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3aa5fe732f
Add two targeted branch-coverage tests that exercise the previously uncovered adjustment paths in litellm/llms/fireworks_ai/cost_calculator.py: - test_fireworks_ai_cache_read_token_pricing: passes cache_read_input_tokens=100 against fireworks_ai/kimi-k2p5 (which carries cache_read_input_token_cost=1e-7 in the pricing config) and asserts the exact differential-adjusted cost. - test_fireworks_ai_cache_creation_token_pricing: injects a synthetic cache_creation_input_token_cost=8e-7 into the local model_cost copy and asserts the upward adjustment; scoped to the test's own get_model_cost_map call so it does not pollute other tests. All three fireworks cache tests (original + two new) pass locally. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2976 lines
188 KiB
Python
2976 lines
188 KiB
Python
import os
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import sys
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import traceback
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import litellm.cost_calculator
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sys.path.insert(0, os.path.abspath("../..")) # Adds the parent directory to the system path
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import asyncio
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import os
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import time
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from typing import Optional
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from unittest.mock import AsyncMock, MagicMock, patch
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import base64
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import pytest
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import litellm
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from litellm import (
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TranscriptionResponse,
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completion_cost,
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cost_per_token,
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get_max_tokens,
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model_cost,
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open_ai_chat_completion_models,
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)
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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import json
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import httpx
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from litellm.types.utils import PromptTokensDetails
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from litellm.litellm_core_utils.litellm_logging import CustomLogger
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class CustomLoggingHandler(CustomLogger):
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response_cost: Optional[float] = None
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def __init__(self):
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super().__init__()
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def log_success_event(self, kwargs, response_obj, start_time, end_time):
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self.response_cost = kwargs["response_cost"]
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async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
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print(f"kwargs - {kwargs}")
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print(f"kwargs response cost - {kwargs.get('response_cost')}")
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self.response_cost = kwargs["response_cost"]
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print(f"response_cost: {self.response_cost} ")
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def log_failure_event(self, kwargs, response_obj, start_time, end_time):
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print("Reaches log failure event!")
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self.response_cost = kwargs["response_cost"]
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async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
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print("Reaches async log failure event!")
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self.response_cost = kwargs["response_cost"]
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@pytest.mark.parametrize("sync_mode", [True, False])
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@pytest.mark.asyncio
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async def test_custom_pricing(sync_mode):
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new_handler = CustomLoggingHandler()
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litellm.callbacks = [new_handler]
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if sync_mode:
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response = litellm.completion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey!"}],
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mock_response="What do you want?",
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input_cost_per_token=0.0,
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output_cost_per_token=0.0,
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)
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time.sleep(5)
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else:
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response = await litellm.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey!"}],
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mock_response="What do you want?",
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input_cost_per_token=0.0,
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output_cost_per_token=0.0,
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)
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await asyncio.sleep(5)
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print(f"new_handler.response_cost: {new_handler.response_cost}")
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assert new_handler.response_cost is not None
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assert new_handler.response_cost == 0
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@pytest.mark.parametrize(
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"sync_mode",
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[True, False],
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)
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@pytest.mark.asyncio
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async def test_failure_completion_cost(sync_mode):
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new_handler = CustomLoggingHandler()
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litellm.callbacks = [new_handler]
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if sync_mode:
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try:
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response = litellm.completion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey!"}],
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mock_response=Exception("this should trigger an error"),
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)
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except Exception:
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pass
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time.sleep(5)
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else:
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try:
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response = await litellm.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey!"}],
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mock_response=Exception("this should trigger an error"),
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)
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except Exception:
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pass
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await asyncio.sleep(5)
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print(f"new_handler.response_cost: {new_handler.response_cost}")
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assert new_handler.response_cost is not None
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assert new_handler.response_cost == 0
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def test_custom_pricing_as_completion_cost_param():
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from litellm import Choices, Message, ModelResponse
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from litellm.utils import Usage
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resp = ModelResponse(
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id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac",
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choices=[
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Choices(
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finish_reason=None,
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index=0,
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message=Message(
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content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a",
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role="assistant",
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),
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)
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],
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created=1700775391,
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model="ft:gpt-3.5-turbo:my-org:custom_suffix:id",
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object="chat.completion",
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system_fingerprint=None,
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usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38),
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)
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cost = litellm.completion_cost(
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completion_response=resp,
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custom_cost_per_token={
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"input_cost_per_token": 1000,
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"output_cost_per_token": 20,
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},
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)
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expected_cost = 1000 * 21 + 17 * 20
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assert round(cost, 5) == round(expected_cost, 5)
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def test_get_gpt3_tokens():
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max_tokens = get_max_tokens("gpt-3.5-turbo")
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print(max_tokens)
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assert max_tokens == 4096
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# print(results)
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# test_get_gpt3_tokens()
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def test_get_gemini_tokens():
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# # 🦄🦄🦄🦄🦄🦄🦄🦄
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max_tokens = get_max_tokens("gemini/gemini-1.5-flash")
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assert max_tokens == 8192
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print(max_tokens)
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# test_get_palm_tokens()
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def test_zephyr_hf_tokens():
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max_tokens = get_max_tokens("huggingface/HuggingFaceH4/zephyr-7b-beta")
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print(max_tokens)
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assert max_tokens == 32768
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# test_zephyr_hf_tokens()
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def test_cost_ft_gpt_35():
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try:
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# this tests if litellm.completion_cost can calculate cost for ft:gpt-3.5-turbo:my-org:custom_suffix:id
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# it needs to lookup ft:gpt-3.5-turbo in the litellm model_cost map to get the correct cost
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from litellm import Choices, Message, ModelResponse
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from litellm.utils import Usage
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litellm.set_verbose = True
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resp = ModelResponse(
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id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac",
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choices=[
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Choices(
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finish_reason=None,
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index=0,
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message=Message(
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content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a",
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role="assistant",
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),
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)
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],
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created=1700775391,
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model="ft:gpt-3.5-turbo:my-org:custom_suffix:id",
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object="chat.completion",
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system_fingerprint=None,
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usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38),
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)
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cost = litellm.completion_cost(completion_response=resp, custom_llm_provider="openai")
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print("\n Calculated Cost for ft:gpt-3.5", cost)
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input_cost = model_cost["ft:gpt-3.5-turbo"]["input_cost_per_token"]
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output_cost = model_cost["ft:gpt-3.5-turbo"]["output_cost_per_token"]
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print(input_cost, output_cost)
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expected_cost = (input_cost * resp.usage.prompt_tokens) + (output_cost * resp.usage.completion_tokens)
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print("\n Excpected cost", expected_cost)
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assert cost == expected_cost
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except Exception as e:
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print(f"Error: {e}")
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pytest.fail(f"Cost Calc failed for ft:gpt-3.5. Expected {expected_cost}, Calculated cost {cost}")
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# test_cost_ft_gpt_35()
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def test_cost_azure_gpt_35():
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try:
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# this tests if litellm.completion_cost can calculate cost for azure/chatgpt-deployment-2 which maps to azure/gpt-3.5-turbo
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# for this test we check if passing `model` to completion_cost overrides the completion cost
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from litellm import Choices, Message, ModelResponse
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from litellm.utils import Usage
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resp = ModelResponse(
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id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac",
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choices=[
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Choices(
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finish_reason=None,
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index=0,
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message=Message(
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content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a",
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role="assistant",
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),
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)
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],
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model="azure/gpt-35-turbo", # azure always has model written like this
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usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38),
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)
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cost = litellm.completion_cost(completion_response=resp, model="azure/chatgpt-deployment-2")
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print("\n Calculated Cost for azure/gpt-3.5-turbo", cost)
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input_cost = model_cost["azure/gpt-35-turbo"]["input_cost_per_token"]
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output_cost = model_cost["azure/gpt-35-turbo"]["output_cost_per_token"]
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expected_cost = (input_cost * resp.usage.prompt_tokens) + (output_cost * resp.usage.completion_tokens)
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print("\n Excpected cost", expected_cost)
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assert cost == expected_cost
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except Exception as e:
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pytest.fail(f"Cost Calc failed for azure/gpt-3.5-turbo. {str(e)}")
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# test_cost_azure_gpt_35()
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def test_cost_azure_embedding():
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try:
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import asyncio
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litellm.set_verbose = True
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async def _test():
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response = await litellm.aembedding(
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model="azure/text-embedding-ada-002",
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input=["good morning from litellm", "gm"],
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)
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print(response)
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return response
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response = asyncio.run(_test())
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cost = litellm.completion_cost(completion_response=response)
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print("Cost", cost)
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expected_cost = float("7e-07")
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assert cost == expected_cost
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except Exception as e:
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pytest.fail(f"Cost Calc failed for azure/gpt-3.5-turbo. Expected {expected_cost}, Calculated cost {cost}")
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# test_cost_azure_embedding()
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def test_cost_bedrock_pricing_actual_calls():
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litellm.set_verbose = True
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model = "anthropic.claude-3-5-sonnet-20240620-v1:0"
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messages = [{"role": "user", "content": "Hey, how's it going?"}]
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response = litellm.completion(model=model, messages=messages, mock_response="hello cool one")
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print("response", response)
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cost = litellm.completion_cost(
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model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
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completion_response=response,
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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)
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assert cost > 0
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def test_whisper_openai():
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litellm.set_verbose = True
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transcription = TranscriptionResponse(
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text="Four score and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure."
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)
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setattr(transcription, "duration", 3)
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transcription._hidden_params = {
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"model": "whisper-1",
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"custom_llm_provider": "openai",
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"optional_params": {},
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"model_id": None,
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}
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_total_time_in_seconds = 3
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cost = litellm.completion_cost(model="whisper-1", completion_response=transcription)
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print(f"cost: {cost}")
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print(f"whisper dict: {litellm.model_cost['whisper-1']}")
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expected_cost = round(
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litellm.model_cost["whisper-1"]["output_cost_per_second"] * _total_time_in_seconds,
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5,
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)
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assert round(cost, 5) == round(expected_cost, 5)
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def test_whisper_azure():
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litellm.set_verbose = True
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transcription = TranscriptionResponse(
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text="Four score and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure."
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)
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transcription._hidden_params = {
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"model": "whisper-1",
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"custom_llm_provider": "azure",
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"optional_params": {},
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"model_id": None,
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}
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_total_time_in_seconds = 3
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setattr(transcription, "duration", _total_time_in_seconds)
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cost = litellm.completion_cost(model="azure/azure-whisper", completion_response=transcription)
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print(f"cost: {cost}")
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print(f"whisper dict: {litellm.model_cost['whisper-1']}")
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expected_cost = round(
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litellm.model_cost["whisper-1"]["output_cost_per_second"] * _total_time_in_seconds,
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5,
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)
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assert round(cost, 5) == round(expected_cost, 5)
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def test_dalle_3_azure_cost_tracking():
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litellm.set_verbose = True
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# model = "azure/dall-e-3-test"
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# response = litellm.image_generation(
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# model=model,
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# prompt="A cute baby sea otter",
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# api_version="2023-12-01-preview",
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# api_base=os.getenv("AZURE_SWEDEN_API_BASE"),
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# api_key=os.getenv("AZURE_SWEDEN_API_KEY"),
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# base_model="dall-e-3",
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# )
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# print(f"response: {response}")
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response = litellm.ImageResponse(
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created=1710265780,
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data=[
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{
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"b64_json": None,
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"revised_prompt": "A close-up image of an adorable baby sea otter. Its fur is thick and fluffy to provide buoyancy and insulation against the cold water. Its eyes are round, curious and full of life. It's lying on its back, floating effortlessly on the calm sea surface under the warm sun. Surrounding the otter are patches of colorful kelp drifting along the gentle waves, giving the scene a touch of vibrancy. The sea otter has its small paws folded on its chest, and it seems to be taking a break from its play.",
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"url": "test-azure-blob-url-with-sas-token",
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}
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],
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)
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response.usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
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response._hidden_params = {"model": "dall-e-3", "model_id": None}
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print(f"response hidden params: {response._hidden_params}")
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cost = litellm.completion_cost(completion_response=response, call_type="image_generation")
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assert cost > 0
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def test_replicate_llama3_cost_tracking():
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litellm.set_verbose = True
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model = "replicate/meta/meta-llama-3-8b-instruct"
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litellm.register_model(
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{
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"replicate/meta/meta-llama-3-8b-instruct": {
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"input_cost_per_token": 0.00000005,
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"output_cost_per_token": 0.00000025,
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"litellm_provider": "replicate",
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}
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}
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)
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response = litellm.ModelResponse(
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id="chatcmpl-cad7282f-7f68-41e7-a5ab-9eb33ae301dc",
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choices=[
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litellm.utils.Choices(
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finish_reason="stop",
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index=0,
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message=litellm.utils.Message(
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content="I'm doing well, thanks for asking! I'm here to help you with any questions or tasks you may have. How can I assist you today?",
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role="assistant",
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),
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)
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],
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created=1714401369,
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model="replicate/meta/meta-llama-3-8b-instruct",
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object="chat.completion",
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system_fingerprint=None,
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usage=litellm.utils.Usage(prompt_tokens=48, completion_tokens=31, total_tokens=79),
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)
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cost = litellm.completion_cost(
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completion_response=response,
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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)
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print(f"cost: {cost}")
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cost = round(cost, 5)
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expected_cost = round(
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litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"]["input_cost_per_token"] * 48
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+ litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"]["output_cost_per_token"] * 31,
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5,
|
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)
|
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assert cost == expected_cost
|
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|
|
|
|
@pytest.mark.parametrize("is_streaming", [True, False]) #
|
|
def test_groq_response_cost_tracking(is_streaming):
|
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from litellm.utils import (
|
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CallTypes,
|
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Choices,
|
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Delta,
|
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Message,
|
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ModelResponse,
|
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StreamingChoices,
|
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Usage,
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)
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|
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response = ModelResponse(
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id="chatcmpl-876cce24-e520-4cf8-8649-562a9be11c02",
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choices=[
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Choices(
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finish_reason="stop",
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index=0,
|
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message=Message(
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content="Hi! I'm an AI, so I don't have emotions or feelings like humans do, but I'm functioning properly and ready to help with any questions or topics you'd like to discuss! How can I assist you today?",
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role="assistant",
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),
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)
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],
|
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created=1717519830,
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model="llama3-70b-8192",
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object="chat.completion",
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system_fingerprint="fp_c1a4bcec29",
|
|
usage=Usage(completion_tokens=46, prompt_tokens=17, total_tokens=63),
|
|
)
|
|
response._hidden_params["custom_llm_provider"] = "groq"
|
|
print(response)
|
|
|
|
response_cost = litellm.response_cost_calculator(
|
|
response_object=response,
|
|
model="groq/llama-3.3-70b-versatile",
|
|
custom_llm_provider="groq",
|
|
call_type=CallTypes.acompletion.value,
|
|
optional_params={},
|
|
)
|
|
|
|
assert isinstance(response_cost, float)
|
|
assert response_cost > 0.0
|
|
|
|
print(f"response_cost: {response_cost}")
|
|
|
|
|
|
from litellm.types.utils import CallTypes
|
|
|
|
|
|
def test_together_ai_qwen_completion_cost():
|
|
input_kwargs = {
|
|
"completion_response": litellm.ModelResponse(
|
|
**{
|
|
"id": "890db0c33c4ef94b-SJC",
|
|
"choices": [
|
|
{
|
|
"finish_reason": "eos",
|
|
"index": 0,
|
|
"message": {
|
|
"content": "I am Qwen, a large language model created by Alibaba Cloud.",
|
|
"role": "assistant",
|
|
},
|
|
}
|
|
],
|
|
"created": 1717900130,
|
|
"model": "together_ai/qwen/Qwen2-72B-Instruct",
|
|
"object": "chat.completion",
|
|
"system_fingerprint": None,
|
|
"usage": {
|
|
"completion_tokens": 15,
|
|
"prompt_tokens": 23,
|
|
"total_tokens": 38,
|
|
},
|
|
}
|
|
),
|
|
"model": "qwen/Qwen2-72B-Instruct",
|
|
"prompt": "",
|
|
"messages": [],
|
|
"completion": "",
|
|
"total_time": 0.0,
|
|
"call_type": "completion",
|
|
"custom_llm_provider": "together_ai",
|
|
"region_name": None,
|
|
"size": None,
|
|
"quality": None,
|
|
"n": None,
|
|
"custom_cost_per_token": None,
|
|
"custom_cost_per_second": None,
|
|
}
|
|
|
|
response = litellm.cost_calculator.get_model_params_and_category(
|
|
model_name="qwen/Qwen2-72B-Instruct", call_type=CallTypes.completion
|
|
)
|
|
|
|
assert response == "together-ai-41.1b-80b"
|
|
|
|
|
|
@pytest.mark.parametrize("provider", ["gemini"])
|
|
def test_gemini_completion_cost(provider):
|
|
"""
|
|
Check if cost correctly calculated for gemini models based on context window
|
|
"""
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
model_name = "gemini-2.0-flash"
|
|
prompt_tokens = 128.0
|
|
output_tokens = 228.0
|
|
## GET MODEL FROM LITELLM.MODEL_INFO
|
|
model_info = litellm.get_model_info(model=model_name, custom_llm_provider=provider)
|
|
|
|
## EXPECTED COST
|
|
input_cost = prompt_tokens * model_info["input_cost_per_token"]
|
|
output_cost = output_tokens * model_info["output_cost_per_token"]
|
|
|
|
## CALCULATED COST
|
|
calculated_input_cost, calculated_output_cost = cost_per_token(
|
|
model=model_name,
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=output_tokens,
|
|
custom_llm_provider=provider,
|
|
)
|
|
|
|
assert calculated_input_cost == input_cost
|
|
assert calculated_output_cost == output_cost
|
|
|
|
|
|
def _count_characters(text):
|
|
# Remove white spaces and count characters
|
|
filtered_text = "".join(char for char in text if not char.isspace())
|
|
return len(filtered_text)
|
|
|
|
|
|
def test_vertex_ai_completion_cost():
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
prompt_tokens = 100
|
|
|
|
model_info = litellm.get_model_info(model="gemini-2.0-flash")
|
|
|
|
print("\nExpected model info:\n{}\n\n".format(model_info))
|
|
|
|
expected_input_cost = prompt_tokens * model_info["input_cost_per_token"]
|
|
|
|
## CALCULATED COST
|
|
calculated_input_cost, calculated_output_cost = cost_per_token(
|
|
model="gemini-2.0-flash",
|
|
custom_llm_provider="vertex_ai",
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=0,
|
|
)
|
|
|
|
assert round(expected_input_cost, 6) == round(calculated_input_cost, 6)
|
|
print("expected_input_cost: {}".format(expected_input_cost))
|
|
print("calculated_input_cost: {}".format(calculated_input_cost))
|
|
|
|
|
|
@pytest.mark.skip(reason="new test - WIP, working on fixing this")
|
|
def test_vertex_ai_medlm_completion_cost():
|
|
"""Test for medlm completion cost ."""
