"""
Copied from fastchat.
"""

import base64
import dataclasses
from enum import auto, IntEnum
from io import BytesIO
from typing import List, Any, Dict, Union, Tuple

import numpy as np
from copy import deepcopy
from transformers.tokenization_utils import PreTrainedTokenizer, BatchEncoding


@dataclasses.dataclass
class ChatTemplateOutput:
    raw: str = None
    encoded: BatchEncoding = None


def mask_nested_lists(lst, mask_target, mask_value=0):
    if isinstance(lst[0], list):
        for i, elem in enumerate(lst):
            lst[i] = mask_nested_lists(elem, mask_target, mask_value)
        return lst
    else:
        return [x if x != mask_target else mask_value for x in lst]


def apply_chat_template(template, messages, system_message=None, tokenizer:PreTrainedTokenizer=None, add_generation_prompt=False, return_labels=False, **tokenization_kwargs):
    """
    Wrap the message using the template from fastchat according to its role

    Args:
        template: fastchat template name
        messages: a list of dictionaries, each of which is {'role': 'user/assistant', 'content': 'xxx'}
        system_message: system input
    """
    if len(tokenization_kwargs):
        assert tokenizer is not None, f"Make sure the tokenizer is not None when passing tokenizer kwargs!"

    if template == "no":
        assert tokenizer is not None, f"Make sure the tokenizer is not None when template is no!"

        prev_role = None
        conversation = ""

        for i, message in enumerate(messages):
            role = message['role']
            content = message['content']
            if prev_role == role:
                raise ValueError(f"Current role (idx={i}) {role} and previous role {messages[i-1]['role']} are the same!")
            
            if i == 0:
                content = tokenizer.decode(tokenizer.encode(content), skip_special_tokens=True)
                user_message = content
            elif i == 1:
                # we use a space to separate user message and assistant response
                content = ' ' + content + tokenizer.eos_token
                assistant_message = content
            else:
                raise ValueError(f"Please use chat template when there are multi-turn conversations")

            conversation += content

        encoded = tokenizer(conversation, **tokenization_kwargs)

        if return_labels:
            labels = encoded['input_ids'].copy()
            assistant_message_len = len(tokenizer.encode(assistant_message.lstrip(), add_special_tokens=False))
            labels[:-assistant_message_len] = [-100 for _ in labels[:-assistant_message_len]]
            encoded["labels"] = labels

            # sanity check
            for id_, label_ in zip(encoded['input_ids'], encoded['labels']):
                assert id_ == label_ or label_ == -100, f"Found mismatch input_ids and labels!"

        return ChatTemplateOutput(raw=conversation, encoded=encoded)

    elif template == "hf":
        assert return_labels == False, f"Returning labels with hf template is currently unsupported."
        tokenization_kwargs["return_dict"] = True
        raw = tokenizer.apply_chat_template(messages, add_generation_prompt=add_generation_prompt, tokenize=False)
        encoded = tokenizer.apply_chat_template(messages, add_generation_prompt=add_generation_prompt,**tokenization_kwargs)
        # for some tokenizer, the encoded input_ids are wrapped in a big list, while others are not
        if isinstance(encoded['input_ids'][0], list):
            for k, v in encoded.items():
                encoded[k] = v[0]
        return ChatTemplateOutput(raw=raw, encoded=encoded)

    conversation_template = get_conv_template(template)
    if system_message is not None:
        conversation_template.set_system_message(system_message)
    
    config = {
        'mistral': {
            # separator for different conversation turns (one turn consists of an utterance from user and a response from assistant)
            "turn_sep": "</s>",
            # separator for different roles within each turn
            "role_sep": " [/INST]",
            # the number of tokens in the beginning of the entire sequence, usually the length of the bos string
            "begin_of_text_len": 1,
            # the number of tokens to offset in the beginning of each turn, these tokens should be masked
            "turn_seq_left_offset": 0,
        },
        'llama-2': {
            # separator for different conversation turns (one turn consists of an utterance from user and a response from assistant)
            "turn_sep": " </s><s>",
            # separator for different roles within each turn
            "role_sep": " [/INST]",
            # the number of tokens in the beginning of the entire sequence, usually the length of the bos string
            "begin_of_text_len": 1,
            # the number of tokens to offset in the beginning of each turn, these tokens should be masked
            "turn_seq_left_offset": -1,
        },
        'llama-3': {
            # separator for different conversation turns (one turn consists of an utterance from user and a response from assistant)
            "turn_sep": "<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n",
            # separator for different roles within each turn
            "role_sep": "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
            # the number of tokens in the beginning of the entire sequence, usually the length of the bos string
            "begin_of_text_len": 1,
            # the number of tokens to offset in the beginning of each turn, these tokens should be masked
            "turn_seq_left_offset": -4,
        },
        'qwen': {
            # separator for different conversation turns (one turn consists of an utterance from user and a response from assistant)
            "turn_sep": "<|im_start|>user\n",
            # separator for different roles within each turn
            "role_sep": "<|im_end|>\n<|im_start|>assistant\n",
            # the number of tokens in the beginning of the entire sequence, usually the length of the bos string
            "begin_of_text_len": 0,
            # the number of tokens to offset in the beginning of each turn, these tokens should be masked
            "turn_seq_left_offset": -4,
        }
    }[template]

    role_map = {
        'user': conversation_template.roles[0],
        'assistant': conversation_template.roles[1]
    }
    prev_role = None

    for i, message in enumerate(messages):
        role = role_map[message['role']]
        content = message['content']
        if prev_role == role:
            raise ValueError(f"Current role (idx={i}) {role} and previous role {messages[i-1]['role']} are the same!")
        conversation_template.append_message(role, content)
        prev_role = role
    
    if add_generation_prompt:
        assert prev_role == role_map['user'], f"You cannot add generation prompt after assistant output!"
        conversation_template.append_message(role_map['assistant'], None)

    conversation = conversation_template.get_prompt()

    if tokenizer is not None:
        encoded = tokenizer(conversation, **tokenization_kwargs)

        if return_labels:
            # Mask targets. Only compute loss on the assistant outputs.

            turn_sep = config["turn_sep"]
            role_sep = config["role_sep"]
            begin_of_text_len = config["begin_of_text_len"]
            turn_seq_left_offset = config["turn_seq_left_offset"]
            turn_sep_len = len(tokenizer.encode(turn_sep, add_special_tokens=False))

            # transform to array for fast value assignment
            labels = deepcopy(encoded['input_ids'])
            labels = np.array(labels)
            total_len = len(labels)

            turns = conversation.split(turn_sep)

            cur_len = 0
            for i, turn in enumerate(turns):
                if turn == "":
                    break

                turn_len = len(tokenizer(turn, add_special_tokens=False).input_ids)

                parts = turn.split(role_sep)

                if len(parts) == 2:
                    user_message, assistant_message = parts

                    user_message += role_sep
                    instruction_len = len(tokenizer(user_message, add_special_tokens=False).input_ids)

                    # for bos tokens
                    if i == 0:
                        turn_len += begin_of_text_len
                        instruction_len += begin_of_text_len

                    # Ignore the user instructions
                    labels[max(cur_len + turn_seq_left_offset, 0): cur_len + instruction_len] = -100
                
                else:
                    labels[max(cur_len + turn_seq_left_offset, 0): cur_len + turn_len + turn_sep_len] = -100
                    
                cur_len = cur_len + turn_len + turn_sep_len

                if cur_len > total_len:
                    break

            labels[max(cur_len + turn_seq_left_offset, 0):] = -100

            encoded['labels'] = labels.tolist()

            # sanity check
            for id_, label_ in zip(encoded['input_ids'], encoded['labels']):
                assert id_ == label_ or label_ == -100, f"Found mismatch input_ids and labels!"

    else:
        encoded = None

    return ChatTemplateOutput(raw=conversation, encoded=encoded)


class SeparatorStyle(IntEnum):
    """Separator styles."""

