litellm/tests/unit/test_video_generation.py

import asyncio
import io
import json
import os
from unittest.mock import AsyncMock, MagicMock, patch

import pytest


import litellm
from litellm.cost_calculator import default_video_cost_calculator
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.litellm_logging import Logging as LitellmLogging
from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
from litellm.llms.gemini.videos.transformation import GeminiVideoConfig
from litellm.llms.openai.videos.transformation import OpenAIVideoConfig
from litellm.types.videos.main import VideoObject, VideoResponse
from litellm.videos import main as videos_main
from litellm.videos.main import (
    avideo_generation,
    avideo_status,
    video_generation,
    video_status,
)


class TestVideoGeneration:
    """Test suite for video generation functionality."""

    def test_video_generation_basic(self):
        """Test basic video generation functionality."""
        # Use mock_response parameter for reliable testing
        response = video_generation(
            prompt="Show them running around the room",
            model="sora-2",
            seconds="8",
            size="720x1280",
            mock_response={
                "id": "video_123",
                "object": "video",
                "status": "queued",
                "created_at": 1712697600,
                "model": "sora-2",
                "size": "720x1280",
                "seconds": "8",
            },
        )

        assert isinstance(response, VideoObject)
        assert response.id == "video_123"
        assert response.status == "queued"
        assert response.model == "sora-2"
        assert response.size == "720x1280"
        assert response.seconds == "8"

    def test_video_generation_with_mock_response(self):
        """Test video generation with mock response."""
        mock_data = {
            "id": "video_456",
            "object": "video",
            "status": "completed",
            "created_at": 1712697600,
            "completed_at": 1712697660,
            "model": "sora-2",
            "size": "1280x720",
            "seconds": "10",
        }

        response = video_generation(
            prompt="A beautiful sunset over the ocean",
            model="sora-2",
            seconds="10",
            size="1280x720",
            mock_response=mock_data,
        )

        assert isinstance(response, VideoObject)
        assert response.id == "video_456"
        assert response.status == "completed"
        assert response.model == "sora-2"
        assert response.size == "1280x720"
        assert response.seconds == "10"

    def test_video_generation_async(self):
        """Test async video generation functionality."""
        mock_response = VideoObject(
            id="video_async_123",
            object="video",
            status="processing",
            created_at=1712697600,
            model="sora-2",
            progress=50,
        )

        # Mock the async_video_generation_handler to return the mock_response
        async_mock = AsyncMock(return_value=mock_response)
        with patch.object(
            videos_main.base_llm_http_handler,
            "async_video_generation_handler",
            async_mock,
        ):
            with patch.object(
                videos_main.base_llm_http_handler,
                "video_generation_handler",
                side_effect=lambda **kwargs: async_mock(**kwargs),
            ):
                import asyncio

                async def test_async():
                    response = await avideo_generation(
                        prompt="A cat playing with a ball",
                        model="sora-2",
                        seconds="5",
                        size="720x1280",
                    )
                    return response

                response = asyncio.run(test_async())

                assert isinstance(response, VideoObject)
                assert response.id == "video_async_123"
                assert response.status == "processing"
                assert response.progress == 50

    def test_video_generation_parameter_validation(self):
        """Test video generation parameter validation."""
        # Test with minimal required parameters
        response = video_generation(
            prompt="Test video",
            model="sora-2",
            mock_response={
                "id": "test",
                "object": "video",
                "status": "queued",
                "created_at": 1712697600,
            },
        )

        assert isinstance(response, VideoObject)
        assert response.id == "test"

    def test_video_generation_error_handling(self):
        """Test video generation error handling."""
        with patch.object(
            videos_main.base_llm_http_handler,
            "video_generation_handler",
            side_effect=Exception("API Error"),
        ):
            with pytest.raises(litellm.APIConnectionError):
                video_generation(prompt="Test video", model="sora-2")

    def test_video_generation_provider_config(self):
        """Test video generation provider configuration."""
        config = OpenAIVideoConfig()

        # Test supported parameters
        supported_params = config.get_supported_openai_params("sora-2")
        assert "prompt" in supported_params
        assert "model" in supported_params
        assert "seconds" in supported_params
        assert "size" in supported_params

    def test_video_generation_request_transformation(self):
        """Test video generation request transformation."""
        config = OpenAIVideoConfig()

        # Test request transformation
        data, files, returned_api_base = config.transform_video_create_request(
            model="sora-2",
            prompt="Test video prompt",
            api_base="https://api.openai.com/v1/videos",
            video_create_optional_request_params={"seconds": "8", "size": "720x1280"},
            litellm_params=MagicMock(),
            headers={},
        )

        assert data["model"] == "sora-2"
        assert data["prompt"] == "Test video prompt"
        assert data["seconds"] == "8"
        assert data["size"] == "720x1280"
        assert files == []
        assert returned_api_base == "https://api.openai.com/v1/videos"

    def test_video_generation_request_decodes_encoded_character_ids(self):
        """Encoded character IDs should be decoded before upstream create-video call."""
        from litellm.types.videos.utils import encode_character_id_with_provider

        config = OpenAIVideoConfig()
        encoded_character_id = encode_character_id_with_provider(
            character_id="char_123",
            provider="openai",
            model_id="sora-2",
        )

        data, files, returned_api_base = config.transform_video_create_request(
            model="sora-2",
            prompt="Test video prompt",
            api_base="https://api.openai.com/v1/videos",
            video_create_optional_request_params={
                "seconds": "8",
                "size": "720x1280",
                "characters": [{"id": encoded_character_id}],
            },
            litellm_params=MagicMock(),
            headers={},
        )

        assert data["characters"] == [{"id": "char_123"}]
        assert files == []
        assert returned_api_base == "https://api.openai.com/v1/videos"

    def test_video_generation_response_transformation(self):
        """Test video generation response transformation."""
        config = OpenAIVideoConfig()

        # Mock HTTP response
        mock_http_response = MagicMock()
        mock_http_response.json.return_value = {
            "id": "video_789",
            "object": "video",
            "status": "completed",
            "created_at": 1712697600,
            "model": "sora-2",
            "size": "1280x720",
            "seconds": "12",
        }

        response = config.transform_video_create_response(
            model="sora-2", raw_response=mock_http_response, logging_obj=MagicMock()
        )

        assert isinstance(response, VideoObject)
        assert response.id == "video_789"
        assert response.status == "completed"
        assert response.model == "sora-2"


    def test_video_generation_cost_calculation_unknown_model(self):
        """Test video generation cost calculation for unknown model."""
        with pytest.raises(Exception, match="Model not found in cost map"):
            default_video_cost_calculator(
                model="unknown-model",
                duration_seconds=5.0,
                custom_llm_provider="openai",
            )

    def test_video_generation_cost_with_custom_model_info(self):
        """Test that custom model_info pricing is applied for video generation.

        When a deployment has custom pricing via model_info, it should be used
        instead of looking up the global litellm.model_cost map.

        Related: https://github.com/BerriAI/litellm/issues/21907
        """
        model_info = {
            "output_cost_per_video_per_second": 0.05,
        }
        cost = default_video_cost_calculator(
            model="my-custom-video-model",
            duration_seconds=10.0,
            model_info=model_info,
        )
        assert cost == 0.5

    def test_video_generation_cost_custom_model_info_fallback_to_per_second(self):
        """Test that output_cost_per_second is used as fallback when
        output_cost_per_video_per_second is not set in custom model_info.

        Related: https://github.com/BerriAI/litellm/issues/21907
        """
        model_info = {
            "output_cost_per_second": 0.10,
        }
        cost = default_video_cost_calculator(
            model="my-custom-video-model",
            duration_seconds=5.0,
            model_info=model_info,
        )
        assert cost == 0.5

    def test_video_generation_cost_1080p_tier_via_default_calculator(self):
        """default_video_cost_calculator uses output_cost_per_second_1080p when requested."""
        from litellm.cost_calculator import default_video_cost_calculator

        model_info = {
            "output_cost_per_second": 0.05,
            "output_cost_per_second_1080p": 0.08,
        }
        cost = default_video_cost_calculator(
            model="my-custom-video-model",
            duration_seconds=10.0,
            model_info=model_info,
            video_resolution="1080p",
        )
        assert cost == 0.8

    def test_video_generation_cost_custom_pricing_through_completion_cost(self):
        """Test that custom video pricing flows through completion_cost via litellm_logging_obj.

        This tests the full cost calculation path: completion_cost extracts model_info
        from litellm_logging_obj.litellm_params.metadata.model_info and passes it to
        the video cost calculator.

