diff --git a/.github/workflows/deploy-website.yml b/.github/workflows/deploy-website.yml
index cf0f89e6a7..0d5cd637dc 100644
--- a/.github/workflows/deploy-website.yml
+++ b/.github/workflows/deploy-website.yml
@@ -25,12 +25,12 @@ jobs:
-
-
+
+
Page Redirection
- If you are not redirected automatically, follow this link to the new documentation.
+ If you are not redirected automatically, follow this link to the new documentation.
EOF
diff --git a/autogen/_pydantic.py b/autogen/_pydantic.py
index a7caffe1d9..09d272508e 100644
--- a/autogen/_pydantic.py
+++ b/autogen/_pydantic.py
@@ -30,7 +30,7 @@ def type2schema(t: Any) -> JsonSchemaValue:
"""
return TypeAdapter(t).json_schema()
- def model_dump(model: BaseModel) -> Dict[str, Any]:
+ def model_dump(model: BaseModel) -> dict[str, Any]:
"""Convert a pydantic model to a dict
Args:
@@ -59,7 +59,7 @@ def model_dump_json(model: BaseModel) -> str:
from pydantic import schema_of
from pydantic.typing import evaluate_forwardref as evaluate_forwardref # type: ignore[no-redef]
- JsonSchemaValue = Dict[str, Any] # type: ignore[misc]
+ JsonSchemaValue = dict[str, Any] # type: ignore[misc]
def type2schema(t: Any) -> JsonSchemaValue:
"""Convert a type to a JSON schema
@@ -75,6 +75,7 @@ def type2schema(t: Any) -> JsonSchemaValue:
return {"type": "null"}
elif get_origin(t) is Union:
return {"anyOf": [type2schema(tt) for tt in get_args(t)]}
+ # we need to support both syntaxes for Tuple
elif get_origin(t) in [Tuple, tuple]:
prefixItems = [type2schema(tt) for tt in get_args(t)]
return {
@@ -92,7 +93,7 @@ def type2schema(t: Any) -> JsonSchemaValue:
return d
- def model_dump(model: BaseModel) -> Dict[str, Any]:
+ def model_dump(model: BaseModel) -> dict[str, Any]:
"""Convert a pydantic model to a dict
Args:
diff --git a/autogen/agentchat/agent.py b/autogen/agentchat/agent.py
index 3f2a494564..655ad388f1 100644
--- a/autogen/agentchat/agent.py
+++ b/autogen/agentchat/agent.py
@@ -28,7 +28,7 @@ def description(self) -> str:
def send(
self,
- message: Union[Dict[str, Any], str],
+ message: Union[dict[str, Any], str],
recipient: "Agent",
request_reply: Optional[bool] = None,
) -> None:
@@ -44,7 +44,7 @@ def send(
async def a_send(
self,
- message: Union[Dict[str, Any], str],
+ message: Union[dict[str, Any], str],
recipient: "Agent",
request_reply: Optional[bool] = None,
) -> None:
@@ -60,7 +60,7 @@ async def a_send(
def receive(
self,
- message: Union[Dict[str, Any], str],
+ message: Union[dict[str, Any], str],
sender: "Agent",
request_reply: Optional[bool] = None,
) -> None:
@@ -75,7 +75,7 @@ def receive(
async def a_receive(
self,
- message: Union[Dict[str, Any], str],
+ message: Union[dict[str, Any], str],
sender: "Agent",
request_reply: Optional[bool] = None,
) -> None:
@@ -91,10 +91,10 @@ async def a_receive(
def generate_reply(
self,
- messages: Optional[List[Dict[str, Any]]] = None,
+ messages: Optional[list[dict[str, Any]]] = None,
sender: Optional["Agent"] = None,
**kwargs: Any,
- ) -> Union[str, Dict[str, Any], None]:
+ ) -> Union[str, dict[str, Any], None]:
"""Generate a reply based on the received messages.
Args:
@@ -109,10 +109,10 @@ def generate_reply(
async def a_generate_reply(
self,
- messages: Optional[List[Dict[str, Any]]] = None,
+ messages: Optional[list[dict[str, Any]]] = None,
sender: Optional["Agent"] = None,
**kwargs: Any,
- ) -> Union[str, Dict[str, Any], None]:
+ ) -> Union[str, dict[str, Any], None]:
"""(Async) Generate a reply based on the received messages.
Args:
diff --git a/autogen/agentchat/assistant_agent.py b/autogen/agentchat/assistant_agent.py
index abae2fb9c2..f87fc9dcd9 100644
--- a/autogen/agentchat/assistant_agent.py
+++ b/autogen/agentchat/assistant_agent.py
@@ -41,8 +41,8 @@ def __init__(
self,
name: str,
system_message: Optional[str] = DEFAULT_SYSTEM_MESSAGE,
- llm_config: Optional[Union[Dict, Literal[False]]] = None,
- is_termination_msg: Optional[Callable[[Dict], bool]] = None,
+ llm_config: Optional[Union[dict, Literal[False]]] = None,
+ is_termination_msg: Optional[Callable[[dict], bool]] = None,
max_consecutive_auto_reply: Optional[int] = None,
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "NEVER",
description: Optional[str] = None,
diff --git a/autogen/agentchat/chat.py b/autogen/agentchat/chat.py
index f105b63a31..5f1e18a511 100644
--- a/autogen/agentchat/chat.py
+++ b/autogen/agentchat/chat.py
@@ -18,7 +18,7 @@
from .utils import consolidate_chat_info
logger = logging.getLogger(__name__)
-Prerequisite = Tuple[int, int]
+Prerequisite = tuple[int, int]
@dataclass
@@ -27,21 +27,21 @@ class ChatResult:
chat_id: int = None
"""chat id"""
- chat_history: List[Dict[str, Any]] = None
+ chat_history: list[dict[str, Any]] = None
"""The chat history."""
summary: str = None
"""A summary obtained from the chat."""
- cost: Dict[str, dict] = None # keys: "usage_including_cached_inference", "usage_excluding_cached_inference"
+ cost: dict[str, dict] = None # keys: "usage_including_cached_inference", "usage_excluding_cached_inference"
"""The cost of the chat.
The value for each usage type is a dictionary containing cost information for that specific type.
- "usage_including_cached_inference": Cost information on the total usage, including the tokens in cached inference.
- "usage_excluding_cached_inference": Cost information on the usage of tokens, excluding the tokens in cache. No larger than "usage_including_cached_inference".
"""
- human_input: List[str] = None
+ human_input: list[str] = None
"""A list of human input solicited during the chat."""
-def _validate_recipients(chat_queue: List[Dict[str, Any]]) -> None:
+def _validate_recipients(chat_queue: list[dict[str, Any]]) -> None:
"""
Validate recipients exits and warn repetitive recipients.
"""
@@ -56,7 +56,7 @@ def _validate_recipients(chat_queue: List[Dict[str, Any]]) -> None:
)
-def __create_async_prerequisites(chat_queue: List[Dict[str, Any]]) -> List[Prerequisite]:
+def __create_async_prerequisites(chat_queue: list[dict[str, Any]]) -> list[Prerequisite]:
"""
Create list of Prerequisite (prerequisite_chat_id, chat_id)
"""
@@ -73,7 +73,7 @@ def __create_async_prerequisites(chat_queue: List[Dict[str, Any]]) -> List[Prere
return prerequisites
-def __find_async_chat_order(chat_ids: Set[int], prerequisites: List[Prerequisite]) -> List[int]:
+def __find_async_chat_order(chat_ids: set[int], prerequisites: list[Prerequisite]) -> list[int]:
"""Find chat order for async execution based on the prerequisite chats
args:
@@ -122,7 +122,7 @@ def _post_process_carryover_item(carryover_item):
return str(carryover_item)
-def __post_carryover_processing(chat_info: Dict[str, Any]) -> None:
+def __post_carryover_processing(chat_info: dict[str, Any]) -> None:
iostream = IOStream.get_default()
if "message" not in chat_info:
@@ -158,7 +158,7 @@ def __post_carryover_processing(chat_info: Dict[str, Any]) -> None:
iostream.print(colored("\n" + "*" * 80, "blue"), flush=True, sep="")
-def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
+def initiate_chats(chat_queue: list[dict[str, Any]]) -> list[ChatResult]:
"""Initiate a list of chats.
Args:
chat_queue (List[Dict]): A list of dictionaries containing the information about the chats.
@@ -234,7 +234,7 @@ def _on_chat_future_done(chat_future: asyncio.Future, chat_id: int):
async def _dependent_chat_future(
- chat_id: int, chat_info: Dict[str, Any], prerequisite_chat_futures: Dict[int, asyncio.Future]
+ chat_id: int, chat_info: dict[str, Any], prerequisite_chat_futures: dict[int, asyncio.Future]
) -> asyncio.Task:
"""
Create an async Task for each chat.
@@ -272,7 +272,7 @@ async def _dependent_chat_future(
return chat_res_future
-async def a_initiate_chats(chat_queue: List[Dict[str, Any]]) -> Dict[int, ChatResult]:
+async def a_initiate_chats(chat_queue: list[dict[str, Any]]) -> dict[int, ChatResult]:
"""(async) Initiate a list of chats.
args:
diff --git a/autogen/agentchat/contrib/agent_builder.py b/autogen/agentchat/contrib/agent_builder.py
index 822e7176dc..1c235e52e7 100644
--- a/autogen/agentchat/contrib/agent_builder.py
+++ b/autogen/agentchat/contrib/agent_builder.py
@@ -22,7 +22,7 @@
logger = logging.getLogger(__name__)
-def _config_check(config: Dict):
+def _config_check(config: dict):
# check config loading
assert config.get("coding", None) is not None, 'Missing "coding" in your config.'
assert config.get("default_llm_config", None) is not None, 'Missing "default_llm_config" in your config.'
@@ -220,11 +220,11 @@ def __init__(
self.config_file_location = config_file_location
self.building_task: str = None
- self.agent_configs: List[Dict] = []
- self.open_ports: List[str] = []
- self.agent_procs: Dict[str, Tuple[sp.Popen, str]] = {}
- self.agent_procs_assign: Dict[str, Tuple[autogen.ConversableAgent, str]] = {}
- self.cached_configs: Dict = {}
+ self.agent_configs: list[dict] = []
+ self.open_ports: list[str] = []
+ self.agent_procs: dict[str, tuple[sp.Popen, str]] = {}
+ self.agent_procs_assign: dict[str, tuple[autogen.ConversableAgent, str]] = {}
+ self.cached_configs: dict = {}
self.max_agents = max_agents
@@ -236,8 +236,8 @@ def set_agent_model(self, model: str):
def _create_agent(
self,
- agent_config: Dict,
- member_name: List[str],
+ agent_config: dict,
+ member_name: list[str],
llm_config: dict,
use_oai_assistant: Optional[bool] = False,
) -> autogen.AssistantAgent:
@@ -366,14 +366,14 @@ def clear_all_agents(self, recycle_endpoint: Optional[bool] = True):
def build(
self,
building_task: str,
- default_llm_config: Dict,
+ default_llm_config: dict,
coding: Optional[bool] = None,
- code_execution_config: Optional[Dict] = None,
+ code_execution_config: Optional[dict] = None,
use_oai_assistant: Optional[bool] = False,
user_proxy: Optional[autogen.ConversableAgent] = None,
max_agents: Optional[int] = None,
**kwargs,
- ) -> Tuple[List[autogen.ConversableAgent], Dict]:
+ ) -> tuple[list[autogen.ConversableAgent], dict]:
"""
Auto build agents based on the building task.
@@ -496,15 +496,15 @@ def build_from_library(
self,
building_task: str,
library_path_or_json: str,
- default_llm_config: Dict,
+ default_llm_config: dict,
top_k: int = 3,
coding: Optional[bool] = None,
- code_execution_config: Optional[Dict] = None,
+ code_execution_config: Optional[dict] = None,
use_oai_assistant: Optional[bool] = False,
embedding_model: Optional[str] = "all-mpnet-base-v2",
user_proxy: Optional[autogen.ConversableAgent] = None,
**kwargs,
- ) -> Tuple[List[autogen.ConversableAgent], Dict]:
+ ) -> tuple[list[autogen.ConversableAgent], dict]:
"""
Build agents from a library.
The library is a list of agent configs, which contains the name and system_message for each agent.
@@ -551,7 +551,7 @@ def build_from_library(
try:
agent_library = json.loads(library_path_or_json)
except json.decoder.JSONDecodeError:
- with open(library_path_or_json, "r") as f:
+ with open(library_path_or_json) as f:
agent_library = json.load(f)
except Exception as e:
raise e
@@ -663,7 +663,7 @@ def build_from_library(
def _build_agents(
self, use_oai_assistant: Optional[bool] = False, user_proxy: Optional[autogen.ConversableAgent] = None, **kwargs
- ) -> Tuple[List[autogen.ConversableAgent], Dict]:
+ ) -> tuple[list[autogen.ConversableAgent], dict]:
"""
Build agents with generated configs.
@@ -731,7 +731,7 @@ def load(
config_json: Optional[str] = None,
use_oai_assistant: Optional[bool] = False,
**kwargs,
- ) -> Tuple[List[autogen.ConversableAgent], Dict]:
+ ) -> tuple[list[autogen.ConversableAgent], dict]:
"""
Load building configs and call the build function to complete building without calling online LLMs' api.
diff --git a/autogen/agentchat/contrib/agent_eval/agent_eval.py b/autogen/agentchat/contrib/agent_eval/agent_eval.py
index 479a58fc9c..d6f3711cbf 100644
--- a/autogen/agentchat/contrib/agent_eval/agent_eval.py
+++ b/autogen/agentchat/contrib/agent_eval/agent_eval.py
@@ -15,7 +15,7 @@
def generate_criteria(
- llm_config: Optional[Union[Dict, Literal[False]]] = None,
+ llm_config: Optional[Union[dict, Literal[False]]] = None,
task: Task = None,
additional_instructions: str = "",
max_round=2,
@@ -67,8 +67,8 @@ def generate_criteria(
def quantify_criteria(
- llm_config: Optional[Union[Dict, Literal[False]]] = None,
- criteria: List[Criterion] = None,
+ llm_config: Optional[Union[dict, Literal[False]]] = None,
+ criteria: list[Criterion] = None,
task: Task = None,
test_case: str = "",
ground_truth: str = "",
diff --git a/autogen/agentchat/contrib/agent_eval/criterion.py b/autogen/agentchat/contrib/agent_eval/criterion.py
index 9d089d08bb..9e682fcc95 100644
--- a/autogen/agentchat/contrib/agent_eval/criterion.py
+++ b/autogen/agentchat/contrib/agent_eval/criterion.py
@@ -21,8 +21,8 @@ class Criterion(BaseModel):
name: str
description: str
- accepted_values: List[str]
- sub_criteria: List[Criterion] = list()
+ accepted_values: list[str]
+ sub_criteria: list[Criterion] = list()
@staticmethod
def parse_json_str(criteria: str):
diff --git a/autogen/agentchat/contrib/agent_optimizer.py b/autogen/agentchat/contrib/agent_optimizer.py
index 2257cda69f..7291e5e4cd 100644
--- a/autogen/agentchat/contrib/agent_optimizer.py
+++ b/autogen/agentchat/contrib/agent_optimizer.py
@@ -217,7 +217,7 @@ def __init__(
)
self._client = autogen.OpenAIWrapper(**self.llm_config)
- def record_one_conversation(self, conversation_history: List[Dict], is_satisfied: bool = None):
+ def record_one_conversation(self, conversation_history: list[dict], is_satisfied: bool = None):
"""
record one conversation history.
Args:
@@ -234,10 +234,10 @@ def record_one_conversation(self, conversation_history: List[Dict], is_satisfied
], "The input is invalid. Please input 1 or 0. 1 represents satisfied. 0 represents not satisfied."
is_satisfied = True if reply == "1" else False
self._trial_conversations_history.append(
- {"Conversation {i}".format(i=len(self._trial_conversations_history)): conversation_history}
+ {f"Conversation {len(self._trial_conversations_history)}": conversation_history}
)
self._trial_conversations_performance.append(
- {"Conversation {i}".format(i=len(self._trial_conversations_performance)): 1 if is_satisfied else 0}
+ {f"Conversation {len(self._trial_conversations_performance)}": 1 if is_satisfied else 0}
)
def step(self):
@@ -290,8 +290,8 @@ def step(self):
incumbent_functions = self._update_function_call(incumbent_functions, actions)
remove_functions = list(
- set([key for dictionary in self._trial_functions for key in dictionary.keys()])
- - set([key for dictionary in incumbent_functions for key in dictionary.keys()])
+ {key for dictionary in self._trial_functions for key in dictionary.keys()}
+ - {key for dictionary in incumbent_functions for key in dictionary.keys()}
)
register_for_llm = []
diff --git a/autogen/agentchat/contrib/capabilities/generate_images.py b/autogen/agentchat/contrib/capabilities/generate_images.py
index 2dc9f22a2f..429a466945 100644
--- a/autogen/agentchat/contrib/capabilities/generate_images.py
+++ b/autogen/agentchat/contrib/capabilities/generate_images.py
@@ -73,7 +73,7 @@ class DalleImageGenerator:
def __init__(
self,
- llm_config: Dict,
+ llm_config: dict,
resolution: Literal["256x256", "512x512", "1024x1024", "1792x1024", "1024x1792"] = "1024x1024",
quality: Literal["standard", "hd"] = "standard",
num_images: int = 1,
@@ -149,7 +149,7 @@ def __init__(
self,
image_generator: ImageGenerator,
cache: Optional[AbstractCache] = None,
- text_analyzer_llm_config: Optional[Dict] = None,
+ text_analyzer_llm_config: Optional[dict] = None,
text_analyzer_instructions: str = PROMPT_INSTRUCTIONS,
verbosity: int = 0,
register_reply_position: int = 2,
@@ -212,10 +212,10 @@ def add_to_agent(self, agent: ConversableAgent):
def _image_gen_reply(
self,
recipient: ConversableAgent,
- messages: Optional[List[Dict]],
+ messages: Optional[list[dict]],
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
if messages is None:
return False, None
@@ -268,13 +268,13 @@ def _cache_set(self, prompt: str, image: Image):
key = self._image_generator.cache_key(prompt)
self._cache.set(key, img_utils.pil_to_data_uri(image))
- def _extract_analysis(self, analysis: Union[str, Dict, None]) -> str:
- if isinstance(analysis, Dict):
+ def _extract_analysis(self, analysis: Union[str, dict, None]) -> str:
+ if isinstance(analysis, dict):
return code_utils.content_str(analysis["content"])
else:
return code_utils.content_str(analysis)
- def _generate_content_message(self, prompt: str, image: Image) -> Dict[str, Any]:
+ def _generate_content_message(self, prompt: str, image: Image) -> dict[str, Any]:
return {
"content": [
{"type": "text", "text": f"I generated an image with the prompt: {prompt}"},
diff --git a/autogen/agentchat/contrib/capabilities/teachability.py b/autogen/agentchat/contrib/capabilities/teachability.py
index ccbbfedebc..5429b3df03 100644
--- a/autogen/agentchat/contrib/capabilities/teachability.py
+++ b/autogen/agentchat/contrib/capabilities/teachability.py
@@ -42,7 +42,7 @@ def __init__(
path_to_db_dir: Optional[str] = "./tmp/teachable_agent_db",
recall_threshold: Optional[float] = 1.5,
max_num_retrievals: Optional[int] = 10,
- llm_config: Optional[Union[Dict, bool]] = None,
+ llm_config: Optional[Union[dict, bool]] = None,
):
"""
Args:
@@ -92,7 +92,7 @@ def prepopulate_db(self):
"""Adds a few arbitrary memos to the DB."""
self.memo_store.prepopulate()
- def process_last_received_message(self, text: Union[Dict, str]):
+ def process_last_received_message(self, text: Union[dict, str]):
"""
Appends any relevant memos to the message text, and stores any apparent teachings in new memos.
Uses TextAnalyzerAgent to make decisions about memo storage and retrieval.
@@ -109,7 +109,7 @@ def process_last_received_message(self, text: Union[Dict, str]):
# Return the (possibly) expanded message text.
return expanded_text
- def _consider_memo_storage(self, comment: Union[Dict, str]):
+ def _consider_memo_storage(self, comment: Union[dict, str]):
"""Decides whether to store something from one user comment in the DB."""
memo_added = False
@@ -167,7 +167,7 @@ def _consider_memo_storage(self, comment: Union[Dict, str]):
# Yes. Save them to disk.
self.memo_store._save_memos()
- def _consider_memo_retrieval(self, comment: Union[Dict, str]):
+ def _consider_memo_retrieval(self, comment: Union[dict, str]):
"""Decides whether to retrieve memos from the DB, and add them to the chat context."""
# First, use the comment directly as the lookup key.
@@ -231,7 +231,7 @@ def _concatenate_memo_texts(self, memo_list: list) -> str:
memo_texts = memo_texts + "\n" + info
return memo_texts
- def _analyze(self, text_to_analyze: Union[Dict, str], analysis_instructions: Union[Dict, str]):
+ def _analyze(self, text_to_analyze: Union[dict, str], analysis_instructions: Union[dict, str]):
"""Asks TextAnalyzerAgent to analyze the given text according to specific instructions."""
self.analyzer.reset() # Clear the analyzer's list of messages.
self.teachable_agent.send(
@@ -280,7 +280,7 @@ def __init__(
self.last_memo_id = 0
if (not reset) and os.path.exists(self.path_to_dict):
print(colored("\nLOADING MEMORY FROM DISK", "light_green"))
- print(colored(" Location = {}".format(self.path_to_dict), "light_green"))
+ print(colored(f" Location = {self.path_to_dict}", "light_green"))
with open(self.path_to_dict, "rb") as f:
self.uid_text_dict = pickle.load(f)
self.last_memo_id = len(self.uid_text_dict)
@@ -298,7 +298,7 @@ def list_memos(self):
input_text, output_text = text
print(
colored(
- " ID: {}\n INPUT TEXT: {}\n OUTPUT TEXT: {}".format(uid, input_text, output_text),
+ f" ID: {uid}\n INPUT TEXT: {input_text}\n OUTPUT TEXT: {output_text}",
"light_green",
)
)
diff --git a/autogen/agentchat/contrib/capabilities/text_compressors.py b/autogen/agentchat/contrib/capabilities/text_compressors.py
index 290d9929b6..1e861c1170 100644
--- a/autogen/agentchat/contrib/capabilities/text_compressors.py
+++ b/autogen/agentchat/contrib/capabilities/text_compressors.py
@@ -19,7 +19,7 @@
class TextCompressor(Protocol):
"""Defines a protocol for text compression to optimize agent interactions."""
- def compress_text(self, text: str, **compression_params) -> Dict[str, Any]:
+ def compress_text(self, text: str, **compression_params) -> dict[str, Any]:
"""This method takes a string as input and returns a dictionary containing the compressed text and other
relevant information. The compressed text should be stored under the 'compressed_text' key in the dictionary.
To calculate the number of saved tokens, the dictionary should include 'origin_tokens' and 'compressed_tokens' keys.
@@ -36,7 +36,7 @@ class LLMLingua:
def __init__(
self,
- prompt_compressor_kwargs: Dict = dict(
+ prompt_compressor_kwargs: dict = dict(
model_name="microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank",
use_llmlingua2=True,
device_map="cpu",
@@ -68,5 +68,5 @@ def __init__(
else self._prompt_compressor.compress_prompt
)
- def compress_text(self, text: str, **compression_params) -> Dict[str, Any]:
+ def compress_text(self, text: str, **compression_params) -> dict[str, Any]:
return self._compression_method([text], **compression_params)
diff --git a/autogen/agentchat/contrib/capabilities/transform_messages.py b/autogen/agentchat/contrib/capabilities/transform_messages.py
index 9546433468..78b4478647 100644
--- a/autogen/agentchat/contrib/capabilities/transform_messages.py
+++ b/autogen/agentchat/contrib/capabilities/transform_messages.py
@@ -47,7 +47,7 @@ class TransformMessages:
```
"""
- def __init__(self, *, transforms: List[MessageTransform] = [], verbose: bool = True):
+ def __init__(self, *, transforms: list[MessageTransform] = [], verbose: bool = True):
"""
Args:
transforms: A list of message transformations to apply.
@@ -66,7 +66,7 @@ def add_to_agent(self, agent: ConversableAgent):
"""
agent.register_hook(hookable_method="process_all_messages_before_reply", hook=self._transform_messages)
- def _transform_messages(self, messages: List[Dict]) -> List[Dict]:
+ def _transform_messages(self, messages: list[dict]) -> list[dict]:
post_transform_messages = copy.deepcopy(messages)
system_message = None
diff --git a/autogen/agentchat/contrib/capabilities/transforms.py b/autogen/agentchat/contrib/capabilities/transforms.py
index a5912cb248..740a81c366 100644
--- a/autogen/agentchat/contrib/capabilities/transforms.py
+++ b/autogen/agentchat/contrib/capabilities/transforms.py
@@ -26,7 +26,7 @@ class MessageTransform(Protocol):
that takes a list of messages and returns the transformed list.
"""
- def apply_transform(self, messages: List[Dict]) -> List[Dict]:
+ def apply_transform(self, messages: list[dict]) -> list[dict]:
"""Applies a transformation to a list of messages.
Args:
@@ -37,7 +37,7 @@ def apply_transform(self, messages: List[Dict]) -> List[Dict]:
"""
...
- def get_logs(self, pre_transform_messages: List[Dict], post_transform_messages: List[Dict]) -> Tuple[str, bool]:
+ def get_logs(self, pre_transform_messages: list[dict], post_transform_messages: list[dict]) -> tuple[str, bool]:
"""Creates the string including the logs of the transformation
Alongside the string, it returns a boolean indicating whether the transformation had an effect or not.
@@ -70,7 +70,7 @@ def __init__(self, max_messages: Optional[int] = None, keep_first_message: bool
self._max_messages = max_messages
self._keep_first_message = keep_first_message
- def apply_transform(self, messages: List[Dict]) -> List[Dict]:
+ def apply_transform(self, messages: list[dict]) -> list[dict]:
"""Truncates the conversation history to the specified maximum number of messages.
This method returns a new list containing the most recent messages up to the specified
@@ -110,7 +110,7 @@ def apply_transform(self, messages: List[Dict]) -> List[Dict]:
return truncated_messages
- def get_logs(self, pre_transform_messages: List[Dict], post_transform_messages: List[Dict]) -> Tuple[str, bool]:
+ def get_logs(self, pre_transform_messages: list[dict], post_transform_messages: list[dict]) -> tuple[str, bool]:
pre_transform_messages_len = len(pre_transform_messages)
post_transform_messages_len = len(post_transform_messages)
@@ -161,7 +161,7 @@ def __init__(
max_tokens: Optional[int] = None,
min_tokens: Optional[int] = None,
model: str = "gpt-3.5-turbo-0613",
- filter_dict: Optional[Dict] = None,
+ filter_dict: Optional[dict] = None,
exclude_filter: bool = True,
):
"""
@@ -185,7 +185,7 @@ def __init__(
self._filter_dict = filter_dict
self._exclude_filter = exclude_filter
- def apply_transform(self, messages: List[Dict]) -> List[Dict]:
+ def apply_transform(self, messages: list[dict]) -> list[dict]:
"""Applies token truncation to the conversation history.
Args:
@@ -237,7 +237,7 @@ def apply_transform(self, messages: List[Dict]) -> List[Dict]:
return processed_messages
- def get_logs(self, pre_transform_messages: List[Dict], post_transform_messages: List[Dict]) -> Tuple[str, bool]:
+ def get_logs(self, pre_transform_messages: list[dict], post_transform_messages: list[dict]) -> tuple[str, bool]:
pre_transform_messages_tokens = sum(
transforms_util.count_text_tokens(msg["content"]) for msg in pre_transform_messages if "content" in msg
)
@@ -253,7 +253,7 @@ def get_logs(self, pre_transform_messages: List[Dict], post_transform_messages:
return logs_str, True
return "No tokens were truncated.", False
- def _truncate_str_to_tokens(self, contents: Union[str, List], n_tokens: int) -> Union[str, List]:
+ def _truncate_str_to_tokens(self, contents: Union[str, list], n_tokens: int) -> Union[str, list]:
if isinstance(contents, str):
return self._truncate_tokens(contents, n_tokens)
elif isinstance(contents, list):
@@ -261,7 +261,7 @@ def _truncate_str_to_tokens(self, contents: Union[str, List], n_tokens: int) ->
else:
raise ValueError(f"Contents must be a string or a list of dictionaries. Received type: {type(contents)}")
- def _truncate_multimodal_text(self, contents: List[Dict[str, Any]], n_tokens: int) -> List[Dict[str, Any]]:
+ def _truncate_multimodal_text(self, contents: list[dict[str, Any]], n_tokens: int) -> list[dict[str, Any]]:
"""Truncates text content within a list of multimodal elements, preserving the overall structure."""
tmp_contents = []
for content in contents:
@@ -324,9 +324,9 @@ def __init__(
self,
text_compressor: Optional[TextCompressor] = None,
min_tokens: Optional[int] = None,
- compression_params: Dict = dict(),
+ compression_params: dict = dict(),
cache: Optional[AbstractCache] = None,
- filter_dict: Optional[Dict] = None,
+ filter_dict: Optional[dict] = None,
exclude_filter: bool = True,
):
"""
@@ -364,7 +364,7 @@ def __init__(
# Optimizing savings calculations to optimize log generation
self._recent_tokens_savings = 0
- def apply_transform(self, messages: List[Dict]) -> List[Dict]:
+ def apply_transform(self, messages: list[dict]) -> list[dict]:
"""Applies compression to messages in a conversation history based on the specified configuration.
The function processes each message according to the `compression_args` and `min_tokens` settings, applying
@@ -414,13 +414,13 @@ def apply_transform(self, messages: List[Dict]) -> List[Dict]:
self._recent_tokens_savings = total_savings
return processed_messages
- def get_logs(self, pre_transform_messages: List[Dict], post_transform_messages: List[Dict]) -> Tuple[str, bool]:
+ def get_logs(self, pre_transform_messages: list[dict], post_transform_messages: list[dict]) -> tuple[str, bool]:
if self._recent_tokens_savings > 0:
return f"{self._recent_tokens_savings} tokens saved with text compression.", True
else:
return "No tokens saved with text compression.", False
- def _compress(self, content: MessageContentType) -> Tuple[MessageContentType, int]:
+ def _compress(self, content: MessageContentType) -> tuple[MessageContentType, int]:
"""Compresses the given text or multimodal content using the specified compression method."""
if isinstance(content, str):
return self._compress_text(content)
@@ -429,7 +429,7 @@ def _compress(self, content: MessageContentType) -> Tuple[MessageContentType, in
else:
return content, 0
- def _compress_multimodal(self, content: MessageContentType) -> Tuple[MessageContentType, int]:
+ def _compress_multimodal(self, content: MessageContentType) -> tuple[MessageContentType, int]:
tokens_saved = 0
for item in content:
if isinstance(item, dict) and "text" in item:
@@ -442,7 +442,7 @@ def _compress_multimodal(self, content: MessageContentType) -> Tuple[MessageCont
return content, tokens_saved
- def _compress_text(self, text: str) -> Tuple[str, int]:
+ def _compress_text(self, text: str) -> tuple[str, int]:
"""Compresses the given text using the specified compression method."""
compressed_text = self._text_compressor.compress_text(text, **self._compression_args)
@@ -483,7 +483,7 @@ def __init__(
position: str = "start",
format_string: str = "{name}:\n",
deduplicate: bool = True,
- filter_dict: Optional[Dict] = None,
+ filter_dict: Optional[dict] = None,
exclude_filter: bool = True,
):
"""
@@ -510,7 +510,7 @@ def __init__(
# Track the number of messages changed for logging
self._messages_changed = 0
- def apply_transform(self, messages: List[Dict]) -> List[Dict]:
+ def apply_transform(self, messages: list[dict]) -> list[dict]:
"""Applies the name change to the message based on the position and format string.
Args:
@@ -558,7 +558,7 @@ def apply_transform(self, messages: List[Dict]) -> List[Dict]:
self._messages_changed = messages_changed
return processed_messages
- def get_logs(self, pre_transform_messages: List[Dict], post_transform_messages: List[Dict]) -> Tuple[str, bool]:
+ def get_logs(self, pre_transform_messages: list[dict], post_transform_messages: list[dict]) -> tuple[str, bool]:
if self._messages_changed > 0:
return f"{self._messages_changed} message(s) changed to incorporate name.", True
else:
diff --git a/autogen/agentchat/contrib/capabilities/transforms_util.py b/autogen/agentchat/contrib/capabilities/transforms_util.py
index 279054f2f1..62decfa091 100644
--- a/autogen/agentchat/contrib/capabilities/transforms_util.py
+++ b/autogen/agentchat/contrib/capabilities/transforms_util.py
@@ -4,7 +4,8 @@
#
# Portions derived from https://github.com/microsoft/autogen are under the MIT License.
# SPDX-License-Identifier: MIT
-from typing import Any, Dict, Hashable, List, Optional, Tuple
+from collections.abc import Hashable
+from typing import Any, Dict, List, Optional, Tuple
from autogen import token_count_utils
from autogen.cache.abstract_cache_base import AbstractCache
@@ -23,7 +24,7 @@ def cache_key(content: MessageContentType, *args: Hashable) -> str:
return "".join(str_keys)
-def cache_content_get(cache: Optional[AbstractCache], key: str) -> Optional[Tuple[MessageContentType, ...]]:
+def cache_content_get(cache: Optional[AbstractCache], key: str) -> Optional[tuple[MessageContentType, ...]]:
"""Retrieves cachedd content from the cache.
Args:
@@ -50,7 +51,7 @@ def cache_content_set(cache: Optional[AbstractCache], key: str, content: Message
cache.set(key, cache_value)
-def min_tokens_reached(messages: List[Dict], min_tokens: Optional[int]) -> bool:
+def min_tokens_reached(messages: list[dict], min_tokens: Optional[int]) -> bool:
"""Returns True if the total number of tokens in the messages is greater than or equal to the specified value.
Args:
@@ -106,7 +107,7 @@ def is_content_text_empty(content: MessageContentType) -> bool:
return True
-def should_transform_message(message: Dict[str, Any], filter_dict: Optional[Dict[str, Any]], exclude: bool) -> bool:
+def should_transform_message(message: dict[str, Any], filter_dict: Optional[dict[str, Any]], exclude: bool) -> bool:
"""Validates whether the transform should be applied according to the filter dictionary.
Args:
diff --git a/autogen/agentchat/contrib/capabilities/vision_capability.py b/autogen/agentchat/contrib/capabilities/vision_capability.py
index ec227391b6..c0fe7a53eb 100644
--- a/autogen/agentchat/contrib/capabilities/vision_capability.py
+++ b/autogen/agentchat/contrib/capabilities/vision_capability.py
@@ -49,7 +49,7 @@ class VisionCapability(AgentCapability):
def __init__(
self,
- lmm_config: Dict,
+ lmm_config: dict,
description_prompt: Optional[str] = DEFAULT_DESCRIPTION_PROMPT,
custom_caption_func: Callable = None,
) -> None:
@@ -105,7 +105,7 @@ def add_to_agent(self, agent: ConversableAgent) -> None:
# Register a hook for processing the last message.
agent.register_hook(hookable_method="process_last_received_message", hook=self.process_last_received_message)
- def process_last_received_message(self, content: Union[str, List[dict]]) -> str:
+ def process_last_received_message(self, content: Union[str, list[dict]]) -> str:
"""
Processes the last received message content by normalizing and augmenting it
with descriptions of any included images. The function supports input content
diff --git a/autogen/agentchat/contrib/captainagent.py b/autogen/agentchat/contrib/captainagent.py
index 3e15d576d1..0229db02fa 100644
--- a/autogen/agentchat/contrib/captainagent.py
+++ b/autogen/agentchat/contrib/captainagent.py
@@ -135,12 +135,12 @@ def __init__(
self,
name: str,
system_message: Optional[str] = None,
- llm_config: Optional[Union[Dict, Literal[False]]] = None,
- is_termination_msg: Optional[Callable[[Dict], bool]] = None,
+ llm_config: Optional[Union[dict, Literal[False]]] = None,
+ is_termination_msg: Optional[Callable[[dict], bool]] = None,
max_consecutive_auto_reply: Optional[int] = None,
human_input_mode: Optional[str] = "NEVER",
- code_execution_config: Optional[Union[Dict, Literal[False]]] = False,
- nested_config: Optional[Dict] = None,
+ code_execution_config: Optional[Union[dict, Literal[False]]] = False,
+ nested_config: Optional[dict] = None,
agent_lib: Optional[str] = None,
tool_lib: Optional[str] = None,
agent_config_save_path: Optional[str] = None,
@@ -220,7 +220,7 @@ def __init__(
)
@staticmethod
- def _update_config(default_dict: Dict, update_dict: Optional[Dict]) -> Dict:
+ def _update_config(default_dict: dict, update_dict: Optional[dict]) -> dict:
"""
Recursively updates the default_dict with values from update_dict.
"""
@@ -290,15 +290,15 @@ class CaptainUserProxyAgent(ConversableAgent):
def __init__(
self,
name: str,
- nested_config: Dict,
+ nested_config: dict,
agent_config_save_path: str = None,
- is_termination_msg: Optional[Callable[[Dict], bool]] = None,
+ is_termination_msg: Optional[Callable[[dict], bool]] = None,
max_consecutive_auto_reply: Optional[int] = None,
human_input_mode: Optional[str] = "NEVER",
- code_execution_config: Optional[Union[Dict, Literal[False]]] = None,
- default_auto_reply: Optional[Union[str, Dict, None]] = DEFAULT_AUTO_REPLY,
- llm_config: Optional[Union[Dict, Literal[False]]] = False,
- system_message: Optional[Union[str, List]] = "",
+ code_execution_config: Optional[Union[dict, Literal[False]]] = None,
+ default_auto_reply: Optional[Union[str, dict, None]] = DEFAULT_AUTO_REPLY,
+ llm_config: Optional[Union[dict, Literal[False]]] = False,
+ system_message: Optional[Union[str, list]] = "",
description: Optional[str] = None,
):
"""
diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/image_qa.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/image_qa.py
index 24fce8edf1..95f2be6dfb 100644
--- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/image_qa.py
+++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/image_qa.py
@@ -39,7 +39,7 @@ def image_processing(img):
def text_processing(file_path):
# Check the file extension
if file_path.endswith(".txt"):
- with open(file_path, "r") as file:
+ with open(file_path) as file:
content = file.read()
else:
# if the file is not .txt, then it is a string, directly return the string
diff --git a/autogen/agentchat/contrib/gpt_assistant_agent.py b/autogen/agentchat/contrib/gpt_assistant_agent.py
index 2e818c6365..4c2ac731f7 100644
--- a/autogen/agentchat/contrib/gpt_assistant_agent.py
+++ b/autogen/agentchat/contrib/gpt_assistant_agent.py
@@ -32,8 +32,8 @@ def __init__(
self,
name="GPT Assistant",
instructions: Optional[str] = None,
- llm_config: Optional[Union[Dict, bool]] = None,
- assistant_config: Optional[Dict] = None,
+ llm_config: Optional[Union[dict, bool]] = None,
+ assistant_config: Optional[dict] = None,
overwrite_instructions: bool = False,
overwrite_tools: bool = False,
**kwargs,
@@ -184,10 +184,10 @@ def __init__(
def _invoke_assistant(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""
Invokes the OpenAI assistant to generate a reply based on the given messages.
@@ -441,7 +441,7 @@ def pretty_print_thread(self, thread):
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
@property
- def oai_threads(self) -> Dict[Agent, Any]:
+ def oai_threads(self) -> dict[Agent, Any]:
"""Return the threads of the agent."""
return self._openai_threads
@@ -475,15 +475,15 @@ def find_matching_assistant(self, candidate_assistants, instructions, tools):
matching_assistants = []
# Preprocess the required tools for faster comparison
- required_tool_types = set(
+ required_tool_types = {
"file_search" if tool.get("type") in ["retrieval", "file_search"] else tool.get("type") for tool in tools
- )
+ }
- required_function_names = set(
+ required_function_names = {
tool.get("function", {}).get("name")
for tool in tools
if tool.get("type") not in ["code_interpreter", "retrieval", "file_search"]
- )
+ }
for assistant in candidate_assistants:
# Check if instructions are similar
@@ -496,10 +496,10 @@ def find_matching_assistant(self, candidate_assistants, instructions, tools):
continue
# Preprocess the assistant's tools
- assistant_tool_types = set(
+ assistant_tool_types = {
"file_search" if tool.type in ["retrieval", "file_search"] else tool.type for tool in assistant.tools
- )
- assistant_function_names = set(tool.function.name for tool in assistant.tools if hasattr(tool, "function"))
+ }
+ assistant_function_names = {tool.function.name for tool in assistant.tools if hasattr(tool, "function")}
# Check if the tool types, function names match
if required_tool_types != assistant_tool_types or required_function_names != assistant_function_names:
diff --git a/autogen/agentchat/contrib/graph_rag/falkor_graph_query_engine.py b/autogen/agentchat/contrib/graph_rag/falkor_graph_query_engine.py
index d374c9ed46..607a2e3215 100644
--- a/autogen/agentchat/contrib/graph_rag/falkor_graph_query_engine.py
+++ b/autogen/agentchat/contrib/graph_rag/falkor_graph_query_engine.py
@@ -88,7 +88,7 @@ def connect_db(self):
else:
raise ValueError(f"Knowledge graph '{self.name}' does not exist")
- def init_db(self, input_doc: List[Document]):
+ def init_db(self, input_doc: list[Document]):
"""
Build the knowledge graph with input documents.
"""
@@ -124,7 +124,7 @@ def init_db(self, input_doc: List[Document]):
else:
raise ValueError("No input documents could be loaded.")
- def add_records(self, new_records: List) -> bool:
+ def add_records(self, new_records: list) -> bool:
raise NotImplementedError("This method is not supported by FalkorDB SDK yet.")
def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryResult:
@@ -168,12 +168,12 @@ def _save_ontology_to_db(self, ontology: Ontology):
Save graph ontology to a separate table with {graph_name}_ontology
"""
if self.ontology_table_name in self.falkordb.list_graphs():
- raise ValueError("Knowledge graph {} is already created.".format(self.name))
+ raise ValueError(f"Knowledge graph {self.name} is already created.")
graph = self.__get_ontology_storage_graph()
ontology.save_to_graph(graph)
def _load_ontology_from_db(self) -> Ontology:
if self.ontology_table_name not in self.falkordb.list_graphs():
- raise ValueError("Knowledge graph {} has not been created.".format(self.name))
+ raise ValueError(f"Knowledge graph {self.name} has not been created.")
graph = self.__get_ontology_storage_graph()
return Ontology.from_graph(graph)
diff --git a/autogen/agentchat/contrib/graph_rag/falkor_graph_rag_capability.py b/autogen/agentchat/contrib/graph_rag/falkor_graph_rag_capability.py
index b5432403c5..fd6eb1a5d4 100644
--- a/autogen/agentchat/contrib/graph_rag/falkor_graph_rag_capability.py
+++ b/autogen/agentchat/contrib/graph_rag/falkor_graph_rag_capability.py
@@ -47,10 +47,10 @@ def add_to_agent(self, agent: UserProxyAgent):
def _reply_using_falkordb_query(
self,
recipient: ConversableAgent,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""
Query FalkorDB and return the message. Internally, it utilises OpenAI to generate a reply based on the given messages.
The history with FalkorDB is also logged and updated.
@@ -74,7 +74,7 @@ def _reply_using_falkordb_query(
return True, result.answer if result.answer else "I'm sorry, I don't have an answer for that."
- def _messages_summary(self, messages: Union[Dict, str], system_message: str) -> str:
+ def _messages_summary(self, messages: Union[dict, str], system_message: str) -> str:
"""Summarize the messages in the conversation history. Excluding any message with 'tool_calls' and 'tool_responses'
Includes the 'name' (if it exists) and the 'content', with a new line between each one, like:
customer:
@@ -90,7 +90,7 @@ def _messages_summary(self, messages: Union[Dict, str], system_message: str) ->
else:
return messages
- elif isinstance(messages, List):
+ elif isinstance(messages, list):
summary = ""
for message in messages:
if "content" in message and "tool_calls" not in message and "tool_responses" not in message:
diff --git a/autogen/agentchat/contrib/graph_rag/graph_query_engine.py b/autogen/agentchat/contrib/graph_rag/graph_query_engine.py
index b15866f2db..b10562e7ee 100644
--- a/autogen/agentchat/contrib/graph_rag/graph_query_engine.py
+++ b/autogen/agentchat/contrib/graph_rag/graph_query_engine.py
@@ -29,7 +29,7 @@ class GraphQueryEngine(Protocol):
This interface defines the basic methods for graph-based RAG.
"""
- def init_db(self, input_doc: List[Document] | None = None):
+ def init_db(self, input_doc: list[Document] | None = None):
"""
This method initializes graph database with the input documents or records.
Usually, it takes the following steps,
@@ -43,7 +43,7 @@ def init_db(self, input_doc: List[Document] | None = None):
"""
pass
- def add_records(self, new_records: List) -> bool:
+ def add_records(self, new_records: list) -> bool:
"""
Add new records to the underlying database and add to the graph if required.
"""
diff --git a/autogen/agentchat/contrib/graph_rag/neo4j_graph_query_engine.py b/autogen/agentchat/contrib/graph_rag/neo4j_graph_query_engine.py
index 5b9753c10a..a91a3f23e6 100644
--- a/autogen/agentchat/contrib/graph_rag/neo4j_graph_query_engine.py
+++ b/autogen/agentchat/contrib/graph_rag/neo4j_graph_query_engine.py
@@ -6,7 +6,10 @@
from llama_index.core import PropertyGraphIndex, SimpleDirectoryReader
from llama_index.core.base.embeddings.base import BaseEmbedding
-from llama_index.core.indices.property_graph import SchemaLLMPathExtractor
+from llama_index.core.indices.property_graph import (
+ DynamicLLMPathExtractor,
+ SchemaLLMPathExtractor,
+)
from llama_index.core.indices.property_graph.transformations.schema_llm import Triple
from llama_index.core.llms import LLM
from llama_index.embeddings.openai import OpenAIEmbedding
@@ -19,14 +22,21 @@
class Neo4jGraphQueryEngine(GraphQueryEngine):
"""
- This class serves as a wrapper for a Neo4j database-backed PropertyGraphIndex query engine,
- facilitating the creation, updating, and querying of graphs.
+ This class serves as a wrapper for a property graph query engine backed by LlamaIndex and Neo4j,
+ facilitating the creating, connecting, updating, and querying of LlamaIndex property graphs.
- It builds a PropertyGraph Index from input documents,
- storing and retrieving data from a property graph in the Neo4j database.
+ It builds a property graph Index from input documents,
+ storing and retrieving data from the property graph in the Neo4j database.
- Using SchemaLLMPathExtractor, it defines schemas with entities, relationships, and other properties based on the input,
- which are added into the preprty graph.
+ It extracts triplets, i.e., [entity] -> [relationship] -> [entity] sets,
+ from the input documents using llamIndex extractors.
+
+ Users can provide custom entities, relationships, and schema to guide the extraction process.
+
+ If strict is True, the engine will extract triplets following the schema
+ of allowed relationships for each entity specified in the schema.
+
+ It also leverages LlamaIndex's chat engine which has a conversation history internally to provide context-aware responses.
For usage, please refer to example notebook/agentchat_graph_rag_neo4j.ipynb
"""
@@ -42,8 +52,8 @@ def __init__(
embedding: BaseEmbedding = OpenAIEmbedding(model_name="text-embedding-3-small"),
entities: Optional[TypeAlias] = None,
relations: Optional[TypeAlias] = None,
- validation_schema: Optional[Union[Dict[str, str], List[Triple]]] = None,
- strict: Optional[bool] = True,
+ schema: Optional[Union[dict[str, str], list[Triple]]] = None,
+ strict: Optional[bool] = False,
):
"""
Initialize a Neo4j Property graph.
@@ -56,12 +66,12 @@ def __init__(
database (str): Neo4j database name.
username (str): Neo4j username.
password (str): Neo4j password.
- llm (LLM): Language model to use for extracting tripletss.
+ llm (LLM): Language model to use for extracting triplets.
embedding (BaseEmbedding): Embedding model to use constructing index and query
- entities (Optional[TypeAlias]): Custom possible entities to include in the graph.
- relations (Optional[TypeAlias]): Custom poissble relations to include in the graph.
- validation_schema (Optional[Union[Dict[str, str], List[Triple]]): Custom schema to validate the extracted triplets
- strict (Optional[bool]): If false, allows for values outside of the schema, useful for using the schema as a suggestion.
+ entities (Optional[TypeAlias]): Custom suggested entities to include in the graph.
+ relations (Optional[TypeAlias]): Custom suggested relations to include in the graph.
+ schema (Optional[Union[Dict[str, str], List[Triple]]): Custom schema to specify allowed relationships for each entity.
+ strict (Optional[bool]): If false, allows for values outside of the input schema.
"""
self.host = host
self.port = port
@@ -72,19 +82,15 @@ def __init__(
self.embedding = embedding
self.entities = entities
self.relations = relations
- self.validation_schema = validation_schema
+ self.schema = schema
self.strict = strict
- def init_db(self, input_doc: List[Document] | None = None):
+ def init_db(self, input_doc: list[Document] | None = None):
"""
Build the knowledge graph with input documents.
"""
- self.input_files = []
- for doc in input_doc:
- if os.path.exists(doc.path_or_url):
- self.input_files.append(doc.path_or_url)
- else:
- raise ValueError(f"Document file not found: {doc.path_or_url}")
+
+ self.documents = self._load_doc(input_doc)
self.graph_store = Neo4jPropertyGraphStore(
username=self.username,
@@ -96,19 +102,8 @@ def init_db(self, input_doc: List[Document] | None = None):
# delete all entities and relationships in case a graph pre-exists
self._clear()
- self.documents = SimpleDirectoryReader(input_files=self.input_files).load_data()
-
- # Extract paths following a strict schema of allowed entities, relationships, and which entities can be connected to which relationships.
- # To add more extractors, please refer to https://docs.llamaindex.ai/en/latest/module_guides/indexing/lpg_index_guide/#construction
- self.kg_extractors = [
- SchemaLLMPathExtractor(
- llm=self.llm,
- possible_entities=self.entities,
- possible_relations=self.relations,
- kg_validation_schema=self.validation_schema,
- strict=self.strict,
- )
- ]
+ # Create knowledge graph extractors.
+ self.kg_extractors = self._create_kg_extractors()
self.index = PropertyGraphIndex.from_documents(
self.documents,
@@ -118,7 +113,27 @@ def init_db(self, input_doc: List[Document] | None = None):
show_progress=True,
)
- def add_records(self, new_records: List) -> bool:
+ def connect_db(self):
+ """
+ Connect to an existing knowledge graph database.
+ """
+ self.graph_store = Neo4jPropertyGraphStore(
+ username=self.username,
+ password=self.password,
+ url=self.host + ":" + str(self.port),
+ database=self.database,
+ )
+
+ self.kg_extractors = self._create_kg_extractors()
+
+ self.index = PropertyGraphIndex.from_existing(
+ property_graph_store=self.graph_store,
+ kg_extractors=self.kg_extractors,
+ embed_model=self.embedding,
+ show_progress=True,
+ )
+
+ def add_records(self, new_records: list) -> bool:
"""
Add new records to the knowledge graph. Must be local files.
@@ -129,7 +144,7 @@ def add_records(self, new_records: List) -> bool:
bool: True if successful, False otherwise.
"""
if self.graph_store is None:
- raise ValueError("Knowledge graph is not initialized. Please call init_db first.")
+ raise ValueError("Knowledge graph is not initialized. Please call init_db or connect_db first.")
try:
"""
@@ -149,7 +164,11 @@ def add_records(self, new_records: List) -> bool:
def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryResult:
"""
- Query the knowledge graph with a question.
+ Query the property graph with a question using LlamaIndex chat engine.
+ We use the condense_plus_context chat mode
+ which condenses the conversation history and the user query into a standalone question,
+ and then build a context for the standadlone question
+ from the property graph to generate a response.
Args:
question: a human input question.
@@ -158,23 +177,15 @@ def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryR
Returns:
A GrapStoreQueryResult object containing the answer and related triplets.
"""
- if self.graph_store is None:
- raise ValueError("Knowledge graph is not created.")
+ if not hasattr(self, "index"):
+ raise ValueError("Property graph index is not created.")
- # query the graph to get the answer
- query_engine = self.index.as_query_engine(include_text=True)
- response = str(query_engine.query(question))
+ # Initialize chat engine if not already initialized
+ if not hasattr(self, "chat_engine"):
+ self.chat_engine = self.index.as_chat_engine(chat_mode="condense_plus_context", llm=self.llm)
- # retrieve source triplets that are semantically related to the question
- retriever = self.index.as_retriever(include_text=False)
- nodes = retriever.retrieve(question)
- triplets = []
- for node in nodes:
- entities = [sub.split("(")[0].strip() for sub in node.text.split("->")]
- triplet = " -> ".join(entities)
- triplets.append(triplet)
-
- return GraphStoreQueryResult(answer=response, results=triplets)
+ response = self.chat_engine.chat(question)
+ return GraphStoreQueryResult(answer=str(response))
def _clear(self) -> None:
"""
@@ -183,3 +194,50 @@ def _clear(self) -> None:
"""
with self.graph_store._driver.session() as session:
session.run("MATCH (n) DETACH DELETE n;")
+
+ def _load_doc(self, input_doc: list[Document]) -> list[Document]:
+ """
+ Load documents from the input files.
+ """
+ input_files = []
+ for doc in input_doc:
+ if os.path.exists(doc.path_or_url):
+ input_files.append(doc.path_or_url)
+ else:
+ raise ValueError(f"Document file not found: {doc.path_or_url}")
+
+ return SimpleDirectoryReader(input_files=input_files).load_data()
+
+ def _create_kg_extractors(self):
+ """
+ If strict is True,
+ extract paths following a strict schema of allowed relationships for each entity.
+
+ If strict is False,
+ auto-create relationships and schema that fit the graph
+
+ # To add more extractors, please refer to https://docs.llamaindex.ai/en/latest/module_guides/indexing/lpg_index_guide/#construction
+ """
+
+ #
+ kg_extractors = [
+ SchemaLLMPathExtractor(
+ llm=self.llm,
+ possible_entities=self.entities,
+ possible_relations=self.relations,
+ kg_validation_schema=self.schema,
+ strict=self.strict,
+ ),
+ ]
+
+ # DynamicLLMPathExtractor will auto-create relationships and schema that fit the graph
+ if not self.strict:
+ kg_extractors.append(
+ DynamicLLMPathExtractor(
+ llm=self.llm,
+ allowed_entity_types=self.entities,
+ allowed_relation_types=self.relations,
+ )
+ )
+
+ return kg_extractors
diff --git a/autogen/agentchat/contrib/graph_rag/neo4j_graph_rag_capability.py b/autogen/agentchat/contrib/graph_rag/neo4j_graph_rag_capability.py
index c4d952437d..fea72719ed 100644
--- a/autogen/agentchat/contrib/graph_rag/neo4j_graph_rag_capability.py
+++ b/autogen/agentchat/contrib/graph_rag/neo4j_graph_rag_capability.py
@@ -49,10 +49,10 @@ def add_to_agent(self, agent: UserProxyAgent):
def _reply_using_neo4j_query(
self,
recipient: ConversableAgent,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""
Query neo4j and return the message. Internally, it queries the Property graph
and returns the answer from the graph query engine.
@@ -73,11 +73,11 @@ def _reply_using_neo4j_query(
return True, result.answer
- def _get_last_question(self, message: Union[Dict, str]):
+ def _get_last_question(self, message: Union[dict, str]):
"""Retrieves the last message from the conversation history."""
if isinstance(message, str):
return message
- if isinstance(message, Dict):
+ if isinstance(message, dict):
if "content" in message:
return message["content"]
return None
diff --git a/autogen/agentchat/contrib/img_utils.py b/autogen/agentchat/contrib/img_utils.py
index 9b9a01b89c..1cf718d53a 100644
--- a/autogen/agentchat/contrib/img_utils.py
+++ b/autogen/agentchat/contrib/img_utils.py
@@ -107,7 +107,7 @@ def get_image_data(image_file: Union[str, Image.Image], use_b64=True) -> bytes:
return content
-def llava_formatter(prompt: str, order_image_tokens: bool = False) -> Tuple[str, List[str]]:
+def llava_formatter(prompt: str, order_image_tokens: bool = False) -> tuple[str, list[str]]:
"""
Formats the input prompt by replacing image tags and returns the new prompt along with image locations.
@@ -189,7 +189,7 @@ def _get_mime_type_from_data_uri(base64_image):
return data_uri
-def gpt4v_formatter(prompt: str, img_format: str = "uri") -> List[Union[str, dict]]:
+def gpt4v_formatter(prompt: str, img_format: str = "uri") -> list[Union[str, dict]]:
"""
Formats the input prompt by replacing image tags and returns a list of text and images.
@@ -274,7 +274,7 @@ def _to_pil(data: str) -> Image.Image:
return Image.open(BytesIO(base64.b64decode(data)))
-def message_formatter_pil_to_b64(messages: List[Dict]) -> List[Dict]:
+def message_formatter_pil_to_b64(messages: list[dict]) -> list[dict]:
"""
Converts the PIL image URLs in the messages to base64 encoded data URIs.
diff --git a/autogen/agentchat/contrib/llamaindex_conversable_agent.py b/autogen/agentchat/contrib/llamaindex_conversable_agent.py
index c1a51cc491..d563a525dd 100644
--- a/autogen/agentchat/contrib/llamaindex_conversable_agent.py
+++ b/autogen/agentchat/contrib/llamaindex_conversable_agent.py
@@ -80,10 +80,10 @@ def __init__(
def _generate_oai_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[OpenAIWrapper] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""Generate a reply using autogen.oai."""
user_message, history = self._extract_message_and_history(messages=messages, sender=sender)
@@ -95,10 +95,10 @@ def _generate_oai_reply(
async def _a_generate_oai_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[OpenAIWrapper] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""Generate a reply using autogen.oai."""
user_message, history = self._extract_message_and_history(messages=messages, sender=sender)
@@ -111,8 +111,8 @@ async def _a_generate_oai_reply(
return (True, extracted_response)
def _extract_message_and_history(
- self, messages: Optional[List[Dict]] = None, sender: Optional[Agent] = None
- ) -> Tuple[str, List[ChatMessage]]:
+ self, messages: Optional[list[dict]] = None, sender: Optional[Agent] = None
+ ) -> tuple[str, list[ChatMessage]]:
"""Extract the message and history from the messages."""
if not messages:
messages = self._oai_messages[sender]
@@ -123,7 +123,7 @@ def _extract_message_and_history(
message = messages[-1].get("content", "")
history = messages[:-1]
- history_messages: List[ChatMessage] = []
+ history_messages: list[ChatMessage] = []
for history_message in history:
content = history_message.get("content", "")
role = history_message.get("role", "user")
diff --git a/autogen/agentchat/contrib/llava_agent.py b/autogen/agentchat/contrib/llava_agent.py
index d5ae5530c1..f2bf77e533 100644
--- a/autogen/agentchat/contrib/llava_agent.py
+++ b/autogen/agentchat/contrib/llava_agent.py
@@ -30,7 +30,7 @@ class LLaVAAgent(MultimodalConversableAgent):
def __init__(
self,
name: str,
- system_message: Optional[Tuple[str, List]] = DEFAULT_LLAVA_SYS_MSG,
+ system_message: Optional[tuple[str, list]] = DEFAULT_LLAVA_SYS_MSG,
*args,
**kwargs,
):
diff --git a/autogen/agentchat/contrib/math_user_proxy_agent.py b/autogen/agentchat/contrib/math_user_proxy_agent.py
index 65350371e5..8cdbd1bafc 100644
--- a/autogen/agentchat/contrib/math_user_proxy_agent.py
+++ b/autogen/agentchat/contrib/math_user_proxy_agent.py
@@ -140,10 +140,10 @@ def __init__(
self,
name: Optional[str] = "MathChatAgent", # default set to MathChatAgent
is_termination_msg: Optional[
- Callable[[Dict], bool]
+ Callable[[dict], bool]
] = _is_termination_msg_mathchat, # terminate if \boxed{} in message
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "NEVER", # Fully automated
- default_auto_reply: Optional[Union[str, Dict, None]] = DEFAULT_REPLY,
+ default_auto_reply: Optional[Union[str, dict, None]] = DEFAULT_REPLY,
max_invalid_q_per_step=3, # a parameter needed in MathChat
**kwargs,
):
@@ -292,7 +292,7 @@ def execute_one_wolfram_query(self, query: str):
def _generate_math_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
):
@@ -364,7 +364,7 @@ def _generate_math_reply(
# THE SOFTWARE.
-def get_from_dict_or_env(data: Dict[str, Any], key: str, env_key: str, default: Optional[str] = None) -> str:
+def get_from_dict_or_env(data: dict[str, Any], key: str, env_key: str, default: Optional[str] = None) -> str:
"""Get a value from a dictionary or an environment variable."""
if key in data and data[key]:
return data[key]
@@ -402,7 +402,7 @@ class Config:
extra = Extra.forbid
@root_validator(skip_on_failure=True)
- def validate_environment(cls, values: Dict) -> Dict:
+ def validate_environment(cls, values: dict) -> dict:
"""Validate that api key and python package exists in environment."""
wolfram_alpha_appid = get_from_dict_or_env(values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID")
values["wolfram_alpha_appid"] = wolfram_alpha_appid
@@ -417,7 +417,7 @@ def validate_environment(cls, values: Dict) -> Dict:
return values
- def run(self, query: str) -> Tuple[str, bool]:
+ def run(self, query: str) -> tuple[str, bool]:
"""Run query through WolframAlpha and parse result."""
from urllib.error import HTTPError
diff --git a/autogen/agentchat/contrib/multimodal_conversable_agent.py b/autogen/agentchat/contrib/multimodal_conversable_agent.py
index a5cbada75c..b4ffcd48dd 100644
--- a/autogen/agentchat/contrib/multimodal_conversable_agent.py
+++ b/autogen/agentchat/contrib/multimodal_conversable_agent.py
@@ -29,7 +29,7 @@ class MultimodalConversableAgent(ConversableAgent):
def __init__(
self,
name: str,
- system_message: Optional[Union[str, List]] = DEFAULT_LMM_SYS_MSG,
+ system_message: Optional[Union[str, list]] = DEFAULT_LMM_SYS_MSG,
is_termination_msg: str = None,
*args,
**kwargs,
@@ -64,7 +64,7 @@ def __init__(
MultimodalConversableAgent.a_generate_oai_reply,
)
- def update_system_message(self, system_message: Union[Dict, List, str]):
+ def update_system_message(self, system_message: Union[dict, list, str]):
"""Update the system message.
Args:
@@ -74,7 +74,7 @@ def update_system_message(self, system_message: Union[Dict, List, str]):
self._oai_system_message[0]["role"] = "system"
@staticmethod
- def _message_to_dict(message: Union[Dict, List, str]) -> Dict:
+ def _message_to_dict(message: Union[dict, list, str]) -> dict:
"""Convert a message to a dictionary. This implementation
handles the GPT-4V formatting for easier prompts.
@@ -103,10 +103,10 @@ def _message_to_dict(message: Union[Dict, List, str]) -> Dict:
def generate_oai_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[OpenAIWrapper] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""Generate a reply using autogen.oai."""
client = self.client if config is None else config
if client is None:
diff --git a/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py b/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py
index 9e24629f7e..ab12d2c1a8 100644
--- a/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py
+++ b/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py
@@ -31,8 +31,8 @@ def __init__(
self,
name="RetrieveChatAgent", # default set to RetrieveChatAgent
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "ALWAYS",
- is_termination_msg: Optional[Callable[[Dict], bool]] = None,
- retrieve_config: Optional[Dict] = None, # config for the retrieve agent
+ is_termination_msg: Optional[Callable[[dict], bool]] = None,
+ retrieve_config: Optional[dict] = None, # config for the retrieve agent
**kwargs,
):
"""
@@ -169,7 +169,7 @@ def create_qdrant_from_dir(
must_break_at_empty_line: bool = True,
embedding_model: str = "BAAI/bge-small-en-v1.5",
custom_text_split_function: Callable = None,
- custom_text_types: List[str] = TEXT_FORMATS,
+ custom_text_types: list[str] = TEXT_FORMATS,
recursive: bool = True,
extra_docs: bool = False,
parallel: int = 0,
@@ -177,7 +177,7 @@ def create_qdrant_from_dir(
quantization_config: Optional[models.QuantizationConfig] = None,
hnsw_config: Optional[models.HnswConfigDiff] = None,
payload_indexing: bool = False,
- qdrant_client_options: Optional[Dict] = {},
+ qdrant_client_options: Optional[dict] = {},
):
"""Create a Qdrant collection from all the files in a given directory, the directory can also be a single file or a
url to a single file.
@@ -266,14 +266,14 @@ def create_qdrant_from_dir(
def query_qdrant(
- query_texts: List[str],
+ query_texts: list[str],
n_results: int = 10,
client: QdrantClient = None,
collection_name: str = "all-my-documents",
search_string: str = "",
embedding_model: str = "BAAI/bge-small-en-v1.5",
- qdrant_client_options: Optional[Dict] = {},
-) -> List[List[QueryResponse]]:
+ qdrant_client_options: Optional[dict] = {},
+) -> list[list[QueryResponse]]:
"""Perform a similarity search with filters on a Qdrant collection
Args:
diff --git a/autogen/agentchat/contrib/reasoning_agent.py b/autogen/agentchat/contrib/reasoning_agent.py
index 1f623592b1..ac43fe3f48 100644
--- a/autogen/agentchat/contrib/reasoning_agent.py
+++ b/autogen/agentchat/contrib/reasoning_agent.py
@@ -81,7 +81,7 @@ def __init__(self, content: str, parent: Optional["ThinkNode"] = None) -> None:
self.parent.children.append(self)
@property
- def _trajectory_arr(self) -> List[str]:
+ def _trajectory_arr(self) -> list[str]:
"""Get the full path from root to this node as a list of strings.
Returns:
@@ -118,7 +118,7 @@ def __str__(self) -> str:
def __repr__(self) -> str:
return self.__str__()
- def to_dict(self) -> Dict:
+ def to_dict(self) -> dict:
"""Convert ThinkNode to dictionary representation.
Returns:
@@ -135,7 +135,7 @@ def to_dict(self) -> Dict:
}
@classmethod
- def from_dict(cls, data: Dict, parent: Optional["ThinkNode"] = None) -> "ThinkNode":
+ def from_dict(cls, data: dict, parent: Optional["ThinkNode"] = None) -> "ThinkNode":
"""Create ThinkNode from dictionary representation.
Args:
@@ -624,7 +624,7 @@ def _mtcs_reply(self, prompt, ground_truth=""):
(child.value / (child.visits + EPSILON)) +
# exploration term
self._exploration_constant
- * math.sqrt((2 * math.log(node.visits + EPSILON) / (child.visits + EPSILON)))
+ * math.sqrt(2 * math.log(node.visits + EPSILON) / (child.visits + EPSILON))
for child in node.children
]
node = node.children[choices_weights.index(max(choices_weights))]
@@ -657,7 +657,7 @@ def _mtcs_reply(self, prompt, ground_truth=""):
best_ans_node = max(answer_nodes, key=lambda node: node.value)
return best_ans_node.content
- def _expand(self, node: ThinkNode) -> List:
+ def _expand(self, node: ThinkNode) -> list:
"""
Expand the node by generating possible next steps based on the current trajectory.
diff --git a/autogen/agentchat/contrib/retrieve_assistant_agent.py b/autogen/agentchat/contrib/retrieve_assistant_agent.py
index 8bea9e46c3..e2e6c0a5cf 100644
--- a/autogen/agentchat/contrib/retrieve_assistant_agent.py
+++ b/autogen/agentchat/contrib/retrieve_assistant_agent.py
@@ -33,10 +33,10 @@ def __init__(self, *args, **kwargs):
def _generate_retrieve_assistant_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
if config is None:
config = self
if messages is None:
diff --git a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
index 49a72f3946..bf5e417156 100644
--- a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
+++ b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
@@ -100,8 +100,8 @@ def __init__(
self,
name="RetrieveChatAgent", # default set to RetrieveChatAgent
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "ALWAYS",
- is_termination_msg: Optional[Callable[[Dict], bool]] = None,
- retrieve_config: Optional[Dict] = None, # config for the retrieve agent
+ is_termination_msg: Optional[Callable[[dict], bool]] = None,
+ retrieve_config: Optional[dict] = None, # config for the retrieve agent
**kwargs,
):
r"""
@@ -371,12 +371,10 @@ def _init_db(self):
logger.info(f"Found {len(chunks)} chunks.")
if self._new_docs:
- all_docs_ids = set(
- [
- doc["id"]
- for doc in self._vector_db.get_docs_by_ids(ids=None, collection_name=self._collection_name)
- ]
- )
+ all_docs_ids = {
+ doc["id"]
+ for doc in self._vector_db.get_docs_by_ids(ids=None, collection_name=self._collection_name)
+ }
else:
all_docs_ids = set()
@@ -525,10 +523,10 @@ def _check_update_context(self, message):
def _generate_retrieve_user_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""In this function, we will update the context and reset the conversation based on different conditions.
We'll update the context and reset the conversation if update_context is True and either of the following:
(1) the last message contains "UPDATE CONTEXT",
diff --git a/autogen/agentchat/contrib/society_of_mind_agent.py b/autogen/agentchat/contrib/society_of_mind_agent.py
index e6f2b5f4dd..fbf2f15cc9 100644
--- a/autogen/agentchat/contrib/society_of_mind_agent.py
+++ b/autogen/agentchat/contrib/society_of_mind_agent.py
@@ -38,13 +38,13 @@ def __init__(
name: str,
chat_manager: GroupChatManager,
response_preparer: Optional[Union[str, Callable]] = None,
- is_termination_msg: Optional[Callable[[Dict], bool]] = None,
+ is_termination_msg: Optional[Callable[[dict], bool]] = None,
max_consecutive_auto_reply: Optional[int] = None,
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "TERMINATE",
- function_map: Optional[Dict[str, Callable]] = None,
- code_execution_config: Union[Dict, Literal[False]] = False,
- llm_config: Optional[Union[Dict, Literal[False]]] = False,
- default_auto_reply: Optional[Union[str, Dict, None]] = "",
+ function_map: Optional[dict[str, Callable]] = None,
+ code_execution_config: Union[dict, Literal[False]] = False,
+ llm_config: Optional[Union[dict, Literal[False]]] = False,
+ default_auto_reply: Optional[Union[str, dict, None]] = "",
**kwargs,
):
super().__init__(
@@ -162,10 +162,10 @@ def update_chat_manager(self, chat_manager: Union[GroupChatManager, None]):
def generate_inner_monologue_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[OpenAIWrapper] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""Generate a reply by running the group chat"""
if self.chat_manager is None:
return False, None
diff --git a/autogen/agentchat/contrib/swarm_agent.py b/autogen/agentchat/contrib/swarm_agent.py
index 6ad536ae07..f604c13a57 100644
--- a/autogen/agentchat/contrib/swarm_agent.py
+++ b/autogen/agentchat/contrib/swarm_agent.py
@@ -66,7 +66,7 @@ class ON_CONDITION:
If a string, it will look up the value of the context variable with that name, which should be a bool.
"""
- target: Union["SwarmAgent", Dict[str, Any]] = None
+ target: Union["SwarmAgent", dict[str, Any]] = None
condition: str = ""
available: Optional[Union[Callable, str]] = None
@@ -74,7 +74,7 @@ def __post_init__(self):
# Ensure valid types
if self.target is not None:
assert isinstance(self.target, SwarmAgent) or isinstance(
- self.target, Dict
+ self.target, dict
), "'target' must be a SwarmAgent or a Dict"
# Ensure they have a condition
@@ -118,13 +118,13 @@ def __post_init__(self):
def initiate_swarm_chat(
initial_agent: "SwarmAgent",
- messages: Union[List[Dict[str, Any]], str],
- agents: List["SwarmAgent"],
+ messages: Union[list[dict[str, Any]], str],
+ agents: list["SwarmAgent"],
user_agent: Optional[UserProxyAgent] = None,
max_rounds: int = 20,
- context_variables: Optional[Dict[str, Any]] = None,
+ context_variables: Optional[dict[str, Any]] = None,
after_work: Optional[Union[AFTER_WORK, Callable]] = AFTER_WORK(AfterWorkOption.TERMINATE),
-) -> Tuple[ChatResult, Dict[str, Any], "SwarmAgent"]:
+) -> tuple[ChatResult, dict[str, Any], "SwarmAgent"]:
"""Initialize and run a swarm chat
Args:
@@ -248,10 +248,10 @@ def determine_next_agent(last_speaker: SwarmAgent, groupchat: GroupChat):
else:
raise ValueError("Invalid After Work condition or return value from callable")
- def create_nested_chats(agent: SwarmAgent, nested_chat_agents: List[SwarmAgent]):
+ def create_nested_chats(agent: SwarmAgent, nested_chat_agents: list[SwarmAgent]):
"""Create nested chat agents and register nested chats"""
for i, nested_chat_handoff in enumerate(agent._nested_chat_handoffs):
- nested_chats: Dict[str, Any] = nested_chat_handoff["nested_chats"]
+ nested_chats: dict[str, Any] = nested_chat_handoff["nested_chats"]
condition = nested_chat_handoff["condition"]
available = nested_chat_handoff["available"]
@@ -365,7 +365,7 @@ class SwarmResult(BaseModel):
values: str = ""
agent: Optional[Union["SwarmAgent", str]] = None
- context_variables: Dict[str, Any] = {}
+ context_variables: dict[str, Any] = {}
class Config: # Add this inner class
arbitrary_types_allowed = True
@@ -388,15 +388,15 @@ def __init__(
self,
name: str,
system_message: Optional[str] = "You are a helpful AI Assistant.",
- llm_config: Optional[Union[Dict, Literal[False]]] = None,
- functions: Union[List[Callable], Callable] = None,
- is_termination_msg: Optional[Callable[[Dict], bool]] = None,
+ llm_config: Optional[Union[dict, Literal[False]]] = None,
+ functions: Union[list[Callable], Callable] = None,
+ is_termination_msg: Optional[Callable[[dict], bool]] = None,
max_consecutive_auto_reply: Optional[int] = None,
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "NEVER",
description: Optional[str] = None,
code_execution_config=False,
update_agent_state_before_reply: Optional[
- Union[List[Union[Callable, UPDATE_SYSTEM_MESSAGE]], Callable, UPDATE_SYSTEM_MESSAGE]
+ Union[list[Union[Callable, UPDATE_SYSTEM_MESSAGE]], Callable, UPDATE_SYSTEM_MESSAGE]
] = None,
**kwargs,
) -> None:
@@ -439,7 +439,7 @@ def __init__(
if name != __TOOL_EXECUTOR_NAME__:
self.register_hook("update_agent_state", self._update_conditional_functions)
- def register_update_agent_state_before_reply(self, functions: Optional[Union[List[Callable], Callable]]):
+ def register_update_agent_state_before_reply(self, functions: Optional[Union[list[Callable], Callable]]):
"""
Register functions that will be called when the agent is selected and before it speaks.
You can add your own validation or precondition functions here.
@@ -464,8 +464,8 @@ def register_update_agent_state_before_reply(self, functions: Optional[Union[Lis
# Outer function to create a closure with the update function
def create_wrapper(update_func: UPDATE_SYSTEM_MESSAGE):
def update_system_message_wrapper(
- agent: ConversableAgent, messages: List[Dict[str, Any]]
- ) -> List[Dict[str, Any]]:
+ agent: ConversableAgent, messages: list[dict[str, Any]]
+ ) -> list[dict[str, Any]]:
if isinstance(update_func.update_function, str):
# Templates like "My context variable passport is {passport}" will
# use the context_variables for substitution
@@ -502,7 +502,7 @@ def __str__(self):
def register_hand_off(
self,
- hand_to: Union[List[Union[ON_CONDITION, AFTER_WORK]], ON_CONDITION, AFTER_WORK],
+ hand_to: Union[list[Union[ON_CONDITION, AFTER_WORK]], ON_CONDITION, AFTER_WORK],
):
"""Register a function to hand off to another agent.
@@ -555,7 +555,7 @@ def transfer_to_agent() -> "SwarmAgent":
# Store function to add/remove later based on it being 'available'
self._conditional_functions[func_name] = (transfer_func, transit)
- elif isinstance(transit.target, Dict):
+ elif isinstance(transit.target, dict):
# Transition to a nested chat
# We will store them here and establish them in the initiate_swarm_chat
self._nested_chat_handoffs.append(
@@ -566,7 +566,7 @@ def transfer_to_agent() -> "SwarmAgent":
raise ValueError("Invalid hand off condition, must be either ON_CONDITION or AFTER_WORK")
@staticmethod
- def _update_conditional_functions(agent: Agent, messages: Optional[List[Dict]] = None) -> None:
+ def _update_conditional_functions(agent: Agent, messages: Optional[list[dict]] = None) -> None:
"""Updates the agent's functions based on the ON_CONDITION's available condition."""
for func_name, (func, on_condition) in agent._conditional_functions.items():
is_available = True
@@ -588,10 +588,10 @@ def _update_conditional_functions(agent: Agent, messages: Optional[List[Dict]] =
def generate_swarm_tool_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[OpenAIWrapper] = None,
- ) -> Tuple[bool, dict]:
+ ) -> tuple[bool, dict]:
"""Pre-processes and generates tool call replies.
This function:
@@ -697,15 +697,15 @@ def add_single_function(self, func: Callable, name=None, description=""):
self.update_tool_signature(f_no_context, is_remove=False)
self.register_function({func._name: func})
- def add_functions(self, func_list: List[Callable]):
+ def add_functions(self, func_list: list[Callable]):
for func in func_list:
self.add_single_function(func)
@staticmethod
def process_nested_chat_carryover(
- chat: Dict[str, Any],
+ chat: dict[str, Any],
recipient: ConversableAgent,
- messages: List[Dict[str, Any]],
+ messages: list[dict[str, Any]],
sender: ConversableAgent,
config: Any,
trim_n_messages: int = 0,
@@ -730,7 +730,7 @@ def process_nested_chat_carryover(
trim_n_messages: The number of latest messages to trim from the messages list
"""
- def concat_carryover(chat_message: str, carryover_message: Union[str, List[Dict[str, Any]]]) -> str:
+ def concat_carryover(chat_message: str, carryover_message: Union[str, list[dict[str, Any]]]) -> str:
"""Concatenate the carryover message to the chat message."""
prefix = f"{chat_message}\n" if chat_message else ""
@@ -799,8 +799,8 @@ def concat_carryover(chat_message: str, carryover_message: Union[str, List[Dict[
@staticmethod
def _summary_from_nested_chats(
- chat_queue: List[Dict[str, Any]], recipient: Agent, messages: Union[str, Callable], sender: Agent, config: Any
- ) -> Tuple[bool, Union[str, None]]:
+ chat_queue: list[dict[str, Any]], recipient: Agent, messages: Union[str, Callable], sender: Agent, config: Any
+ ) -> tuple[bool, Union[str, None]]:
"""Overridden _summary_from_nested_chats method from ConversableAgent.
