llama_chat_format.py

from __future__ import annotations

import dataclasses
import datetime
import json
import os
import random
import string
import sys

from typing import (
    Any,
    Dict,
    Iterator,
    List,
    Literal,
    Optional,
    Tuple,
    Union,
    Protocol,
    cast,
)

import jinja2
from jinja2.ext import Extension
from jinja2.sandbox import ImmutableSandboxedEnvironment

import numpy as np
import numpy.typing as npt

import llama_cpp.llama as llama_core
import llama_cpp.llama_types as llama_types
import llama_cpp.llama_grammar as llama_grammar

from ._logger import logger
from ._utils import suppress_stdout_stderr, Singleton

### Common Chat Templates and Special Tokens ###

# Source: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json
CHATML_CHAT_TEMPLATE = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
CHATML_BOS_TOKEN = "<s>"
CHATML_EOS_TOKEN = "<|im_end|>"

# Source: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
MISTRAL_INSTRUCT_CHAT_TEMPLATE = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
MISTRAL_INSTRUCT_BOS_TOKEN = "<s>"
MISTRAL_INSTRUCT_EOS_TOKEN = "</s>"

# Source: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1/blob/main/tokenizer_config.json
MIXTRAL_INSTRUCT_CHAT_TEMPLATE = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"

# Source: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/tokenizer_config.json
LLAMA3_INSTRUCT_CHAT_TEMPLATE = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"

# Source: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct/blob/main/tokenizer_config.json
LLAMA4_INSTRUCT_BOS_TOKEN = "<|begin_of_text|>"
LLAMA4_INSTRUCT_EOS_TOKEN = "<|eot|>"
LLAMA4_INSTRUCT_CHAT_TEMPLATE = "{% if custom_tools is defined %}\n    {% set tools = custom_tools %}\n{% endif %}\n{% if not tools_in_user_message is defined %}\n    {% set tools_in_user_message = true %}\n{% endif %}\n{% if not date_string is defined %}\n    {% if strftime_now is defined %}\n        {% set date_string = strftime_now(\"%d %b %Y\") %}\n    {% else %}\n        {% set date_string = \"26 Jul 2024\" %}\n    {% endif %}\n{% endif %}\n{% if not tools is defined %}\n    {% set tools = none %}\n{% endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{% if messages[0]['role'] == 'system' %}    \n    {% if messages[0]['content'] is string %}\n        {% set system_message = messages[0]['content']|trim %}\n    {% else %}\n        {#- FIXME: The processor requires an array, always. #}\n        {% set system_message = messages[0]['content'][0]['text']|trim %}\n    {% endif %}\n    {% set messages = messages[1:] %}\n    {% set user_supplied_system_message = true %}\n{% else %}\n    {% set system_message = \"\" %}\n    {% set user_supplied_system_message = false %}\n{% endif %}\n\n{#- System message if the user supplied one #}\n{% if user_supplied_system_message %}\n    {{ \"<|header_start|>system<|header_end|>\\n\\n\" }}\n    {% if tools is not none %}\n        {{ \"Environment: ipython\\n\" }}\n    {% endif %}\n    {% if tools is not none and not tools_in_user_message %}\n        {{ \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n        {{ 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n        {{ \"Do not use variables.\\n\\n\" }}\n        {% for t in tools %}\n            {{ t | tojson(indent=4) }}\n            {{ \"\\n\\n\" }}\n        {% endfor %}\n    {% endif %}\n    {{ system_message }}\n    {{ \"<|eot|>\" }}\n{% endif %}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{% if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {% if messages | length != 0 %}\n        {% set first_user_message = messages[0]['content']|trim %}\n        {% set messages = messages[1:] %}\n    {% else %}\n        {{ raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{% endif %}\n    {{ '<|header_start|>user<|header_end|>\\n\\n' -}}\n    {{ \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{ \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{ 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{ \"Do not use variables.\\n\\n\" }}\n    {% for t in tools %}\n        {{ t | tojson(indent=4) }}\n        {{ \"\\n\\n\" }}\n    {% endfor %}\n    {{ first_user_message + \"<|eot|>\"}}\n{% endif %}\n\n{% for message in messages %}\n    {% if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n    {{ '<|header_start|>' + message['role'] + '<|header_end|>\\n\\n' }}\n        {% if message['content'] is string %}\n            {{ message['content'] }}\n        {% else %}\n            {% for content in message['content'] %}\n                {% if content['type'] == 'image' %}\n                    {{ '<|image|>' }}\n                {% elif content['type'] == 'text' %}\n                    {{ content['text'] }}\n                {% endif %}\n            {% endfor %}\n        {% endif %}\n        {{ \"<|eot|>\" }}\n    {% elif 'tool_calls' in message and message.tool_calls|length > 0 %}\n       {{ '<|header_start|>assistant<|header_end|>\\n\\n' -}}\n       {{ '<|python_start|>' }}\n        {% if message['content'] is string %}\n            {{ message['content'] }}\n        {% else %}\n            {% for content in message['content'] %}\n                {% if content['type'] == 'image' %}\n                    {{ '<|image|>' }}\n                {% elif content['type'] == 'text' %}\n                    {{ content['text'] }}\n                {% endif %}\n            {% endfor %}\n        {% endif %}\n       {{ '<|python_end|>' }}\n        {% for tool_call in message.tool_calls %}\n           {{ '{\"name\": \"' + tool_call.function.name + '\", ' }}\n           {{ '\"parameters\": ' }}\n           {{ tool_call.function.arguments | tojson }}\n           {{ \"}\" }}\n        {% endfor %}\n       {{ \"<|eot|>\" }}\n    {% elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{ \"<|header_start|>ipython<|header_end|>\\n\\n\" }}\n        {% if message.content is mapping or message.content is iterable %}\n            {{ message.content | tojson }}\n        {% else %}\n            {{ message.content }}\n        {% endif %}\n        {{ \"<|eot|>\" }}\n    {% endif %}\n{% endfor %}\n{% if add_generation_prompt %}\n    {{ '<|header_start|>assistant<|header_end|>\\n\\n' }}\n{% endif %}\n"


# Source: https://huggingface.co/openai/gpt-oss-20b/blob/main/tokenizer_config.json
GPT_OSS_BOS_TOKEN = "<|startoftext|>"
GPT_OSS_EOS_TOKEN = "<|return|>"
GPT_OSS_PAD_TOKEN = "<|endoftext|>"

### Chat Completion Handler ###


class LlamaChatCompletionHandler(Protocol):
    """Base Protocol for a llama chat completion handler.

    Very generic protocol that can be used to implement any chat format.
    The only hard requirement is that it must return a ChatCompletion when
    stream=False and an iterator of ChatCompletionChunks when stream=True."""

    def __call__(
        self,
        *,
        # llama.cpp instance
        llama: llama_core.Llama,
        # openai api parameters
        messages: List[llama_types.ChatCompletionRequestMessage],
        functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
        function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
        tools: Optional[List[llama_types.ChatCompletionTool]] = None,
        tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
        temperature: float = 0.2,
        top_p: float = 0.95,
        top_k: int = 40,
        top_n_sigma: float = -1.00,
        stream: bool = False,
        stop: Optional[Union[str, List[str]]] = [],
        seed: Optional[int] = None,
        response_format: Optional[
            llama_types.ChatCompletionRequestResponseFormat
        ] = None,
        max_tokens: Optional[int] = None,
        present_penalty: float = 0.0,
        frequency_penalty: float = 0.0,
        repeat_penalty: float = 1.1,
        model: Optional[str] = None,
        logit_bias: Optional[Dict[str, float]] = None,
        # llama.cpp parameters
        min_p: float = 0.05,
        typical_p: float = 1.0,
        mirostat_mode: int = 0,
        mirostat_tau: float = 5.0,
        mirostat_eta: float = 0.1,
        xtc_threshold: float = 0.1,
        xtc_probability: float = 0.0,
        dry_multiplier: float = 0.0,
        dry_base: float = 1.75,
        dry_allowed_length: int = 2,
        dry_penalty_last_n:int = 0,
        dry_seq_breakers: list[str] = ["\n", ":", "\"", "*"],
        adaptive_target : float = -1.0,
        adaptive_decay : float = 0.9,
        use_infill: bool = False,
        logits_processor: Optional[llama_core.LogitsProcessorList] = None,
        grammar: Optional[llama_grammar.LlamaGrammar] = None,
        logprobs: Optional[bool] = None,
        top_logprobs: Optional[int] = None,
        assistant_prefill: bool = False,
        # Reasoning Budget Params
        #
        # Generic first-reasoning-block budget control. These parameters are
        # passed through to llama.create_completion() without model-specific
        # inference or template guessing.
        reasoning_budget: int = -1,
        reasoning_start: str = "<think>",
        reasoning_end: str = "</think>",
        reasoning_budget_message: Optional[str] = None,
        reasoning_start_in_prompt: bool = False,
        reasoning_start_max_tokens: Optional[int] = 32,
        **kwargs,  # type: ignore
    ) -> Union[
        llama_types.CreateChatCompletionResponse,
        Iterator[llama_types.CreateChatCompletionStreamResponse],
    ]: ...


class LlamaChatCompletionHandlerNotFoundException(Exception):
    pass


class LlamaChatCompletionHandlerRegistry(Singleton):
    _chat_handlers: Dict[str, LlamaChatCompletionHandler] = {}

    def register_chat_completion_handler(
        self,
        name: str,
        chat_handler: LlamaChatCompletionHandler,
        overwrite: bool = False,
    ):
        if not overwrite and name in self._chat_handlers:
            raise ValueError(
                f"Formatter with name '{name}' is already registered. Use `overwrite=True` to overwrite it."
            )
        self._chat_handlers[name] = chat_handler

    def unregister_chat_handler(self, name: str):
        if name in self._chat_handlers:
            del self._chat_handlers[name]
        else:
            raise ValueError(f"No formatter registered under the name '{name}'.")

    def get_chat_completion_handler_by_name(
        self, name: str
    ) -> LlamaChatCompletionHandler:
        try:
            chat_handler = self._chat_handlers[name]
            return chat_handler
        except KeyError:
            raise LlamaChatCompletionHandlerNotFoundException(
                f"Invalid chat handler: {name} (valid formats: {list(self._chat_handlers.keys())})"
            )


def get_chat_completion_handler(name: str) -> LlamaChatCompletionHandler:
    return LlamaChatCompletionHandlerRegistry().get_chat_completion_handler_by_name(
        name
    )


def register_chat_completion_handler(name: str):
    def decorator(f: LlamaChatCompletionHandler):
        LlamaChatCompletionHandlerRegistry().register_chat_completion_handler(name, f)
        return f

    return decorator


### Chat Formatter ###


@dataclasses.dataclass
class ChatFormatterResponse:
    """Dataclass that stores completion parameters for a given chat format and
    create_chat_completion request.

    prompt contains the formatted prompt generated from the chat format and messages.
    stop contains the stop token or list of stop tokens to use for the chat format."""

    prompt: str
    stop: Optional[Union[str, List[str]]] = None
    stopping_criteria: Optional[llama_core.StoppingCriteriaList] = None
    added_special: bool = False


class ChatFormatter(Protocol):
    """Base Protocol for a chat formatter. A chat formatter is a function that
    takes a list of messages and returns a chat format response which can be used
    to generate a completion. The response can also include a stop token or list
    of stop tokens to use for the completion."""

    def __call__(
        self,
        *,
        messages: List[llama_types.ChatCompletionRequestMessage],
        **kwargs: Any,
    ) -> ChatFormatterResponse: ...


class Jinja2ChatFormatter(ChatFormatter):
    class IgnoreGenerationTags(Extension):
        """Render HuggingFace `{% generation %}` blocks without tracking.

        HuggingFace chat templates may wrap assistant text with:

            {% generation %}
            ...
            {% endgeneration %}

        Transformers uses this tag to compute assistant-token masks. In
        llama-cpp-python chat formatting we only need the final rendered prompt,
        so this extension simply removes the tag pair and renders the inner
        content as normal Jinja template content.

        This keeps compatibility with HF templates while avoiding the overhead
        of span tracking.

        More information see:
        https://github.com/huggingface/transformers/blob/39603d0e5cdb6f00e8d473d7fcbb01032d709181/src/transformers/utils/chat_template_utils.py#L425
        """

        tags = {"generation"}

        def parse(self, parser: jinja2.parser.Parser):
            # Consume the opening `{% generation %}` token.
            lineno = next(parser.stream).lineno

            # Parse and return the block body until `{% endgeneration %}`.
            # Returning the body directly makes the tag a transparent wrapper.
            body = parser.parse_statements(
                ("name:endgeneration",),
                drop_needle=True,
            )

            # Preserve line numbers for better template error messages.
            for node in body:
                node.set_lineno(lineno)

            return body

    def __init__(
        self,
        template: str,
        eos_token: str,
        bos_token: str,
        add_generation_prompt: bool = True,
        stop_token_ids: Optional[List[int]] = None,
        special_tokens_map: Optional[Dict[str, str]] = None,
    ):
        """Format chat messages with a HuggingFace-style Jinja2 chat template.

