paddlenlp/transformers/chinesebert/tokenizer.py
def encode(self,
               text,
               text_pair=None,
               max_seq_len=512,
               pad_to_max_seq_len=False,
               truncation_strategy="longest_first",
               return_position_ids=False,
               return_token_type_ids=True,
               return_attention_mask=False,
               return_length=False,
               return_overflowing_tokens=False,
               return_special_tokens_mask=False):
        """
        Performs tokenization and uses the tokenized tokens to prepare model
        inputs. It supports sequence or sequence pair as input, and batch input
        is not allowed.

        Args:
            text (str, List[str] or List[int]):
                The sequence to be processed. One sequence is a string, a list
                of strings, or a list of integers depending on whether it has
                been pretokenized and converted to ids. 
            text_pair (str, List[str] or List[List[str]]):
                Same as `text` argument, while it represents for the latter
                sequence of the sequence pair.
            max_seq_len (int, optional):
                If set to a number, will limit the total sequence returned so
                that it has a maximum length. If there are overflowing tokens,
                those overflowing tokens will be added to the returned dictionary
                when `return_overflowing_tokens` is `True`. Defaults to `None`.
            stride (int, optional):
                Only available for batch input of sequence pair and mainly for
                question answering usage. When for QA, `text` represents questions
                and `text_pair` represents contexts. If `stride` is set to a
                positive number, the context will be split into multiple spans
                where `stride` defines the number of (tokenized) tokens to skip
                from the start of one span to get the next span, thus will produce
                a bigger batch than inputs to include all spans. Moreover, 'overflow_to_sample'
                and 'offset_mapping' preserving the original example and position
                information will be added to the returned dictionary. Defaults to 0.
            pad_to_max_seq_len (bool, optional):
                If set to `True`, the returned sequences would be padded up to
                `max_seq_len` specified length according to padding side
                (`self.padding_side`) and padding token id. Defaults to `False`.
            truncation_strategy (str, optional):
                String selected in the following options:

                - 'longest_first' (default) Iteratively reduce the inputs sequence
                until the input is under `max_seq_len` starting from the longest
                one at each token (when there is a pair of input sequences).
                - 'only_first': Only truncate the first sequence.
                - 'only_second': Only truncate the second sequence.
                - 'do_not_truncate': Do not truncate (raise an error if the input
                sequence is longer than `max_seq_len`).

                Defaults to 'longest_first'.
            return_position_ids (bool, optional):
                Whether to include tokens position ids in the returned dictionary.
                Defaults to `False`.
            return_token_type_ids (bool, optional):
                Whether to include token type ids in the returned dictionary.
                Defaults to `True`.
            return_attention_mask (bool, optional):
                Whether to include the attention mask in the returned dictionary.
                Defaults to `False`.
            return_length (bool, optional):
                Whether to include the length of each encoded inputs in the
                returned dictionary. Defaults to `False`.
            return_overflowing_tokens (bool, optional):
                Whether to include overflowing token information in the returned
                dictionary. Defaults to `False`.
            return_special_tokens_mask (bool, optional):
                Whether to include special tokens mask information in the returned
                dictionary. Defaults to `False`.

        Returns:
            dict:
                The dict has the following optional items:

                - **input_ids** (list[int]): List of token ids to be fed to a model.
                - **pinyin_ids** (list[int]): List of pinyin ids to be fed to a model.
                - **position_ids** (list[int], optional): List of token position ids to be
                  fed to a model. Included when `return_position_ids` is `True`
                - **token_type_ids** (list[int], optional): List of token type ids to be
                  fed to a model. Included when `return_token_type_ids` is `True`.
                - **attention_mask** (list[int], optional): List of integers valued 0 or 1,
                  where 0 specifies paddings and should not be attended to by the
                  model. Included when `return_attention_mask` is `True`.
                - **seq_len** (int, optional): The input_ids length. Included when `return_length`
                  is `True`.
                - **overflowing_tokens** (list[int], optional): List of overflowing tokens.
                  Included when if `max_seq_len` is specified and `return_overflowing_tokens`
                  is True.
                - **num_truncated_tokens** (int, optional): The number of overflowing tokens.
                  Included when if `max_seq_len` is specified and `return_overflowing_tokens`
                  is True.
                - **special_tokens_mask** (list[int], optional): List of integers valued 0 or 1,
                  with 0 specifying special added tokens and 1 specifying sequence tokens.
                  Included when `return_special_tokens_mask` is `True`.
        """

