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Dima-Mediator
312 lines
11 KiB
Python
312 lines
11 KiB
Python
import base64
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import json
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import os
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import sys
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import pytest
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from fastapi.testclient import TestClient
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sys.path.insert(
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0, os.path.abspath("../../..")
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) # Adds the parent directory to the system path
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import litellm
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from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
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from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
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from litellm.responses.utils import ResponseAPILoggingUtils, ResponsesAPIRequestUtils
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from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams
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from litellm.types.utils import Usage
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class TestResponsesAPIRequestUtils:
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def test_get_optional_params_responses_api(self):
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"""Test that optional parameters are correctly processed for responses API"""
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# Setup
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model = "gpt-4o"
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config = OpenAIResponsesAPIConfig()
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optional_params = ResponsesAPIOptionalRequestParams(
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{
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"temperature": 0.7,
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"max_output_tokens": 100,
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"prompt": {"id": "pmpt_123"},
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}
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)
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# Execute
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result = ResponsesAPIRequestUtils.get_optional_params_responses_api(
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model=model,
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responses_api_provider_config=config,
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response_api_optional_params=optional_params,
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)
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# Assert
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assert result == optional_params
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assert "temperature" in result
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assert result["temperature"] == 0.7
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assert "max_output_tokens" in result
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assert result["max_output_tokens"] == 100
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assert "prompt" in result
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assert result["prompt"] == {"id": "pmpt_123"}
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def test_get_optional_params_responses_api_unsupported_param(self):
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"""Test that unsupported parameters raise an error"""
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# Setup
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model = "gpt-4o"
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config = OpenAIResponsesAPIConfig()
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optional_params = ResponsesAPIOptionalRequestParams(
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{"temperature": 0.7, "unsupported_param": "value"}
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)
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# Execute and Assert
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with pytest.raises(litellm.UnsupportedParamsError) as excinfo:
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ResponsesAPIRequestUtils.get_optional_params_responses_api(
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model=model,
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responses_api_provider_config=config,
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response_api_optional_params=optional_params,
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)
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assert "unsupported_param" in str(excinfo.value)
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assert model in str(excinfo.value)
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def test_get_requested_response_api_optional_param(self):
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"""Test filtering parameters to only include those in ResponsesAPIOptionalRequestParams"""
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# Setup
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params = {
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"temperature": 0.7,
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"max_output_tokens": 100,
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"prompt": {"id": "pmpt_456"},
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"invalid_param": "value",
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"model": "gpt-4o", # This is not in ResponsesAPIOptionalRequestParams
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}
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# Execute
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result = ResponsesAPIRequestUtils.get_requested_response_api_optional_param(
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params
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)
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# Assert
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assert "temperature" in result
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assert "max_output_tokens" in result
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assert "invalid_param" not in result
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assert "model" not in result
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assert result["temperature"] == 0.7
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assert result["max_output_tokens"] == 100
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assert result["prompt"] == {"id": "pmpt_456"}
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def test_decode_previous_response_id_to_original_previous_response_id(self):
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"""Test decoding a LiteLLM encoded previous_response_id to the original previous_response_id"""
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# Setup
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test_provider = "openai"
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test_model_id = "gpt-4o"
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original_response_id = "resp_abc123"
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# Use the helper method to build an encoded response ID
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encoded_id = ResponsesAPIRequestUtils._build_responses_api_response_id(
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custom_llm_provider=test_provider,
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model_id=test_model_id,
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response_id=original_response_id,
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)
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# Execute
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result = ResponsesAPIRequestUtils.decode_previous_response_id_to_original_previous_response_id(
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encoded_id
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)
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# Assert
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assert result == original_response_id
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# Test with a non-encoded ID
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plain_id = "resp_xyz789"
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result_plain = ResponsesAPIRequestUtils.decode_previous_response_id_to_original_previous_response_id(
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plain_id
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)
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assert result_plain == plain_id
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def test_update_responses_api_response_id_with_model_id_handles_dict(self):
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"""Ensure _update_responses_api_response_id_with_model_id works with dict input"""
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responses_api_response = {"id": "resp_abc123"}
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litellm_metadata = {"model_info": {"id": "gpt-4o"}}
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updated = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
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responses_api_response=responses_api_response,
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custom_llm_provider="openai",
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litellm_metadata=litellm_metadata,
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)
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assert updated["id"] != "resp_abc123"
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decoded = ResponsesAPIRequestUtils._decode_responses_api_response_id(updated["id"])
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assert decoded.get("response_id") == "resp_abc123"
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assert decoded.get("model_id") == "gpt-4o"
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assert decoded.get("custom_llm_provider") == "openai"
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class TestResponseAPILoggingUtils:
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def test_is_response_api_usage_true(self):
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"""Test identification of Response API usage format"""
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# Setup
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usage = {"input_tokens": 10, "output_tokens": 20}
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# Execute
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result = ResponseAPILoggingUtils._is_response_api_usage(usage)
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# Assert
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assert result is True
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def test_is_response_api_usage_false(self):
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"""Test identification of non-Response API usage format"""
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# Setup
