def _adjust_bin_edges(datetime_bins, offset, closed, index, labels):
    """This is required for determining the bin edges resampling with
    daily frequencies greater than one day, month end, and year end
    frequencies.

    Consider the following example.  Let's say you want to downsample the
    time series with the following coordinates to month end frequency:

    CFTimeIndex([2000-01-01 12:00:00, 2000-01-31 12:00:00,
                 2000-02-01 12:00:00], dtype='object')

    Without this adjustment, _get_time_bins with month-end frequency will
    return the following index for the bin edges (default closed='right' and
    label='right' in this case):

    CFTimeIndex([1999-12-31 00:00:00, 2000-01-31 00:00:00,
                 2000-02-29 00:00:00], dtype='object')

    If 2000-01-31 is used as a bound for a bin, the value on
    2000-01-31T12:00:00 (at noon on January 31st), will not be included in the
    month of January.  To account for this, pandas adds a day minus one worth
    of microseconds to the bin edges generated by cftime range, so that we do
    bin the value at noon on January 31st in the January bin.  This results in
    an index with bin edges like the following:

    CFTimeIndex([1999-12-31 23:59:59, 2000-01-31 23:59:59,
                 2000-02-29 23:59:59], dtype='object')

    The labels are still:

    CFTimeIndex([2000-01-31 00:00:00, 2000-02-29 00:00:00], dtype='object')

    This is also required for daily frequencies longer than one day and
    year-end frequencies.
    """
    is_super_daily = (isinstance(offset, (MonthEnd, QuarterEnd, YearEnd)) or
                      (isinstance(offset, Day) and offset.n > 1))
    if is_super_daily:
        if closed == 'right':
            datetime_bins = datetime_bins + datetime.timedelta(days=1,
                                                               microseconds=-1)
        if datetime_bins[-2] > index.max():
            datetime_bins = datetime_bins[:-1]
            labels = labels[:-1]

    return datetime_bins, labels