# +-----------------------------------------------+
# |                                               |
# |           Give Feedback / Get Help            |
# | https://github.com/BerriAI/litellm/issues/new |
# |                                               |
# +-----------------------------------------------+
#
#  Thank you ! We ❤️ you! - Krrish & Ishaan

import asyncio
import copy
import enum
import hashlib
import inspect
import json
import logging
import re
import threading
import time
import traceback
from collections import defaultdict
from functools import lru_cache
from typing import (
    TYPE_CHECKING,
    Any,
    AsyncGenerator,
    Callable,
    Dict,
    Generator,
    List,
    Literal,
    Optional,
    Set,
    Tuple,
    Union,
    cast,
)

import anyio
import httpx
import openai
from openai import AsyncOpenAI
from pydantic import BaseModel
from typing_extensions import overload

import litellm
import litellm.litellm_core_utils
import litellm.litellm_core_utils.exception_mapping_utils
from litellm import get_secret_str
from litellm._logging import verbose_router_logger
from litellm._uuid import uuid
from litellm.caching.caching import (
    DualCache,
    InMemoryCache,
    RedisCache,
    RedisClusterCache,
)
from litellm.constants import (
    DEFAULT_HEALTH_CHECK_INTERVAL,
    DEFAULT_HEALTH_CHECK_STALENESS_MULTIPLIER,
    DEFAULT_MAX_LRU_CACHE_SIZE,
)
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.asyncify import run_async_function
from litellm.litellm_core_utils.core_helpers import (
    _get_parent_otel_span_from_kwargs,
    get_metadata_variable_name_from_kwargs,
)
from litellm.litellm_core_utils.coroutine_checker import coroutine_checker
from litellm.litellm_core_utils.credential_accessor import CredentialAccessor
from litellm.litellm_core_utils.dd_tracing import tracer
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
from litellm.litellm_core_utils.sensitive_data_masker import SensitiveDataMasker
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
from litellm.router_strategy.budget_limiter import RouterBudgetLimiting
from litellm.router_strategy.least_busy import LeastBusyLoggingHandler
from litellm.router_strategy.lowest_cost import LowestCostLoggingHandler
from litellm.router_strategy.lowest_latency import LowestLatencyLoggingHandler
from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler
from litellm.router_strategy.lowest_tpm_rpm_v2 import LowestTPMLoggingHandler_v2
from litellm.router_strategy.simple_shuffle import simple_shuffle
from litellm.router_strategy.tag_based_routing import get_deployments_for_tag
from litellm.router_utils.add_retry_fallback_headers import (
    add_fallback_headers_to_response,
    add_retry_headers_to_response,
)
from litellm.router_utils.batch_utils import (
    _get_router_metadata_variable_name,
    replace_model_in_jsonl,
    should_replace_model_in_jsonl,
)
from litellm.router_utils.client_initalization_utils import InitalizeCachedClient
from litellm.router_utils.clientside_credential_handler import (
    get_dynamic_litellm_params,
    is_clientside_credential,
)
from litellm.router_utils.common_utils import (
    filter_team_based_models,
    filter_web_search_deployments,
)
from litellm.router_utils.cooldown_cache import CooldownCache
from litellm.router_utils.cooldown_handlers import (
    DEFAULT_COOLDOWN_TIME_SECONDS,
    _async_get_cooldown_deployments,
    _async_get_cooldown_deployments_with_debug_info,
    _get_cooldown_deployments,
    _set_cooldown_deployments,
)
from litellm.router_utils.fallback_event_handlers import (
    _check_non_standard_fallback_format,
    get_fallback_model_group,
    run_async_fallback,
)
from litellm.router_utils.get_retry_from_policy import (
    get_num_retries_from_retry_policy as _get_num_retries_from_retry_policy,
)
from litellm.router_utils.handle_error import (
    async_raise_no_deployment_exception,
    send_llm_exception_alert,
)
from litellm.router_utils.health_state_cache import DeploymentHealthCache
from litellm.router_utils.pre_call_checks.deployment_affinity_check import (
    DeploymentAffinityCheck,
)
from litellm.router_utils.pre_call_checks.model_rate_limit_check import (
    ModelRateLimitingCheck,
)
from litellm.router_utils.pre_call_checks.prompt_caching_deployment_check import (
    PromptCachingDeploymentCheck,
)
from litellm.router_utils.router_callbacks.track_deployment_metrics import (
    increment_deployment_failures_for_current_minute,
    increment_deployment_successes_for_current_minute,
)
from litellm.scheduler import FlowItem, Scheduler
from litellm.types.llms.openai import (
    AllMessageValues,
    FileTypes,
    OpenAIFileObject,
    OpenAIFilesPurpose,
)
from litellm.types.router import (
    CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS,
    VALID_LITELLM_ENVIRONMENTS,
    AlertingConfig,
    AllowedFailsPolicy,
    AssistantsTypedDict,
    CredentialLiteLLMParams,
    CustomRoutingStrategyBase,
    Deployment,
    DeploymentTypedDict,
    GuardrailTypedDict,
    LiteLLM_Params,
    MockRouterTestingParams,
    ModelGroupInfo,
    OptionalPreCallChecks,
    RetryPolicy,
    RouterCacheEnum,
    RouterGeneralSettings,
    RouterModelGroupAliasItem,
    RouterRateLimitError,
    RouterRateLimitErrorBasic,
    RoutingGroup,
    RoutingStrategy,
    SearchToolTypedDict,
)
from litellm.types.services import ServiceTypes
from litellm.types.utils import (
    CustomPricingLiteLLMParams,
    GenericBudgetConfigType,
    LiteLLMBatch,
)
from litellm.types.utils import ModelInfo
from litellm.types.utils import ModelInfo as ModelMapInfo
from litellm.types.utils import (
    ModelResponseStream,
    StandardLoggingPayload,
    Usage,
)
from litellm.utils import (
    CustomStreamWrapper,
    EmbeddingResponse,
    ModelResponse,
    Rules,
    function_setup,
    get_llm_provider,
    get_non_default_completion_params,
    get_secret,
    get_utc_datetime,
    is_region_allowed,
)

from .router_utils.pattern_match_deployments import PatternMatchRouter

if TYPE_CHECKING:
    from opentelemetry.trace import Span as _Span

    from litellm.router_strategy.auto_router.auto_router import (
        AutoRouter,
        PreRoutingHookResponse,
    )
    from litellm.router_strategy.complexity_router.complexity_router import (
        ComplexityRouter,
    )
    from litellm.router_strategy.adaptive_router.adaptive_router import (
        AdaptiveRouter,
    )
    from litellm.router_strategy.quality_router.quality_router import (
        QualityRouter,
    )
    from litellm.responses.streaming_iterator import (
        BaseResponsesAPIStreamingIterator,
    )
    from litellm.types.llms.base import BaseLiteLLMOpenAIResponseObject
    from litellm.types.llms.openai import (
        ResponseAPIUsage,
        ResponseInputParam,
        ResponsesAPIResponse,
    )

    Span = Union[_Span, Any]
else:
    Span = Any
    AutoRouter = Any
    ComplexityRouter = Any
    AdaptiveRouter = Any
    QualityRouter = Any
    PreRoutingHookResponse = Any


class RoutingArgs(enum.Enum):
    ttl = 60  # 1min (RPM/TPM expire key)


class Router:
    model_names: set = set()
    cache_responses: Optional[bool] = False
    default_cache_time_seconds: int = 1 * 60 * 60  # 1 hour
    tenacity = None
    leastbusy_logger: Optional[LeastBusyLoggingHandler] = None
    lowesttpm_logger: Optional[LowestTPMLoggingHandler] = None
    optional_callbacks: Optional[List[Union[CustomLogger, Callable, str]]] = None

    def __init__(
        self,
        model_list: Optional[
            Union[List[DeploymentTypedDict], List[Dict[str, Any]]]
        ] = None,
        ## ASSISTANTS API ##
        assistants_config: Optional[AssistantsTypedDict] = None,
        ## SEARCH API ##
        search_tools: Optional[List[SearchToolTypedDict]] = None,
        ## GUARDRAIL API ##
        guardrail_list: Optional[List[GuardrailTypedDict]] = None,
        ## CACHING ##
        redis_url: Optional[str] = None,
        redis_host: Optional[str] = None,
        redis_port: Optional[int] = None,
        redis_password: Optional[str] = None,
        redis_db: Optional[int] = None,
        cache_responses: Optional[bool] = False,
        cache_kwargs: dict = {},  # additional kwargs to pass to RedisCache (see caching.py)
        caching_groups: Optional[
            List[tuple]
        ] = None,  # if you want to cache across model groups
        client_ttl: int = 3600,  # ttl for cached clients - will re-initialize after this time in seconds
        ## SCHEDULER ##
        polling_interval: Optional[float] = None,
        default_priority: Optional[int] = None,
        ## RELIABILITY ##
        num_retries: Optional[int] = None,
        max_fallbacks: Optional[
            int
        ] = None,  # max fallbacks to try before exiting the call. Defaults to 5.
        timeout: Optional[float] = None,
        stream_timeout: Optional[float] = None,
        default_litellm_params: Optional[
            dict
        ] = None,  # default params for Router.chat.completion.create
        default_max_parallel_requests: Optional[int] = None,
        set_verbose: bool = False,
        debug_level: Literal["DEBUG", "INFO"] = "INFO",
        default_fallbacks: Optional[
            List[str]
        ] = None,  # generic fallbacks, works across all deployments
        fallbacks: List = [],
        context_window_fallbacks: List = [],
        content_policy_fallbacks: List = [],
        model_group_alias: Optional[
            Dict[str, Union[str, RouterModelGroupAliasItem]]
        ] = {},
        enable_pre_call_checks: bool = False,
        enable_tag_filtering: bool = False,
        tag_filtering_match_any: bool = True,
        retry_after: int = 0,  # min time to wait before retrying a failed request
        retry_policy: Optional[
            Union[RetryPolicy, dict]
        ] = None,  # set custom retries for different exceptions
        model_group_retry_policy: Dict[
            str, RetryPolicy
        ] = {},  # set custom retry policies based on model group
        allowed_fails: Optional[
            int
        ] = None,  # Number of times a deployment can failbefore being added to cooldown
        allowed_fails_policy: Optional[
            AllowedFailsPolicy
        ] = None,  # set custom allowed fails policy
        cooldown_time: Optional[
            float
        ] = None,  # (seconds) time to cooldown a deployment after failure
        disable_cooldowns: Optional[bool] = None,
        routing_strategy: Literal[
            "simple-shuffle",
            "least-busy",
            "usage-based-routing",
            "latency-based-routing",
            "cost-based-routing",
            "usage-based-routing-v2",
        ] = "simple-shuffle",
        optional_pre_call_checks: Optional[OptionalPreCallChecks] = None,
        routing_strategy_args: dict = {},  # just for latency-based
        routing_groups: Optional[List[Union[RoutingGroup, dict]]] = None,
        provider_budget_config: Optional[GenericBudgetConfigType] = None,
        alerting_config: Optional[AlertingConfig] = None,
        router_general_settings: Optional[
            RouterGeneralSettings
        ] = RouterGeneralSettings(),
        deployment_affinity_ttl_seconds: int = 3600,
        model_group_affinity_config: Optional[Dict[str, List[str]]] = None,
        ignore_invalid_deployments: bool = False,
        enable_health_check_routing: bool = False,
        health_check_staleness_threshold: Optional[int] = None,
        health_check_ignore_transient_errors: bool = False,
        enable_weighted_failover: bool = False,
    ) -> None:
        """
        Initialize the Router class with the given parameters for caching, reliability, and routing strategy.

        Args:
            model_list (Optional[list]): List of models to be used. Defaults to None.
            redis_url (Optional[str]): URL of the Redis server. Defaults to None.
            redis_host (Optional[str]): Hostname of the Redis server. Defaults to None.
            redis_port (Optional[int]): Port of the Redis server. Defaults to None.
            redis_password (Optional[str]): Password of the Redis server. Defaults to None.
            cache_responses (Optional[bool]): Flag to enable caching of responses. Defaults to False.
            cache_kwargs (dict): Additional kwargs to pass to RedisCache. Defaults to {}.
            caching_groups (Optional[List[tuple]]): List of model groups for caching across model groups. Defaults to None.
            client_ttl (int): Time-to-live for cached clients in seconds. Defaults to 3600.
            polling_interval: (Optional[float]): frequency of polling queue. Only for '.scheduler_acompletion()'. Default is 3ms.
            default_priority: (Optional[int]): the default priority for a request. Only for '.scheduler_acompletion()'. Default is None.
            num_retries (Optional[int]): Number of retries for failed requests. Defaults to 2.
            timeout (Optional[float]): Timeout for requests. Defaults to None.
            default_litellm_params (dict): Default parameters for Router.chat.completion.create. Defaults to {}.
            set_verbose (bool): Flag to set verbose mode. Defaults to False.
            debug_level (Literal["DEBUG", "INFO"]): Debug level for logging. Defaults to "INFO".
            fallbacks (List): List of fallback options. Defaults to [].
            context_window_fallbacks (List): List of context window fallback options. Defaults to [].
            enable_pre_call_checks (boolean): Filter out deployments which are outside context window limits for a given prompt
            model_group_alias (Optional[dict]): Alias for model groups. Defaults to {}.
            retry_after (int): Minimum time to wait before retrying a failed request. Defaults to 0.
            allowed_fails (Optional[int]): Number of allowed fails before adding to cooldown. Defaults to None.
            cooldown_time (float): Time to cooldown a deployment after failure in seconds. Defaults to 1.
            routing_strategy (Literal["simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing", "cost-based-routing"]): Routing strategy used for the implicit "default" group (any model not claimed by an entry in `routing_groups`). Defaults to "simple-shuffle".
            routing_strategy_args (dict): Additional args for the default group's routing strategy (e.g. latency window). Defaults to {}.
            routing_groups (Optional[List[RoutingGroup]]): Named subsets of `model_name`s that use a per-group routing strategy and args. Each model belongs to at most one explicit group; everything else lands in the implicit "default" group driven by `routing_strategy` / `routing_strategy_args`. Defaults to None.
            alerting_config (AlertingConfig): Slack alerting configuration. Defaults to None.
            provider_budget_config (ProviderBudgetConfig): Provider budget configuration. Use this to set llm_provider budget limits. example $100/day to OpenAI, $100/day to Azure, etc. Defaults to None.
            deployment_affinity_ttl_seconds (int): TTL for user-key -> deployment affinity mapping. Defaults to 3600.
            ignore_invalid_deployments (bool): Ignores invalid deployments, and continues with other deployments. Default is to raise an error.
            enable_weighted_failover (bool): When True and the routing strategy is "simple-shuffle", a retryable failure on one deployment causes the request to re-pick (weighted) across the other deployments in the same model group before any cross-group fallback runs. Bounded by `max_fallbacks`. Async-only: currently honored by `router.acompletion()` and other async entrypoints. The sync `router.completion()` path falls back to the regular fallback flow. Defaults to False.
        Returns:
            Router: An instance of the litellm.Router class.

        Example Usage:
        ```python
        from litellm import Router
        model_list = [
        {
            "model_name": "azure-gpt-3.5-turbo", # model alias
            "litellm_params": { # params for litellm completion/embedding call
                "model": "azure/<your-deployment-name-1>",
                "api_key": <your-api-key>,
                "api_version": <your-api-version>,
                "api_base": <your-api-base>
            },
        },
        {
            "model_name": "azure-gpt-3.5-turbo", # model alias
            "litellm_params": { # params for litellm completion/embedding call
                "model": "azure/<your-deployment-name-2>",
                "api_key": <your-api-key>,
                "api_version": <your-api-version>,
                "api_base": <your-api-base>
            },
        },
        {
            "model_name": "openai-gpt-3.5-turbo", # model alias
            "litellm_params": { # params for litellm completion/embedding call
                "model": "gpt-3.5-turbo",
                "api_key": <your-api-key>,
            },
        ]

        router = Router(model_list=model_list, fallbacks=[{"azure-gpt-3.5-turbo": "openai-gpt-3.5-turbo"}])
        ```
        """

        self.set_verbose = set_verbose
        self.ignore_invalid_deployments = ignore_invalid_deployments
        self.debug_level = debug_level
        self.enable_pre_call_checks = enable_pre_call_checks
        self.enable_tag_filtering = enable_tag_filtering
        self.tag_filtering_match_any = tag_filtering_match_any
        from litellm._service_logger import ServiceLogging

        self.service_logger_obj: ServiceLogging = ServiceLogging()
        litellm.suppress_debug_info = True  # prevents 'Give Feedback/Get help' message from being emitted on Router - Relevant Issue: https://github.com/BerriAI/litellm/issues/5942
        if self.set_verbose is True:
            if debug_level == "INFO":
                verbose_router_logger.setLevel(logging.INFO)
            elif debug_level == "DEBUG":
                verbose_router_logger.setLevel(logging.DEBUG)
        self.router_general_settings: RouterGeneralSettings = (
            router_general_settings or RouterGeneralSettings()
        )

        self.assistants_config = assistants_config
        self.search_tools = search_tools or []
        self.guardrail_list = guardrail_list or []
        self.deployment_names: List = (
            []
        )  # names of models under litellm_params. ex. azure/chatgpt-v-2
        self.deployment_latency_map = {}
        ### CACHING ###
        cache_type: Literal["local", "redis", "redis-semantic", "s3", "disk"] = (
            "local"  # default to an in-memory cache
        )
        redis_cache = None
        cache_config: Dict[str, Any] = {}

        self.client_ttl = client_ttl
        if redis_url is not None or (redis_host is not None and redis_port is not None):
            cache_type = "redis"

            if redis_url is not None:
                cache_config["url"] = redis_url

            if redis_host is not None:
                cache_config["host"] = redis_host

            if redis_port is not None:
                cache_config["port"] = str(redis_port)  # type: ignore

            if redis_password is not None:
                cache_config["password"] = redis_password

            if redis_db is not None:
                verbose_router_logger.warning(
                    "Deprecated 'redis_db' argument used. Please remove 'redis_db' from your config/database and use 'cache_kwargs' instead."
                )
                cache_config["db"] = str(redis_db)

            # Add additional key-value pairs from cache_kwargs
            cache_config.update(cache_kwargs)
            redis_cache = self._create_redis_cache(cache_config)

        if cache_responses:
            if litellm.cache is None:
                # the cache can be initialized on the proxy server. We should not overwrite it
                litellm.cache = litellm.Cache(type=cache_type, **cache_config)  # type: ignore
            self.cache_responses = cache_responses
        self.cache = DualCache(
            redis_cache=redis_cache, in_memory_cache=InMemoryCache()
        )  # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc.

        ### SCHEDULER ###
        self.scheduler = Scheduler(
            polling_interval=polling_interval, redis_cache=redis_cache
        )
        self.default_priority = default_priority
        self.default_deployment = None  # use this to track the users default deployment, when they want to use model = *
        self.default_max_parallel_requests = default_max_parallel_requests
        self.provider_default_deployment_ids: List[str] = []
        self.pattern_router = PatternMatchRouter()
        self.team_pattern_routers: Dict[str, PatternMatchRouter] = (
            {}
        )  # {"TEAM_ID": PatternMatchRouter}
        self.auto_routers: Dict[str, "AutoRouter"] = {}
        self.complexity_routers: Dict[str, "ComplexityRouter"] = {}
        self.adaptive_routers: Dict[str, "AdaptiveRouter"] = {}
        self.quality_routers: Dict[str, "QualityRouter"] = {}

        # Initialize model_group_alias early since it's used in set_model_list
        self.model_group_alias: Dict[str, Union[str, RouterModelGroupAliasItem]] = (
            model_group_alias or {}
        )  # dict to store aliases for router, ex. {"gpt-4": "gpt-3.5-turbo"}, all requests with gpt-4 -> get routed to gpt-3.5-turbo group

        # Initialize model ID to deployment index mapping for O(1) lookups
        self.model_id_to_deployment_index_map: Dict[str, int] = {}
        # Initialize model name to deployment indices mapping for O(1) lookups
        # Maps model_name -> list of indices in model_list
        self.model_name_to_deployment_indices: Dict[str, List[int]] = {}
        # Maps (team_id, team_public_model_name) -> list of indices in model_list
        self.team_model_to_deployment_indices: Dict[Tuple[str, str], List[int]] = {}

        # Initialize cache attributes that ``_invalidate_model_group_info_cache``
        # touches *before* the first ``set_model_list`` below (which calls
        # that invalidation as part of building the model index).
        self._access_groups_cache: Optional[Dict[str, List[str]]] = None
        # Per-router cache for the proxy auth-layer "is this model explicitly
        # zero-cost?" check. Lives on the router so it is invalidated alongside
        # ``_cached_get_model_group_info`` and dies with the router (no
        # ``id()``-reuse risk after GC). See
        # ``litellm.proxy.auth.auth_checks._is_model_cost_zero``.
        self._zero_cost_cache: Dict[str, bool] = {}

        if model_list is not None:
            # set_model_list will build indices automatically
            self.set_model_list(model_list)
            self.healthy_deployments: List = self.model_list  # type: ignore
            for m in model_list:
                if "model" in m["litellm_params"]:
                    self.deployment_latency_map[m["litellm_params"]["model"]] = 0
        else:
            self.model_list: List = (
                []
            )  # initialize an empty list - to allow _add_deployment and delete_deployment to work

        if allowed_fails is not None:
            self.allowed_fails = allowed_fails
        else:
            self.allowed_fails = litellm.allowed_fails
        self.cooldown_time = cooldown_time or DEFAULT_COOLDOWN_TIME_SECONDS
        self.cooldown_cache = CooldownCache(
            cache=self.cache, default_cooldown_time=self.cooldown_time
        )
        self.disable_cooldowns = disable_cooldowns
        self.enable_health_check_routing = enable_health_check_routing
        self.enable_weighted_failover = enable_weighted_failover
        self.health_check_ignore_transient_errors = health_check_ignore_transient_errors
        _staleness = health_check_staleness_threshold or (
            DEFAULT_HEALTH_CHECK_INTERVAL * DEFAULT_HEALTH_CHECK_STALENESS_MULTIPLIER
        )
        self.health_state_cache = DeploymentHealthCache(
            cache=self.cache, staleness_threshold=float(_staleness)
        )
        self.failed_calls = (
            InMemoryCache()
        )  # cache to track failed call per deployment, if num failed calls within 1 minute > allowed fails, then add it to cooldown

        if num_retries is not None:
            self.num_retries = num_retries
        elif litellm.num_retries is not None:
            self.num_retries = litellm.num_retries
        else:
            self.num_retries = openai.DEFAULT_MAX_RETRIES

        if max_fallbacks is not None:
            self.max_fallbacks = max_fallbacks
        elif litellm.max_fallbacks is not None:
            self.max_fallbacks = litellm.max_fallbacks
        else:
            self.max_fallbacks = litellm.ROUTER_MAX_FALLBACKS

        self._explicit_timeout = timeout  # None when user did not pass timeout
        self.timeout = timeout or litellm.request_timeout
        self.stream_timeout = stream_timeout

        self.retry_after = retry_after
        self.routing_strategy = self._normalize_strategy(routing_strategy)
        self._routing_groups_input: Optional[List[Union[RoutingGroup, dict]]] = (
            routing_groups
        )

        ## SETTING FALLBACKS ##
        ### validate if it's set + in correct format
        _fallbacks = fallbacks or litellm.fallbacks

        self.validate_fallbacks(fallback_param=_fallbacks)
        ### set fallbacks
        self.fallbacks = _fallbacks

        if default_fallbacks is not None or litellm.default_fallbacks is not None:
            _fallbacks = default_fallbacks or litellm.default_fallbacks
            if self.fallbacks is not None:
                self.fallbacks.append({"*": _fallbacks})
            else:
                self.fallbacks = [{"*": _fallbacks}]

        self.context_window_fallbacks = (
            context_window_fallbacks or litellm.context_window_fallbacks
        )

        _content_policy_fallbacks = (
            content_policy_fallbacks or litellm.content_policy_fallbacks
        )
        self.validate_fallbacks(fallback_param=_content_policy_fallbacks)
        self.content_policy_fallbacks = _content_policy_fallbacks
        self.total_calls: defaultdict = defaultdict(
            int
        )  # dict to store total calls made to each model
        self.fail_calls: defaultdict = defaultdict(
            int
        )  # dict to store fail_calls made to each model
        self.success_calls: defaultdict = defaultdict(
            int
        )  # dict to store success_calls  made to each model
        self.previous_models: List = (
            []
        )  # list to store failed calls (passed in as metadata to next call)

        # make Router.chat.completions.create compatible for openai.chat.completions.create
        default_litellm_params = default_litellm_params or {}
        self.chat = litellm.Chat(params=default_litellm_params, router_obj=self)

        # default litellm args
        self.default_litellm_params = default_litellm_params
        self.default_litellm_params.setdefault("timeout", timeout)
        self.default_litellm_params.setdefault("max_retries", 0)
        self.default_litellm_params.setdefault("metadata", {}).update(
            {"caching_groups": caching_groups}
        )

        self.deployment_stats: dict = {}  # used for debugging load balancing
        """
        deployment_stats = {
            "122999-2828282-277:
            {
                "model": "gpt-3",
                "api_base": "http://localhost:4000",
                "num_requests": 20,
                "avg_latency": 0.001,
                "num_failures": 0,
                "num_successes": 20
            }
        }
        """

        ### ROUTING SETUP ###
        self.routing_strategy_init(
            routing_strategy=routing_strategy,
            routing_strategy_args=routing_strategy_args,
        )
        self._init_routing_groups(self._routing_groups_input)
        self.access_groups = None
        ## USAGE TRACKING ##
        if isinstance(litellm._async_success_callback, list):
            litellm.logging_callback_manager.add_litellm_async_success_callback(
                self.deployment_callback_on_success
            )
        else:
            litellm.logging_callback_manager.add_litellm_async_success_callback(
                self.deployment_callback_on_success
            )
        if isinstance(litellm.success_callback, list):
            litellm.logging_callback_manager.add_litellm_success_callback(
                self.sync_deployment_callback_on_success
            )
        else:
            litellm.success_callback = [self.sync_deployment_callback_on_success]
        if isinstance(litellm._async_failure_callback, list):
            litellm.logging_callback_manager.add_litellm_async_failure_callback(
                self.async_deployment_callback_on_failure
            )
        else:
            litellm._async_failure_callback = [
                self.async_deployment_callback_on_failure
            ]
        ## COOLDOWNS ##
        if isinstance(litellm.failure_callback, list):
            litellm.logging_callback_manager.add_litellm_failure_callback(
                self.deployment_callback_on_failure
            )
        else:
            litellm.failure_callback = [self.deployment_callback_on_failure]
        self.routing_strategy_args = routing_strategy_args
        self.provider_budget_config = provider_budget_config
        self.deployment_affinity_ttl_seconds = deployment_affinity_ttl_seconds
        self.router_budget_logger: Optional[RouterBudgetLimiting] = None
        if RouterBudgetLimiting.should_init_router_budget_limiter(
            model_list=model_list, provider_budget_config=self.provider_budget_config
        ):
            if optional_pre_call_checks is not None:
                optional_pre_call_checks.append("router_budget_limiting")
            else:
                optional_pre_call_checks = ["router_budget_limiting"]
        self.retry_policy: Optional[RetryPolicy] = None
        if retry_policy is not None:
            if isinstance(retry_policy, dict):
                self.retry_policy = RetryPolicy(**retry_policy)
            elif isinstance(retry_policy, RetryPolicy):
                self.retry_policy = retry_policy
            if self.retry_policy is not None:
                verbose_router_logger.info(
                    "\033[32mRouter Custom Retry Policy Set:\n{}\033[0m".format(
                        self.retry_policy.model_dump(exclude_none=True)
                    )
                )

        self.model_group_retry_policy: Optional[Dict[str, RetryPolicy]] = (
            model_group_retry_policy
        )
        self.model_group_affinity_config: Optional[Dict[str, List[str]]] = (
            model_group_affinity_config
        )

        self.allowed_fails_policy: Optional[AllowedFailsPolicy] = None
        if allowed_fails_policy is not None:
            if isinstance(allowed_fails_policy, dict):
                self.allowed_fails_policy = AllowedFailsPolicy(**allowed_fails_policy)
            elif isinstance(allowed_fails_policy, AllowedFailsPolicy):
                self.allowed_fails_policy = allowed_fails_policy

            if self.allowed_fails_policy is not None:
                verbose_router_logger.info(
                    "\033[32mRouter Custom Allowed Fails Policy Set:\n{}\033[0m".format(
                        self.allowed_fails_policy.model_dump(exclude_none=True)
                    )
                )

        self.alerting_config: Optional[AlertingConfig] = alerting_config

        if optional_pre_call_checks is not None:
            self.add_optional_pre_call_checks(optional_pre_call_checks)

        # If model_group_affinity_config is set but no global affinity checks were
        # enabled, we still need the DeploymentAffinityCheck callback (with global
        # flags all False) so per-group config can activate affinity per model group.
        if self.model_group_affinity_config and not any(
            isinstance(cb, DeploymentAffinityCheck)
            for cb in (self.optional_callbacks or [])
        ):
            if self.optional_callbacks is None:
                self.optional_callbacks = []
            affinity_callback = DeploymentAffinityCheck(
                cache=self.cache,
                ttl_seconds=self.deployment_affinity_ttl_seconds,
                enable_user_key_affinity=False,
                enable_responses_api_affinity=False,
                enable_session_id_affinity=False,
                model_group_affinity_config=self.model_group_affinity_config,
            )
            self.optional_callbacks.append(affinity_callback)
            litellm.logging_callback_manager.add_litellm_callback(affinity_callback)

        if self.alerting_config is not None:
            self._initialize_alerting()

        self.initialize_assistants_endpoint()
        self.initialize_router_endpoints()
        self.apply_default_settings()

    @staticmethod
    def get_valid_args() -> List[str]:
        """
        Returns a list of valid arguments for the Router.__init__ method.
        """
        arg_spec = inspect.getfullargspec(Router.__init__)
        valid_args = arg_spec.args + arg_spec.kwonlyargs
        if "self" in valid_args:
            valid_args.remove("self")
        return valid_args

    def apply_default_settings(self):
        """
        Apply the default settings to the router.
        """

        default_pre_call_checks: OptionalPreCallChecks = []
        self.add_optional_pre_call_checks(default_pre_call_checks)
        return None

    def discard(self):
        """
        Pseudo-destructor to be invoked to clean up global data structures when router is no longer used.
        For now, unhook router's callbacks from all lists
        """
        litellm.logging_callback_manager.remove_callback_from_list_by_object(
            litellm._async_success_callback, self
        )
        litellm.logging_callback_manager.remove_callback_from_list_by_object(
            litellm.success_callback, self
        )
        litellm.logging_callback_manager.remove_callback_from_list_by_object(
            litellm._async_failure_callback, self
        )
        litellm.logging_callback_manager.remove_callback_from_list_by_object(
            litellm.failure_callback, self
        )
        litellm.logging_callback_manager.remove_callback_from_list_by_object(
            litellm.input_callback, self
        )
        litellm.logging_callback_manager.remove_callback_from_list_by_object(
            litellm.service_callback, self
        )
        litellm.logging_callback_manager.remove_callback_from_list_by_object(
            litellm.callbacks, self
        )

        # Remove ForwardClientSideHeadersByModelGroup if it exists
        if self.optional_callbacks is not None:
            for callback in self.optional_callbacks:
                litellm.logging_callback_manager.remove_callback_from_list_by_object(
                    litellm.callbacks, callback, require_self=False
                )

    @staticmethod
    def _create_redis_cache(
        cache_config: Dict[str, Any],
    ) -> Union[RedisCache, RedisClusterCache]:
        """
        Initializes either a RedisCache or RedisClusterCache based on the cache_config.
        """
        startup_nodes = cache_config.get("startup_nodes")
        if not startup_nodes:
            _env_cluster_nodes = get_secret("REDIS_CLUSTER_NODES")
            if _env_cluster_nodes is not None and isinstance(_env_cluster_nodes, str):
                startup_nodes = json.loads(_env_cluster_nodes)

        if startup_nodes:
            return RedisClusterCache(**{**cache_config, "startup_nodes": startup_nodes})
        else:
            return RedisCache(**cache_config)

    def _update_redis_cache(self, cache: RedisCache):
        """
        Update the redis cache for the router, if none set.

        Allows proxy user to just do
        ```yaml
        litellm_settings:
            cache: true
        ```
        and caching to just work.
        """
        if self.cache.redis_cache is None:
            self.cache.redis_cache = cache

    # Maps a routing strategy string to the attribute on `self` that holds
    # the default group's strategy selector for that strategy. (The selectors
    # double as `CustomLogger` callbacks, hence the legacy `*_logger` attrs.)
    _DEFAULT_SELECTOR_ATTR_BY_STRATEGY: Dict[str, str] = {
        "least-busy": "leastbusy_logger",
        "usage-based-routing": "lowesttpm_logger",
        "usage-based-routing-v2": "lowesttpm_logger_v2",
        "latency-based-routing": "lowestlatency_logger",
        "cost-based-routing": "lowestcost_logger",
    }

    @staticmethod
    def _normalize_strategy(
        strategy: Union[RoutingStrategy, str, None],
    ) -> Optional[str]:
        if strategy is None:
            return None
        if isinstance(strategy, RoutingStrategy):
            return strategy.value
        return strategy

    def _validate_routing_strategy(
        self, routing_strategy: Union[RoutingStrategy, str, None]
    ) -> None:
        # See: https://github.com/BerriAI/litellm/issues/11330
        valid_strategy_strings = ["simple-shuffle"] + [s.value for s in RoutingStrategy]
        if routing_strategy is None:
            return
        is_valid_string = (
            isinstance(routing_strategy, str)
            and routing_strategy in valid_strategy_strings
        )
        is_valid_enum = isinstance(routing_strategy, RoutingStrategy)
        if not is_valid_string and not is_valid_enum:
            raise ValueError(
                f"Invalid routing_strategy: '{routing_strategy}'. "
                f"Valid options: {valid_strategy_strings}. "
                f"Check 'router_settings.routing_strategy' in your config.yaml "
                f"or the 'routing_strategy' parameter if using the Router SDK directly."
            )

    def _build_strategy_selector(
        self,
        strategy: Union[RoutingStrategy, str],
        routing_strategy_args: dict,
        register_callbacks: bool = True,
    ) -> Optional[Any]:
        """
        Constructs a strategy selector for a given strategy.
        Returns None for `simple-shuffle` (no selector needed) and unknown
        strategies.
        """
        selector: Optional[Any] = None
        match self._normalize_strategy(strategy):
            case RoutingStrategy.LEAST_BUSY.value:
                selector = LeastBusyLoggingHandler(router_cache=self.cache)
                if register_callbacks:
                    if isinstance(litellm.input_callback, list):
                        litellm.input_callback.append(selector)  # type: ignore
                    else:
                        litellm.input_callback = [selector]  # type: ignore
            case RoutingStrategy.USAGE_BASED_ROUTING.value:
                selector = LowestTPMLoggingHandler(
                    router_cache=self.cache,
                    routing_args=routing_strategy_args,
                )
            case RoutingStrategy.USAGE_BASED_ROUTING_V2.value:
                selector = LowestTPMLoggingHandler_v2(
                    router_cache=self.cache,
                    routing_args=routing_strategy_args,
                )
            case RoutingStrategy.LATENCY_BASED.value:
                selector = LowestLatencyLoggingHandler(
                    router_cache=self.cache,
                    routing_args=routing_strategy_args,
                )
            case RoutingStrategy.COST_BASED.value:
                selector = LowestCostLoggingHandler(
                    router_cache=self.cache,
                    routing_args={},
                )

        if (
            selector is not None
            and register_callbacks
            and isinstance(litellm.callbacks, list)
        ):
            litellm.logging_callback_manager.add_litellm_callback(selector)  # type: ignore

        return selector

    def _unregister_router_selectors(self, selectors: List[Any]) -> None:
        """
        Drop router-owned strategy selectors from litellm's global callback
        lists by identity. Used before re-init (`routing_strategy_init` /
        `_init_routing_groups`) so repeated `update_settings` calls don't
        accumulate dead selectors that keep receiving callback events.
        """
        selector_ids = {id(s) for s in selectors if s is not None}
        if not selector_ids:
            return
        if isinstance(litellm.callbacks, list):
            litellm.callbacks = [
                c for c in litellm.callbacks if id(c) not in selector_ids
            ]
        if isinstance(litellm.input_callback, list):
            litellm.input_callback = [
                c for c in litellm.input_callback if id(c) not in selector_ids
            ]

    def routing_strategy_init(
        self, routing_strategy: Union[RoutingStrategy, str], routing_strategy_args: dict
    ):
        verbose_router_logger.info(f"Routing strategy: {routing_strategy}")
        self._validate_routing_strategy(routing_strategy)

        self._unregister_router_selectors(
            [
                getattr(self, attr, None)
                for attr in self._DEFAULT_SELECTOR_ATTR_BY_STRATEGY.values()
            ]
        )

        self.leastbusy_logger: Optional[LeastBusyLoggingHandler] = None
        self.lowesttpm_logger: Optional[LowestTPMLoggingHandler] = None
        self.lowesttpm_logger_v2: Optional[LowestTPMLoggingHandler_v2] = None
        self.lowestlatency_logger: Optional[LowestLatencyLoggingHandler] = None
        self.lowestcost_logger: Optional[LowestCostLoggingHandler] = None

        selector = self._build_strategy_selector(
            strategy=routing_strategy,
            routing_strategy_args=routing_strategy_args,
        )
        attr = self._DEFAULT_SELECTOR_ATTR_BY_STRATEGY.get(
            self._normalize_strategy(routing_strategy) or ""
        )
        # TODO: legacy `self.<strategy>_logger` attributes are read directly by
        # `get_settings()` and external callers. Fold the default group into
        # `self._group_selectors["default"]` and drop these attribute writes —
        # the dual storage is an antipattern preserved only for back-compat.
        if attr is not None:
            setattr(self, attr, selector)

    def _init_routing_groups(
        self,
        groups_input: Optional[List[Union[RoutingGroup, dict]]],
    ) -> None:
        """
        Validates and indexes `routing_groups`. Each `model_name` may belong to
        at most one explicit group. Constructs per-group strategy selectors so
        groups with different `routing_strategy_args` track independent state.

        Models not claimed by any explicit group are served by the implicit
        `"default"` group, whose selectors are the `self.<strategy>_logger`
        attributes set up in `routing_strategy_init`.
        """
        self._unregister_router_selectors(
            [
                sel
                for selectors in getattr(self, "_group_selectors", {}).values()
                for sel in selectors.values()
            ]
        )

        self._routing_groups: Dict[str, RoutingGroup] = {}
        self._model_to_group: Dict[str, str] = {}
        self._group_selectors: Dict[str, Dict[str, Any]] = {}

        if not groups_input:
            return

        known_model_names = {
            m.get("model_name") for m in (self.model_list or []) if m.get("model_name")
        }

        seen_group_names: set = set()
        for raw in groups_input:
            group = raw if isinstance(raw, RoutingGroup) else RoutingGroup(**raw)

            if not group.group_name:
                raise ValueError("routing_groups: group_name must be non-empty.")
            if group.group_name == "default":
                raise ValueError(
                    "routing_groups: 'default' is reserved for the implicit fallback group."
                )
            if group.group_name in seen_group_names:
                raise ValueError(
                    f"routing_groups: group names must be unique, duplicate group_name '{group.group_name}'."
                )
            seen_group_names.add(group.group_name)

            self._validate_routing_strategy(group.routing_strategy)

            for model_name in group.models:
                if model_name in self._model_to_group:
                    raise ValueError(
                        f"routing_groups: model_name '{model_name}' appears in "
                        f"both '{self._model_to_group[model_name]}' and "
                        f"'{group.group_name}'. Each model may belong to at most one group."
                    )
                if known_model_names and model_name not in known_model_names:
                    verbose_router_logger.warning(
                        "routing_groups: model_name '%s' (group '%s') is not in model_list; "
                        "the group entry will only take effect once a deployment with that "
                        "model_name is added.",
                        model_name,
                        group.group_name,
                    )
                self._model_to_group[model_name] = group.group_name

            self._routing_groups[group.group_name] = group

            strategy_value = self._normalize_strategy(group.routing_strategy) or ""
            group_selector = self._build_strategy_selector(
                strategy=group.routing_strategy,
                routing_strategy_args=group.routing_strategy_args or {},
            )
            self._group_selectors[group.group_name] = (
                {strategy_value: group_selector} if group_selector is not None else {}
            )

    def _get_routing_context(self, model: str) -> Tuple[Optional[str], Optional[Any]]:
        """
        Resolves the routing strategy and selector to use for the given model.

        Every model belongs to exactly one group: an explicit entry from
        `routing_groups`, or the implicit `"default"` group driven by the
        router's top-level `routing_strategy` / `routing_strategy_args`.