        def get_input_ids(text):
            if isinstance(text, str):
                tokens = self.tokenize(text)
                return self.convert_tokens_to_ids(tokens)
            elif isinstance(text,
                            (list, tuple)) and len(text) > 0 and isinstance(
                                text[0], str):
                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."
                )

        ids = get_input_ids(text)
        pair_ids = get_input_ids(text_pair) if text_pair is not None else None

        pair = bool(pair_ids is not None)
        len_ids = len(ids)
        len_pair_ids = len(pair_ids) if pair else 0

        encoded_inputs = {}

        # Truncation: Handle max sequence length
        total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(
            pair=pair))

        token_offset_mapping = self.get_offset_mapping(text)

        if pair:
            token_pair_offset_mapping = self.get_offset_mapping(text_pair)
        else:
            token_pair_offset_mapping = None

        if max_seq_len and total_len > max_seq_len:
            ids, pair_ids, token_offset_mapping, token_pair_offset_mapping, overflowing_tokens = self.truncate_sequences(
                ids,
                pair_ids=pair_ids,
                token_offset_mapping=token_offset_mapping,
                token_pair_offset_mapping=token_pair_offset_mapping,
                num_tokens_to_remove=total_len - max_seq_len,
                truncation_strategy=truncation_strategy, )

            if return_overflowing_tokens:
                encoded_inputs["overflowing_tokens"] = overflowing_tokens
                encoded_inputs["num_truncated_tokens"] = total_len - max_seq_len

        # Add special tokens

        sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
        token_type_ids = self.create_token_type_ids_from_sequences(ids,
                                                                   pair_ids)

        offset_mapping = self.build_offset_mapping_with_special_tokens(
            token_offset_mapping, token_pair_offset_mapping)

        # Build output dictionnary
        encoded_inputs["input_ids"] = sequence
        encoded_inputs["pinyin_ids"] = self.get_pinyin_ids(text, text_pair,
                                                           offset_mapping)

        if return_token_type_ids:
            encoded_inputs["token_type_ids"] = token_type_ids
        if return_special_tokens_mask:
            encoded_inputs[
                "special_tokens_mask"] = self.get_special_tokens_mask(ids,
                                                                      pair_ids)
        if return_length:
            encoded_inputs["seq_len"] = len(encoded_inputs["input_ids"])

        # Check lengths
        assert max_seq_len is None or len(encoded_inputs[
            "input_ids"]) <= max_seq_len

        # Padding
        needs_to_be_padded = pad_to_max_seq_len and \
                             max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len

        if needs_to_be_padded:
            difference = max_seq_len - len(encoded_inputs["input_ids"])
            encoded_inputs["pinyin_ids"] = encoded_inputs[
                "pinyin_ids"] + self.special_tokens_pinyin_ids * difference
            if self.padding_side == 'right':
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
                        "input_ids"]) + [0] * difference
                if return_token_type_ids:
                    encoded_inputs["token_type_ids"] = (
                        encoded_inputs["token_type_ids"] +
                        [self.pad_token_type_id] * difference)
                if return_special_tokens_mask:
                    encoded_inputs["special_tokens_mask"] = encoded_inputs[
                        "special_tokens_mask"] + [1] * difference
                encoded_inputs["input_ids"] = encoded_inputs[
                    "input_ids"] + [self.pad_token_id] * difference
            elif self.padding_side == 'left':
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = [0] * difference + [
                        1
                    ] * len(encoded_inputs["input_ids"])
                if return_token_type_ids:
                    encoded_inputs["token_type_ids"] = (
                        [self.pad_token_type_id] * difference +
                        encoded_inputs["token_type_ids"])
                if return_special_tokens_mask:
                    encoded_inputs["special_tokens_mask"] = [
                        1
                    ] * difference + encoded_inputs["special_tokens_mask"]
                encoded_inputs["input_ids"] = [
                    self.pad_token_id
                ] * difference + encoded_inputs["input_ids"]
        else:
            if return_attention_mask:
                encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
                    "input_ids"])

        if return_position_ids:
            encoded_inputs["position_ids"] = list(
                range(len(encoded_inputs["input_ids"])))

        return encoded_inputs

        Performs tokenization and uses the tokenized tokens to prepare model
        inputs. It supports sequence or sequence pair as input, and batch input
        is not allowed.

