protocol.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# Adapted from
# https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/protocol/openai_api_protocol.py
import json
import time
from http import HTTPStatus
from typing import Annotated, Any, ClassVar, Literal, Optional, Union

import regex as re
import torch
from fastapi import HTTPException, UploadFile
# yapf: disable
from openai.types.chat.chat_completion_audio import (
    ChatCompletionAudio as OpenAIChatCompletionAudio)
from openai.types.chat.chat_completion_message import (
    Annotation as OpenAIAnnotation)
# yapf: enable
from openai.types.responses import (ResponseFunctionToolCall,
                                    ResponseInputItemParam, ResponseOutputItem,
                                    ResponsePrompt, ResponseReasoningItem,
                                    ResponseStatus)

# Backward compatibility for OpenAI client versions
try:  # For older openai versions (< 1.100.0)
    from openai.types.responses import ResponseTextConfig
except ImportError:  # For newer openai versions (>= 1.100.0)
    from openai.types.responses import (ResponseFormatTextConfig as
                                        ResponseTextConfig)

from openai.types.responses.response import ToolChoice
from openai.types.responses.tool import Tool
from openai.types.shared import Metadata, Reasoning
from pydantic import (BaseModel, ConfigDict, Field, TypeAdapter,
                      ValidationInfo, field_validator, model_validator)
from typing_extensions import TypeAlias

from vllm import envs
from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
                                         random_tool_call_id)
from vllm.entrypoints.score_utils import (ScoreContentPartParam,
                                          ScoreMultiModalParam)
from vllm.logger import init_logger
from vllm.pooling_params import PoolingParams
from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
                                  RequestOutputKind, SamplingParams)
from vllm.sequence import Logprob
from vllm.utils import random_uuid, resolve_obj_by_qualname

logger = init_logger(__name__)

_LONG_INFO = torch.iinfo(torch.long)


class OpenAIBaseModel(BaseModel):
    # OpenAI API does allow extra fields
    model_config = ConfigDict(extra="allow")

    # Cache class field names
    field_names: ClassVar[Optional[set[str]]] = None

    @model_validator(mode="wrap")
    @classmethod
    def __log_extra_fields__(cls, data, handler):
        result = handler(data)
        if not isinstance(data, dict):
            return result
        field_names = cls.field_names
        if field_names is None:
            # Get all class field names and their potential aliases
            field_names = set()
            for field_name, field in cls.model_fields.items():
                field_names.add(field_name)
                if alias := getattr(field, "alias", None):
                    field_names.add(alias)
            cls.field_names = field_names

        # Compare against both field names and aliases
        if any(k not in field_names for k in data):
            logger.warning(
                "The following fields were present in the request "
                "but ignored: %s",
                data.keys() - field_names,
            )
        return result


class ErrorInfo(OpenAIBaseModel):
    message: str
    type: str
    param: Optional[str] = None
    code: int


class ErrorResponse(OpenAIBaseModel):
    error: ErrorInfo


class ModelPermission(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"modelperm-{random_uuid()}")
    object: str = "model_permission"
    created: int = Field(default_factory=lambda: int(time.time()))
    allow_create_engine: bool = False
    allow_sampling: bool = True
    allow_logprobs: bool = True
    allow_search_indices: bool = False
    allow_view: bool = True
    allow_fine_tuning: bool = False
    organization: str = "*"
    group: Optional[str] = None
    is_blocking: bool = False


class ModelCard(OpenAIBaseModel):
    id: str
    object: str = "model"
    created: int = Field(default_factory=lambda: int(time.time()))
    owned_by: str = "vllm"
    root: Optional[str] = None
    parent: Optional[str] = None
    max_model_len: Optional[int] = None
    permission: list[ModelPermission] = Field(default_factory=list)


class ModelList(OpenAIBaseModel):
    object: str = "list"
    data: list[ModelCard] = Field(default_factory=list)


class PromptTokenUsageInfo(OpenAIBaseModel):
    cached_tokens: Optional[int] = None


class UsageInfo(OpenAIBaseModel):
    prompt_tokens: int = 0
    total_tokens: int = 0
    completion_tokens: Optional[int] = 0
    prompt_tokens_details: Optional[PromptTokenUsageInfo] = None


class RequestResponseMetadata(BaseModel):
    request_id: str
    final_usage_info: Optional[UsageInfo] = None


class JsonSchemaResponseFormat(OpenAIBaseModel):
    name: str
    description: Optional[str] = None
    # schema is the field in openai but that causes conflicts with pydantic so
    # instead use json_schema with an alias
    json_schema: Optional[dict[str, Any]] = Field(default=None, alias='schema')
    strict: Optional[bool] = None


class StructuralTag(OpenAIBaseModel):
    begin: str
    # schema is the field, but that causes conflicts with pydantic so
    # instead use structural_tag_schema with an alias
    structural_tag_schema: Optional[dict[str, Any]] = Field(default=None,
                                                            alias="schema")
    end: str


class StructuralTagResponseFormat(OpenAIBaseModel):
    type: Literal["structural_tag"]
    structures: list[StructuralTag]
    triggers: list[str]


class ResponseFormat(OpenAIBaseModel):
    # type must be "json_schema", "json_object", or "text"
    type: Literal["text", "json_object", "json_schema"]
    json_schema: Optional[JsonSchemaResponseFormat] = None


AnyResponseFormat = Union[ResponseFormat, StructuralTagResponseFormat]


class StreamOptions(OpenAIBaseModel):
    include_usage: Optional[bool] = True
    continuous_usage_stats: Optional[bool] = False


class FunctionDefinition(OpenAIBaseModel):
    name: str
    description: Optional[str] = None
    parameters: Optional[dict[str, Any]] = None


class ChatCompletionToolsParam(OpenAIBaseModel):
    type: Literal["function"] = "function"
    function: FunctionDefinition


class ChatCompletionNamedFunction(OpenAIBaseModel):
    name: str


class ChatCompletionNamedToolChoiceParam(OpenAIBaseModel):
    function: ChatCompletionNamedFunction
    type: Literal["function"] = "function"


# extra="forbid" is a workaround to have kwargs as a field,
# see https://github.com/pydantic/pydantic/issues/3125
class LogitsProcessorConstructor(BaseModel):
    qualname: str
    args: Optional[list[Any]] = None
    kwargs: Optional[dict[str, Any]] = None

    model_config = ConfigDict(extra="forbid")


LogitsProcessors = list[Union[str, LogitsProcessorConstructor]]


def get_logits_processors(processors: Optional[LogitsProcessors],
                          pattern: Optional[str]) -> Optional[list[Any]]:
    if processors and pattern:
        logits_processors = []
        for processor in processors:
            qualname = processor if isinstance(processor,
                                               str) else processor.qualname
            if not re.match(pattern, qualname):
                raise ValueError(
                    f"Logits processor '{qualname}' is not allowed by this "
                    "server. See --logits-processor-pattern engine argument "
                    "for more information.")
            try:
                logits_processor = resolve_obj_by_qualname(qualname)
            except Exception as e:
                raise ValueError(
                    f"Logits processor '{qualname}' could not be resolved: {e}"
                ) from e
            if isinstance(processor, LogitsProcessorConstructor):
                logits_processor = logits_processor(*processor.args or [],
                                                    **processor.kwargs or {})
            logits_processors.append(logits_processor)
        return logits_processors
    elif processors:
        raise ValueError(
            "The `logits_processors` argument is not supported by this "
            "server. See --logits-processor-pattern engine argugment "
            "for more information.")
    return None


ResponseInputOutputItem: TypeAlias = Union[ResponseInputItemParam,
                                           ResponseReasoningItem,
                                           ResponseFunctionToolCall]


class ResponsesRequest(OpenAIBaseModel):
    # Ordered by official OpenAI API documentation
    # https://platform.openai.com/docs/api-reference/responses/create
    background: Optional[bool] = False
    include: Optional[list[
        Literal[
            "code_interpreter_call.outputs",
            "computer_call_output.output.image_url",
            "file_search_call.results",
            "message.input_image.image_url",
            "message.output_text.logprobs",
            "reasoning.encrypted_content",
        ],
    ]] = None
    input: Union[str, list[ResponseInputOutputItem]]
    instructions: Optional[str] = None
    max_output_tokens: Optional[int] = None
    max_tool_calls: Optional[int] = None
    metadata: Optional[Metadata] = None
    model: Optional[str] = None
    parallel_tool_calls: Optional[bool] = True
    previous_response_id: Optional[str] = None
    prompt: Optional[ResponsePrompt] = None
    reasoning: Optional[Reasoning] = None
    service_tier: Literal["auto", "default", "flex", "scale",
                          "priority"] = "auto"
    store: Optional[bool] = True
    stream: Optional[bool] = False
    temperature: Optional[float] = None
    text: Optional[ResponseTextConfig] = None
    tool_choice: ToolChoice = "auto"
    tools: list[Tool] = Field(default_factory=list)
    top_logprobs: Optional[int] = 0
    top_p: Optional[float] = None
    truncation: Optional[Literal["auto", "disabled"]] = "disabled"
    user: Optional[str] = None

