∋dndddlmZddlZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl Z ddl m Z ddlmZmZmZddlmZmZerddlmZmZejeZGddZGd d ZGd d Zd(dZGddeZGddeej Z!Gdde!Z"Gdde!Z#GddeZ$GddeZ%Gddej&Z'Gd d!eZ(Gd"d#eZ)Gd$d%Z*Gd&d'eZ+dS))) annotationsN) Awaitable) TYPE_CHECKINGAnyClassVar)funcnametmpfile) SchedulerTaskStateStatecjeZdZdZd)dZd*dZd*dZd+dZd)dZd,dZ d-d!Z d-d"Z d.d#Z d.d$Z d/d'Zd(S)0SchedulerPluginaInterface to extend the Scheduler A plugin enables custom hooks to run when specific events occur. The scheduler will run the methods of this plugin whenever the corresponding method of the scheduler is run. This runs user code within the scheduler thread that can perform arbitrary operations in synchrony with the scheduler itself. Plugins are often used for diagnostics and measurement, but have full access to the scheduler and could in principle affect core scheduling. To implement a plugin: 1. subclass this class 2. override some of its methods 3. add the plugin to the scheduler with ``Scheduler.add_plugin(myplugin)``. Examples -------- >>> class Counter(SchedulerPlugin): ... def __init__(self): ... self.counter = 0 ... ... def transition(self, key, start, finish, *args, **kwargs): ... if start == 'processing' and finish == 'memory': ... self.counter += 1 ... ... def restart(self, scheduler): ... self.counter = 0 >>> plugin = Counter() >>> scheduler.add_plugin(plugin) # doctest: +SKIP schedulerr returnNonec KdS)zhRun when the scheduler starts up This runs at the end of the Scheduler startup process Nselfrs >lib/python3.11/site-packages/distributed/diagnostics/plugin.pystartzSchedulerPlugin.start: c KdS)z*Runs prior to any Scheduler shutdown logicNrrs r before_closezSchedulerPlugin.before_close@rrc KdS)zRun when the scheduler closes down This runs at the beginning of the Scheduler shutdown process, but after workers have been asked to shut down gracefully Nrrs rclosezSchedulerPlugin.closeCrrclientstrkeysset[str]tasks list[str]rdict[str, dict[str, Any]]priority"dict[str, tuple[int | float, ...]] dependenciesdict[str, set]kwargsrc dS)aRun when a new graph / tasks enter the scheduler Parameters ---------- scheduler: The `Scheduler` instance. client: The unique Client id. keys: The keys the Client is interested in when calling `update_graph`. tasks: The annotations: Fully resolved annotations as applied to the tasks in the format:: { "annotation": { "key": "value, ... }, ... } priority: Task calculated priorities as assigned to the tasks. dependencies: A mapping that maps a key to its dependencies. **kwargs: It is recommended to allow plugins to accept more parameters to ensure future compatibility. Nr) rrrr r"rr%r'r)s r update_graphzSchedulerPlugin.update_graphJrcdS)z&Run when the scheduler restarts itselfNrrs rrestartzSchedulerPlugin.restartur,rkeyrr finishargscdS)aRun whenever a task changes state For a description of the transition mechanism and the available states, see :ref:`Scheduler task states `. .. warning:: This is an advanced feature and the transition mechanism and details of task states are subject to change without deprecation cycle. Parameters ---------- key : string start : string Start state of the transition. One of released, waiting, processing, memory, error. finish : string Final state of the transition. *args, **kwargs : More options passed when transitioning This may include worker ID, compute time, etc. Nr)rr/rr0r1r)s r transitionzSchedulerPlugin.transitionxr,rworkerNone | Awaitable[None]cdS)a>Run when a new worker enters the cluster If this method is synchronous, it is immediately and synchronously executed without ``Scheduler.add_worker`` ever yielding to the event loop. If it is asynchronous, it will be awaited after all synchronous ``SchedulerPlugin.add_worker`` hooks have executed. .. warning:: There are no guarantees about the execution order between individual ``SchedulerPlugin.add_worker`` hooks and the ordering may be subject to change without deprecation cycle. Nrrrr4s r add_workerzSchedulerPlugin.add_workerr,rcdS)aCRun when a worker leaves the cluster If this method is synchronous, it is immediately and synchronously executed without ``Scheduler.remove_worker`` ever yielding to the event loop. If it is asynchronous, it will be awaited after all synchronous ``SchedulerPlugin.remove_worker`` hooks have executed. .. warning:: There are no guarantees about the execution order between individual ``SchedulerPlugin.remove_worker`` hooks and the ordering may be subject to change without deprecation cycle. Nrr7s r remove_workerzSchedulerPlugin.remove_workerr,rcdS)zRun when a new client connectsNrrrrs r add_clientzSchedulerPlugin.add_clientr,rcdS)zRun when a client disconnectsNrr<s r remove_clientzSchedulerPlugin.remove_clientr,rtopicmsgcdS)zRun when an event is loggedNr)rr@rAs r log_eventzSchedulerPlugin.log_eventr,rNrr rrrr)rr rrr r!r"r#rr$r%r&r'r(r)rrr) r/rrr r0r r1rr)rrr)rr r4rrr5)rr rrrr)r@rrArrr)__name__ __module__ __qualname____doc__rrrr+r.r3r8r:r=r?rCrrrr r sB    9999    ) ) ) ) V5555    >         "----,,,,******rr c$eZdZdZdZdZdZdS) WorkerPluginaWInterface to extend the Worker A worker plugin enables custom code to run at different stages of the Workers' lifecycle. A plugin enables custom code to run at each of step of a Workers's life. Whenever such an event happens, the corresponding method on this class will be called. Note that the user code always runs within the Worker's main thread. To implement a plugin implement some of the methods of this class and register the plugin to your client in order to have it attached to every existing and future workers with ``Client.register_worker_plugin``. Examples -------- >>> class ErrorLogger(WorkerPlugin): ... def __init__(self, logger): ... self.logger = logger ... ... def setup(self, worker): ... self.worker = worker ... ... def transition(self, key, start, finish, *args, **kwargs): ... if finish == 'error': ... ts = self.worker.tasks[key] ... exc_info = (type(ts.exception), ts.exception, ts.traceback) ... self.logger.error( ... "Error during computation of '%s'.", key, ... exc_info=exc_info ... ) >>> import logging >>> plugin = ErrorLogger(logging) >>> client.register_worker_plugin(plugin) # doctest: +SKIP cdS)z Run when the plugin is attached to a worker. This happens when the plugin is registered and attached to existing workers, or when a worker is created after the plugin has been registered. Nrrr4s rsetupzWorkerPlugin.setupr,rcdS)zeRun when the worker to which the plugin is attached is closed, or when the plugin is removed.NrrMs rteardownzWorkerPlugin.teardownr,rc dS)ar Throughout the lifecycle of a task (see :doc:`Worker State `), Workers are instructed by the scheduler to compute certain tasks, resulting in transitions in the state of each task. The Worker owning the task is then notified of this state transition. Whenever a task changes its state, this method will be called. .. warning:: This is an advanced feature and the transition mechanism and details of task states are subject to change without deprecation cycle. Parameters ---------- key : string start : string Start state of the transition. One of waiting, ready, executing, long-running, memory, error. finish : string Final state of