playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import src.sandbox as sandbox\n",
    "from datasets import load_dataset\n",
    "from datasets import Dataset, DatasetDict\n",
    "from collections import defaultdict, Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If the dataset is gated/private, make sure you have run huggingface-cli login\n",
    "ds = load_dataset(\"Elfsong/Caduceus_v7\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_1(index):\n",
    "    instance = eval_list[index]\n",
    "    sb = sandbox.Sandbox()\n",
    "    results = list()\n",
    "    \n",
    "    for index, solution in enumerate(instance['solutions']):        \n",
    "        sample = {\n",
    "            \"solution\": solution['solution'],\n",
    "            \"convert_offline\": instance['convert_offline'],\n",
    "            \"evaluate_offline\": instance['evaluate_offline'],\n",
    "            \"entry_point\": instance['entry_point'],\n",
    "            \"test_cases\": json.loads(instance['test_cases']),\n",
    "            \"solution_index\": index,\n",
    "            \"timeout\": 30\n",
    "        }\n",
    "    \n",
    "        results += [sb.run_sample(sample)]\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict, Counter\n",
    "\n",
    "failed_sample = defaultdict(list)\n",
    "\n",
    "for s_index in tqdm(range(256)):\n",
    "    results = test_1(s_index)\n",
    "    for index, result in enumerate(results):\n",
    "        if result['result'] != 'passed':\n",
    "            print(s_index, result)\n",
    "            failed_sample[s_index] += [result]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = Counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for instance in ds['eval']:\n",
    "    l = len(instance['solutions'])\n",
    "    counter[l] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_list, c_list = list(), list()\n",
    "t_list = list()\n",
    "\n",
    "for c in counter:\n",
    "    n_list += [c]\n",
    "    c_list += [counter[c]]\n",
    "    t_list += [c] * counter[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(t_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'count': c_list, 'num_of_solution': n_list}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_dict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = Counter()\n",
    "for instance in ds['eval']:\n",
    "    difficulty = instance['difficulty']\n",
    "    counter[difficulty] += 1\n",
    "print(counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [7.50, 3.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt = sns.barplot(data=df, x='num_of_solution', y='count')\n",
    "plt.set(xlabel=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = Dataset.from_list(train_list)\n",
    "eval_ds = Dataset.from_list(eval_list)\n",
    "final_ds = DatasetDict({\"train\":train_ds, \"eval\":eval_ds})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_ds.push_to_hub('Elfsong/Caduceus_v7', private=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "statistics.mean(t_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "statistics.median(t_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "statistics.mode(t_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "min(t_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max(t_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('        if not matrix: return []\\n        R, C = len(matrix), len(matrix[0])\\n        visited = [[False] * C for _ in matrix]\\n        dr = [0, 1, 0, -1]\\n        dc = [1, 0, -1, 0]\\n        ans = []\\n        r = c = di = 0\\n        for _ in range(R * C):\\n            ans.append(matrix[r][c])\\n            visited[r][c] = True\\n            cr, cc = r + dr[di], c + dc[di]\\n            if 0 <= cr < R and 0 <= cc < C and not visited[cr][cc]:\\n                r, c = cr, cc\\n            else:\\n                di = (di + 1) % 4\\n                r, c = r + dr[di], c + dc[di]\\n        return ans')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import sample \n",