        Args:
            text (str, List[str] or List[int]):
                The sequence to be processed. One sequence is a string, a list
                of strings, or a list of integers depending on whether it has
                been pretokenized and converted to ids. 
            text_pair (str, List[str] or List[List[str]]):
                Same as `text` argument, while it represents for the latter
                sequence of the sequence pair.
            max_seq_len (int, optional):
                If set to a number, will limit the total sequence returned so
                that it has a maximum length. If there are overflowing tokens,
                those overflowing tokens will be added to the returned dictionary
                when `return_overflowing_tokens` is `True`. Defaults to `None`.
            stride (int, optional):
                Only available for batch input of sequence pair and mainly for
                question answering usage. When for QA, `text` represents questions
                and `text_pair` represents contexts. If `stride` is set to a
                positive number, the context will be split into multiple spans
                where `stride` defines the number of (tokenized) tokens to skip
                from the start of one span to get the next span, thus will produce
                a bigger batch than inputs to include all spans. Moreover, 'overflow_to_sample'
                and 'offset_mapping' preserving the original example and position
                information will be added to the returned dictionary. Defaults to 0.
            pad_to_max_seq_len (bool, optional):
                If set to `True`, the returned sequences would be padded up to
                `max_seq_len` specified length according to padding side
                (`self.padding_side`) and padding token id. Defaults to `False`.
            truncation_strategy (str, optional):
                String selected in the following options:

                - 'longest_first' (default) Iteratively reduce the inputs sequence
                until the input is under `max_seq_len` starting from the longest
                one at each token (when there is a pair of input sequences).
                - 'only_first': Only truncate the first sequence.
                - 'only_second': Only truncate the second sequence.
                - 'do_not_truncate': Do not truncate (raise an error if the input
                sequence is longer than `max_seq_len`).

                Defaults to 'longest_first'.
            return_position_ids (bool, optional):
                Whether to include tokens position ids in the returned dictionary.
                Defaults to `False`.
            return_token_type_ids (bool, optional):
                Whether to include token type ids in the returned dictionary.
                Defaults to `True`.
            return_attention_mask (bool, optional):
                Whether to include the attention mask in the returned dictionary.
                Defaults to `False`.
            return_length (bool, optional):
                Whether to include the length of each encoded inputs in the
                returned dictionary. Defaults to `False`.
            return_overflowing_tokens (bool, optional):
                Whether to include overflowing token information in the returned
                dictionary. Defaults to `False`.
            return_special_tokens_mask (bool, optional):
                Whether to include special tokens mask information in the returned
                dictionary. Defaults to `False`.

        Returns:
            dict:
                The dict has the following optional items:

                - **input_ids** (list[int]): List of token ids to be fed to a model.
                - **pinyin_ids** (list[int]): List of pinyin ids to be fed to a model.
                - **position_ids** (list[int], optional): List of token position ids to be
                  fed to a model. Included when `return_position_ids` is `True`
                - **token_type_ids** (list[int], optional): List of token type ids to be
                  fed to a model. Included when `return_token_type_ids` is `True`.
                - **attention_mask** (list[int], optional): List of integers valued 0 or 1,
                  where 0 specifies paddings and should not be attended to by the
                  model. Included when `return_attention_mask` is `True`.
                - **seq_len** (int, optional): The input_ids length. Included when `return_length`
                  is `True`.
                - **overflowing_tokens** (list[int], optional): List of overflowing tokens.
                  Included when if `max_seq_len` is specified and `return_overflowing_tokens`
                  is True.
                - **num_truncated_tokens** (int, optional): The number of overflowing tokens.
                  Included when if `max_seq_len` is specified and `return_overflowing_tokens`
                  is True.
                - **special_tokens_mask** (list[int], optional): List of integers valued 0 or 1,
                  with 0 specifying special added tokens and 1 specifying sequence tokens.
                  Included when `return_special_tokens_mask` is `True`.
        