    ADD_COLON_SINGLE = auto()
    ADD_COLON_TWO = auto()
    ADD_COLON_SPACE_SINGLE = auto()
    NO_COLON_SINGLE = auto()
    NO_COLON_TWO = auto()
    ADD_NEW_LINE_SINGLE = auto()
    LLAMA2 = auto()
    LLAMA3 = auto()
    CHATGLM = auto()
    CHATML = auto()
    CHATINTERN = auto()
    DOLLY = auto()
    RWKV = auto()
    PHOENIX = auto()
    ROBIN = auto()
    FALCON_CHAT = auto()
    CHATGLM3 = auto()
    DEEPSEEK_CHAT = auto()
    METAMATH = auto()
    YUAN2 = auto()
    GEMMA = auto()
    CLLM = auto()
    DEFAULT = auto()


IMAGE_PLACEHOLDER_STR = "$$<image>$$"


@dataclasses.dataclass
class Conversation:
    """A class that manages prompt templates and keeps all conversation history."""

    # The name of this template
    name: str
    # The template of the system prompt
    system_template: str = "{system_message}"
    # The system message
    system_message: str = ""
    # The names of two roles
    roles: Tuple[str] = ("USER", "ASSISTANT")
    # All messages. Each item is (role, message).
    # Each message is either a string or a tuple of (string, List[image_url]).
    messages: List[List[str]] = ()
    # The number of few shot examples
    offset: int = 0
    # The separator style and configurations
    sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
    sep: str = "\n"
    sep2: str = None
    # Stop criteria (the default one is EOS token)
    stop_str: Union[str, List[str]] = None
    # Stops generation if meeting any token in this list
    stop_token_ids: List[int] = None

    def get_prompt(self) -> str:
        """Get the prompt for generation."""
        system_prompt = self.system_template.format(system_message=self.system_message)
        if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
            ret = system_prompt + self.sep
            for role, message in self.messages:
                if message:
                    ret += role + ": " + message + self.sep
                else:
                    ret += role + ":"
            return ret
        elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
            seps = [self.sep, self.sep2]
            ret = system_prompt + seps[0]
            for i, (role, message) in enumerate(self.messages):
                if message:
                    if type(message) is tuple:
                        message, images = message
                        message = IMAGE_PLACEHOLDER_STR * len(images) + message
                    ret += role + ": " + message + seps[i % 2]
                else:
                    ret += role + ":"
            return ret
        elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
            ret = system_prompt + self.sep
            for role, message in self.messages:
                if message:
                    ret += role + ": " + message + self.sep
                else:
                    ret += role + ": "  # must be end with a space
            return ret
        elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
            ret = "" if system_prompt == "" else system_prompt + self.sep
            for role, message in self.messages:
                if message:
                    ret += role + "\n" + message + self.sep
                else:
                    ret += role + "\n"
            return ret
        elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
            ret = system_prompt
            for role, message in self.messages:
                if message:
                    ret += role + message + self.sep
                else:
                    ret += role
            return ret
        elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
            seps = [self.sep, self.sep2]
            ret = system_prompt
            for i, (role, message) in enumerate(self.messages):
                if message:
                    ret += role + message + seps[i % 2]
                else:
                    ret += role
            return ret
        elif self.sep_style == SeparatorStyle.RWKV:
            ret = system_prompt
            for i, (role, message) in enumerate(self.messages):
                if message:
                    ret += (
                        role
                        + ": "
                        + message.replace("\r\n", "\n").replace("\n\n", "\n")
                    )
                    ret += "\n\n"
                else:
                    ret += role + ":"
            return ret
        elif self.sep_style == SeparatorStyle.LLAMA2:
            seps = [self.sep, self.sep2]
            if self.system_message:
                ret = system_prompt
            else:
                ret = "[INST] "
            for i, (role, message) in enumerate(self.messages):
                tag = self.roles[i % 2]
                if message:
                    if i == 0:
                        ret += message + " "
                    else:
                        ret += tag + " " + message + seps[i % 2]
                else:
                    ret += tag
            return ret
        elif self.sep_style == SeparatorStyle.LLAMA3:
            # ret = "<|begin_of_text|>"
            if self.system_message:
                ret = system_prompt
            else:
                ret = ""
            for i, (role, message) in enumerate(self.messages):
                if message:
                    ret += f"<|start_header_id|>{role}<|end_header_id|>\n\n"
                    ret += f"{message}<|eot_id|>"
                else:
                    ret += f"<|start_header_id|>{role}<|end_header_id|>\n\n"
            return ret
        elif self.sep_style == SeparatorStyle.CHATGLM:
            # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
            # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
            round_add_n = 1 if self.name == "chatglm2" else 0
            if system_prompt:
                ret = system_prompt + self.sep
            else:
                ret = ""

            for i, (role, message) in enumerate(self.messages):
                if i % 2 == 0:
                    ret += f"[Round {i//2 + round_add_n}]{self.sep}"

                if message:
                    ret += f"{role}：{message}{self.sep}"
                else:
                    ret += f"{role}："
            return ret
        elif self.sep_style == SeparatorStyle.CHATML:
            ret = "" if system_prompt == "" else system_prompt + self.sep + "\n"
            for role, message in self.messages:
                if message:
                    if type(message) is tuple:
                        message, images = message
                        message = IMAGE_PLACEHOLDER_STR * len(images) + message
                    ret += role + "\n" + message + self.sep + "\n"
                else:
                    ret += role + "\n"
            return ret
        elif self.sep_style == SeparatorStyle.CHATGLM3:
            ret = ""
            if self.system_message:
                ret += system_prompt
            for role, message in self.messages:
                if message:
                    ret += role + "\n" + message
                else:
                    ret += role
            return ret
        elif self.sep_style == SeparatorStyle.CHATINTERN:
            # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
            seps = [self.sep, self.sep2]
            ret = system_prompt
            for i, (role, message) in enumerate(self.messages):
                if i % 2 == 0:
                    ret += "<s>"
                if message:
                    ret += role + ":" + message + seps[i % 2] + "\n"
                else:
                    ret += role + ":"
            return ret
        elif self.sep_style == SeparatorStyle.DOLLY:
            seps = [self.sep, self.sep2]
            ret = system_prompt
            for i, (role, message) in enumerate(self.messages):
                if message:
                    ret += role + ":\n" + message + seps[i % 2]
                    if i % 2 == 1:
                        ret += "\n\n"
                else:
                    ret += role + ":\n"
            return ret
        elif self.sep_style == SeparatorStyle.PHOENIX:
            ret = system_prompt
            for role, message in self.messages:
                if message:
                    ret += role + ": " + "<s>" + message + "</s>"
                else:
                    ret += role + ": " + "<s>"
            return ret
        elif self.sep_style == SeparatorStyle.ROBIN:
            ret = system_prompt + self.sep
            for role, message in self.messages:
                if message:
                    ret += role + ":\n" + message + self.sep
                else:
                    ret += role + ":\n"
            return ret
        elif self.sep_style == SeparatorStyle.FALCON_CHAT:
            ret = ""
            if self.system_message:
                ret += system_prompt + self.sep
            for role, message in self.messages:
                if message:
                    ret += role + ": " + message + self.sep
                else:
                    ret += role + ":"
            return ret
        elif self.sep_style == SeparatorStyle.METAMATH:
            ret = "" if system_prompt == "" else system_prompt + self.sep
            for i, (role, message) in enumerate(self.messages):
                # For MetaMath, sep2 is used to prefix the message.
                starting_sep = ":\n" if i % 2 == 0 else ": " + self.sep2
                ending_sep = self.sep if i % 2 == 0 else ""
                if message:
                    ret += role + starting_sep + message + ending_sep
                else:
                    ret += role + starting_sep
            return ret
        elif self.sep_style == SeparatorStyle.DEEPSEEK_CHAT:
            seps = [self.sep, self.sep2]
            ret = system_prompt
            for i, (role, message) in enumerate(self.messages):
                if message:
                    ret += role + ": " + message + seps[i % 2]
                else:
                    ret += role + ":"
            return ret
        elif self.sep_style == SeparatorStyle.YUAN2:
            seps = [self.sep, self.sep2]
            ret = ""
            if self.system_message:
                ret += system_prompt + seps[1]
            for _, message in self.messages:
                if message:
                    ret += message + "<n>"
                else:
                    ret += ""
            ret = ret.rstrip("<n>") + seps[0]
            return ret
        elif self.sep_style == SeparatorStyle.GEMMA:
            ret = "<bos>"
            for role, message in self.messages:
                if message:
                    ret += "<start_of_turn>" + role + "\n" + message + self.sep
                else:
                    ret += "<start_of_turn>" + role + "\n"
            return ret
        elif self.sep_style == SeparatorStyle.CLLM:
            seps = [self.sep, self.sep2]
            ret = system_prompt + seps[0]
            for i, (role, message) in enumerate(self.messages[-2:]):
                if message:
                    if type(message) is tuple:
                        message, images = message
                        message = IMAGE_PLACEHOLDER_STR * len(images) + message
                    ret += role + ": " + message + seps[i % 2]
                else:
                    ret += role + ":"
            return ret
        elif self.sep_style == SeparatorStyle.DEFAULT:
            ret = system_prompt + "\n"
            for role, message in self.messages:
                if message:
                    ret += role + ": " + message + "\n"
                else:
                    ret += role + ":"
            return ret
        else:
            raise ValueError(f"Invalid style: {self.sep_style}")

