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import json
import time
from typing import TYPE_CHECKING, Any, List, Optional, Union
import httpx
from litellm.litellm_core_utils.prompt_templates.common_utils import (
convert_content_list_to_str,
)
from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
from litellm.types.llms.openai import AllMessageValues
from litellm.utils import ModelResponse, Usage
from ..common_utils import NLPCloudError
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
LoggingClass = LiteLLMLoggingObj
else:
LoggingClass = Any
class NLPCloudConfig(BaseConfig):
"""
Reference: https://docs.nlpcloud.com/#generation
- `max_length` (int): Optional. The maximum number of tokens that the generated text should contain.
- `length_no_input` (boolean): Optional. Whether `min_length` and `max_length` should not include the length of the input text.
- `end_sequence` (string): Optional. A specific token that should be the end of the generated sequence.
- `remove_end_sequence` (boolean): Optional. Whether to remove the `end_sequence` string from the result.
- `remove_input` (boolean): Optional. Whether to remove the input text from the result.
- `bad_words` (list of strings): Optional. List of tokens that are not allowed to be generated.
- `temperature` (float): Optional. Temperature sampling. It modulates the next token probabilities.
- `top_p` (float): Optional. Top P sampling. Below 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.
- `top_k` (int): Optional. Top K sampling. The number of highest probability vocabulary tokens to keep for top k filtering.
- `repetition_penalty` (float): Optional. Prevents the same word from being repeated too many times.
- `num_beams` (int): Optional. Number of beams for beam search.
- `num_return_sequences` (int): Optional. The number of independently computed returned sequences.
"""
max_length: Optional[int] = None
length_no_input: Optional[bool] = None
end_sequence: Optional[str] = None
remove_end_sequence: Optional[bool] = None
remove_input: Optional[bool] = None
bad_words: Optional[list] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
top_k: Optional[int] = None
repetition_penalty: Optional[float] = None
num_beams: Optional[int] = None
num_return_sequences: Optional[int] = None
def __init__(
self,
max_length: Optional[int] = None,
length_no_input: Optional[bool] = None,
end_sequence: Optional[str] = None,
remove_end_sequence: Optional[bool] = None,
remove_input: Optional[bool] = None,
bad_words: Optional[list] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
repetition_penalty: Optional[float] = None,
num_beams: Optional[int] = None,
num_return_sequences: Optional[int] = 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 validate_environment(
self,
headers: dict,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
headers = {
"accept": "application/json",
"content-type": "application/json",
}
if api_key:
headers["Authorization"] = f"Token {api_key}"
return headers
def get_supported_openai_params(self, model: str) -> List:
return [
"max_tokens",
"stream",
"temperature",
"top_p",
"presence_penalty",
"frequency_penalty",
"n",
"stop",
]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
) -> dict:
for param, value in non_default_params.items():
if param == "max_tokens":
optional_params["max_length"] = value
if param == "stream":
optional_params["stream"] = value
if param == "temperature":
optional_params["temperature"] = value
if param == "top_p":
optional_params["top_p"] = value
if param == "presence_penalty":
optional_params["presence_penalty"] = value
if param == "frequency_penalty":
optional_params["frequency_penalty"] = value
if param == "n":
optional_params["num_return_sequences"] = value
if param == "stop":
optional_params["stop_sequences"] = value
return optional_params
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
) -> BaseLLMException:
return NLPCloudError(
status_code=status_code, message=error_message, headers=headers
)
def transform_request(
self,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
headers: dict,
) -> dict:
text = " ".join(convert_content_list_to_str(message) for message in messages)
data = {
"text": text,
**optional_params,
}
return data
def transform_response(
self,
model: str,
raw_response: httpx.Response,
model_response: ModelResponse,
logging_obj: LoggingClass,
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:
## LOGGING
logging_obj.post_call(
input=None,
api_key=api_key,
original_response=raw_response.text,
additional_args={"complete_input_dict": request_data},
)
## RESPONSE OBJECT
try:
completion_response = raw_response.json()
except Exception:
raise NLPCloudError(
message=raw_response.text, status_code=raw_response.status_code
)
if "error" in completion_response:
raise NLPCloudError(
message=completion_response["error"],
status_code=raw_response.status_code,
)
else:
try:
if len(completion_response["generated_text"]) > 0:
model_response.choices[0].message.content = ( # type: ignore
completion_response["generated_text"]
)
except Exception:
raise NLPCloudError(
message=json.dumps(completion_response),
status_code=raw_response.status_code,
)
## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
prompt_tokens = completion_response["nb_input_tokens"]
completion_tokens = completion_response["nb_generated_tokens"]
model_response.created = int(time.time())
model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
setattr(model_response, "usage", usage)
return model_response
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