:j ddlmZddlZddlmZddlmZddlmZmZddl m Z  ddedeed e d ee d ef d Z ddedeed e d edd ee d ef dZy))OptionalN)Tensor)Literal) _check_input_reduce_distance_matrix)TorchMetricsUserErrorxyexponent zero_diagonalreturnct|||\}}}t|ttfr|dk\st d||j }|j tj}|j tj}|jd|jdz jj|jdjd|z }|r|jd|j |S)acCalculate the pairwise minkowski distance matrix. Args: x: tensor of shape ``[N,d]`` y: tensor of shape ``[M,d]`` exponent: int or float larger than 1, exponent to which the difference between preds and target is to be raised zero_diagonal: determines if the diagonal of the distance matrix should be set to zero z>Argument ``p`` must be a float or int greater than 1, but got rg?)r isinstancefloatintrdtypetotorchfloat64 unsqueezeabspowsumfill_diagonal_)r r r r _orig_dtypedistances /home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/pairwise/minkowski.py#_pairwise_minkowski_distance_updater s'q!];Aq- x% .8q=#&demdn$opp''K U]]A U]]A AQ/446::8DHHLPPQTW_Q_`H" ;;{ ## reduction)meanrnoneNc6t||||}t||S)aCalculate pairwise minkowski distances. .. math:: d_{minkowski}(x,y,p) = ||x - y||_p = \sqrt[p]{\sum_{d=1}^D (x_d - y_d)^p} If both :math:`x` and :math:`y` are passed in, the calculation will be performed pairwise between the rows of :math:`x` and :math:`y`. If only :math:`x` is passed in, the calculation will be performed between the rows of :math:`x`. Args: x: Tensor with shape ``[N, d]`` y: Tensor with shape ``[M, d]``, optional exponent: int or float larger than 1, exponent to which the difference between preds and target is to be raised reduction: reduction to apply along the last dimension. Choose between `'mean'`, `'sum'` (applied along column dimension) or `'none'`, `None` for no reduction zero_diagonal: if the diagonal of the distance matrix should be set to 0. If only `x` is given this defaults to `True` else if `y` is also given it defaults to `False` Returns: A ``[N,N]`` matrix of distances if only ``x`` is given, else a ``[N,M]`` matrix Example: >>> import torch >>> from torchmetrics.functional.pairwise import pairwise_minkowski_distance >>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32) >>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32) >>> pairwise_minkowski_distance(x, y, exponent=4) tensor([[3.0092, 2.0000], [5.0317, 4.0039], [8.1222, 7.0583]]) >>> pairwise_minkowski_distance(x, exponent=4) tensor([[0.0000, 2.0305, 5.1547], [2.0305, 0.0000, 3.1383], [5.1547, 3.1383, 0.0000]]) )r r)r r r r"r rs rpairwise_minkowski_distancer&1s"V31a=QH "8Y 77r!)NN)Nr'NN)typingrrrtyping_extensionsr(torchmetrics.functional.pairwise.helpersrr!torchmetrics.utilities.exceptionsrrboolr r&r!rr.s %ZCae$ $6"$5:$OWX\~$ $66:$( ,8 ,8,8,823 ,8 D> ,8  ,8r!