# Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import re
from copy import deepcopy
from dataclasses import dataclass
from typing import TYPE_CHECKING, Optional, Union

from typing_extensions import override

from ..extras import logging
from .data_utils import Role
from .formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter
from .mm_plugin import get_mm_plugin


if TYPE_CHECKING:
    from transformers import PreTrainedTokenizer

    from ..hparams import DataArguments
    from .formatter import SLOTS, Formatter
    from .mm_plugin import BasePlugin
    from .tool_utils import FunctionCall


logger = logging.get_logger(__name__)


@dataclass
class Template:
    format_user: "Formatter"
    format_assistant: "Formatter"
    format_system: "Formatter"
    format_function: "Formatter"
    format_observation: "Formatter"
    format_tools: "Formatter"
    format_prefix: "Formatter"
    default_system: str
    stop_words: list[str]
    thought_words: tuple[str, str]
    efficient_eos: bool
    replace_eos: bool
    replace_jinja_template: bool
    enable_thinking: Optional[bool]
    mm_plugin: "BasePlugin"

    def encode_oneturn(
        self,
        tokenizer: "PreTrainedTokenizer",
        messages: list[dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
    ) -> tuple[list[int], list[int]]:
        r"""Return a single pair of token ids representing prompt and response respectively."""
        encoded_messages = self._encode(tokenizer, messages, system, tools)
        prompt_ids = []
        for encoded_ids in encoded_messages[:-1]:
            prompt_ids += encoded_ids

        response_ids = encoded_messages[-1]
        return prompt_ids, response_ids

    def encode_multiturn(
        self,
        tokenizer: "PreTrainedTokenizer",
        messages: list[dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
    ) -> list[tuple[list[int], list[int]]]:
        r"""Return multiple pairs of token ids representing prompts and responses respectively."""
        encoded_messages = self._encode(tokenizer, messages, system, tools)
        return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]

    def extract_tool(self, content: str) -> Union[str, list["FunctionCall"]]:
        r"""Extract tool message."""
        return self.format_tools.extract(content)

    def get_stop_token_ids(self, tokenizer: "PreTrainedTokenizer") -> list[int]:
        r"""Return stop token ids."""
        stop_token_ids = {tokenizer.eos_token_id}
        for token in self.stop_words:
            stop_token_ids.add(tokenizer.convert_tokens_to_ids(token))

        return list(stop_token_ids)

    def add_thought(self, content: str = "") -> str:
        r"""Add empty thought to assistant message."""
        return f"{self.thought_words[0]}\n\n{self.thought_words[1]}\n\n" + content

    def remove_thought(self, content: str) -> str:
        r"""Remove thought from assistant message."""
        pattern = re.compile(f"{re.escape(self.thought_words[0])}(.*?){re.escape(self.thought_words[1])}", re.DOTALL)
        return re.sub(pattern, "", content).lstrip("\n")

    def get_thought_word_ids(self, tokenizer: "PreTrainedTokenizer") -> list[int]:
        r"""Get the token ids of thought words."""
        return tokenizer.encode(self.add_thought(), add_special_tokens=False)

    def _convert_elements_to_ids(self, tokenizer: "PreTrainedTokenizer", elements: "SLOTS") -> list[int]:
        r"""Convert elements to token ids."""
        token_ids = []
        for elem in elements:
            if isinstance(elem, str):
                if len(elem) != 0:
                    token_ids += tokenizer.encode(elem, add_special_tokens=False)
            elif isinstance(elem, dict):
                token_ids += [tokenizer.convert_tokens_to_ids(elem.get("token"))]
            elif isinstance(elem, set):
                if "bos_token" in elem and tokenizer.bos_token_id is not None:
                    token_ids += [tokenizer.bos_token_id]
                elif "eos_token" in elem and tokenizer.eos_token_id is not None:
                    token_ids += [tokenizer.eos_token_id]
            else:
                raise ValueError(f"Input must be string, set[str] or dict[str, str], got {type(elem)}")

        return token_ids

    def _encode(
        self,
        tokenizer: "PreTrainedTokenizer",
        messages: list[dict[str, str]],
        system: Optional[str],
        tools: Optional[str],
    ) -> list[list[int]]:
        r"""Encode formatted inputs to pairs of token ids.

        Turn 0: prefix + system + query        resp
        Turn t: query                          resp.
        """
        system = system or self.default_system
        encoded_messages = []
        for i, message in enumerate(messages):
            elements = []

            if i == 0:
                elements += self.format_prefix.apply()
                if system or tools:
                    tool_text = self.format_tools.apply(content=tools)[0] if tools else ""
                    elements += self.format_system.apply(content=(system + tool_text))

            if message["role"] == Role.USER:
                elements += self.format_user.apply(content=message["content"], idx=str(i // 2))
            elif message["role"] == Role.ASSISTANT:
                elements += self.format_assistant.apply(content=message["content"])
            elif message["role"] == Role.OBSERVATION:
                elements += self.format_observation.apply(content=message["content"])
            elif message["role"] == Role.FUNCTION:
                elements += self.format_function.apply(content=message["content"])
            else:
                raise NotImplementedError("Unexpected role: {}".format(message["role"]))

            encoded_messages.append(self._convert_elements_to_ids(tokenizer, elements))

        return encoded_messages

    @staticmethod
    def _add_or_replace_eos_token(tokenizer: "PreTrainedTokenizer", eos_token: str) -> None:
        r"""Add or replace eos token to the tokenizer."""
        if tokenizer.eos_token == eos_token:
            return

        is_added = tokenizer.eos_token_id is None
        num_added_tokens = tokenizer.add_special_tokens({"eos_token": eos_token})

        if is_added:
            logger.info_rank0(f"Add eos token: {tokenizer.eos_token}.")
        else:
            logger.info_rank0(f"Replace eos token: {tokenizer.eos_token}.")

        if num_added_tokens > 0:
            logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")

    def fix_special_tokens(self, tokenizer: "PreTrainedTokenizer") -> None:
        r"""Add eos token and pad token to the tokenizer."""
        stop_words = self.stop_words
        if self.replace_eos:
            if not stop_words:
                raise ValueError("Stop words are required to replace the EOS token.")

            self._add_or_replace_eos_token(tokenizer, eos_token=stop_words[0])
            stop_words = stop_words[1:]

        if tokenizer.eos_token_id is None:
            self._add_or_replace_eos_token(tokenizer, eos_token="<|endoftext|>")

        if tokenizer.pad_token_id is None:
            tokenizer.pad_token = tokenizer.eos_token
            logger.info_rank0(f"Add pad token: {tokenizer.pad_token}")

        if stop_words:
            num_added_tokens = tokenizer.add_special_tokens(
                dict(additional_special_tokens=stop_words), replace_additional_special_tokens=False
            )
            logger.info_rank0("Add {} to stop words.".format(",".join(stop_words)))
            if num_added_tokens > 0:
                logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")

    @staticmethod
    def _jinja_escape(content: str) -> str:
        r"""Escape single quotes in content."""
        return content.replace("'", r"\'")