        `self.routing_strategy` may be either a string or a `RoutingStrategy`
        enum member (the constructor accepts both), so it is normalized to a
        string here. Downstream call sites and `_select_deployment_*` arms
        compare against string literals.
        """
        group_name = self._model_to_group.get(model)
        if group_name is None:
            strategy = self._normalize_strategy(self.routing_strategy)
            attr = self._DEFAULT_SELECTOR_ATTR_BY_STRATEGY.get(strategy or "")
            selector = getattr(self, attr, None) if attr is not None else None
            verbose_router_logger.debug(
                "routing_group=default model=%s strategy=%s", model, strategy
            )
            return strategy, selector

        group = self._routing_groups[group_name]
        strategy = self._normalize_strategy(group.routing_strategy)
        selector = self._group_selectors.get(group_name, {}).get(strategy or "")
        verbose_router_logger.debug(
            "routing_group=%s model=%s strategy=%s", group_name, model, strategy
        )
        return strategy, selector

    async def _select_deployment_async(
        self,
        *,
        strategy: Optional[str],
        selector: Optional[Any],
        model: str,
        healthy_deployments: list,
        messages: Optional[List[Dict[str, str]]],
        input: Optional[Union[str, List]],
        request_kwargs: Optional[Dict],
    ) -> Optional[Any]:
        """
        Asks the strategy selector for a deployment. Caller handles
        `simple-shuffle` separately (it does not flow through a selector).
        Returns None for unknown strategies or when the selector is missing.
        """
        if selector is None:
            return None
        match strategy:
            case "least-busy":
                return await selector.async_get_available_deployments(
                    model_group=model,
                    healthy_deployments=healthy_deployments,
                )
            case "usage-based-routing":
                # `LowestTPMLoggingHandler` (v1) only exposes the sync
                # `get_available_deployments`. Mirror the pre-routing-groups
                # top-level fallback by calling it inline so groups using v1
                # still work from async callers.
                return selector.get_available_deployments(
                    model_group=model,
                    healthy_deployments=healthy_deployments,
                    messages=messages,
                    input=input,
                )
            case "usage-based-routing-v2" | "cost-based-routing":
                return await selector.async_get_available_deployments(
                    model_group=model,
                    healthy_deployments=healthy_deployments,
                    messages=messages,
                    input=input,
                )
            case "latency-based-routing":
                return await selector.async_get_available_deployments(
                    model_group=model,
                    healthy_deployments=healthy_deployments,
                    messages=messages,
                    input=input,
                    request_kwargs=request_kwargs,
                )
            case _:
                return None

    def _select_deployment_sync(
        self,
        *,
        strategy: Optional[str],
        selector: Optional[Any],
        model: str,
        healthy_deployments: list,
        messages: Optional[List[Dict[str, str]]],
        input: Optional[Union[str, List]],
        request_kwargs: Optional[Dict],
    ) -> Optional[Any]:
        """
        Sync sibling of `_select_deployment_async`. Caller handles
        `simple-shuffle` separately.
        """
        if selector is None:
            return None

        # `cost-based-routing` is intentionally omitted —
        # `LowestCostLoggingHandler` only implements
        # `async_get_available_deployments`
        match strategy:
            case "least-busy":
                return selector.get_available_deployments(
                    model_group=model,
                    healthy_deployments=healthy_deployments,
                )
            case "usage-based-routing" | "usage-based-routing-v2":
                return selector.get_available_deployments(
                    model_group=model,
                    healthy_deployments=healthy_deployments,
                    messages=messages,
                    input=input,
                )
            case "latency-based-routing":
                return selector.get_available_deployments(
                    model_group=model,
                    healthy_deployments=healthy_deployments,
                    messages=messages,
                    input=input,
                    request_kwargs=request_kwargs,
                )
            case _:
                return None

    def initialize_assistants_endpoint(self):
        ## INITIALIZE PASS THROUGH ASSISTANTS ENDPOINT ##
        self.acreate_assistants = self.factory_function(litellm.acreate_assistants)
        self.adelete_assistant = self.factory_function(litellm.adelete_assistant)
        self.aget_assistants = self.factory_function(litellm.aget_assistants)
        self.acreate_thread = self.factory_function(litellm.acreate_thread)
        self.aget_thread = self.factory_function(litellm.aget_thread)
        self.a_add_message = self.factory_function(litellm.a_add_message)
        self.aget_messages = self.factory_function(litellm.aget_messages)
        self.arun_thread = self.factory_function(litellm.arun_thread)

    def _initialize_core_endpoints(self):
        """Helper to initialize core router endpoints."""
        self.amoderation = self.factory_function(
            litellm.amoderation, call_type="moderation"
        )
        self.aanthropic_messages = self.factory_function(
            litellm.anthropic_messages, call_type="anthropic_messages"
        )
        self.anthropic_messages = self.factory_function(
            litellm.anthropic_messages, call_type="anthropic_messages"
        )
        self.agenerate_content = self.factory_function(
            litellm.agenerate_content, call_type="agenerate_content"
        )
        self.aadapter_generate_content = self.factory_function(
            litellm.aadapter_generate_content, call_type="aadapter_generate_content"
        )
        self.aresponses = self.factory_function(
            litellm.aresponses, call_type="aresponses"
        )
        self.afile_delete = self.factory_function(
            litellm.afile_delete, call_type="afile_delete"
        )
        self.afile_content = self.factory_function(
            litellm.afile_content, call_type="afile_content"
        )
        self.responses = self.factory_function(litellm.responses, call_type="responses")
        self.aget_responses = self.factory_function(
            litellm.aget_responses, call_type="aget_responses"
        )
        self.acancel_responses = self.factory_function(
            litellm.acancel_responses, call_type="acancel_responses"
        )
        self.acompact_responses = self.factory_function(
            litellm.acompact_responses, call_type="acompact_responses"
        )
        self.adelete_responses = self.factory_function(
            litellm.adelete_responses, call_type="adelete_responses"
        )
        self.alist_input_items = self.factory_function(
            litellm.alist_input_items, call_type="alist_input_items"
        )
        self._arealtime = self.factory_function(
            litellm._arealtime, call_type="_arealtime"
        )
        self._aresponses_websocket = self.factory_function(
            litellm._aresponses_websocket, call_type="_aresponses_websocket"
        )
        self.acreate_fine_tuning_job = self.factory_function(
            litellm.acreate_fine_tuning_job, call_type="acreate_fine_tuning_job"
        )
        self.acancel_fine_tuning_job = self.factory_function(
            litellm.acancel_fine_tuning_job, call_type="acancel_fine_tuning_job"
        )
        self.alist_fine_tuning_jobs = self.factory_function(
            litellm.alist_fine_tuning_jobs, call_type="alist_fine_tuning_jobs"
        )
        self.aretrieve_fine_tuning_job = self.factory_function(
            litellm.aretrieve_fine_tuning_job, call_type="aretrieve_fine_tuning_job"
        )
        self.afile_list = self.factory_function(
            litellm.afile_list, call_type="alist_files"
        )
        self.aimage_edit = self.factory_function(
            litellm.aimage_edit, call_type="aimage_edit"
        )
        self.allm_passthrough_route = self.factory_function(
            litellm.allm_passthrough_route, call_type="allm_passthrough_route"
        )
        # Note: acancel_batch is defined as a method on the Router class (not using factory_function)
        # to properly handle model-to-provider mapping like acreate_batch and aretrieve_batch

    def _initialize_vector_store_endpoints(self):
        """Initialize vector store endpoints."""
        from litellm.vector_stores.main import (
            adelete,
            alist,
            aretrieve,
            asearch,
            aupdate,
            create,
            delete,
            list,
            retrieve,
            search,
            update,
        )

        self.avector_store_search = self.factory_function(
            asearch, call_type="avector_store_search"
        )
        self.vector_store_search = self.factory_function(
            search, call_type="vector_store_search"
        )
        self.vector_store_create = self.factory_function(
            create, call_type="vector_store_create"
        )
        self.avector_store_retrieve = self.factory_function(
            aretrieve, call_type="avector_store_retrieve"
        )
        self.vector_store_retrieve = self.factory_function(
            retrieve, call_type="vector_store_retrieve"
        )
        self.avector_store_list = self.factory_function(
            alist, call_type="avector_store_list"
        )
        self.vector_store_list = self.factory_function(
            list, call_type="vector_store_list"
        )
        self.avector_store_update = self.factory_function(
            aupdate, call_type="avector_store_update"
        )
        self.vector_store_update = self.factory_function(
            update, call_type="vector_store_update"
        )
        self.avector_store_delete = self.factory_function(
            adelete, call_type="avector_store_delete"
        )
        self.vector_store_delete = self.factory_function(
            delete, call_type="vector_store_delete"
        )

    def _initialize_vector_store_file_endpoints(self):
        """Initialize vector store file endpoints."""
        from litellm.vector_store_files.main import (
            acreate as avector_store_file_create_fn,
        )
        from litellm.vector_store_files.main import (
            adelete as avector_store_file_delete_fn,
        )
        from litellm.vector_store_files.main import alist as avector_store_file_list_fn
        from litellm.vector_store_files.main import (
            aretrieve as avector_store_file_retrieve_fn,
        )
        from litellm.vector_store_files.main import (
            aretrieve_content as avector_store_file_content_fn,
        )
        from litellm.vector_store_files.main import (
            aupdate as avector_store_file_update_fn,
        )
        from litellm.vector_store_files.main import (
            create as vector_store_file_create_fn,
        )
        from litellm.vector_store_files.main import (
            delete as vector_store_file_delete_fn,
        )
        from litellm.vector_store_files.main import list as vector_store_file_list_fn
        from litellm.vector_store_files.main import (
            retrieve as vector_store_file_retrieve_fn,
        )
        from litellm.vector_store_files.main import (
            retrieve_content as vector_store_file_content_fn,
        )
        from litellm.vector_store_files.main import (
            update as vector_store_file_update_fn,
        )

        self.avector_store_file_create = self.factory_function(
            avector_store_file_create_fn, call_type="avector_store_file_create"
        )
        self.vector_store_file_create = self.factory_function(
            vector_store_file_create_fn, call_type="vector_store_file_create"
        )
        self.avector_store_file_list = self.factory_function(
            avector_store_file_list_fn, call_type="avector_store_file_list"
        )
        self.vector_store_file_list = self.factory_function(
            vector_store_file_list_fn, call_type="vector_store_file_list"
        )
        self.avector_store_file_retrieve = self.factory_function(
            avector_store_file_retrieve_fn, call_type="avector_store_file_retrieve"
        )
        self.vector_store_file_retrieve = self.factory_function(
            vector_store_file_retrieve_fn, call_type="vector_store_file_retrieve"
        )
        self.avector_store_file_content = self.factory_function(
            avector_store_file_content_fn, call_type="avector_store_file_content"
        )
        self.vector_store_file_content = self.factory_function(
            vector_store_file_content_fn, call_type="vector_store_file_content"
        )
        self.avector_store_file_update = self.factory_function(
            avector_store_file_update_fn, call_type="avector_store_file_update"
        )
        self.vector_store_file_update = self.factory_function(
            vector_store_file_update_fn, call_type="vector_store_file_update"
        )
        self.avector_store_file_delete = self.factory_function(
            avector_store_file_delete_fn, call_type="avector_store_file_delete"
        )
        self.vector_store_file_delete = self.factory_function(
            vector_store_file_delete_fn, call_type="vector_store_file_delete"
        )

    def _initialize_google_genai_endpoints(self):
        """Initialize Google GenAI endpoints."""
        from litellm.google_genai import (
            agenerate_content,
            agenerate_content_stream,
            generate_content,
            generate_content_stream,
        )

        self.agenerate_content = self.factory_function(
            agenerate_content, call_type="agenerate_content"
        )
        self.generate_content = self.factory_function(
            generate_content, call_type="generate_content"
        )
        self.agenerate_content_stream = self.factory_function(
            agenerate_content_stream, call_type="agenerate_content_stream"
        )
        self.generate_content_stream = self.factory_function(
            generate_content_stream, call_type="generate_content_stream"
        )

    def _initialize_ocr_search_endpoints(self):
        """Initialize OCR and search endpoints."""
        from litellm.ocr import aocr, ocr

        self.aocr = self.factory_function(aocr, call_type="aocr")
        self.ocr = self.factory_function(ocr, call_type="ocr")

        from litellm.search import asearch, search

        self.asearch = self.factory_function(asearch, call_type="asearch")
        self.search = self.factory_function(search, call_type="search")

    def _initialize_video_endpoints(self):
        """Initialize video endpoints."""
        from litellm.videos import (
            avideo_content,
            avideo_create_character,
            avideo_edit,
            avideo_extension,
            avideo_generation,
            avideo_get_character,
            avideo_list,
            avideo_remix,
            avideo_status,
            video_content,
            video_create_character,
            video_edit,
            video_extension,
            video_generation,
            video_get_character,
            video_list,
            video_remix,
            video_status,
        )

        self.avideo_generation = self.factory_function(
            avideo_generation, call_type="avideo_generation"
        )
        self.video_generation = self.factory_function(
            video_generation, call_type="video_generation"
        )
        self.avideo_list = self.factory_function(avideo_list, call_type="avideo_list")
        self.video_list = self.factory_function(video_list, call_type="video_list")
        self.avideo_status = self.factory_function(
            avideo_status, call_type="avideo_status"
        )
        self.video_status = self.factory_function(
            video_status, call_type="video_status"
        )
        self.avideo_content = self.factory_function(
            avideo_content, call_type="avideo_content"
        )
        self.video_content = self.factory_function(
            video_content, call_type="video_content"
        )
        self.avideo_remix = self.factory_function(
            avideo_remix, call_type="avideo_remix"
        )
        self.video_remix = self.factory_function(video_remix, call_type="video_remix")
        self.avideo_create_character = self.factory_function(
            avideo_create_character, call_type="avideo_create_character"
        )
        self.video_create_character = self.factory_function(
            video_create_character, call_type="video_create_character"
        )
        self.avideo_get_character = self.factory_function(
            avideo_get_character, call_type="avideo_get_character"
        )
        self.video_get_character = self.factory_function(
            video_get_character, call_type="video_get_character"
        )
        self.avideo_edit = self.factory_function(avideo_edit, call_type="avideo_edit")
        self.video_edit = self.factory_function(video_edit, call_type="video_edit")
        self.avideo_extension = self.factory_function(
            avideo_extension, call_type="avideo_extension"
        )
        self.video_extension = self.factory_function(
            video_extension, call_type="video_extension"
        )

    def _initialize_container_endpoints(self):
        """Initialize container endpoints."""
        from litellm.containers import (
            acreate_container,
            adelete_container,
            alist_containers,
            aretrieve_container,
            create_container,
            delete_container,
            list_containers,
            retrieve_container,
        )
        from litellm.containers.endpoint_factory import (
            _generated_endpoints as container_file_endpoints,
        )

        self.acreate_container = self.factory_function(
            acreate_container, call_type="acreate_container"
        )
        self.create_container = self.factory_function(
            create_container, call_type="create_container"
        )
        self.alist_containers = self.factory_function(
            alist_containers, call_type="alist_containers"
        )
        self.list_containers = self.factory_function(
            list_containers, call_type="list_containers"
        )
        self.aretrieve_container = self.factory_function(
            aretrieve_container, call_type="aretrieve_container"
        )
        self.retrieve_container = self.factory_function(
            retrieve_container, call_type="retrieve_container"
        )
        self.adelete_container = self.factory_function(
            adelete_container, call_type="adelete_container"
        )
        self.delete_container = self.factory_function(
            delete_container, call_type="delete_container"
        )

        # Auto-register JSON-generated container file endpoints
        for name, func in container_file_endpoints.items():
            setattr(self, name, self.factory_function(func, call_type=name))  # type: ignore[arg-type]

    def _initialize_skills_endpoints(self):
        """Initialize Anthropic Skills API endpoints."""
        self.acreate_skill = self.factory_function(
            litellm.acreate_skill, call_type="acreate_skill"
        )
        self.alist_skills = self.factory_function(
            litellm.alist_skills, call_type="alist_skills"
        )
        self.aget_skill = self.factory_function(
            litellm.aget_skill, call_type="aget_skill"
        )
        self.adelete_skill = self.factory_function(
            litellm.adelete_skill, call_type="adelete_skill"
        )

    def _initialize_interactions_endpoints(self):
        """Initialize Google Interactions API endpoints."""
        from litellm.interactions import acancel as acancel_interaction
        from litellm.interactions import acreate as acreate_interaction
        from litellm.interactions import adelete as adelete_interaction
        from litellm.interactions import aget as aget_interaction
        from litellm.interactions import cancel as cancel_interaction
        from litellm.interactions import create as create_interaction
        from litellm.interactions import delete as delete_interaction
        from litellm.interactions import get as get_interaction

        self.acreate_interaction = self.factory_function(
            acreate_interaction, call_type="acreate_interaction"
        )
        self.create_interaction = self.factory_function(
            create_interaction, call_type="create_interaction"
        )
        self.aget_interaction = self.factory_function(
            aget_interaction, call_type="aget_interaction"
        )
        self.get_interaction = self.factory_function(
            get_interaction, call_type="get_interaction"
        )
        self.adelete_interaction = self.factory_function(
            adelete_interaction, call_type="adelete_interaction"
        )
        self.delete_interaction = self.factory_function(
            delete_interaction, call_type="delete_interaction"
        )
        self.acancel_interaction = self.factory_function(
            acancel_interaction, call_type="acancel_interaction"
        )
        self.cancel_interaction = self.factory_function(
            cancel_interaction, call_type="cancel_interaction"
        )

    def _initialize_managed_agents_endpoints(self):
        """Initialize Google Managed Agents API endpoints (v1beta/agents)."""
        from litellm.interactions.agents import acreate as acreate_agent
        from litellm.interactions.agents import adelete as adelete_agent
        from litellm.interactions.agents import aget as aget_agent
        from litellm.interactions.agents import alist as alist_agents
        from litellm.interactions.agents import alist_versions as alist_agent_versions
        from litellm.interactions.agents import create as create_agent
        from litellm.interactions.agents import delete as delete_agent
        from litellm.interactions.agents import get as get_agent
        from litellm.interactions.agents import list as list_agents
        from litellm.interactions.agents import list_versions as list_agent_versions

        self.acreate_agent = self.factory_function(
            acreate_agent, call_type="acreate_agent"
        )
        self.create_agent = self.factory_function(
            create_agent, call_type="create_agent"
        )
        self.alist_agents = self.factory_function(
            alist_agents, call_type="alist_agents"
        )
        self.list_agents = self.factory_function(list_agents, call_type="list_agents")
        self.aget_agent = self.factory_function(aget_agent, call_type="aget_agent")
        self.get_agent = self.factory_function(get_agent, call_type="get_agent")
        self.adelete_agent = self.factory_function(
            adelete_agent, call_type="adelete_agent"
        )
        self.delete_agent = self.factory_function(
            delete_agent, call_type="delete_agent"
        )
        self.alist_agent_versions = self.factory_function(
            alist_agent_versions, call_type="alist_agent_versions"
        )
        self.list_agent_versions = self.factory_function(
            list_agent_versions, call_type="list_agent_versions"
        )

    def _initialize_specialized_endpoints(self):
        """Helper to initialize specialized router endpoints (vector store, OCR, search, video, container, skills, interactions)."""
        self._initialize_vector_store_endpoints()
        self._initialize_vector_store_file_endpoints()
        self._initialize_google_genai_endpoints()
        self._initialize_ocr_search_endpoints()
        # Override vector store methods with router-aware implementations
        self._override_vector_store_methods_for_router()
        self._initialize_video_endpoints()
        self._initialize_container_endpoints()
        self._initialize_skills_endpoints()
        self._initialize_interactions_endpoints()
        self._initialize_managed_agents_endpoints()

    def initialize_router_endpoints(self):
        self._initialize_core_endpoints()
        self._initialize_specialized_endpoints()

    def validate_fallbacks(self, fallback_param: Optional[List]):
        """
        Validate the fallbacks parameter.
        """
        if fallback_param is None:
            return
        for fallback_dict in fallback_param:
            if not isinstance(fallback_dict, dict):
                raise ValueError(f"Item '{fallback_dict}' is not a dictionary.")
            if len(fallback_dict) != 1:
                raise ValueError(
                    f"Dictionary '{fallback_dict}' must have exactly one key, but has {len(fallback_dict)} keys."
                )

    def _add_encrypted_content_affinity_check(
        self, enable_global_affinity: bool
    ) -> None:
        from litellm.router_utils.pre_call_checks.encrypted_content_affinity_check import (
            EncryptedContentAffinityCheck,
        )

        def _move_before_deployment_affinity(
            callback_list: List[Any],
            callback_to_move: EncryptedContentAffinityCheck,
        ) -> None:
            if callback_to_move not in callback_list:
                return
            callback_list.remove(callback_to_move)
            insert_index = next(
                (
                    idx
                    for idx, callback in enumerate(callback_list)
                    if isinstance(callback, DeploymentAffinityCheck)
                ),
                len(callback_list),
            )
            callback_list.insert(insert_index, callback_to_move)

        if (
            enable_global_affinity
            or EncryptedContentAffinityCheck.has_model_group_affinity_enabled(
                self.model_group_affinity_config
            )
        ):
            if self.optional_callbacks is None:
                self.optional_callbacks = []

            existing_ec_callback: Optional[EncryptedContentAffinityCheck] = None
            for cb in self.optional_callbacks:
                if isinstance(cb, EncryptedContentAffinityCheck):
                    existing_ec_callback = cb
                    break

            if existing_ec_callback is not None:
                existing_ec_callback.router = self
                existing_ec_callback.enable_global_affinity = (
                    existing_ec_callback.enable_global_affinity
                    or enable_global_affinity
                )
                existing_ec_callback.model_group_affinity_config = (
                    self.model_group_affinity_config or {}
                )
                ec_callback = existing_ec_callback
            else:
                ec_callback = EncryptedContentAffinityCheck(
                    router=self,
                    enable_global_affinity=enable_global_affinity,
                    model_group_affinity_config=self.model_group_affinity_config,
                )
                self.optional_callbacks.append(ec_callback)
                litellm.logging_callback_manager.add_litellm_callback(ec_callback)

            _move_before_deployment_affinity(self.optional_callbacks, ec_callback)
            _move_before_deployment_affinity(litellm.callbacks, ec_callback)

    def add_optional_pre_call_checks(
        self, optional_pre_call_checks: Optional[OptionalPreCallChecks]
    ):
        if optional_pre_call_checks is None:
            return

        # ---------------------------------------------------------------------
        # Unified deployment affinity (session stickiness)
        # ---------------------------------------------------------------------
        enable_user_key_affinity = "deployment_affinity" in optional_pre_call_checks
        enable_responses_api_affinity = (
            "responses_api_deployment_check" in optional_pre_call_checks
        )
        enable_session_id_affinity = "session_affinity" in optional_pre_call_checks
        if (
            enable_user_key_affinity
            or enable_responses_api_affinity
            or enable_session_id_affinity
        ):
            if self.optional_callbacks is None:
                self.optional_callbacks = []

            existing_affinity_callback: Optional[DeploymentAffinityCheck] = None
            for cb in self.optional_callbacks:
                if isinstance(cb, DeploymentAffinityCheck):
                    existing_affinity_callback = cb
                    break

            if existing_affinity_callback is not None:
                existing_affinity_callback.enable_user_key_affinity = (
                    existing_affinity_callback.enable_user_key_affinity
                    or enable_user_key_affinity
                )
                existing_affinity_callback.enable_responses_api_affinity = (
                    existing_affinity_callback.enable_responses_api_affinity
                    or enable_responses_api_affinity
                )
                existing_affinity_callback.enable_session_id_affinity = (
                    existing_affinity_callback.enable_session_id_affinity
                    or enable_session_id_affinity
                )
                existing_affinity_callback.ttl_seconds = (
                    self.deployment_affinity_ttl_seconds
                )
                if self.model_group_affinity_config:
                    existing_affinity_callback.model_group_affinity_config = (
                        self.model_group_affinity_config
                    )
            else:
                affinity_callback = DeploymentAffinityCheck(
                    cache=self.cache,
                    ttl_seconds=self.deployment_affinity_ttl_seconds,
                    enable_user_key_affinity=enable_user_key_affinity,
                    enable_responses_api_affinity=enable_responses_api_affinity,
                    enable_session_id_affinity=enable_session_id_affinity,
                    model_group_affinity_config=self.model_group_affinity_config,
                )
                self.optional_callbacks.append(affinity_callback)
                litellm.logging_callback_manager.add_litellm_callback(affinity_callback)

        # ---------------------------------------------------------------------
        # Encrypted content affinity
        # ---------------------------------------------------------------------
        self._add_encrypted_content_affinity_check(
            enable_global_affinity=(
                "encrypted_content_affinity" in optional_pre_call_checks
            )
        )

        # ---------------------------------------------------------------------
        # Remaining optional pre-call checks
        # ---------------------------------------------------------------------
        for pre_call_check in optional_pre_call_checks:
            _callback: Optional[CustomLogger] = None
            if pre_call_check in (
                "deployment_affinity",
                "responses_api_deployment_check",
                "session_affinity",
                "encrypted_content_affinity",
            ):
                continue
            if pre_call_check == "prompt_caching":
                _callback = PromptCachingDeploymentCheck(cache=self.cache)
            elif pre_call_check == "router_budget_limiting":
                if self._get_router_deployment_budget_limiter() is not None:
                    continue
                _callback = RouterBudgetLimiting(
                    dual_cache=self.cache,
                    provider_budget_config=self.provider_budget_config,
                    model_list=self.model_list,
                )
                self.router_budget_logger = _callback
            elif pre_call_check == "enforce_model_rate_limits":
                _callback = ModelRateLimitingCheck(dual_cache=self.cache)

            if _callback is None:
                continue

            if self.optional_callbacks is None:
                self.optional_callbacks = []
            self.optional_callbacks.append(_callback)
            litellm.logging_callback_manager.add_litellm_callback(_callback)

    def print_deployment(self, deployment: dict):
        """
        returns a copy of the deployment with the api key masked

        Only returns 2 characters of the api key and masks the rest with * (10 *).
        """
        try:
            _deployment_copy = copy.deepcopy(deployment)
            litellm_params: dict = _deployment_copy["litellm_params"]

            if litellm.redact_user_api_key_info:
                masker = SensitiveDataMasker(visible_prefix=2, visible_suffix=0)
                _deployment_copy["litellm_params"] = masker.mask_dict(litellm_params)
            elif "api_key" in litellm_params:
                litellm_params["api_key"] = litellm_params["api_key"][:2] + "*" * 10

            return _deployment_copy
        except Exception as e:
            verbose_router_logger.debug(
                f"Error occurred while printing deployment - {str(e)}"
            )
            raise e

    ### COMPLETION, EMBEDDING, IMG GENERATION FUNCTIONS

    def completion(
        self, model: str, messages: List[Dict[str, str]], **kwargs
    ) -> Union[ModelResponse, CustomStreamWrapper]:
        """
        Example usage:
        response = router.completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}]
        """
        try:
            verbose_router_logger.debug(f"router.completion(model={model},..)")
            kwargs["model"] = model
            kwargs["messages"] = messages
            kwargs["original_function"] = self._completion
            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)

            response = self.function_with_fallbacks(**kwargs)
            return response
        except Exception as e:
            raise e

    def _completion(
        self, model: str, messages: List[Dict[str, str]], **kwargs
    ) -> Union[ModelResponse, CustomStreamWrapper]:
        model_name = None
        deployment = None
        try:
            # Capture kwargs before deployment selection so the streaming
            # fallback iterator can re-dispatch with the original model group.
            input_kwargs_for_streaming_fallback = kwargs.copy()
            input_kwargs_for_streaming_fallback["model"] = model

            # pick the one that is available (lowest TPM/RPM)
            deployment = self.get_available_deployment(
                model=model,
                messages=messages,
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )
            # Check for silent model experiment
            # Make a local copy of litellm_params to avoid mutating the Router's state
            litellm_params = deployment["litellm_params"].copy()
            silent_model = litellm_params.pop("silent_model", None)

            if silent_model is not None:
                # Mirroring traffic to a secondary model
                # Use threading.Thread (not ThreadPoolExecutor) - executor.submit()
                # requires pickling args, which fails when kwargs contain unpicklable
                # objects (e.g. _thread.RLock from OTEL spans, loggers) in deployment.
                thread = threading.Thread(
                    target=self._silent_experiment_completion,
                    args=(silent_model, messages),
                    kwargs=kwargs,
                    daemon=True,
                )
                thread.start()

            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
            kwargs.pop("silent_model", None)  # Ensure it's not in kwargs either
            model_name = litellm_params["model"]
            potential_model_client = self._get_client(
                deployment=deployment, kwargs=kwargs
            )
            # check if provided keys == client keys #
            dynamic_api_key = kwargs.get("api_key", None)
            if (
                dynamic_api_key is not None
                and potential_model_client is not None
                and dynamic_api_key != potential_model_client.api_key
            ):
                model_client = None
            else:
                model_client = potential_model_client

            ### DEPLOYMENT-SPECIFIC PRE-CALL CHECKS ### (e.g. update rpm pre-call. Raise error, if deployment over limit)
            ## only run if model group given, not model id
            if not self.has_model_id(model):
                self.routing_strategy_pre_call_checks(deployment=deployment)

            input_kwargs = {
                **litellm_params,
                "messages": messages,
                "caching": self.cache_responses,
                "client": model_client,
                **kwargs,
            }
            response = litellm.completion(**input_kwargs)
            verbose_router_logger.info(
                f"litellm.completion(model={model_name})\033[32m 200 OK\033[0m"
            )

            ## CHECK CONTENT FILTER ERROR ##
            if isinstance(response, ModelResponse):
                _should_raise = self._should_raise_content_policy_error(
                    model=model, response=response, kwargs=kwargs
                )
                if _should_raise:
                    raise litellm.ContentPolicyViolationError(
                        message="Response output was blocked.",
                        model=model,
                        llm_provider="",
                    )

            # Wrap streaming responses so MidStreamFallbackError (raised
            # during iteration) triggers the Router's fallback chain.
            if isinstance(response, CustomStreamWrapper):
                return self._completion_streaming_iterator(
                    model_response=response,
                    messages=messages,
                    initial_kwargs=input_kwargs_for_streaming_fallback,
                )

            return response
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.completion(model={model_name})\033[31m Exception {str(e)}\033[0m"
            )
            # Set per-deployment num_retries on exception for retry logic
            if deployment is not None:
                self._set_deployment_num_retries_on_exception(e, deployment)
                self._set_failed_deployment_id_on_exception(e, deployment)
            raise e

    def _get_silent_experiment_kwargs(self, **kwargs) -> dict:
        """
        Prepare kwargs for a silent experiment by ensuring isolation from the primary call.

        Guarantee metadata isolation: safe_deep_copy falls back to the original
        reference when deepcopy fails (e.g. metadata contains UserAPIKeyAuth with
        parent_otel_span — an OTel Span that is not deepcopy-able). Force a shallow
        copy of the metadata dict so mutations (model_group, is_silent_experiment)
        never corrupt the main call's metadata.
        """
        from litellm.litellm_core_utils.core_helpers import safe_deep_copy

        silent_kwargs = safe_deep_copy(kwargs)

        # safe_deep_copy may fall back to the original metadata reference when
        # deepcopy fails (UserAPIKeyAuth.parent_otel_span is not deepcopy-able).
        # Detect this via identity check and force a shallow copy so that setting
        # model_group / is_silent_experiment on the silent dict doesn't corrupt
        # the primary call's metadata.
        original_metadata = kwargs.get("metadata")
        if (
            original_metadata is not None
            and silent_kwargs.get("metadata") is original_metadata
        ):
            silent_kwargs["metadata"] = dict(original_metadata)

        if "metadata" not in silent_kwargs:
            silent_kwargs["metadata"] = {}

        # OTel spans are not safe to use across event loops. The silent
        # experiment runs in a new event loop, so strip the span to prevent
        # cross-loop tracing races or span corruption.
        silent_kwargs["metadata"].pop("litellm_parent_otel_span", None)

        silent_kwargs["metadata"]["is_silent_experiment"] = True

        # Force stream=False so the response is fully consumed and callbacks fire
        silent_kwargs["stream"] = False

        # Pop logging objects and call IDs to ensure a fresh logging context
        # This prevents collisions in the Proxy's database (spend_logs)
        silent_kwargs.pop("litellm_call_id", None)
        silent_kwargs.pop("litellm_logging_obj", None)
        silent_kwargs.pop("standard_logging_object", None)
        # DON'T pop proxy_server_request — it's needed for spend log metadata

        return silent_kwargs

    def _silent_experiment_completion(
        self, silent_model: str, messages: List[Any], **kwargs
    ):
        """
        Run a silent experiment in the background (thread).
        """
        try:
            # Prevent infinite recursion if silent model also has a silent model
            if kwargs.get("metadata", {}).get("is_silent_experiment", False):
                return

            messages = copy.deepcopy(messages)

            verbose_router_logger.info(
                f"Starting silent experiment for model {silent_model}"
            )

            silent_kwargs = self._get_silent_experiment_kwargs(**kwargs)

            # Override model_group to correctly attribute metrics to the silent model
            silent_kwargs["metadata"]["model_group"] = silent_model

            # Create a new event loop for this thread so that async success
            # callbacks (e.g. _ProxyDBLogger) can schedule and run DB writes.
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
            try:

                async def _run_silent_completion():
                    await self.acompletion(
                        model=silent_model,
                        messages=cast(List[AllMessageValues], messages),
                        **silent_kwargs,
                    )
                    # Drain any fire-and-forget tasks (e.g. alerting hooks)
                    # scheduled via asyncio.create_task during acompletion.
                    pending = asyncio.all_tasks()
                    current = asyncio.current_task()
                    if current is not None:
                        pending.discard(current)
                    if pending:
                        await asyncio.gather(*pending, return_exceptions=True)

                loop.run_until_complete(_run_silent_completion())
            finally:
                loop.close()
        except Exception as e:
            verbose_router_logger.error(
                f"Silent experiment failed for model {silent_model}: {str(e)}"
            )

    # fmt: off

    @overload
    async def acompletion(
        self, model: str, messages: List[AllMessageValues], stream: Literal[True], **kwargs
    ) -> CustomStreamWrapper: 
        ...

    @overload
    async def acompletion(
        self, model: str, messages: List[AllMessageValues], stream: Literal[False] = False, **kwargs
    ) -> ModelResponse: 
        ...

    @overload
    async def acompletion(
        self, model: str, messages: List[AllMessageValues], stream: Union[Literal[True], Literal[False]] = False, **kwargs
    ) -> Union[CustomStreamWrapper, ModelResponse]: 
        ...

    # fmt: on

    # The actual implementation of the function
    async def acompletion(
        self,
        model: str,
        messages: List[AllMessageValues],
        stream: bool = False,
        **kwargs,
    ):
        try:
            kwargs["model"] = model
            kwargs["messages"] = messages
            kwargs["stream"] = stream
            kwargs["original_function"] = self._acompletion

            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
            request_priority = kwargs.get("priority") or self.default_priority
            start_time = time.time()
            _is_prompt_management_model = self._is_prompt_management_model(model)

            if _is_prompt_management_model:
                return await self._prompt_management_factory(
                    model=model,
                    messages=messages,
                    kwargs=kwargs,
                )
            if request_priority is not None and isinstance(request_priority, int):
                response = await self.schedule_acompletion(**kwargs)
            else:
                response = await self.async_function_with_fallbacks(**kwargs)
            end_time = time.time()
            _duration = end_time - start_time
            asyncio.create_task(
                self.service_logger_obj.async_service_success_hook(
                    service=ServiceTypes.ROUTER,
                    duration=_duration,
                    call_type="acompletion",
                    start_time=start_time,
                    end_time=end_time,
                    parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
                )
            )

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    @staticmethod
    def _combine_fallback_usage(
        fallback_item: ModelResponseStream,
        complete_response_object_usage: Optional[Usage],
    ) -> None:
        """Merge partial-stream usage with fallback-stream usage on the chunk."""
        from litellm.cost_calculator import BaseTokenUsageProcessor

        usage = cast(Optional[Usage], getattr(fallback_item, "usage", None))
        usage_objects = [usage] if usage is not None else []
        if (
            complete_response_object_usage is not None
            and hasattr(complete_response_object_usage, "usage")
            and complete_response_object_usage.usage is not None  # type: ignore
        ):
            usage_objects.append(complete_response_object_usage)
        combined_usage = BaseTokenUsageProcessor.combine_usage_objects(
            usage_objects=usage_objects
        )
        setattr(fallback_item, "usage", combined_usage)

    async def _acompletion_streaming_iterator(
        self,
        model_response: CustomStreamWrapper,
        messages: List[Dict[str, str]],
        initial_kwargs: dict,
    ) -> CustomStreamWrapper:
        """
        Helper to iterate over a streaming response.

        Catches errors for fallbacks using the router's fallback system
        """
        from litellm.exceptions import MidStreamFallbackError

        class FallbackStreamWrapper(CustomStreamWrapper):
            def __init__(self, async_generator: AsyncGenerator):
                # Copy attributes from the original model_response
                super().__init__(
                    completion_stream=async_generator,
                    model=model_response.model,
                    custom_llm_provider=model_response.custom_llm_provider,
                    logging_obj=model_response.logging_obj,
                )
                self._async_generator = async_generator
                # Preserve hidden params (including litellm_overhead_time_ms) from original response
                if hasattr(model_response, "_hidden_params"):
                    self._hidden_params = model_response._hidden_params.copy()

            def __aiter__(self):
                return self

            async def __anext__(self):
                return await self._async_generator.__anext__()

        async def stream_with_fallbacks():
            fallback_response = None  # Track for cleanup in finally
            try:
                async for item in model_response:
                    yield item
            except MidStreamFallbackError as e:
                from litellm.main import stream_chunk_builder

                complete_response_object = stream_chunk_builder(
                    chunks=model_response.chunks
                )
                complete_response_object_usage = cast(
                    Optional[Usage],
                    getattr(complete_response_object, "usage", None),
                )
                try:
                    # Use the router's fallback system
                    model_group = cast(str, initial_kwargs.get("model"))
                    fallbacks: Optional[List] = initial_kwargs.get(
                        "fallbacks", self.fallbacks
                    )
                    context_window_fallbacks: Optional[List] = initial_kwargs.get(
                        "context_window_fallbacks", self.context_window_fallbacks
                    )
                    content_policy_fallbacks: Optional[List] = initial_kwargs.get(
                        "content_policy_fallbacks", self.content_policy_fallbacks
                    )
                    initial_kwargs["original_function"] = self._acompletion
                    if e.is_pre_first_chunk or not e.generated_content:
                        # No content was generated before the error (e.g. a
                        # rate-limit 429 on the very first chunk).  Retry with
                        # the original messages — adding a continuation prompt
                        # would waste tokens and confuse the model.
                        initial_kwargs["messages"] = messages
                    else:
                        initial_kwargs["messages"] = messages + [
                            {
                                "role": "system",
                                "content": "You are a helpful assistant. You are given a message and you need to respond to it. You are also given a generated content. You need to respond to the message in continuation of the generated content. Do not repeat the same content. Your response should be in continuation of this text: ",
                            },
                            {
                                "role": "assistant",
                                "content": e.generated_content,
                                "prefix": True,
                            },
                        ]
                    self._update_kwargs_before_fallbacks(
                        model=model_group, kwargs=initial_kwargs
                    )
                    fallback_response = (
                        await self.async_function_with_fallbacks_common_utils(
                            e=e,
                            disable_fallbacks=False,
                            fallbacks=fallbacks,
                            context_window_fallbacks=context_window_fallbacks,
                            content_policy_fallbacks=content_policy_fallbacks,
                            model_group=model_group,
                            args=(),
                            kwargs=initial_kwargs,
                        )
                    )

                    # If fallback returns a streaming response, iterate over it
                    if hasattr(fallback_response, "__aiter__"):
                        async for fallback_item in fallback_response:  # type: ignore
                            if (
                                fallback_item
                                and isinstance(fallback_item, ModelResponseStream)
                                and hasattr(fallback_item, "usage")
                            ):
                                self._combine_fallback_usage(
                                    fallback_item, complete_response_object_usage
                                )
                            yield fallback_item
                    else:
                        # If fallback returns a non-streaming response, yield None
                        yield None

                except Exception as fallback_error:
                    # If fallback also fails, log and re-raise original error
                    verbose_router_logger.error(
                        f"Fallback also failed: {fallback_error}"
                    )
                    raise fallback_error
            finally:
                # Close the underlying streams to release HTTP connections
                # back to the connection pool when the generator is closed
                # (e.g. on client disconnect).
                # Shield from anyio cancellation so the awaits can complete.
                with anyio.CancelScope(shield=True):
                    if hasattr(model_response, "aclose"):
                        try:
                            await model_response.aclose()
                        except BaseException as e:
                            verbose_router_logger.debug(
                                "stream_with_fallbacks: error closing model_response: %s",
                                e,
                            )
                    if fallback_response is not None and hasattr(
                        fallback_response, "aclose"
                    ):
                        try:
                            await fallback_response.aclose()
                        except BaseException as e:
                            verbose_router_logger.debug(
                                "stream_with_fallbacks: error closing fallback_response: %s",
                                e,
                            )

        return FallbackStreamWrapper(stream_with_fallbacks())

    @staticmethod
    def _extract_partial_responses_usage(
        source_iterator: "BaseResponsesAPIStreamingIterator",
    ) -> Optional["ResponseAPIUsage"]:
        """
        Best-effort: pull partial token usage from a Responses-API streaming
        iterator that errored mid-stream, normalized to ResponseAPIUsage so
        the caller can combine without crossing token-naming conventions.

        Two sources, in priority order:
          1. The bridge path (LiteLLMCompletionStreamingIterator) accumulates
             chat-completion chunks while streaming — feed them through
             stream_chunk_builder to recover chat Usage, then translate
             (prompt_tokens → input_tokens, completion_tokens → output_tokens).
          2. The native path (ResponsesAPIStreamingIterator) only has a
             completed_response object if the stream reached
             RESPONSE_COMPLETED before erroring — uncommon mid-stream but
             worth checking. Already ResponseAPIUsage-shaped.

        Returns None when no partial usage is recoverable.
        """
        from litellm.responses.litellm_completion_transformation.streaming_iterator import (
            LiteLLMCompletionStreamingIterator,
        )
        from litellm.types.llms.openai import (
            ResponseAPIUsage,
            ResponseCompletedEvent,
            ResponseFailedEvent,
            ResponseIncompleteEvent,
        )

        # Bridge subclass is the only iterator that accumulates chat-completion
        # chunks. isinstance narrows the type so we can read the attribute
        # directly instead of getattr-ing on the base class.
        if isinstance(source_iterator, LiteLLMCompletionStreamingIterator):
            chunks = source_iterator.collected_chat_completion_chunks
            if chunks:
                try:
                    from litellm.main import stream_chunk_builder

                    built = stream_chunk_builder(chunks=chunks)
                    # stream_chunk_builder returns ModelResponse |
                    # TextCompletionResponse | None. ModelResponse sets .usage
                    # in __init__ rather than declaring it as a class field, so
                    # static narrowing doesn't expose it. Mirror the sync path
                    # (_completion_streaming_iterator) and pull via getattr.
                    chat = getattr(built, "usage", None) if built is not None else None
                    if chat is not None:
                        # getattr-with-default because the test path may
                        # substitute a SimpleNamespace lacking some fields;
                        # real Usage instances always have them.
                        prompt = int(getattr(chat, "prompt_tokens", 0) or 0)
                        completion = int(getattr(chat, "completion_tokens", 0) or 0)
                        total = int(
                            getattr(chat, "total_tokens", prompt + completion)
                            or (prompt + completion)
                        )
                        return ResponseAPIUsage(
                            input_tokens=prompt,
                            output_tokens=completion,
                            total_tokens=total,
                        )
                except Exception:
                    # Builder is best-effort — fall through to native path.
                    pass

        # Native path: completed_response is set only if RESPONSE_COMPLETED
        # arrived before the error (uncommon mid-stream but worth checking).
        # Already ResponseAPIUsage-shaped — return as-is.
        completed = source_iterator.completed_response
        if isinstance(
            completed,
            (ResponseCompletedEvent, ResponseFailedEvent, ResponseIncompleteEvent),
        ):
            return completed.response.usage
        return None

    @staticmethod
    def _combine_responses_fallback_usage(
        fallback_item: "BaseLiteLLMOpenAIResponseObject",
        partial_usage: "ResponseAPIUsage",
    ) -> None:
        """
        Merge partial-stream usage with fallback-stream usage on a
        Responses-API streaming event.

        Only mutates events that carry a `response` with a `usage` field
        (response.completed / response.failed / response.incomplete). Other
        events pass through unchanged.

        Both inputs are ResponseAPIUsage-shaped (see
        _extract_partial_responses_usage which normalizes the bridge path),
        so we can sum input_tokens / output_tokens / total_tokens directly
        and produce a clean ResponseAPIUsage — no token-naming split, no
        setattr bypass.
        """
        from litellm.types.llms.openai import (
            ResponseAPIUsage,
            ResponseCompletedEvent,
            ResponseFailedEvent,
            ResponseIncompleteEvent,
        )

        if not isinstance(
            fallback_item,
            (ResponseCompletedEvent, ResponseFailedEvent, ResponseIncompleteEvent),
        ):
            return
        response = fallback_item.response
        if response.usage is None:
            return

        fb = response.usage
        response.usage = ResponseAPIUsage(
            input_tokens=(partial_usage.input_tokens or 0) + (fb.input_tokens or 0),
            output_tokens=(partial_usage.output_tokens or 0) + (fb.output_tokens or 0),
            total_tokens=(partial_usage.total_tokens or 0) + (fb.total_tokens or 0),
        )

    @staticmethod
    def _build_responses_continuation_input(
        input_val: Optional[Union[str, "ResponseInputParam"]],
        generated_content: str,
    ) -> "ResponseInputParam":
        """
        Convert Responses-API input + partial assistant output into a
        continuation input that asks the fallback model to pick up where the
        prior assistant message stopped.

        Best effort across providers. The chat-completions path uses
        Anthropic's `prefix: True` prefill trick on the assistant message;
        the Responses-API input schema has no direct equivalent, so we
        append an instruction (developer role) plus a prior assistant
        message containing the partial output. Providers without prefill
        semantics (OpenAI, Vertex) treat this as conversational context
        and may regenerate — same trade-off as the chat-completions path
        for non-Anthropic fallbacks.
        """
        # base/continuation are List[Any] because ResponseInputParam items
        # are a wide Union of TypedDicts (EasyInputMessageParam, Message,
        # ResponseOutputMessageParam, ...) — annotating as List[Dict[str, Any]]
        # rejects the list() spread of input_val. We cast the combined list to
        # ResponseInputParam at the return.
        base: List[Any]
        if isinstance(input_val, str):
            base = [
                {
                    "type": "message",
                    "role": "user",
                    "content": [{"type": "input_text", "text": input_val}],
                }
            ]
        elif isinstance(input_val, list):
            base = list(input_val)
        else:
            base = []
        continuation: List[Any] = [
            {
                "type": "message",
                "role": "developer",
                "content": [
                    {
                        "type": "input_text",
                        "text": (
                            "The previous assistant response was interrupted "
                            "mid-stream. Continue exactly where it stopped — "
                            "do not repeat any of its content. Your response "
                            "must read as a seamless continuation."
                        ),
                    }
                ],
            },
            {
                "type": "message",
                "role": "assistant",
                "content": [{"type": "output_text", "text": generated_content}],
            },
        ]
        return cast("ResponseInputParam", base + continuation)

    async def _aresponses_streaming_iterator(
        self,
        response: "BaseResponsesAPIStreamingIterator",
        initial_kwargs: Dict[str, Any],
    ) -> "BaseResponsesAPIStreamingIterator":
        """
        Wrap a Responses-API streaming iterator so MidStreamFallbackError
        triggers the Router's fallback chain (parity with
        _acompletion_streaming_iterator for the chat-completions path).

        The Responses-API streaming path goes through
        _ageneric_api_call_with_fallbacks rather than _acompletion, so the
        returned iterator is never wrapped by the chat completions
        fallback handler. Without this wrapper, MidStreamFallbackError
        raised mid-stream from the underlying CustomStreamWrapper (used by
        LiteLLMCompletionStreamingIterator when the Responses API is
        served via the completion bridge) propagates unhandled and the
        configured cross-provider fallback never fires.

        Full parity with the chat-completions path:
          - Pre-first-chunk: retry with the original input unchanged.
          - Partial content: inject a developer instruction + prior
            assistant message carrying the generated text so the fallback
            model continues rather than restarts.
          - Usage combining: merge partial-stream usage onto the fallback's
            response.completed event so accounting reflects both attempts.
          - Stream cleanup: shielded aclose() on both source and fallback
            iterators on terminate.
        """
        from litellm.exceptions import MidStreamFallbackError
        from litellm.responses.streaming_iterator import (
            BaseResponsesAPIStreamingIterator,
            _get_openai_response_types,
        )

        source_iterator = response

        # Pre-resolve the set of terminal stream event types so the
        # per-chunk type check inside FallbackResponsesStreamWrapper
        # stays cheap; mirrors the source-iterator filter at
        # responses/streaming_iterator.py:243-247.
        _openai_types = _get_openai_response_types()
        _RESPONSES_TERMINAL_EVENT_TYPES = (
            _openai_types.ResponsesAPIStreamEvents.RESPONSE_COMPLETED,
            _openai_types.ResponsesAPIStreamEvents.RESPONSE_INCOMPLETE,
            _openai_types.ResponsesAPIStreamEvents.RESPONSE_FAILED,
        )

        class FallbackResponsesStreamWrapper(BaseResponsesAPIStreamingIterator):
            """
            Subclasses BaseResponsesAPIStreamingIterator only for isinstance
            compatibility (proxy + interactions code paths check the type).
            Bypasses the parent constructor and delegates iteration to an
            async generator.
            """

            def __init__(self, async_generator: AsyncGenerator):
                import time
                from datetime import datetime

                self._async_generator = async_generator
                # Mirror every attribute BaseResponsesAPIStreamingIterator.__init__
                # would have set. The wrapper bypasses super().__init__ (it has no
                # httpx.Response of its own and no provider config to drive), so
                # we copy from source_iterator where applicable and use safe
                # defaults elsewhere. This keeps inherited methods (e.g.
                # _check_max_streaming_duration, _handle_failure) safe to call.
                #
                # The bridge path (LiteLLMCompletionStreamingIterator used by
                # Anthropic/Bedrock/Vertex) does not call super().__init__ and
                # is missing many of these attributes — use getattr fallbacks
                # so wrapper construction never raises AttributeError. The
                # bridge stores the logging object as `litellm_logging_obj`.
                # base class declares non-Optional types for these
                # fields but the bridge path (LiteLLMCompletionStreamingIterator)
                # can legitimately omit them at runtime — keep the None
                # fallback. Same lines passed mypy on the pre-fix file
                # because the surrounding function body wasn't fully
                # type-narrowed; the new typed terminal-event tuple above
                # is what made these surface.
                self.response = getattr(source_iterator, "response", None)  # type: ignore[assignment]
                self.model = getattr(source_iterator, "model", None)  # type: ignore[assignment]
                self.logging_obj = getattr(  # type: ignore[assignment]
                    source_iterator,
                    "logging_obj",
                    getattr(source_iterator, "litellm_logging_obj", None),
                )
                self.finished = False
                self.responses_api_provider_config = getattr(
                    source_iterator, "responses_api_provider_config", None
                )
                self.completed_response = None
                self.start_time = getattr(source_iterator, "start_time", datetime.now())
                self._failure_handled = False
                self._completed_response_cached = False
                self._completed_response_logged = False
                self._completed_response_cache_hit = None
                self._persist_completed_response_before_logging = True
                self._stream_created_time = time.time()
                self.litellm_metadata = getattr(
                    source_iterator, "litellm_metadata", None
                )
                self.custom_llm_provider = getattr(
                    source_iterator, "custom_llm_provider", None
                )
                self.request_data = getattr(source_iterator, "request_data", {}) or {}
                self.call_type = getattr(source_iterator, "call_type", None)
                # Preserve hidden params so response headers (model_id,
                # api_base, additional_headers) keep flowing.
                self._hidden_params = dict(
                    getattr(source_iterator, "_hidden_params", None) or {}
                )

            def __aiter__(self):
                return self

            async def __anext__(self):
                chunk = await self._async_generator.__anext__()
                # Sniff the terminal stream event off each forwarded chunk
                # so ``self.completed_response`` is populated regardless of
                # which inner iterator produced it (source_iterator,
                # fallback_iterator, or any future wrapper). Without this
                # the proxy's container-ownership hook (which reads
                # ``getattr(stream_response, "completed_response", None)``
                # via _extract_completed_responses_response) silently
                # records nothing on streaming /v1/responses calls — every
                # follow-up /v1/containers/<id>/files call then 403s for
                # the very key that created the container (#30210).
                if (
                    self.completed_response is None
                    and getattr(chunk, "type", None) in _RESPONSES_TERMINAL_EVENT_TYPES
                ):
                    self.completed_response = chunk
                return chunk

            async def aclose(self):
                # async generators always expose aclose — no defensive check needed.
                await self._async_generator.aclose()

        async def stream_with_fallbacks():
            fallback_response = None
            try:
                async for item in source_iterator:
                    yield item
            except MidStreamFallbackError as e:
                partial_usage = Router._extract_partial_responses_usage(source_iterator)
                try:
                    model_group = cast(str, initial_kwargs.get("model"))
                    fallbacks: Optional[List] = initial_kwargs.get(
                        "fallbacks", self.fallbacks
                    )
                    context_window_fallbacks: Optional[List] = initial_kwargs.get(
                        "context_window_fallbacks", self.context_window_fallbacks
                    )
                    content_policy_fallbacks: Optional[List] = initial_kwargs.get(
                        "content_policy_fallbacks", self.content_policy_fallbacks
                    )
                    # Re-enter via the per-attempt helper so the fallback chain
                    # picks deployments through
                    # _ageneric_api_call_with_fallbacks_helper.
                    # original_generic_function is preserved by the caller so
                    # the helper knows what underlying API to invoke per attempt.
                    initial_kwargs["original_function"] = (
                        self._ageneric_api_call_with_fallbacks_helper
                    )
                    if e.is_pre_first_chunk or not e.generated_content:
                        # No content generated before the error — retry with the
                        # original input. Adding a continuation prompt would
                        # waste tokens and confuse the model.
                        pass
                    else:
                        initial_kwargs["input"] = (
                            Router._build_responses_continuation_input(
                                initial_kwargs.get("input"),
                                e.generated_content,
                            )
                        )
                    # The Responses-API path stores observability metadata
                    # under "litellm_metadata" (not the default "metadata") —
                    # see _ageneric_api_call_with_fallbacks. Mirroring that
                    # here ensures model_group, model_group_alias, and trace
                    # ids land in the same key litellm.aresponses reads from.
                    self._update_kwargs_before_fallbacks(
                        model=model_group,
                        kwargs=initial_kwargs,
                        metadata_variable_name="litellm_metadata",
                    )
                    fallback_response = (
                        await self.async_function_with_fallbacks_common_utils(
                            e=e,
                            disable_fallbacks=False,
                            fallbacks=fallbacks,
                            context_window_fallbacks=context_window_fallbacks,
                            content_policy_fallbacks=content_policy_fallbacks,
                            model_group=model_group,
                            args=(),
                            kwargs=initial_kwargs,
                        )
                    )

                    if hasattr(fallback_response, "__aiter__"):
                        async for fallback_item in fallback_response:  # type: ignore
                            if partial_usage is not None:
                                Router._combine_responses_fallback_usage(
                                    fallback_item, partial_usage
                                )
                            yield fallback_item
                    else:
                        yield fallback_response
                except Exception as fallback_error:
                    verbose_router_logger.error(
                        f"Responses streaming fallback also failed: {fallback_error}"
                    )
                    raise fallback_error
            finally:
                with anyio.CancelScope(shield=True):
                    if hasattr(source_iterator, "aclose"):
                        try:
                            await source_iterator.aclose()  # type: ignore[func-returns-value]
                        except BaseException as exc:
                            verbose_router_logger.debug(
                                "stream_with_fallbacks(aresponses): error closing source: %s",
                                exc,
                            )
                    if fallback_response is not None and hasattr(
                        fallback_response, "aclose"
                    ):
                        try:
                            await fallback_response.aclose()
                        except BaseException as exc:
                            verbose_router_logger.debug(
                                "stream_with_fallbacks(aresponses): error closing fallback: %s",
                                exc,
                            )

        return FallbackResponsesStreamWrapper(stream_with_fallbacks())

    def _completion_streaming_iterator(
        self,
        model_response: CustomStreamWrapper,
        messages: List[Dict[str, str]],
        initial_kwargs: dict,
    ) -> CustomStreamWrapper:
        """
        Sync equivalent of _acompletion_streaming_iterator.

