__author__ = "Johannes Köster"
__copyright__ = "Copyright 2015-2019, Johannes Köster"
__email__ = "koester@jimmy.harvard.edu"
__license__ = "MIT"

import os, signal, sys
import threading
import operator
import time
import math

from functools import partial
from collections import defaultdict
from itertools import chain, accumulate, product
from contextlib import ContextDecorator


from snakemake.executors import DryrunExecutor, TouchExecutor, CPUExecutor
from snakemake.executors import (
    GenericClusterExecutor,
    SynchronousClusterExecutor,
    DRMAAExecutor,
    KubernetesExecutor,
    TibannaExecutor,
)
from snakemake.executors.google_lifesciences import GoogleLifeSciencesExecutor
from snakemake.exceptions import RuleException, WorkflowError, print_exception
from snakemake.shell import shell

from snakemake.logging import logger

from fractions import Fraction


def cumsum(iterable, zero=[0]):
    return list(chain(zero, accumulate(iterable)))


_ERROR_MSG_FINAL = (
    "Exiting because a job execution failed. " "Look above for error message"
)


class DummyRateLimiter(ContextDecorator):
    def __enter__(self):
        return self

    def __exit__(self, *args):
        return False


class JobScheduler:
    def __init__(
        self,
        workflow,
        dag,
        cores,
        local_cores=1,
        dryrun=False,
        touch=False,
        cluster=None,
        cluster_status=None,
        cluster_config=None,
        cluster_sync=None,
        drmaa=None,
        drmaa_log_dir=None,
        kubernetes=None,
        container_image=None,
        tibanna=None,
        tibanna_sfn=None,
        google_lifesciences=None,
        google_lifesciences_regions=None,
        google_lifesciences_location=None,
        google_lifesciences_cache=False,
        precommand="",
        preemption_default=None,
        preemptible_rules=None,
        tibanna_config=False,
        jobname=None,
        quiet=False,
        printreason=False,
        printshellcmds=False,
        keepgoing=False,
        max_jobs_per_second=None,
        max_status_checks_per_second=100,
        latency_wait=3,
        greediness=1.0,
        force_use_threads=False,
        assume_shared_fs=True,
        keepincomplete=False,
        keepmetadata=True,
        scheduler_type=None,
        scheduler_ilp_solver=None,
    ):
        """ Create a new instance of KnapsackJobScheduler. """
        from ratelimiter import RateLimiter

        self.cluster = cluster
        self.cluster_config = cluster_config
        self.cluster_sync = cluster_sync
        self.dag = dag
        self.workflow = workflow
        self.dryrun = dryrun
        self.touch = touch
        self.quiet = quiet
        self.keepgoing = keepgoing
        self.running = set()
        self.failed = set()
        self.finished_jobs = 0
        self.greediness = 1
        self.max_jobs_per_second = max_jobs_per_second
        self.keepincomplete = keepincomplete
        self.keepmetadata = keepmetadata
        self.scheduler_type = scheduler_type
        self.scheduler_ilp_solver = scheduler_ilp_solver

        self.global_resources = {
            name: (sys.maxsize if res is None else res)
            for name, res in workflow.global_resources.items()
        }
        self.resources = dict(self.global_resources)

        use_threads = (
            force_use_threads
            or (os.name != "posix")
            or cluster
            or cluster_sync
            or drmaa
        )
        self._open_jobs = threading.Semaphore(0)
        self._lock = threading.Lock()

        self._errors = False
        self._finished = False
        self._job_queue = None
        self._submit_callback = self._noop
        self._finish_callback = partial(
            self._proceed,
            update_dynamic=not self.dryrun,
            print_progress=not self.quiet and not self.dryrun,
        )