    @staticmethod
    def _convert_slots_to_jinja(slots: "SLOTS", tokenizer: "PreTrainedTokenizer", placeholder: str = "content") -> str:
        r"""Convert slots to jinja template."""
        slot_items = []
        for slot in slots:
            if isinstance(slot, str):
                slot_pieces = slot.split("{{content}}")
                if slot_pieces[0]:
                    slot_items.append("'" + Template._jinja_escape(slot_pieces[0]) + "'")
                if len(slot_pieces) > 1:
                    slot_items.append(placeholder)
                    if slot_pieces[1]:
                        slot_items.append("'" + Template._jinja_escape(slot_pieces[1]) + "'")
            elif isinstance(slot, set):  # do not use {{ eos_token }} since it may be replaced
                if "bos_token" in slot and tokenizer.bos_token_id is not None:
                    slot_items.append("'" + tokenizer.bos_token + "'")
                elif "eos_token" in slot and tokenizer.eos_token_id is not None:
                    slot_items.append("'" + tokenizer.eos_token + "'")
            elif isinstance(slot, dict):
                raise ValueError("Dict is not supported.")

        return " + ".join(slot_items)

    def _get_jinja_template(self, tokenizer: "PreTrainedTokenizer") -> str:
        r"""Return the jinja template."""
        prefix = self._convert_slots_to_jinja(self.format_prefix.apply(), tokenizer)
        system = self._convert_slots_to_jinja(self.format_system.apply(), tokenizer, placeholder="system_message")
        user = self._convert_slots_to_jinja(self.format_user.apply(), tokenizer)
        assistant = self._convert_slots_to_jinja(self.format_assistant.apply(), tokenizer)
        jinja_template = ""
        if prefix:
            jinja_template += "{{ " + prefix + " }}"

        if self.default_system:
            jinja_template += "{% set system_message = '" + self._jinja_escape(self.default_system) + "' %}"

        jinja_template += (
            "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}"
            "{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}"
            "{% if system_message is defined %}{{ " + system + " }}{% endif %}"
            "{% for message in loop_messages %}"
            "{% set content = message['content'] %}"
            "{% if message['role'] == 'user' %}"
            "{{ " + user + " }}"
            "{% elif message['role'] == 'assistant' %}"
            "{{ " + assistant + " }}"
            "{% endif %}"
            "{% endfor %}"
        )
        return jinja_template

    def fix_jinja_template(self, tokenizer: "PreTrainedTokenizer") -> None:
        r"""Replace the jinja template in the tokenizer."""
        if tokenizer.chat_template is None or self.replace_jinja_template:
            try:
                tokenizer.chat_template = self._get_jinja_template(tokenizer)
            except ValueError as e:
                logger.info_rank0(f"Cannot add this chat template to tokenizer: {e}.")

    @staticmethod
    def _convert_slots_to_ollama(
        slots: "SLOTS", tokenizer: "PreTrainedTokenizer", placeholder: str = "content"
    ) -> str:
        r"""Convert slots to ollama template."""
        slot_items = []
        for slot in slots:
            if isinstance(slot, str):
                slot_pieces = slot.split("{{content}}")
                if slot_pieces[0]:
                    slot_items.append(slot_pieces[0])
                if len(slot_pieces) > 1:
                    slot_items.append("{{ " + placeholder + " }}")
                    if slot_pieces[1]:
                        slot_items.append(slot_pieces[1])
            elif isinstance(slot, set):  # do not use {{ eos_token }} since it may be replaced
                if "bos_token" in slot and tokenizer.bos_token_id is not None:
                    slot_items.append(tokenizer.bos_token)
                elif "eos_token" in slot and tokenizer.eos_token_id is not None:
                    slot_items.append(tokenizer.eos_token)
            elif isinstance(slot, dict):
                raise ValueError("Dict is not supported.")

        return "".join(slot_items)

    def _get_ollama_template(self, tokenizer: "PreTrainedTokenizer") -> str:
        r"""Return the ollama template."""
        prefix = self._convert_slots_to_ollama(self.format_prefix.apply(), tokenizer)
        system = self._convert_slots_to_ollama(self.format_system.apply(), tokenizer, placeholder=".System")
        user = self._convert_slots_to_ollama(self.format_user.apply(), tokenizer, placeholder=".Content")
        assistant = self._convert_slots_to_ollama(self.format_assistant.apply(), tokenizer, placeholder=".Content")
        return (
            f"{prefix}{{{{ if .System }}}}{system}{{{{ end }}}}"
            f"""{{{{ range .Messages }}}}{{{{ if eq .Role "user" }}}}{user}"""
            f"""{{{{ else if eq .Role "assistant" }}}}{assistant}{{{{ end }}}}{{{{ end }}}}"""
        )

    def get_ollama_modelfile(self, tokenizer: "PreTrainedTokenizer") -> str:
        r"""Return the ollama modelfile.

        TODO: support function calling.
        """
        modelfile = "# ollama modelfile auto-generated by llamafactory\n\n"
        modelfile += f'FROM .\n\nTEMPLATE """{self._get_ollama_template(tokenizer)}"""\n\n'

        if self.default_system:
            modelfile += f'SYSTEM """{self.default_system}"""\n\n'

        for stop_token_id in self.get_stop_token_ids(tokenizer):
            modelfile += f'PARAMETER stop "{tokenizer.convert_ids_to_tokens(stop_token_id)}"\n'

        modelfile += "PARAMETER num_ctx 4096\n"
        return modelfile


@dataclass
class Llama2Template(Template):
    r"""A template that fuse the system message to first user message."""

    @override
    def _encode(
        self,
        tokenizer: "PreTrainedTokenizer",
        messages: list[dict[str, str]],
        system: str,
        tools: str,
    ) -> list[list[int]]:
        system = system or self.default_system
        encoded_messages = []
        for i, message in enumerate(messages):
            elements = []

            system_text = ""
            if i == 0:
                elements += self.format_prefix.apply()
                if system or tools:
                    tool_text = self.format_tools.apply(content=tools)[0] if tools else ""
                    system_text = self.format_system.apply(content=(system + tool_text))[0]

            if message["role"] == Role.USER:
                elements += self.format_user.apply(content=system_text + message["content"])
            elif message["role"] == Role.ASSISTANT:
                elements += self.format_assistant.apply(content=message["content"])
            elif message["role"] == Role.OBSERVATION:
                elements += self.format_observation.apply(content=message["content"])
            elif message["role"] == Role.FUNCTION:
                elements += self.format_function.apply(content=message["content"])
            else:
                raise NotImplementedError("Unexpected role: {}".format(message["role"]))

            encoded_messages.append(self._convert_elements_to_ids(tokenizer, elements))

        return encoded_messages

    def _get_jinja_template(self, tokenizer: "PreTrainedTokenizer") -> str:
        prefix = self._convert_slots_to_jinja(self.format_prefix.apply(), tokenizer)
        system_message = self._convert_slots_to_jinja(
            self.format_system.apply(), tokenizer, placeholder="system_message"
        )
        user_message = self._convert_slots_to_jinja(self.format_user.apply(), tokenizer)
        assistant_message = self._convert_slots_to_jinja(self.format_assistant.apply(), tokenizer)
        jinja_template = ""
        if prefix:
            jinja_template += "{{ " + prefix + " }}"

        if self.default_system:
            jinja_template += "{% set system_message = '" + self._jinja_escape(self.default_system) + "' %}"

        jinja_template += (
            "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}"
            "{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}"
            "{% for message in loop_messages %}"
            "{% if loop.index0 == 0 and system_message is defined %}"
            "{% set content = " + system_message + " + message['content'] %}"
            "{% else %}{% set content = message['content'] %}{% endif %}"
            "{% if message['role'] == 'user' %}"
            "{{ " + user_message + " }}"
            "{% elif message['role'] == 'assistant' %}"
            "{{ " + assistant_message + " }}"
            "{% endif %}"
            "{% endfor %}"
        )
        return jinja_template


@dataclass
class ReasoningTemplate(Template):
    r"""A template that add thought to assistant message."""