This function initiates one or a sequence of chats between the "recipient" and the agents in the chat_queue.
diff --git a/autogen/agentchat/contrib/text_analyzer_agent.py b/autogen/agentchat/contrib/text_analyzer_agent.py
index 79edcf3b7b..914e020851 100644
--- a/autogen/agentchat/contrib/text_analyzer_agent.py
+++ b/autogen/agentchat/contrib/text_analyzer_agent.py
@@ -23,7 +23,7 @@ def __init__(
name="analyzer",
system_message: Optional[str] = system_message,
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "NEVER",
- llm_config: Optional[Union[Dict, bool]] = None,
+ llm_config: Optional[Union[dict, bool]] = None,
**kwargs,
):
"""
@@ -48,10 +48,10 @@ def __init__(
def _analyze_in_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""Analyzes the given text as instructed, and returns the analysis as a message.
Assumes exactly two messages containing the text to analyze and the analysis instructions.
See Teachability.analyze for an example of how to use this method."""
diff --git a/autogen/agentchat/contrib/tool_retriever.py b/autogen/agentchat/contrib/tool_retriever.py
index daa32c4364..4ca2ddcda4 100644
--- a/autogen/agentchat/contrib/tool_retriever.py
+++ b/autogen/agentchat/contrib/tool_retriever.py
@@ -81,7 +81,7 @@ def get_full_tool_description(py_file):
"""
Retrieves the function signature for a given Python file.
"""
- with open(py_file, "r") as f:
+ with open(py_file) as f:
code = f.read()
exec(code)
function_name = os.path.splitext(os.path.basename(py_file))[0]
diff --git a/autogen/agentchat/contrib/vectordb/base.py b/autogen/agentchat/contrib/vectordb/base.py
index 1454d65318..d2f3e0685d 100644
--- a/autogen/agentchat/contrib/vectordb/base.py
+++ b/autogen/agentchat/contrib/vectordb/base.py
@@ -4,14 +4,13 @@
#
# Portions derived from https://github.com/microsoft/autogen are under the MIT License.
# SPDX-License-Identifier: MIT
+from collections.abc import Mapping, Sequence
from typing import (
Any,
Callable,
List,
- Mapping,
Optional,
Protocol,
- Sequence,
Tuple,
TypedDict,
Union,
@@ -42,7 +41,7 @@ class Document(TypedDict):
A query is a list containing one string while queries is a list containing multiple strings.
The response is a list of query results, each query result is a list of tuples containing the document and the distance.
"""
-QueryResults = List[List[Tuple[Document, float]]]
+QueryResults = list[list[tuple[Document, float]]]
@runtime_checkable
@@ -67,7 +66,7 @@ class VectorDB(Protocol):
active_collection: Any = None
type: str = ""
- embedding_function: Optional[Callable[[List[str]], List[List[float]]]] = (
+ embedding_function: Optional[Callable[[list[str]], list[list[float]]]] = (
None # embeddings = embedding_function(sentences)
)
@@ -114,7 +113,7 @@ def delete_collection(self, collection_name: str) -> Any:
"""
...
- def insert_docs(self, docs: List[Document], collection_name: str = None, upsert: bool = False, **kwargs) -> None:
+ def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: bool = False, **kwargs) -> None:
"""
Insert documents into the collection of the vector database.
@@ -129,7 +128,7 @@ def insert_docs(self, docs: List[Document], collection_name: str = None, upsert:
"""
...
- def update_docs(self, docs: List[Document], collection_name: str = None, **kwargs) -> None:
+ def update_docs(self, docs: list[Document], collection_name: str = None, **kwargs) -> None:
"""
Update documents in the collection of the vector database.
@@ -143,7 +142,7 @@ def update_docs(self, docs: List[Document], collection_name: str = None, **kwarg
"""
...
- def delete_docs(self, ids: List[ItemID], collection_name: str = None, **kwargs) -> None:
+ def delete_docs(self, ids: list[ItemID], collection_name: str = None, **kwargs) -> None:
"""
Delete documents from the collection of the vector database.
@@ -159,7 +158,7 @@ def delete_docs(self, ids: List[ItemID], collection_name: str = None, **kwargs)
def retrieve_docs(
self,
- queries: List[str],
+ queries: list[str],
collection_name: str = None,
n_results: int = 10,
distance_threshold: float = -1,
@@ -183,8 +182,8 @@ def retrieve_docs(
...
def get_docs_by_ids(
- self, ids: List[ItemID] = None, collection_name: str = None, include=None, **kwargs
- ) -> List[Document]:
+ self, ids: list[ItemID] = None, collection_name: str = None, include=None, **kwargs
+ ) -> list[Document]:
"""
Retrieve documents from the collection of the vector database based on the ids.
diff --git a/autogen/agentchat/contrib/vectordb/chromadb.py b/autogen/agentchat/contrib/vectordb/chromadb.py
index c6e082fc22..f01b32b898 100644
--- a/autogen/agentchat/contrib/vectordb/chromadb.py
+++ b/autogen/agentchat/contrib/vectordb/chromadb.py
@@ -169,7 +169,7 @@ def _batch_insert(
else:
collection.add(**collection_kwargs)
- def insert_docs(self, docs: List[Document], collection_name: str = None, upsert: bool = False) -> None:
+ def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: bool = False) -> None:
"""
Insert documents into the collection of the vector database.
@@ -204,7 +204,7 @@ def insert_docs(self, docs: List[Document], collection_name: str = None, upsert:
metadatas = [doc.get("metadata") for doc in docs]
self._batch_insert(collection, embeddings, ids, metadatas, documents, upsert)
- def update_docs(self, docs: List[Document], collection_name: str = None) -> None:
+ def update_docs(self, docs: list[Document], collection_name: str = None) -> None:
"""
Update documents in the collection of the vector database.
@@ -217,7 +217,7 @@ def update_docs(self, docs: List[Document], collection_name: str = None) -> None
"""
self.insert_docs(docs, collection_name, upsert=True)
- def delete_docs(self, ids: List[ItemID], collection_name: str = None, **kwargs) -> None:
+ def delete_docs(self, ids: list[ItemID], collection_name: str = None, **kwargs) -> None:
"""
Delete documents from the collection of the vector database.
@@ -234,7 +234,7 @@ def delete_docs(self, ids: List[ItemID], collection_name: str = None, **kwargs)
def retrieve_docs(
self,
- queries: List[str],
+ queries: list[str],
collection_name: str = None,
n_results: int = 10,
distance_threshold: float = -1,
@@ -269,7 +269,7 @@ def retrieve_docs(
return results
@staticmethod
- def _chroma_get_results_to_list_documents(data_dict) -> List[Document]:
+ def _chroma_get_results_to_list_documents(data_dict) -> list[Document]:
"""Converts a dictionary with list values to a list of Document.
Args:
@@ -305,8 +305,8 @@ def _chroma_get_results_to_list_documents(data_dict) -> List[Document]:
return results
def get_docs_by_ids(
- self, ids: List[ItemID] = None, collection_name: str = None, include=None, **kwargs
- ) -> List[Document]:
+ self, ids: list[ItemID] = None, collection_name: str = None, include=None, **kwargs
+ ) -> list[Document]:
"""
Retrieve documents from the collection of the vector database based on the ids.
diff --git a/autogen/agentchat/contrib/vectordb/mongodb.py b/autogen/agentchat/contrib/vectordb/mongodb.py
index aef05e35d7..b1a199c495 100644
--- a/autogen/agentchat/contrib/vectordb/mongodb.py
+++ b/autogen/agentchat/contrib/vectordb/mongodb.py
@@ -4,9 +4,10 @@
#
# Portions derived from https://github.com/microsoft/autogen are under the MIT License.
# SPDX-License-Identifier: MIT
+from collections.abc import Iterable, Mapping
from copy import deepcopy
from time import monotonic, sleep
-from typing import Any, Callable, Dict, Iterable, List, Literal, Mapping, Set, Tuple, Union
+from typing import Any, Callable, Dict, List, Literal, Set, Tuple, Union
import numpy as np
from pymongo import MongoClient, UpdateOne, errors
@@ -25,7 +26,7 @@
_DELAY = 0.5
-def with_id_rename(docs: Iterable) -> List[Dict[str, Any]]:
+def with_id_rename(docs: Iterable) -> list[dict[str, Any]]:
"""Utility changes _id field from Collection into id for Document."""
return [{**{k: v for k, v in d.items() if k != "_id"}, "id": d["_id"]} for d in docs]
@@ -271,7 +272,7 @@ def create_vector_search_index(
def insert_docs(
self,
- docs: List[Document],
+ docs: list[Document],
collection_name: str = None,
upsert: bool = False,
batch_size=DEFAULT_INSERT_BATCH_SIZE,
@@ -341,8 +342,8 @@ def insert_docs(
self._wait_for_document(collection, self.index_name, docs[-1])
def _insert_batch(
- self, collection: Collection, texts: List[str], metadatas: List[Mapping[str, Any]], ids: List[ItemID]
- ) -> Set[ItemID]:
+ self, collection: Collection, texts: list[str], metadatas: list[Mapping[str, Any]], ids: list[ItemID]
+ ) -> set[ItemID]:
"""Compute embeddings for and insert a batch of Documents into the Collection.
For performance reasons, we chose to call self.embedding_function just once,
@@ -373,7 +374,7 @@ def _insert_batch(
insert_result = collection.insert_many(to_insert) # type: ignore
return insert_result.inserted_ids # TODO Remove this. Replace by log like update_docs
- def update_docs(self, docs: List[Document], collection_name: str = None, **kwargs: Any) -> None:
+ def update_docs(self, docs: list[Document], collection_name: str = None, **kwargs: Any) -> None:
"""Update documents, including their embeddings, in the Collection.
Optionally allow upsert as kwarg.
@@ -413,7 +414,7 @@ def update_docs(self, docs: List[Document], collection_name: str = None, **kwarg
result.upserted_count,
)
- def delete_docs(self, ids: List[ItemID], collection_name: str = None, **kwargs):
+ def delete_docs(self, ids: list[ItemID], collection_name: str = None, **kwargs):
"""
Delete documents from the collection of the vector database.
@@ -425,8 +426,8 @@ def delete_docs(self, ids: List[ItemID], collection_name: str = None, **kwargs):
return collection.delete_many({"_id": {"$in": ids}})
def get_docs_by_ids(
- self, ids: List[ItemID] = None, collection_name: str = None, include: List[str] = None, **kwargs
- ) -> List[Document]:
+ self, ids: list[ItemID] = None, collection_name: str = None, include: list[str] = None, **kwargs
+ ) -> list[Document]:
"""
Retrieve documents from the collection of the vector database based on the ids.
@@ -457,7 +458,7 @@ def get_docs_by_ids(
def retrieve_docs(
self,
- queries: List[str],
+ queries: list[str],
collection_name: str = None,
n_results: int = 10,
distance_threshold: float = -1,
@@ -509,14 +510,14 @@ def retrieve_docs(
def _vector_search(
- embedding_vector: List[float],
+ embedding_vector: list[float],
n_results: int,
collection: Collection,
index_name: str,
distance_threshold: float = -1.0,
oversampling_factor=10,
include_embedding=False,
-) -> List[Tuple[Dict, float]]:
+) -> list[tuple[dict, float]]:
"""Core $vectorSearch Aggregation pipeline.
Args:
diff --git a/autogen/agentchat/contrib/vectordb/pgvectordb.py b/autogen/agentchat/contrib/vectordb/pgvectordb.py
index de7d4e5179..431fa9a543 100644
--- a/autogen/agentchat/contrib/vectordb/pgvectordb.py
+++ b/autogen/agentchat/contrib/vectordb/pgvectordb.py
@@ -88,7 +88,7 @@ def set_collection_name(self, collection_name) -> str:
self.name = name
return self.name
- def add(self, ids: List[ItemID], documents: List, embeddings: List = None, metadatas: List = None) -> None:
+ def add(self, ids: list[ItemID], documents: list, embeddings: list = None, metadatas: list = None) -> None:
"""
Add documents to the collection.
@@ -131,7 +131,7 @@ def add(self, ids: List[ItemID], documents: List, embeddings: List = None, metad
cursor.executemany(sql_string, sql_values)
cursor.close()
- def upsert(self, ids: List[ItemID], documents: List, embeddings: List = None, metadatas: List = None) -> None:
+ def upsert(self, ids: list[ItemID], documents: list, embeddings: list = None, metadatas: list = None) -> None:
"""
Upsert documents into the collection.
@@ -240,7 +240,7 @@ def get(
where: Optional[str] = None,
limit: Optional[Union[int, str]] = None,
offset: Optional[Union[int, str]] = None,
- ) -> List[Document]:
+ ) -> list[Document]:
"""
Retrieve documents from the collection.
@@ -312,7 +312,7 @@ def get(
cursor.close()
return retrieved_documents
- def update(self, ids: List, embeddings: List, metadatas: List, documents: List) -> None:
+ def update(self, ids: list, embeddings: list, metadatas: list, documents: list) -> None:
"""
Update documents in the collection.
@@ -341,7 +341,7 @@ def update(self, ids: List, embeddings: List, metadatas: List, documents: List)
cursor.close()
@staticmethod
- def euclidean_distance(arr1: List[float], arr2: List[float]) -> float:
+ def euclidean_distance(arr1: list[float], arr2: list[float]) -> float:
"""
Calculate the Euclidean distance between two vectors.
@@ -356,7 +356,7 @@ def euclidean_distance(arr1: List[float], arr2: List[float]) -> float:
return dist
@staticmethod
- def cosine_distance(arr1: List[float], arr2: List[float]) -> float:
+ def cosine_distance(arr1: list[float], arr2: list[float]) -> float:
"""
Calculate the cosine distance between two vectors.
@@ -371,7 +371,7 @@ def cosine_distance(arr1: List[float], arr2: List[float]) -> float:
return dist
@staticmethod
- def inner_product_distance(arr1: List[float], arr2: List[float]) -> float:
+ def inner_product_distance(arr1: list[float], arr2: list[float]) -> float:
"""
Calculate the Euclidean distance between two vectors.
@@ -387,7 +387,7 @@ def inner_product_distance(arr1: List[float], arr2: List[float]) -> float:
def query(
self,
- query_texts: List[str],
+ query_texts: list[str],
collection_name: Optional[str] = None,
n_results: Optional[int] = 10,
distance_type: Optional[str] = "euclidean",
@@ -458,7 +458,7 @@ def query(
return results
@staticmethod
- def convert_string_to_array(array_string: str) -> List[float]:
+ def convert_string_to_array(array_string: str) -> list[float]:
"""
Convert a string representation of an array to a list of floats.
@@ -494,7 +494,7 @@ def modify(self, metadata, collection_name: Optional[str] = None) -> None:
)
cursor.close()
- def delete(self, ids: List[ItemID], collection_name: Optional[str] = None) -> None:
+ def delete(self, ids: list[ItemID], collection_name: Optional[str] = None) -> None:
"""
Delete documents from the collection.
@@ -836,7 +836,7 @@ def _batch_insert(
else:
collection.add(**collection_kwargs)
- def insert_docs(self, docs: List[Document], collection_name: str = None, upsert: bool = False) -> None:
+ def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: bool = False) -> None:
"""
Insert documents into the collection of the vector database.
@@ -874,7 +874,7 @@ def insert_docs(self, docs: List[Document], collection_name: str = None, upsert:
self._batch_insert(collection, embeddings, ids, metadatas, documents, upsert)
- def update_docs(self, docs: List[Document], collection_name: str = None) -> None:
+ def update_docs(self, docs: list[Document], collection_name: str = None) -> None:
"""
Update documents in the collection of the vector database.
@@ -887,7 +887,7 @@ def update_docs(self, docs: List[Document], collection_name: str = None) -> None
"""
self.insert_docs(docs, collection_name, upsert=True)
- def delete_docs(self, ids: List[ItemID], collection_name: str = None) -> None:
+ def delete_docs(self, ids: list[ItemID], collection_name: str = None) -> None:
"""
Delete documents from the collection of the vector database.
@@ -904,7 +904,7 @@ def delete_docs(self, ids: List[ItemID], collection_name: str = None) -> None:
def retrieve_docs(
self,
- queries: List[str],
+ queries: list[str],
collection_name: str = None,
n_results: int = 10,
distance_threshold: float = -1,
@@ -936,8 +936,8 @@ def retrieve_docs(
return results
def get_docs_by_ids(
- self, ids: List[ItemID] = None, collection_name: str = None, include=None, **kwargs
- ) -> List[Document]:
+ self, ids: list[ItemID] = None, collection_name: str = None, include=None, **kwargs
+ ) -> list[Document]:
"""
Retrieve documents from the collection of the vector database based on the ids.
diff --git a/autogen/agentchat/contrib/vectordb/qdrant.py b/autogen/agentchat/contrib/vectordb/qdrant.py
index 65ede2ec55..70564056d8 100644
--- a/autogen/agentchat/contrib/vectordb/qdrant.py
+++ b/autogen/agentchat/contrib/vectordb/qdrant.py
@@ -7,7 +7,8 @@
import abc
import logging
import os
-from typing import Callable, List, Optional, Sequence, Tuple, Union
+from collections.abc import Sequence
+from typing import Callable, List, Optional, Tuple, Union
from .base import Document, ItemID, QueryResults, VectorDB
from .utils import get_logger
@@ -24,7 +25,7 @@
class EmbeddingFunction(abc.ABC):
@abc.abstractmethod
- def __call__(self, inputs: List[str]) -> List[Embeddings]:
+ def __call__(self, inputs: list[str]) -> list[Embeddings]:
raise NotImplementedError
@@ -67,7 +68,7 @@ def __init__(
self._parallel = parallel
self._model = TextEmbedding(model_name=model_name, cache_dir=cache_dir, threads=threads, **kwargs)
- def __call__(self, inputs: List[str]) -> List[Embeddings]:
+ def __call__(self, inputs: list[str]) -> list[Embeddings]:
embeddings = self._model.embed(inputs, batch_size=self._batch_size, parallel=self._parallel)
return [embedding.tolist() for embedding in embeddings]
@@ -161,7 +162,7 @@ def delete_collection(self, collection_name: str) -> None:
"""
return self.client.delete_collection(collection_name)
- def insert_docs(self, docs: List[Document], collection_name: str = None, upsert: bool = False) -> None:
+ def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: bool = False) -> None:
"""
Insert documents into the collection of the vector database.
@@ -186,7 +187,7 @@ def insert_docs(self, docs: List[Document], collection_name: str = None, upsert:
self.client.upsert(collection_name, points=self._documents_to_points(docs))
- def update_docs(self, docs: List[Document], collection_name: str = None) -> None:
+ def update_docs(self, docs: list[Document], collection_name: str = None) -> None:
if not docs:
return
if any(doc.get("id") is None for doc in docs):
@@ -198,7 +199,7 @@ def update_docs(self, docs: List[Document], collection_name: str = None) -> None
raise ValueError("Some IDs do not exist. Skipping update")
- def delete_docs(self, ids: List[ItemID], collection_name: str = None, **kwargs) -> None:
+ def delete_docs(self, ids: list[ItemID], collection_name: str = None, **kwargs) -> None:
"""
Delete documents from the collection of the vector database.
@@ -214,7 +215,7 @@ def delete_docs(self, ids: List[ItemID], collection_name: str = None, **kwargs)
def retrieve_docs(
self,
- queries: List[str],
+ queries: list[str],
collection_name: str = None,
n_results: int = 10,
distance_threshold: float = 0,
@@ -251,8 +252,8 @@ def retrieve_docs(
return [self._scored_points_to_documents(results) for results in batch_results]
def get_docs_by_ids(
- self, ids: List[ItemID] = None, collection_name: str = None, include=True, **kwargs
- ) -> List[Document]:
+ self, ids: list[ItemID] = None, collection_name: str = None, include=True, **kwargs
+ ) -> list[Document]:
"""
Retrieve documents from the collection of the vector database based on the ids.
@@ -280,13 +281,13 @@ def _point_to_document(self, point) -> Document:
"embedding": point.vector,
}
- def _points_to_documents(self, points) -> List[Document]:
+ def _points_to_documents(self, points) -> list[Document]:
return [self._point_to_document(point) for point in points]
- def _scored_point_to_document(self, scored_point: models.ScoredPoint) -> Tuple[Document, float]:
+ def _scored_point_to_document(self, scored_point: models.ScoredPoint) -> tuple[Document, float]:
return self._point_to_document(scored_point), scored_point.score
- def _documents_to_points(self, documents: List[Document]):
+ def _documents_to_points(self, documents: list[Document]):
contents = [document["content"] for document in documents]
embeddings = self.embedding_function(contents)
points = [
@@ -302,10 +303,10 @@ def _documents_to_points(self, documents: List[Document]):
]
return points
- def _scored_points_to_documents(self, scored_points: List[models.ScoredPoint]) -> List[Tuple[Document, float]]:
+ def _scored_points_to_documents(self, scored_points: list[models.ScoredPoint]) -> list[tuple[Document, float]]:
return [self._scored_point_to_document(scored_point) for scored_point in scored_points]
- def _validate_update_ids(self, collection_name: str, ids: List[str]) -> bool:
+ def _validate_update_ids(self, collection_name: str, ids: list[str]) -> bool:
"""
Validates all the IDs exist in the collection
"""
@@ -319,7 +320,7 @@ def _validate_update_ids(self, collection_name: str, ids: List[str]) -> bool:
return True
- def _validate_upsert_ids(self, collection_name: str, ids: List[str]) -> bool:
+ def _validate_upsert_ids(self, collection_name: str, ids: list[str]) -> bool:
"""
Validate none of the IDs exist in the collection
"""
diff --git a/autogen/agentchat/contrib/vectordb/utils.py b/autogen/agentchat/contrib/vectordb/utils.py
index f0e5f00bce..6fb8cbacdf 100644
--- a/autogen/agentchat/contrib/vectordb/utils.py
+++ b/autogen/agentchat/contrib/vectordb/utils.py
@@ -64,7 +64,7 @@ def filter_results_by_distance(results: QueryResults, distance_threshold: float
return results
-def chroma_results_to_query_results(data_dict: Dict[str, List[List[Any]]], special_key="distances") -> QueryResults:
+def chroma_results_to_query_results(data_dict: dict[str, list[list[Any]]], special_key="distances") -> QueryResults:
"""Converts a dictionary with list-of-list values to a list of tuples.
Args:
diff --git a/autogen/agentchat/contrib/web_surfer.py b/autogen/agentchat/contrib/web_surfer.py
index b6dd58162f..e8c5da2c21 100644
--- a/autogen/agentchat/contrib/web_surfer.py
+++ b/autogen/agentchat/contrib/web_surfer.py
@@ -10,9 +10,7 @@
import re
from dataclasses import dataclass
from datetime import datetime
-from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
-
-from typing_extensions import Annotated
+from typing import Annotated, Any, Callable, Dict, List, Literal, Optional, Tuple, Union
from ... import Agent, AssistantAgent, ConversableAgent, GroupChat, GroupChatManager, OpenAIWrapper, UserProxyAgent
from ...browser_utils import SimpleTextBrowser
@@ -36,17 +34,17 @@ class WebSurferAgent(ConversableAgent):
def __init__(
self,
name: str,
- system_message: Optional[Union[str, List[str]]] = DEFAULT_PROMPT,
+ system_message: Optional[Union[str, list[str]]] = DEFAULT_PROMPT,
description: Optional[str] = DEFAULT_DESCRIPTION,
- is_termination_msg: Optional[Callable[[Dict[str, Any]], bool]] = None,
+ is_termination_msg: Optional[Callable[[dict[str, Any]], bool]] = None,
max_consecutive_auto_reply: Optional[int] = None,
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "TERMINATE",
- function_map: Optional[Dict[str, Callable]] = None,
- code_execution_config: Union[Dict, Literal[False]] = False,
- llm_config: Optional[Union[Dict, Literal[False]]] = None,
- summarizer_llm_config: Optional[Union[Dict, Literal[False]]] = None,
- default_auto_reply: Optional[Union[str, Dict, None]] = "",
- browser_config: Optional[Union[Dict, None]] = None,
+ function_map: Optional[dict[str, Callable]] = None,
+ code_execution_config: Union[dict, Literal[False]] = False,
+ llm_config: Optional[Union[dict, Literal[False]]] = None,
+ summarizer_llm_config: Optional[Union[dict, Literal[False]]] = None,
+ default_auto_reply: Optional[Union[str, dict, None]] = "",
+ browser_config: Optional[Union[dict, None]] = None,
**kwargs,
):
super().__init__(
@@ -94,7 +92,7 @@ def __init__(
self.register_reply([Agent, None], ConversableAgent.generate_function_call_reply)
self.register_reply([Agent, None], ConversableAgent.check_termination_and_human_reply)
- def _create_summarizer_client(self, summarizer_llm_config: Dict[str, Any], llm_config: Dict[str, Any]) -> None:
+ def _create_summarizer_client(self, summarizer_llm_config: dict[str, Any], llm_config: dict[str, Any]) -> None:
# If the summarizer_llm_config is None, we copy it from the llm_config
if summarizer_llm_config is None:
if llm_config is None: # Nothing to copy
@@ -127,7 +125,7 @@ def _register_functions(self) -> None:
"""Register the functions for the inner assistant and user proxy."""
# Helper functions
- def _browser_state() -> Tuple[str, str]:
+ def _browser_state() -> tuple[str, str]:
header = f"Address: {self.browser.address}\n"
if self.browser.page_title is not None:
header += f"Title: {self.browser.page_title}\n"
@@ -266,10 +264,10 @@ def _summarize_page(
def generate_surfer_reply(
self,
- messages: Optional[List[Dict[str, str]]] = None,
+ messages: Optional[list[dict[str, str]]] = None,
sender: Optional[Agent] = None,
config: Optional[OpenAIWrapper] = None,
- ) -> Tuple[bool, Optional[Union[str, Dict[str, str]]]]:
+ ) -> tuple[bool, Optional[Union[str, dict[str, str]]]]:
"""Generate a reply using autogen.oai."""
if messages is None:
messages = self._oai_messages[sender]
diff --git a/autogen/agentchat/conversable_agent.py b/autogen/agentchat/conversable_agent.py
index b2f22ce9c5..747990a7cb 100644
--- a/autogen/agentchat/conversable_agent.py
+++ b/autogen/agentchat/conversable_agent.py
@@ -69,23 +69,23 @@ class ConversableAgent(LLMAgent):
DEFAULT_SUMMARY_PROMPT = "Summarize the takeaway from the conversation. Do not add any introductory phrases."
DEFAULT_SUMMARY_METHOD = "last_msg"
- llm_config: Union[Dict, Literal[False]]
+ llm_config: Union[dict, Literal[False]]
def __init__(
self,
name: str,
- system_message: Optional[Union[str, List]] = "You are a helpful AI Assistant.",
- is_termination_msg: Optional[Callable[[Dict], bool]] = None,
+ system_message: Optional[Union[str, list]] = "You are a helpful AI Assistant.",
+ is_termination_msg: Optional[Callable[[dict], bool]] = None,
max_consecutive_auto_reply: Optional[int] = None,
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "TERMINATE",
- function_map: Optional[Dict[str, Callable]] = None,
- code_execution_config: Union[Dict, Literal[False]] = False,
- llm_config: Optional[Union[Dict, Literal[False]]] = None,
- default_auto_reply: Union[str, Dict] = "",
+ function_map: Optional[dict[str, Callable]] = None,
+ code_execution_config: Union[dict, Literal[False]] = False,
+ llm_config: Optional[Union[dict, Literal[False]]] = None,
+ default_auto_reply: Union[str, dict] = "",
description: Optional[str] = None,
- chat_messages: Optional[Dict[Agent, List[Dict]]] = None,
+ chat_messages: Optional[dict[Agent, list[dict]]] = None,
silent: Optional[bool] = None,
- context_variables: Optional[Dict[str, Any]] = None,
+ context_variables: Optional[dict[str, Any]] = None,
):
"""
Args:
@@ -260,7 +260,7 @@ def __init__(
# Registered hooks are kept in lists, indexed by hookable method, to be called in their order of registration.
# New hookable methods should be added to this list as required to support new agent capabilities.
- self.hook_lists: Dict[str, List[Callable]] = {
+ self.hook_lists: dict[str, list[Callable]] = {
"process_last_received_message": [],
"process_all_messages_before_reply": [],
"process_message_before_send": [],
@@ -309,7 +309,7 @@ def code_executor(self) -> Optional[CodeExecutor]:
def register_reply(
self,
- trigger: Union[Type[Agent], str, Agent, Callable[[Agent], bool], List],
+ trigger: Union[type[Agent], str, Agent, Callable[[Agent], bool], list],
reply_func: Callable,
position: int = 0,
config: Optional[Any] = None,
@@ -392,8 +392,8 @@ def replace_reply_func(self, old_reply_func: Callable, new_reply_func: Callable)
@staticmethod
def _get_chats_to_run(
- chat_queue: List[Dict[str, Any]], recipient: Agent, messages: Union[str, Callable], sender: Agent, config: Any
- ) -> List[Dict[str, Any]]:
+ chat_queue: list[dict[str, Any]], recipient: Agent, messages: Union[str, Callable], sender: Agent, config: Any
+ ) -> list[dict[str, Any]]:
"""A simple chat reply function.
This function initiate one or a sequence of chats between the "recipient" and the agents in the
chat_queue.
@@ -424,8 +424,8 @@ def _get_chats_to_run(
@staticmethod
def _summary_from_nested_chats(
- chat_queue: List[Dict[str, Any]], recipient: Agent, messages: Union[str, Callable], sender: Agent, config: Any
- ) -> Tuple[bool, Union[str, None]]:
+ chat_queue: list[dict[str, Any]], recipient: Agent, messages: Union[str, Callable], sender: Agent, config: Any
+ ) -> tuple[bool, Union[str, None]]:
"""A simple chat reply function.
This function initiate one or a sequence of chats between the "recipient" and the agents in the
chat_queue.
@@ -443,8 +443,8 @@ def _summary_from_nested_chats(
@staticmethod
async def _a_summary_from_nested_chats(
- chat_queue: List[Dict[str, Any]], recipient: Agent, messages: Union[str, Callable], sender: Agent, config: Any
- ) -> Tuple[bool, Union[str, None]]:
+ chat_queue: list[dict[str, Any]], recipient: Agent, messages: Union[str, Callable], sender: Agent, config: Any
+ ) -> tuple[bool, Union[str, None]]:
"""A simple chat reply function.
This function initiate one or a sequence of chats between the "recipient" and the agents in the
chat_queue.
@@ -463,8 +463,8 @@ async def _a_summary_from_nested_chats(
def register_nested_chats(
self,
- chat_queue: List[Dict[str, Any]],
- trigger: Union[Type[Agent], str, Agent, Callable[[Agent], bool], List],
+ chat_queue: list[dict[str, Any]],
+ trigger: Union[type[Agent], str, Agent, Callable[[Agent], bool], list],
reply_func_from_nested_chats: Union[str, Callable] = "summary_from_nested_chats",
position: int = 2,
use_async: Union[bool, None] = None,
@@ -548,7 +548,7 @@ def set_context(self, key: str, value: Any) -> None:
"""
self._context_variables[key] = value
- def update_context(self, context_variables: Dict[str, Any]) -> None:
+ def update_context(self, context_variables: dict[str, Any]) -> None:
"""
Update multiple context variables at once.
Args:
@@ -599,15 +599,15 @@ def max_consecutive_auto_reply(self, sender: Optional[Agent] = None) -> int:
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]]:
+ def chat_messages(self) -> dict[Agent, list[dict]]:
"""A dictionary of conversations from agent to list of messages."""
return self._oai_messages
- def chat_messages_for_summary(self, agent: Agent) -> List[Dict]:
+ def chat_messages_for_summary(self, agent: Agent) -> list[dict]:
"""A list of messages as a conversation to summarize."""
return self._oai_messages[agent]
- def last_message(self, agent: Optional[Agent] = None) -> Optional[Dict]:
+ def last_message(self, agent: Optional[Agent] = None) -> Optional[dict]:
"""The last message exchanged with the agent.
Args:
@@ -640,7 +640,7 @@ def use_docker(self) -> Union[bool, str, None]:
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]) -> Dict:
+ def _message_to_dict(message: Union[dict, str]) -> dict:
"""Convert a message to a dictionary.
The message can be a string or a dictionary. The string will be put in the "content" field of the new dictionary.
@@ -674,7 +674,7 @@ def _assert_valid_name(name):
raise ValueError(f"Invalid name: {name}. Name must be less than 64 characters.")
return name
- def _append_oai_message(self, message: Union[Dict, str], role, conversation_id: Agent, is_sending: bool) -> bool:
+ def _append_oai_message(self, message: Union[dict, str], role, conversation_id: Agent, is_sending: bool) -> 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.
@@ -731,8 +731,8 @@ def _append_oai_message(self, message: Union[Dict, str], role, conversation_id:
return True
def _process_message_before_send(
- self, message: Union[Dict, str], recipient: Agent, silent: bool
- ) -> Union[Dict, str]:
+ self, message: Union[dict, str], recipient: Agent, silent: bool
+ ) -> Union[dict, str]:
"""Process the message before sending it to the recipient."""
hook_list = self.hook_lists["process_message_before_send"]
for hook in hook_list:
@@ -743,7 +743,7 @@ def _process_message_before_send(
def send(
self,
- message: Union[Dict, str],
+ message: Union[dict, str],
recipient: Agent,
request_reply: Optional[bool] = None,
silent: Optional[bool] = False,
@@ -793,7 +793,7 @@ def send(
async def a_send(
self,
- message: Union[Dict, str],
+ message: Union[dict, str],
recipient: Agent,
request_reply: Optional[bool] = None,
silent: Optional[bool] = False,
@@ -841,7 +841,7 @@ async def a_send(
"Message can't be converted into a valid ChatCompletion message. Either content or function_call must be provided."
)
- def _print_received_message(self, message: Union[Dict, str], sender: Agent, skip_head: bool = False):
+ def _print_received_message(self, message: Union[dict, str], sender: Agent, skip_head: bool = False):
iostream = IOStream.get_default()
# print the message received
if not skip_head:
@@ -903,7 +903,7 @@ def _print_received_message(self, message: Union[Dict, str], sender: Agent, skip
iostream.print("\n", "-" * 80, flush=True, sep="")
- def _process_received_message(self, message: Union[Dict, str], sender: Agent, silent: bool):
+ def _process_received_message(self, message: Union[dict, str], sender: Agent, silent: bool):
# When the agent receives a message, the role of the message is "user". (If 'role' exists and is 'function', it will remain unchanged.)
valid = self._append_oai_message(message, "user", sender, is_sending=False)
if logging_enabled():
@@ -919,7 +919,7 @@ def _process_received_message(self, message: Union[Dict, str], sender: Agent, si
def receive(
self,
- message: Union[Dict, str],
+ message: Union[dict, str],
sender: Agent,
request_reply: Optional[bool] = None,
silent: Optional[bool] = False,
@@ -956,7 +956,7 @@ def receive(
async def a_receive(
self,
- message: Union[Dict, str],
+ message: Union[dict, str],
sender: Agent,
request_reply: Optional[bool] = None,
silent: Optional[bool] = False,
@@ -1034,7 +1034,7 @@ def initiate_chat(
max_turns: Optional[int] = None,
summary_method: Optional[Union[str, Callable]] = DEFAULT_SUMMARY_METHOD,
summary_args: Optional[dict] = {},
- message: Optional[Union[Dict, str, Callable]] = None,
+ message: Optional[Union[dict, str, Callable]] = None,
**kwargs,
) -> ChatResult:
"""Initiate a chat with the recipient agent.
@@ -1355,7 +1355,7 @@ def _reflection_with_llm(
response = self._generate_oai_reply_from_client(llm_client=llm_client, messages=messages, cache=cache)
return response
- def _check_chat_queue_for_sender(self, chat_queue: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+ def _check_chat_queue_for_sender(self, chat_queue: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""
Check the chat queue and add the "sender" key if it's missing.
@@ -1372,7 +1372,7 @@ def _check_chat_queue_for_sender(self, chat_queue: List[Dict[str, Any]]) -> List
chat_queue_with_sender.append(chat_info)
return chat_queue_with_sender
- def initiate_chats(self, chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
+ def initiate_chats(self, chat_queue: list[dict[str, Any]]) -> list[ChatResult]:
"""(Experimental) Initiate chats with multiple agents.
Args:
@@ -1385,12 +1385,12 @@ def initiate_chats(self, chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
self._finished_chats = initiate_chats(_chat_queue)
return self._finished_chats
- async def a_initiate_chats(self, chat_queue: List[Dict[str, Any]]) -> Dict[int, ChatResult]:
+ async def a_initiate_chats(self, chat_queue: list[dict[str, Any]]) -> dict[int, ChatResult]:
_chat_queue = self._check_chat_queue_for_sender(chat_queue)
self._finished_chats = await a_initiate_chats(_chat_queue)
return self._finished_chats
- def get_chat_results(self, chat_index: Optional[int] = None) -> Union[List[ChatResult], ChatResult]:
+ def get_chat_results(self, chat_index: Optional[int] = None) -> Union[list[ChatResult], ChatResult]:
"""A summary from the finished chats of particular agents."""
if chat_index is not None:
return self._finished_chats[chat_index]
@@ -1462,10 +1462,10 @@ def clear_history(self, recipient: Optional[Agent] = None, nr_messages_to_preser
def generate_oai_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[OpenAIWrapper] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""Generate a reply using autogen.oai."""
client = self.client if config is None else config
if client is None:
@@ -1477,7 +1477,7 @@ def generate_oai_reply(
)
return (False, None) if extracted_response is None else (True, extracted_response)
- def _generate_oai_reply_from_client(self, llm_client, messages, cache) -> Union[str, Dict, None]:
+ def _generate_oai_reply_from_client(self, llm_client, messages, cache) -> Union[str, dict, None]:
# unroll tool_responses
all_messages = []
for message in messages:
@@ -1522,16 +1522,16 @@ def _generate_oai_reply_from_client(self, llm_client, messages, cache) -> Union[
async def a_generate_oai_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""Generate a reply using autogen.oai asynchronously."""
iostream = IOStream.get_default()
def _generate_oai_reply(
self, iostream: IOStream, *args: Any, **kwargs: Any
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
with IOStream.set_default(iostream):
return self.generate_oai_reply(*args, **kwargs)
@@ -1544,9 +1544,9 @@ def _generate_oai_reply(
def _generate_code_execution_reply_using_executor(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
- config: Optional[Union[Dict, Literal[False]]] = None,
+ config: Optional[Union[dict, Literal[False]]] = None,
):
"""Generate a reply using code executor."""
iostream = IOStream.get_default()
@@ -1613,9 +1613,9 @@ def _generate_code_execution_reply_using_executor(
def generate_code_execution_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
- config: Optional[Union[Dict, Literal[False]]] = None,
+ config: Optional[Union[dict, Literal[False]]] = None,
):
"""Generate a reply using code execution."""
code_execution_config = config if config is not None else self._code_execution_config
@@ -1665,10 +1665,10 @@ def generate_code_execution_reply(
def generate_function_call_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[Dict, None]]:
+ ) -> tuple[bool, Union[dict, None]]:
"""
Generate a reply using function call.
@@ -1703,10 +1703,10 @@ def generate_function_call_reply(
async def a_generate_function_call_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[Dict, None]]:
+ ) -> tuple[bool, Union[dict, None]]:
"""
Generate a reply using async function call.
@@ -1735,10 +1735,10 @@ def _str_for_tool_response(self, tool_response):
def generate_tool_calls_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[Dict, None]]:
+ ) -> tuple[bool, Union[dict, None]]:
"""Generate a reply using tool call."""
if config is None:
config = self
@@ -1802,10 +1802,10 @@ async def _a_execute_tool_call(self, tool_call):
async def a_generate_tool_calls_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[Dict, None]]:
+ ) -> tuple[bool, Union[dict, None]]:
"""Generate a reply using async function call."""
if config is None:
config = self
@@ -1827,10 +1827,10 @@ async def a_generate_tool_calls_reply(
def check_termination_and_human_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, None]]:
+ ) -> tuple[bool, Union[str, None]]:
"""Check if the conversation should be terminated, and if human reply is provided.
This method checks for conditions that require the conversation to be terminated, such as reaching
@@ -1940,10 +1940,10 @@ def check_termination_and_human_reply(
async def a_check_termination_and_human_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, None]]:
+ ) -> tuple[bool, Union[str, None]]:
"""(async) Check if the conversation should be terminated, and if human reply is provided.
This method checks for conditions that require the conversation to be terminated, such as reaching
@@ -2053,10 +2053,10 @@ async def a_check_termination_and_human_reply(
def generate_reply(
self,
- messages: Optional[List[Dict[str, Any]]] = None,
+ messages: Optional[list[dict[str, Any]]] = None,
sender: Optional["Agent"] = None,
**kwargs: Any,
- ) -> Union[str, Dict, None]:
+ ) -> Union[str, dict, None]:
"""Reply based on the conversation history and the sender.
Either messages or sender must be provided.
@@ -2126,10 +2126,10 @@ def generate_reply(
async def a_generate_reply(
self,
- messages: Optional[List[Dict[str, Any]]] = None,
+ messages: Optional[list[dict[str, Any]]] = None,
sender: Optional["Agent"] = None,
**kwargs: Any,
- ) -> Union[str, Dict[str, Any], None]:
+ ) -> Union[str, dict[str, Any], None]:
"""(async) Reply based on the conversation history and the sender.
Either messages or sender must be provided.
@@ -2192,7 +2192,7 @@ async def a_generate_reply(
return reply
return self._default_auto_reply
- def _match_trigger(self, trigger: Union[None, str, type, Agent, Callable, List], sender: Optional[Agent]) -> bool:
+ def _match_trigger(self, trigger: Union[None, str, type, Agent, Callable, list], sender: Optional[Agent]) -> bool:
"""Check if the sender matches the trigger.
Args:
@@ -2348,7 +2348,7 @@ def _format_json_str(jstr):
result.append(char)
return "".join(result)
- def execute_function(self, func_call, verbose: bool = False) -> Tuple[bool, Dict[str, Any]]:
+ def execute_function(self, func_call, verbose: bool = False) -> tuple[bool, dict[str, Any]]:
"""Execute a function call and return the result.
Override this function to modify the way to execute function and tool calls.
@@ -2460,7 +2460,7 @@ async def a_execute_function(self, func_call):
"content": content,
}
- def generate_init_message(self, message: Union[Dict, str, None], **kwargs) -> Union[str, Dict]:
+ def generate_init_message(self, message: Union[dict, str, None], **kwargs) -> Union[str, dict]:
"""Generate the initial message for the agent.
If message is None, input() will be called to get the initial message.
@@ -2478,7 +2478,7 @@ def generate_init_message(self, message: Union[Dict, str, None], **kwargs) -> Un
return self._handle_carryover(message, kwargs)
- def _handle_carryover(self, message: Union[str, Dict], kwargs: dict) -> Union[str, Dict]:
+ def _handle_carryover(self, message: Union[str, dict], kwargs: dict) -> Union[str, dict]:
if not kwargs.get("carryover"):
return message
@@ -2515,7 +2515,7 @@ def _process_carryover(self, content: str, kwargs: dict) -> str:
)
return content
- def _process_multimodal_carryover(self, content: List[Dict], kwargs: dict) -> List[Dict]:
+ def _process_multimodal_carryover(self, content: list[dict], kwargs: dict) -> list[dict]:
"""Prepends the context to a multimodal message."""
# Makes sure there's a carryover
if not kwargs.get("carryover"):
@@ -2523,7 +2523,7 @@ def _process_multimodal_carryover(self, content: List[Dict], kwargs: dict) -> Li
return [{"type": "text", "text": self._process_carryover("", kwargs)}] + content
- async def a_generate_init_message(self, message: Union[Dict, str, None], **kwargs) -> Union[str, Dict]:
+ async def a_generate_init_message(self, message: Union[dict, str, None], **kwargs) -> Union[str, dict]:
"""Generate the initial message for the agent.
If message is None, input() will be called to get the initial message.
@@ -2538,7 +2538,7 @@ async def a_generate_init_message(self, message: Union[Dict, str, None], **kwarg
return self._handle_carryover(message, kwargs)
- def register_function(self, function_map: Dict[str, Union[Callable, None]]):
+ def register_function(self, function_map: dict[str, Union[Callable, None]]):
"""Register functions to the agent.
Args:
@@ -2553,7 +2553,7 @@ def register_function(self, function_map: Dict[str, Union[Callable, None]]):
self._function_map.update(function_map)
self._function_map = {k: v for k, v in self._function_map.items() if v is not None}
- def update_function_signature(self, func_sig: Union[str, Dict], is_remove: None):
+ def update_function_signature(self, func_sig: Union[str, dict], is_remove: None):
"""update a function_signature in the LLM configuration for function_call.
Args:
@@ -2571,7 +2571,7 @@ def update_function_signature(self, func_sig: Union[str, Dict], is_remove: None)
if is_remove:
if "functions" not in self.llm_config.keys():
- error_msg = "The agent config doesn't have function {name}.".format(name=func_sig)
+ error_msg = f"The agent config doesn't have function {func_sig}."
logger.error(error_msg)
raise AssertionError(error_msg)
else:
@@ -2600,7 +2600,7 @@ def update_function_signature(self, func_sig: Union[str, Dict], is_remove: None)
self.client = OpenAIWrapper(**self.llm_config)
- def update_tool_signature(self, tool_sig: Union[str, Dict], is_remove: bool):
+ def update_tool_signature(self, tool_sig: Union[str, dict], is_remove: bool):
"""update a tool_signature in the LLM configuration for tool_call.
Args:
@@ -2615,7 +2615,7 @@ def update_tool_signature(self, tool_sig: Union[str, Dict], is_remove: bool):
if is_remove:
if "tools" not in self.llm_config.keys():
- error_msg = "The agent config doesn't have tool {name}.".format(name=tool_sig)
+ error_msg = f"The agent config doesn't have tool {tool_sig}."
logger.error(error_msg)
raise AssertionError(error_msg)
else:
@@ -2644,13 +2644,13 @@ def update_tool_signature(self, tool_sig: Union[str, Dict], is_remove: bool):
self.client = OpenAIWrapper(**self.llm_config)
- def can_execute_function(self, name: Union[List[str], str]) -> bool:
+ def can_execute_function(self, name: Union[list[str], str]) -> bool:
"""Whether the agent can execute the function."""
names = name if isinstance(name, list) else [name]
return all([n in self._function_map for n in names])
@property
- def function_map(self) -> Dict[str, Callable]:
+ def function_map(self) -> dict[str, Callable]:
"""Return the function map."""
return self._function_map
@@ -2854,7 +2854,7 @@ def register_hook(self, hookable_method: str, hook: Callable):
assert hook not in hook_list, f"{hook} is already registered as a hook."
hook_list.append(hook)
- def update_agent_state_before_reply(self, messages: List[Dict]) -> None:
+ def update_agent_state_before_reply(self, messages: list[dict]) -> None:
"""
Calls any registered capability hooks to update the agent's state.
Primarily used to update context variables.
@@ -2866,7 +2866,7 @@ def update_agent_state_before_reply(self, messages: List[Dict]) -> None:
for hook in hook_list:
hook(self, messages)
- def process_all_messages_before_reply(self, messages: List[Dict]) -> List[Dict]:
+ def process_all_messages_before_reply(self, messages: list[dict]) -> list[dict]:
"""
Calls any registered capability hooks to process all messages, potentially modifying the messages.
"""
@@ -2881,7 +2881,7 @@ def process_all_messages_before_reply(self, messages: List[Dict]) -> List[Dict]:
processed_messages = hook(processed_messages)
return processed_messages
- def process_last_received_message(self, messages: List[Dict]) -> List[Dict]:
+ def process_last_received_message(self, messages: list[dict]) -> list[dict]:
"""
Calls any registered capability hooks to use and potentially modify the text of the last message,
as long as the last message is not a function call or exit command.
@@ -2924,7 +2924,7 @@ def process_last_received_message(self, messages: List[Dict]) -> List[Dict]:
messages[-1]["content"] = processed_user_content
return messages
- def print_usage_summary(self, mode: Union[str, List[str]] = ["actual", "total"]) -> None:
+ def print_usage_summary(self, mode: Union[str, list[str]] = ["actual", "total"]) -> None:
"""Print the usage summary."""
iostream = IOStream.get_default()
@@ -2934,14 +2934,14 @@ def print_usage_summary(self, mode: Union[str, List[str]] = ["actual", "total"])
iostream.print(f"Agent '{self.name}':")
self.client.print_usage_summary(mode)
- def get_actual_usage(self) -> Union[None, Dict[str, int]]:
+ def get_actual_usage(self) -> Union[None, dict[str, int]]:
"""Get the actual usage summary."""
if self.client is None:
return None
else:
return self.client.actual_usage_summary
- def get_total_usage(self) -> Union[None, Dict[str, int]]:
+ def get_total_usage(self) -> Union[None, dict[str, int]]:
"""Get the total usage summary."""
if self.client is None:
return None
diff --git a/autogen/agentchat/groupchat.py b/autogen/agentchat/groupchat.py
index 0e14bf35f8..4a2bc18241 100644
--- a/autogen/agentchat/groupchat.py
+++ b/autogen/agentchat/groupchat.py
@@ -111,15 +111,15 @@ def custom_speaker_selection_func(
- role_for_select_speaker_messages: sets the role name for speaker selection when in 'auto' mode, typically 'user' or 'system'. (default: 'system')
"""
- agents: List[Agent]
- messages: List[Dict]
+ agents: list[Agent]
+ messages: list[dict]
max_round: int = 10
admin_name: str = "Admin"
func_call_filter: bool = True
speaker_selection_method: Union[Literal["auto", "manual", "random", "round_robin"], Callable] = "auto"
max_retries_for_selecting_speaker: int = 2
- allow_repeat_speaker: Optional[Union[bool, List[Agent]]] = None
- allowed_or_disallowed_speaker_transitions: Optional[Dict] = None
+ allow_repeat_speaker: Optional[Union[bool, list[Agent]]] = None
+ allowed_or_disallowed_speaker_transitions: Optional[dict] = None
speaker_transitions_type: Literal["allowed", "disallowed", None] = None
enable_clear_history: bool = False
send_introductions: bool = False
@@ -145,8 +145,8 @@ def custom_speaker_selection_func(
Respond with ONLY the name of the speaker and DO NOT provide a reason."""
select_speaker_transform_messages: Optional[transform_messages.TransformMessages] = None
select_speaker_auto_verbose: Optional[bool] = False
- select_speaker_auto_model_client_cls: Optional[Union[ModelClient, List[ModelClient]]] = None
- select_speaker_auto_llm_config: Optional[Union[Dict, Literal[False]]] = None
+ select_speaker_auto_model_client_cls: Optional[Union[ModelClient, list[ModelClient]]] = None
+ select_speaker_auto_llm_config: Optional[Union[dict, Literal[False]]] = None
role_for_select_speaker_messages: Optional[str] = "system"
_VALID_SPEAKER_SELECTION_METHODS = ["auto", "manual", "random", "round_robin"]
@@ -157,7 +157,7 @@ def custom_speaker_selection_func(
"Hello everyone. We have assembled a great team today to answer questions and solve tasks. In attendance are:"
)
- allowed_speaker_transitions_dict: Dict = field(init=False)
+ allowed_speaker_transitions_dict: dict = field(init=False)
def __post_init__(self):
# Post init steers clears of the automatically generated __init__ method from dataclass
@@ -277,7 +277,7 @@ def __post_init__(self):
raise ValueError("select_speaker_auto_verbose cannot be None or non-bool")
@property
- def agent_names(self) -> List[str]:
+ def agent_names(self) -> list[str]:
"""Return the names of the agents in the group chat."""
return [agent.name for agent in self.agents]
@@ -285,7 +285,7 @@ def reset(self):
"""Reset the group chat."""
self.messages.clear()
- def append(self, message: Dict, speaker: Agent):
+ def append(self, message: dict, speaker: Agent):
"""Append a message to the group chat.
We cast the content to str here so that it can be managed by text-based
model.
@@ -311,7 +311,7 @@ def agent_by_name(
return filtered_agents[0] if filtered_agents else None
- def nested_agents(self) -> List[Agent]:
+ def nested_agents(self) -> list[Agent]:
"""Returns all agents in the group chat manager."""
agents = self.agents.copy()
for agent in agents:
@@ -320,7 +320,7 @@ def nested_agents(self) -> List[Agent]:
agents.extend(agent.groupchat.nested_agents())
return agents
- def next_agent(self, agent: Agent, agents: Optional[List[Agent]] = None) -> Agent:
+ def next_agent(self, agent: Agent, agents: Optional[list[Agent]] = None) -> Agent:
"""Return the next agent in the list."""
if agents is None:
agents = self.agents
@@ -344,7 +344,7 @@ def next_agent(self, agent: Agent, agents: Optional[List[Agent]] = None) -> Agen
# Explicitly handle cases where no valid next agent exists in the provided subset.
raise UndefinedNextAgent()
- def select_speaker_msg(self, agents: Optional[List[Agent]] = None) -> str:
+ def select_speaker_msg(self, agents: Optional[list[Agent]] = None) -> str:
"""Return the system message for selecting the next speaker. This is always the *first* message in the context."""
if agents is None:
agents = self.agents
@@ -355,7 +355,7 @@ def select_speaker_msg(self, agents: Optional[List[Agent]] = None) -> str:
return_msg = self.select_speaker_message_template.format(roles=roles, agentlist=agentlist)
return return_msg
- def select_speaker_prompt(self, agents: Optional[List[Agent]] = None) -> str:
+ def select_speaker_prompt(self, agents: Optional[list[Agent]] = None) -> str:
"""Return the floating system prompt selecting the next speaker.
This is always the *last* message in the context.
Will return None if the select_speaker_prompt_template is None."""
@@ -371,7 +371,7 @@ def select_speaker_prompt(self, agents: Optional[List[Agent]] = None) -> str:
return_prompt = self.select_speaker_prompt_template.format(agentlist=agentlist)
return return_prompt
- def introductions_msg(self, agents: Optional[List[Agent]] = None) -> str:
+ def introductions_msg(self, agents: Optional[list[Agent]] = None) -> str:
"""Return the system message for selecting the next speaker. This is always the *first* message in the context."""
if agents is None:
agents = self.agents
@@ -382,7 +382,7 @@ def introductions_msg(self, agents: Optional[List[Agent]] = None) -> str:
return f"{intro_msg}\n\n{participant_roles}"
- def manual_select_speaker(self, agents: Optional[List[Agent]] = None) -> Union[Agent, None]:
+ def manual_select_speaker(self, agents: Optional[list[Agent]] = None) -> Union[Agent, None]:
"""Manually select the next speaker."""
iostream = IOStream.get_default()
@@ -415,7 +415,7 @@ def manual_select_speaker(self, agents: Optional[List[Agent]] = None) -> Union[A
iostream.print(f"Invalid input. Please enter a number between 1 and {_n_agents}.")
return None
- def random_select_speaker(self, agents: Optional[List[Agent]] = None) -> Union[Agent, None]:
+ def random_select_speaker(self, agents: Optional[list[Agent]] = None) -> Union[Agent, None]:
"""Randomly select the next speaker."""
if agents is None:
agents = self.agents
@@ -424,7 +424,7 @@ def random_select_speaker(self, agents: Optional[List[Agent]] = None) -> Union[A
def _prepare_and_select_agents(
self,
last_speaker: Agent,
- ) -> Tuple[Optional[Agent], List[Agent], Optional[List[Dict]]]:
+ ) -> tuple[Optional[Agent], list[Agent], Optional[list[dict]]]:
# If self.speaker_selection_method is a callable, call it to get the next speaker.
# If self.speaker_selection_method is a string, return it.
speaker_selection_method = self.speaker_selection_method
@@ -575,7 +575,7 @@ async def a_select_speaker(self, last_speaker: Agent, selector: ConversableAgent
# auto speaker selection with 2-agent chat
return await self.a_auto_select_speaker(last_speaker, selector, messages, agents)
- def _finalize_speaker(self, last_speaker: Agent, final: bool, name: str, agents: Optional[List[Agent]]) -> Agent:
+ def _finalize_speaker(self, last_speaker: Agent, final: bool, name: str, agents: Optional[list[Agent]]) -> Agent:
if not final:
# the LLM client is None, thus no reply is generated. Use round robin instead.
return self.next_agent(last_speaker, agents)
@@ -593,7 +593,7 @@ def _finalize_speaker(self, last_speaker: Agent, final: bool, name: str, agents:
agent = self.agent_by_name(name)
return agent if agent else self.next_agent(last_speaker, agents)
- def _register_client_from_config(self, agent: Agent, config: Dict):
+ def _register_client_from_config(self, agent: Agent, config: dict):
model_client_cls_to_match = config.get("model_client_cls")
if model_client_cls_to_match:
if not self.select_speaker_auto_model_client_cls:
@@ -670,8 +670,8 @@ def _auto_select_speaker(
self,
last_speaker: Agent,
selector: ConversableAgent,
- messages: Optional[List[Dict]],
- agents: Optional[List[Agent]],
+ messages: Optional[list[dict]],
+ agents: Optional[list[Agent]],
) -> Agent:
"""Selects next speaker for the "auto" speaker selection method. Utilises its own two-agent chat to determine the next speaker and supports requerying.
@@ -706,7 +706,7 @@ def _auto_select_speaker(
attempt = 0
# Registered reply function for checking_agent, checks the result of the response for agent names
- def validate_speaker_name(recipient, messages, sender, config) -> Tuple[bool, Union[str, Dict, None]]:
+ def validate_speaker_name(recipient, messages, sender, config) -> tuple[bool, Union[str, dict, None]]:
# The number of retries left, starting at max_retries_for_selecting_speaker
nonlocal attempts_left
nonlocal attempt
@@ -754,8 +754,8 @@ async def a_auto_select_speaker(
self,
last_speaker: Agent,
selector: ConversableAgent,
- messages: Optional[List[Dict]],
- agents: Optional[List[Agent]],
+ messages: Optional[list[dict]],
+ agents: Optional[list[Agent]],
) -> Agent:
"""(Asynchronous) Selects next speaker for the "auto" speaker selection method. Utilises its own two-agent chat to determine the next speaker and supports requerying.
@@ -789,7 +789,7 @@ async def a_auto_select_speaker(
attempt = 0
# Registered reply function for checking_agent, checks the result of the response for agent names
- def validate_speaker_name(recipient, messages, sender, config) -> Tuple[bool, Union[str, Dict, None]]:
+ def validate_speaker_name(recipient, messages, sender, config) -> tuple[bool, Union[str, dict, None]]:
# The number of retries left, starting at max_retries_for_selecting_speaker
nonlocal attempts_left
nonlocal attempt
@@ -834,7 +834,7 @@ def validate_speaker_name(recipient, messages, sender, config) -> Tuple[bool, Un
def _validate_speaker_name(
self, recipient, messages, sender, config, attempts_left, attempt, agents
- ) -> Tuple[bool, Union[str, Dict, None]]:
+ ) -> tuple[bool, Union[str, dict, None]]:
"""Validates the speaker response for each round in the internal 2-agent
chat within the auto select speaker method.
@@ -928,7 +928,7 @@ def _validate_speaker_name(
return True, None
- def _process_speaker_selection_result(self, result, last_speaker: ConversableAgent, agents: Optional[List[Agent]]):
+ def _process_speaker_selection_result(self, result, last_speaker: ConversableAgent, agents: Optional[list[Agent]]):
"""Checks the result of the auto_select_speaker function, returning the
agent to speak.
@@ -948,7 +948,7 @@ def _process_speaker_selection_result(self, result, last_speaker: ConversableAge
# No agent, return the failed reason
return next_agent
- def _participant_roles(self, agents: List[Agent] = None) -> str:
+ def _participant_roles(self, agents: list[Agent] = None) -> str:
# Default to all agents registered
if agents is None:
agents = self.agents
@@ -962,7 +962,7 @@ def _participant_roles(self, agents: List[Agent] = None) -> str:
roles.append(f"{agent.name}: {agent.description}".strip())
return "\n".join(roles)
- def _mentioned_agents(self, message_content: Union[str, List], agents: Optional[List[Agent]]) -> Dict:
+ def _mentioned_agents(self, message_content: Union[str, list], agents: Optional[list[Agent]]) -> dict:
"""Counts the number of times each agent is mentioned in the provided message content.
Agent names will match under any of the following conditions (all case-sensitive):
- Exact name match
@@ -1013,7 +1013,7 @@ def __init__(
# unlimited consecutive auto reply by default
max_consecutive_auto_reply: Optional[int] = sys.maxsize,
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "NEVER",
- system_message: Optional[Union[str, List]] = "Group chat manager.",
+ system_message: Optional[Union[str, list]] = "Group chat manager.",
silent: bool = False,
**kwargs,
):
@@ -1058,7 +1058,7 @@ def groupchat(self) -> GroupChat:
"""Returns the group chat managed by the group chat manager."""
return self._groupchat
- def chat_messages_for_summary(self, agent: Agent) -> List[Dict]:
+ def chat_messages_for_summary(self, agent: Agent) -> list[dict]:
"""The list of messages in the group chat as a conversation to summarize.
The agent is ignored.
"""
@@ -1129,10 +1129,10 @@ def print_messages(recipient, messages, sender, config):
def run_chat(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[GroupChat] = None,
- ) -> Tuple[bool, Optional[str]]:
+ ) -> tuple[bool, Optional[str]]:
"""Run a group chat."""
if messages is None:
messages = self._oai_messages[sender]
@@ -1209,7 +1209,7 @@ def run_chat(
async def a_run_chat(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[GroupChat] = None,
):
@@ -1275,10 +1275,10 @@ async def a_run_chat(
def resume(
self,
- messages: Union[List[Dict], str],
+ messages: Union[list[dict], str],
remove_termination_string: Optional[Union[str, Callable[[str], str]]] = None,
silent: Optional[bool] = False,
- ) -> Tuple[ConversableAgent, Dict]:
+ ) -> tuple[ConversableAgent, dict]:
"""Resumes a group chat using the previous messages as a starting point. Requires the agents, group chat, and group chat manager to be established
as per the original group chat.
@@ -1383,10 +1383,10 @@ def resume(
async def a_resume(
self,
- messages: Union[List[Dict], str],
+ messages: Union[list[dict], str],
remove_termination_string: Optional[Union[str, Callable[[str], str]]] = None,
silent: Optional[bool] = False,
- ) -> Tuple[ConversableAgent, Dict]:
+ ) -> tuple[ConversableAgent, dict]:
"""Resumes a group chat using the previous messages as a starting point, asynchronously. Requires the agents, group chat, and group chat manager to be established
as per the original group chat.
@@ -1489,7 +1489,7 @@ async def a_resume(
return previous_last_agent, last_message
- def _valid_resume_messages(self, messages: List[Dict]):
+ def _valid_resume_messages(self, messages: list[dict]):
"""Validates the messages used for resuming
args:
@@ -1515,7 +1515,7 @@ def _valid_resume_messages(self, messages: List[Dict]):
raise Exception(f"Agent name in message doesn't exist as agent in group chat: {message['name']}")
def _process_resume_termination(
- self, remove_termination_string: Union[str, Callable[[str], str]], messages: List[Dict]
+ self, remove_termination_string: Union[str, Callable[[str], str]], messages: list[dict]
):
"""Removes termination string, if required, and checks if termination may occur.
@@ -1548,7 +1548,7 @@ def _remove_termination_string(content: str) -> str:
if self._is_termination_msg(last_message):
logger.warning("WARNING: Last message meets termination criteria and this may terminate the chat.")
- def messages_from_string(self, message_string: str) -> List[Dict]:
+ def messages_from_string(self, message_string: str) -> list[dict]:
"""Reads the saved state of messages in Json format for resume and returns as a messages list
args:
@@ -1564,7 +1564,7 @@ def messages_from_string(self, message_string: str) -> List[Dict]:
return state
- def messages_to_string(self, messages: List[Dict]) -> str:
+ def messages_to_string(self, messages: list[dict]) -> str:
"""Converts the provided messages into a Json string that can be used for resuming the chat.
The state is made up of a list of messages
diff --git a/autogen/agentchat/realtime_agent/realtime_agent.py b/autogen/agentchat/realtime_agent/realtime_agent.py
index 09dadab27e..aadbc1f283 100644
--- a/autogen/agentchat/realtime_agent/realtime_agent.py
+++ b/autogen/agentchat/realtime_agent/realtime_agent.py
@@ -9,7 +9,8 @@
import json
import logging
from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, Generator, List, Literal, Optional, Tuple, TypeVar, Union
+from collections.abc import Generator
+from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, TypeVar, Union
import anyio
import websockets
@@ -51,8 +52,8 @@ def __init__(
*,
name: str,
audio_adapter: RealtimeObserver,
- system_message: Optional[Union[str, List]] = "You are a helpful AI Assistant.",
- llm_config: Optional[Union[Dict, Literal[False]]] = None,
+ system_message: Optional[Union[str, list]] = "You are a helpful AI Assistant.",
+ llm_config: Optional[Union[dict, Literal[False]]] = None,
voice: str = "alloy",
):
"""(Experimental) Agent for interacting with the Realtime Clients.
@@ -102,7 +103,7 @@ def register_swarm(
self,
*,
initial_agent: SwarmAgent,
- agents: List[SwarmAgent],
+ agents: list[SwarmAgent],
system_message: Optional[str] = None,
) -> None:
"""Register a swarm of agents with the Realtime Agent.
@@ -207,10 +208,10 @@ async def _check_event_set(timeout: int = question_timeout) -> None:
def check_termination_and_human_reply(
self,
- messages: Optional[List[Dict]] = None,
+ messages: Optional[list[dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
- ) -> Tuple[bool, Union[str, None]]:
+ ) -> tuple[bool, Union[str, None]]:
"""Check if the conversation should be terminated and if the agent should reply.
Called when its agents turn in the chat conversation.
diff --git a/autogen/agentchat/user_proxy_agent.py b/autogen/agentchat/user_proxy_agent.py
index 6602f232b8..75f6e354b7 100644
--- a/autogen/agentchat/user_proxy_agent.py
+++ b/autogen/agentchat/user_proxy_agent.py
@@ -32,14 +32,14 @@ class UserProxyAgent(ConversableAgent):
def __init__(
self,
name: str,
- is_termination_msg: Optional[Callable[[Dict], bool]] = None,
+ is_termination_msg: Optional[Callable[[dict], bool]] = None,
max_consecutive_auto_reply: Optional[int] = None,
human_input_mode: Literal["ALWAYS", "TERMINATE", "NEVER"] = "ALWAYS",
- function_map: Optional[Dict[str, Callable]] = None,
- code_execution_config: Union[Dict, Literal[False]] = {},
- default_auto_reply: Optional[Union[str, Dict, None]] = "",
- llm_config: Optional[Union[Dict, Literal[False]]] = False,
- system_message: Optional[Union[str, List]] = "",
+ function_map: Optional[dict[str, Callable]] = None,
+ code_execution_config: Union[dict, Literal[False]] = {},
+ default_auto_reply: Optional[Union[str, dict, None]] = "",
+ llm_config: Optional[Union[dict, Literal[False]]] = False,
+ system_message: Optional[Union[str, list]] = "",
description: Optional[str] = None,
**kwargs,
):
diff --git a/autogen/agentchat/utils.py b/autogen/agentchat/utils.py
index 490bd46f18..94b2d7c5e5 100644
--- a/autogen/agentchat/utils.py
+++ b/autogen/agentchat/utils.py
@@ -32,7 +32,7 @@ def consolidate_chat_info(chat_info, uniform_sender=None) -> None:
), "llm client must be set in either the recipient or sender when summary_method is reflection_with_llm."
-def gather_usage_summary(agents: List[Agent]) -> Dict[Dict[str, Dict], Dict[str, Dict]]:
+def gather_usage_summary(agents: list[Agent]) -> dict[dict[str, dict], dict[str, dict]]:
r"""Gather usage summary from all agents.
Args:
@@ -74,7 +74,7 @@ def gather_usage_summary(agents: List[Agent]) -> Dict[Dict[str, Dict], Dict[str,
If none of the agents incurred any cost (not having a client), then the usage_including_cached_inference and usage_excluding_cached_inference will be `{'total_cost': 0}`.
"""
- def aggregate_summary(usage_summary: Dict[str, Any], agent_summary: Dict[str, Any]) -> None:
+ def aggregate_summary(usage_summary: dict[str, Any], agent_summary: dict[str, Any]) -> None:
if agent_summary is None:
return
usage_summary["total_cost"] += agent_summary.get("total_cost", 0)
@@ -102,7 +102,7 @@ def aggregate_summary(usage_summary: Dict[str, Any], agent_summary: Dict[str, An
}
-def parse_tags_from_content(tag: str, content: Union[str, List[Dict[str, Any]]]) -> List[Dict[str, Dict[str, str]]]:
+def parse_tags_from_content(tag: str, content: Union[str, list[dict[str, Any]]]) -> list[dict[str, dict[str, str]]]:
"""Parses HTML style tags from message contents.
The parsing is done by looking for patterns in the text that match the format of HTML tags. The tag to be parsed is
@@ -142,7 +142,7 @@ def parse_tags_from_content(tag: str, content: Union[str, List[Dict[str, Any]]])
return results
-def _parse_tags_from_text(tag: str, text: str) -> List[Dict[str, str]]:
+def _parse_tags_from_text(tag: str, text: str) -> list[dict[str, str]]:
pattern = re.compile(f"<{tag} (.*?)>")
results = []
@@ -180,7 +180,7 @@ def _append_src_value(content, value):
return content
-def _reconstruct_attributes(attrs: List[str]) -> List[str]:
+def _reconstruct_attributes(attrs: list[str]) -> list[str]:
"""Reconstructs attributes from a list of strings where some attributes may be split across multiple elements."""
def is_attr(attr: str) -> bool:
diff --git a/autogen/browser_utils.py b/autogen/browser_utils.py
index dd8d9ca2b7..c624d13749 100644
--- a/autogen/browser_utils.py
+++ b/autogen/browser_utils.py
@@ -44,15 +44,15 @@ def __init__(
downloads_folder: Optional[Union[str, None]] = None,
bing_base_url: str = "https://api.bing.microsoft.com/v7.0/search",
bing_api_key: Optional[Union[str, None]] = None,
- request_kwargs: Optional[Union[Dict[str, Any], None]] = None,
+ request_kwargs: Optional[Union[dict[str, Any], None]] = None,
):
self.start_page: str = start_page if start_page else "about:blank"
self.viewport_size = viewport_size # Applies only to the standard uri types
self.downloads_folder = downloads_folder
- self.history: List[str] = list()
+ self.history: list[str] = list()
self.page_title: Optional[str] = None
self.viewport_current_page = 0
- self.viewport_pages: List[Tuple[int, int]] = list()
+ self.viewport_pages: list[tuple[int, int]] = list()
self.set_address(self.start_page)
self.bing_base_url = bing_base_url
self.bing_api_key = bing_api_key
@@ -132,7 +132,7 @@ def _split_pages(self) -> None:
self.viewport_pages.append((start_idx, end_idx))
start_idx = end_idx
- def _bing_api_call(self, query: str) -> Dict[str, Dict[str, List[Dict[str, Union[str, Dict[str, str]]]]]]:
+ def _bing_api_call(self, query: str) -> dict[str, dict[str, list[dict[str, Union[str, dict[str, str]]]]]]:
# Make sure the key was set
if self.bing_api_key is None:
raise ValueError("Missing Bing API key.")
@@ -162,7 +162,7 @@ def _bing_api_call(self, query: str) -> Dict[str, Dict[str, List[Dict[str, Union
def _bing_search(self, query: str) -> None:
results = self._bing_api_call(query)
- web_snippets: List[str] = list()
+ web_snippets: list[str] = list()
idx = 0
for page in results["webPages"]["value"]:
idx += 1
diff --git a/autogen/cache/abstract_cache_base.py b/autogen/cache/abstract_cache_base.py
index 6a5b88823d..d7d864508f 100644
--- a/autogen/cache/abstract_cache_base.py
+++ b/autogen/cache/abstract_cache_base.py
@@ -63,7 +63,7 @@ def __enter__(self) -> Self:
def __exit__(
self,
- exc_type: Optional[Type[BaseException]],
+ exc_type: Optional[type[BaseException]],
exc_value: Optional[BaseException],
traceback: Optional[TracebackType],
) -> None:
diff --git a/autogen/cache/cache.py b/autogen/cache/cache.py
index 1e64e5d5f0..1b2bc26f6b 100644
--- a/autogen/cache/cache.py
+++ b/autogen/cache/cache.py
@@ -40,7 +40,7 @@ class Cache(AbstractCache):
]
@staticmethod
- def redis(cache_seed: Union[str, int] = 42, redis_url: str = "redis://localhost:6379/0") -> "Cache":
+ def redis(cache_seed: str | int = 42, redis_url: str = "redis://localhost:6379/0") -> Cache:
"""
Create a Redis cache instance.
@@ -54,7 +54,7 @@ def redis(cache_seed: Union[str, int] = 42, redis_url: str = "redis://localhost:
return Cache({"cache_seed": cache_seed, "redis_url": redis_url})
@staticmethod
- def disk(cache_seed: Union[str, int] = 42, cache_path_root: str = ".cache") -> "Cache":
+ def disk(cache_seed: str | int = 42, cache_path_root: str = ".cache") -> Cache:
"""
Create a Disk cache instance.
@@ -69,11 +69,11 @@ def disk(cache_seed: Union[str, int] = 42, cache_path_root: str = ".cache") -> "
@staticmethod
def cosmos_db(
- connection_string: Optional[str] = None,
- container_id: Optional[str] = None,
- cache_seed: Union[str, int] = 42,
- client: Optional[any] = None,
- ) -> "Cache":
+ connection_string: str | None = None,
+ container_id: str | None = None,
+ cache_seed: str | int = 42,
+ client: any | None = None,
+ ) -> Cache:
"""
Create a Cosmos DB cache instance with 'autogen_cache' as database ID.
@@ -93,7 +93,7 @@ def cosmos_db(
}
return Cache({"cache_seed": str(cache_seed), "cosmos_db_config": cosmos_db_config})
- def __init__(self, config: Dict[str, Any]):
+ def __init__(self, config: dict[str, Any]):
"""
Initialize the Cache with the given configuration.
@@ -121,7 +121,7 @@ def __init__(self, config: Dict[str, Any]):
cosmosdb_config=self.config.get("cosmos_db_config"),
)
- def __enter__(self) -> "Cache":
+ def __enter__(self) -> Cache:
"""
Enter the runtime context related to the cache object.
@@ -132,9 +132,9 @@ def __enter__(self) -> "Cache":
def __exit__(
self,
- exc_type: Optional[Type[BaseException]],
- exc_value: Optional[BaseException],
- traceback: Optional[TracebackType],
+ exc_type: type[BaseException] | None,
+ exc_value: BaseException | None,
+ traceback: TracebackType | None,
) -> None:
"""
Exit the runtime context related to the cache object.
@@ -149,7 +149,7 @@ def __exit__(
"""
return self.cache.__exit__(exc_type, exc_value, traceback)
- def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]:
+ def get(self, key: str, default: Any | None = None) -> Any | None:
"""
Retrieve an item from the cache.
diff --git a/autogen/cache/cache_factory.py b/autogen/cache/cache_factory.py
index 8401c189d7..b64328cfe7 100644
--- a/autogen/cache/cache_factory.py
+++ b/autogen/cache/cache_factory.py
@@ -18,7 +18,7 @@ def cache_factory(
seed: Union[str, int],
redis_url: Optional[str] = None,
cache_path_root: str = ".cache",
- cosmosdb_config: Optional[Dict[str, Any]] = None,
+ cosmosdb_config: Optional[dict[str, Any]] = None,
) -> AbstractCache:
"""
Factory function for creating cache instances.
diff --git a/autogen/cache/disk_cache.py b/autogen/cache/disk_cache.py
index adb0720287..2838447cb6 100644
--- a/autogen/cache/disk_cache.py
+++ b/autogen/cache/disk_cache.py
@@ -92,7 +92,7 @@ def __enter__(self) -> Self:
def __exit__(
self,
- exc_type: Optional[Type[BaseException]],
+ exc_type: Optional[type[BaseException]],
exc_value: Optional[BaseException],
traceback: Optional[TracebackType],
) -> None:
diff --git a/autogen/cache/in_memory_cache.py b/autogen/cache/in_memory_cache.py
index 8469554d04..f080530e56 100644
--- a/autogen/cache/in_memory_cache.py
+++ b/autogen/cache/in_memory_cache.py
@@ -20,7 +20,7 @@ class InMemoryCache(AbstractCache):
def __init__(self, seed: Union[str, int] = ""):
self._seed = str(seed)
- self._cache: Dict[str, Any] = {}
+ self._cache: dict[str, Any] = {}
def _prefixed_key(self, key: str) -> str:
separator = "_" if self._seed else ""
@@ -48,7 +48,7 @@ def __enter__(self) -> Self:
return self
def __exit__(
- self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
+ self, exc_type: Optional[type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
) -> None:
"""
Exit the runtime context related to the object.
diff --git a/autogen/cache/redis_cache.py b/autogen/cache/redis_cache.py
index b1c4c10557..b87863f083 100644
--- a/autogen/cache/redis_cache.py
+++ b/autogen/cache/redis_cache.py
@@ -113,7 +113,7 @@ def __enter__(self) -> Self:
return self
def __exit__(
- self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
+ self, exc_type: Optional[type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
) -> None:
"""
Exit the runtime context related to the object.
diff --git a/autogen/code_utils.py b/autogen/code_utils.py
index 0587e87728..94eb988bf3 100644
--- a/autogen/code_utils.py
+++ b/autogen/code_utils.py
@@ -48,7 +48,7 @@
logger = logging.getLogger(__name__)
-def content_str(content: Union[str, List[Union[UserMessageTextContentPart, UserMessageImageContentPart]], None]) -> str:
+def content_str(content: Union[str, list[Union[UserMessageTextContentPart, UserMessageImageContentPart]], None]) -> str:
"""Converts the `content` field of an OpenAI message into a string format.
