cCi8v%SrSSKrSSKrSSKJr SSKJr SSKJrJ r J r SSK r SSK JrJrJr SSKJrJr SS KJrJrJrJrJrJrJrJrJrJrJr SS K J!r!J"r"J#r#J$r$ SS K%J&r& \#"5(aSSK'r'\$RP"\)5r*\r+\r,/S Qr-/S Qr.Sr/\0\1S'"SS5r2\&"SS9"SS\55r3S/r4g)z Image processor class for Flava.N)Iterable) lru_cache)AnyOptionalUnion)BaseImageProcessor BatchFeature get_size_dict)resizeto_channel_dimension_format) OPENAI_CLIP_MEANOPENAI_CLIP_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availablelogging)requires)rr)?rrg?LOGIT_LAPLACE_EPSc\rSrSrSS\\\\\44S\S\\S\S\\S\\4 S jjr S r S r S r S r Srg)FlavaMaskingGenerator<N input_sizetotal_mask_patchesmask_group_max_patchesmask_group_min_patchesmask_group_min_aspect_ratiomask_group_max_aspect_ratiocB[U[5(dU4S-nUuUlUlURUR-UlX lX@lUcUOUUlU=(d SU- n[R"U5[R"U54Ul g)N) isinstancetupleheightwidth num_patchesr%r'r&mathloglog_aspect_ratio)selfr$r%r&r'r(r)s j/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/flava/image_processing_flava.py__init__FlavaMaskingGenerator.__init__=s*e,,$*J", TZ;;3"4&<# [%d ~ %d], max = %d, %.3f ~ %.3f)rr,)r/r0r'r&r%r4)r5repr_strs r6__repr__FlavaMaskingGenerator.__repr__Ss^Q KK JJ  ' '  ' '  # #  ! !! $  ! !! $U  r9c2URUR4$)Nr/r0)r5s r6 get_shapeFlavaMaskingGenerator.get_shape_s{{DJJ&&r9c HSn[S5GHn[R"URU5n[R "[R"UR 65n[[[R"XV-555n[[[R"XV- 555nXR:dMXpR:dM[R"SURU- 5n [R"SURU- 5n XX-2XU-24R5n SXx-U - s=:aU::aDO OA[XU-5H/n [XU-5Hn XU 4S:XdMSXU 4'US- nM M1 US:dGM U$ U$)Nr r,)rangerandomuniformr'r2expr4introundsqrtr0r/randintsum)r5maskmax_mask_patchesdelta_attempt target_area aspect_ratior/r0topleft num_maskedijs r6_maskFlavaMaskingGenerator._maskbs\b H ..)D)DFVWK88FNND4I4I$JKLtyy)CDEFFdii (BCDEEzz!f{{&:nnQ f(<=~~ae);<! "4dE\6I"IJNNP v~ 2F6FF"3f 5!&tE\!:A#qDzQ-.T % ";6 19 )"( r9c[R"UR5[S9nSnX R:aSURU- n[ X0R 5nURX5nUS:XaU$X$- nX R:aMSU$)N)shapedtyper)npzerosr@rHr%minr&rX)r5rM mask_countrNrOs r6__call__FlavaMaskingGenerator.__call__zsxxdnn.c: 222#66C "#35P5PQ JJt6Ez # 222 r9)r/r4r&r'r1r%r0)KN333333?N)__name__ __module__ __qualname____firstlineno__rrHr.rfloatr7r<r@rXra__static_attributes__r9r6r"r"<s35"$04&(7:7;o#uS#X./o o!) o !$ o &.e_ o&.e_o, '0 r9r")vision)backendscG^\rSrSrSrS/rSS\RSSSSSSSSSS S SS SSSS\RSSSSSSSS4S \ S \ \ \ \ 4S\S\ S\ \ \ \ 4S\ S\\ \4S\ S\ \\\\4S\ \\\\4S\ S\ S\ S\ S\ \ S\S\ \S\ S\ S\ \ S \ S!\ S"\ \ S#\ S$\\ \4S%\ S&\ S'\ \\\\4S(\ \\\\4S)S4[TU]"S0UD6 UbUOSSS.n[U5nUbUOSSS.n[USS9nUbUOSSS.n[USS9nUbUOSSS.n[USS9nXlX lX0lX`lXplX@lXPl Xl U bU O[Ul U bU O[UlXlXlXlXlXlUUlUUlUUlUUlUUlUUlUUlUUlUUlUUlUUlUUlUUl UbUO[BUl UbUUl#g[DUl#g) Nr?rx param_nameprrrm)$superr7r rtrurvryrzrwrxr{FLAVA_IMAGE_MEANr|FLAVA_IMAGE_STDr}r~rr%r'r&r(r)rrrrrrrrrrrFLAVA_CODEBOOK_MEANFLAVA_CODEBOOK_STDr) r5rtrurvrwrxryrzr{r|r}r~rr%r'r&r(r)rrrrrrrrrrrrkwargs __class__s r6r7FlavaImageProcessor.__init__sF "6"'tc-JT"!