import torch
#from kogpt2Test.dialogLM.Kogpt2 import DialogKoGPT2
from KoELECTRA import *
#from kogpt2_transformers import get_kogpt2_tokenizer
from transformers import ElectraModel, ElectraTokenizer
from transformers import (
	ElectraConfig,
	ElectraTokenizer,

)
save_ckpt_path = './sentiment-classification.pth'

ctx = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(ctx)

model = koElectraForSequenceClassification()
checkpoint = torch.load(save_ckpt_path, map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
tokenizer = ElectraTokenizer()

## repo
MODEL_SAVE_REPO = 'Koelectra-five-sentiment-classification' # ex) 'my-bert-fine-tuned'
HUGGINGFACE_AUTH_TOKEN = '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' # https://huggingface.co/settings/token

MODEL_SAVE_HUB_PATH = '.'


## Push to huggingface-hub
model.kogpt2.push_to_hub(
			MODEL_SAVE_REPO, 
			use_temp_dir=True, 
			use_auth_token=HUGGINGFACE_AUTH_TOKEN
)
tokenizer.push_to_hub(
			MODEL_SAVE_REPO, 
			use_temp_dir=True, 
			use_auth_token=HUGGINGFACE_AUTH_TOKEN
)