""" Processor class for TaiVisionLM. """ import transformers import logging from typing import List, Optional, Union from transformers.feature_extraction_utils import BatchFeature from transformers.image_utils import ImageInput, is_valid_image from transformers.processing_utils import ProcessorMixin from transformers.tokenization_utils import ( AddedToken, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy, ) from transformers.utils import TensorType from .configuration_taivisionlm import TaiVisionLMConfig logger = logging.getLogger(__name__) IMAGE_TOKEN = "" # Copied from transformers.models.idefics2.processing_idefics2.is_url def is_url(val) -> bool: return isinstance(val, str) and val.startswith("http") # Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url def is_image_or_image_url(elem): return is_url(elem) or is_valid_image(elem) # Copied from transformers.models.paligemma.processing_paligemma._is_str_or_image def _is_str_or_image(elem): return isinstance(elem, (str)) or is_image_or_image_url(elem) def build_string_from_input(image_seq_len, image_token): """ Builds a string from the input prompt and image tokens. For example, for the call: build_string_from_input( image_seq_len=3, image_token="", ) The output will be: "" Args: image_seq_len (`int`): The length of the image sequence. image_token (`str`): The image token. """ return f"{image_token * image_seq_len}" class TaiVisionProcessor(ProcessorMixin): r""" Constructs a TraVision processor which wraps a SigLIP image processor and a GPT2 tokenizer into a single processor. [`TaiVisionProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`LlamaTokenizerFast`]. See the [`~TaiVisionProcessor.__call__`] and [`~TaiVisionProcessor.decode`] for more information. Args: image_processor ([`SiglipImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`], *optional*): The tokenizer is a required input. 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 = ["chat_template"] image_processor_class = "SiglipImageProcessor" tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast") def __init__( self, image_processor=None, tokenizer=None, chat_template=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`.") if not hasattr(image_processor, "image_seq_length"): raise ValueError("Image processor is missing an `image_seq_length` attribute.") self.image_seq_length = image_processor.image_seq_length image_token = AddedToken(IMAGE_TOKEN, normalized=False, special=True) tokens_to_add = {"additional_special_tokens": [image_token]} tokenizer.add_special_tokens(tokens_to_add) self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN) tokenizer.add_bos_token = False tokenizer.add_eos_token = False super().__init__(image_processor, tokenizer, chat_template=chat_template) def __call__( self, prompts: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, images: ImageInput = None, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length=None, return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, do_resize: bool = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821 input_data_format: Optional[ Union[str, "ChannelDimension"] # noqa: F821 ] = None, resample: "PILImageResampling" = None, # noqa: F821 do_convert_rgb: bool = None, do_thumbnail: bool = None, do_align_long_axis: bool = None, do_rescale: bool = None, labels: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None, ) -> 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 GPT2TokenizerFast's [`~GPT2TokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring of the above two methods for more information. The usage for TraVisionLM fine-tuning preparation follows a standard 4D causal mask where only the prompt and label tokens are attended in an auto-regressive manner. The label in `text` are to be passed separately to the __call__ function and will be placed after the prompt, which is the instruction to steer the model generation. Args: prompts (`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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are \image height and width. tokenize_newline_separately (`bool`, defaults to `False`): Adds a separately tokenized '\n' at the end of the prompt. 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). 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. labels (`str`, `List[str]`, `List[List[str]]`): The label or batch of labels to be encoded. Only necessary for training. text (`str`, `List[str]`, `List[List[str]]`): The text or batch of text to be encoded. If provided, the prompt and label should be 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`. If `label` is provided, the `input_ids` will also contain the label input ids. - **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`. - **labels** -- Labels compatible with training if `label` is not None """ # return_token_type_ids = True if labels is not None else False return_token_type_ids = True if images is None: raise ValueError("`images` are expected as arguments to a `TraVisionProcessor` instance.") images = [images] if not isinstance(images, list) else images if prompts is None: logger.warning_once( "You are using TaiVisionLM without a text prefix. It will perform as a picture-captioning model." ) prompts = "描述這張圖片" # default prompt if it is not provided as an argument if len(images) != 1: prompts = [prompts] * len(images) if isinstance(prompts, List) and isinstance(images, List): if len(images) < len(text): raise ValueError( f"Received {len(images)} images for {len(prompts)} prompts. Each prompt should be associated with an image." ) if _is_str_or_image(prompts): prompts = [prompts] elif isinstance(prompts, list) and _is_str_or_image(prompts[0]): pass # add \n after image tokens prompts = [f"\n<|user|>\n{prompt}{self.tokenizer.eos_token}\n" for prompt in prompts] # TODO: tokenize the prompt twice, and check if the prompt is too long prompt_length = [len(self.tokenizer.tokenize(prompt)) + self.image_seq_length for prompt in prompts] if labels is not None: if _is_str_or_image(labels): labels = [labels] # convert it to list if it is a string labels = [f"<|assistant|>\n{label}{self.tokenizer.eos_token}" for label in labels] text = [f"{prompt}{label}" for prompt, label in zip(prompts, labels)] else: text = prompts assert len(images) == len(text), "The number of images and text should be the same." input_strings = [ build_string_from_input( image_seq_len=self.image_seq_length, image_token=IMAGE_TOKEN, ) for _ in text ] # this will do some image processing, like resizing, normalizing, etc. pixel_values = self.image_processor( images, do_resize=do_resize, do_normalize=do_normalize, return_tensors=return_tensors, image_mean=image_mean, image_std=image_std, input_data_format=input_data_format, data_format=data_format, resample=resample, do_convert_rgb=do_convert_rgb, )["pixel_values"] if max_length is not None: max_length += self.image_seq_length # max_length has to account for the image tokens inputs = self.tokenizer( input_strings, text_pair=text, return_tensors=return_tensors, padding=padding, max_length=max_length, truncation=truncation, return_token_type_ids=return_token_type_ids, ) return_data = {**inputs, "pixel_values": pixel_values} # we are doing training, so we need to return the labels if labels is not None: # fill the labels with -100 where we don't have to compute the loss # mask the padding part labels = inputs["input_ids"].masked_fill(inputs["attention_mask"] == 0, -100) # mask the image + prompt part, so that we don't train the model to predict the image tokens import torch prompt_length_tensor = torch.tensor(prompt_length) labels = labels.masked_fill(torch.arange(labels.size(1)).unsqueeze(0) < prompt_length_tensor.unsqueeze(1), -100) return_data.update({"labels": labels}) return BatchFeature(data=return_data) # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->GPT2 def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to GPT2TokenizerFast'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->GPT2 def decode(self, *args, **kwargs): """ This method forwards all its arguments to GPT2TokenizerFast'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 with CLIP->TraVision 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)) # if __name__ == '__main__': # config = TaiVisionLMConfig.from_pretrained("./") # preprocessor = transformers.SiglipImageProcessor.from_pretrained("google/siglip-base-patch16-224") # preprocessor.image_seq_length = config.num_image_tokens # tokenizer = transformers.AutoTokenizer.from_pretrained("benchang1110/Taiwan-tinyllama-v1.0-chat") # processor = TaiVisionProcessor(tokenizer=tokenizer, image_processor=preprocessor) # processor.save_pretrained("./") # from PIL import Image # import requests # processor = TaiVisionProcessor.from_pretrained("./") # url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg" # image = Image.open(requests.get(url, stream=True).raw).convert("RGB") # prompt = "Hello< what is your name?" # label = "I am fine, thank you." # inputs = processor(prompts=prompt, labels=label,images=image, return_tensors="pt",padding="max_length",max_length=512) # for key, value in inputs.items(): # print(f"{key}: {value}") # print(processor.decode(inputs.input_ids.tolist()[0]))