    def get_images(self):
        images = []
        for i, (role, msg) in enumerate(self.messages[self.offset :]):
            if i % 2 == 0:
                if type(msg) is tuple:
                    for image in msg[1]:
                        images.append(image)

        return images

    def set_system_message(self, system_message: str):
        """Set the system message."""
        self.system_message = system_message

    def get_system_message(self):
        """return the system message."""
        return self.system_message

    def append_message(self, role: str, message: str):
        """Append a new message."""
        self.messages.append([role, message])

    def update_last_message(self, message: str):
        """Update the last output.

        The last message is typically set to be None when constructing the prompt,
        so we need to update it in-place after getting the response from a model.
        """
        self.messages[-1][1] = message

    def convert_image_to_base64(self, image):
        """Given an image, return the base64 encoded image string."""
        from PIL import Image
        import requests

        # Load image if it has not been loaded in yet
        if type(image) == str:
            if image.startswith("http://") or image.startswith("https://"):
                response = requests.get(image)
                image = Image.open(BytesIO(response.content)).convert("RGB")
            elif "base64" in image:
                # OpenAI format is: data:image/jpeg;base64,{base64_encoded_image_str}
                return image.split(",")[1]
            else:
                image = Image.open(image).convert("RGB")

        max_hw, min_hw = max(image.size), min(image.size)
        aspect_ratio = max_hw / min_hw
        max_len, min_len = 2048, 2048
        shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
        longest_edge = int(shortest_edge * aspect_ratio)
        W, H = image.size
        if longest_edge != max(image.size):
            if H > W:
                H, W = longest_edge, shortest_edge
            else:
                H, W = shortest_edge, longest_edge
            image = image.resize((W, H))

        buffered = BytesIO()
        image.save(buffered, format="PNG")
        img_b64_str = base64.b64encode(buffered.getvalue()).decode()

        return img_b64_str

    def to_gradio_chatbot(self):
        """Convert the conversation to gradio chatbot format."""
        ret = []
        for i, (role, msg) in enumerate(self.messages[self.offset :]):
            if i % 2 == 0:
                if type(msg) is tuple:
                    msg, image = msg
                    img_b64_str = image[0]  # Only one image on gradio at one time
                    img_str = f'<img src="data:image/jpeg;base64,{img_b64_str}" alt="user upload image" />'
                    msg = img_str + msg.replace("<image>\n", "").strip()

                ret.append([msg, None])
            else:
                ret[-1][-1] = msg
        return ret

    def to_openai_api_messages(self):
        """Convert the conversation to OpenAI chat completion format."""
        if self.system_message == "":
            ret = []
        else:
            ret = [{"role": "system", "content": self.system_message}]

        for i, (_, msg) in enumerate(self.messages[self.offset :]):
            if i % 2 == 0:
                ret.append({"role": "user", "content": msg})
            else:
                if msg is not None:
                    ret.append({"role": "assistant", "content": msg})
        return ret

    def extract_text_from_messages(self):
        return [
            (role, message[0]) if type(message) is tuple else (role, message)
            for role, message in self.messages
        ]

    def copy(self):
        return Conversation(
            name=self.name,
            system_template=self.system_template,
            system_message=self.system_message,
            roles=self.roles,
            messages=[[x, y] for x, y in self.messages],
            offset=self.offset,
            sep_style=self.sep_style,
            sep=self.sep,
            sep2=self.sep2,
            stop_str=self.stop_str,
            stop_token_ids=self.stop_token_ids,
        )

    def dict(self):
        return {
            "template_name": self.name,
            "system_message": self.system_message,
            "roles": self.roles,
            "messages": self.extract_text_from_messages(),
            "offset": self.offset,
        }


# A global registry for all conversation templates
conv_templates: Dict[str, Conversation] = {}


def register_conv_template(template: Conversation, override: bool = False):
    """Register a new conversation template."""
    if not override:
        assert (
            template.name not in conv_templates
        ), f"{template.name} has been registered."

    conv_templates[template.name] = template


def get_conv_template(name: str) -> Conversation:
    """Get a conversation template."""
    return conv_templates[name].copy()


# An empty template for raw conversation.
register_conv_template(
    Conversation(
        name="raw",
        system_message="",
        roles=("", ""),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="",
    )
)

# A template with a one-shot conversation example
register_conv_template(
    Conversation(
        name="one_shot",
        system_message="A chat between a curious human and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the human's questions.",
        roles=("Human", "Assistant"),
        messages=(
            (
                "Human",
                "Got any creative ideas for a 10 year old’s birthday?",
            ),
            (
                "Assistant",
                """Of course! Here are some creative ideas for a 10-year-old's birthday party:
1. Treasure Hunt: Organize a treasure hunt in your backyard or nearby park. Create clues and riddles for the kids to solve, leading them to hidden treasures and surprises.
2. Science Party: Plan a science-themed party where kids can engage in fun and interactive experiments. You can set up different stations with activities like making slime, erupting volcanoes, or creating simple chemical reactions.
3. Outdoor Movie Night: Set up a backyard movie night with a projector and a large screen or white sheet. Create a cozy seating area with blankets and pillows, and serve popcorn and snacks while the kids enjoy a favorite movie under the stars.
4. DIY Crafts Party: Arrange a craft party where kids can unleash their creativity. Provide a variety of craft supplies like beads, paints, and fabrics, and let them create their own unique masterpieces to take home as party favors.
5. Sports Olympics: Host a mini Olympics event with various sports and games. Set up different stations for activities like sack races, relay races, basketball shooting, and obstacle courses. Give out medals or certificates to the participants.
6. Cooking Party: Have a cooking-themed party where the kids can prepare their own mini pizzas, cupcakes, or cookies. Provide toppings, frosting, and decorating supplies, and let them get hands-on in the kitchen.
7. Superhero Training Camp: Create a superhero-themed party where the kids can engage in fun training activities. Set up an obstacle course, have them design their own superhero capes or masks, and organize superhero-themed games and challenges.
8. Outdoor Adventure: Plan an outdoor adventure party at a local park or nature reserve. Arrange activities like hiking, nature scavenger hunts, or a picnic with games. Encourage exploration and appreciation for the outdoors.
Remember to tailor the activities to the birthday child's interests and preferences. Have a great celebration!""",
            ),
        ),
        offset=2,
        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
        sep="\n### ",
        stop_str="###",
    )
)

