# coding=utf-8
# Copyright (c) 2022 NVIDIA CORPORATION. All rights reserved.
# Copyright 2020 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 python tokenizers.
    For fast tokenizers (provided by HuggingFace's tokenizers library) see tokenization_utils_fast.py
"""

import itertools
import logging
import re
import unicodedata
from typing import Any, Dict, List, Optional, Tuple, Union, overload

from utils.file_utils import add_end_docstrings
from bart.tokenization.tokenization_utils_base import (
    ENCODE_KWARGS_DOCSTRING,
    ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING,
    INIT_TOKENIZER_DOCSTRING,
    AddedToken,
    BatchEncoding,
    EncodedInput,
    EncodedInputPair,
    PaddingStrategy,
    PreTokenizedInput,
    PreTokenizedInputPair,
    PreTrainedTokenizerBase,
    TensorType,
    TextInput,
    TextInputPair,
    TruncationStrategy,
)


logger = logging.getLogger(__name__)


def _is_whitespace(char):
    """Checks whether `char` 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 `char` 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.startswith("C"):
        return True
    return False


def _is_punctuation(char):
    """Checks whether `char` 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


def _is_end_of_word(text):
    """Checks whether the last character in text is one of a punctuation, control or whitespace character."""
    last_char = text[-1]
    return bool(_is_control(last_char) | _is_punctuation(last_char) | _is_whitespace(last_char))


def _is_start_of_word(text):
    """Checks whether the first character in text is one of a punctuation, control or whitespace character."""
    first_char = text[0]
    return bool(_is_control(first_char) | _is_punctuation(first_char) | _is_whitespace(first_char))


@add_end_docstrings(INIT_TOKENIZER_DOCSTRING, """    .. automethod:: __call__""")
class PreTrainedTokenizer(PreTrainedTokenizerBase):
    """
    Base class for all slow tokenizers.

    Inherits from :class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase`.

    Handle all the shared methods for tokenization and special tokens as well as methods
    downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.

    This class also contain the added tokens in a unified way on top of all tokenizers so we don't
    have to handle the specific vocabulary augmentation methods of the various underlying
    dictionary structures (BPE, sentencepiece...).
    """

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

        # Added tokens - We store this for both slow and fast tokenizers
        # until the serialization of Fast tokenizers is updated
        self.added_tokens_encoder: Dict[str, int] = {}
        self.added_tokens_decoder: Dict[int, str] = {}
        self.unique_no_split_tokens: List[str] = []

    @property
    def is_fast(self) -> bool:
        return False

    @property
    def vocab_size(self) -> int:
        """
        :obj:`int`: Size of the base vocabulary (without the added tokens).
        """
        raise NotImplementedError

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

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

        Returns:
            :obj:`Dict[str, int]`: The vocabulary.
        """
        raise NotImplementedError()

    def get_added_vocab(self) -> Dict[str, int]:
        """
        Returns the added tokens in the vocabulary as a dictionary of token to index.

        Returns:
            :obj:`Dict[str, int]`: The added tokens.
        """
        return self.added_tokens_encoder

    def __len__(self):
        """
        Size of the full vocabulary with the added tokens.
        """
        return self.vocab_size + len(self.added_tokens_encoder)

    def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
        """
        Add a list of new tokens to the tokenizer class. If the new tokens are not in the
        vocabulary, they are added to it with indices starting from length of the current vocabulary.

        Args:
            new_tokens (:obj:`List[str]`or :obj:`List[tokenizers.AddedToken]`):
                Token(s) to add in vocabulary. A token is only added if it's not already in the vocabulary (tested by
                checking if the tokenizer assign the index of the ``unk_token`` to them).
            special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not the tokens should be added as special tokens.

        Returns:
            :obj:`int`: The number of tokens actually added to the vocabulary.

        Examples::

            # Let's see how to increase the vocabulary of Bert model and tokenizer
            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
            model = BertModel.from_pretrained('bert-base-uncased')

            num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
            print('We have added', num_added_toks, 'tokens')
            # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
            model.resize_token_embeddings(len(tokenizer))
        """
        new_tokens = [str(tok) for tok in new_tokens]

        tokens_to_add = []
        for token in new_tokens:
            assert isinstance(token, str)
            if not special_tokens and self.init_kwargs.get("do_lower_case", False):
                token = token.lower()
            if (
                token != self.unk_token
                and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token)
                and token not in tokens_to_add
            ):
                tokens_to_add.append(token)
                if self.verbose:
                    logger.info("Adding %s to the vocabulary", token)

        added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add))
        added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
        self.added_tokens_encoder.update(added_tok_encoder)
        self.added_tokens_decoder.update(added_tok_decoder)

        # Make sure we don't split on any special tokens (even they were already in the vocab before e.g. for Albert)
        if special_tokens:
            self.unique_no_split_tokens = sorted(set(self.unique_no_split_tokens).union(set(new_tokens)))
        else:
            # Or on the newly added tokens
            self.unique_no_split_tokens = sorted(set(self.unique_no_split_tokens).union(set(tokens_to_add)))

        return len(tokens_to_add)

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

        .. note::
            This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not
            put this inside your training loop.

