# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# 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.
#
#
# 한국어 WordPiece 단위 BERT를 위한 Tokenization Class
# 수정: joonho.lim
# 일자: 2019-05-23
#
"""Tokenization classes."""

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

import collections
import unicodedata
import os
import logging

from transformers.file_utils import cached_path
# from pytorch_pretrained_bert.file_utils import cached_path
# from .file_utils import cached_path

logger = logging.getLogger(__name__)

PRETRAINED_VOCAB_ARCHIVE_MAP = {
	'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
	'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
	'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
	'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
	'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
	'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
	'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
}
PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
	'bert-base-uncased': 512,
	'bert-large-uncased': 512,
	'bert-base-cased': 512,
	'bert-large-cased': 512,
	'bert-base-multilingual-uncased': 512,
	'bert-base-multilingual-cased': 512,
	'bert-base-chinese': 512,
}
VOCAB_NAME = 'vocab.txt'


def load_vocab(vocab_file):
	"""Loads a vocabulary file into a dictionary."""
	vocab = collections.OrderedDict()
	index = 0
	with open(vocab_file, "r", encoding="utf-8") as reader:
		while True:
			token = reader.readline()
			if not token:
				break
			
			### joonho.lim @ 2019-03-15
			if token.find('n_iters=') == 0 or token.find('max_length=') == 0 :
				continue
			token = token.split('\t')[0]
			
			token = token.strip()
			vocab[token] = index
			index += 1
	return vocab


def whitespace_tokenize(text):
	"""Runs basic whitespace cleaning and splitting on a peice of text."""
	text = text.strip()
	if not text:
		return []
	tokens = text.split()
	return tokens


class BertTokenizer(object):
	"""Runs end-to-end tokenization: punctuation splitting + wordpiece"""

	def __init__(self, vocab_file, do_lower_case=True, max_len=None,
				 never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
		if not os.path.isfile(vocab_file):
			raise ValueError(
				"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
				"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
		self.vocab = load_vocab(vocab_file)
		self.ids_to_tokens = collections.OrderedDict(
			[(ids, tok) for tok, ids in self.vocab.items()])
		self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
											  never_split=never_split)
		self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
		self.max_len = max_len if max_len is not None else int(1e12)

	def tokenize(self, text):
		split_tokens = []
		for token in self.basic_tokenizer.tokenize(text):
			### joonho.lim @ 2019-03-15
			token += '_'
			for sub_token in self.wordpiece_tokenizer.tokenize(token):
				split_tokens.append(sub_token)
		return split_tokens

	def convert_tokens_to_ids(self, tokens):
		"""Converts a sequence of tokens into ids using the vocab."""
		ids = []
		for token in tokens:
			ids.append(self.vocab[token])
		if len(ids) > self.max_len:
			raise ValueError(
				"Token indices sequence length is longer than the specified maximum "
				" sequence length for this BERT model ({} > {}). Running this"
				" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
			)
		return ids

	def convert_ids_to_tokens(self, ids):
		"""Converts a sequence of ids in wordpiece tokens using the vocab."""
		tokens = []
		for i in ids:
			tokens.append(self.ids_to_tokens[i])
		return tokens

	@classmethod
	def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs):
		"""
		Instantiate a PreTrainedBertModel from a pre-trained model file.
		Download and cache the pre-trained model file if needed.
		"""
		if pretrained_model_name in PRETRAINED_VOCAB_ARCHIVE_MAP:
			vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name]
		else:
			vocab_file = pretrained_model_name
		if os.path.isdir(vocab_file):
			vocab_file = os.path.join(vocab_file, VOCAB_NAME)
		# redirect to the cache, if necessary
		try:
			resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
		except FileNotFoundError:
			logger.error(
				"Model name '{}' was not found in model name list ({}). "
				"We assumed '{}' was a path or url but couldn't find any file "
				"associated to this path or url.".format(
					pretrained_model_name,
					', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
					vocab_file))
			return None
		if resolved_vocab_file == vocab_file:
			logger.info("loading vocabulary file {}".format(vocab_file))
		else:
			logger.info("loading vocabulary file {} from cache at {}".format(
				vocab_file, resolved_vocab_file))
		if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
			# if we're using a pretrained model, ensure the tokenizer wont index sequences longer
			# than the number of positional embeddings
			max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name]
			kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
		# Instantiate tokenizer.
		tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
		return tokenizer


class BasicTokenizer(object):
	"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""

	def __init__(self,
				 do_lower_case=True,
				 never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
		"""Constructs a BasicTokenizer.

		Args:
		  do_lower_case: Whether to lower case the input.
		"""
		self.do_lower_case = do_lower_case
		self.never_split = never_split

	def tokenize(self, text):
		"""Tokenizes a piece of text."""
		text = self._clean_text(text)
		### joonho.lim @ 2019-03-15
		# # # This was added on November 1st, 2018 for the multilingual and Chinese
		# # # models. This is also applied to the English models now, but it doesn't
		# # # matter since the English models were not trained on any Chinese data
		# # # and generally don't have any Chinese data in them (there are Chinese
		# # # characters in the vocabulary because Wikipedia does have some Chinese
		# # # words in the English Wikipedia.).
		# # text = self._tokenize_chinese_chars(text)
		orig_tokens = whitespace_tokenize(text)
		split_tokens = []
		for token in orig_tokens:
			if self.do_lower_case and token not in self.never_split:
				token = token.lower()
				token = self._run_strip_accents(token)
			split_tokens.extend(self._run_split_on_punc(token))

