# Copyright 2018 MLBenchmark Group. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Defines Subtokenizer class to encode and decode strings."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import re
import sys
import unicodedata

import numpy as np
import six
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

from mlperf_compliance import mlperf_log


PAD = "<pad>"
PAD_ID = 0
EOS = "<EOS>"
EOS_ID = 1
RESERVED_TOKENS = [PAD, EOS]

# Set of characters that will be used in the function _escape_token() (see func
# docstring for more details).
# This set is added to the alphabet list to ensure that all escaped tokens can
# be encoded.
_ESCAPE_CHARS = set(u"\\_u;0123456789")
# Regex for the function _unescape_token(), the inverse of _escape_token().
# This is used to find "\u", "\\", and "\###;" substrings in the token.
_UNESCAPE_REGEX = re.compile(r"\\u|\\\\|\\([0-9]+);")

_UNDEFINED_UNICODE = u"\u3013"

# Set contains all letter and number characters.
_ALPHANUMERIC_CHAR_SET = set(
    six.unichr(i) for i in xrange(sys.maxunicode)
    if (unicodedata.category(six.unichr(i)).startswith("L") or
        unicodedata.category(six.unichr(i)).startswith("N")))

# min_count is the minimum number of times a subtoken must appear in the data
# before before it is added to the vocabulary. The value is found using binary
# search to obtain the target vocabulary size.
_MIN_MIN_COUNT = 1     # min value to use when binary searching for min_count
_MAX_MIN_COUNT = 1000  # max value to use when binary searching for min_count


class Subtokenizer(object):
  """Encodes and decodes strings to/from integer IDs."""

  def __init__(self, vocab_file, reserved_tokens=None):
    """Initializes class, creating a vocab file if data_files is provided."""
    tf.logging.info("Initializing Subtokenizer from file %s." % vocab_file)

    if reserved_tokens is None:
      reserved_tokens = RESERVED_TOKENS

    self.subtoken_list = _load_vocab_file(vocab_file, reserved_tokens)
    self.alphabet = _generate_alphabet_dict(self.subtoken_list)
    self.subtoken_to_id_dict = _list_to_index_dict(self.subtoken_list)

    self.max_subtoken_length = 0
    for subtoken in self.subtoken_list:
      self.max_subtoken_length = max(self.max_subtoken_length, len(subtoken))

    # Create cache to speed up subtokenization
    self._cache_size = 2 ** 20
    self._cache = [(None, None)] * self._cache_size

  @staticmethod
  def init_from_files(
      vocab_file, files, target_vocab_size, threshold, min_count=None,
      file_byte_limit=1e6, reserved_tokens=None):
    """Create subtoken vocabulary based on files, and save vocab to file.

    Args:
      vocab_file: String name of vocab file to store subtoken vocabulary.
      files: List of file paths that will be used to generate vocabulary.
      target_vocab_size: target vocabulary size to generate.
      threshold: int threshold of vocabulary size to accept.
      min_count: int minimum count to use for generating the vocabulary. The min
        count is the minimum number of times a subtoken should appear in the
        files before it is added to the vocabulary. If set to none, this value
        is found using binary search.
      file_byte_limit: (Default 1e6) Maximum number of bytes of sample text that
        will be drawn from the files.
      reserved_tokens: List of string tokens that are guaranteed to be at the
        beginning of the subtoken vocabulary list.

    Returns:
      Subtokenizer object
    """
    if reserved_tokens is None:
      reserved_tokens = RESERVED_TOKENS

    if tf.gfile.Exists(vocab_file):
      tf.logging.info("Vocab file already exists (%s)" % vocab_file)
    else:
      tf.logging.info("Begin steps to create subtoken vocabulary...")
      token_counts = _count_tokens(files, file_byte_limit)
      alphabet = _generate_alphabet_dict(token_counts)
      subtoken_list = _generate_subtokens_with_target_vocab_size(
          token_counts, alphabet, target_vocab_size, threshold, min_count,
          reserved_tokens)
      tf.logging.info("Generated vocabulary with %d subtokens." %
                      len(subtoken_list))
      mlperf_log.transformer_print(key=mlperf_log.PREPROC_VOCAB_SIZE,
                                   value=len(subtoken_list))
      _save_vocab_file(vocab_file, subtoken_list)
    return Subtokenizer(vocab_file)

