a  d@sddlmZmZddlZddlmZddlmZddlmZm Z ddl m Z deeeee e feeedd d Zdeeeee e fed eeed d dZdS))OptionalUnionN)Tensor)Literal) _check_input_reduce_distance_matrix)TorchMetricsUserError)xyexponent zero_diagonalreturncCst|||\}}}t|ttfr(|dks6td||j}|tj}|tj}| d| d | d d|}|r| d||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 isinstancefloatintrZdtypetotorchfloat64Z unsqueezeabspowsumZfill_diagonal_)r r r r Z _orig_dtypedistancers/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/pairwise/minkowski.py#_pairwise_minkowski_distance_updates   . r)meanrnoneN)r r r reductionr rcCst||||}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]]) )rr)r r r r r rrrrpairwise_minkowski_distance1s+r!)Nr N)Nr NN)typingrrrrZtyping_extensionsrZ(torchmetrics.functional.pairwise.helpersrrZ!torchmetrics.utilities.exceptionsrrrboolrr!rrrrs,