# coding=utf-8 # Copyright 2024 Microsoft 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 Phi3-V. """ import re from typing import List, Optional, Union import torch import transformers from transformers.feature_extraction_utils import BatchFeature from transformers.image_utils import ImageInput from transformers.processing_utils import ProcessorMixin from transformers.tokenization_utils_base import PaddingStrategy, TextInput, TruncationStrategy from transformers.utils import TensorType from .image_processing_phi3_v import Phi3VImageProcessor transformers.Phi3VImageProcessor = Phi3VImageProcessor class Phi3VProcessor(ProcessorMixin): r""" Constructs a Phi3-V processor which wraps a Phi3-V image processor and a LLaMa tokenizer into a single processor. [`Phi3VProcessor`] offers all the functionalities of [`Phi3VImageProcessor`] and [`LlamaTokenizerFast`]. See the [`~Phi3VProcessor.__call__`] and [`~Phi3VProcessor.decode`] for more information. Args: image_processor ([`Phi3VImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`], *optional*): The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "Phi3VImageProcessor" tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast") special_image_token = "<|image|>" def __init__(self, image_processor, tokenizer): self.image_processor = image_processor self.tokenizer = tokenizer self.num_img_tokens = image_processor.num_img_tokens self.img_tokens = [f"<|image_{i+1}|>" for i in range(1000000)] def __call__( self, text: Union[TextInput, List[TextInput]], 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, ) -> 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 Phi3ImageProcessor's [`~Phi3ImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring of the above two methods for more information. Args: 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). 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. 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. 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 images is not None: image_inputs = self.image_processor(images, return_tensors=return_tensors) else: image_inputs = {} inputs = self._convert_images_texts_to_inputs(image_inputs, text, padding=padding, truncation=truncation, max_length=max_length, return_tensors=return_tensors) return inputs def calc_num_image_tokens(self, images: ImageInput): """ Calculate the number of image tokens for each image. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. """ return self.image_processor.calc_num_image_tokens(images) def calc_num_image_tokens_from_image_size(self, width, height): """ Calculate the number of image token for an image with given width and height. Args: width (`int`): Width of the image. height (`int`): Height of the image. """ return self.image_processor.calc_num_image_tokens_from_image_size(width, height) @property def special_image_token_id(self): return self.tokenizer.convert_tokens_to_ids(self.special_image_token) def get_special_image_token_id(self): return self.tokenizer.convert_tokens_to_ids(self.special_image_token) def _convert_images_texts_to_inputs(self, images, texts, padding=False, truncation=None, max_length=None, return_tensors=None): if not len(images): model_inputs = self.tokenizer(texts, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length) return BatchFeature(data={**model_inputs}) pattern = r"<\|image_\d+\|>" prompt_chunks = [self.tokenizer(chunk).input_ids for chunk in re.split(pattern, texts)] if 'num_img_tokens' in images: num_img_tokens = images['num_img_tokens'] else: assert 'num_crops' in images, 'num_crops must be provided in images if num_img_tokens is not provided' num_crops = images['num_crops'] num_img_tokens = [_num_crops * self.num_img_tokens for _num_crops in num_crops] images, image_sizes = images['pixel_values'], images['image_sizes'] # image_tags needs to start from 1 to n image_tags = re.findall(pattern, texts) # image_ids = [int(s.split("|")[1].split("_")[-1]) * -1 for s in image_tags] # image_ids_pad = [[iid]*num_img_tokens[i] for i, iid in enumerate(image_ids)] image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags] unique_image_ids = sorted(list(set(image_ids))) # image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be [1, 4, 5] # check the condition assert unique_image_ids == list(range(1, len(unique_image_ids)+1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}" # total images must be the same as the number of image tags assert len(unique_image_ids) == len(images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(images)} images" image_ids_pad = [[-iid]*num_img_tokens[iid-1] for iid in image_ids] def insert_separator(X, sep_list): if len(X) > len(sep_list): sep_list.append([]) return [ele for sublist in zip(X, sep_list) for ele in sublist] input_ids = [] offset = 0 for x in insert_separator(prompt_chunks, image_ids_pad): input_ids.extend(x[offset:]) input_ids = torch.tensor(input_ids, dtype=torch.long).unsqueeze(0) attention_mask = (input_ids > -1000000).to(torch.long) return BatchFeature(data={"input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": images, "image_sizes": image_sizes}) # 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))