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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
import os
token_key = os.environ.get("HF_ACCESS_TOKEN")
# if torch.cuda.is_available():
# m = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-tuned-alpha-7b",use_auth_token=token_key, torch_dtype=torch.float16).cuda()
# tok = AutoTokenizer.from_pretrained("stabilityai/stablelm-tuned-alpha-7b",use_auth_token=token_key)
# else:
# m = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-tuned-alpha-7b",use_auth_token=token_key, torch_dtype=torch.float16)
# tok = AutoTokenizer.from_pretrained("stabilityai/stablelm-tuned-alpha-7b",use_auth_token=token_key)
# 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():
# if torch.cuda_is_available():
# tokens = tok(text, return_tensors="pt")['input_ids'].cuda()[:,:4096-1024]
# bad_tokens = tok(bad_text, return_tensors="pt")['input_ids'].cuda()[:,:4096-1024]
# else:
# tokens = tok(text, return_tensors="pt")['input_ids'][:,:4096-1024]
# bad_tokens = tok(bad_text, return_tensors="pt")['input_ids'][:,: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:
# gr.Markdown("###StableLM-tuned-Alpha-7B Chat")
# with gr.Row():
# with gr.Column():
# chatbot = gr.Chatbot([])
# clear = gr.Button("Clear")
# with gr.Column():
# system_msg = start_message#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) |