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test_generators.py
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2089 lines (1763 loc) · 54.3 KB
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import copy
import gc
import pickle
import sys
import unittest
import weakref
from test import support
class FinalizationTest(unittest.TestCase):
def test_frame_resurrect(self):
# A generator frame can be resurrected by a generator's finalization.
def gen():
nonlocal frame
try:
yield
finally:
frame = sys._getframe()
g = gen()
wr = weakref.ref(g)
next(g)
del g
support.gc_collect()
self.assertIs(wr(), None)
self.assertTrue(frame)
del frame
support.gc_collect()
def test_refcycle(self):
# A generator caught in a refcycle gets finalized anyway.
old_garbage = gc.garbage[:]
finalized = False
def gen():
nonlocal finalized
try:
g = yield
yield 1
finally:
finalized = True
g = gen()
next(g)
g.send(g)
self.assertGreater(sys.getrefcount(g), 2)
self.assertFalse(finalized)
del g
support.gc_collect()
self.assertTrue(finalized)
self.assertEqual(gc.garbage, old_garbage)
def test_lambda_generator(self):
# Issue #23192: Test that a lambda returning a generator behaves
# like the equivalent function
f = lambda: (yield 1)
def g(): return (yield 1)
# test 'yield from'
f2 = lambda: (yield from g())
def g2(): return (yield from g())
f3 = lambda: (yield from f())
def g3(): return (yield from f())
for gen_fun in (f, g, f2, g2, f3, g3):
gen = gen_fun()
self.assertEqual(next(gen), 1)
with self.assertRaises(StopIteration) as cm:
gen.send(2)
self.assertEqual(cm.exception.value, 2)
class GeneratorTest(unittest.TestCase):
def test_copy(self):
def f():
yield 1
g = f()
with self.assertRaises(TypeError):
copy.copy(g)
def test_pickle(self):
def f():
yield 1
g = f()
for proto in range(pickle.HIGHEST_PROTOCOL + 1):
with self.assertRaises((TypeError, pickle.PicklingError)):
pickle.dumps(g, proto)
class ExceptionTest(unittest.TestCase):
# Tests for the issue #23353: check that the currently handled exception
# is correctly saved/restored in PyEval_EvalFrameEx().
def test_except_throw(self):
def store_raise_exc_generator():
try:
self.assertEqual(sys.exc_info()[0], None)
yield
except Exception as exc:
# exception raised by gen.throw(exc)
self.assertEqual(sys.exc_info()[0], ValueError)
self.assertIsNone(exc.__context__)
yield
# ensure that the exception is not lost
self.assertEqual(sys.exc_info()[0], ValueError)
yield
# we should be able to raise back the ValueError
raise
make = store_raise_exc_generator()
next(make)
try:
raise ValueError()
except Exception as exc:
try:
make.throw(exc)
except Exception:
pass
next(make)
with self.assertRaises(ValueError) as cm:
next(make)
self.assertIsNone(cm.exception.__context__)
self.assertEqual(sys.exc_info(), (None, None, None))
def test_except_next(self):
def gen():
self.assertEqual(sys.exc_info()[0], ValueError)
yield "done"
g = gen()
try:
raise ValueError
except Exception:
self.assertEqual(next(g), "done")
self.assertEqual(sys.exc_info(), (None, None, None))
def test_except_gen_except(self):
def gen():
try:
self.assertEqual(sys.exc_info()[0], None)
yield
# we are called from "except ValueError:", TypeError must
# inherit ValueError in its context
raise TypeError()
except TypeError as exc:
self.assertEqual(sys.exc_info()[0], TypeError)
self.assertEqual(type(exc.__context__), ValueError)
# here we are still called from the "except ValueError:"
self.assertEqual(sys.exc_info()[0], ValueError)
yield
self.assertIsNone(sys.exc_info()[0])
yield "done"
g = gen()
next(g)
try:
raise ValueError
except Exception:
next(g)
self.assertEqual(next(g), "done")
self.assertEqual(sys.exc_info(), (None, None, None))
def test_except_throw_exception_context(self):
def gen():
try:
try:
self.assertEqual(sys.exc_info()[0], None)
