#!/usr/bin/env python3
"""
SWE Runner with Hermes Trajectory Format

A runner that uses Hermes-Agent's built-in execution environments
(local, docker, modal) and outputs trajectories in the Hermes-Agent format
compatible with batch_runner.py and trajectory_compressor.py.

Features:
- Uses Hermes-Agent's Docker, Modal, or Local environments for command execution
- Outputs trajectories in Hermes format (from/value pairs with <tool_call>/<tool_response> XML)
- Compatible with the trajectory compression pipeline
- Supports batch processing from JSONL prompt files

Usage:
    # Run a single task with local environment
    python mini_swe_runner.py --task "Create a hello world Python script" --env local
    
    # Run with Docker
    python mini_swe_runner.py --task "List files in /tmp" --env docker --image python:3.11-slim
    
    # Run with Modal (cloud)
    python mini_swe_runner.py --task "Install numpy and test it" --env modal --image python:3.11-slim
    
    # Batch mode from JSONL file
    python mini_swe_runner.py --prompts_file prompts.jsonl --output_file trajectories.jsonl --env docker
"""

import json
import logging
import os
from datetime import datetime
from typing import List, Dict, Any, Optional

import fire
from dotenv import load_dotenv
from agent.tool_dispatch_helpers import make_tool_result_message

# Load environment variables
load_dotenv()


def _effective_temperature_for_model(
    model: str,
    base_url: Optional[str] = None,
) -> Optional[float]:
    """Return a fixed temperature for models with strict sampling contracts.

    Returns ``None`` when the model manages temperature server-side (Kimi);
    callers must omit the ``temperature`` kwarg entirely in that case.
    """
    try:
        from agent.auxiliary_client import _fixed_temperature_for_model, OMIT_TEMPERATURE
    except Exception:
        return None
    result = _fixed_temperature_for_model(model, base_url)
    if result is OMIT_TEMPERATURE:
        return None  # caller must omit temperature
    return result




# ============================================================================
# Terminal Tool Definition (matches Hermes-Agent format)
# ============================================================================

TERMINAL_TOOL_DEFINITION = {
    "type": "function",
    "function": {
        "name": "terminal",
        "description": """Execute bash commands in a sandboxed environment.

**Environment:**
- Isolated execution environment (local, Docker, or Modal cloud)
- Filesystem persists between tool calls within the same task
- Internet access available

**Command Execution:**
- Provide the command to execute via the 'command' parameter
- Optional 'timeout' parameter in seconds (default: 60)

**Examples:**
- Run command: `{"command": "ls -la"}`
- With timeout: `{"command": "long_task.sh", "timeout": 300}`

**Best Practices:**
- Use non-interactive commands (avoid vim, nano, interactive python)
- Pipe to cat if output might be large
- Install tools with apt-get or pip as needed

**Completion:**
- When task is complete, output: echo "MINI_SWE_AGENT_FINAL_OUTPUT" followed by your result
""",
        "parameters": {
            "type": "object",
            "properties": {
                "command": {
                    "type": "string",
                    "description": "The bash command to execute"
                },
                "timeout": {
                    "type": "integer",
                    "description": "Command timeout in seconds (default: 60)"
                }
            },
            "required": ["command"]
        }
    }
}


# ============================================================================
# Environment Factory
# ============================================================================

def create_environment(
    env_type: str = "local",
    image: str = "python:3.11-slim",
    cwd: str = "/tmp",
    timeout: int = 60,
    **kwargs
):
    """
    Create an execution environment using Hermes-Agent's built-in backends.
    
    Args:
        env_type: One of "local", "docker", "modal"
        image: Docker/Modal image name (ignored for local)
        cwd: Working directory
        timeout: Default command timeout
        **kwargs: Additional environment-specific options
        
    Returns:
        Environment instance with execute() and cleanup() methods
    """
    if env_type == "local":
        from tools.environments.local import LocalEnvironment
        return LocalEnvironment(cwd=cwd, timeout=timeout)
    
    elif env_type == "docker":
        from tools.environments.docker import DockerEnvironment
        return DockerEnvironment(image=image, cwd=cwd, timeout=timeout, **kwargs)
    
    elif env_type == "modal":
        from tools.environments.modal import ModalEnvironment
        return ModalEnvironment(image=image, cwd=cwd, timeout=timeout, **kwargs)
    
    else:
        raise ValueError(f"Unknown environment type: {env_type}. Use 'local', 'docker', or 'modal'")


# ============================================================================
# Mini-SWE Runner with Hermes Trajectory Format
# ============================================================================

class MiniSWERunner:
    """
    Agent runner that uses Hermes-Agent's built-in execution environments
    and outputs trajectories in Hermes-Agent format.
    """
    
    def __init__(
        self,
        model: str = "anthropic/claude-sonnet-4.6",
        base_url: str = None,
        api_key: str = None,
        env_type: str = "local",
        image: str = "python:3.11-slim",
        cwd: str = "/tmp",
        max_iterations: int = 15,
        command_timeout: int = 60,
        verbose: bool = False,
    ):
        """
        Initialize the Mini-SWE Runner.
        
