Update docstrings

src/transformers/models/llava/image_processing_llava.py

@@ -183,13 +183,13 @@ def pad_to_square(
  input_data_format: Optional[Union[str, ChannelDimension]] = None,
  ) -> np.array:
  """
- Pads an image to make it square.
+ Pads an image to a square.
 
  Args:
  image (`np.ndarray`):
  The image to pad.
  background_color (`int` or `Tuple[int, int, int]`, *optional*, defaults to 0):
-
+ The color to use for the padding. Can be an integer or a tuple of integers representing the RGB values.
  input_data_format (`str` or `ChannelDimension`, *optional*):
  The channel dimension format for the input image. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.

src/transformers/models/llava/processing_llava.py

@@ -30,13 +30,13 @@
 
 class LlavaProcessor(ProcessorMixin):
  r"""
- Constructs a Llava processor which wraps a Llava image processor and a Llava tokenizer into a single processor.
+ Constructs a LLaVa processor which wraps a LLaVa image processor and a LLaMa tokenizer into a single processor.
 
- [`LlavaProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
+ [`LlavaProcessor`] offers all the functionalities of [`LlavaImageProcessor`] and [`LlamaTokenizerFast`]. See the
  [`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information.
 
  Args:
- image_processor ([`CLIPImageProcessor`], *optional*):
+ image_processor ([`LlavaImageProcessor`], *optional*):
  The image processor is a required input.
  tokenizer ([`LlamaTokenizerFast`], *optional*):
  The tokenizer is a required input.