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import io
import json
import os
import pathlib
import re
import time
from typing import List, Tuple
import datasets
from accelerate import Accelerator, DistributedType
from loguru import logger as eval_logger
from PIL import Image
from tqdm import tqdm
from lmms_eval.api.instance import Instance
from lmms_eval.api.model import lmms
from lmms_eval.api.registry import register_model
try:
import google.generativeai as genai
from google.generativeai.types import HarmBlockThreshold, HarmCategory
NUM_SECONDS_TO_SLEEP = 30
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
genai.configure(api_key=GOOGLE_API_KEY)
except Exception as e:
eval_logger.error(f"Error importing generativeai: {str(e)}")
genai = None
try:
import soundfile as sf
except Exception as e:
eval_logger.warning(f"Error importing soundfile, audio generation will not work: {str(e)}")
@register_model("gemini_api")
class GeminiAPI(lmms):
def __init__(
self,
model_version: str = "gemini-1.5-pro",
# modality: str = "image",
timeout: int = 120,
continual_mode: bool = True,
response_persistent_folder: str = "./logs/gemini_persistent_folder",
interleave: bool = False,
# We will cache the Gemini API response in this path and use it for future requests
**kwargs,
) -> None:
super().__init__()
self.model_version = model_version
self.timeout = timeout
self.model = genai.GenerativeModel(model_version)
self.continual_mode = continual_mode
self.response_persistent_file = ""
self.interleave = interleave
# if self.continual_mode and response_persistent_folder is None:
# raise ValueError("Continual mode requires a persistent path for the response. We will cache the Gemini API response in this path and use it for future requests. Please provide a valid path.")
if self.continual_mode:
self.response_persistent_folder = response_persistent_folder
if not os.path.exists(self.response_persistent_folder):
os.makedirs(self.response_persistent_folder)
self.response_persistent_file = os.path.join(self.response_persistent_folder, f"{self.model_version}_response.json")
if os.path.exists(self.response_persistent_file):
with open(self.response_persistent_file, "r") as f:
self.response_cache = json.load(f)
self.cache_mode = "resume"
else:
self.response_cache = {}
self.cache_mode = "start"
accelerator = Accelerator()
if accelerator.num_processes > 1:
assert self.continual_mode is False, "Continual mode is not supported with distributed inference."
assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
self.accelerator = accelerator
if self.accelerator.is_local_main_process:
eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
self._rank = self.accelerator.local_process_index
self._world_size = self.accelerator.num_processes
else:
self.accelerator = accelerator
self._rank = self.accelerator.local_process_index
self._world_size = self.accelerator.num_processes
self.device = self.accelerator.device
# self.modality = modality
self.video_pool = []
def free_video(self):
for video in self.video_pool:
video.delete()
self.video_pool = []
def flatten(self, input):
new_list = []
for i in input:
for j in i:
new_list.append(j)
return new_list
def get_image_size(self, image):
# Create a BytesIO object to store the image bytes
img_byte_array = io.BytesIO()
# Save the image to the BytesIO object
image.save(img_byte_array, format="PNG")
# Get the size of the BytesIO object
img_size = img_byte_array.tell()
return img_size
def encode_video(self, video_path):
uploaded_obj = genai.upload_file(path=video_path)
time.sleep(5)
self.video_pool.append(uploaded_obj)
return uploaded_obj
def encode_audio(self, audio):
audio_io = io.BytesIO()
sf.write(audio_io, audio["array"], audio["sampling_rate"], format="WAV")
return genai.upload_file(audio_io, mime_type="audio/wav")
def convert_modality(self, images):
for idx, img in enumerate(images):
if isinstance(img, dict) and "sampling_rate" in img: # audio
audio = self.encode_audio(img)
images[idx] = audio
elif isinstance(img, str): # video
try:
images[idx] = self.encode_video(img)
except Exception as e:
eval_logger.error(f"Error converting video: {str(e)}")
return images
def construct_interleaved_input(self, content, media):
pattern = r"<media_(\d+)>"
parts = re.split(pattern, content)
result = []
for i, part in enumerate(parts):
if i % 2 == 0:
if part == "":
continue
result.append(part)
else:
result.append(media[int(part)])
