# 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 InstructBLIP. Largely copy of Blip2Processor with addition of a tokenizer for the Q-Former. """ import os from typing import List, Union from ...image_processing_utils import BatchFeature from ...image_utils import ImageInput from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack from ...tokenization_utils_base import ( AddedToken, BatchEncoding, PreTokenizedInput, TextInput, ) from ...utils import logging from ..auto import AutoTokenizer logger = logging.get_logger(__name__) class InstructBlipProcessorKwargs(ProcessingKwargs, total=False): _defaults = { "text_kwargs": { "add_special_tokens": True, "padding": False, "stride": 0, "return_overflowing_tokens": False, "return_special_tokens_mask": False, "return_offsets_mapping": False, "return_token_type_ids": False, "return_length": False, "verbose": True, }, "images_kwargs": {}, } class InstructBlipProcessor(ProcessorMixin): r""" Constructs an InstructBLIP processor which wraps a BLIP image processor and a LLaMa/T5 tokenizer into a single processor. [`InstructBlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the docstring of [`~BlipProcessor.__call__`] and [`~BlipProcessor.decode`] for more information. Args: image_processor (`BlipImageProcessor`): An instance of [`BlipImageProcessor`]. The image processor is a required input. tokenizer (`AutoTokenizer`): An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input. qformer_tokenizer (`AutoTokenizer`): An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input. num_query_tokens (`int`, *optional*):" Number of tokens used by the Qformer as queries, should be same as in model's config. """ attributes = ["image_processor", "tokenizer", "qformer_tokenizer"] valid_kwargs = ["num_query_tokens"] image_processor_class = ("BlipImageProcessor", "BlipImageProcessorFast") tokenizer_class = "AutoTokenizer" qformer_tokenizer_class = "AutoTokenizer" def __init__(self, image_processor, tokenizer, qformer_tokenizer, num_query_tokens=None, **kwargs): if not hasattr(tokenizer, "image_token"): self.image_token = AddedToken("", normalized=False, special=True) tokenizer.add_tokens([self.image_token], special_tokens=True) else: self.image_token = tokenizer.image_token self.num_query_tokens = num_query_tokens super().__init__(image_processor, tokenizer, qformer_tokenizer) def __call__( self, images: ImageInput = None, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, audio=None, videos=None, **kwargs: Unpack[InstructBlipProcessorKwargs], ) -> BatchFeature: """ This method uses [`BlipImageProcessor.__call__`] method to prepare image(s) for the model, and [`BertTokenizerFast.__call__`] to prepare text for the model. Please refer to the docstring of the above two methods for more information. Args: images (`ImageInput`): 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 (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`): 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). """ if images is None and text is None: raise ValueError("You have to specify at least images or text.") output_kwargs = self._merge_kwargs( InstructBlipProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) encoding = 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") # we have to concatenate lists - so we keep track of return_tensors here return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) _text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None) output_kwargs["text_kwargs"]["return_tensors"] = return_tensors # if we know how many query tokens, expand text inside processor. We need this hacky manipulation # because BLIP expects image tokens to be at the beginning even before BOS token if self.num_query_tokens is not None and images is not None: text_encoding = {} image_tokens = self.image_token.content * self.num_query_tokens image_token_encoding = self.tokenizer( [image_tokens] * len(text), add_special_tokens=False, return_tensors=None ) for k in _text_encoding: text_encoding[k] = [ img_encoding + txt_encoding for img_encoding, txt_encoding in zip(image_token_encoding[k], _text_encoding[k]) ] else: text_encoding = _text_encoding if images is not None: logger.warning_once( "Expanding inputs for image tokens in InstructBLIP should be done in processing. " "Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your InstructBLIP model. " "Using processors without these attributes in the config is deprecated and will throw an error in v4.50." ) # cast to desired return tensors type after concatenating text_encoding = BatchEncoding(text_encoding, tensor_type=return_tensors) encoding.update(text_encoding) qformer_text_encoding = self.qformer_tokenizer(text, **output_kwargs["text_kwargs"]) encoding["qformer_input_ids"] = qformer_text_encoding.pop("input_ids") encoding["qformer_attention_mask"] = qformer_text_encoding.pop("attention_mask") if images is not None: image_encoding = self.image_processor(images, **output_kwargs["images_kwargs"]) encoding.update(image_encoding) return encoding # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to PreTrainedTokenizer'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.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer def decode(self, *args, **kwargs): """ This method forwards all its arguments to PreTrainedTokenizer'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.blip.processing_blip.BlipProcessor.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)) # overwrite to save the Q-Former tokenizer in a separate folder def save_pretrained(self, save_directory, **kwargs): if os.path.isfile(save_directory): raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file") os.makedirs(save_directory, exist_ok=True) qformer_tokenizer_path = os.path.join(save_directory, "qformer_tokenizer") self.qformer_tokenizer.save_pretrained(qformer_tokenizer_path) # We modify the attributes so that only the tokenizer and image processor are saved in the main folder qformer_present = "qformer_tokenizer" in self.attributes if qformer_present: self.attributes.remove("qformer_tokenizer") outputs = super().save_pretrained(save_directory, **kwargs) if qformer_present: self.attributes += ["qformer_tokenizer"] return outputs # overwrite to load the Q-Former tokenizer from a separate folder @classmethod def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs) # if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs' if isinstance(processor, tuple): processor = processor[0] qformer_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="qformer_tokenizer") processor.qformer_tokenizer = qformer_tokenizer return processor __all__ = ["InstructBlipProcessor"]