mvl-sib200.py

import csv
import random
from itertools import combinations
from pathlib import Path
from typing import Any, Dict, List, Union

import datasets
import numpy as np
import pandas as pd


# fmt: off
LANGS = [
    "ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", 
    "aka_Latn", "als_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "arb_Latn", "ars_Arab", 
    "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", 
    "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", 
    "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", 
    "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", 
    "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", 
    "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "fij_Latn", "fin_Latn", 
    "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gaz_Latn", "gla_Latn", "gle_Latn", 
    "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", 
    "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", 
    "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", 
    "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "kaz_Cyrl", "kbp_Latn", "kea_Latn", 
    "khk_Cyrl", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kmr_Latn", 
    "knc_Arab", "knc_Latn", "kon_Latn", "kor_Hang", "lao_Laoo", "lij_Latn", "lim_Latn", 
    "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", 
    "luo_Latn", "lus_Latn", "lvs_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", 
    "min_Arab", "min_Latn", "mkd_Cyrl", "mlt_Latn", "mni_Beng", "mos_Latn", "mri_Latn", 
    "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nqo_Nkoo", "nso_Latn", 
    "nus_Latn", "nya_Latn", "oci_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", 
    "pbt_Arab", "pes_Arab", "plt_Latn", "pol_Latn", "por_Latn", "prs_Arab", "quy_Latn", 
    "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Olck", "scn_Latn", 
    "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", 
    "som_Latn", "sot_Latn", "spa_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", 
    "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "taq_Latn", "taq_Tfng", "tat_Cyrl", 
    "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "tpi_Latn", "tsn_Latn", 
    "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", 
    "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", 
    "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", 
    "zsm_Latn", "zul_Latn"
]
# fmt: on

# For interactive usage:
# Attempt to find the script directory if __file__ is defined, otherwise default to current working directory.
try:
    cwd = Path(__file__).parent
except NameError as _:
    cwd = Path.cwd()

SEED: int = 42
N: int = 1004  # length of pooled train, dev, and test splits
UPSAMPLING_FACTOR: int = 3
NUM_NEGATIVES: int = 3
NUM_REFERENCES: int = 5
NUM_EXAMPLES_PER_OPTION: int = 1

CATEGORIES: List[str] = [
    "entertainment",
    "geography",
    "health",
    "politics",
    "science",
    "sports",
    "travel",
]

# URLs for downloading SIB .tsv data and images.
_SIB_URL: str = "https://huggingface.co/datasets/wuenlp/mvl-sib200/resolve/main/data/sib200/{lang}/{split}.tsv"
_IMG_URL: str = "https://huggingface.co/datasets/wuenlp/mvl-sib200/resolve/main/data/images/sib200/{category}_{no}.jpg"

# Placeholder for dataset description: fill or extend as needed.
_DESCRIPTION: str = (
    "MVLSIB is a multilingual dataset designed to provide sentence-image pairs "
    "spanning multiple languages and categories. The goal is to support tasks such as "
    "multimodal classification, cross-lingual information retrieval, and more. "
    "Each row contains a textual entry (sentence) along with category information, "
    "and the dataset also includes image references for the same set of categories."
)


def read_tsv_to_dict_list(file_path: Union[str, Path]) -> List[Dict[str, Any]]:
    """
    Reads a TSV file with columns 'index_id', 'category', and 'text' into a list of dictionaries.

    The TSV is expected to have the following columns (in order):
        1. index_id
        2. category
        3. text

    Parameters
    ----------
    file_path : Union[str, Path]
        The path to the TSV file.