        Related: https://github.com/BerriAI/litellm/issues/21907
        """
        from litellm.cost_calculator import completion_cost

        # Create mock response with usage containing duration_seconds
        mock_response = MagicMock()
        mock_response.usage = MagicMock()
        mock_response.usage.duration_seconds = 10.0
        type(mock_response)._hidden_params = {}

        # Create mock litellm_logging_obj with custom pricing
        mock_logging_obj = MagicMock()
        mock_logging_obj.litellm_params = {
            "metadata": {
                "model_info": {
                    "output_cost_per_video_per_second": 0.05,
                }
            }
        }

        cost = completion_cost(
            completion_response=mock_response,
            model="openai/hunyuanvideo",
            call_type="create_video",
            custom_llm_provider="openai",
            custom_pricing=True,
            litellm_logging_obj=mock_logging_obj,
        )
        assert cost == 0.5

    def test_completion_cost_video_generation_1080p_tier(self):
        """create_video cost uses output_cost_per_second_1080p when usage.video_resolution is 1080p."""
        from litellm.cost_calculator import completion_cost

        mock_response = MagicMock()
        mock_response.usage = MagicMock()
        mock_response.usage.duration_seconds = 10.0
        mock_response.usage.video_resolution = "1080p"
        type(mock_response)._hidden_params = {}

        mock_logging_obj = MagicMock()
        mock_logging_obj.litellm_params = {
            "metadata": {
                "model_info": {
                    "output_cost_per_second": 0.05,
                    "output_cost_per_second_1080p": 0.08,
                }
            }
        }

        cost = completion_cost(
            completion_response=mock_response,
            model="gemini/veo-3.1-lite-generate-preview",
            call_type="create_video",
            custom_llm_provider="gemini",
            custom_pricing=True,
            litellm_logging_obj=mock_logging_obj,
        )
        assert abs(cost - 0.8) < 0.001

    def test_completion_cost_video_edit_uses_video_calculator(self):
        """video_edit is charged via the same video cost path as create_video."""
        from litellm.cost_calculator import completion_cost

        mock_response = MagicMock()
        mock_response.usage = MagicMock()
        mock_response.usage.duration_seconds = 10.0
        type(mock_response)._hidden_params = {}

        mock_logging_obj = MagicMock()
        mock_logging_obj.litellm_params = {
            "metadata": {
                "model_info": {
                    "output_cost_per_video_per_second": 0.05,
                }
            }
        }

        cost = completion_cost(
            completion_response=mock_response,
            model="vertex_ai/veo-3.1-generate-001",
            call_type="video_edit",
            custom_llm_provider="vertex_ai",
            custom_pricing=True,
            litellm_logging_obj=mock_logging_obj,
        )
        assert cost == 0.5

    def test_completion_cost_video_custom_pricing_under_litellm_metadata(self):
        """Video routes store deployment model_info under litellm_metadata, not metadata.

        Regression for https://github.com/BerriAI/litellm/issues/36483: custom video
        pricing was silently ignored because completion_cost only read metadata.
        """
        from litellm.cost_calculator import completion_cost

        mock_response = MagicMock()
        mock_response.usage = {"duration_seconds": 10.0}
        type(mock_response)._hidden_params = {}

        mock_logging_obj = MagicMock()
        mock_logging_obj.litellm_params = {
            "litellm_metadata": {
                "model_info": {
                    "output_cost_per_video_per_second": 0.18,
                }
            }
        }

        cost = completion_cost(
            completion_response=mock_response,
            model="runwayml/seedance2",
            call_type="create_video",
            custom_llm_provider="runwayml",
            custom_pricing=True,
            litellm_logging_obj=mock_logging_obj,
        )
        assert abs(cost - 1.8) < 0.001

    def test_completion_cost_video_uses_provider_reported_cost_without_custom_pricing(self):
        """With no custom pricing, the provider's own reported cost wins over a duration estimate."""
        from litellm.cost_calculator import completion_cost

        mock_response = MagicMock()
        mock_response.usage = {
            "duration_seconds": 5.0,
            "video_resolution": "720p",
            "provider_reported_cost_usd": 0.31,
        }
        type(mock_response)._hidden_params = {}

        cost = completion_cost(
            completion_response=mock_response,
            model="runwayml/gen4_turbo",
            call_type="create_video",
            custom_llm_provider="runwayml",
        )
        assert cost == 0.31

    def test_completion_cost_video_custom_pricing_beats_provider_reported_cost(self):
        """Deployment-level custom pricing overrides the provider's reported cost."""
        from litellm.cost_calculator import completion_cost

        mock_response = MagicMock()
        mock_response.usage = {
            "duration_seconds": 10.0,
            "provider_reported_cost_usd": 0.31,
        }
        type(mock_response)._hidden_params = {}

        mock_logging_obj = MagicMock()
        mock_logging_obj.litellm_params = {
            "metadata": {
                "model_info": {
                    "output_cost_per_video_per_second": 0.18,
                }
            }
        }

        cost = completion_cost(
            completion_response=mock_response,
            model="runwayml/seedance2",
            call_type="create_video",
            custom_llm_provider="runwayml",
            custom_pricing=True,
            litellm_logging_obj=mock_logging_obj,
        )
        assert abs(cost - 1.8) < 0.001


    def test_video_generation_with_files(self):
        """Test video generation with file uploads."""
        config = OpenAIVideoConfig()

        # Mock file data
        mock_file = MagicMock()
        mock_file.read.return_value = b"fake_image_data"

        data, files, returned_api_base = config.transform_video_create_request(
            model="sora-2",
            prompt="Test video with image",
            api_base="https://api.openai.com/v1/videos",
            video_create_optional_request_params={
                "input_reference": mock_file,
                "seconds": "8",
                "size": "720x1280",
            },
            litellm_params=MagicMock(),
            headers={},
        )

        assert data["model"] == "sora-2"
        assert data["prompt"] == "Test video with image"
        assert len(files) > 0  # Should have files when input_reference is provided

    def test_video_generation_environment_validation(self):
        """Test video generation environment validation."""
        config = OpenAIVideoConfig()

        # Test environment validation
        headers = config.validate_environment(
            headers={}, model="sora-2", api_key="test-api-key"
        )

        assert "Authorization" in headers
        assert headers["Authorization"] == "Bearer test-api-key"

    def test_video_generation_uses_api_key_from_litellm_params(self):
        """Test that video generation handler uses api_key from litellm_params when function parameter is None."""
        handler = BaseLLMHTTPHandler()
        config = OpenAIVideoConfig()

        # Mock the validate_environment method to capture the api_key passed to it
        with patch.object(config, "validate_environment") as mock_validate:
            mock_validate.return_value = {"Authorization": "Bearer deployment-api-key"}

            # Mock the transform and HTTP client
            with patch.object(
                config, "transform_video_create_request"
            ) as mock_transform:
                mock_transform.return_value = (
                    {"model": "sora-2", "prompt": "test"},
                    [],
                    "https://api.openai.com/v1/videos",
                )

                # Mock the transform_video_create_response to avoid needing a real response
                with patch.object(
                    config, "transform_video_create_response"
                ) as mock_transform_response:
                    mock_video_object = MagicMock()
                    mock_video_object.id = "video_123"
                    mock_video_object.object = "video"
                    mock_video_object.status = "queued"
                    mock_transform_response.return_value = mock_video_object

                    mock_response = MagicMock()
                    mock_response.json.return_value = {
                        "id": "video_123",
                        "object": "video",
                        "status": "queued",
                        "created_at": 1712697600,
                        "model": "sora-2",
                    }
                    mock_response.status_code = 200

                    mock_client = MagicMock()
                    mock_client.post.return_value = mock_response

                    with patch(
                        "litellm.llms.custom_httpx.llm_http_handler._get_httpx_client",
                        return_value=mock_client,
                    ):
                        result = handler.video_generation_handler(
                            model="sora-2",
                            prompt="test prompt",
                            video_generation_provider_config=config,
                            video_generation_optional_request_params={},
                            custom_llm_provider="openai",
                            litellm_params={
                                "api_key": "deployment-api-key",
                                "api_base": "https://api.openai.com/v1",
                            },
                            logging_obj=MagicMock(),
                            timeout=5.0,
                            api_key=None,  # Function parameter is None
                            _is_async=False,
                        )

                    # Verify validate_environment was called with api_key from litellm_params
                    mock_validate.assert_called_once()
                    call_args = mock_validate.call_args
                assert call_args.kwargs["api_key"] == "deployment-api-key"

    def test_video_generation_url_generation(self):
        """Test video generation URL generation."""
        config = OpenAIVideoConfig()

        # Test URL generation
        url = config.get_complete_url(
            model="sora-2", api_base="https://api.openai.com/v1", litellm_params={}
        )

        assert url == "https://api.openai.com/v1/videos"

    def test_video_generation_parameter_mapping(self):
        """Test video generation parameter mapping."""
        config = OpenAIVideoConfig()

        # Test parameter mapping
        mapped_params = config.map_openai_params(
            video_create_optional_params={
                "seconds": "8",
                "size": "720x1280",
                "user": "test-user",
            },
            model="sora-2",
            drop_params=False,
        )

        assert mapped_params["seconds"] == "8"
        assert mapped_params["size"] == "720x1280"
        assert mapped_params["user"] == "test-user"

    def test_video_generation_unsupported_parameters(self):
        """Test video generation with provider-specific parameters via extra_body."""
        from litellm.videos.utils import VideoGenerationRequestUtils

        # Test that provider-specific parameters can be passed via extra_body
        # This allows support for Vertex AI and Gemini specific parameters
        result = VideoGenerationRequestUtils.get_optional_params_video_generation(
            model="sora-2",
            video_generation_provider_config=OpenAIVideoConfig(),
            video_generation_optional_params={
                "seconds": "8",
                "extra_body": {"vertex_ai_param": "value", "gemini_param": "value2"},
            },
        )