    # --8<-- [start:responses-extra-params]
    request_id: str = Field(
        default_factory=lambda: f"resp_{random_uuid()}",
        description=(
            "The request_id related to this request. If the caller does "
            "not set it, a random_uuid will be generated. This id is used "
            "through out the inference process and return in response."),
    )
    mm_processor_kwargs: Optional[dict[str, Any]] = Field(
        default=None,
        description=("Additional kwargs to pass to the HF processor."),
    )
    priority: int = Field(
        default=0,
        description=(
            "The priority of the request (lower means earlier handling; "
            "default: 0). Any priority other than 0 will raise an error "
            "if the served model does not use priority scheduling."),
    )
    cache_salt: Optional[str] = Field(
        default=None,
        description=(
            "If specified, the prefix cache will be salted with the provided "
            "string to prevent an attacker to guess prompts in multi-user "
            "environments. The salt should be random, protected from "
            "access by 3rd parties, and long enough to be "
            "unpredictable (e.g., 43 characters base64-encoded, corresponding "
            "to 256 bit). Not supported by vLLM engine V0."))
    # --8<-- [end:responses-extra-params]

    _DEFAULT_SAMPLING_PARAMS = {
        "temperature": 1.0,
        "top_p": 1.0,
    }

    def to_sampling_params(
        self,
        default_max_tokens: int,
        default_sampling_params: Optional[dict] = None,
    ) -> SamplingParams:
        if self.max_output_tokens is None:
            max_tokens = default_max_tokens
        else:
            max_tokens = min(self.max_output_tokens, default_max_tokens)

        default_sampling_params = default_sampling_params or {}
        if (temperature := self.temperature) is None:
            temperature = default_sampling_params.get(
                "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
        if (top_p := self.top_p) is None:
            top_p = default_sampling_params.get(
                "top_p", self._DEFAULT_SAMPLING_PARAMS["top_p"])
        stop_token_ids = default_sampling_params.get("stop_token_ids")

        # Structured output
        guided_decoding = None
        if self.text is not None and self.text.format is not None:
            response_format = self.text.format
            if response_format.type == "json_schema":
                guided_decoding = GuidedDecodingParams.from_optional(
                    json=response_format.schema_)
            elif response_format.type == "json_object":
                raise NotImplementedError("json_object is not supported")

        # TODO: add more parameters
        return SamplingParams.from_optional(
            temperature=temperature,
            top_p=top_p,
            max_tokens=max_tokens,
            logprobs=self.top_logprobs,
            stop_token_ids=stop_token_ids,
            output_kind=(RequestOutputKind.DELTA
                         if self.stream else RequestOutputKind.FINAL_ONLY),
            guided_decoding=guided_decoding,
        )

    @model_validator(mode="before")
    def validate_background(cls, data):
        if not data.get("background"):
            return data
        if not data.get("store", True):
            raise ValueError(
                "background can only be used when `store` is true")
        return data

    @model_validator(mode="before")
    def validate_prompt(cls, data):
        if data.get("prompt") is not None:
            raise ValueError("prompt template is not supported")
        return data

    @model_validator(mode="before")
    def check_cache_salt_support(cls, data):
        if data.get("cache_salt") is not None:
            if not envs.VLLM_USE_V1:
                raise ValueError(
                    "Parameter 'cache_salt' is not supported with "
                    "this instance of vLLM, which uses engine V0.")
            if not isinstance(data["cache_salt"],
                              str) or not data["cache_salt"]:
                raise ValueError("Parameter 'cache_salt' must be a "
                                 "non-empty string if provided.")
        return data


class ChatCompletionRequest(OpenAIBaseModel):
    # Ordered by official OpenAI API documentation
    # https://platform.openai.com/docs/api-reference/chat/create
    messages: list[ChatCompletionMessageParam]
    model: Optional[str] = None
    frequency_penalty: Optional[float] = 0.0
    logit_bias: Optional[dict[str, float]] = None
    logprobs: Optional[bool] = False
    top_logprobs: Optional[int] = 0
    max_tokens: Optional[int] = Field(
        default=None,
        deprecated=
        'max_tokens is deprecated in favor of the max_completion_tokens field')
    max_completion_tokens: Optional[int] = None
    n: Optional[int] = 1
    presence_penalty: Optional[float] = 0.0
    response_format: Optional[AnyResponseFormat] = None
    seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
    stop: Optional[Union[str, list[str]]] = []
    stream: Optional[bool] = False
    stream_options: Optional[StreamOptions] = None
    temperature: Optional[float] = None
    top_p: Optional[float] = None
    tools: Optional[list[ChatCompletionToolsParam]] = None
    tool_choice: Optional[Union[
        Literal["none"],
        Literal["auto"],
        Literal["required"],
        ChatCompletionNamedToolChoiceParam,
    ]] = "none"
    reasoning_effort: Optional[Literal["low", "medium", "high"]] = None
    include_reasoning: bool = True

    # NOTE this will be ignored by vLLM -- the model determines the behavior
    parallel_tool_calls: Optional[bool] = False
    user: Optional[str] = None

    # --8<-- [start:chat-completion-sampling-params]
    best_of: Optional[int] = None
    use_beam_search: bool = False
    top_k: Optional[int] = None
    min_p: Optional[float] = None
    repetition_penalty: Optional[float] = None
    length_penalty: float = 1.0
    stop_token_ids: Optional[list[int]] = []
    include_stop_str_in_output: bool = False
    ignore_eos: bool = False
    min_tokens: int = 0
    skip_special_tokens: bool = True
    spaces_between_special_tokens: bool = True
    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
    prompt_logprobs: Optional[int] = None
    allowed_token_ids: Optional[list[int]] = None
    bad_words: list[str] = Field(default_factory=list)
    # --8<-- [end:chat-completion-sampling-params]

    # --8<-- [start:chat-completion-extra-params]
    echo: bool = Field(
        default=False,
        description=(
            "If true, the new message will be prepended with the last message "
            "if they belong to the same role."),
    )
    add_generation_prompt: bool = Field(
        default=True,
        description=
        ("If true, the generation prompt will be added to the chat template. "
         "This is a parameter used by chat template in tokenizer config of the "
         "model."),
    )
    continue_final_message: bool = Field(
        default=False,
        description=
        ("If this is set, the chat will be formatted so that the final "
         "message in the chat is open-ended, without any EOS tokens. The "
         "model will continue this message rather than starting a new one. "
         "This allows you to \"prefill\" part of the model's response for it. "
         "Cannot be used at the same time as `add_generation_prompt`."),
    )
    add_special_tokens: bool = Field(
        default=False,
        description=(
            "If true, special tokens (e.g. BOS) will be added to the prompt "
            "on top of what is added by the chat template. "
            "For most models, the chat template takes care of adding the "
            "special tokens so this should be set to false (as is the "
            "default)."),
    )
    documents: Optional[list[dict[str, str]]] = Field(
        default=None,
        description=
        ("A list of dicts representing documents that will be accessible to "
         "the model if it is performing RAG (retrieval-augmented generation)."
         " If the template does not support RAG, this argument will have no "
         "effect. We recommend that each document should be a dict containing "
         "\"title\" and \"text\" keys."),
    )
    chat_template: Optional[str] = Field(
        default=None,
        description=(
            "A Jinja template to use for this conversion. "
            "As of transformers v4.44, default chat template is no longer "
            "allowed, so you must provide a chat template if the tokenizer "
            "does not define one."),
    )
    chat_template_kwargs: Optional[dict[str, Any]] = Field(
        default=None,
        description=(
            "Additional keyword args to pass to the template renderer. "
            "Will be accessible by the chat template."),
    )
    mm_processor_kwargs: Optional[dict[str, Any]] = Field(
        default=None,
        description=("Additional kwargs to pass to the HF processor."),
    )
    guided_json: Optional[Union[str, dict, BaseModel]] = Field(
        default=None,
        description=("If specified, the output will follow the JSON schema."),
    )
    guided_regex: Optional[str] = Field(
        default=None,
        description=(
            "If specified, the output will follow the regex pattern."),
    )
    guided_choice: Optional[list[str]] = Field(
        default=None,
        description=(
            "If specified, the output will be exactly one of the choices."),
    )
    guided_grammar: Optional[str] = Field(
        default=None,
        description=(
            "If specified, the output will follow the context free grammar."),
    )
    structural_tag: Optional[str] = Field(
        default=None,
        description=(
            "If specified, the output will follow the structural tag schema."),
    )
    guided_decoding_backend: Optional[str] = Field(
        default=None,
        description=(
            "If specified, will override the default guided decoding backend "
            "of the server for this specific request. If set, must be either "
            "'outlines' / 'lm-format-enforcer'"),
    )
    guided_whitespace_pattern: Optional[str] = Field(
        default=None,
        description=(
            "If specified, will override the default whitespace pattern "
            "for guided json decoding."),
    )
    priority: int = Field(
        default=0,
        description=(
            "The priority of the request (lower means earlier handling; "
            "default: 0). Any priority other than 0 will raise an error "
            "if the served model does not use priority scheduling."),
    )
    request_id: str = Field(
        default_factory=lambda: f"{random_uuid()}",
        description=(
            "The request_id related to this request. If the caller does "
            "not set it, a random_uuid will be generated. This id is used "
            "through out the inference process and return in response."),
    )
    logits_processors: Optional[LogitsProcessors] = Field(
        default=None,
        description=(
            "A list of either qualified names of logits processors, or "
            "constructor objects, to apply when sampling. A constructor is "
            "a JSON object with a required 'qualname' field specifying the "
            "qualified name of the processor class/factory, and optional "
            "'args' and 'kwargs' fields containing positional and keyword "
            "arguments. For example: {'qualname': "
            "'my_module.MyLogitsProcessor', 'args': [1, 2], 'kwargs': "
            "{'param': 'value'}}."))
    return_tokens_as_token_ids: Optional[bool] = Field(
        default=None,
        description=(
            "If specified with 'logprobs', tokens are represented "
            " as strings of the form 'token_id:{token_id}' so that tokens "
            "that are not JSON-encodable can be identified."))
    cache_salt: Optional[str] = Field(
        default=None,
        description=(
            "If specified, the prefix cache will be salted with the provided "
            "string to prevent an attacker to guess prompts in multi-user "
            "environments. The salt should be random, protected from "
            "access by 3rd parties, and long enough to be "
            "unpredictable (e.g., 43 characters base64-encoded, corresponding "
            "to 256 bit). Not supported by vLLM engine V0."))
    kv_transfer_params: Optional[dict[str, Any]] = Field(
        default=None,
        description="KVTransfer parameters used for disaggregated serving.")

