cCiwfSSKJr SSKJrJr SSKrSSKJrJ r J r SSK J r J r Jr SSKJrJrJrJrJrJrJrJrJrJrJrJr SSKJrJrJrJ r \"5(aSSK!r!\ RD"\#5r$"S S \5r%S /r&g) )Iterable)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)convert_to_rgbresizeto_channel_dimension_format) OPENAI_CLIP_MEANOPENAI_CLIP_STDChannelDimension ImageInputPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availableloggingc#R^\rSrSrSrS/rSSS\RSSSSSSS4 S\S \ \ \ \ 4S \ S \S \S \ \ \4S\S\ \ \\\4S\ \ \\\4S\ \S\ \SS4U4Sjjjr\RSS4S\R$S \ \ \ \ 4\ 4S \S\ \ \ \4S\ \ \ \4S\R$4 Sjjr\"5SSSSSSSSSSSS\R,S4S\S\ \S \ \ \ \ 4S \ \S \ \S \ \S\ \S\ \ \\\4S\ \ \\\4S\ \ \ \4S\ \S\ \ \ \\ \ \ 44S\ \S\S\ \ \ \4S\R6R64 Sjj5rS!S\R$S\ \ \\ \ \ 44S\ \ \ \4S\ \ \ \4S\R$4 SjjrS"S\S \ \S \ \S\ \S\ \\S\ \\S\ \ S\ \ 4SjjrS#S\R$S\ \\\4S\ \\\4S\ \ \ \4S\R$4 Sjjr S r!U=r"$)$JanusImageProcessor5a Constructs a JANUS 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`, *optional*, defaults to `{"height": 384, "width": 384}`): Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. min_size (`int`, *optional*, defaults to 14): The minimum allowed size for the resized image. Ensures that neither the height nor width falls below this value after resizing. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be overridden by the `resample` 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 `1/255`): Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be overridden by the `rescale_factor` parameter 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. 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. 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. Can be overridden by the `image_std` parameter in the `preprocess` method. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. do_pad (`bool`, *optional*, defaults to `True`): Whether to pad the image to square or not. pixel_valuesTNgp? do_resizesizemin_sizeresample do_rescalerescale_factor do_normalize image_mean image_stddo_convert_rgbdo_padreturnc  >>[T U]"S0U D6 UbUOSSS.n[USS9nXlX lX@lXPlX`lXplUbUO[Ul U bU O[Ul Xl XlX0lUcSUlg[#SU55Ulg)Ni)heightwidthTdefault_to_square)r3r3c3># UHn[US-5v M g7f)N)int).0xs j/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/janus/image_processing_janus.py /JanusImageProcessor.__init__..s)K 1#a#g,, s)super__init__r r"r#r%r&r'r(r r)rr*r+r,r$background_colortuple)selfr"r#r$r%r&r'r(r)r*r+r,kwargs __class__s r9r>JanusImageProcessor.__init___s "6"'tc-JTT:"  $,((2(>*DT&/&;,   $3D !$))K )K$KD !image data_formatinput_data_formatc dUc [U5n[X5upx[Xx5n [USS9nUSUS:wa[ SUSSUS35eUSnX)- n [[ Xz-5UR 5[[ X-5UR 5/n [U4U UUUS.UD6nU$)a Resize an image to dynamically calculated size. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]` or `int`): The size to resize the image to. If a dictionary, it should have the keys `"height"` and `"width"`. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. 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`: will be inferred from input 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. Returns: `np.ndarray`: The resized image. Tr1r/r0z5Output height and width must be the same. Got height=z and width=)r#r%rGrH)rrmaxr ValueErrorr6r$r ) rArFr#r%rGrHrBr/r0max_sizedeltaoutput_size_nonpaddeds r9r JanusImageProcessor.resizesF  $ >u E &u@ v%TT: >T'] *GXGWWbcghocpbqr H~ FN#T]] 3 EM"DMM 2!    &#/     rEimagesreturn_tensorsr?c UbUO URnUbUO URnUbUO URnUbUO URnUbUO URnUbUO UR nU bU O UR n U bU O URn U bU O URn U bU O URn UbUO URn[USS9nURU5n[U5n[U5(d [S5e[!UUUUU UUUS9 U (aUVs/sHn[#U5PM nnUVs/sHn[%U5PM nnU(a(['US5(a[(R+S5 Uc[-US5nU(a!UVs/sHnUR/UX4US9PM nnU (a!UVs/sHnUR1UU US9PM nnU(a UVs/sHnUR3UXoS 9PM nnU(a!UVs/sHnUR5UXUS 9PM nnUVs/sH n[7UXS 9PM nn[9S U0U S 9nU$s snfs snfs snfs snfs snfs snfs snf)a Preprocess an image or batch of images. 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`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`dict[str, int]`, *optional*, defaults to `self.size`): Controls the size of the image after `resize`. The shortest edge of the image is resized to `size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest edge equal to `int(size["shortest_edge"] * (1333 / 800))`. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. 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 values between [0 - 1]. 