o yi @sddlmZddlZddlmZddlmZ ddededed eeeffd d Zd ed ed efddZ deded efddZ dS))TupleN)Tensor)_check_same_shape-`>predstargetepsilonreturncCsJt||t||}|tjt||d}t|}|}||fS)adUpdates and returns variables required to compute Mean Percentage Error. Checks for same shape of input tensors. Args: preds: Predicted tensor target: Ground truth tensor epsilon: Specifies the lower bound for target values. Any target value below epsilon is set to epsilon (avoids ``ZeroDivisionError``). )min)rtorchabsclampsumnumel)rrrabs_diff abs_per_errorsum_abs_per_errornum_obsr[/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/regression/mape.py&_mean_absolute_percentage_error_updates  rrrcCs||S)aFComputes Mean Absolute Percentage Error. Args: sum_abs_per_error: Sum of absolute value of percentage errors over all observations ``(percentage error = (target - prediction) / target)`` num_obs: Number of predictions or observations Example: >>> target = torch.tensor([1, 10, 1e6]) >>> preds = torch.tensor([0.9, 15, 1.2e6]) >>> sum_abs_per_error, num_obs = _mean_absolute_percentage_error_update(preds, target) >>> _mean_absolute_percentage_error_compute(sum_abs_per_error, num_obs) tensor(0.2667) r)rrrrr'_mean_absolute_percentage_error_compute2srcCst||\}}t||}|S)aComputes mean absolute percentage error. Args: preds: estimated labels target: ground truth labels Return: Tensor with MAPE Note: The epsilon value is taken from `scikit-learn's implementation of MAPE`_. Example: >>> from torchmetrics.functional import mean_absolute_percentage_error >>> target = torch.tensor([1, 10, 1e6]) >>> preds = torch.tensor([0.9, 15, 1.2e6]) >>> mean_absolute_percentage_error(preds, target) tensor(0.2667) )rr)rrrrmean_aperrrmean_absolute_percentage_errorEs r)r) typingrr rtorchmetrics.utilities.checksrfloatintrrrrrrrs