.venv_haddock/lib/python3.12/site-packages/haddock/libs/libgrid.py

import glob
import math
import os
import random
import re
import shutil
import subprocess
import tempfile
import uuid
import warnings
import zipfile
from abc import ABC, abstractmethod
from concurrent.futures import ThreadPoolExecutor
from datetime import datetime
from enum import Enum
from pathlib import Path
from typing import Optional, Tuple, Union

from haddock import log
from haddock.core.defaults import cns_exec_linux as CNS_EXEC
from haddock.libs.libsubprocess import CNSJob
from haddock.libs.libutil import parse_ncores

# NOTE: When creating the payload.zip, this warning appears because we are replicating `toppar_path`
#  subdirectory structure. It can be ignored safely, but keep an eye on it to make sure
#  not many operations are being duplicated
warnings.filterwarnings("ignore", message="Duplicate name:*", category=UserWarning)
# =====================================================================#
# EXPLANATION #
# =====================================================================#
# `JOB_TYPE` is an environment variable that can be set to specify the
#  type of job associated with a given VO. This is usually important for
#  accounting purposes, as different job types may have different resources
#  associated with it.
# There is an explanation about this in the user manual.
# =====================================================================#
JOB_TYPE = os.getenv("HADDOCK3_GRID_JOB_TYPE", "WeNMR-DEV")
MAX_RETRIES = 10

# Important patterns to adjust the paths in the `.inp` file
OUTPUT_PATTERN = r'\$output_\w+\s*=\s*"([^"]+)"|\$output_\w+\s*=\s*([^\s;]+)'
VAR_PATTERN = r"\(\$\s*[^=]*=(?!.?\$)(.*)\)"  # https://regex101.com/r/dYqlZP/1
AT_PATTERN = r"@@(?!\$)(.*)"


def ping_dirac() -> bool:
    """Ping the Dirac server to check if it's reachable."""
    if not validate_dirac():
        return False

    result = subprocess.run(["dirac-proxy-info"], capture_output=True)
    if result.returncode != 0:
        log.error(f"Dirac proxy info failed: {result.stderr.decode().strip()}")
        return False

    return True


def validate_dirac() -> bool:
    """Check if the DIRAC client is valid and configured."""
    expected_cmds = [
        "dirac-proxy-info",
        "dirac-wms-job-submit",
        "dirac-wms-job-status",
        "dirac-wms-job-get-output",
    ]

    for cmd in expected_cmds:
        # Expect the commands to be in the PATH
        which_cmd = shutil.which(cmd)
        if not which_cmd:
            log.error(f"Command '{cmd}' not found in PATH.")
            return False

    return True


class JobStatus(Enum):
    WAITING = "Waiting"
    RUNNING = "Running"
    UNKNOWN = "Unknown"
    DONE = "Done"
    MATCHED = "Matched"
    COMPLETING = "Completing"
    FAILED = "Failed"
    STAGED = "Staged"

    @classmethod
    def from_string(cls, value):
        """Convert string to JobStatus enum."""
        value = value.strip().lower()
        for status in cls:
            if status.value.lower() == value:
                return status
        return cls.UNKNOWN


class Tag(Enum):
    PROBING = "Probing"
    DEFAULT = "Default"


class GridInterface(ABC):

    def __init__(
        self,
        input: Union[Path, str, list[str]],
        toppar_path: Path,
        module_path: Path,
    ) -> None:
        # Common attributes for all grid jobs can be defined here
        # Unique name for the job
        self.name = str(uuid.uuid4())
        # Create a temporary directory for the job
        self.loc = Path(tempfile.mkdtemp(prefix="haddock_grid_"))
        # working directory where the GridJob was created, this is important
        #  so we know where to put the files coming from the grid
        self.wd = Path.cwd()
        # `id` is given by DIRAC
        self.id = None
        # Site where the job is running
        self.site = None
        # Script that will be executed in the grid
        self.job_script = self.loc / "job.sh"
        # JDL file that describes the job to DIRAC
        self.jdl = self.loc / "job.jdl"
        # Internal status of the job
        self.status = JobStatus.STAGED
        # `expected_outputs` is what will be produced by this `.inp` file
        self.expected_outputs = []
        # Output and error files generated by the job.sh execution
        self.stdout_f = None
        self.stderr_f = None
        # List of files to be included in the payload
        self.payload_fnames = []
        # Counter for retries
        self.retries = 0
        # Tracker for timings
        self.timings: dict[JobStatus, datetime] = {}
        # Tag for the job
        self.tag = Tag.DEFAULT
        # `input_str` is the content of the `.inp` file
        self.input_str = ""
        # `input_str_list` is a list of separate recipes`
        self.input_str_list = []  # Initialize the list to store separate recipes
        # `module_path` is the path to the CNS module files
        self.module_path = module_path
        # `toppar_path` is the path to the TOPPAR files`
        self.toppar_path = toppar_path
        # Indicator if this job has been packaged
        self.packaged = False

