# 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 TrtConvertSkipLayernormTest(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 attrs[0]['begin_norm_axis'] >= 0:
            if len(inputs['skip_layernorm_inputX_data'].shape) <= attrs[0][
                    'begin_norm_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, 6, 128, 768]).astype(np.float32)
            elif self.dims == 3:
                return np.ones([batch, 128, 768]).astype(np.float32)
            elif self.dims == 2:
                return np.ones([batch, 128, 768]).astype(np.float32)

        def generate_input2(attrs: List[Dict[str, Any]], batch):
            if self.dims == 4:
                return np.ones([batch, 6, 128, 768]).astype(np.float32)
            elif self.dims == 3:
                return np.ones([batch, 128, 768]).astype(np.float32)
            elif self.dims == 2:
                return np.ones([batch, 128, 768]).astype(np.float32)

        def generate_weight1(attrs: List[Dict[str, Any]]):
            return np.random.random([768]).astype(np.float32)

        def generate_weight2(attrs: List[Dict[str, Any]]):
            return np.random.random([768]).astype(np.float32)

        for dims in [3, 4]:
            for batch in [1, 2, 4]:
                for epsilon in [1e-5]:
                    for begin_norm_axis in [0, 1, 2, -1]:
                        for enable_int8 in [False, True]:
                            self.dims = dims
                            dics = [{
                                "epsilon": epsilon,
                                "begin_norm_axis": begin_norm_axis,
                                "enable_int8": enable_int8
                            }, {}]
                            ops_config = [{
                                "op_type": "skip_layernorm",
                                "op_inputs": {
                                    "X": ["skip_layernorm_inputX_data"],
                                    "Y": ["skip_layernorm_inputY_data"],
                                    "Bias": ["Bias"],
                                    "Scale": ["Scale"]
                                },
                                "op_outputs": {
                                    "Out": ["skip_layernorm_out"]
                                },
                                "op_attrs": dics[0]
                            }]
                            ops = self.generate_op_config(ops_config)
                            program_config = ProgramConfig(
                                ops=ops,
                                weights={
                                    "Bias": TensorConfig(data_gen=partial(
                                        generate_weight1, dics)),
                                    "Scale": TensorConfig(data_gen=partial(
                                        generate_weight2, dics))
                                },
                                inputs={
                                    "skip_layernorm_inputX_data": TensorConfig(
                                        data_gen=partial(generate_input1, dics,
                                                         batch)),
                                    "skip_layernorm_inputY_data": TensorConfig(
                                        data_gen=partial(generate_input2, dics,
                                                         batch))
                                },
                                outputs=["skip_layernorm_out"])

                            yield program_config

    def sample_predictor_configs(
            self, program_config) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            if self.dims == 4:
                self.dynamic_shape.min_input_shape = {
                    "skip_layernorm_inputX_data": [1, 6, 128, 768],
                    "skip_layernorm_inputY_data": [1, 6, 128, 768],
                    "Bias": [768],
                    "Scale": [768]
                }
                self.dynamic_shape.max_input_shape = {
                    "skip_layernorm_inputX_data": [4, 6, 768, 3072],
                    "skip_layernorm_inputY_data": [4, 6, 768, 3072],
                    "Bias": [3072],
                    "Scale": [3072]
                }
                self.dynamic_shape.opt_input_shape = {
                    "skip_layernorm_inputX_data": [1, 6, 128, 768],
                    "skip_layernorm_inputY_data": [1, 6, 128, 768],
                    "Bias": [768],
                    "Scale": [768]
                }
            elif self.dims == 3:
                self.dynamic_shape.min_input_shape = {
                    "skip_layernorm_inputX_data": [1, 128, 768],
                    "skip_layernorm_inputY_data": [1, 128, 768],
                    "Bias": [768],
                    "Scale": [768]
                }
                self.dynamic_shape.max_input_shape = {
                    "skip_layernorm_inputX_data": [4, 768, 3072],
                    "skip_layernorm_inputY_data": [4, 768, 3072],
                    "Bias": [3072],
                    "Scale": [3072]
                }
                self.dynamic_shape.opt_input_shape = {
                    "skip_layernorm_inputX_data": [1, 128, 768],
                    "skip_layernorm_inputY_data": [1, 128, 768],
                    "Bias": [768],
                    "Scale": [768]
                }
            elif self.dims == 2:
                self.dynamic_shape.min_input_shape = {
                    "skip_layernorm_inputX_data": [1, 768],
                    "skip_layernorm_inputY_data": [1, 768],
                    "Bias": [768],
                    "Scale": [768]
                }
                self.dynamic_shape.max_input_shape = {
                    "skip_layernorm_inputX_data": [4, 3072],
                    "skip_layernorm_inputY_data": [4, 3072],
                    "Bias": [3072],
                    "Scale": [3072]
                }
                self.dynamic_shape.opt_input_shape = {
                    "skip_layernorm_inputX_data": [1, 768],
                    "skip_layernorm_inputY_data": [1, 768],
                    "Bias": [768],
                    "Scale": [768]
                }

        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:
                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(
            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):
        pass

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


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