"""
# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""

from abc import ABC, abstractmethod

import numpy as np
from paddleformers.generation import GenerationConfig
from paddleformers.transformers import Llama3Tokenizer, LlamaTokenizer

from fastdeploy import envs
from fastdeploy.utils import data_processor_logger

_SAMPLING_EPS = 1e-5


class BaseDataProcessor(ABC):
    """base class for data processor"""

    def __init__(self):
        """
        Returns:
            None
        """
        self.tokenizer = self._load_tokenizer()
        self.tokenizer.bos_token_id = self.tokenizer._convert_token_to_id(self.tokenizer.bos_token)
        self.tokenizer.cls_token_id = self.tokenizer._convert_token_to_id(self.tokenizer.cls_token)
        self.tokenizer.sep_token_id = self.tokenizer._convert_token_to_id(self.tokenizer.sep_token)
        self.tokenizer.eos_token_id = self.tokenizer._convert_token_to_id(self.tokenizer.eos_token)
        self.tokenizer.mask_token_id = self.tokenizer._convert_token_to_id(self.tokenizer.mask_token)
        data_processor_logger.info(
            (
                f"tokenizer information: bos_token is {self.tokenizer.bos_token}, {self.tokenizer.bos_token_id}, ",
                f"cls_token is {self.tokenizer.cls_token}, {self.tokenizer.cls_token_id}, "
                f"sep_token is {self.tokenizer.sep_token}, {self.tokenizer.sep_token_id}, "
                f"eos_token is {self.tokenizer.eos_token}, {self.tokenizer.eos_token_id}, "
                f"mask_token is {self.tokenizer.mask_token}, {self.tokenizer.mask_token_id}",
            )
        )

    def _apply_default_parameters(self, request):
        """
        Apply default value for parameters in request
        """

        def set_value(req, key, value):
            value = getattr(self.generation_config, key, value)
            if isinstance(req, dict):
                if key not in req:
                    req[key] = value
            else:
                if req.get(key) is None:
                    req.set(key, value)

        set_value(request, "top_p", 0.7)
        set_value(request, "temperature", 1.0)
        set_value(request, "repetition_penalty", 1.0)
        set_value(request, "frequency_penalty", 0.0)
        set_value(request, "presence_penalty", 0.0)
        return request

    @abstractmethod
    def process_request(self, request, **kwargs):
        """
        Preprocess the request

        Args:
            request (Dict): may contain text and messages fields
            **kwargs: others

        Returns:
            bool: Whether preprocessing is successful
            str: error message
        """
        raise NotImplementedError

    @abstractmethod
    def process_response(self, response_dict):
        """
        Preprocess the response

        Args:
            response_dict (Dict): response for engine, contain ids fields

        Returns:
            Dict: response contain text fields
        """
        raise NotImplementedError

    def text2ids(self, text, max_model_len=None):
        """
        text to token ids

        Args:
            text (str): text

        Returns:
            List[int]: token ids list
        """
        raise NotImplementedError

    def messages2ids(self, messages):
        """
        Convert multi-turn messages into ID sequences.

        Args:
            messages (List[List[Dict[str, Any]]]): multi-turn messages.

        Returns:
            List[int]: ID sequences
        """
        raise NotImplementedError

    def ids2tokens(self, token_id, task_id=None):
        """
        token ids to strings

        Args:
            token_id (List[int]): token id
                        task_id (str): task id

        Returns:
            List[str]: strings
        """
        raise NotImplementedError

    @abstractmethod
    def _load_tokenizer(self):
        """
        load tokenizer

        Returns:
            tokenizer (AutoTokenizer)
        """
        raise NotImplementedError


class DataProcessor(BaseDataProcessor):
    def __init__(self, model_name_or_path, reasoning_parser_obj=None, tool_parser_obj=None):
        """
            Initializes the DecodeStatus object.

        Args:
            model_name_or_path (str): The name or path of the pre-trained model to be loaded.
                Can also be a path to a directory containing the pre-trained model file.

