Zj6!SrSSKrSSKJr SSKJr SSKJr SSK J r J r SSK J r JrJrJrJrJr SS KJrJr SS KJrJr "S S \S S9r\"SS\55rS/rg)zImage processor class for TVP.N) functional)TorchvisionBackend) BatchFeature)group_images_by_shapereorder_images)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STD ImageInputPILImageResamplingSizeDictmake_nested_list_of_images) ImagesKwargsUnpack) TensorTypeauto_docstringcP\rSrSr%Sr\\S'\\\-S-\S'\ S-\S'Sr g)TvpImageProcessorKwargs"u do_flip_channel_order (`bool`, *optional*, defaults to `self.do_flip_channel_order`): Whether to flip the channel order of the image from RGB to BGR. constant_values (`float` or `List[float]`, *optional*, defaults to `self.constant_values`): Value used to fill the padding area when `pad_mode` is `'constant'`. pad_mode (`str`, *optional*, defaults to `self.pad_mode`): Padding mode to use — `'constant'`, `'edge'`, `'reflect'`, or `'symmetric'`. do_flip_channel_orderNconstant_valuespad_mode) __name__ __module__ __qualname____firstlineno____doc__bool__annotations__floatliststr__static_attributes__r}/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/tvp/image_processing_tvp.pyrr"s- T%[(4//Djr%rF)totalc&^\rSrSr\R r\r\ r SS0r Sr SSS.r SrSrSrSrSrSSS.rSrS rSrSr\rS \\4U4S jjr\S \\\-\\\-S \\S \4U4Sjj5r S\S \4Sjr!S+SSS\"SSS\#S S4 U4Sjjjr$S,Sjr%S\\SS\#S\"SSS\#S\"S\#S\&S\#S \"S!\&\\&-S"\'S#\#S$\&\\&-S-S%\&\\&-S-S&\#S'\'\(-S-S(\#S-S \4&S)jr)S*r*U=r+$)-TvpImageProcessor1 longest_edgeiFheightwidthTgp?rconstantkwargsc &>[TU]"S0UD6 g)Nr)super__init__)selfr0 __class__s r&r3TvpImageProcessor.__init__Es "6"r%videosreturnc &>[TU]"U40UD6$)zd videos (`ImageInput` or `list[ImageInput]` or `list[list[ImageInput]]`): Frames to preprocess. )r2 preprocess)r4r7r0r5s r&r:TvpImageProcessor.preprocessHsw!&3F33r%imagesc <URU5n[U40UD6$)z Prepare the images structure for processing. Args: images (`ImageInput`): The input images to process. Returns: `ImageInput`: The images with a valid nesting. ) fetch_imagesr)r4r<r0s r&_prepare_images_structure+TvpImageProcessor._prepare_images_structureTs$""6*)&;F;;r%Nimage torch.Tensorsizeresamplez7PILImageResampling | tvF.InterpolationMode | int | None antialiasc *>UR(aoURSSupgXg:a"US-U- nURn [X-5n O!US-U- nURn [X-5n [T U]U[ XS9X4S9$[T U]"X4X4S.UD6$)a Resize an image to the specified size. Args: image (`torch.Tensor`): Image to resize. size (`SizeDict`): Size dictionary. If `size` has `longest_edge`, resize the longest edge to that value while maintaining aspect ratio. Otherwise, use the base class resize method. resample (`tvF.InterpolationMode`, *optional*): Interpolation method to use. antialias (`bool`, *optional*, defaults to `True`): Whether to use antialiasing. Returns: `torch.Tensor`: The resized image. Ng?r,)rDrE)r+shapeintr2resizer ) r4rArCrDrEr0current_height current_widthratio new_height new_widthr5s r&rJTvpImageProcessor.resizefs6   ,1KK,< )N.%+n<!..  23 &,}< --  !23 7>xzCh"  w~e\H\U[\\r%c(URS5nU$)a5 Flip channel order from RGB to BGR. The slow processor puts the red channel at the end (BGR format), but the channel order is different. We need to match the exact channel order of the slow processor: Slow processor: - Channel 0: Blue (originally Red) - Channel 1: Green - Channel 2: Red (originally Blue) )flip)r4framess r&_flip_channel_order%TvpImageProcessor._flip_channel_ordersR r% do_resizedo_center_crop crop_size do_rescalerescale_factordo_padpad_sizerr do_normalize image_mean image_stdrreturn_tensorsdisable_groupingc .[UUSS9unn0nUR5HunnU(aURUX45nU(aURUU5nUR UXxXU5nU (a&UR UXU S9n[ R"USS9nU(aURU5nUUU'M [UUSS9nUS:Xa:UVs/sHn[ R"USS9PM nn[ R"USS9n[SU0US 9$s snf) z Preprocess videos using the fast image processor. This method processes each video frame through the same pipeline as the original TVP image processor but uses torchvision operations for better performance. T)rb is_nested) fill_valuerr)dim)rdpt pixel_values)data tensor_type) ritemsrJ center_croprescale_and_normalizepadtorchstackrUrr)r4r<rWrCrDrXrYrZr[r\r]rrr^r_r`rrarbr0grouped_imagesgrouped_images_indexprocessed_images_groupedrHstacked_framesprocessed_imagess r& _preprocessTvpImageProcessor._preprocesss780E %50 ,,$& %3%9%9%; !E>!%^T!L!%!1!1.)!L"77 LV_N !%.(iq!r!&^!C%!%!9!9.!I.< $U +/&<2**BDXdhi T !IYZIYv F :IY Z${{+;C .2B!CQ_`` [sDr)NT)rTrBr8rB),rrrrr BILINEARrDr r_r r`rCdefault_to_squarerYrWrXrZr[r\r]rrr^rr valid_kwargsrr3rr r"rr:r?r rrJrUr!r#rrvr$ __classcell__)r5s@r&r)r)1sR!**H'J%I C D-IINJN F,HOHL *L#(?!@# 4T*--T*5E0FF 401 4  4 4<<  <,OS .].].]L .]  .] .].]`$>aT.)*>a>a >a L >a  >a>a>a>a>a>ae,>a>a>aDK'$.>a 4;&-!>a" $#>a$j(4/%>a&+'>a* +>a>ar%r))rrotorchvision.transforms.v2rtvFimage_processing_backendsrimage_processing_utilsrimage_transformsrr image_utilsr r r r r rprocessing_utilsrrutilsrrrr)__all__rr%r&rsf% 7;2E5/ l% ta*tatan  r%