        Wraps a sync streaming response so that MidStreamFallbackError
        (raised by CustomStreamWrapper.__next__) triggers the Router's
        fallback chain instead of surfacing directly to the caller.
        """
        from litellm.exceptions import MidStreamFallbackError

        class SyncFallbackStreamWrapper(CustomStreamWrapper):
            def __init__(self, sync_generator: Generator):
                super().__init__(
                    completion_stream=sync_generator,
                    model=model_response.model,
                    custom_llm_provider=model_response.custom_llm_provider,
                    logging_obj=model_response.logging_obj,
                )
                self._sync_generator = sync_generator
                if hasattr(model_response, "_hidden_params"):
                    self._hidden_params = model_response._hidden_params.copy()

            def __iter__(self):
                return self

            def __next__(self):
                return next(self._sync_generator)

        router_self = self

        def stream_with_fallbacks():
            fallback_response = None
            try:
                for item in model_response:
                    yield item
            except MidStreamFallbackError as e:
                from litellm.main import stream_chunk_builder

                complete_response_object = stream_chunk_builder(
                    chunks=model_response.chunks
                )
                complete_response_object_usage = cast(
                    Optional[Usage],
                    getattr(complete_response_object, "usage", None),
                )
                try:
                    model_group = cast(str, initial_kwargs.get("model"))
                    fallbacks: Optional[List] = initial_kwargs.get(
                        "fallbacks", router_self.fallbacks
                    )
                    context_window_fallbacks: Optional[List] = initial_kwargs.get(
                        "context_window_fallbacks",
                        router_self.context_window_fallbacks,
                    )
                    content_policy_fallbacks: Optional[List] = initial_kwargs.get(
                        "content_policy_fallbacks",
                        router_self.content_policy_fallbacks,
                    )
                    initial_kwargs["original_function"] = router_self._completion
                    if e.is_pre_first_chunk or not e.generated_content:
                        initial_kwargs["messages"] = messages
                    else:
                        initial_kwargs["messages"] = messages + [
                            {
                                "role": "system",
                                "content": "You are a helpful assistant. You are given a message and you need to respond to it. You are also given a generated content. You need to respond to the message in continuation of the generated content. Do not repeat the same content. Your response should be in continuation of this text: ",
                            },
                            {
                                "role": "assistant",
                                "content": e.generated_content,
                                "prefix": True,
                            },
                        ]
                    router_self._update_kwargs_before_fallbacks(
                        model=model_group, kwargs=initial_kwargs
                    )
                    fallback_response = router_self.function_with_fallbacks(
                        **initial_kwargs,
                        fallbacks=fallbacks,
                        context_window_fallbacks=context_window_fallbacks,
                        content_policy_fallbacks=content_policy_fallbacks,
                    )

                    if hasattr(fallback_response, "__iter__"):
                        for fallback_item in fallback_response:
                            if (
                                fallback_item
                                and isinstance(fallback_item, ModelResponseStream)
                                and hasattr(fallback_item, "usage")
                            ):
                                router_self._combine_fallback_usage(
                                    fallback_item, complete_response_object_usage
                                )
                            yield fallback_item
                    else:
                        yield None

                except Exception as fallback_error:
                    verbose_router_logger.error(
                        f"Fallback also failed: {fallback_error}"
                    )
                    raise fallback_error
            finally:
                if hasattr(model_response, "close"):
                    try:
                        model_response.close()  # type: ignore[reportAttributeAccessIssue]
                    except BaseException as close_err:
                        verbose_router_logger.debug(
                            "stream_with_fallbacks: error closing model_response: %s",
                            close_err,
                        )
                if fallback_response is not None and hasattr(
                    fallback_response, "close"
                ):
                    try:
                        fallback_response.close()
                    except BaseException as close_err:
                        verbose_router_logger.debug(
                            "stream_with_fallbacks: error closing fallback_response: %s",
                            close_err,
                        )

        return SyncFallbackStreamWrapper(stream_with_fallbacks())

    async def _silent_experiment_acompletion(
        self, silent_model: str, messages: List[Any], **kwargs
    ):
        """
        Run a silent experiment in the background.
        """
        try:
            # Prevent infinite recursion if silent model also has a silent model
            if kwargs.get("metadata", {}).get("is_silent_experiment", False):
                return

            messages = copy.deepcopy(messages)

            verbose_router_logger.info(
                f"Starting silent experiment for model {silent_model}"
            )

            silent_kwargs = self._get_silent_experiment_kwargs(**kwargs)
            # Override model_group to correctly attribute metrics to the silent model
            silent_kwargs["metadata"]["model_group"] = silent_model

            # Trigger the silent request
            await self.acompletion(
                model=silent_model,
                messages=cast(List[AllMessageValues], messages),
                **silent_kwargs,
            )
        except Exception as e:
            verbose_router_logger.error(
                f"Silent experiment failed for model {silent_model}: {str(e)}"
            )

    async def _acompletion(
        self, model: str, messages: List[Dict[str, str]], **kwargs
    ) -> Union[
        ModelResponse,
        CustomStreamWrapper,
    ]:
        """
        - Get an available deployment
        - call it with a semaphore over the call
        - semaphore specific to it's rpm
        - in the semaphore,  make a check against it's local rpm before running
        """
        model_name = None
        deployment = None
        _timeout_debug_deployment_dict = (
            {}
        )  # this is a temporary dict to debug timeout issues
        try:
            input_kwargs_for_streaming_fallback = kwargs.copy()
            input_kwargs_for_streaming_fallback["model"] = model

            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            start_time = time.time()
            deployment = await self.async_get_available_deployment(
                model=model,
                messages=messages,
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )

            _timeout_debug_deployment_dict = deployment
            end_time = time.time()
            _duration = end_time - start_time
            asyncio.create_task(
                self.service_logger_obj.async_service_success_hook(
                    service=ServiceTypes.ROUTER,
                    duration=_duration,
                    call_type="async_get_available_deployment",
                    start_time=start_time,
                    end_time=end_time,
                    parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
                )
            )

            # debug how often this deployment picked

            self._track_deployment_metrics(
                deployment=deployment, parent_otel_span=parent_otel_span
            )

            # Check for silent model experiment
            # Make a local copy of litellm_params to avoid mutating the Router's state
            litellm_params = deployment["litellm_params"].copy()
            silent_model = litellm_params.pop("silent_model", None)

            if silent_model is not None:
                # Mirroring traffic to a secondary model
                # This is a silent experiment, so we don't want to block the primary request
                asyncio.create_task(
                    self._silent_experiment_acompletion(
                        silent_model=silent_model,
                        messages=messages,  # Use messages instead of *args
                        **kwargs,
                    )
                )

            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
            kwargs.pop("silent_model", None)  # Ensure it's not in kwargs either

            model_name = litellm_params["model"]

            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )
            self.total_calls[model_name] += 1

            input_kwargs = {
                **litellm_params,
                "messages": messages,
                "caching": self.cache_responses,
                "client": model_client,
                **kwargs,
            }
            input_kwargs.pop("silent_model", None)

            _response = litellm.acompletion(**input_kwargs)

            logging_obj: Optional[LiteLLMLogging] = kwargs.get(
                "litellm_logging_obj", None
            )

            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )
            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    """
                    - Check rpm limits before making the call
                    - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                    """
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment,
                        logging_obj=logging_obj,
                        parent_otel_span=parent_otel_span,
                    )
                    response = await _response
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment,
                    logging_obj=logging_obj,
                    parent_otel_span=parent_otel_span,
                )

                response = await _response

            ## CHECK CONTENT FILTER ERROR ##
            if isinstance(response, ModelResponse):
                _should_raise = self._should_raise_content_policy_error(
                    model=model, response=response, kwargs=kwargs
                )
                if _should_raise:
                    raise litellm.ContentPolicyViolationError(
                        message="Response output was blocked.",
                        model=model,
                        llm_provider="",
                    )

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.acompletion(model={model_name})\033[32m 200 OK\033[0m"
            )
            # debug how often this deployment picked
            self._track_deployment_metrics(
                deployment=deployment,
                response=response,
                parent_otel_span=parent_otel_span,
            )

            if isinstance(response, CustomStreamWrapper):
                return await self._acompletion_streaming_iterator(
                    model_response=response,
                    messages=messages,
                    initial_kwargs=input_kwargs_for_streaming_fallback,
                )

            return response
        except litellm.Timeout as e:
            deployment_request_timeout_param = _timeout_debug_deployment_dict.get(
                "litellm_params", {}
            ).get("request_timeout", None)
            deployment_timeout_param = _timeout_debug_deployment_dict.get(
                "litellm_params", {}
            ).get("timeout", None)
            if litellm.expose_router_debug_in_errors:
                e.message += f"\n\nDeployment Info: request_timeout: {deployment_request_timeout_param}\ntimeout: {deployment_timeout_param}"
            # Set per-deployment num_retries on exception for retry logic
            if deployment is not None:
                self._set_deployment_num_retries_on_exception(e, deployment)
                self._set_failed_deployment_id_on_exception(e, deployment)
            raise e
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.acompletion(model={model_name})\033[31m Exception {str(e)}\033[0m"
            )
            if model_name is not None:
                self.fail_calls[model_name] += 1
            # Set per-deployment num_retries on exception for retry logic
            if deployment is not None:
                self._set_deployment_num_retries_on_exception(e, deployment)
                self._set_failed_deployment_id_on_exception(e, deployment)
            raise e

    def _update_kwargs_before_fallbacks(
        self,
        model: str,
        kwargs: dict,
        metadata_variable_name: Optional[str] = "metadata",
    ) -> None:
        """
        Adds/updates to kwargs:
        - num_retries
        - litellm_trace_id
        - metadata
        """
        kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
        kwargs.setdefault("litellm_trace_id", str(uuid.uuid4()))
        model_group_alias: Optional[str] = None
        if self._get_model_from_alias(model=model):
            model_group_alias = model
        kwargs.setdefault(metadata_variable_name, {}).update(
            {"model_group": model, "model_group_alias": model_group_alias}
        )

    def _set_deployment_num_retries_on_exception(
        self, exception: Exception, deployment: dict
    ) -> None:
        """
        Set num_retries from deployment litellm_params on the exception.

        This allows the retry logic in async_function_with_retries to use
        per-deployment retry settings instead of the global setting.
        """
        # Only set if exception doesn't already have num_retries
        if hasattr(exception, "num_retries") and exception.num_retries is not None:  # type: ignore
            return

        litellm_params = deployment.get("litellm_params", {})
        dep_num_retries = litellm_params.get("num_retries")
        if dep_num_retries is not None:
            try:
                exception.num_retries = int(dep_num_retries)  # type: ignore  # Handle both int and str
            except (ValueError, TypeError):
                pass  # Skip if value can't be converted to int

    def _set_failed_deployment_id_on_exception(
        self, exception: Exception, deployment: dict
    ) -> None:
        """
        Stamp the failed deployment's `model_info.id` on the exception so the
        fallback layer can exclude it from subsequent re-picks within the same
        request (used by weighted-routing failover).

        Idempotent: never overwrites an existing value, so the id of the
        deployment that *first* failed in a chain is preserved if multiple
        layers re-raise.
        """
        if getattr(exception, "failed_deployment_id", None):
            return
        deployment_id = (deployment.get("model_info") or {}).get("id")
        if deployment_id:
            try:
                exception.failed_deployment_id = deployment_id  # type: ignore[attr-defined]
            except Exception:
                pass

    def _update_kwargs_with_default_litellm_params(
        self, kwargs: dict, metadata_variable_name: Optional[str] = "metadata"
    ) -> None:
        """
        Adds default litellm params to kwargs, if set.

        Handles inserting this as either "metadata" or "litellm_metadata" depending on the metadata_variable_name
        """
        # 1) copy your defaults and pull out metadata
        defaults = self.default_litellm_params.copy()
        metadata_defaults = defaults.pop("metadata", {}) or {}

        # 2) add any non-metadata defaults that aren't already in kwargs
        for key, value in defaults.items():
            if value is None:
                continue
            kwargs.setdefault(key, value)

        # 3) merge in metadata, this handles inserting this as either "metadata" or "litellm_metadata"
        kwargs.setdefault(metadata_variable_name, {}).update(metadata_defaults)

    def _handle_clientside_credential(
        self, deployment: dict, kwargs: dict, function_name: Optional[str] = None
    ) -> Deployment:
        """
        Handle clientside credential
        """
        model_info = deployment.get("model_info", {}).copy()
        litellm_params = deployment["litellm_params"].copy()
        dynamic_litellm_params = get_dynamic_litellm_params(
            litellm_params=litellm_params, request_kwargs=kwargs
        )
        # Use deployment model_name as model_group for generating model_id
        metadata_variable_name = _get_router_metadata_variable_name(
            function_name=function_name,
        )
        model_group = kwargs.get(metadata_variable_name, {}).get("model_group")
        _model_id = self._generate_model_id(
            model_group=model_group, litellm_params=dynamic_litellm_params
        )
        original_model_id = model_info.get("id")
        model_info["id"] = _model_id
        model_info["original_model_id"] = original_model_id
        deployment_pydantic_obj = Deployment(
            model_name=model_group,
            litellm_params=LiteLLM_Params(**dynamic_litellm_params),
            model_info=model_info,
        )
        self.upsert_deployment(
            deployment=deployment_pydantic_obj
        )  # add new deployment to router
        return deployment_pydantic_obj

    @staticmethod
    def _merge_tools_from_deployment(deployment: dict, kwargs: dict) -> None:
        """
        Merge tools from deployment litellm_params with request kwargs.
        When both have tools, concatenate them (deployment tools first, then request tools).
        tool_choice: use request value if provided, else deployment's.
        """
        dep_params_raw = deployment.get("litellm_params", {}) or {}
        if isinstance(dep_params_raw, dict):
            dep_params = dep_params_raw
        else:
            dep_params = dep_params_raw.model_dump(exclude_none=True)
        dep_tools = dep_params.get("tools") or []
        req_tools = kwargs.get("tools") or []
        if dep_tools or req_tools:
            merged = list(dep_tools) + list(req_tools)
            kwargs["tools"] = merged
        if "tool_choice" not in kwargs and dep_params.get("tool_choice") is not None:
            kwargs["tool_choice"] = dep_params["tool_choice"]

    def _update_kwargs_with_deployment(
        self,
        deployment: dict,
        kwargs: dict,
        function_name: Optional[str] = None,
    ) -> None:
        """
        3 jobs:
        - Adds selected deployment, model_info and api_base to kwargs["metadata"] (used for logging)
        - Adds default litellm params to kwargs, if set.
        - Merges tools from deployment with request (proxy-configured tools + request tools).
        """
        self._merge_tools_from_deployment(deployment=deployment, kwargs=kwargs)

        model_info = deployment.get("model_info", {}).copy()
        deployment_litellm_model_name = deployment["litellm_params"]["model"]
        deployment_api_base = deployment["litellm_params"].get("api_base")
        deployment_model_name = deployment["model_name"]
        if is_clientside_credential(request_kwargs=kwargs):
            deployment_pydantic_obj = self._handle_clientside_credential(
                deployment=deployment, kwargs=kwargs, function_name=function_name
            )
            model_info = deployment_pydantic_obj.model_info.model_dump()
            deployment_litellm_model_name = deployment_pydantic_obj.litellm_params.model
            deployment_api_base = deployment_pydantic_obj.litellm_params.api_base

        metadata_variable_name = _get_router_metadata_variable_name(
            function_name=function_name,
        )

        kwargs.setdefault(metadata_variable_name, {}).update(
            {
                "deployment": deployment_litellm_model_name,
                "model_info": model_info,
                "api_base": deployment_api_base,
                "deployment_model_name": deployment_model_name,
            }
        )

        ## DEPLOYMENT-LEVEL TAGS
        deployment_tags = deployment.get("litellm_params", {}).get("tags")
        if deployment_tags:
            existing_tags = kwargs[metadata_variable_name].get("tags") or []
            merged_tags = list(existing_tags)
            for tag in deployment_tags:
                if tag not in merged_tags:
                    merged_tags.append(tag)
            kwargs[metadata_variable_name]["tags"] = merged_tags

        ## CREDENTIAL NAME AS TAG
        credential_name = deployment.get("litellm_params", {}).get(
            "litellm_credential_name"
        )
        if credential_name:
            credential_tag = f"Credential: {credential_name}"
            existing_tags = kwargs[metadata_variable_name].get("tags") or []
            if credential_tag not in existing_tags:
                existing_tags.append(credential_tag)
            kwargs[metadata_variable_name]["tags"] = existing_tags

        kwargs["model_info"] = model_info

        if function_name == "_ageneric_api_call_with_fallbacks":
            from litellm.passthrough.timeout_utils import (
                resolve_llm_passthrough_timeout,
            )

            _router_timeout = (
                float(self._explicit_timeout)
                if isinstance(self._explicit_timeout, (int, float))
                else None
            )
            kwargs["timeout"] = resolve_llm_passthrough_timeout(
                kwargs=kwargs,
                litellm_params=deployment["litellm_params"],
                router_timeout=_router_timeout,
            )
        else:
            kwargs["timeout"] = self._get_timeout(
                kwargs=kwargs, data=deployment["litellm_params"]
            )

        self._update_kwargs_with_default_litellm_params(
            kwargs=kwargs, metadata_variable_name=metadata_variable_name
        )

    def _get_async_openai_model_client(self, deployment: dict, kwargs: dict):
        """
        Helper to get AsyncOpenAI or AsyncAzureOpenAI client that was created for the deployment

        The same OpenAI client is re-used to optimize latency / performance in production

        If dynamic api key is provided:
            Do not re-use the client. Pass model_client=None. The OpenAI/ AzureOpenAI client will be recreated in the handler for the llm provider
        """
        potential_model_client = self._get_client(
            deployment=deployment, kwargs=kwargs, client_type="async"
        )

        # check if provided keys == client keys #
        dynamic_api_key = kwargs.get("api_key", None)
        if (
            dynamic_api_key is not None
            and potential_model_client is not None
            and dynamic_api_key != potential_model_client.api_key
        ):
            model_client = None
        else:
            model_client = potential_model_client

        return model_client

    def _get_stream_timeout(
        self, kwargs: dict, data: dict
    ) -> Optional[Union[float, int]]:
        """Helper to get stream timeout from kwargs or deployment params"""
        return (
            kwargs.get("stream_timeout", None)  # the params dynamically set by user
            or data.get(
                "stream_timeout", None
            )  # timeout set on litellm_params for this deployment
            or self.stream_timeout  # timeout set on router
            or self.default_litellm_params.get("stream_timeout", None)
        )

    def _get_non_stream_timeout(
        self, kwargs: dict, data: dict
    ) -> Optional[Union[float, int]]:
        """Helper to get non-stream timeout from kwargs or deployment params"""
        timeout = (
            kwargs.get("timeout", None)  # the params dynamically set by user
            or kwargs.get("request_timeout", None)  # the params dynamically set by user
            or data.get(
                "timeout", None
            )  # timeout set on litellm_params for this deployment
            or data.get(
                "request_timeout", None
            )  # timeout set on litellm_params for this deployment
            or self.timeout  # timeout set on router
            or self.default_litellm_params.get("timeout", None)
        )
        return timeout

    def _get_timeout(self, kwargs: dict, data: dict) -> Optional[Union[float, int]]:
        """Helper to get timeout from kwargs or deployment params"""
        timeout: Optional[Union[float, int]] = None
        if kwargs.get("stream", False):
            timeout = self._get_stream_timeout(kwargs=kwargs, data=data)
        if timeout is None:
            timeout = self._get_non_stream_timeout(
                kwargs=kwargs, data=data
            )  # default to this if no stream specific timeout set
        return timeout

    async def abatch_completion(
        self,
        models: List[str],
        messages: Union[List[Dict[str, str]], List[List[Dict[str, str]]]],
        **kwargs,
    ):
        """
        Async Batch Completion. Used for 2 scenarios:
        1. Batch Process 1 request to N models on litellm.Router. Pass messages as List[Dict[str, str]] to use this
        2. Batch Process N requests to M models on litellm.Router. Pass messages as List[List[Dict[str, str]]] to use this

        Example Request for 1 request to N models:
        ```
            response = await router.abatch_completion(
                models=["gpt-3.5-turbo", "groq-llama"],
                messages=[
                    {"role": "user", "content": "is litellm becoming a better product ?"}
                ],
                max_tokens=15,
            )
        ```


        Example Request for N requests to M models:
        ```
            response = await router.abatch_completion(
                models=["gpt-3.5-turbo", "groq-llama"],
                messages=[
                    [{"role": "user", "content": "is litellm becoming a better product ?"}],
                    [{"role": "user", "content": "who is this"}],
                ],
            )
        ```
        """
        ############## Helpers for async completion ##################

        async def _async_completion_no_exceptions(
            model: str, messages: List[AllMessageValues], **kwargs
        ):
            """
            Wrapper around self.async_completion that catches exceptions and returns them as a result
            """
            try:
                return await self.acompletion(model=model, messages=messages, **kwargs)
            except Exception as e:
                return e

        async def _async_completion_no_exceptions_return_idx(
            model: str,
            messages: List[AllMessageValues],
            idx: int,  # index of message this response corresponds to
            **kwargs,
        ):
            """
            Wrapper around self.async_completion that catches exceptions and returns them as a result
            """
            try:
                return (
                    await self.acompletion(model=model, messages=messages, **kwargs),
                    idx,
                )
            except Exception as e:
                return e, idx

        ############## Helpers for async completion ##################

        if isinstance(messages, list) and all(isinstance(m, dict) for m in messages):
            _tasks = []
            for model in models:
                # add each task but if the task fails
                _tasks.append(_async_completion_no_exceptions(model=model, messages=messages, **kwargs))  # type: ignore
            response = await asyncio.gather(*_tasks)
            return response
        elif isinstance(messages, list) and all(isinstance(m, list) for m in messages):
            _tasks = []
            for idx, message in enumerate(messages):
                for model in models:
                    # Request Number X, Model Number Y
                    _tasks.append(
                        _async_completion_no_exceptions_return_idx(
                            model=model,
                            idx=idx,
                            messages=message,  # type: ignore[arg-type]
                            **kwargs,
                        )
                    )
            responses = await asyncio.gather(*_tasks)
            final_responses: List[List[Any]] = [[] for _ in range(len(messages))]
            for response in responses:
                if isinstance(response, tuple):
                    final_responses[response[1]].append(response[0])
                else:
                    final_responses[0].append(response)
            return final_responses

    async def abatch_completion_one_model_multiple_requests(
        self, model: str, messages: List[List[AllMessageValues]], **kwargs
    ):
        """
        Async Batch Completion - Batch Process multiple Messages to one model_group on litellm.Router

        Use this for sending multiple requests to 1 model

        Args:
            model (List[str]): model group
            messages (List[List[Dict[str, str]]]): list of messages. Each element in the list is one request
            **kwargs: additional kwargs
        Usage:
            response = await self.abatch_completion_one_model_multiple_requests(
                model="gpt-3.5-turbo",
                messages=[
                    [{"role": "user", "content": "hello"}, {"role": "user", "content": "tell me something funny"}],
                    [{"role": "user", "content": "hello good mornign"}],
                ]
            )
        """

        async def _async_completion_no_exceptions(
            model: str, messages: List[AllMessageValues], **kwargs
        ):
            """
            Wrapper around self.async_completion that catches exceptions and returns them as a result
            """
            try:
                return await self.acompletion(model=model, messages=messages, **kwargs)
            except Exception as e:
                return e

        _tasks = []
        for message_request in messages:
            # add each task but if the task fails
            _tasks.append(
                _async_completion_no_exceptions(
                    model=model, messages=message_request, **kwargs
                )
            )

        response = await asyncio.gather(*_tasks)
        return response

    # fmt: off

    @overload
    async def abatch_completion_fastest_response(
        self, model: str, messages: List[Dict[str, str]], stream: Literal[True], **kwargs
    ) -> CustomStreamWrapper:
        ...



    @overload
    async def abatch_completion_fastest_response(
        self, model: str, messages: List[Dict[str, str]], stream: Literal[False] = False, **kwargs
    ) -> ModelResponse:
        ...

    # fmt: on

    async def abatch_completion_fastest_response(
        self,
        model: str,
        messages: List[Dict[str, str]],
        stream: bool = False,
        **kwargs,
    ):
        """
        model - List of comma-separated model names. E.g. model="gpt-4, gpt-3.5-turbo"

        Returns fastest response from list of model names. OpenAI-compatible endpoint.
        """
        models = [m.strip() for m in model.split(",")]

        async def _async_completion_no_exceptions(
            model: str, messages: List[Dict[str, str]], stream: bool, **kwargs: Any
        ) -> Union[ModelResponse, CustomStreamWrapper, Exception]:
            """
            Wrapper around self.acompletion that catches exceptions and returns them as a result
            """
            try:
                result = await self.acompletion(model=model, messages=messages, stream=stream, **kwargs)  # type: ignore
                return result
            except asyncio.CancelledError:
                verbose_router_logger.debug(
                    "Received 'task.cancel'. Cancelling call w/ model={}.".format(model)
                )
                raise
            except Exception as e:
                return e

        pending_tasks = []  # type: ignore

        async def check_response(task: asyncio.Task):
            nonlocal pending_tasks
            try:
                result = await task
                if isinstance(result, (ModelResponse, CustomStreamWrapper)):
                    verbose_router_logger.debug(
                        "Received successful response. Cancelling other LLM API calls."
                    )
                    # If a desired response is received, cancel all other pending tasks
                    for t in pending_tasks:
                        t.cancel()
                    return result
            except Exception:
                # Ignore exceptions, let the loop handle them
                pass
            finally:
                # Remove the task from pending tasks if it finishes
                try:
                    pending_tasks.remove(task)
                except KeyError:
                    pass

        for model in models:
            task = asyncio.create_task(
                _async_completion_no_exceptions(
                    model=model, messages=messages, stream=stream, **kwargs
                )
            )
            pending_tasks.append(task)

        # Await the first task to complete successfully
        while pending_tasks:
            done, pending_tasks = await asyncio.wait(  # type: ignore
                pending_tasks, return_when=asyncio.FIRST_COMPLETED
            )
            for completed_task in done:
                result = await check_response(completed_task)

                if result is not None:
                    # Return the first successful result
                    result._hidden_params["fastest_response_batch_completion"] = True
                    return result

        # If we exit the loop without returning, all tasks failed
        raise Exception("All tasks failed")

    ### SCHEDULER ###

    # fmt: off

    @overload
    async def schedule_acompletion(
        self, model: str, messages: List[AllMessageValues], priority: int, stream: Literal[False] = False, **kwargs
    ) -> ModelResponse: 
        ...

    @overload
    async def schedule_acompletion(
        self, model: str, messages: List[AllMessageValues], priority: int, stream: Literal[True], **kwargs
    ) -> CustomStreamWrapper: 
        ...

    # fmt: on

    async def schedule_acompletion(
        self,
        model: str,
        messages: List[AllMessageValues],
        priority: int,
        stream=False,
        **kwargs,
    ):
        parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
        ### FLOW ITEM ###
        _request_id = str(uuid.uuid4())
        item = FlowItem(
            priority=priority,  # 👈 SET PRIORITY FOR REQUEST
            request_id=_request_id,  # 👈 SET REQUEST ID
            model_name=model,  # 👈 SAME as 'Router'
        )
        ### [fin] ###

        ## ADDS REQUEST TO QUEUE ##
        await self.scheduler.add_request(request=item)

        ## POLL QUEUE
        end_time = time.monotonic() + self.timeout
        curr_time = time.monotonic()
        poll_interval = self.scheduler.polling_interval  # poll every 3ms
        make_request = False

        while curr_time < end_time:
            _healthy_deployments, _ = await self._async_get_healthy_deployments(
                model=model, parent_otel_span=parent_otel_span
            )
            make_request = await self.scheduler.poll(  ## POLL QUEUE ## - returns 'True' if there's healthy deployments OR if request is at top of queue
                id=item.request_id,
                model_name=item.model_name,
                health_deployments=_healthy_deployments,
            )
            if make_request:  ## IF TRUE -> MAKE REQUEST
                break
            else:  ## ELSE -> loop till default_timeout
                await asyncio.sleep(poll_interval)
                curr_time = time.monotonic()

        if make_request:
            try:
                _response = await self.acompletion(
                    model=model, messages=messages, stream=stream, **kwargs
                )
                _response._hidden_params.setdefault("additional_headers", {})
                _response._hidden_params["additional_headers"].update(
                    {"x-litellm-request-prioritization-used": True}
                )
                return _response
            except Exception as e:
                setattr(e, "priority", priority)
                raise e
        else:
            # Clean up the request from the scheduler queue also before raising the timeout exception
            await self.scheduler.remove_request(
                request_id=item.request_id, model_name=item.model_name
            )
            raise litellm.Timeout(
                message="Request timed out while polling queue",
                model=model,
                llm_provider="openai",
            )

    async def _schedule_factory(
        self,
        model: str,
        priority: int,
        original_function: Callable,
        args: Tuple[Any, ...],
        kwargs: Dict[str, Any],
    ):
        parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
        ### FLOW ITEM ###
        _request_id = str(uuid.uuid4())
        item = FlowItem(
            priority=priority,  # 👈 SET PRIORITY FOR REQUEST
            request_id=_request_id,  # 👈 SET REQUEST ID
            model_name=model,  # 👈 SAME as 'Router'
        )
        ### [fin] ###

        ## ADDS REQUEST TO QUEUE ##
        await self.scheduler.add_request(request=item)

        ## POLL QUEUE
        end_time = time.monotonic() + self.timeout
        curr_time = time.monotonic()
        poll_interval = self.scheduler.polling_interval  # poll every 3ms
        make_request = False

        while curr_time < end_time:
            _healthy_deployments, _ = await self._async_get_healthy_deployments(
                model=model, parent_otel_span=parent_otel_span
            )
            make_request = await self.scheduler.poll(  ## POLL QUEUE ## - returns 'True' if there's healthy deployments OR if request is at top of queue
                id=item.request_id,
                model_name=item.model_name,
                health_deployments=_healthy_deployments,
            )
            if make_request:  ## IF TRUE -> MAKE REQUEST
                break
            else:  ## ELSE -> loop till default_timeout
                await asyncio.sleep(poll_interval)
                curr_time = time.monotonic()

        if make_request:
            try:
                _response = await original_function(*args, **kwargs)
                if isinstance(_response._hidden_params, dict):
                    _response._hidden_params.setdefault("additional_headers", {})
                    _response._hidden_params["additional_headers"].update(
                        {"x-litellm-request-prioritization-used": True}
                    )
                return _response
            except Exception as e:
                setattr(e, "priority", priority)
                raise e
        else:
            # Clean up the request from the scheduler queue also before raising the timeout exception
            await self.scheduler.remove_request(
                request_id=item.request_id, model_name=item.model_name
            )
            raise litellm.Timeout(
                message="Request timed out while polling queue",
                model=model,
                llm_provider="openai",
            )

    def _is_prompt_management_model(self, model: str) -> bool:
        model_list = self.get_model_list(model_name=model)
        if model_list is None or len(model_list) != 1:
            return False

        litellm_model = model_list[0]["litellm_params"].get("model", None)
        if litellm_model is None or "/" not in litellm_model:
            return False

        split_litellm_model = litellm_model.split("/")[0]
        return split_litellm_model in litellm._known_custom_logger_compatible_callbacks

    async def _prompt_management_factory(
        self,
        model: str,
        messages: List[AllMessageValues],
        kwargs: Dict[str, Any],
    ):
        litellm_logging_object = kwargs.get("litellm_logging_obj", None)
        if litellm_logging_object is None:
            litellm_logging_object, kwargs = function_setup(
                **{
                    "original_function": "acompletion",
                    "rules_obj": Rules(),
                    "start_time": get_utc_datetime(),
                    **kwargs,
                }
            )
        litellm_logging_object = cast(LiteLLMLogging, litellm_logging_object)
        prompt_management_deployment = self.get_available_deployment(
            model=model,
            messages=[{"role": "user", "content": "prompt"}],
            specific_deployment=kwargs.pop("specific_deployment", None),
        )

        self._update_kwargs_with_deployment(
            deployment=prompt_management_deployment, kwargs=kwargs
        )
        data = prompt_management_deployment["litellm_params"].copy()

        litellm_model = data.get("model", None)

        # litellm_agent/ prefix only strips the model name, no prompt_id needed
        is_litellm_agent_model = isinstance(
            litellm_model, str
        ) and litellm_model.startswith("litellm_agent/")

        prompt_id = kwargs.get("prompt_id") or prompt_management_deployment[
            "litellm_params"
        ].get("prompt_id", None)
        prompt_variables = kwargs.get(
            "prompt_variables"
        ) or prompt_management_deployment["litellm_params"].get(
            "prompt_variables", None
        )
        prompt_label = kwargs.get("prompt_label", None) or prompt_management_deployment[
            "litellm_params"
        ].get("prompt_label", None)

        if not is_litellm_agent_model and (
            prompt_id is None or not isinstance(prompt_id, str)
        ):
            raise ValueError(
                f"Prompt ID is not set or not a string. Got={prompt_id}, type={type(prompt_id)}"
            )
        if prompt_variables is not None and not isinstance(prompt_variables, dict):
            raise ValueError(
                f"Prompt variables is set but not a dictionary. Got={prompt_variables}, type={type(prompt_variables)}"
            )

        (
            model,
            messages,
            optional_params,
        ) = litellm_logging_object.get_chat_completion_prompt(
            model=litellm_model,
            messages=messages,
            non_default_params=get_non_default_completion_params(kwargs=kwargs),
            prompt_id=prompt_id,
            prompt_variables=prompt_variables,
            prompt_label=prompt_label,
        )

        # Filter out prompt management specific parameters from data before merging
        prompt_management_params = {
            "bitbucket_config",
            "dotprompt_config",
            "prompt_id",
            "prompt_variables",
            "prompt_label",
            "prompt_version",
        }
        filtered_data = {
            k: v for k, v in data.items() if k not in prompt_management_params
        }

        kwargs = {**filtered_data, **kwargs, **optional_params}
        kwargs["model"] = model
        kwargs["messages"] = messages
        kwargs["litellm_logging_obj"] = litellm_logging_object
        kwargs["prompt_id"] = prompt_id
        kwargs["prompt_variables"] = prompt_variables
        kwargs["prompt_label"] = prompt_label

        _model_list = self.get_model_list(model_name=model)
        if _model_list is None or len(_model_list) == 0:  # if direct call to model
            kwargs.pop("original_function")
            return await litellm.acompletion(**kwargs)

        return await self.async_function_with_fallbacks(**kwargs)

    def image_generation(self, prompt: str, model: str, **kwargs):
        try:
            kwargs["model"] = model
            kwargs["prompt"] = prompt
            kwargs["original_function"] = self._image_generation
            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
            kwargs.setdefault("metadata", {}).update({"model_group": model})
            response = self.function_with_fallbacks(**kwargs)

            return response
        except Exception as e:
            raise e

    def _image_generation(self, prompt: str, model: str, **kwargs):
        model_name = ""
        try:
            verbose_router_logger.debug(
                f"Inside _image_generation()- model: {model}; kwargs: {kwargs}"
            )
            deployment = self.get_available_deployment(
                model=model,
                messages=[{"role": "user", "content": "prompt"}],
                specific_deployment=kwargs.pop("specific_deployment", None),
            )
            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
            data = deployment["litellm_params"].copy()

            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )

            self.total_calls[model_name] += 1

            ### DEPLOYMENT-SPECIFIC PRE-CALL CHECKS ### (e.g. update rpm pre-call. Raise error, if deployment over limit)
            self.routing_strategy_pre_call_checks(deployment=deployment)

            response = litellm.image_generation(
                **{
                    **data,
                    "prompt": prompt,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )
            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.image_generation(model={model_name})\033[32m 200 OK\033[0m"
            )
            return response
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.image_generation(model={model_name})\033[31m Exception {str(e)}\033[0m"
            )
            if model_name is not None:
                self.fail_calls[model_name] += 1
            raise e

    async def aimage_generation(self, prompt: str, model: str, **kwargs):
        try:
            kwargs["model"] = model
            kwargs["prompt"] = prompt
            kwargs["original_function"] = self._aimage_generation
            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
            response = await self.async_function_with_fallbacks(**kwargs)

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def _aimage_generation(self, prompt: str, model: str, **kwargs):
        model_name = model
        try:
            verbose_router_logger.debug(
                f"Inside _image_generation()- model: {model}; kwargs: {kwargs}"
            )
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            deployment = await self.async_get_available_deployment(
                model=model,
                messages=[{"role": "user", "content": "prompt"}],
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )
            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)

            data = deployment["litellm_params"].copy()
            model_name = data["model"]

            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )

            self.total_calls[model_name] += 1
            response = litellm.aimage_generation(
                **{
                    **data,
                    "prompt": prompt,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )

            ### CONCURRENCY-SAFE RPM CHECKS ###
            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )

            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    """
                    - Check rpm limits before making the call
                    - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                    """
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment, parent_otel_span=parent_otel_span
                )
                response = await response

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.aimage_generation(model={model_name})\033[32m 200 OK\033[0m"
            )
            return response
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.aimage_generation(model={model_name})\033[31m Exception {str(e)}\033[0m"
            )
            if model_name is not None:
                self.fail_calls[model_name] += 1
            raise e

    async def atranscription(self, file: FileTypes, model: str, **kwargs):
        """
        Example Usage:

        ```
        from litellm import Router
        client = Router(model_list = [
            {
                "model_name": "whisper",
                "litellm_params": {
                    "model": "whisper-1",
                },
            },
        ])

        audio_file = open("speech.mp3", "rb")
        transcript = await client.atranscription(
        model="whisper",
        file=audio_file
        )

        ```
        """
        try:
            kwargs["model"] = model
            kwargs["file"] = file
            kwargs["original_function"] = self._atranscription
            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
            response = await self.async_function_with_fallbacks(**kwargs)

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def _atranscription(self, file: FileTypes, model: str, **kwargs):
        model_name = model
        try:
            verbose_router_logger.debug(
                f"Inside _atranscription()- model: {model}; kwargs: {kwargs}"
            )
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            deployment = await self.async_get_available_deployment(
                model=model,
                messages=[{"role": "user", "content": "prompt"}],
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )

            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
            data = deployment["litellm_params"].copy()
            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )

            self.total_calls[model_name] += 1
            response = litellm.atranscription(
                **{
                    **data,
                    "file": file,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )

            ### CONCURRENCY-SAFE RPM CHECKS ###
            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )

            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    """
                    - Check rpm limits before making the call
                    - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                    """
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment, parent_otel_span=parent_otel_span
                )
                response = await response

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.atranscription(model={model_name})\033[32m 200 OK\033[0m"
            )
            return response
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.atranscription(model={model_name})\033[31m Exception {str(e)}\033[0m"
            )
            if model_name is not None:
                self.fail_calls[model_name] += 1
            raise e

    async def aspeech(self, model: str, input: str, voice: str, **kwargs):
        """
        Example Usage:

        ```
        from litellm import Router
        client = Router(model_list = [
            {
                "model_name": "tts",
                "litellm_params": {
                    "model": "tts-1",
                },
            },
        ])

        async with client.aspeech(
            model="tts",
            voice="alloy",
            input="the quick brown fox jumped over the lazy dogs",
            api_base=None,
            api_key=None,
            organization=None,
            project=None,
            max_retries=1,
            timeout=600,
            client=None,
            optional_params={},
        ) as response:
            response.stream_to_file(speech_file_path)

        ```
        """
        try:
            kwargs["model"] = model
            kwargs["input"] = input
            kwargs["voice"] = voice
            kwargs["original_function"] = self._aspeech
            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
            response = await self.async_function_with_fallbacks(**kwargs)

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def _aspeech(self, model: str, input: str, voice: str, **kwargs):
        model_name = model
        try:
            verbose_router_logger.debug(
                f"Inside _aspeech()- model: {model}; kwargs: {kwargs}"
            )
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            deployment = await self.async_get_available_deployment(
                model=model,
                messages=[{"role": "user", "content": "prompt"}],
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )

            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
            data = deployment["litellm_params"].copy()
            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )

            self.total_calls[model_name] += 1
            response = litellm.aspeech(
                **{
                    **data,
                    "input": input,
                    "voice": voice,
                    "client": model_client,
                    **kwargs,
                }
            )

            ### CONCURRENCY-SAFE RPM CHECKS ###
            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )

            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    """
                    - Check rpm limits before making the call
                    - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                    """
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment, parent_otel_span=parent_otel_span
                )
                response = await response

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.aspeech(model={model_name})\033[32m 200 OK\033[0m"
            )
            return response
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.aspeech(model={model_name})\033[31m Exception {str(e)}\033[0m"
            )
            if model_name is not None:
                self.fail_calls[model_name] += 1
            raise e

    async def arerank(self, model: str, **kwargs):
        try:
            kwargs["model"] = model
            kwargs["input"] = input
            kwargs["original_function"] = self._arerank
            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)

            response = await self.async_function_with_fallbacks(**kwargs)

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def _arerank(self, model: str, **kwargs):
        model_name = None
        try:
            verbose_router_logger.debug(
                f"Inside _rerank()- model: {model}; kwargs: {kwargs}"
            )
            deployment = await self.async_get_available_deployment(
                model=model,
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )
            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
            data = deployment["litellm_params"].copy()
            model_name = data["model"]

            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )
            self.total_calls[model_name] += 1

            response = await litellm.arerank(
                **{
                    **data,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.arerank(model={model_name})\033[32m 200 OK\033[0m"
            )
            return response
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.arerank(model={model_name})\033[31m Exception {str(e)}\033[0m"
            )
            if model_name is not None:
                self.fail_calls[model_name] += 1
            raise e

    def text_completion(
        self,
        model: str,
        prompt: str,
        is_retry: Optional[bool] = False,
        is_fallback: Optional[bool] = False,
        is_async: Optional[bool] = False,
        **kwargs,
    ):
        messages = [{"role": "user", "content": prompt}]
        try:
            kwargs["model"] = model
            kwargs["prompt"] = prompt
            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
            kwargs.setdefault("metadata", {}).update({"model_group": model})

            # pick the one that is available (lowest TPM/RPM)
            deployment = self.get_available_deployment(
                model=model,
                messages=messages,
                specific_deployment=kwargs.pop("specific_deployment", None),
            )

            data = deployment["litellm_params"].copy()
            for k, v in self.default_litellm_params.items():
                if (
                    k not in kwargs
                ):  # prioritize model-specific params > default router params
                    kwargs[k] = v
                elif k == "metadata":
                    kwargs[k].update(v)

            # call via litellm.completion()
            return litellm.text_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs})  # type: ignore
        except Exception as e:
            raise e

    async def atext_completion(
        self,
        model: str,
        prompt: str,
        is_retry: Optional[bool] = False,
        is_fallback: Optional[bool] = False,
        is_async: Optional[bool] = False,
        **kwargs,
    ):
        if kwargs.get("priority", None) is not None:
            return await self._schedule_factory(
                model=model,
                priority=kwargs.pop("priority"),
                original_function=self.atext_completion,
                args=(model, prompt),
                kwargs=kwargs,
            )
        try:
            kwargs["model"] = model
            kwargs["prompt"] = prompt
            kwargs["original_function"] = self._atext_completion

            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
            response = await self.async_function_with_fallbacks(**kwargs)

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def _atext_completion(self, model: str, prompt: str, **kwargs):
        try:
            verbose_router_logger.debug(
                f"Inside _atext_completion()- model: {model}; kwargs: {kwargs}"
            )
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            deployment = await self.async_get_available_deployment(
                model=model,
                messages=[{"role": "user", "content": prompt}],
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )
            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)

            data = deployment["litellm_params"].copy()
            model_name = data["model"]

            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )
            self.total_calls[model_name] += 1

            response = litellm.atext_completion(
                **{
                    **data,
                    "prompt": prompt,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )

            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )

            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    """
                    - Check rpm limits before making the call
                    - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                    """
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment, parent_otel_span=parent_otel_span
                )
                response = await response

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.atext_completion(model={model_name})\033[32m 200 OK\033[0m"
            )
            return response
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.atext_completion(model={model})\033[31m Exception {str(e)}\033[0m"
            )
            if model is not None:
                self.fail_calls[model] += 1
            raise e

    async def aadapter_completion(
        self,
        adapter_id: str,
        model: str,
        is_retry: Optional[bool] = False,
        is_fallback: Optional[bool] = False,
        is_async: Optional[bool] = False,
        **kwargs,
    ):
        try:
            kwargs["model"] = model
            kwargs["adapter_id"] = adapter_id
            kwargs["original_function"] = self._aadapter_completion
            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
            kwargs.setdefault("metadata", {}).update({"model_group": model})
            response = await self.async_function_with_fallbacks(**kwargs)

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def _aadapter_completion(self, adapter_id: str, model: str, **kwargs):
        try:
            verbose_router_logger.debug(
                f"Inside _aadapter_completion()- model: {model}; kwargs: {kwargs}"
            )
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            deployment = await self.async_get_available_deployment(
                model=model,
                messages=[{"role": "user", "content": "default text"}],
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )
            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)

            data = deployment["litellm_params"].copy()
            model_name = data["model"]

            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )
            self.total_calls[model_name] += 1

            response = litellm.aadapter_completion(
                **{
                    **data,
                    "adapter_id": adapter_id,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )

            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )

            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    """
                    - Check rpm limits before making the call
                    - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                    """
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response  # type: ignore
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment, parent_otel_span=parent_otel_span
                )
                response = await response  # type: ignore

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.aadapter_completion(model={model_name})\033[32m 200 OK\033[0m"
            )
            return response
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.aadapter_completion(model={model})\033[31m Exception {str(e)}\033[0m"
            )
            if model is not None:
                self.fail_calls[model] += 1
            raise e

    async def _asearch_with_fallbacks(self, original_function: Callable, **kwargs):
        """
        Helper function to make a search API call through the router with load balancing and fallbacks.
        Reuses the router's retry/fallback infrastructure.
        """
        from litellm.router_utils.search_api_router import SearchAPIRouter

        return await SearchAPIRouter.async_search_with_fallbacks(
            router_instance=self,
            original_function=original_function,
            **kwargs,
        )

    async def _asearch_with_fallbacks_helper(
        self, model: str, original_generic_function: Callable, **kwargs
    ):
        """
        Helper function for search API calls - selects a search tool and calls the original function.
        Called by async_function_with_fallbacks for each retry attempt.
        """
        from litellm.router_utils.search_api_router import SearchAPIRouter

        return await SearchAPIRouter.async_search_with_fallbacks_helper(
            router_instance=self,
            model=model,
            original_generic_function=original_generic_function,
            **kwargs,
        )

    async def aguardrail(
        self,
        guardrail_name: str,
        original_function: Callable,
        **kwargs,
    ):
        """
        Execute a guardrail with load balancing and fallbacks.