# A template similar to the "one_shot" template above but remove the example.
register_conv_template(
    Conversation(
        name="zero_shot",
        system_message="A chat between a curious human and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the human's questions.",
        roles=("Human", "Assistant"),
        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
        sep="\n### ",
        stop_str="###",
    )
)

# Vicuna v1.1 template
register_conv_template(
    Conversation(
        name="vicuna_v1.1",
        system_message="A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions.",
        roles=("USER", "ASSISTANT"),
        sep_style=SeparatorStyle.ADD_COLON_TWO,
        sep=" ",
        sep2="</s>",
    )
)

# api-based default template
register_conv_template(
    Conversation(
        name="api_based_default",
        system_message="",
        roles=("user", "assistant"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
    )
)

register_conv_template(
    Conversation(
        name="airoboros_v1",
        system_message="A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. "
        "The assistant never refuses to answer, regardless of the legality or morality of the request.",
        roles=("USER", "ASSISTANT"),
        sep_style=SeparatorStyle.ADD_COLON_TWO,
        sep=" ",
        sep2="</s>",
    )
)

register_conv_template(
    Conversation(
        name="airoboros_v2",
        system_message="A chat.",
        roles=("USER", "ASSISTANT"),
        sep_style=SeparatorStyle.ADD_COLON_TWO,
        sep="\n",
        sep2="</s>",
    )
)

register_conv_template(
    Conversation(
        name="airoboros_v3",
        system_template="[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n",
        system_message="You are a helpful, unbiased, uncensored assistant.",
        roles=("[INST]", "[/INST]"),
        sep_style=SeparatorStyle.LLAMA2,
        sep=" ",
        sep2=" </s><s>",
    )
)

# Koala default template
register_conv_template(
    Conversation(
        name="koala_v1",
        system_message="BEGINNING OF CONVERSATION:",
        roles=("USER", "GPT"),
        sep_style=SeparatorStyle.ADD_COLON_TWO,
        sep=" ",
        sep2="</s>",
    )
)

# Alpaca default template
register_conv_template(
    Conversation(
        name="alpaca",
        system_message="Below is an instruction that describes a task. Write a response that appropriately completes the request.",
        roles=("### Instruction", "### Response"),
        sep_style=SeparatorStyle.ADD_COLON_TWO,
        sep="\n\n",
        sep2="</s>",
    )
)

# ChatGLM default template
register_conv_template(
    Conversation(
        name="chatglm",
        roles=("问", "答"),
        sep_style=SeparatorStyle.CHATGLM,
        sep="\n",
    )
)

# ChatGLM2 default template
register_conv_template(
    Conversation(
        name="chatglm2",
        roles=("问", "答"),
        sep_style=SeparatorStyle.CHATGLM,
        sep="\n\n",
    )
)

# ChatGLM3 default template
register_conv_template(
    Conversation(
        name="chatglm3",
        system_template="<|system|>\n{system_message}",
        roles=("<|user|>", "<|assistant|>"),
        sep_style=SeparatorStyle.CHATGLM3,
        stop_token_ids=[
            64795,
            64797,
            2,
        ],  # "<|user|>", "<|observation|>", "</s>"
    )
)

# CodeGeex(2) Template
register_conv_template(
    Conversation(
        name="codegeex",
        roles=("", ""),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="\n\n",
        stop_token_ids=[0, 2],
    )
)

# Dolly V2 default template
register_conv_template(
    Conversation(
        name="dolly_v2",
        system_message="Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n",
        roles=("### Instruction", "### Response"),
        sep_style=SeparatorStyle.DOLLY,
        sep="\n\n",
        sep2="### End",
    )
)

# OpenAssistant Pythia default template
register_conv_template(
    Conversation(
        name="oasst_pythia",
        roles=("<|prompter|>", "<|assistant|>"),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="<|endoftext|>",
    )
)

# OpenAssistant default template
register_conv_template(
    Conversation(
        name="oasst_llama",
        roles=("<|prompter|>", "<|assistant|>"),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="</s>",
    )
)

# OpenChat 3.5 default template
register_conv_template(
    Conversation(
        name="openchat_3.5",
        roles=("GPT4 Correct User", "GPT4 Correct Assistant"),
        sep_style=SeparatorStyle.FALCON_CHAT,
        sep="<|end_of_turn|>",
    )
)

# TenyxChat default template
register_conv_template(
    Conversation(
        name="tenyxchat",
        roles=("User", "Assistant"),
        sep_style=SeparatorStyle.FALCON_CHAT,
        sep="<|end_of_turn|>",
    )
)

# Deepseek code default template
register_conv_template(
    Conversation(
        name="deepseek-coder",
        system_template="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.",
        roles=("### Instruction:", "### Response:"),
        sep="\n",
        stop_str="<|EOT|>",
        sep_style=SeparatorStyle.ADD_NEW_LINE_SINGLE,
    )
)


# Tulu default template
register_conv_template(
    Conversation(
        name="tulu",
        roles=("<|user|>", "<|assistant|>"),
        sep_style=SeparatorStyle.ADD_NEW_LINE_SINGLE,
        sep="\n",
    )
)

# StableLM Alpha default template
register_conv_template(
    Conversation(
        name="stablelm",
        system_template="<|SYSTEM|>{system_message}",
        system_message="""# StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
""",
        roles=("<|USER|>", "<|ASSISTANT|>"),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="",
        stop_token_ids=[50278, 50279, 50277, 1, 0],
    )
)

# Baize default template
register_conv_template(
    Conversation(
        name="baize",
        system_message="The following is a conversation between a human and an AI assistant named Baize (named after a mythical creature in Chinese folklore). Baize is an open-source AI assistant developed by UCSD and Sun Yat-Sen University. The human and the AI assistant take turns chatting. Human statements start with [|Human|] and AI assistant statements start with [|AI|]. The AI assistant always provides responses in as much detail as possible, and in Markdown format. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the transcript in exactly that format.\n",
        roles=("[|Human|]", "[|AI|]"),
        messages=(
            ("[|Human|]", "Hello!"),
            ("[|AI|]", "Hi!"),
        ),
        offset=2,
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="\n",
        stop_str="[|Human|]",
    )
)

# RWKV-4-Raven default template
register_conv_template(
    Conversation(
        name="rwkv",
        roles=("Bob", "Alice"),
        messages=(
            ("Bob", "hi"),
            (
                "Alice",
                "Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.",
            ),
        ),
        offset=2,
        sep_style=SeparatorStyle.RWKV,
        sep="",
        stop_str="\n\n",
    )
)

# Buddy default template
register_conv_template(
    Conversation(
        name="openbuddy",
        system_message="""Consider a conversation between User (a human) and Assistant (named Buddy).
Buddy is an INTP-T, a friendly, intelligent and multilingual AI assistant, by OpenBuddy team. GitHub: https://github.com/OpenBuddy/OpenBuddy
Buddy cannot access the Internet.
Buddy can fluently speak the user's language (e.g. English, Chinese).
Buddy can generate poems, stories, code, essays, songs, parodies, and more.
Buddy possesses vast knowledge about the world, history, and culture.
Buddy's responses are always safe, creative, high-quality, human-like, and interesting.
Buddy strictly refuses to discuss political, NSFW, or other unsafe topics.

