# Copyright (c) 2022 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 unittest
from functools import partial
from typing import List

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest

import paddle.inference as paddle_infer


class TrtConvertActivationTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        ver = paddle_infer.get_trt_compile_version()
        if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8415:
            return False
        return True

    def sample_program_configs(self):
        self.trt_param.workspace_size = 1073741824

        def generate_input1(dims, batch):
            if dims == 1:
                return np.zeros((batch)).astype(np.float32)
            elif dims == 2:
                return np.ones((batch, 4)).astype(np.float32)
            elif dims == 3:
                return np.ones((batch, 4, 6)).astype(np.float32)
            else:
                return np.ones((batch, 4, 6, 8)).astype(np.float32)

        def generate_input2(dims, batch):
            if dims == 1:
                return np.zeros((batch)).astype(np.float32)
            elif dims == 2:
                return np.ones((batch, 4)).astype(np.float32)
            elif dims == 3:
                return np.ones((batch, 4, 6)).astype(np.float32)
            else:
                return np.ones((batch, 4, 6, 8)).astype(np.float32)

        def generate_input3(dims, batch):
            if dims == 1:
                return np.zeros((batch)).astype(np.float32)
            elif dims == 2:
                return np.ones((batch, 4)).astype(np.float32)
            elif dims == 3:
                return np.ones((batch, 4, 6)).astype(np.float32)
            else:
                return np.ones((batch, 4, 6, 8)).astype(np.float32)

        for dims in [1, 2, 3, 4]:
            for batch in [1, 2]:
                self.dims = dims
                dics = [{}]
                ops_config = [
                    {
                        "op_type": "cast",
                        "op_inputs": {"X": ["condition_data"]},
                        "op_outputs": {"Out": ["condition_data_bool"]},
                        "op_attrs": {"in_dtype": 5, "out_dtype": 0},
                        "outputs_dtype": {"condition_data_bool": np.bool},
                    },
                    {
                        "op_type": "where",
                        "op_inputs": {
                            "Condition": ["condition_data_bool"],
                            "X": ["input_x_data"],
                            "Y": ["input_y_data"],
                        },
                        "op_outputs": {"Out": ["output_data"]},
                        "op_attrs": dics[0],
                        "outputs_dtype": {"condition_data_bool": np.bool},
                    },
                ]
                ops = self.generate_op_config(ops_config)

                program_config = ProgramConfig(
                    ops=ops,
                    weights={},
                    inputs={
                        "condition_data": TensorConfig(
                            data_gen=partial(generate_input1, dims, batch)
                        ),
                        "input_x_data": TensorConfig(
                            data_gen=partial(generate_input2, dims, batch)
                        ),
                        "input_y_data": TensorConfig(
                            data_gen=partial(generate_input3, dims, batch)
                        ),
                    },
                    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.dims == 1:
                self.dynamic_shape.min_input_shape = {
                    "condition_data": [1],
                    "condition_data_bool": [1],
                    "input_x_data": [1],
                    "input_y_data": [1],
                }
                self.dynamic_shape.max_input_shape = {
                    "condition_data": [2],
                    "condition_data_bool": [2],
                    "input_x_data": [2],
                    "input_y_data": [2],
                }
                self.dynamic_shape.opt_input_shape = {
                    "condition_data": [1],
                    "condition_data_bool": [1],
                    "input_x_data": [1],
                    "input_y_data": [1],
                }
            elif self.dims == 2:
                self.dynamic_shape.min_input_shape = {
                    "condition_data": [1, 4],
                    "condition_data_bool": [1, 4],
                    "input_x_data": [1, 4],
                    "input_y_data": [1, 4],
                }
                self.dynamic_shape.max_input_shape = {
                    "condition_data": [2, 4],
                    "condition_data_bool": [2, 4],
                    "input_x_data": [2, 4],
                    "input_y_data": [2, 4],
                }
                self.dynamic_shape.opt_input_shape = {
                    "condition_data": [1, 4],
                    "condition_data_bool": [1, 4],
                    "input_x_data": [1, 4],
                    "input_y_data": [1, 4],
                }
            elif self.dims == 3:
                self.dynamic_shape.min_input_shape = {
                    "condition_data": [1, 4, 6],
                    "condition_data_bool": [1, 4, 6],
                    "input_x_data": [1, 4, 6],
                    "input_y_data": [1, 4, 6],
                }
                self.dynamic_shape.max_input_shape = {
                    "condition_data": [2, 4, 6],
                    "condition_data_bool": [2, 4, 6],
                    "input_x_data": [2, 4, 6],
                    "input_y_data": [2, 4, 6],
                }
                self.dynamic_shape.opt_input_shape = {
                    "condition_data": [1, 4, 6],
                    "condition_data_bool": [1, 4, 6],
                    "input_x_data": [1, 4, 6],
                    "input_y_data": [1, 4, 6],
                }
            elif self.dims == 4:
                self.dynamic_shape.min_input_shape = {
                    "condition_data": [1, 4, 6, 8],
                    "condition_data_bool": [1, 4, 6, 8],
                    "input_x_data": [1, 4, 6, 8],
                    "input_y_data": [1, 4, 6, 8],
                }
                self.dynamic_shape.max_input_shape = {
                    "condition_data": [2, 4, 6, 8],
                    "condition_data_bool": [2, 4, 6, 8],
                    "input_x_data": [2, 4, 6, 8],
                    "input_y_data": [2, 4, 6, 8],
                }
                self.dynamic_shape.opt_input_shape = {
                    "condition_data": [1, 4, 6, 8],
                    "condition_data_bool": [1, 4, 6, 8],
                    "input_x_data": [1, 4, 6, 8],
                    "input_y_data": [1, 4, 6, 8],
                }

        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 not dynamic_shape:
                return 0, 6
            return 1, 4

        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 test(self):
        self.run_test()


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