ValidationError: 2 validation errors for Usage
input_tokens_details
  Input should be a valid dictionary or instance of InputTokensDetails [type=model_type, input_value=None, input_type=NoneType]
    For further information visit https://errors.pydantic.dev/2.11/v/model_type
output_tokens_details
  Input should be a valid dictionary or instance of OutputTokensDetails [type=model_type, input_value=None, input_type=NoneType]
    For further information visit https://errors.pydantic.dev/2.11/v/model_type

File ~/miniconda3/lib/python3.12/site-packages/agents/models/openai_responses.py:118, in OpenAIResponsesModel.get_response
    usage = Usage(
        requests=1,
        input_tokens=response.usage.input_tokens,
        output_tokens=response.usage.output_tokens,
        total_tokens=response.usage.total_tokens,
        input_tokens_details=response.usage.input_tokens_details,  # ← None causes crash
        output_tokens_details=response.usage.output_tokens_details,  # ← None causes crash
    )

from agents import (
    ModelSettings,
    Agent,
    OpenAIResponsesModel,
    Runner,
)
from openai.types.shared import Reasoning
from openai import AsyncOpenAI

# LiteLLM proxy client
litellm_client = AsyncOpenAI(
    base_url="http://0.0.0.0:4000",
)

# Create agent with ResponseAPI model
test_agent = Agent(
    name="Test Agent",
    instructions="You are a helpful agent.",
    model=OpenAIResponsesModel("claude-4.5-sonnet", openai_client=litellm_client),
    model_settings=ModelSettings(
        reasoning=Reasoning(effort="low", summary="detailed"),
        include_usage=True  # This triggers the usage field requirement
    ),
)

# This would crash before the fix with ValidationError
result = await Runner.run(test_agent, "What do you think about LiteLLM")
result.to_input_list()

response_api_usage: ResponseAPIUsage = (
    ResponseAPIUsage(**usage) if isinstance(usage, dict) else usage
)

if isinstance(usage, dict):
    usage_clean = usage.copy()

    # Ensure numeric fields default to zero rather than None
    for numeric_key in ("input_tokens", "output_tokens", "total_tokens"):
        if usage_clean.get(numeric_key) is None:
            usage_clean[numeric_key] = 0

    # Drop detail fields when provider returns None, or clean nested None values
    for detail_key in ("input_tokens_details", "output_tokens_details"):
        detail_value = usage_clean.get(detail_key)
        if detail_value is None:
            usage_clean.pop(detail_key, None)
        elif isinstance(detail_value, dict):
            usage_clean[detail_key] = {
                k: v for k, v in detail_value.items() if v is not None
            }

    response_api_usage: ResponseAPIUsage = ResponseAPIUsage(**usage_clean)
else:
    response_api_usage = usage

# Normalise token detail fields so they match OpenAI format
input_details = response_api_usage.input_tokens_details
if input_details is None:
    input_details = InputTokensDetails(cached_tokens=0)
elif input_details.cached_tokens is None:
    input_details.cached_tokens = 0
response_api_usage.input_tokens_details = input_details

output_details = response_api_usage.output_tokens_details
if output_details is None:
    output_details = OutputTokensDetails(reasoning_tokens=0)
elif output_details.reasoning_tokens is None:
    output_details.reasoning_tokens = 0
response_api_usage.output_tokens_details = output_details

def test_response_api_transform_usage_with_none_values(self):
    """Test transformation handles None values properly"""
    usage = {
        "input_tokens": None,  # None should become 0
        "output_tokens": 20,
        "total_tokens": None,  # None should become 0
        "input_tokens_details": None,  # None should be normalized
        "output_tokens_details": {"reasoning_tokens": None},  # Nested None
    }

    result = ResponseAPILoggingUtils.transform_usage_to_litellm_usage(usage)

    assert result.prompt_tokens == 0
    assert result.completion_tokens == 20
    assert result.total_tokens == 20
    assert result.prompt_tokens_details is not None
    assert result.prompt_tokens_details.cached_tokens == 0