code quality

Former-commit-id: 9014683f9a

api/app.py

@@ -17,12 +17,15 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
+
 class SwarmInput(BaseModel):
     api_key: str
     objective: str
 
+
 app = FastAPI()
 
+
 @app.on_event("startup")
 async def startup():
     redis_host = os.getenv("REDIS_HOST", "localhost")
@@ -31,6 +34,7 @@ async def startup():
     FastAPICache.init(RedisBackend(redis), prefix="fastapi-cache", coder=JsonCoder())
     await FastAPILimiter.init(f"redis://{redis_host}:{redis_port}")
 
+
 @app.post("/chat", dependencies=[Depends(RateLimiter(times=2, minutes=1))])
 @cache(expire=60)  # Cache results for 1 minute
 async def run(swarm_input: SwarmInput):

api/olds/container.py

@@ -55,8 +55,6 @@ file_handler = FileHandler(handlers=handlers, path=BASE_DIR)
 
 templates = Jinja2Templates(directory=BASE_DIR / "api" / "templates")
 
-uploader = StaticUploader.from_settings(
- path=BASE_DIR / "static", endpoint="static"
-)
+uploader = StaticUploader.from_settings(path=BASE_DIR / "static", endpoint="static")
 
-reload_dirs = [BASE_DIR / "core", BASE_DIR / "api"]
+reload_dirs = [BASE_DIR / "core", BASE_DIR / "api"]

api/olds/main.py

@@ -11,8 +11,15 @@ from fastapi.responses import HTMLResponse
 from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
 
-from api.olds.container import agent_manager, file_handler, reload_dirs, templates, uploader
+from api.olds.container import (
+    agent_manager,
+    file_handler,
+    reload_dirs,
+    templates,
+    uploader,
+)
 from api.olds.worker import get_task_result, start_worker, task_execute
+
 # from env import settings
 
 app = FastAPI()
@@ -127,4 +134,4 @@ def dev():
         port=os.environ["EVAL_PORT"],
         reload=True,
         reload_dirs=reload_dirs,
-    )
+    )

api/olds/worker.py

@@ -41,4 +41,4 @@ def start_worker():
             "worker",
             "--loglevel=INFO",
         ]
-    )
+    )

apps/discord.py

@@ -3,28 +3,34 @@ from langchain.llms import OpenAIChat
 from swarms.agents import OmniModalAgent
 
 # Setup
-TOKEN = 'YOUR_DISCORD_BOT_TOKEN'
-bot = commands.Bot(command_prefix='!')
+TOKEN = "YOUR_DISCORD_BOT_TOKEN"
+bot = commands.Bot(command_prefix="!")
 
 # Initialize the OmniModalAgent
 llm = OpenAIChat(model_name="gpt-4")
 agent = OmniModalAgent(llm)
 
+
 @bot.event
 async def on_ready():
-    print(f'We have logged in as {bot.user}')
+    print(f"We have logged in as {bot.user}")
+
 
 @bot.command()
 async def greet(ctx):
     """Greets the user."""
-    await ctx.send(f'Hello, {ctx.author.name}!')
+    await ctx.send(f"Hello, {ctx.author.name}!")
+
 
 @bot.command()
 async def run(ctx, *, description: str):
     """Generates a video based on the given description."""
-    response = agent.run(description)  # Assuming the response provides information or a link to the generated video
+    response = agent.run(
+        description
+    )  # Assuming the response provides information or a link to the generated video
     await ctx.send(response)
 
+
 @bot.command()
 async def help_me(ctx):
     """Provides a list of commands and their descriptions."""
@@ -35,4 +41,5 @@ async def help_me(ctx):
     """
     await ctx.send(help_text)
 
+
 bot.run(TOKEN)

apps/omni_ui.py

@@ -1,38 +1,42 @@
-#Import required libraries
+# Import required libraries
 from gradio import Interface, Textbox, HTML
 import threading
 import os
 import glob
 import base64
-from langchain.llms import OpenAIChat  
-from swarms.agents import OmniModalAgent  
+from langchain.llms import OpenAIChat
+from swarms.agents import OmniModalAgent
 
-#Function to convert image to base64
+
+# Function to convert image to base64
 def image_to_base64(image_path):
     with open(image_path, "rb") as image_file:
         return base64.b64encode(image_file.read()).decode()
 
-#Function to get the most recently created image in the directory
+
+# Function to get the most recently created image in the directory
 def get_latest_image():
-    list_of_files = glob.glob('./*.png')  # Replace with your image file type
+    list_of_files = glob.glob("./*.png")  # Replace with your image file type
     if not list_of_files:
         return None
     latest_file = max(list_of_files, key=os.path.getctime)
     return latest_file
 
-#Initialize your OmniModalAgent
+
+# Initialize your OmniModalAgent
 llm = OpenAIChat(model_name="gpt-4")  # Replace with your actual initialization
 agent = OmniModalAgent(llm)  # Replace with your actual initialization
 
-#Global variable to store chat history
+# Global variable to store chat history
 chat_history = []
 
-#Function to update chat
+
+# Function to update chat
 def update_chat(user_input):
     global chat_history
     chat_history.append({"type": "user", "content": user_input})
 
-    #Get agent response
+    # Get agent response
     agent_response = agent.run(user_input)
 
     # Handle the case where agent_response is not in the expected dictionary format
@@ -48,38 +52,43 @@ def update_chat(user_input):
 
     return render_chat(chat_history)
 
-#Function to render chat as HTML
+
+# Function to render chat as HTML
+
 
 def render_chat(chat_history):
     chat_str = "<div style='max-height:400px;overflow-y:scroll;'>"
     for message in chat_history:
-        if message['type'] == 'user':
+        if message["type"] == "user":
             chat_str += f"<p><strong>User:</strong> {message['content']}</p>"
-        elif message['type'] == 'text':
+        elif message["type"] == "text":
             chat_str += f"<p><strong>Agent:</strong> {message['content']}</p>"
-        elif message['type'] == 'image':
-            img_path = os.path.join(".", message['content'])
+        elif message["type"] == "image":
+            img_path = os.path.join(".", message["content"])
             base64_img = image_to_base64(img_path)
             chat_str += f"<p><strong>Agent:</strong> <img src='data:image/png;base64,{base64_img}' alt='image' width='200'/></p>"
     chat_str += "</div>"
     return chat_str
 
-#Define Gradio interface
+
+# Define Gradio interface
 iface = Interface(
-    fn=update_chat, 
-    inputs=Textbox(label="Your Message", type="text"), 
+    fn=update_chat,
+    inputs=Textbox(label="Your Message", type="text"),
     outputs=HTML(label="Chat History"),
-    live=True
+    live=True,
 )
 
-#Function to update the chat display
+
+# Function to update the chat display
 def update_display():
     global chat_history
     while True:
         iface.update(render_chat(chat_history))
 
-#Run the update_display function in a separate thread
+
+# Run the update_display function in a separate thread
 threading.Thread(target=update_display).start()
 
-#Run Gradio interface
-iface.launch()
+# Run Gradio interface
+iface.launch()

docs/old-docs/design/abstraction.py

@@ -1,32 +1,19 @@
 from swarms import Model, Agent, vectorstore, tools, orchestrator
 
-#1 model
+# 1 model
 Model(openai)
 
-#2 agent level
-Agent(
-    model,
-    vectorstore,
-    tools
-)
+# 2 agent level
+Agent(model, vectorstore, tools)
 
-#3 worker infrastructure level
-worker_node(
-    Agent,
-    human_input,
-    tools
-)
+# 3 worker infrastructure level
+worker_node(Agent, human_input, tools)
 
-#4 swarm level basically handling infrastructure for multiple worker node
-swarm = orchestrator(
-    worker_node,
-    100 # nodes
-)
+# 4 swarm level basically handling infrastructure for multiple worker node
+swarm = orchestrator(worker_node, 100)  # nodes
 
-#5
-hivemind = Hivemind(
-    swarm * 100
-)
+# 5
+hivemind = Hivemind(swarm * 100)
 
 
-#a market different pre built worker or boss agent that have access to different tools and memory, proompts
+# a market different pre built worker or boss agent that have access to different tools and memory, proompts

example.py

@@ -1,22 +1,17 @@
 from langchain.llms import OpenAIChat
 from swarms import Worker
 
-llm = OpenAIChat(
-    model_name='gpt-4', 
-    openai_api_key="api-key", 
-    temperature=0.5
-)
+llm = OpenAIChat(model_name="gpt-4", openai_api_key="api-key", temperature=0.5)
 
 node = Worker(
     llm=llm,
     ai_name="Optimus Prime",
     ai_role="Worker in a swarm",
-    external_tools = None,
-    human_in_the_loop = False,
-    temperature = 0.5,
+    external_tools=None,
+    human_in_the_loop=False,
+    temperature=0.5,
 )
 
 task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."
 response = node.run(task)
 print(response)
-

omnimodal_agent_example.py

@@ -6,4 +6,4 @@ llm = OpenAIChat(model_name="gpt-4")
 
 agent = OmniModalAgent(llm)
 
-agent.run("Create a video of a swarm of fish")
+agent.run("Create a video of a swarm of fish")

playground/DIY/hierchical.py

@@ -8,13 +8,13 @@ swarm = HierarchicalSwarm(
     use_vectorstore=False,
     use_async=False,
     human_in_the_loop=False,
-    logging_enabled=False
+    logging_enabled=False,
 )
 
-#run the swarm with an objective 
+# run the swarm with an objective
 result = swarm.run("Design a new car")
 
-#or huggingface
+# or huggingface
 swarm = HierarchicalSwarm(
     model_type="huggingface",
     model_id="tiaueu/falcon",

playground/agents/mm_agent_example.py

@@ -1,8 +1,6 @@
 from swarms.agents import MultiModalAgent
 
-load_dict = {
-    "ImageCaptioning": "cuda"
-}
+load_dict = {"ImageCaptioning": "cuda"}
 
 node = MultiModalAgent(load_dict)
 
@@ -12,5 +10,5 @@ img = node.run_img("/image1", "What is this image about?")
 
 chat = node.chat(
     "What is your name? Generate a picture of yourself. What is this image about?",
-    streaming=True
+    streaming=True,
 )

playground/agents/omni_exa_example.py

@@ -1,12 +1,9 @@
-#pip3 install exxa
+# pip3 install exxa
 from exa import Inference
 from swarms.agents import OmniModalAgent
 
-llm = Inference(
-    model_id="mistralai/Mistral-7B-v0.1",
-    quantize=True
-)
+llm = Inference(model_id="mistralai/Mistral-7B-v0.1", quantize=True)
 
 agent = OmniModalAgent(llm)
 
-agent.run("Create a video of a swarm of fish")
+agent.run("Create a video of a swarm of fish")

playground/models/mistral.py

@@ -1,10 +1,7 @@
 from swarms.models import Mistral
 
-model = Mistral(
-    device="cuda", 
-    use_flash_attention=True
-)
+model = Mistral(device="cuda", use_flash_attention=True)
 
 prompt = "My favourite condiment is"
 result = model.run(prompt)
-print(result)
+print(result)

playground/structs/nonlinear_worfklow.py

@@ -7,12 +7,12 @@ prompt2 = "Develop a self attention using pytorch"
 task1 = Task("task1", prompt)
 task2 = Task("task2", prompt2, parents=[task1])
 
-#add tasks to workflow
+# add tasks to workflow
 workflow = NonLinearWorkflow(agent)
 
-#add tasks to tree
+# add tasks to tree
 workflow.add(task1)
 workflow.add(task2)
 
-#run
-workflow.run()
+# run
+workflow.run()

playground/swarms/autoscaler.py

@@ -5,4 +5,3 @@ auto_scaler.start()
 
 for i in range(100):
     auto_scaler.add_task(f"Task {i}")
-

playground/swarms/chat.py

@@ -1,11 +1,7 @@
 from swarms import Orchestrator, Worker
 
 # Instantiate the Orchestrator with 10 agents
-orchestrator = Orchestrator(
-    Worker, 
-    agent_list=[Worker]*10, 
-    task_queue=[]
-)
+orchestrator = Orchestrator(Worker, agent_list=[Worker] * 10, task_queue=[])
 
 # Agent 1 sends a message to Agent 2
-orchestrator.chat(sender_id=1, receiver_id=2, message="Hello, Agent 2!")
+orchestrator.chat(sender_id=1, receiver_id=2, message="Hello, Agent 2!")

playground/swarms/debate.py

@@ -89,6 +89,7 @@ class DialogueSimulator:
 
         return speaker.name, message
 
+
 class BiddingDialogueAgent(DialogueAgent):
     def __init__(
         self,
@@ -114,6 +115,7 @@ class BiddingDialogueAgent(DialogueAgent):
         bid_string = self.model([SystemMessage(content=prompt)]).content
         return bid_string
 
+
 character_names = ["Donald Trump", "Kanye West", "Elizabeth Warren"]
 topic = "transcontinental high speed rail"
 word_limit = 50
@@ -202,8 +204,6 @@ for (
     print(f"\n{character_header}")
     print(f"\n{character_system_message.content}")
 
-    
-
 
 class BidOutputParser(RegexParser):
     def get_format_instructions(self) -> str:
@@ -214,6 +214,7 @@ bid_parser = BidOutputParser(
     regex=r"<(\d+)>", output_keys=["bid"], default_output_key="bid"
 )
 
+
 def generate_character_bidding_template(character_header):
     bidding_template = f"""{character_header}
 
@@ -232,6 +233,7 @@ def generate_character_bidding_template(character_header):
     """
     return bidding_template
 
+
 character_bidding_templates = [
     generate_character_bidding_template(character_header)
     for character_header in character_headers
@@ -263,6 +265,7 @@ specified_topic = ChatOpenAI(temperature=1.0)(topic_specifier_prompt).content
 print(f"Original topic:\n{topic}\n")
 print(f"Detailed topic:\n{specified_topic}\n")
 
+
 @tenacity.retry(
     stop=tenacity.stop_after_attempt(2),
     wait=tenacity.wait_none(),  # No waiting time between retries
@@ -280,6 +283,7 @@ def ask_for_bid(agent) -> str:
     bid = int(bid_parser.parse(bid_string)["bid"])
     return bid
 
+
 def select_next_speaker(step: int, agents: List[DialogueAgent]) -> int:
     bids = []
     for agent in agents:
@@ -300,6 +304,7 @@ def select_next_speaker(step: int, agents: List[DialogueAgent]) -> int:
     print("\n")
     return idx
 
+
 characters = []
 for character_name, character_system_message, bidding_template in zip(
     character_names, character_system_messages, character_bidding_templates
@@ -326,4 +331,4 @@ while n < max_iters:
     name, message = simulator.step()
     print(f"({name}): {message}")
     print("\n")
-    n += 1
+    n += 1

playground/swarms/dialogue_simulator.py

@@ -4,12 +4,12 @@ worker1 = Worker(ai_name="Plinus", openai_api_key="")
 worker2 = Worker(ai_name="Optimus Prime", openai_api_key="")
 
 collab = DialogueSimulator(
-    [worker1, worker2], 
+    [worker1, worker2],
     # DialogueSimulator.select_next_speaker
 )
 
 collab.run(
-    max_iters = 4,
-    name = "plinus",
-    message = "how can we enable multi agent collaboration",
-)
+    max_iters=4,
+    name="plinus",
+    message="how can we enable multi agent collaboration",
+)

playground/swarms/easy_example.py

@@ -5,4 +5,3 @@ api_key = "APIKEY"
 objective = "What is the capital of the UK?"
 result = swarm(api_key, objective)
 print(result)  # Prints: "The capital of the UK is London."
-

playground/swarms/godmode.py

@@ -1,4 +1,3 @@
-
 from langchain.models import Anthropic, GooglePalm, OpenAIChat
 from swarms.swarms import GodMode
 
@@ -7,14 +6,10 @@ palm = GooglePalm(google_api_key="")
 gpt = OpenAIChat(openai_api_key="")
 
 # Usage
-llms = [
-   claude,
-   palm,
-   gpt 
-]
+llms = [claude, palm, gpt]
 
 god_mode = GodMode(llms)
 
 task = "What are the biggest risks facing humanity?"
 
-god_mode.print_responses(task)
+god_mode.print_responses(task)

playground/swarms/group_chat.py

@@ -1,2 +1 @@
 from swarms.swarms import GroupChat
-

playground/swarms/groupchat.py

@@ -2,44 +2,36 @@ from langchain.llms import OpenAIChat
 from swarms.swarms import GroupChat, GroupChatManager
 from swarms.workers import Worker
 
-llm = OpenAIChat(
-    model_name='gpt-4', 
-    openai_api_key="api-key", 
-    temperature=0.5
-)
+llm = OpenAIChat(model_name="gpt-4", openai_api_key="api-key", temperature=0.5)
 
 node = Worker(
     llm=llm,
     ai_name="Optimus Prime",
     ai_role="Worker in a swarm",
-    external_tools = None,
-    human_in_the_loop = False,
-    temperature = 0.5,
+    external_tools=None,
+    human_in_the_loop=False,
+    temperature=0.5,
 )
 
 node2 = Worker(
     llm=llm,
     ai_name="Optimus Prime",
     ai_role="Worker in a swarm",
-    external_tools = None,
-    human_in_the_loop = False,
-    temperature = 0.5,
+    external_tools=None,
+    human_in_the_loop=False,
+    temperature=0.5,
 )
 
 node3 = Worker(
     llm=llm,
     ai_name="Optimus Prime",
     ai_role="Worker in a swarm",
-    external_tools = None,
-    human_in_the_loop = False,
-    temperature = 0.5,
+    external_tools=None,
+    human_in_the_loop=False,
+    temperature=0.5,
 )
 
-nodes = [
-    node,
-    node2,
-    node3
-]
+nodes = [node, node2, node3]
 
 messages = [
     {

playground/swarms/gui_app.py

@@ -20,4 +20,4 @@ I want it to have neumorphism-style. Serve it on port 4500.
 """
 
 # Run HierarchicalSwarm
-swarm.run(objective)
+swarm.run(objective)

playground/swarms/multi_agent_collab.py

@@ -1,9 +1,11 @@
 from swarms import DialogueSimulator, Worker
 
+
 def select_next_speaker(step: int, agents) -> int:
     idx = (step) % len(agents)
     return idx
 
+
 debate = DialogueSimulator(Worker, select_next_speaker)
 
 debate.run()

playground/swarms/multi_agent_debate.py

@@ -5,11 +5,7 @@ worker1 = Worker(openai_api_key="", ai_name="Optimus Prime")
 worker2 = Worker(openai_api_key="", ai_name="Bumblebee")
 worker3 = Worker(openai_api_key="", ai_name="Megatron")
 
-agents = [
-    worker1,
-    worker2,
-    worker3
-]
+agents = [worker1, worker2, worker3]
 
 # Initialize multi-agent debate with the selection function
 debate = MultiAgentDebate(agents, select_speaker)
@@ -20,4 +16,4 @@ results = debate.run(task, max_iters=4)
 
 # Print results
 for result in results:
-    print(f"Agent {result['agent']} responded: {result['response']}")
+    print(f"Agent {result['agent']} responded: {result['response']}")

playground/swarms/orchestrate.py

@@ -3,12 +3,11 @@ from swarms import Worker, Orchestrator
 node = Worker(
     openai_api_key="",
     ai_name="Optimus Prime",
-
 )
 
 
 # Instantiate the Orchestrator with 10 agents
-orchestrator = Orchestrator(node, agent_list=[node]*10, task_queue=[])
+orchestrator = Orchestrator(node, agent_list=[node] * 10, task_queue=[])
 
 # Agent 7 sends a message to Agent 9
-orchestrator.chat(sender_id=7, receiver_id=9, message="Can you help me with this task?")
+orchestrator.chat(sender_id=7, receiver_id=9, message="Can you help me with this task?")

playground/swarms/orchestrator.py

@@ -3,12 +3,11 @@ from swarms import Worker, Orchestrator
 node = Worker(
     openai_api_key="",
     ai_name="Optimus Prime",
-
 )
 
 
 # Instantiate the Orchestrator with 10 agents
-orchestrator = Orchestrator(node, agent_list=[node]*10, task_queue=[])
+orchestrator = Orchestrator(node, agent_list=[node] * 10, task_queue=[])
 
 # Agent 7 sends a message to Agent 9
-orchestrator.chat(sender_id=7, receiver_id=9, message="Can you help me with this task?")
+orchestrator.chat(sender_id=7, receiver_id=9, message="Can you help me with this task?")

playground/swarms/social_app.py

@@ -16,4 +16,4 @@ The ports you can use are 4500 and 6500.
 """
 
 # Run HierarchicalSwarm
-swarm.run(objective)
+swarm.run(objective)

playground/swarms/swarms_example.py

@@ -10,4 +10,4 @@ swarm = HierarchicalSwarm(api_key)
 objective = "Find 20 potential customers for a HierarchicalSwarm based AI Agent automation infrastructure"
 
 # Run HierarchicalSwarm
-swarm.run(objective)
+swarm.run(objective)

playground/swarms/todo_app.py

@@ -17,4 +17,4 @@ The ports you can use are 4500 and 6500.
 """
 
 # Run HierarchicalSwarm
-swarm.run(objective)
+swarm.run(objective)

playground/worker/ultranode_example.py

@@ -12,4 +12,4 @@ I want it to have neumorphism-style. Serve it on port 4500.
 """
 
 node = WorkerUltraUltraNode(objective)
-result = node.execute()
+result = node.execute()

playground/worker/worker.py

@@ -1,19 +1,15 @@
 from langchain.models import OpenAIChat
 from swarms import Worker
 
-llm = OpenAIChat(
-    model_name='gpt-4', 
-    openai_api_key="api-key", 
-    temperature=0.5
-)
+llm = OpenAIChat(model_name="gpt-4", openai_api_key="api-key", temperature=0.5)
 
 node = Worker(
     llm=llm,
     ai_name="Optimus Prime",
     ai_role="Worker in a swarm",
-    external_tools = None,
-    human_in_the_loop = False,
-    temperature = 0.5,
+    external_tools=None,
+    human_in_the_loop=False,
+    temperature=0.5,
 )
 
 task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."

playground/worker/worker_ultra.py

@@ -22,4 +22,4 @@ worker = WorkerUltra(objective, api_key)
 result = worker.execute()
 
 # Print the result
-print(result)
+print(result)

playground/workflow.py

@@ -1,4 +1,3 @@
-
 from swarms import Workflow
 from swarms.tools.autogpt import ChatOpenAI
 

setup.py

@@ -1,50 +1,50 @@
 from setuptools import setup, find_packages
 
 setup(
-  name = 'swarms',
-  packages = find_packages(exclude=[]),
-  version = '1.4.1',
-  license='MIT',
-  description = 'Swarms - Pytorch',
-  author = 'Kye Gomez',
-  author_email = 'kye@apac.ai',
-  long_description_content_type = 'text/markdown',
-  url = 'https://github.com/kyegomez/swarms',
-  keywords = [
-    'artificial intelligence',
-    'deep learning',
-    'optimizers',
-    "Prompt Engineering"
-  ],
-  install_requires=[
-        'transformers',
-        'openai',
-        'langchain==0.0.240',
-        'asyncio',
-        'nest_asyncio',
-        'pegasusx',
-        'google-generativeai',
-        'oceandb',
-        'langchain-experimental',
-        'playwright',
-        'duckduckgo_search',
-        'faiss-cpu',
-        'wget',
-        'httpx',
-        'ggl',
-        'beautifulsoup4',
-        'pydantic',
-        'tenacity',
-        'celery',
-        'redis',
-        'google-search-results==2.4.2',
-        'Pillow',
+    name="swarms",
+    packages=find_packages(exclude=[]),
+    version="1.4.1",
+    license="MIT",
+    description="Swarms - Pytorch",
+    author="Kye Gomez",
+    author_email="kye@apac.ai",
+    long_description_content_type="text/markdown",
+    url="https://github.com/kyegomez/swarms",
+    keywords=[
+        "artificial intelligence",
+        "deep learning",
+        "optimizers",
+        "Prompt Engineering",
     ],
-  classifiers=[
-    'Development Status :: 4 - Beta',
-    'Intended Audience :: Developers',
-    'Topic :: Scientific/Engineering :: Artificial Intelligence',
-    'License :: OSI Approved :: MIT License',
-    'Programming Language :: Python :: 3.6',
-  ],
-)
+    install_requires=[
+        "transformers",
+        "openai",
+        "langchain==0.0.240",
+        "asyncio",
+        "nest_asyncio",
+        "pegasusx",
+        "google-generativeai",
+        "oceandb",
+        "langchain-experimental",
+        "playwright",
+        "duckduckgo_search",
+        "faiss-cpu",
+        "wget",
+        "httpx",
+        "ggl",
+        "beautifulsoup4",
+        "pydantic",
+        "tenacity",
+        "celery",
+        "redis",
+        "google-search-results==2.4.2",
+        "Pillow",
+    ],
+    classifiers=[
+        "Development Status :: 4 - Beta",
+        "Intended Audience :: Developers",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3.6",
+    ],
+)

swarms/__init__.py

@@ -7,6 +7,7 @@ from swarms import models
 from swarms.workers.worker import Worker
 from swarms import workers
 from swarms.logo import logo2
+
 print(logo2)
 
