Create trainer.py

trainer.py

@@ -1,40 +1,13 @@
-from models.text2video_model import Text2VideoModel
-from training.trainer import Text2VideoTrainer
-from config.model_config import CONFIG
-import torch
-
-def main():
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    
-    model = Text2VideoModel(
-        vocab_size=CONFIG['vocab_size'],
-        embed_dim=CONFIG['embed_dim'],
-        latent_dim=CONFIG['latent_dim'],
-        num_frames=CONFIG['num_frames'],
-        frame_size=CONFIG['frame_size']
-    ).to(device)
-    
-    optimizer = torch.optim.Adam(model.parameters(), lr=CONFIG['learning_rate'])
-    trainer = Text2VideoTrainer(model, optimizer, device)
-    
-    # Add your data loading and training loop here
-
-if __name__ == '__main__':
-    main()
-    
-class Text2VideoTrainer:
-    def __init__(self, model, optimizer, device):
-        self.model = model
-        self.optimizer = optimizer
-        self.device = device
-        
-    def train_step(self, text_batch, video_batch):
-        self.optimizer.zero_grad()
-        
-        generated_video = self.model(text_batch)
-        loss = F.mse_loss(generated_video, video_batch)
-        
-        loss.backward()
-        self.optimizer.step()
-        
-        return loss.item()
+class EliteTrainer:
+    def __init__(self):
+        self.training_params = {
+            "epochs": 500,
+            "batch_size": 16,
+            "learning_rate": 2e-5,
+            "warmup_steps": 1000
+        }
+        
+    def train(self, dataset):
+        # Advanced training pipeline
+        pass
+