### Hide pydantic namespace conflict warnings globally ###
from __future__ import annotations

import warnings

warnings.filterwarnings("ignore", message=".*conflict with protected namespace.*")
# Suppress Pydantic 2.11+ deprecation warning about accessing model_fields on instances
# This warning can accumulate during streaming and cause memory leaks
warnings.filterwarnings(
    "ignore", message=".*Accessing the.*attribute on the instance is deprecated.*"
)
### INIT VARIABLES #########################
import threading
import os

# Load .env before any other litellm imports so env vars (e.g. LITELLM_UI_SESSION_DURATION) are available
import dotenv as _dotenv

if os.getenv("LITELLM_MODE", "DEV") == "DEV":
    _dotenv.load_dotenv()

from typing import (
    Callable,
    List,
    Optional,
    Dict,
    Union,
    Any,
    Literal,
    get_args,
    TYPE_CHECKING,
    Tuple,
    overload,
    Type,
)
from litellm.types.integrations.datadog import DatadogInitParams
from litellm._logging import (
    set_verbose,
    _turn_on_debug,
    verbose_logger,
    json_logs,
    _turn_on_json,
    log_level,
)
import re
from litellm.constants import (
    DEFAULT_BATCH_SIZE,
    DEFAULT_FLUSH_INTERVAL_SECONDS,
    ROUTER_MAX_FALLBACKS,
    DEFAULT_MAX_RETRIES,
    DEFAULT_REPLICATE_POLLING_RETRIES,
    DEFAULT_REPLICATE_POLLING_DELAY_SECONDS,
    LITELLM_CHAT_PROVIDERS,
    HUMANLOOP_PROMPT_CACHE_TTL_SECONDS,
    OPENAI_CHAT_COMPLETION_PARAMS,
    OPENAI_CHAT_COMPLETION_PARAMS as _openai_completion_params,  # backwards compatibility
    OPENAI_FINISH_REASONS,
    OPENAI_FINISH_REASONS as _openai_finish_reasons,  # backwards compatibility
    openai_compatible_endpoints,
    openai_compatible_providers,
    openai_text_completion_compatible_providers,
    _openai_like_providers,
    replicate_models,
    clarifai_models,
    huggingface_models,
    empower_models,
    together_ai_models,
    baseten_models,
    WANDB_MODELS,
    REPEATED_STREAMING_CHUNK_LIMIT,
    request_timeout,
    open_ai_embedding_models,
    cohere_embedding_models,
    bedrock_embedding_models,
    known_tokenizer_config,
    BEDROCK_INVOKE_PROVIDERS_LITERAL,
    BEDROCK_EMBEDDING_PROVIDERS_LITERAL,
    BEDROCK_CONVERSE_MODELS,
    DEFAULT_MAX_TOKENS,
    DEFAULT_SOFT_BUDGET,
    DEFAULT_ALLOWED_FAILS,
)
import httpx

# register_async_client_cleanup is lazy-loaded and called on first access

litellm_mode = os.getenv("LITELLM_MODE", "DEV")  # "PRODUCTION", "DEV"


####################################################
if set_verbose:
    _turn_on_debug()
####################################################
### Callbacks /Logging / Success / Failure Handlers #####
CALLBACK_TYPES = Union[str, Callable, "CustomLogger"]  # CustomLogger is lazy-loaded
input_callback: List[CALLBACK_TYPES] = []
success_callback: List[CALLBACK_TYPES] = []
failure_callback: List[CALLBACK_TYPES] = []
service_callback: List[CALLBACK_TYPES] = []
# logging_callback_manager is lazy-loaded via __getattr__
_custom_logger_compatible_callbacks_literal = Literal[
    "lago",
    "openmeter",
    "logfire",
    "literalai",
    "litellm_agent",
    "dynamic_rate_limiter",
    "dynamic_rate_limiter_v3",
    "langsmith",
    "prometheus",
    "otel",
    "datadog",
    "datadog_metrics",
    "datadog_llm_observability",
    "galileo",
    "braintrust",
    "arize",
    "arize_phoenix",
    "langtrace",
    "gcs_bucket",
    "azure_storage",
    "opik",
    "argilla",
    "mlflow",
    "langfuse",
    "langfuse_otel",
    "weave_otel",
    "pagerduty",
    "humanloop",
    "azure_sentinel",
    "gcs_pubsub",
    "agentops",
    "anthropic_cache_control_hook",
    "generic_api",
    "resend_email",
    "sendgrid_email",
    "smtp_email",
    "deepeval",
    "s3_v2",
    "aws_sqs",
    "vector_store_pre_call_hook",
    "dotprompt",
    "bitbucket",
    "gitlab",
    "cloudzero",
    "focus",
    "posthog",
    "levo",
]
cold_storage_custom_logger: Optional[_custom_logger_compatible_callbacks_literal] = None
logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None
_known_custom_logger_compatible_callbacks: List = list(
    get_args(_custom_logger_compatible_callbacks_literal)
)
callbacks: List[
    Union[
        Callable, _custom_logger_compatible_callbacks_literal, "CustomLogger"
    ]  # CustomLogger is lazy-loaded
] = []
callback_settings: Dict[str, Dict[str, Any]] = {}
initialized_langfuse_clients: int = 0
langfuse_default_tags: Optional[List[str]] = None
langsmith_batch_size: Optional[int] = None
prometheus_initialize_budget_metrics: Optional[bool] = False
require_auth_for_metrics_endpoint: Optional[bool] = False
argilla_batch_size: Optional[int] = None
datadog_use_v1: Optional[bool] = False  # if you want to use v1 datadog logged payload.
gcs_pub_sub_use_v1: Optional[
    bool
] = False  # if you want to use v1 gcs pubsub logged payload
generic_api_use_v1: Optional[
    bool
] = False  # if you want to use v1 generic api logged payload
argilla_transformation_object: Optional[Dict[str, Any]] = None
_async_input_callback: List[
    Union[str, Callable, "CustomLogger"]
] = (  # CustomLogger is lazy-loaded
    []
)  # internal variable - async custom callbacks are routed here.
_async_success_callback: List[
    Union[str, Callable, "CustomLogger"]
] = (  # CustomLogger is lazy-loaded
    []
)  # internal variable - async custom callbacks are routed here.
_async_failure_callback: List[
    Union[str, Callable, "CustomLogger"]
] = (  # CustomLogger is lazy-loaded
    []
)  # internal variable - async custom callbacks are routed here.
pre_call_rules: List[Callable] = []
post_call_rules: List[Callable] = []
turn_off_message_logging: Optional[bool] = False
standard_logging_payload_excluded_fields: Optional[
    List[str]
] = None  # Fields to exclude from StandardLoggingPayload before callbacks receive it
log_raw_request_response: bool = False
redact_messages_in_exceptions: Optional[bool] = False
redact_user_api_key_info: Optional[bool] = False
filter_invalid_headers: Optional[bool] = False
add_user_information_to_llm_headers: Optional[
    bool
] = None  # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
store_audit_logs = False  # Enterprise feature, allow users to see audit logs
### end of callbacks #############

email: Optional[
    str
] = None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
token: Optional[
    str
] = None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
telemetry = True
max_tokens: int = DEFAULT_MAX_TOKENS  # OpenAI Defaults
drop_params = bool(os.getenv("LITELLM_DROP_PARAMS", False))
modify_params = bool(os.getenv("LITELLM_MODIFY_PARAMS", False))
use_chat_completions_url_for_anthropic_messages: bool = bool(
    os.getenv("LITELLM_USE_CHAT_COMPLETIONS_URL_FOR_ANTHROPIC_MESSAGES", False)
)  # When True, routes OpenAI /v1/messages requests to chat/completions instead of the Responses API
retry = True
### AUTH ###
api_key: Optional[str] = None
openai_key: Optional[str] = None
groq_key: Optional[str] = None
gigachat_key: Optional[str] = None
databricks_key: Optional[str] = None
openai_like_key: Optional[str] = None
azure_key: Optional[str] = None
anthropic_key: Optional[str] = None
replicate_key: Optional[str] = None
bytez_key: Optional[str] = None
cohere_key: Optional[str] = None
infinity_key: Optional[str] = None
clarifai_key: Optional[str] = None
maritalk_key: Optional[str] = None
ai21_key: Optional[str] = None
ollama_key: Optional[str] = None
openrouter_key: Optional[str] = None
datarobot_key: Optional[str] = None
predibase_key: Optional[str] = None
huggingface_key: Optional[str] = None
vertex_project: Optional[str] = None
vertex_location: Optional[str] = None
predibase_tenant_id: Optional[str] = None
togetherai_api_key: Optional[str] = None
cloudflare_api_key: Optional[str] = None
vercel_ai_gateway_key: Optional[str] = None
baseten_key: Optional[str] = None
llama_api_key: Optional[str] = None
aleph_alpha_key: Optional[str] = None
nlp_cloud_key: Optional[str] = None
novita_api_key: Optional[str] = None
snowflake_key: Optional[str] = None
gradient_ai_api_key: Optional[str] = None
nebius_key: Optional[str] = None
wandb_key: Optional[str] = None
heroku_key: Optional[str] = None
cometapi_key: Optional[str] = None
ovhcloud_key: Optional[str] = None
lemonade_key: Optional[str] = None
sap_service_key: Optional[str] = None
amazon_nova_api_key: Optional[str] = None
common_cloud_provider_auth_params: dict = {
    "params": ["project", "region_name", "token"],
    "providers": ["vertex_ai", "bedrock", "watsonx", "azure", "vertex_ai_beta"],
}
use_litellm_proxy: bool = (
    False  # when True, requests will be sent to the specified litellm proxy endpoint
)
use_client: bool = False
ssl_verify: Union[str, bool] = True
ssl_security_level: Optional[str] = None
ssl_certificate: Optional[str] = None
ssl_ecdh_curve: Optional[
    str
] = None  # Set to 'X25519' to disable PQC and improve performance
disable_streaming_logging: bool = False
disable_token_counter: bool = False
disable_add_transform_inline_image_block: bool = False
disable_add_user_agent_to_request_tags: bool = False
disable_anthropic_gemini_context_caching_transform: bool = False
extra_spend_tag_headers: Optional[List[str]] = None
in_memory_llm_clients_cache: "LLMClientCache"
safe_memory_mode: bool = False
enable_azure_ad_token_refresh: Optional[bool] = False
# Proxy Authentication - auto-obtain/refresh OAuth2/JWT tokens for LiteLLM Proxy
proxy_auth: Optional[Any] = None
### DEFAULT AZURE API VERSION ###
AZURE_DEFAULT_API_VERSION = "2025-02-01-preview"  # this is updated to the latest
### DEFAULT WATSONX API VERSION ###
WATSONX_DEFAULT_API_VERSION = "2024-03-13"
### COHERE EMBEDDINGS DEFAULT TYPE ###
COHERE_DEFAULT_EMBEDDING_INPUT_TYPE: "COHERE_EMBEDDING_INPUT_TYPES" = "search_document"
### CREDENTIALS ###
credential_list: List["CredentialItem"] = []
### GUARDRAILS ###
llamaguard_model_name: Optional[str] = None
openai_moderations_model_name: Optional[str] = None
presidio_ad_hoc_recognizers: Optional[str] = None
google_moderation_confidence_threshold: Optional[float] = None
llamaguard_unsafe_content_categories: Optional[str] = None
blocked_user_list: Optional[Union[str, List]] = None
banned_keywords_list: Optional[Union[str, List]] = None
llm_guard_mode: Literal["all", "key-specific", "request-specific"] = "all"
guardrail_name_config_map: Dict[str, GuardrailItem] = {}
include_cost_in_streaming_usage: bool = False
reasoning_auto_summary: bool = False
### PROMPTS ####
from litellm.types.prompts.init_prompts import PromptSpec

