InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-29-0ccc04ea4c90> in <module>
      5 bert_tokenizer = BertTokenizer.from_pretrained(pretrained_weights)
      6 
----> 7 bert_barebone = TFBertModel.from_pretrained(pretrained_weights)
      8 bert_for_nq = TFBertForNQDemo.from_pretrained(pretrained_weights)
      9 

/kaggle/input/nq-competition/transformers/modeling_tf_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    288             return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file)
    289 
--> 290         ret = model(model.dummy_inputs, training=False)  # build the network with dummy inputs
    291 
    292         assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file)

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    889           with base_layer_utils.autocast_context_manager(
    890               self._compute_dtype):
--> 891             outputs = self.call(cast_inputs, *args, **kwargs)
    892           self._handle_activity_regularization(inputs, outputs)
    893           self._set_mask_metadata(inputs, outputs, input_masks)

/kaggle/input/nq-competition/transformers/modeling_tf_bert.py in call(self, inputs, **kwargs)
    682 
    683     def call(self, inputs, **kwargs):
--> 684         outputs = self.bert(inputs, **kwargs)
    685         return outputs
    686 

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    889           with base_layer_utils.autocast_context_manager(
    890               self._compute_dtype):
--> 891             outputs = self.call(cast_inputs, *args, **kwargs)
    892           self._handle_activity_regularization(inputs, outputs)
    893           self._set_mask_metadata(inputs, outputs, input_masks)

/kaggle/input/nq-competition/transformers/modeling_tf_bert.py in call(self, inputs, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, training)
    541             # head_mask = tf.constant([0] * self.num_hidden_layers)
    542 
--> 543         embedding_output = self.embeddings([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
    544         encoder_outputs = self.encoder([embedding_output, extended_attention_mask, head_mask], training=training)
    545 

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    889           with base_layer_utils.autocast_context_manager(
    890               self._compute_dtype):
--> 891             outputs = self.call(cast_inputs, *args, **kwargs)
    892           self._handle_activity_regularization(inputs, outputs)
    893           self._set_mask_metadata(inputs, outputs, input_masks)

/kaggle/input/nq-competition/transformers/modeling_tf_bert.py in call(self, inputs, mode, training)
    135         """
    136         if mode == "embedding":
--> 137             return self._embedding(inputs, training=training)
    138         elif mode == "linear":
    139             return self._linear(inputs)

/kaggle/input/nq-competition/transformers/modeling_tf_bert.py in _embedding(self, inputs, training)
    161         token_type_embeddings = self.token_type_embeddings(token_type_ids)
    162 
--> 163         embeddings = inputs_embeds + position_embeddings + token_type_embeddings
    164         embeddings = self.LayerNorm(embeddings)
    165         embeddings = self.dropout(embeddings, training=training)

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py in binary_op_wrapper(x, y)
    897     with ops.name_scope(None, op_name, [x, y]) as name:
    898       if isinstance(x, ops.Tensor) and isinstance(y, ops.Tensor):
--> 899         return func(x, y, name=name)
    900       elif not isinstance(y, sparse_tensor.SparseTensor):
    901         try:

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py in _add_dispatch(x, y, name)
   1195       return gen_math_ops.add(x, y, name=name)
   1196     else:
-> 1197       return gen_math_ops.add_v2(x, y, name=name)
   1198   else:
   1199     return gen_math_ops.add(x, y, name=name)

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/gen_math_ops.py in add_v2(x, y, name)
    544       else:
    545         message = e.message
--> 546       _six.raise_from(_core._status_to_exception(e.code, message), None)
    547   # Add nodes to the TensorFlow graph.
    548   _, _, _op = _op_def_lib._apply_op_helper(

/opt/conda/lib/python3.6/site-packages/six.py in raise_from(value, from_value)

InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was expected to be a half tensor but is a float tensor [Op:AddV2] name: tf_bert_model_1/bert/embeddings/add/