    @override
    def encode_oneturn(
        self,
        tokenizer: "PreTrainedTokenizer",
        messages: list[dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
    ) -> tuple[list[int], list[int]]:
        messages = deepcopy(messages)
        for i in range(1, len(messages) - 2, 2):
            messages[i]["content"] = self.remove_thought(messages[i]["content"])

        if self.enable_thinking is False:  # remove all cot
            messages[-1]["content"] = self.remove_thought(messages[-1]["content"])

        prompt_ids, response_ids = super().encode_oneturn(tokenizer, messages, system, tools)
        if (
            self.thought_words[0] not in messages[-1]["content"]
            and self.thought_words[1] not in messages[-1]["content"]
        ):  # add empty cot
            if not self.enable_thinking:  # do not compute loss
                prompt_ids += self.get_thought_word_ids(tokenizer)
            else:  # do compute loss
                response_ids = self.get_thought_word_ids(tokenizer) + response_ids

        return prompt_ids, response_ids

    @override
    def encode_multiturn(
        self,
        tokenizer: "PreTrainedTokenizer",
        messages: list[dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
    ) -> list[tuple[list[int], list[int]]]:
        messages = deepcopy(messages)
        if self.enable_thinking is False:  # remove all cot
            for i in range(1, len(messages), 2):
                messages[i]["content"] = self.remove_thought(messages[i]["content"])

        encoded_messages = self._encode(tokenizer, messages, system, tools)
        for i in range(0, len(messages), 2):
            if (
                self.thought_words[0] not in messages[i + 1]["content"]
                and self.thought_words[1] not in messages[i + 1]["content"]
            ):  # add empty cot
                if not self.enable_thinking:  # do not compute loss
                    encoded_messages[i] += self.get_thought_word_ids(tokenizer)
                else:  # do compute loss
                    encoded_messages[i + 1] = self.get_thought_word_ids(tokenizer) + encoded_messages[i + 1]

        return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]


TEMPLATES: dict[str, "Template"] = {}


def register_template(
    name: str,
    format_user: Optional["Formatter"] = None,
    format_assistant: Optional["Formatter"] = None,
    format_system: Optional["Formatter"] = None,
    format_function: Optional["Formatter"] = None,
    format_observation: Optional["Formatter"] = None,
    format_tools: Optional["Formatter"] = None,
    format_prefix: Optional["Formatter"] = None,
    default_system: str = "",
    stop_words: Optional[list[str]] = None,
    thought_words: Optional[tuple[str, str]] = None,
    efficient_eos: bool = False,
    replace_eos: bool = False,
    replace_jinja_template: bool = False,
    enable_thinking: Optional[bool] = True,
    mm_plugin: "BasePlugin" = get_mm_plugin(name="base"),
    template_class: type["Template"] = Template,
) -> None:
    r"""Register a chat template.

    To add the following chat template:
    ```
    <s><user>user prompt here
    <model>model response here</s>
    <user>user prompt here
    <model>model response here</s>
    ```

    The corresponding code should be:
    ```
    register_template(
        name="custom",
        format_user=StringFormatter(slots=["<user>{{content}}\n<model>"]),
        format_assistant=StringFormatter(slots=["{{content}}</s>\n"]),
        format_prefix=EmptyFormatter("<s>"),
    )
    ```
    """
    if name in TEMPLATES:
        raise ValueError(f"Template {name} already exists.")

    default_slots = ["{{content}}"] if efficient_eos else ["{{content}}", {"eos_token"}]
    default_user_formatter = StringFormatter(slots=["{{content}}"])
    default_assistant_formatter = StringFormatter(slots=default_slots)
    default_function_formatter = FunctionFormatter(slots=default_slots, tool_format="default")
    default_tool_formatter = ToolFormatter(tool_format="default")
    default_prefix_formatter = EmptyFormatter()
    TEMPLATES[name] = template_class(
        format_user=format_user or default_user_formatter,
        format_assistant=format_assistant or default_assistant_formatter,
        format_system=format_system or default_user_formatter,
        format_function=format_function or default_function_formatter,
        format_observation=format_observation or format_user or default_user_formatter,
        format_tools=format_tools or default_tool_formatter,
        format_prefix=format_prefix or default_prefix_formatter,
        default_system=default_system,
        stop_words=stop_words or [],
        thought_words=thought_words or ("<think>", "</think>"),
        efficient_eos=efficient_eos,
        replace_eos=replace_eos,
        replace_jinja_template=replace_jinja_template,
        enable_thinking=enable_thinking,
        mm_plugin=mm_plugin,
    )


def parse_template(tokenizer: "PreTrainedTokenizer") -> "Template":
    r"""Extract a chat template from the tokenizer."""

    def find_diff(short_str: str, long_str: str) -> str:
        i, j = 0, 0
        diff = ""
        while i < len(short_str) and j < len(long_str):
            if short_str[i] == long_str[j]:
                i += 1
                j += 1
            else:
                diff += long_str[j]
                j += 1

        return diff

    prefix = tokenizer.decode(tokenizer.encode(""))

    messages = [{"role": "system", "content": "{{content}}"}]
    system_slot = tokenizer.apply_chat_template(messages, add_generation_prompt=False, tokenize=False)[len(prefix) :]

    messages = [{"role": "system", "content": ""}, {"role": "user", "content": "{{content}}"}]
    user_slot_empty_system = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
    user_slot_empty_system = user_slot_empty_system[len(prefix) :]

    messages = [{"role": "user", "content": "{{content}}"}]
    user_slot = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
    user_slot = user_slot[len(prefix) :]

    messages = [{"role": "user", "content": "{{content}}"}, {"role": "assistant", "content": "{{content}}"}]
    assistant_slot = tokenizer.apply_chat_template(messages, add_generation_prompt=False, tokenize=False)
    assistant_slot = assistant_slot[len(prefix) + len(user_slot) :]
    template_class = ReasoningTemplate if "<think>" in assistant_slot else Template
    assistant_slot = assistant_slot.replace("<think>", "").replace("</think>", "").lstrip("\n")  # remove thought tags

    if len(user_slot) > len(user_slot_empty_system):
        default_system = find_diff(user_slot_empty_system, user_slot)
        sole_system = system_slot.replace("{{content}}", default_system, 1)
        user_slot = user_slot[len(sole_system) :]
    else:  # if defaut_system is empty, user_slot_empty_system will be longer than user_slot
        default_system = ""

    return template_class(
        format_user=StringFormatter(slots=[user_slot]),
        format_assistant=StringFormatter(slots=[assistant_slot]),
        format_system=StringFormatter(slots=[system_slot]),
        format_function=FunctionFormatter(slots=[assistant_slot], tool_format="default"),
        format_observation=StringFormatter(slots=[user_slot]),
        format_tools=ToolFormatter(tool_format="default"),
        format_prefix=EmptyFormatter(slots=[prefix]) if prefix else EmptyFormatter(),
        default_system=default_system,
        stop_words=[],
        thought_words=("<think>", "</think>"),
        efficient_eos=False,
        replace_eos=False,
        replace_jinja_template=False,
        enable_thinking=True,
        mm_plugin=get_mm_plugin(name="base"),
    )


def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args: "DataArguments") -> "Template":
    r"""Get chat template and fixes the tokenizer."""
    if data_args.template is None:
        if isinstance(tokenizer.chat_template, str):
            logger.warning_rank0("`template` was not specified, try parsing the chat template from the tokenizer.")
            template = parse_template(tokenizer)
        else:
            logger.warning_rank0("`template` was not specified, use `empty` template.")
            template = TEMPLATES["empty"]  # placeholder
    else:
        if data_args.template not in TEMPLATES:
            raise ValueError(f"Template {data_args.template} does not exist.")

