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from typing import List, Optional, Union
from httpx._models import Headers
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
class MaritalkError(BaseLLMException):
def __init__(
self,
status_code: int,
message: str,
headers: Optional[Union[dict, Headers]] = None,
):
super().__init__(status_code=status_code, message=message, headers=headers)
class MaritalkConfig(OpenAIGPTConfig):
def __init__(
self,
frequency_penalty: Optional[float] = None,
presence_penalty: Optional[float] = None,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
stop: Optional[List[str]] = None,
stream: Optional[bool] = None,
stream_options: Optional[dict] = None,
tools: Optional[List[dict]] = None,
tool_choice: Optional[Union[str, dict]] = None,
) -> None:
locals_ = locals().copy()
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 super().get_config()
def get_supported_openai_params(self, model: str) -> List:
return [
"frequency_penalty",
"presence_penalty",
"top_p",
"top_k",
"temperature",
"max_tokens",
"n",
"stop",
"stream",
"stream_options",
"tools",
"tool_choice",
]
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, Headers]
) -> BaseLLMException:
return MaritalkError(
status_code=status_code, message=error_message, headers=headers
)
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