# 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 logging
import unittest


class TrtConvertGatherTest(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))
        ]
        if len(inputs['input_data'].shape) <= attrs[0]['axis']:
            return False

        return True

    def sample_program_configs(self):
        def generate_input1(shape):
            return np.random.random(shape).astype(np.float32)

        def generate_input2(index):
            return np.array(index).astype(np.int32)

        def generate_input3(axis):
            return np.array([axis]).astype(np.int32)

        for shape in [[32], [16, 64], [32, 16, 16], [32, 64, 16, 32]]:
            for index in [[1, 4], [4, 8]]:
                for axis in [0, 1, 2, 3]:
                    for overwrite in [True, False]:
                        for input in [{
                                "X": ["input_data"],
                                "Index": ["index_data"]
                        }, {
                                "X": ["input_data"],
                                "Index": ["index_data"],
                                "Axis": ["axis_data"]
                        }]:
                            self.shape = shape
                            self.axis = axis
                            self.input_num = len(input)
                            dics = [{"overwrite": overwrite, "axis": axis}]
                            ops_config = [{
                                "op_type": "gather",
                                "op_inputs": input,
                                "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_input1, shape)),
                                    "index_data": TensorConfig(data_gen=partial(
                                        generate_input2, index)),
                                } if len(input) == 2 else {
                                    "input_data": TensorConfig(data_gen=partial(
                                        generate_input1, shape)),
                                    "index_data": TensorConfig(data_gen=partial(
                                        generate_input2, index)),
                                    "axis_data": TensorConfig(data_gen=partial(
                                        generate_input3, axis)),
                                },
                                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 len(self.shape) == 1:
                self.dynamic_shape.min_input_shape = {
                    "input_data": [4],
                    "index_data": [1]
                }
                self.dynamic_shape.max_input_shape = {
                    "input_data": [128],
                    "index_data": [4]
                }
                self.dynamic_shape.opt_input_shape = {
                    "input_data": [16],
                    "index_data": [2]
                }
            elif len(self.shape) == 2:
                self.dynamic_shape.min_input_shape = {
                    "input_data": [2, 4],
                    "index_data": [1]
                }
                self.dynamic_shape.max_input_shape = {
                    "input_data": [256, 256],
                    "index_data": [4]
                }
                self.dynamic_shape.opt_input_shape = {
                    "input_data": [64, 32],
                    "index_data": [2]
                }
            elif len(self.shape) == 3:
                self.dynamic_shape.min_input_shape = {
                    "input_data": [2, 4, 4],
                    "index_data": [1]
                }
                self.dynamic_shape.max_input_shape = {
                    "input_data": [128, 256, 256],
                    "index_data": [4]
                }
                self.dynamic_shape.opt_input_shape = {
                    "input_data": [16, 64, 32],
                    "index_data": [2]
                }
            elif len(self.shape) == 4:
                self.dynamic_shape.min_input_shape = {
                    "input_data": [2, 4, 4, 2],
                    "index_data": [1]
                }
                self.dynamic_shape.max_input_shape = {
                    "input_data": [128, 256, 128, 256],
                    "index_data": [4]
                }
                self.dynamic_shape.opt_input_shape = {
                    "input_data": [16, 64, 16, 32],
                    "index_data": [2]
                }

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

        def generate_trt_nodes_num(dynamic_shape):
            if self.input_num == 3:
                return 0, 5
            else:
                if dynamic_shape and self.axis == 0:
                    return 1, 3
                else:
                    return 0, 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(
            False), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), generate_trt_nodes_num(
            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(True), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5

    def add_skip_trt_case(self):
        def teller1(program_config, predictor_config):
            if len(self.dynamic_shape.min_input_shape) != 0:
                inputs = program_config.inputs
                if len(inputs['input_data'].shape) == 1 or len(inputs[
                        'index_data'].shape) == 1:
                    return True
            return False

        self.add_skip_case(
            teller1, SkipReasons.TRT_NOT_SUPPORT,
            "Need to repair the case: trt reshape out failed for dynamic shape mode when inputs' dims==1."
        )

        def teller2(program_config, predictor_config):
            inputs = program_config.inputs
            if "axis_data" in inputs.keys():
                return True
            return False

        self.add_skip_case(
            teller2, SkipReasons.TRT_NOT_SUPPORT,
            "Need to repair the case: trt do not support axis tensor input.")

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


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