python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
import pytest
from unittest.mock import Mock
from swarms.workers.worker_agent_ultra import WorkerUltraNode # import your module here
def test_create_agent():
mock_llm = Mock()
mock_toolset = { 'test_toolset': Mock() }
mock_vectorstore = Mock()
worker = WorkerUltraNode(mock_llm, mock_toolset, mock_vect... | swarms-master | tests/agents/workers/worker_agent_ultra.py |
import pytest
from unittest.mock import Mock, patch
from swarms.workers.worker_agent_ultra import WorkerUltraNode, WorkerUltraNodeInitializer
@pytest.fixture
def llm_mock():
return Mock()
@pytest.fixture
def toolsets_mock():
return Mock()
@pytest.fixture
def vectorstore_mock():
return Mock()
@pytest.f... | swarms-master | tests/agents/workers/worker_ultra.py |
import pytest
from unittest.mock import Mock
from swarms.workers.multi_modal_worker import MultiModalVisualAgent, MultiModalVisualAgentTool
@pytest.fixture
def multimodal_agent():
# Mock the MultiModalVisualAgent
mock_agent = Mock(spec=MultiModalVisualAgent)
mock_agent.run_text.return_value = "Expected o... | swarms-master | tests/agents/workers/multi_model_worker.py |
import pytest
from swarms.worker.omni_worker import OmniWorkerAgent
@pytest.fixture
def omni_worker():
api_key = 'test-key'
api_endpoint = 'test-endpoint'
api_type = 'test-type'
return OmniWorkerAgent(api_key, api_endpoint, api_type)
@pytest.mark.parametrize("data, expected_response", [
(
... | swarms-master | tests/agents/workers/omni_worker.py |
import pytest
from unittest.mock import Mock, patch
from swarms.tools.agent_tools import *
from swarms.boss.boss_node import BossNodeInitializer, BossNode
# For initializing BossNodeInitializer in multiple tests
@pytest.fixture
def mock_boss_node_initializer():
llm = Mock()
vectorstore = Mock()
agent_execut... | swarms-master | tests/boss/boss_node.py |
from swarms import Model, Agent, WorkerNode, vectorstore, tools, orchestrator
#1 model
Model(openai)
#2 agent level
Agent(
model,
vectorstore,
tools
)
#3 worker infrastructure level
worker_node(
Agent,
human_input,
tools
)
#4 swarm level basically handling infrastructure for multiple worker ... | swarms-master | docs/old-docs/design/abstraction.py |
swarms-master | api/__init__.py | |
import logging
import os
from fastapi import FastAPI, HTTPException, Depends
from fastapi_cache.decorator import cache
from fastapi_cache.coder import JsonCoder
from fastapi_cache import FastAPICache
from fastapi_cache.backends.redis import RedisBackend
from aioredis import Redis
from pydantic import BaseModel
from s... | swarms-master | api/app.py |
import os
from celery import Celery
from celery.result import AsyncResult
from api.olds.container import agent_manager
celery_app = Celery(__name__)
celery_app.conf.broker_url = os.environ["CELERY_BROKER_URL"]
celery_app.conf.result_backend = os.environ["CELERY_BROKER_URL"]
celery_app.conf.update(
task_track_st... | swarms-master | api/olds/worker.py |
import os
from pathlib import Path
from typing import Dict, List
from fastapi.templating import Jinja2Templates
from swarms.agents.utils.agent_creator import AgentManager
from swarms.utils.main import BaseHandler, FileHandler, FileType
from swarms.tools.main import ExitConversation, RequestsGet, CodeEditor, Terminal
... | swarms-master | api/olds/container.py |
import os
import re
from multiprocessing import Process
from tempfile import NamedTemporaryFile
from typing import List, TypedDict
import uvicorn
from fastapi import FastAPI, Request, UploadFile
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from... | swarms-master | api/olds/main.py |
