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__) tokenizer = None async def get_full_prompt(simple_prompt: str, bot, chat_history): # Prompt without history prompt = bot.name + "'s Persona: " + bot.get_persona() + "\n" prompt += "Scenario: " + bot.get_scenario() + "\n\n" for dialogue_item in bot.get_example_dialogue(): prompt += "" + "\n" dialogue_item = dialogue_item.replace('{{user}}', 'You') dialogue_item = dialogue_item.replace('{{char}}', bot.name) prompt += dialogue_item + "\n\n" prompt += "" + "\n" #prompt += bot.name + ": " + bot.greeting + "\n" #prompt += "You: " + simple_prompt + "\n" #prompt += bot.name + ":" MAX_TOKENS = 2048 WINDOW = 800 max_new_tokens = 200 total_num_tokens = await num_tokens(prompt) input_num_tokens = await num_tokens(f"You: " + simple_prompt + "\n{bot.name}:") total_num_tokens += input_num_tokens visible_history = [] num_message = 0 for key, chat_item in reversed(chat_history.chat_history.items()): num_message += 1 if num_message == 1: # skip current_message continue if chat_item.stop_here: break 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_history.chat_history[key].num_tokens = await num_tokens("{}: {}".format(chat_item.user_name, chat_item.message["en"])) chat_item = chat_history.chat_history[key] # 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 - WINDOW - max_new_tokens: visible_history.append(chat_item) total_num_tokens += chat_item.num_tokens else: break visible_history = reversed(visible_history) if not hasattr(bot, "greeting_num_tokens"): bot.greeting_num_tokens = await num_tokens(bot.greeting) if total_num_tokens + bot.greeting_num_tokens <= MAX_TOKENS - WINDOW - max_new_tokens: prompt += bot.name + ": " + bot.greeting + "\n" total_num_tokens += bot.greeting_num_tokens for chat_item in visible_history: if chat_item.is_own_message: line = bot.name + ": " + chat_item.message["en"] + "\n" else: line = "You" + ": " + chat_item.message["en"] + "\n" prompt += line if chat_history.getSavedPrompt() and not chat_item.is_in_saved_prompt: logger.info(f"adding to saved prompt: \"{line}\"") chat_history.setSavedPrompt( chat_history.getSavedPrompt() + line, chat_history.saved_context_num_tokens + chat_item.num_tokens ) chat_item.is_in_saved_prompt = True if chat_history.saved_context_num_tokens: logger.info(f"saved_context has {chat_history.saved_context_num_tokens+input_num_tokens} tokens. new context would be {total_num_tokens}. Limit is {MAX_TOKENS}") if chat_history.getSavedPrompt(): if chat_history.saved_context_num_tokens+input_num_tokens > MAX_TOKENS - max_new_tokens: chat_history.setFastForward(False) if chat_history.getFastForward(): logger.info("using saved prompt") prompt = chat_history.getSavedPrompt() if not chat_history.getSavedPrompt() or not chat_history.getFastForward(): logger.info("regenerating prompt") chat_history.setSavedPrompt(prompt, total_num_tokens) for key, chat_item in chat_history.chat_history.items(): if key != list(chat_history.chat_history)[-1]: # exclude current item chat_history.chat_history[key].is_in_saved_prompt = True chat_history.setFastForward(True) 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") global tokenizer if not tokenizer: 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