eThґjSSKJrJr SSKJr SSKJrJr SSKr SSK J r J r J r JrJr SSKJrJrJrJrJr SSKJrJrJrJrJr \"5(a SSKrSS K Jr \"5(aSSKr\"5(aSSKr \"5(aSSK!Jr" SHS \ RFS \\ \$4S \\\ \$4S \ RF4Sjjr%S\ RLS4S \ RFS\'S\\ S\ RPS\\\$\ 4S \ RF4 Sjjr)Sr*SIS \\ RFSSSS4S\\+S\\$S\\\$\ 4S S4 Sjjr,SHS \-\.\.44Sjjr/SJS\ RFS\\.\-\.\.4\0\.\-\.4S\+S \\.S\\\$\ 4S \-4 S!jjr1SKS \ RFS\-\.\.4S"S#S$\\.S\\ S%\+S\\\$\ 4S \ RF4S&jjr2SLS \ RFS'\\'\\'4S(\\'\\'4S\\ S\\\$\ 4S \ RF4 S)jjr3SLS \ RFS\-\.\.4S\\\$\ 4S\\\$\ 4S \ RF4 S*jjr4SMS,jr5S+\ RFS \ RF4S-jr6SNS.jr7S+\S \4S/jr8SOS1jr9S0\ RFS \ RF4S2jr:SPS3jr;S0\S \4S4jr"S7S8\5r?\?RS9SS4S \ RFS:\\.\-\.\.4\\-\.\.44S;\?S<\\'\\'4S\\\$\ 4S\\\$\ 4S \ RF4S=jjrAS \ S \ 4S>jrBSLS \ RFS\\ S\\\$\ 4S \ RF4S?jjrCS@rDSA\0SS \-\E\-\.\.4\0S4\E\.\-\-\.\.4\.4444SBjrFSC\E\-\.\.4S4SD\E\.\-\.\.44S \0S4SEjrG"SFSG5rHg)Q) CollectionIterable)ceil)OptionalUnionN)ChannelDimension ImageInputget_channel_dimension_axisget_image_sizeinfer_channel_dimension_format) ExplicitEnum TensorType is_jax_tensor is_tf_tensoris_torch_tensor)is_flax_availableis_tf_availableis_torch_availableis_vision_availablerequires_backends)PILImageResamplingimage channel_diminput_channel_dimreturnc[U[R5(d[S[ U535eUc [ U5n[ U5nX#:XaU$U[ R:Xaa[[URS- 55URS- URS- URS- /-nURU5nU$U[ R:Xaa[[URS- 55URS- URS- URS- /-nURU5nU$[SU35e)az Converts `image` to the channel dimension format specified by `channel_dim`. The input can have arbitrary number of leading dimensions. Only last three dimension will be permuted to format the `image`. Args: image (`numpy.ndarray`): The image to have its channel dimension set. channel_dim (`ChannelDimension`): The channel dimension format to use. input_channel_dim (`ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred from the input image. Returns: `np.ndarray`: The image with the channel dimension set to `channel_dim`. ,Input image must be of type np.ndarray, got rz&Unsupported channel dimension format: ) isinstancenpndarray TypeErrortyper r FIRSTlistrangendim transposeLAST ValueError)rrrtarget_channel_dimaxess U/var/www/auris/envauris/lib/python3.13/site-packages/transformers/image_transforms.pyto_channel_dimension_formatr05s** eRZZ ( (FtE{mTUU :5A)+6. -333E%**q.)*ejj1nejj1nejj[\n-]]% L /44 4E%**q.)*ejj1nejj1nejj[\n-]]% LA+OPPscale data_formatdtypeinput_data_formatc[U[R5(d[S[ U535eUR [R 5U-nUb [XRU5nUR U5nU$)a Rescales `image` by `scale`. Args: image (`np.ndarray`): The image to rescale. scale (`float`): The scale to use for rescaling the image. data_format (`ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. dtype (`np.dtype`, *optional*, defaults to `np.float32`): The dtype of the output image. Defaults to `np.float32`. Used for backwards compatibility with feature extractors. input_data_format (`ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred from the input image. Returns: `np.ndarray`: The rescaled image. r)r!r"r#r$r%astypefloat64r0)rr2r3r4r5rescaled_images r/rescaler:`sl4 eRZZ ( (FtE{mTUU\\"**-5N4^Rcd#**51N r1cZUR[R:XaSnU$[R"XR [ 55(an[R "SU:*5(a"[R "US:*5(aSnU$[SUR5SUR5S35e[R "SU:*5(a"[R "US:*5(aSnU$[S UR5SUR5S35e) z Detects whether or not the image needs to be rescaled before being converted to a PIL image. The assumption is that if the image is of type `np.float` and all values are between 0 and 1, it needs to be rescaled. FrzZThe image to be converted to a PIL image contains values outside the range [0, 255], got [z, z%] which cannot be converted to uint8.rTzXThe image to be converted to a PIL image contains values outside the range [0, 1], got [) r4r"uint8allcloser7intallr,minmax)r do_rescales r/_rescale_for_pil_conversionrDs  {{bhh  ULL- . . 