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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`.
Nbboxesnum_image_tokensfirst_image_token_id)__name__
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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.
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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 ) r< joinr= rr rI rw rt rk r zipr )
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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.
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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`.r^ )rk r floorceil) r r x1y1x2y2ul_xul_ylr_xlr_yul_idxlr_idxs r r r s RR GRopp ::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).
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|||gf ( r |S ) 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 r4 zr^ r5 z>r N)rw r sub)r rY entity_namestartr adjusted_start adjusted_endadjusted_entitys r adjust_entity_positionsr i s_ & K %T&5\:;Nrvvgr4:67L"^\$BCO r c | j }t | t | j z
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>>> 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 r^ )r r r r r rK r ) rY r processed_textr r itemr r r r bboxes_in_coordss r r r s VVGR.N"Ed"K H+ ? aDG1&$?jpqbf 5d1gtAwH\ ]qq+;*==> ? >8 44 rs B) )&r rv r r image_processing_utilsr image_utilsr processing_utilsr r r r r
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