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#!/bin/bash

if [ "$(uname)" == "Darwin" ]; then
    # This is a Mac, so use Homebrew
    echo "Installing jq with Homebrew..."
    brew install jq
else
    # Assume this is a Linux system and use apt-get
    echo "Installing jq with apt-get..."
    sudo apt-get update

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gsettings set org.gnome.shell.extensions.dash-to-dock extend-height false
gsettings set org.gnome.shell.extensions.dash-to-dock dock-position BOTTOM
gsettings set org.gnome.shell.extensions.dash-to-dock transparency-mode FIXED
gsettings set org.gnome.shell.extensions.dash-to-dock dash-max-icon-size 64
gsettings set org.gnome.shell.extensions.dash-to-dock unity-backlit-items true

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def pad_nulls(arraylike, seq_len, input_size):
  '''
  Pad nulls to the end of the arraylike, so that the length of the arraylike
  is divisible by the batch_size.
  '''
  if len(arraylike)==0: 
    arraylike = np.zeros( (seq_len, input_size), dtype=np.float32)

  remainder = len(arraylike) % seq_len
  if remainder != 0:

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initializer=keras.initializers.he_uniform

#@title Encoder
def create_encoder(input_shape):
  inputs = keras.layers.Input(shape=input_shape)
  x = keras.layers.Conv1D(filters=HIDDEN_SIZE,
                          kernel_size=4,
                          strides=1,
                          input_shape=input_shape,
                          kernel_initializer=initializer, 

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class GenModel(nn.Module):
    def _init_weights(self, module):
        if module.state_dict().get('weight') != None:
          if type(module) == nn.BatchNorm1d:
            nn.init.zeros_(module.bias)
          else:
            nn.init.kaiming_uniform_(module.weight)

    def __init__(self, input_size, hidden_size,  num_layers, bidirectional):
        super().__init__()

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class GenModel(nn.Module):
    def _init_weights(self, module):
        if module.state_dict().get('weight') != None:
          if type(module) == nn.BatchNorm1d:
            nn.init.zeros_(module.bias)
          else:
            nn.init.kaiming_uniform_(module.weight)

    def __init__(self, input_size, hidden_size,  num_layers, bidirectional):
        super().__init__()

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