# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors 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.

import os

from paddlenlp.transformers import (
    BasicTokenizer,
    PretrainedTokenizer,
    WordpieceTokenizer,
)

__all__ = ["NeZhaTokenizer"]

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "nezha-base-chinese": 512,
    "nezha-large-chinese": 512,
    "nezha-base-wwm-chinese": 512,
    "nezha-large-wwm-chinese": 512,
}


class NeZhaTokenizer(PretrainedTokenizer):
    """
    Constructs a NeZha tokenizer. It uses a basic tokenizer to do punctuation
    splitting, lower casing and so on, and follows a WordPiece tokenizer to
    tokenize as subwords.

    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`.
        do_lower_case (bool):
            Whether or not to lowercase the input when tokenizing.
            Defaults to`True`.
        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 NeZhaTokenizer
            tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese')

            inputs = tokenizer('欢迎使用百度飞桨！')
            print(inputs)

            '''
            {'input_ids': [101, 3614, 6816, 886, 4500, 4636, 2428, 7607, 3444, 8013, 102],
            'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
            '''

    """

    resource_files_names = {"vocab_file": "vocab.txt"}  # for save_pretrained
    pretrained_resource_files_map = {
        "vocab_file": {
            "nezha-base-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-chinese-vocab.txt",
            "nezha-base-wwm-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-chinese-vocab.txt",
            "nezha-large-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-chinese-vocab.txt",
            "nezha-large-wwm-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-chinese-vocab.txt",
        }
    }
    pretrained_init_configuration = {
        "nezha-base-chinese": {"do_lower_case": False},
        "nezha-base-wwm-chinese": {"do_lower_case": False},
        "nezha-large-chinese": {"do_lower_case": False},
        "nezha-large-wwm-chinese": {"do_lower_case": False},
    }
    padding_side = "right"
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

    def __init__(
        self,
        vocab_file,
        do_lower_case=True,
        unk_token="[UNK]",
        sep_token="[SEP]",
        pad_token="[PAD]",
        cls_token="[CLS]",
        mask_token="[MASK]",
        **kwargs
    ):

        if not os.path.isfile(vocab_file):
            raise ValueError(
                "Can't find a vocabulary file at path '{}'. To load the "
                "vocabulary from a pretrained model please use "
                "`tokenizer = NeZhaTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
            )
        self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token)

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

        Returns:
            int: The size of vocabulary.
        """
        return len(self.vocab)

    def _tokenize(self, text):
        """
        End-to-end tokenization for NeZha models.
        Args:
            text (str): The text to be tokenized.

        Returns:
            list: A list of string representing converted tokens.
        """
        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_string(self, tokens):
        """
        Converts a sequence of tokens (list of string) to a single string. Since
        the usage of WordPiece introducing `##` to concat subwords, also removes
        `##` when converting.

        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 NeZhaTokenizer

                tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese')
                tokens = tokenizer.tokenize('欢迎使用百度飞桨！')
                '''
                ['欢', '迎', '使', '用', '百', '度', '飞', '桨', '！']
                '''
                strings = tokenizer.convert_tokens_to_string(tokens)
                '''
                欢 迎 使 用 百 度 飞 桨 ！
                '''
        """
        out_string = " ".join(tokens).replace(" ##", "").strip()
        return out_string

    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.
        """
        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):
        """
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
        adding special tokens.

        A NeZha 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.
        """
        if token_ids_1 is None:
            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
        _cls = [self.cls_token_id]
        _sep = [self.sep_token_id]
        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):
        """
        Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.

        A NeZha 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.
        """
        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 create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task.

        A NeZha 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
            | first sequence    | second sequence |

        If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).

        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.

        Returns:
            List[int]: List of token_type_id according to the given sequence(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 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 `False`.

        Returns:
            List[int]: The list of integers either be 0 or 1: 1 for a special token, 0 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 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 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
