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| # coding=utf-8 | |
| # Copyright 2022 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. | |
| """ | |
| from typing import List, Optional, Union | |
| from ...image_utils import ImageInput | |
| from ...processing_utils import ProcessorMixin | |
| from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy | |
| from ...utils import TensorType | |
| class BlipProcessor(ProcessorMixin): | |
| r""" | |
| Constructs a BLIP processor which wraps a BERT tokenizer and BLIP image processor into a single processor. | |
| [`BlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`BertTokenizerFast`]. 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 (`BertTokenizerFast`): | |
| An instance of ['BertTokenizerFast`]. The tokenizer is a required input. | |
| """ | |
| attributes = ["image_processor", "tokenizer"] | |
| image_processor_class = "BlipImageProcessor" | |
| tokenizer_class = ("BertTokenizer", "BertTokenizerFast") | |
| def __init__(self, image_processor, tokenizer): | |
| tokenizer.return_token_type_ids = False | |
| super().__init__(image_processor, tokenizer) | |
| self.current_processor = self.image_processor | |
| def __call__( | |
| self, | |
| images: ImageInput = None, | |
| text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, | |
| add_special_tokens: bool = True, | |
| padding: Union[bool, str, PaddingStrategy] = False, | |
| truncation: Union[bool, str, TruncationStrategy] = None, | |
| max_length: Optional[int] = None, | |
| stride: int = 0, | |
| pad_to_multiple_of: Optional[int] = None, | |
| return_attention_mask: Optional[bool] = None, | |
| return_overflowing_tokens: bool = False, | |
| return_special_tokens_mask: bool = False, | |
| return_offsets_mapping: bool = False, | |
| return_token_type_ids: bool = False, | |
| return_length: bool = False, | |
| verbose: bool = True, | |
| return_tensors: Optional[Union[str, TensorType]] = None, | |
| **kwargs, | |
| ) -> 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. | |
| """ | |
| if images is None and text is None: | |
| raise ValueError("You have to specify either images or text.") | |
| # Get only text | |
| if images is None: | |
| self.current_processor = self.tokenizer | |
| text_encoding = self.tokenizer( | |
| text=text, | |
| add_special_tokens=add_special_tokens, | |
| padding=padding, | |
| truncation=truncation, | |
| max_length=max_length, | |
| stride=stride, | |
| pad_to_multiple_of=pad_to_multiple_of, | |
| return_attention_mask=return_attention_mask, | |
| return_overflowing_tokens=return_overflowing_tokens, | |
| return_special_tokens_mask=return_special_tokens_mask, | |
| return_offsets_mapping=return_offsets_mapping, | |
| return_token_type_ids=return_token_type_ids, | |
| return_length=return_length, | |
| verbose=verbose, | |
| return_tensors=return_tensors, | |
| **kwargs, | |
| ) | |
| return text_encoding | |
| # add pixel_values | |
| encoding_image_processor = self.image_processor(images, return_tensors=return_tensors) | |
| if text is not None: | |
| text_encoding = self.tokenizer( | |
| text=text, | |
| add_special_tokens=add_special_tokens, | |
| padding=padding, | |
| truncation=truncation, | |
| max_length=max_length, | |
| stride=stride, | |
| pad_to_multiple_of=pad_to_multiple_of, | |
| return_attention_mask=return_attention_mask, | |
| return_overflowing_tokens=return_overflowing_tokens, | |
| return_special_tokens_mask=return_special_tokens_mask, | |
| return_offsets_mapping=return_offsets_mapping, | |
| return_token_type_ids=return_token_type_ids, | |
| return_length=return_length, | |
| verbose=verbose, | |
| return_tensors=return_tensors, | |
| **kwargs, | |
| ) | |
| else: | |
| text_encoding = None | |
| if text_encoding is not None: | |
| encoding_image_processor.update(text_encoding) | |
| return encoding_image_processor | |
| 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 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)) | |