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rAKA akantu
test_tensors.cc
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/**
* @file test_tensors.cc
*
* @author Nicolas Richart <nicolas.richart@epfl.ch>
*
* @date creation: Tue Nov 14 2017
* @date last modification: Mon Jan 22 2018
*
* @brief test the tensors types
*
*
* Copyright (©) 2016-2018 EPFL (Ecole Polytechnique Fédérale de Lausanne)
* Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
*
* Akantu is free software: you can redistribute it and/or modify it under the
* terms of the GNU Lesser General Public License as published by the Free
* Software Foundation, either version 3 of the License, or (at your option) any
* later version.
*
* Akantu 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 Lesser General Public License for more
* details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Akantu. If not, see <http://www.gnu.org/licenses/>.
*
*/
/* -------------------------------------------------------------------------- */
#include "aka_array.hh"
#include "aka_iterators.hh"
#include "aka_types.hh"
/* -------------------------------------------------------------------------- */
#include <cstdlib>
#include <gtest/gtest.h>
#include <memory>
/* -------------------------------------------------------------------------- */
using
namespace
akantu
;
namespace
{
/* -------------------------------------------------------------------------- */
class
TensorConstructorFixture
:
public
::
testing
::
Test
{
public
:
void
SetUp
()
override
{
for
(
auto
&
r
:
reference
)
{
r
=
rand
();
// google-test seeds srand()
}
}
void
TearDown
()
override
{}
template
<
typename
V
>
void
compareToRef
(
const
V
&
v
)
{
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
{
EXPECT_DOUBLE_EQ
(
reference
[
i
],
v
.
storage
()[
i
]);
}
}
protected
:
const
int
size_
{
24
};
const
std
::
array
<
int
,
2
>
mat_size
{{
4
,
6
}};
// const std::array<int, 3> tens3_size{{4, 2, 3}};
std
::
array
<
double
,
24
>
reference
;
};
/* -------------------------------------------------------------------------- */
class
TensorFixture
:
public
TensorConstructorFixture
{
public
:
TensorFixture
()
:
vref
(
reference
.
data
(),
size_
),
mref
(
reference
.
data
(),
mat_size
[
0
],
mat_size
[
1
])
{}
protected
:
Vector
<
double
>
vref
;
Matrix
<
double
>
mref
;
};
/* -------------------------------------------------------------------------- */
// Vector ----------------------------------------------------------------------
TEST_F
(
TensorConstructorFixture
,
VectorDefaultConstruct
)
{
Vector
<
double
>
v
;
EXPECT_EQ
(
0
,
v
.
size
());
EXPECT_EQ
(
nullptr
,
v
.
storage
());
EXPECT_EQ
(
false
,
v
.
isWrapped
());
}
TEST_F
(
TensorConstructorFixture
,
VectorConstruct1
)
{
double
r
=
rand
();
Vector
<
double
>
v
(
size_
,
r
);
EXPECT_EQ
(
size_
,
v
.
size
());
EXPECT_EQ
(
false
,
v
.
isWrapped
());
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
{
EXPECT_DOUBLE_EQ
(
r
,
v
(
i
));
EXPECT_DOUBLE_EQ
(
r
,
v
[
i
]);
}
}
TEST_F
(
TensorConstructorFixture
,
VectorConstructWrapped
)
{
Vector
<
double
>
v
(
reference
.
data
(),
size_
);
EXPECT_EQ
(
size_
,
v
.
size
());
EXPECT_EQ
(
true
,
v
.
isWrapped
());
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
{
EXPECT_DOUBLE_EQ
(
reference
[
i
],
v
(
i
));
EXPECT_DOUBLE_EQ
(
reference
[
i
],
v
[
i
]);
}
}
TEST_F
(
TensorConstructorFixture
,
VectorConstructInitializer
)
{
Vector
<
double
>
v
{
0.
,
1.
,
2.
,
3.
,
4.
,
5.