    Returns
    -------
    List[Dict[str, Any]]
        A list of dictionaries, where each element has keys:
        - 'index_id': int
        - 'category': str
        - 'text': str

    Raises
    ------
    ValueError
        If the TSV headers do not match the expected format.
    """
    data: List[Dict[str, Any]] = []
    expected_headers = ["index_id", "category", "text"]

    with open(file_path, mode="r", encoding="utf-8") as tsvfile:
        reader = csv.DictReader(tsvfile, delimiter="\t")

        # Validate headers
        if reader.fieldnames != expected_headers:
            raise ValueError(
                f"Expected headers {expected_headers}, but got {reader.fieldnames}"
            )

        # Start enumerating from line 2 to account for the header line
        for _, row in enumerate(reader, start=2):
            #
            if all(
                (row[key].strip() == key) or (row[key].strip() == "")
                for key in expected_headers
            ):
                continue
            # Convert index_id to integer
            index_id = int(row["index_id"])
            # Strip leading/trailing whitespace
            category = row["category"].strip()
            text = row["text"].strip()

            # Append the processed row to data
            data.append({"index_id": index_id, "category": category, "text": text})

    return data


def read_lang_tsv(filepaths: List[str]) -> List[Dict[str, Any]]:
    """
    Reads a list of TSV file paths containing SIB data in the same language
    and merges them into a single, sorted list of dictionaries.

    Specifically:
    1. Calls `read_tsv_to_dict_list` for each file path.
    2. Merges all resulting dictionaries.
    3. Sorts by 'index_id'.

    Also normalizes the category "science/technology" to "science" for internal consistency.

    Parameters
    ----------
    filepaths : List[str]
        A list of TSV file paths for a specific language.

    Returns
    -------
    List[Dict[str, Any]]
        A list of dictionaries sorted by 'index_id' with normalized categories.
    """
    # Read each file into a list of dicts
    dicos = [read_tsv_to_dict_list(path) for path in filepaths]
    # Flatten and sort by index_id
    out: List[Dict[str, Any]] = sorted(
        [line for dico in dicos for line in dico], key=lambda row: row["index_id"]
    )
    # Normalize "science/technology" to "science"
    for line in out:
        if line["category"] == "science/technology":
            line["category"] = "science"
    return out


def replicate_and_negatives(
    df: pd.DataFrame,
    num_replicates: int = 3,
    num_negatives: int = 4,
    num_positives: int = 4,
    seed: int = 42,
) -> pd.DataFrame:
    """
    Create multiple replicated rows from the input DataFrame `df` and
    sample negative and positive examples for each row.

    *Negative* samples are drawn from rows whose category is different
    from the row's category. **Additionally, each negative example for
    a given row is drawn from a distinct category among the negatives,
    if there are enough categories to do so without replacement.**

    *Positive* samples are drawn from rows of the same category (excluding
    the row's own 'index_id').

    Parameters
    ----------
    df : pd.DataFrame
        The original input DataFrame with columns ['index_id', 'category', 'text'].
    num_replicates : int, optional
        Number of times to replicate each row, by default 2.
    num_negatives : int, optional
        Number of negative samples to pick for each row, by default 2.
    num_positives : int, optional
        Number of positive samples to pick for each row, by default 2.
    seed : int, optional
        Seed for random operations, by default 42.

    Returns
    -------
    pd.DataFrame
        A new DataFrame containing replicated rows plus columns:
        - neg_id_i, neg_cat_i, neg_text_i for i in [0 .. num_negatives-1]
        - pos_id_i, pos_cat_i, pos_text_i for i in [0 .. num_positives-1]

    Notes
    -----
    - Negative examples for a row are taken from distinct categories
      (other than the row's category) if enough categories exist. If
      fewer categories exist than `num_negatives`, we sample categories
      with replacement, so some duplicates may appear.
    - Positive sampling excludes the row's own 'index_id'.
      If there are fewer available positives than `num_positives`,
      we sample with replacement.
    """

    rng = np.random.default_rng(seed=seed)

    # --- 1) Replicate the DataFrame k (=num_replicates) times ---
    df_new = pd.concat([df] * num_replicates, ignore_index=True)

    # --- 2) Create empty columns for negative and positive samples ---
    for i in range(num_negatives):
        df_new[f"neg_id_{i}"] = None
        df_new[f"neg_cat_{i}"] = None
        df_new[f"neg_text_{i}"] = None

    for i in range(num_positives):
        df_new[f"pos_id_{i}"] = None
        df_new[f"pos_cat_{i}"] = None
        df_new[f"pos_text_{i}"] = None

