from argparse import ArgumentParser
from random import choice

from mastodon import Mastodon
from transformers import AutoTokenizer, AutoModelForCausalLM


parser = ArgumentParser()
parser.add_argument('-i', '--instance', help='Mastodon instance hosting the bot')
parser.add_argument('-t', '--token', help='Mastodon application access token')
parser.add_argument('-n', '--input', help='initial input text')
parser.add_argument('-m', '--model', default='model',
                    help='path to load saved model')
args = parser.parse_args()


tokenizer = AutoTokenizer.from_pretrained('distilgpt2')
model = AutoModelForCausalLM.from_pretrained(args.model)


if args.input is None:
    # Create random input
    args.input = choice([
        'I am',
        'My life is',
        'Computers are',
        'This is',
        'My',
        'I\'ve',
        'No one',
        'I love',
        'I will die of',
        'I',
        'The',
        'Anime'
    ])


# Run the input through the model
inputs = tokenizer.encode(args.input, return_tensors="pt")
output = tokenizer.decode(model.generate(
    inputs, do_sample=True, max_length=100, top_p=0.9)[0])
print(output)


# Post it to Mastodon
mastodon = Mastodon(
    access_token=args.token,
    api_base_url=args.instance
)
post = output.split('\n')[0]
if len(post) < 100:
    post = output.split('\n')[0] + '\n' + output.split('\n')[1]
mastodon.status_post(post[:500])