# Created on 2018/12
# Author: Kaituo XU

import math

import torch
import numpy as np


def init_fn(worker_id):

    """
    Function to make the pytorch dataloader deterministic
    :param worker_id: id of the parallel worker
    :return:
    """

    np.random.seed(0 + worker_id)


def overlap_and_add(signal, frame_step):
    """Reconstructs a signal from a framed representation.

    Adds potentially overlapping frames of a signal with shape
    `[..., frames, frame_length]`, offsetting subsequent frames by `frame_step`.
    The resulting tensor has shape `[..., output_size]` where

        output_size = (frames - 1) * frame_step + frame_length

    Args:
        signal: A [..., frames, frame_length] Tensor. All dimensions may be unknown, and rank must be at least 2.
        frame_step: An integer denoting overlap offsets. Must be less than or equal to frame_length.

    Returns:
        A Tensor with shape [..., output_size] containing the overlap-added frames of signal's inner-most two dimensions.
        output_size = (frames - 1) * frame_step + frame_length

    Based on https://github.com/tensorflow/tensorflow/blob/r1.12/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
    """
    outer_dimensions = signal.size()[:-2]
    frames, frame_length = signal.size()[-2:]

    subframe_length = math.gcd(frame_length, frame_step)  # gcd=Greatest Common Divisor
    subframe_step = frame_step // subframe_length
    subframes_per_frame = frame_length // subframe_length
    output_size = frame_step * (frames - 1) + frame_length
    output_subframes = output_size // subframe_length

    subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)

    frame = torch.arange(0, output_subframes).unfold(0, subframes_per_frame, subframe_step).to(signal.device).long()
    frame = frame.contiguous().view(-1)

    result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)
    result.index_add_(-2, frame, subframe_signal)
    result = result.view(*outer_dimensions, -1)
    return result


def remove_pad(inputs, inputs_lengths):
    """
    Args:
        inputs: torch.Tensor, [B, C, T] or [B, T], B is batch size
        inputs_lengths: torch.Tensor, [B]
    Returns:
        results: a list containing B items, each item is [C, T], T varies
    """
    results = []
    dim = inputs.dim()
    if dim == 3:
        C = inputs.size(1)
    for input, length in zip(inputs, inputs_lengths):
        if dim == 3: # [B, C, T]
            results.append(input[:,:length].view(C, -1).cpu().numpy())
        elif dim == 2:  # [B, T]
            results.append(input[:length].view(-1).cpu().numpy())
    return results


if __name__ == '__main__':
    torch.manual_seed(123)
    M, C, K, N = 2, 2, 3, 4
    frame_step = 2
    signal = torch.randint(5, (M, C, K, N))
    result = overlap_and_add(signal, frame_step)
    print(signal)
    print(result)