    "  \n",
    "# Prints list of random items of given length \n",
    "list1 = [1, 2]  \n",
    "  \n",
    "print(sample(list1,3)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [2,3,4,5,6,3,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = sorted(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('./data/mbpp/sanitized-mbpp.json', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 0\n",
    "for d in data:\n",
    "    task_id = d['task_id']\n",
    "    if task_id >= 11 and task_id <= 510:\n",
    "        c += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_questions():\n",
    "    url = \"https://leetcode.com/api/problems/algorithms/\"\n",
    "    payload = {}\n",
    "    headers = {\n",
    "        'authority': 'leetcode.cn',\n",
    "        'accept': '*/*',\n",
    "        'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8,zh-CN;q=0.7,zh-HK;q=0.6,zh-TW;q=0.5,zh;q=0.4',\n",
    "        'content-type': 'application/json',\n",
    "        'cookie': '_gid=GA1.2.1098388302.1701430581; gr_user_id=7410146c-e5bc-4739-a7c8-40cb0e580219; a2873925c34ecbd2_gr_session_id=5c028fe5-e819-4177-bc61-685d662a8ea3; a2873925c34ecbd2_gr_session_id_sent_vst=5c028fe5-e819-4177-bc61-685d662a8ea3; Hm_lvt_f0faad39bcf8471e3ab3ef70125152c3=1701430581; _bl_uid=Rtlq0pdemvUjdwuU0mna4wefs90p; csrftoken=AZRVgBFAYbZ8j6ud6Er7hfSUYBoaMMiZyImW25xy9HYJ7Kluvhok6RKzjAofXb3H; LEETCODE_SESSION=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJfYXV0aF91c2VyX2lkIjoiMTgwMDQyMyIsIl9hdXRoX3VzZXJfYmFja2VuZCI6ImRqYW5nby5jb250cmliLmF1dGguYmFja2VuZHMuTW9kZWxCYWNrZW5kIiwiX2F1dGhfdXNlcl9oYXNoIjoiYjg1YTc3ZmYxMDgxNTAwMzg3NzY1YzE3ZGQ0M2I5YTEyYTNhOWYxNWI4YmRhNDVjNjc1ZjFiYmExNGU1YzFmNyIsImlkIjoxODAwNDIzLCJlbWFpbCI6ImR1bWluZ3poZUAxMjYuY29tIiwidXNlcm5hbWUiOiJlbGZzb25nLXYiLCJ1c2VyX3NsdWciOiJlbGZzb25nLXYiLCJhdmF0YXIiOiJodHRwczovL2Fzc2V0cy5sZWV0Y29kZS5jbi9hbGl5dW4tbGMtdXBsb2FkL3VzZXJzL2VsZnNvbmctdi9hdmF0YXJfMTYwMDU0MTMyNy5wbmciLCJwaG9uZV92ZXJpZmllZCI6dHJ1ZSwiX3RpbWVzdGFtcCI6MTcwMTQzMDYzNy4yMjI4ODYsImV4cGlyZWRfdGltZV8iOjE3MDM5NjI4MDAsInZlcnNpb25fa2V5XyI6Mn0.xj1NZG6DUHjo12TmwApzDnzbTIS33WSfKWBAy_UkCJg; a2873925c34ecbd2_gr_last_sent_sid_with_cs1=5c028fe5-e819-4177-bc61-685d662a8ea3; a2873925c34ecbd2_gr_last_sent_cs1=elfsong-v; _ga=GA1.1.695723023.1701430580; Hm_lpvt_f0faad39bcf8471e3ab3ef70125152c3=1701430639; a2873925c34ecbd2_gr_cs1=elfsong-v; _ga_PDVPZYN3CW=GS1.1.1701430580.1.1.1701431322.52.0.0; LEETCODE_SESSION=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.e30.KpufdHIo8CeGduwC5DCQoba8bmWCjJ9mUTYQ4npFdlk; csrftoken=AZRVgBFAYbZ8j6ud6Er7hfSUYBoaMMiZyImW25xy9HYJ7Kluvhok6RKzjAofXb3H',\n",
    "        'referer': 'https://leetcode.cn/problems/two-sum/submissions/486127358/',\n",
    "        'sec-ch-ua': '\"Google Chrome\";v=\"119\", \"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"',\n",
    "        'sec-ch-ua-mobile': '?0',\n",
    "        'sec-ch-ua-platform': '\"macOS\"',\n",
    "        'sec-fetch-dest': 'empty',\n",
    "        'sec-fetch-mode': 'cors',\n",
    "        'sec-fetch-site': 'same-origin',\n",
    "        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',\n",
    "        'x-csrftoken': 'AZRVgBFAYbZ8j6ud6Er7hfSUYBoaMMiZyImW25xy9HYJ7Kluvhok6RKzjAofXb3H'\n",
    "    }\n",
    "\n",
    "    response = requests.request(\"GET\", url, headers=headers, data=payload)\n",
    "    questions = response.json()\n",
    "    question_dict= dict()\n",
    "\n",
    "    for question in questions[\"stat_status_pairs\"]:\n",
    "        # if not question[\"paid_only\"]:\n",
    "        slug_name = question[\"stat\"][\"question__title_slug\"]\n",
    "        paid_only = question[\"paid_only\"]\n",
    "        question_dict[slug_name] = paid_only\n",
    "        \n",
    "    return question_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_list = ds['train'].to_list()\n",
    "eval_list = ds['eval'].to_list()\n",
    "all_questions = get_questions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "paid_index = list()\n",
    "for index, instance in enumerate(train_list):\n",
