import time
import queue
import warnings
import numpy as np
import reip


class Rebuffer(reip.Block):
    '''Collect numpy arrays and convert them to a different resolution'''
    def __init__(self, size=None, duration=None, *a, sr_key='sr', **kw):
        assert size or duration, 'You must specify a size or duration.'
        self.size = size
        self.duration = duration
        self.sr_key = sr_key
        super().__init__(**kw)

    def init(self):
        self._q = queue.Queue()
        self.sr = None
        self.current_size = 0

    def _calc_size(self, meta):
        # calculate the size the first time using the sr in metadata
        if not self.size and meta:
            self.sr = self.sr or meta[self.sr_key]
            self.size = self.duration * self.sr

    def process(self, x, meta):
        self._calc_size(meta)

        # place buffer into queue. If buffer is full, gather buffers and emit.
        if self._place_buffer(x, meta):
            self.log.debug(f"full: {self.current_size} >= {self.size}")
            return self._gather_buffers()

    def _place_buffer(self, x, meta):
        '''put items on the queue and return if the buffer is full.'''
        xsize = self._get_size(x)
        self.current_size += xsize
        self._q.put((x, meta, xsize))
        return self.current_size >= self.size

    def _gather_buffers(self):
        '''Read items from the queue and emit once it's reached full size.'''
        size = 0
        xs, metas = [], []
        while size < self.size:
            x, meta, xsize = self._q.get()
            xs.append(x)
            metas.append(meta)
            size += xsize
            self.current_size -= xsize
        return [self._merge_buffers(xs)], self._merge_meta(metas)

    def _get_size(self, buf):
        '''Return the size of a buffer, based on it's type.'''
        return len(buf)

    def _merge_buffers(self, bufs):
        return np.concatenate(bufs)

    def _merge_meta(self, metas):
        return metas[0]


class FastRebuffer(reip.Block):
    def __init__(self, size=None, **kw):
        assert size , 'You must specify a size.'
        self.size = size
        super().__init__(**kw)

    def init(self):
        self.buffers, self.meta = None, None
        self.current_pos = 0

    def process(self, x, meta):
        # calculate the size the first time using the sr in metadata
        if self.buffers is None:
            shape = list(x.shape)
            shape[0] = shape[0] * self.size
            # self.buffers = np.concatenate([np.zeros_like(x)] * self.size)
            self.buffers = np.zeros(tuple(shape), dtype=x.dtype)
            self.meta = dict(meta)
            self.current_pos = 0

        l, i = x.shape[0], self.current_pos
        
        self.buffers[i*l:(i+1)*l, ...] = x[...]
        self.current_pos = (self.current_pos + 1) % self.size

        if self.current_pos == 0:
            ret = [self.buffers], self.meta
            self.buffers, self.meta = None, None
        else:
            ret = None

        return ret


class GatedRebuffer(reip.Block):
    '''Rebuffer while also sampling sparsely in time. For example, sampling audio files 
    while leaving gaps between them for privacy reasons.'''
    TIME_KEY = 'time'
    def __init__(self, sampler, size=10, **kw):
        self.sampler = (
            sampler if callable(sampler) else
            (lambda: sampler) if sampler else None)
        self.size = size
        self.pause_until = 0
        self.allow_index = None
        super().__init__(**kw)

    def check_input_skip(self, meta):
        if not self.sampler:
            return

        t0 = meta.get(self.TIME_KEY) or time.time()

        # check if we are currently allowing buffers
        if self.allow_index is not None:
            self.log.debug(f'allow: {self.processed} - {self.allow_index} >= {self.size}')
            if self.processed - self.allow_index < self.size:  # under the limit
                return
            # we've exceeded
            self.allow_index = None
            s = self.sampler()
            self.pause_until = t0 + s
            self.log.debug(f'will be sleeping for {s:.3g}s')

        # check if we should be pausing
        if t0 < self.pause_until:
            return True

        # if not, record the processed count
        self.allow_index = self.processed
        self.log.debug(f'allow_index: {self.allow_index}')

    def init(self):
        self.buffers = []
        self.meta = []

    def finish(self):
        self.clear()

    def clear(self):
        self.buffers.clear()
        self.meta.clear()

    def process(self, x, meta):
        if self.check_input_skip(meta):
            return
        self.log.debug(f'collecting buffer: {x.shape}')
        self.buffers.append(x)
        self.meta.append(meta)

        if len(self.buffers) >= self.size:
            X = np.concatenate(self.buffers)
            meta = self.meta[0]
            self.clear()
            self.log.debug(f'submitting: {X.shape}')
            return [X], meta




def temporal_coverage(clip_duration=10, coverage=0.5, min_silence=5.0, sampling='normal'):
    '''Given a clip_duration and desired coverage fraction, compute
    how long the silence between clips needs to be to satisfy the desired
    coverage and add some random variance to the silence based so that it
    gives values sampled from the distribution specified by sampling.

