a  d- @sNddlmZddlZddlmZmZddlmZdeeeeedddZdS) )OptionalN)Tensortensor)"_check_retrieval_functional_inputs)predstargettop_kreturncCst||\}}|p|jd}t|ts>|dkr>td|d||jt||jddddd}|sxtd|j d St j dt |d|j t j d |dk}t t j t ||j t j d d|S) aXCompute average precision (for information retrieval), as explained in `IR Average precision`_. ``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 average precision (AP) of the predictions ``preds`` w.r.t. the labels ``target``. Raises: ValueError: If ``top_k`` is not ``None`` or an integer larger than 0. Example: >>> from torchmetrics.functional.retrieval import retrieval_average_precision >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_average_precision(preds, target) tensor(0.8333) rzAArgument ``top_k`` has to be a positive integer or None, but got .T)sortedZdimg)device)rZdtype)rshape isinstanceint ValueErrorZtopkminsumrrtorchZarangelenfloat32divmean)rrrZ positionsr|/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/retrieval/average_precision.pyretrieval_average_precisions$&r)N) typingrrrrZtorchmetrics.utilities.checksrrrrrrrs