# 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 BLIP-2. """ from typing import List, Optional, 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 logger = logging.get_logger(__name__) class Blip2ProcessorKwargs(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 Blip2Processor(ProcessorMixin): r""" Constructs a BLIP-2 processor which wraps a BLIP image processor and an OPT/T5 tokenizer into a single processor. [`BlipProcessor`] 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. 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"] valid_kwargs = ["num_query_tokens"] image_processor_class = ("BlipImageProcessor", "BlipImageProcessorFast") tokenizer_class = "AutoTokenizer" def __init__(self, image_processor, tokenizer, num_query_tokens=None, **kwargs): tokenizer.return_token_type_ids = False self.current_processor = image_processor 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) def __call__( self, images: ImageInput = None, text: Optional[Union[str, List[str], TextInput, PreTokenizedInput]] = None, audio=None, videos=None, **kwargs: Unpack[Blip2ProcessorKwargs], ) -> BatchEncoding: """ 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). 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. """ if images is None and text is None: raise ValueError("You have to specify either images or text.") output_kwargs = self._merge_kwargs( Blip2ProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) # BC for explicit return_tensors if "return_tensors" in output_kwargs["common_kwargs"]: return_tensors = output_kwargs["common_kwargs"].pop("return_tensors", None) else: return_tensors = None encoding = BatchFeature(tensor_type=return_tensors) 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") text_encoding = {} 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: 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 logger.warning_once( "Expanding inputs for image tokens in BLIP-2 should be done in processing. " "Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your BLIP-2 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 encoding.update(BatchEncoding(text_encoding, tensor_type=return_tensors)) # add pixel_values encoding. If we also have text_encoding, update image encoding and return it. # else, return the text encoding. 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)) __all__ = ["Blip2Processor"]