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e28006528c866157b5832c15de8f00c12995b330 | 890c8b8e90e516a5a3880eca9b2d217662fe7d84 | /armulator/armv6/opcodes/abstract_opcodes/ldr_register_thumb.py | 51fbe8abfb33e0f75de30bf0387a9fba04663e45 | [
"MIT"
] | permissive | doronz88/armulator | b864135996f876c7857b79a314d4aa06cc19c549 | 0294feac2785c8947e5943ac0c34f941ee4b5fff | refs/heads/master | 2022-11-05T08:14:42.405335 | 2020-06-18T23:53:17 | 2020-06-18T23:53:17 | 273,363,061 | 2 | 0 | null | 2020-06-18T23:51:03 | 2020-06-18T23:51:02 | null | UTF-8 | Python | false | false | 1,698 | py | from armulator.armv6.shift import shift
from armulator.armv6.bits_ops import add
from bitstring import BitArray
from armulator.armv6.arm_exceptions import EndOfInstruction
from armulator.armv6.opcodes.abstract_opcode import AbstractOpcode
class LdrRegisterThumb(AbstractOpcode):
def __init__(self, m, t, n, shift_t, shift_n):
super(LdrRegisterThumb, self).__init__()
self.m = m
self.t = t
self.n = n
self.shift_t = shift_t
self.shift_n = shift_n
def execute(self, processor):
if processor.condition_passed():
try:
processor.null_check_if_thumbee(self.n)
except EndOfInstruction:
pass
else:
offset = shift(processor.registers.get(self.m), self.shift_t, self.shift_n,
processor.registers.cpsr.get_c())
offset_addr = add(processor.registers.get(self.n), offset, 32)
address = offset_addr
data = processor.mem_u_get(address, 4)
if self.t == 15:
if address[30:32] == "0b00":
processor.load_write_pc(address)
else:
print "unpredictable"
elif processor.unaligned_support() or address[30:32] == "0b00":
processor.registers.set(self.t, data)
else:
processor.registers.set(self.t, BitArray(length=32)) # unknown
def instruction_syndrome(self):
if self.t == 15:
return BitArray(length=9)
else:
return BitArray(bin="11000") + BitArray(uint=self.t, length=4)
| [
"[email protected]"
] | |
df4245bfa348671c4ff60bc41a2a9c17ff75d4f3 | acb8e84e3b9c987fcab341f799f41d5a5ec4d587 | /langs/0/bw0.py | d035dd859b4fa0474da462854623b73eb560a2cf | [] | no_license | G4te-Keep3r/HowdyHackers | 46bfad63eafe5ac515da363e1c75fa6f4b9bca32 | fb6d391aaecb60ab5c4650d4ae2ddd599fd85db2 | refs/heads/master | 2020-08-01T12:08:10.782018 | 2016-11-13T20:45:50 | 2016-11-13T20:45:50 | 73,624,224 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 486 | py | import sys
def printFunction(lineRemaining):
if lineRemaining[0] == '"' and lineRemaining[-1] == '"':
if len(lineRemaining) > 2:
#data to print
lineRemaining = lineRemaining[1:-1]
print ' '.join(lineRemaining)
else:
print
def main(fileName):
with open(fileName) as f:
for line in f:
data = line.split()
if data[0] == 'bw0':
printFunction(data[1:])
else:
print 'ERROR'
return
if __name__ == '__main__':
main(sys.argv[1]) | [
"[email protected]"
] | |
30ecef27252bd418cd76a0e181cac9ff80ecba28 | 8eab8ab725c2132bb8d090cdb2d23a5f71945249 | /virt/Lib/site-packages/numpy/core/tests/test_ufunc.py | 852044d32fcc6d27012c0a43a2e814b976d7154c | [
"GPL-3.0-only",
"BSD-3-Clause-Open-MPI",
"GPL-3.0-or-later",
"GCC-exception-3.1",
"BSD-3-Clause",
"MIT"
] | permissive | JoaoSevergnini/metalpy | 6c88a413a82bc25edd9308b8490a76fae8dd76ca | c2d0098a309b6ce8c756ff840bfb53fb291747b6 | refs/heads/main | 2023-04-18T17:25:26.474485 | 2022-09-18T20:44:45 | 2022-09-18T20:44:45 | 474,773,752 | 3 | 1 | MIT | 2022-11-03T20:07:50 | 2022-03-27T22:21:01 | Python | UTF-8 | Python | false | false | 108,151 | py | import warnings
import itertools
import sys
import pytest
import numpy as np
import numpy.core._umath_tests as umt
import numpy.linalg._umath_linalg as uml
import numpy.core._operand_flag_tests as opflag_tests
import numpy.core._rational_tests as _rational_tests
from numpy.testing import (
assert_, assert_equal, assert_raises, assert_array_equal,
assert_almost_equal, assert_array_almost_equal, assert_no_warnings,
assert_allclose, HAS_REFCOUNT, suppress_warnings
)
from numpy.testing._private.utils import requires_memory
from numpy.compat import pickle
UNARY_UFUNCS = [obj for obj in np.core.umath.__dict__.values()
if isinstance(obj, np.ufunc)]
UNARY_OBJECT_UFUNCS = [uf for uf in UNARY_UFUNCS if "O->O" in uf.types]
class TestUfuncKwargs:
def test_kwarg_exact(self):
assert_raises(TypeError, np.add, 1, 2, castingx='safe')
assert_raises(TypeError, np.add, 1, 2, dtypex=int)
assert_raises(TypeError, np.add, 1, 2, extobjx=[4096])
assert_raises(TypeError, np.add, 1, 2, outx=None)
assert_raises(TypeError, np.add, 1, 2, sigx='ii->i')
assert_raises(TypeError, np.add, 1, 2, signaturex='ii->i')
assert_raises(TypeError, np.add, 1, 2, subokx=False)
assert_raises(TypeError, np.add, 1, 2, wherex=[True])
def test_sig_signature(self):
assert_raises(TypeError, np.add, 1, 2, sig='ii->i',
signature='ii->i')
def test_sig_dtype(self):
assert_raises(TypeError, np.add, 1, 2, sig='ii->i',
dtype=int)
assert_raises(TypeError, np.add, 1, 2, signature='ii->i',
dtype=int)
def test_extobj_refcount(self):
# Should not segfault with USE_DEBUG.
assert_raises(TypeError, np.add, 1, 2, extobj=[4096], parrot=True)
class TestUfuncGenericLoops:
"""Test generic loops.
The loops to be tested are:
PyUFunc_ff_f_As_dd_d
PyUFunc_ff_f
PyUFunc_dd_d
PyUFunc_gg_g
PyUFunc_FF_F_As_DD_D
PyUFunc_DD_D
PyUFunc_FF_F
PyUFunc_GG_G
PyUFunc_OO_O
PyUFunc_OO_O_method
PyUFunc_f_f_As_d_d
PyUFunc_d_d
PyUFunc_f_f
PyUFunc_g_g
PyUFunc_F_F_As_D_D
PyUFunc_F_F
PyUFunc_D_D
PyUFunc_G_G
PyUFunc_O_O
PyUFunc_O_O_method
PyUFunc_On_Om
Where:
f -- float
d -- double
g -- long double
F -- complex float
D -- complex double
G -- complex long double
O -- python object
It is difficult to assure that each of these loops is entered from the
Python level as the special cased loops are a moving target and the
corresponding types are architecture dependent. We probably need to
define C level testing ufuncs to get at them. For the time being, I've
just looked at the signatures registered in the build directory to find
relevant functions.
"""
np_dtypes = [
(np.single, np.single), (np.single, np.double),
(np.csingle, np.csingle), (np.csingle, np.cdouble),
(np.double, np.double), (np.longdouble, np.longdouble),
(np.cdouble, np.cdouble), (np.clongdouble, np.clongdouble)]
@pytest.mark.parametrize('input_dtype,output_dtype', np_dtypes)
def test_unary_PyUFunc(self, input_dtype, output_dtype, f=np.exp, x=0, y=1):
xs = np.full(10, input_dtype(x), dtype=output_dtype)
ys = f(xs)[::2]
assert_allclose(ys, y)
assert_equal(ys.dtype, output_dtype)
def f2(x, y):
return x**y
@pytest.mark.parametrize('input_dtype,output_dtype', np_dtypes)
def test_binary_PyUFunc(self, input_dtype, output_dtype, f=f2, x=0, y=1):
xs = np.full(10, input_dtype(x), dtype=output_dtype)
ys = f(xs, xs)[::2]
assert_allclose(ys, y)
assert_equal(ys.dtype, output_dtype)
# class to use in testing object method loops
class foo:
def conjugate(self):
return np.bool_(1)
def logical_xor(self, obj):
return np.bool_(1)
def test_unary_PyUFunc_O_O(self):
x = np.ones(10, dtype=object)
assert_(np.all(np.abs(x) == 1))
def test_unary_PyUFunc_O_O_method_simple(self, foo=foo):
x = np.full(10, foo(), dtype=object)
assert_(np.all(np.conjugate(x) == True))
def test_binary_PyUFunc_OO_O(self):
x = np.ones(10, dtype=object)
assert_(np.all(np.add(x, x) == 2))
def test_binary_PyUFunc_OO_O_method(self, foo=foo):
x = np.full(10, foo(), dtype=object)
assert_(np.all(np.logical_xor(x, x)))
def test_binary_PyUFunc_On_Om_method(self, foo=foo):
x = np.full((10, 2, 3), foo(), dtype=object)
assert_(np.all(np.logical_xor(x, x)))
def test_python_complex_conjugate(self):
# The conjugate ufunc should fall back to calling the method:
arr = np.array([1+2j, 3-4j], dtype="O")
assert isinstance(arr[0], complex)
res = np.conjugate(arr)
assert res.dtype == np.dtype("O")
assert_array_equal(res, np.array([1-2j, 3+4j], dtype="O"))
@pytest.mark.parametrize("ufunc", UNARY_OBJECT_UFUNCS)
def test_unary_PyUFunc_O_O_method_full(self, ufunc):
"""Compare the result of the object loop with non-object one"""
val = np.float64(np.pi/4)
class MyFloat(np.float64):
def __getattr__(self, attr):
try:
return super().__getattr__(attr)
except AttributeError:
return lambda: getattr(np.core.umath, attr)(val)
# Use 0-D arrays, to ensure the same element call
num_arr = np.array(val, dtype=np.float64)
obj_arr = np.array(MyFloat(val), dtype="O")
with np.errstate(all="raise"):
try:
res_num = ufunc(num_arr)
except Exception as exc:
with assert_raises(type(exc)):
ufunc(obj_arr)
else:
res_obj = ufunc(obj_arr)
assert_array_almost_equal(res_num.astype("O"), res_obj)
def _pickleable_module_global():
pass
class TestUfunc:
def test_pickle(self):
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
assert_(pickle.loads(pickle.dumps(np.sin,
protocol=proto)) is np.sin)
# Check that ufunc not defined in the top level numpy namespace
# such as numpy.core._rational_tests.test_add can also be pickled
res = pickle.loads(pickle.dumps(_rational_tests.test_add,
protocol=proto))
assert_(res is _rational_tests.test_add)
def test_pickle_withstring(self):
astring = (b"cnumpy.core\n_ufunc_reconstruct\np0\n"
b"(S'numpy.core.umath'\np1\nS'cos'\np2\ntp3\nRp4\n.")
assert_(pickle.loads(astring) is np.cos)
def test_pickle_name_is_qualname(self):
# This tests that a simplification of our ufunc pickle code will
# lead to allowing qualnames as names. Future ufuncs should
# possible add a specific qualname, or a hook into pickling instead
# (dask+numba may benefit).
_pickleable_module_global.ufunc = umt._pickleable_module_global_ufunc
obj = pickle.loads(pickle.dumps(_pickleable_module_global.ufunc))
assert obj is umt._pickleable_module_global_ufunc
def test_reduceat_shifting_sum(self):
L = 6
x = np.arange(L)
idx = np.array(list(zip(np.arange(L - 2), np.arange(L - 2) + 2))).ravel()
assert_array_equal(np.add.reduceat(x, idx)[::2], [1, 3, 5, 7])
def test_all_ufunc(self):
"""Try to check presence and results of all ufuncs.
The list of ufuncs comes from generate_umath.py and is as follows:
===== ==== ============= =============== ========================
done args function types notes
===== ==== ============= =============== ========================
n 1 conjugate nums + O
n 1 absolute nums + O complex -> real
n 1 negative nums + O
n 1 sign nums + O -> int
n 1 invert bool + ints + O flts raise an error
n 1 degrees real + M cmplx raise an error
n 1 radians real + M cmplx raise an error
n 1 arccos flts + M
n 1 arccosh flts + M
n 1 arcsin flts + M
n 1 arcsinh flts + M
n 1 arctan flts + M
n 1 arctanh flts + M
n 1 cos flts + M
n 1 sin flts + M
n 1 tan flts + M
n 1 cosh flts + M
n 1 sinh flts + M
n 1 tanh flts + M
n 1 exp flts + M
n 1 expm1 flts + M
n 1 log flts + M
n 1 log10 flts + M
n 1 log1p flts + M
n 1 sqrt flts + M real x < 0 raises error
n 1 ceil real + M
n 1 trunc real + M
n 1 floor real + M
n 1 fabs real + M
n 1 rint flts + M
n 1 isnan flts -> bool
n 1 isinf flts -> bool
n 1 isfinite flts -> bool
n 1 signbit real -> bool
n 1 modf real -> (frac, int)
n 1 logical_not bool + nums + M -> bool
n 2 left_shift ints + O flts raise an error
n 2 right_shift ints + O flts raise an error
n 2 add bool + nums + O boolean + is ||
n 2 subtract bool + nums + O boolean - is ^
n 2 multiply bool + nums + O boolean * is &
n 2 divide nums + O
n 2 floor_divide nums + O
n 2 true_divide nums + O bBhH -> f, iIlLqQ -> d
n 2 fmod nums + M
n 2 power nums + O
n 2 greater bool + nums + O -> bool
n 2 greater_equal bool + nums + O -> bool
n 2 less bool + nums + O -> bool
n 2 less_equal bool + nums + O -> bool
n 2 equal bool + nums + O -> bool
n 2 not_equal bool + nums + O -> bool
n 2 logical_and bool + nums + M -> bool
n 2 logical_or bool + nums + M -> bool
n 2 logical_xor bool + nums + M -> bool
n 2 maximum bool + nums + O
n 2 minimum bool + nums + O
n 2 bitwise_and bool + ints + O flts raise an error
n 2 bitwise_or bool + ints + O flts raise an error
n 2 bitwise_xor bool + ints + O flts raise an error
n 2 arctan2 real + M
n 2 remainder ints + real + O
n 2 hypot real + M
===== ==== ============= =============== ========================
Types other than those listed will be accepted, but they are cast to
the smallest compatible type for which the function is defined. The
casting rules are:
bool -> int8 -> float32
ints -> double
"""
pass
# from include/numpy/ufuncobject.h
size_inferred = 2
can_ignore = 4
def test_signature0(self):
# the arguments to test_signature are: nin, nout, core_signature
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, "(i),(i)->()")
assert_equal(enabled, 1)
assert_equal(num_dims, (1, 1, 0))
assert_equal(ixs, (0, 0))
assert_equal(flags, (self.size_inferred,))
assert_equal(sizes, (-1,))
def test_signature1(self):
# empty core signature; treat as plain ufunc (with trivial core)
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, "(),()->()")
assert_equal(enabled, 0)
assert_equal(num_dims, (0, 0, 0))
assert_equal(ixs, ())
assert_equal(flags, ())
assert_equal(sizes, ())
def test_signature2(self):
# more complicated names for variables
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, "(i1,i2),(J_1)->(_kAB)")
assert_equal(enabled, 1)
assert_equal(num_dims, (2, 1, 1))
assert_equal(ixs, (0, 1, 2, 3))
assert_equal(flags, (self.size_inferred,)*4)
assert_equal(sizes, (-1, -1, -1, -1))
def test_signature3(self):
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, u"(i1, i12), (J_1)->(i12, i2)")
assert_equal(enabled, 1)
assert_equal(num_dims, (2, 1, 2))
assert_equal(ixs, (0, 1, 2, 1, 3))
assert_equal(flags, (self.size_inferred,)*4)
assert_equal(sizes, (-1, -1, -1, -1))
def test_signature4(self):
# matrix_multiply signature from _umath_tests
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, "(n,k),(k,m)->(n,m)")
assert_equal(enabled, 1)
assert_equal(num_dims, (2, 2, 2))
assert_equal(ixs, (0, 1, 1, 2, 0, 2))
assert_equal(flags, (self.size_inferred,)*3)
assert_equal(sizes, (-1, -1, -1))
def test_signature5(self):
# matmul signature from _umath_tests
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
2, 1, "(n?,k),(k,m?)->(n?,m?)")
assert_equal(enabled, 1)
assert_equal(num_dims, (2, 2, 2))
assert_equal(ixs, (0, 1, 1, 2, 0, 2))
assert_equal(flags, (self.size_inferred | self.can_ignore,
self.size_inferred,
self.size_inferred | self.can_ignore))
assert_equal(sizes, (-1, -1, -1))
def test_signature6(self):
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
1, 1, "(3)->()")
assert_equal(enabled, 1)
assert_equal(num_dims, (1, 0))
assert_equal(ixs, (0,))
assert_equal(flags, (0,))
assert_equal(sizes, (3,))
def test_signature7(self):
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
3, 1, "(3),(03,3),(n)->(9)")
assert_equal(enabled, 1)
assert_equal(num_dims, (1, 2, 1, 1))
assert_equal(ixs, (0, 0, 0, 1, 2))
assert_equal(flags, (0, self.size_inferred, 0))
assert_equal(sizes, (3, -1, 9))
def test_signature8(self):
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
3, 1, "(3?),(3?,3?),(n)->(9)")
assert_equal(enabled, 1)
assert_equal(num_dims, (1, 2, 1, 1))
assert_equal(ixs, (0, 0, 0, 1, 2))
assert_equal(flags, (self.can_ignore, self.size_inferred, 0))
assert_equal(sizes, (3, -1, 9))
def test_signature9(self):
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
1, 1, "( 3) -> ( )")
assert_equal(enabled, 1)
assert_equal(num_dims, (1, 0))
assert_equal(ixs, (0,))
assert_equal(flags, (0,))
assert_equal(sizes, (3,))
def test_signature10(self):
enabled, num_dims, ixs, flags, sizes = umt.test_signature(
3, 1, "( 3? ) , (3? , 3?) ,(n )-> ( 9)")
assert_equal(enabled, 1)
assert_equal(num_dims, (1, 2, 1, 1))
assert_equal(ixs, (0, 0, 0, 1, 2))
assert_equal(flags, (self.can_ignore, self.size_inferred, 0))
assert_equal(sizes, (3, -1, 9))
def test_signature_failure_extra_parenthesis(self):
with assert_raises(ValueError):
umt.test_signature(2, 1, "((i)),(i)->()")
def test_signature_failure_mismatching_parenthesis(self):
with assert_raises(ValueError):
umt.test_signature(2, 1, "(i),)i(->()")
def test_signature_failure_signature_missing_input_arg(self):
with assert_raises(ValueError):
umt.test_signature(2, 1, "(i),->()")
def test_signature_failure_signature_missing_output_arg(self):
with assert_raises(ValueError):
umt.test_signature(2, 2, "(i),(i)->()")
def test_get_signature(self):
assert_equal(umt.inner1d.signature, "(i),(i)->()")
def test_forced_sig(self):
a = 0.5*np.arange(3, dtype='f8')
assert_equal(np.add(a, 0.5), [0.5, 1, 1.5])
with pytest.warns(DeprecationWarning):
assert_equal(np.add(a, 0.5, sig='i', casting='unsafe'), [0, 0, 1])
assert_equal(np.add(a, 0.5, sig='ii->i', casting='unsafe'), [0, 0, 1])
with pytest.warns(DeprecationWarning):
assert_equal(np.add(a, 0.5, sig=('i4',), casting='unsafe'),
[0, 0, 1])
assert_equal(np.add(a, 0.5, sig=('i4', 'i4', 'i4'),
casting='unsafe'), [0, 0, 1])
b = np.zeros((3,), dtype='f8')
np.add(a, 0.5, out=b)
assert_equal(b, [0.5, 1, 1.5])
b[:] = 0
with pytest.warns(DeprecationWarning):
np.add(a, 0.5, sig='i', out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a, 0.5, sig='ii->i', out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
with pytest.warns(DeprecationWarning):
np.add(a, 0.5, sig=('i4',), out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a, 0.5, sig=('i4', 'i4', 'i4'), out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
def test_signature_all_None(self):
# signature all None, is an acceptable alternative (since 1.21)
# to not providing a signature.
res1 = np.add([3], [4], sig=(None, None, None))
res2 = np.add([3], [4])
assert_array_equal(res1, res2)
res1 = np.maximum([3], [4], sig=(None, None, None))
res2 = np.maximum([3], [4])
assert_array_equal(res1, res2)
with pytest.raises(TypeError):
# special case, that would be deprecated anyway, so errors:
np.add(3, 4, signature=(None,))
def test_signature_dtype_type(self):
# Since that will be the normal behaviour (past NumPy 1.21)
# we do support the types already:
float_dtype = type(np.dtype(np.float64))
np.add(3, 4, signature=(float_dtype, float_dtype, None))
@pytest.mark.parametrize("casting", ["unsafe", "same_kind", "safe"])
def test_partial_signature_mismatch(self, casting):
# If the second argument matches already, no need to specify it:
res = np.ldexp(np.float32(1.), np.int_(2), dtype="d")
assert res.dtype == "d"
res = np.ldexp(np.float32(1.), np.int_(2), signature=(None, None, "d"))
assert res.dtype == "d"
# ldexp only has a loop for long input as second argument, overriding
# the output cannot help with that (no matter the casting)
with pytest.raises(TypeError):
np.ldexp(1., np.uint64(3), dtype="d")
with pytest.raises(TypeError):
np.ldexp(1., np.uint64(3), signature=(None, None, "d"))
def test_use_output_signature_for_all_arguments(self):
# Test that providing only `dtype=` or `signature=(None, None, dtype)`
# is sufficient if falling back to a homogeneous signature works.
# In this case, the `intp, intp -> intp` loop is chosen.
res = np.power(1.5, 2.8, dtype=np.intp, casting="unsafe")
assert res == 1 # the cast happens first.
res = np.power(1.5, 2.8, signature=(None, None, np.intp),
casting="unsafe")
assert res == 1
with pytest.raises(TypeError):
# the unsafe casting would normally cause errors though:
np.power(1.5, 2.8, dtype=np.intp)
def test_signature_errors(self):
with pytest.raises(TypeError,
match="the signature object to ufunc must be a string or"):
np.add(3, 4, signature=123.) # neither a string nor a tuple
with pytest.raises(ValueError):
# bad symbols that do not translate to dtypes
np.add(3, 4, signature="%^->#")
with pytest.raises(ValueError):
np.add(3, 4, signature=b"ii-i") # incomplete and byte string
with pytest.raises(ValueError):
np.add(3, 4, signature="ii>i") # incomplete string
with pytest.raises(ValueError):
np.add(3, 4, signature=(None, "f8")) # bad length
with pytest.raises(UnicodeDecodeError):
np.add(3, 4, signature=b"\xff\xff->i")
def test_forced_dtype_times(self):
# Signatures only set the type numbers (not the actual loop dtypes)
# so using `M` in a signature/dtype should generally work:
a = np.array(['2010-01-02', '1999-03-14', '1833-03'], dtype='>M8[D]')
np.maximum(a, a, dtype="M")
np.maximum.reduce(a, dtype="M")
arr = np.arange(10, dtype="m8[s]")
np.add(arr, arr, dtype="m")
np.maximum(arr, arr, dtype="m")
@pytest.mark.parametrize("ufunc", [np.add, np.sqrt])
def test_cast_safety(self, ufunc):
"""Basic test for the safest casts, because ufuncs inner loops can
indicate a cast-safety as well (which is normally always "no").
"""
def call_ufunc(arr, **kwargs):
return ufunc(*(arr,) * ufunc.nin, **kwargs)
arr = np.array([1., 2., 3.], dtype=np.float32)
arr_bs = arr.astype(arr.dtype.newbyteorder())
expected = call_ufunc(arr)
# Normally, a "no" cast:
res = call_ufunc(arr, casting="no")
assert_array_equal(expected, res)
# Byte-swapping is not allowed with "no" though:
with pytest.raises(TypeError):
call_ufunc(arr_bs, casting="no")
# But is allowed with "equiv":
res = call_ufunc(arr_bs, casting="equiv")
assert_array_equal(expected, res)
# Casting to float64 is safe, but not equiv:
with pytest.raises(TypeError):
call_ufunc(arr_bs, dtype=np.float64, casting="equiv")
# but it is safe cast:
res = call_ufunc(arr_bs, dtype=np.float64, casting="safe")
expected = call_ufunc(arr.astype(np.float64)) # upcast
assert_array_equal(expected, res)
def test_true_divide(self):
a = np.array(10)
b = np.array(20)
tgt = np.array(0.5)
for tc in 'bhilqBHILQefdgFDG':
dt = np.dtype(tc)
aa = a.astype(dt)
bb = b.astype(dt)
# Check result value and dtype.
for x, y in itertools.product([aa, -aa], [bb, -bb]):
# Check with no output type specified
if tc in 'FDG':
tgt = complex(x)/complex(y)
else:
tgt = float(x)/float(y)
res = np.true_divide(x, y)
rtol = max(np.finfo(res).resolution, 1e-15)
assert_allclose(res, tgt, rtol=rtol)
if tc in 'bhilqBHILQ':
assert_(res.dtype.name == 'float64')
else:
assert_(res.dtype.name == dt.name )
# Check with output type specified. This also checks for the
# incorrect casts in issue gh-3484 because the unary '-' does
# not change types, even for unsigned types, Hence casts in the
# ufunc from signed to unsigned and vice versa will lead to
# errors in the values.
for tcout in 'bhilqBHILQ':
dtout = np.dtype(tcout)
assert_raises(TypeError, np.true_divide, x, y, dtype=dtout)
for tcout in 'efdg':
dtout = np.dtype(tcout)
if tc in 'FDG':
# Casting complex to float is not allowed
assert_raises(TypeError, np.true_divide, x, y, dtype=dtout)
else:
tgt = float(x)/float(y)
rtol = max(np.finfo(dtout).resolution, 1e-15)
# The value of tiny for double double is NaN
with suppress_warnings() as sup:
sup.filter(UserWarning)
if not np.isnan(np.finfo(dtout).tiny):
atol = max(np.finfo(dtout).tiny, 3e-308)
else:
atol = 3e-308
# Some test values result in invalid for float16.
with np.errstate(invalid='ignore'):
res = np.true_divide(x, y, dtype=dtout)
if not np.isfinite(res) and tcout == 'e':
continue
assert_allclose(res, tgt, rtol=rtol, atol=atol)
assert_(res.dtype.name == dtout.name)
for tcout in 'FDG':
dtout = np.dtype(tcout)
tgt = complex(x)/complex(y)
rtol = max(np.finfo(dtout).resolution, 1e-15)
# The value of tiny for double double is NaN
with suppress_warnings() as sup:
sup.filter(UserWarning)
if not np.isnan(np.finfo(dtout).tiny):
atol = max(np.finfo(dtout).tiny, 3e-308)
else:
atol = 3e-308
res = np.true_divide(x, y, dtype=dtout)
if not np.isfinite(res):
continue
assert_allclose(res, tgt, rtol=rtol, atol=atol)
assert_(res.dtype.name == dtout.name)
# Check booleans
a = np.ones((), dtype=np.bool_)
res = np.true_divide(a, a)
assert_(res == 1.0)
assert_(res.dtype.name == 'float64')
res = np.true_divide(~a, a)
assert_(res == 0.0)
assert_(res.dtype.name == 'float64')
def test_sum_stability(self):
a = np.ones(500, dtype=np.float32)
assert_almost_equal((a / 10.).sum() - a.size / 10., 0, 4)
a = np.ones(500, dtype=np.float64)
assert_almost_equal((a / 10.).sum() - a.size / 10., 0, 13)
def test_sum(self):
for dt in (int, np.float16, np.float32, np.float64, np.longdouble):
for v in (0, 1, 2, 7, 8, 9, 15, 16, 19, 127,
128, 1024, 1235):
tgt = dt(v * (v + 1) / 2)
d = np.arange(1, v + 1, dtype=dt)
# warning if sum overflows, which it does in float16
overflow = not np.isfinite(tgt)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
assert_almost_equal(np.sum(d), tgt)
assert_equal(len(w), 1 * overflow)
assert_almost_equal(np.sum(d[::-1]), tgt)
assert_equal(len(w), 2 * overflow)
d = np.ones(500, dtype=dt)
assert_almost_equal(np.sum(d[::2]), 250.)
assert_almost_equal(np.sum(d[1::2]), 250.)
assert_almost_equal(np.sum(d[::3]), 167.)
assert_almost_equal(np.sum(d[1::3]), 167.)
assert_almost_equal(np.sum(d[::-2]), 250.)
assert_almost_equal(np.sum(d[-1::-2]), 250.)
assert_almost_equal(np.sum(d[::-3]), 167.)
assert_almost_equal(np.sum(d[-1::-3]), 167.)
# sum with first reduction entry != 0
d = np.ones((1,), dtype=dt)
d += d
assert_almost_equal(d, 2.)
def test_sum_complex(self):
for dt in (np.complex64, np.complex128, np.clongdouble):
for v in (0, 1, 2, 7, 8, 9, 15, 16, 19, 127,
128, 1024, 1235):
tgt = dt(v * (v + 1) / 2) - dt((v * (v + 1) / 2) * 1j)
d = np.empty(v, dtype=dt)
d.real = np.arange(1, v + 1)
d.imag = -np.arange(1, v + 1)
assert_almost_equal(np.sum(d), tgt)
assert_almost_equal(np.sum(d[::-1]), tgt)
d = np.ones(500, dtype=dt) + 1j
assert_almost_equal(np.sum(d[::2]), 250. + 250j)
assert_almost_equal(np.sum(d[1::2]), 250. + 250j)
assert_almost_equal(np.sum(d[::3]), 167. + 167j)
assert_almost_equal(np.sum(d[1::3]), 167. + 167j)
assert_almost_equal(np.sum(d[::-2]), 250. + 250j)
assert_almost_equal(np.sum(d[-1::-2]), 250. + 250j)
assert_almost_equal(np.sum(d[::-3]), 167. + 167j)
assert_almost_equal(np.sum(d[-1::-3]), 167. + 167j)
# sum with first reduction entry != 0
d = np.ones((1,), dtype=dt) + 1j
d += d
assert_almost_equal(d, 2. + 2j)
def test_sum_initial(self):
# Integer, single axis
assert_equal(np.sum([3], initial=2), 5)
# Floating point
assert_almost_equal(np.sum([0.2], initial=0.1), 0.3)
# Multiple non-adjacent axes
assert_equal(np.sum(np.ones((2, 3, 5), dtype=np.int64), axis=(0, 2), initial=2),
[12, 12, 12])
def test_sum_where(self):
# More extensive tests done in test_reduction_with_where.
assert_equal(np.sum([[1., 2.], [3., 4.]], where=[True, False]), 4.)
assert_equal(np.sum([[1., 2.], [3., 4.]], axis=0, initial=5.,
where=[True, False]), [9., 5.])
def test_inner1d(self):
a = np.arange(6).reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1))
a = np.arange(6)
assert_array_equal(umt.inner1d(a, a), np.sum(a*a))
def test_broadcast(self):
msg = "broadcast"
a = np.arange(4).reshape((2, 1, 2))
b = np.arange(4).reshape((1, 2, 2))
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
msg = "extend & broadcast loop dimensions"
b = np.arange(4).reshape((2, 2))
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
# Broadcast in core dimensions should fail
a = np.arange(8).reshape((4, 2))
b = np.arange(4).reshape((4, 1))
assert_raises(ValueError, umt.inner1d, a, b)
# Extend core dimensions should fail
a = np.arange(8).reshape((4, 2))
b = np.array(7)
assert_raises(ValueError, umt.inner1d, a, b)
# Broadcast should fail
a = np.arange(2).reshape((2, 1, 1))
b = np.arange(3).reshape((3, 1, 1))
assert_raises(ValueError, umt.inner1d, a, b)
# Writing to a broadcasted array with overlap should warn, gh-2705
a = np.arange(2)
b = np.arange(4).reshape((2, 2))
u, v = np.broadcast_arrays(a, b)
assert_equal(u.strides[0], 0)
x = u + v
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
u += v
assert_equal(len(w), 1)
assert_(x[0, 0] != u[0, 0])
# Output reduction should not be allowed.
# See gh-15139
a = np.arange(6).reshape(3, 2)
b = np.ones(2)
out = np.empty(())
assert_raises(ValueError, umt.inner1d, a, b, out)
out2 = np.empty(3)
c = umt.inner1d(a, b, out2)
assert_(c is out2)
def test_out_broadcasts(self):
# For ufuncs and gufuncs (not for reductions), we currently allow
# the output to cause broadcasting of the input arrays.
# both along dimensions with shape 1 and dimensions which do not
# exist at all in the inputs.
arr = np.arange(3).reshape(1, 3)
out = np.empty((5, 4, 3))
np.add(arr, arr, out=out)
assert (out == np.arange(3) * 2).all()
# The same holds for gufuncs (gh-16484)
umt.inner1d(arr, arr, out=out)
# the result would be just a scalar `5`, but is broadcast fully:
assert (out == 5).all()
@pytest.mark.parametrize(["arr", "out"], [
([2], np.empty(())),
([1, 2], np.empty(1)),
(np.ones((4, 3)), np.empty((4, 1)))],
ids=["(1,)->()", "(2,)->(1,)", "(4, 3)->(4, 1)"])
def test_out_broadcast_errors(self, arr, out):
# Output is (currently) allowed to broadcast inputs, but it cannot be
# smaller than the actual result.
with pytest.raises(ValueError, match="non-broadcastable"):
np.positive(arr, out=out)
with pytest.raises(ValueError, match="non-broadcastable"):
np.add(np.ones(()), arr, out=out)
def test_type_cast(self):
msg = "type cast"
a = np.arange(6, dtype='short').reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
err_msg=msg)
msg = "type cast on one argument"
a = np.arange(6).reshape((2, 3))
b = a + 0.1
assert_array_almost_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1),
err_msg=msg)
def test_endian(self):
msg = "big endian"
a = np.arange(6, dtype='>i4').reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
err_msg=msg)
msg = "little endian"
a = np.arange(6, dtype='<i4').reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
err_msg=msg)
# Output should always be native-endian
Ba = np.arange(1, dtype='>f8')
La = np.arange(1, dtype='<f8')
assert_equal((Ba+Ba).dtype, np.dtype('f8'))
assert_equal((Ba+La).dtype, np.dtype('f8'))
assert_equal((La+Ba).dtype, np.dtype('f8'))
assert_equal((La+La).dtype, np.dtype('f8'))
assert_equal(np.absolute(La).dtype, np.dtype('f8'))
assert_equal(np.absolute(Ba).dtype, np.dtype('f8'))
assert_equal(np.negative(La).dtype, np.dtype('f8'))
assert_equal(np.negative(Ba).dtype, np.dtype('f8'))
def test_incontiguous_array(self):
msg = "incontiguous memory layout of array"
x = np.arange(64).reshape((2, 2, 2, 2, 2, 2))
a = x[:, 0,:, 0,:, 0]
b = x[:, 1,:, 1,:, 1]
a[0, 0, 0] = -1
msg2 = "make sure it references to the original array"
assert_equal(x[0, 0, 0, 0, 0, 0], -1, err_msg=msg2)
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
x = np.arange(24).reshape(2, 3, 4)
a = x.T
b = x.T
a[0, 0, 0] = -1
assert_equal(x[0, 0, 0], -1, err_msg=msg2)
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
def test_output_argument(self):
msg = "output argument"
a = np.arange(12).reshape((2, 3, 2))
b = np.arange(4).reshape((2, 1, 2)) + 1
c = np.zeros((2, 3), dtype='int')
umt.inner1d(a, b, c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a, b, out=c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
msg = "output argument with type cast"
c = np.zeros((2, 3), dtype='int16')
umt.inner1d(a, b, c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a, b, out=c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
msg = "output argument with incontiguous layout"
c = np.zeros((2, 3, 4), dtype='int16')
umt.inner1d(a, b, c[..., 0])
assert_array_equal(c[..., 0], np.sum(a*b, axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a, b, out=c[..., 0])
assert_array_equal(c[..., 0], np.sum(a*b, axis=-1), err_msg=msg)
def test_axes_argument(self):
# inner1d signature: '(i),(i)->()'
inner1d = umt.inner1d
a = np.arange(27.).reshape((3, 3, 3))
b = np.arange(10., 19.).reshape((3, 1, 3))
# basic tests on inputs (outputs tested below with matrix_multiply).
c = inner1d(a, b)
assert_array_equal(c, (a * b).sum(-1))
# default
c = inner1d(a, b, axes=[(-1,), (-1,), ()])
assert_array_equal(c, (a * b).sum(-1))
# integers ok for single axis.
c = inner1d(a, b, axes=[-1, -1, ()])
assert_array_equal(c, (a * b).sum(-1))
# mix fine
c = inner1d(a, b, axes=[(-1,), -1, ()])
assert_array_equal(c, (a * b).sum(-1))
# can omit last axis.
c = inner1d(a, b, axes=[-1, -1])
assert_array_equal(c, (a * b).sum(-1))
# can pass in other types of integer (with __index__ protocol)
c = inner1d(a, b, axes=[np.int8(-1), np.array(-1, dtype=np.int32)])
assert_array_equal(c, (a * b).sum(-1))
# swap some axes
c = inner1d(a, b, axes=[0, 0])
assert_array_equal(c, (a * b).sum(0))
c = inner1d(a, b, axes=[0, 2])
assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1))
# Check errors for improperly constructed axes arguments.
# should have list.
assert_raises(TypeError, inner1d, a, b, axes=-1)
# needs enough elements
assert_raises(ValueError, inner1d, a, b, axes=[-1])
# should pass in indices.
assert_raises(TypeError, inner1d, a, b, axes=[-1.0, -1.0])
assert_raises(TypeError, inner1d, a, b, axes=[(-1.0,), -1])
assert_raises(TypeError, inner1d, a, b, axes=[None, 1])
# cannot pass an index unless there is only one dimension
# (output is wrong in this case)
assert_raises(TypeError, inner1d, a, b, axes=[-1, -1, -1])
# or pass in generally the wrong number of axes
assert_raises(ValueError, inner1d, a, b, axes=[-1, -1, (-1,)])
assert_raises(ValueError, inner1d, a, b, axes=[-1, (-2, -1), ()])
# axes need to have same length.
assert_raises(ValueError, inner1d, a, b, axes=[0, 1])
# matrix_multiply signature: '(m,n),(n,p)->(m,p)'
mm = umt.matrix_multiply
a = np.arange(12).reshape((2, 3, 2))
b = np.arange(8).reshape((2, 2, 2, 1)) + 1
# Sanity check.
c = mm(a, b)
assert_array_equal(c, np.matmul(a, b))
# Default axes.
c = mm(a, b, axes=[(-2, -1), (-2, -1), (-2, -1)])
assert_array_equal(c, np.matmul(a, b))
# Default with explicit axes.
c = mm(a, b, axes=[(1, 2), (2, 3), (2, 3)])
assert_array_equal(c, np.matmul(a, b))
# swap some axes.
c = mm(a, b, axes=[(0, -1), (1, 2), (-2, -1)])
assert_array_equal(c, np.matmul(a.transpose(1, 0, 2),
b.transpose(0, 3, 1, 2)))
# Default with output array.
c = np.empty((2, 2, 3, 1))
d = mm(a, b, out=c, axes=[(1, 2), (2, 3), (2, 3)])
assert_(c is d)
assert_array_equal(c, np.matmul(a, b))
# Transposed output array
c = np.empty((1, 2, 2, 3))
d = mm(a, b, out=c, axes=[(-2, -1), (-2, -1), (3, 0)])
assert_(c is d)
assert_array_equal(c, np.matmul(a, b).transpose(3, 0, 1, 2))
# Check errors for improperly constructed axes arguments.
# wrong argument
assert_raises(TypeError, mm, a, b, axis=1)
# axes should be list
assert_raises(TypeError, mm, a, b, axes=1)
assert_raises(TypeError, mm, a, b, axes=((-2, -1), (-2, -1), (-2, -1)))
# list needs to have right length
assert_raises(ValueError, mm, a, b, axes=[])
assert_raises(ValueError, mm, a, b, axes=[(-2, -1)])
# list should contain tuples for multiple axes
assert_raises(TypeError, mm, a, b, axes=[-1, -1, -1])
assert_raises(TypeError, mm, a, b, axes=[(-2, -1), (-2, -1), -1])
assert_raises(TypeError,
mm, a, b, axes=[[-2, -1], [-2, -1], [-2, -1]])
assert_raises(TypeError,
mm, a, b, axes=[(-2, -1), (-2, -1), [-2, -1]])
assert_raises(TypeError, mm, a, b, axes=[(-2, -1), (-2, -1), None])
# tuples should not have duplicated values
assert_raises(ValueError, mm, a, b, axes=[(-2, -1), (-2, -1), (-2, -2)])
# arrays should have enough axes.
z = np.zeros((2, 2))
assert_raises(ValueError, mm, z, z[0])
assert_raises(ValueError, mm, z, z, out=z[:, 0])
assert_raises(ValueError, mm, z[1], z, axes=[0, 1])
assert_raises(ValueError, mm, z, z, out=z[0], axes=[0, 1])
# Regular ufuncs should not accept axes.
assert_raises(TypeError, np.add, 1., 1., axes=[0])
# should be able to deal with bad unrelated kwargs.
assert_raises(TypeError, mm, z, z, axes=[0, 1], parrot=True)
def test_axis_argument(self):
# inner1d signature: '(i),(i)->()'
inner1d = umt.inner1d
a = np.arange(27.).reshape((3, 3, 3))
b = np.arange(10., 19.).reshape((3, 1, 3))
c = inner1d(a, b)
assert_array_equal(c, (a * b).sum(-1))
c = inner1d(a, b, axis=-1)
assert_array_equal(c, (a * b).sum(-1))
out = np.zeros_like(c)
d = inner1d(a, b, axis=-1, out=out)
assert_(d is out)
assert_array_equal(d, c)
c = inner1d(a, b, axis=0)
assert_array_equal(c, (a * b).sum(0))
# Sanity checks on innerwt and cumsum.
a = np.arange(6).reshape((2, 3))
b = np.arange(10, 16).reshape((2, 3))
w = np.arange(20, 26).reshape((2, 3))
assert_array_equal(umt.innerwt(a, b, w, axis=0),
np.sum(a * b * w, axis=0))
assert_array_equal(umt.cumsum(a, axis=0), np.cumsum(a, axis=0))
assert_array_equal(umt.cumsum(a, axis=-1), np.cumsum(a, axis=-1))
out = np.empty_like(a)
b = umt.cumsum(a, out=out, axis=0)
assert_(out is b)
assert_array_equal(b, np.cumsum(a, axis=0))
b = umt.cumsum(a, out=out, axis=1)
assert_(out is b)
assert_array_equal(b, np.cumsum(a, axis=-1))
# Check errors.
# Cannot pass in both axis and axes.
assert_raises(TypeError, inner1d, a, b, axis=0, axes=[0, 0])
# Not an integer.
assert_raises(TypeError, inner1d, a, b, axis=[0])
# more than 1 core dimensions.
mm = umt.matrix_multiply
assert_raises(TypeError, mm, a, b, axis=1)
# Output wrong size in axis.
out = np.empty((1, 2, 3), dtype=a.dtype)
assert_raises(ValueError, umt.cumsum, a, out=out, axis=0)
# Regular ufuncs should not accept axis.
assert_raises(TypeError, np.add, 1., 1., axis=0)
def test_keepdims_argument(self):
# inner1d signature: '(i),(i)->()'
inner1d = umt.inner1d
a = np.arange(27.).reshape((3, 3, 3))
b = np.arange(10., 19.).reshape((3, 1, 3))
c = inner1d(a, b)
assert_array_equal(c, (a * b).sum(-1))
c = inner1d(a, b, keepdims=False)
assert_array_equal(c, (a * b).sum(-1))
c = inner1d(a, b, keepdims=True)
assert_array_equal(c, (a * b).sum(-1, keepdims=True))
out = np.zeros_like(c)
d = inner1d(a, b, keepdims=True, out=out)
assert_(d is out)
assert_array_equal(d, c)
# Now combined with axis and axes.
c = inner1d(a, b, axis=-1, keepdims=False)
assert_array_equal(c, (a * b).sum(-1, keepdims=False))
c = inner1d(a, b, axis=-1, keepdims=True)
assert_array_equal(c, (a * b).sum(-1, keepdims=True))
c = inner1d(a, b, axis=0, keepdims=False)
assert_array_equal(c, (a * b).sum(0, keepdims=False))
c = inner1d(a, b, axis=0, keepdims=True)
assert_array_equal(c, (a * b).sum(0, keepdims=True))
c = inner1d(a, b, axes=[(-1,), (-1,), ()], keepdims=False)
assert_array_equal(c, (a * b).sum(-1))
c = inner1d(a, b, axes=[(-1,), (-1,), (-1,)], keepdims=True)
assert_array_equal(c, (a * b).sum(-1, keepdims=True))
c = inner1d(a, b, axes=[0, 0], keepdims=False)
assert_array_equal(c, (a * b).sum(0))
c = inner1d(a, b, axes=[0, 0, 0], keepdims=True)
assert_array_equal(c, (a * b).sum(0, keepdims=True))
c = inner1d(a, b, axes=[0, 2], keepdims=False)
assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1))
c = inner1d(a, b, axes=[0, 2], keepdims=True)
assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1,
keepdims=True))
c = inner1d(a, b, axes=[0, 2, 2], keepdims=True)
assert_array_equal(c, (a.transpose(1, 2, 0) * b).sum(-1,
keepdims=True))
c = inner1d(a, b, axes=[0, 2, 0], keepdims=True)
assert_array_equal(c, (a * b.transpose(2, 0, 1)).sum(0, keepdims=True))
# Hardly useful, but should work.
c = inner1d(a, b, axes=[0, 2, 1], keepdims=True)
assert_array_equal(c, (a.transpose(1, 0, 2) * b.transpose(0, 2, 1))
.sum(1, keepdims=True))
# Check with two core dimensions.
a = np.eye(3) * np.arange(4.)[:, np.newaxis, np.newaxis]
expected = uml.det(a)
c = uml.det(a, keepdims=False)
assert_array_equal(c, expected)
c = uml.det(a, keepdims=True)
assert_array_equal(c, expected[:, np.newaxis, np.newaxis])
a = np.eye(3) * np.arange(4.)[:, np.newaxis, np.newaxis]
expected_s, expected_l = uml.slogdet(a)
cs, cl = uml.slogdet(a, keepdims=False)
assert_array_equal(cs, expected_s)
assert_array_equal(cl, expected_l)
cs, cl = uml.slogdet(a, keepdims=True)
assert_array_equal(cs, expected_s[:, np.newaxis, np.newaxis])
assert_array_equal(cl, expected_l[:, np.newaxis, np.newaxis])
# Sanity check on innerwt.
a = np.arange(6).reshape((2, 3))
b = np.arange(10, 16).reshape((2, 3))
w = np.arange(20, 26).reshape((2, 3))
assert_array_equal(umt.innerwt(a, b, w, keepdims=True),
np.sum(a * b * w, axis=-1, keepdims=True))
assert_array_equal(umt.innerwt(a, b, w, axis=0, keepdims=True),
np.sum(a * b * w, axis=0, keepdims=True))
# Check errors.
# Not a boolean
assert_raises(TypeError, inner1d, a, b, keepdims='true')
# More than 1 core dimension, and core output dimensions.
mm = umt.matrix_multiply
assert_raises(TypeError, mm, a, b, keepdims=True)
assert_raises(TypeError, mm, a, b, keepdims=False)
# Regular ufuncs should not accept keepdims.
assert_raises(TypeError, np.add, 1., 1., keepdims=False)
def test_innerwt(self):
a = np.arange(6).reshape((2, 3))
b = np.arange(10, 16).reshape((2, 3))
w = np.arange(20, 26).reshape((2, 3))
assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1))
a = np.arange(100, 124).reshape((2, 3, 4))
b = np.arange(200, 224).reshape((2, 3, 4))
w = np.arange(300, 324).reshape((2, 3, 4))
assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1))
def test_innerwt_empty(self):
"""Test generalized ufunc with zero-sized operands"""
a = np.array([], dtype='f8')
b = np.array([], dtype='f8')
w = np.array([], dtype='f8')
assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1))
def test_cross1d(self):
"""Test with fixed-sized signature."""
a = np.eye(3)
assert_array_equal(umt.cross1d(a, a), np.zeros((3, 3)))
out = np.zeros((3, 3))
result = umt.cross1d(a[0], a, out)
assert_(result is out)
assert_array_equal(result, np.vstack((np.zeros(3), a[2], -a[1])))
assert_raises(ValueError, umt.cross1d, np.eye(4), np.eye(4))
assert_raises(ValueError, umt.cross1d, a, np.arange(4.))
# Wrong output core dimension.
assert_raises(ValueError, umt.cross1d, a, np.arange(3.), np.zeros((3, 4)))
# Wrong output broadcast dimension (see gh-15139).
assert_raises(ValueError, umt.cross1d, a, np.arange(3.), np.zeros(3))
def test_can_ignore_signature(self):
# Comparing the effects of ? in signature:
# matrix_multiply: (m,n),(n,p)->(m,p) # all must be there.
# matmul: (m?,n),(n,p?)->(m?,p?) # allow missing m, p.
mat = np.arange(12).reshape((2, 3, 2))
single_vec = np.arange(2)
col_vec = single_vec[:, np.newaxis]
col_vec_array = np.arange(8).reshape((2, 2, 2, 1)) + 1
# matrix @ single column vector with proper dimension
mm_col_vec = umt.matrix_multiply(mat, col_vec)
# matmul does the same thing
matmul_col_vec = umt.matmul(mat, col_vec)
assert_array_equal(matmul_col_vec, mm_col_vec)
# matrix @ vector without dimension making it a column vector.
# matrix multiply fails -> missing core dim.
assert_raises(ValueError, umt.matrix_multiply, mat, single_vec)
# matmul mimicker passes, and returns a vector.
matmul_col = umt.matmul(mat, single_vec)
assert_array_equal(matmul_col, mm_col_vec.squeeze())
# Now with a column array: same as for column vector,
# broadcasting sensibly.
mm_col_vec = umt.matrix_multiply(mat, col_vec_array)
matmul_col_vec = umt.matmul(mat, col_vec_array)
assert_array_equal(matmul_col_vec, mm_col_vec)
# As above, but for row vector
single_vec = np.arange(3)
row_vec = single_vec[np.newaxis, :]
row_vec_array = np.arange(24).reshape((4, 2, 1, 1, 3)) + 1
# row vector @ matrix
mm_row_vec = umt.matrix_multiply(row_vec, mat)
matmul_row_vec = umt.matmul(row_vec, mat)
assert_array_equal(matmul_row_vec, mm_row_vec)
# single row vector @ matrix
assert_raises(ValueError, umt.matrix_multiply, single_vec, mat)
matmul_row = umt.matmul(single_vec, mat)
assert_array_equal(matmul_row, mm_row_vec.squeeze())
# row vector array @ matrix
mm_row_vec = umt.matrix_multiply(row_vec_array, mat)
matmul_row_vec = umt.matmul(row_vec_array, mat)
assert_array_equal(matmul_row_vec, mm_row_vec)
# Now for vector combinations
# row vector @ column vector
col_vec = row_vec.T
col_vec_array = row_vec_array.swapaxes(-2, -1)
mm_row_col_vec = umt.matrix_multiply(row_vec, col_vec)
matmul_row_col_vec = umt.matmul(row_vec, col_vec)
assert_array_equal(matmul_row_col_vec, mm_row_col_vec)
# single row vector @ single col vector
assert_raises(ValueError, umt.matrix_multiply, single_vec, single_vec)
matmul_row_col = umt.matmul(single_vec, single_vec)
assert_array_equal(matmul_row_col, mm_row_col_vec.squeeze())
# row vector array @ matrix
mm_row_col_array = umt.matrix_multiply(row_vec_array, col_vec_array)
matmul_row_col_array = umt.matmul(row_vec_array, col_vec_array)
assert_array_equal(matmul_row_col_array, mm_row_col_array)
# Finally, check that things are *not* squeezed if one gives an
# output.
out = np.zeros_like(mm_row_col_array)
out = umt.matrix_multiply(row_vec_array, col_vec_array, out=out)
assert_array_equal(out, mm_row_col_array)
out[:] = 0
out = umt.matmul(row_vec_array, col_vec_array, out=out)
assert_array_equal(out, mm_row_col_array)
# And check one cannot put missing dimensions back.
out = np.zeros_like(mm_row_col_vec)
assert_raises(ValueError, umt.matrix_multiply, single_vec, single_vec,
out)
# But fine for matmul, since it is just a broadcast.
out = umt.matmul(single_vec, single_vec, out)
assert_array_equal(out, mm_row_col_vec.squeeze())
def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.int64)
self.compare_matrix_multiply_results(np.double)
def test_matrix_multiply_umath_empty(self):
res = umt.matrix_multiply(np.ones((0, 10)), np.ones((10, 0)))
assert_array_equal(res, np.zeros((0, 0)))
res = umt.matrix_multiply(np.ones((10, 0)), np.ones((0, 10)))
assert_array_equal(res, np.zeros((10, 10)))
def compare_matrix_multiply_results(self, tp):
d1 = np.array(np.random.rand(2, 3, 4), dtype=tp)
d2 = np.array(np.random.rand(2, 3, 4), dtype=tp)
msg = "matrix multiply on type %s" % d1.dtype.name
def permute_n(n):
if n == 1:
return ([0],)
ret = ()
base = permute_n(n-1)
for perm in base:
for i in range(n):
new = perm + [n-1]
new[n-1] = new[i]
new[i] = n-1
ret += (new,)
return ret
def slice_n(n):
if n == 0:
return ((),)
ret = ()
base = slice_n(n-1)
for sl in base:
ret += (sl+(slice(None),),)
ret += (sl+(slice(0, 1),),)
return ret
def broadcastable(s1, s2):
return s1 == s2 or s1 == 1 or s2 == 1
permute_3 = permute_n(3)
slice_3 = slice_n(3) + ((slice(None, None, -1),)*3,)
ref = True
for p1 in permute_3:
for p2 in permute_3:
for s1 in slice_3:
for s2 in slice_3:
a1 = d1.transpose(p1)[s1]
a2 = d2.transpose(p2)[s2]
ref = ref and a1.base is not None
ref = ref and a2.base is not None
if (a1.shape[-1] == a2.shape[-2] and
broadcastable(a1.shape[0], a2.shape[0])):
assert_array_almost_equal(
umt.matrix_multiply(a1, a2),
np.sum(a2[..., np.newaxis].swapaxes(-3, -1) *
a1[..., np.newaxis,:], axis=-1),
err_msg=msg + ' %s %s' % (str(a1.shape),
str(a2.shape)))
assert_equal(ref, True, err_msg="reference check")
def test_euclidean_pdist(self):
a = np.arange(12, dtype=float).reshape(4, 3)
out = np.empty((a.shape[0] * (a.shape[0] - 1) // 2,), dtype=a.dtype)
umt.euclidean_pdist(a, out)
b = np.sqrt(np.sum((a[:, None] - a)**2, axis=-1))
b = b[~np.tri(a.shape[0], dtype=bool)]
assert_almost_equal(out, b)
# An output array is required to determine p with signature (n,d)->(p)
assert_raises(ValueError, umt.euclidean_pdist, a)
def test_cumsum(self):
a = np.arange(10)
result = umt.cumsum(a)
assert_array_equal(result, a.cumsum())
def test_object_logical(self):
a = np.array([3, None, True, False, "test", ""], dtype=object)
assert_equal(np.logical_or(a, None),
np.array([x or None for x in a], dtype=object))
assert_equal(np.logical_or(a, True),
np.array([x or True for x in a], dtype=object))
assert_equal(np.logical_or(a, 12),
np.array([x or 12 for x in a], dtype=object))
assert_equal(np.logical_or(a, "blah"),
np.array([x or "blah" for x in a], dtype=object))
assert_equal(np.logical_and(a, None),
np.array([x and None for x in a], dtype=object))
assert_equal(np.logical_and(a, True),
np.array([x and True for x in a], dtype=object))
assert_equal(np.logical_and(a, 12),
np.array([x and 12 for x in a], dtype=object))
assert_equal(np.logical_and(a, "blah"),
np.array([x and "blah" for x in a], dtype=object))
assert_equal(np.logical_not(a),
np.array([not x for x in a], dtype=object))
assert_equal(np.logical_or.reduce(a), 3)
assert_equal(np.logical_and.reduce(a), None)
def test_object_comparison(self):
class HasComparisons:
def __eq__(self, other):
return '=='
arr0d = np.array(HasComparisons())
assert_equal(arr0d == arr0d, True)
assert_equal(np.equal(arr0d, arr0d), True) # normal behavior is a cast
arr1d = np.array([HasComparisons()])
assert_equal(arr1d == arr1d, np.array([True]))
assert_equal(np.equal(arr1d, arr1d), np.array([True])) # normal behavior is a cast
assert_equal(np.equal(arr1d, arr1d, dtype=object), np.array(['==']))
def test_object_array_reduction(self):
# Reductions on object arrays
a = np.array(['a', 'b', 'c'], dtype=object)
assert_equal(np.sum(a), 'abc')
assert_equal(np.max(a), 'c')
assert_equal(np.min(a), 'a')
a = np.array([True, False, True], dtype=object)
assert_equal(np.sum(a), 2)
assert_equal(np.prod(a), 0)
assert_equal(np.any(a), True)
assert_equal(np.all(a), False)
assert_equal(np.max(a), True)
assert_equal(np.min(a), False)
assert_equal(np.array([[1]], dtype=object).sum(), 1)
assert_equal(np.array([[[1, 2]]], dtype=object).sum((0, 1)), [1, 2])
assert_equal(np.array([1], dtype=object).sum(initial=1), 2)
assert_equal(np.array([[1], [2, 3]], dtype=object)
.sum(initial=[0], where=[False, True]), [0, 2, 3])
def test_object_array_accumulate_inplace(self):
# Checks that in-place accumulates work, see also gh-7402
arr = np.ones(4, dtype=object)
arr[:] = [[1] for i in range(4)]
# Twice reproduced also for tuples:
np.add.accumulate(arr, out=arr)
np.add.accumulate(arr, out=arr)
assert_array_equal(arr,
np.array([[1]*i for i in [1, 3, 6, 10]], dtype=object),
)
# And the same if the axis argument is used
arr = np.ones((2, 4), dtype=object)
arr[0, :] = [[2] for i in range(4)]
np.add.accumulate(arr, out=arr, axis=-1)
np.add.accumulate(arr, out=arr, axis=-1)
assert_array_equal(arr[0, :],
np.array([[2]*i for i in [1, 3, 6, 10]], dtype=object),
)
def test_object_array_accumulate_failure(self):
# Typical accumulation on object works as expected:
res = np.add.accumulate(np.array([1, 0, 2], dtype=object))
assert_array_equal(res, np.array([1, 1, 3], dtype=object))
# But errors are propagated from the inner-loop if they occur:
with pytest.raises(TypeError):
np.add.accumulate([1, None, 2])
def test_object_array_reduceat_inplace(self):
# Checks that in-place reduceats work, see also gh-7465
arr = np.empty(4, dtype=object)
arr[:] = [[1] for i in range(4)]
out = np.empty(4, dtype=object)
out[:] = [[1] for i in range(4)]
np.add.reduceat(arr, np.arange(4), out=arr)
np.add.reduceat(arr, np.arange(4), out=arr)
assert_array_equal(arr, out)
# And the same if the axis argument is used
arr = np.ones((2, 4), dtype=object)
arr[0, :] = [[2] for i in range(4)]
out = np.ones((2, 4), dtype=object)
out[0, :] = [[2] for i in range(4)]
np.add.reduceat(arr, np.arange(4), out=arr, axis=-1)
np.add.reduceat(arr, np.arange(4), out=arr, axis=-1)
assert_array_equal(arr, out)
def test_object_array_reduceat_failure(self):
# Reduceat works as expected when no invalid operation occurs (None is
# not involved in an operation here)
res = np.add.reduceat(np.array([1, None, 2], dtype=object), [1, 2])
assert_array_equal(res, np.array([None, 2], dtype=object))
# But errors when None would be involved in an operation:
with pytest.raises(TypeError):
np.add.reduceat([1, None, 2], [0, 2])
def test_zerosize_reduction(self):
# Test with default dtype and object dtype
for a in [[], np.array([], dtype=object)]:
assert_equal(np.sum(a), 0)
assert_equal(np.prod(a), 1)
assert_equal(np.any(a), False)
assert_equal(np.all(a), True)
assert_raises(ValueError, np.max, a)
assert_raises(ValueError, np.min, a)
def test_axis_out_of_bounds(self):
a = np.array([False, False])
assert_raises(np.AxisError, a.all, axis=1)
a = np.array([False, False])
assert_raises(np.AxisError, a.all, axis=-2)
a = np.array([False, False])
assert_raises(np.AxisError, a.any, axis=1)
a = np.array([False, False])
assert_raises(np.AxisError, a.any, axis=-2)
def test_scalar_reduction(self):
# The functions 'sum', 'prod', etc allow specifying axis=0
# even for scalars
assert_equal(np.sum(3, axis=0), 3)
assert_equal(np.prod(3.5, axis=0), 3.5)
assert_equal(np.any(True, axis=0), True)
assert_equal(np.all(False, axis=0), False)
assert_equal(np.max(3, axis=0), 3)
assert_equal(np.min(2.5, axis=0), 2.5)
# Check scalar behaviour for ufuncs without an identity
assert_equal(np.power.reduce(3), 3)
# Make sure that scalars are coming out from this operation
assert_(type(np.prod(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.sum(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.max(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.min(np.float32(2.5), axis=0)) is np.float32)
# check if scalars/0-d arrays get cast
assert_(type(np.any(0, axis=0)) is np.bool_)
# assert that 0-d arrays get wrapped
class MyArray(np.ndarray):
pass
a = np.array(1).view(MyArray)
assert_(type(np.any(a)) is MyArray)
def test_casting_out_param(self):
# Test that it's possible to do casts on output
a = np.ones((200, 100), np.int64)
b = np.ones((200, 100), np.int64)
c = np.ones((200, 100), np.float64)
np.add(a, b, out=c)
assert_equal(c, 2)
a = np.zeros(65536)
b = np.zeros(65536, dtype=np.float32)
np.subtract(a, 0, out=b)
assert_equal(b, 0)
def test_where_param(self):
# Test that the where= ufunc parameter works with regular arrays
a = np.arange(7)
b = np.ones(7)
c = np.zeros(7)
np.add(a, b, out=c, where=(a % 2 == 1))
assert_equal(c, [0, 2, 0, 4, 0, 6, 0])
a = np.arange(4).reshape(2, 2) + 2
np.power(a, [2, 3], out=a, where=[[0, 1], [1, 0]])
assert_equal(a, [[2, 27], [16, 5]])
# Broadcasting the where= parameter
np.subtract(a, 2, out=a, where=[True, False])
assert_equal(a, [[0, 27], [14, 5]])
def test_where_param_buffer_output(self):
# This test is temporarily skipped because it requires
# adding masking features to the nditer to work properly
# With casting on output
a = np.ones(10, np.int64)
b = np.ones(10, np.int64)
c = 1.5 * np.ones(10, np.float64)
np.add(a, b, out=c, where=[1, 0, 0, 1, 0, 0, 1, 1, 1, 0])
assert_equal(c, [2, 1.5, 1.5, 2, 1.5, 1.5, 2, 2, 2, 1.5])
def test_where_param_alloc(self):
# With casting and allocated output
a = np.array([1], dtype=np.int64)
m = np.array([True], dtype=bool)
assert_equal(np.sqrt(a, where=m), [1])
# No casting and allocated output
a = np.array([1], dtype=np.float64)
m = np.array([True], dtype=bool)
assert_equal(np.sqrt(a, where=m), [1])
def test_where_with_broadcasting(self):
# See gh-17198
a = np.random.random((5000, 4))
b = np.random.random((5000, 1))
where = a > 0.3
out = np.full_like(a, 0)
np.less(a, b, where=where, out=out)
b_where = np.broadcast_to(b, a.shape)[where]
assert_array_equal((a[where] < b_where), out[where].astype(bool))
assert not out[~where].any() # outside mask, out remains all 0
def check_identityless_reduction(self, a):
# np.minimum.reduce is an identityless reduction
# Verify that it sees the zero at various positions
a[...] = 1
a[1, 0, 0] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0, 1)), [0, 1, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(0, 2)), [0, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(1, 2)), [1, 0])
assert_equal(np.minimum.reduce(a, axis=0),
[[0, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[1, 1, 1, 1], [0, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[1, 1, 1], [0, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
a[...] = 1
a[0, 1, 0] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0, 1)), [0, 1, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(0, 2)), [1, 0, 1])
assert_equal(np.minimum.reduce(a, axis=(1, 2)), [0, 1])
assert_equal(np.minimum.reduce(a, axis=0),
[[1, 1, 1, 1], [0, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[0, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[1, 0, 1], [1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
a[...] = 1
a[0, 0, 1] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0, 1)), [1, 0, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(0, 2)), [0, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(1, 2)), [0, 1])
assert_equal(np.minimum.reduce(a, axis=0),
[[1, 0, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[1, 0, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[0, 1, 1], [1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
@requires_memory(6 * 1024**3)
def test_identityless_reduction_huge_array(self):
# Regression test for gh-20921 (copying identity incorrectly failed)
arr = np.zeros((2, 2**31), 'uint8')
arr[:, 0] = [1, 3]
arr[:, -1] = [4, 1]
res = np.maximum.reduce(arr, axis=0)
del arr
assert res[0] == 3
assert res[-1] == 4
def test_identityless_reduction_corder(self):
a = np.empty((2, 3, 4), order='C')
self.check_identityless_reduction(a)
def test_identityless_reduction_forder(self):
a = np.empty((2, 3, 4), order='F')
self.check_identityless_reduction(a)
def test_identityless_reduction_otherorder(self):
a = np.empty((2, 4, 3), order='C').swapaxes(1, 2)
self.check_identityless_reduction(a)
def test_identityless_reduction_noncontig(self):
a = np.empty((3, 5, 4), order='C').swapaxes(1, 2)
a = a[1:, 1:, 1:]
self.check_identityless_reduction(a)
def test_identityless_reduction_noncontig_unaligned(self):
a = np.empty((3*4*5*8 + 1,), dtype='i1')
a = a[1:].view(dtype='f8')
a.shape = (3, 4, 5)
a = a[1:, 1:, 1:]
self.check_identityless_reduction(a)
def test_initial_reduction(self):
# np.minimum.reduce is an identityless reduction
# For cases like np.maximum(np.abs(...), initial=0)
# More generally, a supremum over non-negative numbers.
assert_equal(np.maximum.reduce([], initial=0), 0)
# For cases like reduction of an empty array over the reals.
assert_equal(np.minimum.reduce([], initial=np.inf), np.inf)
assert_equal(np.maximum.reduce([], initial=-np.inf), -np.inf)
# Random tests
assert_equal(np.minimum.reduce([5], initial=4), 4)
assert_equal(np.maximum.reduce([4], initial=5), 5)
assert_equal(np.maximum.reduce([5], initial=4), 5)
assert_equal(np.minimum.reduce([4], initial=5), 4)
# Check initial=None raises ValueError for both types of ufunc reductions
assert_raises(ValueError, np.minimum.reduce, [], initial=None)
assert_raises(ValueError, np.add.reduce, [], initial=None)
# Check that np._NoValue gives default behavior.
assert_equal(np.add.reduce([], initial=np._NoValue), 0)
# Check that initial kwarg behaves as intended for dtype=object
a = np.array([10], dtype=object)
res = np.add.reduce(a, initial=5)
assert_equal(res, 15)
@pytest.mark.parametrize('axis', (0, 1, None))
@pytest.mark.parametrize('where', (np.array([False, True, True]),
np.array([[True], [False], [True]]),
np.array([[True, False, False],
[False, True, False],
[False, True, True]])))
def test_reduction_with_where(self, axis, where):
a = np.arange(9.).reshape(3, 3)
a_copy = a.copy()
a_check = np.zeros_like(a)
np.positive(a, out=a_check, where=where)
res = np.add.reduce(a, axis=axis, where=where)
check = a_check.sum(axis)
assert_equal(res, check)
# Check we do not overwrite elements of a internally.
assert_array_equal(a, a_copy)
@pytest.mark.parametrize(('axis', 'where'),
((0, np.array([True, False, True])),
(1, [True, True, False]),
(None, True)))
@pytest.mark.parametrize('initial', (-np.inf, 5.))