        # `parse_input` is an abstract compatibility method that will handle
        #  different types of input. It can be either a string, a path or
        #  a list of paths/strings
        self.parse_input(input)

    def package(self) -> None:

        # We need to process the input file first to adjust paths and identify outputs
        self.process_input_f()

        # CREATE JOB FILE
        self.create_job_script()

        # CREATE THE JDL
        self.create_jdl()

        # CREATE PAYLOAD
        self.prepare_payload(
            cns_script_path=Path(self.module_path),
            toppar_path=Path(self.toppar_path),
        )

        self.packaged = True

    @abstractmethod
    def parse_input(self, input: Union[Path, str, list[str]]) -> None:
        """placeholder"""
        pass

    @abstractmethod
    def create_job_script(self) -> None:
        """Create the job script that will be executed in the grid."""
        pass

    @abstractmethod
    def process_input_f(self) -> None:
        """Process the input file to adjust paths and identify outputs."""
        pass

    def create_jdl(self) -> None:
        """Create the JDL file that describes the job to DIRAC."""
        output_sandbox = ["job.out", "job.err"]
        output_sandbox.extend(self.expected_outputs)
        output_sandbox_str = ", ".join(f'"{fname}"' for fname in output_sandbox)
        jdl_lines = [
            f'JobName = "{self.name}";',
            'Executable = "job.sh";',
            'Arguments = "";',
            'StdOutput = "job.out";',
            'StdError = "job.err";',
            f'JobType = "{JOB_TYPE}";',
            'InputSandbox = {"job.sh", "payload.zip"};',
            "OutputSandbox = {" + f"{output_sandbox_str}" + "};",
        ]

        jdl_string = "[\n    " + "\n    ".join(jdl_lines) + "\n]\n"
        # log.debug(f"JDL for job {self.name}:\n{jdl_string}")

        with open(self.jdl, "w") as f:
            f.write(jdl_string)

    def update_status(self) -> None:
        """Update the status of the job by querying DIRAC."""
        try:
            result = subprocess.run(
                ["dirac-wms-job-status", str(self.id)],
                shell=False,
                capture_output=True,
                text=True,
            )
        except subprocess.CalledProcessError as e:
            log.error(
                f"Updating the status failed: {e}\nStdout: {e.stdout}\nStderr: {e.stderr}"
            )
            raise

        output_dict = self.parse_output(result.stdout)

        self.id = output_dict["JobID"]
        self.status = JobStatus.from_string(output_dict["Status"])
        self.site = output_dict.get("Site", "Unknown")

        log.debug(self)

        if self.status == JobStatus.RUNNING and JobStatus.RUNNING not in self.timings:
            # job is running and we have not tracket it yet, save the time
            self.timings[JobStatus.RUNNING] = datetime.now()
        elif self.status == JobStatus.DONE and JobStatus.DONE not in self.timings:
            # job is done and we have not tracked it yet, save the time
            self.timings[JobStatus.DONE] = datetime.now()

    def prepare_payload(self, cns_script_path: Path, toppar_path: Path) -> None:
        """Prepare the payload.zip file containing all necessary files."""

        # Find the CNS scripts that should be inside the payload
        for f in cns_script_path.glob("*"):
            self.payload_fnames.append(Path(f))

        # Find the TOPPAR files that should be inside the payload
        for f in toppar_path.rglob("*"):
            if f.is_file():  # Only add files, not directories
                self.payload_fnames.append(f)

        # Create the payload.zip
        with zipfile.ZipFile(f"{self.loc}/payload.zip", "w") as z:
            #  It must contain the CNS executable and the input file,
            # z.write(self.input_f, arcname=f"{self.input_f.name}")
            z.write(CNS_EXEC, arcname="cns")
            for f in set(self.payload_fnames):
                # NOTE: Preserve the relative path structure from `toppar_path`!
                #  This is important because some CNS scripts have relative paths
                #   hardcoded in it
                if f.is_relative_to(toppar_path):
                    relative_path = f.relative_to(toppar_path)
                    z.write(f, arcname=str(relative_path))
                else:
                    z.write(f, arcname=Path(f).name)

    def submit(self) -> None:
        """Interface to submit the job to DIRAC."""