    vllm_xargs: Optional[dict[str, Union[str, int, float]]] = Field(
        default=None,
        description=("Additional request parameters with string or "
                     "numeric values, used by custom extensions."),
    )

    # --8<-- [end:chat-completion-extra-params]

    # Default sampling parameters for chat completion requests
    _DEFAULT_SAMPLING_PARAMS: dict = {
        "repetition_penalty": 1.0,
        "temperature": 1.0,
        "top_p": 1.0,
        "top_k": 0,
        "min_p": 0.0,
    }

    def to_beam_search_params(
            self, max_tokens: int,
            default_sampling_params: dict) -> BeamSearchParams:

        n = self.n if self.n is not None else 1
        if (temperature := self.temperature) is None:
            temperature = default_sampling_params.get(
                "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])

        return BeamSearchParams(
            beam_width=n,
            max_tokens=max_tokens,
            ignore_eos=self.ignore_eos,
            temperature=temperature,
            length_penalty=self.length_penalty,
            include_stop_str_in_output=self.include_stop_str_in_output,
        )

    def to_sampling_params(
        self,
        max_tokens: int,
        logits_processor_pattern: Optional[str],
        default_sampling_params: dict,
    ) -> SamplingParams:

        # Default parameters
        if (repetition_penalty := self.repetition_penalty) is None:
            repetition_penalty = default_sampling_params.get(
                "repetition_penalty",
                self._DEFAULT_SAMPLING_PARAMS["repetition_penalty"],
            )
        if (temperature := self.temperature) is None:
            temperature = default_sampling_params.get(
                "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
        if (top_p := self.top_p) is None:
            top_p = default_sampling_params.get(
                "top_p", self._DEFAULT_SAMPLING_PARAMS["top_p"])
        if (top_k := self.top_k) is None:
            top_k = default_sampling_params.get(
                "top_k", self._DEFAULT_SAMPLING_PARAMS["top_k"])
        if (min_p := self.min_p) is None:
            min_p = default_sampling_params.get(
                "min_p", self._DEFAULT_SAMPLING_PARAMS["min_p"])

        prompt_logprobs = self.prompt_logprobs
        if prompt_logprobs is None and self.echo:
            prompt_logprobs = self.top_logprobs

        guided_json_object = None
        if self.response_format is not None:
            if self.response_format.type == "json_object":
                guided_json_object = True
            elif self.response_format.type == "json_schema":
                json_schema = self.response_format.json_schema
                assert json_schema is not None
                self.guided_json = json_schema.json_schema
            elif self.response_format.type == "structural_tag":
                structural_tag = self.response_format
                assert structural_tag is not None and isinstance(
                    structural_tag, StructuralTagResponseFormat)
                s_tag_obj = structural_tag.model_dump(by_alias=True)
                self.structural_tag = json.dumps(s_tag_obj)

        guided_decoding = GuidedDecodingParams.from_optional(
            json=self._get_guided_json_from_tool() or self.guided_json,
            regex=self.guided_regex,
            choice=self.guided_choice,
            grammar=self.guided_grammar,
            json_object=guided_json_object,
            backend=self.guided_decoding_backend,
            whitespace_pattern=self.guided_whitespace_pattern,
            structural_tag=self.structural_tag,
        )

        extra_args: dict[str, Any] = self.vllm_xargs if self.vllm_xargs else {}
        if self.kv_transfer_params:
            # Pass in kv_transfer_params via extra_args
            extra_args["kv_transfer_params"] = self.kv_transfer_params
        return SamplingParams.from_optional(
            n=self.n,
            best_of=self.best_of,
            presence_penalty=self.presence_penalty,
            frequency_penalty=self.frequency_penalty,
            repetition_penalty=repetition_penalty,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            min_p=min_p,
            seed=self.seed,
            stop=self.stop,
            stop_token_ids=self.stop_token_ids,
            logprobs=self.top_logprobs if self.logprobs else None,
            prompt_logprobs=prompt_logprobs,
            ignore_eos=self.ignore_eos,
            max_tokens=max_tokens,
            min_tokens=self.min_tokens,
            skip_special_tokens=self.skip_special_tokens,
            spaces_between_special_tokens=self.spaces_between_special_tokens,
            logits_processors=get_logits_processors(self.logits_processors,
                                                    logits_processor_pattern),
            include_stop_str_in_output=self.include_stop_str_in_output,
            truncate_prompt_tokens=self.truncate_prompt_tokens,
            output_kind=RequestOutputKind.DELTA if self.stream \
                else RequestOutputKind.FINAL_ONLY,
            guided_decoding=guided_decoding,
            logit_bias=self.logit_bias,
            bad_words= self.bad_words,
            allowed_token_ids=self.allowed_token_ids,
            extra_args=extra_args or None,
        )

    def _get_guided_json_from_tool(
            self) -> Optional[Union[str, dict, BaseModel]]:
        # user has chosen to not use any tool
        if self.tool_choice == "none" or self.tools is None:
            return None

        # user has chosen to use a named tool
        if type(self.tool_choice) is ChatCompletionNamedToolChoiceParam:
            tool_name = self.tool_choice.function.name
            tools = {tool.function.name: tool.function for tool in self.tools}
            if tool_name not in tools:
                raise ValueError(
                    f"Tool '{tool_name}' has not been passed in `tools`.")
            tool = tools[tool_name]
            return tool.parameters

        if self.tool_choice == "required":
            # Pydantic schema generation cannot be used since the JSON schema
            # has to be constructed for a specific instantiation of a tool list
            # so that parameters of a function are correctly generated
            # based on the chosen function name
            def get_tool_schema(tool: ChatCompletionToolsParam) -> dict:
                return {
                    "properties": {
                        "name": {
                            "type": "string",
                            "enum": [tool.function.name]
                        },
                        # parameters are always generated as '{}' in the final
                        # output if they are missing from the request
                        # (i.e. are None or '{}') so the schema is
                        # updated to produce an empty object in that case
                        "parameters": tool.function.parameters
                        if tool.function.parameters else {
                            "type": "object",
                            "properties": {}
                        }
                    },
                    "required": ["name", "parameters"]
                }

            def get_tool_schema_defs(
                    tools: list[ChatCompletionToolsParam]) -> dict:
                all_defs = dict[str, dict[str, Any]]()
                for tool in tools:
                    if tool.function.parameters is None:
                        continue
                    defs = tool.function.parameters.pop("$defs", {})
                    for def_name, def_schema in defs.items():
                        if def_name in all_defs and all_defs[
                                def_name] != def_schema:
                            raise ValueError(
                                f"Tool definition '{def_name}' has "
                                "multiple schemas, which is not "
                                "supported.")
                        else:
                            all_defs[def_name] = def_schema
                return all_defs

            json_schema = {
                "type": "array",
                "minItems": 1,
                "items": {
                    "type": "object",
                    "anyOf": [get_tool_schema(tool) for tool in self.tools]
                }
            }
            json_schema_defs = get_tool_schema_defs(self.tools)
            if json_schema_defs:
                json_schema["$defs"] = json_schema_defs
            return json_schema

        return None

    @model_validator(mode="before")
    @classmethod
    def validate_stream_options(cls, data):
        if data.get("stream_options") and not data.get("stream"):
            raise ValueError(
                "Stream options can only be defined when `stream=True`.")

        return data

    @model_validator(mode="before")
    @classmethod
    def check_logprobs(cls, data):
        if (prompt_logprobs := data.get("prompt_logprobs")) is not None:
            if data.get("stream") and prompt_logprobs > 0:
                raise ValueError(
                    "`prompt_logprobs` are not available when `stream=True`.")

            if prompt_logprobs < 0:
                raise ValueError("`prompt_logprobs` must be a positive value.")

        if (top_logprobs := data.get("top_logprobs")) is not None:
            if top_logprobs < 0:
                raise ValueError("`top_logprobs` must be a positive value.")

            if top_logprobs > 0 and not data.get("logprobs"):
                raise ValueError(
                    "when using `top_logprobs`, `logprobs` must be set to true."
                )

        return data

    @model_validator(mode="before")
    @classmethod
    def check_guided_decoding_count(cls, data):
        if isinstance(data, ValueError):
            raise data

        guide_count = sum([
            "guided_json" in data and data["guided_json"] is not None,
            "guided_regex" in data and data["guided_regex"] is not None,
            "guided_choice" in data and data["guided_choice"] is not None
        ])
        # you can only use one kind of guided decoding
        if guide_count > 1:
            raise ValueError(
                "You can only use one kind of guided decoding "
                "('guided_json', 'guided_regex' or 'guided_choice').")
        # you can only either use guided decoding or tools, not both
        if guide_count > 1 and data.get("tool_choice", "none") not in (
                "none",
                "auto",
                "required",
        ):
            raise ValueError(
                "You can only either use guided decoding or tools, not both.")
        return data

    @model_validator(mode="before")
    @classmethod
    def check_tool_usage(cls, data):

        # if "tool_choice" is not specified but tools are provided,
        # default to "auto" tool_choice
        if "tool_choice" not in data and data.get("tools"):
            data["tool_choice"] = "auto"

        # if "tool_choice" is "none" -- no validation is needed for tools
        if "tool_choice" in data and data["tool_choice"] == "none":
            return data

        # if "tool_choice" is specified -- validation
        if "tool_choice" in data and data["tool_choice"] is not None:

            # ensure that if "tool choice" is specified, tools are present
            if "tools" not in data or data["tools"] is None:
                raise ValueError(
                    "When using `tool_choice`, `tools` must be set.")

