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146 lines
6.1 KiB
146 lines
6.1 KiB
# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from ...feature_extraction_utils import BatchFeature
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from ...image_utils import ImageInput, make_nested_list_of_images
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from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils_base import PreTokenizedInput, TextInput
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from ...utils import auto_docstring
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class Gemma3nProcessorKwargs(ProcessingKwargs, total=False):
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_defaults = {
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"text_kwargs": {"padding": False},
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}
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@auto_docstring
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class Gemma3nProcessor(ProcessorMixin):
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def __init__(
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self,
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feature_extractor,
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image_processor,
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tokenizer,
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chat_template=None,
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audio_seq_length: int = 188,
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image_seq_length: int = 256,
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**kwargs,
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):
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r"""
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audio_seq_length (int, *optional*, defaults to 188):
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The number of audio soft tokens that will be added to the text prompt
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image_seq_length (int, *optional*, defaults to 256):
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The number of image soft tokens that should be added to
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"""
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self.audio_seq_length = audio_seq_length
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self.audio_token_id = tokenizer.audio_token_id
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self.boa_token = tokenizer.boa_token
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self.audio_token = tokenizer.audio_token
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audio_tokens_expanded = "".join([tokenizer.audio_token] * audio_seq_length)
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self.full_audio_sequence = f"\n\n{tokenizer.boa_token}{audio_tokens_expanded}{tokenizer.eoa_token}\n\n"
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self.image_seq_length = image_seq_length
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self.image_token_id = tokenizer.image_token_id
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self.boi_token = tokenizer.boi_token
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self.image_token = tokenizer.image_token
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image_tokens_expanded = "".join([tokenizer.image_token] * image_seq_length)
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self.full_image_sequence = f"\n\n{tokenizer.boi_token}{image_tokens_expanded}{tokenizer.eoi_token}\n\n"
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super().__init__(
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feature_extractor=feature_extractor,
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image_processor=image_processor,
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tokenizer=tokenizer,
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chat_template=chat_template,
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**kwargs,
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)
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@auto_docstring
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def __call__(
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self,
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images: ImageInput | None = None,
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text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
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audio: np.ndarray | list[float] | list[np.ndarray] | list[list[float]] | None = None,
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**kwargs: Unpack[Gemma3nProcessorKwargs],
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) -> BatchFeature:
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if text is None and images is None and audio is None:
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raise ValueError("Provide at least one of `text`, `images`, or `audio`.")
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output_kwargs = self._merge_kwargs(
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Gemma3nProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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if isinstance(text, str):
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text = [text]
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elif not isinstance(text, list) and not isinstance(text[0], str):
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raise TypeError("Invalid input text. Please provide a string, or a list of strings")
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if audio is not None:
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audio_inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
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if not text:
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text = [self.audio_token for _ in audio]
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# Expand placeholder audio tokens to the full audio token sequence
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text = [prompt.replace(self.audio_token, self.full_audio_sequence) for prompt in text]
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else:
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audio_inputs = {}
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if images is not None:
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images = self.image_processor.fetch_images(images)
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batched_images = make_nested_list_of_images(images)
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image_inputs = self.image_processor(batched_images, **output_kwargs["images_kwargs"])
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# Create empty text to be replaced with placeholders
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if not text:
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text = [" ".join([self.image_token] * len(images)) for images in batched_images]
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if len(batched_images) != len(text):
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raise ValueError(
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f"Received inconsistently sized batches of images ({len(batched_images)}) and text ({len(text)})."
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)
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# Expand placeholder image tokens to the full image token sequence
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text = [prompt.replace(self.image_token, self.full_image_sequence) for prompt in text]
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else:
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image_inputs = {}
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return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
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text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"], return_tensors="np")
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self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
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# Add token type ids manually, as tokenizer can't do arbitrary position token types
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array_ids = text_inputs["input_ids"]
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token_type_ids = np.zeros_like(array_ids)
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token_type_ids[array_ids == self.image_token_id] = 1
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token_type_ids[array_ids == self.audio_token_id] = 3
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text_inputs = {k: v.tolist() for k, v in text_inputs.items()} # in case user requested list inputs
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text_inputs["token_type_ids"] = token_type_ids.tolist()
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return BatchFeature(data={**text_inputs, **image_inputs, **audio_inputs}, tensor_type=return_tensors)
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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names + ["token_type_ids"]
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image_processor_input_names = self.image_processor.model_input_names
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audio_processor_input_names = self.feature_extractor.model_input_names
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image_processor_input_names = [name for name in image_processor_input_names if name != "num_crops"]
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return list(tokenizer_input_names + image_processor_input_names + audio_processor_input_names)
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__all__ = ["Gemma3nProcessor"]
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