2022-02-21 21:03:28 +00:00
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from argparse import ArgumentParser
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2022-02-23 04:25:03 +00:00
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from random import choice
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2022-02-21 18:30:20 +00:00
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from mastodon import Mastodon
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2022-02-22 22:58:19 +00:00
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from transformers import AutoTokenizer, AutoModelForCausalLM
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2022-02-21 22:40:27 +00:00
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2022-02-21 21:03:28 +00:00
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parser = ArgumentParser()
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2022-02-23 00:00:10 +00:00
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parser.add_argument('-i', '--instance', help='Mastodon instance hosting the bot')
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2022-02-21 21:03:28 +00:00
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parser.add_argument('-t', '--token', help='Mastodon application access token')
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2022-02-23 00:00:10 +00:00
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parser.add_argument('-n', '--input', help='initial input text')
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2022-02-22 22:58:19 +00:00
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parser.add_argument('-m', '--model', default='model',
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2022-02-21 22:47:59 +00:00
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help='path to load saved model')
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2022-02-21 21:03:28 +00:00
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args = parser.parse_args()
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2022-02-22 22:58:19 +00:00
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tokenizer = AutoTokenizer.from_pretrained('distilgpt2')
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model = AutoModelForCausalLM.from_pretrained(args.model)
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2022-02-21 18:30:20 +00:00
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2022-02-21 21:03:28 +00:00
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2022-02-23 04:25:03 +00:00
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if args.input is None:
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# Create random input
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args.input = choice([
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'I am',
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'My life is',
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'Computers are',
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'This is',
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'My',
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'I\'ve',
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'No one',
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'I love',
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'I will die of',
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'I',
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'The',
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'Anime'
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])
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2022-02-22 22:58:19 +00:00
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# Run the input through the model
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inputs = tokenizer.encode(args.input, return_tensors="pt")
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2022-02-22 23:55:04 +00:00
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output = tokenizer.decode(model.generate(
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2022-02-23 04:25:03 +00:00
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inputs, do_sample=True, max_length=100, top_p=0.9)[0])
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2022-02-22 22:58:19 +00:00
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print(output)
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2022-02-21 22:40:27 +00:00
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2022-02-22 22:58:19 +00:00
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# Post it to Mastodon
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mastodon = Mastodon(
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access_token=args.token,
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2022-02-23 00:00:10 +00:00
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api_base_url=args.instance
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2022-02-22 22:58:19 +00:00
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)
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2022-02-23 04:01:25 +00:00
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post = output.split('\n')[0]
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if len(post) < 100:
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post = output.split('\n')[0] + '\n' + output.split('\n')[1]
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2022-02-23 04:25:03 +00:00
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mastodon.status_post(post[:500])
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