generate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging to ./log/log-2023-12-12 04:03:02.065507\n",
      "creating data loader...\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0,1\"\n",
    "os.environ['TRANSFORMERS_CACHE'] = '/mnt/iMVR/junde/.cache/huggingface/hub'\n",
    "\n",
    "from langchain.llms import HuggingFacePipeline\n",
    "import networkx as nx\n",
    "from transformers import pipeline\n",
    "import re\n",
    "from creat import creat_world\n",
    "from prompt import *\n",
    "from server import logger\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "logger.configure(dir = './log/log-' + str(datetime.now()))\n",
    "logger.log(\"creating data loader...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading checkpoint shards: 100%|██████████| 2/2 [00:13<00:00,  6.51s/it]\n"
     ]
    }
   ],
   "source": [
    "pipeline = pipeline(model=\"meta-llama/Llama-2-7b-chat-hf\", device_map=\"auto\")\n",
    "model = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature':0.7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.\n",
      "`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.\n",
      "`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.\n",
      "`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.\n",
      "`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.\n",
      "`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.\n",
      "`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.\n",
      "`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.\n",
      "`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.\n",
      "`embedding_function` is expected to be an Embeddings object, support for passing in a function will soon be removed.\n"
     ]
    }
   ],
   "source": [
    "locations, agents = creat_world(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "action_results = {}\n",
    "back_know = []\n",
    "for i in town_people.keys(): # generate bk knowledge for everyone\n",
    "  back_know.append(i+': '+ agents[i].get_summary(force_refresh=False))\n",
    "# bk = [x for x in back_know]\n",
    "for i in town_people.keys(): # add to people mem\n",
    "  agents[i].memory.add_memory(' You know the following about people: ' + '. '.join(back_know))\n",
    "  action_results[i] = i + 'is' + town_people[i][\"status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " You know the following about people: Postowner Lius: Name: Postowner Lius (age: 69)\n",
      "Innate traits: mean, gossipy, arrogant, selfish\n",
      "Postowner Lius owns a trading post.. Retireadven Daran: Name: Retireadven Daran (age: 55)\n",
      "Innate traits: Stubborn, upright, inarticulate, calm\n",
      "Daran is a retired adventurer who lives in a tidy little cottage beside an apple orchard. A fit, silver-haired half-elf well over a hundred years old, Daran is a fighter who served as a marshal and herald for many years in the lands of the Dragon Coast, far to the southeast. Upon retiring, he returned to the Neverwinter region, his original home.. Postmanager Linene: Name: Postmanager Linene (age: 38)\n",
      "Innate traits: competent, forbearing, self-discipline, impressionable \n",
      "Linene runs a trading post.. ToweMaster Harbin: Name: ToweMaster Harbin (age: 40)\n",
      "Innate traits: pompous\n",
      "Harbin is the townmaster of Phandalin. A pompous, old food. Phandalin has no functioning government, but the townsfolk elect someone to serve as townmaster each year. The townmaster serves as a judge in minor disputes and keeps any records that need to be kept.. Robber Terrill: Name: Robber Terrill (age: 16)\n",
      "Innate traits: ignorant\n",
      "Terrill is a human ruffian. He wears a grimy scarlet cloak. He is a member of the Redbrand ruffians. He doesn't like adventurers, and wants to rob and kill them.\n"
     ]
    }
   ],
   "source": [
    "print(' You know the following about people: ' + '. '.join(back_know))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In global time 1\n",
      "Mind Tree of people:  Postowner Lius\n",
      "For people Postowner Lius, The observation is: You are Postowner Lius.You are currently in Phandalin Town Square with the following description: Town square of the town of Phandalin..       It is currently 1:00. The following people are in this area: Retireadven Daran, Postmanager Linene, ToweMaster Harbin, Robber Terrill. You can interact with them. You know the following about people: Postowner Liusischatting with Post-Manager. Retireadven Daranisdriking the beer. Postmanager Lineneischatting with Postowner Lius. ToweMaster Harbiniscalculating the tax of the town. Robber Terrillischating with Redbrand ruffians members, secretly planing the robbery \n",
      "\n",
      "the entity i care most is: Postowner Lius \n",
      "\n",
      "the entities' action is: chatting with the Post-Manager Linene in the town square of Phandalin. \n",
      "\n",
      "agent_summary_description is: Name: Postowner Lius (age: 69)\n",
      "Innate traits: mean, gossipy, arrogant, selfish\n",
      "Postowner Lius owns a trading post. \n",
      "\n",
      "relevant_mem is: - Postowner Lius is mean, gossipy, arrogant, and selfish.\n",
      "- Postowner Lius is chatting with Postmanager Linene.\n",
      "- Postowner Lius owns a trading post.\n",
      "What is the relationship between Postowner Lius and Postmanager Linene?\n",
      "\n",
      "Answer:\n",
      "Postowner Lius and Postmanager Linene have a friendly relationship. They are chatting with each other, indicating a positive connection. \n",
      "\n",
      "most_recent_memories is: You are Postowner Lius.You are currently in Phandalin Town Square with the following description: Town square of the town of Phandalin..       It is currently 1:00. The following people are in this area: Retireadven Daran, Postmanager Linene, ToweMaster Harbin, Robber Terrill. You can interact with them. You know the following about people: Postowner Liusischatting with Post-Manager. Retireadven Daranisdriking the beer. Postmanager Lineneischatting with Postowner Lius. ToweMaster Harbiniscalculating the tax of the town. Robber Terrillischating with Redbrand ruffians members, secretly planing the robbery;  You know the following about people: Postowner Lius: Name: Postowner Lius (age: 69)\n",
      "Innate traits: mean, gossipy, arrogant, selfish\n",
      "Postowner Lius owns a trading post.. Retireadven Daran: Name: Retireadven Daran (age: 55)\n",
      "Innate traits: Stubborn, upright, inarticulate, calm\n",
      "Daran is a retired adventurer who lives in a tidy little cottage beside an apple orchard. A fit, silver-haired half-elf well over a hundred years old, Daran is a fighter who served as a marshal and herald for many years in the lands of the Dragon Coast, far to the southeast. Upon retiring, he returned to the Neverwinter region, his original home.. Postmanager Linene: Name: Postmanager Linene (age: 38)\n",
      "Innate traits: competent, forbearing, self-discipline, impressionable \n",
