distance
        - `Fisher-Rao distance`_

    `InfoLM`_ is a family of untrained embedding-based metrics which addresses some famous flaws of standard
    string-based metrics thanks to the usage of pre-trained masked language models. This family of metrics is mainly
    designed for summarization and data-to-text tasks.

    If you want to use IDF scaling over the whole dataset, please use the class metric.

    The implementation of this metric is fully based HuggingFace `transformers`' package.

    Args:
        preds:
            An iterable of hypothesis corpus.
        target:
            An iterable of reference corpus.
        model_name_or_path:
            A name or a model path used to load `transformers` pretrained model.
        temperature:
            A temperature for calibrating language modelling. For more information, please reference `InfoLM`_ paper.
        information_measure:
            A name of information measure to be used. Please use one of: ['kl_divergence', 'alpha_divergence',
            'beta_divergence', 'ab_divergence', 'renyi_divergence', 'l1_distance', 'l2_distance', 'l_infinity_distance',
            'fisher_rao_distance']
        idf:
            An indication of whether normalization using inverse document frequencies should be used.
        alpha:
            Alpha parameter of the divergence used for alpha, AB and Rényi divergence measures.
        beta:
            Beta parameter of the divergence used for beta and AB divergence measures.
        device:
            A device to be used for calculation.
        max_length:
            A maximum length of input sequences. Sequences longer than `max_length` are to be trimmed.
        batch_size:
            A batch size used for model processing.
        num_threads:
            A number of threads to use for a dataloader.
        verbose:
            An indication of whether a progress bar to be displayed during the embeddings calculation.
        return_sentence_level_score:
            An indication whether a sentence-level InfoLM score to be returned.

    Returns:
        A corpus-level InfoLM score.
        (Optionally) A list of sentence-level InfoLM scores if `return_sentence_level_score=True`.

    Example:
        >>> from torchmetrics.functional.text.infolm import infolm
        >>> preds = ['he read the book because he was interested in world history']
        >>> target = ['he was interested in world history because he read the book']
        >>> infolm(preds, target, model_name_or_path='google/bert_uncased_L-2_H-128_A-2', idf=False)
        tensor(-0.1784)

    References:
        [1] InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation by Pierre Colombo, Chloé Clavel and
        Pablo Piantanida `InfoLM`_

    )	r