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Litellm ruff linting enforcement (#5992)

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* ci(config.yml): add a 'check_code_quality' step

Addresses https://github.com/BerriAI/litellm/issues/5991

* ci(config.yml): check why circle ci doesn't pick up this test

* ci(config.yml): fix to run 'check_code_quality' tests

* fix(__init__.py): fix unprotected import

* fix(__init__.py): don't remove unused imports

* build(ruff.toml): update ruff.toml to ignore unused imports

* fix: fix: ruff + pyright - fix linting + type-checking errors

* fix: fix linting errors

* fix(lago.py): fix module init error

* fix: fix linting errors

* ci(config.yml): cd into correct dir for checks

* fix(proxy_server.py): fix linting error

* fix(utils.py): fix bare except

causes ruff linting errors

* fix: ruff - fix remaining linting errors

* fix(clickhouse.py): use standard logging object

* fix(__init__.py): fix unprotected import

* fix: ruff - fix linting errors

* fix: fix linting errors

* ci(config.yml): cleanup code qa step (formatting handled in local_testing)

* fix(_health_endpoints.py): fix ruff linting errors

* ci(config.yml): just use ruff in check_code_quality pipeline for now

* build(custom_guardrail.py): include missing file

* style(embedding_handler.py): fix ruff check
This commit is contained in:
Krish Dholakia 2024-10-01 16:44:20 -07:00 • committed by GitHub
parent 3fc4ae0d65
commit d57be47b0f
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263 changed files with 1687 additions and 3320 deletions
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21
litellm/llms/huggingface_restapi.py
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@ -13,6 +13,7 @@ import requests
import litellm
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.secret_managers.main import get_secret_str
from litellm.types.completion import ChatCompletionMessageToolCallParam
from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage
@ -181,7 +182,7 @@ class HuggingfaceConfig:
return optional_params
def get_hf_api_key(self) -> Optional[str]:
return litellm.utils.get_secret("HUGGINGFACE_API_KEY")
return get_secret_str("HUGGINGFACE_API_KEY")
def output_parser(generated_text: str):
@ -240,7 +241,7 @@ def read_tgi_conv_models():
# Cache the set for future use
conv_models_cache = conv_models
return tgi_models, conv_models
except:
except Exception:
return set(), set()
@ -372,7 +373,7 @@ class Huggingface(BaseLLM):
]["finish_reason"]
sum_logprob = 0
for token in completion_response[0]["details"]["tokens"]:
if token["logprob"] != None:
if token["logprob"] is not None:
sum_logprob += token["logprob"]
setattr(model_response.choices[0].message, "_logprob", sum_logprob) # type: ignore
if "best_of" in optional_params and optional_params["best_of"] > 1:
@ -386,7 +387,7 @@ class Huggingface(BaseLLM):
):
sum_logprob = 0
for token in item["tokens"]:
if token["logprob"] != None:
if token["logprob"] is not None:
sum_logprob += token["logprob"]
if len(item["generated_text"]) > 0:
message_obj = Message(
@ -417,7 +418,7 @@ class Huggingface(BaseLLM):
prompt_tokens = len(
encoding.encode(input_text)
) ##[TODO] use the llama2 tokenizer here
except:
except Exception:
# this should remain non blocking we should not block a response returning if calculating usage fails
pass
output_text = model_response["choices"][0]["message"].get("content", "")
@ -429,7 +430,7 @@ class Huggingface(BaseLLM):
model_response["choices"][0]["message"].get("content", "")
)
) ##[TODO] use the llama2 tokenizer here
except:
except Exception:
# this should remain non blocking we should not block a response returning if calculating usage fails
pass
else:
@ -559,7 +560,7 @@ class Huggingface(BaseLLM):
True
if "stream" in optional_params
and isinstance(optional_params["stream"], bool)
and optional_params["stream"] == True # type: ignore
and optional_params["stream"] is True # type: ignore
else False
),
}
@ -595,7 +596,7 @@ class Huggingface(BaseLLM):
data["stream"] = ( # type: ignore
True # type: ignore
if "stream" in optional_params
and optional_params["stream"] == True
and optional_params["stream"] is True
else False
)
input_text = prompt
@ -631,7 +632,7 @@ class Huggingface(BaseLLM):
### ASYNC COMPLETION
return self.acompletion(api_base=completion_url, data=data, headers=headers, model_response=model_response, task=task, encoding=encoding, input_text=input_text, model=model, optional_params=optional_params, timeout=timeout) # type: ignore
### SYNC STREAMING
if "stream" in optional_params and optional_params["stream"] == True:
if "stream" in optional_params and optional_params["stream"] is True:
response = requests.post(
completion_url,
headers=headers,
@ -691,7 +692,7 @@ class Huggingface(BaseLLM):
completion_response = response.json()
if isinstance(completion_response, dict):
completion_response = [completion_response]
except:
except Exception:
import traceback
raise HuggingfaceError(