a  dI@s:ddlZddlmZmZddlmZeeedddZdS)N)Tensortensor)"_check_retrieval_functional_inputs)predstargetreturncCsTt||\}}|}|s(td|jdS|tj|dddd|}||S)aCompute the r-precision metric for information retrieval. R-Precision is the fraction of relevant documents among all the top ``k`` retrieved documents where ``k`` is equal to the total number of relevant documents. ``preds`` and ``target`` should be of the same shape and live on the same device. If no ``target`` is ``True``, ``0`` is returned. ``target`` must be either `bool` or `integers` and ``preds`` must be ``float``, otherwise an error is raised. If you want to measure Precision@K, ``top_k`` must be a positive integer. Args: preds: estimated probabilities of each document to be relevant. target: ground truth about each document being relevant or not. Returns: A single-value tensor with the r-precision of the predictions ``preds`` w.r.t. the labels ``target``. Example: >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_r_precision(preds, target) tensor(0.5000) g)deviceT)ZdimZ descendingN)rsumrrtorchZargsortfloat)rrZrelevant_numberZrelevantr v/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/retrieval/r_precision.pyretrieval_r_precisions $r)r rrZtorchmetrics.utilities.checksrrr r r rs