        Args:
            guardrail_name: Name of the guardrail to execute
            original_function: The guardrail's execution function (e.g., async_pre_call_hook)
            **kwargs: Additional arguments passed to the guardrail

        Returns:
            Result from the guardrail execution
        """
        kwargs["model"] = guardrail_name  # For fallback system compatibility
        kwargs["original_generic_function"] = original_function
        kwargs["original_function"] = self._aguardrail_helper
        self._update_kwargs_before_fallbacks(
            model=guardrail_name,
            kwargs=kwargs,
            metadata_variable_name="litellm_metadata",
        )
        verbose_router_logger.debug(
            f"Inside aguardrail() - guardrail_name: {guardrail_name}; kwargs: {kwargs}"
        )
        response = await self.async_function_with_fallbacks(**kwargs)
        return response

    async def _aguardrail_helper(
        self,
        model: str,
        original_generic_function: Callable,
        **kwargs,
    ):
        """
        Helper for aguardrail - selects a guardrail deployment and executes it.
        Called by async_function_with_fallbacks for each retry attempt.

        Args:
            model: The guardrail_name (named 'model' for fallback system compatibility)
            original_generic_function: The guardrail's execution function
            **kwargs: Additional arguments
        """
        guardrail_name = model
        selected_guardrail = self.get_available_guardrail(
            guardrail_name=guardrail_name,
        )

        verbose_router_logger.debug(
            f"Selected guardrail deployment: {selected_guardrail.get('litellm_params', {}).get('guardrail')}"
        )

        # Pass the selected guardrail config to the original function
        kwargs["selected_guardrail"] = selected_guardrail
        response = await original_generic_function(**kwargs)
        return response

    def get_available_guardrail(
        self,
        guardrail_name: str,
    ) -> "GuardrailTypedDict":
        """
        Select a guardrail deployment using the router's load balancing strategy.

        Args:
            guardrail_name: Name of the guardrail to select

        Returns:
            Selected guardrail configuration dict
        """
        from litellm.router_strategy.simple_shuffle import simple_shuffle

        healthy_deployments = [
            g for g in self.guardrail_list if g.get("guardrail_name") == guardrail_name
        ]

        if not healthy_deployments:
            raise ValueError(f"No guardrail found with name: {guardrail_name}")

        if len(healthy_deployments) == 1:
            return healthy_deployments[0]

        # Use simple_shuffle for weighted selection
        return cast(
            GuardrailTypedDict,
            simple_shuffle(
                llm_router_instance=self,
                healthy_deployments=healthy_deployments,
                model=guardrail_name,
            ),
        )

    async def _ageneric_api_call_with_fallbacks(
        self, model: str, original_function: Callable, **kwargs
    ):
        """
        Helper function to make a generic LLM API call through the router, this allows you to use retries/fallbacks with litellm router
        """
        try:
            kwargs["model"] = model
            kwargs["original_generic_function"] = original_function
            kwargs["original_function"] = self._ageneric_api_call_with_fallbacks_helper
            self._update_kwargs_before_fallbacks(
                model=model, kwargs=kwargs, metadata_variable_name="litellm_metadata"
            )
            verbose_router_logger.debug(
                f"Inside ageneric_api_call_with_fallbacks() - model: {model}; kwargs: {kwargs}"
            )
            response = await self.async_function_with_fallbacks(**kwargs)
            return response

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    def _add_deployment_model_to_endpoint_for_llm_passthrough_route(
        self, kwargs: Dict[str, Any], model: str, model_name: str
    ) -> Dict[str, Any]:
        """
        Add the deployment model to the endpoint for LLM passthrough route.

        e.g for bedrock invoke users can pass endpoint as /model/special-bedrock-model/invoke
          it should be actually sent as /model/us.anthropic.claude-3-5-sonnet-20240620-v1:0/invoke
        """
        if "endpoint" in kwargs and kwargs["endpoint"]:
            # For provider-specific endpoints, strip the provider prefix from model_name
            # e.g., "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0" -> "us.anthropic.claude-3-5-sonnet-20240620-v1:0"
            from litellm import get_llm_provider

            try:
                # get_llm_provider returns (model_without_prefix, provider, api_key, api_base)
                stripped_model_name, _, _, _ = get_llm_provider(
                    model=model_name,
                    custom_llm_provider=kwargs.get("custom_llm_provider"),
                    api_base=kwargs.get("api_base"),
                )
                replacement_model_name = stripped_model_name
            except Exception:
                # If get_llm_provider fails, fall back to using model_name as-is
                replacement_model_name = model_name

            kwargs["endpoint"] = kwargs["endpoint"].replace(
                model, replacement_model_name
            )
        return kwargs

    async def _ageneric_api_call_with_fallbacks_helper(
        self, model: str, original_generic_function: Callable, **kwargs
    ):
        """
        Helper function to make a generic LLM API call through the router, this allows you to use retries/fallbacks with litellm router
        """

        passthrough_on_no_deployment = kwargs.pop("passthrough_on_no_deployment", False)
        function_name = "_ageneric_api_call_with_fallbacks"
        try:
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            try:
                deployment = await self.async_get_available_deployment(
                    model=model,
                    request_kwargs=kwargs,
                    messages=kwargs.get("messages", None),
                    specific_deployment=kwargs.pop("specific_deployment", None),
                )
            except Exception as e:
                if passthrough_on_no_deployment:
                    return await original_generic_function(model=model, **kwargs)
                raise e

            self._update_kwargs_with_deployment(
                deployment=deployment, kwargs=kwargs, function_name=function_name
            )

            data = deployment["litellm_params"].copy()
            model_name = data["model"]
            self.total_calls[model_name] += 1

            self._add_deployment_model_to_endpoint_for_llm_passthrough_route(
                kwargs=kwargs, model=model, model_name=model_name
            )

            # Get custom_llm_provider from deployment params
            try:
                custom_llm_provider = data.get("custom_llm_provider")
                _, inferred_custom_llm_provider, _, _ = get_llm_provider(
                    model=data["model"],
                    custom_llm_provider=custom_llm_provider,
                )
                custom_llm_provider = (
                    custom_llm_provider or inferred_custom_llm_provider
                )
            except Exception:
                custom_llm_provider = None

            response_kwargs = {
                **data,
                "caching": self.cache_responses,
                **kwargs,
                "model": model_name,
            }
            # Only set custom_llm_provider if it's not None
            if custom_llm_provider is not None:
                response_kwargs["custom_llm_provider"] = custom_llm_provider

            response = original_generic_function(**response_kwargs)

            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )

            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    """
                    - Check rpm limits before making the call
                    - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                    """
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response  # type: ignore
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment, parent_otel_span=parent_otel_span
                )
                response = await response  # type: ignore

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"ageneric_api_call_with_fallbacks(model={model_name})\033[32m 200 OK\033[0m"
            )

            return response
        except Exception as e:
            verbose_router_logger.info(
                f"ageneric_api_call_with_fallbacks(model={model})\033[31m Exception {str(e)}\033[0m"
            )
            if model is not None:
                self.fail_calls[model] += 1
            raise e

    async def _aresponses_with_streaming_fallbacks(
        self, original_function: Callable, **kwargs: Any
    ) -> Union["ResponsesAPIResponse", "BaseResponsesAPIStreamingIterator"]:
        """
        _ageneric_api_call_with_fallbacks for the Responses API, with the
        addition of mid-stream fallback handling.

        When stream=True and the underlying call returns a
        BaseResponsesAPIStreamingIterator, wrap it with
        _aresponses_streaming_iterator so MidStreamFallbackError raised
        during iteration triggers the Router's cross-provider fallback chain.
        """
        from litellm.responses.streaming_iterator import (
            BaseResponsesAPIStreamingIterator,
        )

        from litellm.litellm_core_utils.core_helpers import safe_deep_copy

        # Snapshot the request kwargs before _ageneric_api_call_with_fallbacks
        # mutates them. A shallow copy alone is not enough: the primary
        # attempt mutates nested dicts in place — notably `litellm_metadata`,
        # which `_update_kwargs_with_deployment` populates with
        # deployment-specific fields (`deployment`, `model_info`, `api_base`,
        # tags, etc.). Without an explicit copy of that dict, the shallow
        # copy would still share its reference, leaking primary-deployment
        # metadata into the mid-stream fallback request.
        #
        # We avoid deep-copying the full kwargs because it can contain
        # non-deepcopyable objects (logging handles, async clients, etc.);
        # `safe_deep_copy` deep-copies the metadata dicts key-by-key with a
        # fallback to the original reference for any non-picklable value.
        # The original_generic_function is preserved so the per-attempt
        # helper knows which underlying API to call on fallback.
        fallback_kwargs: Dict[str, Any] = kwargs.copy()
        if isinstance(fallback_kwargs.get("litellm_metadata"), dict):
            fallback_kwargs["litellm_metadata"] = safe_deep_copy(
                fallback_kwargs["litellm_metadata"]
            )
        if isinstance(fallback_kwargs.get("metadata"), dict):
            fallback_kwargs["metadata"] = safe_deep_copy(fallback_kwargs["metadata"])
        fallback_kwargs["original_generic_function"] = original_function

        response = await self._ageneric_api_call_with_fallbacks(
            original_function=original_function, **kwargs
        )

        if kwargs.get("stream") and isinstance(
            response, BaseResponsesAPIStreamingIterator
        ):
            return await self._aresponses_streaming_iterator(
                response=response,
                initial_kwargs=fallback_kwargs,
            )
        return response

    def _generic_api_call_with_fallbacks(
        self, model: str, original_function: Callable, **kwargs
    ):
        """
        Make a generic LLM API call through the router, this allows you to use retries/fallbacks with litellm router
        Args:
            model: The model to use
            original_function: The handler function to call (e.g., litellm.completion)
            **kwargs: Additional arguments to pass to the handler function
        Returns:
            The response from the handler function
        """
        handler_name = original_function.__name__
        metadata_variable_name = _get_router_metadata_variable_name(
            function_name="generic_api_call"
        )
        try:
            verbose_router_logger.debug(
                f"Inside _generic_api_call() - handler: {handler_name}, model: {model}; kwargs: {kwargs}"
            )
            self._update_kwargs_before_fallbacks(
                model=model,
                kwargs=kwargs,
                metadata_variable_name=metadata_variable_name,
            )
            deployment = self.get_available_deployment(
                model=model,
                messages=kwargs.get("messages", None),
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )
            self._update_kwargs_with_deployment(
                deployment=deployment, kwargs=kwargs, function_name="generic_api_call"
            )

            data = deployment["litellm_params"].copy()
            model_name = data["model"]

            self.total_calls[model_name] += 1

            # For passthrough routes, use the actual model from deployment
            # and swap model name in endpoint if present
            if "endpoint" in kwargs and kwargs["endpoint"]:
                kwargs["endpoint"] = kwargs["endpoint"].replace(model, model_name)
            kwargs["model"] = model_name

            # Perform pre-call checks for routing strategy
            self.routing_strategy_pre_call_checks(deployment=deployment)

            try:
                custom_llm_provider = data.get("custom_llm_provider")
                _, inferred_custom_llm_provider, _, _ = get_llm_provider(
                    model=data["model"],
                    custom_llm_provider=custom_llm_provider,
                )
                custom_llm_provider = (
                    custom_llm_provider or inferred_custom_llm_provider
                )
            except Exception:
                custom_llm_provider = None

            response = original_function(
                **{
                    **data,
                    "custom_llm_provider": custom_llm_provider,
                    "caching": self.cache_responses,
                    **kwargs,
                }
            )

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"{handler_name}(model={model_name})\033[32m 200 OK\033[0m"
            )
            return response
        except Exception as e:
            verbose_router_logger.info(
                f"{handler_name}(model={model})\033[31m Exception {str(e)}\033[0m"
            )
            if model is not None:
                self.fail_calls[model] += 1
            raise e

    def embedding(
        self,
        model: str,
        input: Union[str, List],
        is_async: Optional[bool] = False,
        **kwargs,
    ) -> EmbeddingResponse:
        try:
            kwargs["model"] = model
            kwargs["input"] = input
            kwargs["original_function"] = self._embedding
            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
            response = self.function_with_fallbacks(**kwargs)
            return response
        except Exception as e:
            raise e

    def _embedding(self, input: Union[str, List], model: str, **kwargs):
        model_name = None
        try:
            verbose_router_logger.debug(
                f"Inside embedding()- model: {model}; kwargs: {kwargs}"
            )
            deployment = self.get_available_deployment(
                model=model,
                input=input,
                specific_deployment=kwargs.pop("specific_deployment", None),
            )
            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
            data = deployment["litellm_params"].copy()
            model_name = data["model"]

            potential_model_client = self._get_client(
                deployment=deployment, kwargs=kwargs, client_type="sync"
            )
            # check if provided keys == client keys #
            dynamic_api_key = kwargs.get("api_key", None)
            if (
                dynamic_api_key is not None
                and potential_model_client is not None
                and dynamic_api_key != potential_model_client.api_key
            ):
                model_client = None
            else:
                model_client = potential_model_client

            self.total_calls[model_name] += 1

            ### DEPLOYMENT-SPECIFIC PRE-CALL CHECKS ### (e.g. update rpm pre-call. Raise error, if deployment over limit)
            self.routing_strategy_pre_call_checks(deployment=deployment)

            response = litellm.embedding(
                **{
                    **data,
                    "input": input,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )
            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.embedding(model={model_name})\033[32m 200 OK\033[0m"
            )
            return response
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.embedding(model={model_name})\033[31m Exception {str(e)}\033[0m"
            )
            if model_name is not None:
                self.fail_calls[model_name] += 1
            raise e

    async def aembedding(
        self,
        model: str,
        input: Union[str, List],
        is_async: Optional[bool] = True,
        **kwargs,
    ) -> EmbeddingResponse:
        try:
            kwargs["model"] = model
            kwargs["input"] = input
            kwargs["original_function"] = self._aembedding
            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
            response = await self.async_function_with_fallbacks(**kwargs)
            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def _aembedding(self, input: Union[str, List], model: str, **kwargs):
        model_name = None
        try:
            verbose_router_logger.debug(
                f"Inside _aembedding()- model: {model}; kwargs: {kwargs}"
            )
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            deployment = await self.async_get_available_deployment(
                model=model,
                input=input,
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )
            self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
            data = deployment["litellm_params"].copy()
            model_name = data["model"]
            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )

            self.total_calls[model_name] += 1
            response = litellm.aembedding(
                **{
                    **data,
                    "input": input,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )

            ### CONCURRENCY-SAFE RPM CHECKS ###
            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )

            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    """
                    - Check rpm limits before making the call
                    - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                    """
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment, parent_otel_span=parent_otel_span
                )
                response = await response

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.aembedding(model={model_name})\033[32m 200 OK\033[0m"
            )
            return response
        except Exception as e:
            verbose_router_logger.info(
                f"litellm.aembedding(model={model_name})\033[31m Exception {str(e)}\033[0m"
            )
            if model_name is not None:
                self.fail_calls[model_name] += 1
            raise e

    #### FILES API ####
    async def acreate_file(
        self,
        model: str,
        **kwargs,
    ) -> OpenAIFileObject:
        try:
            kwargs["model"] = model
            kwargs["original_function"] = self._acreate_file
            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
            response = await self.async_function_with_fallbacks(**kwargs)

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def _acreate_file(
        self,
        model: str,
        **kwargs,
    ) -> OpenAIFileObject:
        try:
            from litellm.router_utils.common_utils import add_model_file_id_mappings

            verbose_router_logger.debug(
                f"Inside _atext_completion()- model: {model}; kwargs: {kwargs}"
            )
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            healthy_deployments = await self.async_get_healthy_deployments(
                model=model,
                messages=[{"role": "user", "content": "files-api-fake-text"}],
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
                parent_otel_span=parent_otel_span,
            )

            async def create_file_for_deployment(deployment: dict) -> OpenAIFileObject:
                from litellm.litellm_core_utils.core_helpers import safe_deep_copy

                kwargs_copy = safe_deep_copy(kwargs)
                self._update_kwargs_with_deployment(
                    deployment=deployment,
                    kwargs=kwargs_copy,
                    function_name="acreate_file",
                )
                data = deployment["litellm_params"].copy()
                model_name = data["model"]

                model_client = self._get_async_openai_model_client(
                    deployment=deployment,
                    kwargs=kwargs_copy,
                )
                self.total_calls[model_name] += 1

                ## REPLACE MODEL IN FILE WITH SELECTED DEPLOYMENT ##
                # For DB/config deployments, use provider from deployment params
                custom_llm_provider = data.get("custom_llm_provider")
                stripped_model, inferred_custom_llm_provider, _, _ = get_llm_provider(
                    model=data["model"],
                    custom_llm_provider=custom_llm_provider,
                )
                # Preserve explicitly stored provider, fallback to inferred
                custom_llm_provider = (
                    custom_llm_provider or inferred_custom_llm_provider
                )

                ## REPLACE MODEL IN FILE WITH SELECTED DEPLOYMENT ##
                purpose = cast(Optional[OpenAIFilesPurpose], kwargs.get("purpose"))
                file = cast(Optional[FileTypes], kwargs.get("file"))
                if not file or not purpose:
                    raise Exception(
                        "file and file_purpose are required for create_file"
                    )

                replace_model_in_jsonl_bool = should_replace_model_in_jsonl(
                    purpose=purpose,
                )
                if replace_model_in_jsonl_bool:
                    file = replace_model_in_jsonl(
                        file_content=file,
                        new_model_name=stripped_model,
                    )

                    kwargs_copy["file"] = file
                if (
                    "gcs_bucket_name" in data
                ):  # TODO: Remove this once we have a better way to handle GCS bucket name:  Problem is that we need to pass the gcs_bucket_name to the router for the create_file call but it doesn't show up there
                    kwargs_copy.setdefault("litellm_metadata", {})[
                        "gcs_bucket_name"
                    ] = data["gcs_bucket_name"]
                response = litellm.acreate_file(
                    **{
                        **data,
                        "custom_llm_provider": custom_llm_provider,
                        "caching": self.cache_responses,
                        "client": model_client,
                        **kwargs_copy,
                    }
                )

                rpm_semaphore = self._get_client(
                    deployment=deployment,
                    kwargs=kwargs_copy,
                    client_type="max_parallel_requests",
                )

                if rpm_semaphore is not None and isinstance(
                    rpm_semaphore, asyncio.Semaphore
                ):
                    async with rpm_semaphore:
                        """
                        - Check rpm limits before making the call
                        - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                        """
                        await self.async_routing_strategy_pre_call_checks(
                            deployment=deployment, parent_otel_span=parent_otel_span
                        )
                        response = await response  # type: ignore
                else:
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response  # type: ignore

                self.success_calls[model_name] += 1
                verbose_router_logger.info(
                    f"litellm.acreate_file(model={model_name})\033[32m 200 OK\033[0m"
                )

                return response

            tasks = []

            if isinstance(healthy_deployments, dict):
                tasks.append(create_file_for_deployment(healthy_deployments))
            else:
                for deployment in healthy_deployments:
                    tasks.append(create_file_for_deployment(deployment))

            responses = await asyncio.gather(*tasks)

            if len(responses) == 0:
                raise Exception("No healthy deployments found.")

            model_file_id_mapping = add_model_file_id_mappings(
                healthy_deployments=healthy_deployments, responses=responses
            )
            returned_response = cast(OpenAIFileObject, responses[0])
            returned_response._hidden_params["model_file_id_mapping"] = (
                model_file_id_mapping
            )
            return returned_response
        except Exception as e:
            verbose_router_logger.exception(
                f"litellm.acreate_file(model={model}, {kwargs})\033[31m Exception {str(e)}\033[0m"
            )
            if model is not None:
                self.fail_calls[model] += 1
            raise e

    #### VECTOR STORES API ####
    async def avector_store_create(
        self,
        model: Union[str, None],
        **kwargs,
    ):
        """
        Create a vector store for a specific model.

        Args:
            model: Model name from router config
            **kwargs: Vector store creation parameters

        Returns:
            VectorStoreCreateResponse
        """
        try:
            # If model is None, use the factory function approach (direct SDK call)
            if model is None:
                from litellm.vector_stores.main import acreate

                # Use the factory function to handle the call
                factory_fn = self.factory_function(
                    acreate, call_type="avector_store_create"
                )
                return await factory_fn(**kwargs)

            from litellm.vector_stores import acreate as avector_store_create_sdk

            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            deployment = await self.async_get_available_deployment(
                model=model,
                messages=[{"role": "user", "content": "vector-store-api-fake-text"}],
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )
            data = deployment["litellm_params"].copy()
            model_name = data["model"]
            self._update_kwargs_with_deployment(
                deployment=deployment,
                kwargs=kwargs,
                function_name="avector_store_create",
            )

            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )
            self.total_calls[model_name] += 1

            # Get custom provider from deployment params
            custom_llm_provider = data.get("custom_llm_provider")
            _, inferred_custom_llm_provider, _, _ = get_llm_provider(
                model=data["model"],
                custom_llm_provider=custom_llm_provider,
            )
            custom_llm_provider = custom_llm_provider or inferred_custom_llm_provider

            response = avector_store_create_sdk(
                **{
                    **data,
                    "custom_llm_provider": custom_llm_provider,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )

            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )

            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment, parent_otel_span=parent_otel_span
                )
                response = await response

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.avector_store_create(model={model_name})\033[32m 200 OK\033[0m"
            )

            return response
        except Exception as e:
            verbose_router_logger.exception(
                f"litellm.avector_store_create(model={model})\033[31m Exception {str(e)}\033[0m"
            )
            if model is not None:
                self.fail_calls[model] += 1
            raise e

    def _override_vector_store_methods_for_router(self):
        """
        Override factory-generated vector store methods with router-aware implementations.
        This is called after _initialize_vector_store_endpoints() to ensure our custom
        methods that handle deployment selection and credential injection are used instead
        of the generic factory-generated ones.
        """
        # Store references to the custom methods defined above
        # These methods handle proper routing through deployments
        pass  # The methods are already defined as instance methods above

    async def acreate_batch(
        self,
        model: str,
        **kwargs,
    ) -> LiteLLMBatch:
        try:
            kwargs["model"] = model
            kwargs["original_function"] = self._acreate_batch
            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
            metadata_variable_name = _get_router_metadata_variable_name(
                function_name="_acreate_batch"
            )
            self._update_kwargs_before_fallbacks(
                model=model,
                kwargs=kwargs,
                metadata_variable_name=metadata_variable_name,
            )
            response = await self.async_function_with_fallbacks(**kwargs)

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def _acreate_batch(
        self,
        model: str,
        **kwargs,
    ) -> LiteLLMBatch:
        try:
            verbose_router_logger.debug(
                f"Inside _acreate_batch()- model: {model}; kwargs: {kwargs}"
            )
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            deployment = await self.async_get_available_deployment(
                model=model,
                messages=[{"role": "user", "content": "files-api-fake-text"}],
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )

            data = deployment["litellm_params"].copy()
            model_name = data["model"]
            self._update_kwargs_with_deployment(
                deployment=deployment, kwargs=kwargs, function_name="_acreate_batch"
            )

            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )
            self.total_calls[model_name] += 1

            ## SET CUSTOM PROVIDER TO SELECTED DEPLOYMENT ##
            custom_llm_provider = data.get("custom_llm_provider")
            _, inferred_custom_llm_provider, _, _ = get_llm_provider(
                model=data["model"],
                custom_llm_provider=custom_llm_provider,
            )
            custom_llm_provider = custom_llm_provider or inferred_custom_llm_provider

            response = litellm.acreate_batch(
                **{
                    **data,
                    "custom_llm_provider": custom_llm_provider,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )

            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )

            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    """
                    - Check rpm limits before making the call
                    - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                    """
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response  # type: ignore
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment, parent_otel_span=parent_otel_span
                )
                response = await response  # type: ignore

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.acreate_batch(model={model_name})\033[32m 200 OK\033[0m"
            )

            return response  # type: ignore
        except Exception as e:
            verbose_router_logger.exception(
                f"litellm._acreate_batch(model={model}, {kwargs})\033[31m Exception {str(e)}\033[0m"
            )
            if model is not None:
                self.fail_calls[model] += 1
            raise e

    async def aretrieve_batch(
        self,
        model: Optional[str] = None,
        **kwargs,
    ) -> LiteLLMBatch:
        """
        Iterate through all models in a model group to check for batch

        Future Improvement - cache the result.
        """
        try:
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            if model is not None:
                filtered_model_list: Optional[
                    Union[List[DeploymentTypedDict], List[Dict], Dict]
                ] = await self.async_get_healthy_deployments(
                    model=model,
                    messages=[{"role": "user", "content": "retrieve-api-fake-text"}],
                    specific_deployment=kwargs.pop("specific_deployment", None),
                    request_kwargs=kwargs,
                    parent_otel_span=parent_otel_span,
                )
            else:
                filtered_model_list = self.get_model_list()
            if filtered_model_list is None:
                raise Exception("Router not yet initialized.")

            receieved_exceptions = []

            async def try_retrieve_batch(model_name: DeploymentTypedDict):
                try:
                    from litellm.litellm_core_utils.core_helpers import safe_deep_copy

                    model = model_name["litellm_params"].get("model")
                    data = model_name["litellm_params"].copy()
                    custom_llm_provider = data.get("custom_llm_provider")
                    if model is None:
                        raise Exception(
                            f"Model not found in litellm_params for deployment: {model_name}"
                        )
                    # Update kwargs with the current model name or any other model-specific adjustments
                    ## SET CUSTOM PROVIDER TO SELECTED DEPLOYMENT ##
                    if not custom_llm_provider:
                        _, custom_llm_provider, _, _ = get_llm_provider(  # type: ignore
                            model=model
                        )
                    new_kwargs = safe_deep_copy(kwargs)
                    self._update_kwargs_with_deployment(
                        deployment=cast(dict, model_name),
                        kwargs=new_kwargs,
                        function_name="aretrieve_batch",
                    )
                    new_kwargs.pop("custom_llm_provider", None)
                    data.pop("custom_llm_provider", None)
                    return await litellm.aretrieve_batch(
                        **{
                            **data,
                            "custom_llm_provider": custom_llm_provider,
                            **new_kwargs,  # type: ignore
                        },
                    )
                except Exception as e:
                    import traceback

                    traceback.print_exc()
                    receieved_exceptions.append(e)
                    return None

            # Check all models in parallel
            if (
                filtered_model_list is not None
                and isinstance(filtered_model_list, list)
                and len(filtered_model_list) > 0
            ):
                results = await asyncio.gather(
                    *[
                        try_retrieve_batch(cast(DeploymentTypedDict, model))
                        for model in filtered_model_list
                    ],
                    return_exceptions=True,
                )
            elif filtered_model_list is not None and isinstance(
                filtered_model_list, dict
            ):
                results = await try_retrieve_batch(
                    cast(DeploymentTypedDict, filtered_model_list)
                )
            else:
                raise Exception("No healthy deployments found.")

            # Check for successful responses and handle exceptions
            if results is not None:
                if isinstance(results, LiteLLMBatch):
                    return results
                elif isinstance(results, list):
                    for result in results:
                        if isinstance(result, LiteLLMBatch):
                            return result

            # If no valid Batch response was found, raise the first encountered exception
            if receieved_exceptions:
                raise receieved_exceptions[0]  # Raising the first exception encountered

            # If no exceptions were encountered, raise a generic exception
            raise Exception(
                "Unable to find batch in any model. Received errors - {}".format(
                    receieved_exceptions
                )
            )
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def acancel_batch(
        self,
        model: str,
        **kwargs,
    ) -> LiteLLMBatch:
        """
        Cancel a batch through the router with proper model-to-provider mapping.
        """
        try:
            kwargs["model"] = model
            kwargs["original_function"] = self._acancel_batch
            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
            metadata_variable_name = _get_router_metadata_variable_name(
                function_name="_acancel_batch"
            )
            self._update_kwargs_before_fallbacks(
                model=model,
                kwargs=kwargs,
                metadata_variable_name=metadata_variable_name,
            )
            response = await self.async_function_with_fallbacks(**kwargs)

            return response
        except Exception as e:
            asyncio.create_task(
                send_llm_exception_alert(
                    litellm_router_instance=self,
                    request_kwargs=kwargs,
                    error_traceback_str=traceback.format_exc(),
                    original_exception=e,
                )
            )
            raise e

    async def _acancel_batch(
        self,
        model: str,
        **kwargs,
    ) -> LiteLLMBatch:
        try:
            verbose_router_logger.debug(
                f"Inside _acancel_batch()- model: {model}; kwargs: {kwargs}"
            )
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            deployment = await self.async_get_available_deployment(
                model=model,
                messages=[{"role": "user", "content": "batch-api-fake-text"}],
                specific_deployment=kwargs.pop("specific_deployment", None),
                request_kwargs=kwargs,
            )

            selected_deployment_id = (deployment.get("model_info") or {}).get("id")
            data = deployment["litellm_params"].copy()
            resolved_credentials = self.get_deployment_credentials_with_provider(
                model_id=selected_deployment_id or model
            )
            if resolved_credentials is not None:
                data.update(resolved_credentials)
            data.pop("litellm_credential_name", None)
            model_name = data["model"]
            self._update_kwargs_with_deployment(
                deployment=deployment, kwargs=kwargs, function_name="_acancel_batch"
            )

            model_client = self._get_async_openai_model_client(
                deployment=deployment,
                kwargs=kwargs,
            )
            self.total_calls[model_name] += 1

            ## SET CUSTOM PROVIDER TO SELECTED DEPLOYMENT ##
            custom_llm_provider = data.get("custom_llm_provider")
            _, inferred_custom_llm_provider, _, _ = get_llm_provider(
                model=data["model"],
                custom_llm_provider=custom_llm_provider,
            )
            custom_llm_provider = custom_llm_provider or inferred_custom_llm_provider

            response = litellm.acancel_batch(
                **{
                    **data,
                    "custom_llm_provider": custom_llm_provider,
                    "caching": self.cache_responses,
                    "client": model_client,
                    **kwargs,
                }
            )

            rpm_semaphore = self._get_client(
                deployment=deployment,
                kwargs=kwargs,
                client_type="max_parallel_requests",
            )

            if rpm_semaphore is not None and isinstance(
                rpm_semaphore, asyncio.Semaphore
            ):
                async with rpm_semaphore:
                    """
                    - Check rpm limits before making the call
                    - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
                    """
                    await self.async_routing_strategy_pre_call_checks(
                        deployment=deployment, parent_otel_span=parent_otel_span
                    )
                    response = await response  # type: ignore
            else:
                await self.async_routing_strategy_pre_call_checks(
                    deployment=deployment, parent_otel_span=parent_otel_span
                )
                response = await response  # type: ignore

            self.success_calls[model_name] += 1
            verbose_router_logger.info(
                f"litellm.acancel_batch(model={model_name})\033[32m 200 OK\033[0m"
            )

            return response  # type: ignore
        except Exception as e:
            verbose_router_logger.exception(
                f"litellm._acancel_batch(model={model}, {kwargs})\033[31m Exception {str(e)}\033[0m"
            )
            if model is not None:
                self.fail_calls[model] += 1
            raise e

    async def alist_batches(
        self,
        model: str,
        **kwargs,
    ):
        """
        Return all the batches across all deployments of a model group.
        """

        filtered_model_list = self.get_model_list(model_name=model)
        if filtered_model_list is None:
            raise Exception("Router not yet initialized.")

        async def try_retrieve_batch(model: DeploymentTypedDict):
            try:
                # Update kwargs with the current model name or any other model-specific adjustments
                return await litellm.alist_batches(
                    **{**model["litellm_params"], **kwargs}
                )
            except Exception:
                return None

        # Check all models in parallel
        results = await asyncio.gather(
            *[try_retrieve_batch(model) for model in filtered_model_list]
        )

        final_results: Dict = {
            "object": "list",
            "data": [],
            "first_id": None,
            "last_id": None,
            "has_more": False,
        }

        for result in results:
            if result is not None:
                ## check batch id
                if final_results["first_id"] is None and hasattr(result, "first_id"):
                    final_results["first_id"] = getattr(result, "first_id")
                final_results["last_id"] = getattr(result, "last_id")
                final_results["data"].extend(result.data)  # type: ignore

                ## check 'has_more'
                if getattr(result, "has_more", False) is True:
                    final_results["has_more"] = True

        return final_results

    #### PASSTHROUGH API ####

    async def _pass_through_moderation_endpoint_factory(
        self,
        original_function: Callable,
        custom_llm_provider: Optional[str] = None,
        **kwargs,
    ):
        # update kwargs with model_group
        self._update_kwargs_before_fallbacks(
            model=kwargs.get("model", ""),
            kwargs=kwargs,
        )
        if kwargs.get("model") and self.get_model_list(model_name=kwargs["model"]):
            deployment = await self.async_get_available_deployment(
                model=kwargs["model"],
                request_kwargs=kwargs,
            )
            kwargs["model"] = deployment["litellm_params"]["model"]
            data = deployment["litellm_params"].copy()
            self._update_kwargs_with_deployment(
                deployment=deployment,
                kwargs=kwargs,
            )
            kwargs.update(data)

        return await original_function(**kwargs)

    def factory_function(
        self,
        original_function: Callable,
        call_type: Literal[
            "assistants",
            "moderation",
            "anthropic_messages",
            "aresponses",
            "acancel_responses",
            "acompact_responses",
            "responses",
            "aget_responses",
            "adelete_responses",
            "afile_delete",
            "afile_content",
            "_arealtime",
            "_aresponses_websocket",
            "acreate_fine_tuning_job",
            "acancel_fine_tuning_job",
            "alist_fine_tuning_jobs",
            "aretrieve_fine_tuning_job",
            "alist_files",
            "aimage_edit",
            "allm_passthrough_route",
            "alist_input_items",
            "agenerate_content",
            "generate_content",
            "agenerate_content_stream",
            "generate_content_stream",
            "avector_store_search",
            "avector_store_create",
            "avector_store_retrieve",
            "avector_store_list",
            "avector_store_update",
            "avector_store_delete",
            "avector_store_file_create",
            "avector_store_file_list",
            "avector_store_file_retrieve",
            "avector_store_file_content",
            "avector_store_file_update",
            "avector_store_file_delete",
            "vector_store_search",
            "vector_store_create",
            "vector_store_retrieve",
            "vector_store_list",
            "vector_store_update",
            "vector_store_delete",
            "vector_store_file_create",
            "vector_store_file_list",
            "vector_store_file_retrieve",
            "vector_store_file_content",
            "vector_store_file_update",
            "vector_store_file_delete",
            "aocr",
            "ocr",
            "asearch",
            "search",
            "aadapter_generate_content",
            "avideo_generation",
            "video_generation",
            "avideo_list",
            "video_list",
            "avideo_status",
            "video_status",
            "avideo_content",
            "video_content",
            "avideo_remix",
            "video_remix",
            "avideo_create_character",
            "video_create_character",
            "avideo_get_character",
            "video_get_character",
            "avideo_edit",
            "video_edit",
            "avideo_extension",
            "video_extension",
            "acreate_container",
            "create_container",
            "alist_containers",
            "list_containers",
            "aretrieve_container",
            "retrieve_container",
            "adelete_container",
            "delete_container",
            "aupload_container_file",
            "upload_container_file",
            "alist_container_files",
            "list_container_files",
            "aretrieve_container_file",
            "retrieve_container_file",
            "adelete_container_file",
            "delete_container_file",
            "acreate_skill",
            "alist_skills",
            "aget_skill",
            "adelete_skill",
            "acreate_interaction",
            "create_interaction",
            "aget_interaction",
            "get_interaction",
            "adelete_interaction",
            "delete_interaction",
            "acancel_interaction",
            "cancel_interaction",
            "acreate_agent",
            "create_agent",
            "alist_agents",
            "list_agents",
            "aget_agent",
            "get_agent",
            "adelete_agent",
            "delete_agent",
            "alist_agent_versions",
            "list_agent_versions",
        ] = "assistants",
    ):
        """
        Creates appropriate wrapper functions for different API call types.

        Returns:
            - A synchronous function for synchronous call types
            - An asynchronous function for asynchronous call types
        """
        # Handle synchronous call types
        if call_type in (
            "responses",
            "generate_content",
            "generate_content_stream",
            "vector_store_search",
            "vector_store_create",
            "ocr",
            "search",
            "video_generation",
            "video_list",
            "video_status",
            "video_content",
            "video_remix",
            "create_container",
            "list_containers",
            "retrieve_container",
            "delete_container",
        ):

            def sync_wrapper(
                custom_llm_provider: Optional[str] = None,
                client: Optional[Any] = None,
                **kwargs,
            ):
                return self._generic_api_call_with_fallbacks(
                    original_function=original_function, **kwargs
                )

            return sync_wrapper

        if call_type in (
            "vector_store_retrieve",
            "vector_store_list",
            "vector_store_update",
            "vector_store_delete",
        ):

            def vector_store_sync_wrapper(
                custom_llm_provider: Optional[str] = None,
                client: Optional[Any] = None,
                **kwargs,
            ):
                if custom_llm_provider and "custom_llm_provider" not in kwargs:
                    kwargs["custom_llm_provider"] = custom_llm_provider
                if kwargs.get("model"):
                    return self._generic_api_call_with_fallbacks(
                        original_function=original_function, **kwargs
                    )
                return original_function(**kwargs)

            return vector_store_sync_wrapper

        if call_type in (
            "vector_store_file_create",
            "vector_store_file_list",
            "vector_store_file_retrieve",
            "vector_store_file_content",
            "vector_store_file_update",
            "vector_store_file_delete",
        ):

            def vector_store_file_sync_wrapper(
                custom_llm_provider: Optional[str] = None,
                client: Optional[Any] = None,
                **kwargs,
            ):
                return original_function(
                    custom_llm_provider=custom_llm_provider,
                    client=client,
                    **kwargs,
                )

            return vector_store_file_sync_wrapper

        if call_type in (
            "create_agent",
            "list_agents",
            "get_agent",
            "delete_agent",
            "list_agent_versions",
        ):

            def managed_agents_sync_wrapper(
                custom_llm_provider: Optional[str] = None,
                client: Optional[Any] = None,
                **kwargs,
            ):
                if custom_llm_provider and "custom_llm_provider" not in kwargs:
                    kwargs["custom_llm_provider"] = custom_llm_provider
                if "custom_llm_provider" not in kwargs:
                    kwargs["custom_llm_provider"] = "gemini"
                return original_function(**kwargs)

            return managed_agents_sync_wrapper

        # Handle asynchronous call types
        async def async_wrapper(
            custom_llm_provider: Optional[str] = None,
            client: Optional[Any] = None,
            **kwargs,
        ):
            if call_type == "assistants":
                return await self._pass_through_assistants_endpoint_factory(
                    original_function=original_function,
                    custom_llm_provider=custom_llm_provider,
                    client=client,
                    **kwargs,
                )
            elif call_type == "moderation":
                return await self._pass_through_moderation_endpoint_factory(
                    original_function=original_function, **kwargs
                )
            elif call_type in ("asearch", "search"):
                return await self._asearch_with_fallbacks(
                    original_function=original_function,
                    **kwargs,
                )
            elif call_type in (
                "avector_store_file_create",
                "avector_store_file_list",
                "avector_store_file_retrieve",
                "avector_store_file_content",
                "avector_store_file_update",
                "avector_store_file_delete",
            ):
                return await self._init_vector_store_api_endpoints(
                    original_function=original_function,
                    custom_llm_provider=custom_llm_provider,
                    **kwargs,
                )
            elif call_type == "aresponses":
                return await self._aresponses_with_streaming_fallbacks(
                    original_function=original_function,
                    **kwargs,
                )
            elif call_type in (
                "anthropic_messages",
                "_arealtime",
                "_aresponses_websocket",
                "acreate_fine_tuning_job",
                "acancel_fine_tuning_job",
                "alist_fine_tuning_jobs",
                "aretrieve_fine_tuning_job",
                "alist_files",
                "aimage_edit",
                "agenerate_content",
                "agenerate_content_stream",
                "aocr",
                "ocr",
                "avideo_generation",
                "avideo_list",
                "avideo_status",
                "avideo_content",
                "avideo_remix",
                "avideo_create_character",
                "avideo_get_character",
                "avideo_edit",
                "avideo_extension",
                "acreate_skill",
                "alist_skills",
                "aget_skill",
                "adelete_skill",
            ):
                return await self._ageneric_api_call_with_fallbacks(
                    original_function=original_function,
                    **kwargs,
                )
            elif call_type in (
                "acreate_container",
                "alist_containers",
                "aretrieve_container",
                "adelete_container",
                "aupload_container_file",
                "alist_container_files",
                "aretrieve_container_file",
                "adelete_container_file",
                "aretrieve_container_file_content",
            ):
                return await self._init_containers_api_endpoints(
                    original_function=original_function,
                    custom_llm_provider=custom_llm_provider,
                    **kwargs,
                )
            elif call_type == "allm_passthrough_route":
                return await self._ageneric_api_call_with_fallbacks(
                    original_function=original_function,
                    passthrough_on_no_deployment=True,
                    **kwargs,
                )
            elif call_type in (
                "aget_responses",
                "acancel_responses",
                "acompact_responses",
                "adelete_responses",
                "alist_input_items",
            ):
                return await self._init_responses_api_endpoints(
                    original_function=original_function,
                    **kwargs,
                )
            elif call_type in (
                "avector_store_search",
                "avector_store_create",
                "avector_store_retrieve",
                "avector_store_list",
                "avector_store_update",
                "avector_store_delete",
            ):
                return await self._init_vector_store_api_endpoints(
                    original_function=original_function,
                    custom_llm_provider=custom_llm_provider,
                    **kwargs,
                )
            elif call_type in ("afile_delete", "afile_content"):
                return await self._ageneric_api_call_with_fallbacks(
                    original_function=original_function,
                    custom_llm_provider=custom_llm_provider,
                    client=client,
                    **kwargs,
                )
            elif call_type in (
                "acreate_interaction",
                "create_interaction",
                "aget_interaction",
                "adelete_interaction",
                "acancel_interaction",
            ):
                return await self._init_interactions_api_endpoints(
                    original_function=original_function,
                    custom_llm_provider=custom_llm_provider,
                    **kwargs,
                )
            elif call_type in (
                "acreate_agent",
                "alist_agents",
                "aget_agent",
                "adelete_agent",
                "alist_agent_versions",
            ):
                return await self._init_managed_agents_api_endpoints(
                    original_function=original_function,
                    custom_llm_provider=custom_llm_provider,
                    **kwargs,
                )

        return async_wrapper

    async def _init_vector_store_api_endpoints(
        self,
        original_function: Callable,
        custom_llm_provider: Optional[str] = None,
        **kwargs,
    ):
        """
        Initialize the Vector Store API endpoints on the router.

        If a model is provided in kwargs, use model-based routing to get
        the deployment credentials. Otherwise, call the original function directly.
        """
        if custom_llm_provider and "custom_llm_provider" not in kwargs:
            kwargs["custom_llm_provider"] = custom_llm_provider

        # If model is provided, use generic API call with fallbacks for proper routing
        if kwargs.get("model"):
            return await self._ageneric_api_call_with_fallbacks(
                original_function=original_function,
                **kwargs,
            )

        # Otherwise, call the original function directly
        return await original_function(**kwargs)

    async def _init_containers_api_endpoints(
        self,
        original_function: Callable,
        custom_llm_provider: Optional[str] = None,
        **kwargs,
    ):
        """
        Initialize the Containers API endpoints on the router.

        LiteLLM-managed container IDs (``cntr_...``) encode ``model_id`` and provider
        metadata. When present, decode the ID, replace ``container_id`` with the
        upstream value, and route through ``_ageneric_api_call_with_fallbacks`` so
        deployment credentials (e.g. regional ``api_base`` for Azure) match
        :meth:`_init_responses_api_endpoints`. Otherwise call the handler directly.
        """
        if custom_llm_provider and "custom_llm_provider" not in kwargs:
            kwargs["custom_llm_provider"] = custom_llm_provider

        from litellm.responses.utils import ResponsesAPIRequestUtils

        container_id = kwargs.get("container_id")
        _forwarded_model_id = kwargs.get("model_id")
        if isinstance(container_id, str):
            decoded = ResponsesAPIRequestUtils._decode_container_id(container_id)
            original_id = decoded.get("response_id", container_id)
            if original_id != container_id:
                kwargs["container_id"] = original_id
            decoded_provider = decoded.get("custom_llm_provider")
            if decoded_provider and kwargs.get("custom_llm_provider") == "openai":
                kwargs["custom_llm_provider"] = decoded_provider
            # Fall back to the model_id forwarded by the proxy when the container_id
            # is a native upstream ID (e.g. Azure hex cntr_) that carries no LiteLLM
            # routing payload, so deployment credentials (api_base, api_key) are applied.
            model_id = decoded.get("model_id") or (
                _forwarded_model_id.strip()
                if isinstance(_forwarded_model_id, str) and _forwarded_model_id.strip()
                else None
            )
            if model_id:
                kwargs["model"] = model_id
                return await self._ageneric_api_call_with_fallbacks(
                    original_function=original_function,
                    **kwargs,
                )

        return await original_function(**kwargs)

    async def _init_responses_api_endpoints(
        self,
        original_function: Callable,
        **kwargs,
    ):
        """
        Initialize the Responses API endpoints on the router.