        # extra_body params should be merged into the result
        assert result["seconds"] == "8"
        assert result["vertex_ai_param"] == "value"
        assert result["gemini_param"] == "value2"
        # extra_body itself should be removed from the result
        assert "extra_body" not in result

    def test_video_generation_types(self):
        """Test video generation type definitions."""
        # Test VideoObject
        video_obj = VideoObject(
            id="test_id",
            object="video",
            status="completed",
            created_at=1712697600,
            model="sora-2",
        )

        assert video_obj.id == "test_id"
        assert video_obj.object == "video"
        assert video_obj.status == "completed"

        # Test dictionary-like access
        assert video_obj["id"] == "test_id"
        assert video_obj["status"] == "completed"
        assert "id" in video_obj
        assert video_obj.get("id") == "test_id"
        assert video_obj.get("nonexistent", "default") == "default"

        # Test JSON serialization
        json_data = video_obj.json()
        assert json_data["id"] == "test_id"
        assert json_data["object"] == "video"

    def test_video_generation_response_types(self):
        """Test video generation response types."""
        # Test VideoResponse
        video_obj = VideoObject(
            id="test_id", object="video", status="completed", created_at=1712697600
        )

        response = VideoResponse(data=[video_obj])

        assert len(response.data) == 1
        assert response.data[0].id == "test_id"

        # Test dictionary-like access
        assert response["data"][0]["id"] == "test_id"
        assert "data" in response
        assert response.get("data")[0]["id"] == "test_id"

        # Test JSON serialization
        json_data = response.json()
        assert len(json_data["data"]) == 1
        assert json_data["data"][0]["id"] == "test_id"

    def test_video_status_basic(self):
        """Test basic video status functionality."""
        # Use mock_response parameter for reliable testing
        response = video_status(
            video_id="video_123",
            model="sora-2",
            mock_response={
                "id": "video_123",
                "object": "video",
                "status": "completed",
                "created_at": 1712697600,
                "completed_at": 1712697660,
                "model": "sora-2",
                "progress": 100,
                "size": "720x1280",
                "seconds": "8",
            },
        )

        assert isinstance(response, VideoObject)
        assert response.id == "video_123"
        assert response.status == "completed"
        assert response.progress == 100
        assert response.model == "sora-2"

    def test_video_status_with_mock_response(self):
        """Test video status with mock response."""
        mock_data = {
            "id": "video_456",
            "object": "video",
            "status": "processing",
            "created_at": 1712697600,
            "model": "sora-2",
            "progress": 75,
            "size": "1280x720",
            "seconds": "10",
        }

        response = video_status(
            video_id="video_456", model="sora-2", mock_response=mock_data
        )

        assert isinstance(response, VideoObject)
        assert response.id == "video_456"
        assert response.status == "processing"
        assert response.progress == 75
        assert response.model == "sora-2"

    def test_video_status_async(self):
        """Test async video status functionality."""
        mock_response = VideoObject(
            id="video_async_123",
            object="video",
            status="queued",
            created_at=1712697600,
            model="sora-2",
            progress=0,
        )

        # Mock the async_video_status_handler to return the mock_response
        async_mock = AsyncMock(return_value=mock_response)
        with patch.object(
            videos_main.base_llm_http_handler, "async_video_status_handler", async_mock
        ):
            with patch.object(
                videos_main.base_llm_http_handler,
                "video_status_handler",
                side_effect=lambda **kwargs: async_mock(**kwargs),
            ):
                import asyncio

                async def test_async():
                    response = await avideo_status(
                        video_id="video_async_123", model="sora-2"
                    )
                    return response

                response = asyncio.run(test_async())

                assert isinstance(response, VideoObject)
                assert response.id == "video_async_123"
                assert response.status == "queued"
                assert response.progress == 0

    def test_video_status_parameter_validation(self):
        """Test video status parameter validation."""
        # Test with minimal required parameters
        response = video_status(
            video_id="test_video_id",
            model="sora-2",
            mock_response={
                "id": "test",
                "object": "video",
                "status": "completed",
                "created_at": 1712697600,
            },
        )

        assert isinstance(response, VideoObject)
        assert response.id == "test"

    def test_video_status_error_handling(self):
        """Test video status error handling."""
        with patch.object(
            videos_main.base_llm_http_handler,
            "video_status_handler",
            side_effect=Exception("API Error"),
        ):
            with pytest.raises(litellm.APIConnectionError):
                video_status(video_id="test_video_id", model="sora-2")

    def test_video_status_request_transformation(self):
        """Test video status request transformation."""
        config = OpenAIVideoConfig()

        # Test request transformation
        url, data = config.transform_video_status_retrieve_request(
            video_id="video_123",
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
        )

        assert url == "https://api.openai.com/v1/videos/video_123"
        assert data == {}

    def test_video_status_response_transformation(self):
        """Test video status response transformation."""
        config = OpenAIVideoConfig()

        # Mock HTTP response
        mock_http_response = MagicMock()
        mock_http_response.json.return_value = {
            "id": "video_789",
            "object": "video",
            "status": "completed",
            "created_at": 1712697600,
            "completed_at": 1712697660,
            "model": "sora-2",
            "progress": 100,
            "size": "1280x720",
            "seconds": "12",
        }

        response = config.transform_video_status_retrieve_response(
            raw_response=mock_http_response, logging_obj=MagicMock()
        )

        assert isinstance(response, VideoObject)
        assert response.id == "video_789"
        assert response.status == "completed"
        assert response.progress == 100
        assert response.model == "sora-2"

    def test_video_status_different_states(self):
        """Test video status with different video states."""
        # Test queued state
        queued_response = video_status(
            video_id="video_queued",
            model="sora-2",
            mock_response={
                "id": "video_queued",
                "object": "video",
                "status": "queued",
                "created_at": 1712697600,
                "model": "sora-2",
                "progress": 0,
            },
        )
        assert queued_response.status == "queued"
        assert queued_response.progress == 0

        # Test processing state
        processing_response = video_status(
            video_id="video_processing",
            model="sora-2",
            mock_response={
                "id": "video_processing",
                "object": "video",
                "status": "processing",
                "created_at": 1712697600,
                "model": "sora-2",
                "progress": 50,
            },
        )
        assert processing_response.status == "processing"
        assert processing_response.progress == 50

        # Test completed state
        completed_response = video_status(
            video_id="video_completed",
            model="sora-2",
            mock_response={
                "id": "video_completed",
                "object": "video",
                "status": "completed",
                "created_at": 1712697600,
                "completed_at": 1712697660,
                "model": "sora-2",
                "progress": 100,
            },
        )
        assert completed_response.status == "completed"
        assert completed_response.progress == 100

    def test_video_status_with_remix_info(self):
        """Test video status with remix information."""
        mock_data = {
            "id": "video_remix_123",
            "object": "video",
            "status": "completed",
            "created_at": 1712697600,
            "completed_at": 1712697660,
            "model": "sora-2",
            "progress": 100,
            "remixed_from_video_id": "video_original_123",
            "size": "720x1280",
            "seconds": "8",
        }

        response = video_status(
            video_id="video_remix_123", model="sora-2", mock_response=mock_data
        )

        assert isinstance(response, VideoObject)
        assert response.id == "video_remix_123"
        assert response.status == "completed"
        assert hasattr(response, "remixed_from_video_id")
        assert response.remixed_from_video_id == "video_original_123"

    def test_video_status_async_inside_async_function(self):
        """Test that sync video_status works inside async functions (no asyncio.run issues)."""
        import asyncio

        async def test_sync_in_async():
            # This should work without asyncio.run() issues
            # Use mock_response parameter for reliable testing
            response = video_status(
                video_id="video_sync_in_async",
                model="sora-2",
                mock_response={
                    "id": "video_sync_in_async",
                    "object": "video",
                    "status": "completed",
                    "created_at": 1712697600,
                    "model": "sora-2",
                    "progress": 100,
                },
            )
            return response

        response = asyncio.run(test_sync_in_async())

        assert isinstance(response, VideoObject)
        assert response.id == "video_sync_in_async"
        assert response.status == "completed"

    def test_video_status_url_construction(self):
        """Test video status URL construction."""
        config = OpenAIVideoConfig()

        # Test with different API bases
        test_cases = [
            (
                "https://api.openai.com/v1/videos",
                "video_123",
                "https://api.openai.com/v1/videos/video_123",
            ),
            (
                "https://api.openai.com/v1/videos/",
                "video_123",
                "https://api.openai.com/v1/videos/video_123",
            ),
            (
                "https://custom-api.com/v1/videos",
                "video_456",
                "https://custom-api.com/v1/videos/video_456",
            ),
        ]

        for api_base, video_id, expected_url in test_cases:
            url, data = config.transform_video_status_retrieve_request(
                video_id=video_id,
                api_base=api_base,
                litellm_params=MagicMock(),
                headers={},
            )
            assert url == expected_url
            assert data == {}


class TestVideoLogging:
    """Test video generation logging functionality."""

    class TestVideoLogger(CustomLogger):
        def __init__(self):
            self.standard_logging_payload = None

        async def async_log_success_event(
            self, kwargs, response_obj, start_time, end_time
        ):
            self.standard_logging_payload = kwargs.get("standard_logging_object")

    @pytest.mark.asyncio
    async def test_video_generation_logging(self):
        """Test that video generation creates proper logging payload with cost tracking.