		output_tokens = whitespace_tokenize(" ".join(split_tokens))
		return output_tokens

	def _run_strip_accents(self, text):
		"""Strips accents from a piece of text."""
		text = unicodedata.normalize("NFD", text)
		output = []
		for char in text:
			cat = unicodedata.category(char)
			if cat == "Mn":
				continue
			output.append(char)
		return "".join(output)

	def _run_split_on_punc(self, text):
		"""Splits punctuation on a piece of text."""
		if text in self.never_split:
			return [text]
		chars = list(text)
		i = 0
		start_new_word = True
		output = []
		while i < len(chars):
			char = chars[i]
			if _is_punctuation(char):
				output.append([char])
				start_new_word = True
			else:
				if start_new_word:
					output.append([])
				start_new_word = False
				output[-1].append(char)
			i += 1

		return ["".join(x) for x in output]

	def _tokenize_chinese_chars(self, text):
		"""Adds whitespace around any CJK character."""
		output = []
		for char in text:
			cp = ord(char)
			if self._is_chinese_char(cp):
				output.append(" ")
				output.append(char)
				output.append(" ")
			else:
				output.append(char)
		return "".join(output)

	def _is_chinese_char(self, cp):
		"""Checks whether CP is the codepoint of a CJK character."""
		# This defines a "chinese character" as anything in the CJK Unicode block:
		#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
		#
		# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
		# despite its name. The modern Korean Hangul alphabet is a different block,
		# as is Japanese Hiragana and Katakana. Those alphabets are used to write
		# space-separated words, so they are not treated specially and handled
		# like the all of the other languages.
		if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
				(cp >= 0x3400 and cp <= 0x4DBF) or  #
				(cp >= 0x20000 and cp <= 0x2A6DF) or  #
				(cp >= 0x2A700 and cp <= 0x2B73F) or  #
				(cp >= 0x2B740 and cp <= 0x2B81F) or  #
				(cp >= 0x2B820 and cp <= 0x2CEAF) or
				(cp >= 0xF900 and cp <= 0xFAFF) or  #
				(cp >= 0x2F800 and cp <= 0x2FA1F)):  #
			return True

		return False

	def _clean_text(self, text):
		"""Performs invalid character removal and whitespace cleanup on text."""
		output = []
		for char in text:
			cp = ord(char)
			if cp == 0 or cp == 0xfffd or _is_control(char):
				continue
			if _is_whitespace(char):
				output.append(" ")
			else:
				output.append(char)
		return "".join(output)


class WordpieceTokenizer(object):
	"""Runs WordPiece tokenization."""

	def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
		self.vocab = vocab
		self.unk_token = unk_token
		self.max_input_chars_per_word = max_input_chars_per_word

	def tokenize(self, text):
		"""Tokenizes a piece of text into its word pieces.

		This uses a greedy longest-match-first algorithm to perform tokenization
		using the given vocabulary.

		For example:
		  input = "unaffable"
		  output = ["un", "##aff", "##able"]

		Args:
		  text: A single token or whitespace separated tokens. This should have
			already been passed through `BasicTokenizer`.

		Returns:
		  A list of wordpiece tokens.
		"""

		output_tokens = []
		for token in whitespace_tokenize(text):
			chars = list(token)
			if len(chars) > self.max_input_chars_per_word:
				output_tokens.append(self.unk_token)
				continue

			is_bad = False
			start = 0
			sub_tokens = []
			while start < len(chars):
				end = len(chars)
				cur_substr = None
				while start < end:
					substr = "".join(chars[start:end])
					### joonho.lim @ 2019-03-15
					# if start > 0:
						# substr = "##" + substr
					# print ( '[substr]\t%s\t%s\t%d\t%d' % ( substr, substr in self.vocab, start, end))
					if substr in self.vocab:
						cur_substr = substr
						break
					end -= 1
				if cur_substr is None:
					is_bad = True
					break
				sub_tokens.append(cur_substr)
				start = end

			if is_bad:
				output_tokens.append(self.unk_token)
			else:
				output_tokens.extend(sub_tokens)
		return output_tokens


def _is_whitespace(char):
	"""Checks whether `chars` is a whitespace character."""
	# \t, \n, and \r are technically contorl characters but we treat them
	# as whitespace since they are generally considered as such.
	if char == " " or char == "\t" or char == "\n" or char == "\r":
		return True
	cat = unicodedata.category(char)
	if cat == "Zs":
		return True
	return False


def _is_control(char):
	"""Checks whether `chars` is a control character."""
	# These are technically control characters but we count them as whitespace
	# characters.
	if char == "\t" or char == "\n" or char == "\r":
		return False
	cat = unicodedata.category(char)
	if cat.startswith("C"):
		return True
	return False


def _is_punctuation(char):
	### joonho.lim @ 2019-03-15
	return char == ' '
	
	# """Checks whether `chars` is a punctuation character."""
	# cp = ord(char)
	# # We treat all non-letter/number ASCII as punctuation.
	# # Characters such as "^", "$", and "`" are not in the Unicode
	# # Punctuation class but we treat them as punctuation anyways, for
	# # consistency.
	# if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
			# (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
		# return True
	# cat = unicodedata.category(char)
	# if cat.startswith("P"):
		# return True
	# return False