    "    slug_name = instance['slug_name']\n",
    "    if all_questions[slug_name]:\n",
    "        paid_index += [index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index in paid_index[::-1]:\n",
    "    del train_list[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "paid_index = list()\n",
    "for index, instance in enumerate(eval_list):\n",
    "    slug_name = instance['slug_name']\n",
    "    if all_questions[slug_name]:\n",
    "        paid_index += [index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index in paid_index[::-1]:\n",
    "    del eval_list[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(train_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(eval_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = Dataset.from_list(train_list)\n",
    "eval_ds = Dataset.from_list(eval_list)\n",
    "final_ds = DatasetDict({\"train\":train_ds, \"eval\":eval_ds})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_ds.push_to_hub('Elfsong/Caduceus_v8', private=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = load_dataset(\"Elfsong/Caduceus_v8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1633 + 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'Difficulty': ['Easy', 'Medium', 'Hard'],\n",
    "    'Train': [446, 968, 219],\n",
    "    'Eval': [88, 81, 87]\n",
    "})\n",
    "\n",
    "# Create a pie chart for Train\n",
    "plt.pie(df['Train'], labels=df['Difficulty'], autopct='%1.1f%%', startangle=90, pctdistance=0.85, radius=1)\n",
    "\n",
    "# Create a pie chart for Eval inside the previous one\n",
    "plt.pie(df['Eval'], labels=df['Difficulty'], autopct='%1.1f%%', startangle=90, pctdistance=0.85, radius=0.75)\n",
    "\n",
    "# Draw a white circle at the center\n",
    "centre_circle = plt.Circle((0, 0), 0.50, fc='white')\n",
    "fig = plt.gcf()\n",
    "\n",
    "# Equal aspect ratio ensures that pie is drawn as a circle\n",
    "fig.gca().add_artist(centre_circle)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from datasets import load_dataset\n",
    "from collections import defaultdict, Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = load_dataset(\"Elfsong/Caduceus_v8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slug_name_dict = dict()\n",
    "for instance in ds['eval']:\n",
    "    slug_name = instance['slug_name']\n",
    "    difficulty = instance['difficulty']\n",
    "    slug_name_dict[slug_name] = difficulty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./data/deepseek-ai/deepseek-coder-6.7b-instruct_eval.json') as data_f:\n",
    "    data = json.load(data_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_a, sum_a = 0, 0\n",
    "total_e, sum_e = 0, 0\n",
    "total_m, sum_m = 0, 0\n",
    "total_h, sum_h = 0, 0\n",
    "\n",
    "for slug_name in data:\n",
    "    results = data[slug_name]\n",
    "    beyonds = [result['beyond_p'] for result in results]\n",
    "    beyond = max(beyonds[:1])\n",
    "    \n",
    "    total_a += 1\n",
    "    sum_a += beyond\n",
    "    \n",
    "    if slug_name_dict[slug_name] == 'Easy':\n",
    "        total_e += 1\n",
    "        sum_e += beyond\n",
    "    elif slug_name_dict[slug_name] == 'Medium':\n",
    "        total_m += 1\n",
    "        sum_m += beyond\n",
    "    elif slug_name_dict[slug_name] == 'Hard':\n",
    "        total_h += 1\n",
    "        sum_h += beyond\n",
    "        \n",
    "    \n",
    "final_beyond_a = sum_a / total_a * 100\n",
    "print(final_beyond_a)\n",
    "\n",
    "final_beyond_e = sum_e / total_e * 100\n",
    "print(final_beyond_e)\n",
    "\n",
    "final_beyond_m = sum_m / total_m * 100\n",
    "print(final_beyond_m)\n",
    "\n",
    "final_beyond_h = sum_h / total_h * 100\n",
    "print(final_beyond_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./data/codellama/CodeLlama-7b-Instruct-hf_eval_new.json') as data_f:\n",
    "    data = json.load(data_f)\n",
    "    \n",
    "\n",
    "for d in ['Easy', 'Medium', 'Hard']:\n",
    "    status = defaultdict(int)\n",
    "    for slug_name in data:\n",
