class_docstr: Calculate `BLEU score`_ of machine translated text with one or more references.
  
  This implementation follows the behaviour of `SacreBLEU`_. The SacreBLEU implementation differs from the NLTK BLEU
  implementation in tokenization techniques.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~Sequence`): An iterable of machine translated corpus
  - ``target`` (:class:`~Sequence`): An iterable of iterables of reference corpus
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``sacre_bleu`` (:class:`~torch.Tensor`): A tensor with the SacreBLEU Score
  
  Args:
      n_gram: Gram value ranged from 1 to 4
      smooth: Whether to apply smoothing, see `SacreBLEU`_
      tokenize: Tokenization technique to be used. Choose between ``'none'``, ``'13a'``, ``'zh'``, ``'intl'``,
          ``'char'``, ``'ja-mecab'``, ``'ko-mecab'``, ``'flores101'`` and ``'flores200'``.
      lowercase:  If ``True``, BLEU score over lowercased text is calculated.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
      weights:
          Weights used for unigrams, bigrams, etc. to calculate BLEU score.
          If not provided, uniform weights are used.
  
  Raises:
      ValueError:
          If ``tokenize`` not one of 'none', '13a', 'zh', 'intl' or 'char'
      ValueError:
          If ``tokenize`` is set to 'intl' and `regex` is not installed
      ValueError:
          If a length of a list of weights is not ``None`` and not equal to ``n_gram``.
  
  
  Example:
      >>> from torchmetrics.text import SacreBLEUScore
      >>> preds = ['the cat is on the mat']
      >>> target = [['there is a cat on the mat', 'a cat is on the mat']]
      >>> sacre_bleu = SacreBLEUScore()
      >>> sacre_bleu(preds, target)
      tensor(0.7598)
  
  Additional References:
  
      - Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence
        and Skip-Bigram Statistics by Chin-Yew Lin and Franz Josef Och `Machine Translation Evolution`_
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 import SacreBLEUScore
      >>> metric = SacreBLEUScore()
      >>> preds = ['the cat is on the mat']
      >>> target = [['there is a cat on the mat', 'a cat is on the mat']]
      >>> metric.update(preds, target)
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> from torchmetrics.text import SacreBLEUScore
      >>> metric = SacreBLEUScore()
      >>> preds = ['the cat is on the mat']
      >>> target = [['there is a cat on the mat', 'a cat is on the mat']]
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(preds, target))
      >>> fig_, ax_ = metric.plot(values)
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 import SacreBLEUScore
          >>> metric = SacreBLEUScore()
          >>> preds = ['the cat is on the mat']
          >>> target = [['there is a cat on the mat', 'a cat is on the mat']]
          >>> metric.update(preds, target)
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> from torchmetrics.text import SacreBLEUScore
          >>> metric = SacreBLEUScore()
          >>> preds = ['the cat is on the mat']
          >>> target = [['there is a cat on the mat', 'a cat is on the mat']]
          >>> 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/sacre_bleu.py
imports: from typing import Any, Optional, Sequence, Union
  from torch import Tensor
  from torchmetrics.functional.text.bleu import _bleu_score_update
  from torchmetrics.functional.text.sacre_bleu import _SacreBLEUTokenizer, _TokenizersLiteral
  from torchmetrics.text.bleu import BLEUScore
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 3 for calling the SacreBLEUScore obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self