        # If this is a re-submission the timings will already be set,
        #  make sure it is clean
        self.clean_timings()

        if not self.packaged:
            self.package()

        self.timings[JobStatus.WAITING] = datetime.now()

        try:
            # The SUBMIT_CMD returns the job ID in stdout
            result = subprocess.run(
                ["dirac-wms-job-submit", f"{self.loc}/job.jdl"],
                shell=False,
                capture_output=True,
                text=True,
                cwd=self.loc,
                check=True,
            )
        except subprocess.CalledProcessError as e:
            log.error(
                f"Job submission failed: {e}\nStdout: {e.stdout}\nStderr: {e.stderr}"
            )
            # TODO: Add some sort of retry/fallback mechanism here?
            raise

        # Add the ID to the object
        self.id = int(result.stdout.split()[-1])

        # Update the status
        self.update_status()

    def retrieve_output(self) -> None:
        """Retrieve the output files from DIRAC.

        The `dirac-wms-job-get-output` command downloads the output sandbox, this means that
         anything that was specified in the `OutputSandbox` section of the JDL file will be
         put in the current working directory following the pattern: `working_dir/job_id/`
        """
        try:
            subprocess.run(
                ["dirac-wms-job-get-output", str(self.id)],
                shell=False,
                capture_output=True,
                text=True,
                cwd=self.loc,
            )
        except subprocess.CalledProcessError as e:
            log.error(
                f"Retrieving output failed: {e}\nStdout: {e.stdout}\nStderr: {e.stderr}"
            )
            raise

        # NOTE: This is the output of the `job.sh`
        self.stdout_f = Path(f"{self.loc}/{self.id}/job.out")
        self.stderr_f = Path(f"{self.loc}/{self.id}/job.err")

        # If the job failed for some reason, save the output
        if self.stderr_f.exists():
            dst = Path(self.wd / f"{self.id}_dirac.err")
            shutil.copy(self.stderr_f, dst)
            log.debug(f"ID stderr: {self.stderr_f.read_text()}")

        ls = glob.glob(f"{self.loc}/{self.id}/*")
        log.debug(f"Files in the output sandbox: {ls}")
        log.debug(f"Expected outputs: {self.expected_outputs}")

        # Copy the output to the expected location
        for output_f in self.expected_outputs:
            src = Path(f"{self.loc}/{self.id}/{output_f}")
            dst = Path(self.wd / f"{output_f}")
            shutil.copy(src, dst)

    def clean_timings(self) -> None:
        """Clean the timings dictionary."""
        self.timings = {}

    def clean(self) -> None:
        """Clean up the temporary directory where the job lives."""
        shutil.rmtree(self.loc)

    @staticmethod
    def parse_output(output_str: str) -> dict[str, str]:
        """Parse the output string from DIRAC commands into a dictionary."""
        items = output_str.replace(";", "")
        status_dict = {}
        for item in items.split(" "):
            if "=" in item:
                key, value = item.split("=", 1)
                status_dict[key.strip()] = value.strip()
        return status_dict

    @staticmethod
    def _process_line(line: str) -> Tuple[str, Optional[str]]:
        """Process a line to identify and adjust paths."""

        match_var = re.findall(VAR_PATTERN, line)
        match_at = re.findall(AT_PATTERN, line)

        # NOTE: In CNS it cannot match both patterns at the same time
        if match_at:
            item = match_at[0].strip('"').strip("'")
        elif match_var:
            item = match_var[0].strip('"').strip("'")
        else:
            # no match
            return line, None

        if Path(item).exists():
            # This is a path
            return line.replace(item, Path(item).name), item
        else:
            # This is not a path
            return line, None

    @staticmethod
    def _find_output(line) -> Optional[str]:
        """Parse the line and identify if this contains an output file declaration."""
        match = re.search(OUTPUT_PATTERN, line)
        if match:
            return match.group(1) if match.group(1) else match.group(2)
        return None

    def __repr__(self) -> str:
        return f"ID: {self.id} Name: {self.name} Output: {self.expected_outputs} Status: {self.status.value} Site: {self.site}"


class GridJob(GridInterface):
    """GridJob is a class tha represents a job to be run on the GRID via DIRAC."""