the transition. kwargs : More options passed when transitioning Nr)rr/rr0r)s rr3zWorkerPlugin.transitionr,rN)rFrGrHrIrNrPr3rrrrKrKsL""H   '''     rrKc"eZdZdZdZdZdZdS) NannyPluginaInterface to extend the Nanny A worker plugin enables custom code to run at different stages of the Workers' lifecycle. A nanny plugin does the same thing, but benefits from being able to run code before the worker is started, or to restart the worker if necessary. To implement a plugin implement some of the methods of this class and register the plugin to your client in order to have it attached to every existing and future nanny by passing ``nanny=True`` to :meth:`Client.register_worker_plugin`. The ``restart`` attribute is used to control whether or not a running ``Worker`` needs to be restarted when registering the plugin. See Also -------- WorkerPlugin SchedulerPlugin FcdS)z Run when the plugin is attached to a nanny. This happens when the plugin is registered and attached to existing nannies, or when a nanny is created after the plugin has been registered. Nrrnannys rrNzNannyPlugin.setup#r,rcdS)z?Run when the nanny to which the plugin is attached to is closedNrrUs rrPzNannyPlugin.teardown*r,rN)rFrGrHrIr.rNrPrrrrSrS sH*G   NNNNNrrSplugin,SchedulerPlugin | WorkerPlugin | NannyPluginrrct|dr|jStt|dzt t jzS)zUReturn plugin name. If plugin has no name attribute a random name is used. name-)hasattrr[rtyperuuiduuid4)rXs r_get_plugin_namera.sK vv@{V %%+c$*,,.?.???rc$eZdZdZdddZdd Zd S)SchedulerUploadFile upload_fileTfilepathrloadboolctj||_||_t |d5}||_ddddS#1swxYwYdSS Initialize the plugin by reading in the data from the given file. rbNospathbasenamefilenamerfopenreaddatarrerffs r__init__zSchedulerUploadFile.__init__=((22  (D ! ! !QDI ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !A##A'*A'rr rrcdK||j|j|jd{VdS)N)rf)rdrprsrfrs rrzSchedulerUploadFile.startFs?##DM4949#MMMMMMMMMMMrNTrerrfrgrD)rFrGrHr[rvrrrrrcrc:sL D!!!!!NNNNNNrrcceZdZUdZded<ded<ded<ded <dd Zd ZejddZ dZ dZ dZ dZ dZdZdS)PackageInstalla1Abstract parent class for a worker plugin to install a set of packages This accepts a set of packages to install on all workers. You can also optionally ask for the worker to restart itself after performing this installation. .. note:: This will increase the time it takes to start up each worker. If possible, we recommend including the libraries in the worker environment or image. This is primarily intended for experimentation and debugging. Parameters ---------- packages A list of packages (with optional versions) to install restart Whether or not to restart the worker after installing the packages Only functions if the worker has an attached nanny process See Also -------- CondaInstall PipInstall z ClassVar[str] INSTALLERrr[r#packagesrgr.cf||_||_|jdtj|_dS)Nz -install-)rr.r~r_r`r[)rrr.s rrvzPackageInstall.__init__ls3 !  ~>> >> rcKddlm}|dtjd|j|jd{V4d{V||d{VsVtd|j |j | |d{V| n td|j |j r|jrz||d{Vs_td||d{V|j|jd|jd  dddd{VdS#1d{VswxYwYdS) Nr) SemaphoreT) max_leasesr[register scheduler_rpcloopz(%s installing the following packages: %sz6The following packages have already been installed: %sz)Restarting worker to refresh interpreter.z-setup)r.reason)distributed.semaphorersocket gethostnamerr _is_installedloggerinfor~r_set_installedinstallr.rV _is_restarted_set_restarted add_callbackclose_gracefullyr[)rr4rs rrNzPackageInstall.setupusu333333)'))$.