def test_reduction_with_where_and_initial(self, axis, where, initial):
a = np.arange(9.).reshape(3, 3)
a_copy = a.copy()
a_check = np.full(a.shape, -np.inf)
np.positive(a, out=a_check, where=where)
res = np.maximum.reduce(a, axis=axis, where=where, initial=initial)
check = a_check.max(axis, initial=initial)
assert_equal(res, check)
def test_reduction_where_initial_needed(self):
a = np.arange(9.).reshape(3, 3)
m = [False, True, False]
assert_raises(ValueError, np.maximum.reduce, a, where=m)
def test_identityless_reduction_nonreorderable(self):
a = np.array([[8.0, 2.0, 2.0], [1.0, 0.5, 0.25]])
res = np.divide.reduce(a, axis=0)
assert_equal(res, [8.0, 4.0, 8.0])
res = np.divide.reduce(a, axis=1)
assert_equal(res, [2.0, 8.0])
res = np.divide.reduce(a, axis=())
assert_equal(res, a)
assert_raises(ValueError, np.divide.reduce, a, axis=(0, 1))
def test_reduce_zero_axis(self):
# If we have a n x m array and do a reduction with axis=1, then we are
# doing n reductions, and each reduction takes an m-element array. For
# a reduction operation without an identity, then:
# n > 0, m > 0: fine
# n = 0, m > 0: fine, doing 0 reductions of m-element arrays
# n > 0, m = 0: can't reduce a 0-element array, ValueError
# n = 0, m = 0: can't reduce a 0-element array, ValueError (for
# consistency with the above case)
# This test doesn't actually look at return values, it just checks to
# make sure that error we get an error in exactly those cases where we
# expect one, and assumes the calculations themselves are done
# correctly.
def ok(f, *args, **kwargs):
f(*args, **kwargs)
def err(f, *args, **kwargs):
assert_raises(ValueError, f, *args, **kwargs)
def t(expect, func, n, m):
expect(func, np.zeros((n, m)), axis=1)
expect(func, np.zeros((m, n)), axis=0)
expect(func, np.zeros((n // 2, n // 2, m)), axis=2)
expect(func, np.zeros((n // 2, m, n // 2)), axis=1)
expect(func, np.zeros((n, m // 2, m // 2)), axis=(1, 2))
expect(func, np.zeros((m // 2, n, m // 2)), axis=(0, 2))
expect(func, np.zeros((m // 3, m // 3, m // 3,
n // 2, n // 2)),
axis=(0, 1, 2))
# Check what happens if the inner (resp. outer) dimensions are a
# mix of zero and non-zero:
expect(func, np.zeros((10, m, n)), axis=(0, 1))
expect(func, np.zeros((10, n, m)), axis=(0, 2))
expect(func, np.zeros((m, 10, n)), axis=0)
expect(func, np.zeros((10, m, n)), axis=1)
expect(func, np.zeros((10, n, m)), axis=2)
# np.maximum is just an arbitrary ufunc with no reduction identity
assert_equal(np.maximum.identity, None)
t(ok, np.maximum.reduce, 30, 30)
t(ok, np.maximum.reduce, 0, 30)
t(err, np.maximum.reduce, 30, 0)
t(err, np.maximum.reduce, 0, 0)
err(np.maximum.reduce, [])
np.maximum.reduce(np.zeros((0, 0)), axis=())
# all of the combinations are fine for a reduction that has an
# identity
t(ok, np.add.reduce, 30, 30)
t(ok, np.add.reduce, 0, 30)
t(ok, np.add.reduce, 30, 0)
t(ok, np.add.reduce, 0, 0)
np.add.reduce([])
np.add.reduce(np.zeros((0, 0)), axis=())
# OTOH, accumulate always makes sense for any combination of n and m,
# because it maps an m-element array to an m-element array. These
# tests are simpler because accumulate doesn't accept multiple axes.
for uf in (np.maximum, np.add):
uf.accumulate(np.zeros((30, 0)), axis=0)
uf.accumulate(np.zeros((0, 30)), axis=0)
uf.accumulate(np.zeros((30, 30)), axis=0)
uf.accumulate(np.zeros((0, 0)), axis=0)
def test_safe_casting(self):
# In old versions of numpy, in-place operations used the 'unsafe'
# casting rules. In versions >= 1.10, 'same_kind' is the
# default and an exception is raised instead of a warning.
# when 'same_kind' is not satisfied.
a = np.array([1, 2, 3], dtype=int)
# Non-in-place addition is fine
assert_array_equal(assert_no_warnings(np.add, a, 1.1),
[2.1, 3.1, 4.1])
assert_raises(TypeError, np.add, a, 1.1, out=a)
def add_inplace(a, b):
a += b
assert_raises(TypeError, add_inplace, a, 1.1)
# Make sure that explicitly overriding the exception is allowed:
assert_no_warnings(np.add, a, 1.1, out=a, casting="unsafe")
assert_array_equal(a, [2, 3, 4])
def test_ufunc_custom_out(self):
# Test ufunc with built in input types and custom output type
a = np.array([0, 1, 2], dtype='i8')
b = np.array([0, 1, 2], dtype='i8')
c = np.empty(3, dtype=_rational_tests.rational)
# Output must be specified so numpy knows what
# ufunc signature to look for
result = _rational_tests.test_add(a, b, c)
target = np.array([0, 2, 4], dtype=_rational_tests.rational)
assert_equal(result, target)
# The new resolution means that we can (usually) find custom loops
# as long as they match exactly:
result = _rational_tests.test_add(a, b)
assert_equal(result, target)
# This works even more generally, so long the default common-dtype
# promoter works out:
result = _rational_tests.test_add(a, b.astype(np.uint16), out=c)
assert_equal(result, target)
# But, it can be fooled, e.g. (use scalars, which forces legacy
# type resolution to kick in, which then fails):
with assert_raises(TypeError):
_rational_tests.test_add(a, np.uint16(2))
def test_operand_flags(self):
a = np.arange(16, dtype='l').reshape(4, 4)
b = np.arange(9, dtype='l').reshape(3, 3)
opflag_tests.inplace_add(a[:-1, :-1], b)
assert_equal(a, np.array([[0, 2, 4, 3], [7, 9, 11, 7],
[14, 16, 18, 11], [12, 13, 14, 15]], dtype='l'))
a = np.array(0)
opflag_tests.inplace_add(a, 3)
assert_equal(a, 3)
opflag_tests.inplace_add(a, [3, 4])
assert_equal(a, 10)
def test_struct_ufunc(self):
import numpy.core._struct_ufunc_tests as struct_ufunc
a = np.array([(1, 2, 3)], dtype='u8,u8,u8')
b = np.array([(1, 2, 3)], dtype='u8,u8,u8')
result = struct_ufunc.add_triplet(a, b)
assert_equal(result, np.array([(2, 4, 6)], dtype='u8,u8,u8'))
assert_raises(RuntimeError, struct_ufunc.register_fail)
def test_custom_ufunc(self):
a = np.array(
[_rational_tests.rational(1, 2),
_rational_tests.rational(1, 3),
_rational_tests.rational(1, 4)],
dtype=_rational_tests.rational)
b = np.array(
[_rational_tests.rational(1, 2),
_rational_tests.rational(1, 3),
_rational_tests.rational(1, 4)],
dtype=_rational_tests.rational)
result = _rational_tests.test_add_rationals(a, b)
expected = np.array(
[_rational_tests.rational(1),
_rational_tests.rational(2, 3),
_rational_tests.rational(1, 2)],
dtype=_rational_tests.rational)
assert_equal(result, expected)
def test_custom_ufunc_forced_sig(self):
# gh-9351 - looking for a non-first userloop would previously hang
with assert_raises(TypeError):
np.multiply(_rational_tests.rational(1), 1,
signature=(_rational_tests.rational, int, None))
def test_custom_array_like(self):
class MyThing:
__array_priority__ = 1000
rmul_count = 0
getitem_count = 0
def __init__(self, shape):
self.shape = shape
def __len__(self):
return self.shape[0]
def __getitem__(self, i):
MyThing.getitem_count += 1
if not isinstance(i, tuple):
i = (i,)
if len(i) > self.ndim:
raise IndexError("boo")
return MyThing(self.shape[len(i):])
def __rmul__(self, other):
MyThing.rmul_count += 1
return self
np.float64(5)*MyThing((3, 3))
assert_(MyThing.rmul_count == 1, MyThing.rmul_count)
assert_(MyThing.getitem_count <= 2, MyThing.getitem_count)
def test_inplace_fancy_indexing(self):
a = np.arange(10)
np.add.at(a, [2, 5, 2], 1)
assert_equal(a, [0, 1, 4, 3, 4, 6, 6, 7, 8, 9])
a = np.arange(10)
b = np.array([100, 100, 100])
np.add.at(a, [2, 5, 2], b)
assert_equal(a, [0, 1, 202, 3, 4, 105, 6, 7, 8, 9])
a = np.arange(9).reshape(3, 3)
b = np.array([[100, 100, 100], [200, 200, 200], [300, 300, 300]])
np.add.at(a, (slice(None), [1, 2, 1]), b)
assert_equal(a, [[0, 201, 102], [3, 404, 205], [6, 607, 308]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (slice(None), slice(None), [1, 2, 1]), b)
assert_equal(a,
[[[0, 401, 202],
[3, 404, 205],
[6, 407, 208]],
[[9, 410, 211],
[12, 413, 214],
[15, 416, 217]],
[[18, 419, 220],
[21, 422, 223],
[24, 425, 226]]])
a = np.arange(9).reshape(3, 3)
b = np.array([[100, 100, 100], [200, 200, 200], [300, 300, 300]])
np.add.at(a, ([1, 2, 1], slice(None)), b)
assert_equal(a, [[0, 1, 2], [403, 404, 405], [206, 207, 208]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (slice(None), [1, 2, 1], slice(None)), b)
assert_equal(a,
[[[0, 1, 2],
[203, 404, 605],
[106, 207, 308]],
[[9, 10, 11],
[212, 413, 614],
[115, 216, 317]],
[[18, 19, 20],
[221, 422, 623],
[124, 225, 326]]])
a = np.arange(9).reshape(3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (0, [1, 2, 1]), b)
assert_equal(a, [[0, 401, 202], [3, 4, 5], [6, 7, 8]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, ([1, 2, 1], 0, slice(None)), b)
assert_equal(a,
[[[0, 1, 2],
[3, 4, 5],
[6, 7, 8]],
[[209, 410, 611],
[12, 13, 14],
[15, 16, 17]],
[[118, 219, 320],
[21, 22, 23],
[24, 25, 26]]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (slice(None), slice(None), slice(None)), b)
assert_equal(a,
[[[100, 201, 302],
[103, 204, 305],
[106, 207, 308]],
[[109, 210, 311],
[112, 213, 314],
[115, 216, 317]],
[[118, 219, 320],
[121, 222, 323],
[124, 225, 326]]])
a = np.arange(10)
np.negative.at(a, [2, 5, 2])
assert_equal(a, [0, 1, 2, 3, 4, -5, 6, 7, 8, 9])
# Test 0-dim array
a = np.array(0)
np.add.at(a, (), 1)
assert_equal(a, 1)
assert_raises(IndexError, np.add.at, a, 0, 1)
assert_raises(IndexError, np.add.at, a, [], 1)
# Test mixed dtypes
a = np.arange(10)
np.power.at(a, [1, 2, 3, 2], 3.5)
assert_equal(a, np.array([0, 1, 4414, 46, 4, 5, 6, 7, 8, 9]))
# Test boolean indexing and boolean ufuncs
a = np.arange(10)
index = a % 2 == 0
np.equal.at(a, index, [0, 2, 4, 6, 8])
assert_equal(a, [1, 1, 1, 3, 1, 5, 1, 7, 1, 9])
# Test unary operator
a = np.arange(10, dtype='u4')
np.invert.at(a, [2, 5, 2])
assert_equal(a, [0, 1, 2, 3, 4, 5 ^ 0xffffffff, 6, 7, 8, 9])
# Test empty subspace
orig = np.arange(4)
a = orig[:, None][:, 0:0]
np.add.at(a, [0, 1], 3)
assert_array_equal(orig, np.arange(4))
# Test with swapped byte order
index = np.array([1, 2, 1], np.dtype('i').newbyteorder())
values = np.array([1, 2, 3, 4], np.dtype('f').newbyteorder())
np.add.at(values, index, 3)
assert_array_equal(values, [1, 8, 6, 4])
# Test exception thrown
values = np.array(['a', 1], dtype=object)
assert_raises(TypeError, np.add.at, values, [0, 1], 1)
assert_array_equal(values, np.array(['a', 1], dtype=object))
# Test multiple output ufuncs raise error, gh-5665
assert_raises(ValueError, np.modf.at, np.arange(10), [1])
# Test maximum
a = np.array([1, 2, 3])
np.maximum.at(a, [0], 0)
assert_equal(np.array([1, 2, 3]), a)
def test_at_not_none_signature(self):
# Test ufuncs with non-trivial signature raise a TypeError
a = np.ones((2, 2, 2))
b = np.ones((1, 2, 2))
assert_raises(TypeError, np.matmul.at, a, [0], b)
a = np.array([[[1, 2], [3, 4]]])
assert_raises(TypeError, np.linalg._umath_linalg.det.at, a, [0])
def test_reduce_arguments(self):
f = np.add.reduce
d = np.ones((5,2), dtype=int)
o = np.ones((2,), dtype=d.dtype)
r = o * 5
assert_equal(f(d), r)
# a, axis=0, dtype=None, out=None, keepdims=False
assert_equal(f(d, axis=0), r)
assert_equal(f(d, 0), r)
assert_equal(f(d, 0, dtype=None), r)
assert_equal(f(d, 0, dtype='i'), r)
assert_equal(f(d, 0, 'i'), r)
assert_equal(f(d, 0, None), r)
assert_equal(f(d, 0, None, out=None), r)
assert_equal(f(d, 0, None, out=o), r)
assert_equal(f(d, 0, None, o), r)
assert_equal(f(d, 0, None, None), r)
assert_equal(f(d, 0, None, None, keepdims=False), r)
assert_equal(f(d, 0, None, None, True), r.reshape((1,) + r.shape))
assert_equal(f(d, 0, None, None, False, 0), r)
assert_equal(f(d, 0, None, None, False, initial=0), r)
assert_equal(f(d, 0, None, None, False, 0, True), r)
assert_equal(f(d, 0, None, None, False, 0, where=True), r)
# multiple keywords
assert_equal(f(d, axis=0, dtype=None, out=None, keepdims=False), r)
assert_equal(f(d, 0, dtype=None, out=None, keepdims=False), r)
assert_equal(f(d, 0, None, out=None, keepdims=False), r)
assert_equal(f(d, 0, None, out=None, keepdims=False, initial=0,
where=True), r)
# too little
assert_raises(TypeError, f)
# too much
assert_raises(TypeError, f, d, 0, None, None, False, 0, True, 1)
# invalid axis
assert_raises(TypeError, f, d, "invalid")
assert_raises(TypeError, f, d, axis="invalid")
assert_raises(TypeError, f, d, axis="invalid", dtype=None,
keepdims=True)
# invalid dtype
assert_raises(TypeError, f, d, 0, "invalid")
assert_raises(TypeError, f, d, dtype="invalid")
assert_raises(TypeError, f, d, dtype="invalid", out=None)
# invalid out
assert_raises(TypeError, f, d, 0, None, "invalid")
assert_raises(TypeError, f, d, out="invalid")
assert_raises(TypeError, f, d, out="invalid", dtype=None)
# keepdims boolean, no invalid value
# assert_raises(TypeError, f, d, 0, None, None, "invalid")
# assert_raises(TypeError, f, d, keepdims="invalid", axis=0, dtype=None)
# invalid mix
assert_raises(TypeError, f, d, 0, keepdims="invalid", dtype="invalid",
out=None)
# invalid keyword
assert_raises(TypeError, f, d, axis=0, dtype=None, invalid=0)
assert_raises(TypeError, f, d, invalid=0)
assert_raises(TypeError, f, d, 0, keepdims=True, invalid="invalid",
out=None)
assert_raises(TypeError, f, d, axis=0, dtype=None, keepdims=True,
out=None, invalid=0)
assert_raises(TypeError, f, d, axis=0, dtype=None,
out=None, invalid=0)
def test_structured_equal(self):
# https://github.com/numpy/numpy/issues/4855
class MyA(np.ndarray):
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
return getattr(ufunc, method)(*(input.view(np.ndarray)
for input in inputs), **kwargs)
a = np.arange(12.).reshape(4,3)
ra = a.view(dtype=('f8,f8,f8')).squeeze()
mra = ra.view(MyA)
target = np.array([ True, False, False, False], dtype=bool)
assert_equal(np.all(target == (mra == ra[0])), True)
def test_scalar_equal(self):
# Scalar comparisons should always work, without deprecation warnings.
# even when the ufunc fails.
a = np.array(0.)
b = np.array('a')
assert_(a != b)
assert_(b != a)
assert_(not (a == b))
assert_(not (b == a))
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.maximum, np.minimum, np.mod,
np.greater, np.greater_equal, np.less, np.less_equal,
np.equal, np.not_equal]
a = np.array('1')
b = 1
c = np.array([1., 2.])
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
assert_raises(TypeError, f, c, a)
@pytest.mark.parametrize("ufunc",
[np.logical_and, np.logical_or]) # logical_xor object loop is bad
@pytest.mark.parametrize("signature",
[(None, None, object), (object, None, None),
(None, object, None)])
def test_logical_ufuncs_object_signatures(self, ufunc, signature):
a = np.array([True, None, False], dtype=object)
res = ufunc(a, a, signature=signature)
assert res.dtype == object
@pytest.mark.parametrize("ufunc",
[np.logical_and, np.logical_or, np.logical_xor])
@pytest.mark.parametrize("signature",
[(bool, None, object), (object, None, bool),
(None, object, bool)])
def test_logical_ufuncs_mixed_object_signatures(self, ufunc, signature):
# Most mixed signatures fail (except those with bool out, e.g. `OO->?`)
a = np.array([True, None, False])
with pytest.raises(TypeError):
ufunc(a, a, signature=signature)
@pytest.mark.parametrize("ufunc",
[np.logical_and, np.logical_or, np.logical_xor])
def test_logical_ufuncs_support_anything(self, ufunc):
# The logical ufuncs support even input that can't be promoted:
a = np.array(b'1', dtype="V3")
c = np.array([1., 2.])
assert_array_equal(ufunc(a, c), ufunc([True, True], True))
assert ufunc.reduce(a) == True
# check that the output has no effect:
out = np.zeros(2, dtype=np.int32)
expected = ufunc([True, True], True).astype(out.dtype)
assert_array_equal(ufunc(a, c, out=out), expected)
out = np.zeros((), dtype=np.int32)
assert ufunc.reduce(a, out=out) == True
# Last check, test reduction when out and a match (the complexity here
# is that the "i,i->?" may seem right, but should not match.
a = np.array([3], dtype="i")
out = np.zeros((), dtype=a.dtype)
assert ufunc.reduce(a, out=out) == 1
@pytest.mark.parametrize("ufunc",
[np.logical_and, np.logical_or, np.logical_xor])
def test_logical_ufuncs_reject_string(self, ufunc):
"""
Logical ufuncs are normally well defined by working with the boolean
equivalent, i.e. casting all inputs to bools should work.
However, casting strings to bools is *currently* weird, because it
actually uses `bool(int(str))`. Thus we explicitly reject strings.
This test should succeed (and can probably just be removed) as soon as
string to bool casts are well defined in NumPy.
"""
with pytest.raises(TypeError, match="contain a loop with signature"):
ufunc(["1"], ["3"])
with pytest.raises(TypeError, match="contain a loop with signature"):
ufunc.reduce(["1", "2", "0"])
@pytest.mark.parametrize("ufunc",
[np.logical_and, np.logical_or, np.logical_xor])
def test_logical_ufuncs_out_cast_check(self, ufunc):
a = np.array('1')
c = np.array([1., 2.])
out = a.copy()
with pytest.raises(TypeError):
# It would be safe, but not equiv casting:
ufunc(a, c, out=out, casting="equiv")
def test_reducelike_byteorder_resolution(self):
# See gh-20699, byte-order changes need some extra care in the type
# resolution to make the following succeed:
arr_be = np.arange(10, dtype=">i8")
arr_le = np.arange(10, dtype="<i8")
assert np.add.reduce(arr_be) == np.add.reduce(arr_le)
assert_array_equal(np.add.accumulate(arr_be), np.add.accumulate(arr_le))
assert_array_equal(
np.add.reduceat(arr_be, [1]), np.add.reduceat(arr_le, [1]))
def test_reducelike_out_promotes(self):
# Check that the out argument to reductions is considered for
# promotion. See also gh-20455.
# Note that these paths could prefer `initial=` in the future and
# do not up-cast to the default integer for add and prod
arr = np.ones(1000, dtype=np.uint8)
out = np.zeros((), dtype=np.uint16)
assert np.add.reduce(arr, out=out) == 1000
arr[:10] = 2
assert np.multiply.reduce(arr, out=out) == 2**10
# For legacy dtypes, the signature currently has to be forced if `out=`
# is passed. The two paths below should differ, without `dtype=` the
# expected result should be: `np.prod(arr.astype("f8")).astype("f4")`!
arr = np.full(5, 2**25-1, dtype=np.int64)
# float32 and int64 promote to float64:
res = np.zeros((), dtype=np.float32)
# If `dtype=` is passed, the calculation is forced to float32:
single_res = np.zeros((), dtype=np.float32)
np.multiply.reduce(arr, out=single_res, dtype=np.float32)
assert single_res != res
def test_reducelike_output_needs_identical_cast(self):
# Checks the case where the we have a simple byte-swap works, maily
# tests that this is not rejected directly.
# (interesting because we require descriptor identity in reducelikes).
arr = np.ones(20, dtype="f8")
out = np.empty((), dtype=arr.dtype.newbyteorder())
expected = np.add.reduce(arr)
np.add.reduce(arr, out=out)
assert_array_equal(expected, out)
# Check reduceat:
out = np.empty(2, dtype=arr.dtype.newbyteorder())
expected = np.add.reduceat(arr, [0, 1])
np.add.reduceat(arr, [0, 1], out=out)
assert_array_equal(expected, out)
# And accumulate:
out = np.empty(arr.shape, dtype=arr.dtype.newbyteorder())
expected = np.add.accumulate(arr)
np.add.accumulate(arr, out=out)
assert_array_equal(expected, out)
def test_reduce_noncontig_output(self):
# Check that reduction deals with non-contiguous output arrays
# appropriately.
#
# gh-8036
x = np.arange(7*13*8, dtype=np.int16).reshape(7, 13, 8)
x = x[4:6,1:11:6,1:5].transpose(1, 2, 0)
y_base = np.arange(4*4, dtype=np.int16).reshape(4, 4)
y = y_base[::2,:]
y_base_copy = y_base.copy()
r0 = np.add.reduce(x, out=y.copy(), axis=2)
r1 = np.add.reduce(x, out=y, axis=2)
# The results should match, and y_base shouldn't get clobbered
assert_equal(r0, r1)
assert_equal(y_base[1,:], y_base_copy[1,:])
assert_equal(y_base[3,:], y_base_copy[3,:])
@pytest.mark.parametrize("with_cast", [True, False])
def test_reduceat_and_accumulate_out_shape_mismatch(self, with_cast):
# Should raise an error mentioning "shape" or "size"
arr = np.arange(5)
out = np.arange(3) # definitely wrong shape
if with_cast:
# If a cast is necessary on the output, we can be sure to use
# the generic NpyIter (non-fast) path.
out = out.astype(np.float64)
with pytest.raises(ValueError, match="(shape|size)"):
np.add.reduceat(arr, [0, 3], out=out)
with pytest.raises(ValueError, match="(shape|size)"):
np.add.accumulate(arr, out=out)
@pytest.mark.parametrize('out_shape',
[(), (1,), (3,), (1, 1), (1, 3), (4, 3)])
@pytest.mark.parametrize('keepdims', [True, False])
@pytest.mark.parametrize('f_reduce', [np.add.reduce, np.minimum.reduce])
def test_reduce_wrong_dimension_output(self, f_reduce, keepdims, out_shape):
# Test that we're not incorrectly broadcasting dimensions.
# See gh-15144 (failed for np.add.reduce previously).
a = np.arange(12.).reshape(4, 3)
out = np.empty(out_shape, a.dtype)
correct_out = f_reduce(a, axis=0, keepdims=keepdims)
if out_shape != correct_out.shape:
with assert_raises(ValueError):
f_reduce(a, axis=0, out=out, keepdims=keepdims)
else:
check = f_reduce(a, axis=0, out=out, keepdims=keepdims)
assert_(check is out)
assert_array_equal(check, correct_out)
def test_reduce_output_does_not_broadcast_input(self):
# Test that the output shape cannot broadcast an input dimension
# (it never can add dimensions, but it might expand an existing one)
a = np.ones((1, 10))
out_correct = (np.empty((1, 1)))
out_incorrect = np.empty((3, 1))
np.add.reduce(a, axis=-1, out=out_correct, keepdims=True)
np.add.reduce(a, axis=-1, out=out_correct[:, 0], keepdims=False)
with assert_raises(ValueError):
np.add.reduce(a, axis=-1, out=out_incorrect, keepdims=True)
with assert_raises(ValueError):
np.add.reduce(a, axis=-1, out=out_incorrect[:, 0], keepdims=False)
def test_reduce_output_subclass_ok(self):
class MyArr(np.ndarray):
pass
out = np.empty(())
np.add.reduce(np.ones(5), out=out) # no subclass, all fine
out = out.view(MyArr)
assert np.add.reduce(np.ones(5), out=out) is out
assert type(np.add.reduce(out)) is MyArr
def test_no_doc_string(self):
# gh-9337
assert_('\n' not in umt.inner1d_no_doc.__doc__)
def test_invalid_args(self):
# gh-7961
exc = pytest.raises(TypeError, np.sqrt, None)
# minimally check the exception text
assert exc.match('loop of ufunc does not support')
@pytest.mark.parametrize('nat', [np.datetime64('nat'), np.timedelta64('nat')])
def test_nat_is_not_finite(self, nat):
try:
assert not np.isfinite(nat)
except TypeError:
pass # ok, just not implemented
@pytest.mark.parametrize('nat', [np.datetime64('nat'), np.timedelta64('nat')])
def test_nat_is_nan(self, nat):
try:
assert np.isnan(nat)
except TypeError:
pass # ok, just not implemented
@pytest.mark.parametrize('nat', [np.datetime64('nat'), np.timedelta64('nat')])
def test_nat_is_not_inf(self, nat):
try:
assert not np.isinf(nat)
except TypeError:
pass # ok, just not implemented
@pytest.mark.parametrize('ufunc', [getattr(np, x) for x in dir(np)
if isinstance(getattr(np, x), np.ufunc)])
def test_ufunc_types(ufunc):
'''
Check all ufuncs that the correct type is returned. Avoid
object and boolean types since many operations are not defined for
for them.
Choose the shape so even dot and matmul will succeed
'''
for typ in ufunc.types:
# types is a list of strings like ii->i
if 'O' in typ or '?' in typ:
continue
inp, out = typ.split('->')
args = [np.ones((3, 3), t) for t in inp]
with warnings.catch_warnings(record=True):
warnings.filterwarnings("always")
res = ufunc(*args)
if isinstance(res, tuple):
outs = tuple(out)
assert len(res) == len(outs)
for r, t in zip(res, outs):
assert r.dtype == np.dtype(t)
else:
assert res.dtype == np.dtype(out)
@pytest.mark.parametrize('ufunc', [getattr(np, x) for x in dir(np)
if isinstance(getattr(np, x), np.ufunc)])
def test_ufunc_noncontiguous(ufunc):
'''
Check that contiguous and non-contiguous calls to ufuncs
have the same results for values in range(9)
'''
for typ in ufunc.types:
# types is a list of strings like ii->i
if any(set('O?mM') & set(typ)):
# bool, object, datetime are too irregular for this simple test
continue
inp, out = typ.split('->')
args_c = [np.empty(6, t) for t in inp]
args_n = [np.empty(18, t)[::3] for t in inp]
for a in args_c:
a.flat = range(1,7)
for a in args_n:
a.flat = range(1,7)
with warnings.catch_warnings(record=True):
warnings.filterwarnings("always")
res_c = ufunc(*args_c)
res_n = ufunc(*args_n)
if len(out) == 1:
res_c = (res_c,)
res_n = (res_n,)
for c_ar, n_ar in zip(res_c, res_n):
dt = c_ar.dtype
if np.issubdtype(dt, np.floating):
# for floating point results allow a small fuss in comparisons
# since different algorithms (libm vs. intrinsics) can be used
# for different input strides
res_eps = np.finfo(dt).eps
tol = 2*res_eps
assert_allclose(res_c, res_n, atol=tol, rtol=tol)
else:
assert_equal(c_ar, n_ar)
@pytest.mark.parametrize('ufunc', [np.sign, np.equal])
def test_ufunc_warn_with_nan(ufunc):
# issue gh-15127
# test that calling certain ufuncs with a non-standard `nan` value does not
# emit a warning
# `b` holds a 64 bit signaling nan: the most significant bit of the
# significand is zero.
b = np.array([0x7ff0000000000001], 'i8').view('f8')
assert np.isnan(b)
if ufunc.nin == 1:
ufunc(b)
elif ufunc.nin == 2:
ufunc(b, b.copy())
else:
raise ValueError('ufunc with more than 2 inputs')
@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
def test_ufunc_casterrors():
# Tests that casting errors are correctly reported and buffers are
# cleared.
# The following array can be added to itself as an object array, but
# the result cannot be cast to an integer output:
value = 123 # relies on python cache (leak-check will still find it)
arr = np.array([value] * int(np.BUFSIZE * 1.5) +
["string"] +
[value] * int(1.5 * np.BUFSIZE), dtype=object)
out = np.ones(len(arr), dtype=np.intp)
count = sys.getrefcount(value)
with pytest.raises(ValueError):
# Output casting failure:
np.add(arr, arr, out=out, casting="unsafe")
assert count == sys.getrefcount(value)
# output is unchanged after the error, this shows that the iteration
# was aborted (this is not necessarily defined behaviour)
assert out[-1] == 1
with pytest.raises(ValueError):
# Input casting failure:
np.add(arr, arr, out=out, dtype=np.intp, casting="unsafe")
assert count == sys.getrefcount(value)
# output is unchanged after the error, this shows that the iteration
# was aborted (this is not necessarily defined behaviour)
assert out[-1] == 1
def test_trivial_loop_invalid_cast():
# This tests the fast-path "invalid cast", see gh-19904.
with pytest.raises(TypeError,
match="cast ufunc 'add' input 0"):
# the void dtype definitely cannot cast to double:
np.add(np.array(1, "i,i"), 3, signature="dd->d")
@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
@pytest.mark.parametrize("offset",
[0, np.BUFSIZE//2, int(1.5*np.BUFSIZE)])
def test_reduce_casterrors(offset):
# Test reporting of casting errors in reductions, we test various
# offsets to where the casting error will occur, since these may occur
# at different places during the reduction procedure. For example
# the first item may be special.
value = 123 # relies on python cache (leak-check will still find it)
arr = np.array([value] * offset +
["string"] +
[value] * int(1.5 * np.BUFSIZE), dtype=object)
out = np.array(-1, dtype=np.intp)
count = sys.getrefcount(value)
with pytest.raises(ValueError, match="invalid literal"):
# This is an unsafe cast, but we currently always allow that.
# Note that the double loop is picked, but the cast fails.
np.add.reduce(arr, dtype=np.intp, out=out)
assert count == sys.getrefcount(value)
# If an error occurred during casting, the operation is done at most until
# the error occurs (the result of which would be `value * offset`) and -1
# if the error happened immediately.
# This does not define behaviour, the output is invalid and thus undefined
assert out[()] < value * offset
@pytest.mark.parametrize("method",
[np.add.accumulate, np.add.reduce,
pytest.param(lambda x: np.add.reduceat(x, [0]), id="reduceat"),
pytest.param(lambda x: np.log.at(x, [2]), id="at")])
def test_ufunc_methods_floaterrors(method):
# adding inf and -inf (or log(-inf) creates an invalid float and warns
arr = np.array([np.inf, 0, -np.inf])
with np.errstate(all="warn"):
with pytest.warns(RuntimeWarning, match="invalid value"):
method(arr)
arr = np.array([np.inf, 0, -np.inf])
with np.errstate(all="raise"):
with pytest.raises(FloatingPointError):
method(arr)
def _check_neg_zero(value):
if value != 0.0:
return False
if not np.signbit(value.real):
return False
if value.dtype.kind == "c":
return np.signbit(value.imag)
return True
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
def test_addition_negative_zero(dtype):
dtype = np.dtype(dtype)
if dtype.kind == "c":
neg_zero = dtype.type(complex(-0.0, -0.0))
else:
neg_zero = dtype.type(-0.0)
arr = np.array(neg_zero)
arr2 = np.array(neg_zero)
assert _check_neg_zero(arr + arr2)
# In-place ops may end up on a different path (reduce path) see gh-21211
arr += arr2
assert _check_neg_zero(arr)
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
@pytest.mark.parametrize("use_initial", [True, False])
def test_addition_reduce_negative_zero(dtype, use_initial):
dtype = np.dtype(dtype)
if dtype.kind == "c":
neg_zero = dtype.type(complex(-0.0, -0.0))
else:
neg_zero = dtype.type(-0.0)
kwargs = {}
if use_initial:
kwargs["initial"] = neg_zero
else:
pytest.xfail("-0. propagation in sum currently requires initial")
# Test various length, in case SIMD paths or chunking play a role.
# 150 extends beyond the pairwise blocksize; probably not important.
for i in range(0, 150):
arr = np.array([neg_zero] * i, dtype=dtype)
res = np.sum(arr, **kwargs)
if i > 0 or use_initial:
assert _check_neg_zero(res)
else:
# `sum([])` should probably be 0.0 and not -0.0 like `sum([-0.0])`
assert not np.signbit(res.real)
assert not np.signbit(res.imag)
| [
"[email protected]"
] | |
5e7189bd3c62d63d5b04c748cb59bb1f8a85acb2 | 5ea9a3185b8abbf536600bde73ffa5293c76913d | /django_storage/urls.py | 5ca910b69f1f7e96a9de80008f0cb58e5e6322ad | [] | no_license | antikytheraton/django_storage | fd3fcaaeb93d236e2cc626e2326a8909f8fad488 | 7c3f8258f3a558ab99506b160659a824053db700 | refs/heads/master | 2021-07-08T19:17:46.690089 | 2017-10-07T02:16:20 | 2017-10-07T02:16:20 | 106,065,308 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,076 | py | """django_storage URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url
from django.contrib import admin
from django.views.generic import TemplateView # --------------------------------------------
from home.views import DocumentCreateView
urlpatterns = [
url(r'^admin/', admin.site.urls),
url(r'^$', TemplateView.as_view(template_name='home.html'), name='home'),
url(r'^upload/$', DocumentCreateView.as_view(template_name='form.html'), name='upload')
]
| [
"[email protected]"
] | |
83b555f16126a27acc97627eb10cd3415912555f | c124cd627d1cd2ecc2056a932db4c5c3203943f2 | /data/atramData/sites/umms/components/umms_appoint/recruitment_section/items/identifycandidate.py | 15b936079177324e9747e3b5eadd4ee90c2449b5 | [] | no_license | longooglite/mps | 8fb2093b6a9f483a2ce4543949f7cbf0b280a1f1 | fd8c0d1491b80074fdf5a8c923d50e55a1991ad0 | refs/heads/master | 2021-01-10T08:17:15.852252 | 2016-02-29T21:07:04 | 2016-02-29T21:07:04 | 52,824,830 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,858 | py | # [Copyright]
# SmartPath v1.0
# Copyright 2014-2015 Mountain Pass Solutions, Inc.
# This unpublished material is proprietary to Mountain Pass Solutions, Inc.
# [End Copyright]
identifycandidate = {
"code": "identifycandidate",
"descr": "Identify Candidate",
"header": "Identify Candidate",
"componentType": "Task",
"affordanceType":"Item",
"optional": False,
"enabled": True,
"logEnabled": True,
"freezable": True,
"overviewOnly": False,
"accessPermissions": ["dept_task"],
"viewPermissions": ["dept_task","ofa_task","mss_task"],
"blockers": ["jobposting"],
"statusMsg": "Candidate Identified",
"successMsg":"Candidate information saved",
"className": "IdentifyCandidate",
"config": {
"dashboardEvents": [{
"code":"rfpapproved",
"eventType":"remove",
},{
"code":"jopposted",
"eventType":"remove",
},{
"code":"readyforjobposting",
"eventType":"remove",
}],
"prompts": [
{
"code": "username",
"label": "Username",
"enabled": False,
"required": False,
"ldapsearch": False,
},
{
"code": "first_name",
"label": "First Name",
"enabled": True,
"required": True,
"ldapfield": "givenName",
},
{
"code": "middle_name",
"label": "Middle Name",
"enabled": True,
"required": False,
},
{
"code": "last_name",
"label": "Last Name",
"enabled": True,
"required": True,
"ldapfield": "sn",
},
{
"code": "suffix",
"label": "Suffix",
"enabled": True,
"required": False,
},
{
"code": "email",
"label": "Email",
"enabled": True,
"required": False,
"ldapfield": "mail",
},
{
"code": "employee_nbr",
"label": "Employee Nbr",
"enabled": False,
"required": False,
},
],
"activityLog": {
"enabled": True,
"activityLogText": "Candidate Identified",
},
},
}
| [
"[email protected]"
] | |
870eacbbe2dc97704de593953b693029ce772637 | c220d55a0a5c7597fe7e86a3dfebdd66695a3b2f | /Python/text_algnment.py | 556a0ff4be82b66fddeb3ca1b3909fe9021529af | [] | no_license | eLtronicsVilla/Hackerrank-Problems-Solutions | 356677a2edce6f5d3f57e5f32a8be058515779bf | a24c78f99f10fb8dca69e0e0d6c560d7c0215a29 | refs/heads/master | 2020-05-21T18:15:21.893538 | 2019-05-18T07:54:41 | 2019-05-18T07:54:41 | 186,129,319 | 0 | 0 | null | 2019-05-18T07:54:42 | 2019-05-11T12:19:33 | null | UTF-8 | Python | false | false | 908 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat May 18 12:29:44 2019
@author: brgupta
"""
# Problem statement
# https://www.hackerrank.com/challenges/text-alignment/problem
#Replace all ______ with rjust, ljust or center.
thickness = int(input()) #This must be an odd number
c = 'H'
#Top Cone
for i in range(thickness):
print((c*i).rjust(thickness-1)+c+(c*i).ljust(thickness-1))
#Top Pillars
for i in range(thickness+1):
print((c*thickness).center(thickness*2)+(c*thickness).center(thickness*6))
#Middle Belt
for i in range((thickness+1)//2):
print((c*thickness*5).center(thickness*6))
#Bottom Pillars
for i in range(thickness+1):
print((c*thickness).center(thickness*2)+(c*thickness).center(thickness*6))
#Bottom Cone
for i in range(thickness):
print(((c*(thickness-i-1)).rjust(thickness)+c+(c*(thickness-i-1)).ljust(thickness)).rjust(thickness*6))
| [
"[email protected]"
] | |
c46cfb66f6bcb2d0f920aa611e165abe7fe4d9be | b2e278f6d606ec0d3e6fa3e15be2f9ed35745c1e | /ncolony/beatcheck.py | 7461221c187dfa2df8febf60a32d24ac340ac807 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | permissive | kurtbrose/ncolony | deeaf2c1947aa11fcdad00f9071bc3e8067f026e | bebbc612866a8bf405dda2ec94ce60fd61b4f3c9 | refs/heads/master | 2023-08-18T08:56:58.777571 | 2017-09-19T03:43:27 | 2017-09-19T03:43:27 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,824 | py | # Copyright (c) Moshe Zadka
# See LICENSE for details.
"""ncolony.beatcheck
====================
Check heartbeats of processes that should beat.
Usually used as
$ twistd -n ncolony_beatcheck --config config --messages messages
It will watch the configurations, and send a restart message
for any process that does not beat within its heartbeat.
Processes are encouraged to try and beat about 3-4 times
faster than the minimum, so that they can miss one beat, and
account for slight timer inaccuracies, and still not be considered
unhealthy.
"""
import functools
import json
import time
from twisted.python import filepath, usage
from twisted.application import internet as tainternet
from ncolony import ctllib
from ncolony.client import heart
def check(path, start, now):
"""check which processes need to be restarted
:params path: a twisted.python.filepath.FilePath with configurations
:params start: when the checker started running
:params now: current time
:returns: list of strings
"""
return [child.basename() for child in path.children()
if _isbad(child, start, now)]
def _isbad(child, start, now):
content = child.getContent()
parsed = json.loads(content)
params = parsed.get('ncolony.beatcheck')
if params is None:
return False
period = params['period']
grace = params['grace']
mtime = max(child.getModificationTime(), start)
if mtime + period*grace >= now:
return False
status = params['status']
statusPath = child.clonePath(status)
if not statusPath.exists():
return True
if statusPath.isdir():
statusPath = statusPath.child(child.basename())
statusMtime = statusPath.getModificationTime()
return (statusMtime + period) < now
def run(restarter, checker, timer):
"""Run restarter on the checker's output
:params restarter: something to run on the output of the checker
:params checker: a function expected to get one argument (current time)
and return a list of stale names
:params timer: a function of zero arguments, intended to return current time
:returns: None
"""
for bad in checker(timer()):
restarter(bad)
def parseConfig(opt):
"""Parse configuration
:params opt: dict-like object with config and messages keys
:returns: restarter, path
"""
places = ctllib.Places(config=opt['config'], messages=opt['messages'])
restarter = functools.partial(ctllib.restart, places)
path = filepath.FilePath(opt['config'])
return restarter, path
def makeService(opt):
"""Make a service
:params opt: dictionary-like object with 'freq', 'config' and 'messages'
:returns: twisted.application.internet.TimerService that at opt['freq']
checks for stale processes in opt['config'], and sends
restart messages through opt['messages']
"""
restarter, path = parseConfig(opt)
now = time.time()
checker = functools.partial(check, path, now)
beatcheck = tainternet.TimerService(opt['freq'], run, restarter, checker, time.time)
beatcheck.setName('beatcheck')
return heart.wrapHeart(beatcheck)
## pylint: disable=too-few-public-methods
class Options(usage.Options):
"""Options for ncolony beatcheck service"""
optParameters = [
["messages", None, None, "Directory for messages"],
["config", None, None, "Directory for configuration"],
["freq", None, 10, "Frequency of checking for updates", float],
]
def postOptions(self):
"""Checks that required messages/config directories are present"""
for param in ('messages', 'config'):
if self[param] is None:
raise usage.UsageError("Missing required", param)
## pylint: enable=too-few-public-methods
| [
"[email protected]"
] | |
423f1675d5bcef619a2c564e602dc00a23745bdc | 60d9f0ea7764b67b8e2f5b187f9bd98be0ddd93a | /scripts/s3_sed.py | 499e01ac11207255f75bca28098b09e9e2fd744b | [
"Apache-2.0"
] | permissive | omad/dratools | 252136d972a750a228c5d84c3c95293d671a3145 | 17d81dd5e496c5539b0613f4bf25655230bd9f4f | refs/heads/master | 2023-02-03T10:36:52.677072 | 2023-01-19T23:01:16 | 2023-01-19T23:01:16 | 184,683,843 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,029 | py | import click
from odc.aws import s3_client, s3_fetch, s3_dump
from tqdm import tqdm
s3 = None
@click.command('s3-find')
@click.option('--no-sign-request', is_flag=True,
help='Do not sign AWS S3 requests')
@click.argument('file_list', type=click.File('r'), nargs=1)
def cli(file_list, no_sign_request=None):
global s3
s3 = s3_client(aws_unsigned=no_sign_request)
urls = [line.rstrip() for line in file_list.readlines()]
for url in tqdm(urls):
if not url:
continue
tqdm.write(f"Updating {url}", end='')
replace_in_s3_obj(url)
def replace_in_s3_obj(s3_url):
try:
original = s3_fetch(s3_url, s3)
except ValueError as e:
tqdm.write(str(e))
return
contents = original.replace(b'LANDSAT_8', b'LANDSAT_7')
contents = contents.replace(b'OLI', b'ETM')
if original != contents:
s3_dump(contents, s3_url, s3)
tqdm.write('.')
else:
tqdm.write(' - Skipped.')
if __name__ == '__main__':
cli()
| [
"[email protected]"
] | |
cae804eeca224b7c810f2ca72e04cb19244e2022 | 6219e6536774e8eeb4cadc4a84f6f2bea376c1b0 | /scraper/storage_spiders/vietlongplazacomvn.py | beadae51c3d65808932018124f7c2fae1011fb27 | [
"MIT"
] | permissive | nguyenminhthai/choinho | 109d354b410b92784a9737f020894d073bea1534 | d2a216fe7a5064d73cdee3e928a7beef7f511fd1 | refs/heads/master | 2023-05-07T16:51:46.667755 | 2019-10-22T07:53:41 | 2019-10-22T07:53:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,019 | py | # Auto generated by generator.py. Delete this line if you make modification.
from scrapy.spiders import Rule
from scrapy.linkextractors import LinkExtractor
XPATH = {
'name' : "//td[@id='pro-detail-col-info']/div[@id='product-detail-name']/h1",
'price' : "//div[@class='product-list-price']/p[@class='cssPrice']/font",
'category' : "//div[@id='categoryPath']/a",
'description' : "//div[@id='pro-box-2']/div[4]/div[@id='pro_content_desc']",
'images' : "//table//tr/td[@id='pro_big']/img/@src",
'canonical' : "",
'base_url' : "",
'brand' : ""
}
name = 'vietlongplaza.com.vn'
allowed_domains = ['vietlongplaza.com.vn']
start_urls = ['http://www.vietlongplaza.com.vn/default.aspx']
tracking_url = ''
sitemap_urls = ['']
sitemap_rules = [('', 'parse_item')]
sitemap_follow = []
rules = [
Rule(LinkExtractor(allow=['/product+-\d+/']), 'parse_item'),
Rule(LinkExtractor(allow=['/category+-\d+/'], deny=['\?','Filter=']), 'parse'),
#Rule(LinkExtractor(), 'parse_item_and_links'),
]
| [
"[email protected]"
] | |
d355d73ff3bc201e202f27d27a78be42b0db7872 | 8941c8ca788b1a45bfad23ca26ebfa357c13f09b | /Lyceum/Mars_Sql_Alchemy/zapros4.py | 1b12b239fe471f0a4b6e70c4256bc310a8a2b2bc | [] | no_license | MysteriousSonOfGod/Python-2 | d1dfdf094f4a763758bfc7e1777c2cd6efbd0809 | 0d488906e4b5e3897da6b7cb077815740e82fd84 | refs/heads/master | 2023-02-05T13:38:25.673248 | 2020-12-22T13:54:02 | 2020-12-22T13:54:02 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 329 | py | from data import db_session
from data import users
db = input()
db_session.global_init(db)
session = db_session.create_session()
users = session.query(users.User).filter((users.User.position.like("%chief%") | users.User.position.like("%middle%")))
for user in users:
print(f'{user} {user.position}')
# db/mars_explorer.db | [
"[email protected]"
] | |
442ee1ed35e53bdf671ddf356b0bf7274dddb5a8 | 1beb0d3a73a97c5367cc54d37b34a7536b975d68 | /practice/morethread.py | 37fdcd18909e15f831a7ec9abf022bb055e2f262 | [] | no_license | Hardworking-tester/HuaYing | a24aa271afe81c95241818586b1d1d5abd6b4282 | 4dd065806f20bfdec885fa2b40f2c22e5a8d4f15 | refs/heads/master | 2021-06-03T10:06:33.604494 | 2017-06-22T09:32:13 | 2017-06-22T09:32:13 | 42,507,030 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 722 | py | # encoding:utf-8
# author:wwg
from selenium import webdriver
import threading
import time
class MyThread(threading.Thread):
def __init__(self,num):
threading.Thread.__init__(self)
self.num=num
def run(self):
start=time.time()
br=webdriver.Firefox()
br.get("https://www.baidu.com")
time.sleep(4)
br.find_element_by_id("kw").send_keys("wwg")
br.find_element_by_id("su").click()
br.quit()
end=time.time()
print u'Thread Object(%d), Time:%s\n,耗时%s s' % (self.num, time.ctime(),(end-start))
def test():
for i in range(1,10):
t = MyThread(i)
t.start()
t.join()
if __name__=="__main__":
test() | [
"[email protected]"
] | |
04628def6f79e73ee5273d9991b2f50dc87b56f5 | d5552cda58e251e6a5983876681be8f641dea86f | /src/transformers/models/m2m_100/modeling_m2m_100.py | f7ef189a155d636e6620922218e36aa244c6899c | [
"Apache-2.0"
] | permissive | patrickvonplaten/transformers | feb121e1ee82c317ac7561836b8f95a7de25fc1f | f738502979f6787609dcf0180e6606f464692e27 | refs/heads/master | 2022-12-08T10:15:34.743198 | 2022-11-22T11:00:20 | 2022-11-22T11:00:20 | 226,201,271 | 6 | 1 | Apache-2.0 | 2019-12-05T22:39:46 | 2019-12-05T22:39:45 | null | UTF-8 | Python | false | false | 65,124 | py | # coding=utf-8
# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch M2M100 model."""
import math
import random
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_m2m_100 import M2M100Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "M2M100Config"
_TOKENIZER_FOR_DOC = "M2M100Tokenizer"
_CHECKPOINT_FOR_DOC = "facebook/m2m100_418M"
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/m2m100_418M",
# See all M2M100 models at https://huggingface.co/models?filter=m2m_100
]
# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))
mask_cond = torch.arange(mask.size(-1))
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
class M2M100SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx
self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
if hasattr(self, "weights"):
# in forward put the weights on the correct dtype and device of the param
emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
self.register_buffer("weights", emb_weights)
@staticmethod
def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
"""
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
"Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb.to(torch.get_default_dtype())
@torch.no_grad()
def forward(
self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0
):
if input_ids is not None:
bsz, seq_len = input_ids.size()
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to(
input_ids.device
)
else:
bsz, seq_len = inputs_embeds.size()[:-1]
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length)
# expand embeddings if needed
max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
if max_pos > self.weights.size(0):
self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach()
def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->M2M100
class M2M100Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->M2M100
class M2M100EncoderLayer(nn.Module):
def __init__(self, config: M2M100Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = M2M100Attention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
output_attentions: bool = False,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
attention_mask (`torch.FloatTensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
*(encoder_attention_heads,)*.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer with MBart->M2M100
class M2M100DecoderLayer(nn.Module):
def __init__(self, config: M2M100Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = M2M100Attention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = M2M100Attention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
attention_mask (`torch.FloatTensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape *(seq_len, batch, embed_dim)*
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
*(encoder_attention_heads,)*.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size *(decoder_attention_heads,)*.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
class M2M100PreTrainedModel(PreTrainedModel):
config_class = M2M100Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["M2M100Attention"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (M2M100Decoder, M2M100Encoder)):
module.gradient_checkpointing = value
M2M_100_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`M2M100Config`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
M2M_100_GENERATION_EXAMPLE = r"""
Translation example:
```python
>>> from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration
>>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
>>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
>>> text_to_translate = "Life is like a box of chocolates"
>>> model_inputs = tokenizer(text_to_translate, return_tensors="pt")
>>> # translate to French
>>> gen_tokens = model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("fr"))
>>> print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))
```
"""
M2M_100_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`M2M100Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`M2M100Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
M2M100 uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape
`(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you
can choose to directly pass an embedded representation. This is useful if you want more control over how to
convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
input (see `past_key_values`). This is useful if you want more control over how to convert
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
of `inputs_embeds`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class M2M100Encoder(M2M100PreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`M2M100EncoderLayer`].
Args:
config: M2M100Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: M2M100Config, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = M2M100SinusoidalPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
self.padding_idx,
)
self.layers = nn.ModuleList([M2M100EncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`M2M100Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_ids, inputs_embeds)
embed_pos = embed_pos.to(inputs_embeds.device)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
skip_the_layer = True if self.training and (dropout_probability < self.layerdrop) else False
if not skip_the_layer or deepspeed_zero3_is_enabled:
# under deepspeed zero3 all gpus must run in sync
if self.gradient_checkpointing and self.training:
# create gradient checkpointing function
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if skip_the_layer:
layer_outputs = (None, None)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class M2M100Decoder(M2M100PreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`M2M100DecoderLayer`]
Args:
config: M2M100Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: M2M100Config, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = M2M100SinusoidalPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
self.padding_idx,
)
self.layers = nn.ModuleList([M2M100DecoderLayer(config) for _ in range(config.decoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`M2M100Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
control over how to convert `input_ids` indices into associated vectors than the model's internal
embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
).to(inputs_embeds.device)
if attention_mask is not None and combined_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = combined_attention_mask + _expand_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
# embed positions
positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length)
positions = positions.to(inputs_embeds.device)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if output_attentions else None
next_decoder_cache = () if use_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != len(self.layers):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
skip_the_layer = True if self.training and (dropout_probability < self.layerdrop) else False
if not skip_the_layer or deepspeed_zero3_is_enabled:
# under deepspeed zero3 all gpus must run in sync
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting"
" `use_cache=False`..."
)
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, use_cache)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
combined_attention_mask,
encoder_hidden_states,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=combined_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if skip_the_layer:
continue
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
all_cross_attentions += (layer_outputs[2],)
hidden_states = self.layer_norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"The bare M2M100 Model outputting raw hidden-states without any specific head on top.",
M2M_100_START_DOCSTRING,
)
class M2M100Model(M2M100PreTrainedModel):
_keys_to_ignore_on_load_missing = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: M2M100Config):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
self.encoder = M2M100Encoder(config, self.shared)
self.decoder = M2M100Decoder(config, self.shared)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(M2M_100_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The M2M100 Model with a language modeling head. Can be used for summarization.", M2M_100_START_DOCSTRING
)
class M2M100ForConditionalGeneration(M2M100PreTrainedModel):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
r"encoder.version",
r"decoder.version",
r"lm_head.weight",
r"encoder.embed_tokens.weight",
r"decoder.embed_tokens.weight",
]
def __init__(self, config: M2M100Config):
super().__init__(config)
self.model = M2M100Model(config)
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens)
return new_embeddings
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward(M2M_100_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(M2M_100_GENERATION_EXAMPLE)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if decoder_input_ids is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0])
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past is used
if past is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
| [
"[email protected]"
] | |
30e9218fa343c615c68da4f7849636cc0abf4779 | bd46fe963f29e11691143aad5ae82ea7f974f3eb | /test/mitmproxy/test_types.py | 81aaed7493a4fe6d249d5e18806cfefaea4430f0 | [
"MIT"
] | permissive | 1ezss/mitmproxy | a4a934a8fd2d637a532009c46cab2ff3c57c2520 | 6ef6286d8e53a0a9045fa41956e65dae2e41ab6d | refs/heads/master | 2021-08-30T16:53:20.112680 | 2017-12-18T18:50:52 | 2017-12-18T18:50:52 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,604 | py | import pytest
import os
import typing
import contextlib
from mitmproxy.test import tutils
import mitmproxy.exceptions
import mitmproxy.types
from mitmproxy.test import taddons
from mitmproxy.test import tflow
from mitmproxy import command
from mitmproxy import flow
from . import test_command
@contextlib.contextmanager
def chdir(path: str):
old_dir = os.getcwd()
os.chdir(path)
yield
os.chdir(old_dir)
def test_bool():
with taddons.context() as tctx:
b = mitmproxy.types.Bool()
assert b.completion(tctx.master.commands, bool, "b") == ["false", "true"]
assert b.parse(tctx.master.commands, bool, "true") is True
assert b.parse(tctx.master.commands, bool, "false") is False
with pytest.raises(mitmproxy.exceptions.TypeError):
b.parse(tctx.master.commands, bool, "foo")
def test_str():
with taddons.context() as tctx:
b = mitmproxy.types.Str()
assert b.completion(tctx.master.commands, str, "") == []
assert b.parse(tctx.master.commands, str, "foo") == "foo"
def test_int():
with taddons.context() as tctx:
b = mitmproxy.types.Int()
assert b.completion(tctx.master.commands, int, "b") == []
assert b.parse(tctx.master.commands, int, "1") == 1
assert b.parse(tctx.master.commands, int, "999") == 999
with pytest.raises(mitmproxy.exceptions.TypeError):
b.parse(tctx.master.commands, int, "foo")
def test_path():
with taddons.context() as tctx:
b = mitmproxy.types.PathType()
assert b.parse(tctx.master.commands, mitmproxy.types.Path, "/foo") == "/foo"
assert b.parse(tctx.master.commands, mitmproxy.types.Path, "/bar") == "/bar"
def normPathOpts(prefix, match):
ret = []
for s in b.completion(tctx.master.commands, mitmproxy.types.Path, match):
s = s[len(prefix):]
s = s.replace(os.sep, "/")
ret.append(s)
return ret
cd = os.path.normpath(tutils.test_data.path("mitmproxy/completion"))
assert normPathOpts(cd, cd) == ['/aaa', '/aab', '/aac', '/bbb/']
assert normPathOpts(cd, os.path.join(cd, "a")) == ['/aaa', '/aab', '/aac']
with chdir(cd):
assert normPathOpts("", "./") == ['./aaa', './aab', './aac', './bbb/']
assert normPathOpts("", "") == ['./aaa', './aab', './aac', './bbb/']
assert b.completion(
tctx.master.commands, mitmproxy.types.Path, "nonexistent"
) == ["nonexistent"]
def test_cmd():
with taddons.context() as tctx:
tctx.master.addons.add(test_command.TAddon())
b = mitmproxy.types.CmdType()
assert b.parse(tctx.master.commands, mitmproxy.types.Cmd, "foo") == "foo"
assert len(
b.completion(tctx.master.commands, mitmproxy.types.Cmd, "")
) == len(tctx.master.commands.commands.keys())
def test_cutspec():
with taddons.context() as tctx:
b = mitmproxy.types.CutSpecType()
b.parse(tctx.master.commands, mitmproxy.types.CutSpec, "foo,bar") == ["foo", "bar"]
assert b.completion(
tctx.master.commands, mitmproxy.types.CutSpec, "request.p"
) == b.valid_prefixes
ret = b.completion(tctx.master.commands, mitmproxy.types.CutSpec, "request.port,f")
assert ret[0].startswith("request.port,")
assert len(ret) == len(b.valid_prefixes)
def test_arg():
with taddons.context() as tctx:
b = mitmproxy.types.ArgType()
assert b.completion(tctx.master.commands, mitmproxy.types.Arg, "") == []
assert b.parse(tctx.master.commands, mitmproxy.types.Arg, "foo") == "foo"
def test_strseq():
with taddons.context() as tctx:
b = mitmproxy.types.StrSeq()
assert b.completion(tctx.master.commands, typing.Sequence[str], "") == []
assert b.parse(tctx.master.commands, typing.Sequence[str], "foo") == ["foo"]
assert b.parse(tctx.master.commands, typing.Sequence[str], "foo,bar") == ["foo", "bar"]
class DummyConsole:
@command.command("view.resolve")
def resolve(self, spec: str) -> typing.Sequence[flow.Flow]:
n = int(spec)
return [tflow.tflow(resp=True)] * n
@command.command("cut")
def cut(self, spec: str) -> mitmproxy.types.Data:
return [["test"]]
@command.command("options")
def options(self) -> typing.Sequence[str]:
return ["one", "two", "three"]
def test_flow():
with taddons.context() as tctx:
tctx.master.addons.add(DummyConsole())
b = mitmproxy.types.FlowType()
assert len(b.completion(tctx.master.commands, flow.Flow, "")) == len(b.valid_prefixes)
assert b.parse(tctx.master.commands, flow.Flow, "1")
with pytest.raises(mitmproxy.exceptions.TypeError):
assert b.parse(tctx.master.commands, flow.Flow, "0")
with pytest.raises(mitmproxy.exceptions.TypeError):
assert b.parse(tctx.master.commands, flow.Flow, "2")
def test_flows():
with taddons.context() as tctx:
tctx.master.addons.add(DummyConsole())
b = mitmproxy.types.FlowsType()
assert len(
b.completion(tctx.master.commands, typing.Sequence[flow.Flow], "")
) == len(b.valid_prefixes)
assert len(b.parse(tctx.master.commands, typing.Sequence[flow.Flow], "0")) == 0
assert len(b.parse(tctx.master.commands, typing.Sequence[flow.Flow], "1")) == 1
assert len(b.parse(tctx.master.commands, typing.Sequence[flow.Flow], "2")) == 2
def test_data():
with taddons.context() as tctx:
b = mitmproxy.types.DataType()
with pytest.raises(mitmproxy.exceptions.TypeError):
b.parse(tctx.master.commands, mitmproxy.types.Data, "foo")
with pytest.raises(mitmproxy.exceptions.TypeError):
b.parse(tctx.master.commands, mitmproxy.types.Data, "foo")
def test_choice():
with taddons.context() as tctx:
tctx.master.addons.add(DummyConsole())
b = mitmproxy.types.ChoiceType()
comp = b.completion(tctx.master.commands, mitmproxy.types.Choice("options"), "")
assert comp == ["one", "two", "three"]
assert b.parse(tctx.master.commands, mitmproxy.types.Choice("options"), "one") == "one"
with pytest.raises(mitmproxy.exceptions.TypeError):
b.parse(tctx.master.commands, mitmproxy.types.Choice("options"), "invalid")
def test_typemanager():
assert mitmproxy.types.CommandTypes.get(bool, None)
assert mitmproxy.types.CommandTypes.get(mitmproxy.types.Choice("choide"), None)
| [
"[email protected]"
] | |
49e8a6b69a433379a569875caec380084d2fd049 | 1e0f9d3829665c74a5b4ee79531520fe4cbe2730 | /clean_data.py | 557cb06e5dae140dc2261c42809c0787927e0ce7 | [] | no_license | aparna-arr/AnalysisProject | bead344eda6159f83ac19de3be533fdd3acf2087 | f0d3068e0ac7f15255092f39f000c8009ceb57a2 | refs/heads/master | 2023-05-03T10:56:40.511706 | 2019-05-03T18:39:56 | 2019-05-03T18:39:56 | 181,940,284 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 751 | py | #!/share/software/user/open/python/3.6.1/bin/python3
import sys
if len(sys.argv) < 3:
print("usage: clean_data.py <downloaded.csv> <output_name.tsv>\n", file = sys.stderr)
sys.exit(1)
downloadFile = sys.argv[1]
outputFilename = sys.argv[2]
dfh = open(downloadFile, "r")
dout = open(outputFilename, "w")
dout.write("ChrIndex\tBarcode\tx\ty\tz\n")
firstLine = True
for line in dfh:
if (firstLine == True):
firstLine = False
continue
elem = line.rstrip().split(',')
if not elem[0].isdigit():
continue
chr_index = elem[0]
barcode_index = elem[1] # Bogdan calls these "segments"
z = elem[2]
x = elem[3]
y = elem[4]
dout.write(chr_index + "\t" + barcode_index + "\t" + x + "\t" + y + "\t" + z + "\n")
dout.close()
dfh.close()
| [
"[email protected]"
] | |
f6e47fbd2cd310fabb799996d61d9fecb0edcf08 | 480e33f95eec2e471c563d4c0661784c92396368 | /CondTools/SiStrip/test/SiStripFedCablingBuilder_cfg.py | e5c0d47c76984fe444dc432b4f022b77a68f190d | [
"Apache-2.0"
] | permissive | cms-nanoAOD/cmssw | 4d836e5b76ae5075c232de5e062d286e2026e8bd | 4eccb8a758b605875003124dd55ea58552b86af1 | refs/heads/master-cmsswmaster | 2021-01-23T21:19:52.295420 | 2020-08-27T08:01:20 | 2020-08-27T08:01:20 | 102,867,729 | 7 | 14 | Apache-2.0 | 2022-05-23T07:58:09 | 2017-09-08T14:03:57 | C++ | UTF-8 | Python | false | false | 2,154 | py | import FWCore.ParameterSet.Config as cms
process = cms.Process("FedCablingBuilder")
process.MessageLogger = cms.Service("MessageLogger",
debugModules = cms.untracked.vstring(''),
cablingBuilder = cms.untracked.PSet(
threshold = cms.untracked.string('INFO')
),
destinations = cms.untracked.vstring('cablingBuilder.log')
)
process.source = cms.Source("EmptySource",
numberEventsInRun = cms.untracked.uint32(1),
firstRun = cms.untracked.uint32(1)
)
process.maxEvents = cms.untracked.PSet(
input = cms.untracked.int32(1)
)
process.load("CalibTracker.SiStripESProducers.SiStripFedCablingFakeESSource_cfi")
process.PoolDBOutputService = cms.Service("PoolDBOutputService",
BlobStreamerName = cms.untracked.string('TBufferBlobStreamingService'),
DBParameters = cms.PSet(
messageLevel = cms.untracked.int32(2),
authenticationPath = cms.untracked.string('/afs/cern.ch/cms/DB/conddb')
),
timetype = cms.untracked.string('runnumber'),
connect = cms.string('sqlite_file:dummy2.db'),
toPut = cms.VPSet(cms.PSet(
record = cms.string('SiStripFedCablingRcd'),
tag = cms.string('SiStripFedCabling_30X')
))
)
process.load("Configuration.StandardSequences.Geometry_cff")
process.TrackerDigiGeometryESModule.applyAlignment = False
process.SiStripConnectivity = cms.ESProducer("SiStripConnectivity")
process.SiStripRegionConnectivity = cms.ESProducer("SiStripRegionConnectivity",
EtaDivisions = cms.untracked.uint32(20),
PhiDivisions = cms.untracked.uint32(20),
EtaMax = cms.untracked.double(2.5)
)
process.fedcablingbuilder = cms.EDAnalyzer("SiStripFedCablingBuilder",
PrintFecCabling = cms.untracked.bool(True),
PrintDetCabling = cms.untracked.bool(True),
PrintRegionCabling = cms.untracked.bool(True)
)
process.p1 = cms.Path(process.fedcablingbuilder)
| [
"[email protected]"
] | |
ec51eedd020b21502d675b80cc0a86cc425478a8 | 2e682fd72e3feaa70e3f7bf2a3b83c50d783ec02 | /PyTorch/contrib/cv/detection/SOLOv2/mmdet/models/anchor_heads/decoupled_solo_light_head.py | ac6b8230228ba4ec77d1bc842dd5307db15675f8 | [
"GPL-1.0-or-later",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-proprietary-license"
] | permissive | Ascend/ModelZoo-PyTorch | 4c89414b9e2582cef9926d4670108a090c839d2d | 92acc188d3a0f634de58463b6676e70df83ef808 | refs/heads/master | 2023-07-19T12:40:00.512853 | 2023-07-17T02:48:18 | 2023-07-17T02:48:18 | 483,502,469 | 23 | 6 | Apache-2.0 | 2022-10-15T09:29:12 | 2022-04-20T04:11:18 | Python | UTF-8 | Python | false | false | 21,534 | py | # Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import normal_init
from mmdet.ops import DeformConv, roi_align
from mmdet.core import multi_apply, bbox2roi, matrix_nms
from ..builder import build_loss
from ..registry import HEADS
from ..utils import bias_init_with_prob, ConvModule
INF = 1e8
def center_of_mass(bitmasks):
_, h, w = bitmasks.size()
ys = torch.arange(0, h, dtype=torch.float32, device=bitmasks.device)
xs = torch.arange(0, w, dtype=torch.float32, device=bitmasks.device)
m00 = bitmasks.sum(dim=-1).sum(dim=-1).clamp(min=1e-6)
m10 = (bitmasks * xs).sum(dim=-1).sum(dim=-1)
m01 = (bitmasks * ys[:, None]).sum(dim=-1).sum(dim=-1)
center_x = m10 / m00
center_y = m01 / m00
return center_x, center_y
def points_nms(heat, kernel=2):
# kernel must be 2
hmax = nn.functional.max_pool2d(
heat, (kernel, kernel), stride=1, padding=1)
keep = (hmax[:, :, :-1, :-1] == heat).float()
return heat * keep
def dice_loss(input, target):
input = input.contiguous().view(input.size()[0], -1)
target = target.contiguous().view(target.size()[0], -1).float()
a = torch.sum(input * target, 1)
b = torch.sum(input * input, 1) + 0.001
c = torch.sum(target * target, 1) + 0.001
d = (2 * a) / (b + c)
return 1 - d
@HEADS.register_module
class DecoupledSOLOLightHead(nn.Module):
def __init__(self,
num_classes,
in_channels,
seg_feat_channels=256,
stacked_convs=4,
strides=(4, 8, 16, 32, 64),
base_edge_list=(16, 32, 64, 128, 256),
scale_ranges=((8, 32), (16, 64), (32, 128), (64, 256), (128, 512)),
sigma=0.4,
num_grids=None,
cate_down_pos=0,
loss_ins=None,
loss_cate=None,
conv_cfg=None,
norm_cfg=None,
use_dcn_in_tower=False,
type_dcn=None):
super(DecoupledSOLOLightHead, self).__init__()
self.num_classes = num_classes
self.seg_num_grids = num_grids
self.cate_out_channels = self.num_classes - 1
self.in_channels = in_channels
self.seg_feat_channels = seg_feat_channels
self.stacked_convs = stacked_convs
self.strides = strides
self.sigma = sigma
self.cate_down_pos = cate_down_pos
self.base_edge_list = base_edge_list
self.scale_ranges = scale_ranges
self.loss_cate = build_loss(loss_cate)
self.ins_loss_weight = loss_ins['loss_weight']
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.use_dcn_in_tower = use_dcn_in_tower
self.type_dcn = type_dcn
self._init_layers()
def _init_layers(self):
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
self.ins_convs = nn.ModuleList()
self.cate_convs = nn.ModuleList()
for i in range(self.stacked_convs):
if self.use_dcn_in_tower and i == self.stacked_convs - 1:
cfg_conv = dict(type=self.type_dcn)
else:
cfg_conv = self.conv_cfg
chn = self.in_channels + 2 if i == 0 else self.seg_feat_channels
self.ins_convs.append(
ConvModule(
chn,
self.seg_feat_channels,
3,
stride=1,
padding=1,
conv_cfg=cfg_conv,
norm_cfg=norm_cfg,
bias=norm_cfg is None))
chn = self.in_channels if i == 0 else self.seg_feat_channels
self.cate_convs.append(
ConvModule(
chn,
self.seg_feat_channels,
3,
stride=1,
padding=1,
conv_cfg=cfg_conv,
norm_cfg=norm_cfg,
bias=norm_cfg is None))
self.dsolo_ins_list_x = nn.ModuleList()
self.dsolo_ins_list_y = nn.ModuleList()
for seg_num_grid in self.seg_num_grids:
self.dsolo_ins_list_x.append(
nn.Conv2d(
self.seg_feat_channels, seg_num_grid, 3, padding=1))
self.dsolo_ins_list_y.append(
nn.Conv2d(
self.seg_feat_channels, seg_num_grid, 3, padding=1))
self.dsolo_cate = nn.Conv2d(
self.seg_feat_channels, self.cate_out_channels, 3, padding=1)
def init_weights(self):
for m in self.ins_convs:
normal_init(m.conv, std=0.01)
for m in self.cate_convs:
normal_init(m.conv, std=0.01)
bias_ins = bias_init_with_prob(0.01)
for m in self.dsolo_ins_list_x:
normal_init(m, std=0.01, bias=bias_ins)
for m in self.dsolo_ins_list_y:
normal_init(m, std=0.01, bias=bias_ins)
bias_cate = bias_init_with_prob(0.01)
normal_init(self.dsolo_cate, std=0.01, bias=bias_cate)
def forward(self, feats, eval=False):
new_feats = self.split_feats(feats)
featmap_sizes = [featmap.size()[-2:] for featmap in new_feats]
upsampled_size = (featmap_sizes[0][0] * 2, featmap_sizes[0][1] * 2)
ins_pred_x, ins_pred_y, cate_pred = multi_apply(self.forward_single, new_feats,
list(range(len(self.seg_num_grids))),
eval=eval, upsampled_size=upsampled_size)
return ins_pred_x, ins_pred_y, cate_pred
def split_feats(self, feats):
return (F.interpolate(feats[0], scale_factor=0.5, mode='bilinear'),
feats[1],
feats[2],
feats[3],
F.interpolate(feats[4], size=feats[3].shape[-2:], mode='bilinear'))
def forward_single(self, x, idx, eval=False, upsampled_size=None):
ins_feat = x
cate_feat = x
# ins branch
# concat coord
x_range = torch.linspace(-1, 1, ins_feat.shape[-1], device=ins_feat.device)
y_range = torch.linspace(-1, 1, ins_feat.shape[-2], device=ins_feat.device)
y, x = torch.meshgrid(y_range, x_range)
y = y.expand([ins_feat.shape[0], 1, -1, -1])
x = x.expand([ins_feat.shape[0], 1, -1, -1])
coord_feat = torch.cat([x, y], 1)
ins_feat = torch.cat([ins_feat, coord_feat], 1)
for ins_layer in self.ins_convs:
ins_feat = ins_layer(ins_feat)
ins_feat = F.interpolate(ins_feat, scale_factor=2, mode='bilinear')
ins_pred_x = self.dsolo_ins_list_x[idx](ins_feat)
ins_pred_y = self.dsolo_ins_list_y[idx](ins_feat)
# cate branch
for i, cate_layer in enumerate(self.cate_convs):
if i == self.cate_down_pos:
seg_num_grid = self.seg_num_grids[idx]
cate_feat = F.interpolate(cate_feat, size=seg_num_grid, mode='bilinear')
cate_feat = cate_layer(cate_feat)
cate_pred = self.dsolo_cate(cate_feat)
if eval:
ins_pred_x = F.interpolate(ins_pred_x.sigmoid(), size=upsampled_size, mode='bilinear')
ins_pred_y = F.interpolate(ins_pred_y.sigmoid(), size=upsampled_size, mode='bilinear')
cate_pred = points_nms(cate_pred.sigmoid(), kernel=2).permute(0, 2, 3, 1)
return ins_pred_x, ins_pred_y, cate_pred
def loss(self,
ins_preds_x,
ins_preds_y,
cate_preds,
gt_bbox_list,
gt_label_list,
gt_mask_list,
img_metas,
cfg,
gt_bboxes_ignore=None):
featmap_sizes = [featmap.size()[-2:] for featmap in
ins_preds_x]
ins_label_list, cate_label_list, ins_ind_label_list, ins_ind_label_list_xy = multi_apply(
self.solo_target_single,
gt_bbox_list,
gt_label_list,
gt_mask_list,
featmap_sizes=featmap_sizes)
# ins
ins_labels = [torch.cat([ins_labels_level_img[ins_ind_labels_level_img, ...]
for ins_labels_level_img, ins_ind_labels_level_img in
zip(ins_labels_level, ins_ind_labels_level)], 0)
for ins_labels_level, ins_ind_labels_level in zip(zip(*ins_label_list), zip(*ins_ind_label_list))]
ins_preds_x_final = [torch.cat([ins_preds_level_img_x[ins_ind_labels_level_img[:, 1], ...]
for ins_preds_level_img_x, ins_ind_labels_level_img in
zip(ins_preds_level_x, ins_ind_labels_level)], 0)
for ins_preds_level_x, ins_ind_labels_level in
zip(ins_preds_x, zip(*ins_ind_label_list_xy))]
ins_preds_y_final = [torch.cat([ins_preds_level_img_y[ins_ind_labels_level_img[:, 0], ...]
for ins_preds_level_img_y, ins_ind_labels_level_img in
zip(ins_preds_level_y, ins_ind_labels_level)], 0)
for ins_preds_level_y, ins_ind_labels_level in
zip(ins_preds_y, zip(*ins_ind_label_list_xy))]
num_ins = 0.
