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, Dict
  
  import torch
  from torch import Tensor, tensor
  
  from torchmetrics.functional.regression.mae import _mean_absolute_error_compute, _mean_absolute_error_update
  from torchmetrics.metric import Metric
  
  
  class MeanAbsoluteError(Metric):
      r"""`Computes Mean Absolute Error`_ (MAE):
  
      .. math:: \text{MAE} = \frac{1}{N}\sum_i^N | y_i - \hat{y_i} |
  
      Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
  
      Args:
          kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
      Example:
          >>> from torchmetrics import MeanAbsoluteError
          >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
          >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
          >>> mean_absolute_error = MeanAbsoluteError()
          >>> mean_absolute_error(preds, target)
          tensor(0.5000)
      """
      is_differentiable = True
      higher_is_better = False
      sum_abs_error: Tensor
      total: Tensor
  
      def __init__(
          self,
          **kwargs: Dict[str, Any],
      ) -> None:
          super().__init__(**kwargs)
  
          self.add_state("sum_abs_error", default=tensor(0.0), dist_reduce_fx="sum")
          self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
  
      def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
          """Update state with predictions and targets.
  
          Args:
              preds: Predictions from model
              target: Ground truth values
          """
          sum_abs_error, n_obs = _mean_absolute_error_update(preds, target)
  
          self.sum_abs_error += sum_abs_error
          self.total += n_obs
  
      def compute(self) -> Tensor:
          """Computes mean absolute error over state."""
          return _mean_absolute_error_compute(self.sum_abs_error, self.total)
  