cCi^SrSSKrSSKJrJr SSKrSSKJrJ r SSK J r J r J r SSKJrJrJrJrJrJrJrJrJrJrJrJr SSKJrJr SS KJr \R@"\!5r"SS \#S \#S \#S \#S\#S\#S\#4Sjjr$"SS\5r%S/r&g)z#Image processor class for GLM-4.1V.N)OptionalUnion)BaseImageProcessor BatchFeature)convert_to_rgbresizeto_channel_dimension_format) OPENAI_CLIP_MEANOPENAI_CLIP_STDChannelDimension ImageInputPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypelogging) VideoInput num_framesheightwidthtemporal_factorfactor min_pixels max_pixelscX:a[SUSU35eX:dX$:a[SUSUSU35e[X5[X5- S:a#[S[X5[X5- 35e[X- 5U-n[X$- 5U-n[X- 5U-n X-U-U:aq[R "X-U-U- 5n [U[R "X- U- 5U-5n[U[R "X*- U- 5U-5nXx4$X-U-U:aZ[R "XPU-U-- 5n [R"X-U- 5U-n[R"X*-U- 5U-nXx4$)Nzt:z% must be larger than temporal_factor:zheight:z or width:z must be larger than factor:z4absolute aspect ratio must be smaller than 200, got ) ValueErrormaxminroundmathsqrtfloorceil) rrrrrrr h_barw_bart_barbetas j/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/glm4v/image_processing_glm4v.py smart_resizer01s#2j\)NN_`aa %.76(*UG;WX^W_`aa V c&0 03 6B3vCUX[\bXjCjBk l   &/ "V +E %. !F *E *. // AE }uz)yy*-5CDFDJJv}v'=>GHFDJJu|f'<=FG <   +yyF':U'BCD &-&01F: %,/069 <c&(^\rSrSrSrSS/rSS\RSSSSSSSS S 4 S \S \ \ \ \ 4S \S \S\ \ \4S\S\ \ \\\4S\ \ \\\4S\S\ S\ S\ SS4U4SjjjrSSSSSSSSSSSS\R$S4S\ \\4S \ \S \ \ \ \ 4S \ \S \ \S\ \S\ \S\ \ \\\4S\ \ \\\4S\ \ S\ \ S\ \ S\ \S\ \S\ \ \ \44SjjrSSSSSSSSSSSSSS\R$S4S\S\ \S \ \S \ \ \ \ 4S \ \S \ \S\ \S\ \S\ \ \\\4S\ \ \\\4S\ \ S\ \ S\ \ S\ \S\ \ \ \4S\ \S\ \ \ \44"SjjrS#S\ S \ 4S!jjrS"rU=r$)$Glm4vImageProcessorRa) Constructs a GLM-4V image processor that dynamically resizes images based on the original images. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions. size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 112 * 112, "longest_edge": 28 * 28 * 15000}`): Size of the image's `(height, width)` dimensions after resizing. Can be overridden by the `size` parameter in the `preprocess` method. Available options are: - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`. Do NOT keep the aspect ratio. - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge less or equal to `longest_edge`. - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to `max_width`. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use when resizing the image. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`): Mean to use if normalizing the image. This is a float or list of floats for each channel in the image. image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`): Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. patch_size (`int`, *optional*, defaults to 14): The spatial patch size of the vision encoder. temporal_patch_size (`int`, *optional*, defaults to 2): The temporal patch size of the vision encoder. merge_size (`int`, *optional*, defaults to 2): The merge size of the vision encoder to llm encoder. pixel_valuesimage_grid_thwTNgp? do_resizesizeresample do_rescalerescale_factor do_normalize image_mean image_stddo_convert_rgb patch_sizetemporal_patch_size merge_sizereturnc  ">[TU]"S0U D6 UbSU;dSU;a [S5eUcSSS.nX lXlX0lX@lXPlX`lUbUO[Ul UbUO[Ul Xl XlXlXlg)N shortest_edge longest_edge:size must contain 'shortest_edge' and 'longest_edge' keys.1qrGrH)super__init__r#r:r9r;r<r=r>r r?r r@rBrCrDrA)selfr9r:r;r<r=r>r?r@rArBrCrDkwargs __class__s r/rOGlm4vImageProcessor.__init__|s "6"  !<VZ@ZYZ Z \%.PD " $,((2(>*DT&/&;$#6 $,r1images data_formatinput_data_formatc [U5nU (aUVs/sHn[U5PM nnUVs/sHn[U5PM nnU(a([US5(a[R S5 Uc[ US5n[USUS9unnUUnn/nUH{nU(a'[U UUU X-USUSS9unn[UUU4XOS9nU(aURUXoS9nU(aURUXUS 9n[UXS 9nURU5 M} [R"U5nU[ R":XaUR%SS S S 5nUR&SU -S:waT[R("US[R*U UR&SU -- SS9n[R,"UU/SS9nUR&S nUR&SU -nUU -UU -nnUR/UU UUU -U U UU -U U 5 nUR%SS SSSS S SS5 nUR/UU-U-UU -U -U -5nUUUU44$s snfs snf)a6 Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`. Args: images (`ImageInput`): Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`. vision_info (`List[Dict]`, *optional*): Optional list of dictionaries containing additional information about vision inputs. 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 resizing. `shortest_edge` and `longest_edge` keys must be present. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Scale factor to use if rescaling the image. 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`): Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image. patch_size (`int`, *optional*, defaults to `self.patch_size`): The spatial patch size of the vision encoder. temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`): The temporal patch size of the vision encoder. merge_size (`int`, *optional*, defaults to `self.merge_size`): The merge size of the vision encoder to llm encoder. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output 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. - Unset: Use the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for 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. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. rzIt 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.) channel_dimrGrH)rrrrrrr )r:r;rV)scalerV)imagemeanstdrV)input_channel_dimrr8)axis)rrrrlogger warning_oncerrr0r rescale normalizer appendnparrayr LAST transposeshaperepeatnewaxis concatenatereshape)rPrTr9r:r;r<r=r>r?r@rBrCrDrArUrVrZrrresized_height resized_widthprocessed_imagespatchesrepeatschannelgrid_tgrid_hgrid_wflatten_patchess r/ _preprocessGlm4vImageProcessor._preprocesssz*&1 9?@nU+F@6<vay I &vay>OP (. E0<2!$7%2#O4#N31-  ?( U. fjSd'0{hE  # #E *14((+, *// /''1a3G == 1 1Q 6ii BJJ')< a@PSf@f)gnoGnngw%7a@G--"q!%88':5} 7R//    j   j    ##Aq!Q1aA>!// VOf $g0C&Cj&PS]&]  888MA=s IIvideosreturn_tensorscUbUO URnUbSU;dSU;a [S5eUcSSS.nUbUO URnUbUO URnUbUO URnUbUO UR nUbUO UR nU bU O URn U bU O URn U bU O URn U bU O URn U bU O URn UbUO URnUbURU5n[U5nUb[U5(d [S5e[!UUU U UUUS9 0nUb//nnUHEnUR#UUUUUUUU U U U U UUUS 9unnUR%U5 UR'U5 MG [(R*"U5n[(R*"U5nUR-UUS .5 [/UUS 9$) a Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. videos (`VideoInput`): Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If passing in videos 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 resizing. Shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. resample (`int`, *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_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. 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 to use for normalization. Only has an effect if `do_normalize` is set to `True`. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. The max pixels of the image to resize the image. patch_size (`int`, *optional*, defaults to `self.patch_size`): The spatial patch size of the vision encoder. temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`): The temporal patch size of the vision encoder. merge_size (`int`, *optional*, defaults to `self.merge_size`): The merge size of the vision encoder to llm encoder. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. 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: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the 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. rGrHrIrJrKrLzkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r=r>r?r@r9r:r;)r9r:r;r<r=r>r?r@rBrCrDrUrArV)r5r6)data tensor_type)r:r#r9r;r<r=r>r?r@rBrCrDrA fetch_imagesrrrr~extendrjrkrlupdater)rPrTrr9r:r;r<r=r>r?r@rBrCrDrArrUrVrr5vision_grid_thwsrZrwr6s r/ preprocessGlm4vImageProcessor.preprocess(s&Z'tTYY  !<VZ@ZYZ Z \%.PD!*!6IDNN '38#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN #-#9Zt 5H5T1Z^ZrZr#-#9Zt +9+E4K^K^  &&v.F-f5F  l6&:&::  &)%!   -/*L*.*:*:'%)#1!-)')(;) +#1&7+;+'"##G, ''7' (88L1L!xx(89  KKIYZ [>BBr1rrc URSUR5nURSUR5nURSSSS.5nXE-n[URUUUUSUSURS 9upX-X-pX-$) aY A utility that returns number of image patches for a given image size. Args: height (`int`): Height of the input image. width (`int`): Width of the input image. images_kwargs (`dict`, *optional*) Any kwargs to override defaults of the image processor. Returns: `int`: Number of image patches per image. rBrDr:rJrKrLrGrH)rrrrrr r)getrBrDr0rC) rPrr images_kwargsrBrDr:rrtrur{r|s r/get_number_of_image_patches/Glm4vImageProcessor.get_number_of_image_patchess#&&|T__E "&&|T__E   9Ve)fg((4//O,N+ 44) %(5}7Rr1) rAr>r<r9r?r@rDrBr;r=r:rC)N)__name__ __module__ __qualname____firstlineno____doc__model_input_namesrBICUBICboolrdictstrintrfloatlistrOr FIRSTrrr~rrr__static_attributes__ __classcell__)rRs@r/r3r3Rs%N()9:)-'9'A'A,3!:>9=##$"-"-tCH~&"-% "-  "- c5j) "-"-U5$u+#567"-E%e"456"-"-"-!"-"- "-"-N%))-15%)*.'+:>9=$(-1$()-2B2H2HDH!F9j*,-F9D>F9tCH~& F9 -. F9 TN F9!F9tnF9U5$u+#567F9E%e"456F9SMF9&c]F9SMF9!F9./F9 $E#/?*?$@A!F9V(,$()-15%)*.'+:>9=$(-1$()-;?2B2H2HDH%NCNC$NCD> NC tCH~& NC -. NCTNNC!NCtnNCU5$u+#567NCE%e"456NCSMNC&c]NCSMNC!NC !sJ!78!NC"./#NC$$E#/?*?$@A%NC`#cr1r3)r8rJi)'rr'typingrrnumpyrkimage_processing_utilsrrimage_transformsrr r image_utilsr r r rrrrrrrrrutilsrr video_utilsr get_loggerrrfrr0r3__all__rMr1r/rs* "F    )%   H %0       BC,CL ! !r1