# coding=utf-8

# Copyright 2018 The Google AI Language Team Authors.

#

# 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.

"""Tokenization classes."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import collections

import re

import unicodedata

import six

import tensorflow as tf

def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):

    """Checks whether the casing config is consistent with the checkpoint name."""

    # The casing has to be passed in by the user and there is no explicit check

    # as to whether it matches the checkpoint. The casing information probably

    # should have been stored in the bert_config.json file, but it's not, so

    # we have to heuristically detect it to validate.

    if not init_checkpoint:

        return

    m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)

    if m is None:

        return

    model_name = m.group(1)

    lower_models = [

        "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",

        "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"

    ]

    cased_models = [

        "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",

        "multi_cased_L-12_H-768_A-12"

    ]

    is_bad_config = False

    if model_name in lower_models and not do_lower_case:

        is_bad_config = True

        actual_flag = "False"

        case_name = "lowercased"

        opposite_flag = "True"

    if model_name in cased_models and do_lower_case:

        is_bad_config = True

        actual_flag = "True"

        case_name = "cased"

        opposite_flag = "False"

    if is_bad_config:

        raise ValueError(

            "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "

            "However, `%s` seems to be a %s model, so you "

            "should pass in `--do_lower_case=%s` so that the fine-tuning matches "

            "how the model was pre-training. If this error is wrong, please "

            "just comment out this check." % (actual_flag, init_checkpoint,

                                              model_name, case_name, opposite_flag))

def convert_to_unicode(text):

    """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""

    if six.PY3:

        if isinstance(text, str):

            return text

        elif isinstance(text, bytes):

            return text.decode("utf-8", "ignore")

        else:

            raise ValueError("Unsupported string type: %s" % (type(text)))

    elif six.PY2:

        if isinstance(text, str):

            return text.decode("utf-8", "ignore")

        elif isinstance(text, unicode):

            return text

        else:

            raise ValueError("Unsupported string type: %s" % (type(text)))

    else:

        raise ValueError("Not running on Python2 or Python 3?")

def printable_text(text):

    """Returns text encoded in a way suitable for print or `tf.logging`."""

    # These functions want `str` for both Python2 and Python3, but in one case

    # it's a Unicode string and in the other it's a byte string.

    if six.PY3:

        if isinstance(text, str):

            return text

        elif isinstance(text, bytes):

            return text.decode("utf-8", "ignore")

        else:

            raise ValueError("Unsupported string type: %s" % (type(text)))

    elif six.PY2:

        if isinstance(text, str):

            return text

        elif isinstance(text, unicode):

            return text.encode("utf-8")

        else:

            raise ValueError("Unsupported string type: %s" % (type(text)))

    else:

        raise ValueError("Not running on Python2 or Python 3?")

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 = convert_to_unicode(reader.readline())

            if not token:

                break

            token = token.strip()

            vocab[token] = index

            index += 1

    return vocab

def convert_by_vocab(vocab, items):

    """Converts a sequence of [tokens|ids] using the vocab."""

    output = []

    for item in items:

        output.append(vocab[item])

    return output

def convert_tokens_to_ids(vocab, tokens):

    return convert_by_vocab(vocab, tokens)

def convert_ids_to_tokens(inv_vocab, ids):

    return convert_by_vocab(inv_vocab, ids)

def whitespace_tokenize(text):

    """Runs basic whitespace cleaning and splitting on a piece of text."""

    text = text.strip()

    if not text:

        return []

    tokens = text.split()

    return tokens

class FullTokenizer(object):

    """Runs end-to-end tokenziation."""

    def __init__(self, vocab_file, do_lower_case=True):

        self.vocab = load_vocab(vocab_file)

        self.inv_vocab = {v: k for k, v in self.vocab.items()}

        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)

        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)

    def tokenize(self, text):

        split_tokens = []

        for token in self.basic_tokenizer.tokenize(text):

            for sub_token in self.wordpiece_tokenizer.tokenize(token):

                split_tokens.append(sub_token)

        return split_tokens

    def convert_tokens_to_ids(self, tokens):

        return convert_by_vocab(self.vocab, tokens)

    def convert_ids_to_tokens(self, ids):

        return convert_by_vocab(self.inv_vocab, ids)

class BasicTokenizer(object):

    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""

    def __init__(self, do_lower_case=True):

        """Constructs a BasicTokenizer.

        Args:

          do_lower_case: Whether to lower case the input.

        """

        self.do_lower_case = do_lower_case

    def tokenize(self, text):

        """Tokenizes a piece of text."""

        text = convert_to_unicode(text)

        text = self._clean_text(text)

        # 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:

                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."""

        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 tokenziation."""

    def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):

        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.

        """

        text = convert_to_unicode(text)

        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])

                    if start > 0:

                        substr = "##" + substr

                    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 in ("Cc", "Cf"):

        return True

    return False

def _is_punctuation(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