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.
  
  import sys
  sys.path.append('../')
  
  from auto_scan_test import AutoScanTest, IgnoreReasons
  from program_config import TensorConfig, ProgramConfig, OpConfig, CxxConfig, TargetType, PrecisionType, DataLayoutType, Place
  import unittest
  
  import hypothesis
  from hypothesis import given, settings, seed, example, assume, reproduce_failure
  import hypothesis.strategies as st
  import numpy as np
  from functools import partial
  
  
  class TestEqualOp(AutoScanTest):
      def __init__(self, *args, **kwargs):
          AutoScanTest.__init__(self, *args, **kwargs)
          self.enable_testing_on_place(
              TargetType.Host,
              [PrecisionType.FP32, PrecisionType.INT64, PrecisionType.INT32],
              DataLayoutType.NCHW,
              thread=[1, 4])
  
      def is_program_valid(self,
                           program_config: ProgramConfig,
                           predictor_config: CxxConfig) -> bool:
          target_type = predictor_config.target()
          input_data_type = program_config.inputs["input_data_x"].dtype
          if predictor_config.precision(
          ) == PrecisionType.INT64 and input_data_type != np.int64:
              return False
          if predictor_config.precision(
          ) == PrecisionType.FP32 and input_data_type != np.float32:
              return False
          if predictor_config.precision(
          ) == PrecisionType.INT32 and input_data_type != np.int32:
              return False
          return True
  
      def sample_program_configs(self, draw):
          in_shape = draw(
              st.lists(
                  st.integers(
                      min_value=1, max_value=8), min_size=1, max_size=6))
          data_type = draw(st.sampled_from(['float32', 'int32', 'int64']))
  
          def gen_input_data(*args, **kwargs):
              if kwargs['dtype'] == 'float32':
                  return np.random.normal(0.0, 1.0,
                                          kwargs['shape']).astype(np.float32)
              elif kwargs['dtype'] == 'int32':
                  return np.random.randint(1, 40,
                                           kwargs['shape']).astype(np.int32)
              elif kwargs['dtype'] == 'int64':
                  return np.random.randint(1, 40,
                                           kwargs['shape']).astype(np.int64)
  
          equal_op = OpConfig(
              type="equal",
              inputs={"X": ["input_data_x"],
                      "Y": ["input_data_y"]},
              outputs={"Out": ["output_data"]},
              attrs={})
          cast_op = OpConfig(
              type="cast",
              inputs={"X": ["output_data"]},
              outputs={"Out": ["cast_output_data"]},
              attrs={'in_dtype': 0,
                     'out_dtype': 5})  # 0: bool , 5: float
          program_config = ProgramConfig(
              ops=[equal_op, cast_op],
              weights={},
              inputs={
                  "input_data_x": TensorConfig(data_gen=partial(
                      gen_input_data, dtype=data_type, shape=in_shape)),
                  "input_data_y": TensorConfig(data_gen=partial(
                      gen_input_data, dtype=data_type, shape=in_shape))
              },
              outputs=["cast_output_data"])
          return program_config
  
      def sample_predictor_configs(self):
          return self.get_predictor_configs(), ["equal"], (1e-5, 1e-5)
  
      def add_ignore_pass_case(self):
          pass
  
      def test(self, *args, **kwargs):
          self.run_and_statis(quant=False, max_examples=300)
  
  
  if __name__ == "__main__":
      unittest.main(argv=[''])
  