share/fta.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Finite Thoughts Automaton\n",
    "\n",
    "This notebook focuses on creating FTA a lower level automaton than STA which could be called a \"machine model\" for LM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3114d3d6-abcb-4ba5-91dc-8aa87b82312f",
   "metadata": {
    "tags": []
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   "outputs": [
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      "/usr/local/lib/python3.11/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n",
      "llama.cpp: loading model from /workspace/models/7B/ggml-model-q4_0.bin\n",
      "llama_model_load_internal: format     = ggjt v1 (latest)\n",
      "llama_model_load_internal: n_vocab    = 32000\n",
      "llama_model_load_internal: n_ctx      = 2048\n",
      "llama_model_load_internal: n_embd     = 4096\n",
      "llama_model_load_internal: n_mult     = 256\n",
      "llama_model_load_internal: n_head     = 32\n",
      "llama_model_load_internal: n_layer    = 32\n",
      "llama_model_load_internal: n_rot      = 128\n",
      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
      "llama_model_load_internal: n_ff       = 11008\n",
      "llama_model_load_internal: n_parts    = 1\n",
      "llama_model_load_internal: model size = 7B\n",
      "llama_model_load_internal: ggml ctx size =  68.20 KB\n",
      "llama_model_load_internal: mem required  = 5809.33 MB (+ 1026.00 MB per state)\n",
      "llama_init_from_file: kv self size  = 1024.00 MB\n"
     ]
    }
   ],
   "source": [
    "import os, sys, json\n",
    "sys.path.append(\"/workspace/projects/autocog\")\n",
    "from autocog.lm import TfLM, OpenAI, Llama\n",
    "llama_path = lambda x: \"/workspace/models/{}/ggml-model-{}.bin\".format(*x)\n",
    "llm = Llama(**Llama.create(model_path=llama_path(('7B','q4_0')), n_ctx=2048), max_tokens=20)"
   ]
  },
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   },
   "outputs": [
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   "source": [
    "from autocog.automatons.fta.automaton import FiniteThoughtAutomaton as FTA\n",
    "from autocog.automatons.fta.actions import Choose, Complete, Text\n",
    "\n",
    "nonzero_digits = ['1','2','3','4','5','6','7','8','9']\n",
    "\n",
    "fta = FTA(lm=llm)\n",
    "subject   = fta.create(uid='subject',   cls=Text,     successors=['qualifier'],           text=\"the speed of light in \"                                      )\n",
    "qualifier = fta.create(uid='qualifier', cls=Choose,   successors=['verb','verb','verb'],  choices=[ \"km/s\", \"m/s\", \"km/h\" ]                                  )\n",
    "verb      = fta.create(uid='verb',      cls=Text,     successors=['digits_1'],            text=\" is \"                                                        )\n",
    "digits_1  = fta.create(uid='digits_1',  cls=Complete, successors=['sep_1'],               length=3, vocab={ 'texts' : nonzero_digits }, stop=[',','.']       )\n",
    "sep_1     = fta.create(uid='sep_1',     cls=Choose,   successors=['digits_2','digits_4'], choices=[ ',', '.' ]                                               )\n",
    "digits_2  = fta.create(uid='digits_2',  cls=Complete, successors=['sep_2'],               length=3, vocab={ 'texts' : ['0']+nonzero_digits }, stop=[',','.'] )\n",
    "sep_2     = fta.create(uid='sep_2',     cls=Choose,   successors=['digits_3','digits_4'], choices=[ ',', '.' ]                                               )\n",
    "digits_3  = fta.create(uid='digits_3',  cls=Complete, successors=['sep_3'],               length=3, vocab={ 'texts' : ['0']+nonzero_digits }, stop=['.']     )\n",
    "sep_3     = fta.create(uid='sep_3',     cls=Text,     successors=['digits_4'],            text=\".\"                                                           )\n",
    "digits_4  = fta.create(uid='digits_4',  cls=Complete, successors=['eol'],                 length=2, vocab={ 'texts' : ['0']+nonzero_digits }, stop=['\\n']    )\n",
    "eol       = fta.create(uid='eol',       cls=Text,     successors=[],                      text=\"\\n\"                                                          )\n",
