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230 lines
7.7 KiB
230 lines
7.7 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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class TestSumOp(OpTest):
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def setUp(self):
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self.op_type = "sum"
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self.init_kernel_type()
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self.use_mkldnn = False
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self.init_kernel_type()
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x0 = np.random.random((3, 4)).astype(self.dtype)
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x1 = np.random.random((3, 4)).astype(self.dtype)
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x2 = np.random.random((3, 4)).astype(self.dtype)
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self.inputs = {"X": [("x0", x0), ("x1", x1), ("x2", x2)]}
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y = x0 + x1 + x2
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self.outputs = {'Out': y}
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self.attrs = {'use_mkldnn': self.use_mkldnn}
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def init_kernel_type(self):
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['x0'], 'Out')
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def init_kernel_type(self):
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pass
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class TestSelectedRowsSumOp(OpTest):
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def setUp(self):
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self.height = 10
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self.row_numel = 12
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self.rows = [0, 1, 2, 3, 4, 5, 6]
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self.dtype = np.float32
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self.init_kernel_type()
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def check_with_place(self, place, inplace):
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self.check_input_and_optput(core.Scope(), place, inplace, True, True,
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True)
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self.check_input_and_optput(core.Scope(), place, inplace, False, True,
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True)
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self.check_input_and_optput(core.Scope(), place, inplace, False, False,
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True)
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self.check_input_and_optput(core.Scope(), place, inplace, False, False,
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False)
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def init_kernel_type(self):
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pass
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def _get_array(self, rows, row_numel):
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array = np.ones((len(rows), row_numel)).astype(self.dtype)
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for i in range(len(rows)):
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array[i] *= rows[i]
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return array
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def check_input_and_optput(self,
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scope,
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place,
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inplace,
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w1_has_data=False,
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w2_has_data=False,
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w3_has_data=False):
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self.create_selected_rows(scope, place, "W1", w1_has_data)
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self.create_selected_rows(scope, place, "W2", w2_has_data)
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self.create_selected_rows(scope, place, "W3", w3_has_data)
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# create Out Variable
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if inplace:
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out_var_name = "W1"
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else:
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out_var_name = "Out"
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out = scope.var(out_var_name).get_selected_rows()
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# create and run sum operator
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sum_op = Operator("sum", X=["W1", "W2", "W3"], Out=out_var_name)
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sum_op.run(scope, place)
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has_data_w_num = 0
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for has_data in [w1_has_data, w2_has_data, w3_has_data]:
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if has_data:
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has_data_w_num += 1
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if has_data_w_num > 0:
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self.assertEqual(len(out.rows()), 7)
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self.assertTrue(
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np.array_equal(
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np.array(out.get_tensor()),
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self._get_array(self.rows, self.row_numel) *
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has_data_w_num))
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else:
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self.assertEqual(len(out.rows()), 0)
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def create_selected_rows(self, scope, place, var_name, has_data):
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# create and initialize W Variable
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if has_data:
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rows = self.rows
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else:
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rows = []
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var = scope.var(var_name)
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w_selected_rows = var.get_selected_rows()
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w_selected_rows.set_height(self.height)
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w_selected_rows.set_rows(rows)
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w_array = self._get_array(self.rows, self.row_numel)
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w_tensor = w_selected_rows.get_tensor()
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w_tensor.set(w_array, place)
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return var
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def test_w_is_selected_rows(self):
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(core.CUDAPlace(0))
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for place in places:
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for inplace in [True, False]:
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self.check_with_place(place, inplace)
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class TestLoDTensorAndSelectedRowsOp(TestSelectedRowsSumOp):
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def setUp(self):
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self.height = 10
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self.row_numel = 12
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self.rows = [0, 1, 2, 2, 4, 5, 6]
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def check_with_place(self, place, inplace):
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scope = core.Scope()
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if inplace:
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self.create_lod_tensor(scope, place, "x1")
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self.create_selected_rows(scope, place, "x2", True)
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out = scope.var("x1").get_tensor()
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out_name = "x1"
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else:
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self.create_selected_rows(scope, place, "x1", True)
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self.create_lod_tensor(scope, place, "x2")
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out = scope.var("out").get_tensor()
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out_name = "out"
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# create and run sum operator
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sum_op = Operator("sum", X=["x1", "x2"], Out=out_name)
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sum_op.run(scope, place)
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result = np.ones((1, self.height)).astype(np.int32).tolist()[0]
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for ele in self.rows:
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result[ele] += 1
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out_t = np.array(out)
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self.assertEqual(out_t.shape[0], self.height)
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self.assertTrue(
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np.array_equal(out_t,
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self._get_array([i for i in range(
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self.height)], self.row_numel) * np.tile(
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np.array(result).reshape(self.height, 1),
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self.row_numel)))
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def create_lod_tensor(self, scope, place, var_name):
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var = scope.var(var_name)
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w_tensor = var.get_tensor()
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w_array = self._get_array([i for i in range(self.height)],
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self.row_numel)
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w_tensor.set(w_array, place)
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return var
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#----------- test fp16 -----------
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestFP16SumOp(TestSumOp):
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def init_kernel_type(self):
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self.dtype = np.float16
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def test_check_output(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-2)
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# FIXME: Because of the precision fp16, max_relative_error
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# should be 0.15 here.
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def test_check_grad(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_grad(['x0'], 'Out', max_relative_error=0.15)
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def create_test_sum_fp16_class(parent):
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestSumFp16Case(parent):
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def init_kernel_type(self):
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self.dtype = np.float16
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def test_w_is_selected_rows(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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for inplace in [True, False]:
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self.check_with_place(place, inplace)
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cls_name = "{0}_{1}".format(parent.__name__, "SumFp16Test")
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TestSumFp16Case.__name__ = cls_name
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globals()[cls_name] = TestSumFp16Case
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create_test_sum_fp16_class(TestSelectedRowsSumOp)
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create_test_sum_fp16_class(TestLoDTensorAndSelectedRowsOp)
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if __name__ == "__main__":
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unittest.main()
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