*!6IsUX*DT&/&;!2"4"4&<#&<#+F(+F(&<#"4*!2'>$"4#6 '>$&<#%:"#6 :M:Y#6_r 8J8V"4\nr9image_processor_dictc >UR5nSU;aURS5US'SU;aURS5US'[TU] "U40UD6$)z Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is created using from_dict and kwargs e.g. `FlavaImageProcessor.from_pretrained(checkpoint, codebook_size=600)` rr)copypopr from_dict)clsrrrs r6rFlavaImageProcessor.from_dict4sd 488: f $4:JJ4O  1 6 )9?DX9Y !5 6w !5@@@r9c [UUUUUUS9$)N)r$r%r'r&r(r))r")r5rr%r'r&r(r)s r6masking_generator%FlavaImageProcessor.masking_generatorAs#%)1#9#9(C(C   r9image data_formatinput_data_formatc [U5nSU;dSU;a[SUR535eUSUS4n[U4UUUUS.UD6$)a Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. 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"` or `ChannelDimension.NONE`: image in (height, width) format. 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. r/r0zFThe `size` dictionary must contain the keys `height` and `width`. Got )rurvrr)r ValueErrorkeysr )r5rrurvrrr output_sizes r6r FlavaImageProcessor.resizeUsyFT" 4 7$#6efjfofofqerst tH~tG}5   #/     r9c.SS[-- U-[-$)Nr,r+)r )r5rs r6 map_pixelsFlavaImageProcessor.map_pixelssA)))U25FFFr9 do_map_pixelsc [UUU U U UUUUUS9 [U5nU(a%[U5(a[R S5 Uc [ U5nU(aUR XXNS9nU(aURXUS9nU(aURXUS9nU (aURXXS9nU (aURU5nU b [XUS9nU$)zPreprocesses a single image.) ryrzr{r|r}rwrxrtrurvzIt 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.)rrurvr)rrur)rscaler)rmeanstdr)input_channel_dim) rrrlogger warning_oncerr center_croprescale normalizerr )r5rrtrurvrwrxryrzr{r|r}rrrs r6_preprocess_image%FlavaImageProcessor._preprocess_images& &!)%!) u% /%00   s   $ >u E  KKeKoE $$5Te$fE LLuVgLhE NNYNtE OOE*E  "/VghE r9imagesreturn_tensorsc"UbUO URnUbUO URn[U5nUbUO URnUbUO URnUbUO UR n[USS9nUbUO UR nUbUO URnU bU O URn U bU O URn U bU O URn U bU O URn U bU O URn UbUO URnUbUO URnUbUO URnUbUO UR nUbUO UR"nUbUO UR$nUbUO UR&nUbUO UR(n[USS9nUbUO UR*nUbUO UR,nUbUO UR.nUbUO UR0nUbUO UR2n[USS9nUbUO UR4nUbUO UR6nUbUO UR8nUbUO UR:n[=U5n[?U5(d [AS5eUV"s/sHn"URCU"UUUUUUUU U U SU U!S9PM! n#n"SU#0n$U(a1UV"s/sHn"URCU"UUUUUUUUUUUU U!S9PM! n%n"U%U$S 'U (a0UREU UUUUUS 9n&UV's/sH n'U&"5PM n(n'U(U$S '[GU$US 9$s sn"fs sn"fs sn'f) 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`): Size of the image. 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_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the center crop. Only has an effect if `do_center_crop` 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. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. return_image_mask (`bool`, *optional*, defaults to `self.return_image_mask`): Whether to return the image mask. input_size_patches (`int`, *optional*, defaults to `self.input_size_patches`): Size of the patches to extract from the image. total_mask_patches (`int`, *optional*, defaults to `self.total_mask_patches`): Total number of patches to extract from the image. mask_group_min_patches (`int`, *optional*, defaults to `self.mask_group_min_patches`): Minimum number of patches to extract from the image. mask_group_max_patches (`int`, *optional*, defaults to `self.mask_group_max_patches`): Maximum number of patches to extract from the image. mask_group_min_aspect_ratio (`float`, *optional*, defaults to `self.mask_group_min_aspect_ratio`): Minimum aspect ratio of the patches to extract from the image. mask_group_max_aspect_ratio (`float`, *optional*, defaults to `self.mask_group_max_aspect_ratio`): Maximum aspect ratio of the patches to extract from the image. return_codebook_pixels (`bool`, *optional*, defaults to `self.return_codebook_pixels`): Whether to return the codebook pixels. codebook_do_resize (`bool`, *optional*, defaults to `self.codebook_do_resize`): Whether to resize the codebook pixels. codebook_size (`dict[str, int]`, *optional*, defaults to `self.codebook_size`): Size of the codebook pixels. codebook_resample (`int`, *optional*, defaults to `self.codebook_resample`): Resampling filter to use if resizing the codebook pixels. This can be one of the enum `PILImageResampling`, Only has an effect if `codebook_do_resize` is set to `True`. codebook_do_center_crop (`bool`, *optional*, defaults to `self.codebook_do_center_crop`): Whether to center crop the codebook pixels. codebook_crop_size (`dict[str, int]`, *optional*, defaults to `self.codebook_crop_size`): Size of the center crop of the codebook pixels. Only has an effect if `codebook_do_center_crop` is set to `True`. codebook_do_rescale (`bool`, *optional*, defaults to `self.codebook_do_rescale`): Whether to rescale the codebook pixels values between [0 - 1]. codebook_rescale_factor (`float`, *optional*, defaults to `self.codebook_rescale_factor`): Rescale factor to rescale the codebook pixels by if `codebook_do_rescale` is set to `True`. codebook_do_map_pixels (`bool`, *optional*, defaults to `self.codebook_do_map_pixels`): Whether to map the codebook pixels values. codebook_do_normalize (`bool`, *optional*, defaults to `self.codebook_do_normalize`): Whether to normalize the codebook pixels. codebook_image_mean (`float` or `list[float]`, *optional*, defaults to `self.codebook_image_mean`): Codebook pixels mean to normalize the codebook pixels by if `codebook_do_normalize` is set to `True`. codebook_image_std (`float` or `list[float]`, *optional*, defaults to `self.codebook_image_std`): Codebook pixels standard deviation to normalize the codebook pixels by if `codebook_do_normalize` is set to `True`. 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. 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. rxrrrzkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.F)rrtrurvrwrxryrzr{r|r}rrrrscodebook_pixel_values)rr%r'r&r(r)bool_masked_pos)data tensor_type)$rtrur rvrwrxryrzr{r|r}r~rr%r'r&r(r)rrrrrrrrrrrrrrrrrr ))r5rrtrurvrwrxryrzr{r|r}r~rr%r'r&r(r)rrrrrrrrrrrrrrrimgprocessed_imagesrcodebook_imagesmask_generator_maskss) r6 preprocessFlavaImageProcessor.preprocesss|"+!6IDNN 'tTYYT"'38+9+E4K^K^!*!6IDNN !) 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