Cleanup and reformat with autopep8

Changes

4 changed files (+17/-9)

• bot.py
  • @@ -1,10 +1,17 @@
    - #!/usr/bin/python3 + from argparse import ArgumentParser from mastodon import Mastodon + + parser = ArgumentParser() + parser.add_argument('-t', '--token', help='Mastodon application access token') + args = parser.parse_args() + + mastodon = Mastodon( - access_token = 'token.secret', + access_token = args.token, api_base_url = 'https://social.exozy.me/' ) - mastodon.status_post('i am pretty horrible but i can keep reading the same dtb thing to be a really cool people there a notification in every fediverse in liberal democracies and games iaposd looks like to enjoy course at that goes and is it in case youaposre always welcome this time ago maybe you can start things that i think you want me to screenshot you use straw as an owl party platform in the algerian population for that have the list i just wanted to be a registry that you are out where the app actually the') + + mastodon.status_post('i am pretty horrible but i can keep reading the same dtb thing to be a really cool people there a notification in every fediverse in liberal democracies and games iaposd looks like to enjoy course at that goes and is it in case youaposre always welcome this time ago maybe you can start things that i think you want me to screenshot you use straw as an owl party platform in the algerian population for that have the list i just wanted to be a registry that you are out where the app actually the')
• data.py
  • @@ -1,6 +1,6 @@
    - from re import sub - from html import unescape from argparse import ArgumentParser + from html import unescape + from re import sub from psycopg2 import connect
  • @@ -29,6 +29,7 @@ words = [sub(r'[^a-z0-9]', '', word.lower())
    words = [word for word in words if word != ''] + # Save to output file with open(args.output, 'w') as f: for word in words: f.write(word + '\n')
• dataset.py
  • @@ -25,4 +25,4 @@ class Dataset(torch.utils.data.Dataset):
    def __getitem__(self, index): return (torch.tensor(self.words_indexes[index:index+self.seq_size]), - torch.tensor(self.words_indexes[index+1:index+self.seq_size+1])) + torch.tensor(self.words_indexes[index+1:index+self.seq_size+1]))
• model.py
  • @@ -4,14 +4,14 @@ import torch.nn as nn
    class Model(nn.Module): def __init__(self, dataset, embedding_size, lstm_size, num_layers, dropout): super(Model, self).__init__() - + self.seq_size = dataset.seq_size self.lstm_size = lstm_size self.num_layers = num_layers n_vocab = len(dataset.uniq_words) self.embedding = nn.Embedding(n_vocab, embedding_size) - + self.lstm = nn.LSTM( input_size=embedding_size, hidden_size=lstm_size,
  • @@ -30,4 +30,4 @@ class Model(nn.Module):
    def zero_state(self, batch_size): return (torch.zeros(self.num_layers, batch_size, self.lstm_size), - torch.zeros(self.num_layers, batch_size, self.lstm_size)) + torch.zeros(self.num_layers, batch_size, self.lstm_size))