cCiAK"SrSSKJrJr SSKrSSKJr SSKJ r SSK J r J r J r SSKJrJrJrJr SS KJrJrJrJrJrJrJrJrJrJrJr SS KJ r J!r! \"5(aSSK"r"\!RF"\$5r%S \&\&\4S jr'"S S\ 5r(S/r)g)z Image processor class for Vivit.)OptionalUnionN)is_vision_available) TensorType)BaseImageProcessor BatchFeature get_size_dict)get_resize_output_image_sizerescaleresizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imageis_valid_imageto_numpy_array valid_imagesvalidate_preprocess_arguments)filter_out_non_signature_kwargsloggingreturncJ[U[[45(a6[US[[45(a[USS5(aU$[U[[45(a[US5(aU/$[U5(aU//$[ SU35e)Nrz"Could not make batched video from ) isinstancelisttupler ValueError)videoss j/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/vivit/image_processing_vivit.py make_batchedr$5s&4-((Zq D%=-Q-QVdeklmenopeqVrVr FT5M * *~fQi/H/Hx   z 9&B CCc#J^\rSrSrSrS/rSS\RSSSSSSSS4 S\S\ \ \ \ 4S \S \S \ \ \ \ 4S \S \ \ \4S\S\S\ \ \\\4S\ \ \\\4SS4U4Sjjjr\RSS4S\R$S\ \ \ 4S \S\ \ \ \4S\ \ \ \4S\R$4 SjjrSS\R$S\ \ \4S\S\ \ \ \4S\ \ \ \44 SjjrSSSSSSSSSSS\R,S4 S\S\ \S\ \ \ \ 4S \ \S \ \S \ \ \ \ 4S \ \S \ \S\ \S\ \S\ \ \\\4S\ \ \\\4S\ \S\ \ \ \4S\R$4Sjjr\"5SSSSSSSSSSSS\R,S4S\S\ \S\ \ \ \ 4S \ \S \ \S \ \ \ \ 4S \ \S \ \S\ \S\ \S\ \ \\\4S\ \ \\\4S\ \ \ \4S\S\ \ \ \4S\R8R84 Sjj5rSrU=r$) VivitImageProcessorBa Constructs a Vivit image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 256}`): Size of the output image after resizing. The shortest edge of the image will be resized to `size["shortest_edge"]` while maintaining the aspect ratio of the original image. Can be overridden by `size` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `True`): Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after applying the center crop. Can be overridden by the `crop_size` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/127.5`): Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. offset (`bool`, *optional*, defaults to `True`): Whether to scale the image in both negative and positive directions. Can be overridden by the `offset` in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. pixel_valuesTNg? do_resizesizeresampledo_center_crop crop_size do_rescalerescale_factoroffset do_normalize image_mean image_stdrc  *>[T U]"S 0U D6 UbUOSS0n[USS9nUbUOSSS.n[USS9nXlX lX@lXPlX0lX`lXpl Xl Xl U bU O[Ul U bXlg[Ulg) N shortest_edgeFdefault_to_square)heightwidthr. param_name)super__init__r r*r+r-r.r,r/r0r1r2rr3rr4)selfr*r+r,r-r.r/r0r1r2r3r4kwargs __class__s r#rAVivitImageProcessor.__init__ms "6"'tos-CTU;!*!6IsUX*DZ&/&;AVr%image data_formatinput_data_formatc [USS9nSU;a[XSSUS9nO3SU;aSU;a USUS4nO[SUR535e[ U4UUUUS.UD6$) a Resize an image. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will have the size `(h, w)`. If `size` is of the form `{"shortest_edge": s}`, the output image will have its shortest edge of length `s` while keeping the aspect ratio of the original image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Fr8r6)r9rHr;r<zDSize must have 'height' and 'width' or 'shortest_edge' as keys. Got )r+r,rGrH)r r r!keysr )rBrFr+r,rGrHrC output_sizes r#r VivitImageProcessor.resizes4TU; d "6O,YjK 'T/>4=9Kcdhdmdmdocpqr r  #/     r%scalec <[U4X$US.UD6nU(aUS- nU$)a~ Rescale an image by a scale factor. If `offset` is `True`, the image has its values rescaled by `scale` and then offset by 1. If `scale` is 1/127.5, the image is rescaled between [-1, 1]. image = image * scale - 1 If `offset` is `False`, and `scale` is 1/255, the image is rescaled between [0, 1]. image = image * scale Args: image (`np.ndarray`): Image to rescale. scale (`int` or `float`): Scale to apply to the image. offset (`bool`, *optional*): Whether to scale the image in both negative and positive directions. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. )rMrGrH)r )rBrFrMr1rGrHrCrescaled_images r#r VivitImageProcessor.rescales9>!  K\ `f  +a/Nr%c [UUU U U UUUUUS9 U (aU(d [S5e[U5nU(a%[U5(a[R S5 Uc [ U5nU(aURXXNS9nU(aURXUS9nU(aURXXS9nU (aURXXS9n[XUS9nU$) zPreprocesses a single image.) r/r0r2r3r4r-r.r*r+r,z0For offset, do_rescale must also be set to True.zIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)rFr+r,rH)r+rH)rFrMr1rH)rFmeanstdrH)input_channel_dim) rr!rrlogger warning_oncerr center_cropr normalizer)rBrFr*r+r,r-r.r/r0r1r2r3r4rGrHs r#_preprocess_image%VivitImageProcessor._preprocess_images& &!)%!)  *OP Pu% /%00   s   $ >u E  KKeKoE $$UN_$`E LLu6LwE NNYNtE+ERcd r%r"return_tensorscUbUO URnUbUO URnUbUO URnUbUO URnUbUO URnU bU O UR n U bU O UR n U bU O URn U bU O URn UbUO URn[USS9nUbUO URn[USS9n[U5(d [S5e[U5nUVVs/sH0nUVs/sHnURUUUUUUUUU U U U UUS9PM! snPM2 nnnSU0n[!UU S9$s snfs snnf) a Preprocess an image or batch of images. Args: videos (`ImageInput`): Video frames to preprocess. Expects a single or batch of video frames with pixel values ranging from 0 to 255. If passing in frames with pixel values between 0 and 1, set `do_rescale=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after applying resize. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_center_crop (`bool`, *optional*, defaults to `self.do_centre_crop`): Whether to centre crop the image. crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the image after applying the centre crop. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between `[-1 - 1]` if `offset` is `True`, `[0, 1]` otherwise. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. offset (`bool`, *optional*, defaults to `self.offset`): Whether to scale the image in both negative and positive directions. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`): Image mean. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the inferred channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. Fr8r.r=zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)rFr*r+r,r-r.r/r0r1r2r3r4rGrHr))data tensor_type)r*r,r-r/r0r1r2r3r4r+r r.rr!r$rZr )rBr"r*r+r,r-r.r/r0r1r2r3r4r\rGrHvideoimgr^s r# preprocessVivitImageProcessor.preprocess!sH"+!6IDNN '38+9+E4K^K^#-#9Zt +9+E4K^K^!-4;;'3'?|TEVEV #-#9Zt !*!6IDNN 'tTYYTU;!*!6IDNN !) D F##:  f%, ) ( !# "!C!&&'%#1')#1!!-)' +&7' !# & )  .'>BB/  s; E&E +EE) r.r-r2r/r*r3r4r1r,r0r+)TNN) __name__ __module__ __qualname____firstlineno____doc__model_input_namesrBILINEARboolrdictstrintrfloatrrAnpndarrayrr r FIRSTrrZrrPILImagerb__static_attributes__ __classcell__)rDs@r#r'r'Bs&P(()-'9'B'B#.2,5!:>9=WWtCH~&W% W  W DcN+ WWc5j)WWWU5$u+#567WE%e"456W WWJ(:'B'B>BDH * zz* 38n* % * eC)9$9:; * $E#/?*?$@A *  * b>BDH &zz&S%Z & & eC)9$9:; & $E#/?*?$@A &V%))-15)-.2%)*.!%'+:>9=2B2H2HDH<<D><tCH~& < -. < ! <DcN+<TN<!<<tn<U5$u+#567<E%e"456<./<$E#/?*?$@A< !<|%&%))-15)-.2%)*.!%'+:>9=;?(8(>(>DH!rCrCD>rCtCH~& rC -. rC ! rCDcN+rCTNrC!rCrCtnrCU5$u+#567rCE%e"456rC!sJ!78rC&rC $E#/?*?$@A!rC" #rC'rCr%r')*rhtypingrrnumpyrptransformers.utilsrtransformers.utils.genericrimage_processing_utilsrr r image_transformsr r r r image_utilsrrrrrrrrrrrutilsrrrs get_loggerrdrVrr$r'__all__r?r%r#rs'"21UU    >   H % DDj!12 DRC,RCj ! !r%