import threading
import uuid
from typing import Any, Dict, List, Optional, Type

from langchain_openai import ChatOpenAI
from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
from pydantic_core import PydanticCustomError

from crewai.agent import Agent
from crewai.tasks.task_output import TaskOutput
from crewai.utilities import I18N, Converter, ConverterError, Printer
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser


class Task(BaseModel):
    """Class that represent a task to be executed."""

    class Config:
        arbitrary_types_allowed = True

    __hash__ = object.__hash__  # type: ignore
    used_tools: int = 0
    tools_errors: int = 0
    delegations: int = 0
    i18n: I18N = I18N()
    thread: threading.Thread = None
    description: str = Field(description="Description of the actual task.")
    expected_output: str = Field(
        description="Clear definition of expected output for the task."
    )
    config: Optional[Dict[str, Any]] = Field(
        description="Configuration for the agent",
        default=None,
    )
    callback: Optional[Any] = Field(
        description="Callback to be executed after the task is completed.", default=None
    )
    agent: Optional[Agent] = Field(
        description="Agent responsible for execution the task.", default=None
    )
    context: Optional[List["Task"]] = Field(
        description="Other tasks that will have their output used as context for this task.",
        default=None,
    )
    async_execution: Optional[bool] = Field(
        description="Whether the task should be executed asynchronously or not.",
        default=False,
    )
    output_json: Optional[Type[BaseModel]] = Field(
        description="A Pydantic model to be used to create a JSON output.",
        default=None,
    )
    output_pydantic: Optional[Type[BaseModel]] = Field(
        description="A Pydantic model to be used to create a Pydantic output.",
        default=None,
    )
    output_file: Optional[str] = Field(
        description="A file path to be used to create a file output.",
        default=None,
    )
    output: Optional[TaskOutput] = Field(
        description="Task output, it's final result after being executed", default=None
    )
    tools: Optional[List[Any]] = Field(
        default_factory=list,
        description="Tools the agent is limited to use for this task.",
    )
    id: UUID4 = Field(
        default_factory=uuid.uuid4,
        frozen=True,
        description="Unique identifier for the object, not set by user.",
    )

    _original_description: str | None = None
    _original_expected_output: str | None = None

    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) -> "Task":
        """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 check_tools(self):
        """Check if the tools are set."""
        if not self.tools and self.agent and self.agent.tools:
            self.tools.extend(self.agent.tools)
        return self

    @model_validator(mode="after")
    def check_output(self):
        """Check if an output type is set."""
        output_types = [self.output_json, self.output_pydantic]
        if len([type for type in output_types if type]) > 1:
            raise PydanticCustomError(
                "output_type",
                "Only one output type can be set, either output_pydantic or output_json.",
                {},
            )
        return self

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

        Returns:
            Output of the task.
        """

        agent = agent or self.agent
        if not agent:
            raise Exception(
                f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
            )

        if self.context:
            context = []
            for task in self.context:
                if task.async_execution:
                    task.thread.join()
                if task and task.output:
                    context.append(task.output.raw_output)
            context = "\n".join(context)

        tools = tools or self.tools

        if self.async_execution:
            self.thread = threading.Thread(
                target=self._execute, args=(agent, self, context, tools)
            )
            self.thread.start()
        else:
            result = self._execute(
                task=self,
                agent=agent,
                context=context,
                tools=tools,
            )
            return result

    def _execute(self, agent, task, context, tools):
        result = agent.execute_task(
            task=task,
            context=context,
            tools=tools,
        )

        exported_output = self._export_output(result)

        self.output = TaskOutput(
            description=self.description,
            exported_output=exported_output,
            raw_output=result,
        )

        if self.callback:
            self.callback(self.output)

        return exported_output

    def prompt(self) -> str:
        """Prompt the task.

        Returns:
            Prompt of the task.
        """
        tasks_slices = [self.description]

        output = self.i18n.slice("expected_output").format(
            expected_output=self.expected_output
        )
        tasks_slices = [self.description, output]
        return "\n".join(tasks_slices)

    def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
        """Interpolate inputs into the task description and expected output."""
        if self._original_description is None:
            self._original_description = self.description
        if self._original_expected_output is None:
            self._original_expected_output = self.expected_output

        if inputs:
            self.description = self._original_description.format(**inputs)
            self.expected_output = self._original_expected_output.format(**inputs)

    def increment_tools_errors(self) -> None:
        """Increment the tools errors counter."""
        self.tools_errors += 1

    def increment_delegations(self) -> None:
        """Increment the delegations counter."""
        self.delegations += 1

    def _export_output(self, result: str) -> Any:
        exported_result = result
        instructions = "I'm gonna convert this raw text into valid JSON."

        if self.output_pydantic or self.output_json:
            model = self.output_pydantic or self.output_json
            llm = self.agent.function_calling_llm or self.agent.llm

            if not self._is_gpt(llm):
                model_schema = PydanticSchemaParser(model=model).get_schema()
                instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"

            converter = Converter(
                llm=llm, text=result, model=model, instructions=instructions
            )

            if self.output_pydantic:
                exported_result = converter.to_pydantic()
            elif self.output_json:
                exported_result = converter.to_json()

            if isinstance(exported_result, ConverterError):
                Printer().print(
                    content=f"{exported_result.message} Using raw output instead.",
                    color="red",
                )
                exported_result = result

        if self.output_file:
            content = (
                exported_result if not self.output_pydantic else exported_result.json()
            )
            self._save_file(content)

        return exported_result

    def _is_gpt(self, llm) -> bool:
        return isinstance(llm, ChatOpenAI) and llm.openai_api_base == None

    def _save_file(self, result: Any) -> None:
        with open(self.output_file, "w") as file:
            file.write(result)
        return None

    def __repr__(self):
        return f"Task(description={self.description}, expected_output={self.expected_output})"
