# 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.

import os
import six
import glob
import json
import logging
import argparse
import pandas as pd
import multiprocessing
from multiprocessing import Process

import google.protobuf.text_format as text_format
import paddle.fluid.proto.profiler.profiler_pb2 as profiler_pb2

from CspChromeTraceFormatter import ChromeTraceFormatter

from CspFileReader import FileReader
from CspFileReader import getLogger
from CspFileReader import TIME_PATH, DCGM_PATH, NET_PATH, PROFILE_PATH
from CspFileReader import NETINFO_TRACE_NUM, DCGMINFO_TRACE_NUM, PIPELINEINFO_TRACE_NUM
from CspFileReader import FILEORGANIZEFORM_BYRANK, FILEORGANIZEFORM_BYTRAINER, FILEORGANIZEFORM_BYOTHER, FILEORGANIZEFORM


class profileFileReader(FileReader):
    def _parseSingleFile(self, profile):
        with open(profile, 'rb') as f:
            profile_s = f.read()
            profile_pb = profiler_pb2.Profile()
            profile_pb.ParseFromString(profile_s)

            return profile_pb

    def _parseTask(self, taskList, q=None):
        profile_dict = {}

        for fileName in taskList:
            rankId = self.getRankId(fileName)
            profile_dict["trainerRank.%03d" %
                         (rankId)] = self._parseSingleFile(fileName)
            self._logger.info("I finish processing %s!" % fileName)

        if not q is None:
            q.put(profile_dict)

        return profile_dict

    def _is_forwardBackwardInfo(self, items):
        if items["name"] == "marker/compute/MarkerCUDA":
            if items.has_key("args"):
                if isinstance(items["args"], dict):
                    args = items["args"]
                    if args.has_key("detail_info"):
                        if args["detail_info"] == "marker_forward_B" or \
                           args["detail_info"] == "marker_forward_E" or \
                           args["detail_info"] == "marker_backward_B" or \
                           args["detail_info"] == "marker_backward_E":
                            return True
        return False

    def _allocate_forwardBackwardInfo(self, restList, pid, tid):
        def _cmp_ele(items):
            return items["ts"]

        restList.sort(key=_cmp_ele)
        newList = []

        lastEle = {}
        for items in restList:
            if items["args"]["detail_info"].endswith("E"):
                if not lastEle:
                    continue
                else:
                    lastEle["dur"] = items["ts"] - lastEle["ts"]
                    name = lastEle["args"]["detail_info"]
                    name = name[:name.rfind('_')]
                    name = name.split('_')[1]
                    lastEle["name"] = name
                    lastEle["args"]["detail_info"] = name
                    lastEle["args"]["name"] = name
                    if name == "backward":
                        lastEle["cname"] = "good"
                    else:
                        lastEle["cname"] = "bad"

                    lastEle["tid"] = tid
                    lastEle["pid"] = pid

                    newList.append(lastEle)
            else:
                lastEle = items

        return newList

    def _getPipeLineInfo(self, profileList, q=None):

        res = {}
        for profile in profileList:
            rankId = self.getRankId(profile)

            profile_pb = self._parseSingleFile(profile)
            traceEventList = []
            pid = 0
            tid = rankId

            for event in profile_pb.events:
                args = {'name': event.name}
                if event.memcopy.bytes > 0:
                    args['mem_bytes'] = event.memcopy.bytes
                if hasattr(event, "detail_info") and event.detail_info:
                    args['detail_info'] = event.detail_info

                traceEvent = {}
                traceEvent['ph'] = 'X'
                traceEvent['cat'] = 'Op'
                traceEvent['name'] = event.name
                traceEvent['pid'] = pid
                traceEvent['tid'] = tid
                traceEvent['ts'] = self._align_ts(event.start_ns)
                traceEvent['dur'] = (event.end_ns - event.start_ns) / 1.0
                traceEvent['args'] = args

                if self._is_forwardBackwardInfo(traceEvent):
                    traceEventList.append(traceEvent)

            pipeLineList = self._allocate_forwardBackwardInfo(traceEventList,
                                                              pid, tid)

            res[str(rankId)] = pipeLineList

        if not q is None:
            q.put(res)

