# 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 TrtConvertHardSigmoidTest_dim_2(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input(shape):
            return np.random.random(shape).astype(np.float32)

        for batch in [1, 2, 4]:
            for shape in [[batch, 64], [batch, 32, 64], [batch, 64, 32, 128]]:
                self.input_dim = len(shape)
                for slope in [0.1, 0.5]:
                    for offset in [0.2, 0.7]:
                        dics = [{"slope": slope, "offset": offset}]
                        ops_config = [{
                            "op_type": "hard_sigmoid",
                            "op_inputs": {
                                "X": ["input_data"],
                            },
                            "op_outputs": {
                                "Out": ["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_input, shape))
                            },
                            outputs=["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.input_dim == 2:
                self.dynamic_shape.min_input_shape = {"input_data": [1, 8]}
                self.dynamic_shape.max_input_shape = {"input_data": [64, 128]}
                self.dynamic_shape.opt_input_shape = {"input_data": [2, 16]}
            elif self.input_dim == 3:
                self.dynamic_shape.min_input_shape = {"input_data": [1, 8, 8]}
                self.dynamic_shape.max_input_shape = {
                    "input_data": [64, 128, 256]
                }
                self.dynamic_shape.opt_input_shape = {"input_data": [2, 16, 64]}
            elif self.input_dim == 4:
                self.dynamic_shape.min_input_shape = {
                    "input_data": [1, 8, 8, 4]
                }
                self.dynamic_shape.max_input_shape = {
                    "input_data": [64, 128, 256, 512]
                }
                self.dynamic_shape.opt_input_shape = {
                    "input_data": [2, 16, 64, 128]
                }

        def clear_dynamic_shape():
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        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(), (1, 2), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (1, 2), 1e-5

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), (1, 2), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (1, 2), 1e-5

    def add_skip_trt_case(self):
        def teller(program_config, predictor_config):
            if len(self.dynamic_shape.
                   min_input_shape) == 0 and self.input_dim == 2:
                return True
            return False

        self.add_skip_case(
            teller, SkipReasons.TRT_NOT_SUPPORT,
            "Need to repair the case: the output of trt and GPU has diff when inputs' dims is 2 in static shape mode."
        )

    def test(self):
        self.add_skip_trt_case()
        self.run_test()


if __name__ == "__main__":
    unittest.main()
