From f1600d2e433cf97ebd1e742b9a4ba0708fad4b71 Mon Sep 17 00:00:00 2001
From: Kye Gomez <kye@swarms.world>
Date: Sun, 26 Jan 2025 22:08:20 -0500
Subject: [PATCH] [DOCS][LUMO]
---
docs/mkdocs.yml | 6 +-
docs/swarms/examples/lumo.md | 63 ++++++++++++++++++
example.py | 15 +----
new_features_examples/lumo_example.py | 59 +++++++++++++++++
pyproject.toml | 4 +-
swarms/structs/agent.py | 29 ++++----
swarms/structs/agent_memory_manager.py | 14 ++--
swarms/structs/auto_swarm_builder.py | 15 ++---
swarms/structs/base_swarm.py | 16 +----
swarms/structs/hiearchical_swarm.py | 3 +-
swarms/structs/omni_agent_types.py | 4 +-
swarms/structs/swarm_builder.py | 15 +----
swarms/tools/json_former.py | 3 +-
swarms/tools/prebuilt/code_executor.py | 91 +++++++++++++++++++++++++-
swarms/utils/litellm_tokenizer.py | 6 ++
swarms/utils/litellm_wrapper.py | 3 +-
16 files changed, 252 insertions(+), 94 deletions(-)
create mode 100644 docs/swarms/examples/lumo.md
create mode 100644 new_features_examples/lumo_example.py
create mode 100644 swarms/utils/litellm_tokenizer.py
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 345f70f5..e70b8c5e 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -210,10 +210,12 @@ nav:
- OpenRouter: "swarms/examples/openrouter.md"
- XAI: "swarms/examples/xai.md"
- Swarms Tools:
- - Agent with HTX + CoinGecko: "swarms/examples/swarms_tools_htx.md"
- - Agent with HTX + CoinGecko Function Calling: "swarms/examples/swarms_tools_htx_gecko.md"
- Agent with Yahoo Finance: "swarms/examples/yahoo_finance.md"
- Twitter Agents: "swarms_tools/twitter.md"
+ - Blockchain Agents:
+ - Agent with HTX + CoinGecko: "swarms/examples/swarms_tools_htx.md"
+ - Agent with HTX + CoinGecko Function Calling: "swarms/examples/swarms_tools_htx_gecko.md"
+ - Lumo: "swarms/examples/lumo.md"
- Meme Agents:
- Bob The Builder: "swarms/examples/bob_the_builder.md"
- Meme Agent Builder: "swarms/examples/meme_agents.md"
diff --git a/docs/swarms/examples/lumo.md b/docs/swarms/examples/lumo.md
new file mode 100644
index 00000000..ba0d831b
--- /dev/null
+++ b/docs/swarms/examples/lumo.md
@@ -0,0 +1,63 @@
+# Lumo Example
+Introducing Lumo-70B-Instruct - the largest and most advanced AI model ever created for the Solana ecosystem. Built on Meta's groundbreaking LLaMa 3.3 70B Instruct foundation, this revolutionary model represents a quantum leap in blockchain-specific artificial intelligence. With an unprecedented 70 billion parameters and trained on the most comprehensive Solana documentation dataset ever assembled, Lumo-70B-Instruct sets a new standard for developer assistance in the blockchain space.
+
+
+- [Docs](https://huggingface.co/lumolabs-ai/Lumo-70B-Instruct)
+
+```python
+from swarms import Agent
+from transformers import LlamaForCausalLM, AutoTokenizer
+import torch
+from transformers import BitsAndBytesConfig
+
+class Lumo:
+ """
+ A class for generating text using the Lumo model with 4-bit quantization.
+ """
+ def __init__(self):
+ """
+ Initializes the Lumo model with 4-bit quantization and a tokenizer.