yield
except ValueError:
# we are called from "except ValueError:"
self.assertEqual(sys.exc_info()[0], ValueError)
raise TypeError()
except Exception as exc:
self.assertEqual(sys.exc_info()[0], TypeError)
self.assertEqual(type(exc.__context__), ValueError)
# we are still called from "except ValueError:"
self.assertEqual(sys.exc_info()[0], ValueError)
yield
self.assertIsNone(sys.exc_info()[0])
yield "done"
g = gen()
next(g)
try:
raise ValueError
except Exception as exc:
g.throw(exc)
self.assertEqual(next(g), "done")
self.assertEqual(sys.exc_info(), (None, None, None))
tutorial_tests = """
Let's try a simple generator:
>>> def f():
... yield 1
... yield 2
>>> for i in f():
... print(i)
1
2
>>> g = f()
>>> next(g)
1
>>> next(g)
2
"Falling off the end" stops the generator:
>>> next(g)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "<stdin>", line 2, in g
StopIteration
"return" also stops the generator:
>>> def f():
... yield 1
... return
... yield 2 # never reached
...
>>> g = f()
>>> next(g)
1
>>> next(g)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "<stdin>", line 3, in f
StopIteration
>>> next(g) # once stopped, can't be resumed
Traceback (most recent call last):
File "<stdin>", line 1, in ?
StopIteration
"raise StopIteration" stops the generator too:
>>> def f():
... yield 1
... raise StopIteration
... yield 2 # never reached
...
>>> g = f()
>>> next(g)
1
>>> next(g)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
StopIteration
>>> next(g)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
StopIteration
However, they are not exactly equivalent:
>>> def g1():
... try:
... return
... except:
... yield 1
...
>>> list(g1())
[]
>>> def g2():
... try:
... raise StopIteration
... except:
... yield 42
>>> print(list(g2()))
[42]
This may be surprising at first:
>>> def g3():
... try:
... return
... finally:
... yield 1
...
>>> list(g3())
[1]
Let's create an alternate range() function implemented as a generator:
>>> def yrange(n):
... for i in range(n):
... yield i
...
>>> list(yrange(5))
[0, 1, 2, 3, 4]
Generators always return to the most recent caller:
>>> def creator():
... r = yrange(5)
... print("creator", next(r))
... return r
...
>>> def caller():
... r = creator()
... for i in r:
... print("caller", i)
...
>>> caller()
creator 0
caller 1
caller 2
caller 3
caller 4
Generators can call other generators:
>>> def zrange(n):
... for i in yrange(n):
... yield i
...
>>> list(zrange(5))
[0, 1, 2, 3, 4]
"""
# The examples from PEP 255.
pep_tests = """
Specification: Yield
Restriction: A generator cannot be resumed while it is actively
running:
>>> def g():
... i = next(me)
... yield i
>>> me = g()
>>> next(me)
Traceback (most recent call last):
...
File "<string>", line 2, in g
ValueError: generator already executing
Specification: Return
Note that return isn't always equivalent to raising StopIteration: the
difference lies in how enclosing try/except constructs are treated.
For example,
>>> def f1():
... try:
... return
... except:
... yield 1
>>> print(list(f1()))
[]
because, as in any function, return simply exits, but
>>> def f2():
... try:
... raise StopIteration
... except:
... yield 42
>>> print(list(f2()))
[42]
because StopIteration is captured by a bare "except", as is any
exception.
Specification: Generators and Exception Propagation
>>> def f():
... return 1//0
>>> def g():
... yield f() # the zero division exception propagates
... yield 42 # and we'll never get here
>>> k = g()
>>> next(k)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "<stdin>", line 2, in g
File "<stdin>", line 2, in f
ZeroDivisionError: integer division or modulo by zero
>>> next(k) # and the generator cannot be resumed
Traceback (most recent call last):
File "<stdin>", line 1, in ?
