"""
Common utilities used across bedrock chat/embedding/image generation
"""

import os
import re
import types
from enum import Enum
from typing import Any, List, Optional, Union

import httpx

import litellm
from litellm.llms.base_llm.chat.transformation import (
    BaseConfig,
    BaseLLMException,
    LiteLLMLoggingObj,
)
from litellm.secret_managers.main import get_secret
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import ModelResponse


class BedrockError(BaseLLMException):
    pass


class AmazonBedrockGlobalConfig:
    def __init__(self):
        pass

    def get_mapped_special_auth_params(self) -> dict:
        """
        Mapping of common auth params across bedrock/vertex/azure/watsonx
        """
        return {"region_name": "aws_region_name"}

    def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
        mapped_params = self.get_mapped_special_auth_params()
        for param, value in non_default_params.items():
            if param in mapped_params:
                optional_params[mapped_params[param]] = value
        return optional_params

    def get_eu_regions(self) -> List[str]:
        """
        Source: https://www.aws-services.info/bedrock.html
        """
        return [
            "eu-west-1",
            "eu-west-3",
            "eu-central-1",
        ]

    def get_us_regions(self) -> List[str]:
        """
        Source: https://www.aws-services.info/bedrock.html
        """
        return [
            "us-east-2",
            "us-east-1",
            "us-west-2",
            "us-gov-west-1",
        ]


class AmazonInvokeMixin:
    """
    Base class for bedrock models going through invoke_handler.py
    """

    def get_error_class(
        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
    ) -> BaseLLMException:
        return BedrockError(
            message=error_message,
            status_code=status_code,
            headers=headers,
        )

    def transform_request(
        self,
        model: str,
        messages: List[AllMessageValues],
        optional_params: dict,
        litellm_params: dict,
        headers: dict,
    ) -> dict:
        raise NotImplementedError(
            "transform_request not implemented for config. Done in invoke_handler.py"
        )

    def transform_response(
        self,
        model: str,
        raw_response: httpx.Response,
        model_response: ModelResponse,
        logging_obj: LiteLLMLoggingObj,
        request_data: dict,
        messages: List[AllMessageValues],
        optional_params: dict,
        litellm_params: dict,
        encoding: Any,
        api_key: Optional[str] = None,
        json_mode: Optional[bool] = None,
    ) -> ModelResponse:
        raise NotImplementedError(
            "transform_response not implemented for config. Done in invoke_handler.py"
        )

    def validate_environment(
        self,
        headers: dict,
        model: str,
        messages: List[AllMessageValues],
        optional_params: dict,
        api_key: Optional[str] = None,
        api_base: Optional[str] = None,
    ) -> dict:
        raise NotImplementedError(
            "validate_environment not implemented for config. Done in invoke_handler.py"
        )


class AmazonTitanConfig(AmazonInvokeMixin, BaseConfig):
    """
    Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-text-express-v1

    Supported Params for the Amazon Titan models:

    - `maxTokenCount` (integer) max tokens,
    - `stopSequences` (string[]) list of stop sequence strings
    - `temperature` (float) temperature for model,
    - `topP` (int) top p for model
    """

    maxTokenCount: Optional[int] = None
    stopSequences: Optional[list] = None
    temperature: Optional[float] = None
    topP: Optional[int] = None

    def __init__(
        self,
        maxTokenCount: Optional[int] = None,
        stopSequences: Optional[list] = None,
        temperature: Optional[float] = None,
        topP: Optional[int] = None,
    ) -> None:
        locals_ = locals()
        for key, value in locals_.items():
            if key != "self" and value is not None:
                setattr(self.__class__, key, value)

