Update OR-Tools to v9.1
This update, despite ostensibly being a minor version, includes breaking changes that must be accounted for. Overflow is much more strictly checked, so the magnitude of certain constants has been decreased. CP-SAT's default number of workers has been changed to reflect the default of this script. As such, the script no longer needs to change that parameter unless a specific number of workers has been specified. In light of the breaking changes, the OR-Tools version is now pinned at 9.1 instead of being permitted to use future minor versions.
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@ -4,7 +4,7 @@ verify_ssl = true
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name = "pypi"
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[packages]
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ortools = "~=9.0"
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ortools = "9.1"
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windows-curses = {platform_system = "== 'Windows'"}
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[scripts]
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@ -28,7 +28,7 @@ Each parameter is interpreted as a BoundedLinearExpression, and a layer of indir
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Each parameter is interpreted as a BoundedLinearExpression, and a layer of indirection is applied such that each Constraint in the returned tuple can accept an enforcement literal."""
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intermediate_target, target_constraint = self.NewIntermediateIntVar(target, f'{repr(target)} == {repr(num)} // {repr(denom)}: target')
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intermediate_num, num_constraint = self.NewIntermediateIntVar(num, f'{repr(target)} == {repr(num)} // {repr(denom)}: num', lb = 0)
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intermediate_denom, denom_constraint = self.NewIntermediateIntVar(denom, f'{repr(target)} == {repr(num)} // {repr(denom)}: denom', lb = 0)
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intermediate_denom, denom_constraint = self.NewIntermediateIntVar(denom, f'{repr(target)} == {repr(num)} // {repr(denom)}: denom', lb = 1)
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super().AddDivisionEquality(intermediate_target, intermediate_num, intermediate_denom)
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return (target_constraint, num_constraint, denom_constraint)
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@ -41,7 +41,7 @@ Each parameter is interpreted as a BoundedLinearExpression, and a layer of indir
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`multiple` defaults to the same value as `denom` if unspecified."""
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quotient = self.NewIntVar(f'{repr(target)} == ({repr(num)} // {repr(denom)}) * {repr(multiple)}: quotient')
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intermediate_num, num_constraint = self.NewIntermediateIntVar(num, f'{repr(target)} == ({repr(num)} // {repr(denom)}) * {repr(multiple)}: num', lb = 0)
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intermediate_denom, denom_constraint = self.NewIntermediateIntVar(denom, f'{repr(target)} == ({repr(num)} // {repr(denom)}) * {repr(multiple)}: denom', lb = 0)
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intermediate_denom, denom_constraint = self.NewIntermediateIntVar(denom, f'{repr(target)} == ({repr(num)} // {repr(denom)}) * {repr(multiple)}: denom', lb = 1)
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intermediate_target, target_constraint = self.NewIntermediateIntVar(target, f'{repr(target)} == ({repr(num)} // {repr(denom)}) * {repr(multiple)}: target')
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if multiple:
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intermediate_multiple, multiple_constraint = self.NewIntermediateIntVar(multiple, f'{repr(target)} == ({repr(num)} // {repr(denom)}) * {repr(multiple)}: multiple')
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@ -110,7 +110,7 @@ Each parameter is interpreted as a BoundedLinearExpression, and a layer of indir
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self.AddLinearExpressionInDomain(linear_exp, domain.Complement()).OnlyEnforceIf(intermediate.Not())
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return intermediate
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def NewIntermediateIntVar(self, linear_exp, name, *, lb = cp_model.INT_MIN//8, ub = cp_model.INT_MAX//8):
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def NewIntermediateIntVar(self, linear_exp, name, *, lb = cp_model.INT32_MIN, ub = cp_model.INT32_MAX):
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"""Creates an integer variable equivalent to the given expression and returns a tuple consisting of the variable and constraint for use with enforcement literals."""
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intermediate = super().NewIntVar(lb, ub, name)
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@ -4,7 +4,6 @@ __all__ = ['Adjustment', 'Appendage', 'Buyer', 'Declaration', 'DiplomatFascinati
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__author__ = "Jeremy Saklad"
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from functools import partialmethod
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from os import cpu_count
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from ortools.sat.python import cp_model
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@ -22,7 +21,7 @@ from .data.torsos import Torso
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from .objects.bone_market_model import BoneMarketModel
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# This multiplier is applied to the profit margin to avoid losing precision due to rounding.
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PROFIT_MARGIN_MULTIPLIER = 10000000
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PROFIT_MARGIN_MULTIPLIER = 10000
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# This is the highest number of attribute to calculate fractional exponents for.
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MAXIMUM_ATTRIBUTE = 100
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@ -31,7 +30,7 @@ MAXIMUM_ATTRIBUTE = 100
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DIFFICULTY_SCALER = 0.6
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def Solve(shadowy_level, bone_market_fluctuations = None, zoological_mania = None, occasional_buyer = None, diplomat_fascination = None, desired_buyers = [], maximum_cost = cp_model.INT32_MAX, maximum_exhaustion = cp_model.INT32_MAX, time_limit = float('inf'), workers = cpu_count(), blacklist = [], stdscr = None):
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def Solve(shadowy_level, bone_market_fluctuations = None, zoological_mania = None, occasional_buyer = None, diplomat_fascination = None, desired_buyers = [], maximum_cost = cp_model.INT32_MAX, maximum_exhaustion = cp_model.INT32_MAX, time_limit = float('inf'), workers = None, blacklist = [], stdscr = None):
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model = BoneMarketModel()
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actions = {}
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@ -1202,7 +1201,8 @@ Exhaustion: {solver.Value(exhaustion):n}"""
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printer = SkeletonPrinter()
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solver = cp_model.CpSolver()
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solver.parameters.num_search_workers = workers
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if workers:
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solver.parameters.num_search_workers = workers
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solver.parameters.max_time_in_seconds = time_limit
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# There's no window in verbose mode
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@ -1210,7 +1210,7 @@ Exhaustion: {solver.Value(exhaustion):n}"""
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solver.parameters.log_search_progress = True
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solver.Solve(model)
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else:
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solver.SolveWithSolutionCallback(model, printer)
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solver.Solve(model, printer)
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status = solver.StatusName()
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