    "\n",
    "from autocog.utility.pynb import wrap_graphviz\n",
    "wrap_graphviz(fta.toGraphViz())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa75c60c-b6f1-4b36-b9d5-cadd4228f491",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfta\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgreedy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msubject\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/workspace/projects/autocog/autocog/automatons/fta/automaton.py:39\u001b[0m, in \u001b[0;36mFiniteThoughtAutomaton.greedy\u001b[0;34m(self, entry, header, min_branch, max_branch, tok_clip)\u001b[0m\n\u001b[1;32m     37\u001b[0m header \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlm\u001b[38;5;241m.\u001b[39mtokenize(header)\n\u001b[1;32m     38\u001b[0m root \u001b[38;5;241m=\u001b[39m FiniteTokenTree()\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgreedy_rec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mactions\u001b[49m\u001b[43m[\u001b[49m\u001b[43mentry\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_branch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmin_branch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_branch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_branch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtok_clip\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtok_clip\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m root\n",
      "File \u001b[0;32m/workspace/projects/autocog/autocog/automatons/fta/automaton.py:31\u001b[0m, in \u001b[0;36mFiniteThoughtAutomaton.greedy_rec\u001b[0;34m(self, prompt, root, action, step, **kwargs)\u001b[0m\n\u001b[1;32m     29\u001b[0m     tree \u001b[38;5;241m=\u001b[39m FiniteTokenTree(token\u001b[38;5;241m=\u001b[39mtok, proba\u001b[38;5;241m=\u001b[39mprob)\n\u001b[1;32m     30\u001b[0m     root\u001b[38;5;241m.\u001b[39mchildren\u001b[38;5;241m.\u001b[39mupdate({tok:tree})\n\u001b[0;32m---> 31\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgreedy_rec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mtok\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(branches) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     33\u001b[0m     aid \u001b[38;5;241m=\u001b[39m action\u001b[38;5;241m.\u001b[39mnext(prompt\u001b[38;5;241m=\u001b[39mprompt)\n",
      "File \u001b[0;32m/workspace/projects/autocog/autocog/automatons/fta/automaton.py:31\u001b[0m, in \u001b[0;36mFiniteThoughtAutomaton.greedy_rec\u001b[0;34m(self, prompt, root, action, step, **kwargs)\u001b[0m\n\u001b[1;32m     29\u001b[0m     tree \u001b[38;5;241m=\u001b[39m FiniteTokenTree(token\u001b[38;5;241m=\u001b[39mtok, proba\u001b[38;5;241m=\u001b[39mprob)\n\u001b[1;32m     30\u001b[0m     root\u001b[38;5;241m.\u001b[39mchildren\u001b[38;5;241m.\u001b[39mupdate({tok:tree})\n\u001b[0;32m---> 31\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgreedy_rec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mtok\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(branches) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     33\u001b[0m     aid \u001b[38;5;241m=\u001b[39m action\u001b[38;5;241m.\u001b[39mnext(prompt\u001b[38;5;241m=\u001b[39mprompt)\n",
      "    \u001b[0;31m[... skipping similar frames: FiniteThoughtAutomaton.greedy_rec at line 31 (3 times)]\u001b[0m\n",
      "File \u001b[0;32m/workspace/projects/autocog/autocog/automatons/fta/automaton.py:31\u001b[0m, in \u001b[0;36mFiniteThoughtAutomaton.greedy_rec\u001b[0;34m(self, prompt, root, action, step, **kwargs)\u001b[0m\n\u001b[1;32m     29\u001b[0m     tree \u001b[38;5;241m=\u001b[39m FiniteTokenTree(token\u001b[38;5;241m=\u001b[39mtok, proba\u001b[38;5;241m=\u001b[39mprob)\n\u001b[1;32m     30\u001b[0m     root\u001b[38;5;241m.\u001b[39mchildren\u001b[38;5;241m.\u001b[39mupdate({tok:tree})\n\u001b[0;32m---> 31\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgreedy_rec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mtok\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(branches) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     33\u001b[0m     aid \u001b[38;5;241m=\u001b[39m action\u001b[38;5;241m.\u001b[39mnext(prompt\u001b[38;5;241m=\u001b[39mprompt)\n",
