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import types
from typing import Any, Dict, List, Optional
from openai.types.image import Image
from litellm.types.llms.bedrock import (
AmazonNovaCanvasColorGuidedGenerationParams,
AmazonNovaCanvasColorGuidedRequest,
AmazonNovaCanvasImageGenerationConfig,
AmazonNovaCanvasRequestBase,
AmazonNovaCanvasTextToImageParams,
AmazonNovaCanvasTextToImageRequest,
AmazonNovaCanvasTextToImageResponse,
)
from litellm.types.utils import ImageResponse
class AmazonNovaCanvasConfig:
"""
Reference: https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/model-catalog/serverless/amazon.nova-canvas-v1:0
"""
@classmethod
def get_config(cls):
return {
k: v
for k, v in cls.__dict__.items()
if not k.startswith("__")
and not isinstance(
v,
(
types.FunctionType,
types.BuiltinFunctionType,
classmethod,
staticmethod,
),
)
and v is not None
}
@classmethod
def get_supported_openai_params(cls, model: Optional[str] = None) -> List:
""" """
return ["n", "size", "quality"]
@classmethod
def _is_nova_model(cls, model: Optional[str] = None) -> bool:
"""
Returns True if the model is a Nova Canvas model
Nova models follow this pattern:
"""
if model:
if "amazon.nova-canvas" in model:
return True
return False
@classmethod
def transform_request_body(
cls, text: str, optional_params: dict
) -> AmazonNovaCanvasRequestBase:
"""
Transform the request body for Amazon Nova Canvas model
"""
task_type = optional_params.pop("taskType", "TEXT_IMAGE")
image_generation_config = optional_params.pop("imageGenerationConfig", {})
image_generation_config = {**image_generation_config, **optional_params}
if task_type == "TEXT_IMAGE":
text_to_image_params: Dict[str, Any] = image_generation_config.pop(
"textToImageParams", {}
)
text_to_image_params = {"text": text, **text_to_image_params}
try:
text_to_image_params_typed = AmazonNovaCanvasTextToImageParams(
**text_to_image_params # type: ignore
)
except Exception as e:
raise ValueError(
f"Error transforming text to image params: {e}. Got params: {text_to_image_params}, Expected params: {AmazonNovaCanvasTextToImageParams.__annotations__}"
)
try:
image_generation_config_typed = AmazonNovaCanvasImageGenerationConfig(
**image_generation_config
)
except Exception as e:
raise ValueError(
f"Error transforming image generation config: {e}. Got params: {image_generation_config}, Expected params: {AmazonNovaCanvasImageGenerationConfig.__annotations__}"
)
return AmazonNovaCanvasTextToImageRequest(
textToImageParams=text_to_image_params_typed,
taskType=task_type,
imageGenerationConfig=image_generation_config_typed,
)
if task_type == "COLOR_GUIDED_GENERATION":
color_guided_generation_params: Dict[
str, Any
] = image_generation_config.pop("colorGuidedGenerationParams", {})
color_guided_generation_params = {
"text": text,
**color_guided_generation_params,
}
try:
color_guided_generation_params_typed = AmazonNovaCanvasColorGuidedGenerationParams(
**color_guided_generation_params # type: ignore
)
except Exception as e:
raise ValueError(
f"Error transforming color guided generation params: {e}. Got params: {color_guided_generation_params}, Expected params: {AmazonNovaCanvasColorGuidedGenerationParams.__annotations__}"
)
try:
image_generation_config_typed = AmazonNovaCanvasImageGenerationConfig(
**image_generation_config
)
except Exception as e:
raise ValueError(
f"Error transforming image generation config: {e}. Got params: {image_generation_config}, Expected params: {AmazonNovaCanvasImageGenerationConfig.__annotations__}"
)
return AmazonNovaCanvasColorGuidedRequest(
taskType=task_type,
colorGuidedGenerationParams=color_guided_generation_params_typed,
imageGenerationConfig=image_generation_config_typed,
)
raise NotImplementedError(f"Task type {task_type} is not supported")
@classmethod
def map_openai_params(cls, non_default_params: dict, optional_params: dict) -> dict:
"""
Map the OpenAI params to the Bedrock params
"""
_size = non_default_params.get("size")
if _size is not None:
width, height = _size.split("x")
optional_params["width"], optional_params["height"] = int(width), int(
height
)
if non_default_params.get("n") is not None:
optional_params["numberOfImages"] = non_default_params.get("n")
if non_default_params.get("quality") is not None:
if non_default_params.get("quality") in ("hd", "premium"):
optional_params["quality"] = "premium"
if non_default_params.get("quality") == "standard":
optional_params["quality"] = "standard"
return optional_params
@classmethod
def transform_response_dict_to_openai_response(
cls, model_response: ImageResponse, response_dict: dict
) -> ImageResponse:
"""
Transform the response dict to the OpenAI response
"""
nova_response = AmazonNovaCanvasTextToImageResponse(**response_dict)
openai_images: List[Image] = []
for _img in nova_response.get("images", []):
openai_images.append(Image(b64_json=_img))
model_response.data = openai_images
return model_response
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