# coding=utf-8 # Copyright 2024 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 IDEFICS2. """ from typing import TYPE_CHECKING, List, Optional, Union from ...feature_extraction_utils import BatchFeature from ...image_utils import ImageInput, is_valid_image, load_image from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import AddedToken, BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy from ...utils import TensorType, logging if TYPE_CHECKING: from ...tokenization_utils_base import PreTokenizedInput logger = logging.get_logger(__name__) def is_url(val) -> bool: return isinstance(val, str) and val.startswith("http") def is_image_or_image_url(elem): return is_url(elem) or is_valid_image(elem) class Idefics2Processor(ProcessorMixin): r""" Constructs a IDEFICS2 processor which wraps a LLama tokenizer and IDEFICS2 image processor into a single processor. [`IdeficsProcessor`] offers all the functionalities of [`Idefics2ImageProcessor`] and [`LlamaTokenizerFast`]. See the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information. Args: image_processor (`Idefics2ImageProcessor`): An instance of [`Idefics2ImageProcessor`]. The image processor is a required input. tokenizer (`PreTrainedTokenizerBase`, *optional*): An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input. image_seq_len (`int`, *optional*, defaults to 64): The length of the image sequence i.e. the number of tokens per image in the input. This parameter is used to build the string from the input prompt and image tokens and should match the config.perceiver_config.resampler_n_latents value for the model used. 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 = ["image_seq_len", "chat_template"] image_processor_class = "Idefics2ImageProcessor" tokenizer_class = "AutoTokenizer" def __init__(self, image_processor, tokenizer=None, image_seq_len: int = 64, chat_template: str = 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`.") self.fake_image_token = AddedToken("", normalized=False, special=True) self.image_token = AddedToken("", normalized=False, special=True) self.end_of_utterance_token = AddedToken("", normalized=False, special=True) self.image_seq_len = image_seq_len tokens_to_add = { "additional_special_tokens": [self.fake_image_token, self.image_token, self.end_of_utterance_token] } tokenizer.add_special_tokens(tokens_to_add) super().__init__(image_processor, tokenizer, chat_template=chat_template) def _extract_images_from_prompts(self, prompts): prompt_images = [] for prompt in prompts: images = [] for elem in prompt: if is_valid_image(elem): images.append(elem) elif is_url(elem): images.append(load_image(elem)) prompt_images.append(images) return prompt_images def __call__( self, text: Union[TextInput, "PreTokenizedInput", List[TextInput], List["PreTokenizedInput"]] = None, images: Union[ImageInput, List[ImageInput], List[List[ImageInput]]] = None, image_seq_len: Optional[int] = None, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, is_split_into_words: bool = False, add_special_tokens: bool = True, return_tensors: Optional[Union[str, TensorType]] = None, ) -> BatchEncoding: """ Processes the input prompts and returns a BatchEncoding. Example: ```python >>> import requests >>> from transformers import Idefics2Processor >>> from transformers.image_utils import load_image >>> processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2) >>> processor.image_processor.do_image_splitting = False # Force as False to simplify the example >>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" >>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg" >>> image1, image2 = load_image(url1), load_image(url2) >>> images = [[image1], [image2]] >>> text = [ ... "In this image, we see", ... "bla bla bla", ... ] >>> outputs = processor(text=text, images=images, return_tensors="pt", padding=True) >>> input_ids = outputs.input_ids >>> input_tokens = processor.tokenizer.batch_decode(input_ids) >>> print(input_tokens) [' In this image, we see', ' bla bla bla'] ``` Args: text (`Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]`, *optional*): 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). Wherever an image token, `` is encountered it is expanded to `` + `` * `image_seq_len` * `. images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, *optional*): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. If is of type `List[ImageInput]`, it's assumed that this is for a single prompt i.e. of batch size 1. image_seq_len (`int`, *optional*): The length of the image sequence. If not provided, the default value is used. padding (`Union[bool, str, PaddingStrategy]`, *optional*, defaults to `False`): Padding strategy applied to the input ids. See [`PreTrainedTokenizerFast.pad`] for more information. truncation (`Union[bool, str, TruncationStrategy]`, *optional*): Truncation strategy applied to the input ids. See [`PreTrainedTokenizerFast.truncate`] for more information. max_length (`int`, *optional*): Maximum length of the returned list and optionally padding/truncation length. See [`PreTrainedTokenizerFast.__call__`] for more information. is_split_into_words (`bool`, *optional*, defaults to `False`): Whether the input text is split into words or not. If set to `True`, the tokenizer will skip the tokenization process and assume the input is already tokenized. add_special_tokens (`bool`, *optional*, defaults to `True`): Whether to add special tokens or not. See [`PreTrainedTokenizerFast.__call__`] for more information. return_tensors (`Union[str, TensorType]`, *optional*): If set, will return tensors of a particular framework. See [`PreTrainedTokenizerFast.__call__`] for more information. """ image_seq_len = image_seq_len if image_seq_len is not None else self.image_seq_len n_images_in_text = [] inputs = BatchFeature() if text 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") # Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len` fake_image_token = self.fake_image_token.content image_token = self.image_token.content image_str = f"{fake_image_token}{image_token * image_seq_len}{fake_image_token}" if self.image_processor.do_image_splitting: # A single image token is split into 4 patches + 1 original image image_str = image_str * 5 prompt_strings = [] for sample in text: n_images_in_text.append(sample.count(image_token)) sample = sample.replace(image_token, image_str) # Remove any double fake tokens if images are adjacent sample = sample.replace(f"{fake_image_token}{fake_image_token}", f"{fake_image_token}") prompt_strings.append(sample) text_inputs = self.tokenizer( text=prompt_strings, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, is_split_into_words=is_split_into_words, return_tensors=return_tensors, ) inputs.update(text_inputs) if images is not None: if is_image_or_image_url(images): images = [[images]] elif isinstance(images, list) and is_image_or_image_url(images[0]): images = [images] elif ( not isinstance(images, list) and not isinstance(images[0], list) and not is_image_or_image_url(images[0][0]) ): raise ValueError( "Invalid input images. Please provide a single image or a list of images or a list of list of images." ) n_images_in_images = [len(sample) for sample in images] if text is not None and not n_images_in_images == n_images_in_text: raise ValueError( f"The number of images in the text {n_images_in_text} and images {n_images_in_images} should be the same." ) # Load images if they are URLs images = [[load_image(im) for im in sample] for sample in images] image_inputs = self.image_processor(images, return_tensors=return_tensors) inputs.update(image_inputs) return inputs 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) 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 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))