# coding=utf-8
# 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.

from time import sleep
from squad_QSL import get_squad_QSL
from transformers import BertConfig, BertForQuestionAnswering
import onnxruntime
import numpy as np
import mlperf_loadgen as lg
import threading
import array
import json
import os
import sys

sys.path.insert(0, os.getcwd())


class BERT_ONNXRuntime_SUT:
    def __init__(self, args):
        self.profile = args.profile
        self.network = args.network
        self.options = onnxruntime.SessionOptions()
        self.options.enable_profiling = args.profile

        print("Loading ONNX model...")
        self.quantized = args.quantized

        model_path = os.environ.get("ML_MODEL_FILE_WITH_PATH")
        if not model_path:
            if self.quantized:
                model_path = "build/data/bert_tf_v1_1_large_fp32_384_v2/bert_large_v1_1_fake_quant.onnx"
            else:
                model_path = "build/data/bert_tf_v1_1_large_fp32_384_v2/model.onnx"
        if len(onnxruntime.get_all_providers()) > 1 and os.environ.get(
            "USE_GPU", "yes"
        ).lower() not in ["0", "false", "off", "no"]:
            preferred_execution_provider = os.environ.get(
                "ONNXRUNTIME_PREFERRED_EXECUTION_PROVIDER", "CUDAExecutionProvider"
            )
            self.sess = onnxruntime.InferenceSession(
                model_path, self.options, providers=[
                    preferred_execution_provider]
            )
        else:
            self.sess = onnxruntime.InferenceSession(
                model_path, self.options, providers=["CPUExecutionProvider"]
            )

        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):
        max_num_threads = int(
            os.environ.get(
                "CM_MAX_NUM_THREADS",
                os.cpu_count()))

        for i in range(len(query_samples)):
            eval_features = self.qsl.get_features(query_samples[i].index)
            n = threading.active_count()
            while n >= max_num_threads:
                # sleep(0.01)
                n = threading.active_count()
            threading.Thread(
                target=self.process_sample, args=[
                    eval_features, query_samples[i].id]
            ).start()

    def process_sample(self, eval_features, query_id=None):
        """For Loadgen over the network"""
        if self.network == "sut":
            input_ids = eval_features["input_ids"]
            input_mask = eval_features["input_mask"]
            segment_ids = eval_features["segment_ids"]
        else:
            input_ids = eval_features.input_ids
            input_mask = eval_features.input_mask
            segment_ids = eval_features.segment_ids

        if self.quantized:
            fd = {
                "input_ids": np.array(input_ids).astype(np.int64)[np.newaxis, :],
                "attention_mask": np.array(input_mask).astype(np.int64)[np.newaxis, :],
                "token_type_ids": np.array(segment_ids).astype(np.int64)[np.newaxis, :],
            }
        else:
            fd = {
                "input_ids": np.array(input_ids).astype(np.int64)[np.newaxis, :],
                "input_mask": np.array(input_mask).astype(np.int64)[np.newaxis, :],
                "segment_ids": np.array(segment_ids).astype(np.int64)[np.newaxis, :],
            }

        scores = self.sess.run([o.name for o in self.sess.get_outputs()], fd)
        output = np.stack(scores, axis=-1)[0]

        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):
        if self.profile:
            print(
                "ONNX runtime profile dumped to: '{}'".format(
                    self.sess.end_profiling())
            )
        print("Finished destroying SUT.")


def get_onnxruntime_sut(args):
    return BERT_ONNXRuntime_SUT(args)