import itertools

from .utils import db_to_float


def detect_silence(audio_segment, min_silence_len=1000, silence_thresh=-16, seek_step=1):
    seg_len = len(audio_segment)

    # you can't have a silent portion of a sound that is longer than the sound
    if seg_len < min_silence_len:
        return []

    # convert silence threshold to a float value (so we can compare it to rms)
    silence_thresh = db_to_float(silence_thresh) * audio_segment.max_possible_amplitude

    # find silence and add start and end indicies to the to_cut list
    silence_starts = []

    # check successive (1 sec by default) chunk of sound for silence
    # try a chunk at every "seek step" (or every chunk for a seek step == 1)
    last_slice_start = seg_len - min_silence_len
    slice_starts = range(0, last_slice_start + 1, seek_step)

    # guarantee last_slice_start is included in the range
    # to make sure the last portion of the audio is searched
    if last_slice_start % seek_step:
        slice_starts = itertools.chain(slice_starts, [last_slice_start])

    for i in slice_starts:
        audio_slice = audio_segment[i:i + min_silence_len]
        if audio_slice.rms <= silence_thresh:
            silence_starts.append(i)

    # short circuit when there is no silence
    if not silence_starts:
        return []

    # combine the silence we detected into ranges (start ms - end ms)
    silent_ranges = []

    prev_i = silence_starts.pop(0)
    current_range_start = prev_i

    for silence_start_i in silence_starts:
        continuous = (silence_start_i == prev_i + seek_step)

        # sometimes two small blips are enough for one particular slice to be
        # non-silent, despite the silence all running together. Just combine
        # the two overlapping silent ranges.
        silence_has_gap = silence_start_i > (prev_i + min_silence_len)

        if not continuous and silence_has_gap:
            silent_ranges.append([current_range_start,
                                  prev_i + min_silence_len])
            current_range_start = silence_start_i
        prev_i = silence_start_i

    silent_ranges.append([current_range_start,
                          prev_i + min_silence_len])

    return silent_ranges


def detect_nonsilent(audio_segment, min_silence_len=1000, silence_thresh=-16, seek_step=1):
    silent_ranges = detect_silence(audio_segment, min_silence_len, silence_thresh, seek_step)
    len_seg = len(audio_segment)

    # if there is no silence, the whole thing is nonsilent
    if not silent_ranges:
        return [[0, len_seg]]

    # short circuit when the whole audio segment is silent
    if silent_ranges[0][0] == 0 and silent_ranges[0][1] == len_seg:
        return []

    prev_end_i = 0
    nonsilent_ranges = []
    for start_i, end_i in silent_ranges:
        nonsilent_ranges.append([prev_end_i, start_i])
        prev_end_i = end_i

    if end_i != len_seg:
        nonsilent_ranges.append([prev_end_i, len_seg])

    if nonsilent_ranges[0] == [0, 0]:
        nonsilent_ranges.pop(0)

    return nonsilent_ranges


def split_on_silence(audio_segment, min_silence_len=1000, silence_thresh=-16, keep_silence=100,
                     seek_step=1):
    """
    audio_segment - original pydub.AudioSegment() object

    min_silence_len - (in ms) minimum length of a silence to be used for
        a split. default: 1000ms

    silence_thresh - (in dBFS) anything quieter than this will be
        considered silence. default: -16dBFS

    keep_silence - (in ms or True/False) leave some silence at the beginning
        and end of the chunks. Keeps the sound from sounding like it
        is abruptly cut off.
        When the length of the silence is less than the keep_silence duration
        it is split evenly between the preceding and following non-silent
        segments.
        If True is specified, all the silence is kept, if False none is kept.
        default: 100ms
    """

    # from the itertools documentation
    def pairwise(iterable):
        "s -> (s0,s1), (s1,s2), (s2, s3), ..."
        a, b = itertools.tee(iterable)
        next(b, None)
        return zip(a, b)

    if isinstance(keep_silence, bool):
        keep_silence = len(audio_segment) if keep_silence else 0

    output_ranges = [
        [ start - keep_silence, end + keep_silence ]
        for (start,end)
            in detect_nonsilent(audio_segment, min_silence_len, silence_thresh, seek_step)
    ]

    for range_i, range_ii in pairwise(output_ranges):
        last_end = range_i[1]
        next_start = range_ii[0]
        if next_start < last_end:
            range_i[1] = (last_end+next_start)//2
            range_ii[0] = range_i[1]

    return [
        audio_segment[ max(start,0) : min(end,len(audio_segment)) ]
        for start,end in output_ranges
    ]


def detect_leading_silence(sound, silence_threshold=-50.0, chunk_size=10):
    '''
    sound is a pydub.AudioSegment
    silence_threshold in dB
    chunk_size in ms
    iterate over chunks until you find the first one with sound
    '''
    trim_ms = 0 # ms
    assert chunk_size > 0 # to avoid infinite loop
    while sound[trim_ms:trim_ms+chunk_size].dBFS < silence_threshold and trim_ms < len(sound):
        trim_ms += chunk_size

    return trim_ms