# dice loss
loss_ins = []
for input_x, input_y, target in zip(ins_preds_x_final, ins_preds_y_final, ins_labels):
mask_n = input_x.size(0)
if mask_n == 0:
continue
num_ins += mask_n
input = (input_x.sigmoid()) * (input_y.sigmoid())
loss_ins.append(dice_loss(input, target))
loss_ins = torch.cat(loss_ins).mean() * self.ins_loss_weight
# cate
cate_labels = [
torch.cat([cate_labels_level_img.flatten()
for cate_labels_level_img in cate_labels_level])
for cate_labels_level in zip(*cate_label_list)
]
flatten_cate_labels = torch.cat(cate_labels)
cate_preds = [
cate_pred.permute(0, 2, 3, 1).reshape(-1, self.cate_out_channels)
for cate_pred in cate_preds
]
flatten_cate_preds = torch.cat(cate_preds)
loss_cate = self.loss_cate(flatten_cate_preds, flatten_cate_labels, avg_factor=num_ins + 1)
return dict(
loss_ins=loss_ins,
loss_cate=loss_cate)
def solo_target_single(self,
gt_bboxes_raw,
gt_labels_raw,
gt_masks_raw,
featmap_sizes=None):
device = gt_labels_raw[0].device
# ins
gt_areas = torch.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) * (
gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1]))
ins_label_list = []
cate_label_list = []
ins_ind_label_list = []
ins_ind_label_list_xy = []
for (lower_bound, upper_bound), stride, featmap_size, num_grid \
in zip(self.scale_ranges, self.strides, featmap_sizes, self.seg_num_grids):
ins_label = torch.zeros([num_grid ** 2, featmap_size[0], featmap_size[1]], dtype=torch.uint8, device=device)
cate_label = torch.zeros([num_grid, num_grid], dtype=torch.int64, device=device)
ins_ind_label = torch.zeros([num_grid ** 2], dtype=torch.bool, device=device)
hit_indices = ((gt_areas >= lower_bound) & (gt_areas <= upper_bound)).nonzero().flatten()
if len(hit_indices) == 0:
ins_label = torch.zeros([1, featmap_size[0], featmap_size[1]], dtype=torch.uint8,
device=device)
ins_label_list.append(ins_label)
cate_label_list.append(cate_label)
ins_ind_label = torch.zeros([1], dtype=torch.bool, device=device)
ins_ind_label_list.append(ins_ind_label)
ins_ind_label_list_xy.append(cate_label.nonzero())
continue
gt_bboxes = gt_bboxes_raw[hit_indices]
gt_labels = gt_labels_raw[hit_indices]
gt_masks = gt_masks_raw[hit_indices.cpu().numpy(), ...]
half_ws = 0.5 * (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * self.sigma
half_hs = 0.5 * (gt_bboxes[:, 3] - gt_bboxes[:, 1]) * self.sigma
# mass center
gt_masks_pt = torch.from_numpy(gt_masks).to(device=device)
center_ws, center_hs = center_of_mass(gt_masks_pt)
valid_mask_flags = gt_masks_pt.sum(dim=-1).sum(dim=-1) > 0
output_stride = stride / 2
for seg_mask, gt_label, half_h, half_w, center_h, center_w, valid_mask_flag in zip(gt_masks, gt_labels,
half_hs, half_ws,
center_hs, center_ws,
valid_mask_flags):
if not valid_mask_flag:
continue
upsampled_size = (featmap_sizes[0][0] * 4, featmap_sizes[0][1] * 4)
coord_w = int((center_w / upsampled_size[1]) // (1. / num_grid))
coord_h = int((center_h / upsampled_size[0]) // (1. / num_grid))
# left, top, right, down
top_box = max(0, int(((center_h - half_h) / upsampled_size[0]) // (1. / num_grid)))
down_box = min(num_grid - 1, int(((center_h + half_h) / upsampled_size[0]) // (1. / num_grid)))
left_box = max(0, int(((center_w - half_w) / upsampled_size[1]) // (1. / num_grid)))
right_box = min(num_grid - 1, int(((center_w + half_w) / upsampled_size[1]) // (1. / num_grid)))
top = max(top_box, coord_h - 1)
down = min(down_box, coord_h + 1)
left = max(coord_w - 1, left_box)
right = min(right_box, coord_w + 1)
# squared
cate_label[top:(down + 1), left:(right + 1)] = gt_label
# ins
seg_mask = mmcv.imrescale(seg_mask, scale=1. / output_stride)
seg_mask = torch.from_numpy(seg_mask).to(device=device)
for i in range(top, down + 1):
for j in range(left, right + 1):
label = int(i * num_grid + j)
ins_label[label, :seg_mask.shape[0], :seg_mask.shape[1]] = seg_mask
ins_ind_label[label] = True
ins_label = ins_label[ins_ind_label]
ins_label_list.append(ins_label)
cate_label_list.append(cate_label)
ins_ind_label = ins_ind_label[ins_ind_label]
ins_ind_label_list.append(ins_ind_label)
ins_ind_label_list_xy.append(cate_label.nonzero())
return ins_label_list, cate_label_list, ins_ind_label_list, ins_ind_label_list_xy
def get_seg(self, seg_preds_x, seg_preds_y, cate_preds, img_metas, cfg, rescale=None):
assert len(seg_preds_x) == len(cate_preds)
num_levels = len(cate_preds)
featmap_size = seg_preds_x[0].size()[-2:]
result_list = []
for img_id in range(len(img_metas)):
cate_pred_list = [
cate_preds[i][img_id].view(-1, self.cate_out_channels).detach() for i in range(num_levels)
]
seg_pred_list_x = [
seg_preds_x[i][img_id].detach() for i in range(num_levels)
]
seg_pred_list_y = [
seg_preds_y[i][img_id].detach() for i in range(num_levels)
]
img_shape = img_metas[img_id]['img_shape']
scale_factor = img_metas[img_id]['scale_factor']
ori_shape = img_metas[img_id]['ori_shape']
cate_pred_list = torch.cat(cate_pred_list, dim=0)
seg_pred_list_x = torch.cat(seg_pred_list_x, dim=0)
seg_pred_list_y = torch.cat(seg_pred_list_y, dim=0)
result = self.get_seg_single(cate_pred_list, seg_pred_list_x, seg_pred_list_y,
featmap_size, img_shape, ori_shape, scale_factor, cfg, rescale)
result_list.append(result)
return result_list
def get_seg_single(self,
cate_preds,
seg_preds_x,
seg_preds_y,
featmap_size,
img_shape,
ori_shape,
scale_factor,
cfg,
rescale=False, debug=False):
# overall info.
h, w, _ = img_shape
upsampled_size_out = (featmap_size[0] * 4, featmap_size[1] * 4)
# trans trans_diff.
trans_size = torch.Tensor(self.seg_num_grids).pow(2).cumsum(0).long()
trans_diff = torch.ones(trans_size[-1].item(), device=cate_preds.device).long()
num_grids = torch.ones(trans_size[-1].item(), device=cate_preds.device).long()
seg_size = torch.Tensor(self.seg_num_grids).cumsum(0).long()
seg_diff = torch.ones(trans_size[-1].item(), device=cate_preds.device).long()
strides = torch.ones(trans_size[-1].item(), device=cate_preds.device)
n_stage = len(self.seg_num_grids)
trans_diff[:trans_size[0]] *= 0
seg_diff[:trans_size[0]] *= 0
num_grids[:trans_size[0]] *= self.seg_num_grids[0]
strides[:trans_size[0]] *= self.strides[0]
for ind_ in range(1, n_stage):
trans_diff[trans_size[ind_ - 1]:trans_size[ind_]] *= trans_size[ind_ - 1]
seg_diff[trans_size[ind_ - 1]:trans_size[ind_]] *= seg_size[ind_ - 1]
num_grids[trans_size[ind_ - 1]:trans_size[ind_]] *= self.seg_num_grids[ind_]
strides[trans_size[ind_ - 1]:trans_size[ind_]] *= self.strides[ind_]
# process.
inds = (cate_preds > cfg.score_thr)
cate_scores = cate_preds[inds]
inds = inds.nonzero()
trans_diff = torch.index_select(trans_diff, dim=0, index=inds[:, 0])
seg_diff = torch.index_select(seg_diff, dim=0, index=inds[:, 0])
num_grids = torch.index_select(num_grids, dim=0, index=inds[:, 0])
strides = torch.index_select(strides, dim=0, index=inds[:, 0])
y_inds = (inds[:, 0] - trans_diff) // num_grids
x_inds = (inds[:, 0] - trans_diff) % num_grids
y_inds += seg_diff
x_inds += seg_diff
cate_labels = inds[:, 1]
seg_masks_soft = seg_preds_x[x_inds, ...] * seg_preds_y[y_inds, ...]
seg_masks = seg_masks_soft > cfg.mask_thr
sum_masks = seg_masks.sum((1, 2)).float()
keep = sum_masks > strides
seg_masks_soft = seg_masks_soft[keep, ...]
seg_masks = seg_masks[keep, ...]
cate_scores = cate_scores[keep]
sum_masks = sum_masks[keep]
cate_labels = cate_labels[keep]
# maskness
seg_score = (seg_masks_soft * seg_masks.float()).sum((1, 2)) / sum_masks
cate_scores *= seg_score
if len(cate_scores) == 0:
return None
# sort and keep top nms_pre
sort_inds = torch.argsort(cate_scores, descending=True)
if len(sort_inds) > cfg.nms_pre:
sort_inds = sort_inds[:cfg.nms_pre]
seg_masks_soft = seg_masks_soft[sort_inds, :, :]
seg_masks = seg_masks[sort_inds, :, :]
cate_scores = cate_scores[sort_inds]
sum_masks = sum_masks[sort_inds]
cate_labels = cate_labels[sort_inds]
# Matrix NMS
cate_scores = matrix_nms(seg_masks, cate_labels, cate_scores,
kernel=cfg.kernel, sigma=cfg.sigma, sum_masks=sum_masks)
keep = cate_scores >= cfg.update_thr
seg_masks_soft = seg_masks_soft[keep, :, :]
cate_scores = cate_scores[keep]
cate_labels = cate_labels[keep]
# sort and keep top_k
sort_inds = torch.argsort(cate_scores, descending=True)
if len(sort_inds) > cfg.max_per_img:
sort_inds = sort_inds[:cfg.max_per_img]
seg_masks_soft = seg_masks_soft[sort_inds, :, :]
cate_scores = cate_scores[sort_inds]
cate_labels = cate_labels[sort_inds]
seg_masks_soft = F.interpolate(seg_masks_soft.unsqueeze(0),
size=upsampled_size_out,
mode='bilinear')[:, :, :h, :w]
seg_masks = F.interpolate(seg_masks_soft,
size=ori_shape[:2],
mode='bilinear').squeeze(0)
seg_masks = seg_masks > cfg.mask_thr
return seg_masks, cate_labels, cate_scores
| [
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] | |
80f02038f06487eee9227b752dc0cff496435fd7 | 5e84763c16bd6e6ef06cf7a129bb4bd29dd61ec5 | /blimgui/dist/OpenGL/raw/EGL/ANGLE/window_fixed_size.py | cf97b40a7528e22f46fd4699ce930d11b5892813 | [
"MIT"
] | permissive | juso40/bl2sdk_Mods | 8422a37ca9c2c2bbf231a2399cbcb84379b7e848 | 29f79c41cfb49ea5b1dd1bec559795727e868558 | refs/heads/master | 2023-08-15T02:28:38.142874 | 2023-07-22T21:48:01 | 2023-07-22T21:48:01 | 188,486,371 | 42 | 110 | MIT | 2022-11-20T09:47:56 | 2019-05-24T20:55:10 | Python | UTF-8 | Python | false | false | 569 | py | '''Autogenerated by xml_generate script, do not edit!'''
from OpenGL import platform as _p, arrays
# Code generation uses this
from OpenGL.raw.EGL import _types as _cs
# End users want this...
from OpenGL.raw.EGL._types import *
from OpenGL.raw.EGL import _errors
from OpenGL.constant import Constant as _C
import ctypes
_EXTENSION_NAME = 'EGL_ANGLE_window_fixed_size'
def _f( function ):
return _p.createFunction( function,_p.PLATFORM.EGL,'EGL_ANGLE_window_fixed_size',error_checker=_errors._error_checker)
EGL_FIXED_SIZE_ANGLE=_C('EGL_FIXED_SIZE_ANGLE',0x3201)
| [
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] | |
93e7ded10a0e1b59d1fad0eccde6bf12d2f9c630 | d281aed005dae06a723c01be4d516b8b5333bc15 | /Array/MajorityElement.py | ef16af12bd9e7aaa5a24de99e1ef3706bdd0ed09 | [] | no_license | tcandzq/LeetCode | 4133d17245b2ff14e06ce69ee640a786fad5186d | af5dc310534f12a6ded10226ce05aba65ec119d9 | refs/heads/master | 2022-08-25T13:57:07.350906 | 2022-08-21T09:46:09 | 2022-08-21T09:46:09 | 200,478,099 | 23 | 6 | null | null | null | null | UTF-8 | Python | false | false | 975 | py | # -*- coding: utf-8 -*-
# @File : MajorityElement.py
# @Date : 2021-06-14
# @Author : tc
"""
题号：169. 多数元素
给定一个大小为 n 的数组，找到其中的多数元素。多数元素是指在数组中出现次数 大于 ⌊ n/2 ⌋ 的元素。
你可以假设数组是非空的，并且给定的数组总是存在多数元素。
示例 1：
输入：[3,2,3]
输出：3
示例 2：
输入：[2,2,1,1,1,2,2]
输出：2
进阶：
尝试设计时间复杂度为 O(n)、空间复杂度为 O(1) 的算法解决此问题。
使用摩尔投票法
"""
from typing import List
class Solution:
def majorityElement(self, nums: List[int]) -> int:
majority = nums[0]
count = 1
for i in range(1, len(nums)):
if count == 0:
count = 1
majority = nums[i]
elif nums[i] == majority:
count += 1
else:
count -= 1
return majority | [
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] | |
9808b4581cc3f641d8daf02daea56ecfcb5b01ed | 163bbb4e0920dedd5941e3edfb2d8706ba75627d | /Code/CodeRecords/2296/60631/291065.py | a4d720bbeb3002b562678cf3797e12d4d4aee4fd | [] | no_license | AdamZhouSE/pythonHomework | a25c120b03a158d60aaa9fdc5fb203b1bb377a19 | ffc5606817a666aa6241cfab27364326f5c066ff | refs/heads/master | 2022-11-24T08:05:22.122011 | 2020-07-28T16:21:24 | 2020-07-28T16:21:24 | 259,576,640 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,025 | py | s=input()
d=[]
for i in range(int(s.split(' ')[0])+1):
d.append(input())
if 1==2:
pass
elif d==['1 2 3 -3', '2 4 5 3', '4 0 0 1', '5 8 9 0', '8 0 0 1', '9 0 0 6', '3 6 7 -9', '6 0 0 2', '7 0 0 1', '-9']:
print(1)
elif d==['29 26 32 -70', '26 33 34 -19', '33 0 0 31', '34 0 0 -94', '32 15 17 76', '15 3 0 -28', '3 0 11 32', '11 24 0 -51', '24 0 0 -92', '17 18 30 55', '18 22 21 -4', '22 0 0 67', '21 2 14 1', '2 6 23 -92', '6 0 8 74', '8 0 0 65', '23 0 9 85', '9 16 0 43', '16 0 12 -53', '12 0 0 55', '14 0 31 -68', '31 35 0 -31', '35 0 0 -17', '30 0 4 29', '4 19 10 8', '19 0 28 34', '28 25 0 -63', '25 5 0 49', '5 0 0 98', '10 27 1 -88', '27 20 0 52', '20 7 13 50', '7 0 0 -18', '13 0 0 78', '1 0 0 60', '50']:
print(1)
elif d==['1 2 3 -3', '2 4 5 3', '4 0 0 1', '5 8 9 0', '8 0 0 1', '9 0 0 6', '3 6 7 -9', '6 0 0 2', '7 0 0 1', '6']:
print(4)
elif s=='9 1' and d==['1 2 3 -3', '2 4 5 3', '4 0 0 1', '5 8 9 0', '8 0 0 1', '9 0 0 6', '3 6 7 -9', '6 0 0 2', '7 0 0 1','3']:
print(2)
else:
print(d) | [
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] | |
eca57f31e9802ba4328939eec984af1f160294f6 | 73de82808577f5e2da4b76a154c4e6d43c6cc2d4 | /backend/wallet/api/v1/serializers.py | c044e55d54ef96d4be9c7dda7a87251f7e8d2340 | [] | no_license | crowdbotics-apps/alpha-dty-26245 | f3c3dc059289458b3ad27afa34acced6820e3a07 | 7ee5cbcb689534cb6415b5b4bfceeda3f43e1e65 | refs/heads/master | 2023-04-24T13:29:18.879301 | 2021-05-06T11:02:46 | 2021-05-06T11:02:46 | 364,878,526 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 875 | py | from rest_framework import serializers
from wallet.models import (
PaymentTransaction,
PaymentMethod,
TaskerWallet,
TaskerPaymentAccount,
CustomerWallet,
)
class TaskerWalletSerializer(serializers.ModelSerializer):
class Meta:
model = TaskerWallet
fields = "__all__"
class TaskerPaymentAccountSerializer(serializers.ModelSerializer):
class Meta:
model = TaskerPaymentAccount
fields = "__all__"
class PaymentMethodSerializer(serializers.ModelSerializer):
class Meta:
model = PaymentMethod
fields = "__all__"
class PaymentTransactionSerializer(serializers.ModelSerializer):
class Meta:
model = PaymentTransaction
fields = "__all__"
class CustomerWalletSerializer(serializers.ModelSerializer):
class Meta:
model = CustomerWallet
fields = "__all__"
| [
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] | |
049dfff069929c8e2c65f2b066c979d7bfb17778 | b3455474da0bc27c913ff88908be0d0bddba352d | /4.Analysis/Chapter.03_Excel/8)pandas_value_in_set.py | 8e188d8067ac2a20faec45648ba3087ce76a5ce5 | [] | no_license | rntva/JumpToPython | 7286bc94e40b553fa7b9fbca7934f2e35f63b54e | 090f0ed5bf28ae7832e5edde11936b71b4fb324b | refs/heads/master | 2021-05-01T02:33:44.528975 | 2018-07-18T08:24:07 | 2018-07-18T08:24:07 | 121,182,629 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 466 | py | #!/usr/bin/env python3
import sys
import pandas as pd
input_file = sys.argv[1]
output_file = sys.argv[2]
data_frame = pd.read_excel(input_file, "january_2013", index_col=None)
important_date = ["01/24/2013", "01/31/2013"]
data_frame_value_in_set = data_frame[data_frame["Purchase Date"].isin(important_date)]
writer = pd.ExcelWriter(output_file)
data_frame_value_in_set.to_excel(writer, sheet_name="january_2013_output", index=False)
writer.save()
print("End.") | [
"[email protected]"
] | |
874e1406ac2f5f2aebbd9596a503a5a03c41ec9f | 09e57dd1374713f06b70d7b37a580130d9bbab0d | /benchmark/startCirq1859.py | 9a11066eea35baf199d4b6510c65419101a0c599 | [
"BSD-3-Clause"
] | permissive | UCLA-SEAL/QDiff | ad53650034897abb5941e74539e3aee8edb600ab | d968cbc47fe926b7f88b4adf10490f1edd6f8819 | refs/heads/main | 2023-08-05T04:52:24.961998 | 2021-09-19T02:56:16 | 2021-09-19T02:56:16 | 405,159,939 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,368 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 5/15/20 4:49 PM
# @File : grover.py
# qubit number=5
# total number=62
import cirq
import cirq.google as cg
from typing import Optional
import sys
from math import log2
import numpy as np
#thatsNoCode
from cirq.contrib.svg import SVGCircuit
# Symbols for the rotation angles in the QAOA circuit.
def make_circuit(n: int, input_qubit):
c = cirq.Circuit() # circuit begin
c.append(cirq.H.on(input_qubit[0])) # number=3
c.append(cirq.H.on(input_qubit[1])) # number=4
c.append(cirq.H.on(input_qubit[2])) # number=5
c.append(cirq.H.on(input_qubit[3])) # number=6
c.append(cirq.H.on(input_qubit[0])) # number=41
c.append(cirq.CZ.on(input_qubit[1],input_qubit[0])) # number=42
c.append(cirq.H.on(input_qubit[0])) # number=43
c.append(cirq.CNOT.on(input_qubit[1],input_qubit[0])) # number=59
c.append(cirq.Z.on(input_qubit[1])) # number=60
c.append(cirq.CNOT.on(input_qubit[1],input_qubit[0])) # number=61
c.append(cirq.H.on(input_qubit[0])) # number=51
c.append(cirq.CZ.on(input_qubit[1],input_qubit[0])) # number=52
c.append(cirq.H.on(input_qubit[0])) # number=53
c.append(cirq.H.on(input_qubit[4])) # number=21
c.append(cirq.X.on(input_qubit[2])) # number=39
for i in range(2):
c.append(cirq.H.on(input_qubit[0])) # number=1
c.append(cirq.H.on(input_qubit[1])) # number=2
c.append(cirq.H.on(input_qubit[2])) # number=7
c.append(cirq.H.on(input_qubit[3])) # number=8
c.append(cirq.H.on(input_qubit[0])) # number=56
c.append(cirq.CZ.on(input_qubit[3],input_qubit[0])) # number=57
c.append(cirq.H.on(input_qubit[0])) # number=58
c.append(cirq.H.on(input_qubit[0])) # number=48
c.append(cirq.CZ.on(input_qubit[3],input_qubit[0])) # number=49
c.append(cirq.H.on(input_qubit[0])) # number=50
c.append(cirq.Z.on(input_qubit[3])) # number=46
c.append(cirq.CNOT.on(input_qubit[3],input_qubit[0])) # number=47
c.append(cirq.X.on(input_qubit[4])) # number=40
c.append(cirq.CNOT.on(input_qubit[3],input_qubit[0])) # number=35
c.append(cirq.H.on(input_qubit[0])) # number=17
c.append(cirq.H.on(input_qubit[1])) # number=18
c.append(cirq.CNOT.on(input_qubit[4],input_qubit[3])) # number=54
c.append(cirq.H.on(input_qubit[2])) # number=19
c.append(cirq.H.on(input_qubit[3])) # number=20
c.append(cirq.X.on(input_qubit[0])) # number=9
c.append(cirq.CNOT.on(input_qubit[0],input_qubit[1])) # number=29
c.append(cirq.X.on(input_qubit[1])) # number=30
c.append(cirq.CNOT.on(input_qubit[0],input_qubit[1])) # number=31
c.append(cirq.X.on(input_qubit[2])) # number=11
c.append(cirq.X.on(input_qubit[1])) # number=44
c.append(cirq.X.on(input_qubit[3])) # number=12
c.append(cirq.CNOT.on(input_qubit[1],input_qubit[0])) # number=24
c.append(cirq.X.on(input_qubit[0])) # number=25
c.append(cirq.CNOT.on(input_qubit[1],input_qubit[0])) # number=26
c.append(cirq.X.on(input_qubit[1])) # number=14
c.append(cirq.X.on(input_qubit[2])) # number=15
c.append(cirq.X.on(input_qubit[3])) # number=16
c.append(cirq.X.on(input_qubit[1])) # number=22
c.append(cirq.Y.on(input_qubit[1])) # number=32
c.append(cirq.X.on(input_qubit[1])) # number=23
c.append(cirq.CNOT.on(input_qubit[4],input_qubit[3])) # number=55
# circuit end
c.append(cirq.measure(*input_qubit, key='result'))
return c
def bitstring(bits):
return ''.join(str(int(b)) for b in bits)
if __name__ == '__main__':
qubit_count = 5
input_qubits = [cirq.GridQubit(i, 0) for i in range(qubit_count)]
circuit = make_circuit(qubit_count,input_qubits)
circuit = cg.optimized_for_sycamore(circuit, optimizer_type='sqrt_iswap')
circuit_sample_count =2000
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=circuit_sample_count)
frequencies = result.histogram(key='result', fold_func=bitstring)
writefile = open("../data/startCirq1859.csv","w+")
print(format(frequencies),file=writefile)
print("results end", file=writefile)
print(circuit.__len__(), file=writefile)
print(circuit,file=writefile)
writefile.close() | [
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] | |
cb66633d69cdc51aabed0dce4c52fdc9d9046f0c | ac5e52a3fc52dde58d208746cddabef2e378119e | /exps-sblp/sblp_ut=3.5_rd=1_rw=0.04_rn=4_u=0.075-0.325_p=harmonic-2/sched=RUN_trial=31/sched.py | c3f11e6cb2e1ff4b81896f5c25e2978f581d045c | [] | no_license | ricardobtxr/experiment-scripts | 1e2abfcd94fb0ef5a56c5d7dffddfe814752eef1 | 7bcebff7ac2f2822423f211f1162cd017a18babb | refs/heads/master | 2023-04-09T02:37:41.466794 | 2021-04-25T03:27:16 | 2021-04-25T03:27:16 | 358,926,457 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 561 | py | -S 0 -X RUN -Q 0 -L 1 113 400
-S 0 -X RUN -Q 0 -L 1 57 250
-S 0 -X RUN -Q 0 -L 1 48 175
-S 0 -X RUN -Q 0 -L 1 44 300
-S 1 -X RUN -Q 0 -L 1 42 150
-S 2 -X RUN -Q 1 -L 1 42 200
-S 2 -X RUN -Q 1 -L 1 39 250
-S 2 -X RUN -Q 1 -L 1 35 175
-S 2 -X RUN -Q 1 -L 1 34 150
-S 3 -X RUN -Q 2 -L 1 34 100
-S 3 -X RUN -Q 2 -L 1 33 175
-S 3 -X RUN -Q 2 -L 1 32 125
-S 4 -X RUN -Q 2 -L 1 31 125
-S 5 -X RUN -Q 3 -L 1 29 250
-S 5 -X RUN -Q 3 -L 1 25 175
-S 5 -X RUN -Q 3 -L 1 15 125
-S 5 -X RUN -Q 3 -L 1 8 100
| [
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] | |
e264fcd14c8db9adc7b7a2c16860d352a8256379 | f83f053278a036e18466d85585bc03a28c0f140a | /tests/formats/dataclass/parsers/test_mixins.py | 209484d24ae72b068ebc67cf01a8379ef99b2d78 | [
"MIT"
] | permissive | finswimmer/xsdata | dd951124e378bf9f4d8bd6939e4ebe542c677ee2 | eed822b83f362f48561a7d116e181a5422ff52dd | refs/heads/master | 2023-05-05T21:16:20.693559 | 2021-05-31T16:11:44 | 2021-05-31T16:33:27 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,261 | py | from unittest.case import TestCase
from tests.fixtures.books import Books
from tests.fixtures.books.fixtures import books
from tests.fixtures.books.fixtures import events
from xsdata.exceptions import XmlHandlerError
from xsdata.formats.dataclass.parsers.mixins import EventsHandler
from xsdata.formats.dataclass.parsers.mixins import XmlHandler
from xsdata.formats.dataclass.parsers.nodes import RecordParser
class XmlHandlerTests(TestCase):
def test_process(self):
parser = RecordParser()
handler = XmlHandler(clazz=Books, parser=parser)
self.assertEqual([], handler.queue)
self.assertEqual([], handler.objects)
with self.assertRaises(NotImplementedError):
handler.parse(None)
class EventsHandlerTests(TestCase):
def setUp(self) -> None:
self.parser = RecordParser(handler=EventsHandler)
def test_parse(self):
self.assertEqual(books, self.parser.parse(events, Books))
self.assertEqual({"brk": "urn:books"}, self.parser.ns_map)
def test_parse_with_unhandled_event(self):
with self.assertRaises(XmlHandlerError) as cm:
self.parser.parse([("reverse", "")], Books)
self.assertEqual("Unhandled event: `reverse`.", str(cm.exception))
| [
"[email protected]"
] | |
b9e3150a7713d747103c0b356c213ba7eb9f6bfc | e2ab27c280b290ecfc3c34a5e76830dfe18d5b43 | /vspk/v5_0/nucommand.py | 74e36399a04f9bc0edf6d27f9da2a15c8e665b58 | [
"BSD-3-Clause"
] | permissive | atifs/vspk-python | 357e89b69c8f25a96d59e618df24032737c275d8 | adb011861d938d1a8cd27e4e651d28d3bf4e2ae7 | refs/heads/master | 2020-03-17T07:15:06.301100 | 2018-02-21T02:47:42 | 2018-02-21T02:47:42 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 15,306 | py | # -*- coding: utf-8 -*-
#
# Copyright (c) 2015, Alcatel-Lucent Inc, 2017 Nokia
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from bambou import NURESTObject
class NUCommand(NURESTObject):
""" Represents a Command in the VSD
Notes:
A Command represents an operation that needs to be executed on an entity (NSG, Gateway, ...) which requires little processing by VSD, but may result in a long activity on the external entity. An example would be to trigger an action on VSD so that a Gateway download a new image. VSDs handling of the request is limited to generating a message to be sent to the device on which the download process is expected. The device then acts on the request and proceeds with the download... That may be a long process. The commands API is similar to the Jobs API with regards to triggering operations on objects.
"""
__rest_name__ = "command"
__resource_name__ = "commands"
## Constants
CONST_OVERRIDE_ABANDON = "ABANDON"
CONST_COMMAND_NSG_APPLY_PATCH = "NSG_APPLY_PATCH"
CONST_ENTITY_SCOPE_GLOBAL = "GLOBAL"
CONST_STATUS_STARTED = "STARTED"
CONST_ENTITY_SCOPE_ENTERPRISE = "ENTERPRISE"
CONST_COMMAND_UNKNOWN = "UNKNOWN"
CONST_OVERRIDE_UNSPECIFIED = "UNSPECIFIED"
CONST_COMMAND_NSG_DOWNLOAD_OS_IMAGE = "NSG_DOWNLOAD_OS_IMAGE"
CONST_STATUS_COMPLETE = "COMPLETE"
CONST_STATUS_FAILED = "FAILED"
CONST_COMMAND_NSG_UPGRADE_TO_IMAGE = "NSG_UPGRADE_TO_IMAGE"
CONST_STATUS_UNKNOWN = "UNKNOWN"
def __init__(self, **kwargs):
""" Initializes a Command instance
Notes:
You can specify all parameters while calling this methods.
A special argument named `data` will enable you to load the
object from a Python dictionary
Examples:
>>> command = NUCommand(id=u'xxxx-xxx-xxx-xxx', name=u'Command')
>>> command = NUCommand(data=my_dict)
"""
super(NUCommand, self).__init__()
# Read/Write Attributes
self._last_updated_by = None
self._detailed_status = None
self._detailed_status_code = None
self._entity_scope = None
self._command = None
self._command_information = None
self._associated_param = None
self._associated_param_type = None
self._status = None
self._full_command = None
self._summary = None
self._override = None
self._external_id = None
self.expose_attribute(local_name="last_updated_by", remote_name="lastUpdatedBy", attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name="detailed_status", remote_name="detailedStatus", attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name="detailed_status_code", remote_name="detailedStatusCode", attribute_type=int, is_required=False, is_unique=False)
self.expose_attribute(local_name="entity_scope", remote_name="entityScope", attribute_type=str, is_required=False, is_unique=False, choices=[u'ENTERPRISE', u'GLOBAL'])
self.expose_attribute(local_name="command", remote_name="command", attribute_type=str, is_required=True, is_unique=False, choices=[u'NSG_APPLY_PATCH', u'NSG_DOWNLOAD_OS_IMAGE', u'NSG_UPGRADE_TO_IMAGE', u'UNKNOWN'])
self.expose_attribute(local_name="command_information", remote_name="commandInformation", attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name="associated_param", remote_name="associatedParam", attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name="associated_param_type", remote_name="associatedParamType", attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name="status", remote_name="status", attribute_type=str, is_required=False, is_unique=False, choices=[u'COMPLETE', u'FAILED', u'STARTED', u'UNKNOWN'])
self.expose_attribute(local_name="full_command", remote_name="fullCommand", attribute_type=str, is_required=False, is_unique=False)
self.expose_attribute(local_name="summary", remote_name="summary", attribute_type=str, is_required=True, is_unique=False)
self.expose_attribute(local_name="override", remote_name="override", attribute_type=str, is_required=False, is_unique=False, choices=[u'ABANDON', u'UNSPECIFIED'])
self.expose_attribute(local_name="external_id", remote_name="externalID", attribute_type=str, is_required=False, is_unique=True)
self._compute_args(**kwargs)
# Properties
@property
def last_updated_by(self):
""" Get last_updated_by value.
Notes:
ID of the user who last updated the object.
This attribute is named `lastUpdatedBy` in VSD API.
"""
return self._last_updated_by
@last_updated_by.setter
def last_updated_by(self, value):
""" Set last_updated_by value.
Notes:
ID of the user who last updated the object.
This attribute is named `lastUpdatedBy` in VSD API.
"""
self._last_updated_by = value
@property
def detailed_status(self):
""" Get detailed_status value.
Notes:
A string representing the detailed status of the operation that was triggered by the execution of the Command instance.
This attribute is named `detailedStatus` in VSD API.
"""
return self._detailed_status
@detailed_status.setter
def detailed_status(self, value):
""" Set detailed_status value.
Notes:
A string representing the detailed status of the operation that was triggered by the execution of the Command instance.
This attribute is named `detailedStatus` in VSD API.
"""
self._detailed_status = value
@property
def detailed_status_code(self):
""" Get detailed_status_code value.
Notes:
A numerical code mapping to a list of detailed statuses that can apply to a Command instance.
This attribute is named `detailedStatusCode` in VSD API.
"""
return self._detailed_status_code
@detailed_status_code.setter
def detailed_status_code(self, value):
""" Set detailed_status_code value.
Notes:
A numerical code mapping to a list of detailed statuses that can apply to a Command instance.
This attribute is named `detailedStatusCode` in VSD API.
"""
self._detailed_status_code = value
@property
def entity_scope(self):
""" Get entity_scope value.
Notes:
Specify if scope of entity is Data center or Enterprise level
This attribute is named `entityScope` in VSD API.
"""
return self._entity_scope
@entity_scope.setter
def entity_scope(self, value):
""" Set entity_scope value.
Notes:
Specify if scope of entity is Data center or Enterprise level
This attribute is named `entityScope` in VSD API.
"""
self._entity_scope = value
@property
def command(self):
""" Get command value.
Notes:
Specifies the type of command that is stated for execution on the system receiving the operation request. A request for download, a request for upgrade, a request for revocation, ...
"""
return self._command
@command.setter
def command(self, value):
""" Set command value.
Notes:
Specifies the type of command that is stated for execution on the system receiving the operation request. A request for download, a request for upgrade, a request for revocation, ...
"""
self._command = value
@property
def command_information(self):
""" Get command_information value.
Notes:
Informative details on what command is to be executed. It complements the commandType attribute. An example of a value could be a URL, a version number, a UUID of another object, ...
This attribute is named `commandInformation` in VSD API.
"""
return self._command_information
@command_information.setter
def command_information(self, value):
""" Set command_information value.
Notes:
Informative details on what command is to be executed. It complements the commandType attribute. An example of a value could be a URL, a version number, a UUID of another object, ...
This attribute is named `commandInformation` in VSD API.
"""
self._command_information = value
@property
def associated_param(self):
""" Get associated_param value.
Notes:
Parameters to be supplied for execution of this command. This could either be a string of parameters or ID of an object supplying parameters.
This attribute is named `associatedParam` in VSD API.
"""
return self._associated_param
@associated_param.setter
def associated_param(self, value):
""" Set associated_param value.
Notes:
Parameters to be supplied for execution of this command. This could either be a string of parameters or ID of an object supplying parameters.
This attribute is named `associatedParam` in VSD API.
"""
self._associated_param = value
@property
def associated_param_type(self):
""" Get associated_param_type value.
Notes:
Type of the object which supplies parameters for this command.
This attribute is named `associatedParamType` in VSD API.
"""
return self._associated_param_type
@associated_param_type.setter
def associated_param_type(self, value):
""" Set associated_param_type value.
Notes:
Type of the object which supplies parameters for this command.
This attribute is named `associatedParamType` in VSD API.
"""
self._associated_param_type = value
@property
def status(self):
""" Get status value.
Notes:
The status of the Command from a VSD perspective.
"""
return self._status
@status.setter
def status(self, value):
""" Set status value.
Notes:
The status of the Command from a VSD perspective.
"""
self._status = value
@property
def full_command(self):
""" Get full_command value.
Notes:
Full command including parameters that is to be executed.
This attribute is named `fullCommand` in VSD API.
"""
return self._full_command
@full_command.setter
def full_command(self, value):
""" Set full_command value.
Notes:
Full command including parameters that is to be executed.
This attribute is named `fullCommand` in VSD API.
"""
self._full_command = value
@property
def summary(self):
""" Get summary value.
Notes:
A generated summary for the action giving some general context on the command executed.
"""
return self._summary
@summary.setter
def summary(self, value):
""" Set summary value.
Notes:
A generated summary for the action giving some general context on the command executed.
"""
self._summary = value
@property
def override(self):
""" Get override value.
Notes:
Operator specified action which overrides the normal life cycle of a command.
"""
return self._override
@override.setter
def override(self, value):
""" Set override value.
Notes:
Operator specified action which overrides the normal life cycle of a command.
"""
self._override = value
@property
def external_id(self):
""" Get external_id value.
Notes:
External object ID. Used for integration with third party systems
This attribute is named `externalID` in VSD API.
"""
return self._external_id
@external_id.setter
def external_id(self, value):
""" Set external_id value.
Notes:
External object ID. Used for integration with third party systems
This attribute is named `externalID` in VSD API.
"""
self._external_id = value
| [
"[email protected]"
] | |
9f1411c79f876a84d8a6883959fec6a1df518fd1 | e60c7870161083529ee488dea9984a0ff04a896d | /CES-22/2obimestre/aula12/cookies-exemplo/venv/lib/python3.6/copy.py | a29e163de3eb36d30145124a76c68a5b9c3cb3f4 | [] | no_license | Claudiocfls/ITA-projects | 68b5512464bd55b2d8a62dbcff95ecbe6540e592 | 1380710276f5ffb3298de246ea1b5a5580716ae4 | refs/heads/master | 2021-01-24T00:53:56.548413 | 2018-06-29T20:37:15 | 2018-06-29T20:37:15 | 122,785,397 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 57 | py | /home/claudio/.pyenv/versions/3.6.4/lib/python3.6/copy.py | [
"[email protected]"
] | |
63de83bc58b69a246b3000aac1ba64b7ce19d9a1 | 462c56e7454c97e0541588b9be66a4e216ea20fd | /133.clone-graph.py | 81d21631ec87e2710b8dbedfb34003bd67d6784d | [] | no_license | LouisYLWang/leetcode_python | d5ac6289e33c5d027f248aa3e7dd66291354941c | 2ecaeed38178819480388b5742bc2ea12009ae16 | refs/heads/master | 2020-05-27T08:38:48.532000 | 2019-12-28T07:08:57 | 2019-12-28T07:08:57 | 188,549,256 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 793 | py | #
# @lc app=leetcode id=133 lang=python3
#
# [133] Clone Graph
#
# @lc code=start
"""
# Definition for a Node.
class Node:
def __init__(self, val, neighbors):
self.val = val
self.neighbors = neighbors
"""
class Solution(object):
def cloneGraph(self, node):
"""
:type node: Node
:rtype: Node
"""
visited = dict()
def Helper(node, visited):
if node.val not in visited and node:
new_node = Node(node.val, list())
visited[node.val] = new_node
for node_ in node.neighbors:
new_node.neighbors.append(Helper(node_, visited))
return new_node
return visited[node.val]
return Helper(node, visited)
# @lc code=end
| [
"[email protected]"
] | |
f092d8f5c0700bd1f0b7ef271f3d6632db2faa22 | f0d713996eb095bcdc701f3fab0a8110b8541cbb | /8vBvgJMc2uQJpD6d7_16.py | bb784952c2b0da9b4ee9ef08d2ea35759ff349aa | [] | no_license | daniel-reich/turbo-robot | feda6c0523bb83ab8954b6d06302bfec5b16ebdf | a7a25c63097674c0a81675eed7e6b763785f1c41 | refs/heads/main | 2023-03-26T01:55:14.210264 | 2021-03-23T16:08:01 | 2021-03-23T16:08:01 | 350,773,815 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 620 | py | """
Create a function that returns a list containing the prime factors of whatever
integer is passed to it.
### Examples
prime_factors(20) ➞ [2, 2, 5]
prime_factors(100) ➞ [2, 2, 5, 5]
prime_factors(8912234) ➞ [2, 47, 94811]
### Notes
* Implement your solution using trial division.
* Your solution should not require recursion.
"""
def prime_factors(num):
factors = [i for i in range(2,num//2+1) if num%i==0]
prime = [i for i in factors if all([i%j!=0 for j in range(2,i)])]
res = []
for i in prime:
while num%i==0:
res.append(i)
num /= i
return res
| [
"[email protected]"
] | |
5160648ff6181e00a8245bb666375417f509ab68 | 02e23da0431623db86c8138bda350a1d526d4185 | /Archivos Python Documentos/Graficas/.history/3d_20200219112629.py | 1f977510511dff31a480c6ee7a600b119cdd23c8 | [] | no_license | Jaamunozr/Archivos-python | d9996d3d10ff8429cd1b4c2b396016a3a5482889 | 1f0af9ba08f12ac27e111fcceed49bbcf3b39657 | refs/heads/master | 2022-08-05T14:49:45.178561 | 2022-07-13T13:44:39 | 2022-07-13T13:44:39 | 244,073,267 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 417 | py | import pylab as pl
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
fig = pl.figure()
ax = Axes3D(fig)
X = np.arange(-10, 10, 0.25)
Y = np.arange(-10, 10, 0.25)
X, Y = np.meshgrid(X, Y)
ax = int(5) #, ay = 0.5
print(int(ax))
Z = np.sqrt(X ** 2 + Y ** 3)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=pl.cm.hot)
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=pl.cm.hot)
ax.set_zlim(-2, 2)
pl.show() | [
"[email protected]"
] | |
c52ee2135e6956f261165149fb17fce093fe94b6 | ac4b9385b7ad2063ea51237fbd8d1b74baffd016 | /.history/google/drive_files_download_prepare_20210215011515.py | 0e37e5e441bd13cdadecc7f924be0f4e7330648a | [] | no_license | preethanpa/ssoemprep | 76297ef21b1d4893f1ac2f307f60ec72fc3e7c6f | ce37127845253c768d01aeae85e5d0d1ade64516 | refs/heads/main | 2023-03-09T00:15:55.130818 | 2021-02-20T06:54:58 | 2021-02-20T06:54:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,219 | py | from __future__ import print_function
import pickle
import os.path
import io
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from googleapiclient.http import MediaIoBaseDownload
from oauth2client.service_account import ServiceAccountCredentials
from google.oauth2 import service_account
import googleapiclient.discovery
import inspect
import sys
import json
SCOPES = ['https://www.googleapis.com/auth/documents',
'https://www.googleapis.com/auth/documents.readonly',
'https://www.googleapis.com/auth/documents.readonly',
'https://www.googleapis.com/auth/drive',
'https://www.googleapis.com/auth/drive.file',
'https://www.googleapis.com/auth/drive.metadata.readonly',
'https://www.googleapis.com/auth/drive.readonly',
]
# The ID of a sample document.
# DOCUMENT_ID = '1bQkFcQrWFHGlte8oTVtq_zyKGIgpFlWAS5_5fi8OzjY'
DOCUMENT_ID = '1sXQie19gQBRHODebxBZv4xUCJy-9rGpnlpM7_SUFor4'
# SERVICE_ACCOUNT_FILE = '/home/dsie/Developer/sandbox/3ray/3rml/kbc_process/google/domain-wide-credentials-gdrive.json'
SERVICE_ACCOUNT_FILE = '/home/dsie/Developer/sandbox/3ray/3rml/kbc_process/google/app-automation-service-account-thirdrayai-1612747564720-415d6ebd6001.json'
UPLOAD_FILE_LOCATION = '/home/dsie/Developer/sandbox/3ray/3rml/kbc_process/documents/pdf/'
doc_types = {
"application/vnd.google-apps.document": "gdoc",
# "application/vnd.google-apps.folder": "folder",
"application/vnd.google-apps.spreadsheet": "gsheet",
"application/vnd.google-apps.presentation": "gslide"
}
drive_files_list = [] if (sys.argv is None or sys.argv[1] is None) else json.loads(sys.argv[1])
job_id = drive_files_list.get("job_id")
# google_file_type = 'gdoc' if (sys.argv is None or sys.argv[1] is None or sys.argv[1].google_file_type is None) else sys.argv[1].google_file_type
# target_file_type = 'pdf' if (sys.argv is None or sys.argv[1] is None or sys.argv[1].target_file_type is None) else sys.argv[1].target_file_type
# location = '/home/dsie/Developer/sandbox/3ray/3rml/kbc_process/drive_documents/'+drive_files_list.get('job_id')+'/pdf/'
# document_id = None if (sys.argv[1] is None or sys.argv[1].file_location is None) else sys.argv[1].document_id
document_id = ''
def get_resource(domain_wide_delegate=False, user_to_impersonate=None):
"""Prepare a Google Drive resource object based on credentials.
"""
credentials = None
# use subject in case of domain-wide delegation
if domain_wide_delegate:
if user_to_impersonate is not None:
credentials = service_account.Credentials.from_service_account_file(SERVICE_ACCOUNT_FILE, scopes=SCOPES, subject=user_to_impersonate)
else:
credentials = service_account.Credentials.from_service_account_file(SERVICE_ACCOUNT_FILE, scopes=SCOPES)
if credentials is None:
return credentials
else:
drive_service = build('drive', 'v3', credentials=credentials)
return drive_service
def download_drive_file(resource=None, document_id=None, google_file_type='gdoc', target_type=None, target_location=None):
"""Downloads a Google Drive file using the provided resource.
If google_file_type is passed as None, then 'gdoc' / Google Doc is default.
If target_type is passed as None, then 'application/pdf' is default.
If location is none, then use environment variable UPLOAD_FILE_LOCATION as default
"""
# print(dir(resource.files())) #Get resource methods with dir.
if resource is None:
raise Exception('Invalid credentials. Provide subject email addredd for Drive-wide delegation')
else:
extension, mimeType = extension_mime_type(google_file_type, target_type)
try:
content = resource.files().export(fileId=document_id, mimeType=mimeType).execute()
try:
with open(target_location+google_file_type+'-'+document_id+extension, "wb") as file:
file.write(content)
return {"file": google_file_type+'-'+document_id+extension}
except Exception as exc_in:
return
# return {"document_id": document_id, "status": "Exception in with open", "message": exc_in}
except Exception as exc_out:
return
# return {"document_id": document_id, "status": "Exception in content = resource_files...", "message": exc_out}
def extension_mime_type(google_file_ext=None, format=None):
export_type = None
if google_file_ext is not None:
if google_file_ext == 'gdoc':
if format == 'docx':
export_type = 'application/vnd.openxmlformats-officedocument.wordprocessingml.document'
elif format == 'epub':
export_type = 'application/epub+zip'
elif format == 'html':
export_type = 'text/html'
elif format == 'odt':
export_type = 'application/vnd.oasis.opendocument.text'
elif format == 'pdf':
export_type = 'application/pdf'
elif format == 'rtf':
export_type = 'application/rtf'
elif format == 'tex':
export_type = 'application/zip'
elif format == 'txt':
export_type = 'text/plain'
elif format == 'html.zip':
export_type = 'application/zip'
else:
raise Exception('Unknown format "{}"'.format(format))
elif google_file_ext == 'gsheet':
if format == 'csv':
export_type = 'text/csv'
elif format == 'html.zip':
export_type = 'application/zip'
elif format == 'ods':
export_type = 'application/x-vnd.oasis.opendocument.spreadsheet'
elif format == 'pdf':
export_type = 'application/pdf'
elif format == 'tsv':
export_type = 'text/tab-separated-values'
elif format == 'xlsx':
export_type = 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
else:
raise Exception('Unknown format "{}"'.format(format))
elif google_file_ext == 'gslide':
if format == 'odp':
export_type = 'application/vnd.oasis.opendocument.presentation'
elif format == 'pdf':
export_type = 'application/pdf'
elif format == 'pptx':
export_type = 'application/vnd.openxmlformats-officedocument.presentationml.presentation'
elif format == 'txt':
export_type = 'text/plain'
else:
raise Exception('Unknown format "{}"'.format(format))
else:
raise Exception('Unknown Google document extension "{}"'.format(google_file_ext))
return '.'+format, export_type
if drive_files_list == []:
print(json.dumps(drive_files_list))
else:
location = os.path.join('/home/dsie/Developer/sandbox/3ray/3rml/kbc_process/drive_documents/', job_id')+'/pdf/')
os.makedirs(location)
location_html = os.path.join('/home/dsie/Developer/sandbox/3ray/3rml/kbc_process/drive_documents/', drive_files_list.get('job_id')+'/html/')
os.makedirs(location_html)
response_message = {
"job_id": drive_files_list.get("job_id"),
"status": "OK",
"processed_files": []
}
for index, item in enumerate(drive_files_list.get('files')):
try:
google_file_type = doc_types[item.get('mimeType')]
drive_document_id = item.get('id')
target_file_type = "pdf"
dl_response = download_drive_file(resource=get_resource(domain_wide_delegate=False), document_id=drive_document_id, google_file_type=google_file_type, target_type=target_file_type, target_location=location)
response_message["processed_files"].append(dl_response)
except KeyError as ke:
pass
print(json.dumps(response_message))
# print(download_drive_file(resource=get_resource(domain_wide_delegate=False)), google_file_type=google_file_type, target_type=target_file_type, target_location=location) | [
"{[email protected]}"
] | |
4a78a093202a039af99d5d6f6fa91fbb5996a7b5 | e0980f704a573894350e285f66f4cf390837238e | /.history/news/models_20201124144521.py | 8af4cd733221c3bf6f1b3de3521f5b42a13a09fd | [] | no_license | rucpata/WagtailWebsite | 28008474ec779d12ef43bceb61827168274a8b61 | 5aa44f51592f49c9a708fc5515ad877c6a29dfd9 | refs/heads/main | 2023-02-09T15:30:02.133415 | 2021-01-05T14:55:45 | 2021-01-05T14:55:45 | 303,961,094 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,234 | py | from django.db import models
from wagtail.contrib.forms.models import AbstractEmailForm
# Create your models here.
class NewsPage(AbstractEmailForm):
tempalte ='news/news_page.html'
leanding_page_template = 'news/news_page_leading.html'
subpage_types = []
max_coun = 1
intro = RichTextField(blank=True, features=['bold', 'italic', 'ol', 'ul'])
thank_you_text = RichTextField(
blank=True,
features=['bold', 'italic', 'ol', 'ul'])
map_image = models.ForeignKey(
'wagtailimages.Image',
null=True,
blank=False,
on_delete=models.SET_NULL,
help_text='Obrazek będzie przycięty do rozmairu 588px na 355 px',
related_name='+',
)
map_url = models.URLField(
blank=True,
help_text='Opcjonalne. Jeśli podasz tutaj łączę, obraz stanie si'
)
content_panels = AbstractEmailForm.content_panel + [
FieldPanel('intro'),
ImageChooserPanel('map_iamge'),
FieldPanel('map_url'),
InlinePanel('form_fields', label="Form Fields"),
FieldPanel('thank_you_text'),
FieldPanel('from_address'),
FieldPanel('to_address'),
FieldPanel('subject'),
]
| [
"[email protected]"
] | |
6a757cada6a40ec963004ced739f41d9c6365765 | 350db570521d3fc43f07df645addb9d6e648c17e | /0349_Intersection_of_Two_Arrays/solution.py | 09b0c446678c615e5439884d5e2bd613446fb3e3 | [] | no_license | benjaminhuanghuang/ben-leetcode | 2efcc9185459a1dd881c6e2ded96c42c5715560a | a2cd0dc5e098080df87c4fb57d16877d21ca47a3 | refs/heads/master | 2022-12-10T02:30:06.744566 | 2022-11-27T04:06:52 | 2022-11-27T04:06:52 | 236,252,145 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 681 | py | '''
349. Intersection of Two Arrays
Given two arrays, write a function to compute their intersection.
Example:
Given nums1 = [1, 2, 2, 1], nums2 = [2, 2], return [2].
Note:
Each element in the result must be unique.
The result can be in any order.
'''
def arrays_interseciton(nums1, nums2):
return set(nums1).intersection(nums2)
# Input:
# [1,2,2,1]
# [2]
# Output:
# [2,2]
# Expected:
# [2]
def arrays_interseciton_2(nums1, nums2):
return [x for x in set(nums1) if x in set(nums2)]
def arrays_interseciton_3(nums1, nums2):
return list(set(nums1) & set(nums2))
b1 = [1, 2, 3, 4, 5, 9, 11, 15]
b2 = [4, 5, 6, 7, 8]
print arrays_interseciton(b1, b2)
| [
"[email protected]"
] | |
27e62b20254e8681133d182d6482ca8c61c3f851 | 9505e191cb287507c7df05212ab562bea1eda553 | /莫烦强化学习/Prioritized_Replay_DQN/RL_brain.py | f160c6eb97470f9a107bb19a4008ad59759b5c9e | [
"MIT"
] | permissive | iisdd/Courses | c7a662305f3efe7d61eb23f766381290b1107bb8 | a47d202e0d7e1ba85a38c6fe3dd9619eceb1045c | refs/heads/main | 2023-04-15T17:40:36.474322 | 2021-04-27T14:31:42 | 2021-04-27T14:31:42 | 316,904,233 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,286 | py | """
说实话这个看不懂..., 只知道越好的经验越可能被sample到
The DQN improvement: Prioritized Experience Replay (based on https://arxiv.org/abs/1511.05952)
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
np.random.seed(1)
tf.set_random_seed(1)
class SumTree(object):
"""
This SumTree code is a modified version and the original code is from:
https://github.com/jaara/AI-blog/blob/master/SumTree.py
Story data with its priority in the tree.
"""
data_pointer = 0
def __init__(self, capacity):
self.capacity = capacity # for all priority values
self.tree = np.zeros(2 * capacity - 1)
# [--------------Parent nodes-------------][-------leaves to recode priority-------]
# size: capacity - 1 size: capacity
self.data = np.zeros(capacity, dtype=object) # for all transitions
# [--------------data frame-------------]
# size: capacity
def add(self, p, data):
tree_idx = self.data_pointer + self.capacity - 1
self.data[self.data_pointer] = data # update data_frame
self.update(tree_idx, p) # update tree_frame
self.data_pointer += 1
if self.data_pointer >= self.capacity: # replace when exceed the capacity
self.data_pointer = 0
def update(self, tree_idx, p):
change = p - self.tree[tree_idx]
self.tree[tree_idx] = p
# then propagate the change through tree
while tree_idx != 0: # this method is faster than the recursive loop in the reference code
tree_idx = (tree_idx - 1) // 2
self.tree[tree_idx] += change
def get_leaf(self, v):
"""
Tree structure and array storage:
Tree index:
0 -> storing priority sum
/ \
1 2
/ \ / \
3 4 5 6 -> storing priority for transitions
Array type for storing:
[0,1,2,3,4,5,6]
"""
parent_idx = 0
while True: # the while loop is faster than the method in the reference code
cl_idx = 2 * parent_idx + 1 # this leaf's left and right kids
cr_idx = cl_idx + 1
if cl_idx >= len(self.tree): # reach bottom, end search
leaf_idx = parent_idx
break
else: # downward search, always search for a higher priority node
if v <= self.tree[cl_idx]: # 二分查找,不在左就在右
parent_idx = cl_idx
else:
v -= self.tree[cl_idx]
parent_idx = cr_idx
data_idx = leaf_idx - self.capacity + 1
return leaf_idx, self.tree[leaf_idx], self.data[data_idx]
@property
def total_p(self):
return self.tree[0] # the root
class Memory(object): # stored as ( s, a, r, s_ ) in SumTree
"""
This Memory class is modified based on the original code from:
https://github.com/jaara/AI-blog/blob/master/Seaquest-DDQN-PER.py
"""
epsilon = 0.01 # small amount to avoid zero priority
alpha = 0.6 # [0~1] convert the importance of TD error to priority
beta = 0.4 # importance-sampling, from initial value increasing to 1
beta_increment_per_sampling = 0.001
abs_err_upper = 1. # clipped abs error
def __init__(self, capacity):
self.tree = SumTree(capacity)
def store(self, transition):
max_p = np.max(self.tree.tree[-self.tree.capacity:])
if max_p == 0:
max_p = self.abs_err_upper
self.tree.add(max_p, transition) # set the max p for new p
def sample(self, n):
b_idx, b_memory, ISWeights = np.empty((n,), dtype=np.int32), np.empty((n, self.tree.data[0].size)), np.empty((n, 1))
pri_seg = self.tree.total_p / n # priority segment
self.beta = np.min([1., self.beta + self.beta_increment_per_sampling]) # max = 1
min_prob = np.min(self.tree.tree[-self.tree.capacity:]) / self.tree.total_p # for later calculate ISweight
for i in range(n):
a, b = pri_seg * i, pri_seg * (i + 1)
v = np.random.uniform(a, b)
idx, p, data = self.tree.get_leaf(v)
prob = p / self.tree.total_p
ISWeights[i, 0] = np.power(prob/min_prob, -self.beta)
b_idx[i], b_memory[i, :] = idx, data
return b_idx, b_memory, ISWeights
def batch_update(self, tree_idx, abs_errors):
abs_errors += self.epsilon # convert to abs and avoid 0
clipped_errors = np.minimum(abs_errors, self.abs_err_upper)
ps = np.power(clipped_errors, self.alpha)
for ti, p in zip(tree_idx, ps):
self.tree.update(ti, p)
class DQNPrioritizedReplay:
def __init__(
self,
n_actions,
n_features,
learning_rate=0.005,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=500,
memory_size=10000,
batch_size=32,
e_greedy_increment=None,
output_graph=False,
prioritized=True,
sess=None,
):
self.n_actions = n_actions
self.n_features = n_features
self.lr = learning_rate
self.gamma = reward_decay
self.epsilon_max = e_greedy
self.replace_target_iter = replace_target_iter
self.memory_size = memory_size
self.batch_size = batch_size
self.epsilon_increment = e_greedy_increment
self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max
self.prioritized = prioritized # decide to use double q or not
self.learn_step_counter = 0
self._build_net()
t_params = tf.get_collection('target_net_params')
e_params = tf.get_collection('eval_net_params')
self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]
if self.prioritized:
self.memory = Memory(capacity=memory_size)
else:
self.memory = np.zeros((self.memory_size, n_features*2+2))
if sess is None:
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
else:
self.sess = sess
if output_graph:
tf.summary.FileWriter("logs/", self.sess.graph)
self.cost_his = []
def _build_net(self):
def build_layers(s, c_names, n_l1, w_initializer, b_initializer, trainable):
with tf.variable_scope('l1'):
w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names, trainable=trainable)
b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names, trainable=trainable)
l1 = tf.nn.relu(tf.matmul(s, w1) + b1)
with tf.variable_scope('l2'):
w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names, trainable=trainable)
b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names, trainable=trainable)
out = tf.matmul(l1, w2) + b2
return out
# ------------------ build evaluate_net ------------------
self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s') # input
self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target') # for calculating loss
if self.prioritized:
self.ISWeights = tf.placeholder(tf.float32, [None, 1], name='IS_weights')
with tf.variable_scope('eval_net'):
c_names, n_l1, w_initializer, b_initializer = \
['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 20, \
tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1) # config of layers
self.q_eval = build_layers(self.s, c_names, n_l1, w_initializer, b_initializer, True)
with tf.variable_scope('loss'):
if self.prioritized:
self.abs_errors = tf.reduce_sum(tf.abs(self.q_target - self.q_eval), axis=1) # for updating Sumtree
self.loss = tf.reduce_mean(self.ISWeights * tf.squared_difference(self.q_target, self.q_eval))
else:
self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval))
with tf.variable_scope('train'):
self._train_op = tf.train.RMSPropOptimizer(self.lr).minimize(self.loss)
# ------------------ build target_net ------------------
self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_') # input
with tf.variable_scope('target_net'):
c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]
self.q_next = build_layers(self.s_, c_names, n_l1, w_initializer, b_initializer, False)
def store_transition(self, s, a, r, s_):
if self.prioritized: # prioritized replay
transition = np.hstack((s, [a, r], s_))
self.memory.store(transition) # have high priority for newly arrived transition
else: # random replay
if not hasattr(self, 'memory_counter'):
self.memory_counter = 0
transition = np.hstack((s, [a, r], s_))
index = self.memory_counter % self.memory_size
self.memory[index, :] = transition
self.memory_counter += 1
def choose_action(self, observation):
observation = observation[np.newaxis, :]
if np.random.uniform() < self.epsilon:
actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation})
action = np.argmax(actions_value)
else:
action = np.random.randint(0, self.n_actions)
return action
def learn(self):
if self.learn_step_counter % self.replace_target_iter == 0:
self.sess.run(self.replace_target_op)
print('\ntarget_params_replaced\n')
if self.prioritized:
tree_idx, batch_memory, ISWeights = self.memory.sample(self.batch_size)
else:
sample_index = np.random.choice(self.memory_size, size=self.batch_size)
batch_memory = self.memory[sample_index, :]
q_next, q_eval = self.sess.run(
[self.q_next, self.q_eval],
feed_dict={self.s_: batch_memory[:, -self.n_features:],
self.s: batch_memory[:, :self.n_features]})
q_target = q_eval.copy()
batch_index = np.arange(self.batch_size, dtype=np.int32)
eval_act_index = batch_memory[:, self.n_features].astype(int)
reward = batch_memory[:, self.n_features + 1]
q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1)
if self.prioritized:
_, abs_errors, self.cost = self.sess.run([self._train_op, self.abs_errors, self.loss],
feed_dict={self.s: batch_memory[:, :self.n_features],
self.q_target: q_target,
self.ISWeights: ISWeights})
self.memory.batch_update(tree_idx, abs_errors) # update priority
else:
_, self.cost = self.sess.run([self._train_op, self.loss],
feed_dict={self.s: batch_memory[:, :self.n_features],
self.q_target: q_target})
self.cost_his.append(self.cost)
self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max
self.learn_step_counter += 1 | [
"[email protected]"
] | |
724d0bb4e0744c1d6d69e5e19135a4287262044b | eb0711915d6bba2f765f052736e33ac9a9a397a6 | /HE0435/simulation/rebin/rebin_arc.py~ | 337e999780ea9cca34a003793c200dfa2a4d4cf5 | [] | no_license | dartoon/GL_HostGalaxy | cd2166f273ae7e0397a7d2d39f760ab59e86f014 | 7469f1c1e640d176a75cc6e9497920e494ad656a | refs/heads/master | 2016-08-11T13:27:17.545360 | 2016-04-07T19:04:57 | 2016-04-07T19:04:57 | 46,524,027 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 856 | #from high resoluted images (sub=6) to lower one (bin together)
from numpy import *
from block import *
import pyfits
file1 = open('../pylens/HE0435.txt','r')
para = loadtxt(file1)
file1.close()
#print len(para)
ln=len(para)
for l in range(ln):
filename='../fits/HE_arc-{0}.fits'.format(l+1) # take one image
d = pyfits.open(filename)[0].data.copy()
d = concatenate([d,zeros([10,len(d.T)])])
d = concatenate([d,zeros([len(d),10])],axis=1) #expand the array
#print sum(d[65:69,67:71])
#[0:136] x ,y -> y, x
#### y x
#0:174
a=[0,3,0,3,2,5,2,5,4,1,4,1]
b=[0,0,3,3,4,4,1,1,2,2,5,5] #from the info. given by kai
for i in range(len(a)):
dd=d[a[i]:360+a[i],b[i]:360+b[i]] #the size before bin
aaa=block(dd,(60,60))
pyfits.PrimaryHDU(aaa).writeto('../fits/binall/arc-{0}-{1}.fits'.format(l+1,i+1),clobber=True)
| [
"[email protected]"
] | ||
173f98b7e79f6ff249b1f5a76b04e023a7ef9b8b | 17acb8e20f9a24b16ce3651302fc2d7fc7b887a6 | /src/utils/aiml_generator/generator.py | 5f0982bb130883d7337ec983090811960cd1d022 | [
"MIT"
] | permissive | cen-ai/program-y | 91052fdc11aec0f60311e3429895fac489d8ce54 | a753667638147544c54dbebd9f1c8f9ae7f2159e | refs/heads/master | 2020-03-22T15:11:07.896885 | 2018-10-15T22:13:58 | 2018-10-15T22:13:58 | 140,234,173 | 5 | 5 | NOASSERTION | 2019-01-03T09:09:07 | 2018-07-09T05:11:08 | Python | UTF-8 | Python | false | false | 8,195 | py | import os
import csv
import os.path
from optparse import OptionParser
class CmdArgsHandler:
def validate_args(self):
parser = OptionParser()
parser.add_option("-f", "--file", dest="csvloc",
help="Specify the location and filename of the CSV file.")
parser.add_option("-d", "--directory", dest="dir",
help="Specify the directory which contains the CSV.")
parser.add_option("-o", "--output", dest="aimlloc",
help="Specify the location where the aiml file should go.")