StopIteration
>>>
Specification: Try/Except/Finally
>>> def f():
... try:
... yield 1
... try:
... yield 2
... 1//0
... yield 3 # never get here
... except ZeroDivisionError:
... yield 4
... yield 5
... raise
... except:
... yield 6
... yield 7 # the "raise" above stops this
... except:
... yield 8
... yield 9
... try:
... x = 12
... finally:
... yield 10
... yield 11
>>> print(list(f()))
[1, 2, 4, 5, 8, 9, 10, 11]
>>>
Guido's binary tree example.
>>> # A binary tree class.
>>> class Tree:
...
... def __init__(self, label, left=None, right=None):
... self.label = label
... self.left = left
... self.right = right
...
... def __repr__(self, level=0, indent=" "):
... s = level*indent + repr(self.label)
... if self.left:
... s = s + "\\n" + self.left.__repr__(level+1, indent)
... if self.right:
... s = s + "\\n" + self.right.__repr__(level+1, indent)
... return s
...
... def __iter__(self):
... return inorder(self)
>>> # Create a Tree from a list.
>>> def tree(list):
... n = len(list)
... if n == 0:
... return []
... i = n // 2
... return Tree(list[i], tree(list[:i]), tree(list[i+1:]))
>>> # Show it off: create a tree.
>>> t = tree("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
>>> # A recursive generator that generates Tree labels in in-order.
>>> def inorder(t):
... if t:
... for x in inorder(t.left):
... yield x
... yield t.label
... for x in inorder(t.right):
... yield x
>>> # Show it off: create a tree.
>>> t = tree("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
>>> # Print the nodes of the tree in in-order.
>>> for x in t:
... print(' '+x, end='')
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
>>> # A non-recursive generator.
>>> def inorder(node):
... stack = []
... while node:
... while node.left:
... stack.append(node)
... node = node.left
... yield node.label
... while not node.right:
... try:
... node = stack.pop()
... except IndexError:
... return
... yield node.label
... node = node.right
>>> # Exercise the non-recursive generator.
>>> for x in t:
... print(' '+x, end='')
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
"""
# Examples from Iterator-List and Python-Dev and c.l.py.
email_tests = """
The difference between yielding None and returning it.
>>> def g():
... for i in range(3):
... yield None
... yield None
... return
>>> list(g())
[None, None, None, None]
Ensure that explicitly raising StopIteration acts like any other exception
in try/except, not like a return.
>>> def g():
... yield 1
... try:
... raise StopIteration
... except:
... yield 2
... yield 3
>>> list(g())
[1, 2, 3]
Next one was posted to c.l.py.
>>> def gcomb(x, k):
... "Generate all combinations of k elements from list x."
...
... if k > len(x):
... return
... if k == 0:
... yield []
... else:
... first, rest = x[0], x[1:]
... # A combination does or doesn't contain first.
... # If it does, the remainder is a k-1 comb of rest.
... for c in gcomb(rest, k-1):
... c.insert(0, first)
... yield c
... # If it doesn't contain first, it's a k comb of rest.
... for c in gcomb(rest, k):
... yield c
>>> seq = list(range(1, 5))
>>> for k in range(len(seq) + 2):
... print("%d-combs of %s:" % (k, seq))
... for c in gcomb(seq, k):
... print(" ", c)
0-combs of [1, 2, 3, 4]:
[]
1-combs of [1, 2, 3, 4]:
[1]
[2]
[3]
[4]
2-combs of [1, 2, 3, 4]:
[1, 2]
[1, 3]
[1, 4]
[2, 3]
[2, 4]
[3, 4]
3-combs of [1, 2, 3, 4]:
[1, 2, 3]
[1, 2, 4]
[1, 3, 4]
[2, 3, 4]
4-combs of [1, 2, 3, 4]:
[1, 2, 3, 4]
5-combs of [1, 2, 3, 4]:
From the Iterators list, about the types of these things.
>>> def g():
... yield 1
...