    Arguments:
        clip_duration (float): Duration of non-silent clip in seconds
        coverage (float): Fraction of time to be recorded (non-silence), must
            be in range (0,1].
        min_silence (float): Minimum silence allowed between clips, in seconds
        sampling (str): The distribution from which silence durations will be
            sampled, must be 'uniform' or 'normal'.

    Returns:
        silence (float): The amount of silence to insert between the current
            and next clip, in seconds.

    '''
    if clip_duration <= 0:
        raise ValueError('Clip duration must be positive.')
    if not (0 < coverage <= 1):
        raise ValueError('Coverage outside the allowed range of (0,1].')
    if sampling not in ['normal', 'uniform']:
        raise ValueError('Unknown sampling method.')

    # Compute exact silence duration
    silence = (1 - coverage) / float(coverage) * clip_duration

    # If the silence required to obtain the specified coverage is shorter
    # than min_silence we default back to min_silence and report the
    # estimated coverage
    if silence < min_silence:
        warnings.warn(
            "Coverage too high to meet min_silence of {:.2f} seconds, "
            "returning {:.2f}. Estimated coverage is {:.2%}".format(
                min_silence, min_silence,
                clip_duration / float(clip_duration + min_silence)))
        return min_silence

    # Add some variance
    sigma = np.abs(silence - min_silence)
    max_silence = silence + sigma

    if sampling == 'uniform':
        silence += sigma * (np.random.random_sample() * 2 - 1) # +[-sig, +sig]
    elif sampling == 'normal':
        silence += sigma / 3. * np.random.randn() # +N(0, sig/3)

    # Make sure silence is within limits
    return min(max(silence, min_silence), max_silence)

import time
import queue
import warnings
import numpy as np
import reip


class Rebuffer(reip.Block):
    '''Collect numpy arrays and convert them to a different resolution'''
    def __init__(self, size=None, duration=None, *a, sr_key='sr', **kw):
        assert size or duration, 'You must specify a size or duration.'
        self.size = size
        self.duration = duration
        self.sr_key = sr_key
        super().__init__(**kw)

    def init(self):
        self._q = queue.Queue()
        self.sr = None
        self.current_size = 0

    def _calc_size(self, meta):
        # calculate the size the first time using the sr in metadata
        if not self.size and meta:
            self.sr = self.sr or meta[self.sr_key]
            self.size = self.duration * self.sr

    def process(self, x, meta):
        self._calc_size(meta)

        # place buffer into queue. If buffer is full, gather buffers and emit.
        if self._place_buffer(x, meta):
            self.log.debug(f"full: {self.current_size} >= {self.size}")
            return self._gather_buffers()

    def _place_buffer(self, x, meta):
        '''put items on the queue and return if the buffer is full.'''
        xsize = self._get_size(x)
        self.current_size += xsize
        self._q.put((x, meta, xsize))
        return self.current_size >= self.size

    def _gather_buffers(self):
        '''Read items from the queue and emit once it's reached full size.'''
        size = 0
        xs, metas = [], []
        while size < self.size:
            x, meta, xsize = self._q.get()
            xs.append(x)
            metas.append(meta)
            size += xsize
            self.current_size -= xsize
        return [self._merge_buffers(xs)], self._merge_meta(metas)

    def _get_size(self, buf):
        '''Return the size of a buffer, based on it's type.'''
        return len(buf)

    def _merge_buffers(self, bufs):
        return np.concatenate(bufs)

    def _merge_meta(self, metas):
        return metas[0]


class FastRebuffer(reip.Block):
    def __init__(self, size=None, **kw):
        assert size , 'You must specify a size.'
        self.size = size
        super().__init__(**kw)

    def init(self):
        self.buffers, self.meta = None, None
        self.current_pos = 0

    def process(self, x, meta):
        # calculate the size the first time using the sr in metadata
        if self.buffers is None:
            shape = list(x.shape)
            shape[0] = shape[0] * self.size
            # self.buffers = np.concatenate([np.zeros_like(x)] * self.size)
            self.buffers = np.zeros(tuple(shape), dtype=x.dtype)
            self.meta = dict(meta)
            self.current_pos = 0

        l, i = x.shape[0], self.current_pos
        
        self.buffers[i*l:(i+1)*l, ...] = x[...]
        self.current_pos = (self.current_pos + 1) % self.size

        if self.current_pos == 0:
            ret = [self.buffers], self.meta
            self.buffers, self.meta = None, None
        else:
            ret = None

        return ret


class GatedRebuffer(reip.Block):
    '''Rebuffer while also sampling sparsely in time. For example, sampling audio files 
    while leaving gaps between them for privacy reasons.'''
    TIME_KEY = 'time'
    def __init__(self, sampler, size=10, **kw):
        self.sampler = (
            sampler if callable(sampler) else
            (lambda: sampler) if sampler else None)
        self.size = size
        self.pause_until = 0
        self.allow_index = None
        super().__init__(**kw)

    def check_input_skip(self, meta):
        if not self.sampler:
            return

        t0 = meta.get(self.TIME_KEY) or time.time()