        self._local_executor = None
        if dryrun:
            self._executor = DryrunExecutor(
                workflow,
                dag,
                printreason=printreason,
                quiet=quiet,
                printshellcmds=printshellcmds,
                latency_wait=latency_wait,
            )
        elif touch:
            self._executor = TouchExecutor(
                workflow,
                dag,
                printreason=printreason,
                quiet=quiet,
                printshellcmds=printshellcmds,
                latency_wait=latency_wait,
            )
        elif cluster or cluster_sync or (drmaa is not None):
            if not workflow.immediate_submit:
                # No local jobs when using immediate submit!
                # Otherwise, they will fail due to missing input
                self._local_executor = CPUExecutor(
                    workflow,
                    dag,
                    local_cores,
                    printreason=printreason,
                    quiet=quiet,
                    printshellcmds=printshellcmds,
                    latency_wait=latency_wait,
                    cores=local_cores,
                    keepincomplete=keepincomplete,
                    keepmetadata=keepmetadata,
                )
            if cluster or cluster_sync:
                if cluster_sync:
                    constructor = SynchronousClusterExecutor
                else:
                    constructor = partial(
                        GenericClusterExecutor,
                        statuscmd=cluster_status,
                        max_status_checks_per_second=max_status_checks_per_second,
                    )

                self._executor = constructor(
                    workflow,
                    dag,
                    None,
                    submitcmd=(cluster or cluster_sync),
                    cluster_config=cluster_config,
                    jobname=jobname,
                    printreason=printreason,
                    quiet=quiet,
                    printshellcmds=printshellcmds,
                    latency_wait=latency_wait,
                    assume_shared_fs=assume_shared_fs,
                    keepincomplete=keepincomplete,
                    keepmetadata=keepmetadata,
                )
                if workflow.immediate_submit:
                    self._submit_callback = partial(
                        self._proceed,
                        update_dynamic=False,
                        print_progress=False,
                        update_resources=False,
                        handle_job_success=False,
                    )
            else:
                self._executor = DRMAAExecutor(
                    workflow,
                    dag,
                    None,
                    drmaa_args=drmaa,
                    drmaa_log_dir=drmaa_log_dir,
                    jobname=jobname,
                    printreason=printreason,
                    quiet=quiet,
                    printshellcmds=printshellcmds,
                    latency_wait=latency_wait,
                    cluster_config=cluster_config,
                    assume_shared_fs=assume_shared_fs,
                    max_status_checks_per_second=max_status_checks_per_second,
                    keepincomplete=keepincomplete,
                    keepmetadata=keepmetadata,
                )
        elif kubernetes:
            self._local_executor = CPUExecutor(
                workflow,
                dag,
                local_cores,
                printreason=printreason,
                quiet=quiet,
                printshellcmds=printshellcmds,
                latency_wait=latency_wait,
                cores=local_cores,
                keepincomplete=keepincomplete,
                keepmetadata=keepmetadata,
            )

            self._executor = KubernetesExecutor(
                workflow,
                dag,
                kubernetes,
                container_image=container_image,
                printreason=printreason,
                quiet=quiet,
                printshellcmds=printshellcmds,
                latency_wait=latency_wait,
                cluster_config=cluster_config,
                keepincomplete=keepincomplete,
                keepmetadata=keepmetadata,
            )
        elif tibanna:
            self._local_executor = CPUExecutor(
                workflow,
                dag,
                local_cores,
                printreason=printreason,
                quiet=quiet,
                printshellcmds=printshellcmds,
                use_threads=use_threads,
                latency_wait=latency_wait,
                cores=local_cores,
                keepincomplete=keepincomplete,
                keepmetadata=keepmetadata,
            )

            self._executor = TibannaExecutor(
                workflow,
                dag,
                cores,
                tibanna_sfn,
                precommand=precommand,
                tibanna_config=tibanna_config,
                container_image=container_image,
                printreason=printreason,
                quiet=quiet,
                printshellcmds=printshellcmds,
                latency_wait=latency_wait,
                keepincomplete=keepincomplete,
                keepmetadata=keepmetadata,
            )
        elif google_lifesciences:
            self._local_executor = CPUExecutor(
                workflow,
                dag,
                local_cores,
                printreason=printreason,
                quiet=quiet,
                printshellcmds=printshellcmds,
                latency_wait=latency_wait,
                cores=local_cores,
            )

