litellm-mirror/litellm/utils.py

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

import ast
import asyncio
import base64
import binascii
import copy
import datetime
import hashlib
import inspect
import io
import itertools
import json
import logging
import os
import random  # type: ignore
import re
import struct
import subprocess

# What is this?
## Generic utils.py file. Problem-specific utils (e.g. 'cost calculation), should all be in `litellm_core_utils/`.
import sys
import textwrap
import threading
import time
import traceback
import uuid
from dataclasses import dataclass, field
from functools import lru_cache, wraps
from inspect import iscoroutine
from os.path import abspath, dirname, join

import aiohttp
import dotenv
import httpx
import openai
import requests
import tiktoken
from httpx import Proxy
from httpx._utils import get_environment_proxies
from openai.lib import _parsing, _pydantic
from openai.types.chat.completion_create_params import ResponseFormat
from pydantic import BaseModel
from tokenizers import Tokenizer

import litellm
import litellm._service_logger  # for storing API inputs, outputs, and metadata
import litellm.litellm_core_utils
import litellm.litellm_core_utils.audio_utils.utils
import litellm.litellm_core_utils.json_validation_rule
from litellm.caching.caching import DualCache
from litellm.caching.caching_handler import CachingHandlerResponse, LLMCachingHandler
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.core_helpers import map_finish_reason
from litellm.litellm_core_utils.exception_mapping_utils import (
    _get_response_headers,
    exception_type,
    get_error_message,
)
from litellm.litellm_core_utils.get_llm_provider_logic import (
    _is_non_openai_azure_model,
    get_llm_provider,
)
from litellm.litellm_core_utils.llm_request_utils import _ensure_extra_body_is_safe
from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
    _handle_invalid_parallel_tool_calls,
    convert_to_model_response_object,
    convert_to_streaming_response,
    convert_to_streaming_response_async,
)
from litellm.litellm_core_utils.llm_response_utils.get_headers import (
    get_response_headers,
)
from litellm.litellm_core_utils.redact_messages import (
    LiteLLMLoggingObject,
    redact_message_input_output_from_logging,
)
from litellm.litellm_core_utils.token_counter import get_modified_max_tokens
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.secret_managers.main import get_secret
from litellm.types.llms.openai import (
    AllMessageValues,
    ChatCompletionAssistantToolCall,
    ChatCompletionNamedToolChoiceParam,
    ChatCompletionToolParam,
    ChatCompletionToolParamFunctionChunk,
)
from litellm.types.rerank import RerankResponse
from litellm.types.utils import FileTypes  # type: ignore
from litellm.types.utils import (
    OPENAI_RESPONSE_HEADERS,
    CallTypes,
    ChatCompletionDeltaToolCall,
    ChatCompletionMessageToolCall,
    Choices,
    CostPerToken,
    Delta,
    Embedding,
    EmbeddingResponse,
    Function,
    ImageResponse,
    Message,
    ModelInfo,
    ModelResponse,
    ProviderField,
    StreamingChoices,
    TextChoices,
    TextCompletionResponse,
    TranscriptionResponse,
    Usage,
)

try:
    # New and recommended way to access resources
    from importlib import resources

    filename = str(resources.files(litellm).joinpath("llms/tokenizers"))
except (ImportError, AttributeError):
    # Old way to access resources, which setuptools deprecated some time ago
    import pkg_resources  # type: ignore

    filename = pkg_resources.resource_filename(__name__, "llms/tokenizers")

os.environ["TIKTOKEN_CACHE_DIR"] = os.getenv(
    "CUSTOM_TIKTOKEN_CACHE_DIR", filename
)  # use local copy of tiktoken b/c of - https://github.com/BerriAI/litellm/issues/1071
from tiktoken import Encoding

encoding = tiktoken.get_encoding("cl100k_base")
from importlib import resources

with resources.open_text("litellm.llms.tokenizers", "anthropic_tokenizer.json") as f:
    json_data = json.load(f)
# Convert to str (if necessary)
claude_json_str = json.dumps(json_data)
import importlib.metadata
from concurrent.futures import ThreadPoolExecutor
from typing import (
    Any,
    Callable,
    Dict,
    Iterable,
    List,
    Literal,
    Optional,
    Tuple,
    Type,
    Union,
    cast,
    get_args,
)

from openai import OpenAIError as OriginalError

from ._logging import verbose_logger
from .caching.caching import (
    Cache,
    QdrantSemanticCache,
    RedisCache,
    RedisSemanticCache,
    S3Cache,
)
from .exceptions import (
    APIConnectionError,
    APIError,
    AuthenticationError,
    BadRequestError,
    BudgetExceededError,
    ContentPolicyViolationError,
    ContextWindowExceededError,
    NotFoundError,
    OpenAIError,
    PermissionDeniedError,
    RateLimitError,
    ServiceUnavailableError,
    Timeout,
    UnprocessableEntityError,
    UnsupportedParamsError,
)
from .proxy._types import AllowedModelRegion, KeyManagementSystem
from .types.llms.openai import (
    ChatCompletionDeltaToolCallChunk,
    ChatCompletionToolCallChunk,
    ChatCompletionToolCallFunctionChunk,
)
from .types.router import LiteLLM_Params

####### ENVIRONMENT VARIABLES ####################
# Adjust to your specific application needs / system capabilities.
MAX_THREADS = 100

# Create a ThreadPoolExecutor
executor = ThreadPoolExecutor(max_workers=MAX_THREADS)
sentry_sdk_instance = None
capture_exception = None
add_breadcrumb = None
posthog = None
slack_app = None
alerts_channel = None
heliconeLogger = None
athinaLogger = None
promptLayerLogger = None
langsmithLogger = None
logfireLogger = None
weightsBiasesLogger = None
customLogger = None
langFuseLogger = None
openMeterLogger = None
lagoLogger = None
dataDogLogger = None
prometheusLogger = None
dynamoLogger = None
s3Logger = None
genericAPILogger = None
greenscaleLogger = None
lunaryLogger = None
aispendLogger = None
supabaseClient = None
callback_list: Optional[List[str]] = []
user_logger_fn = None
additional_details: Optional[Dict[str, str]] = {}
local_cache: Optional[Dict[str, str]] = {}
last_fetched_at = None
last_fetched_at_keys = None
######## Model Response #########################


# All liteLLM Model responses will be in this format, Follows the OpenAI Format
# https://docs.litellm.ai/docs/completion/output
# {
#   'choices': [
#      {
#         'finish_reason': 'stop',
#         'index': 0,
#         'message': {
#            'role': 'assistant',
#             'content': " I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic."
#         }
#       }
#     ],
#  'created': 1691429984.3852863,
#  'model': 'claude-instant-1',
#  'usage': {'prompt_tokens': 18, 'completion_tokens': 23, 'total_tokens': 41}
# }


############################################################
def print_verbose(
    print_statement,
    logger_only: bool = False,
    log_level: Literal["DEBUG", "INFO", "ERROR"] = "DEBUG",
):
    try:
        if log_level == "DEBUG":
            verbose_logger.debug(print_statement)
        elif log_level == "INFO":
            verbose_logger.info(print_statement)
        elif log_level == "ERROR":
            verbose_logger.error(print_statement)
        if litellm.set_verbose is True and logger_only is False:
            print(print_statement)  # noqa
    except Exception:
        pass


####### RULES ###################


class Rules:
    """
    Fail calls based on the input or llm api output

    Example usage:
    import litellm
    def my_custom_rule(input): # receives the model response
            if "i don't think i can answer" in input: # trigger fallback if the model refuses to answer
                    return False
            return True

    litellm.post_call_rules = [my_custom_rule] # have these be functions that can be called to fail a call

    response = litellm.completion(model="gpt-3.5-turbo", messages=[{"role": "user",
        "content": "Hey, how's it going?"}], fallbacks=["openrouter/mythomax"])
    """

    def __init__(self) -> None:
        pass

    def pre_call_rules(self, input: str, model: str):
        for rule in litellm.pre_call_rules:
            if callable(rule):
                decision = rule(input)
                if decision is False:
                    raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider="", model=model)  # type: ignore
        return True

    def post_call_rules(self, input: Optional[str], model: str) -> bool:
        if input is None:
            return True
        for rule in litellm.post_call_rules:
            if callable(rule):
                decision = rule(input)
                if isinstance(decision, bool):
                    if decision is False:
                        raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider="", model=model)  # type: ignore
                elif isinstance(decision, dict):
                    decision_val = decision.get("decision", True)
                    decision_message = decision.get(
                        "message", "LLM Response failed post-call-rule check"
                    )
                    if decision_val is False:
                        raise litellm.APIResponseValidationError(message=decision_message, llm_provider="", model=model)  # type: ignore
        return True


####### CLIENT ###################
# make it easy to log if completion/embedding runs succeeded or failed + see what happened | Non-Blocking
def custom_llm_setup():
    """
    Add custom_llm provider to provider list
    """
    for custom_llm in litellm.custom_provider_map:
        if custom_llm["provider"] not in litellm.provider_list:
            litellm.provider_list.append(custom_llm["provider"])

        if custom_llm["provider"] not in litellm._custom_providers:
            litellm._custom_providers.append(custom_llm["provider"])


def function_setup(  # noqa: PLR0915
    original_function: str, rules_obj, start_time, *args, **kwargs
):  # just run once to check if user wants to send their data anywhere - PostHog/Sentry/Slack/etc.
    ### NOTICES ###
    from litellm import Logging as LiteLLMLogging
    from litellm.litellm_core_utils.litellm_logging import set_callbacks

    if litellm.set_verbose is True:
        verbose_logger.warning(
            "`litellm.set_verbose` is deprecated. Please set `os.environ['LITELLM_LOG'] = 'DEBUG'` for debug logs."
        )
    try:
        global callback_list, add_breadcrumb, user_logger_fn, Logging

        ## CUSTOM LLM SETUP ##
        custom_llm_setup()

        ## LOGGING SETUP
        function_id: Optional[str] = kwargs["id"] if "id" in kwargs else None

        if len(litellm.callbacks) > 0:
            for callback in litellm.callbacks:
                # check if callback is a string - e.g. "lago", "openmeter"
                if isinstance(callback, str):
                    callback = litellm.litellm_core_utils.litellm_logging._init_custom_logger_compatible_class(  # type: ignore
                        callback, internal_usage_cache=None, llm_router=None
                    )
                    if callback is None or any(
                        isinstance(cb, type(callback))
                        for cb in litellm._async_success_callback
                    ):  # don't double add a callback
                        continue
                if callback not in litellm.input_callback:
                    litellm.input_callback.append(callback)  # type: ignore
                if callback not in litellm.success_callback:
                    litellm.success_callback.append(callback)  # type: ignore
                if callback not in litellm.failure_callback:
                    litellm.failure_callback.append(callback)  # type: ignore
                if callback not in litellm._async_success_callback:
                    litellm._async_success_callback.append(callback)  # type: ignore
                if callback not in litellm._async_failure_callback:
                    litellm._async_failure_callback.append(callback)  # type: ignore
            print_verbose(
                f"Initialized litellm callbacks, Async Success Callbacks: {litellm._async_success_callback}"
            )

        if (
            len(litellm.input_callback) > 0
            or len(litellm.success_callback) > 0
            or len(litellm.failure_callback) > 0
        ) and len(
            callback_list  # type: ignore
        ) == 0:  # type: ignore
            callback_list = list(
                set(
                    litellm.input_callback  # type: ignore
                    + litellm.success_callback
                    + litellm.failure_callback
                )
            )
            set_callbacks(callback_list=callback_list, function_id=function_id)
        ## ASYNC CALLBACKS
        if len(litellm.input_callback) > 0:
            removed_async_items = []
            for index, callback in enumerate(litellm.input_callback):  # type: ignore
                if inspect.iscoroutinefunction(callback):
                    litellm._async_input_callback.append(callback)
                    removed_async_items.append(index)

            # Pop the async items from input_callback in reverse order to avoid index issues
            for index in reversed(removed_async_items):
                litellm.input_callback.pop(index)
        if len(litellm.success_callback) > 0:
            removed_async_items = []
            for index, callback in enumerate(litellm.success_callback):  # type: ignore
                if inspect.iscoroutinefunction(callback):
                    litellm._async_success_callback.append(callback)
                    removed_async_items.append(index)
                elif callback == "dynamodb" or callback == "openmeter":
                    # dynamo is an async callback, it's used for the proxy and needs to be async
                    # we only support async dynamo db logging for acompletion/aembedding since that's used on proxy
                    litellm._async_success_callback.append(callback)
                    removed_async_items.append(index)
                elif callback in litellm._known_custom_logger_compatible_callbacks:
                    callback_class = litellm.litellm_core_utils.litellm_logging._init_custom_logger_compatible_class(  # type: ignore
                        callback, internal_usage_cache=None, llm_router=None  # type: ignore
                    )

                    # don't double add a callback
                    if callback_class is not None and not any(
                        isinstance(cb, type(callback_class)) for cb in litellm.callbacks
                    ):
                        litellm.callbacks.append(callback_class)  # type: ignore
                        litellm.input_callback.append(callback_class)  # type: ignore
                        litellm.success_callback.append(callback_class)  # type: ignore
                        litellm.failure_callback.append(callback_class)  # type: ignore
                        litellm._async_success_callback.append(callback_class)  # type: ignore
                        litellm._async_failure_callback.append(callback_class)  # type: ignore

            # Pop the async items from success_callback in reverse order to avoid index issues
            for index in reversed(removed_async_items):
                litellm.success_callback.pop(index)

        if len(litellm.failure_callback) > 0:
            removed_async_items = []
            for index, callback in enumerate(litellm.failure_callback):  # type: ignore
                if inspect.iscoroutinefunction(callback):
                    litellm._async_failure_callback.append(callback)
                    removed_async_items.append(index)

            # Pop the async items from failure_callback in reverse order to avoid index issues
            for index in reversed(removed_async_items):
                litellm.failure_callback.pop(index)
        ### DYNAMIC CALLBACKS ###
        dynamic_success_callbacks: Optional[
            List[Union[str, Callable, CustomLogger]]
        ] = None
        dynamic_async_success_callbacks: Optional[
            List[Union[str, Callable, CustomLogger]]
        ] = None
        dynamic_failure_callbacks: Optional[
            List[Union[str, Callable, CustomLogger]]
        ] = None
        dynamic_async_failure_callbacks: Optional[
            List[Union[str, Callable, CustomLogger]]
        ] = None
        if kwargs.get("success_callback", None) is not None and isinstance(
            kwargs["success_callback"], list
        ):
            removed_async_items = []
            for index, callback in enumerate(kwargs["success_callback"]):
                if (
                    inspect.iscoroutinefunction(callback)
                    or callback == "dynamodb"
                    or callback == "s3"
                ):
                    if dynamic_async_success_callbacks is not None and isinstance(
                        dynamic_async_success_callbacks, list
                    ):
                        dynamic_async_success_callbacks.append(callback)
                    else:
                        dynamic_async_success_callbacks = [callback]
                    removed_async_items.append(index)
            # Pop the async items from success_callback in reverse order to avoid index issues
            for index in reversed(removed_async_items):
                kwargs["success_callback"].pop(index)
            dynamic_success_callbacks = kwargs.pop("success_callback")
        if kwargs.get("failure_callback", None) is not None and isinstance(
            kwargs["failure_callback"], list
        ):
            dynamic_failure_callbacks = kwargs.pop("failure_callback")

        if add_breadcrumb:
            try:
                details_to_log = copy.deepcopy(kwargs)
            except Exception:
                details_to_log = kwargs

            if litellm.turn_off_message_logging:
                # make a copy of the _model_Call_details and log it
                details_to_log.pop("messages", None)
                details_to_log.pop("input", None)
                details_to_log.pop("prompt", None)
            add_breadcrumb(
                category="litellm.llm_call",
                message=f"Positional Args: {args}, Keyword Args: {details_to_log}",
                level="info",
            )
        if "logger_fn" in kwargs:
            user_logger_fn = kwargs["logger_fn"]
        # INIT LOGGER - for user-specified integrations
        model = args[0] if len(args) > 0 else kwargs.get("model", None)
        call_type = original_function
        if (
            call_type == CallTypes.completion.value
            or call_type == CallTypes.acompletion.value
        ):
            messages = None
            if len(args) > 1:
                messages = args[1]
            elif kwargs.get("messages", None):
                messages = kwargs["messages"]
            ### PRE-CALL RULES ###
            if (
                isinstance(messages, list)
                and len(messages) > 0
                and isinstance(messages[0], dict)
                and "content" in messages[0]
            ):
                rules_obj.pre_call_rules(
                    input="".join(
                        m.get("content", "")
                        for m in messages
                        if "content" in m and isinstance(m["content"], str)
                    ),
                    model=model,
                )
        elif (
            call_type == CallTypes.embedding.value
            or call_type == CallTypes.aembedding.value
        ):
            messages = args[1] if len(args) > 1 else kwargs.get("input", None)
        elif (
            call_type == CallTypes.image_generation.value
            or call_type == CallTypes.aimage_generation.value
        ):
            messages = args[0] if len(args) > 0 else kwargs["prompt"]
        elif (
            call_type == CallTypes.moderation.value
            or call_type == CallTypes.amoderation.value
        ):
            messages = args[1] if len(args) > 1 else kwargs["input"]
        elif (
            call_type == CallTypes.atext_completion.value
            or call_type == CallTypes.text_completion.value
        ):
            messages = args[0] if len(args) > 0 else kwargs["prompt"]
        elif (
            call_type == CallTypes.rerank.value or call_type == CallTypes.arerank.value
        ):
            messages = kwargs.get("query")
        elif (
            call_type == CallTypes.atranscription.value
            or call_type == CallTypes.transcription.value
        ):
            _file_obj: FileTypes = args[1] if len(args) > 1 else kwargs["file"]
            file_checksum = (
                litellm.litellm_core_utils.audio_utils.utils.get_audio_file_name(
                    file_obj=_file_obj
                )
            )
            if "metadata" in kwargs:
                kwargs["metadata"]["file_checksum"] = file_checksum
            else:
                kwargs["metadata"] = {"file_checksum": file_checksum}
            messages = file_checksum
        elif (
            call_type == CallTypes.aspeech.value or call_type == CallTypes.speech.value
        ):
            messages = kwargs.get("input", "speech")
        else:
            messages = "default-message-value"
        stream = True if "stream" in kwargs and kwargs["stream"] is True else False
        logging_obj = LiteLLMLogging(
            model=model,
            messages=messages,
            stream=stream,
            litellm_call_id=kwargs["litellm_call_id"],
            function_id=function_id or "",
            call_type=call_type,
            start_time=start_time,
            dynamic_success_callbacks=dynamic_success_callbacks,
            dynamic_failure_callbacks=dynamic_failure_callbacks,
            dynamic_async_success_callbacks=dynamic_async_success_callbacks,
            dynamic_async_failure_callbacks=dynamic_async_failure_callbacks,
            kwargs=kwargs,
        )

        ## check if metadata is passed in
        litellm_params: Dict[str, Any] = {"api_base": ""}
        if "metadata" in kwargs:
            litellm_params["metadata"] = kwargs["metadata"]
        logging_obj.update_environment_variables(
            model=model,
            user="",
            optional_params={},
            litellm_params=litellm_params,
            stream_options=kwargs.get("stream_options", None),
        )
        return logging_obj, kwargs
    except Exception as e:
        verbose_logger.error(
            f"litellm.utils.py::function_setup() - [Non-Blocking] {traceback.format_exc()}; args - {args}; kwargs - {kwargs}"
        )
        raise e


def client(original_function):  # noqa: PLR0915
    rules_obj = Rules()

    def check_coroutine(value) -> bool:
        if inspect.iscoroutine(value):
            return True
        elif inspect.iscoroutinefunction(value):
            return True
        else:
            return False

    def post_call_processing(original_response, model, optional_params: Optional[dict]):
        try:
            if original_response is None:
                pass
            else:
                call_type = original_function.__name__
                if (
                    call_type == CallTypes.completion.value
                    or call_type == CallTypes.acompletion.value
                ):
                    is_coroutine = check_coroutine(original_response)
                    if is_coroutine is True:
                        pass
                    else:
                        if (
                            isinstance(original_response, ModelResponse)
                            and len(original_response.choices) > 0
                        ):
                            model_response: Optional[str] = original_response.choices[
                                0
                            ].message.content  # type: ignore
                            if model_response is not None:
                                ### POST-CALL RULES ###
                                rules_obj.post_call_rules(
                                    input=model_response, model=model
                                )
                                ### JSON SCHEMA VALIDATION ###
                                if litellm.enable_json_schema_validation is True:
                                    try:
                                        if (
                                            optional_params is not None
                                            and "response_format" in optional_params
                                            and optional_params["response_format"]
                                            is not None
                                        ):
                                            json_response_format: Optional[dict] = None
                                            if (
                                                isinstance(
                                                    optional_params["response_format"],
                                                    dict,
                                                )
                                                and optional_params[
                                                    "response_format"
                                                ].get("json_schema")
                                                is not None
                                            ):
                                                json_response_format = optional_params[
                                                    "response_format"
                                                ]
                                            elif _parsing._completions.is_basemodel_type(
                                                optional_params["response_format"]  # type: ignore
                                            ):
                                                json_response_format = (
                                                    type_to_response_format_param(
                                                        response_format=optional_params[
                                                            "response_format"
                                                        ]
                                                    )
                                                )
                                            if json_response_format is not None:
                                                litellm.litellm_core_utils.json_validation_rule.validate_schema(
                                                    schema=json_response_format[
                                                        "json_schema"
                                                    ]["schema"],
                                                    response=model_response,
                                                )
                                    except TypeError:
                                        pass
                                if (
                                    optional_params is not None
                                    and "response_format" in optional_params
                                    and isinstance(
                                        optional_params["response_format"], dict
                                    )
                                    and "type" in optional_params["response_format"]
                                    and optional_params["response_format"]["type"]
                                    == "json_object"
                                    and "response_schema"
                                    in optional_params["response_format"]
                                    and isinstance(
                                        optional_params["response_format"][
                                            "response_schema"
                                        ],
                                        dict,
                                    )
                                    and "enforce_validation"
                                    in optional_params["response_format"]
                                    and optional_params["response_format"][
                                        "enforce_validation"
                                    ]
                                    is True
                                ):
                                    # schema given, json response expected, and validation enforced
                                    litellm.litellm_core_utils.json_validation_rule.validate_schema(
                                        schema=optional_params["response_format"][
                                            "response_schema"
                                        ],
                                        response=model_response,
                                    )

        except Exception as e:
            raise e

    @wraps(original_function)
    def wrapper(*args, **kwargs):  # noqa: PLR0915
        # DO NOT MOVE THIS. It always needs to run first
        # Check if this is an async function. If so only execute the async function
        if (
            kwargs.get("acompletion", False) is True
            or kwargs.get("aembedding", False) is True
            or kwargs.get("aimg_generation", False) is True
            or kwargs.get("amoderation", False) is True
            or kwargs.get("atext_completion", False) is True
            or kwargs.get("atranscription", False) is True
            or kwargs.get("arerank", False) is True
            or kwargs.get("_arealtime", False) is True
        ):
            # [OPTIONAL] CHECK MAX RETRIES / REQUEST
            if litellm.num_retries_per_request is not None:
                # check if previous_models passed in as ['litellm_params']['metadata]['previous_models']
                previous_models = kwargs.get("metadata", {}).get(
                    "previous_models", None
                )
                if previous_models is not None:
                    if litellm.num_retries_per_request <= len(previous_models):
                        raise Exception("Max retries per request hit!")

            # MODEL CALL
            result = original_function(*args, **kwargs)
            if "stream" in kwargs and kwargs["stream"] is True:
                if (
                    "complete_response" in kwargs
                    and kwargs["complete_response"] is True
                ):
                    chunks = []
                    for idx, chunk in enumerate(result):
                        chunks.append(chunk)
                    return litellm.stream_chunk_builder(
                        chunks, messages=kwargs.get("messages", None)
                    )
                else:
                    return result

            return result

        # Prints Exactly what was passed to litellm function - don't execute any logic here - it should just print
        print_args_passed_to_litellm(original_function, args, kwargs)
        start_time = datetime.datetime.now()
        result = None
        logging_obj: Optional[LiteLLMLoggingObject] = kwargs.get(
            "litellm_logging_obj", None
        )

        # only set litellm_call_id if its not in kwargs
        call_type = original_function.__name__
        if "litellm_call_id" not in kwargs:
            kwargs["litellm_call_id"] = str(uuid.uuid4())

        model: Optional[str] = None
        try:
            model = args[0] if len(args) > 0 else kwargs["model"]
        except Exception:
            model = None
            if (
                call_type != CallTypes.image_generation.value
                and call_type != CallTypes.text_completion.value
            ):
                raise ValueError("model param not passed in.")

        try:
            if logging_obj is None:
                logging_obj, kwargs = function_setup(
                    original_function.__name__, rules_obj, start_time, *args, **kwargs
                )
            kwargs["litellm_logging_obj"] = logging_obj
            _llm_caching_handler: LLMCachingHandler = LLMCachingHandler(
                original_function=original_function,
                request_kwargs=kwargs,
                start_time=start_time,
            )
            logging_obj._llm_caching_handler = _llm_caching_handler

            # CHECK FOR 'os.environ/' in kwargs
            for k, v in kwargs.items():
                if v is not None and isinstance(v, str) and v.startswith("os.environ/"):
                    kwargs[k] = litellm.get_secret(v)
            # [OPTIONAL] CHECK BUDGET
            if litellm.max_budget:
                if litellm._current_cost > litellm.max_budget:
                    raise BudgetExceededError(
                        current_cost=litellm._current_cost,
                        max_budget=litellm.max_budget,
                    )

            # [OPTIONAL] CHECK MAX RETRIES / REQUEST
            if litellm.num_retries_per_request is not None:
                # check if previous_models passed in as ['litellm_params']['metadata]['previous_models']
                previous_models = kwargs.get("metadata", {}).get(
                    "previous_models", None
                )
                if previous_models is not None:
                    if litellm.num_retries_per_request <= len(previous_models):
                        raise Exception("Max retries per request hit!")

            # [OPTIONAL] CHECK CACHE
            print_verbose(
                f"SYNC kwargs[caching]: {kwargs.get('caching', False)}; litellm.cache: {litellm.cache}; kwargs.get('cache')['no-cache']: {kwargs.get('cache', {}).get('no-cache', False)}"
            )
            # if caching is false or cache["no-cache"]==True, don't run this
            if (
                (
                    (
                        (
                            kwargs.get("caching", None) is None
                            and litellm.cache is not None
                        )
                        or kwargs.get("caching", False) is True
                    )
                    and kwargs.get("cache", {}).get("no-cache", False) is not True
                )
                and kwargs.get("aembedding", False) is not True
                and kwargs.get("atext_completion", False) is not True
                and kwargs.get("acompletion", False) is not True
                and kwargs.get("aimg_generation", False) is not True
                and kwargs.get("atranscription", False) is not True
                and kwargs.get("arerank", False) is not True
                and kwargs.get("_arealtime", False) is not True
            ):  # allow users to control returning cached responses from the completion function
                # checking cache
                print_verbose("INSIDE CHECKING CACHE")
                caching_handler_response: CachingHandlerResponse = (
                    _llm_caching_handler._sync_get_cache(
                        model=model or "",
                        original_function=original_function,
                        logging_obj=logging_obj,
                        start_time=start_time,
                        call_type=call_type,
                        kwargs=kwargs,
                        args=args,
                    )
                )
                if caching_handler_response.cached_result is not None:
                    return caching_handler_response.cached_result

            # CHECK MAX TOKENS
            if (
                kwargs.get("max_tokens", None) is not None
                and model is not None
                and litellm.modify_params
                is True  # user is okay with params being modified
                and (
                    call_type == CallTypes.acompletion.value
                    or call_type == CallTypes.completion.value
                )
            ):
                try:
                    base_model = model
                    if kwargs.get("hf_model_name", None) is not None:
                        base_model = f"huggingface/{kwargs.get('hf_model_name')}"
                    messages = None
                    if len(args) > 1:
                        messages = args[1]
                    elif kwargs.get("messages", None):
                        messages = kwargs["messages"]
                    user_max_tokens = kwargs.get("max_tokens")
                    modified_max_tokens = get_modified_max_tokens(
                        model=model,
                        base_model=base_model,
                        messages=messages,
                        user_max_tokens=user_max_tokens,
                        buffer_num=None,
                        buffer_perc=None,
                    )
                    kwargs["max_tokens"] = modified_max_tokens
                except Exception as e:
                    print_verbose(f"Error while checking max token limit: {str(e)}")
            # MODEL CALL
            result = original_function(*args, **kwargs)
            end_time = datetime.datetime.now()
            if "stream" in kwargs and kwargs["stream"] is True:
                if (
                    "complete_response" in kwargs
                    and kwargs["complete_response"] is True
                ):
                    chunks = []
                    for idx, chunk in enumerate(result):
                        chunks.append(chunk)
                    return litellm.stream_chunk_builder(
                        chunks, messages=kwargs.get("messages", None)
                    )
                else:
                    return result
            elif "acompletion" in kwargs and kwargs["acompletion"] is True:
                return result
            elif "aembedding" in kwargs and kwargs["aembedding"] is True:
                return result
            elif "aimg_generation" in kwargs and kwargs["aimg_generation"] is True:
                return result
            elif "atranscription" in kwargs and kwargs["atranscription"] is True:
                return result
            elif "aspeech" in kwargs and kwargs["aspeech"] is True:
                return result

            ### POST-CALL RULES ###
            post_call_processing(
                original_response=result,
                model=model or None,
                optional_params=kwargs,
            )

            # [OPTIONAL] ADD TO CACHE
            _llm_caching_handler.sync_set_cache(
                result=result,
                args=args,
                kwargs=kwargs,
            )

            # LOG SUCCESS - handle streaming success logging in the _next_ object, remove `handle_success` once it's deprecated
            verbose_logger.info("Wrapper: Completed Call, calling success_handler")
            threading.Thread(
                target=logging_obj.success_handler, args=(result, start_time, end_time)
            ).start()
            # RETURN RESULT
            if hasattr(result, "_hidden_params"):
                result._hidden_params["model_id"] = kwargs.get("model_info", {}).get(
                    "id", None
                )
                result._hidden_params["api_base"] = get_api_base(
                    model=model or "",
                    optional_params=getattr(logging_obj, "optional_params", {}),
                )
                result._hidden_params["response_cost"] = (
                    logging_obj._response_cost_calculator(result=result)
                )

                result._hidden_params["additional_headers"] = process_response_headers(
                    result._hidden_params.get("additional_headers") or {}
                )  # GUARANTEE OPENAI HEADERS IN RESPONSE
            result._response_ms = (
                end_time - start_time
            ).total_seconds() * 1000  # return response latency in ms like openai
            return result
        except Exception as e:
            call_type = original_function.__name__
            if call_type == CallTypes.completion.value:
                num_retries = (
                    kwargs.get("num_retries", None) or litellm.num_retries or None
                )
                litellm.num_retries = (
                    None  # set retries to None to prevent infinite loops
                )
                context_window_fallback_dict = kwargs.get(
                    "context_window_fallback_dict", {}
                )

                _is_litellm_router_call = "model_group" in kwargs.get(
                    "metadata", {}
                )  # check if call from litellm.router/proxy
                if (
                    num_retries and not _is_litellm_router_call
                ):  # only enter this if call is not from litellm router/proxy. router has it's own logic for retrying
                    if (
                        isinstance(e, openai.APIError)
                        or isinstance(e, openai.Timeout)
                        or isinstance(e, openai.APIConnectionError)
                    ):
                        kwargs["num_retries"] = num_retries
                        return litellm.completion_with_retries(*args, **kwargs)
                elif (
                    isinstance(e, litellm.exceptions.ContextWindowExceededError)
                    and context_window_fallback_dict
                    and model in context_window_fallback_dict
                    and not _is_litellm_router_call
                ):
                    if len(args) > 0:
                        args[0] = context_window_fallback_dict[model]  # type: ignore
                    else:
                        kwargs["model"] = context_window_fallback_dict[model]
                    return original_function(*args, **kwargs)
            traceback_exception = traceback.format_exc()
            end_time = datetime.datetime.now()

            # LOG FAILURE - handle streaming failure logging in the _next_ object, remove `handle_failure` once it's deprecated
            if logging_obj:
                logging_obj.failure_handler(
                    e, traceback_exception, start_time, end_time
                )  # DO NOT MAKE THREADED - router retry fallback relies on this!
            raise e

