# Copyright (c) 2020 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 json
import os
import shutil
from typing import Dict, List, Optional

from paddle.utils import try_import

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

__all__ = [
    "SkepTokenizer",
]


def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a significant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    bs = list(range(33, 126 + 1)) + list(range(161, 172 + 1)) + list(range(174, 255 + 1))
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8 + n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


def get_pairs(word):
    """Return set of symbol pairs in a word.
    Word is represented as tuple of symbols (symbols being variable-length strings).
    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


class BpeEncoder(object):
    """BpeEncoder"""

    def __init__(self, encoder_json_file, vocab_bpe_file, errors="replace", unk_token="<|endoftext|>", **kwargs):
        """
        Constructs a BpeEncoder.

        Args:
            encoder_json_file (`str`): The path to bpe encode json file.
            vocab_bpe_file (`str`): The path to bpe vocab file.
            errors (`str`): the error handler
            unk_token (`str`): the unk token
        """
        self.encoder = self.__get_encoder(encoder_json_file)
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.errors = errors  # how to handle errors in decoding
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        self.bpe_ranks = self.__get_bpe_ranks(vocab_bpe_file)
        self.unk_token = unk_token
        self.cache = {}
        re = try_import("regex")
        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")

    def __get_encoder(self, encoder_json_file):
        with open(encoder_json_file, "r") as f:
            encoder = json.load(f)
        return encoder

    def __get_bpe_ranks(self, vocab_bpe_file):
        with open(vocab_bpe_file, "r", encoding="utf-8") as f:
            bpe_data = f.read()
        bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
        bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
        return bpe_ranks

    def bpe(self, token):
        """
        bpe
        """
        if token in self.cache:
            return self.cache[token]
        word = tuple(token)
        pairs = get_pairs(word)

        if not pairs:
            return token

        while True:
            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
            if bigram not in self.bpe_ranks:
                break
            first, second = bigram
            new_word = []
            i = 0
            while i < len(word):
                try:
                    j = word.index(first, i)
                    new_word.extend(word[i:j])
                    i = j
                except Exception:
                    new_word.extend(word[i:])
                    break

                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
                    new_word.append(first + second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            new_word = tuple(new_word)
            word = new_word
            if len(word) == 1:
                break
            else:
                pairs = get_pairs(word)
        word = " ".join(word)
        self.cache[token] = word
        return word

    def encode(self, text: str) -> List[int]:
        """
        encode the text to token_ids
        TODO(wj-Mcat): to be deprecated
        """
        bpe_tokens = []
        re = try_import("regex")
        for token in re.findall(self.pat, text):
            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" "))
        return bpe_tokens

    def decode(self, tokens: List[str]) -> str:
        """
        decode
        TODO(wj-Mcat): to be deprecated
        """
        text = "".join([self.decoder[token] for token in tokens])
        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
        return text

    def _tokenize(self, text: str) -> List[str]:
        """tokenize text into tokens with bpe algo

        Args:
            text (str): the content of text

        Returns:
            List[str]: the sub token of text
        """
        bpe_tokens = []
        re = try_import("regex")
        for token in re.findall(self.pat, text):
            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
        return bpe_tokens

    def _convert_token_to_id(self, token: str) -> int:
        """Converts a token (str) in an id using the vocab."""
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index: int) -> str:
        """Converts an index (integer) in a token (str) using the vocab."""
        return self.decoder.get(index)

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """Converts a sequence of tokens (string) in a single string."""
        text = "".join(tokens)
        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
        return text


