litellm/tests/test_anthropic_compaction_usage.py

from litellm.llms.anthropic.chat.transformation import AnthropicConfig


def test_anthropic_compaction_usage_calculation():
    """
    Test that calculate_usage correctly sums tokens from the iterations array
    as requested in Issue #27060.
    """
    anthropic_config = AnthropicConfig()

    # Mock usage object with compaction iterations
    usage_object = {
        "input_tokens": 100,  # Top-level (excludes compaction)
        "output_tokens": 50,  # Top-level (excludes compaction)
        "iterations": [
            {
                "iteration": 1,
                "type": "compaction",
                "input_tokens": 1000,
                "output_tokens": 500,
            },
            {
                "iteration": 2,
                "type": "message",
                "input_tokens": 100,
                "output_tokens": 50,
            },
        ],
    }

    usage = anthropic_config.calculate_usage(
        usage_object=usage_object, reasoning_content=None
    )

    # Assertions
    # Total prompt tokens should be 1000 + 100 = 1100
    assert usage.prompt_tokens == 1100
    # Total completion tokens should be 500 + 50 = 550
    assert usage.completion_tokens == 550
    # Total tokens should be 1650
    assert usage.total_tokens == 1650

    # Assert details
    assert usage.prompt_tokens_details.text_tokens == 1100

    # Assert iterations passthrough
    assert usage.iterations is not None
    assert len(usage.iterations) == 2
    assert usage.iterations[0]["type"] == "compaction"


def test_anthropic_compaction_usage_with_iteration_cache():
    """
    Test that calculate_usage correctly sums caching tokens FROM iterations.
    This covers the specific case mentioned by JasonPan.
    """
    anthropic_config = AnthropicConfig()

    usage_object = {
        "input_tokens": 100,
        "output_tokens": 50,
        "iterations": [
            {
                "type": "compaction",
                "input_tokens": 500,
                "output_tokens": 200,
                "cache_creation_input_tokens": 50,
                "cache_read_input_tokens": 17000,
            },
            {
                "type": "message",
                "input_tokens": 100,
                "output_tokens": 50,
                "cache_creation_input_tokens": 10,
                "cache_read_input_tokens": 20,
            },
        ],
    }

    usage = anthropic_config.calculate_usage(
        usage_object=usage_object, reasoning_content=None
    )

    # input_tokens sum = 500 + 100 = 600
    # cache_creation sum = 50 + 10 = 60
    # cache_read sum = 17000 + 20 = 17020
    # Total prompt tokens = 600 + 60 + 17020 = 17680
    assert usage.prompt_tokens == 17680
    assert usage.completion_tokens == 250
    assert usage.prompt_tokens_details.cache_creation_tokens == 60
    assert usage.prompt_tokens_details.cached_tokens == 17020


if __name__ == "__main__":
    test_anthropic_compaction_usage_calculation()
    test_anthropic_compaction_usage_with_iteration_cache()