# 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


class TrtConvertPreluTest(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)

        def generate_alpha(attrs: List[Dict[str, Any]], dim1, dim2, dim3):
            if attrs[0]["mode"] == "all":
                return np.random.random(size=(1)).astype(np.float32)
            elif attrs[0]["mode"] == "channel":
                shape = [1]
                if dim1 != 0:
                    shape.append(dim1)
                if dim2 != 0:
                    shape.append(1)
                if dim3 != 0:
                    shape.append(1)
                return np.random.random(size=shape).astype(np.float32)
            elif attrs[0]["mode"] == "element":
                shape = [1]
                if dim1 != 0:
                    shape.append(dim1)
                if dim2 != 0:
                    shape.append(dim2)
                if dim3 != 0:
                    shape.append(dim3)
                return np.random.random(size=shape).astype(np.float32)

        for batch in [1, 4]:
            for dim1 in [0, 3]:
                for dim2 in [0, 16]:
                    for dim3 in [0, 32]:
                        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

                        for mode in ["all", "channel", "element"]:
                            if mode == "channel" and dim1 == 0:
                                continue
                            dics = [{"mode": mode}]
                            ops_config = [{
                                "op_type": "prelu",
                                "op_inputs": {
                                    "X": ["input_data"],
                                    "Alpha": ["alpha_weight"]
                                },
                                "op_outputs": {
                                    "Out": ["output_data"]
                                },
                                "op_attrs": dics[0]
                            }]
                            ops = self.generate_op_config(ops_config)

                            program_config = ProgramConfig(
                                ops=ops,
                                weights={
                                    "alpha_weight": TensorConfig(
                                        data_gen=partial(generate_alpha, dics,
                                                         dim1, dim2, dim3))
                                },
                                inputs={
                                    "input_data": TensorConfig(
                                        data_gen=partial(generate_input, batch,
                                                         dim1, dim2, dim3)),
                                },
                                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.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.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 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 teller2(program_config, predictor_config):
            if (len(self.dynamic_shape.min_input_shape) == 0):
                if self.dim1 != 0 and self.dim2 == 0 and self.dim3 == 0:
                    return True
            return False

        self.add_skip_case(
            teller2, SkipReasons.TRT_NOT_SUPPORT,
            "Need to repair the case: the output of GPU and tensorrt has diff when the input dimension is 2 in static shape mode."
        )

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


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