365 lines
16 KiB
Python
365 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team.
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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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"""
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Processor class for PaliGemma.
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"""
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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, is_valid_image, make_flat_list_of_images
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from ...processing_utils import (
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ImagesKwargs,
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MultiModalData,
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ProcessingKwargs,
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ProcessorMixin,
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TextKwargs,
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Unpack,
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)
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from ...tokenization_utils_base import AddedToken, PreTokenizedInput, TextInput
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from ...utils import logging
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logger = logging.get_logger(__name__)
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IMAGE_TOKEN = "<image>"
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EXTRA_TOKENS = [f"<loc{i:0>4}>" for i in range(1024)] + [f"<seg{i:0>3}>" for i in range(128)]
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class PaliGemmaTextKwargs(TextKwargs):
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suffix: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]]
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class PaliGemmaImagesKwargs(ImagesKwargs):
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do_convert_rgb: Optional[bool]
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class PaliGemmaProcessorKwargs(ProcessingKwargs, total=False):
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text_kwargs: PaliGemmaTextKwargs
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images_kwargs: PaliGemmaImagesKwargs
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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": False,
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},
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"images_kwargs": {
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"data_format": "channels_first",
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},
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}
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# Copied from transformers.models.idefics2.processing_idefics2.is_url
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def is_url(val) -> bool:
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return isinstance(val, str) and val.startswith("http")
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# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url
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def is_image_or_image_url(elem):
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return is_url(elem) or is_valid_image(elem)
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def _is_str_or_image(elem):
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return isinstance(elem, (str)) or is_image_or_image_url(elem)
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def build_string_from_input(prompt, bos_token, image_seq_len, image_token, num_images):
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"""
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Builds a string from the input prompt and image tokens.
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For example, for the call:
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build_string_from_input(
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prompt="Prefix str"
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bos_token="<s>",
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image_seq_len=3,
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image_token="<im>",
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)
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The output will be:
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"<im><im><im><s>Initial str"
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Args:
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prompt (`list[Union[str, ImageInput]]`): The input prompt.
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bos_token (`str`): The beginning of sentence token.
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image_seq_len (`int`): The length of the image sequence.
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image_token (`str`): The image token.
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num_images (`int`): Number of images in the prompt.
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"""
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return f"{image_token * image_seq_len * num_images}{bos_token}{prompt}\n"
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class PaliGemmaProcessor(ProcessorMixin):
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r"""
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Constructs a PaliGemma processor which wraps a PaliGemma image processor and a PaliGemma tokenizer into a single processor.
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[`PaliGemmaProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`GemmaTokenizerFast`]. See the
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[`~PaliGemmaProcessor.__call__`] and [`~PaliGemmaProcessor.decode`] for more information.
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Args:
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image_processor ([`SiglipImageProcessor`], *optional*):
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The image processor is a required input.
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tokenizer ([`GemmaTokenizerFast`], *optional*):
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The tokenizer is a required input.
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chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
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in a chat into a tokenizable string.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = ("SiglipImageProcessor", "SiglipImageProcessorFast")
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tokenizer_class = ("GemmaTokenizer", "GemmaTokenizerFast")
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def __init__(
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self,
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image_processor=None,
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tokenizer=None,
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chat_template=None,
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**kwargs,
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):
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if image_processor is None:
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raise ValueError("You need to specify an `image_processor`.")
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if tokenizer is None:
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raise ValueError("You need to specify a `tokenizer`.")
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if not hasattr(image_processor, "image_seq_length"):
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raise ValueError("Image processor is missing an `image_seq_length` attribute.")
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self.image_seq_length = image_processor.image_seq_length
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if not hasattr(tokenizer, "image_token"):
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image_token = AddedToken(IMAGE_TOKEN, normalized=False, special=True)
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tokens_to_add = {"additional_special_tokens": [image_token]}
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tokenizer.add_special_tokens(tokens_to_add)
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self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
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self.image_token = IMAGE_TOKEN
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else:
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self.image_token_id = tokenizer.image_token_id
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self.image_token = tokenizer.image_token
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tokenizer.add_tokens(EXTRA_TOKENS)
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tokenizer.add_bos_token = False
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tokenizer.add_eos_token = False
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super().__init__(image_processor, tokenizer, chat_template=chat_template)
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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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audio=None,
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videos=None,
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**kwargs: Unpack[PaliGemmaProcessorKwargs],
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) -> BatchFeature:
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"""
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Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
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and `kwargs` arguments to GemmaTokenizerFast's [`~GemmaTokenizerFast.__call__`] if `text` is not `None` to encode
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the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
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SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
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of the above two methods for more information.
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The usage for PaliGemma fine-tuning preparation is slightly different than usual. suffix passed are suffixes to
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the prompt in `text`, and will be placed after the prompt. This is because attention is handled differently for
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the prefix and the suffix. For instance,
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```python
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image = PIL_cow_image
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prompt = "answer en Where is the cow standing?"
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suffix = "on the beach"
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inputs = processor(text=prompt, images=image, suffix=suffix)
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```
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Here `inputs` will contain the `input_ids` and `token_type_ids` that follow
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```python
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inputs["input_ids"][:, 256:]
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# tensor([[ 2, 6006, 603, 573, 13910, 9980, 235336, 108, 477, 573, 8318]])
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inputs["token_type_ids"][:, 256:]
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tensor([[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1]])
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```
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Meaning the last three tokens are of "label" ("suffix") type while the other ones are of "prefix" type.