This function processes content that may be a string, a list of mixed text and image URLs, or None,
@@ -108,8 +108,8 @@ def infer_lang(code: str) -> str:
# TODO: In the future move, to better support https://spec.commonmark.org/0.30/#fenced-code-blocks
# perhaps by using a full Markdown parser.
def extract_code(
- text: Union[str, List], pattern: str = CODE_BLOCK_PATTERN, detect_single_line_code: bool = False
-) -> List[Tuple[str, str]]:
+ text: Union[str, list], pattern: str = CODE_BLOCK_PATTERN, detect_single_line_code: bool = False
+) -> list[tuple[str, str]]:
"""Extract code from a text.
Args:
@@ -146,7 +146,7 @@ def extract_code(
return extracted
-def generate_code(pattern: str = CODE_BLOCK_PATTERN, **config) -> Tuple[str, float]:
+def generate_code(pattern: str = CODE_BLOCK_PATTERN, **config) -> tuple[str, float]:
"""(openai<1) Generate code.
Args:
@@ -175,7 +175,7 @@ def improve_function(file_name, func_name, objective, **config):
"""(openai<1) Improve the function to achieve the objective."""
params = {**_IMPROVE_FUNCTION_CONFIG, **config}
# read the entire file into a str
- with open(file_name, "r") as f:
+ with open(file_name) as f:
file_string = f.read()
response = oai.Completion.create(
{"func_name": func_name, "objective": objective, "file_string": file_string}, **params
@@ -208,7 +208,7 @@ def improve_code(files, objective, suggest_only=True, **config):
code = ""
for file_name in files:
# read the entire file into a string
- with open(file_name, "r") as f:
+ with open(file_name) as f:
file_string = f.read()
code += f"""{file_name}:
{file_string}
@@ -358,9 +358,9 @@ def execute_code(
timeout: Optional[int] = None,
filename: Optional[str] = None,
work_dir: Optional[str] = None,
- use_docker: Union[List[str], str, bool] = SENTINEL,
+ use_docker: Union[list[str], str, bool] = SENTINEL,
lang: Optional[str] = "python",
-) -> Tuple[int, str, Optional[str]]:
+) -> tuple[int, str, Optional[str]]:
"""Execute code in a docker container.
This function is not tested on MacOS.
@@ -552,7 +552,7 @@ def execute_code(
}
-def generate_assertions(definition: str, **config) -> Tuple[str, float]:
+def generate_assertions(definition: str, **config) -> tuple[str, float]:
"""(openai<1) Generate assertions for a function.
Args:
@@ -582,14 +582,14 @@ def _remove_check(response):
def eval_function_completions(
- responses: List[str],
+ responses: list[str],
definition: str,
test: Optional[str] = None,
entry_point: Optional[str] = None,
- assertions: Optional[Union[str, Callable[[str], Tuple[str, float]]]] = None,
+ assertions: Optional[Union[str, Callable[[str], tuple[str, float]]]] = None,
timeout: Optional[float] = 3,
use_docker: Optional[bool] = True,
-) -> Dict:
+) -> dict:
"""(openai<1) Select a response from a list of responses for the function completion task (using generated assertions), and/or evaluate if the task is successful using a gold test.
Args:
@@ -692,9 +692,9 @@ def pass_assertions(self, context, response, **_):
def implement(
definition: str,
- configs: Optional[List[Dict]] = None,
- assertions: Optional[Union[str, Callable[[str], Tuple[str, float]]]] = generate_assertions,
-) -> Tuple[str, float]:
+ configs: Optional[list[dict]] = None,
+ assertions: Optional[Union[str, Callable[[str], tuple[str, float]]]] = generate_assertions,
+) -> tuple[str, float]:
"""(openai<1) Implement a function from a definition.
Args:
diff --git a/autogen/coding/base.py b/autogen/coding/base.py
index 57af08ac5c..edb3a6e320 100644
--- a/autogen/coding/base.py
+++ b/autogen/coding/base.py
@@ -6,7 +6,8 @@
# SPDX-License-Identifier: MIT
from __future__ import annotations
-from typing import Any, List, Literal, Mapping, Optional, Protocol, TypedDict, Union, runtime_checkable
+from collections.abc import Mapping
+from typing import Any, List, Literal, Optional, Protocol, TypedDict, Union, runtime_checkable
from pydantic import BaseModel, Field
@@ -35,8 +36,8 @@ class CodeExtractor(Protocol):
"""(Experimental) A code extractor class that extracts code blocks from a message."""
def extract_code_blocks(
- self, message: Union[str, List[Union[UserMessageTextContentPart, UserMessageImageContentPart]], None]
- ) -> List[CodeBlock]:
+ self, message: str | list[Union[UserMessageTextContentPart, UserMessageImageContentPart]] | None
+ ) -> list[CodeBlock]:
"""(Experimental) Extract code blocks from a message.
Args:
@@ -57,7 +58,7 @@ def code_extractor(self) -> CodeExtractor:
"""(Experimental) The code extractor used by this code executor."""
... # pragma: no cover
- def execute_code_blocks(self, code_blocks: List[CodeBlock]) -> CodeResult:
+ def execute_code_blocks(self, code_blocks: list[CodeBlock]) -> CodeResult:
"""(Experimental) Execute code blocks and return the result.
This method should be implemented by the code executor.
@@ -83,7 +84,7 @@ def restart(self) -> None:
class IPythonCodeResult(CodeResult):
"""(Experimental) A code result class for IPython code executor."""
- output_files: List[str] = Field(
+ output_files: list[str] = Field(
default_factory=list,
description="The list of files that the executed code blocks generated.",
)
@@ -95,7 +96,7 @@ class IPythonCodeResult(CodeResult):
"executor": Union[Literal["ipython-embedded", "commandline-local"], CodeExecutor],
"last_n_messages": Union[int, Literal["auto"]],
"timeout": int,
- "use_docker": Union[bool, str, List[str]],
+ "use_docker": Union[bool, str, list[str]],
"work_dir": str,
"ipython-embedded": Mapping[str, Any],
"commandline-local": Mapping[str, Any],
diff --git a/autogen/coding/docker_commandline_code_executor.py b/autogen/coding/docker_commandline_code_executor.py
index 2576f28ed7..395f61da27 100644
--- a/autogen/coding/docker_commandline_code_executor.py
+++ b/autogen/coding/docker_commandline_code_executor.py
@@ -45,7 +45,7 @@ def _wait_for_ready(container: Any, timeout: int = 60, stop_time: float = 0.1) -
class DockerCommandLineCodeExecutor(CodeExecutor):
- DEFAULT_EXECUTION_POLICY: ClassVar[Dict[str, bool]] = {
+ DEFAULT_EXECUTION_POLICY: ClassVar[dict[str, bool]] = {
"bash": True,
"shell": True,
"sh": True,
@@ -57,18 +57,18 @@ class DockerCommandLineCodeExecutor(CodeExecutor):
"html": False,
"css": False,
}
- LANGUAGE_ALIASES: ClassVar[Dict[str, str]] = {"py": "python", "js": "javascript"}
+ LANGUAGE_ALIASES: ClassVar[dict[str, str]] = {"py": "python", "js": "javascript"}
def __init__(
self,
image: str = "python:3-slim",
- container_name: Optional[str] = None,
+ container_name: str | None = None,
timeout: int = 60,
- work_dir: Union[Path, str] = Path("."),
- bind_dir: Optional[Union[Path, str]] = None,
+ work_dir: Path | str = Path("."),
+ bind_dir: Path | str | None = None,
auto_remove: bool = True,
stop_container: bool = True,
- execution_policies: Optional[Dict[str, bool]] = None,
+ execution_policies: dict[str, bool] | None = None,
):
"""(Experimental) A code executor class that executes code through
a command line environment in a Docker container.
@@ -183,7 +183,7 @@ def code_extractor(self) -> CodeExtractor:
"""(Experimental) Export a code extractor that can be used by an agent."""
return MarkdownCodeExtractor()
- def execute_code_blocks(self, code_blocks: List[CodeBlock]) -> CommandLineCodeResult:
+ def execute_code_blocks(self, code_blocks: list[CodeBlock]) -> CommandLineCodeResult:
"""(Experimental) Execute the code blocks and return the result.
Args:
@@ -257,6 +257,6 @@ def __enter__(self) -> Self:
return self
def __exit__(
- self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
+ self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: TracebackType | None
) -> None:
self.stop()
diff --git a/autogen/coding/func_with_reqs.py b/autogen/coding/func_with_reqs.py
index 7a755ffdce..1f842c9193 100644
--- a/autogen/coding/func_with_reqs.py
+++ b/autogen/coding/func_with_reqs.py
@@ -20,7 +20,7 @@
P = ParamSpec("P")
-def _to_code(func: Union[FunctionWithRequirements[T, P], Callable[P, T], FunctionWithRequirementsStr]) -> str:
+def _to_code(func: FunctionWithRequirements[T, P] | Callable[P, T] | FunctionWithRequirementsStr) -> str:
if isinstance(func, FunctionWithRequirementsStr):
return func.func
@@ -40,7 +40,7 @@ class Alias:
@dataclass
class ImportFromModule:
module: str
- imports: List[Union[str, Alias]]
+ imports: list[str | Alias]
Import = Union[str, ImportFromModule, Alias]
@@ -53,7 +53,7 @@ def _import_to_str(im: Import) -> str:
return f"import {im.name} as {im.alias}"
else:
- def to_str(i: Union[str, Alias]) -> str:
+ def to_str(i: str | Alias) -> str:
if isinstance(i, str):
return i
else:
@@ -82,10 +82,10 @@ class FunctionWithRequirementsStr:
func: str
_compiled_func: Callable[..., Any]
_func_name: str
- python_packages: List[str] = field(default_factory=list)
- global_imports: List[Import] = field(default_factory=list)
+ python_packages: list[str] = field(default_factory=list)
+ global_imports: list[Import] = field(default_factory=list)
- def __init__(self, func: str, python_packages: List[str] = [], global_imports: List[Import] = []):
+ def __init__(self, func: str, python_packages: list[str] = [], global_imports: list[Import] = []):
self.func = func
self.python_packages = python_packages
self.global_imports = global_imports
@@ -117,18 +117,18 @@ def __call__(self, *args: Any, **kwargs: Any) -> None:
@dataclass
class FunctionWithRequirements(Generic[T, P]):
func: Callable[P, T]
- python_packages: List[str] = field(default_factory=list)
- global_imports: List[Import] = field(default_factory=list)
+ python_packages: list[str] = field(default_factory=list)
+ global_imports: list[Import] = field(default_factory=list)
@classmethod
def from_callable(
- cls, func: Callable[P, T], python_packages: List[str] = [], global_imports: List[Import] = []
+ cls, func: Callable[P, T], python_packages: list[str] = [], global_imports: list[Import] = []
) -> FunctionWithRequirements[T, P]:
return cls(python_packages=python_packages, global_imports=global_imports, func=func)
@staticmethod
def from_str(
- func: str, python_packages: List[str] = [], global_imports: List[Import] = []
+ func: str, python_packages: list[str] = [], global_imports: list[Import] = []
) -> FunctionWithRequirementsStr:
return FunctionWithRequirementsStr(func=func, python_packages=python_packages, global_imports=global_imports)
@@ -138,7 +138,7 @@ def __call__(self, *args: P.args, **kwargs: P.kwargs) -> T:
def with_requirements(
- python_packages: List[str] = [], global_imports: List[Import] = []
+ python_packages: list[str] = [], global_imports: list[Import] = []
) -> Callable[[Callable[P, T]], FunctionWithRequirements[T, P]]:
"""Decorate a function with package and import requirements
@@ -162,10 +162,10 @@ def wrapper(func: Callable[P, T]) -> FunctionWithRequirements[T, P]:
def _build_python_functions_file(
- funcs: List[Union[FunctionWithRequirements[Any, P], Callable[..., Any], FunctionWithRequirementsStr]]
+ funcs: list[FunctionWithRequirements[Any, P] | Callable[..., Any] | FunctionWithRequirementsStr]
) -> str:
# First collect all global imports
- global_imports: Set[str] = set()
+ global_imports: set[str] = set()
for func in funcs:
if isinstance(func, (FunctionWithRequirements, FunctionWithRequirementsStr)):
global_imports.update(map(_import_to_str, func.global_imports))
@@ -178,7 +178,7 @@ def _build_python_functions_file(
return content
-def to_stub(func: Union[Callable[..., Any], FunctionWithRequirementsStr]) -> str:
+def to_stub(func: Callable[..., Any] | FunctionWithRequirementsStr) -> str:
"""Generate a stub for a function as a string
Args:
diff --git a/autogen/coding/jupyter/docker_jupyter_server.py b/autogen/coding/jupyter/docker_jupyter_server.py
index 2a73a7b307..f90090dee6 100644
--- a/autogen/coding/jupyter/docker_jupyter_server.py
+++ b/autogen/coding/jupyter/docker_jupyter_server.py
@@ -59,12 +59,12 @@ class GenerateToken:
def __init__(
self,
*,
- custom_image_name: Optional[str] = None,
- container_name: Optional[str] = None,
+ custom_image_name: str | None = None,
+ container_name: str | None = None,
auto_remove: bool = True,
stop_container: bool = True,
- docker_env: Dict[str, str] = {},
- token: Union[str, GenerateToken] = GenerateToken(),
+ docker_env: dict[str, str] = {},
+ token: str | GenerateToken = GenerateToken(),
):
"""Start a Jupyter kernel gateway server in a Docker container.
@@ -159,6 +159,6 @@ def __enter__(self) -> Self:
return self
def __exit__(
- self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
+ self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: TracebackType | None
) -> None:
self.stop()
diff --git a/autogen/coding/jupyter/embedded_ipython_code_executor.py b/autogen/coding/jupyter/embedded_ipython_code_executor.py
index 231dca0ffd..4e0a8d828c 100644
--- a/autogen/coding/jupyter/embedded_ipython_code_executor.py
+++ b/autogen/coding/jupyter/embedded_ipython_code_executor.py
@@ -78,7 +78,7 @@ def code_extractor(self) -> CodeExtractor:
"""(Experimental) Export a code extractor that can be used by an agent."""
return MarkdownCodeExtractor()
- def execute_code_blocks(self, code_blocks: List[CodeBlock]) -> IPythonCodeResult:
+ def execute_code_blocks(self, code_blocks: list[CodeBlock]) -> IPythonCodeResult:
"""(Experimental) Execute a list of code blocks and return the result.
This method executes a list of code blocks as cells in an IPython kernel
diff --git a/autogen/coding/jupyter/jupyter_client.py b/autogen/coding/jupyter/jupyter_client.py
index e1df947969..5482c8537f 100644
--- a/autogen/coding/jupyter/jupyter_client.py
+++ b/autogen/coding/jupyter/jupyter_client.py
@@ -40,7 +40,7 @@ def __init__(self, connection_info: JupyterConnectionInfo):
retries = Retry(total=5, backoff_factor=0.1)
self._session.mount("http://", HTTPAdapter(max_retries=retries))
- def _get_headers(self) -> Dict[str, str]:
+ def _get_headers(self) -> dict[str, str]:
if self._connection_info.token is None:
return {}
return {"Authorization": f"token {self._connection_info.token}"}
@@ -54,13 +54,13 @@ def _get_ws_base_url(self) -> str:
port = f":{self._connection_info.port}" if self._connection_info.port else ""
return f"ws://{self._connection_info.host}{port}"
- def list_kernel_specs(self) -> Dict[str, Dict[str, str]]:
+ def list_kernel_specs(self) -> dict[str, dict[str, str]]:
response = self._session.get(f"{self._get_api_base_url()}/api/kernelspecs", headers=self._get_headers())
- return cast(Dict[str, Dict[str, str]], response.json())
+ return cast(dict[str, dict[str, str]], response.json())
- def list_kernels(self) -> List[Dict[str, str]]:
+ def list_kernels(self) -> list[dict[str, str]]:
response = self._session.get(f"{self._get_api_base_url()}/api/kernels", headers=self._get_headers())
- return cast(List[Dict[str, str]], response.json())
+ return cast(list[dict[str, str]], response.json())
def start_kernel(self, kernel_spec_name: str) -> str:
"""Start a new kernel.
@@ -109,7 +109,7 @@ class DataItem:
is_ok: bool
output: str
- data_items: List[DataItem]
+ data_items: list[DataItem]
def __init__(self, websocket: WebSocket):
self._session_id: str = uuid.uuid4().hex
@@ -119,14 +119,14 @@ def __enter__(self) -> Self:
return self
def __exit__(
- self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
+ self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: TracebackType | None
) -> None:
self.stop()
def stop(self) -> None:
self._websocket.close()
- def _send_message(self, *, content: Dict[str, Any], channel: str, message_type: str) -> str:
+ def _send_message(self, *, content: dict[str, Any], channel: str, message_type: str) -> str:
timestamp = datetime.datetime.now().isoformat()
message_id = uuid.uuid4().hex
message = {
@@ -147,17 +147,17 @@ def _send_message(self, *, content: Dict[str, Any], channel: str, message_type:
self._websocket.send_text(json.dumps(message))
return message_id
- def _receive_message(self, timeout_seconds: Optional[float]) -> Optional[Dict[str, Any]]:
+ def _receive_message(self, timeout_seconds: float | None) -> dict[str, Any] | None:
self._websocket.settimeout(timeout_seconds)
try:
data = self._websocket.recv()
if isinstance(data, bytes):
data = data.decode("utf-8")
- return cast(Dict[str, Any], json.loads(data))
+ return cast(dict[str, Any], json.loads(data))
except websocket.WebSocketTimeoutException:
return None
- def wait_for_ready(self, timeout_seconds: Optional[float] = None) -> bool:
+ def wait_for_ready(self, timeout_seconds: float | None = None) -> bool:
message_id = self._send_message(content={}, channel="shell", message_type="kernel_info_request")
while True:
message = self._receive_message(timeout_seconds)
@@ -170,7 +170,7 @@ def wait_for_ready(self, timeout_seconds: Optional[float] = None) -> bool:
):
return True
- def execute(self, code: str, timeout_seconds: Optional[float] = None) -> ExecutionResult:
+ def execute(self, code: str, timeout_seconds: float | None = None) -> ExecutionResult:
message_id = self._send_message(
content={
"code": code,
diff --git a/autogen/coding/jupyter/jupyter_code_executor.py b/autogen/coding/jupyter/jupyter_code_executor.py
index 862885d797..afee16963d 100644
--- a/autogen/coding/jupyter/jupyter_code_executor.py
+++ b/autogen/coding/jupyter/jupyter_code_executor.py
@@ -82,7 +82,7 @@ def code_extractor(self) -> CodeExtractor:
"""(Experimental) Export a code extractor that can be used by an agent."""
return MarkdownCodeExtractor()
- def execute_code_blocks(self, code_blocks: List[CodeBlock]) -> IPythonCodeResult:
+ def execute_code_blocks(self, code_blocks: list[CodeBlock]) -> IPythonCodeResult:
"""(Experimental) Execute a list of code blocks and return the result.
This method executes a list of code blocks as cells in the Jupyter kernel.
@@ -156,6 +156,6 @@ def __enter__(self) -> Self:
return self
def __exit__(
- self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
+ self, exc_type: Optional[type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
) -> None:
self.stop()
diff --git a/autogen/coding/jupyter/local_jupyter_server.py b/autogen/coding/jupyter/local_jupyter_server.py
index 6aa1378313..6ea55c1f0a 100644
--- a/autogen/coding/jupyter/local_jupyter_server.py
+++ b/autogen/coding/jupyter/local_jupyter_server.py
@@ -32,8 +32,8 @@ class GenerateToken:
def __init__(
self,
ip: str = "127.0.0.1",
- port: Optional[int] = None,
- token: Union[str, GenerateToken] = GenerateToken(),
+ port: int | None = None,
+ token: str | GenerateToken = GenerateToken(),
log_file: str = "jupyter_gateway.log",
log_level: str = "INFO",
log_max_bytes: int = 1048576,
@@ -59,8 +59,7 @@ def __init__(
subprocess.run(
[sys.executable, "-m", "jupyter", "kernelgateway", "--version"],
check=True,
- stdout=subprocess.PIPE,
- stderr=subprocess.PIPE,
+ capture_output=True,
text=True,
)
except subprocess.CalledProcessError:
@@ -163,6 +162,6 @@ def __enter__(self) -> Self:
return self
def __exit__(
- self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
+ self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: TracebackType | None
) -> None:
self.stop()
diff --git a/autogen/coding/local_commandline_code_executor.py b/autogen/coding/local_commandline_code_executor.py
index 889182bfb1..bcbab20ef2 100644
--- a/autogen/coding/local_commandline_code_executor.py
+++ b/autogen/coding/local_commandline_code_executor.py
@@ -36,7 +36,7 @@
class LocalCommandLineCodeExecutor(CodeExecutor):
- SUPPORTED_LANGUAGES: ClassVar[List[str]] = [
+ SUPPORTED_LANGUAGES: ClassVar[list[str]] = [
"bash",
"shell",
"sh",
@@ -48,7 +48,7 @@ class LocalCommandLineCodeExecutor(CodeExecutor):
"html",
"css",
]
- DEFAULT_EXECUTION_POLICY: ClassVar[Dict[str, bool]] = {
+ DEFAULT_EXECUTION_POLICY: ClassVar[dict[str, bool]] = {
"bash": True,
"shell": True,
"sh": True,
@@ -74,9 +74,9 @@ def __init__(
timeout: int = 60,
virtual_env_context: Optional[SimpleNamespace] = None,
work_dir: Union[Path, str] = Path("."),
- functions: List[Union[FunctionWithRequirements[Any, A], Callable[..., Any], FunctionWithRequirementsStr]] = [],
+ functions: list[Union[FunctionWithRequirements[Any, A], Callable[..., Any], FunctionWithRequirementsStr]] = [],
functions_module: str = "functions",
- execution_policies: Optional[Dict[str, bool]] = None,
+ execution_policies: Optional[dict[str, bool]] = None,
):
"""(Experimental) A code executor class that executes or saves LLM generated code a local command line
environment.
@@ -168,7 +168,7 @@ def functions_module(self) -> str:
@property
def functions(
self,
- ) -> List[Union[FunctionWithRequirements[Any, A], Callable[..., Any], FunctionWithRequirementsStr]]:
+ ) -> list[Union[FunctionWithRequirements[Any, A], Callable[..., Any], FunctionWithRequirementsStr]]:
"""(Experimental) The functions that are available to the code executor."""
return self._functions
@@ -244,7 +244,7 @@ def _setup_functions(self) -> None:
raise ValueError(f"Functions failed to load: {exec_result.output}")
self._setup_functions_complete = True
- def execute_code_blocks(self, code_blocks: List[CodeBlock]) -> CommandLineCodeResult:
+ def execute_code_blocks(self, code_blocks: list[CodeBlock]) -> CommandLineCodeResult:
"""(Experimental) Execute the code blocks and return the result.
Args:
@@ -256,7 +256,7 @@ def execute_code_blocks(self, code_blocks: List[CodeBlock]) -> CommandLineCodeRe
self._setup_functions()
return self._execute_code_dont_check_setup(code_blocks)
- def _execute_code_dont_check_setup(self, code_blocks: List[CodeBlock]) -> CommandLineCodeResult:
+ def _execute_code_dont_check_setup(self, code_blocks: list[CodeBlock]) -> CommandLineCodeResult:
logs_all = ""
file_names = []
for code_block in code_blocks:
diff --git a/autogen/coding/markdown_code_extractor.py b/autogen/coding/markdown_code_extractor.py
index 01dda0df52..8342ea2f92 100644
--- a/autogen/coding/markdown_code_extractor.py
+++ b/autogen/coding/markdown_code_extractor.py
@@ -18,8 +18,8 @@ class MarkdownCodeExtractor(CodeExtractor):
"""(Experimental) A class that extracts code blocks from a message using Markdown syntax."""
def extract_code_blocks(
- self, message: Union[str, List[Union[UserMessageTextContentPart, UserMessageImageContentPart]], None]
- ) -> List[CodeBlock]:
+ self, message: Union[str, list[Union[UserMessageTextContentPart, UserMessageImageContentPart]], None]
+ ) -> list[CodeBlock]:
"""(Experimental) Extract code blocks from a message. If no code blocks are found,
return an empty list.
diff --git a/autogen/formatting_utils.py b/autogen/formatting_utils.py
index fefc7f05ad..9c6ccb1676 100644
--- a/autogen/formatting_utils.py
+++ b/autogen/formatting_utils.py
@@ -6,7 +6,8 @@
# SPDX-License-Identifier: MIT
from __future__ import annotations
-from typing import Iterable, Literal
+from collections.abc import Iterable
+from typing import Literal
try:
from termcolor import colored
diff --git a/autogen/function_utils.py b/autogen/function_utils.py
index f4a6531fe5..8553e3e8e2 100644
--- a/autogen/function_utils.py
+++ b/autogen/function_utils.py
@@ -8,10 +8,10 @@
import inspect
import json
from logging import getLogger
-from typing import Any, Callable, Dict, ForwardRef, List, Optional, Set, Tuple, Type, TypeVar, Union
+from typing import Annotated, Any, Callable, Dict, ForwardRef, List, Optional, Set, Tuple, Type, TypeVar, Union
from pydantic import BaseModel, Field
-from typing_extensions import Annotated, Literal, get_args, get_origin
+from typing_extensions import Literal, get_args, get_origin
from ._pydantic import JsonSchemaValue, evaluate_forwardref, model_dump, model_dump_json, type2schema
@@ -20,7 +20,7 @@
T = TypeVar("T")
-def get_typed_annotation(annotation: Any, globalns: Dict[str, Any]) -> Any:
+def get_typed_annotation(annotation: Any, globalns: dict[str, Any]) -> Any:
"""Get the type annotation of a parameter.
Args:
@@ -79,7 +79,7 @@ def get_typed_return_annotation(call: Callable[..., Any]) -> Any:
return get_typed_annotation(annotation, globalns)
-def get_param_annotations(typed_signature: inspect.Signature) -> Dict[str, Union[Annotated[Type[Any], str], Type[Any]]]:
+def get_param_annotations(typed_signature: inspect.Signature) -> dict[str, Union[Annotated[type[Any], str], type[Any]]]:
"""Get the type annotations of the parameters of a function
Args:
@@ -97,8 +97,8 @@ class Parameters(BaseModel):
"""Parameters of a function as defined by the OpenAI API"""
type: Literal["object"] = "object"
- properties: Dict[str, JsonSchemaValue]
- required: List[str]
+ properties: dict[str, JsonSchemaValue]
+ required: list[str]
class Function(BaseModel):
@@ -116,7 +116,7 @@ class ToolFunction(BaseModel):
function: Annotated[Function, Field(description="Function under tool")]
-def get_parameter_json_schema(k: str, v: Any, default_values: Dict[str, Any]) -> JsonSchemaValue:
+def get_parameter_json_schema(k: str, v: Any, default_values: dict[str, Any]) -> JsonSchemaValue:
"""Get a JSON schema for a parameter as defined by the OpenAI API
Args:
@@ -128,7 +128,7 @@ def get_parameter_json_schema(k: str, v: Any, default_values: Dict[str, Any]) ->
A Pydanitc model for the parameter
"""
- def type2description(k: str, v: Union[Annotated[Type[Any], str], Type[Any]]) -> str:
+ def type2description(k: str, v: Union[Annotated[type[Any], str], type[Any]]) -> str:
# handles Annotated
if hasattr(v, "__metadata__"):
retval = v.__metadata__[0]
@@ -149,7 +149,7 @@ def type2description(k: str, v: Union[Annotated[Type[Any], str], Type[Any]]) ->
return schema
-def get_required_params(typed_signature: inspect.Signature) -> List[str]:
+def get_required_params(typed_signature: inspect.Signature) -> list[str]:
"""Get the required parameters of a function
Args:
@@ -161,7 +161,7 @@ def get_required_params(typed_signature: inspect.Signature) -> List[str]:
return [k for k, v in typed_signature.parameters.items() if v.default == inspect.Signature.empty]
-def get_default_values(typed_signature: inspect.Signature) -> Dict[str, Any]:
+def get_default_values(typed_signature: inspect.Signature) -> dict[str, Any]:
"""Get default values of parameters of a function
Args:
@@ -174,9 +174,9 @@ def get_default_values(typed_signature: inspect.Signature) -> Dict[str, Any]:
def get_parameters(
- required: List[str],
- param_annotations: Dict[str, Union[Annotated[Type[Any], str], Type[Any]]],
- default_values: Dict[str, Any],
+ required: list[str],
+ param_annotations: dict[str, Union[Annotated[type[Any], str], type[Any]]],
+ default_values: dict[str, Any],
) -> Parameters:
"""Get the parameters of a function as defined by the OpenAI API
@@ -197,7 +197,7 @@ def get_parameters(
)
-def get_missing_annotations(typed_signature: inspect.Signature, required: List[str]) -> Tuple[Set[str], Set[str]]:
+def get_missing_annotations(typed_signature: inspect.Signature, required: list[str]) -> tuple[set[str], set[str]]:
"""Get the missing annotations of a function
Ignores the parameters with default values as they are not required to be annotated, but logs a warning.
@@ -214,7 +214,7 @@ def get_missing_annotations(typed_signature: inspect.Signature, required: List[s
return missing, unannotated_with_default
-def get_function_schema(f: Callable[..., Any], *, name: Optional[str] = None, description: str) -> Dict[str, Any]:
+def get_function_schema(f: Callable[..., Any], *, name: Optional[str] = None, description: str) -> dict[str, Any]:
"""Get a JSON schema for a function as defined by the OpenAI API
Args:
@@ -289,7 +289,7 @@ def f(a: Annotated[str, "Parameter a"], b: int = 2, c: Annotated[float, "Paramet
return model_dump(function)
-def get_load_param_if_needed_function(t: Any) -> Optional[Callable[[Dict[str, Any], Type[BaseModel]], BaseModel]]:
+def get_load_param_if_needed_function(t: Any) -> Optional[Callable[[dict[str, Any], type[BaseModel]], BaseModel]]:
"""Get a function to load a parameter if it is a Pydantic model
Args:
@@ -302,7 +302,7 @@ def get_load_param_if_needed_function(t: Any) -> Optional[Callable[[Dict[str, An
if get_origin(t) is Annotated:
return get_load_param_if_needed_function(get_args(t)[0])
- def load_base_model(v: Dict[str, Any], t: Type[BaseModel]) -> BaseModel:
+ def load_base_model(v: dict[str, Any], t: type[BaseModel]) -> BaseModel:
return t(**v)
return load_base_model if isinstance(t, type) and issubclass(t, BaseModel) else None
diff --git a/autogen/graph_utils.py b/autogen/graph_utils.py
index 82e8bf4ae9..a495fb4131 100644
--- a/autogen/graph_utils.py
+++ b/autogen/graph_utils.py
@@ -10,7 +10,7 @@
from autogen.agentchat import Agent
-def has_self_loops(allowed_speaker_transitions: Dict) -> bool:
+def has_self_loops(allowed_speaker_transitions: dict) -> bool:
"""
Returns True if there are self loops in the allowed_speaker_transitions_Dict.
"""
@@ -18,8 +18,8 @@ def has_self_loops(allowed_speaker_transitions: Dict) -> bool:
def check_graph_validity(
- allowed_speaker_transitions_dict: Dict,
- agents: List[Agent],
+ allowed_speaker_transitions_dict: dict,
+ agents: list[Agent],
):
"""
allowed_speaker_transitions_dict: A dictionary of keys and list as values. The keys are the names of the agents, and the values are the names of the agents that the key agent can transition to.
@@ -100,7 +100,7 @@ def check_graph_validity(
)
-def invert_disallowed_to_allowed(disallowed_speaker_transitions_dict: dict, agents: List[Agent]) -> dict:
+def invert_disallowed_to_allowed(disallowed_speaker_transitions_dict: dict, agents: list[Agent]) -> dict:
"""
Start with a fully connected allowed_speaker_transitions_dict of all agents. Remove edges from the fully connected allowed_speaker_transitions_dict according to the disallowed_speaker_transitions_dict to form the allowed_speaker_transitions_dict.
"""
@@ -117,7 +117,7 @@ def invert_disallowed_to_allowed(disallowed_speaker_transitions_dict: dict, agen
def visualize_speaker_transitions_dict(
- speaker_transitions_dict: dict, agents: List[Agent], export_path: Optional[str] = None
+ speaker_transitions_dict: dict, agents: list[Agent], export_path: Optional[str] = None
):
"""
Visualize the speaker_transitions_dict using networkx.
diff --git a/autogen/interop/interoperability.py b/autogen/interop/interoperability.py
index b86285d6a6..067571de5c 100644
--- a/autogen/interop/interoperability.py
+++ b/autogen/interop/interoperability.py
@@ -40,7 +40,7 @@ def convert_tool(cls, *, tool: Any, type: str, **kwargs: Any) -> Tool:
return interop.convert_tool(tool, **kwargs)
@classmethod
- def get_interoperability_class(cls, type: str) -> Type[Interoperable]:
+ def get_interoperability_class(cls, type: str) -> type[Interoperable]:
"""
Retrieves the interoperability class corresponding to the specified type.
@@ -63,7 +63,7 @@ def get_interoperability_class(cls, type: str) -> Type[Interoperable]:
return cls.registry.get_class(type)
@classmethod
- def get_supported_types(cls) -> List[str]:
+ def get_supported_types(cls) -> list[str]:
"""
Returns a sorted list of all supported interoperability types.
diff --git a/autogen/interop/pydantic_ai/pydantic_ai_tool.py b/autogen/interop/pydantic_ai/pydantic_ai_tool.py
index 629f65e7ad..7ff50181ba 100644
--- a/autogen/interop/pydantic_ai/pydantic_ai_tool.py
+++ b/autogen/interop/pydantic_ai/pydantic_ai_tool.py
@@ -25,7 +25,7 @@ class PydanticAITool(Tool):
"""
def __init__(
- self, name: str, description: str, func: Callable[..., Any], parameters_json_schema: Dict[str, Any]
+ self, name: str, description: str, func: Callable[..., Any], parameters_json_schema: dict[str, Any]
) -> None:
"""
Initializes a PydanticAITool object with the provided name, description,
diff --git a/autogen/interop/registry.py b/autogen/interop/registry.py
index 443dcb5beb..cb10b701ac 100644
--- a/autogen/interop/registry.py
+++ b/autogen/interop/registry.py
@@ -8,12 +8,12 @@
__all__ = ["register_interoperable_class", "InteroperableRegistry"]
-InteroperableClass = TypeVar("InteroperableClass", bound=Type[Interoperable])
+InteroperableClass = TypeVar("InteroperableClass", bound=type[Interoperable])
class InteroperableRegistry:
def __init__(self) -> None:
- self._registry: Dict[str, Type[Interoperable]] = {}
+ self._registry: dict[str, type[Interoperable]] = {}
def register(self, short_name: str, cls: InteroperableClass) -> InteroperableClass:
if short_name in self._registry:
@@ -23,15 +23,15 @@ def register(self, short_name: str, cls: InteroperableClass) -> InteroperableCla
return cls
- def get_short_names(self) -> List[str]:
+ def get_short_names(self) -> list[str]:
return sorted(self._registry.keys())
- def get_supported_types(self) -> List[str]:
+ def get_supported_types(self) -> list[str]:
short_names = self.get_short_names()
supported_types = [name for name in short_names if self._registry[name].get_unsupported_reason() is None]
return supported_types
- def get_class(self, short_name: str) -> Type[Interoperable]:
+ def get_class(self, short_name: str) -> type[Interoperable]:
return self._registry[short_name]
@classmethod
diff --git a/autogen/io/base.py b/autogen/io/base.py
index 5c79832751..39b857f416 100644
--- a/autogen/io/base.py
+++ b/autogen/io/base.py
@@ -5,9 +5,10 @@
# Portions derived from https://github.com/microsoft/autogen are under the MIT License.
# SPDX-License-Identifier: MIT
import logging
+from collections.abc import Iterator
from contextlib import contextmanager
from contextvars import ContextVar
-from typing import Any, Iterator, Optional, Protocol, runtime_checkable
+from typing import Any, Optional, Protocol, runtime_checkable
__all__ = ("OutputStream", "InputStream", "IOStream")
diff --git a/autogen/io/websockets.py b/autogen/io/websockets.py
index d30bcc69c5..2135727c8c 100644
--- a/autogen/io/websockets.py
+++ b/autogen/io/websockets.py
@@ -7,10 +7,11 @@
import logging
import ssl
import threading
+from collections.abc import Iterable, Iterator
from contextlib import contextmanager
from functools import partial
from time import sleep
-from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, Iterator, Optional, Protocol, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Protocol, Union
from .base import IOStream
@@ -134,7 +135,7 @@ def run_server_in_thread(
Yields:
str: The URI of the websocket server.
"""
- server_dict: Dict[str, WebSocketServer] = {}
+ server_dict: dict[str, WebSocketServer] = {}
def _run_server() -> None:
if _import_error is not None:
diff --git a/autogen/logger/base_logger.py b/autogen/logger/base_logger.py
index 93a7c617eb..b01c112da7 100644
--- a/autogen/logger/base_logger.py
+++ b/autogen/logger/base_logger.py
@@ -18,8 +18,8 @@
from autogen import Agent, ConversableAgent, OpenAIWrapper
F = TypeVar("F", bound=Callable[..., Any])
-ConfigItem = Dict[str, Union[str, List[str]]]
-LLMConfig = Dict[str, Union[None, float, int, ConfigItem, List[ConfigItem]]]
+ConfigItem = dict[str, Union[str, list[str]]]
+LLMConfig = dict[str, Union[None, float, int, ConfigItem, list[ConfigItem]]]
class BaseLogger(ABC):
@@ -39,9 +39,9 @@ def log_chat_completion(
invocation_id: uuid.UUID,
client_id: int,
wrapper_id: int,
- source: Union[str, Agent],
- request: Dict[str, Union[float, str, List[Dict[str, str]]]],
- response: Union[str, ChatCompletion],
+ source: str | Agent,
+ request: dict[str, float | str | list[dict[str, str]]],
+ response: str | ChatCompletion,
is_cached: int,
cost: float,
start_time: str,
@@ -67,7 +67,7 @@ def log_chat_completion(
...
@abstractmethod
- def log_new_agent(self, agent: ConversableAgent, init_args: Dict[str, Any]) -> None:
+ def log_new_agent(self, agent: ConversableAgent, init_args: dict[str, Any]) -> None:
"""
Log the birth of a new agent.
@@ -78,7 +78,7 @@ def log_new_agent(self, agent: ConversableAgent, init_args: Dict[str, Any]) -> N
...
@abstractmethod
- def log_event(self, source: Union[str, Agent], name: str, **kwargs: Dict[str, Any]) -> None:
+ def log_event(self, source: str | Agent, name: str, **kwargs: dict[str, Any]) -> None:
"""
Log an event for an agent.
@@ -90,7 +90,7 @@ def log_event(self, source: Union[str, Agent], name: str, **kwargs: Dict[str, An
...
@abstractmethod
- def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: Dict[str, Union[LLMConfig, List[LLMConfig]]]) -> None:
+ def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: dict[str, LLMConfig | list[LLMConfig]]) -> None:
"""
Log the birth of a new OpenAIWrapper.
@@ -101,9 +101,7 @@ def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: Dict[str, Union[LLM
...
@abstractmethod
- def log_new_client(
- self, client: Union[AzureOpenAI, OpenAI], wrapper: OpenAIWrapper, init_args: Dict[str, Any]
- ) -> None:
+ def log_new_client(self, client: AzureOpenAI | OpenAI, wrapper: OpenAIWrapper, init_args: dict[str, Any]) -> None:
"""
Log the birth of a new OpenAIWrapper.
@@ -114,7 +112,7 @@ def log_new_client(
...
@abstractmethod
- def log_function_use(self, source: Union[str, Agent], function: F, args: Dict[str, Any], returns: Any) -> None:
+ def log_function_use(self, source: str | Agent, function: F, args: dict[str, Any], returns: Any) -> None:
"""
Log the use of a registered function (could be a tool)
@@ -133,7 +131,7 @@ def stop(self) -> None:
...
@abstractmethod
- def get_connection(self) -> Union[None, sqlite3.Connection]:
+ def get_connection(self) -> None | sqlite3.Connection:
"""
Return a connection to the logging database.
"""
diff --git a/autogen/logger/file_logger.py b/autogen/logger/file_logger.py
index 625a96892c..249dd11015 100644
--- a/autogen/logger/file_logger.py
+++ b/autogen/logger/file_logger.py
@@ -51,7 +51,7 @@ def default(o: Any) -> str:
class FileLogger(BaseLogger):
- def __init__(self, config: Dict[str, Any]):
+ def __init__(self, config: dict[str, Any]):
self.config = config
self.session_id = str(uuid.uuid4())
@@ -85,9 +85,9 @@ def log_chat_completion(
invocation_id: uuid.UUID,
client_id: int,
wrapper_id: int,
- source: Union[str, Agent],
- request: Dict[str, Union[float, str, List[Dict[str, str]]]],
- response: Union[str, ChatCompletion],
+ source: str | Agent,
+ request: dict[str, float | str | list[dict[str, str]]],
+ response: str | ChatCompletion,
is_cached: int,
cost: float,
start_time: str,
@@ -122,7 +122,7 @@ def log_chat_completion(
except Exception as e:
self.logger.error(f"[file_logger] Failed to log chat completion: {e}")
- def log_new_agent(self, agent: ConversableAgent, init_args: Dict[str, Any] = {}) -> None:
+ def log_new_agent(self, agent: ConversableAgent, init_args: dict[str, Any] = {}) -> None:
"""
Log a new agent instance.
"""
@@ -147,7 +147,7 @@ def log_new_agent(self, agent: ConversableAgent, init_args: Dict[str, Any] = {})
except Exception as e:
self.logger.error(f"[file_logger] Failed to log new agent: {e}")
- def log_event(self, source: Union[str, Agent], name: str, **kwargs: Dict[str, Any]) -> None:
+ def log_event(self, source: str | Agent, name: str, **kwargs: dict[str, Any]) -> None:
"""
Log an event from an agent or a string source.
"""
@@ -191,9 +191,7 @@ def log_event(self, source: Union[str, Agent], name: str, **kwargs: Dict[str, An
except Exception as e:
self.logger.error(f"[file_logger] Failed to log event {e}")
- def log_new_wrapper(
- self, wrapper: OpenAIWrapper, init_args: Dict[str, Union[LLMConfig, List[LLMConfig]]] = {}
- ) -> None:
+ def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: dict[str, LLMConfig | list[LLMConfig]] = {}) -> None:
"""
Log a new wrapper instance.
"""
@@ -229,7 +227,7 @@ def log_new_client(
| BedrockClient
),
wrapper: OpenAIWrapper,
- init_args: Dict[str, Any],
+ init_args: dict[str, Any],
) -> None:
"""
Log a new client instance.
@@ -252,7 +250,7 @@ def log_new_client(
except Exception as e:
self.logger.error(f"[file_logger] Failed to log event {e}")
- def log_function_use(self, source: Union[str, Agent], function: F, args: Dict[str, Any], returns: Any) -> None:
+ def log_function_use(self, source: str | Agent, function: F, args: dict[str, Any], returns: Any) -> None:
"""
Log a registered function(can be a tool) use from an agent or a string source.
"""
diff --git a/autogen/logger/logger_factory.py b/autogen/logger/logger_factory.py
index f25cd1d6af..c3bab860a9 100644
--- a/autogen/logger/logger_factory.py
+++ b/autogen/logger/logger_factory.py
@@ -16,7 +16,7 @@
class LoggerFactory:
@staticmethod
def get_logger(
- logger_type: Literal["sqlite", "file"] = "sqlite", config: Optional[Dict[str, Any]] = None
+ logger_type: Literal["sqlite", "file"] = "sqlite", config: Optional[dict[str, Any]] = None
) -> BaseLogger:
if config is None:
config = {}
diff --git a/autogen/logger/logger_utils.py b/autogen/logger/logger_utils.py
index 5c226d3d3a..f80f1eb426 100644
--- a/autogen/logger/logger_utils.py
+++ b/autogen/logger/logger_utils.py
@@ -17,9 +17,9 @@ def get_current_ts() -> str:
def to_dict(
- obj: Union[int, float, str, bool, Dict[Any, Any], List[Any], Tuple[Any, ...], Any],
- exclude: Tuple[str, ...] = (),
- no_recursive: Tuple[Any, ...] = (),
+ obj: Union[int, float, str, bool, dict[Any, Any], list[Any], tuple[Any, ...], Any],
+ exclude: tuple[str, ...] = (),
+ no_recursive: tuple[Any, ...] = (),
) -> Any:
if isinstance(obj, (int, float, str, bool)):
return obj
diff --git a/autogen/logger/sqlite_logger.py b/autogen/logger/sqlite_logger.py
index 31510ccfec..24bd7447e3 100644
--- a/autogen/logger/sqlite_logger.py
+++ b/autogen/logger/sqlite_logger.py
@@ -55,7 +55,7 @@ def default(o: Any) -> str:
class SqliteLogger(BaseLogger):
schema_version = 1
- def __init__(self, config: Dict[str, Any]):
+ def __init__(self, config: dict[str, Any]):
self.config = config
try:
@@ -169,7 +169,7 @@ class TEXT, -- type or class name of cli
finally:
return self.session_id
- def _get_current_db_version(self) -> Union[None, int]:
+ def _get_current_db_version(self) -> None | int:
self.cur.execute("SELECT version_number FROM version ORDER BY id DESC LIMIT 1")
result = self.cur.fetchone()
return result[0] if result is not None else None
@@ -188,7 +188,7 @@ def _apply_migration(self, migrations_dir: str = "./migrations") -> None:
migrations_to_apply = [m for m in migrations if int(m.split("_")[0]) > current_version]
for script in migrations_to_apply:
- with open(script, "r") as f:
+ with open(script) as f:
migration_sql = f.read()
self._run_query_script(script=migration_sql)
@@ -197,7 +197,7 @@ def _apply_migration(self, migrations_dir: str = "./migrations") -> None:
args = (latest_version,)
self._run_query(query=query, args=args)
- def _run_query(self, query: str, args: Tuple[Any, ...] = ()) -> None:
+ def _run_query(self, query: str, args: tuple[Any, ...] = ()) -> None:
"""
Executes a given SQL query.
@@ -231,9 +231,9 @@ def log_chat_completion(
invocation_id: uuid.UUID,
client_id: int,
wrapper_id: int,
- source: Union[str, Agent],
- request: Dict[str, Union[float, str, List[Dict[str, str]]]],
- response: Union[str, ChatCompletion],
+ source: str | Agent,
+ request: dict[str, float | str | list[dict[str, str]]],
+ response: str | ChatCompletion,
is_cached: int,
cost: float,
start_time: str,
@@ -275,7 +275,7 @@ def log_chat_completion(
self._run_query(query=query, args=args)
- def log_new_agent(self, agent: ConversableAgent, init_args: Dict[str, Any]) -> None:
+ def log_new_agent(self, agent: ConversableAgent, init_args: dict[str, Any]) -> None:
from autogen import Agent
if self.con is None:
@@ -317,7 +317,7 @@ class = excluded.class,
)
self._run_query(query=query, args=args)
- def log_event(self, source: Union[str, Agent], name: str, **kwargs: Dict[str, Any]) -> None:
+ def log_event(self, source: str | Agent, name: str, **kwargs: dict[str, Any]) -> None:
from autogen import Agent
if self.con is None:
@@ -352,7 +352,7 @@ def log_event(self, source: Union[str, Agent], name: str, **kwargs: Dict[str, An
)
self._run_query(query=query, args=args_str_based)
- def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: Dict[str, Union[LLMConfig, List[LLMConfig]]]) -> None:
+ def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: dict[str, LLMConfig | list[LLMConfig]]) -> None:
if self.con is None:
return
@@ -382,7 +382,7 @@ def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: Dict[str, Union[LLM
)
self._run_query(query=query, args=args)
- def log_function_use(self, source: Union[str, Agent], function: F, args: Dict[str, Any], returns: Any) -> None:
+ def log_function_use(self, source: str | Agent, function: F, args: dict[str, Any], returns: Any) -> None:
if self.con is None:
return
@@ -390,7 +390,7 @@ def log_function_use(self, source: Union[str, Agent], function: F, args: Dict[st
query = """
INSERT INTO function_calls (source_id, source_name, function_name, args, returns, timestamp) VALUES (?, ?, ?, ?, ?, ?)
"""
- query_args: Tuple[Any, ...] = (
+ query_args: tuple[Any, ...] = (
id(source),
source.name if hasattr(source, "name") else source,
function.__name__,
@@ -402,21 +402,21 @@ def log_function_use(self, source: Union[str, Agent], function: F, args: Dict[st
def log_new_client(
self,
- client: Union[
- AzureOpenAI,
- OpenAI,
- CerebrasClient,
- GeminiClient,
- AnthropicClient,
- MistralAIClient,
- TogetherClient,
- GroqClient,
- CohereClient,
- OllamaClient,
- BedrockClient,
- ],
+ client: (
+ AzureOpenAI
+ | OpenAI
+ | CerebrasClient
+ | GeminiClient
+ | AnthropicClient
+ | MistralAIClient
+ | TogetherClient
+ | GroqClient
+ | CohereClient
+ | OllamaClient
+ | BedrockClient
+ ),
wrapper: OpenAIWrapper,
- init_args: Dict[str, Any],
+ init_args: dict[str, Any],
) -> None:
if self.con is None:
return
@@ -453,7 +453,7 @@ def stop(self) -> None:
if self.con:
self.con.close()
- def get_connection(self) -> Union[None, sqlite3.Connection]:
+ def get_connection(self) -> None | sqlite3.Connection:
if self.con:
return self.con
return None
diff --git a/autogen/math_utils.py b/autogen/math_utils.py
index 0ef12d8f2d..069747f7c7 100644
--- a/autogen/math_utils.py
+++ b/autogen/math_utils.py
@@ -138,7 +138,7 @@ def _fix_a_slash_b(string: str) -> str:
try:
a = int(a_str)
b = int(b_str)
- if not string == "{}/{}".format(a, b):
+ if not string == f"{a}/{b}":
raise AssertionError
new_string = "\\frac{" + str(a) + "}{" + str(b) + "}"
return new_string
diff --git a/autogen/oai/anthropic.py b/autogen/oai/anthropic.py
index 44dc7bd60d..a3a67baf85 100644
--- a/autogen/oai/anthropic.py
+++ b/autogen/oai/anthropic.py
@@ -56,7 +56,7 @@
import os
import time
import warnings
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Annotated, Any, Dict, List, Optional, Tuple, Union
from anthropic import Anthropic, AnthropicBedrock
from anthropic import __version__ as anthropic_version
@@ -65,7 +65,6 @@
from openai.types.chat.chat_completion import ChatCompletionMessage, Choice
from openai.types.completion_usage import CompletionUsage
from pydantic import BaseModel
-from typing_extensions import Annotated
from autogen.oai.client_utils import validate_parameter
@@ -134,7 +133,7 @@ def __init__(self, **kwargs: Any):
self._last_tooluse_status = {}
- def load_config(self, params: Dict[str, Any]):
+ def load_config(self, params: dict[str, Any]):
"""Load the configuration for the Anthropic API client."""
anthropic_params = {}
@@ -183,7 +182,7 @@ def aws_session_token(self):
def aws_region(self):
return self._aws_region
- def create(self, params: Dict[str, Any]) -> ChatCompletion:
+ def create(self, params: dict[str, Any]) -> ChatCompletion:
if "tools" in params:
converted_functions = self.convert_tools_to_functions(params["tools"])
params["functions"] = params.get("functions", []) + converted_functions
@@ -270,7 +269,7 @@ def create(self, params: Dict[str, Any]) -> ChatCompletion:
return response_oai
- def message_retrieval(self, response) -> List:
+ def message_retrieval(self, response) -> list:
"""
Retrieve and return a list of strings or a list of Choice.Message from the response.
@@ -286,7 +285,7 @@ def openai_func_to_anthropic(openai_func: dict) -> dict:
return res
@staticmethod
- def get_usage(response: ChatCompletion) -> Dict:
+ def get_usage(response: ChatCompletion) -> dict:
"""Get the usage of tokens and their cost information."""
return {
"prompt_tokens": response.usage.prompt_tokens if response.usage is not None else 0,
@@ -297,7 +296,7 @@ def get_usage(response: ChatCompletion) -> Dict:
}
@staticmethod
- def convert_tools_to_functions(tools: List) -> List:
+ def convert_tools_to_functions(tools: list) -> list:
functions = []
for tool in tools:
if tool.get("type") == "function" and "function" in tool:
@@ -306,7 +305,7 @@ def convert_tools_to_functions(tools: List) -> List:
return functions
-def oai_messages_to_anthropic_messages(params: Dict[str, Any]) -> list[dict[str, Any]]:
+def oai_messages_to_anthropic_messages(params: dict[str, Any]) -> list[dict[str, Any]]:
"""Convert messages from OAI format to Anthropic format.
We correct for any specific role orders and types, etc.
"""
diff --git a/autogen/oai/bedrock.py b/autogen/oai/bedrock.py
index 5d8f34fe51..b624cc9125 100644
--- a/autogen/oai/bedrock.py
+++ b/autogen/oai/bedrock.py
@@ -120,7 +120,7 @@ def message_retrieval(self, response):
"""Retrieve the messages from the response."""
return [choice.message for choice in response.choices]
- def parse_custom_params(self, params: Dict[str, Any]):
+ def parse_custom_params(self, params: dict[str, Any]):
"""
Parses custom parameters for logic in this client class
"""
@@ -129,7 +129,7 @@ def parse_custom_params(self, params: Dict[str, Any]):
# This is required because not all models support a system prompt (e.g. Mistral Instruct).
self._supports_system_prompts = params.get("supports_system_prompts", True)
- def parse_params(self, params: Dict[str, Any]) -> tuple[Dict[str, Any], Dict[str, Any]]:
+ def parse_params(self, params: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]:
"""
Loads the valid parameters required to invoke Bedrock Converse
Returns a tuple of (base_params, additional_params)
@@ -273,7 +273,7 @@ def cost(self, response: ChatCompletion) -> float:
return calculate_cost(response.usage.prompt_tokens, response.usage.completion_tokens, response.model)
@staticmethod
- def get_usage(response) -> Dict:
+ def get_usage(response) -> dict:
"""Get the usage of tokens and their cost information."""
return {
"prompt_tokens": response.usage.prompt_tokens,
@@ -284,7 +284,7 @@ def get_usage(response) -> Dict:
}
-def extract_system_messages(messages: List[dict]) -> List:
+def extract_system_messages(messages: list[dict]) -> list:
"""Extract the system messages from the list of messages.
Args:
@@ -309,8 +309,8 @@ def extract_system_messages(messages: List[dict]) -> List:
def oai_messages_to_bedrock_messages(
- messages: List[Dict[str, Any]], has_tools: bool, supports_system_prompts: bool
-) -> List[Dict]:
+ messages: list[dict[str, Any]], has_tools: bool, supports_system_prompts: bool
+) -> list[dict]:
"""
Convert messages from OAI format to Bedrock format.
We correct for any specific role orders and types, etc.
@@ -453,9 +453,9 @@ def oai_messages_to_bedrock_messages(
def parse_content_parts(
- message: Dict[str, Any],
-) -> List[dict]:
- content: str | List[Dict[str, Any]] = message.get("content")
+ message: dict[str, Any],
+) -> list[dict]:
+ content: str | list[dict[str, Any]] = message.get("content")
if isinstance(content, str):
return [
{
@@ -487,7 +487,7 @@ def parse_content_parts(
return content_parts
-def parse_image(image_url: str) -> Tuple[bytes, str]:
+def parse_image(image_url: str) -> tuple[bytes, str]:
"""Try to get the raw data from an image url.
Ref: https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ImageSource.html
@@ -516,7 +516,7 @@ def parse_image(image_url: str) -> Tuple[bytes, str]:
raise RuntimeError("Unable to access the image url")
-def format_tools(tools: List[Dict[str, Any]]) -> Dict[Literal["tools"], List[Dict[str, Any]]]:
+def format_tools(tools: list[dict[str, Any]]) -> dict[Literal["tools"], list[dict[str, Any]]]:
converted_schema = {"tools": []}
for tool in tools:
diff --git a/autogen/oai/cerebras.py b/autogen/oai/cerebras.py
index 201fb2ee55..cce38f1ca2 100644
--- a/autogen/oai/cerebras.py
+++ b/autogen/oai/cerebras.py
@@ -67,7 +67,7 @@ def __init__(self, api_key=None, **kwargs):
if "response_format" in kwargs and kwargs["response_format"] is not None:
warnings.warn("response_format is not supported for Crebras, it will be ignored.", UserWarning)
- def message_retrieval(self, response: ChatCompletion) -> List:
+ def message_retrieval(self, response: ChatCompletion) -> list:
"""
Retrieve and return a list of strings or a list of Choice.Message from the response.
@@ -81,7 +81,7 @@ def cost(self, response: ChatCompletion) -> float:
return response.cost
@staticmethod
- def get_usage(response: ChatCompletion) -> Dict:
+ def get_usage(response: ChatCompletion) -> dict:
"""Return usage summary of the response using RESPONSE_USAGE_KEYS."""
# ... # pragma: no cover
return {
@@ -92,7 +92,7 @@ def get_usage(response: ChatCompletion) -> Dict:
"model": response.model,
}
- def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
+ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]:
"""Loads the parameters for Cerebras API from the passed in parameters and returns a validated set. Checks types, ranges, and sets defaults"""
cerebras_params = {}
@@ -115,7 +115,7 @@ def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
return cerebras_params
- def create(self, params: Dict) -> ChatCompletion:
+ def create(self, params: dict) -> ChatCompletion:
messages = params.get("messages", [])
# Convert AutoGen messages to Cerebras messages
@@ -243,7 +243,7 @@ def create(self, params: Dict) -> ChatCompletion:
return response_oai
-def oai_messages_to_cerebras_messages(messages: list[Dict[str, Any]]) -> list[dict[str, Any]]:
+def oai_messages_to_cerebras_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Convert messages from OAI format to Cerebras's format.
We correct for any specific role orders and types.
"""
diff --git a/autogen/oai/client.py b/autogen/oai/client.py
index de83c7c1b0..fb2a0a4358 100644
--- a/autogen/oai/client.py
+++ b/autogen/oai/client.py
@@ -205,14 +205,14 @@ class Message(Protocol):
message: Message
- choices: List[Choice]
+ choices: list[Choice]
model: str
- def create(self, params: Dict[str, Any]) -> ModelClientResponseProtocol: ... # pragma: no cover
+ def create(self, params: dict[str, Any]) -> ModelClientResponseProtocol: ... # pragma: no cover
def message_retrieval(
self, response: ModelClientResponseProtocol
- ) -> Union[List[str], List[ModelClient.ModelClientResponseProtocol.Choice.Message]]:
+ ) -> Union[list[str], list[ModelClient.ModelClientResponseProtocol.Choice.Message]]:
"""
Retrieve and return a list of strings or a list of Choice.Message from the response.
@@ -224,7 +224,7 @@ def message_retrieval(
def cost(self, response: ModelClientResponseProtocol) -> float: ... # pragma: no cover
@staticmethod
- def get_usage(response: ModelClientResponseProtocol) -> Dict:
+ def get_usage(response: ModelClientResponseProtocol) -> dict:
"""Return usage summary of the response using RESPONSE_USAGE_KEYS."""
... # pragma: no cover
@@ -251,7 +251,7 @@ def __init__(self, client: Union[OpenAI, AzureOpenAI], response_format: Optional
def message_retrieval(
self, response: Union[ChatCompletion, Completion]
- ) -> Union[List[str], List[ChatCompletionMessage]]:
+ ) -> Union[list[str], list[ChatCompletionMessage]]:
"""Retrieve the messages from the response."""
choices = response.choices
if isinstance(response, Completion):
@@ -279,7 +279,7 @@ def _format_content(content: str) -> str:
for choice in choices
]
- def create(self, params: Dict[str, Any]) -> ChatCompletion:
+ def create(self, params: dict[str, Any]) -> ChatCompletion:
"""Create a completion for a given config using openai's client.
Args:
@@ -314,8 +314,8 @@ def _create_or_parse(*args, **kwargs):
iostream.print("\033[32m", end="")
# Prepare for potential function call
- full_function_call: Optional[Dict[str, Any]] = None
- full_tool_calls: Optional[List[Optional[Dict[str, Any]]]] = None
+ full_function_call: Optional[dict[str, Any]] = None
+ full_tool_calls: Optional[list[Optional[dict[str, Any]]]] = None
# Send the chat completion request to OpenAI's API and process the response in chunks
for chunk in create_or_parse(**params):
@@ -445,7 +445,7 @@ def cost(self, response: Union[ChatCompletion, Completion]) -> float:
return tmp_price1K * (n_input_tokens + n_output_tokens) / 1000 # type: ignore [operator]
@staticmethod
- def get_usage(response: Union[ChatCompletion, Completion]) -> Dict:
+ def get_usage(response: Union[ChatCompletion, Completion]) -> dict:
return {
"prompt_tokens": response.usage.prompt_tokens if response.usage is not None else 0,
"completion_tokens": response.usage.completion_tokens if response.usage is not None else 0,
@@ -479,13 +479,13 @@ class OpenAIWrapper:
openai_kwargs = set(inspect.getfullargspec(OpenAI.__init__).kwonlyargs)
aopenai_kwargs = set(inspect.getfullargspec(AzureOpenAI.__init__).kwonlyargs)
openai_kwargs = openai_kwargs | aopenai_kwargs
- total_usage_summary: Optional[Dict[str, Any]] = None
- actual_usage_summary: Optional[Dict[str, Any]] = None
+ total_usage_summary: Optional[dict[str, Any]] = None
+ actual_usage_summary: Optional[dict[str, Any]] = None
def __init__(
self,
*,
- config_list: Optional[List[Dict[str, Any]]] = None,
+ config_list: Optional[list[dict[str, Any]]] = None,
**base_config: Any,
):
"""
@@ -526,8 +526,8 @@ def __init__(
# It's OK if "model" is not provided in base_config or config_list
# Because one can provide "model" at `create` time.
- self._clients: List[ModelClient] = []
- self._config_list: List[Dict[str, Any]] = []
+ self._clients: list[ModelClient] = []
+ self._config_list: list[dict[str, Any]] = []
if config_list:
config_list = [config.copy() for config in config_list] # make a copy before modifying
@@ -541,19 +541,19 @@ def __init__(
self._config_list = [extra_kwargs]
self.wrapper_id = id(self)
- def _separate_openai_config(self, config: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+ def _separate_openai_config(self, config: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]:
"""Separate the config into openai_config and extra_kwargs."""
openai_config = {k: v for k, v in config.items() if k in self.openai_kwargs}
extra_kwargs = {k: v for k, v in config.items() if k not in self.openai_kwargs}
return openai_config, extra_kwargs
- def _separate_create_config(self, config: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+ def _separate_create_config(self, config: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]:
"""Separate the config into create_config and extra_kwargs."""
create_config = {k: v for k, v in config.items() if k not in self.extra_kwargs}
extra_kwargs = {k: v for k, v in config.items() if k in self.extra_kwargs}
return create_config, extra_kwargs
- def _configure_azure_openai(self, config: Dict[str, Any], openai_config: Dict[str, Any]) -> None:
+ def _configure_azure_openai(self, config: dict[str, Any], openai_config: dict[str, Any]) -> None:
openai_config["azure_deployment"] = openai_config.get("azure_deployment", config.get("model"))
if openai_config["azure_deployment"] is not None:
openai_config["azure_deployment"] = openai_config["azure_deployment"].replace(".", "")
@@ -567,7 +567,7 @@ def _configure_azure_openai(self, config: Dict[str, Any], openai_config: Dict[st
azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
)
- def _configure_openai_config_for_bedrock(self, config: Dict[str, Any], openai_config: Dict[str, Any]) -> None:
+ def _configure_openai_config_for_bedrock(self, config: dict[str, Any], openai_config: dict[str, Any]) -> None:
"""Update openai_config with AWS credentials from config."""
required_keys = ["aws_access_key", "aws_secret_key", "aws_region"]
optional_keys = ["aws_session_token", "aws_profile_name"]
@@ -578,7 +578,7 @@ def _configure_openai_config_for_bedrock(self, config: Dict[str, Any], openai_co
if key in config:
openai_config[key] = config[key]
- def _register_default_client(self, config: Dict[str, Any], openai_config: Dict[str, Any]) -> None:
+ def _register_default_client(self, config: dict[str, Any], openai_config: dict[str, Any]) -> None:
"""Create a client with the given config to override openai_config,
after removing extra kwargs.
@@ -690,8 +690,8 @@ def register_model_client(self, model_client_cls: ModelClient, **kwargs):
@classmethod
def instantiate(
cls,
- template: Optional[Union[str, Callable[[Dict[str, Any]], str]]],
- context: Optional[Dict[str, Any]] = None,
+ template: Optional[Union[str, Callable[[dict[str, Any]], str]]],
+ context: Optional[dict[str, Any]] = None,
allow_format_str_template: Optional[bool] = False,
) -> Optional[str]:
if not context or template is None:
@@ -700,11 +700,11 @@ def instantiate(
return template.format(**context) if allow_format_str_template else template
return template(context)
- def _construct_create_params(self, create_config: Dict[str, Any], extra_kwargs: Dict[str, Any]) -> Dict[str, Any]:
+ def _construct_create_params(self, create_config: dict[str, Any], extra_kwargs: dict[str, Any]) -> dict[str, Any]:
"""Prime the create_config with additional_kwargs."""
# Validate the config
prompt: Optional[str] = create_config.get("prompt")
- messages: Optional[List[Dict[str, Any]]] = create_config.get("messages")
+ messages: Optional[list[dict[str, Any]]] = create_config.get("messages")
if (prompt is None) == (messages is None):
raise ValueError("Either prompt or messages should be in create config but not both.")
context = extra_kwargs.get("context")
@@ -961,7 +961,7 @@ def yes_or_no_filter(context, response):
@staticmethod
def _cost_with_customized_price(
- response: ModelClient.ModelClientResponseProtocol, price_1k: Tuple[float, float]
+ response: ModelClient.ModelClientResponseProtocol, price_1k: tuple[float, float]
) -> None:
"""If a customized cost is passed, overwrite the cost in the response."""
n_input_tokens = response.usage.prompt_tokens if response.usage is not None else 0 # type: ignore [union-attr]
@@ -971,7 +971,7 @@ def _cost_with_customized_price(
return (n_input_tokens * price_1k[0] + n_output_tokens * price_1k[1]) / 1000
@staticmethod
- def _update_dict_from_chunk(chunk: BaseModel, d: Dict[str, Any], field: str) -> int:
+ def _update_dict_from_chunk(chunk: BaseModel, d: dict[str, Any], field: str) -> int:
"""Update the dict from the chunk.
Reads `chunk.field` and if present updates `d[field]` accordingly.
@@ -1007,9 +1007,9 @@ def _update_dict_from_chunk(chunk: BaseModel, d: Dict[str, Any], field: str) ->
@staticmethod
def _update_function_call_from_chunk(
function_call_chunk: Union[ChoiceDeltaToolCallFunction, ChoiceDeltaFunctionCall],
- full_function_call: Optional[Dict[str, Any]],
+ full_function_call: Optional[dict[str, Any]],
completion_tokens: int,
- ) -> Tuple[Dict[str, Any], int]:
+ ) -> tuple[dict[str, Any], int]:
"""Update the function call from the chunk.
Args:
@@ -1038,9 +1038,9 @@ def _update_function_call_from_chunk(
@staticmethod
def _update_tool_calls_from_chunk(
tool_calls_chunk: ChoiceDeltaToolCall,
- full_tool_call: Optional[Dict[str, Any]],
+ full_tool_call: Optional[dict[str, Any]],
completion_tokens: int,
- ) -> Tuple[Dict[str, Any], int]:
+ ) -> tuple[dict[str, Any], int]:
"""Update the tool call from the chunk.
Args:
@@ -1113,11 +1113,11 @@ def update_usage(usage_summary, response_usage):
if actual_usage is not None:
self.actual_usage_summary = update_usage(self.actual_usage_summary, actual_usage)
- def print_usage_summary(self, mode: Union[str, List[str]] = ["actual", "total"]) -> None:
+ def print_usage_summary(self, mode: Union[str, list[str]] = ["actual", "total"]) -> None:
"""Print the usage summary."""
iostream = IOStream.get_default()
- def print_usage(usage_summary: Optional[Dict[str, Any]], usage_type: str = "total") -> None:
+ def print_usage(usage_summary: Optional[dict[str, Any]], usage_type: str = "total") -> None:
word_from_type = "including" if usage_type == "total" else "excluding"
if usage_summary is None:
iostream.print("No actual cost incurred (all completions are using cache).", flush=True)
@@ -1174,7 +1174,7 @@ def clear_usage_summary(self) -> None:
@classmethod
def extract_text_or_completion_object(
cls, response: ModelClient.ModelClientResponseProtocol
- ) -> Union[List[str], List[ModelClient.ModelClientResponseProtocol.Choice.Message]]:
+ ) -> Union[list[str], list[ModelClient.ModelClientResponseProtocol.Choice.Message]]:
"""Extract the text or ChatCompletion objects from a completion or chat response.
Args:
diff --git a/autogen/oai/client_utils.py b/autogen/oai/client_utils.py
index fac01286cc..6f417c90ba 100644
--- a/autogen/oai/client_utils.py
+++ b/autogen/oai/client_utils.py
@@ -12,12 +12,12 @@
def validate_parameter(
- params: Dict[str, Any],
+ params: dict[str, Any],
param_name: str,
- allowed_types: Tuple,
+ allowed_types: tuple,
allow_None: bool,
default_value: Any,
- numerical_bound: Tuple,
+ numerical_bound: tuple,
allowed_values: list,
) -> Any:
"""
@@ -106,7 +106,7 @@ def validate_parameter(
return param_value
-def should_hide_tools(messages: List[Dict[str, Any]], tools: List[Dict[str, Any]], hide_tools_param: str) -> bool:
+def should_hide_tools(messages: list[dict[str, Any]], tools: list[dict[str, Any]], hide_tools_param: str) -> bool:
"""
Determines if tools should be hidden. This function is used to hide tools when they have been run, minimising the chance of the LLM choosing them when they shouldn't.
Parameters:
diff --git a/autogen/oai/cohere.py b/autogen/oai/cohere.py
index b7d411454d..e725d8e156 100644
--- a/autogen/oai/cohere.py
+++ b/autogen/oai/cohere.py
@@ -83,7 +83,7 @@ def __init__(self, **kwargs):
if "response_format" in kwargs and kwargs["response_format"] is not None:
warnings.warn("response_format is not supported for Cohere, it will be ignored.", UserWarning)
- def message_retrieval(self, response) -> List:
+ def message_retrieval(self, response) -> list:
"""
Retrieve and return a list of strings or a list of Choice.Message from the response.
@@ -96,7 +96,7 @@ def cost(self, response) -> float:
return response.cost
@staticmethod
- def get_usage(response) -> Dict:
+ def get_usage(response) -> dict:
"""Return usage summary of the response using RESPONSE_USAGE_KEYS."""
# ... # pragma: no cover
return {
@@ -107,7 +107,7 @@ def get_usage(response) -> Dict:
"model": response.model,
}
- def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
+ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]:
"""Loads the parameters for Cohere API from the passed in parameters and returns a validated set. Checks types, ranges, and sets defaults"""
cohere_params = {}
@@ -151,7 +151,7 @@ def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
return cohere_params
- def create(self, params: Dict) -> ChatCompletion:
+ def create(self, params: dict) -> ChatCompletion:
messages = params.get("messages", [])
client_name = params.get("client_name") or "autogen-cohere"
# Parse parameters to the Cohere API's parameters
@@ -263,7 +263,7 @@ def create(self, params: Dict) -> ChatCompletion:
return response_oai
-def extract_to_cohere_tool_results(tool_call_id: str, content_output: str, all_tool_calls) -> List[Dict[str, Any]]:
+def extract_to_cohere_tool_results(tool_call_id: str, content_output: str, all_tool_calls) -> list[dict[str, Any]]:
temp_tool_results = []
for tool_call in all_tool_calls:
@@ -281,7 +281,7 @@ def extract_to_cohere_tool_results(tool_call_id: str, content_output: str, all_t
def oai_messages_to_cohere_messages(
- messages: list[Dict[str, Any]], params: Dict[str, Any], cohere_params: Dict[str, Any]
+ messages: list[dict[str, Any]], params: dict[str, Any], cohere_params: dict[str, Any]
) -> tuple[list[dict[str, Any]], str, str]:
"""Convert messages from OAI format to Cohere's format.
We correct for any specific role orders and types.
diff --git a/autogen/oai/completion.py b/autogen/oai/completion.py
index 72886dd857..300abfda53 100644
--- a/autogen/oai/completion.py
+++ b/autogen/oai/completion.py
@@ -172,7 +172,7 @@ def clear_cache(cls, seed: Optional[int] = None, cache_path_root: Optional[str]
cache.clear()
@classmethod
- def _book_keeping(cls, config: Dict, response):
+ def _book_keeping(cls, config: dict, response):
"""Book keeping for the created completions."""
if response != -1 and "cost" not in response:
response["cost"] = cls.cost(response)
@@ -212,7 +212,7 @@ def _book_keeping(cls, config: Dict, response):
cls._count_create += 1
@classmethod
- def _get_response(cls, config: Dict, raise_on_ratelimit_or_timeout=False, use_cache=True):
+ def _get_response(cls, config: dict, raise_on_ratelimit_or_timeout=False, use_cache=True):
"""Get the response from the openai api call.
Try cache first. If not found, call the openai api. If the api call fails, retry after retry_wait_time.
@@ -335,7 +335,7 @@ def _pop_subspace(cls, config, always_copy=True):
return config.copy() if always_copy else config
@classmethod
- def _get_params_for_create(cls, config: Dict) -> Dict:
+ def _get_params_for_create(cls, config: dict) -> dict:
"""Get the params for the openai api call from a config in the search space."""
params = cls._pop_subspace(config)
if cls._prompts:
@@ -526,7 +526,7 @@ def _eval(cls, config: dict, prune=True, eval_only=False):
@classmethod
def tune(
cls,
- data: List[Dict],
+ data: list[dict],
metric: str,
mode: str,
eval_func: Callable,
@@ -726,10 +726,10 @@ def eval_func(responses, **data):
@classmethod
def create(
cls,
- context: Optional[Dict] = None,
+ context: Optional[dict] = None,
use_cache: Optional[bool] = True,
- config_list: Optional[List[Dict]] = None,
- filter_func: Optional[Callable[[Dict, Dict], bool]] = None,
+ config_list: Optional[list[dict]] = None,
+ filter_func: Optional[Callable[[dict, dict], bool]] = None,
raise_on_ratelimit_or_timeout: Optional[bool] = True,
allow_format_str_template: Optional[bool] = False,
**config,
@@ -861,7 +861,7 @@ def yes_or_no_filter(context, config, response):
def instantiate(
cls,
template: Union[str, None],
- context: Optional[Dict] = None,
+ context: Optional[dict] = None,
allow_format_str_template: Optional[bool] = False,
):
if not context or template is None:
@@ -1069,7 +1069,7 @@ def cost(cls, response: dict):
return price1K * (n_input_tokens + n_output_tokens) / 1000
@classmethod
- def extract_text(cls, response: dict) -> List[str]:
+ def extract_text(cls, response: dict) -> list[str]:
"""Extract the text from a completion or chat response.
Args:
@@ -1084,7 +1084,7 @@ def extract_text(cls, response: dict) -> List[str]:
return [choice["message"].get("content", "") for choice in choices]
@classmethod
- def extract_text_or_function_call(cls, response: dict) -> List[str]:
+ def extract_text_or_function_call(cls, response: dict) -> list[str]:
"""Extract the text or function calls from a completion or chat response.
Args:
@@ -1103,12 +1103,12 @@ def extract_text_or_function_call(cls, response: dict) -> List[str]:
@classmethod
@property
- def logged_history(cls) -> Dict:
+ def logged_history(cls) -> dict:
"""Return the book keeping dictionary."""
return cls._history_dict
@classmethod
- def print_usage_summary(cls) -> Dict:
+ def print_usage_summary(cls) -> dict:
"""Return the usage summary."""
if cls._history_dict is None:
print("No usage summary available.", flush=True)
@@ -1147,7 +1147,7 @@ def print_usage_summary(cls) -> Dict:
@classmethod
def start_logging(
- cls, history_dict: Optional[Dict] = None, compact: Optional[bool] = True, reset_counter: Optional[bool] = True
+ cls, history_dict: Optional[dict] = None, compact: Optional[bool] = True, reset_counter: Optional[bool] = True
):
"""Start book keeping.
diff --git a/autogen/oai/gemini.py b/autogen/oai/gemini.py
index f89e40cf84..02fa5df54f 100644
--- a/autogen/oai/gemini.py
+++ b/autogen/oai/gemini.py
@@ -46,8 +46,9 @@
import re
import time
import warnings
+from collections.abc import Mapping
from io import BytesIO
-from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
import google.generativeai as genai
import PIL
@@ -151,7 +152,7 @@ def __init__(self, **kwargs):
if "response_format" in kwargs and kwargs["response_format"] is not None:
warnings.warn("response_format is not supported for Gemini. It will be ignored.", UserWarning)
- def message_retrieval(self, response) -> List:
+ def message_retrieval(self, response) -> list:
"""
Retrieve and return a list of strings or a list of Choice.Message from the response.
@@ -164,7 +165,7 @@ def cost(self, response) -> float:
return response.cost
@staticmethod
- def get_usage(response) -> Dict:
+ def get_usage(response) -> dict:
"""Return usage summary of the response using RESPONSE_USAGE_KEYS."""