            # make sure that tool choice is either a named tool
            # OR that it's set to "auto" or "required"
            if data["tool_choice"] not in [
                    "auto", "required"
            ] and not isinstance(data["tool_choice"], dict):
                raise ValueError(
                    f'Invalid value for `tool_choice`: {data["tool_choice"]}! '\
                    'Only named tools, "none", "auto" or "required" '\
                    'are supported.'
                )

            # if tool_choice is "required" but the "tools" list is empty,
            # override the data to behave like "none" to align with
            # OpenAI’s behavior.
            if data["tool_choice"] == "required" and isinstance(
                    data["tools"], list) and len(data["tools"]) == 0:
                data["tool_choice"] = "none"
                del data["tools"]
                return data

            # ensure that if "tool_choice" is specified as an object,
            # it matches a valid tool
            correct_usage_message = 'Correct usage: `{"type": "function",' \
                ' "function": {"name": "my_function"}}`'
            if isinstance(data["tool_choice"], dict):
                valid_tool = False
                function = data["tool_choice"].get("function")
                if not isinstance(function, dict):
                    raise ValueError(
                        f"Invalid value for `function`: `{function}` in "
                        f"`tool_choice`! {correct_usage_message}")
                if "name" not in function:
                    raise ValueError(f"Expected field `name` in `function` in "
                                     f"`tool_choice`! {correct_usage_message}")
                function_name = function["name"]
                if not isinstance(function_name,
                                  str) or len(function_name) == 0:
                    raise ValueError(
                        f"Invalid `name` in `function`: `{function_name}`"
                        f" in `tool_choice`! {correct_usage_message}")
                for tool in data["tools"]:
                    if tool["function"]["name"] == function_name:
                        valid_tool = True
                        break
                if not valid_tool:
                    raise ValueError(
                        "The tool specified in `tool_choice` does not match any"
                        " of the specified `tools`")
        return data

    @model_validator(mode="before")
    @classmethod
    def check_generation_prompt(cls, data):
        if data.get("continue_final_message") and data.get(
                "add_generation_prompt"):
            raise ValueError("Cannot set both `continue_final_message` and "
                             "`add_generation_prompt` to True.")
        return data

    @model_validator(mode="before")
    @classmethod
    def check_cache_salt_support(cls, data):
        if data.get("cache_salt") is not None:
            if not envs.VLLM_USE_V1:
                raise ValueError(
                    "Parameter 'cache_salt' is not supported with "
                    "this instance of vLLM, which uses engine V0.")
            if not isinstance(data["cache_salt"],
                              str) or not data["cache_salt"]:
                raise ValueError("Parameter 'cache_salt' must be a "
                                 "non-empty string if provided.")
        return data


class CompletionRequest(OpenAIBaseModel):
    # Ordered by official OpenAI API documentation
    # https://platform.openai.com/docs/api-reference/completions/create
    model: Optional[str] = None
    prompt: Optional[Union[list[int], list[list[int]], str, list[str]]] = None
    prompt_embeds: Optional[Union[bytes, list[bytes]]] = None
    best_of: Optional[int] = None
    echo: Optional[bool] = False
    frequency_penalty: Optional[float] = 0.0
    logit_bias: Optional[dict[str, float]] = None
    logprobs: Optional[int] = None
    max_tokens: Optional[int] = 16
    n: int = 1
    presence_penalty: Optional[float] = 0.0
    seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
    stop: Optional[Union[str, list[str]]] = []
    stream: Optional[bool] = False
    stream_options: Optional[StreamOptions] = None
    suffix: Optional[str] = None
    temperature: Optional[float] = None
    top_p: Optional[float] = None
    user: Optional[str] = None

    # --8<-- [start:completion-sampling-params]
    use_beam_search: bool = False
    top_k: Optional[int] = None
    min_p: Optional[float] = None
    repetition_penalty: Optional[float] = None
    length_penalty: float = 1.0
    stop_token_ids: Optional[list[int]] = []
    include_stop_str_in_output: bool = False
    ignore_eos: bool = False
    min_tokens: int = 0
    skip_special_tokens: bool = True
    spaces_between_special_tokens: bool = True
    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
    allowed_token_ids: Optional[list[int]] = None
    prompt_logprobs: Optional[int] = None
    # --8<-- [end:completion-sampling-params]

    # --8<-- [start:completion-extra-params]
    add_special_tokens: bool = Field(
        default=True,
        description=(
            "If true (the default), special tokens (e.g. BOS) will be added to "
            "the prompt."),
    )
    response_format: Optional[AnyResponseFormat] = Field(
        default=None,
        description=(
            "Similar to chat completion, this parameter specifies the format "
            "of output. Only {'type': 'json_object'}, {'type': 'json_schema'}"
            ", {'type': 'structural_tag'}, or {'type': 'text' } is supported."
        ),
    )
    guided_json: Optional[Union[str, dict, BaseModel]] = Field(
        default=None,
        description="If specified, the output will follow the JSON schema.",
    )
    guided_regex: Optional[str] = Field(
        default=None,
        description=(
            "If specified, the output will follow the regex pattern."),
    )
    guided_choice: Optional[list[str]] = Field(
        default=None,
        description=(
            "If specified, the output will be exactly one of the choices."),
    )
    guided_grammar: Optional[str] = Field(
        default=None,
        description=(
            "If specified, the output will follow the context free grammar."),
    )
    guided_decoding_backend: Optional[str] = Field(
        default=None,
        description=(
            "If specified, will override the default guided decoding backend "
            "of the server for this specific request. If set, must be one of "
            "'outlines' / 'lm-format-enforcer'"),
    )
    guided_whitespace_pattern: Optional[str] = Field(
        default=None,
        description=(
            "If specified, will override the default whitespace pattern "
            "for guided json decoding."),
    )
    priority: int = Field(
        default=0,
        description=(
            "The priority of the request (lower means earlier handling; "
            "default: 0). Any priority other than 0 will raise an error "
            "if the served model does not use priority scheduling."),
    )
    request_id: str = Field(
        default_factory=lambda: f"{random_uuid()}",
        description=(
            "The request_id related to this request. If the caller does "
            "not set it, a random_uuid will be generated. This id is used "
            "through out the inference process and return in response."),
    )
    logits_processors: Optional[LogitsProcessors] = Field(
        default=None,
        description=(
            "A list of either qualified names of logits processors, or "
            "constructor objects, to apply when sampling. A constructor is "
            "a JSON object with a required 'qualname' field specifying the "
            "qualified name of the processor class/factory, and optional "
            "'args' and 'kwargs' fields containing positional and keyword "
            "arguments. For example: {'qualname': "
            "'my_module.MyLogitsProcessor', 'args': [1, 2], 'kwargs': "
            "{'param': 'value'}}."))

    return_tokens_as_token_ids: Optional[bool] = Field(
        default=None,
        description=(
            "If specified with 'logprobs', tokens are represented "
            " as strings of the form 'token_id:{token_id}' so that tokens "
            "that are not JSON-encodable can be identified."))

    cache_salt: Optional[str] = Field(
        default=None,
        description=(
            "If specified, the prefix cache will be salted with the provided "
            "string to prevent an attacker to guess prompts in multi-user "
            "environments. The salt should be random, protected from "
            "access by 3rd parties, and long enough to be "
            "unpredictable (e.g., 43 characters base64-encoded, corresponding "
            "to 256 bit). Not supported by vLLM engine V0."))

    kv_transfer_params: Optional[dict[str, Any]] = Field(
        default=None,
        description="KVTransfer parameters used for disaggregated serving.")

    vllm_xargs: Optional[dict[str, Union[str, int, float]]] = Field(
        default=None,
        description=("Additional request parameters with string or "
                     "numeric values, used by custom extensions."),
    )

