"""gr.HighlightedText() component."""

from __future__ import annotations

from typing import Any, Callable, List, Union

from gradio_client.documentation import document

from gradio.components.base import Component
from gradio.data_classes import GradioModel, GradioRootModel
from gradio.events import Events


class HighlightedToken(GradioModel):
    token: str
    class_or_confidence: Union[str, float, None] = None


class HighlightedTextData(GradioRootModel):
    root: List[HighlightedToken]

from gradio.events import Dependency

@document()
class HighlightedText(Component):
    """
    Displays text that contains spans that are highlighted by category or numerical value.

    Demos: diff_texts
    Guides: named-entity-recognition
    """

    data_model = HighlightedTextData
    EVENTS = [Events.change, Events.select]

    def __init__(
        self,
        value: list[tuple[str, str | float | None]] | dict | Callable | None = None,
        *,
        color_map: dict[str, str]
        | None = None,  # Parameter moved to HighlightedText.style()
        show_legend: bool = False,
        combine_adjacent: bool = False,
        adjacent_separator: str = "",
        label: str | None = None,
        every: float | None = None,
        show_label: bool | None = None,
        container: bool = True,
        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,
        interactive: bool | None = None,
    ):
        """
        Parameters:
            value: Default value to show. If callable, the function will be called whenever the app loads to set the initial value of the component.
            color_map: A dictionary mapping labels to colors. The colors may be specified as hex codes or by their names. For example: {"person": "red", "location": "#FFEE22"}
            show_legend: whether to show span categories in a separate legend or inline.
            combine_adjacent: If True, will merge the labels of adjacent tokens belonging to the same category.
            adjacent_separator: Specifies the separator to be used between tokens if combine_adjacent is True.
            label: The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
            show_label: if True, will display label.
            container: If True, will place the component in a container - providing some extra padding around the border.
            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.
            interactive: If True, the component will be editable, and allow user to select spans of text and label them.
        """
        self.color_map = color_map
        self.show_legend = show_legend
        self.combine_adjacent = combine_adjacent
        self.adjacent_separator = adjacent_separator
        super().__init__(
            label=label,
            every=every,
            show_label=show_label,
            container=container,
            scale=scale,
            min_width=min_width,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            render=render,
            value=value,
            interactive=interactive,
        )

    def example_payload(self) -> Any:
        return [
            {"token": "The", "class_or_confidence": None},
            {"token": "quick", "class_or_confidence": "adj"},
        ]

    def example_value(self) -> Any:
        return [("The", None), ("quick", "adj"), ("brown", "adj"), ("fox", "noun")]

    def preprocess(
        self, payload: HighlightedTextData | None
    ) -> list[tuple[str, str | float | None]] | None:
        """
        Parameters:
            payload: An instance of HighlightedTextData
        Returns:
            Passes the value as a list of tuples as a `list[tuple]` into the function. Each `tuple` consists of a `str` substring of the text (so the entire text is included) and `str | float | None` label, which is the category or confidence of that substring.
        """
        if payload is None:
            return None
        return payload.model_dump()  # type: ignore

    def postprocess(
        self, value: list[tuple[str, str | float | None]] | dict | None
    ) -> HighlightedTextData | None:
        """
        Parameters:
            value: Expects a list of (word, category) tuples, or a dictionary of two keys: "text", and "entities", which itself is a list of dictionaries, each of which have the keys: "entity" (or "entity_group"), "start", and "end"
        Returns:
            An instance of HighlightedTextData
        """
        if value is None:
            return None
        if isinstance(value, dict):
            try:
                text = value["text"]
                entities = value["entities"]
            except KeyError as ke:
                raise ValueError(
                    "Expected a dictionary with keys 'text' and 'entities' "
                    "for the value of the HighlightedText component."
                ) from ke
            if len(entities) == 0:
                value = [(text, None)]
            else:
                list_format = []
                index = 0
                entities = sorted(entities, key=lambda x: x["start"])
                for entity in entities:
                    list_format.append((text[index : entity["start"]], None))
                    entity_category = entity.get("entity") or entity.get("entity_group")
                    list_format.append(
                        (text[entity["start"] : entity["end"]], entity_category)
                    )
                    index = entity["end"]
                list_format.append((text[index:], None))
                value = list_format
        if self.combine_adjacent:
            output = []
            running_text, running_category = None, None
            for text, category in value:
                if running_text is None:
                    running_text = text
                    running_category = category
                elif category == running_category:
                    running_text += self.adjacent_separator + text
                elif not text:
                    # Skip fully empty item, these get added in processing
                    # of dictionaries.
                    pass
                else:
                    output.append((running_text, running_category))
                    running_text = text
                    running_category = category
            if running_text is not None:
                output.append((running_text, running_category))
            return HighlightedTextData(
                root=[
                    HighlightedToken(token=o[0], class_or_confidence=o[1])
                    for o in output
                ]
            )
        else:
            return HighlightedTextData(
                root=[
                    HighlightedToken(token=o[0], class_or_confidence=o[1])
                    for o in value
                ]
            )

    
    def change(self,
        fn: Callable | None,
        inputs: Component | Sequence[Component] | set[Component] | None = None,
        outputs: Component | Sequence[Component] | 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: 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: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds.
            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 to use this event. If fn is None, show_api will automatically be set to False.
        """
        ...
    
    def select(self,
        fn: Callable | None,
        inputs: Component | Sequence[Component] | set[Component] | None = None,
        outputs: Component | Sequence[Component] | 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: 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: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds.
            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 to use this event. If fn is None, show_api will automatically be set to False.
        """
        ...