# coding=utf-8 # Copyright 2023 The Intel AIA Team Authors, and 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 TVP. """ from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class TvpProcessor(ProcessorMixin): r""" Constructs an TVP processor which wraps a TVP image processor and a Bert tokenizer into a single processor. [`TvpProcessor`] offers all the functionalities of [`TvpImageProcessor`] and [`BertTokenizerFast`]. See the [`~TvpProcessor.__call__`] and [`~TvpProcessor.decode`] for more information. Args: image_processor ([`TvpImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`BertTokenizerFast`], *optional*): The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "TvpImageProcessor" tokenizer_class = ("BertTokenizer", "BertTokenizerFast") def __init__(self, image_processor=None, tokenizer=None, **kwargs): 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, videos=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 BertTokenizerFast's [`~BertTokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `videos` and `kwargs` arguments to TvpImageProcessor's [`~TvpImageProcessor.__call__`] if `videos` is not `None`. Please refer to the docstring 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). videos (`List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, `List[List[PIL.Image.Image]]`, `List[List[np.ndarray]]`,: `List[List[torch.Tensor]]`): The video or batch of videos to be prepared. Each video should be a list of frames, which can be either PIL images or NumPy arrays. In case of NumPy arrays/PyTorch tensors, each frame should be of shape (H, W, C), where H and W are frame height and width, and C is a number of channels. 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 `videos` is not `None`. """ max_text_length = kwargs.pop("max_text_length", None) if text is None and videos is None: raise ValueError("You have to specify either text or videos. Both cannot be none.") encoding = {} if text is not None: textual_input = self.tokenizer.batch_encode_plus( text, truncation=True, padding="max_length", max_length=max_text_length, pad_to_max_length=True, return_tensors=return_tensors, return_token_type_ids=False, **kwargs, ) encoding.update(textual_input) if videos is not None: image_features = self.image_processor(videos, return_tensors=return_tensors, **kwargs) encoding.update(image_features) return BatchEncoding(data=encoding, tensor_type=return_tensors) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to BertTokenizerFast'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 BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) def post_process_video_grounding(self, logits, video_durations): """ Compute the time of the video. Args: logits (`torch.Tensor`): The logits output of TvpForVideoGrounding. video_durations (`float`): The video's duration. Returns: start (`float`): The start time of the video. end (`float`): The end time of the video. """ start, end = ( round(logits.tolist()[0][0] * video_durations, 1), round(logits.tolist()[0][1] * video_durations, 1), ) return start, end @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__ = ["TvpProcessor"]