        Args:
            model: Model name for OpenAI-compatible API
            base_url: API base URL (optional, uses env vars if not provided)
            api_key: API key (optional, uses env vars if not provided)
            env_type: Environment type - "local", "docker", or "modal"
            image: Docker/Modal image (ignored for local)
            cwd: Working directory for commands
            max_iterations: Maximum tool-calling iterations
            command_timeout: Default timeout for commands
            verbose: Enable verbose logging
        """
        self.model = model
        self.max_iterations = max_iterations
        self.command_timeout = command_timeout
        self.verbose = verbose
        self.env_type = env_type
        self.image = image
        self.cwd = cwd
        
        self.logger = logging.getLogger(__name__)
        
        # Initialize LLM client via centralized provider router.
        # If explicit api_key/base_url are provided (e.g. from CLI args),
        # construct directly.  Otherwise use the router for OpenRouter.
        if api_key or base_url:
            from openai import OpenAI
            client_kwargs = {
                "base_url": base_url or "https://openrouter.ai/api/v1",
                "api_key": api_key or os.getenv(
                    "OPENROUTER_API_KEY",
                    os.getenv("ANTHROPIC_API_KEY",
                              os.getenv("OPENAI_API_KEY", ""))),
            }
            self.client = OpenAI(**client_kwargs)
        else:
            from agent.auxiliary_client import resolve_provider_client
            self.client, _ = resolve_provider_client("openrouter", model=model)
            if self.client is None:
                # Fallback: try auto-detection
                self.client, _ = resolve_provider_client("auto", model=model)
            if self.client is None:
                from openai import OpenAI
                self.client = OpenAI(
                    base_url="https://openrouter.ai/api/v1",
                    api_key=os.getenv("OPENROUTER_API_KEY", ""))
        
        # Environment will be created per-task
        self.env = None
        
        # Tool definition
        self.tools = [TERMINAL_TOOL_DEFINITION]
        
        print("🤖 Mini-SWE Runner initialized")
        print(f"   Model: {self.model}")
        print(f"   Environment: {self.env_type}")
        if self.env_type != "local":
            print(f"   Image: {self.image}")
        print(f"   Max iterations: {self.max_iterations}")
    
    def _create_env(self):
        """Create the execution environment."""
        print(f"🔧 Creating {self.env_type} environment...")
        self.env = create_environment(
            env_type=self.env_type,
            image=self.image,
            cwd=self.cwd,
            timeout=self.command_timeout
        )
        print("✅ Environment ready")
    
    def _cleanup_env(self):
        """Cleanup the execution environment."""
        if self.env is not None:
            if hasattr(self.env, 'cleanup'):
                self.env.cleanup()
            elif hasattr(self.env, 'stop'):
                self.env.stop()
            self.env = None
    
    def _execute_command(self, command: str, timeout: int = None) -> Dict[str, Any]:
        """
        Execute a command in the environment.
        
        Args:
            command: Bash command to execute
            timeout: Optional timeout override
            
        Returns:
            Dict with 'output' and 'returncode'
        """
        if self.env is None:
            self._create_env()
        
        try:
            result = self.env.execute(command, timeout=timeout or self.command_timeout)
            return {
                "output": result.get("output", ""),
                "exit_code": result.get("returncode", 0),
                "error": None
            }
        except Exception as e:
            return {
                "output": "",
                "exit_code": -1,
                "error": str(e)
            }
    
    def _format_tools_for_system_message(self) -> str:
        """Format tool definitions for the system message."""
        formatted_tools = []
        for tool in self.tools:
            func = tool["function"]
            formatted_tools.append({
                "name": func["name"],
                "description": func.get("description", ""),
                "parameters": func.get("parameters", {}),
                "required": None
            })
        return json.dumps(formatted_tools, ensure_ascii=False)
    
    def _convert_to_hermes_format(
        self,
        messages: List[Dict[str, Any]],
        user_query: str,
        completed: bool
    ) -> List[Dict[str, Any]]:
        """
        Convert internal message format to Hermes trajectory format.
        
