# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/aria/modular_aria.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_aria.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2024 The Rhymes-AI Teams Authors 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. from typing import Dict, List, Optional, Union from ...image_processing_utils import BatchFeature from ...image_utils import ImageInput from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack from ...tokenization_utils import PreTokenizedInput, TextInput from ...utils import TensorType from ..auto import AutoTokenizer class AriaProcessorKwargs(ProcessingKwargs, total=False): _defaults = { "text_kwargs": { "padding": False, }, "images_kwargs": { "max_image_size": 980, "split_image": False, }, "return_tensors": TensorType.PYTORCH, } class AriaProcessor(ProcessorMixin): """ AriaProcessor is a processor for the Aria model which wraps the Aria image preprocessor and the LLama slow tokenizer. Args: image_processor (`AriaImageProcessor`, *optional*): The AriaImageProcessor to use for image preprocessing. tokenizer (`PreTrainedTokenizerBase`, *optional*): An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. 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. size_conversion (`Dict`, *optional*): A dictionary indicating size conversions for images. """ attributes = ["image_processor", "tokenizer"] valid_kwargs = ["chat_template", "size_conversion"] image_processor_class = "AriaImageProcessor" tokenizer_class = "AutoTokenizer" def __init__( self, image_processor=None, tokenizer: Union[AutoTokenizer, str] = None, chat_template: Optional[str] = None, size_conversion: Optional[Dict[Union[float, int], int]] = None, ): if size_conversion is None: size_conversion = {490: 128, 980: 256} self.size_conversion = {int(k): v for k, v in size_conversion.items()} if tokenizer is not None and tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.unk_token super().__init__(image_processor, tokenizer, chat_template=chat_template) def __call__( self, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], images: Optional[ImageInput] = None, audio=None, videos=None, **kwargs: Unpack[AriaProcessorKwargs], ) -> BatchFeature: """ Main method to prepare for the model one or several sequences(s) and image(s). Args: 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). 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. 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`. - **pixel_mask** -- Pixel mask to be fed to a model. Returned when `images` is not `None`. """ output_kwargs = self._merge_kwargs( AriaProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) 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") if images is not None: image_inputs = self.image_processor( images, **output_kwargs["images_kwargs"], ) # expand the image_token according to the num_crops and tokens per image tokens_per_image = self.size_conversion[image_inputs.pixel_values.shape[2]] prompt_strings = [] num_crops = image_inputs.pop("num_crops") * tokens_per_image for sample in text: sample = sample.replace(self.tokenizer.image_token, self.tokenizer.image_token * num_crops) prompt_strings.append(sample) else: image_inputs = {} prompt_strings = text text_inputs = self.tokenizer( prompt_strings, **output_kwargs["text_kwargs"], ) return BatchFeature(data={**text_inputs, **image_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 # Remove `num_crops`, it is popped and used only when processing. Make a copy of list when remocing # otherwise `self.image_processor.model_input_names` is also modified image_processor_input_names = [name for name in image_processor_input_names if name != "num_crops"] return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) __all__ = ["AriaProcessor"]