    # --8<-- [end:completion-extra-params]

    # Default sampling parameters for completion requests
    _DEFAULT_SAMPLING_PARAMS: dict = {
        "repetition_penalty": 1.0,
        "temperature": 1.0,
        "top_p": 1.0,
        "top_k": 0,
        "min_p": 0.0,
    }

    def to_beam_search_params(
        self,
        max_tokens: int,
        default_sampling_params: Optional[dict] = None,
    ) -> BeamSearchParams:

        if default_sampling_params is None:
            default_sampling_params = {}
        n = self.n if self.n is not None else 1

        if (temperature := self.temperature) is None:
            temperature = default_sampling_params.get("temperature", 1.0)

        return BeamSearchParams(
            beam_width=n,
            max_tokens=max_tokens,
            ignore_eos=self.ignore_eos,
            temperature=temperature,
            length_penalty=self.length_penalty,
            include_stop_str_in_output=self.include_stop_str_in_output,
        )

    def to_sampling_params(
        self,
        max_tokens: int,
        logits_processor_pattern: Optional[str],
        default_sampling_params: Optional[dict] = None,
    ) -> SamplingParams:

        if default_sampling_params is None:
            default_sampling_params = {}

        # Default parameters
        if (repetition_penalty := self.repetition_penalty) is None:
            repetition_penalty = default_sampling_params.get(
                "repetition_penalty",
                self._DEFAULT_SAMPLING_PARAMS["repetition_penalty"],
            )
        if (temperature := self.temperature) is None:
            temperature = default_sampling_params.get(
                "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
        if (top_p := self.top_p) is None:
            top_p = default_sampling_params.get(
                "top_p", self._DEFAULT_SAMPLING_PARAMS["top_p"])
        if (top_k := self.top_k) is None:
            top_k = default_sampling_params.get(
                "top_k", self._DEFAULT_SAMPLING_PARAMS["top_k"])
        if (min_p := self.min_p) is None:
            min_p = default_sampling_params.get(
                "min_p", self._DEFAULT_SAMPLING_PARAMS["min_p"])

        prompt_logprobs = self.prompt_logprobs
        if prompt_logprobs is None and self.echo:
            prompt_logprobs = self.logprobs

        echo_without_generation = self.echo and self.max_tokens == 0

        guided_json_object = None
        if (self.response_format is not None
                and self.response_format.type == "json_object"):
            guided_json_object = True

        guided_decoding = GuidedDecodingParams.from_optional(
            json=self.guided_json,
            regex=self.guided_regex,
            choice=self.guided_choice,
            grammar=self.guided_grammar,
            json_object=guided_json_object,
            backend=self.guided_decoding_backend,
            whitespace_pattern=self.guided_whitespace_pattern,
        )

        extra_args: dict[str, Any] = self.vllm_xargs if self.vllm_xargs else {}
        if self.kv_transfer_params:
            # Pass in kv_transfer_params via extra_args
            extra_args["kv_transfer_params"] = self.kv_transfer_params
        return SamplingParams.from_optional(
            n=self.n,
            best_of=self.best_of,
            presence_penalty=self.presence_penalty,
            frequency_penalty=self.frequency_penalty,
            repetition_penalty=repetition_penalty,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            min_p=min_p,
            seed=self.seed,
            stop=self.stop,
            stop_token_ids=self.stop_token_ids,
            logprobs=self.logprobs,
            ignore_eos=self.ignore_eos,
            max_tokens=max_tokens if not echo_without_generation else 1,
            min_tokens=self.min_tokens,
            prompt_logprobs=prompt_logprobs,
            skip_special_tokens=self.skip_special_tokens,
            spaces_between_special_tokens=self.spaces_between_special_tokens,
            include_stop_str_in_output=self.include_stop_str_in_output,
            logits_processors=get_logits_processors(self.logits_processors,
                                                    logits_processor_pattern),
            truncate_prompt_tokens=self.truncate_prompt_tokens,
            output_kind=RequestOutputKind.DELTA if self.stream \
                else RequestOutputKind.FINAL_ONLY,
            guided_decoding=guided_decoding,
            logit_bias=self.logit_bias,
            allowed_token_ids=self.allowed_token_ids,
            extra_args=extra_args or None,
            )

    @model_validator(mode="before")
    @classmethod
    def check_guided_decoding_count(cls, data):
        guide_count = sum([
            "guided_json" in data and data["guided_json"] is not None,
            "guided_regex" in data and data["guided_regex"] is not None,
            "guided_choice" in data and data["guided_choice"] is not None
        ])
        if guide_count > 1:
            raise ValueError(
                "You can only use one kind of guided decoding "
                "('guided_json', 'guided_regex' or 'guided_choice').")
        return data

    @model_validator(mode="before")
    @classmethod
    def check_logprobs(cls, data):
        if (prompt_logprobs := data.get("prompt_logprobs")) is not None:
            if data.get("stream") and prompt_logprobs > 0:
                raise ValueError(
                    "`prompt_logprobs` are not available when `stream=True`.")

            if prompt_logprobs < 0:
                raise ValueError("`prompt_logprobs` must be a positive value.")

        if (logprobs := data.get("logprobs")) is not None and logprobs < 0:
            raise ValueError("`logprobs` must be a positive value.")

        return data

    @model_validator(mode="before")
    @classmethod
    def validate_stream_options(cls, data):
        if data.get("stream_options") and not data.get("stream"):
            raise ValueError(
                "Stream options can only be defined when `stream=True`.")

        return data

    @model_validator(mode="before")
    @classmethod
    def validate_prompt_and_prompt_embeds(cls, data):
        if data.get("prompt") is None and data.get("prompt_embeds") is None:
            raise ValueError(
                "At least one of `prompt` or `prompt_embeds` must be set.")
        return data

    @model_validator(mode="before")
    @classmethod
    def check_cache_salt_support(cls, data):
        if data.get("cache_salt") is not None:
            if not envs.VLLM_USE_V1:
                raise ValueError(
                    "Parameter 'cache_salt' is not supported with "
                    "this instance of vLLM, which uses engine V0.")
            if not isinstance(data["cache_salt"],
                              str) or not data["cache_salt"]:
                raise ValueError("Parameter 'cache_salt' must be a "
                                 "non-empty string if provided.")
        return data


class EmbeddingCompletionRequest(OpenAIBaseModel):
    # Ordered by official OpenAI API documentation
    # https://platform.openai.com/docs/api-reference/embeddings
    model: Optional[str] = None
    input: Union[list[int], list[list[int]], str, list[str]]
    encoding_format: Literal["float", "base64"] = "float"
    dimensions: Optional[int] = None
    user: Optional[str] = None
    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None

    # --8<-- [start:embedding-extra-params]
    add_special_tokens: bool = Field(
        default=True,
        description=(
            "If true (the default), special tokens (e.g. BOS) will be added to "
            "the prompt."),
    )
    priority: int = Field(
        default=0,
        description=(
            "The priority of the request (lower means earlier handling; "
            "default: 0). Any priority other than 0 will raise an error "
            "if the served model does not use priority scheduling."),
    )
    request_id: str = Field(
        default_factory=lambda: f"{random_uuid()}",
        description=(
            "The request_id related to this request. If the caller does "
            "not set it, a random_uuid will be generated. This id is used "
            "through out the inference process and return in response."),
    )
    normalize: Optional[bool] = None

    # --8<-- [end:embedding-extra-params]

    def to_pooling_params(self):
        return PoolingParams(dimensions=self.dimensions,
                             normalize=self.normalize)


class EmbeddingChatRequest(OpenAIBaseModel):
    model: Optional[str] = None
    messages: list[ChatCompletionMessageParam]

    encoding_format: Literal["float", "base64"] = "float"
    dimensions: Optional[int] = None
    user: Optional[str] = None
    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None

    # --8<-- [start:chat-embedding-extra-params]
    add_special_tokens: bool = Field(
        default=False,
        description=(
            "If true, special tokens (e.g. BOS) will be added to the prompt "
            "on top of what is added by the chat template. "
            "For most models, the chat template takes care of adding the "
            "special tokens so this should be set to false (as is the "
            "default)."),
    )
    chat_template: Optional[str] = Field(
        default=None,
        description=(
            "A Jinja template to use for this conversion. "
            "As of transformers v4.44, default chat template is no longer "
            "allowed, so you must provide a chat template if the tokenizer "
            "does not define one."),
    )
    chat_template_kwargs: Optional[dict[str, Any]] = Field(
        default=None,
        description=(
            "Additional keyword args to pass to the template renderer. "
            "Will be accessible by the chat template."),
    )
    mm_processor_kwargs: Optional[dict[str, Any]] = Field(
        default=None,
        description=("Additional kwargs to pass to the HF processor."),
    )
    priority: int = Field(
        default=0,
        description=(
            "The priority of the request (lower means earlier handling; "
            "default: 0). Any priority other than 0 will raise an error "
            "if the served model does not use priority scheduling."),
    )
    request_id: str = Field(
        default_factory=lambda: f"{random_uuid()}",
        description=(
            "The request_id related to this request. If the caller does "
            "not set it, a random_uuid will be generated. This id is used "
            "through out the inference process and return in response."),
    )
    normalize: Optional[bool] = None
    # --8<-- [end:chat-embedding-extra-params]

    @model_validator(mode="before")
    @classmethod
    def check_generation_prompt(cls, data):
        if data.get("continue_final_message") and data.get(
                "add_generation_prompt"):
            raise ValueError("Cannot set both `continue_final_message` and "
                             "`add_generation_prompt` to True.")
        return data

    def to_pooling_params(self):
        return PoolingParams(dimensions=self.dimensions,
                             normalize=self.normalize)


EmbeddingRequest = Union[EmbeddingCompletionRequest, EmbeddingChatRequest]

PoolingCompletionRequest = EmbeddingCompletionRequest
PoolingChatRequest = EmbeddingChatRequest
PoolingRequest = Union[PoolingCompletionRequest, PoolingChatRequest]


class ScoreRequest(OpenAIBaseModel):
    model: Optional[str] = None
    text_1: Union[list[str], str, ScoreMultiModalParam]
    text_2: Union[list[str], str, ScoreMultiModalParam]
    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None

    # --8<-- [start:score-extra-params]

    mm_processor_kwargs: Optional[dict[str, Any]] = Field(
        default=None,
        description=("Additional kwargs to pass to the HF processor."),
    )

    priority: int = Field(
        default=0,
        description=(
            "The priority of the request (lower means earlier handling; "
            "default: 0). Any priority other than 0 will raise an error "
            "if the served model does not use priority scheduling."),
    )

    activation: Optional[bool] = None

    # --8<-- [end:score-extra-params]

    def to_pooling_params(self):
        return PoolingParams(activation=self.activation)


class RerankRequest(OpenAIBaseModel):
    model: Optional[str] = None
    query: Union[str, ScoreMultiModalParam]
    documents: Union[list[str], ScoreMultiModalParam]
    top_n: int = Field(default_factory=lambda: 0)
    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None