    # --- Precompute a dictionary of all rows by category (for negatives sampling) ---
    # Key: category -> DataFrame of that category
    unique_cats = df_new["category"].unique()
    cat_to_df: Dict[str, pd.DataFrame] = {}
    for c in unique_cats:
        cat_to_df[c] = df_new[df_new["category"] == c].reset_index(drop=True)

    # --- 4) Build a "positive pool" dictionary by category ---
    # For positive sampling, we exclude the row's own 'index_id' in each row's step
    pos_pool_by_cat = {}
    for c in unique_cats:
        pos_pool_by_cat[c] = df.loc[
            df["category"] == c, ["index_id", "category", "text"]
        ].reset_index(drop=True)

    # --- 5) Group df_new by category and populate negative/positive samples ---
    grouped = df_new.groupby("category", group_keys=False)
    output_chunks: List[pd.DataFrame] = []

    for cat, group_df in grouped:
        g_size = len(group_df)

        # The preallocated arrays for negative and positive columns will be filled for each row individually, i.e., sampling of negative categories and samples will be done per row
        # Prepare arrays for final negative columns
        neg_id_cols = [np.empty(g_size, dtype=object) for _ in range(num_negatives)]
        neg_cat_cols = [np.empty(g_size, dtype=object) for _ in range(num_negatives)]
        neg_text_cols = [np.empty(g_size, dtype=object) for _ in range(num_negatives)]

        # Prepare arrays for final positive columns
        pos_id_cols = [np.empty(g_size, dtype=object) for _ in range(num_positives)]
        pos_cat_cols = [np.empty(g_size, dtype=object) for _ in range(num_positives)]
        pos_text_cols = [np.empty(g_size, dtype=object) for _ in range(num_positives)]

        # For convenience, get all categories *except* the current one (cat)
        # We'll sample from these as negative categories
        negative_candidate_cats = [c for c in unique_cats if c != cat]

        # For each row in the current group
        row_ids_for_group = group_df["index_id"].to_numpy()
        for i_row in range(g_size):
            row_id = row_ids_for_group[i_row]

            # ------------- Negative Sampling -------------
            # 1) Choose distinct categories if possible. If not enough categories
            #    exist to cover num_negatives, we sample categories with replacement.
            replace_for_cats = len(negative_candidate_cats) < num_negatives
            chosen_neg_cats = rng.choice(
                negative_candidate_cats, size=num_negatives, replace=replace_for_cats
            )

            # 2) For each chosen negative category, pick a random row
            for j, neg_cat in enumerate(chosen_neg_cats):
                neg_pool = cat_to_df[neg_cat]
                pick_idx = rng.integers(len(neg_pool))  # random index
                neg_id_cols[j][i_row] = neg_pool["index_id"].iloc[pick_idx]
                neg_cat_cols[j][i_row] = neg_pool["category"].iloc[pick_idx]
                neg_text_cols[j][i_row] = neg_pool["text"].iloc[pick_idx]

            # ------------- Positive Sampling -------------
            pos_pool_cat = pos_pool_by_cat[cat]
            # Exclude the row's own ID in the sampling
            valid_mask = pos_pool_cat["index_id"] != row_id
            valid_pos_pool = pos_pool_cat[valid_mask]
            # If not enough positives remain, sample with replacement
            replace_pos_for_row = len(valid_pos_pool) < num_positives

            if len(valid_pos_pool) == 0:
                # Edge case: if there's literally no other row of the same category,
                # we won't be able to sample. You could decide to fill with NaN
                # or replicate the single example. Here we do the "safe" approach
                # of sampling from the entire cat's pool if possible.
                valid_pos_pool = pos_pool_cat
                replace_pos_for_row = True

            valid_idx_array = valid_pos_pool.index.to_numpy()
            chosen_indices = rng.choice(
                valid_idx_array, size=num_positives, replace=replace_pos_for_row
            )
            for j in range(num_positives):
                pick_idx = chosen_indices[j]
                pos_id_cols[j][i_row] = valid_pos_pool["index_id"].loc[pick_idx]
                pos_cat_cols[j][i_row] = valid_pos_pool["category"].loc[pick_idx]
                pos_text_cols[j][i_row] = valid_pos_pool["text"].loc[pick_idx]