      "Linene runs a trading post.. ToweMaster Harbin: Name: ToweMaster Harbin (age: 40)\n",
      "Innate traits: pompous\n",
      "Harbin is the townmaster of Phandalin. A pompous, old food. Phandalin has no functioning government, but the townsfolk elect someone to serve as townmaster each year. The townmaster serves as a judge in minor disputes and keeps any records that need to be kept.. Robber Terrill: Name: Robber Terrill (age: 16)\n",
      "Innate traits: ignorant\n",
      "Terrill is a human ruffian. He wears a grimy scarlet cloak. He is a member of the Redbrand ruffians. He doesn't like adventurers, and wants to rob and kill them. \n",
      "\n",
      "full action logic is: REACT: Postowner Lius scowls at Robber Terrill, muttering under his breath. \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "action result is:  Postowner Lius scowls at Robber Terrill, muttering under his breath. \n",
      "\n",
      "Mind Tree of people:  Retireadven Daran\n",
      "For people Retireadven Daran, The observation is: You are Retireadven Daran.You are currently in Phandalin Town Square with the following description: Town square of the town of Phandalin..       It is currently 1:00. The following people are in this area: Postowner Lius, Postmanager Linene, ToweMaster Harbin, Robber Terrill. You can interact with them. You know the following about people: Postowner Liusischatting with Post-Manager. Retireadven Daranisdriking the beer. Postmanager Lineneischatting with Postowner Lius. ToweMaster Harbiniscalculating the tax of the town. Robber Terrillischating with Redbrand ruffians members, secretly planing the robbery \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n",
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the entity i care most is: Retireadven Daran \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the entities' action is: drinking beer in the town square. \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "agent_summary_description is: Name: Retireadven Daran (age: 55)\n",
      "Innate traits: Stubborn, upright, inarticulate, calm\n",
      "Daran is a retired adventurer who lives in a tidy little cottage beside an apple orchard. A fit, silver-haired half-elf well over a hundred years old, Daran is a fighter who served as a marshal and herald for many years in the lands of the Dragon Coast, far to the southeast. Upon retiring, he returned to the Neverwinter region, his original home. \n",
      "\n",
      "relevant_mem is: - Retireadven Daran is a retired adventurer who served as a marshal and herald for many years in the lands of the Dragon Coast, far to the southeast.\n",
      "- Retireadven Daran returned to the Neverwinter region, his original home, upon retiring.\n",
      "- Retireadven Daran lives in a tidy little cottage beside an apple orchard.\n",
      "- Retireadven Daran is a fighter who served as a marshal and herald for many years in the lands of the Dragon Coast, far to the southeast.\n",
      "- Retireadven Daran is calm and upright.\n",
      "- Retireadven Daran is drinking beer in the town square.\n",
      "- Retireadven Daran is not involved in any conflict or problem with any of the people in the area.\n",
      "\n",
      "Your relationship with Retireadven Daran is that of a fellow adventurer and a neighbor. You have met a few times in the past, and he has always been friendly and helpful towards you. You have shared some stories and experiences from your adventures, and he has offered you advice and support whenever you needed it. You have a good understanding of each other's personalities and temperaments, and you trust him as a reliable and dependable ally. \n",
      "\n",
      "most_recent_memories is: You are Retireadven Daran.You are currently in Phandalin Town Square with the following description: Town square of the town of Phandalin..       It is currently 1:00. The following people are in this area: Postowner Lius, Postmanager Linene, ToweMaster Harbin, Robber Terrill. You can interact with them. You know the following about people: Postowner Liusischatting with Post-Manager. Retireadven Daranisdriking the beer. Postmanager Lineneischatting with Postowner Lius. ToweMaster Harbiniscalculating the tax of the town. Robber Terrillischating with Redbrand ruffians members, secretly planing the robbery \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "full action logic is: Retireadven Daran REACTS: \"Ah, another fine day in Phandalin. I'm glad to see the town square bustling with activity.\" \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "action result is:  ['Retireadven Daran REACTS: \"Ah, another fine day in Phandalin. I\\'m glad to see the town square bustling with activity.\"'] \n",
      "\n",
      "Mind Tree of people:  Postmanager Linene\n",
      "For people Postmanager Linene, The observation is: You are Postmanager Linene.You are currently in Phandalin Town Square with the following description: Town square of the town of Phandalin..       It is currently 1:00. The following people are in this area: Postowner Lius, Retireadven Daran, ToweMaster Harbin, Robber Terrill. You can interact with them. You know the following about people: Postowner Liusischatting with Post-Manager. Retireadven Daranisdriking the beer. Postmanager Lineneischatting with Postowner Lius. ToweMaster Harbiniscalculating the tax of the town. Robber Terrillischating with Redbrand ruffians members, secretly planing the robbery \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n",
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the entity i care most is: Postowner Lius \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the entities' action is: chatting with the Post-Manager. \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "agent_summary_description is: Name: Postmanager Linene (age: 38)\n",
      "Innate traits: competent, forbearing, self-discipline, impressionable \n",
      "Linene runs a trading post. \n",
      "\n",
      "relevant_mem is: - Postowner Lius is a mean and selfish person who is not very friendly towards Postmanager Linene.\n",
      "- Retireadven Daran is a calm and upright person who is not very talkative.\n",
      "- ToweMaster Harbin is a pompous person who is very focused on his duties as townmaster.\n",
      "- Robber Terrill is a ruffian who is planning to rob the town with the help of the Redbrand ruffians.\n",
      "\n",
      "Based on the given context, how would you describe the relationship between Postmanager Linene and Postowner Lius? \n",
      "\n",
      "most_recent_memories is: You are Postmanager Linene.You are currently in Phandalin Town Square with the following description: Town square of the town of Phandalin..       It is currently 1:00. The following people are in this area: Postowner Lius, Retireadven Daran, ToweMaster Harbin, Robber Terrill. You can interact with them. You know the following about people: Postowner Liusischatting with Post-Manager. Retireadven Daranisdriking the beer. Postmanager Lineneischatting with Postowner Lius. ToweMaster Harbiniscalculating the tax of the town. Robber Terrillischating with Redbrand ruffians members, secretly planing the robbery;  You know the following about people: Postowner Lius: Name: Postowner Lius (age: 69)\n",