        Args:
            template:
                Raw HuggingFace chat template string.
            eos_token:
                Text form of the model EOS token.
            bos_token:
                Text form of the model BOS token.
            add_generation_prompt:
                Whether to ask the template to append the assistant generation
                prefix. This mirrors Transformers' `add_generation_prompt`.
            stop_token_ids:
                Optional token ids that should stop generation when they appear
                as the last generated token. This is llama-cpp-python specific.
            special_tokens_map:
                Optional tokenizer special-token map. Some HF templates may
                reference extra variables such as `pad_token`, `unk_token`,
                `sep_token`, or model-specific special tokens.
        """
        self.template = template
        self.eos_token = eos_token
        self.bos_token = bos_token
        self.add_generation_prompt = add_generation_prompt
        self.special_tokens_map = special_tokens_map or {}

        self.stop_token_ids = (
            {int(token_id) for token_id in stop_token_ids}
            if stop_token_ids is not None
            else None
        )

        environment = ImmutableSandboxedEnvironment(
            loader=jinja2.BaseLoader(),
            trim_blocks=True,
            lstrip_blocks=True,
            # Keep this aligned with Transformers' chat-template Jinja setup:
            # - IgnoreGenerationTags supports `{% generation %}` blocks.
            # - loopcontrols supports `{% break %}` and `{% continue %}`.
            extensions=[
                Jinja2ChatFormatter.IgnoreGenerationTags,
                jinja2.ext.loopcontrols,
            ],
        )

        # Match Transformers' chat-template JSON behavior.
        # Jinja's default `tojson` escapes HTML characters, which is not what
        # plain-text chat templates usually expect.
        environment.filters["tojson"] = self.tojson

        # Register these as globals once instead of passing them on every render.
        environment.globals["raise_exception"] = self.raise_exception
        environment.globals["strftime_now"] = self.strftime_now

        self._environment = environment
        self._template = environment.from_string(self.template)

        # Precompute static stop fields once. This avoids rebuilding closures and
        # StoppingCriteriaList objects for every chat completion request.
        self._stop = [self.eos_token] if self.eos_token else []
        self._stopping_criteria = self._build_stopping_criteria()

    @staticmethod
    def raise_exception(message: str):
        """Raise a Jinja template error from inside a chat template."""
        raise jinja2.exceptions.TemplateError(message)

    @staticmethod
    def strftime_now(format_string: str = "%Y-%m-%d %H:%M:%S") -> str:
        """Return the current local time formatted with `datetime.strftime`."""
        return datetime.datetime.now().strftime(format_string)

    @staticmethod
    def tojson(
        x: Any,
        ensure_ascii: bool = False,
        indent: Optional[int] = None,
        separators: Optional[Tuple[str, str]] = None,
        sort_keys: bool = False,
    ) -> str:
        """Serialize an object to JSON for chat-template rendering.

        This intentionally bypasses Jinja's built-in `tojson` filter because
        the built-in filter escapes HTML-sensitive characters. HuggingFace chat
        templates expect plain JSON text instead.
        """
        return json.dumps(
            x,
            ensure_ascii=ensure_ascii,
            indent=indent,
            separators=separators,
            sort_keys=sort_keys,
        )

    def _build_stopping_criteria(self):
        """Create stopping criteria once during initialization."""
        if self.stop_token_ids is None:
            return None

        stop_token_ids = self.stop_token_ids

        def stop_on_last_token(
            tokens: npt.NDArray[np.intc],
            logits: npt.NDArray[np.single],
        ) -> bool:
            # Defensive guard: generation normally calls this with at least one
            # token, but the callback should never crash on empty input.
            return len(tokens) > 0 and int(tokens[-1]) in stop_token_ids

        return llama_core.StoppingCriteriaList([stop_on_last_token])

    def __call__(
        self,
        *,
        messages: List[llama_types.ChatCompletionRequestMessage],
        functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
        function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
        tools: Optional[List[llama_types.ChatCompletionTool]] = None,
        tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
        documents: Optional[List[Dict[str, Any]]] = None,
        **kwargs: Any,
    ) -> ChatFormatterResponse:
        """Render OpenAI-style chat messages into a model prompt.

        The method builds the variable context expected by HuggingFace-style
        Jinja chat templates and renders the final prompt string used by
        llama-cpp-python.

        Template variables provided by default:
            messages:
                The chat history to render. Each item is expected to be an
                OpenAI-style message dictionary, usually containing at least
                `role` and `content`.

            eos_token:
                The model's end-of-sequence token string.

            bos_token:
                The model's beginning-of-sequence token string.

            add_generation_prompt:
                Whether the template should append the assistant generation
                prefix. This mirrors Transformers' `add_generation_prompt`.

            functions:
                Legacy OpenAI-compatible function definitions, if provided.

            function_call:
                Legacy OpenAI-compatible function-call selection, if provided.

            tools:
                OpenAI/HuggingFace-compatible tool definitions, if provided.
                This formatter expects tools to already be normalized into
                JSON-schema-like dictionaries. It does not auto-convert Python
                callables into JSON schemas like Transformers can.

            tool_choice:
                Optional tool-choice instruction, such as `"auto"`, `"none"`,
                or a specific tool/function selection object.

            documents:
                Optional RAG/document context. Some HF chat templates reference
                this variable when rendering retrieval-augmented prompts.

            **kwargs:
                Extra model-specific or template-specific variables. These are
                merged into the template context last, so they can intentionally
                override the defaults above when needed.

        Additional variables:
            Values from `special_tokens_map` are also exposed to the template,
            such as `pad_token`, `unk_token`, `sep_token`, or custom
            model-specific special tokens. Core variables like `messages`,
            `eos_token`, and `bos_token` override `special_tokens_map` entries
            by default.

        Returns:
            ChatFormatterResponse:
                Contains the rendered prompt, text stop sequences, optional
                token-id stopping criteria, and `added_special=True` because the
                chat template is responsible for adding model special tokens.

        Raises:
            jinja2.exceptions.TemplateError:
                If the template calls `raise_exception(...)` or Jinja rendering
                fails.
        """
        template_kwargs: Dict[str, Any] = {}

        # Make extra tokenizer special tokens available to templates, e.g.
        # `pad_token`, `unk_token`, `sep_token`, or model-specific tokens.
        template_kwargs.update(self.special_tokens_map)

        # Explicit core variables should override values from special_tokens_map.
        template_kwargs.update(
            {
                "messages": messages,
                "eos_token": self.eos_token,
                "bos_token": self.bos_token,
                "add_generation_prompt": self.add_generation_prompt,
                "functions": functions,
                "function_call": function_call,
                "tools": tools,
                "tool_choice": tool_choice,
                "documents": documents,
            }
        )

        # Let caller-provided kwargs extend the template context.
        # If a caller intentionally passes a same-name key, it will override the
        # defaults above. This is useful for model-specific template variables.
        template_kwargs.update(kwargs)

        prompt = self._template.render(**template_kwargs)

        return ChatFormatterResponse(
            prompt=prompt,
            stop=self._stop,
            stopping_criteria=self._stopping_criteria,
            added_special=True,
        )

    def to_chat_handler(self) -> LlamaChatCompletionHandler:
        return chat_formatter_to_chat_completion_handler(self)


def _convert_text_completion_logprobs_to_chat(
    logprobs: Optional[llama_types.CompletionLogprobs],
) -> llama_types.ChatCompletionLogprobs:
    if logprobs is None:
        return None

    return {
        "content": [
            {
                "token": token,
                "bytes": None,
                "logprob": logprob,
                "top_logprobs": [
                    {
                        "token": top_token,
                        "logprob": top_logprob,
                        "bytes": None,
                    }
                    for top_token, top_logprob in top_logprobs.items()
                ],
            } for (token, logprob, top_logprobs) in zip(logprobs["tokens"], logprobs["token_logprobs"], logprobs["top_logprobs"])
        ],
        "refusal": None,
    }

def _convert_text_completion_to_chat(
    completion: llama_types.Completion,
) -> llama_types.ChatCompletion:
    assert "usage" in completion
    return {
        "id": "chat" + completion["id"],
        "object": "chat.completion",
        "created": completion["created"],
        "model": completion["model"],
        "choices": [
            {
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": completion["choices"][0]["text"],
                },
                "logprobs": _convert_text_completion_logprobs_to_chat(completion["choices"][0]["logprobs"]),
                "finish_reason": completion["choices"][0]["finish_reason"],
            }
        ],
        "usage": completion["usage"],
    }


def _convert_text_completion_chunks_to_chat(
    chunks: Iterator[llama_types.CreateCompletionStreamResponse],
) -> Iterator[llama_types.ChatCompletionChunk]:
    for i, chunk in enumerate(chunks):
        if i == 0:
            yield {
                "id": "chat" + chunk["id"],
                "model": chunk["model"],
                "created": chunk["created"],
                "object": "chat.completion.chunk",
                "choices": [
                    {
                        "index": 0,
                        "delta": {
                            "role": "assistant",
                        },
                        "logprobs": None,
                        "finish_reason": None,
                    }
                ],
            }
        yield {
            "id": "chat" + chunk["id"],
            "model": chunk["model"],
            "created": chunk["created"],
            "object": "chat.completion.chunk",
            "choices": [
                {
                    "index": 0,
                    "delta": (
                        {
                            "content": chunk["choices"][0]["text"],
                        }
                        if chunk["choices"][0]["finish_reason"] is None
                        else {}
                    ),
                    "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]),
                    "finish_reason": chunk["choices"][0]["finish_reason"],
                }
            ],
        }


def _convert_completion_to_chat(
    completion_or_chunks: Union[
        llama_types.CreateCompletionResponse,
        Iterator[llama_types.CreateCompletionStreamResponse],
    ],
    stream: bool = False,
) -> Union[
    llama_types.CreateChatCompletionResponse, Iterator[llama_types.ChatCompletionChunk]
]:
    if stream:
        chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks  # type: ignore
        return _convert_text_completion_chunks_to_chat(chunks)
    else:
        completion: llama_types.Completion = completion_or_chunks  # type: ignore
        return _convert_text_completion_to_chat(completion)


def _convert_completion_to_chat_function(
    tool_name: str,
    completion_or_chunks: Union[
        llama_types.CreateCompletionResponse,
        Iterator[llama_types.CreateCompletionStreamResponse],
    ],
    stream: bool,
):
    if not stream:
        completion: llama_types.CreateCompletionResponse = completion_or_chunks  # type: ignore
        assert "usage" in completion
        tool_id = "call_" + "_0_" + tool_name + "_" + completion["id"]
        # TODO: Fix for legacy function calls
        chat_completion: llama_types.CreateChatCompletionResponse = {
            "id": "chat" + completion["id"],
            "object": "chat.completion",
            "created": completion["created"],
            "model": completion["model"],
            "choices": [
                {
                    "index": 0,
                    "message": {
                        "role": "assistant",
                        "content": None,
                        "function_call": {
                            "name": tool_name,
                            "arguments": completion["choices"][0]["text"],
                        },
                        "tool_calls": [
                            {
                                "id": tool_id,
                                "type": "function",
                                "function": {
                                    "name": tool_name,
                                    "arguments": completion["choices"][0]["text"],
                                },
                            }
                        ],
                    },
                    "logprobs": _convert_text_completion_logprobs_to_chat(completion["choices"][0]["logprobs"]),
                    "finish_reason": "tool_calls",
                }
            ],
            "usage": completion["usage"],
        }
        return chat_completion
    else:
        chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks  # type: ignore

        def _stream_response_to_function_stream(
            chunks: Iterator[llama_types.CreateCompletionStreamResponse],
        ) -> Iterator[llama_types.CreateChatCompletionStreamResponse]:
            # blank first message
            first = True
            id_ = None
            created = None
            model = None
            tool_id = None
            for chunk in chunks:
                if first:
                    id_ = "chat" + chunk["id"]
                    created = chunk["created"]
                    model = chunk["model"]
                    tool_id = "call_" + "_0_" + tool_name + "_" + chunk["id"]
                    yield {
                        "id": id_,
                        "object": "chat.completion.chunk",
                        "created": created,
                        "model": model,
                        "choices": [
                            {
                                "index": 0,
                                "finish_reason": None,
                                "logprobs": None,
                                "delta": {
                                    "role": "assistant",
                                    "content": None,
                                    "function_call": None,
                                    "tool_calls": None,
                                },
                            }
                        ],
                    }
                    yield {
                        "id": "chat" + chunk["id"],
                        "object": "chat.completion.chunk",
                        "created": chunk["created"],
                        "model": chunk["model"],
                        "choices": [
                            {
                                "index": 0,
                                "finish_reason": None,
                                "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]),
                                "delta": {
                                    "role": None,
                                    "content": None,
                                    "function_call": {
                                        "name": tool_name,
                                        "arguments": chunk["choices"][0]["text"],
                                    },
                                    "tool_calls": [
                                        {
                                            "index": 0,
                                            "id": tool_id,
                                            "type": "function",
                                            "function": {
                                                "name": tool_name,
                                                "arguments": chunk["choices"][0][
                                                    "text"
                                                ],
                                            },
                                        }
                                    ],
                                },
                            }
                        ],
                    }
                    first = False
                    continue
                assert tool_id is not None
                yield {
                    "id": "chat" + chunk["id"],
                    "object": "chat.completion.chunk",
                    "created": chunk["created"],
                    "model": chunk["model"],
                    "choices": [
                        {
                            "index": 0,
                            "finish_reason": None,
                            "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]),
                            "delta": {
                                "role": None,
                                "content": None,
                                "function_call": {
                                    "name": tool_name,
                                    "arguments": chunk["choices"][0]["text"],
                                },
                                "tool_calls": [
                                    {
                                        "index": 0,
                                        "id": tool_id,
                                        "type": "function",
                                        "function": {
                                            "name": tool_name,
                                            "arguments": chunk["choices"][0]["text"],
                                        },
                                    }
                                ],
                            },
                        }
                    ],
                }

            if id_ is not None and created is not None and model is not None:
                yield {
                    "id": id_,
                    "object": "chat.completion.chunk",
                    "created": created,
                    "model": model,
                    "choices": [
                        {
                            "index": 0,
                            "finish_reason": "tool_calls",
                            "logprobs": None,
                            "delta": {
                                "role": None,
                                "content": None,
                                "function_call": None,
                                "tool_calls": None,
                            },
                        }
                    ],
                }

        return _stream_response_to_function_stream(chunks)


def chat_formatter_to_chat_completion_handler(
    chat_formatter: ChatFormatter,
) -> LlamaChatCompletionHandler:
    def chat_completion_handler(
        *,
        llama: llama_core.Llama,
        messages: List[llama_types.ChatCompletionRequestMessage],
        functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
        function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
        tools: Optional[List[llama_types.ChatCompletionTool]] = None,
        tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
        temperature: float = 0.2,
        top_p: float = 0.95,
        top_k: int = 40,
        min_p: float = 0.05,
        typical_p: float = 1.0,
        stream: bool = False,
        stop: Optional[Union[str, List[str]]] = [],
        seed: Optional[int] = None,
        response_format: Optional[
            llama_types.ChatCompletionRequestResponseFormat
        ] = None,
        max_tokens: Optional[int] = None,
        present_penalty: float = 0.0,
        frequency_penalty: float = 0.0,
        repeat_penalty: float = 1.1,
        top_n_sigma: float = -1.00,
        mirostat_mode: int = 0,
        mirostat_tau: float = 5.0,
        mirostat_eta: float = 0.1,
        xtc_threshold: float = 0.1,
        xtc_probability: float = 0.0,
        dry_multiplier: float = 0.0,
        dry_base: float = 1.75,
        dry_allowed_length: int = 2,
        dry_penalty_last_n:int = 0,
        dry_seq_breakers: list[str] = ["\n", ":", "\"", "*"],
        adaptive_target : float = -1.0,
        adaptive_decay : float = 0.9,
        use_infill: bool = False,
        model: Optional[str] = None,
        logits_processor: Optional[llama_core.LogitsProcessorList] = None,
        grammar: Optional[llama_grammar.LlamaGrammar] = None,
        logit_bias: Optional[Dict[str, float]] = None,
        logprobs: Optional[bool] = None,
        top_logprobs: Optional[int] = None,
        assistant_prefill: bool = False,
        # Reasoning Budget Params
        #
        # Generic first-reasoning-block budget control. These parameters are
        # passed through to llama.create_completion() without model-specific
        # inference or template guessing.
        reasoning_budget: int = -1,
        reasoning_start: str = "<think>",
        reasoning_end: str = "</think>",
        reasoning_budget_message: Optional[str] = None,
        reasoning_start_in_prompt: bool = False,
        reasoning_start_max_tokens: Optional[int] = 32,
        **kwargs,  # type: ignore
    ) -> Union[
        llama_types.CreateChatCompletionResponse,
        Iterator[llama_types.CreateChatCompletionStreamResponse],
    ]:

        # JIT Interception for Assistant Prefill (Continue Generation)
        partial_assistant_text = ""
        if assistant_prefill:
            if not messages:
                if llama.verbose:
                    print("Llama.create_chat_completion: Warning! 'assistant_prefill=True' but messages list is empty. Ignoring prefill.", file=sys.stderr)
            elif messages[-1].get("role") != "assistant":
                if llama.verbose:
                    print(f"Llama.create_chat_completion: Warning! 'assistant_prefill=True' but last message role is '{messages[-1].get('role')}'. Expected 'assistant'. Ignoring prefill.", file=sys.stderr)
            else:
                # Safe to prefill: pop the last message without mutating the user's original list
                messages = messages.copy()
                partial_message = messages.pop()
                partial_assistant_text = partial_message.get("content", "") or ""
                if not partial_assistant_text and llama.verbose:
                    print("Llama.create_chat_completion: Warning! 'assistant_prefill=True' but the assistant message has no content.", file=sys.stderr)

        result = chat_formatter(
            messages=messages,
            functions=functions,
            function_call=function_call,
            tools=tools,
            tool_choice=tool_choice,
        )

        # Seamlessly append the partial assistant text to the standard generated Jinja template
        if partial_assistant_text:
            result.prompt += partial_assistant_text

        prompt = llama.tokenize(
            result.prompt.encode("utf-8"),
            add_bos=not result.added_special,
            special=True,
        )
        if result.stop is not None:
            stop = [] if stop is None else [stop] if isinstance(stop, str) else stop
            rstop = result.stop if isinstance(result.stop, list) else [result.stop]
            stop = stop + rstop

        stopping_criteria = None
        if result.stopping_criteria is not None:
            stopping_criteria = result.stopping_criteria

        if response_format is not None and response_format["type"] == "json_object":
            grammar = _grammar_for_response_format(
                response_format, verbose=llama.verbose
            )

        # Convert legacy functions to tools
        if functions is not None:
            tools = [
                {
                    "type": "function",
                    "function": function,
                }
                for function in functions
            ]

        # Convert legacy function_call to tool_choice
        if function_call is not None:
            if isinstance(function_call, str) and (
                function_call == "none" or function_call == "auto"
            ):
                tool_choice = function_call
            if isinstance(function_call, dict) and "name" in function_call:
                tool_choice = {
                    "type": "function",
                    "function": {
                        "name": function_call["name"],
                    },
                }

        tool = None
        if (
            tool_choice is not None
            and isinstance(tool_choice, dict)
            and tools is not None
        ):
            name = tool_choice["function"]["name"]
            tool = next((t for t in tools if t["function"]["name"] == name), None)
            if tool is None:
                raise ValueError(f"Tool choice '{name}' not found in tools.")
            schema = tool["function"]["parameters"]
            try:
                # create grammar from json schema
                grammar = llama_grammar.LlamaGrammar.from_json_schema(
                    json.dumps(schema), verbose=llama.verbose
                )
            except Exception as e:
                if llama.verbose:
                    print(str(e), file=sys.stderr)
                grammar = llama_grammar.LlamaGrammar.from_string(
                    llama_grammar.JSON_GBNF, verbose=llama.verbose
                )

        completion_or_chunks = llama.create_completion(
            prompt=prompt,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            min_p=min_p,
            typical_p=typical_p,
            logprobs=top_logprobs if logprobs else None,
            stream=stream,
            stop=stop,
            seed=seed,
            max_tokens=max_tokens,
            present_penalty=present_penalty,
            frequency_penalty=frequency_penalty,
            repeat_penalty=repeat_penalty,
            top_n_sigma=top_n_sigma,
            mirostat_mode=mirostat_mode,
            mirostat_tau=mirostat_tau,
            mirostat_eta=mirostat_eta,
            xtc_threshold=xtc_threshold,
            xtc_probability=xtc_probability,
            dry_multiplier=dry_multiplier,
            dry_base=dry_base,
            dry_allowed_length=dry_allowed_length,
            dry_penalty_last_n=dry_penalty_last_n,
            dry_seq_breakers=dry_seq_breakers,
            adaptive_target=adaptive_target,
            adaptive_decay=adaptive_decay,
            use_infill=use_infill,
            model=model,
            logits_processor=logits_processor,
            stopping_criteria=stopping_criteria,
            grammar=grammar,
            logit_bias=logit_bias,
            reasoning_budget=reasoning_budget,
            reasoning_start=reasoning_start,
            reasoning_end=reasoning_end,
            reasoning_budget_message=reasoning_budget_message,
            reasoning_start_in_prompt=reasoning_start_in_prompt,
            reasoning_start_max_tokens=reasoning_start_max_tokens,
        )
        if tool is not None:
            tool_name = tool["function"]["name"]
            return _convert_completion_to_chat_function(
                tool_name, completion_or_chunks, stream
            )
        return _convert_completion_to_chat(completion_or_chunks, stream=stream)

    return chat_completion_handler


def hf_autotokenizer_to_chat_formatter(
    pretrained_model_name_or_path: Union[str, os.PathLike[str]]
) -> ChatFormatter:
    # https://huggingface.co/docs/transformers/main/chat_templating
    # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format
    # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
    from transformers import AutoTokenizer  # type: ignore

    tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)  # type: ignore

    def format_autotokenizer(
        messages: List[llama_types.ChatCompletionRequestMessage],
        **kwargs: Any,
    ) -> ChatFormatterResponse:
        tokenizer.use_default_system_prompt = False  # type: ignore
        prompt: str = tokenizer.apply_chat_template(messages, tokenize=False)  # type: ignore
        assert isinstance(prompt, str)
        # Return formatted prompt and eos token by default
        return ChatFormatterResponse(
            prompt=prompt, stop=tokenizer.eos_token, added_special=True
        )

    return format_autotokenizer


def hf_autotokenizer_to_chat_completion_handler(
    pretrained_model_name_or_path: Union[str, os.PathLike[str]]
) -> LlamaChatCompletionHandler:
    chat_formatter = hf_autotokenizer_to_chat_formatter(pretrained_model_name_or_path)
    return chat_formatter_to_chat_completion_handler(chat_formatter)


def hf_tokenizer_config_to_chat_formatter(
    tokenizer_config: Dict[str, Any],
    add_generation_prompt: bool = True,
) -> ChatFormatter:
    assert isinstance(tokenizer_config, dict)

    assert "chat_template" in tokenizer_config
    assert isinstance(tokenizer_config["chat_template"], str)
    chat_template = tokenizer_config["chat_template"]

    assert "bos_token" in tokenizer_config
    assert isinstance(tokenizer_config["bos_token"], str)
    bos_token = tokenizer_config["bos_token"]

    assert "eos_token" in tokenizer_config
    assert isinstance(tokenizer_config["eos_token"], str)
    eos_token = tokenizer_config["eos_token"]

    env = ImmutableSandboxedEnvironment(
        trim_blocks=True,
        lstrip_blocks=True,
    ).from_string(chat_template)

    def format_tokenizer_config(
        messages: List[llama_types.ChatCompletionRequestMessage],
        **kwargs: Any,
    ) -> ChatFormatterResponse:
        # TODO: veryify this is correct
        # Add a blank assistant message to the end of the messages to prompt the model to generate a response
        if add_generation_prompt:
            messages = [
                *messages,
                llama_types.ChatCompletionRequestAssistantMessage(
                    role="assistant", content=""
                ),
            ]
        prompt = env.render(
            messages=messages,
            bos_token=bos_token,
            eos_token=eos_token,
        )
        return ChatFormatterResponse(
            prompt=prompt, stop=[eos_token, bos_token], added_special=True
        )

    return format_tokenizer_config


def hf_tokenizer_config_to_chat_completion_handler(
    tokenizer_config: Dict[str, Any],
    add_generation_prompt: bool = True,
) -> LlamaChatCompletionHandler:
    chat_formatter = hf_tokenizer_config_to_chat_formatter(
        tokenizer_config, add_generation_prompt=add_generation_prompt
    )
    return chat_formatter_to_chat_completion_handler(chat_formatter)


def guess_chat_format_from_gguf_metadata(metadata: Dict[str, str]) -> Optional[str]:
    if "tokenizer.chat_template" not in metadata:
        return None

    if metadata["tokenizer.chat_template"] == CHATML_CHAT_TEMPLATE:
        return "chatml"

    if (
        metadata["tokenizer.chat_template"] == MISTRAL_INSTRUCT_CHAT_TEMPLATE
        or metadata["tokenizer.chat_template"] == MIXTRAL_INSTRUCT_CHAT_TEMPLATE
    ):
        return "mistral-instruct"

    if metadata["tokenizer.chat_template"] == LLAMA3_INSTRUCT_CHAT_TEMPLATE:
        return "llama-3"

    return None


### Utility functions for formatting chat prompts ###
# TODO: Replace these with jinja2 templates


def _get_system_message(
    messages: List[llama_types.ChatCompletionRequestMessage],
) -> str:
    """Get the first system message."""
    for message in messages:
        if message["role"] == "system":
            return message["content"] or ""
    return ""


def _map_roles(
    messages: List[llama_types.ChatCompletionRequestMessage],
    role_map: Dict[str, str],
) -> List[Tuple[str, Optional[str]]]:
    """Map the message roles."""
    output: List[Tuple[str, Optional[str]]] = []
    for message in messages:
        role = message["role"]
        if role in role_map:
            content: str | None = (
                message["content"] if isinstance(message["content"], str) else None
            )
            output.append((role_map[role], content))
    return output


def _format_llama2(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str, sep2: str
) -> str:
    """Format the prompt with the llama2 style."""
    seps = [sep, sep2]
    ret = system_message + sep
    for i, (role, message) in enumerate(messages):
        if system_message and i == 0:
            m = message or ""
            ret += m + seps[i % 2]
        elif message:
            ret += role + message + " " + seps[i % 2]
        else:
            ret += role + " "
    return ret


def _format_add_colon_single(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
) -> str:
    """Format the prompt with the add-colon-single style."""
    ret = system_message + sep
    for role, message in messages:
        if message:
            ret += role + ": " + message + sep
        else:
            ret += role + ":"
    return ret


def _format_add_colon_two(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str, sep2: str
) -> str:
    """Format the prompt with the add-colon-two style."""
    seps = [sep, sep2]
    ret = system_message + seps[0]
    for i, (role, message) in enumerate(messages):
        if message:
            ret += role + ": " + message + seps[i % 2]
        else:
            ret += role + ":"
    return ret


def _format_no_colon_single(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
) -> str:
    """Format the prompt with the no-colon-single style."""
    ret = system_message
    for role, message in messages:
        if message:
            ret += role + message + sep
        else:
            ret += role
    return ret


def _format_add_colon_space_single(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
) -> str:
    """Format the prompt with the add-colon-space-single style."""
    ret = system_message + sep
    for role, message in messages:
        if message:
            ret += role + ": " + message + sep
        else:
            ret += role + ": "  # must be end with a space
    return ret


def _format_chatml(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
) -> str:
    """Format the prompt with the chatml style."""
    ret = "" if system_message == "" else system_message + sep + "\n"
    for role, message in messages:
        if message:
            ret += role + "\n" + message + sep + "\n"
        else:
            ret += role + "\n"
    return ret


def _format_chatglm3(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
) -> str:
    """Format the prompt with the chatglm3 style."""
    ret = ""
    if system_message:
        ret += system_message
    for role, message in messages:
        if message:
            ret += role + "\n" + " " + message
        else:
            ret += role
    return ret


def _grammar_for_json(verbose: bool = False):
    return llama_grammar.LlamaGrammar.from_string(
        llama_grammar.JSON_GBNF, verbose=verbose
    )


def _grammar_for_json_schema(
    schema: str, verbose: bool = False, fallback_to_json: bool = True
):
    try:
        return llama_grammar.LlamaGrammar.from_json_schema(schema, verbose=verbose)
    except Exception as e:
        if fallback_to_json:
            return _grammar_for_json(verbose=verbose)
        else:
            raise e


def _grammar_for_response_format(
    response_format: llama_types.ChatCompletionRequestResponseFormat,
    verbose: bool = False,
):
    if response_format["type"] != "json_object":
        return None

    if "schema" in response_format:
        return _grammar_for_json_schema(
            json.dumps(response_format["schema"]), verbose=verbose
        )
    else:
        return _grammar_for_json(verbose=verbose)