  def encode(self, raw_string, add_eos=False):
    """Encodes a string into a list of int subtoken ids."""
    ret = []
    tokens = _split_string_to_tokens(_native_to_unicode(raw_string))
    for token in tokens:
      ret.extend(self._token_to_subtoken_ids(token))
    if add_eos:
      ret.append(EOS_ID)
    return ret

  def _token_to_subtoken_ids(self, token):
    """Encode a single token into a list of subtoken ids."""
    cache_location = hash(token) % self._cache_size
    cache_key, cache_value = self._cache[cache_location]
    if cache_key == token:
      return cache_value

    ret = _split_token_to_subtokens(
        _escape_token(token, self.alphabet), self.subtoken_to_id_dict,
        self.max_subtoken_length)
    ret = [self.subtoken_to_id_dict[subtoken_id] for subtoken_id in ret]

    self._cache[cache_location] = (token, ret)
    return ret

  def decode(self, subtokens):
    """Converts list of int subtokens ids into a string."""
    if isinstance(subtokens, np.ndarray):
      # Note that list(subtokens) converts subtokens to a python list, but the
      # items remain as np.int32. This converts both the array and its items.
      subtokens = subtokens.tolist()

    if not subtokens:
      return ""

    assert isinstance(subtokens, list) and isinstance(subtokens[0], int), (
        "Subtokens argument passed into decode() must be a list of integers.")

    return _unicode_to_native(
        _join_tokens_to_string(self._subtoken_ids_to_tokens(subtokens)))

  def _subtoken_ids_to_tokens(self, subtokens):
    """Convert list of int subtoken ids to a list of string tokens."""
    escaped_tokens = "".join([
        self.subtoken_list[s] for s in subtokens
        if s < len(self.subtoken_list)])
    escaped_tokens = escaped_tokens.split("_")

    # All tokens in the vocabulary list have been escaped (see _escape_token())
    # so each token must be unescaped when decoding.
    ret = []
    for token in escaped_tokens:
      if token:
        ret.append(_unescape_token(token))
    return ret


def _save_vocab_file(vocab_file, subtoken_list):
  """Save subtokens to file."""
  with tf.gfile.Open(vocab_file, mode="w") as f:
    for subtoken in subtoken_list:
      f.write("'%s'\n" % _unicode_to_native(subtoken))


def _load_vocab_file(vocab_file, reserved_tokens=None):
  """Load vocabulary while ensuring reserved tokens are at the top."""
  if reserved_tokens is None:
    reserved_tokens = RESERVED_TOKENS

  subtoken_list = []
  with tf.gfile.Open(vocab_file, mode="r") as f:
    for line in f:
      subtoken = _native_to_unicode(line.strip())
      subtoken = subtoken[1:-1]  # Remove surrounding single-quotes
      if subtoken in reserved_tokens:
        continue
      subtoken_list.append(_native_to_unicode(subtoken))
  return reserved_tokens + subtoken_list


def _native_to_unicode(s):
  """Convert string to unicode (required in Python 2)."""
  if six.PY2:
    return s if isinstance(s, unicode) else s.decode("utf-8")
  else:
    return s


def _unicode_to_native(s):
  """Convert string from unicode to native format (required in Python 2)."""
  if six.PY2:
    return s.encode("utf-8") if isinstance(s, unicode) else s
  else:
    return s


def _split_string_to_tokens(text):
  """Splits text to a list of string tokens."""
  if not text:
    return []
  ret = []
  token_start = 0
  # Classify each character in the input string
  is_alnum = [c in _ALPHANUMERIC_CHAR_SET for c in text]
  for pos in xrange(1, len(text)):
    if is_alnum[pos] != is_alnum[pos - 1]:
      token = text[token_start:pos]
      if token != u" " or token_start == 0:
        ret.append(token)
      token_start = pos
  final_token = text[token_start:]
  ret.append(final_token)
  return ret


def _join_tokens_to_string(tokens):
  """Join a list of string tokens into a single string."""
  token_is_alnum = [t[0] in _ALPHANUMERIC_CHAR_SET for t in tokens]
  ret = []
  for i, token in enumerate(tokens):
    if i > 0 and token_is_alnum[i - 1] and token_is_alnum[i]:
      ret.append(u" ")
    ret.append(token)
  return "".join(ret)


def _escape_token(token, alphabet):
  r"""Replace characters that aren't in the alphabet and append "_" to token.