    @wraps(original_function)
    async def wrapper_async(*args, **kwargs):  # noqa: PLR0915
        print_args_passed_to_litellm(original_function, args, kwargs)
        start_time = datetime.datetime.now()
        result = None
        logging_obj: Optional[LiteLLMLoggingObject] = kwargs.get(
            "litellm_logging_obj", None
        )
        _llm_caching_handler: LLMCachingHandler = LLMCachingHandler(
            original_function=original_function,
            request_kwargs=kwargs,
            start_time=start_time,
        )
        # only set litellm_call_id if its not in kwargs
        call_type = original_function.__name__
        if "litellm_call_id" not in kwargs:
            kwargs["litellm_call_id"] = str(uuid.uuid4())

        model = ""
        try:
            model = args[0] if len(args) > 0 else kwargs["model"]
        except Exception:
            if (
                call_type != CallTypes.aimage_generation.value  # model optional
                and call_type != CallTypes.atext_completion.value  # can also be engine
            ):
                raise ValueError("model param not passed in.")

        try:
            if logging_obj is None:
                logging_obj, kwargs = function_setup(
                    original_function.__name__, rules_obj, start_time, *args, **kwargs
                )
            kwargs["litellm_logging_obj"] = logging_obj
            logging_obj._llm_caching_handler = _llm_caching_handler
            # [OPTIONAL] CHECK BUDGET
            if litellm.max_budget:
                if litellm._current_cost > litellm.max_budget:
                    raise BudgetExceededError(
                        current_cost=litellm._current_cost,
                        max_budget=litellm.max_budget,
                    )

            # [OPTIONAL] CHECK CACHE
            print_verbose(
                f"ASYNC kwargs[caching]: {kwargs.get('caching', False)}; litellm.cache: {litellm.cache}; kwargs.get('cache'): {kwargs.get('cache', None)}"
            )
            _caching_handler_response: CachingHandlerResponse = (
                await _llm_caching_handler._async_get_cache(
                    model=model,
                    original_function=original_function,
                    logging_obj=logging_obj,
                    start_time=start_time,
                    call_type=call_type,
                    kwargs=kwargs,
                    args=args,
                )
            )
            if (
                _caching_handler_response.cached_result is not None
                and _caching_handler_response.final_embedding_cached_response is None
            ):
                return _caching_handler_response.cached_result

            elif _caching_handler_response.embedding_all_elements_cache_hit is True:
                return _caching_handler_response.final_embedding_cached_response

            # MODEL CALL
            result = await original_function(*args, **kwargs)
            end_time = datetime.datetime.now()
            if "stream" in kwargs and kwargs["stream"] is True:
                if (
                    "complete_response" in kwargs
                    and kwargs["complete_response"] is True
                ):
                    chunks = []
                    for idx, chunk in enumerate(result):
                        chunks.append(chunk)
                    return litellm.stream_chunk_builder(
                        chunks, messages=kwargs.get("messages", None)
                    )
                else:
                    return result
            elif call_type == CallTypes.arealtime.value:
                return result

            # ADD HIDDEN PARAMS - additional call metadata
            if hasattr(result, "_hidden_params"):
                result._hidden_params["litellm_call_id"] = getattr(
                    logging_obj, "litellm_call_id", None
                )
                result._hidden_params["model_id"] = kwargs.get("model_info", {}).get(
                    "id", None
                )
                result._hidden_params["api_base"] = get_api_base(
                    model=model,
                    optional_params=kwargs,
                )
                result._hidden_params["response_cost"] = (
                    logging_obj._response_cost_calculator(result=result)
                )
                result._hidden_params["additional_headers"] = process_response_headers(
                    result._hidden_params.get("additional_headers") or {}
                )  # GUARANTEE OPENAI HEADERS IN RESPONSE
            if (
                isinstance(result, ModelResponse)
                or isinstance(result, EmbeddingResponse)
                or isinstance(result, TranscriptionResponse)
            ):
                setattr(
                    result,
                    "_response_ms",
                    (end_time - start_time).total_seconds() * 1000,
                )  # return response latency in ms like openai

            ### POST-CALL RULES ###
            post_call_processing(
                original_response=result, model=model, optional_params=kwargs
            )

            ## Add response to cache
            await _llm_caching_handler.async_set_cache(
                result=result,
                original_function=original_function,
                kwargs=kwargs,
                args=args,
            )

            # LOG SUCCESS - handle streaming success logging in the _next_ object
            print_verbose(
                f"Async Wrapper: Completed Call, calling async_success_handler: {logging_obj.async_success_handler}"
            )
            # check if user does not want this to be logged
            asyncio.create_task(
                logging_obj.async_success_handler(result, start_time, end_time)
            )
            threading.Thread(
                target=logging_obj.success_handler,
                args=(result, start_time, end_time),
            ).start()

            # REBUILD EMBEDDING CACHING
            if (
                isinstance(result, EmbeddingResponse)
                and _caching_handler_response.final_embedding_cached_response
                is not None
            ):
                return _llm_caching_handler._combine_cached_embedding_response_with_api_result(
                    _caching_handler_response=_caching_handler_response,
                    embedding_response=result,
                    start_time=start_time,
                    end_time=end_time,
                )

            return result
        except Exception as e:
            traceback_exception = traceback.format_exc()
            end_time = datetime.datetime.now()
            if logging_obj:
                try:
                    logging_obj.failure_handler(
                        e, traceback_exception, start_time, end_time
                    )  # DO NOT MAKE THREADED - router retry fallback relies on this!
                except Exception as e:
                    raise e
                try:
                    await logging_obj.async_failure_handler(
                        e, traceback_exception, start_time, end_time
                    )
                except Exception as e:
                    raise e

            call_type = original_function.__name__
            if call_type == CallTypes.acompletion.value:
                num_retries = (
                    kwargs.get("num_retries", None) or litellm.num_retries or None
                )
                litellm.num_retries = (
                    None  # set retries to None to prevent infinite loops
                )
                context_window_fallback_dict = kwargs.get(
                    "context_window_fallback_dict", {}
                )

                _is_litellm_router_call = "model_group" in kwargs.get(
                    "metadata", {}
                )  # check if call from litellm.router/proxy
                if (
                    num_retries and not _is_litellm_router_call
                ):  # only enter this if call is not from litellm router/proxy. router has it's own logic for retrying
                    try:
                        kwargs["num_retries"] = num_retries
                        kwargs["original_function"] = original_function
                        if isinstance(
                            e, openai.RateLimitError
                        ):  # rate limiting specific error
                            kwargs["retry_strategy"] = "exponential_backoff_retry"
                        elif isinstance(e, openai.APIError):  # generic api error
                            kwargs["retry_strategy"] = "constant_retry"
                        return await litellm.acompletion_with_retries(*args, **kwargs)
                    except Exception:
                        pass
                elif (
                    isinstance(e, litellm.exceptions.ContextWindowExceededError)
                    and context_window_fallback_dict
                    and model in context_window_fallback_dict
                ):
                    if len(args) > 0:
                        args[0] = context_window_fallback_dict[model]  # type: ignore
                    else:
                        kwargs["model"] = context_window_fallback_dict[model]
                    return await original_function(*args, **kwargs)
            raise e

    is_coroutine = inspect.iscoroutinefunction(original_function)

    # Return the appropriate wrapper based on the original function type
    if is_coroutine:
        return wrapper_async
    else:
        return wrapper


@lru_cache(maxsize=128)
def _select_tokenizer(model: str):
    if model in litellm.cohere_models and "command-r" in model:
        # cohere
        cohere_tokenizer = Tokenizer.from_pretrained(
            "Xenova/c4ai-command-r-v01-tokenizer"
        )
        return {"type": "huggingface_tokenizer", "tokenizer": cohere_tokenizer}
    # anthropic
    elif model in litellm.anthropic_models and "claude-3" not in model:
        claude_tokenizer = Tokenizer.from_str(claude_json_str)
        return {"type": "huggingface_tokenizer", "tokenizer": claude_tokenizer}
    # llama2
    elif "llama-2" in model.lower() or "replicate" in model.lower():
        tokenizer = Tokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
        return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}
    # llama3
    elif "llama-3" in model.lower():
        tokenizer = Tokenizer.from_pretrained("Xenova/llama-3-tokenizer")
        return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}
    # default - tiktoken
    else:
        tokenizer = None
        if (
            model in litellm.open_ai_chat_completion_models
            or model in litellm.open_ai_text_completion_models
            or model in litellm.open_ai_embedding_models
        ):
            return {"type": "openai_tokenizer", "tokenizer": encoding}

        try:
            tokenizer = Tokenizer.from_pretrained(model)
            return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}
        except Exception:
            return {"type": "openai_tokenizer", "tokenizer": encoding}


def encode(model="", text="", custom_tokenizer: Optional[dict] = None):
    """
    Encodes the given text using the specified model.

    Args:
        model (str): The name of the model to use for tokenization.
        custom_tokenizer (Optional[dict]): A custom tokenizer created with the `create_pretrained_tokenizer` or `create_tokenizer` method. Must be a dictionary with a string value for `type` and Tokenizer for `tokenizer`. Default is None.
        text (str): The text to be encoded.

    Returns:
        enc: The encoded text.
    """
    tokenizer_json = custom_tokenizer or _select_tokenizer(model=model)
    if isinstance(tokenizer_json["tokenizer"], Encoding):
        enc = tokenizer_json["tokenizer"].encode(text, disallowed_special=())
    else:
        enc = tokenizer_json["tokenizer"].encode(text)
    return enc


def decode(model="", tokens: List[int] = [], custom_tokenizer: Optional[dict] = None):
    tokenizer_json = custom_tokenizer or _select_tokenizer(model=model)
    dec = tokenizer_json["tokenizer"].decode(tokens)
    return dec


def openai_token_counter(  # noqa: PLR0915
    messages: Optional[list] = None,
    model="gpt-3.5-turbo-0613",
    text: Optional[str] = None,
    is_tool_call: Optional[bool] = False,
    tools: Optional[List[ChatCompletionToolParam]] = None,
    tool_choice: Optional[ChatCompletionNamedToolChoiceParam] = None,
    count_response_tokens: Optional[
        bool
    ] = False,  # Flag passed from litellm.stream_chunk_builder, to indicate counting tokens for LLM Response. We need this because for LLM input we add +3 tokens per message - based on OpenAI's token counter
):
    """
    Return the number of tokens used by a list of messages.

    Borrowed from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb.
    """
    print_verbose(f"LiteLLM: Utils - Counting tokens for OpenAI model={model}")
    try:
        if "gpt-4o" in model:
            encoding = tiktoken.get_encoding("o200k_base")
        else:
            encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        print_verbose("Warning: model not found. Using cl100k_base encoding.")
        encoding = tiktoken.get_encoding("cl100k_base")
    if model == "gpt-3.5-turbo-0301":
        tokens_per_message = (
            4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
        )
        tokens_per_name = -1  # if there's a name, the role is omitted
    elif model in litellm.open_ai_chat_completion_models:
        tokens_per_message = 3
        tokens_per_name = 1
    elif model in litellm.azure_llms:
        tokens_per_message = 3
        tokens_per_name = 1
    else:
        raise NotImplementedError(
            f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
        )
    num_tokens = 0
    includes_system_message = False

    if is_tool_call and text is not None:
        # if it's a tool call we assembled 'text' in token_counter()
        num_tokens = len(encoding.encode(text, disallowed_special=()))
    elif messages is not None:
        for message in messages:
            num_tokens += tokens_per_message
            if message.get("role", None) == "system":
                includes_system_message = True
            for key, value in message.items():
                if isinstance(value, str):
                    num_tokens += len(encoding.encode(value, disallowed_special=()))
                    if key == "name":
                        num_tokens += tokens_per_name
                elif isinstance(value, List):
                    for c in value:
                        if c["type"] == "text":
                            text += c["text"]
                            num_tokens += len(
                                encoding.encode(c["text"], disallowed_special=())
                            )
                        elif c["type"] == "image_url":
                            if isinstance(c["image_url"], dict):
                                image_url_dict = c["image_url"]
                                detail = image_url_dict.get("detail", "auto")
                                url = image_url_dict.get("url")
                                num_tokens += calculage_img_tokens(
                                    data=url, mode=detail
                                )
                            elif isinstance(c["image_url"], str):
                                image_url_str = c["image_url"]
                                num_tokens += calculage_img_tokens(
                                    data=image_url_str, mode="auto"
                                )
    elif text is not None and count_response_tokens is True:
        # This is the case where we need to count tokens for a streamed response. We should NOT add +3 tokens per message in this branch
        num_tokens = len(encoding.encode(text, disallowed_special=()))
        return num_tokens
    elif text is not None:
        num_tokens = len(encoding.encode(text, disallowed_special=()))
    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>

    if tools:
        num_tokens += len(encoding.encode(_format_function_definitions(tools)))
        num_tokens += 9  # Additional tokens for function definition of tools
    # If there's a system message and tools are present, subtract four tokens
    if tools and includes_system_message:
        num_tokens -= 4
    # If tool_choice is 'none', add one token.
    # If it's an object, add 4 + the number of tokens in the function name.
    # If it's undefined or 'auto', don't add anything.
    if tool_choice == "none":
        num_tokens += 1
    elif isinstance(tool_choice, dict):
        num_tokens += 7
        num_tokens += len(encoding.encode(tool_choice["function"]["name"]))

    return num_tokens


def resize_image_high_res(width, height):
    # Maximum dimensions for high res mode
    max_short_side = 768
    max_long_side = 2000

    # Return early if no resizing is needed
    if width <= 768 and height <= 768:
        return width, height

    # Determine the longer and shorter sides
    longer_side = max(width, height)
    shorter_side = min(width, height)

    # Calculate the aspect ratio
    aspect_ratio = longer_side / shorter_side

    # Resize based on the short side being 768px
    if width <= height:  # Portrait or square
        resized_width = max_short_side
        resized_height = int(resized_width * aspect_ratio)
        # if the long side exceeds the limit after resizing, adjust both sides accordingly
        if resized_height > max_long_side:
            resized_height = max_long_side
            resized_width = int(resized_height / aspect_ratio)
    else:  # Landscape
        resized_height = max_short_side
        resized_width = int(resized_height * aspect_ratio)
        # if the long side exceeds the limit after resizing, adjust both sides accordingly
        if resized_width > max_long_side:
            resized_width = max_long_side
            resized_height = int(resized_width / aspect_ratio)

    return resized_width, resized_height


# Test the function with the given example
def calculate_tiles_needed(
    resized_width, resized_height, tile_width=512, tile_height=512
):
    tiles_across = (resized_width + tile_width - 1) // tile_width
    tiles_down = (resized_height + tile_height - 1) // tile_height
    total_tiles = tiles_across * tiles_down
    return total_tiles


def get_image_type(image_data: bytes) -> Union[str, None]:
    """take an image (really only the first ~100 bytes max are needed)
    and return 'png' 'gif' 'jpeg' 'heic' or None. method added to
    allow deprecation of imghdr in 3.13"""

    if image_data[0:8] == b"\x89\x50\x4e\x47\x0d\x0a\x1a\x0a":
        return "png"

    if image_data[0:4] == b"GIF8" and image_data[5:6] == b"a":
        return "gif"

    if image_data[0:3] == b"\xff\xd8\xff":
        return "jpeg"

    if image_data[4:8] == b"ftyp":
        return "heic"

    return None


def get_image_dimensions(data):
    img_data = None

    try:
        # Try to open as URL
        # Try to open as URL
        client = HTTPHandler(concurrent_limit=1)
        response = client.get(data)
        img_data = response.read()
    except Exception:
        # If not URL, assume it's base64
        header, encoded = data.split(",", 1)
        img_data = base64.b64decode(encoded)

    img_type = get_image_type(img_data)

    if img_type == "png":
        w, h = struct.unpack(">LL", img_data[16:24])
        return w, h
    elif img_type == "gif":
        w, h = struct.unpack("<HH", img_data[6:10])
        return w, h
    elif img_type == "jpeg":
        with io.BytesIO(img_data) as fhandle:
            fhandle.seek(0)
            size = 2
            ftype = 0
            while not 0xC0 <= ftype <= 0xCF or ftype in (0xC4, 0xC8, 0xCC):
                fhandle.seek(size, 1)
                byte = fhandle.read(1)
                while ord(byte) == 0xFF:
                    byte = fhandle.read(1)
                ftype = ord(byte)
                size = struct.unpack(">H", fhandle.read(2))[0] - 2
            fhandle.seek(1, 1)
            h, w = struct.unpack(">HH", fhandle.read(4))
        return w, h
    else:
        return None, None


def calculage_img_tokens(
    data,
    mode: Literal["low", "high", "auto"] = "auto",
    base_tokens: int = 85,  # openai default - https://openai.com/pricing
):
    if mode == "low" or mode == "auto":
        return base_tokens
    elif mode == "high":
        width, height = get_image_dimensions(data=data)
        resized_width, resized_height = resize_image_high_res(
            width=width, height=height
        )
        tiles_needed_high_res = calculate_tiles_needed(resized_width, resized_height)
        tile_tokens = (base_tokens * 2) * tiles_needed_high_res
        total_tokens = base_tokens + tile_tokens
        return total_tokens


def create_pretrained_tokenizer(
    identifier: str, revision="main", auth_token: Optional[str] = None
):
    """
    Creates a tokenizer from an existing file on a HuggingFace repository to be used with `token_counter`.

    Args:
    identifier (str): The identifier of a Model on the Hugging Face Hub, that contains a tokenizer.json file
    revision (str, defaults to main): A branch or commit id
    auth_token (str, optional, defaults to None): An optional auth token used to access private repositories on the Hugging Face Hub

    Returns:
    dict: A dictionary with the tokenizer and its type.
    """

    tokenizer = Tokenizer.from_pretrained(
        identifier, revision=revision, auth_token=auth_token
    )
    return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}


def create_tokenizer(json: str):
    """
    Creates a tokenizer from a valid JSON string for use with `token_counter`.

    Args:
    json (str): A valid JSON string representing a previously serialized tokenizer

    Returns:
    dict: A dictionary with the tokenizer and its type.
    """

    tokenizer = Tokenizer.from_str(json)
    return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}


def _format_function_definitions(tools):
    """Formats tool definitions in the format that OpenAI appears to use.
    Based on https://github.com/forestwanglin/openai-java/blob/main/jtokkit/src/main/java/xyz/felh/openai/jtokkit/utils/TikTokenUtils.java
    """
    lines = []
    lines.append("namespace functions {")
    lines.append("")
    for tool in tools:
        function = tool.get("function")
        if function_description := function.get("description"):
            lines.append(f"// {function_description}")
        function_name = function.get("name")
        parameters = function.get("parameters", {})
        properties = parameters.get("properties")
        if properties and properties.keys():
            lines.append(f"type {function_name} = (_: {{")
            lines.append(_format_object_parameters(parameters, 0))
            lines.append("}) => any;")
        else:
            lines.append(f"type {function_name} = () => any;")
        lines.append("")
    lines.append("} // namespace functions")
    return "\n".join(lines)


def _format_object_parameters(parameters, indent):
    properties = parameters.get("properties")
    if not properties:
        return ""
    required_params = parameters.get("required", [])
    lines = []
    for key, props in properties.items():
        description = props.get("description")
        if description:
            lines.append(f"// {description}")
        question = "?"
        if required_params and key in required_params:
            question = ""
        lines.append(f"{key}{question}: {_format_type(props, indent)},")
    return "\n".join([" " * max(0, indent) + line for line in lines])


def _format_type(props, indent):
    type = props.get("type")
    if type == "string":
        if "enum" in props:
            return " | ".join([f'"{item}"' for item in props["enum"]])
        return "string"
    elif type == "array":
        # items is required, OpenAI throws an error if it's missing
        return f"{_format_type(props['items'], indent)}[]"
    elif type == "object":
        return f"{{\n{_format_object_parameters(props, indent + 2)}\n}}"
    elif type in ["integer", "number"]:
        if "enum" in props:
            return " | ".join([f'"{item}"' for item in props["enum"]])
        return "number"
    elif type == "boolean":
        return "boolean"
    elif type == "null":
        return "null"
    else:
        # This is a guess, as an empty string doesn't yield the expected token count
        return "any"


def token_counter(
    model="",
    custom_tokenizer: Optional[dict] = None,
    text: Optional[Union[str, List[str]]] = None,
    messages: Optional[List] = None,
    count_response_tokens: Optional[bool] = False,
    tools: Optional[List[ChatCompletionToolParam]] = None,
    tool_choice: Optional[ChatCompletionNamedToolChoiceParam] = None,
) -> int:
    """
    Count the number of tokens in a given text using a specified model.

    Args:
    model (str): The name of the model to use for tokenization. Default is an empty string.
    custom_tokenizer (Optional[dict]): A custom tokenizer created with the `create_pretrained_tokenizer` or `create_tokenizer` method. Must be a dictionary with a string value for `type` and Tokenizer for `tokenizer`. Default is None.
    text (str): The raw text string to be passed to the model. Default is None.
    messages (Optional[List[Dict[str, str]]]): Alternative to passing in text. A list of dictionaries representing messages with "role" and "content" keys. Default is None.

    Returns:
    int: The number of tokens in the text.
    """
    # use tiktoken, anthropic, cohere, llama2, or llama3's tokenizer depending on the model
    is_tool_call = False
    num_tokens = 0
    if text is None:
        if messages is not None:
            print_verbose(f"token_counter messages received: {messages}")
            text = ""
            for message in messages:
                if message.get("content", None) is not None:
                    content = message.get("content")
                    if isinstance(content, str):
                        text += message["content"]
                    elif isinstance(content, List):
                        for c in content:
                            if c["type"] == "text":
                                text += c["text"]
                            elif c["type"] == "image_url":
                                if isinstance(c["image_url"], dict):
                                    image_url_dict = c["image_url"]
                                    detail = image_url_dict.get("detail", "auto")
                                    url = image_url_dict.get("url")
                                    num_tokens += calculage_img_tokens(
                                        data=url, mode=detail
                                    )
                                elif isinstance(c["image_url"], str):
                                    image_url_str = c["image_url"]
                                    num_tokens += calculage_img_tokens(
                                        data=image_url_str, mode="auto"
                                    )
                if message.get("tool_calls"):
                    is_tool_call = True
                    for tool_call in message["tool_calls"]:
                        if "function" in tool_call:
                            function_arguments = tool_call["function"]["arguments"]
                            text += function_arguments
        else:
            raise ValueError("text and messages cannot both be None")
    elif isinstance(text, List):
        text = "".join(t for t in text if isinstance(t, str))
    elif isinstance(text, str):
        count_response_tokens = True  # user just trying to count tokens for a text. don't add the chat_ml +3 tokens to this

    if model is not None or custom_tokenizer is not None:
        tokenizer_json = custom_tokenizer or _select_tokenizer(model=model)
        if tokenizer_json["type"] == "huggingface_tokenizer":
            enc = tokenizer_json["tokenizer"].encode(text)
            num_tokens = len(enc.ids)
        elif tokenizer_json["type"] == "openai_tokenizer":
            if (
                model in litellm.open_ai_chat_completion_models
                or model in litellm.azure_llms
            ):
                if model in litellm.azure_llms:
                    # azure llms use gpt-35-turbo instead of gpt-3.5-turbo 🙃
                    model = model.replace("-35", "-3.5")

                print_verbose(
                    f"Token Counter - using OpenAI token counter, for model={model}"
                )
                num_tokens = openai_token_counter(
                    text=text,  # type: ignore
                    model=model,
                    messages=messages,
                    is_tool_call=is_tool_call,
                    count_response_tokens=count_response_tokens,
                    tools=tools,
                    tool_choice=tool_choice,
                )
            else:
                print_verbose(
                    f"Token Counter - using generic token counter, for model={model}"
                )
                num_tokens = openai_token_counter(
                    text=text,  # type: ignore
                    model="gpt-3.5-turbo",
                    messages=messages,
                    is_tool_call=is_tool_call,
                    count_response_tokens=count_response_tokens,
                    tools=tools,
                    tool_choice=tool_choice,
                )
    else:
        num_tokens = len(encoding.encode(text, disallowed_special=()))  # type: ignore
    return num_tokens


def supports_httpx_timeout(custom_llm_provider: str) -> bool:
    """
    Helper function to know if a provider implementation supports httpx timeout
    """
    supported_providers = ["openai", "azure", "bedrock"]

    if custom_llm_provider in supported_providers:
        return True

    return False


def supports_system_messages(model: str, custom_llm_provider: Optional[str]) -> bool:
    """
    Check if the given model supports system messages and return a boolean value.

    Parameters:
    model (str): The model name to be checked.
    custom_llm_provider (str): The provider to be checked.

    Returns:
    bool: True if the model supports system messages, False otherwise.

    Raises:
    Exception: If the given model is not found in model_prices_and_context_window.json.
    """
    try:
        model_info = litellm.get_model_info(
            model=model, custom_llm_provider=custom_llm_provider
        )
        if model_info.get("supports_system_messages", False) is True:
            return True
        return False
    except Exception:
        raise Exception(
            f"Model not supports system messages. You passed model={model}, custom_llm_provider={custom_llm_provider}."
        )


def supports_response_schema(model: str, custom_llm_provider: Optional[str]) -> bool:
    """
    Check if the given model + provider supports 'response_schema' as a param.

    Parameters:
    model (str): The model name to be checked.
    custom_llm_provider (str): The provider to be checked.

    Returns:
    bool: True if the model supports response_schema, False otherwise.

    Does not raise error. Defaults to 'False'. Outputs logging.error.
    """
    try:
        ## GET LLM PROVIDER ##
        model, custom_llm_provider, _, _ = get_llm_provider(
            model=model, custom_llm_provider=custom_llm_provider
        )

        if custom_llm_provider == "predibase":  # predibase supports this globally
            return True

        ## GET MODEL INFO
        model_info = litellm.get_model_info(
            model=model, custom_llm_provider=custom_llm_provider
        )

        if model_info.get("supports_response_schema", False) is True:
            return True
        return False
    except Exception:
        verbose_logger.error(
            f"Model not supports response_schema. You passed model={model}, custom_llm_provider={custom_llm_provider}."
        )
        return False


def supports_function_calling(
    model: str, custom_llm_provider: Optional[str] = None
) -> bool:
    """
    Check if the given model supports function calling and return a boolean value.

    Parameters:
    model (str): The model name to be checked.
    custom_llm_provider (Optional[str]): The provider to be checked.

    Returns:
    bool: True if the model supports function calling, False otherwise.

    Raises:
    Exception: If the given model is not found or there's an error in retrieval.
    """
    try:
        model, custom_llm_provider, _, _ = litellm.get_llm_provider(
            model=model, custom_llm_provider=custom_llm_provider
        )

        ## CHECK IF MODEL SUPPORTS FUNCTION CALLING ##
        model_info = litellm.get_model_info(
            model=model, custom_llm_provider=custom_llm_provider
        )

        if model_info.get("supports_function_calling", False) is True:
            return True
        return False
    except Exception as e:
        raise Exception(
            f"Model not found or error in checking function calling support. You passed model={model}, custom_llm_provider={custom_llm_provider}. Error: {str(e)}"
        )


def _supports_factory(model: str, custom_llm_provider: Optional[str], key: str) -> bool:
    """
    Check if the given model supports function calling and return a boolean value.

    Parameters:
    model (str): The model name to be checked.
    custom_llm_provider (Optional[str]): The provider to be checked.

    Returns:
    bool: True if the model supports function calling, False otherwise.

    Raises:
    Exception: If the given model is not found or there's an error in retrieval.
    """
    try:
        model, custom_llm_provider, _, _ = litellm.get_llm_provider(
            model=model, custom_llm_provider=custom_llm_provider
        )

        model_info = litellm.get_model_info(
            model=model, custom_llm_provider=custom_llm_provider
        )

        if model_info.get(key, False) is True:
            return True
        return False
    except Exception as e:
        raise Exception(
            f"Model not found or error in checking {key} support. You passed model={model}, custom_llm_provider={custom_llm_provider}. Error: {str(e)}"
        )


def supports_audio_input(model: str, custom_llm_provider: Optional[str] = None) -> bool:
    """Check if a given model supports audio input in a chat completion call"""
    return _supports_factory(
        model=model, custom_llm_provider=custom_llm_provider, key="supports_audio_input"
    )


def supports_audio_output(
    model: str, custom_llm_provider: Optional[str] = None
) -> bool:
    """Check if a given model supports audio output in a chat completion call"""
    return _supports_factory(
        model=model, custom_llm_provider=custom_llm_provider, key="supports_audio_input"
    )


def supports_prompt_caching(
    model: str, custom_llm_provider: Optional[str] = None
) -> bool:
    """
    Check if the given model supports prompt caching and return a boolean value.

    Parameters:
    model (str): The model name to be checked.
    custom_llm_provider (Optional[str]): The provider to be checked.

    Returns:
    bool: True if the model supports prompt caching, False otherwise.

    Raises:
    Exception: If the given model is not found or there's an error in retrieval.
    """
    try:
        model, custom_llm_provider, _, _ = litellm.get_llm_provider(
            model=model, custom_llm_provider=custom_llm_provider
        )

        model_info = litellm.get_model_info(
            model=model, custom_llm_provider=custom_llm_provider
        )

        if model_info.get("supports_prompt_caching", False) is True:
            return True
        return False
    except Exception as e:
        raise Exception(
            f"Model not found or error in checking prompt caching support. You passed model={model}, custom_llm_provider={custom_llm_provider}. Error: {str(e)}"
        )


def supports_vision(model: str, custom_llm_provider: Optional[str] = None) -> bool:
    """
    Check if the given model supports vision and return a boolean value.

    Parameters:
    model (str): The model name to be checked.
    custom_llm_provider (Optional[str]): The provider to be checked.

    Returns:
    bool: True if the model supports vision, False otherwise.
    """
    try:
        model, custom_llm_provider, _, _ = litellm.get_llm_provider(
            model=model, custom_llm_provider=custom_llm_provider
        )

        model_info = litellm.get_model_info(
            model=model, custom_llm_provider=custom_llm_provider
        )

        if model_info.get("supports_vision", False) is True:
            return True
        return False
    except Exception as e:
        verbose_logger.error(
            f"Model not found or error in checking vision support. You passed model={model}, custom_llm_provider={custom_llm_provider}. Error: {str(e)}"
        )
        return False


def supports_parallel_function_calling(model: str):
    """
    Check if the given model supports parallel function calling and return True if it does, False otherwise.

    Parameters:
        model (str): The model to check for support of parallel function calling.

    Returns:
        bool: True if the model supports parallel function calling, False otherwise.

