text: #
  # Copyright 2016 The BigDL Authors.
  #
  # 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.
  #
  
  import torch
  from numpy import ndarray
  from functools import partial
  from torchmetrics.functional import mean_squared_error, mean_absolute_error,\
      mean_absolute_percentage_error, r2_score, symmetric_mean_absolute_percentage_error
  
  
  TORCHMETRICS_REGRESSION_MAP = {
      'mae': mean_absolute_error,
      'mse': mean_squared_error,
      'rmse': partial(mean_squared_error, squared=False),
      'mape': mean_absolute_percentage_error,
      'smape': symmetric_mean_absolute_percentage_error,
      'r2': r2_score,
  }
  
  
  def _standard_input(metrics, y_true, y_pred):
      """
      Standardize input functions. Format metrics,
      check the ndim of y_pred and y_true,
      converting 1-3 dim y_true and y_pred to 2 dim.
      """
      if isinstance(metrics, str):
          metrics = [metrics]
      metrics = list(map(lambda x: x.lower(), metrics))
      assert all(metric in TORCHMETRICS_REGRESSION_MAP.keys() for metric in metrics),\
          f"metric should be one of {TORCHMETRICS_REGRESSION_MAP.keys()}, "\
          f"but get {metrics}."
      assert type(y_true) is type(y_pred) and isinstance(y_pred, ndarray),\
          "y_pred and y_true type must be numpy.ndarray, "\
          f"but found {type(y_pred)} and {type(y_true)}."
      y_true, y_pred = torch.from_numpy(y_true), torch.from_numpy(y_pred)
  
      assert y_true.shape == y_pred.shape,\
          "y_true and y_pred should have the same shape, "\
          f"but get {y_true.shape} and {y_pred.shape}."
  
      if y_true.ndim == 1:
          y_true = y_true.reshape(-1, 1)
          y_pred = y_pred.reshape(-1, 1)
          original_shape = y_true.shape
      elif y_true.ndim == 3:
          original_shape = y_true.shape
          y_true = y_true.reshape(y_true.shape[0], y_true.shape[1]*y_true.shape[2])
          y_pred = y_pred.reshape(y_pred.shape[0], y_pred.shape[1]*y_pred.shape[2])
      else:
          original_shape = y_true.shape
      return metrics, y_true, y_pred, original_shape
  
  
  class Evaluator(object):
      """
      Evaluate metrics for y_true and y_pred.
      """
  
      @staticmethod
      def evaluate(metrics, y_true, y_pred, aggregate='mean'):
          """
          Evaluate a specific metrics for y_true and y_pred.
  
          :param metrics: String or list in ['mae', 'mse', 'rmse', 'r2', 'mape', 'smape']
          :param y_true: Array-like of shape = (n_samples, \*). Ground truth (correct) target values.
          :param y_pred: Array-like of shape = (n_samples, \*). Estimated target values.
          :param aggregate: aggregation method. Currently, "mean" and None are supported,
                 'mean' represents aggregating by mean, while None will return the element-wise
                 result. The value defaults to 'mean'.
  
          :return: Float or ndarray of floats.
                   A floating point value, or an
                   array of floating point values, one for each individual target.
          """
          metrics, y_true, y_pred, original_shape = _standard_input(metrics, y_true, y_pred)
  
          res_list = []
          for metric in metrics:
              if len(original_shape) in [2, 3] and aggregate is None:
                  res = torch.zeros(y_true.shape[-1])
                  for i in range(y_true.shape[-1]):
                      res[i] = TORCHMETRICS_REGRESSION_MAP[metric](y_pred[..., i], y_true[..., i])
                  res = res.reshape(original_shape[1:])
              else:
                  res = TORCHMETRICS_REGRESSION_MAP[metric](y_pred, y_true)
              res_list.append(res.numpy())
          return res_list
  