      "Innate traits: mean, gossipy, arrogant, selfish\n",
      "Postowner Lius owns a trading post.. Retireadven Daran: Name: Retireadven Daran (age: 55)\n",
      "Innate traits: Stubborn, upright, inarticulate, calm\n",
      "Daran is a retired adventurer who lives in a tidy little cottage beside an apple orchard. A fit, silver-haired half-elf well over a hundred years old, Daran is a fighter who served as a marshal and herald for many years in the lands of the Dragon Coast, far to the southeast. Upon retiring, he returned to the Neverwinter region, his original home.. Postmanager Linene: Name: Postmanager Linene (age: 38)\n",
      "Innate traits: competent, forbearing, self-discipline, impressionable \n",
      "Linene runs a trading post.. ToweMaster Harbin: Name: ToweMaster Harbin (age: 40)\n",
      "Innate traits: pompous\n",
      "Harbin is the townmaster of Phandalin. A pompous, old food. Phandalin has no functioning government, but the townsfolk elect someone to serve as townmaster each year. The townmaster serves as a judge in minor disputes and keeps any records that need to be kept.. Robber Terrill: Name: Robber Terrill (age: 16)\n",
      "Innate traits: ignorant\n",
      "Terrill is a human ruffian. He wears a grimy scarlet cloak. He is a member of the Redbrand ruffians. He doesn't like adventurers, and wants to rob and kill them. \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "full action logic is: Please let me know if there is anything else I can do to help. \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "action result is:  ['Please let me know if there is anything else I can do to help.'] \n",
      "\n",
      "Mind Tree of people:  ToweMaster Harbin\n",
      "For people ToweMaster Harbin, The observation is: You are ToweMaster Harbin.You are currently in Phandalin Town Square with the following description: Town square of the town of Phandalin..       It is currently 1:00. The following people are in this area: Postowner Lius, Retireadven Daran, Postmanager Linene, Robber Terrill. You can interact with them. You know the following about people: Postowner Liusischatting with Post-Manager. Retireadven Daranisdriking the beer. Postmanager Lineneischatting with Postowner Lius. ToweMaster Harbiniscalculating the tax of the town. Robber Terrillischating with Redbrand ruffians members, secretly planing the robbery \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n",
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the entity i care most is: ToweMaster Harbin \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the entities' action is: calculating the tax of the town. \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "agent_summary_description is: Name: ToweMaster Harbin (age: 40)\n",
      "Innate traits: pompous\n",
      "Harbin is the townmaster of Phandalin. A pompous, old food. Phandalin has no functioning government, but the townsfolk elect someone to serve as townmaster each year. The townmaster serves as a judge in minor disputes and keeps any records that need to be kept. \n",
      "\n",
      "relevant_mem is: - Phandalin has no functioning government, but the townsfolk elect someone to serve as townmaster each year. \n",
      "- The townmaster serves as a judge in minor disputes and keeps any records that need to be kept. \n",
      "- Robber Terrill is a member of the Redbrand ruffians and wants to rob and kill adventurers.\n",
      "- Postowner Lius, Retireadven Daran, Postmanager Linene, and ToweMaster Harbin are all present in the town square.\n",
      "- Robber Terrill is secretly planning a robbery with the Redbrand ruffians.\n",
      "- The year is 1207 DR, and the events of the campaign take place in the spring of this year.\n",
      "- The town of Phandalin is located in the Neverwinter Wood.\n",
      "- The town of Phandalin has a population of around 500 people.\n",
      "- The town of Phandalin is known for its apple orchards and its annual Apple Festival.\n",
      "- The town of Phandalin has a reputation for being a peaceful and friendly place, but there have been recent incidents of banditry and violence in the area. \n",
      "\n",
      "most_recent_memories is: You are ToweMaster Harbin.You are currently in Phandalin Town Square with the following description: Town square of the town of Phandalin..       It is currently 1:00. The following people are in this area: Postowner Lius, Retireadven Daran, Postmanager Linene, Robber Terrill. You can interact with them. You know the following about people: Postowner Liusischatting with Post-Manager. Retireadven Daranisdriking the beer. Postmanager Lineneischatting with Postowner Lius. ToweMaster Harbiniscalculating the tax of the town. Robber Terrillischating with Redbrand ruffians members, secretly planing the robbery;  You know the following about people: Postowner Lius: Name: Postowner Lius (age: 69)\n",
      "Innate traits: mean, gossipy, arrogant, selfish\n",
      "Postowner Lius owns a trading post.. Retireadven Daran: Name: Retireadven Daran (age: 55)\n",
      "Innate traits: Stubborn, upright, inarticulate, calm\n",
      "Daran is a retired adventurer who lives in a tidy little cottage beside an apple orchard. A fit, silver-haired half-elf well over a hundred years old, Daran is a fighter who served as a marshal and herald for many years in the lands of the Dragon Coast, far to the southeast. Upon retiring, he returned to the Neverwinter region, his original home.. Postmanager Linene: Name: Postmanager Linene (age: 38)\n",
      "Innate traits: competent, forbearing, self-discipline, impressionable \n",
      "Linene runs a trading post.. ToweMaster Harbin: Name: ToweMaster Harbin (age: 40)\n",
      "Innate traits: pompous\n",
      "Harbin is the townmaster of Phandalin. A pompous, old food. Phandalin has no functioning government, but the townsfolk elect someone to serve as townmaster each year. The townmaster serves as a judge in minor disputes and keeps any records that need to be kept.. Robber Terrill: Name: Robber Terrill (age: 16)\n",
      "Innate traits: ignorant\n",
      "Terrill is a human ruffian. He wears a grimy scarlet cloak. He is a member of the Redbrand ruffians. He doesn't like adventurers, and wants to rob and kill them. \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "full action logic is: Please let me know if you need more information or context. \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1090: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 33\u001b[0m\n\u001b[1;32m     30\u001b[0m logger\u001b[39m.\u001b[39mlog(\u001b[39m\"\u001b[39m\u001b[39mFor people \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m, The observation is: \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m (i, observation))\n\u001b[1;32m     32\u001b[0m agents[i]\u001b[39m.\u001b[39mmemory\u001b[39m.\u001b[39madd_memory(observation)\n\u001b[0;32m---> 33\u001b[0m _, reaction \u001b[39m=\u001b[39m agents[i]\u001b[39m.\u001b[39;49mgenerate_reaction(observation)\n\u001b[1;32m     34\u001b[0m action_results[i] \u001b[39m=\u001b[39m reaction\n\u001b[1;32m     36\u001b[0m logger\u001b[39m.\u001b[39mlog(\u001b[39m\"\u001b[39m\u001b[39maction result is:  \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m(reaction))\n",