#swarms
#from swarms.orchestrator.autoscaler import AutoScaler
# worker
# from swarms.workers.worker_node import WorkerNode
#boss
from swarms.boss.boss_node import Boss
#models
from swarms.models.anthropic import Anthropic
from swarms.models.huggingface import HFLLM
# from swarms.models.palm import GooglePalm
from ... | swarms-master | swarms/__init__.py |
swarms-master | swarms/artifacts/__init__.py | |
from __future__ import annotations
from attr import define, field
from swarms.artifacts.base import BaseArtifact
@define(frozen=True)
class ErrorArtifact(BaseArtifact):
value: str = field(converter=str)
def __add__(self, other: ErrorArtifact) -> ErrorArtifact:
return ErrorArtifact(self.value + other.... | swarms-master | swarms/artifacts/error_artifact.py |
from __future__ import annotations
import pprint
import json
from typing import Optional
from pydantic import BaseModel, Field, StrictStr
class Artifact(BaseModel):
"""
Artifact that has the task has been produced
"""
artifact_id: StrictStr = Field(
...,
description="ID of the artifa... | swarms-master | swarms/artifacts/main.py |
from __future__ import annotations
import json
import uuid
from abc import ABC, abstractmethod
from attr import define, field, Factory
from marshmallow import class_registry
from marshmallow.exceptions import RegistryError
@define
class BaseArtifact(ABC):
id: str = field(default=Factory(lambda: uuid.uuid4().hex),... | swarms-master | swarms/artifacts/base.py |
#props to shroominic
from swarms.tools.base import Tool, ToolException
from typing import Callable, Any, List
from codeinterpreterapi import CodeInterpreterSession, File, ToolException
class CodeInterpreter(Tool):
def __init__(self, name: str, description: str):
super().__init__(name, description, self.run... | swarms-master | swarms/tools/code_intepretor.py |
# from swarms.tools.base import BaseTool, Tool, StructuredTool, ToolWrapper, BaseToolSet, ToolCreator, GlobalToolsCreator, SessionToolsCreator, ToolsFactory
# from swarms.tools.autogpt import pushd, process_csv, async_load_playwright, run_async, browse_web_page, WebpageQATool, web_search, query_website_tool
# from swar... | swarms-master | swarms/tools/__init__.py |
import asyncio
import os
# Tools
from contextlib import contextmanager
from typing import Optional
import pandas as pd
from langchain.agents import tool
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain... | swarms-master | swarms/tools/autogpt.py |
import os
import uuid
import numpy as np
import torch
from diffusers import (
EulerAncestralDiscreteScheduler,
StableDiffusionInpaintPipeline,
StableDiffusionInstructPix2PixPipeline,
StableDiffusionPipeline,
)
from PIL import Image
from transformers import (
BlipForConditionalGeneration,
BlipFo... | swarms-master | swarms/tools/mm_models.py |
import requests
from bs4 import BeautifulSoup
from swarms.tools.base import BaseToolSet, tool
from swarms.utils.logger import logger
class RequestsGet(BaseToolSet):
@tool(
name="Requests Get",
description="A portal to the internet. "
"Use this when you need to get specific content from a... | swarms-master | swarms/tools/requests.py |
import os
import re
import signal
import subprocess
import time
from datetime import datetime
from pathlib import Path
from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
from ptrace.debugger import (
NewProcessEvent,
ProcessExecution,
ProcessExit,
ProcessSignal,
PtraceDebugge... | swarms-master | swarms/tools/developer.py |
from langchain.tools import tool