    "        if slug_name_dict[slug_name] == d:\n",
    "            result = data[slug_name][0]['result']['result']\n",
    "            status[result] += 1\n",
    "    print(d)\n",
    "    print(status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "53 + 39 + 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"openai_humaneval\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_sum = 0\n",
    "for data in dataset['test']:\n",
    "    test = data['test']\n",
    "    cases = test.count('assert')\n",
    "    cases_sum += cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_sum / 164"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset_sanitized = load_dataset(\"mbpp\", \"sanitized\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 0\n",
    "for data in dataset_sanitized['test']:\n",
    "    s += len(data['test_list'])\n",
    "    \n",
    "print(s/257)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "import json\n",
    "\n",
    "dataset = load_dataset(\"codeparrot/apps\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 0\n",
    "v = 0\n",
    "for data in dataset['test']:\n",
    "    # if data['solutions']:\n",
    "    #     s += len(json.loads(data['solutions']))\n",
    "    if data['input_output']:\n",
    "        input_output = json.loads(data['input_output'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s / 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"OPENAI_API_KEY\"] = 'YOUR_OPENAI_KEY'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate evaluator with model_name\n",
    "# Set do_generate to True if you are going to load the specific language model during evaluator initialization.\n",
    "from src import evaluator as Evaluator\n",
    "evaluator = Evaluator.DistributeWiseEvaluator(model_name_or_path='bigcode/starcoder2-7b', do_generate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate code samples\n",
    "# evaluator.generate(num_samples_per_task=1)\n",
    "\n",
    "# Evaluate code samples using the Mercury benchmark\n",
    "evaluator.evaluate(num_samples_per_task=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"\"\"\\nclass Solution(object):\\n    def spiralOrder(self, matrix):\\n        \\\"\\\"\\\"\\n        :type matrix: List[List[int]]\\n        :rtype: List[int]\\n        \\\"\\\"\\\"\\n        \\n        if not matrix:\\n            return []\\n        \\n        m = len(matrix)\\n        n = len(matrix[0])\\n        res = []\\n        top = 0\\n        bottom = m - 1\\n        left = 0\\n        right = n - 1\\n        \\n        while top <= bottom and left <= right:\\n            for i in range(left, right + 1):\\n                res.append(matrix[top][i])\\n            top += 1\\n            for i in range(top, bottom + 1):\\n                res.append(matrix[i][right])\\n            right -= 1\\n            if top <= bottom:\\n                for i in range(right, left - 1, -1):\\n                    res.append(matrix[bottom][i])\\n                bottom -= 1\\n            if left <= right:\\n                for i in range(bottom, top - 1, -1):\\n                    res.append(matrix[i][left])\\n                left += 1\\n        return res\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"```Given an m x n matrix, return all elements of the matrix in spiral order.\\n\\n\\u00a0\\nExample 1:\\n\\nInput: matrix = [[1,2,3],[4,5,6],[7,8,9]]\\nOutput: [1,2,3,6,9,8,7,4,5]\\n\\n\\nExample 2:\\n\\nInput: matrix = [[1,2,3,4],[5,6,7,8],[9,10,11,12]]\\nOutput: [1,2,3,4,8,12,11,10,9,5,6,7]\\n\\n\\n\\u00a0\\nConstraints:\\n\\n\\n\\tm == matrix.length\\n\\tn == matrix[i].length\\n\\t1 <= m, n <= 10\\n\\t-100 <= matrix[i][j] <= 100\\n\\n\\n```\\nclass Solution(object):\\n    def spiralOrder(self, matrix):\\n        \\\"\\\"\\\"\\n        :type matrix: List[List[int]]\\n        :rtype: List[int]\\n        \\\"\\\"\\\"\\n        \\n        if not matrix:\\n            return []\\n        \\n        m = len(matrix)\\n        n = len(matrix[0])\\n        res = []\\n        i = 0\\n        j = 0\\n        