text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  #
  # Copyright (c) 2021 Intel Corporation
  #
  # 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 onnx
  from .base_operator import QuantOperatorBase
  from .qdq_base_operator import QDQOperatorBase
  from onnxruntime.quantization.quant_utils import QuantizedValueType, \
                                                   attribute_to_kwarg, ms_domain
  from onnx import onnx_pb as onnx_proto
  from neural_compressor.adaptor.ox_utils.util import QuantizedValue
  
  
  class QLinearActivation(QuantOperatorBase):
      def __init__(self, onnx_quantizer, onnx_node):
          super().__init__(onnx_quantizer, onnx_node)
  
      def convert(self):
          node = self.node
          if node.op_type in ['Relu', 'Clip']:
              return
              
          if len(self.quantizer.model.get_children(node)) == 0:
              return
          # No assert on op_type as it is controlled by registry
          # only try to quantize when given quantization parameters for it
          parent = self.quantizer.model.get_parents(node)[0]
          child = self.quantizer.model.get_children(node)[0]
  
          inputs = []
          inputs.extend(parent.input)
          inputs.extend(child.input[1:])
  
          qlinear_activation_output = child.output[0]
          qlinear_activation_name = ""
          if node.name != "":
              qlinear_activation_name = node.name + "_quant"
          kwargs = {}
          for attribute in node.attribute:
              kwargs.update(attribute_to_kwarg(attribute))
          kwargs["domain"] = ms_domain
  
          qlinear_activation_node = onnx.helper.make_node(
              "QLinear" + node.op_type, inputs,
              [qlinear_activation_output], qlinear_activation_name, **kwargs)
  
          self.quantizer.new_nodes.append(qlinear_activation_node)
          self.quantizer.remove_nodes.extend([parent, child, node])
  
  class QDQRemovableActivation(QDQOperatorBase):
      def __init__(self, onnx_quantizer, onnx_node):
          super().__init__(onnx_quantizer, onnx_node)
  
      def quantize(self):
          node = self.node
          if node.input[0] not in self.quantizer.quantized_value_map:
              return
          elif node.output[0] in [i.name for i in self.quantizer.model.model.graph.output]:
              self.quantizer.dequantize_tensor(node, node.input[0])
          else:
              self.quantizer.model.replace_input_of_all_nodes(node.output[0], node.input[0])
              self.quantizer.remove_nodes.append(node)
  
  class QDQActivation(QDQOperatorBase):
      def __init__(self, onnx_quantizer, onnx_node):
          super().__init__(onnx_quantizer, onnx_node)
  
      def quantize(self):
          node = self.node
          data_found, _, _, _, _ = self.quantizer._get_quantization_params(node.output[0])
          if not data_found:
              return
          super().quantize()
  