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from __future__ import annotations

from collections.abc import Sequence
from typing import TYPE_CHECKING

from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.cloud.aiplatform_v1 import PredictionServiceClient

from airflow.providers.google.common.consts import CLIENT_INFO
from airflow.providers.google.common.hooks.base_google import PROVIDE_PROJECT_ID, GoogleBaseHook

if TYPE_CHECKING:
    from google.api_core.retry import Retry
    from google.cloud.aiplatform_v1.types import PredictResponse


class PredictionServiceHook(GoogleBaseHook):
    """Hook for Google Cloud Vertex AI Prediction API."""

    def _get_api_endpoint(
        self,
        region: str | None = None,
    ) -> str | None:
        if region and region != "global" and self.is_default_universe():
            return f"{region}-aiplatform.googleapis.com:443"
        return None

    def get_prediction_service_client(self, region: str | None = None) -> PredictionServiceClient:
        """
        Return PredictionServiceClient object.

        :param region: The ID of the Google Cloud region that the service belongs to. Default is None.

        :return: `google.cloud.aiplatform_v1.services.prediction_service.client.PredictionServiceClient` instance.
        """
        return PredictionServiceClient(
            credentials=self.get_credentials(),
            client_info=CLIENT_INFO,
            client_options=self.get_client_options(
                api_endpoint_override=self._get_api_endpoint(region=region)
            ),
        )

    @GoogleBaseHook.fallback_to_default_project_id
    def predict(
        self,
        endpoint_id: str,
        instances: list[str],
        location: str,
        project_id: str = PROVIDE_PROJECT_ID,
        parameters: dict[str, str] | None = None,
        retry: Retry | _MethodDefault = DEFAULT,
        timeout: float | None = None,
        metadata: Sequence[tuple[str, str]] = (),
    ) -> PredictResponse:
        """
        Perform an online prediction and returns the prediction result in the response.

        :param endpoint_id: Name of the endpoint_id requested to serve the prediction.
        :param instances: Required. The instances that are the input to the prediction call. A DeployedModel
            may have an upper limit on the number of instances it supports per request, and when it is
            exceeded the prediction call errors in case of AutoML Models, or, in case of customer created
            Models, the behaviour is as documented by that Model.
        :param parameters: Additional domain-specific parameters, any string must be up to 25000 characters long.
        :param project_id: ID of the Google Cloud project where model is located if None then
            default project_id is used.
        :param location: The location of the project.
        :param retry: A retry object used to retry requests. If `None` is specified, requests will not be
            retried.
        :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
            `retry` is specified, the timeout applies to each individual attempt.
        :param metadata: Additional metadata that is provided to the method.
        """
        client = self.get_prediction_service_client(location)
        endpoint = f"projects/{project_id}/locations/{location}/endpoints/{endpoint_id}"
        return client.predict(
            request={"endpoint": endpoint, "instances": instances, "parameters": parameters},
            retry=retry,
            timeout=timeout,
            metadata=metadata,
        )
