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| #!/usr/bin/env python | |
| # coding=utf-8 | |
| # Copyright 2023 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. | |
| import re | |
| from ..models.auto import AutoProcessor | |
| from ..models.vision_encoder_decoder import VisionEncoderDecoderModel | |
| from ..utils import is_vision_available | |
| from .base import PipelineTool | |
| if is_vision_available(): | |
| from PIL import Image | |
| class DocumentQuestionAnsweringTool(PipelineTool): | |
| default_checkpoint = "naver-clova-ix/donut-base-finetuned-docvqa" | |
| description = ( | |
| "This is a tool that answers a question about an document (pdf). It takes an input named `document` which " | |
| "should be the document containing the information, as well as a `question` that is the question about the " | |
| "document. It returns a text that contains the answer to the question." | |
| ) | |
| name = "document_qa" | |
| pre_processor_class = AutoProcessor | |
| model_class = VisionEncoderDecoderModel | |
| inputs = ["image", "text"] | |
| outputs = ["text"] | |
| def __init__(self, *args, **kwargs): | |
| if not is_vision_available(): | |
| raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool.") | |
| super().__init__(*args, **kwargs) | |
| def encode(self, document: "Image", question: str): | |
| task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>" | |
| prompt = task_prompt.replace("{user_input}", question) | |
| decoder_input_ids = self.pre_processor.tokenizer( | |
| prompt, add_special_tokens=False, return_tensors="pt" | |
| ).input_ids | |
| pixel_values = self.pre_processor(document, return_tensors="pt").pixel_values | |
| return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values} | |
| def forward(self, inputs): | |
| return self.model.generate( | |
| inputs["pixel_values"].to(self.device), | |
| decoder_input_ids=inputs["decoder_input_ids"].to(self.device), | |
| max_length=self.model.decoder.config.max_position_embeddings, | |
| early_stopping=True, | |
| pad_token_id=self.pre_processor.tokenizer.pad_token_id, | |
| eos_token_id=self.pre_processor.tokenizer.eos_token_id, | |
| use_cache=True, | |
| num_beams=1, | |
| bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]], | |
| return_dict_in_generate=True, | |
| ).sequences | |
| def decode(self, outputs): | |
| sequence = self.pre_processor.batch_decode(outputs)[0] | |
| sequence = sequence.replace(self.pre_processor.tokenizer.eos_token, "") | |
| sequence = sequence.replace(self.pre_processor.tokenizer.pad_token, "") | |
| sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token | |
| sequence = self.pre_processor.token2json(sequence) | |
| return sequence["answer"] | |