        Returns:
            None.

        Raises:
            None.
        """

        self.model_name_or_path = model_name_or_path

        # Generation config
        try:
            self.generation_config = GenerationConfig.from_pretrained(self.model_name_or_path)
        except Exception as e:
            data_processor_logger.warning(
                f"Can't find generation config: {e}, so it will not use generation_config field in the model config"
            )
            self.generation_config = None

        self.decode_status = dict()
        self.tool_parser_dict = dict()
        self.tokenizer = self._load_tokenizer()
        data_processor_logger.info(
            f"tokenizer information: bos_token is {self.tokenizer.bos_token}, {self.tokenizer.bos_token_id}, \
                                eos_token is {self.tokenizer.eos_token}, {self.tokenizer.eos_token_id} "
        )

        from paddleformers.trl.llm_utils import get_eos_token_id

        self.eos_token_ids = get_eos_token_id(self.tokenizer, self.generation_config)
        self.eos_token_id_len = len(self.eos_token_ids)
        self.pad_token_id = self.get_pad_id()
        self.reasoning_parser = None
        self.tool_parser_obj = tool_parser_obj
        if reasoning_parser_obj:
            self.reasoning_parser = reasoning_parser_obj(self.tokenizer)
        self.tokenizer.pad_token_id = self.pad_token_id

    def process_request(self, request, max_model_len=None, **kwargs):
        """
        Preprocess the request

        Args:
            request (Dict): may contain text and messages fields

        Returns:
            bool: Whether preprocessing is successful
            str: error message
        """
        data_processor_logger.info(f"Start processing request: {request}")
        request.chat_template = kwargs.get("chat_template")
        request = self._apply_default_parameters(request)
        if request.get("eos_token_ids") is None or len(request.eos_token_ids) == 0:
            request.eos_token_ids = self.eos_token_ids

        # processing stop_sequences
        stop_sequences = request.get("stop", [])
        if stop_sequences is not None and len(stop_sequences) != 0:
            stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences)
            request.set("stop_token_ids", stop_seqs)
            request.set("stop_seqs_len", stop_seqs_len)

        # processing bad_words
        bad_words = request.get("bad_words")
        bad_words_token_ids = request.get("bad_words_token_ids")
        if bad_words:
            bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
            request["bad_words_token_ids"] = bad_words_token_ids

        # processing prompt_token_ids
        if request.prompt_token_ids is None or len(request.prompt_token_ids) == 0:
            if request.prompt is not None:
                prompt = request.prompt
                assert isinstance(prompt, str) or (
                    isinstance(prompt, list) and all([isinstance(t, int) for t in prompt])
                ), f"prompt must be a string or a list of integers, but got {type(prompt)}"
                if isinstance(prompt, list):  # if prompt is a token id list
                    request.prompt_token_ids = prompt
                else:
                    request.prompt_token_ids = self.text2ids(request.prompt, max_model_len)
            elif request.messages is not None:
                if self.tokenizer.chat_template is None:
                    raise ValueError("This model does not support chat_template.")
                task = request.to_dict()
                chat_template_kwargs = kwargs.get("chat_template_kwargs")
                if chat_template_kwargs:
                    if isinstance(chat_template_kwargs, dict):
                        for k, v in chat_template_kwargs.items():
                            if k not in task:
                                task[k] = v
                    else:
                        raise ValueError("Invalid input: chat_template_kwargs must be a dict")
                task.setdefault("enable_thinking", True)
                request.prompt_token_ids = self.messages2ids(task)
            else:
                raise ValueError(f"The request should have `input_ids`, `text` or `messages`: {request}.")

        if len(request.prompt_token_ids) == 0:
            raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")