    @classmethod
    def get_config(cls):
        return {
            k: v
            for k, v in cls.__dict__.items()
            if not k.startswith("__")
            and not k.startswith("_abc")
            and not isinstance(
                v,
                (
                    types.FunctionType,
                    types.BuiltinFunctionType,
                    classmethod,
                    staticmethod,
                ),
            )
            and v is not None
        }

    def _map_and_modify_arg(
        self,
        supported_params: dict,
        provider: str,
        model: str,
        stop: Union[List[str], 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

    def get_supported_openai_params(self, model: str) -> List[str]:
        return [
            "max_tokens",
            "max_completion_tokens",
            "stop",
            "temperature",
            "top_p",
            "stream",
        ]

    def map_openai_params(
        self,
        non_default_params: dict,
        optional_params: dict,
        model: str,
        drop_params: bool,
    ) -> dict:
        for k, v in non_default_params.items():
            if k == "max_tokens" or k == "max_completion_tokens":
                optional_params["maxTokenCount"] = v
            if k == "temperature":
                optional_params["temperature"] = v
            if k == "stop":
                filtered_stop = self._map_and_modify_arg(
                    {"stop": v}, provider="bedrock", model=model, stop=v
                )
                optional_params["stopSequences"] = filtered_stop["stop"]
            if k == "top_p":
                optional_params["topP"] = v
            if k == "stream":
                optional_params["stream"] = v
        return optional_params


class AmazonAnthropicClaude3Config:
    """
    Reference:
        https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=claude
        https://docs.anthropic.com/claude/docs/models-overview#model-comparison

    Supported Params for the Amazon / Anthropic Claude 3 models:

    - `max_tokens` Required (integer) max tokens. Default is 4096
    - `anthropic_version` Required (string) version of anthropic for bedrock - e.g. "bedrock-2023-05-31"
    - `system` Optional (string) the system prompt, conversion from openai format to this is handled in factory.py
    - `temperature` Optional (float) The amount of randomness injected into the response
    - `top_p` Optional (float) Use nucleus sampling.
    - `top_k` Optional (int) Only sample from the top K options for each subsequent token
    - `stop_sequences` Optional (List[str]) Custom text sequences that cause the model to stop generating
    """

    max_tokens: Optional[int] = 4096  # Opus, Sonnet, and Haiku default
    anthropic_version: Optional[str] = "bedrock-2023-05-31"
    system: Optional[str] = None
    temperature: Optional[float] = None
    top_p: Optional[float] = None
    top_k: Optional[int] = None
    stop_sequences: Optional[List[str]] = None

    def __init__(
        self,
        max_tokens: Optional[int] = None,
        anthropic_version: Optional[str] = None,
    ) -> None:
        locals_ = locals()
        for key, value in locals_.items():
            if key != "self" and value is not None:
                setattr(self.__class__, key, value)

    @classmethod
    def get_config(cls):
        return {
            k: v
            for k, v in cls.__dict__.items()
            if not k.startswith("__")
            and not isinstance(
                v,
                (
                    types.FunctionType,
                    types.BuiltinFunctionType,
                    classmethod,
                    staticmethod,
                ),
            )
            and v is not None
        }

    def get_supported_openai_params(self):
        return [
            "max_tokens",
            "max_completion_tokens",
            "tools",
            "tool_choice",
            "stream",
            "stop",
            "temperature",
            "top_p",
            "extra_headers",
        ]

    def map_openai_params(self, non_default_params: dict, optional_params: dict):
        for param, value in non_default_params.items():
            if param == "max_tokens" or param == "max_completion_tokens":
                optional_params["max_tokens"] = value
            if param == "tools":
                optional_params["tools"] = value
            if param == "stream":
                optional_params["stream"] = value
            if param == "stop":
                optional_params["stop_sequences"] = value
            if param == "temperature":
                optional_params["temperature"] = value
            if param == "top_p":
                optional_params["top_p"] = value
        return optional_params


class AmazonAnthropicConfig:
    """
    Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=claude

    Supported Params for the Amazon / Anthropic models:

    - `max_tokens_to_sample` (integer) max tokens,
    - `temperature` (float) model temperature,
    - `top_k` (integer) top k,
    - `top_p` (integer) top p,
    - `stop_sequences` (string[]) list of stop sequences - e.g. ["\\n\\nHuman:"],
    - `anthropic_version` (string) version of anthropic for bedrock - e.g. "bedrock-2023-05-31"
    """

    max_tokens_to_sample: Optional[int] = litellm.max_tokens
    stop_sequences: Optional[list] = None
    temperature: Optional[float] = None
    top_k: Optional[int] = None
    top_p: Optional[int] = None
    anthropic_version: Optional[str] = None

    def __init__(
        self,
        max_tokens_to_sample: Optional[int] = None,
        stop_sequences: Optional[list] = None,
        temperature: Optional[float] = None,
        top_k: Optional[int] = None,
        top_p: Optional[int] = None,
        anthropic_version: Optional[str] = None,
    ) -> None:
        locals_ = locals()
        for key, value in locals_.items():
            if key != "self" and value is not None:
                setattr(self.__class__, key, value)