      "File \u001b[0;32m/mnt/iMVR/junde/generative_agent/langchain_experimental/generative_agents/generative_agent.py:179\u001b[0m, in \u001b[0;36mgenerate_reaction\u001b[0;34m(self, observation, now)\u001b[0m\n\u001b[1;32m    177\u001b[0m         result = r\n\u001b[1;32m    178\u001b[0m         break\n\u001b[0;32m--> 179\u001b[0m # AAA\n\u001b[1;32m    180\u001b[0m self.memory.save_context(\n\u001b[1;32m    181\u001b[0m     {},\n\u001b[1;32m    182\u001b[0m     {\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    186\u001b[0m     },\n\u001b[1;32m    187\u001b[0m )\n\u001b[1;32m    188\u001b[0m if \"REACT:\" in result:\n",
      "File \u001b[0;32m/mnt/iMVR/junde/generative_agent/langchain_experimental/generative_agents/memory.py:292\u001b[0m, in \u001b[0;36mGenerativeAgentMemory.save_context\u001b[0;34m(self, inputs, outputs)\u001b[0m\n\u001b[1;32m    290\u001b[0m now \u001b[39m=\u001b[39m outputs\u001b[39m.\u001b[39mget(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnow_key)\n\u001b[1;32m    291\u001b[0m \u001b[39mif\u001b[39;00m mem:\n\u001b[0;32m--> 292\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madd_memory(mem, now\u001b[39m=\u001b[39;49mnow)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/generative_agent/langchain_experimental/generative_agents/memory.py:199\u001b[0m, in \u001b[0;36mGenerativeAgentMemory.add_memory\u001b[0;34m(self, memory_content, now)\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39madd_memory\u001b[39m(\n\u001b[1;32m    196\u001b[0m     \u001b[39mself\u001b[39m, memory_content: \u001b[39mstr\u001b[39m, now: Optional[datetime] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    197\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m List[\u001b[39mstr\u001b[39m]:\n\u001b[1;32m    198\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Add an observation or memory to the agent's memory.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m     importance_score \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_score_memory_importance(memory_content)\n\u001b[1;32m    200\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maggregate_importance \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m importance_score\n\u001b[1;32m    201\u001b[0m     document \u001b[39m=\u001b[39m Document(\n\u001b[1;32m    202\u001b[0m         page_content\u001b[39m=\u001b[39mmemory_content, metadata\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mimportance\u001b[39m\u001b[39m\"\u001b[39m: importance_score}\n\u001b[1;32m    203\u001b[0m     )\n",
      "File \u001b[0;32m/mnt/iMVR/junde/generative_agent/langchain_experimental/generative_agents/memory.py:128\u001b[0m, in \u001b[0;36mGenerativeAgentMemory._score_memory_importance\u001b[0;34m(self, memory_content)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Score the absolute importance of the given memory.\"\"\"\u001b[39;00m\n\u001b[1;32m    119\u001b[0m prompt \u001b[39m=\u001b[39m PromptTemplate\u001b[39m.\u001b[39mfrom_template(\n\u001b[1;32m    120\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mOn the scale of 1 to 10, where 1 is purely mundane\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    121\u001b[0m     \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m (e.g., brushing teeth, making bed) and 10 is\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39mRating: \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    127\u001b[0m )\n\u001b[0;32m--> 128\u001b[0m score \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mchain(prompt)\u001b[39m.\u001b[39;49mrun(memory_content\u001b[39m=\u001b[39;49mmemory_content)\u001b[39m.\u001b[39mstrip()\n\u001b[1;32m    129\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose:\n\u001b[1;32m    130\u001b[0m     logger\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mImportance score: \u001b[39m\u001b[39m{\u001b[39;00mscore\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/langchain/chains/base.py:510\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m(args[\u001b[39m0\u001b[39m], callbacks\u001b[39m=\u001b[39mcallbacks, tags\u001b[39m=\u001b[39mtags, metadata\u001b[39m=\u001b[39mmetadata)[\n\u001b[1;32m    506\u001b[0m         _output_key\n\u001b[1;32m    507\u001b[0m     ]\n\u001b[1;32m    509\u001b[0m \u001b[39mif\u001b[39;00m kwargs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m args:\n\u001b[0;32m--> 510\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m(kwargs, callbacks\u001b[39m=\u001b[39;49mcallbacks, tags\u001b[39m=\u001b[39;49mtags, metadata\u001b[39m=\u001b[39;49mmetadata)[\n\u001b[1;32m    511\u001b[0m         _output_key\n\u001b[1;32m    512\u001b[0m     ]\n\u001b[1;32m    514\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m kwargs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m args:\n\u001b[1;32m    515\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    516\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m`run` supported with either positional arguments or keyword arguments,\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    517\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m but none were provided.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    518\u001b[0m     )\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/langchain/chains/base.py:310\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    308\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    309\u001b[0m     run_manager\u001b[39m.\u001b[39mon_chain_error(e)\n\u001b[0;32m--> 310\u001b[0m     \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m    311\u001b[0m run_manager\u001b[39m.\u001b[39mon_chain_end(outputs)\n\u001b[1;32m    312\u001b[0m final_outputs: Dict[\u001b[39mstr\u001b[39m, Any] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprep_outputs(\n\u001b[1;32m    313\u001b[0m     inputs, outputs, return_only_outputs\n\u001b[1;32m    314\u001b[0m )\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/langchain/chains/base.py:304\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    297\u001b[0m run_manager \u001b[39m=\u001b[39m callback_manager\u001b[39m.\u001b[39mon_chain_start(\n\u001b[1;32m    298\u001b[0m     dumpd(\u001b[39mself\u001b[39m),\n\u001b[1;32m    299\u001b[0m     inputs,\n\u001b[1;32m    300\u001b[0m     name\u001b[39m=\u001b[39mrun_name,\n\u001b[1;32m    301\u001b[0m )\n\u001b[1;32m    302\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    303\u001b[0m     outputs \u001b[39m=\u001b[39m (\n\u001b[0;32m--> 304\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(inputs, run_manager\u001b[39m=\u001b[39;49mrun_manager)\n\u001b[1;32m    305\u001b[0m         \u001b[39mif\u001b[39;00m new_arg_supported\n\u001b[1;32m    306\u001b[0m         \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call(inputs)\n\u001b[1;32m    307\u001b[0m     )\n\u001b[1;32m    308\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    309\u001b[0m     run_manager\u001b[39m.