change order of params in Blip2QFormerModel.forward

src/transformers/models/blip_2/modeling_blip_2.py

@@ -1265,20 +1265,27 @@ def get_extended_attention_mask(
 
  def forward(
  self,
- query_embeds: Optional[torch.FloatTensor] = None,
+ input_ids: Optional[torch.FloatTensor] = None,
  attention_mask: Optional[torch.FloatTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
  head_mask: Optional[torch.FloatTensor] = None,
+ query_embeds: Optional[torch.FloatTensor] = None,
  encoder_hidden_states: Optional[torch.FloatTensor] = None,
  encoder_attention_mask: Optional[torch.FloatTensor] = None,
  past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
  use_cache: Optional[bool] = None,
  output_attentions: Optional[bool] = None,
  output_hidden_states: Optional[bool] = None,
  return_dict: Optional[bool] = None,
- input_ids: Optional[torch.FloatTensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
+
+
  ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
  r"""
+ input_ids (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of input sequence tokens in the vocabulary of the language model.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
  encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, `optional`):
  Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
  the model is configured as a decoder.
@@ -1296,11 +1303,6 @@ def forward(
  use_cache (`bool`, `optional`):
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- input_ids (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Indices of input sequence tokens in the vocabulary of the language model.
- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
- config.max_position_embeddings - 1]`.
  """
  output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
  output_hidden_states = (