# coding=utf-8
# Copyright 2021 Arm Limited and affiliates.
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.

import array
import json
import os
import sys

sys.path.insert(
    0,
    os.path.join(
        os.getcwd(), "DeepLearningExamples", "PyTorch", "LanguageModeling", "BERT"
    ),
)
sys.path.insert(0, os.getcwd())

try:
    from squad_QSL import get_squad_QSL
    from transformers import BertConfig, BertForQuestionAnswering
    import transformers
    import torch
    import numpy as np
    import mlperf_loadgen as lg
except ImportError:
    raise Exception("Error importing local modules")


class BERT_PyTorch_SUT:
    def __init__(self, args):
        print("Loading BERT configs...")
        with open("bert_config.json") as f:
            config_json = json.load(f)

        config = BertConfig(
            attention_probs_dropout_prob=config_json["attention_probs_dropout_prob"],
            hidden_act=config_json["hidden_act"],
            hidden_dropout_prob=config_json["hidden_dropout_prob"],
            hidden_size=config_json["hidden_size"],
            initializer_range=config_json["initializer_range"],
            intermediate_size=config_json["intermediate_size"],
            max_position_embeddings=config_json["max_position_embeddings"],
            num_attention_heads=config_json["num_attention_heads"],
            num_hidden_layers=config_json["num_hidden_layers"],
            type_vocab_size=config_json["type_vocab_size"],
            vocab_size=config_json["vocab_size"],
        )

        self.network = args.network
        self.dev = (
            torch.device("cuda:0") if torch.cuda.is_available(
            ) else torch.device("cpu")
        )
        self.version = transformers.__version__

        print("Loading PyTorch model...")
        self.model = BertForQuestionAnswering(config)
        self.model.to(self.dev)
        self.model.eval()
        model_file = os.environ.get(
            "ML_MODEL_FILE_WITH_PATH",
            "build/data/bert_tf_v1_1_large_fp32_384_v2/model.pytorch",
        )
        self.model.load_state_dict(torch.load(model_file), strict=False)

        print("Constructing SUT...")
        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
        print("Finished constructing SUT.")

        self.qsl = get_squad_QSL(args.max_examples)

    def issue_queries(self, query_samples):
        for i in range(len(query_samples)):
            eval_features = self.qsl.get_features(query_samples[i].index)
            self.process_sample(eval_features, query_samples[i].id)

    def process_sample(self, sample_input, query_id=None):

        if self.network == "sut":
            input_ids = sample_input["input_ids"]
            input_mask = sample_input["input_mask"]
            segment_ids = sample_input["segment_ids"]
        else:
            input_ids = sample_input.input_ids
            input_mask = sample_input.input_mask
            segment_ids = sample_input.segment_ids

        with torch.no_grad():
            model_output = self.model.forward(
                input_ids=torch.LongTensor(
                    input_ids).unsqueeze(0).to(self.dev),
                attention_mask=torch.LongTensor(
                    input_mask).unsqueeze(0).to(self.dev),
                token_type_ids=torch.LongTensor(
                    segment_ids).unsqueeze(0).to(self.dev),
            )
            if self.version >= "4.0.0":
                start_scores = model_output.start_logits
                end_scores = model_output.end_logits
            else:
                start_scores, end_scores = model_output
            output = (
                torch.stack([start_scores, end_scores], axis=-1)
                .squeeze(0)
                .cpu()
                .numpy()
            )

            if self.network == "sut":
                return output.tolist()

            response_array = array.array("B", output.tobytes())
            bi = response_array.buffer_info()
            response = lg.QuerySampleResponse(query_id, bi[0], bi[1])
            lg.QuerySamplesComplete([response])

    def flush_queries(self):
        pass

    def __del__(self):
        print("Finished destroying SUT.")


def get_pytorch_sut(args):
    return BERT_PyTorch_SUT(args)