        Note: Uses AsyncMock with side_effect pattern for reliable parallel execution.
        """
        custom_logger = self.TestVideoLogger()
        litellm.logging_callback_manager._reset_all_callbacks()
        await asyncio.wait_for(GLOBAL_LOGGING_WORKER.flush(), timeout=10.0)
        litellm.callbacks = [custom_logger]

        # Mock video generation response
        mock_response = VideoObject(
            id="video_test_123",
            object="video",
            status="queued",
            created_at=1712697600,
            model="sora-2",
            size="720x1280",
            seconds="8",
        )

        # Create async mock function to return the mock_response
        async def mock_async_handler(*args, **kwargs):
            return mock_response

        # Patch the async_video_generation_handler method on base_llm_http_handler
        with patch.object(
            videos_main.base_llm_http_handler,
            "async_video_generation_handler",
            side_effect=mock_async_handler,
        ):
            response = await litellm.avideo_generation(
                prompt="A cat running in a garden",
                model="sora-2",
                seconds="8",
                size="720x1280",
            )

            await asyncio.sleep(1)  # Allow logging to complete

            # Verify logging payload was created
            assert custom_logger.standard_logging_payload is not None

            payload = custom_logger.standard_logging_payload

            # Verify basic logging fields
            assert payload["call_type"] == "avideo_generation"
            assert payload["status"] == "success"
            assert payload["model"] == "sora-2"
            assert payload["custom_llm_provider"] == "openai"

            # Verify response object is recognized for logging
            assert payload["response"] is not None
            assert payload["response"]["id"] == "video_test_123"
            assert payload["response"]["object"] == "video"

            # Verify cost tracking is present (may be 0 in test environment)
            assert payload["response_cost"] is not None
            # Note: Cost calculation may not work in test environment due to mocking
            # The important thing is that the logging payload is created and recognized


def test_openai_transform_video_content_request_empty_params():
    """OpenAI content transform should return empty params to ensure GET is used."""
    config = OpenAIVideoConfig()
    url, params = config.transform_video_content_request(
        video_id="video_123",
        api_base="https://api.openai.com/v1/videos",
        litellm_params={},
        headers={},
    )

    assert url == "https://api.openai.com/v1/videos/video_123/content"
    assert params == {}


@pytest.mark.parametrize(
    "variant,expected_suffix",
    [
        ("thumbnail", "?variant=thumbnail"),
        ("spritesheet", "?variant=spritesheet"),
    ],
)
def test_openai_transform_video_content_request_with_variant(variant, expected_suffix):
    """OpenAI content transform should append ?variant= when variant is provided."""
    config = OpenAIVideoConfig()
    url, params = config.transform_video_content_request(
        video_id="video_123",
        api_base="https://api.openai.com/v1/videos",
        litellm_params={},
        headers={},
        variant=variant,
    )

    assert url == f"https://api.openai.com/v1/videos/video_123/content{expected_suffix}"
    assert params == {}


def test_openai_transform_video_content_request_variant_none_no_query_param():
    """OpenAI content transform should NOT append ?variant= when variant is None."""
    config = OpenAIVideoConfig()
    url, params = config.transform_video_content_request(
        video_id="video_123",
        api_base="https://api.openai.com/v1/videos",
        litellm_params={},
        headers={},
        variant=None,
    )

    assert "variant" not in url
    assert url == "https://api.openai.com/v1/videos/video_123/content"


def test_video_content_handler_passes_variant_to_url():
    """HTTP handler should pass variant through to the final URL."""
    from litellm.llms.custom_httpx.http_handler import HTTPHandler
    from litellm.types.router import GenericLiteLLMParams

    if hasattr(litellm, "in_memory_llm_clients_cache"):
        litellm.in_memory_llm_clients_cache.flush_cache()

    handler = BaseLLMHTTPHandler()
    config = OpenAIVideoConfig()

    mock_client = MagicMock(spec=HTTPHandler)
    mock_response = MagicMock()
    mock_response.content = b"thumbnail-bytes"
    mock_client.get.return_value = mock_response

    with patch(
        "litellm.llms.custom_httpx.llm_http_handler._get_httpx_client",
        return_value=mock_client,
    ):
        result = handler.video_content_handler(
            video_id="video_abc",
            video_content_provider_config=config,
            custom_llm_provider="openai",
            litellm_params=GenericLiteLLMParams(api_base="https://api.openai.com/v1"),
            logging_obj=MagicMock(),
            timeout=5.0,
            api_key="sk-test",
            client=mock_client,
            _is_async=False,
            variant="thumbnail",
        )

    assert result == b"thumbnail-bytes"
    called_url = mock_client.get.call_args.kwargs["url"]
    assert (
        called_url
        == "https://api.openai.com/v1/videos/video_abc/content?variant=thumbnail"
    )


def test_video_content_handler_uses_get_for_openai():
    """HTTP handler must use GET (not POST) for OpenAI content download."""
    from litellm.llms.custom_httpx.http_handler import HTTPHandler
    from litellm.types.router import GenericLiteLLMParams

    # Clear the HTTP client cache to prevent test isolation issues
    # In CI, a cached real HTTPHandler from a previous test might bypass the mock
    if hasattr(litellm, "in_memory_llm_clients_cache"):
        litellm.in_memory_llm_clients_cache.flush_cache()

    handler = BaseLLMHTTPHandler()
    config = OpenAIVideoConfig()

    # Use spec=HTTPHandler so isinstance(mock_client, HTTPHandler) returns True,
    # ensuring the handler uses our mock directly instead of creating a new client.
    mock_client = MagicMock(spec=HTTPHandler)
    mock_response = MagicMock()
    mock_response.content = b"mp4-bytes"
    mock_client.get.return_value = mock_response

    # Patch _get_httpx_client to ensure no real HTTP client is created
    # This prevents test isolation issues where isinstance check might fail
    with patch(
        "litellm.llms.custom_httpx.llm_http_handler._get_httpx_client"
    ) as mock_get_client:
        mock_get_client.return_value = mock_client

        result = handler.video_content_handler(
            video_id="video_abc",
            video_content_provider_config=config,
            custom_llm_provider="openai",
            litellm_params=GenericLiteLLMParams(api_base="https://api.openai.com/v1"),
            logging_obj=MagicMock(),
            timeout=5.0,
            api_key="sk-test",
            client=mock_client,
            _is_async=False,
        )

    assert result == b"mp4-bytes"
    mock_client.get.assert_called_once()
    assert not mock_client.post.called
    called_url = mock_client.get.call_args.kwargs["url"]
    assert called_url == "https://api.openai.com/v1/videos/video_abc/content"


def test_video_content_respects_api_base_and_api_key_from_kwargs():
    """Test that video_content respects api_base and api_key from kwargs (simulating database entry)."""
    from litellm.videos.main import video_content

    # Mock the handler to capture litellm_params
    captured_litellm_params = None

    def capture_litellm_params(*args, **kwargs):
        nonlocal captured_litellm_params
        captured_litellm_params = kwargs.get("litellm_params")
        return b"mp4-bytes"

    with patch("litellm.videos.main.base_llm_http_handler") as mock_handler:
        mock_handler.video_content_handler = capture_litellm_params

        # Call video_content with api_base and api_key in kwargs (simulating database entry)
        # This simulates how the router passes model config from database via **kwargs
        result = video_content(
            video_id="video_test_123",
            custom_llm_provider="azure",
            api_base="https://test-resource.openai.azure.com/",  # Passed via kwargs by router
            api_key="test-api-key-from-db",  # Passed via kwargs by router
        )

    # Verify that api_base and api_key from kwargs were included in litellm_params
    assert captured_litellm_params is not None
    assert (
        captured_litellm_params.get("api_base")
        == "https://test-resource.openai.azure.com/"
    )
    assert captured_litellm_params.get("api_key") == "test-api-key-from-db"
    assert result == b"mp4-bytes"


def test_openai_video_config_has_async_transform():
    """Ensure OpenAIVideoConfig exposes async_transform_video_content_response at runtime."""
    cfg = OpenAIVideoConfig()
    assert callable(getattr(cfg, "async_transform_video_content_response", None))


def test_gemini_video_config_has_async_transform():
    """Ensure GeminiVideoConfig exposes async_transform_video_content_response at runtime."""
    cfg = GeminiVideoConfig()
    assert callable(getattr(cfg, "async_transform_video_content_response", None))


def test_encode_video_id_with_provider_handles_azure_video_prefix():
    """
    Test that encode_video_id_with_provider correctly encodes Azure/OpenAI video IDs
    that start with 'video_' prefix.