        GET, DELETE, CANCEL Responses API Requests encode the model_id in the response_id, this function decodes the response_id and sets the model to the model_id.
        """
        from litellm.responses.utils import ResponsesAPIRequestUtils

        model_id = ResponsesAPIRequestUtils.get_model_id_from_response_id(
            kwargs.get("response_id")
        )
        if model_id is not None:
            kwargs["model"] = model_id
        return await self._ageneric_api_call_with_fallbacks(
            original_function=original_function,
            **kwargs,
        )

    async def _init_interactions_api_endpoints(
        self,
        original_function: Callable,
        custom_llm_provider: Optional[str] = None,
        **kwargs,
    ):
        """
        Initialize the Interactions API endpoints on the router.

        GET, DELETE, CANCEL Interactions API Requests don't need model-based routing,
        so we call the original function directly with the custom_llm_provider.
        """
        if custom_llm_provider and "custom_llm_provider" not in kwargs:
            kwargs["custom_llm_provider"] = custom_llm_provider
        # Default to gemini for interactions API
        if "custom_llm_provider" not in kwargs:
            kwargs["custom_llm_provider"] = "gemini"
        # If the proxy accidentally passed agent name as model, clear it
        if kwargs.get("agent") and kwargs.get("model") == kwargs.get("agent"):
            kwargs["model"] = None
        # Model-based interactions use deployment routing + fallbacks; agent-only calls
        # must not enter model-group lookup (agent name is not a LiteLLM deployment).
        if kwargs.get("model"):
            return await self._ageneric_api_call_with_fallbacks(
                original_function=original_function,
                **kwargs,
            )
        return await original_function(**kwargs)

    async def _init_managed_agents_api_endpoints(
        self,
        original_function: Callable,
        custom_llm_provider: Optional[str] = None,
        **kwargs,
    ):
        """
        Initialize the Managed Agents API endpoints on the router (v1beta/agents).

        CRUD operations for Gemini managed agents don't need model-based routing,
        so we call the original function directly with the custom_llm_provider.
        """
        if custom_llm_provider and "custom_llm_provider" not in kwargs:
            kwargs["custom_llm_provider"] = custom_llm_provider
        if "custom_llm_provider" not in kwargs:
            kwargs["custom_llm_provider"] = "gemini"
        return await original_function(**kwargs)

    async def _pass_through_assistants_endpoint_factory(
        self,
        original_function: Callable,
        custom_llm_provider: Optional[str] = None,
        client: Optional[AsyncOpenAI] = None,
        **kwargs,
    ):
        """Internal helper function to pass through the assistants endpoint"""
        if custom_llm_provider is None:
            if self.assistants_config is not None:
                custom_llm_provider = self.assistants_config["custom_llm_provider"]
                kwargs.update(self.assistants_config["litellm_params"])
            else:
                raise Exception(
                    "'custom_llm_provider' must be set. Either via:\n `Router(assistants_config={'custom_llm_provider': ..})` \nor\n `router.arun_thread(custom_llm_provider=..)`"
                )
        return await original_function(  # type: ignore
            custom_llm_provider=custom_llm_provider, client=client, **kwargs
        )

    #### [END] ASSISTANTS API ####

    async def _maybe_run_weighted_failover(
        self,
        exception: Exception,
        original_model_group: str,
        all_deployments: List[DeploymentTypedDict],
        args: tuple,
        kwargs: dict,
        input_kwargs: dict,
    ) -> Optional[Any]:
        """Same-model-group retry after a failed deployment; returns None if not applicable."""
        strategy, _ = self._get_routing_context(original_model_group)
        if strategy != "simple-shuffle":
            return None

        failed_id: Optional[str] = getattr(exception, "failed_deployment_id", None)
        if not failed_id:
            return None

        metadata_variable_name = self._get_metadata_variable_name_from_kwargs(kwargs)
        meta = kwargs.get(metadata_variable_name)
        if meta is None:
            meta = {}
            kwargs[metadata_variable_name] = meta
        if not isinstance(meta, dict):
            return None
        prev_excluded = set(meta.get("_failover_excluded_ids") or [])
        excluded = prev_excluded | {failed_id}

        all_ids = {
            (d.get("model_info") or {}).get("id")
            for d in all_deployments
            if (d.get("model_info") or {}).get("id") is not None
        }
        # Only consider deployments that are currently healthy (not in cooldown).
        # Using all_ids here would cause a wasteful run_async_fallback invocation
        # that fails with RouterRateLimitError whenever the "remaining" entries
        # are all in cooldown — the inner async_get_healthy_deployments call
        # would find an empty list and raise immediately.
        cooldown_ids = set(
            await _async_get_cooldown_deployments(
                litellm_router_instance=self, parent_otel_span=None
            )
        )
        remaining = (all_ids - cooldown_ids) - excluded
        if not remaining:
            return None

        verbose_router_logger.debug(
            f"Weighted failover: exclude={excluded!r}, remaining={len(remaining)} "
            f"for model_group={original_model_group!r}"
        )

        meta["_failover_excluded_ids"] = list(excluded)

        entry = {
            "model": original_model_group,
            "_excluded_deployment_ids": list(excluded),
        }
        # Build a local copy so the weighted-failover keys do not leak back to
        # the caller's shared kwargs dict (any downstream fallback path reads
        # the same dict and must not inherit our `_excluded_deployment_ids`
        # entry).
        failover_kwargs = {
            **input_kwargs,
            "fallback_model_group": [entry],
            "original_model_group": original_model_group,
        }
        try:
            return await run_async_fallback(*args, **failover_kwargs)
        except (openai.APIError, RouterRateLimitError, RouterRateLimitErrorBasic):
            # Expected model-level failure on the retried deployment. All
            # litellm provider errors derive from openai.APIError; if every
            # remaining deployment in the group is in cooldown the router
            # raises RouterRateLimitError (a ValueError, not an APIError).
            # In either case defer to the regular fallback path. Programming
            # errors (AttributeError, KeyError, TypeError, etc.) intentionally
            # propagate so they remain visible.
            return None

    async def async_function_with_fallbacks_common_utils(
        self,
        e: Exception,
        disable_fallbacks: Optional[bool],
        fallbacks: Optional[List],
        context_window_fallbacks: Optional[List],
        content_policy_fallbacks: Optional[List],
        model_group: Optional[str],
        args: tuple,
        kwargs: dict,
    ):
        """
        Common utilities for async_function_with_fallbacks
        """
        verbose_router_logger.debug(f"Traceback{traceback.format_exc()}")
        original_exception = e
        fallback_model_group = None
        original_model_group: Optional[str] = kwargs.get("model")  # type: ignore
        fallback_failure_exception_str = ""

        if disable_fallbacks is True or original_model_group is None:
            raise e

        input_kwargs = {
            "litellm_router": self,
            "original_exception": original_exception,
            **kwargs,
        }

        if "max_fallbacks" not in input_kwargs:
            input_kwargs["max_fallbacks"] = self.max_fallbacks
        if "fallback_depth" not in input_kwargs:
            input_kwargs["fallback_depth"] = 0

        # ORDER-BASED FALLBACKS: prepend higher order levels to the fallback list
        # Skip for error types that have their own dedicated fallback handlers
        _skip_order_fallback = isinstance(
            e,
            (litellm.ContextWindowExceededError, litellm.ContentPolicyViolationError),
        )
        _request_team_id: Optional[str] = (kwargs.get("metadata", {}) or {}).get(
            "user_api_key_team_id"
        )
        # Use wildcard-aware lookup so order-based fallback also works for model
        # groups resolved via pattern routing (e.g. `openai/*` -> `openai/gpt-4.1-mini`).
        all_deployments = (
            self.get_model_list(
                model_name=original_model_group, team_id=_request_team_id
            )
            or []
        )
        _order_set: set = {
            litellm.utils._get_deployment_order(d)
            for d in all_deployments
            if litellm.utils._get_deployment_order(d) is not None
        }
        order_values: list = sorted(_order_set)
        if len(order_values) > 1 and not _skip_order_fallback:
            # Determine which order levels have already been tried
            current_target = kwargs.get("_target_order")
            skip_up_to = (
                current_target if current_target is not None else order_values[0]
            )
            # Build order-based fallback entries (skip already-tried levels)
            order_fallback_entries: List = [
                {"model": original_model_group, "_target_order": o}
                for o in order_values
                if o > skip_up_to
            ]
            # Get external fallbacks — handle both standard and non-standard formats
            external_fallback_group: Optional[List] = None
            if fallbacks is not None and model_group is not None:
                if _check_non_standard_fallback_format(fallbacks=fallbacks):
                    # Non-standard formats (e.g. ["claude-3-haiku"] or
                    # [{"model": "...", "messages": [...]}]) are passed through directly
                    external_fallback_group = fallbacks
                else:
                    external_fallback_group, generic_idx = get_fallback_model_group(
                        fallbacks=fallbacks,
                        model_group=cast(str, model_group),
                    )
                    if external_fallback_group is None and generic_idx is not None:
                        external_fallback_group = fallbacks[generic_idx]["*"]

            # Combined list: order fallbacks first, then external
            combined_fallbacks = order_fallback_entries + (
                external_fallback_group or []
            )

            if combined_fallbacks:
                input_kwargs.update(
                    {
                        "fallback_model_group": combined_fallbacks,
                        "original_model_group": original_model_group,
                    }
                )
                response = await run_async_fallback(
                    *args,
                    **input_kwargs,
                )
                return response

        # Weighted intra-group failover (simple-shuffle only); see _maybe_run_weighted_failover.
        if (
            self.enable_weighted_failover
            and not _skip_order_fallback
            and original_model_group is not None
        ):
            response = await self._maybe_run_weighted_failover(
                exception=e,
                original_model_group=original_model_group,
                all_deployments=all_deployments,
                args=args,
                kwargs=kwargs,
                input_kwargs=input_kwargs,
            )
            if response is not None:
                return response

        try:
            verbose_router_logger.info("Trying to fallback b/w models")

            # check if client-side fallbacks are used (e.g. fallbacks = ["gpt-3.5-turbo", "claude-3-haiku"] or fallbacks=[{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Hey, how's it going?"}]}]
            is_non_standard_fallback_format = _check_non_standard_fallback_format(
                fallbacks=fallbacks
            )

            if is_non_standard_fallback_format:
                input_kwargs.update(
                    {
                        "fallback_model_group": fallbacks,
                        "original_model_group": original_model_group,
                    }
                )

                response = await run_async_fallback(
                    *args,
                    **input_kwargs,
                )

                return response

            if isinstance(e, litellm.ContextWindowExceededError):
                if context_window_fallbacks is not None:
                    context_window_fallback_model_group: Optional[List[str]] = (
                        self._get_fallback_model_group_from_fallbacks(
                            fallbacks=context_window_fallbacks,
                            model_group=model_group,
                        )
                    )
                    if context_window_fallback_model_group is None:
                        raise original_exception

                    input_kwargs.update(
                        {
                            "fallback_model_group": context_window_fallback_model_group,
                            "original_model_group": original_model_group,
                        }
                    )

                    response = await run_async_fallback(
                        *args,
                        **input_kwargs,
                    )
                    return response

                else:
                    error_message = "model={}. context_window_fallbacks={}. fallbacks={}.\n\nSet 'context_window_fallback' - https://docs.litellm.ai/docs/routing#fallbacks".format(
                        model_group, context_window_fallbacks, fallbacks
                    )
                    verbose_router_logger.info(
                        msg="Got 'ContextWindowExceededError'. No context_window_fallback set. Defaulting \
                        to fallbacks, if available.{}".format(
                            error_message
                        )
                    )

                    if litellm.expose_router_debug_in_errors:
                        e.message += "\n{}".format(error_message)
            elif isinstance(e, litellm.ContentPolicyViolationError):
                if content_policy_fallbacks is not None:
                    content_policy_fallback_model_group: Optional[List[str]] = (
                        self._get_fallback_model_group_from_fallbacks(
                            fallbacks=content_policy_fallbacks,
                            model_group=model_group,
                        )
                    )
                    if content_policy_fallback_model_group is None:
                        raise original_exception

                    input_kwargs.update(
                        {
                            "fallback_model_group": content_policy_fallback_model_group,
                            "original_model_group": original_model_group,
                        }
                    )

                    response = await run_async_fallback(
                        *args,
                        **input_kwargs,
                    )
                    return response
                else:
                    error_message = "model={}. content_policy_fallback={}. fallbacks={}.\n\nSet 'content_policy_fallback' - https://docs.litellm.ai/docs/routing#fallbacks".format(
                        model_group, content_policy_fallbacks, fallbacks
                    )
                    verbose_router_logger.info(
                        msg="Got 'ContentPolicyViolationError'. No content_policy_fallback set. Defaulting \
                        to fallbacks, if available.{}".format(
                            error_message
                        )
                    )

                    if litellm.expose_router_debug_in_errors:
                        e.message += "\n{}".format(error_message)
            if fallbacks is not None and model_group is not None:
                verbose_router_logger.debug(f"inside model fallbacks: {fallbacks}")
                (
                    fallback_model_group,
                    generic_fallback_idx,
                ) = get_fallback_model_group(
                    fallbacks=fallbacks,  # if fallbacks = [{"gpt-3.5-turbo": ["claude-3-haiku"]}]
                    model_group=cast(str, model_group),
                )
                ## if none, check for generic fallback
                if fallback_model_group is None and generic_fallback_idx is not None:
                    fallback_model_group = fallbacks[generic_fallback_idx]["*"]

                if fallback_model_group is None:
                    verbose_router_logger.info(
                        f"No fallback model group found for original model_group={model_group}. Fallbacks={fallbacks}"
                    )
                    if (
                        hasattr(original_exception, "message")
                        and litellm.expose_router_debug_in_errors
                    ):
                        original_exception.message += f"No fallback model group found for original model_group={model_group}. Fallbacks={fallbacks}"  # type: ignore
                    raise original_exception

                input_kwargs.update(
                    {
                        "fallback_model_group": fallback_model_group,
                        "original_model_group": original_model_group,
                    }
                )

                response = await run_async_fallback(
                    *args,
                    **input_kwargs,
                )

                return response
        except Exception as new_exception:
            parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
            verbose_router_logger.error(
                "litellm.router.py::async_function_with_fallbacks() - Error occurred while trying to do fallbacks - {}\n{}\n\nDebug Information:\nCooldown Deployments={}".format(
                    str(new_exception),
                    traceback.format_exc(),
                    await _async_get_cooldown_deployments_with_debug_info(
                        litellm_router_instance=self,
                        parent_otel_span=parent_otel_span,
                    ),
                )
            )
            fallback_failure_exception_str = str(new_exception)

        if (
            hasattr(original_exception, "message")
            and litellm.expose_router_debug_in_errors
        ):
            # add the available fallbacks to the exception
            original_exception.message += ". Received Model Group={}\nAvailable Model Group Fallbacks={}".format(  # type: ignore
                model_group,
                fallback_model_group,
            )
            if len(fallback_failure_exception_str) > 0:
                original_exception.message += (  # type: ignore
                    "\nError doing the fallback: {}".format(
                        fallback_failure_exception_str
                    )
                )

        raise original_exception

    @tracer.wrap()
    async def async_function_with_fallbacks(self, *args, **kwargs):
        """
        Try calling the function_with_retries
        If it fails after num_retries, fall back to another model group
        """
        model_group: Optional[str] = kwargs.get("model")
        disable_fallbacks: Optional[bool] = kwargs.pop("disable_fallbacks", False)
        fallbacks: Optional[List] = kwargs.get("fallbacks", self.fallbacks)
        context_window_fallbacks: Optional[List] = kwargs.get(
            "context_window_fallbacks", self.context_window_fallbacks
        )
        content_policy_fallbacks: Optional[List] = kwargs.get(
            "content_policy_fallbacks", self.content_policy_fallbacks
        )

        mock_timeout = kwargs.pop("mock_timeout", None)

        try:
            self._handle_mock_testing_fallbacks(
                kwargs=kwargs,
                model_group=model_group,
                fallbacks=fallbacks,
                context_window_fallbacks=context_window_fallbacks,
                content_policy_fallbacks=content_policy_fallbacks,
            )

            if mock_timeout is not None:
                response = await self.async_function_with_retries(
                    *args, **kwargs, mock_timeout=mock_timeout
                )
            else:
                response = await self.async_function_with_retries(*args, **kwargs)
            if verbose_router_logger.isEnabledFor(logging.DEBUG):
                verbose_router_logger.debug(f"Async Response: {response}")
            response = add_fallback_headers_to_response(
                response=response,
                attempted_fallbacks=0,
            )
            return response
        except Exception as e:
            return await self.async_function_with_fallbacks_common_utils(
                e,
                disable_fallbacks,
                fallbacks,
                context_window_fallbacks,
                content_policy_fallbacks,
                model_group,
                args,
                kwargs,
            )

    def _handle_mock_testing_fallbacks(
        self,
        kwargs: dict,
        model_group: Optional[str] = None,
        fallbacks: Optional[List] = None,
        context_window_fallbacks: Optional[List] = None,
        content_policy_fallbacks: Optional[List] = None,
    ):
        """
        Helper function to raise a litellm Error for mock testing purposes.

        Raises:
            litellm.InternalServerError: when `mock_testing_fallbacks=True` passed in request params
            litellm.ContextWindowExceededError: when `mock_testing_context_fallbacks=True` passed in request params
            litellm.ContentPolicyViolationError: when `mock_testing_content_policy_fallbacks=True` passed in request params
        """
        mock_testing_params = MockRouterTestingParams.from_kwargs(kwargs)
        if (
            mock_testing_params.mock_testing_fallbacks is not None
            and mock_testing_params.mock_testing_fallbacks is True
        ):
            raise litellm.InternalServerError(
                model=model_group,
                llm_provider="",
                message=f"This is a mock exception for model={model_group}, to trigger a fallback. Fallbacks={fallbacks}",
            )
        elif (
            mock_testing_params.mock_testing_context_fallbacks is not None
            and mock_testing_params.mock_testing_context_fallbacks is True
        ):
            raise litellm.ContextWindowExceededError(
                model=model_group,
                llm_provider="",
                message=f"This is a mock exception for model={model_group}, to trigger a fallback. \
                    Context_Window_Fallbacks={context_window_fallbacks}",
            )
        elif (
            mock_testing_params.mock_testing_content_policy_fallbacks is not None
            and mock_testing_params.mock_testing_content_policy_fallbacks is True
        ):
            raise litellm.ContentPolicyViolationError(
                model=model_group,
                llm_provider="",
                message=f"This is a mock exception for model={model_group}, to trigger a fallback. \
                    Context_Policy_Fallbacks={content_policy_fallbacks}",
            )

    @tracer.wrap()
    async def async_function_with_retries(self, *args, **kwargs):
        verbose_router_logger.debug("Inside async function with retries.")
        original_function = kwargs.pop("original_function")
        fallbacks = kwargs.pop("fallbacks", self.fallbacks)
        parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
        context_window_fallbacks = kwargs.pop(
            "context_window_fallbacks", self.context_window_fallbacks
        )
        content_policy_fallbacks = kwargs.pop(
            "content_policy_fallbacks", self.content_policy_fallbacks
        )
        # Support per-request model_group_retry_policy override (from key/team settings)
        model_group_retry_policy = kwargs.pop(
            "model_group_retry_policy", self.model_group_retry_policy
        )
        model_group: Optional[str] = kwargs.get("model")
        num_retries = kwargs.pop("num_retries")

        ## ADD MODEL GROUP SIZE TO METADATA - used for model_group_rate_limit_error tracking
        _metadata: dict = kwargs.get("litellm_metadata", kwargs.get("metadata")) or {}
        if "model_group" in _metadata and isinstance(_metadata["model_group"], str):
            model_list = self.get_model_list(model_name=_metadata["model_group"])
            if model_list is not None:
                _metadata.update({"model_group_size": len(model_list)})

        verbose_router_logger.debug(
            f"async function w/ retries: original_function - {original_function}, num_retries - {num_retries}"
        )
        ## ADD RETRY TRACKING TO METADATA - used for spend logs retry tracking
        _metadata["attempted_retries"] = 0
        _metadata["max_retries"] = (
            num_retries  # Updated after overrides in exception handler
        )
        try:
            self._handle_mock_testing_rate_limit_error(
                model_group=model_group, kwargs=kwargs
            )
            # if the function call is successful, no exception will be raised and we'll break out of the loop
            response = await self.make_call(original_function, *args, **kwargs)
            response = add_retry_headers_to_response(
                response=response, attempted_retries=0, max_retries=None
            )
            return response
        except Exception as e:
            current_attempt = None
            original_exception = e
            deployment_num_retries = getattr(e, "num_retries", None)

            if deployment_num_retries is not None and isinstance(
                deployment_num_retries, int
            ):
                num_retries = deployment_num_retries
            """
            Retry Logic
            """
            (
                _healthy_deployments,
                _all_deployments,
            ) = await self._async_get_healthy_deployments(
                model=kwargs.get("model") or "",
                parent_otel_span=parent_otel_span,
            )

            # Check retry policy FIRST, before should_retry_this_error
            # This allows retry policies to override the healthy deployments check
            _retry_policy_applies = False
            if self.retry_policy is not None or model_group_retry_policy is not None:
                # get num_retries from retry policy
                # Use the model_group captured at the start of the function, or get it from metadata
                # kwargs.get("model") at this point is the deployment model, not the model_group
                _model_group_for_retry_policy = (
                    model_group or _metadata.get("model_group") or kwargs.get("model")
                )
                # Use per-request model_group_retry_policy if provided, otherwise use self
                _retry_policy_retries = _get_num_retries_from_retry_policy(
                    exception=original_exception,
                    model_group=_model_group_for_retry_policy,
                    model_group_retry_policy=model_group_retry_policy,
                    retry_policy=self.retry_policy,
                )
                if _retry_policy_retries is not None:
                    num_retries = _retry_policy_retries
                    _retry_policy_applies = True

            # raises an exception if this error should not be retries
            # Skip this check if retry policy applies (retry policy takes precedence)
            if not _retry_policy_applies:
                self.should_retry_this_error(
                    error=e,
                    healthy_deployments=_healthy_deployments,
                    all_deployments=_all_deployments,
                    context_window_fallbacks=context_window_fallbacks,
                    regular_fallbacks=fallbacks,
                    content_policy_fallbacks=content_policy_fallbacks,
                )
            # Update max_retries after overrides (deployment_num_retries / retry_policy)
            _metadata["max_retries"] = num_retries

            ## LOGGING
            if num_retries > 0:
                kwargs = self.log_retry(kwargs=kwargs, e=original_exception)
            else:
                raise

            verbose_router_logger.debug(
                f"Retrying request with num_retries: {num_retries}"
            )
            # decides how long to sleep before retry
            retry_after = self._time_to_sleep_before_retry(
                e=original_exception,
                remaining_retries=num_retries,
                num_retries=num_retries,
                healthy_deployments=_healthy_deployments,
                all_deployments=_all_deployments,
            )

            await asyncio.sleep(retry_after)

            for current_attempt in range(num_retries):
                try:
                    # Update retry tracking metadata before each retry attempt
                    _metadata["attempted_retries"] = current_attempt + 1
                    _metadata["max_retries"] = num_retries
                    # if the function call is successful, no exception will be raised and we'll break out of the loop
                    response = await self.make_call(original_function, *args, **kwargs)
                    if coroutine_checker.is_async_callable(
                        response
                    ):  # async errors are often returned as coroutines
                        response = await response

                    response = add_retry_headers_to_response(
                        response=response,
                        attempted_retries=current_attempt + 1,
                        max_retries=num_retries,
                    )
                    return response

                except Exception as e:
                    # Always track the latest error so we raise the most
                    # recent exception instead of the first one.
                    original_exception = e

                    ## LOGGING
                    kwargs = self.log_retry(kwargs=kwargs, e=e)
                    remaining_retries = num_retries - current_attempt - 1
                    _model: Optional[str] = kwargs.get("model")  # type: ignore
                    if _model is not None:
                        (
                            _healthy_deployments,
                            _,
                        ) = await self._async_get_healthy_deployments(
                            model=_model,
                            parent_otel_span=parent_otel_span,
                        )
                    else:
                        _healthy_deployments = []

                    # Check if this error is non-retryable (e.g., 400 context
                    # window exceeded). If so, raise immediately instead of
                    # continuing the retry loop. Respect retry policy
                    # precedence - only check when no retry policy applies.
                    if not _retry_policy_applies:
                        try:
                            self.should_retry_this_error(
                                error=e,
                                healthy_deployments=_healthy_deployments,
                                all_deployments=_all_deployments,
                                context_window_fallbacks=context_window_fallbacks,
                                regular_fallbacks=fallbacks,
                                content_policy_fallbacks=content_policy_fallbacks,
                            )
                        except Exception:
                            raise e

                    _timeout = self._time_to_sleep_before_retry(
                        e=e,
                        remaining_retries=remaining_retries,
                        num_retries=num_retries,
                        healthy_deployments=_healthy_deployments,
                        all_deployments=_all_deployments,
                    )
                    await asyncio.sleep(_timeout)

            if type(original_exception) in litellm.LITELLM_EXCEPTION_TYPES:
                setattr(original_exception, "max_retries", num_retries)
                # current_attempt is 0-indexed (0 to num_retries-1), so after loop completion
                # it represents the last attempt index. The actual number of retries attempted
                # is current_attempt + 1, which equals num_retries when all retries are exhausted.
                # We've already verified num_retries > 0 before entering the loop, so current_attempt
                # will always be set (never None) when we reach this point.
                actual_retries_attempted = (
                    current_attempt + 1 if current_attempt is not None else num_retries
                )
                setattr(original_exception, "num_retries", actual_retries_attempted)

            raise original_exception

    async def make_call(self, original_function: Any, *args, **kwargs):
        """
        Handler for making a call to the .completion()/.embeddings()/etc. functions.
        """
        model_group = kwargs.get("model")
        response = original_function(*args, **kwargs)
        if coroutine_checker.is_async_callable(response) or inspect.isawaitable(
            response
        ):
            response = await response
        ## PROCESS RESPONSE HEADERS
        response = await self.set_response_headers(
            response=response, model_group=model_group, request_kwargs=kwargs
        )

        return response

    def _handle_mock_testing_rate_limit_error(
        self, kwargs: dict, model_group: Optional[str] = None
    ):
        """
        Helper function to raise a mock litellm.RateLimitError error for testing purposes.

        Raises:
            litellm.RateLimitError error when `mock_testing_rate_limit_error=True` passed in request params
        """
        mock_testing_rate_limit_error: Optional[bool] = kwargs.pop(
            "mock_testing_rate_limit_error", None
        )

        available_models = self.get_model_list(model_name=model_group)
        num_retries: Optional[int] = None

        if available_models is not None and len(available_models) == 1:
            num_retries = cast(
                Optional[int], available_models[0]["litellm_params"].get("num_retries")
            )

        if (
            mock_testing_rate_limit_error is not None
            and mock_testing_rate_limit_error is True
        ):
            verbose_router_logger.info(
                f"litellm.router.py::_mock_rate_limit_error() - Raising mock RateLimitError for model={model_group}"
            )
            raise litellm.RateLimitError(
                model=model_group,
                llm_provider="",
                message=f"This is a mock exception for model={model_group}, to trigger a rate limit error.",
                num_retries=num_retries,
            )

    def should_retry_this_error(
        self,
        error: Exception,
        healthy_deployments: Optional[List] = None,
        all_deployments: Optional[List] = None,
        context_window_fallbacks: Optional[List] = None,
        content_policy_fallbacks: Optional[List] = None,
        regular_fallbacks: Optional[List] = None,
    ):
        """
        1. raise an exception for ContextWindowExceededError if context_window_fallbacks is not None
        2. raise an exception for ContentPolicyViolationError if content_policy_fallbacks is not None

        2. raise an exception for RateLimitError if
            - there are no fallbacks
            - there are no healthy deployments in the same model group
        """
        _num_healthy_deployments = 0
        if healthy_deployments is not None and isinstance(healthy_deployments, list):
            _num_healthy_deployments = len(healthy_deployments)

        _num_all_deployments = 0
        if all_deployments is not None and isinstance(all_deployments, list):
            _num_all_deployments = len(all_deployments)

        ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR / CONTENT POLICY VIOLATION ERROR w/ fallbacks available / Bad Request Error
        if (
            isinstance(error, litellm.ContextWindowExceededError)
            and context_window_fallbacks is not None
        ):
            raise error

        if (
            isinstance(error, litellm.ContentPolicyViolationError)
            and content_policy_fallbacks is not None
        ):
            raise error

        status_code = getattr(error, "status_code", None)
        if status_code is not None and not litellm._should_retry(status_code):
            # 401/403 are special cases - allow retry if multiple deployments exist (handled below)
            if status_code not in (401, 403):
                raise error

        if isinstance(error, litellm.NotFoundError):
            raise error
        # Error we should only retry if there are other deployments
        if isinstance(error, openai.RateLimitError):
            if (
                _num_healthy_deployments <= 0  # if no healthy deployments
                and regular_fallbacks is not None  # and fallbacks available
                and len(regular_fallbacks) > 0
            ):
                raise error  # then raise the error

        if isinstance(error, openai.AuthenticationError):
            """
            - if other deployments available -> retry
            - else -> raise error
            """
            if (
                _num_all_deployments <= 1
            ):  # if there is only 1 deployment for this model group then don't retry
                raise error  # then raise error

        # Do not retry if there are no healthy deployments
        # just raise the error
        if _num_healthy_deployments <= 0:  # if no healthy deployments
            raise error

        return True

    def function_with_fallbacks(self, *args, **kwargs):
        """
        Sync wrapper for async_function_with_fallbacks

        Wrapped to reduce code duplication and prevent bugs.
        """
        return run_async_function(self.async_function_with_fallbacks, *args, **kwargs)

    def _get_fallback_model_group_from_fallbacks(
        self,
        fallbacks: List[Dict[str, List[str]]],
        model_group: Optional[str] = None,
    ) -> Optional[List[str]]:
        """
        Returns the list of fallback models to use for a given model group

        If no fallback model group is found, returns None

        Example:
            fallbacks = [{"gpt-3.5-turbo": ["gpt-4"]}, {"gpt-4o": ["gpt-3.5-turbo"]}]
            model_group = "gpt-3.5-turbo"
            returns: ["gpt-4"]
        """
        if model_group is None:
            return None

        fallback_model_group: Optional[List[str]] = None
        for item in fallbacks:  # [{"gpt-3.5-turbo": ["gpt-4"]}]
            if list(item.keys())[0] == model_group:
                fallback_model_group = item[model_group]
                break
        return fallback_model_group

    def _get_first_default_fallback(self) -> Optional[str]:
        """
        Returns the first model from the default_fallbacks list, if it exists.
        """
        if self.fallbacks is None:
            return None
        for fallback in self.fallbacks:
            if isinstance(fallback, dict) and "*" in fallback:
                default_list = fallback["*"]
                if isinstance(default_list, list) and len(default_list) > 0:
                    return default_list[0]
        return None

    def _time_to_sleep_before_retry(
        self,
        e: Exception,
        remaining_retries: int,
        num_retries: int,
        healthy_deployments: Optional[List] = None,
        all_deployments: Optional[List] = None,
    ) -> Union[int, float]:
        """
        Calculate back-off, then retry

        It should instantly retry only when:
            1. there are healthy deployments in the same model group
            2. there are fallbacks for the completion call
        """

        ## base case - single deployment
        if all_deployments is not None and len(all_deployments) == 1:
            pass
        elif (
            healthy_deployments is not None
            and isinstance(healthy_deployments, list)
            and len(healthy_deployments) > 0
        ):
            return 0

        response_headers: Optional[httpx.Headers] = None
        if hasattr(e, "response") and hasattr(e.response, "headers"):  # type: ignore
            response_headers = e.response.headers  # type: ignore
        if hasattr(e, "litellm_response_headers"):
            response_headers = e.litellm_response_headers  # type: ignore

        if response_headers is not None:
            timeout = litellm._calculate_retry_after(
                remaining_retries=remaining_retries,
                max_retries=num_retries,
                response_headers=response_headers,
                min_timeout=self.retry_after,
            )

        else:
            timeout = litellm._calculate_retry_after(
                remaining_retries=remaining_retries,
                max_retries=num_retries,
                min_timeout=self.retry_after,
            )

        return timeout

    ### HELPER FUNCTIONS

    async def deployment_callback_on_success(
        self,
        kwargs,  # kwargs to completion
        completion_response,  # response from completion
        start_time,
        end_time,  # start/end time
    ):
        """
        Track remaining tpm/rpm quota for model in model_list
        """
        from litellm.types.caching import RedisPipelineIncrementOperation

        try:
            # WS session wrappers fire with result=None; per-turn costs tracked by inner calls.
            if kwargs.get("call_type") in ("_aresponses_websocket", "_arealtime"):
                return
            standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
                "standard_logging_object", None
            )
            if standard_logging_object is None:
                raise ValueError("standard_logging_object is None")
            if kwargs["litellm_params"].get("metadata") is None:
                pass
            else:
                deployment_name = kwargs["litellm_params"]["metadata"].get(
                    "deployment", None
                )  # stable name - works for wildcard routes as well
                # Get model_group and id from kwargs like the sync version does
                model_group = kwargs["litellm_params"]["metadata"].get(
                    "model_group", None
                )
                model_info = kwargs["litellm_params"].get("model_info", {}) or {}
                id = model_info.get("id", None)
                if model_group is None or id is None:
                    return
                elif isinstance(id, int):
                    id = str(id)

                ## get deployment info
                deployment_info = self.get_deployment(model_id=id)

                if deployment_info is None:
                    return
                else:
                    deployment_model_info = self.get_router_model_info(
                        deployment=deployment_info,
                        received_model_name=model_group,
                    )
                    # get tpm/rpm from deployment info
                    tpm = deployment_info.get("tpm", None)
                    rpm = deployment_info.get("rpm", None)

                    ## check tpm/rpm in litellm_params
                    tpm_litellm_params = deployment_info.litellm_params.tpm
                    rpm_litellm_params = deployment_info.litellm_params.rpm

                    ## check tpm/rpm in model_info
                    tpm_model_info = deployment_model_info.get("tpm", None)
                    rpm_model_info = deployment_model_info.get("rpm", None)

                # Always track deployment successes for cooldown logic, regardless of TPM/RPM limits
                increment_deployment_successes_for_current_minute(
                    litellm_router_instance=self,
                    deployment_id=id,
                )

                ## if all are none, return - no need to track current tpm/rpm usage for models with no tpm/rpm set
                if (
                    tpm is None
                    and rpm is None
                    and tpm_litellm_params is None
                    and rpm_litellm_params is None
                    and tpm_model_info is None
                    and rpm_model_info is None
                ):
                    return

                parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
                total_tokens: float = standard_logging_object.get("total_tokens", 0)

                # ------------
                # Setup values
                # ------------
                dt = get_utc_datetime()
                current_minute = dt.strftime(
                    "%H-%M"
                )  # use the same timezone regardless of system clock

                tpm_key = RouterCacheEnum.TPM.value.format(
                    id=id, current_minute=current_minute, model=deployment_name
                )
                # ------------
                # Update usage
                # ------------
                # update cache
                pipeline_operations: List[RedisPipelineIncrementOperation] = []

                ## TPM
                pipeline_operations.append(
                    RedisPipelineIncrementOperation(
                        key=tpm_key,
                        increment_value=total_tokens,
                        ttl=RoutingArgs.ttl.value,
                    )
                )

                ## RPM
                rpm_key = RouterCacheEnum.RPM.value.format(
                    id=id, current_minute=current_minute, model=deployment_name
                )
                pipeline_operations.append(
                    RedisPipelineIncrementOperation(
                        key=rpm_key,
                        increment_value=1,
                        ttl=RoutingArgs.ttl.value,
                    )
                )

                await self.cache.async_increment_cache_pipeline(
                    increment_list=pipeline_operations,
                    parent_otel_span=parent_otel_span,
                )

                return tpm_key

        except Exception as e:
            verbose_router_logger.debug(
                "litellm.router.Router::deployment_callback_on_success(): Exception occured - {}".format(
                    str(e)
                )
            )
            pass

    def sync_deployment_callback_on_success(
        self,
        kwargs,  # kwargs to completion
        completion_response,  # response from completion
        start_time,
        end_time,  # start/end time
    ) -> Optional[str]:
        """
        Tracks the number of successes for a deployment in the current minute (using in-memory cache)

        Returns:
        - key: str - The key used to increment the cache
        - None: if no key is found
        """
        id = None
        if kwargs["litellm_params"].get("metadata") is None:
            pass
        else:
            model_group = kwargs["litellm_params"]["metadata"].get("model_group", None)
            model_info = kwargs["litellm_params"].get("model_info", {}) or {}
            id = model_info.get("id", None)
            if model_group is None or id is None:
                return None
            elif isinstance(id, int):
                id = str(id)

        if id is not None:
            key = increment_deployment_successes_for_current_minute(
                litellm_router_instance=self,
                deployment_id=id,
            )
            return key

        return None

    def deployment_callback_on_failure(
        self,
        kwargs,  # kwargs to completion
        completion_response,  # response from completion
        start_time,
        end_time,  # start/end time
    ) -> bool:
        """
        2 jobs:
        - Tracks the number of failures for a deployment in the current minute (using in-memory cache)
        - Puts the deployment in cooldown if it exceeds the allowed fails / minute

        Returns:
        - True if the deployment should be put in cooldown
        - False if the deployment should not be put in cooldown
        """
        verbose_router_logger.debug("Router: Entering 'deployment_callback_on_failure'")
        try:
            exception = kwargs.get("exception", None)
            exception_status = getattr(exception, "status_code", "")

            # Cache litellm_params to avoid repeated dict lookups
            litellm_params = kwargs.get("litellm_params", {})
            _model_info = litellm_params.get("model_info", {})

            exception_headers = litellm.litellm_core_utils.exception_mapping_utils._get_response_headers(
                original_exception=exception
            )

            # Determine cooldown time with priority: deployment config > response header > router default
            deployment_cooldown = litellm_params.get("cooldown_time", None)

            header_cooldown = None
            if exception_headers is not None:
                header_cooldown = litellm.utils._get_retry_after_from_exception_header(
                    response_headers=exception_headers
                )
            ##############################################
            # Logic to determine cooldown time
            # 1. Check if a cooldown time is set in the deployment config
            # 2. Check if a cooldown time is set in the response header
            # 3. If no cooldown time is set, use the router default cooldown time
            ##############################################
            if deployment_cooldown is not None and deployment_cooldown >= 0:
                _time_to_cooldown = deployment_cooldown
            elif header_cooldown is not None and header_cooldown >= 0:
                _time_to_cooldown = header_cooldown
            else:
                _time_to_cooldown = self.cooldown_time

            if isinstance(_model_info, dict):
                deployment_id: Optional[str] = _model_info.get("id")
                if deployment_id is None:
                    return False
                increment_deployment_failures_for_current_minute(
                    litellm_router_instance=self,
                    deployment_id=deployment_id,
                )
                result = _set_cooldown_deployments(
                    litellm_router_instance=self,
                    exception_status=exception_status,
                    original_exception=exception,
                    deployment=deployment_id,
                    time_to_cooldown=_time_to_cooldown,
                )  # setting deployment_id in cooldown deployments

                return result
            else:
                verbose_router_logger.debug(
                    "Router: Exiting 'deployment_callback_on_failure' without cooldown. No model_info found."
                )
                return False

        except Exception as e:
            raise e

    async def async_deployment_callback_on_failure(
        self, kwargs, completion_response: Optional[Any], start_time, end_time
    ):
        """
        Update RPM usage for a deployment
        """
        deployment_name = kwargs["litellm_params"]["metadata"].get(
            "deployment", None
        )  # handles wildcard routes - by giving the original name sent to `litellm.completion`
        model_group = kwargs["litellm_params"]["metadata"].get("model_group", None)
        model_info = kwargs["litellm_params"].get("model_info", {}) or {}
        id = model_info.get("id", None)
        if model_group is None or id is None:
            return
        elif isinstance(id, int):
            id = str(id)
        parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)

        dt = get_utc_datetime()
        current_minute = dt.strftime(
            "%H-%M"
        )  # use the same timezone regardless of system clock

        ## RPM
        rpm_key = RouterCacheEnum.RPM.value.format(
            id=id, current_minute=current_minute, model=deployment_name
        )
        await self.cache.async_increment_cache(
            key=rpm_key,
            value=1,
            parent_otel_span=parent_otel_span,
            ttl=RoutingArgs.ttl.value,
        )

    def _get_metadata_variable_name_from_kwargs(
        self, kwargs: dict
    ) -> Literal["metadata", "litellm_metadata"]:
        """
        Helper to return what the "metadata" field should be called in the request data

        - New endpoints return `litellm_metadata`
        - Old endpoints return `metadata`

        Context:
        - LiteLLM used `metadata` as an internal field for storing metadata
        - OpenAI then started using this field for their metadata
        - LiteLLM is now moving to using `litellm_metadata` for our metadata
        """
        return get_metadata_variable_name_from_kwargs(kwargs)

    def log_retry(self, kwargs: dict, e: Exception) -> dict:
        """
        When a retry or fallback happens, log the details of the just failed model call - similar to Sentry breadcrumbing
        """
        try:
            _metadata_var = (
                "litellm_metadata" if "litellm_metadata" in kwargs else "metadata"
            )
            # Log failed model as the previous model
            previous_model = {
                "exception_type": type(e).__name__,
                "exception_string": str(e),
            }
            for (
                k,
                v,
            ) in (
                kwargs.items()
            ):  # log everything in kwargs except the old previous_models value - prevent nesting
                if k not in [_metadata_var, "messages", "original_function"]:
                    previous_model[k] = v
                elif k == _metadata_var and isinstance(v, dict):
                    previous_model[_metadata_var] = {}  # type: ignore
                    for metadata_k, metadata_v in kwargs[_metadata_var].items():
                        if metadata_k != "previous_models":
                            previous_model[k][metadata_k] = metadata_v  # type: ignore

            # check current size of self.previous_models, if it's larger than 3, remove the first element
            if len(self.previous_models) > 3:
                self.previous_models.pop(0)

            self.previous_models.append(previous_model)
            kwargs[_metadata_var]["previous_models"] = self.previous_models
            return kwargs
        except Exception as e:
            raise e

    def _update_usage(
        self, deployment_id: str, parent_otel_span: Optional[Span]
    ) -> int:
        """
        Update deployment rpm for that minute

        Returns:
        - int: request count
        """
        rpm_key = deployment_id

        request_count = self.cache.get_cache(
            key=rpm_key, parent_otel_span=parent_otel_span, local_only=True
        )
        if request_count is None:
            request_count = 1
            self.cache.set_cache(
                key=rpm_key, value=request_count, local_only=True, ttl=60
            )  # only store for 60s
        else:
            request_count += 1
            self.cache.set_cache(
                key=rpm_key, value=request_count, local_only=True
            )  # don't change existing ttl

        return request_count

    def _has_default_fallbacks(self) -> bool:
        if self.fallbacks is None:
            return False
        for fallback in self.fallbacks:
            if isinstance(fallback, dict):
                if "*" in fallback:
                    return True
        return False

    def _should_raise_content_policy_error(
        self, model: str, response: ModelResponse, kwargs: dict
    ) -> bool:
        """
        Determines if a content policy error should be raised.

        Only raised if a fallback is available.

        Else, original response is returned.
        """
        if response.choices and len(response.choices) > 0:
            if response.choices[0].finish_reason != "content_filter":
                return False

        content_policy_fallbacks = kwargs.get(
            "content_policy_fallbacks", self.content_policy_fallbacks
        )

        ### ONLY RAISE ERROR IF CP FALLBACK AVAILABLE ###
        if content_policy_fallbacks is not None:
            fallback_model_group = None
            for item in content_policy_fallbacks:  # [{"gpt-3.5-turbo": ["gpt-4"]}]
                if list(item.keys())[0] == model:
                    fallback_model_group = item[model]
                    break

            if fallback_model_group is not None:
                return True
        elif self._has_default_fallbacks():  # default fallbacks set
            return True

        verbose_router_logger.debug(
            "Content Policy Error occurred. No available fallbacks. Returning original response. model={}, content_policy_fallbacks={}".format(
                model, content_policy_fallbacks
            )
        )
        return False

    def _get_healthy_deployments(self, model: str, parent_otel_span: Optional[Span]):
        _all_deployments: list = []
        try:
            _, _all_deployments = self._common_checks_available_deployment(  # type: ignore
                model=model,
            )
            if isinstance(_all_deployments, dict):
                return []
        except Exception:
            pass

        unhealthy_deployments = _get_cooldown_deployments(
            litellm_router_instance=self, parent_otel_span=parent_otel_span
        )
        unhealthy_set = set(unhealthy_deployments)
        healthy_deployments: list = [
            d for d in _all_deployments if d["model_info"]["id"] not in unhealthy_set
        ]
        healthy_deployments = self._filter_blocked_deployments(healthy_deployments)

        return healthy_deployments, _all_deployments

    async def _async_get_healthy_deployments(
        self, model: str, parent_otel_span: Optional[Span]
    ) -> Tuple[List[Dict], List[Dict]]:
        """
        Returns Tuple of:
        - Tuple[List[Dict], List[Dict]]:
            1. healthy_deployments: list of healthy deployments
            2. all_deployments: list of all deployments
        """
        _all_deployments: list = []
        try:
            _, _all_deployments = self._common_checks_available_deployment(  # type: ignore
                model=model,
            )
            if isinstance(_all_deployments, dict):
                return [], _all_deployments
        except Exception:
            pass

        unhealthy_deployments = await _async_get_cooldown_deployments(
            litellm_router_instance=self, parent_otel_span=parent_otel_span
        )
        # Convert to set for O(1) lookup instead of O(n)
        unhealthy_deployments_set = set(unhealthy_deployments)
        healthy_deployments: list = [
            d
            for d in _all_deployments
            if d["model_info"]["id"] not in unhealthy_deployments_set
        ]
        healthy_deployments = self._filter_blocked_deployments(healthy_deployments)
        return healthy_deployments, _all_deployments

    def routing_strategy_pre_call_checks(self, deployment: dict):
        """
        Mimics 'async_routing_strategy_pre_call_checks'

        Ensures consistent update rpm implementation for 'usage-based-routing-v2'

        Returns:
        - None

        Raises:
        - Rate Limit Exception - If the deployment is over it's tpm/rpm limits
        """
        for _callback in litellm.callbacks:
            if isinstance(_callback, CustomLogger):
                _callback.pre_call_check(deployment)

    async def async_routing_strategy_pre_call_checks(
        self,
        deployment: dict,
        parent_otel_span: Optional[Span],
        logging_obj: Optional[LiteLLMLogging] = None,
    ):
        """
        For usage-based-routing-v2, enables running rpm checks before the call is made, inside the semaphore.