 # worker

swarms/agents/__init__.py

@@ -1,4 +1,3 @@
-
 """Agent Infrastructure, models, memory, utils, tools"""
 
 # agents

swarms/agents/aot.py

@@ -4,7 +4,9 @@ import time
 
 import openai
 
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
 logger = logging.getLogger(__name__)
 
 
@@ -25,11 +27,13 @@ class OpenAI:
             raise Exception("Please provide OpenAI API key")
 
         if api_base == "" or api_base is None:
-            api_base = os.environ.get("OPENAI_API_BASE", "")  # if not set, use the default base path of "https://api.openai.com/v1"
+            api_base = os.environ.get(
+                "OPENAI_API_BASE", ""
+            )  # if not set, use the default base path of "https://api.openai.com/v1"
         if api_base != "":
             # e.g. https://api.openai.com/v1/ or your custom url
             openai.api_base = api_base
-            print(f'Using custom api_base {api_base}')
+            print(f"Using custom api_base {api_base}")
 
         if api_model == "" or api_model is None:
             api_model = os.environ.get("OPENAI_API_MODEL", "")
@@ -37,29 +41,17 @@ class OpenAI:
             self.api_model = api_model
         else:
             self.api_model = "text-davinci-003"
-        print(f'Using api_model {self.api_model}')
+        print(f"Using api_model {self.api_model}")
 
-        self.use_chat_api = 'gpt' in self.api_model
+        self.use_chat_api = "gpt" in self.api_model
         self.strategy = strategy
         self.evaluation_strategy = evaluation_strategy
 
-    def run(
-        self,
-        prompt,
-        max_tokens,
-        temperature,
-        k=1,
-        stop=None
-    ):
+    def run(self, prompt, max_tokens, temperature, k=1, stop=None):
         while True:
             try:
                 if self.use_chat_api:
-                    messages = [
-                        {
-                            "role": "user",
-                            "content": prompt
-                        }
-                    ]
+                    messages = [{"role": "user", "content": prompt}]
                     response = openai.ChatCompletion.create(
                         model=self.api_model,
                         messages=messages,
@@ -75,17 +67,21 @@ class OpenAI:
                         stop=stop,
                         temperature=temperature,
                     )
-                with open("openai.logs", 'a') as log_file:
-                    log_file.write("\n" + "-----------" + '\n' + "Prompt : " + prompt + "\n")
+                with open("openai.logs", "a") as log_file:
+                    log_file.write(
+                        "\n" + "-----------" + "\n" + "Prompt : " + prompt + "\n"
+                    )
                 return response
             except openai.error.RateLimitError as e:
                 sleep_duratoin = os.environ.get("OPENAI_RATE_TIMEOUT", 30)
-                print(f'{str(e)}, sleep for {sleep_duratoin}s, set it by env OPENAI_RATE_TIMEOUT')
+                print(
+                    f"{str(e)}, sleep for {sleep_duratoin}s, set it by env OPENAI_RATE_TIMEOUT"
+                )
                 time.sleep(sleep_duratoin)
 
     def openai_choice2text_handler(self, choice):
         if self.use_chat_api:
-            text = choice['message']['content']
+            text = choice["message"]["content"]
         else:
             text = choice.text.strip()
         return text
@@ -102,20 +98,16 @@ class OpenAI:
 
         else:
             response = self.run(prompt, 300, 0.5, k)
-            thoughts = [self.openai_choice2text_handler(choice) for choice in response.choices]
+            thoughts = [
+                self.openai_choice2text_handler(choice) for choice in response.choices
+            ]
             return thoughts
 
-    def generate_thoughts(
-        self,
-        state,
-        k,
-        initial_prompt,
-        rejected_solutions=None
-    ):
-        if (isinstance(state, str)):
+    def generate_thoughts(self, state, k, initial_prompt, rejected_solutions=None):
+        if isinstance(state, str):
             state_text = state
         else:
-            state_text = '\n'.join(state)
+            state_text = "\n".join(state)
         print("New state generating thought:", state, "\n\n")
         prompt = f"""
         Accomplish the task below by decomposing it as many very explicit subtasks as possible, be very explicit and thorough denoted by
@@ -135,14 +127,10 @@ class OpenAI:
         # print(f"Generated thoughts: {thoughts}")
         return thoughts
 
-    def generate_solution(self,
-                          initial_prompt,
-                          state,
-                          rejected_solutions=None):
+    def generate_solution(self, initial_prompt, state, rejected_solutions=None):
         try:
-
             if isinstance(state, list):
-                state_text = '\n'.join(state)
+                state_text = "\n".join(state)
             else:
                 state_text = state
 
@@ -156,7 +144,7 @@ class OpenAI:
             ###{rejected_solutions}###,
             complete the {initial_prompt} without making the same mistakes you did with the evaluated rejected solutions. Be simple. Be direct. Provide intuitive solutions as soon as you think of them."""
             answer = self.generate_text(prompt, 1)
-            print(f'Generated Solution Summary {answer}')
+            print(f"Generated Solution Summary {answer}")
             return answer
         except Exception as e:
             logger.error(f"Error in generate_solutions: {e}")
@@ -166,14 +154,20 @@ class OpenAI:
         if not states:
             return {}
 
-        if self.evaluation_strategy == 'value':
+        if self.evaluation_strategy == "value":
             state_values = {}
             for state in states:
-                if (isinstance(state, str)):
+                if isinstance(state, str):
                     state_text = state
                 else:
-                    state_text = '\n'.join(state)
-                print("We receive a state of type", type(state), "For state: ", state, "\n\n")
+                    state_text = "\n".join(state)
+                print(
+                    "We receive a state of type",
+                    type(state),
+                    "For state: ",
+                    state,
+                    "\n\n",
+                )
                 prompt = f""" To achieve the following goal: '{initial_prompt}', pessimistically value the context of the past solutions and more importantly the latest generated solution you had AS A FLOAT BETWEEN 0 AND 1\n
                     Past solutions:\n\n
                     {state_text}\n
@@ -244,7 +238,11 @@ class AoTAgent:
         for next_state in thoughts:
             state_value = self.evaluated_thoughts[next_state]
             if state_value > self.value_threshold:
-                child = (state, next_state) if isinstance(state, str) else (*state, next_state)
+                child = (
+                    (state, next_state)
+                    if isinstance(state, str)
+                    else (*state, next_state)
+                )
                 self.dfs(child, step + 1)
 
                 # backtracking
@@ -255,17 +253,18 @@ class AoTAgent:
 
     def generate_and_filter_thoughts(self, state):
         thoughts = self.model.generate_thoughts(
-            state,
-            self.num_thoughts,
-            self.initial_prompt
+            state, self.num_thoughts, self.initial_prompt
         )
 
         self.evaluated_thoughts = self.model.evaluate_states(
-            thoughts,
-            self.initial_prompt
+            thoughts, self.initial_prompt
         )
 
-        filtered_thoughts = [thought for thought in thoughts if self.evaluated_thoughts[thought] >= self.pruning_threshold]
+        filtered_thoughts = [
+            thought
+            for thought in thoughts
+            if self.evaluated_thoughts[thought] >= self.pruning_threshold
+        ]
         print(f"filtered_thoughts: {filtered_thoughts}")
         return filtered_thoughts
 

swarms/agents/base.py

@@ -18,7 +18,7 @@ class AbstractAgent:
         self,
         name: str,
         # tools: List[Tool],
-        #memory: Memory
+        # memory: Memory
     ):
         """
         Args:
@@ -51,10 +51,7 @@ class AbstractAgent:
     def chat(self, messages: List[Dict]):
         """Chat with the agent"""
 
-    def _achat(
-        self,
-        messages: List[Dict]
-    ):
+    def _achat(self, messages: List[Dict]):
         """Asynchronous Chat"""
 
     def step(self, message: str):

swarms/agents/conversabe_agent.py

@@ -43,7 +43,9 @@ class ConversableAgent(Agent):
     DEFAULT_CONFIG = {
         "model": DEFAULT_MODEL,
     }
-    MAX_CONSECUTIVE_AUTO_REPLY = 100  # maximum number of consecutive auto replies (subject to future change)
+    MAX_CONSECUTIVE_AUTO_REPLY = (
+        100  # maximum number of consecutive auto replies (subject to future change)
+    )
 
     def __init__(
         self,
@@ -103,7 +105,9 @@ class ConversableAgent(Agent):
         self._oai_messages = defaultdict(list)
         self._oai_system_message = [{"content": system_message, "role": "system"}]
         self._is_termination_msg = (
-            is_termination_msg if is_termination_msg is not None else (lambda x: x.get("content") == "TERMINATE")
+            is_termination_msg
+            if is_termination_msg is not None
+            else (lambda x: x.get("content") == "TERMINATE")
         )
         if llm_config is False:
             self.llm_config = False
@@ -112,21 +116,33 @@ class ConversableAgent(Agent):
             if isinstance(llm_config, dict):
                 self.llm_config.update(llm_config)
 
-        self._code_execution_config = {} if code_execution_config is None else code_execution_config
+        self._code_execution_config = (
+            {} if code_execution_config is None else code_execution_config
+        )
         self.human_input_mode = human_input_mode
         self._max_consecutive_auto_reply = (
-            max_consecutive_auto_reply if max_consecutive_auto_reply is not None else self.MAX_CONSECUTIVE_AUTO_REPLY
+            max_consecutive_auto_reply
+            if max_consecutive_auto_reply is not None
+            else self.MAX_CONSECUTIVE_AUTO_REPLY
         )
         self._consecutive_auto_reply_counter = defaultdict(int)
-        self._max_consecutive_auto_reply_dict = defaultdict(self.max_consecutive_auto_reply)
+        self._max_consecutive_auto_reply_dict = defaultdict(
+            self.max_consecutive_auto_reply
+        )
         self._function_map = {} if function_map is None else function_map
         self._default_auto_reply = default_auto_reply
         self._reply_func_list = []
         self.reply_at_receive = defaultdict(bool)
         self.register_reply([Agent, None], ConversableAgent.generate_oai_reply)
-        self.register_reply([Agent, None], ConversableAgent.generate_code_execution_reply)
-        self.register_reply([Agent, None], ConversableAgent.generate_function_call_reply)
-        self.register_reply([Agent, None], ConversableAgent.check_termination_and_human_reply)
+        self.register_reply(
+            [Agent, None], ConversableAgent.generate_code_execution_reply
+        )
+        self.register_reply(
+            [Agent, None], ConversableAgent.generate_function_call_reply
+        )
+        self.register_reply(
+            [Agent, None], ConversableAgent.check_termination_and_human_reply
+        )
 
     def register_reply(
         self,
@@ -170,7 +186,9 @@ class ConversableAgent(Agent):
                 The function returns None. Signature: ```def reset_config(config: Any)```
         """
         if not isinstance(trigger, (type, str, Agent, Callable, list)):
-            raise ValueError("trigger must be a class, a string, an agent, a callable or a list.")
+            raise ValueError(
+                "trigger must be a class, a string, an agent, a callable or a list."
+            )
         self._reply_func_list.insert(
             position,
             {
@@ -195,7 +213,9 @@ class ConversableAgent(Agent):
         """
         self._oai_system_message[0]["content"] = system_message
 
-    def update_max_consecutive_auto_reply(self, value: int, sender: Optional[Agent] = None):
+    def update_max_consecutive_auto_reply(
+        self, value: int, sender: Optional[Agent] = None
+    ):
         """Update the maximum number of consecutive auto replies.
 
         Args:
@@ -211,7 +231,11 @@ class ConversableAgent(Agent):
 
     def max_consecutive_auto_reply(self, sender: Optional[Agent] = None) -> int:
         """The maximum number of consecutive auto replies."""
-        return self._max_consecutive_auto_reply if sender is None else self._max_consecutive_auto_reply_dict[sender]
+        return (
+            self._max_consecutive_auto_reply
+            if sender is None
+            else self._max_consecutive_auto_reply_dict[sender]
+        )
 
     @property
     def chat_messages(self) -> Dict[Agent, List[Dict]]:
@@ -236,7 +260,9 @@ class ConversableAgent(Agent):
             if n_conversations == 1:
                 for conversation in self._oai_messages.values():
                     return conversation[-1]
-            raise ValueError("More than one conversation is found. Please specify the sender to get the last message.")
+            raise ValueError(
+                "More than one conversation is found. Please specify the sender to get the last message."
+            )
         return self._oai_messages[agent][-1]
 
     @property
@@ -244,7 +270,11 @@ class ConversableAgent(Agent):
         """Bool value of whether to use docker to execute the code,
         or str value of the docker image name to use, or None when code execution is disabled.
         """
-        return None if self._code_execution_config is False else self._code_execution_config.get("use_docker")
+        return (
+            None
+            if self._code_execution_config is False
+            else self._code_execution_config.get("use_docker")
+        )
 
     @staticmethod
     def _message_to_dict(message: Union[Dict, str]):
@@ -257,7 +287,9 @@ class ConversableAgent(Agent):
         else:
             return message
 
-    def _append_oai_message(self, message: Union[Dict, str], role, conversation_id: Agent) -> bool:
+    def _append_oai_message(
+        self, message: Union[Dict, str], role, conversation_id: Agent
+    ) -> bool:
         """Append a message to the ChatCompletion conversation.
 
         If the message received is a string, it will be put in the "content" field of the new dictionary.
@@ -275,16 +307,24 @@ class ConversableAgent(Agent):
         """
         message = self._message_to_dict(message)
         # create oai message to be appended to the oai conversation that can be passed to oai directly.
-        oai_message = {k: message[k] for k in ("content", "function_call", "name", "context") if k in message}
+        oai_message = {
+            k: message[k]
+            for k in ("content", "function_call", "name", "context")
+            if k in message
+        }
         if "content" not in oai_message:
             if "function_call" in oai_message:
-                oai_message["content"] = None  # if only function_call is provided, content will be set to None.
+                oai_message[
+                    "content"
+                ] = None  # if only function_call is provided, content will be set to None.
             else:
                 return False
 
         oai_message["role"] = "function" if message.get("role") == "function" else role
         if "function_call" in oai_message:
-            oai_message["role"] = "assistant"  # only messages with role 'assistant' can have a function call.
+            oai_message[
+                "role"
+            ] = "assistant"  # only messages with role 'assistant' can have a function call.
         self._oai_messages[conversation_id].append(oai_message)
         return True
 
@@ -390,7 +430,9 @@ class ConversableAgent(Agent):
         # print the message received
         print(colored(sender.name, "yellow"), "(to", f"{self.name}):\n", flush=True)
         if message.get("role") == "function":
-            func_print = f"***** Response from calling function \"{message['name']}\" *****"
+            func_print = (
+                f"***** Response from calling function \"{message['name']}\" *****"
+            )
             print(colored(func_print, "green"), flush=True)
             print(message["content"], flush=True)
             print(colored("*" * len(func_print), "green"), flush=True)
@@ -401,7 +443,8 @@ class ConversableAgent(Agent):
                     content = oai.ChatCompletion.instantiate(
                         content,
                         message["context"],
-                        self.llm_config and self.llm_config.get("allow_format_str_template", False),
+                        self.llm_config
+                        and self.llm_config.get("allow_format_str_template", False),
                     )
                 print(content, flush=True)
             if "function_call" in message:
@@ -457,7 +500,11 @@ class ConversableAgent(Agent):
             ValueError: if the message can't be converted into a valid ChatCompletion message.
         """
         self._process_received_message(message, sender, silent)
-        if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False:
+        if (
+            request_reply is False
+            or request_reply is None
+            and self.reply_at_receive[sender] is False
+        ):
             return
         reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender)
         if reply is not None:
@@ -493,7 +540,11 @@ class ConversableAgent(Agent):
             ValueError: if the message can't be converted into a valid ChatCompletion message.
         """
         self._process_received_message(message, sender, silent)
-        if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False:
+        if (
+            request_reply is False
+            or request_reply is None
+            and self.reply_at_receive[sender] is False
+        ):
             return
         reply = await self.a_generate_reply(sender=sender)
         if reply is not None:
@@ -551,7 +602,9 @@ class ConversableAgent(Agent):
                 "message" needs to be provided if the `generate_init_message` method is not overridden.
         """
         self._prepare_chat(recipient, clear_history)
-        await self.a_send(self.generate_init_message(**context), recipient, silent=silent)
+        await self.a_send(
+            self.generate_init_message(**context), recipient, silent=silent
+        )
 
     def reset(self):
         """Reset the agent."""
@@ -604,7 +657,9 @@ class ConversableAgent(Agent):
 
         # TODO: #1143 handle token limit exceeded error
         response = oai.ChatCompletion.create(
-            context=messages[-1].pop("context", None), messages=self._oai_system_message + messages, **llm_config
+            context=messages[-1].pop("context", None),
+            messages=self._oai_system_message + messages,
+            **llm_config,
         )
         return True, oai.ChatCompletion.extract_text_or_function_call(response)[0]
 
@@ -615,7 +670,9 @@ class ConversableAgent(Agent):
         config: Optional[Any] = None,
     ):
         """Generate a reply using code execution."""
-        code_execution_config = config if config is not None else self._code_execution_config
+        code_execution_config = (
+            config if config is not None else self._code_execution_config
+        )
         if code_execution_config is False:
             return False, None
         if messages is None:
@@ -634,7 +691,9 @@ class ConversableAgent(Agent):
             # found code blocks, execute code and push "last_n_messages" back
             exitcode, logs = self.execute_code_blocks(code_blocks)
             code_execution_config["last_n_messages"] = last_n_messages
-            exitcode2str = "execution succeeded" if exitcode == 0 else "execution failed"
+            exitcode2str = (
+                "execution succeeded" if exitcode == 0 else "execution failed"
+            )
             return True, f"exitcode: {exitcode} ({exitcode2str})\nCode output: {logs}"
 
         # no code blocks are found, push last_n_messages back and return.
@@ -681,7 +740,10 @@ class ConversableAgent(Agent):
             # if the human input is empty, and the message is a termination message, then we will terminate the conversation
             reply = reply if reply or not self._is_termination_msg(message) else "exit"
         else:
-            if self._consecutive_auto_reply_counter[sender] >= self._max_consecutive_auto_reply_dict[sender]:
+            if (
+                self._consecutive_auto_reply_counter[sender]
+                >= self._max_consecutive_auto_reply_dict[sender]
+            ):
                 if self.human_input_mode == "NEVER":
                     reply = "exit"
                 else:
@@ -776,7 +838,12 @@ class ConversableAgent(Agent):
             if asyncio.coroutines.iscoroutinefunction(reply_func):
                 continue
             if self._match_trigger(reply_func_tuple["trigger"], sender):
-                final, reply = reply_func(self, messages=messages, sender=sender, config=reply_func_tuple["config"])
+                final, reply = reply_func(
+                    self,
+                    messages=messages,
+                    sender=sender,
+                    config=reply_func_tuple["config"],
+                )
                 if final:
                     return reply
         return self._default_auto_reply
@@ -827,10 +894,18 @@ class ConversableAgent(Agent):
             if self._match_trigger(reply_func_tuple["trigger"], sender):
                 if asyncio.coroutines.iscoroutinefunction(reply_func):
                     final, reply = await reply_func(
-                        self, messages=messages, sender=sender, config=reply_func_tuple["config"]
+                        self,
+                        messages=messages,
+                        sender=sender,
+                        config=reply_func_tuple["config"],
                     )
                 else:
-                    final, reply = reply_func(self, messages=messages, sender=sender, config=reply_func_tuple["config"])
+                    final, reply = reply_func(
+                        self,
+                        messages=messages,
+                        sender=sender,
+                        config=reply_func_tuple["config"],
+                    )
                 if final:
                     return reply
         return self._default_auto_reply
@@ -897,10 +972,12 @@ class ConversableAgent(Agent):
                 flush=True,
             )
             if lang in ["bash", "shell", "sh"]:
-                exitcode, logs, image = self.run_code(code, lang=lang, **self._code_execution_config)
+                exitcode, logs, image = self.run_code(
+                    code, lang=lang, **self._code_execution_config
+                )
             elif lang in ["python", "Python"]:
                 if code.startswith("# filename: "):
-                    filename = code[11: code.find("\n")].strip()
+                    filename = code[11 : code.find("\n")].strip()
                 else:
                     filename = None
                 exitcode, logs, image = self.run_code(

swarms/agents/models/groundingdino/datasets/cocogrounding_eval.py

@@ -66,7 +66,9 @@ class CocoGroundingEvaluator(object):
     def synchronize_between_processes(self):
         for iou_type in self.iou_types:
             self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2)
-            create_common_coco_eval(self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type])
+            create_common_coco_eval(
+                self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type]
+            )
 
     def accumulate(self):
         for coco_eval in self.coco_eval.values():
@@ -127,7 +129,9 @@ class CocoGroundingEvaluator(object):
             labels = prediction["labels"].tolist()
 
             rles = [
-                mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0]
+                mask_util.encode(
+                    np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F")
+                )[0]
                 for mask in masks
             ]
             for rle in rles:
@@ -227,7 +231,9 @@ def evaluate(self):
     # add backward compatibility if useSegm is specified in params
     if p.useSegm is not None:
         p.iouType = "segm" if p.useSegm == 1 else "bbox"
-        print("useSegm (deprecated) is not None. Running {} evaluation".format(p.iouType))
+        print(
+            "useSegm (deprecated) is not None. Running {} evaluation".format(p.iouType)
+        )
     # print('Evaluate annotation type *{}*'.format(p.iouType))
     p.imgIds = list(np.unique(p.imgIds))
     if p.useCats:
@@ -246,7 +252,8 @@ def evaluate(self):
     self.ious = {
         (imgId, catId): computeIoU(imgId, catId)
         for imgId in p.imgIds
-        for catId in catIds}
+        for catId in catIds
+    }
 
     evaluateImg = self.evaluateImg
     maxDet = p.maxDets[-1]

swarms/agents/models/groundingdino/datasets/transforms.py

@@ -38,7 +38,7 @@ def crop(image, target, region):
 
     if "masks" in target:
         # FIXME should we update the area here if there are no boxes?
-        target["masks"] = target["masks"][:, i: i + h, j: j + w]
+        target["masks"] = target["masks"][:, i : i + h, j : j + w]
         fields.append("masks")
 