    Raises:
        Exception: If the model is not found in the model_cost dictionary.
    """
    if model in litellm.model_cost:
        model_info = litellm.model_cost[model]
        if model_info.get("supports_parallel_function_calling", False) is True:
            return True
        return False
    else:
        raise Exception(
            f"Model not supports parallel function calling. You passed model={model}."
        )


####### HELPER FUNCTIONS ################
def _update_dictionary(existing_dict: Dict, new_dict: dict) -> dict:
    for k, v in new_dict.items():
        existing_dict[k] = v

    return existing_dict


def register_model(model_cost: Union[str, dict]):  # noqa: PLR0915
    """
    Register new / Override existing models (and their pricing) to specific providers.
    Provide EITHER a model cost dictionary or a url to a hosted json blob
    Example usage:
    model_cost_dict = {
        "gpt-4": {
            "max_tokens": 8192,
            "input_cost_per_token": 0.00003,
            "output_cost_per_token": 0.00006,
            "litellm_provider": "openai",
            "mode": "chat"
        },
    }
    """
    loaded_model_cost = {}
    if isinstance(model_cost, dict):
        loaded_model_cost = model_cost
    elif isinstance(model_cost, str):
        loaded_model_cost = litellm.get_model_cost_map(url=model_cost)

    for key, value in loaded_model_cost.items():
        ## get model info ##
        try:
            existing_model: Union[ModelInfo, dict] = get_model_info(model=key)
            model_cost_key = existing_model["key"]
        except Exception:
            existing_model = {}
            model_cost_key = key
        ## override / add new keys to the existing model cost dictionary
        litellm.model_cost.setdefault(model_cost_key, {}).update(
            _update_dictionary(existing_model, value)  # type: ignore
        )
        verbose_logger.debug(f"{key} added to model cost map")
        # add new model names to provider lists
        if value.get("litellm_provider") == "openai":
            if key not in litellm.open_ai_chat_completion_models:
                litellm.open_ai_chat_completion_models.append(key)
        elif value.get("litellm_provider") == "text-completion-openai":
            if key not in litellm.open_ai_text_completion_models:
                litellm.open_ai_text_completion_models.append(key)
        elif value.get("litellm_provider") == "cohere":
            if key not in litellm.cohere_models:
                litellm.cohere_models.append(key)
        elif value.get("litellm_provider") == "anthropic":
            if key not in litellm.anthropic_models:
                litellm.anthropic_models.append(key)
        elif value.get("litellm_provider") == "openrouter":
            split_string = key.split("/", 1)
            if key not in litellm.openrouter_models:
                litellm.openrouter_models.append(split_string[1])
        elif value.get("litellm_provider") == "vertex_ai-text-models":
            if key not in litellm.vertex_text_models:
                litellm.vertex_text_models.append(key)
        elif value.get("litellm_provider") == "vertex_ai-code-text-models":
            if key not in litellm.vertex_code_text_models:
                litellm.vertex_code_text_models.append(key)
        elif value.get("litellm_provider") == "vertex_ai-chat-models":
            if key not in litellm.vertex_chat_models:
                litellm.vertex_chat_models.append(key)
        elif value.get("litellm_provider") == "vertex_ai-code-chat-models":
            if key not in litellm.vertex_code_chat_models:
                litellm.vertex_code_chat_models.append(key)
        elif value.get("litellm_provider") == "ai21":
            if key not in litellm.ai21_models:
                litellm.ai21_models.append(key)
        elif value.get("litellm_provider") == "nlp_cloud":
            if key not in litellm.nlp_cloud_models:
                litellm.nlp_cloud_models.append(key)
        elif value.get("litellm_provider") == "aleph_alpha":
            if key not in litellm.aleph_alpha_models:
                litellm.aleph_alpha_models.append(key)
        elif value.get("litellm_provider") == "bedrock":
            if key not in litellm.bedrock_models:
                litellm.bedrock_models.append(key)
    return model_cost


def get_litellm_params(
    api_key=None,
    force_timeout=600,
    azure=False,
    logger_fn=None,
    verbose=False,
    hugging_face=False,
    replicate=False,
    together_ai=False,
    custom_llm_provider=None,
    api_base=None,
    litellm_call_id=None,
    model_alias_map=None,
    completion_call_id=None,
    metadata=None,
    model_info=None,
    proxy_server_request=None,
    acompletion=None,
    preset_cache_key=None,
    no_log=None,
    input_cost_per_second=None,
    input_cost_per_token=None,
    output_cost_per_token=None,
    output_cost_per_second=None,
    cooldown_time=None,
    text_completion=None,
    azure_ad_token_provider=None,
    user_continue_message=None,
    base_model=None,
):
    litellm_params = {
        "acompletion": acompletion,
        "api_key": api_key,
        "force_timeout": force_timeout,
        "logger_fn": logger_fn,
        "verbose": verbose,
        "custom_llm_provider": custom_llm_provider,
        "api_base": api_base,
        "litellm_call_id": litellm_call_id,
        "model_alias_map": model_alias_map,
        "completion_call_id": completion_call_id,
        "metadata": metadata,
        "model_info": model_info,
        "proxy_server_request": proxy_server_request,
        "preset_cache_key": preset_cache_key,
        "no-log": no_log,
        "stream_response": {},  # litellm_call_id: ModelResponse Dict
        "input_cost_per_token": input_cost_per_token,
        "input_cost_per_second": input_cost_per_second,
        "output_cost_per_token": output_cost_per_token,
        "output_cost_per_second": output_cost_per_second,
        "cooldown_time": cooldown_time,
        "text_completion": text_completion,
        "azure_ad_token_provider": azure_ad_token_provider,
        "user_continue_message": user_continue_message,
        "base_model": base_model
        or _get_base_model_from_litellm_call_metadata(metadata=metadata),
    }

    return litellm_params


def _should_drop_param(k, additional_drop_params) -> bool:
    if (
        additional_drop_params is not None
        and isinstance(additional_drop_params, list)
        and k in additional_drop_params
    ):
        return True  # allow user to drop specific params for a model - e.g. vllm - logit bias

    return False


def _get_non_default_params(
    passed_params: dict, default_params: dict, additional_drop_params: Optional[bool]
) -> dict:
    non_default_params = {}
    for k, v in passed_params.items():
        if (
            k in default_params
            and v != default_params[k]
            and _should_drop_param(k=k, additional_drop_params=additional_drop_params)
            is False
        ):
            non_default_params[k] = v

    return non_default_params


def get_optional_params_transcription(
    model: str,
    language: Optional[str] = None,
    prompt: Optional[str] = None,
    response_format: Optional[str] = None,
    temperature: Optional[int] = None,
    custom_llm_provider: Optional[str] = None,
    drop_params: Optional[bool] = None,
    **kwargs,
):
    # retrieve all parameters passed to the function
    passed_params = locals()
    custom_llm_provider = passed_params.pop("custom_llm_provider")
    drop_params = passed_params.pop("drop_params")
    special_params = passed_params.pop("kwargs")
    for k, v in special_params.items():
        passed_params[k] = v

    default_params = {
        "language": None,
        "prompt": None,
        "response_format": None,
        "temperature": None,  # openai defaults this to 0
    }

    non_default_params = {
        k: v
        for k, v in passed_params.items()
        if (k in default_params and v != default_params[k])
    }
    optional_params = {}

    ## raise exception if non-default value passed for non-openai/azure embedding calls
    def _check_valid_arg(supported_params):
        if len(non_default_params.keys()) > 0:
            keys = list(non_default_params.keys())
            for k in keys:
                if (
                    drop_params is True or litellm.drop_params is True
                ) and k not in supported_params:  # drop the unsupported non-default values
                    non_default_params.pop(k, None)
                elif k not in supported_params:
                    raise UnsupportedParamsError(
                        status_code=500,
                        message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.",
                    )
            return non_default_params

    if custom_llm_provider == "openai" or custom_llm_provider == "azure":
        optional_params = non_default_params
    elif custom_llm_provider == "groq":
        supported_params = litellm.GroqSTTConfig().get_supported_openai_params_stt()
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.GroqSTTConfig().map_openai_params_stt(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
            drop_params=drop_params if drop_params is not None else False,
        )
    for k in passed_params.keys():  # pass additional kwargs without modification
        if k not in default_params.keys():
            optional_params[k] = passed_params[k]
    return optional_params


def get_optional_params_image_gen(
    n: Optional[int] = None,
    quality: Optional[str] = None,
    response_format: Optional[str] = None,
    size: Optional[str] = None,
    style: Optional[str] = None,
    user: Optional[str] = None,
    custom_llm_provider: Optional[str] = None,
    additional_drop_params: Optional[bool] = None,
    **kwargs,
):
    # retrieve all parameters passed to the function
    passed_params = locals()
    custom_llm_provider = passed_params.pop("custom_llm_provider")
    additional_drop_params = passed_params.pop("additional_drop_params", None)
    special_params = passed_params.pop("kwargs")
    for k, v in special_params.items():
        if k.startswith("aws_") and (
            custom_llm_provider != "bedrock" and custom_llm_provider != "sagemaker"
        ):  # allow dynamically setting boto3 init logic
            continue
        elif k == "hf_model_name" and custom_llm_provider != "sagemaker":
            continue
        elif (
            k.startswith("vertex_")
            and custom_llm_provider != "vertex_ai"
            and custom_llm_provider != "vertex_ai_beta"
        ):  # allow dynamically setting vertex ai init logic
            continue
        passed_params[k] = v

    default_params = {
        "n": None,
        "quality": None,
        "response_format": None,
        "size": None,
        "style": None,
        "user": None,
    }

    non_default_params = _get_non_default_params(
        passed_params=passed_params,
        default_params=default_params,
        additional_drop_params=additional_drop_params,
    )
    optional_params = {}

    ## raise exception if non-default value passed for non-openai/azure embedding calls
    def _check_valid_arg(supported_params):
        if len(non_default_params.keys()) > 0:
            keys = list(non_default_params.keys())
            for k in keys:
                if (
                    litellm.drop_params is True and k not in supported_params
                ):  # drop the unsupported non-default values
                    non_default_params.pop(k, None)
                elif k not in supported_params:
                    raise UnsupportedParamsError(
                        status_code=500,
                        message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.",
                    )
            return non_default_params

    if (
        custom_llm_provider == "openai"
        or custom_llm_provider == "azure"
        or custom_llm_provider in litellm.openai_compatible_providers
    ):
        optional_params = non_default_params
    elif custom_llm_provider == "bedrock":
        supported_params = ["size"]
        _check_valid_arg(supported_params=supported_params)
        if size is not None:
            width, height = size.split("x")
            optional_params["width"] = int(width)
            optional_params["height"] = int(height)
    elif custom_llm_provider == "vertex_ai":
        supported_params = ["n"]
        """
        All params here: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/imagegeneration?project=adroit-crow-413218
        """
        _check_valid_arg(supported_params=supported_params)
        if n is not None:
            optional_params["sampleCount"] = int(n)

    for k in passed_params.keys():
        if k not in default_params.keys():
            optional_params[k] = passed_params[k]
    return optional_params


def get_optional_params_embeddings(  # noqa: PLR0915
    # 2 optional params
    model: str,
    user: Optional[str] = None,
    encoding_format: Optional[str] = None,
    dimensions: Optional[int] = None,
    custom_llm_provider="",
    drop_params: Optional[bool] = None,
    additional_drop_params: Optional[bool] = None,
    **kwargs,
):
    # retrieve all parameters passed to the function
    passed_params = locals()
    custom_llm_provider = passed_params.pop("custom_llm_provider", None)
    special_params = passed_params.pop("kwargs")
    for k, v in special_params.items():
        passed_params[k] = v

    drop_params = passed_params.pop("drop_params", None)
    additional_drop_params = passed_params.pop("additional_drop_params", None)

    default_params = {"user": None, "encoding_format": None, "dimensions": None}

    def _check_valid_arg(supported_params: Optional[list]):
        if supported_params is None:
            return
        unsupported_params = {}
        for k in non_default_params.keys():
            if k not in supported_params:
                unsupported_params[k] = non_default_params[k]
        if unsupported_params:
            if litellm.drop_params is True or (
                drop_params is not None and drop_params is True
            ):
                pass
            else:
                raise UnsupportedParamsError(
                    status_code=500,
                    message=f"{custom_llm_provider} does not support parameters: {unsupported_params}, for model={model}. To drop these, set `litellm.drop_params=True` or for proxy:\n\n`litellm_settings:\n drop_params: true`\n",
                )

    non_default_params = _get_non_default_params(
        passed_params=passed_params,
        default_params=default_params,
        additional_drop_params=additional_drop_params,
    )
    ## raise exception if non-default value passed for non-openai/azure embedding calls
    if custom_llm_provider == "openai":
        # 'dimensions` is only supported in `text-embedding-3` and later models

        if (
            model is not None
            and "text-embedding-3" not in model
            and "dimensions" in non_default_params.keys()
        ):
            raise UnsupportedParamsError(
                status_code=500,
                message="Setting dimensions is not supported for OpenAI `text-embedding-3` and later models. To drop it from the call, set `litellm.drop_params = True`.",
            )
    elif custom_llm_provider == "triton":
        keys = list(non_default_params.keys())
        for k in keys:
            non_default_params.pop(k, None)
        final_params = {**non_default_params, **kwargs}
        return final_params
    elif custom_llm_provider == "databricks":
        supported_params = get_supported_openai_params(
            model=model or "",
            custom_llm_provider="databricks",
            request_type="embeddings",
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.DatabricksEmbeddingConfig().map_openai_params(
            non_default_params=non_default_params, optional_params={}
        )
        final_params = {**optional_params, **kwargs}
        return final_params
    elif custom_llm_provider == "nvidia_nim":
        supported_params = get_supported_openai_params(
            model=model or "",
            custom_llm_provider="nvidia_nim",
            request_type="embeddings",
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.nvidiaNimEmbeddingConfig.map_openai_params(
            non_default_params=non_default_params, optional_params={}, kwargs=kwargs
        )
        return optional_params
    elif custom_llm_provider == "vertex_ai":
        supported_params = get_supported_openai_params(
            model=model,
            custom_llm_provider="vertex_ai",
            request_type="embeddings",
        )
        _check_valid_arg(supported_params=supported_params)
        (
            optional_params,
            kwargs,
        ) = litellm.VertexAITextEmbeddingConfig().map_openai_params(
            non_default_params=non_default_params, optional_params={}, kwargs=kwargs
        )
        final_params = {**optional_params, **kwargs}
        return final_params
    elif custom_llm_provider == "bedrock":
        # if dimensions is in non_default_params -> pass it for model=bedrock/amazon.titan-embed-text-v2
        if "amazon.titan-embed-text-v1" in model:
            object: Any = litellm.AmazonTitanG1Config()
        elif "amazon.titan-embed-image-v1" in model:
            object = litellm.AmazonTitanMultimodalEmbeddingG1Config()
        elif "amazon.titan-embed-text-v2:0" in model:
            object = litellm.AmazonTitanV2Config()
        elif "cohere.embed-multilingual-v3" in model:
            object = litellm.BedrockCohereEmbeddingConfig()
        else:  # unmapped model
            supported_params = []
            _check_valid_arg(supported_params=supported_params)
            final_params = {**kwargs}
            return final_params

        supported_params = object.get_supported_openai_params()
        _check_valid_arg(supported_params=supported_params)
        optional_params = object.map_openai_params(
            non_default_params=non_default_params, optional_params={}
        )
        final_params = {**optional_params, **kwargs}
        return final_params
    elif custom_llm_provider == "mistral":
        supported_params = get_supported_openai_params(
            model=model,
            custom_llm_provider="mistral",
            request_type="embeddings",
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.MistralEmbeddingConfig().map_openai_params(
            non_default_params=non_default_params, optional_params={}
        )
        final_params = {**optional_params, **kwargs}
        return final_params
    elif custom_llm_provider == "fireworks_ai":
        supported_params = get_supported_openai_params(
            model=model,
            custom_llm_provider="fireworks_ai",
            request_type="embeddings",
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.FireworksAIEmbeddingConfig().map_openai_params(
            non_default_params=non_default_params, optional_params={}, model=model
        )
        final_params = {**optional_params, **kwargs}
        return final_params

    elif (
        custom_llm_provider != "openai"
        and custom_llm_provider != "azure"
        and custom_llm_provider not in litellm.openai_compatible_providers
    ):
        if len(non_default_params.keys()) > 0:
            if (
                litellm.drop_params is True or drop_params is True
            ):  # drop the unsupported non-default values
                keys = list(non_default_params.keys())
                for k in keys:
                    non_default_params.pop(k, None)
            else:
                raise UnsupportedParamsError(
                    status_code=500,
                    message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.",
                )
    final_params = {**non_default_params, **kwargs}
    return final_params


def _remove_additional_properties(schema):
    """
    clean out 'additionalProperties = False'. Causes vertexai/gemini OpenAI API Schema errors - https://github.com/langchain-ai/langchainjs/issues/5240

    Relevant Issues: https://github.com/BerriAI/litellm/issues/6136, https://github.com/BerriAI/litellm/issues/6088
    """
    if isinstance(schema, dict):
        # Remove the 'additionalProperties' key if it exists and is set to False
        if "additionalProperties" in schema and schema["additionalProperties"] is False:
            del schema["additionalProperties"]

        # Recursively process all dictionary values
        for key, value in schema.items():
            _remove_additional_properties(value)

    elif isinstance(schema, list):
        # Recursively process all items in the list
        for item in schema:
            _remove_additional_properties(item)

    return schema


def _remove_strict_from_schema(schema):
    """
    Relevant Issues: https://github.com/BerriAI/litellm/issues/6136, https://github.com/BerriAI/litellm/issues/6088
    """
    if isinstance(schema, dict):
        # Remove the 'additionalProperties' key if it exists and is set to False
        if "strict" in schema:
            del schema["strict"]

        # Recursively process all dictionary values
        for key, value in schema.items():
            _remove_strict_from_schema(value)

    elif isinstance(schema, list):
        # Recursively process all items in the list
        for item in schema:
            _remove_strict_from_schema(item)

    return schema


def get_optional_params(  # noqa: PLR0915
    # use the openai defaults
    # https://platform.openai.com/docs/api-reference/chat/create
    model: str,
    functions=None,
    function_call=None,
    temperature=None,
    top_p=None,
    n=None,
    stream=False,
    stream_options=None,
    stop=None,
    max_tokens=None,
    max_completion_tokens=None,
    modalities=None,
    audio=None,
    presence_penalty=None,
    frequency_penalty=None,
    logit_bias=None,
    user=None,
    custom_llm_provider="",
    response_format=None,
    seed=None,
    tools=None,
    tool_choice=None,
    max_retries=None,
    logprobs=None,
    top_logprobs=None,
    extra_headers=None,
    api_version=None,
    parallel_tool_calls=None,
    drop_params=None,
    additional_drop_params=None,
    messages: Optional[List[AllMessageValues]] = None,
    **kwargs,
):
    # retrieve all parameters passed to the function
    passed_params = locals().copy()
    special_params = passed_params.pop("kwargs")
    for k, v in special_params.items():
        if k.startswith("aws_") and (
            custom_llm_provider != "bedrock" and custom_llm_provider != "sagemaker"
        ):  # allow dynamically setting boto3 init logic
            continue
        elif k == "hf_model_name" and custom_llm_provider != "sagemaker":
            continue
        elif (
            k.startswith("vertex_")
            and custom_llm_provider != "vertex_ai"
            and custom_llm_provider != "vertex_ai_beta"
        ):  # allow dynamically setting vertex ai init logic
            continue
        passed_params[k] = v

    optional_params: Dict = {}

    common_auth_dict = litellm.common_cloud_provider_auth_params
    if custom_llm_provider in common_auth_dict["providers"]:
        """
        Check if params = ["project", "region_name", "token"]
        and correctly translate for = ["azure", "vertex_ai", "watsonx", "aws"]
        """
        if custom_llm_provider == "azure":
            optional_params = litellm.AzureOpenAIConfig().map_special_auth_params(
                non_default_params=passed_params, optional_params=optional_params
            )
        elif custom_llm_provider == "bedrock":
            optional_params = (
                litellm.AmazonBedrockGlobalConfig().map_special_auth_params(
                    non_default_params=passed_params, optional_params=optional_params
                )
            )
        elif (
            custom_llm_provider == "vertex_ai"
            or custom_llm_provider == "vertex_ai_beta"
        ):
            optional_params = litellm.VertexAIConfig().map_special_auth_params(
                non_default_params=passed_params, optional_params=optional_params
            )
        elif custom_llm_provider == "watsonx":
            optional_params = litellm.IBMWatsonXAIConfig().map_special_auth_params(
                non_default_params=passed_params, optional_params=optional_params
            )

    default_params = {
        "functions": None,
        "function_call": None,
        "temperature": None,
        "top_p": None,
        "n": None,
        "stream": None,
        "stream_options": None,
        "stop": None,
        "max_tokens": None,
        "max_completion_tokens": None,
        "modalities": None,
        "audio": None,
        "presence_penalty": None,
        "frequency_penalty": None,
        "logit_bias": None,
        "user": None,
        "model": None,
        "custom_llm_provider": "",
        "response_format": None,
        "seed": None,
        "tools": None,
        "tool_choice": None,
        "max_retries": None,
        "logprobs": None,
        "top_logprobs": None,
        "extra_headers": None,
        "api_version": None,
        "parallel_tool_calls": None,
        "drop_params": None,
        "additional_drop_params": None,
        "messages": None,
    }

    # filter out those parameters that were passed with non-default values
    non_default_params = {
        k: v
        for k, v in passed_params.items()
        if (
            k != "model"
            and k != "custom_llm_provider"
            and k != "api_version"
            and k != "drop_params"
            and k != "additional_drop_params"
            and k != "messages"
            and k in default_params
            and v != default_params[k]
            and _should_drop_param(k=k, additional_drop_params=additional_drop_params)
            is False
        )
    }

    ## raise exception if function calling passed in for a provider that doesn't support it
    if (
        "functions" in non_default_params
        or "function_call" in non_default_params
        or "tools" in non_default_params
    ):
        if (
            custom_llm_provider == "ollama"
            and custom_llm_provider != "text-completion-openai"
            and custom_llm_provider != "azure"
            and custom_llm_provider != "vertex_ai"
            and custom_llm_provider != "anyscale"
            and custom_llm_provider != "together_ai"
            and custom_llm_provider != "groq"
            and custom_llm_provider != "nvidia_nim"
            and custom_llm_provider != "cerebras"
            and custom_llm_provider != "ai21_chat"
            and custom_llm_provider != "volcengine"
            and custom_llm_provider != "deepseek"
            and custom_llm_provider != "codestral"
            and custom_llm_provider != "mistral"
            and custom_llm_provider != "anthropic"
            and custom_llm_provider != "cohere_chat"
            and custom_llm_provider != "cohere"
            and custom_llm_provider != "bedrock"
            and custom_llm_provider != "ollama_chat"
            and custom_llm_provider != "openrouter"
            and custom_llm_provider not in litellm.openai_compatible_providers
        ):
            if custom_llm_provider == "ollama":
                # ollama actually supports json output
                optional_params["format"] = "json"
                litellm.add_function_to_prompt = (
                    True  # so that main.py adds the function call to the prompt
                )
                if "tools" in non_default_params:
                    optional_params["functions_unsupported_model"] = (
                        non_default_params.pop("tools")
                    )
                    non_default_params.pop(
                        "tool_choice", None
                    )  # causes ollama requests to hang
                elif "functions" in non_default_params:
                    optional_params["functions_unsupported_model"] = (
                        non_default_params.pop("functions")
                    )
            elif (
                litellm.add_function_to_prompt
            ):  # if user opts to add it to prompt instead
                optional_params["functions_unsupported_model"] = non_default_params.pop(
                    "tools", non_default_params.pop("functions", None)
                )
            else:
                raise UnsupportedParamsError(
                    status_code=500,
                    message=f"Function calling is not supported by {custom_llm_provider}.",
                )

    if "response_format" in non_default_params:
        non_default_params["response_format"] = type_to_response_format_param(
            response_format=non_default_params["response_format"]
        )
    if "tools" in non_default_params and isinstance(
        non_default_params, list
    ):  # fixes https://github.com/BerriAI/litellm/issues/4933
        tools = non_default_params["tools"]
        for (
            tool
        ) in (
            tools
        ):  # clean out 'additionalProperties = False'. Causes vertexai/gemini OpenAI API Schema errors - https://github.com/langchain-ai/langchainjs/issues/5240
            tool_function = tool.get("function", {})
            parameters = tool_function.get("parameters", None)
            if parameters is not None:
                new_parameters = copy.deepcopy(parameters)
                if (
                    "additionalProperties" in new_parameters
                    and new_parameters["additionalProperties"] is False
                ):
                    new_parameters.pop("additionalProperties", None)
                tool_function["parameters"] = new_parameters

    def _check_valid_arg(supported_params):
        verbose_logger.info(
            f"\nLiteLLM completion() model= {model}; provider = {custom_llm_provider}"
        )
        verbose_logger.debug(
            f"\nLiteLLM: Params passed to completion() {passed_params}"
        )
        verbose_logger.debug(
            f"\nLiteLLM: Non-Default params passed to completion() {non_default_params}"
        )
        unsupported_params = {}
        for k in non_default_params.keys():
            if k not in supported_params:
                if k == "user" or k == "stream_options" or k == "stream":
                    continue
                if k == "n" and n == 1:  # langchain sends n=1 as a default value
                    continue  # skip this param
                if (
                    k == "max_retries"
                ):  # TODO: This is a patch. We support max retries for OpenAI, Azure. For non OpenAI LLMs we need to add support for max retries
                    continue  # skip this param
                # Always keeps this in elif code blocks
                else:
                    unsupported_params[k] = non_default_params[k]
        if unsupported_params:
            if litellm.drop_params is True or (
                drop_params is not None and drop_params is True
            ):
                pass
            else:
                raise UnsupportedParamsError(
                    status_code=500,
                    message=f"{custom_llm_provider} does not support parameters: {unsupported_params}, for model={model}. To drop these, set `litellm.drop_params=True` or for proxy:\n\n`litellm_settings:\n drop_params: true`\n",
                )

    def _map_and_modify_arg(supported_params: dict, provider: str, model: str):
        """
        filter params to fit the required provider format, drop those that don't fit if user sets `litellm.drop_params = True`.
        """
        filtered_stop = None
        if "stop" in supported_params and litellm.drop_params:
            if provider == "bedrock" and "amazon" in model:
                filtered_stop = []
                if isinstance(stop, list):
                    for s in stop:
                        if re.match(r"^(\|+|User:)$", s):
                            filtered_stop.append(s)
        if filtered_stop is not None:
            supported_params["stop"] = filtered_stop

        return supported_params

    ## raise exception if provider doesn't support passed in param
    if custom_llm_provider == "anthropic":
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.AnthropicConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            messages=messages,
        )
    elif custom_llm_provider == "cohere":
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        # handle cohere params
        if stream:
            optional_params["stream"] = stream
        if temperature is not None:
            optional_params["temperature"] = temperature
        if max_tokens is not None:
            optional_params["max_tokens"] = max_tokens
        if n is not None:
            optional_params["num_generations"] = n
        if logit_bias is not None:
            optional_params["logit_bias"] = logit_bias
        if top_p is not None:
            optional_params["p"] = top_p
        if frequency_penalty is not None:
            optional_params["frequency_penalty"] = frequency_penalty
        if presence_penalty is not None:
            optional_params["presence_penalty"] = presence_penalty
        if stop is not None:
            optional_params["stop_sequences"] = stop
    elif custom_llm_provider == "cohere_chat":
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        # handle cohere params
        if stream:
            optional_params["stream"] = stream
        if temperature is not None:
            optional_params["temperature"] = temperature
        if max_tokens is not None:
            optional_params["max_tokens"] = max_tokens
        if n is not None:
            optional_params["num_generations"] = n
        if top_p is not None:
            optional_params["p"] = top_p
        if frequency_penalty is not None:
            optional_params["frequency_penalty"] = frequency_penalty
        if presence_penalty is not None:
            optional_params["presence_penalty"] = presence_penalty
        if stop is not None:
            optional_params["stop_sequences"] = stop
        if tools is not None:
            optional_params["tools"] = tools
        if seed is not None:
            optional_params["seed"] = seed
    elif custom_llm_provider == "maritalk":
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        # handle cohere params
        if stream:
            optional_params["stream"] = stream
        if temperature is not None:
            optional_params["temperature"] = temperature
        if max_tokens is not None:
            optional_params["max_tokens"] = max_tokens
        if logit_bias is not None:
            optional_params["logit_bias"] = logit_bias
        if top_p is not None:
            optional_params["p"] = top_p
        if presence_penalty is not None:
            optional_params["repetition_penalty"] = presence_penalty
        if stop is not None:
            optional_params["stopping_tokens"] = stop
    elif custom_llm_provider == "replicate":
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        if stream:
            optional_params["stream"] = stream
            # return optional_params
        if max_tokens is not None:
            if "vicuna" in model or "flan" in model:
                optional_params["max_length"] = max_tokens
            elif "meta/codellama-13b" in model:
                optional_params["max_tokens"] = max_tokens
            else:
                optional_params["max_new_tokens"] = max_tokens
        if temperature is not None:
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["top_p"] = top_p
        if stop is not None:
            optional_params["stop_sequences"] = stop
    elif custom_llm_provider == "predibase":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.PredibaseConfig().map_openai_params(
            non_default_params=non_default_params, optional_params=optional_params
        )
    elif custom_llm_provider == "huggingface":
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.HuggingfaceConfig().map_openai_params(
            non_default_params=non_default_params, optional_params=optional_params
        )
    elif custom_llm_provider == "together_ai":
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        if stream:
            optional_params["stream"] = stream
        if temperature is not None:
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["top_p"] = top_p
        if max_tokens is not None:
            optional_params["max_tokens"] = max_tokens
        if frequency_penalty is not None:
            optional_params["frequency_penalty"] = frequency_penalty
        if stop is not None:
            optional_params["stop"] = stop
        if tools is not None:
            optional_params["tools"] = tools
        if tool_choice is not None:
            optional_params["tool_choice"] = tool_choice
        if response_format is not None:
            optional_params["response_format"] = response_format
    elif custom_llm_provider == "ai21":
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        if stream:
            optional_params["stream"] = stream
        if n is not None:
            optional_params["numResults"] = n
        if max_tokens is not None:
            optional_params["maxTokens"] = max_tokens
        if temperature is not None:
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["topP"] = top_p
        if stop is not None:
            optional_params["stopSequences"] = stop
        if frequency_penalty is not None:
            optional_params["frequencyPenalty"] = {"scale": frequency_penalty}
        if presence_penalty is not None:
            optional_params["presencePenalty"] = {"scale": presence_penalty}
    elif (
        custom_llm_provider == "palm"
    ):  # https://developers.generativeai.google/tutorials/curl_quickstart
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        if temperature is not None:
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["top_p"] = top_p
        if stream:
            optional_params["stream"] = stream
        if n is not None:
            optional_params["candidate_count"] = n
        if stop is not None:
            if isinstance(stop, str):
                optional_params["stop_sequences"] = [stop]
            elif isinstance(stop, list):
                optional_params["stop_sequences"] = stop
        if max_tokens is not None:
            optional_params["max_output_tokens"] = max_tokens
    elif custom_llm_provider == "vertex_ai" and (
        model in litellm.vertex_chat_models
        or model in litellm.vertex_code_chat_models
        or model in litellm.vertex_text_models
        or model in litellm.vertex_code_text_models
        or model in litellm.vertex_language_models
        or model in litellm.vertex_vision_models
    ):
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        optional_params = litellm.VertexGeminiConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
            drop_params=(
                drop_params
                if drop_params is not None and isinstance(drop_params, bool)
                else False
            ),
        )

        if litellm.vertex_ai_safety_settings is not None:
            optional_params["safety_settings"] = litellm.vertex_ai_safety_settings
    elif custom_llm_provider == "gemini":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.GoogleAIStudioGeminiConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
            drop_params=(
                drop_params
                if drop_params is not None and isinstance(drop_params, bool)
                else False
            ),
        )
    elif custom_llm_provider == "vertex_ai_beta" or (
        custom_llm_provider == "vertex_ai" and "gemini" in model
    ):
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.VertexGeminiConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
            drop_params=(
                drop_params
                if drop_params is not None and isinstance(drop_params, bool)
                else False
            ),
        )
        if litellm.vertex_ai_safety_settings is not None:
            optional_params["safety_settings"] = litellm.vertex_ai_safety_settings
    elif litellm.VertexAIAnthropicConfig.is_supported_model(
        model=model, custom_llm_provider=custom_llm_provider
    ):
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.VertexAIAnthropicConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
        )
    elif custom_llm_provider == "vertex_ai" and model in litellm.vertex_llama3_models:
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.VertexAILlama3Config().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
            drop_params=(
                drop_params
                if drop_params is not None and isinstance(drop_params, bool)
                else False
            ),
        )
    elif custom_llm_provider == "vertex_ai" and model in litellm.vertex_mistral_models:
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        if "codestral" in model:
            optional_params = litellm.MistralTextCompletionConfig().map_openai_params(
                non_default_params=non_default_params, optional_params=optional_params
            )
        else:
            optional_params = litellm.MistralConfig().map_openai_params(
                non_default_params=non_default_params,
                optional_params=optional_params,
            )
    elif custom_llm_provider == "vertex_ai" and model in litellm.vertex_ai_ai21_models:
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.VertexAIAi21Config().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
            drop_params=(
                drop_params
                if drop_params is not None and isinstance(drop_params, bool)
                else False
            ),
        )
    elif custom_llm_provider == "sagemaker":
        ## check if unsupported param passed in
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        # temperature, top_p, n, stream, stop, max_tokens, n, presence_penalty default to None
        if temperature is not None:
            if temperature == 0.0 or temperature == 0:
                # hugging face exception raised when temp==0
                # Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive
                if not passed_params.get("aws_sagemaker_allow_zero_temp", False):
                    temperature = 0.01
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["top_p"] = top_p
        if n is not None:
            optional_params["best_of"] = n
            optional_params["do_sample"] = (
                True  # Need to sample if you want best of for hf inference endpoints
            )
        if stream is not None:
            optional_params["stream"] = stream
        if stop is not None:
            optional_params["stop"] = stop
        if max_tokens is not None:
            # HF TGI raises the following exception when max_new_tokens==0
            # Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive
            if max_tokens == 0:
                max_tokens = 1
            optional_params["max_new_tokens"] = max_tokens
        passed_params.pop("aws_sagemaker_allow_zero_temp", None)
    elif custom_llm_provider == "bedrock":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        base_model = litellm.AmazonConverseConfig()._get_base_model(model)
        if base_model in litellm.BEDROCK_CONVERSE_MODELS:
            _check_valid_arg(supported_params=supported_params)
            optional_params = litellm.AmazonConverseConfig().map_openai_params(
                model=model,
                non_default_params=non_default_params,
                optional_params=optional_params,
                drop_params=(
                    drop_params
                    if drop_params is not None and isinstance(drop_params, bool)
                    else False
                ),
                messages=messages,
            )
        elif "ai21" in model:
            _check_valid_arg(supported_params=supported_params)
            # params "maxTokens":200,"temperature":0,"topP":250,"stop_sequences":[],
            # https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=j2-ultra
            if max_tokens is not None:
                optional_params["maxTokens"] = max_tokens
            if temperature is not None:
                optional_params["temperature"] = temperature
            if top_p is not None:
                optional_params["topP"] = top_p
            if stream:
                optional_params["stream"] = stream
        elif "anthropic" in model:
            _check_valid_arg(supported_params=supported_params)
            if "aws_bedrock_client" in passed_params:  # deprecated boto3.invoke route.
                if model.startswith("anthropic.claude-3"):
                    optional_params = (
                        litellm.AmazonAnthropicClaude3Config().map_openai_params(
                            non_default_params=non_default_params,
                            optional_params=optional_params,
                        )
                    )
            else:
                optional_params = litellm.AmazonAnthropicConfig().map_openai_params(
                    non_default_params=non_default_params,
                    optional_params=optional_params,
                )
        elif "amazon" in model:  # amazon titan llms
            _check_valid_arg(supported_params=supported_params)
            # see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large
            if max_tokens is not None:
                optional_params["maxTokenCount"] = max_tokens
            if temperature is not None:
                optional_params["temperature"] = temperature
            if stop is not None:
                filtered_stop = _map_and_modify_arg(
                    {"stop": stop}, provider="bedrock", model=model
                )
                optional_params["stopSequences"] = filtered_stop["stop"]
            if top_p is not None:
                optional_params["topP"] = top_p
            if stream:
                optional_params["stream"] = stream
        elif "meta" in model:  # amazon / meta llms
            _check_valid_arg(supported_params=supported_params)
            # see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large
            if max_tokens is not None:
                optional_params["max_gen_len"] = max_tokens
            if temperature is not None:
                optional_params["temperature"] = temperature
            if top_p is not None:
                optional_params["top_p"] = top_p
            if stream:
                optional_params["stream"] = stream
        elif "cohere" in model:  # cohere models on bedrock
            _check_valid_arg(supported_params=supported_params)
            # handle cohere params
            if stream:
                optional_params["stream"] = stream
            if temperature is not None:
                optional_params["temperature"] = temperature
            if max_tokens is not None:
                optional_params["max_tokens"] = max_tokens
        elif "mistral" in model:
            _check_valid_arg(supported_params=supported_params)
            # mistral params on bedrock
            # \"max_tokens\":400,\"temperature\":0.7,\"top_p\":0.7,\"stop\":[\"\\\\n\\\\nHuman:\"]}"
            if max_tokens is not None:
                optional_params["max_tokens"] = max_tokens
            if temperature is not None:
                optional_params["temperature"] = temperature
            if top_p is not None:
                optional_params["top_p"] = top_p
            if stop is not None:
                optional_params["stop"] = stop
            if stream is not None:
                optional_params["stream"] = stream
    elif custom_llm_provider == "aleph_alpha":
        supported_params = [
            "max_tokens",
            "stream",
            "top_p",
            "temperature",
            "presence_penalty",
            "frequency_penalty",
            "n",
            "stop",
        ]
        _check_valid_arg(supported_params=supported_params)
        if max_tokens is not None:
            optional_params["maximum_tokens"] = max_tokens
        if stream:
            optional_params["stream"] = stream
        if temperature is not None:
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["top_p"] = top_p
        if presence_penalty is not None:
            optional_params["presence_penalty"] = presence_penalty
        if frequency_penalty is not None:
            optional_params["frequency_penalty"] = frequency_penalty
        if n is not None:
            optional_params["n"] = n
        if stop is not None:
            optional_params["stop_sequences"] = stop
    elif custom_llm_provider == "cloudflare":
        # https://developers.cloudflare.com/workers-ai/models/text-generation/#input
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        if max_tokens is not None:
            optional_params["max_tokens"] = max_tokens
        if stream is not None:
            optional_params["stream"] = stream
    elif custom_llm_provider == "ollama":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        if max_tokens is not None:
            optional_params["num_predict"] = max_tokens
        if stream:
            optional_params["stream"] = stream
        if temperature is not None:
            optional_params["temperature"] = temperature
        if seed is not None:
            optional_params["seed"] = seed
        if top_p is not None:
            optional_params["top_p"] = top_p
        if frequency_penalty is not None:
            optional_params["repeat_penalty"] = frequency_penalty
        if stop is not None:
            optional_params["stop"] = stop
        if response_format is not None and response_format["type"] == "json_object":
            optional_params["format"] = "json"
    elif custom_llm_provider == "ollama_chat":
        supported_params = litellm.OllamaChatConfig().get_supported_openai_params()