[               ++F33333333  >NM ))&111111111  LM |   4;M;Mf;U;U5U5U5U5U5U5U  GHHH))&111111111 ((+TTYBVBVBV)5                              sDE-- E7:E7rrcdS)zInstall the requested packagesNrrs rrzPackageInstall.installr,rcnK|j|dd{VSNF)default)r get_metadata_compose_installed_keyrMs rrzPackageInstall._is_installedsT]//  ' ' ) )50         rcpK|j|dd{VdSNT)r set_metadatarrMs rrzPackageInstall._set_installedsZm((  ' ' ) )            rc8|jdtjgS)N installed)r[rrrs rrz%PackageInstall._compose_installed_keys I      rcpK|j||dd{VSr)rr_compose_restarted_keyrMs rrzPackageInstall._is_restartedsW]//  ' ' / /0         rcrK|j||dd{VdSr)rrrrMs rrzPackageInstall._set_restarteds\m((  ' ' / /            rc |jd|jgS)N restarted)r[rVrMs rrz%PackageInstall._compose_restarted_keys ; 55rN)rr#r.rgrE)rFrGrHrI__annotations__rvrNabcabstractmethodrrrrrrrrrrr}r}Js6 IIIMMM????B ----               66666rr}cBeZdZUdZdZded< ddfd ZddZxZS) CondaInstallamA Worker Plugin to conda install a set of packages This accepts a set of packages to install on all workers as well as options to use when installing. You can also optionally ask for the worker to restart itself after performing this installation. .. note:: This will increase the time it takes to start up each worker. If possible, we recommend including the libraries in the worker environment or image. This is primarily intended for experimentation and debugging. Parameters ---------- packages A list of packages (with optional versions) to install using conda conda_options Additional options to pass to conda restart Whether or not to restart the worker after installing the packages Only functions if the worker has an attached nanny process Examples -------- >>> from dask.distributed import CondaInstall >>> plugin = CondaInstall(packages=["scikit-learn"], conda_options=["--update-deps"]) >>> client.register_worker_plugin(plugin) See Also -------- PackageInstall PipInstall condar# conda_optionsNFrlist[str] | Noner.rgc`t|||pg|_dSN)r.)superrvr)rrrr. __class__s rrvzCondaInstall.__init__s5 7333*0brrrc  ddlm}m}n>#t$r1}d}t|t ||d}~wwxYw ||j|j|j z\}}}n>#t$r1}d}t|t ||d}~wwxYw|dkrSd| d}t|t |dS)Nr)Commands run_commandz`conda install failed because conda could not be found. Please make sure that conda is installed.zconda install failedzconda install failed with '') conda.cli.python_apirrModuleNotFoundErrorrerror RuntimeErrorINSTALLrr Exceptiondecodestrip)rrrerA_stderr returncodes rrzCondaInstall.installs> + B B B B B B B B B" + + +<  LL   s## *  + +$/K $"4t}"D%% !Avzz + + +(C LL   s## * + ??J 0E0E0G0GJJJC LL   s## # ?s, A,AA "A-- B(7,B##B(NF)rr#rrr.rgrE rFrGrHrIr~rrvr __classcell__rs@rrrs~##JI +/ 1111111$$$$$$$$rrcBeZdZUdZdZded< ddfd ZddZxZS) PipInstalla]A Worker Plugin to pip install a set of packages This accepts a set of packages to install on all workers as well as options to use when installing. You can also optionally ask for the worker to restart itself after performing this installation. .. note:: This will increase the time it takes to start up each worker. If possible, we recommend including the libraries in the worker environment or image. This is primarily intended for experimentation and debugging. Parameters ---------- packages A list of packages (with optional versions) to install using pip pip_options Additional options to pass to pip restart Whether or not to restart the worker after installing the packages Only functions if the worker has an attached nanny process Examples -------- >>> from dask.distributed import PipInstall >>> plugin = PipInstall(packages=["scikit-learn"], pip_options=["--upgrade"]) >>> client.register_worker_plugin(plugin) See Also -------- PackageInstall CondaInstall pipr# pip_optionsNFrrr.rgc`t|||pg|_dSr)rrvr)rrrr.rs rrvzPipInstall.