Uniformize kwargs for Paligemma processor and update docs (#33571)

* Uniformize paligemma processor

* nit

docs/source/en/model_doc/paligemma.md

@@ -41,7 +41,7 @@ processor = AutoProcessor.from_pretrained(model_id)
 prompt = "What is on the flower?"
 image_file = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg?download=true"
 raw_image = Image.open(requests.get(image_file, stream=True).raw)
-inputs = processor(prompt, raw_image, return_tensors="pt")
+inputs = processor(raw_image, prompt, return_tensors="pt")
 output = model.generate(**inputs, max_new_tokens=20)
 
 print(processor.decode(output[0], skip_special_tokens=True)[len(prompt):])
@@ -53,7 +53,7 @@ print(processor.decode(output[0], skip_special_tokens=True)[len(prompt):])
 ```python
 prompt = "What is on the flower?"
 answer = "a bee"
-inputs = processor(text=prompt, images=raw_image, suffix=answer, return_tensors="pt")
+inputs = processor(images=raw_image, text=prompt, suffix=answer, return_tensors="pt")
 ```
 
 ## Resources

src/transformers/models/paligemma/modeling_paligemma.py

@@ -443,7 +443,7 @@ def forward(
         >>> url = "https://huggingface.co/gv-hf/PaliGemma-test-224px-hf/resolve/main/cow_beach_1.png"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> inputs = processor(text=prompt, images=image, return_tensors="pt")
+        >>> inputs = processor(images=image, text=prompt,  return_tensors="pt")
 
         >>> # Generate
         >>> generate_ids = model.generate(**inputs, max_length=30)

src/transformers/models/paligemma/processing_paligemma.py

@@ -21,15 +21,19 @@
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, is_valid_image
-from ...processing_utils import ProcessorMixin
+from ...processing_utils import (
+    ImagesKwargs,
+    ProcessingKwargs,
+    ProcessorMixin,
+    TextKwargs,
+    Unpack,
+    _validate_images_text_input_order,
+)
 from ...tokenization_utils_base import (
     AddedToken,
-    PaddingStrategy,
     PreTokenizedInput,
     TextInput,
-    TruncationStrategy,
 )
-from ...utils import TensorType
 
 
 logger = logging.getLogger(__name__)
@@ -38,6 +42,27 @@
 EXTRA_TOKENS = [f"<loc{i:0>4}>" for i in range(1024)] + [f"<seg{i:0>3}>" for i in range(128)]
 
 
+class PaliGemmaTextKwargs(TextKwargs):
+    suffix: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]]
+
+
+class PaliGemmaImagesKwargs(ImagesKwargs):
+    do_convert_rgb: Optional[bool]
+
+
+class PaliGemmaProcessorKwargs(ProcessingKwargs, total=False):
+    text_kwargs: PaliGemmaTextKwargs
+    images_kwargs: PaliGemmaImagesKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "images_kwargs": {
+            "data_format": "channels_first",
+        },
+    }
+
+
 # Copied from transformers.models.idefics2.processing_idefics2.is_url
 def is_url(val) -> bool:
     return isinstance(val, str) and val.startswith("http")
@@ -122,27 +147,11 @@ def __init__(
 
     def __call__(
         self,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
         images: ImageInput = None,
-        tokenize_newline_separately: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length=None,
-        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
-        do_resize: bool = None,
-        do_normalize: bool = None,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        data_format: Optional["ChannelDimension"] = "channels_first",  # noqa: F821
-        input_data_format: Optional[
-            Union[str, "ChannelDimension"]  # noqa: F821
-        ] = None,
-        resample: "PILImageResampling" = None,  # noqa: F821
-        do_convert_rgb: bool = None,
-        do_thumbnail: bool = None,
-        do_align_long_axis: bool = None,
-        do_rescale: bool = None,
-        suffix: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        audio=None,
+        videos=None,
+        **kwargs: Unpack[PaliGemmaProcessorKwargs],
     ) -> BatchFeature:
         """
         Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
@@ -171,29 +180,14 @@ def __call__(
 