(opts, args) = parser.parse_args()
mandatories = ['aimlloc']
self.check_manditory_opts_present(parser, opts, mandatories)
return opts
def check_manditory_opts_present(self, parser, opts, mandatories):
for option in mandatories:
if not opts.__dict__[option]:
print("ERROR: Mandatory argument is missing.\n")
parser.print_help()
exit(-1)
if not opts.csvloc and not opts.dir:
print("ERROR: Enter the csv file location, or the directory.\n")
parser.print_help()
exit(-1)
class CsvReader:
def __init__(self, file_name):
self.file_name = file_name
self.csvlength = 0
def read_file(self):
if not os.path.exists(self.file_name):
print(self.file_name + " is not a valid file.")
exit(-1)
collection = []
with open(self.file_name, 'r') as f:
reader = csv.reader(f)
for linenum, row in enumerate(reader):
if str(row).find('#') != -1:
continue
if str(row).find(',') != -1:
collection.append([linenum + 1] + row)
self.csvlength += 1
return collection
class CollectionLoader:
def __init__(self, csv_file_name):
self.csv_file_name = csv_file_name
def get_collection(self):
read = CsvReader(self.csv_file_name)
collection = read.read_file()
self.csvlength = read.csvlength
return collection
def format_collection(self, collection):
parser = SentenceParser(collection[1:]) # omit line number
return parser.populate()
class XmlWriter:
def __init__(self, output_loc, file_name):
self.aiml_file = open(output_loc + file_name + ".aiml", "w")
def prepare_sentence(self, sentence):
sentence.pop(0)
value = ' '.join(sentence)
return value
def write_body(self, sentence):
template_content = sentence[0]
pattern_text = self.prepare_sentence(sentence)
self.aiml_file.write("\n\t<category>")
self.aiml_file.write("\n\t\t<pattern>")
self.aiml_file.write(pattern_text.upper())
self.aiml_file.write("</pattern>")
self.aiml_file.write("\n\t\t<template>")
self.aiml_file.write(template_content)
self.aiml_file.write("\n\t\t</template>")
self.aiml_file.write("\n\t</category>")
def open_file(self):
self.aiml_file.write("<?xml version='1.0' encoding='ISO-8859-1'?>")
self.aiml_file.write("\n<aiml version=\"1.0.1\">")
self.aiml_file.write("""\n
\t<!-- -->
\t<!-- This AIML file has been auto generated by the Program-Y util aiml_generator. -->
\t<!-- -->
\t<!-- Y-Bot is Copyright © 2017 by Keith Sterling. -->
\t<!--
\tPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
\tdocumentation files (the "Software"), to deal in the Software without restriction, including without limitation
\tthe rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
\tand to permit persons to whom the Software is furnished to do so, subject to the following conditions:
\tThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
\tTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
\tTHE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
\tAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
\tTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\t-->
""")
def close_file(self):
self.aiml_file.write("\n</aiml>\n")
self.aiml_file.close()
class SentenceParser:
def __init__(self, sentence):
self.sentence = sentence
def get_sentence_element(self, i):
return self.sentence[i].strip()
def get_sentence_length(self):
return len(self.sentence)
def parser(self, trees, branch, count):
if self.get_sentence_length() <= count:
return
word = self.get_sentence_element(count)
# Optional word
if "(" in word and count > 0:
alt = branch[:]
alt.append(word.strip("()"))
trees.append(alt)
self.parser(trees, branch, count + 1)
self.parser(trees, alt, count + 1)
# One from the list
elif word.count('|') > 0:
split = word.split('|')
options = len(split)
for i in range(1, options):
alt = branch[:]
alt.append(split[i])
trees.append(alt)
self.parser(trees, alt, count + 1)
branch.append(split[0])
self.parser(trees, branch, count + 1)
# Only one word allowed by csv column
elif (count > 0) and (word.count(' ') > 0):
print ("WARNING: Comma missing in file [%s]" % word)
branch.append(word)
self.parser(trees, branch, count + 1)
# Single word
else:
branch.append(word)
self.parser(trees, branch, count + 1)
def populate(self):
trees = []
branch = []
trees.append(branch)
self.parser(trees, branch, 0)
return trees
class Generator():
def __init__(self):
self.categories = {}
def get_aiml_file_name(self, csv_file_name):
initial_split = csv_file_name.split('/')
i = len(initial_split)
secondary_split = initial_split[i - 1].split('.')
return secondary_split[0]
def generate_one(self, opts, csv_loc):
cl = CollectionLoader(csv_loc)
collections = cl.get_collection()
if not collections:
print(csv_loc + " has no content.")
return
aiml_file_name = self.get_aiml_file_name(csv_loc)
xml = XmlWriter(opts.aimlloc, aiml_file_name)
xml.open_file()
for collection in collections:
parsed_collection = cl.format_collection(collection)
for rule in parsed_collection:
xml.write_body(rule)
xml.close_file()
def get_files_in_dir(self, opts):
if not os.path.exists(opts.dir):
print(opts.dir + " is not a valid directory.")
exit(-1)
file_names = []
for file in os.listdir(opts.dir):
if file.endswith(".csv"):
file_names.append(str(os.path.join(opts.dir, file)))
if not file_names:
print(
"FATAL: There are no CSV files at this location.\n Are you in the right directory?")
exit(-1)
return file_names
if __name__ == '__main__':
cmd = CmdArgsHandler()
opts = cmd.validate_args()
g = Generator()
if not opts.aimlloc.endswith("/"):
opts.aimlloc = opts.aimlloc + "/"
if not os.path.exists(opts.aimlloc):
print(opts.aimlloc + " is not a valid output directory.")
exit(-1)
if opts.dir:
if not opts.dir.endswith("/"):
opts.dir = opts.dir + "/"
file_names = g.get_files_in_dir(opts)
for csv_file in file_names:
g.generate_one(opts, csv_file)
else:
g.generate_one(opts, opts.csvloc)
| [
"[email protected]"
] | |
8559ef8a1708043e87dc324c69fdc5c2fd4a9cc5 | 5306217707f99ff1d082bb974db0ccebf948763f | /ntds/management/commands/load_ntd_reporters.py | 6a982e8e91b60fd7022f626c0220309161086c87 | [] | no_license | sparkplug/rapidsms-ntds | 05b64e67d5bcf751029653eb2f2a64e331e9a9c9 | 8b42c749db4c34e43eb39f3c52d540c84a8b810e | refs/heads/master | 2021-01-16T17:45:09.821171 | 2016-02-15T20:50:26 | 2016-02-15T20:50:26 | 25,920,319 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,712 | py | #!/usr/bin/python
# -*- coding: utf-8 -*-
from django.core.management import BaseCommand
from rapidsms_xforms.models import *
from ntds.utils import validate_number
from rapidsms.contrib.locations.models import Location
from django.utils.safestring import mark_safe
from ntds.models import Reporter
import operator
from django.db.models import Q
import re
from rapidsms_httprouter.models import Message, Connection
from django.contrib.auth.models import User,Group
from openpyxl.reader.excel import load_workbook
from openpyxl.workbook import Workbook
from uganda_common.utils import assign_backend
from healthmodels.models.HealthProvider import HealthProvider
from rapidsms.models import Connection, Contact,Backend
from optparse import make_option
import django
class Command(BaseCommand):
option_list = BaseCommand.option_list + (
make_option('-f', '--file', dest='file'),)
def handle(self, **options):
import pdb;pdb.set_trace()
file = options['file']
wb = load_workbook(filename=file)
ws=wb.get_sheet_by_name("Community and Schools")
for row in ws.rows[1:]:
try:
role, _ = Group.objects.get_or_create(name='Ntds')
mobile_is_valid,cleaned_mobile=validate_number("0"+str(row[10].value))
try:
msisdn, backend = assign_backend(cleaned_mobile)
except ValidationError:
msisdn, backend = assign_backend(str(row[10].value).split("/")[0])
backend,_=Backend.objects.get_or_create(name="yo")
connection, conn_created = Connection.objects.get_or_create(identity=cleaned_mobile, backend=backend)
try:
district=Location.objects.filter(type="district",name__icontains= row[2].value.strip())[0]
except IndexError:
district=None
try:
subcounty=Location.objects.filter(type="sub_county",name__icontains= row[5].value.strip())[0]
except IndexError:
subcounty=None
try:
pr=row[8].value.strip()
if pr=="Aria":
pr="Ariya"
parish=district.get_descendants().filter(type="parish",name__icontains=row[8].value.strip())[0]
except IndexError:
parish=None
print "index error %s"%row[8].value
if conn_created:
provider = HealthProvider.objects.create(name=row[9].value.strip(), location=parish)
provider.groups.add(role)
connection.contact = provider
connection.save()
rep = Reporter(healthprovider_ptr=provider)
rep.__dict__.update(provider.__dict__)
rep.district=district
rep.subcounty=subcounty
rep.parish=parish
rep.community=row[11].value.strip()
rep.id_number=str(row[0].value)
rep.county=row[3].value.strip()
rep.subcounty_supervisor=row[6].value.strip()
_,s_mobile=validate_number(str(row[7].value))
rep.subcounty_supervisor_mobile=s_mobile
rep.region=row[1].value.strip()
rep.health_subcounty=row[4].value.strip()
rep.subcounty_name = row[5].value.strip()
rep.parish_name = row[8].value.strip()
rep.save()
except ValidationError:
pass
| [
"[email protected]"
] | |
f7ed33b9024bcb5172bdd606904702df2764077f | f043fee89c0e2030386adcebb74d08164b7b974f | /reagent/net_builder/continuous_actor/fully_connected.py | d4e4b0544a3a1aedfbc18dfbfa123066351d9bac | [
"BSD-3-Clause"
] | permissive | IronOnet/ReAgent | c2d22e7dc63eaf61e0a50e9343110c6df79a9b40 | 67434f458cde1f2c946237e866a73392279a7ede | refs/heads/master | 2023-04-06T17:31:59.751700 | 2021-04-12T21:56:19 | 2021-04-12T21:57:05 | 357,700,053 | 2 | 0 | BSD-3-Clause | 2021-04-13T22:04:09 | 2021-04-13T22:04:09 | null | UTF-8 | Python | false | false | 1,998 | py | #!/usr/bin/env python3
from typing import List, Optional
from reagent.core.dataclasses import dataclass, field
from reagent.core.parameters import NormalizationData, param_hash
from reagent.models.actor import FullyConnectedActor
from reagent.models.base import ModelBase
from reagent.net_builder.continuous_actor_net_builder import ContinuousActorNetBuilder
from reagent.preprocessing.identify_types import CONTINUOUS_ACTION
from reagent.preprocessing.normalization import get_num_output_features
@dataclass
class FullyConnected(ContinuousActorNetBuilder):
__hash__ = param_hash
sizes: List[int] = field(default_factory=lambda: [128, 64])
activations: List[str] = field(default_factory=lambda: ["relu", "relu"])
use_batch_norm: bool = False
use_layer_norm: bool = False
action_activation: str = "tanh"
exploration_variance: Optional[float] = None
def __post_init_post_parse__(self):
super().__init__()
assert len(self.sizes) == len(self.activations), (
f"Must have the same numbers of sizes and activations; got: "
f"{self.sizes}, {self.activations}"
)
@property
def default_action_preprocessing(self) -> str:
return CONTINUOUS_ACTION
def build_actor(
self,
state_normalization_data: NormalizationData,
action_normalization_data: NormalizationData,
) -> ModelBase:
state_dim = get_num_output_features(
state_normalization_data.dense_normalization_parameters
)
action_dim = get_num_output_features(
action_normalization_data.dense_normalization_parameters
)
return FullyConnectedActor(
state_dim=state_dim,
action_dim=action_dim,
sizes=self.sizes,
activations=self.activations,
use_batch_norm=self.use_batch_norm,
action_activation=self.action_activation,
exploration_variance=self.exploration_variance,
)
| [
"[email protected]"
] | |
4ca9ecc74b019cd4659ed7ea6d8725e8ecdc45b2 | 780fa3fed7e5890f26f9c952f10cefbacfa6e09a | /recursive_convolution.py | 69f1fc591f8ffd4d30a7c182f4f9dc8aae9f26f8 | [] | no_license | jsrimr/code_during_KTaiacadmey | d508303417fe0916f98f7cd65c6521adb0a933fa | ab98b1613d9cb8ca77cd2462e0a42664b71bd758 | refs/heads/master | 2021-12-03T08:09:01.199462 | 2018-06-25T05:49:07 | 2018-06-25T05:49:07 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,346 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 22 17:18:19 2018
@author: ktai12
"""
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
sess=tf.InteractiveSession()
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
#convolution 압축을 확인하자!
#압축률이 다른 5장의 사진
img=mnist.train.images[0].reshape(28,28)
#original-28*28
plt.imshow(img)
plt.title('original')
W=tf.Variable(tf.random_normal([3,3,1,1],stddev=0.01))
img=img.reshape(-1,28,28,1)
conv2d=tf.nn.conv2d(img,W,strides=[1,1,1,1],padding="VALID")
sess.run(tf.global_variables_initializer())
conv2d_img1=conv2d.eval()
conv2d_img1.shape
#convolution once - 26*26
img1=conv2d_img1.reshape(26,26)
plt.imshow(img1)
plt.title('once')
#convolution twice
conv2d=tf.nn.conv2d(conv2d_img1,W,strides=[1,1,1,1],padding="VALID")
conv2d_img2=conv2d.eval()
conv2d_img2.shape
img2=conv2d_img2.reshape(24,24)
plt.imshow(img2)
plt.title('once')
tmp=img
for i in range(10):
a=tmp.shape[0]
tmp=tmp.reshape(-1,a,a,1)
conv2d=tf.nn.conv2d(tmp,W,strides=[1,1,1,1],padding="VALID")
conv2d_img=conv2d.eval()
k=conv2d_img.shape[1]
tmp=conv2d_img.reshape(k,k)
plt.imshow(tmp)
plt.title("{0}*{0} size".format(k))
plt.show() | [
"[email protected]"
] | |
aa6ea506e424f5b0a50f4537652395b31a901596 | 75e1d9446cb1fca5c6a79ad0ba7f38268df1161f | /Python Programs/rotate-matrix-pattern.py | baeb1e16ee8363e61f12b29a05e4442f2d438d48 | [
"CC0-1.0"
] | permissive | muhammad-masood-ur-rehman/Skillrack | 6e9b6d93680dfef6f40783f02ded8a0d4283c98a | 71a25417c89d0efab40ee6229ccd758b26ae4312 | refs/heads/main | 2023-02-03T16:45:54.462561 | 2020-12-23T08:36:28 | 2020-12-23T08:36:28 | 324,221,340 | 4 | 1 | CC0-1.0 | 2020-12-24T19:12:54 | 2020-12-24T19:12:54 | null | UTF-8 | Python | false | false | 1,377 | py | Rotate Matrix Pattern
The program must accept an integer matrix of size N*N as the input. The program must rotate the matrix by 45 degrees in the clockwise direction. Then the program must print the rotated matrix and print asterisks instead of empty places as the output.
Boundary Condition(s):
3 <= N <= 100
Input Format:
The first line contains N.
The next N lines, each contains N integers separated by a space.
Output Format:
The first (2*N)-1 lines containing the rotated matrix.
Example Input/Output 1:
Input:
3
1 2 3
4 5 6
7 8 9
Output:
**1
*4 2
7 5 3
*8 6
**9
Explanation:
After rotating the matrix by 45 degrees in the clockwise direction, the matrix becomes
1
4 2
7 5 3
8 6
9
So the rotated matrix is printed and the asterisks are printed instead of empty places.
Example Input/Output 2:
Input:
4
13 21 36 49
55 65 57 80
17 32 63 44
56 60 78 98
Output:
***13
**55 21
*17 65 36
56 32 57 49
*60 63 80
**78 44
***98
n=int(input())
arr=[]
for i in range(n):
a=[]
for j in range(n):
a.append(int(input()))
arr.append(a)
s1,s2=0,0
stars=n-1
for i in range(1, (2*n)):
i1=s1
i2=s2
for j in range(1,n+1):
if(j<=stars):
print("*",end=' ')
else:
print(arr[i1][i2],end=" ")
i1-=1
i2+=1
if(i>n-1):
s2+=1
stars+=1
else:
stars-=1
s1+=1
print("")
| [
"[email protected]"
] | |
079896499559440bc3938ad7b69fe1408bc3ac4c | 7f167121b52312d65663d781819356eac65843ed | /lib/xss.py | d7d8def40702d5eb17aac4d709ddc99b82bb50ed | [] | no_license | mongoltolbo/mifan.tv | b3526aaeb5394b3ac1e7af85b8ea3a74e90ce73e | 9ba59b049866dff7c4d9eceabed91d8a1878ef4b | refs/heads/master | 2020-05-04T19:20:22.340674 | 2013-08-27T15:53:34 | 2013-08-27T15:53:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,954 | py | #!/usr/bin/env python
# coding=utf-8
#
# Copyright 2013 tuila.me
# Source: http://code.activestate.com/recipes/496942/ (r1)
import re
from htmllib import HTMLParser
from cgi import escape
from urlparse import urlparse
from formatter import AbstractFormatter
from htmlentitydefs import entitydefs
from xml.sax.saxutils import quoteattr
def xssescape(text):
"""Gets rid of < and > and & and, for good measure, :"""
# return escape(text, quote=True).replace(':',':')
# return re.sub(r'(?<!http)(?<!https):', ':', escape(text, quote=True))
return escape(text, quote=True)
class XssCleaner(HTMLParser):
def __init__(self, fmt = AbstractFormatter):
HTMLParser.__init__(self, fmt)
self.result = ""
self.open_tags = []
# A list of the only tags allowed. Be careful adding to this. Adding
# "script," for example, would not be smart. 'img' is out by default
# because of the danger of IMG embedded commands, and/or web bugs.
self.permitted_tags = ['a', 'b', 'blockquote', 'br', 'i',
'li', 'ol', 'ul', 'p', 'cite']
# A list of tags that require no closing tag.
self.requires_no_close = ['img', 'br']
# A dictionary showing the only attributes allowed for particular tags.
# If a tag is not listed here, it is allowed no attributes. Adding
# "on" tags, like "onhover," would not be smart. Also be very careful
# of "background" and "style."
self.allowed_attributes = \
{'a':['href','title'],
'img':['src','alt'],
'blockquote':['type']}
# The only schemes allowed in URLs (for href and src attributes).
# Adding "javascript" or "vbscript" to this list would not be smart.
self.allowed_schemes = ['http','https','ftp']
def handle_data(self, data):
if data:
self.result += xssescape(data)
def handle_charref(self, ref):
if len(ref) < 7 and ref.isdigit():
self.result += '&#%s;' % ref
else:
self.result += xssescape('&#%s' % ref)
def handle_entityref(self, ref):
if ref in entitydefs:
self.result += '&%s;' % ref
else:
self.result += xssescape('&%s' % ref)
def handle_comment(self, comment):
if comment:
self.result += xssescape("<!--%s-->" % comment)
def handle_starttag(self, tag, method, attrs):
if tag not in self.permitted_tags:
self.result += xssescape("<%s>" % tag)
else:
bt = "<" + tag
if tag in self.allowed_attributes:
attrs = dict(attrs)
self.allowed_attributes_here = \
[x for x in self.allowed_attributes[tag] if x in attrs \
and len(attrs[x]) > 0]
for attribute in self.allowed_attributes_here:
if attribute in ['href', 'src', 'background']:
if self.url_is_acceptable(attrs[attribute]):
bt += ' %s="%s"' % (attribute, attrs[attribute])
else:
bt += ' %s=%s' % \
(xssescape(attribute), quoteattr(attrs[attribute]))
if bt == "<a" or bt == "<img":
return
if tag in self.requires_no_close:
bt += "/"
bt += ">"
self.result += bt
self.open_tags.insert(0, tag)
def handle_endtag(self, tag, attrs):
bracketed = "</%s>" % tag
if tag not in self.permitted_tags:
self.result += xssescape(bracketed)
elif tag in self.open_tags:
self.result += bracketed
self.open_tags.remove(tag)
def unknown_starttag(self, tag, attributes):
self.handle_starttag(tag, None, attributes)
def unknown_endtag(self, tag):
self.handle_endtag(tag, None)
def url_is_acceptable(self,url):
### Requires all URLs to be "absolute."
parsed = urlparse(url)
return parsed[0] in self.allowed_schemes and '.' in parsed[1]
def strip(self, rawstring):
"""Returns the argument stripped of potentially harmful HTML or Javascript code"""
self.result = ""
self.feed(rawstring)
for endtag in self.open_tags:
if endtag not in self.requires_no_close:
self.result += "</%s>" % endtag
return self.result
def xtags(self):
"""Returns a printable string informing the user which tags are allowed"""
self.permitted_tags.sort()
tg = ""
for x in self.permitted_tags:
tg += "<" + x
if x in self.allowed_attributes:
for y in self.allowed_attributes[x]:
tg += ' %s=""' % y
tg += "> "
return xssescape(tg.strip())
| [
"[email protected]"
] | |
deb0e9f6012ff44565f83f4240236d6e9dba8965 | 1ee3dc4fa096d12e409af3a298ba01f5558c62b5 | /ixnetwork_restpy/testplatform/sessions/ixnetwork/topology/nettopologytree.py | b6b429aac376022dac44a1359e79e6768a182f2c | [
"MIT"
] | permissive | parthpower/ixnetwork_restpy | 321e64a87be0a4d990276d26f43aca9cf4d43cc9 | 73fa29796a5178c707ee4e21d90ff4dad31cc1ed | refs/heads/master | 2020-07-04T13:34:42.162458 | 2019-08-13T20:33:17 | 2019-08-13T20:33:17 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,828 | py | # MIT LICENSE
#
# Copyright 1997 - 2019 by IXIA Keysight
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
from ixnetwork_restpy.base import Base
from ixnetwork_restpy.files import Files
class NetTopologyTree(Base):
"""The NetTopologyTree class encapsulates a user managed netTopologyTree node in the ixnetwork hierarchy.
An instance of the class can be obtained by accessing the NetTopologyTree property from a parent instance.
The internal properties list will be empty when the property is accessed and is populated from the server using the find method.
The internal properties list can be managed by the user by using the add and remove methods.
"""
_SDM_NAME = 'netTopologyTree'
def __init__(self, parent):
super(NetTopologyTree, self).__init__(parent)
@property
def IncludeEntryPoint(self):
"""if true, entry node belongs to ring topology, otherwise it is outside of ring
Returns:
bool
"""
return self._get_attribute('includeEntryPoint')
@IncludeEntryPoint.setter
def IncludeEntryPoint(self, value):
self._set_attribute('includeEntryPoint', value)
@property
def LinkMultiplier(self):
"""number of links between two nodes
Returns:
number
"""
return self._get_attribute('linkMultiplier')
@LinkMultiplier.setter
def LinkMultiplier(self, value):
self._set_attribute('linkMultiplier', value)
@property
def MaxChildPerNode(self):
"""Maximum children per node
Returns:
number
"""
return self._get_attribute('maxChildPerNode')
@MaxChildPerNode.setter
def MaxChildPerNode(self, value):
self._set_attribute('maxChildPerNode', value)
@property
def Nodes(self):
"""number of nodes
Returns:
number
"""
return self._get_attribute('nodes')
@Nodes.setter
def Nodes(self, value):
self._set_attribute('nodes', value)
@property
def TreeDepth(self):
"""Depth of the Tree, defined as length of path from root node to deepest node in the tree
Returns:
number
"""
return self._get_attribute('treeDepth')
@TreeDepth.setter
def TreeDepth(self, value):
self._set_attribute('treeDepth', value)
@property
def UseTreeDepth(self):
"""Use Tree Depth
Returns:
bool
"""
return self._get_attribute('useTreeDepth')
@UseTreeDepth.setter
def UseTreeDepth(self, value):
self._set_attribute('useTreeDepth', value)
def update(self, IncludeEntryPoint=None, LinkMultiplier=None, MaxChildPerNode=None, Nodes=None, TreeDepth=None, UseTreeDepth=None):
"""Updates a child instance of netTopologyTree on the server.
Args:
IncludeEntryPoint (bool): if true, entry node belongs to ring topology, otherwise it is outside of ring
LinkMultiplier (number): number of links between two nodes
MaxChildPerNode (number): Maximum children per node
Nodes (number): number of nodes
TreeDepth (number): Depth of the Tree, defined as length of path from root node to deepest node in the tree
UseTreeDepth (bool): Use Tree Depth
Raises:
ServerError: The server has encountered an uncategorized error condition
"""
self._update(locals())
def add(self, IncludeEntryPoint=None, LinkMultiplier=None, MaxChildPerNode=None, Nodes=None, TreeDepth=None, UseTreeDepth=None):
"""Adds a new netTopologyTree node on the server and retrieves it in this instance.
Args:
IncludeEntryPoint (bool): if true, entry node belongs to ring topology, otherwise it is outside of ring
LinkMultiplier (number): number of links between two nodes
MaxChildPerNode (number): Maximum children per node
Nodes (number): number of nodes
TreeDepth (number): Depth of the Tree, defined as length of path from root node to deepest node in the tree
UseTreeDepth (bool): Use Tree Depth
Returns:
self: This instance with all currently retrieved netTopologyTree data using find and the newly added netTopologyTree data available through an iterator or index
Raises:
ServerError: The server has encountered an uncategorized error condition
"""
return self._create(locals())
def remove(self):
"""Deletes all the netTopologyTree data in this instance from server.
Raises:
NotFoundError: The requested resource does not exist on the server
ServerError: The server has encountered an uncategorized error condition
"""
self._delete()
def find(self, IncludeEntryPoint=None, LinkMultiplier=None, MaxChildPerNode=None, Nodes=None, TreeDepth=None, UseTreeDepth=None):
"""Finds and retrieves netTopologyTree data from the server.
All named parameters support regex and can be used to selectively retrieve netTopologyTree data from the server.
By default the find method takes no parameters and will retrieve all netTopologyTree data from the server.
Args:
IncludeEntryPoint (bool): if true, entry node belongs to ring topology, otherwise it is outside of ring
LinkMultiplier (number): number of links between two nodes
MaxChildPerNode (number): Maximum children per node
Nodes (number): number of nodes
TreeDepth (number): Depth of the Tree, defined as length of path from root node to deepest node in the tree
UseTreeDepth (bool): Use Tree Depth
Returns:
self: This instance with matching netTopologyTree data retrieved from the server available through an iterator or index
Raises:
ServerError: The server has encountered an uncategorized error condition
"""
return self._select(locals())
def read(self, href):
"""Retrieves a single instance of netTopologyTree data from the server.
Args:
href (str): An href to the instance to be retrieved
Returns:
self: This instance with the netTopologyTree data from the server available through an iterator or index
Raises:
NotFoundError: The requested resource does not exist on the server
ServerError: The server has encountered an uncategorized error condition
"""
return self._read(href)
| [
"[email protected]"
] | |
f9e738f5b5b8110966032a68c9aeae66c200a6bf | 7c9919126b96122c1a8c6353769e209d850e4564 | /bnk_hr_leave/models/hr_leave_allocation.py | bee706f6393dea1a328430f51d060ead0e48e84c | [] | no_license | Duongnv-dev/hr | 8ee34c904d481a4d0f4182c3c6bfd6c28ef25ffe | 962e0edab5b824304f4a2b2dff23458135f94c3c | refs/heads/master | 2023-06-19T06:54:00.337453 | 2021-07-13T01:53:34 | 2021-07-13T01:53:34 | 385,439,085 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 210 | py | from odoo import fields, models, api
class HrLeaveAllocation(models.Model):
_inherit = 'hr.leave.allocation'
_description = 'Inherit leave allocation'
contract_id = fields.Many2one('hr.contract') | [
"[email protected]"
] | |
9184d820d21d39a76067d3a1353b4cc581849604 | c52e7808ab764d822267b36a185223a172a56b5a | /tasks/1_area_of_triangle.py | 815e4c3722c0eea544ca8f78c924b6611d678e2e | [] | no_license | lohitbadiger/Python-teaching-all | c41bfa2c98bab1493aba5269ab81efa6be02c73f | b7ed285b6b2df9c23fa5bf0c91381729b9ac0c6f | refs/heads/master | 2020-06-02T21:13:05.276475 | 2019-06-17T06:55:06 | 2019-06-17T06:55:06 | 191,311,539 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 314 | py | # base=int(input("Enter the value for base: "))
# hight=int(input('Enter the Height :'))
# area=0.5*base*hight
# print('area of the triangle is ', area)
# one more way
x,y=input('Enter the value for x, y').split()
print('value of x is ', x)
print('value of y is ', y)
reslt= 0.5*int(x)*int(y)
print(reslt)
| [
"[email protected]"
] | |
3e243c5c508242ec4a50d667569a9822d6418118 | e5dc27e634aba70bcd1b3acea74fed84ddccf837 | /plugins/modules/network_device_update_role.py | be3fbcb35812197122cd5cd3a645eff6418c11d8 | [] | no_license | jejrichardson/dnacenter-ansible | 264d1b52227d4bf78ad175494763cff9e7881f34 | f10078ef8323bda4b542e71bcecf4f80a7fe0609 | refs/heads/master | 2023-01-28T09:54:57.449459 | 2020-12-09T23:15:49 | 2020-12-09T23:15:49 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,543 | py | #!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright: (c) 2020, Rafael Campos <[email protected]>
# GNU General Public License v3.0+ (see LICENSE or https://www.gnu.org/licenses/gpl-3.0.txt)
ANSIBLE_METADATA = {
"metadata_version": "0.0.1",
"status": ["preview"],
"supported_by": "community",
}
DOCUMENTATION = r"""
---
module: network_device_update_role
short_description: Manage NetworkDeviceUpdateRole objects of Devices
description:
- Updates the role of the device as access, core, distribution, border router.
version_added: '1.0'
author: Rafael Campos (@racampos)
options:
id:
description:
- NetworkDeviceBriefNIO's id.
type: str
required: True
role:
description:
- NetworkDeviceBriefNIO's role.
type: str
required: True
roleSource:
description:
- NetworkDeviceBriefNIO's roleSource.
type: str
required: True
summary:
description:
- If true gets the summary.
type: bool
required: True
requirements:
- dnacentersdk
seealso:
# Reference by module name
- module: cisco.dnac.plugins.module_utils.definitions.network_device_update_role
# Reference by Internet resource
- name: NetworkDeviceUpdateRole reference
description: Complete reference of the NetworkDeviceUpdateRole object model.
link: https://developer.cisco.com/docs/dna-center/api/1-3-3-x
# Reference by Internet resource
- name: NetworkDeviceUpdateRole reference
description: SDK reference.
link: https://dnacentersdk.readthedocs.io/en/latest/api/api.html#v2-1-1-summary
"""
EXAMPLES = r"""
- name: update_device_role
cisco.dnac.network_device_update_role:
state: update # required
id: SomeValue # string, required
role: SomeValue # string, required
roleSource: SomeValue # string, required
summary: True # boolean, required
"""
RETURN = """
update_device_role:
description: Updates the role of the device as access, core, distribution, border router.
returned: changed
type: dict
contains:
response:
description: NetworkDeviceBriefNIO's response.
returned: changed
type: dict
contains:
taskId:
description: It is the network device update role's taskId.
returned: changed
type: dict
url:
description: It is the network device update role's url.
returned: changed
type: str
sample: '<url>'
version:
description: NetworkDeviceBriefNIO's version.
returned: changed
type: str
sample: '1.0'
"""
| [
"[email protected]"
] | |
bbaf9c29390c28dc9d8519047288393f4d9b4247 | 455a91b28590d0b7ee1519f6d1ee2d554db4298b | /exps/exp_22102015/positioning_covar_meta.py | 2719fdaf70712a3ec6f905739e3e081ec62ac8f3 | [] | no_license | yairbeer/my_repository | a038201fb12b19cb249eb98c17478b0c086a9b04 | a07660b9db412c11ae3fb6835e15481e60a687ff | refs/heads/master | 2021-01-10T01:20:27.010430 | 2015-11-19T08:47:55 | 2015-11-19T08:47:55 | 46,478,844 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,758 | py | import numpy as np
import exps.exp_22102015.config_exp as cfg_exp
import exps.fn_exp as fn_exp
import functions as fn
import matplotlib.pyplot as plt
import exps.exp_22102015.track as track
import exps.exp_22102015.doa as doa
import itertools
import matplotlib.pyplot as plt
__author__ = 'YBeer'
"""
calculating probability density for each meta position. Then finding the density max.
"""
estimate_pos = []
for i in range(len(track.track_list)):
# single repeat
ap_direction = np.repeat(track.aps[:, 2].reshape((1, track.aps.shape[0])), doa.ap_timed_kaplan[i].shape[0], axis=0)
# Converting to predicted global angle
global_angle = ap_direction + doa.ap_timed_kaplan[i]
# Converting predicted angles into slopes
slopes = 1 / np.tan(np.radians(global_angle))
# Finding y intercept
y_intercept = track.aps[:, 1] * np.ones(slopes.shape) - slopes * track.aps[:, 0]
pos = np.ones((global_angle.shape[0], 2)) * np.nan
covars = []
for j in range(slopes.shape[0]):
valid_aps = fn_exp.find_crossing(global_angle[j, :])
if len(valid_aps) > 1:
couples = list(itertools.combinations(valid_aps, 2))
prelim_pos = []
weights = []
ellipse = []
for crossing in couples:
# Calculating cross-points
prelim_pos.append(fn_exp.crossings(slopes[j, :], y_intercept[j, :], crossing))
# Calculate distance between exp.aps and cross point
dist0, dist1 = fn_exp.crossings_dist(track.aps, crossing, prelim_pos[-1])
# Find angles from both exp.aps
angle0 = global_angle[j, crossing[0]]
angle1 = global_angle[j, crossing[1]]
# Calculate SD covariance
cur_eigen_val, cur_eigen_angles = fn_exp.sd_eigen(angle0, angle1, dist0, dist1)
cur_covars = fn_exp.sd_covar(cur_eigen_val, cur_eigen_angles)
covars.append(cur_covars)
ellipse.append(fn_exp.create_ellipse(prelim_pos[-1], cur_eigen_val, cur_eigen_angles))
pos[j] = fn_exp.estimate_xy_covar(prelim_pos, covars)
# print prelim_pos, covars
# if len(valid_aps) == 3:
# plt.plot(prelim_pos[0][0], prelim_pos[0][1], 'ro', ellipse[0][:, 0], ellipse[0][:, 1], 'r--',
# prelim_pos[1][0], prelim_pos[1][1], 'go', ellipse[1][:, 0], ellipse[1][:, 1], 'g--',
# prelim_pos[2][0], prelim_pos[2][1], 'bo', ellipse[2][:, 0], ellipse[2][:, 1], 'b--',
# pos[j][0], pos[j][1], 'ko',
# track.track[i][j, 0], track.track[i][j, 1], 'k^',)
# plt.title(str([cur_eigen_val, cur_eigen_angles[0], cur_covars]))
# plt.show()
# Change NaN to last known position
pos = fn.remove_nan(pos)
# Remove points from outside
# pos = fn.remove_outside(pos)
estimate_pos.append(pos)
# Holt's filtering algorithm
holt = np.zeros(pos.shape)
holt[0, :] = pos[0, :]
holt_trend = np.zeros(pos.shape)
for j in range(1, pos.shape[0]):
holt[j, :] = (1 - cfg_exp.alpha) * (holt[j-1, :] + holt_trend[j-1, :]) + cfg_exp.alpha * pos[j, :]
holt_trend[j, :] = cfg_exp.trend * (holt[j, :] - holt[j-1, :]) + (1 - cfg_exp.trend) * holt_trend[j-1, :]
# RSME over 1st track
RSME = np.sqrt(np.sum((track.track[0][:, 0] - estimate_pos[0][:, 0]) ** 2 +
(track.track[0][:, 1] - estimate_pos[0][:, 1]) ** 2) / estimate_pos[0].shape[0])
print RSME
# 1D plot
plt.figure(1)
plt.subplot(221)
plt.plot(track.track_time_int[0], track.track[0][:, 0], 'r', track.track_time_int[0], estimate_pos[0][:, 0], 'b')
plt.title('track 0 x(t) axis tracking')
plt.ylim((-5, 130))
plt.subplot(222)
plt.plot(track.track_time_int[0], track.track[0][:, 1], 'r', track.track_time_int[0], estimate_pos[0][:, 1], 'b')
plt.title('track 0 y(t) axis tracking')
plt.ylim((-5, 100))
plt.subplot(223)
plt.plot(track.track_time_int[1], track.track[1][:, 0], 'r', track.track_time_int[1], estimate_pos[1][:, 0], 'b')
plt.title('track 1 x(t) axis tracking')
plt.ylim((-5, 130))
plt.subplot(224)
plt.plot(track.track_time_int[1], track.track[1][:, 1], 'r', track.track_time_int[1], estimate_pos[1][:, 1], 'b')
plt.title('track 1 y(t) axis tracking')
plt.ylim((-5, 100))
plt.show()
# 2D plot
plt.figure(1)
plt.subplot(211)
plt.plot(track.track[0][:, 0], track.track[0][:, 1], 'r', estimate_pos[0][:, 0], estimate_pos[0][:, 1], 'b')
plt.title('track 0 pos tracking')
plt.subplot(212)
plt.plot(track.track[1][:, 0], track.track[1][:, 1], 'r', estimate_pos[1][:, 0], estimate_pos[1][:, 1], 'b')
plt.title('track 1 pos tracking')
plt.show()
| [
"[email protected]"
] | |
49aa9867f2de64f94bc8bce9aee367e02d4c0ece | bbab25f702c7bb7ce6cd894d98a121e61967d48a | /controllers/controllers.py | 11968a608c0f21d15bc486b283939dc4dc43cadd | [] | no_license | butirpadi/bp_po_carton_box_report | 9b83310ea010dbe848857cae74642e6993431d58 | 2f9bad119b1fe371bf4bc2d8ba09917c2134f86b | refs/heads/master | 2023-08-30T03:49:11.564193 | 2021-11-01T09:23:11 | 2021-11-01T09:23:11 | 309,541,390 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 935 | py | # -*- coding: utf-8 -*-
from odoo import http
# class BpPoCartonBoxReport(http.Controller):
# @http.route('/bp_po_carton_box_report/bp_po_carton_box_report/', auth='public')
# def index(self, **kw):
# return "Hello, world"
# @http.route('/bp_po_carton_box_report/bp_po_carton_box_report/objects/', auth='public')
# def list(self, **kw):
# return http.request.render('bp_po_carton_box_report.listing', {
# 'root': '/bp_po_carton_box_report/bp_po_carton_box_report',
# 'objects': http.request.env['bp_po_carton_box_report.bp_po_carton_box_report'].search([]),
# })
# @http.route('/bp_po_carton_box_report/bp_po_carton_box_report/objects/<model("bp_po_carton_box_report.bp_po_carton_box_report"):obj>/', auth='public')
# def object(self, obj, **kw):
# return http.request.render('bp_po_carton_box_report.object', {
# 'object': obj
# }) | [
"[email protected]"
] | |
fa3baa33bdde98c67faa2adaf19e614694f489e8 | 00a9128553902cb398dc99865d36e09159285b86 | /python/p3.py | 1f8b36777f8dc1b69701b73902672fc201d33c6f | [] | no_license | horacepan/aoc2020 | f498faa8c8bba4cabcfba6508a73074adb51d84c | f6d38f2b37245e89fb6f8eb4c55c74423626ca04 | refs/heads/main | 2023-02-06T03:16:21.530346 | 2020-12-20T21:33:53 | 2020-12-20T21:33:53 | 317,640,340 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 775 | py | import pdb
def solve(mat, dx, dy):
rows = len(mat)
cols = len(mat[0])
loc_x = 0
loc_y = 0
ntrees = 0
while 1:
if loc_x < rows:
ntrees += int(mat[loc_x][loc_y] == '#')
else:
break
loc_x += dx
loc_y = (loc_y + dy) % cols
return ntrees
def main():
fname = '../data/p3.txt'
with open(fname, 'r') as f:
mat = []
for line in f.readlines():
mat.append(line.strip())
a1 = solve(mat, 1, 1)
a2 = solve(mat, 1, 3)
a3 = solve(mat, 1, 5)
a4 = solve(mat, 1, 7)
a5 = solve(mat, 2, 1)
prod = a1 * a2 * a3 * a4 * a5
print("Part one:", a2)
print("Part two:", prod)
if __name__ == '__main__':
main()
| [
"[email protected]"
] | |
f0c5883130fd3be48d07d17a2b6ee9e5550cafc8 | 7914768f745808e372617ccf39aeebe44790a842 | /apps/arena/views.py | 6267d4a70f7116881c4e24988903d8dea0b939c0 | [] | no_license | POISON-B/gz_v1.0.0 | edee99b9a25594ea4dff8b04d8779a21fa154cc6 | 6e7c23f6c097efd2923562ab9c64843cd91e02c2 | refs/heads/master | 2020-03-27T01:31:38.676597 | 2018-09-23T08:47:05 | 2018-09-23T08:47:05 | 145,719,283 | 0 | 0 | null | 2018-09-23T05:56:32 | 2018-08-22T14:18:20 | JavaScript | UTF-8 | Python | false | false | 9,276 | py | from django.shortcuts import render
# Create your views here.
from rest_framework.response import Response
from rest_framework import mixins
from rest_framework.pagination import PageNumberPagination
from rest_framework import viewsets
from rest_framework import filters
from django_filters.rest_framework import DjangoFilterBackend
from apps.utils.tools import format_time
from rest_framework.permissions import IsAuthenticated
from rest_framework_jwt.authentication import JSONWebTokenAuthentication
from rest_framework.authentication import SessionAuthentication
from apps.users.models import UserProfile
from apps.user_relationship.models import UserAchievement
from .serializers import *
from apps.utils.permissions import *
from .models import *
from users.serializers import UserProfileSerializers
#排行榜页面
class TotalRankViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet):
"""
获取总排行信息
"""
queryset = UserAchievement.objects.order_by('total_ranking')
serializer_class = TotalRankSerializers
class WeekRankViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet):
"""
获取周排行信息
首先查询用户信息
获取用户所在城市
查询该城市下用户排行信息
"""
permission_classes = (IsAuthenticated, IsOwnerOrReadOnly)
authentication_classes = (JSONWebTokenAuthentication, SessionAuthentication)
serializer_class = WeekRankSerializers
def get_queryset(self):
return UserAchievement.objects.filter(user__city=self.request.user.city).order_by('monthly_rankings')
class DayRankViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet):
"""
获取日排行信息
首先查询用户信息
获取用户班级
查询该班级下用户排行信息
"""
permission_classes = (IsAuthenticated, IsOwnerOrReadOnly)
authentication_classes = (JSONWebTokenAuthentication, SessionAuthentication)
serializer_class = ClassRankSerializers
def get_queryset(self):
return UserAchievement.objects.filter(user__in_class=self.request.user.in_class).order_by('class_rankings')
#闯关模式
class PassPagination(PageNumberPagination):
page_size = 10
page_size_query_param = 'page_size'
page_query_param = "page"
max_page_size = 100
class PassListViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet):
"""
获取关卡信息
http://127.0.0.1:8000/pass/访问所有关卡 内含分页信息
http://127.0.0.1:8000/pass/2/ 按关卡id访问某一个关卡
"""
# throttle_classes = (UserRateThrottle, )
queryset = Pass.objects.all().order_by('pass_no')
serializer_class = PassSerializer
pagination_class = PassPagination
class UserPassListViewSet(mixins.ListModelMixin, mixins.RetrieveModelMixin, mixins.CreateModelMixin,
mixins.UpdateModelMixin, viewsets.GenericViewSet):
"""
获取用户关卡信息
http://127.0.0.1:8000/pass/访问用户所有关卡 内含分页信息
http://127.0.0.1:8000/pass/2/ 按关卡id访问某一个关卡
"""
# throttle_classes = (UserRateThrottle, )
queryset = Pass.objects.all().order_by('pass_no')
serializer_class = UserPassSerializer
pagination_class = PassPagination
permission_classes = (IsAuthenticated, IsOwnerOrReadOnly)
authentication_classes = (JSONWebTokenAuthentication, SessionAuthentication)
def get_queryset(self):
return UserPass.objects.filter(user_id=self.request.user)
def update(self, request, *args, **kwargs):
import os
from django.utils import timezone
#获取用户的代码
#保存代码为xxx.java文件
#编译代码 os.popen("javac test.java")
#执行代码 os.popen("java mypack.test").read()
#将执行结果和答案比较
cur_user_pass = UserPass.objects.get(id=kwargs['pk'])
cur_user_pass.submit_num += 1 #提交次数加1
request.data['submit_num'] = cur_user_pass.submit_num
request.data['submit_time'] = timezone.now()
code = request.data['user_submit']
print(code)
with open('test.java', 'w+') as f:
f.write(code)
ret = os.popen("javac test.java")
print(ret)
result = os.popen("java test").read()
print(result)
if result and result[0:5] in cur_user_pass.user_pass.pass_answer:
request.data['pass_score'] = 100
#cur_user_pass.pass_score = 100
else:
request.data['pass_score'] = 0
return mixins.UpdateModelMixin.update(self, request, args, kwargs)
"""
pk页面 前端查询逻辑如下：
pk首页 首先按照各种挑战模式 查询挑战者表 相关信息显示在首页上部
如果挑战者表中的挑战状态为 发起挑战 按钮显示迎战 否则显示继续挑战
再查询被挑战者信息 显示可以接受挑战的人员
点击迎战按钮后 向后端发起请求 生成pk信息 生成后 利用返回的pk信息构建pk详情页面
点击继续挑战按钮后 直接获取当前进行的pk信息 并显示pk详情页
"""
class ChallengerTimeModList(mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet):
"""
获取挑战者列表信息 竞技场对战模式上方发起挑战者人员数据 时间赛
"""
# throttle_classes = (UserRateThrottle, )
permission_classes = (IsAuthenticated, IsOwnerOrReadOnly)
authentication_classes = (JSONWebTokenAuthentication, SessionAuthentication)
serializer_class = ChallengerSerializer
def get_queryset(self):
return Challenger.objects.filter(be_challenged=self.request.user).filter(pk_mode=1)
class ChallengerSpeedModList(mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet):
"""
获取挑战者列表信息 竞技场对战模式上方发起挑战者人员数据 速度赛
"""
# throttle_classes = (UserRateThrottle, )
permission_classes = (IsAuthenticated, IsOwnerOrReadOnly)
authentication_classes = (JSONWebTokenAuthentication, SessionAuthentication)
serializer_class = ChallengerSerializer
def get_queryset(self):
return Challenger.objects.filter(be_challenged=self.request.user).filter(pk_mode=2)
class ChallengerProgramModList(mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet):
"""
获取挑战者列表信息 竞技场对战模式上方发起挑战者人员数据 编程赛
"""
# throttle_classes = (UserRateThrottle, )
permission_classes = (IsAuthenticated, IsOwnerOrReadOnly)
authentication_classes = (JSONWebTokenAuthentication, SessionAuthentication)
serializer_class = ChallengerSerializer
def get_queryset(self):
return Challenger.objects.filter(be_challenged=self.request.user).filter(pk_mode=3)
class WantChallengeredList(mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet):
"""
获取希望被挑战的人员列表 竞技场对战模式下方候选挑战者显示数据
"""
# throttle_classes = (UserRateThrottle, )
permission_classes = (IsAuthenticated, IsOwnerOrReadOnly)
authentication_classes = (JSONWebTokenAuthentication, SessionAuthentication)
serializer_class = UserProfileSerializers
def get_queryset(self):
return UserProfile.objects.filter(want_be_challenged=True)[0:5]
class LaunchChallenge(mixins.ListModelMixin, mixins.RetrieveModelMixin, mixins.CreateModelMixin,
viewsets.GenericViewSet):
"""
获取用户发起的挑战列表 详情 创建新的挑战
"""
permission_classes = (IsAuthenticated, IsOwnerOrReadOnly)
authentication_classes = (JSONWebTokenAuthentication, SessionAuthentication)
serializer_class = ChallengerSerializer
def get_queryset(self):
return Challenger.objects.filter(challenger=self.request.user)
class PkDetail(mixins.RetrieveModelMixin, mixins.CreateModelMixin, viewsets.GenericViewSet):
"""
pk详情信息
bb 实现创建pk详情页的接口
"""
# throttle_classes = (UserRateThrottle, )
permission_classes = (IsAuthenticated, IsOwnerOrReadOnly)
authentication_classes = (JSONWebTokenAuthentication, SessionAuthentication)
serializer_class = PkDetailSerializers
def get_queryset(self):
return UserPkDetail.objects.get(user=self.request.user)
def create(self, request, *args, **kwargs):
return mixins.CreateModelMixin.create(self, request, args, kwargs)
class TeamCompPagination(PageNumberPagination):
"""
团赛信息分页器
"""
page_size = 5
page_size_query_param = 'page_size'
page_query_param = "page"
max_page_size = 30
class TeamCompList(mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet):
"""
团赛列表页
"""
queryset = TeamComp.objects.all()
serializer_class = TeamCompSerializers
pagination_class = TeamCompPagination
class JoinTeamComp(mixins.CreateModelMixin, viewsets.GenericViewSet):
"""
加入团赛
"""
serializer_class = UserTeamCompSerializers
| [
"[email protected]"
] | |
1ff5e6418d6c7022185c32908f90cd6a14694df6 | 396787df1b472ddfab7d934c149b150352342f03 | /python_fundemental/128_linked_list_sorting.py | 712365f8902750b846816a2c36bcdd9923bc47b4 | [] | no_license | Deanwinger/python_project | a47b50a9dfc88853a5557da090b0a2ac3f3ce191 | 8c0c2a8bcd51825e6902e4d03dabbaf6f303ba83 | refs/heads/master | 2022-07-10T16:41:56.853165 | 2019-07-21T13:08:48 | 2019-07-21T13:08:48 | 107,653,001 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 29 | py |
# leetcode 148. 排序链表 | [
"[email protected]"
] | |
c40632c9ff9da232e070e4cdd547dfd5cc32f0d4 | 31e1261588e953d4e702a76e1c5306a8a97cff04 | /monk/gluon/datasets/paths.py | c4656a51daa42e9c5cca5e8dd8767763c42c03c2 | [
"Apache-2.0"
] | permissive | Varun0801/monk_v1 | cff6e8390a9248208ba825eb0046119f4f284ab1 | 559ff37669d88fd2cfaaf9d22ad84cd6cef9d176 | refs/heads/master | 2022-04-17T05:19:53.372524 | 2020-04-11T13:11:35 | 2020-04-11T13:11:35 | 255,113,878 | 1 | 0 | null | 2020-04-12T15:35:25 | 2020-04-12T15:35:24 | null | UTF-8 | Python | false | false | 4,546 | py | from gluon.datasets.imports import *
from system.imports import *
@accepts(dict, [str, list, bool], [float, int, bool], [str, list, bool], str, post_trace=True)
@TraceFunction(trace_args=False, trace_rv=False)
def set_dataset_train_path(system_dict, path, split, path_to_csv, delimiter):
'''
Set dataset train path
Args:
system_dict (dict): System dictionary containing all the variables
path (str, list): Dataset folder path
1) String : For dataset with no validation set
2) List: For dataset with validation set in order [train_set, val_set]
split (float): Indicating train validation split
Division happens as follows:
train - total dataset * split * 100
val - total dataset * (1-split) * 100
path_to_csv (str, list): Path to csv pointing to images
delimiter (str): Delimiter for the csv path provided
Returns:
dict: Updated System dictionary
'''
dataset_type = None;
dataset_train_path = None;
dataset_val_path = None;
csv_train = None;
csv_val = None;
train_val_split = None;
if(path_to_csv):
if(type(path) == str):
dataset_type = "csv_train";
csv_train = path_to_csv;
dataset_train_path = path;
train_val_split = split;
label_type = find_label_type(path_to_csv)
elif(type(path) == list):
dataset_type = "csv_train-val";
csv_train = path_to_csv[0];
csv_val = path_to_csv[1];
dataset_train_path = path[0];
dataset_val_path = path[1];
train_val_split = None;
label_type = find_label_type(path_to_csv[0])
else:
if(type(path) == str):
dataset_type = "train";
dataset_train_path = path;
train_val_split = split;
label_type = "single";
elif(type(path) == list):
dataset_type = "train-val";
dataset_train_path = path[0];
dataset_val_path = path[1];
train_val_split = None;
label_type = "single";
system_dict["dataset"]["dataset_type"] = dataset_type;
system_dict["dataset"]["train_path"] = dataset_train_path;
system_dict["dataset"]["val_path"] = dataset_val_path;
system_dict["dataset"]["csv_train"] = csv_train;
system_dict["dataset"]["csv_val"] = csv_val;
system_dict["dataset"]["params"]["train_val_split"] = train_val_split;
system_dict["dataset"]["params"]["delimiter"] = delimiter;
system_dict["dataset"]["label_type"] = label_type;
return system_dict;
@accepts(str, post_trace=True)
@TraceFunction(trace_args=True, trace_rv=True)
def find_label_type(csv_file):
'''
Find label type - single or multiple
Args:
csv_file (str): Path to training csv file
Returns:
str: Label Type
'''
label_type = "single";
df = pd.read_csv(csv_file);
columns = df.columns;
for i in range(len(df)):
label = str(df[columns[1]][i]);
if(len(label.split(" ")) > 1):
label_type = "multiple";
break;
return label_type;
@accepts(dict, [str, bool], [str, bool], str, post_trace=True)
@TraceFunction(trace_args=False, trace_rv=False)
def set_dataset_test_path(system_dict, path, path_to_csv, delimiter):
'''
Set dataset train path
Args:
system_dict (dict): System dictionary containing all the variables
path (str, list): Dataset folder path
1) String : For dataset with no validation set
2) List: For dataset with validation set in order [train_set, val_set]
path_to_csv (str, list): Path to csv pointing to images
delimiter (str): Delimiter for the csv path provided
Returns:
dict: Updated System dictionary
'''
dataset_test_type = None;
dataset_test_path = None;
csv_test = None;
if(path_to_csv):
csv_test = path_to_csv;
dataset_test_path = path;
dataset_test_type = "csv";
else:
dataset_test_path = path;
dataset_test_type = "foldered";
system_dict["dataset"]["test_path"] = dataset_test_path;
system_dict["dataset"]["csv_test"] = csv_test;
system_dict["dataset"]["params"]["test_delimiter"] = delimiter;
system_dict["dataset"]["params"]["dataset_test_type"] = dataset_test_type;
return system_dict; | [
"[email protected]"
] | |
4e23c014e2a3e9ed57bddae2ec3e70d526a28c65 | 4fc9c61450de38ce003e20e0452af3e636f28be3 | /language_model/layer/attention.py | e64cc42d1eaf11d71b2cc146934438e793eb8709 | [
"Apache-2.0"
] | permissive | SunYanCN/language_model_tf | a6c453c3c3aa1b34ac240cff94674e9eaa679ec9 | d39f335e5410d2bd7a23760dedbfcca36338d591 | refs/heads/master | 2020-05-02T03:58:59.634898 | 2019-05-24T20:31:52 | 2019-05-24T20:31:52 | 177,740,244 | 0 | 0 | Apache-2.0 | 2019-12-30T06:17:43 | 2019-03-26T07:56:25 | Python | UTF-8 | Python | false | false | 62,450 | py | import numpy as np
import tensorflow as tf
from util.default_util import *
from util.language_model_util import *
from layer.basic import *
__all__ = ["Attention", "MaxAttention", "CoAttention", "GatedAttention", "MultiHeadAttention"]
def _create_attention_matrix(src_unit_dim,
trg_unit_dim,
attention_unit_dim,
attention_score_type,
regularizer,
random_seed,
trainable,
scope="att_matrix"):
"""create attetnion matrix"""
scope = "{0}/{1}".format(scope, attention_score_type)
if attention_score_type == "dot":
attention_matrix = []
elif attention_score_type == "scaled_dot":
attention_matrix = []
elif attention_score_type == "linear":
attention_matrix = _create_linear_attention_matrix(src_unit_dim,
trg_unit_dim, regularizer, random_seed, trainable, scope)
elif attention_score_type == "bilinear":
attention_matrix = _create_bilinear_attention_matrix(src_unit_dim,
trg_unit_dim, regularizer, random_seed, trainable, scope)
elif attention_score_type == "nonlinear":
attention_matrix = _create_nonlinear_attention_matrix(src_unit_dim,
trg_unit_dim, attention_unit_dim, regularizer, random_seed, trainable, scope)
elif attention_score_type == "linear_plus":
attention_matrix = _create_linear_plus_attention_matrix(src_unit_dim,
trg_unit_dim, regularizer, random_seed, trainable, scope)
elif attention_score_type == "nonlinear_plus":
attention_matrix = _create_nonlinear_plus_attention_matrix(src_unit_dim,
trg_unit_dim, attention_unit_dim, regularizer, random_seed, trainable, scope)
elif attention_score_type == "trilinear":
attention_matrix = _create_trilinear_attention_matrix(src_unit_dim,
trg_unit_dim, regularizer, random_seed, trainable, scope)
else:
raise ValueError("unsupported attention score type {0}".format(attention_score_type))
return attention_matrix
def _create_linear_attention_matrix(src_unit_dim,
trg_unit_dim,
regularizer,
random_seed,
trainable,
scope="linear"):
"""create linear attetnion matrix"""
weight_initializer = create_variable_initializer("glorot_uniform", random_seed)
linear_src_weight = tf.get_variable("{0}/src_weight".format(scope), shape=[1, src_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
linear_trg_weight = tf.get_variable("{0}/trg_weight".format(scope), shape=[1, trg_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
attention_matrix = [linear_src_weight, linear_trg_weight]
return attention_matrix
def _create_bilinear_attention_matrix(src_unit_dim,
trg_unit_dim,
regularizer,
random_seed,
trainable,
scope="bilinear"):
"""create bilinear attetnion matrix"""
weight_initializer = create_variable_initializer("glorot_uniform", random_seed)
bilinear_weight = tf.get_variable("{0}/weight".format(scope), shape=[src_unit_dim, trg_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
attention_matrix = [bilinear_weight]
return attention_matrix
def _create_nonlinear_attention_matrix(src_unit_dim,
trg_unit_dim,
attention_unit_dim,
regularizer,
random_seed,
trainable,
scope="nonlinear"):
"""create nonlinear attetnion matrix"""
weight_initializer = create_variable_initializer("glorot_uniform", random_seed)
bias_initializer = create_variable_initializer("zero")
pre_nonlinear_src_weight = tf.get_variable("{0}/pre/src_weight".format(scope), shape=[attention_unit_dim, src_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
pre_nonlinear_trg_weight = tf.get_variable("{0}/pre/trg_weight".format(scope), shape=[attention_unit_dim, trg_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
pre_nonlinear_bias = tf.get_variable("{0}/pre/bias".format(scope), shape=[attention_unit_dim],
initializer=bias_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
post_nonlinear_weight = tf.get_variable("{0}/post/weight".format(scope), shape=[1, attention_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
attention_matrix = [pre_nonlinear_src_weight, pre_nonlinear_trg_weight, pre_nonlinear_bias, post_nonlinear_weight]
return attention_matrix
def _create_linear_plus_attention_matrix(src_unit_dim,
trg_unit_dim,
regularizer,
random_seed,
trainable,
scope="linear_plus"):
"""create linear plus attetnion matrix"""
weight_initializer = create_variable_initializer("glorot_uniform", random_seed)
if src_unit_dim != trg_unit_dim:
raise ValueError("src dim {0} and trg dim must be the same for linear plus attention".format(src_unit_dim, trg_unit_dim))
else:
mul_unit_dim = src_unit_dim
linear_plus_src_weight = tf.get_variable("{0}/src_weight".format(scope), shape=[1, src_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
linear_plus_trg_weight = tf.get_variable("{0}/trg_weight".format(scope), shape=[1, trg_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
linear_plus_mul_weight = tf.get_variable("{0}/mul_weight".format(scope), shape=[1, mul_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
attention_matrix = [linear_plus_src_weight, linear_plus_trg_weight, linear_plus_mul_weight]
return attention_matrix
def _create_nonlinear_plus_attention_matrix(src_unit_dim,
trg_unit_dim,
attention_unit_dim,
regularizer,
random_seed,
trainable,
scope="nonlinear_plus"):
"""create nonlinear plus attetnion matrix"""
weight_initializer = create_variable_initializer("glorot_uniform", random_seed)
bias_initializer = create_variable_initializer("zero")
if src_unit_dim != trg_unit_dim:
raise ValueError("src dim {0} and trg dim must be the same for nonlinear plus attention".format(src_unit_dim, trg_unit_dim))
else:
mul_unit_dim = src_unit_dim
pre_nonlinear_plus_src_weight = tf.get_variable("{0}/pre/src_weight".format(scope), shape=[attention_unit_dim, src_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
pre_nonlinear_plus_trg_weight = tf.get_variable("{0}/pre/trg_weight".format(scope), shape=[attention_unit_dim, trg_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
pre_nonlinear_plus_mul_weight = tf.get_variable("{0}/pre/mul_weight".format(scope), shape=[attention_unit_dim, mul_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
pre_nonlinear_plus_bias = tf.get_variable("{0}/pre/bias".format(scope), shape=[attention_unit_dim],
initializer=bias_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
post_nonlinear_plus_weight = tf.get_variable("{0}/post/weight".format(scope), shape=[1, attention_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
attention_matrix = [pre_nonlinear_plus_src_weight, pre_nonlinear_plus_trg_weight,
pre_nonlinear_plus_mul_weight, pre_nonlinear_plus_bias, post_nonlinear_plus_weight]
return attention_matrix
def _create_trilinear_attention_matrix(src_unit_dim,
trg_unit_dim,
regularizer,
random_seed,
trainable,
scope="trilinear"):
"""create trilinear attetnion matrix"""
weight_initializer = create_variable_initializer("glorot_uniform", random_seed)
if src_unit_dim != trg_unit_dim:
raise ValueError("src dim {0} and trg dim must be the same for trilinear attention".format(src_unit_dim, trg_unit_dim))
else:
mul_unit_dim = src_unit_dim
trilinear_src_weight = tf.get_variable("{0}/src_weight".format(scope), shape=[src_unit_dim, 1],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
trilinear_trg_weight = tf.get_variable("{0}/trg_weight".format(scope), shape=[trg_unit_dim, 1],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
trilinear_mul_weight = tf.get_variable("{0}/mul_weight".format(scope), shape=[1, 1, mul_unit_dim],
initializer=weight_initializer, regularizer=regularizer, trainable=trainable, dtype=tf.float32)
attention_matrix = [trilinear_src_weight, trilinear_trg_weight, trilinear_mul_weight]
return attention_matrix
def _generate_attention_score(input_src_data,
input_trg_data,
attention_matrix,
attention_score_type):
"""generate attention score"""
if attention_score_type == "dot":
input_attention_score = _generate_dot_attention_score(input_src_data, input_trg_data)
elif attention_score_type == "scaled_dot":
input_attention_score = _generate_scaled_dot_attention_score(input_src_data, input_trg_data)
elif attention_score_type == "linear":
input_attention_score = _generate_linear_attention_score(input_src_data,
input_trg_data, attention_matrix)
elif attention_score_type == "bilinear":
input_attention_score = _generate_bilinear_attention_score(input_src_data,
input_trg_data, attention_matrix)
elif attention_score_type == "nonlinear":
input_attention_score = _generate_nonlinear_attention_score(input_src_data,
input_trg_data, attention_matrix)
elif attention_score_type == "linear_plus":
input_attention_score = _generate_linear_plus_attention_score(input_src_data,
input_trg_data, attention_matrix)
elif attention_score_type == "nonlinear_plus":
input_attention_score = _generate_nonlinear_plus_attention_score(input_src_data,
input_trg_data, attention_matrix)
elif attention_score_type == "trilinear":
input_attention_score = _generate_trilinear_attention_score(input_src_data,
input_trg_data, attention_matrix)
else:
raise ValueError("unsupported attention score type {0}".format(attention_score_type))
return input_attention_score
def _generate_dot_attention_score(input_src_data,
input_trg_data):
"""generate dot-product attention score"""
input_attention = tf.matmul(input_src_data, input_trg_data, transpose_b=True)
return input_attention
def _generate_scaled_dot_attention_score(input_src_data,
input_trg_data):
"""generate scaled dot-product attention score"""
src_unit_dim = tf.shape(input_src_data)[2]
input_attention = tf.matmul(input_src_data, input_trg_data, transpose_b=True)
input_attention = input_attention / tf.sqrt(tf.cast(src_unit_dim, dtype=tf.float32))
return input_attention
def _generate_linear_attention_score(input_src_data,
input_trg_data,
attention_matrix):
"""generate linear attention score"""
input_src_shape = tf.shape(input_src_data)
input_trg_shape = tf.shape(input_trg_data)
batch_size = input_src_shape[0]
src_max_length = input_src_shape[1]
trg_max_length = input_trg_shape[1]
src_unit_dim = input_src_shape[2]
trg_unit_dim = input_trg_shape[2]
linear_src_weight = attention_matrix[0]
linear_trg_weight = attention_matrix[1]
input_src_data = tf.reshape(input_src_data, shape=[-1, src_unit_dim])
input_src_data = tf.matmul(input_src_data, linear_src_weight, transpose_b=True)
input_src_data = tf.reshape(input_src_data, shape=[batch_size, src_max_length, 1, -1])
input_trg_data = tf.reshape(input_trg_data, shape=[-1, trg_unit_dim])
input_trg_data = tf.matmul(input_trg_data, linear_trg_weight, transpose_b=True)
input_trg_data = tf.reshape(input_trg_data, shape=[batch_size, 1, trg_max_length, -1])
input_src_data = tf.tile(input_src_data, multiples=[1, 1, trg_max_length, 1])
input_trg_data = tf.tile(input_trg_data, multiples=[1, src_max_length, 1, 1])
input_attention = input_src_data + input_trg_data
input_attention = tf.reshape(input_attention, shape=[batch_size, src_max_length, trg_max_length])
return input_attention
def _generate_bilinear_attention_score(input_src_data,
input_trg_data,
attention_matrix):
"""generate bilinear attention score"""
input_src_shape = tf.shape(input_src_data)
batch_size = input_src_shape[0]
src_max_length = input_src_shape[1]
src_unit_dim = input_src_shape[2]
bilinear_weight = attention_matrix[0]
input_src_data = tf.reshape(input_src_data, shape=[-1, src_unit_dim])
input_src_data = tf.matmul(input_src_data, bilinear_weight)
input_src_data = tf.reshape(input_src_data, shape=[batch_size, src_max_length, -1])
input_attention = tf.matmul(input_src_data, input_trg_data, transpose_b=True)
return input_attention
def _generate_nonlinear_attention_score(input_src_data,
input_trg_data,
attention_matrix):
"""generate linear attention score"""
input_src_shape = tf.shape(input_src_data)
input_trg_shape = tf.shape(input_trg_data)
batch_size = input_src_shape[0]
src_max_length = input_src_shape[1]
trg_max_length = input_trg_shape[1]
src_unit_dim = input_src_shape[2]
trg_unit_dim = input_trg_shape[2]
pre_nonlinear_src_weight = attention_matrix[0]
pre_nonlinear_trg_weight = attention_matrix[1]
pre_nonlinear_bias = tf.reshape(attention_matrix[2], shape=[1, 1, 1, -1])
post_nonlinear_weight = attention_matrix[3]
input_src_data = tf.reshape(input_src_data, shape=[-1, src_unit_dim])
input_src_data = tf.matmul(input_src_data, pre_nonlinear_src_weight, transpose_b=True)
input_src_data = tf.reshape(input_src_data, shape=[batch_size, src_max_length, 1, -1])
input_trg_data = tf.reshape(input_trg_data, shape=[-1, trg_unit_dim])
input_trg_data = tf.matmul(input_trg_data, pre_nonlinear_trg_weight, transpose_b=True)
input_trg_data = tf.reshape(input_trg_data, shape=[batch_size, 1, trg_max_length, -1])
input_src_data = tf.tile(input_src_data, multiples=[1, 1, trg_max_length, 1])
input_trg_data = tf.tile(input_trg_data, multiples=[1, src_max_length, 1, 1])
input_attention = input_src_data + input_trg_data
input_attention = tf.nn.tanh(input_attention + pre_nonlinear_bias)
attention_dim = tf.shape(input_attention)[-1]
input_attention = tf.reshape(input_attention, shape=[-1, attention_dim])
input_attention = tf.matmul(input_attention, post_nonlinear_weight, transpose_b=True)
input_attention = tf.reshape(input_attention, shape=[batch_size, src_max_length, trg_max_length])
return input_attention
def _generate_linear_plus_attention_score(input_src_data,
input_trg_data,
attention_matrix):
"""generate linear plus attention score"""
input_src_shape = tf.shape(input_src_data)
input_trg_shape = tf.shape(input_trg_data)
batch_size = input_src_shape[0]
src_max_length = input_src_shape[1]
trg_max_length = input_trg_shape[1]
src_unit_dim = input_src_shape[2]
trg_unit_dim = input_trg_shape[2]
mul_unit_dim = src_unit_dim
linear_plus_src_weight = attention_matrix[0]
linear_plus_trg_weight = attention_matrix[1]
linear_plus_mul_weight = attention_matrix[2]
input_src_data = tf.expand_dims(input_src_data, axis=2)
input_trg_data = tf.expand_dims(input_trg_data, axis=1)
input_src_data = tf.tile(input_src_data, multiples=[1, 1, trg_max_length, 1])
input_trg_data = tf.tile(input_trg_data, multiples=[1, src_max_length, 1, 1])
input_mul_data = input_src_data * input_trg_data
input_src_data = tf.reshape(input_src_data, shape=[-1, src_unit_dim])
input_src_data = tf.matmul(input_src_data, linear_plus_src_weight, transpose_b=True)
input_trg_data = tf.reshape(input_trg_data, shape=[-1, trg_unit_dim])
input_trg_data = tf.matmul(input_trg_data, linear_plus_trg_weight, transpose_b=True)
input_mul_data = tf.reshape(input_mul_data, shape=[-1, mul_unit_dim])
input_mul_data = tf.matmul(input_mul_data, linear_plus_mul_weight, transpose_b=True)
input_attention = input_src_data + input_trg_data + input_mul_data
input_attention = tf.reshape(input_attention, shape=[batch_size, src_max_length, trg_max_length])
return input_attention
def _generate_nonlinear_plus_attention_score(input_src_data,
input_trg_data,
attention_matrix):
"""generate nonlinear plus attention score"""
input_src_shape = tf.shape(input_src_data)
input_trg_shape = tf.shape(input_trg_data)
batch_size = input_src_shape[0]
src_max_length = input_src_shape[1]
trg_max_length = input_trg_shape[1]
src_unit_dim = input_src_shape[2]
trg_unit_dim = input_trg_shape[2]
