a  d @s~ddlmZmZddlZddlmZddlmZdeeeeeefdddZ eeeefed d d Z eeed d dZ dS))TupleUnionN)Tensor)_check_same_shape-`>)predstargetepsilonreturncCsJt||t||}|tjt||d}t|}|}||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)rtorchabsclampsumZnumel)rrr Zabs_diffZ abs_per_errorsum_abs_per_errornum_obsrp/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/regression/mape.py&_mean_absolute_percentage_error_updates   r)rrr cCs||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) r)rrrrr'_mean_absolute_percentage_error_compute2sr)rrr cCst||\}}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)rrrrrrrmean_absolute_percentage_errorEsr)r) typingrrr rZtorchmetrics.utilities.checksrfloatintrrrrrrrs