66!u*  "&&#"6"6J  }Buyy{m3XZ  U  uz 2 2   IIK=599;-/T V  r1zPIL.Image.Image torch.Tensor tf.Tensorz jnp.ndarrayrC image_modec[[S/5 [U[RR5(aU$[ U5(d[ U5(aUR5nO][U5(a[R"U5nO6[U[R5(d[S[U535e[U[R U5nUR"SS:Xa[R$"USS9OUnUc ['U5OUnU(a [)US5nUR+[R,5n[RR/XS9$)a Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed. Args: image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor` or `tf.Tensor`): The image to convert to the `PIL.Image` format. do_rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type and casting to `int` would result in a loss of precision, and `False` otherwise. image_mode (`str`, *optional*): The mode to use for the PIL image. If unset, will use the default mode for the input image type. input_data_format (`ChannelDimension`, *optional*): The channel dimension format of the input image. If unset, will use the inferred format from the input. Returns: `PIL.Image.Image`: The converted image. visionz Input image type not supported: raxisr<mode)r to_pil_imager!PILImagerrnumpyrr"arrayr#r,r%r0r r+shapesqueezerDr:r7r= fromarray)rrCrGr5s r/rOrOs2lXJ/%)) ue!4!4  u   rzz * *;DK=IJJ (/?/D/DFW XE+0++b/Q*>BJJu2 &EE8B7I,U3zJs# LL "E 99  u  66r1cUup4SnUbP[[X4455n[[X4455nXv- U-U:aX&-U- n[[ U55nX4::aX1:Xd XC::a XA:XaX4pX4$XC:a/Un UbUb[XS-U- 5nX4$[X-U- 5nX4$UnUbUb[XT-U- 5n X4$[X-U- 5n X4$)a Computes the output image size given the input image size and the desired output size. Args: image_size (`Tuple[int, int]`): The input image size. size (`int`): The desired output size. max_size (`int`, *optional*): The maximum allowed output size. N)floatrArBr?round) image_sizesizemax_sizeheightwidthraw_sizemin_original_sizemax_original_sizeohows r/get_size_with_aspect_ratiordsMFH!#vo"67!#vo"67  04 7( B36GGHuX'DFNEMB 8O    H$8X&./B 8OT]U*+B 8O   H$8X%./B 8OT\F*+B 8Or1 input_imager[default_to_squarer\c[U[[45(a:[U5S:Xa [U5$[U5S:XaUSnO [ S5eU(aX4$[ X5upVXe::aXe4OXV4upxUn U [ X-U- 5pUb,X9::a[ SUSU35eX:a[ X:-U - 5UpXe::aX4$X4$)aV Find the target (height, width) dimension of the output image after resizing given the input image and the desired size. Args: input_image (`np.ndarray`): The image to resize. size (`int` or `Tuple[int, int]` or List[int] or `Tuple[int]`): The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to this. If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size). default_to_square (`bool`, *optional*, defaults to `True`): How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square (`size`,`size`). If set to `False`, will replicate [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize) with support for resizing only the smallest edge and providing an optional `max_size`. max_size (`int`, *optional*): The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than `max_size` after being resized according to `size`, then the image is resized again so that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter than `size`. Only used if `default_to_square` is `False`. input_data_format (`ChannelDimension`, *optional*): The channel dimension format of the input image. If unset, will use the inferred format from the input. Returns: `tuple`: The target (height, width) dimension of the output image after resizing. r rrz7size must have 1 or 2 elements if it is a list or tuplez max_size = zN must be strictly greater than the requested size for the smaller edge size = )r!tupler'lenr,r r?) rer[rfr\r5r]r^shortlongrequested_new_short new_shortnew_longs r/get_resize_output_image_sizerosJ$ && t9>;  Y!^7DVW W|";BMF%*_5/6/KE-s3F3MPU3U/Vx  *hZ(4486;   "%h&:X&E"Fx$)OH N)9NNr1resampler reducing_gap return_numpyc<[[S/5 UbUO[Rn[ U5S:Xd [ S5eUc [ U5nUcUOUnSn[U[RR5(d[U5n[XUS9nUupURX4X#S9n U(aj[R"U 5n U RS:Xa[R"U SS9OU n [!X["R$S 9n U(a ['U S 5OU n U $) aw Resizes `image` to `(height, width)` specified by `size` using the PIL library. Args: image (`np.ndarray`): The image to resize. size (`Tuple[int, int]`): The size to use for resizing the image. resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. reducing_gap (`int`, *optional*): Apply optimization by resizing the image in two steps. The bigger `reducing_gap`, the closer the result to the fair resampling. See corresponding Pillow documentation for more details. data_format (`ChannelDimension`, *optional*): The