        # check if we are currently allowing buffers
        if self.allow_index is not None:
            self.log.debug(f'allow: {self.processed} - {self.allow_index} >= {self.size}')
            if self.processed - self.allow_index < self.size:  # under the limit
                return
            # we've exceeded
            self.allow_index = None
            s = self.sampler()
            self.pause_until = t0 + s
            self.log.debug(f'will be sleeping for {s:.3g}s')

        # check if we should be pausing
        if t0 < self.pause_until:
            return True

        # if not, record the processed count
        self.allow_index = self.processed
        self.log.debug(f'allow_index: {self.allow_index}')

    def init(self):
        self.buffers = []
        self.meta = []

    def finish(self):
        self.clear()

    def clear(self):
        self.buffers.clear()
        self.meta.clear()

    def process(self, x, meta):
        if self.check_input_skip(meta):
            return
        self.log.debug(f'collecting buffer: {x.shape}')
        self.buffers.append(x)
        self.meta.append(meta)

        if len(self.buffers) >= self.size:
            X = np.concatenate(self.buffers)
            meta = self.meta[0]
            self.clear()
            self.log.debug(f'submitting: {X.shape}')
            return [X], meta




def temporal_coverage(clip_duration=10, coverage=0.5, min_silence=5.0, sampling='normal'):
    '''Given a clip_duration and desired coverage fraction, compute
    how long the silence between clips needs to be to satisfy the desired
    coverage and add some random variance to the silence based so that it
    gives values sampled from the distribution specified by sampling.

    Arguments:
        clip_duration (float): Duration of non-silent clip in seconds
        coverage (float): Fraction of time to be recorded (non-silence), must
            be in range (0,1].
        min_silence (float): Minimum silence allowed between clips, in seconds
        sampling (str): The distribution from which silence durations will be
            sampled, must be 'uniform' or 'normal'.

    Returns:
        silence (float): The amount of silence to insert between the current
            and next clip, in seconds.

    '''
    if clip_duration <= 0:
        raise ValueError('Clip duration must be positive.')
    if not (0 < coverage <= 1):
        raise ValueError('Coverage outside the allowed range of (0,1].')
    if sampling not in ['normal', 'uniform']:
        raise ValueError('Unknown sampling method.')

    # Compute exact silence duration
    silence = (1 - coverage) / float(coverage) * clip_duration

    # If the silence required to obtain the specified coverage is shorter
    # than min_silence we default back to min_silence and report the
    # estimated coverage
    if silence < min_silence:
        warnings.warn(
            "Coverage too high to meet min_silence of {:.2f} seconds, "
            "returning {:.2f}. Estimated coverage is {:.2%}".format(
                min_silence, min_silence,
                clip_duration / float(clip_duration + min_silence)))
        return min_silence

    # Add some variance
    sigma = np.abs(silence - min_silence)
    max_silence = silence + sigma

    if sampling == 'uniform':
        silence += sigma * (np.random.random_sample() * 2 - 1) # +[-sig, +sig]
    elif sampling == 'normal':
        silence += sigma / 3. * np.random.randn() # +N(0, sig/3)

    # Make sure silence is within limits
    return min(max(silence, min_silence), max_silence)

Collect numpy arrays and convert them to a different resolution # calculate the size the first time using the sr in metadata # place buffer into queue. If buffer is full, gather buffers and emit. put items on the queue and return if the buffer is full. Read items from the queue and emit once it's reached full size. Return the size of a buffer, based on it's type. # calculate the size the first time using the sr in metadata # self.buffers = np.concatenate([np.zeros_like(x)] * self.size) Rebuffer while also sampling sparsely in time. For example, sampling audio files      while leaving gaps between them for privacy reasons. # check if we are currently allowing buffers # under the limit # we've exceeded # check if we should be pausing # if not, record the processed count Given a clip_duration and desired coverage fraction, compute     how long the silence between clips needs to be to satisfy the desired     coverage and add some random variance to the silence based so that it     gives values sampled from the distribution specified by sampling.      Arguments:         clip_duration (float): Duration of non-silent clip in seconds         coverage (float): Fraction of time to be recorded (non-silence), must             be in range (0,1].         min_silence (float): Minimum silence allowed between clips, in seconds         sampling (str): The distribution from which silence durations will be             sampled, must be 'uniform' or 'normal'.      Returns:         silence (float): The amount of silence to insert between the current             and next clip, in seconds. # Compute exact silence duration # If the silence required to obtain the specified coverage is shorter # than min_silence we default back to min_silence and report the # estimated coverage # Add some variance # +[-sig, +sig] # +N(0, sig/3) # Make sure silence is within limits