o yi @sddlmZddlZddlmZddlmZddlmZmZddl m Z  ddedeed ee d efd d Z   ddedeed edd ee d ef ddZ dS))OptionalN)Tensor)Literal) _check_input_reduce_distance_matrix) _safe_matmulxy zero_diagonalreturncCsjt|||\}}}tj|ddd}||d}tj|ddd}||d}t||}|r3|d|S)zCalculates the pairwise cosine similarity 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 )pdimr)rtorchnorm unsqueezerfill_diagonal_)rr r rdistancer[/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/pairwise/cosine.py"_pairwise_cosine_similarity_updates   r reduction)meansumnoneNcCst|||}t||S)aeCalculates pairwise cosine similarity: .. math:: s_{cos}(x,y) = \frac{}{||x|| \cdot ||y||} = \frac{\sum_{d=1}^D x_d \cdot y_d }{\sqrt{\sum_{d=1}^D x_i^2} \cdot \sqrt{\sum_{d=1}^D y_i^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 :math:`x` is given this defaults to ``True`` else if :math:`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_cosine_similarity >>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32) >>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32) >>> pairwise_cosine_similarity(x, y) tensor([[0.5547, 0.8682], [0.5145, 0.8437], [0.5300, 0.8533]]) >>> pairwise_cosine_similarity(x) tensor([[0.0000, 0.9989, 0.9996], [0.9989, 0.0000, 0.9998], [0.9996, 0.9998, 0.0000]]) )rr)rr rr rrrrpairwise_cosine_similarity/s ) r)NN)NNN)typingrrrtyping_extensionsr(torchmetrics.functional.pairwise.helpersrrtorchmetrics.utilities.computerboolrrrrrrs<