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  - - . . $ $ /% /r"r1rr(rjrh)r*rDrrrr.r1r)rr1rt depickled_objs r test_propertyzCloudPickleTest.test_propertys  / /&J ((A---''//3JJJJ ]]> * *()I % *%& "' 2 ,,111--222]]> * *()I % **+ &--222 ]++337NN! * * * *sC?4D ?D Dctjdgd}|ddd}|ddd}t|||g|j\}}}t ||sJ||k(sJ||usJt ||sJ||k(sJy)NMyTuple)rArJrQrrjrhr+)r namedtupler1r,r)rr5t1t2 depickled_t1depickled_MyTuple depickled_t2s r test_namedtuplezCloudPickleTest.test_namedtuples((OD Q1  Q1 8G "  975 ',000r!!! G+++,000r!!!r"z'fails sometimes for pypy on conda-forgecxdj|j}ttj|y)NaO from srsly.tests.cloudpickle.testutils import subprocess_pickle_echo CONSTANT = 42 class Foo(object): def method(self, x): return x foo = Foo() def f0(x): return x ** 2 def f1(): return Foo def f2(x): return Foo().method(x) def f3(): return Foo().method(CONSTANT) def f4(x): return foo.method(x) def f5(x): # Recursive call to a dynamically defined function. if x <= 0: return f4(x) return f5(x - 1) + 1 cloned = subprocess_pickle_echo(lambda x: x**2, protocol={protocol}) assert cloned(3) == 9 cloned = subprocess_pickle_echo(f0, protocol={protocol}) assert cloned(3) == 9 cloned = subprocess_pickle_echo(Foo, protocol={protocol}) assert cloned().method(2) == Foo().method(2) cloned = subprocess_pickle_echo(Foo(), protocol={protocol}) assert cloned.method(2) == Foo().method(2) cloned = subprocess_pickle_echo(f1, protocol={protocol}) assert cloned()().method('a') == f1()().method('a') cloned = subprocess_pickle_echo(f2, protocol={protocol}) assert cloned(2) == f2(2) cloned = subprocess_pickle_echo(f3, protocol={protocol}) assert cloned() == f3() cloned = subprocess_pickle_echo(f4, protocol={protocol}) assert cloned(2) == f4(2) cloned = subprocess_pickle_echo(f5, protocol={protocol}) assert cloned(7) == f5(7) == 7 r+rr,rrrrrs r #test_interactively_defined_functionz3CloudPickleTest.test_interactively_defined_function's4 ; v FDMMF *w x !!67r"cd}dD]=}|j|j|}ttj|?y)Na; from srsly.tests.cloudpickle.testutils import subprocess_pickle_echo from srsly.cloudpickle import dumps, loads def local_clone(obj, protocol=None): return loads(dumps(obj, protocol=protocol)) VARIABLE = "default_value" def f0(): global VARIABLE VARIABLE = "changed_by_f0" def f1(): return VARIABLE assert f0.__globals__ is f1.__globals__ # pickle f0 and f1 inside the same pickle_string cloned_f0, cloned_f1 = {clone_func}([f0, f1], protocol={protocol}) # cloned_f0 and cloned_f1 now share a global namespace that is isolated # from any previously existing namespace assert cloned_f0.__globals__ is cloned_f1.__globals__ assert cloned_f0.__globals__ is not f0.__globals__ # pickle f1 another time, but in a new pickle string pickled_f1 = dumps(f1, protocol={protocol}) # Change the value of the global variable in f0's new global namespace cloned_f0() # thanks to cloudpickle isolation, depickling and calling f0 and f1 # should not affect the globals of already existing modules assert VARIABLE == "default_value", VARIABLE # Ensure that cloned_f1 and cloned_f0 share the same globals, as f1 and # f0 shared the same globals at pickling time, and cloned_f1 was # depickled from the same pickle string as cloned_f0 shared_global_var = cloned_f1() assert shared_global_var == "changed_by_f0", shared_global_var # f1 is unpickled another time, but because it comes from another # pickle string than pickled_f1 and pickled_f0, it will not share the # same globals as the latter two. new_cloned_f1 = loads(pickled_f1) assert new_cloned_f1.__globals__ is not cloned_f1.__globals__ assert new_cloned_f1.__globals__ is not f1.__globals__ # get the value of new_cloned_f1's VARIABLE new_global_var = new_cloned_f1() assert new_global_var == "default_value", new_global_var ) local_cloner)r, clone_funcr>)r code_templaterCrs r *test_interactively_defined_global_variablez:CloudPickleTest.test_interactively_defined_global_variablejsK4 jD .f0s(7%r"ctSrrIr)r"r rzKCloudPickleTest.test_closure_interacting_with_a_global_variable..f1s,,r"r+rH)rJr1r, __globals__r.r/rr-) r orig_valuerKr cloned_f0 cloned_f1 pickled_f1shared_global_var new_cloned_f1new_global_vars r /test_closure_interacting_with_a_global_variablez?CloudPickleTest.test_closure_interacting_with_a_global_variables/$777* , / 8 -$3B8<@MM$K Iy ((I,A,AA AA((> >>%**2 FJ K)O; ;; !* $7 J9J J7 #LL4M ,,I4I4II II ,,BNNB BB+_N!