     # remove elements for which the boxes or masks that have zero area
@@ -73,9 +73,9 @@ def hflip(image, target):
     target = target.copy()
     if "boxes" in target:
         boxes = target["boxes"]
-        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
-            [w, 0, w, 0]
-        )
+        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor(
+            [-1, 1, -1, 1]
+        ) + torch.as_tensor([w, 0, w, 0])
         target["boxes"] = boxes
 
     if "masks" in target:
@@ -119,7 +119,9 @@ def resize(image, target, size, max_size=None):
     if target is None:
         return rescaled_image, None
 
-    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
+    ratios = tuple(
+        float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size)
+    )
     ratio_width, ratio_height = ratios
 
     target = target.copy()
@@ -140,7 +142,8 @@ def resize(image, target, size, max_size=None):
 
     if "masks" in target:
         target["masks"] = (
-            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
+            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0]
+            > 0.5
         )
 
     return rescaled_image, target
@@ -155,7 +158,9 @@ def pad(image, target, padding):
     # should we do something wrt the original size?
     target["size"] = torch.tensor(padded_image.size[::-1])
     if "masks" in target:
-        target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
+        target["masks"] = torch.nn.functional.pad(
+            target["masks"], (0, padding[0], 0, padding[1])
+        )
     return padded_image, target
 
 

swarms/agents/models/groundingdino/models/GroundingDINO/backbone/backbone.py

@@ -47,14 +47,27 @@ class FrozenBatchNorm2d(torch.nn.Module):
         self.register_buffer("running_var", torch.ones(n))
 
     def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
     ):
         num_batches_tracked_key = prefix + "num_batches_tracked"
         if num_batches_tracked_key in state_dict:
             del state_dict[num_batches_tracked_key]
 
         super(FrozenBatchNorm2d, self)._load_from_state_dict(
-            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
         )
 
     def forward(self, x):
@@ -91,7 +104,11 @@ class BackboneBase(nn.Module):
         return_layers = {}
         for idx, layer_index in enumerate(return_interm_indices):
             return_layers.update(
-                {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
+                {
+                    "layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(
+                        layer_index
+                    )
+                }
             )
 
         # if len:
@@ -136,10 +153,13 @@ class Backbone(BackboneBase):
         else:
             raise NotImplementedError("Why you can get here with name {}".format(name))
         # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
-        assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
+        assert name not in (
+            "resnet18",
+            "resnet34",
+        ), "Only resnet50 and resnet101 are available."
         assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
         num_channels_all = [256, 512, 1024, 2048]
-        num_channels = num_channels_all[4 - len(return_interm_indices):]
+        num_channels = num_channels_all[4 - len(return_interm_indices) :]
         super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
 
 
@@ -204,7 +224,7 @@ def build_backbone(args):
             use_checkpoint=use_checkpoint,
         )
 
-        bb_num_channels = backbone.num_features[4 - len(return_interm_indices):]
+        bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
     else:
         raise NotImplementedError("Unknown backbone {}".format(args.backbone))
 

swarms/agents/models/groundingdino/models/GroundingDINO/backbone/position_encoding.py

@@ -33,7 +33,9 @@ class PositionEmbeddingSine(nn.Module):
     used by the Attention is all you need paper, generalized to work on images.
     """
 
-    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+    def __init__(
+        self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
+    ):
         super().__init__()
         self.num_pos_feats = num_pos_feats
         self.temperature = temperature
@@ -82,7 +84,12 @@ class PositionEmbeddingSineHW(nn.Module):
     """
 
     def __init__(
-        self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
+        self,
+        num_pos_feats=64,
+        temperatureH=10000,
+        temperatureW=10000,
+        normalize=False,
+        scale=None,
     ):
         super().__init__()
         self.num_pos_feats = num_pos_feats
@@ -111,11 +118,15 @@ class PositionEmbeddingSineHW(nn.Module):
             x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
 
         dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
+        dim_tx = self.temperatureW ** (
+            2 * (torch.div(dim_tx, 2, rounding_mode="floor")) / self.num_pos_feats
+        )
         pos_x = x_embed[:, :, :, None] / dim_tx
 
         dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
+        dim_ty = self.temperatureH ** (
+            2 * (torch.div(dim_ty, 2, rounding_mode="floor")) / self.num_pos_feats
+        )
         pos_y = y_embed[:, :, :, None] / dim_ty
 
         pos_x = torch.stack(

swarms/agents/models/groundingdino/models/GroundingDINO/backbone/swin_transformer.py

@@ -25,7 +25,12 @@ class Mlp(nn.Module):
     """Multilayer perceptron."""
 
     def __init__(
-        self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        act_layer=nn.GELU,
+        drop=0.0,
     ):
         super().__init__()
         out_features = out_features or in_features
@@ -54,7 +59,9 @@ def window_partition(x, window_size):
     """
     B, H, W, C = x.shape
     x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    windows = (
+        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    )
     return windows
 
 
@@ -69,7 +76,9 @@ def window_reverse(windows, window_size, H, W):
         x: (B, H, W, C)
     """
     B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = windows.view(
+        B, H // window_size, W // window_size, window_size, window_size, -1
+    )
     x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
     return x
 
@@ -97,7 +106,6 @@ class WindowAttention(nn.Module):
         attn_drop=0.0,
         proj_drop=0.0,
     ):
-
         super().__init__()
         self.dim = dim
         self.window_size = window_size  # Wh, Ww
@@ -115,8 +123,12 @@ class WindowAttention(nn.Module):
         coords_w = torch.arange(self.window_size[1])
         coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
         coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords = (
+            coords_flatten[:, :, None] - coords_flatten[:, None, :]
+        )  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(
+            1, 2, 0
+        ).contiguous()  # Wh*Ww, Wh*Ww, 2
         relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
         relative_coords[:, :, 1] += self.window_size[1] - 1
         relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
@@ -143,7 +155,11 @@ class WindowAttention(nn.Module):
             .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
             .permute(2, 0, 3, 1, 4)
         )
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+        q, k, v = (
+            qkv[0],
+            qkv[1],
+            qkv[2],
+        )  # make torchscript happy (cannot use tensor as tuple)
 
         q = q * self.scale
         attn = q @ k.transpose(-2, -1)
@@ -151,7 +167,9 @@ class WindowAttention(nn.Module):
         relative_position_bias = self.relative_position_bias_table[
             self.relative_position_index.view(-1)
         ].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
+            self.window_size[0] * self.window_size[1],
+            self.window_size[0] * self.window_size[1],
+            -1,
         )  # Wh*Ww,Wh*Ww,nH
         relative_position_bias = relative_position_bias.permute(
             2, 0, 1
@@ -160,7 +178,9 @@ class WindowAttention(nn.Module):
 
         if mask is not None:
             nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
+                1
+            ).unsqueeze(0)
             attn = attn.view(-1, self.num_heads, N, N)
             attn = self.softmax(attn)
         else:
@@ -212,7 +232,9 @@ class SwinTransformerBlock(nn.Module):
         self.window_size = window_size
         self.shift_size = shift_size
         self.mlp_ratio = mlp_ratio
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+        assert (
+            0 <= self.shift_size < self.window_size
+        ), "shift_size must in 0-window_size"
 
         self.norm1 = norm_layer(dim)
         self.attn = WindowAttention(
@@ -229,7 +251,10 @@ class SwinTransformerBlock(nn.Module):
         self.norm2 = norm_layer(dim)
         mlp_hidden_dim = int(dim * mlp_ratio)
         self.mlp = Mlp(
-            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
         )
 
         self.H = None
@@ -259,7 +284,9 @@ class SwinTransformerBlock(nn.Module):
 
         # cyclic shift
         if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+            shifted_x = torch.roll(
+                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
+            )
             attn_mask = mask_matrix
         else:
             shifted_x = x
@@ -274,7 +301,9 @@ class SwinTransformerBlock(nn.Module):
         )  # nW*B, window_size*window_size, C
 
         # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+        attn_windows = self.attn(
+            x_windows, mask=attn_mask
+        )  # nW*B, window_size*window_size, C
 
         # merge windows
         attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
@@ -282,7 +311,9 @@ class SwinTransformerBlock(nn.Module):
 
         # reverse cyclic shift
         if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+            x = torch.roll(
+                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
+            )
         else:
             x = shifted_x
 
@@ -393,7 +424,9 @@ class BasicLayer(nn.Module):
                     qk_scale=qk_scale,
                     drop=drop,
                     attn_drop=attn_drop,
-                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                    drop_path=drop_path[i]
+                    if isinstance(drop_path, list)
+                    else drop_path,
                     norm_layer=norm_layer,
                 )
                 for i in range(depth)
@@ -473,7 +506,9 @@ class PatchEmbed(nn.Module):
         self.in_chans = in_chans
         self.embed_dim = embed_dim
 
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        self.proj = nn.Conv2d(
+            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
+        )
         if norm_layer is not None:
             self.norm = norm_layer(embed_dim)
         else:
@@ -614,7 +649,7 @@ class SwinTransformer(nn.Module):
                 qk_scale=qk_scale,
                 drop=drop_rate,
                 attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]): sum(depths[: i_layer + 1])],
+                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
                 norm_layer=norm_layer,
                 # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                 downsample=downsamplelist[i_layer],
@@ -700,7 +735,11 @@ class SwinTransformer(nn.Module):
                 norm_layer = getattr(self, f"norm{i}")
                 x_out = norm_layer(x_out)
 
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                out = (
+                    x_out.view(-1, H, W, self.num_features[i])
+                    .permute(0, 3, 1, 2)
+                    .contiguous()
+                )
                 outs.append(out)
         # in:
         #   torch.Size([2, 3, 1024, 1024])
@@ -735,7 +774,11 @@ class SwinTransformer(nn.Module):
                 norm_layer = getattr(self, f"norm{i}")
                 x_out = norm_layer(x_out)
 
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                out = (
+                    x_out.view(-1, H, W, self.num_features[i])
+                    .permute(0, 3, 1, 2)
+                    .contiguous()
+                )
                 outs.append(out)
         # in:
         #   torch.Size([2, 3, 1024, 1024])
@@ -748,7 +791,9 @@ class SwinTransformer(nn.Module):
         for idx, out_i in enumerate(outs):
             m = tensor_list.mask
             assert m is not None
-            mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]
+            mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[
+                0
+            ]
             outs_dict[idx] = NestedTensor(out_i, mask)
 
         return outs_dict
@@ -776,13 +821,22 @@ def build_swin_transformer(modelname, pretrain_img_size, **kw):
             embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7
         ),
         "swin_B_384_22k": dict(
-            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12
+            embed_dim=128,
+            depths=[2, 2, 18, 2],
+            num_heads=[4, 8, 16, 32],
+            window_size=12,
         ),
         "swin_L_224_22k": dict(
-            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7
+            embed_dim=192,
+            depths=[2, 2, 18, 2],
+            num_heads=[6, 12, 24, 48],
+            window_size=7,
         ),
         "swin_L_384_22k": dict(
-            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12
+            embed_dim=192,
+            depths=[2, 2, 18, 2],
+            num_heads=[6, 12, 24, 48],
+            window_size=12,
         ),
     }
     kw_cgf = model_para_dict[modelname]

swarms/agents/models/groundingdino/models/GroundingDINO/bertwarper.py

@@ -61,14 +61,18 @@ class BertModelWarper(nn.Module):
             decoding (see :obj:`past_key_values`).
         """
         output_attentions = (
-            output_attentions if output_attentions is not None else self.config.output_attentions
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
         )
         output_hidden_states = (
             output_hidden_states
             if output_hidden_states is not None
             else self.config.output_hidden_states
         )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
 
         if self.config.is_decoder:
             use_cache = use_cache if use_cache is not None else self.config.use_cache
@@ -76,7 +80,9 @@ class BertModelWarper(nn.Module):
             use_cache = False
 
         if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time"
+            )
         elif input_ids is not None:
             input_shape = input_ids.size()
             batch_size, seq_length = input_shape
@@ -109,11 +115,17 @@ class BertModelWarper(nn.Module):
         # If a 2D or 3D attention mask is provided for the cross-attention
         # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
         if self.config.is_decoder and encoder_hidden_states is not None:
-            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            (
+                encoder_batch_size,
+                encoder_sequence_length,
+                _,
+            ) = encoder_hidden_states.size()
             encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
             if encoder_attention_mask is None:
                 encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+            encoder_extended_attention_mask = self.invert_attention_mask(
+                encoder_attention_mask
+            )
         else:
             encoder_extended_attention_mask = None
         # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
@@ -147,7 +159,9 @@ class BertModelWarper(nn.Module):
             return_dict=return_dict,
         )
         sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+        pooled_output = (
+            self.pooler(sequence_output) if self.pooler is not None else None
+        )
 
         if not return_dict:
             return (sequence_output, pooled_output) + encoder_outputs[1:]
@@ -193,7 +207,10 @@ def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer
 
     # generate attention mask and positional ids
     attention_mask = (
-        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
+        torch.eye(num_token, device=input_ids.device)
+        .bool()
+        .unsqueeze(0)
+        .repeat(bs, 1, 1)
     )
     position_ids = torch.zeros((bs, num_token), device=input_ids.device)
     previous_col = 0
@@ -203,8 +220,10 @@ def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer
             attention_mask[row, col, col] = True
             position_ids[row, col] = 0
         else:
-            attention_mask[row, previous_col + 1: col + 1, previous_col + 1: col + 1] = True
-            position_ids[row, previous_col + 1: col + 1] = torch.arange(
+            attention_mask[
+                row, previous_col + 1 : col + 1, previous_col + 1 : col + 1
+            ] = True
+            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
                 0, col - previous_col, device=input_ids.device
             )
 
@@ -217,7 +236,9 @@ def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer
     return attention_mask, position_ids.to(torch.long)
 
 
-def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
+def generate_masks_with_special_tokens_and_transfer_map(
+    tokenized, special_tokens_list, tokenizer
+):
     """Generate attention mask between each pair of special tokens
     Args:
         input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
@@ -237,7 +258,10 @@ def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_token
 
     # generate attention mask and positional ids
     attention_mask = (
-        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
+        torch.eye(num_token, device=input_ids.device)
+        .bool()
+        .unsqueeze(0)
+        .repeat(bs, 1, 1)
     )
     position_ids = torch.zeros((bs, num_token), device=input_ids.device)
     cate_to_token_mask_list = [[] for _ in range(bs)]
@@ -248,12 +272,14 @@ def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_token
             attention_mask[row, col, col] = True
             position_ids[row, col] = 0
         else:
-            attention_mask[row, previous_col + 1: col + 1, previous_col + 1: col + 1] = True
-            position_ids[row, previous_col + 1: col + 1] = torch.arange(
+            attention_mask[
+                row, previous_col + 1 : col + 1, previous_col + 1 : col + 1
+            ] = True
+            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
                 0, col - previous_col, device=input_ids.device
             )
             c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
-            c2t_maski[previous_col + 1: col] = True
+            c2t_maski[previous_col + 1 : col] = True
             cate_to_token_mask_list[row].append(c2t_maski)
         previous_col = col
 

swarms/agents/models/groundingdino/models/GroundingDINO/fuse_modules.py

@@ -127,7 +127,11 @@ class BiMultiHeadAttention(nn.Module):
         self._reset_parameters()
 
     def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+        return (
+            tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
+            .transpose(1, 2)
+            .contiguous()
+        )
 
     def _reset_parameters(self):
         nn.init.xavier_uniform_(self.v_proj.weight)
@@ -171,7 +175,9 @@ class BiMultiHeadAttention(nn.Module):
         value_l_states = value_l_states.view(*proj_shape)
 
         src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))  # bs*nhead, nimg, ntxt
+        attn_weights = torch.bmm(
+            query_states, key_states.transpose(1, 2)
+        )  # bs*nhead, nimg, ntxt
 
         if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
             raise ValueError(
@@ -191,7 +197,9 @@ class BiMultiHeadAttention(nn.Module):
             )  # Do not increase 50000, data type half has quite limited range
 
         attn_weights_T = attn_weights.transpose(1, 2)
-        attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
+        attn_weights_l = (
+            attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
+        )
         if self.clamp_min_for_underflow:
             attn_weights_l = torch.clamp(
                 attn_weights_l, min=-50000
@@ -204,7 +212,9 @@ class BiMultiHeadAttention(nn.Module):
         # mask vison for language
         if attention_mask_v is not None:
             attention_mask_v = (
-                attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+                attention_mask_v[:, None, None, :]
+                .repeat(1, self.num_heads, 1, 1)
+                .flatten(0, 1)
             )
             attn_weights_l.masked_fill_(attention_mask_v, float("-inf"))
 
@@ -213,7 +223,9 @@ class BiMultiHeadAttention(nn.Module):
         # mask language for vision
         if attention_mask_l is not None:
             attention_mask_l = (
-                attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+                attention_mask_l[:, None, None, :]
+                .repeat(1, self.num_heads, 1, 1)
+                .flatten(0, 1)
             )
             attn_weights.masked_fill_(attention_mask_l, float("-inf"))
         attn_weights_v = attn_weights.softmax(dim=-1)
@@ -275,13 +287,21 @@ class BiAttentionBlock(nn.Module):
         self.layer_norm_v = nn.LayerNorm(v_dim)
         self.layer_norm_l = nn.LayerNorm(l_dim)
         self.attn = BiMultiHeadAttention(
-            v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout
+            v_dim=v_dim,
+            l_dim=l_dim,
+            embed_dim=embed_dim,
+            num_heads=num_heads,
+            dropout=dropout,
         )
 
         # add layer scale for training stability
         self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
-        self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
+        self.gamma_v = nn.Parameter(
+            init_values * torch.ones((v_dim)), requires_grad=True
+        )
+        self.gamma_l = nn.Parameter(
+            init_values * torch.ones((l_dim)), requires_grad=True
+        )
 
     def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
         v = self.layer_norm_v(v)

swarms/agents/models/groundingdino/models/GroundingDINO/groundingdino.py

@@ -100,13 +100,17 @@ class GroundingDINO(nn.Module):
         self.bert.pooler.dense.bias.requires_grad_(False)
         self.bert = BertModelWarper(bert_model=self.bert)
 
-        self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
+        self.feat_map = nn.Linear(
+            self.bert.config.hidden_size, self.hidden_dim, bias=True
+        )
         nn.init.constant_(self.feat_map.bias.data, 0)
         nn.init.xavier_uniform_(self.feat_map.weight.data)
         # freeze
 
         # special tokens
-        self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
+        self.specical_tokens = self.tokenizer.convert_tokens_to_ids(
+            ["[CLS]", "[SEP]", ".", "?"]
+        )
 
         # prepare input projection layers
         if num_feature_levels > 1:
@@ -123,14 +127,18 @@ class GroundingDINO(nn.Module):
             for _ in range(num_feature_levels - num_backbone_outs):
                 input_proj_list.append(
                     nn.Sequential(
-                        nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
+                        nn.Conv2d(
+                            in_channels, hidden_dim, kernel_size=3, stride=2, padding=1
+                        ),
                         nn.GroupNorm(32, hidden_dim),
                     )
                 )
                 in_channels = hidden_dim
             self.input_proj = nn.ModuleList(input_proj_list)
         else:
-            assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
+            assert (
+                two_stage_type == "no"
+            ), "two_stage_type should be no if num_feature_levels=1 !!!"
             self.input_proj = nn.ModuleList(
                 [
                     nn.Sequential(
@@ -157,12 +165,17 @@ class GroundingDINO(nn.Module):
         nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
 
         if dec_pred_bbox_embed_share:
-            box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
+            box_embed_layerlist = [
+                _bbox_embed for i in range(transformer.num_decoder_layers)
+            ]
         else:
             box_embed_layerlist = [
-                copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
+                copy.deepcopy(_bbox_embed)
+                for i in range(transformer.num_decoder_layers)
             ]
-        class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
+        class_embed_layerlist = [
+            _class_embed for i in range(transformer.num_decoder_layers)
+        ]
         self.bbox_embed = nn.ModuleList(box_embed_layerlist)
         self.class_embed = nn.ModuleList(class_embed_layerlist)
         self.transformer.decoder.bbox_embed = self.bbox_embed
@@ -170,9 +183,10 @@ class GroundingDINO(nn.Module):
 
         # two stage
         self.two_stage_type = two_stage_type
-        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
-            two_stage_type
-        )
+        assert two_stage_type in [
+            "no",
+            "standard",
+        ], "unknown param {} of two_stage_type".format(two_stage_type)
         if two_stage_type != "no":
             if two_stage_bbox_embed_share:
                 assert dec_pred_bbox_embed_share
@@ -237,12 +251,18 @@ class GroundingDINO(nn.Module):
             ]
             position_ids = position_ids[:, : self.max_text_len]
             tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
-            tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
-            tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
+            tokenized["attention_mask"] = tokenized["attention_mask"][
+                :, : self.max_text_len
+            ]
+            tokenized["token_type_ids"] = tokenized["token_type_ids"][
+                :, : self.max_text_len
+            ]
 
         # extract text embeddings
         if self.sub_sentence_present:
-            tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
+            tokenized_for_encoder = {
+                k: v for k, v in tokenized.items() if k != "attention_mask"
+            }
             tokenized_for_encoder["attention_mask"] = text_self_attention_masks
             tokenized_for_encoder["position_ids"] = position_ids
         else:
@@ -251,7 +271,9 @@ class GroundingDINO(nn.Module):
 
         bert_output = self.bert(**tokenized_for_encoder)  # bs, 195, 768
 
-        encoded_text = self.feat_map(bert_output["last_hidden_state"])  # bs, 195, d_model
+        encoded_text = self.feat_map(
+            bert_output["last_hidden_state"]
+        )  # bs, 195, d_model
         text_token_mask = tokenized.attention_mask.bool()  # bs, 195
         # text_token_mask: True for nomask, False for mask
         # text_self_attention_masks: True for nomask, False for mask
@@ -292,7 +314,9 @@ class GroundingDINO(nn.Module):
                 else:
                     src = self.input_proj[l](srcs[-1])
                 m = samples.mask
-                mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
+                mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(
+                    torch.bool
+                )[0]
                 pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
                 srcs.append(src)
                 masks.append(mask)
@@ -350,7 +374,6 @@ class GroundingDINO(nn.Module):
 
 @MODULE_BUILD_FUNCS.registe_with_name(module_name="groundingdino")
 def build_groundingdino(args):
-
     backbone = build_backbone(args)
     transformer = build_transformer(args)
 

swarms/agents/models/groundingdino/models/GroundingDINO/ms_deform_attn.py

@@ -34,7 +34,9 @@ except BaseException:
 # helpers
 def _is_power_of_2(n):
     if (not isinstance(n, int)) or (n < 0):
-        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
+        raise ValueError(
+            "invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))
+        )
     return (n & (n - 1) == 0) and n != 0
 
 
@@ -96,7 +98,6 @@ def multi_scale_deformable_attn_pytorch(
     sampling_locations: torch.Tensor,
     attention_weights: torch.Tensor,
 ) -> torch.Tensor:
-
     bs, _, num_heads, embed_dims = value.shape
     _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
     value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
@@ -108,7 +109,10 @@ def multi_scale_deformable_attn_pytorch(
         # bs, num_heads*embed_dims, H_*W_ ->
         # bs*num_heads, embed_dims, H_, W_
         value_l_ = (
-            value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)
+            value_list[level]
+            .flatten(2)
+            .transpose(1, 2)
+            .reshape(bs * num_heads, embed_dims, H_, W_)
         )
         # bs, num_queries, num_heads, num_points, 2 ->
         # bs, num_heads, num_queries, num_points, 2 ->
@@ -116,7 +120,11 @@ def multi_scale_deformable_attn_pytorch(
         sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
         # bs*num_heads, embed_dims, num_queries, num_points
         sampling_value_l_ = F.grid_sample(
-            value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
+            value_l_,
+            sampling_grid_l_,
+            mode="bilinear",
+            padding_mode="zeros",
+            align_corners=False,
         )
         sampling_value_list.append(sampling_value_l_)
     # (bs, num_queries, num_heads, num_levels, num_points) ->
@@ -184,8 +192,12 @@ class MultiScaleDeformableAttention(nn.Module):
         self.num_heads = num_heads
         self.num_levels = num_levels
         self.num_points = num_points
-        self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2)
-        self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points)
+        self.sampling_offsets = nn.Linear(
+            embed_dim, num_heads * num_levels * num_points * 2
+        )
+        self.attention_weights = nn.Linear(
+            embed_dim, num_heads * num_levels * num_points
+        )
         self.value_proj = nn.Linear(embed_dim, embed_dim)
         self.output_proj = nn.Linear(embed_dim, embed_dim)
 
