################################################################################
#
# Copyright (C) 2016-2022 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
################################################################################

from .Common import print1, print2, HR, printExit, defaultAnalysisParameters, globalParameters, \
  setWorkingPath, popWorkingPath, assignParameterWithDefault, startTime, ProgressBar, printWarning
from .SolutionStructs import Solution
from . import LibraryIO
from . import SolutionSelectionLibrary

from copy import deepcopy
from sys import stdout

import array
import csv
import os
import time

################################################################################
# Analyze Problem Type
################################################################################
def analyzeProblemType( problemType, problemSizeGroups, inputParameters ):
  print2(HR)
  print1("# Analyzing: %s" % problemType)

  enableTileSelection = problemType["TileAwareSelection"]
  solutionsList = []
  problemSizesList = []
  dataFileNameList = []
  selectionFileNameList = []

  for problemSizeGroup in problemSizeGroups:
    problemSizes = problemSizeGroup[0]
    dataFileName = problemSizeGroup[1]
    dataFileNameList.append(dataFileName)
    solutionsFileName = problemSizeGroup[2]
    #print "  problemSizes:", problemSizes
    #print "# DataFileName:", dataFileName
    #print "  solutionsFileName:", solutionsFileName
    if enableTileSelection:
      selectionFileName = problemSizeGroup[3]
      selectionFileNameList.append(selectionFileName)

    ######################################
    # Read Solutions
    # (problemSizes, solutions) are already read and kept in problemSizeGroups, no need to call LibraryIO.readSolutions(solutionsFileName) again
    solutions = problemSizeGroup[4]
    problemSizesList.append(problemSizes)
    solutionsList.append(solutions)
    solutionMinNaming = Solution.getMinNaming(solutions)
    print1("# Read: %s" % (solutionsFileName))
    print2("# ProblemSizes: %s" % problemSizes)
    print2("# Solutions:")
    solutionIdx = 0
    for solution in solutions:
      print2("#  (%u) %s" % (solutionIdx, Solution.getNameMin(solution, \
          solutionMinNaming)))
      solutionIdx += 1
    print2(HR)

  ######################################
  # Create Logic Analyzer
  logicAnalyzer = LogicAnalyzer( problemType, problemSizesList, solutionsList, \
      dataFileNameList, inputParameters)

  selectionSolutionsIdsList = None
  selectionSolutions = None

  validSelectionSolutions = []

  ######################################
  # Remove invalid solutions
  logicAnalyzer.removeInvalidSolutions()

  ######################################
  # Remove least important solutions
  if globalParameters["SolutionSelectionAlg"] == 0:
    logicAnalyzer.removeLeastImportantSolutions()
  elif globalParameters["SolutionSelectionAlg"] == 1:
    logicAnalyzer.keepWinnerSolutions()
  else:
    printExit("Bad KeepLogic=%u"%globalParameters["KeepLogic"])

  # print raw data
  if globalParameters["PrintLevel"] >= 2:
    line = "After Removals:\n"
    numOther = 1
    for size in logicAnalyzer.numProblemSizes:
      numOther *= size
    numCols = logicAnalyzer.numProblemSizes[1]
    if numCols == 0: numCols = 1
    numOther //= numCols
    for row in range(0, numOther):
      for col in range(0, numCols):
        for sol in range(0, logicAnalyzer.numSolutions):
         line += "% 5.0f" % logicAnalyzer.data[sol + logicAnalyzer.numSolutions*(col + row*numCols)]
        line += "; "
      line += "\n"
    print(line)

  for i in range(0, len(logicAnalyzer.solutions)):
    s = logicAnalyzer.solutions[i]
    s["SolutionIndex"] = i
    s["SolutionNameMin"] = Solution.getNameMin(s, solutionMinNaming)
    print1("(%2u) %s : %s" % (i, Solution.getNameMin(s, solutionMinNaming), Solution.getNameFull(s)))

  if enableTileSelection:
    validSelectionSolutions = SolutionSelectionLibrary.analyzeSolutionSelection(problemType, selectionFileNameList, \
        logicAnalyzer.numSolutionsPerGroup,  logicAnalyzer.solutionGroupMap, solutionsList)

    validSelectionSolutionsIncluded = []
    validSelectionSolutionsRemainder = []
    selectionSolutionsIds = set([])
    for validSelectionSolution in validSelectionSolutions:
      (validSolution, validSolutionInfo) = validSelectionSolution
      if validSolution in logicAnalyzer.solutions:
        validExactSolutionIndex = logicAnalyzer.solutions.index(validSolution)
        selectionSolutionsIds.add(validExactSolutionIndex)
        validExactSolution = logicAnalyzer.solutions[validExactSolutionIndex]
        validSelectionSolutionsIncluded.append((validExactSolution, validSolutionInfo))
      else:
        validSelectionSolutionsRemainder.append(validSelectionSolution)

    selectionSolutions = []
    for i in range(0 ,len(validSelectionSolutionsIncluded)):
      validSelectionSolution = validSelectionSolutionsIncluded[i]
      (validSolution, validSolutionInfo) = validSelectionSolution
      validSolution["Ideals"] = validSolutionInfo

    solutionsStartIndex = len(logicAnalyzer.solutions)

    for i in range(0, len(validSelectionSolutionsRemainder)):
      validSelectionSolution = validSelectionSolutionsRemainder[i]
      (validSolution, validSolutionInfo) = validSelectionSolution
      selectionSolutionIndex = solutionsStartIndex + i
      selectionSolutionsIds.add(selectionSolutionIndex)
      validSolution["SolutionNameMin"] = Solution.getNameMin(validSolution, solutionMinNaming)
      validSolution["Ideals"] = validSolutionInfo
      selectionSolutions.append(validSolution)

    selectionSolutionsIdsList = list(selectionSolutionsIds)

  ######################################
  # Correct outliers
  """
  if inputParameters["SmoothOutliers"]:
    logicAnalyzer.smoothOutliers()
  """
  numProblemSizes = logicAnalyzer.numProblemSizes
  # print all 2D
  numPermutations = 1
  permutations = []
  for i in range(0, logicAnalyzer.numIndices):
    if i != logicAnalyzer.idx0 and i != logicAnalyzer.idx1:
      numPermutations *= numProblemSizes[i]
  #print numPermutations
  for j in range(0, numPermutations):
    pIdx = j
    permutation = []
    for i in range(0, logicAnalyzer.numIndices):
      if i != logicAnalyzer.idx0 and i != logicAnalyzer.idx1:
        npsi = numProblemSizes[i]
        permutation.append(pIdx%npsi)
        pIdx /= numProblemSizes[i]
    permutations.append(permutation)
  #print permutations
  for permutation in permutations:
    logicAnalyzer.print2D(permutation)

