# coding=utf-8

# Copyright 2018 Google AI, Google Brain and the HuggingFace 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.

""" Tokenization classes for ALBERT model."""

from __future__ import (absolute_import, division, print_function,

                        unicode_literals)



from .tokenization_utils import PreTrainedTokenizer

import logging

import unicodedata

import six

import os

from shutil import copyfile



logger = logging.getLogger(__name__)

VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'}



PRETRAINED_VOCAB_FILES_MAP = {

    'vocab_file':

    {

        'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-spiece.model",

        'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-spiece.model",

        'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-spiece.model",

        'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-spiece.model",

        'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-spiece.model",

        'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-spiece.model",

        'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-spiece.model",

        'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-spiece.model",

    }

}



PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {

    'albert-base-v1': 512,

    'albert-large-v1': 512,

    'albert-xlarge-v1': 512,

    'albert-xxlarge-v1': 512,

    'albert-base-v2': 512,

    'albert-large-v2': 512,

    'albert-xlarge-v2': 512,

    'albert-xxlarge-v2': 512,

}



SPIECE_UNDERLINE = u'▁'



class AlbertTokenizer(PreTrainedTokenizer):

    """

        SentencePiece based tokenizer. Peculiarities:



            - requires `SentencePiece <https://github.com/google/sentencepiece>`_

    """

    vocab_files_names = VOCAB_FILES_NAMES

    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP

    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



    def __init__(self, vocab_file,

                 do_lower_case=True, remove_space=True, keep_accents=False,

                 bos_token="[CLS]", eos_token="[SEP]", unk_token="<unk>", sep_token="[SEP]",

                 pad_token="<pad>", cls_token="[CLS]", mask_token="[MASK]", **kwargs):

        super(AlbertTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token,

                                             unk_token=unk_token, sep_token=sep_token,

                                             pad_token=pad_token, cls_token=cls_token,

                                             mask_token=mask_token, **kwargs)



        self.max_len_single_sentence = self.max_len - 2  # take into account special tokens

        self.max_len_sentences_pair = self.max_len - 3  # take into account special tokens



        try:

            import sentencepiece as spm

        except ImportError:

            logger.warning("You need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiece"

                           "pip install sentencepiece")



        self.do_lower_case = do_lower_case

        self.remove_space = remove_space

        self.keep_accents = keep_accents

        self.vocab_file = vocab_file



        self.sp_model = spm.SentencePieceProcessor()

        self.sp_model.Load(vocab_file)



    @property

    def vocab_size(self):

        return len(self.sp_model)



    def __getstate__(self):

        state = self.__dict__.copy()

        state["sp_model"] = None

        return state



    def __setstate__(self, d):

        self.__dict__ = d

        try:

            import sentencepiece as spm

        except ImportError:

            logger.warning("You need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiece"

                           "pip install sentencepiece")

        self.sp_model = spm.SentencePieceProcessor()

        self.sp_model.Load(self.vocab_file)



    def preprocess_text(self, inputs):

        if self.remove_space:

            outputs = ' '.join(inputs.strip().split())

        else:

            outputs = inputs

        outputs = outputs.replace("``", '"').replace("''", '"')



        if six.PY2 and isinstance(outputs, str):

            outputs = outputs.decode('utf-8')



        if not self.keep_accents:

            outputs = unicodedata.normalize('NFKD', outputs)

            outputs = ''.join([c for c in outputs if not unicodedata.combining(c)])

        if self.do_lower_case:

            outputs = outputs.lower()



        return outputs



    def _tokenize(self, text, return_unicode=True, sample=False):

        """ Tokenize a string.

            return_unicode is used only for py2

        """

        text = self.preprocess_text(text)

        # note(zhiliny): in some systems, sentencepiece only accepts str for py2

        if six.PY2 and isinstance(text, unicode):

            text = text.encode('utf-8')



        if not sample:

            pieces = self.sp_model.EncodeAsPieces(text)

        else:

            pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1)

        new_pieces = []

        for piece in pieces:

            if len(piece) > 1 and piece[-1] == str(',') and piece[-2].isdigit():

                cur_pieces = self.sp_model.EncodeAsPieces(

                    piece[:-1].replace(SPIECE_UNDERLINE, ''))

                if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:

                    if len(cur_pieces[0]) == 1:

                        cur_pieces = cur_pieces[1:]

                    else:

                        cur_pieces[0] = cur_pieces[0][1:]

                cur_pieces.append(piece[-1])

                new_pieces.extend(cur_pieces)

            else:

                new_pieces.append(piece)



        # note(zhiliny): convert back to unicode for py2

        if six.PY2 and return_unicode:

            ret_pieces = []

            for piece in new_pieces:

                if isinstance(piece, str):

                    piece = piece.decode('utf-8')

                ret_pieces.append(piece)

            new_pieces = ret_pieces



        return new_pieces



    def _convert_token_to_id(self, token):

        """ Converts a token (str/unicode) in an id using the vocab. """

        return self.sp_model.PieceToId(token)



    def _convert_id_to_token(self, index, return_unicode=True):

        """Converts an index (integer) in a token (string/unicode) using the vocab."""

        token = self.sp_model.IdToPiece(index)

        if six.PY2 and return_unicode and isinstance(token, str):

            token = token.decode('utf-8')

        return token



    def convert_tokens_to_string(self, tokens):

        """Converts a sequence of tokens (strings for sub-words) in a single string."""

        out_string = ''.join(tokens).replace(SPIECE_UNDERLINE, ' ').strip()

        return out_string



    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):

        """

        Build model inputs from a sequence or a pair of sequence for sequence classification tasks

        by concatenating and adding special tokens.

        An ALBERT sequence has the following format:

            single sequence: [CLS] X [SEP]

            pair of sequences: [CLS] A [SEP] B [SEP]

        """

        sep = [self.sep_token_id]

        cls = [self.cls_token_id]

        if token_ids_1 is None:

            return cls + token_ids_0 + sep

        return cls + token_ids_0 + sep + token_ids_1 + sep



    def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):

        """

        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding

        special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.



        Args:

            token_ids_0: list of ids (must not contain special tokens)

            token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids

                for sequence pairs

            already_has_special_tokens: (default False) Set to True if the token list is already formated with

                special tokens for the model



        Returns:

            A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.

        """



        if already_has_special_tokens:

            if token_ids_1 is not None:

                raise ValueError("You should not supply a second sequence if the provided sequence of "

                                 "ids is already formated with special tokens for the model.")

            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))



        if token_ids_1 is not None:

            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]

        return [1] + ([0] * len(token_ids_0)) + [1]



    def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):

        """

        Creates a mask from the two sequences passed to be used in a sequence-pair classification task.

        An ALBERT sequence pair mask has the following format:

        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1

        | first sequence    | second sequence



        if token_ids_1 is None, only returns the first portion of the mask (0's).

        """

        sep = [self.sep_token_id]

        cls = [self.cls_token_id]



        if token_ids_1 is None:

            return len(cls + token_ids_0 + sep) * [0]

        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]



    def save_vocabulary(self, save_directory):

        """ Save the sentencepiece vocabulary (copy original file) and special tokens file

            to a directory.

        """

        if not os.path.isdir(save_directory):

            logger.error("Vocabulary path ({}) should be a directory".format(save_directory))

            return

        out_vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])



        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):

            copyfile(self.vocab_file, out_vocab_file)



        return (out_vocab_file,)