        # Attach negative columns to group_df
        for j in range(num_negatives):
            group_df[f"neg_id_{j}"] = neg_id_cols[j]
            group_df[f"neg_cat_{j}"] = neg_cat_cols[j]
            group_df[f"neg_text_{j}"] = neg_text_cols[j]

        # Attach positive columns to group_df
        for j in range(num_positives):
            group_df[f"pos_id_{j}"] = pos_id_cols[j]
            group_df[f"pos_cat_{j}"] = pos_cat_cols[j]
            group_df[f"pos_text_{j}"] = pos_text_cols[j]

        output_chunks.append(group_df)

    # --- 6) Combine all chunks and restore index order ---
    df_out = pd.concat(output_chunks, axis=0)
    df_out.sort_index(inplace=True)
    return df_out


def get_reference_image_ids(
    N: int, num_images: int, k: int, seed: int
) -> List[List[int]]:
    """
    Generates reference image ID combinations for each row in a dataset of size N.

    We pick (k)-combinations from the range [1 .. num_images-1]. Then we sample
    from these combinations (with replacement) for each of N rows, and shuffle them
    in a reproducible manner.

    Parameters
    ----------
    N : int
        Number of rows in the dataset.
    num_images : int
        Total number of images available per category.
    k : int
        Number of images to select in each combination.
    seed : int
        Global seed for random operations.

    Returns
    -------
    List[List[int]]
        A list of length N, where each element is a list of k unique image IDs.

    Notes
    -----
    - We use Python's `random.choices` to draw from all possible k-combinations.
    - Each combination is then locally shuffled to remove ordering biases.
    """
    all_combinations = list(combinations(range(0, num_images), k))
    random.seed(seed)
    sampled_combinations = [list(x) for x in random.choices(all_combinations, k=N)]

    for i, tuple_ in enumerate(sampled_combinations):
        # Use a unique seed for each shuffle to ensure reproducibility
        random.seed(seed + i)
        random.shuffle(tuple_)
    return sampled_combinations


class MVLSIBConfig(datasets.BuilderConfig):
    """
    Configuration class for the MVLSIB (Multilingual Visual Language SIB) dataset.

    Parameters
    ----------
    name : str
        The configuration name, typically in the format "task.lang".
    upsampling_factor : int, optional
        How many times to replicate each row for additional sampling variety, default: 3.
    num_references : int, optional
        Number of positive references to sample for each row, default: 5.
    num_negatives : int, optional
        Number of negative samples to pair with each row, default: 3.
    seed : int, optional
        Seed for random operations, default: 42.
    """

    def __init__(
        self,
        name: str,
        upsampling_factor: int = UPSAMPLING_FACTOR,
        num_references: int = NUM_REFERENCES,
        num_negatives: int = NUM_NEGATIVES,
        seed: int = SEED,
        **kwargs: Any,
    ):
        super(MVLSIBConfig, self).__init__(**kwargs)
        self.name: str = name
        self.task, self.lang = name.split(".")
        self.upsampling_factor: int = upsampling_factor
        self.num_references: int = num_references
        self.num_negatives: int = num_negatives
        self.seed: int = seed


def _builder_configs() -> List[MVLSIBConfig]:
    """
    Internal helper to build the list of MVLSIBConfig objects
    for all tasks ('img2sent', 'sent2img') and all available languages in LANGS.

    Returns
    -------
    List[MVLSIBConfig]
        A list of dataset configuration objects, each specifying a (task, language) pair.
    """
    configs: List[MVLSIBConfig] = []
    for task in ("img2sent", "sent2img"):
        for lang in LANGS:
            cfg = MVLSIBConfig(
                name=f"{task}.{lang}",
                version=datasets.Version("1.0.0"),
                description=f"MVLSIB: {task}.{lang}",
            )
            configs.append(cfg)
    return configs


class MVLSIB(datasets.GeneratorBasedBuilder):
    """
    MVLSIB is a multilingual dataset that provides matched
    (sentence -> image) or (image -> sentence) examples for
    classification or retrieval tasks.