prompt_name_config_map: Dict[str, PromptSpec] = {}

##################
### PREVIEW FEATURES ###
enable_preview_features: bool = False
return_response_headers: bool = (
    False  # get response headers from LLM Api providers - example x-remaining-requests,
)
enable_json_schema_validation: bool = False
enable_key_alias_format_validation: bool = (
    False  # opt-in validation of key_alias format on /key/generate and /key/update
)
####################
logging: bool = True
enable_loadbalancing_on_batch_endpoints: Optional[bool] = None
enable_caching_on_provider_specific_optional_params: bool = (
    False  # feature-flag for caching on optional params - e.g. 'top_k'
)
caching: bool = False  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
caching_with_models: bool = False  # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
cache: Optional[
    "Cache"
] = None  # cache object <- use this - https://docs.litellm.ai/docs/caching
default_in_memory_ttl: Optional[float] = None
default_redis_ttl: Optional[float] = None
default_redis_batch_cache_expiry: Optional[float] = None
model_alias_map: Dict[str, str] = {}
model_group_settings: Optional["ModelGroupSettings"] = None
max_budget: float = 0.0  # set the max budget across all providers
budget_duration: Optional[
    str
] = None  # proxy only - resets budget after fixed duration. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
default_soft_budget: float = (
    DEFAULT_SOFT_BUDGET  # by default all litellm proxy keys have a soft budget of 50.0
)
forward_traceparent_to_llm_provider: bool = False


_current_cost = 0.0  # private variable, used if max budget is set
error_logs: Dict = {}
add_function_to_prompt: bool = False  # if function calling not supported by api, append function call details to system prompt
client_session: Optional[httpx.Client] = None
aclient_session: Optional[httpx.AsyncClient] = None
model_fallbacks: Optional[List] = None  # Deprecated for 'litellm.fallbacks'
model_cost_map_url: str = os.getenv(
    "LITELLM_MODEL_COST_MAP_URL",
    "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json",
)
blog_posts_url: str = os.getenv(
    "LITELLM_BLOG_POSTS_URL",
    "https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/blog_posts.json",
)
anthropic_beta_headers_url: str = os.getenv(
    "LITELLM_ANTHROPIC_BETA_HEADERS_URL",
    "https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/anthropic_beta_headers_config.json",
)
suppress_debug_info = False
dynamodb_table_name: Optional[str] = None
s3_callback_params: Optional[Dict] = None
datadog_llm_observability_params: Optional[Union[DatadogLLMObsInitParams, Dict]] = None
datadog_params: Optional[Union[DatadogInitParams, Dict]] = None
aws_sqs_callback_params: Optional[Dict] = None
generic_logger_headers: Optional[Dict] = None
default_key_generate_params: Optional[Dict] = None
upperbound_key_generate_params: Optional[LiteLLM_UpperboundKeyGenerateParams] = None
key_generation_settings: Optional["StandardKeyGenerationConfig"] = None
default_internal_user_params: Optional[Dict] = None
default_team_params: Optional[Union[DefaultTeamSSOParams, Dict]] = None
default_team_settings: Optional[List] = None
max_user_budget: Optional[float] = None
default_max_internal_user_budget: Optional[float] = None
max_internal_user_budget: Optional[float] = None
max_ui_session_budget: Optional[float] = 0.25  # $0.25 USD budgets for UI Chat sessions
internal_user_budget_duration: Optional[str] = None
tag_budget_config: Optional[Dict[str, "BudgetConfig"]] = None
max_end_user_budget: Optional[float] = None
max_end_user_budget_id: Optional[str] = None
disable_end_user_cost_tracking: Optional[bool] = None
disable_end_user_cost_tracking_prometheus_only: Optional[bool] = None
enable_end_user_cost_tracking_prometheus_only: Optional[bool] = None
custom_prometheus_metadata_labels: List[str] = []
custom_prometheus_tags: List[str] = []
prometheus_metrics_config: Optional[List] = None
prometheus_emit_stream_label: bool = False
disable_add_prefix_to_prompt: bool = (
    False  # used by anthropic, to disable adding prefix to prompt
)
disable_copilot_system_to_assistant: bool = False  # If false (default), converts all 'system' role messages to 'assistant' for GitHub Copilot compatibility. Set to true to disable this behavior.
public_mcp_servers: Optional[List[str]] = None
public_model_groups: Optional[List[str]] = None
public_agent_groups: Optional[List[str]] = None
# Supports both old format (Dict[str, str]) and new format (Dict[str, Dict[str, Any]])
# New format: { "displayName": { "url": "...", "index": 0 } }
# Old format: { "displayName": "url" } (for backward compatibility)
public_model_groups_links: Dict[str, Union[str, Dict[str, Any]]] = {}
#### REQUEST PRIORITIZATION #######
priority_reservation: Optional[
    Dict[str, Union[float, "PriorityReservationDict"]]
] = None
# priority_reservation_settings is lazy-loaded via __getattr__
# Only declare for type checking - at runtime __getattr__ handles it
if TYPE_CHECKING:
    priority_reservation_settings: Optional["PriorityReservationSettings"] = None


######## Networking Settings ########
use_aiohttp_transport: bool = True  # Older variable, aiohttp is now the default. use disable_aiohttp_transport instead.
aiohttp_trust_env: bool = False  # set to true to use HTTP_ Proxy settings
disable_aiohttp_transport: bool = False  # Set this to true to use httpx instead
disable_aiohttp_trust_env: bool = (
    False  # When False, aiohttp will respect HTTP(S)_PROXY env vars
)
force_ipv4: bool = False  # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
network_mock: bool = False  # When True, use mock transport — no real network calls

####### STOP SEQUENCE LIMIT #######
disable_stop_sequence_limit: bool = False  # when True, stop sequence limit is disabled

#### RETRIES ####
num_retries: Optional[int] = None  # per model endpoint
max_fallbacks: Optional[int] = None
default_fallbacks: Optional[List] = None
fallbacks: Optional[List] = None
context_window_fallbacks: Optional[List] = None
content_policy_fallbacks: Optional[List] = None
allowed_fails: int = 3
allow_dynamic_callback_disabling: bool = True
num_retries_per_request: Optional[
    int
] = None  # for the request overall (incl. fallbacks + model retries)
####### SECRET MANAGERS #####################
secret_manager_client: Optional[
    Any
] = None  # list of instantiated key management clients - e.g. azure kv, infisical, etc.
_google_kms_resource_name: Optional[str] = None
_key_management_system: Optional["KeyManagementSystem"] = None
# Note: KeyManagementSettings must be eagerly imported because _key_management_settings
# is accessed during import time in secret_managers/main.py
# We'll import it after the lazy import system is set up
# We can't define it here because KeyManagementSettings is lazy-loaded
#### PII MASKING ####
output_parse_pii: bool = False
#############################################
from litellm.litellm_core_utils.get_model_cost_map import get_model_cost_map

model_cost = get_model_cost_map(url=model_cost_map_url)
cost_discount_config: Dict[
    str, float
] = {}  # Provider-specific cost discounts {"vertex_ai": 0.05} = 5% discount
cost_margin_config: Dict[
    str, Union[float, Dict[str, float]]
] = {}  # Provider-specific or global cost margins. Examples:
# Percentage: {"openai": 0.10} = 10% margin
# Fixed: {"openai": {"fixed_amount": 0.001}} = $0.001 per request
# Global: {"global": 0.05} = 5% global margin on all providers
# Combined: {"vertex_ai": {"percentage": 0.08, "fixed_amount": 0.0005}}
custom_prompt_dict: Dict[str, dict] = {}
check_provider_endpoint = False


####### THREAD-SPECIFIC DATA ####################
class MyLocal(threading.local):
    def __init__(self):
        self.user = "Hello World"


_thread_context = MyLocal()


def identify(event_details):
    # Store user in thread local data
    if "user" in event_details:
        _thread_context.user = event_details["user"]


####### ADDITIONAL PARAMS ################### configurable params if you use proxy models like Helicone, map spend to org id, etc.
api_base: Optional[str] = None
headers = None
api_version: Optional[str] = None
organization = None
project = None
config_path = None
vertex_ai_safety_settings: Optional[dict] = None