  Apply three transformations to the token:
    1. Replace underline character "_" with "\u", and backslash "\" with "\\".
    2. Replace characters outside of the alphabet with "\###;", where ### is the
       character's Unicode code point.
    3. Appends "_" to mark the end of a token.

  Args:
    token: unicode string to be escaped
    alphabet: list of all known characters

  Returns:
    escaped string
  """
  token = token.replace(u"\\", u"\\\\").replace(u"_", u"\\u")
  ret = [c if c in alphabet and c != u"\n" else r"\%d;" % ord(c) for c in token]
  return u"".join(ret) + "_"


def _unescape_token(token):
  r"""Replaces escaped characters in the token with their unescaped versions.

  Applies inverse transformations as _escape_token():
    1. Replace "\u" with "_", and "\\" with "\".
    2. Replace "\###;" with the unicode character the ### refers to.

  Args:
    token: escaped string

  Returns:
    unescaped string
  """

  def match(m):
    r"""Returns replacement string for matched object.

    Matched objects contain one of the strings that matches the regex pattern:
      r"\\u|\\\\|\\([0-9]+);"
    The strings can be '\u', '\\', or '\###;' (### is any digit number).

    m.group(0) refers to the entire matched string ('\u', '\\', or '\###;').
    m.group(1) refers to the first parenthesized subgroup ('###').

    m.group(0) exists for all match objects, while m.group(1) exists only for
    the string '\###;'.

    This function looks to see if m.group(1) exists. If it doesn't, then the
    matched string must be '\u' or '\\' . In this case, the corresponding
    replacement ('_' and '\') are returned. Note that in python, a single
    backslash is written as '\\', and double backslash as '\\\\'.

    If m.goup(1) exists, then use the integer in m.group(1) to return a
    unicode character.

    Args:
      m: match object

    Returns:
      String to replace matched object with.
    """
    # Check if the matched strings are '\u' or '\\'.
    if m.group(1) is None:
      return u"_" if m.group(0) == u"\\u" else u"\\"

    # If m.group(1) exists, try and return unicode character.
    try:
      return six.unichr(int(m.group(1)))
    except (ValueError, OverflowError) as _:
      return _UNDEFINED_UNICODE

  # Use match function to replace escaped substrings in the token.
  return _UNESCAPE_REGEX.sub(match, token)


def _count_tokens(files, file_byte_limit=1e6):
  """Return token counts of words in the files.

  Samples file_byte_limit bytes from each file, and counts the words that appear
  in the samples. The samples are semi-evenly distributed across the file.

  Args:
    files: List of filepaths
    file_byte_limit: Max number of bytes that will be read from each file.

  Returns:
    Dictionary mapping tokens to the number of times they appear in the sampled
    lines from the files.
  """
  token_counts = collections.defaultdict(int)

  for filepath in files:
    with tf.gfile.Open(filepath, mode="r") as reader:
      file_byte_budget = file_byte_limit
      counter = 0
      lines_to_skip = int(reader.size() / (file_byte_budget * 2))
      for line in reader:
        if counter < lines_to_skip:
          counter += 1
        else:
          if file_byte_budget < 0:
            break
          line = line.strip()
          file_byte_budget -= len(line)
          counter = 0

          # Add words to token counts
          for token in _split_string_to_tokens(_native_to_unicode(line)):
            token_counts[token] += 1
  return token_counts


def _list_to_index_dict(lst):
  """Create dictionary mapping list items to their indices in the list."""
  return {item: n for n, item in enumerate(lst)}


def _split_token_to_subtokens(token, subtoken_dict, max_subtoken_length):
  """Splits a token into subtokens defined in the subtoken dict."""
  ret = []
  start = 0
  token_len = len(token)
  while start < token_len:
    # Find the longest subtoken, so iterate backwards.
    for end in xrange(min(token_len, start + max_subtoken_length), start, -1):
      subtoken = token[start:end]
      if subtoken in subtoken_dict:
        ret.append(subtoken)
        start = end
        break
    else:  # Did not break
      # If there is no possible encoding of the escaped token then one of the
      # characters in the token is not in the alphabet. This should be
      # impossible and would be indicative of a bug.
      raise ValueError("Was unable to split token \"%s\" into subtokens." %
                       token)
  return ret


