200 lines
8.3 KiB
Python
200 lines
8.3 KiB
Python
# coding=utf-8
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# 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 re
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from typing import Optional, Union
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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 ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils_base import PreTokenizedInput, TextInput
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from ...utils import to_py_obj
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class Gemma3ImagesKwargs(ImagesKwargs):
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do_pan_and_scan: Optional[bool]
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pan_and_scan_min_crop_size: Optional[int]
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pan_and_scan_max_num_crops: Optional[int]
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pan_and_scan_min_ratio_to_activate: Optional[float]
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do_convert_rgb: Optional[bool]
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class Gemma3ProcessorKwargs(ProcessingKwargs, total=False):
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images_kwargs: Gemma3ImagesKwargs
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_defaults = {
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"text_kwargs": {
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"padding": False,
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"return_mm_token_type_ids": True,
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},
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"images_kwargs": {
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"do_convert_rgb": True,
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"do_pan_and_scan": False,
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"pan_and_scan_min_crop_size": 256,
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"pan_and_scan_max_num_crops": 4,
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"pan_and_scan_min_ratio_to_activate": 1.2,
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},
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}
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class Gemma3Processor(ProcessorMixin):
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "AutoImageProcessor"
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tokenizer_class = "AutoTokenizer"
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def __init__(
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self,
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image_processor,
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tokenizer,
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chat_template=None,
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image_seq_length: int = 256,
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**kwargs,
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):
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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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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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def __call__(
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self,
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images: ImageInput = None,
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text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
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videos=None,
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audio=None,
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**kwargs: Unpack[Gemma3ProcessorKwargs],
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) -> BatchFeature:
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if text is None and images is None:
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raise ValueError("Provide at least one of `text` or `images`.")
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output_kwargs = self._merge_kwargs(
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Gemma3ProcessorKwargs,
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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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image_inputs = {}
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if images is not None:
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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.boi_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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# Replace image tokens by the full expanded sequence
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num_crops = to_py_obj(image_inputs.pop("num_crops"))
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batch_num_crops = [[num_crops.pop(0) for _ in range(len(images))] for images in batched_images]
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for batch_idx, (prompt, images, num_crops) in enumerate(zip(text, batched_images, batch_num_crops)):
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image_indexes = [m.start() for m in re.finditer(self.boi_token, prompt)]
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if len(images) != len(image_indexes):
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raise ValueError(
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f"Prompt contained {len(image_indexes)} image tokens but received {len(images)} images."
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)
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# Insert additional image tokens for Pan-and-Scan crops
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for num, idx in reversed(list(zip(num_crops, image_indexes))):
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if num:
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formatted_image_text = (
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f"Here is the original image {self.boi_token} and here are some crops to help you see better "
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+ " ".join([self.boi_token] * num)
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)
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prompt = prompt[:idx] + formatted_image_text + prompt[idx + len(self.boi_token) :]
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text[batch_idx] = prompt
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# Expand placeholder image tokens to the full image token sequence
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text = [prompt.replace(self.boi_token, self.full_image_sequence) for prompt in text]
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return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
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return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
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text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
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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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if return_mm_token_type_ids:
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array_ids = np.array(text_inputs["input_ids"])
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mm_token_type_ids = np.zeros_like(array_ids)
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mm_token_type_ids[array_ids == self.image_token_id] = 1
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text_inputs["token_type_ids"] = mm_token_type_ids.tolist()
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return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
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def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
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"""
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Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
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Args:
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image_sizes (`list[list[int]]`, *optional*):
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The input sizes formatted as (height, width) per each image.
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Returns:
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`MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
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input modalities, along with other useful data.
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"""
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vision_data = {}
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if image_sizes is not None:
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# NOTE: no image cropping supported yet
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num_image_tokens = [self.image_seq_length] * len(image_sizes)
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num_image_patches = [1] * len(image_sizes)
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vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
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return MultiModalData(**vision_data)
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# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Gemma
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to GemmaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Gemma
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to GemmaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
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the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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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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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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__all__ = ["Gemma3Processor"]
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