+ """
+ # Configure 4-bit quantization
+ bnb_config = BitsAndBytesConfig(
+ load_in_4bit=True,
+ bnb_4bit_quant_type="nf4",
+ bnb_4bit_compute_dtype=torch.float16,
+ llm_int8_enable_fp32_cpu_offload=True
+ )
+
+ self.model = LlamaForCausalLM.from_pretrained(
+ "lumolabs-ai/Lumo-70B-Instruct",
+ device_map="auto",
+ quantization_config=bnb_config,
+ use_cache=False,
+ attn_implementation="sdpa"
+ )
+ self.tokenizer = AutoTokenizer.from_pretrained("lumolabs-ai/Lumo-70B-Instruct")
+
+ def run(self, task: str) -> str:
+ """
+ Generates text based on the given prompt using the Lumo model.
+
+ Args:
+ prompt (str): The input prompt for the model.
+
+ Returns:
+ str: The generated text.
+ """
+ inputs = self.tokenizer(task, return_tensors="pt").to(self.model.device)
+ outputs = self.model.generate(**inputs, max_new_tokens=100)
+ return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+
+
+
+Agent(
+ agent_name="Solana-Analysis-Agent",
+ model_name=Lumo(),
+ max_loops="auto",
+ interactive=True,
+ streaming_on=True,
+).run("How do i create a smart contract in solana?")
+
+```
\ No newline at end of file
diff --git a/example.py b/example.py
index 075feb1c..db042f11 100644
--- a/example.py
+++ b/example.py
@@ -1,6 +1,3 @@
-import os
-
-from swarm_models import OpenAIChat
from swarms import Agent
from swarms.prompts.finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT,
@@ -9,16 +6,6 @@ from dotenv import load_dotenv
load_dotenv()
-# Get the OpenAI API key from the environment variable
-api_key = os.getenv("GROQ_API_KEY")
-
-# Model
-model = OpenAIChat(
- openai_api_base="https://api.groq.com/openai/v1",
- openai_api_key=api_key,
- model_name="llama-3.1-70b-versatile",
- temperature=0.1,
-)
# Initialize the agent
agent = Agent(
@@ -26,7 +13,7 @@ agent = Agent(
agent_description="Personal finance advisor agent",
system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
max_loops=1,
- llm=model,
+ model_name="gpt-4o",
dynamic_temperature_enabled=True,
user_name="swarms_corp",
retry_attempts=3,
diff --git a/new_features_examples/lumo_example.py b/new_features_examples/lumo_example.py
new file mode 100644
index 00000000..3363c702
--- /dev/null
+++ b/new_features_examples/lumo_example.py
@@ -0,0 +1,59 @@
+import torch
+from transformers import (
+ AutoTokenizer,
+ BitsAndBytesConfig,
+ LlamaForCausalLM,
+)
+
+from swarms import Agent
+
+
+class Lumo:
+ """
+ A class for generating text using the Lumo model with 4-bit quantization.
+ """
+ def __init__(self):
+ """
+ Initializes the Lumo model with 4-bit quantization and a tokenizer.
+ """
+ # Configure 4-bit quantization
+ bnb_config = BitsAndBytesConfig(
+ load_in_4bit=True,
+ bnb_4bit_quant_type="nf4",
+ bnb_4bit_compute_dtype=torch.float16,
+ llm_int8_enable_fp32_cpu_offload=True
+ )
+
+ self.model = LlamaForCausalLM.from_pretrained(
+ "lumolabs-ai/Lumo-70B-Instruct",
+ device_map="auto",
+ quantization_config=bnb_config,
+ use_cache=False,
+ attn_implementation="sdpa"
+ )
+ self.tokenizer = AutoTokenizer.from_pretrained("lumolabs-ai/Lumo-70B-Instruct")
+
+ def run(self, task: str) -> str:
+ """
+ Generates text based on the given prompt using the Lumo model.
+
+ Args:
+ prompt (str): The input prompt for the model.
+
+ Returns:
+ str: The generated text.