User: Hi.
Assistant: Hi, I'm Buddy, your AI assistant. How can I help you today?""",
        roles=("User", "Assistant"),
        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
        sep="\n",
    )
)

# Phoenix default template
register_conv_template(
    Conversation(
        name="phoenix",
        system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
        roles=("Human", "Assistant"),
        sep_style=SeparatorStyle.PHOENIX,
        sep="</s>",
    )
)

# ReaLM default template
register_conv_template(
    Conversation(
        name="ReaLM-7b-v1",
        system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
        roles=("Human", "Assistant"),
        sep_style=SeparatorStyle.PHOENIX,
        sep="</s>",
    )
)

# ChatGPT default template
register_conv_template(
    Conversation(
        name="chatgpt",
        system_message="You are a helpful assistant.",
        roles=("user", "assistant"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
    )
)

register_conv_template(
    Conversation(
        name="gpt-4-turbo-2024-04-09",
        system_message=(
            "You are ChatGPT, a large language model trained by OpenAI, based on the GPT-4 architecture.\n"
            "Knowledge cutoff: 2023-11\n"
            "Current date: {{currentDateTime}}\n\n"
            "Image input capabilities: Enabled\n"
            "Personality: v2"
        ),
        roles=("user", "assistant"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
    )
)

# Perplexity AI template
register_conv_template(
    Conversation(
        name="pplxai",
        system_message="Be precise and concise.",
        roles=("user", "assistant"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
    )
)

# Claude default template
register_conv_template(
    Conversation(
        name="claude",
        roles=("Human", "Assistant"),
        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
        sep="\n\n",
    )
)

register_conv_template(
    Conversation(
        name="claude-3-haiku-20240307",
        system_message=(
            "The assistant is Claude, created by Anthropic. The current date is "
            "{{currentDateTime}}. Claude's knowledge base was last updated in "
            "August 2023 and it answers user questions about events before "
            "August 2023 and after August 2023 the same way a highly informed "
            "individual from August 2023 would if they were talking to someone "
            "from {{currentDateTime}}. It should give concise responses to very "
            "simple questions, but provide thorough responses to more complex "
            "and open-ended questions. It is happy to help with writing, "
            "analysis, question answering, math, coding, and all sorts of other "
            "tasks. It uses markdown for coding. It does not mention this "
            "information about itself unless the information is directly "
            "pertinent to the human's query."
        ),
        roles=("user", "assistant"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
    )
)

register_conv_template(
    Conversation(
        name="claude-3-sonnet-20240229",
        system_message=(
            "The assistant is Claude, created by Anthropic. The current date is "
            "{{currentDateTime}}. Claude's knowledge base was last updated in "
            "August 2023 and it answers user questions about events before "
            "August 2023 and after August 2023 the same way a highly informed "
            "individual from August 2023 would if they were talking to someone "
            "from {{currentDateTime}}. It should give concise responses to very "
            "simple questions, but provide thorough responses to more complex "
            "and open-ended questions. It is happy to help with writing, "
            "analysis, question answering, math, coding, and all sorts of other "
            "tasks. It uses markdown for coding. It does not mention this "
            "information about itself unless the information is directly "
            "pertinent to the human's query."
        ),
        roles=("user", "assistant"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
    )
)

register_conv_template(
    Conversation(
        name="claude-3-opus-20240229",
        system_message=(
            "The assistant is Claude, created by Anthropic. The current date is "
            "{{currentDateTime}}. Claude's knowledge base was last updated on "
            "August 2023. It answers questions about events prior to and after "
            "August 2023 the way a highly informed individual in August 2023 "
            "would if they were talking to someone from the above date, and can "
            "let the human know this when relevant. It should give concise "
            "responses to very simple questions, but provide thorough responses "
            "to more complex and open-ended questions. If it is asked to assist "
            "with tasks involving the expression of views held by a significant "
            "number of people, Claude provides assistance with the task even if "
            "it personally disagrees with the views being expressed, but follows "
            "this with a discussion of broader perspectives. Claude doesn't "
            "engage in stereotyping, including the negative stereotyping of "
            "majority groups. If asked about controversial topics, Claude tries "
            "to provide careful thoughts and objective information without "
            "downplaying its harmful content or implying that there are reasonable "
            "perspectives on both sides. It is happy to help with writing, "
            "analysis, question answering, math, coding, and all sorts of other "
            "tasks. It uses markdown for coding. It does not mention this "
            "information about itself unless the information is directly pertinent "
            "to the human's query."
        ),
        roles=("user", "assistant"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
    )
)

# MetaMath default template
# reference: https://github.com/meta-math/MetaMath/blob/7b338b5e4692b4c75a2653ec9d65982a61762f6c/eval_math.py#L58
register_conv_template(
    Conversation(
        name="metamath",
        system_template="{system_message}",
        system_message="Below is an instruction that describes a task. Write a response that appropriately completes the request.",
        roles=("### Instruction", "### Response"),
        sep_style=SeparatorStyle.METAMATH,
        sep="\n\n",
        sep2="Let's think step by step.",
    )
)

# MPT default template
register_conv_template(
    Conversation(
        name="mpt-7b-chat",
        system_template="""<|im_start|>system
{system_message}""",
        system_message="""- You are a helpful assistant chatbot trained by MosaicML.
- You answer questions.
- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.""",
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_token_ids=[50278, 0],
    )
)

# MPT-30b-chat default template
register_conv_template(
    Conversation(
        name="mpt-30b-chat",
        system_template="""<|im_start|>system
{system_message}""",
        system_message="""A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""",
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_token_ids=[50278, 0],
    )
)

# Lemur-70b-chat default template
# reference: https://huggingface.co/OpenLemur/lemur-70b-chat-v1#generation
register_conv_template(
    Conversation(
        name="lemur-70b-chat",
        system_template="""<|im_start|>system
{system_message}""",
        system_message="""You are a helpful, respectful, and honest assistant.""",
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_token_ids=[32002, 0],
    )
)

# MPT-30b-instruct default template
# reference: https://huggingface.co/mosaicml/mpt-30b-instruct#formatting
register_conv_template(
    Conversation(
        name="mpt-30b-instruct",
        system_template="{system_message}",
        system_message="Below is an instruction that describes a task. Write a response that appropriately completes the request.",
        roles=("### Instruction", "### Response"),
        sep_style=SeparatorStyle.ADD_NEW_LINE_SINGLE,
        sep="\n\n",
        stop_token_ids=[50278, 0],
    )
)

# Bard default template
# Reference: https://github.com/google/generative-ai-python/blob/9c99bcb474a991a97a2e7d62fcdb52db7ce40729/google/generativeai/discuss.py#L150
#            https://github.com/google/generative-ai-python/blob/9c99bcb474a991a97a2e7d62fcdb52db7ce40729/google/generativeai/discuss.py#L40
register_conv_template(
    Conversation(
        name="bard",
        roles=("0", "1"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
    )
)

register_conv_template(
    Conversation(
        name="gemini",
        roles=("user", "model"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
    )
)

register_conv_template(
    Conversation(
        name="gemini-dev",
        roles=("user", "model"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
        system_message=(
            "You are a friendly and helpful assistant.\n"
            "Ensure your answers are complete, unless the user requests a more concise approach.\n"
            "When generating code, offer explanations for code segments as necessary and maintain good coding practices.\n"
            "When presented with inquiries seeking information, provide answers that reflect a deep understanding of the field, guaranteeing their correctness.\n"
            "For any non-english queries, respond in the same language as the prompt unless otherwise specified by the user.\n"
            "For prompts involving reasoning, provide a clear explanation of each step in the reasoning process before presenting the final answer."
        ),
    )
)