|
|
|
|
with pytest.raises(Exception) as e:
|
|
model = "vertex_ai/medlm-medium"
|
|
messages = [{"role": "user", "content": "Test MedLM completion cost."}]
|
|
predictive_cost = completion_cost(model=model, messages=messages, custom_llm_provider="vertex_ai")
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
model = "vertex_ai/medlm-medium"
|
|
messages = [{"role": "user", "content": "Test MedLM completion cost."}]
|
|
predictive_cost = completion_cost(model=model, messages=messages, custom_llm_provider="vertex_ai")
|
|
assert predictive_cost > 0
|
|
|
|
model = "vertex_ai/medlm-large"
|
|
messages = [{"role": "user", "content": "Test MedLM completion cost."}]
|
|
predictive_cost = completion_cost(model=model, messages=messages)
|
|
assert predictive_cost > 0
|
|
|
|
|
|
def test_vertex_ai_claude_completion_cost():
|
|
from litellm import Choices, Message, ModelResponse
|
|
from litellm.utils import Usage
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
litellm.set_verbose = True
|
|
input_tokens = litellm.token_counter(
|
|
model="vertex_ai/claude-3-sonnet@20240229",
|
|
messages=[{"role": "user", "content": "Hey, how's it going?"}],
|
|
)
|
|
print(f"input_tokens: {input_tokens}")
|
|
output_tokens = litellm.token_counter(
|
|
model="vertex_ai/claude-3-sonnet@20240229",
|
|
text="It's all going well",
|
|
count_response_tokens=True,
|
|
)
|
|
print(f"output_tokens: {output_tokens}")
|
|
response = ModelResponse(
|
|
id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac",
|
|
choices=[
|
|
Choices(
|
|
finish_reason=None,
|
|
index=0,
|
|
message=Message(
|
|
content="It's all going well",
|
|
role="assistant",
|
|
),
|
|
)
|
|
],
|
|
created=1700775391,
|
|
model="claude-3-sonnet",
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
usage=Usage(
|
|
prompt_tokens=input_tokens,
|
|
completion_tokens=output_tokens,
|
|
total_tokens=input_tokens + output_tokens,
|
|
),
|
|
)
|
|
cost = litellm.completion_cost(
|
|
model="vertex_ai/claude-3-sonnet",
|
|
completion_response=response,
|
|
messages=[{"role": "user", "content": "Hey, how's it going?"}],
|
|
)
|
|
predicted_cost = input_tokens * 0.000003 + 0.000015 * output_tokens
|
|
assert cost == predicted_cost
|
|
|
|
|
|
def test_vertex_ai_embedding_completion_cost(caplog):
|
|
"""
|
|
Relevant issue - https://github.com/BerriAI/litellm/issues/4630
|
|
"""
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
text = "The quick brown fox jumps over the lazy dog."
|
|
input_tokens = litellm.token_counter(model="vertex_ai/text-embedding-004", text=text)
|
|
|
|
model_info = litellm.get_model_info(model="vertex_ai/text-embedding-004")
|
|
|
|
print("\nExpected model info:\n{}\n\n".format(model_info))
|
|
|
|
expected_input_cost = input_tokens * model_info["input_cost_per_token"]
|
|
|
|
## CALCULATED COST
|
|
calculated_input_cost, calculated_output_cost = cost_per_token(
|
|
model="text-embedding-004",
|
|
custom_llm_provider="vertex_ai",
|
|
prompt_tokens=input_tokens,
|
|
call_type="aembedding",
|
|
)
|
|
|
|
assert round(expected_input_cost, 6) == round(calculated_input_cost, 6)
|
|
print("expected_input_cost: {}".format(expected_input_cost))
|
|
print("calculated_input_cost: {}".format(calculated_input_cost))
|
|
|
|
captured_logs = [rec.message for rec in caplog.records]
|
|
for item in captured_logs:
|
|
print("\nitem:{}\n".format(item))
|
|
if "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): Exception occured " in item:
|
|
raise Exception("Error log raised for calculating embedding cost")
|
|
|
|
|
|
# def test_vertex_ai_embedding_completion_cost_e2e():
|
|
# """
|
|
# Relevant issue - https://github.com/BerriAI/litellm/issues/4630
|
|
# """
|
|
# from test_amazing_vertex_completion import load_vertex_ai_credentials
|
|
|
|
# load_vertex_ai_credentials()
|
|
# os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
# litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
# text = "The quick brown fox jumps over the lazy dog."
|
|
# input_tokens = litellm.token_counter(
|
|
# model="vertex_ai/textembedding-gecko", text=text
|
|
# )
|
|
|
|
# model_info = litellm.get_model_info(model="vertex_ai/textembedding-gecko")
|
|
|
|
# print("\nExpected model info:\n{}\n\n".format(model_info))
|
|
|
|
# expected_input_cost = input_tokens * model_info["input_cost_per_token"]
|
|
|
|
# ## CALCULATED COST
|
|
# resp = litellm.embedding(model="textembedding-gecko", input=[text])
|
|
|
|
# calculated_input_cost = resp._hidden_params["response_cost"]
|
|
|
|
# assert round(expected_input_cost, 6) == round(calculated_input_cost, 6)
|
|
# print("expected_input_cost: {}".format(expected_input_cost))
|
|
# print("calculated_input_cost: {}".format(calculated_input_cost))
|
|
|
|
# assert False
|
|
|
|
|
|
@pytest.mark.parametrize("sync_mode", [True, False])
|
|
@pytest.mark.asyncio
|
|
async def test_completion_cost_hidden_params(sync_mode):
|
|
litellm.return_response_headers = True
|
|
if sync_mode:
|
|
response = litellm.completion(
|
|
model="gpt-3.5-turbo",
|
|
messages=[{"role": "user", "content": "Hey, how's it going?"}],
|
|
mock_response="Hello world",
|
|
)
|
|
else:
|
|
response = await litellm.acompletion(
|
|
model="gpt-3.5-turbo",
|
|
messages=[{"role": "user", "content": "Hey, how's it going?"}],
|
|
mock_response="Hello world",
|
|
)
|
|
|
|
assert "response_cost" in response._hidden_params
|
|
assert isinstance(response._hidden_params["response_cost"], float)
|
|
|
|
|
|
def test_vertex_ai_gemini_predict_cost():
|
|
model = "gemini-2.0-flash"
|
|
messages = [{"role": "user", "content": "Hey, hows it going???"}]
|
|
predictive_cost = completion_cost(model=model, messages=messages)
|
|
|
|
assert predictive_cost > 0
|
|
|
|
|
|
def test_vertex_ai_llama_predict_cost():
|
|
model = "meta/llama3-405b-instruct-maas"
|
|
messages = [{"role": "user", "content": "Hey, hows it going???"}]
|
|
custom_llm_provider = "vertex_ai"
|
|
predictive_cost = completion_cost(model=model, messages=messages, custom_llm_provider=custom_llm_provider)
|
|
|
|
assert predictive_cost == 0
|
|
|
|
|
|
@pytest.mark.parametrize("usage", ["litellm_usage", "openai_usage"])
|
|
def test_vertex_ai_mistral_predict_cost(usage):
|
|
from litellm.types.utils import Choices, Message, ModelResponse, Usage
|
|
|
|
if usage == "litellm_usage":
|
|
response_usage = Usage(prompt_tokens=32, completion_tokens=55, total_tokens=87)
|
|
else:
|
|
from openai.types.completion_usage import CompletionUsage
|
|
|
|
response_usage = CompletionUsage(prompt_tokens=32, completion_tokens=55, total_tokens=87)
|
|
response_object = ModelResponse(
|
|
id="26c0ef045020429d9c5c9b078c01e564",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="stop",
|
|
index=0,
|
|
message=Message(
|
|
content="Hello! I'm Litellm Bot, your helpful assistant. While I can't provide real-time weather updates, I can help you find a reliable weather service or guide you on how to check the weather on your device. Would you like assistance with that?",
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
),
|
|
)
|
|
],
|
|
created=1722124652,
|
|
model="vertex_ai/mistral-large",
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
usage=response_usage,
|
|
)
|
|
model = "mistral-large@2407"
|
|
messages = [{"role": "user", "content": "Hey, hows it going???"}]
|
|
custom_llm_provider = "vertex_ai"
|
|
predictive_cost = completion_cost(
|
|
completion_response=response_object,
|
|
model=model,
|
|
messages=messages,
|
|
custom_llm_provider=custom_llm_provider,
|
|
)
|
|
|
|
assert predictive_cost > 0
|
|
|
|
|
|
@pytest.mark.parametrize("model", ["openai/tts-1", "azure/tts-1", "openai/gpt-4o-mini-tts"])
|
|
def test_completion_cost_tts(model):
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
cost = completion_cost(
|
|
model=model,
|
|
prompt="the quick brown fox jumped over the lazy dogs",
|
|
call_type="speech",
|
|
)
|
|
|
|
assert cost > 0
|
|
|
|
|
|
def test_completion_cost_anthropic():
|
|
"""
|
|
model_name: claude-3-haiku-20240307
|
|
litellm_params:
|
|
model: anthropic/claude-3-haiku-20240307
|
|
max_tokens: 4096
|
|
"""
|
|
router = litellm.Router(
|
|
model_list=[
|
|
{
|
|
"model_name": "claude-3-haiku-20240307",
|
|
"litellm_params": {
|
|
"model": "anthropic/claude-3-haiku-20240307",
|
|
"max_tokens": 4096,
|
|
},
|
|
}
|
|
]
|
|
)
|
|
data = {
|
|
"model": "claude-3-haiku-20240307",
|
|
"prompt_tokens": 21,
|
|
"completion_tokens": 20,
|
|
"response_time_ms": 871.7040000000001,
|
|
"custom_llm_provider": "anthropic",
|
|
"region_name": None,
|
|
"prompt_characters": 0,
|
|
"completion_characters": 0,
|
|
"custom_cost_per_token": None,
|
|
"custom_cost_per_second": None,
|
|
"call_type": "acompletion",
|
|
}
|
|
|
|
input_cost, output_cost = cost_per_token(**data)
|
|
|
|
assert input_cost > 0
|
|
assert output_cost > 0
|
|
|
|
print(input_cost)
|
|
print(output_cost)
|
|
|
|
|
|
def test_completion_cost_azure_common_deployment_name():
|
|
from litellm.utils import (
|
|
CallTypes,
|
|
Choices,
|
|
Delta,
|
|
Message,
|
|
ModelResponse,
|
|
StreamingChoices,
|
|
Usage,
|
|
)
|
|
|
|
router = litellm.Router(
|
|
model_list=[
|
|
{
|
|
"model_name": "gpt-4",
|
|
"litellm_params": {
|
|
"model": "azure/gpt-4-0314",
|
|
"max_tokens": 4096,
|
|
"api_key": os.getenv("AZURE_API_KEY"),
|
|
"api_base": os.getenv("AZURE_API_BASE"),
|
|
},
|
|
"model_info": {"base_model": "azure/gpt-4"},
|
|
}
|
|
]
|
|
)
|
|
|
|
response = ModelResponse(
|
|
id="chatcmpl-876cce24-e520-4cf8-8649-562a9be11c02",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="stop",
|
|
index=0,
|
|
message=Message(
|
|
content="Hi! I'm an AI, so I don't have emotions or feelings like humans do, but I'm functioning properly and ready to help with any questions or topics you'd like to discuss! How can I assist you today?",
|
|
role="assistant",
|
|
),
|
|
)
|
|
],
|
|
created=1717519830,
|
|
model="gpt-4",
|
|
object="chat.completion",
|
|
system_fingerprint="fp_c1a4bcec29",
|
|
usage=Usage(completion_tokens=46, prompt_tokens=17, total_tokens=63),
|
|
)
|
|
response._hidden_params["custom_llm_provider"] = "azure"
|
|
print(response)
|
|
|
|
with patch.object(litellm.cost_calculator, "completion_cost", new=MagicMock()) as mock_client:
|
|
_ = litellm.response_cost_calculator(
|
|
response_object=response,
|
|
model="gpt-4-0314",
|
|
custom_llm_provider="azure",
|
|
call_type=CallTypes.acompletion.value,
|
|
optional_params={},
|
|
base_model="azure/gpt-4",
|
|
)
|
|
|
|
mock_client.assert_called()
|
|
|
|
print(f"mock_client.call_args: {mock_client.call_args.kwargs}")
|
|
assert "azure/gpt-4" == mock_client.call_args.kwargs["base_model"]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model, custom_llm_provider",
|
|
[
|
|
("claude-sonnet-4-6", "anthropic"),
|
|
("claude-haiku-4-5", "anthropic"),
|
|
],
|
|
)
|
|
def test_completion_cost_prompt_caching(model, custom_llm_provider):
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
from litellm.utils import Choices, Message, ModelResponse, Usage
|
|
|
|
## WRITE TO CACHE ## (MORE EXPENSIVE)
|
|
response_1 = ModelResponse(
|
|
id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="length",
|
|
index=0,
|
|
message=Message(
|
|
content="Hello! I'm doing well, thank you for",
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
),
|
|
)
|
|
],
|
|
created=1725036547,
|
|
model=model,
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
usage=Usage(
|
|
completion_tokens=10,
|
|
prompt_tokens=114,
|
|
total_tokens=124,
|
|
prompt_tokens_details=PromptTokensDetails(cached_tokens=0),
|
|
cache_creation_input_tokens=100,
|
|
cache_read_input_tokens=0,
|
|
),
|
|
)
|
|
|
|
cost_1 = completion_cost(model=model, completion_response=response_1)
|
|
|
|
_model_info = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider)
|
|
expected_cost = (
|
|
(
|
|
response_1.usage.prompt_tokens
|
|
- response_1.usage.prompt_tokens_details.cached_tokens
|
|
- response_1.usage.prompt_tokens_details.cache_creation_tokens
|
|
)
|
|
* _model_info["input_cost_per_token"]
|
|
+ (response_1.usage.prompt_tokens_details.cached_tokens or 0) * _model_info["cache_read_input_token_cost"]
|
|
+ (response_1.usage.cache_creation_input_tokens or 0) * _model_info["cache_creation_input_token_cost"]
|
|
+ (response_1.usage.completion_tokens or 0) * _model_info["output_cost_per_token"]
|
|
) # Cost of processing (non-cache hit + cache hit) + Cost of cache-writing (cache writing)
|
|
|
|
assert round(expected_cost, 5) == round(cost_1, 5)
|
|
|
|
print(f"expected_cost: {expected_cost}, cost_1: {cost_1}")
|
|
|
|
## READ FROM CACHE ## (LESS EXPENSIVE)
|
|
response_2 = ModelResponse(
|
|
id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="length",
|
|
index=0,
|
|
message=Message(
|
|
content="Hello! I'm doing well, thank you for",
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
),
|
|
)
|
|
],
|
|
created=1725036547,
|
|
model=model,
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
usage=Usage(
|
|
completion_tokens=10,
|
|
prompt_tokens=114,
|
|
total_tokens=134,
|
|
prompt_tokens_details=PromptTokensDetails(cached_tokens=100),
|
|
cache_creation_input_tokens=0,
|
|
cache_read_input_tokens=100,
|
|
),
|
|
)
|
|
|
|
cost_2 = completion_cost(model=model, completion_response=response_2)
|
|
|
|
assert cost_1 > cost_2
|
|
|
|
|
|
@pytest.mark.flaky(retries=6, delay=2)
|
|
@pytest.mark.parametrize(
|
|
"model",
|
|
[
|
|
"databricks/databricks-meta-llama-3.2-3b-instruct",
|
|
"databricks/databricks-meta-llama-3-70b-instruct",
|
|
"databricks/databricks-dbrx-instruct",
|
|
# "databricks/databricks-mixtral-8x7b-instruct",
|
|
],
|
|
)
|
|
@pytest.mark.skip(reason="databricks is having an active outage")
|
|
def test_completion_cost_databricks(model):
|
|
litellm._turn_on_debug()
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
model, messages = model, [{"role": "user", "content": "What is 2+2?"}]
|
|
|
|
resp = litellm.completion(model=model, messages=messages) # works fine
|
|
|
|
print(resp)
|
|
print(f"hidden_params: {resp._hidden_params}")
|
|
assert resp._hidden_params["response_cost"] > 0
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model",
|
|
[
|
|
"databricks/databricks-bge-large-en",
|
|
"databricks/databricks-gte-large-en",
|
|
],
|
|
)
|
|
def test_completion_cost_databricks_embedding(model, monkeypatch):
|
|
"""
|
|
Test completion cost calculation for Databricks embedding models using mocked HTTP responses.
|
|
"""
|
|
base_url = "https://my.workspace.cloud.databricks.com/serving-endpoints"
|
|
api_key = "dapimykey"
|
|
monkeypatch.setenv("DATABRICKS_API_BASE", base_url)
|
|
monkeypatch.setenv("DATABRICKS_API_KEY", api_key)
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
mock_response_data = {
|
|
"object": "list",
|
|
"model": model.split("/")[1],
|
|
"data": [
|
|
{
|
|
"index": 0,
|
|
"object": "embedding",
|
|
"embedding": [
|
|
0.06768798828125,
|
|
-0.01291656494140625,
|
|
-0.0501708984375,
|
|
0.0245361328125,
|
|
-0.030364990234375,
|
|
],
|
|
}
|
|
],
|
|
"usage": {
|
|
"prompt_tokens": 8,
|
|
"total_tokens": 8,
|
|
"completion_tokens": 0,
|
|
"completion_tokens_details": None,
|
|
"prompt_tokens_details": None,
|
|
},
|
|
}
|
|
|
|
mock_response = MagicMock(spec=httpx.Response)
|
|
mock_response.status_code = 200
|
|
mock_response.json.return_value = mock_response_data
|
|
|
|
sync_handler = HTTPHandler()
|
|
|
|
with patch.object(HTTPHandler, "post", return_value=mock_response):
|
|
resp = litellm.embedding(model=model, input=["hey, how's it going?"], client=sync_handler)
|
|
|
|
print(resp)
|
|
cost = completion_cost(completion_response=resp)
|
|
|
|
|
|
from litellm.llms.fireworks_ai.cost_calculator import get_base_model_for_pricing
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model, base_model",
|
|
[
|
|
("fireworks_ai/llama-v3p3-70b-instruct", "fireworks-ai-above-16b"),
|
|
],
|
|
)
|
|
def test_get_model_params_fireworks_ai(model, base_model):
|
|
pricing_model = get_base_model_for_pricing(model_name=model)
|
|
assert base_model == pricing_model
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model",
|
|
[
|
|
"fireworks_ai/llama-v3p3-70b-instruct",
|
|
],
|
|
)
|
|
def test_completion_cost_fireworks_ai(model):
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
messages = [{"role": "user", "content": "Hey, how's it going?"}]
|
|
resp = litellm.completion(model=model, messages=messages) # works fine
|
|
|
|
print(resp)
|
|
cost = completion_cost(completion_response=resp)
|
|
|
|
|
|
def test_fireworks_ai_cache_token_pricing():
|
|
"""Test that Fireworks AI cost calculator accounts for cache read/creation tokens.