    This test verifies the fix for the issue where Azure returns video IDs like
    'video_69323201cf6081909263f751f89991e6', which were previously skipped
    from encoding, causing video status retrieval to default to 'openai' provider.
    """
    from litellm.types.videos.utils import (
        decode_video_id_with_provider,
        encode_video_id_with_provider,
    )

    # Test case: Azure returns a video ID starting with 'video_'
    raw_azure_video_id = "video_69323201cf6081909263f751f89991e6"
    provider = "azure"
    model_id = "azure/sora-2"

    # Encode the video ID with provider information
    encoded_id = encode_video_id_with_provider(
        video_id=raw_azure_video_id, provider=provider, model_id=model_id
    )

    # Verify the ID was encoded (should be different from the original)
    assert encoded_id != raw_azure_video_id
    assert encoded_id.startswith("video_")

    # Decode the encoded ID to verify provider information is preserved
    decoded = decode_video_id_with_provider(encoded_id)
    assert decoded.get("custom_llm_provider") == provider
    assert decoded.get("model_id") == model_id
    assert decoded.get("video_id") == raw_azure_video_id

    # Verify that encoding an already-encoded ID doesn't double-encode it
    encoded_twice = encode_video_id_with_provider(
        video_id=encoded_id, provider=provider, model_id=model_id
    )
    assert encoded_twice == encoded_id  # Should return the same encoded ID


class TestVideoListTransformation:
    """Tests for video list request/response transformation with provider ID encoding."""

    def test_transform_video_list_response_encodes_first_id_and_last_id(self):
        """Verify that first_id and last_id are encoded with provider metadata."""
        config = OpenAIVideoConfig()

        mock_http_response = MagicMock()
        mock_http_response.json.return_value = {
            "object": "list",
            "data": [
                {
                    "id": "video_aaa",
                    "object": "video",
                    "model": "sora-2",
                    "status": "completed",
                },
                {
                    "id": "video_bbb",
                    "object": "video",
                    "model": "sora-2",
                    "status": "completed",
                },
            ],
            "first_id": "video_aaa",
            "last_id": "video_bbb",
            "has_more": False,
        }

        result = config.transform_video_list_response(
            raw_response=mock_http_response,
            logging_obj=MagicMock(),
            custom_llm_provider="azure",
        )

        from litellm.types.videos.utils import decode_video_id_with_provider

        # data[].id should be encoded
        for item in result["data"]:
            decoded = decode_video_id_with_provider(item["id"])
            assert decoded["custom_llm_provider"] == "azure"

        # first_id and last_id should also be encoded
        first_decoded = decode_video_id_with_provider(result["first_id"])
        assert first_decoded["custom_llm_provider"] == "azure"
        assert first_decoded["video_id"] == "video_aaa"
        assert first_decoded["model_id"] == "sora-2"

        last_decoded = decode_video_id_with_provider(result["last_id"])
        assert last_decoded["custom_llm_provider"] == "azure"
        assert last_decoded["video_id"] == "video_bbb"
        assert last_decoded["model_id"] == "sora-2"

    def test_transform_video_list_response_no_provider_leaves_ids_unchanged(self):
        """When custom_llm_provider is None, all IDs should remain unchanged."""
        config = OpenAIVideoConfig()

        mock_http_response = MagicMock()
        mock_http_response.json.return_value = {
            "object": "list",
            "data": [
                {
                    "id": "video_aaa",
                    "object": "video",
                    "model": "sora-2",
                    "status": "completed",
                },
            ],
            "first_id": "video_aaa",
            "last_id": "video_aaa",
            "has_more": False,
        }

        result = config.transform_video_list_response(
            raw_response=mock_http_response,
            logging_obj=MagicMock(),
            custom_llm_provider=None,
        )

        assert result["data"][0]["id"] == "video_aaa"
        assert result["first_id"] == "video_aaa"
        assert result["last_id"] == "video_aaa"

    def test_transform_video_list_response_missing_pagination_fields(self):
        """first_id / last_id may be absent or null; should not raise."""
        config = OpenAIVideoConfig()

        mock_http_response = MagicMock()
        mock_http_response.json.return_value = {
            "object": "list",
            "data": [
                {
                    "id": "video_aaa",
                    "object": "video",
                    "model": "sora-2",
                    "status": "completed",
                },
            ],
            "has_more": False,
        }

        result = config.transform_video_list_response(
            raw_response=mock_http_response,
            logging_obj=MagicMock(),
            custom_llm_provider="azure",
        )

        # data[].id should still be encoded
        from litellm.types.videos.utils import decode_video_id_with_provider

        decoded = decode_video_id_with_provider(result["data"][0]["id"])
        assert decoded["custom_llm_provider"] == "azure"

        # first_id / last_id should not be present
        assert "first_id" not in result
        assert "last_id" not in result

    def test_transform_video_list_request_decodes_after_parameter(self):
        """Encoded 'after' cursor should be decoded back to the raw provider ID."""
        from litellm.types.videos.utils import encode_video_id_with_provider

        config = OpenAIVideoConfig()

        raw_id = "video_69888baee890819086dd3366bfc372fe"
        encoded_id = encode_video_id_with_provider(raw_id, "azure", "sora-2")

        url, params = config.transform_video_list_request(
            api_base="https://my-resource.openai.azure.com/openai/v1/videos",
            litellm_params=MagicMock(),
            headers={},
            after=encoded_id,
            limit=10,
        )

        assert params["after"] == raw_id
        assert params["limit"] == "10"

    def test_transform_video_list_request_passes_through_plain_after(self):
        """A plain (non-encoded) 'after' value should pass through unchanged."""
        config = OpenAIVideoConfig()

        url, params = config.transform_video_list_request(
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
            after="video_plain_id",
        )

        assert params["after"] == "video_plain_id"

    def test_transform_video_list_roundtrip(self):
        """first_id from list response should decode correctly when used as after parameter."""
        config = OpenAIVideoConfig()

        # Simulate a list response
        mock_http_response = MagicMock()
        mock_http_response.json.return_value = {
            "object": "list",
            "data": [
                {
                    "id": "video_aaa",
                    "object": "video",
                    "model": "sora-2",
                    "status": "completed",
                },
                {
                    "id": "video_bbb",
                    "object": "video",
                    "model": "sora-2",
                    "status": "completed",
                },
            ],
            "first_id": "video_aaa",
            "last_id": "video_bbb",
            "has_more": True,
        }

        list_result = config.transform_video_list_response(
            raw_response=mock_http_response,
            logging_obj=MagicMock(),
            custom_llm_provider="azure",
        )

        # Use the encoded last_id as the 'after' cursor for the next page
        _, params = config.transform_video_list_request(
            api_base="https://my-resource.openai.azure.com/openai/v1/videos",
            litellm_params=MagicMock(),
            headers={},
            after=list_result["last_id"],
        )

        # The after param sent to the upstream API should be the raw video ID
        assert params["after"] == "video_bbb"


class TestVideoEndpointsProxyLitellmParams:
    """Test that video proxy endpoints (status, content, remix) respect litellm_params from proxy config."""

    @pytest.fixture
    def client_with_vertex_config(self, monkeypatch):
        """Create a test client with a proxy config that includes Vertex AI model with litellm_params."""
        import asyncio
        import tempfile

        import yaml
        from fastapi import FastAPI
        from fastapi.testclient import TestClient

        from litellm.proxy.proxy_server import (
            cleanup_router_config_variables,
            initialize,
            router,
        )
        from litellm.proxy.video_endpoints.endpoints import router as video_router

        # Clean up any existing router config
        cleanup_router_config_variables()

        # Create inline config
        config = {
            "model_list": [
                {
                    "model_name": "vertex-ai-sora-2",
                    "litellm_params": {
                        "model": "vertex_ai/veo-2.0-generate-001",
                        "vertex_project": "test-project-123",
                        "vertex_location": "global",
                        "vertex_credentials": "/path/to/test-credentials.json",
                    },
                }
            ]
        }

        # Write config to temporary file
        with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f:
            yaml.dump(config, f)
            config_fp = f.name

        try:
            # Initialize the proxy with the test config
            app = FastAPI()
            asyncio.run(initialize(config=config_fp, debug=True))
            app.include_router(router)
            app.include_router(video_router)

            return TestClient(app)
        finally:
            # Clean up temporary file
            import os

            if os.path.exists(config_fp):
                os.unlink(config_fp)

    @pytest.fixture
    def mock_video_generation_response(self):
        """Mock video generation response with encoded video_id."""
        from litellm.types.videos.utils import encode_video_id_with_provider

        # Create an encoded video_id that includes provider and model_id
        original_video_id = "projects/test-project-123/locations/global/publishers/google/models/veo-2.0-generate-001/operations/test-operation-123"
        encoded_video_id = encode_video_id_with_provider(
            video_id=original_video_id,
            provider="vertex_ai",
            model_id="veo-2.0-generate-001",
        )

        return VideoObject(
            id=encoded_video_id,
            object="video",
            status="processing",
            created_at=1712697600,
            model="vertex_ai/veo-2.0-generate-001",
        )

    @pytest.fixture
    def mock_video_status_response(self):
        """Mock video status response."""
        return VideoObject(
            id="video_test_123",
            object="video",
            status="completed",
            created_at=1712697600,
            completed_at=1712697660,
            model="vertex_ai/veo-2.0-generate-001",
            progress=100,
        )

    @pytest.fixture
    def mock_video_content_response(self):
        """Mock video content response (raw bytes)."""
        return b"fake_video_content_bytes"

    @pytest.mark.asyncio
    async def test_video_status_respects_litellm_params(
        self,
        client_with_vertex_config,
        mock_video_generation_response,
        mock_video_status_response,
    ):
        """Test that video_status endpoint uses litellm_params from proxy config."""
        from unittest.mock import AsyncMock, MagicMock, patch

        # Create an encoded video_id
        encoded_video_id = mock_video_generation_response.id

        # Mock the router instance
        mock_router_instance = MagicMock()
        mock_router_instance.resolve_model_name_from_model_id.return_value = (
            "vertex-ai-sora-2"
        )
        mock_router_instance.model_names = {"vertex-ai-sora-2"}
        mock_router_instance.has_model_id.return_value = False