        # truncate prompts that exceed the length limit
        if max_model_len is not None and len(request.prompt_token_ids) > max_model_len:
            request.prompt_token_ids = request.prompt_token_ids[: max_model_len - 1]
        if request.get("max_tokens") is None:
            request.set("max_tokens", max(1, max_model_len - len(request.prompt_token_ids)))
        if request.get("temperature") < _SAMPLING_EPS:
            # zero temperature is equivalent to greedy sampling
            request.set("temperature", 1)
        if request.get("top_p") < _SAMPLING_EPS:
            request.set("top_p", _SAMPLING_EPS)

        data_processor_logger.info(f"Processed request: {request}")
        return request

    def process_request_dict(self, request, max_model_len=None, **kwargs):
        """
        Preprocess the request

        Args:
            request (Dict): may contain text and messages fields

        Returns:
            bool: Whether preprocessing is successful
            str: error message
        """
        data_processor_logger.info(f"Start processing request dict: {request}")
        request = self._apply_default_parameters(request)
        if not request.get("eos_token_ids"):
            request["eos_token_ids"] = self.eos_token_ids

        # processing stop_sequences
        stop_sequences = request.get("stop", [])
        if stop_sequences:
            stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences)
            request["stop_token_ids"] = stop_seqs
            request["stop_seqs_len"] = stop_seqs_len

        # processing bad_words
        bad_words = request.get("bad_words")
        bad_words_token_ids = request.get("bad_words_token_ids")
        if bad_words:
            bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
            request["bad_words_token_ids"] = bad_words_token_ids

        # processing prompt_token_ids
        if not request.get("prompt_token_ids"):
            if request.get("prompt"):
                prompt = request.get("prompt")
                assert isinstance(prompt, str) or (
                    isinstance(prompt, list) and all([isinstance(t, int) for t in prompt])
                ), f"prompt must be a string or a list of integers, but got {type(prompt)}"
                if isinstance(prompt, list):  # if prompt is a token id list
                    request["prompt_token_ids"] = prompt
                else:
                    request["prompt_token_ids"] = self.text2ids(request["prompt"], max_model_len).tolist()
            elif request.get("messages"):
                if self.tokenizer.chat_template is None:
                    raise ValueError("This model does not support chat_template.")
                chat_template_kwargs = request.get("chat_template_kwargs")
                if chat_template_kwargs:
                    if isinstance(chat_template_kwargs, dict):
                        for k, v in chat_template_kwargs.items():
                            if k not in request:
                                request[k] = v
                    else:
                        raise ValueError("Invalid input: chat_template_kwargs must be a dict")
                request.setdefault("enable_thinking", True)
                request["prompt_token_ids"] = self.messages2ids(request)
            else:
                raise ValueError(f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}")

        if len(request["prompt_token_ids"]) == 0:
            raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")

        # truncate prompts that exceed the length limit
        if max_model_len is not None and len(request["prompt_token_ids"]) > max_model_len:
            request["prompt_token_ids"] = request["prompt_token_ids"][: max_model_len - 1]
        if request.get("max_tokens") is None:
            request["max_tokens"] = max(1, max_model_len - len(request["prompt_token_ids"]))
        if request.get("temperature") < _SAMPLING_EPS:
            # zero temperature is equivalent to greedy sampling
            request["temperature"] = 1
        if request.get("top_p") < _SAMPLING_EPS:
            request["top_p"] = _SAMPLING_EPS

        data_processor_logger.info(f"Processed request dict: {request}")
        return request

    def process_logprob_response(self, token_ids, **kwargs):
        full_text = self.tokenizer.decode(token_ids, **kwargs)
        return full_text

    def process_response(self, response_dict, **kwargs):
        """
        Preprocess the response

        Args:
            response_dict (Dict): response for engine, contain ids fields

        Returns:
            Dict: response contain text fields
        """
        req_id = response_dict.request_id
        token_ids = response_dict.outputs.token_ids
        if token_ids[-1] == self.tokenizer.eos_token_id:
            token_ids = token_ids[:-1]
        full_text = self.tokenizer.decode(token_ids)