|
|
|
|
Regression test for https://github.com/BerriAI/litellm/issues/24774
|
|
"""
|
|
from litellm.llms.fireworks_ai.cost_calculator import cost_per_token
|
|
from litellm.types.utils import Usage
|
|
|
|
# Simulate usage with cache read tokens
|
|
usage_with_cache = Usage(
|
|
prompt_tokens=1000,
|
|
completion_tokens=500,
|
|
cache_read_input_tokens=800,
|
|
cache_creation_input_tokens=0,
|
|
)
|
|
|
|
# Simulate usage without cache tokens
|
|
usage_no_cache = Usage(
|
|
prompt_tokens=1000,
|
|
completion_tokens=500,
|
|
)
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
# Use kimi-k2p5 which has cache_read_input_token_cost in the pricing config
|
|
prompt_cost_cached, completion_cost_cached = cost_per_token(model="fireworks_ai/kimi-k2p5", usage=usage_with_cache)
|
|
prompt_cost_no_cache, completion_cost_no_cache = cost_per_token(
|
|
model="fireworks_ai/kimi-k2p5", usage=usage_no_cache
|
|
)
|
|
|
|
# Completion cost should be the same regardless of cache
|
|
assert completion_cost_cached == completion_cost_no_cache
|
|
|
|
# kimi-k2p5 has cache_read_input_token_cost (1e-07) < input_cost_per_token (6e-07),
|
|
# so prompt cost with 800 cache-read tokens must be cheaper
|
|
assert prompt_cost_cached < prompt_cost_no_cache, (
|
|
"Prompt cost with 800 cache-read tokens should be less than full-price for the same total prompt tokens"
|
|
)
|
|
|
|
|
|
def test_fireworks_ai_cache_read_token_pricing():
|
|
"""Deterministic branch-coverage test for the cache_read_input_tokens adjustment path.
|
|
|
|
Exercises lines 86-91 of litellm/llms/fireworks_ai/cost_calculator.py.
|
|
|
|
kimi-k2p5 pricing (from model_cost map):
|
|
input_cost_per_token = 6e-7
|
|
cache_read_input_token_cost = 1e-7 (cheaper — cache hit discount)
|
|
output_cost_per_token = 3e-6
|
|
|
|
With prompt_tokens=1000 and cache_read_input_tokens=100:
|
|
base = 1000 * 6e-7 = 6e-4
|
|
cache adjustment = 100 * (1e-7 - 6e-7) = -5e-5
|
|
expected prompt_cost = 5.5e-4
|
|
"""
|
|
from litellm.llms.fireworks_ai.cost_calculator import cost_per_token
|
|
from litellm.types.utils import Usage
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
INPUT_COST = 6e-7
|
|
CACHE_READ_COST = 1e-7
|
|
PROMPT_TOKENS = 1000
|
|
CACHE_READ_TOKENS = 100
|
|
COMPLETION_TOKENS = 50
|
|
|
|
usage = Usage(
|
|
prompt_tokens=PROMPT_TOKENS,
|
|
completion_tokens=COMPLETION_TOKENS,
|
|
cache_read_input_tokens=CACHE_READ_TOKENS,
|
|
)
|
|
|
|
prompt_cost, completion_cost_val = cost_per_token(model="fireworks_ai/kimi-k2p5", usage=usage)
|
|
|
|
expected_prompt_cost = PROMPT_TOKENS * INPUT_COST + CACHE_READ_TOKENS * (CACHE_READ_COST - INPUT_COST)
|
|
expected_completion_cost = COMPLETION_TOKENS * 3e-6
|
|
|
|
assert abs(prompt_cost - expected_prompt_cost) < 1e-12, (
|
|
f"Cache-read prompt cost {prompt_cost} != expected {expected_prompt_cost}"
|
|
)
|
|
assert abs(completion_cost_val - expected_completion_cost) < 1e-12, (
|
|
f"Completion cost {completion_cost_val} != expected {expected_completion_cost}"
|
|
)
|
|
# Sanity: cheaper than paying full rate for all 1000 tokens
|
|
assert prompt_cost < PROMPT_TOKENS * INPUT_COST, "Cache-read should reduce prompt cost below full input rate"
|
|
|
|
|
|
def test_fireworks_ai_cache_creation_token_pricing():
|
|
"""Deterministic branch-coverage test for the cache_creation_input_tokens adjustment path.
|
|
|
|
Exercises lines 94-99 of litellm/llms/fireworks_ai/cost_calculator.py.
|
|
|
|
No live fireworks model currently has cache_creation_input_token_cost set, so this
|
|
test injects a synthetic value (8e-7) directly into litellm.model_cost to isolate
|
|
the branch. The injection is scoped to the local model_cost copy created by
|
|
get_model_cost_map and does not persist beyond this test.
|
|
|
|
Injected pricing for fireworks_ai/kimi-k2p5:
|
|
input_cost_per_token = 6e-7
|
|
cache_creation_input_token_cost = 8e-7 (premium — first-write penalty)
|
|
|
|
With prompt_tokens=1000 and cache_creation_input_tokens=100:
|
|
base = 1000 * 6e-7 = 6e-4
|
|
creation adjustment = 100 * (8e-7 - 6e-7) = +2e-5
|
|
expected prompt_cost = 6.2e-4
|
|
"""
|
|
from litellm.llms.fireworks_ai.cost_calculator import cost_per_token
|
|
from litellm.types.utils import Usage
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
INPUT_COST = 6e-7
|
|
CACHE_CREATION_COST = 8e-7
|
|
PROMPT_TOKENS = 1000
|
|
CACHE_CREATION_TOKENS = 100
|
|
COMPLETION_TOKENS = 50
|
|
|
|
# Inject cache_creation_input_token_cost into the local model_cost copy
|
|
litellm.model_cost["fireworks_ai/kimi-k2p5"]["cache_creation_input_token_cost"] = CACHE_CREATION_COST
|
|
|
|
usage = Usage(
|
|
prompt_tokens=PROMPT_TOKENS,
|
|
completion_tokens=COMPLETION_TOKENS,
|
|
cache_creation_input_tokens=CACHE_CREATION_TOKENS,
|
|
)
|
|
|
|
prompt_cost, completion_cost_val = cost_per_token(model="fireworks_ai/kimi-k2p5", usage=usage)
|
|
|
|
expected_prompt_cost = PROMPT_TOKENS * INPUT_COST + CACHE_CREATION_TOKENS * (CACHE_CREATION_COST - INPUT_COST)
|
|
expected_completion_cost = COMPLETION_TOKENS * 3e-6
|
|
|
|
assert abs(prompt_cost - expected_prompt_cost) < 1e-12, (
|
|
f"Cache-creation prompt cost {prompt_cost} != expected {expected_prompt_cost}"
|
|
)
|
|
assert abs(completion_cost_val - expected_completion_cost) < 1e-12, (
|
|
f"Completion cost {completion_cost_val} != expected {expected_completion_cost}"
|
|
)
|
|
# Sanity: more expensive than paying the plain input rate for all 1000 tokens
|
|
assert prompt_cost > PROMPT_TOKENS * INPUT_COST, "Cache-creation should increase prompt cost above full input rate"
|
|
|
|
|
|
def test_cost_azure_openai_prompt_caching():
|
|
from litellm.utils import Choices, Message, ModelResponse, Usage
|
|
from litellm.types.utils import (
|
|
PromptTokensDetailsWrapper,
|
|
CompletionTokensDetailsWrapper,
|
|
)
|
|
from litellm import get_model_info
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
model = "azure/o1-mini"
|
|
|
|
## LLM API CALL ## (MORE EXPENSIVE)
|
|
response_1 = ModelResponse(
|
|
id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="length",
|
|
index=0,
|
|
message=Message(
|
|
content="Hello! I'm doing well, thank you for",
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
),
|
|
)
|
|
],
|
|
created=1725036547,
|
|
model=model,
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
usage=Usage(
|
|
completion_tokens=10,
|
|
prompt_tokens=14,
|
|
total_tokens=24,
|
|
completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=2),
|
|
),
|
|
)
|
|
|
|
## PROMPT CACHE HIT ## (LESS EXPENSIVE)
|
|
response_2 = ModelResponse(
|
|
id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="length",
|
|
index=0,
|
|
message=Message(
|
|
content="Hello! I'm doing well, thank you for",
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
),
|
|
)
|
|
],
|
|
created=1725036547,
|
|
model=model,
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
usage=Usage(
|
|
completion_tokens=10,
|
|
prompt_tokens=0,
|
|
total_tokens=10,
|
|
prompt_tokens_details=PromptTokensDetailsWrapper(
|
|
cached_tokens=14,
|
|
),
|
|
completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=2),
|
|
),
|
|
)
|
|
|
|
cost_1 = completion_cost(model=model, completion_response=response_1)
|
|
cost_2 = completion_cost(model=model, completion_response=response_2)
|
|
assert cost_1 > cost_2
|
|
|
|
model_info = get_model_info(model=model, custom_llm_provider="azure")
|
|
usage = response_2.usage
|
|
|
|
_expected_cost2 = (
|
|
(usage.prompt_tokens - usage.prompt_tokens_details.cached_tokens) * model_info["input_cost_per_token"]
|
|
+ (usage.completion_tokens * model_info["output_cost_per_token"])
|
|
+ (usage.prompt_tokens_details.cached_tokens * model_info["cache_read_input_token_cost"])
|
|
)
|
|
|
|
print("_expected_cost2", _expected_cost2)
|
|
print("cost_2", cost_2)
|
|
|
|
assert abs(cost_2 - _expected_cost2) < 1e-5 # Allow for small floating-point differences
|
|
|
|
|
|
def test_completion_cost_vertex_llama3():
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
from litellm.utils import Choices, Message, ModelResponse, Usage
|
|
|
|
response = ModelResponse(
|
|
id="2024-09-19|14:52:01.823070-07|3.10.13.64|-333502972",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="stop",
|
|
index=0,
|
|
message=Message(
|
|
content="My name is Litellm Bot, and I'm here to help you with any questions or tasks you may have. As for the weather, I'd be happy to provide you with the current conditions and forecast for your location. However, I'm a large language model, I don't have real-time access to your location, so I'll need you to tell me where you are or provide me with a specific location you're interested in knowing the weather for.\\n\\nOnce you provide me with that information, I can give you the current weather conditions, including temperature, humidity, wind speed, and more, as well as a forecast for the next few days. Just let me know how I can assist you!",
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
),
|
|
)
|
|
],
|
|
created=1726782721,
|
|
model="vertex_ai/meta/llama3-405b-instruct-maas",
|
|
object="chat.completion",
|
|
system_fingerprint="",
|
|
usage=Usage(
|
|
completion_tokens=152,
|
|
prompt_tokens=27,
|
|
total_tokens=179,
|
|
completion_tokens_details=None,
|
|
),
|
|
)
|
|
|
|
model = "vertex_ai/meta/llama3-8b-instruct-maas"
|
|
cost = completion_cost(model=model, completion_response=response)
|
|
|
|
assert cost == 0
|
|
|
|
|
|
def test_cost_openai_prompt_caching():
|
|
from litellm.utils import Choices, Message, ModelResponse, Usage
|
|
from litellm import get_model_info
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
model = "gpt-4o-mini-2024-07-18"
|
|
|
|
## LLM API CALL ## (MORE EXPENSIVE)
|
|
response_1 = ModelResponse(
|
|
id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="length",
|
|
index=0,
|
|
message=Message(
|
|
content="Hello! I'm doing well, thank you for",
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
),
|
|
)
|
|
],
|
|
created=1725036547,
|
|
model=model,
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
usage=Usage(
|
|
completion_tokens=10,
|
|
prompt_tokens=14,
|
|
total_tokens=24,
|
|
),
|
|
)
|
|
|
|
## PROMPT CACHE HIT ## (LESS EXPENSIVE)
|
|
response_2 = ModelResponse(
|
|
id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="length",
|
|
index=0,
|
|
message=Message(
|
|
content="Hello! I'm doing well, thank you for",
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
),
|
|
)
|
|
],
|
|
created=1725036547,
|
|
model=model,
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
usage=Usage(
|
|
completion_tokens=10,
|
|
prompt_tokens=14,
|
|
total_tokens=10,
|
|
prompt_tokens_details=PromptTokensDetails(
|
|
cached_tokens=14,
|
|
),
|
|
),
|
|
)
|
|
|
|
cost_1 = completion_cost(model=model, completion_response=response_1)
|
|
cost_2 = completion_cost(model=model, completion_response=response_2)
|
|
assert cost_1 > cost_2
|
|
|
|
model_info = get_model_info(model=model, custom_llm_provider="openai")
|
|
usage = response_2.usage
|
|
|
|
_expected_cost2 = (
|
|
(usage.prompt_tokens - usage.prompt_tokens_details.cached_tokens) * model_info["input_cost_per_token"]
|
|
+ usage.completion_tokens * model_info["output_cost_per_token"]
|
|
+ usage.prompt_tokens_details.cached_tokens * model_info["cache_read_input_token_cost"]
|
|
)
|
|
|
|
print("_expected_cost2", _expected_cost2)
|
|
print("cost_2", cost_2)
|
|
|
|
assert cost_2 == _expected_cost2
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model",
|
|
[
|
|
"cohere/rerank-english-v3.0",
|
|
"azure_ai/cohere-rerank-v3-english",
|
|
],
|
|
)
|
|
def test_completion_cost_azure_ai_rerank(model):
|
|
from litellm import RerankResponse, rerank
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
response = RerankResponse(
|
|
id="b01dbf2e-63c8-4981-9e69-32241da559ed",
|
|
results=[
|
|
{
|
|
"document": {
|
|
"id": "1",
|
|
"text": "Paris is the capital of France.",
|
|
},
|
|
"index": 0,
|
|
"relevance_score": 0.990732,
|
|
},
|
|
],
|
|
meta={
|
|
"billed_units": {
|
|
"search_units": 1,
|
|
}
|
|
},
|
|
)
|
|
print("response", response)
|
|
model = model
|
|
cost = completion_cost(model=model, completion_response=response, call_type="arerank")
|
|
assert cost > 0
|
|
|
|
|
|
def test_together_ai_embedding_completion_cost():
|
|
from litellm.utils import Choices, EmbeddingResponse, Message, ModelResponse, Usage
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
response = EmbeddingResponse(
|
|
model="togethercomputer/m2-bert-80M-8k-retrieval",
|
|
data=[
|
|
{
|
|
"embedding": [
|
|
-0.18039076,
|
|
0.11614138,
|
|
0.37174946,
|
|
0.27238843,
|
|
-0.21933095,
|
|
-0.15207036,
|
|
0.17764972,
|
|
-0.08700938,
|
|
-0.23863377,
|
|
-0.24203257,
|
|
0.20441775,
|
|
0.04630023,
|
|
-0.07832973,
|
|
-0.193581,
|
|
0.2009999,
|
|
-0.30106494,
|
|
0.21179546,
|
|
-0.23836501,
|
|
-0.14919636,
|
|
-0.045276586,
|
|
0.08645845,
|
|
-0.027714893,
|
|
-0.009854938,
|
|
0.25298217,
|
|
-0.1081501,
|
|
-0.2383125,
|
|
0.23080236,
|
|
0.011114239,
|
|
0.06954927,
|