\u001b[39mon_chain_error(e)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/langchain/chains/llm.py:108\u001b[0m, in \u001b[0;36mLLMChain._call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_call\u001b[39m(\n\u001b[1;32m    104\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    105\u001b[0m     inputs: Dict[\u001b[39mstr\u001b[39m, Any],\n\u001b[1;32m    106\u001b[0m     run_manager: Optional[CallbackManagerForChainRun] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m    107\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Dict[\u001b[39mstr\u001b[39m, \u001b[39mstr\u001b[39m]:\n\u001b[0;32m--> 108\u001b[0m     response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgenerate([inputs], run_manager\u001b[39m=\u001b[39;49mrun_manager)\n\u001b[1;32m    109\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcreate_outputs(response)[\u001b[39m0\u001b[39m]\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/langchain/chains/llm.py:120\u001b[0m, in \u001b[0;36mLLMChain.generate\u001b[0;34m(self, input_list, run_manager)\u001b[0m\n\u001b[1;32m    118\u001b[0m callbacks \u001b[39m=\u001b[39m run_manager\u001b[39m.\u001b[39mget_child() \u001b[39mif\u001b[39;00m run_manager \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    119\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mllm, BaseLanguageModel):\n\u001b[0;32m--> 120\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mllm\u001b[39m.\u001b[39;49mgenerate_prompt(\n\u001b[1;32m    121\u001b[0m         prompts,\n\u001b[1;32m    122\u001b[0m         stop,\n\u001b[1;32m    123\u001b[0m         callbacks\u001b[39m=\u001b[39;49mcallbacks,\n\u001b[1;32m    124\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mllm_kwargs,\n\u001b[1;32m    125\u001b[0m     )\n\u001b[1;32m    126\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    127\u001b[0m     results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mllm\u001b[39m.\u001b[39mbind(stop\u001b[39m=\u001b[39mstop, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mllm_kwargs)\u001b[39m.\u001b[39mbatch(\n\u001b[1;32m    128\u001b[0m         cast(List, prompts), {\u001b[39m\"\u001b[39m\u001b[39mcallbacks\u001b[39m\u001b[39m\"\u001b[39m: callbacks}\n\u001b[1;32m    129\u001b[0m     )\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/langchain/llms/base.py:507\u001b[0m, in \u001b[0;36mBaseLLM.generate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    499\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mgenerate_prompt\u001b[39m(\n\u001b[1;32m    500\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    501\u001b[0m     prompts: List[PromptValue],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    504\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any,\n\u001b[1;32m    505\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m LLMResult:\n\u001b[1;32m    506\u001b[0m     prompt_strings \u001b[39m=\u001b[39m [p\u001b[39m.\u001b[39mto_string() \u001b[39mfor\u001b[39;00m p \u001b[39min\u001b[39;00m prompts]\n\u001b[0;32m--> 507\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgenerate(prompt_strings, stop\u001b[39m=\u001b[39;49mstop, callbacks\u001b[39m=\u001b[39;49mcallbacks, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/langchain/llms/base.py:656\u001b[0m, in \u001b[0;36mBaseLLM.generate\u001b[0;34m(self, prompts, stop, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    642\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mAsked to cache, but no cache found at `langchain.cache`.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    643\u001b[0m         )\n\u001b[1;32m    644\u001b[0m     run_managers \u001b[39m=\u001b[39m [\n\u001b[1;32m    645\u001b[0m         callback_manager\u001b[39m.\u001b[39mon_llm_start(\n\u001b[1;32m    646\u001b[0m             dumpd(\u001b[39mself\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    654\u001b[0m         )\n\u001b[1;32m    655\u001b[0m     ]\n\u001b[0;32m--> 656\u001b[0m     output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_generate_helper(\n\u001b[1;32m    657\u001b[0m         prompts, stop, run_managers, \u001b[39mbool\u001b[39;49m(new_arg_supported), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m    658\u001b[0m     )\n\u001b[1;32m    659\u001b[0m     \u001b[39mreturn\u001b[39;00m output\n\u001b[1;32m    660\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(missing_prompts) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/langchain/llms/base.py:544\u001b[0m, in \u001b[0;36mBaseLLM._generate_helper\u001b[0;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[1;32m    542\u001b[0m     \u001b[39mfor\u001b[39;00m run_manager \u001b[39min\u001b[39;00m run_managers:\n\u001b[1;32m    543\u001b[0m         run_manager\u001b[39m.\u001b[39mon_llm_error(e)\n\u001b[0;32m--> 544\u001b[0m     \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m    545\u001b[0m flattened_outputs \u001b[39m=\u001b[39m output\u001b[39m.\u001b[39mflatten()\n\u001b[1;32m    546\u001b[0m \u001b[39mfor\u001b[39;00m manager, flattened_output \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(run_managers, flattened_outputs):\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/langchain/llms/base.py:531\u001b[0m, in \u001b[0;36mBaseLLM._generate_helper\u001b[0;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[1;32m    521\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_generate_helper\u001b[39m(\n\u001b[1;32m    522\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    523\u001b[0m     prompts: List[\u001b[39mstr\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    527\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any,\n\u001b[1;32m    528\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m LLMResult:\n\u001b[1;32m    529\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    530\u001b[0m         output \u001b[39m=\u001b[39m (\n\u001b[0;32m--> 531\u001b[0m             \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_generate(\n\u001b[1;32m    532\u001b[0m                 prompts,\n\u001b[1;32m    533\u001b[0m                 stop\u001b[39m=\u001b[39;49mstop,\n\u001b[1;32m    534\u001b[0m                 \u001b[39m# TODO: support multiple run managers\u001b[39;49;00m\n\u001b[1;32m    535\u001b[0m                 run_manager\u001b[39m=\u001b[39;49mrun_managers[\u001b[39m0\u001b[39;49m] \u001b[39mif\u001b[39;49;00m run_managers \u001b[39melse\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m    536\u001b[0m                 \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m    537\u001b[0m             )\n\u001b[1;32m    538\u001b[0m             \u001b[39mif\u001b[39;00m