di = 0\\n        dj = 1\\n        for _ in range(m*n):\\n            res.append(matrix[i][j])\\n            if i+di >= m or i+di < 0 or j+dj >= n or j+dj < 0:\\n                break\\n            if matrix[i+di][j+dj] == 0:\\n                break\\n            matrix[i+di][j+dj] = 0\\n            i += di\\n            j += dj\\n            if di == 0:\\n                di = 1\\n                dj = 0\\n            elif di == 1:\\n                di = 0\\n                dj = -1\\n            elif di == -1:\\n                di = 0\\n                dj = 1\\n            elif di == 0:\\n                di = -1\\n                dj = 0\\n        return res\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_pass_at_k(num_samples, num_correct, k):\n",
    "    \"\"\"Estimates pass@k of each problem and returns them in an array.\"\"\"\n",
    "\n",
    "    def estimator(n: int, c: int, k: int) -> float:\n",
    "        \"\"\"Calculates 1 - comb(n - c, k) / comb(n, k).\"\"\"\n",
    "        if n - c < k:\n",
    "            return 1.0\n",
    "        return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))\n",
    "\n",
    "    if isinstance(num_samples, int):\n",
    "        num_samples_it = itertools.repeat(num_samples, len(num_correct))\n",
    "    else:\n",
    "        assert len(num_samples) == len(num_correct)\n",
    "        num_samples_it = iter(num_samples)\n",
    "\n",
    "    return np.array([estimator(float(n), float(c), k) for n, c in zip(num_samples_it, num_correct)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cf(n, k):\n",
    "    return math.gamma(n+1) / (math.gamma(k+1) * (math.gamma(n-k+1)))\n",
    "\n",
    "def e1(n ,c, k):\n",
    "    if n - c < k:\n",
    "        return 1.0\n",
    "    return 1 - cf(n-c, k) / cf(n, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def e2(n: int, c: int, k: int) -> float:\n",
    "    \"\"\"Calculates 1 - comb(n - c, k) / comb(n, k).\"\"\"\n",
    "    if n - c < k:\n",
    "        return 1.0\n",
    "    return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e1(5, 0.1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e2(5, 4, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_pass_at_k([5,5,5], [3,2,5], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks = [1,3,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not isinstance(ks, (list, tuple)):\n",
    "    ks = [ks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{f\"pass@{k}\": estimate_pass_at_k([5,5,5], [3,2,5], 1).mean() for k in ks if ([5,5,5] >= k).all()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = np.array([5,5,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = {1:1}\n",
    "b = {2:2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pynvml as N\n",
    "\n",
    "MB = 1024 * 1024\n",
    "\n",
    "def get_usage(device_index, my_pid):\n",
    "    N.nvmlInit()\n",
    "\n",
    "    handle = N.nvmlDeviceGetHandleByIndex(device_index)\n",
    "\n",
    "    usage = [nv_process.usedGpuMemory // MB for nv_process in\n",
    "             N.nvmlDeviceGetComputeRunningProcesses(handle) + N.nvmlDeviceGetGraphicsRunningProcesses(handle) if\n",
    "             nv_process.pid == my_pid]\n",
    "\n",
    "    if len(usage) == 1:\n",
    "        usage = usage[0]\n",
    "    else:\n",
    "        raise KeyError(\"PID not found\")\n",
    "\n",
    "    return usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"Elfsong/Mercury\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    # \"deepseek-ai/deepseek-coder-6.7b-instruct\",\n",
    "    # \"./checkpoints/checkpoint-200\",\n",
    "    \"./checkpoints\",\n",
    "    # quantization_config=bnb_config,\n",
    "    device_map=\"cuda:0\",\n",
    "    trust_remote_code=True,\n",
    "    low_cpu_mem_usage=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"./checkpoints\", legacy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "instance = dataset['eval'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "instance[\"entry_point\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "instance[\"solutions\"][0][\"solution\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_text = f'{instance[\"pretty_content\"][0]}\\n{instance[\"prompt\"]}'\n",