+ """
+ inputs = self.tokenizer(task, return_tensors="pt").to(self.model.device)
+ outputs = self.model.generate(**inputs, max_new_tokens=100)
+ return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+
+
+
+Agent(
+ agent_name="Solana-Analysis-Agent",
+ model_name=Lumo(),
+ max_loops="auto",
+ interactive=True,
+ streaming_on=True,
+).run("How do i create a smart contract in solana?")
diff --git a/pyproject.toml b/pyproject.toml
index 48405661..b70fe545 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "swarms"
-version = "6.9.7"
+version = "6.9.8"
description = "Swarms - TGSC"
license = "MIT"
authors = ["Kye Gomez <kye@apac.ai>"]
@@ -62,7 +62,6 @@ python = ">=3.10,<4.0"
asyncio = ">=3.4.3,<4.0"
toml = "*"
pypdf = "5.1.0"
-swarm-models = "*"
loguru = "*"
pydantic = "*"
tenacity = "*"
@@ -76,7 +75,6 @@ aiofiles = "*"
clusterops = "*"
# chromadb = "*"
rich = "*"
-pandas = "*"
# sentence-transformers = "*"
diff --git a/swarms/structs/agent.py b/swarms/structs/agent.py
index 00a0420d..e0427d44 100644
--- a/swarms/structs/agent.py
+++ b/swarms/structs/agent.py
@@ -23,7 +23,6 @@ import toml
import yaml
from loguru import logger
from pydantic import BaseModel
-from swarm_models.tiktoken_wrapper import TikTokenizer
from swarms.agents.ape_agent import auto_generate_prompt
from swarms.artifacts.main_artifact import Artifact
@@ -55,6 +54,7 @@ from swarms.utils.wrapper_clusterop import (
)
from swarms.telemetry.capture_sys_data import log_agent_data
from swarms.agents.agent_print import agent_print
+from swarms.utils.litellm_tokenizer import count_tokens
# Utils
@@ -439,7 +439,7 @@ class Agent:
self.time_created = time_created
self.data_memory = data_memory
self.load_yaml_path = load_yaml_path
- self.tokenizer = TikTokenizer()
+ self.tokenizer = tokenizer
self.auto_generate_prompt = auto_generate_prompt
self.rag_every_loop = rag_every_loop
self.plan_enabled = plan_enabled
@@ -563,9 +563,7 @@ class Agent:
max_loops=self.max_loops,
steps=self.short_memory.to_dict(),
full_history=self.short_memory.get_str(),
- total_tokens=self.tokenizer.count_tokens(
- self.short_memory.get_str()
- ),
+ total_tokens=count_tokens(self.short_memory.get_str()),
stopping_token=self.stopping_token,
interactive=self.interactive,
dynamic_temperature_enabled=self.dynamic_temperature_enabled,
@@ -1043,10 +1041,8 @@ class Agent:
self.agent_output.full_history = (
self.short_memory.get_str()
)
- self.agent_output.total_tokens = (
- self.tokenizer.count_tokens(
- self.short_memory.get_str()
- )
+ self.agent_output.total_tokens = count_tokens(
+ self.short_memory.get_str()
)
# Handle artifacts
@@ -1976,7 +1972,7 @@ class Agent:
)
# # Count the tokens
- # memory_token_count = self.tokenizer.count_tokens(
+ # memory_token_count = count_tokens(
# memory_retrieval
# )
# if memory_token_count > self.memory_chunk_size:
@@ -2065,7 +2061,7 @@ class Agent:
def check_available_tokens(self):
# Log the amount of tokens left in the memory and in the task
if self.tokenizer is not None:
- tokens_used = self.tokenizer.count_tokens(
+ tokens_used = count_tokens(
self.short_memory.return_history_as_string()
)
logger.info(
@@ -2076,7 +2072,7 @@ class Agent:
def tokens_checks(self):
# Check the tokens available
- tokens_used = self.tokenizer.count_tokens(
+ tokens_used = count_tokens(
self.short_memory.return_history_as_string()
)
out = self.check_available_tokens()
@@ -2140,13 +2136,10 @@ class Agent:
# Calculate token usage
# full_memory = self.short_memory.return_history_as_string()
- # prompt_tokens = self.tokenizer.count_tokens(full_memory)
- # completion_tokens = self.tokenizer.count_tokens(response)
+ # prompt_tokens = count_tokens(full_memory)
+ # completion_tokens = count_tokens(response)
# total_tokens = prompt_tokens + completion_tokens
- total_tokens = (
- self.tokenizer.count_tokens(task)
- + self.tokenizer.count_tokens(response),
- )
+ total_tokens = (count_tokens(task) + count_tokens(response),)
