text: # Copyright The PyTorch Lightning team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from typing import Any, Callable, List, Optional, Union
  
  import torch
  from torch import Tensor, tensor
  
  from torchmetrics.functional.text.cer import _cer_compute, _cer_update
  from torchmetrics.metric import Metric
  
  
  class CharErrorRate(Metric):
      r"""
      Character error rate (CharErrorRate_) is a metric of the performance of an automatic speech recognition
      (ASR) system. This value indicates the percentage of characters that were incorrectly predicted.
      The lower the value, the better the performance of the ASR system with a CharErrorRate of 0 being
      a perfect score.
      Character error rate can then be computed as:
  
      .. math::
          CharErrorRate = \frac{S + D + I}{N} = \frac{S + D + I}{S + D + C}
  
      where:
          - S is the number of substitutions,
          - D is the number of deletions,
          - I is the number of insertions,
          - C is the number of correct characters,
          - N is the number of characters in the reference (N=S+D+C).
  
      Compute CharErrorRate score of transcribed segments against references.
  
      Args:
          compute_on_step:
              Forward only calls ``update()`` and return None if this is set to False. default: True
          dist_sync_on_step:
              Synchronize metric state across processes at each ``forward()``
              before returning the value at the step. default: False
          process_group:
              Specify the process group on which synchronization is called. default: None (which selects the entire world)
          dist_sync_fn:
              Callback that performs the allgather operation on the metric state. When ``None``, DDP
              will be used to perform the allgather
  
      Returns:
          (Tensor) Character error rate
  
      Examples:
          >>> predictions = ["this is the prediction", "there is an other sample"]
          >>> references = ["this is the reference", "there is another one"]
          >>> metric = CharErrorRate()
          >>> metric(predictions, references)
          tensor(0.3415)
      """
      is_differentiable = False
      higher_is_better = False
      error: Tensor
      total: Tensor
  
      def __init__(
          self,
          compute_on_step: bool = True,
          dist_sync_on_step: bool = False,
          process_group: Optional[Any] = None,
          dist_sync_fn: Callable = None,
      ):
          super().__init__(
              compute_on_step=compute_on_step,
              dist_sync_on_step=dist_sync_on_step,
              process_group=process_group,
              dist_sync_fn=dist_sync_fn,
          )
          self.add_state("errors", tensor(0, dtype=torch.float), dist_reduce_fx="sum")
          self.add_state("total", tensor(0, dtype=torch.float), dist_reduce_fx="sum")
  
      def update(self, predictions: Union[str, List[str]], references: Union[str, List[str]]) -> None:  # type: ignore
          """Store references/predictions for computing Character Error Rate scores.
  
          Args:
              predictions: Transcription(s) to score as a string or list of strings
              references: Reference(s) for each speech input as a string or list of strings
          """
          errors, total = _cer_update(predictions, references)
          self.errors += errors
          self.total += total
  
      def compute(self) -> Tensor:
          """Calculate the character error rate.
  
          Returns:
              (Tensor) Character error rate
          """
          return _cer_compute(self.errors, self.total)
  