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import inspect
import re
from typing import List, Optional, Union
from transformers import AutoTokenizer, BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils import (
PaddingStrategy,
PreTokenizedInput,
TensorType,
TextInput,
TruncationStrategy,
)
from .vision_processor import AriaVisionProcessor
class AriaProcessor(ProcessorMixin):
"""
AriaProcessor is a processor for the Aria model which wraps the Aria image preprocessor and the LLama slow tokenizer.
Args:
image_processor(AriaVisionProcessor): The AriaVisionProcessor to use for image preprocessing.
tokenizer(AutoTokenizer): The AutoTokenizer to use for tokenizing the text.
patch_size(int): The patch size to use for the image processor.
chat_template(str): The chat template to use for the tokenizer.
image_token(str): The image token to use for the tokenizer.
"""
attributes = []
valid_kwargs = ["chat_template", "patch_size", "image_token"]
image_processor_class = None
tokenizer_class = "AutoTokenizer"
def __init__(
self,
image_processor: AriaVisionProcessor = None,
tokenizer: Union[AutoTokenizer, str] = None,
patch_size: int = 490,
chat_template: str = None,
image_token: str = "<|img|>",
):
super().__init__(chat_template=chat_template)
if image_processor is None:
self.image_processor = AriaVisionProcessor(max_image_size=patch_size)
else:
self.image_processor = image_processor
if isinstance(tokenizer, str):
self.tokenizer = AutoTokenizer.from_pretrained(
tokenizer, trust_remote_code=True, use_fast=False
)
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.unk_token
else:
self.tokenizer = tokenizer
self.image_token = image_token
# Copied from transformers.models.llava_next.processing_llave_next.LlavaNextProcessor.__call__
def __call__(
self,
text: Union[
TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
],
images: ImageInput = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
max_image_size: Optional[int] = 980,
split_image: Optional[bool] = False,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and image(s). Please refer to the doctsring
of the above two methods for more information.
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. Both channels-first and channels-last formats are supported.
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).
max_image_size (`int`, *optional*):
Maximum size of the image to be processed.
split_image (`bool`, *optional*):
Whether to split the image into patches before processing.
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.
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`.
- **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`.
- **pixel_mask** -- Pixel mask to be fed to a model. Returned when `images` is not `None`.
"""
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise ValueError(
"Invalid input text. Please provide a string, or a list of strings"
)
if images is not None:
image_inputs = self.image_processor(
images,
return_tensors=return_tensors,
max_image_size=max_image_size,
split_image=split_image,
)
# expand the image_token according to the num_crops of image
prompt_strings = []
crop_iter = iter(image_inputs.pop("num_crops"))
for prompt in text:
prompt_strings.append(
re.sub(
re.escape(self.image_token),
lambda _: next(crop_iter) * self.image_token,
prompt,
)
)
else:
image_inputs = {}
text_inputs = self.tokenizer(
prompt_strings,
return_tensors=return_tensors,
padding=padding,
truncation=truncation,
max_length=max_length,
)
return BatchFeature(data={**text_inputs, **image_inputs})
@staticmethod
def _extract_kwargs(func: callable, **kwargs) -> dict:
"""
Extract the kwargs that are valid for the given function.
"""
return {
k: v for k, v in kwargs.items() if k in inspect.signature(func).parameters
}
def save_pretrained(self, save_directory, **kwargs):
"""
Save both the image processor and tokenizer.
"""
if self.image_processor is not None:
self.image_processor.save_pretrained(
save_directory,
**self._extract_kwargs(self.image_processor.save_pretrained, **kwargs),
)
if self.tokenizer is not None:
self.tokenizer.save_pretrained(
save_directory,
**self._extract_kwargs(self.tokenizer.save_pretrained, **kwargs),
)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path,
tokenizer_path=None,
image_processor_path=None,
**kwargs,
):
"""
Load both the image processor and tokenizer from a pretrained model path.
"""
tokenizer_path = (
tokenizer_path
if tokenizer_path is not None
else pretrained_model_name_or_path
)
image_processor_path = (
image_processor_path
if image_processor_path is not None
else pretrained_model_name_or_path
)
image_processor = AriaVisionProcessor.from_pretrained(
image_processor_path,
**cls._extract_kwargs(AriaVisionProcessor.from_pretrained, **kwargs),
)
try:
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_path,
**cls._extract_kwargs(AutoTokenizer.from_pretrained, **kwargs),
)
chat_template = tokenizer.chat_template
except Exception:
tokenizer = None
chat_template = None
return cls(
image_processor=image_processor,
tokenizer=tokenizer,
chat_template=chat_template,
)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
if self.tokenizer is None:
raise ValueError(
"Tokenizer is not initialized. Please provide a valid tokenizer."
)
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
if self.tokenizer is None:
raise ValueError(
"Tokenizer is not initialized. Please provide a valid tokenizer."
)
return self.tokenizer.decode(*args, **kwargs)
@property
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
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))