"""Tools for spawning and managing sub-agents."""

from typing import Any, Dict, List, Union

from langchain_core.tools import tool
from rich.console import Console

from ra_aid.agent_context import get_completion_message, reset_completion_flags
from ra_aid.console.formatting import print_error
from ra_aid.exceptions import AgentInterrupt
from ra_aid.tools.memory import _global_memory

from ..console import print_task_header
from ..llm import initialize_llm
from .human import ask_human
from .memory import get_memory_value, get_related_files, get_work_log

ResearchResult = Dict[str, Union[str, bool, Dict[int, Any], List[Any], None]]

CANCELLED_BY_USER_REASON = "The operation was explicitly cancelled by the user. This typically is an indication that the action requested was not aligned with the user request."

RESEARCH_AGENT_RECURSION_LIMIT = 3

console = Console()


@tool("request_research")
def request_research(query: str) -> ResearchResult:
    """Spawn a research-only agent to investigate the given query.

    This function creates a new research agent to investigate the given query. It includes
    recursion depth limiting to prevent infinite recursive research calls.

    Args:
        query: The research question or project description
    """
    # Initialize model from config
    config = _global_memory.get("config", {})
    model = initialize_llm(
        config.get("provider", "anthropic"),
        config.get("model", "claude-3-7-sonnet-20250219"),
        temperature=config.get("temperature"),
    )

    # Check recursion depth
    current_depth = _global_memory.get("agent_depth", 0)
    if current_depth >= RESEARCH_AGENT_RECURSION_LIMIT:
        print_error("Maximum research recursion depth reached")
        return {
            "completion_message": "Research stopped - maximum recursion depth reached",
            "key_facts": get_memory_value("key_facts"),
            "related_files": get_related_files(),
            "research_notes": get_memory_value("research_notes"),
            "key_snippets": get_memory_value("key_snippets"),
            "success": False,
            "reason": "max_depth_exceeded",
        }

    success = True
    reason = None

    try:
        # Run research agent
        from ..agent_utils import run_research_agent

        _result = run_research_agent(
            query,
            model,
            expert_enabled=True,
            research_only=True,
            hil=config.get("hil", False),
            console_message=query,
            config=config,
        )
    except AgentInterrupt:
        print()
        response = ask_human.invoke({"question": "Why did you interrupt me?"})
        success = False
        reason = response if response.strip() else CANCELLED_BY_USER_REASON
    except KeyboardInterrupt:
        raise
    except Exception as e:
        print_error(f"Error during research: {str(e)}")
        success = False
        reason = f"error: {str(e)}"
    finally:
        # Get completion message if available
        completion_message = get_completion_message() or ("Task was completed successfully." if success else None)

        work_log = get_work_log()

        # Clear completion state
        reset_completion_flags()

    response_data = {
        "completion_message": completion_message,
        "key_facts": get_memory_value("key_facts"),
        "related_files": get_related_files(),
        "research_notes": get_memory_value("research_notes"),
        "key_snippets": get_memory_value("key_snippets"),
        "success": success,
        "reason": reason,
    }
    if work_log is not None:
        response_data["work_log"] = work_log
    return response_data


@tool("request_web_research")
def request_web_research(query: str) -> ResearchResult:
    """Spawn a web research agent to investigate the given query using web search.

    Args:
        query: The research question or project description
    """
    # Initialize model from config
    config = _global_memory.get("config", {})
    model = initialize_llm(
        config.get("provider", "anthropic"),
        config.get("model", "claude-3-7-sonnet-20250219"),
        temperature=config.get("temperature"),
    )

    success = True
    reason = None

    try:
        # Run web research agent
        from ..agent_utils import run_web_research_agent

        _result = run_web_research_agent(
            query,
            model,
            expert_enabled=True,
            hil=config.get("hil", False),
            console_message=query,
            config=config,
        )
    except AgentInterrupt:
        print()
        response = ask_human.invoke({"question": "Why did you interrupt me?"})
        success = False
        reason = response if response.strip() else CANCELLED_BY_USER_REASON
    except KeyboardInterrupt:
        raise
    except Exception as e:
        print_error(f"Error during web research: {str(e)}")
        success = False
        reason = f"error: {str(e)}"
    finally:
        # Get completion message if available
        completion_message = get_completion_message() or ("Task was completed successfully." if success else None)

        work_log = get_work_log()

        # Clear completion state
        reset_completion_flags()

    response_data = {
        "completion_message": completion_message,
        "key_snippets": get_memory_value("key_snippets"),
        "research_notes": get_memory_value("research_notes"),
        "success": success,
        "reason": reason,
    }
    if work_log is not None:
        response_data["work_log"] = work_log
    return response_data


@tool("request_research_and_implementation")
def request_research_and_implementation(query: str) -> Dict[str, Any]:
    """Spawn a research agent to investigate and implement the given query.

