WASM Python integration milestone - game.py runs in browser
Major milestone for issue #158 (Emscripten/WebAssembly build target): - Python 3.14 successfully initializes and runs in WASM - mcrfpy module loads and works correctly - Game scripts execute with full level generation - Entities (boulders, rats, cyclops, spawn points) placed correctly Key changes: - CMakeLists.txt: Add 2MB stack, Emscripten link options, preload files - platform.h: Add WASM-specific implementations for executable paths - HeadlessTypes.h: Make Texture/Font/Sound stubs return success - CommandLineParser.cpp: Guard filesystem operations for WASM - McRFPy_API.cpp: Add WASM path configuration, debug output - game.py: Make 'code' module import optional (not available in WASM) - wasm_stdlib/: Add minimal Python stdlib for WASM (~4MB) Build with: emmake make (from build-emscripten/) Test with: node mcrogueface.js Next steps: - Integrate VRSFML for actual WebGL rendering - Create HTML page to host WASM build - Test in actual browsers Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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wasm_stdlib/lib/python3.14/copy.py
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286
wasm_stdlib/lib/python3.14/copy.py
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"""Generic (shallow and deep) copying operations.
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Interface summary:
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import copy
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x = copy.copy(y) # make a shallow copy of y
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x = copy.deepcopy(y) # make a deep copy of y
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x = copy.replace(y, a=1, b=2) # new object with fields replaced, as defined by `__replace__`
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For module specific errors, copy.Error is raised.
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The difference between shallow and deep copying is only relevant for
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compound objects (objects that contain other objects, like lists or
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class instances).
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- A shallow copy constructs a new compound object and then (to the
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extent possible) inserts *the same objects* into it that the
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original contains.
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- A deep copy constructs a new compound object and then, recursively,
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inserts *copies* into it of the objects found in the original.
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Two problems often exist with deep copy operations that don't exist
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with shallow copy operations:
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a) recursive objects (compound objects that, directly or indirectly,
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contain a reference to themselves) may cause a recursive loop
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b) because deep copy copies *everything* it may copy too much, e.g.
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administrative data structures that should be shared even between
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copies
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Python's deep copy operation avoids these problems by:
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a) keeping a table of objects already copied during the current
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copying pass
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b) letting user-defined classes override the copying operation or the
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set of components copied
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This version does not copy types like module, class, function, method,
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nor stack trace, stack frame, nor file, socket, window, nor any
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similar types.
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Classes can use the same interfaces to control copying that they use
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to control pickling: they can define methods called __getinitargs__(),
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__getstate__() and __setstate__(). See the documentation for module
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"pickle" for information on these methods.
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"""
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import types
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import weakref
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from copyreg import dispatch_table
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class Error(Exception):
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pass
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error = Error # backward compatibility
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__all__ = ["Error", "copy", "deepcopy", "replace"]
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def copy(x):
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"""Shallow copy operation on arbitrary Python objects.
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See the module's __doc__ string for more info.
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"""
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cls = type(x)
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if cls in _copy_atomic_types:
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return x
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if cls in _copy_builtin_containers:
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return cls.copy(x)
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if issubclass(cls, type):
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# treat it as a regular class:
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return x
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copier = getattr(cls, "__copy__", None)
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if copier is not None:
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return copier(x)
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reductor = dispatch_table.get(cls)
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if reductor is not None:
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rv = reductor(x)
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else:
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reductor = getattr(x, "__reduce_ex__", None)
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if reductor is not None:
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rv = reductor(4)
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else:
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reductor = getattr(x, "__reduce__", None)
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if reductor:
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rv = reductor()
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else:
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raise Error("un(shallow)copyable object of type %s" % cls)
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if isinstance(rv, str):
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return x
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return _reconstruct(x, None, *rv)
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_copy_atomic_types = {types.NoneType, int, float, bool, complex, str, tuple,
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bytes, frozenset, type, range, slice, property,
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types.BuiltinFunctionType, types.EllipsisType,
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types.NotImplementedType, types.FunctionType, types.CodeType,
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weakref.ref, super}
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_copy_builtin_containers = {list, dict, set, bytearray}
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def deepcopy(x, memo=None, _nil=[]):
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"""Deep copy operation on arbitrary Python objects.
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See the module's __doc__ string for more info.
