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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install -U -q transformers datasets accelerate sentencepiece\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "n9CP-h2s6_O_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hh451UTX63oX"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# If the dataset is gated/private, make sure you have run huggingface-cli login\n",
        "input_dataset = load_dataset(\"sablo/SHP-2-dpo\")\n",
        "input_dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from itertools import chain\n",
        "\n",
        "def count_words(text):\n",
        "    words = re.findall(r\"\\b\\w+\\b\", text)\n",
        "    return len(words)"
      ],
      "metadata": {
        "id": "tIKKhFkD9ngD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tiktoken -q\n",
        "import os\n",
        "\n",
        "import tiktoken\n",
        "\n",
        "# To get the tokeniser corresponding to a specific model in the OpenAI API:\n",
        "enc = tiktoken.encoding_for_model(\"gpt-4\")\n",
        "\n",
        "\n",
        "def count_tokens(text:str):\n",
        "    if len(str(text).strip()) < 2:\n",
        "        return 0\n",
        "\n",
        "    return len(\n",
        "            enc.encode(text, allowed_special={\"<|endoftext|>\"})\n",
        "        )\n",
        "count_tokens('ayyyy lmao!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mfFtOrAl94WM",
        "outputId": "82222a49-0d83-42f6-a234-92dd7475eea1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def check_within_ctx(example, max_ctx: int = 4096):\n",
        "\n",
        "    try:\n",
        "        words_full_chosen = count_tokens(example[\"prompt\"]) + count_tokens(\n",
        "            example[\"chosen\"]\n",
        "        )\n",
        "        words_full_rejected = count_tokens(example[\"prompt\"]) + count_tokens(\n",
        "            example[\"rejected\"]\n",
        "        )\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        "        return False\n",
        "\n",
        "    return words_full_chosen <= max_ctx and words_full_rejected <= max_ctx\n",
        "\n",
        "\n",
        "check_within_ctx(input_dataset[\"train\"][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kAzWemy-ZBa",
        "outputId": "c3a26892-5f84-4575-ea6f-299c13b47bd7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "input_dataset = input_dataset.filter(check_within_ctx, num_proc=16)\n",
        "input_dataset"
      ],
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      "execution_count": 7,
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              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['post_id', 'domain', 'upvote_ratio', 'seconds_difference', 'score_ratio', 'prompt', 'chosen', 'rejected', 'score_chosen', 'score_rejected'],\n",
              "        num_rows: 3490705\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['post_id', 'domain', 'upvote_ratio', 'seconds_difference', 'score_ratio', 'prompt', 'chosen', 'rejected', 'score_chosen', 'score_rejected'],\n",
              "        num_rows: 238624\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['post_id', 'domain', 'upvote_ratio', 'seconds_difference', 'score_ratio', 'prompt', 'chosen', 'rejected', 'score_chosen', 'score_rejected'],\n",
              "        num_rows: 241159\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, DatasetDict\n",
        "import os\n",
        "from collections import Counter\n",
        "import json\n",
        "from datasets.utils.logging import disable_progress_bar, enable_progress_bar\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "\n",
        "def get_workers(safety:int= 4):\n",
        "    return max(1, os.cpu_count()-safety)\n",
        "\n",
        "total_rows_small = {\"train\": 90000, \"validation\": 5000, \"test\": 5000}\n",
        "\n",
        "\n",
        "\n",
        "# Function to sample data for a given split\n",
        "def sample_split_data(split, total_rows):\n",
        "    # Count domain occurrences\n",
        "    domain_counts = Counter(input_dataset[split][\"domain\"])\n",
        "\n",
        "    # Calculate the total number of rows in the current split\n",
        "    total_rows_current_split = len(input_dataset[split])\n",
        "    disable_progress_bar()\n",
        "    # Calculate the number of rows to sample for each domain\n",
        "    sample_counts = {\n",
        "        domain: int((count / total_rows_current_split) * total_rows)\n",
        "        for domain, count in domain_counts.items()\n",
        "    }\n",
        "\n",