        return res

    def getPipeLineInfo(self, groupId, processNum=8):
        fileFist = self.getFileListByGroup(groupId)

        self._logger.info(
            "using [%d] process to do this work, total task num is %d!" %
            (processNum, len(fileFist)))
        processPool = []
        pidList = []

        manager = multiprocessing.Manager()
        q = manager.Queue()

        taskList = self._splitTaskListForMultiProcess(fileFist, processNum)
        for task in taskList:
            subproc = Process(
                target=self._getPipeLineInfo, args=(
                    task,
                    q, ))
            processPool.append(subproc)
            subproc.start()
            pidList.append(subproc.pid)
            self._logger.info(
                "[pipeline info]: process [%d] has been started, total task num is %d ..."
                % (subproc.pid, len(task)))

        for t in processPool:
            t.join()
            pidList.remove(t.pid)
            self._logger.info(
                "[pipeline info]: process [%d] has exited! remained %d process!"
                % (t.pid, len(pidList)))

        pipeLineInfo = {}

        metaInfo = {}
        metaInfo['name'] = 'process_name'
        metaInfo['ph'] = 'M'
        metaInfo['pid'] = 0
        metaInfo['args'] = {
            'name': "%02d_pipeLineInfo" % PIPELINEINFO_TRACE_NUM
        }

        for t in processPool:
            for k, v in q.get().items():
                rankId = int(k)
                gpuId = rankId % self._gpuPerTrainer
                if str(gpuId) not in pipeLineInfo.keys():
                    pipeLineInfo[str(gpuId)] = [metaInfo]
                pipeLineInfo[str(gpuId)].extend(v)

        return pipeLineInfo

    def _allocate_pids(self, profile_dict, gpuId, initPid):
        chrome_trace = ChromeTraceFormatter()
        devices = dict()
        mem_devices = dict()

        initLineNum = initPid + 1
        lineDelta = len(profile_dict.keys())
        i = 0
        for k, profile_pb in six.iteritems(profile_dict):
            lineNum = initLineNum
            for event in profile_pb.events:
                if event.type == profiler_pb2.Event.CPU:
                    if (k, event.device_id, "CPU") not in devices:
                        pid = initPid
                        initPid = initPid + 1
                        devices[(k, event.device_id, "CPU")] = pid
                        # -1 device id represents CUDA API(RunTime) call.(e.g. cudaLaunch, cudaMemcpy)
                        if event.device_id == -1:
                            chrome_trace.emit_pid("%02d_%s:cuda_api" %
                                                  (lineNum, k), pid)
                            lineNum = lineNum + 1
                        else:
                            chrome_trace.emit_pid("%02d_%s:cpu:block:%d" %
                                                  (lineNum, k, event.device_id),
                                                  pid)
                            lineNum = lineNum + 1
                elif event.type == profiler_pb2.Event.GPUKernel:
                    if (k, event.device_id, "GPUKernel") not in devices:
                        if gpuId == event.device_id:
                            pid = initPid
                            initPid = initPid + 1

                            devices[(k, event.device_id, "GPUKernel")] = pid
                            chrome_trace.emit_pid("%02d_%s:gpu:%d" %
                                                  (lineNum, k, event.device_id),
                                                  pid)
                            lineNum = lineNum + 1

            if not hasattr(profile_pb, "mem_events"):
                continue
            for mevent in profile_pb.mem_events:
                if mevent.place == profiler_pb2.MemEvent.CUDAPlace:
                    if (k, mevent.device_id, "GPU") not in mem_devices:
                        if gpuId == mevent.device_id:
                            pid = initPid
                            initPid = initPid + 1

                            mem_devices[(k, mevent.device_id, "GPU")] = pid
                            chrome_trace.emit_pid(
                                "%02d_memory usage on %s:gpu:%d" %
                                (lineNum, k, mevent.device_id), pid)
                            lineNum = lineNum + 1
                elif mevent.place == profiler_pb2.MemEvent.CPUPlace:
                    if (k, mevent.device_id, "CPU") not in mem_devices:
                        pid = initPid
                        initPid = initPid + 1