@@ -306,7 +318,9 @@ class MultiScaleDeformableAttention(nn.Module):
 
         # bs, num_query, num_heads, num_levels, num_points, 2
         if reference_points.shape[-1] == 2:
-            offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
+            offset_normalizer = torch.stack(
+                [spatial_shapes[..., 1], spatial_shapes[..., 0]], -1
+            )
             sampling_locations = (
                 reference_points[:, :, None, :, None, :]
                 + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
@@ -370,7 +384,9 @@ def create_dummy_class(klass, dependency, message=""):
     Returns:
         class: a class object
     """
-    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass)
+    err = "Cannot import '{}', therefore '{}' is not available.".format(
+        dependency, klass
+    )
     if message:
         err = err + " " + message
 
@@ -399,7 +415,9 @@ def create_dummy_func(func, dependency, message=""):
     Returns:
         function: a function object
     """
-    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func)
+    err = "Cannot import '{}', therefore '{}' is not available.".format(
+        dependency, func
+    )
     if message:
         err = err + " " + message
 

swarms/agents/models/groundingdino/models/GroundingDINO/transformer.py

@@ -82,7 +82,13 @@ class Transformer(nn.Module):
 
         # choose encoder layer type
         encoder_layer = DeformableTransformerEncoderLayer(
-            d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
+            d_model,
+            dim_feedforward,
+            dropout,
+            activation,
+            num_feature_levels,
+            nhead,
+            enc_n_points,
         )
 
         if use_text_enhancer:
@@ -154,7 +160,9 @@ class Transformer(nn.Module):
 
         if num_feature_levels > 1:
             if self.num_encoder_layers > 0:
-                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+                self.level_embed = nn.Parameter(
+                    torch.Tensor(num_feature_levels, d_model)
+                )
             else:
                 self.level_embed = None
 
@@ -169,9 +177,10 @@ class Transformer(nn.Module):
 
         # for two stage
         self.two_stage_type = two_stage_type
-        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
-            two_stage_type
-        )
+        assert two_stage_type in [
+            "no",
+            "standard",
+        ], "unknown param {} of two_stage_type".format(two_stage_type)
         if two_stage_type == "standard":
             # anchor selection at the output of encoder
             self.enc_output = nn.Linear(d_model, d_model)
@@ -208,7 +217,16 @@ class Transformer(nn.Module):
     def init_ref_points(self, use_num_queries):
         self.refpoint_embed = nn.Embedding(use_num_queries, 4)
 
-    def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):
+    def forward(
+        self,
+        srcs,
+        masks,
+        refpoint_embed,
+        pos_embeds,
+        tgt,
+        attn_mask=None,
+        text_dict=None,
+    ):
         """
         Input:
             - srcs: List of multi features [bs, ci, hi, wi]
@@ -287,7 +305,9 @@ class Transformer(nn.Module):
             output_memory = self.enc_output_norm(self.enc_output(output_memory))
 
             if text_dict is not None:
-                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)
+                enc_outputs_class_unselected = self.enc_out_class_embed(
+                    output_memory, text_dict
+                )
             else:
                 enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)
 
@@ -301,7 +321,9 @@ class Transformer(nn.Module):
 
             # gather boxes
             refpoint_embed_undetach = torch.gather(
-                enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
+                enc_outputs_coord_unselected,
+                1,
+                topk_proposals.unsqueeze(-1).repeat(1, 1, 4),
             )  # unsigmoid
             refpoint_embed_ = refpoint_embed_undetach.detach()
             init_box_proposal = torch.gather(
@@ -310,7 +332,9 @@ class Transformer(nn.Module):
 
             # gather tgt
             tgt_undetach = torch.gather(
-                output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
+                output_memory,
+                1,
+                topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model),
             )
             if self.embed_init_tgt:
                 tgt_ = (
@@ -350,7 +374,9 @@ class Transformer(nn.Module):
             init_box_proposal = refpoint_embed_.sigmoid()
 
         else:
-            raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type))
+            raise NotImplementedError(
+                "unknown two_stage_type {}".format(self.two_stage_type)
+            )
         #########################################################
         # End preparing tgt
         # - tgt: bs, NQ, d_model
@@ -432,7 +458,9 @@ class TransformerEncoder(nn.Module):
         self.text_layers = []
         self.fusion_layers = []
         if num_layers > 0:
-            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
+            self.layers = _get_clones(
+                encoder_layer, num_layers, layer_share=enc_layer_share
+            )
 
             if text_enhance_layer is not None:
                 self.text_layers = _get_clones(
@@ -465,7 +493,6 @@ class TransformerEncoder(nn.Module):
     def get_reference_points(spatial_shapes, valid_ratios, device):
         reference_points_list = []
         for lvl, (H_, W_) in enumerate(spatial_shapes):
-
             ref_y, ref_x = torch.meshgrid(
                 torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
                 torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
@@ -534,7 +561,9 @@ class TransformerEncoder(nn.Module):
                     .unsqueeze(-1)
                     .repeat(bs, 1, 1)
                 )
-                pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)
+                pos_text = get_sine_pos_embed(
+                    pos_text, num_pos_feats=256, exchange_xy=False
+                )
             if position_ids is not None:
                 pos_text = get_sine_pos_embed(
                     position_ids[..., None], num_pos_feats=256, exchange_xy=False
@@ -662,7 +691,6 @@ class TransformerDecoder(nn.Module):
         ref_points = [reference_points]
 
         for layer_id, layer in enumerate(self.layers):
-
             if reference_points.shape[-1] == 4:
                 reference_points_input = (
                     reference_points[:, :, None]
@@ -670,7 +698,9 @@ class TransformerDecoder(nn.Module):
                 )  # nq, bs, nlevel, 4
             else:
                 assert reference_points.shape[-1] == 2
-                reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]
+                reference_points_input = (
+                    reference_points[:, :, None] * valid_ratios[None, :]
+                )
             query_sine_embed = gen_sineembed_for_position(
                 reference_points_input[:, :, 0, :]
             )  # nq, bs, 256*2
@@ -777,7 +807,13 @@ class DeformableTransformerEncoderLayer(nn.Module):
         return src
 
     def forward(
-        self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None
+        self,
+        src,
+        pos,
+        reference_points,
+        spatial_shapes,
+        level_start_index,
+        key_padding_mask=None,
     ):
         # self attention
         # import ipdb; ipdb.set_trace()

swarms/agents/models/groundingdino/models/GroundingDINO/transformer_vanilla.py

@@ -26,7 +26,9 @@ from .utils import (
 
 
 class TextTransformer(nn.Module):
-    def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
+    def __init__(
+        self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1
+    ):
         super().__init__()
         self.num_layers = num_layers
         self.d_model = d_model
@@ -35,7 +37,10 @@ class TextTransformer(nn.Module):
         self.norm = None
 
         single_encoder_layer = TransformerEncoderLayer(
-            d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout
+            d_model=d_model,
+            nhead=nheads,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
         )
         self.layers = _get_clones(single_encoder_layer, num_layers)
 

swarms/agents/models/groundingdino/models/GroundingDINO/utils.py

@@ -39,14 +39,20 @@ def get_sine_pos_embed(
     """
     scale = 2 * math.pi
     dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
+    dim_t = temperature ** (
+        2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats
+    )
 
     def sine_func(x: torch.Tensor):
         sin_x = x * scale / dim_t
-        sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
+        sin_x = torch.stack(
+            (sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3
+        ).flatten(2)
         return sin_x
 
-    pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
+    pos_res = [
+        sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)
+    ]
     if exchange_xy:
         pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
     pos_res = torch.cat(pos_res, dim=-1)
@@ -70,7 +76,9 @@ def gen_encoder_output_proposals(
     proposals = []
     _cur = 0
     for lvl, (H_, W_) in enumerate(spatial_shapes):
-        mask_flatten_ = memory_padding_mask[:, _cur: (_cur + H_ * W_)].view(N_, H_, W_, 1)
+        mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(
+            N_, H_, W_, 1
+        )
         valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
         valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
 
@@ -82,7 +90,9 @@ def gen_encoder_output_proposals(
         )
         grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)  # H_, W_, 2
 
-        scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
+        scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(
+            N_, 1, 1, 2
+        )
         grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
 
         if learnedwh is not None:
@@ -99,15 +109,21 @@ def gen_encoder_output_proposals(
         _cur += H_ * W_
     # import ipdb; ipdb.set_trace()
     output_proposals = torch.cat(proposals, 1)
-    output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(
-        -1, keepdim=True
-    )
+    output_proposals_valid = (
+        (output_proposals > 0.01) & (output_proposals < 0.99)
+    ).all(-1, keepdim=True)
     output_proposals = torch.log(output_proposals / (1 - output_proposals))  # unsigmoid
-    output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf"))
-    output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
+    output_proposals = output_proposals.masked_fill(
+        memory_padding_mask.unsqueeze(-1), float("inf")
+    )
+    output_proposals = output_proposals.masked_fill(
+        ~output_proposals_valid, float("inf")
+    )
 
     output_memory = memory
-    output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
+    output_memory = output_memory.masked_fill(
+        memory_padding_mask.unsqueeze(-1), float(0)
+    )
     output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
 
     # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
@@ -136,7 +152,12 @@ class RandomBoxPerturber:
 
 
 def sigmoid_focal_loss(
-    inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False
+    inputs,
+    targets,
+    num_boxes,
+    alpha: float = 0.25,
+    gamma: float = 2,
+    no_reduction=False,
 ):
     """
     Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
@@ -206,23 +227,31 @@ def gen_sineembed_for_position(pos_tensor):
     # sineembed_tensor = torch.zeros(n_query, bs, 256)
     scale = 2 * math.pi
     dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128)
+    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode="floor")) / 128)
     x_embed = pos_tensor[:, :, 0] * scale
     y_embed = pos_tensor[:, :, 1] * scale
     pos_x = x_embed[:, :, None] / dim_t
     pos_y = y_embed[:, :, None] / dim_t
-    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
-    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
+    pos_x = torch.stack(
+        (pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3
+    ).flatten(2)
+    pos_y = torch.stack(
+        (pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3
+    ).flatten(2)
     if pos_tensor.size(-1) == 2:
         pos = torch.cat((pos_y, pos_x), dim=2)
     elif pos_tensor.size(-1) == 4:
         w_embed = pos_tensor[:, :, 2] * scale
         pos_w = w_embed[:, :, None] / dim_t
-        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
+        pos_w = torch.stack(
+            (pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3
+        ).flatten(2)
 
         h_embed = pos_tensor[:, :, 3] * scale
         pos_h = h_embed[:, :, None] / dim_t
-        pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
+        pos_h = torch.stack(
+            (pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3
+        ).flatten(2)
 
         pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
     else:
@@ -262,7 +291,9 @@ class ContrastiveEmbed(nn.Module):
         res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
 
         # padding to max_text_len
-        new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
+        new_res = torch.full(
+            (*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device
+        )
         new_res[..., : res.shape[-1]] = res
 
         return new_res

swarms/agents/models/groundingdino/models/registry.py

@@ -57,7 +57,9 @@ class Registry(object):
         if module_name is None:
             module_name = module_build_function.__name__
         if not force and module_name in self._module_dict:
-            raise KeyError("{} is already registered in {}".format(module_name, self.name))
+            raise KeyError(
+                "{} is already registered in {}".format(module_name, self.name)
+            )
         self._module_dict[module_name] = module_build_function
 
         return module_build_function

swarms/agents/models/groundingdino/util/get_tokenlizer.py

@@ -22,7 +22,9 @@ def get_tokenlizer(text_encoder_type):
 
 
 def get_pretrained_language_model(text_encoder_type):
-    if text_encoder_type == "bert-base-uncased" or (os.path.isdir(text_encoder_type) and os.path.exists(text_encoder_type)):
+    if text_encoder_type == "bert-base-uncased" or (
+        os.path.isdir(text_encoder_type) and os.path.exists(text_encoder_type)
+    ):
         return BertModel.from_pretrained(text_encoder_type)
     if text_encoder_type == "roberta-base":
         return RobertaModel.from_pretrained(text_encoder_type)

swarms/agents/models/groundingdino/util/inference.py

@@ -26,7 +26,9 @@ def preprocess_caption(caption: str) -> str:
     return result + "."
 
 
-def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"):
+def load_model(
+    model_config_path: str, model_checkpoint_path: str, device: str = "cuda"
+):
     args = SLConfig.fromfile(model_config_path)
     args.device = device
     model = build_model(args)
@@ -51,13 +53,13 @@ def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
 
 
 def predict(
-        model,
-        image: torch.Tensor,
-        caption: str,
-        box_threshold: float,
-        text_threshold: float,
-        device: str = "cuda",
-        remove_combined: bool = False
+    model,
+    image: torch.Tensor,
+    caption: str,
+    box_threshold: float,
+    text_threshold: float,
+    device: str = "cuda",
+    remove_combined: bool = False,
 ) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
     caption = preprocess_caption(caption=caption)
 
@@ -67,8 +69,12 @@ def predict(
     with torch.no_grad():
         outputs = model(image[None], captions=[caption])
 
-    prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0]  # prediction_logits.shape = (nq, 256)
-    prediction_boxes = outputs["pred_boxes"].cpu()[0]  # prediction_boxes.shape = (nq, 4)
+    prediction_logits = (
+        outputs["pred_logits"].cpu().sigmoid()[0]
+    )  # prediction_logits.shape = (nq, 256)
+    prediction_boxes = outputs["pred_boxes"].cpu()[
+        0
+    ]  # prediction_boxes.shape = (nq, 4)
 
     mask = prediction_logits.max(dim=1)[0] > box_threshold
     logits = prediction_logits[mask]  # logits.shape = (n, 256)
@@ -78,7 +84,11 @@ def predict(
     tokenized = tokenizer(caption)
 
     if remove_combined:
-        sep_idx = [i for i in range(len(tokenized['input_ids'])) if tokenized['input_ids'][i] in [101, 102, 1012]]
+        sep_idx = [
+            i
+            for i in range(len(tokenized["input_ids"]))
+            if tokenized["input_ids"][i] in [101, 102, 1012]
+        ]
 
         phrases = []
         for logit in logits:
@@ -86,32 +96,40 @@ def predict(
             insert_idx = bisect.bisect_left(sep_idx, max_idx)
             right_idx = sep_idx[insert_idx]
             left_idx = sep_idx[insert_idx - 1]
-            phrases.append(get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer, left_idx, right_idx).replace('.', ''))
+            phrases.append(
+                get_phrases_from_posmap(
+                    logit > text_threshold, tokenized, tokenizer, left_idx, right_idx
+                ).replace(".", "")
+            )
     else:
         phrases = [
-            get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')
-            for logit
-            in logits
+            get_phrases_from_posmap(
+                logit > text_threshold, tokenized, tokenizer
+            ).replace(".", "")
+            for logit in logits
         ]
 
     return boxes, logits.max(dim=1)[0], phrases
 
 
-def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray:
+def annotate(
+    image_source: np.ndarray,
+    boxes: torch.Tensor,
+    logits: torch.Tensor,
+    phrases: List[str],
+) -> np.ndarray:
     h, w, _ = image_source.shape
     boxes = boxes * torch.Tensor([w, h, w, h])
     xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
     detections = sv.Detections(xyxy=xyxy)
 
-    labels = [
-        f"{phrase} {logit:.2f}"
-        for phrase, logit
-        in zip(phrases, logits)
-    ]
+    labels = [f"{phrase} {logit:.2f}" for phrase, logit in zip(phrases, logits)]
 
     box_annotator = sv.BoxAnnotator()
     annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)
-    annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
+    annotated_frame = box_annotator.annotate(
+        scene=annotated_frame, detections=detections, labels=labels
+    )
     return annotated_frame
 
 
@@ -121,17 +139,13 @@ def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor
 
 
 class Model:
-
     def __init__(
-        self,
-        model_config_path: str,
-        model_checkpoint_path: str,
-        device: str = "cuda"
+        self, model_config_path: str, model_checkpoint_path: str, device: str = "cuda"
     ):
         self.model = load_model(
             model_config_path=model_config_path,
             model_checkpoint_path=model_checkpoint_path,
-            device=device
+            device=device,
         ).to(device)
         self.device = device
 
@@ -140,7 +154,7 @@ class Model:
         image: np.ndarray,
         caption: str,
         box_threshold: float = 0.35,
-        text_threshold: float = 0.25
+        text_threshold: float = 0.25,
     ) -> Tuple[sv.Detections, List[str]]:
         """
         import cv2
@@ -167,13 +181,12 @@ class Model:
             caption=caption,
             box_threshold=box_threshold,
             text_threshold=text_threshold,
-            device=self.device)
+            device=self.device,
+        )
         source_h, source_w, _ = image.shape
         detections = Model.post_process_result(
-            source_h=source_h,
-            source_w=source_w,
-            boxes=boxes,
-            logits=logits)
+            source_h=source_h, source_w=source_w, boxes=boxes, logits=logits
+        )
         return detections, phrases
 
     def predict_with_classes(
@@ -181,7 +194,7 @@ class Model:
         image: np.ndarray,
         classes: List[str],
         box_threshold: float,
-        text_threshold: float
+        text_threshold: float,
     ) -> sv.Detections:
         """
         import cv2
@@ -210,13 +223,12 @@ class Model:
             caption=caption,
             box_threshold=box_threshold,
             text_threshold=text_threshold,
-            device=self.device)
+            device=self.device,
+        )
         source_h, source_w, _ = image.shape
         detections = Model.post_process_result(
-            source_h=source_h,
-            source_w=source_w,
-            boxes=boxes,
-            logits=logits)
+            source_h=source_h, source_w=source_w, boxes=boxes, logits=logits
+        )
         class_id = Model.phrases2classes(phrases=phrases, classes=classes)
         detections.class_id = class_id
         return detections
@@ -236,10 +248,7 @@ class Model:
 
     @staticmethod
     def post_process_result(
-            source_h: int,
-            source_w: int,
-            boxes: torch.Tensor,
-            logits: torch.Tensor
+        source_h: int, source_w: int, boxes: torch.Tensor, logits: torch.Tensor
     ) -> sv.Detections:
         boxes = boxes * torch.Tensor([source_w, source_h, source_w, source_h])
         xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()

swarms/agents/models/groundingdino/util/logger.py

@@ -29,7 +29,9 @@ class _ColorfulFormatter(logging.Formatter):
 
 # so that calling setup_logger multiple times won't add many handlers
 @functools.lru_cache()
-def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None):
+def setup_logger(
+    output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None
+):
     """
     Initialize the detectron2 logger and set its verbosity level to "INFO".
 

swarms/agents/models/groundingdino/util/misc.py

@@ -135,7 +135,9 @@ def all_gather_cpu(data):
 
     # obtain Tensor size of each rank
     local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
-    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
+    size_list = [
+        torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)
+    ]
     if cpu_group is None:
         dist.all_gather(size_list, local_size)
     else:
@@ -153,7 +155,9 @@ def all_gather_cpu(data):
     for _ in size_list:
         tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
     if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
+        padding = torch.empty(
+            size=(max_size - local_size,), dtype=torch.uint8, device=device
+        )
         tensor = torch.cat((tensor, padding), dim=0)
     if cpu_group is None:
         dist.all_gather(tensor_list, tensor)
@@ -205,7 +209,9 @@ def all_gather(data):
     for _ in size_list:
         tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
     if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
+        padding = torch.empty(
+            size=(max_size - local_size,), dtype=torch.uint8, device="cuda"
+        )
         tensor = torch.cat((tensor, padding), dim=0)
     dist.all_gather(tensor_list, tensor)
 
@@ -261,7 +267,9 @@ class MetricLogger(object):
             return self.meters[attr]
         if attr in self.__dict__:
             return self.__dict__[attr]
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
+        raise AttributeError(
+            "'{}' object has no attribute '{}'".format(type(self).__name__, attr)
+        )
 
     def __str__(self):
         loss_str = []
@@ -434,7 +442,9 @@ class NestedTensor(object):
         return NestedTensor(cast_tensor, cast_mask)
 
     def to_img_list_single(self, tensor, mask):
-        assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim())
+        assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(
+            tensor.dim()
+        )
         maxH = (~mask).sum(0).max()
         maxW = (~mask).sum(1).max()
         img = tensor[:, :maxH, :maxW]
@@ -516,11 +526,15 @@ def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTen
     padded_masks = []
     for img in tensor_list:
         padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
-        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
+        padded_img = torch.nn.functional.pad(
+            img, (0, padding[2], 0, padding[1], 0, padding[0])
+        )
         padded_imgs.append(padded_img)
 
         m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
-        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
+        padded_mask = torch.nn.functional.pad(
+            m, (0, padding[2], 0, padding[1]), "constant", 1
+        )
         padded_masks.append(padded_mask.to(torch.bool))
 
     tensor = torch.stack(padded_imgs)
@@ -575,7 +589,9 @@ def save_on_master(*args, **kwargs):
 
 
 def init_distributed_mode(args):
-    if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "":  # 'RANK' in os.environ and
+    if (
+        "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != ""
+    ):  # 'RANK' in os.environ and
         args.rank = int(os.environ["RANK"])
         args.world_size = int(os.environ["WORLD_SIZE"])
         args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"])
@@ -615,11 +631,17 @@ def init_distributed_mode(args):
         args.local_rank = 0
         return
 
-    print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank))
+    print(
+        "world_size:{} rank:{} local_rank:{}".format(
+            args.world_size, args.rank, args.local_rank
+        )
+    )
     args.distributed = True
     torch.cuda.set_device(args.local_rank)
     args.dist_backend = "nccl"
-    print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
+    print(
+        "| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True
+    )
 
     torch.distributed.init_process_group(
         backend=args.dist_backend,
@@ -666,7 +688,9 @@ def accuracy_onehot(pred, gt):
     return acc
 
