from transformers.models.auto.tokenization_auto import AutoTokenizer
from transformers.models.auto.modeling_auto import AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-medium')
model = AutoModelForCausalLM.from_pretrained('../output-medium')

# chatting 5 times with beam search
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        num_beams=3,
        early_stopping=True,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"Cartman: {output}")