Performs tokenization and uses the tokenized tokens to prepare model
inputs. It supports sequence or sequence pair as input, and batch input
is not allowed.
Performs tokenization and uses the tokenized tokens to prepare model inputs . It supports sequence or sequence pair as input and batch input is not allowed .
def encode(self,
               text,
               text_pair=None,
               max_seq_len=512,
               pad_to_max_seq_len=False,
               truncation_strategy="longest_first",
               return_position_ids=False,
               return_token_type_ids=True,
               return_attention_mask=False,
               return_length=False,
               return_overflowing_tokens=False,
               return_special_tokens_mask=False):
        def get_input_ids(text):
            if isinstance(text, str):
                tokens = self.tokenize(text)
                return self.convert_tokens_to_ids(tokens)
            elif isinstance(text,
                            (list, tuple)) and len(text) > 0 and isinstance(
                                text[0], str):
                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."
                )
        ids = get_input_ids(text)
        pair_ids = get_input_ids(text_pair) if text_pair is not None else None
        pair = bool(pair_ids is not None)
        len_ids = len(ids)
        len_pair_ids = len(pair_ids) if pair else 0
        encoded_inputs = {}
        total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(
            pair=pair))
        token_offset_mapping = self.get_offset_mapping(text)
        if pair:
            token_pair_offset_mapping = self.get_offset_mapping(text_pair)
        else:
            token_pair_offset_mapping = None
        if max_seq_len and total_len > max_seq_len:
            ids, pair_ids, token_offset_mapping, token_pair_offset_mapping, overflowing_tokens = self.truncate_sequences(
                ids,
                pair_ids=pair_ids,
                token_offset_mapping=token_offset_mapping,
                token_pair_offset_mapping=token_pair_offset_mapping,
                num_tokens_to_remove=total_len - max_seq_len,
                truncation_strategy=truncation_strategy, )
            if return_overflowing_tokens:
                encoded_inputs["overflowing_tokens"] = overflowing_tokens
                encoded_inputs["num_truncated_tokens"] = total_len - max_seq_len
        sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
        token_type_ids = self.create_token_type_ids_from_sequences(ids,
                                                                   pair_ids)
        offset_mapping = self.build_offset_mapping_with_special_tokens(
            token_offset_mapping, token_pair_offset_mapping)
        encoded_inputs["input_ids"] = sequence
        encoded_inputs["pinyin_ids"] = self.get_pinyin_ids(text, text_pair,
                                                           offset_mapping)
        if return_token_type_ids:
            encoded_inputs["token_type_ids"] = token_type_ids
        if return_special_tokens_mask:
            encoded_inputs[
                "special_tokens_mask"] = self.get_special_tokens_mask(ids,
                                                                      pair_ids)
        if return_length:
            encoded_inputs["seq_len"] = len(encoded_inputs["input_ids"])
        assert max_seq_len is None or len(encoded_inputs[
            "input_ids"]) <= max_seq_len
        needs_to_be_padded = pad_to_max_seq_len and \
                             max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len
        if needs_to_be_padded:
            difference = max_seq_len - len(encoded_inputs["input_ids"])
            encoded_inputs["pinyin_ids"] = encoded_inputs[
                "pinyin_ids"] + self.special_tokens_pinyin_ids * difference
            if self.padding_side == 'right':
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
                        "input_ids"]) + [0] * difference
                if return_token_type_ids:
                    encoded_inputs["token_type_ids"] = (
                        encoded_inputs["token_type_ids"] +
                        [self.pad_token_type_id] * difference)
                if return_special_tokens_mask:
                    encoded_inputs["special_tokens_mask"] = encoded_inputs[
                        "special_tokens_mask"] + [1] * difference
                encoded_inputs["input_ids"] = encoded_inputs[
                    "input_ids"] + [self.pad_token_id] * difference
            elif self.padding_side == 'left':
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = [0] * difference + [
                        1
                    ] * len(encoded_inputs["input_ids"])
                if return_token_type_ids:
                    encoded_inputs["token_type_ids"] = (
                        [self.pad_token_type_id] * difference +
                        encoded_inputs["token_type_ids"])
                if return_special_tokens_mask:
                    encoded_inputs["special_tokens_mask"] = [