                        mem_devices[(k, mevent.device_id, "CPU")] = pid
                        chrome_trace.emit_pid("%02d_memory usage on %s:cpu:%d" %
                                              (lineNum, k, mevent.device_id),
                                              pid)
                        lineNum = lineNum + 1
                elif mevent.place == profiler_pb2.MemEvent.CUDAPinnedPlace:
                    if (k, mevent.device_id, "CUDAPinnedPlace"
                        ) not in mem_devices:
                        if gpuId == mevent.device_id:
                            pid = initPid
                            initPid = initPid + 1

                            mem_devices[(k, mevent.device_id,
                                         "CUDAPinnedPlace")] = pid
                            chrome_trace.emit_pid(
                                "%02d_memory usage on %s:cudapinnedplace:%d" %
                                (lineNum, k, mevent.device_id), pid)
                            lineNum = lineNum + 1
                if (k, 0, "CPU") not in mem_devices:
                    pid = initPid
                    initPid = initPid + 1

                    mem_devices[(k, 0, "CPU")] = pid
                    chrome_trace.emit_pid("%02d_memory usage on %s:cpu:%d" %
                                          (lineNum, k, 0), pid)
                    lineNum = lineNum + 1
                if (k, 0, "GPU") not in mem_devices:
                    # if gpuId == mevent.device_id:
                    pid = initPid
                    initPid = initPid + 1

                    mem_devices[(k, 0, "GPU")] = pid
                    chrome_trace.emit_pid("%02d_memory usage on %s:gpu:%d" %
                                          (lineNum, k, 0), pid)
                    lineNum = lineNum + 1
                if (k, 0, "CUDAPinnedPlace") not in mem_devices:
                    pid = initPid
                    initPid = initPid + 1

                    mem_devices[(k, 0, "CUDAPinnedPlace")] = pid
                    chrome_trace.emit_pid(
                        "%02d_memory usage on %s:cudapinnedplace:%d" %
                        (lineNum, k, 0), pid)
                    lineNum = lineNum + 1
            i = i + 1
        return chrome_trace, devices, mem_devices

    def _allocate_events(self, profile_dict, devices, gpuId):
        chrome_trace = ChromeTraceFormatter()
        for k, profile_pb in six.iteritems(profile_dict):

            rankId = int(k.split(".")[-1])

            for event in profile_pb.events:
                if event.type == profiler_pb2.Event.CPU:
                    type = "CPU"
                elif event.type == profiler_pb2.Event.GPUKernel:
                    type = "GPUKernel"

                if event.type == profiler_pb2.Event.GPUKernel and event.device_id != gpuId and rankId % self._gpuPerTrainer != gpuId:
                    continue

                pid = devices[(k, event.device_id, type)]
                args = {'name': event.name}
                if event.memcopy.bytes > 0:
                    args['mem_bytes'] = event.memcopy.bytes
                if hasattr(event, "detail_info") and event.detail_info:
                    args['detail_info'] = event.detail_info
                # TODO(panyx0718): Chrome tracing only handles ms. However, some
                # ops takes micro-seconds. Hence, we keep the ns here.
                chrome_trace.emit_region(
                    self._align_ts(event.start_ns),
                    (event.end_ns - event.start_ns) / 1.0, pid,
                    event.sub_device_id, 'Op', event.name, args)
        return chrome_trace

    def _allocate_memory_event(self, profile_dict, mem_devices, gpuId):
        chrome_trace = ChromeTraceFormatter()
        if not hasattr(profiler_pb2, "MemEvent"):
            return
        place_to_str = {
            profiler_pb2.MemEvent.CPUPlace: "CPU",
            profiler_pb2.MemEvent.CUDAPlace: "GPU",
            profiler_pb2.MemEvent.CUDAPinnedPlace: "CUDAPinnedPlace"
        }
        for k, profile_pb in six.iteritems(profile_dict):
            rankId = int(k.split(".")[-1])

            trainerId = rankId / self._gpuPerTrainer

            if trainerId >= self._displaySize:
                continue

            mem_list = []
            end_profiler = 0
            for mevent in profile_pb.mem_events:
                crt_info = dict()
                crt_info['time'] = mevent.start_ns
                crt_info['size'] = mevent.bytes
                if mevent.place in place_to_str:
                    place = place_to_str[mevent.place]
                else:
                    place = "UnDefine"