        # Mock route_request to capture the data being passed
        # route_request should return a coroutine (not await it), so we return a coroutine
        async def mock_route_request_func(*args, **kwargs):
            return mock_video_status_response

        # Create a coroutine that will be added to tasks
        def create_mock_coroutine(*args, **kwargs):
            return mock_route_request_func(*args, **kwargs)

        with patch("litellm.proxy.proxy_server.llm_router", mock_router_instance):
            with patch(
                "litellm.proxy.common_request_processing.route_request",
                side_effect=create_mock_coroutine,
            ) as mock_route_request:
                # Make request to video_status endpoint
                response = client_with_vertex_config.get(
                    f"/v1/videos/{encoded_video_id}",
                    headers={"Authorization": "Bearer sk-1234"},
                )

                # Verify the endpoint was called
                assert response.status_code == 200, f"Response: {response.text}"

                # Verify that route_request was called
                assert mock_route_request.called
                call_args = mock_route_request.call_args
                # route_request is called with data as a keyword argument
                data_passed = (
                    call_args.kwargs.get("data", {})
                    if call_args.kwargs
                    else (
                        call_args.args[0]
                        if call_args.args and len(call_args.args) > 0
                        else {}
                    )
                )

                # Verify that model was resolved and added to data
                assert data_passed.get("model") == "vertex-ai-sora-2", (
                    f"Expected model to be 'vertex-ai-sora-2', got '{data_passed.get('model')}'. "
                    f"Full data: {data_passed}, call_args: {call_args}"
                )
                # Verify that custom_llm_provider is set from decoded video_id
                assert data_passed.get("custom_llm_provider") == "vertex_ai", (
                    f"Expected custom_llm_provider to be 'vertex_ai', got '{data_passed.get('custom_llm_provider')}'. "
                    f"Full data: {data_passed}"
                )

    @pytest.mark.asyncio
    async def test_video_content_respects_litellm_params(
        self,
        client_with_vertex_config,
        mock_video_generation_response,
        mock_video_content_response,
    ):
        """Test that video_content endpoint uses litellm_params from proxy config."""
        from unittest.mock import AsyncMock, MagicMock, patch

        # Create an encoded video_id
        encoded_video_id = mock_video_generation_response.id

        # Mock the router instance
        mock_router_instance = MagicMock()
        mock_router_instance.resolve_model_name_from_model_id.return_value = (
            "vertex-ai-sora-2"
        )
        mock_router_instance.model_names = {"vertex-ai-sora-2"}
        mock_router_instance.has_model_id.return_value = False

        # Mock route_request to capture the data being passed
        # route_request should return a coroutine (not await it), so we return a coroutine
        async def mock_route_request_func(*args, **kwargs):
            return mock_video_content_response

        # Create a coroutine that will be added to tasks
        def create_mock_coroutine(*args, **kwargs):
            return mock_route_request_func(*args, **kwargs)

        with patch("litellm.proxy.proxy_server.llm_router", mock_router_instance):
            with patch(
                "litellm.proxy.common_request_processing.route_request",
                side_effect=create_mock_coroutine,
            ) as mock_route_request:
                # Make request to video_content endpoint
                response = client_with_vertex_config.get(
                    f"/v1/videos/{encoded_video_id}/content",
                    headers={"Authorization": "Bearer sk-1234"},
                )

                # Verify the endpoint was called
                assert response.status_code == 200, f"Response: {response.text}"

                # Verify that route_request was called
                assert mock_route_request.called
                call_args = mock_route_request.call_args
                # route_request is called with data as a keyword argument
                data_passed = (
                    call_args.kwargs.get("data", {})
                    if call_args.kwargs
                    else (
                        call_args.args[0]
                        if call_args.args and len(call_args.args) > 0
                        else {}
                    )
                )

                # Verify that model was resolved and added to data
                assert data_passed.get("model") == "vertex-ai-sora-2", (
                    f"Expected model to be 'vertex-ai-sora-2', got '{data_passed.get('model')}'. "
                    f"Full data: {data_passed}, call_args: {call_args}"
                )
                # Verify that custom_llm_provider is correctly set from decoded video_id (not "openai")
                assert data_passed.get("custom_llm_provider") == "vertex_ai", (
                    f"Expected custom_llm_provider to be 'vertex_ai', got '{data_passed.get('custom_llm_provider')}'. "
                    f"Full data: {data_passed}"
                )

    @pytest.mark.asyncio
    async def test_video_content_preserves_custom_llm_provider_from_decoded_id(
        self,
        client_with_vertex_config,
        mock_video_generation_response,
        mock_video_content_response,
    ):
        """Test that video_content preserves custom_llm_provider from decoded video_id."""
        from unittest.mock import AsyncMock, MagicMock, patch

        # Create an encoded video_id
        encoded_video_id = mock_video_generation_response.id

        # Mock the router instance
        mock_router_instance = MagicMock()
        mock_router_instance.resolve_model_name_from_model_id.return_value = (
            "vertex-ai-sora-2"
        )
        mock_router_instance.model_names = {"vertex-ai-sora-2"}
        mock_router_instance.has_model_id.return_value = False

        # Mock route_request to capture the data being passed
        # route_request should return a coroutine (not await it), so we return a coroutine
        async def mock_route_request_func(*args, **kwargs):
            return mock_video_content_response

        # Create a coroutine that will be added to tasks
        def create_mock_coroutine(*args, **kwargs):
            return mock_route_request_func(*args, **kwargs)

        with patch("litellm.proxy.proxy_server.llm_router", mock_router_instance):
            with patch(
                "litellm.proxy.common_request_processing.route_request",
                side_effect=create_mock_coroutine,
            ) as mock_route_request:
                # Make request to video_content endpoint
                response = client_with_vertex_config.get(
                    f"/v1/videos/{encoded_video_id}/content",
                    headers={"Authorization": "Bearer sk-1234"},
                )

                # Verify the endpoint was called
                assert response.status_code == 200, f"Response: {response.text}"

                # Verify that route_request was called
                assert mock_route_request.called
                call_args = mock_route_request.call_args
                # route_request is called with data as a keyword argument
                data_passed = (
                    call_args.kwargs.get("data", {})
                    if call_args.kwargs
                    else (
                        call_args.args[0]
                        if call_args.args and len(call_args.args) > 0
                        else {}
                    )
                )

                # Most importantly: verify that custom_llm_provider is "vertex_ai" not "openai"
                # This was the bug we fixed - it was defaulting to "openai" before
                assert data_passed.get("custom_llm_provider") == "vertex_ai", (
                    f"Expected custom_llm_provider to be 'vertex_ai', "
                    f"but got '{data_passed.get('custom_llm_provider')}'. "
                    f"Full data: {data_passed}, call_args: {call_args}"
                )


def test_video_remix_handler_uses_api_key_from_litellm_params():
    """Sync remix handler should fall back to litellm_params api_key when api_key param is None."""
    handler = BaseLLMHTTPHandler()
    config = OpenAIVideoConfig()

    with patch.object(config, "validate_environment") as mock_validate:
        mock_validate.return_value = {"Authorization": "Bearer deployment-key"}

        with patch.object(config, "transform_video_remix_request") as mock_transform:
            mock_transform.return_value = (
                "https://api.openai.com/v1/videos/video_123/remix",
                {"prompt": "remix it"},
            )

            with patch.object(config, "transform_video_remix_response") as mock_resp:
                mock_resp.return_value = MagicMock()

                mock_client = MagicMock()
                mock_client.post.return_value = MagicMock(status_code=200)

                with patch(
                    "litellm.llms.custom_httpx.llm_http_handler._get_httpx_client",
                    return_value=mock_client,
                ):
                    handler.video_remix_handler(
                        video_id="video_123",
                        prompt="remix it",
                        video_remix_provider_config=config,
                        custom_llm_provider="openai",
                        litellm_params={
                            "api_key": "deployment-key",
                            "api_base": "https://api.openai.com/v1",
                        },
                        logging_obj=MagicMock(),
                        timeout=5.0,
                        api_key=None,
                        _is_async=False,
                    )

        mock_validate.assert_called_once()
        assert mock_validate.call_args.kwargs["api_key"] == "deployment-key"