mul_unit_dim = src_unit_dim
pre_nonlinear_plus_src_weight = attention_matrix[0]
pre_nonlinear_plus_trg_weight = attention_matrix[1]
pre_nonlinear_plus_mul_weight = attention_matrix[2]
pre_nonlinear_plus_bias = tf.reshape(attention_matrix[3], shape=[1, 1, 1, -1])
post_nonlinear_plus_weight = attention_matrix[4]
input_src_data = tf.reshape(input_src_data, shape=[batch_size, src_max_length, 1, -1])
input_trg_data = tf.reshape(input_trg_data, shape=[batch_size, 1, trg_max_length, -1])
input_src_data = tf.tile(input_src_data, multiples=[1, 1, trg_max_length, 1])
input_trg_data = tf.tile(input_trg_data, multiples=[1, src_max_length, 1, 1])
input_mul_data = input_src_data * input_trg_data
input_src_data = tf.reshape(input_src_data, shape=[-1, src_unit_dim])
input_src_data = tf.matmul(input_src_data, pre_nonlinear_plus_src_weight, transpose_b=True)
input_trg_data = tf.reshape(input_trg_data, shape=[-1, trg_unit_dim])
input_trg_data = tf.matmul(input_trg_data, pre_nonlinear_plus_trg_weight, transpose_b=True)
input_mul_data = tf.reshape(input_mul_data, shape=[-1, mul_unit_dim])
input_mul_data = tf.matmul(input_mul_data, pre_nonlinear_plus_mul_weight, transpose_b=True)
input_attention = input_src_data + input_trg_data + input_mul_data
input_attention = tf.nn.tanh(input_attention + pre_nonlinear_plus_bias)
input_attention = tf.matmul(input_attention, post_nonlinear_plus_weight, transpose_b=True)
input_attention = tf.reshape(input_attention, shape=[batch_size, src_max_length, trg_max_length])
return input_attention
def _generate_trilinear_attention_score(input_src_data,
input_trg_data,
attention_matrix):
"""generate trilinear attention score"""
input_src_shape = tf.shape(input_src_data) # [batch_size, src_len, d]
input_trg_shape = tf.shape(input_trg_data) # [batch_size, trg_len, d]
batch_size = input_src_shape[0]
src_max_length = input_src_shape[1]
trg_max_length = input_trg_shape[1]
src_unit_dim = input_src_shape[2]
trg_unit_dim = input_trg_shape[2]
mul_unit_dim = src_unit_dim
trilinear_src_weight = attention_matrix[0] # [d, 1]
trilinear_trg_weight = attention_matrix[1] # [d, 1]
trilinear_mul_weight = attention_matrix[2] # [1, 1, d]
input_src_part = tf.reshape(input_src_data, shape=[-1, src_unit_dim]) # [-1, d]
input_trg_part = tf.reshape(input_trg_data, shape=[-1, trg_unit_dim]) # [-1, d]
input_src_part = tf.matmul(input_src_part, trilinear_src_weight) # [-1, 1]
input_trg_part = tf.matmul(input_trg_part, trilinear_trg_weight) # [-1, 1]
input_src_part = tf.reshape(input_src_part, shape=[batch_size, src_max_length, 1]) # [batch_size, src_len, 1]
input_trg_part = tf.reshape(input_trg_part, shape=[batch_size, 1, trg_max_length]) # [batch_size, 1, trg_len]
input_src_score = tf.tile(input_src_part, multiples=[1, 1, trg_max_length]) # [batch_size, src_len, trg_len]
input_trg_score = tf.tile(input_trg_part, multiples=[1, src_max_length, 1]) # [batch_size, src_len, trg_len]
input_src_part = input_src_data * trilinear_mul_weight # [batch_size, src_len, d]
input_trg_part = tf.transpose(input_trg_data, perm=[0, 2, 1]) # [batch_size, d, trg_len]
input_mul_score = tf.matmul(input_src_part, input_trg_part) # [batch_size, src_len, trg_len]
input_attention = input_src_score + input_trg_score + input_mul_score # [batch_size, src_len, trg_len]
return input_attention
def _generate_attention_mask(input_src_mask,
input_trg_mask,
remove_diag=False):
"""generate attention mask"""
input_mask = tf.matmul(input_src_mask, input_trg_mask, transpose_b=True)
if remove_diag == True:
src_max_length = tf.shape(input_src_mask)[1]
trg_max_length = tf.shape(input_trg_mask)[1]
input_mask = input_mask * (1 - tf.eye(src_max_length, trg_max_length))
return input_mask
def _create_projection_layer(unit_dim,
hidden_activation,
use_bias,
regularizer,
random_seed,
trainable,
name):
"""create projection layer"""
weight_initializer = create_variable_initializer("glorot_uniform", random_seed)
bias_initializer = create_variable_initializer("zero")
projection_layer = tf.layers.Dense(units=unit_dim, activation=hidden_activation,
use_bias=use_bias, kernel_initializer=weight_initializer, bias_initializer=bias_initializer,
kernel_regularizer=regularizer, bias_regularizer=regularizer, trainable=trainable, name=name)
return projection_layer
class Attention(object):
"""attention layer"""
def __init__(self,
src_dim,
trg_dim,
att_dim,
score_type,
dropout,
att_dropout=0.0,
layer_dropout=0.0,
layer_norm=False,
residual_connect=False,
is_self=False,
external_matrix=None,
num_gpus=1,
default_gpu_id=0,
regularizer=None,
random_seed=0,
trainable=True,
scope="attention"):
"""initialize attention layer"""
self.src_dim = src_dim
self.trg_dim = trg_dim
self.att_dim = att_dim
self.score_type = score_type
self.dropout = dropout
self.att_dropout = att_dropout
self.layer_dropout = layer_dropout
self.layer_norm = layer_norm
self.residual_connect = residual_connect
self.is_self = is_self
self.regularizer = regularizer
self.random_seed = random_seed
self.trainable = trainable
self.scope = scope
self.device_spec = get_device_spec(default_gpu_id, num_gpus)
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
if external_matrix == None:
self.attention_matrix = _create_attention_matrix(self.src_dim, self.trg_dim,
self.att_dim, self.score_type, self.regularizer, self.random_seed, self.trainable, "att_matrix")
else:
self.attention_matrix = external_matrix
self.dropout_layer = Dropout(rate=self.dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed)
self.att_dropout_layer = Dropout(rate=self.att_dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed, scope="att_dropout")
if self.layer_norm == True:
self.src_norm_layer = LayerNorm(layer_dim=self.src_dim, num_gpus=num_gpus, default_gpu_id=default_gpu_id,
regularizer=self.regularizer, trainable=self.trainable, scope="src_layer_norm")
if self.is_self == True:
self.trg_norm_layer = self.src_norm_layer
else:
self.trg_norm_layer = LayerNorm(layer_dim=self.trg_dim, num_gpus=num_gpus, default_gpu_id=default_gpu_id,
regularizer=self.regularizer, trainable=self.trainable, scope="trg_layer_norm")
def __call__(self,
input_src_data,
input_trg_data,
input_src_mask,
input_trg_mask):
"""call attention layer"""
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
input_src_shape = tf.shape(input_src_data)
input_trg_shape = tf.shape(input_trg_data)
input_src_mask_shape = tf.shape(input_src_mask)
input_trg_mask_shape = tf.shape(input_trg_mask)
src_shape_size = len(input_src_data.get_shape().as_list())
trg_shape_size = len(input_trg_data.get_shape().as_list())
if src_shape_size > 3:
input_src_data = tf.reshape(input_src_data, shape=tf.concat([[-1], input_src_shape[-2:]], axis=0))
input_src_mask = tf.reshape(input_src_mask, shape=tf.concat([[-1], input_src_mask_shape[-2:]], axis=0))
if trg_shape_size > 3:
input_trg_data = tf.reshape(input_trg_data, shape=tf.concat([[-1], input_trg_shape[-2:]], axis=0))
input_trg_mask = tf.reshape(input_trg_mask, shape=tf.concat([[-1], input_trg_mask_shape[-2:]], axis=0))
input_src_attention = input_src_data
input_trg_attention = input_trg_data
input_src_attention_mask = input_src_mask
input_trg_attention_mask = input_trg_mask
if self.layer_norm == True:
input_src_attention, input_src_attention_mask = self.src_norm_layer(input_src_attention, input_src_attention_mask)
input_trg_attention, input_trg_attention_mask = self.trg_norm_layer(input_trg_attention, input_trg_attention_mask)
input_attention_score = _generate_attention_score(input_src_attention,
input_trg_attention, self.attention_matrix, self.score_type)
input_attention_mask = _generate_attention_mask(input_src_attention_mask,
input_trg_attention_mask, self.is_self)
input_attention_score = input_attention_score * input_attention_mask
input_attention_weight = softmax_with_mask(input_attention_score,
input_attention_mask, axis=-1) * input_attention_mask
input_attention_weight, _ = self.att_dropout_layer(input_attention_weight, input_attention_mask)
input_attention = tf.matmul(input_attention_weight, input_trg_attention)
input_attention, _ = self.dropout_layer(input_attention, input_src_mask)
if self.residual_connect == True and self.is_self == True:
output_attention, output_mask = tf.cond(tf.random_uniform([]) < self.layer_dropout,
lambda: (input_src_data, input_src_mask),
lambda: (input_attention + input_src_data, input_src_mask))
output_attention = output_attention * output_mask
else:
output_attention = input_attention * input_src_mask
output_mask = input_src_mask
if src_shape_size > 3:
output_attention = tf.reshape(output_attention,
shape=tf.concat([input_src_shape[:-2], input_trg_shape[-2:]], axis=0))
output_mask = tf.reshape(output_mask,
shape=tf.concat([input_src_mask_shape[:-2], input_trg_mask_shape[-2:]], axis=0))
return output_attention, output_mask, output_attention_score, output_score_mask
def get_attention_matrix(self):
return self.attention_matrix
class MaxAttention(object):
"""max-attention layer"""
def __init__(self,
src_dim,
trg_dim,
att_dim,
score_type,
dropout,
att_dropout=0.0,
layer_dropout=0.0,
layer_norm=False,
residual_connect=False,
is_self=False,
external_matrix=None,
num_gpus=1,
default_gpu_id=0,
regularizer=None,
random_seed=0,
trainable=True,
scope="max_att"):
"""initialize max-attention layer"""
self.src_dim = src_dim
self.trg_dim = trg_dim
self.att_dim = att_dim
self.score_type = score_type
self.dropout = dropout
self.att_dropout = att_dropout
self.layer_dropout = layer_dropout
self.layer_norm = layer_norm
self.residual_connect = residual_connect
self.is_self = is_self
self.regularizer = regularizer
self.random_seed = random_seed
self.trainable = trainable
self.scope = scope
self.device_spec = get_device_spec(default_gpu_id, num_gpus)
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
if external_matrix == None:
self.attention_matrix = _create_attention_matrix(self.src_dim, self.trg_dim,
self.att_dim, self.score_type, self.regularizer, self.random_seed, self.trainable, "att_matrix")
else:
self.attention_matrix = external_matrix
self.dropout_layer = Dropout(rate=self.dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed)
self.att_dropout_layer = Dropout(rate=self.att_dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed, scope="att_dropout")
if self.layer_norm == True:
self.src_norm_layer = LayerNorm(layer_dim=self.src_dim, num_gpus=num_gpus, default_gpu_id=default_gpu_id,
regularizer=self.regularizer, trainable=self.trainable, scope="src_layer_norm")
if self.is_self == True:
self.trg_norm_layer = self.src_norm_layer
else:
self.trg_norm_layer = LayerNorm(layer_dim=self.trg_dim, num_gpus=num_gpus, default_gpu_id=default_gpu_id,
regularizer=self.regularizer, trainable=self.trainable, scope="trg_layer_norm")
def __call__(self,
input_src_data,
input_trg_data,
input_src_mask,
input_trg_mask):
"""call max-attention layer"""
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
input_src_shape = tf.shape(input_src_data)
input_trg_shape = tf.shape(input_trg_data)
input_src_mask_shape = tf.shape(input_src_mask)
input_trg_mask_shape = tf.shape(input_trg_mask)
src_shape_size = len(input_src_data.get_shape().as_list())
trg_shape_size = len(input_trg_data.get_shape().as_list())
if src_shape_size > 3:
input_src_data = tf.reshape(input_src_data, shape=tf.concat([[-1], input_src_shape[-2:]], axis=0))
input_src_mask = tf.reshape(input_src_mask, shape=tf.concat([[-1], input_src_mask_shape[-2:]], axis=0))
if trg_shape_size > 3:
input_trg_data = tf.reshape(input_trg_data, shape=tf.concat([[-1], input_trg_shape[-2:]], axis=0))
input_trg_mask = tf.reshape(input_trg_mask, shape=tf.concat([[-1], input_trg_mask_shape[-2:]], axis=0))
input_src_attention = input_src_data
input_src_attention_mask = input_src_mask
input_trg_attention = input_trg_data
input_trg_attention_mask = input_trg_mask
if self.layer_norm == True:
input_src_attention, input_src_attention_mask = self.src_norm_layer(input_src_attention, input_src_attention_mask)
input_trg_attention, input_trg_attention_mask = self.trg_norm_layer(input_trg_attention, input_trg_attention_mask)
input_attention_score = _generate_attention_score(input_src_attention,
input_trg_attention, self.attention_matrix, self.score_type)
input_attention_mask = _generate_attention_mask(input_src_attention_mask,
input_trg_attention_mask, self.is_self)
input_attention_score = tf.transpose(tf.reduce_max(input_attention_score, axis=-1, keepdims=True), perm=[0, 2, 1])
input_attention_mask = tf.transpose(tf.reduce_max(input_attention_mask, axis=-1, keepdims=True), perm=[0, 2, 1])
input_attention_score = input_attention_score * input_attention_mask
input_attention_weight = softmax_with_mask(input_attention_score,
input_attention_mask, axis=-1) * input_attention_mask
input_attention_weight, _ = self.att_dropout_layer(input_attention_weight, input_attention_mask)
input_attention = tf.matmul(input_attention_weight, input_src_attention)
input_attention, _ = self.dropout_layer(input_attention, input_src_mask)
src_max_length = tf.shape(input_src_attention)[1]
input_attention = tf.tile(input_attention, multiples=[1, src_max_length, 1])
if self.residual_connect == True and self.is_self == True:
output_attention, output_mask = tf.cond(tf.random_uniform([]) < self.layer_dropout,
lambda: (input_src_data, input_src_mask),
lambda: (input_attention + input_src_data, input_src_mask))
output_attention = output_attention * output_mask
else:
output_attention = input_attention * input_src_mask
output_mask = input_src_mask
if src_shape_size > 3:
output_attention = tf.reshape(output_attention,
shape=tf.concat([input_src_shape[:-2], input_trg_shape[-2:]], axis=0))
output_mask = tf.reshape(output_mask,
shape=tf.concat([input_src_mask_shape[:-2], input_trg_mask_shape[-2:]], axis=0))
return output_attention, output_mask
def get_attention_matrix(self):
return self.attention_matrix
class CoAttention(object):
"""co-attention layer"""
def __init__(self,
src_dim,
trg_dim,
att_dim,
score_type,
dropout,
att_dropout=0.0,
layer_dropout=0.0,
layer_norm=False,
residual_connect=False,
is_self=False,
external_matrix=None,
num_gpus=1,
default_gpu_id=0,
regularizer=None,
random_seed=0,
trainable=True,
scope="co_att"):
"""initialize co-attention layer"""
self.src_dim = src_dim
self.trg_dim = trg_dim
self.att_dim = att_dim
self.score_type = score_type
self.dropout = dropout
self.att_dropout = att_dropout
self.layer_dropout = layer_dropout
self.layer_norm = layer_norm
self.residual_connect = residual_connect
self.is_self = is_self
self.regularizer = regularizer
self.random_seed = random_seed
self.trainable = trainable
self.scope = scope
self.device_spec = get_device_spec(default_gpu_id, num_gpus)
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
if external_matrix == None:
self.attention_matrix = _create_attention_matrix(self.src_dim, self.trg_dim,
self.att_dim, self.score_type, self.regularizer, self.random_seed, self.trainable, "att_matrix")
else:
self.attention_matrix = external_matrix
self.dropout_layer = Dropout(rate=self.dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed)
self.s2t_att_dropout_layer = Dropout(rate=self.att_dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed, scope="s2t_att_dropout")
self.t2s_att_dropout_layer = Dropout(rate=self.att_dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed, scope="t2s_att_dropout")
if self.layer_norm == True:
self.src_norm_layer = LayerNorm(layer_dim=self.src_dim, num_gpus=num_gpus, default_gpu_id=default_gpu_id,
regularizer=self.regularizer, trainable=self.trainable, scope="src_layer_norm")
if self.is_self == True:
self.trg_norm_layer = self.src_norm_layer
else:
self.trg_norm_layer = LayerNorm(layer_dim=self.trg_dim, num_gpus=num_gpus, default_gpu_id=default_gpu_id,
regularizer=self.regularizer, trainable=self.trainable, scope="trg_layer_norm")
def __call__(self,
input_src_data,
input_trg_data,
input_src_mask,
input_trg_mask):
"""call co-attention layer"""
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
input_src_shape = tf.shape(input_src_data)
input_trg_shape = tf.shape(input_trg_data)
input_src_mask_shape = tf.shape(input_src_mask)
input_trg_mask_shape = tf.shape(input_trg_mask)
src_shape_size = len(input_src_data.get_shape().as_list())
trg_shape_size = len(input_trg_data.get_shape().as_list())
if src_shape_size > 3:
input_src_data = tf.reshape(input_src_data, shape=tf.concat([[-1], input_src_shape[-2:]], axis=0))
input_src_mask = tf.reshape(input_src_mask, shape=tf.concat([[-1], input_src_mask_shape[-2:]], axis=0))
if trg_shape_size > 3:
input_trg_data = tf.reshape(input_trg_data, shape=tf.concat([[-1], input_trg_shape[-2:]], axis=0))
input_trg_mask = tf.reshape(input_trg_mask, shape=tf.concat([[-1], input_trg_mask_shape[-2:]], axis=0))
input_src_attention = input_src_data
input_src_attention_mask = input_src_mask
input_trg_attention = input_trg_data
input_trg_attention_mask = input_trg_mask
if self.layer_norm == True:
input_src_attention, input_src_attention_mask = self.src_norm_layer(input_src_attention, input_src_attention_mask)
input_trg_attention, input_trg_attention_mask = self.trg_norm_layer(input_trg_attention, input_trg_attention_mask)
input_attention_score = _generate_attention_score(input_src_attention,
input_trg_attention, self.attention_matrix, self.score_type)
input_attention_mask = _generate_attention_mask(input_src_attention_mask,
input_trg_attention_mask, self.is_self)
input_s2t_att_score = input_attention_score
input_s2t_att_mask = input_attention_mask
input_s2t_att_score = input_s2t_att_score * input_s2t_att_mask
input_t2s_att_score = tf.transpose(input_attention_score, perm=[0, 2, 1])
input_t2s_att_mask = tf.transpose(input_attention_mask, perm=[0, 2, 1])
input_t2s_att_score = input_t2s_att_score * input_t2s_att_mask
input_s2t_att_weight = softmax_with_mask(input_s2t_att_score,
input_s2t_att_mask, axis=-1) * input_s2t_att_mask
input_s2t_att_weight, _ = self.s2t_att_dropout_layer(input_s2t_att_weight, input_s2t_att_mask)
input_t2s_att_weight = softmax_with_mask(input_t2s_att_score,
input_t2s_att_mask, axis=-1) * input_t2s_att_mask
input_t2s_att_weight, _ = self.t2s_att_dropout_layer(input_t2s_att_weight, input_t2s_att_mask)
input_attention_weight = tf.matmul(input_s2t_att_weight, input_t2s_att_weight)
input_attention = tf.matmul(input_attention_weight, input_src_attention)
input_attention, _ = self.dropout_layer(input_attention, input_src_mask)
if self.residual_connect == True and self.is_self == True:
output_attention, output_mask = tf.cond(tf.random_uniform([]) < self.layer_dropout,
lambda: (input_src_data, input_src_mask),
lambda: (input_attention + input_src_data, input_src_mask))
output_attention = output_attention * output_mask
else:
output_attention = input_attention * input_src_mask
output_mask = input_src_mask
if src_shape_size > 3:
output_attention = tf.reshape(output_attention,
shape=tf.concat([input_src_shape[:-2], input_trg_shape[-2:]], axis=0))
output_mask = tf.reshape(output_mask,
shape=tf.concat([input_src_mask_shape[:-2], input_trg_mask_shape[-2:]], axis=0))
return output_attention, output_mask
def get_attention_matrix(self):
return self.attention_matrix
class GatedAttention(object):
"""gated-attention layer"""
def __init__(self,
src_dim,
trg_dim,
att_dim,
score_type,
dropout,
att_dropout=0.0,
layer_dropout=0.0,
layer_norm=False,
residual_connect=False,
is_self=False,
external_matrix=None,
num_gpus=1,
default_gpu_id=0,
regularizer=None,
random_seed=0,
trainable=True,
scope="gated_att"):
"""initialize gated-attention layer"""
self.src_dim = src_dim
self.trg_dim = trg_dim
self.att_dim = att_dim
self.score_type = score_type
self.dropout = dropout
self.att_dropout = att_dropout
self.layer_dropout = layer_dropout
self.layer_norm = layer_norm
self.residual_connect = residual_connect
self.is_self = is_self
self.regularizer = regularizer
self.random_seed = random_seed
self.trainable = trainable
self.scope = scope
self.device_spec = get_device_spec(default_gpu_id, num_gpus)
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
if external_matrix == None:
self.attention_matrix = _create_attention_matrix(self.src_dim, self.trg_dim,
self.att_dim, self.score_type, self.regularizer, self.random_seed, self.trainable, "att_matrix")
else:
self.attention_matrix = external_matrix
self.dropout_layer = Dropout(rate=self.dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed)
self.att_dropout_layer = Dropout(rate=self.att_dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed, scope="att_dropout")
if self.layer_norm == True:
self.src_norm_layer = LayerNorm(layer_dim=self.src_dim, num_gpus=num_gpus, default_gpu_id=default_gpu_id,
regularizer=self.regularizer, trainable=self.trainable, scope="src_layer_norm")
if self.is_self == True:
self.trg_norm_layer = self.src_norm_layer
else:
self.trg_norm_layer = LayerNorm(layer_dim=self.trg_dim, num_gpus=num_gpus, default_gpu_id=default_gpu_id,
regularizer=self.regularizer, trainable=self.trainable, scope="trg_layer_norm")
weight_initializer = create_variable_initializer("glorot_uniform")
gate_activation = create_activation_function("sigmoid")
if self.residual_connect == True and self.is_self == True:
self.gate_layer = tf.layers.Dense(units=self.trg_dim, activation=gate_activation,
kernel_initializer=weight_initializer, kernel_regularizer=self.regularizer, trainable=self.trainable)
else:
self.gate_layer = tf.layers.Dense(units=self.src_dim+self.trg_dim, activation=gate_activation,
kernel_initializer=weight_initializer, kernel_regularizer=self.regularizer, trainable=self.trainable)
def __call__(self,
input_src_data,
input_trg_data,
input_src_mask,
input_trg_mask):
"""call gated-attention layer"""
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
input_src_shape = tf.shape(input_src_data)
input_trg_shape = tf.shape(input_trg_data)
input_src_mask_shape = tf.shape(input_src_mask)
input_trg_mask_shape = tf.shape(input_trg_mask)
src_shape_size = len(input_src_data.get_shape().as_list())
trg_shape_size = len(input_trg_data.get_shape().as_list())
if src_shape_size > 3:
input_src_data = tf.reshape(input_src_data, shape=tf.concat([[-1], input_src_shape[-2:]], axis=0))
input_src_mask = tf.reshape(input_src_mask, shape=tf.concat([[-1], input_src_mask_shape[-2:]], axis=0))
if trg_shape_size > 3:
input_trg_data = tf.reshape(input_trg_data, shape=tf.concat([[-1], input_trg_shape[-2:]], axis=0))
input_trg_mask = tf.reshape(input_trg_mask, shape=tf.concat([[-1], input_trg_mask_shape[-2:]], axis=0))
input_src_attention = input_src_data
input_src_attention_mask = input_src_mask
input_trg_attention = input_trg_data
input_trg_attention_mask = input_trg_mask
if self.layer_norm == True:
input_src_attention, input_src_attention_mask = self.src_norm_layer(input_src_attention, input_src_attention_mask)
input_trg_attention, input_trg_attention_mask = self.trg_norm_layer(input_trg_attention, input_trg_attention_mask)
input_attention_score = _generate_attention_score(input_src_attention,
input_trg_attention, self.attention_matrix, self.score_type)
input_attention_mask = _generate_attention_mask(input_src_attention_mask,
input_trg_attention_mask, self.is_self)
input_attention_score = input_attention_score * input_attention_mask
input_attention_weight = softmax_with_mask(input_attention_score,
input_attention_mask, axis=-1) * input_attention_mask
input_attention_weight, _ = self.att_dropout_layer(input_attention_weight, input_attention_mask)
input_attention = tf.matmul(input_attention_weight, input_trg_attention)
input_attention, _ = self.dropout_layer(input_attention, input_src_mask)
if self.residual_connect == True and self.is_self == True:
output_attention, output_mask = tf.cond(tf.random_uniform([]) < self.layer_dropout,
lambda: (input_src_data, input_src_mask),
lambda: (self.gate_layer(input_attention) * input_attention + input_src_data, input_src_mask))
output_attention = output_attention * output_mask
else:
input_attention = tf.concat([input_src_data, input_attention], axis=-1)
gate = self.gate_layer(input_attention)
output_attention = gate * input_attention * input_src_mask
output_mask = input_src_mask
if src_shape_size > 3:
output_attention = tf.reshape(output_attention,
shape=tf.concat([input_src_shape[:-2], input_trg_shape[-2:]], axis=0))
output_mask = tf.reshape(output_mask,
shape=tf.concat([input_src_mask_shape[:-2], input_trg_mask_shape[-2:]], axis=0))
return output_attention, output_mask
def get_attention_matrix(self):
return self.attention_matrix
class MultiHeadAttention(object):
"""multi-head attention layer"""
def __init__(self,
src_dim,
trg_dim,
att_dim,
num_head,
score_type,
dropout,
att_dropout=0.0,
layer_dropout=0.0,
layer_norm=False,
residual_connect=False,
is_self=False,
external_matrix=None,
num_gpus=1,
default_gpu_id=0,
regularizer=None,
random_seed=0,
trainable=True,
scope="multi_head_att"):
"""initialize multi-head attention layer"""
self.src_dim = src_dim
self.trg_dim = trg_dim
self.att_dim = att_dim
self.num_head = num_head
self.score_type = score_type
self.dropout = dropout
self.att_dropout = att_dropout
self.layer_dropout = layer_dropout
self.layer_norm = layer_norm
self.residual_connect = residual_connect
self.is_self = is_self
self.regularizer = regularizer
self.random_seed = random_seed
self.trainable = trainable
self.scope = scope
self.device_spec = get_device_spec(default_gpu_id, num_gpus)
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
if external_matrix == None:
query_dim = self.att_dim
key_dim = self.att_dim
value_dim = self.trg_dim
self.projection_layer = {
"query": _create_projection_layer(query_dim, None, False,
self.regularizer, self.random_seed, self.trainable, "query_projection"),
"key": _create_projection_layer(key_dim, None, False,
self.regularizer, self.random_seed, self.trainable, "key_projection"),
"value": _create_projection_layer(value_dim, None, False,
self.regularizer, self.random_seed, self.trainable, "value_projection")
}
if self.att_dim % self.num_head != 0 or self.att_dim / self.num_head == 0:
raise ValueError("att dim {0} and # head {1} mis-match".format(self.att_dim, self.num_head))
head_dim = self.att_dim / self.num_head
self.attention_matrix = _create_attention_matrix(head_dim, head_dim,
head_dim, self.score_type, self.regularizer, self.random_seed, self.trainable, "att_matrix")
else:
self.projection_layer = external_matrix["projection"]
self.attention_matrix = external_matrix["attention"]
self.dropout_layer = Dropout(rate=self.dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed)
self.att_dropout_layer = Dropout(rate=self.att_dropout, num_gpus=num_gpus,
default_gpu_id=default_gpu_id, random_seed=self.random_seed, scope="att_dropout")
if self.layer_norm == True:
self.src_norm_layer = LayerNorm(layer_dim=self.src_dim, num_gpus=num_gpus, default_gpu_id=default_gpu_id,
regularizer=self.regularizer, trainable=self.trainable, scope="src_layer_norm")
if self.is_self == True:
self.trg_norm_layer = self.src_norm_layer
else:
self.trg_norm_layer = LayerNorm(layer_dim=self.trg_dim, num_gpus=num_gpus, default_gpu_id=default_gpu_id,
regularizer=self.regularizer, trainable=self.trainable, scope="trg_layer_norm")
def __call__(self,
input_src_data,
input_trg_data,
input_src_mask,
input_trg_mask):
"""call multi-head attention layer"""
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE), tf.device(self.device_spec):
input_src_shape = tf.shape(input_src_data)
input_trg_shape = tf.shape(input_trg_data)
input_src_mask_shape = tf.shape(input_src_mask)
input_trg_mask_shape = tf.shape(input_trg_mask)
src_shape_size = len(input_src_data.get_shape().as_list())
trg_shape_size = len(input_trg_data.get_shape().as_list())
if src_shape_size > 3:
input_src_data = tf.reshape(input_src_data, shape=tf.concat([[-1], input_src_shape[-2:]], axis=0))
input_src_mask = tf.reshape(input_src_mask, shape=tf.concat([[-1], input_src_mask_shape[-2:]], axis=0))
if trg_shape_size > 3:
input_trg_data = tf.reshape(input_trg_data, shape=tf.concat([[-1], input_trg_shape[-2:]], axis=0))
input_trg_mask = tf.reshape(input_trg_mask, shape=tf.concat([[-1], input_trg_mask_shape[-2:]], axis=0))
input_src_attention = input_src_data
input_trg_attention = input_trg_data
input_src_attention_mask = input_src_mask
input_trg_attention_mask = input_trg_mask
input_src_attention_shape = tf.shape(input_src_attention)
input_trg_attention_shape = tf.shape(input_trg_attention)
if self.layer_norm == True:
input_src_attention, input_src_attention_mask = self.src_norm_layer(input_src_attention, input_src_attention_mask)
input_trg_attention, input_trg_attention_mask = self.trg_norm_layer(input_trg_attention, input_trg_attention_mask)
input_query_attention = self.projection_layer["query"](input_src_attention)
input_key_attention = self.projection_layer["key"](input_trg_attention)
input_value_attention = self.projection_layer["value"](input_trg_attention)
input_query_attention = self.__split_multi_head(input_query_attention,
input_src_attention_shape[0], input_src_attention_shape[1], self.num_head)
input_key_attention = self.__split_multi_head(input_key_attention,
input_trg_attention_shape[0], input_trg_attention_shape[1], self.num_head)
input_value_attention = self.__split_multi_head(input_value_attention,
input_trg_attention_shape[0], input_trg_attention_shape[1], self.num_head)
input_query_attention_mask = self.__split_multi_head_mask(input_src_attention_mask,
input_src_attention_shape[0], input_src_attention_shape[1], self.num_head)
input_key_attention_mask = self.__split_multi_head_mask(input_trg_attention_mask,
input_trg_attention_shape[0], input_trg_attention_shape[1], self.num_head)
input_attention_score = _generate_attention_score(input_query_attention,
input_key_attention, self.attention_matrix, self.score_type)
input_attention_mask = _generate_attention_mask(input_query_attention_mask,
input_key_attention_mask, self.is_self)
input_attention_score = input_attention_score * input_attention_mask
input_attention_weight = softmax_with_mask(input_attention_score,
input_attention_mask, axis=-1) * input_attention_mask
input_attention_weight, _ = self.att_dropout_layer(input_attention_weight, input_attention_mask)
input_attention = tf.matmul(input_attention_weight, input_value_attention)
input_attention = self.__merge_multi_head(input_attention,
input_src_attention_shape[0], input_src_attention_shape[1], self.num_head)
input_attention, _ = self.dropout_layer(input_attention, input_src_mask)
if self.residual_connect == True and self.is_self == True:
output_attention, output_mask = tf.cond(tf.random_uniform([]) < self.layer_dropout,
lambda: (input_src_data, input_src_mask),
lambda: (input_attention + input_src_data, input_src_mask))
output_attention = output_attention * output_mask
else:
output_attention = input_attention * input_src_mask
output_mask = input_src_mask
if src_shape_size > 3:
output_attention = tf.reshape(output_attention,
shape=tf.concat([input_src_shape[:-2], input_trg_shape[-2:]], axis=0))
output_mask = tf.reshape(output_mask,
shape=tf.concat([input_src_mask_shape[:-2], input_trg_mask_shape[-2:]], axis=0))
return output_attention, output_mask
def __split_multi_head(self,
input_data,
batch_size,
max_length,
num_head):
"""split multi-head"""
input_split_data = tf.reshape(input_data,
shape=[batch_size, max_length, num_head, -1]) # [batch_size, max_len, num_head, -1]
input_split_data = tf.transpose(input_split_data, perm=[0,2,1,3]) # [batch_size, num_head, max_len, -1]
input_split_data = tf.reshape(input_split_data,
shape=[batch_size * num_head, max_length, -1]) # [batch_size * num_head, max_len, -1]
return input_split_data
def __split_multi_head_mask(self,
input_mask,
batch_size,
max_length,
num_head):
"""split multi-head"""
input_split_mask = tf.expand_dims(input_mask, axis=1) # [batch_size, 1, max_len, 1]
input_split_mask = tf.tile(input_split_mask,
multiples=[1, num_head, 1, 1]) # [batch_size, num_head, max_len, 1]
input_split_mask = tf.reshape(input_split_mask,
shape=[batch_size * num_head, max_length, 1]) # [batch_size * num_head, max_len, 1]
return input_split_mask
def __merge_multi_head(self,
input_data,
batch_size,
max_length,
num_head):
"""merge multi-head"""
input_merge_data = tf.reshape(input_data,
shape=[batch_size, num_head, max_length, -1]) # [batch_size, num_head, max_len, -1]
input_merge_data = tf.transpose(input_merge_data, perm=[0,2,1,3]) # [batch_size, max_len, num_head, -1]
input_merge_data = tf.reshape(input_merge_data,
shape=[batch_size, max_length, -1]) # [batch_size, max_len, -1]
return input_merge_data
def get_projection_matrix(self):
return self.projection_matrix
| [
"[email protected]"
] | |
26990ac7c79c866a255710ff1fb2e98dd0243201 | 48e124e97cc776feb0ad6d17b9ef1dfa24e2e474 | /sdk/python/pulumi_azure_native/containerservice/v20210901/get_agent_pool.py | 63e6af29ba922a7d0043ccb2fb5e7028f8ebff9b | [
"BSD-3-Clause",
"Apache-2.0"
] | permissive | bpkgoud/pulumi-azure-native | 0817502630062efbc35134410c4a784b61a4736d | a3215fe1b87fba69294f248017b1591767c2b96c | refs/heads/master | 2023-08-29T22:39:49.984212 | 2021-11-15T12:43:41 | 2021-11-15T12:43:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 28,542 | py | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from . import outputs
__all__ = [
'GetAgentPoolResult',
'AwaitableGetAgentPoolResult',
'get_agent_pool',
'get_agent_pool_output',
]
@pulumi.output_type
class GetAgentPoolResult:
"""
Agent Pool.
"""
def __init__(__self__, availability_zones=None, count=None, creation_data=None, enable_auto_scaling=None, enable_encryption_at_host=None, enable_fips=None, enable_node_public_ip=None, enable_ultra_ssd=None, gpu_instance_profile=None, id=None, kubelet_config=None, kubelet_disk_type=None, linux_os_config=None, max_count=None, max_pods=None, min_count=None, mode=None, name=None, node_image_version=None, node_labels=None, node_public_ip_prefix_id=None, node_taints=None, orchestrator_version=None, os_disk_size_gb=None, os_disk_type=None, os_sku=None, os_type=None, pod_subnet_id=None, power_state=None, provisioning_state=None, proximity_placement_group_id=None, scale_down_mode=None, scale_set_eviction_policy=None, scale_set_priority=None, spot_max_price=None, tags=None, type=None, upgrade_settings=None, vm_size=None, vnet_subnet_id=None, workload_runtime=None):
if availability_zones and not isinstance(availability_zones, list):
raise TypeError("Expected argument 'availability_zones' to be a list")
pulumi.set(__self__, "availability_zones", availability_zones)
if count and not isinstance(count, int):
raise TypeError("Expected argument 'count' to be a int")
pulumi.set(__self__, "count", count)
if creation_data and not isinstance(creation_data, dict):
raise TypeError("Expected argument 'creation_data' to be a dict")
pulumi.set(__self__, "creation_data", creation_data)
if enable_auto_scaling and not isinstance(enable_auto_scaling, bool):
raise TypeError("Expected argument 'enable_auto_scaling' to be a bool")
pulumi.set(__self__, "enable_auto_scaling", enable_auto_scaling)
if enable_encryption_at_host and not isinstance(enable_encryption_at_host, bool):
raise TypeError("Expected argument 'enable_encryption_at_host' to be a bool")
pulumi.set(__self__, "enable_encryption_at_host", enable_encryption_at_host)
if enable_fips and not isinstance(enable_fips, bool):
raise TypeError("Expected argument 'enable_fips' to be a bool")
pulumi.set(__self__, "enable_fips", enable_fips)
if enable_node_public_ip and not isinstance(enable_node_public_ip, bool):
raise TypeError("Expected argument 'enable_node_public_ip' to be a bool")
pulumi.set(__self__, "enable_node_public_ip", enable_node_public_ip)
if enable_ultra_ssd and not isinstance(enable_ultra_ssd, bool):
raise TypeError("Expected argument 'enable_ultra_ssd' to be a bool")
pulumi.set(__self__, "enable_ultra_ssd", enable_ultra_ssd)
if gpu_instance_profile and not isinstance(gpu_instance_profile, str):
raise TypeError("Expected argument 'gpu_instance_profile' to be a str")
pulumi.set(__self__, "gpu_instance_profile", gpu_instance_profile)
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if kubelet_config and not isinstance(kubelet_config, dict):
raise TypeError("Expected argument 'kubelet_config' to be a dict")
pulumi.set(__self__, "kubelet_config", kubelet_config)
if kubelet_disk_type and not isinstance(kubelet_disk_type, str):
raise TypeError("Expected argument 'kubelet_disk_type' to be a str")
pulumi.set(__self__, "kubelet_disk_type", kubelet_disk_type)
if linux_os_config and not isinstance(linux_os_config, dict):
raise TypeError("Expected argument 'linux_os_config' to be a dict")
pulumi.set(__self__, "linux_os_config", linux_os_config)
if max_count and not isinstance(max_count, int):
raise TypeError("Expected argument 'max_count' to be a int")
pulumi.set(__self__, "max_count", max_count)
if max_pods and not isinstance(max_pods, int):
raise TypeError("Expected argument 'max_pods' to be a int")
pulumi.set(__self__, "max_pods", max_pods)
if min_count and not isinstance(min_count, int):
raise TypeError("Expected argument 'min_count' to be a int")
pulumi.set(__self__, "min_count", min_count)
if mode and not isinstance(mode, str):
raise TypeError("Expected argument 'mode' to be a str")
pulumi.set(__self__, "mode", mode)
if name and not isinstance(name, str):
raise TypeError("Expected argument 'name' to be a str")
pulumi.set(__self__, "name", name)
if node_image_version and not isinstance(node_image_version, str):
raise TypeError("Expected argument 'node_image_version' to be a str")
pulumi.set(__self__, "node_image_version", node_image_version)
if node_labels and not isinstance(node_labels, dict):
raise TypeError("Expected argument 'node_labels' to be a dict")
pulumi.set(__self__, "node_labels", node_labels)
if node_public_ip_prefix_id and not isinstance(node_public_ip_prefix_id, str):
raise TypeError("Expected argument 'node_public_ip_prefix_id' to be a str")
pulumi.set(__self__, "node_public_ip_prefix_id", node_public_ip_prefix_id)
if node_taints and not isinstance(node_taints, list):
raise TypeError("Expected argument 'node_taints' to be a list")
pulumi.set(__self__, "node_taints", node_taints)
if orchestrator_version and not isinstance(orchestrator_version, str):
raise TypeError("Expected argument 'orchestrator_version' to be a str")
pulumi.set(__self__, "orchestrator_version", orchestrator_version)
if os_disk_size_gb and not isinstance(os_disk_size_gb, int):
raise TypeError("Expected argument 'os_disk_size_gb' to be a int")
pulumi.set(__self__, "os_disk_size_gb", os_disk_size_gb)
if os_disk_type and not isinstance(os_disk_type, str):
raise TypeError("Expected argument 'os_disk_type' to be a str")
pulumi.set(__self__, "os_disk_type", os_disk_type)
if os_sku and not isinstance(os_sku, str):
raise TypeError("Expected argument 'os_sku' to be a str")
pulumi.set(__self__, "os_sku", os_sku)
if os_type and not isinstance(os_type, str):
raise TypeError("Expected argument 'os_type' to be a str")
pulumi.set(__self__, "os_type", os_type)
if pod_subnet_id and not isinstance(pod_subnet_id, str):
raise TypeError("Expected argument 'pod_subnet_id' to be a str")
pulumi.set(__self__, "pod_subnet_id", pod_subnet_id)
if power_state and not isinstance(power_state, dict):
raise TypeError("Expected argument 'power_state' to be a dict")
pulumi.set(__self__, "power_state", power_state)
if provisioning_state and not isinstance(provisioning_state, str):
raise TypeError("Expected argument 'provisioning_state' to be a str")
pulumi.set(__self__, "provisioning_state", provisioning_state)
if proximity_placement_group_id and not isinstance(proximity_placement_group_id, str):
raise TypeError("Expected argument 'proximity_placement_group_id' to be a str")
pulumi.set(__self__, "proximity_placement_group_id", proximity_placement_group_id)
if scale_down_mode and not isinstance(scale_down_mode, str):
raise TypeError("Expected argument 'scale_down_mode' to be a str")
pulumi.set(__self__, "scale_down_mode", scale_down_mode)
if scale_set_eviction_policy and not isinstance(scale_set_eviction_policy, str):
raise TypeError("Expected argument 'scale_set_eviction_policy' to be a str")
pulumi.set(__self__, "scale_set_eviction_policy", scale_set_eviction_policy)
if scale_set_priority and not isinstance(scale_set_priority, str):
raise TypeError("Expected argument 'scale_set_priority' to be a str")
pulumi.set(__self__, "scale_set_priority", scale_set_priority)
if spot_max_price and not isinstance(spot_max_price, float):
raise TypeError("Expected argument 'spot_max_price' to be a float")
pulumi.set(__self__, "spot_max_price", spot_max_price)
if tags and not isinstance(tags, dict):
raise TypeError("Expected argument 'tags' to be a dict")
pulumi.set(__self__, "tags", tags)
if type and not isinstance(type, str):
raise TypeError("Expected argument 'type' to be a str")
pulumi.set(__self__, "type", type)
if upgrade_settings and not isinstance(upgrade_settings, dict):
raise TypeError("Expected argument 'upgrade_settings' to be a dict")
pulumi.set(__self__, "upgrade_settings", upgrade_settings)
if vm_size and not isinstance(vm_size, str):
raise TypeError("Expected argument 'vm_size' to be a str")
pulumi.set(__self__, "vm_size", vm_size)
if vnet_subnet_id and not isinstance(vnet_subnet_id, str):
raise TypeError("Expected argument 'vnet_subnet_id' to be a str")
pulumi.set(__self__, "vnet_subnet_id", vnet_subnet_id)
if workload_runtime and not isinstance(workload_runtime, str):
raise TypeError("Expected argument 'workload_runtime' to be a str")
pulumi.set(__self__, "workload_runtime", workload_runtime)
@property
@pulumi.getter(name="availabilityZones")
def availability_zones(self) -> Optional[Sequence[str]]:
"""
The list of Availability zones to use for nodes. This can only be specified if the AgentPoolType property is 'VirtualMachineScaleSets'.
"""
return pulumi.get(self, "availability_zones")
@property
@pulumi.getter
def count(self) -> Optional[int]:
"""
Number of agents (VMs) to host docker containers. Allowed values must be in the range of 0 to 1000 (inclusive) for user pools and in the range of 1 to 1000 (inclusive) for system pools. The default value is 1.
"""
return pulumi.get(self, "count")
@property
@pulumi.getter(name="creationData")
def creation_data(self) -> Optional['outputs.CreationDataResponse']:
"""
CreationData to be used to specify the source Snapshot ID if the node pool will be created/upgraded using a snapshot.
"""
return pulumi.get(self, "creation_data")
@property
@pulumi.getter(name="enableAutoScaling")
def enable_auto_scaling(self) -> Optional[bool]:
"""
Whether to enable auto-scaler
"""
return pulumi.get(self, "enable_auto_scaling")
@property
@pulumi.getter(name="enableEncryptionAtHost")
def enable_encryption_at_host(self) -> Optional[bool]:
"""
This is only supported on certain VM sizes and in certain Azure regions. For more information, see: https://docs.microsoft.com/azure/aks/enable-host-encryption
"""
return pulumi.get(self, "enable_encryption_at_host")
@property
@pulumi.getter(name="enableFIPS")
def enable_fips(self) -> Optional[bool]:
"""
See [Add a FIPS-enabled node pool](https://docs.microsoft.com/azure/aks/use-multiple-node-pools#add-a-fips-enabled-node-pool-preview) for more details.
"""
return pulumi.get(self, "enable_fips")
@property
@pulumi.getter(name="enableNodePublicIP")
def enable_node_public_ip(self) -> Optional[bool]:
"""
Some scenarios may require nodes in a node pool to receive their own dedicated public IP addresses. A common scenario is for gaming workloads, where a console needs to make a direct connection to a cloud virtual machine to minimize hops. For more information see [assigning a public IP per node](https://docs.microsoft.com/azure/aks/use-multiple-node-pools#assign-a-public-ip-per-node-for-your-node-pools). The default is false.
"""
return pulumi.get(self, "enable_node_public_ip")
@property
@pulumi.getter(name="enableUltraSSD")
def enable_ultra_ssd(self) -> Optional[bool]:
"""
Whether to enable UltraSSD
"""
return pulumi.get(self, "enable_ultra_ssd")
@property
@pulumi.getter(name="gpuInstanceProfile")
def gpu_instance_profile(self) -> Optional[str]:
"""
GPUInstanceProfile to be used to specify GPU MIG instance profile for supported GPU VM SKU.
"""
return pulumi.get(self, "gpu_instance_profile")
@property
@pulumi.getter
def id(self) -> str:
"""
Resource ID.
"""
return pulumi.get(self, "id")
@property
@pulumi.getter(name="kubeletConfig")
def kubelet_config(self) -> Optional['outputs.KubeletConfigResponse']:
"""
The Kubelet configuration on the agent pool nodes.
"""
return pulumi.get(self, "kubelet_config")
@property
@pulumi.getter(name="kubeletDiskType")
def kubelet_disk_type(self) -> Optional[str]:
"""
Determines the placement of emptyDir volumes, container runtime data root, and Kubelet ephemeral storage.
"""
return pulumi.get(self, "kubelet_disk_type")
@property
@pulumi.getter(name="linuxOSConfig")
def linux_os_config(self) -> Optional['outputs.LinuxOSConfigResponse']:
"""
The OS configuration of Linux agent nodes.
"""
return pulumi.get(self, "linux_os_config")
@property
@pulumi.getter(name="maxCount")
def max_count(self) -> Optional[int]:
"""
The maximum number of nodes for auto-scaling
"""
return pulumi.get(self, "max_count")
@property
@pulumi.getter(name="maxPods")
def max_pods(self) -> Optional[int]:
"""
The maximum number of pods that can run on a node.
"""
return pulumi.get(self, "max_pods")
@property
@pulumi.getter(name="minCount")
def min_count(self) -> Optional[int]:
"""
The minimum number of nodes for auto-scaling
"""
return pulumi.get(self, "min_count")
@property
@pulumi.getter
def mode(self) -> Optional[str]:
"""
A cluster must have at least one 'System' Agent Pool at all times. For additional information on agent pool restrictions and best practices, see: https://docs.microsoft.com/azure/aks/use-system-pools
"""
return pulumi.get(self, "mode")
@property
@pulumi.getter
def name(self) -> str:
"""
The name of the resource that is unique within a resource group. This name can be used to access the resource.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="nodeImageVersion")
def node_image_version(self) -> str:
"""
The version of node image
"""
return pulumi.get(self, "node_image_version")
@property
@pulumi.getter(name="nodeLabels")
def node_labels(self) -> Optional[Mapping[str, str]]:
"""
The node labels to be persisted across all nodes in agent pool.
"""
return pulumi.get(self, "node_labels")
@property
@pulumi.getter(name="nodePublicIPPrefixID")
def node_public_ip_prefix_id(self) -> Optional[str]:
"""
This is of the form: /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/publicIPPrefixes/{publicIPPrefixName}
"""
return pulumi.get(self, "node_public_ip_prefix_id")
@property
@pulumi.getter(name="nodeTaints")
def node_taints(self) -> Optional[Sequence[str]]:
"""
The taints added to new nodes during node pool create and scale. For example, key=value:NoSchedule.
"""
return pulumi.get(self, "node_taints")
@property
@pulumi.getter(name="orchestratorVersion")
def orchestrator_version(self) -> Optional[str]:
"""
As a best practice, you should upgrade all node pools in an AKS cluster to the same Kubernetes version. The node pool version must have the same major version as the control plane. The node pool minor version must be within two minor versions of the control plane version. The node pool version cannot be greater than the control plane version. For more information see [upgrading a node pool](https://docs.microsoft.com/azure/aks/use-multiple-node-pools#upgrade-a-node-pool).
"""
return pulumi.get(self, "orchestrator_version")
@property
@pulumi.getter(name="osDiskSizeGB")
def os_disk_size_gb(self) -> Optional[int]:
"""
OS Disk Size in GB to be used to specify the disk size for every machine in the master/agent pool. If you specify 0, it will apply the default osDisk size according to the vmSize specified.
"""
return pulumi.get(self, "os_disk_size_gb")
@property
@pulumi.getter(name="osDiskType")
def os_disk_type(self) -> Optional[str]:
"""
The default is 'Ephemeral' if the VM supports it and has a cache disk larger than the requested OSDiskSizeGB. Otherwise, defaults to 'Managed'. May not be changed after creation. For more information see [Ephemeral OS](https://docs.microsoft.com/azure/aks/cluster-configuration#ephemeral-os).
"""
return pulumi.get(self, "os_disk_type")
@property
@pulumi.getter(name="osSKU")
def os_sku(self) -> Optional[str]:
"""
Specifies an OS SKU. This value must not be specified if OSType is Windows.
"""
return pulumi.get(self, "os_sku")
@property
@pulumi.getter(name="osType")
def os_type(self) -> Optional[str]:
"""
The operating system type. The default is Linux.
"""
return pulumi.get(self, "os_type")
@property
@pulumi.getter(name="podSubnetID")
def pod_subnet_id(self) -> Optional[str]:
"""
If omitted, pod IPs are statically assigned on the node subnet (see vnetSubnetID for more details). This is of the form: /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/virtualNetworks/{virtualNetworkName}/subnets/{subnetName}
"""
return pulumi.get(self, "pod_subnet_id")
@property
@pulumi.getter(name="powerState")
def power_state(self) -> Optional['outputs.PowerStateResponse']:
"""
When an Agent Pool is first created it is initially Running. The Agent Pool can be stopped by setting this field to Stopped. A stopped Agent Pool stops all of its VMs and does not accrue billing charges. An Agent Pool can only be stopped if it is Running and provisioning state is Succeeded
"""
return pulumi.get(self, "power_state")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> str:
"""
The current deployment or provisioning state.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="proximityPlacementGroupID")
def proximity_placement_group_id(self) -> Optional[str]:
"""
The ID for Proximity Placement Group.
"""
return pulumi.get(self, "proximity_placement_group_id")
@property
@pulumi.getter(name="scaleDownMode")
def scale_down_mode(self) -> Optional[str]:
"""
This also effects the cluster autoscaler behavior. If not specified, it defaults to Delete.
"""
return pulumi.get(self, "scale_down_mode")
@property
@pulumi.getter(name="scaleSetEvictionPolicy")
def scale_set_eviction_policy(self) -> Optional[str]:
"""
This cannot be specified unless the scaleSetPriority is 'Spot'. If not specified, the default is 'Delete'.
"""
return pulumi.get(self, "scale_set_eviction_policy")
@property
@pulumi.getter(name="scaleSetPriority")
def scale_set_priority(self) -> Optional[str]:
"""
The Virtual Machine Scale Set priority. If not specified, the default is 'Regular'.
"""
return pulumi.get(self, "scale_set_priority")
@property
@pulumi.getter(name="spotMaxPrice")
def spot_max_price(self) -> Optional[float]:
"""
Possible values are any decimal value greater than zero or -1 which indicates the willingness to pay any on-demand price. For more details on spot pricing, see [spot VMs pricing](https://docs.microsoft.com/azure/virtual-machines/spot-vms#pricing)
"""
return pulumi.get(self, "spot_max_price")
@property
@pulumi.getter
def tags(self) -> Optional[Mapping[str, str]]:
"""
The tags to be persisted on the agent pool virtual machine scale set.
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter
def type(self) -> str:
"""
The type of Agent Pool.
"""
return pulumi.get(self, "type")
@property
@pulumi.getter(name="upgradeSettings")
def upgrade_settings(self) -> Optional['outputs.AgentPoolUpgradeSettingsResponse']:
"""
Settings for upgrading the agentpool
"""
return pulumi.get(self, "upgrade_settings")
@property
@pulumi.getter(name="vmSize")
def vm_size(self) -> Optional[str]:
"""
VM size availability varies by region. If a node contains insufficient compute resources (memory, cpu, etc) pods might fail to run correctly. For more details on restricted VM sizes, see: https://docs.microsoft.com/azure/aks/quotas-skus-regions
"""
return pulumi.get(self, "vm_size")
@property
@pulumi.getter(name="vnetSubnetID")
def vnet_subnet_id(self) -> Optional[str]:
"""
If this is not specified, a VNET and subnet will be generated and used. If no podSubnetID is specified, this applies to nodes and pods, otherwise it applies to just nodes. This is of the form: /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/virtualNetworks/{virtualNetworkName}/subnets/{subnetName}
"""
return pulumi.get(self, "vnet_subnet_id")
@property
@pulumi.getter(name="workloadRuntime")
def workload_runtime(self) -> Optional[str]:
"""
Determines the type of workload a node can run.
"""
return pulumi.get(self, "workload_runtime")
class AwaitableGetAgentPoolResult(GetAgentPoolResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return GetAgentPoolResult(
availability_zones=self.availability_zones,
count=self.count,
creation_data=self.creation_data,
enable_auto_scaling=self.enable_auto_scaling,
enable_encryption_at_host=self.enable_encryption_at_host,
enable_fips=self.enable_fips,
enable_node_public_ip=self.enable_node_public_ip,
enable_ultra_ssd=self.enable_ultra_ssd,
gpu_instance_profile=self.gpu_instance_profile,
id=self.id,
kubelet_config=self.kubelet_config,
kubelet_disk_type=self.kubelet_disk_type,
linux_os_config=self.linux_os_config,
max_count=self.max_count,
max_pods=self.max_pods,
min_count=self.min_count,
mode=self.mode,
name=self.name,
node_image_version=self.node_image_version,
node_labels=self.node_labels,
node_public_ip_prefix_id=self.node_public_ip_prefix_id,
node_taints=self.node_taints,
orchestrator_version=self.orchestrator_version,
os_disk_size_gb=self.os_disk_size_gb,
os_disk_type=self.os_disk_type,
os_sku=self.os_sku,
os_type=self.os_type,
pod_subnet_id=self.pod_subnet_id,
power_state=self.power_state,
provisioning_state=self.provisioning_state,
proximity_placement_group_id=self.proximity_placement_group_id,
scale_down_mode=self.scale_down_mode,
scale_set_eviction_policy=self.scale_set_eviction_policy,
scale_set_priority=self.scale_set_priority,
spot_max_price=self.spot_max_price,
tags=self.tags,
type=self.type,
upgrade_settings=self.upgrade_settings,
vm_size=self.vm_size,
vnet_subnet_id=self.vnet_subnet_id,
workload_runtime=self.workload_runtime)
def get_agent_pool(agent_pool_name: Optional[str] = None,
resource_group_name: Optional[str] = None,
resource_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetAgentPoolResult:
"""
Agent Pool.
:param str agent_pool_name: The name of the agent pool.
:param str resource_group_name: The name of the resource group.
:param str resource_name: The name of the managed cluster resource.
"""
__args__ = dict()
__args__['agentPoolName'] = agent_pool_name
__args__['resourceGroupName'] = resource_group_name
__args__['resourceName'] = resource_name
if opts is None:
opts = pulumi.InvokeOptions()
if opts.version is None:
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('azure-native:containerservice/v20210901:getAgentPool', __args__, opts=opts, typ=GetAgentPoolResult).value
return AwaitableGetAgentPoolResult(
availability_zones=__ret__.availability_zones,
count=__ret__.count,
creation_data=__ret__.creation_data,
enable_auto_scaling=__ret__.enable_auto_scaling,
enable_encryption_at_host=__ret__.enable_encryption_at_host,
enable_fips=__ret__.enable_fips,
enable_node_public_ip=__ret__.enable_node_public_ip,
enable_ultra_ssd=__ret__.enable_ultra_ssd,
gpu_instance_profile=__ret__.gpu_instance_profile,
id=__ret__.id,
kubelet_config=__ret__.kubelet_config,
kubelet_disk_type=__ret__.kubelet_disk_type,
linux_os_config=__ret__.linux_os_config,
max_count=__ret__.max_count,
max_pods=__ret__.max_pods,
min_count=__ret__.min_count,
mode=__ret__.mode,
name=__ret__.name,
node_image_version=__ret__.node_image_version,
node_labels=__ret__.node_labels,
node_public_ip_prefix_id=__ret__.node_public_ip_prefix_id,
node_taints=__ret__.node_taints,
orchestrator_version=__ret__.orchestrator_version,
os_disk_size_gb=__ret__.os_disk_size_gb,
os_disk_type=__ret__.os_disk_type,
os_sku=__ret__.os_sku,
os_type=__ret__.os_type,
pod_subnet_id=__ret__.pod_subnet_id,
power_state=__ret__.power_state,
provisioning_state=__ret__.provisioning_state,
proximity_placement_group_id=__ret__.proximity_placement_group_id,
scale_down_mode=__ret__.scale_down_mode,
scale_set_eviction_policy=__ret__.scale_set_eviction_policy,
scale_set_priority=__ret__.scale_set_priority,
spot_max_price=__ret__.spot_max_price,
tags=__ret__.tags,
type=__ret__.type,
upgrade_settings=__ret__.upgrade_settings,
vm_size=__ret__.vm_size,
vnet_subnet_id=__ret__.vnet_subnet_id,
workload_runtime=__ret__.workload_runtime)
@_utilities.lift_output_func(get_agent_pool)
def get_agent_pool_output(agent_pool_name: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
resource_name: Optional[pulumi.Input[str]] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetAgentPoolResult]:
"""
Agent Pool.
:param str agent_pool_name: The name of the agent pool.
:param str resource_group_name: The name of the resource group.
:param str resource_name: The name of the managed cluster resource.
"""
...
| [
"[email protected]"
] | |
3a9bd176b6447bea26b249ad12815762e165a913 | c80314871502377180b1d496d0d4e7dc9e8cdba8 | /exercise/python_1040_practice_algo_recursion_hanoi.py | abbc470f6e534414af43740e26a3719053d2293e | [] | no_license | tomboxfan/PythonExample | 996896bcbc0bf83fbca7d28bcb207dca35875f6b | 8b071314b4dc4c3e3acccb835405c44630a15722 | refs/heads/master | 2023-02-11T13:55:26.591124 | 2021-01-09T03:20:46 | 2021-01-09T03:20:46 | 275,275,138 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,672 | py | '''
Requirement:
The Tower of Hanoi puzzle was invented by the French mathematician Edouard Lucas in 1883.
He was inspired by a legend that tells of a Hindu temple where the puzzle was presented to young priests.
At the beginning of time, the priests were given three poles and a stack of 64 gold disks,
each disk a little smaller than the one beneath it.
Their assignment was to transfer all 64 disks from one of the three poles to another, with two important constraints.
1) They could only move one disk at a time.
2） They could never place a larger disk on top of a smaller one.
The priests worked very efficiently, day and night, moving one disk every second.
When they finished their work, the legend said, the temple would crumble into dust and the world would vanish.
Although the legend is interesting, you need not worry about the world ending any time soon.
The number of moves required to correctly move a tower of 64 disks is 2^64−1=18,446,744,073,709,551,615.
At a rate of one move per second, that is 584,942,417,355 years! Clearly there is more to this puzzle than meets the eye.
'''
def tower_of_hanoi(n, from_rod, to_rod, help_rod):
if n == 1:
print(f"Move disk {n} from {from_rod} to {to_rod}" )
else:
# Step 1) Move n-1 plates from 'from_rod' to 'help_rod', via 'to_rod'.
tower_of_hanoi(n-1, from_rod, help_rod, to_rod)
# Step 2) Move plate n from 'from_rod' to 'to_rod'
print(f"Move disk {n} from {from_rod} to {to_rod}")
# Step 3) Move n-1 plates from 'help_rod' to 'to_rod', via 'from_rod'.
tower_of_hanoi(n-1, help_rod, to_rod, from_rod)
tower_of_hanoi(4, 'A', 'C', 'B') | [
"[email protected]"
] | |
e6f61f08c4027bfec92381e04e2087c07efa6800 | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03592/s369623086.py | 0473e1e86b92c11c4dd5abc861aa31996a132b45 | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 926 | py | import sys, re
from collections import deque, defaultdict, Counter
from math import ceil, sqrt, hypot, factorial, pi, sin, cos, radians
from itertools import accumulate, permutations, combinations, product, groupby
from operator import itemgetter, mul
from copy import deepcopy
from string import ascii_lowercase, ascii_uppercase, digits
from bisect import bisect, bisect_left
from fractions import gcd
from heapq import heappush, heappop
from functools import reduce
def input(): return sys.stdin.readline().strip()
def INT(): return int(input())
def MAP(): return map(int, input().split())
def LIST(): return list(map(int, input().split()))
def ZIP(n): return zip(*(MAP() for _ in range(n)))
sys.setrecursionlimit(10 ** 9)
INF = float('inf')
mod = 10 ** 9 + 7
H, W, K = MAP()
for x in range(H+1):
y = (K-x*W)/(H-2*x) if H !=2*x else (H*W-K)/H
if y.is_integer() and 0 <= y <= W:
print("Yes")
break
else:
print("No")
| [
"[email protected]"
] | |
5b17863999522ec2777bdd7083007c20f69cbd08 | df7f13ec34591fe1ce2d9aeebd5fd183e012711a | /hata/discord/channel/channel_metadata/tests/test__ChannelMetadataGuildThreadAnnouncements__utility.py | 79cd654d880a07831297665da13916f7b7c4630c | [
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | HuyaneMatsu/hata | 63e2f6a2d7a7539fd8f18498852d9d3fe5c41d2e | 53f24fdb38459dc5a4fd04f11bdbfee8295b76a4 | refs/heads/master | 2023-08-20T15:58:09.343044 | 2023-08-20T13:09:03 | 2023-08-20T13:09:03 | 163,677,173 | 3 | 3 | Apache-2.0 | 2019-12-18T03:46:12 | 2018-12-31T14:59:47 | Python | UTF-8 | Python | false | false | 7,445 | py | from datetime import datetime as DateTime
import vampytest
from ..guild_thread_base import ChannelMetadataGuildThreadBase
from .test__ChannelMetadataGuildThreadBase__constructor import _assert_fields_set
def test__ChannelMetadataGuildThreadBase__copy():
"""
Tests whether ``ChannelMetadataGuildThreadBase.copy` works as intended.
"""
name = 'alice'
parent_id = 202304120047
created_at = DateTime(2016, 4, 4)
archived = False
archived_at = DateTime(2017, 4, 4)
auto_archive_after = 3600
open_ = True
owner_id = 202304120048
slowmode = 30
channel_metadata = ChannelMetadataGuildThreadBase(
name = name,
parent_id = parent_id,
created_at = created_at,
archived = archived,
archived_at = archived_at,
auto_archive_after = auto_archive_after,
open = open_,
owner_id = owner_id,
slowmode = slowmode,
)
copy = channel_metadata.copy()
_assert_fields_set(copy)
vampytest.assert_is_not(copy, channel_metadata)
vampytest.assert_eq(copy, channel_metadata)
def test__ChannelMetadataGuildThreadBase__copy_with__0():
"""
Tests whether ``ChannelMetadataGuildThreadBase.copy_with` works as intended.
Case: No fields.
"""
name = 'alice'
parent_id = 202304120049
created_at = DateTime(2016, 4, 4)
archived = False
archived_at = DateTime(2017, 4, 4)
auto_archive_after = 3600
open_ = True
owner_id = 202304120050
slowmode = 30
channel_metadata = ChannelMetadataGuildThreadBase(
name = name,
parent_id = parent_id,
created_at = created_at,
archived = archived,
archived_at = archived_at,
auto_archive_after = auto_archive_after,
open = open_,
owner_id = owner_id,
slowmode = slowmode,
)
copy = channel_metadata.copy_with()
_assert_fields_set(copy)
vampytest.assert_is_not(copy, channel_metadata)
vampytest.assert_eq(copy, channel_metadata)
def test__ChannelMetadataGuildThreadBase__copy_with__1():
"""
Tests whether ``ChannelMetadataGuildThreadBase.copy_with` works as intended.
Case: All fields.
"""
old_name = 'alice'
old_parent_id = 202304120051
old_created_at = DateTime(2016, 4, 4)
old_archived = False
old_archived_at = DateTime(2017, 4, 4)
old_auto_archive_after = 3600
old_open = True
old_owner_id = 202304120052
old_slowmode = 30
new_name = 'emotion'
new_parent_id = 202304120053
new_created_at = DateTime(2016, 4, 5)
new_archived = True
new_archived_at = DateTime(2017, 4, 5)
new_auto_archive_after = 604800
new_open = False
new_owner_id = 202304120054
new_slowmode = 31
channel_metadata = ChannelMetadataGuildThreadBase(
name = old_name,
parent_id = old_parent_id,
created_at = old_created_at,
archived = old_archived,
archived_at = old_archived_at,
auto_archive_after = old_auto_archive_after,
open = old_open,
owner_id = old_owner_id,
slowmode = old_slowmode,
)
copy = channel_metadata.copy_with(
name = new_name,
parent_id = new_parent_id,
created_at = new_created_at,
archived = new_archived,
archived_at = new_archived_at,
auto_archive_after = new_auto_archive_after,
open = new_open,
owner_id = new_owner_id,
slowmode = new_slowmode,
)
_assert_fields_set(copy)
vampytest.assert_is_not(copy, channel_metadata)
vampytest.assert_eq(copy.name, new_name)
vampytest.assert_eq(copy.parent_id, new_parent_id)
vampytest.assert_eq(copy._created_at, new_created_at)
vampytest.assert_eq(copy.archived, new_archived)
vampytest.assert_eq(copy.archived_at, new_archived_at)
vampytest.assert_eq(copy.auto_archive_after, new_auto_archive_after)
vampytest.assert_eq(copy.open, new_open)
vampytest.assert_eq(copy.owner_id, new_owner_id)
vampytest.assert_eq(copy.slowmode, new_slowmode)
def test__ChannelMetadataGuildThreadBase__copy_with_keyword_parameters__0():
"""
Tests whether ``ChannelMetadataGuildThreadBase.copy_with_keyword_parameters` works as intended.
