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# Copyright 2010 Hakan Kjellerstrand hakank@gmail.com
#
# 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
#
#
# 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.
 
"""
 
  Nonogram  (Painting by numbers) in Google CP Solver.
 
  '''
  Nonograms or Paint by Numbers are picture logic puzzles in which cells in a
  grid have to be colored or left blank according to numbers given at the
  side of the grid to reveal a hidden picture. In this puzzle type, the
  numbers measure how many unbroken lines of filled-in squares there are
  in any given row or column. For example, a clue of '4 8 3' would mean
  there are sets of four, eight, and three filled squares, in that order,
  with at least one blank square between successive groups.
 
  '''
 
  See problem 12 at http://www.csplib.org/.
 
 
  Haskell solution:
 
  Brunetti, Sara & Daurat, Alain (2003)
  'An algorithm reconstructing convex lattice sets'
  http://geodisi.u-strasbg.fr/~daurat/papiers/tomoqconv.pdf
 
 
  was a major influence when writing this Google CP solver model.
 
  I have also blogged about the development of a Nonogram solver in Comet
  using the regular constraint.
  * 'Comet: Nonogram improved: solving problem P200 from 1:30 minutes
     to about 1 second'
 
  * 'Comet: regular constraint, a much faster Nonogram with the regular constraint,
     some OPL models, and more'
 
  Compare with the other models:
  * Gecode/R: http://www.hakank.org/gecode_r/nonogram.rb (using 'regexps')
    Note: nonogram_create_automaton2.mzn is the preferred model
 
  This model was created by Hakan Kjellerstrand (hakank@bonetmail.com)
  Also see my other Google CP Solver models: http://www.hakank.org/google_or_tools/
 
"""
 
import sys
 
from constraint_solver import pywrapcp
 
 
#
# Make a transition (automaton) list of tuples from a
# single pattern, e.g. [3,2,1]
#
def make_transition_tuples(pattern):
  p_len = len(pattern)
  num_states = p_len + sum(pattern)
 
  tuples = pywrapcp.IntTupleSet(3)
 
  # this is for handling 0-clues. It generates
  # just the minimal state
  if num_states == 0:
    tuples.Insert3(1, 0, 1);
    return (tuples, 1)
 
  # convert pattern to a 0/1 pattern for easy handling of
  # the states
  tmp = [0];
  c = 0
  for pattern_index in range(p_len):
    tmp.extend([1] * pattern[pattern_index])
    tmp.append(0)
 
  for i in range(num_states):
    state = i + 1
    if tmp[i] == 0:
      tuples.Insert3(state, 0, state)
      tuples.Insert3(state, 1, state + 1)
    else:
      if i < num_states - 1:
        if tmp[i + 1] == 1:
          tuples.Insert3(state, 1, state + 1)
        else:
          tuples.Insert3(state, 0, state + 1)
  tuples.Insert3(num_states, 0, num_states)
  return (tuples, num_states)
 
 
#
# check each rule by creating an automaton and transition constraint.
#
def check_rule(rules, y):
  cleaned_rule = [rules[i] for i in range(len(rules)) if rules[i] > 0]
  (transition_tuples, last_state) = make_transition_tuples(cleaned_rule)
 
  initial_state = 1
  accepting_states = [last_state]
 
  solver = y[0].solver()
  solver.Add(solver.TransitionConstraint(y,
                                         transition_tuples,
                                         initial_state,
                                         accepting_states))
 
 
def main(rows, row_rule_len, row_rules, cols, col_rule_len, col_rules):
 
  # Create the solver.
  solver = pywrapcp.Solver('Regular test')
 
  #
  # variables
  #
  board = {}
  for i in range(rows):
    for j in range(cols):
      board[i, j] = solver.IntVar(0, 1, 'board[%i, %i]' % (i, j))
  board_flat = [board[i, j] for i in range(rows) for j in range(cols)]
 
  # Flattened board for labeling.
  # This labeling was inspired by a suggestion from
  # Pascal Van Hentenryck about my Comet nonogram model.
  board_label = []
  if rows * row_rule_len < cols * col_rule_len:
    for i in range(rows):
      for j in range(cols):
        board_label.append(board[i, j])
  else:
    for j in range(cols):
      for i in range(rows):
        board_label.append(board[i, j])
 
 
  #
  # constraints
  #
  for i in range(rows):
    check_rule(row_rules[i], [board[i, j] for j in range(cols)])
 
  for j in range(cols):
    check_rule(col_rules[j], [board[i, j] for i in range(rows)])
 
 
  #
  # solution and search
  #
  db = solver.Phase(board_label,
                    solver.CHOOSE_FIRST_UNBOUND,
                    solver.ASSIGN_MIN_VALUE)
 
  print 'before solver, wall time = ', solver.WallTime(), 'ms'
  solver.NewSearch(db)
 
  num_solutions = 0
  while solver.NextSolution():
    print
    num_solutions += 1
    for i in range(rows):
      row = [board[i, j].Value() for j in range(cols)]
      row_pres = []
      for j in row:
        if j == 1:
          row_pres.append('#')
        else:
          row_pres.append(' ')
      print '  ', ''.join(row_pres)
 
    print
    print '  ', '-' * cols
 
    if num_solutions >= 2:
      print '2 solutions is enough...'
      break
 
  solver.EndSearch()
  print
  print 'num_solutions:', num_solutions
  print 'failures:', solver.Failures()
  print 'branches:', solver.Branches()
  print 'WallTime:', solver.WallTime(), 'ms'
 
 
 
#
# Default problem
#
# The lambda picture
#
rows = 12
row_rule_len = 3
row_rules = [
    [0,0,2],
    [0,1,2],
    [0,1,1],
    [0,0,2],
    [0,0,1],
    [0,0,3],
    [0,0,3],
    [0,2,2],
    [0,2,1],
    [2,2,1],
    [0,2,3],
    [0,2,2]
    ]
 
cols = 10
col_rule_len = 2
col_rules = [
    [2,1],
    [1,3],
    [2,4],
    [3,4],
    [0,4],
    [0,3],
    [0,3],
    [0,3],
    [0,2],
    [0,2]
    ]
 
 
if __name__ == '__main__':
  if len(sys.argv) > 1:
    file = sys.argv[1]
    execfile(file)
  main(rows, row_rule_len, row_rules, cols, col_rule_len, col_rules)