        -> makes the calls concurrency-safe, when rpm limits are set for a deployment

        Returns:
        - None

        Raises:
        - Rate Limit Exception - If the deployment is over it's tpm/rpm limits
        """
        for _callback in litellm.callbacks:
            if isinstance(_callback, CustomLogger):
                try:
                    await _callback.async_pre_call_check(deployment, parent_otel_span)
                except litellm.RateLimitError as e:
                    ## LOG FAILURE EVENT
                    if logging_obj is not None:
                        asyncio.create_task(
                            logging_obj.async_failure_handler(
                                exception=e,
                                traceback_exception=traceback.format_exc(),
                                end_time=time.time(),
                            )
                        )
                        ## LOGGING
                        threading.Thread(
                            target=logging_obj.failure_handler,
                            args=(e, traceback.format_exc()),
                        ).start()  # log response
                    _set_cooldown_deployments(
                        litellm_router_instance=self,
                        exception_status=e.status_code,
                        original_exception=e,
                        deployment=deployment["model_info"]["id"],
                        time_to_cooldown=self.cooldown_time,
                    )
                    raise e
                except Exception as e:
                    ## LOG FAILURE EVENT
                    if logging_obj is not None:
                        asyncio.create_task(
                            logging_obj.async_failure_handler(
                                exception=e,
                                traceback_exception=traceback.format_exc(),
                                end_time=time.time(),
                            )
                        )
                        ## LOGGING
                        threading.Thread(
                            target=logging_obj.failure_handler,
                            args=(e, traceback.format_exc()),
                        ).start()  # log response
                    raise e

    async def async_callback_filter_deployments(
        self,
        model: str,
        healthy_deployments: List[dict],
        messages: Optional[List[AllMessageValues]],
        parent_otel_span: Optional[Span],
        request_kwargs: Optional[dict] = None,
        logging_obj: Optional[LiteLLMLogging] = None,
    ):
        """
        For usage-based-routing-v2, enables running rpm checks before the call is made, inside the semaphore.

        -> makes the calls concurrency-safe, when rpm limits are set for a deployment

        Returns:
        - None

        Raises:
        - Rate Limit Exception - If the deployment is over it's tpm/rpm limits
        """
        returned_healthy_deployments = healthy_deployments
        for _callback in litellm.callbacks:
            if isinstance(_callback, CustomLogger):
                try:
                    returned_healthy_deployments = (
                        await _callback.async_filter_deployments(
                            model=model,
                            healthy_deployments=returned_healthy_deployments,
                            messages=messages,
                            request_kwargs=request_kwargs,
                            parent_otel_span=parent_otel_span,
                        )
                    )
                except Exception as e:
                    ## LOG FAILURE EVENT
                    if logging_obj is not None:
                        asyncio.create_task(
                            logging_obj.async_failure_handler(
                                exception=e,
                                traceback_exception=traceback.format_exc(),
                                end_time=time.time(),
                            )
                        )
                        ## LOGGING
                        threading.Thread(
                            target=logging_obj.failure_handler,
                            args=(e, traceback.format_exc()),
                        ).start()  # log response
                    raise e
        return returned_healthy_deployments

    def _generate_model_id(self, model_group: str, litellm_params: dict):
        """
        Helper function to consistently generate the same id for a deployment

        - create a string from all the litellm params
        - hash
        - use hash as id
        """
        # Optimized: Use list and join instead of string concatenation in loop
        # This avoids creating many temporary string objects (O(n) vs O(n²) complexity)
        parts = [model_group]
        for k, v in litellm_params.items():
            if isinstance(k, str):
                parts.append(k)
            elif isinstance(k, dict):
                parts.append(json.dumps(k))
            else:
                parts.append(str(k))

            if isinstance(v, str):
                parts.append(v)
            elif isinstance(v, dict):
                parts.append(json.dumps(v))
            else:
                parts.append(str(v))

        concat_str = "".join(parts)
        hash_object = hashlib.sha256(concat_str.encode())

        return hash_object.hexdigest()

    @staticmethod
    def _inherit_builtin_cache_pricing(
        model_info: dict, backend_model: str, custom_llm_provider: Optional[str]
    ) -> None:
        """Fill missing cache pricing on a custom-priced deployment entry from
        the backend model's built-in cost map entry, so a deployment that
        only spells out ``input_cost_per_token``/``output_cost_per_token``
        does not silently bill cache_read/cache_creation at 0.

        User-specified cache fields always win; only ``None``/missing entries
        are inherited. No-op when the backend model has no canonical entry.
        """
        cache_fields = (
            "cache_creation_input_token_cost",
            "cache_creation_input_token_cost_above_1hr",
            "cache_creation_input_token_cost_above_200k_tokens",
            "cache_read_input_token_cost",
            "cache_read_input_token_cost_above_200k_tokens",
        )
        if all(model_info.get(f) is not None for f in cache_fields):
            return
        try:
            backend_info = litellm.get_model_info(
                model=backend_model, custom_llm_provider=custom_llm_provider
            )
        except Exception:
            return
        for field in cache_fields:
            if model_info.get(field) is None:
                backend_value = backend_info.get(field)
                if backend_value is not None:
                    model_info[field] = backend_value

    def _create_deployment(
        self,
        deployment_info: dict,
        _model_name: str,
        _litellm_params: dict,
        _model_info: dict,
    ) -> Optional[Deployment]:
        """
        Create a deployment object and add it to the model list

        If the deployment is not active for the current environment, it is ignored

        Returns:
        - Deployment: The deployment object
        - None: If the deployment is not active for the current environment (if 'supported_environments' is set in litellm_params)
        """
        try:
            litellm_params: LiteLLM_Params = LiteLLM_Params(**_litellm_params)
            deployment = Deployment(
                **deployment_info,
                model_name=_model_name,
                litellm_params=litellm_params,
                model_info=_model_info,
            )
            for field in CustomPricingLiteLLMParams.model_fields.keys():
                if deployment.litellm_params.get(field) is not None:
                    _model_info[field] = deployment.litellm_params[field]

            if _model_info.get("input_cost_per_token") is not None:
                Router._inherit_builtin_cache_pricing(
                    model_info=_model_info,
                    backend_model=deployment.litellm_params.model,
                    custom_llm_provider=deployment.litellm_params.custom_llm_provider,
                )

            ## REGISTER MODEL INFO IN LITELLM MODEL COST MAP
            model_id = deployment.model_info.id
            if model_id is not None:
                litellm.register_model(
                    model_cost={
                        model_id: _model_info,
                    }
                )

            ## OLD MODEL REGISTRATION ## Kept to prevent breaking changes
            _model_name = deployment.litellm_params.model
            if deployment.litellm_params.custom_llm_provider is not None:
                _model_name = (
                    deployment.litellm_params.custom_llm_provider + "/" + _model_name
                )

            # For the shared backend key, strip custom pricing fields so that
            # one deployment's pricing overrides don't pollute another
            # deployment sharing the same backend model name.
            # Each deployment's full pricing is already stored under its
            # unique model_id above.
            _custom_pricing_fields = CustomPricingLiteLLMParams.model_fields.keys()
            _shared_model_info = {
                k: v for k, v in _model_info.items() if k not in _custom_pricing_fields
            }
            _existing_shared_mode = (
                cast(Optional[dict], litellm.model_cost.get(_model_name, {})) or {}
            ).get("mode")
            _deployment_mode = _shared_model_info.get("mode")
            # Keep the built-in bridge mode stable for shared backend keys.
            # Multiple aliases can point at the same provider/model backend,
            # but their deployment-level overrides should not downgrade the
            # backend from responses -> chat via last-write-wins registration.
            # Only preserve in that specific direction so legitimate upgrades
            # (e.g. chat -> responses) and unrelated mode changes still apply,
            # and so a missing deployment mode does not silently clear the
            # existing shared backend mode.
            _is_responses_to_chat_downgrade = (
                _existing_shared_mode == "responses" and _deployment_mode == "chat"
            )
            _would_clear_existing_mode = (
                _existing_shared_mode is not None and _deployment_mode is None
            )
            if _is_responses_to_chat_downgrade or _would_clear_existing_mode:
                if _deployment_mode is not None:
                    verbose_router_logger.warning(
                        "Router: preserving existing mode=%s for shared backend "
                        "key %s instead of the deployment-specified mode=%s "
                        "(prevents alias registration from downgrading the "
                        "shared backend mode).",
                        _existing_shared_mode,
                        _model_name,
                        _deployment_mode,
                    )
                _shared_model_info["mode"] = _existing_shared_mode

            # Always register the (possibly mode-preserved) shared backend info.
            _backend_alias_cost = {_model_name: _shared_model_info}
            if "responses/" in _model_name:
                _stripped_model_name = _model_name.replace("responses/", "")
                _backend_alias_cost[_stripped_model_name] = _shared_model_info
            litellm.register_model(model_cost=_backend_alias_cost)

            ## Check if LLM Deployment is allowed for this deployment
            if (
                self.deployment_is_active_for_environment(deployment=deployment)
                is not True
            ):
                verbose_router_logger.warning(
                    f"Ignoring deployment {deployment.model_name} as it is not active for environment {deployment.model_info['supported_environments']}"
                )
                return None

            # Validate tag_regex patterns BEFORE adding the deployment so we never
            # have partially-initialised router state if a pattern is invalid.
            _tag_regex = deployment.litellm_params.get("tag_regex") or []
            for pattern in _tag_regex:
                try:
                    re.compile(pattern)
                except re.error as exc:
                    raise ValueError(
                        f"Invalid regex in tag_regex for model '{deployment.model_name}': {pattern!r} — {exc}"
                    ) from exc

            deployment = self._add_deployment(deployment=deployment)

            model = deployment.to_json(exclude_none=True)

            self._add_model_to_list_and_index_map(
                model=model, model_id=deployment.model_info.id
            )
            return deployment
        except Exception as e:
            if self.ignore_invalid_deployments:
                verbose_router_logger.exception(
                    f"Error creating deployment: {e}, ignoring and continuing with other deployments."
                )
                return None
            else:
                raise e

    def _is_auto_router_deployment(self, litellm_params: LiteLLM_Params) -> bool:
        """
        Check if the deployment is an auto-router deployment (semantic router).

        Returns True if the litellm_params model starts with "auto_router/"
        but NOT "auto_router/complexity_router" or "auto_router/adaptive_router"
        (which use the complexity-router and adaptive-router strategies).
        """
        if litellm_params.model.startswith("auto_router/complexity_router"):
            return False  # This is handled by complexity_router
        if litellm_params.model.startswith("auto_router/adaptive_router"):
            return False  # This is handled by adaptive_router
        if litellm_params.model.startswith("auto_router/quality_router"):
            return False  # This is handled by quality_router
        if litellm_params.model.startswith("auto_router/"):
            return True
        return False

    def init_auto_router_deployment(self, deployment: Deployment):
        """
        Initialize the auto-router deployment.

        This will initialize the auto-router and add it to the auto-routers dictionary.
        """
        from litellm.router_strategy.auto_router.auto_router import AutoRouter

        auto_router_config_path: Optional[str] = (
            deployment.litellm_params.auto_router_config_path
        )
        auto_router_config: Optional[str] = deployment.litellm_params.auto_router_config
        if auto_router_config_path is None and auto_router_config is None:
            raise ValueError(
                "auto_router_config_path or auto_router_config is required for auto-router deployments. Please set it in the litellm_params"
            )

        default_model: Optional[str] = (
            deployment.litellm_params.auto_router_default_model
        )
        if default_model is None:
            raise ValueError(
                "auto_router_default_model is required for auto-router deployments. Please set it in the litellm_params"
            )

        embedding_model: Optional[str] = (
            deployment.litellm_params.auto_router_embedding_model
        )
        if embedding_model is None:
            raise ValueError(
                "auto_router_embedding_model is required for auto-router deployments. Please set it in the litellm_params"
            )

        autor_router: AutoRouter = AutoRouter(
            model_name=deployment.model_name,
            auto_router_config_path=auto_router_config_path,
            auto_router_config=auto_router_config,
            default_model=default_model,
            embedding_model=embedding_model,
            litellm_router_instance=self,
        )
        if deployment.model_name in self.auto_routers:
            raise ValueError(
                f"Auto-router deployment {deployment.model_name} already exists. Please use a different model name."
            )
        self.auto_routers[deployment.model_name] = autor_router

    def _is_complexity_router_deployment(self, litellm_params: LiteLLM_Params) -> bool:
        """
        Check if the deployment is a complexity-router deployment.

        Returns True if the litellm_params model starts with "auto_router/complexity_router"
        """
        if litellm_params.model.startswith("auto_router/complexity_router"):
            return True
        return False

    def init_complexity_router_deployment(self, deployment: Deployment):
        """
        Initialize the complexity-router deployment.

        This will initialize the complexity-router and add it to the complexity-routers dictionary.
        """
        # Import here to avoid circular imports — ComplexityRouter is a CustomLogger
        # subclass that imports litellm internals which depend on router.py.
        # This matches the AutoRouter pattern in init_auto_router_deployment above.
        from litellm.router_strategy.complexity_router.complexity_router import (
            ComplexityRouter,
        )

        complexity_router_config: Optional[dict] = (
            deployment.litellm_params.complexity_router_config
        )

        default_model: Optional[str] = (
            deployment.litellm_params.complexity_router_default_model
        )

        # If no default model specified, try to get from config tiers
        if default_model is None and complexity_router_config:
            tiers = complexity_router_config.get("tiers", {})
            # Use MEDIUM tier as fallback default
            default_model = tiers.get("MEDIUM") or tiers.get("SIMPLE")

        if default_model is None:
            raise ValueError(
                "complexity_router_default_model is required for complexity-router deployments, "
                "or configure tiers in complexity_router_config. Please set it in the litellm_params"
            )

        complexity_router: ComplexityRouter = ComplexityRouter(
            model_name=deployment.model_name,
            default_model=default_model,
            litellm_router_instance=self,
            complexity_router_config=complexity_router_config,
        )
        if deployment.model_name in self.complexity_routers:
            raise ValueError(
                f"Complexity-router deployment {deployment.model_name} already exists. Please use a different model name."
            )
        self.complexity_routers[deployment.model_name] = complexity_router

    def _is_adaptive_router_deployment(self, litellm_params: LiteLLM_Params) -> bool:
        """True when this deployment opts in via the `auto_router/adaptive_router` model prefix."""
        return litellm_params.model.startswith("auto_router/adaptive_router")

    def _finalize_adaptive_router_if_configured(self) -> None:
        """Locate every adaptive-router deployment in the finalized model_list and
        build an AdaptiveRouter for each. Safe no-op when none are configured.
        Idempotent: skips any deployment whose model_name is already initialized."""
        # Drop any adaptive-router hooks left over from a previous Router
        # instance (e.g. after `/config/reload` replaced `llm_router`). Without
        # this, stale AdaptiveRouterPostCallHook callbacks from the old Router
        # remain wired up in `litellm.callbacks` and double-fire signal
        # recording for every request.
        from litellm.router_strategy.adaptive_router.hooks import (
            AdaptiveRouterPostCallHook,
        )

        for _cb_list in (
            litellm.callbacks,
            litellm.success_callback,
            litellm.failure_callback,
            litellm._async_success_callback,
            litellm._async_failure_callback,
        ):
            litellm.logging_callback_manager.remove_callbacks_by_type(
                _cb_list, AdaptiveRouterPostCallHook
            )

        for entry in self.model_list or []:
            lp = (
                entry.get("litellm_params")
                if isinstance(entry, dict)
                else entry.litellm_params
            )
            lp_model = (
                (lp.get("model") if isinstance(lp, dict) else lp.model) if lp else None
            )
            if not (lp_model and lp_model.startswith("auto_router/adaptive_router")):
                continue
            model_name = (
                entry.get("model_name") if isinstance(entry, dict) else entry.model_name
            )
            if not model_name or not lp:
                continue
            if model_name in self.adaptive_routers:
                continue
            deployment = Deployment(
                model_name=model_name,
                litellm_params=(
                    lp if not isinstance(lp, dict) else LiteLLM_Params(**lp)
                ),
                model_info=(
                    entry.get("model_info")
                    if isinstance(entry, dict)
                    else entry.model_info
                ),
            )
            self.init_adaptive_router_deployment(deployment=deployment)

    def init_adaptive_router_deployment(self, deployment: Deployment) -> None:
        """
        Build an AdaptiveRouter instance for this deployment and register its
        post-call hook. Multiple adaptive routers can coexist on a single Router,
        keyed by `deployment.model_name`.

        `model_to_prefs` and `model_to_cost` are derived from the OTHER models
        already registered in `self.model_list` whose `model_name` appears in
        `available_models`. Models not yet registered fall back to defaults.
        """
        # Local import: AdaptiveRouter -> hooks -> classifier all import litellm
        # internals which transitively import this module. (AGENTS.md exception clause.)
        from litellm.router_strategy.adaptive_router.adaptive_router import (
            AdaptiveRouter,
        )
        from litellm.router_strategy.adaptive_router.hooks import (
            AdaptiveRouterPostCallHook,
        )
        from litellm.types.router import (
            AdaptiveRouterConfig,
            AdaptiveRouterPreferences,
        )

        raw_config = deployment.litellm_params.adaptive_router_config
        if raw_config is None:
            raise ValueError(
                "adaptive_router_config is required for adaptive-router deployments."
            )

        config = AdaptiveRouterConfig(**raw_config)

        model_to_prefs: Dict[str, AdaptiveRouterPreferences] = {}
        model_to_cost: Dict[str, float] = {}
        # O(k) via the name→indices map: only touch deployments whose name
        # is listed in `available_models`, instead of scanning model_list.
        for name in config.available_models:
            indices = self.model_name_to_deployment_indices.get(name, [])
            if not indices:
                continue
            d = (self.model_list or [])[indices[0]]
            mi = d.get("model_info") if isinstance(d, dict) else d.model_info
            mi_dict: Dict[str, Any] = (
                mi if isinstance(mi, dict) else (mi.model_dump() if mi else {})
            )
            prefs_raw = mi_dict.get("adaptive_router_preferences")
            if prefs_raw is not None:
                model_to_prefs[name] = AdaptiveRouterPreferences(**prefs_raw)

            # `input_cost_per_token` is a LiteLLM_Params field per types/router.py.
            lp = d.get("litellm_params") if isinstance(d, dict) else d.litellm_params
            lp_dict: Dict[str, Any] = (
                lp if isinstance(lp, dict) else (lp.model_dump() if lp else {})
            )
            cost = lp_dict.get("input_cost_per_token")
            if cost is not None:
                model_to_cost[name] = float(cost)

        if deployment.model_name in self.adaptive_routers:
            raise ValueError(
                f"Adaptive-router deployment {deployment.model_name} already exists. Please use a different model name."
            )

        adaptive_router = AdaptiveRouter(
            router_name=deployment.model_name,
            config=config,
            model_to_prefs=model_to_prefs,
            model_to_cost=model_to_cost,
        )
        self.adaptive_routers[deployment.model_name] = adaptive_router
        litellm.logging_callback_manager.add_litellm_callback(
            AdaptiveRouterPostCallHook(adaptive_router=adaptive_router)
        )
        verbose_router_logger.info(
            "AdaptiveRouter[%s] initialized with %d models",
            deployment.model_name,
            len(config.available_models),
        )

    def _is_quality_router_deployment(self, litellm_params: LiteLLM_Params) -> bool:
        """
        Check if the deployment is a quality-router deployment.

        Returns True if the litellm_params model starts with "auto_router/quality_router".
        """
        if litellm_params.model.startswith("auto_router/quality_router"):
            return True
        return False

    def init_quality_router_deployment(self, deployment: Deployment):
        """
        Initialize the quality-router deployment.

        Resolves the default model from either `quality_router_default_model` or
        `quality_router_config["default_model"]`, then instantiates the
        QualityRouter and stores it in `self.quality_routers`.
        """
        # Import here to mirror the AutoRouter / ComplexityRouter init pattern
        # and avoid circular imports.
        from litellm.router_strategy.quality_router.quality_router import (
            QualityRouter,
        )

        quality_router_config: Optional[dict] = (
            deployment.litellm_params.quality_router_config
        )

        default_model: Optional[str] = (
            deployment.litellm_params.quality_router_default_model
        )
        if default_model is None and quality_router_config:
            default_model = quality_router_config.get("default_model")

        if default_model is None:
            raise ValueError(
                "quality_router_default_model is required for quality-router deployments, "
                "or set default_model in quality_router_config. Please configure it in the litellm_params"
            )

        quality_router: QualityRouter = QualityRouter(
            model_name=deployment.model_name,
            default_model=default_model,
            litellm_router_instance=self,
            quality_router_config=quality_router_config,
        )
        if deployment.model_name in self.quality_routers:
            raise ValueError(
                f"Quality-router deployment {deployment.model_name} already exists. Please use a different model name."
            )
        self.quality_routers[deployment.model_name] = quality_router

    def deployment_is_active_for_environment(self, deployment: Deployment) -> bool:
        """
        Function to check if a llm deployment is active for a given environment. Allows using the same config.yaml across multople environments

        Requires `LITELLM_ENVIRONMENT` to be set in .env. Valid values for environment:
            - development
            - staging
            - production

        Raises:
        - ValueError: If LITELLM_ENVIRONMENT is not set in .env or not one of the valid values
        - ValueError: If supported_environments is not set in model_info or not one of the valid values
        """
        if (
            deployment.model_info is None
            or "supported_environments" not in deployment.model_info
            or deployment.model_info["supported_environments"] is None
        ):
            return True
        litellm_environment = get_secret_str(secret_name="LITELLM_ENVIRONMENT")
        if litellm_environment is None:
            raise ValueError(
                "Set 'supported_environments' for model but not 'LITELLM_ENVIRONMENT' set in .env"
            )

        if litellm_environment not in VALID_LITELLM_ENVIRONMENTS:
            raise ValueError(
                f"LITELLM_ENVIRONMENT must be one of {VALID_LITELLM_ENVIRONMENTS}. but set as: {litellm_environment}"
            )

        for _env in deployment.model_info["supported_environments"]:
            if _env not in VALID_LITELLM_ENVIRONMENTS:
                raise ValueError(
                    f"supported_environments must be one of {VALID_LITELLM_ENVIRONMENTS}. but set as: {_env} for deployment: {deployment}"
                )

        if litellm_environment in deployment.model_info["supported_environments"]:
            return True
        return False

    def set_model_list(self, model_list: list):
        original_model_list = copy.deepcopy(model_list)
        self.model_list = []
        self.model_id_to_deployment_index_map = {}  # Reset the index
        self.model_name_to_deployment_indices = {}  # Reset the model_name index
        self.team_model_to_deployment_indices = {}  # Reset the team_model index
        # Reset per-strategy router registries so hot-reload doesn't leave
        # stale routers pointing at the old model_list.
        self.quality_routers = {}
        self.complexity_routers = {}
        self.auto_routers = {}
        self._invalidate_model_group_info_cache()
        self._invalidate_access_groups_cache()
        # we add api_base/api_key each model so load balancing between azure/gpt on api_base1 and api_base2 works

        for model in original_model_list:
            _model_name = model.pop("model_name")
            _litellm_params = model.pop("litellm_params")
            ## check if litellm params in os.environ
            if isinstance(_litellm_params, dict):
                for k, v in _litellm_params.items():
                    if isinstance(v, str) and v.startswith("os.environ/"):
                        _litellm_params[k] = get_secret(v)

            _model_info: dict = model.pop("model_info", {})

            # check if model info has id
            if "id" not in _model_info:
                _id = self._generate_model_id(_model_name, _litellm_params)
                _model_info["id"] = _id

            if _litellm_params.get("organization", None) is not None and isinstance(
                _litellm_params["organization"], list
            ):  # Addresses https://github.com/BerriAI/litellm/issues/3949
                for org in _litellm_params["organization"]:
                    _litellm_params["organization"] = org
                    self._create_deployment(
                        deployment_info=model,
                        _model_name=_model_name,
                        _litellm_params=_litellm_params,
                        _model_info=_model_info,
                    )
            else:
                self._create_deployment(
                    deployment_info=model,
                    _model_name=_model_name,
                    _litellm_params=_litellm_params,
                    _model_info=_model_info,
                )

        verbose_router_logger.debug(
            f"\nInitialized Model List {self.get_model_names()}"
        )
        self.model_names = {m["model_name"] for m in model_list}

        # Note: model_name_to_deployment_indices is already built incrementally
        # by _create_deployment -> _add_model_to_list_and_index_map

        # Deferred: build the AdaptiveRouter strategy now that all underlying
        # deployments have been registered.
        self._finalize_adaptive_router_if_configured()

    def _add_deployment(self, deployment: Deployment) -> Deployment:
        import os

        #### VALIDATE MODEL ########
        # Check if this is a prompt management model before validating as LLM provider
        litellm_model = deployment.litellm_params.model
        is_prompt_management_model = False

        if "/" in litellm_model:
            split_litellm_model = litellm_model.split("/")[0]
            if split_litellm_model in litellm._known_custom_logger_compatible_callbacks:
                is_prompt_management_model = True

        if is_prompt_management_model:
            # For prompt management models, skip LLM provider validation
            # The actual model will be resolved at runtime from the prompt file
            _model = litellm_model
            custom_llm_provider = None
            dynamic_api_key = None
            api_base = None
        else:
            # check if model provider in supported providers
            (
                _model,
                custom_llm_provider,
                dynamic_api_key,
                api_base,
            ) = litellm.get_llm_provider(
                model=deployment.litellm_params.model,
                custom_llm_provider=deployment.litellm_params.get(
                    "custom_llm_provider", None
                ),
            )
            # done reading model["litellm_params"]
            # Check if provider is supported: either in enum or JSON-configured
            if (
                custom_llm_provider not in litellm.provider_list
                and not JSONProviderRegistry.exists(custom_llm_provider)
            ):
                raise Exception(f"Unsupported provider - {custom_llm_provider}")

        #### DEPLOYMENT NAMES INIT ########
        self.deployment_names.append(deployment.litellm_params.model)
        ############ Users can either pass tpm/rpm as a litellm_param or a router param ###########
        # for get_available_deployment, we use the litellm_param["rpm"]
        # in this snippet we also set rpm to be a litellm_param
        if (
            deployment.litellm_params.rpm is None
            and getattr(deployment, "rpm", None) is not None
        ):
            deployment.litellm_params.rpm = getattr(deployment, "rpm")

        if (
            deployment.litellm_params.tpm is None
            and getattr(deployment, "tpm", None) is not None
        ):
            deployment.litellm_params.tpm = getattr(deployment, "tpm")

        # Check if user is trying to use model_name == "*"
        # this is a catch all model for their specific api key
        # if deployment.model_name == "*":
        #     if deployment.litellm_params.model == "*":
        #         # user wants to pass through all requests to litellm.acompletion for unknown deployments
        #         self.router_general_settings.pass_through_all_models = True
        #     else:
        #         self.default_deployment = deployment.to_json(exclude_none=True)
        # Check if user is using provider specific wildcard routing
        # example model_name = "databricks/*" or model_name = "anthropic/*"
        if "*" in deployment.model_name:
            # store this as a regex pattern - all deployments matching this pattern will be sent to this deployment
            # Store deployment.model_name as a regex pattern
            self.pattern_router.add_pattern(
                deployment.model_name, deployment.to_json(exclude_none=True)
            )
            if deployment.model_info.id:
                self.provider_default_deployment_ids.append(deployment.model_info.id)

        _team_id = deployment.model_info.get("team_id")
        _team_public_model_name = deployment.model_info.get("team_public_model_name")
        if (
            _team_id is not None
            and _team_public_model_name is not None
            and "*" in _team_public_model_name
        ):
            if _team_id not in self.team_pattern_routers:
                self.team_pattern_routers[_team_id] = PatternMatchRouter()
            self.team_pattern_routers[_team_id].add_pattern(
                _team_public_model_name, deployment.to_json(exclude_none=True)
            )

        # Azure GPT-Vision Enhancements, users can pass os.environ/
        data_sources = deployment.litellm_params.get("dataSources", []) or []

        for data_source in data_sources:
            params = data_source.get("parameters", {})
            for param_key in ["endpoint", "key"]:
                # if endpoint or key set for Azure GPT Vision Enhancements, check if it's an env var
                if param_key in params and params[param_key].startswith("os.environ/"):
                    env_name = params[param_key].replace("os.environ/", "")
                    params[param_key] = os.environ.get(env_name, "")

        # # init OpenAI, Azure clients
        # InitalizeOpenAISDKClient.set_client(
        #     litellm_router_instance=self, model=deployment.to_json(exclude_none=True)
        # )

        if custom_llm_provider is not None:
            self._initialize_deployment_for_pass_through(
                deployment=deployment,
                custom_llm_provider=custom_llm_provider,
                model=deployment.litellm_params.model,
            )

        #########################################################
        # Check if this is an auto-router deployment
        #########################################################
        if self._is_auto_router_deployment(litellm_params=deployment.litellm_params):
            self.init_auto_router_deployment(deployment=deployment)

        #########################################################
        # Check if this is a complexity-router deployment
        #########################################################
        if self._is_complexity_router_deployment(
            litellm_params=deployment.litellm_params
        ):
            self.init_complexity_router_deployment(deployment=deployment)

        # NOTE: adaptive-router deployments are deferred to the end of
        # set_model_list() because their init needs visibility into the OTHER
        # deployments listed in `available_models` (which may not yet have
        # been processed when this one is created).
        #########################################################
        # Check if this is a quality-router deployment
        #########################################################
        if self._is_quality_router_deployment(litellm_params=deployment.litellm_params):
            self.init_quality_router_deployment(deployment=deployment)

        return deployment

    def _initialize_deployment_for_pass_through(
        self, deployment: Deployment, custom_llm_provider: str, model: str
    ):
        """
        Optional: Initialize deployment for pass-through endpoints if `deployment.litellm_params.use_in_pass_through` is True

        Each provider uses diff .env vars for pass-through endpoints, this helper uses the deployment credentials to set the .env vars for pass-through endpoints
        """
        if deployment.litellm_params.use_in_pass_through is True:
            from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import (
                passthrough_endpoint_router,
            )

            if deployment.litellm_params.litellm_credential_name is not None:
                credential_values = CredentialAccessor.get_credential_values(
                    deployment.litellm_params.litellm_credential_name
                )
            else:
                credential_values = {}

            if custom_llm_provider == "vertex_ai":
                vertex_project = (
                    credential_values.get("vertex_project")
                    or deployment.litellm_params.vertex_project
                )
                vertex_location = (
                    credential_values.get("vertex_location")
                    or deployment.litellm_params.vertex_location
                )
                vertex_credentials = (
                    credential_values.get("vertex_credentials")
                    or deployment.litellm_params.vertex_credentials
                )

                if vertex_project is None or vertex_location is None:
                    raise ValueError(
                        "vertex_project, and vertex_location must be set in litellm_params for pass-through endpoints."
                    )
                passthrough_endpoint_router.add_vertex_credentials(
                    project_id=vertex_project,
                    location=vertex_location,
                    vertex_credentials=vertex_credentials,
                )
            else:
                api_base = (
                    credential_values.get("api_base")
                    or deployment.litellm_params.api_base
                )
                api_key = (
                    credential_values.get("api_key")
                    or deployment.litellm_params.api_key
                )
                if api_key is None:
                    verbose_router_logger.debug(
                        "Skipping pass-through credential setup for deployment model=%s, custom_llm_provider=%s; no api_key set. Providers like bedrock resolve credentials at request time.",
                        model,
                        custom_llm_provider,
                    )
                    return
                passthrough_endpoint_router.set_pass_through_credentials(
                    custom_llm_provider=custom_llm_provider,
                    api_base=api_base,
                    api_key=api_key,
                )
            pass
        pass

    def add_deployment(self, deployment: Deployment) -> Optional[Deployment]:
        """
        Parameters:
        - deployment: Deployment - the deployment to be added to the Router

        Returns:
        - The added deployment
        - OR None (if deployment already exists)
        """
        # check if deployment already exists

        _deployment_model_id = deployment.model_info.id
        if _deployment_model_id and self.has_model_id(_deployment_model_id):
            return None

        # add to model list
        _deployment = deployment.to_json(exclude_none=True)
        # initialize client
        self._add_deployment(deployment=deployment)

        _model_info_dict: dict = deployment.model_info.model_dump(exclude_none=True)
        for field in CustomPricingLiteLLMParams.model_fields.keys():
            field_value = deployment.litellm_params.get(field)
            if field_value is not None:
                _model_info_dict[field] = field_value

        if _model_info_dict.get("input_cost_per_token") is not None:
            Router._inherit_builtin_cache_pricing(
                model_info=_model_info_dict,
                backend_model=deployment.litellm_params.model,
                custom_llm_provider=deployment.litellm_params.custom_llm_provider,
            )

        # Register custom pricing in litellm.model_cost.
        # Mirrors _create_deployment() logic to ensure dynamically-added deployments
        # (e.g., loaded from DB) also have their custom pricing registered.
        # Without this, _is_model_cost_zero() cannot detect explicitly-configured
        # zero-cost models, causing budget checks to block free models.
        _model_id = deployment.model_info.id
        if _model_id is not None:
            litellm.register_model(model_cost={_model_id: _model_info_dict})

        ## REGISTER MODEL INFO IN LITELLM MODEL COST MAP
        ## OLD MODEL REGISTRATION ## Kept to prevent breaking changes
        _model_name = deployment.litellm_params.model
        if deployment.litellm_params.custom_llm_provider is not None:
            _model_name = (
                deployment.litellm_params.custom_llm_provider + "/" + _model_name
            )

        # For the shared backend key, strip custom pricing fields so that
        # one deployment's pricing overrides don't pollute another
        # deployment sharing the same backend model name.
        # Each deployment's full pricing is already stored under its
        # unique model_id above (when present).
        _custom_pricing_fields = CustomPricingLiteLLMParams.model_fields.keys()
        _shared_model_info = {
            k: v for k, v in _model_info_dict.items() if k not in _custom_pricing_fields
        }
        _backend_alias_cost = {_model_name: _shared_model_info}
        if "responses/" in _model_name:
            _stripped_model_name = _model_name.replace("responses/", "")
            _backend_alias_cost[_stripped_model_name] = _shared_model_info
        litellm.register_model(model_cost=_backend_alias_cost)

        # add to model names
        self._add_model_to_list_and_index_map(
            model=_deployment, model_id=deployment.model_info.id
        )
        self.model_names.add(deployment.model_name)
        self._sync_deployment_budget_config(deployment=deployment)
        return deployment

    def _update_deployment_indices_after_removal(
        self, model_id: str, removal_idx: int
    ) -> None:
        """
        Helper method to update deployment indices after a deployment has been removed from model_list.

        Parameters:
        - model_id: str - the id of the deployment that was removed
        - removal_idx: int - the index where the deployment was removed from model_list
        """
        # Update indices for all models after the removed one
        for deployment_id, idx in self.model_id_to_deployment_index_map.items():
            if idx > removal_idx:
                self.model_id_to_deployment_index_map[deployment_id] = idx - 1
        # Remove the deleted model from index
        if model_id in self.model_id_to_deployment_index_map:
            del self.model_id_to_deployment_index_map[model_id]

        # Update model_name_to_deployment_indices
        for model_name, indices in list(self.model_name_to_deployment_indices.items()):
            # Build new list without mutating the original
            updated_indices = []
            for idx in indices:
                if idx == removal_idx:
                    # Skip the removed index
                    continue
                elif idx > removal_idx:
                    # Decrement indices after removal
                    updated_indices.append(idx - 1)
                else:
                    # Keep indices before removal unchanged
                    updated_indices.append(idx)

            # Update or remove the entry
            if len(updated_indices) > 0:
                self.model_name_to_deployment_indices[model_name] = updated_indices
            else:
                del self.model_name_to_deployment_indices[model_name]

        # Update team_model_to_deployment_indices
        for key, indices in list(self.team_model_to_deployment_indices.items()):
            # Build new list without mutating the original
            updated_indices = []
            for idx in indices:
                if idx == removal_idx:
                    # Skip the removed index
                    continue
                elif idx > removal_idx:
                    # Decrement indices after removal
                    updated_indices.append(idx - 1)
                else:
                    # Keep indices before removal unchanged
                    updated_indices.append(idx)

            # Update or remove the entry
            if len(updated_indices) > 0:
                self.team_model_to_deployment_indices[key] = updated_indices
            else:
                del self.team_model_to_deployment_indices[key]

    def _update_team_model_index(self, model: dict, idx: int) -> None:
        """
        Helper to update team_model_to_deployment_indices for a single deployment.

        Parameters:
        - model: dict - the deployment to index
        - idx: int - the index in model_list
        """
        team_id = (model.get("model_info") or {}).get("team_id")
        team_public_model_name = (model.get("model_info") or {}).get(
            "team_public_model_name"
        )
        if team_id and team_public_model_name:
            key = (team_id, team_public_model_name)
            if key not in self.team_model_to_deployment_indices:
                self.team_model_to_deployment_indices[key] = []
            if idx not in self.team_model_to_deployment_indices[key]:
                self.team_model_to_deployment_indices[key].append(idx)

    def _add_model_to_list_and_index_map(
        self, model: dict, model_id: Optional[str] = None
    ) -> None:
        """
        Helper method to add a model to the model_list and update both indices.

        Parameters:
        - model: dict - the model to add to the list
        - model_id: Optional[str] - the model ID to use for indexing. If None, will try to get from model["model_info"]["id"]
        """
        idx = len(self.model_list)
        self.model_list.append(model)
        self._invalidate_model_group_info_cache()
        self._invalidate_access_groups_cache()

        # Update model_id index for O(1) lookup
        if model_id is not None:
            self.model_id_to_deployment_index_map[model_id] = idx
        elif model.get("model_info", {}).get("id") is not None:
            self.model_id_to_deployment_index_map[model["model_info"]["id"]] = idx

        # Update model_name index for O(1) lookup
        model_name = model.get("model_name")
        if model_name:
            if model_name not in self.model_name_to_deployment_indices:
                self.model_name_to_deployment_indices[model_name] = []
            self.model_name_to_deployment_indices[model_name].append(idx)

        # Update team_model index for O(1) team-scoped lookup
        self._update_team_model_index(model, idx)

    def upsert_deployment(self, deployment: Deployment) -> Optional[Deployment]:
        """
        Add or update deployment
        Parameters:
        - deployment: Deployment - the deployment to be added to the Router

        Returns:
        - The added/updated deployment
        """
        try:
            # check if deployment already exists
            _deployment_model_id = deployment.model_info.id or ""

            _deployment_on_router: Optional[Deployment] = self.get_deployment(
                model_id=_deployment_model_id
            )
            if _deployment_on_router is not None:
                # deployment with this model_id exists on the router
                if (
                    deployment.litellm_params == _deployment_on_router.litellm_params
                    and deployment.model_info == _deployment_on_router.model_info
                ):
                    # No need to update
                    return None

                # if there is a new litellm param -> then update the deployment
                # remove the previous deployment
                removal_idx: Optional[int] = None
                deployment_id = deployment.model_info.id
                deployment_fast_mapping = self.model_id_to_deployment_index_map

                if deployment_id in deployment_fast_mapping:
                    removal_idx = deployment_fast_mapping[deployment_id]

                    if removal_idx is not None:
                        self.model_list.pop(removal_idx)
                        self._invalidate_model_group_info_cache()
                        self._invalidate_access_groups_cache()
                        self._update_deployment_indices_after_removal(
                            model_id=deployment_id, removal_idx=removal_idx
                        )

            # if the model_id is not in router
            self.add_deployment(deployment=deployment)
            return deployment
        except Exception as e:
            if self.ignore_invalid_deployments:
                verbose_router_logger.debug(
                    f"Error upserting deployment: {e}, ignoring and continuing with other deployments."
                )
                return None
            else:
                raise e

    def delete_deployment(self, id: str) -> Optional[Deployment]:
        """
        Parameters:
        - id: str - the id of the deployment to be deleted

        Returns:
        - The deleted deployment
        - OR None (if deleted deployment not found)
        """
        deployment_idx = None
        if id in self.model_id_to_deployment_index_map:
            deployment_idx = self.model_id_to_deployment_index_map[id]

        try:
            if deployment_idx is not None:
                # Pop the item from the list first
                item = self.model_list.pop(deployment_idx)
                self._invalidate_model_group_info_cache()
                self._invalidate_access_groups_cache()
                self._update_deployment_indices_after_removal(
                    model_id=id, removal_idx=deployment_idx
                )
                _budget_limiter = self._get_router_deployment_budget_limiter()
                if _budget_limiter is not None:
                    _budget_limiter.unregister_deployment_budget(model_id=id)
                return item
            else:
                return None
        except Exception:
            return None

    def _get_router_deployment_budget_limiter(
        self,
    ) -> Optional[RouterBudgetLimiting]:
        """
        Return the router's deployment-budget callback.

        Uses exact-type matching so proxy subclasses (e.g. virtual-key model budgets)
        registered on litellm.callbacks are not mistaken for router deployment budgets.
        """
        if self.router_budget_logger is not None:
            return self.router_budget_logger

        if self.optional_callbacks:
            for _cb in self.optional_callbacks:
                if type(_cb) is RouterBudgetLimiting:
                    self.router_budget_logger = _cb
                    return _cb
        return None

    def _deployment_has_budget_limits(self, deployment: Deployment) -> bool:
        return (
            deployment.litellm_params.get("max_budget") is not None
            and deployment.litellm_params.get("budget_duration") is not None
            and deployment.model_info.id is not None
        )

    def _sync_deployment_budget_config(self, deployment: Deployment) -> None:
        model_id = deployment.model_info.id
        if model_id is None:
            return

        _budget_limiter = self._get_router_deployment_budget_limiter()

        if not self._deployment_has_budget_limits(deployment=deployment):
            if _budget_limiter is not None:
                _budget_limiter.unregister_deployment_budget(model_id=model_id)
            return

        if _budget_limiter is None:
            self.add_optional_pre_call_checks(
                optional_pre_call_checks=["router_budget_limiting"]
            )
            _budget_limiter = self._get_router_deployment_budget_limiter()

        if _budget_limiter is not None:
            _budget_limiter.register_deployment_budget(
                deployment=deployment.to_json(exclude_none=True)
            )

    def get_deployment(self, model_id: str) -> Optional[Deployment]:
        """
        Returns -> Deployment or None

        Raise Exception -> if model found in invalid format
        """
        # Use O(1) lookup via model_id_to_deployment_index_map only
        if model_id in self.model_id_to_deployment_index_map:
            idx = self.model_id_to_deployment_index_map[model_id]
            model = self.model_list[idx]
            if isinstance(model, dict):
                return Deployment(**model)
            elif isinstance(model, Deployment):
                return model
            else:
                raise Exception("Model invalid format - {}".format(type(model)))

        return None

    def get_deployment_credentials(self, model_id: str) -> Optional[dict]:
        """
        Returns -> dict of credentials for a given model id.

        Returns None if the deployment is paused via `LiteLLM_ProxyModelTable.blocked`,
        so file/batch/passthrough callers that resolve credentials directly cannot keep
        using a paused deployment.
        """
        deployment = self.get_deployment(model_id=model_id)
        if deployment is None or self._is_deployment_blocked(deployment):
            return None
        return CredentialLiteLLMParams(
            **deployment.litellm_params.model_dump(exclude_none=True)
        ).model_dump(exclude_none=True)

    def get_deployment_by_model_group_name(
        self, model_group_name: str
    ) -> Optional[Deployment]:
        """
        Returns -> Deployment or None

        Raise Exception -> if model found in invalid format

        Optimized with O(1) index lookup instead of O(n) linear scan.
        """
        # O(1) lookup in model_name index
        if model_group_name in self.model_name_to_deployment_indices:
            indices = self.model_name_to_deployment_indices[model_group_name]
            if indices:
                # Return first deployment for this model_name
                model = self.model_list[indices[0]]
                if isinstance(model, dict):
                    return Deployment(**model)
                elif isinstance(model, Deployment):
                    return model
                else:
                    raise Exception("Model Name invalid - {}".format(type(model)))
        return None

    def get_deployment_credentials_with_provider(
        self, model_id: str
    ) -> Optional[Dict[str, Any]]:
        """
        Get API credentials and provider info from a model name in model_list.
        Useful for passthrough endpoints (files, batches, etc.) that need credentials.

        This method tries to find a deployment by model_id first, and if not found,
        it tries to find by model_group_name (model_name).

        Args:
            model_id: Model ID or model name from model_list (e.g., "gpt-4o-litellm")

        Returns:
            Dictionary containing api_key, api_base, custom_llm_provider, etc.
            Returns None if model not found, or if the resolved deployment is
            paused via `LiteLLM_ProxyModelTable.blocked` (so passthrough callers
            cannot bypass an admin pause by resolving credentials directly).

        Example:
            credentials = router.get_deployment_credentials_with_provider("gpt-4o-litellm")
            # Returns: {"api_key": "sk-...", "custom_llm_provider": "openai", ...}
        """
        # Try to get deployment by model_id first
        deployment = self.get_deployment(model_id=model_id)

        # If not found, try by model_group_name
        if deployment is None:
            deployment = self.get_deployment_by_model_group_name(
                model_group_name=model_id
            )

        # If still not found, check for wildcard pattern matches
        if deployment is None:
            potential_wildcard_models = self.pattern_router.route(model_id) or []
            if potential_wildcard_models:
                # Use the first matching wildcard deployment
                deployment_dict = potential_wildcard_models[0]
                if isinstance(deployment_dict, dict):
                    deployment = Deployment(**deployment_dict)
                elif isinstance(deployment_dict, Deployment):
                    deployment = deployment_dict

        if deployment is None or self._is_deployment_blocked(deployment):
            return None

        # Get basic credentials
        credentials = CredentialLiteLLMParams(
            **deployment.litellm_params.model_dump(exclude_none=True)
        ).model_dump(exclude_none=True)

        # Resolve litellm_credential_name to actual credentials
        if deployment.litellm_params.litellm_credential_name is not None:
            credential_values = CredentialAccessor.get_credential_values(
                deployment.litellm_params.litellm_credential_name
            )
            if not credential_values:
                verbose_router_logger.warning(
                    f"Credential '{deployment.litellm_params.litellm_credential_name}' not found in credential_list"
                )
            credentials.update(credential_values)
            # Remove the credential name since we've resolved it
            credentials.pop("litellm_credential_name", None)

        # Add custom_llm_provider
        if deployment.litellm_params.custom_llm_provider:
            credentials["custom_llm_provider"] = (
                deployment.litellm_params.custom_llm_provider
            )
        elif "/" in deployment.litellm_params.model:
            # Extract provider from "provider/model" format
            credentials["custom_llm_provider"] = deployment.litellm_params.model.split(
                "/"
            )[0]
        else:
            credentials["custom_llm_provider"] = "openai"  # default

        return credentials

    @overload
    def get_router_model_info(
        self,
        deployment: Union[dict, "Deployment"],
        received_model_name: str,
        id: None = None,
    ) -> ModelMapInfo:
        pass

    @overload
    def get_router_model_info(
        self, deployment: None, received_model_name: str, id: str
    ) -> ModelMapInfo:
        pass

    def get_router_model_info(
        self,
        deployment: Optional[Union[dict, "Deployment"]],
        received_model_name: str,
        id: Optional[str] = None,
    ) -> ModelMapInfo:
        """
        For a given model id, return the model info (max tokens, input cost, output cost, etc.).

