+ jHRt^RIt^RIHt^RIHt^RIHtH t H t ^RI H t H t HtHt^RIHtHtHtHtHtHtHtHtHtHtHt^RIHtHt]!4'd^RIt]P@!]!4t"RR lt#!R R ]4t$R .t%R#) z Image processor class for Vivit.N)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_kwargsloggingcPV^8dQhR\\\,,/#)return)listr)formats"q/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/vivit/image_processing_vivit.py __annotate__r2s D DDj!12 Dcv\V\\34'dE\V^,\\34'd"\V^,^,4'dV#\V\\34'd\V^,4'dV.#\V4'dV..#\ RV 24h)z"Could not make batched video from ) isinstancertupler ValueError)videoss&r make_batchedr&2s&4-((Zq D%=-Q-QVdeklmenopeqVrVr FT5M * *~fQi/H/Hx   z 9&B CCrcZaa]tRt^?toRtR.tRR]PRRRRRRRR3 V3RlV3Rlllt]PRR3V3RlRllt RV3R lR llt RRRRRRRRRRR] PR3 V3R lR llt ]!4RRRRRRRRRRRR] PR3V3R lRll4tRtVtV;t#)VivitImageProcessora 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_valuesTNc<V^8dQhRS[RS[S[S[3,R,RS[RS[RS[S[S[3,R,RS[RS[S[,R S[R S[R S[S[S[,,R,R S[S[S[,,R,R R/ #)r do_resizesizeNresampledo_center_crop crop_size do_rescalerescale_factoroffset do_normalize image_mean image_stdr)booldictstrintrfloatr)r __classdict__s"rr VivitImageProcessor.__annotate__jsWWW38nt#W% W  W S>D( WWe WWWDK'$.W4;&-W Wrc  D<\S V`!R /V BVeTMRR/p\VRR7pVeTMR^R^/p\VRR7pWnW nW@nWPnW0nW`nWpn Wn Wn V eT M\Vn V e WnR#\VnR#) N shortest_edgeFdefault_to_squareheightwidthr/ param_name)super__init__rr+r,r.r/r-r0r1r2r3r r4r r5)selfr+r,r-r.r/r0r1r2r3r4r5kwargs __class__s&&&&&&&&&&&&,rrHVivitImageProcessor.__init__js "6"'tos-CTU;!*!6IXsGUX*DZ&/&;AVrc <V^8dQhRS[PRS[S[S[3,RS[RS[S[,R,RS[S[,R,RS[P/#)rimager,r- data_formatNinput_data_formatr)npndarrayr7r8r9rr )rr;s"rrr<sm* * zz* 38n* % * ++d2 * !11D8 *  * rc \VRR7pRV9d\WR,RVR7pM=RV9dRV9dVR,VR,3pM\RVP4 24h\ V3RVR VR VR V/VB#) 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. Fr@r>)rArPrBrCzDSize must have 'height' and 'width' or 'shortest_edge' as keys. Got r,r-rOrP)rrr$keysr )rIrNr,r-rOrPrJ output_sizes&&&&&&, rr VivitImageProcessor.resizes4TU; d "6O,YjK 'T/>4=9Kcdhdmdmdocpqr r    $  0     rc <V^8dQhRS[PRS[S[,RS[RS[S[,R,RS[S[,R,/#)rrNscaler2rONrP)rQrRr9r:r6r8r )rr;s"rrr<s[&&zz&U{& & ++d2 & !11D8 &rc L\V3RVRVRV/VBpV'd V^, pV#)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. rXrOrP)r)rIrNrXr2rOrPrJrescaled_images&&&&&&, rrVivitImageProcessor.rescalesF>!   ,7 K\ `f  +a/Nrc<V^8dQhRS[RS[R,RS[S[S[3,R,RS[R,RS[R,RS[S[S[3,R,RS[R,R S[R,R S[R,R S[R,R S[S[S[,,R,R S[S[S[,,R,RS[R,RS[S[,R,RS[ P/#)rrNr+Nr,r-r.r/r0r1r2r3r4r5rOrPr) rr6r7r8r9rr:rr rQrR)rr;s"rrr<s<<<$;<38nt# < %t+ < t <S>D(<4K< <t <Tk<DK'$.<4;&-<&,<!11D8< !<rc  \VVV V V VVVVVR7 V 'dV'g \R4h\V4pV'd'\V4'd\P R4Vf \ V4pV'dVPWWNR7pV'dVPWVR7pV'dVPWWR7pV 'dVPWWR7p\WVR7pV#) zPreprocesses a single image.) r0r1r3r4r5r.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.)rNr,r-rP)r,rP)rNrXr2rP)rNmeanstdrP)input_channel_dim) rr$rrlogger warning_oncerr center_cropr normalizer )rIrNr+r,r-r.r/r0r1r2r3r4r5rOrPs&&&&&&&&&&&&&&&r_preprocess_image%VivitImageProcessor._preprocess_images& &!)%!)  *OP Pu% /%00   s   $ >u E  KKeKoE $$UN_$`E LLu6LwE NNYNtE+ERcd rc <V^8dQhRS[RS[R,RS[S[S[3,R,RS[R,RS[R,RS[S[S[3,R,RS[R,R S[R,R S[R,R S[R,R S[S[S[,,R,R S[S[S[,,R,RS[S[,R,RS[ RS[S[ ,R,RS[ PP/#)rr%r+Nr,r-r.r/r0r1r2r3r4r5return_tensorsrOrPr) rr6r7r8r9rr:rrr PILImage)rr;s"rrr<s?mCmCmC$;mC38nt# mC %t+ mC t mCS>D(mC4KmC mCt mCTkmCDK'$.mC4;&-mCj(4/mC&mC !11D8!mC" #mCrc VeTM VPpVeTM VPpVeTM VPpVeTM VPpVeTM VPpV eT M VP p V eT M VP p V eT M VPp V eT M VPp VeTM VPp\VRR7pVeTM VPp\VRR7p\V4'g \R4h\V4pVUUu.uF2pVUu.uF"pVPVVVVVVVVV V V V VVR7NK$ upNK4 pppRV/p\!VV R7#uupiuuppi) 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.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.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. Fr@r/rDzSInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, or torch.Tensor)rNr+r,r-r.r/r0r1r2r3r4r5rOrPr))data tensor_type)r+r-r.r0r1r2r3r4r5r,rr/rr$r&rer)rIr%r+r,r-r.r/r0r1r2r3r4r5rhrOrPvideoimgrls&&&&&&&&&&&&&&&& r preprocessVivitImageProcessor.preprocesssD"+!6IDNN '38+9+E4K^K^#-#9Zt +9+E4K^K^!-4;;'3'?|TEVEV #-#9Zt !*!6IDNN 'tTYYTU;!*!6IDNN !) 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