_4 Dn D4$. !J !s C8DDcRdj|j}t|y)Naif __name__ == "__main__": from srsly.tests.cloudpickle.testutils import subprocess_worker def interactive_function(x): return x + 1 with subprocess_worker(protocol={protocol}) as w: assert w.run(interactive_function, 41) == 42 # Define a new function that will call an updated version of # the previously called function: def wrapper_func(x): return interactive_function(x) def interactive_function(x): return x - 1 # The change in the definition of interactive_function in the main # module of the main process should be reflected transparently # in the worker process: the worker process does not recall the # previous definition of `interactive_function`: assert w.run(wrapper_func, 41) == 40 r+rr,rr?s r &test_interactive_remote_function_callsz6CloudPickleTest.test_interactive_remote_function_callss( 2 FDMMF *3 4 !&r"cRdj|j}t|y)NaSif __name__ == "__main__": from srsly.tests.cloudpickle.testutils import subprocess_worker import sys with subprocess_worker(protocol={protocol}) as w: GLOBAL_VARIABLE = 0 class CustomClass(object): def mutate_globals(self): global GLOBAL_VARIABLE GLOBAL_VARIABLE += 1 return GLOBAL_VARIABLE custom_object = CustomClass() assert w.run(custom_object.mutate_globals) == 1 # The caller global variable is unchanged in the main process. assert GLOBAL_VARIABLE == 0 # Calling the same function again starts again from zero. The # worker process is stateless: it has no memory of the past call: assert w.run(custom_object.mutate_globals) == 1 # The symbols defined in the main process __main__ module are # not set in the worker process main module to leave the worker # as stateless as possible: def is_in_main(name): return hasattr(sys.modules["__main__"], name) assert is_in_main("CustomClass") assert not w.run(is_in_main, "CustomClass") assert is_in_main("GLOBAL_VARIABLE") assert not w.run(is_in_main, "GLOBAL_VARIABLE") r+rWr?s r 5test_interactive_remote_function_calls_no_side_effectzECloudPickleTest.test_interactive_remote_function_calls_no_side_effects+( P FDMMF *Q R !&r"cRdj|j}t|y)Naif __name__ == "__main__": from srsly.tests.cloudpickle.testutils import subprocess_worker with subprocess_worker(protocol={protocol}) as w: class CustomCounter: def __init__(self): self.count = 0 def increment(self): self.count += 1 return self counter = CustomCounter().increment() assert counter.count == 1 returned_counter = w.run(counter.increment) assert returned_counter.count == 2, returned_counter.count # Check that the class definition of the returned instance was # matched back to the original class definition living in __main__. assert isinstance(returned_counter, CustomCounter) # Check that memoization does not break provenance tracking: def echo(*args): return args C1, C2, c1, c2 = w.run(echo, CustomCounter, CustomCounter, CustomCounter(), returned_counter) assert C1 is CustomCounter assert C2 is CustomCounter assert isinstance(c1, CustomCounter) assert isinstance(c2, CustomCounter) r+rWr?s r 2test_interactive_dynamic_type_and_remote_instanceszBCloudPickleTest.test_interactive_dynamic_type_and_remote_instances"s+# F FDMMF *G H !&r"cRdj|j}t|y)zSimulate objects stored on workers to check isinstance semantics Such instances stored in the memory of running worker processes are similar to dask-distributed futures for instance. a if __name__ == "__main__": import srsly.cloudpickle as cloudpickle, uuid from srsly.tests.cloudpickle.testutils import subprocess_worker with subprocess_worker(protocol={protocol}) as w: class A: '''Original class definition''' pass def store(x): storage = getattr(cloudpickle, "_test_storage", None) if storage is None: storage = cloudpickle._test_storage = dict() obj_id = uuid.uuid4().hex storage[obj_id] = x return obj_id def lookup(obj_id): return cloudpickle._test_storage[obj_id] id1 = w.run(store, A()) # The stored object on the worker is matched to a singleton class # definition thanks to provenance tracking: assert w.run(lambda obj_id: isinstance(lookup(obj_id), A), id1) # Retrieving the object from the worker yields a local copy that # is matched back the local class definition this instance # originally stems from. assert isinstance(w.run(lookup, id1), A) # Changing the local class definition should be taken into account # in all subsequent calls. In particular the old instances on the # worker do not map back to the new class definition, neither on # the worker itself, nor locally on the main program when the old # instance is retrieved: class A: '''Updated class definition''' pass assert not w.run(lambda obj_id: isinstance(lookup(obj_id), A), id1) retrieved1 = w.run(lookup, id1) assert not isinstance(retrieved1, A) assert retrieved1.