# ... # pragma: no cover
return {
@@ -175,7 +176,7 @@ def get_usage(response) -> Dict:
"model": response.model,
}
- def create(self, params: Dict) -> ChatCompletion:
+ def create(self, params: dict) -> ChatCompletion:
if self.use_vertexai:
self._initialize_vertexai(**params)
@@ -230,7 +231,7 @@ def create(self, params: Dict) -> ChatCompletion:
autogen_tool_calls = []
# Maps the function call ids to function names so we can inject it into FunctionResponse messages
- self.tool_call_function_map: Dict[str, str] = {}
+ self.tool_call_function_map: dict[str, str] = {}
# A. create and call the chat model.
gemini_messages = self._oai_messages_to_gemini_messages(messages)
@@ -325,7 +326,7 @@ def create(self, params: Dict) -> ChatCompletion:
return response_oai
- def _oai_content_to_gemini_content(self, message: Dict[str, Any]) -> Tuple[List, str]:
+ def _oai_content_to_gemini_content(self, message: dict[str, Any]) -> tuple[list, str]:
"""Convert AutoGen content to Gemini parts, catering for text and tool calls"""
rst = []
@@ -420,7 +421,7 @@ def _oai_content_to_gemini_content(self, message: Dict[str, Any]) -> Tuple[List,
else:
raise Exception("Unable to convert content to Gemini format.")
- def _concat_parts(self, parts: List[Part]) -> List:
+ def _concat_parts(self, parts: list[Part]) -> list:
"""Concatenate parts with the same type.
If two adjacent parts both have the "text" attribute, then it will be joined into one part.
"""
@@ -449,7 +450,7 @@ def _concat_parts(self, parts: List[Part]) -> List:
return concatenated_parts
- def _oai_messages_to_gemini_messages(self, messages: list[Dict[str, Any]]) -> list[dict[str, Any]]:
+ def _oai_messages_to_gemini_messages(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Convert messages from OAI format to Gemini format.
Make sure the "user" role and "model" role are interleaved.
Also, make sure the last item is from the "user" role.
@@ -522,7 +523,7 @@ def _oai_messages_to_gemini_messages(self, messages: list[Dict[str, Any]]) -> li
return rst
- def _tools_to_gemini_tools(self, tools: List[Dict[str, Any]]) -> List[Tool]:
+ def _tools_to_gemini_tools(self, tools: list[dict[str, Any]]) -> list[Tool]:
"""Create Gemini tools (as typically requires Callables)"""
functions = []
@@ -543,7 +544,7 @@ def _tools_to_gemini_tools(self, tools: List[Dict[str, Any]]) -> List[Tool]:
return [Tool(function_declarations=functions)]
@staticmethod
- def _create_gemini_function_declaration(tool: Dict) -> FunctionDeclaration:
+ def _create_gemini_function_declaration(tool: dict) -> FunctionDeclaration:
function_declaration = FunctionDeclaration()
function_declaration.name = tool["function"]["name"]
function_declaration.description = tool["function"]["description"]
@@ -657,7 +658,7 @@ def _to_vertexai_safety_settings(safety_settings):
return safety_settings
@staticmethod
- def _to_json_or_str(data: str) -> Union[Dict, str]:
+ def _to_json_or_str(data: str) -> dict | str:
try:
json_data = json.loads(data)
return json_data
diff --git a/autogen/oai/groq.py b/autogen/oai/groq.py
index e3112619fa..65cc29ec97 100644
--- a/autogen/oai/groq.py
+++ b/autogen/oai/groq.py
@@ -70,7 +70,7 @@ def __init__(self, **kwargs):
warnings.warn("response_format is not supported for Groq API, it will be ignored.", UserWarning)
self.base_url = kwargs.get("base_url", None)
- def message_retrieval(self, response) -> List:
+ def message_retrieval(self, response) -> list:
"""
Retrieve and return a list of strings or a list of Choice.Message from the response.
@@ -83,7 +83,7 @@ def cost(self, response) -> float:
return response.cost
@staticmethod
- def get_usage(response) -> Dict:
+ def get_usage(response) -> dict:
"""Return usage summary of the response using RESPONSE_USAGE_KEYS."""
# ... # pragma: no cover
return {
@@ -94,7 +94,7 @@ def get_usage(response) -> Dict:
"model": response.model,
}
- def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
+ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]:
"""Loads the parameters for Groq API from the passed in parameters and returns a validated set. Checks types, ranges, and sets defaults"""
groq_params = {}
@@ -130,7 +130,7 @@ def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
return groq_params
- def create(self, params: Dict) -> ChatCompletion:
+ def create(self, params: dict) -> ChatCompletion:
messages = params.get("messages", [])
# Convert AutoGen messages to Groq messages
@@ -255,7 +255,7 @@ def create(self, params: Dict) -> ChatCompletion:
return response_oai
-def oai_messages_to_groq_messages(messages: list[Dict[str, Any]]) -> list[dict[str, Any]]:
+def oai_messages_to_groq_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Convert messages from OAI format to Groq's format.
We correct for any specific role orders and types.
"""
diff --git a/autogen/oai/mistral.py b/autogen/oai/mistral.py
index 022210c9aa..4904583b56 100644
--- a/autogen/oai/mistral.py
+++ b/autogen/oai/mistral.py
@@ -76,7 +76,7 @@ def __init__(self, **kwargs):
self._client = Mistral(api_key=self.api_key)
- def message_retrieval(self, response: ChatCompletion) -> Union[List[str], List[ChatCompletionMessage]]:
+ def message_retrieval(self, response: ChatCompletion) -> Union[list[str], list[ChatCompletionMessage]]:
"""Retrieve the messages from the response."""
return [choice.message for choice in response.choices]
@@ -84,7 +84,7 @@ def message_retrieval(self, response: ChatCompletion) -> Union[List[str], List[C
def cost(self, response) -> float:
return response.cost
- def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
+ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]:
"""Loads the parameters for Mistral.AI API from the passed in parameters and returns a validated set. Checks types, ranges, and sets defaults"""
mistral_params = {}
@@ -173,7 +173,7 @@ def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
return mistral_params
- def create(self, params: Dict[str, Any]) -> ChatCompletion:
+ def create(self, params: dict[str, Any]) -> ChatCompletion:
# 1. Parse parameters to Mistral.AI API's parameters
mistral_params = self.parse_params(params)
@@ -224,7 +224,7 @@ def create(self, params: Dict[str, Any]) -> ChatCompletion:
return response_oai
@staticmethod
- def get_usage(response: ChatCompletion) -> Dict:
+ def get_usage(response: ChatCompletion) -> dict:
return {
"prompt_tokens": response.usage.prompt_tokens if response.usage is not None else 0,
"completion_tokens": response.usage.completion_tokens if response.usage is not None else 0,
@@ -236,7 +236,7 @@ def get_usage(response: ChatCompletion) -> Dict:
}
-def tool_def_to_mistral(tool_definitions: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+def tool_def_to_mistral(tool_definitions: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Converts AutoGen tool definition to a mistral tool format"""
mistral_tools = []
diff --git a/autogen/oai/ollama.py b/autogen/oai/ollama.py
index 1b7c3ced79..6c431619c6 100644
--- a/autogen/oai/ollama.py
+++ b/autogen/oai/ollama.py
@@ -88,7 +88,7 @@ def __init__(self, **kwargs):
if "response_format" in kwargs and kwargs["response_format"] is not None:
warnings.warn("response_format is not supported for Ollama, it will be ignored.", UserWarning)
- def message_retrieval(self, response) -> List:
+ def message_retrieval(self, response) -> list:
"""
Retrieve and return a list of strings or a list of Choice.Message from the response.
@@ -101,7 +101,7 @@ def cost(self, response) -> float:
return response.cost
@staticmethod
- def get_usage(response) -> Dict:
+ def get_usage(response) -> dict:
"""Return usage summary of the response using RESPONSE_USAGE_KEYS."""
# ... # pragma: no cover
return {
@@ -112,7 +112,7 @@ def get_usage(response) -> Dict:
"model": response.model,
}
- def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
+ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]:
"""Loads the parameters for Ollama API from the passed in parameters and returns a validated set. Checks types, ranges, and sets defaults"""
ollama_params = {}
@@ -180,7 +180,7 @@ def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
return ollama_params
- def create(self, params: Dict) -> ChatCompletion:
+ def create(self, params: dict) -> ChatCompletion:
messages = params.get("messages", [])
# Are tools involved in this conversation?
@@ -289,7 +289,7 @@ def create(self, params: Dict) -> ChatCompletion:
for tool_call in response["message"]["tool_calls"]:
tool_calls.append(
ChatCompletionMessageToolCall(
- id="ollama_func_{}".format(random_id),
+ id=f"ollama_func_{random_id}",
function={
"name": tool_call["function"]["name"],
"arguments": json.dumps(tool_call["function"]["arguments"]),
@@ -314,7 +314,7 @@ def create(self, params: Dict) -> ChatCompletion:
for json_function in response_toolcalls:
tool_calls.append(
ChatCompletionMessageToolCall(
- id="ollama_manual_func_{}".format(random_id),
+ id=f"ollama_manual_func_{random_id}",
function={
"name": json_function["name"],
"arguments": (
@@ -360,7 +360,7 @@ def create(self, params: Dict) -> ChatCompletion:
return response_oai
- def oai_messages_to_ollama_messages(self, messages: list[Dict[str, Any]], tools: list) -> list[dict[str, Any]]:
+ def oai_messages_to_ollama_messages(self, messages: list[dict[str, Any]], tools: list) -> list[dict[str, Any]]:
"""Convert messages from OAI format to Ollama's format.
We correct for any specific role orders and types, and convert tools to messages (as Ollama can't use tool messages)
"""
@@ -526,7 +526,7 @@ def response_to_tool_call(response_string: str) -> Any:
return None
-def _object_to_tool_call(data_object: Any) -> List[Dict]:
+def _object_to_tool_call(data_object: Any) -> list[dict]:
"""Attempts to convert an object to a valid tool call object List[Dict] and returns it, if it can, otherwise None"""
# If it's a dictionary and not a list then wrap in a list
diff --git a/autogen/oai/openai_utils.py b/autogen/oai/openai_utils.py
index e26096199f..77da5a6279 100644
--- a/autogen/oai/openai_utils.py
+++ b/autogen/oai/openai_utils.py
@@ -84,7 +84,7 @@
}
-def get_key(config: Dict[str, Any]) -> str:
+def get_key(config: dict[str, Any]) -> str:
"""Get a unique identifier of a configuration.
Args:
@@ -122,11 +122,11 @@ def is_valid_api_key(api_key: str) -> bool:
def get_config_list(
- api_keys: List[str],
- base_urls: Optional[List[str]] = None,
+ api_keys: list[str],
+ base_urls: Optional[list[str]] = None,
api_type: Optional[str] = None,
api_version: Optional[str] = None,
-) -> List[Dict[str, Any]]:
+) -> list[dict[str, Any]]:
"""Get a list of configs for OpenAI API client.
Args:
@@ -179,7 +179,7 @@ def config_list_openai_aoai(
openai_api_base_file: Optional[str] = "base_openai.txt",
aoai_api_base_file: Optional[str] = "base_aoai.txt",
exclude: Optional[str] = None,
-) -> List[Dict[str, Any]]:
+) -> list[dict[str, Any]]:
"""Get a list of configs for OpenAI API client (including Azure or local model deployments that support OpenAI's chat completion API).
This function constructs configurations by reading API keys and base URLs from environment variables or text files.
@@ -307,8 +307,8 @@ def config_list_from_models(
aoai_api_key_file: Optional[str] = "key_aoai.txt",
aoai_api_base_file: Optional[str] = "base_aoai.txt",
exclude: Optional[str] = None,
- model_list: Optional[List[str]] = None,
-) -> List[Dict[str, Any]]:
+ model_list: Optional[list[str]] = None,
+) -> list[dict[str, Any]]:
"""
Get a list of configs for API calls with models specified in the model list.
@@ -374,7 +374,7 @@ def config_list_gpt4_gpt35(
aoai_api_key_file: Optional[str] = "key_aoai.txt",
aoai_api_base_file: Optional[str] = "base_aoai.txt",
exclude: Optional[str] = None,
-) -> List[Dict[str, Any]]:
+) -> list[dict[str, Any]]:
"""Get a list of configs for 'gpt-4' followed by 'gpt-3.5-turbo' API calls.
Args:
@@ -398,10 +398,10 @@ def config_list_gpt4_gpt35(
def filter_config(
- config_list: List[Dict[str, Any]],
- filter_dict: Optional[Dict[str, Union[List[Union[str, None]], Set[Union[str, None]]]]],
+ config_list: list[dict[str, Any]],
+ filter_dict: Optional[dict[str, Union[list[Union[str, None]], set[Union[str, None]]]]],
exclude: bool = False,
-) -> List[Dict[str, Any]]:
+) -> list[dict[str, Any]]:
"""This function filters `config_list` by checking each configuration dictionary against the criteria specified in
`filter_dict`. A configuration dictionary is retained if for every key in `filter_dict`, see example below.
@@ -479,8 +479,8 @@ def _satisfies_criteria(value: Any, criteria_values: Any) -> bool:
def config_list_from_json(
env_or_file: str,
file_location: Optional[str] = "",
- filter_dict: Optional[Dict[str, Union[List[Union[str, None]], Set[Union[str, None]]]]] = None,
-) -> List[Dict[str, Any]]:
+ filter_dict: Optional[dict[str, Union[list[Union[str, None]], set[Union[str, None]]]]] = None,
+) -> list[dict[str, Any]]:
"""
Retrieves a list of API configurations from a JSON stored in an environment variable or a file.
@@ -523,7 +523,7 @@ def config_list_from_json(
# The environment variable exists. We should use information from it.
if os.path.exists(env_str):
# It is a file location, and we need to load the json from the file.
- with open(env_str, "r") as file:
+ with open(env_str) as file:
json_str = file.read()
else:
# Else, it should be a JSON string by itself.
@@ -547,7 +547,7 @@ def get_config(
base_url: Optional[str] = None,
api_type: Optional[str] = None,
api_version: Optional[str] = None,
-) -> Dict[str, Any]:
+) -> dict[str, Any]:
"""
Constructs a configuration dictionary for a single model with the provided API configurations.
@@ -587,9 +587,9 @@ def get_config(
def config_list_from_dotenv(
dotenv_file_path: Optional[str] = None,
- model_api_key_map: Optional[Dict[str, Any]] = None,
- filter_dict: Optional[Dict[str, Union[List[Union[str, None]], Set[Union[str, None]]]]] = None,
-) -> List[Dict[str, Union[str, Set[str]]]]:
+ model_api_key_map: Optional[dict[str, Any]] = None,
+ filter_dict: Optional[dict[str, Union[list[Union[str, None]], set[Union[str, None]]]]] = None,
+) -> list[dict[str, Union[str, set[str]]]]:
"""
Load API configurations from a specified .env file or environment variables and construct a list of configurations.
@@ -688,7 +688,7 @@ def config_list_from_dotenv(
return config_list
-def retrieve_assistants_by_name(client: OpenAI, name: str) -> List[Assistant]:
+def retrieve_assistants_by_name(client: OpenAI, name: str) -> list[Assistant]:
"""
Return the assistants with the given name from OAI assistant API
"""
@@ -709,7 +709,7 @@ def detect_gpt_assistant_api_version() -> str:
return "v2"
-def create_gpt_vector_store(client: OpenAI, name: str, fild_ids: List[str]) -> Any:
+def create_gpt_vector_store(client: OpenAI, name: str, fild_ids: list[str]) -> Any:
"""Create a openai vector store for gpt assistant"""
try:
@@ -732,7 +732,7 @@ def create_gpt_vector_store(client: OpenAI, name: str, fild_ids: List[str]) -> A
def create_gpt_assistant(
- client: OpenAI, name: str, instructions: str, model: str, assistant_config: Dict[str, Any]
+ client: OpenAI, name: str, instructions: str, model: str, assistant_config: dict[str, Any]
) -> Assistant:
"""Create a openai gpt assistant"""
@@ -782,7 +782,7 @@ def create_gpt_assistant(
return client.beta.assistants.create(name=name, instructions=instructions, model=model, **assistant_create_kwargs)
-def update_gpt_assistant(client: OpenAI, assistant_id: str, assistant_config: Dict[str, Any]) -> Assistant:
+def update_gpt_assistant(client: OpenAI, assistant_id: str, assistant_config: dict[str, Any]) -> Assistant:
"""Update openai gpt assistant"""
gpt_assistant_api_version = detect_gpt_assistant_api_version()
diff --git a/autogen/oai/together.py b/autogen/oai/together.py
index a823155dd7..b98d64d310 100644
--- a/autogen/oai/together.py
+++ b/autogen/oai/together.py
@@ -32,8 +32,9 @@
import re
import time
import warnings
+from collections.abc import Mapping
from io import BytesIO
-from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
import requests
from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall
@@ -67,7 +68,7 @@ def __init__(self, **kwargs):
self.api_key
), "Please include the api_key in your config list entry for Together.AI or set the TOGETHER_API_KEY env variable."
- def message_retrieval(self, response) -> List:
+ def message_retrieval(self, response) -> list:
"""
Retrieve and return a list of strings or a list of Choice.Message from the response.
@@ -80,7 +81,7 @@ def cost(self, response) -> float:
return response.cost
@staticmethod
- def get_usage(response) -> Dict:
+ def get_usage(response) -> dict:
"""Return usage summary of the response using RESPONSE_USAGE_KEYS."""
# ... # pragma: no cover
return {
@@ -91,7 +92,7 @@ def get_usage(response) -> Dict:
"model": response.model,
}
- def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
+ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]:
"""Loads the parameters for Together.AI API from the passed in parameters and returns a validated set. Checks types, ranges, and sets defaults"""
together_params = {}
@@ -133,7 +134,7 @@ def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
return together_params
- def create(self, params: Dict) -> ChatCompletion:
+ def create(self, params: dict) -> ChatCompletion:
messages = params.get("messages", [])
# Convert AutoGen messages to Together.AI messages
@@ -218,7 +219,7 @@ def create(self, params: Dict) -> ChatCompletion:
return response_oai
-def oai_messages_to_together_messages(messages: list[Dict[str, Any]]) -> list[dict[str, Any]]:
+def oai_messages_to_together_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Convert messages from OAI format to Together.AI format.
We correct for any specific role orders and types.
"""
diff --git a/autogen/retrieve_utils.py b/autogen/retrieve_utils.py
index 774e5e57d3..b6daed232c 100644
--- a/autogen/retrieve_utils.py
+++ b/autogen/retrieve_utils.py
@@ -164,7 +164,7 @@ def split_files_to_chunks(
chunk_mode: str = "multi_lines",
must_break_at_empty_line: bool = True,
custom_text_split_function: Callable = None,
-) -> Tuple[List[str], List[dict]]:
+) -> tuple[list[str], list[dict]]:
"""Split a list of files into chunks of max_tokens."""
chunks = []
@@ -185,7 +185,7 @@ def split_files_to_chunks(
elif file_extension == ".pdf":
text = extract_text_from_pdf(file)
else: # For non-PDF text-based files
- with open(file, "r", encoding="utf-8", errors="ignore") as f:
+ with open(file, encoding="utf-8", errors="ignore") as f:
text = f.read()
if not text.strip(): # Debugging line to check if text is empty after reading
@@ -202,7 +202,7 @@ def split_files_to_chunks(
return chunks, sources
-def get_files_from_dir(dir_path: Union[str, List[str]], types: list = TEXT_FORMATS, recursive: bool = True):
+def get_files_from_dir(dir_path: Union[str, list[str]], types: list = TEXT_FORMATS, recursive: bool = True):
"""Return a list of all the files in a given directory, a url, a file path or a list of them."""
if len(types) == 0:
raise ValueError("types cannot be empty.")
@@ -292,7 +292,7 @@ def _generate_file_name_from_url(url: str, max_length=255) -> str:
return file_name
-def get_file_from_url(url: str, save_path: str = None) -> Tuple[str, str]:
+def get_file_from_url(url: str, save_path: str = None) -> tuple[str, str]:
"""Download a file from a URL."""
if save_path is None:
save_path = "tmp/chromadb"
@@ -339,7 +339,7 @@ def is_url(string: str):
def create_vector_db_from_dir(
- dir_path: Union[str, List[str]],
+ dir_path: Union[str, list[str]],
max_tokens: int = 4000,
client: API = None,
db_path: str = "tmp/chromadb.db",
@@ -350,7 +350,7 @@ def create_vector_db_from_dir(
embedding_model: str = "all-MiniLM-L6-v2",
embedding_function: Callable = None,
custom_text_split_function: Callable = None,
- custom_text_types: List[str] = TEXT_FORMATS,
+ custom_text_types: list[str] = TEXT_FORMATS,
recursive: bool = True,
extra_docs: bool = False,
) -> API:
@@ -432,7 +432,7 @@ def create_vector_db_from_dir(
def query_vector_db(
- query_texts: List[str],
+ query_texts: list[str],
n_results: int = 10,
client: API = None,
db_path: str = "tmp/chromadb.db",
diff --git a/autogen/runtime_logging.py b/autogen/runtime_logging.py
index a4430a4f91..02abf2e80c 100644
--- a/autogen/runtime_logging.py
+++ b/autogen/runtime_logging.py
@@ -38,9 +38,9 @@
def start(
- logger: Optional[BaseLogger] = None,
+ logger: BaseLogger | None = None,
logger_type: Literal["sqlite", "file"] = "sqlite",
- config: Optional[Dict[str, Any]] = None,
+ config: dict[str, Any] | None = None,
) -> str:
"""
Start logging for the runtime.
@@ -72,9 +72,9 @@ def log_chat_completion(
invocation_id: uuid.UUID,
client_id: int,
wrapper_id: int,
- agent: Union[str, Agent],
- request: Dict[str, Union[float, str, List[Dict[str, str]]]],
- response: Union[str, ChatCompletion],
+ agent: str | Agent,
+ request: dict[str, float | str | list[dict[str, str]]],
+ response: str | ChatCompletion,
is_cached: int,
cost: float,
start_time: str,
@@ -88,7 +88,7 @@ def log_chat_completion(
)
-def log_new_agent(agent: ConversableAgent, init_args: Dict[str, Any]) -> None:
+def log_new_agent(agent: ConversableAgent, init_args: dict[str, Any]) -> None:
if autogen_logger is None:
logger.error("[runtime logging] log_new_agent: autogen logger is None")
return
@@ -96,7 +96,7 @@ def log_new_agent(agent: ConversableAgent, init_args: Dict[str, Any]) -> None:
autogen_logger.log_new_agent(agent, init_args)
-def log_event(source: Union[str, Agent], name: str, **kwargs: Dict[str, Any]) -> None:
+def log_event(source: str | Agent, name: str, **kwargs: dict[str, Any]) -> None:
if autogen_logger is None:
logger.error("[runtime logging] log_event: autogen logger is None")
return
@@ -104,7 +104,7 @@ def log_event(source: Union[str, Agent], name: str, **kwargs: Dict[str, Any]) ->
autogen_logger.log_event(source, name, **kwargs)
-def log_function_use(agent: Union[str, Agent], function: F, args: Dict[str, Any], returns: any):
+def log_function_use(agent: str | Agent, function: F, args: dict[str, Any], returns: any):
if autogen_logger is None:
logger.error("[runtime logging] log_function_use: autogen logger is None")
return
@@ -112,7 +112,7 @@ def log_function_use(agent: Union[str, Agent], function: F, args: Dict[str, Any]
autogen_logger.log_function_use(agent, function, args, returns)
-def log_new_wrapper(wrapper: OpenAIWrapper, init_args: Dict[str, Union[LLMConfig, List[LLMConfig]]]) -> None:
+def log_new_wrapper(wrapper: OpenAIWrapper, init_args: dict[str, LLMConfig | list[LLMConfig]]) -> None:
if autogen_logger is None:
logger.error("[runtime logging] log_new_wrapper: autogen logger is None")
return
@@ -121,21 +121,21 @@ def log_new_wrapper(wrapper: OpenAIWrapper, init_args: Dict[str, Union[LLMConfig
def log_new_client(
- client: Union[
- AzureOpenAI,
- OpenAI,
- CerebrasClient,
- GeminiClient,
- AnthropicClient,
- MistralAIClient,
- TogetherClient,
- GroqClient,
- CohereClient,
- OllamaClient,
- BedrockClient,
- ],
+ client: (
+ AzureOpenAI
+ | OpenAI
+ | CerebrasClient
+ | GeminiClient
+ | AnthropicClient
+ | MistralAIClient
+ | TogetherClient
+ | GroqClient
+ | CohereClient
+ | OllamaClient
+ | BedrockClient
+ ),
wrapper: OpenAIWrapper,
- init_args: Dict[str, Any],
+ init_args: dict[str, Any],
) -> None:
if autogen_logger is None:
logger.error("[runtime logging] log_new_client: autogen logger is None")
@@ -151,7 +151,7 @@ def stop() -> None:
is_logging = False
-def get_connection() -> Union[None, sqlite3.Connection]:
+def get_connection() -> None | sqlite3.Connection:
if autogen_logger is None:
logger.error("[runtime logging] get_connection: autogen logger is None")
return None
diff --git a/autogen/token_count_utils.py b/autogen/token_count_utils.py
index 56975a279b..defb163674 100644
--- a/autogen/token_count_utils.py
+++ b/autogen/token_count_utils.py
@@ -67,7 +67,7 @@ def percentile_used(input, model="gpt-3.5-turbo-0613"):
return count_token(input) / get_max_token_limit(model)
-def token_left(input: Union[str, List, Dict], model="gpt-3.5-turbo-0613") -> int:
+def token_left(input: Union[str, list, dict], model="gpt-3.5-turbo-0613") -> int:
"""Count number of tokens left for an OpenAI model.
Args:
@@ -80,7 +80,7 @@ def token_left(input: Union[str, List, Dict], model="gpt-3.5-turbo-0613") -> int
return get_max_token_limit(model) - count_token(input, model=model)
-def count_token(input: Union[str, List, Dict], model: str = "gpt-3.5-turbo-0613") -> int:
+def count_token(input: Union[str, list, dict], model: str = "gpt-3.5-turbo-0613") -> int:
"""Count number of tokens used by an OpenAI model.
Args:
input: (str, list, dict): Input to the model.
@@ -107,7 +107,7 @@ def _num_token_from_text(text: str, model: str = "gpt-3.5-turbo-0613"):
return len(encoding.encode(text))
-def _num_token_from_messages(messages: Union[List, Dict], model="gpt-3.5-turbo-0613"):
+def _num_token_from_messages(messages: Union[list, dict], model="gpt-3.5-turbo-0613"):
"""Return the number of tokens used by a list of messages.
retrieved from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb/
diff --git a/autogen/types.py b/autogen/types.py
index 99546672cd..be865907ab 100644
--- a/autogen/types.py
+++ b/autogen/types.py
@@ -6,7 +6,7 @@
# SPDX-License-Identifier: MIT
from typing import Dict, List, Literal, TypedDict, Union
-MessageContentType = Union[str, List[Union[Dict, str]], None]
+MessageContentType = Union[str, list[Union[dict, str]], None]
class UserMessageTextContentPart(TypedDict):
@@ -17,4 +17,4 @@ class UserMessageTextContentPart(TypedDict):
class UserMessageImageContentPart(TypedDict):
type: Literal["image_url"]
# Ignoring the other "detail param for now"
- image_url: Dict[Literal["url"], str]
+ image_url: dict[Literal["url"], str]
diff --git a/notebook/agentchat_graph_rag_neo4j.ipynb b/notebook/agentchat_graph_rag_neo4j.ipynb
index 00c4723aa5..b9ef0735d8 100644
--- a/notebook/agentchat_graph_rag_neo4j.ipynb
+++ b/notebook/agentchat_graph_rag_neo4j.ipynb
@@ -39,9 +39,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
"source": [
"import os\n",
"\n",
@@ -118,7 +127,7 @@
"source": [
"### A Simple Example\n",
"\n",
- "In this example, the graph schema is auto-generated. This allows you to load data without specifying the specific types of entities and relationships that will make up the database. However, it will only use some default simple relationships including \"WORKED_ON\", \"MENTIONS\", \"LOCATED_IN\" \n",
+ "In this example, the graph schema is auto-generated. Entities and relationship are created as they fit into the data\n",
"\n",
"LlamaIndex supports a lot of extensions including docx, text, pdf, csv, etc. Find more details in Neo4jGraphQueryEngine. You may need to install dependencies for each extension. In this example, we need `pip install docx2txt`\n",
"\n",
@@ -127,7 +136,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -138,10 +147,79 @@
"input_documents = [Document(doctype=DocumentType.TEXT, path_or_url=input_path)]"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 83.68it/s]\n",
+ "Extracting paths from text with schema: 100%|██████████| 3/3 [00:07<00:00, 2.49s/it]\n",
+ "Extracting and inferring knowledge graph from text: 100%|██████████| 3/3 [00:07<00:00, 2.51s/it]\n",
+ "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 1.12it/s]\n",
+ "Generating embeddings: 100%|██████████| 2/2 [00:01<00:00, 1.47it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# pip install docx2txt\n",
+ "# Auto generate graph schema from unstructured data\n",
+ "\n",
+ "\n",
+ "# Create Neo4jGraphQueryEngine\n",
+ "query_engine = Neo4jGraphQueryEngine(\n",
+ " username=\"neo4j\", # Change if you reset username\n",
+ " password=\"password\", # Change if you reset password\n",
+ " host=\"bolt://172.17.0.3\", # Change\n",
+ " port=7687, # if needed\n",
+ " llm=OpenAI(model=\"gpt-4o\", temperature=0.0), # Default, no need to specify\n",
+ " embedding=OpenAIEmbedding(model_name=\"text-embedding-3-small\"), # except you want to use a different model\n",
+ " database=\"neo4j\", # Change if you want to store the graphh in your custom database\n",
+ ")\n",
+ "\n",
+ "# Ingest data and create a new property graph\n",
+ "query_engine.init_db(input_doc=input_documents)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Instead of creating a new property graph, if you want to use an existing graph, you can connect to its database"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+ "from llama_index.llms.openai import OpenAI\n",
+ "\n",
+ "from autogen.agentchat.contrib.graph_rag.neo4j_graph_query_engine import Neo4jGraphQueryEngine\n",
+ "\n",
+ "query_engine = Neo4jGraphQueryEngine(\n",
+ " username=\"neo4j\", # Change if you reset username\n",
+ " password=\"password\", # Change if you reset password\n",
+ " host=\"bolt://172.17.0.3\", # Change\n",
+ " port=7687, # if needed\n",
+ " llm=OpenAI(model=\"gpt-4o\", temperature=0.0), # Default, no need to specify\n",
+ " embedding=OpenAIEmbedding(model_name=\"text-embedding-3-small\"), # except you want to use a different model\n",
+ " database=\"neo4j\", # Change if you want to store the graphh in your custom database\n",
+ ")\n",
+ "\n",
+ "# Connect to the existing graph\n",
+ "query_engine.connect_db()"
+ ]
+ },
{
"attachments": {
"neo4j_property_graph_1.png": {
- "image/png": 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"
+ "image/png": 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"
}
},
"cell_type": "markdown",
@@ -156,15 +234,151 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Schema\n",
- "Define our custom entities, relations and schema"
+ "### Add capability to a ConversableAgent and query them\n",
+ "Notice that we intentionally moved the specific content of Equal Employment Opportunity Policy into a different document to add later"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 19,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001B[33muser_proxy\u001B[0m (to buzz_agent):\n",
+ "\n",
+ "Which company is the employer?\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33mbuzz_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "The employer is BUZZ Co.\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33muser_proxy\u001B[0m (to buzz_agent):\n",
+ "\n",
+ "What policies does it have?\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33mbuzz_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "BUZZ Co. has several policies, including:\n",
+ "\n",
+ "1. Employment Policies and Practices\n",
+ "2. Equal Employment Opportunity\n",
+ "3. Health and Dental Insurance\n",
+ "4. Solicitation Policy\n",
+ "5. Separation Policy\n",
+ "6. Computer and Information Security Policy\n",
+ "7. Bereavement Leave Policy\n",
+ "8. Tax Deferred Annuity Plan\n",
+ "9. Confidentiality Policy\n",
+ "10. Policy Against Workplace Harassment\n",
+ "11. Non-Disclosure of Confidential Information\n",
+ "12. Meetings and Conferences Policy\n",
+ "13. Reimbursement of Expenses Policy\n",
+ "14. Voluntary At-Will Employment\n",
+ "15. Extended Personal Leave Policy\n",
+ "16. Hours of Work Policy\n",
+ "\n",
+ "These policies cover a wide range of employment aspects, from leave and separation to confidentiality and security.\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33muser_proxy\u001B[0m (to buzz_agent):\n",
+ "\n",
+ "What's Buzz's equal employment opprtunity policy?\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33mbuzz_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "The specific content of BUZZ Co.'s Equal Employment Opportunity policy is not provided in the document. However, it is mentioned that BUZZ Co. has an Equal Employment Opportunity policy, which typically would state the company's commitment to providing equal employment opportunities and non-discrimination in the workplace.\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33muser_proxy\u001B[0m (to buzz_agent):\n",
+ "\n",
+ "What does Civic Responsibility state?\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33mbuzz_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "The document does not provide any information about a Civic Responsibility policy. Therefore, I don't have details on what BUZZ Co.'s Civic Responsibility might state.\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33muser_proxy\u001B[0m (to buzz_agent):\n",
+ "\n",
+ "Does Donald Trump work there?\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33mbuzz_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "The document does not provide any information about specific individuals, including whether Donald Trump works at BUZZ Co. Therefore, I don't have any details on the employment of Donald Trump at BUZZ Co.\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "ChatResult(chat_id=None, chat_history=[{'content': 'Which company is the employer?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The employer is BUZZ Co.', 'role': 'user', 'name': 'buzz_agent'}, {'content': 'What policies does it have?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'BUZZ Co. has several policies, including:\\n\\n1. Employment Policies and Practices\\n2. Equal Employment Opportunity\\n3. Health and Dental Insurance\\n4. Solicitation Policy\\n5. Separation Policy\\n6. Computer and Information Security Policy\\n7. Bereavement Leave Policy\\n8. Tax Deferred Annuity Plan\\n9. Confidentiality Policy\\n10. Policy Against Workplace Harassment\\n11. Non-Disclosure of Confidential Information\\n12. Meetings and Conferences Policy\\n13. Reimbursement of Expenses Policy\\n14. Voluntary At-Will Employment\\n15. Extended Personal Leave Policy\\n16. Hours of Work Policy\\n\\nThese policies cover a wide range of employment aspects, from leave and separation to confidentiality and security.', 'role': 'user', 'name': 'buzz_agent'}, {'content': \"What's Buzz's equal employment opprtunity policy?\", 'role': 'assistant', 'name': 'user_proxy'}, {'content': \"The specific content of BUZZ Co.'s Equal Employment Opportunity policy is not provided in the document. However, it is mentioned that BUZZ Co. has an Equal Employment Opportunity policy, which typically would state the company's commitment to providing equal employment opportunities and non-discrimination in the workplace.\", 'role': 'user', 'name': 'buzz_agent'}, {'content': 'What does Civic Responsibility state?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': \"The document does not provide any information about a Civic Responsibility policy. Therefore, I don't have details on what BUZZ Co.'s Civic Responsibility might state.\", 'role': 'user', 'name': 'buzz_agent'}, {'content': 'Does Donald Trump work there?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': \"The document does not provide any information about specific individuals, including whether Donald Trump works at BUZZ Co. Therefore, I don't have any details on the employment of Donald Trump at BUZZ Co.\", 'role': 'user', 'name': 'buzz_agent'}], summary=\"The document does not provide any information about specific individuals, including whether Donald Trump works at BUZZ Co. Therefore, I don't have any details on the employment of Donald Trump at BUZZ Co.\", cost={'usage_including_cached_inference': {'total_cost': 0}, 'usage_excluding_cached_inference': {'total_cost': 0}}, human_input=['What policies does it have?', \"What's Buzz's equal employment opprtunity policy?\", 'What does Civic Responsibility state?', 'Does Donald Trump work there?', 'exit'])"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from autogen.agentchat.contrib.graph_rag.neo4j_graph_rag_capability import Neo4jGraphCapability\n",
+ "\n",
+ "# Create a ConversableAgent (no LLM configuration)\n",
+ "graph_rag_agent = ConversableAgent(\n",
+ " name=\"buzz_agent\",\n",
+ " human_input_mode=\"NEVER\",\n",
+ ")\n",
+ "\n",
+ "# Associate the capability with the agent\n",
+ "graph_rag_capability = Neo4jGraphCapability(query_engine)\n",
+ "graph_rag_capability.add_to_agent(graph_rag_agent)\n",
+ "\n",
+ "# Create a user proxy agent to converse with our RAG agent\n",
+ "user_proxy = UserProxyAgent(\n",
+ " name=\"user_proxy\",\n",
+ " human_input_mode=\"ALWAYS\",\n",
+ ")\n",
+ "\n",
+ "user_proxy.initiate_chat(graph_rag_agent, message=\"Which company is the employer?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Revisit the example by defining custom entities, relations and schema\n",
+ "\n",
+ "By providing custom entities, relations and schema, you could guide the engine to create a graph that better extracts the structure within the data.\n",
+ "\n",
+ "We set `strict=True` to tell the engine to only extracting allowed relationships from the data for each entity\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 71.74it/s]\n",
+ "Extracting paths from text with schema: 100%|██████████| 3/3 [00:47<00:00, 15.87s/it]\n",
+ "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 2.36it/s]\n",
+ "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 1.47it/s]\n"
+ ]
+ }
+ ],
"source": [
"from typing import Literal\n",
"\n",
@@ -183,8 +397,8 @@
" \"REPORTS_TO\",\n",
"]\n",
"\n",
- "# define which entities can have which relations\n",
- "validation_schema = {\n",
+ "# Define which entities can have which relationships. It can also be used a a guidance if strict is False.\n",
+ "schema = {\n",
" \"EMPLOYEE\": [\"FOLLOWS\", \"APPLIES_TO\", \"ASSIGNED_TO\", \"ENTITLED_TO\", \"REPORTS_TO\"],\n",
" \"EMPLOYER\": [\"PROVIDES\", \"DEFINED_AS\", \"MANAGES\", \"REQUIRES\"],\n",
" \"POLICY\": [\"APPLIES_TO\", \"DEFINED_AS\", \"REQUIRES\"],\n",
@@ -233,8 +447,8 @@
" embedding=OpenAIEmbedding(model_name=\"text-embedding-3-small\"),\n",
" entities=entities, # possible entities\n",
" relations=relations, # possible relations\n",
- " validation_schema=validation_schema, # schema to validate the extracted triplets\n",
- " strict=True, # enforce the extracted triplets to be in the schema\n",
+ " schema=schema,\n",
+ " strict=True, # enforce the extracted relationships to be in the schema\n",
")\n",
"\n",
"# Ingest data and initialize the database\n",
@@ -244,7 +458,7 @@
{
"attachments": {
"neo4j_property_graph_2.png": {
- "image/png": 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"
+ "image/png": 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"
}
},
"cell_type": "markdown",
@@ -259,47 +473,80 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Create Neo4j-based RAG agents\n",
- "With the schema defined and data loaded, we can now create a capable agent!"
+ "### Add capability to a ConversableAgent and query them again\n",
+ "You should find the answers are much more detailed and accurate since our schema fits well"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n",
+ "\u001B[33muser_proxy\u001B[0m (to rag_agent):\n",
"\n",
"Which company is the employer?\n",
"\n",
"--------------------------------------------------------------------------------\n",
- "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n",
+ "\u001B[33mrag_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "The employer is BUZZ Co.\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33muser_proxy\u001B[0m (to rag_agent):\n",
"\n",
- "BUZZ Co.\n",
+ "What polices does it have?\n",
"\n",
"--------------------------------------------------------------------------------\n",
- "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n",
+ "\u001B[33mrag_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "BUZZ Co. has several policies outlined in its Employee Handbook, including:\n",
+ "\n",
+ "1. **Voluntary At-Will Employment**: Employment can be terminated by either party at any time, with or without cause.\n",
+ "2. **Equal Employment Opportunity**: Commitment to non-discrimination and equal opportunity.\n",
+ "3. **Policy Against Workplace Harassment**: Prohibits all forms of workplace harassment.\n",
+ "4. **Confidentiality Policy**: Requires non-disclosure of confidential information.\n",
+ "5. **Solicitation Policy**: Restrictions on solicitation during work time and on premises.\n",
+ "6. **Hours of Work, Attendance, and Punctuality**: Guidelines for work hours, attendance, and punctuality.\n",
+ "7. **Overtime Policy**: Overtime pay for non-exempt employees for hours worked over 40 in a week.\n",