    # --8<-- [start:rerank-extra-params]

    mm_processor_kwargs: Optional[dict[str, Any]] = Field(
        default=None,
        description=("Additional kwargs to pass to the HF processor."),
    )

    priority: int = Field(
        default=0,
        description=(
            "The priority of the request (lower means earlier handling; "
            "default: 0). Any priority other than 0 will raise an error "
            "if the served model does not use priority scheduling."),
    )

    activation: Optional[bool] = None

    # --8<-- [end:rerank-extra-params]

    def to_pooling_params(self):
        return PoolingParams(activation=self.activation)


class RerankDocument(BaseModel):
    text: Optional[str] = None
    multi_modal: Optional[ScoreContentPartParam] = None


class RerankResult(BaseModel):
    index: int
    document: RerankDocument
    relevance_score: float


class RerankUsage(BaseModel):
    total_tokens: int


class RerankResponse(OpenAIBaseModel):
    id: str
    model: str
    usage: RerankUsage
    results: list[RerankResult]


class CompletionLogProbs(OpenAIBaseModel):
    text_offset: list[int] = Field(default_factory=list)
    token_logprobs: list[Optional[float]] = Field(default_factory=list)
    tokens: list[str] = Field(default_factory=list)
    top_logprobs: list[Optional[dict[str,
                                     float]]] = Field(default_factory=list)


class CompletionResponseChoice(OpenAIBaseModel):
    index: int
    text: str
    logprobs: Optional[CompletionLogProbs] = None
    finish_reason: Optional[str] = None
    stop_reason: Optional[Union[int, str]] = Field(
        default=None,
        description=(
            "The stop string or token id that caused the completion "
            "to stop, None if the completion finished for some other reason "
            "including encountering the EOS token"),
    )
    prompt_logprobs: Optional[list[Optional[dict[int, Logprob]]]] = None


class CompletionResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
    object: Literal["text_completion"] = "text_completion"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    choices: list[CompletionResponseChoice]
    service_tier: Optional[Literal["auto", "default", "flex", "scale",
                                   "priority"]] = None
    system_fingerprint: Optional[str] = None
    usage: UsageInfo

    # vLLM-specific fields that are not in OpenAI spec
    kv_transfer_params: Optional[dict[str, Any]] = Field(
        default=None, description="KVTransfer parameters.")


class CompletionResponseStreamChoice(OpenAIBaseModel):
    index: int
    text: str
    logprobs: Optional[CompletionLogProbs] = None
    finish_reason: Optional[str] = None
    stop_reason: Optional[Union[int, str]] = Field(
        default=None,
        description=(
            "The stop string or token id that caused the completion "
            "to stop, None if the completion finished for some other reason "
            "including encountering the EOS token"),
    )


class CompletionStreamResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
    object: str = "text_completion"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    choices: list[CompletionResponseStreamChoice]
    usage: Optional[UsageInfo] = Field(default=None)


class EmbeddingResponseData(OpenAIBaseModel):
    index: int
    object: str = "embedding"
    embedding: Union[list[float], str]


class EmbeddingResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"embd-{random_uuid()}")
    object: str = "list"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    data: list[EmbeddingResponseData]
    usage: UsageInfo


class PoolingResponseData(OpenAIBaseModel):
    index: int
    object: str = "pooling"
    data: Union[list[list[float]], list[float], str]


class PoolingResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"pool-{random_uuid()}")
    object: str = "list"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    data: list[PoolingResponseData]
    usage: UsageInfo


class ScoreResponseData(OpenAIBaseModel):
    index: int
    object: str = "score"
    score: float


class ScoreResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"embd-{random_uuid()}")
    object: str = "list"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    data: list[ScoreResponseData]
    usage: UsageInfo


class ClassificationRequest(OpenAIBaseModel):
    model: Optional[str] = None
    input: Union[list[str], str]
    truncate_prompt_tokens: Optional[int] = None
    user: Optional[str] = None

    # --8<-- [start:classification-extra-params]
    priority: int = Field(
        default=0,
        description=(
            "The priority of the request (lower means earlier handling; "
            "default: 0). Any priority other than 0 will raise an error "
            "if the served model does not use priority scheduling."),
    )

    activation: Optional[bool] = None

    # --8<-- [end:classification-extra-params]

    def to_pooling_params(self):
        return PoolingParams(activation=self.activation)


class ClassificationData(OpenAIBaseModel):
    index: int
    label: Optional[str]
    probs: list[float]
    num_classes: int


class ClassificationResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"classify-{random_uuid()}")
    object: str = "list"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    data: list[ClassificationData]
    usage: UsageInfo


class FunctionCall(OpenAIBaseModel):
    name: str
    arguments: str


class ToolCall(OpenAIBaseModel):
    id: str = Field(default_factory=random_tool_call_id)
    type: Literal["function"] = "function"
    function: FunctionCall


class DeltaFunctionCall(BaseModel):
    name: Optional[str] = None
    arguments: Optional[str] = None


# a tool call delta where everything is optional
class DeltaToolCall(OpenAIBaseModel):
    id: Optional[str] = None
    type: Optional[Literal["function"]] = None
    index: int
    function: Optional[DeltaFunctionCall] = None


class ExtractedToolCallInformation(BaseModel):
    # indicate if tools were called
    tools_called: bool

    # extracted tool calls
    tool_calls: list[ToolCall]

    # content - per OpenAI spec, content AND tool calls can be returned rarely
    # But some models will do this intentionally
    content: Optional[str] = None


class ChatMessage(OpenAIBaseModel):
    role: str
    content: Optional[str] = None
    refusal: Optional[str] = None
    annotations: Optional[OpenAIAnnotation] = None
    audio: Optional[OpenAIChatCompletionAudio] = None
    function_call: Optional[FunctionCall] = None
    tool_calls: list[ToolCall] = Field(default_factory=list)

    # vLLM-specific fields that are not in OpenAI spec
    reasoning_content: Optional[str] = None


class ChatCompletionLogProb(OpenAIBaseModel):
    token: str
    logprob: float = -9999.0
    bytes: Optional[list[int]] = None


class ChatCompletionLogProbsContent(ChatCompletionLogProb):
    # Workaround: redefine fields name cache so that it's not
    # shared with the super class.
    field_names: ClassVar[Optional[set[str]]] = None
    top_logprobs: list[ChatCompletionLogProb] = Field(default_factory=list)


class ChatCompletionLogProbs(OpenAIBaseModel):
    content: Optional[list[ChatCompletionLogProbsContent]] = None


class ChatCompletionResponseChoice(OpenAIBaseModel):
    index: int
    message: ChatMessage
    logprobs: Optional[ChatCompletionLogProbs] = None
    # per OpenAI spec this is the default
    finish_reason: Optional[str] = "stop"
    # not part of the OpenAI spec but included in vLLM for legacy reasons
    stop_reason: Optional[Union[int, str]] = None


class ChatCompletionResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
    object: Literal["chat.completion"] = "chat.completion"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    choices: list[ChatCompletionResponseChoice]
    service_tier: Optional[Literal["auto", "default", "flex", "scale",
                                   "priority"]] = None
    system_fingerprint: Optional[str] = None
    usage: UsageInfo

    # vLLM-specific fields that are not in OpenAI spec
    prompt_logprobs: Optional[list[Optional[dict[int, Logprob]]]] = None
    kv_transfer_params: Optional[dict[str, Any]] = Field(
        default=None, description="KVTransfer parameters.")


class DeltaMessage(OpenAIBaseModel):
    role: Optional[str] = None
    content: Optional[str] = None
    reasoning_content: Optional[str] = None
    tool_calls: list[DeltaToolCall] = Field(default_factory=list)


class ChatCompletionResponseStreamChoice(OpenAIBaseModel):
    index: int
    delta: DeltaMessage
    logprobs: Optional[ChatCompletionLogProbs] = None
    finish_reason: Optional[str] = None
    stop_reason: Optional[Union[int, str]] = None


class ChatCompletionStreamResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
    object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    choices: list[ChatCompletionResponseStreamChoice]
    usage: Optional[UsageInfo] = Field(default=None)


class TranscriptionResponseStreamChoice(OpenAIBaseModel):
    delta: DeltaMessage
    finish_reason: Optional[str] = None
    stop_reason: Optional[Union[int, str]] = None


class TranscriptionStreamResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"trsc-{random_uuid()}")
    object: Literal["transcription.chunk"] = "transcription.chunk"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    choices: list[TranscriptionResponseStreamChoice]
    usage: Optional[UsageInfo] = Field(default=None)


class InputTokensDetails(OpenAIBaseModel):
    cached_tokens: int


class OutputTokensDetails(OpenAIBaseModel):
    reasoning_tokens: int


class ResponseUsage(OpenAIBaseModel):
    input_tokens: int
    input_tokens_details: InputTokensDetails
    output_tokens: int
    output_tokens_details: OutputTokensDetails
    total_tokens: int


class ResponsesResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"resp_{random_uuid()}")
    created_at: int = Field(default_factory=lambda: int(time.time()))
    # error: Optional[ResponseError] = None
    # incomplete_details: Optional[IncompleteDetails] = None
    instructions: Optional[str] = None
    metadata: Optional[Metadata] = None
    model: str
    object: Literal["response"] = "response"
    output: list[ResponseOutputItem]
    parallel_tool_calls: bool
    temperature: float
    tool_choice: ToolChoice
    tools: list[Tool]
    top_p: float
    background: bool
    max_output_tokens: int
    max_tool_calls: Optional[int] = None
    previous_response_id: Optional[str] = None
    prompt: Optional[ResponsePrompt] = None
    reasoning: Optional[Reasoning] = None
    service_tier: Literal["auto", "default", "flex", "scale", "priority"]
    status: ResponseStatus
    text: Optional[ResponseTextConfig] = None
    top_logprobs: int
    truncation: Literal["auto", "disabled"]
    usage: Optional[ResponseUsage] = None
    user: Optional[str] = None

