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 trt_layer_auto_scan_test import TrtLayerAutoScanTest, 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
  
  
  class TrtConvertShuffleChannelTest(TrtLayerAutoScanTest):
  
      def is_program_valid(self, program_config: ProgramConfig) -> bool:
          return True
  
      def sample_program_configs(self):
  
          def generate_input1(attrs: List[Dict[str, Any]], batch):
              return np.ones([batch, 6, 24, 24]).astype(np.float32)
  
          for batch in [1, 2, 4]:
              for group in [1, 2, 3]:
                  dics = [{"group": group}, {}]
                  ops_config = [{
                      "op_type": "shuffle_channel",
                      "op_inputs": {
                          "X": ["shuffle_channel_input"]
                      },
                      "op_outputs": {
                          "Out": ["shuffle_channel_out"]
                      },
                      "op_attrs": dics[0]
                  }]
                  ops = self.generate_op_config(ops_config)
                  program_config = ProgramConfig(
                      ops=ops,
                      weights={},
                      inputs={
                          "shuffle_channel_input":
                          TensorConfig(
                              data_gen=partial(generate_input1, dics, batch))
                      },
                      outputs=["shuffle_channel_out"])
  
                  yield program_config
  
      def sample_predictor_configs(
              self, program_config) -> (paddle_infer.Config, List[int], float):
  
          def generate_dynamic_shape(attrs):
              self.dynamic_shape.min_input_shape = {
                  "shuffle_channel_input": [1, 6, 24, 24]
              }
              self.dynamic_shape.max_input_shape = {
                  "shuffle_channel_input": [4, 6, 48, 48]
              }
              self.dynamic_shape.opt_input_shape = {
                  "shuffle_channel_input": [1, 6, 24, 48]
              }
  
          def clear_dynamic_shape():
              self.dynamic_shape.min_input_shape = {}
              self.dynamic_shape.max_input_shape = {}
              self.dynamic_shape.opt_input_shape = {}
  
          def generate_trt_nodes_num(attrs, dynamic_shape):
              if dynamic_shape == True:
                  return 0, 3
              else:
                  return 1, 2
  
          attrs = [
              program_config.ops[i].attrs for i in range(len(program_config.ops))
          ]
          self.trt_param.max_batch_size = 9
          # for static_shape
          clear_dynamic_shape()
          self.trt_param.precision = paddle_infer.PrecisionType.Float32
          yield self.create_inference_config(), generate_trt_nodes_num(
              attrs, False), 1e-5
          self.trt_param.precision = paddle_infer.PrecisionType.Half
          yield self.create_inference_config(), generate_trt_nodes_num(
              attrs, False), 1e-5
  
          # for dynamic_shape
          generate_dynamic_shape(attrs)
          self.trt_param.precision = paddle_infer.PrecisionType.Float32
          yield self.create_inference_config(), generate_trt_nodes_num(
              attrs, True), 1e-5
          self.trt_param.precision = paddle_infer.PrecisionType.Half
          yield self.create_inference_config(), generate_trt_nodes_num(
              attrs, True), 1e-5
  
      def add_skip_trt_case(self):
          pass
  
      def test(self):
          self.add_skip_trt_case()
          self.run_test()
  
  
  if __name__ == "__main__":
      unittest.main()
  