    "inputs = tokenizer(input_text, return_tensors=\"pt\").to(\"cuda:0\")\n",
    "outputs = model.generate(**inputs, max_length=1024, pad_token_id=tokenizer.eos_token_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"Elfsong/Mercury\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['train'][528]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!huggingface-cli download codellama/CodeLlama-34b-hf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!huggingface-cli download bigcode/starcoder2-15b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79+6KjIzUokWLVLFiRS1cuFBer1d33XWXXC6X7xUXF6caNWroyy+/9GsrNDRUDzzwwDn1+8knn0iSBg8e7Ld8yJAhkk79C/bpqlatqnbt2uXZVs+ePf0mwG3SpImMMerTp4/fdk2aNNHvv/+ukydP+padPq+S2+2Wy+VSy5Yt9b///U9ut9tv/9q1a/tN/BsTE6OaNWvqf//7n29ZSkqK6tevn+dIEYfDIenU7R1XXXWVatWq5Xdec0Yd/P28/t24ceM0Z84cNWzYUJ9++qmeeeYZNWrUSNdcc422bt3qV0t0dLQee+yxM9byySefKCgoSElJSX7rhwwZImOMli5d6re8ZcuWql27tu+9MUYpKSnq3LmzjDF+x9OuXTu53W6tX79eklSqVCnt2bNHa9euPevxnUnPnj1VokQJ3/s77rhD5cuX9/0s5Wzz/fff+92uN3v2bMXHx6tly5bn3Nc999yj7du3a+3atb7/PdNoqU8++URxcXF+c4iFhIQoKSlJGRkZWrVqlW+74OBg34gVSQoKCsp1ff766y998cUXuuuuu3TkyBHf+Txw4IDatWunbdu26Y8//jjnY5H8f86zsrJ04MABJSQkqFSpUr7rc7r+/fv7fkYkqUWLFsrOztauXbskScuXL9eJEyf02GOP+W2XM+rMLv369VNQUFC+juXvIiMj/Ua2FCtWTI0bN/b7HJ/Nww8/7Pe+RYsWfvsuW7ZMISEh6tevn2+Z0+nMNbLxTOrUqaONGzfqvvvu086dO/XSSy+pS5cuKleunN58881zaiPHxTh/n3/+uQ4dOqQePXr4fd6DgoLUpEkT3/fXn3/+qc2bN6tnz56KjIz07d+yZUvVrVvXr80rr7xSTZo00ezZs33L/vrrLy1dulT33nuv388YAOD8ESIBAGwzffp0ff755/ryyy/1yy+/6H//+58vrNm2bZuMMapRo4ZiYmL8Xlu3bs01yWvFihXPefLsXbt2yel0KiEhwW95XFycSpUq5ftjNUfVqlXP2NYVV1zh9z4qKkqSFB8fn2u51+v1C4dWr16ttm3bqnjx4ipVqpRiYmL09NNPS1KuEOnv/UhS6dKldfDgQd/71NRUXX311WesVTp1Xrds2ZLrnF555ZWSzm3y3B49eujrr7/WwYMH9dlnn+mee+7Rhg0b1LlzZ9+8P6mpqapZs6aCg898J/yuXbtUoUIFv3BG+r/bVqyuw/79+3Xo0CG98cYbuY4nJ1DMOZ7hw4crMjJSjRs3Vo0aNTRgwACtXr3a8lhz1KhRw++9w+FQQkKC31wud999t0JDQ31/jLrdbn300Ufn/Ydow4YNVatWLc2ZM0ezZ89WXFzcGW8t2rVrl2rUqCGn0/9XtL+fw127dql8+fJ+f1BL8rs1Uzp126QxRqNGjcp1TnMe8X6+EywfO3ZMo0eP9s17FR0drZiYGB06dCjXz7mU+2e9dOnSkuT7Wc85pr9fk5iYGN+2dsjrc3++x/J3lSpVyvWz8PfP8ZmEhYUpJibmrPvmXOe/31b39++6s7nyyiv17rvvyuVy6aefftKECRMUHBys/v37a/ny5efczsU4f9u2bZN0av68v/98fvbZZ76fzZyfkbyOO69lPXv21OrVq337zZ8/X1lZWbr//vvP+XgBAHljTiQAgG0aN27sezrb33m9XjkcDi1dujTXv2ZLyvXH8IU8Le1c/7A/W9t51Xa25eb/TxidmpqqNm3aqFatWnrxxRcVHx+vYsWK6ZNPPtGUKVPk9XrPq71z5fV6VbduXb344ot5rv97+HU2JUuW1M0336ybb75ZISEhevvtt/X999+f16ib8/H365Bzju67774zzvlSr149SadCld9++00fffSRli1bppSUFL366qsaPXq032PL86N06dK69dZbNXv2bI0ePVoLFixQZmbmBc2pcs8992jGjBkqUaKE7r777lwh0cWSc06HDh16xtF35xNISKfmOJo5c6Yef/xxNW3aVFFRUXI4HOrevXuun3PJvp/1/Mrrc3++x/J3+Tm2M+17sQQFBalu3bqqW7eumjZtqtatW2v27Nlq27btOe1/Mc5fzjbvvvuu4uLicq0/W2h9Nt27d9cTTzyh2bNn6+mnn9Z7772na6+9NlfICgA4f4RIAIACkTPxctWqVX2jZOxSuXJleb1ebdu2zTdiQ5L27t2rQ4cO5flkIrt9+OGHyszM1JIlS/xGXljdTnY21atX188//2y5zaZNm9SmTRtbb9O49tpr9fbbbystLc3Xz/fff6+srCy/2/1OlzOJ75EjR/xGI+U8mczqOsTExKhEiRLKzs4+pz9sixcvrrvvvlt33323Tpw4oW7duum5557TiBEjLCe9zhkBkcMYo+3bt/tCqhw9e/bU7bffrrVr12r27Nlq2LChb+Lz83HPPfdo9OjRSktL07vvvnvG7SpXrqyffvpJXq/XL2j6+zmsXLmyVqxYoYyMDL8A9rfffvNrr1q1apJO3RJ3rmGBlQULFqhXr16aPHmyb9nx48fP+GQ4KznHtG3bNl+90qmRaecyoic/7D4Wu1WuXFlffvmlPB6P32ik7du356vdnLA/5/MtnXsIf7pzPX9nart69eqSTk3yf7afz5yfkbyOO69lZcqUUadOnTR79mzde++9Wr16taZOnWp1OACAc8DtbACAAtGtWzcFBQVp3Lhxuf6V3hijAwcOXHDbHTt2lKRcfyTkjM7J6ylYdssZVXD6sbndbs2cOfOC20xMTNSmTZtyPV3u9H7uuusu/fHHH3nOb3Ls2DHfU5Ty4vF4tGbNmjzX5cxflPMv94mJiXK5XHrllVfOWEvHjh2VnZ2da5spU6bI4XBYPq0qKChIiYmJSklJyTM8O/2x8H//eSlWrJhq164tY4yysrLO2o8kvfPOOzpy5Ijv/YIFC5SWlparxg4dOig6OlqTJk3SqlWrLvjJTtWrV9fUqVOVnJysxo0bn3G7jh07Kj093e/JZSdPntTLL7+syMhI36iwjh076uTJk5oxY4Zvu+zsbL388st+7cXGxqpVq1Z6/fXX/QKDHKef03MVFBSU6zP88ssv53o0/blq27atQkJC9PLLL/u1WxB/9Nt9LHZr166dsrKy/D7fXq9X06dPP6f9v/766zw/Dzlzf50+Mqd48eLnHZ6d6/krXry4JOVqv127dipZsqQmTJiQZ505P58VKlTQ1VdfrXfeeUcZGRm+9atWrdLmzZvzrO3+++/XL7/8omHDhikoKEjdu3c/r2MDAOSNkUgAgAJRvXp1PfvssxoxYoR27typLl26qESJEtqxY4