### Chat Formats ###


def register_chat_format(name: str):
    def decorator(f: ChatFormatter):
        chat_completion_handler = chat_formatter_to_chat_completion_handler(f)
        LlamaChatCompletionHandlerRegistry().register_chat_completion_handler(
            name, chat_completion_handler
        )
        return f

    return decorator


# see https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/tokenization_llama.py
# system prompt is "embedded" in the first message
@register_chat_format("llama-2")
def format_llama2(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_template = "[INST] <<SYS>>\n{system_message}\n<</SYS>>"
    _roles = dict(user="<s>[INST]", assistant="[/INST]")
    _messages = _map_roles(messages, _roles)
    system_message = _get_system_message(messages)
    if system_message:
        system_message = _system_template.format(system_message=system_message)
    _prompt = _format_llama2(system_message, _messages, " ", "</s>") + "[/INST]"
    return ChatFormatterResponse(prompt=_prompt)


# Chat format for Llama-3 models, see more details at:
# https://github.com/meta-llama/llama3/blob/main/llama/tokenizer.py#L202-L229
@register_chat_format("llama-3")
def format_llama3(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(
        system="<|start_header_id|>system<|end_header_id|>\n\n",
        user="<|start_header_id|>user<|end_header_id|>\n\n",
        assistant="<|start_header_id|>assistant<|end_header_id|>\n\n",
    )
    _sep = "<|eot_id|>"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single("", _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_sep)


# Chat format for Llama-4 models text only, see more details at:
# https://github.com/meta-llama/llama-models/blob/main/models/llama4/chat_format.py#L61-L316
@register_chat_format("llama-4")
def format_llama4(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(
        system="<|header_start|>system<|header_end|>\n\n",
        user="<|header_start|>user<|header_end|>\n\n",
        assistant="<|header_start|>assistant<|header_end|>\n\n",
    )
    _sep = "<|eot|>"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single("", _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_sep)


@register_chat_format("alpaca")
def format_alpaca(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(user="### Instruction", assistant="### Response")
    _sep = "\n\n"
    _sep2 = "</s>"
    system_message = _get_system_message(messages)
    _messages = _map_roles(messages, _roles)
    _prompt = _format_add_colon_two(system_message, _messages, _sep, _sep2)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("qwen")
def format_qwen(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(user="<|im_start|>user", assistant="<|im_start|>assistant")
    system_message = _get_system_message(messages) or "You are a helpful assistant."
    system_template = "<|im_start|>system\n{system_message}"
    system_message = system_template.format(system_message=system_message)
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _sep = "<|im_end|>"
    _prompt = _format_chatml(system_message, _messages, _sep)
    _sep2 = "<|endoftext|>"
    return ChatFormatterResponse(prompt=_prompt, stop=_sep2)


@register_chat_format("vicuna")
def format(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _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 = dict(user="USER", assistant="ASSISTANT")
    _sep = " "
    _sep2 = "</s>"
    system_message = _system_message
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_two(system_message, _messages, _sep, _sep2)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("oasst_llama")
def format_oasst_llama(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_template = "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n"
    _roles = dict(user="<|prompter|>", assistant="<|assistant|>")
    _sep = "</s>"
    system_message = _get_system_message(messages)
    system_message = _system_template.format(system_message=system_message)
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("baichuan-2")
def format_baichuan2(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_template = "{system_message}"
    _roles = dict(user="<reserved_106>", assistant="<reserved_107>")
    _sep = ""
    system_message = _get_system_message(messages)
    system_message = _system_template.format(system_message=system_message)
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("baichuan")
def format_baichuan(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_template = "{system_message}"
    _roles = dict(user="<reserved_102>", assistant="<reserved_103>")
    _sep = ""
    system_message = _get_system_message(messages)
    system_message = _system_template.format(system_message=system_message)
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("openbuddy")
def format_openbuddy(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_message = """You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
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.
You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.

"""
    _roles = dict(user="User", assistant="Assistant")
    _sep = "\n"
    system_message = _system_message
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("redpajama-incite")
def format_redpajama_incite(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_message = _get_system_message(messages)
    _roles = dict(user="<human>", assistant="<bot>")
    _sep = "\n"
    _stop = "<human>"
    system_message = _system_message
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_stop)


@register_chat_format("snoozy")
def format_snoozy(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_template = "### Instruction:\n{system_message}"
    default_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."
    _system_message = _get_system_message(messages)
    _system_message = (
        _system_message if _system_message != "" else default_system_message
    )
    system_message = system_template.format(system_message=_system_message)
    _roles = dict(user="### Prompt", assistant="### Response")
    _sep = "\n"
    _stop = "###"
    system_message = _system_message
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_stop)


@register_chat_format("phind")
def format_phind(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(user="### User Message", assistant="### Assistant")
    _sep = "\n\n"
    _system_message = "### System Prompt\nYou are an intelligent programming assistant."
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_single(_system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("intel")
def format_intel(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(user="### User:", assistant="### Assistant:")
    _sep = "\n"
    _system_message = "### System:\n{system_message}"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_single(_system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("open-orca")
def format_open_orca(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    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 = "<|end_of_turn|>\n"
    # stop_token_ids=[32000, 32001],  # "<|end_of_turn|>"
    stop_str = "User"
    system_message = system_template.format(system_message=system_message)
    _messages = _map_roles(messages, dict(zip(roles, roles)))
    _messages.append((roles[1], None))
    _prompt = _format_add_colon_space_single(system_message, _messages, sep)
    return ChatFormatterResponse(prompt=_prompt, stop=stop_str)


@register_chat_format("mistrallite")
def format_mistrallite(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(user="<|prompter|>", assistant="</s>\n<|assistant|>")
    _sep = " "
    system_template = """<|system|>{system_message}</s>"""
    system_message = _get_system_message(messages)
    system_message = system_template.format(system_message=system_message)
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("zephyr")
def format_zephyr(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_template = """<|system|>
{system_message}"""
    system_message = _get_system_message(messages)
    system_message = system_template.format(system_message=system_message)
    _roles = dict(user="<|user|>\n", assistant="<|assistant|>\n")
    _sep = "</s>"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_chatml(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_sep)


@register_chat_format("pygmalion")
def format_pygmalion(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_template = """<|system|>{system_message}"""
    system_message = _get_system_message(messages)
    system_message = system_template.format(system_message=system_message)
    _roles = dict(user="<|user|>", assistant="<|model|>")
    _sep = "\n"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_chatml(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_sep)


@register_chat_format("chatml")
def format_chatml(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_template = """<|im_start|>system
{system_message}"""
    system_message = _get_system_message(messages)
    system_message = system_template.format(system_message=system_message)
    _roles = dict(user="<|im_start|>user", assistant="<|im_start|>assistant")
    _sep = "<|im_end|>"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_chatml(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_sep)


@register_chat_format("mistral-instruct")
def format_mistral_instruct(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    eos = "</s>"
    stop = eos
    prompt = ""
    for message in messages:
        if (
            message["role"] == "user"
            and message["content"] is not None
            and isinstance(message["content"], str)
        ):
            prompt += "[INST] " + message["content"]
        elif message["role"] == "assistant" and message["content"] is not None:
            prompt += " [/INST]" + message["content"] + eos
    prompt += " [/INST]"
    return ChatFormatterResponse(prompt=prompt, stop=stop)


@register_chat_format("chatglm3")
def format_chatglm3(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_template = """<|system|>
{system_message}"""
    system_message = _get_system_message(messages)
    system_message = system_template.format(system_message=system_message)
    _roles = dict(user="<|user|>", assistant="<|assistant|>")
    _sep = "</s>"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_chatglm3(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_sep)


@register_chat_format("openchat")
def format_openchat(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_template = "{system_message}<|end_of_turn|>"
    system_message = _get_system_message(messages)
    system_message = system_template.format(system_message=system_message)
    _roles = dict(
        user="GPT4 Correct User: ", assistant="<|end_of_turn|>GPT4 Correct Assistant: "
    )
    _sep = "<|end_of_turn|>"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_chatml(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_sep)


# Chat format for Saiga models, see more details and available models:
# https://huggingface.co/collections/IlyaGusev/saiga2-saigamistral-6505d4ccc3d1e53166b636cd
@register_chat_format("saiga")
def format_saiga(
    messages: list[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _message_template = "<s>{role}\n{content}</s>"
    _roles = dict(user="user", bot="bot", system="system")
    _messages = _map_roles(messages, _roles)

    _prompt = ""
    for role, content in _messages:
        if content:
            _prompt += _message_template.format(role=role, content=content)
        else:
            _prompt += f"<s>{role}\n"
    # Response template
    _prompt += "<s>bot"
    return ChatFormatterResponse(prompt=_prompt.strip())


# Chat format for Google's Gemma models, see more details and available models:
# https://huggingface.co/collections/google/gemma-release-65d5efbccdbb8c4202ec078b
@register_chat_format("gemma")
def format_gemma(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_message = _get_system_message(messages)
    if system_message != "":
        logger.debug(
            "`role='system'` messages are not allowed on Google's Gemma models."
        )
    _roles = dict(user="<start_of_turn>user\n", assistant="<start_of_turn>model\n")
    _sep = "<end_of_turn>\n"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single(system_message="", messages=_messages, sep=_sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_sep)


# Tricky chat formats that require custom chat handlers


@register_chat_completion_handler("functionary")
def functionary_chat_handler(
    llama: llama_core.Llama,
    messages: List[llama_types.ChatCompletionRequestMessage],
    functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
    function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
    tools: Optional[List[llama_types.ChatCompletionTool]] = None,
    tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
    temperature: float = 0.2,
    top_p: float = 0.95,
    top_k: int = 40,
    min_p: float = 0.05,
    typical_p: float = 1.0,
    stream: bool = False,
    stop: Optional[Union[str, List[str]]] = [],
    response_format: Optional[llama_types.ChatCompletionRequestResponseFormat] = None,
    max_tokens: Optional[int] = None,
    present_penalty: float = 0.0,
    frequency_penalty: float = 0.0,
    repeat_penalty: float = 1.1,
    top_n_sigma: float = -1.00,
    mirostat_mode: int = 0,
    mirostat_tau: float = 5.0,
    mirostat_eta: float = 0.1,
    xtc_threshold: float = 0.1,
    xtc_probability: float = 0.0,
    dry_multiplier: float = 0.0,
    dry_base: float = 1.75,
    dry_allowed_length: int = 2,
    dry_penalty_last_n:int = 0,
    dry_seq_breakers: list[str] = ["\n", ":", "\"", "*"],
    adaptive_target : float = -1.0,
    adaptive_decay : float = 0.9,
    use_infill: bool = False,
    model: Optional[str] = None,
    logits_processor: Optional[llama_core.LogitsProcessorList] = None,
    grammar: Optional[llama_grammar.LlamaGrammar] = None,
    **kwargs,  # type: ignore
) -> Union[llama_types.ChatCompletion, Iterator[llama_types.ChatCompletionChunk]]:
    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. The assistant calls functions with appropriate input when necessary"""

    def generate_type_definition(
        param: Dict[str, llama_types.JsonType], indent_level: int, shared_defs
    ) -> str:
        indent = "  " * indent_level
        if "$ref" in param:
            # Reference to a shared definition
            ref_name = param["$ref"].split("/")[
                -1
            ]  # Extract the type name from the reference
            return ref_name
        elif param.get("type") == "array":
            items = param.get("items", {})
            item_type = generate_type_definition(items, indent_level + 1, shared_defs)
            return f"Array<{item_type}>"
        elif param.get("type") == "object":
            properties = param.get("properties", {})
            nested_schema = "{\n"
            for nested_param_name, nested_param in properties.items():
                nested_param_type = generate_type_definition(
                    nested_param, indent_level + 1, shared_defs
                )
                nested_schema += (
                    f"{indent}  {nested_param_name}: {nested_param_type},\n"
                )
            nested_schema += indent + "}"
            return nested_schema
        elif "enum" in param:
            # Enum type
            return " | ".join([f'"{enum_value}"' for enum_value in param["enum"]])
        else:
            # Simple type
            return param.get("type", "any")

    def generate_shared_definitions(shared_defs, indent_level: int) -> str:
        indent = "  " * indent_level
        shared_definitions = ""
        for def_name, def_properties in shared_defs.items():
            shared_definitions += f"{indent}type {def_name} = "
            if def_properties.get("type") == "object":
                shared_definitions += generate_type_definition(
                    def_properties, indent_level, shared_defs
                )
            elif "enum" in def_properties:
                # Enum type
                shared_definitions += " | ".join(
                    [f'"{enum_value}"' for enum_value in def_properties["enum"]]
                )
            shared_definitions += ";\n"
        return shared_definitions

    def generate_schema_from_functions(functions, namespace="functions") -> str:
        schema = (
            "// Supported function definitions that should be called when necessary.\n"
        )
        schema += f"namespace {namespace} {{\n\n"