####### COMPLETION MODELS ###################
from typing import Set

open_ai_chat_completion_models: Set = set()
open_ai_text_completion_models: Set = set()
cohere_models: Set = set()
cohere_chat_models: Set = set()
mistral_chat_models: Set = set()
text_completion_codestral_models: Set = set()
anthropic_models: Set = set()
openrouter_models: Set = set()
datarobot_models: Set = set()
vertex_language_models: Set = set()
vertex_vision_models: Set = set()
vertex_chat_models: Set = set()
vertex_code_chat_models: Set = set()
vertex_ai_image_models: Set = set()
vertex_ai_video_models: Set = set()
vertex_text_models: Set = set()
vertex_code_text_models: Set = set()
vertex_embedding_models: Set = set()
vertex_anthropic_models: Set = set()
vertex_llama3_models: Set = set()
vertex_deepseek_models: Set = set()
vertex_ai_ai21_models: Set = set()
vertex_mistral_models: Set = set()
vertex_openai_models: Set = set()
vertex_minimax_models: Set = set()
vertex_moonshot_models: Set = set()
vertex_zai_models: Set = set()
ai21_models: Set = set()
ai21_chat_models: Set = set()
nlp_cloud_models: Set = set()
aleph_alpha_models: Set = set()
bedrock_models: Set = set()
bedrock_converse_models: Set = set(BEDROCK_CONVERSE_MODELS)
fal_ai_models: Set = set()
fireworks_ai_models: Set = set()
fireworks_ai_embedding_models: Set = set()
deepinfra_models: Set = set()
perplexity_models: Set = set()
watsonx_models: Set = set()
gemini_models: Set = set()
xai_models: Set = set()
zai_models: Set = set()
deepseek_models: Set = set()
runwayml_models: Set = set()
azure_ai_models: Set = set()
jina_ai_models: Set = set()
voyage_models: Set = set()
infinity_models: Set = set()
heroku_models: Set = set()
databricks_models: Set = set()
cloudflare_models: Set = set()
codestral_models: Set = set()
friendliai_models: Set = set()
featherless_ai_models: Set = set()
palm_models: Set = set()
groq_models: Set = set()
azure_models: Set = set()
azure_anthropic_models: Set = set()
azure_text_models: Set = set()
anyscale_models: Set = set()
cerebras_models: Set = set()
galadriel_models: Set = set()
nvidia_nim_models: Set = set()
sambanova_models: Set = set()
sambanova_embedding_models: Set = set()
novita_models: Set = set()
assemblyai_models: Set = set()
snowflake_models: Set = set()
gradient_ai_models: Set = set()
llama_models: Set = set()
nscale_models: Set = set()
nebius_models: Set = set()
nebius_embedding_models: Set = set()
aiml_models: Set = set()
deepgram_models: Set = set()
elevenlabs_models: Set = set()
dashscope_models: Set = set()
moonshot_models: Set = set()
publicai_models: Set = set()
v0_models: Set = set()
morph_models: Set = set()
lambda_ai_models: Set = set()
hyperbolic_models: Set = set()
black_forest_labs_models: Set = set()
recraft_models: Set = set()
cometapi_models: Set = set()
oci_models: Set = set()
vercel_ai_gateway_models: Set = set()
volcengine_models: Set = set()
wandb_models: Set = set(WANDB_MODELS)
ovhcloud_models: Set = set()
ovhcloud_embedding_models: Set = set()
lemonade_models: Set = set()
docker_model_runner_models: Set = set()
amazon_nova_models: Set = set()
stability_models: Set = set()
github_copilot_models: Set = set()
chatgpt_models: Set = set()
minimax_models: Set = set()
aws_polly_models: Set = set()
gigachat_models: Set = set()
llamagate_models: Set = set()
bedrock_mantle_models: Set = set()


def is_bedrock_pricing_only_model(key: str) -> bool:
    """
    Excludes keys with the pattern 'bedrock/<region>/<model>'. These are in the model_prices_and_context_window.json file for pricing purposes only.

    Args:
        key (str): A key to filter.

    Returns:
        bool: True if the key matches the Bedrock pattern, False otherwise.
    """
    # Regex to match 'bedrock/<region>/<model>'
    bedrock_pattern = re.compile(r"^bedrock/[a-zA-Z0-9_-]+/.+$")

    if "month-commitment" in key:
        return True

    is_match = bedrock_pattern.match(key)
    return is_match is not None


def is_openai_finetune_model(key: str) -> bool:
    """
    Excludes model cost keys with the pattern 'ft:<model>'. These are in the model_prices_and_context_window.json file for pricing purposes only.

    Args:
        key (str): A key to filter.

    Returns:
        bool: True if the key matches the OpenAI finetune pattern, False otherwise.
    """
    return key.startswith("ft:") and not key.count(":") > 1


def add_known_models(model_cost_map: Optional[Dict] = None):
    _map = model_cost_map if model_cost_map is not None else model_cost
    for key, value in _map.items():
        if value.get("litellm_provider") == "openai" and not is_openai_finetune_model(
            key
        ):
            open_ai_chat_completion_models.add(key)
        elif value.get("litellm_provider") == "text-completion-openai":
            open_ai_text_completion_models.add(key)
        elif value.get("litellm_provider") == "azure_text":
            azure_text_models.add(key)
        elif value.get("litellm_provider") == "cohere":
            cohere_models.add(key)
        elif value.get("litellm_provider") == "cohere_chat":
            cohere_chat_models.add(key)
        elif value.get("litellm_provider") == "mistral":
            mistral_chat_models.add(key)
        elif value.get("litellm_provider") == "anthropic":
            anthropic_models.add(key)
        elif value.get("litellm_provider") == "empower":
            empower_models.add(key)
        elif value.get("litellm_provider") == "openrouter":
            openrouter_models.add(key)
        elif value.get("litellm_provider") == "vercel_ai_gateway":
            vercel_ai_gateway_models.add(key)
        elif value.get("litellm_provider") == "datarobot":
            datarobot_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-text-models":
            vertex_text_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-code-text-models":
            vertex_code_text_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-language-models":
            vertex_language_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-vision-models":
            vertex_vision_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-chat-models":
            vertex_chat_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-code-chat-models":
            vertex_code_chat_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-embedding-models":
            vertex_embedding_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-anthropic_models":
            key = key.replace("vertex_ai/", "")
            vertex_anthropic_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-llama_models":
            key = key.replace("vertex_ai/", "")
            vertex_llama3_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-deepseek_models":
            key = key.replace("vertex_ai/", "")
            vertex_deepseek_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-mistral_models":
            key = key.replace("vertex_ai/", "")
            vertex_mistral_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-ai21_models":
            key = key.replace("vertex_ai/", "")
            vertex_ai_ai21_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-image-models":
            key = key.replace("vertex_ai/", "")
            vertex_ai_image_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-video-models":
            key = key.replace("vertex_ai/", "")
            vertex_ai_video_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-openai_models":
            key = key.replace("vertex_ai/", "")
            vertex_openai_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-minimax_models":
            key = key.replace("vertex_ai/", "")
            vertex_minimax_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-moonshot_models":
            key = key.replace("vertex_ai/", "")
            vertex_moonshot_models.add(key)
        elif value.get("litellm_provider") == "vertex_ai-zai_models":
            key = key.replace("vertex_ai/", "")
            vertex_zai_models.add(key)
        elif value.get("litellm_provider") == "ai21":
            if value.get("mode") == "chat":
                ai21_chat_models.add(key)
            else:
                ai21_models.add(key)
        elif value.get("litellm_provider") == "nlp_cloud":
            nlp_cloud_models.add(key)
        elif value.get("litellm_provider") == "aleph_alpha":
            aleph_alpha_models.add(key)
        elif value.get(
            "litellm_provider"
        ) == "bedrock" and not is_bedrock_pricing_only_model(key):
            bedrock_models.add(key)
        elif value.get("litellm_provider") == "bedrock_converse":
            bedrock_converse_models.add(key)
        elif value.get("litellm_provider") == "deepinfra":
            deepinfra_models.add(key)
        elif value.get("litellm_provider") == "perplexity":
            perplexity_models.add(key)
        elif value.get("litellm_provider") == "watsonx":
            watsonx_models.add(key)
        elif value.get("litellm_provider") == "gemini":
            gemini_models.add(key)
        elif value.get("litellm_provider") == "fireworks_ai":
            # ignore the 'up-to', '-to-' model names -> not real models. just for cost tracking based on model params.
            if "-to-" not in key and "fireworks-ai-default" not in key:
                fireworks_ai_models.add(key)
        elif value.get("litellm_provider") == "fireworks_ai-embedding-models":
            # ignore the 'up-to', '-to-' model names -> not real models. just for cost tracking based on model params.
            if "-to-" not in key:
                fireworks_ai_embedding_models.add(key)
        elif value.get("litellm_provider") == "text-completion-codestral":
            text_completion_codestral_models.add(key)
        elif value.get("litellm_provider") == "xai":
            xai_models.add(key)
        elif value.get("litellm_provider") == "zai":
            zai_models.add(key)
        elif value.get("litellm_provider") == "fal_ai":
            fal_ai_models.add(key)
        elif value.get("litellm_provider") == "deepseek":
            deepseek_models.add(key)
        elif value.get("litellm_provider") == "runwayml":
            runwayml_models.add(key)
        elif value.get("litellm_provider") == "meta_llama":
            llama_models.add(key)
        elif value.get("litellm_provider") == "nscale":
            nscale_models.add(key)
        elif value.get("litellm_provider") == "azure_ai":
            azure_ai_models.add(key)
        elif value.get("litellm_provider") == "voyage":
            voyage_models.add(key)
        elif value.get("litellm_provider") == "infinity":
            infinity_models.add(key)
        elif value.get("litellm_provider") == "databricks":
            databricks_models.add(key)
        elif value.get("litellm_provider") == "cloudflare":
            cloudflare_models.add(key)
        elif value.get("litellm_provider") == "codestral":
            codestral_models.add(key)
        elif value.get("litellm_provider") == "friendliai":
            friendliai_models.add(key)
        elif value.get("litellm_provider") == "palm":
            palm_models.add(key)
        elif value.get("litellm_provider") == "groq":
            groq_models.add(key)
        elif value.get("litellm_provider") == "azure":
            azure_models.add(key)
        elif value.get("litellm_provider") == "azure_anthropic":
            azure_anthropic_models.add(key)
        elif value.get("litellm_provider") == "anyscale":
            anyscale_models.add(key)
        elif value.get("litellm_provider") == "cerebras":
            cerebras_models.add(key)
        elif value.get("litellm_provider") == "galadriel":
            galadriel_models.add(key)
        elif value.get("litellm_provider") == "nvidia_nim":
            nvidia_nim_models.add(key)
        elif value.get("litellm_provider") == "sambanova":
            sambanova_models.add(key)
        elif value.get("litellm_provider") == "sambanova-embedding-models":
            sambanova_embedding_models.add(key)
        elif value.get("litellm_provider") == "novita":
            novita_models.add(key)
        elif value.get("litellm_provider") == "nebius-chat-models":
            nebius_models.add(key)
        elif value.get("litellm_provider") == "nebius-embedding-models":
            nebius_embedding_models.add(key)
        elif value.get("litellm_provider") == "aiml":
            aiml_models.add(key)
        elif value.get("litellm_provider") == "assemblyai":
            assemblyai_models.add(key)
        elif value.get("litellm_provider") == "jina_ai":
            jina_ai_models.add(key)
        elif value.get("litellm_provider") == "snowflake":
            snowflake_models.add(key)
        elif value.get("litellm_provider") == "gradient_ai":
            gradient_ai_models.add(key)
        elif value.get("litellm_provider") == "featherless_ai":
            featherless_ai_models.add(key)
        elif value.get("litellm_provider") == "deepgram":
            deepgram_models.add(key)
        elif value.get("litellm_provider") == "elevenlabs":
            elevenlabs_models.add(key)
        elif value.get("litellm_provider") == "heroku":
            heroku_models.add(key)
        elif value.get("litellm_provider") == "dashscope":
            dashscope_models.add(key)
        elif value.get("litellm_provider") == "moonshot":
            moonshot_models.add(key)
        elif value.get("litellm_provider") == "publicai":
            publicai_models.add(key)
        elif value.get("litellm_provider") == "v0":
            v0_models.add(key)
        elif value.get("litellm_provider") == "morph":
            morph_models.add(key)
        elif value.get("litellm_provider") == "lambda_ai":
            lambda_ai_models.add(key)
        elif value.get("litellm_provider") == "hyperbolic":
            hyperbolic_models.add(key)
        elif value.get("litellm_provider") == "black_forest_labs":
            black_forest_labs_models.add(key)
        elif value.get("litellm_provider") == "recraft":
            recraft_models.add(key)
        elif value.get("litellm_provider") == "cometapi":
            cometapi_models.add(key)
        elif value.get("litellm_provider") == "oci":
            oci_models.add(key)
        elif value.get("litellm_provider") == "volcengine":
            volcengine_models.add(key)
        elif value.get("litellm_provider") == "wandb":
            wandb_models.add(key)
        elif value.get("litellm_provider") == "ovhcloud":
            ovhcloud_models.add(key)
        elif value.get("litellm_provider") == "ovhcloud-embedding-models":
            ovhcloud_embedding_models.add(key)
        elif value.get("litellm_provider") == "lemonade":
            lemonade_models.add(key)
        elif value.get("litellm_provider") == "docker_model_runner":
            docker_model_runner_models.add(key)
        elif value.get("litellm_provider") == "amazon_nova":
            amazon_nova_models.add(key)
        elif value.get("litellm_provider") == "stability":
            stability_models.add(key)
        elif value.get("litellm_provider") == "github_copilot":
            github_copilot_models.add(key)
        elif value.get("litellm_provider") == "chatgpt":
            chatgpt_models.add(key)
        elif value.get("litellm_provider") == "minimax":
            minimax_models.add(key)
        elif value.get("litellm_provider") == "aws_polly":
            aws_polly_models.add(key)
        elif value.get("litellm_provider") == "gigachat":
            gigachat_models.add(key)
        elif value.get("litellm_provider") == "llamagate":
            llamagate_models.add(key)
        elif value.get("litellm_provider") == "bedrock_mantle":
            bedrock_mantle_models.add(key)


