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from __future__ import annotations

import asyncio
import time

import boto3
from botocore.exceptions import ClientError

from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook

DEFAULT_LOG_SUFFIX = "output"
ERROR_LOG_SUFFIX = "error"


class GlueJobHook(AwsBaseHook):
    """
    Interact with AWS Glue.

    Provide thick wrapper around :external+boto3:py:class:`boto3.client("glue") <Glue.Client>`.

    :param s3_bucket: S3 bucket where logs and local etl script will be uploaded
    :param job_name: unique job name per AWS account
    :param desc: job description
    :param concurrent_run_limit: The maximum number of concurrent runs allowed for a job
    :param script_location: path to etl script on s3
    :param retry_limit: Maximum number of times to retry this job if it fails
    :param num_of_dpus: Number of AWS Glue DPUs to allocate to this Job
    :param region_name: aws region name (example: us-east-1)
    :param iam_role_name: AWS IAM Role for Glue Job Execution. If set `iam_role_arn` must equal None.
    :param iam_role_arn: AWS IAM Role ARN for Glue Job Execution, If set `iam_role_name` must equal None.
    :param create_job_kwargs: Extra arguments for Glue Job Creation
    :param update_config: Update job configuration on Glue (default: False)

    Additional arguments (such as ``aws_conn_id``) may be specified and
    are passed down to the underlying AwsBaseHook.

    .. seealso::
        - :class:`airflow.providers.amazon.aws.hooks.base_aws.AwsBaseHook`
    """

    class LogContinuationTokens:
        """Used to hold the continuation tokens when reading logs from both streams Glue Jobs write to."""

        def __init__(self):
            self.output_stream_continuation: str | None = None
            self.error_stream_continuation: str | None = None

    def __init__(
        self,
        s3_bucket: str | None = None,
        job_name: str | None = None,
        desc: str | None = None,
        concurrent_run_limit: int = 1,
        script_location: str | None = None,
        retry_limit: int = 0,
        num_of_dpus: int | float | None = None,
        iam_role_name: str | None = None,
        iam_role_arn: str | None = None,
        create_job_kwargs: dict | None = None,
        update_config: bool = False,
        job_poll_interval: int | float = 6,
        *args,
        **kwargs,
    ):
        self.job_name = job_name
        self.desc = desc
        self.concurrent_run_limit = concurrent_run_limit
        self.script_location = script_location
        self.retry_limit = retry_limit
        self.s3_bucket = s3_bucket
        self.role_name = iam_role_name
        self.role_arn = iam_role_arn
        self.s3_glue_logs = "logs/glue-logs/"
        self.create_job_kwargs = create_job_kwargs or {}
        self.update_config = update_config
        self.job_poll_interval = job_poll_interval

        worker_type_exists = "WorkerType" in self.create_job_kwargs
        num_workers_exists = "NumberOfWorkers" in self.create_job_kwargs

        if self.role_arn and self.role_name:
            raise ValueError("Cannot set iam_role_arn and iam_role_name simultaneously")
        if worker_type_exists and num_workers_exists:
            if num_of_dpus is not None:
                raise ValueError("Cannot specify num_of_dpus with custom WorkerType")
        elif not worker_type_exists and num_workers_exists:
            raise ValueError("Need to specify custom WorkerType when specifying NumberOfWorkers")
        elif worker_type_exists and not num_workers_exists:
            raise ValueError("Need to specify NumberOfWorkers when specifying custom WorkerType")
        elif num_of_dpus is None:
            self.num_of_dpus: int | float = 10
        else:
            self.num_of_dpus = num_of_dpus

        kwargs["client_type"] = "glue"
        super().__init__(*args, **kwargs)

    def create_glue_job_config(self) -> dict:
        default_command = {
            "Name": "glueetl",
            "ScriptLocation": self.script_location,
        }
        command = self.create_job_kwargs.pop("Command", default_command)
        if not self.role_arn:
            execution_role = self.get_iam_execution_role()
            role_arn = execution_role["Role"]["Arn"]
        else:
            role_arn = self.role_arn

        config = {
            "Name": self.job_name,
            "Description": self.desc,
            "Role": role_arn,
            "ExecutionProperty": {"MaxConcurrentRuns": self.concurrent_run_limit},
            "Command": command,
            "MaxRetries": self.retry_limit,
            **self.create_job_kwargs,
        }

        if hasattr(self, "num_of_dpus"):
            config["MaxCapacity"] = self.num_of_dpus

        if self.s3_bucket is not None:
            config["LogUri"] = f"s3://{self.s3_bucket}/{self.s3_glue_logs}{self.job_name}"

        return config

    def list_jobs(self) -> list:
        """
        Get list of Jobs.

