"""
SHIVIK-Code Model Implementation

This is a modified version of SHIVIK-M4 with:
- Extended context length (32K) via YaRN RoPE scaling
- Tool calling capabilities
- Fill-in-the-Middle support
"""

import torch
import torch.nn as nn
from transformers import LlamaForCausalLM, LlamaConfig
from transformers.modeling_outputs import CausalLMOutputWithPast
from typing import Optional, Tuple, List, Union


class ShivikCodeConfig(LlamaConfig):
    """Configuration for SHIVIK-Code model."""
    
    model_type = "shivik_code"
    
    def __init__(
        self,
        vocab_size=128279,  # Extended for tool tokens
        hidden_size=2048,
        intermediate_size=8192,
        num_hidden_layers=16,
        num_attention_heads=32,
        num_key_value_heads=8,
        max_position_embeddings=32768,  # Extended context
        rope_theta=500000.0,
        rope_scaling=None,
        **kwargs
    ):
        # Default YaRN scaling for 32K context
        if rope_scaling is None:
            rope_scaling = {
                "type": "yarn",
                "factor": 8.0,
                "original_max_position_embeddings": 4096,
            }
        
        super().__init__(
            vocab_size=vocab_size,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            num_hidden_layers=num_hidden_layers,
            num_attention_heads=num_attention_heads,
            num_key_value_heads=num_key_value_heads,
            max_position_embeddings=max_position_embeddings,
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            **kwargs
        )
        
        # Tool token IDs (set after tokenizer is loaded)
        self.tool_call_start_id = None
        self.tool_call_end_id = None
        self.tool_result_start_id = None
        self.tool_result_end_id = None


class ShivikCodeForCausalLM(LlamaForCausalLM):
    """
    SHIVIK-Code: An agentic coding model.
    
    Extends LlamaForCausalLM with:
    - Tool calling support
    - Extended context via YaRN
    - FIM capability
    """
    
    config_class = ShivikCodeConfig
    
    def __init__(self, config: ShivikCodeConfig):
        super().__init__(config)
        
        # Store tool token IDs for easy access
        self.tool_tokens = {
            "call_start": config.tool_call_start_id,
            "call_end": config.tool_call_end_id,
            "result_start": config.tool_result_start_id,
            "result_end": config.tool_result_end_id,
        }
    
    def is_tool_call(self, token_id: int) -> bool:
        """Check if token is a tool call token."""
        return token_id in [
            self.tool_tokens["call_start"],
            self.tool_tokens["call_end"],
        ]
    
    def generate_with_tools(
        self,
        input_ids: torch.Tensor,
        tool_executor,  # Callable that executes tools
        max_new_tokens: int = 512,
        max_tool_calls: int = 10,
        **generate_kwargs
    ):
        """
        Generate with automatic tool execution.
        
        Args:
            input_ids: Input token IDs
            tool_executor: Function that takes tool call JSON and returns result
            max_new_tokens: Max tokens per generation step
            max_tool_calls: Max number of tool calls allowed
        
        Returns:
            Full generated sequence including tool results
        """
        current_ids = input_ids
        tool_call_count = 0
        
        while tool_call_count < max_tool_calls:
            # Generate until tool call or EOS
            outputs = self.generate(
                current_ids,
                max_new_tokens=max_new_tokens,
                stop_strings=["</tool_call>"],
                **generate_kwargs
            )
            
            generated = outputs[0]
            
            # Check if we hit a tool call
            if self._contains_tool_call(generated):
                # Extract and execute tool
                tool_call = self._extract_tool_call(generated)
                tool_result = tool_executor(tool_call)
                
                # Append tool result to context
                result_tokens = self._format_tool_result(tool_result)
                current_ids = torch.cat([generated, result_tokens], dim=-1)
                tool_call_count += 1
            else:
                # No tool call, we're done
                return generated
        
        return current_ids
    
    def _contains_tool_call(self, token_ids: torch.Tensor) -> bool:
        """Check if sequence contains a tool call."""
        # Implementation depends on tokenizer
        pass
    
    def _extract_tool_call(self, token_ids: torch.Tensor) -> dict:
        """Extract tool call JSON from sequence."""
        # Implementation depends on tokenizer
        pass
    
    def _format_tool_result(self, result: str) -> torch.Tensor:
        """Format tool result as tokens."""
        # Implementation depends on tokenizer
        pass


# Register for auto classes
from transformers import AutoConfig, AutoModelForCausalLM

AutoConfig.register("shivik_code", ShivikCodeConfig)
AutoModelForCausalLM.register(ShivikCodeConfig, ShivikCodeForCausalLM)