"""Config flow for Anthropic integration."""

from __future__ import annotations

from functools import partial
import json
import logging
from typing import Any

import anthropic
import voluptuous as vol
from voluptuous_openapi import convert

from homeassistant.components.zone import ENTITY_ID_HOME
from homeassistant.config_entries import (
    ConfigEntry,
    ConfigEntryState,
    ConfigFlow,
    ConfigFlowResult,
    ConfigSubentryFlow,
    SubentryFlowResult,
)
from homeassistant.const import (
    ATTR_LATITUDE,
    ATTR_LONGITUDE,
    CONF_API_KEY,
    CONF_LLM_HASS_API,
    CONF_NAME,
)
from homeassistant.core import HomeAssistant, callback
from homeassistant.helpers import llm
from homeassistant.helpers.selector import (
    NumberSelector,
    NumberSelectorConfig,
    SelectOptionDict,
    SelectSelector,
    SelectSelectorConfig,
    TemplateSelector,
)
from homeassistant.helpers.typing import VolDictType

from .const import (
    CONF_CHAT_MODEL,
    CONF_MAX_TOKENS,
    CONF_PROMPT,
    CONF_RECOMMENDED,
    CONF_TEMPERATURE,
    CONF_THINKING_BUDGET,
    CONF_WEB_SEARCH,
    CONF_WEB_SEARCH_CITY,
    CONF_WEB_SEARCH_COUNTRY,
    CONF_WEB_SEARCH_MAX_USES,
    CONF_WEB_SEARCH_REGION,
    CONF_WEB_SEARCH_TIMEZONE,
    CONF_WEB_SEARCH_USER_LOCATION,
    DEFAULT_AI_TASK_NAME,
    DEFAULT_CONVERSATION_NAME,
    DOMAIN,
    NON_THINKING_MODELS,
    RECOMMENDED_CHAT_MODEL,
    RECOMMENDED_MAX_TOKENS,
    RECOMMENDED_TEMPERATURE,
    RECOMMENDED_THINKING_BUDGET,
    RECOMMENDED_WEB_SEARCH,
    RECOMMENDED_WEB_SEARCH_MAX_USES,
    RECOMMENDED_WEB_SEARCH_USER_LOCATION,
    WEB_SEARCH_UNSUPPORTED_MODELS,
)

_LOGGER = logging.getLogger(__name__)

STEP_USER_DATA_SCHEMA = vol.Schema(
    {
        vol.Required(CONF_API_KEY): str,
    }
)

RECOMMENDED_CONVERSATION_OPTIONS = {
    CONF_RECOMMENDED: True,
    CONF_LLM_HASS_API: [llm.LLM_API_ASSIST],
    CONF_PROMPT: llm.DEFAULT_INSTRUCTIONS_PROMPT,
}

RECOMMENDED_AI_TASK_OPTIONS = {
    CONF_RECOMMENDED: True,
}


async def validate_input(hass: HomeAssistant, data: dict[str, Any]) -> None:
    """Validate the user input allows us to connect.

    Data has the keys from STEP_USER_DATA_SCHEMA with values provided by the user.
    """
    client = await hass.async_add_executor_job(
        partial(anthropic.AsyncAnthropic, api_key=data[CONF_API_KEY])
    )
    await client.models.list(timeout=10.0)


class AnthropicConfigFlow(ConfigFlow, domain=DOMAIN):
    """Handle a config flow for Anthropic."""

    VERSION = 2
    MINOR_VERSION = 3

    async def async_step_user(
        self, user_input: dict[str, Any] | None = None
    ) -> ConfigFlowResult:
        """Handle the initial step."""
        errors: dict[str, str] = {}

        if user_input is not None:
            self._async_abort_entries_match(user_input)
            try:
                await validate_input(self.hass, user_input)
            except anthropic.APITimeoutError:
                errors["base"] = "timeout_connect"
            except anthropic.APIConnectionError:
                errors["base"] = "cannot_connect"
            except anthropic.APIStatusError as e:
                errors["base"] = "unknown"
                if (
                    isinstance(e.body, dict)
                    and (error := e.body.get("error"))
                    and error.get("type") == "authentication_error"
                ):
                    errors["base"] = "authentication_error"
            except Exception:
                _LOGGER.exception("Unexpected exception")
                errors["base"] = "unknown"
            else:
                return self.async_create_entry(
                    title="Claude",
                    data=user_input,
                    subentries=[
                        {
                            "subentry_type": "conversation",
                            "data": RECOMMENDED_CONVERSATION_OPTIONS,
                            "title": DEFAULT_CONVERSATION_NAME,
                            "unique_id": None,
                        },
                        {
                            "subentry_type": "ai_task_data",
                            "data": RECOMMENDED_AI_TASK_OPTIONS,
                            "title": DEFAULT_AI_TASK_NAME,
                            "unique_id": None,
                        },
                    ],
                )

        return self.async_show_form(
            step_id="user", data_schema=STEP_USER_DATA_SCHEMA, errors=errors or None
        )