        _check_valid_arg(supported_params=supported_params)

        optional_params = litellm.OllamaChatConfig().map_openai_params(
            model=model,
            non_default_params=non_default_params,
            optional_params=optional_params,
        )
    elif custom_llm_provider == "nlp_cloud":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        if max_tokens is not None:
            optional_params["max_length"] = max_tokens
        if stream:
            optional_params["stream"] = stream
        if temperature is not None:
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["top_p"] = top_p
        if presence_penalty is not None:
            optional_params["presence_penalty"] = presence_penalty
        if frequency_penalty is not None:
            optional_params["frequency_penalty"] = frequency_penalty
        if n is not None:
            optional_params["num_return_sequences"] = n
        if stop is not None:
            optional_params["stop_sequences"] = stop
    elif custom_llm_provider == "petals":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        # max_new_tokens=1,temperature=0.9, top_p=0.6
        if max_tokens is not None:
            optional_params["max_new_tokens"] = max_tokens
        if temperature is not None:
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["top_p"] = top_p
        if stream:
            optional_params["stream"] = stream
    elif custom_llm_provider == "deepinfra":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.DeepInfraConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
            drop_params=(
                drop_params
                if drop_params is not None and isinstance(drop_params, bool)
                else False
            ),
        )
    elif custom_llm_provider == "perplexity":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        if temperature is not None:
            if (
                temperature == 0 and model == "mistral-7b-instruct"
            ):  # this model does no support temperature == 0
                temperature = 0.0001  # close to 0
            optional_params["temperature"] = temperature
        if top_p:
            optional_params["top_p"] = top_p
        if stream:
            optional_params["stream"] = stream
        if max_tokens:
            optional_params["max_tokens"] = max_tokens
        if presence_penalty:
            optional_params["presence_penalty"] = presence_penalty
        if frequency_penalty:
            optional_params["frequency_penalty"] = frequency_penalty
    elif custom_llm_provider == "anyscale":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        if model in [
            "mistralai/Mistral-7B-Instruct-v0.1",
            "mistralai/Mixtral-8x7B-Instruct-v0.1",
        ]:
            supported_params += [  # type: ignore
                "functions",
                "function_call",
                "tools",
                "tool_choice",
                "response_format",
            ]
        _check_valid_arg(supported_params=supported_params)
        optional_params = non_default_params
        if temperature is not None:
            if temperature == 0 and model in [
                "mistralai/Mistral-7B-Instruct-v0.1",
                "mistralai/Mixtral-8x7B-Instruct-v0.1",
            ]:  # this model does no support temperature == 0
                temperature = 0.0001  # close to 0
            optional_params["temperature"] = temperature
        if top_p:
            optional_params["top_p"] = top_p
        if stream:
            optional_params["stream"] = stream
        if max_tokens:
            optional_params["max_tokens"] = max_tokens
    elif custom_llm_provider == "mistral" or custom_llm_provider == "codestral":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.MistralConfig().map_openai_params(
            non_default_params=non_default_params, optional_params=optional_params
        )
    elif custom_llm_provider == "text-completion-codestral":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.MistralTextCompletionConfig().map_openai_params(
            non_default_params=non_default_params, optional_params=optional_params
        )

    elif custom_llm_provider == "databricks":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.DatabricksConfig().map_openai_params(
            non_default_params=non_default_params, optional_params=optional_params
        )
    elif custom_llm_provider == "nvidia_nim":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.NvidiaNimConfig().map_openai_params(
            model=model,
            non_default_params=non_default_params,
            optional_params=optional_params,
        )
    elif custom_llm_provider == "cerebras":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.CerebrasConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
        )
    elif custom_llm_provider == "ai21_chat":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.AI21ChatConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
        )
    elif custom_llm_provider == "fireworks_ai":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.FireworksAIConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
        )
    elif custom_llm_provider == "volcengine":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.VolcEngineConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
        )
    elif custom_llm_provider == "hosted_vllm":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.HostedVLLMChatConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
            drop_params=(
                drop_params
                if drop_params is not None and isinstance(drop_params, bool)
                else False
            ),
        )

    elif custom_llm_provider == "groq":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        if temperature is not None:
            optional_params["temperature"] = temperature
        if max_tokens is not None:
            optional_params["max_tokens"] = max_tokens
        if top_p is not None:
            optional_params["top_p"] = top_p
        if stream is not None:
            optional_params["stream"] = stream
        if stop is not None:
            optional_params["stop"] = stop
        if tools is not None:
            optional_params["tools"] = tools
        if tool_choice is not None:
            optional_params["tool_choice"] = tool_choice
        if response_format is not None:
            optional_params["response_format"] = response_format
        if seed is not None:
            optional_params["seed"] = seed
    elif custom_llm_provider == "deepseek":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        optional_params = litellm.OpenAIConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
            drop_params=(
                drop_params
                if drop_params is not None and isinstance(drop_params, bool)
                else False
            ),
        )
    elif custom_llm_provider == "openrouter":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)

        if functions is not None:
            optional_params["functions"] = functions
        if function_call is not None:
            optional_params["function_call"] = function_call
        if temperature is not None:
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["top_p"] = top_p
        if n is not None:
            optional_params["n"] = n
        if stream is not None:
            optional_params["stream"] = stream
        if stop is not None:
            optional_params["stop"] = stop
        if max_tokens is not None:
            optional_params["max_tokens"] = max_tokens
        if presence_penalty is not None:
            optional_params["presence_penalty"] = presence_penalty
        if frequency_penalty is not None:
            optional_params["frequency_penalty"] = frequency_penalty
        if logit_bias is not None:
            optional_params["logit_bias"] = logit_bias
        if user is not None:
            optional_params["user"] = user
        if response_format is not None:
            optional_params["response_format"] = response_format
        if seed is not None:
            optional_params["seed"] = seed
        if tools is not None:
            optional_params["tools"] = tools
        if tool_choice is not None:
            optional_params["tool_choice"] = tool_choice
        if max_retries is not None:
            optional_params["max_retries"] = max_retries

        # OpenRouter-only parameters
        extra_body = {}
        transforms = passed_params.pop("transforms", None)
        models = passed_params.pop("models", None)
        route = passed_params.pop("route", None)
        if transforms is not None:
            extra_body["transforms"] = transforms
        if models is not None:
            extra_body["models"] = models
        if route is not None:
            extra_body["route"] = route
        optional_params["extra_body"] = (
            extra_body  # openai client supports `extra_body` param
        )
    elif custom_llm_provider == "watsonx":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        _check_valid_arg(supported_params=supported_params)
        if max_tokens is not None:
            optional_params["max_new_tokens"] = max_tokens
        if stream:
            optional_params["stream"] = stream
        if temperature is not None:
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["top_p"] = top_p
        if frequency_penalty is not None:
            optional_params["repetition_penalty"] = frequency_penalty
        if seed is not None:
            optional_params["random_seed"] = seed
        if stop is not None:
            optional_params["stop_sequences"] = stop

        # WatsonX-only parameters
        extra_body = {}
        if "decoding_method" in passed_params:
            extra_body["decoding_method"] = passed_params.pop("decoding_method")
        if "min_tokens" in passed_params or "min_new_tokens" in passed_params:
            extra_body["min_new_tokens"] = passed_params.pop(
                "min_tokens", passed_params.pop("min_new_tokens")
            )
        if "top_k" in passed_params:
            extra_body["top_k"] = passed_params.pop("top_k")
        if "truncate_input_tokens" in passed_params:
            extra_body["truncate_input_tokens"] = passed_params.pop(
                "truncate_input_tokens"
            )
        if "length_penalty" in passed_params:
            extra_body["length_penalty"] = passed_params.pop("length_penalty")
        if "time_limit" in passed_params:
            extra_body["time_limit"] = passed_params.pop("time_limit")
        if "return_options" in passed_params:
            extra_body["return_options"] = passed_params.pop("return_options")
        optional_params["extra_body"] = (
            extra_body  # openai client supports `extra_body` param
        )
    elif custom_llm_provider == "openai":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider="openai"
        )
        _check_valid_arg(supported_params=supported_params)
        optional_params = litellm.OpenAIConfig().map_openai_params(
            non_default_params=non_default_params,
            optional_params=optional_params,
            model=model,
            drop_params=(
                drop_params
                if drop_params is not None and isinstance(drop_params, bool)
                else False
            ),
        )
    elif custom_llm_provider == "azure":
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider="azure"
        )
        _check_valid_arg(supported_params=supported_params)
        if litellm.AzureOpenAIO1Config().is_o1_model(model=model):
            optional_params = litellm.AzureOpenAIO1Config().map_openai_params(
                non_default_params=non_default_params,
                optional_params=optional_params,
                model=model,
                drop_params=(
                    drop_params
                    if drop_params is not None and isinstance(drop_params, bool)
                    else False
                ),
            )
        else:
            verbose_logger.debug(
                "Azure optional params - api_version: api_version={}, litellm.api_version={}, os.environ['AZURE_API_VERSION']={}".format(
                    api_version, litellm.api_version, get_secret("AZURE_API_VERSION")
                )
            )
            api_version = (
                api_version
                or litellm.api_version
                or get_secret("AZURE_API_VERSION")
                or litellm.AZURE_DEFAULT_API_VERSION
            )
            optional_params = litellm.AzureOpenAIConfig().map_openai_params(
                non_default_params=non_default_params,
                optional_params=optional_params,
                model=model,
                api_version=api_version,  # type: ignore
                drop_params=drop_params,
            )
    else:  # assume passing in params for text-completion openai
        supported_params = get_supported_openai_params(
            model=model, custom_llm_provider="custom_openai"
        )
        _check_valid_arg(supported_params=supported_params)
        if functions is not None:
            optional_params["functions"] = functions
        if function_call is not None:
            optional_params["function_call"] = function_call
        if temperature is not None:
            optional_params["temperature"] = temperature
        if top_p is not None:
            optional_params["top_p"] = top_p
        if n is not None:
            optional_params["n"] = n
        if stream is not None:
            optional_params["stream"] = stream
        if stream_options is not None:
            optional_params["stream_options"] = stream_options
        if stop is not None:
            optional_params["stop"] = stop
        if max_tokens is not None:
            optional_params["max_tokens"] = max_tokens
        if presence_penalty is not None:
            optional_params["presence_penalty"] = presence_penalty
        if frequency_penalty is not None:
            optional_params["frequency_penalty"] = frequency_penalty
        if logit_bias is not None:
            optional_params["logit_bias"] = logit_bias
        if user is not None:
            optional_params["user"] = user
        if response_format is not None:
            optional_params["response_format"] = response_format
        if seed is not None:
            optional_params["seed"] = seed
        if tools is not None:
            optional_params["tools"] = tools
        if tool_choice is not None:
            optional_params["tool_choice"] = tool_choice
        if max_retries is not None:
            optional_params["max_retries"] = max_retries
        if logprobs is not None:
            optional_params["logprobs"] = logprobs
        if top_logprobs is not None:
            optional_params["top_logprobs"] = top_logprobs
        if extra_headers is not None:
            optional_params["extra_headers"] = extra_headers
    if (
        custom_llm_provider
        in ["openai", "azure", "text-completion-openai"]
        + litellm.openai_compatible_providers
    ):
        # for openai, azure we should pass the extra/passed params within `extra_body` https://github.com/openai/openai-python/blob/ac33853ba10d13ac149b1fa3ca6dba7d613065c9/src/openai/resources/models.py#L46
        if (
            _should_drop_param(
                k="extra_body", additional_drop_params=additional_drop_params
            )
            is False
        ):
            extra_body = passed_params.pop("extra_body", {})
            for k in passed_params.keys():
                if k not in default_params.keys():
                    extra_body[k] = passed_params[k]
            optional_params.setdefault("extra_body", {})
            optional_params["extra_body"] = {
                **optional_params["extra_body"],
                **extra_body,
            }

            optional_params["extra_body"] = _ensure_extra_body_is_safe(
                extra_body=optional_params["extra_body"]
            )
    else:
        # if user passed in non-default kwargs for specific providers/models, pass them along
        for k in passed_params.keys():
            if k not in default_params.keys():
                optional_params[k] = passed_params[k]
    print_verbose(f"Final returned optional params: {optional_params}")
    return optional_params


def get_non_default_params(passed_params: dict) -> dict:
    default_params = {
        "functions": None,
        "function_call": None,
        "temperature": None,
        "top_p": None,
        "n": None,
        "stream": None,
        "stream_options": None,
        "stop": None,
        "max_tokens": None,
        "presence_penalty": None,
        "frequency_penalty": None,
        "logit_bias": None,
        "user": None,
        "model": None,
        "custom_llm_provider": "",
        "response_format": None,
        "seed": None,
        "tools": None,
        "tool_choice": None,
        "max_retries": None,
        "logprobs": None,
        "top_logprobs": None,
        "extra_headers": None,
    }
    # filter out those parameters that were passed with non-default values
    non_default_params = {
        k: v
        for k, v in passed_params.items()
        if (
            k != "model"
            and k != "custom_llm_provider"
            and k in default_params
            and v != default_params[k]
        )
    }

    return non_default_params


def calculate_max_parallel_requests(
    max_parallel_requests: Optional[int],
    rpm: Optional[int],
    tpm: Optional[int],
    default_max_parallel_requests: Optional[int],
) -> Optional[int]:
    """
    Returns the max parallel requests to send to a deployment.

    Used in semaphore for async requests on router.

    Parameters:
    - max_parallel_requests - Optional[int] - max_parallel_requests allowed for that deployment
    - rpm - Optional[int] - requests per minute allowed for that deployment
    - tpm - Optional[int] - tokens per minute allowed for that deployment
    - default_max_parallel_requests - Optional[int] - default_max_parallel_requests allowed for any deployment

    Returns:
    - int or None (if all params are None)

    Order:
    max_parallel_requests > rpm > tpm / 6 (azure formula) > default max_parallel_requests

    Azure RPM formula:
    6 rpm per 1000 TPM
    https://learn.microsoft.com/en-us/azure/ai-services/openai/quotas-limits


    """
    if max_parallel_requests is not None:
        return max_parallel_requests
    elif rpm is not None:
        return rpm
    elif tpm is not None:
        calculated_rpm = int(tpm / 1000 / 6)
        if calculated_rpm == 0:
            calculated_rpm = 1
        return calculated_rpm
    elif default_max_parallel_requests is not None:
        return default_max_parallel_requests
    return None


def _get_order_filtered_deployments(healthy_deployments: List[Dict]) -> List:
    min_order = min(
        (
            deployment["litellm_params"]["order"]
            for deployment in healthy_deployments
            if "order" in deployment["litellm_params"]
        ),
        default=None,
    )

    if min_order is not None:
        filtered_deployments = [
            deployment
            for deployment in healthy_deployments
            if deployment["litellm_params"].get("order") == min_order
        ]

        return filtered_deployments
    return healthy_deployments


def _get_model_region(
    custom_llm_provider: str, litellm_params: LiteLLM_Params
) -> Optional[str]:
    """
    Return the region for a model, for a given provider
    """
    if custom_llm_provider == "vertex_ai":
        # check 'vertex_location'
        vertex_ai_location = (
            litellm_params.vertex_location
            or litellm.vertex_location
            or get_secret("VERTEXAI_LOCATION")
            or get_secret("VERTEX_LOCATION")
        )
        if vertex_ai_location is not None and isinstance(vertex_ai_location, str):
            return vertex_ai_location
    elif custom_llm_provider == "bedrock":
        aws_region_name = litellm_params.aws_region_name
        if aws_region_name is not None:
            return aws_region_name
    elif custom_llm_provider == "watsonx":
        watsonx_region_name = litellm_params.watsonx_region_name
        if watsonx_region_name is not None:
            return watsonx_region_name
    return litellm_params.region_name


def _infer_model_region(litellm_params: LiteLLM_Params) -> Optional[AllowedModelRegion]:
    """
    Infer if a model is in the EU or US region

    Returns:
    - str (region) - "eu" or "us"
    - None (if region not found)
    """
    model, custom_llm_provider, _, _ = litellm.get_llm_provider(
        model=litellm_params.model, litellm_params=litellm_params
    )

    model_region = _get_model_region(
        custom_llm_provider=custom_llm_provider, litellm_params=litellm_params
    )

    if model_region is None:
        verbose_logger.debug(
            "Cannot infer model region for model: {}".format(litellm_params.model)
        )
        return None

    if custom_llm_provider == "azure":
        eu_regions = litellm.AzureOpenAIConfig().get_eu_regions()
        us_regions = litellm.AzureOpenAIConfig().get_us_regions()
    elif custom_llm_provider == "vertex_ai":
        eu_regions = litellm.VertexAIConfig().get_eu_regions()
        us_regions = litellm.VertexAIConfig().get_us_regions()
    elif custom_llm_provider == "bedrock":
        eu_regions = litellm.AmazonBedrockGlobalConfig().get_eu_regions()
        us_regions = litellm.AmazonBedrockGlobalConfig().get_us_regions()
    elif custom_llm_provider == "watsonx":
        eu_regions = litellm.IBMWatsonXAIConfig().get_eu_regions()
        us_regions = litellm.IBMWatsonXAIConfig().get_us_regions()
    else:
        eu_regions = []
        us_regions = []

    for region in eu_regions:
        if region in model_region.lower():
            return "eu"
    for region in us_regions:
        if region in model_region.lower():
            return "us"
    return None


def _is_region_eu(litellm_params: LiteLLM_Params) -> bool:
    """
    Return true/false if a deployment is in the EU
    """
    if litellm_params.region_name == "eu":
        return True

    ## Else - try and infer from model region
    model_region = _infer_model_region(litellm_params=litellm_params)
    if model_region is not None and model_region == "eu":
        return True
    return False


def _is_region_us(litellm_params: LiteLLM_Params) -> bool:
    """
    Return true/false if a deployment is in the US
    """
    if litellm_params.region_name == "us":
        return True

    ## Else - try and infer from model region
    model_region = _infer_model_region(litellm_params=litellm_params)
    if model_region is not None and model_region == "us":
        return True
    return False


def is_region_allowed(
    litellm_params: LiteLLM_Params, allowed_model_region: str
) -> bool:
    """
    Return true/false if a deployment is in the EU
    """
    if litellm_params.region_name == allowed_model_region:
        return True
    return False


def get_model_region(
    litellm_params: LiteLLM_Params, mode: Optional[str]
) -> Optional[str]:
    """
    Pass the litellm params for an azure model, and get back the region
    """
    if (
        "azure" in litellm_params.model
        and isinstance(litellm_params.api_key, str)
        and isinstance(litellm_params.api_base, str)
    ):
        _model = litellm_params.model.replace("azure/", "")
        response: dict = litellm.AzureChatCompletion().get_headers(
            model=_model,
            api_key=litellm_params.api_key,
            api_base=litellm_params.api_base,
            api_version=litellm_params.api_version or litellm.AZURE_DEFAULT_API_VERSION,
            timeout=10,
            mode=mode or "chat",
        )

        region: Optional[str] = response.get("x-ms-region", None)
        return region
    return None


def get_api_base(
    model: str, optional_params: Union[dict, LiteLLM_Params]
) -> Optional[str]:
    """
    Returns the api base used for calling the model.

    Parameters:
    - model: str - the model passed to litellm.completion()
    - optional_params - the 'litellm_params' in router.completion *OR* additional params passed to litellm.completion - eg. api_base, api_key, etc. See `LiteLLM_Params` - https://github.com/BerriAI/litellm/blob/f09e6ba98d65e035a79f73bc069145002ceafd36/litellm/router.py#L67

    Returns:
    - string (api_base) or None

    Example:
    ```
    from litellm import get_api_base

    get_api_base(model="gemini/gemini-pro")
    ```
    """

    try:
        if isinstance(optional_params, LiteLLM_Params):
            _optional_params = optional_params
        elif "model" in optional_params:
            _optional_params = LiteLLM_Params(**optional_params)
        else:  # prevent needing to copy and pop the dict
            _optional_params = LiteLLM_Params(
                model=model, **optional_params
            )  # convert to pydantic object
    except Exception as e:
        verbose_logger.debug("Error occurred in getting api base - {}".format(str(e)))
        return None
    # get llm provider

    if _optional_params.api_base is not None:
        return _optional_params.api_base

    if litellm.model_alias_map and model in litellm.model_alias_map:
        model = litellm.model_alias_map[model]
    try:
        (
            model,
            custom_llm_provider,
            dynamic_api_key,
            dynamic_api_base,
        ) = get_llm_provider(
            model=model,
            custom_llm_provider=_optional_params.custom_llm_provider,
            api_base=_optional_params.api_base,
            api_key=_optional_params.api_key,
        )
    except Exception as e:
        verbose_logger.debug("Error occurred in getting api base - {}".format(str(e)))
        custom_llm_provider = None
        dynamic_api_base = None

    if dynamic_api_base is not None:
        return dynamic_api_base

    stream: bool = getattr(optional_params, "stream", False)

    if (
        _optional_params.vertex_location is not None
        and _optional_params.vertex_project is not None
    ):
        from litellm.llms.vertex_ai_and_google_ai_studio.vertex_ai_partner_models.main import (
            VertexPartnerProvider,
            create_vertex_url,
        )

        if "claude" in model:
            _api_base = create_vertex_url(
                vertex_location=_optional_params.vertex_location,
                vertex_project=_optional_params.vertex_project,
                model=model,
                stream=stream,
                partner=VertexPartnerProvider.claude,
            )
        else:

            if stream:
                _api_base = "{}-aiplatform.googleapis.com/v1/projects/{}/locations/{}/publishers/google/models/{}:streamGenerateContent".format(
                    _optional_params.vertex_location,
                    _optional_params.vertex_project,
                    _optional_params.vertex_location,
                    model,
                )
            else:
                _api_base = "{}-aiplatform.googleapis.com/v1/projects/{}/locations/{}/publishers/google/models/{}:generateContent".format(
                    _optional_params.vertex_location,
                    _optional_params.vertex_project,
                    _optional_params.vertex_location,
                    model,
                )
        return _api_base

    if custom_llm_provider is None:
        return None

    if custom_llm_provider == "gemini":
        if stream:
            _api_base = "https://generativelanguage.googleapis.com/v1beta/models/{}:streamGenerateContent".format(
                model
            )
        else:
            _api_base = "https://generativelanguage.googleapis.com/v1beta/models/{}:generateContent".format(
                model
            )
        return _api_base
    elif custom_llm_provider == "openai":
        _api_base = "https://api.openai.com"
        return _api_base
    return None


def get_first_chars_messages(kwargs: dict) -> str:
    try:
        _messages = kwargs.get("messages")
        _messages = str(_messages)[:100]
        return _messages
    except Exception:
        return ""


def get_supported_openai_params(  # noqa: PLR0915
    model: str,
    custom_llm_provider: Optional[str] = None,
    request_type: Literal["chat_completion", "embeddings"] = "chat_completion",
) -> Optional[list]:
    """
    Returns the supported openai params for a given model + provider

    Example:
    ```
    get_supported_openai_params(model="anthropic.claude-3", custom_llm_provider="bedrock")
    ```

    Returns:
    - List if custom_llm_provider is mapped
    - None if unmapped
    """
    if not custom_llm_provider:
        try:
            custom_llm_provider = litellm.get_llm_provider(model=model)[1]
        except BadRequestError:
            return None
    if custom_llm_provider == "bedrock":
        return litellm.AmazonConverseConfig().get_supported_openai_params(model=model)
    elif custom_llm_provider == "ollama":
        return litellm.OllamaConfig().get_supported_openai_params()
    elif custom_llm_provider == "ollama_chat":
        return litellm.OllamaChatConfig().get_supported_openai_params()
    elif custom_llm_provider == "anthropic":
        return litellm.AnthropicConfig().get_supported_openai_params()
    elif custom_llm_provider == "fireworks_ai":
        if request_type == "embeddings":
            return litellm.FireworksAIEmbeddingConfig().get_supported_openai_params(
                model=model
            )
        else:
            return litellm.FireworksAIConfig().get_supported_openai_params()
    elif custom_llm_provider == "nvidia_nim":
        if request_type == "chat_completion":
            return litellm.nvidiaNimConfig.get_supported_openai_params(model=model)
        elif request_type == "embeddings":
            return litellm.nvidiaNimEmbeddingConfig.get_supported_openai_params()
    elif custom_llm_provider == "cerebras":
        return litellm.CerebrasConfig().get_supported_openai_params(model=model)
    elif custom_llm_provider == "ai21_chat":
        return litellm.AI21ChatConfig().get_supported_openai_params(model=model)
    elif custom_llm_provider == "volcengine":
        return litellm.VolcEngineConfig().get_supported_openai_params(model=model)
    elif custom_llm_provider == "groq":
        return litellm.GroqChatConfig().get_supported_openai_params(model=model)
    elif custom_llm_provider == "hosted_vllm":
        return litellm.HostedVLLMChatConfig().get_supported_openai_params(model=model)
    elif custom_llm_provider == "deepseek":
        return [
            # https://platform.deepseek.com/api-docs/api/create-chat-completion
            "frequency_penalty",
            "max_tokens",
            "presence_penalty",
            "response_format",
            "stop",
            "stream",
            "temperature",
            "top_p",
            "logprobs",
            "top_logprobs",
            "tools",
            "tool_choice",
        ]
    elif custom_llm_provider == "cohere":
        return [
            "stream",
            "temperature",
            "max_tokens",
            "logit_bias",
            "top_p",
            "frequency_penalty",
            "presence_penalty",
            "stop",
            "n",
            "extra_headers",
        ]
    elif custom_llm_provider == "cohere_chat":
        return [
            "stream",
            "temperature",
            "max_tokens",
            "top_p",
            "frequency_penalty",
            "presence_penalty",
            "stop",
            "n",
            "tools",
            "tool_choice",
            "seed",
            "extra_headers",
        ]
    elif custom_llm_provider == "maritalk":
        return [
            "stream",
            "temperature",
            "max_tokens",
            "top_p",
            "presence_penalty",
            "stop",
        ]
    elif custom_llm_provider == "openai":
        return litellm.OpenAIConfig().get_supported_openai_params(model=model)
    elif custom_llm_provider == "azure":
        if litellm.AzureOpenAIO1Config().is_o1_model(model=model):
            return litellm.AzureOpenAIO1Config().get_supported_openai_params(
                model=model
            )
        else:
            return litellm.AzureOpenAIConfig().get_supported_openai_params()
    elif custom_llm_provider == "openrouter":
        return [
            "temperature",
            "top_p",
            "frequency_penalty",
            "presence_penalty",
            "repetition_penalty",
            "seed",
            "max_tokens",
            "logit_bias",
            "logprobs",
            "top_logprobs",
            "response_format",
            "stop",
            "tools",
            "tool_choice",
        ]
    elif custom_llm_provider == "mistral" or custom_llm_provider == "codestral":
        # mistal and codestral api have the exact same params
        if request_type == "chat_completion":
            return litellm.MistralConfig().get_supported_openai_params()
        elif request_type == "embeddings":
            return litellm.MistralEmbeddingConfig().get_supported_openai_params()
    elif custom_llm_provider == "text-completion-codestral":
        return litellm.MistralTextCompletionConfig().get_supported_openai_params()
    elif custom_llm_provider == "replicate":
        return [
            "stream",
            "temperature",
            "max_tokens",
            "top_p",
            "stop",
            "seed",
            "tools",
            "tool_choice",
            "functions",
            "function_call",
        ]
    elif custom_llm_provider == "huggingface":
        return litellm.HuggingfaceConfig().get_supported_openai_params()
    elif custom_llm_provider == "together_ai":
        return [
            "stream",
            "temperature",
            "max_tokens",
            "top_p",
            "stop",
            "frequency_penalty",
            "tools",
            "tool_choice",
            "response_format",
        ]
    elif custom_llm_provider == "ai21":
        return [
            "stream",
            "n",
            "temperature",
            "max_tokens",
            "top_p",
            "stop",
            "frequency_penalty",
            "presence_penalty",
        ]
    elif custom_llm_provider == "databricks":
        if request_type == "chat_completion":
            return litellm.DatabricksConfig().get_supported_openai_params()
        elif request_type == "embeddings":
            return litellm.DatabricksEmbeddingConfig().get_supported_openai_params()
    elif custom_llm_provider == "palm" or custom_llm_provider == "gemini":
        return litellm.GoogleAIStudioGeminiConfig().get_supported_openai_params()
    elif custom_llm_provider == "vertex_ai":
        if request_type == "chat_completion":
            if model.startswith("meta/"):
                return litellm.VertexAILlama3Config().get_supported_openai_params()
            if model.startswith("mistral"):
                return litellm.MistralConfig().get_supported_openai_params()
            if model.startswith("codestral"):
                return (
                    litellm.MistralTextCompletionConfig().get_supported_openai_params()
                )
            if model.startswith("claude"):
                return litellm.VertexAIAnthropicConfig().get_supported_openai_params()
            return litellm.VertexAIConfig().get_supported_openai_params()
        elif request_type == "embeddings":
            return litellm.VertexAITextEmbeddingConfig().get_supported_openai_params()
    elif custom_llm_provider == "vertex_ai_beta":
        if request_type == "chat_completion":
            return litellm.VertexGeminiConfig().get_supported_openai_params()
        elif request_type == "embeddings":
            return litellm.VertexAITextEmbeddingConfig().get_supported_openai_params()
    elif custom_llm_provider == "sagemaker":
        return ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
    elif custom_llm_provider == "aleph_alpha":
        return [
            "max_tokens",
            "stream",
            "top_p",
            "temperature",
            "presence_penalty",
            "frequency_penalty",
            "n",
            "stop",
        ]
    elif custom_llm_provider == "cloudflare":
        return ["max_tokens", "stream"]
    elif custom_llm_provider == "nlp_cloud":
        return [
            "max_tokens",
            "stream",
            "temperature",
            "top_p",
            "presence_penalty",
            "frequency_penalty",
            "n",
            "stop",
        ]
    elif custom_llm_provider == "petals":
        return ["max_tokens", "temperature", "top_p", "stream"]
    elif custom_llm_provider == "deepinfra":
        return litellm.DeepInfraConfig().get_supported_openai_params()
    elif custom_llm_provider == "perplexity":
        return [
            "temperature",
            "top_p",
            "stream",
            "max_tokens",
            "presence_penalty",
            "frequency_penalty",
        ]
    elif custom_llm_provider == "anyscale":
        return [
            "temperature",
            "top_p",
            "stream",
            "max_tokens",
            "stop",
            "frequency_penalty",
            "presence_penalty",
        ]
    elif custom_llm_provider == "watsonx":
        return litellm.IBMWatsonXAIConfig().get_supported_openai_params()
    elif custom_llm_provider == "custom_openai" or "text-completion-openai":
        return [
            "functions",
            "function_call",
            "temperature",
            "top_p",
            "n",
            "stream",
            "stream_options",
            "stop",
            "max_tokens",
            "presence_penalty",
            "frequency_penalty",
            "logit_bias",
            "user",
            "response_format",
            "seed",
            "tools",
            "tool_choice",
            "max_retries",
            "logprobs",
            "top_logprobs",
            "extra_headers",
        ]
    return None


def _count_characters(text: str) -> int:
    # Remove white spaces and count characters
    filtered_text = "".join(char for char in text if not char.isspace())
    return len(filtered_text)


def get_formatted_prompt(
    data: dict,
    call_type: Literal[
        "completion",
        "embedding",
        "image_generation",
        "audio_transcription",
        "moderation",
        "text_completion",
    ],
) -> str:
    """
    Extracts the prompt from the input data based on the call type.

