text: """
  Copyright 2020 The OneFlow 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
  from collections import OrderedDict
  
  import math
  import numpy as np
  from automated_test_util import *
  from test_util import GenArgList
  from test_util import GenArgList, type_name_to_flow_type, type_name_to_np_type
  
  import oneflow as flow
  import oneflow.unittest
  
  
  def gen_quant_scale_for_moving_average_min_max_symmetric(
      activation, quantization_bit, momentum, moving_max, moving_min
  ):
      activation_max = np.max(np.abs(activation))
      denominator = 2.0 ** (quantization_bit - 1) - 1
      if moving_max[0] == 0:
          moving_max[0] = activation_max
      else:
          moving_max[0] = moving_max[0] * momentum + activation_max * (1 - momentum)
      moving_min[0] = moving_max[0]
      return (moving_max[0] / denominator, 0)
  
  
  def gen_quant_scale_for_moving_average_min_max_affine(
      activation, quantization_bit, momentum, moving_max, moving_min
  ):
      activation_max = np.max(activation)
      activation_min = np.min(activation)
      denominator = 2.0 ** quantization_bit - 1
      if moving_max[0] == 0:
          moving_max[0] = activation_max
      else:
          moving_max[0] = moving_max[0] * momentum + activation_max * (1 - momentum)
      if moving_min[0] == 0:
          moving_min[0] = activation_min
      else:
          moving_min[0] = moving_min[0] * momentum + activation_min * (1 - momentum)
      scale = (moving_max[0] - moving_min[0]) / denominator
      zero_point = -np.round(moving_min[0] / scale)
      return (scale, zero_point)
  
  
  def gen_quant_scale_for_moving_average_min_max_cambricon(
      activation, quantization_bit, momentum, moving_max, moving_min
  ):
      activation_max = np.max(np.abs(activation))
      if moving_max[0] == 0:
          moving_max[0] = activation_max
      else:
          moving_max[0] = moving_max[0] * momentum + activation_max * (1 - momentum)
      moving_min[0] = moving_max[0]
      return (math.floor(math.log2(moving_max[0])) - (quantization_bit - 2), 0)
  
  
  def _check_moving_average_min_max_observer(
      test_case,
      activation,
      scale_of,
      zero_point_of,
      moving_max_np,
      moving_min_np,
      quantization_bit,
      quantization_scheme,
      quantization_formula,
      momentum,
  ):
      if quantization_formula == "google":
          if quantization_scheme == "symmetric":
              (
                  scale_np,
                  zero_point_np,
              ) = gen_quant_scale_for_moving_average_min_max_symmetric(
                  activation.flatten(),
                  quantization_bit,
                  momentum,
                  moving_max_np,
                  moving_min_np,
              )
          else:
              (
                  scale_np,
                  zero_point_np,
              ) = gen_quant_scale_for_moving_average_min_max_affine(
                  activation.flatten(),
                  quantization_bit,
                  momentum,
                  moving_max_np,
                  moving_min_np,
              )
      else:
          (
              scale_np,
              zero_point_np,
          ) = gen_quant_scale_for_moving_average_min_max_cambricon(
              activation.flatten(),
              quantization_bit,
              momentum,
              moving_max_np,
              moving_min_np,
          )
      test_case.assertTrue(np.allclose(scale_of[0], scale_np, rtol=0.001))
  
      rmse = np.sqrt(np.mean((zero_point_of[0] - zero_point_np) ** 2))
      assert (
          rmse <= 1.0
      ), "moving_average_min_max_observer op zero_point calculate has bug!"
  
  
  def _run_test_moving_average_min_max_observer(
      test_case,
      device_type,
      dtype,
      activation_shape,
      quantization_bit,
      quantization_scheme,
      quantization_formula,
      momentum,
  ):
      moving_max_np = np.zeros((1,))
      moving_min_np = np.zeros((1,))
      current_train_step_tensor = flow.Tensor(
          np.zeros((1,)).astype(np.float32),
          dtype=flow.int64,
          device=flow.device(device_type),
      )
      for i in range(10):
          activation = (np.random.random(activation_shape) - 0.5).astype(
              type_name_to_np_type[dtype]
          )
          activation_tensor = flow.Tensor(activation, device=flow.device(device_type))
          moving_average_min_max_observer = flow.nn.MovingAverageMinMaxObserver(
              training=True,
              quantization_formula=quantization_formula,
              stop_update_after_iters=1,
              quantization_bit=quantization_bit,
              quantization_scheme=quantization_scheme,
              momentum=momentum,
          )
          moving_average_min_max_observer = moving_average_min_max_observer.to(
              device_type
          )
          (scale, zero_point) = moving_average_min_max_observer(
              activation_tensor, current_train_step_tensor
          )
          _check_moving_average_min_max_observer(
              test_case,
              activation,
              scale.numpy(),
              zero_point.numpy(),
              moving_max_np,
              moving_min_np,
              quantization_bit,
              quantization_scheme,
              quantization_formula,
              momentum,
          )
  
  
  class TestMovingAverageMinMaxObserver(flow.unittest.TestCase):
      def test_moving_average_min_max_observer(test_case):
          arg_dict = OrderedDict()
          arg_dict["test_case"] = [test_case]
          arg_dict["device_type"] = ["cpu", "cuda"]
          arg_dict["dtype"] = ["float32", "double"]
          arg_dict["activation_shape"] = [(9, 40, 20, 10)]
          arg_dict["quantization_bit"] = [8, 2]
          arg_dict["quantization_scheme"] = ["symmetric", "affine"]
          arg_dict["quantization_formula"] = ["google"]
          arg_dict["momentum"] = [0.95]
          for arg in GenArgList(arg_dict):
              _run_test_moving_average_min_max_observer(*arg)
  
  
  if __name__ == "__main__":
      unittest.main()
  