>>> type(g)
<class 'function'>
>>> i = g()
>>> type(i)
<class 'generator'>
>>> [s for s in dir(i) if not s.startswith('_')]
['close', 'gi_code', 'gi_frame', 'gi_running', 'send', 'throw']
>>> from test.support import HAVE_DOCSTRINGS
>>> print(i.__next__.__doc__ if HAVE_DOCSTRINGS else 'Implement next(self).')
Implement next(self).
>>> iter(i) is i
True
>>> import types
>>> isinstance(i, types.GeneratorType)
True
And more, added later.
>>> i.gi_running
0
>>> type(i.gi_frame)
<class 'frame'>
>>> i.gi_running = 42
Traceback (most recent call last):
...
AttributeError: readonly attribute
>>> def g():
... yield me.gi_running
>>> me = g()
>>> me.gi_running
0
>>> next(me)
1
>>> me.gi_running
0
A clever union-find implementation from c.l.py, due to David Eppstein.
Sent: Friday, June 29, 2001 12:16 PM
To: python-list@python.org
Subject: Re: PEP 255: Simple Generators
>>> class disjointSet:
... def __init__(self, name):
... self.name = name
... self.parent = None
... self.generator = self.generate()
...
... def generate(self):
... while not self.parent:
... yield self
... for x in self.parent.generator:
... yield x
...
... def find(self):
... return next(self.generator)
...
... def union(self, parent):
... if self.parent:
... raise ValueError("Sorry, I'm not a root!")
... self.parent = parent
...
... def __str__(self):
... return self.name
>>> names = "ABCDEFGHIJKLM"
>>> sets = [disjointSet(name) for name in names]
>>> roots = sets[:]
>>> import random
>>> gen = random.Random(42)
>>> while 1:
... for s in sets:
... print(" %s->%s" % (s, s.find()), end='')
... print()
... if len(roots) > 1:
... s1 = gen.choice(roots)
... roots.remove(s1)
... s2 = gen.choice(roots)
... s1.union(s2)
... print("merged", s1, "into", s2)
... else:
... break
A->A B->B C->C D->D E->E F->F G->G H->H I->I J->J K->K L->L M->M
merged K into B
A->A B->B C->C D->D E->E F->F G->G H->H I->I J->J K->B L->L M->M
merged A into F
A->F B->B C->C D->D E->E F->F G->G H->H I->I J->J K->B L->L M->M
merged E into F
A->F B->B C->C D->D E->F F->F G->G H->H I->I J->J K->B L->L M->M
merged D into C
A->F B->B C->C D->C E->F F->F G->G H->H I->I J->J K->B L->L M->M
merged M into C
A->F B->B C->C D->C E->F F->F G->G H->H I->I J->J K->B L->L M->C
merged J into B
A->F B->B C->C D->C E->F F->F G->G H->H I->I J->B K->B L->L M->C
merged B into C
A->F B->C C->C D->C E->F F->F G->G H->H I->I J->C K->C L->L M->C
merged F into G
A->G B->C C->C D->C E->G F->G G->G H->H I->I J->C K->C L->L M->C
merged L into C
A->G B->C C->C D->C E->G F->G G->G H->H I->I J->C K->C L->C M->C
merged G into I
A->I B->C C->C D->C E->I F->I G->I H->H I->I J->C K->C L->C M->C
merged I into H
A->H B->C C->C D->C E->H F->H G->H H->H I->H J->C K->C L->C M->C
merged C into H
A->H B->H C->H D->H E->H F->H G->H H->H I->H J->H K->H L->H M->H
"""
# Emacs turd '
# Fun tests (for sufficiently warped notions of "fun").
fun_tests = """
Build up to a recursive Sieve of Eratosthenes generator.