                        1
                    ] * difference + encoded_inputs["special_tokens_mask"]
                encoded_inputs["input_ids"] = [
                    self.pad_token_id
                ] * difference + encoded_inputs["input_ids"]
        else:
            if return_attention_mask:
                encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
                    "input_ids"])
        if return_position_ids:
            encoded_inputs["position_ids"] = list(
                range(len(encoded_inputs["input_ids"])))
        return encoded_inputs
def encode ( self , text , text_pair = None , max_seq_len = 512 , pad_to_max_seq_len = False , truncation_strategy = "longest_first" , return_position_ids = False , return_token_type_ids = True , return_attention_mask = False , return_length = False , return_overflowing_tokens = False , return_special_tokens_mask = False ) : def get_input_ids ( text ) : if isinstance ( text , str ) : tokens = self . tokenize ( text ) return self . convert_tokens_to_ids ( tokens ) elif isinstance ( text , ( list , tuple ) ) and len ( text ) > 0 and isinstance ( text [ 0 ] , str ) : 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." ) ids = get_input_ids ( text ) pair_ids = get_input_ids ( text_pair ) if text_pair is not None else None pair = bool ( pair_ids is not None ) len_ids = len ( ids ) len_pair_ids = len ( pair_ids ) if pair else 0 encoded_inputs = { } total_len = len_ids + len_pair_ids + ( self . num_special_tokens_to_add ( pair = pair ) ) token_offset_mapping = self . get_offset_mapping ( text ) if pair : token_pair_offset_mapping = self . get_offset_mapping ( text_pair ) else : token_pair_offset_mapping = None if max_seq_len and total_len > max_seq_len : ids , pair_ids , token_offset_mapping , token_pair_offset_mapping , overflowing_tokens = self . truncate_sequences ( ids , pair_ids = pair_ids , token_offset_mapping = token_offset_mapping , token_pair_offset_mapping = token_pair_offset_mapping , num_tokens_to_remove = total_len - max_seq_len , truncation_strategy = truncation_strategy , ) if return_overflowing_tokens : encoded_inputs [ "overflowing_tokens" ] = overflowing_tokens encoded_inputs [ "num_truncated_tokens" ] = total_len - max_seq_len sequence = self . build_inputs_with_special_tokens ( ids , pair_ids ) token_type_ids = self . create_token_type_ids_from_sequences ( ids , pair_ids ) offset_mapping = self . build_offset_mapping_with_special_tokens ( token_offset_mapping , token_pair_offset_mapping ) encoded_inputs [ "input_ids" ] = sequence encoded_inputs [ "pinyin_ids" ] = self . get_pinyin_ids ( text , text_pair , offset_mapping ) if return_token_type_ids : encoded_inputs [ "token_type_ids" ] = token_type_ids if return_special_tokens_mask : encoded_inputs [ "special_tokens_mask" ] = self . get_special_tokens_mask ( ids , pair_ids ) if return_length : encoded_inputs [ "seq_len" ] = len ( encoded_inputs [ "input_ids" ] ) assert max_seq_len is None or len ( encoded_inputs [ "input_ids" ] ) <= max_seq_len needs_to_be_padded = pad_to_max_seq_len and max_seq_len and len ( encoded_inputs [ "input_ids" ] ) < max_seq_len if needs_to_be_padded : difference = max_seq_len - len ( encoded_inputs [ "input_ids" ] ) encoded_inputs [ "pinyin_ids" ] = encoded_inputs [ "pinyin_ids" ] + self . special_tokens_pinyin_ids * difference if self . padding_side == 'right' : if return_attention_mask : encoded_inputs [ "attention_mask" ] = [ 1 ] * len ( encoded_inputs [ "input_ids" ] ) + [ 0 ] * difference if return_token_type_ids : encoded_inputs [ "token_type_ids" ] = ( encoded_inputs [ "token_type_ids" ] + [ self . pad_token_type_id ] * difference ) if return_special_tokens_mask : encoded_inputs [ "special_tokens_mask" ] = encoded_inputs [ "special_tokens_mask" ] + [ 1 ] * difference encoded_inputs [ "input_ids" ] = encoded_inputs [ "input_ids" ] + [ self . pad_token_id ] * difference elif self . padding_side == 'left' : if return_attention_mask : encoded_inputs [ "attention_mask" ] = [ 0 ] * difference + [ 1 ] * len ( encoded_inputs [ "input_ids" ] ) if return_token_type_ids : encoded_inputs [ "token_type_ids" ] = ( [ self . pad_token_type_id ] * difference + encoded_inputs [ "token_type_ids" ] ) if return_special_tokens_mask : encoded_inputs [ "special_tokens_mask" ] = [ 1 ] * difference + encoded_inputs [ "special_tokens_mask" ] encoded_inputs [ "input_ids" ] = [ self . pad_token_id ] * difference + encoded_inputs [ "input_ids" ] else : if return_attention_mask : encoded_inputs [ "attention_mask" ] = [ 1 ] * len ( encoded_inputs [ "input_ids" ] ) if return_position_ids : encoded_inputs [ "position_ids" ] = list ( range ( len ( encoded_inputs [ "input_ids" ] ) ) ) return encoded_inputs
Performs tokenization and uses the tokenized tokens to prepare model
inputs.
Performs tokenization and uses the tokenized tokens to prepare model inputs .
"""
        Performs tokenization and uses the tokenized tokens to prepare model
        inputs. It supports sequence or sequence pair as input, and batch input
        is not allowed.

