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import torch |
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import io |
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import re |
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from typing import Any, Dict |
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from PIL import Image |
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from transformers import DonutProcessor, VisionEncoderDecoderModel |
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class EndpointHandler: |
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def __init__(self, path=""): |
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self.processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2") |
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self.model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2") |
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self.device = "cuda" if torch.cuda.is_available() else "cpu" |
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def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]: |
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inputs = data.pop("inputs", data) |
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image = inputs["image"] |
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image = Image.open(io.BytesIO(eval(image))) |
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return self.process_document(image) |
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def process_document(self, image:Image) -> dict[str, Any]: |
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pixel_values = self.processor(image, return_tensors="pt").pixel_values |
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task_prompt = "<s_cord-v2>" |
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decoder_input_ids = self.processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids |
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outputs = self.model.generate( |
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pixel_values.to(self.device), |
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decoder_input_ids=decoder_input_ids.to(self.device), |
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max_length=self.model.decoder.config.max_position_embeddings, |
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early_stopping=True, |
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pad_token_id=self.processor.tokenizer.pad_token_id, |
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eos_token_id=self.processor.tokenizer.eos_token_id, |
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use_cache=True, |
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num_beams=1, |
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bad_words_ids=[[self.processor.tokenizer.unk_token_id]], |
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return_dict_in_generate=True, |
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) |
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sequence = self.processor.batch_decode(outputs.sequences)[0] |
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sequence = sequence.replace(self.processor.tokenizer.eos_token, "").replace(self.processor.tokenizer.pad_token, "") |
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sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() |
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return self.processor.token2json(sequence) |
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