import os
import uuid
from typing import Any, Dict, List, Optional, Tuple

from langchain.agents.agent import RunnableAgent
from langchain.agents.tools import tool as LangChainTool
from langchain.memory import ConversationSummaryMemory
from langchain.tools.render import render_text_description
from langchain_core.agents import AgentAction
from langchain_core.callbacks import BaseCallbackHandler
from langchain_openai import ChatOpenAI
from pydantic import (
    UUID4,
    BaseModel,
    ConfigDict,
    Field,
    InstanceOf,
    PrivateAttr,
    field_validator,
    model_validator,
)
from pydantic_core import PydanticCustomError

from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser, ToolsHandler
from crewai.utilities import I18N, Logger, Prompts, RPMController
from crewai.utilities.token_counter_callback import TokenCalcHandler, TokenProcess


class Agent(BaseModel):
    """Represents an agent in a system.

    Each agent has a role, a goal, a backstory, and an optional language model (llm).
    The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.

    Attributes:
            agent_executor: An instance of the CrewAgentExecutor class.
            role: The role of the agent.
            goal: The objective of the agent.
            backstory: The backstory of the agent.
            config: Dict representation of agent configuration.
            llm: The language model that will run the agent.
            function_calling_llm: The language model that will the tool calling for this agent, it overrides the crew function_calling_llm.
            max_iter: Maximum number of iterations for an agent to execute a task.
            memory: Whether the agent should have memory or not.
            max_rpm: Maximum number of requests per minute for the agent execution to be respected.
            verbose: Whether the agent execution should be in verbose mode.
            allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
            tools: Tools at agents disposal
            step_callback: Callback to be executed after each step of the agent execution.
            callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process
    """

    __hash__ = object.__hash__  # type: ignore
    _logger: Logger = PrivateAttr()
    _rpm_controller: RPMController = PrivateAttr(default=None)
    _request_within_rpm_limit: Any = PrivateAttr(default=None)
    _token_process: TokenProcess = TokenProcess()

    formatting_errors: int = 0
    model_config = ConfigDict(arbitrary_types_allowed=True)
    id: UUID4 = Field(
        default_factory=uuid.uuid4,
        frozen=True,
        description="Unique identifier for the object, not set by user.",
    )
    role: str = Field(description="Role of the agent")
    goal: str = Field(description="Objective of the agent")
    backstory: str = Field(description="Backstory of the agent")
    config: Optional[Dict[str, Any]] = Field(
        description="Configuration for the agent",
        default=None,
    )
    max_rpm: Optional[int] = Field(
        default=None,
        description="Maximum number of requests per minute for the agent execution to be respected.",
    )
    memory: bool = Field(
        default=False, description="Whether the agent should have memory or not"
    )
    verbose: bool = Field(
        default=False, description="Verbose mode for the Agent Execution"
    )
    allow_delegation: bool = Field(
        default=True, description="Allow delegation of tasks to agents"
    )
    tools: Optional[List[Any]] = Field(
        default_factory=list, description="Tools at agents disposal"
    )
    max_iter: Optional[int] = Field(
        default=15, description="Maximum iterations for an agent to execute a task"
    )
    agent_executor: InstanceOf[CrewAgentExecutor] = Field(
        default=None, description="An instance of the CrewAgentExecutor class."
    )
    tools_handler: InstanceOf[ToolsHandler] = Field(
        default=None, description="An instance of the ToolsHandler class."
    )
    cache_handler: InstanceOf[CacheHandler] = Field(
        default=CacheHandler(), description="An instance of the CacheHandler class."
    )
    step_callback: Optional[Any] = Field(
        default=None,
        description="Callback to be executed after each step of the agent execution.",
    )
    i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
    llm: Any = Field(
        default_factory=lambda: ChatOpenAI(
            model=os.environ.get("OPENAI_MODEL_NAME", "gpt-4")
        ),
        description="Language model that will run the agent.",
    )
    function_calling_llm: Optional[Any] = Field(
        description="Language model that will run the agent.", default=None
    )
    callbacks: Optional[List[InstanceOf[BaseCallbackHandler]]] = Field(
        default=None, description="Callback to be executed"
    )

    def __init__(__pydantic_self__, **data):
        config = data.pop("config", {})
        super().__init__(**config, **data)

    @field_validator("id", mode="before")
    @classmethod
    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
        if v:
            raise PydanticCustomError(
                "may_not_set_field", "This field is not to be set by the user.", {}
            )

    @model_validator(mode="after")
    def set_attributes_based_on_config(self) -> "Agent":
        """Set attributes based on the agent configuration."""
        if self.config:
            for key, value in self.config.items():
                setattr(self, key, value)
        return self

    @model_validator(mode="after")
    def set_private_attrs(self):
        """Set private attributes."""
        self._logger = Logger(self.verbose)
        if self.max_rpm and not self._rpm_controller:
            self._rpm_controller = RPMController(
                max_rpm=self.max_rpm, logger=self._logger
            )
        return self

    @model_validator(mode="after")
    def set_agent_executor(self) -> "Agent":
        """set agent executor is set."""
        if hasattr(self.llm, "model_name"):
            self.llm.callbacks = [
                TokenCalcHandler(self.llm.model_name, self._token_process)
            ]
        if not self.agent_executor:
            self.set_cache_handler(self.cache_handler)
        return self

    def execute_task(
        self,
        task: Any,
        context: Optional[str] = None,
        tools: Optional[List[Any]] = None,
    ) -> str:
        """Execute a task with the agent.