            self._executor = GoogleLifeSciencesExecutor(
                workflow,
                dag,
                cores,
                container_image=container_image,
                regions=google_lifesciences_regions,
                location=google_lifesciences_location,
                cache=google_lifesciences_cache,
                printreason=printreason,
                quiet=quiet,
                printshellcmds=printshellcmds,
                latency_wait=latency_wait,
                preemption_default=preemption_default,
                preemptible_rules=preemptible_rules,
            )

        else:
            self._executor = CPUExecutor(
                workflow,
                dag,
                cores,
                printreason=printreason,
                quiet=quiet,
                printshellcmds=printshellcmds,
                use_threads=use_threads,
                latency_wait=latency_wait,
                cores=cores,
                keepincomplete=keepincomplete,
                keepmetadata=keepmetadata,
            )
        if self.max_jobs_per_second and not self.dryrun:
            max_jobs_frac = Fraction(self.max_jobs_per_second).limit_denominator()
            self.rate_limiter = RateLimiter(
                max_calls=max_jobs_frac.numerator, period=max_jobs_frac.denominator
            )

        else:
            # essentially no rate limit
            self.rate_limiter = DummyRateLimiter()

        # Choose job selector (greedy or ILP)
        self.job_selector = self.job_selector_greedy
        if scheduler_type == "ilp":
            import pulp

            if pulp.apis.LpSolverDefault is None:
                logger.warning(
                    "Falling back to greedy scheduler because no default "
                    "solver is found for pulp (you have to install either "
                    "coincbc or glpk)."
                )
            else:
                self.job_selector = self.job_selector_ilp

        self._user_kill = None
        try:
            signal.signal(signal.SIGTERM, self.exit_gracefully)
        except ValueError:
            # If this fails, it is due to scheduler not being invoked in the main thread.
            # This can only happen with --gui, in which case it is fine for now.
            pass
        self._open_jobs.release()

    @property
    def stats(self):
        try:
            return self._executor.stats
        except AttributeError:
            raise TypeError("Executor does not support stats")

    @property
    def open_jobs(self):
        """ Return open jobs. """
        jobs = self.dag.ready_jobs

        if not self.dryrun:
            jobs = [
                job
                for job in jobs
                if not job.dynamic_input and not self.dag.dynamic(job)
            ]
        return jobs

    @property
    def remaining_jobs(self):
        """ Return jobs to be scheduled including not yet ready ones. """
        return [
            job
            for job in self.dag.needrun_jobs
            if job not in self.running and not self.dag.finished(job)
        ]

    def schedule(self):
        """ Schedule jobs that are ready, maximizing cpu usage. """
        try:
            while True:
                # work around so that the wait does not prevent keyboard interrupts
                # while not self._open_jobs.acquire(False):
                #    time.sleep(1)
                self._open_jobs.acquire()

                # obtain needrun and running jobs in a thread-safe way
                with self._lock:
                    needrun = set(self.open_jobs)
                    running = list(self.running)
                    errors = self._errors
                    user_kill = self._user_kill

                # handle errors
                if user_kill or (not self.keepgoing and errors):
                    if user_kill == "graceful":
                        logger.info(
                            "Will exit after finishing " "currently running jobs."
                        )

                    if not running:
                        logger.info("Shutting down, this might take some time.")
                        self._executor.shutdown()
                        if not user_kill:
                            logger.error(_ERROR_MSG_FINAL)
                        return False
                    continue

                # normal shutdown because all jobs have been finished
                if not needrun and (not running or self.workflow.immediate_submit):
                    self._executor.shutdown()
                    if errors:
                        logger.error(_ERROR_MSG_FINAL)
                    return not errors

