import torch
from dateutil.parser import parse as parse_date
from sklearn.model_selection import train_test_split
from transformers import (
    pipeline,
    AutoTokenizer,
    AutoModelForSequenceClassification,
    TrainingArguments,
    Trainer
)
from torch.utils.data import Dataset

class GroundingDataset(Dataset):
    def __init__(self, data, tokenizer, max_length=512):
        self.data = data
        self.tokenizer = tokenizer
        self.max_length = max_length
        
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        item = self.data[idx]
        encoding = self.tokenizer(
            item["question"],
            text_pair=item["answer"] + " [SEP] " + item["context"],
            padding="max_length",
            truncation=True,
            max_length=self.max_length,
            return_tensors="pt"
        )
        return {
            "input_ids": encoding["input_ids"].squeeze(),
            "attention_mask": encoding["attention_mask"].squeeze(),
            "labels": torch.tensor(item["label"])
        }

class GroundingTrainer:
    def __init__(self):
        self.tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
        self.model = AutoModelForSequenceClassification.from_pretrained(
            "distilbert-base-uncased", num_labels=2
        )
        
    def train(self, dataset):
        train_data, val_data = train_test_split(dataset, test_size=0.2)
        
        trainer = Trainer(
            model=self.model,
            args=TrainingArguments(
                output_dir="./results",
                num_train_epochs=3,
                per_device_train_batch_size=8,
                evaluation_strategy="epoch",
                logging_dir="./logs"
            ),
            train_dataset=GroundingDataset(train_data, self.tokenizer),
            eval_dataset=GroundingDataset(val_data, self.tokenizer)
        )
        
        trainer.train()
        self.model.save_pretrained("./grounding_detector")
        self.tokenizer.save_pretrained("./grounding_detector")