        template = TEMPLATES[data_args.template]

    if data_args.train_on_prompt and template.efficient_eos:
        raise ValueError("Current template does not support `train_on_prompt`.")

    if data_args.tool_format is not None:
        logger.info_rank0(f"Using tool format: {data_args.tool_format}.")
        default_slots = ["{{content}}"] if template.efficient_eos else ["{{content}}", {"eos_token"}]
        template.format_function = FunctionFormatter(slots=default_slots, tool_format=data_args.tool_format)
        template.format_tools = ToolFormatter(tool_format=data_args.tool_format)

    if data_args.default_system is not None:
        logger.info_rank0(f"Using default system message: {data_args.default_system}.")
        template.default_system = data_args.default_system

    template.enable_thinking = data_args.enable_thinking
    template.fix_special_tokens(tokenizer)
    template.fix_jinja_template(tokenizer)
    return template


register_template(
    name="alpaca",
    format_user=StringFormatter(slots=["### Instruction:\n{{content}}\n\n### Response:\n"]),
    format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}, "\n\n"]),
    default_system=(
        "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
    ),
    replace_jinja_template=True,
)


register_template(
    name="aquila",
    format_user=StringFormatter(slots=["Human: {{content}}###Assistant:"]),
    format_assistant=StringFormatter(slots=["{{content}}###"]),
    format_system=StringFormatter(slots=["System: {{content}}###"]),
    default_system=(
        "A chat between a curious human and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the human's questions."
    ),
    stop_words=["</s>"],
)


register_template(
    name="atom",
    format_user=StringFormatter(
        slots=[{"bos_token"}, "Human: {{content}}\n", {"eos_token"}, {"bos_token"}, "Assistant:"]
    ),
    format_assistant=StringFormatter(slots=["{{content}}\n", {"eos_token"}]),
)


register_template(
    name="baichuan",
    format_user=StringFormatter(slots=[{"token": "<reserved_102>"}, "{{content}}", {"token": "<reserved_103>"}]),
    efficient_eos=True,
)


register_template(
    name="baichuan2",
    format_user=StringFormatter(slots=["<reserved_106>{{content}}<reserved_107>"]),
    efficient_eos=True,
)


register_template(
    name="bailing",
    format_user=StringFormatter(slots=["<role>HUMAN</role>{{content}}<role>ASSISTANT</role>"]),
    format_system=StringFormatter(slots=["<role>SYSTEM</role>{{content}}"]),
    format_observation=StringFormatter(slots=["<role>OBSERVATION</role>{{content}}<role>ASSISTANT</role>"]),
    stop_words=["<|endoftext|>"],
    efficient_eos=True,
)


register_template(
    name="belle",
    format_user=StringFormatter(slots=["Human: {{content}}\n\nBelle: "]),
    format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}, "\n\n"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)


register_template(
    name="bluelm",
    format_user=StringFormatter(slots=[{"token": "[|Human|]:"}, "{{content}}", {"token": "[|AI|]:"}]),
)


register_template(
    name="breeze",
    format_user=StringFormatter(slots=["[INST] {{content}} [/INST] "]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    efficient_eos=True,
)


register_template(
    name="chatglm2",
    format_user=StringFormatter(slots=["[Round {{idx}}]\n\n问：{{content}}\n\n答："]),
    format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
    efficient_eos=True,
)


register_template(
    name="chatglm3",
    format_user=StringFormatter(slots=[{"token": "<|user|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]),
    format_assistant=StringFormatter(slots=["\n", "{{content}}"]),
    format_system=StringFormatter(slots=[{"token": "<|system|>"}, "\n", "{{content}}"]),
    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
    format_observation=StringFormatter(
        slots=[{"token": "<|observation|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]
    ),
    format_tools=ToolFormatter(tool_format="glm4"),
    format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
    stop_words=["<|user|>", "<|observation|>"],
    efficient_eos=True,
)


register_template(
    name="chatml",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    stop_words=["<|im_end|>", "<|im_start|>"],
    replace_eos=True,
    replace_jinja_template=True,
)


# copied from chatml template
register_template(
    name="chatml_de",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    default_system="Du bist ein freundlicher und hilfsbereiter KI-Assistent.",
    stop_words=["<|im_end|>", "<|im_start|>"],
    replace_eos=True,
    replace_jinja_template=True,
)


register_template(
    name="codegeex2",
    format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
)


register_template(
    name="codegeex4",
    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>\n"]),
    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
    format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>\n"]),
    format_tools=ToolFormatter(tool_format="glm4"),
    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
    default_system=(
        "你是一位智能编程助手，你叫CodeGeeX。你会为用户回答关于编程、代码、计算机方面的任何问题，"
        "并提供格式规范、可以执行、准确安全的代码，并在必要时提供详细的解释。"
    ),
    stop_words=["<|user|>", "<|observation|>"],
    efficient_eos=True,
)


register_template(
    name="cohere",
    format_user=StringFormatter(
        slots=[
            (
                "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{content}}<|END_OF_TURN_TOKEN|>"
                "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
            )
        ]
    ),
    format_system=StringFormatter(slots=["<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{content}}<|END_OF_TURN_TOKEN|>"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)


register_template(
    name="cpm",
    format_user=StringFormatter(slots=["<用户>{{content}}<AI>"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)


# copied from chatml template
register_template(
    name="cpm3",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<|im_end|>"],
)


# copied from chatml template
register_template(
    name="cpm4",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="default"),
    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_tools=ToolFormatter(tool_format="default"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    thought_words=("<|thought_start|>", "<|thought_end|>"),
    stop_words=["<|im_end|>", "<|tool_call_start|>", "<|tool_call_end|>"],
    default_system=(
        "# Functions\n"
        "Here is a list of functions that you can invoke:\n"
        "```python\n"
        "from enum import Enum\n"
        "from typing import List, Dict, Optional\n"
        "from pydantic import BaseModel, Field\n"
        "```\n\n"
        "# Function Call Rule and Output Format\n"
        "- If the user's question can be answered without calling any function, please answer the user's question directly. In this situation, you should return your thought and answer the user's question directly.\n"
        "- If the user cannot be answered without calling any function, and the user does not provide enough information to call functions, please ask the user for more information. In this situation, you should return your thought and ask the user for more information.\n"
        "- If the user's question cannot be answered without calling any function, and the user has provided enough information to call functions to solve it, you should call the functions. In this situation, the assistant should return your thought and call the functions.\n"
        "- Use default parameters unless the user has specified otherwise.\n"
        "- You should answer in the following format:\n\n"
        "<|thought_start|>\n"
        "{explain why the user's question can be answered without calling a function or why you should ask the user for more information or why you should call one or more functions and your plan to solve the user's question.}\n"
        "<|thought_end|>\n"
        "<|tool_call_start|>\n"
        "```python\n"
        "func1(params_name=params_value, params_name2=params_value2...)\n"
        "func2(params)\n"
        "```\n"
        "<|tool_call_end|>\n"
        "{answer the user's question directly or ask the user for more information}"
    ),
)