add_known_models()
# known openai compatible endpoints - we'll eventually move this list to the model_prices_and_context_window.json dictionary

# this is maintained for Exception Mapping


# used for Cost Tracking & Token counting
# https://azure.microsoft.com/en-in/pricing/details/cognitive-services/openai-service/
# Azure returns gpt-35-turbo in their responses, we need to map this to azure/gpt-3.5-turbo for token counting
azure_llms = {
    "gpt-35-turbo": "azure/gpt-35-turbo",
    "gpt-35-turbo-16k": "azure/gpt-35-turbo-16k",
    "gpt-35-turbo-instruct": "azure/gpt-35-turbo-instruct",
    "azure/gpt-41": "gpt-4.1",
    "azure/gpt-41-mini": "gpt-4.1-mini",
    "azure/gpt-41-nano": "gpt-4.1-nano",
}

azure_embedding_models = {
    "ada": "azure/ada",
}

petals_models = [
    "petals-team/StableBeluga2",
]

ollama_models = ["llama2"]

maritalk_models = ["maritalk"]

model_list = list(
    open_ai_chat_completion_models
    | open_ai_text_completion_models
    | cohere_models
    | cohere_chat_models
    | anthropic_models
    | set(replicate_models)
    | openrouter_models
    | datarobot_models
    | set(huggingface_models)
    | vertex_chat_models
    | vertex_text_models
    | ai21_models
    | ai21_chat_models
    | set(together_ai_models)
    | set(baseten_models)
    | aleph_alpha_models
    | nlp_cloud_models
    | set(ollama_models)
    | bedrock_models
    | deepinfra_models
    | perplexity_models
    | set(maritalk_models)
    | runwayml_models
    | vertex_language_models
    | watsonx_models
    | gemini_models
    | text_completion_codestral_models
    | xai_models
    | zai_models
    | fal_ai_models
    | deepseek_models
    | azure_ai_models
    | voyage_models
    | infinity_models
    | databricks_models
    | cloudflare_models
    | codestral_models
    | friendliai_models
    | palm_models
    | groq_models
    | azure_models
    | azure_anthropic_models
    | anyscale_models
    | cerebras_models
    | galadriel_models
    | nvidia_nim_models
    | sambanova_models
    | azure_text_models
    | novita_models
    | assemblyai_models
    | jina_ai_models
    | snowflake_models
    | gradient_ai_models
    | llama_models
    | featherless_ai_models
    | nscale_models
    | deepgram_models
    | elevenlabs_models
    | dashscope_models
    | moonshot_models
    | publicai_models
    | v0_models
    | morph_models
    | lambda_ai_models
    | black_forest_labs_models
    | recraft_models
    | cometapi_models
    | oci_models
    | heroku_models
    | vercel_ai_gateway_models
    | volcengine_models
    | wandb_models
    | ovhcloud_models
    | lemonade_models
    | docker_model_runner_models
    | bedrock_mantle_models
    | set(clarifai_models)
)

model_list_set = set(model_list)

# provider_list is lazy-loaded via __getattr__ to avoid importing LlmProviders at import time


models_by_provider: dict = {
    "openai": open_ai_chat_completion_models | open_ai_text_completion_models,
    "text-completion-openai": open_ai_text_completion_models,
    "cohere": cohere_models | cohere_chat_models,
    "cohere_chat": cohere_chat_models,
    "anthropic": anthropic_models,
    "replicate": replicate_models,
    "huggingface": huggingface_models,
    "together_ai": together_ai_models,
    "baseten": baseten_models,
    "openrouter": openrouter_models,
    "vercel_ai_gateway": vercel_ai_gateway_models,
    "datarobot": datarobot_models,
    "vertex_ai": vertex_chat_models
    | vertex_text_models
    | vertex_anthropic_models
    | vertex_vision_models
    | vertex_language_models
    | vertex_deepseek_models
    | vertex_minimax_models
    | vertex_moonshot_models
    | vertex_zai_models,
    "ai21": ai21_models,
    "bedrock": bedrock_models | bedrock_converse_models,
    "petals": petals_models,
    "ollama": ollama_models,
    "ollama_chat": ollama_models,
    "deepinfra": deepinfra_models,
    "perplexity": perplexity_models,
    "maritalk": maritalk_models,
    "watsonx": watsonx_models,
    "gemini": gemini_models,
    "fireworks_ai": fireworks_ai_models | fireworks_ai_embedding_models,
    "aleph_alpha": aleph_alpha_models,
    "text-completion-codestral": text_completion_codestral_models,
    "xai": xai_models,
    "zai": zai_models,
    "fal_ai": fal_ai_models,
    "deepseek": deepseek_models,
    "runwayml": runwayml_models,
    "mistral": mistral_chat_models,
    "azure_ai": azure_ai_models,
    "voyage": voyage_models,
    "infinity": infinity_models,
    "databricks": databricks_models,
    "cloudflare": cloudflare_models,
    "codestral": codestral_models,
    "nlp_cloud": nlp_cloud_models,
    "friendliai": friendliai_models,
    "palm": palm_models,
    "groq": groq_models,
    "azure": azure_models | azure_text_models,
    "azure_anthropic": azure_anthropic_models,
    "azure_text": azure_text_models,
    "anyscale": anyscale_models,
    "cerebras": cerebras_models,
    "galadriel": galadriel_models,
    "nvidia_nim": nvidia_nim_models,
    "sambanova": sambanova_models | sambanova_embedding_models,
    "novita": novita_models,
    "nebius": nebius_models | nebius_embedding_models,
    "aiml": aiml_models,
    "assemblyai": assemblyai_models,
    "jina_ai": jina_ai_models,
    "snowflake": snowflake_models,
    "gradient_ai": gradient_ai_models,
    "meta_llama": llama_models,
    "nscale": nscale_models,
    "featherless_ai": featherless_ai_models,
    "deepgram": deepgram_models,
    "elevenlabs": elevenlabs_models,
    "heroku": heroku_models,
    "dashscope": dashscope_models,
    "moonshot": moonshot_models,
    "publicai": publicai_models,
    "v0": v0_models,
    "morph": morph_models,
    "lambda_ai": lambda_ai_models,
    "hyperbolic": hyperbolic_models,
    "black_forest_labs": black_forest_labs_models,
    "recraft": recraft_models,
    "cometapi": cometapi_models,
    "oci": oci_models,
    "volcengine": volcengine_models,
    "wandb": wandb_models,
    "ovhcloud": ovhcloud_models | ovhcloud_embedding_models,
    "lemonade": lemonade_models,
    "clarifai": clarifai_models,
    "amazon_nova": amazon_nova_models,
    "stability": stability_models,
    "github_copilot": github_copilot_models,
    "chatgpt": chatgpt_models,
    "minimax": minimax_models,
    "aws_polly": aws_polly_models,
    "gigachat": gigachat_models,
    "llamagate": llamagate_models,
    "bedrock_mantle": bedrock_mantle_models,
}

# mapping for those models which have larger equivalents
longer_context_model_fallback_dict: dict = {
    # openai chat completion models
    "gpt-3.5-turbo": "gpt-3.5-turbo-16k",
    "gpt-3.5-turbo-0301": "gpt-3.5-turbo-16k-0301",
    "gpt-3.5-turbo-0613": "gpt-3.5-turbo-16k-0613",
    "gpt-4": "gpt-4-32k",
    "gpt-4-0314": "gpt-4-32k-0314",
    "gpt-4-0613": "gpt-4-32k-0613",
    # anthropic
    "claude-instant-1": "claude-2",
    "claude-instant-1.2": "claude-2",
    # vertexai
    "chat-bison": "chat-bison-32k",
    "chat-bison@001": "chat-bison-32k",
    "codechat-bison": "codechat-bison-32k",
    "codechat-bison@001": "codechat-bison-32k",
    # openrouter
    "openrouter/openai/gpt-3.5-turbo": "openrouter/openai/gpt-3.5-turbo-16k",
    "openrouter/anthropic/claude-instant-v1": "openrouter/anthropic/claude-2",
}

####### EMBEDDING MODELS ###################

all_embedding_models = (
    open_ai_embedding_models
    | set(cohere_embedding_models)
    | set(bedrock_embedding_models)
    | vertex_embedding_models
    | fireworks_ai_embedding_models
    | nebius_embedding_models
    | sambanova_embedding_models
    | ovhcloud_embedding_models
)

####### IMAGE GENERATION MODELS ###################
openai_image_generation_models = ["dall-e-2", "dall-e-3"]