};
EXPECT_EQ
(
6
,
v
.
size
());
EXPECT_EQ
(
false
,
v
.
isWrapped
());
for
(
int
i
=
0
;
i
<
6
;
++
i
)
{
EXPECT_DOUBLE_EQ
(
i
,
v
(
i
));
}
}
TEST_F
(
TensorConstructorFixture
,
VectorConstructCopy1
)
{
Vector
<
double
>
vref
(
reference
.
data
(),
reference
.
size
());
Vector
<
double
>
v
(
vref
);
EXPECT_EQ
(
size_
,
v
.
size
());
EXPECT_EQ
(
false
,
v
.
isWrapped
());
compareToRef
(
v
);
}
TEST_F
(
TensorConstructorFixture
,
VectorConstructCopy2
)
{
Vector
<
double
>
vref
(
reference
.
data
(),
reference
.
size
());
Vector
<
double
>
v
(
vref
,
false
);
EXPECT_EQ
(
size_
,
v
.
size
());
EXPECT_EQ
(
true
,
v
.
isWrapped
());
compareToRef
(
v
);
}
TEST_F
(
TensorConstructorFixture
,
VectorConstructProxy1
)
{
VectorProxy
<
double
>
vref
(
reference
.
data
(),
reference
.
size
());
EXPECT_EQ
(
size_
,
vref
.
size
());
compareToRef
(
vref
);
Vector
<
double
>
v
(
vref
);
EXPECT_EQ
(
size_
,
v
.
size
());
EXPECT_EQ
(
true
,
v
.
isWrapped
());
compareToRef
(
v
);
}
TEST_F
(
TensorConstructorFixture
,
VectorConstructProxy2
)
{
Vector
<
double
>
vref
(
reference
.
data
(),
reference
.
size
());
VectorProxy
<
double
>
v
(
vref
);
EXPECT_EQ
(
size_
,
v
.
size
());
compareToRef
(
v
);
}
/* -------------------------------------------------------------------------- */
TEST_F
(
TensorFixture
,
VectorEqual
)
{
Vector
<
double
>
v
;
v
=
vref
;
compareToRef
(
v
);
EXPECT_EQ
(
size_
,
v
.
size
());
EXPECT_EQ
(
false
,
v
.
isWrapped
());
}
TEST_F
(
TensorFixture
,
VectorEqualProxy
)
{
VectorProxy
<
double
>
vref_proxy
(
vref
);
Vector
<
double
>
v
;
v
=
vref
;
compareToRef
(
v
);
EXPECT_EQ
(
size_
,
v
.
size
());
EXPECT_EQ
(
false
,
v
.
isWrapped
());
}
TEST_F
(
TensorFixture
,
VectorEqualProxy2
)
{
Vector
<
double
>
v_store
(
size_
,
0.
);
VectorProxy
<
double
>
v
(
v_store
);
v
=
vref
;
compareToRef
(
v
);
compareToRef
(
v_store
);
}
/* -------------------------------------------------------------------------- */
TEST_F
(
TensorFixture
,
VectorSet
)
{
Vector
<
double
>
v
(
vref
);
compareToRef
(
v
);
double
r
=
rand
();
v
.
set
(
r
);
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
r
,
v
[
i
]);
}
TEST_F
(
TensorFixture
,
VectorClear
)
{
Vector
<
double
>
v
(
vref
);
compareToRef
(
v
);
v
.
zero
();
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
0
,
v
[
i
]);
}
/* -------------------------------------------------------------------------- */
TEST_F
(
TensorFixture
,
VectorDivide
)
{
Vector
<
double
>
v
;
double
r
=
rand
();
v
=
vref
/
r
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
/
r
,
v
[
i
]);
}
TEST_F
(
TensorFixture
,
VectorMultiply1
)
{
Vector
<
double
>
v
;
double
r
=
rand
();
v
=
vref
*
r
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
r
,
v
[
i
]);
}
TEST_F
(
TensorFixture
,
VectorMultiply2
)
{
Vector
<
double
>
v
;
double
r
=
rand
();
v
=
r
*
vref
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
r
,
v
[
i
]);
}
TEST_F
(
TensorFixture
,
VectorAddition
)
{
Vector
<
double
>
v
;
v
=
vref
+
vref
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
2.
,
v
[
i
]);
}
TEST_F
(
TensorFixture
,
VectorSubstract
)
{
Vector
<
double
>
v
;
v
=
vref
-
vref
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
0.
,
v
[
i
]);
}
TEST_F
(
TensorFixture
,
VectorDivideEqual
)
{
Vector
<
double
>
v
(
vref
);
double
r
=
rand
();
v
/=
r
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
/
r
,
v
[
i
]);
}
TEST_F
(
TensorFixture
,
VectorMultiplyEqual1
)
{
Vector
<
double
>
v
(
vref
);
double
r
=
rand
();
v
*=
r
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
r
,
v
[
i
]);
}
TEST_F
(
TensorFixture
,
VectorMultiplyEqual2
)
{
Vector
<
double
>
v
(
vref
);
v
*=
v
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
reference
[
i
],
v
[
i
]);
}
TEST_F
(
TensorFixture
,
VectorAdditionEqual
)
{
Vector
<
double
>
v
(
vref
);
v
+=
vref
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
2.