  ######################################
  # Range Logic
  rangeLogic = logicAnalyzer.enRule(0, logicAnalyzer.globalIndexRange)
  print2("# Final Range Logic:")
  print2(rangeLogic)
  logicComplexity = [0]*logicAnalyzer.numIndices
  logicAnalyzer.scoreLogicComplexity(rangeLogic, logicComplexity)
  print2("# Range Logic Complexity: %s" % logicComplexity)
  score = logicAnalyzer.scoreRangeForLogic( \
      logicAnalyzer.globalIndexRange, rangeLogic)
  print1("\n# Score: %.0f ms" % (score/1000))
  logicAnalyzer.prepareLogic(rangeLogic) # convert indices to sizes, -1

  ######################################
  # Range Logic
  exactLogic = logicAnalyzer.exactWinners
  print1("# Exact Logic:\n")
  print1("%s"%exactLogic)

  #selectionSolutionsIdsList = list(selectionSolutionsIds)
  return (problemType, logicAnalyzer.solutions, logicAnalyzer.indexOrder, \
       exactLogic, rangeLogic, selectionSolutions, selectionSolutionsIdsList, logicAnalyzer.perfMetric)



################################################################################
# LogicAnalyzer
################################################################################
class LogicAnalyzer:

  ##############################################################################
  ##############################################################################
  ###
  ###  Entry / Top-Level Functions
  ###
  ##############################################################################
  ##############################################################################

  ##############################################################################
  # ENTRY: Init
  ##############################################################################
  def __init__(self, problemType, problemSizesList, solutionsList, \
      dataFileNameList, inputParameters):

    # parameters
    self.parameters = inputParameters

    # problem type
    self.problemType = problemType
    self.idx0 = self.problemType["Index0"]
    self.idx1 = self.problemType["Index1"]
    self.idxU = self.problemType["IndexUnroll"]

    # merge solutions from size groups
    # solutions needs to be a set, and offset needs to be mapping
    print1("# Merging Solutions:")
    self.numSolutionsPerGroup = []
    self.solutionGroupMap = []
    self.solutions = []
    solutionsHash = {} # for accelerating lookups

    totalSolutions = 0
    for solutionGroupIdx in range(0, len(solutionsList)):
      solutionGroup = solutionsList[solutionGroupIdx]
      totalSolutions += len(solutionGroup)
    progressBar = ProgressBar(totalSolutions)
    for solutionGroupIdx in range(0, len(solutionsList)):
      solutionGroup = solutionsList[solutionGroupIdx]
      self.numSolutionsPerGroup.append(len(solutionGroup))
      self.solutionGroupMap.append({})
      for solutionIdx in range(0, len(solutionGroup)):
        solution = solutionGroup[solutionIdx]
        if not solution in solutionsHash:
          sIdx = len(self.solutions) # the one we are about to add
          self.solutions.append(solution)
          solutionsHash[solution] = sIdx
        else:
          sIdx = solutionsHash[solution]

        self.solutionGroupMap[solutionGroupIdx][solutionIdx] = sIdx
        progressBar.increment()
    self.numSolutions = len(self.solutions)
    self.solutionMinNaming = Solution.getMinNaming(self.solutions)
    self.solutionNames = []
    self.solutionTiles = []
    for solution in self.solutions:
      self.solutionNames.append(Solution.getNameMin(solution, \
          self.solutionMinNaming))
      self.solutionTiles.append("%ux%u"%(solution["MacroTile0"], \
          solution["MacroTile1"]))
    self.flopsPerMac = self.problemType["DataType"].flopsPerMac()

    # merge problem sizes from size groups
    #self.numIndices = len(problemSizesList[0].numProblemSizes)
    self.numIndices = self.problemType["TotalIndices"] + problemType["NumIndicesLD"]
    unifiedProblemSizes = []
    for i in range(0, self.numIndices):
      unifiedProblemSizes.append(set())
    self.exactProblemSizes = set()
    self.rangeProblemSizes = set()
    for problemSizes in problemSizesList:
      # add exacts
      for problem in problemSizes.exacts:
        self.exactProblemSizes.add(tuple(problem.sizes))

      # add ranges
      #print "ProblemSizes", problemSizes.sizes
      #FIXME-problem
      self.rangeProblemSizes.update([tuple(problem.sizes) for problem in problemSizes.problems])
      for rangeSize in problemSizes.ranges:

        if globalParameters["ExpandRanges"]:
          # Treat ranges as pile of exacts:
          for rsize in rangeSize.problemSizes:
            self.exactProblemSizes.add(tuple(rsize))
        else:
          # Create the ranges info in the logic file
          #print "RangeSize", rangeSize
          sizedIdx = 0
          mappedIdx = 0
          for i in range(0, self.numIndices):
            if rangeSize.indexIsSized[i]:
              index = rangeSize.indicesSized[sizedIdx]
              sizedIdx += 1
            else:
              index = rangeSize.indicesSized[ \
                rangeSize.indicesMapped[mappedIdx]]
              mappedIdx += 1
            currentSize = index[0]
            currentStride = index[1]
            while currentSize <= index[3]:
              unifiedProblemSizes[i].add(currentSize)
              currentSize += currentStride
              currentStride += index[2]
    for i in range(0, len(unifiedProblemSizes)):
      unifiedProblemSizes[i] = sorted(list(unifiedProblemSizes[i]))
    print2("UnifiedProblemSizes: %s" % unifiedProblemSizes)
    print2("ExactProblemSizes: %s" % self.exactProblemSizes)
    print2("RangeProblemSizes: %s" % self.rangeProblemSizes)

    # problem size index <-> size
    self.problemSizeToIndex = []
    self.problemIndexToSize = []
    self.numProblemSizes = []
    for i in range(0, self.numIndices):
      self.problemSizeToIndex.append({})
      self.problemIndexToSize.append([])
      for j in range(0, len(unifiedProblemSizes[i])):
        size = unifiedProblemSizes[i][j]
        self.problemSizeToIndex[i][size] = j
        self.problemIndexToSize[i].append(size)
      self.numProblemSizes.append(len(unifiedProblemSizes[i]))
    print1("# NumProblemSizes: %s" % self.numProblemSizes)

    # total size of data array
    self.totalProblems = 1
    for numProblems in self.numProblemSizes:
      self.totalProblems *= numProblems
    self.totalSize = self.totalProblems * self.numSolutions
    print2("TotalProblems: %u" % self.totalProblems)
    print2("TotalSolutions: %u" % self.numSolutions)
    print2("TotalSize: %u" % self.totalSize)
    # data is a 2D array [problemIdx][solutionIdx] which stores perf data in gflops for
    # the specified solution
    self.data = array.array('f', [-2]*self.totalSize)