        # Generate shared definitions
        shared_definitions = {}
        for function in functions:
            parameters = function.get("parameters", {})
            shared_definitions.update(parameters.get("$defs", {}))

        schema += generate_shared_definitions(shared_definitions, 1)

        for function in functions:
            function_name = function["name"]
            description = function.get("description", "")
            parameters = function.get("parameters", {})
            required_params = parameters.get("required", [])

            schema += f"  // {description}\n"
            schema += f"  type {function_name} = (_: {{\n"

            for param_name, param in parameters.get("properties", {}).items():
                param_description = param.get("description", "")
                param_type = generate_type_definition(param, 2, shared_definitions)
                optional_indicator = "" if param_name in required_params else "?"
                schema += f"    // {param_description}\n"
                schema += f"    {param_name}{optional_indicator}: {param_type},\n"
            schema += "  }) => any;\n\n"

        schema += "}} // namespace {}\n".format(namespace)
        return schema

    def prepare_messages_for_inference(
        messages: List[llama_types.ChatCompletionRequestMessage],
        functions: Optional[List[llama_types.ChatCompletionFunctions]] = None,
        tools: Optional[List[llama_types.ChatCompletionTool]] = None,
    ):
        all_messages: List[llama_types.ChatCompletionRequestMessage] = []
        if functions is not None:
            all_messages.append(
                llama_types.ChatCompletionRequestSystemMessage(
                    role="system", content=generate_schema_from_functions(functions)
                )
            )

        if tools is not None:
            all_messages.append(
                llama_types.ChatCompletionRequestSystemMessage(
                    role="system",
                    content=generate_schema_from_functions(
                        [
                            tool["function"]
                            for tool in tools
                            if tool["type"] == "function"
                        ]
                    ),
                )
            )

        all_messages.append(
            llama_types.ChatCompletionRequestSystemMessage(
                role="system", content=SYSTEM_MESSAGE
            )
        )

        for message in messages:
            # Function call responses
            if message["role"] == "function" and "name" in message:
                message["name"] = f"functions.{message['name']}"
            # Function call requests by assistant
            if "function_call" in message:
                message["function_call"][
                    "name"
                ] = f"functions.{message['function_call']['name']}"
            all_messages.append(message)

        all_messages.append(
            llama_types.ChatCompletionRequestAssistantMessage(
                role="assistant", content=None
            )
        )

        def message_to_str(msg: llama_types.ChatCompletionRequestMessage):
            if msg["role"] == "system":
                return f"system:\n{msg['content']}\n"

            elif msg["role"] == "function" and "name" in msg:
                return f"function name={msg['name']}:\n{msg['content']}\n"
            elif msg["role"] == "function" and "function_call" in msg:
                return f"function name={msg['function_call']['name']}:\n{msg['function_call']['arguments']}\n"
            elif msg["role"] == "tool":
                if msg["content"] is not None:
                    return f"function name={msg['tool_call_id']}:\n{msg['content']}\n"
                else:
                    return f"function name={msg['tool_call_id']}\n"
            elif msg["role"] == "user":
                if msg["content"] is None:
                    return "user:\n</s></s>\n"
                else:
                    return f"user:\n</s>{msg['content']}</s>\n"
            elif msg["role"] == "assistant":
                if msg["content"] is not None and "function_call" in msg:
                    return f"assistant:\n{msg['content']}\nassistant to={msg['function_call']['name']}:\n{msg['function_call']['arguments']}</s>\n"
                elif "function_call" in msg:
                    return f"assistant to={msg['function_call']['name']}:\n{msg['function_call']['arguments']}</s>\n"
                elif "tool_calls" in msg and len(msg["tool_calls"]) > 0:
                    for tool_call in msg[
                        "tool_calls"
                    ]:  # NOTE: probably doesn't work with the functionary model
                        return f"assistant to={tool_call['id']}:\n{tool_call['function']['arguments']}</s>\n"
                elif msg["content"] is None:
                    return "assistant"
                else:
                    return f"assistant:\n{msg['content']}\n"
            else:
                raise ValueError(f"Unsupported role: {msg['role']}")

        return "".join([message_to_str(msg) for msg in all_messages])

    if tools is not None:
        functions = [tool["function"] for tool in tools if tool["type"] == "function"]

    if tool_choice is not None:
        function_call = (
            tool_choice if isinstance(tool_choice, str) else tool_choice["function"]
        )

    prompt = prepare_messages_for_inference(messages, functions, tools)

    if function_call is None and (functions is None or len(functions) == 0):
        completion_or_completion_chunks = llama.create_completion(
            prompt=prompt + ":\n",
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            min_p=min_p,
            typical_p=typical_p,
            stream=stream,
            stop=["user:", "</s>"],
            max_tokens=max_tokens,
            present_penalty=present_penalty,
            frequency_penalty=frequency_penalty,
            repeat_penalty=repeat_penalty,
            top_n_sigma=top_n_sigma,
            mirostat_mode=mirostat_mode,
            mirostat_tau=mirostat_tau,
            mirostat_eta=mirostat_eta,
            xtc_threshold=xtc_threshold,
            xtc_probability=xtc_probability,
            dry_multiplier=dry_multiplier,
            dry_base=dry_base,
            dry_allowed_length=dry_allowed_length,
            dry_penalty_last_n=dry_penalty_last_n,
            dry_seq_breakers=dry_seq_breakers,
            adaptive_target=adaptive_target,
            adaptive_decay=adaptive_decay,
            use_infill=use_infill,
            model=model,
            logits_processor=logits_processor,
            grammar=grammar,
        )
        return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream)  # type: ignore

    if function_call is None or (
        isinstance(function_call, str) and function_call == "auto"
    ):
        stop = "\n"
        completion: llama_types.Completion = llama.create_completion(
            prompt=prompt, stop=stop, stream=False
        )  # type: ignore
        completion_text = completion["choices"][0]["text"]
        # strip " to=functions." and ending ":"
        function_call = completion_text.split(".")[-1][:-1]
        new_prompt = prompt + completion_text + stop
    elif isinstance(function_call, str) and function_call != "none":
        new_prompt = prompt + ":\n"
    elif isinstance(function_call, dict):
        new_prompt = prompt + f" to=functions.{function_call['name']}:\n"
        function_call = function_call["name"]
    else:
        new_prompt = prompt + ":\n"

    function_body = None
    for function in functions or []:
        if function["name"] == function_call:
            function_body = function["parameters"]
            break
    for tool in tools or []:
        if tool["type"] == "function" and tool["function"]["name"] == function_call:
            function_body = tool["function"]["parameters"]
            break

    if function_body is not None:
        try:
            with suppress_stdout_stderr(disable=llama.verbose):
                grammar_text = llama_grammar.json_schema_to_gbnf(
                    json.dumps(function_body)
                )
                grammar = llama_grammar.LlamaGrammar.from_string(
                    llama_grammar.json_schema_to_gbnf(json.dumps(function_body)),
                    verbose=llama.verbose,
                )
                print(grammar_text)
        except Exception as e:
            if llama.verbose:
                print(
                    "Failed to parse function body as JSON schema, falling back to default grammar"
                )
                print(e)
            with suppress_stdout_stderr(disable=llama.verbose):
                grammar = llama_grammar.LlamaGrammar.from_string(
                    llama_grammar.JSON_GBNF,
                    verbose=llama.verbose,
                )
    else:
        with suppress_stdout_stderr(disable=llama.verbose):
            grammar = llama_grammar.LlamaGrammar.from_string(
                llama_grammar.JSON_GBNF, verbose=llama.verbose
            )

    completion: llama_types.Completion = llama.create_completion(
        prompt=new_prompt,
        stop=["user:", "</s>"],
        stream=False,
        grammar=grammar,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        min_p=min_p,
        typical_p=typical_p,
        present_penalty=present_penalty,
        frequency_penalty=frequency_penalty,
        repeat_penalty=repeat_penalty,
        top_n_sigma=top_n_sigma,
        mirostat_mode=mirostat_mode,
        mirostat_tau=mirostat_tau,
        mirostat_eta=mirostat_eta,
        xtc_threshold=xtc_threshold,
        xtc_probability=xtc_probability,
        dry_multiplier=dry_multiplier,
        dry_base=dry_base,
        dry_allowed_length=dry_allowed_length,
        dry_penalty_last_n=dry_penalty_last_n,
        dry_seq_breakers=dry_seq_breakers,
        adaptive_target=adaptive_target,
        adaptive_decay=adaptive_decay,
        use_infill=use_infill,
        model=model,
        logits_processor=logits_processor,
    )  # type: ignore

    assert "usage" in completion
    assert isinstance(function_call, str)
    assert stream is False  # TODO: support stream mode

    if llama.verbose:
        print(new_prompt)
        print(completion["choices"][0]["text"])

    # TODO: support stream mode
    return llama_types.CreateChatCompletionResponse(
        id="chat" + completion["id"],
        object="chat.completion",
        created=completion["created"],
        model=completion["model"],
        choices=[
            {
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": None,
                    "function_call": {
                        "name": function_call,
                        "arguments": completion["choices"][0]["text"],
                    },
                    "tool_calls": [
                        {
                            "id": function_call,
                            "type": "function",
                            "function": {
                                "name": function_call,
                                "arguments": completion["choices"][0]["text"],
                            },
                        }
                    ],
                },
                "logprobs": _convert_text_completion_logprobs_to_chat(completion["choices"][0]["logprobs"]),
                "finish_reason": "tool_calls",
            }
        ],
        usage=completion["usage"],
    )


@register_chat_completion_handler("functionary-v1")
@register_chat_completion_handler("functionary-v2")
def functionary_v1_v2_chat_handler(
    llama: llama_core.Llama,
    messages: List[llama_types.ChatCompletionRequestMessage],
    functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
    function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
    tools: Optional[List[llama_types.ChatCompletionTool]] = None,
    tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
    temperature: float = 0.2,
    top_p: float = 0.95,
    top_k: int = 40,
    min_p: float = 0.05,
    typical_p: float = 1.0,
    stream: bool = False,
    stop: Optional[Union[str, List[str]]] = [],
    response_format: Optional[llama_types.ChatCompletionRequestResponseFormat] = None,
    max_tokens: Optional[int] = None,
    present_penalty: float = 0.0,
    frequency_penalty: float = 0.0,
    repeat_penalty: float = 1.1,
    top_n_sigma: float = -1.00,
    mirostat_mode: int = 0,
    mirostat_tau: float = 5.0,
    mirostat_eta: float = 0.1,
    xtc_threshold: float = 0.1,
    xtc_probability: float = 0.0,
    dry_multiplier: float = 0.0,
    dry_base: float = 1.75,
    dry_allowed_length: int = 2,
    dry_penalty_last_n:int = 0,
    dry_seq_breakers: list[str] = ["\n", ":", "\"", "*"],
    adaptive_target : float = -1.0,
    adaptive_decay : float = 0.9,
    use_infill: bool = False,
    model: Optional[str] = None,
    logits_processor: Optional[llama_core.LogitsProcessorList] = None,
    grammar: Optional[llama_grammar.LlamaGrammar] = None,
    **kwargs,  # type: ignore
) -> Union[llama_types.ChatCompletion, Iterator[llama_types.ChatCompletionChunk]]:
    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. The assistant calls functions with appropriate input when necessary"""

    tokenizer = llama.tokenizer_
    assert hasattr(
        tokenizer, "hf_tokenizer"
    ), "Please provide a valid hf_tokenizer_path from https://huggingface.co/meetkai when initializing the Llama class"
    from transformers import AutoTokenizer

    if "<|START_OF_FUNCTION_CALL|>" in tokenizer.hf_tokenizer.additional_special_tokens:
        version = "v1"
        END_SYSTEM_TOKEN = "<|END_OF_SYSTEM|>"
        END_USER_TOKEN = "<|END_OF_USER|>"
        END_ASSISTANT_TOKEN = "<|END_OF_ASSISTANT|>"
        END_FUNCTION_RESULT_TOKEN = "<|END_OF_FUNCTION_RESULT|>"
        START_FUNCTION_CALL_TOKEN = "<|START_OF_FUNCTION_CALL|>"
        END_FUNCTION_CALL_TOKEN = "<|END_OF_FUNCTION_CALL|>"
    else:
        version = "v2"
        RECIPIENT_TOKEN = "<|recipient|>"
        FROM_TOKEN = "<|from|>"
        STOP_TOKEN = "<|stop|>"
        CONTENT_TOKEN = "<|content|>"

    def generate_type_definition(
        param: Dict[str, llama_types.JsonType], indent_level: int, shared_defs
    ) -> str:
        indent = "  " * indent_level
        if "$ref" in param:
            # Reference to a shared definition
            ref_name = param["$ref"].split("/")[
                -1
            ]  # Extract the type name from the reference
            return ref_name
        elif param.get("type") == "array":
            items = param.get("items", {})
            item_type = generate_type_definition(items, indent_level + 1, shared_defs)
            return f"Array<{item_type}>"
        elif param.get("type") == "object":
            properties = param.get("properties", {})
            nested_schema = "{\n"
            for nested_param_name, nested_param in properties.items():
                nested_param_type = generate_type_definition(
                    nested_param, indent_level + 1, shared_defs
                )
                nested_schema += (
                    f"{indent}  {nested_param_name}: {nested_param_type},\n"
                )
            nested_schema += indent + "}"
            return nested_schema
        elif "enum" in param:
            # Enum type
            return " | ".join([f'"{enum_value}"' for enum_value in param["enum"]])
        else:
            # Simple type
            return param.get("type", "any")

    def generate_shared_definitions(shared_defs, indent_level: int) -> str:
        indent = "  " * indent_level
        shared_definitions = ""
        for def_name, def_properties in shared_defs.items():
            shared_definitions += f"{indent}type {def_name} = "
            if def_properties.get("type") == "object":
                shared_definitions += generate_type_definition(
                    def_properties, indent_level, shared_defs
                )
            elif "enum" in def_properties:
                # Enum type
                shared_definitions += " | ".join(
                    [f'"{enum_value}"' for enum_value in def_properties["enum"]]
                )
            shared_definitions += ";\n"
        return shared_definitions

    def generate_schema_from_functions(functions, namespace="functions") -> str:
        schema = (
            "// Supported function definitions that should be called when necessary.\n"
        )
        schema += f"namespace {namespace} {{\n\n"