    def __init__(
        self,
        input: Union[Path, str],
        toppar_path: Path,
        module_path: Path,
    ) -> None:
        super().__init__(
            input=input,
            toppar_path=toppar_path,
            module_path=module_path,
        )  # initialize the base class

    def parse_input(self, input: Union[Path, str, list[str]]) -> None:
        """placeholder"""
        # NOTE: `input` can be a string or a path, handle the polymorfism here
        if isinstance(input, Path):
            self.input_str = input.read_text()

        elif isinstance(input, str):
            self.input_str = input

        elif isinstance(input, list):
            raise ValueError("GridJob input cannot be a list, use CompositeGridJob")

    def create_job_script(self) -> None:
        """Create the job script that will be executed in the grid."""
        # NOTE: We use `\n` instead of `os.linesep` because this will
        #  be executed in a Linux environment inside the grid
        inp_name = f"{self.name}.inp"
        cns_out_name = f"{self.name}.out"
        self.expected_outputs.append(cns_out_name)

        instructions = "#!/bin/bash\n"
        instructions += "export MODULE=./\n"
        instructions += "export TOPPAR=./\n"
        instructions += "unzip payload.zip\n"
        instructions += f"./cns < {inp_name} > {cns_out_name}\n"
        # Remove `cns.log` if there is no error
        instructions += f"[ $? -eq 0 ] && rm {cns_out_name} || exit 1\n"

        with open(self.job_script, "w") as f:
            f.write(instructions)

    def process_input_f(self) -> None:
        """Process the input file to adjust paths and identify outputs."""

        # Write the modified lines back
        inp_name = Path(f"{self.loc}/{self.name}.inp")
        self.payload_fnames.append(inp_name)
        with open(inp_name, "w") as f:
            for line in self.input_str.splitlines(keepends=True):

                # Parse this line and try to identify output files
                output = self._find_output(line)
                if output:
                    self.expected_outputs.append(output)

                # Process the line to adjust paths, if any
                new_line, found_fname = self._process_line(line)

                f.write(new_line)

                # Collect the files that need to be in the payload
                if found_fname:
                    src_path = Path(found_fname)
                    dst_path = self.loc / Path(found_fname).name
                    shutil.copy(src_path, dst_path)
                    self.payload_fnames.append(dst_path)


class CompositeGridJob(GridInterface):

    def __init__(
        self,
        input: list[str],
        toppar_path: Path,
        module_path: Path,
    ) -> None:
        super().__init__(
            input=input,
            toppar_path=toppar_path,
            module_path=module_path,
        )  # initialize the base class

    def create_job_script(self) -> None:
        """Create the job script that will be executed in the grid."""
        # NOTE: We use `\n` instead of `os.linesep` because this will
        #  be executed in a Linux environment inside the grid
        instructions = "#!/bin/bash\n"
        instructions += "export MODULE=./\n"
        instructions += "export TOPPAR=./\n"
        instructions += "unzip payload.zip\n"
        for idx, _ in enumerate(self.input_str_list):
            inp_name = f"{idx}_{self.name}.inp"
            cns_out_name = f"{idx}_{self.name}.out"
            self.expected_outputs.append(cns_out_name)
            instructions += f"./cns < {inp_name} > {cns_out_name}\n"
            instructions += f"[ $? -eq 0 ] && rm {cns_out_name} || exit 1\n"

        with open(self.job_script, "w") as f:
            f.write(instructions)

    def parse_input(self, input: Union[Path, str, list[str]]) -> None:
        """Read a concatenated list of `.inp` files and split them into separate recipes."""
        if not isinstance(input, list):
            raise ValueError("CompositeGridJob input must be a list of strings")

        # Join all the `.inp` contents into a single string
        inp = "".join(input)
        _input = ""

        lines_iter = iter(inp.split("\n"))
        for line in lines_iter:
            if line.startswith("stop"):
                _input += "! end of the recipe\nstop\n"
                self.input_str_list.append(_input.rstrip("\n"))
                _input = ""  # Reset for next recipe
            else:
                # If next line is not "stop", we still need to add it back
                _input += line + "\n"

        # If there's remaining content without a termination signal, add it as the last recipe
        if _input.strip():
            _input += "! end of the recipe\nstop\n"
            self.input_str_list.append(_input.rstrip("\n"))