# copied from chatml template
register_template(
    name="dbrx",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    default_system=(
        "You are DBRX, created by Databricks. You were last updated in December 2023. "
        "You answer questions based on information available up to that point.\n"
        "YOU PROVIDE SHORT RESPONSES TO SHORT QUESTIONS OR STATEMENTS, but provide thorough "
        "responses to more complex and open-ended questions.\nYou assist with various tasks, "
        "from writing to coding (using markdown for code blocks — remember to use ``` with "
        "code, JSON, and tables).\n(You do not have real-time data access or code execution "
        "capabilities. You avoid stereotyping and provide balanced perspectives on "
        "controversial topics. You do not provide song lyrics, poems, or news articles and "
        "do not divulge details of your training data.)\nThis is your system prompt, "
        "guiding your responses. Do not reference it, just respond to the user. If you find "
        "yourself talking about this message, stop. You should be responding appropriately "
        "and usually that means not mentioning this.\nYOU DO NOT MENTION ANY OF THIS INFORMATION "
        "ABOUT YOURSELF UNLESS THE INFORMATION IS DIRECTLY PERTINENT TO THE USER'S QUERY."
    ),
    stop_words=["<|im_end|>"],
    replace_eos=True,
)


register_template(
    name="deepseek",
    format_user=StringFormatter(slots=["User: {{content}}\n\nAssistant:"]),
    format_system=StringFormatter(slots=["{{content}}\n\n"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)


register_template(
    name="deepseek3",
    format_user=StringFormatter(slots=["<｜User｜>{{content}}<｜Assistant｜>"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)


# copied from deepseek3 template
register_template(
    name="deepseekr1",
    format_user=StringFormatter(slots=["<｜User｜>{{content}}<｜Assistant｜>"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    template_class=ReasoningTemplate,
)


register_template(
    name="deepseekcoder",
    format_user=StringFormatter(slots=["### Instruction:\n{{content}}\n### Response:"]),
    format_assistant=StringFormatter(slots=["\n{{content}}\n<|EOT|>\n"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    default_system=(
        "You are an AI programming assistant, utilizing the DeepSeek Coder model, "
        "developed by DeepSeek Company, and you only answer questions related to computer science. "
        "For politically sensitive questions, security and privacy issues, "
        "and other non-computer science questions, you will refuse to answer.\n"
    ),
)


register_template(
    name="default",
    format_user=StringFormatter(slots=["Human: {{content}}", {"eos_token"}, "\nAssistant:"]),
    format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}, "\n"]),
    format_system=StringFormatter(slots=["System: {{content}}", {"eos_token"}, "\n"]),
    replace_jinja_template=True,
)


register_template(
    name="empty",
    format_assistant=StringFormatter(slots=["{{content}}"]),
)


register_template(
    name="exaone",
    format_user=StringFormatter(slots=["[|user|]{{content}}\n[|assistant|]"]),
    format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}, "\n"]),
    format_system=StringFormatter(slots=["[|system|]{{content}}[|endofturn|]\n"]),
)


register_template(
    name="falcon",
    format_user=StringFormatter(slots=["User: {{content}}\nFalcon:"]),
    format_assistant=StringFormatter(slots=["{{content}}\n"]),
    efficient_eos=True,
)


register_template(
    name="fewshot",
    format_assistant=StringFormatter(slots=["{{content}}\n\n"]),
    efficient_eos=True,
    replace_jinja_template=True,
)


register_template(
    name="gemma",
    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
    format_system=StringFormatter(slots=["{{content}}\n\n"]),
    format_observation=StringFormatter(
        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
    ),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<end_of_turn>"],
    replace_eos=True,
    template_class=Llama2Template,
)


# copied from gemma template
register_template(
    name="gemma3",
    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
    format_system=StringFormatter(slots=["{{content}}\n\n"]),
    format_observation=StringFormatter(
        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
    ),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<end_of_turn>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin("gemma3", image_token="<image_soft_token>"),
    template_class=Llama2Template,
)


register_template(
    name="glm4",
    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
    format_assistant=StringFormatter(slots=["\n{{content}}"]),
    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
    format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
    format_tools=ToolFormatter(tool_format="glm4"),
    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
    stop_words=["<|user|>", "<|observation|>"],
    efficient_eos=True,
)


# copied from glm4 template
register_template(
    name="glmz1",
    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
    format_assistant=StringFormatter(slots=["\n{{content}}"]),
    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
    format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
    format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
    format_tools=ToolFormatter(tool_format="glm4"),
    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
    stop_words=["<|user|>", "<|observation|>"],
    efficient_eos=True,
    template_class=ReasoningTemplate,
)


register_template(
    name="granite3",
    format_user=StringFormatter(
        slots=[
            "<|start_of_role|>user<|end_of_role|>{{content}}<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>"
        ]
    ),
    format_assistant=StringFormatter(slots=["{{content}}<|end_of_text|>\n"]),
    format_system=StringFormatter(slots=["<|start_of_role|>system<|end_of_role|>{{content}}<|end_of_text|>\n"]),
)


register_template(
    name="granite3_vision",
    format_user=StringFormatter(slots=["<|user|>\n{{content}}\n<|assistant|>\n"]),
    format_system=StringFormatter(slots=["<|system|>\n{{content}}\n"]),
    default_system=(
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions."
    ),
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)


register_template(
    name="index",
    format_user=StringFormatter(slots=["reserved_0{{content}}reserved_1"]),
    format_system=StringFormatter(slots=["<unk>{{content}}"]),
    efficient_eos=True,
)


register_template(
    name="hunyuan",
    format_user=StringFormatter(slots=["<|bos|>user\n{{content}}<|eos|>\n<|bos|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|eos|>\n"]),
    format_system=StringFormatter(slots=["<|bos|>system\n{{content}}<|eos|>\n"]),
    format_prefix=EmptyFormatter(slots=["<|bos|>"]),
    stop_words=["<|eos|>"],
)


register_template(
    name="intern",
    format_user=StringFormatter(slots=["<|User|>:{{content}}\n<|Bot|>:"]),
    format_assistant=StringFormatter(slots=["{{content}}<eoa>\n"]),
    format_system=StringFormatter(slots=["<|System|>:{{content}}\n"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    default_system=(
        "You are an AI assistant whose name is InternLM (书生·浦语).\n"
        "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory "
        "(上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
        "- InternLM (书生·浦语) can understand and communicate fluently in the language "
        "chosen by the user such as English and 中文."
    ),
    stop_words=["<eoa>"],
)