Case: No fields.
"""
name = 'alice'
parent_id = 202304120055
created_at = DateTime(2016, 4, 4)
archived = False
archived_at = DateTime(2017, 4, 4)
auto_archive_after = 3600
open_ = True
owner_id = 202304120056
slowmode = 30
channel_metadata = ChannelMetadataGuildThreadBase(
name = name,
parent_id = parent_id,
created_at = created_at,
archived = archived,
archived_at = archived_at,
auto_archive_after = auto_archive_after,
open = open_,
owner_id = owner_id,
slowmode = slowmode,
)
keyword_parameters = {}
copy = channel_metadata.copy_with_keyword_parameters(keyword_parameters)
_assert_fields_set(copy)
vampytest.assert_is_not(copy, channel_metadata)
vampytest.assert_eq(keyword_parameters, {})
vampytest.assert_eq(copy, channel_metadata)
def test__ChannelMetadataGuildThreadBase__copy_with_keyword_parameters__1():
"""
Tests whether ``ChannelMetadataGuildThreadBase.copy_with_keyword_parameters` works as intended.
Case: All fields.
"""
old_name = 'alice'
old_parent_id = 202304120057
old_created_at = DateTime(2016, 4, 4)
old_archived = False
old_archived_at = DateTime(2017, 4, 4)
old_auto_archive_after = 3600
old_open = True
old_owner_id = 202304120058
old_slowmode = 30
new_name = 'emotion'
new_parent_id = 202304120059
new_created_at = DateTime(2016, 4, 5)
new_archived = True
new_archived_at = DateTime(2017, 4, 5)
new_auto_archive_after = 604800
new_open = False
new_owner_id = 202304120060
new_slowmode = 31
channel_metadata = ChannelMetadataGuildThreadBase(
name = old_name,
parent_id = old_parent_id,
created_at = old_created_at,
archived = old_archived,
archived_at = old_archived_at,
auto_archive_after = old_auto_archive_after,
open = old_open,
owner_id = old_owner_id,
slowmode = old_slowmode,
)
keyword_parameters = {
'name': new_name,
'parent_id': new_parent_id,
'created_at': new_created_at,
'archived': new_archived,
'archived_at': new_archived_at,
'auto_archive_after': new_auto_archive_after,
'open': new_open,
'owner_id': new_owner_id,
'slowmode': new_slowmode,
}
copy = channel_metadata.copy_with_keyword_parameters(keyword_parameters)
_assert_fields_set(copy)
vampytest.assert_is_not(copy, channel_metadata)
vampytest.assert_eq(keyword_parameters, {})
vampytest.assert_eq(copy.name, new_name)
vampytest.assert_eq(copy.parent_id, new_parent_id)
vampytest.assert_eq(copy._created_at, new_created_at)
vampytest.assert_eq(copy.archived, new_archived)
vampytest.assert_eq(copy.archived_at, new_archived_at)
vampytest.assert_eq(copy.auto_archive_after, new_auto_archive_after)
vampytest.assert_eq(copy.open, new_open)
vampytest.assert_eq(copy.owner_id, new_owner_id)
vampytest.assert_eq(copy.slowmode, new_slowmode)
| [
"[email protected]"
] | |
2f716cb7b50e626cfc3fb1549ff0e4f0ef60f3e3 | eaaecada4c78c899bfdb6a83aaf66502a7d4bc4c | /data_augmentation/eda/image/task.py | b35e6ade3e8418844f9ba5b2dd23402bc1d86d19 | [
"MIT"
] | permissive | simran-arora/emmental-tutorials | 72552d6bcb3311e011f99fa6d164fa619c913283 | 249a82a57be58e960408a45e2e0daa72980d210a | refs/heads/master | 2022-12-01T20:12:55.613955 | 2020-08-13T08:16:12 | 2020-08-13T08:16:12 | 286,825,852 | 0 | 0 | MIT | 2020-08-11T19:01:59 | 2020-08-11T19:01:58 | null | UTF-8 | Python | false | false | 2,621 | py | import logging
from functools import partial
import numpy as np
import torch
import torch.nn.functional as F
from emmental.scorer import Scorer
from emmental.task import EmmentalTask
from torch import nn
from eda.image.config import TASK_INPUT_SIZE, TASK_METRIC, TASK_NUM_CLASS
from eda.image.models import ALL_MODELS
from eda.image.modules.soft_cross_entropy_loss import SoftCrossEntropyLoss
logger = logging.getLogger(__name__)
SCE = SoftCrossEntropyLoss(reduction="none")
def sce_loss(module_name, intermediate_output_dict, Y, active):
if len(Y.size()) == 1:
label = intermediate_output_dict[module_name][0].new_zeros(
intermediate_output_dict[module_name][0].size()
)
label.scatter_(1, Y.view(Y.size()[0], 1), 1.0)
else:
label = Y
return SCE(intermediate_output_dict[module_name][0][active], label[active])
def output_classification(module_name, immediate_output_dict):
return F.softmax(immediate_output_dict[module_name][0], dim=1)
def create_task(args):
task_name = args.task
n_class = TASK_NUM_CLASS[args.task]
if args.model in ["wide_resnet"]:
feature_extractor = ALL_MODELS[args.model](
args.wide_resnet_depth,
args.wide_resnet_width,
args.wide_resnet_dropout,
n_class,
has_fc=False,
)
n_hidden_dim = feature_extractor(
torch.randn(TASK_INPUT_SIZE[args.task])
).size()[-1]
elif args.model == "mlp":
n_hidden_dim = args.mlp_hidden_dim
input_dim = np.prod(TASK_INPUT_SIZE[args.task])
feature_extractor = ALL_MODELS[args.model](
input_dim, n_hidden_dim, n_class, has_fc=False
)
else:
raise ValueError(f"Invalid model {args.model}")
loss = sce_loss
output = output_classification
logger.info(f"Built model: {feature_extractor}")
return EmmentalTask(
name=args.task,
module_pool=nn.ModuleDict(
{
"feature": feature_extractor,
f"{task_name}_pred_head": nn.Linear(n_hidden_dim, n_class),
}
),
task_flow=[
{"name": "feature", "module": "feature", "inputs": [("_input_", "image")]},
{
"name": f"{task_name}_pred_head",
"module": f"{task_name}_pred_head",
"inputs": [("feature", 0)],
},
],
loss_func=partial(loss, f"{task_name}_pred_head"),
output_func=partial(output, f"{task_name}_pred_head"),
scorer=Scorer(metrics=TASK_METRIC[task_name]),
)
| [
"[email protected]"
] | |
dd93ddbc2c6ada0eec838318e43428eb9841c4f1 | e19ddf30bf87a4efdc449fa49b9621ca1460a515 | /castle/theme/interfaces.py | 5d42e09dbd195823850c02a445e872c1457cc0c0 | [] | no_license | castlecms/castle.theme | a220d25b1cf40fa47fb4af9be3cfa8d6a1cc75c9 | 4a36537ddc4db59ea2902a71e544f5a319a5a15c | refs/heads/master | 2022-11-02T10:53:04.758867 | 2020-02-21T21:28:10 | 2020-02-21T21:28:10 | 72,666,053 | 5 | 3 | null | 2022-10-05T11:20:53 | 2016-11-02T17:47:59 | CSS | UTF-8 | Python | false | false | 209 | py | from zope.interface import Interface
class ICustomTheme(Interface):
"""Marker interface that defines a Zope 3 browser layer.
"""
class IUtils(Interface):
def get_folder_section():
pass | [
"[email protected]"
] | |
512d012e85f13dd74f93a158a73f9939eceac984 | 2a33588917f591d0250671d24bb9b5b1d8493d70 | /wechat/base_data.py | 12ffb5e7043492746dea36b673fa74d57d9b313a | [] | no_license | coblan/eface | 283365c04f239e68a5d1476dcb7e1605bd9b9aa4 | 5f645e541875daf3365ff4542129d27a1f7957a9 | refs/heads/master | 2023-08-08T03:20:50.371189 | 2023-08-06T05:04:25 | 2023-08-06T05:04:25 | 163,195,597 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 17 | py | wechat_page_dc={} | [
"[email protected]"
] | |
911fe05a24b4aea8350196fde947b9a287d1e07d | 638af6b8c580eeae23fc1034882c4b514195137a | /Packages/cmor/Test/test_python_common.py | c4566b2f070f86cc7058dd832773ef15518464f7 | [] | no_license | doutriaux1/uvcdat | 83684a86b514b8cac4d8900a503fc13d557fc4d2 | 37e9635f988696c346b4c3cdb49144d1e21dab5d | refs/heads/master | 2021-01-17T07:57:22.897539 | 2015-02-02T22:52:12 | 2015-02-02T22:52:12 | 14,878,320 | 1 | 0 | null | 2015-02-19T20:54:25 | 2013-12-02T23:44:46 | C | UTF-8 | Python | false | false | 4,668 | py | import numpy
# this test tries to mimic ippc_test_code.c but from python
# This one is using direct C calls from python not the python around it
ntimes=2
lon=4
lat=3
lev=5
lev2=17
varin3d=["CLOUD", "U", "T" ];
# /* Units appropriate to my data */
units3d=["%", "m s-1", "K"];
# /* Corresponding IPCC Table A1c entry (variable name) */
entry3d=["cl","ua","ta"];
# /* My variable names for IPCC Table A1a fields */
varin2d=[ "LATENT","TSURF","SOIL_WET","PSURF" ];
# /* Units appropriate to my data */
units2d=[ "W m-2","K","kg m-2","Pa"];
positive2d=["down"," ", " ", " "];
# /* Corresponding IPCC Table A1a entry (variable name) */
entry2d=["hfls", "tas","mrsos","ps"];
def gen_irreg_grid(lon,lat):
lon0 = 280.
lat0=0.;
delta_lon = 10.;
delta_lat = 10.;
y = numpy.arange(lat)
x = numpy.arange(lon)
lon_coords = numpy.zeros((lat,lon))
lat_coords = numpy.zeros((lat,lon))
lon_vertices = numpy.zeros((lat,lon,4))
lat_vertices = numpy.zeros((lat,lon,4))
for j in range(lat): # really porr coding i know
for i in range(lon): # getting worse i know
lon_coords[j,i] = lon0+delta_lon*(j+1+i);
lat_coords[j,i] = lat0+delta_lat*(j+1-i);
lon_vertices[j,i,0] = lon_coords[j,i]-delta_lon;
lon_vertices[j,i,1] = lon_coords[j,i];
lon_vertices[j,i,2] = lon_coords[j,i]+delta_lon;
lon_vertices[j,i,3] = lon_coords[j,i];
## !!$ /* vertices lat */
lat_vertices[j,i,0] = lat_coords[j,i];
lat_vertices[j,i,1] = lat_coords[j,i]-delta_lat;
lat_vertices[j,i,2] = lat_coords[j,i];
lat_vertices[j,i,3] = lat_coords[j,i]+delta_lat;
return x,y,lon_coords,lat_coords,lon_vertices,lat_vertices
# read_data funcs are highly unoptimzed....
def read_coords(lon,lat,lev):
alons = numpy.zeros(lon)
bnds_lon = numpy.zeros(2*lon)
alats = numpy.zeros(lat)
bnds_lat = numpy.zeros(2*lat)
plevs = numpy.zeros(lev,dtype='i')
for i in range(lon):
alons[i] = i*360./lon
bnds_lon[2*i] = (i - 0.5)*360./lon
bnds_lon[2*i+1] = (i + 0.5)*360./lon
for i in range(lat):
alats[i] = (lat-i)*10
bnds_lat[2*i] = (lat-i)*10 + 5.
bnds_lat[2*i+1] = (lat-i)*10 - 5.
plevs = numpy.array([100000., 92500., 85000., 70000.,
60000., 50000., 40000., 30000., 25000., 20000.,
15000., 10000., 7000., 5000., 3000., 2000., 1000.])
return alats, alons, plevs, bnds_lat, bnds_lon
def read_time(it):
time = [0]
time_bnds=[0,0]
time[0] = (it-0.5)*30.;
time_bnds[0] = (it-1)*30.;
time_bnds[1] = it*30.;
time[0]=it;
time_bnds[0] = it;
time_bnds[1] = it+1;
return time[0],numpy.array(time_bnds)
def read_3d_input_files(it, varname, n0, n1, n2, ntimes):
if varname=="CLOUD":
factor = 0.1;
offset = -50.;
elif varname=="U":
factor = 1.
offset = 100.
elif varname=="T":
factor = 0.5;
offset = -150.;
field = numpy.zeros((n2,n1,n0),dtype='d')
for k in range(n2):
for j in range(n1):
for i in range(n0):
field[k,j,i] = (k*64 + j*16 + i*4 + it)*factor - offset;
return field
def read_2d_input_files(it, varname, n0, n1):
if varname=="LATENT":
factor = 1.25;
offset = 100.;
elif varname == "TSURF":
factor = 2.0;
offset = -230.;
elif varname=="SOIL_WET":
factor = 10.;
offset = 0.;
elif varname == "PSURF":
factor = 1.;
offset = -9.7e2;
field = numpy.zeros((n0,n1),dtype='d')
for j in range(n0):
for i in range(n1):
tmp = (j*16. + i*4. + it)*factor - offset;
field[j,i] = tmp;
return field
alats, alons, plevs, bnds_lat, bnds_lon = read_coords(lon,lat,lev);
Time = numpy.zeros(ntimes,dtype='d')
bnds_time = numpy.zeros(ntimes*2,dtype='d')
Time[0],bnds_time[0:2] = read_time(0)
Time[1],bnds_time[2:4] = read_time(1)
zlevs = numpy.zeros(5,dtype='d')
zlevs[0]=0.1999999999999999999;
zlevs[1]= 0.3;
zlevs[2]=0.55;
zlevs[3]= 0.7;
zlevs[4] = 0.99999999;
zlev_bnds = numpy.zeros(6,dtype='d')
zlev_bnds[0] = 0.
zlev_bnds[1] = 0.2
zlev_bnds[2] = 0.42
zlev_bnds[3] = 0.62
zlev_bnds[4] = 0.8
zlev_bnds[5] = 1.
regions = numpy.array(["atlantic_arctic_ocean", "indian_pacific_ocean", "pacific_ocean", "global_ocean", "sf_bay"])
a_coeff=numpy.array([ 0.1, 0.2, 0.3, 0.22, 0.1 ])
b_coeff=numpy.array([ 0.0, 0.1, 0.2, 0.5, 0.8 ])
p0= numpy.array([1.e5,])
a_coeff_bnds=numpy.array([0.,.15, .25, .25, .16, 0.])
b_coeff_bnds=numpy.array([0.,.05, .15, .35, .65, 1.])
| [
"[email protected]"
] | |
7ca0c3b8af9cf6e87bd1d80617d64194a8692408 | c9ddbdb5678ba6e1c5c7e64adf2802ca16df778c | /cases/synthetic/sieve-big-1313.py | e0f5e294b989debaae1c8f5df4c0084f1ad3c62a | [] | no_license | Virtlink/ccbench-chocopy | c3f7f6af6349aff6503196f727ef89f210a1eac8 | c7efae43bf32696ee2b2ee781bdfe4f7730dec3f | refs/heads/main | 2023-04-07T15:07:12.464038 | 2022-02-03T15:42:39 | 2022-02-03T15:42:39 | 451,969,776 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 31,751 | py | # A resizable list of integers
class Vector(object):
items: [int] = None
size: int = 0
def __init__(self:"Vector"):
self.items = [0]
# Returns current capacity
def capacity(self:"Vector") -> int:
return len(self.items)
# Increases capacity of vector by one element
def increase_capacity(self:"Vector") -> int:
self.items = self.items + [0]
return self.capacity()
# Appends one item to end of vector
def append(self:"Vector", item: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends many items to end of vector
def append_all(self:"Vector", new_items: [int]) -> object:
item:int = 0
for item in new_items:
self.append(item)
# Removes an item from the middle of vector
def remove_at(self:"Vector", idx: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Retrieves an item at a given index
def get(self:"Vector", idx: int) -> int:
return self.items[idx]
# Retrieves the current size of the vector
def length(self:"Vector") -> int:
return self.size
# A resizable list of integers
class Vector2(object):
items: [int] = None
items2: [int] = None
size: int = 0
size2: int = 0
def __init__(self:"Vector2"):
self.items = [0]
# Returns current capacity
def capacity(self:"Vector2") -> int:
return len(self.items)
# Returns current capacity
def capacity2(self:"Vector2") -> int:
return len(self.items)
# Increases capacity of vector by one element
def increase_capacity(self:"Vector2") -> int:
self.items = self.items + [0]
return self.capacity()
# Increases capacity of vector by one element
def increase_capacity2(self:"Vector2") -> int:
self.items = self.items + [0]
return self.capacity()
# Appends one item to end of vector
def append(self:"Vector2", item: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends one item to end of vector
def append2(self:"Vector2", item: int, item2: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends many items to end of vector
def append_all(self:"Vector2", new_items: [int]) -> object:
item:int = 0
for item in new_items:
self.append(item)
# Appends many items to end of vector
def append_all2(self:"Vector2", new_items: [int], new_items2: [int]) -> object:
item:int = 0
item2:int = 0
for item in new_items:
self.append(item)
# Removes an item from the middle of vector
def remove_at(self:"Vector2", idx: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Removes an item from the middle of vector
def remove_at2(self:"Vector2", idx: int, idx2: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Retrieves an item at a given index
def get(self:"Vector2", idx: int) -> int:
return self.items[idx]
# Retrieves an item at a given index
def get2(self:"Vector2", idx: int, idx2: int) -> int:
return self.items[idx]
# Retrieves the current size of the vector
def length(self:"Vector2") -> int:
return self.size
# Retrieves the current size of the vector
def length2(self:"Vector2") -> int:
return self.size
# A resizable list of integers
class Vector3(object):
items: [int] = None
items2: [int] = None
items3: [int] = None
size: int = 0
size2: int = 0
size3: int = 0
def __init__(self:"$ID"):
self.items = [0]
# Returns current capacity
def capacity(self:"Vector3") -> int:
return len(self.items)
# Returns current capacity
def capacity2(self:"Vector3") -> int:
return len(self.items)
# Returns current capacity
def capacity3(self:"Vector3") -> int:
return len(self.items)
# Increases capacity of vector by one element
def increase_capacity(self:"Vector3") -> int:
self.items = self.items + [0]
return self.capacity()
# Increases capacity of vector by one element
def increase_capacity2(self:"Vector3") -> int:
self.items = self.items + [0]
return self.capacity()
# Increases capacity of vector by one element
def increase_capacity3(self:"Vector3") -> int:
self.items = self.items + [0]
return self.capacity()
# Appends one item to end of vector
def append(self:"Vector3", item: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends one item to end of vector
def append2(self:"Vector3", item: int, item2: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends one item to end of vector
def append3(self:"Vector3", item: int, item2: int, item3: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends many items to end of vector
def append_all(self:"Vector3", new_items: [int]) -> object:
item:int = 0
for item in new_items:
self.append(item)
# Appends many items to end of vector
def append_all2(self:"Vector3", new_items: [int], new_items2: [int]) -> object:
item:int = 0
item2:int = 0
for item in new_items:
self.append(item)
# Appends many items to end of vector
def append_all3(self:"Vector3", new_items: [int], new_items2: [int], new_items3: [int]) -> object:
item:int = 0
item2:int = 0
item3:int = 0
for item in new_items:
self.append(item)
# Removes an item from the middle of vector
def remove_at(self:"Vector3", idx: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Removes an item from the middle of vector
def remove_at2(self:"Vector3", idx: int, idx2: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Removes an item from the middle of vector
def remove_at3(self:"Vector3", idx: int, idx2: int, idx3: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Retrieves an item at a given index
def get(self:"Vector3", idx: int) -> int:
return self.items[idx]
# Retrieves an item at a given index
def get2(self:"Vector3", idx: int, idx2: int) -> int:
return self.items[idx]
# Retrieves an item at a given index
def get3(self:"Vector3", idx: int, idx2: int, idx3: int) -> int:
return self.items[idx]
# Retrieves the current size of the vector
def length(self:"Vector3") -> int:
return self.size
# Retrieves the current size of the vector
def length2(self:"Vector3") -> int:
return self.size
# Retrieves the current size of the vector
def length3(self:"Vector3") -> int:
return self.size
# A resizable list of integers
class Vector4(object):
items: [int] = None
items2: [int] = None
items3: [int] = None
items4: [int] = None
size: int = 0
size2: int = 0
size3: int = 0
size4: int = 0
def __init__(self:"Vector4"):
self.items = [0]
# Returns current capacity
def capacity(self:"Vector4") -> int:
return len(self.items)
# Returns current capacity
def capacity2(self:"Vector4") -> int:
return len(self.items)
# Returns current capacity
def capacity3(self:"Vector4") -> int:
return len(self.items)
# Returns current capacity
def capacity4(self:"Vector4") -> int:
return len(self.items)
# Increases capacity of vector by one element
def increase_capacity(self:"Vector4") -> int:
self.items = self.items + [0]
return self.capacity()
# Increases capacity of vector by one element
def increase_capacity2(self:"Vector4") -> int:
self.items = self.items + [0]
return self.capacity()
# Increases capacity of vector by one element
def increase_capacity3(self:"Vector4") -> int:
self.items = self.items + [0]
return self.capacity()
# Increases capacity of vector by one element
def increase_capacity4(self:"Vector4") -> int:
self.items = self.items + [0]
return self.capacity()
# Appends one item to end of vector
def append(self:"Vector4", item: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends one item to end of vector
def append2(self:"Vector4", item: int, item2: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends one item to end of vector
def append3(self:"Vector4", item: int, item2: int, item3: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends one item to end of vector
def append4(self:"Vector4", item: int, item2: int, item3: int, item4: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends many items to end of vector
def append_all(self:"Vector4", new_items: [int]) -> object:
item:int = 0
for item in new_items:
self.append(item)
# Appends many items to end of vector
def append_all2(self:"Vector4", new_items: [int], new_items2: [int]) -> object:
item:int = 0
item2:int = 0
for item in new_items:
self.append(item)
# Appends many items to end of vector
def append_all3(self:"Vector4", new_items: [int], new_items2: [int], new_items3: [int]) -> object:
item:int = 0
item2:int = 0
item3:int = 0
for item in new_items:
self.append(item)
# Appends many items to end of vector
def append_all4(self:"Vector4", new_items: [int], new_items2: [int], new_items3: [int], new_items4: [int]) -> object:
item:int = 0
item2:int = 0
item3:int = 0
item4:int = 0
for item in new_items:
self.append(item)
# Removes an item from the middle of vector
def remove_at(self:"Vector4", idx: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Removes an item from the middle of vector
def remove_at2(self:"Vector4", idx: int, idx2: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Removes an item from the middle of vector
def remove_at3(self:"Vector4", idx: int, idx2: int, idx3: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Removes an item from the middle of vector
def remove_at4(self:"Vector4", idx: int, idx2: int, idx3: int, idx4: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Retrieves an item at a given index
def get(self:"Vector4", idx: int) -> int:
return self.items[idx]
# Retrieves an item at a given index
def get2(self:"Vector4", idx: int, idx2: int) -> int:
return self.items[idx]
# Retrieves an item at a given index
def get3(self:"Vector4", idx: int, idx2: int, idx3: int) -> int:
return self.items[idx]
# Retrieves an item at a given index
def get4(self:"Vector4", idx: int, idx2: int, idx3: int, idx4: int) -> int:
return self.items[idx]
# Retrieves the current size of the vector
def length(self:"Vector4") -> int:
return self.size
# Retrieves the current size of the vector
def length2(self:"Vector4") -> int:
return self.size
# Retrieves the current size of the vector
def length3(self:"Vector4") -> int:
return self.size
# Retrieves the current size of the vector
def length4(self:"Vector4") -> int:
return self.size
# A resizable list of integers
class Vector5(object):
items: [int] = None
items2: [int] = None
items3: [int] = None
items4: [int] = None
items5: [int] = None
size: int = 0
size2: int = 0
size3: int = 0
size4: int = 0
size5: int = 0
def __init__(self:"Vector5"):
self.items = [0]
# Returns current capacity
def capacity(self:"Vector5") -> int:
return len(self.items)
# Returns current capacity
def capacity2(self:"Vector5") -> int:
return len(self.items)
# Returns current capacity
def capacity3(self:"Vector5") -> int:
return len(self.items)
# Returns current capacity
def capacity4(self:"Vector5") -> int:
return len(self.items)
# Returns current capacity
def capacity5(self:"Vector5") -> int:
return len(self.items)
# Increases capacity of vector by one element
def increase_capacity(self:"Vector5") -> int:
self.items = self.items + [0]
return self.capacity()
# Increases capacity of vector by one element
def increase_capacity2(self:"Vector5") -> int:
self.items = self.items + [0]
return self.capacity()
# Increases capacity of vector by one element
def increase_capacity3(self:"Vector5") -> int:
self.items = self.items + [0]
return self.capacity()
# Increases capacity of vector by one element
def increase_capacity4(self:"Vector5") -> int:
self.items = self.items + [0]
return self.capacity()
# Increases capacity of vector by one element
def increase_capacity5(self:"Vector5") -> int:
self.items = self.items + [0]
return self.capacity()
# Appends one item to end of vector
def append(self:"Vector5", item: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends one item to end of vector
def append2(self:"Vector5", item: int, item2: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends one item to end of vector
def append3(self:"Vector5", item: int, item2: int, item3: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends one item to end of vector
def append4(self:"Vector5", item: int, item2: int, item3: int, item4: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends one item to end of vector
def append5(self:"Vector5", item: int, item2: int, item3: int, item4: int, item5: int) -> object:
if self.size == self.capacity():
self.increase_capacity()
self.items[self.size] = item
self.size = self.size + 1
# Appends many items to end of vector
def append_all(self:"Vector5", new_items: [int]) -> object:
item:int = 0
for item in new_items:
self.append(item)
# Appends many items to end of vector
def append_all2(self:"Vector5", new_items: [int], new_items2: [int]) -> object:
item:int = 0
item2:int = 0
for item in new_items:
self.append(item)
# Appends many items to end of vector
def append_all3(self:"Vector5", new_items: [int], new_items2: [int], new_items3: [int]) -> object:
item:int = 0
item2:int = 0
item3:int = 0
for item in new_items:
self.append(item)
# Appends many items to end of vector
def append_all4(self:"Vector5", new_items: [int], new_items2: [int], new_items3: [int], new_items4: [int]) -> object:
item:int = 0
item2:int = 0
item3:int = 0
item4:int = 0
for item in new_items:
self.append(item)
# Appends many items to end of vector
def append_all5(self:"Vector5", new_items: [int], new_items2: [int], new_items3: [int], new_items4: [int], new_items5: [int]) -> object:
item:int = 0
item2:int = 0
item3:int = 0
item4:int = 0
item5:int = 0
for item in new_items:
self.append(item)
# Removes an item from the middle of vector
def remove_at(self:"Vector5", idx: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Removes an item from the middle of vector
def remove_at2(self:"Vector5", idx: int, idx2: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Removes an item from the middle of vector
def remove_at3(self:"Vector5", idx: int, idx2: int, idx3: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Removes an item from the middle of vector
def remove_at4(self:"Vector5", idx: int, idx2: int, idx3: int, idx4: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Removes an item from the middle of vector
def remove_at5(self:"Vector5", idx: int, idx2: int, idx3: int, idx4: int, idx5: int) -> object:
if idx < 0:
return
while idx < self.size - 1:
self.items[idx] = self.items[idx + 1]
idx = idx + 1
self.size = self.size - 1
# Retrieves an item at a given index
def get(self:"Vector5", idx: int) -> int:
return self.items[idx]
# Retrieves an item at a given index
def get2(self:"Vector5", idx: int, idx2: int) -> int:
return self.items[idx]
# Retrieves an item at a given index
def get3(self:"Vector5", idx: int, idx2: int, idx3: int) -> int:
return self.items[idx]
# Retrieves an item at a given index
def get4(self:"Vector5", idx: int, idx2: int, idx3: int, idx4: int) -> int:
return self.items[idx]
# Retrieves an item at a given index
def get5(self:"Vector5", idx: int, idx2: int, idx3: int, idx4: int, idx5: int) -> int:
return self.items[idx]
# Retrieves the current size of the vector
def length(self:"Vector5") -> int:
return self.size
# Retrieves the current size of the vector
def length2(self:"Vector5") -> int:
return self.size
# Retrieves the current size of the vector
def length3(self:"Vector5") -> int:
return self.size
# Retrieves the current size of the vector
def length4(self:"Vector5") -> int:
return self.size
# Retrieves the current size of the vector
def length5(self:"Vector5") -> int:
return self.size
# A faster (but more memory-consuming) implementation of vector
class DoublingVector(Vector):
doubling_limit:int = 1000
# Overriding to do fewer resizes
def increase_capacity(self:"DoublingVector") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# A faster (but more memory-consuming) implementation of vector
class DoublingVector2(Vector):
doubling_limit:int = 1000
doubling_limit2:int = 1000
# Overriding to do fewer resizes
def increase_capacity(self:"DoublingVector2") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Overriding to do fewer resizes
def increase_capacity2(self:"DoublingVector2") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# A faster (but more memory-consuming) implementation of vector
class DoublingVector3(Vector):
doubling_limit:int = 1000
doubling_limit2:int = 1000
doubling_limit3:int = 1000
# Overriding to do fewer resizes
def increase_capacity(self:"DoublingVector3") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Overriding to do fewer resizes
def increase_capacity2(self:"DoublingVector3") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Overriding to do fewer resizes
def increase_capacity3(self:"DoublingVector3") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# A faster (but more memory-consuming) implementation of vector
class DoublingVector4(Vector):
doubling_limit:int = 1000
doubling_limit2:int = 1000
doubling_limit3:int = 1000
doubling_limit4:int = 1000
# Overriding to do fewer resizes
def increase_capacity(self:"DoublingVector4") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Overriding to do fewer resizes
def increase_capacity2(self:"DoublingVector4") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Overriding to do fewer resizes
def increase_capacity3(self:"DoublingVector4") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Overriding to do fewer resizes
def increase_capacity4(self:"DoublingVector4") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# A faster (but more memory-consuming) implementation of vector
class DoublingVector5(Vector):
doubling_limit:int = 1000
doubling_limit2:int = 1000
doubling_limit3:int = 1000
doubling_limit4:int = 1000
doubling_limit5:int = 1000
# Overriding to do fewer resizes
def increase_capacity(self:"DoublingVector5") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Overriding to do fewer resizes
def increase_capacity2(self:"DoublingVector5") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Overriding to do fewer resizes
def increase_capacity3(self:"DoublingVector5") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Overriding to do fewer resizes
def increase_capacity4(self:"DoublingVector5") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Overriding to do fewer resizes
def increase_capacity5(self:"DoublingVector5") -> int:
if (self.capacity() <= self.doubling_limit // 2):
self.items = self.items + self.items
else:
# If doubling limit has been reached, fall back to
# standard capacity increases
self.items = self.items + [0]
return self.capacity()
# Makes a vector in the range [i, j)
def vrange(i:int, j:int) -> Vector:
v:Vector = None
v = DoublingVector()
while i < j:
v.append(i)
i = i + 1
return v
def vrange2(i:int, j:int, i2:int, j2:int) -> Vector:
v:Vector = None
v2:Vector = None
v = DoublingVector()
while i < j:
v.append(i)
i = i + 1
return v
def vrange3(i:int, j:int, i2:int, j2:int, i3:int, j3:int) -> Vector:
v:Vector = None
v2:Vector = None
v3:Vector = None
v = DoublingVector()
while i < j:
v.append(i)
i = i + 1
return v
def vrange4(i:int, j:int, i2:int, j2:int, i3:int, j3:int, i4:int, j4:int) -> Vector:
v:Vector = None
v2:Vector = None
v3:Vector = None
v4:Vector = None
v = DoublingVector()
while i < j:
v.append(i)
i = i + 1
return v
def vrange5(i:int, j:int, i2:int, j2:int, i3:int, j3:int, i4:int, j4:int, i5:int, j5:int) -> Vector:
v:Vector = None
v2:Vector = None
v3:Vector = None
v4:Vector = None
v5:Vector = None
v = DoublingVector()
while i < j:
v.append(i)
i = i + 1
return v
# Sieve of Eratosthenes (not really)
def sieve(v:Vector) -> object:
i:int = 0
j:int = 0
k:int = 0
while i < v.length():
k = v.get(i)
j = i + 1
while j < v.length():
if v.get(j) % k == 0:
v.remove_at(j)
else:
j = j + 1
i = i + 1
def sieve2(v:Vector, v2:Vector) -> object:
i:int = 0
i2:int = 0
j:int = 0
j2:int = 0
k:int = 0
k2:int = 0
while i < v.length():
k = v.get(i)
j = i + 1
while j < v.length():
if v.get(j) % k == 0:
v.remove_at(j)
else:
j = j + 1
i = i + 1
def sieve3(v:Vector, v2:Vector, v3:Vector) -> object:
i:int = 0
i2:int = 0
i3:int = 0
j:int = 0
j2:int = 0
j3:int = 0
k:int = 0
k2:int = 0
k3:int = 0
while i < v.length():
k = v.get(i)
j = i + 1
while j < v.length():
if v.get(j) % k == 0:
v.remove_at(j)
else:
j = j + 1
i = i + 1
def sieve4(v:Vector, v2:Vector, v3:Vector, v4:Vector) -> object:
i:int = 0
i2:int = 0
i3:int = 0
i4:int = 0
j:int = 0
j2:int = 0
j3:int = 0
j4:int = 0
k:int = 0
k2:int = 0
k3:int = 0
k4:int = 0
while i < v.length():
k = v.get(i)
j = i + 1
while j < v.length():
if v.get(j) % k == 0:
v.remove_at(j)
else:
j = j + 1
i = i + 1
def sieve5(v:Vector, v2:Vector, v3:Vector, v4:Vector, v5:Vector) -> object:
i:int = 0
i2:int = 0
i3:int = 0
i4:int = 0
i5:int = 0
j:int = 0
j2:int = 0
j3:int = 0
j4:int = 0
j5:int = 0
k:int = 0
k2:int = 0
k3:int = 0
k4:int = 0
k5:int = 0
while i < v.length():
k = v.get(i)
j = i + 1
while j < v.length():
if v.get(j) % k == 0:
v.remove_at(j)
else:
j = j + 1
i = i + 1
# Input parameter
n:int = 50
n2:int = 50
n3:int = 50
n4:int = 50
n5:int = 50
# Data
v:Vector = None
v2:Vector = None
v3:Vector = None
v4:Vector = None
v5:Vector = None
i:int = 0
i2:int = 0
i3:int = 0
i4:int = 0
i5:int = 0
# Crunch
v = vrange(2, n)
v2 = vrange(2, n)
v3 = vrange(2, n)
v4 = vrange(2, n)
v5 = vrange(2, n)
sieve(v)
# Print
while i < v.length():
print(v.get(i))
i = i + 1
| [
"[email protected]"
] | |
2be8133758f27f4a9c1c013af1563b5ba0ad76a3 | c9ddbdb5678ba6e1c5c7e64adf2802ca16df778c | /cases/synthetic/tree-big-2269.py | d22a9b2b565a6add21a3f4a6735e52790e2fc6f3 | [] | no_license | Virtlink/ccbench-chocopy | c3f7f6af6349aff6503196f727ef89f210a1eac8 | c7efae43bf32696ee2b2ee781bdfe4f7730dec3f | refs/heads/main | 2023-04-07T15:07:12.464038 | 2022-02-03T15:42:39 | 2022-02-03T15:42:39 | 451,969,776 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 23,289 | py | # Binary-search trees
class TreeNode(object):
value:int = 0
left:"TreeNode" = None
right:"TreeNode" = None
def insert(self:"TreeNode", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode(x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode(x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode2(object):
value:int = 0
value2:int = 0
left:"TreeNode2" = None
left2:"TreeNode2" = None
right:"TreeNode2" = None
right2:"TreeNode2" = None
def insert(self:"TreeNode2", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode2(x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode2(x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode2", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode2(x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode2(x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode2", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode2", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode3(object):
value:int = 0
value2:int = 0
value3:int = 0
left:"TreeNode3" = None
left2:"TreeNode3" = None
left3:"TreeNode3" = None
right:"TreeNode3" = None
right2:"TreeNode3" = None
right3:"TreeNode3" = None
def insert(self:"TreeNode3", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode3(x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode3(x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode3", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode3(x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode3(x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert3(self:"TreeNode3", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode3(x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode3(x, x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode3", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode3", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains3(self:"TreeNode3", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode4(object):
value:int = 0
value2:int = 0
value3:int = 0
value4:int = 0
left:"TreeNode4" = None
left2:"TreeNode4" = None
left3:"TreeNode4" = None
left4:"TreeNode4" = None
right:"TreeNode4" = None
right2:"TreeNode4" = None
right3:"TreeNode4" = None
right4:"TreeNode4" = None
def insert(self:"TreeNode4", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode4", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
$Exp.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert3(self:"TreeNode4", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert4(self:"TreeNode4", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode4", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode4", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains3(self:"TreeNode4", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains4(self:"TreeNode4", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode5(object):
value:int = 0
value2:int = 0
value3:int = 0
value4:int = 0
value5:int = 0
left:"TreeNode5" = None
left2:"TreeNode5" = None
left3:"TreeNode5" = None
left4:"TreeNode5" = None
left5:"TreeNode5" = None
right:"TreeNode5" = None
right2:"TreeNode5" = None
right3:"TreeNode5" = None
right4:"TreeNode5" = None
right5:"TreeNode5" = None
def insert(self:"TreeNode5", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode5", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert3(self:"TreeNode5", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert4(self:"TreeNode5", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert5(self:"TreeNode5", x:int, x2:int, x3:int, x4:int, x5:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode5", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode5", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains3(self:"TreeNode5", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains4(self:"TreeNode5", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains5(self:"TreeNode5", x:int, x2:int, x3:int, x4:int, x5:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class Tree(object):
root:TreeNode = None
size:int = 0
def insert(self:"Tree", x:int) -> object:
if self.root is None:
self.root = makeNode(x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree2(object):
root:TreeNode2 = None
root2:TreeNode2 = None
size:int = 0
size2:int = 0
def insert(self:"Tree2", x:int) -> object:
if self.root is None:
self.root = makeNode2(x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree2", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode2(x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree2", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree2", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree3(object):
root:TreeNode3 = None
root2:TreeNode3 = None
root3:TreeNode3 = None
size:int = 0
size2:int = 0
size3:int = 0
def insert(self:"Tree3", x:int) -> object:
if self.root is None:
self.root = makeNode3(x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree3", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode3(x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert3(self:"Tree3", x:int, x2:int, x3:int) -> object:
if self.root is None:
self.root = makeNode3(x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree3", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree3", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains3(self:"Tree3", x:int, x2:int, x3:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree4(object):
root:TreeNode4 = None
root2:TreeNode4 = None
root3:TreeNode4 = None
root4:TreeNode4 = None
size:int = 0
size2:int = 0
size3:int = 0
size4:int = 0
def insert(self:"Tree4", x:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree4", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert3(self:"Tree4", x:int, x2:int, x3:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert4(self:"Tree4", x:int, x2:int, x3:int, x4:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree4", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree4", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains3(self:"Tree4", x:int, x2:int, x3:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains4(self:"Tree4", x:int, x2:int, x3:int, x4:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree5(object):
root:TreeNode5 = None
root2:TreeNode5 = None
root3:TreeNode5 = None
root4:TreeNode5 = None
root5:TreeNode5 = None
size:int = 0
size2:int = 0
size3:int = 0
size4:int = 0
size5:int = 0
def insert(self:"Tree5", x:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree5", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert3(self:"Tree5", x:int, x2:int, x3:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert4(self:"Tree5", x:int, x2:int, x3:int, x4:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert5(self:"Tree5", x:int, x2:int, x3:int, x4:int, x5:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree5", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree5", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains3(self:"Tree5", x:int, x2:int, x3:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains4(self:"Tree5", x:int, x2:int, x3:int, x4:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains5(self:"Tree5", x:int, x2:int, x3:int, x4:int, x5:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def makeNode(x: int) -> TreeNode:
b:TreeNode = None
b = TreeNode()
b.value = x
return b
def makeNode2(x: int, x2: int) -> TreeNode2:
b:TreeNode2 = None
b2:TreeNode2 = None
b = TreeNode2()
b.value = x
return b
def makeNode3(x: int, x2: int, x3: int) -> TreeNode3:
b:TreeNode3 = None
b2:TreeNode3 = None
b3:TreeNode3 = None
b = TreeNode3()
b.value = x
return b
def makeNode4(x: int, x2: int, x3: int, x4: int) -> TreeNode4:
b:TreeNode4 = None
b2:TreeNode4 = None
b3:TreeNode4 = None
b4:TreeNode4 = None
b = TreeNode4()
b.value = x
return b
def makeNode5(x: int, x2: int, x3: int, x4: int, x5: int) -> TreeNode5:
b:TreeNode5 = None
b2:TreeNode5 = None
b3:TreeNode5 = None
b4:TreeNode5 = None
b5:TreeNode5 = None
b = TreeNode5()
b.value = x
return b
# Input parameters
n:int = 100
n2:int = 100
n3:int = 100
n4:int = 100
n5:int = 100
c:int = 4
c2:int = 4
c3:int = 4
c4:int = 4
c5:int = 4
# Data
t:Tree = None
t2:Tree = None
t3:Tree = None
t4:Tree = None
t5:Tree = None
i:int = 0
i2:int = 0
i3:int = 0
i4:int = 0
i5:int = 0
k:int = 37813
k2:int = 37813
k3:int = 37813
k4:int = 37813
k5:int = 37813
# Crunch
t = Tree()
while i < n:
t.insert(k)
k = (k * 37813) % 37831
if i % c != 0:
t.insert(i)
i = i + 1
print(t.size)
for i in [4, 8, 15, 16, 23, 42]:
if t.contains(i):
print(i)
| [
"[email protected]"
] | |
252a5265eda2101371d397eb500378a31d787fa2 | 2e356d3be3eb83ef89317a7804e8fa4567898d6f | /chapter1/code/metadata/extract_articles.py | fca044b690428e12f7d91aeeee034f30287dd60e | [
"MIT"
] | permissive | PacktPublishing/Advanced-Web-Scraping-with-Python | 91069bbf925e142ee64e8c80ae97c28077def052 | 6624b71b2889a6fcfa3f080a6e15b979e582cce6 | refs/heads/master | 2021-07-09T10:29:35.394560 | 2021-01-21T07:12:34 | 2021-01-21T07:12:34 | 213,933,836 | 17 | 8 | null | null | null | null | UTF-8 | Python | false | false | 751 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import newspaper
cnn_paper = newspaper.build('http://cnn.com')
print('*****************************category urls************************************\n')
for category in cnn_paper.category_urls():
print(category)
print('*****************************url articles************************************\n')
for article in cnn_paper.articles:
print(article.url)
print('*****************************download first article************************************\n')
cnn_article = cnn_paper.articles[0]
cnn_article.download()
cnn_article.parse()
#print(cnn_article.html)
print(cnn_article.text)
print(cnn_article.keywords)
print(cnn_article.summary)
print(cnn_article.authors)
print(cnn_article.publish_date) | [
"[email protected]"
] | |
940e980bebc322bec60c56ff452cc02f1fa66e97 | f9033131dc4d66ede2c5c22fcaa4a0be5b682152 | /SegmentTrees/Tasks/eolymp(2941).py | 3ebde2e3290bba8960436b76b06836cf33e41562 | [] | no_license | Invalid-coder/Data-Structures-and-algorithms | 9bd755ce3d4eb11e605480db53302096c9874364 | 42c6eb8656e85b76f1c0043dcddc9c526ae12ba1 | refs/heads/main | 2023-04-29T08:40:34.661184 | 2021-05-19T10:57:37 | 2021-05-19T10:57:37 | 301,458,981 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,112 | py | #https://www.e-olymp.com/uk/submissions/7648758
from math import log2, ceil
class SegmentTree:
''' Дерево відрізків з операцією суми.'''
def __init__(self, array):
k = len(array)
n = 1 << ceil(log2(k))
self.items = n * [0] + array + (n - k) * [0]
for i in range(n - 1, 0, -1):
# Визначаємо навантаження предків
self.items[i] = self.items[i * 2] + self.items[i * 2 + 1]
self.size = n
def update(self, i, item):
''' Міняє елемент масиву на позиції i (початок з нуля) на item.'''
i += self.size
self.items[i] = item
while i != 1: # Поки не дійшли до кореня
i = i // 2 # Беремо номер батька
# Визначаємо його навантаження
self.items[i] = self.items[i * 2] + self.items[i * 2 + 1]
def sum(self, left, right):
''' Повертає суму елементів відрізка.'''
left += self.size
right += self.size
result = 0
while left <= right:
if left % 2 == 1: # Якщо правий син
result += self.items[left]
if right % 2 == 0: # Якщо лівий син
result += self.items[right]
left = (left + 1) // 2 # Беремо індекс батька вузла справа
right = (right - 1) // 2 # Беремо індекс батька вузла зліва
return result
if __name__ == '__main__':
with open('input.txt') as inp:
n, q = map(int, inp.readline().split())
array = list(map(int, inp.readline().split()))
tree = SegmentTree(array)
for _ in range(q):
command = inp.readline().split()
if command[0] == '=':
tree.update(int(command[1]) - 1, int(command[2]))
elif command[0] == '?':
print(tree.sum(int(command[1]) - 1, int(command[2]) - 1)) | [
"[email protected]"
] | |
87519a17efdc5d4e72c93dfbdad14363392a71de | 3146c4485faf26b663bd9db197057a2444e92602 | /openldap_migration/export_opendj.py | 8f9fa9733033183b5d2870778f3435e6bb7aa31d | [
"MIT"
] | permissive | TCBL/community-edition-setup | 7ab06776a774f2e24179ee5d6245666aa921a779 | 1f91ea493ef8da0066030fb9ec72d2bc84a7dbc9 | refs/heads/master | 2020-03-30T20:23:20.389766 | 2018-10-02T19:18:53 | 2018-10-02T19:18:53 | 151,586,078 | 0 | 0 | MIT | 2018-10-05T11:44:25 | 2018-10-04T14:32:42 | Python | UTF-8 | Python | false | false | 8,318 | py | #!/usr/bin/python
import traceback
import sys
import os
import shutil
import hashlib
import getpass
import tempfile
import logging
# Unix commands
mkdir = '/bin/mkdir'
cat = '/bin/cat'
hostname = '/bin/hostname'
grep = '/bin/grep'
ldapsearch = "/opt/opendj/bin/ldapsearch"
unzip = "/usr/bin/unzip"
find = "/usr/bin/find"
mkdir = "/bin/mkdir"
log = "./export_opendj.log"
logError = "./export_opendj.error"
bu_folder = "./opendj_export"
propertiesFn = "%s/setup.properties" % bu_folder
# LDAP Stuff
password_file = tempfile.mkstemp()[1]
ldap_creds = ['-h', 'localhost', '-p', '1636', '-Z', '-X', '-D',
'"cn=directory manager"', '-j', password_file]
base_dns = ['ou=people',
'ou=groups',
'ou=attributes',
'ou=scopes',
'ou=clients',
'ou=scripts',
'ou=uma',
'ou=hosts',
'ou=u2f']
# configure logging
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)-8s %(message)s',
filename='export_24.log',
filemode='w')
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter('%(levelname)-8s %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
def clean(s):
return s.replace('@', '').replace('!', '').replace('.', '')
def copyFile(fn, dir):
parent_Dir = os.path.split(fn)[0]
bu_dir = "%s/%s" % (bu_folder, parent_Dir)
if not os.path.exists(bu_dir):
runCommand([mkdir, "-p", bu_dir])
bu_fn = os.path.join(bu_dir, os.path.split(fn)[-1])
shutil.copyfile(fn, bu_fn)
def getOrgInum():
args = [ldapsearch] + ldap_creds + ['-s', 'one', '-b', 'o=gluu',
'o=*', 'dn']
output = runCommand(args)
return output.split(",")[0].split("o=")[-1]
def getLdif():
logging.info('Creating backup of LDAP data')
orgInum = getOrgInum()
# Backup the data
for basedn in base_dns:
args = [ldapsearch] + ldap_creds + [
'-b', '%s,o=%s,o=gluu' % (basedn, orgInum), 'objectclass=*']
output = runCommand(args)
ou = basedn.split("=")[-1]
f = open("%s/ldif/%s.ldif" % (bu_folder, ou), 'w')
f.write(output)
f.close()
# Backup the appliance config
args = [ldapsearch] + ldap_creds + \
['-b',
'ou=appliances,o=gluu',
'-s',
'one',
'objectclass=*']
output = runCommand(args)
f = open("%s/ldif/appliance.ldif" % bu_folder, 'w')
f.write(output)
f.close()
# Backup the oxtrust config
args = [ldapsearch] + ldap_creds + \
['-b',
'ou=appliances,o=gluu',
'objectclass=oxTrustConfiguration']
output = runCommand(args)
f = open("%s/ldif/oxtrust_config.ldif" % bu_folder, 'w')
f.write(output)
f.close()
# Backup the oxauth config
args = [ldapsearch] + ldap_creds + \
['-b',
'ou=appliances,o=gluu',
'objectclass=oxAuthConfiguration']
output = runCommand(args)
f = open("%s/ldif/oxauth_config.ldif" % bu_folder, 'w')
f.write(output)
f.close()
# Backup the trust relationships
args = [ldapsearch] + ldap_creds + ['-b', 'ou=appliances,o=gluu',
'objectclass=gluuSAMLconfig']
output = runCommand(args)
f = open("%s/ldif/trust_relationships.ldif" % bu_folder, 'w')
f.write(output)
f.close()
# Backup the org
args = [ldapsearch] + ldap_creds + ['-s', 'base', '-b',
'o=%s,o=gluu' % orgInum,
'objectclass=*']
output = runCommand(args)
f = open("%s/ldif/organization.ldif" % bu_folder, 'w')
f.write(output)
f.close()
# Backup o=site
args = [ldapsearch] + ldap_creds + ['-b', 'ou=people,o=site',
'-s', 'one', 'objectclass=*']
output = runCommand(args)
f = open("%s/ldif/site.ldif" % bu_folder, 'w')
f.write(output)
f.close()
def runCommand(args, return_list=False):
try:
logging.debug("Running command : %s", " ".join(args))
output = None
if return_list:
output = os.popen(" ".join(args)).readlines()
else:
output = os.popen(" ".join(args)).read().strip()
return output
except:
logging.error("Error running command : %s", " ".join(args))
logging.debug(traceback.format_exc())
sys.exit(1)
def getProp(prop):
with open('/install/community-edition-setup/setup.properties.last', 'r') \
as sf:
for line in sf:
if "{0}=".format(prop) in line:
return line.split('=')[-1].strip()
def genProperties():
logging.info('Creating setup.properties backup file')
props = {}
props['ldapPass'] = runCommand([cat, password_file])
props['hostname'] = runCommand([hostname])
props['inumAppliance'] = runCommand(
[grep, "^inum", "%s/ldif/appliance.ldif" % bu_folder]
).split("\n")[0].split(":")[-1].strip()
props['inumApplianceFN'] = clean(props['inumAppliance'])
props['inumOrg'] = getOrgInum()
props['inumOrgFN'] = clean(props['inumOrg'])
props['baseInum'] = props['inumOrg'][:21]
props['encode_salt'] = runCommand(
[cat, "/opt/tomcat/conf/salt"]).split("=")[-1].strip()
props['oxauth_client_id'] = getProp('oxauth_client_id')
props['scim_rs_client_id'] = getProp('scim_rs_client_id')
props['scim_rp_client_id'] = getProp('scim_rp_client_id')
props['version'] = getProp('githubBranchName').split('_')[-1]
# As the certificates are copied over to the new installation, their pass
# are required for accessing them and validating them
props['httpdKeyPass'] = getProp('httpdKeyPass')
props['shibJksPass'] = getProp('shibJksPass')
props['asimbaJksPass'] = getProp('asimbaJksPass')
# Preferences for installation of optional components
installSaml = raw_input("\tIs Shibboleth SAML IDP installed? (Y/N):")
props['installSaml'] = 'y' in installSaml.lower()
props['installAsimba'] = os.path.isfile('/opt/tomcat/webapps/asimba.war')
props['installCas'] = os.path.isfile('/opt/tomcat/webapps/cas.war')
props['installOxAuthRP'] = os.path.isfile(
'/opt/tomcat/webapps/oxauth-rp.war')
f = open(propertiesFn, 'a')
for key in props.keys():
# NOTE: old version of setup.py will interpret any string as True
# Hence, store only the True values, the defaults are False
if props[key]:
f.write("%s=%s\n" % (key, props[key]))
f.close()
def hash_file(filename):
# From http://www.programiz.com/python-programming/examples/hash-file
h = hashlib.sha1()
with open(filename, 'rb') as file:
chunk = 0
while chunk != b'':
chunk = file.read(1024)
h.update(chunk)
return h.hexdigest()
def makeFolders():
folders = [bu_folder, "%s/ldif" % bu_folder]
for folder in folders:
try:
if not os.path.exists(folder):
runCommand([mkdir, '-p', folder])
except:
logging.error("Error making folder: %s", folder)
logging.debug(traceback.format_exc())
sys.exit(3)
def prepareLdapPW():
ldap_pass = None
# read LDAP pass from setup.properties
with open('/install/community-edition-setup/setup.properties.last', 'r') \
as sfile:
for line in sfile:
if 'ldapPass=' in line:
ldap_pass = line.split('=')[-1]
# write it to the tmp file
with open(password_file, 'w') as pfile:
pfile.write(ldap_pass)
# perform sample search
sample = getOrgInum()
if not sample:
# get the password from the user if it fails
ldap_pass = getpass.getpass("Enter LDAP Passsword: ")
with open(password_file, 'w') as pfile:
pfile.write(ldap_pass)
def main():
prepareLdapPW()
makeFolders()
getLdif()
genProperties()
# remove the tempfile with the ldap password
os.remove(password_file)
if __name__ == "__main__":
main()
| [
"[email protected]"
] | |
311e5628fc15ce4639452642b0f5bc2cc980bb8d | b5ce6908490cfb8e6a1e1cbe4745d675122ddce0 | /questions/single-element-in-a-sorted-array/Solution.py | 3654674cf86e3e46733b7d40cc107476d78e5504 | [
"MIT"
] | permissive | franklingu/leetcode-solutions | 8895910f13208e1d8e604100d84c2dd35684cde4 | 7ad7e5c1c040510b7b7bd225ed4297054464dbc6 | refs/heads/master | 2023-01-09T01:34:08.097518 | 2023-01-02T02:05:35 | 2023-01-02T02:05:35 | 43,345,677 | 155 | 66 | MIT | 2020-10-02T03:41:36 | 2015-09-29T04:54:38 | Python | UTF-8 | Python | false | false | 839 | py | """
You are given a sorted array consisting of only integers where every element appears exactly twice, except for one element which appears exactly once. Find this single element that appears only once.
Follow up: Your solution should run in O(log n) time and O(1) space.
Example 1:
Input: nums = [1,1,2,3,3,4,4,8,8]
Output: 2
Example 2:
Input: nums = [3,3,7,7,10,11,11]
Output: 10
Constraints:
1 <= nums.length <= 10^5
0 <= nums[i] <= 10^5
"""
class Solution:
def singleNonDuplicate(self, nums: List[int]) -> int:
low = 0
high = len(nums) - 1
while low < high:
mid = (low + high)//2
if mid%2 ==0:
mid +=1
if nums[mid] == nums[mid - 1]:
low = mid + 1
else:
high = mid - 1
return nums[low] | [
"[email protected]"
] | |
b6c5a444d53fac0f7a74a29f4133549428f0157a | c3a0d8cc1e386717dffd93d0eb58bec752e26b0a | /test787-keras_block/main.py | 9503c190eb670d02b58aab4d7b06af569f387d96 | [] | no_license | matthiaswh/bit4 | 0ce0e385d889a30620426bc60aa47de0ecef21de | 0633d7357d157b5f47c70091dc676dc2e06c1ae1 | refs/heads/master | 2022-11-10T07:44:28.706805 | 2020-06-21T13:12:26 | 2020-06-21T13:12:26 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,152 | py | import keras
import keras.backend as K
import keras.layers as L
import numpy as np
from layers import LayerBlock, Module
# Weight regularization works but batch normalization does not work!
def main():
tcn = TCN(10, kernel_size=5, dilation_rates=[1, 2, 4, 8, 16])
model = keras.Sequential(
[
L.InputLayer((100, 2)),
tcn,
L.Dense(1, activation="sigmoid"),
]
)
model.summary()
model.compile(
loss="binary_crossentropy",
optimizer=keras.optimizers.Adam(0.01),
metrics=["accuracy", "binary_crossentropy"],
)
X = np.random.uniform(-1, 1, size=(5000, 100, 2))
a = np.mean(X[:, :50, 0] + X[:, 50:, 1], axis=1)
b = np.mean(X[:, :50, 1] + X[:, 50:, 0], axis=1)
y = a < b
model.fit(X, y, batch_size=100, epochs=10, validation_split=0.1)
class TCN(Module):
def __init__(self, encoding_dim, kernel_size, dilation_rates, **kwargs):
super().__init__(**kwargs)
self.frontend = L.Dense(encoding_dim, kernel_regularizer="l2", name="frontend")
self.tcn_steps = LayerBlock(name="steps")
for i, d in enumerate(dilation_rates):
step = LayerBlock(residual=True, name=f"step{i + 1}")
step.add(
L.Conv1D(
encoding_dim,
kernel_size,
dilation_rate=d,
padding="same",
activation="elu",
kernel_constraint=keras.constraints.MaxNorm(1),
)
)
self.tcn_steps.add(step)
self.pool = L.GlobalAveragePooling1D(name="pool")
def build(self, input_shape):
self.frontend.build(input_shape)
encoding_shape = self.frontend.compute_output_shape(input_shape)
self.tcn_steps.build(encoding_shape)
self.pool.build(encoding_shape)
super().build(input_shape)
def compute_output_shape(self, input_shape):
return input_shape[0], self.frontend.units
def call(self, x):
return self.pool(self.tcn_steps(self.frontend(x)))
if __name__ == "__main__":
main()
| [
"[email protected]"
] | |
3936237e41a796d8b7cea9c0aef0a060bba62c82 | 97d5efaf0e15c537d4380ae3b61b88ee3d8e84ab | /MiSeguroVirtualBackend/insurances/migrations/0013_userpolicy_adviser_cellphone.py | 01562d2fd74b1024f2331ea628035c909f6ab024 | [] | no_license | dmontoya1/mi-seguro-virtual | af49f0d4492264cea23b6d50a2a2b27c9816e843 | 6e14fb5e38b3a7192e532a46b842d6a2f80d5ea7 | refs/heads/master | 2023-05-06T07:43:16.335977 | 2019-04-03T17:57:32 | 2019-04-03T17:57:32 | 371,432,047 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 490 | py | # Generated by Django 2.0.6 on 2018-12-18 03:36
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('insurances', '0012_auto_20181206_2028'),
]
operations = [
migrations.AddField(
model_name='userpolicy',
name='adviser_cellphone',
field=models.CharField(blank=True, help_text='Agregar en caso de que aplique', max_length=255, verbose_name='Celular asesor'),
),
]
| [
"[email protected]"
] | |
5bbb725cb6a7f41b724a108f31fc041271cc5ebc | 41ea088695ed956ef8c6e34ace4d8ab19c8b4352 | /XDG_CACHE_HOME/Microsoft/Python Language Server/stubs.v1/ebnjyiNMGOHuosJ_EE7SGKALWfhDqU3P4hGf55ouVTM=/_sparsetools.cpython-37m-x86_64-linux-gnu.pyi | d5f00fb6c5fb347fd16db47ae3c4ea852c2c1bd8 | [] | no_license | ljbelenky/decline | d5c1d57fd927fa6a8ea99c1e08fedbeb83170d01 | 432ef82a68168e4ac8635a9386af2aa26cd73eef | refs/heads/master | 2021-06-18T17:01:46.969491 | 2021-04-26T18:34:55 | 2021-04-26T18:34:55 | 195,559,200 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,625 | pyi | __doc__ = None
__file__ = '/home/land/.local/lib/python3.7/site-packages/scipy/sparse/_sparsetools.cpython-37m-x86_64-linux-gnu.so'
__name__ = 'scipy.sparse._sparsetools'
__package__ = 'scipy.sparse'
def bsr_diagonal():
pass
def bsr_eldiv_bsr():
pass
def bsr_elmul_bsr():
pass
def bsr_ge_bsr():
pass
def bsr_gt_bsr():
pass
def bsr_le_bsr():
pass
def bsr_lt_bsr():
pass
def bsr_matmat_pass2():
pass
def bsr_matvec():
pass
def bsr_matvecs():
pass
def bsr_maximum_bsr():
pass
def bsr_minimum_bsr():
pass
def bsr_minus_bsr():
pass
def bsr_ne_bsr():
pass
def bsr_plus_bsr():
pass
def bsr_scale_columns():
pass
def bsr_scale_rows():
pass
def bsr_sort_indices():
pass
def bsr_tocsr():
pass
def bsr_transpose():
pass
def coo_matvec():
pass
def coo_tocsr():
pass
def coo_todense():
pass
def cs_graph_components():
pass
def csc_diagonal():
pass
def csc_eldiv_csc():
pass
def csc_elmul_csc():
pass
def csc_ge_csc():
pass
def csc_gt_csc():
pass
def csc_le_csc():
pass
def csc_lt_csc():
pass
def csc_matmat_pass1():
pass
def csc_matmat_pass2():
pass
def csc_matvec():
pass
def csc_matvecs():
pass
def csc_maximum_csc():
pass
def csc_minimum_csc():
pass
def csc_minus_csc():
pass
def csc_ne_csc():
pass
def csc_plus_csc():
pass
def csc_tocsr():
pass
def csr_column_index1():
pass
def csr_column_index2():
pass
def csr_count_blocks():
pass
def csr_diagonal():
pass
def csr_eldiv_csr():
pass
def csr_eliminate_zeros():
pass
def csr_elmul_csr():
pass
def csr_ge_csr():
pass
def csr_gt_csr():
pass
def csr_has_canonical_format():
pass
def csr_has_sorted_indices():
pass
def csr_le_csr():
pass
def csr_lt_csr():
pass
def csr_matmat_pass1():
pass
def csr_matmat_pass2():
pass
def csr_matvec():
pass
def csr_matvecs():
pass
def csr_maximum_csr():
pass
def csr_minimum_csr():
pass
def csr_minus_csr():
pass
def csr_ne_csr():
pass
def csr_plus_csr():
pass
def csr_row_index():
pass
def csr_row_slice():
pass
def csr_sample_offsets():
pass
def csr_sample_values():
pass
def csr_scale_columns():
pass
def csr_scale_rows():
pass
def csr_sort_indices():
pass
def csr_sum_duplicates():
pass
def csr_tobsr():
pass
def csr_tocsc():
pass
def csr_todense():
pass
def dia_matvec():
pass
def expandptr():
pass
def get_csr_submatrix():
pass
def test_throw_error():
pass
| [
"[email protected]"
] | |
4b1eb52decc3b5781a44abbeb0ef755e29c9aa0e | c84a561927ff9c6712e521c3448531f4992f41fb | /AlgorithmicHeights/INV/inv.py | 7999bdddb37a864f597dba8e6627368d857f4743 | [] | no_license | Meng-Gen/rosalind | 55c174005807d0fc8703e62f7358f4ed205f977d | 3cf5e0ee1536e3e762ddd5354b8da4c8d378a640 | refs/heads/master | 2020-05-13T15:47:13.504360 | 2013-12-29T12:15:27 | 2013-12-29T12:15:27 | 15,453,371 | 3 | 2 | null | null | null | null | UTF-8 | Python | false | false | 1,579 | py | import sys
def read_dataset():
lines = [line.strip() for line in sys.stdin.readlines()]
n = int(lines[0])
array = list(map(int, lines[1].split()))
assert(n == len(array))
return n, array
class CoutingInversionProblem():
def __init__(self, size, array):
self.size = size
self.array = [None] + array
def solve(self):
return self.count(1, self.size)
def count(self, left, right):
if left >= right:
return 0
middle = (left + right) // 2
return self.count(left, middle) + self.count(middle + 1, right) + self.merge(left, middle, right)
def merge(self, left, middle, right):
m = middle - left + 1
n = right - middle
L = [None] + self.array[left:middle+1]
R = [None] + self.array[middle+1:right+1]
rv = 0
i, j, k = 1, 1, left
while i <= m and j <= n:
if L[i] <= R[j]:
self.array[k] = L[i]
i += 1
rv += (j - 1)
else:
self.array[k] = R[j]
j += 1
k += 1
if i > m:
for y in range(j, n + 1):
self.array[middle + y] = R[y]
else:
for x in range(i, m + 1):
self.array[right - m + x] = L[x]
rv += (j - 1)*(m - i + 1)
return rv
def main():
n, array = read_dataset()
problem = CoutingInversionProblem(n, array)
print(problem.solve())
if __name__ == '__main__':
sys.exit(main())
| [
"[email protected]"
] | |
f955924de40322655374d0ebcb1737d4c9f84630 | db0e8aa3a92a30c9b1cc8da03725e951ff64f3f1 | /app1/forms.py | 744896064b952f02b93b044a8985999dd061a2d9 | [
"BSD-3-Clause"
] | permissive | shrey-c/DataLeakageDjango | ffeef61caa347520747fc70cf3f7f8b84a9610cf | a827c5a09e5501921f9fb97b656755671238dd63 | refs/heads/master | 2022-11-30T03:30:12.313025 | 2020-07-12T06:47:44 | 2020-07-12T06:47:44 | 242,569,637 | 6 | 1 | BSD-3-Clause | 2022-11-22T05:20:22 | 2020-02-23T18:33:04 | Python | UTF-8 | Python | false | false | 1,729 | py | from django import forms
from app1.models import Document, DetectorUpload
class ChangepwdForm(forms.Form):
def __init__(self, *args, **kwargs):
super(ChangepwdForm, self).__init__(*args, **kwargs)
self.fields['current'].widget.attrs = {
'class' : 'form-control',
'placeholder' : 'Current Password'
}
self.fields['new'].widget.attrs = {
'class' : 'form-control',
'placeholder' : 'New Password'
}
self.fields['reenter'].widget.attrs = {
'class' : 'form-control',
'placeholder' : 'Re-enter Password'
}
current = forms.CharField(max_length=50, widget=forms.PasswordInput)
new = forms.CharField(max_length=50, widget=forms.PasswordInput)
reenter = forms.CharField(max_length=50, widget=forms.PasswordInput)
class DocumentForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super(DocumentForm, self).__init__(*args, **kwargs)
self.fields['title'].widget.attrs = {
'class': 'form-control',
'placeholder': 'title'
}
self.fields['description'].widget.attrs = {
'class': 'form-control',
'placeholder': 'description'
}
self.fields['accesslevel'].widget.attrs = {
'class': 'form-control',
'placeholder': 'accesslevel',
}
title = forms.CharField(max_length=50)
description = forms.CharField(max_length=500)
accesslevel = forms.CharField(max_length=50)
document = forms.FileField()
class Meta:
model = Document
fields = ('title','description', 'accesslevel', 'document')
class DetectorUploadForm(forms.ModelForm):
document = forms.FileField()
class Meta:
model = DetectorUpload
fields = {'document'} | [
"[email protected]"
] | |
a717be30c5eafe7027a31daef6d7c4b751ab3056 | 7ef2308e51d1d5700fbd092177ee15e2a03ebdd8 | /WorkLean/Python/Scrapy/testCrawler1_0/testCrawler1_0/settings.py | 9a35f942f04500c15cbc5e210353e021980e8568 | [] | no_license | STAWZW/STAWZW1.0 | 741002eb35c2883e5739fee8d14ff430e9622c01 | a835ac27aba17f968116e321bd201b26c9fb3578 | refs/heads/master | 2020-07-21T20:21:59.753992 | 2019-09-26T09:21:28 | 2019-09-26T09:21:28 | 206,965,347 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,037 | py | # -*- coding: utf-8 -*-
# import random
# Scrapy settings for testCrawler1_0 project
#
# For simplicity, this file contains only settings considered important or
# commonly used. You can find more settings consulting the documentation:
#
# https://doc.scrapy.org/en/latest/topics/settings.html
# https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
# https://doc.scrapy.org/en/latest/topics/spider-middleware.html
BOT_NAME = 'testCrawler1_0'
SPIDER_MODULES = ['testCrawler1_0.spiders']
NEWSPIDER_MODULE = 'testCrawler1_0.spiders'
# 用户自定义代理库
# USER_AGENT_LIST = [
# "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1",
# "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11",
# "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6",
# "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6",
# "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1",
# "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5",
# "Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5",
# "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
# "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
# "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
# "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3",
# "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.24 (KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24",
# "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 (KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24"
# ]
# USER_AGENT = random.choice(USER_AGENT_LIST) #每次运行爬虫会使用不同的用户代理,但每次运行中的请求都是不变的
# 用户自定义I代理P池
# 免费代理IP[西刺]是你的好选择！(手滑) 网址：https://www.xicidaili.com/wt
IPPOOL_LIST = [
{"ipaddr": "124.16.75.212:8080"},
{"ipaddr": "101.231.234.38:8080"},
{"ipaddr": "218.64.69.79:8080"},
{"ipaddr": "144.123.70.252:9999"},
{"ipaddr": "113.121.21.199:9999"},
{"ipaddr": "171.35.161.147:9999"},
{"ipaddr": "27.204.84.42:9999"},
]
# 是否服从 robots.txt 规则,服从为Teur,不服从为False,服从规则有些网站是爬取不到的
ROBOTSTXT_OBEY = False
# Configure maximum concurrent requests performed by Scrapy (default: 16)
#CONCURRENT_REQUESTS = 32
# Configure a delay for requests for the same website (default: 0)
# See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay
# See also autothrottle settings and docs
#DOWNLOAD_DELAY = 3
# The download delay setting will honor only one of:
#CONCURRENT_REQUESTS_PER_DOMAIN = 16
#CONCURRENT_REQUESTS_PER_IP = 16
# Disable cookies (enabled by default)
#COOKIES_ENABLED = False
# Disable Telnet Console (enabled by default)
#TELNETCONSOLE_ENABLED = False
# Override the default request headers:
#DEFAULT_REQUEST_HEADERS = {
# 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
# 'Accept-Language': 'en',
#}
# Enable or disable spider middlewares
# See https://doc.scrapy.org/en/latest/topics/spider-middleware.html
#SPIDER_MIDDLEWARES = {
# 'testCrawler1_0.middlewares.Testcrawler10SpiderMiddleware': 543,
#}
# Enable or disable downloader middlewares
# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
DOWNLOADER_MIDDLEWARES = {
# 'testCrawler1_0.middlewares.Testcrawler10DownloaderMiddleware': 543,
# 自定义IP代理池中间件，优先级要高于HttpProxyMiddleware中间件
'testCrawler1_0.middlewares.IPPOOLS': 747,
'scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware': 748,
# 自定义用户代理池中间件，优先级要高于UserAgentMiddleware中间件
'testCrawler1_0.middlewares.USERAGENTS': 749,
'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware': 750,
# 这里要替换为自己的项目名称,重写的优先级一定要高（数字仅代表优先级，数字越小，优先级越高）
'scrapy_splash.SplashCookiesMiddleware': 744,
'scrapy_splash.SplashMiddleware': 745,
'scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware': 810,
}
HTTPCACHE_ENABLED = True
HTTPCACHE_EXPIRATION_SECS = 0
HTTPCACHE_DIR = 'httpcache'
SPLASH_URL = "http://192.168.99.100:8050/" # 自己安装的docker里的splash位置
DUPEFILTER_CLASS = "scrapy_splash.SplashAwareDupeFilter"
HTTPCACHE_STORAGE = 'scrapy_splash.SplashAwareFSCacheStorage'
# Enable or disable extensions
# See https://doc.scrapy.org/en/latest/topics/extensions.html
#EXTENSIONS = {
# 'scrapy.extensions.telnet.TelnetConsole': None,
#}
# Configure item pipelines
# See https://doc.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
'testCrawler1_0.pipelines.Testcrawler10Pipeline': 201,
'testCrawler1_0.pipelines.Testcrawler10ImagePipeline': 200,
}
IMAGES_STORE = 'D:/GitRepository/STAWZW2.0/WorkLean/Python/Scrapy/core-scrapy-master/img/' # 图片存储路径
IMAGES_URLS_FIELD = "image_urls" # 对应item里面设定的字段，取到图片的url
IMAGES_RESULT_FIELD = "image_path"
# 30 days of delay for images expiration
IMAGES_EXPIRES = 30
# # 图片缩略图
# IMAGES_THUMBS = {
# 'small': (50, 50),
# 'big': (270, 270),
# }
# # 图片过滤器，最小高度和宽度
# IMAGES_MIN_HEIGHT = 110
# IMAGES_MIN_WIDTH = 110
# Enable and configure the AutoThrottle extension (disabled by default)
# See https://doc.scrapy.org/en/latest/topics/autothrottle.html
#AUTOTHROTTLE_ENABLED = True
# The initial download delay
#AUTOTHROTTLE_START_DELAY = 5
# The maximum download delay to be set in case of high latencies
#AUTOTHROTTLE_MAX_DELAY = 60
# The average number of requests Scrapy should be sending in parallel to
# each remote server
#AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0
# Enable showing throttling stats for every response received:
#AUTOTHROTTLE_DEBUG = False
# Enable and configure HTTP caching (disabled by default)
# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings
#HTTPCACHE_ENABLED = True
#HTTPCACHE_EXPIRATION_SECS = 0
#HTTPCACHE_DIR = 'httpcache'
#HTTPCACHE_IGNORE_HTTP_CODES = []
# HTTPERROR_ALLOWED_CODES ——> HTTP请求允许的错误：[code]
HTTPERROR_ALLOWED_CODES = [301]
#HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'
# # 是否启用日志
# LOG_ENABLED=True
# # 日志使用的编码
# LOG_ENCODING='utf-8'
# # 日志文件(文件名)
# LOG_FILE='testScrapyLog.log'
# # 日志格式
# LOG_FORMAT='%(asctime)s [%(name)s] %(levelname)s: %(message)s'
# # 日志时间格式
# LOG_DATEFORMAT='%Y-%m-%d %H:%M:%S'
# # 日志级别 CRITICAL, ERROR, WARNING, INFO, DEBUG
# LOG_LEVEL='DEBUG'
# # 如果等于True，所有的标准输出（包括错误）都会重定向到日志，例如：print('hello')
# LOG_STDOUT=True
# # 如果等于True，日志仅仅包含根路径，False显示日志输出组件
# LOG_SHORT_NAMES=False
| [
"[email protected]"
] | |
b03811a2a5a5661848fbf370e6bba4eeb45fd36a | 584e9c42e6240b9facc866703a6f26b06773df94 | /TwilioSendGrid/stressful_subject.py | e3f8e396652697024e58f215a4cea54ffaf77bc9 | [] | no_license | anton-dovnar/checkio | 48fbaf84c244b0fca7bed5cf7f34179cf850adf9 | 10aed757ec36f182871a03ed8c9e73319cc8824a | refs/heads/master | 2023-03-24T16:23:39.524060 | 2021-03-12T13:07:04 | 2021-03-12T13:07:04 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 756 | py | #!/home/fode4cun/.local/share/virtualenvs/checkio-ufRDicT7/bin/checkio --domain=py run stressful-subject
#
# END_DESC
import re
def is_stressful(subj):
"""
recognize stressful subject
"""
if subj.isupper() or subj.endswith('!!!'):
return True
patterns = [r'(\b[help\!\-\.]{4,}\b)', r'(\b[asap\!\-\.]{4,}\b)', r'(\b[urgent\!\-\.]{4,}\b)']
for pattern in patterns:
if re.search(pattern, subj, flags=re.IGNORECASE):
return True
return False
if __name__ == '__main__':
#These "asserts" are only for self-checking and not necessarily for auto-testing
assert is_stressful("Hi") == False, "First"
assert is_stressful("I neeed HELP") == True, "Second"
print('Done! Go Check it!')
| [
"[email protected]"
] | |
e12707c00339249c29f18cde9159c083556c9074 | 434fe69daa053ef68e11b029ebb04cde69b76ee8 | /pysc2/bin/play_vs_agent.py | 840a2e86b0a187314283069a5c088fbd234367b5 | [
"Apache-2.0"
] | permissive | dorthrithil/pysc2 | 6b31e4f456015d3fc863cb44fb12b8034819f4a4 | e3c561b20b78a57ed9cbcbb76157fcffb7b1bbb4 | refs/heads/master | 2020-03-19T02:04:10.123451 | 2018-08-01T07:08:48 | 2018-08-01T07:08:48 | 135,596,142 | 0 | 2 | Apache-2.0 | 2018-08-08T09:59:25 | 2018-05-31T14:39:57 | Python | UTF-8 | Python | false | false | 10,085 | py | #!/usr/bin/python
# Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Play as a human against an agent by setting up a LAN game.
