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 TrtConvertMishTest(TrtLayerAutoScanTest):
      def is_program_valid(self, program_config: ProgramConfig) -> bool:
          return True
  
      def sample_program_configs(self):
          def generate_input(batch, dim1, dim2, dim3):
              shape = [batch]
              if dim1 != 0:
                  shape.append(dim1)
              if dim2 != 0:
                  shape.append(dim2)
              if dim3 != 0:
                  shape.append(dim3)
              return np.random.random(shape).astype(np.float32)
  
          for batch in [1, 4]:
              for dim1 in [0, 3]:
                  for dim2 in [0, 16]:
                      for dim3 in [0, 32]:
                          for thre in [5.0, 20.0]:
                              self.dim1 = dim1
                              self.dim2 = dim2
                              self.dim3 = dim3
  
                              if dim1 == 0 and dim2 != 0:
                                  continue
                              if dim1 == 0 and dim2 == 0 and dim3 != 0:
                                  continue
  
                              ops_config = [{
                                  "op_type": "mish",
                                  "op_inputs": {
                                      "X": ["input_data"]
                                  },
                                  "op_outputs": {
                                      "Out": ["mish_output_data"]
                                  },
                                  "op_attrs": {
                                      "threshold": thre
                                  }
                              }]
  
                              ops = self.generate_op_config(ops_config)
                              program_config = ProgramConfig(
                                  ops=ops,
                                  weights={},
                                  inputs={
                                      "input_data": TensorConfig(
                                          data_gen=partial(generate_input, batch,
                                                           dim1, dim2, dim3))
                                  },
                                  outputs=["mish_output_data"])
  
                              yield program_config
  
      def sample_predictor_configs(self, program_config):
          def generate_dynamic_shape(attrs):
              if self.dim1 == 0:
                  self.dynamic_shape.min_input_shape = {"input_data": [1], }
                  self.dynamic_shape.max_input_shape = {"input_data": [4], }
                  self.dynamic_shape.opt_input_shape = {"input_data": [2], }
              else:
                  if self.dim2 == 0 and self.dim3 == 0:
                      self.dynamic_shape.min_input_shape = {
                          "input_data": [1, 1],
                      }
                      self.dynamic_shape.max_input_shape = {
                          "input_data": [4, 64],
                      }
                      self.dynamic_shape.opt_input_shape = {
                          "input_data": [2, 3],
                      }
                  elif self.dim2 != 0 and self.dim3 != 0:
                      self.dynamic_shape.min_input_shape = {
                          "input_data": [1, 1, 1, 1],
                      }
                      self.dynamic_shape.max_input_shape = {
                          "input_data": [4, 64, 128, 128],
                      }
                      self.dynamic_shape.opt_input_shape = {
                          "input_data": [2, 3, 16, 32],
                      }
                  elif self.dim3 == 0:
                      self.dynamic_shape.min_input_shape = {
                          "input_data": [1, 1, 1],
                      }
                      self.dynamic_shape.max_input_shape = {
                          "input_data": [4, 64, 256],
                      }
                      self.dynamic_shape.opt_input_shape = {
                          "input_data": [2, 3, 128],
                      }
  
          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):
              return 1, 2
  
          attrs = [
              program_config.ops[i].attrs
              for i in range(len(program_config.ops))
          ]
  
          # 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):
          def teller1(program_config, predictor_config):
              if self.dim1 == 0 and self.dim2 == 0 and self.dim3 == 0:
                  return True
              return False
  
          self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
                             "Trt does not support 1-dimensional input.")
  
      def test(self):
          self.add_skip_trt_case()
          self.run_test()
  
  
  if __name__ == "__main__":
      unittest.main()
  