text: # Copyright (c) 2021 PaddlePaddle 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.
  
  from auto_scan_test import PassAutoScanTest, SkipReasons
  from program_config import TensorConfig, ProgramConfig
  import numpy as np
  import paddle.inference as paddle_infer
  from functools import partial
  from typing import Optional, List, Callable, Dict, Any, Set
  import unittest
  
  import hypothesis
  from hypothesis import given, settings, seed, example, assume
  import hypothesis.strategies as st
  
  
  class TestConvHardSigmoidMkldnnFusePass(PassAutoScanTest):
      def is_program_valid(self, program_config: ProgramConfig) -> bool:
          return True
  
      def sample_program_config(self, draw):
          data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
          dilations = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
          padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
          groups = draw(st.sampled_from([1, 2, 4]))
          paddings = draw(st.sampled_from([[0, 3], [1, 2, 3, 4]]))
          strides = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
          slope = draw(st.floats(min_value=0, max_value=10))
          offset = draw(st.floats(min_value=0, max_value=10))
          batch_size = draw(st.integers(min_value=1, max_value=4))
  
          def generate_input():
              if data_format == "NCHW":
                  return np.random.random(
                      [batch_size, 48, 64, 64]).astype(np.float32)
              else:
                  return np.random.random(
                      [batch_size, 64, 64, 48]).astype(np.float32)
  
          def generate_weight():
              return np.random.random(
                  [16, int(48 / groups), 3, 3]).astype(np.float32)
  
          ops_config = [{
              "op_type": "conv2d",
              "op_inputs": {
                  "Input": ["input_data"],
                  "Filter": ["input_weight"]
              },
              "op_outputs": {
                  "Output": ["conv_output"]
              },
              "op_attrs": {
                  "data_format": data_format,
                  "dilations": dilations,
                  "padding_algorithm": padding_algorithm,
                  "groups": groups,
                  "paddings": paddings,
                  "strides": strides
              }
          }, {
              "op_type": "hard_sigmoid",
              "op_inputs": {
                  "X": ["conv_output"]
              },
              "op_outputs": {
                  "Out": ["sigmoid_output"]
              },
              "op_attrs": {
                  "slope": slope,
                  "offset": offset
              },
          }]
  
          ops = self.generate_op_config(ops_config)
  
          program_config = ProgramConfig(
              ops=ops,
              weights={
                  "input_weight": TensorConfig(data_gen=partial(generate_weight))
              },
              inputs={
                  "input_data": TensorConfig(data_gen=partial(generate_input)),
              },
              outputs=["sigmoid_output"])
  
          return program_config
  
      def sample_predictor_configs(self, program_config):
          config = self.create_inference_config(use_mkldnn=True)
          yield config, ["conv2d"], (1e-5, 1e-5)
  
      def test(self):
          self.run_and_statis(
              quant=False, passes=["conv_hard_sigmoid_mkldnn_fuse_pass"])
  
  
  if __name__ == "__main__":
      unittest.main()
  