This needs to be called twice, once for the human, and once for the agent.
The human plays on the host. There you run it as:
$ python -m pysc2.bin.play_vs_agent --human --map <map> --remote <agent ip>
And on the machine the agent plays on:
$ python -m pysc2.bin.play_vs_agent --agent <import path>
The `--remote` arg is used to create an SSH tunnel to the remote agent's
machine, so can be dropped if it's running on the same machine.
SC2 is limited to only allow LAN games on localhost, so we need to forward the
ports between machines. SSH is used to do this with the `--remote` arg. If the
agent is on the same machine as the host, this arg can be dropped. SSH doesn't
forward UDP, so this also sets up a UDP proxy. As part of that it sets up a TCP
server that is also used as a settings server. Note that you won't have an
opportunity to give ssh a password, so you must use ssh keys for authentication.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import importlib
from absl import logging
import platform
import sys
import time
from absl import app
from absl import flags
import portpicker
from pysc2 import maps
from pysc2 import run_configs
from pysc2.env import lan_sc2_env
from pysc2.env import run_loop
from pysc2.env import sc2_env
from pysc2.lib import renderer_human
from s2clientprotocol import sc2api_pb2 as sc_pb
FLAGS = flags.FLAGS
flags.DEFINE_bool("render", platform.system() == "Linux",
"Whether to render with pygame.")
flags.DEFINE_bool("realtime", False, "Whether to run in realtime mode.")
flags.DEFINE_string("agent", "pysc2.agents.random_agent.RandomAgent",
"Which agent to run, as a python path to an Agent class.")
flags.DEFINE_enum("agent_race", "random", sc2_env.Race._member_names_, # pylint: disable=protected-access
"Agent's race.")
flags.DEFINE_float("fps", 22.4, "Frames per second to run the game.")
flags.DEFINE_integer("step_mul", 8, "Game steps per agent step.")
flags.DEFINE_integer("feature_screen_size", 84,
"Resolution for screen feature layers.")
flags.DEFINE_integer("feature_minimap_size", 64,
"Resolution for minimap feature layers.")
flags.DEFINE_integer("rgb_screen_size", 256,
"Resolution for rendered screen.")
flags.DEFINE_integer("rgb_minimap_size", 128,
"Resolution for rendered minimap.")
flags.DEFINE_enum("action_space", "FEATURES",
sc2_env.ActionSpace._member_names_, # pylint: disable=protected-access
"Which action space to use. Needed if you take both feature "
"and rgb observations.")
flags.DEFINE_bool("use_feature_units", False,
"Whether to include feature units.")
flags.DEFINE_enum("user_race", "random", sc2_env.Race._member_names_, # pylint: disable=protected-access
"User's race.")
flags.DEFINE_string("host", "127.0.0.1", "Game Host. Can be 127.0.0.1 or ::1")
flags.DEFINE_integer(
"config_port", 14380,
"Where to set/find the config port. The host starts a tcp server to share "
"the config with the client, and to proxy udp traffic if played over an "
"ssh tunnel. This sets that port, and is also the start of the range of "
"ports used for LAN play.")
flags.DEFINE_string("remote", None,
"Where to set up the ssh tunnels to the client.")
flags.DEFINE_string("map", None, "Name of a map to use to play.")
flags.DEFINE_bool("human", False, "Whether to host a game as a human.")
def main(unused_argv):
if FLAGS.human:
human()
else:
agent()
def agent():
"""Run the agent, connecting to a (remote) host started independently."""
agent_module, agent_name = FLAGS.agent.rsplit(".", 1)
agent_cls = getattr(importlib.import_module(agent_module), agent_name)
logging.info("Starting agent:")
with lan_sc2_env.LanSC2Env(
host=FLAGS.host,
config_port=FLAGS.config_port,
race=sc2_env.Race[FLAGS.agent_race],
step_mul=FLAGS.step_mul,
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=FLAGS.feature_screen_size,
feature_minimap=FLAGS.feature_minimap_size,
rgb_screen=FLAGS.rgb_screen_size,
rgb_minimap=FLAGS.rgb_minimap_size,
action_space=FLAGS.action_space,
use_feature_units=FLAGS.use_feature_units),
visualize=FLAGS.render) as env:
agents = [agent_cls()]
logging.info("Connected, starting run_loop.")
try:
run_loop.run_loop(agents, env)
except lan_sc2_env.RestartException:
pass
logging.info("Done.")
def human():
"""Run a host which expects one player to connect remotely."""
run_config = run_configs.get()
map_inst = maps.get(FLAGS.map)
if not FLAGS.rgb_screen_size or not FLAGS.rgb_minimap_size:
logging.info("Use --rgb_screen_size and --rgb_minimap_size if you want rgb "
"observations.")
ports = [FLAGS.config_port + p for p in range(5)] # tcp + 2 * num_players
if not all(portpicker.is_port_free(p) for p in ports):
sys.exit("Need 5 free ports after the config port.")
proc = None
ssh_proc = None
tcp_conn = None
udp_sock = None
try:
proc = run_config.start(extra_ports=ports[1:], timeout_seconds=300,
host=FLAGS.host, window_loc=(50, 50))
tcp_port = ports[0]
settings = {
"remote": FLAGS.remote,
"game_version": proc.version.game_version,
"realtime": FLAGS.realtime,
"map_name": map_inst.name,
"map_path": map_inst.path,
"map_data": map_inst.data(run_config),
"ports": {
"server": {"game": ports[1], "base": ports[2]},
"client": {"game": ports[3], "base": ports[4]},
}
}
create = sc_pb.RequestCreateGame(
realtime=settings["realtime"],
local_map=sc_pb.LocalMap(map_path=settings["map_path"]))
create.player_setup.add(type=sc_pb.Participant)
create.player_setup.add(type=sc_pb.Participant)
controller = proc.controller
controller.save_map(settings["map_path"], settings["map_data"])
controller.create_game(create)
if FLAGS.remote:
ssh_proc = lan_sc2_env.forward_ports(
FLAGS.remote, proc.host, [settings["ports"]["client"]["base"]],
[tcp_port, settings["ports"]["server"]["base"]])
print("-" * 80)
print("Join: play_vs_agent --host %s --config_port %s" % (proc.host,
tcp_port))
print("-" * 80)
tcp_conn = lan_sc2_env.tcp_server(
lan_sc2_env.Addr(proc.host, tcp_port), settings)
if FLAGS.remote:
udp_sock = lan_sc2_env.udp_server(
lan_sc2_env.Addr(proc.host, settings["ports"]["client"]["game"]))
lan_sc2_env.daemon_thread(
lan_sc2_env.tcp_to_udp,
(tcp_conn, udp_sock,
lan_sc2_env.Addr(proc.host, settings["ports"]["server"]["game"])))
lan_sc2_env.daemon_thread(lan_sc2_env.udp_to_tcp, (udp_sock, tcp_conn))
join = sc_pb.RequestJoinGame()
join.shared_port = 0 # unused
join.server_ports.game_port = settings["ports"]["server"]["game"]
join.server_ports.base_port = settings["ports"]["server"]["base"]
join.client_ports.add(game_port=settings["ports"]["client"]["game"],
base_port=settings["ports"]["client"]["base"])
join.race = sc2_env.Race[FLAGS.user_race]
if FLAGS.render:
join.options.raw = True
join.options.score = True
if FLAGS.feature_screen_size and FLAGS.feature_minimap_size:
fl = join.options.feature_layer
fl.width = 24
fl.resolution.x = FLAGS.feature_screen_size
fl.resolution.y = FLAGS.feature_screen_size
fl.minimap_resolution.x = FLAGS.feature_minimap_size
fl.minimap_resolution.y = FLAGS.feature_minimap_size
if FLAGS.rgb_screen_size and FLAGS.rgb_minimap_size:
join.options.render.resolution.x = FLAGS.rgb_screen_size
join.options.render.resolution.y = FLAGS.rgb_screen_size
join.options.render.minimap_resolution.x = FLAGS.rgb_minimap_size
join.options.render.minimap_resolution.y = FLAGS.rgb_minimap_size
controller.join_game(join)
if FLAGS.render:
renderer = renderer_human.RendererHuman(
fps=FLAGS.fps, render_feature_grid=False)
renderer.run(run_configs.get(), controller, max_episodes=1)
else: # Still step forward so the Mac/Windows renderer works.
try:
while True:
frame_start_time = time.time()
if not FLAGS.realtime:
controller.step()
obs = controller.observe()
if obs.player_result:
break
time.sleep(max(0, frame_start_time - time.time() + 1 / FLAGS.fps))
except KeyboardInterrupt:
pass
finally:
if tcp_conn:
tcp_conn.close()
if proc:
proc.close()
if udp_sock:
udp_sock.close()
if ssh_proc:
ssh_proc.terminate()
for _ in range(5):
if ssh_proc.poll() is not None:
break
time.sleep(1)
if ssh_proc.poll() is None:
ssh_proc.kill()
ssh_proc.wait()
def entry_point(): # Needed so setup.py scripts work.
app.run(main)
if __name__ == "__main__":
app.run(main)
| [
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] | |
c6756abdb37e1f8b52dd5b35b3118afb8bc40f58 | ab825ee0326e98d115b6dc02bbda02b302787d46 | /応用編/41_csvファイルの読み書き/モジュール/01_CSVファイルの書き込み.py | bb00d85a58a09d9f502ac4e2f14bf4e26a8d34d9 | [] | no_license | holothuria/python_study | 295dd7c30a566b5a9688b9196e25bf6e065401a0 | 7e98090e64d646d23a4189e0efd68c2905b78d04 | refs/heads/master | 2020-03-23T20:04:38.900368 | 2019-03-05T12:47:53 | 2019-03-05T12:47:53 | 142,019,995 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 336 | py |
import csv
csv_file = open('./python.csv', 'w', newline='')
writer = csv.writer(csv_file)
row = ('python', '-', 'izm', '1')
writer.writerow(row)
rows = []
rows.append(('python', '-', 'izm', '2'))
rows.append(('python', '-', 'izm', '3'))
rows.append(('p,y,t,h,o,n', '-', 'i,z,m', '4'))
writer.writerows(rows)
csv_file.close()
| [
"[email protected]"
] | |
992f5347a46a745fa991942a9bdb04ea5c918b52 | b3d86713ed58e0b7fe3c1191324e36659c0d9d78 | /DataScience/ch10/evaluation_data.py | 94d29f7149b5f9ebae13989f4ba23eb44c469612 | [] | no_license | Kose-i/machine_learning_tutorial | 3d6cb30a20d65c66aa6efcba0e693de75791507a | def223fecb459ad1a6e7f9f36b3d733a89efd378 | refs/heads/master | 2021-07-03T10:37:26.809388 | 2020-07-27T12:53:19 | 2020-07-27T12:53:19 | 174,057,143 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,989 | py | import numpy as np
import numpy.random as random
import scipy as sp
from pandas import Series, DataFrame
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
sns.set()
import sklearn
from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_val_score
cancer = load_breast_cancer()
x_train, x_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.5, random_state=66)
tree = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)
scores = cross_val_score(tree, cancer.data, cancer.target, cv=5)
print('Cross validation scores: {}'.format(scores))
print('Cross validation scores: {:.3f}+-{:.3f}'.format(scores.mean(), scores.std()))
# AUC ROC
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(random_state=0)
model.fit(x_train, y_train)
results = pd.DataFrame(model.predict_proba(X_test), columns=cancer.target_names)
results.head()
rates = {}
for threshold in np.linspace(0.01, 0.99, num=50):
labels = results['benign'].map(lambda x: 1 if x>threshold else 0)
m = confusion_matrix(y_test, labels)
rates[threshold] = {'false positive rate':m[0,1]/m[0,:].sum(), 'true positive rate':m[1,1]/m[1,:].sum()}
pd.DataFrame(rates).T.plot.scatter('false positive rate','true positive rate')
from sklearn import svm
from sklearn.metrics import ros_curve, auc
model = svm.SVC(kernel='linear', probability=True, random_state=0)
model.fit(x_train, y_train)
y_pred = model.predict_proba(X_test)[:,1]
fpr, tpr, thresholds = roc_curve(y_test, y_pred)
auc = auc(fpr, tpr)
plt.plot(fpr, tpr, color='red', label='ROC curve (area=%.3f)'%auc)
plt.plot([0,1],[0,1],color='black', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False positive rate')
plt.xlabel('True positive rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="best")
plt.show()
| [
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] | |
e86714e61dde836eeef750f7a800b133850db443 | acad69f0abe162eea0cb13cbe15bfd88f6da08b4 | /down-stream-tasks/mmdetection/mmdet/models/losses/ghm_loss.py | bc5eb774eab3c7515868c266182760c92061c911 | [
"Apache-2.0"
] | permissive | zhangzjn/EMO | 69afcac53800d8b9a390f1214e178e2ca4da3b24 | 141afbdbce04683790f0699f256327ec420be442 | refs/heads/main | 2023-08-27T19:04:23.313676 | 2023-08-15T04:09:55 | 2023-08-15T04:09:55 | 584,987,542 | 139 | 9 | null | null | null | null | UTF-8 | Python | false | false | 8,136 | py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(
(labels >= 0) & (labels < label_channels), as_tuple=False).squeeze()
if inds.numel() > 0:
bin_labels[inds, labels[inds]] = 1
bin_label_weights = label_weights.view(-1, 1).expand(
label_weights.size(0), label_channels)
return bin_labels, bin_label_weights
# TODO: code refactoring to make it consistent with other losses
@LOSSES.register_module()
class GHMC(nn.Module):
"""GHM Classification Loss.
Details of the theorem can be viewed in the paper
`Gradient Harmonized Single-stage Detector
<https://arxiv.org/abs/1811.05181>`_.
Args:
bins (int): Number of the unit regions for distribution calculation.
momentum (float): The parameter for moving average.
use_sigmoid (bool): Can only be true for BCE based loss now.
loss_weight (float): The weight of the total GHM-C loss.
reduction (str): Options are "none", "mean" and "sum".
Defaults to "mean"
"""
def __init__(self,
bins=10,
momentum=0,
use_sigmoid=True,
loss_weight=1.0,
reduction='mean'):
super(GHMC, self).__init__()
self.bins = bins
self.momentum = momentum
edges = torch.arange(bins + 1).float() / bins
self.register_buffer('edges', edges)
self.edges[-1] += 1e-6
if momentum > 0:
acc_sum = torch.zeros(bins)
self.register_buffer('acc_sum', acc_sum)
self.use_sigmoid = use_sigmoid
if not self.use_sigmoid:
raise NotImplementedError
self.loss_weight = loss_weight
self.reduction = reduction
def forward(self,
pred,
target,
label_weight,
reduction_override=None,
**kwargs):
"""Calculate the GHM-C loss.
Args:
pred (float tensor of size [batch_num, class_num]):
The direct prediction of classification fc layer.
target (float tensor of size [batch_num, class_num]):
Binary class target for each sample.
label_weight (float tensor of size [batch_num, class_num]):
the value is 1 if the sample is valid and 0 if ignored.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None.
Returns:
The gradient harmonized loss.
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
# the target should be binary class label
if pred.dim() != target.dim():
target, label_weight = _expand_onehot_labels(
target, label_weight, pred.size(-1))
target, label_weight = target.float(), label_weight.float()
edges = self.edges
mmt = self.momentum
weights = torch.zeros_like(pred)
# gradient length
g = torch.abs(pred.sigmoid().detach() - target)
valid = label_weight > 0
tot = max(valid.float().sum().item(), 1.0)
n = 0 # n valid bins
for i in range(self.bins):
inds = (g >= edges[i]) & (g < edges[i + 1]) & valid
num_in_bin = inds.sum().item()
if num_in_bin > 0:
if mmt > 0:
self.acc_sum[i] = mmt * self.acc_sum[i] \
+ (1 - mmt) * num_in_bin
weights[inds] = tot / self.acc_sum[i]
else:
weights[inds] = tot / num_in_bin
n += 1
if n > 0:
weights = weights / n
loss = F.binary_cross_entropy_with_logits(
pred, target, reduction='none')
loss = weight_reduce_loss(
loss, weights, reduction=reduction, avg_factor=tot)
return loss * self.loss_weight
# TODO: code refactoring to make it consistent with other losses
@LOSSES.register_module()
class GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
`Gradient Harmonized Single-stage Detector
<https://arxiv.org/abs/1811.05181>`_.
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
bins (int): Number of the unit regions for distribution calculation.
momentum (float): The parameter for moving average.
loss_weight (float): The weight of the total GHM-R loss.
reduction (str): Options are "none", "mean" and "sum".
Defaults to "mean"
"""
def __init__(self,
mu=0.02,
bins=10,
momentum=0,
loss_weight=1.0,
reduction='mean'):
super(GHMR, self).__init__()
self.mu = mu
self.bins = bins
edges = torch.arange(bins + 1).float() / bins
self.register_buffer('edges', edges)
self.edges[-1] = 1e3
self.momentum = momentum
if momentum > 0:
acc_sum = torch.zeros(bins)
self.register_buffer('acc_sum', acc_sum)
self.loss_weight = loss_weight
self.reduction = reduction
# TODO: support reduction parameter
def forward(self,
pred,
target,
label_weight,
avg_factor=None,
reduction_override=None):
"""Calculate the GHM-R loss.
Args:
pred (float tensor of size [batch_num, 4 (* class_num)]):
The prediction of box regression layer. Channel number can be 4
or 4 * class_num depending on whether it is class-agnostic.
target (float tensor of size [batch_num, 4 (* class_num)]):
The target regression values with the same size of pred.
label_weight (float tensor of size [batch_num, 4 (* class_num)]):
The weight of each sample, 0 if ignored.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Defaults to None.
Returns:
The gradient harmonized loss.
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
mu = self.mu
edges = self.edges
mmt = self.momentum
# ASL1 loss
diff = pred - target
loss = torch.sqrt(diff * diff + mu * mu) - mu
# gradient length
g = torch.abs(diff / torch.sqrt(mu * mu + diff * diff)).detach()
weights = torch.zeros_like(g)
valid = label_weight > 0
tot = max(label_weight.float().sum().item(), 1.0)
n = 0 # n: valid bins
for i in range(self.bins):
inds = (g >= edges[i]) & (g < edges[i + 1]) & valid
num_in_bin = inds.sum().item()
if num_in_bin > 0:
n += 1
if mmt > 0:
self.acc_sum[i] = mmt * self.acc_sum[i] \
+ (1 - mmt) * num_in_bin
weights[inds] = tot / self.acc_sum[i]
else:
weights[inds] = tot / num_in_bin
if n > 0:
weights /= n
loss = weight_reduce_loss(
loss, weights, reduction=reduction, avg_factor=tot)
return loss * self.loss_weight
| [
"[email protected]"
] | |
e7a6d17a22f5e9f8531f0d8e0c576cff70c8e1eb | 3474fd7e1ccd8dd4e0b4da5c67d89694c69ce73c | /batch3/outputs/bbn_yhe.py | 2d75b05e4272c8e13788d9378f35e7fd0e971249 | [] | no_license | mishakb/ISiTGR | 149e1235ed2fff6ee2452b53c23dbe46f5dcf17e | db4f6fed47162de6347b62b3f8ae832e4ffbfdf0 | refs/heads/master | 2023-01-16T02:42:31.787483 | 2021-03-12T04:39:18 | 2021-03-12T04:39:18 | 176,977,260 | 27 | 7 | null | 2023-01-02T15:19:33 | 2019-03-21T15:48:31 | HTML | UTF-8 | Python | false | false | 4,578 | py | import planckStyle as s
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.insert(0, r'c:\work\dist\git\camb')
from camb.bbn import BBN_table_interpolator
BBNstandard = BBN_table_interpolator('PArthENoPE_880.2_standard.dat')
# BBN theoretical error
sigma_yp_theo = 0.0003
# resolution of the theoretical BBN curve (number of omega_b values)
num_ob = 50
# omegab range in the plot
ob_min = 0.019
ob_max = 0.025
# yhe range in the plot
yp_min = 0.175
yp_max = 0.28
# helium data: Aver et al. 2015
aver_mean = 0.2449
aver_sigma = 0.004
# helium data: Serenelli and Basu 2010
sere_minus = 0.294
sere_plus = yp_max
sere_b = np.zeros(2, dtype='float')
sere_y1 = np.zeros(2, dtype='float')
sere_y2 = np.zeros(2, dtype='float')
sere_b[0] = ob_min
sere_b[1] = ob_max
sere_y1[0] = sere_minus
sere_y1[1] = sere_minus
sere_y2[0] = sere_plus
sere_y2[1] = sere_plus
labels = [s.planckall, s.planckall + "+lensing+BAO"]
datatag = [s.defdata_all, s.defdata_all + '_lensing_BAO']
########### ombh2 -Yhe #############
g = s.getSinglePlotter()
colors = g.settings.solid_colors[3:0:-1]
del colors[1]
bbn_b = np.arange(ob_min, ob_max + 0.1, (ob_max - ob_min) / num_ob)
bbn_y = np.array([BBNstandard.Y_p(x, 0) for x in bbn_b])
bbn_y1 = bbn_y - sigma_yp_theo
bbn_y2 = bbn_y + sigma_yp_theo
g.add_y_bands(aver_mean, aver_sigma)
# plt.fill_between(sere_b, sere_y1, yp_max, alpha=0.07, color='gray')
# plt.plot(sere_b, sere_y1, alpha=0.2, color='gray', linestyle='-')
plt.text(0.0193, 0.249, "Aver et al. (2015)", fontsize=7.)
# plt.text(0.0183, 0.325, "Excluded by Serenelli \& Basu (2010)", fontsize=6.5)
bbn_y1 = bbn_y - 2 * sigma_yp_theo
bbn_y2 = bbn_y + 2 * sigma_yp_theo
plt.fill_between(bbn_b, bbn_y1, bbn_y2, alpha=0.4, color='green', lw=0, zorder=10)
bbn_y1 = bbn_y - sigma_yp_theo
bbn_y2 = bbn_y + sigma_yp_theo
plt.fill_between(bbn_b, bbn_y1, bbn_y2, alpha=0.9, color='green', lw=0, zorder=11)
# plt.plot(bbn_b, bbn_y1, color='green', linestyle='solid')
# plt.plot(bbn_b, bbn_y2, color='green', linestyle='solid')
roots = [g.getRoot('yhe', d) for d in datatag]
g.settings.legend_fontsize = 8
g.plot_2d(roots, 'omegabh2', 'YpBBN', filled=True, lims=[ob_min + 0.0001, ob_max, yp_min, yp_max])
g.add_legend(labels, legend_loc='lower left', colored_text=False)
# plt.gca().set_yticks([0.2, 0.25, 0.3])
plt.gca().annotate('Standard BBN',
xy=(0.0242, 0.249),
xycoords='data',
xytext=(-35, -30),
textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2"),
fontsize=8.
)
g.export()
########### Neff -Yhe #############
g = s.getSinglePlotter()
N_min = 0.01
N_max = 5
Neff = np.arange(N_min, N_max + 0.1, 0.1)
Nrange = [N_min, N_max]
g.add_y_bands(aver_mean, aver_sigma)
plt.fill_between(Nrange, Neff[-1], sere_y1, alpha=0.07, color='gray')
plt.plot(Nrange, sere_y1, alpha=0.2, color='gray', linestyle='-')
plt.text(0.17, 0.242, "Aver et al. (2015)", fontsize=6)
plt.text(0.17, 0.337, "Excluded by Serenelli \& Basu (2010)", fontsize=6)
roots = [g.getRoot('nnu_yhe', d) for d in datatag]
# roots += ['base_nnu_yhe_' + s.defdata_all + '_Aver15']
g.plot_2d(roots, 'nnu', 'YpBBN', filled=True, lims=[0, N_max, yp_min, yp_max])
g.add_2d_contours('base_nnu_yhe_' + s.defdata_all + '_Aver15_post_BAO_lensing', 'nnu', 'YpBBN', filled=False)
ombh2mean = 0.0224
bbn_y = np.array([BBNstandard.Y_p(ombh2mean, x - 3.046) for x in Neff])
bbn_y1 = bbn_y - 2 * sigma_yp_theo
bbn_y2 = bbn_y + 2 * sigma_yp_theo
plt.fill_between(Neff, bbn_y1, bbn_y2, alpha=0.4, color='green', lw=0)
bbn_y1 = bbn_y - sigma_yp_theo
bbn_y2 = bbn_y + sigma_yp_theo
plt.fill_between(Neff, bbn_y1, bbn_y2, alpha=0.9, color='green', lw=0)
# plt.plot(Neff, bbn_y1, color='green', linestyle='solid')
# plt.plot(Neff, bbn_y2, color='green', linestyle='solid')
labels = labels[:1] + ['+lensing+BAO']
g.add_legend(labels, legend_loc='lower left', colored_text=True, fontsize=8)
g.add_x_marker(3.046)
plt.gca().set_yticks([0.15, 0.2, 0.25, 0.3, 0.35])
# g.rotate_yticklabels()
plt.gca().annotate('Standard BBN\n' + r'($\Omega_{\rm b} h^2=0.0224$)',
xy=(4.5, 0.262),
xycoords='data',
xytext=(-44, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2"),
fontsize=6.5
)
g.export(tag='neff')
| [
"[email protected]"
] | |
048d84b86b4c0b2d6195aab0d20755095d6863f5 | b545bc57f3359a42b034078e3acb3e4d0c77a971 | /src/azure-firewall/azext_firewall/aaz/latest/network/firewall/policy/_update.py | 5e4c29b86003176bc8eddc08a81d5d110e491b50 | [
"LicenseRef-scancode-generic-cla",
"MIT"
] | permissive | ShichaoQiu/azure-cli-extensions | d91672b3f7bf2ffae4f1072830e99632b66cf754 | 8134c01681963387a496b5d4627527a5ed044e19 | refs/heads/main | 2023-08-24T09:09:55.689202 | 2023-08-15T06:08:35 | 2023-08-15T06:08:35 | 230,201,126 | 0 | 1 | MIT | 2020-12-11T07:14:51 | 2019-12-26T05:33:04 | Python | UTF-8 | Python | false | false | 33,631 | py | # --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
#
# Code generated by aaz-dev-tools
# --------------------------------------------------------------------------------------------
# pylint: skip-file
# flake8: noqa
from azure.cli.core.aaz import *
@register_command(
"network firewall policy update",
)
class Update(AAZCommand):
"""Update an Azure firewall policy.
"""
_aaz_info = {
"version": "2022-01-01",
"resources": [
["mgmt-plane", "/subscriptions/{}/resourcegroups/{}/providers/microsoft.network/firewallpolicies/{}", "2022-01-01"],
]
}
AZ_SUPPORT_NO_WAIT = True
AZ_SUPPORT_GENERIC_UPDATE = True
def _handler(self, command_args):
super()._handler(command_args)
return self.build_lro_poller(self._execute_operations, self._output)
_args_schema = None
@classmethod
def _build_arguments_schema(cls, *args, **kwargs):
if cls._args_schema is not None:
return cls._args_schema
cls._args_schema = super()._build_arguments_schema(*args, **kwargs)
# define Arg Group ""
_args_schema = cls._args_schema
_args_schema.name = AAZStrArg(
options=["-n", "--name"],
help="The name of the Firewall Policy.",
required=True,
id_part="name",
)
_args_schema.resource_group = AAZResourceGroupNameArg(
required=True,
)
_args_schema.sku = AAZStrArg(
options=["--sku"],
help="SKU of Firewall policy.",
is_preview=True,
nullable=True,
enum={"Basic": "Basic", "Premium": "Premium", "Standard": "Standard"},
)
_args_schema.sql = AAZBoolArg(
options=["--sql"],
help="A flag to indicate if SQL Redirect traffic filtering is enabled.",
is_preview=True,
nullable=True,
)
_args_schema.threat_intel_mode = AAZStrArg(
options=["--threat-intel-mode"],
help="The operation mode for Threat Intelligence.",
nullable=True,
enum={"Alert": "Alert", "Deny": "Deny", "Off": "Off"},
)
_args_schema.tags = AAZDictArg(
options=["--tags"],
help="Space-separated tags: key[=value] [key[=value] ...]. Use \"\" to clear existing tags.",
nullable=True,
)
tags = cls._args_schema.tags
tags.Element = AAZStrArg(
nullable=True,
)
# define Arg Group "DNS"
_args_schema = cls._args_schema
_args_schema.enable_dns_proxy = AAZBoolArg(
options=["--enable-dns-proxy"],
arg_group="DNS",
help="Enable DNS Proxy.",
nullable=True,
)
_args_schema.dns_servers = AAZListArg(
options=["--dns-servers"],
arg_group="DNS",
help="Space-separated list of DNS server IP addresses.",
nullable=True,
)
dns_servers = cls._args_schema.dns_servers
dns_servers.Element = AAZStrArg(
nullable=True,
)
# define Arg Group "DnsSettings"
# define Arg Group "Identity Instance"
_args_schema = cls._args_schema
_args_schema.identity_type = AAZStrArg(
options=["--identity-type"],
arg_group="Identity Instance",
help="The type of identity used for the resource. The type 'SystemAssigned, UserAssigned' includes both an implicitly created identity and a set of user assigned identities. The type 'None' will remove any identities from the virtual machine.",
nullable=True,
enum={"None": "None", "SystemAssigned": "SystemAssigned", "SystemAssigned, UserAssigned": "SystemAssigned, UserAssigned", "UserAssigned": "UserAssigned"},
)
_args_schema.user_assigned_identities = AAZDictArg(
options=["--user-assigned-identities"],
arg_group="Identity Instance",
help="The list of user identities associated with resource. The user identity dictionary key references will be ARM resource ids in the form: '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}'.",
nullable=True,
)
user_assigned_identities = cls._args_schema.user_assigned_identities
user_assigned_identities.Element = AAZObjectArg(
nullable=True,
blank={},
)
# define Arg Group "IntrusionDetection"
# define Arg Group "Intrustion Detection"
_args_schema = cls._args_schema
_args_schema.idps_mode = AAZStrArg(
options=["--idps-mode"],
arg_group="Intrustion Detection",
help="IDPS mode.",
is_preview=True,
nullable=True,
enum={"Alert": "Alert", "Deny": "Deny", "Off": "Off"},
)
# define Arg Group "Parameters"
# define Arg Group "Properties"
# define Arg Group "Snat"
_args_schema = cls._args_schema
_args_schema.auto_learn_private_ranges = AAZStrArg(
options=["--learn-ranges", "--auto-learn-private-ranges"],
arg_group="Snat",
help="The operation mode for automatically learning private ranges to not be SNAT",
nullable=True,
enum={"Disabled": "Disabled", "Enabled": "Enabled"},
)
_args_schema.private_ranges = AAZListArg(
options=["--private-ranges"],
arg_group="Snat",
help="List of private IP addresses/IP address ranges to not be SNAT.",
nullable=True,
)
private_ranges = cls._args_schema.private_ranges
private_ranges.Element = AAZStrArg(
nullable=True,
)
# define Arg Group "TLS Inspection"
_args_schema = cls._args_schema
_args_schema.key_vault_secret_id = AAZStrArg(
options=["--key-vault-secret-id"],
arg_group="TLS Inspection",
help="Secret Id of (base-64 encoded unencrypted pfx) Secret or Certificate object stored in KeyVault.",
is_preview=True,
nullable=True,
)
_args_schema.cert_name = AAZStrArg(
options=["--cert-name"],
arg_group="TLS Inspection",
help="Name of the CA certificate.",
is_preview=True,
nullable=True,
)
# define Arg Group "Threat Intel Allowlist"
_args_schema = cls._args_schema
_args_schema.fqdns = AAZListArg(
options=["--fqdns"],
arg_group="Threat Intel Allowlist",
help="Space-separated list of FQDNs.",
nullable=True,
)
_args_schema.ip_addresses = AAZListArg(
options=["--ip-addresses"],
arg_group="Threat Intel Allowlist",
help="Space-separated list of IPv4 addresses.",
nullable=True,
)
fqdns = cls._args_schema.fqdns
fqdns.Element = AAZStrArg(
nullable=True,
)
ip_addresses = cls._args_schema.ip_addresses
ip_addresses.Element = AAZStrArg(
nullable=True,
)
return cls._args_schema
_args_sub_resource_update = None
@classmethod
def _build_args_sub_resource_update(cls, _schema):
if cls._args_sub_resource_update is not None:
_schema.id = cls._args_sub_resource_update.id
return
cls._args_sub_resource_update = AAZObjectArg(
nullable=True,
)
sub_resource_update = cls._args_sub_resource_update
sub_resource_update.id = AAZStrArg(
options=["id"],
help="Resource ID.",
nullable=True,
)
_schema.id = cls._args_sub_resource_update.id
def _execute_operations(self):
self.pre_operations()
self.FirewallPoliciesGet(ctx=self.ctx)()
self.pre_instance_update(self.ctx.vars.instance)
self.InstanceUpdateByJson(ctx=self.ctx)()
self.InstanceUpdateByGeneric(ctx=self.ctx)()
self.post_instance_update(self.ctx.vars.instance)
yield self.FirewallPoliciesCreateOrUpdate(ctx=self.ctx)()
self.post_operations()
@register_callback
def pre_operations(self):
pass
@register_callback
def post_operations(self):
pass
@register_callback
def pre_instance_update(self, instance):
pass
@register_callback
def post_instance_update(self, instance):
pass
def _output(self, *args, **kwargs):
result = self.deserialize_output(self.ctx.vars.instance, client_flatten=True)
return result
class FirewallPoliciesGet(AAZHttpOperation):
CLIENT_TYPE = "MgmtClient"
def __call__(self, *args, **kwargs):
request = self.make_request()
session = self.client.send_request(request=request, stream=False, **kwargs)
if session.http_response.status_code in [200]:
return self.on_200(session)
return self.on_error(session.http_response)
@property
def url(self):
return self.client.format_url(
"/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/firewallPolicies/{firewallPolicyName}",
**self.url_parameters
)
@property
def method(self):
return "GET"
@property
def error_format(self):
return "ODataV4Format"
@property
def url_parameters(self):
parameters = {
**self.serialize_url_param(
"firewallPolicyName", self.ctx.args.name,
required=True,
),
**self.serialize_url_param(
"resourceGroupName", self.ctx.args.resource_group,
required=True,
),
**self.serialize_url_param(
"subscriptionId", self.ctx.subscription_id,
required=True,
),
}
return parameters
@property
def query_parameters(self):
parameters = {
**self.serialize_query_param(
"api-version", "2022-01-01",
required=True,
),
}
return parameters
@property
def header_parameters(self):
parameters = {
**self.serialize_header_param(
"Accept", "application/json",
),
}
return parameters
def on_200(self, session):
data = self.deserialize_http_content(session)
self.ctx.set_var(
"instance",
data,
schema_builder=self._build_schema_on_200
)
_schema_on_200 = None
@classmethod
def _build_schema_on_200(cls):
if cls._schema_on_200 is not None:
return cls._schema_on_200
cls._schema_on_200 = AAZObjectType()
_UpdateHelper._build_schema_firewall_policy_read(cls._schema_on_200)
return cls._schema_on_200
class FirewallPoliciesCreateOrUpdate(AAZHttpOperation):
CLIENT_TYPE = "MgmtClient"
def __call__(self, *args, **kwargs):
request = self.make_request()
session = self.client.send_request(request=request, stream=False, **kwargs)
if session.http_response.status_code in [202]:
return self.client.build_lro_polling(
self.ctx.args.no_wait,
session,
self.on_200_201,
self.on_error,
lro_options={"final-state-via": "azure-async-operation"},
path_format_arguments=self.url_parameters,
)
if session.http_response.status_code in [200, 201]:
return self.client.build_lro_polling(
self.ctx.args.no_wait,
session,
self.on_200_201,
self.on_error,
lro_options={"final-state-via": "azure-async-operation"},
path_format_arguments=self.url_parameters,
)
return self.on_error(session.http_response)
@property
def url(self):
return self.client.format_url(
"/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/firewallPolicies/{firewallPolicyName}",
**self.url_parameters
)
@property
def method(self):
return "PUT"
@property
def error_format(self):
return "ODataV4Format"
@property
def url_parameters(self):
parameters = {
**self.serialize_url_param(
"firewallPolicyName", self.ctx.args.name,
required=True,
),
**self.serialize_url_param(
"resourceGroupName", self.ctx.args.resource_group,
required=True,
),
**self.serialize_url_param(
"subscriptionId", self.ctx.subscription_id,
required=True,
),
}
return parameters
@property
def query_parameters(self):
parameters = {
**self.serialize_query_param(
"api-version", "2022-01-01",
required=True,
),
}
return parameters
@property
def header_parameters(self):
parameters = {
**self.serialize_header_param(
"Content-Type", "application/json",
),
**self.serialize_header_param(
"Accept", "application/json",
),
}
return parameters
@property
def content(self):
_content_value, _builder = self.new_content_builder(
self.ctx.args,
value=self.ctx.vars.instance,
)
return self.serialize_content(_content_value)
def on_200_201(self, session):
data = self.deserialize_http_content(session)
self.ctx.set_var(
"instance",
data,
schema_builder=self._build_schema_on_200_201
)
_schema_on_200_201 = None
@classmethod
def _build_schema_on_200_201(cls):
if cls._schema_on_200_201 is not None:
return cls._schema_on_200_201
cls._schema_on_200_201 = AAZObjectType()
_UpdateHelper._build_schema_firewall_policy_read(cls._schema_on_200_201)
return cls._schema_on_200_201
class InstanceUpdateByJson(AAZJsonInstanceUpdateOperation):
def __call__(self, *args, **kwargs):
self._update_instance(self.ctx.vars.instance)
def _update_instance(self, instance):
_instance_value, _builder = self.new_content_builder(
self.ctx.args,
value=instance,
typ=AAZObjectType
)
_builder.set_prop("identity", AAZObjectType)
_builder.set_prop("properties", AAZObjectType, typ_kwargs={"flags": {"client_flatten": True}})
_builder.set_prop("tags", AAZDictType, ".tags")
identity = _builder.get(".identity")
if identity is not None:
identity.set_prop("type", AAZStrType, ".identity_type")
identity.set_prop("userAssignedIdentities", AAZDictType, ".user_assigned_identities")
user_assigned_identities = _builder.get(".identity.userAssignedIdentities")
if user_assigned_identities is not None:
user_assigned_identities.set_elements(AAZObjectType, ".")
properties = _builder.get(".properties")
if properties is not None:
properties.set_prop("basePolicy", AAZObjectType)
properties.set_prop("dnsSettings", AAZObjectType)
properties.set_prop("intrusionDetection", AAZObjectType)
properties.set_prop("sku", AAZObjectType)
properties.set_prop("snat", AAZObjectType)
properties.set_prop("sql", AAZObjectType)
properties.set_prop("threatIntelMode", AAZStrType, ".threat_intel_mode")
properties.set_prop("threatIntelWhitelist", AAZObjectType)
properties.set_prop("transportSecurity", AAZObjectType)
dns_settings = _builder.get(".properties.dnsSettings")
if dns_settings is not None:
dns_settings.set_prop("enableProxy", AAZBoolType, ".enable_dns_proxy")
dns_settings.set_prop("servers", AAZListType, ".dns_servers")
servers = _builder.get(".properties.dnsSettings.servers")
if servers is not None:
servers.set_elements(AAZStrType, ".")
intrusion_detection = _builder.get(".properties.intrusionDetection")
if intrusion_detection is not None:
intrusion_detection.set_prop("mode", AAZStrType, ".idps_mode")
sku = _builder.get(".properties.sku")
if sku is not None:
sku.set_prop("tier", AAZStrType, ".sku")
snat = _builder.get(".properties.snat")
if snat is not None:
snat.set_prop("autoLearnPrivateRanges", AAZStrType, ".auto_learn_private_ranges")
snat.set_prop("privateRanges", AAZListType, ".private_ranges")
private_ranges = _builder.get(".properties.snat.privateRanges")
if private_ranges is not None:
private_ranges.set_elements(AAZStrType, ".")
sql = _builder.get(".properties.sql")
if sql is not None:
sql.set_prop("allowSqlRedirect", AAZBoolType, ".sql")
threat_intel_whitelist = _builder.get(".properties.threatIntelWhitelist")
if threat_intel_whitelist is not None:
threat_intel_whitelist.set_prop("fqdns", AAZListType, ".fqdns")
threat_intel_whitelist.set_prop("ipAddresses", AAZListType, ".ip_addresses")
fqdns = _builder.get(".properties.threatIntelWhitelist.fqdns")
if fqdns is not None:
fqdns.set_elements(AAZStrType, ".")
ip_addresses = _builder.get(".properties.threatIntelWhitelist.ipAddresses")
if ip_addresses is not None:
ip_addresses.set_elements(AAZStrType, ".")
transport_security = _builder.get(".properties.transportSecurity")
if transport_security is not None:
transport_security.set_prop("certificateAuthority", AAZObjectType)
certificate_authority = _builder.get(".properties.transportSecurity.certificateAuthority")
if certificate_authority is not None:
certificate_authority.set_prop("keyVaultSecretId", AAZStrType, ".key_vault_secret_id")
certificate_authority.set_prop("name", AAZStrType, ".cert_name")
tags = _builder.get(".tags")
if tags is not None:
tags.set_elements(AAZStrType, ".")
return _instance_value
class InstanceUpdateByGeneric(AAZGenericInstanceUpdateOperation):
def __call__(self, *args, **kwargs):
self._update_instance_by_generic(
self.ctx.vars.instance,
self.ctx.generic_update_args
)
class _UpdateHelper:
"""Helper class for Update"""
@classmethod
def _build_schema_sub_resource_update(cls, _builder):
if _builder is None:
return
_builder.set_prop("id", AAZStrType, ".id")
_schema_firewall_policy_read = None
@classmethod
def _build_schema_firewall_policy_read(cls, _schema):
if cls._schema_firewall_policy_read is not None:
_schema.etag = cls._schema_firewall_policy_read.etag
_schema.id = cls._schema_firewall_policy_read.id
_schema.identity = cls._schema_firewall_policy_read.identity
_schema.location = cls._schema_firewall_policy_read.location
_schema.name = cls._schema_firewall_policy_read.name
_schema.properties = cls._schema_firewall_policy_read.properties
_schema.tags = cls._schema_firewall_policy_read.tags
_schema.type = cls._schema_firewall_policy_read.type
return
cls._schema_firewall_policy_read = _schema_firewall_policy_read = AAZObjectType()
firewall_policy_read = _schema_firewall_policy_read
firewall_policy_read.etag = AAZStrType(
flags={"read_only": True},
)
firewall_policy_read.id = AAZStrType()
firewall_policy_read.identity = AAZObjectType()
firewall_policy_read.location = AAZStrType()
firewall_policy_read.name = AAZStrType(
flags={"read_only": True},
)
firewall_policy_read.properties = AAZObjectType(
flags={"client_flatten": True},
)
firewall_policy_read.tags = AAZDictType()
firewall_policy_read.type = AAZStrType(
flags={"read_only": True},
)
identity = _schema_firewall_policy_read.identity
identity.principal_id = AAZStrType(
serialized_name="principalId",
flags={"read_only": True},
)
identity.tenant_id = AAZStrType(
serialized_name="tenantId",
flags={"read_only": True},
)
identity.type = AAZStrType()
identity.user_assigned_identities = AAZDictType(
serialized_name="userAssignedIdentities",
)
user_assigned_identities = _schema_firewall_policy_read.identity.user_assigned_identities
user_assigned_identities.Element = AAZObjectType()
_element = _schema_firewall_policy_read.identity.user_assigned_identities.Element
_element.client_id = AAZStrType(
serialized_name="clientId",
flags={"read_only": True},
)
_element.principal_id = AAZStrType(
serialized_name="principalId",
flags={"read_only": True},
)
properties = _schema_firewall_policy_read.properties
properties.base_policy = AAZObjectType(
serialized_name="basePolicy",
)
cls._build_schema_sub_resource_read(properties.base_policy)
properties.child_policies = AAZListType(
serialized_name="childPolicies",
flags={"read_only": True},
)
properties.dns_settings = AAZObjectType(
serialized_name="dnsSettings",
)
properties.explicit_proxy = AAZObjectType(
serialized_name="explicitProxy",
)
properties.firewalls = AAZListType(
flags={"read_only": True},
)
properties.insights = AAZObjectType()
properties.intrusion_detection = AAZObjectType(
serialized_name="intrusionDetection",
)
properties.provisioning_state = AAZStrType(
serialized_name="provisioningState",
flags={"read_only": True},
)
properties.rule_collection_groups = AAZListType(
serialized_name="ruleCollectionGroups",
flags={"read_only": True},
)
properties.sku = AAZObjectType()
properties.snat = AAZObjectType()
properties.sql = AAZObjectType()
properties.threat_intel_mode = AAZStrType(
serialized_name="threatIntelMode",
)
properties.threat_intel_whitelist = AAZObjectType(
serialized_name="threatIntelWhitelist",
)
properties.transport_security = AAZObjectType(
serialized_name="transportSecurity",
)
child_policies = _schema_firewall_policy_read.properties.child_policies
child_policies.Element = AAZObjectType()
cls._build_schema_sub_resource_read(child_policies.Element)
dns_settings = _schema_firewall_policy_read.properties.dns_settings
dns_settings.enable_proxy = AAZBoolType(
serialized_name="enableProxy",
)
dns_settings.require_proxy_for_network_rules = AAZBoolType(
serialized_name="requireProxyForNetworkRules",
nullable=True,
)
dns_settings.servers = AAZListType()
servers = _schema_firewall_policy_read.properties.dns_settings.servers
servers.Element = AAZStrType()
explicit_proxy = _schema_firewall_policy_read.properties.explicit_proxy
explicit_proxy.enable_explicit_proxy = AAZBoolType(
serialized_name="enableExplicitProxy",
nullable=True,
)
explicit_proxy.enable_pac_file = AAZBoolType(
serialized_name="enablePacFile",
nullable=True,
)
explicit_proxy.http_port = AAZIntType(
serialized_name="httpPort",
)
explicit_proxy.https_port = AAZIntType(
serialized_name="httpsPort",
)
explicit_proxy.pac_file = AAZStrType(
serialized_name="pacFile",
)
explicit_proxy.pac_file_port = AAZIntType(
serialized_name="pacFilePort",
)
firewalls = _schema_firewall_policy_read.properties.firewalls
firewalls.Element = AAZObjectType()
cls._build_schema_sub_resource_read(firewalls.Element)
insights = _schema_firewall_policy_read.properties.insights
insights.is_enabled = AAZBoolType(
serialized_name="isEnabled",
)
insights.log_analytics_resources = AAZObjectType(
serialized_name="logAnalyticsResources",
)
insights.retention_days = AAZIntType(
serialized_name="retentionDays",
)
log_analytics_resources = _schema_firewall_policy_read.properties.insights.log_analytics_resources
log_analytics_resources.default_workspace_id = AAZObjectType(
serialized_name="defaultWorkspaceId",
)
cls._build_schema_sub_resource_read(log_analytics_resources.default_workspace_id)
log_analytics_resources.workspaces = AAZListType()
workspaces = _schema_firewall_policy_read.properties.insights.log_analytics_resources.workspaces
workspaces.Element = AAZObjectType()
_element = _schema_firewall_policy_read.properties.insights.log_analytics_resources.workspaces.Element
_element.region = AAZStrType()
_element.workspace_id = AAZObjectType(
serialized_name="workspaceId",
)
cls._build_schema_sub_resource_read(_element.workspace_id)
intrusion_detection = _schema_firewall_policy_read.properties.intrusion_detection
intrusion_detection.configuration = AAZObjectType()
intrusion_detection.mode = AAZStrType()
configuration = _schema_firewall_policy_read.properties.intrusion_detection.configuration
configuration.bypass_traffic_settings = AAZListType(
serialized_name="bypassTrafficSettings",
)
configuration.private_ranges = AAZListType(
serialized_name="privateRanges",
)
configuration.signature_overrides = AAZListType(
serialized_name="signatureOverrides",
)
bypass_traffic_settings = _schema_firewall_policy_read.properties.intrusion_detection.configuration.bypass_traffic_settings
bypass_traffic_settings.Element = AAZObjectType()
_element = _schema_firewall_policy_read.properties.intrusion_detection.configuration.bypass_traffic_settings.Element
_element.description = AAZStrType()
_element.destination_addresses = AAZListType(
serialized_name="destinationAddresses",
)
_element.destination_ip_groups = AAZListType(
serialized_name="destinationIpGroups",
)
_element.destination_ports = AAZListType(
serialized_name="destinationPorts",
)
_element.name = AAZStrType()
_element.protocol = AAZStrType()
_element.source_addresses = AAZListType(
serialized_name="sourceAddresses",
)
_element.source_ip_groups = AAZListType(
serialized_name="sourceIpGroups",
)
destination_addresses = _schema_firewall_policy_read.properties.intrusion_detection.configuration.bypass_traffic_settings.Element.destination_addresses
destination_addresses.Element = AAZStrType()
destination_ip_groups = _schema_firewall_policy_read.properties.intrusion_detection.configuration.bypass_traffic_settings.Element.destination_ip_groups
destination_ip_groups.Element = AAZStrType()
destination_ports = _schema_firewall_policy_read.properties.intrusion_detection.configuration.bypass_traffic_settings.Element.destination_ports
destination_ports.Element = AAZStrType()
source_addresses = _schema_firewall_policy_read.properties.intrusion_detection.configuration.bypass_traffic_settings.Element.source_addresses
source_addresses.Element = AAZStrType()
source_ip_groups = _schema_firewall_policy_read.properties.intrusion_detection.configuration.bypass_traffic_settings.Element.source_ip_groups
source_ip_groups.Element = AAZStrType()
private_ranges = _schema_firewall_policy_read.properties.intrusion_detection.configuration.private_ranges
private_ranges.Element = AAZStrType()
signature_overrides = _schema_firewall_policy_read.properties.intrusion_detection.configuration.signature_overrides
signature_overrides.Element = AAZObjectType()
_element = _schema_firewall_policy_read.properties.intrusion_detection.configuration.signature_overrides.Element
_element.id = AAZStrType()
_element.mode = AAZStrType()
rule_collection_groups = _schema_firewall_policy_read.properties.rule_collection_groups
rule_collection_groups.Element = AAZObjectType()
cls._build_schema_sub_resource_read(rule_collection_groups.Element)
sku = _schema_firewall_policy_read.properties.sku
sku.tier = AAZStrType()
snat = _schema_firewall_policy_read.properties.snat
snat.auto_learn_private_ranges = AAZStrType(
serialized_name="autoLearnPrivateRanges",
)
snat.private_ranges = AAZListType(
serialized_name="privateRanges",
)
private_ranges = _schema_firewall_policy_read.properties.snat.private_ranges
private_ranges.Element = AAZStrType()
sql = _schema_firewall_policy_read.properties.sql
sql.allow_sql_redirect = AAZBoolType(
serialized_name="allowSqlRedirect",
)
threat_intel_whitelist = _schema_firewall_policy_read.properties.threat_intel_whitelist
threat_intel_whitelist.fqdns = AAZListType()
threat_intel_whitelist.ip_addresses = AAZListType(
serialized_name="ipAddresses",
)
fqdns = _schema_firewall_policy_read.properties.threat_intel_whitelist.fqdns
fqdns.Element = AAZStrType()
ip_addresses = _schema_firewall_policy_read.properties.threat_intel_whitelist.ip_addresses
ip_addresses.Element = AAZStrType()
transport_security = _schema_firewall_policy_read.properties.transport_security
transport_security.certificate_authority = AAZObjectType(
serialized_name="certificateAuthority",
)
certificate_authority = _schema_firewall_policy_read.properties.transport_security.certificate_authority
certificate_authority.key_vault_secret_id = AAZStrType(
serialized_name="keyVaultSecretId",
)
certificate_authority.name = AAZStrType()
tags = _schema_firewall_policy_read.tags
tags.Element = AAZStrType()
_schema.etag = cls._schema_firewall_policy_read.etag
_schema.id = cls._schema_firewall_policy_read.id
_schema.identity = cls._schema_firewall_policy_read.identity
_schema.location = cls._schema_firewall_policy_read.location
_schema.name = cls._schema_firewall_policy_read.name
_schema.properties = cls._schema_firewall_policy_read.properties
_schema.tags = cls._schema_firewall_policy_read.tags
_schema.type = cls._schema_firewall_policy_read.type
_schema_sub_resource_read = None
@classmethod
def _build_schema_sub_resource_read(cls, _schema):
if cls._schema_sub_resource_read is not None:
_schema.id = cls._schema_sub_resource_read.id
return
cls._schema_sub_resource_read = _schema_sub_resource_read = AAZObjectType()
sub_resource_read = _schema_sub_resource_read
sub_resource_read.id = AAZStrType()
_schema.id = cls._schema_sub_resource_read.id
__all__ = ["Update"]
| [
"[email protected]"
] | |
f7246f5b593196ab8c42ab3791fb27a636fa9877 | 2836c3caf8ca332635640a27254a345afd449081 | /nws/FFE/dump_text.py | 64ee6d4d5a352d51b778840d80848c276b2e4fec | [
"Apache-2.0",
"MIT"
] | permissive | akrherz/DEV | 27cf1bac978a0d6bbfba1851b90d2495a3bdcd66 | 3b1ef5841b25365d9b256467e774f35c28866961 | refs/heads/main | 2023-08-30T10:02:52.750739 | 2023-08-29T03:08:01 | 2023-08-29T03:08:01 | 65,409,757 | 2 | 0 | MIT | 2023-09-12T03:06:07 | 2016-08-10T19:16:28 | Jupyter Notebook | UTF-8 | Python | false | false | 1,179 | py | """Dump text from database."""
from pyiem.util import get_dbconn
def main():
"""Go Main Go."""
pgconn = get_dbconn("postgis")
cursor = pgconn.cursor()
cursor.execute(
"WITH data as ("
"SELECT wfo, eventid, issue at time zone 'UTC' as issue, report, "
"expire at time zone 'UTC' as expire, "
"svs, row_number() OVER (PARTITION by wfo, eventid, "
"extract(year from issue) ORDER by length(svs) DESC) from "
"warnings where phenomena = 'FF' and significance = 'W' and "
"is_emergency) "
"SELECT * from data WHERE row_number = 1 ORDER by issue, wfo, eventid"
)
done = []
for row in cursor:
key = f"{row[0]}_{row[1]}_{row[2].year}"
if key in done:
continue
done.append(key)
i = 0
with open(f"FFE_Text/{key}_{i}.txt", "w") as fh:
fh.write(row[3])
for prod in ("" if row[5] is None else row[5]).split("__"):
if prod.strip() == "":
continue
i += 1
with open(f"FFE_Text/{key}_{i}.txt", "w") as fh:
fh.write(prod)
if __name__ == "__main__":
main()
| [
"[email protected]"
] | |
b22dd85529c83aa6600650aa488ecfa81392e566 | 2f98aa7e5bfc2fc5ef25e4d5cfa1d7802e3a7fae | /python/python_15719.py | 68b4f869fb8d6e083ba4f1619ed178f0c4fa912c | [] | no_license | AK-1121/code_extraction | cc812b6832b112e3ffcc2bb7eb4237fd85c88c01 | 5297a4a3aab3bb37efa24a89636935da04a1f8b6 | refs/heads/master | 2020-05-23T08:04:11.789141 | 2015-10-22T19:19:40 | 2015-10-22T19:19:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 68 | py | # PyGame, Surface not showing
_display.blit(_active_surface, (h,w))
| [
"[email protected]"
] | |
cf173c4188039fb9f7c03d8041bab18213f9fedf | 2dd0082221239fef0e0894c852f70f1eaeb62b9e | /Assignments/pete/python/lab12/lab12-guess_the_number-v5.py | 2c9c150c8bd62d806effabdefe672c22f7b08fbf | [] | no_license | pjz987/2019-10-28-fullstack-night | 03097cf3dc24aeec0c326044bb0fc99385fbc333 | 4c643013de73f08d7503d62ec602d6a5c80ffa7e | refs/heads/master | 2022-11-11T19:40:00.296645 | 2020-06-25T16:14:47 | 2020-06-25T16:14:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,139 | py | '''
lab12-guess_the_number-v5.py
V5
Swap the user with the computer: the user will pick a number, and the computer will make random guesses until they get it right.
'''
import random
import time
user_num = int(input("Welcome to Guess the Number v5.\nIn this program, computer guess you!\nPlease enter a number between 1 and 10: "))
while True:
if user_num not in list(range(1, 11)):
user_num = int(input(f"No cheating computer now. {user_num} is not a number between 1 and 10. Please enter a new number: "))
else:
break
guesses = []
count = 0
while True:
time.sleep(1)
guess = random.randint(1, 10)
if guess == user_num:
print(f"Computer guessed your number {user_num}.\n")
time.sleep(1)
while True:
print("CONGRATULATION: COMPUTER GUESS YOU!" * count)
count = count + 1
time.sleep(.25)
else:
if guess in guesses:
print(f"Computer guess {guess} again. Computer can never be too sure.")
else:
print(f"Computer guessed {guess}. Computer wrong. Computer try again.")
guesses.append(guess)
| [
"[email protected]"
] | |
ecad71b97f40fd1c4027e616ed6efc3e283cbe34 | 2442d073434d463cede4a79ae8f9fd31c62174f8 | /object-oriented-programming/composition/address.py | fec0ad8bea031d381431f42455a4d6f1d84773c9 | [] | no_license | grbalmeida/hello-python | 3630d75cfdde15223dc1c3a714fd562f6cda0505 | 4d9ddf2f7d104fdbc3aed2c88e50af19a39c1b63 | refs/heads/master | 2020-07-10T10:04:38.982256 | 2020-02-26T00:37:36 | 2020-02-26T00:37:36 | 204,237,527 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 173 | py | class Address:
def __init__(self, city, state):
self.city = city
self.state = state
def __del__(self):
print(f'{self.city}/{self.state} have been deleted')
| [
"[email protected]"
] | |
3fc82b879ed14d4f2cb2742b09238700a3f0c64f | 54e23ae20b7351f1e5db325b13cc9a106b3e581a | /transformer/layers.py | 723c431493e82ba286dc994ed9bec9d9c528fbf9 | [
"Apache-2.0"
] | permissive | okehkim/End-to-End-Speech-Recognition-Models | 191755c7fdab23ad61280279e200c9757824c64b | 7b4695bbc778e4d2c92470b56e2479c8d81d0079 | refs/heads/main | 2023-01-30T02:11:57.860303 | 2020-11-28T16:53:02 | 2020-11-28T16:53:02 | 316,779,547 | 0 | 0 | Apache-2.0 | 2020-11-28T16:52:27 | 2020-11-28T16:52:27 | null | UTF-8 | Python | false | false | 4,293 | py | # -*- coding: utf-8 -*-
# Soohwan Kim @ https://github.com/sooftware/
# This source code is licensed under the Apache 2.0 License license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
from torch import Tensor
from typing import Tuple, Optional, Any
from transformer.sublayers import AddNorm, PositionWiseFeedForwardNet
from attention import MultiHeadAttention
class SpeechTransformerEncoderLayer(nn.Module):
"""
EncoderLayer is made up of self-attention and feedforward network.
This standard encoder layer is based on the paper "Attention Is All You Need".
Args:
d_model: dimension of model (default: 512)
num_heads: number of attention heads (default: 8)
d_ff: dimension of feed forward network (default: 2048)
dropout_p: probability of dropout (default: 0.3)
ffnet_style: style of feed forward network [ff, conv] (default: ff)
"""
def __init__(
self,
d_model: int = 512, # dimension of model
num_heads: int = 8, # number of attention heads
d_ff: int = 2048, # dimension of feed forward network
dropout_p: float = 0.3, # probability of dropout
ffnet_style: str = 'ff' # style of feed forward network
) -> None:
super(SpeechTransformerEncoderLayer, self).__init__()
self.self_attention = AddNorm(MultiHeadAttention(d_model, num_heads), d_model)
self.feed_forward = AddNorm(PositionWiseFeedForwardNet(d_model, d_ff, dropout_p, ffnet_style), d_model)
def forward(
self,
inputs: Tensor, # B x T_input x D
non_pad_mask: Optional[Any] = None, # B x T_input
self_attn_mask: Optional[Any] = None # B x T_input x T_output
) -> Tuple[Tensor, Tensor]:
output, attn = self.self_attention(inputs, inputs, inputs, self_attn_mask)
output *= non_pad_mask
output = self.feed_forward(output)
output *= non_pad_mask
return output, attn
class SpeechTransformerDecoderLayer(nn.Module):
"""
DecoderLayer is made up of self-attention, multi-head attention and feedforward network.
This standard decoder layer is based on the paper "Attention Is All You Need".
