:j ddlmZddlZddlmZddlmZ ddedededeeeffd Z d ed eeefdefd Z dededefd Z y))UnionN)Tensor)_check_same_shapepredstargetepsilonreturnct||tj||z }|tjtj||z }tj|}|j }||fS)acUpdate and returns variables required to compute Mean Percentage Error. Check 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_obss |/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/regression/mape.py&_mean_absolute_percentage_error_updatersc eV$yy(Hu{{599V+<'JJM -0llnG g %%rrc ||z S)aFCompute 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) )rrs r'_mean_absolute_percentage_error_computer2s w &&rc8t||\}}t||S)aCompute 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.regression 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)rrrrs rmean_absolute_percentage_errorrEs%*"Hv!Vw 23Dg NNr)g-`>) typingrr rtorchmetrics.utilities.checksrfloattupleintrrrrrrr"s ; & & && 63; &8'v'PUVY[aVaPb'gm'&O&O&OVOr