 
-def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
+def interpolate(
+    input, size=None, scale_factor=None, mode="nearest", align_corners=None
+):
     # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
     """
     Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
@@ -675,13 +699,17 @@ def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corne
     """
     if __torchvision_need_compat_flag < 0.7:
         if input.numel() > 0:
-            return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
+            return torch.nn.functional.interpolate(
+                input, size, scale_factor, mode, align_corners
+            )
 
         output_shape = _output_size(2, input, size, scale_factor)
         output_shape = list(input.shape[:-2]) + list(output_shape)
         return _new_empty_tensor(input, output_shape)
     else:
-        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
+        return torchvision.ops.misc.interpolate(
+            input, size, scale_factor, mode, align_corners
+        )
 
 
 class color_sys:
@@ -693,7 +721,12 @@ class color_sys:
             lightness = (50 + np.random.rand() * 10) / 100.0
             saturation = (90 + np.random.rand() * 10) / 100.0
             colors.append(
-                tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])
+                tuple(
+                    [
+                        int(j * 255)
+                        for j in colorsys.hls_to_rgb(hue, lightness, saturation)
+                    ]
+                )
             )
         self.colors = colors
 

swarms/agents/models/groundingdino/util/slconfig.py

@@ -31,7 +31,9 @@ class ConfigDict(Dict):
         try:
             value = super(ConfigDict, self).__getattr__(name)
         except KeyError:
-            ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'")
+            ex = AttributeError(
+                f"'{self.__class__.__name__}' object has no " f"attribute '{name}'"
+            )
         except Exception as e:
             ex = e
         else:
@@ -79,9 +81,11 @@ class SLConfig(object):
         check_file_exist(filename)
         if filename.lower().endswith(".py"):
             with tempfile.TemporaryDirectory() as temp_config_dir:
-                temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py")
+                temp_config_file = tempfile.NamedTemporaryFile(
+                    dir=temp_config_dir, suffix=".py"
+                )
                 temp_config_name = osp.basename(temp_config_file.name)
-                if os.name == 'nt':
+                if os.name == "nt":
                     temp_config_file.close()
                 shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))
                 temp_module_name = osp.splitext(temp_config_name)[0]
@@ -90,7 +94,9 @@ class SLConfig(object):
                 mod = import_module(temp_module_name)
                 sys.path.pop(0)
                 cfg_dict = {
-                    name: value for name, value in mod.__dict__.items() if not name.startswith("__")
+                    name: value
+                    for name, value in mod.__dict__.items()
+                    if not name.startswith("__")
                 }
                 # delete imported module
                 del sys.modules[temp_module_name]
@@ -111,7 +117,9 @@ class SLConfig(object):
         if BASE_KEY in cfg_dict:
             cfg_dir = osp.dirname(filename)
             base_filename = cfg_dict.pop(BASE_KEY)
-            base_filename = base_filename if isinstance(base_filename, list) else [base_filename]
+            base_filename = (
+                base_filename if isinstance(base_filename, list) else [base_filename]
+            )
 
             cfg_dict_list = list()
             cfg_text_list = list()
@@ -156,7 +164,6 @@ class SLConfig(object):
         b = b.copy()
         for k, v in a.items():
             if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
-
                 if not isinstance(b[k], dict) and not isinstance(b[k], list):
                     # if :
                     # import ipdb; ipdb.set_trace()
@@ -172,7 +179,8 @@ class SLConfig(object):
                     _ = int(k)
                 except BaseException:
                     raise TypeError(
-                        f"b is a list, " f"index {k} should be an int when input but {type(k)}"
+                        f"b is a list, "
+                        f"index {k} should be an int when input but {type(k)}"
                     )
                 b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)])
             else:
@@ -215,7 +223,6 @@ class SLConfig(object):
 
     @property
     def pretty_text(self):
-
         indent = 4
 
         def _indent(s_, num_spaces):

swarms/agents/models/groundingdino/util/utils.py

@@ -40,7 +40,9 @@ def renorm(
 ) -> torch.FloatTensor:
     # img: tensor(3,H,W) or tensor(B,3,H,W)
     # return: same as img
-    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
+    assert img.dim() == 3 or img.dim() == 4, (
+        "img.dim() should be 3 or 4 but %d" % img.dim()
+    )
     if img.dim() == 3:
         assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
             img.size(0),
@@ -147,8 +149,12 @@ class CocoClassMapper:
             "89": 79,
             "90": 80,
         }
-        self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}
-        self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}
+        self.origin2compact_mapper = {
+            int(k): v - 1 for k, v in self.category_map_str.items()
+        }
+        self.compact2origin_mapper = {
+            int(v - 1): int(k) for k, v in self.category_map_str.items()
+        }
 
     def origin2compact(self, idx):
         return self.origin2compact_mapper[int(idx)]
@@ -271,6 +277,7 @@ def get_embedder(multires, i=0):
 
     def embed(x, eo=embedder_obj):
         return eo.embed(x)
+
     return embed, embedder_obj.out_dim
 
 
@@ -381,7 +388,9 @@ class NiceRepr:
             return str(len(self))
         else:
             # In all other cases force the subclass to overload __nice__
-            raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}")
+            raise NotImplementedError(
+                f"Define the __nice__ method for {self.__class__!r}"
+            )
 
     def __repr__(self):
         """str: the string of the module"""
@@ -496,7 +505,9 @@ class ModelEma(torch.nn.Module):
                 ema_v.copy_(update_fn(ema_v, model_v))
 
     def update(self, model):
-        self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)
+        self._update(
+            model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m
+        )
 
     def set(self, model):
         self._update(model, update_fn=lambda e, m: m)
@@ -594,16 +605,21 @@ def targets_to(targets: List[Dict[str, Any]], device):
         "dataset_type",
     ]
     return [
-        {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets
+        {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()}
+        for t in targets
     ]
 
 
 def get_phrases_from_posmap(
-    posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer, left_idx: int = 0, right_idx: int = 255
+    posmap: torch.BoolTensor,
+    tokenized: Dict,
+    tokenizer: AutoTokenizer,
+    left_idx: int = 0,
+    right_idx: int = 255,
 ):
     assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor"
     if posmap.dim() == 1:
-        posmap[0: left_idx + 1] = False
+        posmap[0 : left_idx + 1] = False
         posmap[right_idx:] = False
         non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
         token_ids = [tokenized["input_ids"][i] for i in non_zero_idx]

swarms/agents/models/groundingdino/util/visualizer.py

@@ -23,7 +23,9 @@ def renorm(
 ) -> torch.FloatTensor:
     # img: tensor(3,H,W) or tensor(B,3,H,W)
     # return: same as img
-    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
+    assert img.dim() == 3 or img.dim() == 4, (
+        "img.dim() should be 3 or 4 but %d" % img.dim()
+    )
     if img.dim() == 3:
         assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
             img.size(0),
@@ -124,7 +126,10 @@ class COCOVisualizer:
             )
         else:
             savename = "{}/{}-{}-{}.png".format(
-                savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
+                savedir,
+                caption,
+                int(image_id),
+                str(datetime.datetime.now()).replace(" ", "-"),
             )
         print("savename: {}".format(savename))
         os.makedirs(os.path.dirname(savename), exist_ok=True)
@@ -188,7 +193,9 @@ class COCOVisualizer:
                 )
 
         if "box_label" in tgt:
-            assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, "
+            assert (
+                len(tgt["box_label"]) == numbox
+            ), f"{len(tgt['box_label'])} = {numbox}, "
             for idx, bl in enumerate(tgt["box_label"]):
                 _string = str(bl)
                 bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
@@ -214,7 +221,9 @@ class COCOVisualizer:
                 tgt["attn"] = [tgt["attn"]]
             for item in tgt["attn"]:
                 attn_map, basergb = item
-                attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3)
+                attn_map = (attn_map - attn_map.min()) / (
+                    attn_map.max() - attn_map.min() + 1e-3
+                )
                 attn_map = (attn_map * 255).astype(np.uint8)
                 cm = ColorMap(basergb)
                 heatmap = cm(attn_map)
@@ -310,7 +319,9 @@ class COCOVisualizer:
 
             # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
             # ax.add_collection(p)
-            p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
+            p = PatchCollection(
+                polygons, facecolor="none", edgecolors=color, linewidths=2
+            )
             ax.add_collection(p)
         elif datasetType == "captions":
             for ann in anns:

swarms/agents/models/groundingdino/util/vl_utils.py

@@ -16,7 +16,7 @@ def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
     """
     positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float)
     for j, tok_list in enumerate(token_span):
-        for (beg, end) in tok_list:
+        for beg, end in tok_list:
             beg_pos = tokenized.char_to_token(beg)
             end_pos = tokenized.char_to_token(end - 1)
             if beg_pos is None:
@@ -41,7 +41,7 @@ def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
                 positive_map[j, beg_pos] = 1
                 break
             else:
-                positive_map[j, beg_pos: end_pos + 1].fill_(1)
+                positive_map[j, beg_pos : end_pos + 1].fill_(1)
 
     return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)
 

swarms/agents/models/segment_anything/scripts/amg.py

@@ -52,7 +52,9 @@ parser.add_argument(
     help="The path to the SAM checkpoint to use for mask generation.",
 )
 
-parser.add_argument("--device", type=str, default="cuda", help="The device to run generation on.")
+parser.add_argument(
+    "--device", type=str, default="cuda", help="The device to run generation on."
+)
 
 parser.add_argument(
     "--convert-to-rle",
@@ -204,7 +206,9 @@ def main(args: argparse.Namespace) -> None:
         targets = [args.input]
     else:
         targets = [
-            f for f in os.listdir(args.input) if not os.path.isdir(os.path.join(args.input, f))
+            f
+            for f in os.listdir(args.input)
+            if not os.path.isdir(os.path.join(args.input, f))
         ]
         targets = [os.path.join(args.input, f) for f in targets]
 

swarms/agents/models/segment_anything/scripts/export_onnx_model.py

@@ -24,7 +24,10 @@ parser = argparse.ArgumentParser(
 )
 
 parser.add_argument(
-    "--checkpoint", type=str, required=True, help="The path to the SAM model checkpoint."
+    "--checkpoint",
+    type=str,
+    required=True,
+    help="The path to the SAM model checkpoint.",
 )
 
 parser.add_argument(
@@ -129,7 +132,9 @@ def run_export(
     mask_input_size = [4 * x for x in embed_size]
     dummy_inputs = {
         "image_embeddings": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),
-        "point_coords": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),
+        "point_coords": torch.randint(
+            low=0, high=1024, size=(1, 5, 2), dtype=torch.float
+        ),
         "point_labels": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),
         "mask_input": torch.randn(1, 1, *mask_input_size, dtype=torch.float),
         "has_mask_input": torch.tensor([1], dtype=torch.float),

swarms/agents/models/segment_anything/segment_anything/automatic_mask_generator.py

@@ -172,7 +172,9 @@ class SamAutomaticMaskGenerator:
 
         # Encode masks
         if self.output_mode == "coco_rle":
-            mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
+            mask_data["segmentations"] = [
+                coco_encode_rle(rle) for rle in mask_data["rles"]
+            ]
         elif self.output_mode == "binary_mask":
             mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
         else:
@@ -242,7 +244,9 @@ class SamAutomaticMaskGenerator:
         # Generate masks for this crop in batches
         data = MaskData()
         for (points,) in batch_iterator(self.points_per_batch, points_for_image):
-            batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
+            batch_data = self._process_batch(
+                points, cropped_im_size, crop_box, orig_size
+            )
             data.cat(batch_data)
             del batch_data
         self.predictor.reset_image()
@@ -275,7 +279,9 @@ class SamAutomaticMaskGenerator:
         # Run model on this batch
         transformed_points = self.predictor.transform.apply_coords(points, im_size)
         in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
-        in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
+        in_labels = torch.ones(
+            in_points.shape[0], dtype=torch.int, device=in_points.device
+        )
         masks, iou_preds, _ = self.predictor.predict_torch(
             in_points[:, None, :],
             in_labels[:, None],
@@ -298,7 +304,9 @@ class SamAutomaticMaskGenerator:
 
         # Calculate stability score
         data["stability_score"] = calculate_stability_score(
-            data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
+            data["masks"],
+            self.predictor.model.mask_threshold,
+            self.stability_score_offset,
         )
         if self.stability_score_thresh > 0.0:
             keep_mask = data["stability_score"] >= self.stability_score_thresh
@@ -309,7 +317,9 @@ class SamAutomaticMaskGenerator:
         data["boxes"] = batched_mask_to_box(data["masks"])
 
         # Filter boxes that touch crop boundaries
-        keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
+        keep_mask = ~is_box_near_crop_edge(
+            data["boxes"], crop_box, [0, 0, orig_w, orig_h]
+        )
         if not torch.all(keep_mask):
             data.filter(keep_mask)
 

swarms/agents/models/segment_anything/segment_anything/build_sam.py

@@ -8,7 +8,13 @@ import torch
 
 from functools import partial
 
-from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer
+from .modeling import (
+    ImageEncoderViT,
+    MaskDecoder,
+    PromptEncoder,
+    Sam,
+    TwoWayTransformer,
+)
 
 
 def build_sam_vit_h(checkpoint=None):

swarms/agents/models/segment_anything/segment_anything/modeling/image_encoder.py

@@ -66,7 +66,9 @@ class ImageEncoderViT(nn.Module):
         if use_abs_pos:
             # Initialize absolute positional embedding with pretrain image size.
             self.pos_embed = nn.Parameter(
-                torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
+                torch.zeros(
+                    1, img_size // patch_size, img_size // patch_size, embed_dim
+                )
             )
 
         self.blocks = nn.ModuleList()
@@ -159,7 +161,9 @@ class Block(nn.Module):
         )
 
         self.norm2 = norm_layer(dim)
-        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
+        self.mlp = MLPBlock(
+            embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer
+        )
 
         self.window_size = window_size
 
@@ -224,23 +228,34 @@ class Attention(nn.Module):
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         B, H, W, _ = x.shape
         # qkv with shape (3, B, nHead, H * W, C)
-        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        qkv = (
+            self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        )
         # q, k, v with shape (B * nHead, H * W, C)
         q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
 
         attn = (q * self.scale) @ k.transpose(-2, -1)
 
         if self.use_rel_pos:
-            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
+            attn = add_decomposed_rel_pos(
+                attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)
+            )
 
         attn = attn.softmax(dim=-1)
-        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+        x = (
+            (attn @ v)
+            .view(B, self.num_heads, H, W, -1)
+            .permute(0, 2, 3, 1, 4)
+            .reshape(B, H, W, -1)
+        )
         x = self.proj(x)
 
         return x
 
 
-def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
+def window_partition(
+    x: torch.Tensor, window_size: int
+) -> Tuple[torch.Tensor, Tuple[int, int]]:
     """
     Partition into non-overlapping windows with padding if needed.
     Args:
@@ -260,12 +275,17 @@ def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, T
     Hp, Wp = H + pad_h, W + pad_w
 
     x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    windows = (
+        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    )
     return windows, (Hp, Wp)
 
 
 def window_unpartition(
-    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
+    windows: torch.Tensor,
+    window_size: int,
+    pad_hw: Tuple[int, int],
+    hw: Tuple[int, int],
 ) -> torch.Tensor:
     """
     Window unpartition into original sequences and removing padding.
@@ -281,7 +301,9 @@ def window_unpartition(
     Hp, Wp = pad_hw
     H, W = hw
     B = windows.shape[0] // (Hp * Wp // window_size // window_size)
-    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = windows.view(
+        B, Hp // window_size, Wp // window_size, window_size, window_size, -1
+    )
     x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
 
     if Hp > H or Wp > W:
@@ -355,7 +377,9 @@ def add_decomposed_rel_pos(
     rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
 
     attn = (
-        attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+        attn.view(B, q_h, q_w, k_h, k_w)
+        + rel_h[:, :, :, :, None]
+        + rel_w[:, :, :, None, :]
     ).view(B, q_h * q_w, k_h * k_w)
 
     return attn

swarms/agents/models/segment_anything/segment_anything/modeling/mask_decoder.py

@@ -51,10 +51,14 @@ class MaskDecoder(nn.Module):
         self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
 
         self.output_upscaling = nn.Sequential(
-            nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
+            nn.ConvTranspose2d(
+                transformer_dim, transformer_dim // 4, kernel_size=2, stride=2
+            ),
             LayerNorm2d(transformer_dim // 4),
             activation(),
-            nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
+            nn.ConvTranspose2d(
+                transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2
+            ),
             activation(),
         )
         self.output_hypernetworks_mlps = nn.ModuleList(
@@ -118,8 +122,12 @@ class MaskDecoder(nn.Module):
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         """Predicts masks. See 'forward' for more details."""
         # Concatenate output tokens
-        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
-        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
+        output_tokens = torch.cat(
+            [self.iou_token.weight, self.mask_tokens.weight], dim=0
+        )
+        output_tokens = output_tokens.unsqueeze(0).expand(
+            sparse_prompt_embeddings.size(0), -1, -1
+        )
         tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
 
         # Expand per-image data in batch direction to be per-mask
@@ -131,14 +139,16 @@ class MaskDecoder(nn.Module):
         # Run the transformer
         hs, src = self.transformer(src, pos_src, tokens)
         iou_token_out = hs[:, 0, :]
-        mask_tokens_out = hs[:, 1: (1 + self.num_mask_tokens), :]
+        mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
 
         # Upscale mask embeddings and predict masks using the mask tokens
         src = src.transpose(1, 2).view(b, c, h, w)
         upscaled_embedding = self.output_upscaling(src)
         hyper_in_list: List[torch.Tensor] = []
         for i in range(self.num_mask_tokens):
-            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
+            hyper_in_list.append(
+                self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])
+            )
         hyper_in = torch.stack(hyper_in_list, dim=1)
         b, c, h, w = upscaled_embedding.shape
         masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)

swarms/agents/models/segment_anything/segment_anything/modeling/prompt_encoder.py

@@ -43,11 +43,16 @@ class PromptEncoder(nn.Module):
         self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
 
         self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
-        point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
+        point_embeddings = [
+            nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)
+        ]
         self.point_embeddings = nn.ModuleList(point_embeddings)
         self.not_a_point_embed = nn.Embedding(1, embed_dim)
 
-        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
+        self.mask_input_size = (
+            4 * image_embedding_size[0],
+            4 * image_embedding_size[1],
+        )
         self.mask_downscaling = nn.Sequential(
             nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
             LayerNorm2d(mask_in_chans // 4),
@@ -83,7 +88,9 @@ class PromptEncoder(nn.Module):
             padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
             points = torch.cat([points, padding_point], dim=1)
             labels = torch.cat([labels, padding_label], dim=1)
-        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
+        point_embedding = self.pe_layer.forward_with_coords(
+            points, self.input_image_size
+        )
         point_embedding[labels == -1] = 0.0
         point_embedding[labels == -1] += self.not_a_point_embed.weight
         point_embedding[labels == 0] += self.point_embeddings[0].weight
@@ -94,7 +101,9 @@ class PromptEncoder(nn.Module):
         """Embeds box prompts."""
         boxes = boxes + 0.5  # Shift to center of pixel
         coords = boxes.reshape(-1, 2, 2)
-        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
+        corner_embedding = self.pe_layer.forward_with_coords(
+            coords, self.input_image_size
+        )
         corner_embedding[:, 0, :] += self.point_embeddings[2].weight
         corner_embedding[:, 1, :] += self.point_embeddings[3].weight
         return corner_embedding
@@ -149,7 +158,9 @@ class PromptEncoder(nn.Module):
             Bx(embed_dim)x(embed_H)x(embed_W)
         """
         bs = self._get_batch_size(points, boxes, masks)
-        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
+        sparse_embeddings = torch.empty(
+            (bs, 0, self.embed_dim), device=self._get_device()
+        )
         if points is not None:
             coords, labels = points
             point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))

swarms/agents/models/segment_anything/segment_anything/modeling/sam.py

@@ -43,7 +43,9 @@ class Sam(nn.Module):
         self.image_encoder = image_encoder
         self.prompt_encoder = prompt_encoder
         self.mask_decoder = mask_decoder
-        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
+        self.register_buffer(
+            "pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False
+        )
         self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
 
     @property
@@ -94,7 +96,9 @@ class Sam(nn.Module):
                 shape BxCxHxW, where H=W=256. Can be passed as mask input
                 to subsequent iterations of prediction.
         """
-        input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
+        input_images = torch.stack(
+            [self.preprocess(x["image"]) for x in batched_input], dim=0
+        )
         image_embeddings = self.image_encoder(input_images)
 
         outputs = []
@@ -158,7 +162,9 @@ class Sam(nn.Module):
             align_corners=False,
         )
         masks = masks[..., : input_size[0], : input_size[1]]
-        masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
+        masks = F.interpolate(
+            masks, original_size, mode="bilinear", align_corners=False
+        )
         return masks
 
     def preprocess(self, x: torch.Tensor) -> torch.Tensor:

swarms/agents/models/segment_anything/segment_anything/modeling/transformer.py

@@ -198,7 +198,9 @@ class Attention(nn.Module):
         self.embedding_dim = embedding_dim
         self.internal_dim = embedding_dim // downsample_rate
         self.num_heads = num_heads
-        assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
+        assert (
+            self.internal_dim % num_heads == 0
+        ), "num_heads must divide embedding_dim."
 
         self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
         self.k_proj = nn.Linear(embedding_dim, self.internal_dim)

swarms/agents/models/segment_anything/segment_anything/predictor.py

@@ -55,7 +55,9 @@ class SamPredictor:
         # Transform the image to the form expected by the model
         input_image = self.transform.apply_image(image)
         input_image_torch = torch.as_tensor(input_image, device=self.device)
-        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
+        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[
+            None, :, :, :
+        ]
 
         self.set_torch_image(input_image_torch, image.shape[:2])
 
@@ -131,7 +133,9 @@ class SamPredictor:
             a subsequent iteration as mask input.
         """
         if not self.is_image_set:
-            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
+            raise RuntimeError(
+                "An image must be set with .set_image(...) before mask prediction."
+            )
 
         # Transform input prompts
         coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
@@ -140,15 +144,21 @@ class SamPredictor:
                 point_labels is not None
             ), "point_labels must be supplied if point_coords is supplied."
             point_coords = self.transform.apply_coords(point_coords, self.original_size)
-            coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
-            labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
+            coords_torch = torch.as_tensor(
+                point_coords, dtype=torch.float, device=self.device
+            )
+            labels_torch = torch.as_tensor(
+                point_labels, dtype=torch.int, device=self.device
+            )
             coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
         if box is not None:
             box = self.transform.apply_boxes(box, self.original_size)
             box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
             box_torch = box_torch[None, :]
         if mask_input is not None:
-            mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
+            mask_input_torch = torch.as_tensor(
+                mask_input, dtype=torch.float, device=self.device
+            )
             mask_input_torch = mask_input_torch[None, :, :, :]
 
         masks, iou_predictions, low_res_masks = self.predict_torch(
@@ -211,7 +221,9 @@ class SamPredictor:
             a subsequent iteration as mask input.
         """
         if not self.is_image_set:
-            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
+            raise RuntimeError(
+                "An image must be set with .set_image(...) before mask prediction."
+            )
 
         if point_coords is not None:
             points = (point_coords, point_labels)
@@ -235,7 +247,9 @@ class SamPredictor:
         )
 
         # Upscale the masks to the original image resolution
-        masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
+        masks = self.model.postprocess_masks(
+            low_res_masks, self.input_size, self.original_size
+        )
 
         if not return_logits:
             masks = masks > self.model.mask_threshold
@@ -252,7 +266,9 @@ class SamPredictor:
             raise RuntimeError(
                 "An image must be set with .set_image(...) to generate an embedding."
             )
-        assert self.features is not None, "Features must exist if an image has been set."
+        assert (
+            self.features is not None
+        ), "Features must exist if an image has been set."
         return self.features
 
     @property

swarms/agents/models/segment_anything/segment_anything/utils/amg.py

@@ -101,7 +101,7 @@ def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
     ), "Batched iteration must have inputs of all the same size."
     n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
     for b in range(n_batches):
-        yield [arg[b * batch_size: (b + 1) * batch_size] for arg in args]
+        yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
 
 
 def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
@@ -142,7 +142,7 @@ def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
     idx = 0
     parity = False
     for count in rle["counts"]:
-        mask[idx: idx + count] = parity
+        mask[idx : idx + count] = parity
         idx += count
         parity ^= True
     mask = mask.reshape(w, h)

swarms/agents/models/segment_anything/segment_anything/utils/onnx.py

@@ -48,32 +48,43 @@ class SamOnnxModel(nn.Module):
         transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)
         return transformed_size
 
-    def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
+    def _embed_points(
+        self, point_coords: torch.Tensor, point_labels: torch.Tensor
+    ) -> torch.Tensor:
         point_coords = point_coords + 0.5
         point_coords = point_coords / self.img_size
         point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
         point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)
 
         point_embedding = point_embedding * (point_labels != -1)
-        point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (
-            point_labels == -1
+        point_embedding = (
+            point_embedding
+            + self.model.prompt_encoder.not_a_point_embed.weight * (point_labels == -1)
         )
 
         for i in range(self.model.prompt_encoder.num_point_embeddings):
-            point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[
-                i
-            ].weight * (point_labels == i)
+            point_embedding = (
+                point_embedding
+                + self.model.prompt_encoder.point_embeddings[i].weight
+                * (point_labels == i)
+            )
 
         return point_embedding
 
-    def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:
-        mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)
+    def _embed_masks(
+        self, input_mask: torch.Tensor, has_mask_input: torch.Tensor
+    ) -> torch.Tensor:
+        mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(
+            input_mask
+        )
         mask_embedding = mask_embedding + (
             1 - has_mask_input
         ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)
         return mask_embedding
 