        # 模型支持思考,并且支持思考
        if self.reasoning_parser:
            reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
            response_dict.outputs.text = text
            response_dict.outputs.reasoning_content = reasoning_content
        else:
            # 模型不支持思考,并且没单独设置enable_thinking为false
            response_dict.outputs.text = full_text
        if self.tool_parser_obj:
            tool_parser = self.tool_parser_obj(self.tokenizer)
            tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
            if tool_call_info.tools_called:
                response_dict.outputs.tool_calls = tool_call_info.tool_calls
                response_dict.outputs.text = tool_call_info.content
        data_processor_logger.info(f"req_id:{req_id}, token)ids: {token_ids}")

        return response_dict

    def process_response_dict_normal(self, response_dict, **kwargs):
        """
        Preprocess the response

        Args:
            response_dict (Dict): response for engine, contain ids fields

        Returns:
            Dict: response contain text fields
        """
        enable_thinking = kwargs.get("enable_thinking")
        token_ids = response_dict["outputs"]["token_ids"]
        is_end = response_dict["finished"]
        req_id = response_dict["request_id"]
        if is_end and len(token_ids) > 0 and not kwargs.get("include_stop_str_in_output"):
            if token_ids[-1] == self.tokenizer.eos_token_id:
                token_ids = token_ids[:-1]
        delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
        if is_end:
            full_text = previous_texts + delta_text
            response_dict["outputs"]["raw_prediction"] = full_text
            if enable_thinking and self.reasoning_parser:
                reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
                response_dict["outputs"]["text"] = text
                response_dict["outputs"]["reasoning_content"] = reasoning_content
            else:
                response_dict["outputs"]["text"] = full_text
            if self.tool_parser_obj:
                tool_parser = self.tool_parser_obj(self.tokenizer)
                tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
                if tool_call_info.tools_called:
                    response_dict["outputs"]["tool_call"] = tool_call_info.tool_calls
                    response_dict["outputs"]["text"] = tool_call_info.content
            data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
            del self.decode_status[req_id]
        return response_dict

    def process_response_dict_streaming(self, response_dict, **kwargs):
        """
        Preprocess the response

        Args:
            response_dict (Dict): response for engine, contain ids fields

        Returns:
            Dict: response contain text fields
        """
        enable_thinking = kwargs.get("enable_thinking")
        is_end = response_dict["finished"]
        req_id = response_dict["request_id"]
        token_ids = response_dict["outputs"]["token_ids"]

        if is_end and len(token_ids) > 0 and not kwargs.get("include_stop_str_in_output"):
            if token_ids[-1] == self.tokenizer.eos_token_id:
                token_ids = token_ids[:-1]
        delta_text, previous_token_ids, previous_texts = self.ids2tokens(token_ids, req_id)
        response_dict["outputs"]["raw_prediction"] = delta_text
        if self.reasoning_parser and (
            enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
        ):
            reasoning_delta_message = self.reasoning_parser.extract_reasoning_content_streaming(
                previous_texts,
                previous_texts + delta_text,
                delta_text,
                previous_token_ids,
                previous_token_ids + token_ids,
                token_ids,
            )
            response_dict["outputs"]["delta_message"] = reasoning_delta_message
        if self.tool_parser_obj:
            if req_id not in self.tool_parser_dict:
                self.tool_parser_dict[req_id] = self.tool_parser_obj(self.tokenizer)
            tool_parser = self.tool_parser_dict[req_id]
            tool_call = tool_parser.extract_tool_calls_streaming(
                previous_texts,
                previous_texts + delta_text,
                delta_text,
                previous_token_ids,
                previous_token_ids + token_ids,
                token_ids,
                response_dict,
            )
            if tool_call is None or tool_call.tool_calls:
                response_dict["outputs"]["delta_message"] = tool_call
        response_dict["outputs"]["text"] = delta_text
        if is_end:
            data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
            del self.decode_status[req_id]
            if req_id in self.tool_parser_dict:
                del self.tool_parser_dict[req_id]
        return response_dict

    def process_response_dict(self, response_dict, **kwargs):
        """
        Preprocess the response