    @classmethod
    def get_config(cls):
        return {
            k: v
            for k, v in cls.__dict__.items()
            if not k.startswith("__")
            and not isinstance(
                v,
                (
                    types.FunctionType,
                    types.BuiltinFunctionType,
                    classmethod,
                    staticmethod,
                ),
            )
            and v is not None
        }

    def get_supported_openai_params(
        self,
    ):
        return [
            "max_tokens",
            "max_completion_tokens",
            "temperature",
            "stop",
            "top_p",
            "stream",
        ]

    def map_openai_params(self, non_default_params: dict, optional_params: dict):
        for param, value in non_default_params.items():
            if param == "max_tokens" or param == "max_completion_tokens":
                optional_params["max_tokens_to_sample"] = value
            if param == "temperature":
                optional_params["temperature"] = value
            if param == "top_p":
                optional_params["top_p"] = value
            if param == "stop":
                optional_params["stop_sequences"] = value
            if param == "stream" and value is True:
                optional_params["stream"] = value
        return optional_params


class AmazonCohereConfig(AmazonInvokeMixin, BaseConfig):
    """
    Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=command

    Supported Params for the Amazon / Cohere models:

    - `max_tokens` (integer) max tokens,
    - `temperature` (float) model temperature,
    - `return_likelihood` (string) n/a
    """

    max_tokens: Optional[int] = None
    temperature: Optional[float] = None
    return_likelihood: Optional[str] = None

    def __init__(
        self,
        max_tokens: Optional[int] = None,
        temperature: Optional[float] = None,
        return_likelihood: Optional[str] = None,
    ) -> None:
        locals_ = locals()
        for key, value in locals_.items():
            if key != "self" and value is not None:
                setattr(self.__class__, key, value)

    @classmethod
    def get_config(cls):
        return {
            k: v
            for k, v in cls.__dict__.items()
            if not k.startswith("__")
            and not k.startswith("_abc")
            and not isinstance(
                v,
                (
                    types.FunctionType,
                    types.BuiltinFunctionType,
                    classmethod,
                    staticmethod,
                ),
            )
            and v is not None
        }

    def get_supported_openai_params(self, model: str) -> List[str]:
        return [
            "max_tokens",
            "temperature",
            "stream",
        ]

    def map_openai_params(
        self,
        non_default_params: dict,
        optional_params: dict,
        model: str,
        drop_params: bool,
    ) -> dict:
        for k, v in non_default_params.items():
            if k == "stream":
                optional_params["stream"] = v
            if k == "temperature":
                optional_params["temperature"] = v
            if k == "max_tokens":
                optional_params["max_tokens"] = v
        return optional_params


class AmazonAI21Config(AmazonInvokeMixin, BaseConfig):
    """
    Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=j2-ultra

    Supported Params for the Amazon / AI21 models:

    - `maxTokens` (int32): The maximum number of tokens to generate per result. Optional, default is 16. If no `stopSequences` are given, generation stops after producing `maxTokens`.

    - `temperature` (float): Modifies the distribution from which tokens are sampled. Optional, default is 0.7. A value of 0 essentially disables sampling and results in greedy decoding.

    - `topP` (float): Used for sampling tokens from the corresponding top percentile of probability mass. Optional, default is 1. For instance, a value of 0.9 considers only tokens comprising the top 90% probability mass.

    - `stopSequences` (array of strings): Stops decoding if any of the input strings is generated. Optional.

    - `frequencyPenalty` (object): Placeholder for frequency penalty object.

    - `presencePenalty` (object): Placeholder for presence penalty object.