        Args:
            task: Task to execute.
            context: Context to execute the task in.
            tools: Tools to use for the task.

        Returns:
            Output of the agent
        """
        self.tools_handler.last_used_tool = {}

        task_prompt = task.prompt()

        if context:
            task_prompt = self.i18n.slice("task_with_context").format(
                task=task_prompt, context=context
            )

        tools = self._parse_tools(tools or self.tools)
        self.create_agent_executor(tools=tools)
        self.agent_executor.tools = tools
        self.agent_executor.task = task

        self.agent_executor.tools_description = render_text_description(tools)
        self.agent_executor.tools_names = self.__tools_names(tools)

        result = self.agent_executor.invoke(
            {
                "input": task_prompt,
                "tool_names": self.agent_executor.tools_names,
                "tools": self.agent_executor.tools_description,
            }
        )["output"]

        if self.max_rpm:
            self._rpm_controller.stop_rpm_counter()

        return result

    def set_cache_handler(self, cache_handler: CacheHandler) -> None:
        """Set the cache handler for the agent.

        Args:
            cache_handler: An instance of the CacheHandler class.
        """
        self.cache_handler = cache_handler
        self.tools_handler = ToolsHandler(cache=self.cache_handler)
        self.create_agent_executor()

    def set_rpm_controller(self, rpm_controller: RPMController) -> None:
        """Set the rpm controller for the agent.

        Args:
            rpm_controller: An instance of the RPMController class.
        """
        if not self._rpm_controller:
            self._rpm_controller = rpm_controller
            self.create_agent_executor()

    def create_agent_executor(self, tools=None) -> None:
        """Create an agent executor for the agent.

        Returns:
            An instance of the CrewAgentExecutor class.
        """
        tools = tools or self.tools

        agent_args = {
            "input": lambda x: x["input"],
            "tools": lambda x: x["tools"],
            "tool_names": lambda x: x["tool_names"],
            "agent_scratchpad": lambda x: self.format_log_to_str(
                x["intermediate_steps"]
            ),
        }

        executor_args = {
            "llm": self.llm,
            "i18n": self.i18n,
            "tools": self._parse_tools(tools),
            "verbose": self.verbose,
            "handle_parsing_errors": True,
            "max_iterations": self.max_iter,
            "step_callback": self.step_callback,
            "tools_handler": self.tools_handler,
            "function_calling_llm": self.function_calling_llm,
            "callbacks": self.callbacks,
        }

        if self._rpm_controller:
            executor_args[
                "request_within_rpm_limit"
            ] = self._rpm_controller.check_or_wait

        if self.memory:
            summary_memory = ConversationSummaryMemory(
                llm=self.llm, input_key="input", memory_key="chat_history"
            )
            executor_args["memory"] = summary_memory
            agent_args["chat_history"] = lambda x: x["chat_history"]
            prompt = Prompts(i18n=self.i18n, tools=tools).task_execution_with_memory()
        else:
            prompt = Prompts(i18n=self.i18n, tools=tools).task_execution()

        execution_prompt = prompt.partial(
            goal=self.goal,
            role=self.role,
            backstory=self.backstory,
        )

        bind = self.llm.bind(stop=[self.i18n.slice("observation")])
        inner_agent = agent_args | execution_prompt | bind | CrewAgentParser(agent=self)
        self.agent_executor = CrewAgentExecutor(
            agent=RunnableAgent(runnable=inner_agent), **executor_args
        )

    def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
        """Interpolate inputs into the agent description and backstory."""
        if inputs:
            self.role = self.role.format(**inputs)
            self.goal = self.goal.format(**inputs)
            self.backstory = self.backstory.format(**inputs)

    def increment_formatting_errors(self) -> None:
        """Count the formatting errors of the agent."""
        self.formatting_errors += 1

    def format_log_to_str(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        observation_prefix: str = "Observation: ",
        llm_prefix: str = "",
    ) -> str:
        """Construct the scratchpad that lets the agent continue its thought process."""
        thoughts = ""
        for action, observation in intermediate_steps:
            thoughts += action.log
            thoughts += f"\n{observation_prefix}{observation}\n{llm_prefix}"
        return thoughts

    def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:
        """Parse tools to be used for the task."""
        # tentatively try to import from crewai_tools import BaseTool as CrewAITool
        tools_list = []
        try:
            from crewai_tools import BaseTool as CrewAITool

            for tool in tools:
                if isinstance(tool, CrewAITool):
                    tools_list.append(tool.to_langchain())
                else:
                    tools_list.append(tool)
        except ModuleNotFoundError:
            for tool in tools:
                tools_list.append(tool)
        return tools_list

    @staticmethod
    def __tools_names(tools) -> str:
        return ", ".join([t.name for t in tools])

    def __repr__(self):
        return f"Agent(role={self.role}, goal={self.goal}, backstory={self.backstory})"

langchain.memory.ConversationSummaryMemory langchain.agents.agent.RunnableAgent langchain.tools.render.render_text_description
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