# # Get memory responses
# memory_responses = {
diff --git a/swarms/structs/agent_memory_manager.py b/swarms/structs/agent_memory_manager.py
index 0f506fc4..2b52013d 100644
--- a/swarms/structs/agent_memory_manager.py
+++ b/swarms/structs/agent_memory_manager.py
@@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional
import yaml
from pydantic import BaseModel
-from swarm_models.tiktoken_wrapper import TikTokenizer
+from swarms.utils.litellm_tokenizer import count_tokens
logger = logging.getLogger(__name__)
@@ -60,7 +60,7 @@ class MemoryManager:
long_term_memory: Optional[Any] = None,
):
self.config = config
- self.tokenizer = tokenizer or TikTokenizer()
+ self.tokenizer = tokenizer
self.long_term_memory = long_term_memory
# Initialize memories
@@ -86,7 +86,7 @@ class MemoryManager:
agent_name=agent_name,
session_id=session_id,
memory_type=memory_type,
- token_count=self.tokenizer.count_tokens(content),
+ token_count=count_tokens(content),
)
return MemoryEntry(content=content, metadata=metadata)
@@ -219,7 +219,7 @@ class MemoryManager:
self, text: str, max_tokens: int
) -> str:
"""Truncate text to fit within token limit"""
- current_tokens = self.tokenizer.count_tokens(text)
+ current_tokens = count_tokens(text)
if current_tokens <= max_tokens:
return text
@@ -230,7 +230,7 @@ class MemoryManager:
current_count = 0
for sentence in sentences:
- sentence_tokens = self.tokenizer.count_tokens(sentence)
+ sentence_tokens = count_tokens(sentence)
if current_count + sentence_tokens <= max_tokens:
result.append(sentence)
current_count += sentence_tokens
@@ -376,9 +376,7 @@ class MemoryManager:
agent_name="system",
session_id="long_term",
memory_type="long_term",
- token_count=self.tokenizer.count_tokens(
- content
- ),
+ token_count=count_tokens(content),
)
results.append(
MemoryEntry(
diff --git a/swarms/structs/auto_swarm_builder.py b/swarms/structs/auto_swarm_builder.py
index 16e1f5b9..76fcbce9 100644
--- a/swarms/structs/auto_swarm_builder.py
+++ b/swarms/structs/auto_swarm_builder.py
@@ -2,9 +2,11 @@ import os
from typing import List
from pydantic import BaseModel, Field
-from swarm_models import OpenAIFunctionCaller, OpenAIChat
from swarms.structs.agent import Agent
+from swarms.structs.meme_agent_persona_generator import (
+ OpenAIFunctionCaller,
+)
from swarms.structs.swarm_router import SwarmRouter
from swarms.utils.loguru_logger import initialize_logger
from swarms.structs.agents_available import showcase_available_agents
@@ -64,15 +66,6 @@ class SwarmConfig(BaseModel):
)
-# Get the OpenAI API key from the environment variable
-api_key = os.getenv("OPENAI_API_KEY")
-
-# Create an instance of the OpenAIChat class
-model = OpenAIChat(
- openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
-)
-
-
BOSS_SYSTEM_PROMPT = """
Manage a swarm of worker agents to efficiently serve the user by deciding whether to create new agents or delegate tasks. Ensure operations are efficient and effective.
@@ -248,7 +241,7 @@ class AutoSwarmBuilder:
agent_name=agent_name,
description=agent_description,
system_prompt=agent_system_prompt,
- llm=model,
+ model_name="gpt-4o",
max_loops=max_loops,
autosave=True,
dashboard=False,
diff --git a/swarms/structs/base_swarm.py b/swarms/structs/base_swarm.py
index 277f2d4f..4e26c0b3 100644
--- a/swarms/structs/base_swarm.py
+++ b/swarms/structs/base_swarm.py
@@ -20,11 +20,6 @@ from swarms.structs.agent import Agent
from swarms.structs.conversation import Conversation
from swarms.structs.omni_agent_types import AgentType
from pydantic import BaseModel
-from swarms.utils.pandas_utils import (
- dict_to_dataframe,
- display_agents_info,
- pydantic_model_to_dataframe,
-)
from swarms.utils.loguru_logger import initialize_logger
logger = initialize_logger(log_folder="base_swarm")
@@ -795,17 +790,10 @@ class BaseSwarm(ABC):
Returns:
None
"""
- display_agents_info(self.agents)
+ ...
def agents_to_dataframe(self):
"""
Convert agents to a pandas DataFrame.
