from __future__ import annotations

from typing import TYPE_CHECKING, Any, Callable, Sequence

import os
import json
from pathlib import Path
from gradio.components.base import Component
from gradio.events import Events
from gradio.context import Context

if TYPE_CHECKING:
    from gradio.components import Timer

from gradio.events import Dependency

class PathSelector(Component):
    EVENTS = [
        Events.change,
    ]

    def __init__(
        self,
        value: str | Callable | None = None,
        *,
        placeholder: str | None = None,
        label: str | None = None,
        every: Timer | float | None = None,
        show_label: bool | None = None,
        scale: int | None = None,
        min_width: int = 160,
        visible: bool = True,
        elem_id: str | None = None,
        elem_classes: list[str] | str | None = None,
        render: bool = True,
        key: int | str | None = None,
    ):
        """
        Parameters:
            value: default text to provide in textbox. If callable, the function will be called whenever the app loads to set the initial value of the component.
            placeholder: placeholder hint to provide behind textbox.
            label: component name in interface.
            every: Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            show_label: if True, will display label.
            scale: relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
            visible: If False, component will be hidden.
            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
            key: if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.
        """
        self.placeholder = placeholder

        if value is None:
            value = self.init_value()
        super().__init__(
            label=label,
            every=every,
            show_label=show_label,
            scale=scale,
            min_width=min_width,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            value=value,
            render=render,
            key=key,
        )
        if Context.root_block is not None:
            self.subscribe()

    @staticmethod
    def get_listdir(path: Path) -> list[str]:
        # if path is None:
        #     path = Path.cwd()
        listdir = sorted(list(path.iterdir()))
        if len(listdir) > 0:
            dirs = [p.name for p in listdir if p.is_dir()]
            files = [p.name for p in listdir if p.is_file()]
            return dirs, files
        else:
            return [], []

    @staticmethod
    def init_value() -> dict:
        return PathSelector.get_value(Path.cwd())

    @staticmethod
    def get_value(path: Path) -> dict:
        dirs, files = PathSelector.get_listdir(path)
        return {
            "current_path": str(path),
            "directories": dirs,
            "files": files,
            "separator": os.path.sep,
        }

    def subscribe(self):
        self.change(self.refresh_value, self, self)

    def preprocess(self, payload):
        """
        This docstring is used to generate the docs for this custom component.
        Parameters:
            payload: the data to be preprocessed, sent from the frontend
        Returns:
            the data after preprocessing, sent to the user's function in the backend
        """
        if payload is None:
            return None
        else:
            return json.loads(payload)

    def postprocess(self, value):
        """
        This docstring is used to generate the docs for this custom component.
        Parameters:
            payload: the data to be postprocessed, sent from the user's function in the backend
        Returns:
            the data after postprocessing, sent to the frontend
        """
        if value is None:
            return None
        else:
            value["status"] = "download"
            return json.dumps(value)

    @staticmethod
    def refresh_value(D: dict):
        current_path = Path(D["current_path"])
        directory = D["selected_inode"]
        if directory == -1:
            path = current_path.parent
        else:
            path = current_path / directory
        return PathSelector.get_value(path)

    def example_payload(self):
        return {"foo": "bar"}

    def example_value(self):
        return {"foo": "bar"}

    def api_info(self):
        return {"type": {}, "description": "any valid json"}

    
    def change(self,
        fn: Callable | None = None,
        inputs: Block | Sequence[Block] | set[Block] | None = None,
        outputs: Block | Sequence[Block] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: Timer | float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: If True, will scroll to output component on completion
            show_progress: If True, will show progress animation while pending
            queue: If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: If False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            trigger_mode: If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: If set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        """
        ...