new_arg_supported\n\u001b[1;32m    539\u001b[0m             \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_generate(prompts, stop\u001b[39m=\u001b[39mstop)\n\u001b[1;32m    540\u001b[0m         )\n\u001b[1;32m    541\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    542\u001b[0m         \u001b[39mfor\u001b[39;00m run_manager \u001b[39min\u001b[39;00m run_managers:\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/langchain/llms/huggingface_pipeline.py:202\u001b[0m, in \u001b[0;36mHuggingFacePipeline._generate\u001b[0;34m(self, prompts, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    199\u001b[0m batch_prompts \u001b[39m=\u001b[39m prompts[i : i \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbatch_size]\n\u001b[1;32m    201\u001b[0m \u001b[39m# Process batch of prompts\u001b[39;00m\n\u001b[0;32m--> 202\u001b[0m responses \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpipeline(batch_prompts)\n\u001b[1;32m    204\u001b[0m \u001b[39m# Process each response in the batch\u001b[39;00m\n\u001b[1;32m    205\u001b[0m \u001b[39mfor\u001b[39;00m j, response \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(responses):\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/text_generation.py:204\u001b[0m, in \u001b[0;36mTextGenerationPipeline.__call__\u001b[0;34m(self, text_inputs, **kwargs)\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, text_inputs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    164\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    165\u001b[0m \u001b[39m    Complete the prompt(s) given as inputs.\u001b[39;00m\n\u001b[1;32m    166\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[39m          ids of the generated text.\u001b[39;00m\n\u001b[1;32m    203\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 204\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(text_inputs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1110\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1106\u001b[0m \u001b[39mif\u001b[39;00m can_use_iterator:\n\u001b[1;32m   1107\u001b[0m     final_iterator \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_iterator(\n\u001b[1;32m   1108\u001b[0m         inputs, num_workers, batch_size, preprocess_params, forward_params, postprocess_params\n\u001b[1;32m   1109\u001b[0m     )\n\u001b[0;32m-> 1110\u001b[0m     outputs \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(final_iterator)\n\u001b[1;32m   1111\u001b[0m     \u001b[39mreturn\u001b[39;00m outputs\n\u001b[1;32m   1112\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/pt_utils.py:124\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloader_batch_item()\n\u001b[1;32m    123\u001b[0m \u001b[39m# We're out of items within a batch\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m item \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39miterator)\n\u001b[1;32m    125\u001b[0m processed \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minfer(item, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mparams)\n\u001b[1;32m    126\u001b[0m \u001b[39m# We now have a batch of \"inferred things\".\u001b[39;00m\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/pt_utils.py:125\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[39m# We're out of items within a batch\u001b[39;00m\n\u001b[1;32m    124\u001b[0m item \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39miterator)\n\u001b[0;32m--> 125\u001b[0m processed \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minfer(item, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mparams)\n\u001b[1;32m    126\u001b[0m \u001b[39m# We now have a batch of \"inferred things\".\u001b[39;00m\n\u001b[1;32m    127\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloader_batch_size \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    128\u001b[0m     \u001b[39m# Try to infer the size of the batch\u001b[39;00m\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/base.py:1035\u001b[0m, in \u001b[0;36mPipeline.forward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1033\u001b[0m     \u001b[39mwith\u001b[39;00m inference_context():\n\u001b[1;32m   1034\u001b[0m         model_inputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ensure_tensor_on_device(model_inputs, device\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice)\n\u001b[0;32m-> 1035\u001b[0m         model_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_forward(model_inputs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mforward_params)\n\u001b[1;32m   1036\u001b[0m         model_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ensure_tensor_on_device(model_outputs, device\u001b[39m=\u001b[39mtorch\u001b[39m.\u001b[39mdevice(\u001b[39m\"\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m\"\u001b[39m))\n\u001b[1;32m   1037\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/pipelines/text_generation.py:265\u001b[0m, in \u001b[0;36mTextGenerationPipeline._forward\u001b[0;34m(self, model_inputs, **generate_kwargs)\u001b[0m\n\u001b[1;32m    262\u001b[0m         generate_kwargs[\u001b[39m\"\u001b[39m\u001b[39mmin_length\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m prefix_length\n\u001b[1;32m    264\u001b[0m \u001b[39m# BS x SL\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m generated_sequence \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mgenerate(input_ids\u001b[39m=\u001b[39;49minput_ids, attention_mask\u001b[39m=\u001b[39;49mattention_mask, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mgenerate_kwargs)\n\u001b[1;32m    266\u001b[0m out_b \u001b[39m=\u001b[39m generated_sequence\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]\n\u001b[1;32m    267\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mframework \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mpt\u001b[39m\u001b[39m\"\u001b[39m:\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[39mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/generation/utils.py:1642\u001b[0m, in \u001b[0;36mGenerationMixin.generate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)\u001b[0m\n\u001b[1;32m   1634\u001b[0m     input_ids, model_kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_expand_inputs_for_generation(\n\u001b[1;32m   1635\u001b[0m         input_ids\u001b[39m=\u001b[39minput_ids,\n\u001b[1;32m   1636\u001b[0m         expand_size\u001b[39m=\u001b[39mgeneration_config\u001b[39m.\u001b[39mnum_return_sequences,\n\u001b[1;32m   1637\u001b[0m         is_encoder_decoder\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig\u001b[39m.\u001b[39mis_encoder_decoder,\n\u001b[1;32m   1638\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mmodel_kwargs,\n\u001b[1;32m   1639\u001b[0m     )\n\u001b[1;32m   1641\u001b[0m     \u001b[39m# 13. run sample\u001b[39;00m\n\u001b[0;32m-> 1642\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msample(\n\u001b[1;32m   1643\u001b[0m         input_ids,\n\u001b[1;32m   1644\u001b[0m         logits_processor\u001b[39m=\u001b[39;49mlogits_processor,\n\u001b[1;32m   1645\u001b[0m         logits_warper\u001b[39m=\u001b[39;49mlogits_warper,\n\u001b[1;32m   1646\u001b[0m         stopping_criteria\u001b[39m=\u001b[39;49mstopping_criteria,\n\u001b[1;32m   1647\u001b[0m         pad_token_id\u001b[39m=\u001b[39;49mgeneration_config\u001b[39m.