 
         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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                 number of channels, H and W are image height and width.
-            tokenize_newline_separately (`bool`, defaults to `True`):
-                Adds a separately tokenized '\n' at the end of the prompt.
-            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
-                Select a strategy to pad the returned sequences (according to the model's padding side and padding
-                index) among:
-                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
-                  sequence if provided).
-                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
-                  acceptable input length for the model if that argument is not provided.
-                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
-                  lengths).
-            max_length (`int`, *optional*):
-                Maximum length of the returned list and optionally padding length (see above).
-            truncation (`bool`, *optional*):
-                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
+            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).
             return_tensors (`str` or [`~utils.TensorType`], *optional*):
                 If set, will return tensors of a particular framework. Acceptable values are:
 
@@ -216,6 +210,15 @@ def __call__(
             - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
             - **labels** -- Labels compatible with training if `suffix` is not None
         """
+        # check if images and text inputs are reversed for BC
+        images, text = _validate_images_text_input_order(images, text)
+
+        output_kwargs = self._merge_kwargs(
+            PaliGemmaProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        suffix = output_kwargs["text_kwargs"].pop("suffix", None)
 
         return_token_type_ids = True if suffix is not None else False
 
@@ -251,30 +254,17 @@ def __call__(
             for prompt in text
         ]
 
-        pixel_values = self.image_processor(
-            images,
-            do_resize=do_resize,
-            do_normalize=do_normalize,
-            return_tensors=return_tensors,
-            image_mean=image_mean,
-            image_std=image_std,
-            input_data_format=input_data_format,
-            data_format=data_format,
-            resample=resample,
-            do_convert_rgb=do_convert_rgb,
-        )["pixel_values"]
-
-        if max_length is not None:
-            max_length += self.image_seq_length  # max_length has to account for the image tokens
+        pixel_values = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"]
+
+        # max_length has to account for the image tokens
+        if output_kwargs["text_kwargs"].get("max_length", None) is not None:
+            output_kwargs["text_kwargs"]["max_length"] += self.image_seq_length
 
         inputs = self.tokenizer(
             input_strings,
             text_pair=suffix,
-            return_tensors=return_tensors,
-            padding=padding,
-            max_length=max_length,
-            truncation=truncation,
             return_token_type_ids=return_token_type_ids,
+            **output_kwargs["text_kwargs"],
         )
 
         return_data = {**inputs, "pixel_values": pixel_values}

tests/models/paligemma/test_modeling_paligemma.py

@@ -337,7 +337,7 @@ def test_small_model_integration_test(self):
             "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
         )
         raw_image = Image.open(requests.get(image_file, stream=True).raw)
-        inputs = self.processor(text=prompt, images=raw_image, return_tensors="pt")
+        inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt")
         EXPECTED_INPUT_IDS = torch.tensor([[257152] * 256 + [2, 108]])
         self.assertTrue(torch.equal(inputs["input_ids"], EXPECTED_INPUT_IDS))
 
@@ -360,7 +360,7 @@ def test_small_model_integration_test_paligemma_VQA(self):
             "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
         )
         raw_image = Image.open(requests.get(image_file, stream=True).raw)
-        inputs = self.processor(text=prompt, images=raw_image, return_tensors="pt").to(torch.float16)
+        inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt").to(torch.float16)
 
         output = model.generate(**inputs, max_new_tokens=900, do_sample=False)
         EXPECTED_DECODED_TEXT = "answer en Where is the cow standing?\nbeach"  # fmt: skip
@@ -382,7 +382,7 @@ def test_small_model_integration_test_paligemma_empty_prompt(self):
             "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
         )
         raw_image = Image.open(requests.get(image_file, stream=True).raw)
-        inputs = self.processor(text=prompt, images=raw_image, return_tensors="pt").to(torch.float16)
+        inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt").to(torch.float16)
 
         output = model.generate(**inputs, max_new_tokens=900, do_sample=False)
         EXPECTED_DECODED_TEXT = "\ncow on the beach"  # fmt: skip
@@ -412,7 +412,7 @@ def test_small_model_integration_test_paligemma_batched(self):
         )
         image2 = image1
 
