a  þd|ã@sæddlmZmZddlZddlmZddlmZeeedœdd„Zeeedœdd „Zeeed œd d „Z eee e eeeed œdd„Z eeeeefdœdd„Z deeeeedœdd„Zdeee edœdd„Zdeee edœdd„ZdS)é)ÚOptionalÚTupleN)ÚTensor)Ú_TORCH_GREATER_EQUAL_1_9)ÚxÚyÚreturncCs8|jtjks|jtjkr.| ¡|j ¡ ¡S||jS)zSafe calculation of matrix multiplication. If input is float16, will cast to float32 for computation and back again. )ZdtypeÚtorchÚfloat16ÚfloatÚTZhalf©rr©rúg/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/utilities/compute.pyÚ _safe_matmulsrcCs|t |¡}d||dk<|S)z¦Compute x * log(y). Returns 0 if x=0. Example: >>> import torch >>> x = torch.zeros(1) >>> _safe_xlogy(x, 1/x) tensor([0.]) çr)r Úlog)rrÚresrrrÚ _safe_xlogy!s  r)ÚnumÚdenomrcCs<d||dk<| ¡r|n| ¡}| ¡r,|n| ¡}||S)zSafe division, by preventing division by zero. Additionally casts to float if input is not already to secure backwards compatibility. ér)Zis_floating_pointr )rrrrrÚ _safe_divide0s r)ÚscoreÚaverageÚ multilabelÚtpÚfpÚfnrcCsf|dus|dkr|S|dkr&||}n"t |¡}|sHd||||dk<t|||jdddƒ d¡S)NÚnoneZweightedrréÿÿÿÿT)Zkeepdim)r Z ones_likerÚsum)rrrrrrÚweightsrrrÚ_adjust_weights_safe_divide<s  r#cCsŒ|jdkr| ¡n|}|jdkr(| ¡n|}|jdks@|jdkrXtd|j›d|j›ƒ‚| ¡| ¡kr„td| ¡›d| ¡›ƒ‚||fS)z Check that auc input is correct.rzJExpected both `x` and `y` tensor to be 1d, but got tensors with dimension z and zHExpected the same number of elements in `x` and `y` tensor but received )ÚndimZsqueezeÚ ValueErrorZnumelr rrrÚ_auc_format_inputsJsÿÿr&r )rrÚ directionÚaxisrcCs@t ¡$tj|||d|}Wdƒn1s20Y|S)zrCompute area under the curve using the trapezoidal rule. Assumes increasing or decreasing order of `x`. )ZdimN)r Úno_gradZtrapz)rrr'r(Zauc_rrrÚ_auc_compute_without_checkZs 2r*F)rrÚreorderrcCs¨t ¡Œ|r6tr tj|ddnt |¡\}}||}|dd…|dd…}|dk ¡rv|dk ¡rld}qztdƒ‚nd}t|||ƒWdƒS1sš0YdS) NT)Zstablerr rgð¿z_The `x` tensor is neither increasing or decreasing. Try setting the reorder argument to `True`.gð?)r r)rÚsortÚanyÚallr%r*)rrr+Zx_idxZdxr'rrrÚ _auc_computees    ÿr/cCst||ƒ\}}t|||dS)a8Compute Area Under the Curve (AUC) using the trapezoidal rule. Args: x: x-coordinates, must be either increasing or decreasing y: y-coordinates reorder: if True, will reorder the arrays to make it either increasing or decreasing Return: Tensor containing AUC score )r+)r&r/)rrr+rrrÚaucxs r0)r )F)F)Útypingrrr rZtorchmetrics.utilities.importsrrrrÚstrÚboolr#r&r Úintr*r/r0rrrrÚs    þ