text: #
  # Copyright (c) 2021, NVIDIA CORPORATION. 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.
  #
  
  
  """test for str and repr
  Make sure things can print and in a nice form. Put all the print tests together so that running this test file alone
  can inspect all the print messages in the project
  """
  
  import torch
  from torch import nn
  
  from pytorch_quantization import calib
  from pytorch_quantization import tensor_quant
  from pytorch_quantization import nn as quant_nn
  from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer
  
  # pylint:disable=missing-docstring, no-self-use
  
  class TestPrint():
  
      def test_print_descriptor(self):
          test_desc = tensor_quant.QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL
          print(test_desc)
  
      def test_print_tensor_quantizer(self):
          test_quantizer = TensorQuantizer()
          print(test_quantizer)
  
      def test_print_module(self):
          class _TestModule(nn.Module):
              def __init__(self):
                  super(_TestModule, self).__init__()
                  self.conv = nn.Conv2d(33, 65, 3)
                  self.quant_conv = quant_nn.Conv2d(33, 65, 3)
                  self.linear = nn.Linear(33, 65)
                  self.quant_linear = quant_nn.Linear(33, 65)
  
          test_module = _TestModule()
          print(test_module)
  
      def test_print_calibrator(self):
          print(calib.MaxCalibrator(7, 1, False))
          hist_calibrator = calib.HistogramCalibrator(8, None, True)
          hist_calibrator.collect(torch.rand(10))
          print(hist_calibrator)
  