"""Theory:
We have a sequence S of valid board states s_1, ..., s_n.
We search for a picked subsequence S_p of length k and m additional moves M such that:
- S_p augmented by M forms a valid sequence of moves
- k-m is maximal for S_p picked from S
It is relatively cheap to add new items to S.

User can change detector parameters, presumably in case the previous don't fit the (current) situation.
In such a case we assume the new parameters are correct from the current position onwards.
But the change might have been appropriate even earlier (before the user detected and fixed an error).
So we try to find the correct crossover point like this:
- construct a sequence S' = s'_i, ..., s'_n by reanalyzing the positions with a new set of parameters, where s_i is the point of previous user intervention or s_0
- for each s'_j:
	- try to append it to S[:j]
	- try to append it to S'[:j]
	- remember the better variant
- linearize the fork back by discarding s'_j-s preceding the crossover and s_j-s following the crossover
"""
import random

from util import EMPTY
from go.engine import transitionSequence

rand=random.Random()
rand.seed(361)
zobristNums=tuple(
	tuple(
		tuple(rand.getrandbits(32) for i in range(3)) for c in range(19)
	) for r in range(19)
)


## Crude lower bound on edit distance between states.
def estimateDistance(diff):
	# lot of room for improvements
	additions=sum(1 for d in diff if d[2]=="+")
	deletions=sum(1 for d in diff if d[2]=="-")
	replacements=len(diff)-additions-deletions
	if additions>0: return additions+replacements
	elif replacements==0 and deletions>0: return 2 # take n, return 1
	return replacements+1 # ???


class BoardState:
	def __init__(self,board):
		self._board=tuple(tuple(x for x in row) for row in board)
		self.prev=None
		self._next=None
		self.moves=[]
		self.weight=0
		self.diff2Prev=None

	def hash(self):
		res=0
		for (r,row) in enumerate(self._board):
			for (c,item) in enumerate(row):
				res^=zobristNums[r][c][item+1]
		return res

	def __iter__(self): return iter(self._board)

	def __getitem__(self,key): return self._board[key]

	def __sub__(self,x):
		res=[]

		for (r,(row1,row2)) in enumerate(zip(self._board,x)):
			for (c,(item1,item2)) in enumerate(zip(row1,row2)):
				if item1==item2: continue
				elif item2==EMPTY: res.append((r,c,"+",item1))
				elif item1==EMPTY: res.append((r,c,"-",item2))
				else: res.append((r,c,"*",item1)) # ->to
		return res

	def __eq__(self,x):
		# might want to use hash
		return self._board==x._board


class StateBag:
	def __init__(self):
		self._states=[]

	def pushState(self,board):
		sn=BoardState(board)
		if self._states and sn==self._states[-1]: return # no change

		for s in reversed(self._states):
			diff=sn-s
			distEst=estimateDistance(diff)
			if distEst>3: continue # we couldn't find every such move sequence anyway without a clever algorithm
			weightEst=s.weight-distEst
			if weightEst<=sn.weight: continue
			moves=transitionSequence(s,sn,diff)
			weight=s.weight-len(moves)
			if weight<=sn.weight: continue
			sn.prev=s
			sn.diff2Prev=diff
			sn.moves=moves
			sn.weight=weight
		self._states.append(sn)

	def merge(self,branch):
		pass