eThcSSKrSSKrSSKrSSKrSSKJr SSKJrJrJ r J r SSK r SSK r SSKJr SSKJr SSKJrJrJrJrJrJrJrJrJrJrJr \"5(aSSKJ r \ "S S S 9r!\RD"\#5r$"S S \5r"SS \5r%\"\%RL5\%l&\%RLRNb5\%RLRNRQSSSS9\%RLl'gg)N)BytesIO)AnyOptionalTypeVarUnion)custom_object_save) BatchFeature) IMAGE_PROCESSOR_NAMEPushToHubMixinadd_model_info_to_auto_map"add_model_info_to_custom_pipelines cached_file copy_func download_urlis_offline_mode is_remote_urlis_vision_availablelogging)ImageImageProcessorTypeImageProcessingMixin)boundc\rSrSrSrSrg)r 7a Holds the output of the image processor specific `__call__` methods. This class is derived from a python dictionary and can be used as a dictionary. Args: data (`dict`): Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). tensor_type (`Union[None, str, TensorType]`, *optional*): You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at initialization. N)__name__ __module__ __qualname____firstlineno____doc____static_attributes__rZ/var/www/auris/envauris/lib/python3.13/site-packages/transformers/image_processing_base.pyr r 7s r#r c8\rSrSrSrSrSrS\4Sjr\ S!S\ \ S\ \\ R4S \\ \\ R4S \S \S \\ \\4S \S\ 4Sjj5rS"S\ \\ R4S\4Sjjr\ S\ \\ R4S\\\\4\\\444Sj5r\ S\\\44Sj5rS\\\44Sjr\ S\ \\ R44Sj5rS\4SjrS\ \\ R44SjrSr\ S#Sj5rS\ \\\44SjrS r g)$rGz| This is an image processor mixin used to provide saving/loading functionality for sequential and image feature extractors. Nc URSS5 URSS5UlUR5Hup#[XU5 M g![a%n[ R SUSUSU35 UeSnAff=f)z'Set elements of `kwargs` as attributes.feature_extractor_typeNprocessor_classz Can't set z with value z for )pop_processor_classitemssetattrAttributeErrorloggererror)selfkwargskeyvalueerrs r$__init__ImageProcessingMixin.__init__Os~  +T2 & + To test a pull request you made on the Hub, you can pass `revision="refs/pr/"`. return_unused_kwargs (`bool`, *optional*, defaults to `False`): If `False`, then this function returns just the final image processor object. If `True`, then this functions returns a `Tuple(image_processor, unused_kwargs)` where *unused_kwargs* is a dictionary consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of `kwargs` which has not been used to update `image_processor` and is otherwise ignored. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. kwargs (`Dict[str, Any]`, *optional*): The values in kwargs of any keys which are image processor attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are *not* image processor attributes is controlled by the `return_unused_kwargs` keyword parameter. Returns: A image processor of type [`~image_processing_utils.ImageProcessingMixin`]. Examples: ```python # We can't instantiate directly the base class *ImageProcessingMixin* so let's show the examples on a # derived class: *CLIPImageProcessor* image_processor = CLIPImageProcessor.from_pretrained( "openai/clip-vit-base-patch32" ) # Download image_processing_config from huggingface.co and cache. image_processor = CLIPImageProcessor.from_pretrained( "./test/saved_model/" ) # E.g. image processor (or model) was saved using *save_pretrained('./test/saved_model/')* image_processor = CLIPImageProcessor.from_pretrained("./test/saved_model/preprocessor_config.json") image_processor = CLIPImageProcessor.from_pretrained( "openai/clip-vit-base-patch32", do_normalize=False, foo=False ) assert image_processor.do_normalize is False