from swarms.tools.base import BaseToolSet, SessionGetter, ToolScope
from swarms.utils.logger import logger
class ExitConversation(BaseToolSet):
@tool(
name="Exit Conversation",
description="A tool to exit the conversation. "
"Use this when you want to exit... | swarms-master | swarms/tools/exit_conversation.py |
from __future__ import annotations
from enum import Enum
from abc import ABC, abstractmethod
from typing import Any, Callable, Optional, Type, Tuple
from pydantic import BaseModel
from langchain.llms.base import BaseLLM
from langchain.agents.agent import AgentExecutor
from langchain.agents import load_tools
class T... | swarms-master | swarms/tools/base.py |
from langchain.agents.agent_toolkits import FileManagementToolkit
from tempfile import TemporaryDirectory
# We'll make a temporary directory to avoid clutter
working_directory = TemporaryDirectory()
toolkit = FileManagementToolkit(
root_dir=str(working_directory.name)
) # If you don't provide a root_dir, operati... | swarms-master | swarms/tools/file_mangagement.py |
swarms-master | swarms/embeddings/__init__.py | |
import logging
from typing import Union
from pegasus import Pegasus
# import oceandb
# from oceandb.utils.embedding_functions import MultiModalEmbeddingfunction
class PegasusEmbedding:
def __init__(
self,
modality: str,
multi_process: bool = False,
n_processes: ... | swarms-master | swarms/embeddings/pegasus.py |
from __future__ import annotations
import logging
import warnings
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import numpy as np
from pydantic import BaseModel, Extra, Field, root_validator
from tenacity import (
AsyncRe... | swarms-master | swarms/embeddings/openai.py |
"""Interface for embedding models."""
from abc import ABC, abstractmethod
from typing import List
class Embeddings(ABC):
"""Interface for embedding models."""
@abstractmethod
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed search docs."""
@abstractmethod
def em... | swarms-master | swarms/embeddings/base.py |
import uuid
from abc import ABC
from typing import Any, Dict, List, Optional
from swarms.memory.schemas import Artifact, Status
from swarms.memory.schemas import Step as APIStep
from swarms.memory.schemas import Task as APITask
class Step(APIStep):
additional_properties: Optional[Dict[str, str]] = None
class Ta... | swarms-master | swarms/memory/db.py |
swarms-master | swarms/memory/__init__.py | |
from __future__ import annotations
from enum import Enum
from typing import Any, List, Optional
from pydantic import BaseModel, Field
class TaskInput(BaseModel):
__root__: Any = Field(
...,
description="The input parameters for the task. Any value is allowed.",
example='{\n"debug": false... | swarms-master | swarms/memory/schemas.py |
#init ocean
# TODO upload ocean to pip and config it to the abstract class
import logging
from typing import Union, List
import oceandb
from oceandb.utils.embedding_function import MultiModalEmbeddingFunction
class OceanDB:
def __init__(self):
try:
self.client = oceandb.Client()
p... | swarms-master | swarms/memory/ocean.py |
"""Wrapper around ChromaDB embeddings platform."""
from __future__ import annotations
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
import numpy as np
from langchain.docstore.document import Document
fr... | swarms-master | swarms/memory/chroma.py |
from typing import Any, Dict, List
from swarms.memory.base_memory import BaseChatMemory, get_prompt_input_key
from swarms.memory.base import VectorStoreRetriever
class AgentMemory(BaseChatMemory):
retriever: VectorStoreRetriever
"""VectorStoreRetriever object to connect to."""