@pytest.mark.asyncio
async def test_async_video_remix_handler_uses_api_key_from_litellm_params():
    """Async remix handler should fall back to litellm_params api_key when api_key param is None."""
    handler = BaseLLMHTTPHandler()
    config = OpenAIVideoConfig()

    with patch.object(config, "validate_environment") as mock_validate:
        mock_validate.return_value = {"Authorization": "Bearer deployment-key"}

        with patch.object(config, "transform_video_remix_request") as mock_transform:
            mock_transform.return_value = (
                "https://api.openai.com/v1/videos/video_123/remix",
                {"prompt": "remix it"},
            )

            with patch.object(config, "transform_video_remix_response") as mock_resp:
                mock_resp.return_value = MagicMock()

                mock_client = MagicMock(spec=AsyncHTTPHandler)
                mock_response = MagicMock(status_code=200)
                mock_client.post = AsyncMock(return_value=mock_response)

                with patch(
                    "litellm.llms.custom_httpx.llm_http_handler.get_async_httpx_client",
                    return_value=mock_client,
                ):
                    await handler.async_video_remix_handler(
                        video_id="video_123",
                        prompt="remix it",
                        video_remix_provider_config=config,
                        custom_llm_provider="openai",
                        litellm_params={
                            "api_key": "deployment-key",
                            "api_base": "https://api.openai.com/v1",
                        },
                        logging_obj=MagicMock(),
                        timeout=5.0,
                        api_key=None,
                    )

        mock_validate.assert_called_once()
        assert mock_validate.call_args.kwargs["api_key"] == "deployment-key"


def test_video_remix_handler_prefers_explicit_api_key():
    """Sync remix handler should prefer explicit api_key over litellm_params."""
    handler = BaseLLMHTTPHandler()
    config = OpenAIVideoConfig()

    with patch.object(config, "validate_environment") as mock_validate:
        mock_validate.return_value = {"Authorization": "Bearer explicit-key"}

        with patch.object(config, "transform_video_remix_request") as mock_transform:
            mock_transform.return_value = (
                "https://api.openai.com/v1/videos/video_123/remix",
                {"prompt": "remix it"},
            )

            with patch.object(config, "transform_video_remix_response") as mock_resp:
                mock_resp.return_value = MagicMock()

                mock_client = MagicMock()
                mock_client.post.return_value = MagicMock(status_code=200)

                with patch(
                    "litellm.llms.custom_httpx.llm_http_handler._get_httpx_client",
                    return_value=mock_client,
                ):
                    handler.video_remix_handler(
                        video_id="video_123",
                        prompt="remix it",
                        video_remix_provider_config=config,
                        custom_llm_provider="openai",
                        litellm_params={
                            "api_key": "deployment-key",
                            "api_base": "https://api.openai.com/v1",
                        },
                        logging_obj=MagicMock(),
                        timeout=5.0,
                        api_key="explicit-key",
                        _is_async=False,
                    )

        mock_validate.assert_called_once()
        assert mock_validate.call_args.kwargs["api_key"] == "explicit-key"


if __name__ == "__main__":
    pytest.main([__file__])


# ===== Tests for new video endpoints (characters, edits, extensions) =====


class TestVideoCreateCharacter:
    """Tests for video_create_character / avideo_create_character."""

    def test_video_create_character_transform_request(self):
        """Verify multipart form construction for POST /videos/characters."""
        config = OpenAIVideoConfig()
        fake_video = b"fake_video_bytes"

        url, files_list = config.transform_video_create_character_request(
            name="hero",
            video=fake_video,
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
        )

        assert url == "https://api.openai.com/v1/videos/characters"
        # Should have (name field) + (video file field) = 2 entries
        assert len(files_list) == 2
        field_names = [f[0] for f in files_list]
        assert "name" in field_names
        assert "video" in field_names

    def test_video_create_character_sets_video_mimetype(self):
        """Ensure character video upload is sent as video/mp4."""
        config = OpenAIVideoConfig()
        fake_video = io.BytesIO(b"....ftyp....video-bytes")
        fake_video.name = "character.mp4"

        _, files_list = config.transform_video_create_character_request(
            name="hero",
            video=fake_video,
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
        )

        video_parts = [f for f in files_list if f[0] == "video"]
        assert len(video_parts) == 1
        video_tuple = video_parts[0][1]
        assert video_tuple[0] == "character.mp4"
        assert video_tuple[2] == "video/mp4"

    def test_video_create_character_transform_response(self):
        """Verify CharacterObject is returned from response."""
        from litellm.types.videos.main import CharacterObject

        config = OpenAIVideoConfig()
        mock_response = MagicMock()
        mock_response.json.return_value = {
            "id": "char_abc123",
            "object": "character",
            "created_at": 1712697600,
            "name": "hero",
        }

        result = config.transform_video_create_character_response(
            raw_response=mock_response,
            logging_obj=MagicMock(),
        )

        assert isinstance(result, CharacterObject)
        assert result.id == "char_abc123"
        assert result.name == "hero"

    def test_video_create_character_mock_response(self):
        """video_create_character returns CharacterObject on mock_response."""
        from litellm.types.videos.main import CharacterObject
        from litellm.videos.main import video_create_character

        response = video_create_character(
            name="hero",
            video=b"fake",
            mock_response={
                "id": "char_abc",
                "object": "character",
                "created_at": 1712697600,
                "name": "hero",
            },
        )
        assert isinstance(response, CharacterObject)
        assert response.id == "char_abc"


class TestVideoGetCharacter:
    """Tests for video_get_character / avideo_get_character."""

    def test_video_get_character_transform_request(self):
        """Verify URL construction for GET /videos/characters/{character_id}."""
        config = OpenAIVideoConfig()

        url, params = config.transform_video_get_character_request(
            character_id="char_xyz",
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
        )

        assert url == "https://api.openai.com/v1/videos/characters/char_xyz"
        assert params == {}

    def test_video_get_character_transform_response(self):
        """Verify CharacterObject is returned from GET response."""
        from litellm.types.videos.main import CharacterObject

        config = OpenAIVideoConfig()
        mock_response = MagicMock()
        mock_response.json.return_value = {
            "id": "char_xyz",
            "object": "character",
            "created_at": 1712697600,
            "name": "villain",
        }

        result = config.transform_video_get_character_response(
            raw_response=mock_response,
            logging_obj=MagicMock(),
        )

        assert isinstance(result, CharacterObject)
        assert result.id == "char_xyz"
        assert result.name == "villain"

    def test_video_get_character_mock_response(self):
        """video_get_character returns CharacterObject on mock_response."""
        from litellm.types.videos.main import CharacterObject
        from litellm.videos.main import video_get_character

        response = video_get_character(
            character_id="char_xyz",
            mock_response={
                "id": "char_xyz",
                "object": "character",
                "created_at": 1712697600,
                "name": "villain",
            },
        )
        assert isinstance(response, CharacterObject)
        assert response.id == "char_xyz"


class TestVideoEdit:
    """Tests for video_edit / avideo_edit."""

    def test_video_edit_transform_request(self):
        """Verify JSON body with video.id for POST /videos/edits."""
        config = OpenAIVideoConfig()

        url, data, files = config.transform_video_edit_request(
            prompt="make it brighter",
            video_id="video_abc123",
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
        )

        assert url == "https://api.openai.com/v1/videos/edits"
        assert data["prompt"] == "make it brighter"
        assert data["video"]["id"] == "video_abc123"
        assert files is None

    def test_video_edit_transform_request_with_extra_body(self):
        """Extra body params are merged into request data."""
        config = OpenAIVideoConfig()

        url, data, files = config.transform_video_edit_request(
            prompt="darken it",
            video_id="video_abc123",
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
            extra_body={"resolution": "1080p"},
        )

        assert data["resolution"] == "1080p"
        assert files is None

    def test_video_edit_mock_response(self):
        """video_edit returns VideoObject on mock_response."""
        from litellm.videos.main import video_edit

        response = video_edit(
            video_id="video_abc123",
            prompt="make it brighter",
            mock_response={
                "id": "video_edit_001",
                "object": "video",
                "status": "queued",
                "created_at": 1712697600,
            },
        )
        assert isinstance(response, VideoObject)
        assert response.id == "video_edit_001"

    def test_video_edit_strips_encoded_provider_from_video_id(self):
        """Provider-encoded video IDs are decoded before sending to API."""
        from litellm.types.videos.utils import encode_video_id_with_provider

        config = OpenAIVideoConfig()

        encoded_id = encode_video_id_with_provider("raw_video_id", "openai", None)
        url, data, files = config.transform_video_edit_request(
            prompt="test",
            video_id=encoded_id,
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
        )

        # The video.id in the request body should be the raw ID, not the encoded one
        assert data["video"]["id"] == "raw_video_id"
        assert files is None


class TestVideoExtension:
    """Tests for video_extension / avideo_extension."""

    def test_video_extension_transform_request(self):
        """Verify JSON body with video.id + seconds for POST /videos/extensions."""
        config = OpenAIVideoConfig()

        url, data = config.transform_video_extension_request(
            prompt="continue the scene",
            video_id="video_abc123",
            seconds="5",
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
        )

        assert url == "https://api.openai.com/v1/videos/extensions"
        assert data["prompt"] == "continue the scene"
        assert data["seconds"] == "5"
        assert data["video"]["id"] == "video_abc123"

    def test_video_extension_transform_request_with_extra_body(self):
        """Extra body params are merged into request data."""
        config = OpenAIVideoConfig()

        url, data = config.transform_video_extension_request(
            prompt="extend",
            video_id="video_abc123",
            seconds="10",
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
            extra_body={"model": "sora-2"},
        )

        assert data["model"] == "sora-2"

    def test_video_extension_mock_response(self):
        """video_extension returns VideoObject on mock_response."""
        from litellm.videos.main import video_extension

        response = video_extension(
            video_id="video_abc123",
            prompt="continue the scene",
            seconds="5",
            mock_response={
                "id": "video_ext_001",
                "object": "video",
                "status": "queued",
                "created_at": 1712697600,
            },
        )
        assert isinstance(response, VideoObject)
        assert response.id == "video_ext_001"

    def test_video_extension_strips_encoded_provider_from_video_id(self):
        """Provider-encoded video IDs are decoded before sending to API."""
        from litellm.types.videos.utils import encode_video_id_with_provider

        config = OpenAIVideoConfig()

        encoded_id = encode_video_id_with_provider("raw_video_id", "openai", None)
        url, data = config.transform_video_extension_request(
            prompt="extend",
            video_id=encoded_id,
            seconds="5",
            api_base="https://api.openai.com/v1/videos",
            litellm_params=MagicMock(),
            headers={},
        )

        assert data["video"]["id"] == "raw_video_id"


