some data is dropped when encoding by LayoutLMv3Processor
### System Info

I'm using LayoutLMv3Processor to encoding.
when I input (1, 82, 4) as boxes, the processor will extends the boxes to (1, 512, 4), but some boxes of input is dropped by processor as I can't find them in the encoding;
seems the last n boxes is dropped.

**_tokenizer = LayoutLMv3TokenizerFast.from_pretrained('microsoft/layoutlmv3-base')
processor = LayoutLMv3Processor(LayoutLMv3FeatureExtractor(apply_ocr=False), tokenizer)_**

**boxes of before encoding (512, 4)
boxes of after encoding (82, 4)
boxes of before encoding without duplicated  (58, 4)
boxes of after encoding without duplicated (82, 4)**

### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

import numpy as np
from PIL import Image
from transformers import LayoutLMv3Processor, LayoutLMv3TokenizerFast, LayoutLMv3FeatureExtractor, \
    LayoutLMv3ForTokenClassification, AutoModelForTokenClassification, AutoConfig
from inference_util import prepare_annotation,load_original_dataset

image_paths,bboxes,ner_tags=load_original_dataset("cro_vl_fr/","test")


tokenizer = LayoutLMv3TokenizerFast.from_pretrained('microsoft/layoutlmv3-base')
processor = LayoutLMv3Processor(LayoutLMv3FeatureExtractor(apply_ocr=False), tokenizer)

item = image_paths[0]
image = Image.open( item).convert("RGB")

# get word-level annotations
image,words,boxes=prepare_annotation(image,bboxes[0])
boxes_2_points= np.hstack((np.array(boxes)[:,0:2],np.array(boxes)[:,4:6])).astype(int)

encoding = processor(image, words, boxes=boxes_2_points,
                                padding="max_length", truncation=True,
                                return_tensors="pt")

for k,v in encoding.items():
    encoding[k] = v.squeeze()

token_boxes = encoding['bbox'].numpy()

print("boxes of before encoding",np.shape(token_boxes))
print("boxes of after encoding",np.shape(boxes_2_points))
token_boxes=[tuple(a) for a in token_boxes]
token_boxes=np.array(list(set(token_boxes)))
boxes_2_points=[tuple(a) for a in boxes_2_points]
boxes_2_points=np.array(list(set(boxes_2_points)))

print("boxes of before encoding without duplicated ",np.shape(token_boxes))
print("boxes of after encoding without duplicated",np.shape(boxes_2_points))

### Expected behavior

original boxes should not be drop.