        Args:
            text (str, List[str] or List[int]):
                The sequence to be processed. One sequence is a string, a list
                of strings, or a list of integers depending on whether it has
                been pretokenized and converted to ids. 
            text_pair (str, List[str] or List[List[str]]):
                Same as `text` argument, while it represents for the latter
                sequence of the sequence pair.
            max_seq_len (int, optional):
                If set to a number, will limit the total sequence returned so
                that it has a maximum length. If there are overflowing tokens,
                those overflowing tokens will be added to the returned dictionary
                when `return_overflowing_tokens` is `True`. Defaults to `None`.
            stride (int, optional):
                Only available for batch input of sequence pair and mainly for
                question answering usage. When for QA, `text` represents questions
                and `text_pair` represents contexts. If `stride` is set to a
                positive number, the context will be split into multiple spans
                where `stride` defines the number of (tokenized) tokens to skip
                from the start of one span to get the next span, thus will produce
                a bigger batch than inputs to include all spans. Moreover, 'overflow_to_sample'
                and 'offset_mapping' preserving the original example and position
                information will be added to the returned dictionary. Defaults to 0.
            pad_to_max_seq_len (bool, optional):
                If set to `True`, the returned sequences would be padded up to
                `max_seq_len` specified length according to padding side
                (`self.padding_side`) and padding token id. Defaults to `False`.
            truncation_strategy (str, optional):
                String selected in the following options:

                - 'longest_first' (default) Iteratively reduce the inputs sequence
                until the input is under `max_seq_len` starting from the longest
                one at each token (when there is a pair of input sequences).
                - 'only_first': Only truncate the first sequence.
                - 'only_second': Only truncate the second sequence.
                - 'do_not_truncate': Do not truncate (raise an error if the input
                sequence is longer than `max_seq_len`).

                Defaults to 'longest_first'.
            return_position_ids (bool, optional):
                Whether to include tokens position ids in the returned dictionary.
                Defaults to `False`.
            return_token_type_ids (bool, optional):
                Whether to include token type ids in the returned dictionary.
                Defaults to `True`.
            return_attention_mask (bool, optional):
                Whether to include the attention mask in the returned dictionary.
                Defaults to `False`.
            return_length (bool, optional):
                Whether to include the length of each encoded inputs in the
                returned dictionary. Defaults to `False`.
            return_overflowing_tokens (bool, optional):
                Whether to include overflowing token information in the returned
                dictionary. Defaults to `False`.
            return_special_tokens_mask (bool, optional):
                Whether to include special tokens mask information in the returned
                dictionary. Defaults to `False`.

        Returns:
            dict:
                The dict has the following optional items:

                - **input_ids** (list[int]): List of token ids to be fed to a model.
                - **pinyin_ids** (list[int]): List of pinyin ids to be fed to a model.
                - **position_ids** (list[int], optional): List of token position ids to be
                  fed to a model. Included when `return_position_ids` is `True`
                - **token_type_ids** (list[int], optional): List of token type ids to be
                  fed to a model. Included when `return_token_type_ids` is `True`.
                - **attention_mask** (list[int], optional): List of integers valued 0 or 1,
                  where 0 specifies paddings and should not be attended to by the
                  model. Included when `return_attention_mask` is `True`.
                - **seq_len** (int, optional): The input_ids length. Included when `return_length`
                  is `True`.
                - **overflowing_tokens** (list[int], optional): List of overflowing tokens.
                  Included when if `max_seq_len` is specified and `return_overflowing_tokens`
                  is True.
                - **num_truncated_tokens** (int, optional): The number of overflowing tokens.
                  Included when if `max_seq_len` is specified and `return_overflowing_tokens`
                  is True.
                - **special_tokens_mask** (list[int], optional): List of integers valued 0 or 1,
                  with 0 specifying special added tokens and 1 specifying sequence tokens.
                  Included when `return_special_tokens_mask` is `True`.
        """ # Truncation: Handle max sequence length
 # Add special tokens
 # Build output dictionnary
 # Check lengths
 # Padding

The dict has the following optional items.

input_ids** (list[int]): List of token ids to be fed to a model.
pinyin_ids** (list[int]): List of pinyin ids to be fed to a model.
position_ids** (list[int], optional): List of token position ids to be
fed to a model. Included when `return_position_ids` is `True`
token_type_ids** (list[int], optional): List of token type ids to be
fed to a model. Included when `return_token_type_ids` is `True`.
attention_mask** (list[int], optional): List of integers valued 0 or 1,
where 0 specifies paddings and should not be attended to by the
model. Included when `return_attention_mask` is `True`.
seq_len** (int, optional): The input_ids length. Included when `return_length`
is `True`.
overflowing_tokens** (list[int], optional): List of overflowing tokens.
Included when if `max_seq_len` is specified and `return_overflowing_tokens`
is True.
num_truncated_tokens** (int, optional): The number of overflowing tokens.
Included when if `max_seq_len` is specified and `return_overflowing_tokens`
is True.
special_tokens_mask** (list[int], optional): List of integers valued 0 or 1,
with 0 specifying special added tokens and 1 specifying sequence tokens.
Included when `return_special_tokens_mask` is `True`.
The dict has the following optional items . input_ids ** ( list [ int ] ) : List of token ids to be fed to a model . pinyin_ids ** ( list [ int ] ) : List of pinyin ids to be fed to a model . position_ids ** ( list [ int ] optional ) : List of token position ids to be fed to a model . Included when ` return_position_ids ` is ` True ` token_type_ids ** ( list [ int ] optional ) : List of token type ids to be fed to a model . Included when ` return_token_type_ids ` is ` True ` . attention_mask ** ( list [ int ] optional ) : List of integers valued 0 or 1 where 0 specifies paddings and should not be attended to by the model . Included when ` return_attention_mask ` is ` True ` . seq_len ** ( int optional ) : The input_ids length . Included when ` return_length ` is ` True ` . overflowing_tokens ** ( list [ int ] optional ) : List of overflowing tokens . Included when if ` max_seq_len ` is specified and ` return_overflowing_tokens ` is True . num_truncated_tokens ** ( int optional ) : The number of overflowing tokens . Included when if ` max_seq_len ` is specified and ` return_overflowing_tokens ` is True . special_tokens_mask ** ( list [ int ] optional ) : List of integers valued 0 or 1 with 0 specifying special added tokens and 1 specifying sequence tokens . Included when ` return_special_tokens_mask ` is ` True ` .
dict
self
text
The sequence to be processed. One sequence is a string, a list
of strings, or a list of integers depending on whether it has
been pretokenized and converted to ids.
The sequence to be processed . One sequence is a string a list of strings or a list of integers depending on whether it has been pretokenized and converted to ids .
text_pair
Same as `text` argument, while it represents for the latter
sequence of the sequence pair.
Same as ` text ` argument while it represents for the latter sequence of the sequence pair .
max_seq_len
If set to a number, will limit the total sequence returned so
that it has a maximum length. If there are overflowing tokens,
those overflowing tokens will be added to the returned dictionary
when `return_overflowing_tokens` is `True`. Defaults to `None`.
If set to a number will limit the total sequence returned so that it has a maximum length . If there are overflowing tokens those overflowing tokens will be added to the returned dictionary when ` return_overflowing_tokens ` is ` True ` . Defaults to ` None ` .
pad_to_max_seq_len
If set to `True`, the returned sequences would be padded up to
`max_seq_len` specified length according to padding side
(`self.padding_side`) and padding token id. Defaults to `False`.
If set to ` True ` the returned sequences would be padded up to ` max_seq_len ` specified length according to padding side ( ` self . padding_side ` ) and padding token id . Defaults to ` False ` .
truncation_strategy
String selected in the following options.