__class__ is not A assert retrieved1.__class__.__doc__ == "Original class definition" # New instances on the other hand are proper instances of the new # class definition everywhere: a = A() id2 = w.run(store, a) assert w.run(lambda obj_id: isinstance(lookup(obj_id), A), id2) assert isinstance(w.run(lookup, id2), A) # Monkeypatch the class defintion in the main process to a new # class method: A.echo = lambda cls, x: x # Calling this method on an instance will automatically update # the remote class definition on the worker to propagate the monkey # patch dynamically. assert w.run(a.echo, 42) == 42 # The stored instance can therefore also access the new class # method: assert w.run(lambda obj_id: lookup(obj_id).echo(43), id2) == 43 r+NrWr?s r 9test_interactive_dynamic_type_and_stored_remote_instanceszICloudPickleTest.test_interactive_dynamic_type_and_stored_remote_instancesIs, E J FDMMF *K L !&r"z1Seems to have issues outside of linux and CPythoncRdj|j}t|y)Naif __name__ == "__main__": from srsly.tests.cloudpickle.testutils import subprocess_worker import struct with subprocess_worker(protocol={protocol}) as w: reference_size = w.memsize() assert reference_size > 0 def make_big_closure(i): # Generate a byte string of size 1MB itemsize = len(struct.pack("l", 1)) data = struct.pack("l", i) * (int(1e6) // itemsize) def process_data(): return len(data) return process_data for i in range(100): func = make_big_closure(i) result = w.run(func) assert result == int(1e6), result import gc w.run(gc.collect) # By this time the worker process has processed 100MB worth of data # passed in the closures. The worker memory size should not have # grown by more than a few MB as closures are garbage collected at # the end of each remote function call. growth = w.memsize() - reference_size # For some reason, the memory growth after processing 100MB of # data is ~10MB on MacOS, and ~1MB on Linux, so the upper bound on # memory growth we use is only tight for MacOS. However, # - 10MB is still 10x lower than the expected memory growth in case # of a leak (which would be the total size of the processed data, # 100MB) # - the memory usage growth does not increase if using 10000 # iterations instead of 100 as used now (100x more data) assert growth < 1.5e7, growth r+rWr?s r 5test_interactive_remote_function_calls_no_memory_leakzECloudPickleTest.test_interactive_remote_function_calls_no_memory_leaks+* T FDMMF *U V !&r"cttttfD]b}t |d}t j |tjf5tj||jddddy#1swYoxYw)Nrr+) rr9rr_rrrr PicklingErrorr.r/r,)rexc_typer0s r test_pickle_reraisez#CloudPickleTest.test_pickle_reraisesm"J <H ?H %1C&*>*>?@ ?!!# > ? ? ? ? ?s "A==B cddi}t|j|j}|j|dd|j|ddy)NrArr+rJ)r1getr,rz)rr{depickled_methods r test_unhashable_functionz(CloudPickleTest.test_unhashable_functionsL !H*1554==I )#.2 )#.5r")rh zJDeprecation warning in python 3.12 about future deprecation in python 3.14ctjdd}t|t|t||j}|j ||ut dD]&}|jt|t|(y)Nrrj)stepr+rx) itertoolscountnextr1r,rryrz)rcounter new_counterrus r test_itertools_countz$CloudPickleTest.test_itertools_countso//!!, W  W %g F  {23r ?A   T']D,= > ?r"cddlm}d|fd}t||j}|j |j j y)Nrrcyrr)r)r"r rz=CloudPickleTest.test_wraps_preserves_function_name..f r"cyrr)rsr rz=CloudPickleTest.test_wraps_preserves_function_name..g Cr"r+)r:rr1r,rzr&rrrrrs @r "test_wraps_preserves_function_namez2CloudPickleTest.test_wraps_preserves_function_namesM#  q   Q 7 ajj1r"cddlm}d|fd}t||j}|j |j j y)Nrrcy)42Nr)r)r"r rz.fs r"cyrr)rvsr rz.grwr"r+)r:rr1r,rzrDrxs @r !test_wraps_preserves_function_docz1CloudPickleTest.test_wraps_preserves_function_docsM#  q   Q 7 QYY/r"cddtd_tfd}t||j}|j |jjy)Ncyrr)rs r rzDCloudPickleTest.test_wraps_preserves_function_annotations..frtr"r)rreturnc|yrr))rrs r rzDCloudPickleTest.test_wraps_preserves_function_annotations..gs aDr"r+)r__annotations__rr1r,rz)rrrrs @r )test_wraps_preserves_function_annotationsz9CloudPickleTest.test_wraps_preserves_function_annotationss[ #$u5 q   Q 7 ++Q->->?r"c^tjttf}t ||k(sJyr)typingUnionrrUr1)rrs r test_type_hintzCloudPickleTest.test_type_hint s' LLs #q!Q&&&r"cdgdfD]Gfdd}|}t||j}||fD]G}|j|jdt j t 5d|_dddIGdd|}|}tj||j}~tj|}|jdk(rJy#1swYxYw) Nregistered_attributeceZdZWZdZy)@CloudPickleTest.test_instance_with_slots..ClassWithSlotscd|_y)NrH)rr$s r r!zICloudPickleTest.test_instance_with_slots..ClassWithSlots.__init__s 02D-r"N)r&r'r( __slots__r!)slotssr ClassWithSlotsrs ! 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