mirror of
https://github.com/tiennm99/litellm.git
synced 2026-06-18 23:40:42 +00:00
Ishaan Jaff
99feefd614
* add fal-ai provider * fix image_generation_handler * init FalAIImageGenerationConfig * init cost_calculator * init FAL AI * TestFAL_AI_ImageGeneration * fix load_custom_provider_entrypoints * TestFAL_AI_ImageGeneration * add imagen4 transform FAL AI * add FAL AI imagen 4 transform * BaseImageGenTest * test_fal_ai_image_generation_basic * add BRIA + Recraft img gen * add recraft + BRIA * test_fal_ai_image_generation_basic * tests for flux PRO v11 * Add FAL AI SD * test FAL AI SD * docs FAL AI * docs fal ai * Using Model-Specific Parameters * add fal ai model prices * add fall_ai JPG logo * ui fixes FAL AI * fix linting * fix linting * fix bedrock test_get_request_body_stability3 * test_custom_llm_provider_entrypoint
124 lines
4.4 KiB
Python
124 lines
4.4 KiB
Python
import asyncio
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import httpx
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import json
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import pytest
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import sys
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from typing import Any, Dict, List, Optional
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from unittest.mock import MagicMock, Mock, patch
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import os
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import litellm
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from litellm.exceptions import BadRequestError
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from litellm.utils import CustomStreamWrapper
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from openai.types.image import Image
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.types.utils import StandardLoggingPayload
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class TestCustomLogger(CustomLogger):
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def __init__(self):
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super().__init__()
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self.standard_logging_payload: Optional[StandardLoggingPayload] = None
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async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
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self.standard_logging_payload = kwargs.get("standard_logging_object")
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pass
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# test_example.py
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from abc import ABC, abstractmethod
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class BaseImageGenTest(ABC):
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"""
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Abstract base test class that enforces a common test across all test classes.
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"""
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@abstractmethod
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def get_base_image_generation_call_args(self) -> dict:
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"""Must return the base image generation call args"""
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pass
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@pytest.mark.asyncio(scope="module")
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async def test_basic_image_generation(self):
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"""Test basic image generation"""
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try:
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litellm._turn_on_debug()
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custom_logger = TestCustomLogger()
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litellm.logging_callback_manager._reset_all_callbacks()
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litellm.callbacks = [custom_logger]
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base_image_generation_call_args = self.get_base_image_generation_call_args()
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litellm.set_verbose = True
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response = await litellm.aimage_generation(
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**base_image_generation_call_args, prompt="A image of a otter"
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)
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print("FAL AI RESPONSE: ", response)
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await asyncio.sleep(1)
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# assert response._hidden_params["response_cost"] is not None
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# assert response._hidden_params["response_cost"] > 0
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# print("response_cost", response._hidden_params["response_cost"])
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logged_standard_logging_payload = custom_logger.standard_logging_payload
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print("logged_standard_logging_payload", logged_standard_logging_payload)
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assert logged_standard_logging_payload is not None
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assert logged_standard_logging_payload["response_cost"] is not None
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assert logged_standard_logging_payload["response_cost"] > 0
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import openai
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from openai.types.images_response import ImagesResponse
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# print openai version
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print("openai version=", openai.__version__)
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response_dict = dict(response)
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if "usage" in response_dict:
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response_dict["usage"] = dict(response_dict["usage"])
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print("response usage=", response_dict.get("usage"))
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ImagesResponse.model_validate(response_dict)
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for d in response.data:
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assert isinstance(d, Image)
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print("data in response.data", d)
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assert d.b64_json is not None or d.url is not None
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except litellm.RateLimitError as e:
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pass
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except litellm.ContentPolicyViolationError:
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pass # Azure randomly raises these errors - skip when they occur
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except litellm.InternalServerError:
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pass
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except Exception as e:
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if "Your task failed as a result of our safety system." in str(e):
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pass
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else:
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pytest.fail(f"An exception occurred - {str(e)}")
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@pytest.mark.skip(reason="Skipping image edit test, image file not in ci/cd")
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def test_openai_gpt_image_1():
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from litellm import image_edit
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from PIL import Image
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import io
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# Create a simple mask image with alpha channel
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# Create a 512x512 black image with alpha channel
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try:
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response = image_edit(
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model="openai/gpt-image-1",
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image=open("test_image_edit.png", "rb"),
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mask=open("test_image_edit.png", "rb"),
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prompt="Add a red hat to the person in the image",
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n=1,
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size="1024x1024",
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)
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print("response: ", response)
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except Exception as e:
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if "mask image missing alpha channel" in str(e):
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pass
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else:
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raise e
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