image_processor, unused_kwargs = CLIPImageProcessor.from_pretrained( "openai/clip-vit-base-patch32", do_normalize=False, foo=False, return_unused_kwargs=True ) assert image_processor.do_normalize is False assert unused_kwargs == {"foo": False} ```r>r?r@rBuse_auth_tokenNrThe `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.V`token` and `use_auth_token` are both specified. Please set only the argument `token`.rA)r*warningswarn FutureWarning ValueErrorget_image_processor_dict from_dict) r<r=r>r?r@rArBr2rEimage_processor_dicts r$from_pretrained$ImageProcessingMixin.from_pretrainedbst({#1 %5!"%z$4d;  % MME   l#E  #7O'*'C'CDa'lek'l$}}1>. ) ) ?>2BBe!fg g NT2 #ZZ(8$?NjjN,@,@,Mb,QRG'':6:G#99.I     ' tD A')ggll>CW&X# 56 /0K/LMN   ' ' -jj) ( ,,,r#c >URSS5nURSS5nURSS5nURSS5nURSS5nURSS5nURS S5n URS S5n URS S 5n URS [5n URSS5n URSS5nUb+[R"S[5 Ub [ S5eUnSUS.nU bXS'[ 5(aU (d[RS5 Sn [U5n[RRU5n[RRU5(a[RRX5n[RRU5(aUnSnO4[U5(aUn[!U5nOU n[#UUUUUUU UUU U S9 n[)USS9nUR+5nSSS5 [,R."W5nU(a[RS U35 UU4$[RS WS!U35 S"U;a[3US"U5US"'S#U;a[5US#U5US#'UU4$![$a e[&a [%SUSUSU S35ef=f!,(df  N=f![,R0a [%SUS35ef=f)$aa From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a image processor of type [`~image_processor_utils.ImageProcessingMixin`] using `from_dict`. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`): The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. subfolder (`str`, *optional*, defaults to `""`): In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. image_processor_filename (`str`, *optional*, defaults to `"config.json"`): The name of the file in the model directory to use for the image processor config. Returns: `Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the image processor object. r>Nr?Fresume_downloadproxiesrArEr@rB subfolderimage_processor_filename_from_pipeline _from_autorFrGimage processor) file_typefrom_auto_classusing_pipelinez+Offline mode: forcing local_files_only=TrueT) r>r?rnrmr@rA user_agentrBroz Can't load image processor for 'z'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'z2' is the correct path to a directory containing a z fileutf-8encodingz"It looks like the config file at 'z' is not a valid JSON file.zloading configuration file z from cache at auto_mapcustom_pipelines)r*r rHrIrJrKrr/rfstrrZr[isdirrdr\rrrOSError ExceptionopenreadjsonloadsJSONDecodeErrorr r)r<r=r2r>r?rmrnrArEr@rBrorq from_pipelinervrxis_localimage_processor_fileresolved_image_processor_filereadertextrNs r$rL-ImageProcessingMixin.get_image_processor_dictsb(JJ{D1 $4e< **%6=**Y- 7D)$4d;!::&8%@::j$/JJ{B/ #)::.HJ^#_  #3T:  **\59  % MME   l#E#4Y  $+8' (   %5 KKE F# (+,I(J%77==!>? 77==6 7 7#%77<<0M#h 77>>7 8 8,I )H 8 9 9#@ ,89V,W )#;  0;1('#1#$3%5)%' 1-4 3gF&{{}G#'::d#3   KK56S5TU V$V++ KK-.B-C?SpRqr 113M(46S4$Z0"%99;]();<>[<$%78$V++O  67T6UV99V8WX//G.HO GF## 45R4SSno  s0;J: K8K'*K8:*K$' K51K88$LrNc UR5nURSS5nSU;aSU;aURS5US'SU;aSU;aURS5US'U"S0UD6n/nUR5H4upg[XF5(dM[ XFU5 UR U5 M6 UHnURUS5 M [ RSU35 U(aXB4$U$)a Instantiates a type of [`~image_processing_utils.ImageProcessingMixin`] from a Python dictionary of parameters. Args: image_processor_dict (`Dict[str, Any]`): Dictionary that will be used to instantiate the image processor object. Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the [`~image_processing_utils.ImageProcessingMixin.to_dict`] method. kwargs (`Dict[str, Any]`): Additional parameters from which to initialize the image processor object. Returns: [`~image_processing_utils.ImageProcessingMixin`]: The image processor object instantiated from those parameters. return_unused_kwargsFsize crop_sizeNzImage processor r)copyr*r,hasattrr-appendr/rf)r<rNr2rimage_processor to_remover3r4s r$rMImageProcessingMixin.from_dicts" 488:%zz*@%H V *> >+1::f+=  ( & [4H%H06 ;0G  -5 45  ,,.JC,,e4  %)C JJsD !  &&789 "* *" "r#cx[R"UR5nURRUS'U$)z Serializes this instance to a Python dictionary. Returns: `Dict[str, Any]`: Dictionary of all the attributes that make up this image processor instance. image_processor_type)rdeepcopy__dict__ __class__r)r1outputs r$to_dictImageProcessingMixin.to_dicts0t}}-)-)@)@%& r# json_filec[USS9nUR5nSSS5 [R"W5nU"S0UD6$!,(df  N,=f)a Instantiates a image processor of type [`~image_processing_utils.ImageProcessingMixin`] from the path to a JSON file of parameters. Args: json_file (`str` or `os.PathLike`): Path to the JSON file containing the parameters. Returns: A image processor of type [`~image_processing_utils.ImageProcessingMixin`]: The image_processor object instantiated from that JSON file. ryrzNr)rrrr)r<rrrrNs r$from_json_file#ImageProcessingMixin.from_json_filesG)g .&;;=D/#zz$/*)**/ .s A AcUR5nUR5H8up#[U[R5(dM&UR 5X'M: UR SS5nUbXAS'[R"USSS9S-$)z Serializes this instance to a JSON string. Returns: `str`: String containing all the attributes that make up this feature_extractor instance in JSON format. r+Nr)T)indent sort_keys ) rr, isinstancenpndarraytolistr*rdumps)r1 dictionaryr3r4r+s r$to_json_string#ImageProcessingMixin.to_json_strings}\\^ $**,JC%,,"',,. - &>>* This API is experimental and may have some slight breaking changes in the next releases. Args: auto_class (`str` or `type`, *optional*, defaults to `"AutoImageProcessor "`): The auto class to register this new image processor with. rNz is not a valid auto class.) rr~rtransformers.models.automodelsautorrKrc)r< auto_class auto_modules r$register_for_auto_class,ImageProcessingMixin.register_for_auto_classsE *c**#,,J66{// |+FGH H$r#image_url_or_urlscvSS0n[U[5(a!UVs/sHo0RU5PM sn$[U[5(aO[R "USUS9nUR 5 [R"[UR55$[S[U535es snf)z Convert a single or a list of urls into the corresponding `PIL.Image` objects. If a single url is passed, the return value will be a single object. If a list is passed a list of objects is returned. z User-AgentzuMozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36T)streamheadersz=only a single or a list of entries is supported but got type=) rlist fetch_imagesr~requestsrYraise_for_statusrrrcontent TypeErrortype)r1rrxresponses r$r!ImageProcessingMixin.fetch_imagess !  ' . .2CD2CQ%%a(2CD D )3 / /||$5dGTH  % % '::gh&6&678 8[\`ar\s[tuv v EsB6r9)NFFNmain)F)AutoImageProcessor)!rrrr r!rcr6r~r: classmethodrrrrZPathLikerboolrOrjtupledictrrLrMrrrrerrrrr"rr#r$rrGs K 0C08<$!&,0o= $ %o=',S"++-='>o=E#r{{"234o= o=  o= c4i() o=o= o=o=b;-eC4D.E;-TX;-zv,,1#r{{2B,Cv, tCH~tCH~- .v,v,p*#T#s(^*#*#X c3h +uS"++-='>++$GG* 05bkk1A+B 0D%%2weCcN.Cwr#rtrzimage processor file)object object_class object_files))rrrZrHiortypingrrrrnumpyrrdynamic_module_utilsr feature_extraction_utilsr BaseBatchFeatureutilsr r r rrrrrrrrPILrr get_loggerrr/rrRr!formatrr#r$rs  004F    19OP   H % #  `w>`wF$--A-M-M#N ##++7/C/O/O/W/W/^/^ /CRh0_0$$,8r#