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 unittest
  import numpy as np
  import paddle.inference as paddle_infer
  from functools import partial
  from typing import Optional, List, Callable, Dict, Any, Set
  
  
  class TrtConvertDropoutTest(TrtLayerAutoScanTest):
      def is_program_valid(self, program_config: ProgramConfig) -> bool:
          return True
  
      def sample_program_configs(self):
          def generate_input1(dims, batch, attrs: List[Dict[str, Any]]):
              if dims == 1:
                  return np.ones([64]).astype(np.float32)
              elif dims == 2:
                  return np.ones([3, 64]).astype(np.float32)
              elif dims == 3:
                  return np.ones([3, 64, 64]).astype(np.float32)
              else:
                  return np.ones([batch, 3, 64, 64]).astype(np.float32)
  
          for dims in [1, 2, 3, 4]:
              for batch in [1, 2, 4]:
                  for fix_seed in [False, True]:
                      for dropout_implementation in [
                              "downgrade_in_infer", "upscale_in_train"
                      ]:
                          for dropout_prob in [np.random.random()]:
                              for seed in [0, 64, 128, 512]:
                                  self.dims = dims
                                  dics = [{
                                      "fix_seed": fix_seed,
                                      "dropout_implementation":
                                      dropout_implementation,
                                      "dropout_prob": dropout_prob,
                                      "seed": seed,
                                      "is_test": True
                                  }]
  
                                  ops_config = [{
                                      "op_type": "dropout",
                                      "op_inputs": {
                                          "X": ["input_data"],
                                      },
                                      "op_outputs": {
                                          "Out": ["dropout_output_data"]
                                      },
                                      "op_attrs": dics[0]
                                  }]
                                  ops = self.generate_op_config(ops_config)
  
                                  program_config = ProgramConfig(
                                      ops=ops,
                                      weights={},
                                      inputs={
                                          "input_data": TensorConfig(
                                              data_gen=partial(generate_input1,
                                                               dims, batch, dics))
                                      },
                                      outputs=["dropout_output_data"])
  
                                  yield program_config
  
      def sample_predictor_configs(
              self, program_config) -> (paddle_infer.Config, List[int], float):
          def generate_dynamic_shape(attrs):
              if self.dims == 1:
                  self.dynamic_shape.min_input_shape = {"input_data": [1]}
                  self.dynamic_shape.max_input_shape = {"input_data": [128]}
                  self.dynamic_shape.opt_input_shape = {"input_data": [64]}
              elif self.dims == 2:
                  self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
                  self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
                  self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]}
              elif self.dims == 3:
                  self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
                  self.dynamic_shape.max_input_shape = {"input_data": [4, 64, 64]}
                  self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]}
              else:
                  self.dynamic_shape.min_input_shape = {
                      "input_data": [1, 3, 32, 32]
                  }
                  self.dynamic_shape.max_input_shape = {
                      "input_data": [4, 3, 64, 64]
                  }
                  self.dynamic_shape.opt_input_shape = {
                      "input_data": [1, 3, 64, 64]
                  }
  
          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 attrs[0]['dropout_implementation'] == "upscale_in_train":
                  return 0, 2
              elif self.dims == 1:
                  return 0, 3
              else:
                  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 len(
                      program_config.inputs['input_data'].shape
              ) == 2 and not predictor_config.tensorrt_dynamic_shape_enabled():
                  return True
              return False
  
          self.add_skip_case(
              teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
              "The output shape has diff, but we can add shuffle layer to resolve it."
          )
  
      def test(self):
          self.add_skip_trt_case()
          self.run_test()
  
  
  if __name__ == "__main__":
      unittest.main()
  