"""
Module that contains many useful utilities
for validating data or function arguments
"""
import warnings

from pandas.core.dtypes.common import is_bool


def _check_arg_length(fname, args, max_fname_arg_count, compat_args):
    """
    Checks whether 'args' has length of at most 'compat_args'. Raises
    a TypeError if that is not the case, similar to in Python when a
    function is called with too many arguments.

    """
    if max_fname_arg_count < 0:
        raise ValueError("'max_fname_arg_count' must be non-negative")

    if len(args) > len(compat_args):
        max_arg_count = len(compat_args) + max_fname_arg_count
        actual_arg_count = len(args) + max_fname_arg_count
        argument = 'argument' if max_arg_count == 1 else 'arguments'

        raise TypeError(
            "{fname}() takes at most {max_arg} {argument} "
            "({given_arg} given)".format(
                fname=fname, max_arg=max_arg_count,
                argument=argument, given_arg=actual_arg_count))


def _check_for_default_values(fname, arg_val_dict, compat_args):
    """
    Check that the keys in `arg_val_dict` are mapped to their
    default values as specified in `compat_args`.

    Note that this function is to be called only when it has been
    checked that arg_val_dict.keys() is a subset of compat_args

    """
    for key in arg_val_dict:
        # try checking equality directly with '=' operator,
        # as comparison may have been overridden for the left
        # hand object
        try:
            v1 = arg_val_dict[key]
            v2 = compat_args[key]

            # check for None-ness otherwise we could end up
            # comparing a numpy array vs None
            if (v1 is not None and v2 is None) or \
               (v1 is None and v2 is not None):
                match = False
            else:
                match = (v1 == v2)

            if not is_bool(match):
                raise ValueError("'match' is not a boolean")

        # could not compare them directly, so try comparison
        # using the 'is' operator
        except:
            match = (arg_val_dict[key] is compat_args[key])

        if not match:
            raise ValueError(("the '{arg}' parameter is not "
                              "supported in the pandas "
                              "implementation of {fname}()".
                              format(fname=fname, arg=key)))


def validate_args(fname, args, max_fname_arg_count, compat_args):
    """
    Checks whether the length of the `*args` argument passed into a function
    has at most `len(compat_args)` arguments and whether or not all of these
    elements in `args` are set to their default values.

    fname: str
        The name of the function being passed the `*args` parameter

    args: tuple
        The `*args` parameter passed into a function

    max_fname_arg_count: int
        The maximum number of arguments that the function `fname`
        can accept, excluding those in `args`. Used for displaying
        appropriate error messages. Must be non-negative.

    compat_args: OrderedDict
        A ordered dictionary of keys and their associated default values.
        In order to accommodate buggy behaviour in some versions of `numpy`,
        where a signature displayed keyword arguments but then passed those
        arguments **positionally** internally when calling downstream
        implementations, an ordered dictionary ensures that the original
        order of the keyword arguments is enforced. Note that if there is
        only one key, a generic dict can be passed in as well.

    Raises
    ------
    TypeError if `args` contains more values than there are `compat_args`
    ValueError if `args` contains values that do not correspond to those
    of the default values specified in `compat_args`

    """
    _check_arg_length(fname, args, max_fname_arg_count, compat_args)

    # We do this so that we can provide a more informative
    # error message about the parameters that we are not
    # supporting in the pandas implementation of 'fname'
    kwargs = dict(zip(compat_args, args))
    _check_for_default_values(fname, kwargs, compat_args)


def _check_for_invalid_keys(fname, kwargs, compat_args):
    """
    Checks whether 'kwargs' contains any keys that are not
    in 'compat_args' and raises a TypeError if there is one.

    """
    # set(dict) --> set of the dictionary's keys
    diff = set(kwargs) - set(compat_args)

    if diff:
        bad_arg = list(diff)[0]
        raise TypeError(("{fname}() got an unexpected "
                         "keyword argument '{arg}'".
                         format(fname=fname, arg=bad_arg)))


def validate_kwargs(fname, kwargs, compat_args):
    """
    Checks whether parameters passed to the **kwargs argument in a
    function `fname` are valid parameters as specified in `*compat_args`
    and whether or not they are set to their default values.

    Parameters
    ----------
    fname: str
        The name of the function being passed the `**kwargs` parameter

    kwargs: dict
        The `**kwargs` parameter passed into `fname`

    compat_args: dict
        A dictionary of keys that `kwargs` is allowed to have and their
        associated default values

    Raises
    ------
    TypeError if `kwargs` contains keys not in `compat_args`
    ValueError if `kwargs` contains keys in `compat_args` that do not
    map to the default values specified in `compat_args`

    """
    kwds = kwargs.copy()
    _check_for_invalid_keys(fname, kwargs, compat_args)
    _check_for_default_values(fname, kwds, compat_args)


def validate_args_and_kwargs(fname, args, kwargs,
                             max_fname_arg_count,
                             compat_args):
    """
    Checks whether parameters passed to the *args and **kwargs argument in a
    function `fname` are valid parameters as specified in `*compat_args`
    and whether or not they are set to their default values.

    Parameters
    ----------
    fname: str
        The name of the function being passed the `**kwargs` parameter

    args: tuple
        The `*args` parameter passed into a function

    kwargs: dict
        The `**kwargs` parameter passed into `fname`

    max_fname_arg_count: int
        The minimum number of arguments that the function `fname`
        requires, excluding those in `args`. Used for displaying
        appropriate error messages. Must be non-negative.

    compat_args: OrderedDict
        A ordered dictionary of keys that `kwargs` is allowed to
        have and their associated default values. Note that if there
        is only one key, a generic dict can be passed in as well.

    Raises
    ------
    TypeError if `args` contains more values than there are
    `compat_args` OR `kwargs` contains keys not in `compat_args`
    ValueError if `args` contains values not at the default value (`None`)
    `kwargs` contains keys in `compat_args` that do not map to the default
    value as specified in `compat_args`

    See Also
    --------
    validate_args : purely args validation
    validate_kwargs : purely kwargs validation

    """
    # Check that the total number of arguments passed in (i.e.
    # args and kwargs) does not exceed the length of compat_args
    _check_arg_length(fname, args + tuple(kwargs.values()),
                      max_fname_arg_count, compat_args)

    # Check there is no overlap with the positional and keyword
    # arguments, similar to what is done in actual Python functions
    args_dict = dict(zip(compat_args, args))

    for key in args_dict:
        if key in kwargs:
            raise TypeError("{fname}() got multiple values for keyword "
                            "argument '{arg}'".format(fname=fname, arg=key))

    kwargs.update(args_dict)
    validate_kwargs(fname, kwargs, compat_args)


def validate_bool_kwarg(value, arg_name):
    """ Ensures that argument passed in arg_name is of type bool. """
    if not (is_bool(value) or value is None):
        raise ValueError('For argument "{arg}" expected type bool, received '
                         'type {typ}.'.format(arg=arg_name,
                                              typ=type(value).__name__))
    return value


def validate_axis_style_args(data, args, kwargs, arg_name, method_name):
    """Argument handler for mixed index, columns / axis functions

    In an attempt to handle both `.method(index, columns)`, and
    `.method(arg, axis=.)`, we have to do some bad things to argument