        Args:
            pair (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether the number of added tokens should be computed in the case of a sequence pair or a single
                sequence.

        Returns:
            :obj:`int`: Number of special 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 tokenize(self, text: TextInput, **kwargs) -> List[str]:
        """
        Converts a string in a sequence of tokens, using the tokenizer.

        Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
        Takes care of added tokens.

        Args:
            text (:obj:`str`):
                The sequence to be encoded.
            **kwargs (additional keyword arguments):
                Passed along to the model-specific ``prepare_for_tokenization`` preprocessing method.

        Returns:
            :obj:`List[str]`: The list of tokens.
        """
        # Simple mapping string => AddedToken for special tokens with specific tokenization behaviors
        all_special_tokens_extended = dict(
            (str(t), t) for t in self.all_special_tokens_extended if isinstance(t, AddedToken)
        )

        text, kwargs = self.prepare_for_tokenization(text, **kwargs)

        if kwargs:
            logger.warning(f"Keyword arguments {kwargs} not recognized.")

        # TODO: should this be in the base class?
        if self.init_kwargs.get("do_lower_case", False):
            # convert non-special tokens to lowercase
            escaped_special_toks = [re.escape(s_tok) for s_tok in self.all_special_tokens]
            pattern = r"(" + r"|".join(escaped_special_toks) + r")|" + r"(.+?)"
            text = re.sub(pattern, lambda m: m.groups()[0] or m.groups()[1].lower(), text)

        def split_on_token(tok, text):
            result = []
            tok_extended = all_special_tokens_extended.get(tok, None)
            split_text = text.split(tok)
            full_word = ""
            for i, sub_text in enumerate(split_text):
                # AddedToken can control whitespace stripping around them.
                # We use them for GPT2 and Roberta to have different behavior depending on the special token
                # Cf. https://github.com/huggingface/transformers/pull/2778
                # and https://github.com/huggingface/transformers/issues/3788
                if isinstance(tok_extended, AddedToken):
                    if tok_extended.single_word:
                        # Try to avoid splitting on token
                        if (
                            i < len(split_text) - 1
                            and not _is_end_of_word(sub_text)
                            and not _is_start_of_word(split_text[i + 1])
                        ):
                            # Don't extract the special token
                            full_word += sub_text + tok
                        elif full_word:
                            full_word += sub_text
                            result += [full_word]
                            full_word = ""
                            continue
                    # Strip white spaces on the right
                    if tok_extended.rstrip and i > 0:
                        # A bit counter-intuitive but we strip the left of the string
                        # since tok_extended.rstrip means the special token is eating all white spaces on its right
                        sub_text = sub_text.lstrip()
                    # Strip white spaces on the left
                    if tok_extended.lstrip and i < len(split_text) - 1:
                        sub_text = sub_text.rstrip()  # Opposite here
                else:
                    # We strip left and right by default
                    if i < len(split_text) - 1:
                        sub_text = sub_text.rstrip()
                    if i > 0:
                        sub_text = sub_text.lstrip()

                if i == 0 and not sub_text:
                    result += [tok]
                elif i == len(split_text) - 1:
                    if sub_text:
                        result += [sub_text]
                else:
                    if sub_text:
                        result += [sub_text]
                    result += [tok]
            return result

        def split_on_tokens(tok_list, text):
            if not text.strip():
                return []
            if not tok_list:
                return self._tokenize(text)

            tokenized_text = []
            text_list = [text]
            for tok in tok_list:
                tokenized_text = []
                for sub_text in text_list:
                    if sub_text not in self.unique_no_split_tokens:
                        tokenized_text += split_on_token(tok, sub_text)
                    else:
                        tokenized_text += [sub_text]
                text_list = tokenized_text

            return list(
                itertools.chain.from_iterable(
                    (
                        self._tokenize(token) if token not in self.unique_no_split_tokens else [token]
                        for token in tokenized_text
                    )
                )
            )

        no_split_token = self.unique_no_split_tokens
        tokenized_text = split_on_tokens(no_split_token, text)
        return tokenized_text

    def _tokenize(self, text, **kwargs):
        """
        Converts a string in a sequence of tokens (string), using the tokenizer.
        Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies
        (BPE/SentencePieces/WordPieces).