    # Each entry in exactWinners is a 2D array [solutionIdx, perf]
    self.exactWinners = {}

    """
    # map problem sizes -> index
    self.problemSizeToIndex = []
    self.problemIndexToSize = []
    sizedIdx = 0
    mappedIdx = 0
    for i in range(0, self.numIndices):
      self.problemSizeToIndex.append({})
      self.problemIndexToSize.append([])
      if self.problemSizes.indexIsSized[i]:
        index = self.problemSizes.indicesSized[sizedIdx]
        sizedIdx += 1
      else:
        index = self.problemSizes.indicesSized[ \
          self.problemSizes.indicesMapped[mappedIdx]]
        mappedIdx += 1
      currentSize = index[0]
      currentStride = index[1]
      idx = 0
      while currentSize <= index[3]:
        self.problemSizeToIndex[i][currentSize] = idx
        self.problemIndexToSize[i].append(currentSize)
        currentSize += currentStride
        currentStride += index[2]
        idx += 1
    """
    #self.rangeIndicesFree = range(0, self.problemType["NumIndicesC"])
    #self.rangeIndicesSummation = range(self.problemType["NumIndicesC"], \
    #    self.problemType["TotalIndices"])
    self.indexOrder = self.recommendedIndexOrder()
    print2("IndexOrder: %s" % self.indexOrder)
    self.globalIndexRange = []
    for i in range(0, self.numIndices):
      self.globalIndexRange.append([0, self.numProblemSizes[i]])
    self.problemIndicesForGlobalRange \
        = self.problemIndicesForRange(self.globalIndexRange)
    self.tab = [""]*self.numIndices

    ######################################
    # Read Data From CSV
    for fileIdx in range(0, len(dataFileNameList)):
      dataFileName = dataFileNameList[fileIdx]
      self.addFromCSV(dataFileName, self.numSolutionsPerGroup[fileIdx], \
          self.solutionGroupMap[fileIdx])



    #print self.data
    # map exact problem sizes to solutions
    print1("# ExactWinners: %s" % self.exactWinners)


  ##############################################################################
  # ENTRY: Add From CSV
  ##############################################################################
  def addFromCSV(self, dataFileName, numSolutions, solutionMap):

    # open file
    print("reading datafile", dataFileName)
    try:
      dataFile = open(dataFileName, "r")
    except IOError:
      printExit("Can't open \"%s\" to get data" % dataFileName )

    # column indices
    csvFile = csv.reader(dataFile)
    problemSizeStartIdx = 1
    # notice that for OperationType != GEMM, the numIndices = 0
    totalSizeIdx = problemSizeStartIdx + self.numIndices

    # need to take care if the loaded csv is the export-winner-version
    csvHasWinner = "_CSVWinner" in dataFileName
    if csvHasWinner:
      # the column of the two are fixed (GFlops, SizeI/J/K/L, LDD/C/A/B, TotalFlops, WinnerGFlops, WinnerTimeUs, WinnerIdx, WinnerName)
      # the order are implemented in ResultFileReporter.cpp
      columnOfWinnerGFlops = 10
      columnOfWinnerIdx = 12

    # iterate over rows
    rowIdx = 0
    for row in csvFile:
      rowIdx+=1
      if rowIdx == 1:
        # get unit (gflops or gflops/cu) of benchmark data
        perfUnit = row[0]
        if perfUnit == "GFlops":
          self.perfMetric = "DeviceEfficiency"
        elif perfUnit == "GFlopsPerCU":
          self.perfMetric = "CUEfficiency"
        else:
          printWarning("Performance unit %s in %s is unrecognized: assuming GFlops (device efficiency)" % (perfUnit, dataFileName))
          self.perfMetric = "DeviceEfficiency"

        # get the length of each row, and derive the first column of the solution instead of using wrong "solutionStartIdx = totalSizeIdx + 1"
        rowLength = len(row)
        solutionStartIdx = rowLength - numSolutions
        continue
      else:
        #if len(row) < rowLength:
        #  printWarning("CSV File %s row %u doesn't have %u elements; ignoring remainer of file." \
        #      % (dataFileName, rowIdx, rowLength) )
        #  break

        # get problem size
        problemSize = []
        for i in range(problemSizeStartIdx, totalSizeIdx):
          problemSize.append(int(row[i]))
        problemSize = tuple(problemSize)

        # Exact Problem Size
        if problemSize in self.exactProblemSizes:

          if csvHasWinner:
            # Faster. Get the winner info from csv directly, avoid an extra loop
            winnerGFlops = float(row[columnOfWinnerGFlops])
            winnerIdx = int(row[columnOfWinnerIdx])
          else:
            # Old code. TODO - Can we get rid of this in the future?
            # solution gflops
            solutionIdx = 0
            winnerIdx = -1
            winnerGFlops = -1
            for i in range(solutionStartIdx, rowLength):
              gflops = float(row[i])
              if gflops > winnerGFlops:
                winnerIdx = solutionIdx
                winnerGFlops = gflops
              solutionIdx += 1

          if winnerIdx != -1:
            if problemSize in self.exactWinners:
              if winnerGFlops > self.exactWinners[problemSize][1]:
                #print "update exact", problemSize, "CSV index=", winnerIdx, self.exactWinners[problemSize], "->", solutionMap[winnerIdx], winnerGFlops
                self.exactWinners[problemSize] = [solutionMap[winnerIdx], winnerGFlops]
            else:
              self.exactWinners[problemSize] = [solutionMap[winnerIdx], winnerGFlops]
              #print "new exact", problemSize, "CSV index=", winnerIdx, self.exactWinners[problemSize]

        # Range Problem Size
        elif problemSize in self.rangeProblemSizes:
          problemIndices = []
          for i in range(0, self.numIndices):
            problemIndices.append(self.problemSizeToIndex[i][problemSize[i]])
          serialIdx = self.indicesToSerial(0, problemIndices)
          # solution gflops
          solutionIdx = 0
          for i in range(solutionStartIdx, rowLength):
            gflops = float(row[i])
            self.data[serialIdx+solutionMap[solutionIdx]] = gflops
            solutionIdx += 1

        # Unknown Problem Size
        else:
          printExit("Huh? %s has ProblemSize %s which isn't in its yaml" \
              % ( dataFileName, list(problemSize)) )
    #print self.data


  ##############################################################################
  # ENTRY: Remove Invalid Solutions
  ##############################################################################
  def removeInvalidSolutions(self):
    #problemIndices = [0]*self.numIndices
    allSolutionValid = False
    while not allSolutionValid:
      invalidIdx = -1
      for problemIndices in self.problemIndicesForGlobalRange:
        problemSerial = self.indicesToSerial(0, problemIndices)
        for solutionIdx in range(0, self.numSolutions):
          gflops = self.data[problemSerial+solutionIdx]
          if gflops == 0:
            invalidIdx = solutionIdx
            break
      if invalidIdx >= 0:
        print1("# Removing Invalid Solution: %u %s" \
            % (invalidIdx, self.solutionNames[invalidIdx]) )
        self.removeSolution(invalidIdx)
      else:
        allSolutionValid = True