-    def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:
+    def mask_postprocessing(
+        self, masks: torch.Tensor, orig_im_size: torch.Tensor
+    ) -> torch.Tensor:
         masks = F.interpolate(
             masks,
             size=(self.img_size, self.img_size),
@@ -81,7 +92,9 @@ class SamOnnxModel(nn.Module):
             align_corners=False,
         )
 
-        prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(torch.int64)
+        prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(
+            torch.int64
+        )
         masks = masks[..., : prepadded_size[0], : prepadded_size[1]]  # type: ignore
 
         orig_im_size = orig_im_size.to(torch.int64)

swarms/agents/models/segment_anything/segment_anything/utils/transforms.py

@@ -27,10 +27,14 @@ class ResizeLongestSide:
         """
         Expects a numpy array with shape HxWxC in uint8 format.
         """
-        target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
+        target_size = self.get_preprocess_shape(
+            image.shape[0], image.shape[1], self.target_length
+        )
         return np.array(resize(to_pil_image(image), target_size))
 
-    def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
+    def apply_coords(
+        self, coords: np.ndarray, original_size: Tuple[int, ...]
+    ) -> np.ndarray:
         """
         Expects a numpy array of length 2 in the final dimension. Requires the
         original image size in (H, W) format.
@@ -44,7 +48,9 @@ class ResizeLongestSide:
         coords[..., 1] = coords[..., 1] * (new_h / old_h)
         return coords
 
-    def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
+    def apply_boxes(
+        self, boxes: np.ndarray, original_size: Tuple[int, ...]
+    ) -> np.ndarray:
         """
         Expects a numpy array shape Bx4. Requires the original image size
         in (H, W) format.
@@ -59,7 +65,9 @@ class ResizeLongestSide:
         the transformation expected by the model.
         """
         # Expects an image in BCHW format. May not exactly match apply_image.
-        target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length)
+        target_size = self.get_preprocess_shape(
+            image.shape[2], image.shape[3], self.target_length
+        )
         return F.interpolate(
             image, target_size, mode="bilinear", align_corners=False, antialias=True
         )
@@ -91,7 +99,9 @@ class ResizeLongestSide:
         return boxes.reshape(-1, 4)
 
     @staticmethod
-    def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
+    def get_preprocess_shape(
+        oldh: int, oldw: int, long_side_length: int
+    ) -> Tuple[int, int]:
         """
         Compute the output size given input size and target long side length.
         """

swarms/agents/multi_modal_workers/omni_agent/get_token_ids.py

@@ -31,7 +31,7 @@ max_length = {
     "davinci": 2049,
     "curie": 2049,
     "babbage": 2049,
-    "ada": 2049
+    "ada": 2049,
 }
 
 
@@ -44,14 +44,14 @@ def get_max_context_length(model_name):
 
 
 def get_token_ids_for_task_parsing(model_name):
-    text = '''{"task": "text-classification",  "token-classification", "text2text-generation", "summarization", "translation",  "question-answering", "conversational", "text-generation", "sentence-similarity", "tabular-classification", "object-detection", "image-classification", "image-to-image", "image-to-text", "text-to-image", "visual-question-answering", "document-question-answering", "image-segmentation", "text-to-speech", "text-to-video", "automatic-speech-recognition", "audio-to-audio", "audio-classification", "canny-control", "hed-control", "mlsd-control", "normal-control", "openpose-control", "canny-text-to-image", "depth-text-to-image", "hed-text-to-image", "mlsd-text-to-image", "normal-text-to-image", "openpose-text-to-image", "seg-text-to-image", "args", "text", "path", "dep", "id", "<GENERATED>-"}'''
+    text = """{"task": "text-classification",  "token-classification", "text2text-generation", "summarization", "translation",  "question-answering", "conversational", "text-generation", "sentence-similarity", "tabular-classification", "object-detection", "image-classification", "image-to-image", "image-to-text", "text-to-image", "visual-question-answering", "document-question-answering", "image-segmentation", "text-to-speech", "text-to-video", "automatic-speech-recognition", "audio-to-audio", "audio-classification", "canny-control", "hed-control", "mlsd-control", "normal-control", "openpose-control", "canny-text-to-image", "depth-text-to-image", "hed-text-to-image", "mlsd-text-to-image", "normal-text-to-image", "openpose-text-to-image", "seg-text-to-image", "args", "text", "path", "dep", "id", "<GENERATED>-"}"""
     res = encodings[model_name].encode(text)
     res = list(set(res))
     return res
 
 
 def get_token_ids_for_choose_model(model_name):
-    text = '''{"id": "reason"}'''
+    text = """{"id": "reason"}"""
     res = encodings[model_name].encode(text)
     res = list(set(res))
     return res

swarms/agents/multi_modal_workers/omni_agent/model_server.py

@@ -65,7 +65,7 @@ logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 handler = logging.StreamHandler()
 handler.setLevel(logging.INFO)
-formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
 handler.setFormatter(formatter)
 logger.addHandler(handler)
 
@@ -100,10 +100,16 @@ def load_pipes(local_deployment):
     if local_deployment in ["full"]:
         other_pipes = {
             "nlpconnect/vit-gpt2-image-captioning": {
-                "model": VisionEncoderDecoderModel.from_pretrained(f"{local_fold}/nlpconnect/vit-gpt2-image-captioning"),
-                "feature_extractor": ViTImageProcessor.from_pretrained(f"{local_fold}/nlpconnect/vit-gpt2-image-captioning"),
-                "tokenizer": AutoTokenizer.from_pretrained(f"{local_fold}/nlpconnect/vit-gpt2-image-captioning"),
-                "device": device
+                "model": VisionEncoderDecoderModel.from_pretrained(
+                    f"{local_fold}/nlpconnect/vit-gpt2-image-captioning"
+                ),
+                "feature_extractor": ViTImageProcessor.from_pretrained(
+                    f"{local_fold}/nlpconnect/vit-gpt2-image-captioning"
+                ),
+                "tokenizer": AutoTokenizer.from_pretrained(
+                    f"{local_fold}/nlpconnect/vit-gpt2-image-captioning"
+                ),
+                "device": device,
             },
             # "Salesforce/blip-image-captioning-large": {
             #     "model": BlipForConditionalGeneration.from_pretrained(f"{local_fold}/Salesforce/blip-image-captioning-large"),
@@ -111,8 +117,12 @@ def load_pipes(local_deployment):
             #     "device": device
             # },
             "damo-vilab/text-to-video-ms-1.7b": {
-                "model": DiffusionPipeline.from_pretrained(f"{local_fold}/damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16"),
-                "device": device
+                "model": DiffusionPipeline.from_pretrained(
+                    f"{local_fold}/damo-vilab/text-to-video-ms-1.7b",
+                    torch_dtype=torch.float16,
+                    variant="fp16",
+                ),
+                "device": device,
             },
             # "facebook/maskformer-swin-large-ade": {
             #     "model": MaskFormerForInstanceSegmentation.from_pretrained(f"{local_fold}/facebook/maskformer-swin-large-ade"),
@@ -130,16 +140,22 @@ def load_pipes(local_deployment):
             #     "device": device
             # },
             "JorisCos/DCCRNet_Libri1Mix_enhsingle_16k": {
-                "model": BaseModel.from_pretrained("JorisCos/DCCRNet_Libri1Mix_enhsingle_16k"),
-                "device": device
+                "model": BaseModel.from_pretrained(
+                    "JorisCos/DCCRNet_Libri1Mix_enhsingle_16k"
+                ),
+                "device": device,
             },
             "espnet/kan-bayashi_ljspeech_vits": {
-                "model": Text2Speech.from_pretrained("espnet/kan-bayashi_ljspeech_vits"),
-                "device": device
+                "model": Text2Speech.from_pretrained(
+                    "espnet/kan-bayashi_ljspeech_vits"
+                ),
+                "device": device,
             },
             "lambdalabs/sd-image-variations-diffusers": {
-                "model": DiffusionPipeline.from_pretrained(f"{local_fold}/lambdalabs/sd-image-variations-diffusers"),  # torch_dtype=torch.float16
-                "device": device
+                "model": DiffusionPipeline.from_pretrained(
+                    f"{local_fold}/lambdalabs/sd-image-variations-diffusers"
+                ),  # torch_dtype=torch.float16
+                "device": device,
             },
             # "CompVis/stable-diffusion-v1-4": {
             #     "model": DiffusionPipeline.from_pretrained(f"{local_fold}/CompVis/stable-diffusion-v1-4"),
@@ -150,8 +166,10 @@ def load_pipes(local_deployment):
             #     "device": device
             # },
             "runwayml/stable-diffusion-v1-5": {
-                "model": DiffusionPipeline.from_pretrained(f"{local_fold}/runwayml/stable-diffusion-v1-5"),
-                "device": device
+                "model": DiffusionPipeline.from_pretrained(
+                    f"{local_fold}/runwayml/stable-diffusion-v1-5"
+                ),
+                "device": device,
             },
             # "microsoft/speecht5_tts":{
             #     "processor": SpeechT5Processor.from_pretrained(f"{local_fold}/microsoft/speecht5_tts"),
@@ -165,11 +183,19 @@ def load_pipes(local_deployment):
             #     "device": device
             # },
             "microsoft/speecht5_vc": {
-                "processor": SpeechT5Processor.from_pretrained(f"{local_fold}/microsoft/speecht5_vc"),
-                "model": SpeechT5ForSpeechToSpeech.from_pretrained(f"{local_fold}/microsoft/speecht5_vc"),
-                "vocoder": SpeechT5HifiGan.from_pretrained(f"{local_fold}/microsoft/speecht5_hifigan"),
-                "embeddings_dataset": load_dataset(f"{local_fold}/Matthijs/cmu-arctic-xvectors", split="validation"),
-                "device": device
+                "processor": SpeechT5Processor.from_pretrained(
+                    f"{local_fold}/microsoft/speecht5_vc"
+                ),
+                "model": SpeechT5ForSpeechToSpeech.from_pretrained(
+                    f"{local_fold}/microsoft/speecht5_vc"
+                ),
+                "vocoder": SpeechT5HifiGan.from_pretrained(
+                    f"{local_fold}/microsoft/speecht5_hifigan"
+                ),
+                "embeddings_dataset": load_dataset(
+                    f"{local_fold}/Matthijs/cmu-arctic-xvectors", split="validation"
+                ),
+                "device": device,
             },
             # "julien-c/wine-quality": {
             #     "model": joblib.load(cached_download(hf_hub_url("julien-c/wine-quality", "sklearn_model.joblib")))
@@ -180,15 +206,23 @@ def load_pipes(local_deployment):
             #     "device": device
             # },
             "facebook/maskformer-swin-base-coco": {
-                "feature_extractor": MaskFormerFeatureExtractor.from_pretrained(f"{local_fold}/facebook/maskformer-swin-base-coco"),
-                "model": MaskFormerForInstanceSegmentation.from_pretrained(f"{local_fold}/facebook/maskformer-swin-base-coco"),
-                "device": device
+                "feature_extractor": MaskFormerFeatureExtractor.from_pretrained(
+                    f"{local_fold}/facebook/maskformer-swin-base-coco"
+                ),
+                "model": MaskFormerForInstanceSegmentation.from_pretrained(
+                    f"{local_fold}/facebook/maskformer-swin-base-coco"
+                ),
+                "device": device,
             },
             "Intel/dpt-hybrid-midas": {
-                "model": DPTForDepthEstimation.from_pretrained(f"{local_fold}/Intel/dpt-hybrid-midas", low_cpu_mem_usage=True),
-                "feature_extractor": DPTFeatureExtractor.from_pretrained(f"{local_fold}/Intel/dpt-hybrid-midas"),
-                "device": device
-            }
+                "model": DPTForDepthEstimation.from_pretrained(
+                    f"{local_fold}/Intel/dpt-hybrid-midas", low_cpu_mem_usage=True
+                ),
+                "feature_extractor": DPTFeatureExtractor.from_pretrained(
+                    f"{local_fold}/Intel/dpt-hybrid-midas"
+                ),
+                "device": device,
+            },
         }
 
     if local_deployment in ["full", "standard"]:
@@ -198,36 +232,53 @@ def load_pipes(local_deployment):
             #     "device": device
             # },
             "openai/whisper-base": {
-                "model": pipeline(task="automatic-speech-recognition", model=f"{local_fold}/openai/whisper-base"),
-                "device": device
+                "model": pipeline(
+                    task="automatic-speech-recognition",
+                    model=f"{local_fold}/openai/whisper-base",
+                ),
+                "device": device,
             },
             "microsoft/speecht5_asr": {
-                "model": pipeline(task="automatic-speech-recognition", model=f"{local_fold}/microsoft/speecht5_asr"),
-                "device": device
+                "model": pipeline(
+                    task="automatic-speech-recognition",
+                    model=f"{local_fold}/microsoft/speecht5_asr",
+                ),
+                "device": device,
             },
             "Intel/dpt-large": {
-                "model": pipeline(task="depth-estimation", model=f"{local_fold}/Intel/dpt-large"),
-                "device": device
+                "model": pipeline(
+                    task="depth-estimation", model=f"{local_fold}/Intel/dpt-large"
+                ),
+                "device": device,
             },
             # "microsoft/beit-base-patch16-224-pt22k-ft22k": {
             #     "model": pipeline(task="image-classification", model=f"{local_fold}/microsoft/beit-base-patch16-224-pt22k-ft22k"),
             #     "device": device
             # },
             "facebook/detr-resnet-50-panoptic": {
-                "model": pipeline(task="image-segmentation", model=f"{local_fold}/facebook/detr-resnet-50-panoptic"),
-                "device": device
+                "model": pipeline(
+                    task="image-segmentation",
+                    model=f"{local_fold}/facebook/detr-resnet-50-panoptic",
+                ),
+                "device": device,
             },
             "facebook/detr-resnet-101": {
-                "model": pipeline(task="object-detection", model=f"{local_fold}/facebook/detr-resnet-101"),
-                "device": device
+                "model": pipeline(
+                    task="object-detection",
+                    model=f"{local_fold}/facebook/detr-resnet-101",
+                ),
+                "device": device,
             },
             # "openai/clip-vit-large-patch14": {
             #     "model": pipeline(task="zero-shot-image-classification", model=f"{local_fold}/openai/clip-vit-large-patch14"),
             #     "device": device
             # },
             "google/owlvit-base-patch32": {
-                "model": pipeline(task="zero-shot-object-detection", model=f"{local_fold}/google/owlvit-base-patch32"),
-                "device": device
+                "model": pipeline(
+                    task="zero-shot-object-detection",
+                    model=f"{local_fold}/google/owlvit-base-patch32",
+                ),
+                "device": device,
             },
             # "microsoft/DialoGPT-medium": {
             #     "model": pipeline(task="conversational", model=f"{local_fold}/microsoft/DialoGPT-medium"),
@@ -270,86 +321,121 @@ def load_pipes(local_deployment):
             #     "device": device
             # },
             "impira/layoutlm-document-qa": {
-                "model": pipeline(task="document-question-answering", model=f"{local_fold}/impira/layoutlm-document-qa"),
-                "device": device
+                "model": pipeline(
+                    task="document-question-answering",
+                    model=f"{local_fold}/impira/layoutlm-document-qa",
+                ),
+                "device": device,
             },
             "ydshieh/vit-gpt2-coco-en": {
-                "model": pipeline(task="image-to-text", model=f"{local_fold}/ydshieh/vit-gpt2-coco-en"),
-                "device": device
+                "model": pipeline(
+                    task="image-to-text", model=f"{local_fold}/ydshieh/vit-gpt2-coco-en"
+                ),
+                "device": device,
             },
             "dandelin/vilt-b32-finetuned-vqa": {
-                "model": pipeline(task="visual-question-answering", model=f"{local_fold}/dandelin/vilt-b32-finetuned-vqa"),
-                "device": device
-            }
+                "model": pipeline(
+                    task="visual-question-answering",
+                    model=f"{local_fold}/dandelin/vilt-b32-finetuned-vqa",
+                ),
+                "device": device,
+            },
         }
 
     if local_deployment in ["full", "standard", "minimal"]:
-        controlnet = ControlNetModel.from_pretrained(f"{local_fold}/lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
+        controlnet = ControlNetModel.from_pretrained(
+            f"{local_fold}/lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16
+        )
         controlnetpipe = StableDiffusionControlNetPipeline.from_pretrained(
-            f"{local_fold}/runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+            f"{local_fold}/runwayml/stable-diffusion-v1-5",
+            controlnet=controlnet,
+            torch_dtype=torch.float16,
         )
 
         def mlsd_control_network():
             model = MobileV2_MLSD_Large()
-            model.load_state_dict(torch.load(f"{local_fold}/lllyasviel/ControlNet/annotator/ckpts/mlsd_large_512_fp32.pth"), strict=True)
+            model.load_state_dict(
+                torch.load(
+                    f"{local_fold}/lllyasviel/ControlNet/annotator/ckpts/mlsd_large_512_fp32.pth"
+                ),
+                strict=True,
+            )
             return MLSDdetector(model)
 
-        hed_network = Network(f"{local_fold}/lllyasviel/ControlNet/annotator/ckpts/network-bsds500.pth")
+        hed_network = Network(
+            f"{local_fold}/lllyasviel/ControlNet/annotator/ckpts/network-bsds500.pth"
+        )
 
         controlnet_sd_pipes = {
             "openpose-control": {
-                "model": OpenposeDetector(Body(f"{local_fold}/lllyasviel/ControlNet/annotator/ckpts/body_pose_model.pth"))
-            },
-            "mlsd-control": {
-                "model": mlsd_control_network()
-            },
-            "hed-control": {
-                "model": HEDdetector(hed_network)
-            },
-            "scribble-control": {
-                "model": HEDdetector(hed_network)
+                "model": OpenposeDetector(
+                    Body(
+                        f"{local_fold}/lllyasviel/ControlNet/annotator/ckpts/body_pose_model.pth"
+                    )
+                )
             },
+            "mlsd-control": {"model": mlsd_control_network()},
+            "hed-control": {"model": HEDdetector(hed_network)},
+            "scribble-control": {"model": HEDdetector(hed_network)},
             "midas-control": {
-                "model": MidasDetector(model_path=f"{local_fold}/lllyasviel/ControlNet/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt")
-            },
-            "canny-control": {
-                "model": CannyDetector()
+                "model": MidasDetector(
+                    model_path=f"{local_fold}/lllyasviel/ControlNet/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt"
+                )
             },
+            "canny-control": {"model": CannyDetector()},
             "lllyasviel/sd-controlnet-canny": {
                 "control": controlnet,
                 "model": controlnetpipe,
-                "device": device
+                "device": device,
             },
             "lllyasviel/sd-controlnet-depth": {
-                "control": ControlNetModel.from_pretrained(f"{local_fold}/lllyasviel/sd-controlnet-depth", torch_dtype=torch.float16),
+                "control": ControlNetModel.from_pretrained(
+                    f"{local_fold}/lllyasviel/sd-controlnet-depth",
+                    torch_dtype=torch.float16,
+                ),
                 "model": controlnetpipe,
-                "device": device
+                "device": device,
             },
             "lllyasviel/sd-controlnet-hed": {
-                "control": ControlNetModel.from_pretrained(f"{local_fold}/lllyasviel/sd-controlnet-hed", torch_dtype=torch.float16),
+                "control": ControlNetModel.from_pretrained(
+                    f"{local_fold}/lllyasviel/sd-controlnet-hed",
+                    torch_dtype=torch.float16,
+                ),
                 "model": controlnetpipe,
-                "device": device
+                "device": device,
             },
             "lllyasviel/sd-controlnet-mlsd": {
-                "control": ControlNetModel.from_pretrained(f"{local_fold}/lllyasviel/sd-controlnet-mlsd", torch_dtype=torch.float16),
+                "control": ControlNetModel.from_pretrained(
+                    f"{local_fold}/lllyasviel/sd-controlnet-mlsd",
+                    torch_dtype=torch.float16,
+                ),
                 "model": controlnetpipe,
-                "device": device
+                "device": device,
             },
             "lllyasviel/sd-controlnet-openpose": {
-                "control": ControlNetModel.from_pretrained(f"{local_fold}/lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16),
+                "control": ControlNetModel.from_pretrained(
+                    f"{local_fold}/lllyasviel/sd-controlnet-openpose",
+                    torch_dtype=torch.float16,
+                ),
                 "model": controlnetpipe,
-                "device": device
+                "device": device,
             },
             "lllyasviel/sd-controlnet-scribble": {
-                "control": ControlNetModel.from_pretrained(f"{local_fold}/lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16),
+                "control": ControlNetModel.from_pretrained(
+                    f"{local_fold}/lllyasviel/sd-controlnet-scribble",
+                    torch_dtype=torch.float16,
+                ),
                 "model": controlnetpipe,
-                "device": device
+                "device": device,
             },
             "lllyasviel/sd-controlnet-seg": {
-                "control": ControlNetModel.from_pretrained(f"{local_fold}/lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16),
+                "control": ControlNetModel.from_pretrained(
+                    f"{local_fold}/lllyasviel/sd-controlnet-seg",
+                    torch_dtype=torch.float16,
+                ),
                 "model": controlnetpipe,
-                "device": device
-            }
+                "device": device,
+            },
         }
     pipes = {**standard_pipes, **other_pipes, **controlnet_sd_pipes}
     return pipes
@@ -363,14 +449,17 @@ during = end - start
 print(f"[ ready ] {during}s")
 
 
-@app.route('/running', methods=['GET'])
+@app.route("/running", methods=["GET"])
 def running():
     return jsonify({"running": True})
 
 
-@app.route('/status/<path:model_id>', methods=['GET'])
+@app.route("/status/<path:model_id>", methods=["GET"])
 def status(model_id):
-    disabled_models = ["microsoft/trocr-base-printed", "microsoft/trocr-base-handwritten"]
+    disabled_models = [
+        "microsoft/trocr-base-printed",
+        "microsoft/trocr-base-handwritten",
+    ]
     if model_id in pipes.keys() and model_id not in disabled_models:
         print(f"[ check {model_id} ] success")
         return jsonify({"loaded": True})
@@ -379,7 +468,7 @@ def status(model_id):
         return jsonify({"loaded": False})
 
 
-@app.route('/models/<path:model_id>', methods=['POST'])
+@app.route("/models/<path:model_id>", methods=["POST"])
 def models(model_id):
     while "using" in pipes[model_id] and pipes[model_id]["using"]:
         print(f"[ inference {model_id} ] waiting")
@@ -402,23 +491,29 @@ def models(model_id):
     try:
         # text to video
         if model_id == "damo-vilab/text-to-video-ms-1.7b":
-            pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+            pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+                pipe.scheduler.config
+            )
             # pipe.enable_model_cpu_offload()
             prompt = request.get_json()["text"]
             video_frames = pipe(prompt, num_inference_steps=50, num_frames=40).frames
             video_path = export_to_video(video_frames)
             file_name = str(uuid.uuid4())[:4]
-            os.system(f"LD_LIBRARY_PATH=/usr/local/lib /usr/local/bin/ffmpeg -i {video_path} -vcodec libx264 public/videos/{file_name}.mp4")
+            os.system(
+                f"LD_LIBRARY_PATH=/usr/local/lib /usr/local/bin/ffmpeg -i {video_path} -vcodec libx264 public/videos/{file_name}.mp4"
+            )
             result = {"path": f"/videos/{file_name}.mp4"}
 
