Sync from v0.13

vllm/transformers_utils/processors/bagel.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2025 Bytedance Ltd. and/or its affiliates.
+"""BAGEL processor for image and text inputs."""
+
+from transformers import AutoProcessor
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+
+class BagelProcessor(ProcessorMixin):
+    """
+    Constructs a BAGEL processor which wraps a
+    SigLIP image processor and a Qwen2 tokenizer.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "SiglipImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __call__(
+        self,
+        text: TextInput
+        | PreTokenizedInput
+        | list[TextInput]
+        | list[PreTokenizedInput] = None,
+        images: ImageInput = None,
+        **kwargs,
+    ):
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s).
+        """
+        if images is not None:
+            # Process images with the image processor
+            # Ensure return_tensors is set to "pt" for PyTorch tensors
+            image_kwargs = {**kwargs}
+            if "return_tensors" not in image_kwargs:
+                image_kwargs["return_tensors"] = "pt"
+            pixel_values = self.image_processor(images, **image_kwargs)
+        else:
+            pixel_values = None
+
+        text_inputs = self.tokenizer(text, **kwargs) if text is not None else None
+
+        if pixel_values is not None and text_inputs is not None:
+            text_inputs["pixel_values"] = pixel_values["pixel_values"]
+            return text_inputs
+        elif pixel_values is not None:
+            return pixel_values
+        else:
+            return text_inputs
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's batch_decode.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's decode.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+AutoProcessor.register("BagelProcessor", BagelProcessor)