    - `countPenalty` (object): Placeholder for count penalty object.
    """

    maxTokens: Optional[int] = None
    temperature: Optional[float] = None
    topP: Optional[float] = None
    stopSequences: Optional[list] = None
    frequencePenalty: Optional[dict] = None
    presencePenalty: Optional[dict] = None
    countPenalty: Optional[dict] = None

    def __init__(
        self,
        maxTokens: Optional[int] = None,
        temperature: Optional[float] = None,
        topP: Optional[float] = None,
        stopSequences: Optional[list] = None,
        frequencePenalty: Optional[dict] = None,
        presencePenalty: Optional[dict] = None,
        countPenalty: Optional[dict] = None,
    ) -> None:
        locals_ = locals()
        for key, value in locals_.items():
            if key != "self" and value is not None:
                setattr(self.__class__, key, value)

    @classmethod
    def get_config(cls):
        return {
            k: v
            for k, v in cls.__dict__.items()
            if not k.startswith("__")
            and not k.startswith("_abc")
            and not isinstance(
                v,
                (
                    types.FunctionType,
                    types.BuiltinFunctionType,
                    classmethod,
                    staticmethod,
                ),
            )
            and v is not None
        }

    def get_supported_openai_params(self, model: str) -> List:
        return [
            "max_tokens",
            "temperature",
            "top_p",
            "stream",
        ]

    def map_openai_params(
        self,
        non_default_params: dict,
        optional_params: dict,
        model: str,
        drop_params: bool,
    ) -> dict:
        for k, v in non_default_params.items():
            if k == "max_tokens":
                optional_params["maxTokens"] = v
            if k == "temperature":
                optional_params["temperature"] = v
            if k == "top_p":
                optional_params["topP"] = v
            if k == "stream":
                optional_params["stream"] = v
        return optional_params


class AnthropicConstants(Enum):
    HUMAN_PROMPT = "\n\nHuman: "
    AI_PROMPT = "\n\nAssistant: "


class AmazonLlamaConfig(AmazonInvokeMixin, BaseConfig):
    """
    Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=meta.llama2-13b-chat-v1

    Supported Params for the Amazon / Meta Llama models:

    - `max_gen_len` (integer) max tokens,
    - `temperature` (float) temperature for model,
    - `top_p` (float) top p for model
    """

    max_gen_len: Optional[int] = None
    temperature: Optional[float] = None
    topP: Optional[float] = None

    def __init__(
        self,
        maxTokenCount: Optional[int] = None,
        temperature: Optional[float] = None,
        topP: Optional[int] = None,
    ) -> None:
        locals_ = locals()
        for key, value in locals_.items():
            if key != "self" and value is not None:
                setattr(self.__class__, key, value)

    @classmethod
    def get_config(cls):
        return {
            k: v
            for k, v in cls.__dict__.items()
            if not k.startswith("__")
            and not k.startswith("_abc")
            and not isinstance(
                v,
                (
                    types.FunctionType,
                    types.BuiltinFunctionType,
                    classmethod,
                    staticmethod,
                ),
            )
            and v is not None
        }

    def get_supported_openai_params(self, model: str) -> List:
        return [
            "max_tokens",
            "temperature",
            "top_p",
            "stream",
        ]

    def map_openai_params(
        self,
        non_default_params: dict,
        optional_params: dict,
        model: str,
        drop_params: bool,
    ) -> dict:
        for k, v in non_default_params.items():
            if k == "max_tokens":
                optional_params["max_gen_len"] = v
            if k == "temperature":
                optional_params["temperature"] = v
            if k == "top_p":
                optional_params["top_p"] = v
            if k == "stream":
                optional_params["stream"] = v
        return optional_params


class AmazonMistralConfig(AmazonInvokeMixin, BaseConfig):
    """
    Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-mistral.html
    Supported Params for the Amazon / Mistral models:

    - `max_tokens` (integer) max tokens,
    - `temperature` (float) temperature for model,
    - `top_p` (float) top p for model
    - `stop` [string] A list of stop sequences that if generated by the model, stops the model from generating further output.
    - `top_k` (float) top k for model
    """

    max_tokens: Optional[int] = None
    temperature: Optional[float] = None
    top_p: Optional[float] = None
    top_k: Optional[float] = None
    stop: Optional[List[str]] = None

    def __init__(
        self,
        max_tokens: Optional[int] = None,
        temperature: Optional[float] = None,
        top_p: Optional[int] = None,
        top_k: Optional[float] = None,
        stop: Optional[List[str]] = None,
    ) -> None:
        locals_ = locals()
        for key, value in locals_.items():
            if key != "self" and value is not None:
                setattr(self.__class__, key, value)

