chore: initial snapshot for gitea/github upload
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from io import BufferedReader
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
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import httpx
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from httpx._types import RequestFiles
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import litellm
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from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
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from litellm.llms.openai.image_edit.transformation import ImageEditRequestUtils
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import CreateVideoRequest
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from litellm.types.router import GenericLiteLLMParams
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from litellm.types.videos.main import VideoCreateOptionalRequestParams, VideoObject
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from litellm.types.videos.utils import (
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encode_video_id_with_provider,
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extract_original_video_id,
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)
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
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from ...base_llm.chat.transformation import BaseLLMException as _BaseLLMException
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LiteLLMLoggingObj = _LiteLLMLoggingObj
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BaseLLMException = _BaseLLMException
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else:
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LiteLLMLoggingObj = Any
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BaseLLMException = Any
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class OpenAIVideoConfig(BaseVideoConfig):
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"""
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Configuration class for OpenAI video generation.
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"""
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def __init__(self):
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super().__init__()
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def get_supported_openai_params(self, model: str) -> list:
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"""
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Get the list of supported OpenAI parameters for video generation.
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"""
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return [
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"model",
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"prompt",
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"input_reference",
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"seconds",
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"size",
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"user",
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"extra_headers",
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]
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def map_openai_params(
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self,
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video_create_optional_params: VideoCreateOptionalRequestParams,
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model: str,
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drop_params: bool,
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) -> Dict:
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"""No mapping applied since inputs are in OpenAI spec already"""
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return dict(video_create_optional_params)
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def validate_environment(
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self,
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headers: dict,
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model: str,
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api_key: Optional[str] = None,
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litellm_params: Optional[GenericLiteLLMParams] = None,
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) -> dict:
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# Use api_key from litellm_params if available, otherwise fall back to other sources
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if litellm_params and litellm_params.api_key:
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api_key = api_key or litellm_params.api_key
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api_key = (
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api_key
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or litellm.api_key
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or litellm.openai_key
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or get_secret_str("OPENAI_API_KEY")
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)
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headers.update(
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{
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"Authorization": f"Bearer {api_key}",
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}
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)
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return headers
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def get_complete_url(
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self,
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model: str,
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api_base: Optional[str],
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litellm_params: dict,
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) -> str:
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"""
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Get the complete URL for OpenAI video generation.
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"""
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if api_base is None:
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api_base = "https://api.openai.com/v1"
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return f"{api_base.rstrip('/')}/videos"
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def transform_video_create_request(
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self,
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model: str,
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prompt: str,
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api_base: str,
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video_create_optional_request_params: Dict,
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litellm_params: GenericLiteLLMParams,
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headers: dict,
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) -> Tuple[Dict, RequestFiles, str]:
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"""
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Transform the video creation request for OpenAI API.
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"""
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# Remove model and extra_headers from optional params as they're handled separately
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video_create_optional_request_params = {
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k: v
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for k, v in video_create_optional_request_params.items()
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if k not in ["model", "extra_headers", "prompt"]
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}
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# Create the request data
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video_create_request = CreateVideoRequest(
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model=model, prompt=prompt, **video_create_optional_request_params
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)
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request_dict = cast(Dict, video_create_request)
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# Handle input_reference parameter if provided
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_input_reference = video_create_optional_request_params.get("input_reference")
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data_without_files = {
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k: v for k, v in request_dict.items() if k not in ["input_reference"]
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}
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files_list: List[Tuple[str, Any]] = []
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# Handle input_reference parameter
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if _input_reference is not None:
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self._add_image_to_files(
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files_list=files_list,
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image=_input_reference,
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field_name="input_reference",
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)
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return data_without_files, files_list, api_base
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def transform_video_create_response(
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self,
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model: str,
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raw_response: httpx.Response,
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logging_obj: LiteLLMLoggingObj,
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custom_llm_provider: Optional[str] = None,
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request_data: Optional[Dict] = None,
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) -> VideoObject:
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"""Transform the OpenAI video creation response."""