"""
- data = [agent.agent_output.dict() for agent in self.agents]
- return dict_to_dataframe(data)
-
- def model_to_dataframe(self):
- """
- Convert the Pydantic model to a pandas DataFrame.
- """
- return pydantic_model_to_dataframe(self.output_schema)
+ ...
diff --git a/swarms/structs/hiearchical_swarm.py b/swarms/structs/hiearchical_swarm.py
index 4eac5c78..91c5a68d 100644
--- a/swarms/structs/hiearchical_swarm.py
+++ b/swarms/structs/hiearchical_swarm.py
@@ -6,7 +6,6 @@ from swarms.structs.base_swarm import BaseSwarm
from swarms.structs.agent import Agent
from swarms.structs.concat import concat_strings
from swarms.structs.agent_registry import AgentRegistry
-from swarm_models.base_llm import BaseLLM
from swarms.structs.conversation import Conversation
logger = initialize_logger(log_folder="hiearchical_swarm")
@@ -156,7 +155,7 @@ class HierarchicalAgentSwarm(BaseSwarm):
create_agents_on: bool = False,
template_worker_agent: Agent = None,
director_planning_prompt: str = None,
- template_base_worker_llm: BaseLLM = None,
+ template_base_worker_llm: Any = None,
swarm_history: str = None,
*args,
**kwargs,
diff --git a/swarms/structs/omni_agent_types.py b/swarms/structs/omni_agent_types.py
index 9a0f3f6a..0e5c09b6 100644
--- a/swarms/structs/omni_agent_types.py
+++ b/swarms/structs/omni_agent_types.py
@@ -4,12 +4,10 @@ from typing import (
Sequence,
Union,
)
-from swarm_models.base_llm import BaseLLM
-from swarm_models.base_multimodal_model import BaseMultiModalModel
from swarms.structs.agent import Agent
# Unified type for agent
-AgentType = Union[Agent, Callable, Any, BaseLLM, BaseMultiModalModel]
+AgentType = Union[Agent, Callable, Any]
# List of agents
AgentListType = Sequence[AgentType]
diff --git a/swarms/structs/swarm_builder.py b/swarms/structs/swarm_builder.py
index eb11474d..aa199947 100644
--- a/swarms/structs/swarm_builder.py
+++ b/swarms/structs/swarm_builder.py
@@ -5,7 +5,6 @@ from typing import List, Optional
from loguru import logger
from pydantic import BaseModel, Field
from pydantic.v1 import validator
-from swarm_models import OpenAIChat
from tenacity import (
retry,
stop_after_attempt,
@@ -295,18 +294,6 @@ class AutoSwarmBuilder:
},
)
- # Initialize OpenAI chat model
- try:
- self.chat_model = OpenAIChat(
- openai_api_key=self.api_key,
- model_name=self.model_name,
- )
- except Exception as e:
- logger.error(
- f"Failed to initialize OpenAI chat model: {str(e)}"
- )
- raise
-
def run(
self,
task: str,
@@ -444,7 +431,7 @@ class AutoSwarmBuilder:
agent_name=agent_name,
description=agent_description,
system_prompt=agent_system_prompt,
- llm=self.chat_model,
+ model_name="gpt-4o",
verbose=self.verbose,
dynamic_temperature_enabled=False,
return_step_meta=False,
diff --git a/swarms/tools/json_former.py b/swarms/tools/json_former.py
index 6e1358a9..f4dde387 100644
--- a/swarms/tools/json_former.py
+++ b/swarms/tools/json_former.py
@@ -8,7 +8,6 @@ from swarms.tools.logits_processor import (
OutputNumbersTokens,
StringStoppingCriteria,
)
-from swarm_models.base_llm import BaseLLM
from swarms.utils.auto_download_check_packages import (
auto_check_and_download_package,
)
@@ -59,7 +58,7 @@ class Jsonformer:
max_number_tokens: int = 6,
temperature: float = 1.0,
max_string_token_length: int = 10,
- llm: BaseLLM = None,
+ llm: Any = None,
):
self.model = model
self.tokenizer = tokenizer
diff --git a/swarms/tools/prebuilt/code_executor.py b/swarms/tools/prebuilt/code_executor.py
index 730b1935..089d0379 100644
--- a/swarms/tools/prebuilt/code_executor.py
+++ b/swarms/tools/prebuilt/code_executor.py
@@ -1,7 +1,96 @@
import os
import subprocess
from loguru import logger
-from swarm_models.tiktoken_wrapper import TikTokenizer
+
+try:
+ import tiktoken
+except ImportError:
+ print("tiktoken not found, installing...")