    If you are calling this on behalf of a user request, you must *faithfully*
    represent all info the user gave you, sometimes even to the point of repeating the user query verbatim.

    Args:
        query: The research question or project description
    """
    # Initialize model from config
    config = _global_memory.get("config", {})
    model = initialize_llm(
        config.get("provider", "anthropic"),
        config.get("model", "claude-3-7-sonnet-20250219"),
        temperature=config.get("temperature"),
    )

    try:
        # Run research agent
        from ..agent_utils import run_research_agent

        _result = run_research_agent(
            query,
            model,
            expert_enabled=True,
            research_only=False,
            hil=config.get("hil", False),
            console_message=query,
            config=config,
        )

        success = True
        reason = None
    except AgentInterrupt:
        print()
        response = ask_human.invoke({"question": "Why did you interrupt me?"})
        success = False
        reason = response if response.strip() else CANCELLED_BY_USER_REASON
    except KeyboardInterrupt:
        raise
    except Exception as e:
        console.print(f"\n[red]Error during research: {str(e)}[/red]")
        success = False
        reason = f"error: {str(e)}"

    # Get completion message if available
    completion_message = get_completion_message() or ("Task was completed successfully." if success else None)

    work_log = get_work_log()

    # Clear completion state
    reset_completion_flags()

    response_data = {
        "completion_message": completion_message,
        "key_facts": get_memory_value("key_facts"),
        "related_files": get_related_files(),
        "research_notes": get_memory_value("research_notes"),
        "key_snippets": get_memory_value("key_snippets"),
        "success": success,
        "reason": reason,
    }
    if work_log is not None:
        response_data["work_log"] = work_log
    return response_data


@tool("request_task_implementation")
def request_task_implementation(task_spec: str) -> str:
    """Spawn an implementation agent to execute the given task.

    Task specs should have the requirements. Generally, the spec will not include any code.

    Args:
        task_spec: REQUIRED The full task specification (markdown format, typically one part of the overall plan)
    """
    # Initialize model from config
    config = _global_memory.get("config", {})
    model = initialize_llm(
        config.get("provider", "anthropic"),
        config.get("model", "claude-3-5-sonnet-20241022"),
        temperature=config.get("temperature"),
    )

    # Get required parameters
    tasks = [
        _global_memory["tasks"][task_id] for task_id in sorted(_global_memory["tasks"])
    ]
    plan = _global_memory.get("plan", "")
    related_files = list(_global_memory["related_files"].values())

    try:
        print_task_header(task_spec)
        # Run implementation agent
        from ..agent_utils import run_task_implementation_agent

        reset_completion_flags()

        _result = run_task_implementation_agent(
            base_task=_global_memory.get("base_task", ""),
            tasks=tasks,
            task=task_spec,
            plan=plan,
            related_files=related_files,
            model=model,
            expert_enabled=True,
            config=config,
        )

        success = True
        reason = None
    except AgentInterrupt:
        print()
        response = ask_human.invoke({"question": "Why did you interrupt me?"})
        success = False
        reason = response if response.strip() else CANCELLED_BY_USER_REASON
    except KeyboardInterrupt:
        raise
    except Exception as e:
        print_error(f"Error during task implementation: {str(e)}")
        success = False
        reason = f"error: {str(e)}"

    # Get completion message if available
    completion_message = get_completion_message() or ("Task was completed successfully." if success else None)

    # Get and reset work log if at root depth
    work_log = get_work_log()

    # Clear completion state
    reset_completion_flags()

    response_data = {
        "key_facts": get_memory_value("key_facts"),
        "related_files": get_related_files(),
        "key_snippets": get_memory_value("key_snippets"),
        "completion_message": completion_message,
        "success": success,
        "reason": reason,
    }
    if work_log is not None:
        response_data["work_log"] = work_log
    
    # Convert the response data to a markdown string
    markdown_parts = []
    
    # Add header and completion message
    markdown_parts.append("# Task Implementation")
    if response_data.get("completion_message"):
        markdown_parts.append(f"\n## Completion Message\n\n{response_data['completion_message']}")
    
