text: # Copyright 2019 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.
  
  import numpy as np
  from tensorio import compare_tensor
  from akg.utils import kernel_exec as utils
  from test_op import transpose
  from base import get_rtol_atol
  from gen_random import random_gaussian
  
  def transpose_data(shape, axes, dtype):
      input = random_gaussian(shape, miu=1, sigma=0.3).astype(dtype)
      bench_mark = input.transpose(axes)
      return input, bench_mark
  
  
  def transpose_execute(shape, axes, dtype, attrs):
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = transpose_compile(shape, axes, dtype, attrs, kernel_name=kernel_name, tuning=t)
          if t:
              bench_mark, input, output = gen_data(axes, dtype, shape)
              return mod, bench_mark, (input, output)
          else:
              return mod
      else:
          mod = transpose_compile(shape, axes, dtype, attrs)
          bench_mark, input, output = gen_data(axes, dtype, shape)
          output = utils.mod_launch(mod, (input, output), expect=bench_mark)
  
          # compare result
          rtol, atol = get_rtol_atol("transpose", dtype)
          compare_result = compare_tensor(output, bench_mark, rtol=rtol, atol=atol, equal_nan=True)
          return input, output, bench_mark, compare_result
  
  
  def gen_data(axes, dtype, shape):
      # Generate data
      input = random_gaussian(shape, miu=1, sigma=0.3).astype(dtype)
      bench_mark = input.transpose(axes)
      # mod launch
      out_shape = ()
      for i in axes:
          out_shape = out_shape + (shape[i],)
      output = np.full(out_shape, np.nan, dtype)
      return bench_mark, input, output
  
  
  def transpose_compile(shape, axes, dtype, attrs, kernel_name='transpose', tuning=False):
      op_attrs = [axes]
      return utils.op_build_test(transpose.transpose, [shape], [dtype], op_attrs, kernel_name=kernel_name, attrs=attrs, tuning=tuning)