def METHOD_NAME(
        ref_rttm,
        sys_rttm,
        ignore_overlap=False,
        collar=0.25,
        individual_file_scores=False, ):
    """Computes Missed Speaker percentage (MS), False Alarm (FA),
    Speaker Error Rate (SER), and Diarization Error Rate (DER).
    Arguments
    ---------
    ref_rttm : str
        The path of reference/groundtruth RTTM file.
    sys_rttm : str
        The path of the system generated RTTM file.
    individual_file : bool
        If True, returns scores for each file in order.
    collar : float
        Forgiveness collar.
    ignore_overlap : bool
        If True, ignores overlapping speech during evaluation.
    Returns
    -------
    MS : float array
        Missed Speech.
    FA : float array
        False Alarms.
    SER : float array
        Speaker Error Rates.
    DER : float array
        Diarization Error Rates.
    Example
    -------
    >>> import pytest
    >>> pytest.skip('Skipping because of Perl dependency')
    >>> ref_rttm = "../../samples/rttm_samples/ref_rttm/ES2014c.rttm"
    >>> sys_rttm = "../../samples/rttm_samples/sys_rttm/ES2014c.rttm"
    >>> ignore_overlap = True
    >>> collar = 0.25
    >>> individual_file_scores = True
    >>> Scores = DER(ref_rttm, sys_rttm, ignore_overlap, collar, individual_file_scores)
    >>> print (Scores)
    (array([0., 0.]), array([0., 0.]), array([7.16923618, 7.16923618]), array([7.16923618, 7.16923618]))
    """
    curr = os.path.abspath(os.path.dirname(__file__))
    mdEval = os.path.join(curr, "./md-eval.pl")
    cmd = [
        mdEval,
        "-af",
        "-r",
        ref_rttm,
        "-s",
        sys_rttm,
        "-c",
        str(collar),
    ]
    if ignore_overlap:
        cmd.append("-1")
    try:
        stdout = subprocess.check_output(cmd, stderr=subprocess.STDOUT)
    except subprocess.CalledProcessError as ex:
        stdout = ex.output
    else:
        stdout = stdout.decode("utf-8")
        # Get all recording IDs
        file_ids = [m.strip() for m in FILE_IDS.findall(stdout)]
        file_ids = [
            file_id[2:] if file_id.startswith("f=") else file_id
            for file_id in file_ids
        ]
        scored_speaker_times = np.array(
            [float(m) for m in SCORED_SPEAKER_TIME.findall(stdout)])
        miss_speaker_times = np.array(
            [float(m) for m in MISS_SPEAKER_TIME.findall(stdout)])
        fa_speaker_times = np.array(
            [float(m) for m in FA_SPEAKER_TIME.findall(stdout)])
        error_speaker_times = np.array(
            [float(m) for m in ERROR_SPEAKER_TIME.findall(stdout)])
        with np.errstate(invalid="ignore", divide="ignore"):
            tot_error_times = (
                miss_speaker_times + fa_speaker_times + error_speaker_times)
            miss_speaker_frac = miss_speaker_times / scored_speaker_times
            fa_speaker_frac = fa_speaker_times / scored_speaker_times
            sers_frac = error_speaker_times / scored_speaker_times
            ders_frac = tot_error_times / scored_speaker_times
        # Values in percentage of scored_speaker_time
        miss_speaker = rectify(miss_speaker_frac)
        fa_speaker = rectify(fa_speaker_frac)
        sers = rectify(sers_frac)
        ders = rectify(ders_frac)
        if individual_file_scores:
            return miss_speaker, fa_speaker, sers, ders
        else:
            return miss_speaker[-1], fa_speaker[-1], sers[-1], ders[-1]