    @classmethod
    def get_config(cls):
        return {
            k: v
            for k, v in cls.__dict__.items()
            if not k.startswith("__")
            and not k.startswith("_abc")
            and not isinstance(
                v,
                (
                    types.FunctionType,
                    types.BuiltinFunctionType,
                    classmethod,
                    staticmethod,
                ),
            )
            and v is not None
        }

    def get_supported_openai_params(self, model: str) -> List[str]:
        return ["max_tokens", "temperature", "top_p", "stop", "stream"]

    def map_openai_params(
        self,
        non_default_params: dict,
        optional_params: dict,
        model: str,
        drop_params: bool,
    ) -> dict:
        for k, v in non_default_params.items():
            if k == "max_tokens":
                optional_params["max_tokens"] = v
            if k == "temperature":
                optional_params["temperature"] = v
            if k == "top_p":
                optional_params["top_p"] = v
            if k == "stop":
                optional_params["stop"] = v
            if k == "stream":
                optional_params["stream"] = v
        return optional_params


def add_custom_header(headers):
    """Closure to capture the headers and add them."""

    def callback(request, **kwargs):
        """Actual callback function that Boto3 will call."""
        for header_name, header_value in headers.items():
            request.headers.add_header(header_name, header_value)

    return callback


def init_bedrock_client(
    region_name=None,
    aws_access_key_id: Optional[str] = None,
    aws_secret_access_key: Optional[str] = None,
    aws_region_name: Optional[str] = None,
    aws_bedrock_runtime_endpoint: Optional[str] = None,
    aws_session_name: Optional[str] = None,
    aws_profile_name: Optional[str] = None,
    aws_role_name: Optional[str] = None,
    aws_web_identity_token: Optional[str] = None,
    extra_headers: Optional[dict] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
):
    # check for custom AWS_REGION_NAME and use it if not passed to init_bedrock_client
    litellm_aws_region_name = get_secret("AWS_REGION_NAME", None)
    standard_aws_region_name = get_secret("AWS_REGION", None)
    ## CHECK IS  'os.environ/' passed in
    # Define the list of parameters to check
    params_to_check = [
        aws_access_key_id,
        aws_secret_access_key,
        aws_region_name,
        aws_bedrock_runtime_endpoint,
        aws_session_name,
        aws_profile_name,
        aws_role_name,
        aws_web_identity_token,
    ]

    # Iterate over parameters and update if needed
    for i, param in enumerate(params_to_check):
        if param and param.startswith("os.environ/"):
            params_to_check[i] = get_secret(param)  # type: ignore
    # Assign updated values back to parameters
    (
        aws_access_key_id,
        aws_secret_access_key,
        aws_region_name,
        aws_bedrock_runtime_endpoint,
        aws_session_name,
        aws_profile_name,
        aws_role_name,
        aws_web_identity_token,
    ) = params_to_check

    # SSL certificates (a.k.a CA bundle) used to verify the identity of requested hosts.
    ssl_verify = os.getenv("SSL_VERIFY", litellm.ssl_verify)

    ### SET REGION NAME
    if region_name:
        pass
    elif aws_region_name:
        region_name = aws_region_name
    elif litellm_aws_region_name:
        region_name = litellm_aws_region_name
    elif standard_aws_region_name:
        region_name = standard_aws_region_name
    else:
        raise BedrockError(
            message="AWS region not set: set AWS_REGION_NAME or AWS_REGION env variable or in .env file",
            status_code=401,
        )