        Args:
            response_dict (Dict): response for engine, contain ids fields

        Returns:
            Dict: response contain text fields
        """
        enable_thinking = kwargs.pop("enable_thinking", True)
        if enable_thinking is None:
            enable_thinking = True
        stream = kwargs.get("stream", True)
        if stream:
            return self.process_response_dict_streaming(response_dict, enable_thinking=enable_thinking, **kwargs)
        else:
            return self.process_response_dict_normal(
                response_dict=response_dict,
                enable_thinking=enable_thinking,
                **kwargs,
            )

    def text2ids(self, text, max_model_len):
        """
        text to token ids

        Args:
            text (str): text

        Returns:
            List[int]: token ids list
        """
        if envs.FD_USE_HF_TOKENIZER:
            tokens = self.tokenizer(
                text,
                return_tensors="np",
                padding=True,
                truncation=True,
            )
        else:
            text = [text] if isinstance(text, str) else text

            tokens = self.tokenizer(
                text,
                return_tensors="np",
                padding=True,
                truncation=True,
                max_length=max_model_len,
                add_special_tokens=False,
            )

        return tokens["input_ids"][0]

    def messages2ids(self, request):
        """
        Convert multi-turn messages into ID sequences.

        Args:
            messages (List[List[Dict[str, Any]]]): multi-turn messages.

        Returns:
            List[int]: ID sequences
        """

        spliced_message = self.tokenizer.apply_chat_template(
            request,
            tokenize=False,
            split_special_tokens=False,
            add_special_tokens=False,
            return_tensors="pd",
            chat_template=request.get("chat_template", None),
        )
        request["text_after_process"] = spliced_message
        req_id = None
        tokens = self.tokenizer.tokenize(spliced_message)
        if isinstance(request, dict):
            req_id = request.get("request_id", None)
        token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
        data_processor_logger.info(f"req_id:{req_id}, tokens:{tokens}, token_ids: {token_ids}")
        return token_ids

    def ids2tokens(self, token_id, task_id):
        """
        token ids to strings

        Args:
            token_ids (List[int]): token ids
                        task_id (str): task id

        Returns:
            List[str]: strings
        """
        if envs.FD_USE_HF_TOKENIZER:
            if task_id not in self.decode_status:
                # history token ids & history token strings & befer decode str
                self.decode_status[task_id] = [[], [], ""]

            previous_token_ids = self.decode_status[task_id][0]
            decode_str = self.tokenizer.batch_decode(
                [previous_token_ids + token_id],
                skip_special_tokens=True,
                clean_up_tokenization_spaces=False,
            )
            if isinstance(decode_str, list) and len(decode_str):
                new_str = decode_str[0].replace(self.decode_status[task_id][2], "", 1)
                self.decode_status[task_id][1].append(new_str)
                self.decode_status[task_id][2] = decode_str[0]
            else:
                new_str = ""
            self.decode_status[task_id][0] += token_id
            return new_str
        else:
            if task_id not in self.decode_status:
                # prefix offset & read offset & history token ids & history token strings
                self.decode_status[task_id] = [0, 0, [], ""]

            prefix_offset = self.decode_status[task_id][0]
            read_offset = self.decode_status[task_id][1]
            previous_token_ids = self.decode_status[task_id][2]
            previous_texts = self.decode_status[task_id][3]
            decode_str, prefix_offset, read_offset = self.tokenizer.decode_token(
                previous_token_ids + token_id, prefix_offset, read_offset
            )
            self.decode_status[task_id][0] = prefix_offset
            self.decode_status[task_id][1] = read_offset
            self.decode_status[task_id][2] += token_id
            self.decode_status[task_id][3] += decode_str

            return decode_str, previous_token_ids, previous_texts

    def _load_tokenizer(self):
        """
        load tokenizer

        Returns:
            tokenizer (AutoTokenizer)
        """
        if envs.FD_USE_HF_TOKENIZER:
            from transformers import AutoTokenizer

            return AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=False)
        else:
            from paddleformers.transformers import AutoTokenizer

            return AutoTokenizer.from_pretrained(self.model_name_or_path, padding_side="left", use_fast=True)

    def clear_request_status(self, task_id):
        """
        clear request status

