# Uncomment one # to activate deeper profiling:
# # cython: profile=True

"""
Rough todolist:
 - continue optimizing by profiling
 - investigate migrating everyting to "str" instead of "unicode" (we unidecode anyway)
"""

from __future__ import division
from unidecode import unidecode
import re2

RE_SPLIT = re2.compile(r"(?:[^\w]|[_])+")
RE_ISNUM = re2.compile(r"[0-9]")
RE_UNICODE_PRECLEAN = re2.compile(r"""
  (\xd8(?=\s*[0-9]))    # Diameter character
""", flags=re2.VERBOSE)

cdef bint DEBUG = 0

cdef float jaro_winkler_ratio(unicode s1, unicode s2):
  return jaro_winkler(s1, len(s1), s2, len(s2), 0)


cdef float norm(float flt, float from1, float to1, float from2, float to2):
  return ((flt - from1) / (to1 - from1)) * (to2 - from2) + from2


# Resampled because we don't care about really different strings
cdef float jaro(unicode s1, unicode s2):
  j = jaro_winkler_ratio(s1, s2)

  if j < 0.75:
    return 0
  elif j < 0.85:
    return norm(j, 0, 0.85, 0, 0.01)
  else:
    return norm(j, 0.85, 1, 0.01, 1)


cdef tokenize(s):
  cdef unicode u
  if type(s) == unicode:
    u = unicode(unidecode(RE_UNICODE_PRECLEAN.sub(" ", s)))
  else:
    u = unicode(s)
  return [x for x in RE_SPLIT.split(u.lower()) if x]


cdef float w(tokens, weights):
  """ Returns the weight of a list of tokens """
  cdef float s = 0.0
  for t in tokens:
    s += weights[t]
  return s


cdef unicode reassemble(joiner, tokens, whitelist):
  return joiner.join([t for t in tokens if t in whitelist])


def pa_string_distance(str1, str2):

  if not str1 or not str2:
    return 1.0

  tokens1 = tokenize(str1)
  tokens2 = tokenize(str2)

  if len(tokens1) == 0 or len(tokens2) == 0:
    return 1.0

  weights = {}
  cdef float weight1 = 0.0
  cdef float weight2 = 0.0

  for t in tokens1:
    weight1 += len(t)
    if t in weights:
      weights[t] += len(t)
    else:
      weights[t] = len(t)

  for t in tokens2:
    weight2 += len(t)
    if t in weights:
      weights[t] += len(t)
    else:
      weights[t] = len(t)

  tokens1_set = set(tokens1)
  tokens2_set = set(tokens2)

  # print tokens1_set, tokens2_set

  cdef float max_distance = weight1 + weight2
  cdef float distance = 0.0

  diff1 = set()
  diff1_num = set()

  diff2 = set()
  diff2_num = set()

  for t in tokens1_set.difference(tokens2_set):
    if RE_ISNUM.search(t):
      diff1_num.add(t)
    else:
      diff1.add(t)

  for t in tokens2_set.difference(tokens1_set):
    if RE_ISNUM.search(t):
      diff2_num.add(t)
    else:
      diff2.add(t)

  cdef float diff1_weight = w(diff1, weights)
  cdef float diff2_weight = w(diff2, weights)
  cdef float diff1_num_weight = w(diff1_num, weights)
  cdef float diff2_num_weight = w(diff2_num, weights)

  if DEBUG:
    print "intersection tokens ", tokens1_set.intersection(tokens2_set)
    print "diff tokens1", diff1, diff1_num
    print "diff tokens2",  diff2, diff2_num
    print "max diff / max distance", diff1_weight + diff2_weight + diff1_num_weight + diff2_num_weight, "/", max_distance
    print "token weights", weights

  distance += diff1_num_weight + diff2_num_weight

  if len(diff1) == 0 and len(diff2) == 0:
    return distance / max_distance

  #
  # By now, we have eliminated all the simple cases.
  # Compare the remainders with a few different methods and pick the lowest distance.
  #

  # 1: smallest jaro distance in all the tokens of the other string
  cdef float jaro1 = sum([min([1 - jaro(t1, t2) for t2 in tokens2_set]) * weights[t1] for t1 in diff1])
  cdef float jaro2 = sum([min([1 - jaro(t1, t2) for t1 in tokens1_set]) * weights[t2] for t2 in diff2])

  cdef float distance_crossproduct = (distance + jaro1 + jaro2)

  # for t1 in diff1:
  #   print [(t1, t2, jaro(t1, t2)) for t2 in tokens2_set if jaro(t1, t2)]
  # for t2 in diff2:
  #   print [(t1, t2, jaro(t1, t2)) for t1 in tokens1_set if jaro(t1, t2)]


  # 2: original order diffs
  cdef unicode original1 = reassemble(u" ", tokens1, diff1)
  cdef unicode original2 = reassemble(u" ", tokens2, diff2)

  cdef float weight_originalorder = (1 - jaro(original1, original2)) * (diff1_weight + diff2_weight)
  cdef float distance_originalorder = (distance + weight_originalorder)

  # 2: original order diffs, with numerics
  cdef unicode originalnum1 = reassemble(u"", tokens1, diff1.union(diff1_num))
  cdef unicode originalnum2 = reassemble(u"", tokens2, diff2.union(diff2_num))

  cdef float coef = (diff1_weight + diff2_weight + diff1_num_weight + diff2_num_weight)
  cdef float weight_originalorderwithnum = (1 - jaro(originalnum1, originalnum2)) * coef
  cdef float distance_originalorderwithnum = min(weight_originalorderwithnum + 0.01, coef)  # Add an additional fixed distance here to differentiate cases.

  if DEBUG:
    print "distance_crossproduct: %s" % distance_crossproduct
    print "distance_originalorder: %s" % distance_originalorder
    print "distance_originalorderwithnum: %s" % distance_originalorderwithnum

  return min([distance_crossproduct, distance_originalorder, distance_originalorderwithnum]) / max_distance