to those in the numpy signature.
Part of that validation includes whether or not the user attempted
to pass in non-default values for these extraneous parameters. As we
want to discourage users from relying on these parameters when calling
the pandas implementation, we want them only to pass in the default values
for these parameters.

This module provides a set of commonly used default arguments for functions
and methods that are spread throughout the codebase. This module will make it
easier to adjust to future upstream changes in the analogous numpy signatures.
"""

from numpy import ndarray

from pandas.compat import OrderedDict
from pandas.errors import UnsupportedFunctionCall
from pandas.util._validators import (
    validate_args, validate_args_and_kwargs, validate_kwargs)

from pandas.core.dtypes.common import is_bool, is_integer


class CompatValidator(object):

    def __init__(self, defaults, fname=None, method=None,
                 max_fname_arg_count=None):
        self.fname = fname
        self.method = method
        self.defaults = defaults
        self.max_fname_arg_count = max_fname_arg_count

    def __call__(self, args, kwargs, fname=None,
                 max_fname_arg_count=None, method=None):
        if args or kwargs:
            fname = self.fname if fname is None else fname
            max_fname_arg_count = (self.max_fname_arg_count if
                                   max_fname_arg_count is None
                                   else max_fname_arg_count)
            method = self.method if method is None else method

            if method == 'args':
                validate_args(fname, args, max_fname_arg_count, self.defaults)
            elif method == 'kwargs':
                validate_kwargs(fname, kwargs, self.defaults)
            elif method == 'both':
                validate_args_and_kwargs(fname, args, kwargs,
                                         max_fname_arg_count,
                                         self.defaults)
            else:
                raise ValueError("invalid validation method "
                                 "'{method}'".format(method=method))


ARGMINMAX_DEFAULTS = dict(out=None)
validate_argmin = CompatValidator(ARGMINMAX_DEFAULTS, fname='argmin',
                                  method='both', max_fname_arg_count=1)
validate_argmax = CompatValidator(ARGMINMAX_DEFAULTS, fname='argmax',
                                  method='both', max_fname_arg_count=1)


def process_skipna(skipna, args):
    if isinstance(skipna, ndarray) or skipna is None:
        args = (skipna,) + args
        skipna = True

    return skipna, args


def validate_argmin_with_skipna(skipna, args, kwargs):
    """
    If 'Series.argmin' is called via the 'numpy' library,
    the third parameter in its signature is 'out', which
    takes either an ndarray or 'None', so check if the
    'skipna' parameter is either an instance of ndarray or
    is None, since 'skipna' itself should be a boolean
    """

    skipna, args = process_skipna(skipna, args)
    validate_argmin(args, kwargs)
    return skipna


def validate_argmax_with_skipna(skipna, args, kwargs):
    """
    If 'Series.argmax' is called via the 'numpy' library,
    the third parameter in its signature is 'out', which
    takes either an ndarray or 'None', so check if the
    'skipna' parameter is either an instance of ndarray or
    is None, since 'skipna' itself should be a boolean
    """

    skipna, args = process_skipna(skipna, args)
    validate_argmax(args, kwargs)
    return skipna


ARGSORT_DEFAULTS = OrderedDict()
ARGSORT_DEFAULTS['axis'] = -1
ARGSORT_DEFAULTS['kind'] = 'quicksort'
ARGSORT_DEFAULTS['order'] = None
validate_argsort = CompatValidator(ARGSORT_DEFAULTS, fname='argsort',
                                   max_fname_arg_count=0, method='both')

# two different signatures of argsort, this second validation
# for when the `kind` param is supported
ARGSORT_DEFAULTS_KIND = OrderedDict()
ARGSORT_DEFAULTS_KIND['axis'] = -1
ARGSORT_DEFAULTS_KIND['order'] = None
validate_argsort_kind = CompatValidator(ARGSORT_DEFAULTS_KIND, fname='argsort',
                                        max_fname_arg_count=0, method='both')


def validate_argsort_with_ascending(ascending, args, kwargs):
    """
    If 'Categorical.argsort' is called via the 'numpy' library, the
    first parameter in its signature is 'axis', which takes either
    an integer or 'None', so check if the 'ascending' parameter has
    either integer type or is None, since 'ascending' itself should
    be a boolean
    """

    if is_integer(ascending) or ascending is None:
        args = (ascending,) + args
        ascending = True

    validate_argsort_kind(args, kwargs, max_fname_arg_count=3)
    return ascending


CLIP_DEFAULTS = dict(out=None)
validate_clip = CompatValidator(CLIP_DEFAULTS, fname='clip',
                                method='both', max_fname_arg_count=3)


def validate_clip_with_axis(axis, args, kwargs):
    """
    If 'NDFrame.clip' is called via the numpy library, the third
    parameter in its signature is 'out', which can takes an ndarray,
    so check if the 'axis' parameter is an instance of ndarray, since
    'axis' itself should either be an integer or None
    """

    if isinstance(axis, ndarray):
        args = (axis,) + args
        axis = None

    validate_clip(args, kwargs)
    return axis


COMPRESS_DEFAULTS = OrderedDict()
COMPRESS_DEFAULTS['axis'] = None
COMPRESS_DEFAULTS['out'] = None
validate_compress = CompatValidator(COMPRESS_DEFAULTS, fname='compress',
                                    method='both', max_fname_arg_count=1)