>>> def firstn(g, n):
... return [next(g) for i in range(n)]
>>> def intsfrom(i):
... while 1:
... yield i
... i += 1
>>> firstn(intsfrom(5), 7)
[5, 6, 7, 8, 9, 10, 11]
>>> def exclude_multiples(n, ints):
... for i in ints:
... if i % n:
... yield i
>>> firstn(exclude_multiples(3, intsfrom(1)), 6)
[1, 2, 4, 5, 7, 8]
>>> def sieve(ints):
... prime = next(ints)
... yield prime
... not_divisible_by_prime = exclude_multiples(prime, ints)
... for p in sieve(not_divisible_by_prime):
... yield p
>>> primes = sieve(intsfrom(2))
>>> firstn(primes, 20)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71]
Another famous problem: generate all integers of the form
2**i * 3**j * 5**k
in increasing order, where i,j,k >= 0. Trickier than it may look at first!
Try writing it without generators, and correctly, and without generating
3 internal results for each result output.
>>> def times(n, g):
... for i in g:
... yield n * i
>>> firstn(times(10, intsfrom(1)), 10)
[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
>>> def merge(g, h):
... ng = next(g)
... nh = next(h)
... while 1:
... if ng < nh:
... yield ng
... ng = next(g)
... elif ng > nh:
... yield nh
... nh = next(h)
... else:
... yield ng
... ng = next(g)
... nh = next(h)
The following works, but is doing a whale of a lot of redundant work --
it's not clear how to get the internal uses of m235 to share a single
generator. Note that me_times2 (etc) each need to see every element in the
result sequence. So this is an example where lazy lists are more natural
(you can look at the head of a lazy list any number of times).
>>> def m235():
... yield 1
... me_times2 = times(2, m235())
... me_times3 = times(3, m235())
... me_times5 = times(5, m235())
... for i in merge(merge(me_times2,
... me_times3),
... me_times5):
... yield i
Don't print "too many" of these -- the implementation above is extremely
inefficient: each call of m235() leads to 3 recursive calls, and in
turn each of those 3 more, and so on, and so on, until we've descended
enough levels to satisfy the print stmts. Very odd: when I printed 5
lines of results below, this managed to screw up Win98's malloc in "the
usual" way, i.e. the heap grew over 4Mb so Win98 started fragmenting
address space, and it *looked* like a very slow leak.
>>> result = m235()
>>> for i in range(3):
... print(firstn(result, 15))
[1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24]
[25, 27, 30, 32, 36, 40, 45, 48, 50, 54, 60, 64, 72, 75, 80]
[81, 90, 96, 100, 108, 120, 125, 128, 135, 144, 150, 160, 162, 180, 192]
Heh. Here's one way to get a shared list, complete with an excruciating
namespace renaming trick. The *pretty* part is that the times() and merge()
functions can be reused as-is, because they only assume their stream
arguments are iterable -- a LazyList is the same as a generator to times().
>>> class LazyList:
... def __init__(self, g):
... self.sofar = []
... self.fetch = g.__next__
...
... def __getitem__(self, i):
... sofar, fetch = self.sofar, self.fetch
... while i >= len(sofar):
... sofar.append(fetch())
... return sofar[i]
>>> def m235():
... yield 1
... # Gack: m235 below actually refers to a LazyList.
... me_times2 = times(2, m235)
... me_times3 = times(3, m235)
... me_times5 = times(5, m235)
... for i in merge(merge(me_times2,
... me_times3),
... me_times5):
... yield i
Print as many of these as you like -- *this* implementation is memory-
efficient.
>>> m235 = LazyList(m235())
>>> for i in range(5):
... print([m235[j] for j in range(15*i, 15*(i+1))])
[1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24]
[25, 27, 30, 32, 36, 40, 45, 48, 50, 54, 60, 64, 72, 75, 80]
[81, 90, 96, 100, 108, 120, 125, 128, 135, 144, 150, 160, 162, 180, 192]
[200, 216, 225, 240, 243, 250, 256, 270, 288, 300, 320, 324, 360, 375, 384]
[400, 405, 432, 450, 480, 486, 500, 512, 540, 576, 600, 625, 640, 648, 675]
Ye olde Fibonacci generator, LazyList style.
>>> def fibgen(a, b):
...
