text: # Copyright 2021 Huawei Technologies Co., Ltd
  #
  # 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.
  # ============================================================================
  """
  Initialize.
  """
  import math
  from functools import reduce
  import numpy as np
  import mindspore.nn as nn
  from mindspore import Tensor
  from mindspore.common import initializer as init
  
  def _calculate_gain(nonlinearity, param=None):
      r"""
      Return the recommended gain value for the given nonlinearity function.
  
      The values are as follows:
      ================= ====================================================
      nonlinearity      gain
      ================= ====================================================
      Linear / Identity :math:`1`
      Conv{1,2,3}D      :math:`1`
      Sigmoid           :math:`1`
      Tanh              :math:`\frac{5}{3}`
      ReLU              :math:`\sqrt{2}`
      Leaky Relu        :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
      ================= ====================================================
  
      Args:
          nonlinearity: the non-linear function
          param: optional parameter for the non-linear function
  
      Examples:
          >>> gain = calculate_gain('leaky_relu', 0.2)  # leaky_relu with negative_slope=0.2
      """
      linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
      if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
          return 1
      if nonlinearity == 'tanh':
          return 5.0 / 3
      if nonlinearity == 'relu':
          return math.sqrt(2.0)
      if nonlinearity == 'leaky_relu':
          if param is None:
              negative_slope = 0.01
          elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
              negative_slope = param
          else:
              raise ValueError("negative_slope {} not a valid number".format(param))
          return math.sqrt(2.0 / (1 + negative_slope ** 2))
  
      raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
  
  def _assignment(arr, num):
      """Assign the value of `num` to `arr`."""
      if arr.shape == ():
          arr = arr.reshape((1))
          arr[:] = num
          arr = arr.reshape(())
      else:
          if isinstance(num, np.ndarray):
              arr[:] = num[:]
          else:
              arr[:] = num
      return arr
  
  def _calculate_in_and_out(arr):
      """
      Calculate n_in and n_out.
  
      Args:
          arr (Array): Input array.
  
      Returns:
          Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
      """
      dim = len(arr.shape)
      if dim < 2:
          raise ValueError("If initialize data with xavier uniform, the dimension of data must greater than 1.")
  
      n_in = arr.shape[1]
      n_out = arr.shape[0]
  
      if dim > 2:
          counter = reduce(lambda x, y: x * y, arr.shape[2:])
          n_in *= counter
          n_out *= counter
      return n_in, n_out
  
  def _select_fan(array, mode):
      mode = mode.lower()
      valid_modes = ['fan_in', 'fan_out']
      if mode not in valid_modes:
          raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
  
      fan_in, fan_out = _calculate_in_and_out(array)
      return fan_in if mode == 'fan_in' else fan_out
  
  class KaimingInit(init.Initializer):
      r"""
      Base Class. Initialize the array with He kaiming algorithm.
  
      Args:
          a: the negative slope of the rectifier used after this layer (only
              used with ``'leaky_relu'``)
          mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
              preserves the magnitude of the variance of the weights in the
              forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
              backwards pass.
          nonlinearity: the non-linear function, recommended to use only with
              ``'relu'`` or ``'leaky_relu'`` (default).
      """
      def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
          super(KaimingInit, self).__init__()
          self.mode = mode
          self.gain = _calculate_gain(nonlinearity, a)
  
      def _initialize(self, arr):
          pass
  
  
  class KaimingUniform(KaimingInit):
      r"""
      Initialize the array with He kaiming uniform algorithm. The resulting tensor will
      have values sampled from :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
  
      .. math::
          \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
  
      Input:
          arr (Array): The array to be assigned.
  
      Returns:
          Array, assigned array.
  
      Examples:
          >>> w = np.empty(3, 5)
          >>> KaimingUniform(w, mode='fan_in', nonlinearity='relu')
      """
  
      def _initialize(self, arr):
          fan = _select_fan(arr, self.mode)
          bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
          data = np.random.uniform(-bound, bound, arr.shape)
  
          _assignment(arr, data)
  
  
  class KaimingNormal(KaimingInit):
      r"""
      Initialize the array with He kaiming normal algorithm. The resulting tensor will
      have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
  
      .. math::
          \text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
  
      Input:
          arr (Array): The array to be assigned.
  
      Returns:
          Array, assigned array.
  
      Examples:
          >>> w = np.empty(3, 5)
          >>> KaimingNormal(w, mode='fan_out', nonlinearity='relu')
      """
  
      def _initialize(self, arr):
          fan = _select_fan(arr, self.mode)
          std = self.gain / math.sqrt(fan)
          data = np.random.normal(0, std, arr.shape)
  
          _assignment(arr, data)
  
  
  def default_recurisive_init(custom_cell):
      """default_recurisive_init"""
      for _, cell in custom_cell.cells_and_names():
          if isinstance(cell, nn.Conv2d):
              cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)), cell.weight.shape, cell.weight.dtype))
              if cell.bias is not None:
                  fan_in, _ = _calculate_in_and_out(cell.weight.asnumpy())
                  bound = 1 / math.sqrt(fan_in)
                  cell.bias.set_data(Tensor(np.random.uniform(-bound, bound, cell.bias.shape), cell.bias.dtype))
          elif isinstance(cell, nn.Dense):
              cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)), cell.weight.shape, cell.weight.dtype))
              if cell.bias is not None:
                  fan_in, _ = _calculate_in_and_out(cell.weight.asnumpy())
                  bound = 1 / math.sqrt(fan_in)
                  cell.bias.set_data(Tensor(np.random.uniform(-bound, bound, cell.bias.shape), cell.bias.dtype))
          elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
              pass
  