    Returns a string.
    """
    prompt = ""
    if call_type == "completion":
        for message in data["messages"]:
            if message.get("content", None) is not None:
                content = message.get("content")
                if isinstance(content, str):
                    prompt += message["content"]
                elif isinstance(content, List):
                    for c in content:
                        if c["type"] == "text":
                            prompt += c["text"]
            if "tool_calls" in message:
                for tool_call in message["tool_calls"]:
                    if "function" in tool_call:
                        function_arguments = tool_call["function"]["arguments"]
                        prompt += function_arguments
    elif call_type == "text_completion":
        prompt = data["prompt"]
    elif call_type == "embedding" or call_type == "moderation":
        if isinstance(data["input"], str):
            prompt = data["input"]
        elif isinstance(data["input"], list):
            for m in data["input"]:
                prompt += m
    elif call_type == "image_generation":
        prompt = data["prompt"]
    elif call_type == "audio_transcription":
        if "prompt" in data:
            prompt = data["prompt"]
    return prompt


def get_response_string(response_obj: ModelResponse) -> str:
    _choices: List[Union[Choices, StreamingChoices]] = response_obj.choices

    response_str = ""
    for choice in _choices:
        if isinstance(choice, Choices):
            if choice.message.content is not None:
                response_str += choice.message.content
        elif isinstance(choice, StreamingChoices):
            if choice.delta.content is not None:
                response_str += choice.delta.content

    return response_str


def get_api_key(llm_provider: str, dynamic_api_key: Optional[str]):
    api_key = dynamic_api_key or litellm.api_key
    # openai
    if llm_provider == "openai" or llm_provider == "text-completion-openai":
        api_key = api_key or litellm.openai_key or get_secret("OPENAI_API_KEY")
    # anthropic
    elif llm_provider == "anthropic":
        api_key = api_key or litellm.anthropic_key or get_secret("ANTHROPIC_API_KEY")
    # ai21
    elif llm_provider == "ai21":
        api_key = api_key or litellm.ai21_key or get_secret("AI211_API_KEY")
    # aleph_alpha
    elif llm_provider == "aleph_alpha":
        api_key = (
            api_key or litellm.aleph_alpha_key or get_secret("ALEPH_ALPHA_API_KEY")
        )
    # baseten
    elif llm_provider == "baseten":
        api_key = api_key or litellm.baseten_key or get_secret("BASETEN_API_KEY")
    # cohere
    elif llm_provider == "cohere" or llm_provider == "cohere_chat":
        api_key = api_key or litellm.cohere_key or get_secret("COHERE_API_KEY")
    # huggingface
    elif llm_provider == "huggingface":
        api_key = (
            api_key or litellm.huggingface_key or get_secret("HUGGINGFACE_API_KEY")
        )
    # nlp_cloud
    elif llm_provider == "nlp_cloud":
        api_key = api_key or litellm.nlp_cloud_key or get_secret("NLP_CLOUD_API_KEY")
    # replicate
    elif llm_provider == "replicate":
        api_key = api_key or litellm.replicate_key or get_secret("REPLICATE_API_KEY")
    # together_ai
    elif llm_provider == "together_ai":
        api_key = (
            api_key
            or litellm.togetherai_api_key
            or get_secret("TOGETHERAI_API_KEY")
            or get_secret("TOGETHER_AI_TOKEN")
        )
    return api_key


def get_utc_datetime():
    import datetime as dt
    from datetime import datetime

    if hasattr(dt, "UTC"):
        return datetime.now(dt.UTC)  # type: ignore
    else:
        return datetime.utcnow()  # type: ignore


def get_max_tokens(model: str) -> Optional[int]:
    """
    Get the maximum number of output tokens allowed for a given model.

    Parameters:
    model (str): The name of the model.

    Returns:
        int: The maximum number of tokens allowed for the given model.

    Raises:
        Exception: If the model is not mapped yet.

    Example:
        >>> get_max_tokens("gpt-4")
        8192
    """

    def _get_max_position_embeddings(model_name):
        # Construct the URL for the config.json file
        config_url = f"https://huggingface.co/{model_name}/raw/main/config.json"
        try:
            # Make the HTTP request to get the raw JSON file
            response = requests.get(config_url)
            response.raise_for_status()  # Raise an exception for bad responses (4xx or 5xx)

            # Parse the JSON response
            config_json = response.json()
            # Extract and return the max_position_embeddings
            max_position_embeddings = config_json.get("max_position_embeddings")
            if max_position_embeddings is not None:
                return max_position_embeddings
            else:
                return None
        except requests.exceptions.RequestException:
            return None

    try:
        if model in litellm.model_cost:
            if "max_output_tokens" in litellm.model_cost[model]:
                return litellm.model_cost[model]["max_output_tokens"]
            elif "max_tokens" in litellm.model_cost[model]:
                return litellm.model_cost[model]["max_tokens"]
        model, custom_llm_provider, _, _ = get_llm_provider(model=model)
        if custom_llm_provider == "huggingface":
            max_tokens = _get_max_position_embeddings(model_name=model)
            return max_tokens
        if model in litellm.model_cost:  # check if extracted model is in model_list
            if "max_output_tokens" in litellm.model_cost[model]:
                return litellm.model_cost[model]["max_output_tokens"]
            elif "max_tokens" in litellm.model_cost[model]:
                return litellm.model_cost[model]["max_tokens"]
        else:
            raise Exception()
        return None
    except Exception:
        raise Exception(
            f"Model {model} isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json"
        )


def _strip_stable_vertex_version(model_name) -> str:
    return re.sub(r"-\d+$", "", model_name)


def _strip_openai_finetune_model_name(model_name: str) -> str:
    """
    Strips the organization, custom suffix, and ID from an OpenAI fine-tuned model name.

    input: ft:gpt-3.5-turbo:my-org:custom_suffix:id
    output: ft:gpt-3.5-turbo

    Args:
    model_name (str): The full model name

    Returns:
    str: The stripped model name
    """
    return re.sub(r"(:[^:]+){3}$", "", model_name)


def _strip_model_name(model: str) -> str:
    strip_version = _strip_stable_vertex_version(model_name=model)
    strip_finetune = _strip_openai_finetune_model_name(model_name=strip_version)
    return strip_finetune


def _get_model_info_from_model_cost(key: str) -> dict:
    return litellm.model_cost[key]


def get_model_info(  # noqa: PLR0915
    model: str, custom_llm_provider: Optional[str] = None
) -> ModelInfo:
    """
    Get a dict for the maximum tokens (context window), input_cost_per_token, output_cost_per_token  for a given model.

    Parameters:
    - model (str): The name of the model.
    - custom_llm_provider (str | null): the provider used for the model. If provided, used to check if the litellm model info is for that provider.

    Returns:
        dict: A dictionary containing the following information:
            key: Required[str] # the key in litellm.model_cost which is returned
            max_tokens: Required[Optional[int]]
            max_input_tokens: Required[Optional[int]]
            max_output_tokens: Required[Optional[int]]
            input_cost_per_token: Required[float]
            input_cost_per_character: Optional[float]  # only for vertex ai models
            input_cost_per_token_above_128k_tokens: Optional[float]  # only for vertex ai models
            input_cost_per_character_above_128k_tokens: Optional[
                float
            ]  # only for vertex ai models
            input_cost_per_query: Optional[float] # only for rerank models
            input_cost_per_image: Optional[float]  # only for vertex ai models
            input_cost_per_audio_token: Optional[float]
            input_cost_per_audio_per_second: Optional[float]  # only for vertex ai models
            input_cost_per_video_per_second: Optional[float]  # only for vertex ai models
            output_cost_per_token: Required[float]
            output_cost_per_audio_token: Optional[float]
            output_cost_per_character: Optional[float]  # only for vertex ai models
            output_cost_per_token_above_128k_tokens: Optional[
                float
            ]  # only for vertex ai models
            output_cost_per_character_above_128k_tokens: Optional[
                float
            ]  # only for vertex ai models
            output_cost_per_image: Optional[float]
            output_vector_size: Optional[int]
            output_cost_per_video_per_second: Optional[float]  # only for vertex ai models
            output_cost_per_audio_per_second: Optional[float]  # only for vertex ai models
            litellm_provider: Required[str]
            mode: Required[
                Literal[
                    "completion", "embedding", "image_generation", "chat", "audio_transcription"
                ]
            ]
            supported_openai_params: Required[Optional[List[str]]]
            supports_system_messages: Optional[bool]
            supports_response_schema: Optional[bool]
            supports_vision: Optional[bool]
            supports_function_calling: Optional[bool]
            supports_prompt_caching: Optional[bool]
            supports_audio_input: Optional[bool]
            supports_audio_output: Optional[bool]
    Raises:
        Exception: If the model is not mapped yet.

    Example:
        >>> get_model_info("gpt-4")
        {
            "max_tokens": 8192,
            "input_cost_per_token": 0.00003,
            "output_cost_per_token": 0.00006,
            "litellm_provider": "openai",
            "mode": "chat",
            "supported_openai_params": ["temperature", "max_tokens", "top_p", "frequency_penalty", "presence_penalty"]
        }
    """
    supported_openai_params: Union[List[str], None] = []

    def _get_max_position_embeddings(model_name):
        # Construct the URL for the config.json file
        config_url = f"https://huggingface.co/{model_name}/raw/main/config.json"

        try:
            # Make the HTTP request to get the raw JSON file
            response = requests.get(config_url)
            response.raise_for_status()  # Raise an exception for bad responses (4xx or 5xx)

            # Parse the JSON response
            config_json = response.json()

            # Extract and return the max_position_embeddings
            max_position_embeddings = config_json.get("max_position_embeddings")

            if max_position_embeddings is not None:
                return max_position_embeddings
            else:
                return None
        except requests.exceptions.RequestException:
            return None

    try:
        azure_llms = litellm.azure_llms
        if model in azure_llms:
            model = azure_llms[model]
        if custom_llm_provider is not None and custom_llm_provider == "vertex_ai":
            if "meta/" + model in litellm.vertex_llama3_models:
                model = "meta/" + model
            elif model + "@latest" in litellm.vertex_mistral_models:
                model = model + "@latest"
            elif model + "@latest" in litellm.vertex_ai_ai21_models:
                model = model + "@latest"
        ##########################
        if custom_llm_provider is None:
            # Get custom_llm_provider
            try:
                split_model, custom_llm_provider, _, _ = get_llm_provider(model=model)
            except Exception:
                split_model = model
            combined_model_name = model
            stripped_model_name = _strip_model_name(model=model)
            combined_stripped_model_name = stripped_model_name
        else:
            split_model = model
            combined_model_name = "{}/{}".format(custom_llm_provider, model)
            stripped_model_name = _strip_model_name(model=model)
            combined_stripped_model_name = "{}/{}".format(
                custom_llm_provider, _strip_model_name(model=model)
            )
        #########################

        supported_openai_params = litellm.get_supported_openai_params(
            model=model, custom_llm_provider=custom_llm_provider
        )
        if custom_llm_provider == "huggingface":
            max_tokens = _get_max_position_embeddings(model_name=model)
            return ModelInfo(
                key=model,
                max_tokens=max_tokens,  # type: ignore
                max_input_tokens=None,
                max_output_tokens=None,
                input_cost_per_token=0,
                output_cost_per_token=0,
                litellm_provider="huggingface",
                mode="chat",
                supported_openai_params=supported_openai_params,
                supports_system_messages=None,
                supports_response_schema=None,
                supports_function_calling=None,
                supports_assistant_prefill=None,
                supports_prompt_caching=None,
            )
        elif custom_llm_provider == "ollama" or custom_llm_provider == "ollama_chat":
            return litellm.OllamaConfig().get_model_info(model)
        else:
            """
            Check if: (in order of specificity)
            1. 'custom_llm_provider/model' in litellm.model_cost. Checks "groq/llama3-8b-8192" if model="llama3-8b-8192" and custom_llm_provider="groq"
            2. 'model' in litellm.model_cost. Checks "gemini-1.5-pro-002" in  litellm.model_cost if model="gemini-1.5-pro-002" and custom_llm_provider=None
            3. 'combined_stripped_model_name' in litellm.model_cost. Checks if 'gemini/gemini-1.5-flash' in model map, if 'gemini/gemini-1.5-flash-001' given.
            4. 'stripped_model_name' in litellm.model_cost. Checks if 'ft:gpt-3.5-turbo' in model map, if 'ft:gpt-3.5-turbo:my-org:custom_suffix:id' given.
            5. 'split_model' in litellm.model_cost. Checks "llama3-8b-8192" in litellm.model_cost if model="groq/llama3-8b-8192"
            """
            _model_info: Optional[Dict[str, Any]] = None
            key: Optional[str] = None
            if combined_model_name in litellm.model_cost:
                key = combined_model_name
                _model_info = _get_model_info_from_model_cost(key=key)
                _model_info["supported_openai_params"] = supported_openai_params
                if (
                    "litellm_provider" in _model_info
                    and _model_info["litellm_provider"] != custom_llm_provider
                ):
                    if custom_llm_provider == "vertex_ai" and _model_info[
                        "litellm_provider"
                    ].startswith("vertex_ai"):
                        pass
                    else:
                        _model_info = None
            if _model_info is None and model in litellm.model_cost:
                key = model
                _model_info = _get_model_info_from_model_cost(key=key)
                _model_info["supported_openai_params"] = supported_openai_params
                if (
                    "litellm_provider" in _model_info
                    and _model_info["litellm_provider"] != custom_llm_provider
                ):
                    if custom_llm_provider == "vertex_ai" and _model_info[
                        "litellm_provider"
                    ].startswith("vertex_ai"):
                        pass
                    elif custom_llm_provider == "fireworks_ai" and _model_info[
                        "litellm_provider"
                    ].startswith("fireworks_ai"):
                        pass
                    else:
                        _model_info = None
            if (
                _model_info is None
                and combined_stripped_model_name in litellm.model_cost
            ):
                key = combined_stripped_model_name
                _model_info = _get_model_info_from_model_cost(key=key)
                _model_info["supported_openai_params"] = supported_openai_params
                if (
                    "litellm_provider" in _model_info
                    and _model_info["litellm_provider"] != custom_llm_provider
                ):
                    if custom_llm_provider == "vertex_ai" and _model_info[
                        "litellm_provider"
                    ].startswith("vertex_ai"):
                        pass
                    elif custom_llm_provider == "fireworks_ai" and _model_info[
                        "litellm_provider"
                    ].startswith("fireworks_ai"):
                        pass
                    else:
                        _model_info = None
            if _model_info is None and stripped_model_name in litellm.model_cost:
                key = stripped_model_name
                _model_info = _get_model_info_from_model_cost(key=key)
                _model_info["supported_openai_params"] = supported_openai_params
                if (
                    "litellm_provider" in _model_info
                    and _model_info["litellm_provider"] != custom_llm_provider
                ):
                    if custom_llm_provider == "vertex_ai" and _model_info[
                        "litellm_provider"
                    ].startswith("vertex_ai"):
                        pass
                    elif custom_llm_provider == "fireworks_ai" and _model_info[
                        "litellm_provider"
                    ].startswith("fireworks_ai"):
                        pass
                    else:
                        _model_info = None

            if _model_info is None and split_model in litellm.model_cost:
                key = split_model
                _model_info = _get_model_info_from_model_cost(key=key)
                _model_info["supported_openai_params"] = supported_openai_params
                if (
                    "litellm_provider" in _model_info
                    and _model_info["litellm_provider"] != custom_llm_provider
                ):
                    if custom_llm_provider == "vertex_ai" and _model_info[
                        "litellm_provider"
                    ].startswith("vertex_ai"):
                        pass
                    elif custom_llm_provider == "fireworks_ai" and _model_info[
                        "litellm_provider"
                    ].startswith("fireworks_ai"):
                        pass
                    else:
                        _model_info = None
            if _model_info is None or key is None:
                raise ValueError(
                    "This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json"
                )

            ## PROVIDER-SPECIFIC INFORMATION
            if custom_llm_provider == "predibase":
                _model_info["supports_response_schema"] = True

            _input_cost_per_token: Optional[float] = _model_info.get(
                "input_cost_per_token"
            )
            if _input_cost_per_token is None:
                # default value to 0, be noisy about this
                verbose_logger.debug(
                    "model={}, custom_llm_provider={} has no input_cost_per_token in model_cost_map. Defaulting to 0.".format(
                        model, custom_llm_provider
                    )
                )
                _input_cost_per_token = 0

            _output_cost_per_token: Optional[float] = _model_info.get(
                "output_cost_per_token"
            )
            if _output_cost_per_token is None:
                # default value to 0, be noisy about this
                verbose_logger.debug(
                    "model={}, custom_llm_provider={} has no output_cost_per_token in model_cost_map. Defaulting to 0.".format(
                        model, custom_llm_provider
                    )
                )
                _output_cost_per_token = 0

            return ModelInfo(
                key=key,
                max_tokens=_model_info.get("max_tokens", None),
                max_input_tokens=_model_info.get("max_input_tokens", None),
                max_output_tokens=_model_info.get("max_output_tokens", None),
                input_cost_per_token=_input_cost_per_token,
                cache_creation_input_token_cost=_model_info.get(
                    "cache_creation_input_token_cost", None
                ),
                cache_read_input_token_cost=_model_info.get(
                    "cache_read_input_token_cost", None
                ),
                input_cost_per_character=_model_info.get(
                    "input_cost_per_character", None
                ),
                input_cost_per_token_above_128k_tokens=_model_info.get(
                    "input_cost_per_token_above_128k_tokens", None
                ),
                input_cost_per_query=_model_info.get("input_cost_per_query", None),
                input_cost_per_second=_model_info.get("input_cost_per_second", None),
                input_cost_per_audio_token=_model_info.get(
                    "input_cost_per_audio_token", None
                ),
                output_cost_per_token=_output_cost_per_token,
                output_cost_per_audio_token=_model_info.get(
                    "output_cost_per_audio_token", None
                ),
                output_cost_per_character=_model_info.get(
                    "output_cost_per_character", None
                ),
                output_cost_per_token_above_128k_tokens=_model_info.get(
                    "output_cost_per_token_above_128k_tokens", None
                ),
                output_cost_per_character_above_128k_tokens=_model_info.get(
                    "output_cost_per_character_above_128k_tokens", None
                ),
                output_cost_per_second=_model_info.get("output_cost_per_second", None),
                output_vector_size=_model_info.get("output_vector_size", None),
                litellm_provider=_model_info.get(
                    "litellm_provider", custom_llm_provider
                ),
                mode=_model_info.get("mode"),  # type: ignore
                supported_openai_params=supported_openai_params,
                supports_system_messages=_model_info.get(
                    "supports_system_messages", None
                ),
                supports_response_schema=_model_info.get(
                    "supports_response_schema", None
                ),
                supports_vision=_model_info.get("supports_vision", False),
                supports_function_calling=_model_info.get(
                    "supports_function_calling", False
                ),
                supports_assistant_prefill=_model_info.get(
                    "supports_assistant_prefill", False
                ),
                supports_prompt_caching=_model_info.get(
                    "supports_prompt_caching", False
                ),
                supports_audio_input=_model_info.get("supports_audio_input", False),
                supports_audio_output=_model_info.get("supports_audio_output", False),
            )
    except Exception as e:
        if "OllamaError" in str(e):
            raise e
        raise Exception(
            "This model isn't mapped yet. model={}, custom_llm_provider={}. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json.".format(
                model, custom_llm_provider
            )
        )


def json_schema_type(python_type_name: str):
    """Converts standard python types to json schema types

    Parameters
    ----------
    python_type_name : str
        __name__ of type

    Returns
    -------
    str
        a standard JSON schema type, "string" if not recognized.
    """
    python_to_json_schema_types = {
        str.__name__: "string",
        int.__name__: "integer",
        float.__name__: "number",
        bool.__name__: "boolean",
        list.__name__: "array",
        dict.__name__: "object",
        "NoneType": "null",
    }

    return python_to_json_schema_types.get(python_type_name, "string")


def function_to_dict(input_function):  # noqa: C901
    """Using type hints and numpy-styled docstring,
    produce a dictionnary usable for OpenAI function calling

    Parameters
    ----------
    input_function : function
        A function with a numpy-style docstring

    Returns
    -------
    dictionnary
        A dictionnary to add to the list passed to `functions` parameter of `litellm.completion`
    """
    # Get function name and docstring
    try:
        import inspect
        from ast import literal_eval

        from numpydoc.docscrape import NumpyDocString
    except Exception as e:
        raise e

    name = input_function.__name__
    docstring = inspect.getdoc(input_function)
    numpydoc = NumpyDocString(docstring)
    description = "\n".join([s.strip() for s in numpydoc["Summary"]])

    # Get function parameters and their types from annotations and docstring
    parameters = {}
    required_params = []
    param_info = inspect.signature(input_function).parameters

    for param_name, param in param_info.items():
        if hasattr(param, "annotation"):
            param_type = json_schema_type(param.annotation.__name__)
        else:
            param_type = None
        param_description = None
        param_enum = None

        # Try to extract param description from docstring using numpydoc
        for param_data in numpydoc["Parameters"]:
            if param_data.name == param_name:
                if hasattr(param_data, "type"):
                    # replace type from docstring rather than annotation
                    param_type = param_data.type
                    if "optional" in param_type:
                        param_type = param_type.split(",")[0]
                    elif "{" in param_type:
                        # may represent a set of acceptable values
                        # translating as enum for function calling
                        try:
                            param_enum = str(list(literal_eval(param_type)))
                            param_type = "string"
                        except Exception:
                            pass
                    param_type = json_schema_type(param_type)
                param_description = "\n".join([s.strip() for s in param_data.desc])

        param_dict = {
            "type": param_type,
            "description": param_description,
            "enum": param_enum,
        }

        parameters[param_name] = dict(
            [(k, v) for k, v in param_dict.items() if isinstance(v, str)]
        )

        # Check if the parameter has no default value (i.e., it's required)
        if param.default == param.empty:
            required_params.append(param_name)

    # Create the dictionary
    result = {
        "name": name,
        "description": description,
        "parameters": {
            "type": "object",
            "properties": parameters,
        },
    }

    # Add "required" key if there are required parameters
    if required_params:
        result["parameters"]["required"] = required_params

    return result


def modify_url(original_url, new_path):
    url = httpx.URL(original_url)
    modified_url = url.copy_with(path=new_path)
    return str(modified_url)


def load_test_model(
    model: str,
    custom_llm_provider: str = "",
    api_base: str = "",
    prompt: str = "",
    num_calls: int = 0,
    force_timeout: int = 0,
):
    test_prompt = "Hey, how's it going"
    test_calls = 100
    if prompt:
        test_prompt = prompt
    if num_calls:
        test_calls = num_calls
    messages = [[{"role": "user", "content": test_prompt}] for _ in range(test_calls)]
    start_time = time.time()
    try:
        litellm.batch_completion(
            model=model,
            messages=messages,
            custom_llm_provider=custom_llm_provider,
            api_base=api_base,
            force_timeout=force_timeout,
        )
        end_time = time.time()
        response_time = end_time - start_time
        return {
            "total_response_time": response_time,
            "calls_made": 100,
            "status": "success",
            "exception": None,
        }
    except Exception as e:
        end_time = time.time()
        response_time = end_time - start_time
        return {
            "total_response_time": response_time,
            "calls_made": 100,
            "status": "failed",
            "exception": e,
        }


def get_provider_fields(custom_llm_provider: str) -> List[ProviderField]:
    """Return the fields required for each provider"""

    if custom_llm_provider == "databricks":
        return litellm.DatabricksConfig().get_required_params()

    elif custom_llm_provider == "ollama":
        return litellm.OllamaConfig().get_required_params()

    elif custom_llm_provider == "azure_ai":
        return litellm.AzureAIStudioConfig().get_required_params()

    else:
        return []


def create_proxy_transport_and_mounts():
    proxies = {
        key: None if url is None else Proxy(url=url)
        for key, url in get_environment_proxies().items()
    }

    sync_proxy_mounts = {}
    async_proxy_mounts = {}

    # Retrieve NO_PROXY environment variable
    no_proxy = os.getenv("NO_PROXY", None)
    no_proxy_urls = no_proxy.split(",") if no_proxy else []

    for key, proxy in proxies.items():
        if proxy is None:
            sync_proxy_mounts[key] = httpx.HTTPTransport()
            async_proxy_mounts[key] = httpx.AsyncHTTPTransport()
        else:
            sync_proxy_mounts[key] = httpx.HTTPTransport(proxy=proxy)
            async_proxy_mounts[key] = httpx.AsyncHTTPTransport(proxy=proxy)

    for url in no_proxy_urls:
        sync_proxy_mounts[url] = httpx.HTTPTransport()
        async_proxy_mounts[url] = httpx.AsyncHTTPTransport()

    return sync_proxy_mounts, async_proxy_mounts


def validate_environment(  # noqa: PLR0915
    model: Optional[str] = None,
    api_key: Optional[str] = None,
    api_base: Optional[str] = None,
) -> dict:
    """
    Checks if the environment variables are valid for the given model.

    Args:
        model (Optional[str]): The name of the model. Defaults to None.
        api_key (Optional[str]): If the user passed in an api key, of their own.

