a  þd ã@szddlmZmZddlZddlmZddlmZeeeeefdœdd„Zeeeefedœd d „Z eeedœd d „Z dS) é)ÚTupleÚUnionN)ÚTensor)Ú_check_same_shape)ÚpredsÚtargetÚreturncCsRt||ƒ|jr|n| ¡}|jr&|n| ¡}t t ||¡¡}| ¡}||fS)zÆUpdate and returns variables required to compute Mean Absolute Error. Check for same shape of input tensors. Args: preds: Predicted tensor target: Ground truth tensor )rZis_floating_pointÚfloatÚtorchÚsumÚabsZnumel©rrÚ sum_abs_errorÚn_obs©rúo/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/regression/mae.pyÚ_mean_absolute_error_updates r)rrrcCs||S)aÂCompute Mean Absolute Error. Args: sum_abs_error: Sum of absolute value of errors over all observations n_obs: Number of predictions or observations Example: >>> preds = torch.tensor([0., 1, 2, 3]) >>> target = torch.tensor([0., 1, 2, 2]) >>> sum_abs_error, n_obs = _mean_absolute_error_update(preds, target) >>> _mean_absolute_error_compute(sum_abs_error, n_obs) tensor(0.2500) r)rrrrrÚ_mean_absolute_error_compute(srcCst||ƒ\}}t||ƒS)aˆCompute mean absolute error. Args: preds: estimated labels target: ground truth labels Return: Tensor with MAE Example: >>> from torchmetrics.functional.regression import mean_absolute_error >>> x = torch.tensor([0., 1, 2, 3]) >>> y = torch.tensor([0., 1, 2, 2]) >>> mean_absolute_error(x, y) tensor(0.2500) )rrr rrrÚmean_absolute_error:sr) Útypingrrr rZtorchmetrics.utilities.checksrÚintrrrrrrrÚs