        This produces the exact format used by batch_runner.py.
        """
        trajectory = []
        
        # System message with tool definitions
        system_msg = (
            "You are a function calling AI model. You are provided with function signatures within <tools> </tools> XML tags. "
            "You may call one or more functions to assist with the user query. If available tools are not relevant in assisting "
            "with user query, just respond in natural conversational language. Don't make assumptions about what values to plug "
            "into functions. After calling & executing the functions, you will be provided with function results within "
            "<tool_response> </tool_response> XML tags. Here are the available tools:\n"
            f"<tools>\n{self._format_tools_for_system_message()}\n</tools>\n"
            "For each function call return a JSON object, with the following pydantic model json schema for each:\n"
            "{'title': 'FunctionCall', 'type': 'object', 'properties': {'name': {'title': 'Name', 'type': 'string'}, "
            "'arguments': {'title': 'Arguments', 'type': 'object'}}, 'required': ['name', 'arguments']}\n"
            "Each function call should be enclosed within <tool_call> </tool_call> XML tags.\n"
            "Example:\n<tool_call>\n{'name': <function-name>,'arguments': <args-dict>}\n</tool_call>"
        )
        
        trajectory.append({"from": "system", "value": system_msg})
        trajectory.append({"from": "human", "value": user_query})
        
        # Process messages (skip first user message as we already added it)
        i = 1
        while i < len(messages):
            msg = messages[i]
            
            if msg["role"] == "assistant":
                if "tool_calls" in msg and msg["tool_calls"]:
                    # Assistant message with tool calls
                    content = ""
                    
                    # Add reasoning if present
                    if msg.get("reasoning"):
                        content = f"<think>{msg['reasoning']}</think>"
                    
                    if msg.get("content"):
                        content += msg["content"] + "\n"
                    
                    # Add tool calls in XML format
                    for tool_call in msg["tool_calls"]:
                        if not tool_call or not isinstance(tool_call, dict): continue
                        try:
                            arguments = json.loads(tool_call["function"]["arguments"]) \
                                if isinstance(tool_call["function"]["arguments"], str) \
                                else tool_call["function"]["arguments"]
                        except json.JSONDecodeError:
                            arguments = {}
                        
                        tool_call_json = {
                            "name": tool_call["function"]["name"],
                            "arguments": arguments
                        }
                        content += f"<tool_call>\n{json.dumps(tool_call_json, ensure_ascii=False)}\n</tool_call>\n"
                    
                    trajectory.append({"from": "gpt", "value": content.rstrip()})
                    
                    # Collect subsequent tool responses
                    tool_responses = []
                    j = i + 1
                    while j < len(messages) and messages[j]["role"] == "tool":
                        tool_msg = messages[j]
                        tool_content = tool_msg["content"]
                        
                        # Try to parse as JSON
                        try:
                            if tool_content.strip().startswith(("{", "[")):
                                tool_content = json.loads(tool_content)
                        except (json.JSONDecodeError, AttributeError):
                            pass
                        
                        tool_response = "<tool_response>\n"
                        tool_response += json.dumps({
                            "tool_call_id": tool_msg.get("tool_call_id", ""),
                            "name": msg["tool_calls"][len(tool_responses)]["function"]["name"] \
                                if len(tool_responses) < len(msg["tool_calls"]) else "unknown",
                            "content": tool_content
                        }, ensure_ascii=False)
                        tool_response += "\n</tool_response>"
                        tool_responses.append(tool_response)
                        j += 1
                    
                    if tool_responses:
                        trajectory.append({"from": "tool", "value": "\n".join(tool_responses)})
                        i = j - 1
                
                else:
                    # Regular assistant message (no tool calls)
                    content = ""
                    if msg.get("reasoning"):
                        content = f"<think>{msg['reasoning']}</think>"
                    content += msg.get("content") or ""
                    trajectory.append({"from": "gpt", "value": content})
            
            elif msg["role"] == "user":
                trajectory.append({"from": "human", "value": msg["content"]})
            
            i += 1
        
        return trajectory
    
    def run_task(self, task: str) -> Dict[str, Any]:
        """
        Run a single task and return the result with trajectory.
        
        Args:
            task: The task/prompt to execute
            
        Returns:
            Dict with trajectory, completion status, and metadata
        """
        print(f"\n{'='*60}")
        print(f"📝 Task: {task[:80]}{'...' if len(task) > 80 else ''}")
        print(f"{'='*60}")
        
        # Initialize environment
        self._create_env()
        
        # Message history
        messages = [{"role": "user", "content": task}]
        
        # System prompt for the LLM (ephemeral - not saved to trajectory)
        system_prompt = """You are an AI agent that can execute bash commands to complete tasks.