        # Generate shared definitions
        shared_definitions = {}
        for function in functions:
            parameters = function.get("parameters", {})
            shared_definitions.update(parameters.get("$defs", {}))

        schema += generate_shared_definitions(shared_definitions, 1)

        for function in functions:
            function_name = function["name"]
            description = function.get("description", "")
            parameters = function.get("parameters", {})
            required_params = parameters.get("required", [])

            schema += f"// {description}\n"
            schema += f"type {function_name} = (_: {{\n"

            for param_name, param in parameters.get("properties", {}).items():
                param_description = param.get("description", "")
                param_type = generate_type_definition(param, 2, shared_definitions)
                optional_indicator = "" if param_name in required_params else "?"
                schema += f"// {param_description}\n"
                schema += f"{param_name}{optional_indicator}: {param_type},\n"
            schema += "}) => any;\n\n"

        schema += "}} // namespace {}".format(namespace)
        return schema

    def prepare_messages_for_inference(
        messages: List[llama_types.ChatCompletionRequestMessage],
        tokenizer: AutoTokenizer,
        version: Literal["v1", "v2"],
        functions: Optional[List[llama_types.ChatCompletionFunctions]] = None,
        tools: Optional[List[llama_types.ChatCompletionTool]] = None,
        tool_choice: Union[Dict, str] = "auto",
    ):
        all_messages: List[llama_types.ChatCompletionRequestMessage] = []
        if tool_choice == "none":
            all_messages.append(
                llama_types.ChatCompletionRequestSystemMessage(
                    role="system", content=generate_schema_from_functions([])
                )
            )
        else:
            if functions is not None:
                all_messages.append(
                    llama_types.ChatCompletionRequestSystemMessage(
                        role="system", content=generate_schema_from_functions(functions)
                    )
                )
            elif tools is not None and tool_choice != "none":
                all_messages.append(
                    llama_types.ChatCompletionRequestSystemMessage(
                        role="system",
                        content=generate_schema_from_functions(
                            [
                                tool["function"]
                                for tool in tools
                                if tool["type"] == "function"
                            ]
                        ),
                    )
                )

        all_messages.append(
            llama_types.ChatCompletionRequestSystemMessage(
                role="system", content=SYSTEM_MESSAGE
            )
        )

        for message in messages:
            # Function call responses
            if message["role"] == "function" and "name" in message:
                message["name"] = f"functions.{message['name']}"
            # Function call requests by assistant
            if "function_call" in message:
                message["function_call"][
                    "name"
                ] = f"functions.{message['function_call']['name']}"
            all_messages.append(message)

        if version == "v1":
            suffix = "assistant:\n"
        else:
            suffix = "<|from|>assistant\n<|recipient|>"

        return (
            tokenizer.hf_tokenizer.apply_chat_template(all_messages, tokenize=False)
            + suffix
        )

    if tools is not None:
        functions = [tool["function"] for tool in tools if tool["type"] == "function"]

    if tool_choice is not None:
        function_call = (
            tool_choice if isinstance(tool_choice, str) else tool_choice["function"]
        )
    elif function_call is not None:
        pass
    else:
        function_call = "auto"

    prompt = prepare_messages_for_inference(
        messages, tokenizer, version, functions, tools, function_call
    )

    # If no tools/functions are provided
    if function_call == "none" or functions is None or len(functions) == 0:
        if version == "v1":
            stop = END_ASSISTANT_TOKEN
        else:
            stop = STOP_TOKEN
            prompt += "all\n<|content|>"

        completion_or_completion_chunks = llama.create_completion(
            prompt=prompt,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            min_p=min_p,
            typical_p=typical_p,
            stream=stream,
            stop=stop,
            max_tokens=max_tokens,
            present_penalty=present_penalty,
            frequency_penalty=frequency_penalty,
            repeat_penalty=repeat_penalty,
            top_n_sigma=top_n_sigma,
            mirostat_mode=mirostat_mode,
            mirostat_tau=mirostat_tau,
            mirostat_eta=mirostat_eta,
            xtc_threshold=xtc_threshold,
            xtc_probability=xtc_probability,
            dry_multiplier=dry_multiplier,
            dry_base=dry_base,
            dry_allowed_length=dry_allowed_length,
            dry_penalty_last_n=dry_penalty_last_n,
            dry_seq_breakers=dry_seq_breakers,
            adaptive_target=adaptive_target,
            adaptive_decay=adaptive_decay,
            use_infill=use_infill,
            model=model,
            logits_processor=logits_processor,
            grammar=grammar,
        )
        if stream is False:
            completion_or_completion_chunks["choices"][0]["text"] = (
                completion_or_completion_chunks["choices"][0]["text"].lstrip()
            )
        return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream)  # type: ignore

    def get_grammar(function_call):
        function_body = None
        for function in functions or []:
            if function["name"] == function_call:
                function_body = function["parameters"]
                break
        for tool in tools or []:
            if tool["type"] == "function" and tool["function"]["name"] == function_call:
                function_body = tool["function"]["parameters"]
                break

        try:
            with suppress_stdout_stderr(disable=llama.verbose):
                grammar_text = llama_grammar.json_schema_to_gbnf(
                    json.dumps(function_body)
                )
                grammar = llama_grammar.LlamaGrammar.from_string(
                    llama_grammar.json_schema_to_gbnf(json.dumps(function_body))
                )
                print(grammar_text)
        except Exception as e:
            if llama.verbose:
                print(
                    "Failed to parse function body as JSON schema, falling back to default grammar"
                )
                print(e)
            with suppress_stdout_stderr(disable=llama.verbose):
                grammar = llama_grammar.LlamaGrammar.from_string(
                    llama_grammar.JSON_GBNF, verbose=llama.verbose
                )

        return grammar

    def create_completion(prompt, stop, grammar):
        completion = cast(
            llama_types.Completion,
            llama.create_completion(
                prompt=prompt,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                min_p=min_p,
                typical_p=typical_p,
                stream=stream,
                stop=stop,
                max_tokens=max_tokens,
                present_penalty=present_penalty,
                frequency_penalty=frequency_penalty,
                repeat_penalty=repeat_penalty,
                top_n_sigma=top_n_sigma,
                mirostat_mode=mirostat_mode,
                mirostat_tau=mirostat_tau,
                mirostat_eta=mirostat_eta,
                xtc_threshold=xtc_threshold,
                xtc_probability=xtc_probability,
                dry_multiplier=dry_multiplier,
                dry_base=dry_base,
                dry_allowed_length=dry_allowed_length,
                dry_penalty_last_n=dry_penalty_last_n,
                dry_seq_breakers=dry_seq_breakers,
                adaptive_target=adaptive_target,
                adaptive_decay=adaptive_decay,
                use_infill=use_infill,
                model=model,
                logits_processor=logits_processor,
                grammar=grammar,
            ),
        )

        return completion

    content = ""
    function_calls, function_bodies = [], []
    completion_tokens = 0

    def generate_streaming(tools, functions, function_call, prompt):
        assert version == "v2", "Streaming for v1 is not supported"

        chunk_id, chunk_created = None, None

        # If tool_choice/function_call is provided
        if isinstance(function_call, dict):
            prompt += f"{function_call['name']}\n{CONTENT_TOKEN}"
            grammar = get_grammar(function_call["name"])
            stops = [STOP_TOKEN, FROM_TOKEN]
            tool_id = "".join(
                [random.choice(string.ascii_letters + string.digits) for _ in range(24)]
            )
            completion = create_completion(prompt=prompt, stop=stops, grammar=grammar)
            completion_text = ""
            first = True
            for chunk in completion:
                # Yield the tool/function name first
                if first:
                    if tools is not None:
                        func_call_dict = {
                            "tool_calls": [
                                {
                                    "index": 0,
                                    "id": "call_" + tool_id,
                                    "type": "function",
                                    "function": {
                                        "name": function_call["name"],
                                        "arguments": "",
                                    },
                                }
                            ]
                        }
                    else:
                        func_call_dict = {
                            "function_call": {
                                "name": function_call["name"],
                                "arguments": "",
                            }
                        }
                    yield llama_types.CreateChatCompletionStreamResponse(
                        id="chat" + chunk["id"],
                        object="chat.completion.chunk",
                        created=chunk["created"],
                        model=chunk["model"],
                        choices=[
                            {
                                "index": 0,
                                "logprobs": None,
                                "delta": {
                                    "role": None,
                                    "content": None,
                                    **func_call_dict,
                                },
                            }
                        ],
                    )
                    first = False
                if tools is not None:
                    func_call_dict = {
                        "tool_calls": [
                            {
                                "index": 0,
                                "id": "call_" + tool_id,
                                "type": "function",
                                "function": {
                                    "name": None,
                                    "arguments": chunk["choices"][0]["text"].rstrip(),
                                },
                            }
                        ]
                    }
                else:
                    func_call_dict = {
                        "function_call": {
                            "name": None,
                            "arguments": chunk["choices"][0]["text"].rstrip(),
                        }
                    }
                if len(chunk["choices"][0]["text"].rstrip()) > 0:
                    yield llama_types.CreateChatCompletionStreamResponse(
                        id="chat" + chunk["id"],
                        object="chat.completion.chunk",
                        created=chunk["created"],
                        model=chunk["model"],
                        choices=[
                            {
                                "index": 0,
                                "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]),
                                "delta": {
                                    "role": None,
                                    "content": None,
                                    **func_call_dict,
                                },
                            }
                        ],
                    )
            # Yield tool_call/function_call stop message
            yield llama_types.CreateChatCompletionStreamResponse(
                id="chat" + chunk["id"],
                object="chat.completion.chunk",
                created=chunk["created"],
                model=chunk["model"],
                choices=[
                    {
                        "index": 0,
                        "finish_reason": (
                            "tool_calls" if tools is not None else "function_call"
                        ),
                        "logprobs": None,
                        "delta": {
                            "role": None,
                            "content": None,
                            "function_call": None,
                            "tool_calls": None,
                        },
                    }
                ],
            )
        # If "auto" or no tool_choice/function_call
        elif isinstance(function_call, str) and function_call == "auto":
            tool_index = 0
            while True:
                # Generate function name first
                grammar = None
                stops = CONTENT_TOKEN
                completion = create_completion(
                    prompt=prompt, stop=stops, grammar=grammar
                )
                completion_text = ""
                for chunk in completion:
                    completion_text += chunk["choices"][0]["text"]
                if chunk_id is None:
                    chunk_id = chunk["id"]
                if chunk_created is None:
                    chunk_created = chunk["created"]
                function_name = completion_text.strip()
                if function_name == "all":
                    prompt += "all\n<|content|>"
                    # Yield the first empty message for content
                    yield llama_types.CreateChatCompletionStreamResponse(
                        id="chat" + chunk_id,
                        model=chunk["model"],
                        created=chunk_created,
                        object="chat.completion.chunk",
                        choices=[
                            {
                                "index": 0,
                                "delta": {"role": "assistant", "content": ""},
                                "logprobs": None,
                                "finish_reason": None,
                            }
                        ],
                    )
                else:
                    prompt += f"{function_name}\n<|content|>"
                    grammar = get_grammar(function_name)
                    tool_id = "".join(
                        [
                            random.choice(string.ascii_letters + string.digits)
                            for _ in range(24)
                        ]
                    )
                    if tools is not None:
                        func_call_dict = {
                            "tool_calls": [
                                {
                                    "index": tool_index,
                                    "id": "call_" + tool_id,
                                    "type": "function",
                                    "function": {
                                        "name": function_name,
                                        "arguments": "",
                                    },
                                }
                            ]
                        }
                    else:
                        func_call_dict = {
                            "function_call": {"name": function_name, "arguments": ""}
                        }
                    # Stream function name
                    yield llama_types.CreateChatCompletionStreamResponse(
                        id="chat" + chunk_id,
                        object="chat.completion.chunk",
                        created=chunk_created,
                        model=chunk["model"],
                        choices=[
                            {
                                "index": 0,
                                "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]),
                                "delta": {
                                    "role": "assistant",
                                    "content": None,
                                    **func_call_dict,
                                },
                            }
                        ],
                    )
                # Generate content
                stops = [RECIPIENT_TOKEN, STOP_TOKEN]
                completion = create_completion(
                    prompt=prompt, stop=stops, grammar=grammar
                )
                if function_name == "all":
                    completion_text = ""
                    stop_sequence, buffer, is_end = (
                        "\n<|from|>assistant\n<|recipient|>",
                        [],
                        False,
                    )
                    for i, chunk in enumerate(completion):
                        completion_text += chunk["choices"][0]["text"]
                        if is_end:
                            buffer.append(chunk["choices"][0]["text"].strip(" "))
                            if stop_sequence.startswith("".join(buffer)):
                                continue
                            else:
                                buffer.pop()
                                while len(buffer) > 0:
                                    yield llama_types.CreateChatCompletionStreamResponse(
                                        id="chat" + chunk_id,
                                        object="chat.completion.chunk",
                                        created=chunk_created,
                                        model=chunk["model"],
                                        choices=[
                                            {
                                                "index": 0,
                                                "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]),
                                                "delta": {
                                                    "role": "assistant",
                                                    "content": buffer.pop(0),
                                                },
                                            }
                                        ],
                                    )
                                is_end = False
                        elif chunk["choices"][0]["text"] == "\n":
                            is_end = True
                            buffer.append(chunk["choices"][0]["text"].strip(" "))
                            continue