####### VIDEO GENERATION MODELS ###################
openai_video_generation_models = ["sora-2"]

# timeout is lazy-loaded via __getattr__
# get_llm_provider is lazy-loaded via __getattr__
# remove_index_from_tool_calls is lazy-loaded via __getattr__

# Import KeyManagementSettings here (before utils import) because _key_management_settings
# is accessed during import time in secret_managers/main.py (via dd_tracing -> datadog -> _service_logger -> utils)
from litellm.types.secret_managers.main import KeyManagementSettings

_key_management_settings: KeyManagementSettings = KeyManagementSettings()

# client must be imported immediately as it's used as a decorator at function definition time
from .utils import client

# Note: Most other utils imports are lazy-loaded via __getattr__ to avoid loading utils.py
# (which imports tiktoken) at import time

from .llms.custom_llm import CustomLLM
from .llms.anthropic.common_utils import AnthropicModelInfo
from .llms.ai21.chat.transformation import AI21ChatConfig, AI21ChatConfig as AI21Config
from .llms.deprecated_providers.palm import (
    PalmConfig,
)  # here to prevent breaking changes
from .llms.deprecated_providers.aleph_alpha import AlephAlphaConfig
from .llms.gemini.common_utils import GeminiModelInfo


from .llms.vertex_ai.vertex_embeddings.transformation import (
    VertexAITextEmbeddingConfig,
)

vertexAITextEmbeddingConfig = VertexAITextEmbeddingConfig()


from .llms.bedrock.embed.amazon_titan_v2_transformation import (
    AmazonTitanV2Config,
)
from .llms.topaz.common_utils import TopazModelInfo

# OpenAIOSeriesConfig is lazy loaded - openaiOSeriesConfig will be created on first access
# OpenAIGPTConfig, OpenAIGPT5Config, etc. are lazy loaded - instances will be created on first access
from .llms.xai.common_utils import XAIModelInfo

# PublicAI now uses JSON-based configuration (see litellm/llms/openai_like/providers.json)
# All remaining configs are now lazy loaded - see _lazy_imports_registry.py

# Import LlmProviders here (before main import) because it's imported during import time
# in multiple places including openai.py (via main import)
from litellm.types.utils import LlmProviders

## Lazy loading this is not straightforward, will leave it here for now.
from .main import *  # type: ignore

# Skills API
from .skills.main import (
    create_skill,
    acreate_skill,
    list_skills,
    alist_skills,
    get_skill,
    aget_skill,
    delete_skill,
    adelete_skill,
)
from .evals.main import (
    create_eval,
    acreate_eval,
    list_evals,
    alist_evals,
    get_eval,
    aget_eval,
    delete_eval,
    adelete_eval,
    cancel_eval,
    acancel_eval,
    create_run,
    acreate_run,
    list_runs,
    alist_runs,
    get_run,
    aget_run,
    delete_run,
    adelete_run,
    cancel_run,
    acancel_run,
)
from .integrations import *
from .llms.custom_httpx.async_client_cleanup import close_litellm_async_clients
from .exceptions import (
    AuthenticationError,
    InvalidRequestError,
    BadRequestError,
    ImageFetchError,
    NotFoundError,
    PermissionDeniedError,
    RateLimitError,
    ServiceUnavailableError,
    BadGatewayError,
    OpenAIError,
    ContextWindowExceededError,
    ContentPolicyViolationError,
    BudgetExceededError,
    APIError,
    Timeout,
    APIConnectionError,
    UnsupportedParamsError,
    APIResponseValidationError,
    UnprocessableEntityError,
    InternalServerError,
    JSONSchemaValidationError,
    LITELLM_EXCEPTION_TYPES,
    MockException,
)
from .budget_manager import BudgetManager
from .proxy.proxy_cli import run_server
from .router import Router
from .assistants.main import *
from .batches.main import *
from .images.main import *
from .videos.main import *
from .batch_completion.main import *  # type: ignore
from .rerank_api.main import *
from .llms.anthropic.experimental_pass_through.messages.handler import *
from .responses.main import *

# Interactions API is available as litellm.interactions module
# Usage: litellm.interactions.create(), litellm.interactions.get(), etc.
from . import interactions
from .skills.main import (
    create_skill,
    acreate_skill,
    list_skills,
    alist_skills,
    get_skill,
    aget_skill,
    delete_skill,
    adelete_skill,
)
from .containers.main import *
from .ocr.main import *
from .rag.main import *
from .search.main import *
from .realtime_api.main import (
    _arealtime,
    acreate_realtime_client_secret,
    arealtime_calls,
)
from .responses.main import _aresponses_websocket
from .fine_tuning.main import *
from .files.main import *
from .vector_store_files.main import (
    acreate as avector_store_file_create,
    adelete as avector_store_file_delete,
    alist as avector_store_file_list,
    aretrieve as avector_store_file_retrieve,
    aretrieve_content as avector_store_file_content,
    aupdate as avector_store_file_update,
    create as vector_store_file_create,
    delete as vector_store_file_delete,
    list as vector_store_file_list,
    retrieve as vector_store_file_retrieve,
    retrieve_content as vector_store_file_content,
    update as vector_store_file_update,
)
from .scheduler import *

### ADAPTERS ###
from .types.adapter import AdapterItem
import litellm.anthropic_interface as anthropic

adapters: List[AdapterItem] = []

### Vector Store Registry ###
from .vector_stores.vector_store_registry import (
    VectorStoreRegistry,
    VectorStoreIndexRegistry,
)

vector_store_registry: Optional[VectorStoreRegistry] = None
vector_store_index_registry: Optional[VectorStoreIndexRegistry] = None

### RAG ###
from . import rag

### CUSTOM LLMs ###
from .types.llms.custom_llm import CustomLLMItem

custom_provider_map: List[CustomLLMItem] = []
_custom_providers: List[
    str
] = []  # internal helper util, used to track names of custom providers
disable_hf_tokenizer_download: Optional[
    bool
] = None  # disable huggingface tokenizer download. Defaults to openai clk100
global_disable_no_log_param: bool = False

### CLI UTILITIES ###
from litellm.litellm_core_utils.cli_token_utils import get_litellm_gateway_api_key

### PASSTHROUGH ###
from .passthrough import allm_passthrough_route, llm_passthrough_route
from .google_genai import agenerate_content

### GLOBAL CONFIG ###
global_bitbucket_config: Optional[Dict[str, Any]] = None


def set_global_bitbucket_config(config: Dict[str, Any]) -> None:
    """Set global BitBucket configuration for prompt management."""
    global global_bitbucket_config
    global_bitbucket_config = config


### GLOBAL CONFIG ###
global_gitlab_config: Optional[Dict[str, Any]] = None


def set_global_gitlab_config(config: Dict[str, Any]) -> None:
    """Set global BitBucket configuration for prompt management."""
    global global_gitlab_config
    global_gitlab_config = config


# Lazy loading system for heavy modules to reduce initial import time and memory usage

if TYPE_CHECKING:
    from litellm.types.utils import ModelInfo as _ModelInfoType
    from litellm.types.utils import PriorityReservationSettings
    from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
    from litellm.caching.caching import Cache