register_template(
    name="intern2",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    default_system=(
        "You are an AI assistant whose name is InternLM (书生·浦语).\n"
        "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory "
        "(上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
        "- InternLM (书生·浦语) can understand and communicate fluently in the language "
        "chosen by the user such as English and 中文."
    ),
    stop_words=["<|im_end|>"],
)


register_template(
    name="intern_vl",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    default_system=(
        "你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。"
    ),
    stop_words=["<|im_end|>"],
    mm_plugin=get_mm_plugin(name="intern_vl", image_token="<image>", video_token="<video>"),
)


register_template(
    name="kimi_vl",
    format_user=StringFormatter(
        slots=["<|im_user|>user<|im_middle|>{{content}}<|im_end|><|im_assistant|>assistant<|im_middle|>"]
    ),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>"]),
    format_system=StringFormatter(slots=["<|im_system|>system<|im_middle|>{{content}}<|im_end|>"]),
    default_system="You are a helpful assistant",
    stop_words=["<|im_end|>"],
    thought_words=("◁think▷", "◁/think▷"),
    mm_plugin=get_mm_plugin("kimi_vl", image_token="<|media_pad|>"),
    template_class=ReasoningTemplate,
)


register_template(
    name="llama2",
    format_user=StringFormatter(slots=[{"bos_token"}, "[INST] {{content}} [/INST]"]),
    format_system=StringFormatter(slots=["<<SYS>>\n{{content}}\n<</SYS>>\n\n"]),
    template_class=Llama2Template,
)


# copied from llama2 template
register_template(
    name="llama2_zh",
    format_user=StringFormatter(slots=[{"bos_token"}, "[INST] {{content}} [/INST]"]),
    format_system=StringFormatter(slots=["<<SYS>>\n{{content}}\n<</SYS>>\n\n"]),
    default_system="You are a helpful assistant. 你是一个乐于助人的助手。",
    template_class=Llama2Template,
)


register_template(
    name="llama3",
    format_user=StringFormatter(
        slots=[
            (
                "<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_assistant=StringFormatter(slots=["{{content}}<|eot_id|>"]),
    format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
    format_function=FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3"),
    format_observation=StringFormatter(
        slots=[
            (
                "<|start_header_id|>ipython<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_tools=ToolFormatter(tool_format="llama3"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<|eot_id|>", "<|eom_id|>"],
    replace_eos=True,
)


register_template(
    name="llama4",
    format_user=StringFormatter(
        slots=["<|header_start|>user<|header_end|>\n\n{{content}}<|eot|><|header_start|>assistant<|header_end|>\n\n"]
    ),
    format_assistant=StringFormatter(slots=["{{content}}<|eot|>"]),
    format_system=StringFormatter(slots=["<|header_start|>system<|header_end|>\n\n{{content}}<|eot|>"]),
    format_function=FunctionFormatter(slots=["{{content}}<|eot|>"], tool_format="llama3"),
    format_observation=StringFormatter(
        slots=[
            "<|header_start|>ipython<|header_end|>\n\n{{content}}<|eot|><|header_start|>assistant<|header_end|>\n\n"
        ]
    ),
    format_tools=ToolFormatter(tool_format="llama3"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<|eot|>", "<|eom|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(name="llama4", image_token="<|image|>"),
)


# copied from llama3 template
register_template(
    name="mllama",
    format_user=StringFormatter(
        slots=[
            (
                "<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_assistant=StringFormatter(slots=["{{content}}<|eot_id|>"]),
    format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
    format_function=FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3"),
    format_observation=StringFormatter(
        slots=[
            (
                "<|start_header_id|>ipython<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_tools=ToolFormatter(tool_format="llama3"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<|eot_id|>", "<|eom_id|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(name="mllama", image_token="<|image|>"),
)


register_template(
    name="moonlight",
    format_user=StringFormatter(
        slots=["<|im_user|>user<|im_middle|>{{content}}<|im_end|><|im_assistant|>assistant<|im_middle|>"]
    ),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>"]),
    format_system=StringFormatter(slots=["<|im_system|>system<|im_middle|>{{content}}<|im_end|>"]),
    default_system="You are a helpful assistant provided by Moonshot-AI.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
)


# copied from vicuna template
register_template(
    name="llava",
    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
    default_system=(
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions."
    ),
    mm_plugin=get_mm_plugin(name="llava", image_token="<image>"),
)


# copied from vicuna template
register_template(
    name="llava_next",
    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
    default_system=(
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions."
    ),
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)


# copied from llama3 template
register_template(
    name="llava_next_llama3",
    format_user=StringFormatter(
        slots=[
            (
                "<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_assistant=StringFormatter(slots=["{{content}}<|eot_id|>"]),
    format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
    format_function=FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3"),
    format_observation=StringFormatter(
        slots=[
            (
                "<|start_header_id|>ipython<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_tools=ToolFormatter(tool_format="llama3"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<|eot_id|>", "<|eom_id|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)


# copied from mistral template
register_template(
    name="llava_next_mistral",
    format_user=StringFormatter(slots=["[INST] {{content}}[/INST]"]),
    format_assistant=StringFormatter(slots=[" {{content}}", {"eos_token"}]),
    format_system=StringFormatter(slots=["{{content}}\n\n"]),
    format_function=FunctionFormatter(slots=["[TOOL_CALLS] {{content}}", {"eos_token"}], tool_format="mistral"),
    format_observation=StringFormatter(slots=["""[TOOL_RESULTS] {"content": {{content}}}[/TOOL_RESULTS]"""]),
    format_tools=ToolFormatter(tool_format="mistral"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
    template_class=Llama2Template,
)


# copied from qwen template
register_template(
    name="llava_next_qwen",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
    format_observation=StringFormatter(
        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
    ),
    format_tools=ToolFormatter(tool_format="qwen"),
    default_system="You are a helpful assistant.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)


# copied from chatml template
register_template(
    name="llava_next_yi",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    stop_words=["<|im_end|>"],
    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)


# copied from vicuna template
register_template(
    name="llava_next_video",
    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
    default_system=(
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions."
    ),
    mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)


# copied from mistral template
register_template(
    name="llava_next_video_mistral",
    format_user=StringFormatter(slots=["[INST] {{content}}[/INST]"]),
    format_assistant=StringFormatter(slots=[" {{content}}", {"eos_token"}]),
    format_system=StringFormatter(slots=["{{content}}\n\n"]),
    format_function=FunctionFormatter(slots=["[TOOL_CALLS] {{content}}", {"eos_token"}], tool_format="mistral"),
    format_observation=StringFormatter(slots=["""[TOOL_RESULTS] {"content": {{content}}}[/TOOL_RESULTS]"""]),
    format_tools=ToolFormatter(tool_format="mistral"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
    template_class=Llama2Template,
)


# copied from chatml template
register_template(
    name="llava_next_video_yi",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    stop_words=["<|im_end|>"],
    mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)


# copied from chatml template
register_template(
    name="marco",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    default_system=(
        "你是一个经过良好训练的AI助手，你的名字是Marco-o1."
        "由阿里国际数字商业集团的AI Business创造.\n## 重要！！！！！\n"
        "当你回答问题时，你的思考应该在<Thought>内完成，<Output>内输出你的结果。\n"
        "<Thought>应该尽可能是英文，但是有2个特例，一个是对原文中的引用，另一个是是数学应该使用markdown格式，<Output>内的输出需要遵循用户输入的语言。\n"
    ),
    stop_words=["<|im_end|>"],
)