@property
def memory_va... | swarms-master | swarms/agents/memory.py |
"""Agent Infrastructure, models, memory, utils, tools"""
###########
# #tools
# from swarms.tools.base import BaseTool, Tool, StructuredTool, ToolWrapper, BaseToolSet, ToolCreator, GlobalToolsCreator, SessionToolsCreator, ToolsFactory
# from swarms.tools.autogpt import pushd, process_csv, async_load_playwright, run_a... | swarms-master | swarms/agents/__init__.py |
import logging
import os
import time
import openai
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class OpenAI:
def __init__(
self,
api_key,
strategy="cot",
evaluation_strategy="va... | swarms-master | swarms/agents/aot.py |
from __future__ import annotations
from typing import List, Optional
from langchain.chains.llm import LLMChain
from swarms.agents.utils.Agent import AgentOutputParser
from swarms.agents.utils.human_input import HumanInputRun
from swarms.memory.base import VectorStoreRetriever
from swarms.memory.base_memory import Ba... | swarms-master | swarms/agents/agent.py |
from abc import ABC, abstractmethod
from agent_protocol import Agent, Step, Task
class AbstractAgent:
@staticmethod
async def plan(step: Step) -> Step:
task = await Agent.db.get_task(step.task_id)
steps = generate_steps(task.input)
last_step = steps[-1]
for step in steps[:-1]:... | swarms-master | swarms/agents/base.py |
import logging
import os
from typing import Optional
import faiss
from langchain import LLMChain, OpenAI, PromptTemplate
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
from langchain.docstore import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import F... | swarms-master | swarms/boss/boss_node.py |
swarms-master | swarms/boss/__init__.py | |
from abc import ABC
from typing import Any, Dict, List, Literal, TypedDict, Union, cast
from pydantic import BaseModel, PrivateAttr
class BaseSerialized(TypedDict):
"""Base class for serialized objects."""
lc: int
id: List[str]
class SerializedConstructor(BaseSerialized):
"""Serialized constructor... | swarms-master | swarms/utils/serializable.py |
# from swarms.utils.ansi import Code, Color, Style, ANSI, dim_multiline
# from swarms.utils.logger import logger
# from swarms.utils.utils import FileType, AbstractUploader, StaticUploader, BaseHandler, FileHandler, CsvToDataframe
"""Swarms utils""" | swarms-master | swarms/utils/__init__.py |
import logging
logger = logging.getLogger()
formatter = logging.Formatter("%(message)s")
ch = logging.StreamHandler()
ch.setFormatter(formatter)
logger.addHandler(ch)
logger.setLevel(logging.DEBUG)
| swarms-master | swarms/utils/logger.py |
import os
import shutil
from pathlib import Path
# from env import DotEnv
from swarms.utils.main import AbstractUploader
class StaticUploader(AbstractUploader):
def __init__(self, server: str, path: Path, endpoint: str):
self.server = server
self.path = path
self.endpoint = endpoint
... | swarms-master | swarms/utils/static.py |
import os
import random
import uuid
import numpy as np
def seed_everything(seed):
random.seed(seed)
np.random.seed(seed)
try:
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
except:
pass
return seed
def cut_dialogue_history(history_memory, ... | swarms-master | swarms/utils/main.py |
import time
import logging
import threading
import functools
import warnings
def log_decorator(func):
def wrapper(*args, **kwargs):
logging.info(f'Entering {func.__name__}')
result = func(*args, **kwargs)
logging.info(f'Exiting {func.__name__}')
return result
return wrapper
d... | swarms-master | swarms/utils/decorators.py |
# from __future__ import annotations
# import logging
# from swarms.utils.logger import logger
# from typing import Any, Callable, Dict, List, Optional
# from pydantic import BaseModel, model_validator
# from tenacity import (
# before_sleep_log,
# retry,
# retry_if_exception_type,
# stop_after_attemp... | swarms-master | swarms/models/palm.py |
from transformers import AutoTokenizer, AutoModelForCausalLM
class Petals:
"""Petals Bloom models."""
def __init__(
self,
model_name="bigscience/bloom-petals",
temperature=0.7,
max_new_tokens=256,
top_p=0.9,
top_k=None,
... | swarms-master | swarms/models/petals.py |
from swarms.models.anthropic import Anthropic
from swarms.models.huggingface import HFLLM
# from swarms.models.palm import GooglePalm
from swarms.models.petals import Petals
#from swarms.models.openai import OpenAIChat | swarms-master | swarms/models/__init__.py |
# from __future__ import annotations
# import logging
# import sys
# import warnings
# from typing import (
# AbstractSet,
# Any,
# AsyncIterator,
# Collection,
# Dict,
# Iterator,
# List,
# Literal,
# Mapping,
# Optional,
# Tuple,
# Union,
# )
# from langchain.callback... | swarms-master | swarms/models/openai.py |
import logging
import torch
from torch.multiprocessing import set_start_method
from torch.nn.parallel import DistributedDataParallel as DDP
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
GPTQConfig,
)
#set up logging
logging.basicConfig(level=logging.INFO)
logger =... | swarms-master | swarms/models/huggingface.py |
import requests
import os
class Anthropic:
"""Anthropic large language models."""