-        inputs = self.processor(text=prompts, images=[image1, image2], return_tensors="pt", padding=True)
+        inputs = self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)
 
         output = model.generate(**inputs, max_new_tokens=20)
 
@@ -443,7 +443,7 @@ def test_small_model_integration_test_paligemma_batched_bf16(self):
         image2 = image1
 
         inputs = (
-            self.processor(text=prompts, images=[image1, image2], return_tensors="pt", padding=True)
+            self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)
             .to(torch.bfloat16)
             .to(torch_device)
         )
@@ -475,7 +475,7 @@ def test_small_model_integration_test_paligemma_batched_f16(self):
         image2 = image1
 
         inputs = (
-            self.processor(text=prompts, images=[image1, image2], return_tensors="pt", padding=True)
+            self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)
             .to(torch.float16)
             .to(torch_device)
         )
@@ -504,7 +504,7 @@ def test_integration_detection_bug(self):
             ).raw
         )
 
-        inputs = self.processor(text=prompt, images=image, return_tensors="pt").to(torch.bfloat16).to(torch_device)
+        inputs = self.processor(images=image, text=prompt, return_tensors="pt").to(torch.bfloat16).to(torch_device)
 
         output = model.generate(**inputs, max_new_tokens=20)
 
@@ -528,8 +528,8 @@ def test_paligemma_index_error_bug(self):
 
         raw_image = Image.open(requests.get(image_file, stream=True).raw)
         inputs = self.processor(
-            text=prompt,
             images=raw_image,
+            text=prompt,
             return_tensors="pt",
         ).to(torch.float16)
 
@@ -561,7 +561,7 @@ def test_paligemma_finetuning_with_suffixes_bf16(self):
         image2 = image1
 
         inputs = (
-            self.processor(text=prompts, suffix=suffixes, images=[image1, image2], return_tensors="pt", padding=True)
+            self.processor(images=[image1, image2], text=prompts, suffix=suffixes, return_tensors="pt", padding=True)
             .to(torch.bfloat16)
             .to(torch_device)
         )

tests/models/paligemma/test_processor_paligemma.py

@@ -0,0 +1,89 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import shutil
+import tempfile
+import unittest
+
+from transformers import GemmaTokenizer
+from transformers.testing_utils import get_tests_dir, require_torch, require_vision
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import (
+        PaliGemmaProcessor,
+        SiglipImageProcessor,
+        is_vision_available,
+    )
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+
+@require_vision
+class PaliGemmaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = PaliGemmaProcessor
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+        image_processor = SiglipImageProcessor.from_pretrained("google/siglip-so400m-patch14-384")
+        image_processor.image_seq_length = 0
+        tokenizer = GemmaTokenizer(SAMPLE_VOCAB, keep_accents=True)
+        processor = PaliGemmaProcessor(image_processor=image_processor, tokenizer=tokenizer)
+        processor.save_pretrained(self.tmpdirname)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    @require_torch
+    @require_vision
+    def test_image_seq_length(self):
+        input_str = "lower newer"
+        image_input = self.prepare_image_inputs()
+        image_processor = self.get_component("image_processor")
+        tokenizer = self.get_component("tokenizer", max_length=112, padding="max_length")
+        image_processor.image_seq_length = 14
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        inputs = processor(
+            text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length"
+        )
+        self.assertEqual(len(inputs["input_ids"][0]), 112 + 14)
+
+    @require_torch
+    @require_vision
+    def test_unstructured_kwargs_batched(self):
+        if "image_processor" not in self.processor_class.attributes:
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+        image_processor = self.get_component("image_processor")
+        tokenizer = self.get_component("tokenizer")
+
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = ["lower newer", "upper older longer string"]
+        image_input = self.prepare_image_inputs() * 2
+        inputs = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="pt",
+            size={"height": 214, "width": 214},
+            padding="longest",
+            max_length=76,
+        )
+
+        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+
+        self.assertEqual(len(inputs["input_ids"][0]), 10)