a  dw @srddlmZddlZddlmZddlmZeeeeefdddZdeeeed d d Zeeedd d Z dS))TupleN)Tensor)_check_same_shape)predstargetreturncCs.t||||}|}||fS)zUpdate and returns variables required to compute Weighted Absolute Percentage Error. Check for same shape of input tensors. Args: preds: Predicted tensor target: Ground truth tensor )rabssumrr sum_abs_error sum_scaler q/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/regression/wmape.py/_weighted_mean_absolute_percentage_error_updates  r-`>)r r epsilonrcCs|tj||dS)zCompute Weighted Absolute Percentage Error. Args: sum_abs_error: scalar with sum of absolute errors sum_scale: scalar with sum of target values epsilon: small float to prevent division by zero )min)torchclamp)r r rr r r0_weighted_mean_absolute_percentage_error_compute+s rcCst||\}}t||S)aCompute weighted mean absolute percentage error (`WMAPE`_). The output of WMAPE metric is a non-negative floating point, where the optimal value is 0. It is computes as: .. math:: \text{WMAPE} = \frac{\sum_{t=1}^n | y_t - \hat{y}_t | }{\sum_{t=1}^n |y_t| } Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. Args: preds: estimated labels target: ground truth labels Return: Tensor with WMAPE. Example: >>> import torch >>> _ = torch.manual_seed(42) >>> preds = torch.randn(20,) >>> target = torch.randn(20,) >>> weighted_mean_absolute_percentage_error(preds, target) tensor(1.3967) )rrr r r r'weighted_mean_absolute_percentage_error;sr)r) typingrrrZtorchmetrics.utilities.checksrrfloatrrr r r rs