|
-0.21081704,
|
|
0.06937218,
|
|
-0.16756944,
|
|
-0.2030545,
|
|
-0.19809915,
|
|
-0.031914014,
|
|
-0.15959585,
|
|
0.17361341,
|
|
0.30239972,
|
|
-0.09923253,
|
|
0.12680714,
|
|
-0.13018028,
|
|
0.1302273,
|
|
0.19179879,
|
|
0.17068875,
|
|
0.065124996,
|
|
-0.15515316,
|
|
0.08250379,
|
|
0.07309733,
|
|
-0.07283606,
|
|
0.21411736,
|
|
0.15457751,
|
|
-0.08725933,
|
|
0.07227311,
|
|
0.056812778,
|
|
-0.077683985,
|
|
0.06833304,
|
|
0.0328722,
|
|
0.2719641,
|
|
-0.06989647,
|
|
0.22805125,
|
|
0.14953858,
|
|
0.0792393,
|
|
0.07793462,
|
|
0.16176109,
|
|
-0.15616545,
|
|
-0.25149494,
|
|
-0.065352336,
|
|
-0.38410214,
|
|
-0.27288514,
|
|
0.13946335,
|
|
-0.21873806,
|
|
0.1365704,
|
|
0.11738016,
|
|
-0.1141173,
|
|
0.022973377,
|
|
-0.16935326,
|
|
0.026940947,
|
|
-0.09990286,
|
|
-0.05157219,
|
|
0.21006724,
|
|
0.15897459,
|
|
0.011987913,
|
|
0.02576497,
|
|
-0.11819022,
|
|
-0.09184997,
|
|
-0.31881434,
|
|
-0.17055357,
|
|
-0.09523704,
|
|
0.008458802,
|
|
-0.015483258,
|
|
0.038404867,
|
|
0.014673892,
|
|
-0.041162584,
|
|
0.002691519,
|
|
0.04601874,
|
|
0.059108324,
|
|
0.007177156,
|
|
0.066804245,
|
|
0.038554087,
|
|
-0.038720075,
|
|
-0.2145991,
|
|
-0.15713418,
|
|
-0.03712905,
|
|
-0.066650696,
|
|
0.04227769,
|
|
0.018708894,
|
|
-0.26332214,
|
|
0.0012769096,
|
|
-0.13878848,
|
|
-0.33141217,
|
|
0.118736655,
|
|
0.03026654,
|
|
0.1017467,
|
|
-0.08000539,
|
|
0.00092649367,
|
|
0.13062756,
|
|
-0.03785864,
|
|
-0.2038575,
|
|
0.07655428,
|
|
-0.24818295,
|
|
-0.0600955,
|
|
0.114760056,
|
|
0.027571939,
|
|
-0.047068622,
|
|
-0.19806816,
|
|
0.0774084,
|
|
-0.05213658,
|
|
-0.042000014,
|
|
0.051924672,
|
|
-0.14131106,
|
|
-0.2309609,
|
|
0.20305444,
|
|
0.0700591,
|
|
0.13863273,
|
|
-0.06145084,
|
|
-0.039423797,
|
|
-0.055951696,
|
|
0.04732105,
|
|
0.078736484,
|
|
0.2566198,
|
|
0.054494765,
|
|
0.017602794,
|
|
-0.107575715,
|
|
-0.017887019,
|
|
-0.26046592,
|
|
-0.077659994,
|
|
-0.08430523,
|
|
0.18806657,
|
|
-0.12292346,
|
|
0.06288608,
|
|
-0.106739804,
|
|
-0.06600645,
|
|
-0.14719339,
|
|
-0.05070389,
|
|
0.23234129,
|
|
-0.034023043,
|
|
0.056019265,
|
|
-0.03627352,
|
|
0.11740493,
|
|
0.060294818,
|
|
-0.21726903,
|
|
-0.09775424,
|
|
0.27007395,
|
|
0.28328258,
|
|
0.022495652,
|
|
0.13218465,
|
|
0.07199022,
|
|
-0.15933248,
|
|
0.02381037,
|
|
-0.08288268,
|
|
0.020621575,
|
|
0.17395815,
|
|
0.06978612,
|
|
0.18418784,
|
|
-0.12663148,
|
|
-0.21287888,
|
|
0.21239495,
|
|
0.10222956,
|
|
0.03952703,
|
|
-0.066957936,
|
|
-0.035802357,
|
|
0.03683884,
|
|
0.22524163,
|
|
-0.029355489,
|
|
-0.11534147,
|
|
-0.041979663,
|
|
-0.012147716,
|
|
-0.07279564,
|
|
0.17417553,
|
|
0.05546745,
|
|
-0.1773277,
|
|
-0.26984993,
|
|
0.31703642,
|
|
0.05958132,
|
|
-0.14933203,
|
|
-0.084655434,
|
|
0.074604444,
|
|
-0.077568695,
|
|
0.25167143,
|
|
-0.17753932,
|
|
-0.006415411,
|
|
0.068613894,
|
|
-0.0031754146,
|
|
-0.0039771493,
|
|
0.015294107,
|
|
0.11839045,
|
|
-0.04570732,
|
|
0.103238374,
|
|
-0.09678329,
|
|
-0.21713412,
|
|
0.047976546,
|
|
-0.14346297,
|
|
0.17429878,
|
|
-0.31257913,
|
|
0.15445377,
|
|
-0.10576352,
|
|
-0.16792995,
|
|
-0.17988597,
|
|
-0.14238739,
|
|
-0.088244036,
|
|
0.2760547,
|
|
0.088823885,
|
|
-0.08074319,
|
|
-0.028918687,
|
|
0.107819095,
|
|
0.12004892,
|
|
0.13343112,
|
|
-0.1332874,
|
|
-0.0946055,
|
|
-0.20433402,
|
|
0.17760132,
|
|
0.11774745,
|
|
0.16756779,
|
|
-0.0937686,
|
|
0.23887308,
|
|
0.27315456,
|
|
0.08657822,
|
|
0.027402503,
|
|
-0.06605757,
|
|
0.29859266,
|
|
-0.21552202,
|
|
0.026192812,
|
|
0.1328459,
|
|
0.13072926,
|
|
0.19236198,
|
|
0.01760772,
|
|
-0.042355467,
|
|
0.08815041,
|
|
-0.013158761,
|
|
-0.23350924,
|
|
-0.043668386,
|
|
-0.15479062,
|
|
-0.024266671,
|
|
0.08113482,
|
|
0.14451654,
|
|
-0.29152337,
|
|
-0.028919466,
|
|
0.15022752,
|
|
-0.26923147,
|
|
0.23846954,
|
|
0.03292609,
|
|
-0.23572414,
|
|
-0.14883325,
|
|
-0.12743121,
|
|
-0.052229587,
|
|
-0.14230779,
|
|
0.284658,
|
|
0.36885592,
|
|
-0.13176951,
|
|
-0.16442224,
|
|
-0.20283924,
|
|
0.048434418,
|
|
-0.16231743,
|
|
-0.0010730615,
|
|
0.1408047,
|
|
0.09481033,
|
|
0.018139571,
|
|
-0.030843062,
|
|
0.13304341,
|
|
-0.1516288,
|
|
-0.051779557,
|
|
0.46940327,
|
|
-0.07969027,
|
|
-0.051570967,
|
|
-0.038892798,
|
|
0.11187677,
|
|
0.1703113,
|
|
-0.39926252,
|
|
0.06859773,
|
|
0.08364686,
|
|
0.14696898,
|
|
0.026642298,
|
|
0.13225247,
|
|
0.05730332,
|
|
0.35534015,
|
|
0.11189959,
|
|
0.039673142,
|
|
-0.056019083,
|
|
0.15707816,
|
|
-0.11053284,
|
|
0.12823457,
|
|
0.20075114,
|
|
0.040237684,
|
|
-0.19367051,
|
|
0.13039409,
|
|
-0.26038498,
|
|
-0.05770229,
|
|
-0.009781617,
|
|
0.15812513,
|
|
-0.10420735,
|
|
-0.020158196,
|
|
0.13160926,
|
|
-0.20823349,
|
|
-0.045596864,
|
|
-0.2074525,
|
|
0.1546387,
|
|
0.30158705,
|
|
0.13175933,
|
|
0.11967154,
|
|
-0.09094463,
|
|
0.0019428955,
|
|
-0.06745872,
|
|
0.02998099,
|
|
-0.18385777,
|
|
0.014330351,
|
|
0.07141392,
|
|
-0.17461702,
|
|
0.099743806,
|
|
-0.016181415,
|
|
0.1661396,
|
|
0.070834026,
|
|
0.110713825,
|
|
0.14590909,
|
|
0.15404254,
|
|
-0.21658006,
|
|
0.00715122,
|
|
-0.10229453,
|
|
-0.09980027,
|
|
-0.09406554,
|
|
-0.014849227,
|
|
-0.26285952,
|
|
0.069972225,
|
|
0.05732395,
|
|
-0.10685719,
|
|
0.037572138,
|
|
-0.18863359,
|
|
-0.00083297276,
|
|
-0.16088934,
|
|
-0.117982,
|
|
-0.16381365,
|
|
-0.008932539,
|
|
-0.06549256,
|
|
-0.08928683,
|
|
0.29934987,
|
|
0.16532114,
|
|
-0.27117223,
|
|
-0.12302226,
|
|
-0.28685933,
|
|
-0.14041144,
|
|
-0.0062569617,
|
|
-0.20768198,
|
|
-0.15385273,
|
|
0.20506454,
|
|
-0.21685128,
|
|
0.1081962,
|
|
-0.13133131,
|
|
0.18937315,
|
|
0.14751591,
|
|
0.2786974,
|
|
-0.060183275,
|
|
0.10365405,
|
|
0.109799005,
|
|
-0.044105034,
|
|
-0.04260162,
|
|
0.025758557,
|
|
0.07590695,
|
|
0.0726137,
|
|
-0.09882405,
|
|
0.26437432,
|
|
0.15884234,
|
|
0.115702584,
|
|
0.0015900572,
|
|
0.11673009,
|
|
-0.18648374,
|
|
0.3080215,
|
|
-0.26407364,
|
|
-0.15610488,
|
|
0.12658228,
|
|
-0.05672454,
|
|
0.016239772,
|
|
-0.092462406,
|
|
-0.36205122,
|
|
-0.2925843,
|
|
-0.104364775,
|
|
-0.2598659,
|
|
-0.14073578,
|
|
0.10225995,
|
|
-0.2612335,
|
|
-0.17479639,
|
|
0.17488293,
|
|
-0.2437756,
|
|
0.114384405,
|
|
-0.13196659,
|
|
-0.067482576,
|
|
0.024756929,
|
|
0.11779123,
|
|
0.2751749,
|
|
-0.13306957,
|
|
-0.034118645,
|
|
-0.14177705,
|
|
0.27164033,
|
|
0.06266008,
|
|
0.11199439,
|
|
-0.09814594,
|
|
0.13231735,
|
|
0.019105865,
|
|
-0.2652429,
|
|
-0.12924416,
|
|
0.0840029,
|
|
0.098754935,
|
|
0.025883028,
|
|
-0.33059177,
|
|
-0.10544467,
|
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0.1387053,
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0.29880494,
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0.101618655,
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0.14514285,
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0.21512671,
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0.13212202,
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0.08307805,
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0.10144794,
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-0.024808172,
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0.21877879,
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-0.071282186,
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-8.786433e-05,
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-0.014574037,
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-0.11954953,
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-0.096931055,
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0.1090451,
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0.15424186,
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0.22510754,
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0.1566895,
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0.12401036,
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0.024711533,
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0.24737844,
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0.0652544,
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0.15362221,
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0.096014425,
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0.17337392,
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0.015355054,
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0.055885684,
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0.069017515,
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0.32854614,
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0.0063175815,
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-0.09058244,
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0.000681382,
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0.13190223,
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0.009358909,
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-0.12205342,
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0.08268384,
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-0.260608,
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-0.022601532,
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0.14736788,
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0.061933476,
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-0.07815901,
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0.110823035,
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-0.00875032,
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-0.064237975,
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-0.04546554,
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-0.05909862,
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0.23463917,
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-0.20451859,
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0.10957323,
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0.0002913844,
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0.13701706,
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-0.058854006,
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0.30768716,
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0.095908396,
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0.14793666,
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0.30881998,
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-0.018806668,
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-0.15902956,
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0.07953607,
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-0.07259314,
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0.17318867,
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0.123503335,
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-0.11327983,
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-0.092871994,
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0.31053993,
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0.09460377,
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-0.03127119,
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0.2255386,
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0.18821372,
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-0.16890709,
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-0.04607359,
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0.13054903,
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-0.051014878,
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0.054293603,
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-0.07299424,
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-0.06728367,
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-0.052388195,
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-0.29960096,
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0.24709718,
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0.029514981,
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-0.01951599,
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-0.072677284,
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-0.25097945,
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0.03758907,
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0.14380245,
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-0.037721623,
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-0.19958745,
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0.2408246,
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-0.13995907,
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-0.028115002,
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-0.14780775,
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0.17445801,
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0.11311988,
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0.05306163,
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0.0018454103,
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0.00088805315,
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-0.27949628,