class SkepTokenizer(PretrainedTokenizer):
    r"""
    Constructs a Skep 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`.
        bpe_vocab_file (str, optional):
            The vocabulary file path of a `BpeTokenizer`. Defaults to `None`.
        bpe_json_file (str, optional):
            The json file path of a `BpeTokenizer`. Defaults to `None`.
        use_bpe_encoder (bool, optional):
            Whether or not to use BPE Encoder. Defaults to `False`.
        need_token_type_id (bool, optional):
            Whether or not to use token type id. Defaults to `True`.
        add_two_sep_token_inter (bool, optional):
            Whether or not to add two different `sep_token`. Defaults to `False`.
        unk_token (str, optional):
            The special token for unknown words.
            Defaults to "[UNK]".
        sep_token (str, optional):
            The special token for separator token.
            Defaults to "[SEP]".
        pad_token (str, optional):
            The special token for padding.
            Defaults to "[PAD]".
        cls_token (str, optional):
            The special token for cls.
            Defaults to "[CLS]".
        mask_token (str, optional):
            The special token for mask.
            Defaults to "[MASK]".

    Examples:
        .. code-block::

            from paddlenlp.transformers import SkepTokenizer
            tokenizer = SkepTokenizer.from_pretrained('skep_ernie_2.0_large_en')
            encoded_inputs = tokenizer('He was a puppeteer')
            # encoded_inputs:
            # {
            #    'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102],
            #    'token_type_ids': [0, 0, 0, 0, 0, 0, 0]
            # }
    """
    resource_files_names = {
        "vocab_file": "vocab.txt",
        "bpe_vocab_file": "vocab.bpe",
        "bpe_json_file": "encoder.json",
    }  # for save_pretrained
    pretrained_resource_files_map = {
        "vocab_file": {
            "skep_ernie_1.0_large_ch": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_ernie_1.0_large_ch.vocab.txt",
            "skep_ernie_2.0_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_ernie_2.0_large_en.vocab.txt",
            "skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.vocab.txt",
        },
        "bpe_vocab_file": {
            "skep_ernie_1.0_large_ch": None,
            "skep_ernie_2.0_large_en": None,
            "skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.vocab.bpe",
        },
        "bpe_json_file": {
            "skep_ernie_1.0_large_ch": None,
            "skep_ernie_2.0_large_en": None,
            "skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.encoder.json",
        },
    }
    max_model_input_sizes = {
        "skep_ernie_1.0_large_ch": 512,
        "skep_ernie_2.0_large_en": 512,
        "skep_roberta_large_en": 514,
    }

    pretrained_init_configuration = {
        "skep_ernie_1.0_large_ch": {
            "do_lower_case": True,
            "use_bpe_encoder": False,
            "need_token_type_id": True,
            "add_two_sep_token_inter": False,
        },
        "skep_ernie_2.0_large_en": {
            "do_lower_case": True,
            "use_bpe_encoder": False,
            "need_token_type_id": True,
            "add_two_sep_token_inter": False,
        },
        "skep_roberta_large_en": {
            "do_lower_case": True,
            "use_bpe_encoder": True,
            "need_token_type_id": False,
            "add_two_sep_token_inter": True,
        },
    }

    def __init__(
        self,
        vocab_file,
        bpe_vocab_file=None,
        bpe_json_file=None,
        do_lower_case=True,
        use_bpe_encoder=False,
        need_token_type_id=True,
        add_two_sep_token_inter=False,
        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 = SkepTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
            )
        self.vocab_file = vocab_file
        self.bpe_vocab_file = bpe_vocab_file
        self.bpe_json_file = bpe_json_file
        self.vocab = self.load_vocabulary(
            vocab_file,
            unk_token=unk_token,
            pad_token=pad_token,
            bos_token=cls_token,
            eos_token=sep_token,
            mask_token=mask_token,
        )

        self.use_bpe_encoder = use_bpe_encoder
        self.need_token_type_id = need_token_type_id
        self.add_two_sep_token_inter = add_two_sep_token_inter

        if not self.use_bpe_encoder:
            self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
            self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token)
        else:
            assert (bpe_vocab_file and bpe_json_file) is not None, "bpe_vocab_file and bpe_json_file must be not None."
            if os.path.isfile(bpe_vocab_file) and os.path.isfile(bpe_json_file):
                self.bpe_tokenizer = BpeEncoder(bpe_json_file, bpe_vocab_file, unk_token=unk_token)

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

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

    def _tokenize(self, text):
        r"""
        End-to-end tokenization for Skep models.