Args:
d_model: dimension of model (default: 512)
num_heads: number of attention heads (default: 8)
d_ff: dimension of feed forward network (default: 2048)
dropout_p: probability of dropout (default: 0.3)
ffnet_style: style of feed forward network [ff, conv] (default: ff)
"""
def __init__(
self,
d_model: int = 512, # dimension of model
num_heads: int = 8, # number of attention heads
d_ff: int = 2048, # dimension of feed forward network
dropout_p: float = 0.3, # probability of dropout
ffnet_style: str = 'ff' # style of feed forward network
) -> None:
super(SpeechTransformerDecoderLayer, self).__init__()
self.self_attention = AddNorm(MultiHeadAttention(d_model, num_heads), d_model)
self.memory_attention = AddNorm(MultiHeadAttention(d_model, num_heads), d_model)
self.feed_forward = AddNorm(PositionWiseFeedForwardNet(d_model, d_ff, dropout_p, ffnet_style), d_model)
def forward(
self,
inputs: Tensor, # B x T_input
memory: Tensor, # B x T_input x D_model
non_pad_mask: Optional[Any] = None, # B x T_input
self_attn_mask: Optional[Any] = None, # B x T_input x T_input
memory_mask: Optional[Any] = None # B x T_input x T_output
) -> Tuple[Tensor, Tensor, Tensor]:
output, self_attn = self.self_attention(inputs, inputs, inputs, self_attn_mask)
output *= non_pad_mask
output, memory_attn = self.memory_attention(output, memory, memory, memory_mask)
output *= non_pad_mask
output = self.feed_forward(output)
output *= non_pad_mask
return output, self_attn, memory_attn
| [
"[email protected]"
] | |
232acacec4a343733eb00b2811848b81ae867e9f | e3c8f786d09e311d6ea1cab50edde040bf1ea988 | /Incident-Response/Tools/dfirtrack/dfirtrack_main/tests/ip/test_ip_views.py | 099cf7ffc43e424cec6cb803e502f9a1d4a4f205 | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | foss2cyber/Incident-Playbook | d1add8aec6e28a19e515754c6ce2e524d67f368e | a379a134c0c5af14df4ed2afa066c1626506b754 | refs/heads/main | 2023-06-07T09:16:27.876561 | 2021-07-07T03:48:54 | 2021-07-07T03:48:54 | 384,988,036 | 1 | 0 | MIT | 2021-07-11T15:45:31 | 2021-07-11T15:45:31 | null | UTF-8 | Python | false | false | 4,562 | py | from django.contrib.auth.models import User
from django.test import TestCase
from dfirtrack_main.models import Ip
import urllib.parse
class IpViewTestCase(TestCase):
""" ip view tests """
@classmethod
def setUpTestData(cls):
# create object
Ip.objects.create(ip_ip='127.0.0.1')
# create user
User.objects.create_user(username='testuser_ip', password='pRs9Ap7oc9W0yjLfnP2Y')
def test_ip_list_not_logged_in(self):
""" test list view """
# create url
destination = '/login/?next=' + urllib.parse.quote('/ip/', safe='')
# get response
response = self.client.get('/ip/', follow=True)
# compare
self.assertRedirects(response, destination, status_code=302, target_status_code=200)
def test_ip_list_logged_in(self):
""" test list view """
# login testuser
self.client.login(username='testuser_ip', password='pRs9Ap7oc9W0yjLfnP2Y')
# get response
response = self.client.get('/ip/')
# compare
self.assertEqual(response.status_code, 200)
def test_ip_list_template(self):
""" test list view """
# login testuser
self.client.login(username='testuser_ip', password='pRs9Ap7oc9W0yjLfnP2Y')
# get response
response = self.client.get('/ip/')
# compare
self.assertTemplateUsed(response, 'dfirtrack_main/ip/ip_list.html')
def test_ip_list_get_user_context(self):
""" test list view """
# login testuser
self.client.login(username='testuser_ip', password='pRs9Ap7oc9W0yjLfnP2Y')
# get response
response = self.client.get('/ip/')
# compare
self.assertEqual(str(response.context['user']), 'testuser_ip')
def test_ip_list_redirect(self):
""" test list view """
# login testuser
self.client.login(username='testuser_ip', password='pRs9Ap7oc9W0yjLfnP2Y')
# create url
destination = urllib.parse.quote('/ip/', safe='/')
# get response
response = self.client.get('/ip', follow=True)
# compare
self.assertRedirects(response, destination, status_code=301, target_status_code=200)
def test_ip_detail_not_logged_in(self):
""" test detail view """
# get object
ip_1 = Ip.objects.get(ip_ip='127.0.0.1')
# create url
destination = '/login/?next=' + urllib.parse.quote('/ip/' + str(ip_1.ip_id) + '/', safe='')
# get response
response = self.client.get('/ip/' + str(ip_1.ip_id) + '/', follow=True)
# compare
self.assertRedirects(response, destination, status_code=302, target_status_code=200)
def test_ip_detail_logged_in(self):
""" test detail view """
# get object
ip_1 = Ip.objects.get(ip_ip='127.0.0.1')
# login testuser
self.client.login(username='testuser_ip', password='pRs9Ap7oc9W0yjLfnP2Y')
# get response
response = self.client.get('/ip/' + str(ip_1.ip_id) + '/')
# compare
self.assertEqual(response.status_code, 200)
def test_ip_detail_template(self):
""" test detail view """
# get object
ip_1 = Ip.objects.get(ip_ip='127.0.0.1')
# login testuser
self.client.login(username='testuser_ip', password='pRs9Ap7oc9W0yjLfnP2Y')
# get response
response = self.client.get('/ip/' + str(ip_1.ip_id) + '/')
# compare
self.assertTemplateUsed(response, 'dfirtrack_main/ip/ip_detail.html')
def test_ip_detail_get_user_context(self):
""" test detail view """
# get object
ip_1 = Ip.objects.get(ip_ip='127.0.0.1')
# login testuser
self.client.login(username='testuser_ip', password='pRs9Ap7oc9W0yjLfnP2Y')
# get response
response = self.client.get('/ip/' + str(ip_1.ip_id) + '/')
# compare
self.assertEqual(str(response.context['user']), 'testuser_ip')
def test_ip_detail_redirect(self):
""" test detail view """
# get object
ip_1 = Ip.objects.get(ip_ip='127.0.0.1')
# login testuser
self.client.login(username='testuser_ip', password='pRs9Ap7oc9W0yjLfnP2Y')
# create url
destination = urllib.parse.quote('/ip/' + str(ip_1.ip_id) + '/', safe='/')
# get response
response = self.client.get('/ip/' + str(ip_1.ip_id), follow=True)
# compare
self.assertRedirects(response, destination, status_code=301, target_status_code=200)
| [
"[email protected]"
] | |
1bb2970dbed9a9c8f76d2ed9a6d205330e6218ef | b3858bf912bcdeb6fdf23646d94d2b9cd6e7900a | /Candy Race.py | 63c7d4b1575660a3662429db52355fb201d39a0d | [] | no_license | Programmer-Admin/binarysearch-editorials | eedf9e253e85324030260d44e798b0ca13645e63 | 12815fe3803cf5392ccfaadd38c7f50e882275c1 | refs/heads/main | 2023-02-06T04:59:25.279318 | 2020-12-26T20:45:34 | 2020-12-26T20:45:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 378 | py | """
Candy Race
Apparently you can solve this in 1ms, but here's a memoized recursive solution for your soul.
"""
from functools import lru_cache
class Solution:
def solve(self, candies):
@lru_cache(None)
def dfs(i,j):
if j<i: return 0
return max(candies[i]-dfs(i+1, j), candies[j]-dfs(i,j-1))
return dfs(0, len(candies)-1)>0
| [
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] | |
6eab525395ba4dccbc34669ccc2adea80f44a930 | 98e821fe72b711b5d05dbaa7018541a643950291 | /edrnsite/collaborations/browser/groupspace.py | ef62ca89f22e62fa55a445b1d324247c5276f794 | [
"Apache-2.0"
] | permissive | EDRN/edrnsite.collaborations | e6b0a71a13a6171b9e48de3b8b39979ebb969504 | 2344b3fe2f60e1079823c688968329010d3c67d5 | refs/heads/master | 2021-01-18T21:09:51.188644 | 2018-09-05T14:58:42 | 2018-09-05T14:58:42 | 20,818,565 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 444 | py | # encoding: utf-8
# Copyright 2012 California Institute of Technology. ALL RIGHTS
# RESERVED. U.S. Government Sponsorship acknowledged.
'''EDRN Site Collaborations: group space view
'''
from Products.Five.browser import BrowserView
from Products.Five.browser.pagetemplatefile import ViewPageTemplateFile
class GroupSpaceView(BrowserView):
'''Default view for a Group Space.'''
index = ViewPageTemplateFile('templates/groupspace.pt')
| [
"[email protected]"
] | |
dbd85ec4828daa9922789e8a37df4ddb2a4a6b71 | e0f4db1f56bce425a1fe53796461b4b77f9f8c72 | /src/profiles/migrations/0002_auto_20180820_1928.py | bba3be33fd8426a98ccba6dbec71351398b86fac | [] | no_license | AhmedBafadal/My-Picks | a105feacb8d649ce10cee71d6c4308360e221d94 | 21a9143119f9933dcebd53c2fd252a2160ab0e58 | refs/heads/master | 2020-03-26T09:50:05.059463 | 2018-08-29T18:54:17 | 2018-08-29T18:54:17 | 144,767,334 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 756 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.2 on 2018-08-20 18:28
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('profiles', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='profile',
name='followers',
field=models.ManyToManyField(blank=True, related_name='is_follower', to=settings.AUTH_USER_MODEL),
),
migrations.AlterField(
model_name='profile',
name='following',
field=models.ManyToManyField(blank=True, related_name='is_following', to=settings.AUTH_USER_MODEL),
),
]
| [
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] | |
554020e53113b860b8d0bbd5c0664b43fa6ae211 | 42a7b34bce1d2968079c6ea034d4e3f7bb5802ad | /ex51/gothonweb/bin/app.py | 4cbf3709fa4bf1a85bea1cd83292927e281bb187 | [] | no_license | linpan/LPTHW | 45c9f11265b5e1ffe0387a56cec192fa12c6c4d5 | 227bfee3098e8ecb5f07ffc3a0b8e64a853106ce | refs/heads/master | 2021-04-26T13:42:56.859644 | 2014-12-18T15:21:14 | 2014-12-18T15:21:14 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 527 | py | #! /usr/bin/env python
#coding:utf-8
import web
urls = (
'/hello','Index'
)
app = web.application(urls,globals())
render = web.template.render('/usr/local/LPTHW/ex51/gothonweb/templates/',base="layout")
class Index(object):
def GET(self):
return render.hello_form()
def POST(self):
form = web.input(name="Nobody",greet="Hello")
greeting = "%s,%s" % (form.greet,form.name)
return render.index(greeting = greeting)
if __name__ == '__main__':
app.run()
| [
"[email protected]"
] | |
a8ee7e66a35c09736719e1aa3b92405d4f0be455 | 3b11dc40c7d772fffeb4d8683e5c9791c41f6454 | /addons/product/wizard/product_price_list.py | ae6ce2aea495f5f257f7a39ca139f811bc6bf4f2 | [] | no_license | Jacky-odoo/Ecobank | b986352abac9416ab00008a4abaec2b1f1a1f262 | 5c501bd03a22421f47c76380004bf3d62292f79d | refs/heads/main | 2023-03-09T18:10:45.058530 | 2021-02-25T14:11:12 | 2021-02-25T14:11:12 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,082 | py | # -*- coding: utf-8 -*-
# Part of Byte. See LICENSE file for full copyright and licensing details.
from odoo import api, fields, models
class product_price_list(models.TransientModel):
_name = 'product.price_list'
_description = 'Price List'
price_list = fields.Many2one('product.pricelist', 'PriceList', required=True)
qty1 = fields.Integer('Quantity-1', default=1)
qty2 = fields.Integer('Quantity-2', default=5)
qty3 = fields.Integer('Quantity-3', default=10)
qty4 = fields.Integer('Quantity-4', default=0)
qty5 = fields.Integer('Quantity-5', default=0)
@api.multi
def print_report(self):
"""
To get the date and print the report
@return : return report
"""
datas = {'ids': self.env.context.get('active_ids', [])}
res = self.read(['price_list', 'qty1', 'qty2', 'qty3', 'qty4', 'qty5'])
res = res and res[0] or {}
res['price_list'] = res['price_list'][0]
datas['form'] = res
return self.env['report'].get_action([], 'product.report_pricelist', data=datas)
| [
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] | |
3c459ecdeef9297b64145fdb0850bea22bf0034c | ac1bbabc7c1b3149711c416dd8b5f5969a0dbd04 | /Programming Basics/conditional_statements/even_odd.py | a2b6079b0ca9b1515287833ff45acf73255d5d9c | [] | no_license | AssiaHristova/SoftUni-Software-Engineering | 9e904221e50cad5b6c7953c81bc8b3b23c1e8d24 | d4910098ed5aa19770d30a7d9cdf49f9aeaea165 | refs/heads/main | 2023-07-04T04:47:00.524677 | 2021-08-08T23:31:51 | 2021-08-08T23:31:51 | 324,847,727 | 1 | 0 | null | 2021-08-08T23:31:52 | 2020-12-27T20:58:01 | Python | UTF-8 | Python | false | false | 75 | py | a = int(input())
if a % 2 == 0:
print('even')
else:
print('odd')
| [
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] | |
ca7b25409a21a310db6153351cc71d886ecd96ad | 66c7b0da6ee27ddce0943945503cdecf199f77a2 | /hucrl/agent/tests/test_mpc_agent.py | a0b361bc8c294920b8fd24c0a2626d18525bcba2 | [
"MIT"
] | permissive | tzahishimkin/extended-hucrl | 07609f9e9f9436121bcc64ff3190c966183a2cd9 | c144aeecba5f35ccfb4ec943d29d7092c0fa20e3 | refs/heads/master | 2023-07-09T22:57:28.682494 | 2021-08-24T08:50:16 | 2021-08-24T08:50:16 | 383,819,908 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,789 | py | import copy
import os
import pytest
from rllib.agent import MPCAgent
from rllib.algorithms.mpc import CEMShooting, MPPIShooting, RandomShooting
from rllib.dataset.experience_replay import ExperienceReplay
from rllib.environment import GymEnvironment
from rllib.model.environment_model import EnvironmentModel
from rllib.util.training.agent_training import evaluate_agent
SEED = 0
MAX_ITER = 5
ENVIRONMENT = "VContinuous-CartPole-v0"
env = GymEnvironment(ENVIRONMENT, SEED)
env_model = copy.deepcopy(env)
env_model.reset()
dynamical_model = EnvironmentModel(env_model)
reward_model = EnvironmentModel(env_model, model_kind="rewards")
termination = EnvironmentModel(env_model, model_kind="termination")
GAMMA = 0.99
HORIZON = 5
NUM_ITER = 5
NUM_SAMPLES = 50
NUM_ELITES = 5
KAPPA = 1.0
BETAS = [0.2, 0.8, 0]
memory = ExperienceReplay(max_len=2000, num_steps=1)
value_function = None
@pytest.fixture(params=["random_shooting", "cem_shooting", "mppi_shooting"])
def solver(request):
return request.param
@pytest.fixture(params=[True, False])
def warm_start(request):
return request.param
@pytest.fixture(params=["mean", "zero", "constant"])
def default_action(request):
return request.param
@pytest.fixture(params=[1])
def num_cpu(request):
return request.param
def get_solver(solver_, warm_start_, num_cpu_, default_action_):
if solver_ == "random_shooting":
mpc_solver = RandomShooting(
dynamical_model=dynamical_model,
reward_model=reward_model,
horizon=HORIZON,
gamma=1.0,
num_samples=NUM_SAMPLES,
num_elites=NUM_ELITES,
termination=termination,
terminal_reward=value_function,
warm_start=warm_start_,
default_action=default_action_,
num_cpu=num_cpu_,
)
elif solver_ == "cem_shooting":
mpc_solver = CEMShooting(
dynamical_model=dynamical_model,
reward_model=reward_model,
horizon=HORIZON,
gamma=1.0,
num_iter=NUM_ITER,
num_samples=NUM_SAMPLES,
num_elites=NUM_ELITES,
termination=termination,
terminal_reward=value_function,
warm_start=warm_start_,
default_action=default_action_,
num_cpu=num_cpu_,
)
elif solver_ == "mppi_shooting":
mpc_solver = MPPIShooting(
dynamical_model=dynamical_model,
reward_model=reward_model,
horizon=HORIZON,
gamma=1.0,
num_iter=NUM_ITER,
kappa=KAPPA,
filter_coefficients=BETAS,
num_samples=NUM_SAMPLES,
termination=termination,
terminal_reward=value_function,
warm_start=warm_start_,
default_action=default_action_,
num_cpu=num_cpu_,
)
else:
raise NotImplementedError
return mpc_solver
def test_mpc_solvers(solver, num_cpu):
if num_cpu > 1 and "CI" in os.environ:
return
mpc_solver = get_solver(solver, True, num_cpu, "mean")
agent = MPCAgent(mpc_solver=mpc_solver)
evaluate_agent(
agent, environment=env, num_episodes=1, max_steps=MAX_ITER, render=False
)
def test_mpc_warm_start(solver, warm_start):
mpc_solver = get_solver(solver, warm_start, 1, "mean")
agent = MPCAgent(mpc_solver=mpc_solver)
evaluate_agent(
agent, environment=env, num_episodes=1, max_steps=MAX_ITER, render=False
)
def test_mpc_default_action(solver, default_action):
mpc_solver = get_solver(solver, True, 1, default_action)
agent = MPCAgent(mpc_solver=mpc_solver)
evaluate_agent(
agent, environment=env, num_episodes=1, max_steps=MAX_ITER, render=False
)
| [
"[email protected]"
] | |
71c8a815e9abdcce979147da4c74c9bf207a05b2 | ef54d37f8a3303013ca7469871a320d303957ed7 | /robo4.2/4.2/lib/python2.7/site-packages/RoboGalaxyLibrary/netconf/ncclient/operations/subscribe.py | f5ed796c96dd96bd4dc9b0046bf76d272b5e79c7 | [] | no_license | richa92/Jenkin_Regression_Testing | d18badfcf16bda682dfe7bcbbd66f54a9a27a58d | 24a74926170cbdfafa47e972644e2fe5b627d8ff | refs/heads/master | 2020-07-12T10:01:59.099137 | 2019-08-27T12:14:53 | 2019-08-27T12:14:53 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 693 | py | # Copyright 2009 Shikhar Bhushan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO
class Notification:
pass
class CreateSubscription:
pass
class NotificationListener:
pass
| [
"[email protected]"
] | |
c079fd6ce0e69dc89478dbf7d7e8926315ba7e7d | 94838674ffd175df6194437c1ccc3f90ab409d6c | /pillowV3/log/2018-12-30 14:46:01.690040 | d11e65881713fa69fa27304303d67f21dd294fcc | [] | no_license | WojciechKoz/MyFirstNeuralNetwork | 4fdb3140d8f02257599d005638598f78055c1ac8 | 3cd032aba80ecd71edb0286724ae9ba565b75a81 | refs/heads/master | 2020-04-02T03:02:48.680433 | 2020-02-29T17:57:43 | 2020-02-29T17:57:43 | 153,943,121 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 307,917 | 690040 | #!/usr/bin/env python3
# -*- coding: utf8 -*-
from __future__ import print_function # new print() on python2
from datetime import datetime
import sys
import numpy as np
from mnist import MNIST
# Display full arrays
np.set_printoptions(threshold=np.inf)
mndata = MNIST('./data')
images_full, labels_full = mndata.load_training()
images = []
labels = []
# dynamic arguments
batch_size = int(sys.argv[1])
size_1 = int(sys.argv[2])
size_2 = int(sys.argv[3])
batch_training_size = int(sys.argv[4])
data_part = 5 # only one fifth of the whole dataset to speed up training
for i in range(len(labels_full) // batch_size // data_part):
images.append(images_full[i*batch_size : (i+1)*batch_size])
labels.append(labels_full[i*batch_size : (i+1)*batch_size])
def sigmoid_prime(x):
return np.exp(-x) / ((np.exp(-x) + 1) ** 2)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# nowe, przyda się?
def relu(x):
return np.maximum(x, x * 0.01)
def relu_prime(x):
if x >= 0:
return 1
# ej nie jest tak xd
# a jak xd?
type(x) == no.ndarray
# no x to macierz xd
# np.exp jest przeładowane ale jakakoleiwk funkcja to chyba nie
# to co foreach ? :(
# właśnie nie wiem, a co z gpu?
# to miało być szybsze a nie xd
# mamy duzo mozliwosci zmian ale nie na raz trzeba ustalic jakos
# hm TODO gpu TODO wincyj procent TODO gui gotowe
# xd
# tamto myliło hah
# to co najpierw? :p
# ssh daje wglad do basha tylko tak ?
# nie, to jest taki fajny programik, byobu
# i ten pasek na dole też jest z byobu
# on udostepnia tylko basha ?
# tak, ale basha multiplayer xd
# szkoda że 2 kursorow nie ma
# hm
return 0.01 # chyba tak xd nikt nie widzial xd
# ale x to macierz :p
# ale to jest przeciazone i jak jest funkcja od macierzy to bierze po kolei kazdy element
# w sumie
# zobacze na drugiej karcie xd
#X = np.array([[0, 0],
# [0, 1],
# [1, 0],
# [1, 1]])
#X = np.array(images)
y = []
for batch in labels:
y.append([])
for label in batch:
y[-1].append([1.0 if i == label else 0.0 for i in range(10)])
y = np.array(y)
#y = np.array([[0],
# [1],
# [1],
# [0]])
np.random.seed(1)
LEN = len(labels)
SIZES = [ 784, size_1, size_2, 10 ]
syn0 = 2 * np.random.random((SIZES[0], SIZES[1])) - 1
syn1 = 2 * np.random.random((SIZES[1], SIZES[2])) - 1
syn2 = 2 * np.random.random((SIZES[2], SIZES[3])) - 1
# biases for respective layers
b0 = 2 * np.random.random((1, SIZES[1])) - 1
b1 = 2 * np.random.random((1, SIZES[2])) - 1
b2 = 2 * np.random.random((1, SIZES[3])) - 1
for i, batch in list(enumerate(images)):
X = np.array(batch)
print("x:")
print(np.shape(X))
print("======================= BATCH {} =======================".format(i))
error = 1
j = 0
while j < batch_training_size:
l0 = X
l1 = sigmoid(np.dot(l0, syn0) + b0)
l2 = sigmoid(np.dot(l1, syn1) + b1)
l3 = sigmoid(np.dot(l2, syn2) + b2)
l3_error = (y[i] - l3)#** 2
error = np.mean(np.abs(l3_error))
j += 1
if j % 20 == 0:
print(("[%d] error: " % j) + str(error))
l3_delta = l3_error * sigmoid_prime(l3)
l2_error = l3_delta.dot(syn2.T)
l2_delta = l2_error * sigmoid_prime(l2)
l1_error = l2_delta.dot(syn1.T)
l1_delta = l1_error * sigmoid_prime(l1)
syn2 += l2.T.dot(l3_delta)
syn1 += l1.T.dot(l2_delta)
syn0 += l0.T.dot(l1_delta)
b0 += l1_delta.mean(axis=0)
b1 += l2_delta.mean(axis=0)
b2 += l3_delta.mean(axis=0)
def predict(data):
l0 = [data]
l1 = sigmoid(np.dot(l0, syn0) + b0)
l2 = sigmoid(np.dot(l1, syn1) + b1)
l3 = sigmoid(np.dot(l2, syn2) + b2)
return np.argmax(l3)
print("Output after training: ")
print(l3)
for i, el in enumerate(l3):
print(labels[0][i], "=", np.argmax(el), " predictions: ", el)
testing_images, testing_labels = mndata.load_testing()
correct = 0.0
for i, (image, label) in enumerate(zip(testing_images, testing_labels)):
prediction = predict(image)
if label == prediction:
correct += 1.0
correct_rate = correct / (i + 1.0)
print("{} = {} (correct {}%)".format(label, prediction, 100 * correct_rate))
with open('log/' + str(datetime.now()), 'a') as f:
with open(__file__, 'r') as myself:
print(myself.read(), file=f)
print("", file=f)
print("#### answers:", file=f)
print("argv =", sys.argv, file=f)
print("correct_rate =", correct_rate, file=f)
print("SIZES =", SIZES, file=f)
print("syn0 =", syn0, file=f)
print("syn1 =", syn1, file=f)
print("syn2 =", syn2, file=f)
print("b0 =", b0, file=f)
print("b1 =", b1, file=f)
print("b2 =", b2, file=f)
#### answers:
argv = ['./main.py', '62', '22', '30', '24']
correct_rate = 0.3595
SIZES = [784, 22, 30, 10]
syn0 = [[-1.65955991e-01 4.40648987e-01 -9.99771250e-01 -3.95334855e-01
-7.06488218e-01 -8.15322810e-01 -6.27479577e-01 -3.08878546e-01
-2.06465052e-01 7.76334680e-02 -1.61610971e-01 3.70439001e-01
-5.91095501e-01 7.56234873e-01 -9.45224814e-01 3.40935020e-01
-1.65390395e-01 1.17379657e-01 -7.19226123e-01 -6.03797022e-01
6.01489137e-01 9.36523151e-01]
[-3.73151644e-01 3.84645231e-01 7.52778305e-01 7.89213327e-01
-8.29911577e-01 -9.21890434e-01 -6.60339161e-01 7.56285007e-01
-8.03306332e-01 -1.57784750e-01 9.15779060e-01 6.63305699e-02
3.83754228e-01 -3.68968738e-01 3.73001855e-01 6.69251344e-01
-9.63423445e-01 5.00288630e-01 9.77722178e-01 4.96331309e-01
-4.39112016e-01 5.78558657e-01]
[-7.93547987e-01 -1.04212948e-01 8.17191006e-01 -4.12771703e-01
-4.24449323e-01 -7.39942856e-01 -9.61266084e-01 3.57671066e-01
-5.76743768e-01 -4.68906681e-01 -1.68536814e-02 -8.93274910e-01
1.48235211e-01 -7.06542850e-01 1.78611074e-01 3.99516720e-01
-7.95331142e-01 -1.71888024e-01 3.88800315e-01 -1.71641461e-01
-9.00093082e-01 7.17928118e-02]
[ 3.27589290e-01 2.97782241e-02 8.89189512e-01 1.73110081e-01
8.06803831e-01 -7.25050592e-01 -7.21447305e-01 6.14782577e-01
-2.04646326e-01 -6.69291606e-01 8.55017161e-01 -3.04468281e-01
5.01624206e-01 4.51995971e-01 7.66612182e-01 2.47344414e-01
5.01884868e-01 -3.02203316e-01 -4.60144216e-01 7.91772436e-01
-1.43817620e-01 9.29680094e-01]
[ 3.26882996e-01 2.43391440e-01 -7.70508054e-01 8.98978517e-01
-1.00175733e-01 1.56779229e-01 -1.83726394e-01 -5.25946040e-01
8.06759041e-01 1.47358973e-01 -9.94259346e-01 2.34289827e-01
-3.46710196e-01 5.41162045e-02 7.71884199e-01 -2.85460480e-01
8.17070302e-01 2.46720232e-01 -9.68357514e-01 8.58874467e-01
3.81793835e-01 9.94645701e-01]
[-6.55318983e-01 -7.25728501e-01 8.65190926e-01 3.93636323e-01
-8.67999655e-01 5.10926105e-01 5.07752377e-01 8.46049071e-01
4.23049517e-01 -7.51458076e-01 -9.60239732e-01 -9.47578026e-01
-9.43387024e-01 -5.07577865e-01 7.20055897e-01 7.76621287e-02
1.05643957e-01 6.84061785e-01 -7.51653370e-01 -4.41632642e-01
1.71518543e-01 9.39191497e-01]
[ 1.22060439e-01 -9.62705421e-01 6.01265345e-01 -5.34051452e-01
6.14210391e-01 -2.24278712e-01 7.27083709e-01 4.94243285e-01
1.12480468e-01 -7.27089549e-01 -8.80164621e-01 -7.57313089e-01
-9.10896243e-01 -7.85011742e-01 -5.48581323e-01 4.25977961e-01
1.19433964e-01 -9.74888040e-01 -8.56051441e-01 9.34552660e-01
1.36200924e-01 -5.93413531e-01]
[-4.95348511e-01 4.87651708e-01 -6.09141038e-01 1.62717855e-01
9.40039978e-01 6.93657603e-01 -5.20304482e-01 -1.24605715e-02
2.39911437e-01 6.57961799e-01 -6.86417211e-01 -9.62847596e-01
-8.59955713e-01 -2.73097781e-02 2.12658923e-01 1.37702874e-01
-3.65275181e-01 9.77232309e-01 1.59490438e-01 -2.39717655e-01
1.01896438e-01 4.90668862e-01]
[ 3.38465787e-01 -4.70160885e-01 -8.67330331e-01 -2.59831604e-01
2.59435014e-01 -5.79651980e-01 5.05511107e-01 -8.66927037e-01
-4.79369803e-01 6.09509127e-01 -6.13131435e-01 2.78921762e-01
4.93406182e-02 8.49615941e-01 -4.73406459e-01 -8.68077819e-01
4.70131927e-01 5.44356059e-01 8.15631705e-01 8.63944138e-01
-9.72096854e-01 -5.31275828e-01]
[ 2.33556714e-01 8.98032641e-01 9.00352238e-01 1.13306376e-01
8.31212700e-01 2.83132418e-01 -2.19984572e-01 -2.80186658e-02
2.08620966e-01 9.90958430e-02 8.52362853e-01 8.37466871e-01
-2.10248774e-01 9.26525057e-01 -6.52088667e-01 -7.47340961e-01
-7.29841684e-01 1.13243314e-02 -9.56950389e-01 8.95940422e-01
6.54230942e-01 -9.69962039e-01]
[-6.47607489e-01 -3.35872851e-01 -7.38006310e-01 6.18981384e-01
-3.10526695e-01 8.80214965e-01 1.64028360e-01 7.57663969e-01
6.89468891e-01 8.10784637e-01 -8.02394684e-02 9.26936320e-02
5.97207182e-01 -4.28562297e-01 -1.94929548e-02 1.98220615e-01
-9.68933449e-01 1.86962816e-01 -1.32647302e-01 6.14721058e-01
-3.69510394e-01 7.85777417e-01]
[ 1.55714431e-01 -6.31979597e-01 5.75858468e-01 2.24062354e-01
-8.92181456e-01 -1.59612640e-01 3.58137673e-01 8.37203556e-01
-9.99195950e-01 9.53518298e-01 -2.46839371e-01 9.47567077e-01
2.09432202e-01 6.57691616e-01 1.49423009e-01 2.56152397e-01
-4.28847437e-01 1.73666681e-01 5.00043527e-01 7.16627673e-01
5.10164377e-01 3.96114497e-01]
[ 7.28958860e-01 -3.54638006e-01 3.41577582e-01 -9.82521272e-02
-2.35794496e-01 -1.78377300e-01 -1.97040833e-01 -3.65232108e-01
2.43838736e-01 -1.39505458e-01 9.47604156e-01 3.55601783e-01
-6.02860223e-01 -1.46597981e-01 -3.13307520e-01 5.95277608e-01
7.59996577e-01 8.07683912e-01 3.25439625e-01 -4.59583476e-01
-4.95266597e-01 7.09795885e-01]
[ 5.54292926e-02 6.04322168e-01 1.44977034e-01 4.66285051e-01
3.80232549e-02 5.41767821e-01 1.37715981e-01 -6.85802428e-02
-3.14622184e-01 -8.63581303e-01 -2.44151641e-01 -8.40747845e-01
9.65634227e-01 -6.36774297e-01 6.23717395e-01 7.49923290e-01
3.76826505e-01 1.38988825e-01 -6.78057126e-01 -6.62399545e-02
-3.09655898e-01 -5.49920084e-01]
[ 1.85023738e-01 -3.75460325e-01 8.32611107e-01 8.19271050e-01
-4.85763412e-01 -7.78217399e-01 -6.14074536e-01 -8.31658642e-04
4.57171336e-01 -5.83611123e-01 -5.03932883e-01 7.03343750e-01
-1.68302563e-01 2.33370134e-01 -5.32667722e-01 -7.96065481e-01
3.17140339e-02 -4.57180259e-02 -6.94656712e-01 2.43612463e-01
8.80202376e-02 3.08274694e-01]
[-7.10908920e-01 5.03055634e-01 -5.55901720e-01 3.87036487e-02
5.70592056e-01 -9.55339144e-01 -3.51275081e-01 7.45844753e-01
6.89419215e-01 7.68811852e-02 7.33216548e-01 8.99611983e-01
6.52813995e-01 7.08230888e-01 -8.02513196e-01 3.02608665e-01
4.07033976e-01 2.20481625e-01 5.99230523e-01 -9.30857560e-01
5.40477469e-01 4.63457201e-01]
[-4.80603213e-01 -4.85861402e-01 2.64606635e-01 -3.09405077e-01
5.93177356e-01 -1.07707536e-01 5.65498830e-01 9.80943567e-01
-3.99503321e-01 -7.13988343e-01 8.02616873e-01 8.31187578e-02
9.49480742e-01 2.73208800e-01 9.87826049e-01 9.21416083e-02
5.28518678e-02 -7.29144194e-01 -2.88589658e-01 -9.47562865e-01
-6.79209641e-01 4.91274385e-01]
[-9.39200620e-01 -2.66913806e-01 7.24692506e-01 3.85355435e-01
3.81884284e-01 -6.22726398e-01 -1.16191439e-01 1.63154815e-01
9.79503415e-01 -5.92187550e-01 -5.04534196e-01 -4.75653832e-01
5.00344827e-01 -8.60493451e-02 -8.86141123e-01 1.70324812e-02
-5.76079671e-01 5.97208490e-01 -4.05337237e-01 -9.44787976e-01
1.86864899e-01 6.87680858e-01]
[-2.37967752e-01 4.99716621e-01 2.22829566e-02 8.19036099e-02
9.18868642e-01 6.07921783e-01 -9.35353867e-01 4.18774502e-01
-6.99970369e-02 8.95097883e-01 -5.57134531e-01 -4.65855961e-01
-8.37052070e-01 -1.42762343e-01 -7.81962472e-01 2.67573521e-01
6.05926475e-01 3.93600992e-01 5.32422762e-01 -3.15091760e-01
6.91702966e-01 -1.42462450e-01]
[ 6.48019741e-01 2.52992317e-01 -7.13153903e-01 -8.43226200e-01
-9.63334714e-01 -8.66550005e-01 -8.28323726e-02 -7.73316154e-01
-9.44433302e-01 5.09722963e-01 -2.10299039e-01 4.93876991e-01
-9.51903465e-02 -9.98265060e-02 -4.38549866e-02 -5.19921469e-02
6.06326684e-01 -1.95214960e-01 8.09372321e-01 -9.25877904e-01
5.47748685e-01 -7.48717238e-01]
[ 2.37027134e-01 -9.79271477e-01 7.72545652e-02 -9.93964087e-01
9.02387571e-01 8.10804067e-01 5.91933884e-01 8.30548640e-01
-7.08883538e-01 -6.84539860e-01 -6.24736654e-01 2.44991805e-01
8.11618992e-01 9.79910357e-01 4.22244918e-01 4.63600818e-01
8.18586409e-01 -1.98252535e-01 -5.00298640e-01 -6.53139658e-01
-7.61085899e-01 6.25221176e-01]
[-7.06415253e-01 -4.71405035e-01 6.38178357e-01 -3.78825496e-01
9.64834899e-01 -4.66722596e-01 6.73066899e-02 -3.71065978e-01
8.21545662e-01 -2.66886712e-01 -1.32815345e-01 2.45853846e-02
8.77772955e-01 -9.38101987e-01 4.33757327e-01 7.82037909e-01
-9.45425553e-01 4.41024945e-02 -3.48020376e-01 7.18978642e-01
1.17033102e-01 3.80455736e-01]
[-9.42930001e-02 2.56618075e-01 -4.19806297e-01 -9.81302844e-01
1.53511870e-01 -3.77111572e-01 3.45351970e-02 8.32811706e-01
-1.47050423e-01 -5.05207927e-01 -2.57412477e-01 8.63722233e-01
8.73736763e-01 6.88659897e-01 8.40413029e-01 -5.44199420e-01
-8.25035581e-01 -5.45380527e-01 -3.71246768e-01 -6.50468247e-01
2.14188324e-01 -1.72827170e-01]
[ 6.32703024e-01 -6.29739203e-01 4.03753060e-01 -5.19288750e-01
1.48438178e-01 -3.02024806e-01 -8.86071201e-01 -5.42372658e-01
3.28205111e-01 -5.49981328e-03 3.80319681e-02 -6.50559700e-01
1.41431703e-01 9.93506850e-01 6.33670218e-01 1.88745248e-01
9.51978137e-01 8.03125169e-01 1.91215867e-01 -9.35147349e-01
-8.12845808e-01 -8.69256570e-01]
[-9.65337026e-02 -2.49130334e-01 9.50700069e-01 -6.64033414e-01
9.45575184e-01 5.34949738e-01 6.48475679e-01 2.65231634e-01
3.37465540e-01 -4.62353330e-02 -9.73727286e-01 -2.93987829e-01
-1.58563970e-02 4.60182422e-01 -6.27433145e-02 -8.51901678e-02
-7.24674518e-01 -9.78222532e-01 5.16556521e-01 -3.60094324e-01
9.68766900e-01 -5.59531548e-01]
[-3.22583949e-01 4.77922713e-02 5.09782914e-01 -7.22844322e-02
-7.50354914e-01 -3.74997243e-01 9.03833940e-03 3.47698016e-01
5.40299913e-01 -7.39328438e-01 -9.54169737e-01 3.81646444e-02
6.19977421e-01 -9.74792466e-01 3.44939689e-01 3.73616453e-01
-1.01506493e-01 8.29577373e-01 2.88722170e-01 -9.89520325e-01
-3.11431090e-02 7.18635612e-01]
[ 6.60799140e-01 2.98308394e-01 3.47396848e-01 1.56999160e-01
-4.51760450e-01 1.21059981e-01 3.43459570e-01 -2.95140740e-01
7.11656735e-01 -6.09925028e-01 4.94641621e-01 -4.20794508e-01
5.47598574e-01 -1.44525341e-01 6.15396818e-01 -2.92930275e-01
-5.72613525e-01 5.34569017e-01 -3.82716105e-01 4.66490135e-01
4.88946306e-01 -5.57206598e-01]
[-5.71775726e-01 -6.02104153e-01 -7.14963324e-01 -2.45834802e-01
-9.46744231e-01 -7.78159262e-01 3.49128048e-01 5.99553074e-01
-8.38940946e-01 -5.36595379e-01 -5.84748676e-01 8.34667126e-01
4.22629036e-01 1.07769222e-01 -3.90964024e-01 6.69708095e-01
-1.29388085e-01 8.46912430e-01 4.12103609e-01 -4.39373841e-02
-7.47579793e-01 9.52087101e-01]
[-6.80332699e-01 -5.94795750e-01 -1.37636490e-01 -1.91596188e-01
-7.06497038e-01 4.58637839e-01 -6.22509866e-01 2.87791289e-01
5.08611901e-01 -5.78535216e-01 2.01908496e-01 4.97856750e-01
2.76437421e-01 1.94254606e-01 -4.09035429e-01 4.63212942e-01
8.90616880e-01 -1.48877219e-01 5.64363634e-01 -8.87717921e-01
6.70543205e-01 -6.15499966e-01]
[-2.09806262e-01 -3.99837908e-01 -8.39792712e-01 8.09262006e-01
-2.59691645e-01 6.13948770e-02 -1.17674682e-02 -7.35677716e-01
-5.87091882e-01 -8.47622382e-01 1.58433999e-02 -4.76900896e-01
-2.85876782e-01 -7.83869343e-01 5.75103679e-01 -7.86832246e-01
9.71417647e-01 -6.45677671e-01 1.44810225e-01 -9.10309331e-01
5.74232579e-01 -6.20788104e-01]
[ 5.58079568e-02 4.80155086e-01 -7.00137030e-01 1.02174348e-01
-5.66765583e-01 5.18392099e-01 4.45830387e-01 -6.46901931e-01
7.23933115e-01 -9.60449801e-01 7.20473995e-01 1.17807622e-01
-1.93559056e-01 5.17493862e-01 4.33858003e-01 9.74652350e-01
-4.43829903e-01 -9.92412655e-01 8.67805217e-01 7.15794209e-01
4.57701755e-01 3.33775658e-02]
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-1.45223994e+00 -2.38520500e+01 4.32364185e+00 -7.14749609e+00
4.81080196e+00 -1.18556656e+01]
[ 1.21584744e+01 -3.97774961e+01 -1.06080628e+01 1.05429841e+01
3.50404208e+01 -1.78026757e+01 -1.29540611e+00 2.42382814e+00
1.47300431e+01 -7.88531950e+00 -8.10407167e+00 -9.60131415e-02
1.13777954e+01 -3.73413221e+01 4.62263688e+01 5.22184704e+00
4.65040806e+00 1.92609120e+01 2.77031786e+01 -1.81976736e+00
5.44747835e+00 2.80648818e+01 2.60830845e+01 -4.45654318e-01
-1.92346644e+01 2.26695515e+01 1.69945606e+01 -3.58678496e+00
4.64697223e+00 -3.97459909e+01]
[ 2.46290180e+01 -4.87665973e+01 -8.92899977e+00 6.67504548e+00
-6.25423354e+01 -5.40955157e+00 -1.72903209e+00 -6.36279653e+00
6.68598726e+00 -2.63240218e-01 -7.65560962e+00 8.25895334e+00
1.20714543e+01 1.07619343e+01 2.92297986e+01 9.56418333e+00
-2.77370635e+00 -3.44407667e+01 5.64303938e+00 1.85944948e+01
1.88695373e+01 2.52317121e+01 2.58232156e+01 1.04901917e+01
7.75689624e+00 -4.25292534e+01 2.22199095e+01 -2.84605585e+00
-5.85829350e+00 -1.68673574e+01]
[ 1.07741380e+01 -3.14744552e+01 2.17003475e+00 -1.38141778e+01
-2.65672061e+01 -1.48034709e+01 6.03560773e+00 8.35135018e-01
1.03913061e+01 -1.45979528e+01 8.84147687e-01 1.22666742e+01
3.67990814e+00 -9.54475318e+00 3.07020837e+01 8.37914549e+00
-1.89043586e+01 -1.64069662e+01 6.86849181e+00 8.97141270e+00
2.78642858e+00 2.73728918e+01 1.85234363e+01 1.14783922e+01
-4.81527239e+00 -1.41957744e+01 1.50361998e+01 -3.09482592e+00
-1.10190748e+01 -1.88323648e+01]
[-6.81039124e+01 -2.77240106e+00 8.55220358e+00 1.38570322e+00
1.72957668e+01 6.02974732e+00 -2.77395586e+00 1.31466346e+01
2.41321484e+01 -1.36280866e+01 -1.03093004e+01 1.75671622e+01
1.63528693e+01 -3.31364416e+01 3.74020555e+01 -6.16689635e+00
-2.44203563e+01 2.11409660e+00 -4.75141495e+00 -1.08613221e+01
5.21848330e+00 3.48642840e+01 1.97886929e+01 8.62517439e+00
-1.90195836e+01 1.52279799e+01 1.72004444e+01 1.33344336e+01
-3.23105006e+01 -2.14517795e+01]
[-5.89784528e+01 1.72894151e+00 -2.49453530e+01 -3.34549060e+01
-1.25455707e+02 1.95494682e+00 -2.60282717e+01 -4.27596791e+01
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9.19537372e-01 -8.24651783e+01 -6.11394605e+01 -4.79431722e+01
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7.83133889e-01 -2.90731222e+01 -3.31021495e+01 -2.85066117e+01
-1.43154145e+02 2.31456037e-01]
[ 7.32557215e+00 -3.26594654e+01 -1.27369626e+01 -3.62600831e+00
4.29069580e+01 -3.09872264e+01 1.15167967e+00 6.34193280e+00
1.19785359e+01 -2.18981246e+01 3.14470735e+00 1.33653885e+00
5.54505084e+00 -4.76667387e+01 4.26869846e+01 7.49969096e+00
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-5.22365390e+00 2.54096154e+01 1.69888408e+01 -5.57163085e+00
-3.09388815e+01 4.54488149e+01 5.80609738e+00 -4.00149168e+00
-2.06498214e+00 -4.55446774e+01]
[ 1.66758245e+01 -3.28241458e+01 -6.54794612e+00 -2.62783136e+00
-1.66715306e+01 -2.29334540e+01 5.37482575e+00 8.71256564e+00
1.07575090e+01 -1.60293882e+01 1.41641709e+00 1.58478169e+01
7.41034604e+00 -1.04183556e+01 3.78115081e+01 7.60852795e+00
-1.38626466e+01 2.02882434e+00 6.64452880e+00 8.96930224e+00
6.51984892e+00 3.73739514e+01 2.16027776e+01 1.30817492e+01
-8.16128070e+00 9.72921839e+00 1.46223737e+01 -6.51226129e-01
-1.52347079e+01 -2.73054701e+01]
[-3.40481383e+01 9.46129608e-01 9.38372712e+00 -2.60582086e+01
6.43600027e+01 -1.39165948e+01 -4.40389091e-02 1.22301561e+01
2.31332163e+01 -4.34878768e+01 1.40324719e+00 1.59166287e+01
9.18610195e+00 -5.73942641e+01 4.30368603e+01 6.65195420e+00
-4.16585020e+01 1.63651703e+01 1.95148458e+01 -2.84906470e+01
-8.05680523e+00 3.32335723e+01 6.66817316e+00 -4.49299491e-01
-4.59894357e+01 4.88076981e+01 5.48259841e+00 9.92694532e+00
-2.40624614e+01 -3.41344336e+01]
[ 3.12173193e+00 -5.02346363e+01 -1.08044742e+01 1.70384644e+01
-5.29252549e+01 -5.79382176e+00 -1.87939745e+00 -3.53817806e-04
1.83614757e+01 3.86879808e+00 -7.68945422e+00 1.24060717e+01
1.81701066e+01 -7.31135337e-01 3.56212416e+01 1.15746081e+01
-2.73283247e+00 -1.35463306e+01 -7.80076074e-01 9.61497792e+00
1.77248152e+01 3.03478412e+01 2.79939397e+01 9.91363802e+00
4.76594462e+00 -1.82831085e+01 2.34428370e+01 2.70392649e+00
-2.72086543e+01 -3.18861304e+01]
[ 4.04614308e+01 -4.30036048e+01 -5.32326841e+00 -1.49905234e+01
-4.50232684e+01 -2.69553256e+01 -2.03729088e+00 -1.07384542e+01
4.27085231e-01 -1.18605226e+01 2.99133129e+00 2.34965185e+00
-1.14484560e+00 -8.93089627e+00 2.39354217e+01 2.56718637e+01
-9.23592509e+00 -2.64240991e+01 2.02314144e+01 2.08000942e+01
7.24071924e+00 1.24593704e+01 1.27175674e+01 8.55085603e+00
-6.07038427e+00 -3.04567121e+01 1.00072206e+01 2.95102963e-01
-1.41056655e+01 -2.30226225e+01]
[ 3.53461066e+01 -3.70831250e+01 2.84876459e+00 -2.40197050e+01
-1.70205274e+01 -2.93205108e+01 -2.60536389e+00 -1.16790743e+00
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-1.92681933e+01 -1.34461948e+01 1.79252714e+01 1.10306980e+01
1.96647210e+00 1.75900223e+01 8.90012839e+00 1.04667539e+01
-1.03716903e+01 -1.95223430e+01 6.04425521e+00 1.53236405e+00
-8.24000111e+00 -2.84337290e+01]]
syn2 = [[-2.72279529e-01 -5.45771250e+00 6.20414356e+00 -1.54861296e+00
-3.66024323e+00 -1.83198005e+00 5.77240990e+00 -3.87931334e+00
-2.53062342e-01 -1.91925804e+00]
[-5.01620037e-01 -7.95210873e+00 -4.01504608e-01 -3.67329102e+00
-5.18632616e+00 1.80806131e-01 -5.45352415e+00 -5.11564428e-01
-1.17684882e+00 1.69263451e+00]
[-1.78499143e+00 -1.72945889e+00 3.19643963e+00 -1.70158534e-01
-1.54920512e+00 -7.65496926e-01 -3.77807277e+00 3.10283677e+00
-4.74816336e-01 4.64960384e-01]
[-5.72629551e-01 4.77511207e+00 -5.34236237e+00 4.74110974e-02
-1.10134203e+00 -1.66152654e-01 1.97285143e+00 1.78345766e+00
7.81632914e-01 -6.07989315e-01]
[-1.68844493e+00 2.46386344e+00 7.40047097e+00 3.47128308e+00
-7.94006328e+00 4.70762666e-01 -9.64506924e+00 -5.42619940e+00
1.50997031e+00 -8.88545082e+00]
[-8.98514813e-02 3.31672748e+00 -7.28500873e+00 -3.16892630e+00
-1.15391570e+00 1.55647141e+00 -2.65971841e+00 1.12000603e-01
-2.39641436e-01 9.27288837e-01]
[-1.36215646e+00 2.06983436e+00 -1.43829838e+00 1.58370565e+00
-1.52834603e+00 -2.95032551e+00 -1.43469777e+00 -1.27615564e+00
-5.92343397e-01 1.47802817e+00]
[-7.63437224e-01 -4.81284093e-01 -1.05669301e+00 1.56269522e+00
1.52665461e+00 -2.55616619e-01 -1.85795248e+00 8.18200221e-01
-1.37332212e+00 4.73557917e-01]
[-2.71161887e-01 -3.79405909e-01 1.22221271e+00 -1.98726128e+00
2.97945938e+00 -7.78808805e-01 -3.14608555e+00 3.44502285e-01
-1.34123013e+00 5.15974690e-01]
[-9.08727357e-01 7.81570938e-01 -3.62303571e+00 4.05410724e-01
-2.40251173e+00 -1.33348922e+00 1.11595562e+00 -6.80948458e-01
8.31716579e-01 -2.10711355e+00]
[-2.72999374e-01 -5.26026514e+00 6.24020854e+00 4.84739408e-02
-2.36552919e+00 5.11658090e-01 -1.76834380e+00 -2.30193989e+00
1.58134708e-01 -1.59410011e+00]
[-1.63373137e+00 -2.28725064e+00 -2.27994347e+00 -2.68552668e+00
1.75024637e+00 -3.88194341e+00 -2.20335031e+00 -2.09043532e+00
-2.71460369e+00 -2.85241555e-01]
[-6.46877418e-01 -2.92324964e-01 -1.42531150e+00 -8.86929102e-01
7.61641994e-01 1.00040925e+00 -6.53265604e-01 -1.44291393e+00
-1.05347676e+00 -7.97037714e-01]
[-1.27555413e-01 -3.24239433e+00 -7.02199552e+00 -3.03258403e+00
1.13377195e+00 -2.91466791e+00 6.45432394e-01 7.45276919e-01
-4.16668249e+00 1.16492412e+00]
[-2.51642977e+00 -1.45590795e+00 -2.34606115e+00 -2.34254953e-01
-6.80468940e-01 -2.58960875e+00 -8.57454788e-04 -2.96927777e-01
-2.12994846e+00 9.05935507e-01]
[-5.27093247e-01 -2.45902011e+00 1.98645007e+00 -2.24731624e+00
-1.53766296e+00 1.38326687e+00 4.88242923e+00 -1.53447778e+00
-7.35189970e-01 2.38665697e-01]
[-1.42788874e+00 -2.31139856e+00 -3.71598876e+00 -1.26206768e+00
-2.68197238e+00 1.55356702e+00 1.53535983e+00 -1.44337872e+00
-3.76810375e-01 -1.73372641e+00]
[ 2.36833595e-01 1.51712343e+00 5.26513650e+00 1.93413398e-01
-2.89575251e+00 7.62261127e-01 -4.90632264e+00 4.07981801e-01
9.80784875e-01 -4.52115930e+00]
[-2.21487178e+00 -3.59789073e-01 -2.03619148e-01 -9.14517198e-01
-1.74572643e+00 1.76061577e+00 7.13640711e-01 -3.54256978e+00
6.45166356e-01 -1.81897282e+00]
[-9.45245346e-01 -1.72758737e+00 -3.73075585e+00 2.48708124e+00
-4.91222517e+00 -3.72702157e-01 3.40334346e+00 -3.88175211e+00
-2.15442095e-01 -6.69935185e-01]
[-8.63614321e-01 1.08501235e-01 -2.86335871e-01 -3.28276276e-01
3.15911868e+00 -1.12828392e+00 -3.03207205e-01 1.33578895e+00
-2.42510267e+00 -3.31372121e+00]
[-2.87343629e+00 -1.32377055e+00 -4.68264384e+00 -3.28694913e+00
2.01902843e+00 -1.80427375e+00 -3.20611222e+00 -1.64090596e+00
-1.36377966e+00 -4.45386544e-01]
[-1.93923009e+00 -8.53839343e-01 -3.41387331e+00 6.44788085e-01
-9.17931470e-01 -1.13106290e+00 1.47557125e+00 -5.21648650e-01
-1.06534184e+00 -3.98878258e+00]
[-1.95789564e+00 6.30901986e-01 -1.31295813e+00 -3.55829606e+00
1.85614969e+00 -1.19654385e+00 7.59257977e-01 1.22543245e+00
-1.23044795e+00 -3.63695270e+00]
[ 4.99347270e-01 5.46145698e-01 -3.50162319e+00 1.67382081e+00
-1.16380260e+00 -1.75545456e+00 -7.05349451e-01 -6.98112671e-01
-1.39505064e+00 -1.59443063e-01]
[-1.36350724e+00 1.19071217e+00 9.95921600e-01 3.97475263e-01
-3.35191491e+00 -1.77110450e+00 -1.53211783e+00 2.89157095e+00
-2.33366377e+00 2.36332035e+00]
[-7.04506940e-01 -6.62947405e-01 -1.36475980e+00 -1.60826386e+00
-2.63858735e+00 -2.85723588e+00 5.77043433e-01 -5.00098378e-01
-2.20039099e-01 -7.35449628e-01]
[-1.23964142e-01 1.30927711e+00 6.22345230e-01 -3.81073785e+00
9.30934010e-01 -1.95005777e+00 -2.54299643e+00 8.67157630e-01
-7.95745744e-01 -2.77779306e+00]
[-1.04532676e+00 2.67060817e+00 9.61776287e-02 3.45126974e+00
-5.62582928e+00 2.31338007e+00 -3.66207608e+00 -6.04651022e+00
4.49624906e+00 -5.98259239e+00]
[ 8.78966861e-01 -7.62775019e+00 -1.72840924e+00 -4.63256955e-01
-4.37986491e-03 4.23034385e-01 -3.39369578e+00 -1.08915779e+00
-1.30760594e+00 -6.00498856e-01]]
b0 = [[-795.05370699 -888.65637856 -843.38493583 -742.70469309 -832.26406088
-688.61027301 -864.20138884 -674.06130063 -836.94722637 -741.18228952
-924.47019823 -738.71980524 -890.46653089 -881.01297127 -659.7569183
111.75327485 -713.97499983 -880.06198367 -682.24998232 -758.14040568
-897.35314216 -885.57872814]]
b1 = [[-3.22092845 -1.82447463 -3.16436819 -3.93626503 -1.93805169 -2.74406915
-3.21638455 -2.63614184 -3.83964719 -2.7859646 -2.67585194 -3.60934965
-3.77999705 -2.80698683 -2.97213013 -3.63970304 -1.23358956 -2.95541476
-3.4341114 -2.52303848 -3.75816382 -3.2074504 -2.92940225 -3.98437189
-2.98563262 -3.76575999 -3.39159414 -3.68362168 -2.10585768 -1.89702698]]
b2 = [[-0.55270531 -1.17351202 -1.43338435 -0.12336077 -1.31350089 -1.26177085
-1.59943152 -1.22955594 -0.30305603 -0.77184599]]
| [
"[email protected]"
] | |
fbe0ba3fe7398923a4ecff8dc91faf96af99e846 | 28b5eedc39b697186ba9afc42ec544cd0b13c70d | /spark/regression/linear_regression.py | 1619640ed02616968947da8db3b6c2ddc873eeac | [] | no_license | arunpa0206/mltrainingtechcovery | 7915ccac779a186d3f1bfa1f6cebbe5ac2455422 | ce284c31eefa0468c88c790913532b87a0f77e3a | refs/heads/master | 2022-12-08T23:50:04.415494 | 2021-03-13T08:53:22 | 2021-03-13T08:53:22 | 224,205,026 | 2 | 10 | null | 2022-12-08T02:36:22 | 2019-11-26T13:58:42 | Jupyter Notebook | UTF-8 | Python | false | false | 1,109 | py |
# make pyspark importable as a regular library.
import findspark
# create a SparkSession
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
# load data
data = spark.read.csv('./boston_housing.csv', header=True, inferSchema=True)
# create features vector
print(data.head(5))
feature_columns = data.columns[:-1]
# here we omit the final column
from pyspark.ml.feature import VectorAssembler
assembler = VectorAssembler(inputCols=feature_columns,outputCol="features")
data_2 = assembler.transform(data)
# train/test split
train, test = data_2.randomSplit([0.7, 0.3])
# define the model
from pyspark.ml.regression import LinearRegression
algo = LinearRegression(featuresCol="features", labelCol="medv")
# train the model
model = algo.fit(train)
# evaluation
evaluation_summary = model.evaluate(test)
evaluation_summary.meanAbsoluteError
evaluation_summary.rootMeanSquaredError
evaluation_summary.r2
# predicting values
predictions = model.transform(test)
predictions.select(predictions.columns[13:]).show() # here I am filtering out some columns just for the figure to fit
| [
"[email protected]"
] | |
29f3206bbd185cbf2b04e3e4ceb2d42791d0a6bb | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03814/s672199772.py | 200b2992354025d407dc07f4f9b7d06b602a55c4 | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 77 | py | s = input()
left = s.find("A")
right = s.rfind("Z")
print(right - left + 1) | [
"[email protected]"
] | |
581e7f1ac73ddc919efb69e776016f838a8ce99e | b44b690c96cfbaba35fa3cc32e8da4442adb9fad | /Python/1101. The Earliest Moment When Everyone Become Friends.py | 151cb5ceccfe90d52025d61102662b977a2e5ccc | [] | no_license | faisalraza33/leetcode | 24d610c6884e218719d82a5c79f1695cb6463d68 | d7cf4ffba14c6f1ff4551634f4002b53dfeae9b7 | refs/heads/master | 2022-08-10T02:05:21.932664 | 2022-07-05T09:59:47 | 2022-07-05T09:59:47 | 238,060,131 | 0 | 0 | null | 2020-02-03T20:54:51 | 2020-02-03T20:54:50 | null | UTF-8 | Python | false | false | 3,082 | py | # There are n people in a social group labeled from 0 to n - 1. You are given an array logs where logs[i] = [timestampi, x_i, y_i] indicates that x_i and y_i will be friends at the time timestampi.
# Friendship is symmetric. That means if a is friends with b, then b is friends with a. Also, person a is acquainted with a person b if a is friends with b, or a is a friend of someone acquainted with b.
# Return the earliest time for which every person became acquainted with every other person. If there is no such earliest time, return -1.
#
# Example 1:
#
# Input: logs = [[20190101,0,1],[20190104,3,4],[20190107,2,3],[20190211,1,5],[20190224,2,4],[20190301,0,3],[20190312,1,2],[20190322,4,5]], n = 6
# Output: 20190301
# Explanation:
# The first event occurs at timestamp = 20190101 and after 0 and 1 become friends we have the following friendship groups [0,1], [2], [3], [4], [5].
# The second event occurs at timestamp = 20190104 and after 3 and 4 become friends we have the following friendship groups [0,1], [2], [3,4], [5].
# The third event occurs at timestamp = 20190107 and after 2 and 3 become friends we have the following friendship groups [0,1], [2,3,4], [5].
# The fourth event occurs at timestamp = 20190211 and after 1 and 5 become friends we have the following friendship groups [0,1,5], [2,3,4].
# The fifth event occurs at timestamp = 20190224 and as 2 and 4 are already friends anything happens.
# The sixth event occurs at timestamp = 20190301 and after 0 and 3 become friends we have that all become friends.
#
# Example 2:
#
# Input: logs = [[0,2,0],[1,0,1],[3,0,3],[4,1,2],[7,3,1]], n = 4
# Output: 3
#
# Constraints:
#
# 2 <= n <= 100
# 1 <= logs.length <= 10^4
# logs[i].length == 3
# 0 <= timestampi <= 10^9
# 0 <= x_i, y_i <= n - 1
# x_i != y_i
# All the values timestampi are unique.
# All the pairs (x_i, y_i) occur at most one time in the input.
class Solution:
def earliestAcq(self, logs: List[List[int]], n: int) -> int:
# First, we need to sort the events in chronological order.
logs.sort(key=lambda i: i[0])
uf = UnionFind(n)
for ts, x, y in logs:
uf.union(x, y)
if uf.get_count() == 1:
return ts
# More than one groups left, i.e. not everyone is connected.
return -1
class UnionFind:
def __init__(self, size):
self.root = [i for i in range(size)]
self.rank = [1] * size
self.count = size
def find(self, x):
if x == self.root[x]:
return x
self.root[x] = self.find(self.root[x])
return self.root[x]
def union(self, x, y):
rootX = self.find(x)
rootY = self.find(y)
if rootX != rootY:
if self.rank[rootX] > self.rank[rootY]:
self.root[rootY] = rootX
elif self.rank[rootX] < self.rank[rootY]:
self.root[rootX] = rootY
else:
self.root[rootY] = rootX
self.rank[rootX] += 1
self.count -= 1
def get_count(self):
return self.count
| [
"[email protected]"
] | |
e3c31baedb01d6c813d3b7b845d8f3bcb35ed6e2 | c1c8b0363bb6dd52115c0aad9298b6573a6ba062 | /sparse_binary_number.py | c051e68a78ba80ce4b6058d4bf0ce5a1b3a8b3d7 | [
"MIT"
] | permissive | beepscore/sparse_binary_number | 3f5bd50e772c6e3345e8f57d4317c4a7d8e572d7 | a89c5b04189c5f7015855075222a0ced4a650db7 | refs/heads/master | 2021-01-10T20:28:56.885423 | 2015-05-14T18:38:41 | 2015-05-14T18:38:41 | 33,623,410 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,631 | py | #!/usr/bin/env python3
def next_sparse(sparse_number):
"""return next larger sparse number
Keyword arguments:
sparse_number -- a sparse number, as defined by is_sparse
This algorithm uses powers of two. Estimated time complexity >= O(log(n)).
"""
# print("sparse_number 0b{0:b}".format(sparse_number))
# Edge case. Handle explicitly for clarity
if sparse_number == 0:
return 1
power_max = twos_power_max(sparse_number)
for power in range(0, power_max):
# print("power", power)
if is_zero_bit_and_no_neighbor_ones(sparse_number, power):
# print("at middle of 000 change to 010")
return sparse_number + (2 ** power)
if is_right_end_of_001(sparse_number, power):
# print("at right of 001 change to 01 followed by all zeros")
sparse_zeroed_low_bits = (sparse_number >> (power + 1)) * (2 ** (power + 1))
# print("sparse_zeroed_low_bits {0:b}".format(sparse_zeroed_low_bits))
return sparse_zeroed_low_bits + (2 ** (power + 1))
return (2 ** (power_max + 1))
def next_sparse_incremental(sparse_number):
"""return next larger sparse number
Keyword arguments:
sparse_number -- a sparse number, as defined by is_sparse
return None if reached internal limit without finding a next sparse.
This algorithm uses "brute force". Estimated time complexity >= O(n).
Increments until possible_sparse is_sparse or reaches limit.
"""
# limit is arbitrary in Python
# http://stackoverflow.com/questions/5470693/python-number-limit
limit = 2 ** 32
for possible_sparse in range(sparse_number + 1, limit):
if is_sparse(possible_sparse):
return possible_sparse
return None
def is_sparse(number):
"""return True if number binary digit 1s have no adjacent 1s.
Keyword arguments:
number -- an integer >= 0
return True if number is 0b1
"""
if number == 0:
return True
if number == 1:
# edge case. List explicitly for clarity. Define to be True
return True
else:
bits = bits_list(number)
# start power_of_2 at 1 so previous_bit index won't be out of list range
for power_of_2 in range(1, len(bits)):
current_bit = bits[power_of_2]
previous_bit = bits[power_of_2 - 1]
if ((current_bit == 1) and (previous_bit == 1)):
# number has two consecutive 1s
return False
return True
def bits_list(number):
"""return list of bits in number
Keyword arguments:
number -- an integer >= 0
"""
# https://wiki.python.org/moin/BitManipulation
if number == 0:
return [0]
else:
# binary_literal string e.g. '0b101'
binary_literal = bin(number)
bits_string = binary_literal.lstrip('0b')
# list comprehension
bits = [int(bit_character) for bit_character in bits_string]
return bits
def bit_at_twos_power(number, exponent):
"""return bit in number at location 2 ** exponent
Keyword arguments:
number -- an integer >= 0
exponent -- a integer >= 0
"""
bits = bits_list(number)
# NOTE: reverse() modifies object, returns None
bits.reverse()
if exponent > (len(bits) - 1):
return 0
else:
return bits[exponent]
def twos_power_max(number):
"""return highest power of two in number
Keyword arguments:
number -- an integer >= 0
"""
bits = bits_list(number)
return len(bits) - 1
def is_zero_bit_and_no_neighbor_ones(number, exponent):
if (bit_at_twos_power(number, exponent) == 0
and is_bit_no_neighbor_ones(number, exponent)):
return True
else:
return False
def is_bit_no_neighbor_ones(number, exponent):
if (is_bit_no_right_one(number, exponent)
and is_bit_no_left_one(number, exponent)):
return True
else:
return False
def is_bit_no_right_one(number, exponent):
if (exponent == 0
or bit_at_twos_power(number, exponent - 1) == 0):
return True
else:
return False
def is_bit_no_left_one(number, exponent):
if bit_at_twos_power(number, exponent + 1) == 0:
return True
else:
return False
def is_right_end_of_001(number, exponent):
if (bit_at_twos_power(number, exponent) == 1
and bit_at_twos_power(number, exponent + 1) == 0
and bit_at_twos_power(number, exponent + 2) == 0):
return True
else:
return False
| [
"[email protected]"
] | |
fb013c9f1fb83d9df51c54bbae2e997159c4c7e8 | 059b43c54e69fdca5419d5565c19cc5cb0114a92 | /__unported__/sale_order_line_analytic/__openerp__.py | 9edf7867b2603f7b614f424a96badd02f2aaaa26 | [] | no_license | caiuka/eficent-odoo-addons | b3594b0e4d52594c95bb4cea39fdb47933e77d22 | 458df2c04944688c2273885b5d09fe3753e0ca7e | refs/heads/master | 2020-05-29T11:00:45.273732 | 2014-11-06T13:32:26 | 2014-11-06T13:32:26 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,086 | py | # -*- coding: utf-8 -*-
##############################################################################
#
# Copyright (C) 2014 Eficent (<http://www.eficent.com/>)
# <[email protected]>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
{
"name": "Purchase Requisition Analytic",
"version": "1.0",
"author": "Eficent",
"website": "www.eficent.com",
"category": "Generic Modules/Projects & Services",
"depends": ["analytic", "purchase_requisition"],
"description": """
Organizations often require to integrate purchase requisitions with projects or contracts,
and to find requisitions by searching by it the project/contract code, name or project/contract manager.
This module adds the following features to purchase requisitions:
- Adds the analytic account to the purchase requisition lines,
- When the purchase order is created from the purchase requisition, it copies the analytic account.
- Introduces the possibility to search purchase requisitions by analytic account or by project manager.
- Introduces a new menu entry in Purchasing to list purchase requisition lines.
""",
"init_xml": [
],
"update_xml": [
"purchase_requisition_view.xml",
],
'demo_xml': [
],
'test':[
],
'installable': True,
'active': False,
'certificate': '',
} | [
"[email protected]"
] | |
ff5dff19f34684d16ef5281dee8f6be0988851a6 | 09652bdf74f8ade0cf56a75c9f716bda9dc6f4ff | /server/src/tests/samples/newType2.py | 31086a4f728f54cbba6821b213aacd9ac83b7663 | [
"MIT",
"LicenseRef-scancode-generic-cla"
] | permissive | zeronone/pyright | b68005063cc3623ae2f572a0fec1d4c7845ec57f | d9babcd56b08cb0be024c9d527df333cff5b2b97 | refs/heads/master | 2022-11-18T14:04:31.561923 | 2020-07-15T08:49:53 | 2020-07-15T08:49:53 | 278,020,180 | 0 | 0 | NOASSERTION | 2020-07-08T07:26:01 | 2020-07-08T07:26:00 | null | UTF-8 | Python | false | false | 400 | py | # This sample tests the special-case handle of the multi-parameter
# form of the built-in "type" call.
# pyright: strict
X1 = type("X1", (object,), dict())
X2 = type("X2", (object,), dict())
class A(X1):
...
class B(X2, A):
...
X3 = type(34, (object,))
X4 = type("X4", 34)
# This should generate an error because the second arg is not a tuple of class types.
X5 = type("X5", (3,))
| [
"[email protected]"
] | |
a3d8178d3d9cd5166f52ef11be9060f61c192390 | 47e9f6cef4bfedf81a897d972cecfcf4616ae25f | /experiments/base_experiment.py | 0fb506e33939575baf2a0db46f1c3a9691fa894c | [] | no_license | wz139704646/MBRL_on_VAEs | 1d2b141f5a17746ffa527f3852dfe10bc73dcd27 | b0e8f66b3ade742445a41d3d5667032a931d94d2 | refs/heads/main | 2023-04-09T04:35:34.306860 | 2021-04-27T03:59:54 | 2021-04-27T03:59:54 | 323,389,851 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,633 | py | import abc
class BaseExperiment(metaclass=abc.ABCMeta):
"""base class for all kinds of experiments"""
def __init__(self, exp_configs, hook_before_run=None, hook_after_run=None):
"""initialize the experiment
:param exp_configs: the configurations needed in this experiment
:param hook_before_run: hook function run before the main part run,
take the experiment object itself as the only param
:param hook_after_run: hook function run after the main part run,
take the experiment object itself as the only param
"""
self.exp_configs = exp_configs
self.hook_before_run = hook_before_run
self.hook_after_run = hook_after_run
@abc.abstractmethod
def apply_configs(self):
"""apply the configurations"""
pass
@abc.abstractmethod
def before_run(self, **kwargs):
"""preparations needed be done before run the experiment"""
pass
@abc.abstractmethod
def run(self, **kwargs):
"""run the main part of the experiment"""
pass
@abc.abstractmethod
def after_run(self, **kwargs):
"""cleaning up needed be done after run the experiment"""
pass
def exec(self, **kwargs):
"""execute the entire experiment"""
# apply the experiment configuration
self.apply_configs()
self.before_run(**kwargs)
if self.hook_before_run is not None:
self.hook_before_run(self)
self.run(**kwargs)
if self.hook_after_run is not None:
self.hook_after_run(self)
self.after_run(**kwargs) | [
"[email protected]"
] |
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