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| # coding=utf-8 | |
| # Copyright 2022 WenXiang ZhongzhiCheng LedellWu LiuGuang BoWenZhang 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. | |
| """ | |
| Image/Text processor class for AltCLIP | |
| """ | |
| import warnings | |
| from ...processing_utils import ProcessorMixin | |
| from ...tokenization_utils_base import BatchEncoding | |
| class AltCLIPProcessor(ProcessorMixin): | |
| r""" | |
| Constructs a AltCLIP processor which wraps a CLIP image processor and a XLM-Roberta tokenizer into a single | |
| processor. | |
| [`AltCLIPProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`XLMRobertaTokenizerFast`]. See | |
| the [`~AltCLIPProcessor.__call__`] and [`~AltCLIPProcessor.decode`] for more information. | |
| Args: | |
| image_processor ([`CLIPImageProcessor`], *optional*): | |
| The image processor is a required input. | |
| tokenizer ([`XLMRobertaTokenizerFast`], *optional*): | |
| The tokenizer is a required input. | |
| """ | |
| attributes = ["image_processor", "tokenizer"] | |
| image_processor_class = "CLIPImageProcessor" | |
| tokenizer_class = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast") | |
| def __init__(self, image_processor=None, tokenizer=None, **kwargs): | |
| feature_extractor = None | |
| if "feature_extractor" in kwargs: | |
| warnings.warn( | |
| "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" | |
| " instead.", | |
| FutureWarning, | |
| ) | |
| feature_extractor = kwargs.pop("feature_extractor") | |
| image_processor = image_processor if image_processor is not None else feature_extractor | |
| 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`.") | |
| super().__init__(image_processor, tokenizer) | |
| def __call__(self, text=None, images=None, return_tensors=None, **kwargs): | |
| """ | |
| Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` | |
| and `kwargs` arguments to XLMRobertaTokenizerFast's [`~XLMRobertaTokenizerFast.__call__`] if `text` is not | |
| `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to | |
| CLIPImageProcessor's [`~CLIPImageProcessor.__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. 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. | |
| 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: | |
| [`BatchEncoding`]: A [`BatchEncoding`] 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 text is None and images is None: | |
| raise ValueError("You have to specify either text or images. Both cannot be none.") | |
| if text is not None: | |
| encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs) | |
| if images is not None: | |
| image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs) | |
| if text is not None and images is not None: | |
| encoding["pixel_values"] = image_features.pixel_values | |
| return encoding | |
| elif text is not None: | |
| return encoding | |
| else: | |
| return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors) | |
| def batch_decode(self, *args, **kwargs): | |
| """ | |
| This method forwards all its arguments to XLMRobertaTokenizerFast'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 XLMRobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please | |
| refer to the docstring of this method for more information. | |
| """ | |
| return self.tokenizer.decode(*args, **kwargs) | |
| 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)) | |