__init__2s5 7333&,"rrrctjtjdddg|jz|jztjtj}|\}}|}|dkrSd| d}t |t|dS)Nz-mrr)stdoutrrzpip install failed with 'r) subprocessPopensys executablerrPIPE communicatewaitrrrrr)rprocrrrrAs rrzPipInstall.install;s ^T5) 4t7G G$- W??    $$&& 6YY[[ ??Hfmmoo.C.C.E.EHHHC LL   s## # ?rr)rr#rrr.rgrErrs@rrrs~##JI )- ------- $ $ $ $ $ $ $ $rrc&eZdZdZdZd d dZd Zd S) UploadFileaQA WorkerPlugin to upload a local file to workers. Parameters ---------- filepath: str A path to the file (.py, egg, or zip) to upload Examples -------- >>> from distributed.diagnostics.plugin import UploadFile >>> client.register_worker_plugin(UploadFile("/path/to/file.py")) # doctest: +SKIP rdTrerrfrgctj||_||_t |d5}||_ddddS#1swxYwYdSrirlrts rrvzUploadFile.__init__[rwrxcK||j|j|jd{V}t |j|dksJdS)N)rprsrfnbytes)rdrprsrflen)rr4responses rrNzUploadFile.setupdsq++],        49~~(!3333333rNrzr{)rFrGrHrIr[rvrNrrrrrJsM   D!!!!!44444rrc$eZdZdZdZdZdZdS)ForwardLoggingPlugina A ``WorkerPlugin`` to forward python logging records from worker to client. See :meth:`Client.forward_logging` for full documentation and usage. Needs to be used in coordination with :meth:`Client.subscribe_topic`, the details of which :meth:`Client.forward_logging` handles for you. Parameters ---------- logger_name : str The name of the logger to begin forwarding. level : str | int Optionally restrict forwarding to ``LogRecord``s of this level or higher, even if the forwarded logger's own level is lower. topic : str The name of the topic to which to the worker should log the forwarded log records. c>||_||_||_d|_dSN) logger_namelevelr@handler)rrrr@s rrvzForwardLoggingPlugin.__init__s#&   rct||j|j|_t j|j}||jdS)N)r)_ForwardingLogHandlerr@rrlogging getLoggerr addHandlerrr4rs rrNzForwardLoggingPlugin.setupsK,VTZtzRRR "4#344$,'''''rc~|j5tj|j}||jdSdSr)rrrr removeHandlerrs rrPzForwardLoggingPlugin.teardownsA < #&t'788F   . . . . . $ #rN)rFrGrHrIrvrNrPrrrrrksK( ((( /////rrc<eZdZdZejffd ZdZdZxZ S)ra* Handler class that gets installed inside workers by :class:`ForwardLoggingPlugin`. Not intended to be instantiated by the user directly. In each affected worker, ``ForwardLoggingPlugin`` adds an instance of this handler to one or more loggers (possibly the root logger). Tasks running on the worker may then use the affected logger as normal, with the side effect that any ``LogRecord``s handled by the logger (or by a logger below it in the hierarchy) will be published to the dask client as a ``topic`` event. cft|||_||_dSr)rrvr4r@)rr4r@rrs rrvz_ForwardingLogHandler.__init__s-   rc|j}|r||}t|j}||d<d|d<d|d<|dd|S)NrAr1exc_infomessage)rformatdict__dict__ getMessagepop)rrecordeirds rprepare_record_attributesz/_ForwardingLogHandler.prepare_record_attributessu_  $ F##A  ! !$$&&%& *  ircp||}|j|j|dSr)rr4rCr@)rr attributess remitz_ForwardingLogHandler.emits533F;;  dj*55555r) rFrGrHrIrNOTSETrvrrrrs@rrrsl  -4N &6666666rrc"eZdZdZdddZdZdS) EnvironTNenviron dict | NonecT|pi}d|D|_dS)Nc4i|]\}}|t|Sr)r).0kvs r z$Environ.__init__..s$>>>da3q66>>>r)itemsr)rrs rrvzEnviron.__init__s,-R>>gmmoo>>> rcHK|j|jdSr)envupdaterrUs rrNz Environ.setups$ &&&&&rr)rr)rFrGrHr.rvrNrrrrrsAG?????'''''rrc,eZdZdZddddffdZdZdS)UploadDirectoryaUA NannyPlugin to upload a local file to workers. Parameters ---------- path: str A path to the directory to upload Examples -------- >>> from distributed.diagnostics.plugin import UploadDirectory >>> client.register_worker_plugin(UploadDirectory("/path/to/directory"), nanny=True) # doctest: +SKIP F)z.gitz.githubz .pytest_cachetestsdocscTtj|ddkS)Nrz.pyc)rmrnsplitext)fns rzUploadDirectory.s ))"--a0F:rc j tj|}tj|d|_||_||_dtj|dz|_td5}tj |dtj 5}tj |D]\} } | D]} tj || tfd|Dr>tj t fd|Drytjtj || tj |d} ||  d d d n #1swxYwYt#|d 5} | |_d d d n #1swxYwYd d d d S#1swxYwYd S) rjzupload-directory-zip) extensionwc3.K|]}|VdSrr)r predicaterps r z+UploadDirectory.