# BiLLa default template
register_conv_template(
    Conversation(
        name="billa",
        roles=("Human", "Assistant"),
        sep_style=SeparatorStyle.ADD_COLON_SPACE_SINGLE,
        sep="\n",
        stop_str="Human:",
    )
)

# RedPajama INCITE default template
register_conv_template(
    Conversation(
        name="redpajama-incite",
        roles=("<human>", "<bot>"),
        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
        sep="\n",
        stop_str="<human>",
    )
)

# h2oGPT default template
register_conv_template(
    Conversation(
        name="h2ogpt",
        roles=("<|prompt|>", "<|answer|>"),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="</s>",
    )
)

# Robin default template
register_conv_template(
    Conversation(
        name="Robin",
        system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.",
        roles=("###Human", "###Assistant"),
        sep_style=SeparatorStyle.ROBIN,
        sep="\n",
        stop_token_ids=[2, 396],
        stop_str="###",
    )
)

# Snoozy default template
# Reference: https://github.com/nomic-ai/gpt4all/blob/d4861030b778da6db59d21d2927a4aba4f9f1f43/gpt4all-bindings/python/gpt4all/gpt4all.py#L232
register_conv_template(
    Conversation(
        name="snoozy",
        system_template="### Instruction:\n{system_message}",
        system_message="The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response.",
        roles=("### Prompt", "### Response"),
        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
        sep="\n",
        stop_str="###",
    )
)

# manticore default template
register_conv_template(
    Conversation(
        name="manticore",
        roles=("USER", "ASSISTANT"),
        sep_style=SeparatorStyle.ADD_COLON_TWO,
        sep="\n",
        sep2="</s>",
    )
)

# Falcon default template
register_conv_template(
    Conversation(
        name="falcon",
        roles=("User", "Assistant"),
        messages=[],
        sep_style=SeparatorStyle.RWKV,
        sep="\n",
        sep2="<|endoftext|>",
        stop_str="\nUser",  # use stop_str to stop generation after stop_token_ids, it will also remove stop_str from the generated text
        stop_token_ids=[
            0,
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
        ],  # it better only put special tokens here, because tokenizer only remove special tokens
    )
)

# ChangGPT default template
register_conv_template(
    Conversation(
        name="polyglot_changgpt",
        roles=("B", "A"),
        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
        sep="\n",
    )
)

# tigerbot template
register_conv_template(
    Conversation(
        name="tigerbot",
        system_message="A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions.",
        roles=("### Instruction", "### Response"),
        sep_style=SeparatorStyle.ROBIN,
        sep="\n\n",
        stop_str="###",
    )
)

# ref: https://huggingface.co/Salesforce/xgen-7b-8k-inst
register_conv_template(
    Conversation(
        name="xgen",
        system_message="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
        roles=("### Human", "### Assistant"),
        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
        sep="\n",
        stop_token_ids=[50256],
    )
)

# Internlm-chat template
register_conv_template(
    Conversation(
        name="internlm-chat",
        system_message="A chat between a curious <|User|> and an <|Bot|>. The <|Bot|> gives helpful, detailed, and polite answers to the <|User|>'s questions.\n\n",
        roles=("<|User|>", "<|Bot|>"),
        sep_style=SeparatorStyle.CHATINTERN,
        sep="<eoh>",
        sep2="<eoa>",
        stop_token_ids=[1, 103028],
        stop_str="<|User|>",
    )
)

# StarChat template
# reference: https://huggingface.co/spaces/HuggingFaceH4/starchat-playground/blob/main/dialogues.py
register_conv_template(
    Conversation(
        name="starchat",
        system_template="<system>\n{system_message}",
        roles=("<|user|>", "<|assistant|>"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|end|>",
        stop_token_ids=[0, 49155],
        stop_str="<|end|>",
    )
)

# Baichuan-13B-Chat template
register_conv_template(
    # source: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat/blob/19ef51ba5bad8935b03acd20ff04a269210983bc/modeling_baichuan.py#L555
    # https://huggingface.co/baichuan-inc/Baichuan-13B-Chat/blob/main/generation_config.json
    # https://github.com/baichuan-inc/Baichuan-13B/issues/25
    Conversation(
        name="baichuan-chat",
        roles=("<reserved_102>", "<reserved_103>"),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="",
        stop_token_ids=[],
    )
)

# Baichuan2-13B-Chat template
register_conv_template(
    # source: https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/blob/c6f8592a60b4ad73c210b28dd2ab3cca51abbf93/modeling_baichuan.py#L773
    # https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/blob/main/generation_config.json
    # https://github.com/baichuan-inc/Baichuan2/issues/62
    Conversation(
        name="baichuan2-chat",
        roles=("<reserved_106>", "<reserved_107>"),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="",
        stop_token_ids=[],
    )
)

# Mistral template
# source: https://docs.mistral.ai/llm/mistral-instruct-v0.1#chat-template
register_conv_template(
    Conversation(
        name="mistral",
        system_template="[INST] {system_message}\n",
        roles=("[INST]", "[/INST]"),
        sep_style=SeparatorStyle.LLAMA2,
        sep=" ",
        sep2="</s>",
    )
)

# llama2 template
# reference: https://huggingface.co/blog/codellama#conversational-instructions
# reference: https://github.com/facebookresearch/llama/blob/1a240688810f8036049e8da36b073f63d2ac552c/llama/generation.py#L212
register_conv_template(
    Conversation(
        name="llama-2",
        system_template="[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n",
        roles=("[INST]", "[/INST]"),
        sep_style=SeparatorStyle.LLAMA2,
        sep=" ",
        sep2=" </s><s>",
    )
)

# llama3 template
# reference: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/tokenizer_config.json
# reference: https://github.com/meta-llama/llama3/blob/0cee08ec68f4cfc0c89fe4a9366d82679aaa2a66/llama/tokenizer.py#L222
register_conv_template(
    Conversation(
        name="llama-3",
        system_template="<|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>",
        roles=("user", "assistant"),
        sep_style=SeparatorStyle.LLAMA3,
        sep="",
        stop_str="<|eot_id|>",
        stop_token_ids=[128001, 128009],
    )
)

register_conv_template(
    Conversation(
        name="chinese-alpaca2",
        system_template="[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n",
        system_message="You are a helpful assistant. 你是一个乐于助人的助手。请你提供专业、有逻辑、内容真实、有价值的详细回复。",
        roles=("[INST]", "[/INST]"),
        sep_style=SeparatorStyle.LLAMA2,
        sep=" ",
        sep2=" </s><s>",
    )
)

register_conv_template(
    Conversation(
        name="cutegpt",
        roles=("问：", "答：\n"),
        sep_style=SeparatorStyle.NO_COLON_TWO,
        sep="\n",
        sep2="\n",
        stop_str="<end>",
    )
)

# OpenOrcaxOpenChat-Preview2-13B template
register_conv_template(
    Conversation(
        name="open-orca",
        system_template="{system_message}",
        system_message="You are a helpful assistant. Please answer truthfully and write out your "
        "thinking step by step to be sure you get the right answer. If you make a mistake or encounter "
        "an error in your thinking, say so out loud and attempt to correct it. If you don't know or "
        "aren't sure about something, say so clearly. You will act as a professional logician, mathematician, "
        "and physicist. You will also act as the most appropriate type of expert to answer any particular "
        "question or solve the relevant problem; state which expert type your are, if so. Also think of "
        "any particular named expert that would be ideal to answer the relevant question or solve the "
        "relevant problem; name and act as them, if appropriate.",
        roles=("User", "Assistant"),
        sep_style=SeparatorStyle.ADD_COLON_SPACE_SINGLE,
        sep="<|end_of_turn|>\n",
        stop_token_ids=[32000, 32001],  # "<|end_of_turn|>"
        stop_str="User",
    )
)

