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import inspect |
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import re |
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from typing import List, Optional, Union |
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from transformers import AutoTokenizer, BatchFeature |
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from transformers.image_utils import ImageInput |
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from transformers.processing_utils import ProcessorMixin |
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from transformers.tokenization_utils import ( |
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PaddingStrategy, |
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PreTokenizedInput, |
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TensorType, |
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TextInput, |
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TruncationStrategy, |
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) |
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from .vision_processor import AriaVisionProcessor |
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class AriaProcessor(ProcessorMixin): |
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""" |
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AriaProcessor is a processor for the Aria model which wraps the Aria image preprocessor and the LLama slow tokenizer. |
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Args: |
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image_processor(AriaVisionProcessor): The AriaVisionProcessor to use for image preprocessing. |
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tokenizer(AutoTokenizer): The AutoTokenizer to use for tokenizing the text. |
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patch_size(int): The patch size to use for the image processor. |
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chat_template(str): The chat template to use for the tokenizer. |
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image_token(str): The image token to use for the tokenizer. |
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""" |
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attributes = [] |
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valid_kwargs = ["chat_template", "patch_size", "image_token"] |
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image_processor_class = None |
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tokenizer_class = "AutoTokenizer" |
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def __init__( |
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self, |
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image_processor: AriaVisionProcessor = None, |
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tokenizer: Union[AutoTokenizer, str] = None, |
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patch_size: int = 490, |
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chat_template: str = None, |
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image_token: str = "<|img|>", |
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): |
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super().__init__(chat_template=chat_template) |
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if image_processor is None: |
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self.image_processor = AriaVisionProcessor(max_image_size=patch_size) |
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else: |
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self.image_processor = image_processor |
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|
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if isinstance(tokenizer, str): |
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self.tokenizer = AutoTokenizer.from_pretrained( |
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tokenizer, trust_remote_code=True, use_fast=False |
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) |
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else: |
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self.tokenizer = tokenizer |
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if self.tokenizer.pad_token is None: |
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self.tokenizer.pad_token = self.tokenizer.unk_token |
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self.image_token = image_token |
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def __call__( |
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self, |
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text: Union[ |
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TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput] |
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], |
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images: ImageInput = None, |
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padding: Union[bool, str, PaddingStrategy] = False, |
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truncation: Union[bool, str, TruncationStrategy] = None, |
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max_length: Optional[int] = None, |
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max_image_size: Optional[int] = 980, |
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split_image: Optional[bool] = False, |
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return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, |
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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). Please refer to the doctsring |
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of the above two methods for more information. |
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Args: |
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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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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. Both channels-first and channels-last formats are supported. |
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padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): |
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Select a strategy to pad the returned sequences (according to the model's padding side and padding |
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index) among: |
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- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single |
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sequence if provided). |
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- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum |
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acceptable input length for the model if that argument is not provided. |
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- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different |
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lengths). |
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max_length (`int`, *optional*): |
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Maximum length of the returned list and optionally padding length (see above). |
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max_image_size (`int`, *optional*): |
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Maximum size of the image to be processed. |
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split_image (`bool`, *optional*): |
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Whether to split the image into patches before processing. |
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truncation (`bool`, *optional*): |
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Activates truncation to cut input sequences longer than `max_length` to `max_length`. |
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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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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`. |
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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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- **pixel_mask** -- Pixel mask to be fed to a model. Returned when `images` is not `None`. |
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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 ValueError( |
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"Invalid input text. Please provide a string, or a list of strings" |
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) |
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if images is not None: |
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image_inputs = self.image_processor( |
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images, |
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return_tensors=return_tensors, |
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max_image_size=max_image_size, |
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split_image=split_image, |
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) |
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prompt_strings = [] |
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crop_iter = iter(image_inputs.pop("num_crops")) |
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for prompt in text: |
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prompt_strings.append( |
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re.sub( |
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re.escape(self.image_token), |
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lambda _: next(crop_iter) * self.image_token, |
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prompt, |
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) |
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) |
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else: |
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image_inputs = {} |
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prompt_strings = text |
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text_inputs = self.tokenizer( |
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prompt_strings, |
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return_tensors=return_tensors, |
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padding=padding, |
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truncation=truncation, |
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max_length=max_length, |
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) |
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return BatchFeature(data={**text_inputs, **image_inputs}) |
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@staticmethod |
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def _extract_kwargs(func: callable, **kwargs) -> dict: |
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""" |
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Extract the kwargs that are valid for the given function. |
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""" |
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return { |
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k: v for k, v in kwargs.items() if k in inspect.signature(func).parameters |
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} |
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def save_pretrained(self, save_directory, **kwargs): |
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""" |
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Save both the image processor and tokenizer. |
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""" |
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if self.image_processor is not None: |
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self.image_processor.save_pretrained( |
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save_directory, |
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**self._extract_kwargs(self.image_processor.save_pretrained, **kwargs), |
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) |
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if self.tokenizer is not None: |
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self.tokenizer.save_pretrained( |
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save_directory, |
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**self._extract_kwargs(self.tokenizer.save_pretrained, **kwargs), |
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) |
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@classmethod |
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def from_pretrained( |
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cls, |
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pretrained_model_name_or_path, |
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tokenizer_path=None, |
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image_processor_path=None, |
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**kwargs, |
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): |
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""" |
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Load both the image processor and tokenizer from a pretrained model path. |
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""" |
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tokenizer_path = ( |
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tokenizer_path |
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if tokenizer_path is not None |
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else pretrained_model_name_or_path |
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) |
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image_processor_path = ( |
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image_processor_path |
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if image_processor_path is not None |
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else pretrained_model_name_or_path |
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) |
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image_processor = AriaVisionProcessor.from_pretrained( |
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image_processor_path, |
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**cls._extract_kwargs(AriaVisionProcessor.from_pretrained, **kwargs), |
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) |
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if "use_fast" in kwargs: |
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kwargs.pop("use_fast") |
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try: |
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tokenizer = AutoTokenizer.from_pretrained( |
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tokenizer_path, |
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use_fast=False, |
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**cls._extract_kwargs(AutoTokenizer.from_pretrained, **kwargs), |
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) |
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chat_template = tokenizer.chat_template |
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except Exception: |
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tokenizer = None |
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chat_template = None |
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return cls( |
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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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) |
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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 LlamaTokenizerFast'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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if self.tokenizer is None: |
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raise ValueError( |
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"Tokenizer is not initialized. Please provide a valid tokenizer." |
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) |
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return self.tokenizer.batch_decode(*args, **kwargs) |
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def decode(self, *args, **kwargs): |
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""" |
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This method forwards all its arguments to LlamaTokenizerFast'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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if self.tokenizer is None: |
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raise ValueError( |
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"Tokenizer is not initialized. Please provide a valid tokenizer." |
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) |
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return self.tokenizer.decode(*args, **kwargs) |
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|
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@property |
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|
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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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