# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# 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 class for ALBERT model."""

import os
import unicodedata
from shutil import copyfile

import sentencepiece as spm

from .. import AddedToken, BertTokenizer, PretrainedTokenizer

__all__ = ["AlbertTokenizer", "AlbertChineseTokenizer", "AlbertEnglishTokenizer"]

SPIECE_UNDERLINE = "▁"

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,
    "albert-chinese-tiny": 512,
    "albert-chinese-small": 512,
    "albert-chinese-base": 512,
    "albert-chinese-large": 512,
    "albert-chinese-xlarge": 512,
    "albert-chinese-xxlarge": 512,
}


class AlbertTokenizer(PretrainedTokenizer):
    """
    Constructs an Albert tokenizer based on SentencePiece or `BertTokenizer`.

    This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer`
    which contains most of the main methods. For more information regarding those methods,
    please refer to this superclass.

    Args:
        vocab_file (str):
            The vocabulary file path (ends with '.txt') required to instantiate
            a `WordpieceTokenizer`.
        sentence_model_file (str):
            The vocabulary file (ends with '.spm') required to instantiate
            a `SentencePiece <https://github.com/google/sentencepiece>`__ tokenizer.
        do_lower_case (bool):
            Whether or not to lowercase the input when tokenizing. Defaults to `True`.
        remove_space (bool):
            Whether or note to remove space when tokenizing. Defaults to `True`.
        keep_accents (bool):
            Whether or note to keep accents when tokenizing. Defaults to `False`.
        unk_token (str):
            A special token representing the *unknown (out-of-vocabulary)* token.
            An unknown token is set to be `unk_token` inorder to be converted to an ID.
            Defaults to "[UNK]".
        sep_token (str):
            A special token separating two different sentences in the same input.
            Defaults to "[SEP]".
        pad_token (str):
            A special token used to make arrays of tokens the same size for batching purposes.
            Defaults to "[PAD]".
        cls_token (str):
            A special token used for sequence classification. It is the last token
            of the sequence when built with special tokens. Defaults to "[CLS]".
        mask_token (str):
            A special token representing a masked token. This is the token used
            in the masked language modeling task which the model tries to predict the original unmasked ones.
            Defaults to "[MASK]".

    Examples:
        .. code-block::

            from paddlenlp.transformers import AlbertTokenizer
            tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
            tokens = tokenizer('He was a puppeteer')
            '''
            {'input_ids': [2, 24, 23, 21, 10956, 7911, 3],
             'token_type_ids': [0, 0, 0, 0, 0, 0, 0]}
            '''

    """

    resource_files_names = {
        "sentencepiece_model_file": "spiece.model",
        "vocab_file": "vocab.txt",
    }

    pretrained_resource_files_map = {
        "sentencepiece_model_file": {
            "albert-base-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v1.spiece.model",
            "albert-large-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v1.spiece.model",
            "albert-xlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v1.spiece.model",
            "albert-xxlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v1.spiece.model",
            "albert-base-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v2.spiece.model",
            "albert-large-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v2.spiece.model",
            "albert-xlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v2.spiece.model",
            "albert-xxlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v2.spiece.model",
            "albert-chinese-tiny": None,
            "albert-chinese-small": None,
            "albert-chinese-base": None,
            "albert-chinese-large": None,
            "albert-chinese-xlarge": None,
            "albert-chinese-xxlarge": None,
        },
        "vocab_file": {
            "albert-base-v1": None,
            "albert-large-v1": None,
            "albert-xlarge-v1": None,
            "albert-xxlarge-v1": None,
            "albert-base-v2": None,
            "albert-large-v2": None,
            "albert-xlarge-v2": None,
            "albert-xxlarge-v2": None,
            "albert-chinese-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-tiny.vocab.txt",
            "albert-chinese-small": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-small.vocab.txt",
            "albert-chinese-base": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-base.vocab.txt",
            "albert-chinese-large": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-large.vocab.txt",
            "albert-chinese-xlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xlarge.vocab.txt",
            "albert-chinese-xxlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xxlarge.vocab.txt",
        },
    }

    pretrained_init_configuration = {
        "albert-base-v1": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-large-v1": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-xlarge-v1": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-xxlarge-v1": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-base-v2": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-large-v2": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-xlarge-v2": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-xxlarge-v2": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-chinese-tiny": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
        "albert-chinese-small": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
        "albert-chinese-base": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
        "albert-chinese-large": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
        "albert-chinese-xlarge": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
        "albert-chinese-xxlarge": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
    }

