import asyncio import os, tempfile import logging import json import requests from transformers import AutoTokenizer, AutoConfig from huggingface_hub import hf_hub_download import io import base64 from PIL import Image, PngImagePlugin logger = logging.getLogger(__name__) async def get_full_prompt(simple_prompt: str, bot, chat_history): # Prompt without history prompt = bot.name + "'s Persona: " + bot.persona + "\n" prompt += "Scenario: " + bot.scenario + "\n" prompt += "" + "\n" #prompt += bot.name + ": " + bot.greeting + "\n" prompt += "You: " + simple_prompt + "\n" prompt += bot.name + ":" MAX_TOKENS = 2048 max_new_tokens = 200 total_num_tokens = await num_tokens(prompt) visible_history = [] current_message = True for key, chat_item in reversed(chat_history.chat_history.items()): if current_message: current_message = False continue if chat_item.message["en"].startswith('!begin'): break if chat_item.message["en"].startswith('!'): continue if chat_item.message["en"].startswith(''): continue #if chat_item.message["en"] == bot.greeting: # continue if chat_item.num_tokens == None: chat_item.num_tokens = await num_tokens("{}: {}".format(chat_item.user_name, chat_item.message["en"])) # TODO: is it MAX_TOKENS or MAX_TOKENS - max_new_tokens?? logger.debug(f"History: " + str(chat_item) + " [" + str(chat_item.num_tokens) + "]") if total_num_tokens + chat_item.num_tokens < MAX_TOKENS - max_new_tokens: visible_history.append(chat_item) total_num_tokens += chat_item.num_tokens else: break visible_history = reversed(visible_history) prompt = bot.name + "'s Persona: " + bot.persona + "\n" prompt += "Scenario: " + bot.scenario + "\n" prompt += "" + "\n" #prompt += bot.name + ": " + bot.greeting + "\n" for chat_item in visible_history: if chat_item.is_own_message: prompt += bot.name + ": " + chat_item.message["en"] + "\n" else: prompt += "You" + ": " + chat_item.message["en"] + "\n" prompt += "You: " + simple_prompt + "\n" prompt += bot.name + ":" return prompt async def num_tokens(input_text: str): # os.makedirs("./models/pygmalion-6b", exist_ok=True) # hf_hub_download(repo_id="PygmalionAI/pygmalion-6b", filename="config.json", cache_dir="./models/pygmalion-6b") # config = AutoConfig.from_pretrained("./models/pygmalion-6b/config.json") tokenizer = AutoTokenizer.from_pretrained("PygmalionAI/pygmalion-6b") encoding = tokenizer.encode(input_text, add_special_tokens=False) max_input_size = tokenizer.max_model_input_sizes return len(encoding) async def estimate_num_tokens(input_text: str): return len(input_text)//4+1