a  d@sddlmZddlZddlmZddlmZddlmZeedddZeedd d Z eee eeefd d d Z deee edddZ eeedddZdS))TupleN)Tensor) _check_data_shape_to_num_outputs)_check_same_shape)datareturncCs|}|d}ttjdg|jd|dd|ddkg}||}tt|tj|gg|jdg }|dd|dd}|dk}||S)z_Find and return values which have repeats i.e. the same value are more than once in the tensor.rT)deviceN) detachclonesorttorchcatZtensorrZnonzeronumelflatten)rtempZchangeuniqueZ change_idxfreqZatleast2rt/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/regression/spearman.py _find_repeatss  0,rcCsn|}t|}|}tjd|d|j|jd||d|<t|}|D]}||k}||||<qL|S)aCalculate the rank for each element of a tensor. The rank refers to the indices of an element in the corresponding sorted tensor (starting from 1). Duplicates of the same value will be assigned the mean of their rank. Adopted from `Rank of element tensor`_ r )dtyperN) rrZ empty_likeZargsortZarangerrrmean)rnZrankidxZrepeatsr conditionrrr _rank_data$s  &r)predstarget num_outputsrcCs6|r|stdt||t|||||fS)aUpdate and returns variables required to compute Spearman Correlation Coefficient. Check for same shape and type of input tensors. Args: preds: Predicted tensor target: Ground truth tensor num_outputs: Number of outputs in multioutput setting zhExpected `preds` and `target` both to be floating point tensors, but got {pred.dtype} and {target.dtype})Zis_floating_point TypeErrorrr)rr r!rrr_spearman_corrcoef_update9s   r#ư>)rr epsrc Cs|jdkrt|}t|}n0tdd|jDj}tdd|jDj}||d}||d}||d}t||d}t||d}||||}t|ddS)aCompute Spearman Correlation Coefficient. Args: preds: Predicted tensor target: Ground truth tensor eps: Avoids ``ZeroDivisionError``. Example: >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> preds, target = _spearman_corrcoef_update(preds, target, num_outputs=1) >>> _spearman_corrcoef_compute(preds, target) tensor(1.0000) r cSsg|] }t|qSrr).0prrr bz._spearman_corrcoef_compute..cSsg|] }t|qSrr&)r'trrrr)cr*rgg?)ndimrrstackTrsqrtclamp) rr r%Z preds_diffZ target_diffZcovZ preds_stdZ target_stdZcorrcoefrrr_spearman_corrcoef_computeNs  r1)rr rcCs0t|||jdkrdn|jdd\}}t||S)a Compute `spearmans rank correlation coefficient`_. .. math: r_s = = \frac{cov(rg_x, rg_y)}{\sigma_{rg_x} * \sigma_{rg_y}} where :math:`rg_x` and :math:`rg_y` are the rank associated to the variables x and y. Spearmans correlations coefficient corresponds to the standard pearsons correlation coefficient calculated on the rank variables. Args: preds: estimated scores target: ground truth scores Example (single output regression): >>> from torchmetrics.functional.regression import spearman_corrcoef >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> spearman_corrcoef(preds, target) tensor(1.0000) Example (multi output regression): >>> from torchmetrics.functional.regression import spearman_corrcoef >>> target = torch.tensor([[3, -0.5], [2, 7]]) >>> preds = torch.tensor([[2.5, 0.0], [2, 8]]) >>> spearman_corrcoef(preds, target) tensor([1.0000, 1.0000]) r r )r!)r#r,shaper1)rr rrrspearman_corrcoefps&r3)r$)typingrrrZ(torchmetrics.functional.regression.utilsrZtorchmetrics.utilities.checksrrrintr#floatr1r3rrrrs     "