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from airflow.providers.amazon.aws.hooks.sagemaker import SageMakerHook
from airflow.providers.amazon.aws.sensors.sagemaker_base import SageMakerBaseSensor
from airflow.utils.decorators import apply_defaults


class SageMakerTuningSensor(SageMakerBaseSensor):
    """
    Asks for the state of the tuning state until it reaches a terminal state.
    The sensor will error if the job errors, throwing a AirflowException
    containing the failure reason.

    :param job_name: job_name of the tuning instance to check the state of
    :type job_name: str
    """

    template_fields = ["job_name"]
    template_ext = ()

    @apply_defaults
    def __init__(self, *, job_name: str, **kwargs):
        super().__init__(**kwargs)
        self.job_name = job_name

    def non_terminal_states(self):
        return SageMakerHook.non_terminal_states

    def failed_states(self):
        return SageMakerHook.failed_states

    def get_sagemaker_response(self):
        self.log.info("Poking Sagemaker Tuning Job %s", self.job_name)
        return self.get_hook().describe_tuning_job(self.job_name)

    def get_failed_reason_from_response(self, response):
        return response["FailureReason"]

    def state_from_response(self, response):
        return response["HyperParameterTuningJobStatus"]