    # Type stubs for lazy-loaded configs to help mypy
    from .llms.bedrock.chat.converse_transformation import (
        AmazonConverseConfig as AmazonConverseConfig,
    )
    from .llms.openai_like.chat.handler import (
        OpenAILikeChatConfig as OpenAILikeChatConfig,
    )
    from .llms.galadriel.chat.transformation import (
        GaladrielChatConfig as GaladrielChatConfig,
    )
    from .llms.github.chat.transformation import GithubChatConfig as GithubChatConfig
    from .llms.azure_ai.anthropic.transformation import (
        AzureAnthropicConfig as AzureAnthropicConfig,
    )
    from .llms.bytez.chat.transformation import BytezChatConfig as BytezChatConfig
    from .llms.compactifai.chat.transformation import (
        CompactifAIChatConfig as CompactifAIChatConfig,
    )
    from .llms.empower.chat.transformation import EmpowerChatConfig as EmpowerChatConfig
    from .llms.minimax.chat.transformation import MinimaxChatConfig as MinimaxChatConfig
    from .llms.aiohttp_openai.chat.transformation import (
        AiohttpOpenAIChatConfig as AiohttpOpenAIChatConfig,
    )
    from .llms.huggingface.chat.transformation import (
        HuggingFaceChatConfig as HuggingFaceChatConfig,
    )
    from .llms.huggingface.embedding.transformation import (
        HuggingFaceEmbeddingConfig as HuggingFaceEmbeddingConfig,
    )
    from .llms.oobabooga.chat.transformation import OobaboogaConfig as OobaboogaConfig
    from .llms.maritalk import MaritalkConfig as MaritalkConfig
    from .llms.openrouter.chat.transformation import (
        OpenrouterConfig as OpenrouterConfig,
    )
    from .llms.datarobot.chat.transformation import DataRobotConfig as DataRobotConfig
    from .llms.anthropic.chat.transformation import AnthropicConfig as AnthropicConfig
    from .llms.anthropic.completion.transformation import (
        AnthropicTextConfig as AnthropicTextConfig,
    )
    from .llms.groq.stt.transformation import GroqSTTConfig as GroqSTTConfig
    from .llms.triton.completion.transformation import TritonConfig as TritonConfig
    from .llms.triton.completion.transformation import (
        TritonGenerateConfig as TritonGenerateConfig,
    )
    from .llms.triton.completion.transformation import (
        TritonInferConfig as TritonInferConfig,
    )
    from .llms.triton.embedding.transformation import (
        TritonEmbeddingConfig as TritonEmbeddingConfig,
    )
    from .llms.huggingface.rerank.transformation import (
        HuggingFaceRerankConfig as HuggingFaceRerankConfig,
    )
    from .llms.databricks.chat.transformation import (
        DatabricksConfig as DatabricksConfig,
    )
    from .llms.databricks.embed.transformation import (
        DatabricksEmbeddingConfig as DatabricksEmbeddingConfig,
    )
    from .llms.predibase.chat.transformation import PredibaseConfig as PredibaseConfig
    from .llms.replicate.chat.transformation import ReplicateConfig as ReplicateConfig
    from .llms.snowflake.chat.transformation import SnowflakeConfig as SnowflakeConfig
    from .llms.cohere.rerank.transformation import (
        CohereRerankConfig as CohereRerankConfig,
    )
    from .llms.cohere.rerank_v2.transformation import (
        CohereRerankV2Config as CohereRerankV2Config,
    )
    from .llms.azure_ai.rerank.transformation import (
        AzureAIRerankConfig as AzureAIRerankConfig,
    )
    from .llms.infinity.rerank.transformation import (
        InfinityRerankConfig as InfinityRerankConfig,
    )
    from .llms.jina_ai.rerank.transformation import (
        JinaAIRerankConfig as JinaAIRerankConfig,
    )
    from .llms.deepinfra.rerank.transformation import (
        DeepinfraRerankConfig as DeepinfraRerankConfig,
    )
    from .llms.hosted_vllm.rerank.transformation import (
        HostedVLLMRerankConfig as HostedVLLMRerankConfig,
    )
    from .llms.nvidia_nim.rerank.transformation import (
        NvidiaNimRerankConfig as NvidiaNimRerankConfig,
    )
    from .llms.nvidia_nim.rerank.ranking_transformation import (
        NvidiaNimRankingConfig as NvidiaNimRankingConfig,
    )
    from .llms.vertex_ai.rerank.transformation import (
        VertexAIRerankConfig as VertexAIRerankConfig,
    )
    from .llms.fireworks_ai.rerank.transformation import (
        FireworksAIRerankConfig as FireworksAIRerankConfig,
    )
    from .llms.voyage.rerank.transformation import (
        VoyageRerankConfig as VoyageRerankConfig,
    )
    from .llms.watsonx.rerank.transformation import (
        IBMWatsonXRerankConfig as IBMWatsonXRerankConfig,
    )
    from .llms.clarifai.chat.transformation import ClarifaiConfig as ClarifaiConfig
    from .llms.ai21.chat.transformation import AI21ChatConfig as AI21ChatConfig
    from .llms.meta_llama.chat.transformation import LlamaAPIConfig as LlamaAPIConfig
    from .llms.together_ai.completion.transformation import (
        TogetherAITextCompletionConfig as TogetherAITextCompletionConfig,
    )
    from .llms.cloudflare.chat.transformation import (
        CloudflareChatConfig as CloudflareChatConfig,
    )
    from .llms.novita.chat.transformation import NovitaConfig as NovitaConfig
    from .llms.petals.completion.transformation import PetalsConfig as PetalsConfig
    from .llms.ollama.chat.transformation import OllamaChatConfig as OllamaChatConfig
    from .llms.ollama.completion.transformation import OllamaConfig as OllamaConfig
    from .llms.sagemaker.completion.transformation import (
        SagemakerConfig as SagemakerConfig,
    )
    from .llms.sagemaker.chat.transformation import (
        SagemakerChatConfig as SagemakerChatConfig,
    )
    from .llms.cohere.chat.transformation import CohereChatConfig as CohereChatConfig
    from .llms.anthropic.experimental_pass_through.messages.transformation import (
        AnthropicMessagesConfig as AnthropicMessagesConfig,
    )
    from .llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import (
        AmazonAnthropicClaudeMessagesConfig as AmazonAnthropicClaudeMessagesConfig,
    )
    from .llms.together_ai.chat import TogetherAIConfig as TogetherAIConfig
    from .llms.nlp_cloud.chat.handler import NLPCloudConfig as NLPCloudConfig
    from .llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
        VertexGeminiConfig as VertexGeminiConfig,
    )
    from .llms.gemini.chat.transformation import (
        GoogleAIStudioGeminiConfig as GoogleAIStudioGeminiConfig,
    )
    from .llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation import (
        VertexAIAnthropicConfig as VertexAIAnthropicConfig,
    )
    from .llms.vertex_ai.vertex_ai_partner_models.llama3.transformation import (
        VertexAILlama3Config as VertexAILlama3Config,
    )
    from .llms.vertex_ai.vertex_ai_partner_models.ai21.transformation import (
        VertexAIAi21Config as VertexAIAi21Config,
    )
    from .llms.bedrock.chat.invoke_handler import (
        AmazonCohereChatConfig as AmazonCohereChatConfig,
    )
    from .llms.bedrock.common_utils import (
        AmazonBedrockGlobalConfig as AmazonBedrockGlobalConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_ai21_transformation import (
        AmazonAI21Config as AmazonAI21Config,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_nova_transformation import (
        AmazonInvokeNovaConfig as AmazonInvokeNovaConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_qwen2_transformation import (
        AmazonQwen2Config as AmazonQwen2Config,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_qwen3_transformation import (
        AmazonQwen3Config as AmazonQwen3Config,
    )
    from .llms.bedrock.chat.invoke_transformations.anthropic_claude2_transformation import (
        AmazonAnthropicConfig as AmazonAnthropicConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import (
        AmazonAnthropicClaudeConfig as AmazonAnthropicClaudeConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_cohere_transformation import (
        AmazonCohereConfig as AmazonCohereConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_llama_transformation import (
        AmazonLlamaConfig as AmazonLlamaConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_deepseek_transformation import (
        AmazonDeepSeekR1Config as AmazonDeepSeekR1Config,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_mistral_transformation import (
        AmazonMistralConfig as AmazonMistralConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation import (
        AmazonMoonshotConfig as AmazonMoonshotConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_titan_transformation import (
        AmazonTitanConfig as AmazonTitanConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_twelvelabs_pegasus_transformation import (
        AmazonTwelveLabsPegasusConfig as AmazonTwelveLabsPegasusConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
        AmazonInvokeConfig as AmazonInvokeConfig,
    )
    from .llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import (
        AmazonBedrockOpenAIConfig as AmazonBedrockOpenAIConfig,
    )
    from .llms.bedrock.image_generation.amazon_stability1_transformation import (
        AmazonStabilityConfig as AmazonStabilityConfig,
    )
    from .llms.bedrock.image_generation.amazon_stability3_transformation import (
        AmazonStability3Config as AmazonStability3Config,
    )
    from .llms.bedrock.image_generation.amazon_nova_canvas_transformation import (
        AmazonNovaCanvasConfig as AmazonNovaCanvasConfig,
    )
    from .llms.bedrock.embed.amazon_titan_g1_transformation import (
        AmazonTitanG1Config as AmazonTitanG1Config,
    )
    from .llms.bedrock.embed.amazon_titan_multimodal_transformation import (
        AmazonTitanMultimodalEmbeddingG1Config as AmazonTitanMultimodalEmbeddingG1Config,
    )
    from .llms.cohere.chat.v2_transformation import (
        CohereV2ChatConfig as CohereV2ChatConfig,
    )
    from .llms.bedrock.embed.cohere_transformation import (
        BedrockCohereEmbeddingConfig as BedrockCohereEmbeddingConfig,
    )
    from .llms.bedrock.embed.twelvelabs_marengo_transformation import (
        TwelveLabsMarengoEmbeddingConfig as TwelveLabsMarengoEmbeddingConfig,
    )
    from .llms.bedrock.embed.amazon_nova_transformation import (
        AmazonNovaEmbeddingConfig as AmazonNovaEmbeddingConfig,
    )
    from .llms.openai.openai import (
        OpenAIConfig as OpenAIConfig,
        MistralEmbeddingConfig as MistralEmbeddingConfig,
    )
    from .llms.openai.image_variations.transformation import (
        OpenAIImageVariationConfig as OpenAIImageVariationConfig,
    )
    from .llms.deepgram.audio_transcription.transformation import (
        DeepgramAudioTranscriptionConfig as DeepgramAudioTranscriptionConfig,
    )
    from .llms.topaz.image_variations.transformation import (
        TopazImageVariationConfig as TopazImageVariationConfig,
    )
    from litellm.llms.openai.completion.transformation import (
        OpenAITextCompletionConfig as OpenAITextCompletionConfig,
    )
    from .llms.groq.chat.transformation import GroqChatConfig as GroqChatConfig
    from .llms.bedrock_mantle.chat.transformation import (
        BedrockMantleChatConfig as BedrockMantleChatConfig,
    )
    from .llms.a2a.chat.transformation import A2AConfig as A2AConfig
    from .llms.voyage.embedding.transformation import (
        VoyageEmbeddingConfig as VoyageEmbeddingConfig,
    )
    from .llms.voyage.embedding.transformation_contextual import (
        VoyageContextualEmbeddingConfig as VoyageContextualEmbeddingConfig,
    )
    from .llms.infinity.embedding.transformation import (
        InfinityEmbeddingConfig as InfinityEmbeddingConfig,
    )
    from .llms.perplexity.embedding.transformation import (
        PerplexityEmbeddingConfig as PerplexityEmbeddingConfig,
    )
    from .llms.azure_ai.chat.transformation import (
        AzureAIStudioConfig as AzureAIStudioConfig,
    )
    from .llms.mistral.chat.transformation import MistralConfig as MistralConfig
    from .llms.openai.responses.transformation import (
        OpenAIResponsesAPIConfig as OpenAIResponsesAPIConfig,
    )
    from .llms.azure.responses.transformation import (
        AzureOpenAIResponsesAPIConfig as AzureOpenAIResponsesAPIConfig,
    )
    from .llms.azure.responses.o_series_transformation import (
        AzureOpenAIOSeriesResponsesAPIConfig as AzureOpenAIOSeriesResponsesAPIConfig,
    )
    from .llms.xai.responses.transformation import (
        XAIResponsesAPIConfig as XAIResponsesAPIConfig,
    )
    from .llms.litellm_proxy.responses.transformation import (
        LiteLLMProxyResponsesAPIConfig as LiteLLMProxyResponsesAPIConfig,
    )
    from .llms.volcengine.responses.transformation import (
        VolcEngineResponsesAPIConfig as VolcEngineResponsesAPIConfig,
    )
    from .llms.manus.responses.transformation import (
        ManusResponsesAPIConfig as ManusResponsesAPIConfig,
    )
    from .llms.perplexity.responses.transformation import (
        PerplexityResponsesConfig as PerplexityResponsesConfig,
    )
    from .llms.databricks.responses.transformation import (
        DatabricksResponsesAPIConfig as DatabricksResponsesAPIConfig,
    )
    from .llms.openrouter.responses.transformation import (
        OpenRouterResponsesAPIConfig as OpenRouterResponsesAPIConfig,
    )
    from .llms.gemini.interactions.transformation import (
        GoogleAIStudioInteractionsConfig as GoogleAIStudioInteractionsConfig,
    )
    from .llms.openai.chat.o_series_transformation import (
        OpenAIOSeriesConfig as OpenAIOSeriesConfig,
        OpenAIOSeriesConfig as OpenAIO1Config,
    )
    from .llms.anthropic.skills.transformation import (
        AnthropicSkillsConfig as AnthropicSkillsConfig,
    )
    from .llms.base_llm.skills.transformation import (
        BaseSkillsAPIConfig as BaseSkillsAPIConfig,
    )
    from .llms.gradient_ai.chat.transformation import (
        GradientAIConfig as GradientAIConfig,
    )
    from .llms.openai.chat.gpt_transformation import OpenAIGPTConfig as OpenAIGPTConfig
    from .llms.openai.chat.gpt_5_transformation import (
        OpenAIGPT5Config as OpenAIGPT5Config,
    )
    from .llms.openai.transcriptions.whisper_transformation import (
        OpenAIWhisperAudioTranscriptionConfig as OpenAIWhisperAudioTranscriptionConfig,
    )
    from .llms.openai.transcriptions.gpt_transformation import (
        OpenAIGPTAudioTranscriptionConfig as OpenAIGPTAudioTranscriptionConfig,
    )
    from .llms.openai.chat.gpt_audio_transformation import (
        OpenAIGPTAudioConfig as OpenAIGPTAudioConfig,
    )
    from .llms.nvidia_nim.chat.transformation import NvidiaNimConfig as NvidiaNimConfig
    from .llms.nvidia_nim.embed import (
        NvidiaNimEmbeddingConfig as NvidiaNimEmbeddingConfig,
    )