CUM_FUNC_DEFAULTS = OrderedDict()
CUM_FUNC_DEFAULTS['dtype'] = None
CUM_FUNC_DEFAULTS['out'] = None
validate_cum_func = CompatValidator(CUM_FUNC_DEFAULTS, method='both',
                                    max_fname_arg_count=1)
validate_cumsum = CompatValidator(CUM_FUNC_DEFAULTS, fname='cumsum',
                                  method='both', max_fname_arg_count=1)


def validate_cum_func_with_skipna(skipna, args, kwargs, name):
    """
    If this function is called via the 'numpy' library, the third
    parameter in its signature is 'dtype', which takes either a
    'numpy' dtype or 'None', so check if the 'skipna' parameter is
    a boolean or not
    """
    if not is_bool(skipna):
        args = (skipna,) + args
        skipna = True

    validate_cum_func(args, kwargs, fname=name)
    return skipna


ALLANY_DEFAULTS = OrderedDict()
ALLANY_DEFAULTS['dtype'] = None
ALLANY_DEFAULTS['out'] = None
ALLANY_DEFAULTS['keepdims'] = False
validate_all = CompatValidator(ALLANY_DEFAULTS, fname='all',
                               method='both', max_fname_arg_count=1)
validate_any = CompatValidator(ALLANY_DEFAULTS, fname='any',
                               method='both', max_fname_arg_count=1)

LOGICAL_FUNC_DEFAULTS = dict(out=None, keepdims=False)
validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method='kwargs')

MINMAX_DEFAULTS = dict(out=None, keepdims=False)
validate_min = CompatValidator(MINMAX_DEFAULTS, fname='min',
                               method='both', max_fname_arg_count=1)
validate_max = CompatValidator(MINMAX_DEFAULTS, fname='max',
                               method='both', max_fname_arg_count=1)

RESHAPE_DEFAULTS = dict(order='C')
validate_reshape = CompatValidator(RESHAPE_DEFAULTS, fname='reshape',
                                   method='both', max_fname_arg_count=1)

REPEAT_DEFAULTS = dict(axis=None)
validate_repeat = CompatValidator(REPEAT_DEFAULTS, fname='repeat',
                                  method='both', max_fname_arg_count=1)

ROUND_DEFAULTS = dict(out=None)
validate_round = CompatValidator(ROUND_DEFAULTS, fname='round',
                                 method='both', max_fname_arg_count=1)

SORT_DEFAULTS = OrderedDict()
SORT_DEFAULTS['axis'] = -1
SORT_DEFAULTS['kind'] = 'quicksort'
SORT_DEFAULTS['order'] = None
validate_sort = CompatValidator(SORT_DEFAULTS, fname='sort',
                                method='kwargs')

STAT_FUNC_DEFAULTS = OrderedDict()
STAT_FUNC_DEFAULTS['dtype'] = None
STAT_FUNC_DEFAULTS['out'] = None

PROD_DEFAULTS = SUM_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
SUM_DEFAULTS['keepdims'] = False
SUM_DEFAULTS['initial'] = None

MEDIAN_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
MEDIAN_DEFAULTS['overwrite_input'] = False
MEDIAN_DEFAULTS['keepdims'] = False

STAT_FUNC_DEFAULTS['keepdims'] = False

validate_stat_func = CompatValidator(STAT_FUNC_DEFAULTS,
                                     method='kwargs')
validate_sum = CompatValidator(SUM_DEFAULTS, fname='sum',
                               method='both', max_fname_arg_count=1)
validate_prod = CompatValidator(PROD_DEFAULTS, fname="prod",
                                method="both", max_fname_arg_count=1)
validate_mean = CompatValidator(STAT_FUNC_DEFAULTS, fname='mean',
                                method='both', max_fname_arg_count=1)
validate_median = CompatValidator(MEDIAN_DEFAULTS, fname='median',
                                  method='both', max_fname_arg_count=1)