,
v
[
i
]);
}
TEST_F
(
TensorFixture
,
VectorSubstractEqual
)
{
Vector
<
double
>
v
(
vref
);
v
-=
vref
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
0.
,
v
[
i
]);
}
/* -------------------------------------------------------------------------- */
// Matrix ----------------------------------------------------------------------
TEST_F
(
TensorConstructorFixture
,
MatrixDefaultConstruct
)
{
Matrix
<
double
>
m
;
EXPECT_EQ
(
0
,
m
.
size
());
EXPECT_EQ
(
0
,
m
.
rows
());
EXPECT_EQ
(
0
,
m
.
cols
());
EXPECT_EQ
(
nullptr
,
m
.
storage
());
EXPECT_EQ
(
false
,
m
.
isWrapped
());
}
TEST_F
(
TensorConstructorFixture
,
MatrixConstruct1
)
{
double
r
=
rand
();
Matrix
<
double
>
m
(
mat_size
[
0
],
mat_size
[
1
],
r
);
EXPECT_EQ
(
size_
,
m
.
size
());
EXPECT_EQ
(
mat_size
[
0
],
m
.
rows
());
EXPECT_EQ
(
mat_size
[
1
],
m
.
cols
());
EXPECT_EQ
(
false
,
m
.
isWrapped
());
for
(
int
i
=
0
;
i
<
mat_size
[
0
];
++
i
)
{
for
(
int
j
=
0
;
j
<
mat_size
[
1
];
++
j
)
{
EXPECT_EQ
(
r
,
m
(
i
,
j
));
EXPECT_EQ
(
r
,
m
[
i
+
j
*
mat_size
[
0
]]);
}
}
}
TEST_F
(
TensorConstructorFixture
,
MatrixConstructWrapped
)
{
Matrix
<
double
>
m
(
reference
.
data
(),
mat_size
[
0
],
mat_size
[
1
]);
EXPECT_EQ
(
size_
,
m
.
size
());
EXPECT_EQ
(
mat_size
[
0
],
m
.
rows
());
EXPECT_EQ
(
mat_size
[
1
],
m
.
cols
());
EXPECT_EQ
(
true
,
m
.
isWrapped
());
for
(
int
i
=
0
;
i
<
mat_size
[
0
];
++
i
)
{
for
(
int
j
=
0
;
j
<
mat_size
[
1
];
++
j
)
{
EXPECT_DOUBLE_EQ
(
reference
[
i
+
j
*
mat_size
[
0
]],
m
(
i
,
j
));
}
}
compareToRef
(
m
);
}
TEST_F
(
TensorConstructorFixture
,
MatrixConstructInitializer
)
{
Matrix
<
double
>
m
{{
0.
,
1.
,
2.
},
{
3.
,
4.
,
5.
}};
EXPECT_EQ
(
6
,
m
.
size
());
EXPECT_EQ
(
2
,
m
.
rows
());
EXPECT_EQ
(
3
,
m
.
cols
());
EXPECT_EQ
(
false
,
m
.
isWrapped
());
int
c
=
0
;
for
(
int
i
=
0
;
i
<
2
;
++
i
)
{
for
(
int
j
=
0
;
j
<
3
;
++
j
,
++
c
)
{
EXPECT_DOUBLE_EQ
(
c
,
m
(
i
,
j
));
}
}
}
TEST_F
(
TensorConstructorFixture
,
MatrixConstructCopy1
)
{
Matrix
<
double
>
mref
(
reference
.
data
(),
mat_size
[
0
],
mat_size
[
1
]);
Matrix
<
double
>
m
(
mref
);
EXPECT_EQ
(
size_
,
m
.
size
());
EXPECT_EQ
(
mat_size
[
0
],
m
.
rows
());
EXPECT_EQ
(
mat_size
[
1
],
m
.
cols
());
EXPECT_EQ
(
false
,
m
.
isWrapped
());
compareToRef
(
m
);
}
TEST_F
(
TensorConstructorFixture
,
MatrixConstructCopy2
)
{
Matrix
<
double
>
mref
(
reference
.