  ##############################################################################
  # ENTRY: Original KeepLogic algorithm: Remove Least Important Solutions,
  # one at a time.  Stop when leastImportantSolution indicates no more
  # solutions can be removed, which appears to be when the solution
  # is used by an exact problem or is the only possible solution for some
  # problem or doesn't improve the a solution by > SolutionImportanceMin%
  ##############################################################################
  def removeLeastImportantSolutions(self):
    # Remove least important solutions
    start = time.time()
    while len(self.solutions) > 1:
      lisTuple = self.leastImportantSolution()
      if lisTuple != None:
        lisIdx = lisTuple[0]
        lisPercSaved = lisTuple[1]
        lisPercWins = lisTuple[2]
        lisPercTime = lisTuple[3]
        if lisPercSaved < self.parameters["SolutionImportanceMin"] or lisPercWins == 0:
          print1("# Removing Unimportant Solution %u/%u: %s ( %f%% wins, %f%% ms time, %f%% ms saved" \
              % (lisIdx, self.numSolutions, self.solutionNames[lisIdx], 100*lisPercWins, 100*lisPercTime, 100*lisPercSaved) )
          self.removeSolution(lisIdx)
          continue
        else:
          break
      else: # no more lis, remainders are exact winner
        break
    stop = time.time()
    print("removeLeastImportantSolutions elapsed time = %.1f secs" % (stop - start))


  ##############################################################################
  # ENTRY: Alternate KeepLogic algorithm that keeps the fastest for each
  #  exact and range.  Other solutions are removed.
  ##############################################################################
  def keepWinnerSolutions(self):

    # solution indexes for the winners:
    winners = set()

    solutionImportance = []
    for i in range(0, self.numSolutions):
      solutionImportance.append([i, 0, 0, 0, False])
    problemSizes = [0]*self.numIndices
    print("problemIndicesForGlobalRange", self.problemIndicesForGlobalRange)
    for problemIndices in self.problemIndicesForGlobalRange:
      for i in range(0, self.numIndices):
        problemSizes[i] = self.problemIndexToSize[i][problemIndices[i]]
      totalFlops = self.flopsPerMac
      for size in problemSizes:
        totalFlops *= size

      problemSerial = self.indicesToSerial(0, problemIndices)
      winnerIdx = -1
      winnerGFlops = -1e6
      for solutionIdx in range(0, self.numSolutions):
        solutionSerialIdx = problemSerial + solutionIdx
        solutionGFlops = self.data[solutionSerialIdx]
        if solutionGFlops > winnerGFlops:
          winnerIdx = solutionIdx
          winnerGFlops = solutionGFlops

      winners.add(winnerIdx)

    # Always keep the exact sizes:
    for exactProblem in self.exactWinners:
      winnerIdx = self.exactWinners[exactProblem][0]
      #print "keepWinnerSolution adding exact", exactProblem, winnerIdx
      winners.add(winnerIdx)

    print("Winners", winners)
    self.pruneSolutions(winners)



  ##############################################################################
  # ENTRY: En Rule
  # currentIndexIndex = 0, 1, 2, 3...
  # currentIndexRange will have only 1 size for prior indices (unless initial)
  #
  # Rule:
  # [128, [
  #         [64, [
  #                [16, 0],
  #                [2880,1]
  #              ]
  #         ],
  #         [96, [
  #                [16, 0],
  #                [64, 1]
  #              ]
  #         ]
  #       ]
  # ], another
  #
  #
  ##############################################################################
  def enRule(self, currentIndexIndex, currentIndexRange):
    cii = currentIndexIndex
    if currentIndexIndex == 0:
      self.tab[cii] = "[] "
    elif currentIndexIndex == 1:
      self.tab[cii] = "[%2u] " % ( \
          currentIndexRange[self.indexOrder[0]][0])
    elif currentIndexIndex == 2:
      self.tab[cii] = "[%2u,%2u] " % ( \
          currentIndexRange[self.indexOrder[0]][0], \
          currentIndexRange[self.indexOrder[1]][0])
    elif currentIndexIndex == 3:
      self.tab[cii] = "[%2u,%2u,%2u] " % ( \
          currentIndexRange[self.indexOrder[0]][0], \
          currentIndexRange[self.indexOrder[1]][0], \
          currentIndexRange[self.indexOrder[2]][0])
    elif currentIndexIndex == 4:
      self.tab[cii] = "[%2u,%2u,%2u,%2u] " % ( \
          currentIndexRange[self.indexOrder[0]][0], \
          currentIndexRange[self.indexOrder[1]][0], \
          currentIndexRange[self.indexOrder[2]][0], \
          currentIndexRange[self.indexOrder[3]][0])
    tab = self.tab[cii]
    if globalParameters["PrintLevel"] == 1:
      stdout.write("\n%s"%tab)
    currentIndex = self.indexOrder[currentIndexIndex]
    print2("%senRule(%s)" % (tab, currentIndexRange))
    nextIndexIndex = currentIndexIndex+1
    nextIndexRange = deepcopy(currentIndexRange)
    isLastIndex = currentIndexIndex == self.numIndices-1
    ruleList = []

    ########################################
    # SingleProblem
    ########################################
    if currentIndexRange[currentIndex][1] \
        - currentIndexRange[currentIndex][0] == 1:

      ########################################
      # SingleProblem & LastIndex
      # this is last index, so just return fastest solution
      ########################################
      if isLastIndex:
        print2("%sSingleProblem & LastIndex" % tab)
        winnerIdx = self.winnerForRange(currentIndexRange)
        if winnerIdx < 0:
          print2("%sSingleProblem & LastIndex :: winnerIdx<0; returning" % (tab) )
          return None
        ruleList.append([-1, winnerIdx])
        if globalParameters["PrintLevel"] == 1:
          stdout.write("%")

      ########################################
      # SingleProblem & NotLastIndex
      # this isn't last index, so just recursively return next index
      ########################################
      else:
        print2("%sSingleProblem & NotLastIndex" % tab)
        #    % (tab, nextIndexRange) )
        nextRule = self.enRule(nextIndexIndex, nextIndexRange)
        if nextRule == None:
          print2("%sSingleProblem & NotLastIndex :: nextRule==None; returning" % (tab) )
          return None
        rule = [ -1, nextRule ]
        ruleList.append(rule)
        if globalParameters["PrintLevel"] == 1:
          stdout.write("%")

    else:
    ########################################
    # MultiProblem
    # Create Initial Rule
    ########################################

      if isLastIndex:
        ########################################
        # MultiProblem & LastIndex
        # InitialRule using winnerForRange()
        ########################################
        print2("%sMultiProblem & LastIndex" % tab)
        winnerIdx = -1
        for problemIndex in range(currentIndexRange[currentIndex][0], \
            currentIndexRange[currentIndex][1]):
          nextIndexRange[currentIndex][0] = problemIndex
          nextIndexRange[currentIndex][1] = problemIndex+1
          winnerIdx = self.winnerForRange(nextIndexRange)
          initialRule = [ currentIndexRange[currentIndex][0], winnerIdx]
          if winnerIdx >= 0:
            break
        if winnerIdx < 0:
          print2("%sMultiProblem & LastIndex :: winnerIdx<0; returning" % (tab) )
          return None

      else:
        ########################################
        # MultiProblem & NotLastIndex
        # InitialRule using enRule()
        ########################################
        print2("%sMultiProblem & NotLastIndex" % tab)

        # create initial rule
        winnerIdx = -1
        nextRule = None
        for problemIndex in range(currentIndexRange[currentIndex][0], \
            currentIndexRange[currentIndex][1]):
          nextIndexRange[currentIndex][0] = problemIndex
          nextIndexRange[currentIndex][1] = problemIndex+1
          nextRule = self.enRule(nextIndexIndex, nextIndexRange)
          # break when found initial rule
          if nextRule != None:
            break

        if nextRule == None:
          printWarning("%sMultiProblem & NotLastIndex :: nextRule==None; returning" % (tab) )
          return None
        initialRule = [ currentIndexRange[currentIndex][0], nextRule ]
      ruleList.append(initialRule)
      print2("%sMultiProblem::InitialRuleList=%s" % (tab, ruleList))
      if globalParameters["PrintLevel"] == 1:
        stdout.write("#")

      ########################################
      # MultiProblem
      # Append Rules to Initial Rule
      ########################################
      print2("%sMultiProblem::Improving Rule" % tab)
      for problemIndex in range(currentIndexRange[currentIndex][0]+1, \
          currentIndexRange[currentIndex][1]):
        nextIndexRange[currentIndex][0] = problemIndex
        nextIndexRange[currentIndex][1] = problemIndex+1
        priorRule = ruleList[len(ruleList)-1]
        priorRuleForSize = deepcopy(priorRule)
        priorRuleForSize[0] = problemIndex

        if isLastIndex:
          ########################################
          # nextRule using winnersForRange()
          winnerIdx = self.winnerForRange(nextIndexRange)
          print2("%sMultiProblem::ImproveRule[%u]::LastIndex::WinnerIdx=%u for %s" % (tab, problemIndex, winnerIdx, nextIndexRange))
          # if no solutions benchmarked for this problem size, continue
          if winnerIdx < 0:
            ruleList[len(ruleList)-1][0] = problemIndex # NO_UPDATE
            print2("%sUpdating range b/c None" % tab)
            if globalParameters["PrintLevel"] == 1:
              stdout.write(" ")
            continue
          else:
            candidateRule = [ problemIndex, winnerIdx]
        else:
          ########################################
          # nextRule using enRule()
          nextRule = self.enRule(nextIndexIndex, nextIndexRange)
          print2("%sMultiProblem::ImproveRule[%u]::NotLastIndex::NextRule=%s for %s; %s" % (tab, problemIndex, nextRule, nextIndexIndex, nextIndexRange))
          if nextRule == None:
            ruleList[len(ruleList)-1][0] = problemIndex # NO_UPDATE
            print2("%sUpdating b/c None" % tab)
            if globalParameters["PrintLevel"] == 1:
              stdout.write(" ")
            continue
          else:
            candidateRule = [ problemIndex, nextRule ]

        ########################################
        # candidate same as prior
        if candidateRule[1] == priorRule[1]:
          print2("%sCandidateRule==PriorRule; just updating prior" % (tab))
          ruleList[len(ruleList)-1][0] = problemIndex # NO_UPDATE
          if globalParameters["PrintLevel"] == 1:
            stdout.write(" ")
          continue

        ########################################
        # compare candidate vs prior
        else:
          print2("%sCandidateRule!=PriorRule; appending rule assuming its better" % (tab))

          """
          priorRuleScore = self.scoreRangeForLogic(nextIndexRange, \
              [priorRuleForSize])
          logicComplexity = [0]*self.numIndices
          self.scoreLogicComplexity( \
              [priorRuleForSize], logicComplexity)
          priorRuleScore += self.parameters["BranchPenalty"] \
              * sum(logicComplexity)
          # score candidate
          candidateRuleScore = self.scoreRangeForLogic(nextIndexRange, \
              [candidateRule])
          #print "CRS", candidateRuleScore
          logicComplexity = [0]*self.numIndices
          self.scoreLogicComplexity( \
              [candidateRule], logicComplexity)
          candidateRuleScore += self.parameters["BranchPenalty"] \
              * sum(logicComplexity)
          candidateRuleScore += self.parameters["BranchPenalty"] # penalize
          candidateFaster = candidateRuleScore < priorRuleScore
          print2("%sP[%2u]: %s %s~%.0fus < %s~%.0fus" % (tab, problemIndex, \
              "wins" if candidateFaster else "same", \
              candidateRule, candidateRuleScore, priorRuleForSize, \
              priorRuleScore ))
          """

          ########################################
          # candidate wins
          #print candidateRuleScore, priorRuleScore
          if True: # or candidateRuleScore < priorRuleScore:
            ruleList.append(candidateRule)
            print2("%sAppending b/c Different" % tab)
            if globalParameters["PrintLevel"] == 1:
              stdout.write("#")

          ########################################
          # prior wins
          else:
            print2("%sPrior Rule Wins" % tab)
            if globalParameters["PrintLevel"] == 1:
              stdout.write(".")
            ruleList[len(ruleList)-1][0] = problemIndex # NO_UPDATE

    print2("%sReturning RuleList: %s" % (tab, ruleList))
    return ruleList



  ##############################################################################
  ##############################################################################
  ###
  ###  Mid-Level Functions
  ###
  ##############################################################################
  ##############################################################################



  ##############################################################################
  # Prepare Logic
  # convert threshold indices to sizes
  # last threshold = -1
  ##############################################################################
  def prepareLogic(self, logic):
    depth = self.getLogicDepth(logic)
    if depth == 0: return
    indexIndex = self.numIndices - depth
    index = self.indexOrder[indexIndex]
    for i in range(0, len(logic)):
      if i == len(logic)-1:
        logic[i][0] = -1
      else:
        logic[i][0] = self.problemIndexToSize[index][logic[i][0]]
      self.prepareLogic(logic[i][1])


  ##############################################################################
  # Print2D
  ##############################################################################
  def print2D(self, indices ):
    indicesIdx = 0
    problemIndices = []
    for i in range(0, self.numIndices):
      if i == self.idx0:
        problemIndices.append(-1)
      elif i == self.idx1:
        problemIndices.append(-1)
      else:
        problemIndices.append(indices[indicesIdx])
        indicesIdx += 1

    winnerIndices = []
    w = "winner"
    g = "gflops"
    f = "faster"
    s = "second"
    sss = []
    for sIdx in range(0, self.numSolutions):
      sss.append("Sol[%u]" % sIdx)
    for j in range(0, self.numProblemSizes[1]):
      w += ",%4u" % self.problemIndexToSize[1][j]
      g += ",%4u" % self.problemIndexToSize[1][j]
      f += ",%4u" % self.problemIndexToSize[1][j]
      s += ",%4u" % self.problemIndexToSize[1][j]
      for sIdx in range(0, self.numSolutions):
        sss[sIdx] += ",%4u" % self.problemIndexToSize[1][j]
    w += "\n"
    g += "\n"
    f += "\n"
    s += "\n"
    for sIdx in range(0, self.numSolutions):
      sss[sIdx] += "\n"
    for i in range(0, self.numProblemSizes[0]):
      problemIndices[self.idx0] = i
      w += "%4u" % self.problemIndexToSize[0][i]
      g += "%4u" % self.problemIndexToSize[0][i]
      f += "%4u" % self.problemIndexToSize[0][i]
      s += "%4u" % self.problemIndexToSize[0][i]
      for sIdx in range(0, self.numSolutions):
        sss[sIdx] += "%4u" % self.problemIndexToSize[0][i]
      for j in range(0, self.numProblemSizes[1]):
        problemIndices[self.idx1] = j
        problemSerial = self.indicesToSerial(0, problemIndices)
        for sIdx in range(0, self.numSolutions):
          sss[sIdx] += ",%f" % self.data[problemSerial+sIdx]
        winnerIdx = 0
        secondIdx = 1
        winnerGFlops = self.data[problemSerial+0]
        secondGFlops = 1e-9
        for solutionIdx in range(1, self.numSolutions):
          solutionSerialIdx = problemSerial + solutionIdx
          solutionGFlops = self.data[solutionSerialIdx]
          if solutionGFlops > winnerGFlops:
            secondIdx = winnerIdx
            secondGFlops = winnerGFlops
            winnerIdx = solutionIdx
            winnerGFlops = solutionGFlops
          elif solutionGFlops > secondGFlops:
            secondIdx = solutionIdx
            secondGFlops = solutionGFlops

        if winnerIdx not in winnerIndices:
          winnerIndices.append(winnerIdx)
        w += ",%4u" % winnerIdx
        g += ",%f" % winnerGFlops
        f += ",%f" % (winnerGFlops/secondGFlops)
        s += ",%4u" % (secondIdx)
      w += "\n"
      g += "\n"
      f += "\n"
      s += "\n"
      for sIdx in range(0, self.numSolutions):
        sss[sIdx] += "\n"

    w += "\n\n"
    g += "\n\n"
    f += "\n\n"
    s += "\n\n"
    for sIdx in range(0, self.numSolutions):
      sss[sIdx] += "\n\n"
    w += "Winners:\n"
    for winnerIdx in winnerIndices:
      w += "%4u, %s, %s\n" % (winnerIdx, self.solutionTiles[winnerIdx], self.solutionNames[winnerIdx])

    printFileName = "Winner2D"
    for idx in indices:
      printFileName += "_%u" % idx
    printFileName += ".csv"
    printFile = open(os.path.join(globalParameters["WorkingPath"], printFileName), "w")
    printFile.write( w )
    printFile.write( g )
    printFile.write( f )
    printFile.write( s )
    for sIdx in range(0, self.numSolutions):
      printFile.write( sss[sIdx] )
    printFile.close()


  ##############################################################################
  # Least Important Solution
  ##############################################################################
  def leastImportantSolution(self):
    solutionImportance = []
    for i in range(0, self.numSolutions):
      solutionImportance.append([i, 0, 0, 0, False])
    problemSizes = [0]*self.numIndices
    totalSavedMs = 0
    totalExecMs = 0
    totalWins = 0
    for problemIndices in self.problemIndicesForGlobalRange:
      for i in range(0, self.numIndices):
        problemSizes[i] = self.problemIndexToSize[i][problemIndices[i]]
      totalFlops = self.flopsPerMac
      for size in problemSizes:
        totalFlops *= size

      problemSerial = self.indicesToSerial(0, problemIndices)
      winnerIdx = -1
      winnerGFlops = -1e6
      secondGFlops = -1e9
      for solutionIdx in range(0, self.numSolutions):
        solutionSerialIdx = problemSerial + solutionIdx
        solutionGFlops = self.data[solutionSerialIdx]
        if solutionGFlops > winnerGFlops:
          secondGFlops = winnerGFlops
          winnerIdx = solutionIdx
          winnerGFlops = solutionGFlops
        elif solutionGFlops > secondGFlops:
          secondGFlops = solutionGFlops

      winnerTimeMs = totalFlops / winnerGFlops / 1000000.0
      secondTimeMs = totalFlops / secondGFlops / 1000000.0
      if winnerGFlops > 0 and secondGFlops > 0:
        solutionImportance[winnerIdx][1] += (secondTimeMs - winnerTimeMs)
        totalSavedMs += secondTimeMs - winnerTimeMs
      if winnerGFlops > 0:
        solutionImportance[winnerIdx][2] += 1
        solutionImportance[winnerIdx][3] += winnerTimeMs
        totalExecMs += winnerTimeMs
        totalWins += 1
        if secondGFlops <= 0:
          solutionImportance[winnerIdx][4] = True # this is only valid solution for this problem size, keep it


    # print data before sorting
    for i in range(0, self.numSolutions):
      print2("[%2u] %s: %e saved, %u wins, %u time, %s" \
          % (solutionImportance[i][0], \
          self.solutionNames[solutionImportance[i][0]], \
          solutionImportance[i][1], \
          solutionImportance[i][2], \
          solutionImportance[i][3], \
          "singular" if solutionImportance[i][4] else "" ) )

    totalSavedMs = max(1, totalSavedMs)
    solutionImportance.sort(key=lambda x: x[1])
    for i in range(0, self.numSolutions):
      solutionIdx = solutionImportance[i][0]
      canRemove = not solutionImportance[i][4] # don't remove if is only win for any size
      for exactProblem in self.exactWinners:
        winnerIdx = self.exactWinners[exactProblem][0]
        if solutionIdx == winnerIdx: # exact winners are important
          canRemove = False
          break
      if canRemove:
        idx = solutionImportance[i][0]
        if totalSavedMs > 0:
          percSaved = 1.0 * solutionImportance[i][1] / totalSavedMs
        else:
          percSaved = 0
        if totalWins > 0:
          percWins = 1.0 * solutionImportance[i][2] / totalWins
        else:
          percWins = 0
        if totalExecMs > 0:
          percTime = 1.0 * solutionImportance[i][3] / totalExecMs
        else:
          percTime = 0
        return ( idx, percSaved, percWins, percTime )
    return None


  ##############################################################################
  # Remove Solution
  ##############################################################################
  def removeSolution(self, removeSolutionIdx):

    # temporarily move current to old
    oldSolutions = deepcopy(self.solutions)
    oldNumSolutions = self.numSolutions
    oldData = deepcopy(self.data)

    # update solutions
    self.solutions = []
    for i in range(0, oldNumSolutions):
      if i != removeSolutionIdx:
        self.solutions.append(oldSolutions[i])
    self.solutionMinNaming = Solution.getMinNaming(self.solutions)
    self.solutionNames = []
    self.solutionTiles = []
    for solution in self.solutions:
      self.solutionNames.append(Solution.getNameMin(solution, \
          self.solutionMinNaming))
      self.solutionTiles.append("%ux%u"%(solution["MacroTile0"], \
          solution["MacroTile1"]))
    self.numSolutions = len(self.solutions)

    # update data
    self.totalSize = self.totalProblems * self.numSolutions
    self.data = array.array('f', [0]*self.totalSize)
    for problemIndex in range(0, self.totalProblems):
      newSolutionIdx = 0
      for oldSolutionIdx in range(0, oldNumSolutions):
        if oldSolutionIdx != removeSolutionIdx:
          self.data[problemIndex*self.numSolutions+newSolutionIdx] \
              = oldData[problemIndex*oldNumSolutions+oldSolutionIdx]
          newSolutionIdx += 1

    # update exact Winners
    for problemSize in self.exactWinners:
      if self.exactWinners[problemSize][0] >= removeSolutionIdx:
        self.exactWinners[problemSize][0] -= 1


  ##############################################################################
  # Prune a list of solutions, keeping only the indices specified in
  # keepSolutions.  keepSolutions is a set not a list
  ##############################################################################
  def pruneSolutions(self, keepSolutions):

    removeSolutionIdxList = []
    solutionMapNewToOld = [] # dense mapping
    solutionMapOldToNew = [-1] * self.numSolutions

    # temporarily move current to old
    oldSolutions = deepcopy(self.solutions)
    oldNumSolutions = self.numSolutions
    oldData = deepcopy(self.data)
    # update solutions
    self.solutions = []
    for i in range(0, oldNumSolutions):
      if i in keepSolutions:
        solutionMapNewToOld.append(i)
        solutionMapOldToNew[i] = len(self.solutions)
        self.solutions.append(oldSolutions[i])
      else:
        removeSolutionIdxList.append(i)

    self.solutionMinNaming = Solution.getMinNaming(self.solutions)
    self.solutionNames = []
    self.solutionTiles = []
    for solution in self.solutions:
      self.solutionNames.append(Solution.getNameMin(solution, \
          self.solutionMinNaming))
      self.solutionTiles.append("%ux%u"%(solution["MacroTile0"], \
          solution["MacroTile1"]))
    self.numSolutions = len(self.solutions)

    # update data
    self.totalSize = self.totalProblems * self.numSolutions
    self.data = array.array('f', [0]*self.totalSize)
    for problemIndex in range(0, self.totalProblems):
      for newSolutionIdx in range(0, self.numSolutions):
        oldSolutionIdx = solutionMapNewToOld[newSolutionIdx]
        self.data[problemIndex*self.numSolutions+newSolutionIdx] \
            = oldData[problemIndex*oldNumSolutions+oldSolutionIdx]

    # update exact Winners
    for problemSize in self.exactWinners:
      #print "prune updating exacWinner", problemSize, \
      #        "from ", self.exactWinners[problemSize][0], \
      #        "to ", solutionMapOldToNew[self.exactWinners[problemSize][0]]
      self.exactWinners[problemSize][0] = \
          solutionMapOldToNew[self.exactWinners[problemSize][0]]
      if self.exactWinners[problemSize][0] == -1:
        print(("warning: exactWinner[", problemSize, "] == -1"))
      if self.exactWinners[problemSize][0] >= self.numSolutions:
        print(("warning: exactWinner[", problemSize, "] "))


  ##############################################################################
  # Score Range For Logic
  ##############################################################################
  def scoreRangeForLogic(self, indexRange, logic):
    depth = self.getLogicDepth(logic)
    depth = self.numIndices - depth
    fullLogic = deepcopy(logic)
    for i in range(0, depth):
      fullLogic = [[-1, fullLogic]]
    return self.scoreRangeForFullLogic(depth, indexRange, fullLogic)

  ##############################################################################
  # Score Range For Full Logic
  ##############################################################################
  def scoreRangeForFullLogic(self, depth, indexRange, logic):
    score = 0
    for problemIndices in self.problemIndicesForRange(indexRange):
      problemSerial = self.indicesToSerial(0, problemIndices)
      totalFlops = self.totalFlopsForProblemIndices(problemIndices)
      solutionIdx = self.getSolutionForProblemIndicesUsingLogic( \
          problemIndices, logic)
      if solutionIdx == None:
        printWarning("SolutionIdx = None. This should never happen.")
        continue
      solutionGFlops = self.data[problemSerial + solutionIdx]
      solutionGFlops = max(1E-9, solutionGFlops)
      timeUs = totalFlops / solutionGFlops / 1000
      score += timeUs
    return score

  ##############################################################################
  # Get Solution For Problem Indices Using Logic
  ##############################################################################
  def getSolutionForProblemIndicesUsingLogic(self, problemIndices, logic):
    currentProblemIndices = self.toIndexOrder(problemIndices)
    currentLogic = logic
    for i in range(0, self.numIndices):
      currentSizeIndex = currentProblemIndices[0]
      for j in range(0, len(currentLogic)):
        if currentLogic[j][0] < 0:
          currentProblemIndices = currentProblemIndices[1:]
          currentLogic = currentLogic[j][1]
          break
        if currentLogic[j][0] >= 0:
          if currentSizeIndex <= currentLogic[j][0]:
            currentProblemIndices = currentProblemIndices[1:]
            currentLogic = currentLogic[j][1]
            break
    return currentLogic


  ##############################################################################
  ##############################################################################
  ###
  ###  Helper / Low-Level Functions
  ###
  ##############################################################################
  ##############################################################################


  ##############################################################################
  # Get Winner For Problem
  def getWinnerForProblem(self, problemIndices):
    problemSerial = self.indicesToSerial(0, problemIndices)
    winnerIdx = -1
    winnerGFlops = -1
    for solutionIdx in range(0, self.numSolutions):
      solutionSerialIdx = problemSerial + solutionIdx
      solutionGFlops = self.data[solutionSerialIdx]
      solutionGFlops = max(1E-9, solutionGFlops)
      if solutionGFlops > winnerGFlops:
        winnerIdx = solutionIdx
        winnerGFlops = solutionGFlops
    # print "Winner %u %f" % (winnerIdx, winnerGFlops)
    return (winnerIdx, winnerGFlops)


  ##############################################################################
  # Winner For Range, -1 if nothing benchmarked
  def winnerForRange(self, indexRange):
    if self.numSolutions == 1:
      return 0
    else:
      scores = self.scoreRangeForSolutions(indexRange)
      winnerIdx = 0
      hasWinner = False
      for solutionIdx in range(1, self.numSolutions):
        if scores[solutionIdx] < scores[winnerIdx]:
          winnerIdx = solutionIdx
          hasWinner = True
        elif scores[solutionIdx] > scores[winnerIdx]:
          hasWinner = True
        else:
          pass # still no winner

      return winnerIdx if hasWinner else -1


  ##############################################################################
  # Score (microseconds) Range For Solutions
  def scoreRangeForSolutions(self, indexRange):
    scores = [0]*self.numSolutions
    for problemIndices in self.problemIndicesForRange(indexRange):
      problemSerial = self.indicesToSerial(0, problemIndices)
      totalFlops = self.totalFlopsForProblemIndices(problemIndices)
      for solutionIdx in range(0, self.numSolutions):
        gflops = self.data[problemSerial+solutionIdx]
        if gflops > 0:
          timeUs = totalFlops / gflops / 1000
        else: # this solution not benchmarked for this size, therefore set
          # its score to +inf so that its automatically disqualified
          timeUs = float("inf")
        scores[solutionIdx] += timeUs
    return scores


