text: #  Copyright (c) 2021 PaddlePaddle Authors. 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.
  
  import unittest
  
  import numpy as np
  import paddle
  import paddle.fluid as fluid
  import paddle.fluid.compiler as compiler
  import paddle.optimizer
  import paddle.static
  from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest,
                                                            np_dtype_to_fluid_str)
  
  paddle.enable_static()
  
  
  @unittest.skipIf(not paddle.is_compiled_with_ipu(),
                   "core is not compiled with IPU")
  class TestBase(IPUOpTest):
      def setUp(self):
          self.set_atol()
          self.set_training()
          self.set_feed()
          self.set_feed_attr()
          self.set_attrs()
  
      def set_feed(self):
          self.feed = {"x": np.random.uniform(size=[4, 5, 6]).astype('float32'), }
  
      def set_feed_attr(self):
          self.feed_shape = [x.shape for x in self.feed.values()]
          self.feed_list = list(self.feed.keys())
          self.feed_dtype = [
              np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
          ]
  
      def set_attrs(self):
          self.attrs = {
              "axes": [0, 1, 2],
              "starts": [-3, 0, 2],
              "ends": [3, 2, 4],
          }
  
      def _test_base(self, run_ipu=True):
          scope = fluid.core.Scope()
          main_prog = paddle.static.Program()
          startup_prog = paddle.static.Program()
          SEED = self.SEED
          main_prog.random_seed = SEED
          startup_prog.random_seed = SEED
  
          with fluid.scope_guard(scope):
              with paddle.static.program_guard(main_prog, startup_prog):
                  x = paddle.static.data(
                      name=self.feed_list[0],
                      shape=self.feed_shape[0],
                      dtype=self.feed_dtype[0])
                  out = paddle.fluid.layers.slice(x, **self.attrs)
  
                  fetch_list = [out.name]
  
              if run_ipu:
                  place = paddle.IPUPlace()
              else:
                  place = paddle.CPUPlace()
              exe = paddle.static.Executor(place)
              exe.run(startup_prog)
  
              if run_ipu:
                  feed_list = self.feed_list
                  ipu_strategy = paddle.static.IpuStrategy()
                  ipu_strategy.SetGraphConfig(is_training=self.is_training)
                  program = compiler.IPUCompiledProgram(
                      main_prog,
                      ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
              else:
                  program = main_prog
  
              result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
              return result[0]
  
      def test_base(self):
          res0 = self._test_base(False)
          res1 = self._test_base(True)
  
          self.assertTrue(
              np.allclose(
                  res0.flatten(), res1.flatten(), atol=self.atol))
  
          self.assertTrue(res0.shape == res1.shape)
  
  
  class TestCase1(TestBase):
      def set_attrs(self):
          self.attrs = {
              "axes": [0, 1],
              "starts": [0, 0],
              "ends": [-1, 2],
          }
  
  
  @unittest.skip('dynamic graph is not support on IPU')
  class TestCase2(TestBase):
      def set_feed(self):
          self.feed = {
              "x": np.random.uniform(size=[4, 5, 6]).astype('float32'),
              "starts": np.array([0, 0, 2]).astype('int32'),
              "ends": np.array([3, 2, 4]).astype('int32'),
          }
  
      def set_attrs(self):
          self.attrs = {"axes": [0, 1, 2]}
  
      def _test_base(self, run_ipu=True):
          scope = fluid.core.Scope()
          main_prog = paddle.static.Program()
          startup_prog = paddle.static.Program()
          SEED = self.SEED
          main_prog.random_seed = SEED
          startup_prog.random_seed = SEED
  
          with fluid.scope_guard(scope):
              with paddle.static.program_guard(main_prog, startup_prog):
                  x = paddle.static.data(
                      name=self.feed_list[0],
                      shape=self.feed_shape[0],
                      dtype=self.feed_dtype[0])
                  starts = paddle.static.data(
                      name=self.feed_list[1],
                      shape=self.feed_shape[1],
                      dtype=self.feed_dtype[1])
                  ends = paddle.static.data(
                      name=self.feed_list[2],
                      shape=self.feed_shape[2],
                      dtype=self.feed_dtype[2])
                  out = paddle.fluid.layers.slice(
                      x, starts=starts, ends=ends, **self.attrs)
  
                  fetch_list = [out.name]
  
              if run_ipu:
                  place = paddle.IPUPlace()
              else:
                  place = paddle.CPUPlace()
              exe = paddle.static.Executor(place)
              exe.run(startup_prog)
  
              if run_ipu:
                  feed_list = self.feed_list
                  ipu_strategy = paddle.static.IpuStrategy()
                  ipu_strategy.SetGraphConfig(is_training=self.is_training)
                  program = compiler.IPUCompiledProgram(
                      main_prog,
                      ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
              else:
                  program = main_prog
  
              result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
              return result[0]
  
  
  if __name__ == "__main__":
      unittest.main()
  