return result
def generate_until(self, requests) -> List[str]:
res = []
pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
def get_uuid(task, split, doc_id):
return f"{task}___{split}___{doc_id}"
for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
if self.continual_mode and self.cache_mode == "resume":
doc_uuid = get_uuid(task, split, doc_id)
if doc_uuid in self.response_cache:
content = self.response_cache[doc_uuid]
if content:
res.append(content)
pbar.update(1)
continue
if "max_new_tokens" not in gen_kwargs:
gen_kwargs["max_new_tokens"] = 1024
if "temperature" not in gen_kwargs:
gen_kwargs["temperature"] = 0
config = genai.GenerationConfig(
max_output_tokens=gen_kwargs["max_new_tokens"],
temperature=gen_kwargs["temperature"],
)
visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
visuals = self.flatten(visuals)
visuals = self.convert_modality(visuals)
if self.interleave:
message = self.construct_interleaved_input(contexts, visuals)
else:
message = [contexts] + visuals
for attempt in range(5):
try:
content = self.model.generate_content(
message,
generation_config=config,
safety_settings={
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
},
)
content = content.text
break
except Exception as e:
eval_logger.info(f"Attempt {attempt + 1} failed with error: {str(e)}")
if isinstance(e, ValueError):
try:
eval_logger.info(f"Prompt feed_back: {content.prompt_feedback}")
content = ""
break
except Exception:
pass
if attempt < 5 - 1: # If we have retries left, sleep and then continue to next attempt
time.sleep(NUM_SECONDS_TO_SLEEP)
else: # If this was the last attempt, log and return empty
eval_logger.error(f"All 5 attempts failed. Last error message: {str(e)}")
content = ""
res.append(content)
pbar.update(1)
self.free_video()
if self.continual_mode is True: # Cache the response
doc_uuid = get_uuid(task, split, doc_id)
self.response_cache[doc_uuid] = content
with open(self.response_persistent_file, "w") as f:
json.dump(self.response_cache, f)
pbar.close()
return res
def generate_until_multi_round(self, requests) -> List[str]:
raise NotImplementedError("TODO: Implement multi-round generation for Gemini API")
def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
# TODO
assert False, "Gemini API not support"
def get_image_audio_text_interleaved_messsage(self, image_path, audio_path, question):
# image_path for list of image path
# audio_path for list of audio path
# question for question
# fixed image token and no audio in text
for index in range(1, 1 + len(image_path)):
question = question.replace(f"[img{index}]", "<image>")
for index in range(1, 1 + len(audio_path)):
question = question.replace(f"[audio{index}]", "<audio>")
text = question
info_list = []
image_counter = 0
audio_counter = 0
for part in re.split(r"(<image>|<audio>)", text):
if part == "<image>":
info_list.append(Image.open(image_path[image_counter]))
image_counter += 1
elif part == "<audio>":
info_list.append({"mime_type": "audio/wav", "data": pathlib.Path(audio_path[audio_counter]).read_bytes()})
audio_counter += 1
else:
if part == " ":
continue
info_list.append(part)
return info_list
def get_video_audio_text_interleaved_message(self, video_path, audio_path, question):
# image_path for list of image path
# audio_path for list of audio path
# question for question
# fixed video token and no audio in text
for index in range(1, 1 + len(video_path)):
question = question.replace(f"[video{index}]", "<video>")
for index in range(1, 1 + len(audio_path)):
question = question.replace(f"[audio{index}]", "<audio>")
text = question
info_list = []
video_counter = 0
audio_counter = 0
for part in re.split(r"(<video>|<audio>)", text):
if part == "<video>":
current_video_file_name = video_path[video_counter]
current_video_file = genai.upload_file(path=current_video_file_name)
while current_video_file.state.name == "processing":
print("uploading file")
time.sleep(5)
current_video_file = genai.get_file(current_video_file.name)
if current_video_file.state.name == "FAILED":
print("uploading file failed, next question")
return 0
info_list.append(current_video_file)
video_counter += 1
elif part == "<audio>":
info_list.append({"mime_type": "audio/wav", "data": pathlib.Path(audio_path[audio_counter]).read_bytes()})
audio_counter += 1
else:
if part == " ":
continue
info_list.append(part)
return info_list
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