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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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30
litellm/llms/sagemaker/sagemaker.py
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@ -8,7 +8,7 @@ import types
from copy import deepcopy
from enum import Enum
from functools import partial
from typing import Any, AsyncIterator, Callable, Iterator, List, Optional, Union
from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Optional, Union
import httpx # type: ignore
import requests # type: ignore
@ -112,7 +112,7 @@ class SagemakerLLM(BaseAWSLLM):
):
try:
from botocore.credentials import Credentials
except ImportError as e:
except ImportError:
raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
## CREDENTIALS ##
# pop aws_secret_access_key, aws_access_key_id, aws_session_token, aws_region_name from kwargs, since completion calls fail with them
@ -123,7 +123,7 @@ class SagemakerLLM(BaseAWSLLM):
aws_role_name = optional_params.pop("aws_role_name", None)
aws_session_name = optional_params.pop("aws_session_name", None)
aws_profile_name = optional_params.pop("aws_profile_name", None)
aws_bedrock_runtime_endpoint = optional_params.pop(
optional_params.pop(
"aws_bedrock_runtime_endpoint", None
) # https://bedrock-runtime.{region_name}.amazonaws.com
aws_web_identity_token = optional_params.pop("aws_web_identity_token", None)
@ -175,7 +175,7 @@ class SagemakerLLM(BaseAWSLLM):
from botocore.auth import SigV4Auth
from botocore.awsrequest import AWSRequest
from botocore.credentials import Credentials
except ImportError as e:
except ImportError:
raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
sigv4 = SigV4Auth(credentials, "sagemaker", aws_region_name)
@ -244,7 +244,7 @@ class SagemakerLLM(BaseAWSLLM):
hf_model_name = (
hf_model_name or model
) # pass in hf model name for pulling it's prompt template - (e.g. `hf_model_name="meta-llama/Llama-2-7b-chat-hf` applies the llama2 chat template to the prompt)
prompt = prompt_factory(model=hf_model_name, messages=messages)
prompt: str = prompt_factory(model=hf_model_name, messages=messages) # type: ignore
return prompt
@ -256,10 +256,10 @@ class SagemakerLLM(BaseAWSLLM):
print_verbose: Callable,
encoding,
logging_obj,
optional_params: dict,
timeout: Optional[Union[float, httpx.Timeout]] = None,
custom_prompt_dict={},
hf_model_name=None,
optional_params=None,
litellm_params=None,
logger_fn=None,
acompletion: bool = False,
@ -277,7 +277,7 @@ class SagemakerLLM(BaseAWSLLM):
openai_like_chat_completions = DatabricksChatCompletion()
inference_params["stream"] = True if stream is True else False
_data = {
_data: Dict[str, Any] = {
"model": model,
"messages": messages,
**inference_params,
@ -310,7 +310,7 @@ class SagemakerLLM(BaseAWSLLM):
logger_fn=logger_fn,
timeout=timeout,
encoding=encoding,
headers=prepared_request.headers,
headers=prepared_request.headers, # type: ignore
custom_endpoint=True,
custom_llm_provider="sagemaker_chat",
streaming_decoder=custom_stream_decoder, # type: ignore
@ -474,7 +474,7 @@ class SagemakerLLM(BaseAWSLLM):
try:
sync_response = sync_handler.post(
url=prepared_request.url,
headers=prepared_request.headers,
headers=prepared_request.headers, # type: ignore
json=_data,
timeout=timeout,
)
@ -559,7 +559,7 @@ class SagemakerLLM(BaseAWSLLM):
self,
api_base: str,
headers: dict,
data: str,
data: dict,
logging_obj,
client=None,
):
@ -598,7 +598,7 @@ class SagemakerLLM(BaseAWSLLM):
except httpx.HTTPStatusError as err:
error_code = err.response.status_code
raise SagemakerError(status_code=error_code, message=err.response.text)
except httpx.TimeoutException as e:
except httpx.TimeoutException:
raise SagemakerError(status_code=408, message="Timeout error occurred.")
except Exception as e:
raise SagemakerError(status_code=500, message=str(e))
@ -638,7 +638,7 @@ class SagemakerLLM(BaseAWSLLM):
make_call=partial(
self.make_async_call,
api_base=prepared_request.url,
headers=prepared_request.headers,
headers=prepared_request.headers, # type: ignore
data=data,
logging_obj=logging_obj,
),
@ -716,7 +716,7 @@ class SagemakerLLM(BaseAWSLLM):
try:
response = await async_handler.post(
url=prepared_request.url,
headers=prepared_request.headers,
headers=prepared_request.headers, # type: ignore
json=data,
timeout=timeout,
)
@ -794,8 +794,8 @@ class SagemakerLLM(BaseAWSLLM):
print_verbose: Callable,
encoding,
logging_obj,
optional_params: dict,
custom_prompt_dict={},
optional_params=None,
litellm_params=None,
logger_fn=None,
):
@ -1032,7 +1032,7 @@ class AWSEventStreamDecoder:
yield self._chunk_parser_messages_api(chunk_data=_data)
else:
yield self._chunk_parser(chunk_data=_data)
except json.JSONDecodeError as e:
except json.JSONDecodeError:
# Handle or log any unparseable data at the end
verbose_logger.error(
f"Warning: Unparseable JSON data remained: {accumulated_json}"