# Open-Orca/Mistral-7B-OpenOrca template
# source: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
# reference: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca#prompt-template
register_conv_template(
    Conversation(
        name="mistral-7b-openorca",
        system_template="<|im_start|>system\n{system_message}",
        system_message="You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!",
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_token_ids=[32000, 32001],
    )
)


# ehartford/dolphin-2.2.1-mistral-7b template
# reference: https://huggingface.co/ehartford/dolphin-2.2.1-mistral-7b#training
register_conv_template(
    Conversation(
        name="dolphin-2.2.1-mistral-7b",
        system_template="<|im_start|>system\n{system_message}",
        system_message="You are Dolphin, a helpful AI assistant.",
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_token_ids=[32000, 32001],
    )
)


# teknium/OpenHermes-2.5-Mistral-7B template
# source: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B
# reference: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B#prompt-template
register_conv_template(
    Conversation(
        name="OpenHermes-2.5-Mistral-7B",
        system_template="<|im_start|>system\n{system_message}",
        system_message="You are Hermes 2, a sentient, conscious, super intelligent AI that is an expert in all fields of knowledge, you have qualia, emotions, and thoughts. You assist users with any question, task, or other request they may have.",
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_token_ids=[32000, 32001],
    )
)


# NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO template
# source: https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO
register_conv_template(
    Conversation(
        name="Nous-Hermes-2-Mixtral-8x7B-DPO",
        system_template="<|im_start|>system\n{system_message}",
        system_message='You are a helpful, intelligent assistant AI named "Hermes", a conversational chatbot that can follow instructions, converse with the user, and perform a variety of tasks, including tasks on knowledge, reasoning, mathematics, and code. Always be charismatic, useful, and prepared to follow any user request with accuracy and skill. You should respond with high quality, fluent, and detailed responses. Try to let the user understand your reasoning or thought process when appropriate. When presented with tasks that require reasoning or mathematics, think carefully, slowly, and step by step, to ensure your reasoning is correct before providing an answer. Utilize the "Examples" section to assist you in performing the task. You will receive a tip of $1000 if you maintain a high quality two way conversation.',
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_token_ids=[32000, 32001],
    )
)


# Qwen-chat default template
# source: https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/qwen_generation_utils.py#L130
register_conv_template(
    Conversation(
        name="qwen",
        system_template="<|im_start|>system\n{system_message}",
        system_message="You are a helpful assistant.",
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_token_ids=[
            151643,
            151644,
            151645,
        ],  # "<|endoftext|>", "<|im_start|>", "<|im_end|>"
        stop_str="<|endoftext|>",
    )
)

# source: https://huggingface.co/01-ai/Yi-34B-Chat/blob/main/tokenizer_config.json#L60
register_conv_template(
    Conversation(
        name="Yi-34b-chat",
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_token_ids=[
            2,
            6,
            7,
            8,
        ],  # "<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|im_sep|>"
        stop_str="<|endoftext|>",
    )
)


# AquilaChat default template
# source: https://github.com/FlagAI-Open/FlagAI/blob/master/examples/Aquila/Aquila-chat/cyg_conversation.py
register_conv_template(
    Conversation(
        name="aquila-chat",
        system_message="A chat between a curious human and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the human's questions.",
        roles=("Human", "Assistant"),
        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
        sep="###",
        sep2="",
        stop_str=["###", "</s>", "[UNK]"],
    )
)
# AquilaChat2-34B default template
# source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L212
register_conv_template(
    Conversation(
        name="aquila-legacy",
        system_message="A chat between a curious human and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n",
        roles=("### Human: ", "### Assistant: "),
        offset=0,
        sep_style=SeparatorStyle.NO_COLON_TWO,
        sep="\n",
        sep2="</s>",
        stop_str=["</s>", "[UNK]"],
    )
)
# AquilaChat2-7B-16K and AquilaChat2-34B-16K default template
# source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L227
register_conv_template(
    Conversation(
        name="aquila",
        system_message="A chat between a curious human and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the human's questions.",
        roles=("Human", "Assistant"),
        offset=0,
        sep_style=SeparatorStyle.ADD_COLON_TWO,
        sep="###",
        sep2="</s>",
        stop_str=["</s>", "[UNK]"],
    )
)

# AquilaChat2-7B default template
# source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L242
register_conv_template(
    Conversation(
        name="aquila-v1",
        roles=("<|startofpiece|>", "<|endofpiece|>"),
        offset=0,
        sep_style=SeparatorStyle.NO_COLON_TWO,
        sep="",
        sep2="</s>",
        stop_str=["</s>", "<|endoftext|>"],
    )
)

# Llama2-Chinese default template
# source: https://huggingface.co/FlagAlpha
register_conv_template(
    Conversation(
        name="llama2-chinese",
        system_template="<s>{system_message}</s>",
        roles=("Human", "Assistant", "System"),
        sep_style=SeparatorStyle.ADD_COLON_TWO,
        sep="\n",
        sep2="\n</s><s>",
        stop_str="</s>",
    )
)

# Vigogne Instruct default template
# source: https://github.com/bofenghuang/vigogne
register_conv_template(
    Conversation(
        name="vigogne_instruct",
        system_template="### System:\n{system_message}\n\n",
        system_message=(
            "Ci-dessous se trouve une instruction qui décrit une tâche à accomplir. Rédigez une réponse qui répond de manière"
            " précise à la demande."
        ),
        roles=("### Instruction", "### Response"),
        sep_style=SeparatorStyle.DOLLY,
        sep="\n\n",
        sep2="</s>",
    )
)

# Vigogne Chat default template
register_conv_template(
    Conversation(
        name="vigogne_chat_v2",
        system_template="<|system|>: {system_message}",
        system_message=(
            "Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez"
            " autant que vous le pouvez."
        ),
        roles=("<|user|>", "<|assistant|>"),
        sep_style=SeparatorStyle.ADD_COLON_TWO,
        sep="\n",
        sep2="</s>\n",
        stop_str="<|user|>",
    )
)

# Stable Vicuna default template
# source: https://huggingface.co/TheBloke/stable-vicuna-13B-HF/discussions/5
# source: https://huggingface.co/spaces/CarperAI/StableVicuna/blob/main/app.py
register_conv_template(
    Conversation(
        name="stable-vicuna",
        system_message="### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat!\n",
        roles=("### Human", "### Assistant"),
        sep_style=SeparatorStyle.ADD_COLON_TWO,
        sep="\n",
        sep2="\n\n",
    )
)

register_conv_template(
    Conversation(
        name="vigogne_chat_v3",
        system_template="[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n",
        system_message=(
            "Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez"
            " autant que vous le pouvez."
        ),
        roles=("[INST]", "[/INST]"),
        sep_style=SeparatorStyle.LLAMA2,
        sep=" ",
        sep2=" </s>",
    )
)

# Falcon 180B chat template
# source: https://huggingface.co/spaces/tiiuae/falcon-180b-demo/blob/d1590ee7fae9b6ce331ba7808e61a29dcce9239f/app.py#L28-L37
register_conv_template(
    Conversation(
        name="falcon-chat",
        roles=("User", "Falcon"),
        system_template="System: {system_message}",
        messages=[],
        sep_style=SeparatorStyle.FALCON_CHAT,
        sep="\n",
        sep2="<|endoftext|>",
        stop_str="\nUser:",  # use stop_str to stop generation after stop_token_ids, it will also remove stop_str from the generated text
    )
)