cWLVqk/v37a+jQoRfUdv369dWrVy+98cYbOnTokFq2bKkffvhBb7/9trp06aLWrVvbfDS53XLLLSpWrJg6d+6shx56SBkZGXrzzTcVGxub5x/v52LYsGFasGCB7rzzTvXp00eNGjXSX3/9pSVLlui1115T/fr1df/99+v999/Xww8/rC+//FLNmjVTdna2fv31V73//vv69NNPz3iLocfj0Q033KDrr79e7du3V3x8vA4dOqTFixfr66+/VpcuXdSwYUNJp0bkvPPOOxo8eLB++OEHtWjRQkePHtXy5cv16KOP6vbbb1fnzp3VunVrPfPMM9q5c6fq16+vzz77TB988IEef/xx36iDs5k4caK+/PJLNWnSRP369VPt2rX1119/af369Vq+fLn++usv3/mOi4tTs2bNVK5cOW3dulWvvPKKOnXqlGtOpryUKVNGzZs31wMPPKC9e/dq6tSpSkhI8JvAWDo1gqd79+565ZVXFBQU5Dfh9fkaNGiQ5Tb9+/fX66+/rt69e+vHH39UlSpVtGDBAt9Iipxj69y5s5o1a6annnpKO3fuVO3atbVw4cI856GZPn26mjdvrrp166pfv36qVq2a9u7dqzVr1mjPnj3atGnTeR3HrbfeqnfffVdRUVGqXbu21qxZo+XLl6ts2bLn1U6OmJgYDR06VMnJybr11lvVsWNHbdiwQUuXLlV0dPQFtXmu7D4Wu3Xp0kWNGzfWkCFDtH37dtWqVUtLlizxfQ6sRg9NmjRJP/74o7p16+YbZbd+/Xq98847KlOmjN/k5Y0aNdKMGTP07LPPKiEhQbGxsWecuyvHuZ6/Bg0aKCgoSJMmTZLb7VZoaKhuuukmxcbGasaMGbr//vt1zTXXqHv37oqJidHu3bv18ccfq1mzZr5QesKECbr99tvVrFkzPfDAAzp48KBeeeUVXX311X7BUo5OnTqpbNmymj9/vjp06KDY2FjL8w0AOAcF+Sg4AMClKecR0GvXrrXcNiUlxTRv3twUL17cFC9e3NSqVcsMGDDA/Pbbb75tWrZsecbHt/fq1csUL1481/KsrCwzbtw4U7VqVRMSEmLi4+PNiBEj/B5rbowxlStXNp06dcq1f84jvOfPn39Ox3b6I9xzLFmyxNSrV8+EhYWZKlWqmEmTJpm33nrLSDI7duywrKFly5a5HrF94MABM3DgQFOxYkVTrFgxU6lSJdOrVy/jcrl825w4ccJMmjTJ1KlTx4SGhprSpUubRo0amXHjxhm32537JJ52zt58803TpUsXU7lyZRMaGmoiIiJMw4YNzQsvvGAyMzP9tvd4POaZZ57xneO4uDhzxx13mNTUVN82R44cMU888YSpUKGCCQkJMTVq1DAvvPBCrsdq62+Ptj/d3r17zYABA0x8fLyvnzZt2pg33njDt83rr79ubrzxRlO2bFkTGhpqqlevboYNG3bW4zXm/67zf/7zHzNixAgTGxtrwsPDTadOncyuXbvy3OeHH34wkswtt9xy1rZPl9fPR17yOg979+41DzzwgImOjjbFihUzdevWzfNR7gcOHDD333+/KVmypImKijL333+/2bBhQ56Pfk9NTTU9e/Y0cXFxJiQkxFSsWNHceuutZsGCBb5t8nqMfV4OHjzoqy8yMtK0a9fO/Prrr6Zy5cqmV69evu3O9NnJq5/s7Gw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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Define model names and their corresponding performance scores for Base, SFT, and DPO.\n",
    "models = [\n",
    "    \"DeepSeek-Coder-1.3B-Base\", \"StarCoder2-3B\", \"DeepSeek-Coder-6.7B-Base\",\n",
    "    \"StarCoder2-7B\", \"CodeLlama-7B-HF\", \"CodeQwen1.5-7B\", \"StarCoder2-15B\",\n",
    "    \"CodeLlama-13B-HF\", \"DeepSeek-Coder-33B-Base\", \"CodeLlama-34B-HF\"\n",
    "]\n",
    "base_scores = [9.8900, 9.7648, 16.4505, 11.0023, 9.2986, 15.4003, 12.5994, 13.8361, 18.5516, 15.5372]\n",
    "sft_scores = [12.7809, 14.0849, 16.6075, 15.8435, 12.5066, 14.9639, 16.2730, 13.8269, 17.3484, 11.6559]\n",
    "dpo_scores = [9.6807, 16.3823, 18.7910, 17.4637, 10.4908, 12.3153, 9.2181, 8.5135, 5.3844, 8.0684]\n",
    "\n",
    "# Set up the bar width and positions for the clustered bars.\n",
    "bar_width = 0.2\n",
    "r1 = np.arange(len(models))\n",
    "r2 = [x + bar_width for x in r1]\n",
    "r3 = [x + bar_width for x in r2]\n",
    "\n",
    "# Create the clustered bar plot.\n",
    "plt.figure(figsize=(14, 8))\n",
    "plt.bar(r1, base_scores, color='b', width=bar_width, edgecolor='grey', label='Base')\n",
    "plt.bar(r2, sft_scores, color='r', width=bar_width, edgecolor='grey', label='SFT')\n",
    "plt.bar(r3, dpo_scores, color='g', width=bar_width, edgecolor='grey', label='DPO')\n",
    "\n",
    "# Add labels to the plot.\n",
    "plt.xlabel('Models', fontweight='bold')\n",
    "plt.xticks([r + bar_width for r in range(len(models))], models, rotation=45, ha='right')\n",
    "plt.ylabel('Performance Scores')\n",
    "plt.title('Performance Scores by Model and Training Strategy')\n",
    "\n",
    "# Create a legend.\n",
    "plt.legend()\n",
    "\n",
    "# Display the plot.\n",
    "# plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Define model names and their corresponding performance scores for Base, SFT, and DPO.\n",
    "models = [\n",
    "    \"DeepSeek-Coder-1.3B-Base\", \"StarCoder2-3B\", \"DeepSeek-Coder-6.7B-Base\",\n",
    "    \"StarCoder2-7B\", \"CodeLlama-7B-HF\", \"CodeQwen1.5-7B\", \"StarCoder2-15B\",\n",
    "    \"CodeLlama-13B-HF\", \"DeepSeek-Coder-33B-Base\", \"CodeLlama-34B-HF\"\n",
    "]\n",
    "base_scores = [9.8900, 9.7648, 16.4505, 11.0023, 9.2986, 15.4003, 12.5994, 13.8361, 18.5516, 15.5372]\n",