def _generate_subtokens_with_target_vocab_size(
    token_counts, alphabet, target_size, threshold, min_count=None,
    reserved_tokens=None):
  """Generate subtoken vocabulary close to the target size."""
  if reserved_tokens is None:
    reserved_tokens = RESERVED_TOKENS

  if min_count is not None:
    tf.logging.info("Using min_count=%d to generate vocab with target size %d" %
                    (min_count, target_size))
    return _generate_subtokens(
        token_counts, alphabet, min_count, reserved_tokens=reserved_tokens)

  def bisect(min_val, max_val):
    """Recursive function to binary search for subtoken vocabulary."""
    cur_count = (min_val + max_val) // 2
    tf.logging.info("Binary search: trying min_count=%d (%d %d)" %
                    (cur_count, min_val, max_val))
    subtoken_list = _generate_subtokens(
        token_counts, alphabet, cur_count, reserved_tokens=reserved_tokens)

    val = len(subtoken_list)
    tf.logging.info("Binary search: min_count=%d resulted in %d tokens" %
                    (cur_count, val))

    within_threshold = abs(val - target_size) < threshold
    if within_threshold or min_val >= max_val or cur_count < 2:
      return subtoken_list
    if val > target_size:
      other_subtoken_list = bisect(cur_count + 1, max_val)
    else:
      other_subtoken_list = bisect(min_val, cur_count - 1)

    # Return vocabulary dictionary with the closest number of tokens.
    other_val = len(other_subtoken_list)
    if abs(other_val - target_size) < abs(val - target_size):
      return other_subtoken_list
    return subtoken_list

  tf.logging.info("Finding best min_count to get target size of %d" %
                  target_size)
  return bisect(_MIN_MIN_COUNT, _MAX_MIN_COUNT)


def _generate_alphabet_dict(iterable, reserved_tokens=None):
  """Create set of characters that appear in any element in the iterable."""
  if reserved_tokens is None:
    reserved_tokens = RESERVED_TOKENS
  alphabet = {c for token in iterable for c in token}
  alphabet |= {c for token in reserved_tokens for c in token}
  alphabet |= _ESCAPE_CHARS  # Add escape characters to alphabet set.
  return alphabet


def _count_and_gen_subtokens(
    token_counts, alphabet, subtoken_dict, max_subtoken_length):
  """Count number of times subtokens appear, and generate new subtokens.

  Args:
    token_counts: dict mapping tokens to the number of times they appear in the
      original files.
    alphabet: list of allowed characters. Used to escape the tokens, which
      guarantees that all tokens can be split into subtokens.
    subtoken_dict: dict mapping subtokens to ids.
    max_subtoken_length: maximum length of subtoken in subtoken_dict.

  Returns:
    A defaultdict mapping subtokens to the number of times they appear in the
    tokens. The dict may contain new subtokens.
  """
  subtoken_counts = collections.defaultdict(int)
  for token, count in six.iteritems(token_counts):
    token = _escape_token(token, alphabet)
    subtokens = _split_token_to_subtokens(
        token, subtoken_dict, max_subtoken_length)

    # Generate new subtokens by taking substrings from token.
    start = 0
    for subtoken in subtokens:
      for end in xrange(start + 1, len(token) + 1):
        new_subtoken = token[start:end]
        subtoken_counts[new_subtoken] += count
      start += len(subtoken)

  return subtoken_counts


def _filter_and_bucket_subtokens(subtoken_counts, min_count):
  """Return a bucketed list of subtokens that are filtered by count.

  Args:
    subtoken_counts: defaultdict mapping subtokens to their counts
    min_count: int count used to filter subtokens

  Returns:
    List of subtoken sets, where subtokens in set i have the same length=i.
  """
  # Create list of buckets, where subtokens in bucket i have length i.
  subtoken_buckets = []
  for subtoken, count in six.iteritems(subtoken_counts):
    if count < min_count:  # Filter out subtokens that don't appear enough
      continue
    while len(subtoken_buckets) <= len(subtoken):
      subtoken_buckets.append(set())
    subtoken_buckets[len(subtoken)].add(subtoken)
  return subtoken_buckets


def _gen_new_subtoken_list(
    subtoken_counts, min_count, alphabet, reserved_tokens=None):
  """Generate candidate subtokens ordered by count, and new max subtoken length.

  Add subtokens to the candiate list in order of length (longest subtokens
  first). When a subtoken is added, the counts of each of its prefixes are
  decreased. Prefixes that don't appear much outside the subtoken are not added
  to the candidate list.

