import random
from swarms.structs.base_swarm import BaseSwarm
from typing import List
from swarms.structs.agent import Agent
from pydantic import BaseModel, Field
from typing import Optional
from datetime import datetime
from swarms.schemas.agent_step_schemas import ManySteps
import tenacity
from swarms.utils.loguru_logger import initialize_logger

logger = initialize_logger("round-robin")

datetime_stamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")


class MetadataSchema(BaseModel):
    swarm_id: Optional[str] = Field(
        ..., description="Unique ID for the run"
    )
    name: Optional[str] = Field(
        "RoundRobinSwarm", description="Name of the swarm"
    )
    task: Optional[str] = Field(
        ..., description="Task or query given to all agents"
    )
    description: Optional[str] = Field(
        "Concurrent execution of multiple agents",
        description="Description of the workflow",
    )
    agent_outputs: Optional[List[ManySteps]] = Field(
        ..., description="List of agent outputs and metadata"
    )
    timestamp: Optional[str] = Field(
        default_factory=datetime.now,
        description="Timestamp of the workflow execution",
    )
    max_loops: Optional[int] = Field(
        1, description="Maximum number of loops to run"
    )


class RoundRobinSwarm(BaseSwarm):
    """
    A swarm implementation that executes tasks in a round-robin fashion.

    Args:
        agents (List[Agent], optional): List of agents in the swarm. Defaults to None.
        verbose (bool, optional): Flag to enable verbose mode. Defaults to False.
        max_loops (int, optional): Maximum number of loops to run. Defaults to 1.
        callback (callable, optional): Callback function to be called after each loop. Defaults to None.
        return_json_on (bool, optional): Flag to return the metadata as a JSON object. Defaults to False.
        *args: Variable length argument list.
        **kwargs: Arbitrary keyword arguments.

    Attributes:
        agents (List[Agent]): List of agents in the swarm.
        verbose (bool): Flag to enable verbose mode.
        max_loops (int): Maximum number of loops to run.
        index (int): Current index of the agent being executed.

    Methods:
        run(task: str, *args, **kwargs) -> Any: Executes the given task on the agents in a round-robin fashion.

    """

    def __init__(
        self,
        name: str = "RoundRobinSwarm",
        description: str = "A swarm implementation that executes tasks in a round-robin fashion.",
        agents: List[Agent] = None,
        verbose: bool = False,
        max_loops: int = 1,
        callback: callable = None,
        return_json_on: bool = False,
        max_retries: int = 3,
        *args,
        **kwargs,
    ):
        try:
            super().__init__(
                name=name,
                description=description,
                agents=agents,
                *args,
                **kwargs,
            )
            self.name = name
            self.description = description
            self.agents = agents or []
            self.verbose = verbose
            self.max_loops = max_loops
            self.callback = callback
            self.return_json_on = return_json_on
            self.index = 0
            self.max_retries = max_retries

            # Store the metadata for the run
            self.output_schema = MetadataSchema(
                name=self.name,
                swarm_id=datetime_stamp,
                task="",
                description=self.description,
                agent_outputs=[],
                timestamp=datetime_stamp,
                max_loops=self.max_loops,
            )

            # Set the max loops for every agent
            if self.agents:
                for agent in self.agents:
                    agent.max_loops = random.randint(1, 5)

            logger.info(
                f"Successfully initialized {self.name} with {len(self.agents)} agents"
            )

        except Exception as e:
            logger.error(
                f"Failed to initialize {self.name}: {str(e)}"
            )
            raise

    @tenacity.retry(
        stop=tenacity.stop_after_attempt(3),
        wait=tenacity.wait_exponential(multiplier=1, min=4, max=10),
        retry=tenacity.retry_if_exception_type(Exception),
        before_sleep=lambda retry_state: logger.info(
            f"Retrying in {retry_state.next_action.sleep} seconds..."
        ),
    )
    def _execute_agent(
        self, agent: Agent, task: str, *args, **kwargs
    ) -> str:
        """Execute a single agent with retries and error handling"""
        try:
            logger.info(
                f"Running Agent {agent.agent_name} on task: {task}"
            )
            result = agent.run(task, *args, **kwargs)
            self.output_schema.agent_outputs.append(
                agent.agent_output
            )
            return result
        except Exception as e:
            logger.error(
                f"Error executing agent {agent.agent_name}: {str(e)}"
            )
            raise

    def run(self, task: str, *args, **kwargs):
        """
        Executes the given task on the agents in a round-robin fashion.

        Args:
            task (str): The task to be executed.
            *args: Variable length argument list.
            **kwargs: Arbitrary keyword arguments.

        Returns:
            Any: The result of the task execution.

        Raises:
            ValueError: If no agents are configured
            Exception: If an exception occurs during task execution.
        """
        if not self.agents:
            logger.error("No agents configured for the swarm")
            raise ValueError("No agents configured for the swarm")

        try:
            result = task
            self.output_schema.task = task
            n = len(self.agents)
            logger.info(
                f"Starting round-robin execution with task '{task}' on {n} agents"
            )

            for loop in range(self.max_loops):
                logger.debug(
                    f"Starting loop {loop + 1}/{self.max_loops}"
                )

                for _ in range(n):
                    current_agent = self.agents[self.index]
                    try:
                        result = self._execute_agent(
                            current_agent, result, *args, **kwargs
                        )
                    finally:
                        self.index = (self.index + 1) % n

                if self.callback:
                    logger.debug(
                        f"Executing callback for loop {loop + 1}"
                    )
                    try:
                        self.callback(loop, result)
                    except Exception as e:
                        logger.error(
                            f"Callback execution failed: {str(e)}"
                        )

            logger.success(
                f"Successfully completed {self.max_loops} loops of round-robin execution"
            )

            if self.return_json_on:
                return self.export_metadata()
            return result

        except Exception as e:
            logger.error(f"Round-robin execution failed: {str(e)}")
            raise

    def export_metadata(self):
        """Export the execution metadata as JSON"""
        try:
            return self.output_schema.model_dump_json(indent=4)
        except Exception as e:
            logger.error(f"Failed to export metadata: {str(e)}")
            raise