def __init__(
self,
model="claude-2",
max_tokens_to_sample=256,
temperature=None,
top_k=None,
top_p=None,
streaming=False,
de... | swarms-master | swarms/models/anthropic.py |
from abc import ABC, abstractmethod
class AbstractModel(ABC):
#abstract base class for language models
@abstractmethod
def generate(self, prompt):
#generate text using language model
pass
def chat(self, prompt, history):
pass
| swarms-master | swarms/models/base.py |
import json
import re
from abc import abstractmethod
from typing import Dict, NamedTuple
class AgentAction(NamedTuple):
"""Action returned by AgentOutputParser."""
name: str
args: Dict
class BaseAgentOutputParser:
"""Base Output parser for Agent."""
@abstractmethod
def parse(self, text: str) ... | swarms-master | swarms/models/prompts/agent_output_parser.py |
def generate_agent_role_prompt(agent):
""" Generates the agent role prompt.
Args: agent (str): The type of the agent.
Returns: str: The agent role prompt.
"""
prompts = {
"Finance Agent": "You are a seasoned finance analyst AI assistant. Your primary goal is to compose comprehensive, astute,... | swarms-master | swarms/models/prompts/agent_prompts.py |
import time
from typing import Any, Callable, List
from swarms.models.prompts.agent_prompt_generator import get_prompt
class TokenUtils:
@staticmethod
def count_tokens(text: str) -> int:
return len(text.split())
class PromptConstructor:
def __init__(self, ai_name: str, ai_role: str, tools):
... | swarms-master | swarms/models/prompts/agent_prompt_auto.py |
# """PROMPTS MULTI MODAL""" | swarms-master | swarms/models/prompts/__init__.py |
import json
from typing import List
class PromptGenerator:
"""A class for generating custom prompt strings."""
def __init__(self) -> None:
"""Initialize the PromptGenerator object."""
self.constraints: List[str] = []
self.commands: List[str] = []
self.resources: List[str] = []
... | swarms-master | swarms/models/prompts/agent_prompt.py |
import json
from typing import List
from langchain.tools.base import BaseTool
FINISH_NAME = "finish"
class PromptGenerator:
"""A class for generating custom prompt strings.
Does this based on constraints, commands, resources, and performance evaluations.
"""
def __init__(self) -> None:
"""... | swarms-master | swarms/models/prompts/agent_prompt_generator.py |
from __future__ import annotations
from abc import abstractmethod
from typing import Any, Dict, List, Sequence
from pydantic import Field
class Message:
"""
The base abstract Message class.
Messages are the inputs and outputs of ChatModels.
"""
def __init__(self, content: str, role: str, additio... | swarms-master | swarms/models/prompts/chat_prompt.py |
from __future__ import annotations
from abc import abstractmethod
from typing import TYPE_CHECKING, Any, Dict, List, Sequence
from pydantic import Field
from swarms.utils.serializable import Serializable
if TYPE_CHECKING:
from langchain.prompts.chat import ChatPromptTemplate
def get_buffer_string(
messages... | swarms-master | swarms/models/prompts/base.py |
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.