    # check for custom AWS_BEDROCK_RUNTIME_ENDPOINT and use it if not passed to init_bedrock_client
    env_aws_bedrock_runtime_endpoint = get_secret("AWS_BEDROCK_RUNTIME_ENDPOINT")
    if aws_bedrock_runtime_endpoint:
        endpoint_url = aws_bedrock_runtime_endpoint
    elif env_aws_bedrock_runtime_endpoint:
        endpoint_url = env_aws_bedrock_runtime_endpoint
    else:
        endpoint_url = f"https://bedrock-runtime.{region_name}.amazonaws.com"

    import boto3

    if isinstance(timeout, float):
        config = boto3.session.Config(connect_timeout=timeout, read_timeout=timeout)  # type: ignore
    elif isinstance(timeout, httpx.Timeout):
        config = boto3.session.Config(  # type: ignore
            connect_timeout=timeout.connect, read_timeout=timeout.read
        )
    else:
        config = boto3.session.Config()  # type: ignore

    ### CHECK STS ###
    if (
        aws_web_identity_token is not None
        and aws_role_name is not None
        and aws_session_name is not None
    ):
        oidc_token = get_secret(aws_web_identity_token)

        if oidc_token is None:
            raise BedrockError(
                message="OIDC token could not be retrieved from secret manager.",
                status_code=401,
            )

        sts_client = boto3.client("sts")

        # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
        # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
        sts_response = sts_client.assume_role_with_web_identity(
            RoleArn=aws_role_name,
            RoleSessionName=aws_session_name,
            WebIdentityToken=oidc_token,
            DurationSeconds=3600,
        )

        client = boto3.client(
            service_name="bedrock-runtime",
            aws_access_key_id=sts_response["Credentials"]["AccessKeyId"],
            aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"],
            aws_session_token=sts_response["Credentials"]["SessionToken"],
            region_name=region_name,
            endpoint_url=endpoint_url,
            config=config,
            verify=ssl_verify,
        )
    elif aws_role_name is not None and aws_session_name is not None:
        # use sts if role name passed in
        sts_client = boto3.client(
            "sts",
            aws_access_key_id=aws_access_key_id,
            aws_secret_access_key=aws_secret_access_key,
        )

        sts_response = sts_client.assume_role(
            RoleArn=aws_role_name, RoleSessionName=aws_session_name
        )

        client = boto3.client(
            service_name="bedrock-runtime",
            aws_access_key_id=sts_response["Credentials"]["AccessKeyId"],
            aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"],
            aws_session_token=sts_response["Credentials"]["SessionToken"],
            region_name=region_name,
            endpoint_url=endpoint_url,
            config=config,
            verify=ssl_verify,
        )
    elif aws_access_key_id is not None:
        # uses auth params passed to completion
        # aws_access_key_id is not None, assume user is trying to auth using litellm.completion

        client = boto3.client(
            service_name="bedrock-runtime",
            aws_access_key_id=aws_access_key_id,
            aws_secret_access_key=aws_secret_access_key,
            region_name=region_name,
            endpoint_url=endpoint_url,
            config=config,
            verify=ssl_verify,
        )
    elif aws_profile_name is not None:
        # uses auth values from AWS profile usually stored in ~/.aws/credentials

        client = boto3.Session(profile_name=aws_profile_name).client(
            service_name="bedrock-runtime",
            region_name=region_name,
            endpoint_url=endpoint_url,
            config=config,
            verify=ssl_verify,
        )
    else:
        # aws_access_key_id is None, assume user is trying to auth using env variables
        # boto3 automatically reads env variables

        client = boto3.client(
            service_name="bedrock-runtime",
            region_name=region_name,
            endpoint_url=endpoint_url,
            config=config,
            verify=ssl_verify,
        )
    if extra_headers:
        client.meta.events.register(
            "before-sign.bedrock-runtime.*", add_custom_header(extra_headers)
        )

    return client


class ModelResponseIterator:
    def __init__(self, model_response):
        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


def get_bedrock_tool_name(response_tool_name: str) -> str:
    """
    If litellm formatted the input tool name, we need to convert it back to the original name.

    Args:
        response_tool_name (str): The name of the tool as received from the response.

    Returns:
        str: The original name of the tool.
    """

    if response_tool_name in litellm.bedrock_tool_name_mappings.cache_dict:
        response_tool_name = litellm.bedrock_tool_name_mappings.cache_dict[
            response_tool_name
        ]
    return response_tool_name