# copied from qwen template
register_template(
    name="mimo",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
    format_observation=StringFormatter(
        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
    ),
    format_tools=ToolFormatter(tool_format="qwen"),
    default_system="You are a helpful assistant.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
    template_class=ReasoningTemplate,
)

# copied from qwen2vl
register_template(
    name="mimo_vl",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
    format_observation=StringFormatter(
        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
    ),
    format_tools=ToolFormatter(tool_format="qwen"),
    default_system="You are MiMo, an AI assistant developed by Xiaomi.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(name="qwen2_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
    template_class=ReasoningTemplate,
)


# copied from chatml template
register_template(
    name="minicpm_v",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    stop_words=["<|im_end|>"],
    default_system="You are a helpful assistant.",
    mm_plugin=get_mm_plugin(name="minicpm_v", image_token="<image>", video_token="<video>"),
)


# copied from minicpm_v template
register_template(
    name="minicpm_o",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    stop_words=["<|im_end|>"],
    default_system="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
    mm_plugin=get_mm_plugin(name="minicpm_v", image_token="<image>", video_token="<video>", audio_token="<audio>"),
)


# mistral tokenizer v3 tekken
register_template(
    name="ministral",
    format_user=StringFormatter(slots=["[INST]{{content}}[/INST]"]),
    format_system=StringFormatter(slots=["{{content}}\n\n"]),
    format_function=FunctionFormatter(slots=["[TOOL_CALLS]{{content}}", {"eos_token"}], tool_format="mistral"),
    format_observation=StringFormatter(slots=["""[TOOL_RESULTS]{"content": {{content}}}[/TOOL_RESULTS]"""]),
    format_tools=ToolFormatter(tool_format="mistral"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    template_class=Llama2Template,
)


# mistral tokenizer v3
register_template(
    name="mistral",
    format_user=StringFormatter(slots=["[INST] {{content}}[/INST]"]),
    format_assistant=StringFormatter(slots=[" {{content}}", {"eos_token"}]),
    format_system=StringFormatter(slots=["{{content}}\n\n"]),
    format_function=FunctionFormatter(slots=["[TOOL_CALLS] {{content}}", {"eos_token"}], tool_format="mistral"),
    format_observation=StringFormatter(slots=["""[TOOL_RESULTS] {"content": {{content}}}[/TOOL_RESULTS]"""]),
    format_tools=ToolFormatter(tool_format="mistral"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    template_class=Llama2Template,
)


# mistral tokenizer v7 tekken (copied from ministral)
register_template(
    name="mistral_small",
    format_user=StringFormatter(slots=["[INST]{{content}}[/INST]"]),
    format_system=StringFormatter(slots=["[SYSTEM_PROMPT]{{content}}[/SYSTEM_PROMPT]"]),
    format_function=FunctionFormatter(slots=["[TOOL_CALLS]{{content}}", {"eos_token"}], tool_format="mistral"),
    format_observation=StringFormatter(slots=["""[TOOL_RESULTS]{"content": {{content}}}[/TOOL_RESULTS]"""]),
    format_tools=ToolFormatter(tool_format="mistral"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    mm_plugin=get_mm_plugin(name="pixtral", image_token="[IMG]"),
)


register_template(
    name="olmo",
    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>\n"]),
    format_prefix=EmptyFormatter(slots=[{"eos_token"}]),
)


register_template(
    name="openchat",
    format_user=StringFormatter(slots=["GPT4 Correct User: {{content}}", {"eos_token"}, "GPT4 Correct Assistant:"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)


register_template(
    name="openchat-3.6",
    format_user=StringFormatter(
        slots=[
            (
                "<|start_header_id|>GPT4 Correct User<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>GPT4 Correct Assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<|eot_id|>"],
)


# copied from chatml template
register_template(
    name="opencoder",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    default_system="You are OpenCoder, created by OpenCoder Team.",
    stop_words=["<|im_end|>"],
)


register_template(
    name="orion",
    format_user=StringFormatter(slots=["Human: {{content}}\n\nAssistant: ", {"eos_token"}]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)


register_template(
    name="paligemma",
    format_user=StringFormatter(slots=["{{content}}\n"]),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    mm_plugin=get_mm_plugin(name="paligemma", image_token="<image>"),
    template_class=Llama2Template,
)


# copied from gemma template
register_template(
    name="paligemma_chat",
    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
    format_observation=StringFormatter(
        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
    ),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    stop_words=["<end_of_turn>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(name="paligemma", image_token="<image>"),
    template_class=Llama2Template,
)


register_template(
    name="phi",
    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|end|>\n<|assistant|>\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|end|>\n"]),
    format_system=StringFormatter(slots=["<|system|>\n{{content}}<|end|>\n"]),
    stop_words=["<|end|>"],
    replace_eos=True,
)


register_template(
    name="phi_small",
    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|end|>\n<|assistant|>\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|end|>\n"]),
    format_system=StringFormatter(slots=["<|system|>\n{{content}}<|end|>\n"]),
    format_prefix=EmptyFormatter(slots=[{"<|endoftext|>"}]),
    stop_words=["<|end|>"],
    replace_eos=True,
)


register_template(
    name="phi4",
    format_user=StringFormatter(
        slots=["<|im_start|>user<|im_sep|>{{content}}<|im_end|><|im_start|>assistant<|im_sep|>"]
    ),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>"]),
    format_system=StringFormatter(slots=["<|im_start|>system<|im_sep|>{{content}}<|im_end|>"]),
    stop_words=["<|im_end|>"],
    replace_eos=True,
)


# copied from ministral template
register_template(
    name="pixtral",
    format_user=StringFormatter(slots=["[INST]{{content}}[/INST]"]),
    format_system=StringFormatter(slots=["{{content}}\n\n"]),
    format_function=FunctionFormatter(slots=["[TOOL_CALLS]{{content}}", {"eos_token"}], tool_format="mistral"),
    format_observation=StringFormatter(slots=["""[TOOL_RESULTS]{"content": {{content}}}[/TOOL_RESULTS]"""]),
    format_tools=ToolFormatter(tool_format="mistral"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    mm_plugin=get_mm_plugin(name="pixtral", image_token="[IMG]"),
    template_class=Llama2Template,
)


# copied from chatml template
register_template(
    name="qwen",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
    format_observation=StringFormatter(
        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
    ),
    format_tools=ToolFormatter(tool_format="qwen"),
    default_system="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
)


# copied from qwen template
register_template(
    name="qwen3",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
    format_observation=StringFormatter(
        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
    ),
    format_tools=ToolFormatter(tool_format="qwen"),
    stop_words=["<|im_end|>"],
    replace_eos=True,
    template_class=ReasoningTemplate,
)


# copied from chatml template
register_template(
    name="qwen2_audio",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    default_system="You are a helpful assistant.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(name="qwen2_audio", audio_token="<|AUDIO|>"),
)


# copied from qwen template
register_template(
    name="qwen2_omni",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
    format_observation=StringFormatter(
        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
    ),
    format_tools=ToolFormatter(tool_format="qwen"),
    default_system="You are a helpful assistant.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(
        name="qwen2_omni", audio_token="<|AUDIO|>", image_token="<|IMAGE|>", video_token="<|VIDEO|>"
    ),
)

