import torch
from .decent_torch import DecentModel
from .openpeer import OpenPeerClient
from .grammar import LonScriptGrammar
from .modeling_openpeer import OpenPeerLLM
from .configuration_openpeer import OpenPeerConfig
from .tokenization_openpeer import OpenPeerTokenizer
import asyncio
from typing import Dict, Any, Optional

class DecentralizedLLM(DecentModel):
    def __init__(self, network_url: str = "ws://localhost:8000"):
        super().__init__()
        # Initialize our custom LLM
        self.config = OpenPeerConfig()
        self.model = OpenPeerLLM(self.config)
        self.tokenizer = OpenPeerTokenizer()
        self.peer_client = OpenPeerClient(network_url)
        self.grammar = LonScriptGrammar()
        self._ensure_model_on_device()
        
    def _ensure_model_on_device(self):
        """Ensure model is on the correct device"""
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = self.model.to(device)
        
    def forward(self, input_text: str) -> str:
        # Tokenize input
        inputs = self.tokenizer(input_text, return_tensors="pt")
        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
        
        # Generate response using our custom LLM
        with torch.no_grad():
            outputs = self.model(**inputs)
            logits = outputs["logits"]
            
            # Get next token predictions
            next_token_logits = logits[:, -1, :]
            next_tokens = torch.argmax(next_token_logits, dim=-1)
            
            generated_ids = [inputs["input_ids"][0].tolist()]
            for _ in range(100):  # max length
                curr_input = torch.tensor([generated_ids[-1]], device=self.model.device)
                with torch.no_grad():
                    outputs = self.model(curr_input)
                    next_token_logits = outputs["logits"][:, -1, :]
                    next_token = torch.argmax(next_token_logits, dim=-1).item()
                    
                generated_ids.append([next_token])
                
                if next_token == self.tokenizer.eos_token_id:
                    break
        
        # Decode and return results
        decoded_output = self.tokenizer.decode(torch.tensor(generated_ids).flatten(), skip_special_tokens=True)
        return decoded_output
from .grammar import LonScriptGrammar
from .modeling_openpeer import OpenPeerLLM
from .configuration_openpeer import OpenPeerConfig
from .tokenization_openpeer import OpenPeerTokenizer
import asyncio
from typing import Dict, Any, Optional

class DecentralizedLLM(DecentModel):
    def __init__(self, network_url: str = "ws://localhost:8000"):
        super().__init__()
        # Initialize our custom LLM
        self.config = OpenPeerConfig()
        self.model = OpenPeerLLM(self.config)
        self.tokenizer = OpenPeerTokenizer()
        self.peer_client = OpenPeerClient(network_url)
        self.grammar = LonScriptGrammar()
        self._ensure_model_on_device()
        
    def _ensure_model_on_device(self):
        """Ensure model is on the correct device"""
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = self.model.to(device)
        
    async def connect_to_network(self):
        """Connect to the peer network"""
        await self.peer_client.connect(self.peer_id)
        asyncio.create_task(self._handle_peer_updates())
        
    async def _handle_peer_updates(self):
        """Handle incoming updates from peers"""
        async for update in self.peer_client.receive_updates():
            if update["type"] == "model_update":
                await self._process_model_update(update)
                
    async def _process_model_update(self, update: Dict[str, Any]):
        """Process received model updates"""
        state_dict = {k: torch.tensor(v) for k, v in update["state"].items()}
        self.state_updates[update["peer_id"]] = state_dict
        self.aggregate_states()
        
    def forward(self, input_text: str) -> str:
        """Generate response for input text"""
        # Tokenize input
        inputs = self.tokenizer(input_text, return_tensors="pt")
        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
        
        # Generate response
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_length=100,
                num_return_sequences=1,
                pad_token_id=self.tokenizer.eos_token_id
            )
        
        # Decode and return results
        decoded_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return decoded_output
        
    async def train_step(self, batch: Dict[str, torch.Tensor]):
        """Perform a training step and share updates with peers"""
        # Forward pass
        outputs = self.model(**batch)
        loss = outputs.loss
        
        # Backward pass
        loss.backward()
        
        # Optimizer step would go here
        # self.optimizer.step()
        
        # Share updated model state with peers
        await self.peer_client.send_model_update(self.model.state_dict())
        
    def reason(self, context: str, query: str) -> str:
        """Implement deep reasoning capabilities with grammar enhancement"""
        # Combine context and query
        prompt = f"Context: {context}\nQuery: {query}\nReasoned response:"
        
        # Generate initial response
        initial_response = self.forward(prompt)
        
        # Apply grammar rules for enhanced understanding
        enhanced_response = self.grammar.apply_grammar_rules(initial_response)
        
        return enhanced_response