                # continue if no new job needs to be executed
                if not needrun:
                    continue

                # select jobs by solving knapsack problem (omit with dryrun)
                if self.dryrun:
                    run = needrun
                else:
                    logger.debug(
                        "Resources before job selection: {}".format(self.resources)
                    )
                    logger.debug(
                        "Ready jobs ({}):\n\t".format(len(needrun))
                        + "\n\t".join(map(str, needrun))
                    )

                    run = self.job_selector(needrun)

                    logger.debug(
                        "Selected jobs ({}):\n\t".format(len(run))
                        + "\n\t".join(map(str, run))
                    )
                    logger.debug(
                        "Resources after job selection: {}".format(self.resources)
                    )
                # update running jobs
                with self._lock:
                    self.running.update(run)
                    # remove from read_jobs
                    self.dag._ready_jobs -= run

                # actually run jobs
                local_runjobs = [job for job in run if job.is_local]
                runjobs = [job for job in run if not job.is_local]
                self.run(local_runjobs, executor=self._local_executor or self._executor)
                self.run(runjobs)
        except (KeyboardInterrupt, SystemExit):
            logger.info(
                "Terminating processes on user request, this might take some time."
            )
            self._executor.cancel()
            return False

    def run(self, jobs, executor=None):
        if executor is None:
            executor = self._executor
        executor.run_jobs(
            jobs,
            callback=self._finish_callback,
            submit_callback=self._submit_callback,
            error_callback=self._error,
        )

    def get_executor(self, job):
        if job.is_local and self._local_executor is not None:
            return self._local_executor
        return self._executor

    def _noop(self, job):
        pass

    def _free_resources(self, job):
        for name, value in job.resources.items():
            if name in self.resources:
                value = self.calc_resource(name, value)
                self.resources[name] += value

    def _proceed(
        self,
        job,
        update_dynamic=True,
        print_progress=False,
        update_resources=True,
        handle_job_success=True,
    ):
        """ Do stuff after job is finished. """
        with self._lock:
            if handle_job_success:
                # by calling this behind the lock, we avoid race conditions
                try:
                    self.get_executor(job).handle_job_success(job)
                except (RuleException, WorkflowError) as e:
                    # if an error occurs while processing job output,
                    # we do the same as in case of errors during execution
                    print_exception(e, self.workflow.linemaps)
                    self._handle_error(job)
                    return

            try:
                potential_new_ready_jobs = self.dag.finish(
                    job, update_dynamic=update_dynamic
                )
            except (RuleException, WorkflowError) as e:
                # if an error occurs while processing job output,
                # we do the same as in case of errors during execution
                print_exception(e, self.workflow.linemaps)
                self._handle_error(job)
                return

            if update_resources:
                # normal jobs have len=1, group jobs have len>1
                self.finished_jobs += len(job)
                self.running.remove(job)
                self._free_resources(job)

            if print_progress:
                if job.is_group():
                    for j in job:
                        logger.job_finished(jobid=j.jobid)
                else:
                    logger.job_finished(jobid=job.jobid)
                self.progress()

            if self.dryrun:
                if not self.running:
                    # During dryrun, only release when all running jobs are done.
                    # This saves a lot of time, as self.open_jobs has to be
                    # evaluated less frequently.
                    self._open_jobs.release()
            elif (
                not self.running
                or potential_new_ready_jobs
                or self.workflow.immediate_submit
            ):
                # go on scheduling if open jobs are ready or no job is running
                self._open_jobs.release()

    def _error(self, job):
        with self._lock:
            self._handle_error(job)

    def _handle_error(self, job):
        """Clear jobs and stop the workflow.

