:j ddlmZmZddlmZddlmZdedeeeffdZ dedeeefd eed defd Z dded eed defd Z y ))OptionalUnion)Tensor)Literalimgreturncd|jdk7rtd|j|dddddf|dddddfz }|dddddf|dddddfz }|jj gd}|jj gd}||z}||jdfS) z4Compute total variation statistics on current batch.z1Expected input `img` to be an 4D tensor, but got .N)r r)ndim RuntimeErrorshapeabssum)rdiff1diff2res1res2scores u/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/image/tv.py_total_variation_updaters xx1}NsyykZ[[ QR Oc#ssA+. .E Q Oc#q#2#+. .E 99;??9 %D 99;??9 %D 4KE #))A, r num_elements reduction)meanrnonec|dk(r|j|z S|dk(r|jS||dk(r|Std)z$Compute final total variation score.rrrzHExpected argument `reduction` to either be 'sum', 'mean', 'none' or None)r ValueError)rrrs r_total_variation_computer"!sOFyy{\))Eyy{I/ _ ``rc8t|\}}t|||S)a9Compute total variation loss. Args: img: A `Tensor` of shape `(N, C, H, W)` consisting of images reduction: a method to reduce metric score over samples. - ``'mean'``: takes the mean over samples - ``'sum'``: takes the sum over samples - ``None`` or ``'none'``: return the score per sample Returns: A loss scalar value containing the total variation Raises: ValueError: If ``reduction`` is not one of ``'sum'``, ``'mean'``, ``'none'`` or ``None`` RuntimeError: If ``img`` is not 4D tensor Example: >>> from torch import rand >>> from torchmetrics.functional.image import total_variation >>> img = rand(5, 3, 28, 28) >>> total_variation(img) tensor(7546.8018) )rr")rrrrs rtotal_variationr$.s"<2#6E< #E< CCrN)r) typingrrtorchrtyping_extensionsrtupleintrr"r$rrr+s#%  E&#+,>  a  a!&sF{!3 a@HQfIg@h a  aDDHW=R5S,TDagDr