class_docstr: Calculate `InfoLM`_.
  
  InfoLM measures a distance/divergence between predicted and reference sentence discrete distribution using one of
  the following information measures:
  
      - `KL divergence`_
      - `alpha divergence`_
      - `beta divergence`_
      - `AB divergence`_
      - `Rényi divergence`_
      - L1 distance
      - L2 distance
      - L-infinity 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.
  
  The implementation of this metric is fully based HuggingFace ``transformers``' package.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~Sequence`): An iterable of hypothesis corpus
  - ``target`` (:class:`~Sequence`): An iterable of reference corpus
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  -  ``infolm`` (:class:`~torch.Tensor`): If `return_sentence_level_score=True` return a tuple with a tensor
     with the corpus-level InfoLM score and a list of sentence-level InfoLM scores, else return a corpus-level
     InfoLM score
  
  Args:
      model_name_or_path:
          A name or a model path used to load ``transformers`` pretrained model.
          By default the `"bert-base-uncased"` model is used.
      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.
  
  Example:
      >>> from torchmetrics.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 = InfoLM('google/bert_uncased_L-2_H-128_A-2', idf=False)
      >>> infolm(preds, target)
      tensor(-0.1784)
docstr: Plot a single or multiple values from the metric.
  
  Args:
      val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
          If no value is provided, will automatically call `metric.compute` and plot that result.
      ax: An matplotlib axis object. If provided will add plot to that axis
  
  Returns:
      Figure and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting a single value
      >>> from torchmetrics.text.infolm import InfoLM
      >>> metric = InfoLM('google/bert_uncased_L-2_H-128_A-2', idf=False)
      >>> preds = ['he read the book because he was interested in world history']
      >>> target = ['he was interested in world history because he read the book']
      >>> metric.update(preds, target)
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> from torchmetrics.text.infolm import InfoLM
      >>> metric = InfoLM('google/bert_uncased_L-2_H-128_A-2', idf=False)
      >>> preds = ["this is the prediction", "there is an other sample"]
      >>> target = ["this is the reference", "there is another one"]
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(preds, target))
      >>> fig_, ax_ = metric.plot(values)
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code: def plot(
      self,
      val: Optional[Union[Tensor, Sequence[Tensor]]] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
              If no value is provided, will automatically call `metric.compute` and plot that result.
          ax: An matplotlib axis object. If provided will add plot to that axis
  
      Returns:
          Figure and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting a single value
          >>> from torchmetrics.text.infolm import InfoLM
          >>> metric = InfoLM('google/bert_uncased_L-2_H-128_A-2', idf=False)
          >>> preds = ['he read the book because he was interested in world history']
          >>> target = ['he was interested in world history because he read the book']
          >>> metric.update(preds, target)
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> from torchmetrics.text.infolm import InfoLM
          >>> metric = InfoLM('google/bert_uncased_L-2_H-128_A-2', idf=False)
          >>> preds = ["this is the prediction", "there is an other sample"]
          >>> target = ["this is the reference", "there is another one"]
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(preds, target))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/text/infolm.py
imports: import os
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from torchmetrics.functional.text.helper_embedding_metric import _load_tokenizer_and_model
  from torchmetrics.functional.text.infolm import _ALLOWED_INFORMATION_MEASURE_LITERAL, _get_dataloader, _get_special_tokens_map, _infolm_compute, _infolm_update, _InformationMeasure
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import dim_zero_cat
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TRANSFORMERS_GREATER_EQUAL_4_4
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 4 for calling the InfoLM obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self