uniformize git processor

src/transformers/models/git/processing_git.py

@@ -16,8 +16,16 @@
 Image/Text processor class for GIT
 """
 
-from ...processing_utils import ProcessorMixin
-from ...tokenization_utils_base import BatchEncoding
+from typing import List, Optional, Union
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, _validate_images_text_input_order
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+
+
+class GitProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {}
 
 
 class GitProcessor(ProcessorMixin):
@@ -42,7 +50,14 @@ def __init__(self, image_processor, tokenizer):
         super().__init__(image_processor, tokenizer)
         self.current_processor = self.image_processor
 
-    def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
+    def __call__(
+        self,
+        images: Optional[ImageInput] = None,
+        text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
+        audio=None,
+        videos=None,
+        **kwargs: Unpack[GitProcessorKwargs],
+    ) -> BatchFeature:
         """
         Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
         and `kwargs` arguments to BertTokenizerFast's [`~BertTokenizerFast.__call__`] if `text` is not `None` to encode
@@ -51,13 +66,13 @@ def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
         of the above two methods for more information.
 
         Args:
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
-                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
-                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
             images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                 The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                 tensor. Both channels-first and channels-last formats are supported.
+            text (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`, *optional*):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
 
             return_tensors (`str` or [`~utils.TensorType`], *optional*):
                 If set, will return tensors of a particular framework. Acceptable values are:
@@ -68,37 +83,34 @@ def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
                 - `'jax'`: Return JAX `jnp.ndarray` objects.
 
         Returns:
-            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
 
             - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
             - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
               `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
               `None`).
             - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
         """
-        tokenizer_kwargs, image_processor_kwargs = {}, {}
-        if kwargs:
-            tokenizer_kwargs = {k: v for k, v in kwargs.items() if k not in self.image_processor._valid_processor_keys}
-            image_processor_kwargs = {
-                k: v for k, v in kwargs.items() if k in self.image_processor._valid_processor_keys
-            }
-
         if text is None and images is None:
             raise ValueError("You have to specify either text or images. Both cannot be none.")
 
-        if text is not None:
-            encoding = self.tokenizer(text, return_tensors=return_tensors, **tokenizer_kwargs)
+        # check if images and text inputs are reversed for BC
+        images, text = _validate_images_text_input_order(images, text)
+
+        output_kwargs = self._merge_kwargs(
+            GitProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
 
+        data = {}
+        if text is not None:
+            text_features = self.tokenizer(text, **output_kwargs["text_kwargs"])
+            data.update(text_features)
         if images is not None:
-            image_features = self.image_processor(images, return_tensors=return_tensors, **image_processor_kwargs)
-
-        if text is not None and images is not None:
-            encoding["pixel_values"] = image_features.pixel_values
-            return encoding
-        elif text is not None:
-            return encoding
-        else:
-            return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
+            image_features = self.image_processor(images, **output_kwargs["images_kwargs"])
+            data.update(image_features)
+        return BatchFeature(data=data, tensor_type=output_kwargs["common_kwargs"].get("return_tensors"))
 
     def batch_decode(self, *args, **kwargs):
         """