    Each configuration is specified by a task (img2sent or sent2img)
    and a language code, e.g. 'img2sent.eng_Latn'.

    The dataset is structured such that each row includes:
        - A set of reference items (images or sentences, depending on the task).
        - A set of 4 possible answers (1 positive, 3 negative).
        - A label indicating which of the 4 answers is correct.
    """

    BUILDER_CONFIGS = _builder_configs()
    BUILDER_CONFIG_CLASS = MVLSIBConfig

    def _info(self) -> datasets.DatasetInfo:
        """
        Returns the dataset metadata, including features.

        The dataset has two major tasks:
          - 'img2sent': Given reference images, choose the best matching sentence.
          - 'sent2img': Given reference sentences, choose the best matching image.

        Each example row in 'img2sent' includes:
          - images (list of str URLs to images)
          - sentences (list of str, one positive, three negatives)
          - categories (list of str categories matching each sentence)
          - label (int specifying which of the sentences is correct)
          - id (an integer ID)
          - index_id (the original row ID from the SIB .tsv)

        Each example row in 'sent2img' includes:
          - sentences (list of str, the positive reference sentences)
          - images (list of str URLs to images, one positive, three negatives)
          - categories (list of str categories matching each image)
          - label (int specifying which of the images is correct)
          - id (an integer ID)
          - index_id (the original row ID from the SIB .tsv)

        Returns
        -------
        datasets.DatasetInfo
            The Hugging Face DatasetInfo object describing the dataset features,
            licensing, homepage, citation, etc.
        """
        from datasets import DatasetInfo, Features, Sequence, Value

        img2sents = Features(
            {
                "images": Sequence(Value("string")),
                "sentences": Sequence(Value("string")),
                "categories": Sequence(Value("string")),
                "label": Value("int8"),
                "id": Value("int64"),
                "index_id": Value("int64"),
            }
        )
        sent2imgs = Features(
            {
                "sentences": Sequence(Value("string")),
                "images": Sequence(Value("string")),
                "categories": Sequence(Value("string")),
                "label": Value("int8"),
                "id": Value("int64"),
                "index_id": Value("int64"),
            }
        )

        features = {
            "img2sent": img2sents,
            "sent2img": sent2imgs,
        }

        return DatasetInfo(
            description=_DESCRIPTION,
            features=features[self.config.task],
            supervised_keys=None,
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager, *args: Any, **kwargs: Any
    ) -> List[datasets.SplitGenerator]:
        """
        Defines the splits of the dataset. In this case, we only produce a single 'test' split,
        but in principle, you can define train/dev/test or others.

        Parameters
        ----------
        dl_manager : datasets.DownloadManager
            The Hugging Face DownloadManager used to download files.

        Returns
        -------
        List[datasets.SplitGenerator]
            A list of SplitGenerator objects. Each defines a split name
            and a gen_kwargs dict for the `_generate_examples` method.
        """
        # Download SIB tsv files for train, dev, and test
        files = dl_manager.download(
            [
                _SIB_URL.format(lang=self.config.lang, split=split)
                for split in ("train", "dev", "test")
            ]
        )
        # Download images for each category
        images: Dict[str, List[str]] = {}
        for cat in CATEGORIES:
            images[cat] = []
            for i in range(10):
                images[cat].append(
                    dl_manager.download(_IMG_URL.format(category=cat, no=i))
                )

        return [
            datasets.SplitGenerator(
                name="test",
                gen_kwargs={"sib_filepaths": files, "images_filepaths": images},
            ),
        ]

    def _generate_examples(
        self,
        sib_filepaths: List[str],
        images_filepaths: Dict[str, List[str]],
        *args: Any,
        **kwargs: Any,
    ) -> Any:
        """
        Generator function that yields dataset examples in the format needed by
        Hugging Face Datasets.