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Args:
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images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
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The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
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tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
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number of channels, H and W are image height and width.
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text (`str`, `list[str]`, `list[list[str]]`):
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The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
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`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
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return_tensors (`str` or [`~utils.TensorType`], *optional*):
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If set, will return tensors of a particular framework. Acceptable values are:
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- `'tf'`: Return TensorFlow `tf.constant` objects.
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- `'pt'`: Return PyTorch `torch.Tensor` objects.
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- `'np'`: Return NumPy `np.ndarray` objects.
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- `'jax'`: Return JAX `jnp.ndarray` objects.
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suffix (`str`, `list[str]`, `list[list[str]]`):
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The suffixes or batch of suffixes to be encoded. Only necessary for finetuning. See https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/README.md
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for more information. If your prompt is "<image> What is on the image", the suffix corresponds to the expected prediction "a cow sitting on a bench".
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Returns:
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[`BatchFeature`]: A [`BatchFeature`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix`
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is provided, the `input_ids` will also contain the suffix input ids.
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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`None`).
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- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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- **labels** -- Labels compatible with training if `suffix` is not None
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"""
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output_kwargs = self._merge_kwargs(
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PaliGemmaProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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suffix = output_kwargs["text_kwargs"].pop("suffix", None)
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return_token_type_ids = True if suffix is not None else False
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if images is None:
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raise ValueError("`images` are expected as arguments to a `PaliGemmaProcessor` instance.")
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if text is None:
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logger.warning_once(
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"You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."
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)
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text = ""
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if _is_str_or_image(text):
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text = [text]
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elif isinstance(text, list) and _is_str_or_image(text[0]):
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pass
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if text is not None and images is not None:
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if not any(IMAGE_TOKEN in sample for sample in text):
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logger.warning(
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"You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special "
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"image tokens in the text, as many tokens as there are images per each text. It is recommended to "
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"add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images "
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"each text has and add special tokens."
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)
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if isinstance(text, list) and isinstance(images, list):
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if len(images) != len(text):
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raise ValueError(
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f"Received {len(images)} images for {len(text)} prompts. Each prompt should be associated with an image or list of images."
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)
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# make a nested list of lists to be able to iterate over the images and text below
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if is_valid_image(images):
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images = [[images]]
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elif isinstance(images, (list, tuple)) and is_valid_image(images[0]):
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images = [[image] for image in images]
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elif not (
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isinstance(images, (list, tuple))
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and isinstance(images[0], (list, tuple))
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and is_valid_image(images[0][0])
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):
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raise ValueError("images must be an image, list of images or list of list of images")
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input_strings = [
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build_string_from_input(
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prompt=prompt,
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bos_token=self.tokenizer.bos_token,
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image_seq_len=self.image_seq_length,
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image_token=IMAGE_TOKEN,
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num_images=len(image_list) if isinstance(image_list, list) else 1,
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)
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for prompt, image_list in zip(text, images)
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]
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images = make_flat_list_of_images(images)
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else:
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expanded_samples = []
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for sample in text:
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expanded_sample = sample.replace(IMAGE_TOKEN, IMAGE_TOKEN * self.image_seq_length)
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bos_rfind_index = expanded_sample.rfind(IMAGE_TOKEN)
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bos_index = bos_rfind_index + len(IMAGE_TOKEN) if bos_rfind_index != -1 else 0
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expanded_sample = (
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expanded_sample[:bos_index] + self.tokenizer.bos_token + expanded_sample[bos_index:]
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)
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expanded_samples.append(expanded_sample)
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input_strings = [f"{sample}\n" for sample in expanded_samples]
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if suffix is not None and _is_str_or_image(suffix):
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suffix = [suffix]
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if suffix is not None:
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suffix = [sfx + self.tokenizer.eos_token for sfx in suffix]
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pixel_values = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"]
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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", None)
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inputs = self.tokenizer(
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input_strings,
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text_pair=suffix,
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return_token_type_ids=return_token_type_ids,
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**output_kwargs["text_kwargs"],
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)
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self._check_special_mm_tokens(input_strings, inputs, modalities=["image"])
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return_data = {**inputs, "pixel_values": pixel_values}
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if return_token_type_ids:
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labels = np.array(inputs["input_ids"])
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labels[np.array(inputs["token_type_ids"]) == 0] = -100
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return_data.update({"labels": labels})
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if return_mm_token_type_ids:
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array_ids = np.array(return_data["input_ids"])
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mm_token_type_ids = np.zeros_like(return_data["input_ids"])
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mm_token_type_ids[array_ids == self.image_token_id] = 1
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return_data["mm_token_type_ids"] = mm_token_type_ids.tolist()
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return BatchFeature(data=return_data, 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[str]], *optional*):
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The input sizes formatted as (height, width) per each image.
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Returns:
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dict[str, list[int]]: A dictionary mapping each modality ("image", "video", "audio")
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to a list containing the number of placeholder tokens required. If the model doesn't accept
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a certain modality or no input sizes are provided, the dict value is set to an empty list.
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"""
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vision_data = {}
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if image_sizes is not None:
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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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# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->PaliGemma
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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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__ = ["PaliGemmaProcessor"]
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