a  dy @sddlmZddlZddlmZddlmZddlmZmZd eeeee edddZ deeeed ee ed d d Z dS))OptionalN)Tensor)Literal) _check_input_reduce_distance_matrix)xy zero_diagonalreturncCst|||\}}}|j}|tj}|tj}||jddd}||jdd}||d||j|}|r~|d| S)zCalculate the pairwise euclidean distance matrix. Args: x: tensor of shape ``[N,d]`` y: tensor of shape ``[M,d]`` zero_diagonal: determines if the diagonal of the distance matrix should be set to zero T)dimZkeepdim)r r) rZdtypetotorchfloat64summmTZfill_diagonal_sqrt)rrr Z _orig_dtypeZx_normZy_normdistancers/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/pairwise/euclidean.py#_pairwise_euclidean_distance_updates    r)meanrnoneN)rr reductionr r cCst|||}t||S)aCalculate pairwise euclidean distances. .. math:: d_{euc}(x,y) = ||x - y||_2 = \sqrt{\sum_{d=1}^D (x_d - y_d)^2} 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.pairwise import pairwise_euclidean_distance >>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32) >>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32) >>> pairwise_euclidean_distance(x, y) tensor([[3.1623, 2.0000], [5.3852, 4.1231], [8.9443, 7.6158]]) >>> pairwise_euclidean_distance(x) tensor([[0.0000, 2.2361, 5.8310], [2.2361, 0.0000, 3.6056], [5.8310, 3.6056, 0.0000]]) )rr)rrrr rrrrpairwise_euclidean_distance/s) r)NN)NNN) typingrrrZtyping_extensionsrZ(torchmetrics.functional.pairwise.helpersrrboolrrrrrrs&