    "sft_scores = [12.7809, 14.0849, 16.6075, 15.8435, 12.5066, 14.9639, 16.2730, 13.8269, 17.3484, 11.6559]\n",
    "dpo_scores = [9.6807, 16.3823, 18.7910, 17.4637, 10.4908, 12.3153, 9.2181, 8.5135, 5.3844, 8.0684]\n",
    "\n",
    "# Create a DataFrame from the lists\n",
    "data = {\n",
    "    'Model': models,\n",
    "    'Base': base_scores,\n",
    "    'SFT': sft_scores,\n",
    "    'DPO': dpo_scores\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Melt the DataFrame to have a suitable format for Seaborn\n",
    "df_melted = df.melt(id_vars='Model', var_name='Category', value_name='Score')\n",
    "\n",
    "# Create the clustered bar plot using Seaborn\n",
    "plt.figure(figsize=(14, 8))\n",
    "sns.barplot(x='Model', y='Score', hue='Category', data=df_melted)\n",
    "\n",
    "# Add labels to the plot\n",
    "plt.xlabel('Models', fontweight='bold')\n",
    "plt.xticks(rotation=0, ha='right', minor=100)\n",
    "plt.ylabel('Performance Scores')\n",
    "plt.title('Performance Scores by Model and Training Strategy')\n",
    "\n",
    "# Display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from seaborn) (1.26.4)\n",
      "Requirement already satisfied: pandas>=1.2 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from seaborn) (2.2.2)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from seaborn) (3.8.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.0)\n",
      "Requirement already satisfied: pillow>=8 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (6.4.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib!=3.6.1,>=3.4->seaborn) (3.18.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/mingzhe/miniconda3/envs/mercury/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
      "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: seaborn\n",
      "Successfully installed seaborn-0.13.2\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 5000x2500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "# Create a dictionary with your data\n",
    "a_data = {\n",
    "    'Config': ['Micro', 'Small', 'Standard'],\n",
    "    'Easy': [65.8, 66.74, 67.92],\n",
    "    'Medium': [68.19, 67.32, 67.18],\n",
    "    'Hard': [51.66, 52.51, 52.36]\n",
    "}\n",
    "\n",
    "b_data = {\n",
    "    'Config': ['Micro', 'Small', 'Standard'],\n",
    "    'Easy': [63.21, 62.85, 63.55],\n",
    "    'Medium': [57.41, 58.7, 58.8],\n",
    "    'Hard': [43.55, 45.67, 45.72]\n",
    "}\n",
    "\n",
    "# Convert the dictionary into a pandas DataFrame\n",
    "a_df = pd.DataFrame(a_data)\n",
    "b_df = pd.DataFrame(b_data)\n",
    "\n",
    "# Melt the DataFrame so that it is in long-form for seaborn\n",
    "a_df_long = pd.melt(a_df, id_vars='Config', value_vars=['Easy', 'Medium', 'Hard'], var_name='Difficulty', value_name='Score')\n",
    "b_df_long = pd.melt(b_df, id_vars='Config', value_vars=['Easy', 'Medium', 'Hard'], var_name='Difficulty', value_name='Score')\n",
    "\n",
    "# Use seaborn to create the line plot\n",
    "sns.set_theme(style=\"whitegrid\")  # Set the theme for the plot (optional)\n",
    "plt.figure(figsize=(5, 2.5))  # Set the size of the figure (optional)\n",
    "\n",
    "line_plot_a = sns.lineplot(data=a_df_long, x='Difficulty', y='Score', hue='Config', marker='o')\n",
    "line_plot_b = sns.lineplot(data=b_df_long, x='Difficulty', y='Score', hue='Config', marker='o', linestyle='--')\n",
    "\n",
    "line_plot_a.set_xlabel('')\n",
    "line_plot_a.set_ylabel('Beyond')\n",
    "\n",
    "\n",
    "handles, labels = line_plot_a.get_legend_handles_labels()\n",
    "\n",
    "handles = [handles[0], handles[1], handles[2]]  # skip every alternate handle to avoid duplicates\n",
    "labels = [labels[0], labels[1], labels[2]]    # skip every alternate label to have only unique entries\n",
    "\n",
    "# Add legend to the plot with our custom handles and labels\n",
    "plt.legend(handles=handles, labels=labels)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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