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import json
from typing import Any, Dict, List, Optional
import httpx
from openai.types.image import Image
import litellm
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
HTTPHandler,
get_async_httpx_client,
)
from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
from litellm.types.utils import ImageResponse
class VertexImageGeneration(VertexLLM):
def process_image_generation_response(
self,
json_response: Dict[str, Any],
model_response: ImageResponse,
model: Optional[str] = None,
) -> ImageResponse:
if "predictions" not in json_response:
raise litellm.InternalServerError(
message=f"image generation response does not contain 'predictions', got {json_response}",
llm_provider="vertex_ai",
model=model,
)
predictions = json_response["predictions"]
response_data: List[Image] = []
for prediction in predictions:
bytes_base64_encoded = prediction["bytesBase64Encoded"]
image_object = Image(b64_json=bytes_base64_encoded)
response_data.append(image_object)
model_response.data = response_data
return model_response
def image_generation(
self,
prompt: str,
vertex_project: Optional[str],
vertex_location: Optional[str],
vertex_credentials: Optional[str],
model_response: ImageResponse,
logging_obj: Any,
model: Optional[
str
] = "imagegeneration", # vertex ai uses imagegeneration as the default model
client: Optional[Any] = None,
optional_params: Optional[dict] = None,
timeout: Optional[int] = None,
aimg_generation=False,
) -> ImageResponse:
if aimg_generation is True:
return self.aimage_generation( # type: ignore
prompt=prompt,
vertex_project=vertex_project,
vertex_location=vertex_location,
vertex_credentials=vertex_credentials,
model=model,
client=client,
optional_params=optional_params,
timeout=timeout,
logging_obj=logging_obj,
model_response=model_response,
)
if client is None:
_params = {}
if timeout is not None:
if isinstance(timeout, float) or isinstance(timeout, int):
_httpx_timeout = httpx.Timeout(timeout)
_params["timeout"] = _httpx_timeout
else:
_params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
sync_handler: HTTPHandler = HTTPHandler(**_params) # type: ignore
else:
sync_handler = client # type: ignore
url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:predict"
auth_header, _ = self._ensure_access_token(
credentials=vertex_credentials,
project_id=vertex_project,
custom_llm_provider="vertex_ai",
)
optional_params = optional_params or {
"sampleCount": 1
} # default optional params
request_data = {
"instances": [{"prompt": prompt}],
"parameters": optional_params,
}
request_str = f"\n curl -X POST \\\n -H \"Authorization: Bearer {auth_header[:10] + 'XXXXXXXXXX'}\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d {request_data} \\\n \"{url}\""
logging_obj.pre_call(
input=prompt,
api_key=None,
additional_args={
"complete_input_dict": optional_params,
"request_str": request_str,
},
)
logging_obj.pre_call(
input=prompt,
api_key=None,
additional_args={
"complete_input_dict": optional_params,
"request_str": request_str,
},
)
response = sync_handler.post(
url=url,
headers={
"Content-Type": "application/json; charset=utf-8",
"Authorization": f"Bearer {auth_header}",
},
data=json.dumps(request_data),
)
if response.status_code != 200:
raise Exception(f"Error: {response.status_code} {response.text}")
json_response = response.json()
return self.process_image_generation_response(
json_response, model_response, model
)
async def aimage_generation(
self,
prompt: str,
vertex_project: Optional[str],
vertex_location: Optional[str],
vertex_credentials: Optional[str],
model_response: litellm.ImageResponse,
logging_obj: Any,
model: Optional[
str
] = "imagegeneration", # vertex ai uses imagegeneration as the default model
client: Optional[AsyncHTTPHandler] = None,
optional_params: Optional[dict] = None,
timeout: Optional[int] = None,
):
response = None
if client is None:
_params = {}
if timeout is not None:
if isinstance(timeout, float) or isinstance(timeout, int):
_httpx_timeout = httpx.Timeout(timeout)
_params["timeout"] = _httpx_timeout
else:
_params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
self.async_handler = get_async_httpx_client(
llm_provider=litellm.LlmProviders.VERTEX_AI,
params={"timeout": timeout},
)
else:
self.async_handler = client # type: ignore
# make POST request to
# https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration:predict
url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:predict"
"""
Docs link: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/imagegeneration?project=adroit-crow-413218
curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d {
"instances": [
{
"prompt": "a cat"
}
],
"parameters": {
"sampleCount": 1
}
} \
"https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration:predict"
"""
auth_header, _ = self._ensure_access_token(
credentials=vertex_credentials,
project_id=vertex_project,
custom_llm_provider="vertex_ai",
)
optional_params = optional_params or {
"sampleCount": 1
} # default optional params
request_data = {
"instances": [{"prompt": prompt}],
"parameters": optional_params,
}
request_str = f"\n curl -X POST \\\n -H \"Authorization: Bearer {auth_header[:10] + 'XXXXXXXXXX'}\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d {request_data} \\\n \"{url}\""
logging_obj.pre_call(
input=prompt,
api_key=None,
additional_args={
"complete_input_dict": optional_params,
"request_str": request_str,
},
)
response = await self.async_handler.post(
url=url,
headers={
"Content-Type": "application/json; charset=utf-8",
"Authorization": f"Bearer {auth_header}",
},
data=json.dumps(request_data),
)
if response.status_code != 200:
raise Exception(f"Error: {response.status_code} {response.text}")
json_response = response.json()
return self.process_image_generation_response(
json_response, model_response, model
)
def is_image_generation_response(self, json_response: Dict[str, Any]) -> bool:
if "predictions" in json_response:
if "bytesBase64Encoded" in json_response["predictions"][0]:
return True
return False
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