        Args:
            task_id (str): task id

        Returns:
            results_all (str): all token strings
        """
        results_all = ""
        if task_id in self.decode_status:
            if envs.FD_USE_HF_TOKENIZER:
                results_all = self.decode_status[task_id][2]
            else:
                results_all = "".join(self.decode_status[task_id][3])
            del self.decode_status[task_id]
        return results_all

    def get_pad_id(self):
        """
        get pad_token_id, if not pad_token_id, use eos_token

        Returns:
            int: pad_token_id
        """
        if isinstance(self.tokenizer, (LlamaTokenizer, Llama3Tokenizer)) and not self.tokenizer.pad_token_id:
            return self.tokenizer.eos_token
        return self.tokenizer.pad_token_id

    def pad_batch_data(
        self,
        insts,
        pad_id=0,
        return_seq_len=False,
        return_array=True,
        pad_style="right",
    ):
        """Pad the instances to the max sequence length in batch."""
        if len(insts) == 0:
            padded_insts = np.array([[]], dtype=np.int64) if return_array else [[]]
            if return_seq_len:
                seq_len = np.array([], dtype=np.int64) if return_array else []
                return padded_insts, seq_len
            return padded_insts

        max_len = max(map(len, insts))
        if pad_style == "left":
            padded_insts = [[pad_id] * (max_len - len(inst)) + list(inst) for inst in insts]
        else:
            padded_insts = [list(inst) + [pad_id] * (max_len - len(inst)) for inst in insts]
        if return_array:
            padded_insts = np.array(padded_insts, dtype=np.int64).reshape([-1, max_len])

        if return_seq_len:
            seq_len = [len(inst) for inst in insts]
            if return_array:
                seq_len = np.array(seq_len, dtype=np.int64).reshape(-1, 1)
            return padded_insts, seq_len
        return padded_insts

    def update_stop_seq(self, stop_sequences):
        """
        Update stop sequences from request.
        """
        stop_seqs = []
        for seq in stop_sequences:
            if seq != self.tokenizer.eos_token_id:
                stop_seqs.append(self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(seq)))
        stop_seqs, stop_seqs_len = self.pad_batch_data(stop_seqs, pad_id=-1, return_seq_len=True, return_array=False)
        data_processor_logger.debug(f"processed stop_seqs: {stop_seqs}, {stop_seqs_len}")
        return stop_seqs, stop_seqs_len

    def update_bad_words(self, bad_words, bad_words_token_ids):
        """Support bad words"""

        token_ids = bad_words_token_ids

        if token_ids is None:
            token_ids = []
        for bad_word in bad_words:
            # To prohibit words both at the beginning
            # and in the middle of text
            # (related to add_prefix_space tokenizer parameter)
            for add_prefix_space in [False, True]:
                prefix = " " if add_prefix_space else ""
                prompt = prefix + bad_word.lstrip()
                prompt_token_ids = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(prompt))

                if len(prompt_token_ids) != 1:
                    if not add_prefix_space:
                        data_processor_logger.warning(
                            f"Skip bad_words: <{prompt}>."
                            f"Bad words should be a single token."
                            f"Got tokens: {prompt_token_ids}."
                        )
                    continue

                if prompt_token_ids[0] > self.tokenizer.vocab_size:
                    if not add_prefix_space:
                        data_processor_logger.warning(
                            f"Skip bad_words: <{prompt}>."
                            f"All token id values should be satisfying:"
                            f" 0 <= token_id < {self.tokenizer.vocab_size}."
                            f"Got token: {prompt_token_ids}."
                        )
                    continue

                if prompt_token_ids not in token_ids:
                    token_ids.extend(prompt_token_ids)
        return token_ids