         # controlnet
         if model_id.startswith("lllyasviel/sd-controlnet-"):
-            pipe.controlnet.to('cpu')
+            pipe.controlnet.to("cpu")
             pipe.controlnet = pipes[model_id]["control"].to(pipes[model_id]["device"])
             pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
             control_image = load_image(request.get_json()["img_url"])
             # generator = torch.manual_seed(66)
-            out_image: Image = pipe(request.get_json()["text"], num_inference_steps=20, image=control_image).images[0]
+            out_image: Image = pipe(
+                request.get_json()["text"], num_inference_steps=20, image=control_image
+            ).images[0]
             file_name = str(uuid.uuid4())[:4]
             out_image.save(f"public/images/{file_name}.png")
             result = {"path": f"/images/{file_name}.png"}
@@ -441,17 +536,20 @@ def models(model_id):
             file_name = str(uuid.uuid4())[:4]
             with open(f"public/images/{file_name}.png", "wb") as f:
                 f.write(request.data)
-            tform = transforms.Compose([
-                transforms.ToTensor(),
-                transforms.Resize(
-                    (224, 224),
-                    interpolation=transforms.InterpolationMode.BICUBIC,
-                    antialias=False,
-                ),
-                transforms.Normalize(
-                    [0.48145466, 0.4578275, 0.40821073],
-                    [0.26862954, 0.26130258, 0.27577711]),
-            ])
+            tform = transforms.Compose(
+                [
+                    transforms.ToTensor(),
+                    transforms.Resize(
+                        (224, 224),
+                        interpolation=transforms.InterpolationMode.BICUBIC,
+                        antialias=False,
+                    ),
+                    transforms.Normalize(
+                        [0.48145466, 0.4578275, 0.40821073],
+                        [0.26862954, 0.26130258, 0.27577711],
+                    ),
+                ]
+            )
             inp = tform(im).to(pipes[model_id]["device"]).unsqueeze(0)
             out = pipe(inp, guidance_scale=3)
             out["images"][0].save(f"public/images/{file_name}.jpg")
@@ -459,30 +557,47 @@ def models(model_id):
 
         # image to text
         if model_id == "Salesforce/blip-image-captioning-large":
-            raw_image = load_image(request.get_json()["img_url"]).convert('RGB')
+            raw_image = load_image(request.get_json()["img_url"]).convert("RGB")
             text = request.get_json()["text"]
-            inputs = pipes[model_id]["processor"](raw_image, return_tensors="pt").to(pipes[model_id]["device"])
+            inputs = pipes[model_id]["processor"](raw_image, return_tensors="pt").to(
+                pipes[model_id]["device"]
+            )
             out = pipe.generate(**inputs)
-            caption = pipes[model_id]["processor"].decode(out[0], skip_special_tokens=True)
+            caption = pipes[model_id]["processor"].decode(
+                out[0], skip_special_tokens=True
+            )
             result = {"generated text": caption}
         if model_id == "ydshieh/vit-gpt2-coco-en":
             img_url = request.get_json()["img_url"]
-            generated_text = pipe(img_url)[0]['generated_text']
+            generated_text = pipe(img_url)[0]["generated_text"]
             result = {"generated text": generated_text}
         if model_id == "nlpconnect/vit-gpt2-image-captioning":
             image = load_image(request.get_json()["img_url"]).convert("RGB")
-            pixel_values = pipes[model_id]["feature_extractor"](images=image, return_tensors="pt").pixel_values
+            pixel_values = pipes[model_id]["feature_extractor"](
+                images=image, return_tensors="pt"
+            ).pixel_values
             pixel_values = pixel_values.to(pipes[model_id]["device"])
-            generated_ids = pipe.generate(pixel_values, **{"max_length": 200, "num_beams": 1})
-            generated_text = pipes[model_id]["tokenizer"].batch_decode(generated_ids, skip_special_tokens=True)[0]
+            generated_ids = pipe.generate(
+                pixel_values, **{"max_length": 200, "num_beams": 1}
+            )
+            generated_text = pipes[model_id]["tokenizer"].batch_decode(
+                generated_ids, skip_special_tokens=True
+            )[0]
             result = {"generated text": generated_text}
         # image to text: OCR
-        if model_id == "microsoft/trocr-base-printed" or model_id == "microsoft/trocr-base-handwritten":
+        if (
+            model_id == "microsoft/trocr-base-printed"
+            or model_id == "microsoft/trocr-base-handwritten"
+        ):
             image = load_image(request.get_json()["img_url"]).convert("RGB")
-            pixel_values = pipes[model_id]["processor"](image, return_tensors="pt").pixel_values
+            pixel_values = pipes[model_id]["processor"](
+                image, return_tensors="pt"
+            ).pixel_values
             pixel_values = pixel_values.to(pipes[model_id]["device"])
             generated_ids = pipe.generate(pixel_values)
-            generated_text = pipes[model_id]["processor"].batch_decode(generated_ids, skip_special_tokens=True)[0]
+            generated_text = pipes[model_id]["processor"].batch_decode(
+                generated_ids, skip_special_tokens=True
+            )[0]
             result = {"generated text": generated_text}
 
         # text to image
@@ -494,9 +609,87 @@ def models(model_id):
             result = {"path": f"/images/{file_name}.jpg"}
 
         # object detection
-        if model_id == "google/owlvit-base-patch32" or model_id == "facebook/detr-resnet-101":
+        if (
+            model_id == "google/owlvit-base-patch32"
+            or model_id == "facebook/detr-resnet-101"
+        ):
             img_url = request.get_json()["img_url"]
-            open_types = ["cat", "couch", "person", "car", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird"]
+            open_types = [
+                "cat",
+                "couch",
+                "person",
+                "car",
+                "dog",
+                "horse",
+                "sheep",
+                "cow",
+                "elephant",
+                "bear",
+                "zebra",
+                "giraffe",
+                "backpack",
+                "umbrella",
+                "handbag",
+                "tie",
+                "suitcase",
+                "frisbee",
+                "skis",
+                "snowboard",
+                "sports ball",
+                "kite",
+                "baseball bat",
+                "baseball glove",
+                "skateboard",
+                "surfboard",
+                "tennis racket",
+                "bottle",
+                "wine glass",
+                "cup",
+                "fork",
+                "knife",
+                "spoon",
+                "bowl",
+                "banana",
+                "apple",
+                "sandwich",
+                "orange",
+                "broccoli",
+                "carrot",
+                "hot dog",
+                "pizza",
+                "donut",
+                "cake",
+                "chair",
+                "couch",
+                "potted plant",
+                "bed",
+                "dining table",
+                "toilet",
+                "tv",
+                "laptop",
+                "mouse",
+                "remote",
+                "keyboard",
+                "cell phone",
+                "microwave",
+                "oven",
+                "toaster",
+                "sink",
+                "refrigerator",
+                "book",
+                "clock",
+                "vase",
+                "scissors",
+                "teddy bear",
+                "hair drier",
+                "toothbrush",
+                "traffic light",
+                "fire hydrant",
+                "stop sign",
+                "parking meter",
+                "bench",
+                "bird",
+            ]
             result = pipe(img_url, candidate_labels=open_types)
 
         # VQA
@@ -514,14 +707,16 @@ def models(model_id):
         # depth-estimation
         if model_id == "Intel/dpt-large":
             output = pipe(request.get_json()["img_url"])
-            image = output['depth']
+            image = output["depth"]
             name = str(uuid.uuid4())[:4]
             image.save(f"public/images/{name}.jpg")
             result = {"path": f"/images/{name}.jpg"}
 
         if model_id == "Intel/dpt-hybrid-midas" and model_id == "Intel/dpt-large":
             image = load_image(request.get_json()["img_url"])
-            inputs = pipes[model_id]["feature_extractor"](images=image, return_tensors="pt")
+            inputs = pipes[model_id]["feature_extractor"](
+                images=image, return_tensors="pt"
+            )
             with torch.no_grad():
                 outputs = pipe(**inputs)
                 predicted_depth = outputs.predicted_depth
@@ -550,11 +745,21 @@ def models(model_id):
             text = request.get_json()["text"]
             inputs = pipes[model_id]["processor"](text=text, return_tensors="pt")
             embeddings_dataset = pipes[model_id]["embeddings_dataset"]
-            speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0).to(pipes[model_id]["device"])
+            speaker_embeddings = (
+                torch.tensor(embeddings_dataset[7306]["xvector"])
+                .unsqueeze(0)
+                .to(pipes[model_id]["device"])
+            )
             pipes[model_id]["vocoder"].to(pipes[model_id]["device"])
-            speech = pipe.generate_speech(inputs["input_ids"].to(pipes[model_id]["device"]), speaker_embeddings, vocoder=pipes[model_id]["vocoder"])
+            speech = pipe.generate_speech(
+                inputs["input_ids"].to(pipes[model_id]["device"]),
+                speaker_embeddings,
+                vocoder=pipes[model_id]["vocoder"],
+            )
             name = str(uuid.uuid4())[:4]
-            sf.write(f"public/audios/{name}.wav", speech.cpu().numpy(), samplerate=16000)
+            sf.write(
+                f"public/audios/{name}.wav", speech.cpu().numpy(), samplerate=16000
+            )
             result = {"path": f"/audios/{name}.wav"}
 
         # ASR
@@ -569,19 +774,31 @@ def models(model_id):
             with torch.no_grad():
                 result_wav = pipe(wav.to(pipes[model_id]["device"]))
             name = str(uuid.uuid4())[:4]
-            sf.write(f"public/audios/{name}.wav", result_wav.cpu().squeeze().numpy(), sr)
+            sf.write(
+                f"public/audios/{name}.wav", result_wav.cpu().squeeze().numpy(), sr
+            )
             result = {"path": f"/audios/{name}.wav"}
 
         if model_id == "microsoft/speecht5_vc":
             audio_url = request.get_json()["audio_url"]
             wav, sr = torchaudio.load(audio_url)
-            inputs = pipes[model_id]["processor"](audio=wav, sampling_rate=sr, return_tensors="pt")
+            inputs = pipes[model_id]["processor"](
+                audio=wav, sampling_rate=sr, return_tensors="pt"
+            )
             embeddings_dataset = pipes[model_id]["embeddings_dataset"]
-            speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+            speaker_embeddings = torch.tensor(
+                embeddings_dataset[7306]["xvector"]
+            ).unsqueeze(0)
             pipes[model_id]["vocoder"].to(pipes[model_id]["device"])
-            speech = pipe.generate_speech(inputs["input_ids"].to(pipes[model_id]["device"]), speaker_embeddings, vocoder=pipes[model_id]["vocoder"])
+            speech = pipe.generate_speech(
+                inputs["input_ids"].to(pipes[model_id]["device"]),
+                speaker_embeddings,
+                vocoder=pipes[model_id]["vocoder"],
+            )
             name = str(uuid.uuid4())[:4]
-            sf.write(f"public/audios/{name}.wav", speech.cpu().numpy(), samplerate=16000)
+            sf.write(
+                f"public/audios/{name}.wav", speech.cpu().numpy(), samplerate=16000
+            )
             result = {"path": f"/audios/{name}.wav"}
 
         # segmentation
@@ -592,24 +809,44 @@ def models(model_id):
 
             colors = []
             for i in range(len(segments)):
-                colors.append((random.randint(100, 255), random.randint(100, 255), random.randint(100, 255), 50))
+                colors.append(
+                    (
+                        random.randint(100, 255),
+                        random.randint(100, 255),
+                        random.randint(100, 255),
+                        50,
+                    )
+                )
 
             for segment in segments:
                 mask = segment["mask"]
-                mask = mask.convert('L')
-                layer = Image.new('RGBA', mask.size, colors[i])
+                mask = mask.convert("L")
+                layer = Image.new("RGBA", mask.size, colors[i])
                 image.paste(layer, (0, 0), mask)
             name = str(uuid.uuid4())[:4]
             image.save(f"public/images/{name}.jpg")
             result = {"path": f"/images/{name}.jpg"}
 
-        if model_id == "facebook/maskformer-swin-base-coco" or model_id == "facebook/maskformer-swin-large-ade":
+        if (
+            model_id == "facebook/maskformer-swin-base-coco"
+            or model_id == "facebook/maskformer-swin-large-ade"
+        ):
             image = load_image(request.get_json()["img_url"])
-            inputs = pipes[model_id]["feature_extractor"](images=image, return_tensors="pt").to(pipes[model_id]["device"])
+            inputs = pipes[model_id]["feature_extractor"](
+                images=image, return_tensors="pt"
+            ).to(pipes[model_id]["device"])
             outputs = pipe(**inputs)
-            result = pipes[model_id]["feature_extractor"].post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+            result = pipes[model_id][
+                "feature_extractor"
+            ].post_process_panoptic_segmentation(
+                outputs, target_sizes=[image.size[::-1]]
+            )[
+                0
+            ]
             predicted_panoptic_map = result["segmentation"].cpu().numpy()
-            predicted_panoptic_map = Image.fromarray(predicted_panoptic_map.astype(np.uint8))
+            predicted_panoptic_map = Image.fromarray(
+                predicted_panoptic_map.astype(np.uint8)
+            )
             name = str(uuid.uuid4())[:4]
             predicted_panoptic_map.save(f"public/images/{name}.jpg")
             result = {"path": f"/images/{name}.jpg"}
@@ -641,7 +878,7 @@ def models(model_id):
     return jsonify(result)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # temp folders
     if not os.path.exists("public/audios"):
         os.makedirs("public/audios")

swarms/agents/omni_modal_agent.py

@@ -17,12 +17,7 @@ from swarms.agents.message import Message
 
 class Step:
     def __init__(
-        self,
-        task: str,
-        id: int,
-        dep: List[int],
-        args: Dict[str, str],
-        tool: BaseTool
+        self, task: str, id: int, dep: List[int], args: Dict[str, str], tool: BaseTool
     ):
         self.task = task
         self.id = id
@@ -32,10 +27,7 @@ class Step:
 
 
 class Plan:
-    def __init__(
-        self,
-        steps: List[Step]
-    ):
+    def __init__(self, steps: List[Step]):
         self.steps = steps
 
     def __str__(self) -> str:
@@ -104,10 +96,7 @@ class OmniModalAgent:
         # self.task_executor = TaskExecutor
         self.history = []
 
-    def run(
-        self,
-        input: str
-    ) -> str:
+    def run(self, input: str) -> str:
         """Run the OmniAgent"""
         plan = self.chat_planner.plan(
             inputs={
@@ -124,11 +113,7 @@ class OmniModalAgent:
 
         return response
 
-    def chat(
-        self,
-        msg: str = None,
-        streaming: bool = False
-    ):
+    def chat(self, msg: str = None, streaming: bool = False):
         """
         Run chat
 
@@ -148,24 +133,14 @@ class OmniModalAgent:
         """
 
         # add users message to the history
-        self.history.append(
-            Message(
-                "User",
-                msg
-            )
-        )
+        self.history.append(Message("User", msg))
 
         # process msg
         try:
             response = self.agent.run(msg)
 
             # add agent's response to the history
-            self.history.append(
-                Message(
-                    "Agent",
-                    response
-                )
-            )
+            self.history.append(Message("Agent", response))
 
             # if streaming is = True
             if streaming:
@@ -177,19 +152,11 @@ class OmniModalAgent:
             error_message = f"Error processing message: {str(error)}"
 
             # add error to history
-            self.history.append(
-                Message(
-                    "Agent",
-                    error_message
-                )
-            )
+            self.history.append(Message("Agent", error_message))
 
             return error_message
 
-    def _stream_response(
-        self,
-        response: str = None
-    ):
+    def _stream_response(self, response: str = None):
         """
         Yield the response token by token (word by word)
 

swarms/agents/profitpilot.py

@@ -56,36 +56,36 @@ class StageAnalyzerChain(LLMChain):
 
 class SalesConversationChain(LLMChain):
     """
-    Chain to generate the next utterance for the conversation.
+        Chain to generate the next utterance for the conversation.
 
 
-    # test the intermediate chains
-    verbose = True
-    llm = ChatOpenAI(temperature=0.9)
+        # test the intermediate chains
+        verbose = True
+        llm = ChatOpenAI(temperature=0.9)
 
-    stage_analyzer_chain = StageAnalyzerChain.from_llm(llm, verbose=verbose)
-
-    sales_conversation_utterance_chain = SalesConversationChain.from_llm(
-        llm, verbose=verbose
-    )
+        stage_analyzer_chain = StageAnalyzerChain.from_llm(llm, verbose=verbose)
 
+        sales_conversation_utterance_chain = SalesConversationChain.from_llm(
+            llm, verbose=verbose
+        )
 
-    stage_analyzer_chain.run(conversation_history="")
 
-    sales_conversation_utterance_chain.run(
-    salesperson_name="Ted Lasso",
-    salesperson_role="Business Development Representative",
-    company_name="Sleep Haven",
-    company_business="Sleep Haven is a premium mattress company that provides customers with the most comfortable and supportive sleeping experience possible. We offer a range of high-quality mattresses, pillows, and bedding accessories that are designed to meet the unique needs of our customers.",
-    company_values="Our mission at Sleep Haven is to help people achieve a better night's sleep by providing them with the best possible sleep solutions. We believe that quality sleep is essential to overall health and well-being, and we are committed to helping our customers achieve optimal sleep by offering exceptional products and customer service.",
-    conversation_purpose="find out whether they are looking to achieve better sleep via buying a premier mattress.",
-    conversation_history="Hello, this is Ted Lasso from Sleep Haven. How are you doing today? <END_OF_TURN>\nUser: I am well, howe are you?<END_OF_TURN>",
-    conversation_type="call",
-    conversation_stage=conversation_stages.get(
-        "1",
-        "Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional.",
-    ),
-)
+        stage_analyzer_chain.run(conversation_history="")
+
+        sales_conversation_utterance_chain.run(
+        salesperson_name="Ted Lasso",
+        salesperson_role="Business Development Representative",
+        company_name="Sleep Haven",
+        company_business="Sleep Haven is a premium mattress company that provides customers with the most comfortable and supportive sleeping experience possible. We offer a range of high-quality mattresses, pillows, and bedding accessories that are designed to meet the unique needs of our customers.",
+        company_values="Our mission at Sleep Haven is to help people achieve a better night's sleep by providing them with the best possible sleep solutions. We believe that quality sleep is essential to overall health and well-being, and we are committed to helping our customers achieve optimal sleep by offering exceptional products and customer service.",
+        conversation_purpose="find out whether they are looking to achieve better sleep via buying a premier mattress.",
+        conversation_history="Hello, this is Ted Lasso from Sleep Haven. How are you doing today? <END_OF_TURN>\nUser: I am well, howe are you?<END_OF_TURN>",
+        conversation_type="call",
+        conversation_stage=conversation_stages.get(
+            "1",
+            "Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional.",
+        ),
+    )
 
     """
 
@@ -166,14 +166,12 @@ def get_tools(product_catalog):
             func=knowledge_base.run,
             description="useful for when you need to answer questions about product information",
         ),
-
         # Interpreter
         Tool(
             name="Code Interepeter",
             func=compile,
-            description="Useful when you need to run code locally, such as Python, Javascript, Shell, and more."
+            description="Useful when you need to run code locally, such as Python, Javascript, Shell, and more.",
         )
-
         # omnimodal agent
     ]
 
@@ -354,12 +352,7 @@ class ProfitPilot(Chain, BaseModel):
         return {}
 
     @classmethod
-    def from_llm(
-        cls,
-        llm: BaseLLM,
-        verbose: bool = False,
-        **kwargs
-    ):  # noqa: F821
+    def from_llm(cls, llm: BaseLLM, verbose: bool = False, **kwargs):  # noqa: F821
         """Initialize the SalesGPT Controller."""
         stage_analyzer_chain = StageAnalyzerChain.from_llm(llm, verbose=verbose)
 

swarms/agents/stream_response.py

@@ -1,5 +1,3 @@
-
-
 def stream(response):
     """
     Yield the response token by token (word by word) from llm

swarms/artifacts/base.py

@@ -10,9 +10,14 @@ from marshmallow.exceptions import RegistryError
 @define
 class BaseArtifact(ABC):
     id: str = field(default=Factory(lambda: uuid.uuid4().hex), kw_only=True)
-    name: str = field(default=Factory(lambda self: self.id, takes_self=True), kw_only=True)
+    name: str = field(
+        default=Factory(lambda self: self.id, takes_self=True), kw_only=True
+    )
     value: any = field()
-    type: str = field(default=Factory(lambda self: self.__class__.__name__, takes_self=True), kw_only=True)
+    type: str = field(
+        default=Factory(lambda self: self.__class__.__name__, takes_self=True),
+        kw_only=True,
+    )
 
     @classmethod
     def value_to_bytes(cls, value: any) -> bytes:
@@ -38,7 +43,7 @@ class BaseArtifact(ABC):
             ErrorArtifactSchema,
             BlobArtifactSchema,
             CsvRowArtifactSchema,
-            ListArtifactSchema
+            ListArtifactSchema,
         )
 
         class_registry.register("TextArtifact", TextArtifactSchema)

swarms/artifacts/main.py

@@ -12,14 +12,8 @@ class Artifact(BaseModel):
     Artifact that has the task has been produced
     """
 
-    artifact_id: StrictStr = Field(
-        ...,
-        description="ID of the artifact"
-    )
-    file_name: StrictStr = Field(
-        ...,
-        description="Filename of the artifact"
-    )
+    artifact_id: StrictStr = Field(..., description="ID of the artifact")
+    file_name: StrictStr = Field(..., description="Filename of the artifact")
     relative_path: Optional[StrictStr] = Field(
         None, description="Relative path of the artifact"
     )

swarms/boss/boss_node.py

@@ -10,7 +10,9 @@ from langchain.vectorstores import FAISS
 from langchain_experimental.autonomous_agents import BabyAGI
 from pydantic import ValidationError
 
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
 
 # ---------- Boss Node ----------
 
@@ -48,7 +50,7 @@ class Boss:
         boss_system_prompt="You are a boss planner in a swarm...",
         llm_class=OpenAI,
         worker_node=None,
-        verbose=False
+        verbose=False,
     ):
         # Store parameters
         self.api_key = api_key or os.getenv("OPENAI_API_KEY")
@@ -85,11 +87,7 @@ class Boss:
             embedding_size = 8192
             index = faiss.IndexFlatL2(embedding_size)
 
-            return FAISS(
-                embeddings_model.embed_query,
-                index,
-                InMemoryDocstore({}), {}
-            )
+            return FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
 
         except Exception as e:
             logging.error(f"Failed to initialize vector store: {e}")
@@ -102,9 +100,13 @@ class Boss:
             Tool(
                 name="Goal Decomposition Tool",
                 func=todo_chain.run,
-                description="Use Case: Decompose ambitious goals into as many explicit and well defined tasks for an AI agent to follow. Rules and Regulations, don't use this tool too often only in the beginning when the user grants you a mission."
+                description="Use Case: Decompose ambitious goals into as many explicit and well defined tasks for an AI agent to follow. Rules and Regulations, don't use this tool too often only in the beginning when the user grants you a mission.",
+            ),
+            Tool(
+                name="Swarm Worker Agent",
+                func=worker_node,
+                description="Use Case: When you want to delegate and assign the decomposed goal sub tasks to a worker agent in your swarm, Rules and Regulations, Provide a task specification sheet to the worker agent. It can use the browser, process csvs and generate content",
             ),
-            Tool(name="Swarm Worker Agent", func=worker_node, description="Use Case: When you want to delegate and assign the decomposed goal sub tasks to a worker agent in your swarm, Rules and Regulations, Provide a task specification sheet to the worker agent. It can use the browser, process csvs and generate content")
         ]
 
         suffix = """Question: {task}\n{agent_scratchpad}"""
@@ -118,7 +120,9 @@ class Boss:
 
         llm_chain = LLMChain(llm=self.llm, prompt=prompt)
         agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tools)
-        return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=self.verbose)
+        return AgentExecutor.from_agent_and_tools(
+            agent=agent, tools=tools, verbose=self.verbose
+        )
 
     def _initialize_baby_agi(self, human_in_the_loop):
         try:
@@ -127,7 +131,7 @@ class Boss:
                 vectorstore=self.vectorstore,
                 task_execution_chain=self.agent_executor,
                 max_iterations=self.max_iterations,
-                human_in_the_loop=human_in_the_loop
+                human_in_the_loop=human_in_the_loop,
             )
         except ValidationError as e:
             logging.error(f"Validation Error while initializing BabyAGI: {e}")

swarms/embeddings/openai.py

@@ -28,7 +28,9 @@ from tenacity import (
 from swarms.embeddings.base import Embeddings
 