    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

    def __init__(
        self,
        vocab_file,
        sentencepiece_model_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
    ):

        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
        self._build_special_tokens_map_extended(mask_token=mask_token)

        self.do_lower_case = do_lower_case
        self.remove_space = remove_space
        self.keep_accents = keep_accents
        self.vocab_file = vocab_file
        self.sentencepiece_model_file = sentencepiece_model_file

        if vocab_file is not None:
            self.tokenizer = AlbertChineseTokenizer(
                vocab_file=vocab_file,
                do_lower_case=do_lower_case,
                unk_token=unk_token,
                sep_token=sep_token,
                pad_token=pad_token,
                cls_token=cls_token,
                mask_token=mask_token,
                **kwargs,
            )
        elif sentencepiece_model_file is not None:
            self.tokenizer = AlbertEnglishTokenizer(
                sentencepiece_model_file=sentencepiece_model_file,
                do_lower_case=do_lower_case,
                remove_space=remove_space,
                keep_accents=keep_accents,
                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,
            )
        else:
            raise ValueError(
                "You should only specify either one(not both) of 'vocal_file'"
                "and 'sentencepiece_model_file' to construct an albert tokenizer."
                "Specify 'vocal_file' for Chinese tokenizer and "
                "'sentencepiece_model_file' for English tokenizer"
            )

    @property
    def vocab_size(self):
        """
        Return the size of vocabulary.

        Returns:
            int: The size of vocabulary.
        """
        return self.tokenizer.vocab_size

    def _tokenize(self, text):
        return self.tokenizer._tokenize(text)

    def tokenize(self, text):
        """
        Converts a string to a list of tokens.

        Args:
            text (str): The text to be tokenized.

        Returns:
            List(str): A list of string representing converted tokens.

        Examples:
            .. code-block::

                from paddlenlp.transformers import RobertaTokenizer

                tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext')
                tokens = tokenizer.tokenize('He was a puppeteer')

        """

        return self.tokenizer.tokenize(text)

    def _convert_token_to_id(self, token):
        """
        Converts a sequence of tokens (list of string) to a list of ids.

        Args:
            tokens (list): A list of string representing tokens to be converted.

        Returns:
            list: Converted ids from tokens.

        Examples:
            .. code-block::

                from paddlenlp.transformers import AlbertTokenizer

                tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
                tokens = tokenizer.tokenize('He was a puppeteer')
                #['▁he', '▁was', '▁a', '▁puppet', 'eer']

                ids = tokenizer.convert_tokens_to_ids(tokens)
                #[24, 23, 21, 10956, 7911]
        """
        return self.tokenizer._convert_token_to_id(token)

    def _convert_id_to_token(self, index):
        """
        Converts a sequence of tokens (list of string) to a list of ids.

        Args:
            ids (list): A list of ids to be converted.
            skip_special_tokens (bool, optional):
                Whether or not to skip special tokens. Defaults to `False`.

        Returns:
            list: A list of converted tokens.

        Examples:
            .. code-block::

                from paddlenlp.transformers import AlbertTokenizer

                tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
                ids = [24, 23, 21, 10956, 7911]
                tokens = tokenizer.convert_ids_to_tokens(ids)
                #['▁he', '▁was', '▁a', '▁puppet', 'eer']
        """
        return self.tokenizer._convert_id_to_token(index)

    def convert_tokens_to_string(self, tokens):
        """
        Converts a sequence of tokens (list of string) to a single string.

        Args:
            tokens (list): A list of string representing tokens to be converted.

        Returns:
            str: Converted string from tokens.