When you need to run commands, use the 'terminal' tool with your bash command.

**Important:**
- When you have completed the task successfully, run: echo "MINI_SWE_AGENT_FINAL_OUTPUT" followed by a summary
- Be concise and efficient in your approach
- Install any needed tools with apt-get or pip
- Avoid interactive commands (no vim, nano, less, etc.)

Complete the user's task step by step."""
        
        api_call_count = 0
        completed = False
        final_response = None
        
        try:
            while api_call_count < self.max_iterations:
                api_call_count += 1
                print(f"\n🔄 API call #{api_call_count}/{self.max_iterations}")
                
                # Prepare API messages
                api_messages = [{"role": "system", "content": system_prompt}] + messages
                
                # Make API call
                try:
                    api_kwargs = {
                        "model": self.model,
                        "messages": api_messages,
                        "tools": self.tools,
                        "timeout": 300.0,
                    }
                    fixed_temperature = _effective_temperature_for_model(
                        self.model,
                        str(getattr(self.client, "base_url", "") or ""),
                    )
                    if fixed_temperature is not None:
                        api_kwargs["temperature"] = fixed_temperature

                    response = self.client.chat.completions.create(**api_kwargs)
                except Exception as e:
                    self.logger.error("API call failed: %s", e)
                    break
                
                assistant_message = response.choices[0].message
                
                # Log assistant response
                if assistant_message.content:
                    print(f"🤖 Assistant: {assistant_message.content[:100]}...")
                
                # Check for tool calls
                if assistant_message.tool_calls:
                    print(f"🔧 Tool calls: {len(assistant_message.tool_calls)}")
                    
                    # Add assistant message with tool calls
                    messages.append({
                        "role": "assistant",
                        "content": assistant_message.content,
                        "tool_calls": [
                            {
                                "id": tc.id,
                                "type": tc.type,
                                "function": {
                                    "name": tc.function.name,
                                    "arguments": tc.function.arguments
                                }
                            }
                            for tc in assistant_message.tool_calls
                        ]
                    })
                    
                    # Execute each tool call
                    for tc in assistant_message.tool_calls:
                        try:
                            args = json.loads(tc.function.arguments)
                        except json.JSONDecodeError:
                            args = {}
                        
                        command = args.get("command", "echo 'No command provided'")
                        timeout = args.get("timeout", self.command_timeout)
                        
                        print(f"   📞 terminal: {command[:60]}...")
                        
                        # Execute command
                        result = self._execute_command(command, timeout)
                        
                        # Format result
                        result_json = json.dumps({
                            "content": {
                                "output": result["output"],
                                "exit_code": result["exit_code"],
                                "error": result["error"]
                            }
                        }, ensure_ascii=False)
                        
                        # Check for task completion signal
                        if "MINI_SWE_AGENT_FINAL_OUTPUT" in result["output"]:
                            print("   ✅ Task completion signal detected!")
                            completed = True
                        
                        # Add tool response
                        messages.append(make_tool_result_message(
                            tc.function.name, result_json, tc.id,
                        ))
                        
                        print(f"   ✅ exit_code={result['exit_code']}, output={len(result['output'])} chars")
                    
                    # If task completed, we can stop
                    if completed:
                        final_response = assistant_message.content
                        break
                
                else:
                    # No tool calls - final response
                    final_response = assistant_message.content or ""
                    messages.append({
                        "role": "assistant",
                        "content": final_response
                    })
                    completed = True
                    print("🎉 Agent finished (no more tool calls)")
                    break
            
            if api_call_count >= self.max_iterations:
                print(f"⚠️  Reached max iterations ({self.max_iterations})")
        
        finally:
            # Cleanup environment
            self._cleanup_env()
        
        # Convert to Hermes trajectory format
        trajectory = self._convert_to_hermes_format(messages, task, completed)
        
        return {
            "conversations": trajectory,
            "completed": completed,
            "api_calls": api_call_count,
            "metadata": {
                "model": self.model,
                "env_type": self.env_type,
                "timestamp": datetime.now().isoformat()
            }
        }
    
    def run_batch(
        self,
        prompts: List[str],
        output_file: str
    ) -> List[Dict[str, Any]]:
        """
        Run multiple tasks and save trajectories to a JSONL file.
        