                        if len(buffer) == 0 and len(chunk["choices"][0]["text"]) > 0:
                            yield llama_types.CreateChatCompletionStreamResponse(
                                id="chat" + chunk_id,
                                object="chat.completion.chunk",
                                created=chunk_created,
                                model=chunk["model"],
                                choices=[
                                    {
                                        "index": 0,
                                        "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]),
                                        "delta": {
                                            "role": "assistant",
                                            "content": (
                                                chunk["choices"][0]["text"]
                                                if i > 0
                                                else chunk["choices"][0][
                                                    "text"
                                                ].lstrip()
                                            ),
                                        },
                                    }
                                ],
                            )
                    # Check whether the model wants to generate another turn
                    if (
                        "<|from|> assistant" in completion_text
                        or "<|from|>assistant" in completion_text
                    ):
                        if completion_text.endswith("\n<|from|>assistant\n"):
                            cleaned_completion_text = completion_text[
                                : -len("\n<|from|>assistant\n")
                            ].strip()
                        elif completion_text.endswith("\n<|from|> assistant\n"):
                            cleaned_completion_text = completion_text[
                                : -len("\n<|from|> assistant\n")
                            ].strip()
                        else:
                            cleaned_completion_text = completion_text.strip()
                        prompt += f"{cleaned_completion_text}\n<|from|>assistant\n<|recipient|>"
                    else:
                        # Yield stop message
                        yield llama_types.CreateChatCompletionStreamResponse(
                            id="chat" + chunk_id,
                            model=chunk["model"],
                            created=chunk_created,
                            object="chat.completion.chunk",
                            choices=[
                                {
                                    "index": 0,
                                    "delta": {},
                                    "logprobs": None,
                                    "finish_reason": "stop",
                                }
                            ],
                        )
                        break
                else:
                    # Check whether the model wants to generate another turn
                    completion_text = ""
                    for chunk in completion:
                        completion_text += chunk["choices"][0]["text"]
                        if len(chunk["choices"][0]["text"].rstrip()) > 0:
                            if tools is not None:
                                func_call_dict = {
                                    "tool_calls": [
                                        {
                                            "index": tool_index,
                                            "id": "call_" + tool_id,
                                            "type": "function",
                                            "function": {
                                                "name": None,
                                                "arguments": chunk["choices"][0][
                                                    "text"
                                                ].rstrip(),
                                            },
                                        }
                                    ]
                                }
                            else:
                                func_call_dict = {
                                    "function_call": {
                                        "name": None,
                                        "arguments": chunk["choices"][0][
                                            "text"
                                        ].rstrip(),
                                    }
                                }
                            yield llama_types.CreateChatCompletionStreamResponse(
                                id="chat" + chunk_id,
                                object="chat.completion.chunk",
                                created=chunk_created,
                                model=chunk["model"],
                                choices=[
                                    {
                                        "index": 0,
                                        "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]),
                                        "delta": {
                                            "role": None,
                                            "content": None,
                                            **func_call_dict,
                                        },
                                    }
                                ],
                            )
                    prompt += completion_text.strip()
                    grammar = None
                    completion = create_completion(
                        prompt=prompt, stop=stops, grammar=grammar
                    )
                    completion_text += "".join(
                        [chunk["choices"][0]["text"] for chunk in completion]
                    )
                    if (
                        "<|from|> assistant" in completion_text
                        or "<|from|>assistant" in completion_text
                    ) and tools is not None:
                        prompt += "\n<|from|>assistant\n<|recipient|>"
                        tool_index += 1
                    else:
                        # Yield tool_call/function_call stop message
                        yield llama_types.CreateChatCompletionStreamResponse(
                            id="chat" + chunk_id,
                            object="chat.completion.chunk",
                            created=chunk_created,
                            model=chunk["model"],
                            choices=[
                                {
                                    "index": 0,
                                    "finish_reason": (
                                        "tool_calls"
                                        if tools is not None
                                        else "function_call"
                                    ),
                                    "logprobs": None,
                                    "delta": {
                                        "role": None,
                                        "content": None,
                                        "function_call": None,
                                        "tool_calls": None,
                                    },
                                }
                            ],
                        )
                        break

    if stream is not False:
        return generate_streaming(
            tools=tools, functions=functions, function_call=function_call, prompt=prompt
        )
    else:
        if version == "v1":
            # If no or "auto" tool_choice/function_call
            if isinstance(function_call, str) and function_call == "auto":
                stops = ["\n", END_ASSISTANT_TOKEN]
            # If tool_choice/function_call is provided
            elif isinstance(function_call, dict):
                prompt += f"{START_FUNCTION_CALL_TOKEN}{function_call['name']}:\n"
                stops = END_FUNCTION_CALL_TOKEN
                function_call = function_call["name"]
                function_calls.append(function_call)
                grammar = get_grammar(function_call)
            else:
                prompt = prompt
                stops = ["\n", END_ASSISTANT_TOKEN]

            completion = create_completion(prompt=prompt, stop=stops, grammar=grammar)
            completion_text = completion["choices"][0]["text"]
            completion_tokens += completion["usage"]["completion_tokens"]

            # If the generation does not involve a function call
            if (
                START_FUNCTION_CALL_TOKEN not in prompt
                and START_FUNCTION_CALL_TOKEN not in completion_text
            ):
                completion["usage"]["completion_tokens"] = completion_tokens
                return _convert_completion_to_chat(completion, stream=stream)  # type: ignore
            # If the generation involves a function call in completion, generate the parameters
            elif (
                START_FUNCTION_CALL_TOKEN not in prompt
                and START_FUNCTION_CALL_TOKEN in completion_text
            ):
                prompt += (
                    completion_text.replace(
                        f"{START_FUNCTION_CALL_TOKEN} ", START_FUNCTION_CALL_TOKEN
                    )
                    + "\n"
                )
                function_calls.append(
                    completion_text.split(START_FUNCTION_CALL_TOKEN)[-1][:-1].strip()
                )
                grammar = get_grammar(function_calls[-1])
                completion = create_completion(
                    prompt=prompt, stop=END_FUNCTION_CALL_TOKEN, grammar=grammar
                )
                completion_tokens += completion["usage"]["completion_tokens"]
                function_bodies.append(completion["choices"][0]["text"].strip())
            # If the prompt involves a function call, just append generated parameters to function_bodies
            else:
                function_bodies.append(completion_text.strip())
        else:
            # If tool_choice/function_call is provided
            if isinstance(function_call, dict):
                prompt += f"{function_call['name']}\n{CONTENT_TOKEN}"
                function_call = function_call["name"]
                function_calls.append(function_call)
                grammar = get_grammar(function_call)
                stops = [STOP_TOKEN, FROM_TOKEN]
                completion = create_completion(
                    prompt=prompt, stop=stops, grammar=grammar
                )
                completion_text = completion["choices"][0]["text"]
                completion_tokens += completion["usage"]["completion_tokens"]
                function_bodies.append(completion_text.strip())
            # If "auto" or no tool_choice/function_call
            elif isinstance(function_call, str) and function_call == "auto":
                while True:
                    # Generate function name first
                    grammar = None
                    stops = CONTENT_TOKEN
                    completion = create_completion(
                        prompt=prompt, stop=stops, grammar=grammar
                    )
                    completion_text = completion["choices"][0]["text"]
                    completion_tokens += completion["usage"]["completion_tokens"]
                    function_name = completion_text.strip()
                    if function_name == "all":
                        prompt += "all\n<|content|>"
                    else:
                        function_call = completion_text.strip()
                        prompt += f"{function_call}\n<|content|>"
                        function_calls.append(function_call)
                        grammar = get_grammar(function_call)
                    # Generate content
                    stops = [RECIPIENT_TOKEN, STOP_TOKEN]
                    completion = create_completion(
                        prompt=prompt, stop=stops, grammar=grammar
                    )
                    completion_text = completion["choices"][0]["text"]
                    completion_tokens += completion["usage"]["completion_tokens"]
                    if function_name == "all":
                        if completion_text.endswith("\n<|from|>assistant\n"):
                            content += completion_text[: -len("\n<|from|>assistant\n")]
                        if completion_text.endswith("\n<|from|> assistant\n"):
                            content += completion_text[-len("\n<|from|> assistant\n")]
                        else:
                            content += completion_text
                        content = content.lstrip()
                        # Check whether the model wants to generate another turn
                        if (
                            "<|from|> assistant" in completion_text
                            or "<|from|>assistant" in completion_text
                        ):
                            if completion_text.endswith("\n<|from|>assistant\n"):
                                cleaned_completion_text = completion_text[
                                    : -len("\n<|from|>assistant\n")
                                ].strip()
                            elif completion_text.endswith("\n<|from|> assistant\n"):
                                cleaned_completion_text = completion_text[
                                    -len("\n<|from|> assistant\n")
                                ].strip()
                            else:
                                cleaned_completion_text = completion_text.strip()
                            prompt += f"{cleaned_completion_text}\n<|from|>assistant\n<|recipient|>"
                        else:
                            break
                    else:
                        function_bodies.append(completion_text.strip())
                        # Check whether the model wants to generate another turn
                        prompt += completion_text.strip()
                        grammar = None
                        completion = create_completion(
                            prompt=prompt, stop=stops, grammar=grammar
                        )
                        completion_tokens += completion["usage"]["completion_tokens"]
                        if (
                            "<|from|> assistant" in completion["choices"][0]["text"]
                            or "<|from|>assistant" in completion["choices"][0]["text"]
                        ):
                            prompt += "\n<|from|>assistant\n<|recipient|>"
                        else:
                            break

        assert "usage" in completion
        assert len(function_calls) == len(function_bodies)

        tool_calls: List[llama_types.ChatCompletionMessageToolCall] = []
        for function_call, function_body in zip(function_calls, function_bodies):
            tool_calls.append(
                {
                    "id": "call_"
                    + "".join(
                        [
                            random.choice(string.ascii_letters + string.digits)
                            for _ in range(24)
                        ]
                    ),
                    "type": "function",
                    "function": {
                        "name": function_call,
                        "arguments": function_body,
                    },
                }
            )

        # TODO: support stream mode
        function_call_dict: Union[
            Dict[str, str],
            Dict[
                Literal["function_call"],
                llama_types.ChatCompletionRequestAssistantMessageFunctionCall,
            ],
        ] = {}
        if len(tool_calls) > 0:
            if tools is not None:
                function_call_dict["tool_calls"] = tool_calls
            else:
                function_call_dict["function_call"] = {
                    "name": tool_calls[0]["function"]["name"],
                    "arguments": tool_calls[0]["function"]["arguments"],
                }
        completion["usage"]["completion_tokens"] = completion_tokens
        return llama_types.CreateChatCompletionResponse(
            id="chat" + completion["id"],
            object="chat.completion",
            created=completion["created"],
            model=completion["model"],
            choices=[
                {
                    "index": 0,
                    "logprobs": _convert_text_completion_logprobs_to_chat(completion["choices"][0]["logprobs"]),
                    "message": {
                        "role": "assistant",
                        "content": None if content == "" else content,
                        **function_call_dict,
                    },
                    "finish_reason": "tool_calls" if len(tool_calls) > 0 else "stop",
                }
            ],
            usage=completion["usage"],
        )


@register_chat_completion_handler("chatml-function-calling")
def chatml_function_calling(
    llama: llama_core.Llama,
    messages: List[llama_types.ChatCompletionRequestMessage],
    functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
    function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
    tools: Optional[List[llama_types.ChatCompletionTool]] = None,
    tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
    temperature: float = 0.2,
    top_p: float = 0.95,
    top_k: int = 40,
    min_p: float = 0.05,
    typical_p: float = 1.0,
    stream: bool = False,
    stop: Optional[Union[str, List[str]]] = [],
    response_format: Optional[llama_types.ChatCompletionRequestResponseFormat] = None,
    max_tokens: Optional[int] = None,
    present_penalty: float = 0.0,
    frequency_penalty: float = 0.0,
    repeat_penalty: float = 1.1,
    top_n_sigma: float = -1.00,
    mirostat_mode: int = 0,
    mirostat_tau: float = 5.0,
    mirostat_eta: float = 0.1,
    xtc_threshold: float = 0.1,
    xtc_probability: float = 0.0,
    dry_multiplier: float = 0.0,
    dry_base: float = 1.75,
    dry_allowed_length: int = 2,
    dry_penalty_last_n:int = 0,
    dry_seq_breakers: list[str] = ["\n", ":", "\"", "*"],
    adaptive_target : float = -1.0,
    adaptive_decay : float = 0.9,
    use_infill: bool = False,
    model: Optional[str] = None,
    logits_processor: Optional[llama_core.LogitsProcessorList] = None,
    grammar: Optional[llama_grammar.LlamaGrammar] = None,
    logprobs: Optional[bool] = None,
    top_logprobs: Optional[int] = None,
    **kwargs,  # type: ignore
) -> Union[
    llama_types.CreateChatCompletionResponse,
    Iterator[llama_types.CreateChatCompletionStreamResponse],
]:
    function_calling_template = (
        "{% for message in messages %}"
        "<|im_start|>{{ message.role }}\n"
        # System message
        "{% if message.role == 'system' %}"
        "{{ message.content }}"
        "{% if tool_calls %}"
        "\n\nYou have access to the following functions:\n"
        "{% for tool in tools %}"
        "\nfunctions.{{ tool.function.name }}:\n"
        "{{ tool.function.parameters | tojson }}"
        "\n{% endfor %}"
        "\n\nYou can respond to users messages with either a single message or one or more function calls."
        "\n\nTo respond with a message begin the message with 'message:', use the following format:"
        "\n\nmessage:"
        "\n<message>"
        "\n\nTo respond with one or more function calls begin the message with 'functions.<function_name>:', use the following format:"
        "\n\nfunctions.<function_name>:"
        '\n{ "arg1": "value1", "arg2": "value2" }'
        "\nfunctions.<function_name>:"
        '\n{ "arg1": "value1", "arg2": "value2" }'
        "{% endif %}"
        "<|im_end|>\n"
        "{% endif %}"
        # User message
        "{% if message.role == 'user' %}"
        "{{ message.content }}"
        "<|im_end|>\n"
        "{% endif %}"
        # Assistant message
        "{% if message.role == 'assistant' %}"
        ## Reglar message
        "{% if message.content and message.content | length > 0 %}"
        "{% if tool_calls %}"
        "message:\n"
        "{% endif %}"
        "{{ message.content }}"
        "<|im_end|>\n"
        "{% endif %}"
        ## Function calls
        "{% if 'tool_calls' in message %}"
        "{% for tool_call in message.tool_calls %}"
        "functions.{{ tool_call.function.name }}:\n"
        "{{ tool_call.function.arguments }}"
        "{% endfor %}"
        "<|im_end|>\n"
        "{% endif %}"
        "{% endif %}"
        "{% endfor %}"
        "{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
    )
    template_renderer = ImmutableSandboxedEnvironment(
        autoescape=jinja2.select_autoescape(["html", "xml"]),
        undefined=jinja2.StrictUndefined,
    ).from_string(function_calling_template)