        Examples:
            .. code-block::

                from paddlenlp.transformers import AlbertTokenizer

                tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
                tokens = tokenizer.tokenize('He was a puppeteer')
                '''
                ['▁he', '▁was', '▁a', '▁puppet', 'eer']
                '''
                strings = tokenizer.convert_tokens_to_string(tokens)
                '''
                he was a puppeteer
                '''
        """
        return self.tokenizer.convert_tokens_to_string(tokens)

    def num_special_tokens_to_add(self, pair=False):
        """
        Returns the number of added tokens when encoding a sequence with special tokens.

        Args:
            pair(bool):
                Whether the input is a sequence pair or a single sequence.
                Defaults to `False` and the input is a single sequence.

        Returns:
            int: Number of tokens added to sequences.
        """
        return self.tokenizer.num_special_tokens_to_add(pair=pair)

    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]``

        Args:
            token_ids_0 (List[int]):
                List of IDs to which the special tokens will be added.
            token_ids_1 (List[int], optional):
                Optional second list of IDs for sequence pairs. Defaults to None.

        Returns:
            List[int]: List of input_id with the appropriate special tokens.
        """
        return self.tokenizer.build_inputs_with_special_tokens(token_ids_0, token_ids_1=token_ids_1)

    def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
        """
        Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.

        A Albert offset_mapping has the following format:

        - single sequence:      ``(0,0) X (0,0)``
        - pair of sequences:        ``(0,0) A (0,0) B (0,0)``

        Args:
            offset_mapping_ids_0 (List[tuple]):
                List of wordpiece offsets to which the special tokens will be added.
            offset_mapping_ids_1 (List[tuple], optional):
                Optional second list of wordpiece offsets for offset mapping pairs. Defaults to None.

        Returns:
            List[tuple]: A list of wordpiece offsets with the appropriate offsets of special tokens.
        """
        return self.tokenizer.build_offset_mapping_with_special_tokens(
            offset_mapping_0, offset_mapping_1=offset_mapping_1
        )

    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 ``encode`` methods.

        Args:
            token_ids_0 (List[int]):
                A list of `inputs_ids` for the first sequence.
            token_ids_1 (List[int], optional):
                Optional second list of IDs for sequence pairs. Defaults to None.
            already_has_special_tokens (bool, optional): Whether or not the token list is already
                formatted with special tokens for the model. Defaults to None.

        Returns:
            List[int]: The list of integers either be 0 or 1: 1 for a special token, 0 for a sequence token.
        """
        return self.tokenizer.get_special_tokens_mask(
            token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=already_has_special_tokens
        )

    def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
        return self.tokenizer.create_token_type_ids_from_sequences(token_ids_0, token_ids_1=token_ids_1)

    def save_resources(self, save_directory):
        return self.tokenizer.save_resources(save_directory)


class AlbertEnglishTokenizer(PretrainedTokenizer):
    resource_files_names = {
        "sentencepiece_model_file": "spiece.model",
    }

    pretrained_resource_files_map = {
        "sentencepiece_model_file": {
            "albert-base-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v1.spiece.model",
            "albert-large-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v1.spiece.model",
            "albert-xlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v1.spiece.model",
            "albert-xxlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v1.spiece.model",
            "albert-base-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v2.spiece.model",
            "albert-large-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v2.spiece.model",
            "albert-xlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v2.spiece.model",
            "albert-xxlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v2.spiece.model",
        },
    }

    pretrained_init_configuration = {
        "albert-base-v1": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-large-v1": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-xlarge-v1": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-xxlarge-v1": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-base-v2": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-large-v2": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-xlarge-v2": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
        "albert-xxlarge-v2": {
            "do_lower_case": True,
            "remove_space": True,
            "keep_accents": False,
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        },
    }
    max_model_input_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,
    }

    def __init__(
        self,
        sentencepiece_model_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]",
        sp_model_kwargs=None,
        **kwargs
    ):

        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        self.do_lower_case = do_lower_case
        self.remove_space = remove_space
        self.keep_accents = keep_accents
        self.sentencepiece_model_file = sentencepiece_model_file

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(sentencepiece_model_file)