        Do NOT take care of added tokens.
        """
        raise NotImplementedError

    def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
        """
        Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
        vocabulary.

        Args:
            token (:obj:`str` or :obj:`List[str]`): One or several token(s) to convert to token id(s).

        Returns:
            :obj:`int` or :obj:`List[int]`: The token id or list of token ids.
        """
        if tokens is None:
            return None

        if isinstance(tokens, str):
            return self._convert_token_to_id_with_added_voc(tokens)

        ids = []
        for token in tokens:
            ids.append(self._convert_token_to_id_with_added_voc(token))
        return ids

    def _convert_token_to_id_with_added_voc(self, token):
        if token is None:
            return None

        if token in self.added_tokens_encoder:
            return self.added_tokens_encoder[token]
        return self._convert_token_to_id(token)

    def _convert_token_to_id(self, token):
        raise NotImplementedError

    def _encode_plus(
        self,
        text: Union[TextInput, PreTokenizedInput, EncodedInput],
        text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
        add_special_tokens: bool = True,
        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
        max_length: Optional[int] = None,
        stride: int = 0,
        is_pretokenized: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs
    ) -> BatchEncoding:
        def get_input_ids(text):
            if isinstance(text, str):
                tokens = self.tokenize(text, **kwargs)
                return self.convert_tokens_to_ids(tokens)
            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
                if is_pretokenized:
                    tokens = list(itertools.chain(*(self.tokenize(t, is_pretokenized=True, **kwargs) for t in text)))
                    return self.convert_tokens_to_ids(tokens)
                else:
                    return self.convert_tokens_to_ids(text)
            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
                return text
            else:
                if is_pretokenized:
                    raise ValueError(
                        f"Input {text} is not valid. Should be a string or a list/tuple of strings when `is_pretokenized=True`."
                    )
                else:
                    raise ValueError(
                        f"Input {text} is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
                    )

        if return_offsets_mapping:
            raise NotImplementedError(
                "return_offset_mapping is not available when using Python tokenizers."
                "To use this feature, change your tokenizer to one deriving from "
                "transformers.PreTrainedTokenizerFast."
                "More information on available tokenizers at "
                "https://github.com/huggingface/transformers/pull/2674"
            )

        first_ids = get_input_ids(text)
        second_ids = get_input_ids(text_pair) if text_pair is not None else None

        return self.prepare_for_model(
            first_ids,
            pair_ids=second_ids,
            add_special_tokens=add_special_tokens,
            padding=padding_strategy.value,
            truncation=truncation_strategy.value,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            prepend_batch_axis=True,
            return_attention_mask=return_attention_mask,
            return_token_type_ids=return_token_type_ids,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_length=return_length,
            verbose=verbose,
        )

    def _batch_encode_plus(
        self,
        batch_text_or_text_pairs: Union[
            List[TextInput],
            List[TextInputPair],
            List[PreTokenizedInput],
            List[PreTokenizedInputPair],
            List[EncodedInput],
            List[EncodedInputPair],
        ],
        add_special_tokens: bool = True,
        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
        max_length: Optional[int] = None,
        stride: int = 0,
        is_pretokenized: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs
    ) -> BatchEncoding:
        def get_input_ids(text):
            if isinstance(text, str):
                tokens = self.tokenize(text, **kwargs)
                return self.convert_tokens_to_ids(tokens)
            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
                if is_pretokenized:
                    tokens = list(itertools.chain(*(self.tokenize(t, is_pretokenized=True, **kwargs) for t in text)))
                    return self.convert_tokens_to_ids(tokens)
                else:
                    return self.convert_tokens_to_ids(text)
            elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
                return text
            else:
                raise ValueError(
                    "Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
                )

        if return_offsets_mapping:
            raise NotImplementedError(
                "return_offset_mapping is not available when using Python tokenizers."
                "To use this feature, change your tokenizer to one deriving from "
                "transformers.PreTrainedTokenizerFast."
            )

        input_ids = []
        for ids_or_pair_ids in batch_text_or_text_pairs:
            if not isinstance(ids_or_pair_ids, (list, tuple)):
                ids, pair_ids = ids_or_pair_ids, None
            elif is_pretokenized and not isinstance(ids_or_pair_ids[0], (list, tuple)):
                ids, pair_ids = ids_or_pair_ids, None
            else:
                ids, pair_ids = ids_or_pair_ids

            first_ids = get_input_ids(ids)
            second_ids = get_input_ids(pair_ids) if pair_ids is not None else None
            input_ids.append((first_ids, second_ids))

        batch_outputs = self._batch_prepare_for_model(
            input_ids,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
            return_token_type_ids=return_token_type_ids,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_length=return_length,
            return_tensors=return_tensors,
            verbose=verbose,
        )

        return BatchEncoding(batch_outputs)