data
(),
mat_size
[
0
],
mat_size
[
1
]);
Matrix
<
double
>
m
(
mref
);
EXPECT_EQ
(
size_
,
m
.
size
());
EXPECT_EQ
(
mat_size
[
0
],
m
.
rows
());
EXPECT_EQ
(
mat_size
[
1
],
m
.
cols
());
EXPECT_EQ
(
false
,
m
.
isWrapped
());
compareToRef
(
m
);
}
TEST_F
(
TensorConstructorFixture
,
MatrixConstructProxy1
)
{
MatrixProxy
<
double
>
mref
(
reference
.
data
(),
mat_size
[
0
],
mat_size
[
1
]);
EXPECT_EQ
(
size_
,
mref
.
size
());
EXPECT_EQ
(
mat_size
[
0
],
mref
.
size
(
0
));
EXPECT_EQ
(
mat_size
[
1
],
mref
.
size
(
1
));
compareToRef
(
mref
);
Matrix
<
double
>
m
(
mref
);
EXPECT_EQ
(
size_
,
m
.
size
());
EXPECT_EQ
(
mat_size
[
0
],
m
.
rows
());
EXPECT_EQ
(
mat_size
[
1
],
m
.
cols
());
EXPECT_EQ
(
true
,
m
.
isWrapped
());
compareToRef
(
m
);
}
TEST_F
(
TensorConstructorFixture
,
MatrixConstructProxy2
)
{
Matrix
<
double
>
mref
(
reference
.
data
(),
mat_size
[
0
],
mat_size
[
1
]);
MatrixProxy
<
double
>
m
(
mref
);
EXPECT_EQ
(
size_
,
m
.
size
());
EXPECT_EQ
(
mat_size
[
0
],
m
.
size
(
0
));
EXPECT_EQ
(
mat_size
[
1
],
m
.
size
(
1
));
compareToRef
(
m
);
}
/* -------------------------------------------------------------------------- */
TEST_F
(
TensorFixture
,
MatrixEqual
)
{
Matrix
<
double
>
m
;
m
=
mref
;
compareToRef
(
m
);
EXPECT_EQ
(
size_
,
m
.
size
());
EXPECT_EQ
(
mat_size
[
0
],
m
.
rows
());
EXPECT_EQ
(
mat_size
[
1
],
m
.
cols
());
EXPECT_EQ
(
false
,
m
.
isWrapped
());
}
TEST_F
(
TensorFixture
,
MatrixEqualProxy1
)
{
MatrixProxy
<
double
>
mref_proxy
(
mref
);
Matrix
<
double
>
m
;
m
=
mref
;
compareToRef
(
m
);
EXPECT_EQ
(
size_
,
m
.
size
());
EXPECT_EQ
(
mat_size
[
0
],
m
.
rows
());
EXPECT_EQ
(
mat_size
[
1
],
m
.
cols
());
EXPECT_EQ
(
false
,
m
.
isWrapped
());
}
TEST_F
(
TensorFixture
,
MatrixEqualProxy2
)
{
Matrix
<
double
>
m_store
(
mat_size
[
0
],
mat_size
[
1
],
0.
);
MatrixProxy
<
double
>
m
(
m_store
);
m
=
mref
;
compareToRef
(
m
);
compareToRef
(
m_store
);
}
TEST_F
(
TensorFixture
,
MatrixEqualSlice
)
{
Matrix
<
double
>
m
(
mat_size
[
0
],
mat_size
[
1
],
0.
);
for
(
unsigned
int
i
=
0
;
i
<
m
.
cols
();
++
i
)
m
(
i
)
=
Vector
<
Real
>
(
mref
(
i
));
compareToRef
(
m
);
}
/* -------------------------------------------------------------------------- */
TEST_F
(
TensorFixture
,
MatrixSet
)
{
Matrix
<
double
>
m
(
mref
);
compareToRef
(
m
);
double
r
=
rand
();
m
.
set
(
r
);
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
r
,
m
[
i
]);
}
TEST_F
(
TensorFixture
,
MatrixClear
)
{
Matrix
<
double
>
m
(
mref
);
compareToRef
(
m
);
m
.