        If Snakemake is configured to restart jobs then the job might have
        "restart_times" left and we just decrement and let the scheduler
        try to run the job again.
        """
        self.get_executor(job).handle_job_error(job)
        self.running.remove(job)
        self._free_resources(job)
        # attempt starts counting from 1, but the first attempt is not
        # a restart, hence we subtract 1.
        if job.restart_times > job.attempt - 1:
            logger.info("Trying to restart job {}.".format(self.dag.jobid(job)))
            job.attempt += 1
            # add job to those being ready again
            self.dag._ready_jobs.add(job)
        else:
            self._errors = True
            self.failed.add(job)
            if self.keepgoing:
                logger.info("Job failed, going on with independent jobs.")
        self._open_jobs.release()

    def exit_gracefully(self, *args):
        with self._lock:
            self._user_kill = "graceful"
        self._open_jobs.release()

    def job_selector_ilp(self, jobs):
        """
        Job scheduling by optimization of resource usage by solving ILP using pulp
        """
        import pulp
        from pulp import lpSum

        # assert self.resources["_cores"] > 0
        scheduled_jobs = {
            job: pulp.LpVariable(
                "job_{}".format(idx),
                lowBound=0,
                upBound=1,
                cat=pulp.LpInteger,
            )
            for idx, job in enumerate(jobs)
        }

        temp_files = {
            temp_file for job in jobs for temp_file in self.dag.temp_input(job)
        }

        temp_job_improvement = {
            temp_file: pulp.LpVariable(
                "temp_file_{}".format(idx), lowBound=0, upBound=1, cat="Continuous"
            )
            for idx, temp_file in enumerate(temp_files)
        }

        temp_file_deletable = {
            temp_file: pulp.LpVariable(
                "deletable_{}".format(idx),
                lowBound=0,
                upBound=1,
                cat=pulp.LpInteger,
            )
            for idx, temp_file in enumerate(temp_files)
        }
        prob = pulp.LpProblem("JobScheduler", pulp.LpMaximize)

        total_temp_size = max(sum([temp_file.size for temp_file in temp_files]), 1)
        total_core_requirement = sum(
            [max(job.resources.get("_cores", 1), 1) for job in jobs]
        )
        # Objective function
        # Job priority > Core load
        # Core load > temp file removal
        # Instant removal > temp size
        prob += (
            2
            * total_core_requirement
            * 2
            * total_temp_size
            * lpSum([job.priority * scheduled_jobs[job] for job in jobs])
            + 2
            * total_temp_size
            * lpSum(
                [
                    max(job.resources.get("_cores", 1), 1) * scheduled_jobs[job]
                    for job in jobs
                ]
            )
            + total_temp_size
            * lpSum(
                [
                    temp_file_deletable[temp_file] * temp_file.size
                    for temp_file in temp_files
                ]
            )
            + lpSum(
                [
                    temp_job_improvement[temp_file] * temp_file.size
                    for temp_file in temp_files
                ]
            )
        )

        # Constraints:
        for name in self.workflow.global_resources:
            prob += (
                lpSum(
                    [scheduled_jobs[job] * job.resources.get(name, 0) for job in jobs]
                )
                <= self.resources[name]
            )

        # Choose jobs that lead to "fastest" (minimum steps) removal of existing temp file
        remaining_jobs = self.remaining_jobs
        for temp_file in temp_files:
            prob += temp_job_improvement[temp_file] <= lpSum(
                [
                    scheduled_jobs[job] * self.required_by_job(temp_file, job)
                    for job in jobs
                ]
            ) / lpSum([self.required_by_job(temp_file, job) for job in remaining_jobs])

            prob += temp_file_deletable[temp_file] <= temp_job_improvement[temp_file]

        solver = (
            pulp.get_solver(self.scheduler_ilp_solver)
            if self.scheduler_ilp_solver
            else pulp.apis.LpSolverDefault
        )
        solver.msg = False
        # disable extensive logging
        try:
            prob.solve(solver)
        except pulp.apis.core.PulpSolverError as e:
            raise WorkflowError(
                "Failed to solve the job scheduling problem with pulp. "
                "Please report a bug and use --scheduler greedy as a workaround:\n{}".format(
                    e
                )
            )