    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
    def _batch_prepare_for_model(
        self,
        batch_ids_pairs: List[Union[PreTokenizedInputPair, Tuple[List[int], None]]],
        add_special_tokens: bool = True,
        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[str] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_length: bool = False,
        verbose: bool = True,
    ) -> BatchEncoding:
        """
        Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
        It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
        manages a moving window (with user defined stride) for overflowing tokens

        Args:
            batch_ids_pairs: list of tokenized input ids or input ids pairs
        """

        batch_outputs = {}
        for first_ids, second_ids in batch_ids_pairs:
            outputs = self.prepare_for_model(
                first_ids,
                second_ids,
                add_special_tokens=add_special_tokens,
                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterward
                truncation=truncation_strategy.value,
                max_length=max_length,
                stride=stride,
                pad_to_multiple_of=None,  # we pad in batch afterward
                return_attention_mask=False,  # we pad in batch afterward
                return_token_type_ids=return_token_type_ids,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_length=return_length,
                return_tensors=None,  # We convert the whole batch to tensors at the end
                prepend_batch_axis=False,
                verbose=verbose,
            )

            for key, value in outputs.items():
                if key not in batch_outputs:
                    batch_outputs[key] = []
                batch_outputs[key].append(value)

        batch_outputs = self.pad(
            batch_outputs,
            padding=padding_strategy.value,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
        )

        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)

        return batch_outputs

    def prepare_for_tokenization(
        self, text: str, is_pretokenized: bool = False, **kwargs
    ) -> Tuple[str, Dict[str, Any]]:
        """
        Performs any necessary transformations before tokenization.

        This method should pop the arguments from kwargs and return the remaining :obj:`kwargs` as well.
        We test the :obj:`kwargs` at the end of the encoding process to be sure all the arguments have been used.

        Args:
            test (:obj:`str`):
                The text to prepare.
            is_pretokenized (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not the text has been pretokenized.
            kwargs:
                Keyword arguments to use for the tokenization.

        Returns:
            :obj:`Tuple[str, Dict[str, Any]]`: The prepared text and the unused kwargs.
        """
        return (text, kwargs)

    def get_special_tokens_mask(
        self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        """
        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 (:obj:`List[int]`):
                List of ids of the first sequence.
            token_ids_1 (:obj:`List[int]`, `optional`):
                List of ids of the second sequence.
            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Wheter or not the token list is already formated 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.
        """
        return [0] * ((len(token_ids_1) if token_ids_1 else 0) + len(token_ids_0))

    @overload
    def convert_ids_to_tokens(self, ids: int, skip_special_tokens: bool = False) -> str:
        ...

    @overload
    def convert_ids_to_tokens(self, ids: List[int], skip_special_tokens: bool = False) -> List[str]:
        ...

    def convert_ids_to_tokens(
        self, ids: Union[int, List[int]], skip_special_tokens: bool = False
    ) -> Union[str, List[str]]:
        """
        Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary
        and added tokens.

        Args:
            ids (:obj:`int` or :obj:`List[int]`):
                The token id (or token ids) to convert to tokens.
            skip_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to remove special tokens in the decoding.

        Returns:
            :obj:`str` or :obj:`List[str]`: The decoded token(s).
        """
        if isinstance(ids, int):
            if ids in self.added_tokens_decoder:
                return self.added_tokens_decoder[ids]
            else:
                return self._convert_id_to_token(ids)
        tokens = []
        for index in ids:
            index = int(index)
            if skip_special_tokens and index in self.all_special_ids:
                continue
            if index in self.added_tokens_decoder:
                tokens.append(self.added_tokens_decoder[index])
            else:
                tokens.append(self._convert_id_to_token(index))
        return tokens

    def _convert_id_to_token(self, index: int) -> str:
        raise NotImplementedError

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """
        Converts a sequence of token ids in a single string.

        The most simple way to do it is ``" ".join(tokens)`` but we often want to remove
        sub-word tokenization artifacts at the same time.

        Args:
            tokens (:obj:`List[str]`): The token to join in a string.