zero
();
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
0
,
m
[
i
]);
}
/* -------------------------------------------------------------------------- */
TEST_F
(
TensorFixture
,
MatrixDivide
)
{
Matrix
<
double
>
m
;
double
r
=
rand
();
m
=
mref
/
r
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
/
r
,
m
[
i
]);
}
TEST_F
(
TensorFixture
,
MatrixMultiply1
)
{
Matrix
<
double
>
m
;
double
r
=
rand
();
m
=
mref
*
r
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
r
,
m
[
i
]);
}
TEST_F
(
TensorFixture
,
MatrixMultiply2
)
{
Matrix
<
double
>
m
;
double
r
=
rand
();
m
=
r
*
mref
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
r
,
m
[
i
]);
}
TEST_F
(
TensorFixture
,
MatrixAddition
)
{
Matrix
<
double
>
m
;
m
=
mref
+
mref
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
2.
,
m
[
i
]);
}
TEST_F
(
TensorFixture
,
MatrixSubstract
)
{
Matrix
<
double
>
m
;
m
=
mref
-
mref
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
0.
,
m
[
i
]);
}
TEST_F
(
TensorFixture
,
MatrixDivideEqual
)
{
Matrix
<
double
>
m
(
mref
);
double
r
=
rand
();
m
/=
r
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
/
r
,
m
[
i
]);
}
TEST_F
(
TensorFixture
,
MatrixMultiplyEqual1
)
{
Matrix
<
double
>
m
(
mref
);
double
r
=
rand
();
m
*=
r
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
r
,
m
[
i
]);
}
TEST_F
(
TensorFixture
,
MatrixAdditionEqual
)
{
Matrix
<
double
>
m
(
mref
);
m
+=
mref
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
reference
[
i
]
*
2.
,
m
[
i
]);
}
TEST_F
(
TensorFixture
,
MatrixSubstractEqual
)
{
Matrix
<
double
>
m
(
mref
);
m
-=
mref
;
for
(
int
i
=
0
;
i
<
size_
;
++
i
)
EXPECT_DOUBLE_EQ
(
0.
,
m
[
i
]);
}
TEST_F
(
TensorFixture
,
MatrixIterator
)
{
Matrix
<
double
>
m
(
mref
);
UInt
col_count
=
0
;
for
(
auto
&&
col
:
m
)
{
Vector
<
Real
>
col_hand
(
m
.
storage
()
+
col_count
*
m
.
rows
(),
m
.
rows
());
Vector
<
Real
>
col_wrap
(
col
);
auto
comp
=
(
col_wrap
-
col_hand
).
norm
<
L_inf
>
();
EXPECT_DOUBLE_EQ
(
0.
,
comp
);
++
col_count
;
}
}
TEST_F
(
TensorFixture
,
MatrixIteratorZip
)
{
Matrix
<
double
>
m1
(
mref
);
Matrix
<
double
>
m2
(
mref
);
UInt
col_count
=
0
;
for
(
auto
&&
col
:
zip
(
m1
,
m2
))
{
Vector
<
Real
>
col1
(
std
::
get
<
0
>
(
col
));
Vector
<
Real
>
col2
(
std
::
get
<
1
>
(
col
));
auto
comp
=
(
col1
-
col2
).
norm
<
L_inf
>
();
EXPECT_DOUBLE_EQ
(
0.
,
comp
);
++
col_count
;
}
}
#if defined(AKANTU_USE_LAPACK)
TEST_F
(
TensorFixture
,
MatrixEigs
)
{
Matrix
<
double
>
m
{{
0
,
1
,
0
,
0
},
{
1.
,
0
,
0
,
0
},
{
0
,
1
,
0
,
1
},
{
0
,
0
,
4
,
0
}};
Matrix
<
double
>
eig_vects
(
4
,
4
);
Vector
<
double
>
eigs
(
4
);
m
.
eig
(
eigs
,
eig_vects
);
Vector
<
double
>
eigs_ref
{
2
,
1.
,
-
1.
,
-
2
};
auto
lambda_v
=
m
*
eig_vects
;
for
(
int
i
=
0
;
i
<
4
;
++
i
)
{
EXPECT_NEAR
(
eigs_ref
(
i
),
eigs
(
i
),
1e-14
);
for
(
int
j
=
0
;
j
<
4
;
++
j
)
{
EXPECT_NEAR
(
lambda_v
(
i
)(
j
),
eigs
(
i
)
*
eig_vects
(
i
)(
j
),
1e-14
);
}
}
}
#endif
/* -------------------------------------------------------------------------- */
}
// namespace
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