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 paddle
  import numpy as np
  from op_test import OpTest
  import math
  import os
  from paddle.fluid.framework import _test_eager_guard
  
  paddle.enable_static()
  paddle.seed(100)
  
  
  def output_hist(out, lam, a, b):
      prob = []
      bin = []
      for i in range(a, b + 1):
          prob.append((lam**i) * math.exp(-lam) / math.factorial(i))
          bin.append(i)
      bin.append(b + 0.1)
  
      hist, _ = np.histogram(out, bin)
      hist = hist.astype("float32")
      hist = hist / float(out.size)
      return hist, prob
  
  
  class TestPoissonOp1(OpTest):
  
      def setUp(self):
          self.op_type = "poisson"
          self.config()
  
          self.attrs = {}
          self.inputs = {'X': np.full([1024, 1024], self.lam, dtype=self.dtype)}
          self.outputs = {'Out': np.ones([1024, 1024], dtype=self.dtype)}
  
      def config(self):
          self.lam = 10
          self.a = 5
          self.b = 15
          self.dtype = "float64"
  
      def verify_output(self, outs):
          hist, prob = output_hist(np.array(outs[0]), self.lam, self.a, self.b)
          self.assertTrue(np.allclose(hist, prob, rtol=0.01),
                          "actual: {}, expected: {}".format(hist, prob))
  
      def test_check_output(self):
          self.check_output_customized(self.verify_output)
  
      def test_check_grad_normal(self):
          self.check_grad(
              ['X'],
              'Out',
              user_defined_grads=[np.zeros([1024, 1024], dtype=self.dtype)],
              user_defined_grad_outputs=[
                  np.random.rand(1024, 1024).astype(self.dtype)
              ])
  
  
  class TestPoissonOp2(TestPoissonOp1):
  
      def config(self):
          self.lam = 5
          self.a = 1
          self.b = 9
          self.dtype = "float32"
  
  
  class TestPoissonAPI(unittest.TestCase):
  
      def test_static(self):
          with paddle.static.program_guard(paddle.static.Program(),
                                           paddle.static.Program()):
              x_np = np.random.rand(10, 10)
              x = paddle.static.data(name="x", shape=[10, 10], dtype='float64')
              y = paddle.poisson(x)
  
              exe = paddle.static.Executor()
              y_np = exe.run(paddle.static.default_main_program(),
                             feed={"x": x_np},
                             fetch_list=[y])
              self.assertTrue(np.min(y_np) >= 0)
  
      def test_dygraph(self):
          with paddle.fluid.dygraph.base.guard():
              x = paddle.randn([10, 10], dtype='float32')
              y = paddle.poisson(x)
              self.assertTrue(np.min(y.numpy()) >= 0)
  
              with _test_eager_guard():
                  x = paddle.randn([10, 10], dtype='float32')
                  x.stop_gradient = False
                  y = paddle.poisson(x)
                  y.backward()
                  self.assertTrue(np.min(y.numpy()) >= 0)
                  self.assertTrue(np.array_equal(np.zeros_like(x), x.gradient()))
  
      def test_fixed_random_number(self):
          # Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t'
          if not paddle.is_compiled_with_cuda():
              return
  
          print("Test Fixed Random number on GPU------>")
          paddle.disable_static()
          paddle.set_device('gpu')
          paddle.seed(2021)
          x = paddle.full([32, 3, 1024, 768], 10., dtype="float32")
          y = paddle.poisson(x)
          y_np = y.numpy()
  
          expect = [
              13., 13., 11., 8., 12., 6., 9., 15., 16., 6., 13., 12., 9., 15.,
              17., 8., 11., 16., 11., 10.
          ]
          self.assertTrue(np.array_equal(y_np[0, 0, 0, 0:20], expect))
  
          expect = [
              15., 7., 12., 8., 14., 10., 10., 11., 11., 11., 21., 6., 9., 13.,
              13., 11., 6., 9., 12., 12.
          ]
          self.assertTrue(np.array_equal(y_np[8, 1, 300, 200:220], expect))
  
          expect = [
              10., 15., 9., 6., 4., 13., 10., 10., 13., 12., 9., 7., 10., 14., 7.,
              10., 8., 5., 10., 14.
          ]
          self.assertTrue(np.array_equal(y_np[16, 1, 600, 400:420], expect))
  
          expect = [
              10., 9., 14., 12., 8., 9., 7., 8., 11., 10., 13., 8., 12., 9., 7.,
              8., 11., 11., 12., 5.
          ]
          self.assertTrue(np.array_equal(y_np[24, 2, 900, 600:620], expect))
  
          expect = [
              15., 5., 11., 13., 12., 12., 13., 16., 9., 9., 7., 9., 13., 11.,
              15., 6., 11., 9., 10., 10.
          ]
          self.assertTrue(np.array_equal(y_np[31, 2, 1023, 748:768], expect))
  
          x = paddle.full([16, 1024, 1024], 5., dtype="float32")
          y = paddle.poisson(x)
          y_np = y.numpy()
          expect = [
              4., 5., 2., 9., 8., 7., 4., 7., 4., 7., 6., 3., 10., 7., 5., 7., 2.,
              5., 5., 6.
          ]
          self.assertTrue(np.array_equal(y_np[0, 0, 100:120], expect))
  
          expect = [
              1., 4., 8., 11., 6., 5., 4., 4., 7., 4., 4., 7., 11., 6., 5., 3.,
              4., 6., 3., 3.
          ]
          self.assertTrue(np.array_equal(y_np[4, 300, 300:320], expect))
  
          expect = [
              7., 5., 4., 6., 8., 5., 6., 7., 7., 7., 3., 10., 5., 10., 4., 5.,
              8., 7., 5., 7.
          ]
          self.assertTrue(np.array_equal(y_np[8, 600, 600:620], expect))
  
          expect = [
              8., 6., 7., 4., 3., 0., 4., 6., 6., 4., 3., 10., 5., 1., 3., 8., 8.,
              2., 1., 4.
          ]
          self.assertTrue(np.array_equal(y_np[12, 900, 900:920], expect))
  
          expect = [
              2., 1., 14., 3., 6., 5., 2., 2., 6., 5., 7., 4., 8., 4., 8., 4., 5.,
              7., 1., 7.
          ]
          self.assertTrue(np.array_equal(y_np[15, 1023, 1000:1020], expect))
          paddle.enable_static()
  
  
  if __name__ == "__main__":
      unittest.main()
  