\u001b[39;49mpad_token_id,\n\u001b[1;32m   1648\u001b[0m         eos_token_id\u001b[39m=\u001b[39;49mgeneration_config\u001b[39m.\u001b[39;49meos_token_id,\n\u001b[1;32m   1649\u001b[0m         output_scores\u001b[39m=\u001b[39;49mgeneration_config\u001b[39m.\u001b[39;49moutput_scores,\n\u001b[1;32m   1650\u001b[0m         return_dict_in_generate\u001b[39m=\u001b[39;49mgeneration_config\u001b[39m.\u001b[39;49mreturn_dict_in_generate,\n\u001b[1;32m   1651\u001b[0m         synced_gpus\u001b[39m=\u001b[39;49msynced_gpus,\n\u001b[1;32m   1652\u001b[0m         streamer\u001b[39m=\u001b[39;49mstreamer,\n\u001b[1;32m   1653\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mmodel_kwargs,\n\u001b[1;32m   1654\u001b[0m     )\n\u001b[1;32m   1656\u001b[0m \u001b[39melif\u001b[39;00m generation_mode \u001b[39m==\u001b[39m GenerationMode\u001b[39m.\u001b[39mBEAM_SEARCH:\n\u001b[1;32m   1657\u001b[0m     \u001b[39m# 11. prepare beam search scorer\u001b[39;00m\n\u001b[1;32m   1658\u001b[0m     beam_scorer \u001b[39m=\u001b[39m BeamSearchScorer(\n\u001b[1;32m   1659\u001b[0m         batch_size\u001b[39m=\u001b[39mbatch_size,\n\u001b[1;32m   1660\u001b[0m         num_beams\u001b[39m=\u001b[39mgeneration_config\u001b[39m.\u001b[39mnum_beams,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1665\u001b[0m         max_length\u001b[39m=\u001b[39mgeneration_config\u001b[39m.\u001b[39mmax_length,\n\u001b[1;32m   1666\u001b[0m     )\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/generation/utils.py:2724\u001b[0m, in \u001b[0;36mGenerationMixin.sample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, streamer, **model_kwargs)\u001b[0m\n\u001b[1;32m   2721\u001b[0m model_inputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprepare_inputs_for_generation(input_ids, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mmodel_kwargs)\n\u001b[1;32m   2723\u001b[0m \u001b[39m# forward pass to get next token\u001b[39;00m\n\u001b[0;32m-> 2724\u001b[0m outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m(\n\u001b[1;32m   2725\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mmodel_inputs,\n\u001b[1;32m   2726\u001b[0m     return_dict\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m   2727\u001b[0m     output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m   2728\u001b[0m     output_hidden_states\u001b[39m=\u001b[39;49moutput_hidden_states,\n\u001b[1;32m   2729\u001b[0m )\n\u001b[1;32m   2731\u001b[0m \u001b[39mif\u001b[39;00m synced_gpus \u001b[39mand\u001b[39;00m this_peer_finished:\n\u001b[1;32m   2732\u001b[0m     \u001b[39mcontinue\u001b[39;00m  \u001b[39m# don't waste resources running the code we don't need\u001b[39;00m\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/accelerate/hooks.py:164\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    162\u001b[0m         output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39m_old_forward(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    163\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 164\u001b[0m     output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39;49m_old_forward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    165\u001b[0m \u001b[39mreturn\u001b[39;00m module\u001b[39m.\u001b[39m_hf_hook\u001b[39m.\u001b[39mpost_forward(module, output)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py:809\u001b[0m, in \u001b[0;36mLlamaForCausalLM.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    806\u001b[0m return_dict \u001b[39m=\u001b[39m return_dict \u001b[39mif\u001b[39;00m return_dict \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig\u001b[39m.\u001b[39muse_return_dict\n\u001b[1;32m    808\u001b[0m \u001b[39m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[39;00m\n\u001b[0;32m--> 809\u001b[0m outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel(\n\u001b[1;32m    810\u001b[0m     input_ids\u001b[39m=\u001b[39;49minput_ids,\n\u001b[1;32m    811\u001b[0m     attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m    812\u001b[0m     position_ids\u001b[39m=\u001b[39;49mposition_ids,\n\u001b[1;32m    813\u001b[0m     past_key_values\u001b[39m=\u001b[39;49mpast_key_values,\n\u001b[1;32m    814\u001b[0m     inputs_embeds\u001b[39m=\u001b[39;49minputs_embeds,\n\u001b[1;32m    815\u001b[0m     use_cache\u001b[39m=\u001b[39;49muse_cache,\n\u001b[1;32m    816\u001b[0m     output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m    817\u001b[0m     output_hidden_states\u001b[39m=\u001b[39;49moutput_hidden_states,\n\u001b[1;32m    818\u001b[0m     return_dict\u001b[39m=\u001b[39;49mreturn_dict,\n\u001b[1;32m    819\u001b[0m )\n\u001b[1;32m    821\u001b[0m hidden_states \u001b[39m=\u001b[39m outputs[\u001b[39m0\u001b[39m]\n\u001b[1;32m    822\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig\u001b[39m.\u001b[39mpretraining_tp \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py:697\u001b[0m, in \u001b[0;36mLlamaModel.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    690\u001b[0m     layer_outputs \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39mcheckpoint\u001b[39m.\u001b[39mcheckpoint(\n\u001b[1;32m    691\u001b[0m         create_custom_forward(decoder_layer),\n\u001b[1;32m    692\u001b[0m         hidden_states,\n\u001b[1;32m    693\u001b[0m         attention_mask,\n\u001b[1;32m    694\u001b[0m         position_ids,\n\u001b[1;32m    695\u001b[0m     )\n\u001b[1;32m    696\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 697\u001b[0m     layer_outputs \u001b[39m=\u001b[39m decoder_layer(\n\u001b[1;32m    698\u001b[0m         hidden_states,\n\u001b[1;32m    699\u001b[0m         attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m    700\u001b[0m         position_ids\u001b[39m=\u001b[39;49mposition_ids,\n\u001b[1;32m    701\u001b[0m         past_key_value\u001b[39m=\u001b[39;49mpast_key_value,\n\u001b[1;32m    702\u001b[0m         output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m    703\u001b[0m         use_cache\u001b[39m=\u001b[39;49muse_cache,\n\u001b[1;32m    704\u001b[0m     )\n\u001b[1;32m    706\u001b[0m hidden_states \u001b[39m=\u001b[39m layer_outputs[\u001b[39m0\u001b[39m]\n\u001b[1;32m    708\u001b[0m \u001b[39mif\u001b[39;00m use_cache:\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/accelerate/hooks.py:164\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    162\u001b[0m         output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39m_old_forward(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    163\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 164\u001b[0m     output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39;49m_old_forward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    165\u001b[0m \u001b[39mreturn\u001b[39;00m module\u001b[39m.