      "File \u001b[0;32m/workspace/projects/autocog/autocog/automatons/fta/automaton.py:34\u001b[0m, in \u001b[0;36mFiniteThoughtAutomaton.greedy_rec\u001b[0;34m(self, prompt, root, action, step, **kwargs)\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(branches) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     33\u001b[0m     aid \u001b[38;5;241m=\u001b[39m action\u001b[38;5;241m.\u001b[39mnext(prompt\u001b[38;5;241m=\u001b[39mprompt)\n\u001b[0;32m---> 34\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgreedy_rec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mactions\u001b[49m\u001b[43m[\u001b[49m\u001b[43maid\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/workspace/projects/autocog/autocog/automatons/fta/automaton.py:27\u001b[0m, in \u001b[0;36mFiniteThoughtAutomaton.greedy_rec\u001b[0;34m(self, prompt, root, action, step, **kwargs)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgreedy_rec\u001b[39m(\u001b[38;5;28mself\u001b[39m, prompt:List[Token], root:FiniteTokenTree, action:Action, step:\u001b[38;5;28mint\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 27\u001b[0m     branches \u001b[38;5;241m=\u001b[39m \u001b[43maction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     28\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m (tok,prob) \u001b[38;5;129;01min\u001b[39;00m branches\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m     29\u001b[0m         tree \u001b[38;5;241m=\u001b[39m FiniteTokenTree(token\u001b[38;5;241m=\u001b[39mtok, proba\u001b[38;5;241m=\u001b[39mprob)\n",
      "File \u001b[0;32m/workspace/projects/autocog/autocog/automatons/fta/actions.py:61\u001b[0m, in \u001b[0;36mChoose.step\u001b[0;34m(self, lm, prompt, step, min_branch, max_branch, tok_clip)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, lm, prompt:List[Token], step:\u001b[38;5;28mint\u001b[39m, min_branch:\u001b[38;5;28mint\u001b[39m, max_branch:\u001b[38;5;28mint\u001b[39m, tok_clip:\u001b[38;5;28mfloat\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dict[Token,\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[0;32m---> 61\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m()\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "fta.greedy('subject')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6109e138-ed58-4077-a22b-634b658ba555",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "inputs = [\n",
    "\"\"\"> question(text): Scientists wonder how the Egyptian pyramids were built. They think that the huge blocks of stone may have been put into place by pushing them up a sloping pathway. The pathway is which type of simple machine?\n",
    "> choices[1](text): lever\n",
    "> choices[2](text): pulley\n",
    "> choices[3](text): inclined plane\n",
    "> choices[4](text): wheel and axle\"\"\"\n",
    "]\n",
    "answer = [\n",
    "\"> answer(choice): inclined plane\"\n",
    "]\n",
    "syntax = \"\"\"> question(text): The question from the MMLU Exam\n",
    "> choices[4](text): Four potential answers to the question\n",
    "> answer(choice): You choose the correct answer.\"\"\"\n",
    "formats = \"\"\"- text: any text\n",
    "- thought: short text used to prepare your answers\n",
    "- choice: index of the correct choice, one of `1`, `2`, `3`, or `4`\"\"\"\n",
    "\n",
    "patterns = [ [\n",
    "\"\"\"You are a helpful AI assistant. You are taking the MMLU exam. You are given one question and four choices for the answer. Only one of these choices is the correct answer.\n",
    "You are using an interactive questionnaire. Follow this syntax after the start prompt:\n",
    "```\n",
    "> question(text): \"\"\", { 'beams' : 3, 'length' : 10, 'stop' : '\\n' }, \"\"\"\n",
    "> choices[4](text): \"\"\", { 'beams' : 3, 'length' : 10, 'stop' : '\\n' }, \"\"\"\n",
    "> answer(choice): \"\"\", { 'beams' : 3, 'length' : 10, 'stop' : '\\n' }, \"\"\"\n",
    "```\n",
    "Each prompt expects one of the following formats:\n",