channel dimension format of the output image. If unset, will use the inferred format from the input. return_numpy (`bool`, *optional*, defaults to `True`): Whether or not to return the resized image as a numpy array. If False a `PIL.Image.Image` object is returned. input_data_format (`ChannelDimension`, *optional*): The channel dimension format of the input image. If unset, will use the inferred format from the input. Returns: `np.ndarray`: The resized image. rIr zsize must have 2 elementsF)rCr5)rprqrJrKrgp?)rresizerBILINEARrir,r r!rPrQrDrOr"rSr) expand_dimsr0r r+r:) rr[rprqr3rrr5rCr]r^ resized_images r/ruruBsBfxj)#/x5G5P5PH t9>455 :5A'2':# KJ eSYY__ - -07 UM^_MFLL%8L_M/ CPBTBTXYBY}2>_l 3 :J:O:O > NBJJ / /.~>> n % %+N;; .tN/C.DE FFr1c[U[R5(a[UR5S:XaiUR [R :XaUR[R5nUSS2SS2S4SUSS2SS2S4--SUSS2SS2S4--$[USSUS--SUS--5$)z" Converts RGB color to unique ID. rNrrir ) r!r"r#rirTr4r=r7int32r?)colors r/ rgb_to_idrs%$$U[[)9Q)> ;;"(( "LL*EQ1W~eAq!Gn 44y5Aq>7QQQ uQx#a.(9uQx+?? @@r1c[U[R5(axUR5n[ [ UR 5S/-5n[R"U[RS9n[S5HnUS-USU4'US-nM U$/n[S5HnURUS-5 US-nM U$)z" Converts unique ID to RGB color. rr|r.) r!r"r#copyrhr'rTzerosr=r(append)id_map id_map_copy rgb_shapergb_mapir_s r/ id_to_rgbrs&"**%%kkm $v||,s23 ((9BHH5qA)C/GCFO C K E 1X Vc\"3 Lr1c(\rSrSrSrSrSrSrSrSr g) PaddingModeizH Enum class for the different padding modes to use when padding images. constantreflect replicate symmetricN) __name__ __module__ __qualname____firstlineno____doc__CONSTANTREFLECT REPLICATE SYMMETRIC__static_attributes__rr1r/rrsHGIIr1rgpaddingrNconstant_valuesc^^Tc [T5mUU4SjnU"U5nU[R:Xa U"U5n[R"TUSUS9mOU[R :Xa[R"TUSS9mOdU[R :Xa[R"TUSS9mO9U[R:Xa[R"TUSS9mO[SU35eUb[TUT5mT$TmT$) a Pads the `image` with the specified (height, width) `padding` and `mode`. Args: image (`np.ndarray`): The image to pad. padding (`int` or `Tuple[int, int]` or `Iterable[Tuple[int, int]]`): Padding to apply to the edges of the height, width axes. Can be one of three formats: - `((before_height, after_height), (before_width, after_width))` unique pad widths for each axis. - `((before, after),)` yields same before and after pad for height and width. - `(pad,)` or int is a shortcut for before = after = pad width for all axes. mode (`PaddingMode`): The padding mode to use. Can be one of: - `"constant"`: pads with a constant value. - `"reflect"`: pads with the reflection of the vector mirrored on the first and last values of the vector along each axis. - `"replicate"`: pads with the replication of the last value on the edge of the array along each axis. - `"symmetric"`: pads with the reflection of the vector mirrored along the edge of the array. constant_values (`float` or `Iterable[float]`, *optional*): The value to use for the padding if `mode` is `"constant"`. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use the inferred format of the input image. Returns: `np.ndarray`: The padded image. cN>[U[[45(aX4X44nO[U[5(a$[ U5S:XaUSUS4USUS44nO[U[5(a+[ U5S:Xa[US[5(aX4nOM[U[5(a*[ U5S:Xa[US[5(aUnO[ SU35eT[ R:XaS/UQ7O/UQSP7nTRS:XaS/UQ7nU$UnU$)zQ Convert values to be in the format expected by np.pad based on the data format. rrr zUnsupported format: )rr) r!r?rXrhrir,r r&r))valuesrr5s r/_expand_for_data_format$pad.._expand_for_data_formats- fsEl + +&(89F  & &3v;!+;ay&),vay&).DEF  & &3v;!+; 6RS9VY@Z@Z%F  & &3v;!+; 6RS9V[@\@\F3F8<= ='8;K;Q;Q&Q&"6"WhY_WhagWh',jjAo&"6" rs&1"ZY/ AE( ::(',-( &6&; <=(ZZ (\/3jj@D # ::# #*+# 88 #  c+;&; <= # ZZ #L:"& $@D 37 . ]Z [3737 37 c+;&; <= 37  37l$5c?$V#"@D @O@O U38_d3is; <@O@Osm @O  c+;&; <= @O  @OL&*"&.2@DD ::D S/D#D3- D *+ D  D c+;&; <=DZZDV/3@D : ::: z%(( ): uj'' (:*+ :  c+;&; <= : ZZ :@;?@D Q ::Q S/Q%%5 567Q c+;&; <= Q ZZ Qh2::"**FJF:F*" BJJ 2::  GZGJG.A&,$,,58:>@D S ::S 3c3h%S/)BB CS S5(5/12 S %%5 567 S  c+;&; <= SZZSn*,/3@D# ::#*+# c+;&; <=#ZZ #L 0  0 4c3hn!55 6S%cSVhY\H\B]=]8^ ^_0* 5c?N:; SWX[]bcfhkck]lXlSm  . 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