        .. seealso::
            - :external+boto3:py:meth:`Glue.Client.get_jobs`
        """
        return self.conn.get_jobs()

    def get_iam_execution_role(self) -> dict:
        try:
            iam_client = self.get_session(region_name=self.region_name).client(
                "iam", endpoint_url=self.conn_config.endpoint_url, config=self.config, verify=self.verify
            )
            glue_execution_role = iam_client.get_role(RoleName=self.role_name)
            self.log.info("Iam Role Name: %s", self.role_name)
            return glue_execution_role
        except Exception as general_error:
            self.log.error("Failed to create aws glue job, error: %s", general_error)
            raise

    def initialize_job(
        self,
        script_arguments: dict | None = None,
        run_kwargs: dict | None = None,
    ) -> dict[str, str]:
        """
        Initialize connection with AWS Glue to run job.

        .. seealso::
            - :external+boto3:py:meth:`Glue.Client.start_job_run`
        """
        script_arguments = script_arguments or {}
        run_kwargs = run_kwargs or {}

        try:
            if self.update_config:
                job_name = self.create_or_update_glue_job()
            else:
                job_name = self.get_or_create_glue_job()

            return self.conn.start_job_run(JobName=job_name, Arguments=script_arguments, **run_kwargs)
        except Exception as general_error:
            self.log.error("Failed to run aws glue job, error: %s", general_error)
            raise

    def get_job_state(self, job_name: str, run_id: str) -> str:
        """
        Get state of the Glue job; the job state can be running, finished, failed, stopped or timeout.

        .. seealso::
            - :external+boto3:py:meth:`Glue.Client.get_job_run`

        :param job_name: unique job name per AWS account
        :param run_id: The job-run ID of the predecessor job run
        :return: State of the Glue job
        """
        job_run = self.conn.get_job_run(JobName=job_name, RunId=run_id, PredecessorsIncluded=True)
        return job_run["JobRun"]["JobRunState"]

    async def async_get_job_state(self, job_name: str, run_id: str) -> str:
        """
        Get state of the Glue job; the job state can be running, finished, failed, stopped or timeout.

        The async version of get_job_state.
        """
        async with self.async_conn as client:
            job_run = await client.get_job_run(JobName=job_name, RunId=run_id)
        return job_run["JobRun"]["JobRunState"]

    def print_job_logs(
        self,
        job_name: str,
        run_id: str,
        continuation_tokens: LogContinuationTokens,
    ):
        """
        Print the latest job logs to the Airflow task log and updates the continuation tokens.

        :param continuation_tokens: the tokens where to resume from when reading logs.
            The object gets updated with the new tokens by this method.
        """
        log_client = boto3.client("logs")
        paginator = log_client.get_paginator("filter_log_events")

        def display_logs_from(log_group: str, continuation_token: str | None) -> str | None:
            """Mutualize iteration over the 2 different log streams glue jobs write to."""
            fetched_logs = []
            next_token = continuation_token
            try:
                for response in paginator.paginate(
                    logGroupName=log_group,
                    logStreamNames=[run_id],
                    PaginationConfig={"StartingToken": continuation_token},
                ):
                    fetched_logs.extend([event["message"] for event in response["events"]])
                    # if the response is empty there is no nextToken in it
                    next_token = response.get("nextToken") or next_token
            except ClientError as e:
                if e.response["Error"]["Code"] == "ResourceNotFoundException":
                    # we land here when the log groups/streams don't exist yet
                    self.log.warning(
                        "No new Glue driver logs so far.\nIf this persists, check the CloudWatch dashboard "
                        f"at: https://{self.conn_region_name}.console.aws.amazon.com/cloudwatch/home"
                    )
                else:
                    raise

            if len(fetched_logs):
                # Add a tab to indent those logs and distinguish them from airflow logs.
                # Log lines returned already contain a newline character at the end.
                messages = "\t".join(fetched_logs)
                self.log.info("Glue Job Run %s Logs:\n\t%s", log_group, messages)
            else:
                self.log.info("No new log from the Glue Job in %s", log_group)
            return next_token

        log_group_prefix = self.conn.get_job_run(JobName=job_name, RunId=run_id)["JobRun"]["LogGroupName"]
        log_group_default = f"{log_group_prefix}/{DEFAULT_LOG_SUFFIX}"
        log_group_error = f"{log_group_prefix}/{ERROR_LOG_SUFFIX}"

        # one would think that the error log group would contain only errors, but it actually contains
        # a lot of interesting logs too, so it's valuable to have both
        continuation_tokens.output_stream_continuation = display_logs_from(
            log_group_default, continuation_tokens.output_stream_continuation
        )
        continuation_tokens.error_stream_continuation = display_logs_from(
            log_group_error, continuation_tokens.error_stream_continuation
        )

    def job_completion(self, job_name: str, run_id: str, verbose: bool = False) -> dict[str, str]:
        """
        Wait until Glue job with job_name finishes; return final state if finished or raises AirflowException.

