adaptive-lighting/custom_components/adaptive_lighting/_docs_helpers.py

from typing import Any

import homeassistant.helpers.config_validation as cv
import pandas as pd
import voluptuous as vol
from homeassistant.helpers import selector

from .const import (
    DOCS,
    DOCS_APPLY,
    DOCS_MANUAL_CONTROL,
    SET_MANUAL_CONTROL_SCHEMA,
    VALIDATION_TUPLES,
    apply_service_schema,
)


def _format_voluptuous_instance(instance: vol.All) -> str:
    coerce_type = None
    min_val = None
    max_val = None

    for validator in instance.validators:
        if isinstance(validator, vol.Coerce):
            coerce_type = validator.type.__name__
        elif isinstance(validator, vol.Clamp | vol.Range):
            min_val = validator.min
            max_val = validator.max

    if min_val is not None and max_val is not None:
        return f"`{coerce_type}` {min_val}-{max_val}"
    if min_val is not None:
        return f"`{coerce_type} > {min_val}`"
    if max_val is not None:
        return f"`{coerce_type} < {max_val}`"
    return f"`{coerce_type}`"


def _type_to_str(type_: Any) -> str:  # noqa: PLR0911
    """Convert a (voluptuous) type to a string."""
    if type_ == cv.entity_ids:
        return "list of `entity_id`s"
    if type_ in (bool, int, float, str):
        return f"`{type_.__name__}`"
    if type_ == cv.boolean:
        return "bool"
    if isinstance(type_, vol.All):
        return _format_voluptuous_instance(type_)
    if isinstance(type_, vol.Any):
        return " or ".join(_type_to_str(t) for t in type_.validators)
    if isinstance(type_, vol.In):
        return f"one of `{type_.container}`"
    if isinstance(type_, selector.SelectSelector):
        return f"one of `{type_.config['options']}`"
    if isinstance(type_, selector.ColorRGBSelector):
        return "RGB color"
    msg = f"Unknown type: {type_}"
    raise ValueError(msg)


def generate_config_markdown_table() -> str:
    rows: list[dict[str, str]] = []
    for k, default, type_ in VALIDATION_TUPLES:
        description = DOCS[k]
        row = {
            "Variable name": f"`{k}`",
            "Description": description,
            "Default": f"`{default}`",
            "Type": _type_to_str(type_),
        }
        rows.append(row)

    df = pd.DataFrame(rows)
    return df.to_markdown(index=False)


def _schema_to_dict(schema: vol.Schema) -> dict[str, tuple[bool, Any]]:
    result: dict[str, tuple[bool, Any]] = {}
    for key, value in schema.schema.items():
        if isinstance(key, vol.Required | vol.Optional):
            required = isinstance(key, vol.Required) and key.default == vol.UNDEFINED
            result[key.schema] = (required, value)
    return result


def _generate_service_markdown_table(
    schema: vol.Schema,
    alternative_docs: dict[str, str] | None = None,
) -> str:
    rows: list[dict[str, str]] = []
    for k, (required, type_) in _schema_to_dict(schema).items():
        if alternative_docs is not None and k in alternative_docs:
            description = alternative_docs[k]
        else:
            description = DOCS[k]
        row = {
            "Service data attribute": f"`{k}`",
            "Description": description,
            "Required": "✅" if required else "❌",
            "Type": _type_to_str(type_),
        }
        rows.append(row)

    df = pd.DataFrame(rows)
    return df.to_markdown(index=False)


def generate_apply_markdown_table() -> str:
    return _generate_service_markdown_table(apply_service_schema(), DOCS_APPLY)


def generate_set_manual_control_markdown_table() -> str:
    return _generate_service_markdown_table(
        SET_MANUAL_CONTROL_SCHEMA,
        DOCS_MANUAL_CONTROL,
    )