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Constructs an KOSMOS-2 processor which wraps a KOSMOS-2 image processor and a KOSMOS-2 tokenizer into a single
processor.
[`Kosmos2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and some functionalities of
[`XLMRobertaTokenizerFast`]. See the docstring of [`~Kosmos2Processor.__call__`] and [`~Kosmos2Processor.decode`]
for more information.
Args:
image_processor (`CLIPImageProcessor`):
An instance of [`CLIPImageProcessor`]. The image processor is a required input.
tokenizer (`XLMRobertaTokenizerFast`):
An instance of ['XLMRobertaTokenizerFast`]. The tokenizer is a required input.
num_patch_index_tokens (`int`, *optional*, defaults to 1024):
The number of tokens that represent patch indices.
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This method uses [`CLIPImageProcessor.__call__`] method to prepare image(s) for the model, and
[`XLMRobertaTokenizerFast.__call__`] to prepare text for the model.
Please refer to the docstring of the above two methods for more information.
The rest of this documentation shows the arguments specific to `Kosmos2Processor`.
Args:
bboxes (`Union[list[tuple[int]], list[tuple[float]], list[list[tuple[int]]], list[list[tuple[float]]]]`, *optional*):
The bounding bboxes associated to `texts`.
num_image_tokens (`int`, *optional* defaults to 64):
The number of (consecutive) places that are used to mark the placeholders to store image information.
This should be the same as `latent_query_num` in the instance of `Kosmos2Config` you are using.
first_image_token_id (`int`, *optional*):
The token id that will be used for the first place of the subsequence that is reserved to store image
information. If unset, will default to `self.tokenizer.unk_token_id + 1`.
add_eos_token (`bool`, defaults to `False`):
Whether or not to include `EOS` token id in the encoding when `add_special_tokens=True`.
Nz*You have to specify either images or text.tokenizer_init_kwargsr, r r r5 r r+ r& Fr- r. return_tensors)r rd r input_idsattention_maskc U S $ Nr )r] s r! +Kosmos2Processor.__call__.. s defhdir )keyrn rightleft)rj rk image_embeds_position_mask)data tensor_typer )
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Check `bboxes` for a single text example. It could be
- `None`: no bounding box associated to a text.
- A list with each element being the bounding boxes associated to one ` ... ` pair found
in a text. This could be:
- `None`: no bounding box associated to a ` ... ` pair.
- A tuple of 2 integers: A single bounding box specified by patch indices.
- A tuple of 4 float point number: A single bounding box specified by (normalized) coordinates.
- A list containing the above 2 tuple types: Multiple bounding boxes for a
` ... ` pair.
Nz@`bboxes` (for a single text example) should be `None` or a list. c 3 B # U H n[ U[ 5 v M g 7fN)r~ r .0r] s r! AKosmos2Processor._check_bboxes_for_single_text..B s .S7az!S/A/A7 rB c 3 B # U H n[ U[ 5 v M g 7fr )r~ r r s r! r r C s 1XPW1*Q2F2FPWr a' Each element in `bboxes` (for a single text example) should be either `None`, a tuple containing 2 integers or 4 float point numbers, or a list containing such tuples. Also make sure the arguments `texts` and `bboxes` passed to `preprocess_text` are both in batches or both for a single example.)r~ r rw tupler all)r[ r bboxelements r! _check_bboxes_for_single_text.Kosmos2Processor._check_bboxes_for_single_text) s > FD)) _` ` D| d++ v !'511\Q&3.S7.S+S+SG )c1XPW1X.X.X$@ r c \ UR 5 nUb U SU 3nU R X5 nU$ )N )strip_insert_patch_index_tokens)r[ rd imager img_info_tokenss r! _preprocess_single_example+Kosmos2Processor._preprocess_single_exampleL s; zz| %&av.D ..t< r textsr r c \ U R /U- nSR U R /U- U R /- 5 nSn[ U[ 5 ( a SnU/nUc S/[ U5 - nO[ U[ 5 ( d U/n[ U5 [ U5 :w a$ [ S[ U5 S[ U5 S35 eU( d U R U5 U/nOMUb; [ U[ 5 ( d [ S5 eU H nU R U5 M OS/[ U5 - n[ U5 [ U5 :w a$ [ S [ U5 S[ U5 S35 e[ XU5 V V
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n f ) a Add image and bounding box information to `texts` as image and patch index tokens.
Args:
texts (`Union[TextInput, list[TextInput]]`): The texts to be processed.
images (`ImageInput`, *optional*): The images associated to `texts`.
bboxes (`Union[list[tuple[int]], list[tuple[float]], list[list[tuple[int]]], list[list[tuple[float]]]]`, *optional*):
The bounding bboxes associated to `texts`.
num_image_tokens (`int`, *optional*, defaults to 64):
The number of image tokens (used as latent queries). This should corresponds to the `latent_query_num`
attribute in `Kosmos2Config`.