  For example:
    subtoken being added to candidate list: 'translate'
    subtoken_counts: {'translate':10, 't':40, 'tr':16, 'tra':12, ...}
    min_count: 5

  When 'translate' is added, subtoken_counts is updated to:
    {'translate':0, 't':30, 'tr':6, 'tra': 2, ...}

  The subtoken 'tra' will not be added to the candidate list, because it appears
  twice (less than min_count) outside of 'translate'.

  Args:
    subtoken_counts: defaultdict mapping str subtokens to int counts
    min_count: int minumum count requirement for subtokens
    alphabet: set of characters. Each character is added to the subtoken list to
      guarantee that all tokens can be encoded.
    reserved_tokens: list of tokens that will be added to the beginning of the
      returned subtoken list.

  Returns:
    List of candidate subtokens in decreasing count order, and maximum subtoken
    length
  """
  if reserved_tokens is None:
    reserved_tokens = RESERVED_TOKENS

  # Create a list of (count, subtoken) for each candidate subtoken.
  subtoken_candidates = []

  # Use bucketted list to iterate through subtokens in order of length.
  # subtoken_buckets[i] = set(subtokens), where each subtoken has length i.
  subtoken_buckets = _filter_and_bucket_subtokens(subtoken_counts, min_count)
  max_subtoken_length = len(subtoken_buckets) - 1

  # Go through the list in reverse order to consider longer subtokens first.
  for subtoken_len in xrange(max_subtoken_length, 0, -1):
    for subtoken in subtoken_buckets[subtoken_len]:
      count = subtoken_counts[subtoken]

      # Possible if this subtoken is a prefix of another token.
      if count < min_count:
        continue

      # Ignore alphabet/reserved tokens, which will be added manually later.
      if subtoken not in alphabet and subtoken not in reserved_tokens:
        subtoken_candidates.append((count, subtoken))

      # Decrement count of the subtoken's prefixes (if a longer subtoken is
      # added, its prefixes lose priority to be added).
      for end in xrange(1, subtoken_len):
        subtoken_counts[subtoken[:end]] -= count

  # Add alphabet subtokens (guarantees that all strings are encodable).
  subtoken_candidates.extend((subtoken_counts.get(a, 0), a) for a in alphabet)

  # Order subtoken candidates by decreasing count.
  subtoken_list = [t for _, t in sorted(subtoken_candidates, reverse=True)]

  # Add reserved tokens to beginning of the list.
  subtoken_list = reserved_tokens + subtoken_list
  return subtoken_list, max_subtoken_length


def _generate_subtokens(
    token_counts, alphabet, min_count, num_iterations=4,
    reserved_tokens=None):
  """Create a list of subtokens in decreasing order of frequency.

  Args:
    token_counts: dict mapping str tokens -> int count
    alphabet: set of characters
    min_count: int minimum number of times a subtoken must appear before it is
      added to the vocabulary.
    num_iterations: int number of iterations to generate new tokens.
    reserved_tokens: list of tokens that will be added to the beginning to the
      returned subtoken list.

  Returns:
    Sorted list of subtokens (most frequent first)
  """
  if reserved_tokens is None:
    reserved_tokens = RESERVED_TOKENS

  # Use alphabet set to create initial list of subtokens
  subtoken_list = reserved_tokens + list(alphabet)
  max_subtoken_length = 1

  # On each iteration, segment all words using the subtokens defined in
  # subtoken_dict, count how often the resulting subtokens appear, and update
  # the dictionary with subtokens w/ high enough counts.
  for i in xrange(num_iterations):
    tf.logging.info("\tGenerating subtokens: iteration %d" % i)
    # Generate new subtoken->id dictionary using the new subtoken list.
    subtoken_dict = _list_to_index_dict(subtoken_list)

    # Create dict mapping subtoken->count, with additional subtokens created
    # from substrings taken from the tokens.
    subtoken_counts = _count_and_gen_subtokens(
        token_counts, alphabet, subtoken_dict, max_subtoken_length)

    # Generate new list of subtokens sorted by subtoken count.
    subtoken_list, max_subtoken_length = _gen_new_subtoken_list(
        subtoken_counts, min_count, alphabet, reserved_tokens)

    tf.logging.info("\tVocab size: %d" % len(subtoken_list))
  return subtoken_list