... def sum(g, h):
... while 1:
... yield next(g) + next(h)
...
... def tail(g):
... next(g) # throw first away
... for x in g:
... yield x
...
... yield a
... yield b
... for s in sum(iter(fib),
... tail(iter(fib))):
... yield s
>>> fib = LazyList(fibgen(1, 2))
>>> firstn(iter(fib), 17)
[1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584]
Running after your tail with itertools.tee (new in version 2.4)
The algorithms "m235" (Hamming) and Fibonacci presented above are both
examples of a whole family of FP (functional programming) algorithms
where a function produces and returns a list while the production algorithm
suppose the list as already produced by recursively calling itself.
For these algorithms to work, they must:
- produce at least a first element without presupposing the existence of
the rest of the list
- produce their elements in a lazy manner
To work efficiently, the beginning of the list must not be recomputed over
and over again. This is ensured in most FP languages as a built-in feature.
In python, we have to explicitly maintain a list of already computed results
and abandon genuine recursivity.
This is what had been attempted above with the LazyList class. One problem
with that class is that it keeps a list of all of the generated results and
therefore continually grows. This partially defeats the goal of the generator
concept, viz. produce the results only as needed instead of producing them
all and thereby wasting memory.
Thanks to itertools.tee, it is now clear "how to get the internal uses of
m235 to share a single generator".
>>> from itertools import tee
>>> def m235():
... def _m235():
... yield 1
... for n in merge(times(2, m2),
... merge(times(3, m3),
... times(5, m5))):
... yield n
... m1 = _m235()
... m2, m3, m5, mRes = tee(m1, 4)
... return mRes
>>> it = m235()
>>> for i in range(5):
... print(firstn(it, 15))
[1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24]
[25, 27, 30, 32, 36, 40, 45, 48, 50, 54, 60, 64, 72, 75, 80]
[81, 90, 96, 100, 108, 120, 125, 128, 135, 144, 150, 160, 162, 180, 192]
[200, 216, 225, 240, 243, 250, 256, 270, 288, 300, 320, 324, 360, 375, 384]
[400, 405, 432, 450, 480, 486, 500, 512, 540, 576, 600, 625, 640, 648, 675]
The "tee" function does just what we want. It internally keeps a generated
result for as long as it has not been "consumed" from all of the duplicated
iterators, whereupon it is deleted. You can therefore print the hamming
sequence during hours without increasing memory usage, or very little.
The beauty of it is that recursive running-after-their-tail FP algorithms
are quite straightforwardly expressed with this Python idiom.
Ye olde Fibonacci generator, tee style.
>>> def fib():
...
... def _isum(g, h):
... while 1:
... yield next(g) + next(h)
...
... def _fib():
... yield 1
... yield 2
... next(fibTail) # throw first away
... for res in _isum(fibHead, fibTail):
... yield res
...
... realfib = _fib()
... fibHead, fibTail, fibRes = tee(realfib, 3)
... return fibRes
>>> firstn(fib(), 17)
[1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584]
"""
# syntax_tests mostly provokes SyntaxErrors. Also fiddling with #if 0
# hackery.
syntax_tests = """
These are fine:
>>> def f():
... yield 1
... return
>>> def f():
... try:
... yield 1
... finally:
... pass
>>> def f():
... try:
... try:
... 1//0
... except ZeroDivisionError:
... yield 666
... except:
... pass
... finally:
... pass
>>> def f():
... try:
... try:
... yield 12
... 1//0
... except ZeroDivisionError:
... yield 666
... except:
... try:
... x = 12
... finally:
... yield 12
... except:
... return
>>> list(f())
[12, 666]
>>> def f():
... yield
>>> type(f())
<class 'generator'>
>>> def f():
... if 0:
... yield
>>> type(f())
<class 'generator'>
>>> def f():
... if 0:
... yield 1
>>> type(f())
<class 'generator'>
>>> def f():
... if "":
... yield None
>>> type(f())
<class 'generator'>
>>> def f():
... return
... try: