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# Copyright 2010-2018 Google LLC
# 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.
"""Reallocate production to smooth it over years."""

from __future__ import print_function

import collections

from ortools.sat.python import cp_model



# Data
data_0 = [
    [107, 107, 107, 0, 0],  # pr1
    [0, 47, 47, 47, 0],  # pr2
    [10, 10, 10, 0, 0],  # pr3
    [0, 55, 55, 55, 55],  # pr4
]

data_1 = [
    [119444030, 0, 0, 0],
    [34585586, 38358559, 31860661, 0],
    [19654655, 21798799, 18106106, 0],
    [298836792, 0, 0, 0],
    [3713428, 4118530, 4107277, 3072018],
    [6477273, 7183884, 5358471, 0],
    [1485371, 1647412, 1642911, 1228807]
]

data_2 = [
    [1194440, 0, 0, 0],
    [345855, 383585, 318606, 0],
    [196546, 217987, 181061, 0],
    [2988367, 0, 0, 0],
    [37134, 41185, 41072, 30720],
    [64772, 71838, 53584, 0],
    [14853, 16474, 16429, 12288]
]

pr = data_0

num_pr = len(pr)
num_years = len(pr[1])
total = sum(pr[p][y] for p in range(num_pr) for y in range(num_years))
avg = total // num_years

# Model
model = cp_model.CpModel()

# Variables
delta = model.NewIntVar(0, total, 'delta')

contributions_per_years = collections.defaultdict(list)
contributions_per_prs = collections.defaultdict(list)
all_contribs = {}

for p, inner_l in enumerate(pr):
    for y, item in enumerate(inner_l):
        if item != 0:
            contrib = model.NewIntVar(0, total, 'r%d c%d' % (p, y))
            contributions_per_years[y].append(contrib)
            contributions_per_prs[p].append(contrib)
            all_contribs[p, y] = contrib

year_var = [
    model.NewIntVar(0, total, 'y[%i]' % i) for i in range(num_years)
]

# Constraints

# Maintain year_var.
for y in range(num_years):
    model.Add(year_var[y] == sum(contributions_per_years[y]))

# Fixed contributions per pr.
for p in range(num_pr):
    model.Add(sum(pr[p]) == sum(contributions_per_prs[p]))

# Link delta with variables.
for y in range(num_years):
    model.Add(year_var[y] >= avg - delta)

for y in range(num_years):
    model.Add(year_var[y] <= avg + delta)

# Solve and output
model.Minimize(delta)

# Solve model.
solver = cp_model.CpSolver()
status = solver.Solve(model)

# Output solution.
if status == cp_model.OPTIMAL:
    print('Data')
    print('  - total = ', total)
    print('  - year_average = ', avg)
    print('  - number of projects = ', num_pr)
    print('  - number of years = ', num_years)

    print('  - input production')
    for p in range(num_pr):
        for y in range(num_years):
            if pr[p][y] == 0:
                print('        ', end='')
            else:
                print('%10i' % pr[p][y], end='')
        print()

    print('Solution')
    for p in range(num_pr):
        for y in range(num_years):
            if pr[p][y] == 0:
                print('        ', end='')
            else:
                print('%10i' % solver.Value(all_contribs[p, y]), end='')
        print()

    for y in range(num_years):
        print('%10i' % solver.Value(year_var[y]), end='')
    print()