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, Optional
  
  import torch
  from torch import Tensor, tensor
  
  from torchmetrics.functional.regression.symmetric_mean_absolute_percentage_error import (
      _symmetric_mean_absolute_percentage_error_compute,
      _symmetric_mean_absolute_percentage_error_update,
  )
  from torchmetrics.metric import Metric
  
  
  class SymmetricMeanAbsolutePercentageError(Metric):
      r"""
      Computes symmetric mean absolute percentage error (`SMAPE`_).
  
      .. math:: \text{SMAPE} = \frac{2}{n}\sum_1^n max(\frac{|   y_i - \hat{y_i} |}{| y_i | + | \hat{y_i} |, \epsilon})
  
      Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
  
      Args:
          compute_on_step:
              Forward only calls ``update()`` and return None if this is set to False.
          dist_sync_on_step:
              Synchronize metric state across processes at each ``forward()`` before returning the value at the step.
          process_group:
              Specify the process group on which synchronization is called.
  
      Note:
          The epsilon value is taken from `scikit-learn's implementation of SMAPE`_.
  
      Note:
          SMAPE output is a non-negative floating point between 0 and 1. Best result is 0.0 .
  
  
      Example:
          >>> from torchmetrics import SymmetricMeanAbsolutePercentageError
          >>> target = torch.tensor([1, 10, 1e6])
          >>> preds = torch.tensor([0.9, 15, 1.2e6])
          >>> smape = SymmetricMeanAbsolutePercentageError()
          >>> smape(preds, target)
          tensor(0.2290)
      """
      is_differentiable = True
      higher_is_better = False
      sum_abs_per_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,
      ) -> 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("sum_abs_per_error", default=tensor(0.0), dist_reduce_fx="sum")
          self.add_state("total", default=tensor(0.0), 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_per_error, num_obs = _symmetric_mean_absolute_percentage_error_update(preds, target)
  
          self.sum_abs_per_error += sum_abs_per_error
          self.total += num_obs
  
      def compute(self) -> Tensor:
          """Computes mean absolute percentage error over state."""
          return _symmetric_mean_absolute_percentage_error_compute(self.sum_abs_per_error, self.total)
  