    # Type stubs for lazy-loaded config instances
    openaiOSeriesConfig: OpenAIOSeriesConfig
    openAIGPTConfig: OpenAIGPTConfig
    openAIGPTAudioConfig: OpenAIGPTAudioConfig
    openAIGPT5Config: OpenAIGPT5Config
    nvidiaNimConfig: NvidiaNimConfig
    nvidiaNimEmbeddingConfig: NvidiaNimEmbeddingConfig

    # Import config classes that need type stubs (for mypy) - import with _ prefix to avoid circular reference
    from .llms.vllm.completion.transformation import VLLMConfig as _VLLMConfig
    from .llms.deepseek.chat.transformation import (
        DeepSeekChatConfig as _DeepSeekChatConfig,
    )
    from .llms.sap.chat.transformation import (
        GenAIHubOrchestrationConfig as _GenAIHubOrchestrationConfig,
    )
    from .llms.sap.embed.transformation import (
        GenAIHubEmbeddingConfig as _GenAIHubEmbeddingConfig,
    )
    from .llms.azure.chat.o_series_transformation import (
        AzureOpenAIO1Config as _AzureOpenAIO1Config,
    )
    from .llms.perplexity.chat.transformation import (
        PerplexityChatConfig as _PerplexityChatConfig,
    )
    from .llms.nscale.chat.transformation import NscaleConfig as _NscaleConfig
    from .llms.watsonx.chat.transformation import (
        IBMWatsonXChatConfig as _IBMWatsonXChatConfig,
    )
    from .llms.watsonx.completion.transformation import (
        IBMWatsonXAIConfig as _IBMWatsonXAIConfig,
    )
    from .llms.litellm_proxy.chat.transformation import (
        LiteLLMProxyChatConfig as _LiteLLMProxyChatConfig,
    )
    from .llms.deepinfra.chat.transformation import DeepInfraConfig as _DeepInfraConfig
    from .llms.llamafile.chat.transformation import (
        LlamafileChatConfig as _LlamafileChatConfig,
    )
    from .llms.lm_studio.chat.transformation import (
        LMStudioChatConfig as _LMStudioChatConfig,
    )
    from .llms.lm_studio.embed.transformation import (
        LmStudioEmbeddingConfig as _LmStudioEmbeddingConfig,
    )
    from .llms.watsonx.embed.transformation import (
        IBMWatsonXEmbeddingConfig as _IBMWatsonXEmbeddingConfig,
    )
    from .llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
        VertexGeminiConfig as _VertexGeminiConfig,
    )

    # Type stubs for lazy-loaded config classes (to help mypy understand types)
    VLLMConfig: Type[_VLLMConfig]
    DeepSeekChatConfig: Type[_DeepSeekChatConfig]
    GenAIHubOrchestrationConfig: Type[_GenAIHubOrchestrationConfig]
    GenAIHubEmbeddingConfig: Type[_GenAIHubEmbeddingConfig]
    AzureOpenAIO1Config: Type[_AzureOpenAIO1Config]
    PerplexityChatConfig: Type[_PerplexityChatConfig]
    NscaleConfig: Type[_NscaleConfig]
    IBMWatsonXChatConfig: Type[_IBMWatsonXChatConfig]
    IBMWatsonXAIConfig: Type[_IBMWatsonXAIConfig]
    LiteLLMProxyChatConfig: Type[_LiteLLMProxyChatConfig]
    DeepInfraConfig: Type[_DeepInfraConfig]
    LlamafileChatConfig: Type[_LlamafileChatConfig]
    LMStudioChatConfig: Type[_LMStudioChatConfig]
    LmStudioEmbeddingConfig: Type[_LmStudioEmbeddingConfig]
    IBMWatsonXEmbeddingConfig: Type[_IBMWatsonXEmbeddingConfig]
    VertexAIConfig: Type[_VertexGeminiConfig]  # Alias for VertexGeminiConfig

    from .llms.featherless_ai.chat.transformation import (
        FeatherlessAIConfig as FeatherlessAIConfig,
    )
    from .llms.cerebras.chat import CerebrasConfig as CerebrasConfig
    from .llms.baseten.chat import BasetenConfig as BasetenConfig
    from .llms.sambanova.chat import SambanovaConfig as SambanovaConfig
    from .llms.sambanova.embedding.transformation import (
        SambaNovaEmbeddingConfig as SambaNovaEmbeddingConfig,
    )
    from .llms.fireworks_ai.chat.transformation import (
        FireworksAIConfig as FireworksAIConfig,
    )
    from .llms.fireworks_ai.completion.transformation import (
        FireworksAITextCompletionConfig as FireworksAITextCompletionConfig,
    )
    from .llms.fireworks_ai.audio_transcription.transformation import (
        FireworksAIAudioTranscriptionConfig as FireworksAIAudioTranscriptionConfig,
    )
    from .llms.fireworks_ai.embed.fireworks_ai_transformation import (
        FireworksAIEmbeddingConfig as FireworksAIEmbeddingConfig,
    )
    from .llms.friendliai.chat.transformation import (
        FriendliaiChatConfig as FriendliaiChatConfig,
    )
    from .llms.jina_ai.embedding.transformation import (
        JinaAIEmbeddingConfig as JinaAIEmbeddingConfig,
    )
    from .llms.xai.chat.transformation import XAIChatConfig as XAIChatConfig
    from .llms.zai.chat.transformation import ZAIChatConfig as ZAIChatConfig
    from .llms.aiml.chat.transformation import AIMLChatConfig as AIMLChatConfig
    from .llms.volcengine.chat.transformation import (
        VolcEngineChatConfig as VolcEngineChatConfig,
        VolcEngineChatConfig as VolcEngineConfig,
    )
    from .llms.codestral.completion.transformation import (
        CodestralTextCompletionConfig as CodestralTextCompletionConfig,
    )
    from .llms.azure.azure import (
        AzureOpenAIAssistantsAPIConfig as AzureOpenAIAssistantsAPIConfig,
    )
    from .llms.heroku.chat.transformation import HerokuChatConfig as HerokuChatConfig
    from .llms.cometapi.chat.transformation import CometAPIConfig as CometAPIConfig
    from .llms.azure.chat.gpt_transformation import (
        AzureOpenAIConfig as AzureOpenAIConfig,
    )
    from .llms.azure.chat.gpt_5_transformation import (
        AzureOpenAIGPT5Config as AzureOpenAIGPT5Config,
    )
    from .llms.azure.completion.transformation import (
        AzureOpenAITextConfig as AzureOpenAITextConfig,
    )
    from .llms.hosted_vllm.chat.transformation import (
        HostedVLLMChatConfig as HostedVLLMChatConfig,
    )
    from .llms.hosted_vllm.embedding.transformation import (
        HostedVLLMEmbeddingConfig as HostedVLLMEmbeddingConfig,
    )
    from .llms.hosted_vllm.responses.transformation import (
        HostedVLLMResponsesAPIConfig as HostedVLLMResponsesAPIConfig,
    )
    from .llms.github_copilot.chat.transformation import (
        GithubCopilotConfig as GithubCopilotConfig,
    )
    from .llms.github_copilot.responses.transformation import (
        GithubCopilotResponsesAPIConfig as GithubCopilotResponsesAPIConfig,
    )
    from .llms.github_copilot.embedding.transformation import (
        GithubCopilotEmbeddingConfig as GithubCopilotEmbeddingConfig,
    )
    from .llms.chatgpt.chat.transformation import ChatGPTConfig as ChatGPTConfig
    from .llms.chatgpt.responses.transformation import (
        ChatGPTResponsesAPIConfig as ChatGPTResponsesAPIConfig,
    )
    from .llms.gigachat.chat.transformation import GigaChatConfig as GigaChatConfig
    from .llms.gigachat.embedding.transformation import (
        GigaChatEmbeddingConfig as GigaChatEmbeddingConfig,
    )
    from .llms.nebius.chat.transformation import NebiusConfig as NebiusConfig
    from .llms.wandb.chat.transformation import WandbConfig as WandbConfig
    from .llms.dashscope.chat.transformation import (
        DashScopeChatConfig as DashScopeChatConfig,
    )
    from .llms.moonshot.chat.transformation import (
        MoonshotChatConfig as MoonshotChatConfig,
    )
    from .llms.docker_model_runner.chat.transformation import (
        DockerModelRunnerChatConfig as DockerModelRunnerChatConfig,
    )
    from .llms.v0.chat.transformation import V0ChatConfig as V0ChatConfig
    from .llms.oci.chat.transformation import OCIChatConfig as OCIChatConfig
    from .llms.morph.chat.transformation import MorphChatConfig as MorphChatConfig
    from .llms.ragflow.chat.transformation import RAGFlowConfig as RAGFlowConfig
    from .llms.lambda_ai.chat.transformation import (
        LambdaAIChatConfig as LambdaAIChatConfig,
    )
    from .llms.hyperbolic.chat.transformation import (
        HyperbolicChatConfig as HyperbolicChatConfig,
    )
    from .llms.vercel_ai_gateway.chat.transformation import (
        VercelAIGatewayConfig as VercelAIGatewayConfig,
    )
    from .llms.ovhcloud.chat.transformation import (
        OVHCloudChatConfig as OVHCloudChatConfig,
    )
    from .llms.ovhcloud.embedding.transformation import (
        OVHCloudEmbeddingConfig as OVHCloudEmbeddingConfig,
    )
    from .llms.cometapi.embed.transformation import (
        CometAPIEmbeddingConfig as CometAPIEmbeddingConfig,
    )
    from .llms.lemonade.chat.transformation import (
        LemonadeChatConfig as LemonadeChatConfig,
    )
    from .llms.snowflake.embedding.transformation import (
        SnowflakeEmbeddingConfig as SnowflakeEmbeddingConfig,
    )
    from .llms.amazon_nova.chat.transformation import (
        AmazonNovaChatConfig as AmazonNovaChatConfig,
    )
    from litellm.caching.llm_caching_handler import LLMClientCache
    from litellm.types.llms.bedrock import COHERE_EMBEDDING_INPUT_TYPES
    from litellm.types.utils import (
        BudgetConfig,
        CredentialItem,
        PriorityReservationDict,
        StandardKeyGenerationConfig,
    )
    from litellm.types.guardrails import GuardrailItem
    from litellm.types.proxy.management_endpoints.ui_sso import (
        DefaultTeamSSOParams,
        LiteLLM_UpperboundKeyGenerateParams,
    )