        "    # Sample data\n",
        "    sampled_data = []\n",
        "    for domain, sample_count in tqdm(sample_counts.items()):\n",
        "        domain_data = input_dataset[split].filter(\n",
        "            lambda example: example[\"domain\"] == domain,\n",
        "            num_proc=get_workers()\n",
        "        )\n",
        "        sampled_indices = domain_data.shuffle(seed=42).select(\n",
        "            range(min(sample_count, len(domain_data)))\n",
        "        )\n",
        "        sampled_data.extend(sampled_indices)\n",
        "\n",
        "    enable_progress_bar()\n",
        "    return sampled_data\n",
        "\n",
        "\n",
        "# Sample data for each split\n",
        "sampled_train = sample_split_data(\"train\", total_rows_small[\"train\"])\n",
        "sampled_validation = sample_split_data(\"validation\", total_rows_small[\"validation\"])\n",
        "sampled_test = sample_split_data(\"test\", total_rows_small[\"test\"])\n",
        "\n",
        "# Combine the samples to create smaller datasets\n",
        "small_dataset = DatasetDict(\n",
        "    {\n",
        "        \"train\": sampled_train,\n",
        "        \"validation\": sampled_validation,\n",
        "        \"test\": sampled_test,\n",
        "    }\n",
        ")\n",
        "\n",
        "small_dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "b28ef9f96a4f488bb76852f5a9735966",
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        "id": "XGNAySdq64QP",
        "outputId": "272cb820-7b36-47e8-dc83-2882e99396c6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "model_id": "b28ef9f96a4f488bb76852f5a9735966"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
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            }
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import gc\n",
        "\n",
        "gc.collect()\n",
        "\n",
        "from datasets import Dataset\n",
        "_test = Dataset.from_pandas(pd.DataFrame(sampled_train))\n",
        "_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LyjBZ8Nr73Kz",
        "outputId": "c75495a8-b30f-46b7-b087-0df252e598d8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['post_id', 'domain', 'upvote_ratio', 'seconds_difference', 'score_ratio', 'prompt', 'chosen', 'rejected', 'score_chosen', 'score_rejected'],\n",
              "    num_rows: 89971\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine the samples to create smaller datasets\n",
        "small_dataset = DatasetDict(\n",
        "    {\n",
        "        \"train\": Dataset.from_pandas(pd.DataFrame(sampled_train)),\n",
        "        \"validation\": Dataset.from_pandas(pd.DataFrame(sampled_validation)),\n",
        "        \"test\": Dataset.from_pandas(pd.DataFrame(sampled_test)),\n",
        "    }\n",
        ")\n",
        "\n",
        "small_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6Iz2j4AGAQ0",
        "outputId": "ef1d373b-293d-4b00-ceb0-451d02048f2a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['post_id', 'domain', 'upvote_ratio', 'seconds_difference', 'score_ratio', 'prompt', 'chosen', 'rejected', 'score_chosen', 'score_rejected'],\n",
              "        num_rows: 89971\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['post_id', 'domain', 'upvote_ratio', 'seconds_difference', 'score_ratio', 'prompt', 'chosen', 'rejected', 'score_chosen', 'score_rejected'],\n",
              "        num_rows: 4936\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['post_id', 'domain', 'upvote_ratio', 'seconds_difference', 'score_ratio', 'prompt', 'chosen', 'rejected', 'score_chosen', 'score_rejected'],\n",
              "        num_rows: 4938\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "small_dataset.push_to_hub('sablo/SHP-2-dpo',\n",
        "                          config_name='sampled-100k',\n",
        "                          private=True,\n",
        "                          commit_message='stratified sampled ds prop. to domain freq'\n",
        "                          )"
      ],
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          "height": 293,
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            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/datasets/sablo/SHP-2-dpo/commit/6cd324ab8b1f832033c30a996c631cc4d31e27f0', commit_message='stratified sampled ds prop. to domain freq', commit_description='', oid='6cd324ab8b1f832033c30a996c631cc4d31e27f0', pr_url=None, pr_revision=None, pr_num=None)"
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          "metadata": {},
          "execution_count": 16
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}