# coding=utf-8 # Copyright 2024 Meta Inc. and The HuggingFace Inc. team. All rights reserved. # # 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 Chameleon. """ from typing import Optional, Union import numpy as np from ...feature_extraction_utils import BatchFeature from ...image_utils import ImageInput from ...processing_utils import ( MultiModalData, ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack, ) from ...tokenization_utils_base import PreTokenizedInput, TextInput class ChameleonTextKwargs(TextKwargs, total=False): return_for_text_completion: bool class ChameleonProcessorKwargs(ProcessingKwargs, total=False): text_kwargs: ChameleonTextKwargs _defaults = { "text_kwargs": { "padding": False, "return_for_text_completion": False, "return_mm_token_type_ids": False, }, "common_kwargs": { "return_tensors": "pt", }, } class ChameleonProcessor(ProcessorMixin): r""" Constructs a Chameleon processor which wraps a Chameleon image processor and a Chameleon tokenizer into a single processor. [`ChameleonProcessor`] offers all the functionalities of [`ChameleonImageProcessor`] and [`LlamaTokenizerFast`]. See the [`~ChameleonProcessor.__call__`] and [`~ChameleonProcessor.decode`] for more information. Args: image_processor ([`ChameleonImageProcessor`]): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`]): The tokenizer is a required input. image_seq_length (`int`, *optional*, defaults to 1024): Sequence length of one image embedding. image_token (`str`, *optional*, defaults to `""`): The special token used to indicate image in the text. """ attributes = ["image_processor", "tokenizer"] tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast") image_processor_class = "ChameleonImageProcessor" def __init__(self, image_processor, tokenizer, image_seq_length: int = 1024, image_token: str = ""): self.image_seq_length = image_seq_length self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token) self.image_start_token = ( tokenizer.boi_token if hasattr(tokenizer, "boi_token") else "" ) # fixed tokens for start and end, so can hardcode self.image_end_token = tokenizer.eoi_token if hasattr(tokenizer, "eoi_token") else "" self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token) self.image_start_token_id = tokenizer.convert_tokens_to_ids(self.image_start_token) self.image_end_token_id = tokenizer.convert_tokens_to_ids(self.image_end_token) self.image_ids = [self.image_token_id, self.image_start_token_id, self.image_end_token_id] super().__init__(image_processor, tokenizer) def __call__( self, images: Optional[ImageInput] = None, text: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]] = None, audio=None, videos=None, **kwargs: Unpack[ChameleonProcessorKwargs], ) -> 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 isinstance(text, str): text = [text] elif not isinstance(text, list) and not isinstance(text[0], str): raise TypeError("Invalid input text. Please provide a string, or a list of strings") if text is None and images is None: raise ValueError("You must provide either text or images") output_kwargs = self._merge_kwargs( ChameleonProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) return_for_text_completion = output_kwargs["text_kwargs"].pop("return_for_text_completion", False) # Replace the image token with the expanded image token sequence prompt_strings = [] one_img_tokens = self.image_start_token + (self.image_token * self.image_seq_length) + self.image_end_token for sample in text: sample = sample.replace(self.image_token, one_img_tokens) if not return_for_text_completion: sample += self.tokenizer.sep_token # special Chameleon treatment to add sep for chat mode prompt_strings.append(sample) image_inputs = {} if images is not None: image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"]) return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False) text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None) self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"]) if return_mm_token_type_ids: array_ids = np.array(text_inputs["input_ids"]) mm_token_type_ids = np.zeros_like(text_inputs["input_ids"]) mm_token_type_ids[np.isin(array_ids, self.image_ids)] = 1 text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist() return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors) def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs): """ Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. Args: image_sizes (`list[list[int]]`, *optional*): The input sizes formatted as (height, width) per each image. Returns: `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided input modalities, along with other useful data. """ vision_data = {} if image_sizes is not None: # add 2 for BOI and EOI tokens num_image_tokens = [self.image_seq_length + 2] * len(image_sizes) num_image_patches = [1] * len(image_sizes) vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) return MultiModalData(**vision_data) # 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__ = ["ChameleonProcessor"]