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from typing import Dict, List, Any
from transformers import NougatProcessor, VisionEncoderDecoderModel
import torch
# check for GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class EndpointHandler:
def __init__(self, path=""):
# load the model
self.processor = NougatProcessor.from_pretrained(path)
self.model = VisionEncoderDecoderModel.from_pretrained(path)
# move model to device
self.model.to(device)
# self.decoder_input_ids = self.processor.tokenizer(
# "<s_cord-v2>", add_special_tokens=False, return_tensors="pt"
# ).input_ids
def __call__(self, data):
inputs = data.pop("inputs", data)
# preprocess the input
pixel_values = self.processor(inputs, return_tensors="pt").pixel_values
print(type(pixel_values))
# forward pass
outputs = self.model.generate(
pixel_values.to(device),
min_length = 1,
# decoder_input_ids=self.decoder_input_ids.to(device),
max_length=3584,
# early_stopping=True,
# pad_token_id=self.processor.tokenizer.pad_token_id,
# eos_token_id=self.processor.tokenizer.eos_token_id,
# use_cache=True,
# num_beams=1,
bad_words_ids=[[self.processor.tokenizer.unk_token_id]],
# return_dict_in_generate=True,
)
# process output
prediction = self.processor.batch_decode(outputs, skip_special_tokens=True)[0]
prediction = self.processor.post_process_generation(prediction, fix_markdown=False)
return prediction
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