a  do @sNddlmZddlZddlmZmZddlmZdeeeeedddZdS) )OptionalN)Tensortensor)"_check_retrieval_functional_inputs)predstargettop_kreturncCst||\}}|dur |jdn|}t|tr6|dks>tdd|}|s\td|jdS|tj |ddd d| }||S) aCompute the Fall-out for information retrieval, as explained in `IR Fall-out`_. Fall-out is the fraction of non-relevant documents retrieved among all the non-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 Fall-out@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. top_k: consider only the top k elements (default: ``None``, which considers them all) Returns: A single-value tensor with the fall-out (at ``top_k``) of the predictions ``preds`` w.r.t. the labels ``target`` Raises: ValueError: If ``top_k`` parameter is not `None` or an integer larger than 0 Example: >>> from torchmetrics.functional import retrieval_fall_out >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_fall_out(preds, target, top_k=2) tensor(1.) Nrz,`top_k` has to be a positive integer or Noneg)deviceT)ZdimZ descending) rshape isinstanceint ValueErrorsumrr torchZargsortfloat)rrrZrelevantrs/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/retrieval/fall_out.pyretrieval_fall_outs$r)N) typingrrrrZtorchmetrics.utilities.checksrrrrrrrs