+ jmpRt^RIHt^RIt^RIHt^RIHt^RI H t H t H t ^RI HtHtHtHt^RIHt]P(!]4t]!4'd^RItRtR tR tR tR tR tRt]!RR7]!RR]444tR.t R#)z Processor class for SAM3. )deepcopyN) ImageInput)ProcessorMixin) BatchEncodingPreTokenizedInput TextInput) TensorTypeauto_docstringis_torch_availablelogging)requirescVPR4wrr4VRV,, VRV,, VRV,,VRV,,.p\P!VRR7#?dimunbindtorchstackxx_cy_cwhbs& i/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/sam3/processing_sam3.pybox_cxcywh_to_xyxyr #sRXXb\NCa a-3q=C#'MS37]LA ;;qb !!cVPR4wrr4VRV,, VRV,, W4.p\P!VRR7#rrrs& rbox_cxcywh_to_xywhr#)s@XXb\NCa a-3q=A4A ;;qb !!r!c|VPR4wrr#WW,W,.p\P!VRR7#rrrrryrrrs& rbox_xywh_to_xyxyr(/4"JA! AEQU$A ;;qb !!r!cVPR4wrr#VRV,,VRV,,W#.p\P!VRR7#rrr&s& rbox_xywh_to_cxcywhr+5s@"JA! cAg+S1W0A ;;qb !!r!c|VPR4wrr#WW , W1, .p\P!VRR7#r%r)rr'XYrs& rbox_xyxy_to_xywhr/;r)r!cVPR4wrr4W,^, W$,^, W1, WB, .p\P!VRR7#r%r)rx0y0x1y1rs& rbox_xyxy_to_cxcywhr5AsBXXb\NBB 'QA 27)selfrI tokenizerr>r?kwargs __class__s&&&&&,rrHSam3Processor.__init__XsA >v>.*5*A;tG[G[G`G`aiGjr!c<V^8dQhRS[R,RS[S[,S[S[,,S[S[,,R,RS[R,RS[S[S[S[,,,S[P ,R,RS[S[S[S[,,,S[P ,R,RS[S[S[,,S[P ,R,RS[S[ ,R,R S[ /#) r=imagesNtextsegmentation_maps input_boxesinput_boxes_labelsoriginal_sizesreturn_tensorsreturn) rrrlistfloatrTensorr@strrr)rArBs"rrCrDfsjjT!j++d9o=EV@WWZ^^j&, j $tE{+,u||;dB j !d3i1ELL@4G jT%[)ELL84?jj(4/j jr!c ZRp VeVP!V3RVRV/VBp MaVeO\V\P4'dVP 4P 4p\ RV/VR7p MVe \R4hVPW$4pVe-VPW'R^ R7p V eV PV 4MT p VEefV R,pVPV^R R ^R 7p VPV^R R R7p V eV fVPV 4p V eVPV 4R,p V eVPV 4R,pV eV eX X8wd \R4hV etVPV X ^.,4p\P!V\P R7pVP#VVRRR7\%V4pV PRV/4V eLVPV X4p\P!V\P&R7pV PRV/4V #)a images (`ImageInput`, *optional*): The image(s) to process. text (`str`, `list[str]`, `list[list[str]]`, *optional*): The text to process. segmentation_maps (`ImageInput`, *optional*): The segmentation maps to process. input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): The bounding boxes to process. input_boxes_labels (`list[list[int]]`, `torch.Tensor`, *optional*): The labels for the bounding boxes. original_sizes (`list[list[float]]`, `torch.Tensor`, *optional*): The original sizes of the images. Returns: A [`BatchEncoding`] with the following fields: - `pixel_values` (`torch.Tensor`): The processed image(s). - `original_sizes` (`list[list[float]]`): The original sizes of the images. - `labels` (`torch.Tensor`): The processed segmentation maps (if provided). - `input_boxes_labels` (`torch.Tensor`): The processed labels for the bounding boxes. - `input_boxes` (`torch.Tensor`): The processed bounding boxes. NrSrWrV) tensor_typezKEither images or original_sizes must be provided if input_boxes is not None max_length)rWpaddingr_r7z)[image level, box level, box coordinates])expected_depth input_nameexpected_formatexpected_coord_sizelabelsz[image level, box level])rarbrc:Nr=NzaInput boxes and labels have inconsistent dimensions. Please ensure they have the same dimensions.)dtypeT)is_bounding_boxpreserve_paddingrTrU)rI isinstancerr[cputolistr ValueError_resolve_text_promptsrLupdate_validate_single_input_generate_default_box_labels_get_nested_dimensions_pad_nested_listtensorfloat32_normalize_tensor_coordinatesr5int64)rKrQrRrSrTrUrVrWrMencoding text_inputsprocessed_boxesprocessed_boxes_labelsboxes_max_dimsboxes_labels_max_dims padded_boxes final_boxespadded_boxes_labelsfinal_boxes_labelss&&&&&&&&, r__call__Sam3Processor.__call__esnD  ++"3 . H  '.%,,77!/!3!3!5!$&6%GUcdH  $jk k))$<  ..Vboq.rK# ,&  "%&67N"99 " K$% :O&*%@%@" # : &A& "*/E/M)-)J)J?)[&*!%!