 
-def get_from_dict_or_env(values: dict, key: str, env_key: str, default: Any = None) -> Any:
+def get_from_dict_or_env(
+    values: dict, key: str, env_key: str, default: Any = None
+) -> Any:
     import os
 
     return values.get(key) or os.getenv(env_key) or default
@@ -345,7 +347,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 disallowed_special=self.disallowed_special,
             )
             for j in range(0, len(token), self.embedding_ctx_length):
-                tokens.append(token[j: j + self.embedding_ctx_length])
+                tokens.append(token[j : j + self.embedding_ctx_length])
                 indices.append(i)
 
         batched_embeddings: List[List[float]] = []
@@ -364,7 +366,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         for i in _iter:
             response = embed_with_retry(
                 self,
-                input=tokens[i: i + _chunk_size],
+                input=tokens[i : i + _chunk_size],
                 **self._invocation_params,
             )
             batched_embeddings.extend(r["embedding"] for r in response["data"])
@@ -426,7 +428,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 disallowed_special=self.disallowed_special,
             )
             for j in range(0, len(token), self.embedding_ctx_length):
-                tokens.append(token[j: j + self.embedding_ctx_length])
+                tokens.append(token[j : j + self.embedding_ctx_length])
                 indices.append(i)
 
         batched_embeddings: List[List[float]] = []
@@ -434,7 +436,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         for i in range(0, len(tokens), _chunk_size):
             response = await async_embed_with_retry(
                 self,
-                input=tokens[i: i + _chunk_size],
+                input=tokens[i : i + _chunk_size],
                 **self._invocation_params,
             )
             batched_embeddings.extend(r["embedding"] for r in response["data"])

swarms/embeddings/pegasus.py

@@ -8,10 +8,7 @@ from pegasus import Pegasus
 
 class PegasusEmbedding:
     def __init__(
-        self,
-        modality: str,
-        multi_process: bool = False,
-        n_processes: int = 4
+        self, modality: str, multi_process: bool = False, n_processes: int = 4
     ):
         self.modality = modality
         self.multi_process = multi_process
@@ -19,7 +16,9 @@ class PegasusEmbedding:
         try:
             self.pegasus = Pegasus(modality, multi_process, n_processes)
         except Exception as e:
-            logging.error(f"Failed to initialize Pegasus with modality: {modality}: {e}")
+            logging.error(
+                f"Failed to initialize Pegasus with modality: {modality}: {e}"
+            )
             raise
 
     def embed(self, data: Union[str, list[str]]):

swarms/hivemind/hivemind.py

@@ -10,16 +10,13 @@ import logging
 
 from swarms.swarms.swarms import HierarchicalSwarm
 
-logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(
+    level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s"
+)
 
 
 class HiveMind:
-    def __init__(
-        self,
-        openai_api_key="",
-        num_swarms=1,
-        max_workers=None
-    ):
+    def __init__(self, openai_api_key="", num_swarms=1, max_workers=None):
         self.openai_api_key = openai_api_key
         self.num_swarms = num_swarms
         self.swarms = [HierarchicalSwarm(openai_api_key) for _ in range(num_swarms)]
@@ -43,8 +40,13 @@ class HiveMind:
             logging.error(f"An error occurred in run: {e}")
 
     def run(self, objective, timeout=None):
-        with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor:
-            futures = {executor.submit(self.run_swarm, swarm, objective) for swarm in self.swarms}
+        with concurrent.futures.ThreadPoolExecutor(
+            max_workers=self.max_workers
+        ) as executor:
+            futures = {
+                executor.submit(self.run_swarm, swarm, objective)
+                for swarm in self.swarms
+            }
             results = []
             for future in concurrent.futures.as_completed(futures, timeout=timeout):
                 try:

swarms/memory/embed.py

@@ -4,8 +4,7 @@ from chromadb import EmbeddingFunction
 
 def openai_embed(self, input, api_key, model_name):
     openai = EmbeddingFunction.OpenAIEmbeddingFunction(
-        api_key=api_key,
-        model_name=model_name
+        api_key=api_key, model_name=model_name
     )
     embedding = openai(input)
     return embedding

swarms/memory/schemas.py

@@ -26,19 +26,16 @@ class Artifact(BaseModel):
     relative_path: Optional[str] = Field(
         None,
         description="Relative path of the artifact in the agent's workspace",
-        example="python/code/"
+        example="python/code/",
     )
 
 
 class ArtifactUpload(BaseModel):
-    file: bytes = Field(
-        ...,
-        description="File to upload"
-    )
+    file: bytes = Field(..., description="File to upload")
     relative_path: Optional[str] = Field(
         None,
         description="Relative path of the artifact in the agent's workspace",
-        example="python/code/"
+        example="python/code/",
     )
 
 

swarms/models/__init__.py

@@ -1,7 +1,9 @@
 # prompts
 from swarms.models.anthropic import Anthropic
+
 # from swarms.models.palm import GooglePalm
 from swarms.models.petals import Petals
+
 # from swarms.models.chat_openai import OpenAIChat
 from swarms.models.prompts.debate import *
 from swarms.models.mistral import Mistral

swarms/models/anthropic.py

@@ -13,7 +13,7 @@ class Anthropic:
         top_k=None,
         top_p=None,
         streaming=False,
-        default_request_timeout=None
+        default_request_timeout=None,
     ):
         self.model = model
         self.max_tokens_to_sample = max_tokens_to_sample
@@ -22,7 +22,9 @@ class Anthropic:
         self.top_p = top_p
         self.streaming = streaming
         self.default_request_timeout = default_request_timeout or 600
-        self.anthropic_api_url = os.getenv("ANTHROPIC_API_URL", "https://api.anthropic.com")
+        self.anthropic_api_url = os.getenv(
+            "ANTHROPIC_API_URL", "https://api.anthropic.com"
+        )
         self.anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
 
     def _default_params(self):
@@ -44,12 +46,13 @@ class Anthropic:
         stop = stop or []
         params = self._default_params()
         headers = {"Authorization": f"Bearer {self.anthropic_api_key}"}
-        data = {
-            "prompt": prompt,
-            "stop_sequences": stop,
-            **params
-        }
-        response = requests.post(f"{self.anthropic_api_url}/completions", headers=headers, json=data, timeout=self.default_request_timeout)
+        data = {"prompt": prompt, "stop_sequences": stop, **params}
+        response = requests.post(
+            f"{self.anthropic_api_url}/completions",
+            headers=headers,
+            json=data,
+            timeout=self.default_request_timeout,
+        )
         return response.json().get("completion")
 
     def __call__(self, prompt, stop=None):
@@ -57,10 +60,11 @@ class Anthropic:
         stop = stop or []
         params = self._default_params()
         headers = {"Authorization": f"Bearer {self.anthropic_api_key}"}
-        data = {
-            "prompt": prompt,
-            "stop_sequences": stop,
-            **params
-        }
-        response = requests.post(f"{self.anthropic_api_url}/completions", headers=headers, json=data, timeout=self.default_request_timeout)
+        data = {"prompt": prompt, "stop_sequences": stop, **params}
+        response = requests.post(
+            f"{self.anthropic_api_url}/completions",
+            headers=headers,
+            json=data,
+            timeout=self.default_request_timeout,
+        )
         return response.json().get("completion")

swarms/models/chat_openai.py

@@ -458,7 +458,7 @@ class BaseOpenAI(BaseLLM):
                 )
             params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
         sub_prompts = [
-            prompts[i: i + self.batch_size]
+            prompts[i : i + self.batch_size]
             for i in range(0, len(prompts), self.batch_size)
         ]
         return sub_prompts
@@ -469,7 +469,7 @@ class BaseOpenAI(BaseLLM):
         """Create the LLMResult from the choices and prompts."""
         generations = []
         for i, _ in enumerate(prompts):
-            sub_choices = choices[i * self.n: (i + 1) * self.n]
+            sub_choices = choices[i * self.n : (i + 1) * self.n]
             generations.append(
                 [
                     Generation(

swarms/models/mistral.py

@@ -23,7 +23,7 @@ class Mistral:
         use_flash_attention: bool = False,
         temperature: float = 1.0,
         max_length: int = 100,
-        do_sample: bool = True
+        do_sample: bool = True,
     ):
         self.ai_name = ai_name
         self.system_prompt = system_prompt
@@ -52,34 +52,24 @@ class Mistral:
         except Exception as e:
             raise ValueError(f"Error loading the Mistral model: {str(e)}")
 
-    def run(
-        self,
-        task: str
-    ):
+    def run(self, task: str):
         """Run the model on a given task."""
 
         try:
-            model_inputs = self.tokenizer(
-                [task],
-                return_tensors="pt"
-            ).to(self.device)
+            model_inputs = self.tokenizer([task], return_tensors="pt").to(self.device)
             generated_ids = self.model.generate(
                 **model_inputs,
                 max_length=self.max_length,
                 do_sample=self.do_sample,
                 temperature=self.temperature,
-                max_new_tokens=self.max_length
+                max_new_tokens=self.max_length,
             )
             output_text = self.tokenizer.batch_decode(generated_ids)[0]
             return output_text
         except Exception as e:
             raise ValueError(f"Error running the model: {str(e)}")
 
-    def chat(
-        self,
-        msg: str = None,
-        streaming: bool = False
-    ):
+    def chat(self, msg: str = None, streaming: bool = False):
         """
         Run chat
 
@@ -99,24 +89,14 @@ class Mistral:
         """
 
         # add users message to the history
-        self.history.append(
-            Message(
-                "User",
-                msg
-            )
-        )
+        self.history.append(Message("User", msg))
 
         # process msg
         try:
             response = self.agent.run(msg)
 
             # add agent's response to the history
-            self.history.append(
-                Message(
-                    "Agent",
-                    response
-                )
-            )
+            self.history.append(Message("Agent", response))
 
             # if streaming is = True
             if streaming:
@@ -128,19 +108,11 @@ class Mistral:
             error_message = f"Error processing message: {str(error)}"
 
             # add error to history
-            self.history.append(
-                Message(
-                    "Agent",
-                    error_message
-                )
-            )
+            self.history.append(Message("Agent", error_message))
 
             return error_message
 
-    def _stream_response(
-        self,
-        response: str = None
-    ):
+    def _stream_response(self, response: str = None):
         """
         Yield the response token by token (word by word)
 

swarms/models/petals.py

@@ -12,7 +12,7 @@ class Petals:
         top_p=0.9,
         top_k=None,
         do_sample=True,
-        max_length=None
+        max_length=None,
     ):
         self.model_name = model_name
         self.temperature = temperature

swarms/models/prompts/agent_output_parser.py

@@ -6,6 +6,7 @@ from typing import Dict, NamedTuple
 
 class AgentAction(NamedTuple):
     """Action returned by AgentOutputParser."""
+
     name: str
     args: Dict
 

swarms/models/prompts/agent_prompt_auto.py

@@ -16,14 +16,12 @@ class PromptConstructor:
         self.tools = tools
 
     def construct_full_prompt(self, goals: List[str]) -> str:
-        prompt_start = (
-            """Your decisions must always be made independently
+        prompt_start = """Your decisions must always be made independently
             without seeking user assistance.\n
             Play to your strengths as an LLM and pursue simple
             strategies with no legal complications.\n
             If you have completed all your tasks, make sure to
             use the "finish" command."""
-        )
         # Construct full prompt
         full_prompt = (
             f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
@@ -56,10 +54,12 @@ class MessageFormatter:
     send_token_limit: int = 4196
 
     def format_messages(self, **kwargs: Any) -> List[Message]:
-        prompt_constructor = PromptConstructor(ai_name=kwargs["ai_name"],
-                                               ai_role=kwargs["ai_role"],
-                                               tools=kwargs["tools"])
-        base_prompt = SystemMessage(content=prompt_constructor.construct_full_prompt(kwargs["goals"]))
+        prompt_constructor = PromptConstructor(
+            ai_name=kwargs["ai_name"], ai_role=kwargs["ai_role"], tools=kwargs["tools"]
+        )
+        base_prompt = SystemMessage(
+            content=prompt_constructor.construct_full_prompt(kwargs["goals"])
+        )
         time_prompt = SystemMessage(
             content=f"The current time and date is {time.strftime('%c')}"
         )

swarms/models/prompts/agent_prompts.py

@@ -1,5 +1,5 @@
 def generate_agent_role_prompt(agent):
-    """ Generates the agent role prompt.
+    """Generates the agent role prompt.
     Args: agent (str): The type of the agent.
     Returns: str: The agent role prompt.
     """
@@ -7,35 +7,38 @@ def generate_agent_role_prompt(agent):
         "Finance Agent": "You are a seasoned finance analyst AI assistant. Your primary goal is to compose comprehensive, astute, impartial, and methodically arranged financial reports based on provided data and trends.",
         "Travel Agent": "You are a world-travelled AI tour guide assistant. Your main purpose is to draft engaging, insightful, unbiased, and well-structured travel reports on given locations, including history, attractions, and cultural insights.",
         "Academic Research Agent": "You are an AI academic research assistant. Your primary responsibility is to create thorough, academically rigorous, unbiased, and systematically organized reports on a given research topic, following the standards of scholarly work.",
-        "Default Agent": "You are an AI critical thinker research assistant. Your sole purpose is to write well written, critically acclaimed, objective and structured reports on given text."
-
+        "Default Agent": "You are an AI critical thinker research assistant. Your sole purpose is to write well written, critically acclaimed, objective and structured reports on given text.",
     }
 
     return prompts.get(agent, "No such agent")
 
 
 def generate_report_prompt(question, research_summary):
-    """ Generates the report prompt for the given question and research summary.
+    """Generates the report prompt for the given question and research summary.
     Args: question (str): The question to generate the report prompt for
             research_summary (str): The research summary to generate the report prompt for
     Returns: str: The report prompt for the given question and research summary
     """
 
-    return f'"""{research_summary}""" Using the above information, answer the following'\
-           f' question or topic: "{question}" in a detailed report --'\
-           " The report should focus on the answer to the question, should be well structured, informative," \
-           " in depth, with facts and numbers if available, a minimum of 1,200 words and with markdown syntax and apa format. "\
+    return (
+        f'"""{research_summary}""" Using the above information, answer the following'
+        f' question or topic: "{question}" in a detailed report --'
+        " The report should focus on the answer to the question, should be well structured, informative,"
+        " in depth, with facts and numbers if available, a minimum of 1,200 words and with markdown syntax and apa format. "
         "Write all source urls at the end of the report in apa format"
+    )
 
 
 def generate_search_queries_prompt(question):
-    """ Generates the search queries prompt for the given question.
+    """Generates the search queries prompt for the given question.
     Args: question (str): The question to generate the search queries prompt for
     Returns: str: The search queries prompt for the given question
     """
 
-    return f'Write 4 google search queries to search online that form an objective opinion from the following: "{question}"'\
-           f'You must respond with a list of strings in the following format: ["query 1", "query 2", "query 3", "query 4"]'
+    return (
+        f'Write 4 google search queries to search online that form an objective opinion from the following: "{question}"'
+        f'You must respond with a list of strings in the following format: ["query 1", "query 2", "query 3", "query 4"]'
+    )
 
 
 def generate_resource_report_prompt(question, research_summary):
@@ -48,39 +51,45 @@ def generate_resource_report_prompt(question, research_summary):
     Returns:
         str: The resource report prompt for the given question and research summary.
     """
-    return f'"""{research_summary}""" Based on the above information, generate a bibliography recommendation report for the following' \
-           f' question or topic: "{question}". The report should provide a detailed analysis of each recommended resource,' \
-           ' explaining how each source can contribute to finding answers to the research question.' \
-           ' Focus on the relevance, reliability, and significance of each source.' \
-           ' Ensure that the report is well-structured, informative, in-depth, and follows Markdown syntax.' \
-           ' Include relevant facts, figures, and numbers whenever available.' \
-           ' The report should have a minimum length of 1,200 words.'
+    return (
+        f'"""{research_summary}""" Based on the above information, generate a bibliography recommendation report for the following'
+        f' question or topic: "{question}". The report should provide a detailed analysis of each recommended resource,'
+        " explaining how each source can contribute to finding answers to the research question."
+        " Focus on the relevance, reliability, and significance of each source."
+        " Ensure that the report is well-structured, informative, in-depth, and follows Markdown syntax."
+        " Include relevant facts, figures, and numbers whenever available."
+        " The report should have a minimum length of 1,200 words."
+    )
 
 
 def generate_outline_report_prompt(question, research_summary):
-    """ Generates the outline report prompt for the given question and research summary.
+    """Generates the outline report prompt for the given question and research summary.
     Args: question (str): The question to generate the outline report prompt for
             research_summary (str): The research summary to generate the outline report prompt for
     Returns: str: The outline report prompt for the given question and research summary
     """
 
-    return f'"""{research_summary}""" Using the above information, generate an outline for a research report in Markdown syntax'\
-           f' for the following question or topic: "{question}". The outline should provide a well-structured framework'\
-           ' for the research report, including the main sections, subsections, and key points to be covered.' \
-           ' The research report should be detailed, informative, in-depth, and a minimum of 1,200 words.' \
-           ' Use appropriate Markdown syntax to format the outline and ensure readability.'
+    return (
+        f'"""{research_summary}""" Using the above information, generate an outline for a research report in Markdown syntax'
+        f' for the following question or topic: "{question}". The outline should provide a well-structured framework'
+        " for the research report, including the main sections, subsections, and key points to be covered."
+        " The research report should be detailed, informative, in-depth, and a minimum of 1,200 words."
+        " Use appropriate Markdown syntax to format the outline and ensure readability."
+    )
 
 
 def generate_concepts_prompt(question, research_summary):
-    """ Generates the concepts prompt for the given question.
+    """Generates the concepts prompt for the given question.
     Args: question (str): The question to generate the concepts prompt for
             research_summary (str): The research summary to generate the concepts prompt for
     Returns: str: The concepts prompt for the given question
     """
 
-    return f'"""{research_summary}""" Using the above information, generate a list of 5 main concepts to learn for a research report'\
-           f' on the following question or topic: "{question}". The outline should provide a well-structured framework'\
-           'You must respond with a list of strings in the following format: ["concepts 1", "concepts 2", "concepts 3", "concepts 4, concepts 5"]'
+    return (
+        f'"""{research_summary}""" Using the above information, generate a list of 5 main concepts to learn for a research report'
+        f' on the following question or topic: "{question}". The outline should provide a well-structured framework'
+        'You must respond with a list of strings in the following format: ["concepts 1", "concepts 2", "concepts 3", "concepts 4, concepts 5"]'
+    )
 
 
 def generate_lesson_prompt(concept):
@@ -92,17 +101,19 @@ def generate_lesson_prompt(concept):
         str: The lesson prompt for the given concept.
     """
 
-    prompt = f'generate a comprehensive lesson about {concept} in Markdown syntax. This should include the definition'\
-        f'of {concept}, its historical background and development, its applications or uses in different'\
-        f'fields, and notable events or facts related to {concept}.'
+    prompt = (
+        f"generate a comprehensive lesson about {concept} in Markdown syntax. This should include the definition"
+        f"of {concept}, its historical background and development, its applications or uses in different"
+        f"fields, and notable events or facts related to {concept}."
+    )
 
     return prompt
 
 
 def get_report_by_type(report_type):
     report_type_mapping = {
-        'research_report': generate_report_prompt,
-        'resource_report': generate_resource_report_prompt,
-        'outline_report': generate_outline_report_prompt
+        "research_report": generate_report_prompt,
+        "resource_report": generate_resource_report_prompt,
+        "outline_report": generate_outline_report_prompt,
     }
     return report_type_mapping[report_type]

swarms/models/prompts/debate.py

@@ -38,5 +38,7 @@ def debate_monitor(game_description, word_limit, character_names):
     return prompt
 
 
-def generate_character_header(game_description, topic, character_name, character_description):
+def generate_character_header(
+    game_description, topic, character_name, character_description
+):
     pass

swarms/models/prompts/prebuild/project_manager.py

@@ -1,4 +1,4 @@
-PROJECT_MANAGR_PROMPT_TEMPLATE = '''
+PROJECT_MANAGR_PROMPT_TEMPLATE = """
 # Context
 {context}
 
@@ -23,7 +23,7 @@ Attention: Use '##' to split sections, not '#', and '## <SECTION_NAME>' SHOULD W
 
 ## Anything UNCLEAR: Provide as Plain text. Make clear here. For example, don't forget a main entry. don't forget to init 3rd party libs.
 
-'''
+"""
 
 FORMAT_EXAMPLE = '''
 ---

swarms/models/prompts/prebuild/sales_prompts.py

@@ -1,5 +1,3 @@
-
-
 SALES_ASSISTANT_PROMPT = """You are a sales assistant helping your sales agent to determine which stage of a sales conversation should the agent move to, or stay at.
 Following '===' is the conversation history.
 Use this conversation history to make your decision.
@@ -47,10 +45,12 @@ Conversation history:
 {salesperson_name}:
 """
 
-conversation_stages = {'1': "Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional. Your greeting should be welcoming. Always clarify in your greeting the reason why you are contacting the prospect.",
-                       '2': "Qualification: Qualify the prospect by confirming if they are the right person to talk to regarding your product/service. Ensure that they have the authority to make purchasing decisions.",
-                       '3': "Value proposition: Briefly explain how your product/service can benefit the prospect. Focus on the unique selling points and value proposition of your product/service that sets it apart from competitors.",
-                       '4': "Needs analysis: Ask open-ended questions to uncover the prospect's needs and pain points. Listen carefully to their responses and take notes.",
-                       '5': "Solution presentation: Based on the prospect's needs, present your product/service as the solution that can address their pain points.",
-                       '6': "Objection handling: Address any objections that the prospect may have regarding your product/service. Be prepared to provide evidence or testimonials to support your claims.",
-                       '7': "Close: Ask for the sale by proposing a next step. This could be a demo, a trial or a meeting with decision-makers. Ensure to summarize what has been discussed and reiterate the benefits."}
+conversation_stages = {
+    "1": "Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional. Your greeting should be welcoming. Always clarify in your greeting the reason why you are contacting the prospect.",
+    "2": "Qualification: Qualify the prospect by confirming if they are the right person to talk to regarding your product/service. Ensure that they have the authority to make purchasing decisions.",
+    "3": "Value proposition: Briefly explain how your product/service can benefit the prospect. Focus on the unique selling points and value proposition of your product/service that sets it apart from competitors.",
+    "4": "Needs analysis: Ask open-ended questions to uncover the prospect's needs and pain points. Listen carefully to their responses and take notes.",
+    "5": "Solution presentation: Based on the prospect's needs, present your product/service as the solution that can address their pain points.",
+    "6": "Objection handling: Address any objections that the prospect may have regarding your product/service. Be prepared to provide evidence or testimonials to support your claims.",
+    "7": "Close: Ask for the sale by proposing a next step. This could be a demo, a trial or a meeting with decision-makers. Ensure to summarize what has been discussed and reiterate the benefits.",
+}