from argparse import ArgumentParser
from random import randint, choice

from torch import float16
from transformers import AutoTokenizer, AutoModelForCausalLM


parser = ArgumentParser()
parser.add_argument('-b', '--backend', choices=['mastodon', 'misskey', 'matrix', 'none'], default='mastodon',
                    help='fediverse server type')
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('-d', '--data', default='data',
                    help='data for automatic input generation')
parser.add_argument('-m', '--model', default='model',
                    help='path to load saved model')
args = parser.parse_args()


tokenizer = AutoTokenizer.from_pretrained('gpt2-large')
model = AutoModelForCausalLM.from_pretrained(args.model).to('cuda')


if args.input is None:
    # Create random input
    if randint(0, 1) == 0:
        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',
            'I\'m going to die',
            'Hello',
            '@ta180m@exozy.me',
            'Life',
            'My favorite',
            'I\'m not',
            'I hate',
            'I think',
            'In my opinion',
            'Breaking news:',
            'Have I ever told you that',
            'I read on the news that',
            'I never knew that',
            'My dream is',
            'It\'s terrible that'
        ])
    else:
        with open(args.data, 'r') as f:
            # Get a line with at least two words
            lines = f.readlines()
            line = choice(lines).split()
            while len(line) < 2:
                line = choice(lines).split()
            
            # Remove mentions
            if line[0].count('@') > 1:
                line[0] = '@'.join(line[0].split('@')[0:2])
            if line[1].count('@') > 1:
                line[1] = '@'.join(line[1].split('@')[0:2])
            args.input = line[0] + ' ' + line[1]


# Run the input through the model
print(args.input)
inputs = tokenizer.encode(args.input, return_tensors='pt').to('cuda')
output = tokenizer.decode(model.generate(
    inputs, do_sample=True, max_length=150, top_p=0.9)[0])
print(output)


# Prepare the post
output = output.split('\n')
post = output[0]
if len(post) < 200 and len(output) > 1:
    post = output[0] + '\n' + output[1]
post = post[:500]


# Post it!
if args.backend == 'mastodon':
    from mastodon import Mastodon

    mastodon = Mastodon(
        access_token=args.token,
        api_base_url=args.instance
    )
    mastodon.status_post(post)

elif args.backend == 'misskey':
    from Misskey import Misskey

    misskey = Misskey(args.instance, i=args.token)
    misskey.notes_create(post)

elif args.backend == 'matrix':
    import simplematrixbotlib as botlib

    creds = botlib.Creds(args.instance, 'ebooks', args.token)
    bot = botlib.Bot(creds)

    @bot.listener.on_startup
    async def room_joined(room_id):
        await bot.api.send_text_message(room_id=room_id, message=post)

    bot.run()