"IndexError: index out of range in self" in BertForPreTraining

I’m currently working with BERT applied in time sequences. I’m testing whether or not my inputs are proper for BERT to understand using:

config = BertConfig(vocab_size = 30003,
                    num_attention_heads=12,
                    num_hidden_layers=12)
model = BertForPreTraining(config)
outputs = model(torch.LongTensor(inputs["labels"][54581]).view(-1,43))

Where I used this vocab_size because my tokens range from 0 to 30000 and I added two special tokens, which I named 30002 and 30003, this the size, and I’m resizing the input to (1,43) since I’m just trying to predict a single sequence of length 43 (containing CLS and SEP tokens)…
The input above is of the form:

tensor([30002, 12189, 12818, 13938, 15092, 15906, 16238, 16138, 15772, 15349,
        15094, 15193, 15740, 16740, 18137, 19763, 21208, 21979, 21630, 19799,
        16651, 30003, 14003, 13028, 12250, 11881, 12082, 12807, 13975, 15462,
        17065, 18514, 19534, 19937, 19843, 19390, 18737, 18047, 17449, 16976,
        16575, 16139, 30003])

which seems to be the format BERT recognizes. I also added a token -100 representing [MASK], as suggested in the docs. Then the following error shows:

/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
   2041         # remove once script supports set_grad_enabled
   2042         _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 2043     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
   2044 
   2045 

IndexError: index out of range in self

The error is pretty long, so I just put the last iteration. I’m not understanding what I’m supposed to do now. Can anyone give me a clue of what I can do here? Thanks a lot!