    Returns:
        dict: A dictionary containing the following keys:
            - keys_in_environment (bool): True if all the required keys are present in the environment, False otherwise.
            - missing_keys (List[str]): A list of missing keys in the environment.
    """
    keys_in_environment = False
    missing_keys: List[str] = []

    if model is None:
        return {
            "keys_in_environment": keys_in_environment,
            "missing_keys": missing_keys,
        }
    ## EXTRACT LLM PROVIDER - if model name provided
    try:
        _, custom_llm_provider, _, _ = get_llm_provider(model=model)
    except Exception:
        custom_llm_provider = None

    if custom_llm_provider:
        if custom_llm_provider == "openai":
            if "OPENAI_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("OPENAI_API_KEY")
        elif custom_llm_provider == "azure":
            if (
                "AZURE_API_BASE" in os.environ
                and "AZURE_API_VERSION" in os.environ
                and "AZURE_API_KEY" in os.environ
            ):
                keys_in_environment = True
            else:
                missing_keys.extend(
                    ["AZURE_API_BASE", "AZURE_API_VERSION", "AZURE_API_KEY"]
                )
        elif custom_llm_provider == "anthropic":
            if "ANTHROPIC_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("ANTHROPIC_API_KEY")
        elif custom_llm_provider == "cohere":
            if "COHERE_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("COHERE_API_KEY")
        elif custom_llm_provider == "replicate":
            if "REPLICATE_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("REPLICATE_API_KEY")
        elif custom_llm_provider == "openrouter":
            if "OPENROUTER_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("OPENROUTER_API_KEY")
        elif custom_llm_provider == "vertex_ai":
            if "VERTEXAI_PROJECT" in os.environ and "VERTEXAI_LOCATION" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.extend(["VERTEXAI_PROJECT", "VERTEXAI_LOCATION"])
        elif custom_llm_provider == "huggingface":
            if "HUGGINGFACE_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("HUGGINGFACE_API_KEY")
        elif custom_llm_provider == "ai21":
            if "AI21_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("AI21_API_KEY")
        elif custom_llm_provider == "together_ai":
            if "TOGETHERAI_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("TOGETHERAI_API_KEY")
        elif custom_llm_provider == "aleph_alpha":
            if "ALEPH_ALPHA_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("ALEPH_ALPHA_API_KEY")
        elif custom_llm_provider == "baseten":
            if "BASETEN_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("BASETEN_API_KEY")
        elif custom_llm_provider == "nlp_cloud":
            if "NLP_CLOUD_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("NLP_CLOUD_API_KEY")
        elif custom_llm_provider == "bedrock" or custom_llm_provider == "sagemaker":
            if (
                "AWS_ACCESS_KEY_ID" in os.environ
                and "AWS_SECRET_ACCESS_KEY" in os.environ
            ):
                keys_in_environment = True
            else:
                missing_keys.append("AWS_ACCESS_KEY_ID")
                missing_keys.append("AWS_SECRET_ACCESS_KEY")
        elif custom_llm_provider in ["ollama", "ollama_chat"]:
            if "OLLAMA_API_BASE" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("OLLAMA_API_BASE")
        elif custom_llm_provider == "anyscale":
            if "ANYSCALE_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("ANYSCALE_API_KEY")
        elif custom_llm_provider == "deepinfra":
            if "DEEPINFRA_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("DEEPINFRA_API_KEY")
        elif custom_llm_provider == "gemini":
            if "GEMINI_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("GEMINI_API_KEY")
        elif custom_llm_provider == "groq":
            if "GROQ_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("GROQ_API_KEY")
        elif custom_llm_provider == "nvidia_nim":
            if "NVIDIA_NIM_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("NVIDIA_NIM_API_KEY")
        elif custom_llm_provider == "cerebras":
            if "CEREBRAS_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("CEREBRAS_API_KEY")
        elif custom_llm_provider == "ai21_chat":
            if "AI21_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("AI21_API_KEY")
        elif custom_llm_provider == "volcengine":
            if "VOLCENGINE_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("VOLCENGINE_API_KEY")
        elif (
            custom_llm_provider == "codestral"
            or custom_llm_provider == "text-completion-codestral"
        ):
            if "CODESTRAL_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("GROQ_API_KEY")
        elif custom_llm_provider == "deepseek":
            if "DEEPSEEK_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("DEEPSEEK_API_KEY")
        elif custom_llm_provider == "mistral":
            if "MISTRAL_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("MISTRAL_API_KEY")
        elif custom_llm_provider == "palm":
            if "PALM_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("PALM_API_KEY")
        elif custom_llm_provider == "perplexity":
            if "PERPLEXITYAI_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("PERPLEXITYAI_API_KEY")
        elif custom_llm_provider == "voyage":
            if "VOYAGE_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("VOYAGE_API_KEY")
        elif custom_llm_provider == "fireworks_ai":
            if (
                "FIREWORKS_AI_API_KEY" in os.environ
                or "FIREWORKS_API_KEY" in os.environ
                or "FIREWORKSAI_API_KEY" in os.environ
                or "FIREWORKS_AI_TOKEN" in os.environ
            ):
                keys_in_environment = True
            else:
                missing_keys.append("FIREWORKS_AI_API_KEY")
        elif custom_llm_provider == "cloudflare":
            if "CLOUDFLARE_API_KEY" in os.environ and (
                "CLOUDFLARE_ACCOUNT_ID" in os.environ
                or "CLOUDFLARE_API_BASE" in os.environ
            ):
                keys_in_environment = True
            else:
                missing_keys.append("CLOUDFLARE_API_KEY")
                missing_keys.append("CLOUDFLARE_API_BASE")
    else:
        ## openai - chatcompletion + text completion
        if (
            model in litellm.open_ai_chat_completion_models
            or model in litellm.open_ai_text_completion_models
            or model in litellm.open_ai_embedding_models
            or model in litellm.openai_image_generation_models
        ):
            if "OPENAI_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("OPENAI_API_KEY")
        ## anthropic
        elif model in litellm.anthropic_models:
            if "ANTHROPIC_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("ANTHROPIC_API_KEY")
        ## cohere
        elif model in litellm.cohere_models:
            if "COHERE_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("COHERE_API_KEY")
        ## replicate
        elif model in litellm.replicate_models:
            if "REPLICATE_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("REPLICATE_API_KEY")
        ## openrouter
        elif model in litellm.openrouter_models:
            if "OPENROUTER_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("OPENROUTER_API_KEY")
        ## vertex - text + chat models
        elif (
            model in litellm.vertex_chat_models
            or model in litellm.vertex_text_models
            or model in litellm.models_by_provider["vertex_ai"]
        ):
            if "VERTEXAI_PROJECT" in os.environ and "VERTEXAI_LOCATION" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.extend(["VERTEXAI_PROJECT", "VERTEXAI_PROJECT"])
        ## huggingface
        elif model in litellm.huggingface_models:
            if "HUGGINGFACE_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("HUGGINGFACE_API_KEY")
        ## ai21
        elif model in litellm.ai21_models:
            if "AI21_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("AI21_API_KEY")
        ## together_ai
        elif model in litellm.together_ai_models:
            if "TOGETHERAI_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("TOGETHERAI_API_KEY")
        ## aleph_alpha
        elif model in litellm.aleph_alpha_models:
            if "ALEPH_ALPHA_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("ALEPH_ALPHA_API_KEY")
        ## baseten
        elif model in litellm.baseten_models:
            if "BASETEN_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("BASETEN_API_KEY")
        ## nlp_cloud
        elif model in litellm.nlp_cloud_models:
            if "NLP_CLOUD_API_KEY" in os.environ:
                keys_in_environment = True
            else:
                missing_keys.append("NLP_CLOUD_API_KEY")

    if api_key is not None:
        new_missing_keys = []
        for key in missing_keys:
            if "api_key" not in key.lower():
                new_missing_keys.append(key)
        missing_keys = new_missing_keys

    if api_base is not None:
        new_missing_keys = []
        for key in missing_keys:
            if "api_base" not in key.lower():
                new_missing_keys.append(key)
        missing_keys = new_missing_keys

    if len(missing_keys) == 0:  # no missing keys
        keys_in_environment = True

    return {"keys_in_environment": keys_in_environment, "missing_keys": missing_keys}


def acreate(*args, **kwargs):  ## Thin client to handle the acreate langchain call
    return litellm.acompletion(*args, **kwargs)


def prompt_token_calculator(model, messages):
    # use tiktoken or anthropic's tokenizer depending on the model
    text = " ".join(message["content"] for message in messages)
    num_tokens = 0
    if "claude" in model:
        try:
            import anthropic
        except Exception:
            Exception("Anthropic import failed please run `pip install anthropic`")
        from anthropic import AI_PROMPT, HUMAN_PROMPT, Anthropic

        anthropic_obj = Anthropic()
        num_tokens = anthropic_obj.count_tokens(text)
    else:
        num_tokens = len(encoding.encode(text))
    return num_tokens


def valid_model(model):
    try:
        # for a given model name, check if the user has the right permissions to access the model
        if (
            model in litellm.open_ai_chat_completion_models
            or model in litellm.open_ai_text_completion_models
        ):
            openai.models.retrieve(model)
        else:
            messages = [{"role": "user", "content": "Hello World"}]
            litellm.completion(model=model, messages=messages)
    except Exception:
        raise BadRequestError(message="", model=model, llm_provider="")


def check_valid_key(model: str, api_key: str):
    """
    Checks if a given API key is valid for a specific model by making a litellm.completion call with max_tokens=10

    Args:
        model (str): The name of the model to check the API key against.
        api_key (str): The API key to be checked.

    Returns:
        bool: True if the API key is valid for the model, False otherwise.
    """
    messages = [{"role": "user", "content": "Hey, how's it going?"}]
    try:
        litellm.completion(
            model=model, messages=messages, api_key=api_key, max_tokens=10
        )
        return True
    except AuthenticationError:
        return False
    except Exception:
        return False


def _should_retry(status_code: int):
    """
    Retries on 408, 409, 429 and 500 errors.

    Any client error in the 400-499 range that isn't explicitly handled (such as 400 Bad Request, 401 Unauthorized, 403 Forbidden, 404 Not Found, etc.) would not trigger a retry.

    Reimplementation of openai's should retry logic, since that one can't be imported.
    https://github.com/openai/openai-python/blob/af67cfab4210d8e497c05390ce14f39105c77519/src/openai/_base_client.py#L639
    """
    # If the server explicitly says whether or not to retry, obey.
    # Retry on request timeouts.
    if status_code == 408:
        return True

    # Retry on lock timeouts.
    if status_code == 409:
        return True

    # Retry on rate limits.
    if status_code == 429:
        return True

    # Retry internal errors.
    if status_code >= 500:
        return True

    return False


def type_to_response_format_param(
    response_format: Optional[Union[Type[BaseModel], dict]],
) -> Optional[dict]:
    """
    Re-implementation of openai's 'type_to_response_format_param' function

    Used for converting pydantic object to api schema.
    """
    if response_format is None:
        return None

    if isinstance(response_format, dict):
        return response_format

    # type checkers don't narrow the negation of a `TypeGuard` as it isn't
    # a safe default behaviour but we know that at this point the `response_format`
    # can only be a `type`
    if not _parsing._completions.is_basemodel_type(response_format):
        raise TypeError(f"Unsupported response_format type - {response_format}")

    return {
        "type": "json_schema",
        "json_schema": {
            "schema": _pydantic.to_strict_json_schema(response_format),
            "name": response_format.__name__,
            "strict": True,
        },
    }


def _get_retry_after_from_exception_header(
    response_headers: Optional[httpx.Headers] = None,
):
    """
    Reimplementation of openai's calculate retry after, since that one can't be imported.
    https://github.com/openai/openai-python/blob/af67cfab4210d8e497c05390ce14f39105c77519/src/openai/_base_client.py#L631
    """
    try:
        import email  # openai import

        # About the Retry-After header: https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Retry-After
        #
        # <http-date>". See https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Retry-After#syntax for
        # details.
        if response_headers is not None:
            retry_header = response_headers.get("retry-after")
            try:
                retry_after = int(retry_header)
            except Exception:
                retry_date_tuple = email.utils.parsedate_tz(retry_header)  # type: ignore
                if retry_date_tuple is None:
                    retry_after = -1
                else:
                    retry_date = email.utils.mktime_tz(retry_date_tuple)  # type: ignore
                    retry_after = int(retry_date - time.time())
        else:
            retry_after = -1

        return retry_after

    except Exception:
        retry_after = -1


def _calculate_retry_after(
    remaining_retries: int,
    max_retries: int,
    response_headers: Optional[httpx.Headers] = None,
    min_timeout: int = 0,
) -> Union[float, int]:
    retry_after = _get_retry_after_from_exception_header(response_headers)

    # If the API asks us to wait a certain amount of time (and it's a reasonable amount), just do what it says.
    if retry_after is not None and 0 < retry_after <= 60:
        return retry_after

    initial_retry_delay = 0.5
    max_retry_delay = 8.0
    nb_retries = max_retries - remaining_retries

    # Apply exponential backoff, but not more than the max.
    sleep_seconds = min(initial_retry_delay * pow(2.0, nb_retries), max_retry_delay)

    # Apply some jitter, plus-or-minus half a second.
    jitter = 1 - 0.25 * random.random()
    timeout = sleep_seconds * jitter
    return timeout if timeout >= min_timeout else min_timeout


# custom prompt helper function
def register_prompt_template(
    model: str,
    roles: dict,
    initial_prompt_value: str = "",
    final_prompt_value: str = "",
):
    """
    Register a prompt template to follow your custom format for a given model

    Args:
        model (str): The name of the model.
        roles (dict): A dictionary mapping roles to their respective prompt values.
        initial_prompt_value (str, optional): The initial prompt value. Defaults to "".
        final_prompt_value (str, optional): The final prompt value. Defaults to "".

    Returns:
        dict: The updated custom prompt dictionary.
    Example usage:
    ```
    import litellm
    litellm.register_prompt_template(
            model="llama-2",
        initial_prompt_value="You are a good assistant" # [OPTIONAL]
            roles={
            "system": {
                "pre_message": "[INST] <<SYS>>\n", # [OPTIONAL]
                "post_message": "\n<</SYS>>\n [/INST]\n" # [OPTIONAL]
            },
            "user": {
                "pre_message": "[INST] ", # [OPTIONAL]
                "post_message": " [/INST]" # [OPTIONAL]
            },
            "assistant": {
                "pre_message": "\n" # [OPTIONAL]
                "post_message": "\n" # [OPTIONAL]
            }
        }
        final_prompt_value="Now answer as best you can:" # [OPTIONAL]
    )
    ```
    """
    model = get_llm_provider(model=model)[0]
    litellm.custom_prompt_dict[model] = {
        "roles": roles,
        "initial_prompt_value": initial_prompt_value,
        "final_prompt_value": final_prompt_value,
    }
    return litellm.custom_prompt_dict


####### DEPRECATED ################


def get_all_keys(llm_provider=None):
    try:
        global last_fetched_at_keys
        # if user is using hosted product -> instantiate their env with their hosted api keys - refresh every 5 minutes
        print_verbose(f"Reaches get all keys, llm_provider: {llm_provider}")
        user_email = (
            os.getenv("LITELLM_EMAIL")
            or litellm.email
            or litellm.token
            or os.getenv("LITELLM_TOKEN")
        )
        if user_email:
            time_delta = 0
            if last_fetched_at_keys is not None:
                current_time = time.time()
                time_delta = current_time - last_fetched_at_keys
            if (
                time_delta > 300 or last_fetched_at_keys is None or llm_provider
            ):  # if the llm provider is passed in , assume this happening due to an AuthError for that provider
                # make the api call
                last_fetched_at = time.time()
                print_verbose(f"last_fetched_at: {last_fetched_at}")
                response = requests.post(
                    url="http://api.litellm.ai/get_all_keys",
                    headers={"content-type": "application/json"},
                    data=json.dumps({"user_email": user_email}),
                )
                print_verbose(f"get model key response: {response.text}")
                data = response.json()
                # update model list
                for key, value in data[
                    "model_keys"
                ].items():  # follows the LITELLM API KEY format - <UPPERCASE_PROVIDER_NAME>_API_KEY - e.g. HUGGINGFACE_API_KEY
                    os.environ[key] = value
                # set model alias map
                for model_alias, value in data["model_alias_map"].items():
                    litellm.model_alias_map[model_alias] = value
                return "it worked!"
            return None
        return None
    except Exception:
        print_verbose(
            f"[Non-Blocking Error] get_all_keys error - {traceback.format_exc()}"
        )
        pass


def get_model_list():
    global last_fetched_at, print_verbose
    try:
        # if user is using hosted product -> get their updated model list
        user_email = (
            os.getenv("LITELLM_EMAIL")
            or litellm.email
            or litellm.token
            or os.getenv("LITELLM_TOKEN")
        )
        if user_email:
            # make the api call
            last_fetched_at = time.time()
            print_verbose(f"last_fetched_at: {last_fetched_at}")
            response = requests.post(
                url="http://api.litellm.ai/get_model_list",
                headers={"content-type": "application/json"},
                data=json.dumps({"user_email": user_email}),
            )
            print_verbose(f"get_model_list response: {response.text}")
            data = response.json()
            # update model list
            model_list = data["model_list"]
            # # check if all model providers are in environment
            # model_providers = data["model_providers"]
            # missing_llm_provider = None
            # for item in model_providers:
            #     if f"{item.upper()}_API_KEY" not in os.environ:
            #         missing_llm_provider = item
            #         break
            # # update environment - if required
            # threading.Thread(target=get_all_keys, args=(missing_llm_provider)).start()
            return model_list
        return []  # return empty list by default
    except Exception:
        print_verbose(
            f"[Non-Blocking Error] get_model_list error - {traceback.format_exc()}"
        )


######## Streaming Class ############################
# wraps the completion stream to return the correct format for the model
# replicate/anthropic/cohere


def calculate_total_usage(chunks: List[ModelResponse]) -> Usage:
    """Assume most recent usage chunk has total usage uptil then."""
    prompt_tokens: int = 0
    completion_tokens: int = 0
    for chunk in chunks:
        if "usage" in chunk:
            if "prompt_tokens" in chunk["usage"]:
                prompt_tokens = chunk["usage"].get("prompt_tokens", 0) or 0
            if "completion_tokens" in chunk["usage"]:
                completion_tokens = chunk["usage"].get("completion_tokens", 0) or 0

    returned_usage_chunk = Usage(
        prompt_tokens=prompt_tokens,
        completion_tokens=completion_tokens,
        total_tokens=prompt_tokens + completion_tokens,
    )

    return returned_usage_chunk


class CustomStreamWrapper:
    def __init__(
        self,
        completion_stream,
        model,
        logging_obj: Any,
        custom_llm_provider: Optional[str] = None,
        stream_options=None,
        make_call: Optional[Callable] = None,
        _response_headers: Optional[dict] = None,
    ):
        self.model = model
        self.make_call = make_call
        self.custom_llm_provider = custom_llm_provider
        self.logging_obj: LiteLLMLoggingObject = logging_obj
        self.completion_stream = completion_stream
        self.sent_first_chunk = False
        self.sent_last_chunk = False
        self.system_fingerprint: Optional[str] = None
        self.received_finish_reason: Optional[str] = None
        self.special_tokens = [
            "<|assistant|>",
            "<|system|>",
            "<|user|>",
            "<s>",
            "</s>",
            "<|im_end|>",
            "<|im_start|>",
        ]
        self.holding_chunk = ""
        self.complete_response = ""
        self.response_uptil_now = ""
        _model_info = (
            self.logging_obj.model_call_details.get("litellm_params", {}).get(
                "model_info", {}
            )
            or {}
        )
        self._hidden_params = {
            "model_id": (_model_info.get("id", None)),
        }  # returned as x-litellm-model-id response header in proxy

        self._hidden_params["additional_headers"] = process_response_headers(
            _response_headers or {}
        )  # GUARANTEE OPENAI HEADERS IN RESPONSE

        self._response_headers = _response_headers
        self.response_id = None
        self.logging_loop = None
        self.rules = Rules()
        self.stream_options = stream_options or getattr(
            logging_obj, "stream_options", None
        )
        self.messages = getattr(logging_obj, "messages", None)
        self.sent_stream_usage = False
        self.tool_call = False
        self.chunks: List = (
            []
        )  # keep track of the returned chunks - used for calculating the input/output tokens for stream options
        self.is_function_call = self.check_is_function_call(logging_obj=logging_obj)

    def __iter__(self):
        return self

    def __aiter__(self):
        return self

    def check_is_function_call(self, logging_obj) -> bool:
        if hasattr(logging_obj, "optional_params") and isinstance(
            logging_obj.optional_params, dict
        ):
            if (
                "litellm_param_is_function_call" in logging_obj.optional_params
                and logging_obj.optional_params["litellm_param_is_function_call"]
                is True
            ):
                return True

        return False

    def process_chunk(self, chunk: str):
        """
        NLP Cloud streaming returns the entire response, for each chunk. Process this, to only return the delta.
        """
        try:
            chunk = chunk.strip()
            self.complete_response = self.complete_response.strip()

            if chunk.startswith(self.complete_response):
                # Remove last_sent_chunk only if it appears at the start of the new chunk
                chunk = chunk[len(self.complete_response) :]

            self.complete_response += chunk
            return chunk
        except Exception as e:
            raise e

    def safety_checker(self) -> None:
        """
        Fixes - https://github.com/BerriAI/litellm/issues/5158

        if the model enters a loop and starts repeating the same chunk again, break out of loop and raise an internalservererror - allows for retries.

        Raises - InternalServerError, if LLM enters infinite loop while streaming
        """
        if len(self.chunks) >= litellm.REPEATED_STREAMING_CHUNK_LIMIT:
            # Get the last n chunks
            last_chunks = self.chunks[-litellm.REPEATED_STREAMING_CHUNK_LIMIT :]

            # Extract the relevant content from the chunks
            last_contents = [chunk.choices[0].delta.content for chunk in last_chunks]

            # Check if all extracted contents are identical
            if all(content == last_contents[0] for content in last_contents):
                if (
                    last_contents[0] is not None
                    and isinstance(last_contents[0], str)
                    and len(last_contents[0]) > 2
                ):  # ignore empty content - https://github.com/BerriAI/litellm/issues/5158#issuecomment-2287156946
                    # All last n chunks are identical
                    raise litellm.InternalServerError(
                        message="The model is repeating the same chunk = {}.".format(
                            last_contents[0]
                        ),
                        model="",
                        llm_provider="",
                    )

    def check_special_tokens(self, chunk: str, finish_reason: Optional[str]):
        """
        Output parse <s> / </s> special tokens for sagemaker + hf streaming.
        """
        hold = False
        if (
            self.custom_llm_provider != "huggingface"
            and self.custom_llm_provider != "sagemaker"
        ):
            return hold, chunk

        if finish_reason:
            for token in self.special_tokens:
                if token in chunk:
                    chunk = chunk.replace(token, "")
            return hold, chunk

        if self.sent_first_chunk is True:
            return hold, chunk

        curr_chunk = self.holding_chunk + chunk
        curr_chunk = curr_chunk.strip()

        for token in self.special_tokens:
            if len(curr_chunk) < len(token) and curr_chunk in token:
                hold = True
                self.holding_chunk = curr_chunk
            elif len(curr_chunk) >= len(token):
                if token in curr_chunk:
                    self.holding_chunk = curr_chunk.replace(token, "")
                    hold = True
            else:
                pass

        if hold is False:  # reset
            self.holding_chunk = ""
        return hold, curr_chunk

    def handle_anthropic_text_chunk(self, chunk):
        """
        For old anthropic models - claude-1, claude-2.

        Claude-3 is handled from within Anthropic.py VIA ModelResponseIterator()
        """
        str_line = chunk
        if isinstance(chunk, bytes):  # Handle binary data
            str_line = chunk.decode("utf-8")  # Convert bytes to string
        text = ""
        is_finished = False
        finish_reason = None
        if str_line.startswith("data:"):
            data_json = json.loads(str_line[5:])
            type_chunk = data_json.get("type", None)
            if type_chunk == "completion":
                text = data_json.get("completion")
                finish_reason = data_json.get("stop_reason")
                if finish_reason is not None:
                    is_finished = True
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        elif "error" in str_line:
            raise ValueError(f"Unable to parse response. Original response: {str_line}")
        else:
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }

    def handle_vertexai_anthropic_chunk(self, chunk):
        """
        - MessageStartEvent(message=Message(id='msg_01LeRRgvX4gwkX3ryBVgtuYZ', content=[], model='claude-3-sonnet-20240229', role='assistant', stop_reason=None, stop_sequence=None, type='message', usage=Usage(input_tokens=8, output_tokens=1)), type='message_start'); custom_llm_provider: vertex_ai
        - ContentBlockStartEvent(content_block=ContentBlock(text='', type='text'), index=0, type='content_block_start'); custom_llm_provider: vertex_ai
        - ContentBlockDeltaEvent(delta=TextDelta(text='Hello', type='text_delta'), index=0, type='content_block_delta'); custom_llm_provider: vertex_ai
        """
        text = ""
        prompt_tokens = None
        completion_tokens = None
        is_finished = False
        finish_reason = None
        type_chunk = getattr(chunk, "type", None)
        if type_chunk == "message_start":
            message = getattr(chunk, "message", None)
            text = ""  # lets us return a chunk with usage to user
            _usage = getattr(message, "usage", None)
            if _usage is not None:
                prompt_tokens = getattr(_usage, "input_tokens", None)
                completion_tokens = getattr(_usage, "output_tokens", None)
        elif type_chunk == "content_block_delta":
            """
            Anthropic content chunk
            chunk = {'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': 'Hello'}}
            """
            delta = getattr(chunk, "delta", None)
            if delta is not None:
                text = getattr(delta, "text", "")
            else:
                text = ""
        elif type_chunk == "message_delta":
            """
            Anthropic
            chunk = {'type': 'message_delta', 'delta': {'stop_reason': 'max_tokens', 'stop_sequence': None}, 'usage': {'output_tokens': 10}}
            """
            # TODO - get usage from this chunk, set in response
            delta = getattr(chunk, "delta", None)
            if delta is not None:
                finish_reason = getattr(delta, "stop_reason", "stop")
                is_finished = True
            _usage = getattr(chunk, "usage", None)
            if _usage is not None:
                prompt_tokens = getattr(_usage, "input_tokens", None)
                completion_tokens = getattr(_usage, "output_tokens", None)

        return {
            "text": text,
            "is_finished": is_finished,
            "finish_reason": finish_reason,
            "prompt_tokens": prompt_tokens,
            "completion_tokens": completion_tokens,
        }

    def handle_predibase_chunk(self, chunk):
        try:
            if not isinstance(chunk, str):
                chunk = chunk.decode(
                    "utf-8"
                )  # DO NOT REMOVE this: This is required for HF inference API + Streaming
            text = ""
            is_finished = False
            finish_reason = ""
            print_verbose(f"chunk: {chunk}")
            if chunk.startswith("data:"):
                data_json = json.loads(chunk[5:])
                print_verbose(f"data json: {data_json}")
                if "token" in data_json and "text" in data_json["token"]:
                    text = data_json["token"]["text"]
                if data_json.get("details", False) and data_json["details"].get(
                    "finish_reason", False
                ):
                    is_finished = True
                    finish_reason = data_json["details"]["finish_reason"]
                elif data_json.get(
                    "generated_text", False
                ):  # if full generated text exists, then stream is complete
                    text = ""  # don't return the final bos token
                    is_finished = True
                    finish_reason = "stop"
                elif data_json.get("error", False):
                    raise Exception(data_json.get("error"))
                return {
                    "text": text,
                    "is_finished": is_finished,
                    "finish_reason": finish_reason,
                }
            elif "error" in chunk:
                raise ValueError(chunk)
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        except Exception as e:
            raise e

    def handle_huggingface_chunk(self, chunk):
        try:
            if not isinstance(chunk, str):
                chunk = chunk.decode(
                    "utf-8"
                )  # DO NOT REMOVE this: This is required for HF inference API + Streaming
            text = ""
            is_finished = False
            finish_reason = ""
            print_verbose(f"chunk: {chunk}")
            if chunk.startswith("data:"):
                data_json = json.loads(chunk[5:])
                print_verbose(f"data json: {data_json}")
                if "token" in data_json and "text" in data_json["token"]:
                    text = data_json["token"]["text"]
                if data_json.get("details", False) and data_json["details"].get(
                    "finish_reason", False
                ):
                    is_finished = True
                    finish_reason = data_json["details"]["finish_reason"]
                elif data_json.get(
                    "generated_text", False
                ):  # if full generated text exists, then stream is complete
                    text = ""  # don't return the final bos token
                    is_finished = True
                    finish_reason = "stop"
                elif data_json.get("error", False):
                    raise Exception(data_json.get("error"))
                return {
                    "text": text,
                    "is_finished": is_finished,
                    "finish_reason": finish_reason,
                }
            elif "error" in chunk:
                raise ValueError(chunk)
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        except Exception as e:
            raise e

    def handle_ai21_chunk(self, chunk):  # fake streaming
        chunk = chunk.decode("utf-8")
        data_json = json.loads(chunk)
        try:
            text = data_json["completions"][0]["data"]["text"]
            is_finished = True
            finish_reason = "stop"
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        except Exception:
            raise ValueError(f"Unable to parse response. Original response: {chunk}")

    def handle_maritalk_chunk(self, chunk):  # fake streaming
        chunk = chunk.decode("utf-8")
        data_json = json.loads(chunk)
        try:
            text = data_json["answer"]
            is_finished = True
            finish_reason = "stop"
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        except Exception:
            raise ValueError(f"Unable to parse response. Original response: {chunk}")

    def handle_nlp_cloud_chunk(self, chunk):
        text = ""
        is_finished = False
        finish_reason = ""
        try:
            if "dolphin" in self.model:
                chunk = self.process_chunk(chunk=chunk)
            else:
                data_json = json.loads(chunk)
                chunk = data_json["generated_text"]
            text = chunk
            if "[DONE]" in text:
                text = text.replace("[DONE]", "")
                is_finished = True
                finish_reason = "stop"
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        except Exception:
            raise ValueError(f"Unable to parse response. Original response: {chunk}")

    def handle_aleph_alpha_chunk(self, chunk):
        chunk = chunk.decode("utf-8")
        data_json = json.loads(chunk)
        try:
            text = data_json["completions"][0]["completion"]
            is_finished = True
            finish_reason = "stop"
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        except Exception:
            raise ValueError(f"Unable to parse response. Original response: {chunk}")

    def handle_cohere_chunk(self, chunk):
        chunk = chunk.decode("utf-8")
        data_json = json.loads(chunk)
        try:
            text = ""
            is_finished = False
            finish_reason = ""
            index: Optional[int] = None
            if "index" in data_json:
                index = data_json.get("index")
            if "text" in data_json:
                text = data_json["text"]
            elif "is_finished" in data_json:
                is_finished = data_json["is_finished"]
                finish_reason = data_json["finish_reason"]
            else:
                raise Exception(data_json)
            return {
                "index": index,
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        except Exception:
            raise ValueError(f"Unable to parse response. Original response: {chunk}")

    def handle_cohere_chat_chunk(self, chunk):
        chunk = chunk.decode("utf-8")
        data_json = json.loads(chunk)
        print_verbose(f"chunk: {chunk}")
        try:
            text = ""
            is_finished = False
            finish_reason = ""
            if "text" in data_json:
                text = data_json["text"]
            elif "is_finished" in data_json and data_json["is_finished"] is True:
                is_finished = data_json["is_finished"]
                finish_reason = data_json["finish_reason"]
            else:
                return
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        except Exception:
            raise ValueError(f"Unable to parse response. Original response: {chunk}")

    def handle_azure_chunk(self, chunk):
        is_finished = False
        finish_reason = ""
        text = ""
        print_verbose(f"chunk: {chunk}")
        if "data: [DONE]" in chunk:
            text = ""
            is_finished = True
            finish_reason = "stop"
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        elif chunk.startswith("data:"):
            data_json = json.loads(chunk[5:])  # chunk.startswith("data:"):
            try:
                if len(data_json["choices"]) > 0:
                    delta = data_json["choices"][0]["delta"]
                    text = "" if delta is None else delta.get("content", "")
                    if data_json["choices"][0].get("finish_reason", None):
                        is_finished = True
                        finish_reason = data_json["choices"][0]["finish_reason"]
                print_verbose(
                    f"text: {text}; is_finished: {is_finished}; finish_reason: {finish_reason}"
                )
                return {
                    "text": text,
                    "is_finished": is_finished,
                    "finish_reason": finish_reason,
                }
            except Exception:
                raise ValueError(
                    f"Unable to parse response. Original response: {chunk}"
                )
        elif "error" in chunk:
            raise ValueError(f"Unable to parse response. Original response: {chunk}")
        else:
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }

    def handle_replicate_chunk(self, chunk):
        try:
            text = ""
            is_finished = False
            finish_reason = ""
            if "output" in chunk:
                text = chunk["output"]
            if "status" in chunk:
                if chunk["status"] == "succeeded":
                    is_finished = True
                    finish_reason = "stop"
            elif chunk.get("error", None):
                raise Exception(chunk["error"])
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }
        except Exception:
            raise ValueError(f"Unable to parse response. Original response: {chunk}")

    def handle_openai_chat_completion_chunk(self, chunk):
        try:
            print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n")
            str_line = chunk
            text = ""
            is_finished = False
            finish_reason = None
            logprobs = None
            usage = None
            if str_line and str_line.choices and len(str_line.choices) > 0:
                if (
                    str_line.choices[0].delta is not None
                    and str_line.choices[0].delta.content is not None
                ):
                    text = str_line.choices[0].delta.content
                else:  # function/tool calling chunk - when content is None. in this case we just return the original chunk from openai
                    pass
                if str_line.choices[0].finish_reason:
                    is_finished = True
                    finish_reason = str_line.choices[0].finish_reason

                # checking for logprobs
                if (
                    hasattr(str_line.choices[0], "logprobs")
                    and str_line.choices[0].logprobs is not None
                ):
                    logprobs = str_line.choices[0].logprobs
                else:
                    logprobs = None

            usage = getattr(str_line, "usage", None)