        Augment litellm info with additional params set in `model_info`.

        For azure models, ignore the `model:`. Only set max tokens, cost values if base_model is set.

        Returns
        - ModelInfo - If found -> typed dict with max tokens, input cost, etc.

        Raises:
        - ValueError -> If model is not mapped yet
        """
        if id is not None:
            _deployment = self.get_deployment(model_id=id)
            if _deployment is not None:
                deployment = _deployment

        if deployment is None:
            raise ValueError("Deployment not found")

        ## GET BASE MODEL
        base_model = (deployment.get("model_info") or {}).get("base_model", None)
        if base_model is None:
            base_model = (deployment.get("litellm_params") or {}).get(
                "base_model", None
            )

        model = base_model

        ## GET PROVIDER - reuse LiteLLM_Params if already constructed
        litellm_params_data = deployment.get("litellm_params")
        litellm_params: LiteLLM_Params
        if isinstance(litellm_params_data, LiteLLM_Params):
            litellm_params = litellm_params_data
        elif isinstance(litellm_params_data, dict) and "model" in litellm_params_data:
            litellm_params = LiteLLM_Params(**litellm_params_data)
        else:
            raise ValueError(
                f"Deployment missing valid litellm_params. "
                f"Got: {type(litellm_params_data).__name__}, "
                f"deployment_id: {(deployment.get('model_info') or {}).get('id', 'unknown')}"
            )
        _model, custom_llm_provider, _, _ = litellm.get_llm_provider(
            model=litellm_params.model,
            litellm_params=litellm_params,
        )

        ## SET MODEL TO 'model=' - if base_model is None + not azure
        if custom_llm_provider == "azure" and base_model is None:
            verbose_router_logger.error(
                f"Could not identify azure model '{_model}'. Set azure 'base_model' for accurate max tokens, cost tracking, etc.- https://docs.litellm.ai/docs/proxy/cost_tracking#spend-tracking-for-azure-openai-models"
            )
        elif custom_llm_provider != "azure":
            model = _model

            if "*" in model:  # only call pattern_router for wildcard models
                potential_models = self.pattern_router.route(received_model_name)
                if potential_models is not None:
                    for potential_model in potential_models:
                        try:
                            if (potential_model.get("model_info") or {}).get("id") == (
                                deployment.get("model_info") or {}
                            ).get("id"):
                                model = (
                                    potential_model.get("litellm_params") or {}
                                ).get("model")
                                break
                        except Exception:
                            pass

        ## GET LITELLM MODEL INFO - raises exception, if model is not mapped
        if model is None:
            # Handle case where base_model is None (e.g., Azure models without base_model set)
            # Use the original model from litellm_params
            model = _model

        if not model.startswith("{}/".format(custom_llm_provider)):
            model_info_name = "{}/{}".format(custom_llm_provider, model)
        else:
            model_info_name = model

        model_info = litellm.get_model_info(model=model_info_name)

        ## CHECK USER SET MODEL INFO
        user_model_info = deployment.get("model_info") or {}

        if model_info is not None:
            model_info.update(cast(ModelInfo, user_model_info))

        return model_info

    def get_model_info(self, id: str) -> Optional[dict]:
        """
        For a given model id, return the model info

        Returns
        - dict: the model in list with 'model_name', 'litellm_params', Optional['model_info']
        - None: could not find deployment in list

        Optimized with O(1) index lookup instead of O(n) linear scan.
        """
        # O(1) lookup via model_id_to_deployment_index_map
        if id in self.model_id_to_deployment_index_map:
            idx = self.model_id_to_deployment_index_map[id]
            return self.model_list[idx]
        return None

    def get_model_group(self, id: str) -> Optional[List]:
        """
        Return list of all models in the same model group as that model id
        """

        model_info = self.get_model_info(id=id)
        if model_info is None:
            return None

        model_name = model_info["model_name"]
        return self.get_model_list(model_name=model_name)

    def get_deployment_model_info(
        self, model_id: str, model_name: str
    ) -> Optional[ModelInfo]:
        """
        For a given model id, return the model info

        1. Check if model_id is in model info
        2. If not, check if litellm model name is in model info
        3. If not, return None
        """
        from litellm.utils import _update_dictionary

        model_info: Optional[ModelInfo] = None
        custom_model_info: Optional[dict] = None
        litellm_model_name_model_info: Optional[ModelInfo] = None

        try:
            custom_model_info = litellm.model_cost.get(model_id)
        except Exception:
            pass

        try:
            litellm_model_name_model_info = litellm.get_model_info(model=model_name)
        except Exception:
            pass

        ## check for base model
        try:
            if custom_model_info is not None:
                base_model = custom_model_info.get("base_model", None)
                if base_model is not None:
                    ## update litellm model info with base model info
                    base_model_info = litellm.get_model_info(model=base_model)
                    if base_model_info is not None:
                        custom_model_info = custom_model_info or {}
                        # Base model provides defaults, custom model info overrides
                        custom_model_info = _update_dictionary(
                            cast(dict, base_model_info),
                            custom_model_info,
                        )
        except Exception:
            pass

        # Three mutually exclusive scenarios for the model's metadata:
        if custom_model_info is not None and litellm_model_name_model_info is not None:
            # (1) It has both custom model_info set and exists in the built-in map
            # merge with custom overriding built-in
            model_info = cast(
                ModelInfo,
                _update_dictionary(
                    cast(dict, litellm_model_name_model_info).copy(),
                    custom_model_info,
                ),
            )
        elif litellm_model_name_model_info is not None:
            # (2) Built-in only — no custom pricing to merge
            model_info = litellm_model_name_model_info
        elif custom_model_info is not None:
            # (3) Custom only — model not in built-in cost map yet
            # custom_model_info already includes base_model defaults at this point, if applicable
            model_info = cast(ModelInfo, custom_model_info)

        return model_info

    def _set_model_group_info(
        self, model_group: str, user_facing_model_group_name: str
    ) -> Optional[ModelGroupInfo]:
        """
        For a given model group name, return the combined model info

        Returns:
        - ModelGroupInfo if able to construct a model group
        - None if error constructing model group info
        """
        model_group_info: Optional[ModelGroupInfo] = None

        total_tpm: Optional[int] = None
        total_rpm: Optional[int] = None
        configurable_clientside_auth_params: CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS = None
        model_list = self.get_model_list(model_name=model_group)
        if model_list is None:
            return None
        for model in model_list:
            is_match = False
            if (
                "model_name" in model and model["model_name"] == model_group
            ):  # exact match
                is_match = True
            elif (
                "model_name" in model
                and self.pattern_router.route(model_group) is not None
            ):  # wildcard model
                is_match = True

            if not is_match:
                continue
            # model in model group found #
            litellm_params = LiteLLM_Params(**model["litellm_params"])  # type: ignore
            # get configurable clientside auth params
            configurable_clientside_auth_params = (
                litellm_params.configurable_clientside_auth_params
            )

            # Cache nested dict access to avoid repeated temporary dict allocations
            model_litellm_params = model.get("litellm_params", {})
            model_info_dict = model.get("model_info", {})

            # get model tpm
            _deployment_tpm: Optional[int] = None
            if _deployment_tpm is None:
                _deployment_tpm = model.get("tpm", None)  # type: ignore
            if _deployment_tpm is None:
                _deployment_tpm = model_litellm_params.get("tpm", None)  # type: ignore
            if _deployment_tpm is None:
                _deployment_tpm = model_info_dict.get("tpm", None)  # type: ignore

            # get model rpm
            _deployment_rpm: Optional[int] = None
            if _deployment_rpm is None:
                _deployment_rpm = model.get("rpm", None)  # type: ignore
            if _deployment_rpm is None:
                _deployment_rpm = model_litellm_params.get("rpm", None)  # type: ignore
            if _deployment_rpm is None:
                _deployment_rpm = model_info_dict.get("rpm", None)  # type: ignore

            # get model info
            try:
                model_id = model_info_dict.get("id", None)
                if model_id is not None:
                    model_info = self.get_deployment_model_info(
                        model_id=model_id, model_name=litellm_params.model
                    )
                else:
                    model_info = None
            except Exception:
                model_info = None

            # get llm provider
            litellm_model, llm_provider = "", ""
            try:
                litellm_model, llm_provider, _, _ = litellm.get_llm_provider(
                    model=litellm_params.model,
                    custom_llm_provider=litellm_params.custom_llm_provider,
                )
            except litellm.exceptions.BadRequestError as e:
                verbose_router_logger.error(
                    "litellm.router.py::get_model_group_info() - {}".format(str(e))
                )

            if model_info is None:
                supported_openai_params = litellm.get_supported_openai_params(
                    model=litellm_model, custom_llm_provider=llm_provider
                )
                if supported_openai_params is None:
                    supported_openai_params = []

                # Get mode from database model_info if available, otherwise default to "chat"
                db_model_info = model.get("model_info", {})
                mode = db_model_info.get("mode", "chat")
                input_cost_per_token = db_model_info.get("input_cost_per_token")
                output_cost_per_token = db_model_info.get("output_cost_per_token")

                model_info = ModelMapInfo(
                    key=model_group,
                    max_tokens=None,
                    max_input_tokens=None,
                    max_output_tokens=None,
                    input_cost_per_token=input_cost_per_token,
                    output_cost_per_token=output_cost_per_token,
                    litellm_provider=llm_provider,
                    mode=mode,
                    supported_openai_params=supported_openai_params,
                    supports_system_messages=None,
                )

            if model_group_info is None:
                model_group_info = ModelGroupInfo(  # type: ignore
                    **{
                        "model_group": user_facing_model_group_name,
                        "providers": [llm_provider],
                        **model_info,
                    }
                )
            else:
                # if max_input_tokens > curr
                # if max_output_tokens > curr
                # if input_cost_per_token > curr
                # if output_cost_per_token > curr
                # supports_parallel_function_calling == True
                # supports_vision == True
                # supports_function_calling == True
                if llm_provider not in model_group_info.providers:
                    model_group_info.providers.append(llm_provider)
                if (
                    model_info.get("max_input_tokens", None) is not None
                    and model_info["max_input_tokens"] is not None
                    and (
                        model_group_info.max_input_tokens is None
                        or model_info["max_input_tokens"]
                        > model_group_info.max_input_tokens
                    )
                ):
                    model_group_info.max_input_tokens = model_info["max_input_tokens"]
                if (
                    model_info.get("max_output_tokens", None) is not None
                    and model_info["max_output_tokens"] is not None
                    and (
                        model_group_info.max_output_tokens is None
                        or model_info["max_output_tokens"]
                        > model_group_info.max_output_tokens
                    )
                ):
                    model_group_info.max_output_tokens = model_info["max_output_tokens"]
                if model_info.get("input_cost_per_token", None) is not None and (
                    model_group_info.input_cost_per_token is None
                    or (model_info["input_cost_per_token"] or 0.0)
                    > (model_group_info.input_cost_per_token or 0.0)
                ):
                    model_group_info.input_cost_per_token = model_info[
                        "input_cost_per_token"
                    ]
                if model_info.get("output_cost_per_token", None) is not None and (
                    model_group_info.output_cost_per_token is None
                    or (model_info["output_cost_per_token"] or 0.0)
                    > (model_group_info.output_cost_per_token or 0.0)
                ):
                    model_group_info.output_cost_per_token = model_info[
                        "output_cost_per_token"
                    ]
                if (
                    model_info.get("supports_parallel_function_calling", None)
                    is not None
                    and model_info["supports_parallel_function_calling"] is True  # type: ignore
                ):
                    model_group_info.supports_parallel_function_calling = True
                if (
                    model_info.get("supports_vision", None) is not None and model_info["supports_vision"] is True  # type: ignore
                ):
                    model_group_info.supports_vision = True
                if (
                    model_info.get("supports_function_calling", None) is not None
                    and model_info["supports_function_calling"] is True  # type: ignore
                ):
                    model_group_info.supports_function_calling = True
                if (
                    model_info.get("supports_web_search", None) is not None
                    and model_info["supports_web_search"] is True  # type: ignore
                ):
                    model_group_info.supports_web_search = True
                if (
                    model_info.get("supports_url_context", None) is not None
                    and model_info["supports_url_context"] is True  # type: ignore
                ):
                    model_group_info.supports_url_context = True

                if (
                    model_info.get("supports_reasoning", None) is not None and model_info["supports_reasoning"] is True  # type: ignore
                ):
                    model_group_info.supports_reasoning = True
                if (
                    model_info.get("supported_openai_params", None) is not None
                    and model_info["supported_openai_params"] is not None
                ):
                    model_group_info.supported_openai_params = model_info[
                        "supported_openai_params"
                    ]
                if model_info.get("tpm", None) is not None and _deployment_tpm is None:
                    _deployment_tpm = model_info.get("tpm")
                if model_info.get("rpm", None) is not None and _deployment_rpm is None:
                    _deployment_rpm = model_info.get("rpm")

            if _deployment_tpm is not None:
                if total_tpm is None:
                    total_tpm = 0
                total_tpm += _deployment_tpm  # type: ignore

            if _deployment_rpm is not None:
                if total_rpm is None:
                    total_rpm = 0
                total_rpm += _deployment_rpm  # type: ignore
        if model_group_info is not None:
            ## UPDATE WITH TOTAL TPM/RPM FOR MODEL GROUP
            if total_tpm is not None:
                model_group_info.tpm = total_tpm

            if total_rpm is not None:
                model_group_info.rpm = total_rpm

            ## UPDATE WITH CONFIGURABLE CLIENTSIDE AUTH PARAMS FOR MODEL GROUP
            if configurable_clientside_auth_params is not None:
                model_group_info.configurable_clientside_auth_params = (
                    configurable_clientside_auth_params
                )

        return model_group_info

    def get_model_group_info(self, model_group: str) -> Optional[ModelGroupInfo]:
        """
        For a given model group name, return the combined model info

        Returns:
        - ModelGroupInfo if able to construct a model group
        - None if error constructing model group info or hidden model group
        """
        ## Check if model group alias
        if model_group in self.model_group_alias:
            item = self.model_group_alias[model_group]
            if isinstance(item, str):
                _router_model_group = item
            elif isinstance(item, dict):
                if item["hidden"] is True:
                    return None
                else:
                    _router_model_group = item["model"]
            else:
                return None

            return self._set_model_group_info(
                model_group=_router_model_group,
                user_facing_model_group_name=model_group,
            )

        ## Check if actual model
        return self._set_model_group_info(
            model_group=model_group, user_facing_model_group_name=model_group
        )

    async def get_model_group_usage(
        self, model_group: str
    ) -> Tuple[Optional[int], Optional[int]]:
        """
        Returns current tpm/rpm usage for model group

        Parameters:
        - model_group: str - the received model name from the user (can be a wildcard route).

        Returns:
        - usage: Tuple[tpm, rpm]
        """
        dt = get_utc_datetime()
        current_minute = dt.strftime(
            "%H-%M"
        )  # use the same timezone regardless of system clock
        tpm_keys: List[str] = []
        rpm_keys: List[str] = []

        model_list = self.get_model_list(model_name=model_group)
        if model_list is None:  # no matching deployments
            return None, None

        for model in model_list:
            id: Optional[str] = model.get("model_info", {}).get("id")  # type: ignore
            litellm_model: Optional[str] = model["litellm_params"].get(
                "model"
            )  # USE THE MODEL SENT TO litellm.completion() - consistent with how global_router cache is written.
            if id is None or litellm_model is None:
                continue
            tpm_keys.append(
                RouterCacheEnum.TPM.value.format(
                    id=id,
                    model=litellm_model,
                    current_minute=current_minute,
                )
            )
            rpm_keys.append(
                RouterCacheEnum.RPM.value.format(
                    id=id,
                    model=litellm_model,
                    current_minute=current_minute,
                )
            )
        combined_tpm_rpm_keys = tpm_keys + rpm_keys

        combined_tpm_rpm_values = await self.cache.async_batch_get_cache(
            keys=combined_tpm_rpm_keys
        )
        if combined_tpm_rpm_values is None:
            return None, None

        tpm_usage_list: Optional[List] = combined_tpm_rpm_values[: len(tpm_keys)]
        rpm_usage_list: Optional[List] = combined_tpm_rpm_values[len(tpm_keys) :]

        ## TPM
        tpm_usage: Optional[int] = None
        if tpm_usage_list is not None:
            for t in tpm_usage_list:
                if isinstance(t, int):
                    if tpm_usage is None:
                        tpm_usage = 0
                    tpm_usage += t
        ## RPM
        rpm_usage: Optional[int] = None
        if rpm_usage_list is not None:
            for t in rpm_usage_list:
                if isinstance(t, int):
                    if rpm_usage is None:
                        rpm_usage = 0
                    rpm_usage += t
        return tpm_usage, rpm_usage

    @lru_cache(maxsize=DEFAULT_MAX_LRU_CACHE_SIZE)
    def _cached_get_model_group_info(
        self, model_group: str
    ) -> Optional[ModelGroupInfo]:
        """
        Cached version of get_model_group_info, uses @lru_cache wrapper

        This is a speed optimization, since set_response_headers makes a call to get_model_group_info on every request
        """
        return self.get_model_group_info(model_group)

    async def get_remaining_model_group_usage(self, model_group: str) -> Dict[str, int]:
        model_group_info = self._cached_get_model_group_info(model_group)

        if model_group_info is not None and model_group_info.tpm is not None:
            tpm_limit = model_group_info.tpm
        else:
            tpm_limit = None

        if model_group_info is not None and model_group_info.rpm is not None:
            rpm_limit = model_group_info.rpm
        else:
            rpm_limit = None

        if tpm_limit is None and rpm_limit is None:
            return {}

        current_tpm, current_rpm = await self.get_model_group_usage(model_group)

        returned_dict = {}
        if tpm_limit is not None:
            returned_dict["x-ratelimit-remaining-tokens"] = tpm_limit - (
                current_tpm or 0
            )
            returned_dict["x-ratelimit-limit-tokens"] = tpm_limit
        if rpm_limit is not None:
            returned_dict["x-ratelimit-remaining-requests"] = rpm_limit - (
                current_rpm or 0
            )
            returned_dict["x-ratelimit-limit-requests"] = rpm_limit

        return returned_dict

    async def set_response_headers(
        self,
        response: Any,
        model_group: Optional[str] = None,
        request_kwargs: Optional[dict] = None,
    ) -> Any:
        """
        Add the most accurate rate limit headers for a given model response.

        ## TODO: add model group rate limit headers
        # - if healthy_deployments > 1, return model group rate limit headers
        # - else return the model's rate limit headers
        """
        if (
            isinstance(response, BaseModel)
            and hasattr(response, "_hidden_params")
            and isinstance(response._hidden_params, dict)  # type: ignore
        ):
            response._hidden_params.setdefault("additional_headers", {})  # type: ignore
            response._hidden_params["additional_headers"][  # type: ignore
                "x-litellm-model-group"
            ] = model_group

            additional_headers = response._hidden_params["additional_headers"]  # type: ignore

            # Lift QualityRouter routing decision into response headers for
            # transparency. The decision is stashed in request_kwargs.metadata
            # by QualityRouter.async_pre_routing_hook.
            metadata = (
                (request_kwargs.get("metadata") or {})
                if isinstance(request_kwargs, dict)
                else {}
            )
            decision = (
                metadata.get("quality_router_decision")
                if isinstance(metadata, dict)
                else None
            )
            if isinstance(decision, dict):
                # Only emit headers for fields that have a meaningful value.
                # `complexity_tier` and `matched_keyword` are mutually exclusive
                # (the keyword path short-circuits classification), so each
                # request emits one or the other but not both.
                if decision.get("routed_model") is not None:
                    additional_headers["x-litellm-quality-router-model"] = str(
                        decision["routed_model"]
                    )
                if decision.get("quality_tier") is not None:
                    additional_headers["x-litellm-quality-router-tier"] = str(
                        decision["quality_tier"]
                    )
                if decision.get("routed_via") is not None:
                    additional_headers["x-litellm-quality-router-via"] = str(
                        decision["routed_via"]
                    )
                if decision.get("matched_keyword") is not None:
                    additional_headers["x-litellm-quality-router-keyword"] = str(
                        decision["matched_keyword"]
                    )
                if decision.get("complexity_tier") is not None:
                    additional_headers["x-litellm-quality-router-complexity"] = str(
                        decision["complexity_tier"]
                    )

            if (
                "x-ratelimit-remaining-tokens" not in additional_headers
                and "x-ratelimit-remaining-requests" not in additional_headers
                and model_group is not None
            ):
                remaining_usage = await self.get_remaining_model_group_usage(
                    model_group
                )

                # get_remaining_model_group_usage reads the router's TPM/RPM
                # counter, which is incremented post-response by
                # deployment_callback_on_success. So the values returned here
                # are pre-decrement for the current request, while vendor
                # headers (OpenAI/Anthropic/Azure) are post-decrement. Replay
                # the in-flight increment so router-derived headers match
                # vendor-derived semantics — for both the HTTP response sent
                # to the client and the prometheus gauges that read these
                # headers downstream (LIT-2719).
                in_flight_tokens = 0
                usage = getattr(response, "usage", None)
                if usage is not None:
                    in_flight_tokens = getattr(usage, "total_tokens", 0) or 0
                in_flight_delta = {
                    "x-ratelimit-remaining-tokens": in_flight_tokens,
                    "x-ratelimit-remaining-requests": 1,
                }

                for header, value in remaining_usage.items():
                    if value is not None:
                        additional_headers[header] = value - in_flight_delta.get(
                            header, 0
                        )
        return response

    def _build_model_name_index(self, model_list: list) -> None:
        """
        Build model_name -> deployment indices mapping for O(1) lookups.

        This index allows us to find all deployments for a given model_name in O(1) time
        instead of O(n) linear scan through the entire model_list.
        """
        self.model_name_to_deployment_indices.clear()
        self.team_model_to_deployment_indices.clear()

        for idx, model in enumerate(model_list):
            model_name = model.get("model_name")
            if model_name:
                if model_name not in self.model_name_to_deployment_indices:
                    self.model_name_to_deployment_indices[model_name] = []
                self.model_name_to_deployment_indices[model_name].append(idx)

            self._update_team_model_index(model, idx)

    def _build_model_id_to_deployment_index_map(self, model_list: list):
        """
        Build model index from model list to enable O(1) lookups immediately.
        This is called during initialization to avoid the race condition where
        requests arrive before model_id_to_deployment_index_map is populated.
        """
        # First populate the model_list
        self.model_list = []
        self._invalidate_model_group_info_cache()
        self._invalidate_access_groups_cache()
        for _, model in enumerate(model_list):
            # Extract model_info from the model dict
            model_info = model.get("model_info", {})
            model_id = model_info.get("id")

            # If no ID exists, generate one using the same logic as set_model_list
            if model_id is None:
                model_name = model.get("model_name", "")
                litellm_params = model.get("litellm_params", {})
                model_id = self._generate_model_id(model_name, litellm_params)
                # Update the model_info in the original list
                if "model_info" not in model:
                    model["model_info"] = {}
                model["model_info"]["id"] = model_id

            self._add_model_to_list_and_index_map(model=model, model_id=model_id)

    def get_model_ids(
        self, model_name: Optional[str] = None, exclude_team_models: bool = False
    ) -> List[str]:
        """
        if 'model_name' is none, returns all.

        Returns list of model id's.

        Optimized with O(1) or O(k) index lookup when model_name provided,
        instead of O(n) linear scan.
        """
        ids = []

        if model_name is not None:
            # O(1) lookup in model_name index, then O(k) iteration where k = deployments for this model_name
            if model_name in self.model_name_to_deployment_indices:
                indices = self.model_name_to_deployment_indices[model_name]
                for idx in indices:
                    model = self.model_list[idx]
                    if "model_info" in model and "id" in model["model_info"]:
                        if exclude_team_models and model["model_info"].get("team_id"):
                            continue
                        ids.append(model["model_info"]["id"])
        else:
            # When model_name is None, return all model IDs
            # Use the index map keys for O(n) where n = total deployments
            for model_id in self.model_id_to_deployment_index_map.keys():
                idx = self.model_id_to_deployment_index_map[model_id]
                model = self.model_list[idx]
                if "model_info" in model and "id" in model["model_info"]:
                    if exclude_team_models and model["model_info"].get("team_id"):
                        continue
                    ids.append(model_id)

        return ids

    def has_model_id(self, candidate_id: str) -> bool:
        """
        O(1) membership check for a deployment ID without allocating large lists.

        Note: Call sites may pass a variable named `model` when it actually
        contains a deployment ID. This helper expects the deployment ID string.

        Uses the existing `model_id_to_deployment_index_map` which is kept
        in sync by `_build_model_id_to_deployment_index_map` and model-list
        mutation helpers.
        """
        return candidate_id in self.model_id_to_deployment_index_map

    def resolve_model_name_from_model_id(
        self, model_id: Optional[str]
    ) -> Optional[str]:
        """
        Resolve model_name from model_id.

        This method attempts to find the correct model_name to use with the router
        so that litellm_params can be automatically injected from the model config.

        Strategy:
        1. First, check if model_id directly matches a model_name or deployment ID
        2. If not, search through router's model_list to find a match by litellm_params.model
        3. Return the model_name if found, None otherwise

        Args:
            model_id: The model_id extracted from decoded video_id
                     (could be model_name or litellm_params.model value)

        Returns:
            model_name if found, None otherwise. If None, the request will fall through
            to normal flow using environment variables.
        """
        if not model_id:
            return None

        # Strategy 1: Check if model_id directly matches a model_name or deployment ID
        if model_id in self.model_names or self.has_model_id(model_id):
            return model_id

        # Strategy 2: Search through router's model_list to find by litellm_params.model
        all_models = self.get_model_list(model_name=None)
        if not all_models:
            return None

        for deployment in all_models:
            litellm_params = deployment.get("litellm_params", {})
            actual_model = litellm_params.get("model")

            # Match by exact match or by checking if actual_model ends with /model_id or :model_id
            # e.g., model_id="veo-2.0-generate-001" matches actual_model="vertex_ai/veo-2.0-generate-001"
            matches = (
                actual_model == model_id
                or (actual_model and actual_model.endswith(f"/{model_id}"))
                or (actual_model and actual_model.endswith(f":{model_id}"))
            )

            if matches:
                model_name = deployment.get("model_name")
                if model_name:
                    return model_name

        # No match found
        return None

    def map_team_model(
        self, team_model_name: Optional[str], team_id: str
    ) -> Optional[str]:
        """
        Check if team_model_name resolves to team-specific deployments.

        Returns the public model name (unchanged) so the router can find all
        sibling deployments via team_id filtering, instead of collapsing to a
        single internal model_name.

        When team_model_name is None (e.g. vector store / file endpoints that
        don't include a model in their request), returns the first matching
        team deployment's team_public_model_name so the router can inject BYOK
        credentials from the team-scoped deployment.

        Returns:
        - str: the team_model_name if team deployments exist for this team
        - None: if no team-specific model is found
        """
        models = self.get_model_list(model_name=team_model_name, team_id=team_id)
        if not models:
            return None
        for model in models:
            if model.get("model_info", {}).get("team_id") == team_id:
                if team_model_name is None:
                    # No model was specified (e.g. vector store endpoints).
                    # Return the deployment's public model name so the router
                    # can route to it and inject the BYOK API key.
                    return model.get("model_info", {}).get(
                        "team_public_model_name"
                    ) or model.get("model_name")
                return team_model_name

        # No team-scoped deployment found; wildcard/pattern routes are
        # handled downstream by the pattern_router in _common_checks_available_deployment.
        return None

    def should_include_deployment(
        self, model_name: str, model: dict, team_id: Optional[str] = None
    ) -> bool:
        """
        Get the team-specific model name if team_id matches the deployment.
        """
        if (
            team_id is not None
            and (model.get("model_info") or {}).get("team_id") == team_id
            and model_name
            == (model.get("model_info") or {}).get("team_public_model_name")
        ):
            return True
        elif model_name is not None and model["model_name"] == model_name:
            # Fallback: check by internal model_name for non-team deployments
            # or deployments that haven't been migrated to team_public_model_name yet
            model_team_id = (model.get("model_info") or {}).get("team_id")
            if (
                team_id is None  # requester has no team constraint
                or model_team_id is None  # global deployment - accessible to all teams
                or model_team_id == team_id  # deployment belongs to requester's team
            ):
                return True
        # No match: deployment is for a different team or doesn't match the requested model
        return False

    def _get_all_deployments(
        self,
        model_name: str,
        model_alias: Optional[str] = None,
        team_id: Optional[str] = None,
    ) -> List[DeploymentTypedDict]:
        """
        Return all deployments of a model name

        Used for accurate 'get_model_list'.

        if team_id specified, only return team-specific models

        Optimized with O(1) index lookup instead of O(n) linear scan.

        Note: when team_id is provided, O(1) lookup in
        `team_model_to_deployment_indices` only applies when `model_name` is the
        team public model name. If a caller passes an internal deployment model
        name (for example, `model_name_<team_id>_<uuid>`), this method falls back
        to the standard model-name index / scan path.
        """
        returned_models: List[DeploymentTypedDict] = []

        # O(1) lookup in team_model index when team_id is provided
        if team_id is not None:
            key = (team_id, model_name)
            if key in self.team_model_to_deployment_indices:
                indices = self.team_model_to_deployment_indices[key]
                # O(k) where k = team deployments for this model_name (typically 1-10)
                for idx in indices:
                    model = self.model_list[idx]
                    if not self.should_include_deployment(
                        model_name=model_name, model=model, team_id=team_id
                    ):
                        continue
                    if model_alias is not None:
                        alias_model = model.copy()
                        alias_model["model_name"] = model_alias
                        returned_models.append(alias_model)
                    else:
                        returned_models.append(model)
                if returned_models:
                    return returned_models

        # O(1) lookup in model_name index
        if model_name in self.model_name_to_deployment_indices:
            indices = self.model_name_to_deployment_indices[model_name]

            # O(k) where k = deployments for this model_name (typically 1-10)
            for idx in indices:
                model = self.model_list[idx]
                if self.should_include_deployment(
                    model_name=model_name, model=model, team_id=team_id
                ):
                    if model_alias is not None:
                        # Optimized: Use shallow copy since we only modify top-level model_name
                        # This is much faster than deepcopy for nested dict structures
                        alias_model = model.copy()
                        alias_model["model_name"] = model_alias
                        returned_models.append(alias_model)
                    else:
                        returned_models.append(model)
        elif team_id is not None:
            # Fallback: if team_id is provided and model_name not in index,
            # check if model_name matches any team_public_model_name
            # O(n) scan but only when team_id lookup fails
            for idx, model in enumerate(self.model_list):
                if self.should_include_deployment(
                    model_name=model_name, model=model, team_id=team_id
                ):
                    if model_alias is not None:
                        # Optimized: Use shallow copy since we only modify top-level model_name
                        alias_model = model.copy()
                        alias_model["model_name"] = model_alias
                        returned_models.append(alias_model)
                    else:
                        returned_models.append(model)

        return returned_models

    def get_model_names(self, team_id: Optional[str] = None) -> List[str]:
        """
        Returns all possible model names for the router, including models defined via model_group_alias.

        If a team_id is provided, only deployments configured with that team_id (i.e. team‐specific models)
        will yield their team public name.
        """
        deployments = self.get_model_list() or []
        model_names = []

        for deployment in deployments:
            model_info = deployment.get("model_info")
            if self._is_team_specific_model(model_info):
                team_model_name = self._get_team_specific_model(
                    deployment=deployment, team_id=team_id
                )
                if team_model_name:
                    model_names.append(team_model_name)
            else:
                model_names.append(deployment.get("model_name", ""))

        return model_names

    def get_fully_blocked_model_names(self) -> Set[str]:
        """
        Returns the set of model_names where every backing deployment has `blocked=True`.

        Used by `/v1/models` to hide paused models from client listings while still
        surfacing them on admin endpoints (e.g. `/model/info`). A model with at least
        one non-blocked deployment is still serviceable and remains visible.
        """
        deployments = self.get_model_list() or []
        blocked_by_name: Dict[str, bool] = {}
        for deployment in deployments:
            name = deployment.get("model_name") or ""
            if not name:
                continue
            is_blocked = (deployment.get("model_info") or {}).get("blocked") is True
            if name in blocked_by_name:
                blocked_by_name[name] = blocked_by_name[name] and is_blocked
            else:
                blocked_by_name[name] = is_blocked
        return {
            name for name, fully_blocked in blocked_by_name.items() if fully_blocked
        }

    @staticmethod
    def _are_all_deployments_blocked(
        deployments: List[DeploymentTypedDict],
    ) -> bool:
        return len(deployments) > 0 and all(
            (deployment.get("model_info") or {}).get("blocked") is True
            for deployment in deployments
        )

    def _is_model_fully_blocked(self, model: str) -> bool:
        deployments = self.get_model_list(model_name=model) or []
        return self._are_all_deployments_blocked(deployments=deployments)

    async def async_get_fully_unhealthy_model_names(self) -> Set[str]:
        """
        Returns the set of model names where every backing deployment is currently
        marked unhealthy by background health checks (and the health state is not stale).

        Used by `/v1/models?healthy_only=true` to hide models that cannot serve any
        request. A model with at least one healthy (or unknown-health) deployment
        remains visible. Returns an empty set when no health state is available, so
        callers fail open to the unfiltered listing.

        Notes:
            - Mirrors `_async_filter_health_check_unhealthy_deployments`: when
              `allowed_fails_policy` is set, cooldown is the sole routing exclusion
              mechanism, so nothing is hidden here either.
            - Team-specific public model names (`team_public_model_name`) are
              aggregated alongside `model_name`, so team aliases of fully-unhealthy
              deployments are hidden too (unlike `get_fully_blocked_model_names`,
              which matches `model_name` only).
            - Wildcard routes (e.g. `openai/*`) are matched by their literal
              deployment name only; models expanded from a wildcard route are not
              hidden (fail open).
            - Intentionally diverges from the routing-time safety net (which
              bypasses the health filter when every candidate is unhealthy and
              still attempts the request): hiding here is presentation-only —
              it answers "should this model be advertised?", not "should a
              request for it still be attempted?". A hidden model can still be
              called directly.
        """
        if self.allowed_fails_policy is not None:
            return set()
        unhealthy_ids = (
            await self.health_state_cache.async_get_unhealthy_deployment_ids()
        )
        if not unhealthy_ids:
            return set()
        deployments = self.get_model_list() or []
        unhealthy_by_name: Dict[str, bool] = {}
        for deployment in deployments:
            model_info = deployment.get("model_info") or {}
            names = [deployment.get("model_name") or ""]
            team_public_model_name = model_info.get("team_public_model_name")
            if team_public_model_name:
                names.append(team_public_model_name)
            is_unhealthy = model_info.get("id") in unhealthy_ids
            for name in names:
                if not name:
                    continue
                if name in unhealthy_by_name:
                    unhealthy_by_name[name] = unhealthy_by_name[name] and is_unhealthy
                else:
                    unhealthy_by_name[name] = is_unhealthy
        return {
            name
            for name, fully_unhealthy in unhealthy_by_name.items()
            if fully_unhealthy
        }

    def _get_team_specific_model(
        self, deployment: DeploymentTypedDict, team_id: Optional[str] = None
    ) -> Optional[str]:
        """
        Get the team-specific model name if team_id matches the deployment.

        Args:
            deployment: DeploymentTypedDict - The model deployment
            team_id: Optional[str] - If passed, will return router models set with a `team_id` matching the passed `team_id`.

        Returns:
            str: The `team_public_model_name` if team_id matches
            None: If team_id doesn't match or no team info exists
        """
        model_info: Optional[Dict] = deployment.get("model_info") or {}
        if model_info is None:
            return None
        if team_id == model_info.get("team_id"):
            return model_info.get("team_public_model_name")
        return None

    def _is_team_specific_model(self, model_info: Optional[Dict]) -> bool:
        """
        Check if model info contains team-specific configuration.

        Args:
            model_info: Model information dictionary

        Returns:
            bool: True if model has team-specific configuration
        """
        return bool(model_info and model_info.get("team_id"))

    def get_model_list_from_model_alias(
        self, model_name: Optional[str] = None
    ) -> List[DeploymentTypedDict]:
        """
        Helper function to get model list from model alias.

        Used by `.get_model_list` to get model list from model alias.
        """
        returned_models: List[DeploymentTypedDict] = []

        if model_name is not None:
            # Fast path: direct dict lookup avoids scanning all aliases for non-alias model names.
            if model_name not in self.model_group_alias:
                return returned_models
            alias_items = [(model_name, self.model_group_alias[model_name])]
        else:
            alias_items = list(self.model_group_alias.items())

        for model_alias, model_value in alias_items:
            if isinstance(model_value, str):
                _router_model_name: str = model_value
            elif isinstance(model_value, dict):
                _model_value = RouterModelGroupAliasItem(**model_value)  # type: ignore
                if _model_value["hidden"] is True:
                    continue
                else:
                    _router_model_name = _model_value["model"]
            else:
                continue

            returned_models.extend(
                self._get_all_deployments(
                    model_name=_router_model_name, model_alias=model_alias
                )
            )

        return returned_models

    def get_model_list(
        self, model_name: Optional[str] = None, team_id: Optional[str] = None
    ) -> Optional[List[DeploymentTypedDict]]:
        """
        Includes router model_group_alias'es as well

        if team_id specified, returns matching team-specific models
        """
        # Note: model_list and model_group_alias are always initialized in __init__
        # so hasattr checks are unnecessary
        returned_models: List[DeploymentTypedDict] = []

        if model_name is not None:
            returned_models.extend(
                self._get_all_deployments(model_name=model_name, team_id=team_id)
            )

        returned_models.extend(
            self.get_model_list_from_model_alias(model_name=model_name)
        )

        if len(returned_models) == 0:  # check if wildcard route
            potential_wildcard_models = self.pattern_router.route(model_name) or []

            ## check for team-specific wildcard models
            if team_id is not None and team_id in self.team_pattern_routers:
                potential_team_only_wildcard_models = (
                    self.team_pattern_routers[team_id].route(model_name) or []
                )
                potential_wildcard_models.extend(potential_team_only_wildcard_models)

            if model_name is not None and potential_wildcard_models is not None:
                for m in potential_wildcard_models:
                    deployment_typed_dict = DeploymentTypedDict(**m)  # type: ignore
                    deployment_typed_dict["model_name"] = model_name
                    returned_models.append(deployment_typed_dict)

        if model_name is None:
            returned_models += self.model_list

        return returned_models

    def _invalidate_model_group_info_cache(self) -> None:
        """Invalidate the cached model group info.

        Call this whenever self.model_list is modified to ensure the cache is rebuilt.
        Also clears the auth-layer zero-cost cache, which depends on the same
        ``ModelGroupInfo`` data — without this, an in-place pricing update on
        an existing deployment (same model count) would keep a stale ``True``
        result and bypass budget enforcement.
        """
        self._cached_get_model_group_info.cache_clear()
        self._zero_cost_cache.clear()

    def _invalidate_access_groups_cache(self) -> None:
        """Invalidate the cached access groups.

        Call this whenever self.model_list is modified to ensure the cache is rebuilt.
        """
        self._access_groups_cache = None

    def get_model_access_groups(
        self,
        model_name: Optional[str] = None,
        model_access_group: Optional[str] = None,
        team_id: Optional[str] = None,
    ) -> Dict[str, List[str]]:
        """
        If model_name is provided, only return access groups for that model.

        Parameters:
        - model_name: Optional[str] - the received model name from the user (can be a wildcard route). If set, will only return access groups for that model.
        - model_access_group: Optional[str] - the received model access group from the user. If set, will only return models for that access group.
        - team_id: Optional[str] - the team id, to resolve team-specific models
        """
        # Check if this is the no-args hot path (cacheable)
        _use_cache = (
            model_name is None and model_access_group is None and team_id is None
        )

        # Return cached result for the no-args hot path
        if _use_cache and self._access_groups_cache is not None:
            return self._access_groups_cache

        from collections import defaultdict

        access_groups = defaultdict(list)

        model_list = self.get_model_list(model_name=model_name, team_id=team_id)
        if model_list:
            for m in model_list:
                _model_info = m.get("model_info")
                if _model_info:
                    for group in _model_info.get("access_groups", []) or []:
                        if model_access_group is not None:
                            if group == model_access_group:
                                model_name = m["model_name"]
                                access_groups[group].append(model_name)
                        else:
                            model_name = m["model_name"]
                            access_groups[group].append(model_name)

        # Cache the result for the no-args hot path
        if _use_cache:
            self._access_groups_cache = dict(access_groups)
            return self._access_groups_cache

        return access_groups

    def _is_model_access_group_for_wildcard_route(
        self, model_access_group: str
    ) -> bool:
        """
        Return True if model access group is a wildcard route
        """
        # GET ACCESS GROUPS
        access_groups = self.get_model_access_groups(
            model_access_group=model_access_group
        )

        if len(access_groups) == 0:
            return False

        models = access_groups.get(model_access_group, [])

        for model in models:
            # CHECK IF MODEL ACCESS GROUP IS A WILDCARD ROUTE
            if self.pattern_router.route(request=model) is not None:
                return True

        return False

    def get_settings(self):
        """
        Get router settings method, returns a dictionary of the settings and their values.
        For example get the set values for routing_strategy_args, routing_strategy, allowed_fails, cooldown_time, num_retries, timeout, max_retries, retry_after
        """
        _all_vars = vars(self)
        _settings_to_return = {}
        vars_to_include = [
            "routing_strategy_args",
            "routing_strategy",
            "allowed_fails",
            "cooldown_time",
            "num_retries",
            "timeout",
            "max_retries",
            "retry_after",
            "fallbacks",
            "context_window_fallbacks",
            "model_group_retry_policy",
            "retry_policy",
            "model_group_alias",
            "enable_weighted_failover",
        ]

        for var in vars_to_include:
            if var in _all_vars:
                _settings_to_return[var] = _all_vars[var]
            if (
                var == "routing_strategy_args"
                and self.routing_strategy == "latency-based-routing"
                and self.lowestlatency_logger is not None
            ):
                _settings_to_return[var] = self.lowestlatency_logger.routing_args.json()

        _settings_to_return["routing_groups"] = [
            group.model_dump() for group in self._routing_groups.values()
        ]
        return _settings_to_return

    def update_settings(self, **kwargs):
        """
        Update the router settings.
        """
        # only the following settings are allowed to be configured
        _allowed_settings = [
            "routing_strategy_args",
            "routing_strategy",
            "routing_groups",
            "allowed_fails",
            "cooldown_time",
            "num_retries",
            "timeout",
            "max_retries",
            "retry_after",
            "fallbacks",
            "context_window_fallbacks",
            "model_group_retry_policy",
            "model_group_alias",
            "enable_weighted_failover",
        ]

        _int_settings = [
            "timeout",
            "num_retries",
            "retry_after",
            "allowed_fails",
            "cooldown_time",
        ]

        _existing_router_settings = self.get_settings()
        rebuild_routing_groups = False
        for var in kwargs:
            if var in _allowed_settings:
                if var in _int_settings:
                    _casted_value = int(kwargs[var])
                    setattr(self, var, _casted_value)
                elif var == "routing_groups":
                    self._routing_groups_input = kwargs[var]
                    rebuild_routing_groups = True
                else:
                    value = kwargs[var]
                    # only run routing strategy init if it has changed
                    if var == "routing_strategy":
                        value = self._normalize_strategy(value)
                        if _existing_router_settings["routing_strategy"] != value:
                            self.routing_strategy_init(
                                routing_strategy=value,
                                routing_strategy_args=kwargs.get(
                                    "routing_strategy_args", {}
                                ),
                            )
                            rebuild_routing_groups = True
                    setattr(self, var, value)
            else:
                verbose_router_logger.debug("Setting {} is not allowed".format(var))

        if rebuild_routing_groups:
            self._init_routing_groups(self._routing_groups_input)
        verbose_router_logger.debug(f"Updated Router settings: {self.get_settings()}")

    def _get_client(self, deployment, kwargs, client_type=None):
        """
        Returns the appropriate client based on the given deployment, kwargs, and client_type.

        Parameters:
            deployment (dict): The deployment dictionary containing the clients.
            kwargs (dict): The keyword arguments passed to the function.
            client_type (str): The type of client to return.