        Depending on the task (img2sent or sent2img), the function constructs examples where:
        - img2sent: reference images, 4 candidate sentences (1 positive, 3 negative)
        - sent2img: reference sentences, 4 candidate images (1 positive, 3 negative)

        Parameters
        ----------
        sib_filepaths : List[str]
            The downloaded .tsv file paths (train/dev/test) for the specified language.
        images_filepaths : Dict[str, List[str]]
            A dictionary from category -> list of 10 image URLs, as downloaded from `_split_generators`.

        Yields
        ------
        Tuple[int, Dict[str, Any]]
            A tuple where the first element is an integer index,
            and the second is a dictionary matching the features specification
            of the dataset.
        """
        # Read the SIB .tsv files for the given language and combine into a single DataFrame
        records = read_lang_tsv(sib_filepaths)
        df = pd.DataFrame.from_records(records)

        # Expand the dataset with negative and positive samples
        ext_df = replicate_and_negatives(
            df,
            num_replicates=self.config.upsampling_factor,
            num_negatives=self.config.num_negatives,
            # every line already has a positive
            num_positives=self.config.num_references - 1,
            seed=self.config.seed,
        )

        sent_ids = list(range(self.config.num_negatives + 1))
        N = len(ext_df)
        num_images = len(next(iter(images_filepaths.values())))  # e.g., 10 images/cat

        if self.config.task == "img2sent":
            # Pre-generate image ID combinations for each row
            image_ids = get_reference_image_ids(
                N=N,
                num_images=num_images,
                k=self.config.num_references,
                seed=self.config.seed,
            )
            for i, row in ext_df.iterrows():
                # Construct the list of candidate sentences (pos + neg)
                text = [row["text"]]
                categories = [row["category"]]
                for j in range(self.config.num_negatives):
                    text.append(row[f"neg_text_{j}"])
                    categories.append(row[f"neg_cat_{j}"])

                # Shuffle candidate sentences in a reproducible manner
                random.seed(i)
                random.shuffle(sent_ids)
                label = sent_ids[0]

                # Reorder sentences and categories according to the shuffled indices
                _, categories_shuffled = zip(*sorted(zip(sent_ids, categories)))
                _, sentences_shuffled = zip(*sorted(zip(sent_ids, text)))

                # Fetch the reference images for the row
                row_image_ids = image_ids[i]
                cat = row["category"]
                cat_images = images_filepaths[cat]
                row_images = [
                    cat_images[row_image_ids[j]]
                    for j in range(self.config.num_references)
                ]

                yield (
                    i,
                    {
                        "id": i,
                        "index_id": row["index_id"],
                        "images": row_images,
                        "categories": categories_shuffled,
                        "sentences": sentences_shuffled,
                        "label": label,
                    },
                )
        else:
            # sent2img: We first sample image indices (pos + neg) for each row
            rng = np.random.default_rng(seed=self.config.seed)
            choice_image_ids = rng.integers(
                0, num_images, (N, 1 + self.config.num_negatives)
            ).tolist()

            for i, row in ext_df.iterrows():
                # The positive text
                pos_text = [row["text"]]
                # For the negative categories, we gather them similarly
                cats = [row["category"]]
                for j in range(self.config.num_negatives):
                    cats.append(row[f"neg_cat_{j}"])
                for j in range(self.config.num_references - 1):
                    pos_text.append(row[f"pos_text_{j}"])

                random.seed(i)
                random.shuffle(sent_ids)
                label = sent_ids[0]

                # Reorder categories based on the shuffled indices
                # NOTE: positive text is quasi-shuffled already
                _, categories_shuffled = zip(*sorted(zip(sent_ids, cats)))

                # Match the categories to the sampled image indices
                row_image_ids = choice_image_ids[i]
                row_images = [
                    images_filepaths[cat][idx]
                    for idx, cat in zip(row_image_ids, categories_shuffled)
                ]

                yield (
                    i,
                    {
                        "id": i,
                        "index_id": row["index_id"],
                        "images": row_images,
                        "categories": categories_shuffled,
                        "sentences": pos_text,
                        "label": label,
                    },
                )