output_description: On the first line, print the maximum number of tiles that can be placed.
  
  On the next N lines, print a configulation that achieves the maximum.
  Precisely, output the strings t_1,t_2,\cdots,t_N constructed by the following
  way.
  
    * t_i is initialized to S_i.
    * For each (i,j), if there is a tile that occupies (i,j) and (i+1,j), change t_{i,j}:=`v`, t_{i+1,j}:=`^`.
    * For each (i,j), if there is a tile that occupies (i,j) and (i,j+1), change t_{i,j}:=`>`, t_{i,j+1}:=`<`.
  
  See samples for further information.
  
  You may print any configulation that maximizes the number of tiles.
  
  * * *
input_description: Input is given from Standard Input in the following format:
  
      
      
      N M
      S_1
      S_2
      \vdots
      S_N
attempt: code = r"""
  # distutils: language=c++
  # distutils: include_dirs=[/home/contestant/.local/lib/python3.8/site-packages/numpy/core/include, /opt/atcoder-stl]
  # cython: boundscheck=False
  # cython: wraparound=False
  from libcpp.vector cimport vector
  from libcpp cimport bool
  cdef extern from "<atcoder/maxflow>" namespace "atcoder":
      cdef cppclass mf_graph[Cap]:
          mf_graph(int n)
          int add_edge(int fr, int to, Cap cap)
          Cap flow(int s, int t)
          Cap flow(int s, int t, Cap flow_limit)
          vector[bool] min_cut(int s)
          cppclass edge:
              int frm "from"
              int to
              Cap cap
              Cap flow
              edge(edge &e)
          edge get_edge(int i)
          vector[edge] edges()
          void change_edge(int i, Cap new_cap, Cap new_flow)
   
  cdef class MfGraph:
      cdef mf_graph[int] *_thisptr
      def __cinit__(self, int n):
          self._thisptr = new mf_graph[int](n)
      cpdef int add_edge(self, int fr, int to, int cap):
          return self._thisptr.add_edge(fr, to, cap)
      cpdef int flow(self, int s, int t):
          return self._thisptr.flow(s, t)
      cpdef int flow_with_limit(self, int s, int t, int flow_limit):
          return self._thisptr.flow(s, t, flow_limit)
      cpdef vector[bool] min_cut(self, int s):
          return self._thisptr.min_cut(s)
   
      cpdef vector[int] get_edge(self, int i):
          cdef mf_graph[int].edge *e = new mf_graph[int].edge(self._thisptr.get_edge(i))
          cdef vector[int] *ret_e = new vector[int]()        
          ret_e.push_back(e.frm)
          ret_e.push_back(e.to)
          ret_e.push_back(e.cap)
          ret_e.push_back(e.flow)
          return ret_e[0]
   
      cpdef vector[vector[int]] edges(self):
          cdef vector[mf_graph[int].edge] es = self._thisptr.edges()
          cdef vector[vector[int]] *ret_es = new vector[vector[int]](es.size())
          for i in range(es.size()):
              ret_es.at(i).push_back(es.at(i).frm)
              ret_es.at(i).push_back(es.at(i).to)
              ret_es.at(i).push_back(es.at(i).cap)
              ret_es.at(i).push_back(es.at(i).flow)
          return ret_es[0]
      cpdef void change_edge(self, int i, int new_cap, int new_flow):
          self._thisptr.change_edge(i, new_cap, new_flow)
   
   
  """
  import os, sys
  
  if sys.argv[-1] == "ONLINE_JUDGE":
      open("atcoder.pyx", "w").write(code)
      os.system("cythonize -i -3 -b atcoder.pyx")
  from atcoder import MfGraph
  
  N, M = list(map(int, input().split()))
  S = [input() for i in range(N)]
  
  mg = MfGraph(N * M + 2)
  
  s = N * M
  t = N * M + 1
  
  for i in range(N):
      for j in range(M):
          if S[i][j] == "#":
              continue
          v = i * M + j
          if (i + j) % 2 == 0:
              mg.add_edge(s, v, 1)
          else:
              mg.add_edge(v, t, 1)
  
  for i in range(N):
      for j in range(M):
          if (i + j) % 2 == 1 or S[i][j] == "#":
              continue
          v0 = i * M + j
          if i > 0:
              if S[i - 1][j] == ".":
                  v1 = (i - 1) * M + j
                  mg.add_edge(v0, v1, 1)
          if j > 0:
              if S[i][j - 1] == ".":
                  v1 = i * M + j - 1
                  mg.add_edge(v0, v1, 1)
          if i < N - 1:
              if S[i + 1][j] == ".":
                  v1 = (i + 1) * M + j
                  mg.add_edge(v0, v1, 1)
          if j < M - 1:
              if S[i][j + 1] == ".":
                  v1 = i * M + j + 1
                  mg.add_edge(v0, v1, 1)
  
  print(mg.flow(s, t))
  
  edges = mg.edges()
  
  T = [list(__) for __ in S]
  
  for e in edges:
      if e[0] == s or e[1] == t or e[3] == 0:
          continue
      i0 = e[0] // M
      j0 = e[0] % M
      i1 = e[1] // M
      j1 = e[1] % M
      if i0 == i1 + 1:
          T[i1][j1] = "v"
          T[i0][j0] = "^"
      elif j0 == j1 + 1:
          T[i1][j1] = ">"
          T[i0][j0] = "<"
      elif i0 == i1 - 1:
          T[i1][j1] = "^"
          T[i0][j0] = "v"
      else:
          T[i1][j1] = "<"
          T[i0][j0] = ">"
  
  for __ in T:
      print("".join(__))
  
problem_description: Statement
  
  You are given a grid of N rows and M columns. The square at the i-th row and
  j-th column will be denoted as (i,j). Some of the squares contain an object.
  All the remaining squares are empty. The state of the grid is represented by
  strings S_1,S_2,\cdots,S_N. The square (i,j) contains an object if S_{i,j}=
  `#` and is empty if S_{i,j}= `.`.
  
  Consider placing 1 \times 2 tiles on the grid. Tiles can be placed vertically
  or horizontally to cover two adjacent empty squares. Tiles must not stick out
  of the grid, and no two different tiles may intersect. Tiles cannot occupy the
  square with an object.
  
  Calculate the maximum number of tiles that can be placed and any configulation
  that acheives the maximum.
samples: [{"input": "3 3\n    #..\n    ..#\n    ...", "output": "3\n    #><\n    vv#\n    ^^.\n    \n\nThe following output is also treated as a correct answer.\n\n    \n    \n    3\n    #><\n    v.#\n    ^><"}]