    # Cost calculator functions
    cost_per_token: Callable[..., Tuple[float, float]]
    completion_cost: Callable[..., float]
    response_cost_calculator: Any
    modify_integration: Any

    # Utils functions - type stubs for truly lazy loaded functions only
    # (functions NOT imported via "from .main import *")
    get_response_string: Callable[..., str]
    supports_function_calling: Callable[..., bool]
    supports_web_search: Callable[..., bool]
    supports_url_context: Callable[..., bool]
    supports_response_schema: Callable[..., bool]
    supports_parallel_function_calling: Callable[..., bool]
    supports_vision: Callable[..., bool]
    supports_audio_input: Callable[..., bool]
    supports_audio_output: Callable[..., bool]
    supports_system_messages: Callable[..., bool]
    supports_reasoning: Callable[..., bool]
    acreate: Callable[..., Any]
    get_max_tokens: Callable[..., int]
    get_model_info: Callable[..., _ModelInfoType]  # type: ignore[no-redef]
    register_prompt_template: Callable[..., None]
    validate_environment: Callable[..., dict]
    check_valid_key: Callable[..., bool]
    register_model: Callable[..., None]
    encode: Callable[..., list]
    decode: Callable[..., str]
    _calculate_retry_after: Callable[..., float]
    _should_retry: Callable[..., bool]
    get_supported_openai_params: Callable[..., Optional[list]]
    get_api_base: Callable[..., Optional[str]]
    get_first_chars_messages: Callable[..., str]
    get_provider_fields: Callable[..., List]
    get_valid_models: Callable[..., list]
    remove_index_from_tool_calls: Callable[..., None]

    # Response types - truly lazy loaded only (not in main.py or elsewhere)
    ModelResponseListIterator: Type[Any]

    # HTTP handler singletons (created lazily via __getattr__ at runtime)
    module_level_aclient: AsyncHTTPHandler
    module_level_client: HTTPHandler

    # Bedrock tool name mappings instance (lazy-loaded)
    from litellm.caching.caching import InMemoryCache

    bedrock_tool_name_mappings: InMemoryCache

    # Azure exception class (lazy-loaded)
    from litellm.llms.azure.common_utils import AzureOpenAIError

    # Secret manager types (lazy-loaded)
    from litellm.types.secret_managers.main import (
        KeyManagementSystem,
        KeyManagementSettings,  # Not lazy-loaded - needed for _key_management_settings initialization
    )

    # Custom logger class (lazy-loaded)
    from litellm.integrations.custom_logger import CustomLogger

    # Datadog LLM observability params (lazy-loaded)
    from litellm.types.integrations.datadog_llm_obs import DatadogLLMObsInitParams

    # Logging callback manager class and instance (lazy-loaded)
    from litellm.litellm_core_utils.logging_callback_manager import (
        LoggingCallbackManager,
    )

    logging_callback_manager: LoggingCallbackManager

    # provider_list is lazy-loaded
    from litellm.types.utils import LlmProviders

    provider_list: List[Union[LlmProviders, str]]

    # Note: AmazonConverseConfig and OpenAILikeChatConfig are imported above in TYPE_CHECKING block


# Track if async client cleanup has been registered (for lazy loading)
_async_client_cleanup_registered = False

# Eager loading for backwards compatibility with VCR and other HTTP recording tools
# When LITELLM_DISABLE_LAZY_LOADING is set, lazy-loaded attributes are loaded at import time
# For now, this only affects encoding (tiktoken) as it was the only reported issue
# See: https://github.com/BerriAI/litellm/issues/18659
# This ensures encoding is initialized before VCR starts recording HTTP requests
if os.getenv("LITELLM_DISABLE_LAZY_LOADING", "").lower() in ("1", "true", "yes", "on"):
    # Load encoding at import time (pre-#18070 behavior)
    # This ensures encoding is initialized before VCR starts recording
    from .main import encoding


def __getattr__(name: str) -> Any:
    """Lazy import handler with cached registry for improved performance."""
    global _async_client_cleanup_registered
    # Register async client cleanup on first access (only once)
    if not _async_client_cleanup_registered:
        from litellm.llms.custom_httpx.async_client_cleanup import (
            register_async_client_cleanup,
        )

        register_async_client_cleanup()
        _async_client_cleanup_registered = True

    # Use cached registry from _lazy_imports instead of importing tuples every time
    from ._lazy_imports import _get_lazy_import_registry

    registry = _get_lazy_import_registry()

    # Check if name is in registry and call the cached handler function
    if name in registry:
        handler_func = registry[name]
        return handler_func(name)

    # Lazy load encoding from main.py to avoid heavy tiktoken import
    if name == "encoding":
        from ._lazy_imports import _get_litellm_globals

        _globals = _get_litellm_globals()
        # Check if already cached
        if "encoding" not in _globals:
            from .main import encoding as _encoding

            _globals["encoding"] = _encoding
        return _globals["encoding"]

    # Lazy load bedrock_tool_name_mappings instance
    if name == "bedrock_tool_name_mappings":
        from ._lazy_imports import _get_litellm_globals

        _globals = _get_litellm_globals()
        # Check if already cached
        if "bedrock_tool_name_mappings" not in _globals:
            from .llms.bedrock.chat.invoke_handler import (
                bedrock_tool_name_mappings as _bedrock_tool_name_mappings,
            )

            _globals["bedrock_tool_name_mappings"] = _bedrock_tool_name_mappings
        return _globals["bedrock_tool_name_mappings"]

    # Lazy load AzureOpenAIError exception class
    if name == "AzureOpenAIError":
        from ._lazy_imports import _get_litellm_globals

        _globals = _get_litellm_globals()
        # Check if already cached
        if "AzureOpenAIError" not in _globals:
            from .llms.azure.common_utils import AzureOpenAIError as _AzureOpenAIError

            _globals["AzureOpenAIError"] = _AzureOpenAIError
        return _globals["AzureOpenAIError"]

    # Lazy load openaiOSeriesConfig instance
    if name == "openaiOSeriesConfig":
        from ._lazy_imports import _get_litellm_globals

        _globals = _get_litellm_globals()
        if "openaiOSeriesConfig" not in _globals:
            # Import the config class and instantiate it
            config_class = __getattr__("OpenAIOSeriesConfig")
            _globals["openaiOSeriesConfig"] = config_class()
        return _globals["openaiOSeriesConfig"]

    # Lazy load other config instances
    _config_instances = {
        "openAIGPTConfig": "OpenAIGPTConfig",
        "openAIGPTAudioConfig": "OpenAIGPTAudioConfig",
        "openAIGPT5Config": "OpenAIGPT5Config",
        "nvidiaNimConfig": "NvidiaNimConfig",
        "nvidiaNimEmbeddingConfig": "NvidiaNimEmbeddingConfig",
    }
    if name in _config_instances:
        from ._lazy_imports import _get_litellm_globals

        _globals = _get_litellm_globals()
        if name not in _globals:
            # Import the config class and instantiate it
            config_class = __getattr__(_config_instances[name])
            _globals[name] = config_class()
        return _globals[name]

    # Handle OpenAIO1Config alias
    if name == "OpenAIO1Config":
        return __getattr__("OpenAIOSeriesConfig")

    # Lazy load provider_list
    if name == "provider_list":
        from ._lazy_imports import _get_litellm_globals

        _globals = _get_litellm_globals()
        # Check if already cached
        if "provider_list" not in _globals:
            # LlmProviders is eagerly imported above, so we can import it directly
            from litellm.types.utils import LlmProviders

            _globals["provider_list"] = list(LlmProviders)
        return _globals["provider_list"]

    # Lazy load priority_reservation_settings instance
    if name == "priority_reservation_settings":
        from ._lazy_imports import _get_litellm_globals

        _globals = _get_litellm_globals()
        # Check if already cached
        if "priority_reservation_settings" not in _globals:
            # Import the class and instantiate it
            PriorityReservationSettings = __getattr__("PriorityReservationSettings")
            _globals["priority_reservation_settings"] = PriorityReservationSettings()
        return _globals["priority_reservation_settings"]

    # Lazy load logging_callback_manager instance
    if name == "logging_callback_manager":
        from ._lazy_imports import _get_litellm_globals

        _globals = _get_litellm_globals()
        # Check if already cached
        if "logging_callback_manager" not in _globals:
            # Import the class and instantiate it
            LoggingCallbackManager = __getattr__("LoggingCallbackManager")
            _globals["logging_callback_manager"] = LoggingCallbackManager()
        return _globals["logging_callback_manager"]

    # Lazy load _service_logger module
    if name == "_service_logger":
        from ._lazy_imports import _get_litellm_globals

        _globals = _get_litellm_globals()
        # Check if already cached
        if "_service_logger" not in _globals:
            # Import the module lazily
            import litellm._service_logger

            _globals["_service_logger"] = litellm._service_logger
        return _globals["_service_logger"]

    # Lazy load evals module functions
    if name in [
        "acreate_eval",
        "alist_evals",
        "aget_eval",
        "aupdate_eval",
        "adelete_eval",
        "acancel_eval",
        "create_eval",
        "list_evals",
        "get_eval",
        "update_eval",
        "delete_eval",
        "cancel_eval",
        "acreate_run",
        "alist_runs",
        "aget_run",
        "acancel_run",
        "adelete_run",
        "create_run",
        "list_runs",
        "get_run",
        "cancel_run",
        "delete_run",
    ]:
        from litellm.evals.main import (
            acreate_eval,
            alist_evals,
            aget_eval,
            aupdate_eval,
            adelete_eval,
            acancel_eval,
            create_eval,
            list_evals,
            get_eval,
            update_eval,
            delete_eval,
            cancel_eval,
            acreate_run,
            alist_runs,
            aget_run,
            acancel_run,
            adelete_run,
            create_run,
            list_runs,
            get_run,
            cancel_run,
            delete_run,
        )

        return locals()[name]

    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")


# ALL_LITELLM_RESPONSE_TYPES is lazy-loaded via __getattr__ to avoid loading utils at import time