@pytest.fixture
def video_proxy_test_client():
    from fastapi import FastAPI
    from fastapi.testclient import TestClient

    from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
    from litellm.proxy.video_endpoints.endpoints import router as video_router

    app = FastAPI()
    app.include_router(video_router)
    app.dependency_overrides[user_api_key_auth] = lambda: MagicMock()
    return TestClient(app)


def test_character_id_encode_decode_roundtrip():
    from litellm.types.videos.utils import (
        decode_character_id_with_provider,
        encode_character_id_with_provider,
    )

    encoded = encode_character_id_with_provider(
        character_id="char_raw_123",
        provider="vertex_ai",
        model_id="veo-2.0-generate-001",
    )
    decoded = decode_character_id_with_provider(encoded)

    assert decoded["character_id"] == "char_raw_123"
    assert decoded["custom_llm_provider"] == "vertex_ai"
    assert decoded["model_id"] == "veo-2.0-generate-001"


def test_character_id_decode_handles_missing_base64_padding():
    from litellm.types.videos.utils import (
        decode_character_id_with_provider,
        encode_character_id_with_provider,
    )

    encoded = encode_character_id_with_provider(
        character_id="id",
        provider="openai",
        model_id="gpt-4o",
    )
    encoded_without_padding = encoded.rstrip("=")
    decoded = decode_character_id_with_provider(encoded_without_padding)

    assert decoded["character_id"] == "id"
    assert decoded["custom_llm_provider"] == "openai"
    assert decoded["model_id"] == "gpt-4o"


def test_video_create_character_target_model_names_returns_encoded_id(
    video_proxy_test_client,
):
    from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
    from litellm.types.videos.utils import decode_character_id_with_provider

    captured_data = {}

    async def _mock_base_process(self, **kwargs):
        captured_data.update(self.data)
        return {
            "id": "char_upstream_123",
            "object": "character",
            "created_at": 1712697600,
            "name": "hero",
        }

    with patch.object(
        ProxyBaseLLMRequestProcessing,
        "base_process_llm_request",
        new=_mock_base_process,
    ):
        response = video_proxy_test_client.post(
            "/v1/videos/characters",
            headers={"Authorization": "Bearer sk-1234"},
            files={"video": ("character.mp4", b"fake-video", "video/mp4")},
            data={
                "name": "hero",
                "target_model_names": "vertex-ai-sora-2",
                "extra_body": json.dumps({"custom_llm_provider": "vertex_ai"}),
            },
        )

    assert response.status_code == 200, response.text
    response_json = response.json()
    decoded = decode_character_id_with_provider(response_json["id"])
    assert decoded["character_id"] == "char_upstream_123"
    assert decoded["custom_llm_provider"] == "vertex_ai"
    assert decoded["model_id"] == "vertex-ai-sora-2"
    assert captured_data["model"] == "vertex-ai-sora-2"
    assert captured_data["custom_llm_provider"] == "vertex_ai"


def test_video_get_character_accepts_encoded_character_id(video_proxy_test_client):
    from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
    from litellm.types.videos.utils import (
        decode_character_id_with_provider,
        encode_character_id_with_provider,
    )

    captured_data = {}

    async def _mock_base_process(self, **kwargs):
        captured_data.update(self.data)
        return {
            "id": "char_upstream_123",
            "object": "character",
            "created_at": 1712697600,
            "name": "hero",
        }

    encoded_character_id = encode_character_id_with_provider(
        character_id="char_upstream_123",
        provider="vertex_ai",
        model_id="veo-2.0-generate-001",
    )
    mock_router = MagicMock()
    mock_router.resolve_model_name_from_model_id.return_value = "vertex-ai-sora-2"

    with patch("litellm.proxy.proxy_server.llm_router", mock_router):
        with patch.object(
            ProxyBaseLLMRequestProcessing,
            "base_process_llm_request",
            new=_mock_base_process,
        ):
            response = video_proxy_test_client.get(
                f"/v1/videos/characters/{encoded_character_id}",
                headers={"Authorization": "Bearer sk-1234"},
            )

    assert response.status_code == 200, response.text
    assert captured_data["character_id"] == "char_upstream_123"
    assert captured_data["custom_llm_provider"] == "vertex_ai"
    assert captured_data["model"] == "vertex-ai-sora-2"
    response_decoded = decode_character_id_with_provider(response.json()["id"])
    assert response_decoded["character_id"] == "char_upstream_123"
    assert response_decoded["custom_llm_provider"] == "vertex_ai"
    assert response_decoded["model_id"] == "veo-2.0-generate-001"


@pytest.mark.parametrize("endpoint", ["/v1/videos/edits", "/v1/videos/extensions"])
def test_edit_and_extension_support_custom_provider_from_extra_body(
    video_proxy_test_client, endpoint
):
    from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing

    captured_data = {}

    async def _mock_base_process(self, **kwargs):
        captured_data.update(self.data)
        return {
            "id": "video_resp_123",
            "object": "video",
            "status": "queued",
            "created_at": 1712697600,
        }

    payload = {
        "prompt": "test",
        "video": {"id": "video_raw_123"},
        "extra_body": {"custom_llm_provider": "vertex_ai"},
    }
    if endpoint.endswith("extensions"):
        payload["seconds"] = "4"

    with patch.object(
        ProxyBaseLLMRequestProcessing,
        "base_process_llm_request",
        new=_mock_base_process,
    ):
        response = video_proxy_test_client.post(
            endpoint,
            headers={"Authorization": "Bearer sk-1234"},
            json=payload,
        )

    assert response.status_code == 200, response.text
    assert captured_data["custom_llm_provider"] == "vertex_ai"


@pytest.mark.parametrize(
    "handler_name, path, form",
    [
        (
            "video_edit",
            "/v1/videos/edits",
            {"model": "my-video-model", "prompt": "brighter", "video": "video_123"},
        ),
        (
            "video_extension",
            "/v1/videos/extensions",
            {"model": "my-video-model", "prompt": "continue", "seconds": "4", "video": "video_123"},
        ),
    ],
)
@pytest.mark.asyncio
async def test_edit_and_extension_read_cached_body_after_auth_consumes_stream(
    handler_name, path, form
):
    from urllib.parse import urlencode

    from fastapi import Response
    from starlette.requests import Request

    import litellm.proxy.video_endpoints.endpoints as endpoints
    from litellm.proxy._types import ProxyException, UserAPIKeyAuth
    from litellm.proxy.common_utils.http_parsing_utils import _read_request_body

    body = urlencode(form).encode()
    stream = {"sent": False}

    async def receive():
        if stream["sent"]:
            return {"type": "http.request", "body": b"", "more_body": False}
        stream["sent"] = True
        return {"type": "http.request", "body": body, "more_body": False}

    request = Request(
        {
            "type": "http",
            "method": "POST",
            "path": path,
            "headers": [
                (b"content-type", b"application/x-www-form-urlencoded"),
                (b"content-length", str(len(body)).encode()),
            ],
            "query_string": b"",
        },
        receive,
    )

    await _read_request_body(request=request)

    handler = getattr(endpoints, handler_name)
    with pytest.raises(ProxyException) as exc_info:
        await handler(
            request=request,
            fastapi_response=Response(),
            user_api_key_dict=UserAPIKeyAuth(api_key="sk-1234"),
        )

    message = str(exc_info.value)
    assert "Stream consumed" not in message
    assert "my-video-model" in message


@pytest.mark.parametrize("endpoint", ["/v1/videos/edits", "/v1/videos/extensions"])
def test_edit_and_extension_route_with_encoded_video_ids(
    video_proxy_test_client, endpoint
):
    from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
    from litellm.types.videos.utils import encode_video_id_with_provider

    captured_data = {}

    async def _mock_base_process(self, **kwargs):
        captured_data.update(self.data)
        return {
            "id": "video_resp_123",
            "object": "video",
            "status": "queued",
            "created_at": 1712697600,
        }

    encoded_video_id = encode_video_id_with_provider(
        video_id="video_raw_123",
        provider="vertex_ai",
        model_id="veo-2.0-generate-001",
    )
    payload = {"prompt": "test", "video": {"id": encoded_video_id}}
    if endpoint.endswith("extensions"):
        payload["seconds"] = "4"

    mock_router = MagicMock()
    mock_router.resolve_model_name_from_model_id.return_value = "vertex-ai-sora-2"

    with patch("litellm.proxy.proxy_server.llm_router", mock_router):
        with patch.object(
            ProxyBaseLLMRequestProcessing,
            "base_process_llm_request",
            new=_mock_base_process,
        ):
            response = video_proxy_test_client.post(
                endpoint,
                headers={"Authorization": "Bearer sk-1234"},
                json=payload,
            )

    assert response.status_code == 200, response.text
    assert captured_data["video_id"] == encoded_video_id
    assert captured_data["custom_llm_provider"] == "vertex_ai"
    assert captured_data["model"] == "vertex-ai-sora-2"