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import os
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from typing import Dict, List, Any
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from PIL import Image
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import jax
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from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel, VisionEncoderDecoderModel
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import torch
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class PreTrainedPipeline():
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def __init__(self, path=""):
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model_dir = path
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self.model = VisionEncoderDecoderModel.from_pretrained(model_dir)
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self.feature_extractor = ViTFeatureExtractor.from_pretrained(model_dir)
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self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
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max_length = 16
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num_beams = 4
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self.gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "return_dict_in_generate": True, "output_scores": True}
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self.model.to("cpu")
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self.model.eval()
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def _generate(pixel_values):
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with torch.no_grad():
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outputs = self.model.generate(pixel_values, **self.gen_kwargs)
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output_ids = outputs.sequences
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sequences_scores = outputs.sequences_scores
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return output_ids, sequences_scores
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self.generate = _generate
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image_path = os.path.join(path, 'val_000000039769.jpg')
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image = Image.open(image_path)
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self(image)
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image.close()
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def __call__(self, inputs: "Image.Image") -> List[str]:
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"""
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Args:
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Return:
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"""
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pixel_values = self.feature_extractor(images=inputs, return_tensors="pt").pixel_values
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output_ids, sequences_scores = self.generate(pixel_values)
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preds = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)
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preds = [pred.strip() for pred in preds]
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preds = [{"label": preds[0], "score": float(sequences_scores[0])}]
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return preds
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