Only use the text between first and second '===' to ... | swarms-master | swarms/models/prompts/prebuild/sales_prompts.py |
SUMMARIZE_PROMPT = """
Your output should use the following template:
### Summary
### Facts
- [Emoji] Bulletpoint
Your task is to summarize the text I give you in up to seven concise bullet points and start with a short, high-quality
summary. Pick a suitable emoji for every bullet point. Your response should be in {... | swarms-master | swarms/models/prompts/prebuild/summaries_prompts.py |
swarms-master | swarms/models/prompts/prebuild/__init__.py | |
PROJECT_MANAGR_PROMPT_TEMPLATE = '''
# Context
{context}
## Format example
{format_example}
-----
Role: You are a project manager; the goal is to break down tasks according to PRD/technical design, give a task list, and analyze task dependencies to start with the prerequisite modules
Requirements: Based on the context... | swarms-master | swarms/models/prompts/prebuild/project_manager.py |
ERROR_PROMPT = "An error has occurred for the following text: \n{promptedQuery} Please explain this error.\n {e}"
IMAGE_PROMPT = """
provide a figure named {filename}. The description is: {description}.
Please understand and answer the image based on this information. The image understanding is complete, so don't try... | swarms-master | swarms/models/prompts/prebuild/multi_modal_prompts.py |
swarms-master | swarms/hivemind/__init__.py | |
# workers in unison
#kye gomez jul 13 4:01pm, can scale up the number of swarms working on a probkem with `hivemind(swarms=4, or swarms=auto which will scale the agents depending on the complexity)`
#this needs to change, we need to specify exactly what needs to be imported
# add typechecking, documentation, and deepe... | swarms-master | swarms/hivemind/hivemind.py |
from __future__ import annotations
import json
import pprint
import uuid
from abc import ABC, abstractmethod
from enum import Enum
from typing import Any, Optional
from swarms.artifacts.main import Artifact
from pydantic import BaseModel, Field, StrictStr, conlist
from swarms.artifacts.error_artifact import ErrorArt... | swarms-master | swarms/structs/task.py |
swarms-master | swarms/structs/__init__.py | |
from __future__ import annotations
from typing import Any, Dict, List, Optional, Union
from swarms.artifacts.error_artifacts import ErrorArtifact
from swarms.structs.task import BaseTask
import concurrent.futures
class StringTask(BaseTask):
def __init__(
self,
task
):
super().__init__... | swarms-master | swarms/structs/workflow.py |
# from swarms.workers.multi_modal_workers.multi_modal_agent import MultiModalVisualAgent
from swarms.workers.multi_modal_workers.multi_modal_agent import MultiModalVisualAgent
from langchain.tools import BaseTool
class MultiModalVisualAgentTool(BaseTool):
name = "multi_visual_agent"
description = "Multi-Modal ... | swarms-master | swarms/workers/multi_modal_worker.py |
import faiss
from langchain.chat_models import ChatOpenAI
from langchain.docstore import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings
from langchain.tools.human.tool import HumanInputRun
from langchain.vectorstores import FAISS
from langchain_experimental.autonomous_agents import AutoGPT
from swa... | swarms-master | swarms/workers/worker.py |
from swarms.agents.aot import AoTAgent
task = "Create GPT-2"
system = f"""
You are Quoc V. Le, a computer scientist and artificial intelligence researcher who is
widely regarded as one of the leading experts in deep learning and neural network architecture search.