        :param job_name: unique job name per AWS account
        :param run_id: The job-run ID of the predecessor job run
        :param verbose: If True, more Glue Job Run logs show in the Airflow Task Logs.  (default: False)
        :return: Dict of JobRunState and JobRunId
        """
        next_log_tokens = self.LogContinuationTokens()
        while True:
            job_run_state = self.get_job_state(job_name, run_id)
            ret = self._handle_state(job_run_state, job_name, run_id, verbose, next_log_tokens)
            if ret:
                return ret
            else:
                time.sleep(self.job_poll_interval)

    async def async_job_completion(self, job_name: str, run_id: str, verbose: bool = False) -> dict[str, str]:
        """
        Wait until Glue job with job_name finishes; return final state if finished or raises AirflowException.

        :param job_name: unique job name per AWS account
        :param run_id: The job-run ID of the predecessor job run
        :param verbose: If True, more Glue Job Run logs show in the Airflow Task Logs.  (default: False)
        :return: Dict of JobRunState and JobRunId
        """
        next_log_tokens = self.LogContinuationTokens()
        while True:
            job_run_state = await self.async_get_job_state(job_name, run_id)
            ret = self._handle_state(job_run_state, job_name, run_id, verbose, next_log_tokens)
            if ret:
                return ret
            else:
                await asyncio.sleep(self.job_poll_interval)

    def _handle_state(
        self,
        state: str,
        job_name: str,
        run_id: str,
        verbose: bool,
        next_log_tokens: GlueJobHook.LogContinuationTokens,
    ) -> dict | None:
        """Process Glue Job state while polling; used by both sync and async methods."""
        failed_states = ["FAILED", "TIMEOUT"]
        finished_states = ["SUCCEEDED", "STOPPED"]

        if verbose:
            self.print_job_logs(
                job_name=job_name,
                run_id=run_id,
                continuation_tokens=next_log_tokens,
            )

        if state in finished_states:
            self.log.info("Exiting Job %s Run State: %s", run_id, state)
            return {"JobRunState": state, "JobRunId": run_id}
        if state in failed_states:
            job_error_message = f"Exiting Job {run_id} Run State: {state}"
            self.log.info(job_error_message)
            raise AirflowException(job_error_message)
        else:
            self.log.info(
                "Polling for AWS Glue Job %s current run state with status %s",
                job_name,
                state,
            )
            return None

    def has_job(self, job_name) -> bool:
        """
        Check if the job already exists.

        .. seealso::
            - :external+boto3:py:meth:`Glue.Client.get_job`

        :param job_name: unique job name per AWS account
        :return: Returns True if the job already exists and False if not.
        """
        self.log.info("Checking if job already exists: %s", job_name)

        try:
            self.conn.get_job(JobName=job_name)
            return True
        except self.conn.exceptions.EntityNotFoundException:
            return False

    def update_job(self, **job_kwargs) -> bool:
        """
        Update job configurations.

        .. seealso::
            - :external+boto3:py:meth:`Glue.Client.update_job`

        :param job_kwargs: Keyword args that define the configurations used for the job
        :return: True if job was updated and false otherwise
        """
        job_name = job_kwargs.pop("Name")
        current_job = self.conn.get_job(JobName=job_name)["Job"]

        update_config = {
            key: value for key, value in job_kwargs.items() if current_job.get(key) != job_kwargs[key]
        }
        if update_config != {}:
            self.log.info("Updating job: %s", job_name)
            self.conn.update_job(JobName=job_name, JobUpdate=job_kwargs)
            self.log.info("Updated configurations: %s", update_config)
            return True
        else:
            return False

    def get_or_create_glue_job(self) -> str | None:
        """
        Get (or creates) and returns the Job name.

        .. seealso::
            - :external+boto3:py:meth:`Glue.Client.create_job`

        :return:Name of the Job
        """
        if self.has_job(self.job_name):
            return self.job_name

        config = self.create_glue_job_config()
        self.log.info("Creating job: %s", self.job_name)
        self.conn.create_job(**config)

        return self.job_name

    def create_or_update_glue_job(self) -> str | None:
        """
        Create (or update) and return the Job name.

        .. seealso::
            - :external+boto3:py:meth:`Glue.Client.update_job`
            - :external+boto3:py:meth:`Glue.Client.create_job`

        :return:Name of the Job
        """
        config = self.create_glue_job_config()

        if self.has_job(self.job_name):
            self.update_job(**config)
        else:
            self.log.info("Creating job: %s", self.job_name)
            self.conn.create_job(**config)

        return self.job_name
