# 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 TrtConvertConcatTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        inputs = program_config.inputs
        weights = program_config.weights
        outputs = program_config.outputs

        attrs = [
            program_config.ops[i].attrs
            for i in range(len(program_config.ops))
        ]
        #The input dimension should be less than or equal to the set axis.
        if len(inputs['concat_input1'].shape) <= attrs[0]['axis']:
            return False

        return True

    def sample_program_configs(self):
        def generate_input1(attrs: List[Dict[str, Any]], batch):
            if self.dims == 4:
                return np.ones([batch, 3, 24, 24]).astype(np.float32)
            elif self.dims == 3:
                return np.ones([batch, 3, 24]).astype(np.float32)
            elif self.dims == 2:
                return np.ones([batch, 24]).astype(np.float32)
            elif self.dims == 1:
                return np.ones([24]).astype(np.float32)

        def generate_input2(attrs: List[Dict[str, Any]], batch):
            if self.dims == 4:
                return np.ones([batch, 3, 24, 24]).astype(np.float32)
            elif self.dims == 3:
                return np.ones([batch, 3, 24]).astype(np.float32)
            elif self.dims == 2:
                return np.ones([batch, 24]).astype(np.float32)
            elif self.dims == 1:
                return np.ones([24]).astype(np.float32)

        def generate_input3(attrs: List[Dict[str, Any]], batch):
            if self.dims == 4:
                return np.ones([batch, 3, 24, 24]).astype(np.float32)
            elif self.dims == 3:
                return np.ones([batch, 3, 24]).astype(np.float32)
            elif self.dims == 2:
                return np.ones([batch, 24]).astype(np.float32)
            elif self.dims == 1:
                return np.ones([24]).astype(np.float32)

        def generate_weight1(attrs: List[Dict[str, Any]]):
            return np.zeros([1]).astype(np.int32)

        for dims in [1, 2, 3, 4]:
            for num_input in [0, 1]:
                for batch in [1, 2, 4]:
                    for axis in [-1, 0, 1, 2, 3]:
                        self.num_input = num_input
                        self.dims = dims
                        dics = [{"axis": axis}, {}]
                        dics_intput = [{
                            "X": [
                                "concat_input1", "concat_input2",
                                "concat_input3"
                            ],
                            "AxisTensor": ["AxisTensor"],
                        }, {
                            "X": [
                                "concat_input1", "concat_input2",
                                "concat_input3"
                            ]
                        }]
                        dics_inputs = [{
                            "concat_input1": TensorConfig(data_gen=partial(
                                generate_input1, dics, batch)),
                            "concat_input2": TensorConfig(data_gen=partial(
                                generate_input2, dics, batch)),
                            "concat_input3": TensorConfig(data_gen=partial(
                                generate_input3, dics, batch)),
                            "AxisTensor": TensorConfig(data_gen=partial(
                                generate_weight1, dics))
                        }, {
                            "concat_input1": TensorConfig(data_gen=partial(
                                generate_input1, dics, batch)),
                            "concat_input2": TensorConfig(data_gen=partial(
                                generate_input2, dics, batch)),
                            "concat_input3": TensorConfig(data_gen=partial(
                                generate_input3, dics, batch))
                        }]
                        ops_config = [{
                            "op_type": "concat",
                            "op_inputs": dics_intput[num_input],
                            "op_outputs": {
                                "Out": ["concat_output"]
                            },
                            "op_attrs": dics[0]
                        }]
                        ops = self.generate_op_config(ops_config)
                        program_config = ProgramConfig(
                            ops=ops,
                            weights={},
                            inputs=dics_inputs[num_input],
                            outputs=["concat_output"])

