from preprocess import Model, SquadDataset
from transformers import DistilBertForQuestionAnswering
from torch.utils.data import DataLoader
from transformers import AdamW
import torch
import subprocess

data = Model()
data.ModelExecution()
# train_contexts, train_questions, train_answers = data.ArrangeData("livecheckcontainer")
# val_contexts, val_questions, val_answers = data.ArrangeData("livecheckcontainer")
# print(train_answers)

# train_answers, train_contexts = data.add_end_idx(train_answers, train_contexts)
# val_answers, val_contexts = data.add_end_idx(val_answers, val_contexts)

# train_encodings, val_encodings = data.Tokenizer(train_contexts, train_questions, val_contexts, val_questions)

# train_encodings  = data.add_token_positions(train_encodings, train_answers)
# val_encodings = data.add_token_positions(val_encodings, val_answers)

# train_dataset = SquadDataset(train_encodings)
# val_dataset = SquadDataset(val_encodings)



# model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")


# device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')

# model.to(device)
# model.train()

# train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)

# optim = AdamW(model.parameters(), lr=5e-5)

# for epoch in range(2):
#     print(epoch)
#     for batch in train_loader:
#         optim.zero_grad()
#         input_ids = batch['input_ids'].to(device)
#         attention_mask = batch['attention_mask'].to(device)
#         start_positions = batch['start_positions'].to(device)
#         end_positions = batch['end_positions'].to(device)
#         outputs = model(input_ids, attention_mask=attention_mask, start_positions=start_positions, end_positions=end_positions)
#         loss = outputs[0]
#         loss.backward()
#         optim.step()
# print("Done")
# model.eval()
# model.save_pretrained("./")
# data.tokenizer.save_pretrained("./")


# subprocess.call(["git", "add","--all"])
# subprocess.call(["git", "status"])
# subprocess.call(["git", "commit", "-m", "First version of the your-model-name model and tokenizer."])
# subprocess.call(["git", "push"])