# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Processor class for Llava. """ from typing import List, Union from ...feature_extraction_utils import BatchFeature from ...image_utils import ImageInput, get_image_size, to_numpy_array from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, _validate_images_text_input_order from ...tokenization_utils_base import PreTokenizedInput, TextInput from ...utils import logging logger = logging.get_logger(__name__) class LlavaProcessorKwargs(ProcessingKwargs, total=False): _defaults = { "text_kwargs": { "padding": False, }, "images_kwargs": {}, } class LlavaProcessor(ProcessorMixin): r""" Constructs a LLaVa processor which wraps a LLaVa image processor and a LLaMa tokenizer into a single processor. [`LlavaProcessor`] offers all the functionalities of [`LlavaImageProcessor`] and [`LlamaTokenizerFast`]. See the [`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information. Args: image_processor ([`LlavaImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`], *optional*): The tokenizer is a required input. patch_size (`int`, *optional*): Patch size from the vision tower. vision_feature_select_strategy (`str`, *optional*): The feature selection strategy used to select the vision feature from the vision backbone. Shoudl be same as in model's config chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. image_token (`str`, *optional*, defaults to `""`): Special token used to denote image location. num_additional_image_tokens (`int`, *optional*, defaults to 0): Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other extra tokens appended, no need to set this arg. """ attributes = ["image_processor", "tokenizer"] valid_kwargs = [ "chat_template", "patch_size", "vision_feature_select_strategy", "image_token", "num_additional_image_tokens", ] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" def __init__( self, image_processor=None, tokenizer=None, patch_size=None, vision_feature_select_strategy=None, chat_template=None, image_token="", # set the default and let users change if they have peculiar special tokens in rare cases num_additional_image_tokens=0, **kwargs, ): self.patch_size = patch_size self.num_additional_image_tokens = num_additional_image_tokens self.vision_feature_select_strategy = vision_feature_select_strategy self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token self.image_token_id = ( tokenizer.image_token_id if getattr(tokenizer, "image_token_id", None) else tokenizer.convert_tokens_to_ids(self.image_token) ) super().__init__(image_processor, tokenizer, chat_template=chat_template) def __call__( self, images: ImageInput = None, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, audio=None, videos=None, **kwargs: Unpack[LlavaProcessorKwargs], ) -> 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 LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring of the above two methods for more information. Args: 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. 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). 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`. """ if images is None and text is None: raise ValueError("You have to specify at least one of `images` or `text`.") # check if images and text inputs are reversed for BC images, text = _validate_images_text_input_order(images, text) output_kwargs = self._merge_kwargs( LlavaProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if images is not None: image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"]) else: image_inputs = {} 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") # try to expand inputs in processing if we have the necessary parts prompt_strings = text if image_inputs.get("pixel_values") is not None: # Replace the image token with the expanded image token sequence pixel_values = image_inputs["pixel_values"] height, width = get_image_size(to_numpy_array(pixel_values[0])) num_image_tokens = (height // self.patch_size) * ( width // self.patch_size ) + self.num_additional_image_tokens if self.vision_feature_select_strategy == "default": num_image_tokens -= 1 prompt_strings = [] for sample in text: sample = sample.replace(self.image_token, self.image_token * num_image_tokens) prompt_strings.append(sample) text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"]) return BatchFeature(data={**text_inputs, **image_inputs}) # 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. """ 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. """ 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)) __all__ = ["LlavaProcessor"]