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"""
Processor class for TraVisionLM.
"""
import logging
from typing import List, Optional, Union
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput, is_valid_image
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils import (
AddedToken,
PaddingStrategy,
PreTokenizedInput,
TextInput,
TruncationStrategy,
)
from transformers.utils import TensorType
logger = logging.getLogger(__name__)
IMAGE_TOKEN = "<image>"
EXTRA_TOKENS = [f"<loc{i:0>4}>" for i in range(1024)] # for object detection task
# Copied from transformers.models.idefics2.processing_idefics2.is_url
def is_url(val) -> bool:
return isinstance(val, str) and val.startswith("http")
# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url
def is_image_or_image_url(elem):
return is_url(elem) or is_valid_image(elem)
# Copied from transformers.models.paligemma.processing_paligemma._is_str_or_image
def _is_str_or_image(elem):
return isinstance(elem, (str)) or is_image_or_image_url(elem)
def build_string_from_input(image_seq_len, image_token):
"""
Builds a string from the input prompt and image tokens.
For example, for the call:
build_string_from_input(
image_seq_len=3,
image_token="<im>",
)
The output will be:
"<im><im><im>"
Args:
image_seq_len (`int`): The length of the image sequence.
image_token (`str`): The image token.
"""
return f"{image_token * image_seq_len}"
class TraVisionProcessor(ProcessorMixin):
r"""
Constructs a TraVision processor which wraps a SigLIP image processor and a GPT2 tokenizer into a single processor.
[`TraVisionProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`GPT2TokenizerFast`]. See the
[`~TraVisionProcessor.__call__`] and [`~TraVisionProcessor.decode`] for more information.
Args:
image_processor ([`SiglipImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`GPT2TokenizerFast`], *optional*):
The tokenizer is a required input.
chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
in a chat into a tokenizable string.
"""
attributes = ["image_processor", "tokenizer"]
valid_kwargs = ["chat_template"]
image_processor_class = "SiglipImageProcessor"
tokenizer_class = ("GPT2Tokenizer", "GPT2TokenizerFast")
def __init__(
self,
image_processor=None,
tokenizer=None,
chat_template=None,
**kwargs,
):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
if not hasattr(image_processor, "image_seq_length"):
raise ValueError("Image processor is missing an `image_seq_length` attribute.")
self.image_seq_length = image_processor.image_seq_length
image_token = AddedToken(IMAGE_TOKEN, normalized=False, special=True)
tokens_to_add = {"additional_special_tokens": [image_token]}
tokenizer.add_special_tokens(tokens_to_add)
tokenizer.add_tokens(EXTRA_TOKENS)
self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
tokenizer.add_bos_token = False
tokenizer.add_eos_token = False
super().__init__(image_processor, tokenizer, chat_template=chat_template)
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,
labels: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to GPT2TokenizerFast's [`~GPT2TokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
of the above two methods for more information.
The usage for TraVisionLM fine-tuning preparation follows a standard 4D causal mask where only the prompt and label tokens
are attended in an auto-regressive manner. The label in `text` are to be passed separately to the __call__ function and
will be placed after the prompt, which is the instruction to steer the model generation.
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`.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
label (`str`, `List[str]`, `List[List[str]]`):
The label or batch of labels to be encoded. Only necessary for training.
for more information. If your prompt is "<image> Resimde ne var", the label corresponds to the expected prediction "çimlerde uzanan bir köpek".
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `label`
is provided, the `input_ids` will also contain the label input ids.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
- **labels** -- Labels compatible with training if `label` is not None
"""
# return_token_type_ids = True if labels is not None else False
return_token_type_ids = True
if images is None:
raise ValueError("`images` are expected as arguments to a `TraVisionProcessor` instance.")
if text is None:
logger.warning_once(
"You are using TraVisionLM without a text prefix. It will perform as a picture-captioning model."
)
text = "Açıkla" # default prompt if it is not provided as an argument
if isinstance(text, List) and isinstance(images, List):
if len(images) < len(text):
raise ValueError(
f"Received {len(images)} images for {len(text)} prompts. Each prompt should be associated with an image."
)
if _is_str_or_image(text):
text = [text]
elif isinstance(text, list) and _is_str_or_image(text[0]):
pass
text = [f"{prompt}\n" for prompt in text]
if labels is not None and _is_str_or_image(labels):
labels = [labels]
if labels is not None:
labels = [label + self.tokenizer.eos_token for label in labels]
text = [f"{prompt}{label}" for prompt, label in zip(text, labels)]
input_strings = [
build_string_from_input(
image_seq_len=self.image_seq_length,
image_token=IMAGE_TOKEN,
)
for _ 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
inputs = self.tokenizer(
input_strings,
text_pair=text,
return_tensors=return_tensors,
padding=padding,
max_length=max_length,
truncation=truncation,
return_token_type_ids=return_token_type_ids,
)
return_data = {**inputs, "pixel_values": pixel_values}
if labels is not None:
labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100)
return_data.update({"labels": labels})
return BatchFeature(data=return_data)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->GPT2
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to GPT2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->GPT2
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to GPT2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->TraVision
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)) |