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response_data = raw_response.json()
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video_obj = VideoObject(**response_data) # type: ignore[arg-type]
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if custom_llm_provider and video_obj.id:
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video_obj.id = encode_video_id_with_provider(
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video_obj.id, custom_llm_provider, model
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)
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usage_data = {}
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if video_obj:
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if hasattr(video_obj, "seconds") and video_obj.seconds:
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try:
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usage_data["duration_seconds"] = float(video_obj.seconds)
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except (ValueError, TypeError):
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pass
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video_obj.usage = usage_data
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return video_obj
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def transform_video_content_request(
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self,
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video_id: str,
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api_base: str,
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litellm_params: GenericLiteLLMParams,
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headers: dict,
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variant: Optional[str] = None,
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) -> Tuple[str, Dict]:
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"""
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Transform the video content request for OpenAI API.
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OpenAI API expects the following request:
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- GET /v1/videos/{video_id}/content
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- GET /v1/videos/{video_id}/content?variant=thumbnail
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"""
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original_video_id = extract_original_video_id(video_id)
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# Construct the URL for video content download
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url = f"{api_base.rstrip('/')}/{original_video_id}/content"
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if variant is not None:
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url = f"{url}?variant={variant}"
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# No additional data needed for GET content request
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data: Dict[str, Any] = {}
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return url, data
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def transform_video_remix_request(
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self,
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video_id: str,
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prompt: str,
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api_base: str,
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litellm_params: GenericLiteLLMParams,
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headers: dict,
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extra_body: Optional[Dict[str, Any]] = None,
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) -> Tuple[str, Dict]:
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"""
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Transform the video remix request for OpenAI API.
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OpenAI API expects the following request:
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- POST /v1/videos/{video_id}/remix
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"""
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original_video_id = extract_original_video_id(video_id)
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# Construct the URL for video remix
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url = f"{api_base.rstrip('/')}/{original_video_id}/remix"
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# Prepare the request data
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data = {"prompt": prompt}
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# Add any extra body parameters
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if extra_body:
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data.update(extra_body)
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return url, data
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def transform_video_content_response(
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self,
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raw_response: httpx.Response,
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logging_obj: LiteLLMLoggingObj,
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) -> bytes:
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"""Transform the OpenAI video content download response."""
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return raw_response.content
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def transform_video_remix_response(
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self,
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raw_response: httpx.Response,
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logging_obj: LiteLLMLoggingObj,
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custom_llm_provider: Optional[str] = None,
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) -> VideoObject:
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"""
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Transform the OpenAI video remix response.
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"""
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response_data = raw_response.json()
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# Transform the response data
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video_obj = VideoObject(**response_data) # type: ignore[arg-type]
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if custom_llm_provider and video_obj.id:
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video_obj.id = encode_video_id_with_provider(
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video_obj.id, custom_llm_provider, None
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)
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# Create usage object with duration information for cost calculation
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# Video remix API doesn't provide usage, so we create one with duration
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usage_data = {}
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if video_obj:
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if hasattr(video_obj, "seconds") and video_obj.seconds:
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try:
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usage_data["duration_seconds"] = float(video_obj.seconds)
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except (ValueError, TypeError):
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pass
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# Create the response
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video_obj.usage = usage_data
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return video_obj
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def transform_video_list_request(
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self,
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api_base: str,
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litellm_params: GenericLiteLLMParams,
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headers: dict,
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after: Optional[str] = None,
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limit: Optional[int] = None,
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order: Optional[str] = None,
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extra_query: Optional[Dict[str, Any]] = None,
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) -> Tuple[str, Dict]:
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"""
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Transform the video list request for OpenAI API.