'longest_first' (default) Iteratively reduce the inputs sequence
until the input is under `max_seq_len` starting from the longest
one at each token (when there is a pair of input sequences).
'only_first': Only truncate the first sequence.
'only_second': Only truncate the second sequence.
'do_not_truncate': Do not truncate (raise an error if the input
sequence is longer than `max_seq_len`).

Defaults to 'longest_first'.
String selected in the following options . ' longest_first ' ( default ) Iteratively reduce the inputs sequence until the input is under ` max_seq_len ` starting from the longest one at each token ( when there is a pair of input sequences ) . ' only_first ' : Only truncate the first sequence . ' only_second ' : Only truncate the second sequence . ' do_not_truncate ' : Do not truncate ( raise an error if the input sequence is longer than ` max_seq_len ` ) . Defaults to ' longest_first ' .
return_position_ids
Whether to include tokens position ids in the returned dictionary.
Defaults to `False`.
Whether to include tokens position ids in the returned dictionary . Defaults to ` False ` .
return_token_type_ids
Whether to include token type ids in the returned dictionary.
Defaults to `True`.
Whether to include token type ids in the returned dictionary . Defaults to ` True ` .
return_attention_mask
Whether to include the attention mask in the returned dictionary.
Defaults to `False`.
Whether to include the attention mask in the returned dictionary . Defaults to ` False ` .
return_length
Whether to include the length of each encoded inputs in the
returned dictionary. Defaults to `False`.
Whether to include the length of each encoded inputs in the returned dictionary . Defaults to ` False ` .
return_overflowing_tokens
Whether to include overflowing token information in the returned
dictionary. Defaults to `False`.
Whether to include overflowing token information in the returned dictionary . Defaults to ` False ` .
return_special_tokens_mask
Whether to include special tokens mask information in the returned
dictionary. Defaults to `False`.
Whether to include special tokens mask information in the returned dictionary . Defaults to ` False ` .
stride
Only available for batch input of sequence pair and mainly for
question answering usage. When for QA, `text` represents questions
and `text_pair` represents contexts. If `stride` is set to a
positive number, the context will be split into multiple spans
where `stride` defines the number of (tokenized) tokens to skip
from the start of one span to get the next span, thus will produce
a bigger batch than inputs to include all spans. Moreover, 'overflow_to_sample'
and 'offset_mapping' preserving the original example and position
information will be added to the returned dictionary. Defaults to 0.
Only available for batch input of sequence pair and mainly for question answering usage . When for QA ` text ` represents questions and ` text_pair ` represents contexts . If ` stride ` is set to a positive number the context will be split into multiple spans where ` stride ` defines the number of ( tokenized ) tokens to skip from the start of one span to get the next span thus will produce a bigger batch than inputs to include all spans . Moreover ' overflow_to_sample ' and ' offset_mapping ' preserving the original example and position information will be added to the returned dictionary . Defaults to 0 .