        Return: The joined tokens.
        """
        return " ".join(tokens)

    def decode(
        self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True
    ) -> str:
        """
        Converts a sequence of ids in a string, using the tokenizer and vocabulary
        with options to remove special tokens and clean up tokenization spaces.

        Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.

        Args:
            token_ids (:obj:`List[int]`):
                List of tokenized input ids. Can be obtained using the ``__call__`` method.
            skip_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to remove special tokens in the decoding.
            clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to clean up the tokenization spaces.

        Returns:
            :obj:`str`: The decoded sentence.
        """
        filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separatly for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        sub_texts = []
        current_sub_text = []
        for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                continue
            if token in self.added_tokens_encoder:
                if current_sub_text:
                    sub_texts.append(self.convert_tokens_to_string(current_sub_text))
                    current_sub_text = []
                sub_texts.append(token)
            else:
                current_sub_text.append(token)
        if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(current_sub_text))
        text = " ".join(sub_texts)

        if clean_up_tokenization_spaces:
            clean_text = self.clean_up_tokenization(text)
            return clean_text
        else:
            return text

    def save_vocabulary(self, save_directory) -> Tuple[str]:
        """
        Save the tokenizer vocabulary to a directory. This method does *NOT* save added tokens
        and special token mappings.

        .. warning::
            Please use :meth:`~transformers.PreTrainedTokenizer.save_pretrained` to save the full tokenizer state if
            you want to reload it using the :meth:`~transformers.PreTrainedTokenizer.from_pretrained` class method.

        Args:
            save_directory (:obj:`str`): The path to adirectory where the tokenizer will be saved.

        Returns:
            A tuple of :obj:`str`: The files saved.
        """
        raise NotImplementedError

    def prepare_seq2seq_batch(
        self,
        src_texts: List[str],
        tgt_texts: Optional[List[str]] = None,
        max_length: Optional[int] = None,
        max_target_length: Optional[int] = None,
        padding: str = "longest",
        return_tensors: str = "None",
        truncation=True,
        **kwargs,
    ) -> BatchEncoding:
        r"""

        Prepare a batch that can be passed directly to an instance of :class:`~transformers.AutoModelForSeq2SeqLM`.

        Args:
            src_texts: (:obj:`List[str]`):
                List of documents to summarize or source language texts.
            tgt_texts: (:obj:`List[str]`, `optional`):
                List of summaries or target language texts.
            max_length (:obj:`int`, `optional`):
                Controls the maximum length for encoder inputs (documents to summarize or source language texts).
                If left unset or set to :obj:`None`, this will use the predefined model maximum length if a maximum
                length is required by one of the truncation/padding parameters. If the model has no specific maximum
                input length (like XLNet) truncation/padding to a maximum length will be deactivated.
            max_target_length (:obj:`int`, `optional`):
                Controls the maximum length of decoder inputs (target language texts or summaries).
                If left unset or set to :obj:`None`, this will use the max_length value.
            padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`False`):
                Activates and controls padding. Accepts the following values:

                * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
                  single sequence if provided).
                * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
                  maximum acceptable input length for the model if that argument is not provided.
                * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
                  different lengths).
            return_tensors (:obj:`str` or :class:`~transformers.tokenization_utils_base.TensorType`, `optional`, defaults to "pt"):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
                * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
                * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
            truncation (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.TruncationStrategy`, `optional`, defaults to :obj:`True`):
                Activates and controls truncation. Accepts the following values:

                * :obj:`True` or :obj:`'longest_first'`: Truncate to a maximum length specified with the argument
                  :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not
                  provided. This will truncate token by token, removing a token from the longest sequence in the pair
                  if a pair of sequences (or a batch of pairs) is provided.
                * :obj:`'only_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to
                  the maximum acceptable input length for the model if that argument is not provided. This will only
                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                * :obj:`'only_second'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
                  to the maximum acceptable input length for the model if that argument is not provided. This will only
                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                * :obj:`False` or :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with
                  sequence lengths greater than the model maximum admissible input size).
            **kwargs:
                Additional keyword arguments passed along to :obj:`self.__call__`.

        Returns:
            :class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following fields:

            - **input_ids** -- List of token ids to be fed to the encoder.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model.
            - **labels** -- List of token ids for tgt_texts

            The full set of keys ``[input_ids, attention_mask, labels]``,
            will only be returned if tgt_texts is passed. Otherwise, input_ids, attention_mask will be the only keys.
        """
        raise NotImplementedError(
            "If your model requires more than input_ids for a typical forward pass, you should implement this method. "
            "Returned keys should be [input_ids, attention_mask, labels]. See MarianTokenizer or T5Tokenizer for a "
            "reference implementation."
        )