    @classmethod
    def from_request(
        cls,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
        model_name: str,
        created_time: int,
        output: list[ResponseOutputItem],
        status: ResponseStatus,
        usage: Optional[ResponseUsage] = None,
    ) -> "ResponsesResponse":
        return cls(
            id=request.request_id,
            created_at=created_time,
            instructions=request.instructions,
            metadata=request.metadata,
            model=model_name,
            output=output,
            parallel_tool_calls=request.parallel_tool_calls,
            temperature=sampling_params.temperature,
            tool_choice=request.tool_choice,
            tools=request.tools,
            top_p=sampling_params.top_p,
            background=request.background,
            max_output_tokens=sampling_params.max_tokens,
            max_tool_calls=request.max_tool_calls,
            previous_response_id=request.previous_response_id,
            prompt=request.prompt,
            reasoning=request.reasoning,
            service_tier=request.service_tier,
            status=status,
            text=request.text,
            top_logprobs=sampling_params.logprobs,
            truncation=request.truncation,
            user=request.user,
            usage=usage,
        )


BatchRequestInputBody = Union[ChatCompletionRequest, EmbeddingRequest,
                              ScoreRequest, RerankRequest]


class BatchRequestInput(OpenAIBaseModel):
    """
    The per-line object of the batch input file.

    NOTE: Currently only the `/v1/chat/completions` endpoint is supported.
    """

    # A developer-provided per-request id that will be used to match outputs to
    # inputs. Must be unique for each request in a batch.
    custom_id: str

    # The HTTP method to be used for the request. Currently only POST is
    # supported.
    method: str

    # The OpenAI API relative URL to be used for the request. Currently
    # /v1/chat/completions is supported.
    url: str

    # The parameters of the request.
    body: BatchRequestInputBody

    @field_validator('body', mode='plain')
    @classmethod
    def check_type_for_url(cls, value: Any, info: ValidationInfo):
        # Use url to disambiguate models
        url: str = info.data["url"]
        if url == "/v1/chat/completions":
            return ChatCompletionRequest.model_validate(value)
        if url == "/v1/embeddings":
            return TypeAdapter(EmbeddingRequest).validate_python(value)
        if url.endswith("/score"):
            return ScoreRequest.model_validate(value)
        if url.endswith("/rerank"):
            return RerankRequest.model_validate(value)
        return TypeAdapter(BatchRequestInputBody).validate_python(value)


class BatchResponseData(OpenAIBaseModel):
    # HTTP status code of the response.
    status_code: int = 200

    # An unique identifier for the API request.
    request_id: str

    # The body of the response.
    body: Optional[Union[ChatCompletionResponse, EmbeddingResponse,
                         ScoreResponse, RerankResponse]] = None


class BatchRequestOutput(OpenAIBaseModel):
    """
    The per-line object of the batch output and error files
    """

    id: str

    # A developer-provided per-request id that will be used to match outputs to
    # inputs.
    custom_id: str

    response: Optional[BatchResponseData]

    # For requests that failed with a non-HTTP error, this will contain more
    # information on the cause of the failure.
    error: Optional[Any]


class TokenizeCompletionRequest(OpenAIBaseModel):
    model: Optional[str] = None
    prompt: str

    add_special_tokens: bool = Field(
        default=True,
        description=(
            "If true (the default), special tokens (e.g. BOS) will be added to "
            "the prompt."),
    )
    return_token_strs: Optional[bool] = Field(
        default=False,
        description=("If true, also return the token strings "
                     "corresponding to the token ids."),
    )


class TokenizeChatRequest(OpenAIBaseModel):
    model: Optional[str] = None
    messages: list[ChatCompletionMessageParam]

    add_generation_prompt: bool = Field(
        default=True,
        description=
        ("If true, the generation prompt will be added to the chat template. "
         "This is a parameter used by chat template in tokenizer config of the "
         "model."),
    )
    return_token_strs: Optional[bool] = Field(
        default=False,
        description=("If true, also return the token strings "
                     "corresponding to the token ids."),
    )
    continue_final_message: bool = Field(
        default=False,
        description=
        ("If this is set, the chat will be formatted so that the final "
         "message in the chat is open-ended, without any EOS tokens. The "
         "model will continue this message rather than starting a new one. "
         "This allows you to \"prefill\" part of the model's response for it. "
         "Cannot be used at the same time as `add_generation_prompt`."),
    )
    add_special_tokens: bool = Field(
        default=False,
        description=(
            "If true, special tokens (e.g. BOS) will be added to the prompt "
            "on top of what is added by the chat template. "
            "For most models, the chat template takes care of adding the "
            "special tokens so this should be set to false (as is the "
            "default)."),
    )
    chat_template: Optional[str] = Field(
        default=None,
        description=(
            "A Jinja template to use for this conversion. "
            "As of transformers v4.44, default chat template is no longer "
            "allowed, so you must provide a chat template if the tokenizer "
            "does not define one."),
    )
    chat_template_kwargs: Optional[dict[str, Any]] = Field(
        default=None,
        description=(
            "Additional keyword args to pass to the template renderer. "
            "Will be accessible by the chat template."),
    )
    mm_processor_kwargs: Optional[dict[str, Any]] = Field(
        default=None,
        description=("Additional kwargs to pass to the HF processor."),
    )
    tools: Optional[list[ChatCompletionToolsParam]] = Field(
        default=None,
        description=("A list of tools the model may call."),
    )

    @model_validator(mode="before")
    @classmethod
    def check_generation_prompt(cls, data):
        if data.get("continue_final_message") and data.get(
                "add_generation_prompt"):
            raise ValueError("Cannot set both `continue_final_message` and "
                             "`add_generation_prompt` to True.")
        return data


TokenizeRequest = Union[TokenizeCompletionRequest, TokenizeChatRequest]


class TokenizeResponse(OpenAIBaseModel):
    count: int
    max_model_len: int
    tokens: list[int]
    token_strs: Optional[list[str]] = None


class DetokenizeRequest(OpenAIBaseModel):
    model: Optional[str] = None
    tokens: list[int]


class DetokenizeResponse(OpenAIBaseModel):
    prompt: str


class TokenizerInfoResponse(OpenAIBaseModel):
    """
    Response containing tokenizer configuration 
    equivalent to tokenizer_config.json
    """

    model_config = ConfigDict(extra="allow")
    tokenizer_class: str


class LoadLoRAAdapterRequest(BaseModel):
    lora_name: str
    lora_path: str


class UnloadLoRAAdapterRequest(BaseModel):
    lora_name: str
    lora_int_id: Optional[int] = Field(default=None)


## Protocols for Audio
AudioResponseFormat: TypeAlias = Literal["json", "text", "srt", "verbose_json",
                                         "vtt"]


class TranscriptionRequest(OpenAIBaseModel):
    # Ordered by official OpenAI API documentation
    # https://platform.openai.com/docs/api-reference/audio/createTranscription

    file: UploadFile
    """
    The audio file object (not file name) to transcribe, in one of these
    formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.
    """

    model: Optional[str] = None
    """ID of the model to use.
    """

    language: Optional[str] = None
    """The language of the input audio.

    Supplying the input language in
    [ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) format
    will improve accuracy and latency.
    """

    prompt: str = Field(default="")
    """An optional text to guide the model's style or continue a previous audio
    segment.

    The [prompt](https://platform.openai.com/docs/guides/speech-to-text#prompting)
    should match the audio language.
    """

    response_format: AudioResponseFormat = Field(default="json")
    """
    The format of the output, in one of these options: `json`, `text`, `srt`,
    `verbose_json`, or `vtt`.
    """

    ## TODO (varun) : Support if set to 0, certain thresholds are met !!

    timestamp_granularities: list[Literal["word", "segment"]] = Field(
        alias="timestamp_granularities[]", default=[])
    """The timestamp granularities to populate for this transcription.

    `response_format` must be set `verbose_json` to use timestamp granularities.
    Either or both of these options are supported: `word`, or `segment`. Note:
    There is no additional latency for segment timestamps, but generating word
    timestamps incurs additional latency.
    """

    stream: Optional[bool] = False
    """When set, it will enable output to be streamed in a similar fashion
    as the Chat Completion endpoint.
    """
    # --8<-- [start:transcription-extra-params]
    # Flattened stream option to simplify form data.
    stream_include_usage: Optional[bool] = False
    stream_continuous_usage_stats: Optional[bool] = False

    vllm_xargs: Optional[dict[str, Union[str, int, float]]] = Field(
        default=None,
        description=("Additional request parameters with string or "
                     "numeric values, used by custom extensions."),
    )
    # --8<-- [end:transcription-extra-params]

    # --8<-- [start:transcription-sampling-params]
    temperature: float = Field(default=0.0)
    """The sampling temperature, between 0 and 1.