        # Keep the original behavior for backward compatibility
        self.input_str = "\n".join(self.input_str_list)

    def process_input_f(self) -> None:
        """Process each recipe section separately."""
        for recipe_idx, recipe_str in enumerate(self.input_str_list):
            inp_name = Path(f"{self.loc}/{recipe_idx}_{self.name}.inp")
            self.payload_fnames.append(inp_name)
            with open(inp_name, "w") as f:
                for line in recipe_str.splitlines(keepends=True):
                    # Parse this line and try to identify output files
                    output = self._find_output(line)
                    if output:
                        self.expected_outputs.append(output)

                    # Process the line to adjust paths, if any
                    new_line, found_fname = self._process_line(line)

                    # Collect the files that need to be in the payload
                    if found_fname:
                        src_path = Path(found_fname)
                        dst_path = self.loc / Path(found_fname).name
                        shutil.copy(src_path, dst_path)
                        self.payload_fnames.append(dst_path)

                    f.write(new_line)


class GRIDScheduler:
    """Scheduler to manage and run jobs on the GRID via DIRAC."""

    def __init__(
        self, tasks: list[CNSJob], params: dict, probing: float = 0.05
    ) -> None:

        self.probing: bool = True
        self.ncores = parse_ncores(params["ncores"])
        self.batch_size = 1

        self.workload: list[GridJob] = [
            GridJob(
                input=t.input_file,
                toppar_path=t.envvars["TOPPAR"],
                module_path=t.envvars["MODULE"],
            )
            for t in tasks
        ]

        # ===============================================================#
        #
        # ! IMPORTANT !
        #
        # The `subset_size` is the number of jobs to be used for probing the grid
        # In theory we could send any number of jobs, but in practice we need to
        #  consider that the submission itself takes time. So we can come to the
        #  scenario in which we spend more time submitting jobs than measuring,
        #  this means that we would not be able to properly evaluate the grid
        #  capacity.
        # Here we set the `subset_size` to be the minimum between `ncores`
        # and the 5% of the total number of jobs. By setting it to the number
        # of cores, we can be sure that we will be able to measure the metrics.
        # We also need to ensure there is at least one job to probe the grid.
        #
        # HACK: This is not ideal, but it is a compromise between accuracy and speed.
        subset_size = min(self.ncores, max(1, math.ceil(len(self.workload) * probing)))
        # ===============================================================#

        # Randomly select that many jobs
        if len(self.workload) >= subset_size:
            for i in random.sample(range(len(self.workload)), subset_size):
                self.workload[i].tag = Tag.PROBING
        else:
            log.warning("> Not enough jobs to probe the grid, skipping probing step <")
            self.probing = False

    def run(self) -> None:
        """Execute the tasks."""
        log.info("#" * 42)
        log.info("=== Running tasks with GRID Scheduler ===")

        self.probe_grid_efficiency()

        self.create_batches()

        self.submit_jobs()

        self.wait_for_completion()

        log.info("#" * 42)

    def create_batches(self) -> None:
        """Create batches of jobs to be submitted together."""
        log.info("++ Concatenating jobs to increase efficiency...")

        jobs = [j for j in self.workload if j.tag == Tag.DEFAULT]

        toppar_path = jobs[0].toppar_path
        module_path = jobs[0].module_path

        composite_jobs = []
        for i, batch in enumerate(range(0, len(jobs), self.batch_size), start=1):
            input = [j.input_str for j in jobs[batch : batch + self.batch_size]]
            log.debug(f" Payload {i}, n={len(input)} job(s)")
            job = CompositeGridJob(
                input=input,
                toppar_path=toppar_path,
                module_path=module_path,
            )
            composite_jobs.append(job)

        log.info(
            f"++ Created {len(composite_jobs)} payload(s) with up to {self.batch_size} job(s) each"
        )
        self.workload = composite_jobs

    def wait_for_completion(self) -> None:
        """Wait for jobs with status WAITING or RUNNING to complete."""
        log.info("++ Waiting...")
        complete = False
        while not complete:
            jobs_to_check = [
                job
                for job in self.workload
                if job.status
                not in {JobStatus.STAGED, JobStatus.DONE, JobStatus.FAILED}
            ]
            if jobs_to_check:
                log.debug(f"+ Checking status of {len(jobs_to_check)} payload(s)...")
                with ThreadPoolExecutor(max_workers=self.ncores) as executor:
                    executor.map(self.process_job, jobs_to_check)
            else:
                complete = True

    def submit_jobs(self, tag: Tag = Tag.DEFAULT) -> None:
        """Submit jobs to the GRID in parallel."""
        queue = [
            job
            for job in self.workload
            if job.tag == tag and job.status == JobStatus.STAGED
        ]

        log.info(f"++ Submitting {len(queue)} '{tag.value}' payloads to the grid...")
        with ThreadPoolExecutor(max_workers=self.ncores) as executor:
            executor.map(lambda job: job.package(), queue)

        with ThreadPoolExecutor(max_workers=self.ncores) as executor:
            executor.map(lambda job: job.submit(), queue)

    def probe_grid_efficiency(self) -> None:
        """Submit a small number of jobs to probe the efficiency of the GRID."""
        if not self.probing:
            return

        log.info("++ Probing grid efficiency...")