\u001b[39m_hf_hook\u001b[39m.\u001b[39mpost_forward(module, output)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py:413\u001b[0m, in \u001b[0;36mLlamaDecoderLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache)\u001b[0m\n\u001b[1;32m    410\u001b[0m hidden_states \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_layernorm(hidden_states)\n\u001b[1;32m    412\u001b[0m \u001b[39m# Self Attention\u001b[39;00m\n\u001b[0;32m--> 413\u001b[0m hidden_states, self_attn_weights, present_key_value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mself_attn(\n\u001b[1;32m    414\u001b[0m     hidden_states\u001b[39m=\u001b[39;49mhidden_states,\n\u001b[1;32m    415\u001b[0m     attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m    416\u001b[0m     position_ids\u001b[39m=\u001b[39;49mposition_ids,\n\u001b[1;32m    417\u001b[0m     past_key_value\u001b[39m=\u001b[39;49mpast_key_value,\n\u001b[1;32m    418\u001b[0m     output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m    419\u001b[0m     use_cache\u001b[39m=\u001b[39;49muse_cache,\n\u001b[1;32m    420\u001b[0m )\n\u001b[1;32m    421\u001b[0m hidden_states \u001b[39m=\u001b[39m residual \u001b[39m+\u001b[39m hidden_states\n\u001b[1;32m    423\u001b[0m \u001b[39m# Fully Connected\u001b[39;00m\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/accelerate/hooks.py:164\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    162\u001b[0m         output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39m_old_forward(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    163\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 164\u001b[0m     output \u001b[39m=\u001b[39m module\u001b[39m.\u001b[39;49m_old_forward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    165\u001b[0m \u001b[39mreturn\u001b[39;00m module\u001b[39m.\u001b[39m_hf_hook\u001b[39m.\u001b[39mpost_forward(module, output)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py:322\u001b[0m, in \u001b[0;36mLlamaAttention.forward\u001b[0;34m(self, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache)\u001b[0m\n\u001b[1;32m    320\u001b[0m     kv_seq_len \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m past_key_value[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mshape[\u001b[39m-\u001b[39m\u001b[39m2\u001b[39m]\n\u001b[1;32m    321\u001b[0m cos, sin \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrotary_emb(value_states, seq_len\u001b[39m=\u001b[39mkv_seq_len)\n\u001b[0;32m--> 322\u001b[0m query_states, key_states \u001b[39m=\u001b[39m apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)\n\u001b[1;32m    324\u001b[0m \u001b[39mif\u001b[39;00m past_key_value \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    325\u001b[0m     \u001b[39m# reuse k, v, self_attention\u001b[39;00m\n\u001b[1;32m    326\u001b[0m     key_states \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mcat([past_key_value[\u001b[39m0\u001b[39m], key_states], dim\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py:186\u001b[0m, in \u001b[0;36mapply_rotary_pos_emb\u001b[0;34m(q, k, cos, sin, position_ids)\u001b[0m\n\u001b[1;32m    184\u001b[0m cos \u001b[39m=\u001b[39m cos[position_ids]\u001b[39m.\u001b[39munsqueeze(\u001b[39m1\u001b[39m)  \u001b[39m# [bs, 1, seq_len, dim]\u001b[39;00m\n\u001b[1;32m    185\u001b[0m sin \u001b[39m=\u001b[39m sin[position_ids]\u001b[39m.\u001b[39munsqueeze(\u001b[39m1\u001b[39m)  \u001b[39m# [bs, 1, seq_len, dim]\u001b[39;00m\n\u001b[0;32m--> 186\u001b[0m q_embed \u001b[39m=\u001b[39m (q \u001b[39m*\u001b[39m cos) \u001b[39m+\u001b[39m (rotate_half(q) \u001b[39m*\u001b[39m sin)\n\u001b[1;32m    187\u001b[0m k_embed \u001b[39m=\u001b[39m (k \u001b[39m*\u001b[39m cos) \u001b[39m+\u001b[39m (rotate_half(k) \u001b[39m*\u001b[39m sin)\n\u001b[1;32m    188\u001b[0m \u001b[39mreturn\u001b[39;00m q_embed, k_embed\n",
      "File \u001b[0;32m/mnt/iMVR/junde/miniconda3/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py:177\u001b[0m, in \u001b[0;36mrotate_half\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    175\u001b[0m x1 \u001b[39m=\u001b[39m x[\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m, : x\u001b[39m.\u001b[39mshape[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m] \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m \u001b[39m2\u001b[39m]\n\u001b[1;32m    176\u001b[0m x2 \u001b[39m=\u001b[39m x[\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m, x\u001b[39m.\u001b[39mshape[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m] \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m \u001b[39m2\u001b[39m :]\n\u001b[0;32m--> 177\u001b[0m \u001b[39mreturn\u001b[39;00m torch\u001b[39m.\u001b[39;49mcat((\u001b[39m-\u001b[39;49mx2, x1), dim\u001b[39m=\u001b[39;49m\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "global_time = 0\n",
    "for repeats in range(5):\n",
    "  global_time += 1\n",
    "  print(\"In global time\", global_time)\n",
    "  action_prompts = {}\n",
    "  people_description = []\n",
    "  for location in town_areas.keys():\n",
    "    people = []\n",
    "    for i in town_people.keys():\n",
    "      if locations[i] == location:\n",
    "        people.append(i)\n",
    "        people_description.append(action_results[i])\n",
    "\n",
    "    # for name in people:\n",
    "      # prompt = \"You are {}. Your plans are: {}. You are currently in {} with the following description: {}. Your memories are: {}. It is currently {}:00. The following people are in this area: {}. You can interact with them.\".format(name, plans[name], location, town_areas[location], '\\n'.join(compressed_memories_all[name][-5:]), str(global_time), ', '.join(people))\n",
    "    # for i in people: \n",
    "    #   if i not in action_results: # initialize action results as the status, and add agent mem with description\n",
    "    #     action_results[i] = agents[i].get_summary(force_refresh=False)\n",
    "    for i in people: # add observation to memory and react\n",
    "      logger.log(\"Mind Tree of people: \", i)\n",
    "\n",
    "      others = [x for x in people if x != i]\n",
    "      observation = \"You are {}.You are currently in {} with the following description: {}. \\\n",
    "      It is currently {}:00. The following people are in this area: {}. You can interact with them.\". \\\n",
    "      format(i, location, town_areas[location], str(global_time), ', '.join(others))\n",
    "\n",
    "      others_des = [x for x in people_description if i+': ' not in x]\n",
    "      observation += ' You know the following about people: ' + '. '.join(others_des)\n",
    "\n",
    "      logger.log(\"For people %s, The observation is: %s \\n\" % (i, observation))\n",
    "\n",
    "      agents[i].memory.add_memory(observation)\n",
    "      _, reaction = agents[i].generate_reaction(observation)\n",
    "      action_results[i] = reaction\n",
    "\n",
    "      logger.log(\"action result is:  %s \\n\" %(reaction))"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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