        Returns:
            The appropriate client based on the given client_type and kwargs.
        """
        model_id = deployment["model_info"]["id"]
        parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs(kwargs)
        if client_type == "max_parallel_requests":
            cache_key = "{}_max_parallel_requests_client".format(model_id)
            client = self.cache.get_cache(
                key=cache_key, local_only=True, parent_otel_span=parent_otel_span
            )
            if client is None:
                InitalizeCachedClient.set_max_parallel_requests_client(
                    litellm_router_instance=self, model=deployment
                )
                client = self.cache.get_cache(
                    key=cache_key, local_only=True, parent_otel_span=parent_otel_span
                )
            return client
        elif client_type == "async":
            if kwargs.get("stream") is True:
                cache_key = f"{model_id}_stream_async_client"
                client = self.cache.get_cache(
                    key=cache_key, local_only=True, parent_otel_span=parent_otel_span
                )
                return client
            else:
                cache_key = f"{model_id}_async_client"
                client = self.cache.get_cache(
                    key=cache_key, local_only=True, parent_otel_span=parent_otel_span
                )
                return client
        else:
            if kwargs.get("stream") is True:
                cache_key = f"{model_id}_stream_client"
                client = self.cache.get_cache(
                    key=cache_key, parent_otel_span=parent_otel_span
                )
                return client
            else:
                cache_key = f"{model_id}_client"
                client = self.cache.get_cache(
                    key=cache_key, parent_otel_span=parent_otel_span
                )
                return client

    def _pre_call_checks(
        self,
        model: str,
        healthy_deployments: List,
        messages: List[Dict[str, str]],
        request_kwargs: Optional[dict] = None,
    ):
        """
        Filter out model in model group, if:

        - model context window < message length. For azure openai models, requires 'base_model' is set. - https://docs.litellm.ai/docs/proxy/cost_tracking#spend-tracking-for-azure-openai-models
        - filter models above rpm limits
        - if region given, filter out models not in that region / unknown region
        - [TODO] function call and model doesn't support function calling
        """

        verbose_router_logger.debug(
            f"Starting Pre-call checks for deployments in model={model}"
        )

        # Optimized: Use list() shallow copy instead of deepcopy
        # We only pop from the list, not modify deployment dicts - 100x+ faster on hot path (every request)
        _returned_deployments = list(healthy_deployments)

        invalid_model_indices = set()  # Use set for O(1) membership checks

        try:
            input_tokens = litellm.token_counter(messages=messages)
        except Exception as e:
            verbose_router_logger.error(
                "litellm.router.py::_pre_call_checks: failed to count tokens. Returning initial list of deployments. Got - {}".format(
                    str(e)
                )
            )
            return _returned_deployments

        _context_window_error = False
        _potential_error_str = ""
        _rate_limit_error = False
        parent_otel_span = _get_parent_otel_span_from_kwargs(request_kwargs)

        ## get model group RPM ##
        dt = get_utc_datetime()
        current_minute = dt.strftime("%H-%M")
        rpm_key = f"{model}:rpm:{current_minute}"
        model_group_cache = (
            self.cache.get_cache(
                key=rpm_key, local_only=True, parent_otel_span=parent_otel_span
            )
            or {}
        )  # check the in-memory cache used by lowest_latency and usage-based routing. Only check the local cache.
        for idx, deployment in enumerate(_returned_deployments):
            # Cache nested dict access to avoid repeated temporary dict allocations
            _litellm_params = deployment.get("litellm_params", {})
            _model_info = deployment.get("model_info", {})

            # see if we have the info for this model
            _deployment_model = None  # per-deployment model name (avoids overwriting the outer `model` group name)
            try:
                base_model = _model_info.get("base_model", None)
                if base_model is None:
                    base_model = _litellm_params.get("base_model", None)
                model_info = self.get_router_model_info(
                    deployment=deployment, received_model_name=model
                )
                _deployment_model = base_model or _litellm_params.get("model", None)

                if (
                    isinstance(model_info, dict)
                    and model_info.get("max_input_tokens", None) is not None
                ):
                    if (
                        isinstance(model_info["max_input_tokens"], int)
                        and input_tokens > model_info["max_input_tokens"]
                    ):
                        invalid_model_indices.add(idx)
                        _context_window_error = True
                        _potential_error_str += (
                            "Model={}, Max Input Tokens={}, Got={}".format(
                                _deployment_model,
                                model_info["max_input_tokens"],
                                input_tokens,
                            )
                        )
                        continue
            except Exception as e:
                verbose_router_logger.exception("An error occurs - {}".format(str(e)))

            model_id = _model_info.get("id", "")
            ## RPM CHECK ##
            ### get local router cache ###
            current_request_cache_local = (
                self.cache.get_cache(
                    key=model_id, local_only=True, parent_otel_span=parent_otel_span
                )
                or 0
            )
            ### get usage based cache ###
            if (
                isinstance(model_group_cache, dict)
                and self.routing_strategy != "usage-based-routing-v2"
            ):
                model_group_cache[model_id] = model_group_cache.get(model_id, 0)

                current_request = max(
                    current_request_cache_local, model_group_cache[model_id]
                )

                if (
                    isinstance(_litellm_params, dict)
                    and _litellm_params.get("rpm", None) is not None
                ):
                    if (
                        isinstance(_litellm_params["rpm"], int)
                        and _litellm_params["rpm"] <= current_request
                    ):
                        invalid_model_indices.add(idx)
                        _rate_limit_error = True
                        continue

            ## REGION CHECK ##
            if (
                request_kwargs is not None
                and request_kwargs.get("allowed_model_region") is not None
            ):
                allowed_model_region = request_kwargs.get("allowed_model_region")

                if allowed_model_region is not None:
                    if not is_region_allowed(
                        litellm_params=LiteLLM_Params(**_litellm_params),
                        allowed_model_region=allowed_model_region,
                    ):
                        invalid_model_indices.add(idx)
                        continue

            ## INVALID PARAMS ## -> catch 'gpt-3.5-turbo-16k' not supporting 'response_format' param
            if request_kwargs is not None and litellm.drop_params is False:
                # get supported params — use per-deployment model to avoid overwriting the outer model group name
                _dep_model_for_params = _deployment_model or model
                (
                    _dep_model_for_params,
                    custom_llm_provider,
                    _,
                    _,
                ) = litellm.get_llm_provider(
                    model=_dep_model_for_params,
                    litellm_params=LiteLLM_Params(**_litellm_params),
                )

                supported_openai_params = litellm.get_supported_openai_params(
                    model=_dep_model_for_params,
                    custom_llm_provider=custom_llm_provider,
                )

                if supported_openai_params is None:
                    continue
                else:
                    # check the non-default openai params in request kwargs
                    non_default_params = litellm.utils.get_non_default_params(
                        passed_params=request_kwargs
                    )
                    special_params = ["response_format"]
                    # check if all params are supported
                    for k, v in non_default_params.items():
                        if k not in supported_openai_params and k in special_params:
                            # if not -> invalid model
                            verbose_router_logger.debug(
                                f"INVALID MODEL INDEX @ REQUEST KWARG FILTERING, k={k}"
                            )
                            invalid_model_indices.add(idx)

        if len(invalid_model_indices) == len(_returned_deployments):
            """
            - no healthy deployments available b/c context window checks or rate limit error

            - First check for rate limit errors (if this is true, it means the model passed the context window check but failed the rate limit check)
            """

            if _rate_limit_error is True:  # allow generic fallback logic to take place
                raise RouterRateLimitErrorBasic(
                    model=model,
                )

            elif _context_window_error is True:
                raise litellm.ContextWindowExceededError(
                    message="litellm._pre_call_checks: Context Window exceeded for given call. No models have context window large enough for this call.\n{}".format(
                        _potential_error_str
                    ),
                    model=model,
                    llm_provider="",
                )
        if len(invalid_model_indices) > 0:
            # Single-pass filter using set for O(1) lookups (avoids O(n^2) from repeated pops)
            _returned_deployments = [
                d
                for i, d in enumerate(_returned_deployments)
                if i not in invalid_model_indices
            ]

        return _returned_deployments

    def _get_model_from_alias(self, model: str) -> Optional[str]:
        """
        Get the model from the alias.

        Returns:
        - str, the litellm model name
        - None, if model is not in model group alias
        """
        if model not in self.model_group_alias:
            return None

        _item = self.model_group_alias[model]
        if isinstance(_item, str):
            model = _item
        else:
            model = _item["model"]

        return model

    def _get_deployment_by_litellm_model(self, model: str) -> List:
        """
        Get the deployment by litellm model.
        """
        return [m for m in self.model_list if m["litellm_params"]["model"] == model]

    def _try_early_resolve_deployments_for_model_not_in_names(
        self, model: str, request_team_id: Optional[str]
    ) -> Optional[Tuple[str, Union[List, Dict]]]:
        """
        When ``model`` is not in ``self.model_names``, try team routes, pattern routes,
        team pattern routers, then default deployment. Returns None if none apply.
        """
        if model in self.model_names:
            return None
        # Check for team-specific deployments by team_public_model_name.
        # This intentionally takes priority over team pattern routers below,
        # so that named team deployments shadow wildcard/pattern routes.
        if request_team_id is not None:
            team_deployments = self._get_all_deployments(
                model_name=model, team_id=request_team_id
            )
            if team_deployments:
                return model, team_deployments

        pattern_deployments = self.pattern_router.get_deployments_by_pattern(
            model=model,
        )

        if pattern_deployments:
            return model, pattern_deployments

        if request_team_id is not None and request_team_id in self.team_pattern_routers:
            pattern_deployments = self.team_pattern_routers[
                request_team_id
            ].get_deployments_by_pattern(
                model=model,
            )
            if pattern_deployments:
                return model, pattern_deployments

        if self.default_deployment is not None:
            # Shallow copy with nested litellm_params copy (100x+ faster than deepcopy)
            updated_deployment = self.default_deployment.copy()
            updated_deployment["litellm_params"] = self.default_deployment[
                "litellm_params"
            ].copy()
            updated_deployment["litellm_params"]["model"] = model
            return model, updated_deployment

        return None

    def _common_checks_available_deployment(
        self,
        model: str,
        messages: Optional[List[Dict[str, str]]] = None,
        input: Optional[Union[str, List]] = None,
        specific_deployment: Optional[bool] = False,
        request_kwargs: Optional[Dict] = None,
    ) -> Tuple[str, Union[List, Dict]]:
        """
        Common checks for 'get_available_deployment' across sync + async call.

        If 'healthy_deployments' returned is None, this means the user chose a specific deployment

        Returns
        - str, the litellm model name
        - List, if multiple models chosen
        - Dict, if specific model chosen
        """

        request_team_id: Optional[str] = None
        if request_kwargs is not None:
            metadata = request_kwargs.get("metadata") or {}
            litellm_metadata = request_kwargs.get("litellm_metadata") or {}
            request_team_id = metadata.get(
                "user_api_key_team_id"
            ) or litellm_metadata.get("user_api_key_team_id")
        # check if aliases set on litellm model alias map
        if specific_deployment is True:
            return model, self._get_deployment_by_litellm_model(model=model)
        elif self.has_model_id(model):
            deployment = self.get_deployment(model_id=model)
            if deployment is not None:
                deployment_model = deployment.litellm_params.model
                return deployment_model, deployment.model_dump(exclude_none=True)
            raise ValueError(
                f"LiteLLM Router: Trying to call specific deployment, but Model ID :{model} does not exist in Model ID map"
            )

        _model_from_alias = self._get_model_from_alias(model=model)
        if _model_from_alias is not None:
            model = _model_from_alias

        early = self._try_early_resolve_deployments_for_model_not_in_names(
            model=model, request_team_id=request_team_id
        )
        if early is not None:
            return early

        ## get healthy deployments
        ### get all deployments
        healthy_deployments = self._get_all_deployments(
            model_name=model, team_id=request_team_id
        )
        _pre_model_access_group_filter_len = len(healthy_deployments)
        healthy_deployments = self._filter_deployments_by_model_access_groups(
            model=model,
            healthy_deployments=healthy_deployments,
            request_kwargs=request_kwargs,
            request_team_id=request_team_id,
        )
        _access_group_filter_emptied_candidates = (
            _pre_model_access_group_filter_len > 0 and len(healthy_deployments) == 0
        )

        if len(healthy_deployments) == 0:
            # check if the user sent in a deployment name instead
            # Do not fall back when access-group filtering removed every candidate;
            # _get_deployment_by_litellm_model does not re-apply that filter.
            if _pre_model_access_group_filter_len == 0:
                _litellm_model_deployments = self._get_deployment_by_litellm_model(
                    model=model
                )
                healthy_deployments = self._filter_deployments_by_model_access_groups(
                    model=model,
                    healthy_deployments=_litellm_model_deployments,
                    request_kwargs=request_kwargs,
                    request_team_id=request_team_id,
                )
                # If the litellm-model lookup produced candidates that access-group
                # filtering then removed, treat this the same as the by-name path
                # being emptied: prevent default-model fallback from bypassing the
                # restriction (the fallback model may have no access_groups and
                # would short-circuit the filter).
                if (
                    len(_litellm_model_deployments) > 0
                    and len(healthy_deployments) == 0
                ):
                    _access_group_filter_emptied_candidates = True

        if verbose_router_logger.isEnabledFor(logging.DEBUG):
            verbose_router_logger.debug(
                f"initial list of deployments: {healthy_deployments}"
            )

        if len(healthy_deployments) == 0:
            # Check for default fallbacks if no deployments are found for the requested model
            # Do not fall back to another model when access-group filtering removed every
            # candidate for the requested name: re-filtering the fallback model can be a
            # no-op when it has no access_groups, incorrectly serving a different model.
            if (
                self._has_default_fallbacks()
                and not _access_group_filter_emptied_candidates
            ):
                fallback_model = self._get_first_default_fallback()
                if fallback_model:
                    verbose_router_logger.info(
                        f"Model '{model}' not found. Attempting to use default fallback model '{fallback_model}'."
                    )
                    # Re-assign model to the fallback and try to get deployments again
                    model = fallback_model
                    healthy_deployments = self._get_all_deployments(
                        model_name=model, team_id=request_team_id
                    )
                    healthy_deployments = (
                        self._filter_deployments_by_model_access_groups(
                            model=model,
                            healthy_deployments=healthy_deployments,
                            request_kwargs=request_kwargs,
                            request_team_id=request_team_id,
                        )
                    )

            # If still no deployments after checking for fallbacks, raise an error
            if len(healthy_deployments) == 0:
                if self.get_model_list(model_name=model) is None:
                    message = f"You passed in model={model}. There is no 'model_name' with this string".format(
                        model
                    )
                else:
                    message = f"You passed in model={model}. There are no healthy deployments for this model".format(
                        model
                    )

                raise litellm.BadRequestError(
                    message=message,
                    model=model,
                    llm_provider="",
                )

        if litellm.model_alias_map and model in litellm.model_alias_map:
            model = litellm.model_alias_map[
                model
            ]  # update the model to the actual value if an alias has been passed in

        return model, healthy_deployments

    def _filter_deployments_by_model_access_groups(
        self,
        model: str,
        healthy_deployments: List,
        request_kwargs: Optional[Dict],
        request_team_id: Optional[str],
    ) -> List:
        """
        Restrict candidate deployments to caller-authorized model access groups.

        This is only applied when:
        - request metadata includes `user_api_key_auth`, and
        - caller permissions for this model are access-group-only
          (no explicit model, wildcard, or all-proxy grants).
        """
        if not healthy_deployments or request_kwargs is None:
            return healthy_deployments

        metadata = request_kwargs.get("metadata") or {}
        litellm_metadata = request_kwargs.get("litellm_metadata") or {}
        user_api_key_auth = metadata.get("user_api_key_auth") or litellm_metadata.get(
            "user_api_key_auth"
        )
        if user_api_key_auth is None:
            return healthy_deployments

        object_models = set(getattr(user_api_key_auth, "models", []) or [])
        object_team_models = set(getattr(user_api_key_auth, "team_models", []) or [])
        allowed_models = object_models | object_team_models
        if not allowed_models:
            return healthy_deployments

        # If caller has direct model/wildcard/all-proxy access, do not constrain
        # deployment choice by access group.
        if (
            model in allowed_models
            or "*" in allowed_models
            or "all-proxy-models" in allowed_models
        ):
            return healthy_deployments

        access_groups_for_model = self.get_model_access_groups(
            model_name=model, team_id=request_team_id
        )
        if len(access_groups_for_model) == 0:
            return healthy_deployments

        allowed_access_groups = set(access_groups_for_model.keys()) & allowed_models
        if not allowed_access_groups:
            # No overlap means this request was not authorized via model access
            # group membership for this model, so do not force group filtering.
            return healthy_deployments

        filtered_deployments = []
        for deployment in healthy_deployments:
            deployment_model_info = deployment.get("model_info") or {}
            deployment_access_groups = set(
                deployment_model_info.get("access_groups", []) or []
            )
            if deployment_access_groups & allowed_access_groups:
                filtered_deployments.append(deployment)

        return filtered_deployments

    async def async_get_healthy_deployments(
        self,
        model: str,
        request_kwargs: Dict,
        messages: Optional[List[Dict[str, str]]] = None,
        input: Optional[Union[str, List]] = None,
        specific_deployment: Optional[bool] = False,
        parent_otel_span: Optional[Span] = None,
    ) -> Union[List[Dict], Dict]:
        """
        Get the healthy deployments for a model.

        Returns:
        - List[Dict], if multiple models chosen
        *OR*
        - Dict, if specific model chosen
        """

        model, healthy_deployments = self._common_checks_available_deployment(
            model=model,
            messages=messages,
            input=input,
            specific_deployment=specific_deployment,
            request_kwargs=request_kwargs,
        )  # type: ignore

        # IF TEAM ID SPECIFIED ON MODEL, AND REQUEST CONTAINS USER_API_KEY_TEAM_ID, FILTER OUT MODELS THAT ARE NOT IN THE TEAM
        ## THIS PREVENTS WRITING FILES OF OTHER TEAMS TO MODELS THAT ARE TEAM-ONLY MODELS
        healthy_deployments = filter_team_based_models(
            healthy_deployments=healthy_deployments,
            request_kwargs=request_kwargs,
        )

        if verbose_router_logger.isEnabledFor(logging.DEBUG):
            verbose_router_logger.debug(
                f"healthy_deployments after team filter: {healthy_deployments}"
            )

        healthy_deployments = filter_web_search_deployments(
            healthy_deployments=healthy_deployments,
            request_kwargs=request_kwargs,
        )

        if verbose_router_logger.isEnabledFor(logging.DEBUG):
            verbose_router_logger.debug(
                f"healthy_deployments after web search filter: {healthy_deployments}"
            )

        if isinstance(healthy_deployments, dict):
            if (healthy_deployments.get("model_info") or {}).get("blocked") is True:
                raise litellm.ServiceUnavailableError(
                    message=f"Model '{model}' is currently paused and cannot accept requests.",
                    model=model,
                    llm_provider="",
                )
            return healthy_deployments

        # Health-check-based filtering (before cooldown)
        healthy_deployments = (
            await self._async_filter_health_check_unhealthy_deployments(
                healthy_deployments=healthy_deployments,
                parent_otel_span=parent_otel_span,
            )
        )

        cooldown_deployments = await _async_get_cooldown_deployments(
            litellm_router_instance=self, parent_otel_span=parent_otel_span
        )
        if verbose_router_logger.isEnabledFor(logging.DEBUG):
            verbose_router_logger.debug(f"cooldown deployments: {cooldown_deployments}")
        _pre_cooldown_deployments = healthy_deployments
        healthy_deployments = self._filter_cooldown_deployments(
            healthy_deployments=healthy_deployments,
            cooldown_deployments=cooldown_deployments,
        )
        # Safety net: only bypass cooldown filter when health-check routing is
        # driving cooldown (i.e. allowed_fails_policy is set). Without a policy,
        # cooldowns are from real request failures and must not be bypassed.
        if (
            not healthy_deployments
            and self.enable_health_check_routing
            and self.allowed_fails_policy is not None
        ):
            verbose_router_logger.warning(
                "All deployments in cooldown via health-check routing, bypassing cooldown filter"
            )
            healthy_deployments = _pre_cooldown_deployments

        healthy_deployments = self._filter_blocked_deployments(healthy_deployments)

        healthy_deployments = await self.async_callback_filter_deployments(
            model=model,
            healthy_deployments=healthy_deployments,
            messages=(
                cast(List[AllMessageValues], messages) if messages is not None else None
            ),
            request_kwargs=request_kwargs,
            parent_otel_span=parent_otel_span,
        )

        if self.enable_pre_call_checks and messages is not None:
            healthy_deployments = self._pre_call_checks(
                model=model,
                healthy_deployments=cast(List[Dict], healthy_deployments),
                messages=messages,
                request_kwargs=request_kwargs,
            )
        # check if user wants to do tag based routing
        healthy_deployments = await get_deployments_for_tag(  # type: ignore
            llm_router_instance=self,
            model=model,
            request_kwargs=request_kwargs,
            healthy_deployments=healthy_deployments,
            metadata_variable_name=self._get_metadata_variable_name_from_kwargs(
                request_kwargs
            ),
        )

        ## ORDER FILTERING ## -> if user set 'order' in deployments, return deployments with lowest order (e.g. order=1 > order=2)
        _target_order = (request_kwargs or {}).pop("_target_order", None)
        healthy_deployments = litellm.utils._get_order_filtered_deployments(
            cast(List[Dict], healthy_deployments), target_order=_target_order
        )

        ## WEIGHTED FAILOVER EXCLUSION ## -> drop deployments already tried in
        ## this request via weighted-failover. Always honored, regardless of the
        ## router-level flag, so a stale exclusion key on kwargs cannot escape.
        _excluded_deployment_ids = (request_kwargs or {}).pop(
            "_excluded_deployment_ids", None
        )
        healthy_deployments = litellm.utils._get_excluded_filtered_deployments(
            cast(List[Dict], healthy_deployments),
            excluded_deployment_ids=_excluded_deployment_ids,
        )

        if len(healthy_deployments) == 0:
            exception = await async_raise_no_deployment_exception(
                litellm_router_instance=self,
                model=model,
                parent_otel_span=parent_otel_span,
            )
            raise exception

        return healthy_deployments

    async def async_get_available_deployment(
        self,
        model: str,
        request_kwargs: Dict,
        messages: Optional[List[Dict[str, str]]] = None,
        input: Optional[Union[str, List]] = None,
        specific_deployment: Optional[bool] = False,
    ):
        """
        Async implementation of 'get_available_deployments'.

        Allows all cache calls to be made async => 10x perf impact (8rps -> 100 rps).
        """
        if (
            self.routing_strategy != "usage-based-routing-v2"
            and self.routing_strategy != "simple-shuffle"
            and self.routing_strategy != "cost-based-routing"
            and self.routing_strategy != "latency-based-routing"
            and self.routing_strategy != "least-busy"
        ):  # prevent regressions for other routing strategies, that don't have async get available deployments implemented.
            return self.get_available_deployment(
                model=model,
                messages=messages,
                input=input,
                specific_deployment=specific_deployment,
                request_kwargs=request_kwargs,
            )
        try:
            parent_otel_span = _get_parent_otel_span_from_kwargs(request_kwargs)

            #########################################################
            # Execute Pre-Routing Hooks
            # this hook can modify the model, messages before the routing decision is made
            #########################################################
            pre_routing_hook_response = await self.async_pre_routing_hook(
                model=model,
                request_kwargs=request_kwargs,
                messages=messages,
                input=input,
                specific_deployment=specific_deployment,
            )
            if pre_routing_hook_response is not None:
                model = pre_routing_hook_response.model
                messages = pre_routing_hook_response.messages
            #########################################################

            # Resolve the strategy and logger AFTER the pre-routing hook, since
            # the hook can replace `model` and routing-group lookup must key
            # off the final model name.
            strategy, strategy_selector = self._get_routing_context(model)

            healthy_deployments = await self.async_get_healthy_deployments(
                model=model,
                request_kwargs=request_kwargs,
                messages=messages,
                input=input,
                specific_deployment=specific_deployment,
                parent_otel_span=parent_otel_span,
            )
            if isinstance(healthy_deployments, dict):
                return healthy_deployments

            # When encrypted content affinity pins to a specific deployment,
            if (
                request_kwargs.get("_encrypted_content_affinity_pinned")
                and len(healthy_deployments) == 1
            ):
                return healthy_deployments[0]

            start_time = time.time()
            if strategy == "simple-shuffle":
                return simple_shuffle(
                    llm_router_instance=self,
                    healthy_deployments=healthy_deployments,
                    model=model,
                )
            deployment = await self._select_deployment_async(
                strategy=strategy,
                selector=strategy_selector,
                model=model,
                healthy_deployments=healthy_deployments,  # type: ignore
                messages=messages,
                input=input,
                request_kwargs=request_kwargs,
            )
            if deployment is None:
                exception = await async_raise_no_deployment_exception(
                    litellm_router_instance=self,
                    model=model,
                    parent_otel_span=parent_otel_span,
                )
                raise exception
            verbose_router_logger.info(
                f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment)} for model: {model}"
            )

            end_time = time.time()
            _duration = end_time - start_time
            asyncio.create_task(
                self.service_logger_obj.async_service_success_hook(
                    service=ServiceTypes.ROUTER,
                    duration=_duration,
                    call_type="<routing_strategy>.async_get_available_deployments",
                    parent_otel_span=parent_otel_span,
                    start_time=start_time,
                    end_time=end_time,
                )
            )

            return deployment
        except Exception as e:
            traceback_exception = traceback.format_exc()
            # if router rejects call -> log to langfuse/otel/etc.
            if request_kwargs is not None:
                logging_obj = request_kwargs.get("litellm_logging_obj", None)

                if logging_obj is not None:
                    ## LOGGING
                    threading.Thread(
                        target=logging_obj.failure_handler,
                        args=(e, traceback_exception),
                    ).start()  # log response
                    # Handle any exceptions that might occur during streaming
                    asyncio.create_task(
                        logging_obj.async_failure_handler(e, traceback_exception)  # type: ignore
                    )
            raise e

    async def async_get_available_deployment_for_pass_through(
        self,
        model: str,
        request_kwargs: Dict,
        messages: Optional[List[Dict[str, str]]] = None,
        input: Optional[Union[str, List]] = None,
        specific_deployment: Optional[bool] = False,
    ):
        """
        Async version of get_available_deployment_for_pass_through

        Only returns deployments configured with use_in_pass_through=True
        """
        try:
            parent_otel_span = _get_parent_otel_span_from_kwargs(request_kwargs)

            # 1. Execute pre-routing hook
            pre_routing_hook_response = await self.async_pre_routing_hook(
                model=model,
                request_kwargs=request_kwargs,
                messages=messages,
                input=input,
                specific_deployment=specific_deployment,
            )
            if pre_routing_hook_response is not None:
                model = pre_routing_hook_response.model
                messages = pre_routing_hook_response.messages

            # 2. Get healthy deployments
            healthy_deployments = await self.async_get_healthy_deployments(
                model=model,
                request_kwargs=request_kwargs,
                messages=messages,
                input=input,
                specific_deployment=specific_deployment,
                parent_otel_span=parent_otel_span,
            )

            # 3. If specific deployment returned, verify if it supports pass-through
            if isinstance(healthy_deployments, dict):
                if (healthy_deployments.get("model_info") or {}).get("blocked") is True:
                    raise litellm.ServiceUnavailableError(
                        message=f"Model '{model}' is currently paused and cannot accept requests.",
                        model=model,
                        llm_provider="",
                    )
                litellm_params = healthy_deployments.get("litellm_params", {})
                if litellm_params.get("use_in_pass_through"):
                    return healthy_deployments
                else:
                    raise litellm.BadRequestError(
                        message=f"Deployment {healthy_deployments.get('model_info', {}).get('id')} does not support pass-through endpoint (use_in_pass_through=False)",
                        model=model,
                        llm_provider="",
                    )

            # 4. Filter deployments that support pass-through
            pass_through_deployments = self._filter_pass_through_deployments(
                healthy_deployments=healthy_deployments
            )

            if len(pass_through_deployments) == 0:
                raise litellm.BadRequestError(
                    message=f"Model {model} has no deployments configured with use_in_pass_through=True. Please add use_in_pass_through: true to the deployment configuration",
                    model=model,
                    llm_provider="",
                )

            # 5. Apply load balancing strategy
            start_time = time.perf_counter()
            strategy, strategy_selector = self._get_routing_context(model)
            if strategy == "simple-shuffle":
                return simple_shuffle(
                    llm_router_instance=self,
                    healthy_deployments=pass_through_deployments,
                    model=model,
                )
            deployment = await self._select_deployment_async(
                strategy=strategy,
                selector=strategy_selector,
                model=model,
                healthy_deployments=pass_through_deployments,  # type: ignore
                messages=messages,
                input=input,
                request_kwargs=request_kwargs,
            )

            if deployment is None:
                exception = await async_raise_no_deployment_exception(
                    litellm_router_instance=self,
                    model=model,
                    parent_otel_span=parent_otel_span,
                )
                raise exception

            verbose_router_logger.info(
                f"async_get_available_deployment_for_pass_through model: {model}, selected deployment: {self.print_deployment(deployment)}"
            )

            end_time = time.perf_counter()
            _duration = end_time - start_time
            asyncio.create_task(
                self.service_logger_obj.async_service_success_hook(
                    service=ServiceTypes.ROUTER,
                    duration=_duration,
                    call_type="<routing_strategy>.async_get_available_deployments",
                    parent_otel_span=parent_otel_span,
                    start_time=start_time,
                    end_time=end_time,
                )
            )

            return deployment
        except Exception as e:
            traceback_exception = traceback.format_exc()
            if request_kwargs is not None:
                logging_obj = request_kwargs.get("litellm_logging_obj", None)
                if logging_obj is not None:
                    threading.Thread(
                        target=logging_obj.failure_handler,
                        args=(e, traceback_exception),
                    ).start()
                    asyncio.create_task(
                        logging_obj.async_failure_handler(e, traceback_exception)  # type: ignore
                    )
            raise e

    async def async_pre_routing_hook(
        self,
        model: str,
        request_kwargs: Dict,
        messages: Optional[List[Dict[str, Any]]] = None,
        input: Optional[Union[str, List]] = None,
        specific_deployment: Optional[bool] = False,
    ) -> Optional[PreRoutingHookResponse]:
        """
        This hook is called before the routing decision is made.

        Used for the litellm auto-router to modify the request before the routing decision is made.
        """
        #########################################################
        # Check if any auto-router should be used
        #########################################################
        if model in self.auto_routers:
            return await self.auto_routers[model].async_pre_routing_hook(
                model=model,
                request_kwargs=request_kwargs,
                messages=messages,
                input=input,
                specific_deployment=specific_deployment,
            )

        #########################################################
        # Check if any complexity-router should be used
        #########################################################
        if model in self.complexity_routers:
            return await self.complexity_routers[model].async_pre_routing_hook(
                model=model,
                request_kwargs=request_kwargs,
                messages=messages,
                input=input,
                specific_deployment=specific_deployment,
            )

        #########################################################
        # Check if an adaptive-router should be used
        #########################################################
        adaptive_router = self.adaptive_routers.get(model)
        if adaptive_router is not None:
            return await adaptive_router.async_pre_routing_hook(
                model=model,
                request_kwargs=request_kwargs,
                messages=messages,
                input=input,
                specific_deployment=specific_deployment,
            )

        #########################################################
        # Check if any quality-router should be used
        #########################################################
        if model in self.quality_routers:
            return await self.quality_routers[model].async_pre_routing_hook(
                model=model,
                request_kwargs=request_kwargs,
                messages=messages,
                input=input,
                specific_deployment=specific_deployment,
            )

        return None

    def get_available_deployment(
        self,
        model: str,
        messages: Optional[List[Dict[str, str]]] = None,
        input: Optional[Union[str, List]] = None,
        specific_deployment: Optional[bool] = False,
        request_kwargs: Optional[Dict] = None,
    ):
        """
        Returns the deployment based on routing strategy
        """
        # users need to explicitly call a specific deployment, by setting `specific_deployment = True` as completion()/embedding() kwarg
        # When this was no explicit we had several issues with fallbacks timing out

        model, healthy_deployments = self._common_checks_available_deployment(
            model=model,
            messages=messages,
            input=input,
            specific_deployment=specific_deployment,
            request_kwargs=request_kwargs,
        )

        if isinstance(healthy_deployments, dict):
            if (healthy_deployments.get("model_info") or {}).get("blocked") is True:
                raise litellm.ServiceUnavailableError(
                    message=f"Model '{model}' is currently paused and cannot accept requests.",
                    model=model,
                    llm_provider="",
                )
            return healthy_deployments

        parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs(
            request_kwargs
        )

        # Health-check-based filtering (before cooldown)
        healthy_deployments = self._filter_health_check_unhealthy_deployments(
            healthy_deployments=healthy_deployments,
            parent_otel_span=parent_otel_span,
        )

        cooldown_deployments = _get_cooldown_deployments(
            litellm_router_instance=self, parent_otel_span=parent_otel_span
        )
        _pre_cooldown_deployments = healthy_deployments
        healthy_deployments = self._filter_cooldown_deployments(
            healthy_deployments=healthy_deployments,
            cooldown_deployments=cooldown_deployments,
        )
        if (
            not healthy_deployments
            and self.enable_health_check_routing
            and self.allowed_fails_policy is not None
        ):
            verbose_router_logger.warning(
                "All deployments in cooldown via health-check routing, bypassing cooldown filter"
            )
            healthy_deployments = _pre_cooldown_deployments

        healthy_deployments = self._filter_blocked_deployments(healthy_deployments)

        # filter pre-call checks
        if self.enable_pre_call_checks and messages is not None:
            healthy_deployments = self._pre_call_checks(
                model=model,
                healthy_deployments=healthy_deployments,
                messages=messages,
                request_kwargs=request_kwargs,
            )

        ## ORDER FILTERING ## -> if user set 'order' in deployments, return deployments with lowest order (e.g. order=1 > order=2)
        _target_order = (request_kwargs or {}).pop("_target_order", None)
        healthy_deployments = litellm.utils._get_order_filtered_deployments(
            healthy_deployments, target_order=_target_order
        )

        ## WEIGHTED FAILOVER EXCLUSION ## -> drop deployments already tried in
        ## this request via weighted-failover. See async counterpart in
        ## async_get_healthy_deployments for details.
        _excluded_deployment_ids = (request_kwargs or {}).pop(
            "_excluded_deployment_ids", None
        )
        healthy_deployments = litellm.utils._get_excluded_filtered_deployments(
            healthy_deployments,
            excluded_deployment_ids=_excluded_deployment_ids,
        )

        if len(healthy_deployments) == 0:
            model_ids = self.get_model_ids(model_name=model)
            _cooldown_time = self.cooldown_cache.get_min_cooldown(
                model_ids=model_ids, parent_otel_span=parent_otel_span
            )
            _cooldown_list = _get_cooldown_deployments(
                litellm_router_instance=self, parent_otel_span=parent_otel_span
            )
            raise RouterRateLimitError(
                model=model,
                cooldown_time=_cooldown_time,
                enable_pre_call_checks=self.enable_pre_call_checks,
                cooldown_list=_cooldown_list,
            )

        strategy, strategy_selector = self._get_routing_context(model)
        if strategy == "simple-shuffle":
            # if users pass rpm or tpm, we do a random weighted pick - based on rpm/tpm
            ############## Check 'weight' param set for weighted pick #################
            return simple_shuffle(
                llm_router_instance=self,
                healthy_deployments=healthy_deployments,
                model=model,
            )
        deployment = self._select_deployment_sync(
            strategy=strategy,
            selector=strategy_selector,
            model=model,
            healthy_deployments=healthy_deployments,  # type: ignore
            messages=messages,
            input=input,
            request_kwargs=request_kwargs,
        )

        if deployment is None:
            verbose_router_logger.info(
                f"get_available_deployment for model: {model}, No deployment available"
            )
            model_ids = self.get_model_ids(model_name=model)
            _cooldown_time = self.cooldown_cache.get_min_cooldown(
                model_ids=model_ids, parent_otel_span=parent_otel_span
            )
            _cooldown_list = _get_cooldown_deployments(
                litellm_router_instance=self, parent_otel_span=parent_otel_span
            )
            raise RouterRateLimitError(
                model=model,
                cooldown_time=_cooldown_time,
                enable_pre_call_checks=self.enable_pre_call_checks,
                cooldown_list=_cooldown_list,
            )
        verbose_router_logger.info(
            f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment)} for model: {model}"
        )
        return deployment

    def get_available_deployment_for_pass_through(
        self,
        model: str,
        messages: Optional[List[Dict[str, str]]] = None,
        input: Optional[Union[str, List]] = None,
        specific_deployment: Optional[bool] = False,
        request_kwargs: Optional[Dict] = None,
    ):
        """
        Returns deployments available for pass-through endpoints (based on load balancing strategy)

        Similar to get_available_deployment, but only returns deployments with use_in_pass_through=True

        Args:
            model: Model name
            messages: Optional list of messages
            input: Optional input data
            specific_deployment: Whether to find a specific deployment
            request_kwargs: Optional request parameters

        Returns:
            Dict: Selected deployment configuration

        Raises:
            BadRequestError: If no deployment is configured with use_in_pass_through=True
            RouterRateLimitError: If no pass-through deployments are available
        """
        # 1. Perform common checks to get healthy deployments list
        model, healthy_deployments = self._common_checks_available_deployment(
            model=model,
            messages=messages,
            input=input,
            specific_deployment=specific_deployment,
        )

        # 2. If the returned is a specific deployment (Dict), verify and return directly
        if isinstance(healthy_deployments, dict):
            if (healthy_deployments.get("model_info") or {}).get("blocked") is True:
                raise litellm.ServiceUnavailableError(
                    message=f"Model '{model}' is currently paused and cannot accept requests.",
                    model=model,
                    llm_provider="",
                )
            litellm_params = healthy_deployments.get("litellm_params", {})
            if litellm_params.get("use_in_pass_through"):
                return healthy_deployments
            else:
                # Specific deployment does not support pass-through
                raise litellm.BadRequestError(
                    message=f"Deployment {healthy_deployments.get('model_info', {}).get('id')} does not support pass-through endpoint (use_in_pass_through=False)",
                    model=model,
                    llm_provider="",
                )

        # 3. Filter deployments that support pass-through
        pass_through_deployments = self._filter_pass_through_deployments(
            healthy_deployments=healthy_deployments
        )

        if len(pass_through_deployments) == 0:
            # No deployments support pass-through
            raise litellm.BadRequestError(
                message=f"Model {model} has no deployment configured with use_in_pass_through=True. Please add use_in_pass_through: true in the deployment configuration",
                model=model,
                llm_provider="",
            )

        # 4. Apply health-check and cooldown filtering
        parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs(
            request_kwargs
        )
        pass_through_deployments = self._filter_health_check_unhealthy_deployments(
            healthy_deployments=pass_through_deployments,
            parent_otel_span=parent_otel_span,
        )
        cooldown_deployments = _get_cooldown_deployments(
            litellm_router_instance=self, parent_otel_span=parent_otel_span
        )
        pass_through_deployments = self._filter_cooldown_deployments(
            healthy_deployments=pass_through_deployments,
            cooldown_deployments=cooldown_deployments,
        )
        pass_through_deployments = self._filter_blocked_deployments(
            pass_through_deployments
        )

        # 5. Apply pre-call checks (if enabled)
        if self.enable_pre_call_checks and messages is not None:
            pass_through_deployments = self._pre_call_checks(
                model=model,
                healthy_deployments=pass_through_deployments,
                messages=messages,
                request_kwargs=request_kwargs,
            )

        if len(pass_through_deployments) == 0:
            model_ids = self.get_model_ids(model_name=model)
            _cooldown_time = self.cooldown_cache.get_min_cooldown(
                model_ids=model_ids, parent_otel_span=parent_otel_span
            )
            _cooldown_list = _get_cooldown_deployments(
                litellm_router_instance=self, parent_otel_span=parent_otel_span
            )
            raise RouterRateLimitError(
                model=model,
                cooldown_time=_cooldown_time,
                enable_pre_call_checks=self.enable_pre_call_checks,
                cooldown_list=_cooldown_list,
            )

        # 6. Apply load balancing strategy
        strategy, strategy_selector = self._get_routing_context(model)
        if strategy == "simple-shuffle":
            return simple_shuffle(
                llm_router_instance=self,
                healthy_deployments=pass_through_deployments,
                model=model,
            )
        deployment = self._select_deployment_sync(
            strategy=strategy,
            selector=strategy_selector,
            model=model,
            healthy_deployments=pass_through_deployments,  # type: ignore
            messages=messages,
            input=input,
            request_kwargs=request_kwargs,
        )

        if deployment is None:
            verbose_router_logger.info(
                f"get_available_deployment_for_pass_through model: {model}, no available deployments"
            )
            model_ids = self.get_model_ids(model_name=model)
            _cooldown_time = self.cooldown_cache.get_min_cooldown(
                model_ids=model_ids, parent_otel_span=parent_otel_span
            )
            _cooldown_list = _get_cooldown_deployments(
                litellm_router_instance=self, parent_otel_span=parent_otel_span
            )
            raise RouterRateLimitError(
                model=model,
                cooldown_time=_cooldown_time,
                enable_pre_call_checks=self.enable_pre_call_checks,
                cooldown_list=_cooldown_list,
            )

        verbose_router_logger.info(
            f"get_available_deployment_for_pass_through model: {model}, selected deployment: {self.print_deployment(deployment)}"
        )
        return deployment

    def _filter_cooldown_deployments(
        self, healthy_deployments: List[Dict], cooldown_deployments: List[str]
    ) -> List[Dict]:
        """
        Filters out the deployments currently cooling down from the list of healthy deployments

        Args:
            healthy_deployments: List of healthy deployments
            cooldown_deployments: List of model_ids cooling down. cooldown_deployments is a list of model_id's cooling down, cooldown_deployments = ["16700539-b3cd-42f4-b426-6a12a1bb706a", "16700539-b3cd-42f4-b426-7899"]

        Returns:
            List of healthy deployments
        """
        if verbose_router_logger.isEnabledFor(logging.DEBUG):
            verbose_router_logger.debug(f"cooldown deployments: {cooldown_deployments}")
        # Convert to set for O(1) lookup and use list comprehension for O(n) filtering
        cooldown_set = set(cooldown_deployments)
        return [
            deployment
            for deployment in healthy_deployments
            if deployment["model_info"]["id"] not in cooldown_set
        ]

    def _filter_blocked_deployments(
        self, healthy_deployments: List[Dict]
    ) -> List[Dict]:
        """
        Filters out deployments that an admin has paused via `LiteLLM_ProxyModelTable.blocked`.

        Applied alongside the cooldown filter on every routing entry point that calls
        `_common_checks_available_deployment` directly — the primary sync/async path,
        the sync pass-through path, and the retry / health-check helpers — so paused
        deployments never serve a request. The async pass-through path inherits this
        filter through its delegation to `async_get_healthy_deployments`.
        """
        return [
            deployment
            for deployment in healthy_deployments
            if (deployment.get("model_info") or {}).get("blocked") is not True
        ]

    @staticmethod
    def _is_deployment_blocked(deployment: "Deployment") -> bool:
        """
        Returns True when a `Deployment` Pydantic instance carries the admin-paused
        flag. Used by credential-lookup helpers so passthrough file / batch endpoints
        cannot bypass the pause by resolving credentials directly.
        """
        model_info = getattr(deployment, "model_info", None)
        if model_info is None:
            return False
        return getattr(model_info, "blocked", None) is True

    async def _async_filter_health_check_unhealthy_deployments(
        self,
        healthy_deployments: List[Dict],
        parent_otel_span: Optional[Span] = None,
    ) -> List[Dict]:
        """
        Filter out deployments marked unhealthy by background health checks.
        No-op when enable_health_check_routing is False.
        Returns all deployments if health state is unavailable, stale, or would
        exclude every candidate (safety net).
        """
        if not self.enable_health_check_routing:
            return healthy_deployments

        # When allowed_fails_policy is set, cooldown is the sole routing exclusion
        # mechanism -- skip the binary health check filter so the policy threshold
        # is respected before any deployment is excluded.
        if self.allowed_fails_policy is not None:
            return healthy_deployments

        unhealthy_ids = (
            await self.health_state_cache.async_get_unhealthy_deployment_ids(
                parent_otel_span=parent_otel_span
            )
        )
        if not unhealthy_ids:
            return healthy_deployments

        filtered = [
            d for d in healthy_deployments if d["model_info"]["id"] not in unhealthy_ids
        ]

        if not filtered:
            verbose_router_logger.warning(
                "All deployments marked unhealthy by health checks, bypassing health filter"
            )
            return healthy_deployments

        return filtered

    def _filter_health_check_unhealthy_deployments(
        self,
        healthy_deployments: List[Dict],
        parent_otel_span: Optional[Span] = None,
    ) -> List[Dict]:
        """Sync version of _async_filter_health_check_unhealthy_deployments."""
        if not self.enable_health_check_routing:
            return healthy_deployments

        if self.allowed_fails_policy is not None:
            return healthy_deployments

        unhealthy_ids = self.health_state_cache.get_unhealthy_deployment_ids(
            parent_otel_span=parent_otel_span
        )
        if not unhealthy_ids:
            return healthy_deployments

        filtered = [
            d for d in healthy_deployments if d["model_info"]["id"] not in unhealthy_ids
        ]

        if not filtered:
            verbose_router_logger.warning(
                "All deployments marked unhealthy by health checks, bypassing health filter"
            )
            return healthy_deployments

        return filtered

    def _filter_pass_through_deployments(
        self, healthy_deployments: List[Dict]
    ) -> List[Dict]:
        """
        Filter out deployments configured with use_in_pass_through=True

        Args:
            healthy_deployments: List of healthy deployments

        Returns:
            List[Dict]: Only includes a list of deployments that support pass-through
        """
        verbose_router_logger.debug(
            f"Filter pass-through deployments from {len(healthy_deployments)} healthy deployments"
        )

        pass_through_deployments = [
            deployment
            for deployment in healthy_deployments
            if deployment.get("litellm_params", {}).get("use_in_pass_through", False)
        ]

        verbose_router_logger.debug(
            f"Found {len(pass_through_deployments)} deployments with pass-through enabled"
        )

        return pass_through_deployments

    def _track_deployment_metrics(
        self, deployment, parent_otel_span: Optional[Span], response=None
    ):
        """
        Tracks successful requests rpm usage.
        """
        try:
            model_id = deployment.get("model_info", {}).get("id", None)
            if response is None:
                # update self.deployment_stats
                if model_id is not None:
                    self._update_usage(
                        model_id, parent_otel_span
                    )  # update in-memory cache for tracking
        except Exception as e:
            verbose_router_logger.error(f"Error in _track_deployment_metrics: {str(e)}")

    def get_num_retries_from_retry_policy(
        self, exception: Exception, model_group: Optional[str] = None
    ):
        return _get_num_retries_from_retry_policy(
            exception=exception,
            model_group=model_group,
            model_group_retry_policy=self.model_group_retry_policy,
            retry_policy=self.retry_policy,
        )

    def get_allowed_fails_from_policy(self, exception: Exception):
        """
        BadRequestErrorRetries: Optional[int] = None
        AuthenticationErrorRetries: Optional[int] = None
        TimeoutErrorRetries: Optional[int] = None
        RateLimitErrorRetries: Optional[int] = None
        ContentPolicyViolationErrorRetries: Optional[int] = None
        """
        # if we can find the exception then in the retry policy -> return the number of retries
        allowed_fails_policy: Optional[AllowedFailsPolicy] = self.allowed_fails_policy

        if allowed_fails_policy is None:
            return None

        if (
            isinstance(exception, litellm.AuthenticationError)
            and allowed_fails_policy.AuthenticationErrorAllowedFails is not None
        ):
            return allowed_fails_policy.AuthenticationErrorAllowedFails
        if (
            isinstance(exception, litellm.Timeout)
            and allowed_fails_policy.TimeoutErrorAllowedFails is not None
        ):
            return allowed_fails_policy.TimeoutErrorAllowedFails
        if (
            isinstance(exception, litellm.RateLimitError)
            and allowed_fails_policy.RateLimitErrorAllowedFails is not None
        ):
            return allowed_fails_policy.RateLimitErrorAllowedFails
        if (
            isinstance(exception, litellm.ContentPolicyViolationError)
            and allowed_fails_policy.ContentPolicyViolationErrorAllowedFails is not None
        ):
            return allowed_fails_policy.ContentPolicyViolationErrorAllowedFails
        if (
            isinstance(exception, litellm.BadRequestError)
            and allowed_fails_policy.BadRequestErrorAllowedFails is not None
        ):
            return allowed_fails_policy.BadRequestErrorAllowedFails

    def _initialize_alerting(self):
        from litellm.integrations.SlackAlerting.slack_alerting import SlackAlerting

        if self.alerting_config is None:
            return

        router_alerting_config: AlertingConfig = self.alerting_config

        _slack_alerting_logger = SlackAlerting(
            alerting_threshold=router_alerting_config.alerting_threshold,
            alerting=["slack"],
            default_webhook_url=router_alerting_config.webhook_url,
        )

        self.slack_alerting_logger = _slack_alerting_logger

        litellm.logging_callback_manager.add_litellm_callback(_slack_alerting_logger)  # type: ignore
        litellm.logging_callback_manager.add_litellm_success_callback(
            _slack_alerting_logger.response_taking_too_long_callback
        )
        verbose_router_logger.info(
            "\033[94m\nInitialized Alerting for litellm.Router\033[0m\n"
        )

    def set_custom_routing_strategy(
        self, CustomRoutingStrategy: CustomRoutingStrategyBase
    ):
        """
        Sets get_available_deployment and async_get_available_deployment on an instanced of litellm.Router

        Use this to set your custom routing strategy

        Args:
            CustomRoutingStrategy: litellm.router.CustomRoutingStrategyBase
        """

        setattr(
            self,
            "get_available_deployment",
            CustomRoutingStrategy.get_available_deployment,
        )
        setattr(
            self,
            "async_get_available_deployment",
            CustomRoutingStrategy.async_get_available_deployment,
        )

    def flush_cache(self):
        litellm.cache = None
        self.cache.flush_cache()

    def reset(self):
        ## clean up on close
        litellm.success_callback = []
        litellm._async_success_callback = []
        litellm.failure_callback = []
        litellm._async_failure_callback = []
        self.retry_policy = None
        self.flush_cache()