  ##############################################################################
  # Score Logic Complexity
  def scoreLogicComplexity(self, logic, logicComplexity):
    depth = self.getLogicDepth(logic)
    if depth == 0: return
    depth = self.numIndices - depth
    for i in range(0, len(logic)):
      logicComplexity[depth] += 1
      self.scoreLogicComplexity(logic[i][1], logicComplexity)


  ##############################################################################
  # Get Logic Depth
  def getLogicDepth(self, logic):
    obj = logic
    depth = 0
    while isinstance(obj, list):
      obj = obj[0][1]
      depth += 1
    return depth


  ##############################################################################
  # To Index Order
  def toIndexOrder(self, problemIndices):
    ordered = []
    for i in self.indexOrder:
      ordered.append(problemIndices[i])
    return ordered


  ##############################################################################
  # Total Flops For Problem Indices
  def totalFlopsForProblemIndices(self, problemIndices):
    totalFlops = self.flopsPerMac
    for i in range(0, self.numIndices):
      totalFlops *= self.problemIndexToSize[i][problemIndices[i]]
    return totalFlops


  ##############################################################################
  # Recommended Index Order
  # TODO, this may depend on transposes
  def recommendedIndexOrder(self):
    order = []
    for i in range(0, self.problemType["TotalIndices"]):
      if i != self.idxU and i != self.idx1 and i != self.idx0:
        order.append(i)
    order.append(self.idxU)
    order.append(self.idx0)
    order.append(self.idx1)
    return order

  ##############################################################################
  # Problem Indices For Range
  def problemIndicesForRange(self, indexRange):
    problemIndexList = []
    # early return for empty set
    for i in range(0, self.numIndices):
      if indexRange[i][0] == indexRange[i][1]:
        return []
    problemIndices = []
    for idx in indexRange:
      problemIndices.append(idx[0])
    moreProblems = True
    while moreProblems:
      problemIndexList.append(deepcopy(problemIndices))
      # next problem
      problemIndices[0] += 1
      for i in range(0, self.numIndices):
        if problemIndices[i] >= indexRange[i][1]:
          if i == self.numIndices-1:
            moreProblems = False
            break
          else:
            problemIndices[i] = indexRange[i][0]
            problemIndices[i+1] += 1
        else:
          break
    return problemIndexList


  ##############################################################################
  # Get Item
  def __getitem__(self, indexTuple):
    indices = indexTuple[0] # in analysis order
    solutionIdx = indexTuple[1]
    serial = self.indicesToSerial(solutionIdx, indices)
    return self.data[serial]


  ##############################################################################
  # Set Item
  def __setitem__(self, indexTuple, value):
    indices = indexTuple[0] # in analysis order
    solutionIdx = indexTuple[1]
    serial = self.indicesToSerial(solutionIdx, indices )
    self.data[serial] = value


  ##############################################################################
  # Indices -> Serial
  def indicesToSerial(self, solutionIdx, indices ):
    serial = 0
    stride = 1
    serial += solutionIdx * stride
    stride *= self.numSolutions
    for i in range(0, self.numIndices):
      serial += indices[i] * stride
      stride *= self.numProblemSizes[i]
    return serial



def generateLogic(config, benchmarkDataPath, libraryLogicPath):
  print2("# LibraryLogic config: %s" % config)
  print2("# DefaultAnalysisParameters: " % defaultAnalysisParameters)

  setWorkingPath(libraryLogicPath)

  # Assign Defaults
  analysisParameters = {}
  for parameter in defaultAnalysisParameters:
    assignParameterWithDefault(analysisParameters, parameter, config, \
        defaultAnalysisParameters)

  print1("")
  print1(HR)
  currentTime = time.time()
  elapsedTime = currentTime - startTime
  print1("# Analysing data in %s - %.3fs" % (globalParameters["BenchmarkDataPath"], elapsedTime) )
  for parameter in analysisParameters:
    print2("#   %s: %s" % (parameter, analysisParameters[parameter]))
  print1(HR)
  print1("")

  ##############################################################################
  # Determine Which Problem Types
  ##############################################################################
  problemTypes = {}
  if not os.path.exists(benchmarkDataPath):
    printExit("Path doesn't exist: %s" % benchmarkDataPath)
  fileNames = os.listdir(benchmarkDataPath)
  fileNames = sorted(fileNames)
  for fileName in fileNames:
    if os.path.splitext(fileName)[1] == ".csv":
      fileBase = os.path.splitext( \
          os.path.join(benchmarkDataPath, \
          fileName))[0]
      dataFileName = fileBase + ".csv"
      solutionsFileName = fileBase + ".yaml"
      selectionFileName = fileBase + ".gsp"
      if not os.path.exists(dataFileName):
        printExit("%s doesn't exist for %s" % (dataFileName, fileBase) )
      if not os.path.exists(solutionsFileName):
        printExit("%s doesn't exist for %s" % (solutionsFileName, fileBase) )
      (problemSizes, solutions) = LibraryIO.parseSolutionsFile(solutionsFileName)
      if len(solutions) == 0:
        printExit("%s doesn't contains any solutions." % (solutionsFileName) )
      problemType = solutions[0]["ProblemType"]
      if problemType not in problemTypes:
        problemTypes[problemType] = []
      problemTypes[problemType].append( (problemSizes, \
          dataFileName, solutionsFileName, selectionFileName, solutions) )

  for problemType in problemTypes:
    logicTuple = analyzeProblemType(problemType, problemTypes[problemType], analysisParameters)

    filename = os.path.join(globalParameters["WorkingPath"], \
        "{}_{}".format(analysisParameters["ScheduleName"], str(problemType) + ".yaml"))

    print2("# writing library logic YAML {}".format(filename))
    dictFormat = globalParameters["DictLibraryLogic"]
    data = LibraryIO.createLibraryLogic(analysisParameters["ScheduleName"], \
        analysisParameters["ArchitectureName"], analysisParameters["DeviceNames"], logicTuple, dictFormat)

    LibraryIO.writeYAML(filename, data, explicit_start=False, explicit_end=False)

  currentTime = time.time()
  elapsedTime = currentTime - startTime
  print1("%s\n# Finish Analysing data to %s in %.3fs\n%s" % (HR, os.path.split(libraryLogicPath)[0], elapsedTime, HR) )
  popWorkingPath()


################################################################################
################################################################################
###
###   Main
###
################################################################################
################################################################################
def main(  config ):

  benchmarkDataPath = os.path.join(globalParameters["WorkingPath"], \
      globalParameters["BenchmarkDataPath"])

  libraryLogicPath = os.path.join(globalParameters["WorkingPath"], \
      globalParameters["LibraryLogicPath"])

  generateLogic(config, benchmarkDataPath, libraryLogicPath)