__init__..s-IIyyy22IIIIIIrc3 K|]}|vV dSrr)rworddirss rrz+UploadDirectory.__init__..s'CCtt|CCCCCCrz..Nrk)rmrn expandusersplitr. update_pathr[r zipfileZipFile ZIP_DEFLATEDwalkjoinanyseprelpathwriterqrrrs)rrnr.r skip_wordsskiprzrootfilesfile archive_namerurrps @@rrvzUploadDirectory.__init__sw!!$''GMM$''+  &'"'--*=*=b*AA u % % % %S'*>?? 81)+ 8 8%D$ % 8 8#%7<<d#;#;IIIIDIIIII%$'~~bf55CCCC CCCCC%$')wGLLt44bgll46N6N(( ,7777 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8b$ %1FFHH  % % % % % % % % % % % % % % %! % % % % % % % % % % % % % % % % % %s[!H(8D G H(G H(G H(*H H(H H(H H((H,/H,cvKtj|jdt jd}t |d5}||jdddn #1swxYwYddl }|j |5}| |jdddn #1swxYwY|j rXtj|j|j}|tjvr tjd|tj|dS)Nztmp-z.zipwbr)rn)rmrnr$local_directoryr_r`rqr(rsr r! extractallrrinsertremove)rrVrrur r+rns rrNzUploadDirectory.setups W\\%/1J 1J1J1J K K "d^^ q GGDI                    W_R  5A LLe3L 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5   )7<< 5tyAAD38##4((( " s$ A44A8;A8B;;B?B?N)rFrGrHrIrvrNrrrr r sV   H:: < #%#%#%#%Jrr cDeZdZdZdZdZdZdZdZdZ dZ d Z d S) forward_streamc||_i|_tt||_|dkrd|_n!|dkrd|_nt d|d|dkrdnd|_g|_dS)Nrrrz1Expected stream to be 'stdout' or 'stderr'; got 'r)_worker_original_methodsgetattrr_stream_file ValueError_buffer)rstreamr4s rrvzforward_stream.__init__ s !#sF++ X  DJJ x  DJJMFMMM !H,,QQ!  rcF||||dSr)_forward)rwrite_fnrss r_writezforward_stream._writes% drcv|j|d|vsd|vr|dSdS)N  )r@append_send)rrss rrCzforward_stream._forward!sA D!!! 4<<44<< JJLLLLL(r:rC)rrAs rrJzforward_stream._send's:|TZ2NN w,,, rcB||dSrrJ)rflush_fns r_flushzforward_stream._flush,  rcB||dSrrP)rclose_fns r_closezforward_stream._close0rSrct|j|}||j|<t|j|t j||dSr)r<r=r;setattr functoolspartial)r method_name interceptororiginal_methods r _interceptzforward_stream._intercept4sR!$, <<.={+ L+y'8o'V'V     rc|d|j|d|j|d|j|jS)Nr(flushr)r^rErRrVr=rs r __enter__zforward_stream.__enter__;sL --- --- ---|rc|j|jD]\}}t |j||i|_dSr)r=r`r;rrX)rexc_type exc_value tracebackattroriginals r__exit__zforward_stream.__exit__As] "4::<< 2 2ND( DL$ 1 1 1 1!#rN) rFrGrHrvrErCrJrRrVr^rarhrrrr7r7 s       $$$$$rr7ceZdZdZdZdZdS) ForwardOutputa#A Worker Plugin that forwards ``stdout`` and ``stderr`` from workers to clients This plugin forwards all output sent to ``stdout`` and ``stderr` on all workers to all clients where it is written to the respective streams. Analogous to the terminal, this plugin uses line buffering. To ensure that an output is written without a newline, make sure to flush the stream. .. warning:: Using this plugin will forward **all** output in ``stdout`` and ``stderr`` from every worker to every client. If the output is very chatty, this will add significant strain on the scheduler. Proceed with caution! Examples -------- >>> from dask.distributed import ForwardOutput >>> plugin = ForwardOutput() >>> client.register_worker_plugin(plugin) ctj|_|jt d||jt d|dS)Nr)r4r) contextlib ExitStack _exit_stack enter_contextr7rMs rrNzForwardOutput.setup^sa%/11 &&~hv'N'N'NOOO &&~hv'N'N'NOOOOOrc8|jdSr)rnrrMs rrPzForwardOutput.teardowncs      rN)rFrGrHrIrNrPrrrrjrjHs?*PPP !!!!!rrj)rXrYrr), __future__rrrlrYrrmrrrr_r collections.abcrtypingrrr dask.utilsrr distributed.schedulerr r rrFrr rKrSrarcABCr}rrrrHandlerrrr r7rjrrrrxs""""""   %%%%%%//////////((((((((@????????  8 $ $f*f*f*f*f*f*f*f*RG G G G G G G G T N N N N N N N NF @ @ @ @ N N N N N/ N N N o6o6o6o6o6\37o6o6o6dI$I$I$I$I$>I$I$I$X>$>$>$>$>$>$>$>$D44444444B#/#/#/#/#/<#/#/#/L(6(6(6(6(6GO(6(6(6V'''''k'''BBBBBkBBBJ9$9$9$9$9$9$9$9$x!!!!!L!!!!!r