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 normalize the image by if `do_normalize` is set to `True`. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to normalize the image by if `do_normalize` is set to `True`. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. background_color (`tuple[int, int, int]`): The background color to use for the padding. do_pad (`bool`, *optional*, defaults to `self.do_pad`): Whether to pad the image to square or not. 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. Fr1zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r&r'r(r)r*r"r#r%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.)rFr#r%rH)rFr?rH)rFscalerHrFmeanstdrHinput_channel_dimr data tensor_type)r"r%r&r'r(r)r*r+r,r?r#r fetch_imagesrrrKrr rrlogger warning_oncerr pad_to_squarerescale normalizer r)rArPr"r#r%r&r'r(r)r*rQr+r?r,rGrHrFencoded_outputss r9 preprocessJanusImageProcessor.preprocesssL"+!6IDNN '38#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN +9+E4K^K^!-4;;/?/K+QUQfQf'tTYYTU;""6*)&1F##:  &!)%!  9?@nU+F@6<vay I  $#E %dYj k#  $  $E ""%5&7# $  $#E 5 d#  $#EU^op#  ou ntej '{ `nt  '^V,DR`aeA=   s*I3II=I %I% I*-I/c6[X5upVU[R:XaURSOURSnXV:XaUb[ XU5nU$UnU$[ XV5n[ U[5(aU/nO[U5U:wa[SUS35eU[R:Xav[R"XxU4URS9n [U5HupXU SS2SS24'M Xe:aX- S-n XSS2XU-2SS24'U $X- S-n XSS2SS2XU-24'U $[R"XU4URS9n [U5HupXSS2SS2U 4'M Xe:aX- S-n XXU-2SS2SS24'U $X- S-n XSS2XU-2SS24'U $)a Pads an image to a square based on the longest edge. Args: image (`np.ndarray`): The image to pad. background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0): The color to use for the padding. Can be an integer for single channel or a tuple of integers representing for multi-channel images. If passed as integer in multi-channel mode, it will default to `0` in subsequent channels. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. Returns: `np.ndarray`: The padded image. rNz(background_color must have no more than z) elements to match the number of channels)dtype)rrFIRSTshaper rJ isinstancer6lenrKnpzerosrg enumerate) rArFr?rGrHr/r0 num_channelsmax_dimresulticolorstarts r9r_!JanusImageProcessor.pad_to_square`s<'u@ ):>N>T>T)Tu{{1~Z_ZeZefhZi ?*,E@QR  L  Lf$ & , , 01  ! "l 2:<.Hqr   0 6 6 6XX|g>ekkRF%&67"'q!Qw8~ )a/7ekkRF%&67"'q!Qw8~ )a/7US:Xa8[%U [&R(US 9n [RR+U 5n U R-U 5 M S U 0n US:waUOSn[/XS 9$) znApplies post-processing to the decoded image tokens by reversing transformations applied during preprocessing.Ng?r)rFr)r*rH)rSrHr5zPIL.Image.ImagerWr rY)r&r'r(r)r*rrkPILImagerlrr unnormalizer`clipastypermuint8r rLAST fromarrayappendr) rArPr&r'r(r)r*rHrQr rFrZs r9 postprocessJanusImageProcessor.postprocesss$.#9Zt 6D6Lt222R`'3'?|TEVEV #-#9Zt !*!6IDNN )&1 fQi 1 1 [1_6 ;&) ;  $ >vay I  E"5)E(()) UTe f 1c*11"((; ~AR/R3E;K;P;Pduv ++E2    &!$ -+9=N+NTXBBrEcSn[U[5(a*[U5U:wa[SUS[U535eOU/U-n[U[5(a*[U5U:wa[SUS[U535eOU/U-n[ S[ X#555n[ SU55nUR XXtS9nU$)a Unnormalizes `image` using the mean and standard deviation specified by `mean` and `std`. image = (image * image_std) + image_mean Args: image (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)` or `(num_channels, image_size, image_size)`): Batch of pixel values to postprocess. image_mean (`float` or `Iterable[float]`): The mean to use for unnormalization. image_std (`float` or `Iterable[float]`): The standard deviation to use for unnormalization. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. rzmean must have z$ elements if it is an iterable, got zstd must have c32# UH upU*U- v M g7fNr<)r7rUrVs r9r:2JanusImageProcessor.unnormalize..sW,?cdghqdrcs!tuu.# l2IWC rmndarrayrr rrirrr@ryrzrcr_rrr{__static_attributes__ __classcell__)rCs@r9rr5s%N(()-'9'A'A,3!:>9=)-!%"L"LtCH~&"L "L % "L  "Lc5j)"L"LU5$u+#567"LE%e"456"L!"L"L "L"LP(:'A'A>BDH ?zz?DcNC'(?% ? eC)9$9:; ? $E#/?*?$@A ? ?B%&%))-15%)*.'+:>9=;?)-GK!%(8(>(>DH!YYD>YtCH~& Y -. Y TN Y!YtnYU5$u+#567YE%e"456Y!sJ!78Y!Y#5eCcM.B)B#CDYY&Y $E#/?*?$@A!Y" #Y'Y|>?>BDH HzzH U3S=%9 9:HeC)9$9:; H $E#/?*?$@A H  HZ&**.'+,0+/+/(,1C1CTN1C! 1C tn 1C T%[) 1CDK(1C$C=1C! 1CpEI +zz+%%01+/0 + $E#/?*?$@A +  ++rEr)'collections.abcrtypingrrnumpyrmimage_processing_utilsrrr image_transformsr r r image_utilsr rrrrrrrrrrrutilsrrrrry get_loggerrr]r__all__r<rEr9rss,%"UUSS    _^   H %S,Sl ! !rE