        selected_jobs = set(
            job for job, variable in scheduled_jobs.items() if variable.value() == 1.0
        )
        for name in self.workflow.global_resources:
            self.resources[name] -= sum(
                [job.resources.get(name, 0) for job in selected_jobs]
            )
        return selected_jobs

    def required_by_job(self, temp_file, job):
        return 1 if temp_file in self.dag.temp_input(job) else 0

    def job_selector_greedy(self, jobs):
        """
        Using the greedy heuristic from
        "A Greedy Algorithm for the General Multidimensional Knapsack
        Problem", Akcay, Li, Xu, Annals of Operations Research, 2012

        Args:
            jobs (list):    list of jobs
        """
        with self._lock:
            # each job is an item with one copy (0-1 MDKP)
            n = len(jobs)
            x = [0] * n  # selected jobs
            E = set(range(n))  # jobs still free to select
            u = [1] * n
            a = list(map(self.job_weight, jobs))  # resource usage of jobs
            c = list(map(self.job_reward, jobs))  # job rewards

            def calc_reward():
                return [c_j * y_j for c_j, y_j in zip(c, y)]

            b = [
                self.resources[name] for name in self.global_resources
            ]  # resource capacities

            while True:
                # Step 2: compute effective capacities
                y = [
                    (
                        min(
                            (min(u[j], b_i // a_j_i) if a_j_i > 0 else u[j])
                            for b_i, a_j_i in zip(b, a[j])
                            if a_j_i
                        )
                        if j in E
                        else 0
                    )
                    for j in range(n)
                ]
                if not any(y):
                    break
                y = [
                    (max(1, int(self.greediness * y_j)) if y_j > 0 else 0) for y_j in y
                ]

                # Step 3: compute rewards on cumulative sums
                reward = calc_reward()
                j_sel = max(E, key=reward.__getitem__)  # argmax

                # Step 4: batch increment
                y_sel = y[j_sel]

                # Step 5: update information
                x[j_sel] += y_sel
                b = [b_i - (a_j_i * y_sel) for b_i, a_j_i in zip(b, a[j_sel])]
                u[j_sel] -= y_sel
                if not u[j_sel] or self.greediness == 1:
                    E.remove(j_sel)
                if not E:
                    break

            solution = set(job for job, sel in zip(jobs, x) if sel)
            # update resources
            for name, b_i in zip(self.global_resources, b):
                self.resources[name] = b_i
            return solution

    def calc_resource(self, name, value):
        gres = self.global_resources[name]
        if value > gres:
            if name == "_cores":
                name = "threads"
            raise WorkflowError(
                "Job needs {name}={res} but only {name}={gres} "
                "are available. This is likely because two "
                "jobs are connected via a pipe and have to run "
                "simultaneously. Consider providing more "
                "resources (e.g. via --cores).".format(name=name, res=value, gres=gres)
            )
        return value

    def rule_weight(self, rule):
        res = rule.resources
        return [
            self.calc_resource(name, res.get(name, 0)) for name in self.global_resources
        ]

    def job_weight(self, job):
        res = job.resources
        return [
            self.calc_resource(name, res.get(name, 0)) for name in self.global_resources
        ]

    def job_reward(self, job):
        if self.touch or self.dryrun or self.workflow.immediate_submit:
            temp_size = 0
            input_size = 0
        else:
            temp_size = self.dag.temp_size(job)
            input_size = job.inputsize

        # Usually, this should guide the scheduler to first schedule all jobs
        # that remove the largest temp file, then the second largest and so on.
        # Since the weight is summed up, it can in theory be that it sometimes
        # prefers a set of many jobs that all depend on smaller temp files though.
        # A real solution to the problem is therefore to use dummy jobs that
        # ensure selection of groups of jobs that together delete the same temp
        # file.

        return (job.priority, temp_size, input_size)

    def progress(self):
        """ Display the progress. """
        logger.progress(done=self.finished_jobs, total=len(self.dag))
