class_docstr: Perplexity measures how well a language model predicts a text sample.
  
  It's calculated as the average number of bits per word a model needs to represent the sample.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): Logits or a unnormalized score assigned to each token in a sequence with shape
      [batch_size, seq_len, vocab_size], which is the output of a language model. Scores will be normalized internally
      using softmax.
  - ``target`` (:class:`~torch.Tensor`): Ground truth values with a shape [batch_size, seq_len]
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``perp`` (:class:`~torch.Tensor`): A tensor with the perplexity score
  
  Args:
      ignore_index: Integer specifying a target class to ignore.
          If given, this class index does not contribute to the returned score.
      kwargs:
          Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Examples:
      >>> from torchmetrics.text import Perplexity
      >>> import torch
      >>> gen = torch.manual_seed(42)
      >>> preds = torch.rand(2, 8, 5, generator=gen)
      >>> target = torch.randint(5, (2, 8), generator=gen)
      >>> target[0, 6:] = -100
      >>> perp = Perplexity(ignore_index=-100)
      >>> perp(preds, target)
      tensor(5.8540)
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
      >>> import torch
      >>> from torchmetrics.text import Perplexity
      >>> metric = Perplexity()
      >>> metric.update(torch.rand(2, 8, 5), torch.randint(5, (2, 8)))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.text import Perplexity
      >>> metric = Perplexity()
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.rand(2, 8, 5), torch.randint(5, (2, 8))))
      >>> 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
          >>> import torch
          >>> from torchmetrics.text import Perplexity
          >>> metric = Perplexity()
          >>> metric.update(torch.rand(2, 8, 5), torch.randint(5, (2, 8)))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.text import Perplexity
          >>> metric = Perplexity()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.rand(2, 8, 5), torch.randint(5, (2, 8))))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/text/perplexity.py
imports: from typing import Any, Dict, Optional, Sequence, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.text.perplexity import _perplexity_compute, _perplexity_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 4 for calling the Perplexity obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self