a  d @sNddlmZddlZddlmZmZddlmZdeeeeedddZdS) )OptionalN)Tensortensor)"_check_retrieval_functional_inputs)predstargettop_kreturncCst||\}}|p|jd}t|ts>|dkr>td|d||jt||jddddd}|sxtd|j d St | d}d |dd S) aJCompute reciprocal rank (for information retrieval). See `Mean Reciprocal Rank`_. ``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. 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) Return: a single-value tensor with the reciprocal rank (RR) of the predictions ``preds`` wrt the labels ``target``. Raises: ValueError: If ``top_k`` is not ``None`` or an integer larger than 0. Example: >>> from torchmetrics.functional.retrieval import retrieval_reciprocal_rank >>> preds = torch.tensor([0.2, 0.3, 0.5]) >>> target = torch.tensor([False, True, False]) >>> retrieval_reciprocal_rank(preds, target) tensor(0.5000) rzAArgument ``top_k`` has to be a positive integer or None, but got .T)sortedZdimg)deviceg?) rshape isinstanceint ValueErrorZtopkminsumrrtorchZnonzeroview)rrrpositionrz/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/retrieval/reciprocal_rank.pyretrieval_reciprocal_ranks$r)N) typingrrrrZtorchmetrics.utilities.checksrrrrrrrs