STAT_DDOF_FUNC_DEFAULTS = OrderedDict()
STAT_DDOF_FUNC_DEFAULTS['dtype'] = None
STAT_DDOF_FUNC_DEFAULTS['out'] = None
STAT_DDOF_FUNC_DEFAULTS['keepdims'] = False
validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS,
                                          method='kwargs')

TAKE_DEFAULTS = OrderedDict()
TAKE_DEFAULTS['out'] = None
TAKE_DEFAULTS['mode'] = 'raise'
validate_take = CompatValidator(TAKE_DEFAULTS, fname='take',
                                method='kwargs')


def validate_take_with_convert(convert, args, kwargs):
    """
    If this function is called via the 'numpy' library, the third
    parameter in its signature is 'axis', which takes either an
    ndarray or 'None', so check if the 'convert' parameter is either
    an instance of ndarray or is None
    """

    if isinstance(convert, ndarray) or convert is None:
        args = (convert,) + args
        convert = True

    validate_take(args, kwargs, max_fname_arg_count=3, method='both')
    return convert


TRANSPOSE_DEFAULTS = dict(axes=None)
validate_transpose = CompatValidator(TRANSPOSE_DEFAULTS, fname='transpose',
                                     method='both', max_fname_arg_count=0)


def validate_transpose_for_generic(inst, kwargs):
    try:
        validate_transpose(tuple(), kwargs)
    except ValueError as e:
        klass = type(inst).__name__
        msg = str(e)

        # the Panel class actual relies on the 'axes' parameter if called
        # via the 'numpy' library, so let's make sure the error is specific
        # about saying that the parameter is not supported for particular
        # implementations of 'transpose'
        if "the 'axes' parameter is not supported" in msg:
            msg += " for {klass} instances".format(klass=klass)

        raise ValueError(msg)


def validate_window_func(name, args, kwargs):
    numpy_args = ('axis', 'dtype', 'out')
    msg = ("numpy operations are not "
           "valid with window objects. "
           "Use .{func}() directly instead ".format(func=name))

    if len(args) > 0:
        raise UnsupportedFunctionCall(msg)

    for arg in numpy_args:
        if arg in kwargs:
            raise UnsupportedFunctionCall(msg)


def validate_rolling_func(name, args, kwargs):
    numpy_args = ('axis', 'dtype', 'out')
    msg = ("numpy operations are not "
           "valid with window objects. "
           "Use .rolling(...).{func}() instead ".format(func=name))

    if len(args) > 0:
        raise UnsupportedFunctionCall(msg)

    for arg in numpy_args:
        if arg in kwargs:
            raise UnsupportedFunctionCall(msg)


def validate_expanding_func(name, args, kwargs):
    numpy_args = ('axis', 'dtype', 'out')
    msg = ("numpy operations are not "
           "valid with window objects. "
           "Use .expanding(...).{func}() instead ".format(func=name))

    if len(args) > 0:
        raise UnsupportedFunctionCall(msg)

    for arg in numpy_args:
        if arg in kwargs:
            raise UnsupportedFunctionCall(msg)


def validate_groupby_func(name, args, kwargs, allowed=None):
    """
    'args' and 'kwargs' should be empty, except for allowed
    kwargs because all of
    their necessary parameters are explicitly listed in
    the function signature
    """
    if allowed is None:
        allowed = []

    kwargs = set(kwargs) - set(allowed)

    if len(args) + len(kwargs) > 0:
        raise UnsupportedFunctionCall((
            "numpy operations are not valid "
            "with groupby. Use .groupby(...)."
            "{func}() instead".format(func=name)))


RESAMPLER_NUMPY_OPS = ('min', 'max', 'sum', 'prod',
                       'mean', 'std', 'var')


def validate_resampler_func(method, args, kwargs):
    """
    'args' and 'kwargs' should be empty because all of
    their necessary parameters are explicitly listed in
    the function signature
    """
    if len(args) + len(kwargs) > 0:
        if method in RESAMPLER_NUMPY_OPS:
            raise UnsupportedFunctionCall((
                "numpy operations are not valid "
                "with resample. Use .resample(...)."
                "{func}() instead".format(func=method)))
        else:
            raise TypeError("too many arguments passed in")


def validate_minmax_axis(axis):
    """
    Ensure that the axis argument passed to min, max, argmin, or argmax is
    zero or None, as otherwise it will be incorrectly ignored.

    Parameters
    ----------
    axis : int or None

    Raises
    ------
    ValueError
    """
    ndim = 1  # hard-coded for Index
    if axis is None:
        return
    if axis >= ndim or (axis < 0 and ndim + axis < 0):
        raise ValueError("`axis` must be fewer than the number of "
                         "dimensions ({ndim})".format(ndim=ndim))