                        yield program_config

    def sample_predictor_configs(
            self, program_config) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            if self.num_input == 0:
                if self.dims == 4:
                    self.dynamic_shape.min_input_shape = {
                        "concat_input1": [1, 3, 24, 24],
                        "concat_input2": [1, 3, 24, 24],
                        "concat_input3": [1, 3, 24, 24],
                        "AxisTensor": [1]
                    }
                    self.dynamic_shape.max_input_shape = {
                        "concat_input1": [4, 3, 48, 48],
                        "concat_input2": [4, 3, 48, 48],
                        "concat_input3": [4, 3, 48, 48],
                        "AxisTensor": [1]
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "concat_input1": [1, 3, 24, 24],
                        "concat_input2": [1, 3, 24, 24],
                        "concat_input3": [1, 3, 24, 24],
                        "AxisTensor": [1]
                    }
                elif self.dims == 3:
                    self.dynamic_shape.min_input_shape = {
                        "concat_input1": [1, 3, 24],
                        "concat_input2": [1, 3, 24],
                        "concat_input3": [1, 3, 24],
                        "AxisTensor": [1]
                    }
                    self.dynamic_shape.max_input_shape = {
                        "concat_input1": [4, 12, 48],
                        "concat_input2": [4, 12, 48],
                        "concat_input3": [4, 12, 48],
                        "AxisTensor": [1]
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "concat_input1": [1, 3, 24],
                        "concat_input2": [1, 3, 24],
                        "concat_input3": [1, 3, 24],
                        "AxisTensor": [1]
                    }
                elif self.dims == 2:
                    self.dynamic_shape.min_input_shape = {
                        "concat_input1": [1, 24],
                        "concat_input2": [1, 24],
                        "concat_input3": [1, 24],
                        "AxisTensor": [1]
                    }
                    self.dynamic_shape.max_input_shape = {
                        "concat_input1": [4, 48],
                        "concat_input2": [4, 48],
                        "concat_input3": [4, 48],
                        "AxisTensor": [1]
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "concat_input1": [1, 24],
                        "concat_input2": [1, 24],
                        "concat_input3": [1, 24],
                        "AxisTensor": [1]
                    }
                elif self.dims == 1:
                    self.dynamic_shape.min_input_shape = {
                        "concat_input1": [24],
                        "concat_input2": [24],
                        "concat_input3": [24],
                        "AxisTensor": [0]
                    }
                    self.dynamic_shape.max_input_shape = {
                        "concat_input1": [48],
                        "concat_input2": [48],
                        "concat_input3": [48],
                        "AxisTensor": [0]
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "concat_input1": [24],
                        "concat_input2": [24],
                        "concat_input3": [24],
                        "AxisTensor": [0]
                    }
            elif self.num_input == 1:
                if self.dims == 4:
                    self.dynamic_shape.min_input_shape = {
                        "concat_input1": [1, 3, 24, 24],
                        "concat_input2": [1, 3, 24, 24],
                        "concat_input3": [1, 3, 24, 24],
                    }
                    self.dynamic_shape.max_input_shape = {
                        "concat_input1": [4, 3, 48, 48],
                        "concat_input2": [4, 3, 48, 48],
                        "concat_input3": [4, 3, 48, 48]
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "concat_input1": [1, 3, 24, 24],
                        "concat_input2": [1, 3, 24, 24],
                        "concat_input3": [1, 3, 24, 24]
                    }
                elif self.dims == 3:
                    self.dynamic_shape.min_input_shape = {
                        "concat_input1": [1, 3, 24],
                        "concat_input2": [1, 3, 24],
                        "concat_input3": [1, 3, 24]
                    }
                    self.dynamic_shape.max_input_shape = {
                        "concat_input1": [4, 12, 48],
                        "concat_input2": [4, 12, 48],
                        "concat_input3": [4, 12, 48]
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "concat_input1": [1, 3, 24],
                        "concat_input2": [1, 3, 24],
                        "concat_input3": [1, 3, 24]
                    }
                elif self.dims == 2:
                    self.dynamic_shape.min_input_shape = {
                        "concat_input1": [1, 24],
                        "concat_input2": [1, 24],
                        "concat_input3": [1, 24]
                    }
                    self.dynamic_shape.max_input_shape = {
                        "concat_input1": [4, 48],
                        "concat_input2": [4, 48],
                        "concat_input3": [4, 48]
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "concat_input1": [1, 24],
                        "concat_input2": [1, 24],
                        "concat_input3": [1, 24]
                    }
                elif self.dims == 1:
                    self.dynamic_shape.min_input_shape = {
                        "concat_input1": [24],
                        "concat_input2": [24],
                        "concat_input3": [24]
                    }
                    self.dynamic_shape.max_input_shape = {
                        "concat_input1": [48],
                        "concat_input2": [48],
                        "concat_input3": [48]
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "concat_input1": [24],
                        "concat_input2": [24],
                        "concat_input3": [24]
                    }

        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 dynamic_shape == True:
                if attrs[0]['axis'] >= 0:
                    return 1, 4
                else:
                    return 0, 5
            else:
                if attrs[0]['axis'] > 0:
                    return 1, 4
                else:
                    return 0, 5

        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) == 4:
                return True
            return False

        self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
                           "INPUT AxisTensor NOT SUPPORT")

        def teller2(program_config, predictor_config):
            if len(
                    program_config.inputs['concat_input1'].shape
            ) == 2 and not predictor_config.tensorrt_dynamic_shape_enabled():
                return True
            return False

        self.add_skip_case(
            teller2, 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()