                if (mevent.place == profiler_pb2.MemEvent.CUDAPlace or
                        mevent.place == profiler_pb2.MemEvent.CUDAPinnedPlace
                    ) and mevent.device_id != gpuId:
                    continue

                crt_info['place'] = place
                pid = mem_devices[(k, mevent.device_id, place)]
                crt_info['pid'] = pid
                crt_info['thread_id'] = mevent.thread_id
                crt_info['device_id'] = mevent.device_id
                mem_list.append(crt_info)
                crt_info = dict()
                crt_info['place'] = place
                crt_info['pid'] = pid
                crt_info['thread_id'] = mevent.thread_id
                crt_info['device_id'] = mevent.device_id
                crt_info['time'] = mevent.end_ns
                crt_info['size'] = -mevent.bytes
                mem_list.append(crt_info)
                end_profiler = max(end_profiler, crt_info['time'])
            mem_list.sort(key=lambda tmp: (tmp.get('time', 0)))
            i = 0
            total_size = 0
            while i < len(mem_list):
                total_size += mem_list[i]['size']
                while i < len(mem_list) - 1 and mem_list[i]['time'] == mem_list[
                        i + 1]['time']:
                    total_size += mem_list[i + 1]['size']
                    i += 1

                chrome_trace.emit_counter(
                    "Memory", "Memory", mem_list[i]['pid'],
                    self._align_ts(mem_list[i]['time']), 0, total_size)
                i += 1
        return chrome_trace

    def _getOPTraceInfoByGpuId(self, groupId, gpuId):
        fileFist = self.getFileListByGroup(groupId)
        newFileList = []
        for file in fileFist:
            rankId = self.getRankId(file)
            localRank = rankId % self._gpuPerTrainer
            if localRank == gpuId and (rankId / self._gpuPerTrainer
                                       ) % self._groupSize < self._displaySize:
                newFileList.append(file)

        profile_dict = self._parseTask(newFileList)
        initPid = PIPELINEINFO_TRACE_NUM + DCGMINFO_TRACE_NUM + NETINFO_TRACE_NUM
        metaTrace, devicesPid, mem_devicesPid = self._allocate_pids(
            profile_dict, gpuId, initPid)
        eventsTrace = self._allocate_events(profile_dict, devicesPid, gpuId)
        memEventsTrace = self._allocate_memory_event(profile_dict,
                                                     mem_devicesPid, gpuId)

        trace = {}
        trace[
            'traceEvents'] = metaTrace._metadata + eventsTrace._events + memEventsTrace._events
        self.dumpOpInfoDict(trace, groupId, gpuId, True)

        return trace

    def getOPTraceInfo(self, groupId):
        manager = multiprocessing.Manager()
        q = manager.Queue()
        processPool = []
        pidList = []

        for gpuId in range(self._gpuPerTrainer):
            subproc = Process(
                target=self._getOPTraceInfoByGpuId, args=(
                    groupId,
                    gpuId, ))
            processPool.append(subproc)
            subproc.start()
            pidList.append(subproc.pid)
            self._logger.info(
                "[op info]: process [%d] has been started, total task num is %d ..."
                % (subproc.pid, 1))

        for t in processPool:
            t.join()
            pidList.remove(t.pid)
            self._logger.info(
                "[op info]: process [%d] has exited! remained %d process!" %
                (t.pid, len(pidList)))

        opInfo = {}

        return opInfo

    def parseFileByGroup(self, groupId, processNum=8):
        fileFist = self.getFileListByGroup(groupId)
        if processNum == 0:
            return self._parseTask(fileFist)
        else:
            return self._parseTask(fileFist)


def test_profileFileReader():
    args = {
        "dataPath": "data/newdata/profile",
        "groupSize": 4,
        "displaySize": 8,
        "gpuPerTrainer": 8,
        "minTimeStamp": 0,
        "organizeForm": FILEORGANIZEFORM_BYRANK,
    }

    testReader = profileFileReader(getLogger(), args)
    testReader.printArgs()
    data = testReader.getOPTraceInfo(0)

    jsObj = json.dumps(data)
    fileObject = open('jsonFile.json', 'w')
    fileObject.write(jsObj)
    fileObject.close()


if __name__ == "__main__":
    test_profileFileReader()