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from typing import Dict, List, Any |
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from transformers import DonutProcessor, VisionEncoderDecoderModel |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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class EndpointHandler: |
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def __init__(self, path=""): |
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self.processor = DonutProcessor.from_pretrained(path) |
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self.model = VisionEncoderDecoderModel.from_pretrained(path) |
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self.model.to(device) |
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self.decoder_input_ids = self.processor.tokenizer( |
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"<s_cord-v2>", add_special_tokens=False, return_tensors="pt" |
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).input_ids |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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inputs = data.pop("inputs", data) |
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pixel_values = self.processor(inputs, return_tensors="pt").pixel_values |
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outputs = self.model.generate( |
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pixel_values.to(device), |
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decoder_input_ids=self.decoder_input_ids.to(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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prediction = self.processor.batch_decode(outputs.sequences)[0] |
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prediction = self.processor.token2json(prediction) |
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return prediction |
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