        Args:
            prompts: List of task prompts
            output_file: Output JSONL file path
            
        Returns:
            List of results
        """
        results = []
        
        print(f"\n📦 Running batch of {len(prompts)} tasks")
        print(f"📁 Output: {output_file}")
        
        with open(output_file, 'w', encoding='utf-8') as f:
            for i, prompt in enumerate(prompts, 1):
                print(f"\n{'='*60}")
                print(f"📋 Task {i}/{len(prompts)}")
                print(f"{'='*60}")
                
                try:
                    result = self.run_task(prompt)
                    results.append(result)
                    
                    # Write to file immediately
                    f.write(json.dumps(result, ensure_ascii=False) + "\n")
                    f.flush()
                    
                    print(f"✅ Task {i} completed (api_calls={result['api_calls']})")
                    
                except Exception as e:
                    self.logger.error("Error on task %s: %s", i, e)
                    error_result = {
                        "conversations": [],
                        "completed": False,
                        "api_calls": 0,
                        "error": str(e),
                        "metadata": {"timestamp": datetime.now().isoformat()}
                    }
                    results.append(error_result)
                    f.write(json.dumps(error_result, ensure_ascii=False) + "\n")
                    f.flush()
        
        print(f"\n✅ Batch complete! {len(results)} trajectories saved to {output_file}")
        return results


# ============================================================================
# CLI Interface
# ============================================================================

def main(
    task: str = None,
    prompts_file: str = None,
    output_file: str = "swe-runner-test1.jsonl",
    model: str = "claude-sonnet-4-20250514",
    base_url: str = None,
    api_key: str = None,
    env: str = "local",
    image: str = "python:3.11-slim",
    cwd: str = "/tmp",
    max_iterations: int = 15,
    timeout: int = 60,
    verbose: bool = False,
):
    """
    Run SWE tasks with Hermes trajectory format output.
    
    Args:
        task: Single task to run (use this OR prompts_file)
        prompts_file: JSONL file with prompts (each line: {"prompt": "..."})
        output_file: Output JSONL file for trajectories
        model: Model name (default: claude-sonnet-4-20250514)
        base_url: API base URL (optional)
        api_key: API key (optional, uses env vars)
        env: Environment type - "local", "docker", or "modal"
        image: Docker/Modal image (default: python:3.11-slim)
        cwd: Working directory (default: /tmp)
        max_iterations: Maximum tool-calling iterations (default: 15)
        timeout: Command timeout in seconds (default: 60)
        verbose: Enable verbose logging
        
    Examples:
        # Single task with local environment
        python mini_swe_runner.py --task "Create hello.py that prints Hello World"
        
        # Single task with Docker
        python mini_swe_runner.py --task "List files" --env docker
        
        # Batch from file
        python mini_swe_runner.py --prompts_file tasks.jsonl --output_file results.jsonl
    """
    print("🚀 Mini-SWE Runner with Hermes Trajectory Format")
    print("=" * 60)
    
    # Configure root logging at the entry point (not in library __init__).
    logging.basicConfig(
        level=logging.DEBUG if verbose else logging.INFO,
        format='%(asctime)s - %(levelname)s - %(message)s',
        datefmt='%H:%M:%S'
    )
    
    # Initialize runner
    runner = MiniSWERunner(
        model=model,
        base_url=base_url,
        api_key=api_key,
        env_type=env,
        image=image,
        cwd=cwd,
        max_iterations=max_iterations,
        command_timeout=timeout,
        verbose=verbose,
    )
    
    if task:
        # Single task mode
        result = runner.run_task(task)
        
        # Save to file
        with open(output_file, 'w', encoding='utf-8') as f:
            f.write(json.dumps(result, ensure_ascii=False) + "\n")
        
        print(f"\n📁 Trajectory saved to: {output_file}")
        print(f"✅ Completed: {result['completed']}")
        print(f"📞 API calls: {result['api_calls']}")
        print(f"💬 Turns: {len(result['conversations'])}")
        
    elif prompts_file:
        # Batch mode
        prompts = []
        with open(prompts_file, 'r', encoding='utf-8') as f:
            for line in f:
                line = line.strip()
                if line:
                    try:
                        entry = json.loads(line)
                        prompts.append(entry.get("prompt", entry.get("task", "")))
                    except json.JSONDecodeError:
                        prompts.append(line)
        
        if not prompts:
            print(f"❌ No prompts found in {prompts_file}")
            return
        
        runner.run_batch(prompts, output_file)
    
    else:
        print("❌ Please provide either --task or --prompts_file")
        print("   Example: python mini_swe_runner.py --task 'Create a hello world script'")


if __name__ == "__main__":
    fire.Fire(main)