            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
                "logprobs": logprobs,
                "original_chunk": str_line,
                "usage": usage,
            }
        except Exception as e:
            raise e

    def handle_azure_text_completion_chunk(self, chunk):
        try:
            print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n")
            text = ""
            is_finished = False
            finish_reason = None
            choices = getattr(chunk, "choices", [])
            if len(choices) > 0:
                text = choices[0].text
                if choices[0].finish_reason is not None:
                    is_finished = True
                    finish_reason = choices[0].finish_reason
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
            }

        except Exception as e:
            raise e

    def handle_openai_text_completion_chunk(self, chunk):
        try:
            print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n")
            text = ""
            is_finished = False
            finish_reason = None
            usage = None
            choices = getattr(chunk, "choices", [])
            if len(choices) > 0:
                text = choices[0].text
                if choices[0].finish_reason is not None:
                    is_finished = True
                    finish_reason = choices[0].finish_reason
            usage = getattr(chunk, "usage", None)
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
                "usage": usage,
            }

        except Exception as e:
            raise e

    def handle_baseten_chunk(self, chunk):
        try:
            chunk = chunk.decode("utf-8")
            if len(chunk) > 0:
                if chunk.startswith("data:"):
                    data_json = json.loads(chunk[5:])
                    if "token" in data_json and "text" in data_json["token"]:
                        return data_json["token"]["text"]
                    else:
                        return ""
                data_json = json.loads(chunk)
                if "model_output" in data_json:
                    if (
                        isinstance(data_json["model_output"], dict)
                        and "data" in data_json["model_output"]
                        and isinstance(data_json["model_output"]["data"], list)
                    ):
                        return data_json["model_output"]["data"][0]
                    elif isinstance(data_json["model_output"], str):
                        return data_json["model_output"]
                    elif "completion" in data_json and isinstance(
                        data_json["completion"], str
                    ):
                        return data_json["completion"]
                    else:
                        raise ValueError(
                            f"Unable to parse response. Original response: {chunk}"
                        )
                else:
                    return ""
            else:
                return ""
        except Exception as e:
            verbose_logger.exception(
                "litellm.CustomStreamWrapper.handle_baseten_chunk(): Exception occured - {}".format(
                    str(e)
                )
            )
            return ""

    def handle_cloudlfare_stream(self, chunk):
        try:
            print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n")
            chunk = chunk.decode("utf-8")
            str_line = chunk
            text = ""
            is_finished = False
            finish_reason = None

            if "[DONE]" in chunk:
                return {"text": text, "is_finished": True, "finish_reason": "stop"}
            elif str_line.startswith("data:"):
                data_json = json.loads(str_line[5:])
                print_verbose(f"delta content: {data_json}")
                text = data_json["response"]
                return {
                    "text": text,
                    "is_finished": is_finished,
                    "finish_reason": finish_reason,
                }
            else:
                return {
                    "text": text,
                    "is_finished": is_finished,
                    "finish_reason": finish_reason,
                }

        except Exception as e:
            raise e

    def handle_ollama_stream(self, chunk):
        try:
            if isinstance(chunk, dict):
                json_chunk = chunk
            else:
                json_chunk = json.loads(chunk)
            if "error" in json_chunk:
                raise Exception(f"Ollama Error - {json_chunk}")

            text = ""
            is_finished = False
            finish_reason = None
            if json_chunk["done"] is True:
                text = ""
                is_finished = True
                finish_reason = "stop"
                return {
                    "text": text,
                    "is_finished": is_finished,
                    "finish_reason": finish_reason,
                }
            elif json_chunk["response"]:
                print_verbose(f"delta content: {json_chunk}")
                text = json_chunk["response"]
                return {
                    "text": text,
                    "is_finished": is_finished,
                    "finish_reason": finish_reason,
                }
            else:
                raise Exception(f"Ollama Error - {json_chunk}")
        except Exception as e:
            raise e

    def handle_ollama_chat_stream(self, chunk):
        # for ollama_chat/ provider
        try:
            if isinstance(chunk, dict):
                json_chunk = chunk
            else:
                json_chunk = json.loads(chunk)
            if "error" in json_chunk:
                raise Exception(f"Ollama Error - {json_chunk}")

            text = ""
            is_finished = False
            finish_reason = None
            if json_chunk["done"] is True:
                text = ""
                is_finished = True
                finish_reason = "stop"
                return {
                    "text": text,
                    "is_finished": is_finished,
                    "finish_reason": finish_reason,
                }
            elif "message" in json_chunk:
                print_verbose(f"delta content: {json_chunk}")
                text = json_chunk["message"]["content"]
                return {
                    "text": text,
                    "is_finished": is_finished,
                    "finish_reason": finish_reason,
                }
            else:
                raise Exception(f"Ollama Error - {json_chunk}")
        except Exception as e:
            raise e

    def handle_watsonx_stream(self, chunk):
        try:
            if isinstance(chunk, dict):
                parsed_response = chunk
            elif isinstance(chunk, (str, bytes)):
                if isinstance(chunk, bytes):
                    chunk = chunk.decode("utf-8")
                if "generated_text" in chunk:
                    response = chunk.replace("data: ", "").strip()
                    parsed_response = json.loads(response)
                else:
                    return {
                        "text": "",
                        "is_finished": False,
                        "prompt_tokens": 0,
                        "completion_tokens": 0,
                    }
            else:
                print_verbose(f"chunk: {chunk} (Type: {type(chunk)})")
                raise ValueError(
                    f"Unable to parse response. Original response: {chunk}"
                )
            results = parsed_response.get("results", [])
            if len(results) > 0:
                text = results[0].get("generated_text", "")
                finish_reason = results[0].get("stop_reason")
                is_finished = finish_reason != "not_finished"
                return {
                    "text": text,
                    "is_finished": is_finished,
                    "finish_reason": finish_reason,
                    "prompt_tokens": results[0].get("input_token_count", 0),
                    "completion_tokens": results[0].get("generated_token_count", 0),
                }
            return {"text": "", "is_finished": False}
        except Exception as e:
            raise e

    def handle_triton_stream(self, chunk):
        try:
            if isinstance(chunk, dict):
                parsed_response = chunk
            elif isinstance(chunk, (str, bytes)):
                if isinstance(chunk, bytes):
                    chunk = chunk.decode("utf-8")
                if "text_output" in chunk:
                    response = chunk.replace("data: ", "").strip()
                    parsed_response = json.loads(response)
                else:
                    return {
                        "text": "",
                        "is_finished": False,
                        "prompt_tokens": 0,
                        "completion_tokens": 0,
                    }
            else:
                print_verbose(f"chunk: {chunk} (Type: {type(chunk)})")
                raise ValueError(
                    f"Unable to parse response. Original response: {chunk}"
                )
            text = parsed_response.get("text_output", "")
            finish_reason = parsed_response.get("stop_reason")
            is_finished = parsed_response.get("is_finished", False)
            return {
                "text": text,
                "is_finished": is_finished,
                "finish_reason": finish_reason,
                "prompt_tokens": parsed_response.get("input_token_count", 0),
                "completion_tokens": parsed_response.get("generated_token_count", 0),
            }
            return {"text": "", "is_finished": False}
        except Exception as e:
            raise e

    def handle_clarifai_completion_chunk(self, chunk):
        try:
            if isinstance(chunk, dict):
                parsed_response = chunk
            elif isinstance(chunk, (str, bytes)):
                if isinstance(chunk, bytes):
                    parsed_response = chunk.decode("utf-8")
                else:
                    parsed_response = chunk
            else:
                raise ValueError("Unable to parse streaming chunk")
            if isinstance(parsed_response, dict):
                data_json = parsed_response
            else:
                data_json = json.loads(parsed_response)
            text = (
                data_json.get("outputs", "")[0]
                .get("data", "")
                .get("text", "")
                .get("raw", "")
            )
            len(
                encoding.encode(
                    data_json.get("outputs", "")[0]
                    .get("input", "")
                    .get("data", "")
                    .get("text", "")
                    .get("raw", "")
                )
            )
            len(encoding.encode(text))
            return {
                "text": text,
                "is_finished": True,
            }
        except Exception as e:
            verbose_logger.exception(
                "litellm.CustomStreamWrapper.handle_clarifai_chunk(): Exception occured - {}".format(
                    str(e)
                )
            )
            return ""

    def model_response_creator(
        self, chunk: Optional[dict] = None, hidden_params: Optional[dict] = None
    ):
        _model = self.model
        _received_llm_provider = self.custom_llm_provider
        _logging_obj_llm_provider = self.logging_obj.model_call_details.get("custom_llm_provider", None)  # type: ignore
        if (
            _received_llm_provider == "openai"
            and _received_llm_provider != _logging_obj_llm_provider
        ):
            _model = "{}/{}".format(_logging_obj_llm_provider, _model)
        if chunk is None:
            chunk = {}
        else:
            # pop model keyword
            chunk.pop("model", None)

        model_response = ModelResponse(
            stream=True, model=_model, stream_options=self.stream_options, **chunk
        )
        if self.response_id is not None:
            model_response.id = self.response_id
        else:
            self.response_id = model_response.id  # type: ignore
        if self.system_fingerprint is not None:
            model_response.system_fingerprint = self.system_fingerprint
        if hidden_params is not None:
            model_response._hidden_params = hidden_params
        model_response._hidden_params["custom_llm_provider"] = _logging_obj_llm_provider
        model_response._hidden_params["created_at"] = time.time()
        model_response._hidden_params = {
            **model_response._hidden_params,
            **self._hidden_params,
        }

        if (
            len(model_response.choices) > 0
            and getattr(model_response.choices[0], "delta") is not None
        ):
            # do nothing, if object instantiated
            pass
        else:
            model_response.choices = [StreamingChoices(finish_reason=None)]
        return model_response

    def is_delta_empty(self, delta: Delta) -> bool:
        is_empty = True
        if delta.content is not None:
            is_empty = False
        elif delta.tool_calls is not None:
            is_empty = False
        elif delta.function_call is not None:
            is_empty = False
        return is_empty

    def chunk_creator(self, chunk):  # type: ignore  # noqa: PLR0915
        model_response = self.model_response_creator()
        response_obj = {}
        try:
            # return this for all models
            completion_obj = {"content": ""}
            from litellm.litellm_core_utils.streaming_utils import (
                generic_chunk_has_all_required_fields,
            )
            from litellm.types.utils import GenericStreamingChunk as GChunk

            if (
                isinstance(chunk, dict)
                and generic_chunk_has_all_required_fields(
                    chunk=chunk
                )  # check if chunk is a generic streaming chunk
            ) or (
                self.custom_llm_provider
                and (
                    self.custom_llm_provider == "anthropic"
                    or self.custom_llm_provider in litellm._custom_providers
                )
            ):

                if self.received_finish_reason is not None:
                    if "provider_specific_fields" not in chunk:
                        raise StopIteration
                anthropic_response_obj: GChunk = chunk
                completion_obj["content"] = anthropic_response_obj["text"]
                if anthropic_response_obj["is_finished"]:
                    self.received_finish_reason = anthropic_response_obj[
                        "finish_reason"
                    ]

                if anthropic_response_obj["usage"] is not None:
                    model_response.usage = litellm.Usage(
                        **anthropic_response_obj["usage"]
                    )

                if (
                    "tool_use" in anthropic_response_obj
                    and anthropic_response_obj["tool_use"] is not None
                ):
                    completion_obj["tool_calls"] = [anthropic_response_obj["tool_use"]]

                if (
                    "provider_specific_fields" in anthropic_response_obj
                    and anthropic_response_obj["provider_specific_fields"] is not None
                ):
                    for key, value in anthropic_response_obj[
                        "provider_specific_fields"
                    ].items():
                        setattr(model_response, key, value)
                response_obj = anthropic_response_obj
            elif (
                self.custom_llm_provider
                and self.custom_llm_provider == "anthropic_text"
            ):
                response_obj = self.handle_anthropic_text_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider and self.custom_llm_provider == "clarifai":
                response_obj = self.handle_clarifai_completion_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.model == "replicate" or self.custom_llm_provider == "replicate":
                response_obj = self.handle_replicate_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider and self.custom_llm_provider == "huggingface":
                response_obj = self.handle_huggingface_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider and self.custom_llm_provider == "predibase":
                response_obj = self.handle_predibase_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif (
                self.custom_llm_provider and self.custom_llm_provider == "baseten"
            ):  # baseten doesn't provide streaming
                completion_obj["content"] = self.handle_baseten_chunk(chunk)
            elif (
                self.custom_llm_provider and self.custom_llm_provider == "ai21"
            ):  # ai21 doesn't provide streaming
                response_obj = self.handle_ai21_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider and self.custom_llm_provider == "maritalk":
                response_obj = self.handle_maritalk_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider and self.custom_llm_provider == "vllm":
                completion_obj["content"] = chunk[0].outputs[0].text
            elif (
                self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha"
            ):  # aleph alpha doesn't provide streaming
                response_obj = self.handle_aleph_alpha_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider == "nlp_cloud":
                try:
                    response_obj = self.handle_nlp_cloud_chunk(chunk)
                    completion_obj["content"] = response_obj["text"]
                    if response_obj["is_finished"]:
                        self.received_finish_reason = response_obj["finish_reason"]
                except Exception as e:
                    if self.received_finish_reason:
                        raise e
                    else:
                        if self.sent_first_chunk is False:
                            raise Exception("An unknown error occurred with the stream")
                        self.received_finish_reason = "stop"
            elif self.custom_llm_provider and (self.custom_llm_provider == "vertex_ai"):
                import proto  # type: ignore

                if self.model.startswith("claude-3"):
                    response_obj = self.handle_vertexai_anthropic_chunk(chunk=chunk)
                    if response_obj is None:
                        return
                    completion_obj["content"] = response_obj["text"]
                    setattr(model_response, "usage", Usage())
                    if response_obj.get("prompt_tokens", None) is not None:
                        model_response.usage.prompt_tokens = response_obj[
                            "prompt_tokens"
                        ]
                    if response_obj.get("completion_tokens", None) is not None:
                        model_response.usage.completion_tokens = response_obj[
                            "completion_tokens"
                        ]
                    if hasattr(model_response.usage, "prompt_tokens"):
                        model_response.usage.total_tokens = (
                            getattr(model_response.usage, "total_tokens", 0)
                            + model_response.usage.prompt_tokens
                        )
                    if hasattr(model_response.usage, "completion_tokens"):
                        model_response.usage.total_tokens = (
                            getattr(model_response.usage, "total_tokens", 0)
                            + model_response.usage.completion_tokens
                        )

                    if response_obj["is_finished"]:
                        self.received_finish_reason = response_obj["finish_reason"]
                elif hasattr(chunk, "candidates") is True:
                    try:
                        try:
                            completion_obj["content"] = chunk.text
                        except Exception as e:
                            if "Part has no text." in str(e):
                                ## check for function calling
                                function_call = (
                                    chunk.candidates[0].content.parts[0].function_call
                                )

                                args_dict = {}

                                # Check if it's a RepeatedComposite instance
                                for key, val in function_call.args.items():
                                    if isinstance(
                                        val,
                                        proto.marshal.collections.repeated.RepeatedComposite,
                                    ):
                                        # If so, convert to list
                                        args_dict[key] = [v for v in val]
                                    else:
                                        args_dict[key] = val

                                try:
                                    args_str = json.dumps(args_dict)
                                except Exception as e:
                                    raise e
                                _delta_obj = litellm.utils.Delta(
                                    content=None,
                                    tool_calls=[
                                        {
                                            "id": f"call_{str(uuid.uuid4())}",
                                            "function": {
                                                "arguments": args_str,
                                                "name": function_call.name,
                                            },
                                            "type": "function",
                                        }
                                    ],
                                )
                                _streaming_response = StreamingChoices(delta=_delta_obj)
                                _model_response = ModelResponse(stream=True)
                                _model_response.choices = [_streaming_response]
                                response_obj = {"original_chunk": _model_response}
                            else:
                                raise e
                        if (
                            hasattr(chunk.candidates[0], "finish_reason")
                            and chunk.candidates[0].finish_reason.name
                            != "FINISH_REASON_UNSPECIFIED"
                        ):  # every non-final chunk in vertex ai has this
                            self.received_finish_reason = chunk.candidates[
                                0
                            ].finish_reason.name
                    except Exception:
                        if chunk.candidates[0].finish_reason.name == "SAFETY":
                            raise Exception(
                                f"The response was blocked by VertexAI. {str(chunk)}"
                            )
                else:
                    completion_obj["content"] = str(chunk)
            elif self.custom_llm_provider == "cohere":
                response_obj = self.handle_cohere_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider == "cohere_chat":
                response_obj = self.handle_cohere_chat_chunk(chunk)
                if response_obj is None:
                    return
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]

            elif self.custom_llm_provider == "petals":
                if len(self.completion_stream) == 0:
                    if self.received_finish_reason is not None:
                        raise StopIteration
                    else:
                        self.received_finish_reason = "stop"
                chunk_size = 30
                new_chunk = self.completion_stream[:chunk_size]
                completion_obj["content"] = new_chunk
                self.completion_stream = self.completion_stream[chunk_size:]
            elif self.custom_llm_provider == "palm":
                # fake streaming
                response_obj = {}
                if len(self.completion_stream) == 0:
                    if self.received_finish_reason is not None:
                        raise StopIteration
                    else:
                        self.received_finish_reason = "stop"
                chunk_size = 30
                new_chunk = self.completion_stream[:chunk_size]
                completion_obj["content"] = new_chunk
                self.completion_stream = self.completion_stream[chunk_size:]
            elif self.custom_llm_provider == "ollama":
                response_obj = self.handle_ollama_stream(chunk)
                completion_obj["content"] = response_obj["text"]
                print_verbose(f"completion obj content: {completion_obj['content']}")
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider == "ollama_chat":
                response_obj = self.handle_ollama_chat_stream(chunk)
                completion_obj["content"] = response_obj["text"]
                print_verbose(f"completion obj content: {completion_obj['content']}")
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider == "cloudflare":
                response_obj = self.handle_cloudlfare_stream(chunk)
                completion_obj["content"] = response_obj["text"]
                print_verbose(f"completion obj content: {completion_obj['content']}")
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider == "watsonx":
                response_obj = self.handle_watsonx_stream(chunk)
                completion_obj["content"] = response_obj["text"]
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider == "triton":
                response_obj = self.handle_triton_stream(chunk)
                completion_obj["content"] = response_obj["text"]
                print_verbose(f"completion obj content: {completion_obj['content']}")
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider == "text-completion-openai":
                response_obj = self.handle_openai_text_completion_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                print_verbose(f"completion obj content: {completion_obj['content']}")
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
                if response_obj["usage"] is not None:
                    model_response.usage = litellm.Usage(
                        prompt_tokens=response_obj["usage"].prompt_tokens,
                        completion_tokens=response_obj["usage"].completion_tokens,
                        total_tokens=response_obj["usage"].total_tokens,
                    )
            elif self.custom_llm_provider == "text-completion-codestral":
                response_obj = litellm.MistralTextCompletionConfig()._chunk_parser(
                    chunk
                )
                completion_obj["content"] = response_obj["text"]
                print_verbose(f"completion obj content: {completion_obj['content']}")
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
                if "usage" in response_obj is not None:
                    model_response.usage = litellm.Usage(
                        prompt_tokens=response_obj["usage"].prompt_tokens,
                        completion_tokens=response_obj["usage"].completion_tokens,
                        total_tokens=response_obj["usage"].total_tokens,
                    )
            elif self.custom_llm_provider == "azure_text":
                response_obj = self.handle_azure_text_completion_chunk(chunk)
                completion_obj["content"] = response_obj["text"]
                print_verbose(f"completion obj content: {completion_obj['content']}")
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            elif self.custom_llm_provider == "cached_response":
                response_obj = {
                    "text": chunk.choices[0].delta.content,
                    "is_finished": True,
                    "finish_reason": chunk.choices[0].finish_reason,
                    "original_chunk": chunk,
                    "tool_calls": (
                        chunk.choices[0].delta.tool_calls
                        if hasattr(chunk.choices[0].delta, "tool_calls")
                        else None
                    ),
                }

                completion_obj["content"] = response_obj["text"]
                if response_obj["tool_calls"] is not None:
                    completion_obj["tool_calls"] = response_obj["tool_calls"]
                print_verbose(f"completion obj content: {completion_obj['content']}")
                if hasattr(chunk, "id"):
                    model_response.id = chunk.id
                    self.response_id = chunk.id
                if hasattr(chunk, "system_fingerprint"):
                    self.system_fingerprint = chunk.system_fingerprint
                if response_obj["is_finished"]:
                    self.received_finish_reason = response_obj["finish_reason"]
            else:  # openai / azure chat model
                if self.custom_llm_provider == "azure":
                    if hasattr(chunk, "model"):
                        # for azure, we need to pass the model from the orignal chunk
                        self.model = chunk.model
                response_obj = self.handle_openai_chat_completion_chunk(chunk)
                if response_obj is None:
                    return
                completion_obj["content"] = response_obj["text"]
                print_verbose(f"completion obj content: {completion_obj['content']}")
                if response_obj["is_finished"]:
                    if response_obj["finish_reason"] == "error":
                        raise Exception(
                            "{} raised a streaming error - finish_reason: error, no content string given. Received Chunk={}".format(
                                self.custom_llm_provider, response_obj
                            )
                        )
                    self.received_finish_reason = response_obj["finish_reason"]
                if response_obj.get("original_chunk", None) is not None:
                    if hasattr(response_obj["original_chunk"], "id"):
                        model_response.id = response_obj["original_chunk"].id
                        self.response_id = model_response.id
                    if hasattr(response_obj["original_chunk"], "system_fingerprint"):
                        model_response.system_fingerprint = response_obj[
                            "original_chunk"
                        ].system_fingerprint
                        self.system_fingerprint = response_obj[
                            "original_chunk"
                        ].system_fingerprint
                if response_obj["logprobs"] is not None:
                    model_response.choices[0].logprobs = response_obj["logprobs"]

                if response_obj["usage"] is not None:
                    if isinstance(response_obj["usage"], dict):
                        model_response.usage = litellm.Usage(
                            prompt_tokens=response_obj["usage"].get(
                                "prompt_tokens", None
                            )
                            or None,
                            completion_tokens=response_obj["usage"].get(
                                "completion_tokens", None
                            )
                            or None,
                            total_tokens=response_obj["usage"].get("total_tokens", None)
                            or None,
                        )
                    elif isinstance(response_obj["usage"], BaseModel):
                        model_response.usage = litellm.Usage(
                            **response_obj["usage"].model_dump()
                        )

            model_response.model = self.model
            print_verbose(
                f"model_response finish reason 3: {self.received_finish_reason}; response_obj={response_obj}"
            )
            ## FUNCTION CALL PARSING
            if (
                response_obj is not None
                and response_obj.get("original_chunk", None) is not None
            ):  # function / tool calling branch - only set for openai/azure compatible endpoints
                # enter this branch when no content has been passed in response
                original_chunk = response_obj.get("original_chunk", None)
                model_response.id = original_chunk.id
                self.response_id = original_chunk.id
                if original_chunk.choices and len(original_chunk.choices) > 0:
                    delta = original_chunk.choices[0].delta
                    if delta is not None and (
                        delta.function_call is not None or delta.tool_calls is not None
                    ):
                        try:
                            model_response.system_fingerprint = (
                                original_chunk.system_fingerprint
                            )
                            ## AZURE - check if arguments is not None
                            if (
                                original_chunk.choices[0].delta.function_call
                                is not None
                            ):
                                if (
                                    getattr(
                                        original_chunk.choices[0].delta.function_call,
                                        "arguments",
                                    )
                                    is None
                                ):
                                    original_chunk.choices[
                                        0
                                    ].delta.function_call.arguments = ""
                            elif original_chunk.choices[0].delta.tool_calls is not None:
                                if isinstance(
                                    original_chunk.choices[0].delta.tool_calls, list
                                ):
                                    for t in original_chunk.choices[0].delta.tool_calls:
                                        if hasattr(t, "functions") and hasattr(
                                            t.functions, "arguments"
                                        ):
                                            if (
                                                getattr(
                                                    t.function,
                                                    "arguments",
                                                )
                                                is None
                                            ):
                                                t.function.arguments = ""
                            _json_delta = delta.model_dump()
                            print_verbose(f"_json_delta: {_json_delta}")
                            if "role" not in _json_delta or _json_delta["role"] is None:
                                _json_delta["role"] = (
                                    "assistant"  # mistral's api returns role as None
                                )
                            if "tool_calls" in _json_delta and isinstance(
                                _json_delta["tool_calls"], list
                            ):
                                for tool in _json_delta["tool_calls"]:
                                    if (
                                        isinstance(tool, dict)
                                        and "function" in tool
                                        and isinstance(tool["function"], dict)
                                        and ("type" not in tool or tool["type"] is None)
                                    ):
                                        # if function returned but type set to None - mistral's api returns type: None
                                        tool["type"] = "function"
                            model_response.choices[0].delta = Delta(**_json_delta)
                        except Exception as e:
                            verbose_logger.exception(
                                "litellm.CustomStreamWrapper.chunk_creator(): Exception occured - {}".format(
                                    str(e)
                                )
                            )
                            model_response.choices[0].delta = Delta()
                    elif (
                        delta is not None and getattr(delta, "audio", None) is not None
                    ):
                        model_response.choices[0].delta.audio = delta.audio
                    else:
                        try:
                            delta = (
                                dict()
                                if original_chunk.choices[0].delta is None
                                else dict(original_chunk.choices[0].delta)
                            )
                            print_verbose(f"original delta: {delta}")
                            model_response.choices[0].delta = Delta(**delta)
                            print_verbose(
                                f"new delta: {model_response.choices[0].delta}"
                            )
                        except Exception:
                            model_response.choices[0].delta = Delta()
                else:
                    if (
                        self.stream_options is not None
                        and self.stream_options["include_usage"] is True
                    ):
                        return model_response
                    return
            print_verbose(
                f"model_response.choices[0].delta: {model_response.choices[0].delta}; completion_obj: {completion_obj}"
            )
            print_verbose(f"self.sent_first_chunk: {self.sent_first_chunk}")

            ## CHECK FOR TOOL USE
            if "tool_calls" in completion_obj and len(completion_obj["tool_calls"]) > 0:
                if self.is_function_call is True:  # user passed in 'functions' param
                    completion_obj["function_call"] = completion_obj["tool_calls"][0][
                        "function"
                    ]
                    completion_obj["tool_calls"] = None

                self.tool_call = True

            ## RETURN ARG
            if (
                "content" in completion_obj
                and (
                    isinstance(completion_obj["content"], str)
                    and len(completion_obj["content"]) > 0
                )
                or (
                    "tool_calls" in completion_obj
                    and completion_obj["tool_calls"] is not None
                    and len(completion_obj["tool_calls"]) > 0
                )
                or (
                    "function_call" in completion_obj
                    and completion_obj["function_call"] is not None
                )
            ):  # cannot set content of an OpenAI Object to be an empty string
                self.safety_checker()
                hold, model_response_str = self.check_special_tokens(
                    chunk=completion_obj["content"],
                    finish_reason=model_response.choices[0].finish_reason,
                )  # filter out bos/eos tokens from openai-compatible hf endpoints
                print_verbose(
                    f"hold - {hold}, model_response_str - {model_response_str}"
                )
                if hold is False:
                    ## check if openai/azure chunk
                    original_chunk = response_obj.get("original_chunk", None)
                    if original_chunk:
                        model_response.id = original_chunk.id
                        self.response_id = original_chunk.id
                        if len(original_chunk.choices) > 0:
                            choices = []
                            for idx, choice in enumerate(original_chunk.choices):
                                try:
                                    if isinstance(choice, BaseModel):
                                        try:
                                            choice_json = choice.model_dump()
                                        except Exception:
                                            choice_json = choice.dict()
                                        choice_json.pop(
                                            "finish_reason", None
                                        )  # for mistral etc. which return a value in their last chunk (not-openai compatible).
                                        print_verbose(f"choice_json: {choice_json}")
                                        choices.append(StreamingChoices(**choice_json))
                                except Exception:
                                    choices.append(StreamingChoices())
                            print_verbose(f"choices in streaming: {choices}")
                            model_response.choices = choices
                        else:
                            return
                        model_response.system_fingerprint = (
                            original_chunk.system_fingerprint
                        )
                        model_response.citations = getattr(
                            original_chunk, "citations", None
                        )
                        print_verbose(f"self.sent_first_chunk: {self.sent_first_chunk}")
                        if self.sent_first_chunk is False:
                            model_response.choices[0].delta["role"] = "assistant"
                            self.sent_first_chunk = True
                        elif self.sent_first_chunk is True and hasattr(
                            model_response.choices[0].delta, "role"
                        ):
                            _initial_delta = model_response.choices[
                                0
                            ].delta.model_dump()
                            _initial_delta.pop("role", None)
                            model_response.choices[0].delta = Delta(**_initial_delta)
                        print_verbose(
                            f"model_response.choices[0].delta: {model_response.choices[0].delta}"
                        )
                    else:
                        ## else
                        completion_obj["content"] = model_response_str
                        if self.sent_first_chunk is False:
                            completion_obj["role"] = "assistant"
                            self.sent_first_chunk = True

                        model_response.choices[0].delta = Delta(**completion_obj)
                        if completion_obj.get("index") is not None:
                            model_response.choices[0].index = completion_obj.get(
                                "index"
                            )
                    print_verbose(f"returning model_response: {model_response}")
                    return model_response
                else:
                    return
            elif self.received_finish_reason is not None:
                if self.sent_last_chunk is True:
                    # Bedrock returns the guardrail trace in the last chunk - we want to return this here
                    if (
                        self.custom_llm_provider == "bedrock"
                        and "trace" in model_response
                    ):
                        return model_response

                    # Default - return StopIteration
                    raise StopIteration
                # flush any remaining holding chunk
                if len(self.holding_chunk) > 0:
                    if model_response.choices[0].delta.content is None:
                        model_response.choices[0].delta.content = self.holding_chunk
                    else:
                        model_response.choices[0].delta.content = (
                            self.holding_chunk + model_response.choices[0].delta.content
                        )
                    self.holding_chunk = ""
                # if delta is None
                _is_delta_empty = self.is_delta_empty(
                    delta=model_response.choices[0].delta
                )

                if _is_delta_empty:
                    # get any function call arguments
                    model_response.choices[0].finish_reason = map_finish_reason(
                        finish_reason=self.received_finish_reason
                    )  # ensure consistent output to openai

                    self.sent_last_chunk = True

                return model_response
            elif (
                model_response.choices[0].delta.tool_calls is not None
                or model_response.choices[0].delta.function_call is not None
            ):
                if self.sent_first_chunk is False:
                    model_response.choices[0].delta["role"] = "assistant"
                    self.sent_first_chunk = True
                return model_response
            elif (
                len(model_response.choices) > 0
                and hasattr(model_response.choices[0].delta, "audio")
                and model_response.choices[0].delta.audio is not None
            ):
                return model_response
            else:
                if hasattr(model_response, "usage"):
                    self.chunks.append(model_response)
                return
        except StopIteration:
            raise StopIteration
        except Exception as e:
            traceback.format_exc()
            e.message = str(e)
            raise exception_type(
                model=self.model,
                custom_llm_provider=self.custom_llm_provider,
                original_exception=e,
            )

    def set_logging_event_loop(self, loop):
        """
        import litellm, asyncio

        loop = asyncio.get_event_loop() # 👈 gets the current event loop

        response = litellm.completion(.., stream=True)

        response.set_logging_event_loop(loop=loop) # 👈 enables async_success callbacks for sync logging

        for chunk in response:
            ...
        """
        self.logging_loop = loop

    def run_success_logging_and_cache_storage(self, processed_chunk, cache_hit: bool):
        """
        Runs success logging in a thread and adds the response to the cache
        """
        if litellm.disable_streaming_logging is True:
            """
            [NOT RECOMMENDED]
            Set this via `litellm.disable_streaming_logging = True`.

