+ j>V^RIt^RIt^RIt^RIHuHuHt^RI H t ^RI H t ^RI HtHtHtHt^RIHtHtHtHtHt^RIHtHtHtHt^RIHt^RIH t ^R I!H"t"]!4'd^R I#H$t%R t&R t'R t(]!RR] 44t)]]!RR7!RR]"444t*RR.t+R#)N)TorchvisionBackend) BatchFeature)group_images_by_shapepadreorder_imagesto_channel_dimension_format)OPENAI_CLIP_MEANOPENAI_CLIP_STDChannelDimensionPILImageResamplingSizeDict) TensorTypeauto_docstringis_scipy_availablerequires_backends)requires)OwlViTImageProcessor)OwlViTImageProcessorPil)ndimagec|\V4p\V4pVPpW P8d:VRW P, ,, p\P !W4pW3#W P^, 8XdWPR,3,pW3#W P8d \ R4hW3#)aValidate resize output shape according to input image. Args: image (`np.ndarray`): Image to be resized. output_shape (`iterable`): Size of the generated output image `(rows, cols[, ...][, dim])`. If `dim` is not provided, the number of channels is preserved. Returns image (`np.ndarray`): The input image, but with additional singleton dimensions appended in the case where `len(output_shape) > input.ndim`. output_shape (`Tuple`): The output shape converted to tuple. Raises: ValueError: If output_shape length is smaller than the image number of dimensions. Notes: The input image is reshaped if its number of dimensions is not equal to output_shape_length. zIoutput_shape length cannot be smaller than the image number of dimensions))tuplelenshapendimnpreshape ValueError)image output_shape output_ndim input_shapes&& h/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/owlv2/modular_owlv2.py_preprocess_resize_output_shaper%3s.&Ll#K++KZZt{ZZ788  5.    Q &#{{2&88    zz !dee  c:\P!V4p\P!V4'd*\Pp\PpV!V4pM \Pp\P pV!V4p\P !WV4pV#)aClip output image to range of values of input image. Note that this function modifies the values of *output_image* in-place. Taken from: https://github.com/scikit-image/scikit-image/blob/b4b521d6f0a105aabeaa31699949f78453ca3511/skimage/transform/_warps.py#L640. Args: input_image : ndarray Input image. output_image : ndarray Output image, which is modified in-place. )rminisnannanminnanmaxmaxclip) input_image output_imagemin_valmin_funcmax_funcmax_vals&& r$_clip_warp_outputr4Zsoff[!G xx9999;'6666{#G77<':L r&cF\V\\34'dZ\P!VUu.uF q"^,NK up4p\P!VUu.uF q"^,NK up4pM?\V\P 4'dVP ^4wr4M \R4h\P!W44p\P!WUWU.^R7pVP^4PVP4pW,pV#uupiuupi)a Scale batch of bounding boxes to the target sizes. Args: boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`): Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format. target_sizes (`list[tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`): Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format. Returns: `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes. z4`target_sizes` must be a list, tuple or torch.Tensor)dim) isinstancelistrtorchtensorTensorunbind TypeErrorr,stack unsqueezetodevice)boxes target_sizesi image_height image_widthmax_size scale_factors&& r$ _scale_boxesrIxs,u ..||<$@!77 E:N/= $U + &<**BDXY #xx(8_oxp /D /?0 ,"$%3%9%9%; !E>y $ .t _ 0=&u-&<((>@TU0E^fv/w,#% %3%9%9%; !E>!77~ZN/= $U + &<**BDXY.2B!CQ_``r&p?rTN)__name__ __module__ __qualname____firstlineno__r BILINEARrr rr rr`rrrrr crop_sizedo_center_croprerrr__static_attributes____classdictcell__rWs@r$rKrKsx!**H!JI c7C (DNIJL FIN    04O4Ol7a7ar&rK)backendsca]tRtRto]P t]t] t RRRR/t Rt Rt RtRtRtRtRtRV3RlRlltRV3R lR lltV3R lR ltR tVtR#)Owlv2ImageProcessorPili:rLrMrNTNcT<V^8dQhRS[PRS[RS[P/#)rPr rSrT)rndarrayrU)rVrWs"r$rX#Owlv2ImageProcessorPil.__annotate__Is*   U RZZ r&c VPRRwr4\W44pWS, pWT, p\V^V3^V33VR7pV#)r[N)r rcconstant_valuesr])rr,r)r_r rSrLrNr`rarbs&&& r$rOwlv2ImageProcessorPil.padIsU BC( 6!] L _q)n5*   r&cp<V^8dQhRS[PRS[S[S[3,RS[RS[P/#)rPr r`rurT)rrdictrrri)rVrWs"r$rXrXsC77zz738n7 7 7r&c f\VR4VR,VR,3p\V\P4p\ W4wrVP p\ P!Wv4pRp ^p ^p V'dVf'\ P!^V^, ^, 4pM\ P!V4\ P!V4,p\ P!V^84'd \R4h\ P!V^8V^8*,4'd\P!R4\P !WWR7p MTp VU u.uF p ^V , NK pp \P"!WWV RR 7p\%W4p\V\P&4pV#uup i) a Resize an image as per the original implementation. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary containing the height and width to resize the image to. anti_aliasing (`bool`, *optional*, defaults to `True`): Whether to apply anti-aliasing when downsampling the image. anti_aliasing_sigma (`float`, *optional*, defaults to `None`): Standard deviation for Gaussian kernel when downsampling the image. If `None`, it will be calculated automatically. scipyrLrNmirrorrwrx)cvalmodeT)orderrr grid_mode)rrr LASTr%rrdividemaximumr|r}r~rrrndigaussian_filterzoomr4FIRST)r_r r`rurrmr!r#rndi_moderrrf zoom_factorsouts&&&&&, r$rOwlv2ImageProcessorPil.resizeXsh, $(XW 6 +E3C3H3HI=eRkk ))K6 "*&(jjWq[A4E&F#&(mm4G&H2<rs 22;2  +AH$$N<>_a._a_aD :oa4oaoad !": ;r&