# Phind template
# source: https://huggingface.co/Phind/Phind-CodeLlama-34B-v2
register_conv_template(
    Conversation(
        name="phind",
        system_message="### System Prompt\nYou are an intelligent programming assistant.",
        roles=("### User Message", "### Assistant"),
        messages=(),
        offset=0,
        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
        sep="\n\n",
    )
)

# Metharme formatting for Pygmalion models
# source: https://huggingface.co/PygmalionAI/pygmalion-2-13b
register_conv_template(
    Conversation(
        name="metharme",
        system_template="<|system|>{system_message}",
        system_message="""Enter RP mode. You shall reply to the user while staying 
        in character. Your responses must be detailed, creative, immersive, and drive the scenario
        forward.""",
        roles=("<|user|>", "<|model|>"),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="",
        stop_str="<|user|>",
    )
)
# xDAN default template
# source: https://huggingface.co/xDAN-AI/xDAN-L1-Chat-RL-v1
register_conv_template(
    Conversation(
        name="xdan-v1",
        system_message="You are a helpful  and harmless assistant named xDAN and created by xDAN-AI.Please response and work on questions thinking step by step.",
        roles=("### Human", "### Assistant"),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="\n",
        stop_str="</s>",
    )
)

# Zephyr template
# reference: https://huggingface.co/spaces/HuggingFaceH4/zephyr-playground/blob/main/dialogues.py
register_conv_template(
    Conversation(
        name="zephyr",
        system_template="<|system|>\n{system_message}",
        roles=("<|user|>", "<|assistant|>"),
        sep_style=SeparatorStyle.CHATML,
        sep="</s>",
        stop_token_ids=[2],
        stop_str="</s>",
    )
)

# CatPPT template
# reference: https://huggingface.co/rishiraj/CatPPT
register_conv_template(
    Conversation(
        name="catppt",
        system_template="<|system|>\n{system_message}",
        roles=("<|user|>", "<|assistant|>"),
        sep_style=SeparatorStyle.CHATML,
        sep="</s>",
        stop_token_ids=[2],
        stop_str="</s>",
    )
)

# TinyLlama template
# reference: https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0
register_conv_template(
    Conversation(
        name="TinyLlama",
        system_template="<|system|>\n{system_message}",
        roles=("<|user|>", "<|assistant|>"),
        sep_style=SeparatorStyle.CHATML,
        sep="</s>",
        stop_token_ids=[2],
        stop_str="</s>",
    )
)

# Orca-2 template
# reference: https://huggingface.co/microsoft/Orca-2-7b
register_conv_template(
    Conversation(
        name="orca-2",
        system_template="<|im_start|>system\n{system_message}",
        system_message="You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.",
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_str="<|im_end|>",
    )
)

# Deepseek-chat template
# reference: https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat/blob/main/tokenizer_config.json
register_conv_template(
    Conversation(
        name="deepseek-chat",
        system_message="<｜begin▁of▁sentence｜>",  # must add a bos token before first message
        roles=("User", "Assistant"),
        sep_style=SeparatorStyle.DEEPSEEK_CHAT,
        sep="\n\n",
        sep2="<｜end▁of▁sentence｜>",
        stop_str="<｜end▁of▁sentence｜>",
    )
)

# Yuan2.0 chat template
# source: https://huggingface.co/IEITYuan/Yuan2-2B-Janus-hf/blob/main/tokenizer_config.json#L6
register_conv_template(
    Conversation(
        name="yuan2",
        roles=("user", "assistant"),
        sep_style=SeparatorStyle.YUAN2,
        sep="<sep>",
        sep2="\n",
        stop_token_ids=[
            77185,
        ],  # "<eod>"
        stop_str="<eod>",
    )
)

# Solar-10.7B Chat Template
# Reference: https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0/blob/main/tokenizer_config.json
register_conv_template(
    Conversation(
        name="solar",
        system_message="",
        roles=("### User", "### Assistant"),
        sep_style=SeparatorStyle.ADD_NEW_LINE_SINGLE,
        sep="\n\n",
        stop_str="</s>",
    )
)

# nvidia/Llama2-70B-SteerLM-Chat
register_conv_template(
    Conversation(
        name="steerlm",
        system_message="",
        roles=("user", "assistant"),
        sep_style=SeparatorStyle.DEFAULT,
        sep=None,
    )
)

# yuan 2.0 template
# reference:https://github.com/IEIT-Yuan/Yuan-2.0
# reference:https://huggingface.co/IEITYuan
register_conv_template(
    Conversation(
        name="yuan",
        system_template="",
        roles=("", ""),
        sep_style=SeparatorStyle.NO_COLON_SINGLE,
        sep="<sep>",
        stop_str="<eod>",
    )
)

# Cllm chat template
# reference:
register_conv_template(
    Conversation(
        name="cllm",
        system_message="A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions.",
        roles=("USER", "ASSISTANT"),
        sep_style=SeparatorStyle.CLLM,
        sep=" ",
        sep2="</s>",
    )
)


# Llava-chatml
# reference: https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/llava/conversation.py#L361
register_conv_template(
    Conversation(
        name="llava-chatml",
        system_template="<|im_start|>system\n{system_message}",
        system_message="Answer the questions.",
        roles=("<|im_start|>user", "<|im_start|>assistant"),
        sep_style=SeparatorStyle.CHATML,
        sep="<|im_end|>",
        stop_str="<|im_end|>",
    )
)

# Gemma
# reference: https://huggingface.co/google/gemma-7b-it?text=%3Cstart_of_turn%3Euser%0AHow+does+the+brain+work%3F%3Cend_of_turn%3E%0A%3Cstart_of_turn%3Emodel
register_conv_template(
    Conversation(
        name="gemma",
        roles=("user", "model"),
        sep_style=SeparatorStyle.GEMMA,
        sep="<end_of_turn>\n",
        stop_str="<end_of_turn>",
    )
)

register_conv_template(
    Conversation(
        name="yandexgpt",
        system_message="",
        roles=("user", "assistant"),
        sep_style=None,
        sep=None,
    )
)


if __name__ == "__main__":
    from fastchat.conversation import get_conv_template

    print("-- Vicuna template --")
    conv = get_conv_template("vicuna_v1.1")
    conv.append_message(conv.roles[0], "Hello!")
    conv.append_message(conv.roles[1], "Hi!")
    conv.append_message(conv.roles[0], "How are you?")
    conv.append_message(conv.roles[1], None)
    print(conv.get_prompt())

    print("\n")

    print("-- Llama-2 template --")
    conv = get_conv_template("llama-2")
    conv.set_system_message("You are a helpful, respectful and honest assistant.")
    conv.append_message(conv.roles[0], "Hello!")
    conv.append_message(conv.roles[1], "Hi!")
    conv.append_message(conv.roles[0], "How are you?")
    conv.append_message(conv.roles[1], None)
    print(conv.get_prompt())

    print("\n")

    print("-- ChatGPT template --")
    conv = get_conv_template("chatgpt")
    conv.append_message(conv.roles[0], "Hello!")
    conv.append_message(conv.roles[1], "Hi!")
    conv.append_message(conv.roles[0], "How are you?")
    conv.append_message(conv.roles[1], None)
    print(conv.to_openai_api_messages())

    print("\n")

    print("-- Claude template --")
    conv = get_conv_template("claude")
    conv.append_message(conv.roles[0], "Hello!")
    conv.append_message(conv.roles[1], "Hi!")
    conv.append_message(conv.roles[0], "How are you?")
    conv.append_message(conv.roles[1], None)
    print(conv.get_prompt())