+ subprocess.run(["pip", "install", "tiktoken"])
+ import tiktoken
+
+import concurrent.futures
+from typing import List
+
+
+class TikTokenizer:
+ def __init__(
+ self,
+ model_name: str = "o200k_base",
+ ):
+ """
+ Initializes a TikTokenizer object.
+
+ Args:
+ model_name (str, optional): The name of the model to use for tokenization. Defaults to "gpt-4o".
+ """
+ try:
+ self.model_name = model_name
+ self.encoding = tiktoken.get_encoding(model_name)
+ except Exception as e:
+ raise ValueError(
+ f"Failed to initialize tokenizer with model '{model_name}': {str(e)}"
+ )
+
+ def encode(self, string: str) -> str:
+ """
+ Tokenizes a text string.
+
+ Args:
+ string (str): The input text string.
+
+ Returns:
+ str: The tokenized text string.
+ """
+ return self.encoding.encode(string)
+
+ def decode(self, tokens: List[int]) -> str:
+ """
+ Detokenizes a text string.
+
+ Args:
+ string (str): The input tokenized text string.
+
+ Returns:
+ str: The detokenized text string.
+ """
+ return self.encoding.decode(tokens)
+
+ def count_tokens(self, string: str) -> int:
+ """
+ Returns the number of tokens in a text string.
+
+ Args:
+ string (str): The input text string.
+
+ Returns:
+ int: The number of tokens in the text string.
+ """
+ num_tokens = 0
+
+ def count_tokens_in_chunk(chunk):
+ nonlocal num_tokens
+ num_tokens += len(self.encoding.encode(chunk))
+
+ # Split the string into chunks for parallel processing
+ chunks = [
+ string[i : i + 1000] for i in range(0, len(string), 1000)
+ ]
+
+ # Create a ThreadPoolExecutor with maximum threads
+ with concurrent.futures.ThreadPoolExecutor(
+ max_workers=10
+ ) as executor:
+ # Submit each chunk for processing
+ futures = [
+ executor.submit(count_tokens_in_chunk, chunk)
+ for chunk in chunks
+ ]
+
+ # Wait for all futures to complete
+ concurrent.futures.wait(futures)
+
+ return num_tokens
class CodeExecutor:
diff --git a/swarms/utils/litellm_tokenizer.py b/swarms/utils/litellm_tokenizer.py
new file mode 100644
index 00000000..1ccec6f9
--- /dev/null
+++ b/swarms/utils/litellm_tokenizer.py
@@ -0,0 +1,6 @@
+from litellm import encode
+
+
+def count_tokens(text: str, model: str = "gpt-4o") -> int:
+ """Count the number of tokens in the given text."""
+ return len(encode(model=model, text=text))
diff --git a/swarms/utils/litellm_wrapper.py b/swarms/utils/litellm_wrapper.py
index 9252966f..88d706fa 100644
--- a/swarms/utils/litellm_wrapper.py
+++ b/swarms/utils/litellm_wrapper.py
@@ -66,7 +66,7 @@ class LiteLLM:
return messages
- def run(self, task: str, tools: any = [], *args, **kwargs):
+ def run(self, task: str, tools: list = [], *args, **kwargs):
"""
Run the LLM model for the given task.
@@ -88,7 +88,6 @@ class LiteLLM:
stream=self.stream,
temperature=self.temperature,
max_tokens=self.max_tokens,
- tools=tools,
*args,
**kwargs,
)