    @classmethod
    @callback
    def async_get_supported_subentry_types(
        cls, config_entry: ConfigEntry
    ) -> dict[str, type[ConfigSubentryFlow]]:
        """Return subentries supported by this integration."""
        return {
            "conversation": ConversationSubentryFlowHandler,
            "ai_task_data": ConversationSubentryFlowHandler,
        }


class ConversationSubentryFlowHandler(ConfigSubentryFlow):
    """Flow for managing conversation subentries."""

    options: dict[str, Any]

    @property
    def _is_new(self) -> bool:
        """Return if this is a new subentry."""
        return self.source == "user"

    async def async_step_user(
        self, user_input: dict[str, Any] | None = None
    ) -> SubentryFlowResult:
        """Add a subentry."""
        if self._subentry_type == "ai_task_data":
            self.options = RECOMMENDED_AI_TASK_OPTIONS.copy()
        else:
            self.options = RECOMMENDED_CONVERSATION_OPTIONS.copy()
        return await self.async_step_init()

    async def async_step_reconfigure(
        self, user_input: dict[str, Any] | None = None
    ) -> SubentryFlowResult:
        """Handle reconfiguration of a subentry."""
        self.options = self._get_reconfigure_subentry().data.copy()
        return await self.async_step_init()

    async def async_step_init(
        self, user_input: dict[str, Any] | None = None
    ) -> SubentryFlowResult:
        """Set initial options."""
        # abort if entry is not loaded
        if self._get_entry().state != ConfigEntryState.LOADED:
            return self.async_abort(reason="entry_not_loaded")

        hass_apis: list[SelectOptionDict] = [
            SelectOptionDict(
                label=api.name,
                value=api.id,
            )
            for api in llm.async_get_apis(self.hass)
        ]
        if (suggested_llm_apis := self.options.get(CONF_LLM_HASS_API)) and isinstance(
            suggested_llm_apis, str
        ):
            self.options[CONF_LLM_HASS_API] = [suggested_llm_apis]

        step_schema: VolDictType = {}
        errors: dict[str, str] = {}

        if self._is_new:
            if self._subentry_type == "ai_task_data":
                default_name = DEFAULT_AI_TASK_NAME
            else:
                default_name = DEFAULT_CONVERSATION_NAME
            step_schema[vol.Required(CONF_NAME, default=default_name)] = str

        if self._subentry_type == "conversation":
            step_schema.update(
                {
                    vol.Optional(CONF_PROMPT): TemplateSelector(),
                    vol.Optional(
                        CONF_LLM_HASS_API,
                    ): SelectSelector(
                        SelectSelectorConfig(options=hass_apis, multiple=True)
                    ),
                }
            )

        step_schema[
            vol.Required(
                CONF_RECOMMENDED, default=self.options.get(CONF_RECOMMENDED, False)
            )
        ] = bool

        if user_input is not None:
            if not user_input.get(CONF_LLM_HASS_API):
                user_input.pop(CONF_LLM_HASS_API, None)

            if user_input[CONF_RECOMMENDED]:
                if not errors:
                    if self._is_new:
                        return self.async_create_entry(
                            title=user_input.pop(CONF_NAME),
                            data=user_input,
                        )
                    return self.async_update_and_abort(
                        self._get_entry(),
                        self._get_reconfigure_subentry(),
                        data=user_input,
                    )
            else:
                self.options.update(user_input)
                if (
                    CONF_LLM_HASS_API in self.options
                    and CONF_LLM_HASS_API not in user_input
                ):
                    self.options.pop(CONF_LLM_HASS_API)
                if not errors:
                    return await self.async_step_advanced()

        return self.async_show_form(
            step_id="init",
            data_schema=self.add_suggested_values_to_schema(
                vol.Schema(step_schema), self.options
            ),
            errors=errors or None,
        )

    async def async_step_advanced(
        self, user_input: dict[str, Any] | None = None
    ) -> SubentryFlowResult:
        """Manage advanced options."""
        errors: dict[str, str] = {}

        step_schema: VolDictType = {
            vol.Optional(
                CONF_CHAT_MODEL,
                default=RECOMMENDED_CHAT_MODEL,
            ): str,
            vol.Optional(
                CONF_MAX_TOKENS,
                default=RECOMMENDED_MAX_TOKENS,
            ): int,
            vol.Optional(
                CONF_TEMPERATURE,
                default=RECOMMENDED_TEMPERATURE,
            ): NumberSelector(NumberSelectorConfig(min=0, max=1, step=0.05)),
        }

        if user_input is not None:
            self.options.update(user_input)

            if not errors:
                return await self.async_step_model()

        return self.async_show_form(
            step_id="advanced",
            data_schema=self.add_suggested_values_to_schema(
                vol.Schema(step_schema), self.options
            ),
            errors=errors,
        )