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-0.23556526,
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-0.18175222,
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-0.28372183,
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-0.43095905,
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0.22644317,
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0.06072053,
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0.02278773,
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0.021752749,
|
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0.053462002,
|
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-0.30636713,
|
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0.15607472,
|
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-0.16657323,
|
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-0.07240017,
|
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0.1410017,
|
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-0.026987495,
|
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0.15029654,
|
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0.03340291,
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-0.2056912,
|
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0.055395555,
|
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0.11999902,
|
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0.06368412,
|
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-0.025476053,
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-0.1702383,
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-0.23432998,
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0.14855467,
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-0.07505147,
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-0.030296376,
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-0.07001051,
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0.10510949,
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0.10420236,
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0.09809715,
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0.17195594,
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0.19430229,
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-0.16121922,
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0.15032287,
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0.10385191,
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-0.18741366,
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0.008690719,
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-0.12941097,
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-0.027797364,
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-0.2148853,
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0.037788823,
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0.16691138,
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0.099181786,
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-0.0955518,
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-0.0074798446,
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-0.17511943,
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0.14543307,
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-0.029364567,
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-0.21223477,
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-0.05881982,
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0.11064195,
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-0.2877007,
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0.015789302,
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0.07125638,
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0.05703369,
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-0.08454703,
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-0.07080854,
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0.025179204,
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-0.10522502,
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-0.03670824,
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-0.11075579,
|
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0.0681693,
|
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-0.28287485,
|
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0.2769406,
|
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0.026260372,
|
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0.07289979,
|
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0.04669447,
|
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-0.16541554,
|
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0.040775143,
|
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0.035916835,
|
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0.03648039,
|
|
0.11299418,
|
|
0.14765884,
|
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0.031163761,
|
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0.0011800596,
|
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-0.10715472,
|
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0.02665826,
|
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-0.06237457,
|
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0.15672882,
|
|
0.09038829,
|
|
0.0061029866,
|
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-0.2592228,
|
|
-0.21008603,
|
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0.019810716,
|
|
-0.08721265,
|
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0.107840165,
|
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0.28438854,
|
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-0.16649202,
|
|
0.19627784,
|
|
0.040611178,
|
|
0.16516201,
|
|
0.24990341,
|
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-0.16222852,
|
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-0.009037945,
|
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0.053751092,
|
|
0.1647804,
|
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-0.16184275,
|
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-0.29710436,
|
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0.043035872,
|
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0.04667557,
|
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0.14761224,
|
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-0.09030331,
|
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-0.024515491,
|
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0.10857025,
|
|
0.19865094,
|
|
-0.07794062,
|
|
0.17942934,
|
|
0.13322048,
|
|
-0.16857187,
|
|
0.055713065,
|
|
0.18661156,
|
|
-0.07864222,
|
|
0.23296827,
|
|
0.10348465,
|
|
-0.11750994,
|
|
-0.065938555,
|
|
-0.04377608,
|
|
0.14903909,
|
|
0.019000417,
|
|
0.21033548,
|
|
0.12162547,
|
|
0.1273347,
|
|
],
|
|
"index": 0,
|
|
"object": "embedding",
|
|
}
|
|
],
|
|
object="list",
|
|
usage=Usage(
|
|
completion_tokens=0,
|
|
prompt_tokens=0,
|
|
total_tokens=0,
|
|
completion_tokens_details=None,
|
|
),
|
|
)
|
|
|
|
cost = completion_cost(
|
|
completion_response=response,
|
|
custom_llm_provider="together_ai",
|
|
call_type="embedding",
|
|
)
|
|
|
|
|
|
def test_completion_cost_params():
|
|
"""
|
|
Relevant Issue: https://github.com/BerriAI/litellm/issues/6133
|
|
"""
|
|
litellm.set_verbose = True
|
|
resp1_prompt_cost, resp1_completion_cost = cost_per_token(
|
|
model="gemini-2.0-flash",
|
|
prompt_tokens=1000,
|
|
completion_tokens=1000,
|
|
custom_llm_provider="vertex_ai_beta",
|
|
)
|
|
|
|
resp2_prompt_cost, resp2_completion_cost = cost_per_token(
|
|
model="gemini-2.0-flash", prompt_tokens=1000, completion_tokens=1000
|
|
)
|
|
|
|
assert resp2_prompt_cost > 0
|
|
|
|
assert resp1_prompt_cost == resp2_prompt_cost
|
|
assert resp1_completion_cost == resp2_completion_cost
|
|
|
|
resp3_prompt_cost, resp3_completion_cost = cost_per_token(
|
|
model="vertex_ai/gemini-2.0-flash", prompt_tokens=1000, completion_tokens=1000
|
|
)
|
|
|
|
assert resp3_prompt_cost > 0
|
|
|
|
assert resp3_prompt_cost == resp1_prompt_cost
|
|
assert resp3_completion_cost == resp1_completion_cost
|
|
|
|
|
|
def test_completion_cost_params_2():
|
|
"""
|
|
Relevant Issue: https://github.com/BerriAI/litellm/issues/6133
|
|
"""
|
|
litellm.set_verbose = True
|
|
|
|
prompt_tokens = 1000
|
|
completion_tokens = 1000
|
|
resp1_prompt_cost, resp1_completion_cost = cost_per_token(
|
|
model="gemini-2.0-flash",
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
)
|
|
|
|
print(resp1_prompt_cost, resp1_completion_cost)
|
|
|
|
model_info = litellm.get_model_info("gemini-2.0-flash")
|
|
input_cost_per_token = model_info["input_cost_per_token"]
|
|
output_cost_per_token = model_info["output_cost_per_token"]
|
|
|
|
assert resp1_prompt_cost == input_cost_per_token * prompt_tokens
|
|
assert resp1_completion_cost == output_cost_per_token * completion_tokens
|
|
|
|
|
|
def test_completion_cost_params_gemini_3():
|
|
from litellm.utils import Choices, Message, ModelResponse, Usage
|
|
|
|
from litellm.llms.vertex_ai.cost_calculator import cost_per_character
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
usage = Usage(
|
|
completion_tokens=2,
|
|
prompt_tokens=3771,
|
|
total_tokens=3773,
|
|
completion_tokens_details=None,
|
|
prompt_tokens_details=None,
|
|
)
|
|
|
|
response = ModelResponse(
|
|
id="chatcmpl-61043504-4439-48be-9996-e29bdee24dc3",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="stop",
|
|
index=0,
|
|
message=Message(
|
|
content="Sí. \n",
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
),
|
|
)
|
|
],
|
|
created=1728529259,
|
|
model="gemini-2.0-flash",
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
usage=usage,
|
|
vertex_ai_grounding_metadata=[],
|
|
vertex_ai_safety_results=[
|
|
[
|
|
{
|
|
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
|
|
"probability": "NEGLIGIBLE",
|
|
},
|
|
{"category": "HARM_CATEGORY_HATE_SPEECH", "probability": "NEGLIGIBLE"},
|
|
{"category": "HARM_CATEGORY_HARASSMENT", "probability": "NEGLIGIBLE"},
|
|
{
|
|
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
|
|
"probability": "NEGLIGIBLE",
|
|
},
|
|
]
|
|
],
|
|
vertex_ai_citation_metadata=[],
|
|
)
|
|
|
|
pc, cc = cost_per_character(
|
|
**{
|
|
"model": "gemini-2.0-flash",
|
|
"custom_llm_provider": "vertex_ai",
|
|
"prompt_characters": None,
|
|
"completion_characters": 3,
|
|
"usage": usage,
|
|
}
|
|
)
|
|
|
|
model_info = litellm.get_model_info("gemini-2.0-flash")
|
|
|
|
# gemini-2.0-flash has no per-character pricing, so cost_per_character
|
|
# falls back to per-token pricing using usage.prompt_tokens / usage.completion_tokens
|
|
assert round(pc, 10) == round(3771 * model_info["input_cost_per_token"], 10)
|
|
assert round(cc, 10) == round(
|
|
2 * model_info["output_cost_per_token"],
|
|
10,
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
# @pytest.mark.flaky(retries=3, delay=1)
|
|
@pytest.mark.parametrize("stream", [False]) # True,
|
|
async def test_test_completion_cost_gpt4o_audio_output_from_model(stream):
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
from litellm.types.utils import (
|
|
Choices,
|
|
Message,
|
|
ModelResponse,
|
|
Usage,
|
|
ChatCompletionAudioResponse,
|
|
PromptTokensDetails,
|
|
CompletionTokensDetailsWrapper,
|
|
PromptTokensDetailsWrapper,
|
|
)
|
|
|
|
usage_object = Usage(
|
|
completion_tokens=34,
|
|
prompt_tokens=16,
|
|
total_tokens=50,
|
|
completion_tokens_details=CompletionTokensDetailsWrapper(audio_tokens=28, reasoning_tokens=0, text_tokens=6),
|
|
prompt_tokens_details=PromptTokensDetailsWrapper(
|
|
audio_tokens=0, cached_tokens=0, text_tokens=16, image_tokens=0
|
|
),
|
|
)
|
|
completion = ModelResponse(
|
|
id="chatcmpl-AJnhcglpTV5u84s1cTxWFeIkGKAo7",
|
|
choices=[
|
|
Choices(
|
|
finish_reason="stop",
|
|
index=0,
|
|
message=Message(
|
|
content=None,
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
audio=ChatCompletionAudioResponse(
|
|
id="audio_6712c25ce73c819080b41362648bc6cb",
|
|
