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import torch
from torch.utils.data import DataLoader
from transformers import AutoTokenizer,AutoModelForSequenceClassification,BertModel,BertTokenizer
from pathlib import Path
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset
from transformers import DistilBertTokenizerFast,DistilBertForSequenceClassification
from transformers import Trainer, TrainingArguments
from transformers import AdamW,DistilBertForSequenceClassification
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model_name = "distilbert-base-uncased"



def read_imdb_split(split_dir):
    split_dir = Path(split_dir)
    texts = []
    labels = []
    for label_dir in ["pos","neg"]:
        for text_file in (split_dir/label_dir).iterdir():
            texts.append(text_file.read_text())
            labels.append(0 if label_dir == "neg" else 1)
    return texts, labels

# Large Movie Review Dataset
# http://ai.stanford.edu/~amaas/data/sentiment/
train_texts, train_labels = read_imdb_split('aclImdb/train')
test_texts, test_labels = read_imdb_split('aclImdb/test')

train_texts, val_texts, train_labels, val_lalbels = train_test_split(train_texts,train_labels,test_size=0.2,shuffle=True,random_state=42)

class IMDBdataset(Dataset):
    def __init__(self,encodings, labels):
        self.encodings = encodings
        self.labels = labels
    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
    def __len__(self):
        return len(self.labels)
        
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)

train_encodings = tokenizer(train_texts, truncation=True, padding=True)
val_encodings = tokenizer(val_texts, truncation=True, padding=True)
test_encodings = tokenizer(test_texts, truncation=True, padding=True)

train_dataset = IMDBdataset(train_encodings, train_labels)
val_dataset = IMDBdataset(val_encodings, val_lalbels)
test_dataset = IMDBdataset(test_encodings, test_labels)

model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
model.to(device)
model.train()

train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
optim = AdamW(model.parameters(), lr=5e-5)

num_train_epochs = 2
for each in range(num_train_epochs):
    for batch in train_loader:
        optim.zero_grad()
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)

        outputs = model(input_ids, attention_mask=attention_mask,labels=labels)
        loss = outputs[0]
        loss.backwoard()
        optim.step()
model.eval()