        # Submit
        self.submit_jobs(tag=Tag.PROBING)

        # Wait
        self.wait_for_completion()

        # Calculate actual durations from timestamps
        waiting_durations = []
        running_durations = []

        for job in self.workload:
            wait_start = job.timings.get(JobStatus.WAITING)
            run_start = job.timings.get(JobStatus.RUNNING)
            done_time = job.timings.get(JobStatus.DONE)

            if wait_start is None or run_start is None or done_time is None:
                continue  # Skip jobs without complete timing info

            else:
                log.debug(f"Job {job.expected_outputs}")
                log.debug(f"   timings: {job.timings}")

            # Calculate waiting duration (WAITING to RUNNING)
            waiting_duration = (run_start - wait_start).total_seconds()
            waiting_durations.append(waiting_duration)

            # Calculate running duration (RUNNING to DONE)
            running_duration = (done_time - run_start).total_seconds()
            running_durations.append(running_duration)

        if not running_durations or not waiting_durations:
            log.warning(
                "> Average running time is zero, cannot calculate optimal batch size <"
            )
            return

        # Calculate average durations
        avg_waiting = sum(waiting_durations) / len(waiting_durations)
        avg_running = sum(running_durations) / len(running_durations)

        target_efficiency = 0.9
        batch_size = self.calculate_optimal_batch_size(
            N=self.batch_size,
            W=avg_waiting,
            R=avg_running,
            T=target_efficiency,
        )

        # Make sure batch size is not larger than the number of default jobs
        # TODO: Add a warning if this happens?
        batch_size = min(
            batch_size, len([j for j in self.workload if j.tag == Tag.DEFAULT])
        )

        self.batch_size = batch_size

    @staticmethod
    def calculate_optimal_batch_size(N: int, W: float, R: float, T: float) -> int:
        """Calculate the optimal batch size to achieve target efficiency."""
        # The efficiency of a given batch can be described as:
        #
        #  E = N * R / W + N * R
        #
        # Where E is efficiency, N is the number of jobs running at the same time
        #  R is the average running time, W is the average waiting time
        #
        # The current efficiency is then:
        E = (N * R) / (W + N * R)

        log.info(f"+ Current efficiency with {N} job(s) per payload: {E:.1%}")

        # So to achieve a target efficiency T
        # We solve for N, which is the batch size:
        #  E = N * R / W + N * R
        #  T * (W + N * R) = N * R
        #  T * W + T * N * R = N * R
        #  T * W = N * R - T * N * R
        #  T * W = N * R * (1 - T)
        #  N = T * W / R * (1 - T)
        batch_size = (T * W) / (R * (1 - T))

        batch_size = max(1, round(batch_size))  # Ensure at least 1 job
        return batch_size

    @staticmethod
    def process_job(job: GridJob) -> None:
        """Process a single job: update status, retrieve output if done, handle retries if failed.

        NOTE: This function is parallelized, that is why things like cleaning, download, retry
         are here. If you are adding new functionality, consider if it should be here or in the
         sequential part of the code.
        """

        job.update_status()

        if job.status == JobStatus.FAILED:
            # Jobs on the grid can fail for many reasons outside our control
            #  So if the job is failed, resubmit it up to MAX_RETRIES times
            if job.retries < MAX_RETRIES:
                expected_output_str = ",".join(job.expected_outputs)
                job.retries += 1
                log.warning(
                    f"> Job {job.name} ({expected_output_str}) failed on {job.site}, re-submitting - {job.retries}/{MAX_RETRIES} <"
                )
                log.debug(f"job {job.name} at {job.loc}")
                job.submit()

            # TODO: Add some sort of fallback mechanism here if it reaches the MAX_RETRIES

        if job.status == JobStatus.DONE:
            log.debug(f"Job {job.name} is done, retrieving output...")
            job.retrieve_output()
            log.debug(f"job {job.name} at {job.loc}")
            job.clean()