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style: apply black formatting to transformation.py

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This commit is contained in:
Ethan T. 2026-03-14 21:18:35 +08:00
parent 71c9ba0b1b
commit 6658a8ffb3
1 changed files with 14 additions and 11 deletions
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25
litellm/completion_extras/litellm_responses_transformation/transformation.py
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@ -240,10 +240,10 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
if key in ("max_tokens", "max_completion_tokens"):
responses_api_request["max_output_tokens"] = value
elif key == "tools" and value is not None:
responses_api_request[
"tools"
] = self._convert_tools_to_responses_format(
cast(List[Dict[str, Any]], value)
responses_api_request["tools"] = (
self._convert_tools_to_responses_format(
cast(List[Dict[str, Any]], value)
)
)
elif key == "response_format":
text_format = self._transform_response_format_to_text_format(value)
@ -398,6 +398,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
ResponseOutputMessage,
ResponseReasoningItem,
)
try:
from openai.types.responses.response_output_item import (
ResponseApplyPatchToolCall,
@ -460,7 +461,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
accumulated_tool_calls.append(tool_call_dict)
tool_call_index += 1
elif ResponseApplyPatchToolCall is not None and isinstance(item, ResponseApplyPatchToolCall):
elif ResponseApplyPatchToolCall is not None and isinstance(
item, ResponseApplyPatchToolCall
):
from litellm.responses.litellm_completion_transformation.transformation import (
LiteLLMCompletionResponsesConfig,
)
@ -1069,9 +1072,9 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
)
if provider_specific_fields:
function_chunk[
"provider_specific_fields"
] = provider_specific_fields
function_chunk["provider_specific_fields"] = (
provider_specific_fields
)
tool_call_index = parsed_chunk.get("output_index", 0)
tool_call_chunk = ChatCompletionToolCallChunk(
@ -1144,9 +1147,9 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
# Add provider_specific_fields to function if present
if provider_specific_fields:
function_chunk[
"provider_specific_fields"
] = provider_specific_fields
function_chunk["provider_specific_fields"] = (
provider_specific_fields
)
tool_call_index = parsed_chunk.get("output_index", 0)
tool_call_chunk = ChatCompletionToolCallChunk(