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OpenAI API expects the following request:
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- GET /v1/videos
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"""
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# Use the api_base directly for video list
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url = api_base
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# Prepare query parameters
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params = {}
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if after is not None:
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# Decode the wrapped video ID back to the original provider ID
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params["after"] = extract_original_video_id(after)
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if limit is not None:
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params["limit"] = str(limit)
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if order is not None:
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params["order"] = order
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# Add any extra query parameters
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if extra_query:
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params.update(extra_query)
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return url, params
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def transform_video_list_response(
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self,
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raw_response: httpx.Response,
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logging_obj: LiteLLMLoggingObj,
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custom_llm_provider: Optional[str] = None,
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) -> Dict[str, str]:
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response_data = raw_response.json()
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if custom_llm_provider and "data" in response_data:
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for video_obj in response_data.get("data", []):
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if isinstance(video_obj, dict) and "id" in video_obj:
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video_obj["id"] = encode_video_id_with_provider(
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video_obj["id"],
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custom_llm_provider,
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video_obj.get("model"),
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)
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# Encode pagination cursor IDs so they remain consistent
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# with the wrapped data[].id format
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data_list = response_data.get("data", [])
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if response_data.get("first_id"):
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first_model = None
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if data_list and isinstance(data_list[0], dict):
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first_model = data_list[0].get("model")
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response_data["first_id"] = encode_video_id_with_provider(
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response_data["first_id"],
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custom_llm_provider,
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first_model,
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)
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if response_data.get("last_id"):
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last_model = None
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if data_list and isinstance(data_list[-1], dict):
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last_model = data_list[-1].get("model")
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response_data["last_id"] = encode_video_id_with_provider(
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response_data["last_id"],
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custom_llm_provider,
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last_model,
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)
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return response_data
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def transform_video_delete_request(
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self,
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video_id: str,
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api_base: str,
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litellm_params: GenericLiteLLMParams,
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headers: dict,
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) -> Tuple[str, Dict]:
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"""
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Transform the video delete request for OpenAI API.
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OpenAI API expects the following request:
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- DELETE /v1/videos/{video_id}
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"""
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original_video_id = extract_original_video_id(video_id)
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# Construct the URL for video delete
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url = f"{api_base.rstrip('/')}/{original_video_id}"
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# No data needed for DELETE request
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data: Dict[str, Any] = {}
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return url, data
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def transform_video_delete_response(
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self,
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raw_response: httpx.Response,
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logging_obj: LiteLLMLoggingObj,
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) -> VideoObject:
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"""
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Transform the OpenAI video delete response.
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"""
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response_data = raw_response.json()
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# Transform the response data
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video_obj = VideoObject(**response_data) # type: ignore[arg-type] # type: ignore[arg-type]
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return video_obj
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def transform_video_status_retrieve_request(
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self,
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video_id: str,
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api_base: str,
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litellm_params: GenericLiteLLMParams,
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headers: dict,
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) -> Tuple[str, Dict]:
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"""
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Transform the OpenAI video retrieve request.
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"""
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# Extract the original video_id (remove provider encoding if present)
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original_video_id = extract_original_video_id(video_id)
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# For video retrieve, we just need to construct the URL
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url = f"{api_base.rstrip('/')}/{original_video_id}"
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# No additional data needed for GET request
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data: Dict[str, Any] = {}
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return url, data
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def transform_video_status_retrieve_response(
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self,
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raw_response: httpx.Response,
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logging_obj: LiteLLMLoggingObj,
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custom_llm_provider: Optional[str] = None,
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) -> VideoObject:
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"""
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Transform the OpenAI video retrieve response.
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"""
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response_data = raw_response.json()
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# Transform the response data
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video_obj = VideoObject(**response_data) # type: ignore[arg-type]
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if custom_llm_provider and video_obj.id:
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video_obj.id = encode_video_id_with_provider(
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video_obj.id, custom_llm_provider, None
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)
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return video_obj
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def get_error_class(
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self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
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) -> BaseLLMException:
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from ...base_llm.chat.transformation import BaseLLMException
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raise BaseLLMException(
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status_code=status_code,
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message=error_message,
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headers=headers,
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)
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def _add_image_to_files(
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self,
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files_list: List[Tuple[str, Any]],
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image: Any,
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field_name: str,
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) -> None:
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"""Add an image to the files list with appropriate content type"""
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image_content_type = ImageEditRequestUtils.get_image_content_type(image)
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if isinstance(image, BufferedReader):
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files_list.append((field_name, (image.name, image, image_content_type)))
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else:
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files_list.append(
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(field_name, ("input_reference.png", image, image_content_type))
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)
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