    @property
    def vocab_size(self):
        return len(self.sp_model)

    def get_vocab(self):
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def __getstate__(self):
        state = self.__dict__.copy()
        state["sp_model"] = None
        return state

    def __setstate__(self, d):
        self.__dict__ = d
        if not hasattr(self, "sp_model_kwargs"):
            self.sp_model_kwargs = {}

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(self.sentencepiece_model_file)

    def preprocess_text(self, inputs):
        if self.remove_space:
            outputs = " ".join(inputs.strip().split())
        else:
            outputs = inputs
        outputs = outputs.replace("``", '"').replace("''", '"')

        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):
        """Tokenize a string."""
        text = self.preprocess_text(text)
        pieces = self.sp_model.encode(text, out_type=str)
        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)

        return new_pieces

    def _convert_token_to_id(self, token):
        """Converts a token (str) to an id using the vocab."""
        return self.sp_model.PieceToId(token)

    def _convert_id_to_token(self, index):
        """Converts an index (integer) to a token (str) using the vocab."""
        return self.sp_model.IdToPiece(index)

    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 num_special_tokens_to_add(self, pair=False):
        token_ids_0 = []
        token_ids_1 = []
        return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        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 build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
        if offset_mapping_1 is None:
            return [(0, 0)] + offset_mapping_0 + [(0, 0)]

        return [(0, 0)] + offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)]

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

        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 formatted 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):
        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_resources(self, save_directory):
        for name, file_name in self.resource_files_names.items():
            save_path = os.path.join(save_directory, file_name)
            if os.path.abspath(self.sentencepiece_model_file) != os.path.abspath(save_path) and os.path.isfile(
                self.sentencepiece_model_file
            ):
                copyfile(self.sentencepiece_model_file, save_path)
            elif not os.path.isfile(self.sentencepiece_model_file):
                with open(save_path, "wb") as fi:
                    content_spiece_model = self.sp_model.serialized_model_proto()
                    fi.write(content_spiece_model)


class AlbertChineseTokenizer(BertTokenizer):
    resource_files_names = {"vocab_file": "vocab.txt"}
    pretrained_resource_files_map = {
        "vocab_file": {
            "albert-chinese-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-tiny.vocab.txt",
            "albert-chinese-small": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-small.vocab.txt",
            "albert-chinese-base": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-base.vocab.txt",
            "albert-chinese-large": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-large.vocab.txt",
            "albert-chinese-xlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xlarge.vocab.txt",
            "albert-chinese-xxlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xxlarge.vocab.txt",
        }
    }
    pretrained_init_configuration = {
        "albert-chinese-tiny": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
        "albert-chinese-small": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
        "albert-chinese-base": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
        "albert-chinese-large": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
        "albert-chinese-xlarge": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
        "albert-chinese-xxlarge": {
            "do_lower_case": False,
            "unk_token": "[UNK]",
            "pad_token": "[PAD]",
        },
    }
    max_model_input_sizes = {
        "albert-chinese-tiny": 512,
        "albert-chinese-small": 512,
        "albert-chinese-base": 512,
        "albert-chinese-large": 512,
        "albert-chinese-xlarge": 512,
        "albert-chinese-xxlarge": 512,
    }

    def __init__(
        self,
        vocab_file,
        do_lower_case=True,
        do_basic_tokenize=True,
        never_split=None,
        unk_token="[UNK]",
        sep_token="[SEP]",
        pad_token="[PAD]",
        cls_token="[CLS]",
        mask_token="[MASK]",
        tokenize_chinese_chars=True,
        strip_accents=None,
        **kwargs
    ):
        super(AlbertChineseTokenizer, self).__init__(
            vocab_file,
            do_lower_case=do_lower_case,
            do_basic_tokenize=do_basic_tokenize,
            never_split=never_split,
            unk_token=unk_token,
            sep_token=sep_token,
            pad_token=pad_token,
            cls_token=cls_token,
            mask_token=mask_token,
            tokenize_chinese_chars=tokenize_chinese_chars,
            strip_accents=strip_accents,
            **kwargs,
        )