    async def async_step_model(
        self, user_input: dict[str, Any] | None = None
    ) -> SubentryFlowResult:
        """Manage model-specific options."""
        errors: dict[str, str] = {}

        step_schema: VolDictType = {}

        model = self.options[CONF_CHAT_MODEL]

        if not model.startswith(tuple(NON_THINKING_MODELS)):
            step_schema[
                vol.Optional(CONF_THINKING_BUDGET, default=RECOMMENDED_THINKING_BUDGET)
            ] = vol.All(
                NumberSelector(
                    NumberSelectorConfig(
                        min=0,
                        max=self.options.get(CONF_MAX_TOKENS, RECOMMENDED_MAX_TOKENS),
                    )
                ),
                vol.Coerce(int),
            )
        else:
            self.options.pop(CONF_THINKING_BUDGET, None)

        if not model.startswith(tuple(WEB_SEARCH_UNSUPPORTED_MODELS)):
            step_schema.update(
                {
                    vol.Optional(
                        CONF_WEB_SEARCH,
                        default=RECOMMENDED_WEB_SEARCH,
                    ): bool,
                    vol.Optional(
                        CONF_WEB_SEARCH_MAX_USES,
                        default=RECOMMENDED_WEB_SEARCH_MAX_USES,
                    ): int,
                    vol.Optional(
                        CONF_WEB_SEARCH_USER_LOCATION,
                        default=RECOMMENDED_WEB_SEARCH_USER_LOCATION,
                    ): bool,
                }
            )
        else:
            self.options.pop(CONF_WEB_SEARCH, None)
            self.options.pop(CONF_WEB_SEARCH_MAX_USES, None)
            self.options.pop(CONF_WEB_SEARCH_USER_LOCATION, None)

        self.options.pop(CONF_WEB_SEARCH_CITY, None)
        self.options.pop(CONF_WEB_SEARCH_REGION, None)
        self.options.pop(CONF_WEB_SEARCH_COUNTRY, None)
        self.options.pop(CONF_WEB_SEARCH_TIMEZONE, None)

        if not step_schema:
            user_input = {}

        if user_input is not None:
            if user_input.get(CONF_WEB_SEARCH, RECOMMENDED_WEB_SEARCH) and not errors:
                if user_input.get(
                    CONF_WEB_SEARCH_USER_LOCATION, RECOMMENDED_WEB_SEARCH_USER_LOCATION
                ):
                    user_input.update(await self._get_location_data())

            self.options.update(user_input)

            if not errors:
                if self._is_new:
                    return self.async_create_entry(
                        title=self.options.pop(CONF_NAME),
                        data=self.options,
                    )

                return self.async_update_and_abort(
                    self._get_entry(),
                    self._get_reconfigure_subentry(),
                    data=self.options,
                )

        return self.async_show_form(
            step_id="model",
            data_schema=self.add_suggested_values_to_schema(
                vol.Schema(step_schema), self.options
            ),
            errors=errors or None,
            last_step=True,
        )

    async def _get_location_data(self) -> dict[str, str]:
        """Get approximate location data of the user."""
        location_data: dict[str, str] = {}
        zone_home = self.hass.states.get(ENTITY_ID_HOME)
        if zone_home is not None:
            client = await self.hass.async_add_executor_job(
                partial(
                    anthropic.AsyncAnthropic,
                    api_key=self._get_entry().data[CONF_API_KEY],
                )
            )
            location_schema = vol.Schema(
                {
                    vol.Optional(
                        CONF_WEB_SEARCH_CITY,
                        description="Free text input for the city, e.g. `San Francisco`",
                    ): str,
                    vol.Optional(
                        CONF_WEB_SEARCH_REGION,
                        description="Free text input for the region, e.g. `California`",
                    ): str,
                }
            )
            response = await client.messages.create(
                model=RECOMMENDED_CHAT_MODEL,
                messages=[
                    {
                        "role": "user",
                        "content": "Where are the following coordinates located: "
                        f"({zone_home.attributes[ATTR_LATITUDE]},"
                        f" {zone_home.attributes[ATTR_LONGITUDE]})? Please respond "
                        "only with a JSON object using the following schema:\n"
                        f"{convert(location_schema)}",
                    },
                    {
                        "role": "assistant",
                        "content": "{",  # hints the model to skip any preamble
                    },
                ],
                max_tokens=RECOMMENDED_MAX_TOKENS,
            )
            _LOGGER.debug("Model response: %s", response.content)
            location_data = location_schema(
                json.loads(
                    "{"
                    + "".join(
                        block.text
                        for block in response.content
                        if isinstance(block, anthropic.types.TextBlock)
                    )
                )
                or {}
            )

        if self.hass.config.country:
            location_data[CONF_WEB_SEARCH_COUNTRY] = self.hass.config.country
        location_data[CONF_WEB_SEARCH_TIMEZONE] = self.hass.config.time_zone

        _LOGGER.debug("Location data: %s", location_data)

        return location_data