qa_utils.py

import itertools  # noqa: I001
import random
from functools import cache, partial

import datasets
import requests
from tqdm import tqdm
from transformers import AutoTokenizer

from lm_eval.tasks.ruler.utils import SEQ_LENGTHS

# config =  {
#         'tokens_to_generate': 32,
#         'template': """Answer the question based on the given documents. Only give me the answer and do not output any other words.\n\nThe following are given documents.\n\n{context}\n\nAnswer the question based on the given documents. Only give me the answer and do not output any other words.\n\nQuestion: {query}""",
#         'answer_prefix': """ Answer:""",
#     }
SEED = 42
TEMPLATE = """Answer the question based on the given documents. Only give me the answer and do not output any other words.\n\nThe following are given documents.\n\n{context}\n\nAnswer the question based on the given documents. Only give me the answer and do not output any other words.\n\nQuestion: {query}"""
DOCUMENT_PROMPT = "Document {i}:\n{document}"


@cache
def download_json(url):
    response = requests.get(url)
    response.raise_for_status()
    data = response.json()
    return data


@cache
def read_squad(url="https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json"):
    data = download_json(url)
    total_docs = [p["context"] for d in data["data"] for p in d["paragraphs"]]
    total_docs = sorted(list(set(total_docs)))
    total_docs_dict = {c: idx for idx, c in enumerate(total_docs)}

    total_qas = []
    for d in data["data"]:
        more_docs = [total_docs_dict[p["context"]] for p in d["paragraphs"]]
        for p in d["paragraphs"]:
            for qas in p["qas"]:
                if not qas["is_impossible"]:
                    total_qas.append(
                        {
                            "query": qas["question"],
                            "outputs": [a["text"] for a in qas["answers"]],
                            "context": [total_docs_dict[p["context"]]],
                            "more_context": [
                                idx
                                for idx in more_docs
                                if idx != total_docs_dict[p["context"]]
                            ],
                        }
                    )

    return total_qas, total_docs


@cache
def read_hotpotqa(
    url="http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_distractor_v1.json",
):
    data = download_json(url)
    total_docs = [f"{t}\n{''.join(p)}" for d in data for t, p in d["context"]]
    total_docs = sorted(list(set(total_docs)))
    total_docs_dict = {c: idx for idx, c in enumerate(total_docs)}

    total_qas = []
    for d in data:
        total_qas.append(
            {
                "query": d["question"],
                "outputs": [d["answer"]],
                "context": [
                    total_docs_dict[f"{t}\n{''.join(p)}"] for t, p in d["context"]
                ],
            }
        )

    return total_qas, total_docs


def generate_input_output(
    index: int, num_docs: int, qas: list[dict], docs: list[str]
) -> tuple[str, list[str]]:
    curr_q: str = qas[index]["query"]
    curr_a: list[str] = qas[index]["outputs"]
    curr_docs: list[int] = qas[index]["context"]
    curr_more: list[int] = qas[index].get("more_context", [])
    if num_docs < len(docs):
        if (num_docs - len(curr_docs)) > len(curr_more):
            addition_docs = [
                i for i, d in enumerate(docs) if i not in curr_docs + curr_more
            ]
            all_docs = (
                curr_docs
                + curr_more
                + random.sample(
                    addition_docs, max(0, num_docs - len(curr_docs) - len(curr_more))
                )
            )
        else:
            all_docs = curr_docs + random.sample(curr_more, num_docs - len(curr_docs))

        all_docs = [docs[idx] for idx in all_docs]
    else:
        all_docs = docs

    random.Random(SEED).shuffle(all_docs)

    context = "\n\n".join(
        [DOCUMENT_PROMPT.format(i=i + 1, document=d) for i, d in enumerate(all_docs)]
    )
    input_text = TEMPLATE.format(context=context, query=curr_q)
    return input_text, curr_a


def generate_samples(
    tokenizer,
    docs: list[str],
    qas: list[dict],
    max_seq_length: int,
    num_samples: int = 500,
    tokens_to_generate: int = 32,
    pre_samples: int = 0,
    incremental: int = 10,
    remove_newline_tab=False,
) -> list[dict]:
    write_jsons = []
    tokens_to_generate = tokens_to_generate

    # Find the perfect num_docs
    num_docs = incremental

    total_tokens = 0  # Track the total tokens generated for this example
    while total_tokens + tokens_to_generate < max_seq_length:
        input_text, answer = generate_input_output(0, num_docs, qas=qas, docs=docs)
        # Calculate the number of tokens in the example
        total_tokens = len(tokenizer(input_text + f" {answer}").input_ids)
        print(
            f"Max length {max_seq_length} | Current length {total_tokens + tokens_to_generate} | Docs: {num_docs}"
        )
        if total_tokens + tokens_to_generate > max_seq_length:
            num_docs -= incremental
            break

        num_docs += incremental
        if num_docs > len(docs):
            num_docs = len(docs)
            break
    print("Number of documents:", num_docs)

    # Generate samples
    for index in tqdm(range(num_samples)):
        used_docs = num_docs
        while True:
            try:
                input_text, answer = generate_input_output(
                    index + pre_samples, used_docs, qas=qas, docs=docs
                )
                length = len(tokenizer(input_text).input_ids) + tokens_to_generate
                assert length <= max_seq_length, f"{length} exceeds max_seq_length."
                break
            except:  # noqa: E722
                if used_docs > incremental:
                    used_docs -= incremental

        if remove_newline_tab:
            input_text = " ".join(
                input_text.replace("\n", " ").replace("\t", " ").strip().split()
            )

        formatted_output = {
            "index": index,
            "input": input_text,
            "outputs": answer,
            "length": length,
            "max_seq_length": max_seq_length,
            "gen_prefix": "Answer:",
        }
        write_jsons.append(formatted_output)

    return write_jsons


@cache
def get_tokenizer(pretrained):
    return AutoTokenizer.from_pretrained(pretrained, trust_remote_code=True)


def get_dataset(pretrained, docs, qas, max_seq_length=None, **kwargs):
    tokenizer = get_tokenizer(pretrained)
    write_jsons = generate_samples(
        tokenizer=tokenizer,
        docs=docs,
        qas=qas,
        num_samples=500,
        tokens_to_generate=128,
        max_seq_length=max_seq_length,
    )
    return write_jsons


def get_qa_dataset(ds, **kwargs):
    kwargs = kwargs.get("metadata", {})
    pretrained = kwargs.get("tokenizer", kwargs.get("pretrained", {}))
    if ds == "squad":
        qas, docs = read_squad()
    else:
        qas, docs = read_hotpotqa()
    df = (
        get_dataset(pretrained, docs=docs, qas=qas, max_seq_length=seq)
        for seq in SEQ_LENGTHS
    )

    return {
        "test": datasets.Dataset.from_list(
            list(itertools.chain.from_iterable(df)), split=datasets.Split.TEST
        )
    }


get_squad = partial(get_qa_dataset, "squad")
get_hotpotqa = partial(get_qa_dataset, "hotpotqa")