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Q0+/T0I+zE6szq5uv68Wfvku897krr3++08rzzhvZz9Qf54PeW+XT9uwDNB7kLywxLC/4HXQXbBswKpRCqEWcRAhCBDVUNcApOB80DEwFQ/pr7IPso+xr65Ph19YrwBO0a6ZvmBeb45mvmGOWr5cPjGOQk4t7hgeIZ5KbmROdB6ePqLO317VTxRvIu8/TygfLO9cj4nfsV/SL+Iv4S/0L8tvlY+Ef3nfhV98D5B/iL+cD31/MD8Znsw+xK63Xs/O197nTuK+6z6Vjpe+jm6Rrt9+++8uXyy/Sv9YD4Mfs8/7QBcQb2CNoN8xA+Fj0aiBpZHh0cFx+SIFsiciYdJ+YpEywGKVoqIyfdJFsosCQ2KEcmMCNPJaEczB4RGXMYiha9D8wQwwt/EN0OxgltBp3/EQH0A6wIqQnQAn74uemJ5zrrYvWR+hP8f/q5+J31DvDY7eDq2vJE9x0CQgd2B3oDuvud/iQAzQjGCQAJgAisCIwLYgz2D8INUQvwBk8EuQJhAwUDUQKMBEMDzgDD92TvJumC5Xrp8esl71LwPuxX6DTjMuKK4P/gOOMe5XrqrO2T79rt0+1C7BvtAe/A8Mr0Hfgz/Ar+7/6n/w3+T/5r/pb+GgBw/zIBBAFTAQ4A3Pyv+Oj1ofH+8HTxKPOU9R30QvJx6srkC+BH30PkQuiG7Arueu5v7SvrP+oj6pLtcvCl9dn5BP5cADj/eQENAw8JrAycD28RrBK3FDIWHhlcG/AeHiD6Ic4ifyEBIUQf3B+UIB0gKSB+HcEefB0BHQQcshixFrMUTRIwEgETqhJNFM8Qlg/8Cp8IZQeHBdMIjQtpDsQO5AxeCEIH/wbwCGoNHQ3nBxH3iu7x56jvNf4wBdQQTgm0BGT0lepw6lbq/PTU/IsIhw7JDR4EKPqE9fP3zvxvBL4HDQc5BgQEyAT2BCYEMQDA/ez7tPvs+Zr5qfgl94v3GvS78NHqX+Qi4NjfXOUy6nfuDe+F61jpuObp5enmgert7yD3bvxr/hX9f/qi+Df3kvnA+2L+LgD8AAwCwwOABbIETgOq/9n8e/o2+Fb3GfYn91v3GfcT9nnz+u516WjkCeNJ5bzpKu3D7YHum+u86RDm6uTE5yXtyPQo+l394/z8+k/6xvsvAGAEuwd6CoEL3wsfDAgNgQ5IEZIQchGIEjMT3xQ3E+MRPRIyFG4WvhjeGX0a3xrgGQ4YhhfkF/sXORljGLMYoBqeGmEcFhw5HLcaDRg1Fe4ULRXVF6cXHRetGKMWjRSJD5EO4QzvDS8QEwsHDOgHNQGmAj36vvY07FjkcOEZ5ErwtfcZATH+Rvns7YPoQ+hQ7GXzS/tLA70K6BH0DBQKegKt/wgBQQRcCD0LcA0qDK4LDQpoB7sD+f4q+934t/XU8y3vBe556/Xq3emb5ijkSd6X3DXbjd5z4nLmQOom7jny0vQ39ub0s/Xp9m765v64AhsGFQa8BncFTQX8BNED+QJLAlMBTQAOAY8AHQG8/ZH5BPM77l/rHumB6rPqb+357QHtb+mE5Mbh2OAP4/Xnbeyo71zyBvPK88D0pvX29zv6wP34/t8BBQQfBI4G9waUChANdA6hC14HlgU+BH4GqQcwCkoMPg1vDKMLWQvQCycMTg13DmAPPhBKE2QXzhtxH2ggaSH8HpUcPxj8FnwYhxxXIlklCydXJeAisB5IG44YvRZvFEwVXxOaFdMUPxIUD+wKeAomB4UH1AMqAbT9Tvmt9yz4N/pC9vjwI+sC4+7dnNxp4dDx4ABmB5cIhgBE+jPwxO3S70D5uwZDDukY9BkMGoURCgpxBGECRgR0BW8GDQYRBCUDZQQLA2MAofhB8E7nueCX3CLcb96o4dHkWOe+58rlluL93vrdBuBC5oTuoPc1/e8B8QNSBRYF5wMUBAsELgb6BqwIfgmMCHoG9gMsAg0B9v12+f3zku/37ejti+9R73zu8euh6RHnh+WT5NDmqujL6q3st+2F8HrxEvQ59gf6RPyH/aT9cP40/zMAXAF8AvQE2gT3AyYAW/2h+yr8Bf66/rv+n/7K/SD9h/u4+Zb5n/m++jj8Wv9OA+AGBAngCvcN5hHREy8V6hUtGGEcDCB/JWEnwyjSJp0mtSWEJNwhth/9HhQeuhyFGnIZWxisFvISfxDDDdULLwnEB0YHHAh+CLYJWwqlC48MnAxyDUcKsQmtBdEF7QBJ+/L0IfBR78bw6/W1/skHiAjEApP4ePOz75jvmPCu9YX9LATgCZIMOgxaBon+jPnJ+lf+4ABE/wf+F/0t/p7/JgHoAKv9V/oh9vnz/+4N62fn4Ocv6ursXO+w72nu0OoC56Lk2eS65qrqYu9f9qv8mQGQAsYBpP+f/uf+C/+L/6n+2P+BAJICPwO0Ay4D5QDP/eb3ifNT7x/ud+4q8IvyGfQv9PHxXu9C7HzqWOk26XDrbu5U8mT0c/Vy9aL2EPg9+er5wvnO+rT7ff2E/jkAoAF2Av8AGP4r/Kv6qPqd+Tn5Kfor+2D89fsL/Cj7xfr0+Jf26vUm9ZH3+fj/+5T/ZgNRBowHfwf/BnUHOwnyDYoSKhfdGJsahxrEGuAYrRZ6FQYVNxb2FjUYhhieF1wVvxLnEboPOw+DDOkMuAtiDJkMYA1vEB8PKhAHDWgPnw5uELgSKBZnG7ob0BgLEL4JvACa/1/9JwDCAIcCjgSJBKkDz/7r/J34EPe/8ofyP/Rl9hn51fuOAXQFIwfpBO4BQP/r+kP4zPeX+lz/ZAMzBwsKjwm3Bo4B3PzB+F/1WfTL9FH2E/ZR9hD33veb91T0+fE17x3s2uhR5xLpXO1Y8Xj1d/mm+3n7cvgr9QXz9/Ka8631Ufjh+jP99/16/Q78d/lG93b1S/Qf9GT0Dfb/9sn3Zfgv+VH6D/qJ+Ev2EfTT8VXwJ/BQ8SD0Lvao95D4A/nD+Rf5xfem9dn06/Tx9Ub3+fim+//9p//G/93+pPwn+pr3HPgL+nr+aQJDBT4FqQJH/v76e/kS+TX6BfvE/DH9lf1q/W/+qP16/LX6MvuQ+2n82/vR/aMBoAXICQQMMg/gDl8NAQr3CKgJHQoNDIUNbBHSEV8QkgytCo0M9w0EEYMPJA8pBzsA+fiN+uYBfAh2EJsUJhzHG2UYSA7TB9wFiQjeDjsUgxlgGZgWrBBqDfMKogoRCkAK6AkFCX4HjwXgBPUEuwZvCHYJXgjSBY0Cvf8S/vj9r/+bA8YGIgnhCEgHwgQwAXL+1v0xAAwDZgVOBakEBAPAAB7/x/1v/X/83vv5+pP68fkh+W359/lN+xz8Dvw0+wL5oPbN9Cr1Z/Y9+JT5ffpx+7T6oPlH9132OPX19I70zPTd9U725vb19jH3FPcV9v3zRPKp8e3x4fLj8wv2fviw+Tn45/Uc9G3z+vNR9Dn2Cfkr/ND9qP5N/sj9bfvB93z1u/Qg9Yz0sPTT9FX2RPd++IP5Wfma90f13fP48V3xxvEB9dj6V/8GAnkBkv9E/Nb50vdY9/r4L/m8+sb7oP2P/mwASAB+AGr/3fy3/Gn79/uO/QcBDANrBGYDqQG+ANL+Av6h/xgBgQLOAbIAAAEbAxQFxgdPDHsO+BBhD84N8AkOBL39c/up/qcBdwVMBv8ISQj3BhQEiwQiBnEGeAYEBmIHbgeMCCUJTg1oEvcWARm6F/wSPA2lCBUGgwcdChMNaA8NECsP4w2VC7kIcgb0BGgEUgTTApUBhgGxAtQEIQgeC4UN/g2oCyIJiwfZBkMHyAh6Cj0NIw8UD6MNIAvsBwkGtgNVAp4CWwN5A/sDDwT5A7UDDwL2ABcAvv6o/Yj9qvw1/KL7k/uE/D39cf7b/2ABTQEmABn97voe+ir6nPud/FP/nQDh/0D9ffpK+K31FPPR74fwo/ED9L30CvT187TzXfOY8g/zQ/L78aPvR+/98PvyV/Rp9N70ePUw9rb0Rfbe+Ff7Ef3l+wv8mPvc+fn3OPg4+T/8oP4Y/+P/fgC+AIkAkv7s/Kr9b/y6+5H6gPmV+OL36fdv+V/6+flo+dv3zfUI9ff01/WB96X4b/oe+/n7pf26/kf/JALDBPwG/QZiBnIGcAQnAWH+MP4C/d37J/pE+X35XPl6+d360Pp9+vj6m/rv+gX7jfzZ/+gCMARXBawGHgYhBbAD9QPHBEIEPgPsAvADWQM/A4sCbwPaAhwCTwFPAtUDpAQkBjEJEQ3TDjoPqAzJDJkMzAwUDBgMjwsEDKUMLw3WDpcOlQ4JDSMLxQeQBgYEyAFAABYADgPBBRYKDg2PDlMNlQsPCdkG7QQ+BWQICwyaEOoRHxKlD4gOgAz0CysKDQdRBfQEMAh9CqYLtgudDGwJQgYrA+MB+wCO/ywC5wRHB4IFFwXKAnEBMgE1AjwEEwGR/q776vsb/Ov9YABwA84FzQXRBvAEvQNmAScANABLABj+t/pc90D1A/Xu9BD2/PdX+MP2qPXY8/HyK/Fn8Jfxj/RB+Nj64frc+A33qPUd9XL1dva+9p/1QfNS8dPvl+8I8H7xYvTO9lD3L/XZ8fnul+307ETuY/Aw8WXybvOe9SX30fgU+Rr5Gvgc98T2e/WY9dH2s/nE+3P+4v6s/c/6F/n1+dz6NvsX+7L8I/0Z/mL+tv4n/kn9Xv2x/H/77/gi+c74VPk5+YX6qPzJ/rcAWQC1AID+Bf2p+ir6XPtT/JH9mf4gAdkD/QZ+CBQKXQoCCUYHMgTKA9UD0gMxBeAICA1XDh4MxAhECJkF/gENAdYCTwXHBD8CMAScB1IJiQosC7kL4ApPBsoCIAFQ/7YA1wMgCmcRuhZDGKYWFhNbDwsMzAYYBGMDRQT4BCgGwwcOCY4I3QhHCpsL3AthCckGzgMKBLkE/Ae0C6ERExXJFNIRYQ2zCdQEmQF1/jb8r/lW+sX8mP/gAJoB6wJGAnD/8vtr+Vz3Bvb69nj7xADSA70FQwZqBvgEJQF//Br4dPWa8+ryIvPO9U74Pvz3/90Amv1o90Pz5O/B7Mzpq+vx7wH1wPmT/cgAHwFjAHUA5gBs/1b9q/qc+Yb6Av0SAOICPAPjAkMBMf5H+qz1VPLt8dDzD/XJ9wz6N/3O/qb/FwH0AigESwPwAHv9/fy7/tUBMQRXB0kKSQwICsUFEwJe/6L+rf2LAFIFMQmiBvkCYQJTAnwBO/44/9YA2wFgAo0D2gTGA0AEoQU6CQ0KsQmGCZcIcgZcArj/rv0d/W/9bwEbBqUHuAfyB6kI9ga9Asf/5f/DANUBJAXyCEIL2QupC9QM+AzjCuYHQQRAAHr9evs1+9n8VP4HAFwC2wUcCKoHdAWlA5MB7v9G/9f/iAA2AIsA6gEtAzgBXf7N+1/6Kvkj+Lz4VfkP+jf9sAGnAzEDDANmAxwD8QFTADj/GP5Q/db9BP+G/5v/tf4W/S3+/P6J/qP8tfr8+VH6pPuL+7X8cv1i/80Aof+L/cL67feM9q/3Ovk5+jb6V/qV+tf5x/i/+eH7df06/uT92f1t/PX5x/gJ+Jr4CvpC+1b6RviU9RP0hPQ99k75Dv17/lD+1PvF9471/PKZ8rjzJPkX/vUBlgTiAwQCYf44/6H/lv/B/pj7o/hc9iL2e/d6+5/+KQFvA7oCoAC7/HL5MviM+ML7NgDMA/UDZATRBWQH3wa2Bd8E7wB9/MD3ePUf9tz4Tf2yAn4G/QgfCjsGTQE3////OAHwAk4ECQfbCJkHfQf2CAgLOQsjCv8IMAe3A17/5f1O/nIAagMdBcUEOQN0Av8B8QJBAx4EKAQmA0gCLgLTA6IFMwcHCG4JPAkPBq0BbQAeASgCAQUgCKoJaQigBj4EwQKkAuoD8AWvBiIGfQRHAzgCmAKiBaYITwoZCzEL8wgOAwv9rfrG/Ln/2QJTBRkGJwX6Ae//6P+hADoBIwNiBDgE/wJi/9P7K/p1/Ff/+gADAXH/j/4e/fn7dPt//Iv9U/1q++T4Wfcr9V70EPcs/TkCQQQaBeIDWQKAAQAAZP4s/XD9P/z3+Ib1lPQA9cX1NPe2+F77w/t8+Zb2DPaR9k/4qvid+vn8d/4b/6f+mgCoAPcB7wDSAFYAkv4c/Un72fwr/an9I/7n/a/6xvcy9pHzMfHf8Fr0bPdp+Zj7E/81ArMBIAHjAKD/Af7J/Mf92P/jApEEwwajCF0IPwYdAn0A1/4p/in9NPzx+/T5ZPj69+T6SP1u//cAaQFJ/0P75vkU+Zv8DQEYBWUHKgcYBrUDbQPgArcCxgO3BuoHnwOq/p77n/uD/JD/eQRkB5AHAwVHAz0CfAC8/nr+fgFZBE8EugJkARoAvv93AisFqwW6A2QD9gI9AKr9Gf0K/xIBmwJRBekHKgd/BeEF+ggeCigIRATWAED+YfsB+Rr5qPxqAB4FJAqUC+kH1AOFAZ//s/6A/qj+Uf2Z/fH/IgKqBJEFQgZpCNcJXgjcAxEA2fyl+oX7lP75Ac4EpwUxBA8DhgE2/xT8Pvsj/Hn9av67/i4B6AE6AkgDvAMFAwUA0/2V/MT6kfrq+4L9nf5D/ysBowLQA7sD8QCK/j/96/v3+sb7nv0D/wcAWQKBBL4EtwIUATkBJ/9e/nf+aP3c+qv6uv1N/03+lfue+T34iPcN99D2iPhl+i38kwBsAlwDugOcAgkD+wLHAigCoQCc/sL8FP4UAWkAo//7/4QALv9E/DL7FfnH+HX6qPvD/eH/TgD+/cb9ov9o/wL+ef2t/mcAmgGnAFcA6QBNAGIAwAKSBVcE8gFBAWIBOQFIAHAA8wEOAwIDWgM7AhMAUP5E/AL8y/vo+6P9yv9uAW8BpQKSBQ4FQQKSAYgALP6/+x/7UfsG/Mz+ZADWAD8BlgP+BLcBdgBnASEBR/+3/OH9eP0J/kMAgwKPBBwD0QTqA0sD0QRHAoz/7/y2/Gj+zf6xAJ8D6AR0BdoECwV+ATb9IPym+iL9s/1q/Hb+ZgEWBEIEggXmB2EFqQIjAYX/v/4f/8L/ff+9Ax4HuQUBBD4E6AHJ/63/Df8y//z7Dvvo+9b8p/2y/wQCLATHBOUAFgDU/yoAqf5h/90C5gM4BH0CNgF5AVkAQ/4a/wv/wP5a/Aj7Mfs8+kn97fzR/Kf/awDnAYoB4v6G/iD7cPtlAToAbP2n/L39Jv9W/oEABwCp/oP+yP3e/mn+R/6w/AP9uf8+/3wAg/3++9L9Tf28/UsB2QHo/r//wgCo/6P+Uf7W+0D9t/59/0f/vfzB/Wj5PPgh/yL+vPji+pj+Yv/q/FX9NgBn/rP/GQEzAbwBwAKEAXr/RwFbBBkBPvzOAA4CCv0S++78Kfzv+Kv6t/0f/dn9Iv59/v39hP6dACT97//jB+7/bPysAv8BRgJA/QIAgQG3/ZH85P6yAhQAa/2zAVYCIf97A0b/d/+I/iP8SPyr/3cGZ/9z/QwFPgctA+b9mvtdAcP+mP3VAsH/1v/q/Yz9BP0+ArYDwfo2/ygAivtO/GH9iABg/W/+swdOBUv/NwTFBs3+HQDGBaYB6AAWBDz8Fv5tC4gCI//gBrUDzQHoA6EAyvkhArAA7/eDAo4IWQCp+vIAHQEP+MUCPgZL9c0COwnn/cH+4v7m/3f3qP8jCY79fQLVA1f7PfzSAawBUfuZ/XAFEgTZArQDmQQYAR8AxwJS/0j+6gHF/NX53wFgA0v8MP52Bhf9NAGVA9f9/f1x/TgGIv8O/S0FJP9U+w7/iAM/ApcCaQH3+jH7CQCt/Qb/mfzl+g0C4gag/5/+MAaMASb3mwAOAez50f/2/mv9Uv3REG7/JPWODzMC6vfpAOEHZQAD++/86vi9AmYCbvjpAYwDSfvy+9gAOPxw+qsDn/U0+kYEb//9+C/7fwed9x33bQPZAD39Cf0i9YT94AO5+S/92QKLAJn8gQM2/WT73gPU/ff2wfoaBU39Mvhz+IYCn/wH90cDk/tX+OP89QbB+AH3WA6g/LnuIQEN/FYDwAGD8DH6Dwt/9Srrn/7kBAP4svOqAzv2gv2GAlrvZf46CGX3a/Z4A1MDFvrE/uUInvcx+JMHLQA0/tH90viyBpX8gfcoAgn+Pv378jgELf1C9N8I+fxp9HT/uwht/a/2c/dO/1MAW/eO+4P+5vsk+CUHXvr98eMHhgVq8Yr7vgkkAb70BgBNAIz8PwSD+lIDFwei9S/4dQiOBjzvggACB2/6AvsEAlv+fftZA6v/zf1R+1wJlvZN/rICnPgFCE4CM/nE+aEKRvyt9o8IagRx9+cEuAYy+B4BKQcf+A79TwoR9gr+Jv9GBgr1XAag/3H0jxcp81n5+guZ+sH17gyMAvD3BvtUEgr5qO3VHILtbfbWFa3v7ACREFb8wgAXBDwB+gD//5H+PfchC5QE3O/JDJMGnvx++oELaQbI9sH46w+E+uvxcBdX+sb0QRGkAhH6mAT3/6wBsP7IA9EEKPybBwUBmP8h/QkIZvsDAokH2vzPAnsFEAY3914Gsw9n7pMZggWS4BckOv8F7xASRv8+/g3/mgX2AyT2DgK3ETT1swI0Dtv44//GCQn/4vWhD4gB6/ICGKD14PEvI8D00en7H4UALPUXDpj/pQF5Bzj1WwiWC9z15wwkBEP69AAyDF/4AP9fCxn7MAEGA4QBpwfo+4L4ABZm9wf/XQ2m+WgBnQkr/iT6NgmwBYD4dAxQADb4+BKG/sn4FQnkBCT5Dwj7CDD4wwWhDvf4Xvg8InzorvPBJl/wzfeUEMcDT/6ZAVH/xgF1A3wF/gEA/b4KhAKW+j4CFP/YC0r5CwOWBPj8QQYrAzYDM/FyHbD7EulWJqP1m/IdDkMJtPP1Av8Uh+nnDXgBO/vtCOb9IPkbAjwUJPIrALoOD/7I8x0NoxCA74ADKhOw9fXsiCkZ+QPcoiWi9TzyaRT/+Zj/sAIo/37+dgVsA0T9ufmgDjn8xwL0BBz2Ng4K+m76lQzT88IAZwzZ6osMzAWw80gIvQAxAnP4dRS084HulSJg7tz0whj59aH3UQ5x/a/4Cw/D9TP6ABfD8Y763A1m//vykguvBnXqAwgWFGzrufW3G3nw//+u/iH+AAiM9jEGMPzfB5z5QQXK+Vz5ARAh9Nn/gQMN/Ij//PlSCn761vX6DAr0DwZIABn7HwDXAcj9W/gWDB7zK/1dD5rwKvs1BHkHv+9f/0EQ7e+K/dYEU/oi+y8IFQJC58QQ4QkH47QWJvzy9l8MsfeC/wP6GA2t8WfsbB7y8l3t1hxA8I357Qkm+AMGg/ZaCID0RvY4EhTvpP40CQnyuP5eAvf7K/1U/jL7HAO7/O7+GvvjAx/4aQaS+6PvLRUr+83yVP0wD5z4f+XUGHP46vEqDoP36/y2BjP6sQLI/yb2Mgpp+lP9sPUEBCwKke8mA5IDA/yuAjH/hv5h+s8H5/PzAVb9/PsWBjD46/vpBLwDwPBnAr0GqPDPCs3zpvlEGvbo+vhlFE71pwPx8aYEGwoq6U0OjgAA8/4J3ANJ8cEHTgJh/c76KwArCZHrRQ4U+Jv+hwCM93wVnORL/1scYuk0864aXfhB7VcQTQe56e4LtwOY74sIUQcq9p73Dg0A/xbwyAW5BvL0zQAvBxnzOwSdBjLz6AdA/HD7XAiyACrrcAcSEATwFPzqA8MFevBAEMfyNfe7IBboCvqzCnsFQfUqAIYEO/T+DVP+IPmZBkD6iAaU+0n8VwW59s0Sf+Z/+9AtYspwEAkVxN+xE5P3tAO4/pr3oBvS4oT6CilM5rHwLh4F95P1xALPFW/p9v+9Gd7hrgYJCbH4RPz8/DIKafq3/tj9IgDACXf30Aal+XjzPhzj58f6bBpc7z71bAx3DdnybfsUENr65PVBHCHocQMNFZrmkQwZ/yL+4v+i+3AKPvZ5AHkFNwQ5+Fr6CRbI7rn99Bau7fQAbxgx3wwWmPvJ+l8Gs+tXGQX6xu3NFe71QQlK+n4DBAxK5r0hqu/q8y0VXAOB650JngaJ8RcEaQou96jsBSeh7JL5ngwu/SMD6vesDL7+pPXHB60F+vKi/7sUlviL6MUjdvYe8GkUSQFj7HkILxAk8BwACgsH/Eb7UwdsAH75dP6sDAD1LAMCA3T8IAvx7OwS2/yd8tYQtPq/A9b7oQImCjjqJBCbCuPrKg0ZBHD9hvQeDqsGvd3PH0n3JPfFEC32AAfp/rgFW/yN9jUNwfxxAJX/4fxsDZn8UfWEEgL+Xu4lFTf7qPBcDEMFPfFWA2MRSvkZ+f8JOQRU6pkVxfq07C8W/ftj9X0HvAIo/XX9PAN2ALQCqPhbCHEFm+1yEp72Kf5PDdTxig6f82sPG/4r5Vowl+QO8TkdAPW8+dkKYfjX/qoCvgnH7BAEVBjZ4acNnQSg8P4HKgov5rYSTAEo8qQMF/3c/Wn8hQc4/zPw8RHZ+w/5vAHcC2r2S/nEDWn9UPmbAuALrOxfA7cZ4uaa+H4bIexrA6AIJu+yFiHvygAIE4fhkAuvGwvN8hKVIC3PSRkKAdfskRF8AtfungMgENHye/zUB6IDffS7CLj90vo7A/v+wQNT/Tr4lg+S+Rf1aBBL/LDnQBamCmrjxxD1BNTyBgnHBeXwhwlxAjD3ZQx56M0bZvuQ7EYXa/UEBAUEr/VwB3UBqO9vEAX9M++/GAX1we+wFvP74fXrBkwCe/5l/SEG1vw+BK/6rQDLByL/pfeVCNz+xvYiEM3ygQC/BFT78Aeu+Of3xCuR1Nz8mCeF45zzYRld/VLkzxY4CWfj+QaaFyboMPw5FNT2uvVZBeEM1vAV+LURsP927tULMARVAGnyuQqdAiTn2xha+/HuOAsK/7z6Vg0h9kcAIQlT6TMUc/ow7/QU+vje77QCZQ/D+9juoQpXADz6xPt/AekMX+qQBBYPFO3x/2YQQfCg9qwdbeju/j8IoPXREC7k9v6kHzvl8fmqC2UDAvhI9HcUffPWAzv69gHlBuHutBOT5xYLpA9V104W3Aez6okOfv+d+dgEef7+/bsJTu1pAoYPEOiVEV73yPqJCiv91v539d8Q9/Y6+IEMuPr+9goI+P+q9lwLygPV794OCwZL7NoOFvp0+nYVN+sW/kkPu/pl8bEQpftx9lsLW/cd/ekF+P89+tL8kQ1L9o/+ggoz91T8UgFWBvgCxfhkAFgImfuCBWLsKRav+V30fg/v94P/bAIZ/J36tA4A850ENQKzAFgAJe5kI6vfFvvsG87n9QUiBnH4LAUDBWv0CwbaBBr01g1w9mf8hQJXAyb69P1TCJQB/vPW/KcNWfo778EUCflm988UbedrCnkWecvKI4MQ5tFsEFQYOuZt9W8gOuAdBYYVv+MnC5UFwfr8/DH+ivlqHLXfxgKyIcTW7A5dExPjag3+AkT3hAdf+68AaPw0Bgr2gAgFAvH3hgPF/tP8DgD9ACUCvQBL8wUVS/Iy+68KU/4zBafqbQ+lBfvnPg09CkXiJBMvCSHkYweAGrnh9/yGI77a3AxA/wQBzv/y9CIWlu7SCDABM/vTB5j+6/VDBmgLW/DvA34L7/Ux9Sod/O8o8CEl5Oj//eINYfp/B8T3uwb2/uP9l/0JBQf8KwBkALr3bw349ab2/BRi/trsiw2FAir/BP2M/ToD4AQFB5XrigtJA7r85/q5BeQCWfxk/h0BeQQ7934MX/y2+0IGHgkD8OcPpQHm6ywfOu1gADEIgPBvB8wQJ97mATwswNQCARskst84/8wfyOStA+YLbfXWBycAlfiiCW4JueQgD+4JT+8rBnkA7PvAAd8Gm/gq+1EPEvqL8dwVS/nx9jAPv/SOBt7/pPw7A/wGl/rhAxAC8Po9AGcFggCV7Y4K2gjX8MMAPBJb8sjz2xyHAa7ffRe0DRbiDhN/9WT9hw75+b7vkA30BZTvCBFS6ocJ+RFy6oUE1AtO9Fj3+Bh67JT+ihD58tQFHgHS/Db7yAyc7WcMNv/m75wW2+2H/6cSZfBg8sAhhPKV8/0RtfkH9CgMcwfB6vcMu/+G/PERr+j1AOseqODu+gkdV/X06JwZmAzs0wAVYh/609oLwxeH7oP6JQ/G99QAewTa+lUAe/yiBnv10AjK/l33+QqXAyz4zf2zCBIAb/a6CUQIjfBcBE8Ol/qr6HkaewIr6gUCEhMS9i737Q8W8EYP3v4l7LcR7wWQ8bX5bhgv9OHu2RWqCZTmegCbFpH64vFVBlIM8PFC/u8J7foCA3LzMw2dBwfjdQ6YF4Dmw/NJKbj3X+h6GjL7jfNaCzr88vv2CaDzGf0vHIHfMAh3FwToEQPrB/YGu++dB7/7ZgZb/LL9ZQFY++8N1fJmB0759gbMAAv01wlDAVAEJPAZDxMA7O4YEW0M0+LO/5EpL98++18bd+tE+aoUhgHF5bQTcwQm89wIbPqTBhUCbvGRCoADzfaGBJMB/wIBAAQAUfu4DfHzC/10CRX2uQnt+cX7IA5I+Qj27BD++Wb13hGg/4zzzAQ1//4Bd/Ul/EkcMNyYEDQK1+ypDpL7zAC18qUPJfqH/E8EPf9SA5f7Qgbp9WsLQvdP+1kFRQVd+kvzwxsr+8vqbhmn/xHq3hNA/vjwfQV0Ci71If0eEAnxev3GCzzy4gR0CznrFgm3DCbvfQPrBZH+9ACb988ErxOS6n74qB9n6ab9TRG06Y0Kyv8F/FACN/2kAxMA1P2zBh0G6PATCTcEV/qd9jcSZP+a5CUcXPeR+YwKQvguBZ751QTXBlbyBwTqBlD8+AAjAGkCUv12/o4AAf1UBV//0Pf0Bv39jgSDBUj0pAQgBEn7YQcz/X/3ABpW5fD35iTm44z8CxEY99H69ghdDK3pbQjZAGD9awcX+qr7Cgm6CALj/hYADMvndgNIDZr+5uxUEwoASO6sEqL7D/yb/1oHKQG28hcIuAeh8swI4AN37Y8OmQIL+MX9yADxA2AAFfnEBXkBVfNjCiUCcPeMAzcFovWmCO3/S/ZKDtD5FP0JBFQBxf+m+50KLfuF93YRGfVc+3H/8g6++cvj1ypk9cPhohq/+lT3iwcA+WT6mwiY/u36JwJFA+r5vgA7AJP5kQ4x9Tf6/BJV61wNXf94+00IQuZOIpj5PN8aKT75xuUdF+T8MfWpC4X4kfjQCcP5KPxGCJf73P7c/A8Js/XK/6AMtPNq9n4J2Al38WkEVwaV+dkCCgHI9D8LiAAJ8uUKzv4E9gQNd/xR9UEOmPqp/+sARfn+Byz+FvzDAHr/UAWt9sIDRhGr5FsGSg2U7W4HuPzQ/70CmvdMBDIBEfxj/o3+Cga6AAL0lwJLC8X0y/5lCfT76f51AWwG1PDBD4QDe+tvDz/7pv4N/lQEb/phA0kE6vTTBr0DCv/27TEMwgyd7Yv5SRRw/7HqzA3F/pn82P/a/fEGgu+0CcQNJ+E8BcgVXe0z/u0EKf54AdMBeQHA8Q8UjwXn6TUHGgyh77gF0wdT7ej/ew/n+KHxBwWPFk3gcQOTJy7WBfgKKYDxYuUuFykIAulJDoH/sfAaDgT/BvgcBw/6GQDTEHjr/fazHHH/TN9kENUSKul9+f0VNvV+9uAKwvURBRr7wgNJ/m72KgqM/nT+hfvlBk0Hqe7tAbsR4fLr+OUW6/DK+u4SJAKu6sAEchzS36v/Ixqk6zD9KQyb+578iQfg8twHixCl53wBPwzS/zjygQYrCQz2jgEdA7gKle+kAzIR/+oLCBwExf9x/VkBSQJr/lAGX/TwCKIJ/e1s/UccR+mD+88XgPLYAFoFx/wa/ZgH7AHl+kUGzQCM9t0N6QAh6/4Wjv5q8AYKXA1z+G3pyx8x9SX2CRNw+gX8JQneArrxdAVSBbz/Gv4cBHoFJf5rAu8IgvWr9pUZnfop9H0BVg309uH5BQ6v91sJV/uZ/n8Ky/XRAC4DiwM/+af+fweB9mgCygpJ9Tb7SRM69JL9kwXF/z//Yf9ABon49QeD/t35QwaMAwfz7gOFD9zttf9/DkIBp/Mx+NQZPfqL8L4RRfmq+ZkGe/sx+y779Az2/G/s4Rqj9UH3+RFx9rb+nP3tDYfuYP83E/Tw1/gFEtz+LfbtARYJuQDl7