            Disables streaming logging.
            """
            return
        ## ASYNC LOGGING
        # Create an event loop for the new thread
        if self.logging_loop is not None:
            future = asyncio.run_coroutine_threadsafe(
                self.logging_obj.async_success_handler(
                    processed_chunk, None, None, cache_hit
                ),
                loop=self.logging_loop,
            )
            future.result()
        else:
            asyncio.run(
                self.logging_obj.async_success_handler(
                    processed_chunk, None, None, cache_hit
                )
            )
        ## SYNC LOGGING
        self.logging_obj.success_handler(processed_chunk, None, None, cache_hit)

        ## Sync store in cache
        if self.logging_obj._llm_caching_handler is not None:
            self.logging_obj._llm_caching_handler._sync_add_streaming_response_to_cache(
                processed_chunk
            )

    def finish_reason_handler(self):
        model_response = self.model_response_creator()
        if self.received_finish_reason is not None:
            model_response.choices[0].finish_reason = map_finish_reason(
                finish_reason=self.received_finish_reason
            )
        else:
            model_response.choices[0].finish_reason = "stop"

        ## if tool use
        if (
            model_response.choices[0].finish_reason == "stop" and self.tool_call
        ):  # don't overwrite for other - potential error finish reasons
            model_response.choices[0].finish_reason = "tool_calls"
        return model_response

    def __next__(self):  # noqa: PLR0915
        cache_hit = False
        if (
            self.custom_llm_provider is not None
            and self.custom_llm_provider == "cached_response"
        ):
            cache_hit = True
        try:
            if self.completion_stream is None:
                self.fetch_sync_stream()
            while True:
                if (
                    isinstance(self.completion_stream, str)
                    or isinstance(self.completion_stream, bytes)
                    or isinstance(self.completion_stream, ModelResponse)
                ):
                    chunk = self.completion_stream
                else:
                    chunk = next(self.completion_stream)
                if chunk is not None and chunk != b"":
                    print_verbose(
                        f"PROCESSED CHUNK PRE CHUNK CREATOR: {chunk}; custom_llm_provider: {self.custom_llm_provider}"
                    )
                    response: Optional[ModelResponse] = self.chunk_creator(chunk=chunk)
                    print_verbose(f"PROCESSED CHUNK POST CHUNK CREATOR: {response}")

                    if response is None:
                        continue
                    ## LOGGING
                    threading.Thread(
                        target=self.run_success_logging_and_cache_storage,
                        args=(response, cache_hit),
                    ).start()  # log response
                    choice = response.choices[0]
                    if isinstance(choice, StreamingChoices):
                        self.response_uptil_now += choice.delta.get("content", "") or ""
                    else:
                        self.response_uptil_now += ""
                    self.rules.post_call_rules(
                        input=self.response_uptil_now, model=self.model
                    )
                    # HANDLE STREAM OPTIONS
                    self.chunks.append(response)
                    if hasattr(
                        response, "usage"
                    ):  # remove usage from chunk, only send on final chunk
                        # Convert the object to a dictionary
                        obj_dict = response.dict()

                        # Remove an attribute (e.g., 'attr2')
                        if "usage" in obj_dict:
                            del obj_dict["usage"]

                        # Create a new object without the removed attribute
                        response = self.model_response_creator(
                            chunk=obj_dict, hidden_params=response._hidden_params
                        )
                    # add usage as hidden param
                    if self.sent_last_chunk is True and self.stream_options is None:
                        usage = calculate_total_usage(chunks=self.chunks)
                        response._hidden_params["usage"] = usage
                    # RETURN RESULT
                    return response

        except StopIteration:
            if self.sent_last_chunk is True:
                if (
                    self.sent_stream_usage is False
                    and self.stream_options is not None
                    and self.stream_options.get("include_usage", False) is True
                ):
                    # send the final chunk with stream options
                    complete_streaming_response = litellm.stream_chunk_builder(
                        chunks=self.chunks, messages=self.messages
                    )
                    response = self.model_response_creator()
                    if complete_streaming_response is not None:
                        setattr(
                            response,
                            "usage",
                            getattr(complete_streaming_response, "usage"),
                        )
                    ## LOGGING
                    threading.Thread(
                        target=self.logging_obj.success_handler,
                        args=(response, None, None, cache_hit),
                    ).start()  # log response
                    self.sent_stream_usage = True
                    return response
                raise  # Re-raise StopIteration
            else:
                self.sent_last_chunk = True
                processed_chunk = self.finish_reason_handler()
                if self.stream_options is None:  # add usage as hidden param
                    usage = calculate_total_usage(chunks=self.chunks)
                    processed_chunk._hidden_params["usage"] = usage
                ## LOGGING
                threading.Thread(
                    target=self.run_success_logging_and_cache_storage,
                    args=(processed_chunk, cache_hit),
                ).start()  # log response
                return processed_chunk
        except Exception as e:
            traceback_exception = traceback.format_exc()
            # LOG FAILURE - handle streaming failure logging in the _next_ object, remove `handle_failure` once it's deprecated
            threading.Thread(
                target=self.logging_obj.failure_handler, args=(e, traceback_exception)
            ).start()
            if isinstance(e, OpenAIError):
                raise e
            else:
                raise exception_type(
                    model=self.model,
                    original_exception=e,
                    custom_llm_provider=self.custom_llm_provider,
                )

    def fetch_sync_stream(self):
        if self.completion_stream is None and self.make_call is not None:
            # Call make_call to get the completion stream
            self.completion_stream = self.make_call(client=litellm.module_level_client)
            self._stream_iter = self.completion_stream.__iter__()

        return self.completion_stream

    async def fetch_stream(self):
        if self.completion_stream is None and self.make_call is not None:
            # Call make_call to get the completion stream
            self.completion_stream = await self.make_call(
                client=litellm.module_level_aclient
            )
            self._stream_iter = self.completion_stream.__aiter__()

        return self.completion_stream

    async def __anext__(self):  # noqa: PLR0915
        cache_hit = False
        if (
            self.custom_llm_provider is not None
            and self.custom_llm_provider == "cached_response"
        ):
            cache_hit = True
        try:
            if self.completion_stream is None:
                await self.fetch_stream()

            if (
                self.custom_llm_provider == "openai"
                or self.custom_llm_provider == "azure"
                or self.custom_llm_provider == "custom_openai"
                or self.custom_llm_provider == "text-completion-openai"
                or self.custom_llm_provider == "text-completion-codestral"
                or self.custom_llm_provider == "azure_text"
                or self.custom_llm_provider == "anthropic"
                or self.custom_llm_provider == "anthropic_text"
                or self.custom_llm_provider == "huggingface"
                or self.custom_llm_provider == "ollama"
                or self.custom_llm_provider == "ollama_chat"
                or self.custom_llm_provider == "vertex_ai"
                or self.custom_llm_provider == "vertex_ai_beta"
                or self.custom_llm_provider == "sagemaker"
                or self.custom_llm_provider == "sagemaker_chat"
                or self.custom_llm_provider == "gemini"
                or self.custom_llm_provider == "replicate"
                or self.custom_llm_provider == "cached_response"
                or self.custom_llm_provider == "predibase"
                or self.custom_llm_provider == "databricks"
                or self.custom_llm_provider == "bedrock"
                or self.custom_llm_provider == "triton"
                or self.custom_llm_provider == "watsonx"
                or self.custom_llm_provider in litellm.openai_compatible_endpoints
                or self.custom_llm_provider in litellm._custom_providers
            ):
                async for chunk in self.completion_stream:
                    print_verbose(f"value of async chunk: {chunk}")
                    if chunk == "None" or chunk is None:
                        raise Exception
                    elif (
                        self.custom_llm_provider == "gemini"
                        and hasattr(chunk, "parts")
                        and len(chunk.parts) == 0
                    ):
                        continue
                    # chunk_creator() does logging/stream chunk building. We need to let it know its being called in_async_func, so we don't double add chunks.
                    # __anext__ also calls async_success_handler, which does logging
                    print_verbose(f"PROCESSED ASYNC CHUNK PRE CHUNK CREATOR: {chunk}")

                    processed_chunk: Optional[ModelResponse] = self.chunk_creator(
                        chunk=chunk
                    )
                    print_verbose(
                        f"PROCESSED ASYNC CHUNK POST CHUNK CREATOR: {processed_chunk}"
                    )
                    if processed_chunk is None:
                        continue
                    ## LOGGING
                    ## LOGGING
                    threading.Thread(
                        target=self.logging_obj.success_handler,
                        args=(processed_chunk, None, None, cache_hit),
                    ).start()  # log response
                    asyncio.create_task(
                        self.logging_obj.async_success_handler(
                            processed_chunk, cache_hit=cache_hit
                        )
                    )

                    if self.logging_obj._llm_caching_handler is not None:
                        asyncio.create_task(
                            self.logging_obj._llm_caching_handler._add_streaming_response_to_cache(
                                processed_chunk=processed_chunk,
                            )
                        )

                    choice = processed_chunk.choices[0]
                    if isinstance(choice, StreamingChoices):
                        self.response_uptil_now += choice.delta.get("content", "") or ""
                    else:
                        self.response_uptil_now += ""
                    self.rules.post_call_rules(
                        input=self.response_uptil_now, model=self.model
                    )
                    self.chunks.append(processed_chunk)
                    if hasattr(
                        processed_chunk, "usage"
                    ):  # remove usage from chunk, only send on final chunk
                        # Convert the object to a dictionary
                        obj_dict = processed_chunk.dict()

                        # Remove an attribute (e.g., 'attr2')
                        if "usage" in obj_dict:
                            del obj_dict["usage"]

                        # Create a new object without the removed attribute
                        processed_chunk = self.model_response_creator(chunk=obj_dict)
                    print_verbose(f"final returned processed chunk: {processed_chunk}")
                    return processed_chunk
                raise StopAsyncIteration
            else:  # temporary patch for non-aiohttp async calls
                # example - boto3 bedrock llms
                while True:
                    if isinstance(self.completion_stream, str) or isinstance(
                        self.completion_stream, bytes
                    ):
                        chunk = self.completion_stream
                    else:
                        chunk = next(self.completion_stream)
                    if chunk is not None and chunk != b"":
                        print_verbose(f"PROCESSED CHUNK PRE CHUNK CREATOR: {chunk}")
                        processed_chunk: Optional[ModelResponse] = self.chunk_creator(
                            chunk=chunk
                        )
                        print_verbose(
                            f"PROCESSED CHUNK POST CHUNK CREATOR: {processed_chunk}"
                        )
                        if processed_chunk is None:
                            continue
                        ## LOGGING
                        threading.Thread(
                            target=self.logging_obj.success_handler,
                            args=(processed_chunk, None, None, cache_hit),
                        ).start()  # log processed_chunk
                        asyncio.create_task(
                            self.logging_obj.async_success_handler(
                                processed_chunk, cache_hit=cache_hit
                            )
                        )

                        choice = processed_chunk.choices[0]
                        if isinstance(choice, StreamingChoices):
                            self.response_uptil_now += (
                                choice.delta.get("content", "") or ""
                            )
                        else:
                            self.response_uptil_now += ""
                        self.rules.post_call_rules(
                            input=self.response_uptil_now, model=self.model
                        )
                        # RETURN RESULT
                        self.chunks.append(processed_chunk)
                        return processed_chunk
        except StopAsyncIteration:
            if self.sent_last_chunk is True:
                if (
                    self.sent_stream_usage is False
                    and self.stream_options is not None
                    and self.stream_options.get("include_usage", False) is True
                ):
                    # send the final chunk with stream options
                    complete_streaming_response = litellm.stream_chunk_builder(
                        chunks=self.chunks, messages=self.messages
                    )
                    response = self.model_response_creator()
                    if complete_streaming_response is not None:
                        setattr(
                            response,
                            "usage",
                            getattr(complete_streaming_response, "usage"),
                        )
                    ## LOGGING
                    threading.Thread(
                        target=self.logging_obj.success_handler,
                        args=(response, None, None, cache_hit),
                    ).start()  # log response
                    asyncio.create_task(
                        self.logging_obj.async_success_handler(
                            response, cache_hit=cache_hit
                        )
                    )
                    self.sent_stream_usage = True
                    return response
                raise  # Re-raise StopIteration
            else:
                self.sent_last_chunk = True
                processed_chunk = self.finish_reason_handler()
                ## LOGGING
                threading.Thread(
                    target=self.logging_obj.success_handler,
                    args=(processed_chunk, None, None, cache_hit),
                ).start()  # log response
                asyncio.create_task(
                    self.logging_obj.async_success_handler(
                        processed_chunk, cache_hit=cache_hit
                    )
                )
                return processed_chunk
        except StopIteration:
            if self.sent_last_chunk is True:
                if (
                    self.sent_stream_usage is False
                    and self.stream_options is not None
                    and self.stream_options.get("include_usage", False) is True
                ):
                    # send the final chunk with stream options
                    complete_streaming_response = litellm.stream_chunk_builder(
                        chunks=self.chunks, messages=self.messages
                    )
                    response = self.model_response_creator()
                    if complete_streaming_response is not None:
                        setattr(
                            response,
                            "usage",
                            getattr(complete_streaming_response, "usage"),
                        )
                    ## LOGGING
                    threading.Thread(
                        target=self.logging_obj.success_handler,
                        args=(response, None, None, cache_hit),
                    ).start()  # log response
                    asyncio.create_task(
                        self.logging_obj.async_success_handler(
                            response, cache_hit=cache_hit
                        )
                    )
                    self.sent_stream_usage = True
                    return response
                raise StopAsyncIteration
            else:
                self.sent_last_chunk = True
                processed_chunk = self.finish_reason_handler()
                ## LOGGING
                threading.Thread(
                    target=self.logging_obj.success_handler,
                    args=(processed_chunk, None, None, cache_hit),
                ).start()  # log response
                asyncio.create_task(
                    self.logging_obj.async_success_handler(
                        processed_chunk, cache_hit=cache_hit
                    )
                )
                return processed_chunk
        except httpx.TimeoutException as e:  # if httpx read timeout error occues
            traceback_exception = traceback.format_exc()
            ## ADD DEBUG INFORMATION - E.G. LITELLM REQUEST TIMEOUT
            traceback_exception += "\nLiteLLM Default Request Timeout - {}".format(
                litellm.request_timeout
            )
            if self.logging_obj is not None:
                ## LOGGING
                threading.Thread(
                    target=self.logging_obj.failure_handler,
                    args=(e, traceback_exception),
                ).start()  # log response
                # Handle any exceptions that might occur during streaming
                asyncio.create_task(
                    self.logging_obj.async_failure_handler(e, traceback_exception)
                )
            raise e
        except Exception as e:
            traceback_exception = traceback.format_exc()
            if self.logging_obj is not None:
                ## LOGGING
                threading.Thread(
                    target=self.logging_obj.failure_handler,
                    args=(e, traceback_exception),
                ).start()  # log response
                # Handle any exceptions that might occur during streaming
                asyncio.create_task(
                    self.logging_obj.async_failure_handler(e, traceback_exception)  # type: ignore
                )
            ## Map to OpenAI Exception
            raise exception_type(
                model=self.model,
                custom_llm_provider=self.custom_llm_provider,
                original_exception=e,
                completion_kwargs={},
                extra_kwargs={},
            )


class TextCompletionStreamWrapper:
    def __init__(
        self,
        completion_stream,
        model,
        stream_options: Optional[dict] = None,
        custom_llm_provider: Optional[str] = None,
    ):
        self.completion_stream = completion_stream
        self.model = model
        self.stream_options = stream_options
        self.custom_llm_provider = custom_llm_provider

    def __iter__(self):
        return self

    def __aiter__(self):
        return self

    def convert_to_text_completion_object(self, chunk: ModelResponse):
        try:
            response = TextCompletionResponse()
            response["id"] = chunk.get("id", None)
            response["object"] = "text_completion"
            response["created"] = chunk.get("created", None)
            response["model"] = chunk.get("model", None)
            text_choices = TextChoices()
            if isinstance(
                chunk, Choices
            ):  # chunk should always be of type StreamingChoices
                raise Exception
            text_choices["text"] = chunk["choices"][0]["delta"]["content"]
            text_choices["index"] = chunk["choices"][0]["index"]
            text_choices["finish_reason"] = chunk["choices"][0]["finish_reason"]
            response["choices"] = [text_choices]

            # only pass usage when stream_options["include_usage"] is True
            if (
                self.stream_options
                and self.stream_options.get("include_usage", False) is True
            ):
                response["usage"] = chunk.get("usage", None)

            return response
        except Exception as e:
            raise Exception(
                f"Error occurred converting to text completion object - chunk: {chunk}; Error: {str(e)}"
            )

    def __next__(self):
        # model_response = ModelResponse(stream=True, model=self.model)
        TextCompletionResponse()
        try:
            for chunk in self.completion_stream:
                if chunk == "None" or chunk is None:
                    raise Exception
                processed_chunk = self.convert_to_text_completion_object(chunk=chunk)
                return processed_chunk
            raise StopIteration
        except StopIteration:
            raise StopIteration
        except Exception as e:
            raise exception_type(
                model=self.model,
                custom_llm_provider=self.custom_llm_provider or "",
                original_exception=e,
                completion_kwargs={},
                extra_kwargs={},
            )

    async def __anext__(self):
        try:
            async for chunk in self.completion_stream:
                if chunk == "None" or chunk is None:
                    raise Exception
                processed_chunk = self.convert_to_text_completion_object(chunk=chunk)
                return processed_chunk
            raise StopIteration
        except StopIteration:
            raise StopAsyncIteration


def mock_completion_streaming_obj(
    model_response, mock_response, model, n: Optional[int] = None
):
    if isinstance(mock_response, litellm.MockException):
        raise mock_response
    for i in range(0, len(mock_response), 3):
        completion_obj = Delta(role="assistant", content=mock_response[i : i + 3])
        if n is None:
            model_response.choices[0].delta = completion_obj
        else:
            _all_choices = []
            for j in range(n):
                _streaming_choice = litellm.utils.StreamingChoices(
                    index=j,
                    delta=litellm.utils.Delta(
                        role="assistant", content=mock_response[i : i + 3]
                    ),
                )
                _all_choices.append(_streaming_choice)
            model_response.choices = _all_choices
        yield model_response


async def async_mock_completion_streaming_obj(
    model_response, mock_response, model, n: Optional[int] = None
):
    if isinstance(mock_response, litellm.MockException):
        raise mock_response
    for i in range(0, len(mock_response), 3):
        completion_obj = Delta(role="assistant", content=mock_response[i : i + 3])
        if n is None:
            model_response.choices[0].delta = completion_obj
        else:
            _all_choices = []
            for j in range(n):
                _streaming_choice = litellm.utils.StreamingChoices(
                    index=j,
                    delta=litellm.utils.Delta(
                        role="assistant", content=mock_response[i : i + 3]
                    ),
                )
                _all_choices.append(_streaming_choice)
            model_response.choices = _all_choices
        yield model_response


########## Reading Config File ############################
def read_config_args(config_path) -> dict:
    try:
        import os

        os.getcwd()
        with open(config_path, "r") as config_file:
            config = json.load(config_file)

        # read keys/ values from config file and return them
        return config
    except Exception as e:
        raise e


########## experimental completion variants ############################


def completion_with_fallbacks(**kwargs):
    nested_kwargs = kwargs.pop("kwargs", {})
    response = None
    rate_limited_models = set()
    model_expiration_times = {}
    start_time = time.time()
    original_model = kwargs["model"]
    fallbacks = [kwargs["model"]] + nested_kwargs.get("fallbacks", [])
    if "fallbacks" in nested_kwargs:
        del nested_kwargs["fallbacks"]  # remove fallbacks so it's not recursive
    litellm_call_id = str(uuid.uuid4())

    # max time to process a request with fallbacks: default 45s
    while response is None and time.time() - start_time < 45:
        for model in fallbacks:
            # loop thru all models
            try:
                # check if it's dict or new model string
                if isinstance(
                    model, dict
                ):  # completion(model="gpt-4", fallbacks=[{"api_key": "", "api_base": ""}, {"api_key": "", "api_base": ""}])
                    kwargs["api_key"] = model.get("api_key", None)
                    kwargs["api_base"] = model.get("api_base", None)
                    model = model.get("model", original_model)
                elif (
                    model in rate_limited_models
                ):  # check if model is currently cooling down
                    if (
                        model_expiration_times.get(model)
                        and time.time() >= model_expiration_times[model]
                    ):
                        rate_limited_models.remove(
                            model
                        )  # check if it's been 60s of cool down and remove model
                    else:
                        continue  # skip model

                # delete model from kwargs if it exists
                if kwargs.get("model"):
                    del kwargs["model"]

                print_verbose(f"trying to make completion call with model: {model}")
                kwargs["litellm_call_id"] = litellm_call_id
                kwargs = {
                    **kwargs,
                    **nested_kwargs,
                }  # combine the openai + litellm params at the same level
                response = litellm.completion(**kwargs, model=model)
                print_verbose(f"response: {response}")
                if response is not None:
                    return response

            except Exception as e:
                print_verbose(e)
                rate_limited_models.add(model)
                model_expiration_times[model] = (
                    time.time() + 60
                )  # cool down this selected model
                pass
    return response


def process_system_message(system_message, max_tokens, model):
    system_message_event = {"role": "system", "content": system_message}
    system_message_tokens = get_token_count([system_message_event], model)

    if system_message_tokens > max_tokens:
        print_verbose(
            "`tokentrimmer`: Warning, system message exceeds token limit. Trimming..."
        )
        # shorten system message to fit within max_tokens
        new_system_message = shorten_message_to_fit_limit(
            system_message_event, max_tokens, model
        )
        system_message_tokens = get_token_count([new_system_message], model)

    return system_message_event, max_tokens - system_message_tokens


def process_messages(messages, max_tokens, model):
    # Process messages from older to more recent
    messages = messages[::-1]
    final_messages = []

    for message in messages:
        used_tokens = get_token_count(final_messages, model)
        available_tokens = max_tokens - used_tokens
        if available_tokens <= 3:
            break
        final_messages = attempt_message_addition(
            final_messages=final_messages,
            message=message,
            available_tokens=available_tokens,
            max_tokens=max_tokens,
            model=model,
        )

    return final_messages


def attempt_message_addition(
    final_messages, message, available_tokens, max_tokens, model
):
    temp_messages = [message] + final_messages
    temp_message_tokens = get_token_count(messages=temp_messages, model=model)

    if temp_message_tokens <= max_tokens:
        return temp_messages

    # if temp_message_tokens > max_tokens, try shortening temp_messages
    elif "function_call" not in message:
        # fit updated_message to be within temp_message_tokens - max_tokens (aka the amount temp_message_tokens is greate than max_tokens)
        updated_message = shorten_message_to_fit_limit(message, available_tokens, model)
        if can_add_message(updated_message, final_messages, max_tokens, model):
            return [updated_message] + final_messages

    return final_messages


def can_add_message(message, messages, max_tokens, model):
    if get_token_count(messages + [message], model) <= max_tokens:
        return True
    return False


def get_token_count(messages, model):
    return token_counter(model=model, messages=messages)


def shorten_message_to_fit_limit(message, tokens_needed, model: Optional[str]):
    """
    Shorten a message to fit within a token limit by removing characters from the middle.
    """

    # For OpenAI models, even blank messages cost 7 token,
    # and if the buffer is less than 3, the while loop will never end,
    # hence the value 10.
    if model is not None and "gpt" in model and tokens_needed <= 10:
        return message

    content = message["content"]

    while True:
        total_tokens = get_token_count([message], model)

        if total_tokens <= tokens_needed:
            break

        ratio = (tokens_needed) / total_tokens

        new_length = int(len(content) * ratio) - 1
        new_length = max(0, new_length)

        half_length = new_length // 2
        left_half = content[:half_length]
        right_half = content[-half_length:]

        trimmed_content = left_half + ".." + right_half
        message["content"] = trimmed_content
        content = trimmed_content

    return message


# LiteLLM token trimmer
# this code is borrowed from https://github.com/KillianLucas/tokentrim/blob/main/tokentrim/tokentrim.py
# Credits for this code go to Killian Lucas
def trim_messages(
    messages,
    model: Optional[str] = None,
    trim_ratio: float = 0.75,
    return_response_tokens: bool = False,
    max_tokens=None,
):
    """
    Trim a list of messages to fit within a model's token limit.

    Args:
        messages: Input messages to be trimmed. Each message is a dictionary with 'role' and 'content'.
        model: The LiteLLM model being used (determines the token limit).
        trim_ratio: Target ratio of tokens to use after trimming. Default is 0.75, meaning it will trim messages so they use about 75% of the model's token limit.
        return_response_tokens: If True, also return the number of tokens left available for the response after trimming.
        max_tokens: Instead of specifying a model or trim_ratio, you can specify this directly.

    Returns:
        Trimmed messages and optionally the number of tokens available for response.
    """
    # Initialize max_tokens
    # if users pass in max tokens, trim to this amount
    messages = copy.deepcopy(messages)
    try:
        if max_tokens is None:
            # Check if model is valid
            if model in litellm.model_cost:
                max_tokens_for_model = litellm.model_cost[model].get(
                    "max_input_tokens", litellm.model_cost[model]["max_tokens"]
                )
                max_tokens = int(max_tokens_for_model * trim_ratio)
            else:
                # if user did not specify max (input) tokens
                # or passed an llm litellm does not know
                # do nothing, just return messages
                return messages

        system_message = ""
        for message in messages:
            if message["role"] == "system":
                system_message += "\n" if system_message else ""
                system_message += message["content"]

        ## Handle Tool Call ## - check if last message is a tool response, return as is - https://github.com/BerriAI/litellm/issues/4931
        tool_messages = []

        for message in reversed(messages):
            if message["role"] != "tool":
                break
            tool_messages.append(message)
        # # Remove the collected tool messages from the original list
        if len(tool_messages):
            messages = messages[: -len(tool_messages)]

        current_tokens = token_counter(model=model or "", messages=messages)
        print_verbose(f"Current tokens: {current_tokens}, max tokens: {max_tokens}")

        # Do nothing if current tokens under messages
        if current_tokens < max_tokens:
            return messages

        #### Trimming messages if current_tokens > max_tokens
        print_verbose(
            f"Need to trim input messages: {messages}, current_tokens{current_tokens}, max_tokens: {max_tokens}"
        )
        system_message_event: Optional[dict] = None
        if system_message:
            system_message_event, max_tokens = process_system_message(
                system_message=system_message, max_tokens=max_tokens, model=model
            )

            if max_tokens == 0:  # the system messages are too long
                return [system_message_event]

            # Since all system messages are combined and trimmed to fit the max_tokens,
            # we remove all system messages from the messages list
            messages = [message for message in messages if message["role"] != "system"]

        final_messages = process_messages(
            messages=messages, max_tokens=max_tokens, model=model
        )

        # Add system message to the beginning of the final messages
        if system_message_event:
            final_messages = [system_message_event] + final_messages

        if len(tool_messages) > 0:
            final_messages.extend(tool_messages)

        if (
            return_response_tokens
        ):  # if user wants token count with new trimmed messages
            response_tokens = max_tokens - get_token_count(final_messages, model)
            return final_messages, response_tokens
        return final_messages
    except Exception as e:  # [NON-Blocking, if error occurs just return final_messages
        verbose_logger.exception(
            "Got exception while token trimming - {}".format(str(e))
        )
        return messages


def get_valid_models() -> List[str]:
    """
    Returns a list of valid LLMs based on the set environment variables

    Args:
        None

    Returns:
        A list of valid LLMs
    """
    try:
        # get keys set in .env
        environ_keys = os.environ.keys()
        valid_providers = []
        # for all valid providers, make a list of supported llms
        valid_models = []

        for provider in litellm.provider_list:
            # edge case litellm has together_ai as a provider, it should be togetherai
            provider = provider.replace("_", "")

            # litellm standardizes expected provider keys to
            # PROVIDER_API_KEY. Example: OPENAI_API_KEY, COHERE_API_KEY
            expected_provider_key = f"{provider.upper()}_API_KEY"
            if expected_provider_key in environ_keys:
                # key is set
                valid_providers.append(provider)

        for provider in valid_providers:
            if provider == "azure":
                valid_models.append("Azure-LLM")
            else:
                models_for_provider = litellm.models_by_provider.get(provider, [])
                valid_models.extend(models_for_provider)
        return valid_models
    except Exception:
        return []  # NON-Blocking


# used for litellm.text_completion() to transform HF logprobs to OpenAI.Completion() format
def transform_logprobs(hf_response):
    # Initialize an empty list for the transformed logprobs
    transformed_logprobs = []

    # For each Hugging Face response, transform the logprobs
    for response in hf_response:
        # Extract the relevant information from the response
        response_details = response["details"]
        top_tokens = response_details.get("top_tokens", {})

        # Initialize an empty list for the token information
        token_info = {
            "tokens": [],
            "token_logprobs": [],
            "text_offset": [],
            "top_logprobs": [],
        }

        for i, token in enumerate(response_details["prefill"]):
            # Extract the text of the token
            token_text = token["text"]

            # Extract the logprob of the token
            token_logprob = token["logprob"]

            # Add the token information to the 'token_info' list
            token_info["tokens"].append(token_text)
            token_info["token_logprobs"].append(token_logprob)

            # stub this to work with llm eval harness
            top_alt_tokens = {"": -1, "": -2, "": -3}  # noqa: F601
            token_info["top_logprobs"].append(top_alt_tokens)

        # For each element in the 'tokens' list, extract the relevant information
        for i, token in enumerate(response_details["tokens"]):
            # Extract the text of the token
            token_text = token["text"]

            # Extract the logprob of the token
            token_logprob = token["logprob"]

            top_alt_tokens = {}
            temp_top_logprobs = []
            if top_tokens != {}:
                temp_top_logprobs = top_tokens[i]

            # top_alt_tokens should look like this: { "alternative_1": -1, "alternative_2": -2, "alternative_3": -3 }
            for elem in temp_top_logprobs:
                text = elem["text"]
                logprob = elem["logprob"]
                top_alt_tokens[text] = logprob

            # Add the token information to the 'token_info' list
            token_info["tokens"].append(token_text)
            token_info["token_logprobs"].append(token_logprob)
            token_info["top_logprobs"].append(top_alt_tokens)

            # Add the text offset of the token
            # This is computed as the sum of the lengths of all previous tokens
            token_info["text_offset"].append(
                sum(len(t["text"]) for t in response_details["tokens"][:i])
            )

        # Add the 'token_info' list to the 'transformed_logprobs' list
        transformed_logprobs = token_info

    return transformed_logprobs


def print_args_passed_to_litellm(original_function, args, kwargs):
    try:
        # we've already printed this for acompletion, don't print for completion
        if (
            "acompletion" in kwargs
            and kwargs["acompletion"] is True
            and original_function.__name__ == "completion"
        ):
            return
        elif (
            "aembedding" in kwargs
            and kwargs["aembedding"] is True
            and original_function.__name__ == "embedding"
        ):
            return
        elif (
            "aimg_generation" in kwargs
            and kwargs["aimg_generation"] is True
            and original_function.__name__ == "img_generation"
        ):
            return

        args_str = ", ".join(map(repr, args))
        kwargs_str = ", ".join(f"{key}={repr(value)}" for key, value in kwargs.items())
        print_verbose(
            "\n",
        )  # new line before
        print_verbose(
            "\033[92mRequest to litellm:\033[0m",
        )
        if args and kwargs:
            print_verbose(
                f"\033[92mlitellm.{original_function.__name__}({args_str}, {kwargs_str})\033[0m"
            )
        elif args:
            print_verbose(
                f"\033[92mlitellm.{original_function.__name__}({args_str})\033[0m"
            )
        elif kwargs:
            print_verbose(
                f"\033[92mlitellm.{original_function.__name__}({kwargs_str})\033[0m"
            )
        else:
            print_verbose(f"\033[92mlitellm.{original_function.__name__}()\033[0m")
        print_verbose("\n")  # new line after
    except Exception:
        # This should always be non blocking
        pass


def get_logging_id(start_time, response_obj):
    try:
        response_id = (
            "time-" + start_time.strftime("%H-%M-%S-%f") + "_" + response_obj.get("id")
        )
        return response_id
    except Exception:
        return None


def _get_base_model_from_litellm_call_metadata(
    metadata: Optional[dict],
) -> Optional[str]:
    if metadata is None:
        return None

    if metadata is not None:
        model_info = metadata.get("model_info", {})

        if model_info is not None:
            base_model = model_info.get("base_model", None)
            if base_model is not None:
                return base_model
    return None


def _get_base_model_from_metadata(model_call_details=None):
    if model_call_details is None:
        return None
    litellm_params = model_call_details.get("litellm_params", {})

    if litellm_params is not None:
        metadata = litellm_params.get("metadata", {})

        return _get_base_model_from_litellm_call_metadata(metadata=metadata)
    return None


class ModelResponseIterator:
    def __init__(self, model_response: ModelResponse, convert_to_delta: bool = False):
        if convert_to_delta is True:
            self.model_response = ModelResponse(stream=True)
            _delta = self.model_response.choices[0].delta  # type: ignore
            _delta.content = model_response.choices[0].message.content  # type: ignore
        else:
            self.model_response = model_response
        self.is_done = False

    # Sync iterator
    def __iter__(self):
        return self

    def __next__(self):
        if self.is_done:
            raise StopIteration
        self.is_done = True
        return self.model_response

    # Async iterator
    def __aiter__(self):
        return self

    async def __anext__(self):
        if self.is_done:
            raise StopAsyncIteration
        self.is_done = True
        return self.model_response


class ModelResponseListIterator:
    def __init__(self, model_responses):
        self.model_responses = model_responses
        self.index = 0

    # Sync iterator
    def __iter__(self):
        return self

    def __next__(self):
        if self.index >= len(self.model_responses):
            raise StopIteration
        model_response = self.model_responses[self.index]
        self.index += 1
        return model_response

    # Async iterator
    def __aiter__(self):
        return self

    async def __anext__(self):
        if self.index >= len(self.model_responses):
            raise StopAsyncIteration
        model_response = self.model_responses[self.index]
        self.index += 1
        return model_response


class CustomModelResponseIterator(Iterable):
    def __init__(self) -> None:
        super().__init__()


def is_cached_message(message: AllMessageValues) -> bool:
    """
    Returns true, if message is marked as needing to be cached.

    Used for anthropic/gemini context caching.

    Follows the anthropic format {"cache_control": {"type": "ephemeral"}}
    """
    if "content" not in message:
        return False
    if message["content"] is None or isinstance(message["content"], str):
        return False

    for content in message["content"]:
        if (
            content["type"] == "text"
            and content.get("cache_control") is not None
            and content["cache_control"]["type"] == "ephemeral"  # type: ignore
        ):
            return True

    return False


def is_base64_encoded(s: str) -> bool:
    try:
        # Strip out the prefix if it exists
        if s.startswith("data:"):
            s = s.split(",")[1]

        # Try to decode the string
        decoded_bytes = base64.b64decode(s, validate=True)
        # Check if the original string can be re-encoded to the same string
        return base64.b64encode(decoded_bytes).decode("utf-8") == s
    except Exception:
        return False


def has_tool_call_blocks(messages: List[AllMessageValues]) -> bool:
    """
    Returns true, if messages has tool call blocks.

    Used for anthropic/bedrock message validation.
    """
    for message in messages:
        if message.get("tool_calls") is not None:
            return True
    return False


def process_response_headers(response_headers: Union[httpx.Headers, dict]) -> dict:
    openai_headers = {}
    processed_headers = {}
    additional_headers = {}

    for k, v in response_headers.items():
        if k in OPENAI_RESPONSE_HEADERS:  # return openai-compatible headers
            openai_headers[k] = v
        if k.startswith(
            "llm_provider-"
        ):  # return raw provider headers (incl. openai-compatible ones)
            processed_headers[k] = v
        else:
            additional_headers["{}-{}".format("llm_provider", k)] = v

    additional_headers = {
        **openai_headers,
        **processed_headers,
        **additional_headers,
    }
    return additional_headers


def add_dummy_tool(custom_llm_provider: str) -> List[ChatCompletionToolParam]:
    """
    Prevent Anthropic from raising error when tool_use block exists but no tools are provided.

    Relevent Issues: https://github.com/BerriAI/litellm/issues/5388, https://github.com/BerriAI/litellm/issues/5747
    """
    return [
        ChatCompletionToolParam(
            type="function",
            function=ChatCompletionToolParamFunctionChunk(
                name="dummy-tool",
                description="This is a dummy tool call",  # provided to satisfy bedrock constraint.
                parameters={
                    "type": "object",
                    "properties": {},
                },
            ),
        )
    ]