Your work in this area has focused on developin... | swarms-master | swarms/workers/neural_architecture_search_worker.py |
swarms-master | swarms/workers/__init__.py | |
import os
import re
import logging
from pathlib import Path
from typing import Dict, List
from swarms.agents.utils.agent_creator import AgentCreator
from swarms.utils.main import BaseHandler, FileHandler, FileType
from swarms.tools.main import ExitConversation, RequestsGet, CodeEditor, Terminal
from swarms.utils.main ... | swarms-master | swarms/workers/worker_ultra_node.py |
import enum
import os
from pathlib import Path
import sys
import time
import shutil
import argparse
import asyncio
import re
from typing import List, Optional, Callable, Any
import openai
from openai_function_call import openai_function
from tenacity import retry, stop_after_attempt, wait_random_exponential
import log... | swarms-master | swarms/workers/developer_agent.py |
from langchain.tools import tool
from swarms.workers.multi_modal_workers.omni_agent.omni_chat import chat_huggingface
class OmniWorkerAgent:
def __init__(
self,
api_key,
api_endpoint, api_type
):
self.api_key = api_key
self.api_endpoint = api_endpoint
... | swarms-master | swarms/workers/omni_worker.py |
# coding: utf-8
import argparse
import inspect
import math
import os
import random
import re
import uuid
import cv2
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import torch
import wget
from controlnet_aux import HEDdetector, MLSDdetector, OpenposeDetector
from diffusers import (
ControlN... | swarms-master | swarms/workers/multi_modal_workers/multi_modal_agent.py |
swarms-master | swarms/workers/multi_modal_workers/__init__.py | |
import argparse
import logging
import random
import uuid
import numpy as np
from transformers import pipeline
from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
from diffusers import DiffusionPipeline, DPMSolverMult... | swarms-master | swarms/workers/multi_modal_workers/omni_agent/model_server.py |
swarms-master | swarms/workers/multi_modal_workers/omni_agent/__init__.py | |
import tiktoken
encodings = {
"gpt-4": tiktoken.get_encoding("cl100k_base"),
"gpt-4-32k": tiktoken.get_encoding("cl100k_base"),
"gpt-3.5-turbo": tiktoken.get_encoding("cl100k_base"),
"gpt-3.5-turbo-0301": tiktoken.get_encoding("cl100k_base"),
"text-davinci-003": tiktoken.get_encoding("p50k_base"),
... | swarms-master | swarms/workers/multi_modal_workers/omni_agent/get_token_ids.py |
import base64
import copy
from io import BytesIO
import io
import os
import random
import time
import traceback
import uuid
import requests
import re
import json
import logging
import argparse
import yaml
from PIL import Image, ImageDraw
from diffusers.utils import load_image
from pydub import AudioSegment
import threa... | swarms-master | swarms/workers/multi_modal_workers/omni_agent/omni_chat.py |
# from .GroundingDINO.groundingdino.datasets.transforms import T
# from .GroundingDINO.groundingdino.models import build_model
# from .GroundingDINO.groundingdino.util import box_ops, SLConfig
# from .GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
# from .segment_anything.segmen... | swarms-master | swarms/workers/models/__init__.py |
swarms-master | swarms/workers/models/segment_anything/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from setuptools import find_packages, setup
setup(
name="segment_anything",
version="1.0",
install_requires=[... | swarms-master | swarms/workers/models/segment_anything/setup.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from segment_anything.modeling import Sam
from typing import Optional, Tuple
from .util... | swarms-master | swarms/workers/models/segment_anything/segment_anything/predictor.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... | swarms-master | swarms/workers/models/segment_anything/segment_anything/build_sam.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing impor... | swarms-master | swarms/workers/models/segment_anything/segment_anything/automatic_mask_generator.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| swarms-master | swarms/workers/models/segment_anything/segment_anything/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import math
from copy import deepcopy
from itertools import product
from typing import An... | swarms-master | swarms/workers/models/segment_anything/segment_anything/utils/amg.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.transforms.functional import resize,... | swarms-master | swarms/workers/models/segment_anything/segment_anything/utils/transforms.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from torch.nn import functional as F
from typing import Tuple
from ..modeling import ... | swarms-master | swarms/workers/models/segment_anything/segment_anything/utils/onnx.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| swarms-master | swarms/workers/models/segment_anything/segment_anything/utils/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| swarms-master | swarms/workers/models/segment_anything/segment_anything/modeling/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
class MLPBlock(nn.Module):
def __init__(
self,
... | swarms-master | swarms/workers/models/segment_anything/segment_anything/modeling/common.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
clas... | swarms-master | swarms/workers/models/segment_anything/segment_anything/modeling/transformer.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common... | swarms-master | swarms/workers/models/segment_anything/segment_anything/modeling/image_encoder.py |
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