    # Add success status
    status = "Success" if response_data.get("success", False) else "Failed"
    reason_text = f": {response_data.get('reason')}" if response_data.get("reason") else ""
    markdown_parts.append(f"\n## Status\n\n**{status}**{reason_text}")
    
    # Add key facts
    if response_data.get("key_facts"):
        markdown_parts.append(f"\n## Key Facts\n\n{response_data['key_facts']}")
    
    # Add related files
    if response_data.get("related_files"):
        files_list = "\n".join([f"- {file}" for file in response_data["related_files"]])
        markdown_parts.append(f"\n## Related Files\n\n{files_list}")
    
    # Add key snippets
    if response_data.get("key_snippets"):
        markdown_parts.append(f"\n## Key Snippets\n\n{response_data['key_snippets']}")
    
    # Add work log
    if response_data.get("work_log"):
        markdown_parts.append(f"\n## Work Log\n\n{response_data['work_log']}")
        markdown_parts.append(f"\n\nTHE ABOVE WORK HAS ALREADY BEEN COMPLETED --**DO NOT REQUEST IMPLEMENTATION OF IT AGAIN**")
    
    # Join all parts into a single markdown string
    markdown_output = "".join(markdown_parts)
    
    return markdown_output


@tool("request_implementation")
def request_implementation(task_spec: str) -> str:
    """Spawn a planning agent to create an implementation plan for the given task.

    Args:
        task_spec: The task specification to plan implementation for
    """
    # Initialize model from config
    config = _global_memory.get("config", {})
    model = initialize_llm(
        config.get("provider", "anthropic"),
        config.get("model", "claude-3-5-sonnet-20241022"),
        temperature=config.get("temperature"),
    )

    try:
        # Run planning agent
        from ..agent_utils import run_planning_agent

        reset_completion_flags()

        _result = run_planning_agent(
            task_spec,
            model,
            config=config,
            expert_enabled=True,
            hil=config.get("hil", False),
        )

        success = True
        reason = None
    except AgentInterrupt:
        print()
        response = ask_human.invoke({"question": "Why did you interrupt me?"})
        success = False
        reason = response if response.strip() else CANCELLED_BY_USER_REASON
    except KeyboardInterrupt:
        raise
    except Exception as e:
        print_error(f"Error during planning: {str(e)}")
        success = False
        reason = f"error: {str(e)}"

    # Get completion message if available
    completion_message = get_completion_message() or ("Task was completed successfully." if success else None)

    # Get and reset work log if at root depth
    work_log = get_work_log()

    # Clear completion state
    reset_completion_flags()

    response_data = {
        "completion_message": completion_message,
        "key_facts": get_memory_value("key_facts"),
        "related_files": get_related_files(),
        "key_snippets": get_memory_value("key_snippets"),
        "success": success,
        "reason": reason,
    }
    if work_log is not None:
        response_data["work_log"] = work_log
    
    # Convert the response data to a markdown string
    markdown_parts = []
    
    # Add header and completion message
    markdown_parts.append("# Implementation Plan")
    if response_data.get("completion_message"):
        markdown_parts.append(f"\n## Completion Message\n\n{response_data['completion_message']}")
    
    # Add success status
    status = "Success" if response_data.get("success", False) else "Failed"
    reason_text = f": {response_data.get('reason')}" if response_data.get("reason") else ""
    markdown_parts.append(f"\n## Status\n\n**{status}**{reason_text}")
    
    # Add key facts
    if response_data.get("key_facts"):
        markdown_parts.append(f"\n## Key Facts\n\n{response_data['key_facts']}")
    
    # Add related files
    if response_data.get("related_files"):
        files_list = "\n".join([f"- {file}" for file in response_data["related_files"]])
        markdown_parts.append(f"\n## Related Files\n\n{files_list}")
    
    # Add key snippets
    if response_data.get("key_snippets"):
        markdown_parts.append(f"\n## Key Snippets\n\n{response_data['key_snippets']}")
    
    # Add work log
    if response_data.get("work_log"):
        markdown_parts.append(f"\n## Work Log\n\n{response_data['work_log']}")
        markdown_parts.append(f"\n\nTHE ABOVE WORK HAS ALREADY BEEN COMPLETED --**DO NOT REQUEST IMPLEMENTATION OF IT AGAIN**")
    
    # Join all parts into a single markdown string
    markdown_output = "".join(markdown_parts)
    
    return markdown_output