Returns:
`Union[TextInput, list[TextInput]]`: The processed texts with image and patch index tokens.
r TFNzGThe number of examples in `texts` and `images` should be the same. Got v.s. instead.zS`bboxes` should be `None` or a list (as a batch) when `texts` is passed as a batch.zGThe number of examples in `texts` and `bboxes` should be the same. Got r ) rH joinrI r~ rU r r rw r zipr )
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Post-process the output of the model to decode the text.
Args:
generated_outputs (`torch.Tensor` or `np.ndarray`):
The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
or `(sequence_length,)`.
skip_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
**kwargs:
Additional arguments to be passed to the tokenizer's `batch_decode method`.
Returns:
`list[str]`: The decoded text.
skip_special_tokensF)r ) batch_decoder )r[ generated_outputsr r\ generated_textsrd s r! post_process_image_text_to_text0Kosmos2Processor.post_process_image_text_to_text sJ ++,=qSfqjpqZijZiRV,,Tu,MZijjjs 6c j U R R nU R R nX- S/- $ )Nrt )r; model_input_namesr: )r[ tokenizer_input_namesimage_processor_input_namess r! r "Kosmos2Processor.model_input_names s6 $ @ @ &*&:&:&L&L#$BFbEcccr c Ub [ U5 S:X a U$ [ [ R " SUS95 n[ U5 [ U5 :w a$ [ S[ U5 S[ U5 S35 eSn/ n[ X25 H u pgUR
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g 7fr r )r boxs r! r >Kosmos2Processor._insert_patch_index_tokens.. s 7$3$$s zTThe multiple bounding boxes for a single phrase should not contain any `None` value.r z z )
r r refinditerrw r spanrW r~ r r #_convert_bbox_to_patch_index_tokensr )r[ rd r matched_phrasescurr_posbuffermatchedr r endpatch_index_stringsr
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num_patches_per_side (`int`): the number of patches along each side.
Returns:
`tuple[int, int]`: A pair of patch indices representing the upper-left patch and lower-right patch.
zTThe coordinates in `bbox` should be `(x1, y1, x2, y2)` with `x2 > x1` and `y2 > y1`.ri )rw r floorceil) r r x1y1x2y2ul_xul_ylr_xlr_yul_idxlr_idxs r! r r s RR Gopp ::b/ 0D ::b/ 0D 99R.2 3D 99R.2 3D
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Given a grid of length `num_patches_per_side` and the indices of the upper-left and lower-right corners of a
bounding box, returns the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2).
Args:
ul_idx (`int`): the index of the grid cell that corresponds to the upper-left corner of the bounding box.
lr_idx (`int`): the index of the grid cell that corresponds to the lower-right corner of the bounding box.
num_patches_per_side (`int`): the number of patches along each side.
Returns:
`tuple[float]`: the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2).
g ?r r ) r r r cell_sizer r r r r r r r s r! patch_index_to_coordinater s **I (D )D (D )D
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XU/45 M( GM U$ ) a Extract entities contained in `text`. The bounding bboxes is given in the form of patch indices.
This function is only intended to be used within `clean_text_and_extract_entities_with_bboxes` where further
processing happens, including converting to normalized coordinates and whitespace character cleaning up.
Examples:
```python
>>> text = " An image of a snowman warming himself by a fire."
>>> entities = extract_entities_with_patch_indices(text)
>>> entities
[(' a snowman', (31, 41), [(44, 863)]), (' a fire', (130, 137), [(5, 911)])]
```z(?:(([^<]+)))?r Nr r@ zri rA z>r N)r r sub)r rd entity_namestartr adjusted_start adjusted_endadjusted_entitys r! adjust_entity_positionsr | s[ & K %T&5\:;Nrvvgr4:67L"\$BCO r c U R 5 n[ U 5 [ U R 5 5 -
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nUR 5 nUR XVU4U45 M X$4$ )z9Remove the spaces around the text and the entities in it.)r r rD rE rW ) rd entitiesnew_textleading_spaces new_entitiesr r r r entity_name_leading_spacesentity_name_trailing_spacess r! _cleanup_spacesr' s zz|HYT[[]!33NL-5) \e6%(%5K>> text = " An image of a snowman warming himself by a fire."
>>> clean_text, entities = clean_text_and_extract_entities_with_bboxes(text)
>>> clean_text
'An image of a snowman warming himself by a fire.'
>>> entities
[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]
```r r r r ri )r r r r r rW r' ) rd r processed_textr r! itemr r r r bboxes_in_coordss r! r r s VVGR.N"Ed"K H+ aDG1&?jpqjpbf 5d1gtAwH\ ]jpq*==> , > 44 rs B) )&r r r r typingr r image_processing_utilsr image_utilsr processing_utilsr r r
r r tokenization_utilsr
tokenization_utils_baser r r r r r r r r$ r) r8 r r r r r' r __all__r r r! r4 s7 $ " 2 % b b , ?
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