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usage = {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}
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# Execute
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result = ResponseAPILoggingUtils._is_response_api_usage(usage)
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# Assert
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assert result is False
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def test_transform_response_api_usage_to_chat_usage(self):
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"""Test transformation from Response API usage to Chat usage format"""
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# Setup
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usage = {
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"input_tokens": 10,
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"output_tokens": 20,
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"total_tokens": 30,
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"input_tokens_details": {"cached_tokens": 2},
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"output_tokens_details": {"reasoning_tokens": 5},
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}
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# Execute
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result = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
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usage
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)
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# Assert
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assert isinstance(result, Usage)
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assert result.prompt_tokens == 10
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assert result.completion_tokens == 20
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assert result.total_tokens == 30
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assert result.prompt_tokens_details and result.prompt_tokens_details.cached_tokens == 2
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def test_transform_response_api_usage_with_none_values(self):
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"""Test transformation handles None values properly"""
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# Setup
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usage = {
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"input_tokens": 0, # Changed from None to 0
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"output_tokens": 20,
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"total_tokens": 20,
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"output_tokens_details": {"reasoning_tokens": 5},
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}
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# Execute
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result = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
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usage
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)
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# Assert
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assert result.prompt_tokens == 0
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assert result.completion_tokens == 20
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assert result.total_tokens == 20
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def test_transform_response_api_usage_calculates_total_from_input_and_output_tokens_if_available(self):
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"""Test transformation calculates total_tokens when it's None and input / output tokens are present"""
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# Setup
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usage = {
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"input_tokens": 15,
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"output_tokens": 25,
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"total_tokens": None,
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}
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# Execute
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result = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
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usage
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)
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# Assert
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assert result.prompt_tokens == 15
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assert result.completion_tokens == 25
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assert result.total_tokens == 40 # 15 + 25
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def test_transform_response_api_usage_with_image_tokens(self):
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"""Test transformation handles image_tokens from image generation responses.
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Note: _transform_response_api_usage_to_chat_usage() is used by multiple
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endpoints including /images/generations and Response API (/responses),
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both of which use the input_tokens/output_tokens format.
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This tests the fix for image generation responses that include image_tokens
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in both input_tokens_details and output_tokens_details.
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Example from gpt-image-1.5:
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- input: text prompt with 13 tokens
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- output: generated image with 272 image tokens + 100 text tokens
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"""
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# Setup - simulating image generation usage from OpenAI
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usage = {
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"input_tokens": 13,
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"output_tokens": 372,
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"total_tokens": 385,
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"input_tokens_details": {
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"image_tokens": 0,
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"text_tokens": 13,
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},
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"output_tokens_details": {
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"image_tokens": 272,
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"text_tokens": 100,
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},
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}
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# Execute
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result = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
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usage
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)
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# Assert - verify basic token counts
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assert isinstance(result, Usage)
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assert result.prompt_tokens == 13
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assert result.completion_tokens == 372
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assert result.total_tokens == 385
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# Assert - verify prompt_tokens_details includes image_tokens and text_tokens
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assert result.prompt_tokens_details is not None
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assert result.prompt_tokens_details.image_tokens == 0
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assert result.prompt_tokens_details.text_tokens == 13
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# Assert - verify completion_tokens_details includes image_tokens and text_tokens
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assert result.completion_tokens_details is not None
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assert result.completion_tokens_details.image_tokens == 272
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assert result.completion_tokens_details.text_tokens == 100
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def test_transform_response_api_usage_mixed_details(self):
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"""Test transformation handles mixed token details (cached + image + audio)."""
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# Setup - hypothetical usage with mixed token types
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usage = {
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"input_tokens": 100,
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"output_tokens": 200,
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"total_tokens": 300,
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"input_tokens_details": {
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"cached_tokens": 50,
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"audio_tokens": 10,
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"image_tokens": 20,
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"text_tokens": 20,
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},
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"output_tokens_details": {
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"reasoning_tokens": 30,
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"image_tokens": 100,
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"text_tokens": 70,
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},
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}
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# Execute
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result = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
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usage
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)
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# Assert - all token detail types should be preserved
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assert result.prompt_tokens_details is not None
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assert result.prompt_tokens_details.cached_tokens == 50
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assert result.prompt_tokens_details.audio_tokens == 10
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assert result.prompt_tokens_details.image_tokens == 20
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assert result.prompt_tokens_details.text_tokens == 20
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assert result.completion_tokens_details is not None
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assert result.completion_tokens_details.reasoning_tokens == 30
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assert result.completion_tokens_details.image_tokens == 100
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assert result.completion_tokens_details.text_tokens == 70
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