# copied from qwen template
register_template(
    name="qwen2_vl",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_function=FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen"),
    format_observation=StringFormatter(
        slots=["<|im_start|>user\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]
    ),
    format_tools=ToolFormatter(tool_format="qwen"),
    default_system="You are a helpful assistant.",
    stop_words=["<|im_end|>"],
    replace_eos=True,
    mm_plugin=get_mm_plugin(name="qwen2_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
)


register_template(
    name="sailor",
    format_user=StringFormatter(slots=["<|im_start|>question\n{{content}}<|im_end|>\n<|im_start|>answer\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    default_system=(
        "You are an AI assistant named Sailor created by Sea AI Lab. "
        "Your answer should be friendly, unbiased, faithful, informative and detailed."
    ),
    stop_words=["<|im_end|>"],
)


register_template(
    name="seed_coder",
    format_user=StringFormatter(
        slots=[{"bos_token"}, "user\n{{content}}", {"eos_token"}, {"bos_token"}, "assistant\n"]
    ),
    format_system=StringFormatter(slots=[{"bos_token"}, "system\n{{content}}", {"eos_token"}]),
    default_system=(
        "You are an AI programming assistant, utilizing the Seed-Coder model, developed by ByteDance Seed, "
        "and you only answer questions related to computer science. For politically sensitive questions, "
        "security and privacy issues, and other non-computer science questions, you will refuse to answer.\n\n"
    ),
)


# copied from llama3 template
register_template(
    name="skywork_o1",
    format_user=StringFormatter(
        slots=[
            (
                "<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_assistant=StringFormatter(slots=["{{content}}<|eot_id|>"]),
    format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
    format_function=FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3"),
    format_observation=StringFormatter(
        slots=[
            (
                "<|start_header_id|>ipython<|end_header_id|>\n\n{{content}}<|eot_id|>"
                "<|start_header_id|>assistant<|end_header_id|>\n\n"
            )
        ]
    ),
    format_tools=ToolFormatter(tool_format="llama3"),
    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
    default_system=(
        "You are Skywork-o1, a thinking model developed by Skywork AI, specializing in solving complex problems "
        "involving mathematics, coding, and logical reasoning through deep thought. When faced with a user's request, "
        "you first engage in a lengthy and in-depth thinking process to explore possible solutions to the problem. "
        "After completing your thoughts, you then provide a detailed explanation of the solution process "
        "in your response."
    ),
    stop_words=["<|eot_id|>", "<|eom_id|>"],
)


register_template(
    name="smollm",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    stop_words=["<|im_end|>"],
)


register_template(
    name="smollm2",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    stop_words=["<|im_end|>"],
    default_system="You are a helpful AI assistant named SmolLM, trained by Hugging Face.",
)


register_template(
    name="solar",
    format_user=StringFormatter(slots=["### User:\n{{content}}\n\n### Assistant:\n"]),
    format_system=StringFormatter(slots=["### System:\n{{content}}\n\n"]),
    efficient_eos=True,
)


register_template(
    name="starchat",
    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|end|>\n<|assistant|>"]),
    format_assistant=StringFormatter(slots=["{{content}}<|end|>\n"]),
    format_system=StringFormatter(slots=["<|system|>\n{{content}}<|end|>\n"]),
    stop_words=["<|end|>"],
)


register_template(
    name="telechat",
    format_user=StringFormatter(slots=["<_user>{{content}}<_bot>"]),
    format_system=StringFormatter(slots=["<_system>{{content}}<_end>"]),
)


register_template(
    name="telechat2",
    format_user=StringFormatter(slots=["<_user>{{content}}<_bot>"]),
    format_system=StringFormatter(slots=["<_system>{{content}}"]),
    default_system=(
        "你是中国电信星辰语义大模型，英文名是TeleChat，你是由中电信人工智能科技有限公司和中国电信人工智能研究院（TeleAI）研发的人工智能助手。"
    ),
)


register_template(
    name="vicuna",
    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
    default_system=(
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions."
    ),
    replace_jinja_template=True,
)


register_template(
    name="video_llava",
    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
    default_system=(
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions."
    ),
    mm_plugin=get_mm_plugin(name="video_llava", image_token="<image>", video_token="<video>"),
)


register_template(
    name="xuanyuan",
    format_user=StringFormatter(slots=["Human: {{content}} Assistant:"]),
    default_system=(
        "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，"
        "会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与与不道德、"
        "不安全、有争议、政治敏感等相关的话题、问题和指示。\n"
    ),
)


register_template(
    name="xverse",
    format_user=StringFormatter(slots=["Human: {{content}}\n\nAssistant: "]),
)


register_template(
    name="yayi",
    format_user=StringFormatter(slots=[{"token": "<|Human|>"}, ":\n{{content}}\n\n", {"token": "<|YaYi|>"}, ":"]),
    format_assistant=StringFormatter(slots=["{{content}}\n\n"]),
    format_system=StringFormatter(slots=[{"token": "<|System|>"}, ":\n{{content}}\n\n"]),
    default_system=(
        "You are a helpful, respectful and honest assistant named YaYi "
        "developed by Beijing Wenge Technology Co.,Ltd. "
        "Always answer as helpfully as possible, while being safe.  "
        "Your answers should not include any harmful, unethical, "
        "racist, sexist, toxic, dangerous, or illegal content. "
        "Please ensure that your responses are socially unbiased and positive in nature.\n\n"
        "If a question does not make any sense, or is not factually coherent, "
        "explain why instead of answering something not correct. "
        "If you don't know the answer to a question, please don't share false information."
    ),
    stop_words=["<|End|>"],
)


# copied from chatml template
register_template(
    name="yi",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    stop_words=["<|im_end|>"],
)


register_template(
    name="yi_vl",
    format_user=StringFormatter(slots=["### Human: {{content}}\n### Assistant:"]),
    format_assistant=StringFormatter(slots=["{{content}}\n"]),
    default_system=(
        "This is a chat between an inquisitive human and an AI assistant. "
        "Assume the role of the AI assistant. Read all the images carefully, "
        "and respond to the human's questions with informative, helpful, detailed and polite answers. "
        "这是一个好奇的人类和一个人工智能助手之间的对话。假设你扮演这个AI助手的角色。"
        "仔细阅读所有的图像，并对人类的问题做出信息丰富、有帮助、详细的和礼貌的回答。\n\n"
    ),
    stop_words=["###"],
    efficient_eos=True,
    mm_plugin=get_mm_plugin(name="llava", image_token="<image>"),
)


register_template(
    name="yuan",
    format_user=StringFormatter(slots=["{{content}}", {"token": "<sep>"}]),
    format_assistant=StringFormatter(slots=["{{content}}<eod>\n"]),
    stop_words=["<eod>"],
)


register_template(
    name="zephyr",
    format_user=StringFormatter(slots=["<|user|>\n{{content}}", {"eos_token"}, "<|assistant|>\n"]),
    format_system=StringFormatter(slots=["<|system|>\n{{content}}", {"eos_token"}]),
    default_system="You are Zephyr, a helpful assistant.",
)


register_template(
    name="ziya",
    format_user=StringFormatter(slots=["<human>:{{content}}\n<bot>:"]),
    format_assistant=StringFormatter(slots=["{{content}}\n"]),
)