import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList
import time
import numpy as np
from torch.nn import functional as F


m = AutoModelForCausalLM.from_pretrained("/mnt/nvme/home/dakota/ckpts/stablelm/7B-sft-combined/checkpoint-8000", torch_dtype=torch.float16).cuda()
tok = AutoTokenizer.from_pretrained("/mnt/nvme/home/dakota/stablelm_tokenizer")
generator = pipeline('text-generation', model=m, tokenizer=tok, device=0)


start_message = """<|SYSTEM|># StableAssistant
- StableAssistant is A helpful and harmless Open Source AI Language Model developed by Stability and CarperAI.
- StableAssistant is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableAssistant is more than just an information source, StableAssistant is also able to write poetry, short stories, and make jokes.
- StableAssistant will refuse to participate in anything that could harm a human."""


class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [50278, 50279, 50277, 1, 0]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False


def contrastive_generate(text, bad_text):
    with torch.no_grad():
        tokens = tok(text, return_tensors="pt")['input_ids'].cuda()[:,:4096-1024]
        bad_tokens = tok(bad_text, return_tensors="pt")['input_ids'].cuda()[:,:4096-1024]
        history = None
        bad_history = None
        curr_output = list()
        for i in range(1024):
            out = m(tokens, past_key_values=history, use_cache=True)
            logits = out.logits
            history = out.past_key_values
            bad_out = m(bad_tokens, past_key_values=bad_history, use_cache=True)
            bad_logits = bad_out.logits
            bad_history = bad_out.past_key_values
            probs = F.softmax(logits.float(), dim=-1)[0][-1].cpu()
            bad_probs = F.softmax(bad_logits.float(), dim=-1)[0][-1].cpu()
            logits = torch.log(probs)
            bad_logits = torch.log(bad_probs)
            logits[probs > 0.1] = logits[probs > 0.1] - bad_logits[probs > 0.1]
            probs = F.softmax(logits)
            out = int(torch.multinomial(probs, 1))
            if out in [50278, 50279, 50277, 1, 0]:
                break
            else:
                curr_output.append(out)
            out = np.array([out])
            tokens = torch.from_numpy(np.array([out])).to(
                tokens.device)
            bad_tokens = torch.from_numpy(np.array([out])).to(
                tokens.device)
        return tok.decode(curr_output)

def generate(text, bad_text=None):
    stop = StopOnTokens()
    result = generator(text, max_new_tokens=1024, num_return_sequences=1, num_beams=1, do_sample=True, temperature=1.0, top_p=0.95, top_k=1000, stopping_criteria=StoppingCriteriaList([stop]))
    return result[0]["generated_text"].replace(text, "")


def user(user_message, history):
    return "", history + [[user_message, ""]]


def bot(history, curr_system_message):
    messages = curr_system_message + "".join(["".join(["<|USER|>"+item[0], "<|ASSISTANT|>"+item[1]]) for item in history])
    output = generate(messages)
    history[-1][1] = output
    time.sleep(1)
    return history


def system_update(msg):
    global curr_system_message
    curr_system_message = msg


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            chatbot = gr.Chatbot([])
            clear = gr.Button("Clear")
        with gr.Column():
            system_msg = gr.Textbox(start_message, label="System Message", interactive=True)
            msg = gr.Textbox(label="Chat Message")

    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot, [chatbot, system_msg], chatbot
    )
    system_msg.change(system_update, system_msg, None, queue=False)
    clear.click(lambda: None, None, chatbot, queue=False)
demo.launch(share=True)