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

        Returns:
            list: A list of string representing converted tokens.
        """
        split_tokens = []
        if not self.use_bpe_encoder:
            for token in self.basic_tokenizer.tokenize(text):
                for sub_token in self.wordpiece_tokenizer.tokenize(token):
                    split_tokens.append(sub_token)
        else:
            for token in self.bpe_tokenizer._tokenize(text):
                split_tokens.append(str(token))

        return split_tokens

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

        Args:
            pair (bool, optional):
                Returns the number of added tokens in the case of a sequence
                pair if set to True, returns the number of added tokens in the case of a single sequence if set to False.
                Defaults to False.

        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_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.

        Should be overridden in a subclass if the model has a special way of building those.

        Args:
            offset_mapping_0 (List[tuple]):
                List of char offsets to which the special tokens will be added.
            offset_mapping_1 (List[tuple], optional):
                Optional second list of char offsets for offset mapping pairs.

        Returns:
            List[tuple]: List of char 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 build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        r"""
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
        adding special tokens.

        A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence has the following format:

        - single sequence:      ``[CLS] X [SEP]``
        - pair of sequences:        ``[CLS] A [SEP] B [SEP]``

        A skep_roberta_large_en sequence has the following format:

        - single sequence:      ``[CLS] X [SEP]``
        - pair of sequences:        ``[CLS] A [SEP] [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 not self.add_two_sep_token_inter:
            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
        else:
            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 + _sep + token_ids_1 + _sep

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

        A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en 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 `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).

        note: There is no need token type ids for skep_roberta_large_ch model.

        Args:
            token_ids_0 (List[int]):
                List of IDs.
            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).
        """
        if self.need_token_type_id:
            _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]
        else:
            # For model skep-roberta-large-en, token type ids is no need.
            return None

    def save_resources(self, save_directory):
        """
        Save tokenizer related resources to files under `save_directory`.

        Args:
            save_directory (str): Directory to save files into.
        """
        for name, file_name in self.resource_files_names.items():
            save_path = os.path.join(save_directory, file_name)
            source_file = getattr(self, name, None)
            if not source_file:
                continue

            if os.path.abspath(source_file) != os.path.abspath(save_path):
                shutil.copyfile(source_file, save_path)

    def convert_tokens_to_string(self, tokens: List[str]):
        """
        Converts a sequence of tokens (list of string) in 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 RoFormerTokenizer

                tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-base')
                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'

        """
        # to handle the bpe and wordpiece case
        if hasattr(self, "wordpiece_tokenizer"):
            return " ".join(tokens).replace(" ##", "").strip()
        else:
            return self.bpe_tokenizer.convert_tokens_to_string(tokens)

    def _convert_token_to_id(self, token: str) -> int:
        """Converts a token (str) in an id using the vocab."""
        if self.use_bpe_encoder:
            return self.bpe_tokenizer._convert_token_to_id(token)

        return super()._convert_token_to_id(token)

    def _convert_id_to_token(self, index: int) -> str:
        """Converts an index (integer) in a token (str) using the vocab."""
        if self.use_bpe_encoder:
            return self.bpe_tokenizer._convert_id_to_token(index)

        return super()._convert_id_to_token(index)

    def get_special_tokens_mask(
        self,
        token_ids_0: List[int],
        token_ids_1: Optional[List[int]] = None,
        already_has_special_tokens: bool = False,
    ) -> List[int]:
        """
        Retrieve 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` method.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.

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

        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0,
                token_ids_1=token_ids_1,
                already_has_special_tokens=True,
            )

        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) -> Dict[str, int]:
        """
        Returns the vocabulary as a dictionary of token to index.

        `tokenizer.get_vocab()[token]` is equivalent to `tokenizer.convert_tokens_to_ids(token)` when `token` is in the
        vocab.

        Returns:
            `Dict[str, int]`: The vocabulary.
        """
        return dict(self.vocab.token_to_idx, **self.added_tokens_encoder)