nAPk/qaAAMCwPbKCkb6BwNS+DIDGAsD9RED7/4HBcAD2/TACTMCc/bnCcoEouwMCk4FQvlz/u0FWAOZ86oNEgTh6gAO3Q2U8T/zfhGLAa7yBguF/b/5Jg7o+nXzAgu4BPn4+vwRDAX90/H4EG7/m+1RE3oAbPGjCUYOhPBe+ccRLv7t9dr88BMF8RL7MBDM9xX8bAVVA5fz5goWAhP7V/zBA24IV/LKBGIOzPLF+p4MdgGP7x0EaRCN7pj+cBLj9Bj5uRP5/pDpgQ53DxjlvQifBKr6Lf7zBMsCh/UXDen+7fqzAU4CEf0oALAAKwDtAeH76ABhAZQCZ/ma/3IIVv4G9hYF1g4D8k/5oxei7n7+gQ/38xr/WAifAJL6WQOwARz4TQpb/q3yIBSY+G32wQ1h+Xf9HwS1BvX2BQWA/kcEZQM992IESQEGBOrzWwelAbj3fgs/+64AF/f3COz+9vmxD1f1rvcmCfwL7usCBnoM6/e9+mkKIPd6AvkIAfCBBycD3wdT9YcGPAAG/XsB7gOs/VD1Ngs4BFzzgQFnC8zzSf7zD7D1tvuuC2b8UvhgD7j9y/G1BAUPfPIj+G4WK/Nf+UoXMvSP9OISfADV7xoDGgt59Tr+Ugoh9PUDtAU++z4AuAAFAEb+lf18BAkEQO5OCVsScuL0Cu8SF+8X/SwJsf+Z9s8DHAVd+H8CxAOb+OMBHgQ7AMT3hgOGCgHv1P21GDHsoe9WF30Bx+sEEAsGueqrClsJJfpW9nMM1/4q8oEOw/8785sKPwWd+DIBkAZk/AH/8wZk9Z0HHA4e6W8DgxEM/zX2j/pfDgP4OfzjBHL57AUrBn/rhwYFE473IPD2CS8LO/GhBH4Hj/zD+gQIwf5d8l4dC/Zz5BYdOgo16JL40B4U9RvtnRMwAPD1Dw4JAePu3g58B4jvowTMCS//UPdMBJUFkPgNCcD96PjaDUr5cvYWD4n95vPZC6wElvD5CUkFP+8ZAY8VUuzv+JsT5/oW/NAC+f20BAX/AfztBEUADwFB+34BVwbs+XUC/P37/aIEmf0d/LwEbwGEAmj6Uf4lEPv42PQCD3YDWfTtAh8EQwDo+HQBpf+UBCr8Tv3GAhgA2wGh/NECAf9z/HgEhAC/+4X+xAFyAJP6RAK/ALn7oQqI8M8C2w1G74MD6gq/9db+Jwnv/U79CwW0/ZUAgQD4AL4AqvtL/c4LI/v1+mYD9v+BCG/y7AHLDMv3mvqRCQ39HftjBQ3/vftC/1EE+veN/9UEIwVj/ff0oAfeDP3vNf25BYYBQP7D96YKFfrG+AUVLu/P9PwfffgA5jQV/wuo6DIHPAkF+S0CPQOr+lUH4wE0+eH/kwL9Cazup/4eDuH0Rv3FDJz2BfouDj34AwETAzT/vgBy+1cEgQFmAgb1hABVDzHzP/nrCF4CgvyF/ykAUAEYAc799gT2/c8Cn/5y+hsM0vuv+e4HU/15/UgDhfy1Brb5kvrFC2/5bP8UBrL6tP9XAsoACwIh/fT9RweG/YX3zAfSAqr0Fgie/MH4LQzV/7j18ganBVf4zQC++ToNFvwe84UEFglu/qr1PPwKCxwEm/FZAYQObvUn/7UOme1RApIL7PjB+2cH1QkB9aX+bwwT/JH7BAOm/gj5ywChBn3z+wHbCJr0sQCHC7D2g/1NCAH6wP1eAoj9IwCJ/CYAUwJt/b4Dlgb98GwCUgrL9u8C0vyH/pwJNvrf95MMdQPi9ScD5wV++y4BkQGO+agBAwD0/dACtfdqApYE6PtR/rUFtf0m+zIHgvz89icHbAP69mMEoP6PAar46P2lCeb6UfnXAMwJc/sb/iMBzf9E/usBHgEJ/KL9xgjD9l//sAac+8r+pwIb/uUB+vvIATwGZPUm/+UJM/vB+NcEcgJ2/cv/OgLA/b39GQJO+kIBIgYl+6T8TQStBrX2yv96Bpj4ff0LB435jf/dBCb6mgQ1/xT+9AIr/CP/NgMuAqb5swC6BSX6oPxxC43/cfbXBtEBu/qd/RYJePz+8+cNtAOu7akEGAs8+AP+Qwif/tX8MwHs/oYAdP5v+6gEbAQw+AAA9ATL/YECt/4k/LgGs/v7/MsAYADiA/v7jwTG/pz8FAaa/Q8ADgBN/lQA9QQw/EYAnwNq/UH9SP+9AMcAiv4g/moJqfyS+uQHOv68+eYBtP/RAQ38yvzsAicAmfz0/6YAJv5yBnb41QCqB+76ifqzB8MBW/bxADUIqfm++skGPQTG9CH5DxbU84n2yQ8tANPxogAiEhX0i/ZjEq763PRYB+IEF/Q2AEoMPfnY9NsL6Qm96rwByg3I9gL6+gthAcvuswqzA6LzEwn6BZL6TfxPAwUCK/2r/179zv4B/pQHyP3h9xcNG/5S9MUDDwf0/UP8gAIsAHj8OP43Bpv/8/lKAX8ETv8Z+6AGVQEA+bcAMQVd/hcAwAL4+F8AFwlG+3P6Fgv+BOT2P/zJC4P/OvKXCOoGrfi79+8GYARE+t/+Dgip/Kz3Tg5Y/Nz5RAVeAQD8Af4SBpL8uABnAe/8XgPm/yYEUwDV/L0A+gHs/6D/mAGq/XIChABP+8YEHgZp/4H0OQUiDKH1NfvdB6//gfqhA7IDWfvF/Y4FI/8M+hMFrAKU+0MAsQJvAWv5MATTBXj7ZvsbA/UN+/V7/MUHIf7Q++wCLwOm/P//vv7qBNP/d/i9A48Jofz18moHrAgJ9/n7YQUIAB/8PQM2Apj8Sf9GBPb/vv6gAi0BTvwxBIwAYPq4BiwBBPvg/kMHFv/j9owAuAcFAR7zxfxLDrL+9vO0B90Dbvpb/5wC6P7H+vQCvACK+0QA3v+iAD3+QP+OAnH9v/7PAwH/yP6t/4YAAP+KAssAi/kw/7EC4AC//rb8Hv8MAyf8xvtuA+v+ofjb/zIAlP5p/zD9hgOL/HX7XwL5/439Ff/q//H/sP3tAHQALv9V/XT8KQar/j37JP8DBG/9//fnAyIBKftw/wgCffxZ/KQEEwFS9pgC3gVl+TX9PAPs/bb9fgKE/wP6EQGoA2n7XP03AMsA//th/+gBGwDT/8f9xgE7Arn8qgA2AIP/bwIq/p/+5AAoAi/8hgBpBAn8BgASAqz7kP0BAXsD3fwp+m8Hlf9J+3UEPf9P+oQC9ASV+vv9hwa2ANr5JAF/BCwAy/6R/fUDiwFk/Q0C1QT6/CEAwANI/YYCAQDV/ykAvf2dAuMEFPty/GgHJAFs/en7tAKBBUz+M/2dAsL/kvx8BUL8SPkXCFMCIvm6AbIDy/5i/gUB+AAxAFUAIQAuAXgCogDA/xoBrgDXARMBigEj/lj/jwaA+j38Vwnc/0L80AHfAP8Ahf+6AG/+g/8cBN38df/eBdX9H/0JBRgCd/1z/34BKgDZ/c7/GwKA/zgBYASX/hr9CgeIAMX4HwYoA2b7CwJ+BIX+wv5yAhYAqf6+/gsDegGk/TUBgwIW/4D/VgFUA6H/hfzkAp8DnPzV/ccHnABW+gYEwQLs/e0AMQA7/O0AKALy/nr//P2rARgE3fvh/eIDZP4F/T8BIQIY/pv/swM9/Ej92gW7ASf8EQGoBXz8mPwUBH0Ay/wm/1QB3QFUARX9AAH+AFT9yABlAOr9pP4NAJX/5/65/74A3f75/ncA6AACAeH9EP/tAGsARgCV/uQA9wH5/kT80gH6BIn80f+5A0H/2f8rAgz/p/7eAk0ACP+GAUIBev9FAUMAPwAhA3b/eP0ZAwICxf0d/gUD6ACt/KABAQHV//j+KQFCAIH+AAHO/3r/dQHSABD+zwFeAZb/M/9JAIwARP9oAFb/HAMcAEj8QwGUAsP/yP3vAJABJf4h/08CYv6a/IoBawEd/c39CQOv/836BAD0AsP///uf/ngACQCj/rn9EQA+/6r+d/4dAJv+a/3cAEkApvx+ARIEzvw0/vABPQDC/Z3/7AHy/In8mgEG/1P8jADU/yP9DP8KAVv/H/1BAacAZPzAABoBzP16/fb9lwFiAWT8eP6CA1EA6fsx/6kDev4d+z8BWAIv/p7+HgHuABb+Qv95AUf/X//CAPj+7f3J/78CL/8p+xQBKQMP/Wf6qADNAkz9a/2/ABkBBgDN/Y39xAGhAHv9n/9zABADZf8k/TAA3QBVAET8fP4UAZr+8/2P/xkCQ/87/i0A7/4Y/lIBcAHK/G79ngFfAQz8iv4vAocAOP7j/h8CWf8M/+f/Jf+y/qQAHAAQ/Cb+twJlAKH7/v9nA2r/Cv5TAZUBpf6r/ycBwv+4/8AATQF2AI7/JQDrAagAcv8nAS4B9//+/xICswCZ/iIB1QHq/xQADAG1AI//5/8UAR0BAgCV/xMASv/1//MAuv+K/4AAswBPAMX/lf/8ABEBVP8QAG4B2v/K/9IBaAEeAHYBIAOkAAP/rgEsAcb+Pv9uALEA4P9MAS8Bwv96AH0AswDKADcAK/67/2UBTv7R/XcAHwG6/av+5QIiAW7+Bv+uACsAcf/z/0H/uP/nABIA8f7tAP4A+P4QAJsA2v8GAKgAOf/C/kUA3AAa/9b9rf97AGb+tv0dACoANf4H/vD+jv+D/uj8a/1U/4P/dP1//qr/+v2l/V7+g/7u/eH9pv1E/pL+sv6r/5/+x/2y/sX/9f3u/L3+lf7h/e3+V/++/hX/6v7y/UT+Rf89/uj8If6d/8T+4v3Z/jT/w/5X/gX/pf+p/yT/r/79/7EAq/8R/93/QQAvAHIAwgCWAFsAvwDHAPoAbAEvAVIA8ADdAfgAKAGQAaIAwABxAd4A9f9ZAPkAyAB3AAEBuQFGAYAAaACTAY4BCgD9/+QAMAGFAIAAIAFfAaIAagDXAdABgQCwAJEB+ABfAP8AfgFoAMn/egHiAf3/eACIAgoBmf61AIoDkgDq/YYAzALeAL3+qwDmAbQAk/9aAGABwwD1/5IA8QGgABAAbQEsASP/s/8eAjQBLP9MADAC8ADV/wEBwgGOAKL/6QBQARAASAATAcIANwCWAFYBGAFNAAgAcwHaAaUAzAAEAsgB0wCSAdIB7QClAL4B5QGDAJMAigGmAWkALQAKARsBmgA9AP4AJwGJAI4AoQCeAFkApADJAIMAdQBYAMMA5gB2AHEA5AAkAXUAjQBSAUQBVACDAIEBIQFmAFcAIQFSAfMAjgAXATICjQH6/7IA/gK+AYj/ygCHAmcBAACUABABzgBfAFQAjACNAGsA0QDNALH/CwCxAQcB8P4CAO4BHgEAAL8AhwH/AKMAwgDWAK4AigABAesASgCRAIsB+QDE/14AQAGDADX/QQBsASMAR/9rADgBYwDC/4UAcgGwAM3/uACGATYAZP+CAIIAUv9G/zcA2P9C/+b/XgAyAP//AQAVACUA0P9X/+X/EgBX/2f/HwATAFj/e//I/57/c/9c/1b/If9A/1b/MP8N/17/V//M/tD+Qf9w/9f+mf4f/3z/Gf+s/hH/SP/K/mj+wf4G/5r+ev7N/pr+f/7u/hT/gv6X/m3/iP/l/pH+cP+z/9n+nf5Y/8H///6y/mP/tv/x/qn+Uv9o/9r+vv41/0f//P72/iT/IP8B/+f+AP8g/xX/F/82/1D/Qv9O/1z/Sf8q/1b/Z/81/2j/xf+Z//n+Iv8LAO7/u/6T/hEAhABR/9P+w/+SAMX/9v5Q/wUA9/8Y/+j+Xv/N/23/+/4s/5L/4/9//2j/yf/r/6H/fv+s/3j/Uf+S/8r/mv9q/9H/DADV/4j/rP/l/67/bP+L//n/6/+t/+j/LwAaANH/+/8aANv/wv/h/w4A1f+1/9T/9P/n/83/7v/r/+T/4v8LAPr/xf/I/+v/9//N/9T/BQApAAkADABGADsAEgD5/yIADgDo//b/8f/m/9X/6//0/+j/7P/q//r/9/8PADAAIAASACgAPQArABkAHQAYABQAFAAEAPj/9v8DAPn/9v8TACAAIgAjAFIAXgA9AD0AMgAuAB0AGAD//+z/EwAuABsAEABDAFEAKgAwAEoAPQAlACoAKQASAB8AMwAlABgALQBGAEIAQgBZAGAAUgBCAEoAQAAmAB4AFwAWABMAIgAgABcAHAArACUACwAbACcAIQAcAC0AOAAqACsAMgAyACwALgA2ADYANwA1ADkAPAA4ADUANQA1ADAAMgA3ADQALwA4ADcAKgAkACwAMAAiACUALgApABUAFAAQAAIA/v8CAAgAAwAHABYAIAAbACEAMQApACMAIgAwACMAIAArACYAGgAUACAADgAIABEAHwAXAAkAHQAWAA8ABQATABAABwAKAAYACAD8/wUAAAADAAYACwAMAAoAEwAXAB0AEQASABMAFgAKAAsAGAAUABUACwAPAA4AEAASABMAFQAUABoAGAASAA8AFAANAAYABgAGAAQABAADAAYAAgAKAAsACwARABQAGAARABEAEAANAAgABAADAAQAAwAFAA4AEQATABUAEQAQAA4ADwAMAAkACwAIAAMA///8//z/AAAEAAMAAwACAAkABgAIAAsABgAFAAkABgAHAAQABgACAAUAAgABAAUAAgAHAAYACQAGAAkABQACAAAABAABAP//BAAAAAAA//8DAAAAAgAGAAgABwABAAQABQAEAAEA/v8BAP3//f/9//z//f/8//z/+f/5//r//v/9//z/AQD///////8CAAEA/v/9/wAA/v/8//7//f/7//z/+f/6//r/+//4//n/+f/8//v/+P/5//r/+//8//v//f/7//z//P/6//z//P/7//r/+v/7//7/AQAAAP//AAD//////v8BAP3///////3//P/6//3////+//n//P/4//n/9f/3//j/9v/1//P/9f/1//T/+P/2//f/9f/3//v/+//8//r/+//7//3//P/6//v//P/6//r//v8AAP///v/8//3//v/9//z//P/6//r//v////z/+//+/wAA//8AAP3/AQD///7///8DAP7/BgABAAEACQAHAAMABgAFAAUABwAHAAcAAQABAAUABAAEAAYABQAHAAQABAAEAAMABQAGAAYA//8AAAEAAwAAAAEABQAFAAEAAQACAAMABgAIAAYABAABAAMABQAGAAQAAwACAAMABgAHAAMACAAHAAQAAwABAAIABAAEAAQAAwAHAAEA//8AAAIABQAGAAAAAwD9////AgAEAAAAAgAFAAAAAgABAAEAAgAEAAUAAQADAAQABQADAAUAAwAHAAQABwAIAAQABAADAAMABwAFAAYAAwAFAAcABgAJAAYACAAHAAsABQAHAAkACQAKAAYACAAHAAkACgAOAAwADAAMAAsAEAAMAA0ADAAOAA0ADwAOAA4ADQAQABIADQATABEAFgARABMAEQAUAA4AFQATABIAFgAVABQADwAQABEAEwASABIADgAPABEAEgASABEAEgARABAAEAARAA0AEgAUABMAEQATABEAFQASABQAFQAVAA4AEQARABEAFAAVABAAEQARAA8AEAAPABIAFAAWABIAFAAQABUAEwAXABgAEQAUABQAFAASABQAFAAUABEAEAARABAAFAATABMAEwARABAADwAQABEAEQASABIAEAAPABAAEQAUABQADwASAA8AEAANABAAEAASAAwAEgARAA8AEAAPAA8ADgANAAwADAANABAADgANAAwADQAOAAsACwAPAA8ADQAMABAADAAQAA8ABgAKABAADQAOAAwADQAHAAwACwAMAA0ACQAKAA0ACQAGAAcABwAJAAcACQAKAAsACAAKAAcACwAKAAgAAgAFAAoACQALAAsADQAJAAwABgAGAAcACQAKAAoABQAEAAMABAAGAAUACAAFAAgADAAKAAIAAwABAAIAAQAEAAMAAwABAPz/AwAFAAEAAwACAAMABgAGAAUAAQABAAQAAgAFAAcABAADAAMABgADAAMAAQAGAAQAAAACAAIABgAGAAUAAQACAAEABAAAAAAABgAGAP7/AAAAAP7/AwADAAEAAAADAAYAAQAFAAIABAABAAIAAwADAAEAAgAFAAQABAAEAAQABQABAAIABQAFAAMAAwAFAAQAAQAAAAMAAAAIAAUA/////wYAAAAEAAQAAgD//wEAAgAAAP7/BQAEAP//AQD+/wEAAAAAAP//AQADAAEA/v/+/wAA/v8AAAIA/v/7//7/AAAAAP/////9//z/AAABAP///v/6//7/+//8//r/+P/4//n//P/9//3//v/9//z//f/7//7//P8BAPn////4//n//f/9//X//P/7//j/+//8//f/+P/6//n//P/6//v/+f/5//v/+f/3//f/+v/7//r//P/4//f/9v/2//j/+/////f/+f/2//r/+v/6//n/+v/6//b/+f/3//j/+f/3//j/9//z//T/9P/6//b/9//0//n/9P/0//r/9v/9//f/+P/x//b//f/6//X//f/7//b/+//2//b/+P/6//r/+//8//f/+P/1//n/+P/6//f/+v/4//r/+f/7//X//f/4//X/+v/1//X/9//7//n/+f/4//z/+f/9//z/9v/4//z/+P/8//3/+f/1//j/9//6//n/+v/7//z//v/7//n/+P/0//f//P/9//n/9v/1//n/+f8AAP//9//4//3/+v/8/wAA/f/7//r/+//9//n//f/1//n/+P/5//j/+v/8//r/+P/2//r/+//6//j/+P/5//r/+v/+//j/+f/4//n//v/6//j/+//8//b/+P/3//r//P/+/wAA+P/8//n/+//5//r////7//f/+v////3//v/6//3//v/8//z/+v/9//3//f/7//z//f/9//n/+//1//j//f/9//j//P/7//n/+P/6//f/+f/6//v/+v/7//n/+v/7//7//v////v/AAD8//z//P/5//b//P/6//f/+//6//n/+v/7//7/+//+//f////4//n/+v/2//r//f8GAPv/+P/y/wEA+f/9/wAA+f/7////+f/8//3//v/7//z/+//6//z/9//8//r//v/5//v/+f/1//X/+P/+//n/+P/0//b/+v/6//v/+P/4//n//P/7//v/+f/4//7//f////7/+f/5//v/+//7//r/+P/5//r/+f/5//v////7//v/+v/3//j/+f/3//v/9//7//n/+P/5//3//f/5//T/+v/6//X/+f/6/wEA+/8CAP3/AQD8//7//v/8//v/AQADAPr/+f/1//v/+/8GAAQA/P/7//7/AAD8/wAA/P8BAAAA/v/+//3/AAD6//7/+v8BAP3/+//9/wAA/P/8//7/9v/4//3//v8DAP3//P/3//7/+v/+//7//v///wAA/v/+//7//P8AAP//+v/4//v/AAD///n/AAD///v/AAD8//7/+v8AAP7/+/8AAP///v/6//r/BAD9//r/+P/6//z//f8EAAAA/P/1////AAAGAP3/AAD///3//v8AAPn////9///////5//z//v8AAP7/AgABAAMA+v/+//z//P/5//7//P///wEA+//+//v/AAD7//v///////n/+//6//j/AQAAAPn/+P/3//n///8CAPj//P/5//z/+f/9//b/+v/3//v//f8CAPn//f/4//X/AAD5//f/9v////z/9f/6//7////3//r////8//j/9v////z/+v/8/wEAAAD7//3/9//7//b/AAD9//r/+f/2//r//P//////+//9//7/+//8//r/+f/5//z/+//8//7/+f/6//3/+//8////AQAAAPz//P/5//v/+/8EAPn/+//8/////v/9////AgACAP7/AAD/////AAAFAP///v/6/wEAAAACAP7/+f/+/wYA//8BAAYABgAFAAQA//8CAAAABQAFAP//AwACAAMAAwADAAIAAwABAAEAAQAAAAAA/v8CAP7/+//8/wEA//8AAAAAAAAAAAIA//8CAAMAAAD9/wIA/P/+////AgAGAAEA/f/6//3///8DAP7/AwD9//3/AwAEAPv//f/5//n/AQACAP7//f8BAP//AwAEAAAAAgD8/wIA/f8EAP//AQAAAAAABgD5//7/+/8IAAAA+//8/wgAAQADAAQA/v8CAAMAAQABAAQABwAEAAIABAADAAEA//8CAAIAAAABAAIAAgADAP//AwAAAAMABQAIAAIAAwAFAAAAAwACAAQABAAGAAQA/f8BAAQA//8DAAMA/v/7/wIA/P8BAP//AwD//wEA/f8AAAYAAwAHAAQABAD+//7/AAD//wIAAwAKAAYABgAEAAcAAwACAAQAAgAGAAgAAwD+//7/CgAFAAQAAQAFAAcAAgAGAAQABQAAAAQAAAAGAP3/BQAEAAQACgAHAAIAAgADAAEAAwADAAIAAQACAAMAAwAKAAYABgACAAcABwAMAAAABwAAAAIAAwAGAAAAAgAAAP3/AgABAP7//v8CAAEABQADAAUAAwABAAAA//8BAAUAAwACAAUACQAIAAQA/v8AAAQABQAHAAMABwACAAQABQADAAEABAAHAAMABAADAAIAAQABAAIA//8CAAIABAAAAAYABAALAPz/AwD//wIABgADAP//BQAIAAUA/P8AAAEABAAFAAEAAQD5/wAA//8DAAEA/v/9//////8AAAIAAgABAAUAAgADAAUAAgAEAAIAAQD+//z/AwABAP///P8CAAIA/v/9/wEAAAD9/wAAAgADAP7/AgD//wAAAAD///3//f8EAAEA///7//3//////wIA/v8DAP//AwD9////AwAEAPr//f/8//r/AwADAAAA/v/7//7///8DAP3/AQD7//r/AQD///j/AAABAAEA/P////3/AAD+//z////8////+/8FAAMA/P/7/wAAAQD+/wAA/f/9//3/+v/6//z//v/8//7//P/+//7//v///wAA+//6//v/9//5//r//v/9//v//f/8//v//v8BAAIA/v8DAAAA///6//z////9//r//P8CAP///v/8//3//P////7/+f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",
|
|
expires_at=1729286252,
|
|
transcript="Yes.",
|
|
),
|
|
),
|
|
)
|
|
],
|
|
created=1729282652,
|
|
model="gpt-4o-audio-preview",
|
|
object="chat.completion",
|
|
system_fingerprint="fp_4eafc16e9d",
|
|
usage=usage_object,
|
|
service_tier=None,
|
|
)
|
|
|
|
cost = completion_cost(completion, model="gpt-4o-audio-preview")
|
|
|
|
model_info = litellm.get_model_info("gpt-4o-audio-preview")
|
|
print(f"model_info: {model_info}")
|
|
## input cost
|
|
|
|
input_audio_cost = model_info["input_cost_per_audio_token"] * usage_object.prompt_tokens_details.audio_tokens
|
|
input_text_cost = model_info["input_cost_per_token"] * usage_object.prompt_tokens_details.text_tokens
|
|
|
|
total_input_cost = input_audio_cost + input_text_cost
|
|
|
|
## output cost
|
|
|
|
output_audio_cost = model_info["output_cost_per_audio_token"] * usage_object.completion_tokens_details.audio_tokens
|
|
output_text_cost = model_info["output_cost_per_token"] * usage_object.completion_tokens_details.text_tokens
|
|
|
|
total_output_cost = output_audio_cost + output_text_cost
|
|
|
|
assert round(cost, 2) == round(total_input_cost + total_output_cost, 2)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"response_model, custom_llm_provider",
|
|
[
|
|
("azure_ai/Meta-Llama-3.1-70B-Instruct", "azure_ai"),
|
|
("anthropic.claude-3-5-sonnet-20240620-v1:0", "bedrock"),
|
|
],
|
|
)
|
|
def test_completion_cost_model_response_cost(response_model, custom_llm_provider):
|
|
"""
|
|
Relevant issue: https://github.com/BerriAI/litellm/issues/6310
|
|
"""
|
|
from litellm import ModelResponse
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
litellm.set_verbose = True
|
|
response = {
|
|
"id": "cmpl-55db75e0b05344058b0bd8ee4e00bf84",
|
|
"choices": [
|
|
{
|
|
"finish_reason": "stop",
|
|
"index": 0,
|
|
"logprobs": None,
|
|
"message": {
|
|
"content": 'Here\'s one:\n\nWhy did the Linux kernel go to therapy?\n\nBecause it had a lot of "core" issues!\n\nHope that one made you laugh!',