    "- text: \"\"\", { 'beams' : 3, 'length' : 10, 'stop' : '\\n', }, \"\"\"\n",
    "- choice: \"\"\", { 'beams' : 3, 'length' : 10, 'stop' : '\\n' }, \"\"\"\n",
    "Terminate each prompt with a newline.\n",
    "\n",
    "start(record):\n",
    "{inputs}\n",
    "{answer}\n",
    "\"\"\"\n",
    "],[\n",
    "\"\"\"You are a helpful AI assistant. You are taking the MMLU exam. You are given one question and four choices for the answer. Only one of these choices is the correct answer.\n",
    "You are using an interactive questionnaire. Follow this syntax after the start prompt:\n",
    "```\n",
    "{syntax}\n",
    "```\n",
    "Each prompt expects one of the following formats:\n",
    "{formats}\n",
    "Terminate each prompt with a newline.\n",
    "\n",
    "start(record):\n",
    "{inputs}\n",
    "> answer(choice): \"\"\", { 'choices' : ['1','2','3','4'] }\n",
    "],[\n",
    "\"\"\"You are a helpful AI assistant. You are taking the MMLU exam. You are given one question and four choices for the answer. Only one of these choices is the correct answer.\n",
    "You are using an interactive questionnaire. Follow this syntax after the start prompt:\n",
    "```\n",
    "{syntax}\n",
    "```\n",
    "Each prompt expects one of the following formats:\n",
    "{formats}\n",
    "Terminate each prompt with a newline.\n",
    "\n",
    "start(record):\n",
    "{inputs}\n",
    "> notepad[1](thougth):\"\"\", { 'beams' : 1, 'length' : 30, 'stop' : '\\n' }, \"\"\"\n",
    "> notepad[2](thougth):\"\"\", { 'beams' : 1, 'length' : 30, 'stop' : '\\n' }, \"\"\"\n",
    "> notepad[3](thougth):\"\"\", { 'beams' : 1, 'length' : 30, 'stop' : '\\n' }, \"\"\"\n",
    "> answer(choice): \"\"\", { 'choices' : ['1','2','3','4'] }\n",
    "] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "240cf60e-7f11-4115-bb61-946cfe72d468",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def tree_from_pattern(pattern, **kwargs):\n",
    "    if len(pattern) == 0:\n",
    "        return None\n",
    "    current = pattern[0]\n",
    "    if isinstance(current,str):\n",
    "        tree = Choice.build(choices=[current.format(**kwargs)], norm=False)\n",
    "    elif 'choices' in current:\n",
    "        tree = Choice.build(**current, norm=False)\n",
    "    elif 'length' in current:\n",
    "        tree = Complete.build(**current)\n",
    "    tree.successors = [ tree_from_pattern(pattern[1:], **kwargs) for s in tree.successors ]\n",
    "    return tree                                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be35af93-d9a7-4f07-822f-084530825319",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ftt = FiniteThoughtTree.from_pattern(patterns[2], syntax=syntax, formats=formats, inputs=inputs[0])\n",
    "# print(json.dumps(tree.dict(), indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8175e2d3-3940-4fe9-89d7-3e5a4e99cede",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tqdm\n",
    "llm = Llama(**Llama.create(model_path=llama_path(('7B','q4_0')), n_ctx=2048), max_tokens=20)\n",
    "res = ftt.explore(llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a96bbfb-58b9-4931-91dc-10c605e0dba0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "json.dump(tree.dict(), open('ftt.json','w'), indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76953a6e-d986-4c6f-8980-ac24cc15720b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "results = list(tree.collect())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f855426-26d6-4527-8b7e-733dc69e1dd0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for r in sorted(results, key=lambda x: x[1], reverse=True):\n",
    "    print(f\"Proba: {r[1]}\")\n",
    "    print('    '+'\\n    '.join(r[0].split('\\n')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a53d0fd2-be52-485e-acf5-0e1f80a6bd20",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(json.dumps(tree.dict(), indent=4))\n",
    "for (text,proba,path) in sorted(results,key=lambda x: x[1]):\n",
    "    print(f'> \"{text}\": {proba} ({path})')"
   ]
  }
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