# 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
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 Any, Dict, List
import unittest


class TrtConvertReduceSumTest(TrtLayerAutoScanTest):

    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        inputs = program_config.inputs
        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        ## dim should be in (-rank, rank), and not NONE
        rank = len(inputs['input_data'].shape)
        for x in attrs[0]["dim"]:
            if x >= rank or x <= -rank:
                return False
        if len(attrs[0]["dim"]) == 0:
            return False

        return True

    def sample_program_configs(self):

        def generate_input1(dtype, attrs: List[Dict[str, Any]]):
            if dtype == -1 or dtype == 5:
                return np.random.random([1, 3, 32, 32]).astype(np.float32)
            elif dtype == 2:
                return np.random.random([1, 3, 32, 32]).astype(np.int32)

        for keep_dim in [True, False]:
            for dim in [[], [1], [0], [0, 1], [1, 2, 3], [-2, 0, 3], [-3],
                        [-4, 1], [3, 4, 5]]:
                for reduce_all in [True, False]:
                    for out_dtype in [-1, 2, 5]:
                        dics = [{
                            "keep_dim": keep_dim,
                            "dim": dim,
                            "reduce_all": reduce_all,
                            "out_dtype": out_dtype,
                            "in_dtype": out_dtype,
                        }, {}]

                        ops_config = [{
                            "op_type": "reduce_sum",
                            "op_inputs": {
                                "X": ["input_data"]
                            },
                            "op_outputs": {
                                "Out": ["reduce_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, out_dtype, dics))
                            },
                            outputs=["reduce_output_data"])

                        if not self.is_program_valid(program_config):
                            continue

                        yield program_config

    def sample_predictor_configs(self, program_config):

        def generate_dynamic_shape(attrs):
            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, 32, 32]}

        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:
                if (not attrs[0]['keep_dim']) and attrs[0]['reduce_all']:
                    return 0, 3
                else:
                    return 1, 2
            else:
                if 0 in attrs[0]['dim'] or attrs[0]['reduce_all']:
                    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, 1e-5)
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, False), (1e-4, 1e-4)

        # 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, 1e-5)
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, True), (1e-4, 1e-4)

    def add_skip_trt_case(self):
        pass

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


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