|
|
"refusal": None,
|
|
"role": "assistant",
|
|
"audio": None,
|
|
"function_call": None,
|
|
"tool_calls": [],
|
|
},
|
|
}
|
|
],
|
|
"created": 1729243714,
|
|
"model": response_model,
|
|
"object": "chat.completion",
|
|
"service_tier": None,
|
|
"system_fingerprint": None,
|
|
"usage": {
|
|
"completion_tokens": 32,
|
|
"prompt_tokens": 16,
|
|
"total_tokens": 48,
|
|
"completion_tokens_details": None,
|
|
"prompt_tokens_details": None,
|
|
},
|
|
}
|
|
|
|
model_response = ModelResponse(**response)
|
|
cost = completion_cost(model_response, custom_llm_provider=custom_llm_provider)
|
|
|
|
assert cost > 0
|
|
|
|
|
|
def test_completion_cost_azure_tts():
|
|
from unittest.mock import MagicMock
|
|
|
|
args = {
|
|
"response_object": MagicMock,
|
|
"model": "tts-1",
|
|
"cache_hit": None,
|
|
"custom_llm_provider": "azure",
|
|
"base_model": None,
|
|
"call_type": "aspeech",
|
|
"optional_params": {},
|
|
"custom_pricing": False,
|
|
}
|
|
litellm.response_cost_calculator(**args)
|
|
|
|
|
|
def test_select_model_name_for_cost_calc():
|
|
from litellm.cost_calculator import _select_model_name_for_cost_calc
|
|
from litellm.types.utils import ModelResponse, Choices, Usage, Message
|
|
|
|
args = {
|
|
"model": "Mistral-large-nmefg",
|
|
"completion_response": ModelResponse(
|
|
id="127f24aed4984b4c9a4c5e32ad3752f3",
|
|
created=1734406048,
|
|
model="azure_ai/mistral-large",
|
|
object="chat.completion",
|
|
system_fingerprint=None,
|
|
choices=[
|
|
Choices(
|
|
finish_reason="length",
|
|
index=0,
|
|
message=Message(
|
|
content="I'm an artificial intelligence and do not have an LLM (Master",
|
|
role="assistant",
|
|
tool_calls=None,
|
|
function_call=None,
|
|
),
|
|
)
|
|
],
|
|
usage=Usage(
|
|
completion_tokens=15,
|
|
prompt_tokens=8,
|
|
total_tokens=23,
|
|
completion_tokens_details=None,
|
|
prompt_tokens_details=None,
|
|
),
|
|
service_tier=None,
|
|
),
|
|
"base_model": None,
|
|
"custom_pricing": None,
|
|
}
|
|
|
|
return_model = _select_model_name_for_cost_calc(**args)
|
|
assert return_model == "azure_ai/mistral-large"
|
|
|
|
|
|
def test_moderations():
|
|
from litellm import moderation
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
litellm.add_known_models()
|
|
|
|
assert "omni-moderation-latest" in litellm.model_cost
|
|
print(f"litellm.model_cost['omni-moderation-latest']: {litellm.model_cost['omni-moderation-latest']}")
|
|
assert "omni-moderation-latest" in litellm.open_ai_chat_completion_models
|
|
|
|
response = moderation("I am a bad person", model="omni-moderation-latest")
|
|
cost = completion_cost(response, model="omni-moderation-latest")
|
|
assert cost == 0
|
|
|
|
|
|
def test_cost_calculator_azure_embedding():
|
|
from litellm.cost_calculator import response_cost_calculator
|
|
from litellm.types.utils import EmbeddingResponse, Usage
|
|
|
|
kwargs = {
|
|
"response_object": EmbeddingResponse(
|
|
model="text-embedding-3-small",
|
|
data=[{"embedding": [1, 2, 3]}],
|
|
usage=Usage(prompt_tokens=10, completion_tokens=10),
|
|
),
|
|
"model": "text-embedding-3-small",
|
|
"cache_hit": None,
|
|
"custom_llm_provider": None,
|
|
"base_model": "azure/text-embedding-3-small",
|
|
"call_type": "aembedding",
|
|
"optional_params": {},
|
|
"custom_pricing": False,
|
|
"prompt": "Hello, world!",
|
|
}
|
|
|
|
try:
|
|
response_cost_calculator(**kwargs)
|
|
except Exception as e:
|
|
traceback.print_exc()
|
|
pytest.fail(f"Error: {e}")
|
|
|
|
|
|
def test_add_known_models():
|
|
litellm.add_known_models()
|
|
assert "bedrock/us-west-1/meta.llama3-70b-instruct-v1:0" not in litellm.bedrock_models
|
|
|
|
|
|
@pytest.mark.skip(reason="flaky test")
|
|
def test_bedrock_cost_calc_with_region():
|
|
from litellm import completion
|
|
|
|
from litellm import ModelResponse
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
litellm.model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
litellm.add_known_models()
|
|
|
|
hidden_params = {
|
|
"custom_llm_provider": "bedrock",
|
|
"region_name": "us-east-1",
|
|
"optional_params": {},
|
|
"litellm_call_id": "cf371a5d-679b-410f-b862-8084676d6d59",
|
|
"model_id": None,
|
|
"api_base": None,
|
|
"response_cost": 0.0005639999999999999,
|
|
"additional_headers": {},
|
|
}
|
|
|
|
litellm.set_verbose = True
|
|
|
|
bedrock_models = litellm.bedrock_models + litellm.bedrock_converse_models
|
|
|
|
for model in bedrock_models:
|
|
if litellm.model_cost[model]["mode"] == "chat":
|
|
response = {
|
|
"id": "cmpl-55db75e0b05344058b0bd8ee4e00bf84",
|
|
"choices": [
|
|
{
|
|
"finish_reason": "stop",
|
|
"index": 0,
|
|
"logprobs": None,
|
|
"message": {
|
|
"content": 'Here\'s one:\n\nWhy did the Linux kernel go to therapy?\n\nBecause it had a lot of "core" issues!\n\nHope that one made you laugh!',
|
|
"refusal": None,
|
|
"role": "assistant",
|
|
"audio": None,
|
|
"function_call": None,
|
|
"tool_calls": [],
|
|
},
|
|
}
|
|
],
|
|
"created": 1729243714,
|
|
"model": model,
|
|
"object": "chat.completion",
|
|
"service_tier": None,
|
|
"system_fingerprint": None,
|
|
"usage": {
|
|
"completion_tokens": 32,
|
|
"prompt_tokens": 16,
|
|
"total_tokens": 48,
|
|
"completion_tokens_details": None,
|
|
"prompt_tokens_details": None,
|
|
},
|
|
}
|
|
|
|
model_response = ModelResponse(**response)
|
|
model_response._hidden_params = hidden_params
|
|
cost = completion_cost(model_response, custom_llm_provider="bedrock")
|
|
|
|
assert cost > 0
|
|
|
|
|
|
# @pytest.mark.parametrize(
|
|
# "base_model_arg", [
|
|
# {"base_model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0"},
|
|
# {"model_info": "anthropic.claude-3-sonnet-20240229-v1:0"},
|
|
# ]
|
|
# )
|
|
def test_cost_calculator_with_base_model():
|
|
resp = litellm.completion(
|
|
model="bedrock/random-model",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
base_model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
|
|
mock_response="Hello, how are you?",
|
|
)
|
|
assert resp.model == "random-model"
|
|
assert resp._hidden_params["response_cost"] > 0
|
|
|
|
|
|
@pytest.fixture
|
|
def model_item():
|
|
return {
|
|
"model_name": "random-model",
|
|
"litellm_params": {
|
|
"model": "openai/my-fake-model",
|
|
"api_key": "my-fake-key",
|
|
"api_base": "https://exampleopenaiendpoint-production.up.railway.app/",
|
|
},
|
|
"model_info": {},
|
|
}
|
|
|
|
|
|
@pytest.mark.parametrize("base_model_arg", ["litellm_param", "model_info"])
|
|
def test_cost_calculator_with_base_model_with_router(base_model_arg, model_item):
|
|
from litellm import Router
|
|
|
|
|
|
@pytest.mark.parametrize("base_model_arg", ["litellm_param", "model_info"])
|
|
def test_cost_calculator_with_base_model_with_router(base_model_arg):
|
|
from litellm import Router
|
|
|
|
model_item = {
|
|
"model_name": "random-model",
|
|
"litellm_params": {
|
|
"model": "bedrock/random-model",
|
|
},
|
|
}
|
|
|
|
if base_model_arg == "litellm_param":
|
|
model_item["litellm_params"]["base_model"] = "bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
|
|
elif base_model_arg == "model_info":
|
|
model_item["model_info"] = {
|
|
"base_model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
|
|
}
|
|
|
|
router = Router(model_list=[model_item])
|
|
resp = router.completion(
|
|
model="random-model",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
mock_response="Hello, how are you?",
|
|
)
|
|
assert resp.model == "random-model"
|
|
assert resp._hidden_params["response_cost"] > 0
|
|
|
|
|
|
@pytest.mark.parametrize("base_model_arg", ["litellm_param", "model_info"])
|
|
def test_cost_calculator_with_base_model_with_router_embedding(base_model_arg):
|
|
from litellm import Router
|
|
|
|
litellm._turn_on_debug()
|
|
|
|
model_item = {
|
|
"model_name": "random-model",
|
|
"litellm_params": {
|
|
"model": "bedrock/random-model",
|
|
},
|
|
}
|
|
|
|
if base_model_arg == "litellm_param":
|
|
model_item["litellm_params"]["base_model"] = "cohere.embed-english-v3"
|
|
elif base_model_arg == "model_info":
|
|
model_item["model_info"] = {
|
|
"base_model": "cohere.embed-english-v3",
|
|
}
|
|
|
|
router = Router(model_list=[model_item])
|
|
resp = router.embedding(
|
|
model="random-model",
|
|
input="Hello, how are you?",
|
|
mock_response=[1, 2, 3],
|
|
)
|
|
assert resp.model == "random-model"
|
|
assert resp._hidden_params["response_cost"] > 0
|
|
|
|
|
|
def test_cost_calculator_with_custom_pricing():
|
|
resp = litellm.completion(
|
|
model="bedrock/random-model",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
mock_response="Hello, how are you?",
|
|
input_cost_per_token=0.0000008,
|
|
output_cost_per_token=0.0000032,
|
|
)
|
|
assert resp.model == "random-model"
|
|
assert resp._hidden_params["response_cost"] > 0
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"custom_pricing",
|
|
[
|
|
"litellm_params",
|
|
"model_info",
|
|
],
|
|
)
|
|
@pytest.mark.asyncio
|
|
async def test_cost_calculator_with_custom_pricing_router(model_item, custom_pricing):
|
|
from litellm import Router
|
|
|
|
if custom_pricing == "litellm_params":
|
|
model_item["litellm_params"]["input_cost_per_token"] = 0.0000008
|
|
model_item["litellm_params"]["output_cost_per_token"] = 0.0000032
|
|
elif custom_pricing == "model_info":
|
|
model_item["model_info"]["input_cost_per_token"] = 0.0000008
|
|
model_item["model_info"]["output_cost_per_token"] = 0.0000032
|
|
|
|
router = Router(model_list=[model_item])
|
|
resp = await router.acompletion(
|
|
model="random-model",
|
|
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
|
mock_response="Hello, how are you?",
|
|
)
|
|
# assert resp.model == "random-model"
|
|
assert resp._hidden_params["response_cost"] > 0
|
|
|
|
|
|
def test_json_valid_model_cost_map():
|
|
import json
|
|
|
|
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
|
|
|
model_cost = litellm.get_model_cost_map(url="")
|
|
|
|
try:
|
|
# Attempt to serialize and deserialize the JSON
|
|
json_str = json.dumps(model_cost)
|
|
json.loads(json_str)
|
|
except json.JSONDecodeError as e:
|
|
assert False, f"Invalid JSON format: {str(e)}"
|
|
|
|
|
|
def test_batch_cost_calculator():
|
|
|
|
args = {
|
|
"completion_response": {
|
|
"choices": [
|
|
{
|
|
"content_filter_results": {
|
|
"hate": {"filtered": False, "severity": "safe"},
|
|
"protected_material_code": {
|
|
"filtered": False,
|
|
"detected": False,
|
|
},
|
|
"protected_material_text": {
|
|
"filtered": False,
|
|
"detected": False,
|
|
},
|
|
"self_harm": {"filtered": False, "severity": "safe"},
|
|
"sexual": {"filtered": False, "severity": "safe"},
|
|
"violence": {"filtered": False, "severity": "safe"},
|
|
},
|
|
"finish_reason": "stop",
|
|
"index": 0,
|
|
"logprobs": None,
|
|
"message": {
|
|
"content": 'As of my last update in October 2023, there are eight recognized planets in the solar system. They are:\n\n1. **Mercury** - The closest planet to the Sun, known for its extreme temperature fluctuations.\n2. **Venus** - Similar in size to Earth but with a thick atmosphere rich in carbon dioxide, leading to a greenhouse effect that makes it the hottest planet.\n3. **Earth** - The only planet known to support life, with a diverse environment and liquid water.\n4. **Mars** - Known as the Red Planet, it has the largest volcano and canyon in the solar system and features signs of past water.\n5. **Jupiter** - The largest planet in the solar system, known for its Great Red Spot and numerous moons.\n6. **Saturn** - Famous for its stunning rings, it is a gas giant also known for its extensive moon system.\n7. **Uranus** - An ice giant with a unique tilt, it rotates on its side and has a blue color due to methane in its atmosphere.\n8. **Neptune** - Another ice giant, known for its deep blue color and strong winds, it is the farthest planet from the Sun.\n\nPluto was previously classified as the ninth planet but was reclassified as a "dwarf planet" in 2006 by the International Astronomical Union.',
|
|
"refusal": None,
|
|
"role": "assistant",
|
|
},
|
|
}
|
|
],
|
|
"created": 1741135408,
|
|
"id": "chatcmpl-B7X96teepFM4ILP7cm4Ga62eRuV8p",
|
|
"model": "gpt-4o-mini-2024-07-18",
|
|
"object": "chat.completion",
|
|
"prompt_filter_results": [
|
|
{
|
|
"prompt_index": 0,
|
|
"content_filter_results": {
|
|
"hate": {"filtered": False, "severity": "safe"},
|
|
"jailbreak": {"filtered": False, "detected": False},
|
|
"self_harm": {"filtered": False, "severity": "safe"},
|
|
"sexual": {"filtered": False, "severity": "safe"},
|
|
"violence": {"filtered": False, "severity": "safe"},
|
|
},
|
|
}
|
|
],
|
|
"system_fingerprint": "fp_b705f0c291",
|
|
"usage": {
|
|
"completion_tokens": 278,
|
|
"completion_tokens_details": {
|
|
"accepted_prediction_tokens": 0,
|
|
"audio_tokens": 0,
|
|
"reasoning_tokens": 0,
|
|
"rejected_prediction_tokens": 0,
|
|
},
|
|
"prompt_tokens": 20,
|
|
"prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0},
|
|
"total_tokens": 298,
|
|
},
|
|
},
|
|
"model": None,
|
|
}
|
|
|
|
cost = completion_cost(**args)
|
|
assert cost > 0
|