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"""
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cls = type(x)
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if cls in _atomic_types:
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return x
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d = id(x)
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if memo is None:
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memo = {}
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else:
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y = memo.get(d, _nil)
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if y is not _nil:
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return y
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copier = _deepcopy_dispatch.get(cls)
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if copier is not None:
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y = copier(x, memo)
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else:
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if issubclass(cls, type):
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y = x # atomic copy
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else:
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copier = getattr(x, "__deepcopy__", None)
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if copier is not None:
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y = copier(memo)
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else:
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reductor = dispatch_table.get(cls)
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if reductor:
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rv = reductor(x)
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else:
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reductor = getattr(x, "__reduce_ex__", None)
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if reductor is not None:
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rv = reductor(4)
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else:
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reductor = getattr(x, "__reduce__", None)
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if reductor:
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rv = reductor()
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else:
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raise Error(
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"un(deep)copyable object of type %s" % cls)
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if isinstance(rv, str):
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y = x
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else:
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y = _reconstruct(x, memo, *rv)
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# If is its own copy, don't memoize.
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if y is not x:
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memo[d] = y
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_keep_alive(x, memo) # Make sure x lives at least as long as d
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return y
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_atomic_types = {types.NoneType, types.EllipsisType, types.NotImplementedType,
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int, float, bool, complex, bytes, str, types.CodeType, type, range,
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types.BuiltinFunctionType, types.FunctionType, weakref.ref, property}
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_deepcopy_dispatch = d = {}
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def _deepcopy_list(x, memo, deepcopy=deepcopy):
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y = []
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memo[id(x)] = y
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append = y.append
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for a in x:
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append(deepcopy(a, memo))
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return y
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d[list] = _deepcopy_list
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def _deepcopy_tuple(x, memo, deepcopy=deepcopy):
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y = [deepcopy(a, memo) for a in x]
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# We're not going to put the tuple in the memo, but it's still important we
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# check for it, in case the tuple contains recursive mutable structures.
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try:
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return memo[id(x)]
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except KeyError:
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pass
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for k, j in zip(x, y):
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if k is not j:
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y = tuple(y)
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break
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else:
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y = x
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return y
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d[tuple] = _deepcopy_tuple
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def _deepcopy_dict(x, memo, deepcopy=deepcopy):
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y = {}
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memo[id(x)] = y
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for key, value in x.items():
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y[deepcopy(key, memo)] = deepcopy(value, memo)
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return y
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d[dict] = _deepcopy_dict
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def _deepcopy_method(x, memo): # Copy instance methods
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return type(x)(x.__func__, deepcopy(x.__self__, memo))
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d[types.MethodType] = _deepcopy_method
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del d
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def _keep_alive(x, memo):
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"""Keeps a reference to the object x in the memo.
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Because we remember objects by their id, we have
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to assure that possibly temporary objects are kept
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alive by referencing them.
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We store a reference at the id of the memo, which should
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normally not be used unless someone tries to deepcopy
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the memo itself...
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"""
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try:
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memo[id(memo)].append(x)
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except KeyError:
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# aha, this is the first one :-)
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memo[id(memo)]=[x]
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def _reconstruct(x, memo, func, args,
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state=None, listiter=None, dictiter=None,
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*, deepcopy=deepcopy):
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deep = memo is not None
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if deep and args:
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args = (deepcopy(arg, memo) for arg in args)
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y = func(*args)
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if deep:
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memo[id(x)] = y
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if state is not None:
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if deep:
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state = deepcopy(state, memo)
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if hasattr(y, '__setstate__'):
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y.__setstate__(state)
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else:
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if isinstance(state, tuple) and len(state) == 2:
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state, slotstate = state
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else:
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slotstate = None
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if state is not None:
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y.__dict__.update(state)
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if slotstate is not None:
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for key, value in slotstate.items():
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setattr(y, key, value)
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if listiter is not None:
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if deep:
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for item in listiter:
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item = deepcopy(item, memo)
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y.append(item)
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else:
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for item in listiter:
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y.append(item)
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if dictiter is not None:
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if deep:
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for key, value in dictiter:
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key = deepcopy(key, memo)
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value = deepcopy(value, memo)
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y[key] = value
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else:
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for key, value in dictiter:
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y[key] = value
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return y
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del types, weakref
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def replace(obj, /, **changes):
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"""Return a new object replacing specified fields with new values.
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This is especially useful for immutable objects, like named tuples or
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frozen dataclasses.
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"""
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cls = obj.__class__
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func = getattr(cls, '__replace__', None)
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if func is None:
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raise TypeError(f"replace() does not support {cls.__name__} objects")
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return func(obj, **changes)
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