    Higher values like 0.8 will make the output more random, while lower values
    like 0.2 will make it more focused / deterministic. If set to 0, the model
    will use [log probability](https://en.wikipedia.org/wiki/Log_probability)
    to automatically increase the temperature until certain thresholds are hit.
    """

    top_p: Optional[float] = None
    """Enables nucleus (top-p) sampling, where tokens are selected from the
    smallest possible set whose cumulative probability exceeds `p`.
    """

    top_k: Optional[int] = None
    """Limits sampling to the `k` most probable tokens at each step."""

    min_p: Optional[float] = None
    """Filters out tokens with a probability lower than `min_p`, ensuring a
    minimum likelihood threshold during sampling.
    """

    seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
    """The seed to use for sampling."""

    frequency_penalty: Optional[float] = 0.0
    """The frequency penalty to use for sampling."""

    repetition_penalty: Optional[float] = None
    """The repetition penalty to use for sampling."""

    presence_penalty: Optional[float] = 0.0
    """The presence penalty to use for sampling."""
    # --8<-- [end:transcription-sampling-params]

    # Default sampling parameters for transcription requests.
    _DEFAULT_SAMPLING_PARAMS: dict = {
        "repetition_penalty": 1.0,
        "temperature": 1.0,
        "top_p": 1.0,
        "top_k": 0,
        "min_p": 0.0,
    }

    def to_sampling_params(
            self,
            default_max_tokens: int,
            default_sampling_params: Optional[dict] = None) -> SamplingParams:

        max_tokens = default_max_tokens

        if default_sampling_params is None:
            default_sampling_params = {}

        # Default parameters
        if (temperature := self.temperature) is None:
            temperature = default_sampling_params.get(
                "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
        if (top_p := self.top_p) is None:
            top_p = default_sampling_params.get(
                "top_p", self._DEFAULT_SAMPLING_PARAMS["top_p"])
        if (top_k := self.top_k) is None:
            top_k = default_sampling_params.get(
                "top_k", self._DEFAULT_SAMPLING_PARAMS["top_k"])
        if (min_p := self.min_p) is None:
            min_p = default_sampling_params.get(
                "min_p", self._DEFAULT_SAMPLING_PARAMS["min_p"])

        if (repetition_penalty := self.repetition_penalty) is None:
            repetition_penalty = default_sampling_params.get(
                "repetition_penalty",
                self._DEFAULT_SAMPLING_PARAMS["repetition_penalty"])

        return SamplingParams.from_optional(temperature=temperature,
                                            max_tokens=max_tokens,
                                            seed=self.seed,
                                            top_p=top_p,
                                            top_k=top_k,
                                            min_p=min_p,
                                            frequency_penalty=self.frequency_penalty,
                                            repetition_penalty=repetition_penalty,
                                            presence_penalty=self.presence_penalty,
                                            output_kind=RequestOutputKind.DELTA
                                            if self.stream \
                                            else RequestOutputKind.FINAL_ONLY,
                                            extra_args=self.vllm_xargs)

    @model_validator(mode="before")
    @classmethod
    def validate_transcription_request(cls, data):
        if isinstance(data.get("file"), str):
            raise HTTPException(
                status_code=HTTPStatus.UNPROCESSABLE_ENTITY,
                detail="Expected 'file' to be a file-like object, not 'str'.",
            )

        stream_opts = ["stream_include_usage", "stream_continuous_usage_stats"]
        stream = data.get("stream", False)
        if any(bool(data.get(so, False)) for so in stream_opts) and not stream:
            raise ValueError(
                "Stream options can only be defined when `stream=True`.")

        return data


# Transcription response objects
class TranscriptionResponse(OpenAIBaseModel):
    text: str
    """The transcribed text."""


class TranscriptionWord(OpenAIBaseModel):
    end: float
    """End time of the word in seconds."""

    start: float
    """Start time of the word in seconds."""

    word: str
    """The text content of the word."""


class TranscriptionSegment(OpenAIBaseModel):
    id: int
    """Unique identifier of the segment."""

    avg_logprob: float
    """Average logprob of the segment.

    If the value is lower than -1, consider the logprobs failed.
    """

    compression_ratio: float
    """Compression ratio of the segment.

    If the value is greater than 2.4, consider the compression failed.
    """

    end: float
    """End time of the segment in seconds."""

    no_speech_prob: float
    """Probability of no speech in the segment.

    If the value is higher than 1.0 and the `avg_logprob` is below -1, consider
    this segment silent.
    """

    seek: int
    """Seek offset of the segment."""

    start: float
    """Start time of the segment in seconds."""

    temperature: float
    """Temperature parameter used for generating the segment."""

    text: str
    """Text content of the segment."""

    tokens: list[int]
    """Array of token IDs for the text content."""


class TranscriptionResponseVerbose(OpenAIBaseModel):
    duration: str
    """The duration of the input audio."""

    language: str
    """The language of the input audio."""

    text: str
    """The transcribed text."""

    segments: Optional[list[TranscriptionSegment]] = None
    """Segments of the transcribed text and their corresponding details."""

    words: Optional[list[TranscriptionWord]] = None
    """Extracted words and their corresponding timestamps."""


class TranslationResponseStreamChoice(OpenAIBaseModel):
    delta: DeltaMessage
    finish_reason: Optional[str] = None
    stop_reason: Optional[Union[int, str]] = None


class TranslationStreamResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"trsl-{random_uuid()}")
    object: Literal["translation.chunk"] = "translation.chunk"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    choices: list[TranslationResponseStreamChoice]
    usage: Optional[UsageInfo] = Field(default=None)


class TranslationRequest(OpenAIBaseModel):
    # Ordered by official OpenAI API documentation
    # https://platform.openai.com/docs/api-reference/audio/createTranslation

    file: UploadFile
    """
    The audio file object (not file name) to translate, in one of these
    formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.
    """

    model: Optional[str] = None
    """ID of the model to use.
    """

    prompt: str = Field(default="")
    """An optional text to guide the model's style or continue a previous audio
    segment.

    The [prompt](https://platform.openai.com/docs/guides/speech-to-text#prompting)
    should match the audio language.
    """

    response_format: AudioResponseFormat = Field(default="json")
    """
    The format of the output, in one of these options: `json`, `text`, `srt`,
    `verbose_json`, or `vtt`.
    """

    # TODO support additional sampling parameters
    # --8<-- [start:translation-sampling-params]
    temperature: float = Field(default=0.0)
    """The sampling temperature, between 0 and 1.

    Higher values like 0.8 will make the output more random, while lower values
    like 0.2 will make it more focused / deterministic. If set to 0, the model
    will use [log probability](https://en.wikipedia.org/wiki/Log_probability)
    to automatically increase the temperature until certain thresholds are hit.
    """
    # --8<-- [end:translation-sampling-params]

    # --8<-- [start:translation-extra-params]
    language: Optional[str] = None
    """The language of the input audio we translate from.

    Supplying the input language in
    [ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) format
    will improve accuracy.
    """

    stream: Optional[bool] = False
    """Custom field not present in the original OpenAI definition. When set,
    it will enable output to be streamed in a similar fashion as the Chat
    Completion endpoint.
    """
    # Flattened stream option to simplify form data.
    stream_include_usage: Optional[bool] = False
    stream_continuous_usage_stats: Optional[bool] = False
    # --8<-- [end:translation-extra-params]

    # Default sampling parameters for translation requests.
    _DEFAULT_SAMPLING_PARAMS: dict = {
        "temperature": 0,
    }

    def to_sampling_params(
            self,
            default_max_tokens: int,
            default_sampling_params: Optional[dict] = None) -> SamplingParams:

        max_tokens = default_max_tokens

        if default_sampling_params is None:
            default_sampling_params = {}
        # Default parameters
        if (temperature := self.temperature) is None:
            temperature = default_sampling_params.get(
                "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])

        return SamplingParams.from_optional(temperature=temperature,
                                            max_tokens=max_tokens,
                                            output_kind=RequestOutputKind.DELTA
                                            if self.stream \
                                            else RequestOutputKind.FINAL_ONLY)

    @model_validator(mode="before")
    @classmethod
    def validate_stream_options(cls, data):
        stream_opts = ["stream_include_usage", "stream_continuous_usage_stats"]
        stream = data.get("stream", False)
        if any(bool(data.get(so, False)) for so in stream_opts) and not stream:
            raise ValueError(
                "Stream options can only be defined when `stream=True`.")

        return data


# Translation response objects
class TranslationResponse(OpenAIBaseModel):
    text: str
    """The translated text."""


class TranslationWord(OpenAIBaseModel):
    end: float
    """End time of the word in seconds."""

    start: float
    """Start time of the word in seconds."""

    word: str
    """The text content of the word."""


class TranslationSegment(OpenAIBaseModel):
    id: int
    """Unique identifier of the segment."""

    avg_logprob: float
    """Average logprob of the segment.

    If the value is lower than -1, consider the logprobs failed.
    """

    compression_ratio: float
    """Compression ratio of the segment.

    If the value is greater than 2.4, consider the compression failed.
    """

    end: float
    """End time of the segment in seconds."""

    no_speech_prob: float
    """Probability of no speech in the segment.

    If the value is higher than 1.0 and the `avg_logprob` is below -1, consider
    this segment silent.
    """

    seek: int
    """Seek offset of the segment."""

    start: float
    """Start time of the segment in seconds."""

    temperature: float
    """Temperature parameter used for generating the segment."""

    text: str
    """Text content of the segment."""

    tokens: list[int]
    """Array of token IDs for the text content."""


class TranslationResponseVerbose(OpenAIBaseModel):
    duration: str
    """The duration of the input audio."""

    language: str
    """The language of the input audio."""

    text: str
    """The translated text."""

    segments: Optional[list[TranslationSegment]] = None
    """Segments of the translated text and their corresponding details."""

    words: Optional[list[TranslationWord]] = None
    """Extracted words and their corresponding timestamps."""