Zj>LSSKrSSKrSSKrSSKJs Js Jr SSK J r SSK J r SSK JrJrJrJr SSKJrJrJrJrJr SSKJrJrJrJr SSKJr S S KJ r S S K!J"r" \"5(aSS K#J$r% S r&Sr'Sr(\"SS\ 55r)\\"SS9"SS\"555r*SS/r+g)N)TorchvisionBackend) BatchFeature)group_images_by_shapepadreorder_imagesto_channel_dimension_format)OPENAI_CLIP_MEANOPENAI_CLIP_STDChannelDimensionPILImageResamplingSizeDict) TensorTypeauto_docstringis_scipy_availablerequires_backends)requires)OwlViTImageProcessor)OwlViTImageProcessorPil)ndimagecF[U5n[U5nURnX R:a-USX R- -- n[R "X5nX4$X RS- :XaXRS4-nX4$X R:a [ S5eX4$)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. )rzIoutput_shape length cannot be smaller than the image number of dimensions)tuplelenshapendimnpreshape ValueError)image output_shape output_ndim input_shapes x/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/owlv2/modular_owlv2.py_preprocess_resize_output_shaper'3s.&Ll#K++KZZt{ZZ788  5.    Q &#{{2&88    zz !dee  c8[R"U5n[R"U5(a)[Rn[RnU"U5nO [Rn[R nU"U5n[R "XU5nU$)aClip output image to range of values of input image. Note that this function modifies the values of *output_image* in-place. Taken from: https://github.com/scikit-image/scikit-image/blob/b4b521d6f0a105aabeaa31699949f78453ca3511/skimage/transform/_warps.py#L640. Args: input_image : ndarray Input image. output_image : ndarray Output image, which is modified in-place. )rminisnannanminnanmaxmaxclip) input_image output_imagemin_valmin_funcmax_funcmax_vals r&_clip_warp_outputr6Zsoff[!G xx9999;'6666{#G77<':L r(c([U[[45(aS[R"UVs/sHo"SPM sn5n[R"UVs/sHo"SPM sn5nO>[U[R 5(aUR S5up4O [S5e[R"X45n[R"XUXU/SS9nURS5RUR5nX-nU$s snfs snf)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. rrz4`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_boxesrKxs,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/= $U + &<**BDXY.2B!CQ_``r(rTN)__name__ __module__ __qualname____firstlineno__r BILINEARrr rr rr]rrrrr crop_sizedo_center_cropfloatrbr:boolrrrstrrrr__static_attributes__rr(r&rMrMs!**H!JIC (DNIJL FIN . % R` "!$  ^$ +   n   8# 4O4O4O 4O 4Ol7a^$7a7a 7a . 7a  7a7a7a7aDK'$.7a4;&-7a+7aj(4/7a 7ar(rM)r;)backendsct\rSrSr\R r\r\ r SSS.r Sr Sr SrSrSrSrSrSS\R(S\S \R(4S jjrSS\R(S \\\4S \S \R(4S jjrS\\R(S\S \SSS\S\S\S\S\\\-S-S\\\-S-S\\-S-S \4Sjr Sr!g)Owlv2ImageProcessorPili:rOrPrSTNr"rUrVcpURSSup4[X45nXS- nXT- n[USU4SU44US9nU$)rXrYNr)r"r`constant_values)rr.r)r\r"rUrQrRr]r^r_s r&rOwlv2ImageProcessorPil.padIsU BC( 6!] L _q)n5*   r(r]roc [US5 USUS4n[U[R5n[ X5upUR n[ R"Xv5nSn Sn Sn U(aUc[ R"SUS- S- 5nO[ R"U5[ R"U5-n[ R"US:5(a [S5e[ R"US:US:*-5(a[R"S 5 [R "XXS 9n OUn UV s/sHn SU - PM nn [R""XXU S S 9n[%X5n[U[R&5nU$s sn f) 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. scipyrQrRmirrorrrrrqrr)cvalmodeT)orderrr grid_mode)rr r LASTr'rrdividemaximumrvrwrxr!ryrzndigaussian_filterzoomr6FIRST)r\r"r]rorrhr#r%rndi_moderrrf zoom_factorsouts r&rOwlv2ImageProcessorPil.resizeXsf, $(XW 6 +E3C3H3HI=eRkk ))K6 "*&(jjWq[A4E&F#&(mm4G&H2<rs 22;2  +AH$$N<>_a._a_aD :oa4oaoad !": ;r(