\V4\V48wd%\ R\V4 R\V4 R24h\ V4F%wr4VeK V'gKW#,fK!RW&K' V#)zA Resolve text prompts by setting defaults based on prompt types. NvisualzEThe number of text prompts must match the number of input boxes. Got z text prompts and z input boxes.)rirYtuplerrl enumerate)rKrRrTi text_values&&& rrm#Sam3Processor._resolve_text_promptss <*8 4 4$u ..KDz 3t9K(884yk!3C 4D3E]T  't_MA!kkkn6P"- r!c &Vf.p\V\4'gV#\V4^8XdVP\V44M\ V^,\V44V^&\V4^8dVFpVfK \V\4'gK!VP V4p\ V4FQwrVV^,\V48dVPV4K/\ W%^,,V4W%^,&KS K V#)aH Get the maximum dimensions at each level of nesting, skipping None values. Args: nested_list (`list`): Nested list structure (may contain None values). max_dims (`list`, *optional*): Current maximum dimensions (for recursion). Returns: `list`: A list of maximum dimensions for each nesting level. )rirYrappendmaxrqr)rK nested_listmax_dimsrsub_dimsrrs&&& rrq$Sam3Processor._get_nested_dimensions6s  H+t,,O x=A  OOC , -hqk3{+; template_dimstemplate_rs &&&&& rrrSam3Processor._pad_nested_list^s"  ,,I C , , +t,,&-K;' !0  C ,q0 0    {k.HI Ja 3{#3a#77$/0A0C$DM#BB=\H!*{[9J-KKH""kF`@a#b@a1HX$6@a#bc!,A,=,? @ >>}X""k@R#S@R1HX$6@R#ST 3{+a/ /3{+,>)$/0A0C$DM%)%H%H%bKN 55%)%:%:;>;hiXikt%uKN -#$c $Ts 6G?Hc \V4^8XdV.V^,,#\V^,4Uu.uFq0PVR,V4NK up#uupi)z Create an empty nested structure with given dimensions filled with pad_value. Args: dims (`list`): The dimensions of the nested structure. pad_value (`int`): The value to fill the structure with. :rNN)rrr)rKdimsrrs&&& rr,Sam3Processor._create_empty_nested_structuresV t9>;a( (V[\`ab\cVdeVdQR77R)LVde ees!Ac 2\V\4'd<\V4^8Xd^#VF!pVfK ^VPV4,u# ^#\V\P \ P34'd\VP4#^#)z Get the nesting level of a list structure, skipping None values. Args: input_list (`list`): The list to get the nesting level of. ) rirYr_get_nesting_levelrrrr[r)rK input_listrs&& rr Sam3Processor._get_nesting_levelsy j$ ' ':!#"#t66t<<<#  RZZ$> ? ?z''( (r!c <V^8dQhRS[PS[P,S[,RS[RS[RS[RS[R,RS[/#)r=rrarbrcrdNrX)rr[rrrYr@r\)rArBs"rrCrDse0F0FllRZZ'$.0F0F 0F  0F !4Z 0F 0Fr!c  *VfR#\V\P\P34'dVP V8wd#\ RV RV RV RVP R2 4hVe?VPR ,V8wd'\ RV RV RVPR , R24hVPW4#\V\4'dAVPV4pWb8wd\ RV R V R V RV R 2 4hVPW4#R#) aW Validate a single input by ensuring proper nesting and raising an error if the input is not valid. Args: data (`torch.Tensor`, `np.ndarray`, or `list`): Input data to process. expected_depth (`int`): Expected nesting depth. input_name (`str`): Name of the input for error messages. expected_format (`str`): The expected format of the input. expected_coord_size (`int`, *optional*): Expected coordinate size (4 for boxes, None for labels). . NzInput z must be a tensor/array with z, dimensions. The expected nesting format is z. Got z dimensions.z as the last dimension, got .z must be a nested list with z( levels. The expected nesting format is z levels.r) rirr[rrndimrlrrrYr)rKrrarbrcrdrs&&&&&& rro$Sam3Processor._validate_single_inputs0 < dU\\2::6 7 7yyN* ZL(EnEUVBCRBSSYZ^ZcZcYddpq%0::b>%88$  ,IJ]I^^z{|F|FGI|J{KKLM//E E dD ! ! 33D9M. ZL(D^DTT|~M}NNTUbTcckl//E E "r!c V'd"WP8gpVPRRR7p\\V44FpWqP^,8gKV\V48d W',MV^,pVP W,WR7p V'dAXV,p \ P!V PW,4WV,4W&KWV&K R#)a Helper method to normalize coordinates in a tensor across multiple images. Args: tensor (`torch.Tensor`): Input tensor with coordinates. original_sizes (`list`): Original image sizes. is_bounding_box (`bool`, *optional*, defaults to `False`): Whether coordinates are bounding boxes. preserve_padding (`bool`, *optional*, defaults to `False`): Whether to preserve padding values (for boxes). T)rkeepdim)rgNr) r?allrrrrrwhere expand_as) rKrsrVrgrhmask coord_maskimg_idxrnormalized_coordsimg_masks &&&&& rru+Sam3Processor._normalize_tensor_coordinatess 111Db$7JS01Ga(;BSEX;X 7^lmn^o $($?$?O]%@%!$)'2H&+kk **6?;=NW^P_'FO'87O2r!c @VPPVVVVR7#)a Converts the output of [`Sam3Model`] into semantic segmentation maps. Args: outputs ([`Sam3ImageSegmentationOutput`]): Raw outputs of the model containing semantic_seg. target_sizes (`list[tuple]` of length `batch_size`, *optional*): List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, predictions will not be resized. threshold (`float`, *optional*, defaults to 0.5): Threshold for binarizing the semantic segmentation masks. return_segmentation_scores (`bool`, *optional*, defaults to `False`): Whether to return segmentation scores alongside the segmentation map. Returns: semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry is a binary mask (0 or 1). ) target_sizes thresholdreturn_segmentation_scores)rI"post_process_semantic_segmentation)rKoutputsrrrs&&&&&rr0Sam3Processor.post_process_semantic_segmentations/,##FF %'A G  r!c :VPPWV4#)a Converts the raw output of [`Sam3Model`] into final bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. This is a convenience wrapper around the image processor method. Args: outputs ([`Sam3ImageSegmentationOutput`]): Raw outputs of the model containing pred_boxes, pred_logits, and optionally presence_logits. threshold (`float`, *optional*, defaults to 0.3): Score threshold to keep object detection predictions. target_sizes (`list[tuple[int, int]]`, *optional*): List of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the batch. If unset, predictions will not be resized. Returns: `list[dict]`: A list of dictionaries, each dictionary containing the following keys: - **scores** (`torch.Tensor`): The confidence scores for each predicted box on the image. - **boxes** (`torch.Tensor`): Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. Example: ```python >>> from transformers import AutoModel, AutoProcessor >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> model = AutoModel.from_pretrained("facebook/sam3-base") >>> processor = AutoProcessor.from_pretrained("facebook/sam3-base") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> inputs = processor(images=image, text="cat", return_tensors="pt") >>> outputs = model(**inputs) >>> # Post-process to get bounding boxes >>> results = processor.post_process_object_detection(outputs, threshold=0.3, target_sizes=[image.size[::-1]]) >>> boxes = results[0]["boxes"] >>> scores = results[0]["scores"] ``` )rIpost_process_object_detection)rKrrrs&&&&rr+Sam3Processor.post_process_object_detection7sV##AA'Vbccr!c :VPPWW44#)a) Converts the raw output of [`Sam3Model`] into instance segmentation predictions with bounding boxes and masks. This is a convenience wrapper around the image processor method. Args: outputs ([`Sam3ImageSegmentationOutput`]): Raw outputs of the model containing pred_boxes, pred_logits, pred_masks, and optionally presence_logits. threshold (`float`, *optional*, defaults to 0.3): Score threshold to keep instance predictions. mask_threshold (`float`, *optional*, defaults to 0.5): Threshold for binarizing the predicted masks. target_sizes (`list[tuple[int, int]]`, *optional*): List of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the batch. If unset, predictions will not be resized. Returns: `list[dict]`: A list of dictionaries, each dictionary containing the following keys: - **scores** (`torch.Tensor`): The confidence scores for each predicted instance on the image. - **boxes** (`torch.Tensor`): Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. - **masks** (`torch.Tensor`): Binary segmentation masks for each instance, shape (num_instances, height, width). Example: ```python >>> from transformers import AutoModel, AutoProcessor >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> model = AutoModel.from_pretrained("facebook/sam3-base") >>> processor = AutoProcessor.from_pretrained("facebook/sam3-base") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> inputs = processor(images=image, text="cat", return_tensors="pt") >>> outputs = model(**inputs) >>> # Post-process to get instance segmentation >>> results = processor.post_process_instance_segmentation( ... outputs, threshold=0.3, target_sizes=[image.size[::-1]] ... ) >>> masks = results[0]["masks"] >>> boxes = results[0]["boxes"] >>> scores = results[0]["scores"] ``` )rI"post_process_instance_segmentation)rKrrmask_thresholdrs&&&&&rr0Sam3Processor.post_process_instance_segmentationds#r##FF   r!)r?r>)Ni)NNNNNNN)F)r)N)rN)FF)NrF)333333?N)rrN)__name__ __module__ __qualname____firstlineno__rHr rrrprrmrqrrrrrorurrr__static_attributes____classdictcell__ __classcell__)rNrBs@@rr;r;Us k kjjjX6l(ET6&P@D f,0F0Fd!8F :+dZ; ; r!r;)r)!__doc__copyrrr image_utilsrprocessing_utilsrtokenization_utils_baserrrutilsrr r r utils.import_utilsr get_loggerrloggerrr r#r(r+r/r5r8r;__all__rr!rrs%.RRLL*   H %" " " " " " ! :H  NH  H  V  r!