o yi{ @sddlmZddlmZddlmZddlmZmZ ddedeedee d efd d Z   ddedeed ed dee d ef ddZ dS))Optional)Tensor)Literal) _check_input_reduce_distance_matrixNxy zero_diagonalreturncCsTt|||\}}}|d|d|jdddjdd}|r(|d|S)zCalculates the pairwise manhattan similarity matrix. Args: x: tensor of shape ``[N,d]`` y: if provided, a tensor of shape ``[M,d]`` zero_diagonal: determines if the diagonal of the distance matrix should be set to zero r)dim)r unsqueezerepeatshapeabssumfill_diagonal_)rrr distancer^/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/pairwise/manhattan.py#_pairwise_manhattan_distance_updates  0 r reduction)meanrnoneNcCst|||}t||S)aCalculates pairwise manhattan distance: .. math:: d_{man}(x,y) = ||x-y||_1 = \sum_{d=1}^D |x_d - y_d| 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 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 import pairwise_manhattan_distance >>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32) >>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32) >>> pairwise_manhattan_distance(x, y) tensor([[ 4., 2.], [ 7., 5.], [12., 10.]]) >>> pairwise_manhattan_distance(x) tensor([[0., 3., 8.], [3., 0., 5.], [8., 5., 0.]]) )rr)rrrr rrrrpairwise_manhattan_distance(s ( r)NN)NNN) typingrtorchrtyping_extensionsr(torchmetrics.functional.pairwise.helpersrrboolrrrrrrs8