    # Convert legacy functions to tools
    if functions is not None:
        tools = [
            {
                "type": "function",
                "function": function,
            }
            for function in functions
        ]

    # Convert legacy function_call to tool_choice
    if function_call is not None:
        if isinstance(function_call, str) and (
            function_call == "none" or function_call == "auto"
        ):
            tool_choice = function_call
        if isinstance(function_call, dict) and "name" in function_call:
            tool_choice = {
                "type": "function",
                "function": {
                    "name": function_call["name"],
                },
            }

    stop = (
        [stop, "<|im_end|>"]
        if isinstance(stop, str)
        else stop + ["<|im_end|>"] if stop else ["<|im_end|>"]
    )

    # Case 1: No tool choice by user
    if (
        tool_choice is None
        or (isinstance(tool_choice, str) and tool_choice == "none")
        or tools is None
        or len(tools) == 0
    ):
        prompt = template_renderer.render(
            messages=messages,
            tools=[],
            tool_calls=None,
            add_generation_prompt=True,
        )

        if response_format is not None and response_format["type"] == "json_object":
            grammar = _grammar_for_response_format(response_format)

        return _convert_completion_to_chat(
            llama.create_completion(
                prompt=prompt,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                min_p=min_p,
                typical_p=typical_p,
                stream=stream,
                stop=stop,
                max_tokens=max_tokens,
                present_penalty=present_penalty,
                frequency_penalty=frequency_penalty,
                repeat_penalty=repeat_penalty,
                top_n_sigma=top_n_sigma,
                mirostat_mode=mirostat_mode,
                mirostat_tau=mirostat_tau,
                mirostat_eta=mirostat_eta,
                xtc_threshold=xtc_threshold,
                xtc_probability=xtc_probability,
                dry_multiplier=dry_multiplier,
                dry_base=dry_base,
                dry_allowed_length=dry_allowed_length,
                dry_penalty_last_n=dry_penalty_last_n,
                dry_seq_breakers=dry_seq_breakers,
                adaptive_target=adaptive_target,
                adaptive_decay=adaptive_decay,
                use_infill=use_infill,
                model=model,
                logits_processor=logits_processor,
                grammar=grammar,
                logprobs=top_logprobs if logprobs else None,
            ),
            stream=stream,
        )

    # Case 2: Tool choice by user
    if isinstance(tool_choice, dict):
        tool_name = tool_choice["function"]["name"]
        tool = next(
            (tool for tool in tools if tool["function"]["name"] == tool_name), None
        )
        if tool is None:
            raise ValueError(f"Tool with name '{tool_name}' not found in tools")
        prompt = template_renderer.render(
            messages=messages,
            tools=tools,
            tool_calls=True,
            add_generation_prompt=True,
        )
        prompt += f"functions.{tool_name}:\n"
        try:
            grammar = llama_grammar.LlamaGrammar.from_json_schema(
                json.dumps(tool["function"]["parameters"]), verbose=llama.verbose
            )
        except Exception as e:
            grammar = llama_grammar.LlamaGrammar.from_string(
                llama_grammar.JSON_GBNF, verbose=llama.verbose
            )
            if llama.verbose:
                print(
                    "Failed to parse function body as JSON schema, falling back to default grammar"
                )
                print(e)
        completion_or_chunks = llama.create_completion(
            prompt=prompt,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            min_p=min_p,
            typical_p=typical_p,
            stream=stream,
            stop=stop,
            max_tokens=max_tokens,
            present_penalty=present_penalty,
            frequency_penalty=frequency_penalty,
            repeat_penalty=repeat_penalty,
            top_n_sigma=top_n_sigma,
            mirostat_mode=mirostat_mode,
            mirostat_tau=mirostat_tau,
            mirostat_eta=mirostat_eta,
            xtc_threshold=xtc_threshold,
            xtc_probability=xtc_probability,
            dry_multiplier=dry_multiplier,
            dry_base=dry_base,
            dry_allowed_length=dry_allowed_length,
            dry_penalty_last_n=dry_penalty_last_n,
            dry_seq_breakers=dry_seq_breakers,
            adaptive_target=adaptive_target,
            adaptive_decay=adaptive_decay,
            use_infill=use_infill,
            model=model,
            logits_processor=logits_processor,
            grammar=grammar,
        )
        return _convert_completion_to_chat_function(
            tool_name, completion_or_chunks, stream
        )

    # Case 3: Automatic tool choice
    assert isinstance(tool_choice, str) and tool_choice == "auto"
    function_names = " | ".join(
        [f'''"functions.{tool['function']['name']}:"''' for tool in tools]
    )
    initial_gbnf_tool_grammar = (
        """root   ::= functions | "message:"\n"""
        f"""functions ::= {function_names}\n"""
    )
    follow_up_gbnf_tool_grammar = (
        """root   ::= functions | "<|im_end|>"\n"""
        f"""functions ::= {function_names}\n"""
    )
    prompt = template_renderer.render(
        messages=messages,
        tools=tools,
        tool_calls=True,
        add_generation_prompt=True,
    )
    completion_or_chunks = llama.create_completion(
        prompt=prompt,
        temperature=0,
        top_p=top_p,
        top_k=top_k,
        min_p=min_p,
        typical_p=typical_p,
        stream=False,
        stop=[":"],
        max_tokens=None,
        present_penalty=present_penalty,
        frequency_penalty=frequency_penalty,
        repeat_penalty=repeat_penalty,
        top_n_sigma=top_n_sigma,
        mirostat_mode=mirostat_mode,
        mirostat_tau=mirostat_tau,
        mirostat_eta=mirostat_eta,
        xtc_threshold=xtc_threshold,
        xtc_probability=xtc_probability,
        dry_multiplier=dry_multiplier,
        dry_base=dry_base,
        dry_allowed_length=dry_allowed_length,
        dry_penalty_last_n=dry_penalty_last_n,
        dry_seq_breakers=dry_seq_breakers,
        adaptive_target=adaptive_target,
        adaptive_decay=adaptive_decay,
        use_infill=use_infill,
        model=model,
        logits_processor=logits_processor,
        grammar=llama_grammar.LlamaGrammar.from_string(
            initial_gbnf_tool_grammar, verbose=llama.verbose
        ),
    )
    completion: llama_types.CreateCompletionResponse = completion_or_chunks  # type: ignore
    text = completion["choices"][0]["text"]
    if "message" in text:
        return _convert_completion_to_chat(
            llama.create_completion(
                prompt=prompt + "message:\n",
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                min_p=min_p,
                typical_p=typical_p,
                stream=stream,
                stop=["<|im_end|>"],
                logprobs=top_logprobs if logprobs else None,
                max_tokens=None,
                present_penalty=present_penalty,
                frequency_penalty=frequency_penalty,
                repeat_penalty=repeat_penalty,
                top_n_sigma=top_n_sigma,
                mirostat_mode=mirostat_mode,
                mirostat_tau=mirostat_tau,
                mirostat_eta=mirostat_eta,
                xtc_threshold=xtc_threshold,
                xtc_probability=xtc_probability,
                dry_multiplier=dry_multiplier,
                dry_base=dry_base,
                dry_allowed_length=dry_allowed_length,
                dry_penalty_last_n=dry_penalty_last_n,
                dry_seq_breakers=dry_seq_breakers,
                adaptive_target=adaptive_target,
                adaptive_decay=adaptive_decay,
                use_infill=use_infill,
                model=model,
                logits_processor=logits_processor,
                grammar=llama_grammar.LlamaGrammar.from_string(
                    follow_up_gbnf_tool_grammar, verbose=llama.verbose
                ),
            ),
            stream=stream,
        )

    # One or more function calls
    tool_name = text[len("functions.") :]
    tool = next((tool for tool in tools if tool["function"]["name"] == tool_name), None)
    if not stream:
        completions: List[llama_types.CreateCompletionResponse] = []
        completions_tool_name: List[str] = []
        while tool is not None:
            prompt += f"functions.{tool_name}:\n"
            try:
                grammar = llama_grammar.LlamaGrammar.from_json_schema(
                    json.dumps(tool["function"]["parameters"]), verbose=llama.verbose
                )
            except Exception as e:
                grammar = llama_grammar.LlamaGrammar.from_string(
                    llama_grammar.JSON_GBNF, verbose=llama.verbose
                )
                if llama.verbose:
                    print(
                        "Failed to parse function body as JSON schema, falling back to default grammar"
                    )
                    print(e)
            completion_or_chunks = llama.create_completion(
                prompt=prompt,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                min_p=min_p,
                typical_p=typical_p,
                stream=False,
                stop=stop,
                max_tokens=None,
                present_penalty=present_penalty,
                frequency_penalty=frequency_penalty,
                repeat_penalty=repeat_penalty,
                top_n_sigma=top_n_sigma,
                mirostat_mode=mirostat_mode,
                mirostat_tau=mirostat_tau,
                mirostat_eta=mirostat_eta,
                xtc_threshold=xtc_threshold,
                xtc_probability=xtc_probability,
                dry_multiplier=dry_multiplier,
                dry_base=dry_base,
                dry_allowed_length=dry_allowed_length,
                dry_penalty_last_n=dry_penalty_last_n,
                dry_seq_breakers=dry_seq_breakers,
                adaptive_target=adaptive_target,
                adaptive_decay=adaptive_decay,
                use_infill=use_infill,
                model=model,
                logits_processor=logits_processor,
                grammar=grammar,
            )
            completion_or_chunks = cast(
                llama_types.CreateCompletionResponse, completion_or_chunks
            )
            completions.append(completion_or_chunks)
            completions_tool_name.append(tool_name)
            prompt += completion_or_chunks["choices"][0]["text"]
            prompt += "\n"

            response = llama.create_completion(
                prompt=prompt,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                min_p=min_p,
                typical_p=typical_p,
                stream=False,
                stop=stop,
                max_tokens=None,
                present_penalty=present_penalty,
                frequency_penalty=frequency_penalty,
                repeat_penalty=repeat_penalty,
                top_n_sigma=top_n_sigma,
                mirostat_mode=mirostat_mode,
                mirostat_tau=mirostat_tau,
                mirostat_eta=mirostat_eta,
                xtc_threshold=xtc_threshold,
                xtc_probability=xtc_probability,
                dry_multiplier=dry_multiplier,
                dry_base=dry_base,
                dry_allowed_length=dry_allowed_length,
                dry_penalty_last_n=dry_penalty_last_n,
                dry_seq_breakers=dry_seq_breakers,
                adaptive_target=adaptive_target,
                adaptive_decay=adaptive_decay,
                use_infill=use_infill,
                model=model,
                logits_processor=logits_processor,
                grammar=llama_grammar.LlamaGrammar.from_string(
                    follow_up_gbnf_tool_grammar, verbose=llama.verbose
                ),
            )
            response = cast(llama_types.CreateCompletionResponse, response)

            tool_name = response["choices"][0]["text"][len("functions.") :]
            tool = next(
                (tool for tool in tools if tool["function"]["name"] == tool_name), None
            )

        # Merge completions
        function_call_dict: Union[
            Dict[str, str],
            Dict[
                Literal["function_call"],
                llama_types.ChatCompletionRequestAssistantMessageFunctionCall,
            ],
        ] = (
            {
                "function_call": {
                    "name": tool_name,
                    "arguments": completions[0]["choices"][0]["text"],
                }
            }
            if len(completions) == 1
            else {}
        )
        return {
            "id": "chat" + completion["id"],
            "object": "chat.completion",
            "created": completion["created"],
            "model": completion["model"],
            "choices": [
                {
                    "finish_reason": "tool_calls",
                    "index": 0,
                    "logprobs": _convert_text_completion_logprobs_to_chat(completion["choices"][0]["logprobs"]),
                    "message": {
                        "role": "assistant",
                        "content": None,
                        "tool_calls": [
                            {
                                "id": "call_"
                                + f"_{i}_"
                                + tool_name
                                + "_"
                                + completion["id"],
                                "type": "function",
                                "function": {
                                    "name": tool_name,
                                    "arguments": completion["choices"][0]["text"],
                                },
                            }
                            for i, (tool_name, completion) in enumerate(
                                zip(completions_tool_name, completions)
                            )
                        ],
                        **function_call_dict,
                    },
                }
            ],
            "usage": {
                "completion_tokens": sum(
                    (
                        completion["usage"]["completion_tokens"]
                        if "usage" in completion
                        else 0
                    )
                    for completion in completions
                ),
                "prompt_tokens": sum(
                    completion["usage"]["prompt_tokens"] if "usage" in completion else 0
                    for completion in completions
                ),
                "total_tokens": sum(
                    completion["usage"]["total_tokens"] if "usage" in completion else 0
                    for completion in completions
                ),
            },
        }

    raise ValueError("Automatic streaming tool choice is not supported")

# Backward compatibility re-exports.
# These multimodal chat handlers have been moved to `llama_multimodal`.
# New code should import them from `llama_cpp.llama_multimodal` instead of
# `llama_cpp.llama_chat_format`.
from llama_cpp.llama_multimodal import (
    MTMDChatHandler,
    GenericMTMDChatHandler,
    Llava15ChatHandler,
    ObsidianChatHandler,
    MoondreamChatHandler,
    Llava16ChatHandler,
    NanoLlavaChatHandler,
    Llama3VisionAlphaChatHandler,
    Llama3VisionAlpha,
    MiniCPMv26ChatHandler,
    MiniCPMv45ChatHandler,
    MiniCPMV46ChatHandler,
    Gemma3ChatHandler,
    Gemma4ChatHandler,
    GLM41VChatHandler,
    GLM46VChatHandler,
    GraniteDoclingChatHandler,
    LFM2VLChatHandler,
    LFM25VLChatHandler,
    PaddleOCRChatHandler,
    Qwen25VLChatHandler,
    Qwen3ASRChatHandler,
    Qwen3VLChatHandler,
    Qwen35ChatHandler,
    Step3VLChatHandler
)