+ "8. **Position Description and Salary Administration**: Details on job descriptions and salary distribution.\n",
+ "9. **Work Review**: Ongoing and annual performance reviews.\n",
+ "10. **Economic Benefits and Insurance**: Health, dental, and life insurance, retirement plans, and other benefits.\n",
+ "11. **Leave Benefits and Other Work Policies**: Various leave policies including vacation, sick leave, personal leave, and more.\n",
+ "12. **Reimbursement of Expenses**: Guidelines for expense reimbursement.\n",
+ "13. **Separation Policy**: Procedures for resignation or termination.\n",
+ "14. **Return of Property**: Requirement to return company property upon separation.\n",
+ "15. **Review of Personnel Action**: Process for reviewing personnel actions.\n",
+ "16. **Personnel Records**: Confidentiality and accuracy of personnel records.\n",
+ "17. **Outside Employment**: Permitted if it does not interfere with BUZZ Co. job performance.\n",
+ "18. **Non-Disclosure of Confidential Information**: Requirement to sign a non-disclosure agreement.\n",
+ "19. **Computer and Information Security**: Guidelines for the use of company computer systems.\n",
+ "20. **Internet Acceptable Use Policy**: Internet usage for business purposes only.\n",
"\n",
- "Tell me about sick leave\n",
+ "These policies are designed to guide employees in their conduct and responsibilities at BUZZ Co.\n",
"\n",
"--------------------------------------------------------------------------------\n",
- "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n",
+ "\u001B[33muser_proxy\u001B[0m (to rag_agent):\n",
"\n",
- "Employees at BUZZ Co. are entitled to one day of sick leave per month for full-time employees, which is prorated for part-time employees, with a maximum accumulation of up to 30 days.\n",
+ "What does Civic Responsibility state?\n",
"\n",
"--------------------------------------------------------------------------------\n",
- "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n",
+ "\u001B[33mrag_agent\u001B[0m (to user_proxy):\n",
"\n",
- "And vacation?\n",
+ "The Civic Responsibility policy at BUZZ Co. states that the company will pay employees the difference between their salary and any amount paid by the government, unless prohibited by law, for up to a maximum of ten days of jury duty. Additionally, BUZZ Co. will pay employees the difference between their salary and any amount paid by the government or any other source for serving as an Election Day worker at the polls on official election days, not to exceed two elections in one given calendar year.\n",
"\n",
"--------------------------------------------------------------------------------\n",
- "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n",
+ "\u001B[33muser_proxy\u001B[0m (to paul_graham_agent):\n",
"\n",
- "Full-time employees at BUZZ Co. earn 10 days of vacation after the first year, 15 days after the third year, and 20 days after the fourth year. Part-time employees' vacation days are prorated based on their hours worked.\n",
+ "Which policy listed above does civic responsibility belong to?\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001B[33mpaul_graham_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "The Civic Responsibility policy belongs to the \"Leave Benefits and Other Work Policies\" section of the BUZZ Co. Employee Handbook.\n",
"\n",
"--------------------------------------------------------------------------------\n"
]
@@ -307,15 +554,16 @@
{
"data": {
"text/plain": [
- "ChatResult(chat_id=None, chat_history=[{'content': 'Which company is the employer?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'BUZZ Co.', 'role': 'user', 'name': 'rag_agent'}, {'content': 'Tell me about sick leave', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'Employees at BUZZ Co. are entitled to one day of sick leave per month for full-time employees, which is prorated for part-time employees, with a maximum accumulation of up to 30 days.', 'role': 'user', 'name': 'rag_agent'}, {'content': 'And vacation?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': \"Full-time employees at BUZZ Co. earn 10 days of vacation after the first year, 15 days after the third year, and 20 days after the fourth year. Part-time employees' vacation days are prorated based on their hours worked.\", 'role': 'user', 'name': 'rag_agent'}], summary=\"Full-time employees at BUZZ Co. earn 10 days of vacation after the first year, 15 days after the third year, and 20 days after the fourth year. Part-time employees' vacation days are prorated based on their hours worked.\", cost={'usage_including_cached_inference': {'total_cost': 0}, 'usage_excluding_cached_inference': {'total_cost': 0}}, human_input=['Tell me about sick leave', 'And vacation?', 'exit'])"
+ "ChatResult(chat_id=None, chat_history=[{'content': 'Which company is the employer?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The employer is BUZZ Co.', 'role': 'user', 'name': 'paul_graham_agent'}, {'content': 'What polices does it have?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'BUZZ Co. has several policies outlined in its Employee Handbook, including:\\n\\n1. **Voluntary At-Will Employment**: Employment can be terminated by either party at any time, with or without cause.\\n2. **Equal Employment Opportunity**: Commitment to non-discrimination and equal opportunity.\\n3. **Policy Against Workplace Harassment**: Prohibits all forms of workplace harassment.\\n4. **Confidentiality Policy**: Requires non-disclosure of confidential information.\\n5. **Solicitation Policy**: Restrictions on solicitation during work time and on premises.\\n6. **Hours of Work, Attendance, and Punctuality**: Guidelines for work hours, attendance, and punctuality.\\n7. **Overtime Policy**: Overtime pay for non-exempt employees for hours worked over 40 in a week.\\n8. **Position Description and Salary Administration**: Details on job descriptions and salary distribution.\\n9. **Work Review**: Ongoing and annual performance reviews.\\n10. **Economic Benefits and Insurance**: Health, dental, and life insurance, retirement plans, and other benefits.\\n11. **Leave Benefits and Other Work Policies**: Various leave policies including vacation, sick leave, personal leave, and more.\\n12. **Reimbursement of Expenses**: Guidelines for expense reimbursement.\\n13. **Separation Policy**: Procedures for resignation or termination.\\n14. **Return of Property**: Requirement to return company property upon separation.\\n15. **Review of Personnel Action**: Process for reviewing personnel actions.\\n16. **Personnel Records**: Confidentiality and accuracy of personnel records.\\n17. **Outside Employment**: Permitted if it does not interfere with BUZZ Co. job performance.\\n18. **Non-Disclosure of Confidential Information**: Requirement to sign a non-disclosure agreement.\\n19. **Computer and Information Security**: Guidelines for the use of company computer systems.\\n20. **Internet Acceptable Use Policy**: Internet usage for business purposes only.\\n\\nThese policies are designed to guide employees in their conduct and responsibilities at BUZZ Co.', 'role': 'user', 'name': 'paul_graham_agent'}, {'content': 'What does Civic Responsibility state?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The Civic Responsibility policy at BUZZ Co. states that the company will pay employees the difference between their salary and any amount paid by the government, unless prohibited by law, for up to a maximum of ten days of jury duty. Additionally, BUZZ Co. will pay employees the difference between their salary and any amount paid by the government or any other source for serving as an Election Day worker at the polls on official election days, not to exceed two elections in one given calendar year.', 'role': 'user', 'name': 'paul_graham_agent'}, {'content': 'Which policy listed above does civic responsibility belong to?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The Civic Responsibility policy belongs to the \"Leave Benefits and Other Work Policies\" section of the BUZZ Co. Employee Handbook.', 'role': 'user', 'name': 'paul_graham_agent'}], summary='The Civic Responsibility policy belongs to the \"Leave Benefits and Other Work Policies\" section of the BUZZ Co. Employee Handbook.', cost={'usage_including_cached_inference': {'total_cost': 0}, 'usage_excluding_cached_inference': {'total_cost': 0}}, human_input=['What polices does it have?', 'What does Civic Responsibility state?', 'Which policy listed above does civic responsibility belong to?', 'exit'])"
]
},
- "execution_count": 8,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
+ "from autogen import ConversableAgent, UserProxyAgent\n",
"from autogen.agentchat.contrib.graph_rag.neo4j_graph_rag_capability import Neo4jGraphCapability\n",
"\n",
"# Create a ConversableAgent (no LLM configuration)\n",
@@ -337,23 +585,21 @@
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "### Incrementally add new documents to the existing knoweldge graph!"
- ]
+ "source": "### Incrementally add new documents to the existing knoweldge graph."
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 545.99it/s]\n",
- "Extracting paths from text with schema: 100%|██████████| 1/1 [00:30<00:00, 30.24s/it]\n",
- "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 2.61it/s]\n",
- "Generating embeddings: 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]\n"
+ "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 41.46it/s]\n",
+ "Extracting paths from text with schema: 100%|██████████| 1/1 [00:12<00:00, 12.76s/it]\n",
+ "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 1.13it/s]\n",
+ "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 1.87it/s]\n"
]
}
],
@@ -367,13 +613,13 @@
{
"attachments": {
"neo4j_property_graph_3.png": {
- "image/png": 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IgAAImEughrMNnvRVJX3f1KnruIw9RhNYhCWTScr7yjYX+QwFou/apYILQRcn06XiWkAgYQlAiCXsrcPAQQAEQAAEQAAEBkugmpNa/PyL9bS01aU7ZWJuFklWxGQ0qUG2jsVYBxeH1trtO1XQOVMgxrRMsAwCsSAAIRYL6ugTBEAABEAABEDANAJN7B066vP1tKg55AmT+WDT8hxclDk2NcHMuvhuTs2/tq2TmrkgtNbu5QQeZ3IRaBgIgEDsCCR+IHTs2KFnEAABEAABEACBBCBw6tcbdSIsnVXYzPzkF2Fya0RwTudrLTJkTfz9ws30SnVLAtw9DBEEkpcAhFjy3ltcGQiAAAiAAAikPIHffLeRPmloD3JIZ2Eyo8BBkpwjlWxqnr2XGDv92430vcZLmEo8cK0gEA8EIMTi4S5gDCAAAiAAAiAAAhEncO3KGnp2c7Ou3WnsHcpLoPpgusGPcEXEWIEmFNPLiTz+77tN1GaYQzbCbnA6CIDAIAlAiA0SFA4DARAAARAAARBIHAKvbW2la1fU6gY8WYRIinnCdAB4RcRYttUS3Ly63U2/55pqMBAAAfMJQIiZzxw9ggAIgAAIgAAIRJFAPSemOG9Rla6HsdmZVGKYJ6U7IEVWLDxpbAqLMXlX7aUtLXTH2np1Fe8gAAImEYAQMwk0ugEBEAABEAABEDCHwN8Xb6FarhmmmmRGHMdCDNZDQIpWT8rT1027YEk1LTGk9gcvEACB6BKAEIsuX7QOAiAAAiAAAiBgIoGnNzfR81WheWEZnJ1jUq7dxBEkRldFmRlUzoWstfbPpdXaVSyDAAhEmQCEWJQBo3kQAAEQAAEQAAFzCHg4+cTly2t0nU3ggs0ixmC9CYzPySKHZr7Yh3Xt9NCGbb0PxBYQAIGoEIAQiwpWNAoCIAACIAACIGA2gWtX1NCWTm+w22KeEyaeH1jfBMbn6EM2r+EEJ65AV98nYA8IgEDECECIRQwlGgIBEAABEAABEIgVgY1OL/1rdV2we3GCVRhERnAnFoIE8jmLZKk9FKJYx3PrbloV4hg8EAsgAAIRJwAhFnGkaBAEQAAEQAAEQMBsAret0YsHyZJoS8fXnMHcB0lkoo3evJVZNnDmSRgIgEB0CeA3VHT5onUQAAEQAAEQAIEoExBv2AOVoblNNks6jXHoQ+6iPISEbl7m0Gl5+Xiu3V3rkM4+oW8qBp8QBCDEEuI2YZAgAAIgAAIgAAJ9Ebi/skG3y5gNULcTK2EJCDOLxi123/ptJMlPYCAAAtEjACEWPbZoGQRAAARAAARAIMoExHvzyIbGYC/i3SnTzHkK7sBCvwTSucCzllurL0CPIoNiv8ywEwRGSgBCbKQEcT4IgAAIgAAIgEDMCDzJdcNENKimTTyhbsP74AiU2vUZJp/Y1DS4E3EUCIDAsAhAiA0LG04CARAAARAAARCIBwLPbg4Vb5bxlHDKetjwCEhykyINv4XNnfRdU+fwGsNZIAACAxKAEBsQEQ4AARAAARAAARCIRwIbOEnHJw3twaGNyrRSJifqgA2fgNRe09rL1Xqhq92HZRAAgZERwG+rkfHD2SAAAiAAAiAAAjEi8EZNi67nQhRv1vEYzkoB1xWTrJOqvb61VV3EOwiAQIQJhH7SItwwmgMBEAABEAABEACBaBJ4p7ZN13whe8RgIycwisWYaus6PLS4xaWu4h0EQCCCBCDEIggTTYEACIAACIAACJhDwB3oog/rQmGJBSzCJPMfbOQEhKXWPqoPcdZuxzIIgMDICECIjYwfzgYBEAABEAABEIgBgc+2deh6zc/QiwfdTqwMiYCw1EpaI+shNYaDQQAE+iQAIdYnGuwAARAAARAAARCIVwJfNzp1Q8u1WXTrWBk+AXEs5mrCE42sh98yzgQBENASgBDT0sAyCIAACIAACIBAQhD4oSWUVt3CyiHbCiEWyRuXkxHi2eT105p2dySbR1sgAAJMAEIMHwMQAAEQAAEQAIGEI7BEk0DCkYGvM5G+gdlWPdOlrRBikWaM9kBA/1MGHiAAAiAAAiAAAiAQ5wRafAGqdvmCo3RYQt6b4EYsjIiA3eBhXAWP2Ih44mQQCEcAM1vDUcE2EAABEACBuCIgX7y3efzUxu8uzpbn7+aQDp7Hksn/5fAXxlE8P6iUM71JiBos+QlUckp1rWUZvDfafVgeHgG7ppaYtLDewHx4reIsEAABLQEIMS0NLIMACIAACMSUQKXTQwubO0nCzlbyE3ipYbS500ud/q5BjWusPYMm52TS9JwsmpOfRTsXOGiPomxdBrhBNYSD4ppAlcurG19mOgJ8dEAitJLJYszDDz7EjMwj1AWaAYGUJgAhltK3HxcPAiAAArEl0OwN0Du1rfQh1yn6pKGDqlh0jcQkXE1en3FbqmWw12zf4hw6qDSXDh2dR/MK7OouvCcogVq3XzdymwWeUB2QCK3Y+GfHE+hprNYdCgWNUPNoBgRSngCEWMp/BAAABEAABMwlEOjupmermunFLS30dk1r1Dv3dXXTAhZ68rps2VaalZdFx40toF9UjKKZuVlR7x8dRJ5AI4epai0jzkJSfauXUvvtl1PueVdSxqx52qEm1LI8xFCtUVVk6ga8gwAIjJgAhNiIEaIBEAABEACBwRBY2eamhzdso8c3NSlzvQY6R+Z7ydyfLA6PkifzVg4/s/L3wnTern49lKApEXZ+Flveri7yBrrJzaFUajhVuD5kHCvbaun6lbX0U/aQnTmpiI5nYQZLHALt/u1umu1DtmgEQ+JcRfyPVDvnss3APP5HjxGCQPwTgBCL/3uEEYIACIBAQhP4nud83bG2np5nL1h/5uCkG3mcdEPqF0lNKBFgwzURZ05O7NHBXx7b+b2N6yCxVutl79e1kbxms5fs3KmldBaLMlj8E3Cz4NaaiHNY5AloucrDDj//XFnBOvKg0WLKEoAQS9lbjwsHARAAgegSWMuJNm5cVUtPsQesL8u3WWkUZzss4HdJDBApkyf5edymvFRrYTHWzCFtTR6f4kFTt8v7CvaS/eGHzXT3unq6YGYZncxhi7D4JSBCO5z5160g11svkH/zOkovLOWwwJ3IfvTJlJZhUw73rVpMrtefpZxzLiL3+6+Qb8l31O1yUsYOu1L2r35P/i0byfXa0+Rfv4rS7A7K3Ptgsh91MtF28eF88m5Ks2WSbf7+5Hr1Se6nktILirifHcn+s18p+8KNS93m+fRd8nz3KQW4n/RRxWSdvgM55DzuS6zzpf9SYPN6yv3TFUTW0GdX9nXcdyOll5SR44TfyCp1NTdS5/8eI7lmSrdQxkwew2EnKMcoB0TiP4O+Ff0rXmkYCIBAZAjof8oj0yZaAQEQAAEQSHECV6+oUUL/wmGwseAqycpQXpEUX+H60m4TsSevSTwvrJETDzTwq5XFmdaWsyA749uN9CSLx6t2KKddRvV8QdYeg+XYE9B6atTReL//nDoevZ2sk6dT1kHHUNe2enJ/8CoFqjdQ7jkXK8Kmu72N/JWryPnQrdTtcbGg2o8CWzeT56sPqdvrJt/KxWQpH0dZh/yMfMu+J9ebz7GgK1EEmfQjAqqrvZWPX6CIqKxDjlW2uRe8qYi3vL9fz/1kqEPSvTufvIc8X7xP1qmzKOunP6OuhlryfPYe+Vf8SHkX3EhpOXlkGTtBGbNvxSLK2HF+8PxA9SbyLv6GxeIflW2B2mpqu/ViSsuyU+YeB1K330eerxco45cxiMiLiBn0LnKiRIQqGgGBIAEIsSAKLIAACIAACIyUgIT5XbR0Ky1vdfVqSkIPyxw2RYD12mnyhiIWgvKS8MVaToW+zZARTg1ZvGRWGV02u9zk0aG7gQhkGRRBwNVJnS88TLZd9qKcs84PerBsO+9J7fdcR94fviDb7geEmuWHASJ+xJMk1nbzheRd+AU5fv5ryjrseGVb1sHHUMsFvyFJvCGeMdW6mhrIfuxpZD/iJHUTeT59h5zP3M9C6wPKPOCI4HZ1QcSfiDBpRxFT2z1stl33ofa7ryXX2y+S46SzyLbzXtT57APk4bFohZj3x68Vr55t172VJjtfepTS7dmUd/GtihiTjVkHHU2t1/5VEY+qYFP7H+57F4WUmFXmaSIscbgocR4IhCUQuTiQsM1jIwiAAAiAQKoQkIyEx3y+vpcIk7lek/PsNLcwOy5EmPZ+ZPN8tCnbxybCzGjXcUKPgz5eQ0vCCEvjsVg3j0Aui3qtedl71dXWQvbDTwyKMNmfMXc3so6foniLtMdn/fTnQREm2y1jJijn2fYIiTXxNok3TEIXtZbmyGGPGZ+vscz9DqP04tHsKftIszW06PnyQ+4vnRzHna4f35xdlZBCZT8fnpaZRSLOJGSS2MulmnfRV5Qxb3cWXQ7lOn3LFlLmgUcFRZgcJ14wEXoiKLt9oXPVNobzvr2EmHJqLopmDwchzgGBfgnAI9YvHuwEARAAARAYiMBGp5fO4flVH3N6eKONy86ksfyKdxNv3VQWZBIyWcVFpcVTptqXjU7a68PVdM8uFXTGRCTzULnE8r2Qk7pozVdTraw6n7hLJ3RkY6CxjtJYpGktPa9Au8pKzMIiiIUXz/fSGW83moQPpmUYRDt7ikTw+VYtMR6urAfqayi9qFQJPzQeYJkwVTlPQh7Tc/NZTB1CIsy8yxeRjcWXnCuhiY7jzuhpq3aL8u755C3yfvuJrjlpo9vtoq6WbWQpGbkn19fdU8xZOinUzLfUdYoVEACBYROAEBs2OpwIAiAAAiDwQV07nfX9Rqo3FNiVuVjjc7LInmBP0SV5iLyqWYxt4Zdqkhzi7IWbaQ0nILluzhh1M95jRKDM4L3s8riVkUgyC/E8aS29tJzS+pi3pT1usMt9tsV9yDwzCpdIhOejpdnC16wTL5hicg0sxKxTZpKFx+xd+LkixHzsDRPhGKxH5u35XKbnF1JaQaFu2HKtxOenpekZ6A4awopPk53SyHwIzeBQEACBPghAiPUBBptBAARAAAT6J/AEJ7T4f99v6nVQRU4mjXHEvxes18A1G8SLJxkXN7a7qVNTP+nW1XW0pdNLj+0+UXM0Fs0mUMFzDbXmLy4n8V1J6J+EE+qMQ/zCaSPdMUNYCTTUhD26i7dbpO8w86jS2Tsl4YTEte6MQrGrbqvikdOO27bXweR+50UOMfSShCUq89u2C8z00T0PAmx7HUSZex6oH0tXgBN3+AfM3qg/qe81j4x3u1U4DF5AdQfeQQAEhk0gMo9Mht09TgQBEAABEEhEAneua+glwiQD4qwCR8KLMPV+5PL8sTlh5rU9x/XQjuW5cFI4GhYbApOz9ULMNWmmInA83+hD9bo5iUfzP39LzsfviNhAu7bVcXbCH3XtBThc0L9xLVln7Kjbrq5IGv1u9nhJdkatdbU2K0IrQ87TePIy9/wJe9c8ShIQ/6Z1LLh+EjzNUjRamY/mNVyrHNB+1zXUevkfQl45FmbKXDN511pwu+EzrNkun2+tgJ2SACHG2kvEMggkAgEIsUS4SxgjCIAACMQRgX+vqad/LO6Zp6IOS7xHs0dxFjd+TyZL44uRRCPi5dPae5wd8mdfrCcXxJgWi2nLMl+p3B7y0DgLSimTvUiuN57hOmHPkH/DGiXhRccjt1F3ZwdlHXBkxMYm88M6HvgXp55/lwJVlUpyjPY7r1bCH+2HHhe2n8y9DuS0+OPJ+eyDSv0y5TzOhNh+2yXUHfArNci0Jyq1yWbvpNQ0s4ybyGntJ4Z2S9KPY07jeWWLqePBfynzy/zrVnLWyEeU9PVZnMRD9cpJTbWmP/1CycoYaoA4s+LzPdvZ66Y11xvP9Wx/92X2BOtF2gwu+wADARCILIHk+osZWTZoDQRAAARAwEDgbvaE/XNpT2IEdZdkG5REF8lsEmqZwV+AK9tCafk/aeigE7+qpDf3nZrMlx631zY33041rp7sgE4OH80+7WxOdpFH7gVvcFHn55WEGuKhyjn7QqV2V6QuxDplFmc6nKcUXxYvl4geEUq5v/+n4qkK2w+nyc/7x/VKinsRip0vP7b9vAm8/UZO9DG512kiLH2csEPqhBnNtvv+lMOfx86X/8up+b9U2rJOnMbFns8kSbsfCdOG5Ep7whsGAiAQWQJpbp/W8RzZxtEaCIAACIBA8hB4enMTnfXdJt0FlbBXYnJu6nxBa/L4aK0hlf1xYwvomT0n6bhgJfoErlxeQzeuqg12JOURJPulxNNJra80TnyRZtN7MoMHD3Oh7cZ/UJrdQbl/vopD/vwk88XSRxUpaeUH3STPuwpweGN6/iglXX1f50lNMufT91LBDQ8ryTr6Oq67o40vuVvJuNjXMcPZvqqlM1jwPI/DdOuPDR92OZy2cQ4IgEAPAYQm4pMAAiAAAiAwIAHx/qS6CBNIhZkZNC3foeP1v+oWOt8Qqqk7ACtRIbBnUbau3Ta15AB7qJRU8REWYbrOZMVq5XDDiqGJMDmPPVmSFTGYLVG2GY3nark/fpNsc+f3K8LktLScvIiLMGm3zeeXN8WMrNXteAcBEBgZAYQmjowfzgYBEACBpCdQ4/bR/xmyIxZyOGIqecK0N7kw06oUgV6vCVO8h0M2p/I8sj9MMWTs056I5YgS2Lc4R9dem9dPZXZ9Eg/dAQmy0vHAzYrHLFBTRTmn/ykmo25lltp4qf0MrGMyKHQKAklIAB6xJLypuCQQAAEQiCSB33P9LEnZrppkE5yW5HPC1Gvt672YhajUSdPa+T9uoc+3dWg3YTmKBHK4Rt0BJSEx1mIQD9Ho2rbHT8i26z7RaDrYpnjKLFwPLfePl5GlovfcseCBUVwQllo7qDRXu4plEACBCBHAHLEIgUQzIAACIJCMBK5fWUtXrwjVTcpIT6MdODuipKqHkVJnrM4VEqmz87Lou0O4IG+YWlLgFXkCt3EGz4s1yWOmckKJIg4fhY2MwI+NHeTZnhF0ApcKWH34DiNrEGeDAAiEJYC/pGGxYCMIgAAIgMCXjU6dCBMiksodIiz02ZjIKb1zbVJKuMdWtLnp74v1WSXVfXiPPIFjxuTrGm1y6z05up1YGRQBCUtURZiccEy5nvGgGsFBIAACgyIAITYoTDgIBEAABFKPwEVL9IJiLBd0LUiyOmGRuKuTcuxq2SaluXvXN9C7tW2RaBptDEBgGs/L21szf0myWvq6ugc4C7v7I9DIc0K1dvy4UdpVLIMACESQAIRYBGGiKRAAARBIFgJStPnbJmfwcsTrM46FGKw3ATvPVZpgmC92+fKtvQ/ElqgQOKVCLxTq3aFQ0ah0mMSNioht0AgxqR22tyE7ZRJfPi4NBEwnACFmOnJ0CAIgAALxTaCOw7u088JktOOz9Ykp4vsKzB/daM7WV8DZFFVb3OKi29fWq6t4jyKBX00oJLtmzmL99iLPUewyaZuu18x3lIv8NbOFgQAIRI8AhFj02KJlEAABEEhIAjetriXX9on6cgHlDhvlcKZEWP8EKgweQyk23O4P9H8S9o6YgINF2FmTioLtePmzaxQUwZ1Y6JOABHRqE8+IuD1Tw7XPE7EDBEBg2AQgxIaNDieCAAiAQPIRWN/hIamJpZpkSURIokqj/3eH1UJlLFpVa/EGSLL6waJP4OzJ+vptNZpyC9HvPTl6qOn06ObXnT2lmHL5Mw0DARCIHgEIseixRcsgAAIgkHAE7tSIMBn8GEcmpSMV+6Dv41gDrzs5PLHD3zXo83Hg8AhMz82kMyaGvGJu9opBjA2epZ+rN281iNdzp5YOvgEcCQIgMCwCEGLDwoaTQAAEQCD5CNR7/HQfZ/xTzcahSVoPj7od730TsLIHUUI5VRMRdn9liKm6He+RJ3D+dL1wqGYPjx8ZFAcFutrpoYCG1V+Z5Tg76rENCh4OAoEREIAQGwE8nAoCIAACyUTgkQ3bdJdTxgkoYEMnINy0TsSHNzQOvRGcMWQCM7im23nTQmJMhEUVCwxY/wTafQGq1XjD8nk+6IUzyvo/CXtBAAQiQgBCLCIY0QgIgAAIJD6BJzY1BS9CwhFH44l4kMdQFsQrVpoVErGVPO/u9a2tQ2kCxw6TwCWzyqhYk71SknY0s6cX1jeBzR1u3c7LZpdzvUDMDdNBwQoIRIkAhFiUwKJZEAABEEgkAu/XtZEk6lCtJCsDc8NUGMN4LzGI2GerQiJ3GM3hlEESEG/O1TuM0R29iYVGgOdAwXoT2MIeww72iKm2DxfHPneqPvGJug/vIAACkScAIRZ5pmgRBEAABBKOwMvVLboxF7MQgw2fQDZnm9Om/H9pSwtS2Q8f55DOlFT2x4zJD57j4cQdG9r1Xp/gzhReEE+hzA3T2g1z9SJWuw/LIAACkScAIRZ5pmgRBEAABBKOwGua0Dm7NV0nIhLuYuJkwEWZejGL8ETzbswt88aReMdUa3T7OCugXnSo+1LxvUecunSXftUO5bR7YbZuG1ZAAASiSwBCLLp80ToIgAAIxD2BTxraqVEzj6bQICDi/gLidICjNHOVZIjv1LbF6UiTb1gTOHPlHTtX6C6sikNvmzw+3bZUXVnf5tLVDDuyPJ8unIkEHan6ecB1x46ANXZdo2cQAAEQAIF4IPBhfbtuGAW22PxpcD59H3k+e5cKrn+Q0kcV68YkK11NDdRyye8oc//DKPvUc5T96jm9DrZYyFJYShnzdif7kSdRmt38J/2ZnP5fwhPVOThGzr3GjA0RJXByxSha0uKiW9fUBdtd2+qiWaPSKU/jLQvuTJGFtSzCJFOiauNZtN67i160qvvwDgIgEF0CsflrG91rQusgAAIgAAJDIPBZQ0fwaMn4p53bFNwR5wv2o06mNEdIbHW7Osm38kdyf/ga+devpLy/XU/E4sxsy2NRqwox8Tr+0NxJu4xymD2MlO3vOp7ztI7nQb2qmQO5trWTZhY4SObxpZpVtruoicM0tfbQbhNoNOaEapFgGQRMIwAhZhpqdAQCIAAC8UfAx7WWvm50BgeWl5GYfxYy9zmklxdNxFnHAzeTd9FX5N+wmqxTZwev06wF8bxs1XT2TZMTQkzDw4zFx+ZPoJ+6fPQdsxeTIs+rWzpper4jIR86DJeZJCxpYA5ae4TZ7F+So92EZRAAARMJYI6YibDRFQiAAAjEG4Ef+AupNrF3InrD+mNqnTG3Z3eM0pcbeS5kjxjMXAJZHCL6zJ4TSQo+qyYPIFaxZ6zVmxo1xtZxOKLUVNParZzQ5LTxhdpNWAYBEDCZAISYycDRHQiAAAjEEwGZQ6O1bM6YmEzmX7ucyJpB1knTY3JZFi6MLVkoVVvKc5Rg5hMYZ7fRi3tPpik5mcHOAyLG+EHENkOoXvCAJFhQBCdfo2SN1Nr1HLL5R9QL0yLBMgjEhEBixqDEBBU6BQEQAIHkI7DKUF/JHgfzZpxP3UtpttAXZpV6t7fv9OP+ytWUllvTcyi7+Lqd7eRd/A2HJX5NjhN+o4gxtR2z34Wpy9+ldLuyDfWszOav9jeNRdir+0yhk76qJO19kAyCbq41Ni6792dOPTcR3yUhR+X2a9OO/6Ydx9Kfp5VqN2EZBEAgRgQgxGIEHt2CQDwQ8PATYW9XF0nUliRpsHMIT1o8DAxjMI1Apaagq3wGMvgVawvUVVNamMQa3YFQpjfjGDseusW4SVnPOvQ4yjrwqLD7zNooP1eqeflnblOnlyS9Osx8AlNZjL2931T65Tcb6YttoSQ1Uti40x+giTlZZNPcL/NHGJkea/kztqmjt+i/b9fx9JuJRZHpBK2AAAiMmACE2IgRogEQiF8CTn4Kv4jDUpbzU/g17PmodHqpmv9A13H2tmaeGyFfCo0myQWKuf7RGM6iJWmN5YvLrLws2jHfrgvrMZ6H9cQksLkzFLIk6dbjwfLOv7ZX4g0Zl5q+PtwYc876K6Xl5Ad3dbtd5P3uU3K/9z9Ky7CR/ehTgvvMXjByrYIQM/sW6Por499t7+8/jc78biM9V9Uc3NfMvxfbfU4az2KsJEGzCIpnbzMLMLkWrRVy9k5JzHF4WZ52M5ZBAARiTABCLMY3AN2DQCQJyHyA9+vaaAGnI/+cn/YuGkZigDYOZ5FXJRc/NZqkON6rKFvJsnVIaS5N10x+Nx6L9cQgUKuZO2KLA2/YcKlJRkRj7THbTntQy+W/V2qTxVKIGblqmQ/3enHeyAjIR/2x3Scqv8OuWbE9pJWblIyKEs7XzIWfK7KzdPP7RtZj9M+uYYFf5XQrEQ7a3vYpzqH72RMmD9VgIAAC8UUAQiy+7gdGAwLDIvDClmb6H9fJeWNra1gv17AaDXNSHX9pf4X7kZfYjgV2OnZMAZ0wtkDxmoU5BZvimID4Q6W2lWoZ6fHhEVPHM+J3TpSRMX0Oeb78kLrdnZSWFZv6XRLyqbVGb98hltrjsBx9ApfMKqOd+ffYn3/cQuKpVE08Ss2eDirnqIAxjkwldFvdF2/vjSwat3K0g4RWGk3mgsmcMBgIgEB8EoAQi8/7glGBwIAEJNzwvxsb6clNjRxm2PsPcLgG5AuhhEnJPCArf0mVjG7qd0T5Ui6Rin6eMObneWPeQLcygT1cO+o2ybgnr2v5ifKB7CE7fUIhnYp0yCqeuH+Xyfxas+j1gnZXwi6nF/UkJejudMZOiPHPmdbE4wyLHwJHlueTeI0uWFJNj/HvVK2Jl6mOa2+VsSAbzZkXjd5N7bFmL4sAq+PxGX+OZRzi/RIBdhRfGwwEQCB+CUCIxe+9wchAICwBCTm8e12D4gELe8D2jTLhPJfne+VwxjYHvzt43fhkvr/z1X2S7a2TkyQ4+ctjB7/C/dGXYxfUtyuvK1mU/X5KCZ03tTQo8tS28B5fBNyGOYLpBsEQX6Md3mjS7NnKiTK/LL2wZHiNjPAsI1eZxwOLLwL5/DtSwvdEuFy5fCut0GS37OKHU1s5mYe8SuwZPH/MpvxujcUVSPi5pNtvcHuDmTiN4zh/+mi6Zk658qDNuA/rIAAC8UUAQiy+7gdGAwJ9EpBCsDevrqNXt4cFhjtQhFcBJ9oo4InZjgilIZcaSPIqysxQugzwlxIpgtrCoTtN/C61eLS2iUNkLuIny/9ZW09/4y8E56JWjRZPXC3LvdSa3m+j3ZO4y5aSMmXw3sXfkswjiwdjZzMsTgkcOyafw63z6aqFlXTHphZydut/KhrYOyYvB/9OLOTfifL7NjtCv2v7QiJRCvL7VuatNWlCiY3HH88h4hdxqKUkVoKBAAgkBoE0t8/wlzgxxo1RgkDKEGhhL9SVy2vovvUNYa9ZPF+S4auIX9o02WEPjsJGCY+RJ7TyRSGczeUvBZfPLqdj+MsNLL4IyJy/CW8uCw5qLNdRSrZaSsGLi+GCeDF+2NYeHIHMS7qMfyZg8Ulg8dIVtHjpSnJTOi0ZM4VeaCNq7SecVMK982wW9pJZKYcfho3097AkDHHyfC+JPmj3+altgNDzo/l3q0Qg7F+SE59AMSoQAIE+CcAj1ica7ACB2BN4clMTXbysmurdvUWO/MGXOQvFMU6zLJ4yeUkIY51L5lOEJrwLwaWtLqWA6q94/tiNc8cqqfFjTxYjEAJZ/AVSaxKCBYs8ASNXYzr7yPeIFodLQBVhcn4WddFls8rpluIienBDIz26YZsuZFHtw8Ohpg0uefWUgpC5txJFID9f8qBM5pUpc3L5Xfxr8pKfNPFIi3fUp5mTK2Gr0t5AJnN7T59QRL+dXEy7jYpNEpqBxoj9IAACAxOAEBuYEY4AAdMJyNPXv3AWr2c2N/XqO5sFmGTyUkMFex0Qow3yxWMip7OXsfVMcNcLMhGV73Fq/X/Pq6ATxhXEaJToVksg1xBShZA5LZ3ILRtDQHP5ZwUWXwS0Asw4MhFUf+IQa3l9xHNhn+faY5Klti8vmdxvmU8rr0jbvpxU5ET+/XkKJ0Uq4L8FMBAAgcQmACGW2PcPo09CApL04g8/VNEGnhiuNUm0IWFj4gWLZ5On/SLISnlS+xa+Bm1hUfHs/fKbDfRdc6niHYvn60iFsclT9VEcUqVm3ZSQKFjkCUhootakuC4sfgj0J8KMozyIs8PK6z5O7PF2bRu9x68FDe20SpPcw3jOSNbzWGxJyOEhpXl0RHkeTeAHXTAQAIHkIYC/BslzL3ElSUBA5oGJJ8xoMgesIidLSTtv3Bev65IsZHq+Q5k/trnDzeE3oS+jt6+pp+Wtbnpk/gQq4cnusNgRkCLdqhDzcogULPIEtJ99ab0sxuHEkb/CxG3xvQ8/pdq68PNvB7qqI8pYHPFLbCuHJX7b5KRFXM5jGYdjr+HfeRs4cdFQHm6UZlmVtPOzcu00j2ubScjhLgg7HOg2YD8IJDQBfANK6NuHwScTgYuXbqXb1tTpLsnCLouJLMBiPQ9MN6ghrsjY89kDsIm/mDRycgjV3ucwxYM+WUNP7D6JduIvHbDYEKhgD6v6NH8wc1P6G2XnC4+Q57N3aNQdz/d3WEz3+dcuV9LYq/XFzBiMkWuFoycDqRl9o4/wBIbiBQvfgn7rGI4A+DlnLZSX1kSg1UvGQ84w6+R5tB5+ICX/ZM6YlBQRj1cxP4wq59+T2QhZ1aLDMgikBAEIsZS4zbjIeCdwzsLNSnFm7TjzWLxM4hA/Y0IF7TGJsiwFpKfm2ZU0z+IdU21tu4cO/2wtPbfnZDoAGb9ULKa+T8mx0fvb9b94biRVtnxJTErja2u77VKyH34C2X/2K9Mu0aVJviDhoJM4xBgWOwKRFmH9XYkINHnBQAAEQCAcAcwYDkcF20DARAK/+W5jLxFWyl6KWQWOpBBhWpSSyGMGX5e2sHQLp2Y++vN19AF7yGDmE5jBYl9rkv0SFlkCWqYzmXeSytzIQotia/PmzqZ5c2dFsQc0DQIgAAKDIwCP2OA44SgQiAqBs77bRM9ubta1LQk5pJ5TspoUm55VkE3r2jqVlPdyneKJOf7LSnp936nwjJl846XOm9akfpEUBu/XeC6Z6+0XyLfkWwrUVFF66ViyH3Z82FO6fT5yvf40+ZYtpK62FsqYvgNl7n84Zcycpxwv+9tvv5wy9/0pEbfr+exdCtRVk2XMeLLtth9lHXiUrt2upgZyvfkc+Vb+SF3trZTuyKGMObuS46SzKC2rJ423641nqaulibJPO5s6X+O+V/xIGZNnkL9qg9KW5+uPybdmOdmPOFE5V9dBhFdkamQnM1XNyFvdjndzCfSIsdlcL6ynZpi5vaM3EAABEOghAI8YPgkgECMCf/mxip42pKefwE/Lk1mEqagdPBdiJosxScWvmpe/sZ78dSUt5snuMPMI7MweSq0NJuV2x/03kYidtNwCyjr8RBZNFdTx3/+Qb+n32qaI/Cyybr2YPJ++w8JrR8r6yZEssrZS+93XkW/V4p5juwLkr1xF7g9epc4XHlYEWNbBxyr7Op9/iOSlWrfHTe13XEneRV+TddocchxzGllnzCXPlx+S85kH1MMoUL+VAtUbqfN/j5P7/VdY6Xt4rHk8N6xYOSYty96zbIv+A48OLsirNSRf0NKI/bIIssMO3j/2A8EIQAAEUpIAPGIpedtx0bEmcMOqWrpv/TbdMCTle7ynptcNeIQrUuR0BmdVXN3aSc7t9XYkTPHX326gDw+YjmyKI+Q72NMlQcCunJltYXOnckr7ALWPfCsWkZc9YVk//Tk5jj8j2I13572o44GbKS0jNB/GveBN8m/ZQPkX3UKWcROVY+0s3Npuv4w6n76f8q++J3i+eNby/noNC6wdeo476mTqePTf5P74Lcrc71CylI8n3+olipDLPuV3lHnAEcFz2zud5F+7LLguC9Jet8tJ+Zf9hyxl43r28Ryxpu8/J9tOe5g2R8zIc4/CbN04sRJ7AouXrVQGIeGKdfXbhp1FMfZXghGAAAgkGgF4xBLtjmG8CU9AioFetbxGdx3jOTNiKokw9eIliYekuJdi0Kqt4QQe//f9JnUV7yYQ2E+TKMXLiSUkPLEv8377KZHVqiS80B5jYyFmnTBVu4k8X3xAth3nB0WYslPOPewECjTUKJ4w9YSM2TsHRZiyTbLKHftLIhZPnq8W9Jw6ZgLl/uESsu19iHoahzMGqNvrYdGl96SK98xx4lkhERY6w9SlVs6Wp5qEfO5ZBCGm8oiH9zpOXS/p68tGl/C8sdl0KHvHxEMm6zAQAAEQiDYBeMSiTRjtg4CGwAou+nk2Z0jU2hhHJkkSi1Q18YxNzXPQyhb2asiEGjYpknrR0moUfTbpQ3FQSS5JbTfVWjjddjbXgQtngYZashSNpjSem2U0y8RpFNi6XUQHAiy2ahWvVNtNF+gO7fZ5lXXxWlnGTlSWrRP1Ik42phdzPzl51MXtqOsSXuhbuYT861dyX5vJv3ENdbU2B+eHKQfKfyzkMmbvFFyNxYLMfdR6xKQQMCy+CAS9YXNCyTtGswg7lF8i0mAgAAIgEE0CEGLRpIu2QcBA4LxFVaRNZV3EtWMqcqI/T8UwjLhblTljUzi9/eqWnvA4GaAIg/mjsumEcfq6PHE3+CQY0KFclFZCFKXOkZjUPOpzriLP+6I+5lalZYQeKHTLceytSsvO5WQe5b0oWcZOoPT8wtB2a+jc0EbWUxzq2O3p8XZ1NW9TEnt0NTcqc8OsHO5o221fTsbB4ZI8b0xr6bn5rOTCi0ntcdFcbvaE6uZJP4eN7in+G80+0fbgCUiiDtUbJuLLaOG2GY/BOgiAAAiMhACE2Ejo4VwQGAKBq1bU0OfbOoJnSDjeZEPq8ODOFFyQbIoSoqmtM3b+4i30k9IcKuJ9sOgSOHZMAT2zPXmMzNmTTH+OMF4x8VJJxkIRWUah01UfCrlNy8yi9IIisrKXLPv0P+kHz+GG4hVLs7LI2u4d056rHizhhV2tnGlxh12UTa5Xn6IAZ03Mv/R2soweqx7Gc8eWBpeDC9x2rK2RPYtaO2YMi0NY3BBYvHT73DCNNyxuBoeBgAAIpASB0MSMlLhcXCQIxIbA141OumFlT3iVOoJJuZy5LVkL56oXOcR3CdEsZC+hanVuH120pFpdxXsUCRw3Vu953Mbsw5nM5ep2dZLnu890uyWtvHfFD7ptkilREnt0u0OeTjnA9e5L1PznU5R5YuoJ3kVfUrezXV1V3r3ffqIIvowZOyrrgZrNZCkZoxNhxCGQ/nXLdecNtNLNQrCXiQdPXkYb6vbt50vtsDbN/LAjy/ORgMbINobr4g0TkwQd8HzF8EagaxBIcQIQYin+AcDlm0PgsuVbdR1JOOKAtZp0Z6TOykT2ikkSD9We2NREb9S0qqt4jxKBY9lbM8YeEsENfQixzD0PJAuHGnY+dQ+5P3qD/JsryfvDlxwyeAWlWfSeS/uRv+CwQg+13XoJeRd/w8eu5zT1r5H73Zd5/tbOBkHl5+Mu5RDDryhQVUmScdH5PKezL68g2677KFedXjqGsyZu6ak1xvPLfMt/oPY7r6Kupm1K+KKMpV/jBx9pdgf5Vy0hEXld2+qChzf96RckL51JlkXZft7Jus1S70zZ/udT9ds1aw3unnlw6qaTK0api3iPAwJBbxgn6ICBAAiAQKwI6P9qxmoU6BcEkpjAQxu20WcNoZBEEWCSoAMWnoCIMAlRXN8WyoJ3NYd1Hs0eBVh0CfxqQiHdvKpHnEjilDqXt3c2T4uF8v5xI3U8fifX6XqMvUgcfsdhgFmcYl7M8/l7wUGml5RR/oU3kfOJu0hqj0kGRBFxUrxZRJrWpHZYoLGBOh66hb1gPFeNsyvaZu/CYY3nKok35FjHcadToLaanE/fp5wq4Y9ZBx7NtcxO4PpiV1HHvddTwQ0PaZvttZz10+OU2mIdj96utJ3JoZaRti6+znpXyLtWxl5eCLFIUx5+e1pv2PBbwZkgAAIgMHICaW5fuBiNkTeMFkAABPj7JEdATXt7GVVrvpTN5gQU8IYN/OlYw/XFmjVzbG7baRz9YUrvCfUDt4QjBktgo9NLM98JhfnJPMYdC3tnRwy2x2F7AfZGWYpKiVig9Wcy30tCD9ML9fdQtjf/5VSyH3sa2Y84SQl77GptIktxmSLGwrUpyTokjFDmq0l2RLGuthZKs9l6Z08M10CUt23t9FBVhyfYy8Wzyujy2b0TlgQPwIKpBB5/+iWlv9NPO8HUftEZCIAACBgJIDTRSATrIBBBAreuqdOJsFK7DSJskHzHZeu9hresrqMwM3sG2RoOGwyBidk2+jV7xVSTeU5az466PfjOnjDxcA0kwuR4JXmHQYQF29EsSOigUoCZPWJ9WfqoIhJvmyrC5Lj0vIK4EGHy8KW2Ux+WePbk4r4uBdtNJvDeh58qPcrcMBgIgAAIxJoAhFis7wD6T1oCfnY237E2VJtJLnRMCtcLG+qNlox92iLXW9mreKeB51DbxPEDEzh3Knu3NCbeHdjgCQgvqR+m2p+mldJoTQIadTvezSdgLN5s/gjQIwiAAAjoCUCI6XlgDQQiRuC+9duoQRNaJxkBMy34kRsKYGOh63uZKSy6BOYV2On0iUXBTjyBLqp2Rk+MSYKPrJ/+nKyTZgT7TNQFIysrh02ez0IMFh8EwhVvjo+RYRQgAAKpSgDfClP1zuO6o05AknRorQzeMC2OQS2LcNV6xTawIHi2qnlQ5+Kg4RO4cIY+gcUW5q4tRD78lsOcySGIjuPPIEl1n+hWZRCsF84cTeWaTJSJfn2JPH6tNwzp6hP5TmLsIJBcBCDEkut+4mrihMA7tW20qs0dHE0JfxmzpePHLQhkCAsyr05rj2/kRA2wqBKYwuUV/skJJrSmLbSt3Y7lHgJSd61Rk/J/Es9xvBQJOuLm4wFvWNzcCgwEBEBAQwDfDDUwsAgCkSLwzOYmXVOlWXoxoduJlX4JODhzX0FmKHHDR/XttLo9JHL7PRk7h03gChYRM/Oygue3cJitMQlFcGeKL3h5TtimDv1n8uo55RSqhpfigGJ8+fCGxfgGoHsQAIE+CUCI9YkGO0BgeAScnGnuxS0twZNzuG6YvGDDJ1CcGSo0LK28pOE7/FZx5kAEbpg7VneIiI0OX0C3DStEG9tdJHXXVDttfCGdNA4FnFUesX6HNyzWdwD9gwAI9EUAQqwvMtgOAsMk8HpNKwU05fkKDSJimM2m9GlFnHXOsr1elIB4bWtI6KY0mChf/BFleXSeIdlEJYsO7ec7ykOI++Zl/py23t1YDkO+ZZ5ewMb9RSTxAOENS+Kbi0sDgSQgACGWBDcRlxBfBN5mIaa1Qk1YnXY7lodGYJSG448tLlqnKZg7tJZw9FAI3LzjWNqzKDt4itQWW9/mCq6n8kIDzwkzZpS8Y+cKKrSFQmlTmU88XDu8YfFwFzAGEACBvgjgr0VfZLAdBIZJ4AOew6SahCRqU9Z3tbVQy4VnqruV97SMDEovLufCuGWUufchlLHjfN3+tlsvIerqorx/3KDbHs2VblcnBbZsIMvYCZTmyIlmV4NuW4SYJERQ7cO6dprKSSVg0Sdw7y7jab8Fq6mDRZiYeIA28Dy9SbmhOWTRH0V89dDi9VOlQZBezvPqjirPj6+BpvBo4A1L4ZuPSweBBCEAj1iC3CgMMzEILGzupEZN7bC8Pp6MWydNJ8dJZykv+zGnUcb0HairvZXa772e2u+6mrp93uAFW8rGkrzMNH/Vemq77VLyb1pnZrf99pVvYPnJtpDg7fdE7BwxgVmctOOh3Sbo2ql3eSlVMym28Ty5ta2dOh4yL+xiQ6ZJ3QFYMZ0AvGGmI0eHIAACQyQAj9gQgeFwEOiPwFeNTt3uvD6SdFjKKyjroGN0x8qK97vPqOOR28j18mPkOPn/Kfuzf/mHXsel4gaZIyYeRjVZhJF1KjIx85p/PraA5z6No78v3hLstqaz54HB+JzU8YyJCFvT0kma3Bz0k9JcemS+XqgGIWEhJgTgDYsJdnQKAiAwRAIQYkMEhsNBoD8C3zfrhVhuH0KsrzZs8/ejjO8+Jfcnb1PWocdR+qhicj77gBKamH3aOcpp4jWzzd6ZQxh3J9ebz5Jv9VIquOY+ZZ9/3QpyvfUC+Tevo/TCUsqYtRPZjz6Z0jI06fM5kYj7w9fI+8OXFNi6mdJy88nGbdmP/AWlZeewEPw3hyVuVNrrfOERSrNnU87vLqD0/J4scL41y8j9/isUEG8Zt2udOI3Eq6f12vU1xq7WZnK9+hT5li+kbrVzF4kAAEAASURBVJeT0gqKKHP+/mQ/6hdE6QNnltQKsRqXjzY6vTQxW3NtyqjxX7QInDu1hJo5JO+6lbXBLkSMiSiZmAJhihKSubatkzS5eGjnUQ56eo+JQR5YiA8C8IbFx33AKEAABPongNDE/vlgLwgMicASTiKhmsNqoXRNpj91+0DvIorkm16gtsfzEKjeRPJSTQRQoL6GOh68mbzffkrp2bnKLu/3n1Pbvy/nsEaP4m2zjptE7g9epY77byTy+9XTqeO/t1PnS/+ltEw7ZR3yMxZQ41j4vUXtd1+jHJeeX0DpLM7E0nLyWNAVU5qlRyR5vl5A7f+5ggJ11ZS5z0/JttOeJMKs7ca/k4hA1cKOka+p/c6ryLvoS7LtzuKLxZu1fDwLx+ep8+XH1VP7fc9hplpb0hrird2O5egRuIznQf1jxmhdB3UcpriW50uFErjrdifFioRiruFwRK0Im5Nvpxf2mozkHHF2h+ENi7MbguGAAAj0SQAesT7RYAcIDI2ApPRe0RYq6iqFiIdj6QWFymkitsSjFc48X7xP1ulzqOCmR9lj5aBut4s6X3iYbLvsRTlnnc8KqqeUrG3nPan9nuvY+/UFi58DWCytVMRb1iHHkuOEUNIQ15vPkesN9q6t/FHZ7luzlAXWUrIfcWJwDN3uTup88VGysHjK+/t1lJblUIaWdfAx1Hr938j5/MOU/89bgn0bxxiorVYEpeO4X7O37/iey/rpz6njvhs4JPNTcpwYGk+4a5ZtdoMQW8lf/o8dg+QIffGK1vZr5owhK3/GblgV8ow1cSKV5YEumpxrp+F+9qM13pG2K3Ph1DBMta1d2BMmIkzS1cPiiwC8YfF1PzAaEACBvgkM75ti3+1hDwikLIH1hnTqWZZh/nipYYTaR+9GqpxFMef0PykiTHb5li0kychoP/zEoBCS7RlzdyPr+Ckkniwxz9cfEVmtfNxJyrr6X9bBx1L2r/5Aafk9IlDdrn33LfmOup3tPaGO20WY7E8vLCERY4GqSiXTYvAcwxjTCzi0kT1r3oUcEtlQEzws+8y/Ut6ltwfX+1uwG8QtUtj3Ryu6+67YoZyMBZ+dPH9qWXMHSVr3ZDA3C8tVPB/MKMIOHp1Lb+83FSIsDm8yvGFxeFMwJBAAgT4JwCPWJxrsAIGhEdi0PXGBetZwhVhX8zalCQkJ7MssPC9LwgZVU8MYnU/cpRNisj/QWEdpLNKUZfayWYpGK3PBlA3b/0vLsiuhhtptxuVA/VZlk3XCNOMusk6YqmwTL56lYrKybByjeNDsR59KrteeotbL/8Cp8Scq2SIz5u1BGTPm9moz3Abx80k5AA9/QRYzMlc24j/TCPx1eimVs0forO82BpNXyPMDSevexnPJJnASD2t6j3fWtEFFqCMJRdzED1e6DA9EzphYRPfvOj5CvaCZSBOANyzSRNEeCIBANAlAiEWTLtpOKQI17tA8LLlw2zA9Yup8MAsn2+jL1MQZ6v5ur0dZTC8pYxeV3hOXXlpOadae8CmpD5aWObzaW92enrDLcOen2bKU/ru9odBM4xjlAPvhJyjzyryLvlLCIN2fvUfuBW8qtdNyz75wUAk7bPzF3hNQuqOaJPG89FxNYv5/SsUopZ7bOQs30zLNnD2p+Sa1tiqyM6nUnjgJVTr9AapyeqhFU4ZCvTPXzR1Df5uunx+n7sN77AksXrqCausaqGx0CY3mFwwEQAAE4p0AhFi83yGML2EIaOuHyaAzhuEJkPphEkYogsoypqLPa1eFlXqAZfQYZdFx3OlKqKC6XXn3+4IJBixFpeSTpBrylF+TSESEnPudF8k6dTZlcEbGcGYpKVc2B+q2ktRB05rqLbMUsxDcbsYxKn1yuKJkV5S5Z/Lq7mgj55P3kHfxNxxe+UOvYtZqW9r3DEVo9iixbWG+LGuPxbI5BHbj+VJfHjSD/rSoih7b2Bjs1M/pFKXws4iyMSzICgy14IIHxsGCj8e6tdNDtQbPtgxNCof/Z+cKOpjT1MPil8DipSuVwc2bMyt+B4mRgQAIgICGgP7RuWYHFkEABIZGoJXnx2hN6l4NxUQMOR++VREn9iN4Dtcg0rmr7UviDvGEeb75RN2kvIsHrPmfvyXn43co69YpM5V5Xr6l3+uO8379MbnefpFUrxertJ79mrAsq4QPch/uj9/UnSsCy8NZF9M4e6OF56P1Zd4fv6Gmc08kf+Wq4CESXpl50NHKepezrWd7F3Nk8Ugs2sKZ1tFoZB7ueGwzh4B4KiVkT+pplWTqn/G188/Gap5rJfOtxEsWT+blz9lmDkFc1NgeVoT9v8nF9P0hMyHC4ummhRmLeMPE5s2dBW9YGD7YBAIgEJ8E9H8t43OMGBUIJAQB1/Z5S+pg+3OIBWqqyP3R6z2H8hfBrqYGzmz4FSfcaKasA46gzD0OUJsZ1Lt4qzL3OpgzHz6jiJiMObtSd3sruT97l7o7O7jNI5V2MrltqSEmKewdJ57F87QmkG/Fj+RhcSVFpjPm7KYcl+7IVt5F2EmCDqlZZhk9VplH5uE2nbZMytz7ECVVvvu9V7huWSU5fvFbDnvsCVEMN+iMabOVmmTOJ+4mO2dOtJSOZdHJY2QBKEk8MqaxmGSTdPauN5/n9PanKrXNjG1pSwJI/Sp3oJuyLEMTvcY2sR45AqeNL6TDy/Lo8uU19FDlNl3DrSzC5CX14EqyMqiYX9r7qTs4yitSmHkbzwPrK7HIvAI7XbXDGOVaojwUNB8BAkFv2NzZEWgNTYAACICAOQQgxMzhjF5SgIBf4z2Sy01TvUphrt2/YQ3JSzmOsySmcy0v8Wpl7n0QZcycF+aMgTdln3Y21//K4zlXbyhiJi0jg6wzdqQcnntlndoTqpPGAirvgptIxJDzqXvZ69TjxZM+pWC0nCNmGTOBbPN2Jx/P5fJ++4lSMFpEVvap3Aen1xcR6fn8feXYdC7KnH3GeZS554HKel//ifcr94+XKgWqO+69IXiYhCrmnvNPSi82zL3pw6NolFxSNiDowQu2ioVYEijkEMS7OJRP5o/dyCnuP6hr1w2ng0WQvDZyWviizAwq5FcBe9GM91Z3UgRWnDz/S4oyN3l85PKH97gW8Tgu5Dpp503re45mBIaCJiJIQOsNi2CzaAoEQAAEok4gze0zfHuMepfoAASSk8DfF2+hu9Y1BC9ufklubJ7284+0eNjSuCizCK++rNvno67GekVYSdbEoZr0QSIitxd/Hsr5XeytE4+dhDOGS+rheu1psnAmRhGDRtvEX96183iafj6PHNp4ReMJWI85gTdrWumOtfX0SUNHn2MR3Z3PAi43Q14WylYKovd5+KB2SPp5EXwSGimeODXbZriTRYCdO7WE/sICzI7PUzhEcbvt8adfUsZ2+mknxO0YMbDkJbDV5aO1HN68kZP8VPNyAz/safb5yckPe2TuqTwqlDnj2fx7JV+iAfh3zRjONjvBkUnTcjP5PXGSGSXvXYzdlcEjFjv26DnJCEhada3J83b9Fu3eKC7zN9p0TsoxkIn3S7xRwzWpHzZcU8RbHwLOv2kdeXkOWx4Xew5nxkdHMjcJFt8EjirPJ3ktqG+nhzc00otbmnsNWO6rZCrUZiuUunFSBkJ+tmw8P1G+zEg6fPm5SuPPuXzB6eYTxSsqiUG8/BKxJQJMQoUDErs6gM3Nt9OZk4rodzwXTIpUwxKLALxhiXW/En208rvmY36g9OW2Dvq2qZN+5HmvIrxGYiLOJBR6fmE27V2UTQfwQ9wcQ83MkbSPc+ObAIRYfN8fjC6BCOQYhZh8CcTcpaHfwUCA8s6/Nlis2thATyhiz1bxXODLs5FQ/K4fyFkH5XXjjmPp2c1N9FJ1Cy1q7uxzwBI+2FcIYZ8nDWJHgc1Cx40toF+MG6WMZxCn4JA4JCDFmzE3LA5vTJINqZG96a9Wt5J49t+va1Me+ETyEiXp1Kcs7uR16/aGD+Lfk0fyw6tjx+TTeHjMIok77tpCaGLc3RIMKFEJ3Le+gf7y45bg8Ofw0y0Jr4JFloBk31Mz70l4R+WRPUk+ItsLWjOLwIo2N71T26rMI/uUnzKLZysaNjMvi+TLzaGj85CAIxqAY9SmiDEx1A2L0Q1I4m7fYOH11KYm+h8/MBqqSRIieQ4rnnuxHs89T8sW1/8QTX5nncpJkE4dP2qIZ+LwRCAAIZYIdwljTAgCL/Mv69O+3hAc64wCR1zXTQoONMEWljY5SYruiu3M9au+4vpVsOQg8M6Hn9IXDe3kHTOetjnyaCWLtHU890IyHA7W5HvPZK5ZNiM3iyZZOTyxch1N6vbQn0/92WCbwHEgAAIpSkCeA93DD1Uf5Iyvq7kGYn8mJWqylfms6WTnh64SQp3JodMSQt1XNljRYT7OlOzhjiSMWjz+8vesg18DPYSSB4+/nVRMv59SQqPYqw9LDgIITUyO+4iriAMC4/iXpNb6SwygPQ7LQyMgdZ9UMzJXt+M98QjIXJ969m5M46HPG8VzJuZOCF5EE4cG1bj9JEXT2/gLi8z/ki8t8mVH5gjKfArJ1FjKk+DHcRiPOtNLvCXvrnMq7bzHIu/Qg/cPtokFEAABENAS+PeaerqdkwrVcQH6viyPf8/kswjK56RCIsKGavKgyCZzXvlUSUqktU4WZW2c5EMSC2nnyqrHSFKQq1fU0M2r65SkQn+bUUq5iLpR8STsO4RYwt46DDzeCEzip/Bak4QBsMgSkAxU2qeGRuaR7Q2tmUVARJg610f6lOV5mnpQIrLkNVRbvGxl8JRaZT7RCl27wZ1YAAEQSFkCT/F81etW1lIle9/DmQimIq55KGU2xNsVLXPwAyWH1UZldpuSgKiJHzw1sigUYaY1+W4hZUEeYK/dxbPKlGyv2v1YTiwCQ//LlljXh9GCgGkEJCVtMb+2bc+gFI0kA6ZdTJx25NoekqgObzqn/oUlNgFtwoVoX4kq9rQiL9p9on0QAIH4JLCs1UWXLtvKc1Tbwg6whKNcSrNsSgH6sAdEcaOEPUrRe3nJd4l6t5fquQC9hE6qJpECUjbn+apmunbOGNq/JEfdhfcEIgAhlkA3C0ONfwJzOBX2x5yiW0ydxzSUUXc8fCtZx08h65SZ5HrjOfJvXEuW0nKy/+yXSqFn1+vPkHfRl9TV0sTHTSXHSWeRZdzEYBf+ylVKMWf/pvXU7e6k9FHFXCT6YLIffmLwmGAfU2eT603uo3K1UgjaxgWZleMkdmK7eb5eQJ6P36JAXTXPMu4iS3kFZR1xItf32kM9xNR3qcuitTl5Q69/pj0fy7EloIQOcshgOBMv2UgEk7QtXjCjQYwZiWAdBFKPwO0chnjRUv67FsbKOLxZvFLGkjRhDjVlk5TxmJCTRWM56kZqaNbwS5v041ueN33op2vp71yIXgQZLLEIWC69/MorE2vIGC0IxC+B5W0u+rqxZ06KPLkq5qdZUvdosOZ67Snqaqglz2fvkXXyTMqYNY/8a5aR9/vPKbB5PXl/+EIRQSLWvD9+TT6ut5V10NGSmokCWzZS+62XULffT5k77akUQ+5qbiIviykRZNbxk5VhKH20NrPAepOsFVOUPgK1W8i78EuluLO0Leb5/H1yPnEXn1tEmbvtR1YWbv71q8jzxQdk221fSs/JU44z879afiKo9TTevlPFkPiaOVb0NTCBL79ZSB3O8Onry0aXkLyGa05ud/2GTWFPr6vfNiKRF7ZRbIwKgYb6evrfyy/St99+Q+vXr6PS0tGUnZ0dlb7QaPITqOO5pmd+t1FJyGG8Wvl7PS3fMeS/28Z2orUuc2JljtpoFoniGJNi9Vr7kr97fMgPgqUemcyXhSUGAdypxLhPGGWCENiDfwFqrc0boCz70Mo6S0Hj3L9eQxnTe9KyWydNp/Y7rybfqiVUcMNDlGbrCcdLy7ST6+0XqGtbHaWXlJF4r7r9Psr7/cVkGTNeGUbWQcdS8wVnkI/FXOY+hwSH5l+7nHL5uIwd5/ccd9jx1HLBb1iMfUGZ+x6qbHN/+g4LsyLK+9v1RNaeXxUZc3altpsuUASZZfTwi0EHBzLEhXZNrPz8QgcX+x28yB1iVzg8ygQkeUY4j5XarXGemLp9sO/a+WHhzkHyjnBU4mvbg/ffS488/CCVlZWTg8XX1uot9K+bbqDDDj+CLrr4UhZkCMWKrzsW36ORgvK/W7iZqtijpDUHJ7yoyMlMmCzH8nBXPGRFPGetyslZZTV/F79iMbbPR6vpgV3H0y8qkO5ee5/jdRlCLF7vDMaVkAT2KdZ/MZAMSKWGbIoDXZh18oygCJNjVVFl22XvoAjTbu9yOUmknm3+fiysdgseL8d0e1z8Hy+4+V1jlrJxQREmm0XcWaftoIg69TAHh0OmFxQGRZhsl3BH5Z37NNucPD9MknWotq+Btbod7/FPQMIO+xNhZlwBkneYQXn4fbz5xuv01JNP0J1330e7zd9dacjr9dJHH35A/77tX/SrU0+m/z7xFOXnFwy/E5yZMgQe3dhIv2cRZrRyDkMcz6ImES2Hk4jM4jI5W1lYVnWEUu1LMo/Tv91IG5xeunDm6ES8tJQaM4RYSt1uXGy0CUjCjt3YU/N9U49gMWY7Gkz/6XmGp1iWnh9TS5nBA2XRp761TphK3a5OJYxR5pYFqjfxHLM1LJ70IkzGIB40o6Vl2IgCoVCHjB12oa7GevJ8+i75qyq5vY0k3rpYmZHlT0pyYzUU9DsCAkNJzjHceWJ9zQ8zDhvzxYxE4mO9i+ej3nbrzXT5lVcHRZiMzGaz0eFHHEm777EnnXnGL+nG66+lG266JT4GjVHELQFJ9345J+XQmmQ/nJRrp1FJEMI3hsWkZHbc0O7She5fsXwr1Xt8dOu8cdpLx3KcERhazFScDR7DAYF4JHDY6NDcKUm13qIJG4jmeH3LF1HLJf+POp9/iLraWsg6fQfKOfsi6iXgeBBp28Vdf+ORsMeWy39P7o9eVw6z7boP5f7piv5Oieq+5u3ZKKUTSfN7aFmIc1Q7RuMRJVBb3zCiuV+RHIw6B02EGyx+CCxfvoz8Ph/tf8BPwg6qsLCQrrjyGvrg/feoubk57DHYCAJC4IrlNb1EmMyz2mFUdlKIMPUuixCbw9ckafa1dve6BjonjCdQewyWY0sAHrHY8kfvSUjgqPJ8pSaJemlN/ESqYBg1kNTzB/vufOoeJSlH3j9upLQsTTbB7lA432DbkoQhrteepsy9DqLsX5+rJAORc7s4yUcsTApdaicmH82MMTssFndi5H1qMyGqAujdPjMn6uuJDbb3/uaHzZs7i8pKS2j0CBKBDHYcOG54BOpqayk3N4+npvb9FWWnnXdR5oitW7eW5m8PXRxebzgrWQmICLuJ621pTRJyTEnSbLuSzGMqX5sUuZfMiqr9l8My5VvA/TxvDBZ/BPr+LRd/Y8WIQCAhCOwyykGSxl5qlIhJQcaJHIMuvySjZRJ+2NW8jbJ2PEInwiSRR6ChjiyjhxaaEKipUoYqyTkkI6NqvlWL1cXQexeHM3IoEaWzgz1dEy4Z3M7bZJ9qfW1X94d5b2Qxq7XjxmJeiJZHoi5rxZB4pw49eH9SwgrZayaZDUcyj0zaG11arKAR4SWeOAlFhAiL/0/LtGnT+XNQSxs2VNKkSZPDDtjj8ZDH46bi4p57HPYgbExZAjevquslwiQtvSS5SHaTOW+S0KNKU6D6MRZjDks6/XunoX0XSHZW8XB9EGLxcBcwhqQjcDJnK1KFmOSXaGAxJilno2XiAUvPK1BS2mfMna8k2ZA5Yp1vPENp/FQ5wHO9RKhJGvvBWHppTy0SN9cQU85hISVp9F3v/U8RZiLUup0dlMZZy1xvvaDUI7MffQrZjzo52LzrzeeVmmb2Y04l+5G/CG3n+mgS9mg/9pdk55pkg7EGTluvmszDgxBTaST+u+oVU69ExJlWoKnbh/Iugs5oIsTE5H2k7RvbxnpkCHSz9z6NH/yMnzCBdtl1N7rskovowYcfI7td4+Hf3tXHCz6kkpLSPoVaZEaEVhKRgCTmuJznR2ktkZNyaK9jsMtjHJmcxCuNNmmSeNy7voGK+e/nJbN6zxEfbLs4LvIENI+pI984WgSBVCXwy/GcbVBj9S69R0ezK2KL2Wecp7TVftfV1HrtX6jzpUfJ8fNfUxanppdEG52vPDHovmRemZwb4KQfbTdfSG03/oM8335KeX+5SsnoKLXEvD9+Nej2RnKgiFhttsRfTdCzHUnbODd+CMybMyuqgxFPGCy+Cbz37jv0j7/9VRFjMgesvq6OfnnKSfTVV1zEXrzu2+2Hhd8riTrO+8v56ia8g4BC4CNOUW/MjiiesETNjDiS2xruuq9ZUUNPbmoaSbM4N8IE0ty+YUwgifAg0BwIJCOBM7/bRM9sDv3Cm8bhioVc9yOqxmF/XY385J/T0afnh7IvdjU1sMeM1/uZcxFuXN1eD3U1sSeN20qzO3oO4S9ESntFpbqwxXDnR2LbsmYnOTWFKxcfOotm5CZ/eEkk2CVCG2o9scPYixVtT9XjT7+kJAoJ5zFLBFbJPMYAZ2w96YSf0cmnnKa85Fq3bWugKy+/lL75+iv2fpXQuHEV1NrWSps2bqSzz/kjnfl/v01mJLi2IRKo5Yd2+y5YQ1s086OSeU7YYPFIrbGt/FJNZht8ceAMkmkUsNgTQGhi7O8BRpCkBH43uVgnxGTybNSFGM/RCpeaPr1weN4AqS/WK+sihymmF5tTm0Tm12lF2InjRkGEJenPS7RFmGBTsyQmKcKEvqzXX3uVfF4fHX/CScHrKC4uobvuuZ82VK6nBQs+ImdHh1LY+aijj1GKPAcPxAIIMIFzf6jSiTDJjpisiTmGcsMrsjPJy7XFtvHfUzFxv/xxURV9ddCMoTSDY6NEAEIsSmDRLAjsVZRNkkHxzZpWBYZk/ZNfhPKEDjY4AlKoUmvnTh2eoNS2geX4IaAk5uDU8WYJJHjC4ufea0cihZofeuA+mrfTTvwlsXeW10mTp5C8YCDQF4Hb1tTRG9v/1soxUidsCiIngrgmczZFF4sx9cHmouZOOn/xFroNNcaCjGK1gDlisSKPflOCwF+mc/iexqo7Q+EBms1YDEOglkVYpz9UYPqEcQW0J4tbWPIRULMbJt+V4YoGQ+DlFznhD2d+/ejDD+j4nx1Nr/zvZa4tH/rZH0wbOCZ1CSzhDMUXL9Un55BizTbOEgjrISC5jycZhOk9XGPsLY14BavYEMCnNDbc0WuKENivOIckg6Jqbq6HtUUTq61ux7uegJdTTRo5XTADmZ70lBJ/Ta33hUQaiX8vh3sFLpeLHn30Ibrwokvopf+9TrvNn083XHe1Ml/s3XfeDushG25fOC85CVxiEGGSMXAUZweE6QlkWy000SDGLlmmF7D6M7BmBgEIMTMoo4+UJmBMFVvNQsyp8fSkNJw+Lr6KU+4GNCFKEpI4r6B3Cus+TsfmBCNgxvywBEOSMsN97pmnqKiwiH566GE0ZuxYuvLq6+jZF16m6dNnKOnrTzv5RPr0k49ThgcudGgEHt/USO/XtQVPcrDYqMjJDK5jQU9AyuhoRerKNjddt1Jf9Fp/BtaiTQBCLNqE0X7KE5jOT6Aum12u47Cp3a1bx0qIgKSrVycVy9ZyewZduUNPXbPQUVhKBgJSsNms+WHJwCsZr6GkdDT9+a9/U1LWq9cnRZxvvPlWevypZ6l09Gj621/PozNP/yWtX7dOPQTvIKAQMIqI8RBhA34yKrL1WYevW1lDVYb52AM2ggMiRgBCLGIo0RAI9E1AvGK7alLFtnPiDm3V+77PTK09bp5MvNEgUm+YM5ZyrPhVlWyfBGMh52S7PlzP4AhIBsQ99twr7MEzZ86i/9x5Dz34yGOUnZNDeXl5YY/DxtQkcMvqOtrkDCV0KuFEWPmcKRHWPwE7/z0dy5kUVeOZAHQLJzuBxYYAvt3Ehjt6TUEC/zJkJ9rKiTskPTssRKCy3UVdmpDEX3Px5lPGh+bYhY7EUrIQiHYh52ThlMrXsdNOOytp7EtK9cmPUplJql+7j9XD7WvrdRi04kK3Ayu9CAgrbTKT+9dvo/UdSCbWC5QJGyDETICMLkBACOzNGf+unaMPsVvPwkNNJ5vqlDawJ6zdG8qUNrrbR8e0bKbFS1coL3hQkusToibqiPZV+f3+aHeB9kEABEwmcM/6BtrmCf1slztslIksiYO+C5JFUZhpTZjCzCcAH675zNFjChP4+4zR9GOLi17c0qxQEOfPujYXzeKwRRsXSk5VkwyJ9a5QiIlw+LW/gVrq3fzqeeq5mFYqeGROkZruXLLtIdFDYn9qon3/3nzjNbrjP/+miRMn0YQJE2nipEnB5XEVFWSxWBIbIEYPAilI4MHKbbqrLjOICt1OrIQlUMaJO2p4bpgUexZ7gJlezvPZ8zPwOzEssChthBCLElg0CwJ9EXhwt/G0lrMCLmZBJibzotZwHZSZBQ6ypslzqtQy+UMgmSS1dtcuFTS/I5M9YT3iS7tPEjzIS0wVZ7I8b+4sfs2WRVicEzCzkPOBBx5M6fyQY0tVFVXx696771TWpYiw1WqlceMqFHEmIu2wI46kadOmxzm91Bue1+uh2ppaGj9hQupdPK64F4FXqltonSaMrpQFRSo/yOwFaAgbRnMyLHW+uoR7/ndjI/15GkKAh4BwxIdCiI0YIRoAgaERsHP4xH/nT6RDP11LDdtDKyQ8cU1LJ01PMTFWy16wzSxKtXbBzNH020nFvKk4rBDTHqtdRi0qLY3EWFY9m9EcbV5+Ph1z7M+DXZz1m1/T0cccS4cccihVb62m6i1baOOGSnriicfI2elU6lkFD8ZCXBB48YXnqaCgAEIsLu5G7Afx9OYm3SBKWUzAhkegNMsWFGLSwlPMFkJseCyHe1bqxkINlxjOA4EIEJiVl0VP7TGJMtJDHjDJpLiKxZgaJhCBbuK6CUlWYkzj/9vJxXS1JlW9eLkGY3JctEPcBjMOHDM4Aur8sFiI57lzd6SlS5eQCLRZs2ZTUVERSeFgeT/iiKMGdwE4yjQCnZ2d9MnHC+hw3BvTmMdzR3VuP722tTU4xFybhaRQMWx4BKz8HaSYs02qtoQjdb5tcqqreDeBAISYCZDRBQiEI7B/SQ69sNdk3S7xjK1kMdbB78ls4gVTwyHU6/wVZ0i8a+cKdVV5H0yoIUISdcgSaiUW4nnujvNo6ZLF1NbaStdcdQWd87v/oz333pueee4l2nHeTgnFLxEHu2zZUnrnrTdpy5aqQQ3/maefpJNPOU0JJx3UCTgoqQm8urVFd33FmSERoduBlUET0AoxOemV6pDQHXQjOHDYBCDEho0OJ4LAyAkcXpZHr+wzRecZkzljK1qcSZnaPsDZSdbyfDiZF6a10ycW0UO7hZ//MZBXbDBiTdsXlmNPIJaFnOfuuCNt3rSJTjz+WPph4fd034OP0N/+fiFlZobq6sSeUPKNoKuriy755wVKYebLLv0n3X/fPQNeZFtbGy387ls68KCDBzwWB6QGgTdr9CKhEEJsxDdeaq/ZLKHoHCPjEXeABvolgDli/eLBThCIPgERY2/tN5VO+3pDcM6Ymk2x099FFTnJ8QWxzecnSVHv5mvS2rlTS+gWQ4017X4RWuGSdqjHSHp7iDGVRvy/x7IMQW1tDd10w3UKpEMPO4LOPe8vlJWVFf/QkmCEjz7yEH35xedKceaxY8ZS7vbizIt+WEhvvfUGFReX0EknnUyFHCKq2hOPPUq/PuNMSkvBJEYqA7yHCMhDyvfq2oIbREBIaB1s5AQKbBnBzMWr+e/0Kn7NzMXvxpGTHbgFeMQGZoQjQCDqBPYrzqH3D5hGOxXYdX3JPKoVzU5y+hM7VFGyIq5s7uwlwq7humr9iTAVRjivmGyTVPYi0h5/+iWK5Rd8dZx4H5hAbX1PxkuzCzm/+srLdPKJx9GGykq674GH6e8XXBRWhHk8+gyeA18RjhiIgGSofIqToVx3w80kxZmlMLMI4GefeUoJDZVQ0ZdeeI4kkUpzc09pj6bGRlq9aiXttfc+AzWP/SlC4OOGDpKHlKqJEINFhkCBgeXH9e2RaRitDEgAQmxARDgABMwhIE+fFvxkOp1UMUrXoSTx+P/sXQd4FFUXPaT3hIQQOoTeO0gTRZTeBOGnd1BULAgCIkgVRHpvIiC9CaJIR8EG0rv0FiC994T8974wm90ldbObbHn3c5z25s17Z5IwZ+69516h5FmutWVqFpGQhKtEJLXH7kp1SjY38sUYqquWHdP2eCl5Ya1aNhey9dzHwaMniJRdy053sk0+IuAfoFn/J6+GcuniRbTv0Albtu9C3Xr1xW05XO727VvYs3sXpk6ehB7duqBT+9Z5NSSLuc/tWzeFV6tJ02aqObNAyvy532Ls+AnYun03dv64T5w7eviQWLOYysSvpqrayw2JwF9BURoguJNQhzT9IOCmheWfwVKwQz/IZt2L/JyQNUayhUQgzxBgafsfGpYRnrEJl59o3Je9SsFxiSjubK+hcqTRyEh24imExI+8eYGxiS+N6HVvVywiUY6KrjkLuWTypYQoqhMzZZvPpXf+pQHIA/mKgFIDLq+FOtgDxvWoOC+MPTCsnHj1yhVER0fB1tYWlSpVxiuNGmP7ti1Ub+whSpYsla84mdPNrahoNqsfBgYGwtvbG/+ePkXEdyJGvP8hunbrLqbqRqGKnL8XFp4qxsA13thzJk0ioCBwmqIqFOOQRCeplqjAkeu1NYX/OtMHUhYMY5PKibmGNNsdSCKWbahkQ4lA3iHwWUUfNPFywZhLj3EmJO0fH46RvxMRC3+qv1XUyQ7GlqjMBIxrgz3TEuNQkJtYtSgmVCmi7OZozYSLiVZr8oJpG5/j5ZDwiqUSMm6X1y/72uOS+5oIKOGjHFKa1xYYEIBub3cUty3s44OapJ5o72CPgYOHoHeffrCzsxPnzp75F1eIpEkipr8nxCS3XPnyGNC3JypXroq//voDb3d7h7AfqnGTq1evkDDHmxrH5I5EQEHgvBoRk5L1Cir6W7sQsVWI2IPoBJGz7m0vaYL+EE6/JxmamD4u8qhEIN8RaOzljD9aVMIkIi/axvL2rD54mUIWmZQ9Vw+c126cB/scPskE8UJwVLokrEVhV/zxRiWdSZgyhazIlQxVVJAy7nVeFHLWRqBEyZL45tt5+OXXw2KZ+c0cNHylESIiwlUkjK9heftL5DGTlnsEOPTzCIUaptDfp+UrvxMkKzYuVoQjjh03QeMGnMMXEx2DZq++/KFFo6HcsUgEHtDHvfAX3hoGQBIx/f8YONloUoKr9G+6NMMjIKmu4TGWd5AI5AqBL8iDxHljM64/xdaHqYnsSocxJOJxPzIZD6guF9dT8aTCjNpJt0pbfa/ZOxcSn0RLouormvY9SjvbYXzlIhhI8vT6sOx4uGSooj6QNkwf+VnI2crKCm+01PS2sFfsEOUqqRuHzv3zz9/qh+S2jggcPLAfixbMx6vNX4OrqyvGjB2v0dPff/8FD8oFO3XqH6xeuVwQZcUzqdFQ7lg8AjdJxU/dHLVIg/o5ua0bAo5aoZ63IuPBqQTSDIuAJGKGxVf2LhHQCwIVSMJ+XYMyGOZbCPNvBeDnJ5q1VNghFkj5Y7xYU+w8kzE3ivd2tbWBvv7B4hpg7PmKTEimL5NJGZIvnnARIoQfVyiMTyvmT46HDFXUy4+dwTrJDqE22M3VOmYFvzmzZwnBjvLlK4gz4VToOSZaJqqrwaTTZlJSElatWI4hQ4erarQlJycL0Q4mxVxQe8qkLxEcHAQvr0JCUVF6w3SC2iIuuk+hcurmQPnU0vSLgDam97Qw1+/dZG8KApKIKUjItUTABBBoSjL3vJyikMRVd4Ow6UHIS6NOfp4iRD1Y2IONk5o5jIMJGf+htaOXIC7eaEPJuZygyyV6uBILqwIn0//4+kQKKUqgNed8xdLCnrdYrfpf3Le2VXd3xFBfL7xXLu9zgLTHwvscqshKipxbxqqKitpiem3lMcMjkJ+FnNObXYWKlVC6jC+mTfmKFBU74umTJ0Ksoz/VrpKWOwR+2vsj/S1JRue3u6o62r1rh6glNn/hErAq4v6DRxASEixqiKkayQ2JQDoI+GkJP9lJIpYOSrk7ZEvvCvQfpTqk9uNHaQ/SDI+AJGKGx1jeQSKgdwRe8XQGL9OpDheTse2PQ3EpLP147iT6qxpOMvLhBvqbakt/uHuU8kRPCp98y8dN73PNbYcyVDG3COrnekWoQz+96a+Xr2fNxuejP8W338yEh4cHJk2eJkiZ/u5geT2xOuWa1Svx/vsjhSIlI8D12dauWY2PPxmlAoQ9Y1FRUZKIqRCRGxkhEEAh8OrGpEGf9jwiDGFj0/kAY2UNK2cXWJcuD+f/DYNVIR/VbUPHDIC1V2G4jftWdUx9I/HKWUQunQ7nnuQVfq0tkm5fQ8RczdxI9fbKtvvkJUiJDM92W2uf4sqluV7b0u8kf4BlC6DUA2mGR0ASMcNjLO8gETAYAkUpBHA01eLi5UJYDH55GoGDzyIMLj3r42CDNwu7wfnODdR+Ho169vao5VPaYPPMbcfaoYpcy4oLChtLiFxu52cK1+dXIeessOGQxN17fibRjgi4uLiAyYG03CGwc8d2ODs5ox15GRXbvnULecHc0KpNW+UQFde+gz69emDXnn0oWrSY6rjckAhoIxCmJtTB4ff6pWFpd7PxrQi7+mn17kAhtsmBT5Fw6jdEzJsA94mLUMDRKe2CHGwxiXPqPjjdK1JiYxC7fzsK2NkT8XNFir1Dttum26GOBzmCJj5VwR7qmOvYnbwsGwhIIpYNkGQTiYApIFDbw4nqjzkJZcJg8oD9GRQtCNk5kvxl9SP/ON2+bnEIYxU3B9TycES9gk5oTLL6tWmbbcOtM2JtKvW71EMVOUxOhiqKx5cn/8uvQs7ZnRzXsXr08KFQTLxy+SL8n/lj3sLF2b1ctnuBACslbtm0kTxgcWDy1fWd7khMTMD6dWvx5aSvNIjuiuVL0ZqImSRh8scnKwSi1ULjOaTeUGZdtCQc3kj7gKDcx6ZkOURvWYHEy2dg11A3ZU8rD690+yZZUUStmEUxgc/h3P8jFHBxE0QzvXGk11YZoz7WlLWgsmhKSZBmeAQkETM8xvIOEoE8R8CLxDo6FXMXi3JzJmecfMtx30zKQkl0I4r+0HIuGIeE29AfYC4o7U4iH4Wodgh720pRrbJyJBSSnmmHmpkKGZOhiuk9TcMfy69CzhnNjAU5uG4VF3YWBZ4vXUI4FRNmj1i5cuVx69ZNyl8KgaenZ0ZdyOPpIMD4rV2/kcIQV2LhgrnY+MN6VKRcvGLFi+P1Fi1VV9y4cR1/nDyBnbt/Uh2TGxKBjBDgf6cUU+MKyiHDr1/UGYSDg97vFfvrDiRcOg2H1l1hV6dRpv3npG2mHWVwUt3XqI55Bs3lYT0gIImYHkCUXUgETAEBJme81CevlqHMlMhYkcLeYDl1HrMMVTTUT0Rqvwppz49Cztoze+Lnh88+/Qh3KSyOvTcFCxZETEwMWrVui3btO6BqtepwcnJCpw5tRGHn5q+9rt2F3M8AgejoKKrJZg8uATB2/Jfo138QVq1chl/3/wKfIkVwkEoFvNWqtSC7y5YsQqcub6NoMRmSmAGc8rAaAmk0LFVcSu2U4TcpPDH+5EEKSXSGbcUaer1f4tVziP15K2wr14JTpz6Z9p2Ttpl2lM2T6phn8xLZTAcEJBHTATR5iURAIgAoOT/aWJgKGeP8sFa0KKqKMlRR+0nqfz8/Cjlrz6IQkYS69eqj/8BBonhziRIlMeqTkeCCz/UbNFQ15xpj7CmTREwFSZYbSxYvxJ8nT2LYu++R4Ekn4QWbPHUGBgwagpUUhjhxwjisW7tGhCOeO3uGhFGmZtmnbCARYARs1cIRU6UkDINL4n+XEbVqtqrzFCpAnux3nwZgB7ex36CAQ2pYvqpBLjaeB/kjau18WBUsBJehn4G+UGTYW07aZthJNk6oky91zLNxqWyiIwIZP3UdO5SXSQQkApaBQGY5P0zGmOCYgqUKeVQRQzWlcZsCtsoY87OQszIGZc0Fg7mwcNt2HcAkjE0hXUobXpcp44tr166qH5LbWSDQp29/1KpTB9OnTkaPd7rgyOGDlNKSAl/fspg1ey42bNqKwj4+WEresB49e0m1xCzwlKfTEHBSK+D8nAtnGshSyKub/ORB2hL0DCmU7/g8LFgIdqQkaqo36jqMFFIWjeS8sMR4uLw7FgVIoCMjy0nbjPrI7nGuF6qYOubKMbnWPwLSI6Z/TGWPEgGJACFgKp4xflhMxmSoouF/bI1VpbIGeb/WfrdaIycsNjZWFBs2PCrmcwcmttOmz8TAgUOwYvkSjB87BhUrfYcR738ILtZcuXIVLFy8DBcunCdy5ms+E5czMTgCHpS7rBiXZDGU2dVtDOd+H2p2T4XI4377BTE7v6eCmylw7NxX87wOe9EblwlPm3P/D2FTqlymPeSkbaYdZeMk1xFVTB1z5Zhc6x8B6RHTP6ayR4mARSCgiC9kNllT8jCJUEUqAM1Kijw3LgBtKl69zJ6BMZxjPI0hPywjLGrXqUs5+A5YOH8u7ty+jZMnf6e8pp+FaEdG18jjGSNQrnx5fDt3Adb/sFmInXz68YcYPLAfzp75V1xUu3YduLt7ZNyBPCMR0ELAmwSk1C1RjTCoHzfItrU1HFp2gnXx0ki4fFZ1Cw5TTIlPv34nN+KwRmFa4Yxxx35Gwr8nYN+8NewbpwnYpDbW/H9O2mpeqdueOq7amOvWo7wqKwQkEcsKIXleIiARyDUCilhDrjvKgw5kqKJ+QTaFZ29NL1qfjRmHA7/+gp49umLUxyPRqHFTjB2XdfFV/aJlXr2x8MnipSuwcvVaIdDx3vAhmDxJYmpeTzlvZlOMVHzVLYGEdvLciPw9p1BFxawLF0NyUABAHrP0LDkwtS23Uyzp1lXE7FoHrlfm3H2ocjjddU7apttBDg8mkbdPPTSxmKNdDnuQzXVBQPMTgy49yGskAhIBi0Mgq5dr9iqxKVLxpgZQeqGKXINMWs4RUERduIC2MRvXs6pN+U3XKS+sePESqECS69L0gwCLo6xZux5//fkHEhIS9NOp7MWiECjtrEkK4pKp5pZNWriiocFIfnQXyU8fwqZ8VdWtbEqXR+K184g7/jMc3uysOs4bnEsW/8chIe5hXbSEOMd5ZlFr5sDKxRUuwz+nmjEZv4LnpK3GjXOxE0+YqltpKl8jzfAIZPxTYPh7yztIBCQCZoQAh56Zk/Kgtqrihs27ZAFoHX5eMxN10aE7g17i41MEvHBh519+3kfy9bKws74A57DEUqVLqwRS9NWv7McyECivVc8yjgs8p1/iMleAJD99hLhj+9L6IC9R0r2bSKQ6X6xq6NQ5TWKe637Fn/4dMbvX43lwAGxrvQIrV3ckBzxB7P4deB4SCKfug4mMOYn+ojcsxvOIMNjVbYKEc3+l3UNry7ZqHcRsX5PtttZFUomeVjc53o1VK5rNF2tjnuMO5QXZQkASsWzBJBtJBCQC6giovBxqni/Op8pO3ph6P6awrXj1UvPdroshK8dMYfz5PUYlP8xYhToyLexcvoJQVOR6Y1yoWJomAteoIPbKFcsw4oORQohD82zaHnvBvpo4AcPfGyGJWBoscisHCFRydYC9dQHEJ6eKSUQnpR8OmIMu023KpIsXdSvg7CI8YY4de4uQQuVcAXsHuH0yFTE/bULc77+SoMd+5RQKuLjBufd7sG/WSnWMyRobk7DMiJjzgI8EsctuW30RsRgtTKu6OYjxyv8ZFgFJxAyLr+xdImCWCLDCYK3eaSEaPEkmJwpZMTeiwvPhObOAh7TsI5BVCGv2e9J/y6dPnoj6YeqFnavXqIm+/fqD10phZ/3f2Xx6TKJCt4EBAejfpydavNES7434AL5lX1aA271zB+wd7NGho2b4lvkgIWeSFwjUcnfC6ZBocatoLe9Nbu9v5eYBz+U/5rgbq0I+cBk8Cil93hfkKSU6ElZehUVtMKjVPuOO3acsy3b/9o1aZLutvhpGqRGxwg42KCVDE/UFbab9SCKWKTzypERAIpAeAhl5Nzg3TFFKNDcyxnPu37tbenDIY1kgYIz5YV6FCuHBg/vkpXlfFBhWaoplMRV5Wg2BmrVqY9PWHTh08AB5xpaS0Ek3qs/WHsPffV8Uc+amXAbg+7WrhRiK9CqqgSc3c4xAA880IpZA+UycJ+ZgbRyeavaOWRcrleM5GcsF7GeMSkzzMjYo6GwsQzP7cRjHT7DZwywnKBGQCEgELA8BpZCzMc6cCztXqlQZXp5eMlxOhwfE4Ybnzp7BnTu3BZHdsWsvvvhyEknUn0G3tzti1tfTERgYiG1bNsGrkDfeatVah7vISyQCaQg08XJJ26GtiIQkjX25ozsCjKVaLWc08ZJETHc0c3alJGI5w0u2lghIBDJBQPGCsVfM0o1fVNesWoHlSylBOz+klo3oAWTkQc3vIXIh50uXLub3MEzu/gH+/ujZvSveHTYYvcgLNmRQfwQE+KNzl67YtWcfPv70Mxw/fhRdOrYThbK5oHMBrTAtk5u0HHC+I9DcW5OIhSekeXDyfXAmPoBwLVLb3NvVxGdkOsOXRMx0npUcqUTAJBBQpOstuRjyv/+eRq//dcPePT+KF9HHjx6ZxLPT9yAVoQ5996uv/mrUrInLkojlGM7JX32JAlYFsHbdD5g1ey7u37uL8WNHi37Y09izVx/s+Wk/hg1/F3Xq1sOrzV/L8T3kBRIBbQS4wHBjNU9NWEIiUqU7tFvK/ZwiEKZGxIo62oLDQKXlDQIyRyxvcJZ3kQhIBCwAgZDgYMyfPwfHjhzG4KHDUbhwYawmrxjLdluaGbNQh/Is2CPGeWIRERFwc3NTDqe7lsqJqbD8/defuHb1Krbt3C2k/vloXFycKNQcExMDJ6fUFzhHR0cMHDwUA1Mvk/+XCOgFgXZF3fF3cKpgB9VXRkh8IrzsNYs96+VGFtQJ54apS9e3L+JuQbPP/6lKj1j+PwM5AomAWSFgieGJ/JK+a+d2vNO1E/weP8bGLdsxhIjYHydPoEmTZmb1fLM7GVWJAyMu5FykSFGULVeOnpmmx5Kf553bt7Hnx92YNuUr9HjnbUwYPza7UzfbdimURMKhtqwsyfXWFAsKCkQhygNzcJBy1womcm0YBDoX0yQJwXGJhrmRBfUaTGRW3ToV18RY/Zzc1j8C0iOmf0xljxIBi0fAnNUTtR/uzf9uYOaMabhy5bJ4GZ0zdwE8vbyQkBAP9h5MmzFL4xL2HqxeuVwQNSdn802INpVCzlu370ZEeDj++vMPEaZ4+fIlKuR8GdHRUbC1tUXFSpXwyiuN0LBRI43naIk7x48dxf3797B81RqN6f9+/Bg6de4ia61poCJ3DIFARaon9irlip0MjBLdh8YnUW2x51RjTPoVdMU7SI3MsmR9K5/MowN0vY+8Ln0EJBFLHxd5VCIgEZAIZIoAy3KvWLYEW0kVrmnTV7GZZLyXsbegdw/MmPUtoiIjkZiYiAYNGmr0882sGfj5p7347bdjIr+mQoWKGufNZUfJDzNWoQ4F519+3ocplPPE3h5vCiWtSeGKQym3ideVq1SBnZ290tTi15xPxz/3w4cOBgtwNHu1ufD63qMcsQWLs18jyeKBlADkCoHepTxVRIw7CiAiUdJZ/p7qAmpAbAKSOMbzhTG20vIWgQJxieqClXl7c3k3iYBEwHwR2LB5l5icudbeYpLFRIxf1t9q1UbMlV/mf9iwDiuXL0PRYkWFh2zFqu9UD5kJ2JTJE4WX7ObNG9ixbStGfz5OqM2pGpnBBueHcfHrIlR7rVXL5kY9o2fPngoPGAt3qIfbqQ86OTkZN2/+J6TuC/v4qJ+yuO1rV6+IDw6n/vkbXEcskvLrWrdtJzy8FgeGnHC+IJBMf2dL/HwZoS9UE21IOKaOlytoJS2HCFyhAtnRaoWcr7WpirKS1OYQxdw1t/5y0uTJuetCXi0RkAhIBNJHQAlP4xdyczNra2u80qgxypUrr5oaS3TXql0HdevVx6Yf1qPrO91Rm/bZOOdozGef4O2u72DAoMHi2vLlK6R6Y+g8X2MuFh0dgzv3HqBpo/pwcTHu8EsXF1eRJ+bikiaNHRwchNOn/sG+n/bguzWrMGf2TOzYvhUFCxZE7Tp1zeUx5XgeTEiZrLZr3xH16zfAGVIHvXEjtVSFr29ZIU6T407lBRKBHCDAH3n+PnUWzyKicdsqNSeRHTocmehqK4O8cgAlOL/Onzxiiv2vZEEM9i2k7Mp1HiEgf2rzCGh5G4mARMByEODcIvaONWnSVEya1eTGff4Z+GX1k1GpMt98ovlrr4uaS3O//UYUxS1ZspRZgGTMhZzTA/j27VuCVHDoHeeIPX3yRDRjMY/adevizbda4fatWxZbc8zP7zEmfjFOYMMKoEOHvYu27Tpgzdr1+POPk1i+bDEG9u+N115vgfcoZJE/MEiTCOgTASZg/HeFQ57ZWhSwwa/WHqpbPI1JQBFHO/KKSbeYCpQsNhgzdRtRzvw+mKrPz1i3ZXajsT4ZOS6JgIkjYInqicoj+/234yIssULFSuLQzK+n4eHDB1T0NuClulUc3sWk7eGDB6JtJOWWsefBHMzY88MUjLds2ojFCxcgmMoPtG7dFnPnLyKRjsoiV2za9Jno8b9eFH7amkIYLymXWMw6KSkJ48Z8Bv65HPnxpyhTxheTvvyCauTtFhg0bfYqfti0DTO/mYMH9+9j7ZrVFoONnKjhEWACdojCnDnUWck7bU3hzh/16oyPKxRWDYDznPyiNYmF6qTceAkB9oSphyS+XdwDjdRqtL10gTxgMASkR8xg0MqOJQISAUtST1R/2pw/06hJE3Hox907cWD/L5g9Zx6ekKflow/fR7/+A9Czd18h980hjKyeWK16DbBs+tgxo5CYkICvSfCDxSNM0ZQXJlMZO9cTu3PnFtTz+f49fYpI8yVV/l61atURTuqKj0nqvkSJkqYytVyP86e9PyI4JBjrN2wWP4/P+w3AJx99gL0k7d+5S1fRP4fkstfwjZZvIioqVc0u1zeWHVg0AtoeMA5vr0WlMNQ/7nxWyQer7gYhllQT2Z7ExMPLwQZONtYWjV1Wk+ccO7/oeI1mowlLafmDgCRi+YO7vKtEQCJgxghMJ8n6kNAQ3CKBhzmzv0EvIl0t3nhTzJjV+CZ8MRYb1n8PVyoiHBYaikmTp8HDwwOrSNb+3NkzaNjwFfTp1R1TyRvTqHEqoTMVuPgFis2nsOnkGvAz+WbmdFFyQFFJrF6jJtZ+t0oFO5PlsmXLCY+mpRAxLsGwZvVKvP/+SNVHASsrKzg7u4A9ZdrG57IqjK19jdyXCKgjkB0CprQvbG+DL6oUwcQrqaHEfPwREYxK7qlFxZV2cq2JwMOoeCSqKSUOL1sI9QpKzDRRyrs9GZqYd1jLO0kELA4BSw1P5DwaFun4ae8eVKxYER99Mkr17Nn7snP3Xnw2Ziw6deqCNd9vQPsOHYU4xHf00vvBhx9j0dIVGPbuCHz26cdY932a6qKqEyPeUAo5FylsOvkGZXx9YW/vgBvXU4UnGF4mZ/fu3tXw8PCzYy+ZpdjOHdvh7OSMdvTzqRgTsAvnz1GOWHvlkFxLBHKNQEYhiKy6qu4F077RGPLk1HB3VB0Oo7piz9QEKFQn5IZAgOuusWS9Yh521phUtaiyK9f5gID0iOUD6PKWEgFLQsBSwxP5GTPZ4lAtGxvNP7Xsden2Tg/Vj0FQUCAmThgHzrfpS2GLbN179IS3d2GhtOjp6UkFc9/8f+Z6AABAAElEQVRWtTfmDUUpM7OXJ2MbP3tyqteoIcQ4OGePrWixYvD09BJ5YYpXsknTZiTacdPYhm+Q8XCYLOfOxcfHYfvWLUIB1M7ODnsoJNHe3l6IdRjkxrJTi0Lg4uVruHg57QNIeiGIWQEyvXoxdP7zjqrZg8g4UlC0hrMMUVRhwhvsBbsfFadxbAZhV4g8i9LyDwGJfv5hL+8sEZAIWAAC6rLo6U2XX3gnjB+LkJAQDBg4GJxvo9jrLd7A6y1aipdfUyFippYfpmBdg0IRtcU4vvl2rlC6VNqkPo83lF2zXjM5Xbt+I4lvrMTCBXOxkXIZBw8dJvY//OiTlz4umDUYcnJ6RUA7/JA75w927EXX5QNO6yJu+KC8N5beTg2L5v7uERmrXtCZN6W9QOBeZCwSXuTT8aEuJNAxRMrV5/vPhwxNzPdHIAcgETBvBCw1PDG7T3XF8iW4dPEC2lBR3JEfvIf9v+zTuPTevTtwdXVVHfvrzz9U28a2oeSHGdu4sjOeBpSX5+WlmdfGNeHc3N0RSnl869auwdIli1R1s7LTp6m38fb2xtjxX2LXj/tQv0EDyqObgaCgIPGxgJU+pUkEcoKAdvghX8sErH/vbrSuqhMJU+4/t1YJ1PZIC1GMTkzG3YhY5bTFrzl3jsMSFfMmL9g8wkxa/iMgCzrn/zOQI5AIWAQCSsiaORZ31vUB/v3Xn5j19XRRW4zzyEqTNPjMGdPAZIs9ZUsWLcC1q1fxxYRJKFa8OLZu3oSvJk2g+mMtSB5fkzToOgZ9XmdKhZy1582hiBwaGh0dRXWxluA/KlTMeX7x8fHo36cn/vnnLxFmumbVCsr7qwSWcbcUY1EZ9syyMmIISfx//90aHD96RITOWhIOlvK89T1PJmB/URFmDkGMomLv/G8AF3tv2ri+2NbX/WqT4MTae8Gq7mKSUtUU3ewsO/iLc8IekUCHun3fsDQaeEqPoTom+bUtPWL5hby8r0RAImDRCARSTbFJX46nIrhvCFVFBoPlvzdu2S6U52ZMmyJUF6dMm0HeiIa4euUKFi2chwGDhqBS5cpGiZ2pFXJOD8SJE8bj0MFfUZoEV9jOnzsrJOvX/7AF63/YjImTpgiyzETZ0owLks+aPRcbNm1FYR8fjB71MbZs3mhpMMj5ZhMBbQ8YEzCuAZaVAEc2u3+pWX0iYivrldI4zjLtlizeERKfKMI01UH5qlpRdC6WVgxb/ZzcznsECsQlyviCvIdd3lEiYHkIbNi8S0yaw1CkUeJ0YiK+p3C33n36Ib08MqWos7W1NSIiItC3Vw8UKVpU1Lri/B1jNC68yjlipvqMuUbY253aY8v2XShfvoKAmOvAfbd6FX7+9ZDYZwL21hvNMWfeQtSpW88YH0OuxvTD+nX49/Q/RLSKCMl6n8I+gnRxTTve5lBNxS5cOE85dL5wd5cvdQomcg0wAeOPMvy3gE0XAY7c4Dj56lPMuvFMowtfVwcUdrTTOGbuOxyKeDM8RmOaA8p4vURWNRrInTxHwLL9tXkOt7yhRMByEbBk9cT0nrqtrS2Gk0R9RsYEjI1zcSZTOCKr182YORvGSsL45ctUhTqUZ8By9UWLFlORMD7OoYmOjmm5J4w/L8+ePVUuM5s1/6xt37YF7h7uKF6ihKiZ9uujR2KuyocBVkxkb1hhImV9+vaXJMxsnn7uJ8IKiByCnl8ETJnBZPL4BJAnSD1MkcU72IddxELIGHvCboVr5si1LeomSZjyQ2JEa0nEjOhhyKFIBCQCEgFtBDaRWt2ff5zEYqotxuIJGdm2rZvRpEkzlCylGZqTUXtDHTelQs7aGHAx4ngqYqxu7AGzsk7zQN69cweRkZEoRKUFzM1YsXPa17Mw8v13MXDQUCHUwXPk2mED+vVGxUqVUKtWHfj5PcbTJ37gumrSJAL6kKDXN4rL6pZCFOWIbX8UquqaZe2TScK9uLO96pg5bnBOGBNPdWvu7YJtjcqqH5LbRoKAJGJG8iDkMCQC5o4Aq2JxsjYvipKiuc85t/NjNcUlixeSbPhwNHylUYbdnfj9N8yZPQvjJ0zMNyKm5IeZUiFnbUArkAhHNNV9u0W1wipUqChOB/j7w83VTWz/d+MGhg8diHIUtshCHuZoPK+vpkzDVxMniFxF/rn79/Qp+D1+jOUrVmuEJprj/OWcsoeAdvghX5UbCfrs3TVnrTY0LCMuUCdjjylnLIHIGIcqmqPx/DgvTt2YhO1uUg52VmmlUdTPy+38RUASsfzFX95dImBRCCjhifyPuC71YiwJrLCwMIwfNwZ169XHsOHvZTj1p0+eYMpXX+KtVm3QtVt30e7Mv6dRs1YtcOHovDZTfq5OTk6icPbXJJQyichIRHg4fv31F7Tv0FHAaO9gjw4dO4t6Whxaaq725lut8ezpU1FMfNmKNVi+dDH69usvSZi5PvAczCsjAmasH9eYjDmSR3v9/TQ1RfYYxSYlw9fNUZzLwfSNtmkSkUv2gnFIorpxOOJW8oTZSxKmDotRbUuxDqN6HHIwEgHzRkAJYWFCZqz/cBvLE5g+dTJ+2vsjNm3dofLOaI+NBT+GDRkgQuV+2LgVTs7OJHd/BUMHD4Bv2XKkcDcHJUvmTagii7FwUj4ropmyxcbGCgJy6p+/xTRYtGPl6rUWSUJmz/oae/f8CCcnR+zd96v4+TLlZyvHrjsC2gQsrwU4dB956pVfXnmCOf/5a3RjRaG4Zcgz5u1g2h9VWJTjflScRrFmnmh/EuZYpaUiqQGA3DEKBKRHzCgegxyERMAyEJDhidl/zlxXLDQ0BB99MIJEOr4RnjHtqxctmEcS97ew7odN4iWZ1RXHjR2N6tVroDSp2fXr3RMTv5qClm++pX2pXvf5Jc1cjIU5lixbidu3b4ncqEqVKosCxuYyv5zMY/Tn40gB7xnVVLuBmNgYScRyAp6ZtDV1AqY8hunVi6G0kx1Gnn+kHMJzEqfhos/hCUkoSXlj9mq5oKpGRryRTOPn+mD+5OHTtklVi+KLKkW0D8t9I0RAFnQ2wocihyQRMHcEWFmLc4lcXGRByYyeNavTtWrdhk6nYOqUr2BVwAq169RRkYLjx45g/rw5GPfFRDRt2kx0M2H85yKXZymFk7Vp2w5FihTBjGmTERQYKHLMFCXGjO6p63FTLuSsPudHjx6SCmCqPLunpxcVzfZW4a3ezlK2Wbzjtddb4Nixw/h53z7xM2VnZ1kS4JbyrNObJ5ej0C7CzB/TTPXvdj2qM/Z6YVecColBMJEvxWJJ1MM/LoF+1wFXW9PwT3B4JasiRiQmK9MQay97G3zfoDSGlS2kcVzuGC8CaVJQxjtGOTKJgETADBF4FmA+XhRDPR5+Ee7bfyCWr1yNnTu34eOR7yOKxCS43tXUyZPQtl0HdHm7q7j9RlJX/P2345g0eZpKXbFNu/ZYv3ELzpw5jSGD+osQRkOMVRHqMETfednnhnVr8cTPLy9vafT3cnBwwLwFSxATHY2vp081+vHKAb6MAD+73bt24Msvxop8Uq7/lh1TylEYsghzdsahzzbNCrngVMtKGOTrpdEtOZeEd+licBSY5BircQ7YldBokQ+WSHlh6tapmDvNrTK6FJd1/dRxMfZt6REz9ickxycRMDMEOLeAv7KyV0zmiWXv4fqQZ6tDx054TDWdGjRoKAiZvb0D5i5YBBaNuHzpIiZ9OV4Uhr5z5xZeadQYrq6uonMPj4Lo2KmLqEdW10AFiO/ce4Co6Bg0bVw/exMy0lYcChoSEqJRR8xIh5qnw+JwzSbkdeWfJd+yUgI7T8HP5c0uX76Ed4cNFrmjnp6F8OD+fVFIvhqFL2dV6oKjFkzZA5YRdDYkXNGhqDuqkFjHubAYhKl5lZKIkYWRtyw4jkQvyEPmZGNlFF7xQBoPi3E8i0mANgFzs7XGnFolMKtmcfC2NNNCQBIx03pecrQSAbNBQIYn5uxRsmeCCdY/JCLx44+7sGTpSiqqWxjh4WF4/73hqFS5MtZt2ISrJNaxgEIWfX3LonSZMuImNjY24BevuLg4nD9/Dv5UjNjTywt8PLcmckiIWDPBLle2dG67y9fr7Sns7rfjxwTpyNeBGOHN3T08JAkzwueS2ZBY9XPIwH7o2bsvvp71Ld58qxU6dOoMGyoWX7Z8eVKu9cnscpMNQcx0Umonq7o54N2y3kgk8vVXcLTaGaqdR8c4d+xZbKKQu2fyltc5ZNGk7MjE6w7lsTEx1CZgPOAhvoWwvXFZsES9NNNEIPf/CpvmvOWoJQISASNAgMMTTVnuPD8gbPZqc+z5aT8KFiwovFxc74kL7k6nQrysmjh56gxSutsNzhd7p0dPfDjyY3Bu2DMiXyz88fTpE7CHw8rKGp+N+VzI3utjHqZcyFmZfxkir/fu3VV25VoiYFIIJCQkYP/P+yirNAVvd30Hf/31J7gg+cBBQ1TzYA/60EzKYagaWsiGLREsFvLoX9oLc276Y4OazD1DwIIeHKrIiwOJeXhQDpa7HS2US8Y5Zfo2zvliAhhGSogxRMQyMg5DHF3JBw09ZZ51RhiZynFJxEzlSclxSgTMCAGpnpi7h8kkjO0A1bj684+TYHU7L6+05OzOXboKD9ii+XMRExMjwhTXrFoptlmGnL1hv+7/GRMnjBdeMg5d1NWU/DBTLuSszJ1z8uyp9lpCQny2arCxxD3XcevStZvShVxLBPIcgejoKOzauQNbNm0Ee86HvzdCjIG9mJGRkbh+/RqqVKkq8vyuXruK21SwPJzqFJYqXZq8ZK3pZ10KsFR0tRdS7x+V98ayO4FYey+t7pjyQOOSnwsPFXup2FwpDNCFFicbazhSCCMTNetssjMmeNxfLC0xJBYSTQQskhY+npm9U6IgRpQrhKaU6ybNPBCQRMw8nqOchUTA5BCQxZ1z/8hat2mHhw8eYNGC+SKPocf/eqk65fpXi5auUO1z/hPn9zAJY2OhjydEIhYQWeOXMfaS6WLsCeOkfnPxbFauWhXXr11Drdp1soQjKCgIO3ZsMwsixjX+mEyby3PM8uGZQYPg4CBs3bwJO+lnsHiJEhg1egyVqmhF3u5UHbZ6VAy+StVqGNC3F1zd3ESBcj7Hbbl4+Z11t3HyxO+Y+c0cM0BDP1Oo7u6IZXVLgeXf15F3bPPDENyMjE+3cyZOvKibNXnY7GhhQsYL8zJ2nLGuBpMsDnnkEEMuwJxdK+Zoi96lPIXXjgmjNPNCQBZ0Nq/nKWcjETAZBGRxZ/09qn/+/kuIddSr3wBfTpoMZ+fUr6UclsReHl6OHT2CL8aNweq161GjRk1xc1YI7NyxLbbv2iNyyvQ3ItPt6d/TpxAcHCyk2rOaBZPg7t0647eTf+tMZLO6R16cV34XzaEgd17gZQz34N/tLh3bifDkCRO/QqPGTdIdFueFstc8JiYaxYuXQOXKVVT14I4cPojxY8fgp58PoGixYuleLw8CvwdGYa9fGH55Fo4H0aneMEPj4k0hkO1IUKQzhSDyWpr5IiDl68332cqZSQSMGgFFMZEVFKXlDgF+Cdu0dSeCyUPDRZxv/ndDdMjka8xnn4jtN1q+SflgrTF2zCgEBgSIYzdv3hAvZSz6IS0VgQYNX8kWCePWJUqWFJ6FaySQYsqm/A62atnclKdhUWNnz1bXbu8gLCyUvF5VNebOcvXbtm6mEFvKa6JQRS7ozuHHdclDxnmkitWr31BsslKotIwReI2EMObVLoH/2lTDPyR9z+qEHYkg+TjYZnxRDs8UtLNGKx83TK5WFMdfr4hHHWpgZb1SkoTlEEdTbC49Yqb41OSYJQJmgoDyJZ7r1MiQqNw/1OTkZCxfuhgFPT3Rp29/jPp4JFxIxn7q9K9F5/Hx8SRlPUgUh1713Tqhmsgkomq16rm/uQX0wCp0V65cBkuCc8mAq7TNdd0++PAjDBw81CQRUH4HOVRY+ThikhMx40Gz5/XEid9w/9498bvdvkNHlCnjK/K/OrRrhb70uz7s3RGi9MK2LZuwY/tWKuZeFLPnzkeJEiXBhcqv0M8shyOr2/y534rQxJ0//qQKZ1Q/L7ezRuAR5YtdjYjDrag43CdvmR+pLAaQ0AZL4EdT/lfCixBEW4pKYCl8D8opY28XhxuWdrJHBRd7sHpjOVpLs0wEZI6YZT53OWuJgFEhINUT9fM4WB3xw49SPWDcoxUlj/MXccXs7e1FPkjP7l2xZ/cuUlX8nyRhCjjprLnm0tmz/6qIF78Qp1COhzd5EDm8c8iwd8XLMRMzUzXFGyZJmPE9wb8p5Hj999/h0sULqF2nrggfPkXHNm/cgM/HfQEW5enarTt5v7YghHJA9+3dI36fp3/9jUYJhvv374HVVf+7cQONmzRFQIA/9vy4GwH+/li6fJUkYbl49CWd7MBLG7jlohd5qSUjID1ilvz05dwlAkaAwIbNu8Qo+veWynP6fhxHDh/CjGmTKQdsL7y9vVXdc7hiYmIiFixaqjomN15GYNqUr4QyZaVKlVGjZi1aaoq1j08RVWPOKfvyi7E4eOQ31TFT2ZDeMON8Upz/NXRQf9y5ewcswNOrVx+VyA6f4zqBrJK4aet2kQ/auUMbcOmF8RMmqvI/tWd28MCvVMh5NYICg1CkaBG0bNmK6ov1MencRu05yn2JgCkiIImYKT41OWaJgBkhoLwMyvBE/T9U9t7wl3AOS+I6YxyCGBgYiP59euL1N97A2HETsrwpy9zfuX0bIz4YKeqRZXmBGTUII4lvZ8qp4dpLGRnn47R4rSl27/1ZiCFk1M7YjnMh7oNHT4hhyY8g+f90goICcfjQQfSi4stss76eLsIRudyE9s8fhyD37dUDPkWKiI8p06dOBpdS2LNv/0u/o0q+aEX6mCBNIiARMD4EpFiH8T0TOSKJgEUiwOGJ0vSLAKslTp46nXJD2mP40EF4u1N7vNO1E9w93DF48LBs3Yxl8I8dO4L3hg3GubNnsWbVCnphPJCta029kQfVYdJ+CdaeE4sffDJqNOxsTasWk1L/jXPDpOUfApy/dePGddy7exfz5sym37EzYjB9+w8Q4jv8IUTbOAR5ABVpZvIVERGBfv0HinDDgwf2q5qe+fc0FXB/DwP798GZM/+qjssNiYBEwLgQkB4x43oecjQSAYtEQIYnGv6x8wvb+XNnRfHWOnXraeSOZXV3Lhj7yUcf4sL5c4KYfPTxKBHWxDkmkydNwFUS/ChHhO2z0WOpkLT5C39wkdxQUpp7nvIcpUuXEeUBssLQmM4r3jApV5//T6XHO29THb9WGE5iG/2p3hcXa1+4eJkY2BfjPsetm/+J8hL8UUXd2BP7evMmmDV7LlgR9fPRo/DgwT28N+IDrF+3VhA7zh/r3aefyGlUv1ZuSwQkAsaDgBTrMJ5nYTYj4eKH96Lj8Sg2Af5xSQgh9aBIqhzPVeRZQMia/j1xJBEBN1IP8iLJ1iIkAVuKkl3LkmpQaVpLszwEZHFnwz9zNyro+trrLXS6kaMjFX+l8ETFWDI7KSkJoynXLCoqEiPeH4lTp/7G6FEfY+ePe1V1zJT25rL+4+QJbNm8EZwXxmGfbIUKeaNb9x4YMnS4yRAylTesuvSG5ffPZhEfH+H54nEMGDgY4z7/DLdu3USFChXRf+AgKkfxP/z+2zG83qKlxlDZE2tjk/YKx9cO7N8bM2dMo48kfbFoyQrw77w0iYBEwLgRSPstNu5xytEZKQJMuP4IisapkGicC43B5fBYUTVe1+G6EjmrSZXt6xV0QiMvZ7xayEVIveran7zOtBCQ6omGf14s0pFVuJ32KA5T4VcmXK3btEWffgPEy+JRqlH25IkfdpH0NUtl9+jZC106tSOv2zk0e9X86lGtWb0Sq1YsQ7NmzTFj5jfCE/acyNgFmu/qVcvJA3GHjs/Whs7o9tkb9owW9obJkhF583hYYOP0qX/Q8JVGLykUcp4X54extXijJUqWKoUN5NGaNmOWKL78SqPGpJy49iUixmUn+He5JNWyY2NP9DffzkPTZq+C1VGlSQQkAqaBgMwRM43nZFSj/DMoCuMv+6HO4euocuAahp15gDV3gwQRS3xRM0PXAUcmJoP7X3QrAL3/uYeSP19G8+M3MfPGM1whkifNPBFQpLMVKW3znGX+z4q9OKyYyJ6d7FpCQjyWLFooXu6+nDQFVapUxYaNW4X09VsUUsUkjI0LzBYvXoJqGQVnt2uTaccesNUrl4uX43kLF1Nh7DZg8YPKlauIEM2Nm7fjxO+/CZlxY5+U9Ibl/RPivK9PP/4QX0+fqvKkKqPwod+f4OAgscu/Q/37D8Khgwfw9MkTcYxzwbh2nZI7xge5IPuUyRPRrn1HVKhYSbTj/3GIoiRhKjjkhkTAJBCQRMwkHlP+D5ILFU6//hTVD15Dy99vYf7NAFynIobZNQ5vt6b/WVvRQmvNaPfMezlN3rYpV5+i/pEbaHb8Pyy9HYhwImzSzAsBRTSAv9hLMwwCnGfCX81/O36MyNUCsPpaVjZn9jeUDxUsXvCUcLzD9KKYnJykUbOMzz14cF9FzLLq15TOb/xhvajZxB7B9KxosWLi/M4d29M7bTTHpDcs7x5FTEyM6mb1GzQU3tKf9+0V0vOqE7RRhEohBAelfbxoR8WaPT298MOGdaJZA7q2StVqWEf1xNhztnjhfHTv1gX16jUQcvXqfcltiYBEwPQQkKGJpvfM8nTEJ8k7tYq8XTsehWZ5XwfK+3Ki0EInWvO2PS22RLx4sWImpmXJ9OKWRB40rjzP+WO8xCQlI4ZIllKNXusSnAmJEcs48sgN8S2Ed8sWQhWqSi/NfBCQ4YmGfZb8xfzLSZPxy8/78OlHH2DiV1MzTOY/8Ot+/Lh7JyZNnkovhuuF7H3lKlWEzPaQYcMpd+W42E6Ij4eNrQ1i6eWThUDMzVjyv8vbXTOdVukyZXCSvGLGbNIbZtinw97jlcuX4eSJ38FFlFl4gwsof/rZGOGtmjZjJtWcGwcXFxcMI3EONp+iaR4x3rezs0OvPn1FGCy34T6U3LHOHdqKws7zFy1BHSrwLE0iIBEwfQQkETP9Z2iQGZwIjMK8m/448Cwiw/7tiGh52NnAjQQ3XOklzI4IV05MeMhIucPeGnQ9/U/NmJRFJCQjIjEJYfFJYNKmbhwCueJOoFj6lfbEqIo+kpCpA2SC2xyeyKGJvCihiiY4DZMZcnv68l61alV8OWEcBg0eikaNm2iMnV8kZ86Yii5du6Fjpy4iv+W71atwl3KhWImNSRqHSPGLJqlUCBLynH4v1363Gu++975GX6a+Y0Ny4QkJiZlOIzQ0649VmXZg4JPSG2ZYgJ89e4pRH4+kDxK2eJeUC7nsA8vSf0e5hb17dseKld+JkFYWuZk86Uu4uLqKmmFFKEeMc70iwsPh5u4uBtntne74nn6Ptm3ZhPfe/1Dkjv2vZ2+079hJhAYbdiayd4mARCAvEZDy9XmJtgncixUPOQRxewYeMA4tLGRvC08HImBEvvLKQomMBccnIjgu45ehkRUKY2KVIkKNMa/GJe+jXwRkcWf94pmd3mJjY0kM4DsMJ/LEOSpscXFxQoHNysoaa9dtIKl7R1VXLGXfp2cP2DvY4+tZc9CrR1fxhb//gEFo1vw1DBnUH6PHjBXkTXWRiW8sXbII/12/hkVLV6Q7EyZh3bt2xsiPPxEhivzZKPE5yONPC+0QP6UlBXycF/5kZUX/529X9D0LNrS2pTUryhrKDlHxZhbpkIXTDYPwhPGfI5zI1LwFi4VXS7lLVFQURgwfAlvydK1Zu178ju37aQ+4CPOEiV+hTdt2aNa4Ibbt2A3fsuWUy7B08UL8Th5WPq4tXa9qJDckAhIBk0cg796kTR4q858AC2JwLlZ65kweq8IOdvB2tM1Rfld6felyrKC9DXgp4+KAgLgEBMQmIp68Zuq2mAQ+tj4MwbTqxTCwjJf6KbltYgjI8MS8e2COjo7iq7v6HVmqnmtl8Vf8I4cPY/Gi+ahZszYttXDnzi0qIhuOHev2wsurEE6fvSgECJSXxQ9Hfkyqb9+bFRHr8T9ShOzYFitJNXHY8PdUhDWZ1PBOnjqDeYsWo3CV+qjYvCOuhHK4tTqaGW0zJdM0JmIUYECh3amLIx1won+l7XKZzS29YZo463uPQ1cPHzqIlWu+1yBhfB8OQ+TQ3r4kQ3/82FG0fPMt8bvBnjEW73Bychbhh0FBQRpEjL3UHJqo/F7pe8yyP4mARMA4EJAeMeN4Dvk6ChbDGH3RD7zWNg4ZLOpkL0iQ9rn83g+gOmVPaYnjz85a1qW4B+bWKoHiRBylmRYCsrizcTwvReae1dv++OOEUAS8dPGikKxnz1kVCmsU5KxWLdQgguZDogNsN/+7gT69euDg4ePw9DKfDyJnz/yLz8eMgo1LQZSvUQ/JNg54EhIhQsqqVauOviTrb+9gmHxVJmLO9KfMlUiZq21qOHdOfkqkNywnaKXfliXomUjt+XEXLl26iKKU2zXui4moXbsOrpO3tH+fnjhw+Jj4OJFeD8MGD0DxEiUweeoM1ekd27di/txvhbds/Pgv0aZde9U5uSERkAhYBgLSI2YZzznDWS4kL9LYS34vnXewISlqImCFqNiysVphRzvw8jQmAX4x8Ujm+J8XtscvDJzntrhOSXQr4aEclmsTQEAWdzaOh6TUGmNFwO49eoqFSVmnDm3wyajRCAwMIHJ2Ebt27qD8qXi0bdcBU6d/TfL1IeIrPhecNQfjPythCSkoWKk+vvp+Hy5fuYK7d+6IYrqlKroLcRKFhBpqvuxhI4gRSgsHNzrSv9zu9KfZwy7VY5bZfaU3LDN0sj6XkJCA/SRss2HD94iOjkanzm9TrldrbNm0EWNHf4p9+w9SiGmqyzIsNCxDIlazVm1RCFz9jvx7xZ4xFr0pX7Gi+im5LRGQCFgIApKIWciD1p4mv1wMO/sAmx6EaJ9CcWd7lKDFVKyok50gjI+puDR7yRQLSUhCn1P3qMh0EUyullrrSDkn18aPgAxPNL5nxC+NbK+91gLRMdFIoT8kjx49RFysNRo2fEWcY2n8EiVLUl6ZYbxD4iZ58L8ISkcNiU8R5Ef5xOPi6o7GjZuKJQ+GkOEtYukx8PIsNpWUedoBnvasUPvyJVIp8WVMsnvEz+8xhg4aADt7O3AOZMdOnSn0MPXfRi603KFtK3AYbyWqJ+fh4UE5XcdRrnz5DLt3pjBFbevVu68Q7dA+LvclAhIBy0AgnT/bljFxS54l1wRr8fvNl0iYC4UhVivobFIkTHmOLJHv6+qAiu5OlGOh+WM9i3Lf+p2+LxLmlfZybbwIKIqJsriz8T0jVnpzc3PDu8MGkWBHd/xHYYgffzKKQrKOowO9pHKIFoduDaQitKZo/IEqIC4F18JScDsihYhYqriGMc+FCZkflay6TLlp96NSEJXKlcWQpTcsd0+uWLHicKWf+apUx6vbOz1UJIx7dXf3ELX1HCjHkkN12SO8ierNsUdY27jG3m+/HRPS89rn5L5EQCJg2QhovrFaNhYWMfszoTFodeIWTgVr5oOxV4lJGJMxUzYW9KhB8/DUCqnkOmitT95CEKkvSjN+BGRxZ+N5RpwrdvzYEXz26Udo2+oN2NraIZheNufOX4hlK1ajXfuOYMGPy5cv4aMP3hMvmx06djaeCWRjJJxm+iQmBZeIzDymP41xWde5zkaved+EiePN8FQSGU7BAdIblv1n4O//DMuXLhZqoSPp55h/nlkogz1hx44eEZ5fpbeQ4GCwSmLZcuVFCQc+zjLz/KHi048/RHBwkNKUwnYTSJRjisgl7NO3v+q43JAISAQkAoyAFOuwoJ8DLs78zl93EU4Fk9WtrJsjvLWIi/p5U932o1BFDldUt1oejtjVpCxKUG6ZNONFgGXs2RTvmPGO1LxHxjXBNm3cgOfJyaIGUgeqY8S5Lhvpyz+/tHINsVKlS+P+vXv44+QJNKJwrdlzF5hMWCJ7wDi8zz+OPF9K/KGZPNKwsHBcOX8OZQo6olPLxmYyK/1Pg/P9Nqxfi4MHfhU1uspXqIgLhBsTsy3bdqGwjw86k2Jm06avYvDQYeT12iC8vlzAm3MDuXgz1w0bMnS4IGvjx47G40ePqS5fY1hT/bkLF86jUCFvKvUwG8WLl9D/BGSPEgGJgEkjIImYST++7A/+DyJhnf+8g2g1hUE7kkYu7+b0UjHl7Pdq/C2DqO7YnYhYjYFWd3fET03LoZhUVNTARe5IBLQReLNFc6qNFCZeIOvVbyDUEWuRSmIZ37KpL6okWBAQ4C+IF3vGGrzIE9Puxxj3AykE8Sn9aVD7k2iMw9R5TOcvXkEokbG6tarD18cdRR0LCFl8nTs0wwsXLZwniFWrNm0xYOBgUYSZp8l14XpSfby2bdsLYZrNm37A4oXzBQJc+HwAhd6yWiLbls0bsWjBPOzYvRclSpQUHrATVP/rMikrcp5k7br10KBBQyHuIi6Q/5MISAQkAmoISCKmBoa5bl4Ii0W7k7fB4hWKOZIqIudTOWjlUynnzWkdTvO+GR4rCqoq86rv6YRfXy1PctCmHYqpzEeuJQKGQCCGVOKuXr0i5LovXbyAKxSuFRERIfJmqlevIbxjLF1fp25djfwZQ4xFX31GkQiHH4UhRqf9OdRX10bTz70HD3Hv/iMU9CBVRyJiihV1giBkyr6lrw8fOkAhhmOxdftuCjMspwHH4IH9RA2wRUuWIyYmBh3bt8abb7bC+AkTNdpx/tfA/n3gTCqhi6ngN3vBpEkEJAISgewiIHPEsouUibZ7Sh4hVg5UJ2FORD4qe1gGCePH5m5nI+ZrTfH+ip0JiSFc7iu7ci0RkAikgwBL0LOXi8OuFi5ehiPHT2L7rj3CS+BTpAgOURFbzqfx9/dP5+qXD3HIKQtI5JcxAbtJIhzmTMIYWyZhbL6lS4q18r+nJOpxg3LIIomMSgMVV24lvL3r163VgOPkyd9x+9ZN1Hrh9XJyckIPkprn8MXo6CiNtpxH9tnoz4VgB4foSpMISAQkAjlBQHrEcoKWCbZtfeI2fg+MVI2c64NVJk+YvQV4wlSTfrERQZ6xG+ExGrkgQ3wLYWldzZcV7evkvkTAkhFgCe8L58+TQhzV7SvsI8IT+eVTscjISOEhU/YzWjMBO3j0hDjNYix5mf/HSoKPolOE5HtG49P1OOdinaMwQN8yJYn4lNK1G71dp3jDshpPEUegmFPac9TbAEyso107t+Pbb2Ziz779oqbXD+vXiRyw9h06Ce+XUk+PwxU7tmuFYe+OEGGMJjZNOVyJgETASBGQRMxIH4w+hvXR+UdYdTdNvcmGJN6rejhTMVDLdYSGkmriTSJj6vZtreIYWb6w+iG5beQIcAFhpZ6PkQ/VZIfHym/jPx+N8yRcYGdnJ15Snz9/Di7wzPk0LOedU2OPmFKWIK/ImD/lgvlpisTmdNiZtjc2Inbs9z/FeN94rWmm4+aTrlQUupRzAfowl2VTs23Af0s6tm8DJ0cnPHv2FK82f53KLwxG1WppIZ3K5GfP+looKP70ywHxO6Ecl2uJgERAIqArArKgs67IGfl1398P1iBhPNzypI5oySSMMWB5+zJUb+x+JMmkvbAxF/1Qh0I1mxVyUQ7JtREicJNqVh05cghHjxxGw1caYey4CUY4SvMYEufEDOzXB8WKF8fGzdtRoWJFMbG7d+9g757d4BfSe3fvYvTn43I0YfaC8XKIPGNMyHhp3bI5qc9556if7Dbmulos6W5I86A8LDYOB2SP2MOYeDyMjcMTKi4fEJ9IYeGJiEhKRkzycyQSkWVxRg6TdqDaU84UJu5hawNv+rtUhDyOJZzs4evkABcdc1fZG8bG3rDsGIco/kehmqWJjLlbqJAsf9Dp2bMPli1dhO++3yDyHjPCrl//gaJ2WGJigiRiGYEkj0sEJAI5QkB6xHIEl2k0vhkZjwZHriOetZlfGJMPHynZrsCBB1FxeBZDhXZeWA1SUvz3zcrKrlwbCQL/3XhBvg4fEtLQRYsWE2p9LDG9act2Ixml+Q1j65ZNIjxr89adIvdFe4an/vkbH304AivXfK9Sj9Nuk9W+Ib1j8ckQxY0NnQsWR+TqbFgUDt26j1uUj+tvbYtYOpZbK+Foj6qkaFvTzZk+ErnA19khW13mxBum3WEJZ6Cwg2WGKnJ4bQcKO+xLdb449FCaREAiIBHIKwSkRyyvkM7D+3x+6bEGCStMBEySMM0HUNrFgRL2kxGZQG9sZJdJVXHsJT98U7O4ZkO5l+cIXL9+DUeJeLHn6/Fj8jKQVHprkpfm+lUrVyyFp5cnvp27MM/HZUk33P/Lz+jZq2+6JIxxeIXqhbXr0BG7dmzTmYgpOWKKZ4z7VY7xtq7G+WD3IlPI+6RrD5lfF0q5pr8FheFkUAT+CYlQa0zxfXogYdzh41iqgUjLIf9Q0X9J8pQ18XRD80KkgkjELD1juXq27HrDtPvgQtYJ9PGuhAXmjblSIeau3bpj29Yt6EcFnFl2XppEQCIgEcgLBCQRywuU8/Aea+4F4cCztJcDDkVkb5i0lxEo4+KIyyFpClgLbwWgQzF3vCpDFF8GK4+OcD7S8CED4Vu2HNq27yDkollWmqXTPx89Co2bNsX4LybKsCADP49YCk309PTM9C4VyCt59sy/mbbJ6qS+QxXDycl9l8IRDVGc+URQOH71DyHxo/CspqVxnnVNbOl/VpSja4VUjxONkMppEG+jgSapRS5oXKi284jCHbfFBGLb40CUIlLW2scT7Yt4orA9JXmRcZ4a1wxjufrcCIYEUF21ZBpPaRfT94yxOMyzgEAR/lqEQl9bUQhsZta7Tz9se+EJ7tmrT2ZN5TmJgERAIqA3BCQR0xuU+d9ReGIyvrr6VGMgpcjzY/r/pGpMSW87TkRS2TPGYYqKTSb8jr5WQdmV6zxGoBZ5vVgenb1giu3ZvQtz53yDDz/6BP/r2Vs5LNcGRMDewR4PH9zP9A4czmVvr5+PPPySrIQqsrKiLkIeYUzCyBOmb9vhF4jdfsF4EJP2dyK9e9gS0XK2tRZ5X/wBjGs02lMeGIskZWZMGuMpdyyevGkc1hhLFaaj6G95DHns0zPOQVt976lY2hIZe6d4IcQ9SF+uPr3rszoWTDl1nMnma6JkjAnYxSvX8YzWivkULqRsZrj29vbGiPdHokwZ3wzbyBMSAYmAREDfCMgcMX0jmo/9cWgde3UU45BEX+kNU+DIcH09LBoRL0IUudGKeqUwsIxXhu3libxBICkpCXO/nYUjFKY4a/Zc1KvfIG9uLO8ClvRevnQJNmzcIgQ7tCFhpbnOHdph7Pgv8HqLltqndd5XyBh3kBMyZggSxt6nzY8CEEiCGxkZ1yj0oMXNzhpcn1Gfxt4y/rvEBenDaGGilpHVKJCE9q726FS3RkZNcny8IIl3+LpmTiJz3KkBL9AmYOwFq1W9isGEYAw4Fdm1REAiYEEISCJmJg/7TlQ8qh28ppqNNX2Fre3pkuXXWNUFFrwRSV+fr4Wm6VuXdbHHtdZVLRiR/J96SEgIxo0ZhWgKkZszbwFYpENa3iHAROujD98HK1W+9/6HaNOmHdzcU9UB7965gxnTp4jCthmJeeR2pKyqyB6N7LxMRxBPuk3Kf/qywwGhWHvfP0MPmCt5vbwcbOFFYYFZebv0NSbuJ4L+TgWTIEgQLc8ziL3sSt6xoWWKCCVGfdzb0x4oY+SeMUnA9PGkZR8SAYlAfiEgiVh+Ia/n+46kmmGr1WqGcUhiUScL1SPWAdu7kbEIjE378j2/dgmMKGcYSW0dhmdxl/Tr/T/cvn0Lb3d9Bx4FC4rkeUdHR7HmRHoHsZ26X43q/VhRCJg0/SKQmJiIRQvmCbn6uLg4eHh44DnlD4WHh6F+g4bkpZwDd3cP/d5UrbfseMdYFfEWkbBspFmp9Zz+5iMSx1h25wl+p1yw9Mzb0ZZUBe3gQkQsP43nGhiXAH+Sx+cwRm1zJc/ciLLF0KWYfrz63hR9WpLk7Y3NJAEzticixyMRkAjogoAkYrqgZmTXPCIZ9gq/XlWNyp5yE2p7pa+spWokNzQQYBnqi8Fpwh3lySt2RXrFNDDKy52DB37FDVJPjIuLpSVOc4lNPebv/wycp7Rm7XrUql0nL4dnUfeKiY7G33//hZCQYBLBSEH9+g3BAip5YZmRsQTiIDeJhKlFFes8pF1+QVh4xy9d4QxWnOWPWvx31diMvWNP6e9/evlkrLD4afni8CHymFsr5gQUcTQOMiYJWG6fprxeIiARMCYEJBEzpqeh41gmXX2C2Tf8VVeXprywIrJmmAqP7G7coyLPAfSVWbF1DcugZ8mCyq5cGwECgQEBOHr0sJC3v3TpIlxd3fD5uC/QqnUbIxid6Q8hNDQUA/r2Qo2aNWmphZq0VKxUGTY2+afrpP7irR6qyCQsKs2JrRP4LI7xzc1HKpl49U44/LA4KRSy8IaxG3vHHkfHv0Qk3ch7N6ZiSbT0zr3nksU7ClKoYn6ZOinnMaj/LOTXmOR9JQISAYlAbhGQRCy3CBrB9SV/vkwJ5RSjQ8Y5C3ULuUqlRB2eC39VvhySliv2emFXHHi1vA49yUv0iUCAvz+OHT2CI0cOCRl7d8pVev2NlkLankPkrK3zN1RMn3PN7744N2/rlo2E80Vcu3oFseR9tLen4sJVq4k6bkzOeMlK2t4Q81B/ES9WtTY8S6Ypa+pyv2uRMZh+4yHuR2uqIToQ8SpFBZQL2ucf+dRlPizu8ZByhdU/Jin9DCrtg2G+RZVdndYsw1/ZvQAc8/DXjUm4IkGvDFoSMAUJuZYISATMAQFJxEz8KW57RF+wT99XzaIYfcEtSWF10nRD4L/wGIS9ILXcw5k3K6O6u6NuncmrcoXA6VP/YMXypbhy+ZLIT2rxxpto+eZbQj1Rm3wxYeAcMmm5Q+DUP38LHLl49nOSVL918yYuXbpAy0VBgp/4+YkbcHmBbTt/RAF+O89DYzL2z7UHiLR1BRcu1rVm1rHAMEymfpK0RC84DJEjCvJ2VvoFMJT+fnFJDm2VxVY+BTG5Sulc3cyZuGklImOGNnUvqHKvnKhoKtfItURAIiARMHYETOuTn7GjmQ/j205ETN0KUUK5NN0R8KZwJHUituNxqCRiusOZqytt7exQsWIljPhgJOrVq68hyMG5SleuXMaxIxSmSEvTV1/F2HETcnU/eTGwedMPqFO3nvB+sQBKpcqVxdK9R08BT0hwsCBlnJ+X1ySMB1CxalUEOxXDPaqbde/+IypkHEFkrCQR9VRFx+w8w91PgjDn5mONplZEKMsSAeNwRFM39uS52jmDQ61DKIdMsUP+oQghGfyvq5WBi45S+yyO8ig6xWDiHdoETHq/lKcn1xIBiYC5IiA9Yib8ZPkf1WL7Lqtm4Eb1bKp4UFa1tFwhcC4oEokvZNgq0svZpVZVctWfvFg/CAjyRd4xrivGoYrPnj2lGkFFUIVezi9fuoT9B49okDX93NVyemF832zxKpULWCjImDHOXD0v7N6Dh4KM8Tiz6x3jumBLSBlR3bgIczlXR5PIBVMfd3a2/ShvjHPH1K2qmxO+rV4WBenfC12tLNUX88i9Bojq9pKAqaCQGxIBiYCFIaD7X2ILA8oYp/vL0wiNYXnmMqfheXgIrFwpqVtNCjz1GH1ttsrDxACNWeXdjjL/glQfSMmzuElflS+ExaC2JLh59yDU7sTk4NLFC8LrxSIdnC/GwhEcGvf1N9+ievUa4MLPrVq+hgvnz6Euec6k6YbAgwf3qTZYNBHbarp1YOCrnsVqinMoYYnsGeOFTTmW3lC2PAp8iYSx96i8myPYI2aOVtzZHnak9ng3IlY1vWsRMRh95S7m1ygHFvPQxR6TV8zNtgDhpsvVaddIApaGhdySCEgELBMBScRM+Lkf8tckYroml8cd/wVxR/bieUggXIZ8Brv6zRD/11HE/roDz4P84dz7PcDWDtHrF8H9qyWwLlLchFF7eeja8y9Ys5GKiHHrQ/6Rkoi9DFueHGFyNXzoIFHQuVWrNmj5ViuEhoZg+tTJ4PphHB5na2uL5q+1EGRNEjHdHwsTXs4LGzygb6piIuWJsWpiyVKldO9UT1fGJgNPYl7ujIlXQRJvySpUce+TYCwmeXp1K0RhiOWIhJm7cbi1Lf2e3CQCpqTEXaft8VfvYVGtcrDWgYRy6QC/GN1DFCUBM/efOjk/iYBEILsISCKWXaSMsN1vAZGqUXGRUTs1T5bqRBYbKfFxiP1pE2wr1YTDwI9hVbgYyMWAmB83wIZUyVz6j4RVIR8k+z2ATalyKEAvveZk6c3fg0J2+Av58xdvLYzz55V8zGnaJjMXrg/m6eWFseMnoGmzV8W4udBwYkICzp87K4Q7+CCLeMycMQ2fjRkrwxN1fLqXSZCj4SuNBPHi7T0/7hLEjAs5K1L2LOJRlQgwF9XOS3tCL/0ZGeeH1aFFCVUMDQvXCFU8QQWaWaJe3bg4c1kKR7QU8yDPXyV3J7AYkULGzodFYRIJlsygnDFdLJDEJjk80TUH/ySoK1/yPWUOmC7Iy2skAhIBc0JAEjETfZqXwmNVkvU8Bc4P08WSnzxEChXNtWvcAjYVUkOSkp/5ISUqAnYNm6uOWRUsBNvq9XS5hVFfk978ecBudtYq0Y4/gtIKPRv1ZMxwcCwY0aJFSyFdrxCxVA/Y6+JYvfoNxKwbNW4ipNYvklenTp26ZoiE4afEyoiDhwxH6zZtxc1YifIqCaJw/h0rJ278YQPClyzCq81fw7wFiw0/oBd3CKXSfuFp5f0yvK8Slqgeqvi8kDemXH+gcQ17wiyJhCmTd6d/IyoyGaNQa8WOk3rkYsqZG1mOPsDlwJj0Mt5PKVzUlUIUMzP2fkkJ+swQkuckAhIBS0ZAt7d3S0bMSOb+r1q9Kx6Sazqx/vEnDiL+3xNIfnwfTKRsKlaDU+e+KOCYKugRvWkZku7dFDOK3fMD4g7tEdspialvPXH7dyD+5CG4DPoEyf5+iKV9lyGjYOXpndqOCBxfl3jzKp4H+wvPmX2zVnB4jV7k1Lxzz0ODycO2Hkm3r4lcM9vKNeHYuhusvIuIfhIunqZ7/wi3T6ci/vQJJJz+HbZV68Ch1dtIoPHHHfkJyU/pi7adPWxKl4djh56w8a0ors3sf7rM3+W9cZQn5y7wVNQTE0i44zTh3dDTObPbyXMGQoDDEceNGSVywZTCwnxsxtQpGPP5eOEBsyOFxY6dOoMLPkvLOQKRkZG4d/cuqSXWUl3M5QC4ThsvbJyv9/DBAyQmpSnxqRobcONpJt4w7dsqoYrnLl7BXcobW02qsrHJad409gxZQjiiNi7KPnv7OSfutlrO2BYSMPF1ckCHop5Ks0zXiueRGzHewfEp8LJ/mYxphx9yeylBzyhIkwhIBCQCaQhYpW3KLVNC4HxYWvI1j1tbjjh64zJEb1lBZ1Lg8FZnIjDlBKmKmDlaeLv4GiYcQpyDtgu4uBHBKpS6uJFghzjmKvZhbYPnkeFIunsDKRQSxpYSG4OI2eMQ9+dhWJcsI+5RgPLIYravQczu9aIN/4+9a+Ff00s0ET77V1rArk5jMPGKWDAJz0ODRLuUyDDRd8KFU4jeuBTJAU9RgMaWcO4vRK2dD9g7CPLl8GorIpX3ELl4iupa1Y20NnSdf4EXoiTOWvLOF7Tw1rqd3DUgAixdz3XDuK6YYq+80hiFfXwQEOCvHBJhia1at1Hty43sIxAbE4O3u74jcvEyuorz8UqXKYPy5Stk1ETvxwPjUhBH+WE5MQ5VfOO1pjju4omHSWkkzJEKNTMJsXRjif5SLpqhpRy6eSda89+U9HBSJ2GKQIq/1mVMwA4dPYGDtDyjbQ4/bN2yOfr37kZErGp63cpjEgGJgETAYhGQHjETffRXKTRRMXtSxbJRk69iwhRPBMm+SUs49/2AWFbq10q7ek0RuXQ6iXDshFP3wXDs1EcQpMQbF+HwZhfY1Ur98i3I09VzsG/eBvaNWii30VjH/fYLeakewnXEF7CtmRoe5tihFyLnT0Tc8Z/h2L4Hed6cEbPre1jR2u2LuSjgkPoS5PBGB4RP/xSxv2xLHd+LnmN2fAf2SNnVTB1H1Hd0DZEwt0+mqjxsPIfwGaOQeOUs7F9trTEmZSe38+d+tInYVbUvyMp95DpvEBDhiVTMmeuFNWnaTNyUPWAbNm5RDSAsLAy/HTsqwhWHDHtXhieqkMneRmhoKD765NPsNc7DVtov+dm99QGqmXUsUlO2nT1hughTZPeeptSuqJMdeQqTERib6t1MJm/nvFt+WFq7fKbTUMiX0iiM8vGY+DJhfh4ehItXrgvyxedl/peCklxLBCQCEoGMEZBELGNsjPrMzai0lwz+0qturHjIoYFOb/dXkTA+zzleHBbI55mI5cbi/z4mxDsUEqb05dR7BIUgXhWesxQWVSDC5NRjqIqEcTsOk2SSGP/HITj9b7hyKexeeV1FwkQ7z8JgMY24kwfhQCGP5BaBdQlfeMxehwIUppiR6WP+TGxZ9jkhmeTByG5qvdRldG953DAItGnbDseOHdHoPCQkBL8dJ/JFdcXOnvlXhM5x+NzrlFMm88Q0oMpy58P3hyMiIgJly5YTxZwVcY5SpUtnea2hGvDLPavz5dTCEpOw6LamQqIv1QPU/riS037Nrb0viZVEJz5HTFKqy5HFO9Y/8MeA0j7pTvU8hXtqG6tV+tLByxcfoMCz2+K0JGDaKMl9iYBEQCKQMQKSiGWMjdGeCUtMprj8JNX4HIgwqBuH9ll5FRbhhurHeduacqwSb1wSoYYcmqiTPU+mnLBA2DZNyydR+rH2KQZe2BJvpv7DHf/7fpH3pbThNYc6skjI87DU8EQ+ZltdU2SBQxETL/+LmK2rELt3oxAOsa1cC/YNXhWeMr4mPdPX/B2sCxARS73DPa2iqOndVx4zHAJ16tYTRYZDgoOJkB3FsSNEvs6egbOzM0nXv44ZM2djzeoVKFzYB127vWO4gZhpz7v3/oIrVCybBTtYxv7ggV+pplgUeTtIMbFGLZE7xuSMF/ZG5oWxKp8utvzuUzAZU4zFOQo75s2YlXuawprjJMoQQb0WGq0a7sp7T9G8kDt8nTVDFzkkkdUotY2PKcfL+RRHk+rlqMh6ag6xdlu5LxGQCEgEJAIvIyCJ2MuYGP0RvxfhJMpAX5Ktj48lj5HmP6RKWw71E0aeJuhIxFLiyRtHZKyAfcZeKXGPhFSvnZW7Jwp4aCaCWxUuCpSrTFGTaSSS26kby+a7jZ9LXrUzRMho+e8yEi/9K0gZhzAyKUvX9DT/VFxTmZg25uneVx40KAKbN/2AhfPnwsXFBc1fbyGU+1hunVUUp3z1JRLiEwQh41BGaTlDwNXVFY2bNBULX8n1xO7cuU2KiRdFbt6ypYuFx3Hn7p9EnljOes95a1ZKzGluGN/l39BI7HsarLqhLXm2S2vlQ6lOyg0hSsRFn/3UPjStuv8UM6uxnyvV1PPClGPa67q1qqM4ETgfd6Z30iQCEgGJgEQguwhIIpZdpIyoXUCcpmqZnVp+GA/TyruoCAmktylVbpUy/Of+T0SIn6J8qBzPyZpVFzn/63nQywp1rG7ISod2jd6A1QvPmF3jN17ONSMil0L1ytRDDAvYaBWkoRyGAhSOyAIfvLBxqGPkipmIpTpntkTS0jN9zZ9f4hRLJOXEkIQkeOpYJkDpR651R6AxSdSXKeMral0p6onc27atm3Hs6BF8v34TmFBIyz0CLMwRRUqKV69cwd9//4WCBQuibbsOyKtQxWAKS9TFvqfQOnUrSSRMPX9W/ZzcTkWgBBGxkPhExCalxoH+HhiOX0zQDwAAQABJREFUP4Ij0MzLDZwDpp0XlhFu0eSEjKLFRb5VZASRPC4RkAhIBF5CQH46fgkS4z/AoYnqpv2iYVultsitiv/7qHozSqYORcL5v0XxZnV5eY1G2dyxIW9W4tWzIsRQ/ZLY/dsRe2AXrJycYe3lIyTtE079rt5EbEcumYbwSe+zJvZL55QD4TM/Q+SSqcquWHOem03ZyngelVbMWqMB7ehr/tq4hipxito31Npn1TBepOkXAV/KX2KxDnUSxkWdF8ybg6+mTEfZcuX0e0ML7M3P7zFWrVyOLp3a4YMRwxFFv2fTZ8zC/oNH8cmo0eTBTvs4YSh42BMWofmtKVu3OhwQiguU56QY11b0prBEaVkjUEIrFPGHh6mElssAZMc4V4wthKTspUkEJAISAYlA9hGQ366yj5XRtIx68eVSGZCV1suRPRVnjju6D9FbVyMlJloIdCQHBwgvUkpyEhypllhuzbFjL0SQsmLkwq9IfbE3ES8XITefcO5v2JMHjOXw2Zw69kbU9/MRtfpboXLIXi8mg4nXL8KpSz8NMRHtMdmRGiMrPMb8+ANYLbEAhaAlUd6ZkMKnYtMZmb7mr62wFv1CuCO9+2rXzGG5ZmmGRSDA3x/jPv8MffsPxBst39S42RM/P1hR7mSRIhQCKy1TBDgXjAVPftn3Ey5cOI8qVauhb78BaN26LdzcdcwjzfSOmZ/U9WV+yyPNjx8ccictewh4Un01rrGm1E68HB6NtWcuo0z2Lhd5Yuw9syrojpJUbtHwdD2bA5PNJAISAYmAkSMgiZiRP6D0hscFhtVNLYIu9TDVwnIb8zXVEVuJ2H1bUut6EVmzLl6ajs8itcOy6pfrtG1TqhwVYJ6G6M3LEbV8puq+9s3ehFPXgao+7YgwuVDOTszudYKosZS+TZkKcOo2CA4tO6rapbfBhZtZ0COepPLjDu0WTZiM2ddvRmqLw9K7JPWYnuavTXC1ceebaROwjAclz+gTgQSqZzdm9KeoXKUqRrz/oeiavTkscc+k4vq1qxg4aAg+GPmxPm9rdn0x8fqQPF+sNtm9R0+M/3ISfH1z//chN0BxflhO7TcKp7sRGaO6zNPeFm7pFLlXNZAbLyFQzMleRcT45JGoBAzNAaNirxhL2YeSV8wznQLPL91QHpAISAQkAhIBFIhLzCQ2TAJklAh8dy8IH5xLDQXhAVb3dM5YmpnyxJKD/GHlXjBTpcHcTDQlNhrPI8IpFLEwKG4sw65SoiLEC1+O1RqTEpHMxZ+JgFoV9NLIK8vwZsqJXMw/kHLx7qrVDzvZohIaeDqJnrMiYFy8VJrhEJg6eRIunD9H4hzf4BQVej5GBOz69Wvw9vYm79hbaPlWK9SqVZsicGX0dWZP4fChA5g351sEBQXCweH/7F0HYBRFF37pvXdKQuhFmoiIdKlWehE7ohSxoaCoCAgiVbqIWBF7QQUVQfQXC0gPIB1ChySQ3uv/vrnsZe9yl9wll+QuN0+X3Z2dmZ15e3fZb95733Onlq1uoDbF7IhgSERsWHUK4oyOp+guNJly/2cOnqZdiSXuyi0DvAQRhSltZZ0SDRxPydQBY8gr1t7fW1SAxas8ARDzZ4LKhj5mILjyOpXXpQakBqQGarEGjL811+JJ2/rUnPVcEcuE0vwi6gSGwioUEHc48VaewF2xQn+e2Z3RiQlIKiSVmD+sBGpJTUqmLfv3aBOWqq/J4+rTwOaff6KNP3xHvr6+9OD991JoWJhwTXxuygsiBxbimMD6N27sIzTt5Vdl7FgZj6ZvvwGE7crly1rq+n937qCP131IBUyWU79+pKCu79GzF/XipNpVLSm5ut85U+53Ij1LB4T5cWyYj7SGmaK6UnXCmOZfcU/ExY1XE7VADCDLFElhiyZ+OvX+TJnSVNaRGpAakBqwOw1IIGaDj9xTL4FzoR5gsMEpWeWQC/TeCffu2kPhRaaxCOiTdcjcOiWPGCCpMpaqwMBAzlUULsBXH7Z8wXKjTyKB/j05x9ivnG/s8UYTSm4uj3Q0sGjBPLpn4CBq2qw5RdSpQ/0H3C6uZ2dn05H/DmvB2bGjR6sHiJn29dKZw+a4RJ3zUA9J0KGjEDNO/BnEejo7aZM8b2Yg9mzjuuTDZaYKfjZT+DnCMiZFakBqQGpAaqBsDUggVrZ+rPKqn95qb74EYlXynAr09OpBGnpnU272y7btZVYL10t6GhYaXKp+eKhuYtTaAOYyMzPp7jv6kSezan6/6WcdQIa4rk/Wr+M8YT4iVxjyhQWHhFK79u2pXr36Qj+wUgYHB9P7H35M3kxV7+mpcRUtpTwuAEj7eN1H9Pg4CcQM6SeH8wF+8/WXNHT4CO1lPAPov35kJN3Y4SaxaS9W8UEuf72y2DXRXPk1PlnbxJUJWhAfJqXiGgDT5Ll0pq4slm2s30F1gpRTk/ZpeUUMxCrk/2BS/7KS1IDUgNRAbdGABGI2+CSD9XJZIceVFMtrQF+vE0cNpMOHj1LMoaPl3qxt6xY6deLiOcatWK4ytT02teif41oMlX0fWwRz3234hsKYyfDC+XMUE3OA2re/UauGCxcu0C+bf6ZRo++jxMREio09Q/v27iGAN7gdjhh5L6VxbquRw4do28Dy5c9xTM2bt6DxE54QjH+4uIkZAHfv2kWxZ06LfvQJKJKSkiibiWAiIupo+7K3gyMMugBkGzSI1k797dWrqP2NHQTRiVI47YXnmRDlySrPIYaXd3NlFydwvsY5sBQJYuY/KZXTQJAAYtnaTv53rSJATNtcHkgNSA1IDUgNlKEB+VerDOVY66U6eq43ufo+dBYYeMrMSVQQd0m3J3b6d3D3IKfweuTBtPQuLdpqr4OePncfJ35d9rlBMo2inGxKeuZecr3xVvJ+bIpolziRX6j1rE7aDosPvEaPF7T35tTV76Oi57kquvpwfjkBnX3b1i0Jlqqr8QlGARkAEuqZI/qujOhfX2wdzCHm6LNP1tOEJyYJZsPftm3VAWLIWeXm5kbPPf+CdupgR3xj7mxavvRNuuvugZSRrskTtXzlamrcuInIc3Xo0EHa8M3XNO6xMbRk6Uras2eXSPIM0AbZxiyKYx8fr+0TB19yEmjEmm344UdRPmHcWALrotoapxyPGHWvFqycPnWK9u3bQ7AmNeS8Zjd3ukUnr5nOTaz85CAD4VY3tNa6dcLaeJh1CbZJRZAiAEyU016arhRV2R7JgM2VvznxsFqkNUytjYodI5G9msoeJCipnLvSHBZK5ILLYwuni+TKqdhDkK2kBqQG7EYDEojZ4KMGKPBgF5ysYqCQzTE3VSEOXj7kccfwkq75PiIp9N6/KO2tOUxfP4eTKzcruW7mkeewMdzCwCo4vxBmb9nATIzJ5AgmRhZz6ooGFvhHrdcor5KAB7gIKm6CpljHTBmK0p9SV/9cKS9rX5Ngrp8JedO2/rKZcvNyBTkEYpDef3ctTX5uqhYIpDPIAvhRi6urq8hntemH7+laQgIDIM1KPdwTQ0JDxZafn09H2CJ2+PAhGj/uUcH05+fnT5GRUXTPoMH05ReflQJimntp2OBwv6tXrgi2QJBS4Fp6Gm8MDHEMAUhZOP8N+v67Deyu14FwzxXLlggXvmUr3qK6deuJesb++eN/vxcDPW+DYA9ulphrdcqhgzGC3ES557lzZykjI0NrVUT5Qa4TGcVpL/xMI2pQ+qrIPqPEsGVy838TS4CYG/8meuu5bZvckYGK+P1JfuERA1dKipyjm5Lv1PklBTVwlLr4ZWaULeTUJMVpRCwwBsSKqUk7/k1Kpb6h5jFoggFTxolZ4GHILqQGpAZqtQYkELPRx9vY240OpWSJ0WfrJXi21JQcPDzJ/bbSub7cuvSllFmTKOefbZUCYu633WVwqNm/fi9AmHuvO8mlZXtRx5y6Bjs1sxDwUK1X6FtfYPXCFnPoiFHrmH6bqjzXB2/656bc2xJgzth91jET35Chw8mFc8F16dqN3nh9NhNC/MdWmRtEEwAfb58ScKT0s2f3LuFCWK9+fYrhvFcQADa4Ma55exW7OZ7n/EWcnoEtlmMfG8+gaxw9+/QkruNNvfv0pQXz5tLZs7Faqxbaa+5VAvpwjpxkYBA0JAAtX335OX2w7hO6ga1IELhPPvrwAzR/7hxavuptQ820ZZcvX6JdTLMPYJeQEE9IOA23wKysLAHyRt/3AD37nMZSjDxoP23aSOfOnyU3VzdhdYPeAIgsKZgT3D0VgYUMpB2gsVcEZSBDqWqBUR9WFHPkYlYOnc/M0TYBW2JVCMCWK+cuNCSOvuaBE0N9WGMZgJha9ianmw3EMvNlnJhah/JYakBqQGrAkAZ0f20N1ZBlVqmBlr7uWiCWmV8g7ErVFRqNpMoQuClaWvJOHKLMDevIuXFLtoKVvRptTl1zxwmdqgX6Nib6gMwQ8YaxttZWrg/e9M8rOt6dO/6h06dO0qLFS0UXYD1s0qQpwT1RC8TYCnUt4Rq99OJUAaKcOSfdiRPH6T9YujhpM+LBFAtVfHwcwW2xffsONPq+BwmAAUBnzNjHtPXCORYNlrGON3eiv/7crgfEYH0rAX3oV98ap57ryZMnBIBsxkBFEbA3jr7/AfrgvbVKkdH9vaPvJ2yQ7X/8j5579ina9PNW8mJmR8TAMYYU8vrsmfTTj5vowYceocHDhlEG62QLWxIffnA0LXpzmcXIMy5evCCA5Lw35lDbdu2FNfCfv/8Se81INP8CrA0cVBKTp75myeMs3a+bSV0fTMnQqefrajqzn07Dck6cIuobXJAqp5lNX4Z10YPZebOKF/kOJuvq2pTJVeSZmtKvrCM1IDUgNVCbNCCBmI0+zbb+nvTFhSTt6NPZh7+6cudk/+8nkSTGte3N2vtb4qCQkzanr11Ejj7+mjgyR+MvVubUrcjYoE+1tPUzzs6n1FPiwvTZDpXr9rxHXirQ1g+8+3YBaACCYA3K2pZFTz79rFANwFBwSDDHXjUUgAvnTZo2pUGDh9Idd2qsp7BcQcY//qhwD8TxD99vYEDjTVNfnKaN1xIWr2Kg9frc+eTDOcfUtPnoOzw8HM1FvBdcDQ/s38sgzkEAMowPWwizNsIFsg0nh4Z7IkDi4+PG87iaibbDR4wibOYIxgbrHUAYwKUCCH//7Veey3f07vsf6VihBtxxJ81nwIR7//DjZou4MAYGBNK8BYsFgAXY+oXj5fLy8oQe4aIIK1grTu58/PgxnbGYM09z6maz9cRc+S81U6eJj0sN/znjz3fWz19R3sFdVHDlAjmG1iWP/kOoMC2F8mL+JZ9nZ4vxwu067/gh8nnyVZ3x5+7fQdm//kA+E6aRA+dchOSfOUZZP31J+edOU1F2Jie0Dya3W3uTx4BhOm2r4gRungoQO5uZzZT0+eRnho4rwoBZFfOQfUoNSA1IDVizBmr4L5c1q8a6x3ZTgC4wqAogVsQvEOnvLNAqoig/jwrjr1DB9XjyHv8iOTdppb1W6QPuO33NfCrKyiCfyXPI0dffeJfm1DXeS5lX9IFYBz19G2usgDFj1+2x/PixY8JatZYBBsAPQBA2WMjmsVvfSbZ6AdgAoICAQ59YQ60ztINL3x9/7RQACm0AkOCaCAuaIqgHcLZ//z5B1gHL22QmAQGlPSSdiTy8+V7iuBjcxcbG0sWLF8U4NDFi6SJX2aszXxPjWrJsJa1YvpRGjxoucm717HmbAIkNGzUS/Zj6D8bmwXMACFPLB++/RwNuv9Mg8AFr4Q/ff8/umD/R3fcMUjer0DFyrMFtExsE1sVjR48U5w2LEeQna9esFiAxmoFxVYu5bokYz4n0EiAGC44rg+iaFPx+5TIIc2l1I7m37UQFV/mz9OEycuI418Lka9qhFSRcpfyzJ7XnygHibwG88DuLmRRcPEtpS2eQg18AubFrpGNAEOXG7Kas7z8Ri1VuXfooTatk7825wxKoJHAPibM7BpS485Z3U6QjAKFvDT+W8oYpr0sNSA1IDdSoBkreXGp0GPLm5mqgc5CXYPFTcl2l8mplBFk22B8vBAWXz5UMjV94CzPYIsHluTv/R85RjcnRL7DkeiWOMj5fy6u+p8hr1OMcd1bi/mWoS3PqGmpvShlYwhRp5+9BAVXk9qTcozbvP173gXCpa8cucGppy1amd/hl/7dtvxYDsXRBsKGuo38MgORVbOkCwyI2tcDqdYDBF+K3cN/Vb62gOnXr0u133EVNi61YqA8wpFii0CdkygvTtPFfokDvn1s630rYEG8GV0dYkb5g9kXEdo269z692sZP1fdWauXm5ghAilgxQwKyjOYtWghXTUPXzS1bzxbKuvXqCdAXHBwirGyw+mGj4iFcuXxZEIzoA0Zz72VK/ZwK8A2dydAQt6B//ST3ptzT1DqwXqkXpNTt3Nky5RzZkPKO7BcgzL3vIPIc8pC2Sm77zqKt4s6tvWDCQc7O3wUo853wEjnViRQt3G+7h5KmPkR5Jw5TVQMxJHZWC/RtDhBDWzxXD91u1F3KY6kBqQGpAbvXgARiNvoRAMVwjxBv+i1e46qVkptv8TgxuMH4zVxVSkN5x2LYerVAvGD4TplX6rq5BTl/baWcv7eS2y29yK3H7WU2N6dumR2VcTGD48PU1PU9QkxfBS6jW7u8hJd5kE/MX/hmqfnDPa9Ll260jYHYOM4BZgig6DfSuBzqPg+Ar/379tKvv26h37dto+vXNdaH9gyaHuNkzi2YhENf0I8C6HBfiALM9OvifNmSxXTPwEEUzZT1yLuF7f4HHqJlTKu/ZPFCvja4zOTS6j41cyiJT8M15DWDe2RYsbukur5yDFfGZK5XlmzhROJKTjpjeeZymLESlj3oDYJ4PRCVYOvStasArE5OTsLqF1GnevKswXpijsTl5GpZY9HOQw80mNNXeXWLMtJ1F6TUDXI0hEm5u7YTm2TZZXCo+iq5MhDDgpXOgpZODeMnrh27kUubm7QgDDWLcD94cXIOvKoWxIipRU2Moi4v6xjPVQKxsjQkr0kNSA3YuwYkELPhT0CfMF8tEGNjFSXl5FNgNSQ0dWneltw69yLEihWlp4p4BoW4o4hfEByY6U1fkEcMotRTrufHnqCML94hp/oNyXP0BKXY4N6cugY7MLFQTduMJn3CdF/8TexGVmMN4EX+9+3/6DDxqRUz6t7RwiIGwooePXpSs+ZlW0MNgbWPPnif3lq1nOrXj6S7GSx1vrULjRv7iIjBitm/n06fPCnYGL2YaTEsNEywD2r60TxXACNIWUAM9POgmH+UgZhaunXrTrAuASDBZdIUgQVOnxgkgGO2ICnJyUa7QJ6zli3LdgdWiGLKShqexykEohs3o/Oxp6h5q7ZMTR9AF7lvsFOuWb2SnJmMJyy8DoXXqc+ukneRJ7t4KqL0r5xjrx8TWRGCF+ScMkcuZ+XqVHdn18SqEtcbO5PXA5PK7B7uhk5BYeTgWaIrpYFTgyYVAmIAcEVZmZS75y/hylhw6RzvT3CsWNWDMIwdeROx4Kcktr+SratzZX5l7TVta9ZltKzxyWtSA1IDUgM1rQEJxGr6CVTi/reH+9JLhy5pe0jKyasWICZuiNV0Rn+IF3PmwHKnUM3KeSEngTYU31XILyoQpR6OEcSevnYBObi5k8+4F6gs9x1z6qLvykgiA1pF/DhgvS8DXikV14CHh3F2TdClY4PAKlaePP3MZEIOMrXcdfc9GktOcT9I5Nzpls60b+9eAoBCImlnZxcmCXFmKvjONGPWbGF9UoCXYhGb/vI0Zln0EyAJ17DVY3A34PY7aNiIkfTWyuVUt05d6tt/AMFilMGWks8+XS+sY3B/NFUMgUnkEIMe4PLYs9dtpbo6c/q0oOkHw2FZUlaMojo1wV0D+tCPzM64ft17bOVrTOOfeJLzrwXSsePH6QK7Xp47F0tnTp+ka4nJ5MqxQYoo1jblHPsYOqo+LXVszDKnrnjdU9fy5u/vp75c6jghtyR2CRdrOj4M7tpkYAEKY3NwMdFlHKtpKsn7bz+lv7eIHPiz68yLX85NW5E7k39k8sJVdQli7/IKNW7a8WyFNFeqKLOKucOQ9aUGpAakBqxWAxKIWe2jKX9gLZhSHSQSe5M0QevXGYg1KHIXK5nlt65EDXahytn7t3jBcAqvJzpy4tVbSNbW78jHAIlH9u+bNPWiNQQJxH/cM95dxEHsiYI9TEncLCrp/2NOXf22Zp6DpENNXT+obhmkIWb2LatXXgMgmcCmFiWxM8rg4vfs00+I/GSgrUfME+LIQAyydcsvbDWrL+KhfvplG/mwhQvSslUrmvna6xqSDnZTBFCClSwuLk4kOAYQA/U8XPkWLniDZr82g/wDAijx+nUBnpYsXyn6MfUf9K3cW90GlPWvzXxVAL+bOpYwksJauGD+XDEXxKhVVPQtVWPGPEJDhw6mRQvn06xXptLTzz5PI4eVTVWvBnMYx9X4hFLDiYu/pi0ryzKnVCpycKQEt0Tl1OA+QA+YnXbUBTcuesQnBjupwkLH4DDKO3qAV5cYtOixvYLgSEfAXqEHunC9iBNIqyXjk7cESyLcv3U8CQy0Vbez5DEsYookcRyyuYL8cFKkBqQGpAakBoxrQAIx47qxiSvD6gVogRj+Pl/LzqMwD92XlIpOBG4x2b9t1GleeC2Ocnb/KVwSPQbeJ6xZqODSvI2IhwAFc9ryWeTe8w7Cy0lh8nWO//qVcvf9Q6C7d2naWvSXBQpnDjiHSyKonrEZEqeISCbxOGlyXZcWlUs+m8D6U8vQehKIqfVhzcegw580cRyzKWbTJ59/JfKUqcc78t77hMtij569GHzdoL0UEVGH7rxL1yKjvVh8gHg2xISBTAP07qkpKRTKbo4ViaGCRQ/31Jf+A26n2DOn6ZmnnhDsju04R1oC50tDHrHCokJasnSloL3Xb1eZc+RZmz3nDfrzzz9Egu2tWzbTK9NnGp2XPpjTPzdlLPpg7mLcNTqbr+vWmZycqu0qKTmFsKnlkiAmctEWOasAg7awGg+QeB6/cfhtdOvUU3vnwsQEyj2yT+e5OXr5CnbYgoQr5BQSoanLP965THGvCNwPkaLDvc3tOiAMv78FCXHkFKZZAFPqV9XemT/3iqTp5VZUysvaSyBWlnbkNakBqQGpAQ4vlkqwbQ3cGxlA01TuiQlZFgRizJCY+dX7OgqCm41T3ShyY2Ywt866LlReDz5JTuF1GbxtojSsDivCblzufQaSx50jlBIqZJdGSMGFM5TJmzFx69yb/Rk18R+m1K0MEAMDZYIqDqKxtxv1k26Jxh6N1ZVv+3UrxV29Sl99+z272QWUGt8NN7SmEaNG03vvvkOLlywvdd2UAjAIRkc3LFX1uw3fUkzMfuHOiPgvxbURxyD2aNRYYzFGw3fe/UBLlKHfERJXKwmof/5pE+cxC6G77hkoWBn1GSL121bmvFu3HtT+6w609M1FNGrEEJo46SkaMfJeHQBRmf7VbfXBmx+zNjok65lOotQtSh/vPxJLFF8CzpxK8ELpypUswSKR/oKU0qWDl48AXiAayt7yLWWyFQvkHkhIX3jtKmV9t54cnPjPLCxlxeLcuIU4ymSmWLAsgt89Z9smKmLmTEVgAYOLd+6BneTSuiM5+gcSYsQyN33GrorOwiUcQA2ESlUpjiogls9c9Pn8G6kGZ+XdG/T1UqQGpAakBqQGjGtAAjHjurGJK+HuLnRvZCB9dl7j2gPGP5B2BFSStMNvpnnuVlAWXh487rmPAdcoKkiM5xXd6+JlwoktY8wAoKNPr/smEjZTxZy6pvapXy+OCQD4PUMrDzUI0h7LA+vXwB//+41zbA00CMKU0bdvfyP9tf0P5dRieySc7ta9O+cnK3FtVFwcE5MSSZfig9+9y3Cl63BTR8JWHYL8YSABuXjxAl28cEG4bYKdcdGCeXTrrV2pfmRkdQzD7Hs4cUydWhxE5i11ieWOQRKEzZA4RdTXWMB4sQkuhOnrVlDmho84xQe78fFvnnu3fqJZzl9btM1hPQMAy972g6C9Rz23jt3Jc9ADHBO2WFvP66GnKIP7S1v5migDMPMc+ZjIT5a18TPK/O5j8n7kWW39qjhQ4TDRPcg3nKsS9VbFJGSfUgNSA1IDVqwBCcSs+OGYOrRHo4O0QAxtrjKgqCwQM/XeBuvxSwlcbrRuNwYrWVch8NfVzFztoBAbMTa6alebtTeTBxbRgLu7u2BKLK+z1NQSS0p5dU29DrKNoKBgsZnapibqIWfbjh1/C9B14fx5SkiIZxITFyYlqU/16tUXzJOPPjZOc1xNIEy19lETKjF6TwCfwNUbjF7Xv+DApEU+E18WeRYLEq+JRM7M6lLKqwDtkGvM4+7RBNdFJHwG9T0kkBM3KwLA5j93LXsPcBweE4E4MrulIrDAOfpqzn2fe10plnupAakBqQGpARvTgARiNvbADA23a7C3YPbbGqeJq0jlnGKWsIoZuldtLbuSmaOlacYcJzYKkUmcbexh12FGwxMnDFsulKl8+81Xgt5eObe3/Zq3VzEb4mlB/PHIo2MJLokgO0EMXE1JRe6s7x5XZPEsipXQBlu4nEKLY7/K6AYssU5hdcqowZeY+MMxJLxUHcfAkFJlVVGg9hBA//p6r4p7yj6lBqQGpAbsSQOa4Bt7mnEtnetTTXT/MF9iYCHFNA3A3eZSRok1DK2easKr1FJsSgNDhg2nHf/8Rbt3lZAeKBOAC978N+bwtV00kuPE7FX69htAnZl5EaQg8+bOoXtHDhXkIIibg94yMzKqXTUV4dnQzxsmSSGq5rEVqpAYCFHULIqm3LEiz9aUfmUdqQGpAamB2qIBaRGrJU8Sua7uquNHmy5r3K4ymIYdrnbhnrqxFLVkuhadxsWMHGalK3GQmtIsjOp66Ma0WfSGsrMq0UBYWDiNGz+Rnpo0gYYOG0Ht2rdnjy9nOnrkCG384TtKTU2l+QsXU4tykiJXyeCspNOx7HYIKeLPe+yZM3TwYAwd4m3zTz9yMudVwjLWqHETmr9gcbXFh1XkZd3H2UlHoyCSqPFcYjojKjlxbsYMnWz9skUBOYci+jpXysvay3CysrQjr0kNSA1IDTC/Qnae6pdWasSmNYB8Yl1+O66dAxiv2gR6EZJySjGsgRR24zyWnKm9GOrOL+79W5GXs9SZVik2drBjxz+0etUKzh12ijkT8ggui127daeHHhlDwczQpwgSMnt5eSundr8HJf+hQwcFMHvokUeprETcllbWvuslL/ym9L0lLolmHj2nrdrc35P8XOW6olYhFjr4LymDkFsR0tDLndZ31CRfN7X7OpyVINyjIs6npt5B1pMakBqQGrBtDci/XLb9/HRGj+TOT7JL3YqTGmp4WHnOp+dQEz8PnXrypEQD59KzS074aEbLOhKE6WjE9k7geocNCZjz8vJEQmdlFufPnaNff91Cv27dwiAtn7782nQyBqWP2rr39fOjLl27ia2654h1j/xC0+8a7KZrYco1gSe9kBMmJ7/wSJk3cY5uSr5T55dZx5IX844forSlr5LPUzOpMqk3LDkmdV+5BSUPJURP5+p6xo7lepYxzchyqQGpAakBjQYkEKtln4TZrerQxsvJdLY45ikxJ49ZFJ14VVK6KOo/6rNp2ZSlevvrH+5LYKCUUjs0AIp45N46ezZWAK/fOM/YyZMnBDnFtYQEat6Ccz0xWCuLSr52aML6Z+FiJhCr4677e5atAgzlzRZgy1XFTqiurzARqsvs9Rh5FdUAN0JP56boxaUGSWBMGZ+sIzUgNSA1UNMakECspp+Ahe/vzk7589vUpZE7YrU9n2PA4cUxFT4uunEV2gp2eJCQnUfIG6aIE78wQG9SaocGYmPP0Da2esH6BRfFiDp1qHeffvTy9Jn0+2+/ipixRYuXShBmJY/blYFYlhljCWdQAJfrnGIAls35E00V5P5yv+1uU6vbbT31IhWUEOnpbrYuXOWfHLN1JhtIDUgN2JcGJBCrhc97YB1/mtQ4hFae4vwzxXImNYtaBXgRmK/sXRDzAH2o5bkQJ8o5e4biQkMoLKwkjkhdRx7bhgZycnLowftGUWBQEIOvvvTqjNeoZSsmTGCBS+In69fRW2+vpdAwTjQuxSo04FaBkEzELB1N1cR3Zqgs25aYUN6xGMra+Dl5j3+Rsrd+R3kHd1NRVga5tOpAXvdPoPyLZynrh08p//QxcvDwJLdbe3Mi+5Ecda35fc1Yz8QnnPvLlRM1Z32/nvLPnyFH/yB2P2xDHgPvF9fKGmfO9l8oZ/d2KuD7OAYEk3PTVuSJdnyv6pJMPXAbXQEgVpHnWl3zk/eRGpAakBqwBg1IIGYNT6EKxrCobT0CeceO6xo6arjunGLwgaB2exbEPEAPaunjmkeNL5yhmAtEMXRUXApnMBYWqknoHC7BmVpdVn8Md8QvvtpAderqWjhhGXtt5nR69rkp1P7GDlY/D3saoFsFLCfNvD20QAyWMaShMJde3ZiOi9JSKf/MMcp4dzEV5WQxoOpGBZfPU86ObVSUm015R2PIKaIeufcZSHmH91DWj18QcnsBkEEAoArTUrj+7wJEufe5R5Rl//6jAG++z8/lpFy6cW7KWDLWv0U5f28l58YtyL3vQCpMuEo5f3JM45EDHL82j5A4ujpEH9w29TEv1hhWTrnuVx1PSt5DakBqwJY1IIGYLT+9csa+pkMk9fjfCUrK1bjtgCHwNIOQRr7m/UEt5zY2cxkxDyd5/oo7EwbekVkl320ZQFu2MQpTydW4BMIGUcAZjhWAJsEZtGG9og/C0tLSaMpzz1Dvvv1oxMh7rXfg1TSymENHyJo+wx6C59w85sSWPl70HV3XaiwtL58CTSCUAEFG+jsLtO3UB+4DhpFzZMOSInZ/BPhBYmVI6oIXKHfv3+Q56AFy7z9ElLn3vpuSpz5M6FcBYrhQmJhAHveMJo/bh4t6+Cdn+2bK+GwNA61fya3H7dpy5QDgDyAM/Xjd/4TWwubaoQulrZpDWT9/TZ7DxyjVq3QPfSoS6elGAS7mvS4wAa0UqYFKawCLyKc55v0Cp+O5yiEF1/k9Ji2vkLK4HH/TEVbgxr8fSK8QxMypYfzBq89pexp6uZG3ZIuptP5lB1WvAflTWfU6rrE7NPVxp/c7NqDBf5/WjuEa/5CB1j6ar9mTgFTtREqWlooZcwdV/fsdoyjc200ALAV4laUX1IGlTLovlqUl67oGQo5XXnqBfHx9adpL03UGd+S/w7R3zx564KGHdcpr+0nMoaMUF3aN+lmJG65HBf4StfHz0nlMqbzgZAoQK+K0BQWXz+m01Z6w9Ust7n0HaUEYyp3qRFH+2ZPk2qmHtpqDu4ewhsF1US0Ont5sMeP2KnHr1p+y2NUxZ8dvBoFYzj/b+H6O5Dn4QS0IQ3OXGzqQS/M2hOvVAcSwWKWOEWvIODT23HmKjopUzabsQ08Ndi27krwqNaCngT+vpdNO9uTZxx49B/lv9mlmfq6oRDIga82s0Tcyo/QtvOjaNcSb3KSZtqLqlO2qSAMV+PNXRSOR3VaJBm5nJsDVbBmbsPe8tv94Jqko4v8amulqou3Axg6QlPQk/6Cn8kqaIgCj62+OpiYMwiBtb2ihtYApdQztYRFr27qloUuyzEo18PbqlXTs6BH6+JMvOK+uCx0+fEgQeWxjFsUrVy5TY05gPGTYMLvJKRZXbOm1pscFg5g7v7hna4z3Jg0NVpq6Hm50KUvzogaLvyniemNn8npgkilVydHXX7eekxM5uDHw4ngvHeFyfXGqG0UO+omc+XfHObIR5R07qF9dnBfEXyHHoFCD7odOUY1FO7g8Ovr4GWxvqUJ9XYakp1JseiLFnr0gbhHdoL5mXwYw83SW8ciWeh61uR9YuTZeTqHNV1NpW3wa/waUpEyo7LzPsxUN249XUkRX/PWj20J9aAC/F90V4UfRbDWTIjVQ0xqQQKymn0A13P+RBkFsyi+gqQcvae+WkJVH+L1rzKtFtfnPpRIbl1GclFRRwJedo6k7r44pAgsXQFZ5VjEANim2o4Hftv1K6z78gCY88SR9+snHBPB19eoVatKkKQ0cPIT6MJNiVIMGtjMhC4z0arzG5dbaPste/NfIHCAGVdwS6EPfXNIAMXzXM5hgAgyx1iAORmLAEBuGODPiBaJSwhY5B1fD3goObsXlOdy2ioFYsh6oHdKqCRWyJTE5OZWSklO0gAzALMDfj/z9fSmA89D587Einia8Xby7dg1dvXKFOt1yC93c6Rby89MDvkpncl/rNPD5hST6/HyiAGDVNTl85bbFpYltSswl8Q4wqn4g3RcVKC1l1fUQ5H1KacCEn8pSbWSBDWrgKU70XMA/QtMOlYAx5Bg7klQoYsbcORaitgksYCDmQBC/Wj6/JVqshqnLcFyeVaxt6xbSJVFfaVZ8XsR/defNnU0FBQW0cvlSatqsOQ0ZOlwwKUZGRVnxyKt2aHHx16r2BhXs3Zu5K66b6YXUJciXgVjJfBJz8q0GiBUkXDGoiUIud2JiD4VhUV3JMSSCyT/2coAZr5Kxi6JaCuMus2+kk3CDVJdb+jiffy+TWI+K3BTgQ1HBgUTYir82cFOEKMBMgDMqsZa5UiHdGBStdGF0//NPm8jVxZX27dvD7sMvUjP+jva6rQ8NHjqMQZ0EZUYVZ6MX4rLzac2ZBHov9jrFsSWsLIH1CosqHhzn5c6fe6TmceHvBAh5EBcGD0P+n317NGsa8HzBZxe57+Bai4WZLF6YAelMoaFFD263PSFdbFMPXuQcosH0eMNgalTsJVPW2OQ1qQFLakACMUtq08r7erZpqAhqnXzgonakoHL/LylDuCkGuNWej8NVdr9E/jS1uPIvN0DYHeySYEhMtYoZaivLrE8DDvzH+u57BpGPj48g6ahfXze+5bsN3worWQ5bGAYPHkoPjxlrfZOoghHB6itIZ6wkPkyZordwZdNdNFGuGdvfEuhLgRygn1hswbnOL3f1rcTdqPBaHLMrHmDK+nba4RdcvShizNy69tOWqQ9QF2QgYGd069JXe6kwJYly9+8gl2ZtdAFafvHLrL71zdxy7Z0AhnVfkHsEl/691MaK6QEzWMiweRRkUYxLVplu3LBMnz93jt5ctoK6desh3IR37viH3lv7Dn3z9Zf0/ocfa1NM5ObmkCunA5BimxoA6HrzRDwtPxVv0BCszMqXv8t+nHzOh4lhzMp7yojMGbDMiDEclnLEkKYyAU2yapFBuW86g7VlJ+PFNiY6iCY3DaPGEpAp6pH7KtZA7XnzrmJF1ZbuJzYKEcxCD+06q50SVpFOpGRSHY65qG/jPz6Yy9n0bMILmVrAorTu5gbUOUg3wF9dB8fGrGKwhoHgABuOZZyYvuas8/zJp58VA8vPzxegq1279iKnGEg6Xp89k26/4y5q3qKFyC2GOmMfH2+dE7HQqKwxPkyZmhu/RHnwlmVGnBja9gkNoC8vatwtsRIOa05Zi0oFVy5Q9m8bldvq7B28fMitU0+dsoqeID4s/Z2F5DnkQXJu0IQQ/5X57UcEl0WPfoMNduvWuRdlb9tIGZ+vpaJMzlvGBB0F1+Mpa8M6KirIFznI1A0TnxwhTgNXbygp5tV/Uc4LEYFvfVtSzlY2Uc4MkIGrvi4p1ztCsnu19A4t3zKlADPsk9l10S09nn8jm6q7KXW8c8cOcnZ2pg4dOoprERF1aPCQYdS33wC6o39v2rDhGxo3fiJdOH+eRo0YSjd26ECd2H2x0y2dqTG7FmOhRYr1a2Du0as079hVYakyNFo/Bl+BvAgMop2qynMKyxq2CGJbLb4f/BsBjyC15VcZ2/tsrcP2DC9cv9IiQjIvKoqR+yrTgARiVaZa6+14ZP0AqufhQmP3nKfYjBJfoMuZOZTCK0aRvKKMlSlbEzBCnmcQpu+K2INjwd69KUpQ2pY3J0NWsf69u2tdEhUwhn4kGCtPm9Zzff6812nLL5tp3vxFYlAHY2IoODiEXpvD+ZxY2rRtRxMef5SGjxxlF3Eq1hYfJh4C/+PrykBMl7hQuWR03z+sBIihUnx2bplALD/2BGEzJE4R9S0GxJwbtWAg1ZYyv/mQc5GxdZ6Bg1PdBuQzYRo5BocZur1gaPSdMldQ3Gdt/EwAN027KPKdMk+XWt9wD5UqBUmHOp62L+vW30zaesSJtWtYPnj7d+cOat2mLXl66ua29PZmtkl3dypkl2II6kE63NSR/vn7L1r91kryZis3Ysp69rxNuBqLCvIfq9LAd5eSafp/l+lkWsk7hjJAuBaG8jtIiIcrL77ouuAqdapqD6KuYHcXseXy4gTi5UFgBpdGtSxlC95n55PotVYR9BDH2UuRGqgqDThk5xlxnq2qO8p+rUYDyMcxYe8F+uFycqkxhfIPZD0GZJZKkFrqBhYsyGS3g4sMKA2tbk1sHEJvcnJrcwRWg1+2bRdN9K1fyL8EMAbRvyYK5T9Wp4Fr1xJ4hb0PrXxrjXh5wwA/Wb+OvmfXxC+/+U473n69e9C0l6eLGBVtYS072MKfa7gmqhcXrGmK6RyadCJF94XIlPE9GXOKE9ina6u2CvAibxcjfkraWlV3kDpvCjl4eJLP07OI2NKKeDHHgCBycNcFHWWOgF8SC9i90dEvgJkaDRN4lNm+AhfhGaH+HV3WthF15Bgxc8SPwXQjn7KtVUgp0a93T4Jb8E0dbxZWLgCt8PAI2vjDd7RqxTLx3axbtx49P/lpys7OFt9fjAPH+/ftFQDNiWOHFKs3rmVmaFIIeDC4kxYzaKT6JY3d/J6PuUgfnS3J8aeMAhavCPZOCef3CwAia5I4BmNXmGFRnWdUGd+guv60sE1dkxZzlTZyLzVgqgZsz+xh6sxkvXI1gOSHYA9880QcvXTosk59rBBd45XlCHZXxA8nVrCsTfCDiR9O/IDqSxC7OgCAwfpnrihWMeQL07d64RybAsikq6K52q3++pcuXuT4ElctCMMI/P0DKCVVQ2msjMjdw4OuXSshflDKa+Men3FrFG/+iwQXxRwz3RMHRQTrADHEiDZ2sZLE9ex+B0ub2cLEBE6hEWY3q2gDxAurQVhrztNmLgjDvf0ZiJUnx48dpZSUZHrl1ZmUkJBAvzGb6bIlixmz5gtQtnDxUgIIA9HOnt276dGxj2u7hLWs861dxKYUXrx4gdasXiVYUfPy8oTL45133SPaRdSpo1ST+yrWwNa4VHpq/0UdTxvllnV5YRfhDyDZsEYJY3CIDe8Ul3hhF8miFYF174+ENFrWrj6NqMA7hdKP3EsNGNJA9dqEDY1AltW4BhCYuqN3M+rF+TXUAks9fpD2c4LFC7w3tFKkrl9dxwi8jWUijgPX0w2CsNGRgbSvT4sKgTBlDnDd0gdhyjXsNYBMQ2WvcVc8or4sj61IAyEhoYSXs5ycEheZwMBASk5K0o4SFrIrly9Tk6bNtGW18UAh6rDmuQWY8CKvP/6GWakUzkx9iiBGFCk7pJiuAbimq2VY3WD1qUnHWK8LcC3/TRvuhr6cYB1kOmMfG0fvvPchbd76G3Xr3kOAr1u7dBX3O3zoIGUwbX5ScqKIFTM0CHxvxz02hsk+rtCqt9eKfkD0kZqayrFlQwQhiNIOTKog/pBieQ0s5gXdu/86XQqEIfarbZC38LCxVhCm1gYWnjHeEHadVEsSk308yLH1rxzWXbRW15HHUgMV0YAEYhXRWi1s097fk37u1phWtK9PYe66hlKsDF1mIAbgc5Lp4NWrptWlCgTYIoj8aHImHU7MED7d+vdu4+9BX3VuSO93jCo1B/265Z2bYjGQYKw8LVrH9Tp161JoaBj99afG3RSjAjCDwEUKL2arV62gocNGEMg8aqsoRB2w9FqzBLiV/yKvjB806r/98bdg6uvqoEsygd+smhJXJvxw7dClpm5v9n3xm67+XW/h60l9mQTFXAlkEG3Ky/ZOBmIdb+7EDP0lryDIIYbv4J7duwSIwr3//XenYD09sH8/DRtyD91z5wB6fc4s+nXrFsrMzBTDmz3rVQoICBCui+3b30hBQcHUomUrWrDoTQKgm/biFO00kNi9V/eudO7sWW2ZPKi8BibuO08v63nVwIumoa8HNeFcpbaWHgchGQ19PKipnydb6Es+o9DUouNxNGpnrEUTT1f+CcgebFkDum/ctjwTOXaLaOAxzqNxPyc3XMA/NnBZzEHyMZUkMhjCBl9vMJP5C7pZdr2pAtdFBNKm8CoU6GaTcvOM0t7W4xWs55jhaAIzQla3aMCYdFWsbr2be7+HHnmUlixeSA0bNqSAwCD6+svPKTIySrwIghb7408+p+iGjczt1qbqxxzWxDaGh1b/98QcRYE50YcXo9N0cZVOFwBgoElXJLpBfbqNGfuO7TtJh1MzRDGSEoPAB4H51S3uve6s7ltW6n7wfFDLA5FGyETUlQwcB5oAorOYjeVgzAGa+sJLpXrI4BgvfB9B2AGB5axLt+40e84blJ6eziDtX1GGGLIG0dFUxH8jdjNwW7HqbUHwod/hmEcfo9GjhgtrN1wUwdTo7s7swJG6qSzQ7sqVy+TCTJcg8ZFimgaQq+vef2Pp5yupOg3AhBjt414KxOhUsoETvOP4uHrRWfbAUTMxw1WxH5N8fMrpcEB8JkVqoDIakECsMtqrpW3BYjSjZQRNYqKLlacSaM3pa9o8PcqUQRMPtiFsEATGY/NkilhPkYCR4xvMAGfoL4t/1EG8AddDxCtkcdBvWdLEx43GNwyhJ3icNS2KG6NkVazpJ2H4/sOGj6DTp07QyOFDGNAXiRe9199YoK1c20GYdqJ8YIq1V12/Jo6D+YU+LU93EQjjMATAFOp0XH+kQRg9d/AMDoXApRovU+b8Filt7WUPEIbfXEWQJLungdxhynVjey9+mwCALk/c3Nxozdr3GQxFlap68uQJatS4sVggAfCCayIo7SEAZz179Rab0vDTTz4WLKegtDckUQ2iRfHx48cIQOzff0tb4pR277+3lv47fJg+/fwrUXSUrWfIPaiAQqWe3Gs0AFe9YTvO0N8cuqAWuPZFelcPuYz6vlV17MzvMY3Zsof3mgvpJQsWu9gz584/T9HXtzakJjae9qeqdCf7NU0DEoiZpie7rAUyDwCyV1qE0wfMgLT+XCLtvK5ZbdZXCIATNrXAaubKrifYO7G3EViS4HSE1yu4GuZjYwAG2tgC3kyVAeG+9CDTyQ5hJiNrEgnGrOlplB7LC9NeoeEj7qXridepCech8ve3rs9P6RFbtsQW4sOUGQe4EV3OKiHtKA+AKe06c4LnAeGBtPlqoijK5cWdc5zSAm5GUkprAL/ZYJxVy2PREepTk4+DTXz3hjsiaOsNSXp6mhb4wEURrsPGQBbax129SvXq1zPKkJiSoiHk8ePvelmWOPQF61vvPv1wKGTy00/SwEGDafzESXT9Oi9GJiaK3w3luj3vk/lzM+if0/Sv3vtAA7aCgfCiNgqIRuCmeCqFf5iK5Thbygb9fZq+79JIJoBWlCL3ZmtAAjGzVWZ/DbCaPDY6WGwxyVn0LZvlN11Jof9UP0iGtAKQlV+oC84M1TOlDImY767DMQT1/CmKV9ysVaSrorU+Gc24GjZqRNjsTZT4MFuadwi/2P95pLQLotoCZmg+ExlI/HUthdKLrTyw2vtwLqyQGnBRNDQ+ayo7yyBVLWMahFNTb/NBK5gug0xwS1Tfy9DxCy++TEnFJDqwYkGeeeoJBmOcyLlTZ2p/443CdVFpC4AFUg5jsnfPbmFdi2RXxH1794jYUEPADkmjQfpxS7Fl7fSpU8ygmqAFgT9u2kgrli0R8WfIX3ZL585c91YKDLK//FKIGR/BljB9EIZYMBBz1GYJ4vm5BDjQSX73wfsN5DRbyYazPn7iGPsI+RtTmx9/lc1NNwqxym4jO64tGmjLhBizOMHh3j7NKaZfC1rarh6DI04QbWFw1JRX1mD1QiLmM3fcQL/3bEqTOQ7MmkGY+hlrAJlkVVTrRB7XvAasNZGzvmaQHmLzt9/Sudhz4pKIAevRhcoDYagczC9LTzWuq9NlbFqWcHvWKbTzEzDPqpM33+DrRWMZiFVEAJotJSDegIwbP5F+/Hkrjbp3tLB8TX/5RUG0MWniOAGqUAfkHEhPoSbfiD1zGpeEC/KnzIZ6W+8+AkDB4gVXQ0N09riG2DQAPcjOnf+IRNOK5W7njn+oK8eqzZg1WxCDrPvoA7rz9r4089WXddhYReNa/s99/56l7Qm67ojNmOyrtoMw5bH68qJOMybxUOdYPZqaTdCLmvJeqS/3UgPlaUBaxMrTkLxuVAPNGCxhG19MknGJV54P80oRzPVnMnLpAufjuApyD/YlT+PV6RxeQYJLIixs7uyr6MvxZMj3hVWkSAZyjTjPSHNfN2qNlTV2i7R1ka6Ktv4Ea8/4FaIOa5+Rkp9PGWeHxhHkU6+xcmry/i52TzyQnE4/Fbso8s8OneGXpZYBnlaXSNbkSVmwIvKsIVekWiY30QWv6mtlHcMaFuoOp3PLS2hYmKC4B809YjthJQNoQsJmSPsbO1D3Hr1o+ivTaP6CxRQSGkrPskvhqHvvo2PHjlBs7BmaPmOWqIt4MySPNiQAXmprG+7RoUNHkY8MaS9iDuynZ5+bqpO/bAeDsxl8X+Q/m/qihnjkxanPMdCrKyxr6v4M3dMWy5CoGUQVagEIA2mXPQni4cGoeIwToCthFf9wrNwju8/Rupsb2JMq5FwtoAH7+vZYQGGyC+MaqMvsQdj6cwyXFI0GpKui/CRYkwaslahDH4C1bV2Sx++/5CKzEzxD51Ob1qfj6VnsOqSJ6QAhxSlOv4EXKHuWxJw8OseLZWqZ0rQeNfepmF7CzPdkVN/a5GMHXsBr3ryF2NSNYKV6ffYsprcfKFwG/QP8afGi+SJP2VucV0zJDXg17ip179lL3VQc6yeNzs3Npf379tKkp54R13GMMrgjqqVz51upw00d6QCDNAgSU2/jxNSw0v248XvOf5YpwB1cKuFaiXFgDrYqa89cE+Rd6vGDxMLeQJgyf4CxJjz/Y5xSR5EvLyQRvHkQVy9FasBUDUggZqqmZD2pgUpoQFrHKqG8amqKOCprBSqVUQHmZa1EHWUBMGXOEfyif1bXE0q5VObelUmCXm5Wnx7ff1Ibz4FcWWfYTdFeyTtSmdIf8S1qQeLmwXWC1UUmH4MpEQyXNSlgNXxj/kJmRT0lAFRefp5IRXHoYAw1ZlIeReqwpQp5xPQF+QWRNLp7j57iUgxT62dnZ2vjw2Adc3d3Fy6QoWxxgwujIhcvXNACw12c8wxEJG8uW0leXl504vhxwdK4Y8ff9OGH79HmLb8JenylrS3t9yRl0pP7L+gMGcyIQXYeEwWa/kYMxk7zAo8ic45cofYcwnFnhJ9SJPdSA2VqQAKxMtUjL0oNWE4D+mAsLv4a9evd3XI3kD1VWAPrPv1GtH1w9NAK92HtDa0pkbMpAEzRJ3JTJeYUUWoZecWUuvp7WHlmtIii6f+d1V4CeYcj87eC4c2eJI2Z7k7ogTBQ1U9uUq/CaojwrFkQph44aO+xQeDG+PSTE2n3rn+1AOuhR8bQlMnP0KqVy+mBBx8WFrNLly7SsqWLCdatqAYNRNtdDLzCwsKpQYNocQ4ghtxiiFGDlattu3bUqFFjOnHiOMUnxNPiJctFPbg3trqhtZb1sVnz5oTtwYceIVjdnJycBPviqhXLBS0/wF1bTiA/fMQoioyKEn1Y6z9P64GwUGZGBE29FF6IYDCKfGrqXHxPH7hItwZ5U4Ar++1KkRooRwMO2XnwnpciNSA1UJ0aUL+Iqt2wqnMM8l4lGlCeR218Flu2bRcWsf4M+mva4qfoWdG8qeYolDkAAEAASURBVPrOzCeOx6j4n6qvLiXQkpOXlNuKPV4mkXTWHiSFLWEAYYjRVaQVk3OsaNuI43UrxtkFuvpIL+sBYsq8lD2o72GhUgusVm+vXsX5wg4x6UYgU9JfF1az5Svf0iZyfmD0SGrarLmILQNt/YC+t9H8hW9Sr9t6CysXLFzffv0V1atXj5ZzImlnZ2cB/Ab0u426detB016eLkCX+r44xnju575dXV1ozKOPC6vbJ0wmcub0KVqweKkAg/ptrOF8+uHLtPB4nHYocMlrFeClPZcHGg2c4HgxWNwVeSAqkNYy2ZgUqYHyNCAtYuVpSF6XGqgCDehbx3ALpawKbie7lBqoURBWUQCmPDZP/ksFF8UrJR5AyiWT9sPrhoiE8W+fuaKtD7IKsJwhzqU2y3UmTEJsnFoaM0X9Gzc0qDAIc2F8U9eKrGHquSnH+iAM5aCex3b16hWCW6Gvr69O7FZycrIgBHnw4UdEN4q7IUg+EN+lWLluu60PDR18Nx1iAhDEhJ3iRNSJ16/TL5t/oq1bf6GbOHYMNPmguFesXXCLPMlWtHXrP6MWLVuJ/vv07UcvT3uBFs6fS99s2Gh1MWTIG6oGYRi0vVmSxYMy4R/oJS0vQ+sG/THnXYV74iAry3dqwlRklWrWgO5yUTXfXN5OasCeNQDgBSuFlJrXgAKCYw4drfnBWHgENRkfBgAGt09Fr7CAwf1T0bc5U4UbHGKSKioPRobRgxEaanSlD4CUI0kZwrVIKatN+8uZOaVAWBMGYQtbR1MwW2YqKvX4WTDxrc1KeHiEYFCE5UtNoIHcYQBJHW++Rcxt544d1JLPAdjUAjZHSHpamtjDfdHLy5u2/e9PWrFyNQO2FgzKfqYRwwbR+nUfijqeXl7iXgBvigAsArAhjxksZtYmMzneSS31vd34Oyjd7dQ6UY5d+VlGcdycWmb8p6s/9TV5LDWgaKASf9aULuReakBqoKIagKuYKXFJmZmZ9M7bb9Fj48aLP/gVvZ9sZ1wDAAkADAAPFQEKxnuuuSs1lci5shYwYxqrz65wFXFRTE5OodhzF6gp7293cKGfi0riWxA79R+DsWgfJKStHX8SkWsWudOuMdBUSxs/L5rbKrpS6UHgkhjgpu619hw3btyEPvr4U+2EfHx8qDdbrfQFucsgSnL4f9nl8aaOHQWRR5u27Qjb4+MmUHp6OhVyfBgEMWdjHn2MFi2YR8ePHaPxEydRSEgI3XHnnQQSEMSQWZOsZ4vO/+I1QBPjgktiHc9a+uAtpHjEi8E9EcykEKTygUVxSjMNcLfQbWQ3tUwDteOvTi17KHI6UgP6Gpg3dw79/NMmunjxAi16c5n+ZXkuNVCmBqorkXNVATBlcnBRrMfhKRczlBLjewV8JTH4UksXyqOOLZrQa0fPaYvzGbmc5BgPEBCADc6WBfFgZ9OzKTtf18LSM8SfXmsZRc7sYldR8WD9Awzbizw/9UWDUz127KgATmBizM3N4QTTe+mZyc+VqgtGR7UAfIGgY8mbi2jwPXfQw2PGCnB2a5eu6mpWcbxAFReGAdXnPJ9SytdAPbYaKkAMtRcci6PHGgaTPwNZKVIDhjQggZghrcgyqQEr0sD3330rYg+CgoK1sQVWNLxaMxRYwTQWsaO1xiJWXYmcqxqAqT9kSB6clV9E13PUpZpjY+BLXTO6QX2KDgugcF69nnPsPF1WJTa+wknoUzgBfSS/TIGa2pYEVrALGdl0leegL6Prh9KkRnX0i80+j2IQZj8wzLh62rRtS/eOvp/y8/PpwP79Aozt5ATPPt4+IgYtMChIp/HaNaspmK1fg4cME0mh4Y74w3cbaCkng4Z1bOHiJTr1LX2C72d4aIjJcaLvcM6wE6pcc7D0+NrY98HSOjS1Pw8mv4lgy+EVdguGpHH+wqUn4mlmqwhTu5D17EwDTq+8OnOmnc1ZTldqwGY0gNw4U557Rqyc7t+/l56Y9DSze1Us54/NTLqGB4q0ApBwdhu1dfl75x4xhS6db6qSqeAFD6yMis7g3tm/T48q152/q4Ogs88rNvoAgB09fkq4H2ZnG0BoqtkH+PsRtnB3V+rPgOwi1z9X/NKEanmMaODSl8sxO54cD+PM+cisXRJ4vCeZkAPWMLUgCuzxEC96rFkDdXGFjqPYuOPHepfCevDz4wTPtwqrWBD/HrdlV8SU1BTatPEHWrliKf3++2/svXCRPJiiHvFoX335OcXHxdFtvfsI9SEurXmLlszY2ITWMItjly7dhHuiMd3Cxfh07LkKf68OHobL9VEBxry9y2c8fGT3Obqu+iw19vUkFxv4HhjTX3WXe7H1K47TZCgcpQc46fMTjTkHndRhdT8Km7ifbS352YRK5SClBiyjgaysLJr2wvPUjlm52rE7y/cbvmFa5WaW6Vz2Uus1oMSHVQWgrE4LmLEH1cDbgU6kFlFCoib+S98F0Vi76KhI7SU/F2d6g2OmPrsQTytOX9aW4wD5xrDBXREr3Nb4IgoXKFjx0jnOTV86BfpQ56R4Cr6WQcnJoeTP4LOiEuFJFFTDiZsrOvaqbod8YF26dhMb7pUQH08g70BesX/+/ku4InZlWvsF816nc2fPavOVoW54hMZKgiTUZQks2yDdUZPeoL6psaxoC/mFF03KSxnx5YUkHWtYiIcLeThLXjehQBP/gfsvfjeU3GLp7Ca8NvYaPdMk1MQeZDV70oC0iNnT05ZztSkNvD57Jp05c4ZWvrWGfuSV1jBeWe3Rs5fOHMC0hVw0R478Rze0bqNzTZ6YrwGAFrzswMJj6kuO+XepnhZIPotV9EYNoyq8kq4/0pqygOmPA+d4N/RydqBMBze2OoRSckoq52Yq3xoWwXX1pTWTWPQM8aMrbFm6mKXbB0AOwE4+U92DGc0aABksYGfZdQxuiLnwSVQJVt0nNapLz3GiZk+HIgZhrJecHDI0b1Uzo4cg5wBLohTTNODF7IhgY7ytd19mX+wkGiHRNOJ7l3JsWEZGhnDvBFPi8mVLmHzJiz0dntJhb9S/k7eXJyeK9hR10vl7jd8nbMpvVXpxn4asXfjOKhZr9KscG1ugef7gJTqbUeLe2pDTO+BzL8U8DXiwNR2/G4pcyMyjCY1s38tCmY/cW04D0iJmOV3KnqQGLKaBTT98z+QcP9Jbb6+lwMBA+vuvP2nipCd1+j9x/BjNmT2LkJ+miF8SIyOjyBqDvnUGbQMntYU9UYkPQ2xIZcUaLGCG5uDNvnfRPg50Jq2I2re9gfbHHKayLGP+/ro05Oo+G3l50JttGtKPVxPpvbNX6Wp2yUsU6sXxSxU2xI4hZgYMi46VIL5Q39uUYwBCWMDgNgn3SUNyZ3ggjY0OpzA3V3FZsf7Fnr3AbpvnSTk31NZQWQB3Y81Jmw2N2RrLwIg4Y+Zs6tdvAH3Hng1bf9kshtmaY83mTJpXKvG0/hzArqtOxo7vIwSgCtYuYS0jTeoN/H5BylpIKrGstRR1lX8Oc9Lv31VMif78GZd09Yp2zNtjwQZJ45GvEAIGxS1xqdQvzPhvkHl3kLVriwYkEKstT1LOo9ZoIPbMaZrPbiyPPT6eOnBi0EuXLorV1E6dOos55vDqNoK/P//sE6Y+vpuuXL4kAsQVEIZcOMHBlX/5rjUKtfOJqF/gzFWFtQIw9Tz8GSzATfFsevlgzBQgAjBzB2/LYo7TxqRMytKjp0AcFrYzbCTyZ1CGDUQG7hykb0kB1Eotvlcy77P0WBDV9+oa7Ef3MyEH6On1BXOGVQxgDGKKDlDPj/UKkCvFchrofGsXQdZR2R71QZYCzACwFJCl7I3dS7mu7uuri0k61UN4wUFKxTWABRsFiKEXuH1KIFZxfdbWlhKI1dYnK+dlsxr4ga1hrdu0pTFjHxdzgDWsPceJISHont27aO6c18iHE4x+8NF6emvVCnH88iszRN3tf/yP48qm0LOTn6dhI0barA5qcuB4MVFeaNQvKTU5porcG6vkxtyPyuvPFgCYeg6BbjhzoN//O1emRUzdpqxjwI82ydeoBR8kRDehby+z5UHPQsZGaJEzCHmDIG4MxJBryZN9Jj3YAuLOezd26TIlPh8WruyCQrFlMstaBlu/kN+sPLkt1J+G1Qmmdv66NOn67aKj6gu9AIyZAsQAwhpJEKavRqs9V36nsEds6NV4TUyYAraMDVy5rrTfcClZWxUWnUA3CcS0CqnAgY/4PXAifKch37J+3+kQZdJvQgVuJ5vYqAYkELPRByeHXXs18PQzkwlEHY7FfvkAYgBms2fNoK1bNotEoKPuvY8++Xgd7eJEogBkAGlXr16hWTNe4WSibemdNW/Rnj27aPqMWTIBdAU+KrbunqgQdZg7dVsDYOr55SUnUOLJw0QufnQjuykmpTCJR7EVCPVAW2+qwI0P0pTb9I8Mpft52xqfRD9fTaKdiakGu8lhIIXtut5VsC4ieN+JN7UnYwEjuQIGc3kc5wlQZ6qE8stx/7BAguUu0sQEuyDqwPxNcVFEsuZotjBKsU0NlHZj1LgsGpsNwBhcHINvvIlJOkriIyUIM6Yx88qD3J0pM10DxDLZqv3jlRS6u07FiXPMu7usbQsakEDMFp6SHKPdacDDw0PMGW6Ie3bvFoDrpo430xdfbaCIOnXo0MEYWrVyGT0/dZoIDEc+m5fYEubn50+LOeFzRmYmvTxtKt03agS9sWARtWCqZCn2owFlRdzURM62DMCUp4oX0JtbRpNPUCile/hpWQLVYEypW95eaaO2HvUNDSBsV9gy9r+EZPrreirtT04vrytCsuh8LZF1udUNVghh8NUlyJe6swviLYEVizFR5mJobspNQ5iYw54SNivztvc9rOdf/nOQ1SBMy0IdiA+zNynKyqSCi7HkVDeKHDzLtjKbqht/Vxe6QCUAF3FiEoiZqj37qGd/3zT7eK5ylrVEAyDh6Ne/v2DfQjwYJJVX+l96cSr1uq03DRs+QpStWrmcjjN5x/sfaqxjsJC9/c57IkfN2EceoqefnUwjRt4r6sp/ytcAXHVs2T1RYUYrb6a1AYCp56i4WGXzAnQsx4ypwYdyrK5v6FixhhmzoEVw/rF7OR4LW0pePu1JSqeYlHQ6nJpJx9MzzbJuGbo/ygC8Wvh4ipiv9ux2iGNLCHQgrGIG4sXq8C3CPaQlzBJ6tpY+FNdDU8YTk1/yOggSGsQ+2pvkXzhNaUteJZ+nZpBLi3YWmT5cleGmnF0c4/mbigzFIjeQndi8Buzvm2bzj0xOwJ40gBw1M2bN0U4ZwGwmux86OzsTJ2MX5X/++Qe7KX5ELi6udJQZFJs1by7K4do44Ykn6cYON1FMzAFRhvZIJiqlfA3YsnsiVrghxog6ahsA03+a7k5EzXwd6ByDMWLwEcAJeE2VsixG+n0gD1lvjtPCBoG74ZmMbJEg+jJT6cczy2Fibh4nny6gTHZbzOPr+A4SuyPmZ2VTALsW1vH1oWBeNQ/nwP76Hm7UwMtdnOvfy1LnWhfF4ngxxLBFsSsiGBKl1B4NlOWerMSOKhbz4NBgmrAhhiev8ZH1deUvkBSLaQC/E9n5GvbE0+mcQJ7TA0R5yS+cxRRs4x1JIGbjD1AO3740sOHbr0Wy0Pc+/FjEfsXFXaVZr06n3n36CgZFgLS9HBs27eVXydNTs4re6ZbOhA1xZ+Mff5QeengM57jpY1+Ks6PZKi9gysuWeuq1HYCp5wqAAdY/jyy8XJoGxMqzhqn7N3SMOLAm3h5iM3RdXfbbH39TANPMt29ekmBafb2qjhXLIADnlfNnqXebaGZ8rKq7yX5rSgOKe7LyO6CALkOLMzuuZ4hFBGWsPgwcLCW5Mbsoe8sG8n32NcrZtZ1yd/1BLi3bk3u/wVSUl0dZGz+lvMN7qTA1mVyatiK37gPIpXlb7e0z1q8iB1c3cu3YnbK+X0/558+Qo38QW6zakMfA+8U1bWU+yNn+C+Xs3s4uhmfJMSCYnLlPT9TzKLEqp62eS648Bpc2N1PWj59T3vFD5BzdTLRBX5lfvc/1vcj78ank6Beg7r5CxyDtiMsqaborMUMCsRJ12P2R5b5tdq9KqQCpgarXQP8Bt3OMWF1q3rwFFRQUCBdFHx8fYR3z8vKmTz77UpQ9cN9Imjd/ETVp2kw7qPlvzKEj/x1mwo9fJBDTasX4ga27J4bxKrci9gTAlDkre7jbefFfugsZYCZUSg3vzbGGGe7BNkoBxgqS4+n60X103DmrzJxTtjEjOUp9DSB/YNvRLfWLDZ7vT87UKQf7p6WkKC2Z8s8co9wD/xJAFUCUa6eeRPl5lLb4JSq4epHcbu0tYrJy9++gtFWvk88TL2vBGABVYVoK5ez4XYAq9z73CMCU/fuPlH/6GPk+P5ezu2vYHTPWv0U5f28l58YtyL3vQCpMuEo5f26h/CMHyHfqPHLw1sRXFpw7RQXB4ZSzdoHoy6lOJAMufypK8aOCK8y/yvUAwByY/dQS4qWnT+h7eP3KAzxLjE32UfMakECs5p+BHIHUgMkaANjq3PlWUf/tt1YK0o4PPvpEy4wYHh5Ba9/7UBB5zHvjdXrvg3WirpIgOqpBA/Jl6nsppmnAFt0T1Ymc7RmAqZ+wD7+ntfB3oIsMxhKy1VdKjitrDSvpybqPYP2q6+VAN97WkdZ9el7EQuKl3ZClxLpnIkdXlgbMeZ5I5KwWL45psrRkfvUeeY9/kVzZCgXJ3vod5TMxht+Li8ipXgNR5jFgGKUunU6Zn64hv9feEmX4pzAxgTzuGU0etw/XluVs30wZn61h4PUrufW4XYA9gDCAOq/7n2A0pXHBd+3QhcHdHMr6+WvyHD6mpD0AW9MbyH/+B1prWd6JQ4TN4/ZhFosRww2RYxDsqSDugRxONfIjJK7Kf+xNA5b/ttmbBuV8pQZqSAMg6wCD4orlSwhJnBVx4lW8p56eTKvXvCuK1Amio6MbSiCmKKqW73/Ztl2b3BWA8sHRQ+3a8oHXMjACNubYMU8DS5D2YA0LYzLWlgxI/YrTQ+FzAVHAuziR/9idBtS09ciHBxdbSwusYAoIQ98AUK5tOmpBmLgfxz579B9KBQlXBLBSxgAGQ/c+g5RTsXfr1p8cg8PYUvabOM/5ZxtxzhfyHPygFoThgssNHdi61obEdVGz+B+O0/R+8EktCFNfqopjD9arIsfTJBBTdCH3/LGVSpAakBqwTQ20bHUDrf/0SwZWfkxTP1zEjqln4urqStnZ2fTC1Oe1CaJTU1NFAmh1vX937qA5r81k/oBCdbE8Zg0oLHzmsI/VpOJgAVOIOjAOCcBKPw1fBiHN/RyoLoeMII4MUlPWsKTkFM0AqvhfJGjWzFn3BRufb3xG8JnBZ0eKfWrgTEYJvTqsN1UhLjfcWNItu9UXsNsgXAtT50/V2TI5DgxScOWCtr6gk3cpXj1QShksOkc2Ev2I+vFXyJFTVyjuh0o17J2iGlNRdqZwcVTKnRo0MVhXuW7pPZgTFQFZRz5Ie6RIDbAGSj4ZUh1SA1IDNqcBxIctWPQmjRn7OE1+5ilavWqFDqCaP+91QXc/5/V5IkF0GgMxX58S10RY0l59ZZrw4khIiCeQf0jR1YDWamDFL6p4iV736TdaC5i3l6fdWMDgdnvp0kXdh2bCWRjHjt0Q4EChbCWqCWtYACdZrmpBbFwjJizBZsgKiPurFxskGKvqJ2J9/YPp83JWnnZgrk66YF17oZIHjn6B2h6KOD6MCgvIwcuHHEMjdDaALtebu3OMVkl9h+IYMG0HygGXF+WydQmgJieLiTs4EZ4BcXArLs8psURZgoTDwK2MFrmxtU4tFzNLdK4ul8f2pwEDDhr2pwQ5Y6kBW9fAyFGjqU3bdjSNrV+wlPXo2Yt+3LSRfuJt5ep3KDAoSEwxLS1NaxGDBeyVl16kwMAgkRh6PVPgf/bJepr52hzq2q27ravELsaPF2e1tQ6xPmBL63LLTXYxfywkYLHhFo6bXLh4qdlzduZ3zuunj1JQzjWKbHED4RW0NqxTe7PxIMTddEr6/r27k+LKqgAzs5UpG9ikBuJz8nXG7aoHGHQuVuJEDaYAjEDa4cxWKS92D9QRBlVFebmkrg9XRUNSyOVOgSHCFdExJEKwLyI1BFwU1VIYd5nNYk7kiLrFou5fKavKvYtifi++CVJbNJAU9lWpcpvpW/fTajPDlgOVGpAa0NdAixYt6fOvvhUgLDb2DIEl8dHHxlHHjprgaNRPTWOLWDFZx9p33hYsim8sWERubm70KFvVnnjyKXpx6nO0bOmbgpVR/x72eK68mKoBjzXoQW0BU1wQBZKwhsFV0xiCg0NoFS80TJ/xWoXviOfqVFRAvds1ptaBDoTExraaRsmfXRARA9eUN3PygoHYwRYsvxV+yLKhUQ0k5epSiYJUojoEcVu5B3cJl0H1/bJ++YaSnh4l4sSU8sJrcZR3VJMLUykD22L+2ZPk3KyNKEIC5iK2eOXs4FgxlRSmJBHYGF1QTw+gqaoVHxbPvQrcBvX1qq/30mORJfaiAWkRs5cnLedpFxpAAmhIakoKde/Rk8YyEFMEFrDMjAzyZnfG3bv+pffffYeTRc+mBg2ilSo0eMgwCo+oQ089MZ5iDuynufMWEJgY7V3wkooXduToMoeNzBy9ZWSkU05OLlsoS1xyymqvvDgrQBF1lfiwqhpjWeOpzmuIfVQ+67AEV1QUVzxFl7CQge4+nN0Vkzn/amJOkdhXtP/qaAcWxAA3okA3B3KrxNKq8jlSFhyU8+qYg7xHzWkgPV8XiFUFUYeh2XncMYJy9vxFqYtfJo+7RomcX/kn/qPsX74VecacwupqmzlwfFj6OwvJc8iDwopWwPFgmd9+JKxmHpyPDOLWuRdlb9tIGZ+vpaLMDEHQUXA9nrI2rKOignyRc0zboZEDR08vcSXn3z+oKCNN5BnTujUaaWNqsb5e0/NlTLapuqvt9SQQq+1PWM7PLjXQtl17wqaWNLaGQfJyc+mVl1+ku+4eKJJAq+sArH30wXsCfNWtW5fuu3cEzZw1h7p176GuZrfHcPuzNMj568/t9MH779J/hw8JK2RoWBg9MuYxGjZ8RJl61n9RLiuRc5kd1fDF5ORkArGMkoC8vOEcP3aMnnpyAs2YOZtu7dK1vOplXi8LdMC65O8KymmAMgZkHNKRyuDMUuLv70sg60jmzd/MeDE3Bl8g4MD4vC34VxyfKegEm6S0t9STtu5+coop1ZVRVo89jI1TIeHk98J8yvh4JaWvmS/ivJw4Xsyta18CSFOLc6MWIq9Y5jcfCqsXgpqd6jYgnwnTBHOiqOvoRL5T5gpK+6yNnwmgpqkXxeXzmNijobpLg8dOdTg+re3NlMcWNCSe9p/9NlkKiDnymNWSAxdKKVIDrAEL/oRLfUoNSA1YswbAmAhZtGAev/j505QXppUaLtwVYQl7570PqXXrNvT9d99ygugptHjJcrq50y2l6ttLgfoFVR8AVUYHHzIAe48tk+MmPCGAhSMzlv3KCbcXL5xHB2MO0Gtz5prdfdsbNJTkZjesoQavvPQCIXZx+cq3yI+TqpYnXt5eFBISQiGhoeVVLfO6vjXMWGWQnQVzvFUwG5vxzprKgCwtr4jSObQmSze8xlgXlS7HGAC4EPvl4+JAHgzEqkoU6y9ixpDyQErt1oC+F54uXKj83N269mNw1c9gR071osl32mIBrmCBUsdw6Tdw7z+E3HtzMmeOC3MMCCIHd/Yh1hMHDy/yHjOZv6iFVMDujCIps0LUoarrP+991ZnqkF0XvceX/ruoqmGxw9oQi2oxZdh5R5VwZLBzzcnpSw3YmAbS+WUXgvixefMXaV27lGko7opPTHpKgDCUDxw0hD794mtBBLJi2RKC+5y9iuK+plieKquHM6dP02pOyr3yrTV0/wMPUWRUFNWrV58eHjOWlixbSZt//pF++H6DybexxVxQSUlJNPm5qZQQH0ePjXmY4uPiyp0vdPTxJ19QkyZNy61bVoWyrGHG2iF8BpYy5CNrwRT4bTmmrAnHY4EKP4jdA8FSqGKpNtaN0XIsmivWLuT8auDtIPJ+tWF2x4bMfBjKgLAqQRgGhoUG5bOugFWjA5YXbF4D+rFLNQEQBHmHikjDqFI5z5hTRH2DIEynDQMqWNcsZc3S6buCJ0V6iNdZz0JWwW5ls1qgAQnEasFDlFOQGjBFA/XqRwp2uRemvULRDRvpNLl+/ZpwV+zSpRvdx6BALfW53eJF8+nrr76g69euqy/Z5THcEysi58+do+mcKuDokf9E86++/Iw6dLiplAspLoIFcOiw4fT26lXMzGzeq5GlXScrMldT2nz95Rf0wOiRVD8ykt774GPKz8+nsWMeogvnz5dqDvdFWGcVcajkS4wCMBTAofRr7h5M3z5spQIVfhSDpmYMzgCa2jFAQ+JkkGYAQDXwJork8JP6qi3UKYf88lIoyiNf5Phqze3ac7tW3A5083U9HTjuiwgxYNUtAGPhTOChcVM8Ut23l/erRg146NHVF5r5e1ONQ7XpW+k7IqoTPNv0xOTgK60BCcQqrULZgdSAbWgAOceWr1zNsWH36AwYcWHTmcbehXOygLpe/yV3888/0XfffkPTXpourDY6je3oRHFJVCwppk4dwGLG9Jdp5PDBVMS69vXT5I9KTEwUxCjG+hk6fCRbiuLpMMeOmSIg6sDLsy3IL5t/pqVLFtEM/ry5cCB+RJ069O77HwkimbFjHiTEgallzduraO6c1+jUqZPq4gofK89QeaYV7shIQ1jOAKDgSggLGog04N4ISnll83XMJ7eCbEpnQgHk+HKxsr/GiouroisjU5XFNq4BPxddpG9tiYZdO/Uk1w5dbFzLxLGmugtq+nq3+QnKCVRYA1b201/heciGUgNSAyZoQB9kocl7a9fQvn17BUOiAhKUrmDFeeP112jQ4CE04I47lWLhoghmRnsTxYJiinvihQvnaearL9OwIfewleeccPGcM3c+1a1bT6jNmd1sriXEG1Vh48ZNBInFlcuXjdZRLpgyHqVuTe//+fsvem3mqzRn7jyd1ArIdbdm7ftsIYui8Y+Pof3792mH+syzz4s4ReiksmIpa1hlx2Ht7WFZVT7vWzheTErt1ECwqy5VQJ4eYKjpWbv3utNojFlNj82c++sDsWD4IEuRGmANSCAmPwZSA3asgUOHDtK7DMQmclyYPg14bm4OvfjC88Ja8fxUTQDzr1t/odGjhlPPbrdS717daPTIYRzL9JPdabAs90RYumbNnE4jhg4SdPSdmOQErnVRUQ109NSlazfavXuXuKZzofikoAC00g4CjBm6ri5TxqNYMdTXrOkYRDAvTJnMboh5gqBDf2yw2iJmDp/FJyeOoz///ENUQZ47SyUZVyw8VWUN05+TsXOwElq7QEcAY7C2KgDW2scsx2eeBnzZIuatCmzMLdC13JjXW+naRVmZlDhhMGVv0bgW5x0/JM7zjsaUrlxFJWBRxBiYlraK7lB+t/osiRHubC6XIjXAGpBATH4MpAbsWANIAg2XwwcefLiUFhYvXEAX2aozb8FikfB5DccrvTztBWrWvDl99PGn9N3Gn+jugYNoyZsLCdfsQZSXd+Vl3tCcQcXOgV30yWdf0RvzF9Kzz02hS5cu0k+bNupU796jFwF4LGX9GZLtf/xOzi7O1OGmjoYu65TFxV/TObfGk5MnjtOzT0+iceMn0shRo2n2rBn0yfp1pYaK/GBg6ezZ6zaaMvkZwmKBpUQBE4qlx1L91uZ+1J95RX+1eb61ZW7mPKsoL/7NKhZ9wKCUW2rv4O5BzlGNmXCD2WiqSRz9A8U9azLhfU5BSZQYwG+gniWymlQhb2OFGnB65dWZM61wXHJIUgNSA9WgAUdml2rOYEzfZXHrls20csUymj5jFnW8uRNt+uF7EdPDvxfiRRrU4b6+voJd8ZZbOtPr7L4YHR1NDaIbVsOoa/4WAD6waHgzlbq+AIgBRAQUJ2YOCAikc2djCXFRw0dw4lLWOQT1GjRsSCuXL6WsrCy6mfWsPAdYyl6d/pIAyNBvefL3zj0iPkx5aS6vfnVfhxvrIw/fT0OGDqNHOcl451u7kAPrYRV/xmAdw2dMLdBRz169KSw8nLp27a7Vi7pORY4VF7v+fXpUpLlF22RkZNLp2HPiudlCbB8+89is9TNm0YdTCzo7ePgo4XdBWTSKY5IhY5+z3+LT6Xhatpg1DGJ1vZglxlLC32+RpLlFW0I+MIAi0NmDgr66BMBPUOg71Jzt4VJmjjZOrJWfBz0aHVxd05f3sXIN6DoHW/lg5fCkBqQGql4DiG2aM3sW3X3PIJHwGS6Kq1ev5DixoXTPQHbv0JMmTZtxvbsYaGwWL896l2vtqTnJncc+Pp5GDhtMP/24UehVUUq3bj3o9TcW0Ly5s9li9gO1btOWEJcHC9rzU1+kwUOGKVWN7q09Pmw/xx824tiuWa+9LtgglYmMZUDmx8QlC+e/QakpqTT1xZe0IBV1AMYMfd6U9ubuFQuBtIaZqzkNpT1a4aUeepRgzHwd1mQLBYwpe+U7oDzHFj7u9H3xAMGamM3WG3cn80FLwcVYytr4OeXHHmdiogJybd2R3AcM1Zl6/vnTlPnFu+Q54lGNlYqv5u7eTtm//kAFVy7wCpWbKPe4axQ5RzctaYtxbfuBcvf9QwWXz5ODjx+5trlZJH928GJaUpasTZ9TYXIieY0eR5k/fEp5Rw6Q14ixVBB3kXL++Y18n5vDPyzVH5sFnWYhM3yxtPTlpIRSpAaKNSCBmPwoSA1IDeho4IfvNlA4WyLwYgw5sH+/yO/0GIMJY4L4p393fqZzGbTrioVH54KNn+DlRfNCetTkF9IGDaKpX/8BTIzyjgC3Tk4lLwMov4ndD2EFiz1zmgbcfqewjukTp5SnNmuMDwMjJ4BWYVGhiP3SnwMshD4+vjRrxiuEhONIYA0Sk6qQkpfQllXRvdl9KmkGbMGtFJPD5x5jtTY9mq14O2mA3wPE9hkS5RliDyuZn2egTrX0vAKzgVj+qSOUtnK2cDl0vakr04C6Ud7Bfyl91es6fSNmLP/MMSrKzBDlAFbp7y8h5yatCOCrKCuDQdM2Slsxi/ymL2PLmcZylP7hUsrdtZ1cmrcl9z4DKf/cKcr+4ycB+nwn8z34d6Mg/jIVJlylzA3rKPu3TZpcYh4eVJh4TdyTLBv+pjOvsk4yVCAM9dqwRUyK1ICiAfOXPJSWci81IDVQKzXwxJNP0zvvfqBN+AwLTUREHYI7ojGJPXOGwVuE9jISET89aUKtSQCdxsmwP/t0PT337FP04tTnKMjfR8zVHGvU2MfG05Url2nTRmXtWasuAmNg/wG30/iJk6hP335aivuSGsaPrDmRM6xa/2fvOsCjqLro2fRGaCGEGnqvAv4iTXqR3nsREASkF5GOIF16kaZ0kKIgVUUpioINkN47CTVAetn9731hJrMpZJPsJrvJu5/LzLx58+bNmd04Z+6957L4hr29A3r36o579+iNdyxr1LgJ5n6xEJwTN3zoxwgNjQ6RitUtRZvSG5Yi+NSDG9StKdb5AT4p3311ALlidQgIsnbrutG8mIgl1Zj8wNEJnqNnkberD9xadYPnJ3Oh84j+W5nQeOH//i4KL3sOnQqXBq3g2qIrMn08kQhZMCLO/S0Oi7x2UZAwl3rNkWnIZEHYMg0cD9fG7YiIXUHExdPq8OxVi/jvL0HiMk9aAvs8BdR9abXyKiLS6NSVslIFeGkSgdcISCImvwoSAYlAHAQyZ86itnGuU1BQYIKFhTn/h9UU33qrkjjmxvXrwgvy++8n8IjqYNm6nT1zmsIKW+K7b3dSKF0W6Ene+cCBveKyFLVCU67Rt0ABNCCytXbNKlG82JRjktJH8bAk5ZjU6KvI0uf0zok+vXrg6tUrcU7LCpJLlq/EpYsXcJE+5jbFA6CEYpl7/Iw0nhLWZs0vADLS/UjoWpPy96BOmaIoqMkLexmLOCR0DqVd/8SfPE6X4Vy1DuyyxaiB6pxdROig0i++pV02bxjCQhF6/JCqamiftyCyzP4aTm9H53KG/fGz8Hi5NmpnNIRL3eZw7zoAuswxHj0ey63tB7D3iS4TYnRAGm28DI8hto5UZLBq9uhQyjSajjytlSFgmRgQK7tIOR2JgEQg+Qi8TfLrwcHB5LE4glrv1TYaiEPPZs6YLvJ5OnXpKrwZ0ZL3eUSYHQt62LIxyRw5fAi6kqpk9x691Es5uH8fHgWEijCtpDzc9yWvWLs2LfDTD4eM6rKpAydjxRYKOXt4eGAxecbYm9ivTy8sWLQ0TrmEChUqkhLnAbi5mfdtsfSGJeNL9YZDlO+74hVLygP/G4aVu1KIgOKhVAhyQmGJsU/DxJrvaa3wO7gZFCZ2cz5TUvLEoigckM0hf2Gx1P7jUODNtf9cajQgD9afCN66EiG7N4oQRQ4/dK5SQ3jKeKyoRw9hnz0nlFwwZXxWXnSuVl/ZjF7qdHAsVcG4LQ23uCybltjWyuEBLvguTSKgICA9YgoScikRkAjEiwBLrLN6Iqv4HfnlMHmEopOOH/n7YyzVGTt65BfK7ZkBd0qYnjVjGgkvvMBwkmxn4/wfW7M/T50E17ti++OP3xFF16slYdzOxa0Vz4DyAMTtiVl+X198SWGf7BkzhyXl3OY4X2JjvHgRgEULvyDv4S4hOx8cFJ0HwsdxLTAOQWTv18CPPsTvJ36LM5y5SRifwNq9YaY+MMcBKw0b+MGdv/+ShKXNTeDfPX9YBZQ/6zfvxCFa8oe/T8rLmYRUEpVZN6RQU4VY181pHEIYEG4cTqccE++SlBGFcemO2Ebhim8yO6+c8Bw7Dx4fjoZThXcQde8Wgr9ZjYDx/RBx6Yw4lMMUdfT3wxSzIxGPtBDkSGhuAeERXM1EtXo5be//ierk5YpFEJAeMYvAKgeVCKQvBJq83wwRERGk7jcdXF+Maz09eHBfyIsvWLQkWuL++z2kCrgXSynEjEU6+MFb1NR6DQUTuAnjPkHHTl2EOqC1IcRiGSupHtoVCp0bNmwkypOHhlX9XpGIBIfLcc01Jhr//vMPzp/7DxF6qgWTIxdu3LqdpAdS9vyYy5TQSGsR6ti4fh02rPta3PvIyEhB2n1y5UKRwkVJObGIUE/kmnUulEDP+WAszlG/QSNzwRFnHGv3hvGDsi0SMQZaeYCPA7psMDsCyguXhLxdCuFS/g4oBJmPi+/7xf25r9KPJ9zYhwiMxp6HRcDH9c0kSunOZIpN7/9AaVKXehLQeKNRkWUdiRc5VawqPtyXc8NerZiBEMo7cySSZp/dGxEkBiIYDf2/RTEDKfqGHtwBhyKlyAv2+u+qg6Oy2yqWz8KMCW0jH0nErOLGWNEkJBGzopshpyIRsGYEWrRsjcZNmgpRhdCQUCEw8U7Vd0VYIqv9sTeMJckrV3lbeM5ihyWu+nI5fj1+DB/2G2BVl/kXEzCa2+VLF9G+QyfMnb9Q5ILxJCtS3lup0qXRs1tnqhnmIZT9nEheuUTJksiWLbsgYlev30alimWJdJr2xtacF68o7mkfqMw5flLGYk8oi41wXuDhn36gQuBzkSdvPly/dg3Xr9Pn2lX8QPXpHj54IL4zUfQANnniBFSoWAk5csTklSTlnIn1tXZvWGLzl/szHgKJkS5GhIlUTm8vUcvwTb/9+PbxsYroihZdDwcqF5E7C/Y8CBDNnNcUQjL2ribI2NvnzA07IkthJ36CS51mIHUedejQ4z+o6/GtvJgxAnaeWZBp8GR1t2OZSnAoVAL6p9E5xg6FSyD8zEkhwuFYroraL/yPIwg5sEN409RGU1foRRExO5orPQZryB0l8Cat/Q3niyJX2DMitIqxSEcJKhUgTSKgRUASMS0acl0iIBF4IwLs4apXv6FRH1a547ywsmXLiWK9vJNVBjNp8sNOnfxDiFRMnjoNLFphDcZE4OMB/XCOvFtMwGbPnY8sWWJESniOfL3LVqzGb79SnRu6Tpbp5wLYiqfv6LHfcPueH06e/BM1alSPc1lhYWHYsX0bLl44j2mfz4qzP6UNSghSSsdJ6fE7vtmGDRu+xs5vvwffYybhn4weSevTSY6/idHwnG94g4g7EzPvnD4WJGHRoh9KCKnRJOSGRMBKEEiMeCXk7TJ1+ny84hVT8sESOrZt3hgixn2ehEYgn0bEI6HjOBSQpeeD1i0iCXtSP6zfEjpXd6r59RupHR5N8DDe4UTEislU8Lcb4FSJCr07OiLyyjmhhuj8drRKp3OtxqKGGEvYCyGOPL6iRljYkX2wz5UPjmUqv/Ec8e18uWgyIq+eh+fwaSIvTenzcuEksBS/54jpwtMW0z6R2i/Cc+QMKkxdQml+45Lx04Ylts2b9Y395c6MiYAkYhnzvsurlgiYDQGuExUQEECEZZXwdPDAXBPK83V+2NOnTzBh/FhRyJg9amzbtm7GPgpl5HpatWrXQafOXUU4m9hp4X8e3L+PXLlzi3NnJ08Mq0KyJ0dbv4proP1K5KtatRpCPCKh8DlPTw7n8cOLlzG5UDx9JmA7d3xDnsGf0ZqKMnMOnblNeYAz97hJHe/QwQNYMH8u5pMAh4LhiFFjRFjnRLrvr169BNcLU4zzwMqUKSs+SpslltIbZglU5ZgpQUD5zSYUYshjm+rtSuo8OB8sPg+Zdpz2+bJi2Jl7ePo6nO5xSLhpRIwGcX6ntvAshRCherV4qhiWQxZZZv7lF+O1pzFaF7XDQkPApCr0h11iH5MxZ6pF5tahb/Q2RRuwLH7QhqUI2rScYiCjVQhZ1MO9c39B3owGtZKNx0TEtNYxvyRiWjzkejQCutAILV+XsEgEJAISAdMR4Lyv6Z9NEV6PKm//Tz1wxbIluHL5sgjzY2GG58+fYd2GLYJs7dj+Dcnbfy68Z1w4evu2rYigZO9ly1eJcEd1EAuscB2vVs3fx+cz56BO3XrgGmmsYjh23ES0bNVaSPSz+AiHKj5+5I8vV32FQoULC6LJ4Xat27RTi1TztS9a8AWy+RQUM+3euQ3CKWdh547t+OXnw2I8Ls7MtbQsYfxQx8n5pjxgWeL8POaJ337FqBHDMH3GTLxXu26c03yzbQvlFM5C334fibDVOB0s1MC5YUzEEvMAWOj0Jg+rzDMt76HJk5Udk4wA/0Y5j5NDiBWvlHaQlHq7tGMltK6Qv8RImHL8p/89wBdX/JVNFKRQOm8Tc8WUg/QBT0XInxDOUBoTW9L/A6KePyGSZaAiztmhSyDU20C5yhyyaJclmygendiwabX/OZHZKy+C1dMzCfu6SgF1W65IBBQEpEdMQUIuJQISgSQjwCSDFRVjmxKauHrVl0LYYt3GaBLG/ZjQsHLeh/Rwzsahaz27d8V4EvJgr5oljQtTv/tudXC+Wu06dcEqhkyW1q5ZSR4cT6xetRL+fn4kV99DhCsqKn5PnjzGgi/m4g+qjcbeMSaWe/fspvpqQRg6shpu3X0QrWJ2cJ8gkytWrrEYAVPwUd6qK9upvWRlSZaj/+TTcUYkjEM9v6JaaXPmLRAYsmd08qTxQuhk+IjRKpG15HylN8yS6Jo29uPHj/HXnydxilRIOb9yxMgxph1oo70UwqP8LhMiXqbkdpkTAlMJmHLO3gWzGxExP/KKJZWI2WXJrgxn+pJENuxJ/CgxY2+ZvU+exLql+X5/wk1rvQt6aTflukRARUASMRUKuSIRkAiYC4GXFI7GhZ0PHtiHiZM/Q8GChdShixQpCvY6hYeHi1wrFrkYN34SkbHO8PN7CB+fxP9nrA6WjJW+/T9C9y4diRD+SPluDYSnpm3r5vhsyiR0694LHTp1jlPLiufMdbDWELFcvnSxIFvvN22OVm3a4tKV6wARMX7z3a1Hz2TMKGWHJPVBK2Vniz6aizIPGzIIHTp2FiGnyph8z4d8PADduvVQiShL/XtQCQQmbUWLFkPzFq2U7hZZWrtSokUu2goG5XDkf/7+E6dOngSXgLh166bwJrOozT9//0UlID6wWD5gWly+NXi7LHHdhT2c0dU3GzbefiaG55pij5JBxiwxN1sZk71hLzTy/1waoIaXLOJsK/cvtecpiVhqIy7PJxHIAAgEkljHlcuX0LR5C7zftJlQymOSxSqEvT7og0MH92PO7BmCgDEcHJrIxrlZKTXOSfty+TIMGjxUiEbEHo9l6GvUrEXerxWoW68+8uXPT2qQ79PD4t+CSCl5TspxPB57yVh2fvHSFUqzumQZb/bARIfClVLbLbkiHgIp7EkJbbLkueIbO2vWrJRv4oM9e75Dg4aNULRYcXGPBw3sh+bNW6In3WOtsQf06w2bUahQ3IKv2n7mWJfeMHOgmPgYHIb7N/1m/iLSxcTrMv3e2WrWqo3OXbvh3WrVSfwmK7p0aocu3brbNAmzVm9X4ncpeT0GF/VWiRiPcD84HDkoPFGXvOEy3FEPgqMLYysXPriIt7IqlxKBOAjYj584eXKcVtkgEZAISARSgICDg73wbs2cNU8IOHDO2K5dO9CmbTu4klhDaRJrWDh/nigCXYbUFkNCQkg1OBJN3o8W80jBqRFJOQSffTZZ1JxhKf34zLdAQfJurRRv7AsVjq5vxeF0LKNeslRpccizp0/x5YplYMGJKJJxZvL2JuM8EB/vHBSG5f6mbmbZFxQUjOs3b6NwId9UJWP+/n4izMzNzZ0IWGORI7Zx/dcixHPShHGidMHoTz41Cj9cunghjh87iqbNmhu1mwWIWIOwN4zvA+eGpRVJjTWlN276v84f8vBws4n5ai8mnArVjhk5HByC+O671dC5SzdSFz1OLzN6kZe0hfiecDgv52HOmDVXFXLRjmGt60y8+Pd19txF/PbHX2KdtwPpd8cf/m7x77zaO5VRhH6D1apWVn+LqfH7tzRuOV0ccZ+EJk4HhIhTsQw7vyLL7CTf3SeGPYckPgqJEengumHjS1k2yiOxOcn91o2A/FVZ9/2Rs5MI2CQCLHFft14D9cH77t07qKIhRW9VqoxRY8ZS7bHpqFS5svCofDp+YrKulQkT56Fw8WWua+ZJy06dumDLlk3i4ZC3Y1uJEiVR673aJMqxgkQ7yCuWL78ggV+tWY3q1Wtiy+aNQna+bLnyokA1F3c2xTg8MTVCBZU8FCZ+qWUcatb3gx6UW1eP8sLGC28j5/SNHjEUo+mBvGLFt0Av9tR7zvNiksbqkV+u/ipVpim9YakCsziJKxXl3rp9lxqCyo0sevP5tKlUsL0cWJ10+zdbxe+Hi7tbqyXm7VIIfWrndqU1XmNL+GD9rWdgEsb2ICgM2Zwd4E4v2aTFj0A4CY3cDTT2ho0t6RN/Z9kqEXiNgPSIya+CREAiYBEEdJoimRcvXMD58+eM8oM4RPAYeUruEUmLT3EvsUndvXMHX8ydjSkkBHHkl8PYv28vLl68IELlihPR2rp5E6JI5lir5qgd09e3oBCVKFioEAqTV6xI0aL4eu1qbN5E9Wyofth4EiHhAtWm5KzxwxqTgGhvjOXDE5W38/wmPrXsrz//xL69e3D1ymVRB4yJLD9g16fQxNu3blFu0B/In98XnE/Htmf3t1i8aAEWLVmOUq+9jJacq615wxgLDvXie8nfH+WB35IYmXts7W+cxy5cpAhu37klyPeBffsoB7M+OtJLEa3dv39PFE/PkyevtjnV1pPq7eLQY8XznB68XaYCndnRHg70N/zI41fqIaFRBuQgb5m0+BG4+SoUQZHR0vrco3/hHOgjRTriB0u2qghIj5gKhVyRCEgELIVA954kgtG2FdasXonefT5UT5OJRByeP3+ubpuywt61tTTOwQP7hXeGvS3Fi5fAjz8cFFL6+/d+L3LTOnXugs3k2erStTt5y4wLNfN5ipcoIbxiq8grxt67vHnz4eMhwwRp4Fy2pBqHwwkyRmFNlvaKpUUhZ5b7Zyl/JtC/Hj+O4UM/FkWw2TMyfcYs4d2cQMqXr0i0IbtXdtr+HHO/WIBy5SskFcpk9ZfesGTBZvaDRo0eSyUimpCnzB6Dh44wGp9/szM/n0Y1/OywedsOkWdo1MHMG6Z6u8qXKSnObOnfrZkvz+LDjSmRE7sfBOCf59Ey7C9JgOI+ecbymFLk2eKzs64TsKDJU03dsNyujvisjAxJtK67ZJ2zkUTMOu+LnJVEIF0h4OWVA8u/XI2PB32Ev//6U+QXsWeFk/w5fyQpxsTph0MHsGHTVhHSqBzLanxbNm3E0aNHBBHrTARsK4UnbtywHgMHDVa6qUuuA8YS+l06tScVx58FqWPSllJLUngihVUaiLggmB50KLeN1EpAyTSAiyuQyQM6KjZN7jmjKSkPl0aNqbDBpQr6ftgf8ynvZ+nylRg2dBAG9u+LBYuXiTDFseMmiLDQWTOnw9HREZOnTkdVyh1KDZNKiamBsmnn+Pefv/Hs2TMqXzAf/KKFLZjKPMym2oH76CVJi5atqexDICZ8+glWrFor8sr27P4Ot27egCN915nwf9C7r1HIo2lnhighkdZ1u0ydq630m1UuD+ofvapO9x4RMXfylmWR+WIqJuwFY2+Y1hi3TDKMUwuJXE8AAUnEEgBGNksEJALmRaAYea12fvu9CP3jsDV+WGdPCisXvsmYMD2i4srZiJSw1D171Fh18fz580ZELJjITBgpuSm1v/ghsBMJCGzasA5dSbVN8Yqx2tu3u3ZS/tI6Kti8Fkspz6kS5ayl1BJUT4yKguHsWRguXIThKj3Q3LwJw717/NRokkqkjhQKkTsPdAV8oStSGI4k7+8cHITyZWqmdMpJPp7z6VatXIGzZ09jzVoiuAM+xId9emIJSfsz2WbCy7l6LkQkWU0xtczWvWEc0poejCXsp02dLMiUEm58gUKSxxPpCgh4jrGfTkCNWu+BvaisptiHcg5v374lyiBUffddPKLfBNf4CyAv+cjRn7wREuWFhJIvGbtulxLqKb1db4Qx0Z0suz6BxCY+u/BQ7XvjZQhKZ3WHM3k2M7pxDt2Nl8YkrG8hL7TLS3+3pUkETEBAFxrxOhPThM6yi0RAIiARSC0E+KFu13f7qCD0GRz5aT9YMn3qtBlCkGPi+E9x+vQ/tH+vUGPjcMVxn4zGvXt3sebrDWrdMi4s3fz9hmjbrgP6kvfru293ijwwVm7s27c/GjRqnKw37wlhwJ4ZJgXv5/FG1ovnYfjjJAwk8W0gMmlui6LabI7VqsKOvE66WuYlZVFEHkNDQ4SqZex5//TjIeEV+3b3PnCZAvZyBga+El4yDu9MbVMw59BQJsO2Zus37xT5YQ3qmvcepgUOLM7Bdfa279pNL06yY8P6r8U2K6IuWLSUSPxysFDP4CHDRR4ZhynOW7AINWu+p05308b1ou4YFwRnL2x8ptxz7T4mXhlNUEN7/ZZeb/kb1YX0I+/9a/Mgr1ipLO4kzqO0ZMzllRfB4Lphir2V1Q0n6hRXNuVSIpAoApKIJQqR7CARkAikJgJMnlgw48iRo2jcvL04tVdWT2zZsBoPHj4g8vU9XgS8QLs2LfABece4PtnB/ftEaOHQYSPgnTOn0XT5DTs/EHJRYRcXFyHA0ajx+wk+5BkdnIQNw6VL8F+3Ac7Hj8PjeXQx1CQcnqKuOiKWunr1YNeoUYpJGddymzwxWgClYaMmRGLbg72ZirGHslOHNoLctmvfkUhYIIYNHkgk+B52fLs7XvKmHGuJJRMZtu6d21hieIuPmZ6IWASF166k3xuHH/P35OKF8+jdtx9evniBv/46JTzPVy5fxqq16/Drr8foezMIHTt3xYiRo5OEM3vD2BMmvV1Jgi1Fnf0p/6nmkSu4HRSujpOVVBSLZXZTtzPaCocjcm6YYq7kITxauxjKZabSZ3zYAABAAElEQVTQcmkSARMRkKGJJgIlu0kEJAKWR4A9MZ3atxES6CxnzzlGPxw+Bg47qt+0PXZtW4ejR34R6otchHk1hclxCNSmLduFkER8M+TwxF+pvlH7Dp1E4eaE3rLHd6wpbfo9e2DYuQv6v/5CdhMO0FEejM7FGTonZ1B8JnScR2BPHzt+tUwfDlLgcEb6cN6YITwchlCSRA4NFbV84juFgcIyDTQPnosub17YtW4Fu3btQG6J+Lq/sS2I8nmuXbsqartx2FjvXt1RtGgxtCGvYv0GDUR4KKtJcq4Y5/t4eHhgCeWMnfjteKqTMJkb9sZbabGdnAPG6od6+o5yHT4lF4zDjXPSi5D/zp4RLzo+obzBVq3aIJy+w716dMWNmzeEsil7yHLnyi1+54d//AG+pLaZydNTiHi4uroJBVMuGJ6QsahGA/pISz0EuLbY2sq+qKvJF2NP0DUKUyzimfGIx+1AYxLGd2JtFV9JwlLvK5luziQ9YunmVsoLkQikDwQ4tGnb1i3Ys/eAEH/gq9KGItkjEl06dwCHI7Zt1VzUtGrVum2qX7x+w0boN26MzvdK4Ow6Cq3S0QOmjoQ37NwpjMed3h4z6UqOET8zhBDhCgyiTyD0L18JkpbQUCwtruvSBXY9ukOXO3dC3eJtZy/X8CGDcIdKBMyYNQdXSFhl547tePr0CZo1awHGe9TIYWjXvgN9OsY7Rmo02ro3jDGypWvg4s1cnP3ggX0iF5MJFb836NCxE/p9NFCQryuXLwkBHH6JcvbMGSxeupzqipUH16Hr2a0zCXUEie8Mi3owmStRohQePLgvQmHZi8b7edmIXrRMIcEXc784SY3vZXo+x/Z7z9Ht5C2jS8xOJC0jkbFbRML8g2M8YQzGFxXyYgDJ1UuTCCQVgfgDsJM6iuwvEZAISASSiADngN2+dSvOUV269RBtnCuimG++3Lh17aLYjIKDIGZKEea1q1eR44gUB1PJ9Nu3I7JBQ0TNnBkvCdORV8De2xsOxYvBsfJbcChWBPa5fIiQkYJcckkYXxs5yzgE0Y6KONsXKgjHCuXgWKYU7PPmgR21xzYOMWSiGFm/AfRz54FiCGN3SXCbvVyLSYCjZKlSGDFsMD0sl8Q3O77FnLnzSTjlETp3bAsupP0V1V1jb0damPSGWQZ1JkH84XwvJlWKhYSEYCiFoDJp+nr9Jvz48zH8fPQ3KuI8Qbw4WTifvmNkRch7ymIc7LFu1aYtBg3sT/mc/6JAgYJEylbQT8BeFEvn79hqClFcueYrES6cLbsX9uw7hBMn/8akKdNEuPGxo78op5dLK0GARSiWvmWcC8qy7ZcpV0rPrDyd23XyAMYmYZNL55IkLJ3fd0tenizobEl05dgSAYlAvAhwHliLpo2EemHpsmWRJ08etR8XCQ4LC8UWkp5n5b1ttBz/6RiK3DOgO71RD6NaNtdv3BaiGHXr1KHCzRtIsc/L4kWDDSdPQT9mDPRbtwE0/9hmlyUL7PPlgUPBArDLklmEH8buY+5tJn12lPvG5MyO1Ap1Onq3FhxCpzF+IDKcPg3Djh0AeeR0ZcqYNA0HktGvT4Tzxo3rWLZkkSBjb//vHaFyyR4xZ2cneOXIgRo1aokQM5MGNWMnDllla1ivlhlHTf2hWDExMCg4zYRGWKnw999PYPd3u4Qi5pJFC9GtR0+MGTUczhQ+W7nK2wIUJt3nSP2TlUZzUVghG3tduYB3fl9fzJ83B+9Wqy5qg52ishRM0Dn3i6Xr582ZiQoVKqI8fS5Tjljx4sWFeIc9fcfGjhkJfunCRdSvXrkivnPFaP8BKtDORZ/LV0idOnTiguQ/JiHAghRezo5G4h2hUXq8iIhCJkcHOIowa5OGsplOEXoDrr4IMRLm4MmPJ0XJT0smHEZrMxcoJ5pmCMgcsTSDXp5YIpBxEXCnMD2Wm2/Ttj2GfjyAak5NowewGLlzzuviGmCtWzRFCSogPH3GbPGQx4jxm3UlVPGfsxfRuXsfcJ4Jj2URI+9A1OzZ4FDE+MyeSVBOb+jIC5CWpvNwhz1/iAxGPXoMvR/J42s8hYaAAER9Ng2Gwz/DbtQo6IoVTXS67L2Y+hkVZs40U3jGptA6k2MuJdDzgz6JHm+pDtIbljxk2UsaQr+7Xbt24Nx/Z6kMwRk8Jg+nu7sHid3UQdNmzVG2bDlBsHjJ+xX79dhRQcLZkxXbuPZX4SJFsJtUScuUKYsydOwfJ34T3QZ+PEQomw4mdc35C5egHIUp7tixDf/++48Q6wihcNu5XywUhcI7kaf1+z3foXTpsqJkBRMyadaJQP/CXnAmwvXRP3fUCQYRETsfEIRCmVyQjYhaejEuZH2DhDnCiGxq7bMyuTGquLE4lHa/XJcImIKA9IiZgpLsIxGQCJgVAX6TvoNCn7igcqnSpfHZlElUvziTeIDjE0V7xcIo6f8sNmzeRvXCihmdX6kRxN4EFzcPNGnajCTAvY36mGPD8OefiBo4CIYjR+IMZ5/DCw5U18vOKzvYM2U1Rnlpdpk8RDgkJdhQSGJQtADI6wka7t6Ffts26MiDpytXNtFp872qVr2GCFebN2cWlRHIJu5ZogdasEN68YYxRNdv3k4Vj9jaNavwNX0akmIoL1kJk/P7mFTfvXMbq9aso1DU0shOIYJ8z/3JU3bwwH5079lLbC9ZvAB16zdI0PPs9/ChUEbkUhFhJCzDL1K6du8pSBh71VjYY+aMaeIcx0hwZ9/ePSL0NSwsDFdIcZRLSnhlzy5UF7nOYIeOndG6LQnOSLNaBCqSZ6w0KQTuefCCwhKjp8nRic9IxIPDFDM72f67/gfBYbhOdcK4XpjWODxzUBHz/z9Hew65njEQoP9LS5MISAQkAqmPQHYqAMzS863btKOckM9IhW8OFi+crxY57kxeMScnR1GQOb7Zcc0olixnUnb23CUheqAUeY2vf1Lb9F9/jUh6CDVcu2Z0qB3lejmWKgH7ggVSJfzQ6ORJ3ODcNMcKZSlnLW4SedTnnyNq7KcACS6YYkyah40YhV07t6dqTl7suaVXb1hKvrvs6eKPv7+fqJN36VJ0PqUWO1YhPEMeLlY2XES5WiyuwaGE71R9FyzCwb9FrbFHLCgoEDdv3BDNmTJ54jkVWk7IshGJ4rxPNvaIsQIqy9cr1ufD/oJcrVn1pWjyocLkTjSXjZu2oVz5Ctj7/Xe4SgXPuSB7JSJuHw8Zphwql1aMQKs8WfBzraIoRl4wrT0kMYtzz4PA3iRbtKBI+v4GBONuICnWasyLJPt3VyuM3gW9NK1yVSKQfAQkEUs+dvJIiYBEIAUI8NtvPz8/MUKT95uhY6cuWL/uK1HDitXYWBKbydg327YggMLqEjIuhMvFfNkOUd6Q8qCeUH9T2qMmTkLUnLlGXUmDEA6++eFQojh08YRnGXW2pg3Kw7Ev4AtHEg+JHT7JcveRhLvh9cN2YtPme7Ruw2bxMJ9YX0vt54LZbLZYvDk+TLgIcVKNcyw5r4tr5A0mMYy671XHtatX8OTxEyylfL4B/frgwvlzRsMyseJ8Lc7501ppCiVkDxiHKmqNQ4KZtLEUPVvxEiVw6o/ftV2M1tkj5kUvV9i4+DoX99aGNh7+6UfKCd2BLOSJ5Xywdh06kvLpXezfvxejxowlAZDNWLn6KxH6+pAEQaTZDgJVsrnjONXPap03i9GkOVSRycwtCuvjHCtbMJ7m3aAwnHsWl0TW9s5E11kcDX08beFS5BxtBAFJxGzkRslpSgTSGwLZSWCDH97+/PMU+n7QA9+RWAAXD/7l55+EdDqrtHGuGOcpbaECz28yfihXyBg/qCebjNEDblTfD6HfudPodCyI4UDn4FwwWzVdZk84li0N+1jXYCCvRVS37ri2ZZtJuPHDeVqZcl+Ve51W80ir83Jph/ZtWgriNXXSBCorcAVV3n4H8xYshm+BghRuWFzUectK4YYDP/oQ/2nIFQtqZM6cRSVWyjV4UnkFX98CQmpeaeMlkyUeTxmDa8ax+uEZ+sQ2/q0eO3pEhLAq+8qUK2dE7p5T7bFa79XG7r0HcfzEKXQlddSp02cIMnnkl5/FYWfPnKYcsd0Jhj8qY8ul9SGQ2dEem/9XEDPLxQgvKbP0p6LHp58G4j4RHGvmY35inq/wgOYZ28aU8MGBGkVQ0N0p9i65LRFIEQK2H8CbosuXB0sEJAJphQDnomwmgnXo4H5RbHnu/IXiQbFxk6YYQgIe/fp+gIWLl2He/EWUI5Z40n40GSslCkBHk7GLaEjeMi7+aooZyDsX9fFgGC5cMOpu75MT9vmN5ZqNOtjYhj159XQklhJ14xZpK0a/pWYhD99pn+HXtlQTjAintVp684YlhvMno0cIIrTru70ib/LBwwciBHE31dhTlAtjj1GiZEkhmMH1twYN6IdF9BtitUK2MqRQynmXsevucZ2v2B4x7s/tJ3+P9oJxXbC69eoLlcPPZ80VKojch0Ma58yaIYhbl67duUlYe8o/Y0EexdpSzbm26KBsimXFim9RuOtojBoxFEwIObSRc9eGDBsp9nPdOjc3dyGHb3Sg3LBaBIYW9cZ7OTJh7H/38cujGHVZzhm7RwSHyU5OVyfxsQZ1Rc79ehQSASaLscU4GGRWiJxRNjdq0TVJkwhYAgHpEbMEqnJMiYBEIFEEOIwpC4Uw8RtyVlbjt/VsnC+yYuVa1KlTl+SznVDxrUqIT6ktoRMkJ1TR8OABovr1j0PCHCikLz2RMAUzFhhxoDw3HZUK0Fr1HVuhP3hI22Q16+nVG/biRXTYrR8pXWqNw3E5nI/l5S9ejH45wOGFTHy8vRNWauM+7MUaMmwE2pFwBqsVcvFkNiZWSqih9lxlyXvFeWWxa8KxwiEXYuZQSA5fnPLZdHAJg/70kqRd6xbo1L4N2rRsJvYtWLTU6HfK5/rfO1W1p4l3nb3gXE+MhUN4/PUbt4jwxd9+PY7OHdpi5Ypl8R4nG60XgQpZXIX3aGHFfCRzb/y+P5JcYuwZ++fJKxLBCMGLNMohe0VhkxwyyfO4QwWaY5MwJ1KEZFXEE3WKSxJmvV+1dDEz419IurgkeRESAYmALSCQncgAq6txzkhs43wU/iTXlPwhxTPG4yhtccakwsRRAz82EuXgh077oqSIGM/c4hxvow0sd+9QsgSirl2HPjBQvYqoESOgIwKsq1NbbbOGlfTmDeM8yKaNG5AAxjO069KHWFJ0nqOCNSsZsgCGi4sL/jtzRnigmNyE0m/mypXLQu6dCy9z7S0mXlFRkUIMg/twMWYucj5o8FChWsge5i8WLBbS8V8uXyo8T+yBUoyP4f6XiYzxumIsusEiIOfO/YeqJOrh5ORMpSamk9rpAJrDJRLltEehwoVFPphyTHKW7LFTvHZMBhfMn0v1ATeRpH499OrdNzlDymOsAIF+hbzQJX82zLrkh3lXHsUp+PyECkHzx9neDlmJsGUhlUVLKi0y+QogRcfn4REIidQniFD/wjkwmmTpc7umXRh2gpOTO9IdApKIpbtbKi9IIpC2CLDngh+aOY8nQfJDU+TQRA5F4vwSVwvU4DIpVJGU3aKGDoOBHmwV01FOmkOxItBRXlh6Nx2pUjqUKIbIK9egp3uhWNTQobD/ai10lSopTWm6TI/eMC6YzXmSrEx4+OBu1KldywjjO3fuIF++/PDx8VHztIpRiC6Xdli6eKEgTqxKGEW1jUqWKoXqNWqK49mLxWTm8uVLIkSx/4BBIs9y6OCBmPb5TOG9Os/EikINFStUqDDcKFyVCZ2WiHH4Y/MWreBB+7SWO08e8Mfcxt63cWPH4DYtx46bIBRVzX0OOV7qIuDhYCc8SwOL5MDia4+x4vpjBMUiQeyN8iOVRf6QI0oUhfagnDN3B3u40fFM1JJq4fSSIpjOw4IhgaSAyCQsKpEEtX5UG40l6Yt6GEcKJPXcsr9EICkISCKWFLRkX4mARCBBBBQCpnTwiUcyXdnHSyZirIwYHBxkESKmnItDFZW5saqiliBGjRoN/T//KF2ho7waByp0rKM6XBnG+JqLF0Xk5asqGTMwQSVs7Desh84CD9xJxTa9ecOU6y9HIYFMyPz9H+HAvt14i0oNKMYesXz586NEiZIkSb9GNLNwTSmq9XXn9m107toNQ4YOFzlVPIZiOby94Z0zpwhB5OLKbH2p9ICDgyORnE/EktUMtUSM88kqVaosQhCVcZTlhElTlFWLLr/7dhfmzZkpyOeGTVtRkMihtPSDgI+LI6ZTqN+4kj5Ye/MpNtx+ijMBIXEukLkShytqQxaZnDEZc6TvKeeV2VPEArfRfyLLlY/hXC8Oe2QCFh5liFP3K86JXjcUIdLVzTc7ehXMDu9YYZQJHSPbJQLmRCDmr7c5R5VjSQQkAhkGAYXkKBesJTpKW3xLfqO+Z98ho7yS+PqZo03xzGlDFcse/hH6Q8b5UPZF2ROWgUiYAi492DhQKGbkpSvQk8Q5m4Fyk/RUZ8x+/TqlV5os06M3TAGSvU97936PZq06Y/eOjejcuTMKFiwkdt+9eweFixQF53w9eRJd54trb/ExXPeLSwkkZOwVO0vhjJ06d1W79OrdB/bkYeBaffEJc3DoYloYe8U/nzZF5MNxEefBRC5ZsVFa+kTAjQjVIPKO8ef3p0HYdf+5KAh9Oyg8wQtmosWhhCFIOJwwwYPj2ZHTxQFNc2URcvt1SZJemkQgLRGQRCwt0ZfnlgjYMALJJWDKJbMMempKoWtDFQO2bUfUt9uVqYilQ+FCsCOJ9wxr5G2xL1IIhouXYaDQNjb9338D06bDfvy4NIMlvXrDGNCyZSnni0JzHei38F7d+vhsyiQhXMEeqrsUmlibBGtYRZBzs1hkI5qIlRP19ri4Mtfr0hqH+XLoIud2cY5VbOveoxeqULHkPHnyxt6VJttcgLpPrx6U9xaCLxYuRo0axuGZaTIpedJUQ6BqdnfwZ065vPjreTB+JpXF448DiaAFUjiheUgXXwwLb7xD56nh5YE6RLyq0VKaRMBaEJBEzFruhJyHRMBGEEgpAUvry6xfqijCx44ymoZ93jywy57NqC0jbrCKokOhgoi4FJMzp9+yBbry5WDXrFmqQ5IevGH+/o8TLKHAoYesFur34B5q1a6D1cvm45utW9CxcxdwjlheyhHjsEPOAWPZ+foNGgmSxTeCiVmBAgVFXhevc34XF3Ve8/UGQbZuXL8OFgTRhi3ycSUptNFajNUffQsUEOTRFBLGRHPZ0kXo1auPEDKxluuQ80g5ApVJJp4/LJLBdu5FCP6jz6VXYbgeGIa7JC/vRzLzTylsMT6Sxrlk2Ujsw4e8XflIHr8QhRwWz+SCspldUDGLW8onKEeQCFgIAUnELASsHFYikN4QsHUCptwP/YyZsHsdfsdtdlT81j53LmV3hl/qPKl4NdUai7x9R8VCTyIPdu+8A+QwrSabemAKV2zNG8aki+3MuYti6UfbPlTHrsEbatmxdLzfw3vkHXbCuAmTMGLYYBF+yCIe+YmIsXE44unXEvRc9oFFNEaPJJEZyospUrSYCF/sTMXPOSSRyR1bauV2iZMl8x9FEp9l8Hfu+AZt2rZPcCSW1x83djSePnmKmrVqSyKWIFLpY0eZzK7gT3wmQhVJ4IPzwjhfzNmeQqtpKU0iYIsISCJmi3dNzlkikIoIpBcCxpDpd+yE/vBhFT2WaedaYdKMEbDL6Q17IqtR9NDLZnj5AlFz5sJ+9izjjhbcsgVvGBMvrv/l/+gJmHTFNiZhiRmTrK1bNotulSlssGGjJuBCztnoBQErGbKxqMe2LZuEGiLnTw0fOVoI3ZQqXcaiQjfi5Bb+h0V7Jk2ZJq65YsVKQg5fe0omm5s2rseyJYtQomQpLFn6JXLlzq3tItczGAIs1OFOHjBpEoH0gIAkYunhLsprkAhYAIH0RMAEPK9eQb9ggRFS9uT5AQkYSIuLAGOjf/ESBqovxabftw92DepDV69e3M4WaLE2b1h83q7Yl83EK6e3F1gxNKcJJIyPZzGO5UsXUxmHYDHckGHD8euvx8izFfOCgIsor6NCx0pO5XsUxpierFr1GmjZuo3weK3buFnkxPH1PaMaf5MnjsPJk3+gZ6/e+LD/ACHFz/XTVn25HG+R0mOVt/+XnqCQ1yIRkAhkMAQkEctgN1xerkQgMQTSHQF7fcFRS5fCQAIHitlTmJ1d1rjFpJX9GX7J4h358yHy+g0ViqglS+GQCkTMGrxhpnq7ypeJLsRsKvFSwXy9Upq8WhyixyqHLZo2gru7B9Z8tV4slb7cVpRCENOzfTx4GHr16Ipfjx9Hnbr1cOK3XzFl0nghZLL8y9WCdPH1P3z4AOM//UTkyO3+bhc2b9sZb1H49IyVvDaJgEQg/SCgC40gv780iYBEIMMjkF4JGN9Yw7VriGzRUr3HXLTZsXxZ8obJd1EqKAmsRF69Dr2WwFItKruuMbLoCRyWoub1m3eK47t3bpOicUw92FLeLlPPf/rf04C9IyqUsx4hDVPnbs5+4eFhRErtsGTRAmzZvBG13quDCRMnwzNzZnGaH384SFL3U5GVwjanTZ+JFcvpxQD9htNKet+c1y7HkghIBDImApKIZcz7Lq9aIqAikJ4JmHKRUZ+Og373bmUTDuTpsfOJVudSG+VKvAgYgkMQce68uk9HBYMdfvlZ3Tb3ivJ9NLUeXXLOn1reruTMLaMfc/zYUYwdMxLDRoxSxTtYLXHOrBn4fs93aNCwEYmaTIabmxuePXuGTh3aoHefD9G+Q6eMDp28fomARMAGEZCvg23wpskpSwTMgYDywKuMZckHX+UcabFkb5iWhOlcXCQJS8KN0Lm5wp7IV9SjR+IoAy316zfArnu3JIxieldz54altbfL9CuXPRmBGjVr4dvd+5CDvnNsWrXE4iVKICwsTJAw3seCJpOnTseo4UNE6GIRKoAtTSIgEZAI2BICkojZ0t2Sc5UImAGBjELAFKj0W7Yqq2Jpn0t6wowAMWHDjjBTiBh359piliJi/EKAxS6Sa9LblVzkrOc4JmGslrh50wYsXbwQJUqUxOIlK+Ds4ozOHdpix/Zv0LZdtNR91arv0noHfPrJaGza8o0qaGI9VyNnIhGQCEgEEkZAhiYmjI3cIxFIVwhkNAImbh4pJUZWqw5DVJTY5ILFIjcsXd3Z1LmYqFu3iYzFSLTbz5sLu0aNUufkCZwlMW+XIh+fUkGNBE4vmy2IgJ/fQ3Ro2wpv/68qZs6m0gmU18n226/HMWbUcKzfuFWVuo8gZU8W96j1Xm0LzkgOLRGQCEgEzI+AJGLmx1SOqEHgeXgU7oeEwz8sEgG0HhgZhXCqxsgKMVyA0dXeDpkd7eDl7IBcLo7I5+akOVqumgOBDEnAXgOn37ABUTNjal/Z58sL+1w+5oA1w41hCA6mXLEL6nXb1agO+xUr1O3UWJHertRA2XrOsff7PZg/bzYpI+6gcgAxv9sv5s7GqVN/EBnbokrdW8+s5UwkAhIBiYDpCEgiZjpWsmciCPz3IgSnngXh34AQnKP1K6/C8Cw8MpGjjHc7UKXGoh7OKOnpgvKZXVE5mzuqZneHGxE2aUlDICMTMAWpyA4dYTh3TtmE01sVpFKiikbSVyIvXYH+5Uv1QIdDB6HLm1fdNueK9HaZE03bHWv8p2PwyN8fK1athZ1d9P8HwsPDMXf2TPQfMEjkiR355TCFMC4i8Y6neJc84EOGjYCXV/LDW20XLTlziYBEwNYQkETM1u6YFc2XvV37Hr7AD/4v8cujV3hMXi9LWXUvD9TLmQmNfTKjfBZXS50mXYwrCVj0bTRcvoxIKhKrmF32bHAoXEjZlMtkIKB/8hSRN26qR9oPHw673h+o28ldUUiXH4U++j96Aj//mBBIZUxtsWRuS27dLmW8tFxyQWKFVKTlPGzh3EFBgejcsR2aNWuBPh/2jzPlW7duolP7NuAi103eb4Y9u7/DjRvXqBbbBllfLA5askEiIBGwNgQkEbO2O2ID89l+9zm+ufcc3z94kSazLUOesrZ5s6BT/mzwlaGM6j2QBEyFQqzol69A1JIlaqND0SKygLOKRjJXKKw44u9/hJACj6ArXx4OmzcleTCFeJ05d1EcG5t4pffcrqaNG6B6jZqoW68+KlWuIklZIt+g/86eQf8P+2DZipUoX6EiOCeMPyxhf/inH/HJ6BE4evx3uLm7g0lu/74foEDBQvh0/MRERpa7JQISAYlA2iIgiVja4m8zZ78fEoFVN57gq1tP4R8akei8nSmU0NXBDi60dKZwEkcKOeSwQ3vKC6P/QP/RwxzAEgpR/HBH//Pk3LHQKL34BFMumSmlxlsTIetd0At1vTMlOqf02kESsPjvbGSnzjCcPSt26uyogHPlivF3lK1JQiDy2g3oqX6TYo4/H2b3lLIZZ5kY6eIDtN4uW/Z0xbn4BBq+2bYFP/34A86c/heenp7Cm1OnXgNUqfK2KFCcwGEZunnd12uRKVMmtGzVBuPGjkaWrFkx5pNxYI9Yu9YthGJiseIlBEYH9u/FZ1Mm4fiJU6rIR4YGT168REAiYLUISCJmtbfGOiZ2KygcC64+worrcUOFtDP0cLSHp6MDMjnZw8PBXpAu7f7krAdGRIt7vKQ8sxcUBql/AzOrkcMDQ4p6o2muzMk5lU0eIwnYG27b06eIoHpEitlRvSGHIjIsUcEjJcs44YnTp8GuZUt1yMSI18uAZ3j16iVOnjgqjrGDHm3bd0DHTl0ynGcoICAAA/r1QWhoKB4/fgQnUvWsVau28JS9/b93pBS7+q2KWTlPOZ89u3cWQh0lS5UWO7p2bo+Kb1XCiJFjxPbZM6fRu1d3/PjzMRmeGAOdXJMISASsEAFZR8wKb4o1TCkoUo/PL/lh3mX/BKfj6eSAbKR2mJU+Tq+TqBPsnIwdTO744+MaraQYQITsWVgEnoZGxiFlxx8Hgj+cR/ZJCR9wTll6NUnAEr+z+r/+Mupkl9nTaFtuJB8BXSwsQ479it8yZRMDxg4x5EbF2+Xi7IghA/uh6rvvonOXbujbuzsePnyIg/v3YfHC+Th+7CjmzV+kFutN/gxt58gsWbLAm7yJTLratG2PUyf/wLFjRzBt6mQiZyHC49Ooyfu2c0GpMNObN67DxycXFBLGp2zbriMWfDEXgz4eCmcis6tWrhD7GV9pEgGJgETAmhGQRMya704aze1rCj+cdP5hvCGI9hRe6E0y8zlcnEToYWpOMQsRP/4UJI71mMIjH4eGg71mWvvJ/xX407eQFz4rkxtZiMilF5MEzPQ7aTh92qizLlP6JeZGF5oKGzpHR+hcXWEICRFnC/vzT/hVrirW35TbNWzIIFSuUgWTpkxTZ8nKdmXLlkPzFq0weFB/oYQ3cfJUdX9GWHFzc0dQYKAgEDXIi1uyVCnkyOGN9RSKd51IhzRjBAoUKoSnT58ghL5/rvQ9ZGvQoCHmzJohaozVqVsPTUnYg79X0iQCEgGJgLUjIImYtd+hVJzfTQpDHH32XrwiHE6U68WeKf5wjldaGp/f29VRfF6Ql8wvOJxqlBkrNnI+2x4SE5lVLg865sualtNN8bklAUsGhOfOqwcJ4uDiom7LlZQjYOfhgajXRCwTSYY3qlwe3sWKJDhwWFiY8PZs2LQt3j7FS5TA9Bmz8RGF6fXs1Rv5fX3j7ZceG909iIgFUdmPf//B9m1b8cvPP6FwkSIYP3EK6hPBkGaMQOnSZVC0WHGs+2qNkK/nvSzS4e3tjRvXr4GJWMNGjY0PklsSAYmARMBKEZBEzEpvTGpPa+PtZxhy+i44JFFrdsR68rg7Ibebs7bZatYzk4eMP0zEHgSF4ZXGQ8aiIj1P3RIhi4sqUiHftGaQSURNErAkAqbpbrh0Wd3S0UOaNPMioHN3I7d0zJg5nj+ljYSJ2JUrl4XSYoGCBWMOirVWmYQqcuXKjQsXzmcYIhYeHoaXL15gHxUu3rRxPYVtVsOiJctR5e3/xUJHbioI6Ojv+CefjicVxd7woBcCzVu2FuT1/v178C1QQOkmlxIBiYBEwCYQkETMJm6TZSc54sw9LL2meap6fboc5HXK5+4iFA8tO4OUj66ELT4KCcddImSRpMCo2JqbT/D38yCsqOSLCjZQg0wSMOXOJW9puH8fhuAg9WAOo5NmXgRiY2q4fgO6/yVMHvLmzSfkxm9QqF2RIkUTnIw7k+Y3iPIkeKCN7eACxdu/2Yrvvt0pvGENGzdB1649hCfMxi4lTaZbsmQpLFq8DFOnTMTCBV8IkZfWbdqhXn3pQUyTGyJPKhGQCCQbAUnEkg2d7R/4lLxIPU/dxo9UkFlrLDmf38NFiHBo221h3ZtCJ7OSKMCdwFA80cjsnw4IQa0jl/FVlQJoncc6E7glATPTN+zOXaOBdK4yLNEIEDNsxMbUcOfOG0fNSlLjJUqUxKL587CAHqDjK2b85MljKsR7HVXeQOjeeBIb2rllyybsJhLG5KEDqUXmyJHDhmZvHVPlemLf7PgOd+/egbu7h8TQOm6LnIVEQCKQRASkfH0SAUsv3S++DEXnkzfBS615kRBHwUwu4JBEWzf2jt16FYoY31j0Fc0rnxcDi1jPg48kYOb9pul3fYuoCRPUQR1LFoeO6g9JMy8CEX//C0NUtFiOXb16sF+4IM4JuLju8qWL0b3nB/D390OPrp3wv3eqYtyEScie3UvtHxkZiYEffYhs2bJjxqw5ant6XWHlv5yk/McFiWObgTyCweTRDQ4KFl7E3HnyxO4it1OAAH/XuBi0IvSRgqHkoRIBiYBEIMUISI9YiiG0vQH+fBaMdr/fgJ/GY8RX4UteMB+3aKl427uquDNm75g7qSbeILLJBaIV41DMV7TNMvdpaZKAWQj9x7HCbJ3Sz3faQoglb1jG9bVghyE25q9H3L9vL/ZTcd2+/T4SIYlLV6zCxPFj0aJpY1SvUVO0hYWH49CB/XAhQZX5C5ckby42dtSjR4+wfNlSCksMjP4EBiGIyBeLdoQEB4t8Or4kJmG7vz9gY1eXetO9c/t2kvMJ582ZiUDC+bNpM1JvovJMEgGJgEQgAQQkEUsAmPTafOpZEFr+dgPPNCqDLGJRJLOrkIZPb9ftTsWlS2d1w7WXIXgeFqOsOJnk+aPIVTauZOqTMUnALPstM1CRXK3pHOSfOS0e5lpnXFVvcyzM+RzsDVu7eiV69eoDp9dkuAKFk23ZtgOHDh4ggYXD+PnwT4KAderSFa1atxXr5pqfNY/D2ERFRQqvICtERpKH5rtvd2HUmLEizI5z5fz8HmL1yi+t+TLSfG7r131FeWEN8E7Vd02eSw9S5ezcoS327/seTd5vZvJxsqNEQCIgEbAEAjI00RKoWumY516EoMmv1/CICiIr5kz5YEWJhDFhSe92k8IUOVxRa9Oo1tjI4jm1TRZblwTMYtAaDRw1YSL0u3aJNg6wdXy7stF+uWEeBCKvXIP+NQHTZc8OByrIrLW9pAS4aME87D3wo0rEtPvlegwCLN7xfuP6OH7ilEpGr127iu5dOuLEyb9jOso1IwS4ltiQjwdg5ux5FNYaXVTcqEMCGz/9+AM+mzoJm7Z8AxaSkSYRkAhIBNIKAbu0OrE8b+oiwGGInSgnTEvCXB3sUCKLW4YgYYw2577FDr0cf+4B1t5k6e2kGZMqU437rt+8E2f+uygOKV+2JLp3boPyZUuZOoTslxQEqGaVanbyT5yKhblXtNiGajCn8yjesOfPn2NA/76ihpi5T5+exuM6WGwcqqiYOxV65lymcArdlBY/ApznNXzkaMyYPlUN54y/p3GrA3lzIyMiMW7sGHDOmDSJgERAIpBWCMinlLRCPpXP2+PULVx9FfOwxMqIxTK7gZcZyTgPLifljmltwD934ihHavfHXlc8W4mRMUnAYiOXStuvBSTE2dKB6EwqoZb002ix1cfkYPJAnBsWRjWyuCYWC/+wEEefD3pIQpYAyiza4ejoSKqRN9Qejx75i3VJFFRI4l1hNU5WUNxKSpSJGRcWnzVjGkaNGIr3atcWxJfFZKRJBCQCEoG0QiBjPYWnFcppfN6B/9zF0ccxb1od7HQiHDGjkTDlNhQgzxirQ2qtz1+3cSc48TfP/v6PVc+W4uHSjsPrkoDFRiSVt7WemgxQkyqV0dWcTs0QA+nRq+2KN4xzw7hA8co1X2PZl6slIVMRirvCcv7v1a5LhZ13C29iFL1MOPzTj2BPWXzKinFHyNgtXbp2x19/nsKVy5cSBOL6tWvo3rUj9u39HpMmf4bpM2bjc/ps3bIZf546meBxcodEQCIgEbAkAjKL3ZLoWsHYK288ARc01loRT1e4ZYCcMO01x14vTBhEUNHnF69FS/wpb+4j8oztq14kdlej7TPnosMLlUYmXUqIIa9ryRmHICr7lP5ymQoIOGs8npKIWQ5wEpxQTaNMef78OUEmmrdspe6uUuVt8OdPelhetWKZ8JCxF6Nf/wGo8vb/1H4ZeaVVm7YY0K8Pfvv1OJydXfD48SMM+nhoRobE5GvXkdd13PhJ2LHjGxQrXiLOcVw8e8EX81C4cGHKC9uOfPnziz7cd9DgITiwf5/8HsZBTTZIBCQCqYGAFOtIDZTT6BxnSZzj7Z+M3xBynhTLukuDIGLnnwchLCrmgZJVFCeUyhUvPLGJltKJCZckYAoaab+MmjED+o0xYUpOld8y8tik/QzTxwwiL12G/uUrcTE6EjxwOHRAvTDObeJQu4RMIWR58ubFpCnTEuqW4dr/+P0EfvvtODJl8kTLlq3hnTNnhsPAnBf84kUApk6eiOMkJMNeswGDBsf5XnLdNjYmc9IkAhIBiUBqIyA9YqmNeCqebxTVy9Ia50ZJEhaDiCOFaDIxvRQQrDZOv+iH2t6ZUN3LQ23jlYRIWPS+aC+Z9IAZQZbsDc7j+Pefv/Hs2VNR4LcyeVI4ud5U02XOYtTVQEn5Oq2XzGiv3EguAoyralkyq6u88iYSxvsVD1k45ZFJi0GAZdiTIsUec6Rci41AMNUK69yxHaKoZiTnKiaEK/+d8XvohwIFC4jSAbHHkdsSAYmARMCSCJj+dGPJWcixzY7AgquPjPLCOBSRc6OkGSOQ2ckBed2dcS8o5oHw0/8e4FjtYmpHbV6Y2hhrhVUQpSUNAc4lunTxAnwLxDwAcf7G3NkzRG4Mj/aECgVzGNHS5SuRM6eJNd+8vIwnEkG5f5KIGWNiji3yeimmi425siORpZOTcyI90u/uvXt2IyDguSjiHEhqicFBwUI8gos6BwVSoWcu8EyFnoeNGIW69eqnXyAsdGWcX/fRgI/xbrXq8Urbcw7elxQme/PGdTEDftlTtWo1TJ46DZ6ZjV8sWGiKcliJgERAIoCYDGsJRrpB4C6JTkwkWXat+UoSpoXDaD0PETFPp5g6alz0momsYrHzwpR27ZI9ZtLiR+DunTvgRPnYFhkZgV49uuL0v/+KXefO/YfPpkzEuAmTsY9qT/Fnz76DeP7sGUxVNmPSHECS1lozhCUuwqLtL9dNQIDEJAxa2e9c8YfzmjBShu2yhopdc0Higwf2Cw/w0SO/iN9JlixZRJ5T9eo1YU8v0JisSUseAk2bNY9DwjgUcdbM6Rg7ZiSKFCkqxGR++uWYWPLfpD69e+LZ06SXNEneDOVREgGJQEZHQHrE0uE3gMPrwkmIQrFcbk7wdIwhGkq7XMYgkM/dBefDg9SGzy48RJf82fDgyhX40cN9YsY5YhlZmOPmzRuiHo+np2ccz9XQIQMp9Ochliz7EhXfqqRCyd6QnD4+uHPnNqqhBv48eRL58uVHvfoN1D7sBZs9dz6eP3+mtsW3og0dzUShRk01nQyhoZotuWoOBGJjqsuX9KK4XDPL3d04BNgcc7OVMb7ds89oqlMmjYeXVw4M/HiI2n7r1k3hMVMb5EqKEVi9cgV2f7tLFIGuU7eeOl7Zslkwf9FSDB08EF99tRojRo5R98kViYBEQCJgKQSkR8xSyKbRuH8+C8bXt2Le5jlRnTAmGdLejIAHEVUmrIoFReox+d+bRiIcyr6ElundK8ahhLFt/77v0bF9a3Tt1B69undB08YNxIPMndu31a6++QuIRPgRwwarYUDKzgK+BXD71i2xmTVrVkHKTvz2q1Fx1kqVqxA5a6gcEu/S/9ET+OTMIT6FalUHJZWp/QwhIeq6XDEPAoZgY0x1hQomeeBpUyfj4UNjz32SB0lHBzAp5RBFrXEbhylKMw8CL1++xMaN60kpcSi0JEwZ3d7eHr0+6IM9332L4OCY3GFlv1xKBCQCEgFzIxDztGLukeV4aYLA/KvRRUCVk+dxc6aHYGVLLt+EAIcoPg6NQORrb+Ka+4EoonNAdoNGlOANA6QXrxg/rNy+fQtMpm7fvonKld8WVz1syCAc+OFnsNeL7Tt6qzybQnyGDh+J95s2E96N+/fvYTo9YPfv11vIRDO54hywl69eIi8p5H088CN8tX4TcuTIIcbwLVCQwrGuivWGjZvgp59+wJCPB5B8tzO8vXMiCx3PXrJa79WO98FJHEj/NKhbU1kVy8jixWE4f16sGyj3Rpp5ETDEekjVFSuWpBNwvSdXVzfkypU7Scel587ulNN0/bpxCG8IvUQIlR5ds912rhfGBcbbte+Y4Jj5fX0FCWMCLGu4JQiT3CERkAiYCQFJxMwEpDUM8/fzYOy6F6BOxZ28PN6uCUtIqx3likDAnv4HnZuI653AmFC2awVLoXNBYxW+9ACXVijj+LFjoqBpNPm6RWGAzwWp4gcSX/pwToW3tzfCw8MFQStbthwJCwRh3pyZgoS179BJhSRPnrz4YuESdO7QVuyf9vksGqMAFVHdgxUr14DJ3JBBH2HV2q/Vcxw+/KM43pVyuzh8kQngNSJnT58+IeXEZ7hAdak+GT0CH/Tui/4DBqnnetOKrnTpGCJGKowGmrtOU+vqTcfKfYkjYCARCcV0FF5K8ajKpknLr9auNgrBM+mgdN6pMtVT20TeGiZf/Fvg3+i5/86ieIm4dbHSORQWu7ywsFDkoBc8b1L1vHH9uvDgu7q5WWwecmCJgERAIqAgIImYgkQ6WHLxZq35yHphWjhMWufwxIfBYaLGGB+w4WEQPq9UCJltNMeOhTKYQBUuUsTo+hWhjC8WLBZiAOzlYq+Wq4srJowfiwWLl6ICFdxVjOtC2dnZiTBCJmJHj/4iHhSbNW+pdFGXLi4u6N6jF+bNnQWWomdCx+SOHzBnzZ6HD/v2wqjhQ7FwyXJB0h4/eiTeQCtvn7k/f7TGYh3btm5Gv48GmlTvR1ehPPDNN+oQhleB0GXPpm7LlRQgQEIdeiLiivkRSf+8f19xXwZSnaZSpcsou+JdKt6wvFR7TFoMApUpBJe9wF07t8c777yL30/8Jko41NXkMcX0lmvJQYDFOe7fuyv+HrG3PrZF0Xeb/9bUrPUePDwybv5ibFzktkRAImA5BGSOmOWwTdWRH4VFYp0mN8zVwQ5eLtIblpyb4KPJFWPRk680uCZnPEsewySL5ZeP/HIYX3+1RhQv7d2rOzh3i42FMrp37ShU2bTz0ApldOzUBUOGjUCr1m1Rv2EjcJ7EHQpN1Bq/Qc6dOw/lcEW3+/v5CVEOfnMfn739v3dESNV5UkLk0EQ29rixpPTCxctwj0IYp06eIKTpeR8TRi6+2rN7Z1y+dImbjCzvazEIU4uu6ipXNjreQOGW0syDgP6FMZabLp2nh9ZMFLKamQh0AXES9uY8uH/fKNdPOTt7wz7o01fZlMvXCPB3e8q0z8Xv7NTJP1CjZi3s+HYPChYqLDEyEwLFipdAeXrBtGzpojjfTX5RNGbUcNygv6f8wkeaREAiIBFIDQSkRyw1UE6Fc2y8bawqJws3Jx90bxcn3A2MqSu2gbAdWtQ7+QOa6UgO0+N8qsKFiyBb9uxYOH8eNm/aIDxVeSj/Kn9+CiWknKtm5VqgbDnyCJH5klAGkyYWyljz1XqjhzqtUIYyRSZhPJYioKG085K9VGo7PTSGUphPQuaRKZPYxW+Ys2f3EmGITO7KlCkrthcvWQEmjF45vMEeNCZpSgjWHKojNnvOF+IaeZDz585hzaqVaPJ+s4ROF6ddlycPdPT22/A6/0wf8AJSNzQOTMlqMLx4YXTcO0OHohZ5QBVjT87kieNEWKmPTy7M+WIBSpQoKXZLb5iCUvxL9jYvXroi/p2y1SwIjB03AYMG9MPI4UPQrHkLZMrkif8oBHTr5k3i5REXfy5atJhZziUHkQhIBCQCiSEgiVhiCNnI/m13Y4gYPSMjh/SGJfvOOdjpkINy6x6HRBesPf8iBL89CUQ1L8uHqnDuFXuN4rNPPxmFv//6E1M+my5ISWaqN8QPEYd++kV4seI7xlShDO2xnNPFkvKxjds52Z2tVKnS4JDChw8eIFfuuIILvI9NIWRGJI7aeV7zFy7GR/36gMMeb5NMN9tMCl0cPnQwGjesC843Y4lzLnDbuWs39OnbX/Qx9R+7mjUQ9ZqIGegc7BXTvRYaMXUM2S8uAvrnMXmofvRd1ZKwk3/8TvfvYzRu0hT1GzTEN9u2iJcAe/f/IEIX2evT98Ok3ce4M0h/LRzCqxZxpu+7WKcl/z2wo5cjDchTLc08CLD4D7+Umj9vDqZMmoBAEuXgl0WMcc9evdUXQOY5mxxFIiARkAi8GQFJxN6Mj03sPR0QjDMBMXLS2Z0dwcIT0pKPgBdhqBAxHmXX/QCLE7G+H/TA6dP/gvO2OCxJaxzqdfHCeUHSFGl4FtLgcD6WWc702gOlPYbXTRXK0B7HXrXfjh/TNol1Jk87d2wXuWFvVaoMz8yZsXnzhnjr7fxw6AByk1eqZMlS6rHs9dIae+2mz5gtwoGeva4Txh6UzVu3C7EOJnOcMF+qVClwKGVSTVe7NrB2rXqY/tlz2EsipuKRnBUmYdpCzk8rVMaz18XMs2RyE0Vy27brgBGjxojhy5UvjzpUTuDe3bsiDLVoseLJOW26PGbf3u/xxbzZgnSx5zgh49+dJGIJoZO8dq7Xxn97GHd+EcReeWkSAYmARCAtEJBELC1QN/M5v39gHCqUXXrDUoywp5MDnKkGW1iUXoy19+ELzCufN9Fx/an485lzVNy5TEnKoYqWaE/0oNcdmNgw4Vi04Au8W626kZfr5o0bggDVqPmeCOPjQ3x9C4ojmZiVLlNGPFRwXs4tkpx3cHBE1arvJlkoQ4xLIY7b7t0V52OBDsU4/yc8PEyEOrIXbOynEzBu7GgUIOLWuk074fFgwnjwwD6s+3otee4+Vw6lEElfcH2w2May9MdPnIqjYsZJ9fxJieneqghdwYIw3Iz2tumfPIO9b37QRFMybIY+Vv80pkYhA3GXiPZzKmauWIMmLVG3YRNlk/JwIL5H7lL4QMVEWeGvIYvjDB4yTITuMkbu9OKBa4e5e7hHL8njqP0NKsfKpXkQ4FBs/kiTCEgEJAJphYAkYmmFvBnPe8g/JnnensLqshCJkJZyBLI6O8AvOFwMdDsoHFweoFJWtwQH5oLOXEuMLae3V5KJGL/5Zsl2DpXZtXO7Ua2bc+fOCu9SoUKF8PPhn8Q5WMCCH9JmfD5V5Dbcv3dPPPT65MqFOnXqCSLGY7KxN4rzs1goo1ePrkIoYwAp3LGxUIaSn8XbfAy/JX7w4D7V/opRtuOcMjYei4lYvfoNhKrb0sULsWHd1/AmCXOuIxZBAiKfjp+Iho0ai/78T08qktongZA0FgKxlNm93wRRS5aK4Q16Uvt7/AR23kkjyJaam62Na6DwOfYqKmagfKY9/53C4yPPkTdfAbz9bi3kzlcIZ89dEp/yZUvi4vn/RH5YtmxSsVLBTVky4WKxmxYtWytNcikRkAhIBCQCGQwB+cRu4zec1RL/ehZTsFaSMPPdUMZSIWI86i+PXsVLxBQvmB95wxTzf2RcSkBpf9Myf/4C+H73d5gwaSqmTZ1EeWBNxVtxPoYFK0oTkWKSxPlbXNvLyckJTLo4T6x3nw+Fh4zJGbcrllShDD6OwxnZWJhDS8RykEw5PzgyEXuHvG1sXEOMCdc/f/9FuVzBYElo3hf7LbODQ9r8qdG1aAG8JmI8X/2jx5KIMRDJMMZOaw5tWmF5q1bi3nPuVyWSX//vfLTiJb+QiH4p4YD6TdvT+gX4EAFOqpdYe770ts65oJwDKU0iIBGQCEgEMi4CafN0lHHxNvuV/0oiElrLLL1hWjhStM5YcvgQh1exHSesRxbPGb3x+l+tF8xoRzI2mGQFBASI+l2FSBnxqzWrMWjwUDHSOZKB/6B3H+TL5ytqc/n7+4HzqZg0udMDXW3ygCVkpgplKMezIiO/rWfCVa16DaVZLJcuX0m5Pr5GbZkzZ3nj+Y06p/KGjjx3dk2aQL9/vziznvLp2Ktjly1uDaFUnpptnY5yafT+0QIsPPFI+n44tmkjPLJcqkAxT3dnUUKBcxZ1Dq7CC+vsnlmQsjOI9hazp6x82ejcQeW4jLj0EEQsph5bRsTAVq6ZX7bJlwi2crfkPCUCtoWAJGK2db/izPbkU+P/kWey0cLDcS7MShoyOTrgZXikmM3JZ8ZY/3D4GLReMO2UuZ3/583mR54ExUP2ptwx1RNFHq+hw0eCxTvatGtPXqZsQra+dOmyyPK6CCl7qxQixkqKbzImeEyqtBafUIZ2/4ZNW0WoobaN1xVZ/Njt1rxt16mjSsR4nno/f0nEknjDoh76w0D5f4o5d+8GYmFi84dDBwXhYnEWfnPBnjEWkGHBmarvVhN9+IUFm+Ip46UP5VByCG9GJWX8soNrV3FepcwDE18Pq/xn/eadYl4N69aUZMwq75CclETAthGQRMy27x/+IcVExRwpP8yFBCakmQ8BDyK2ChELCI/C5VehyBL8CoeIhCVm8fV5U+4Y59F4UMI+k6ymzZqjbr36WLZkEVq3bY8sJFWvyMRziCCHJ/7vnarCI/btrh2iZtNdamPCxeIdvOSHu1lUjyupQhl8Xfny50/s8mxmv+6tt2D33nvQHzki5qynHDz9k6ew88puM9eQlhM1hEdA//ChOgUD5fTZdesqtp+ReAcX5l5CnlImYgULFlK9uOoBtKKQLV5qSRm/sMiopIx/615eXiK/041EOqRZJwLswRUvEEiEqUESBZis84rkrCQCEgFrQkASMWu6G8mYy39U40oxd+kNU6Aw29LdwZjY7vvvGrxuXTZpfOWNP+fGsJkS2qL1Xg0YOBhtWjVDZGQU2BumGItmMFlj4/5cg6hhvfeEhD171bitZKnSqupgWglliAlayT92vXqpRIynFHX/gSRiJt4bPWHFOYmKOfTpA5DCJ9v6dV+hXPkKIpxW2f/77yfwBxV1HjZilNJktIxNythbzIRMIWX84Mum9DM6OB1tcAjw/kOH1Stizxh7Erl2GOeOBfKSXhpwG5eBUF7EqAfIlVRBQPkeRntzL6T772WqgCpPIhGQCKgISCKmQmF7Kw+o4DB7aRRzld4wBQqzLd0cjKWN/7j3CE1NHP1NYYgJDcGCHXfIm8XGKoRdunbHmtUr0e+jgaKN/ylctChCKaSJrXTpMli1dp3wjLFQRnyWVkIZ8c0lrdp0lSvBrmlT6PfuFVNgBcCoe/dhn5fC6aQliAAXwY56HCPSEUZeHI8P+4r+7A3bsX2b8IZpB/hy+RIqv2CcW6jdr11XHnK5jT1lTMr4gTd6m8pApHNS9kHPbvB7+EAULecwRS3hFSC8/mfi5Klo1ryltkmupyIC/D2NJmJKnqPMcUxF+OWpJALpGgFJxGz49t4ICjOavQxLNILDLBuxMY3MSjktPj7qw+KbTsK5YaZ4wbRjsDfrxx8Oqk3dz91F8gAAQABJREFUe36Al/QwXLduPbVtxMgx6jorr1WoUFHdlisJI2A3YIBKxLhX1IOHsMuSGToiF9LiRyDqzj2jHQvDghE4YRz6UimCPbu/jeMN41px7K1dvPRLo+NM2VBIGedW8m9H++DL6+mRlNWuXZfq/0VS3TCqHUa/Zc4b4980C3nwOtcTc3NzF2qlpmAo+1gOAc4R43Dz6O+iJGKWQ1qOLBHIWAjoQiM0MScZ69pt/mq33HmOXn/eUq+jRBY3SNVEFQ6zrZx+GqgWdq6VwwOHakYXGtY+MMZ3Mg5NbED/806K+fk9xCsiXkWLFU/KYbKviQjoV65C1MKFam87euB1KB0dCqc2yhWBAJOwKD8/FQ1d1ao41LI5eWi/xL27d0WJgnHjJ6FpcyoR8Np6du8svGEf9vtIaUrRMqHfWHokZSkCSh6cKggoKrn8/VNeHKTKieVJJAISgXSLgCRiNnxrF1x9hE/O3levoFw2KhDKOU3ErZ8NaA3XRm3h2qKLuj8lK+FnTiFwxQx4jp4Fh4LFUjKUzR174XkQXkVEh4CWyOSC0w3iPrjH98CYHCJmc+DY4ISjunSF/vRpdeb2FAJq75tP3ZYrpCxJEv+R164bQeGwcwd0JUoIlb8D+/ephKxuvQbCQ8YvEcaNHY09+w6JfEWjg82woRX50A7HD8W2WqPs5B+/4/r1a6/zwoIoLyyQ8sKCKC8sOj8siPLEuI09Zwnl3GmxkOuWR0CSMctjLM8gEchICMjQRBu+289ey6orl+BAqomKOfgWobCrbMqmXKYAAS2uT2NhrgzLIYj84bek8ZEypZ9cpj0CdmM/gb5DR3UiUf7+0Lm5wi6Hl9qWkVcMoaGIunnLCIIlDjrcWLwA7OniEgbvN22Gxk3eh0LIOrZvjUyenujUpZtFSBhPRvFA8FJLykQIo6ZGmS2Rsr179+DIz4dFiQoRmkghihfOnxPhxgUKFhThiZcuXcRjTZ6e0Y2RG6mOQPT3L7pguS1911IdKHlCiYBEwCQEJBEzCSbr7BQYoTeamD1XH2ajpecnc6LX5b8pRkDFlUYKjDTGPL7BtaQsvv2yLW0R0JUpA/tPPkHUzJnqRCKJeDg6O0PnmUlty5Ar5E2Pun4TBirgrJhd/fqo1KoF/vlyOVhc4p2q78ZLyLZt3YxOnaNl7ZVjLbU0lZQp/Sw1j5SOy7lgLVu3gTbvs3nTRujWo5dag23zpg1gz5k060GAvbD8AoBzxrp3bmM9E5MzkQhIBGwOAUnEbO6WxUw4VFNglVtVhxg9TL2c+ymc33kPzjUawhD0Cq+WfQ7XZp2gD3iKsOOHSKjgDnkAcsH1/Q5wKv92zKC0Fv7Xr6JP5J3r5CnwgHPV2rDP7WvUR9kI+/VHhJ34iXJJ7sEhX2E4VaoG55oNld0IPXpAjOfx4WgEb12JyOuXxEQdCpeEW4uuJCGeU+3LK5HXLiBk/3ZE3rlGRXe94ViyAlybdoDO0Un0i7h0BiHfb0Wmj8Yi5MfdiDh7CvoXz+HgWxhuHfrC3iev0Xjm2LBTCC4NFhtzc4wvx0h9BLgOluHSJei/+049eeT1G3AoUQw6V1e1LaOtcDiinsLhFNNRPTn7KZNRk+Tqa9Z6D8eOHsGqBAgZe8nSwhSyFe2psK3C0SzE8fz5cyPYPEikg+XrFWNPGYcoSrMeBJTvnPDGktqnsm09M5QzkQhIBGwFAeMiSbYyazlPgYA+prQPO8GMLPIGPWQ+eyLaDJGR4O2wI/sQ8u16OBQoBud360H//AkCv5xJJComzyz0pz0IXDMP+uBAuNRuCscybxGZOoiQ7zYajc8bwd+sRtDm5bDL7g2X+q0ABwcEbVlBROobta/+6SMiXxfxav5EKqLrB+dq9eBY+i1EnPsHL2aNNjo3E8CX1M8QEQaXOs3gkLcgQn/aLeZIxbTEmIZXL8W1BG1chojzf8OpYlWaY6XocyyeSsktMW/y1UmYcYWlbTSwm3FkOVRqI2A/fRrsKsYoThoiIhB59TpY2j4jWtSNm/Q3IUC9dB39UbH//HO1ZhjvYDK2YfM2zJu/CAFEINhD9vHA/vjv7Bn1uLRc4Qdi/rCXQuSOUbiwUp9s/ead+EGo3kWTtbScp3JuVkvUki5uZ+IVSHliirF6ItcWk2ZdCPD3jPOAFTJmXbOTs5EISARsBQHpEbOVOxXPPLUhc0wQErOIy+eQeeIi2GXNLro6VXwHL+eNQ/i/J+DauJ3wnIXs3QKHIqXgOWQyEStH0c+lXgu8nDnaaPjIW1cRemQ/3Dt+SB6wRuq+4F3rELJ3K3njapNHK0d0O03OLlt28mKNA0mtRY9ZqzERsTHk3doMj76jYAgNQfD2NXB6qyo8Phguwiu5I8/x1bLpCP/nNzi9XSt6PPpX/yoAmTn88vUcQ/bnobG2EEm7QvOPK6ahHpiMFYOGerHXMRbnTcaI8hBrQcBuzmwYevaC4d49MSXOj4q8cg0OxYpAR6GKGcWibtxC1JOnRpfL2OgqVjBqUzaYkGk9ZLt27RC5Y8p+a1hqvRScU2auwtFceNnOzjzvMOPzdjE5Y7EOxXibBTukWR8CXCtSIfra75v1zVTOSCIgEbBWBMzzfxNrvbp0Pi9Xe2NKoE+EjTm9XVMlYQwNhwfqXFwpXPGZQIqVEQ1hoXBt2FolOLzDnkIY2ZOltbATh6FzzyRCH7XtTOjYwk4d1TZTeGEnlYTxDnvydjlXrk4k8Hdxzohzf0P/MkAoPWrde45lK8Mhf2GE/fGL0XjsMVNIGO9gLxub/kX0tYgNM/2j9TzKotlmAtVKhtHlygX7+V9AR6F3ihmosG7kpSswBAcrTel6GXntBpGwaO/5/9m7DsAoqib8pfcQkkBC7x1Bmoo0sVCkWRFRESsWflSsCCJSBEQUFQuIIkVAEFB6ETs2QEV6DT2FEtJ78s+8y172LpeQcpcrmdFjd9/uvn3v20uy387MN9pEPcaNg3vfvtpmkcsuXbspD9lLL79a5DGOsIMfkrmUhOYp4zEZPBkHwJ4ygxJeyTxlI598HCOfGgFWiSyvWfJ2cbhiSnIB8Qrw95fQxPICbaPzDQJNhhd/7G0VEwQEAUGgtAiIR6y0iDnQ8cFeBu+SNqRs8op5m3IzbZdaelSLNNlWhMeDvgL5ifm55w01gzyI+JibZ/0mJk2cE4asTCS+VVBc2HiAmztyok8bN+n1MZGphgXb+WvcJxMsDllU/VF7yqLZRm+YdkLORVK1I5KmN3ezuWg5ZNpc9MeWdz1HR3DNMS9v33K+/RFwa9kSHrNnI+fJJ5GX/wDM4YnZBw7Bo3FDuOtImv1Ha70RcCgmC3PkUt06veU8+wxS+vZBiL6xiPXHH30IDwwbjht63ljEEY7XzKSMP3p1UyZlbLwsTnzhfFwcdu3aqST8z509i8jIGuWaYK1atdC0WXOTPl5/YxK8vAzRCLyD938y9zOTY2TDcRDQPGFaiKK27TgjlJEIAoKAIyMgRMyR784VxhbmbXr7silkxtvdlJyZdMGkqxjLy85Se928DcIY+kONREdrzKQ8GgoLdK9e+EHEm9o4v8toPCZL48oPK2QvXB73R6YIllnYD1/DLf9YrU+3/BBHbduWy2ydS8wcc1teV/quOATc2reDxyefIGfUKORdMnhVWTkw+9AREoKpC/eI6hU3mAq4Ul5SEoXxniiUD8dqkosy0/BFv964e/AQpd4XEmKZkv22/VdEHT+Gjp2uqYARW/8SenVTjZRx+GJxtnXrZjAe7LXKzMy0eCjLz3NBdj2ZsnggNbZr30F9eH8aeWL9SCjGRxcSy2GQvr6+aNKkctVuLAovR21n8qV5WHmMQsYc9U7JuAQBx0Og+CdzxxuvjEiHQKRvwVtTbs4kwuCv21/aVY98BcOc2HP08NnY5PScuHMm2+4RNZGbnIjAh54zaeeNPPKUuZFXzGhE8HIvnS/IGcvfkXPeENrDYh8e1B+b/+3DCh0HOl/nlMo/u+IWmTp1yhpmmFfcKORKtkaA86E8PpuHnNHPIy8qyni57JOn4JGSCo/6delNge57bTzCuVZyY2JJlVTnsc4fvsdkEi+5/TYMJq9gLpFQlk1fsXxZkYRs7pyPlFx9IOUwObtppOxK89i8aSO4gPU/f+9CVv6LK/05mzZuwGtjX0FoaCjWbtgCb3qplUNYehTz4oi9bONfexWHDx/Cth9+Ud2xOuX0aVOQcPmyKhcw/vWJCHZRz6weP2de1yTtmZAJEXPmOyljFwQqFgHnf6qoWLwc6mp1/E2JWEbOlWtcFTcBz+ZtVVhgxs+bTA+jB4mM3783afOiY5Ui4lFDSI+2k+Xp40fdQ8dv05rUkiXz9cZhUayS6BFZC+7BVUkcobV6yM340zS3LC8tFfFjHkXKwvf1p5dsnRUU+WHJXEnR2G6Gl4V2doZl5tA/+WaOudYuS9dAwK1pU3gu+ALunTubTIhzqLL27KN8StMQWZODHHxDhVseOVqIhLmF0M/fnE8UCeMpMLF69PEnsGb9ZgyjelarSYhjIHnIPnjvXVzOnz97w06dPKkKODv4tK02vDNnTqtiy7369IUXEaysTEMEgXaBfXv34s3Jb6BKlRC0anWVImHJRGp739wTr40bg19++YnEXw3qr9o5efSG6WnKOcsgkZgpb05Xzem0Pn7cq2hKXrUxY8fjIn333pwyUTtFlg6KAJMvJmNsWsFxBx2qDEsQEAQcCAHxiDnQzSjtUBoFmqq6pZWg2HBx12CvFKsdKiEObx94d+pOoUtpSN+2lvK4Yk1O9bm2B9K3rELSJ1PJi/WAEtTIPnsS6ZtXUU5NVVI/7GJyPPfB5tXmGuSlpSCdaoAxkQt85HlF/pQgSOebSHFxqSJPLEmfl5SAdCJweSyl3+NWk/5KssH1yNLWf0VCIUNUvTTtnLT1y5XEPtdV87t1sNZM1/4KaRtXwG/gfaQieZdqTycSqjdzzPX7ZN1FEAgLg8e8T0FPxshdssQ4KUVkSFHRo1o43GvXotp2pi9CjAc64EpudCxyzpwhz3LBSwUepnuHDnCfPAlcL4yNa1p9R+F3XlS3r8cNPRUhG0JFmpctWWziIdux4y9wuyt4w9TES/DPls2bEBERibZtr1YkK4teJmkWFxuLF0Y/g/uHDceC+Z+DyRrbD99/hyQqucGFmydPnKCI2I033kT7b0WHDh2xa+cOJfqxYfM2I5ZcvDmTQrUnTZmm2jp07EhEuA8SEkgplkiemOMiwGSMw1vZK8YmnjHHvVcyMkHAURAQIuYod6IM4+B8pRp+XohOMzwQpJXTI8ZDCLj/KaWGyIWaWZ6eBT28yFvFxZJTFn5QMErK+Qp+/k2kfPkxUpbRQyt5ntwCg+FNBIrl7t0CdOFKFM4VOOJlqjlGNcY2rVR9uFcJVTL13qScqFnA0BFwDwpG+g/rFFHiB13PZm3UudaWpNeueaVlqhm5bRHke6VTZL+LIOAx9lW4tWqJ3ElTVHkFbVo55y/QS4RLcK9Jiov0cWTLvRSP3HPRVBcwtdAwT/e8CQ1nv2dsZ/GJYfffqx74ufG9d9/GxMlT0a17j0KEjAndvUTEKpNt3rQBt/TuQ78S6Xci/W7Kys8RYw/W88+NQufru6B27doUveqmpP0ZGyZv11x7HV4eMw4vkrLkTiKwHN748gvPKbn/bt1vQBsidnpCe2D/PjQjgQ6tjQVBWOb+5IkT6tjKhLkzzpXVOQ1KnAcQWb0akff8Mi7OOBkZsyAgCNgcAbf0LLNXpDa/pFzAmggM2n4Mm2MMqmdcV6xjtSDrdE9fC/aCuQWHUD2lK5APCunLvUg5YJxjRmPQG9cVS9+2BqEfEgGjPnNIIdGNvG1MxIo0vjbllLkFVVHHFnlcBew4mZyOmNSCpPwjfVuhjr93BVxZLuEoCOSdPo3c6W8h94cfCg2JhW08IiMMYh5m3/1CB1dggyJgsXFUby+p0FXZ+3WKhDh+9QlQoVTaW3sOkePQw4/nzFOhci+/OBonTkRhHeU6+RMR0IzD7Q4fOoj25NGpLHbs6FEMGXyHkupv3rwFRo18Ej3Js3Xb7XdizMsvqPDBjwi3F59/lgQ3/DF1+gxcItGXvr1uxGvj30D/gYNMoGJvGueGcbji0i8Xk/iHv1JG5Dww9qxVrVoVY1+boM6JjY1B/7698OXS5YUUFk06lQ2HQcBQDoGIGJEwJmZigoAgIAgUhYDkiBWFjJO0d6haIM/BMusp2aahdGWeBj1UsoLhFUkYX4C8Y0rt8EoPorSfQxCLJWHcH1+bBDyYsNnbkrMK8KxF3kchYfa+IxV/fbc6dUje/gN4THwDbmHhJgPII68IC19k/f0vck5R6B8p39nNKIw2l8gX57JlHz1mkYR5DB8Oz7Vr0PChYWqYmuT237t2Ys9/uzHr/Q8RHByMatWr47nnX6SwuiSSeTeUtdDmxZ6aykTCeN6bN29A3Xr1wCSMjUU4WDVx7pyPKW9sH96aOUsVYeawwt59+qhjvv9uq5K5d6OIAH0YI+9kj1rNWrXQqFFjREefw5Ejh/H33ztVn//+84/KMePjmMzNfn8WQilctkHDRtwk5gQIaPlihmLP+51gxDJEQUAQsBcCEppoL+StdN3rwgreVHOXSZk5CPD0sFLvlbsbJrZ6ItY5TBduWbmhqZSzd7/zTrjfeityP/4YOZ99boIBS93nxHA9vBgKrw0i5c+qcCOZczcfG3tPSdEz93ICeZDjley+aQZYwRDdb74Z7k+MgFsLA5HgPXqVt98oDPmuwfegWrWCMKrjJE3PpKxO3XoFHVXStehz54hgFeSpMpH6bstmHCYCNe/zBcqDxaImLDV/fZduCiUOZaxdu44iUjNnTKNaazdRH33R6ZprKXzR8A6UlyxRH0Qh2euIIH+3dQvJ2KeiB9VlY7XFO28bgAyqaffmtLdKJIdfSW+PQ06byZhI2jvkrZFBCQIOhYAQMYe6HaUfTLdwU3KQkJWNSNj44a8Uw/Rq0qpQuGIpTrfroQmZpgpn3aqZYm3XwcnF7YMA1XlyHz0abvfcg9wvFpiIeWgD4nBAFRJIsvfuFHLmRmTGLSiQcigDyi/wQQ/teSkp1H8yidkkIS8hEUWRLx6Pe/fucH9wGNyuu04bnnGphSTyw6KndwAGDDANn9tMUuwDB90OT0/5M8HCGXqhExYz2b37X8wgT5hW44tzv26gcEX2lrEXkfczgbrxplvAHsfNmzfi1TEvISgwCKu+XafI2NYtm1TNsfhLF/HTjz8oifrX35isiB0rU6akJGPxkq/w6dw52PHXn+hFOWqcU8Z5amKOj0BvCkvcvO1nRci0nzfHH7WMUBAQBCoSAckRq0i0bXStfr8exbbYglwQzhPjfDGx8iFwPDEN59MLlNH29m6JxmZKleW7gpzt9AhQnk/uV8uRu3Il8s6fv+J0OKfMzc/XEPJL3jKlvEhEx82DPCRa7T3yxOYR4SKJPVBMGxU7pw95RTjsMY+EIUpi7oMGwX3w3XC7+uorHr5s+WpkkijN3zu2o1H9OqqIc1TUcYx6+gl8u26Tqol1xU4q2QEsZc8YdevWQ838PN37/n1vwawPPkLnztdj8cIvVNjilm0/KS+ZBg/L1x8/dlTlep2h3MOHh9+Phx55TCko3n/fEGz97kdUIU8q24TxY8FqjJx79usvPysi9zOTteAqipD16t0XzZo317qWpYMioOWLsQdayJiD3iQZliBgRwSEiNkRfGtd+oOjcXhx91ljd42C/RAuhYeNeJR1ZeeFJORwITGyNiF++OsmeegpK5aV4bzcTZuQt2Ejcrdts8t03dq0gXvfPnDv3x/Enko1BlZ5Y/tizjtKYr0qnd+9xw14/oWXS9VPZT3433//wTtvv4X5Cxar4s0PDL0H9Rs0UBL0RWEyjIjXIRI9WbdxK8IoB+yWG7uDvWGMO8vX97qpJ54d/QIJgtxh7ILDFG8feCt50zyo1MAl1KhZk0hZXzw4/GEVuqiFPBpPkBWHQEDImEPcBhmEIOCQCIhYh0PeltINakAN09oyFzMKvDil60mO1hC4RBhqJIzbBtY0xVg7TpaCgIaAO4k0eLz/Hrz++AMe06YpQuSmy7nSjrPWkr1p7l26wOOll+C5fh08ly6B+zAS4SgFCeMH+4MHD6BZ4wZqWC+OnayKOCclJuLb1atMijhba9yu2M/VV7fDwsVLFQnjcMKU1BR8T4SclSe/3/adIlbm875ERIrP47w8JlBXt2uv6orxcdt//YVyw9LBNcf0dvr0KbD3bfyEidiy7UcMf+hRnD51Uh3S/9ZeKrxRf7ysOwYCmieMw4BjY6/sOXeMUcsoBAFBoCIQkOD/ikDZxteoH+CNntWD8EOcITzxckY2MqimmA+HO4mVCYELupBE7uCOWkLEygRkZTyJ8sHcB5BXij9kF/7cgYOrvkHTzHSEUS5QXlQU8i5eLBUybv4kylO/HtwaN4YbhaO5XdUabu3alSv/8vixY3h21NNKtY+JwH0PPqbG1Klzd1Ws2byI81MjRymiUaqBV8KDAwICseqbdThwYD+2UN7YzLen440Jr+GGG25E31v74ToKXWTFxYsXLpjUYuvQsRM2EqFm43yzzp27qJwxPYTczgqKHTtdo8hb/wEDwZ8ff9im5PC1fDX9ObLuGAho4jicMzZs6J2OMSgZhSAgCNgdASFidr8F1hnAkDpVjUSMe4wjIlEnwP7y79aZXcX2woWx44nMatadRDpaBvtqm7IUBEqFQE79+kjt1Rt5rVvAQyvuSgWW8yj/B0zIWHCDlPJAuWBUbA+kjgE3Ut8jKT2gaijcqChsabxcJRkcK/WNe/VlhFC9qlfHjcfRo0eU9+vp0a8pYQEuRPvo408YCRmH0Hl4iBprSbDVjmnRoiX4M+rZ0eDQxS2koriBiBYTsd+2/6LUEjt0LKjF1qFDJyqiPVPlhXFO2Lj8OmJaf7zcQoIfN9/cS5EwfTsTtNatr1KS+NzONcqWLf0SPxBB8/TwRC0qND34nntV0Wn9ebJecQjovWIcqqhtV9wI5EqCgCDgiAgIEXPEu1KGMT1QLwyv7DmLeJKvZ4tLy0RtImIi2VF6MBk7vQ0jbMUEgbIiEMFFXTUCpnXCaoqUQwT+kFX0zykTgssJl6lI8Aql0MfkIIGKOe/5dweuurqTUnrjt/ZcM4wJmVjZEWCFw3YUdsgfzc6cOYM6derCXRNooR1NmjZFABXOnkVkjK37DT3VUvtn7949OHf2rBLq0Np4mUqk/peff8LT/3tGNXMR7pFPjkAgeWaHDXuIyHYIjhw+jFeo8HTXrt0w+c3porqoB7AC15l8xcZdUC87+LJCxioQfLmUIOCgCEjsmoPemNIOy52e5B5pUFBsNptEJmJSTQlFafusjMdnmeHGRZzvr1c64YPKiJvM2XkQYG/Y5/Pm4uGHH1MkTBt5cnISzsecUvXFuI3f2ovZBoH7H3gQ/foPpFwy8oTmm5YnxpL23br3gD+Rdb1xXbLIyBpKvl7fzkqKXDD6FvK68r19cfSziKxRg4RDvsRtd9yp6pc9NuJJLP96Nf6k/MUVy5fpT1f1ykwaZMOmCLQlzzgb54uJCQKCgCAgRMyFvgMjGhYQMZ5WdCpJXrvQ/CpiKoyZ3p5oVFDgVt8u64KAsyLA3rB0EoIYeNvtxinwA/wfv/+O/v0HGd/SG4rRChkzgmTlFf8Af6QkJ5v02rVbd+Wt4sLPejt65AjWrfkWt/TuXcibtWXzJrTv0BHh4dWwYd1anCLxjtffmAQ/qnmnt4iISAwb/hBJ6y8wNicmJKDXzTdg6ZLFxjZZsS0C7CHnfDG2LZQvZm4s5mGp3fw42RYEBAHXQECImGvcRzWLOv7eeFJHHNi7cy7FlFi40HStPpV0yg2L1nkRw7w98b/GQsSsDrR0aFcEOM/oAinvvf7aWKppdUyNZdPG9cggyXSNnHEhWjZ5a69gsMk/dw8eAiZeervjzruxbsMWXN+lm75Z5XolE2nr3ftWk3YmUr//vt0YrvgzhShe36WrCns0OTB/g8U8YmKiVTgjN7GiI/ex9ttvLB0ubTZCgEMSmYzFEOnSe555ncU8uF3UFW0EvnQrCDgYAkLEHOyGlHc4o5tWpzemBb2cISLGCopiV0aAsdLb882qw1eUJ/WQyLoLIDDr/Q9VvapDhw5gyOA7MOblFzFv7hySQn8E3lRwmk3/1l7/oOgC03eYKXAooiWrHhFhvA/a/oOkwOhJIi7mBZy//36bOuTGm25Wy6NHD6Nly1baaYWWrNaoNybl3CeLtTAh0xt74D6d87G+SdatiICWH6Z5nvnnTP/iIyZOZO6tCLd0JQg4LAIi1uGwt6ZsA2Ov2CvNIzH1QIyxg9NEMBpTkWexohHgumEXdZL1TYJ8MLppRNEnyB5BwEkRYALQr/8AJaW+ccN6fDZvDrg+1c4dO9Cx4zVo2KiRmpkICzjODR563wPGGmP6UbES47XXdUaVKobyGn5+/sjNKzog/b/d/6JWrdoq/+zChfPYtWsnZr77PnlHX8U///yNHjqBkK+//kqpPmrXY4/cWRIZqUpKm0wWxcqPgCZprydg5e+16B5i07NxjJ4HTlPkRzT9vbtI6sCJ2TlIy8lDDn1v+NUAl70J8nRHKEWERPh6orafNxqS8BeXyRETBAQB6yMgRMz6mNq9x9da1MCyU/GIyvfwMMEIoV+q4b5edh+bIw6AH1xOJZt6wxhDMUGgtAhER5/Dki8XUY2oi5g4+U3lxeA+WMnO28vbKC9e2n5tcbwlQsYesptIHv0xUkpkQtaLQhQXLlmp3tRrb/BtMZbK3GfU8WP4atlSqELQKSm05A8VhU5OoRDCFCTTMpPCRs09aOzd4uLODz9qqP/GGDZu3ARMtiwZ9/nN6pW46+571O7vtm5BcHCwkrRv176DInoaETt79gz27d2LZ557QR37DRX3fnfmW6puXXpaGuqT2udTT48yIW6WriltZUeA1RXLY8nZufj5fBL+uJSCXfGp+O9yGs7ryrKUtm9/ImdXVfFD+xB/XBsWgK7hAYqklbYfOV4QEARMERAiZoqHS2yxguKk1jVw/58njPM5mZyOIC8PKfJsRKRg5QRhow/f5OLNg6kum5ggUBoE+KF4+LD7kEUPyD163qhIGD8svzD6Gfz+23bVFT/oTp3+Nry8HOeliCVCtmjRF3h9wiQ1Zu2tPQsIMDETsy4CqalpOEx12gKoVAArJbLHKiAwAIFUGNqfCnnzek5ODt6a9ibSiARpIhwcRrpi1bdKKVEb0cOPPIahQ+7Gmm9XY+CgAjEWVlUcO+ZlClt3xwMPPqQO52LTTLq5PhwXk16/bo3WDdU826S8Xldf3Q7R585hxvQ3wcqLHL7KhO6j2R/gpReeo5cNU2EuLGLsRFaKRYB/njgXrCgrbl9R5xyjF4proxOwKSYRP8YlFXVYmdpTidj9eTFFfT4+Zhh3mxA/9I4IRv+aVXBtaECZ+pWTBIHKjoAQMRf9BtxVu6r6Zbz45CU1Q5azZ8LRrIqpJLKLTr/E0+KaYefTuJCuwQLord/UNrW0TVkKAiVGYOGCz6kGcxC+WrHaWPz4449mY/++fXj7nfcU+Zr25mSlWvfQI4+WuF9rHPj5Z59i1dcrEBoWhvCwcLUMDQtFGK2HURsvQ0PD0L3HDSpkkWtTaaZ5wrRcFm1b2y/L8iHQqnVrfL5gcbGdMPlhIsYeMo2IaSfoPWX16tdXionTp03B1i2bwZ4uN/pv3bpvybNGBOqTuao2HJOrPXv+w8hRz6pu2rfviFnvvI2kpCT1HebcsV69+iiFRi7mzS8U+vUboI4NIIL44stj1LHzP/9UiJh2I0q4NM8FK+FpRR7GolyL6O/8stOXyANmqsJZ5ElmOzivnP5XKsvFRLaanQXlZWNP24xDsWgR7It76AXmfXVDwSkSYoKAIFAyBISIlQwnpzzq7ba11S/mU/lKgJcpLOEUkbG6gb5OOR9rDzopKwdRSekm3b5DmNWTPyImmMhGyRDY/ssveJy8BuxhYOMcnOUUcjb2tfHGEK7hDz+iHpArmojt/OtPRQTZSxIbG4MYCqH8e9cunIiKQgIVdtbbE0+NxCOPPq5vUpL2BiJ2QLULGTOBx+YbnPvFhaE5XJFJc3HWq3cfyhu7ToU7suz95cvxyjt2KxGpatUMKrCbiWhVq14dV+cXmdaKSf9LeWLskWPxjvEkgc/Wpk1b+Pr64r1ZMzGevKSaoEvdevUox2xHcUORfeVEgJUTWTjHkp2kv+tzyDM1L+oiEulvWXHmSWEyAZ4e8KMXjX6UA8Z5YN6UK8rt/GESphlnGHK+GL+8zeQPiX2l8YdyyVLIK8bbluxAYjom7ItWnyF1q2JEw2roTCGMYoKAIFA8AkLEisfHqfeGUCji++3q4Lbtx4zzYHl2VgKsTgm4ldkyqW7SscQ0Ewi4IPaD9cNM2mRDECgpApcuXUKDhgahCz5n2ZIvUZ0edvv07WfsgnNyMtJNyb9xpw1XJk99C48+NAysvjf2tdfVQz1fbsRjD6Nlq9YYOvR+CpOKQSxJm1/XuYvFkWghipHVLT8YWjxJGq2CAHu9WOrenX53l8RYvINfChRlHJZ4yy29jXln3D97z/4m8Y59e/egTt26RqEO9qK+98FHePnF0Xjgvnvw6tjxaEshixwG2YM8qGKlQ4BfYvDP0O69B4oNTeReWTnRnIjxi9WZh2OJhBWdQ8YeLs4LD+YPPQf4EwkrqTEp86QOPD3c6FmBNuh8vXEYP7/ETMjMVh/2yJkb56jzp1+NKniOlJy7hgeaHyLbgoAgkI+Ax7jxEyYIGq6LQONAH/X26wddvPhl+gXKibd+pfjl7EoI8d+NQwmp9Iav4M0eJx+v6NzQlaYpc6lgBL7ftlV5DtpefTVOnzqFSW+Mx5PkXWrRspVxJGvXrlGeqZ433mRsq4gVDmfr2r07Pnj/XZyPi1MCDbt27sDiRQsw7a2ZCAsPpwe+CBLoaGz0eJiPK1IVom1JoW3yltscG2tuc9jgH1Qb7M8/fsc5Wvfx8SFVxCoUAngriWtUKfelOPzw6xVfKaLGXjHN2IP7C9Uh279/H/rQtTp2ugabibDVrFETdevVR/8Bg3Dk8CHMencmsrIycc21113RO6f1LUtTBPhnqFHDeoqQpVAYcHJKQSiw/kj+meMPWzZ5qSbuj8E9f0Qp8Q39cdp6VR9SOSSFw0bB/kqcK5BIlBeRbGsae9CY2IX6eKGGv4/KPXcn4sZeM3NKdoRy1hZS2OSxlEy0JqEPVmIUEwQEAVME3NKzShMRbHqybDkPAk/sOoUvTlw0GXAzUj/it2aVzQ5eTlVv8rR5VyeJ3u97NAWTVjFBoKwIfLd1s6rJ1aJFS7DqXN269TBv/kJjqCIToHvvuUvl8HTr3qOslynXeYcOHlResHuH3qfkyluRN+x/zzxXrj7lZOsgwETonbdnUOjqJuWpYvKVmJiIPPoTzXXCxrz6GoKpzVZ28OABPDDUoKi4/OvVqFW7Drp27oTZH81RpEu77rbvtmLcqy/jyaf/h2H5wh/aPlmWDQEOQbTkIWMSxgI5y0/HY/y+czhBhMbcvIgYcYRLNVJF5pBDexm/4Dyfnok4yrlOpTBGS/ZGqxp4mcrriAkCgkABAkLECrBw+TUOUWQ1Jb1VJjLGrxzYE8YhFXrb1L0xbqgWpG+SdUGgTAiwl2kT1XYKodAwVqfjUMR///0Hv2//FWvXfEPFc1vgnVkfGEMDy3SRcp60Y8dfeGakIZdtzfrNqi5UObs0ns7qfvzR8oiMO2SlWAQyMjJw3713w8vTC0/9bxSuueY65QnLpRDq3+i7M/uDWQgOCsbsj+faDFu+1sB+fdR3dslXX5PXKwt9bulJRH00brv9DpPxT5k0Abt37wYTtqKsuPymos6p7O3mhCyVKnvtaNoeX+aLbunx8SbSVYMIWKQD5jRzXU5Og0i2kLt2HUWfzGhTG51CRThMfz9lvfIiIKGJlejeDyJZ9l8uJFMxxwKVQK4xxsm7pYkhd0bIOI79UGIqEjNN39Qtva4B+kba7i2zM2IlYy47AjVr1kL37jcoDwKHlLGto3DE33/7Fbf07ovnRr9oswfpko66Vq1aqFe/gfK8sMoek0Nr2KmTJ/HsqKfBdbG6dO1mjS4rTR+fzvkYUVHHMZ/UExs3bmqsP8cCHSyKwTLzixZ8QaQ5FE2bNbMJLnyte+4dqnLRWP2TRWeYIM7/bB7aULhtZGRBbcUVy5fBjwQ8+g0YWORYVq3ZpOrP8QGcdyQhrUVCZdyhD1n8KSETM3Kq4q8E0xqXdJtU+GFTUkDm0ENHNE57YC8d56NzCgCHVWp2hjxm8yk6h3PXOCVATBCo7AiIR6ySfQMuERG5/bdjqhaIfuqspFjDAd+s6cdY1vUUCpM4SsIc6bqcMO5rwTX1ldxuWfuV8wQBRoClvYcOuQtNmzYHS5Gz+EVzIjfmMuOOhhY/TL/91jRV14xD38pjq1auoIK/MxQWQeS52bB5KxFOCfUtKaYsmtKn7624/Y67ijzlvVnvUKHwC6pQeJEH2WDHXCKJC7/4HC0p15FJOxcn/+vPPzDv8wW4qk1bi1dkzw4LTbDSpt5Y8EUUN/WIWF5nOfjX9p4rtDOMwg/rUA6YPUMQCw2qBA1nUjJwlj7mdn+9UHzasZ6JaqP5MbItCLg6AkLEXP0OW5jfZQoXGPz78UI1R/gNVoMg15K2v0Aev+NJaZRnYQrEl9c2wJ21Q0wbZUsQKAMCXHNr5YrlJHKwlxTn9iKapOFZha4hKSgyKdPIWePGTYyejjJcxianfEJ1zlav+hobt3xvVNArzYVYKXLyG6/jdxKXuOvue7Bh/Vq88uo43EI1qMRKjkC/vrfgjYlTlEBGUWfxfdq0cQPmfPp5UYfYrP3ixQv46ccflNcuMDAIt/brjzp16pboelw3i01Pyjj3KaJ6uJAyCwhayudmgYx69LI0nIiYsxqHKZ6k8jnm4YrsFVvQqT7qB1RuJWdnva8y7vIjIESs/Bg6ZQ9cJ+S+P0/gm7OmNYQCKFygPv3Cd9SQh9KAzb/0YyhOXW9VvT2w6JoGuDlCcsL0uMi69RCIj4+nIs5EyujDS/5wG3uItmz7AVwQ15FsN+WwsRx5aY0V9iZPfF0p+U2cMhXz5n5CeU2+eHPaW6XtqtIf/9QTj6mQ1iEkolKUzZwxHekZ6Rg77vWiDnH4diFlRd8iVh28lxQRzfO4Q0gJsQH9TeacMFcwS3+X61I0zhJKE+hYVfLGXOEeyxxKh4DkiJUOL5c5muVm76pdFQkUrvfXpRTjvDiX6jx5kegFHMnSOqeiIotxHElIQzwVsNZb2xA/rLy+EThZWEwQsBUCHJLIionsDQMJOkdHRyMuLpbyproqCXBbXbes/epzf0rSR1paGmZMn4r3KVSOc4Teevtd7KCC0RvWr8N7738IH8odEisdAhco5HDNN6tJor6vRfy4wPL0qZPxwLCHqFad85bZ0OTYtfBEzkuLoTDG2LgLRo9ZLIU0apLtpUPReY+Op5SBQb8dh77MDM+mJsnDNwz2gwf/QXYRY6VmDq3U/31OIG/ZyjOX1d/mei6aIuEit0+mYQMExCNmA1Cdrct5URcw8u/ThYbNXjGuSVLFSSTus4lEcix6bJqpF4wndm/dUHzSoS58XOgPWqEbJg0OgcBeKoj77TersGXzJvj7+aP/wEEYOOi2EodyOcQkihhEXGwsnhjxCFKp7tH4CRNxfZeuiKEi0EPuvhNTpk4XkY4icLtSM4tiPEF5YlxT6uGHH0Wna68FC2acOX0GP/ywDYsXfkG137q6rLeRPWVMxpiUacb5ZGwaadPaXW3JJGQgKRr/ebHghSjPkdMEOF3AVY2LQh8lAa3MnIK8gQCqb/ptl0ZSANpVb7rMyyICQsQswlL5Gv+OT8Wz/54x8Y5pKHCCcE16S+XIyornUjNwjmqscMiluc1sWxtPNzYUxTTfJ9uCgDUQSEi4rDxCTMBOREUpQjLotjvUktXnXMVY4nzuJx9hyND7ERISompcPU1hdbVq18bY1yYUmiYTjD3/7Ub7Dh3LlINWqEMXbuDw1dnvz8LGDeuUdLw2Vf+AADz2+BO4lzB3pe+SNj/9sjiRDz7O1UgZvTtE31+O4KfzyXoY0ISLH1PBZFe3dArHPEwlZVhZUTNOH9jQrTHaUZ1TMUGgMiAgRKwy3OVSzHHCvmhMOxhj8QxOFI6gN3SOkj/GpIu9X5wHxiGV5nYT5YFNu6oWrqI/amKCgC0QOHf2LD6gh+effvxeyXuz54s9YOHhjkH8tZwcWz3ALlv6JZYuWYyly74GEwauPcUewZ0UqriTaqoxCeO2J54aiUcefdwWt8Dl+mQBlJOkTMjErFat2mjcpInLEzBLN7EykDLOCVttlqddmWp78n3PpJc7hy6nmRSBbhjog++6N0FNP9cno5a++9JWuRBwziSgynWPKnS2E6jyff+aVTBh3zl8F5tkcm1WIOQPhyoyKbOXghOrLvE4zqdnwgL/QhglN49vWQMjGoabjF82BAFrI7B162Z8Rx9fyou6ul17BFepAs73CQmp6hAKiZpSnS2I2MkTJ/DhB+9hxBNPYcWKr7CTCkX/+8/fSra/Q8dO6HTNtYgnUnHq1MlKSSTK+l0LDQ0Ffyq7RbCyIn34u6u9UODvs/ad5iWHL0ZWNxznbHi99N/ZSk/C+J55k8JsM3pZelDnGTuenIHhO05gC5ExMUHA1REQj5ir3+FyzG/Z6XjMIO/YvsR0i714UKJ1qK8nqnp7KXJmy/QrrgV2mcQ3LtEnldaLsueaVseY5pGqWGRRx0i7IGAtBDgkce+ePRYVEps1a2aUr2/T9mrl3bDWdUvSDz+8ag+rtiBis955G18uXqhUINt36KCIV8dO11BB4ibY/usvmESKioGBgZg8ZRpaUA0qMUHAGgjoSZm+P2ciZZ9HXcRTf5/SDx+NSJTDXi83TQZipw0OTzxwOcUkuuVRepk6u10dO41ILisIVAwCQsQqBmenvsr8Exfx4dHz2EtKhMUZe8qCSOCDJfADPD3gVUZmxmleTLaS+UPer0T6ZFIseVHGhPDJRuEY1aQ6WAZXTBCwJwLR584pYqbJ1x88sB8339Ibr73+RoUOa+GSlep6w4beaZPrJiYm4vSpU0SyWhrzv9LT0/Heu29j5dcrcMedd+PZ0S8obyEPgAtIHzp4EOPGT7DJeKTTyodAcaTMFi8frIHwP5dT0XnbIZOu6pI8fQ352wVWPD5I+Ojtw/Z18EgDiW7RYyLrroWAEDHXup82nQ3XHGNStjkmsUTXYSLmSzK1XP+E1/nDsvlaNRTO6uLQwmxiXlkUJ87qSRlEuDiBtyTWiOLIh9ULo1/SYQincEQxQcBeCBw/dgxnzpyGv78/GjZqbBJaxgIXaWmpFVo/zNbeMEs4H9i/D6+NG4OkpCS8Nv4NdO3WXR3GxYAnThiP33/bTrXUvImQTlQy7Zb6kDZBoKwIWCJljlg4usePh00UElkZkRUSxQwIxFHed1RSQRQOF7PeeXNzNBeM5CvioggIEXPRG2vLaR2h+O0VFLbIScZ7ruAls/Y4gsnbNohy2LgGWu/IYGt3L/0JAqVC4L/d/+L118YqEqadyMp2LOs+6tnRqF+/gdZcoUtbe8P0k2Gi+cX8z5SaYpeu3cjj9QaqVq2qDvnxh+8xZdIEePv4YMLEKbhI+XNvTZuCpctXUv5PpL4bWRcErIYAkzJzOXxHIGUT90fjzQMFYlgcPdK6qtS1NL/xTMSYkGl2S0Qw1nZtpG3KUhBwKQSEiLnU7az4yeyn/LEtsYn4MS4Jv15IpnDCknmzSjNSLsTco1oQbiYVxF70C1lMEHAEBFic4pn/PY2Bt92mwvDq1atP3qBE7PjzTywlNcHTJFIx89330a59hwodbkV7wzIzMzDi0YfBcv233WEIg0yleljvvP2WqqfG+XHvvjcbwcGGn92xY17CxYsX8dEnnxpDGisUILlYpULAEimzRo0yVnVkMZGSGpeIuf5705DEVkTCHEWFuKTzqIjjOFpmz6VkE1n79yhXTAS4KgJ9uUZFIyBErKIRd/Hr/Uvx3f+QFO0+8pQdTspAFBVYPpOWhbQShBvWIKnaehQn35hCDlsG+6JNFX90DPVHCL01FBMEHA2BJx5/BK1bX4WRo54tNLTs7GzlCdpNHrOvV62pUMJRkd4wbeJ5FF7sRmHHbHv2/IfxY8fg8uV49Ol7KzZt3KA8YJGRNdR+Dl28d/CdGHLvUNw/bLhqk38EgYpAQHtJob9WWUkZ/5zxuSXNReOizVt0Yf21A3xQiz5ilhEwzxcLofpi+3u3RCjloosJAq6EgBAxV7qbDjwXFtyIpw97zLhuCAtycOy3P+WPcbhhOP1ypU0xQcApEOD6YXfc1h8bt3xvDMMzH3hycjIGDeiLSZOnqlBF8/222NYeNEvzgGitcXCI4qdzP8Hn8+YqKf83Jk1RtdXGvfoyzp8/j4/nzDMSUs2buPSrr1G3Xj1rDcGl+tHuZe+bupfK8+JSINhoMuWtUabdGx5eSX7WllMo/7C/ThhnY5OQxJwc5CYnwr2KISzYeDEnXjlBIYpcK1SzkY2r4e22tbVNWQoCLoGAvFpwidvo+JNgssUfMUHAFRBISUmGl5dXkSSM58jS7XXq1AETsooyrcZSSd/SW3Nc7lQP6NDBA6hNc9aTrpfHjFMesC8XLcQDDw5Xl2SZ+9kffYI6detacwjSlyBQIgT0Ncr0pEz7+dHKPnBnln6WtON4v7Zu6TjezzbjUKxhJf/fOlb2hKUsmo3Mf/5AXloKQqbPR+rSOciJPoUqEz40ua6zbdSm6JiLGVnIzi8YOpvUmx8jSftmItzhbLdSxlsMApqAXTGHyC5BQBAQBAQBPQKeRMIyMzNxiXKdirPUlFQibBXzvovf0rNpoVbFjctW+1gRkYnnqpVfGy8RFBQE9o598vGHOHK4IEeGc+dY2v/bb1YZj5UVQaCiEdCKRnOZB4N3q4UaAhMs/nAIIv9sMWFj05ZqI/8fw7GGnz99O68vIKVhvahVmK+h7qb5cWXdzj6yDxm/bYNvz34Ifn4K3P0D4F4tEh41nf8lhyeFO9fyNw3fnHUkrqxQyXmCgEMiUDFPCA45dRmUICAICAJlQ6BBg4aqftasd2cqkqHlR+l7+3vXTsTFxeHaazvrm222XpI38za7eH7HrJbI0vVjXn4BncjrVa9+fbWnQ8dOeOGll8lLGKS29UqLHh6elEvWDz6krChWGIGYuNKJQhTuQVpKioDm1eKl9mLDQLKIlOGA6obVFy1ZUT9/Hx4zEDjtHGvXC8uOMrzc8L15INz8DAqM/nc8qF3O6ZeRlDfO4YlaWZv5VAz7pWYRaGBlr6LTAyUTcFoEPKi45gSnHb0MXBAQBAQBOyFQn8jYJx/Nxi4iXHXq1DXKsTPJ4JpZXFPrjjvuAku629rUG/u4C+qNflEPirYeg9Y/53zFxcZi+VdLMXDQ7ca8sBYtWoK9Y5xfN/q5Udj23RZUCQlBmzZt0X/AQO10WeYjwCmzx6JOgu+nve9pZbwpGu4aOeNtlsRPJi93Ucb7I6tXoxcOBkK05lwCOJxOs3DyhkVQ3TCrGAkCJc4ci6xDeygkMRXZtMw68C+8O3RB2reLkbF9K7zbX68ulfTxm0BqMuWPhSJ1JYUurvgcvjf2R/JnM5F7kTxM9GVLWfQhUr+ah6x/fod7WHV4hEcgbe1SpC6bo/rLPrAbnnUawD04xDj8zB0/I2XhB9TfZ0jftlaNwaN6DbhXDTMeY40V1gG6TMWeNfOmhPKbRUFZg0OWTo6AhCY6+Q2U4QsCgoB9ELj66naY9/kCFZ748PAHcFPPbhh852246YauJGv/FPr3H4inRo6qkMEV9Ta+Qi5u4SLPjn4BnEc395OPTPauW/Mt7h1yF3LoIfLTzxYghcIY77jzbpNjZEMQcDQEmIzxh4VTrmSbt/1sDF/8gsIS9WY1EsadEjtxDw03esHcQ8KMQh05MWeRc/q48dI5J48iJy4ayZ++hcy/foZ7gMEzzcdk7tqO5E+mwZ2Il+9NA5B7+RKS582kz9tI/3kjvJq3hc/1NyHr+AEoQsdKW2SZf/+G5M/fBXx84dd/CHy79ULOmSgkffAGcuMvGK9tjRXGzUun5sW45qeNWaN76UMQsCsCEppoV/jl4oKAIOBsCOSQOhkXbWZr0rQZlpDy30ESqfj377/pbXkyQkKqomu3bkoxsCLmpoVQ2TM3zHyevr6+Si3y0KGDaldCwmWS85+In378Ho88NgKPPPo41ny7GoHkIeva7coPt+b9y7YgYA8EOEy0JMZkrHmX67EhOsF4eDApA1u1Zhj9Dgp85Hmkb1mF1NUnEDDsf0TK/I3XM19hD5ln09ZKzEN/XDaRtKDnJsGL9rF5NmhKZGoisg7+h5Cp8+DmbQgZdvPxQ9rGFci9EKty0DLJc+ZGJCz42Ykgt7c6l71xCVNGI2vvLvh0663arPVPdSJjZ6kcDlt8Zg6WnLqE++uFWqt76UcQsBsCQsTsBr1cWBAQBJwRgdUkRMHCEy1btkQrqiPWslVr+rTCkKH32WU6juYN00Aw4NIav//+Gya+/hqYnM2bvxBXXdVGHbLq6xWqCDSrLeqNQzvN2/T7ZV0QsBcC2s9aSa7/zp8k3uFRQBSqUViiXY1+rgItkDXPhs2MJIzHp4l8cFijRsL07bmkzMg/se6h1ZGXkY70XzbDt2svOtEDHrUbIOStL0zO43OtYRzWqREx7m/5mXghYtYAVvqwOwJCxOx+C2QAgoAg4EwItO/QkQoRP4j9+/ZhLYXazft0jhp+RESkImWtWjMxow8RtYCAQJtOzRG9YfoJM6ma9c4M5SF87vmX4O9veGN/YP8+HCYFxZnvvm88PDMzAx+8N4vCumLx1tvvGNsr+wrnHYnZHwHtZ60kI+F8sv+SKE8sx3C0O4URslqiPc2jfhO4BQYXGoJ7sFndMRLPYfOIrGV6bH4UgNbIoYhZe3ZQDtlclUPm2aSVIYyxUzflKdOOs9bSl2qOViGvIhd6ZuPi2LHpWYiwM67Wmp/0U3kRECJWee+9zFwQEATKgACH2fW9tR+GP/SIOvvixQuKlO3bu4eWe7FwwXwkJiTg5lt6Yer0t8twhZKfor2h1wQFSn5mxRzJnq35XyyGf4BBvEC76qqVK5SISfWICNV0mEIYx40dg/hLFzH2tQnQh39q51S2JcuqizkmApp4SkT1cDVAFuhg0+7ZISpEfHyLQWWR20N9PFkPw65m7ULPnFMWPGYmhSHuJEJGHxYL+W+HImWBT7yiSJm1Jxzq42UkYtz3+uhEPNzAusIg1h6z9CcIXAkBIWJXQkj2CwKCgCCgQ2DG9KkY9uBD6ENkjC0sLBzduvdQH+2wM2dOI5sEKWxp2ht6R8oNszRfcxLGIh6bN23Em9NmgD1mixctUOqTna65lgpBfwovTy/cPqgf3p/9MerXb2CpS2kTBCocASZbbYe2LNF1t8YmmRxXlYiYvc2Nfq6sapQr60ZeMu92ndWH++bcsKRPpiJt9UJ4EUmztjGOUTpot8QKEbM2xtJfxSNg/98OFT9nuaIgIAgIAmVCID09HceOHVWhh8V1ULt2neJ2W2Wfo3vDiprkxg0bUKVKCBo2bIQnRzwK9iQ+9/yLuHvwEOMpLPn/2quvYP7CL+HpKX+mjMDIit0Q0LxdJRnAD3E6tkAnhFBInatZwtTnlZR90KgJxql5te4Az4bNDZL4xlbrrbByYpCXB5KyDEOYF3cAAEAASURBVDGf5jhb70rSkyBQcQiYZklX3HXlSoKAICAIOB0CrI4YQGF2derWtevYncUbZgmk1au+JmXJENx3791IS03F4qXLTUgYn/Pscy+ASe8cM/l7S/25eltMbMmU+lwdB2ea368Xko3DZbVEzhFzNfNu04nqlu0mxcZFyD51HDnRp5Hx00ZkRx2msESDII8t5sx4apZAhGxXfNF13bTjZCkIODICBd9oRx6ljE0QEAQEAQdAgHPAMjIy8eLzz6JVK1ZMbIUWLVshOLhwErwth+us3rDTp0+B88E4d2zY8Icx4omnLHq8uDxA66uuwsIvPsf1XbqiXbv2toTTYfvmXCQhYg57eywObE9CGpggaMYeHFc0rh2Wl56GjB/XKwl9nqOblxd8OnaF/z2P2WzK5nj+dSkFHaoWLdtvs4FIx4KAlRBwS8/Kr85npQ6lG0FAEBAEXBWBsWNeQhyp+gUEBiphjvj4eKqr6obadeoQMWttJGfNmreAj4+h/o61sWBvGBMxzg1zVJGO4ub87swZ6HnTzeCC2Jbs5IkTeG3sKyoEtGOnaxAVdRxLqVabrRUoLY3F3m1bqB4VE7FhQ++091Dk+iVEYNHJS3hs50nj0U2r+MMRcsSMA7L2SnYWcriAM1VYdq8aZhPpev2Qc+iRdef5gtDPYfXDMLeDfSMU9OOTdUGgtAiIR6y0iMnxgoAgUGkRYMn6Z557Hjf0vFFhEH3uHPaRl4w/7C37+acPkErhdrf2G4A3Jk2xCU7O6g3TwOB8sKLs6+VfYda7b6NOnbpYsHipyiMb8ehDYIGUCRNtg2dRY5F2QaAsCOxLTDM5LcDLxTNASATEo1oNkznbcsODXnz5erojPTtXXWYfeSDFBAFnRkCImDPfPRm7ICAIVBgCLEnPaojs+dKsRs2a4A9L1bOxCuCJqCi4UVK5LcyZc8OKw4NLAEx643X8tv1XDL3vATw1chS8vb3VKW9MehP3Dx2M48eOoWGjRsV1I/sEAbsjcDgpwzgGT/o94G1WsNy4U1bKjIAf1RTTiBiXChATBJwZASFiznz3ZOyCgCBQYQhcir+k8pWqVa9e5DU598mWZMHZvWGWgPvpxx8wZdIEeBHx+vDjuWAZe73VrFUL36zZgMysTJw/fx7VqlW++lqxFJ5YGtU+PX6yXrEIRKUUEDEuQixmfQT0uCaTZyw2PZsKO8vjrPWRlh4rAgH5LVERKMs1BAFBwOkRiImOVuISWVlZdpmLK3rDFpAYxwujn0HHTtdSHtjKQiRMA3rXrh0YcvedeP+9d7SmSrHUCgZXism6yCTPpBX8fhBvmG1uqo8ZwT2blmmbC0mvgkAFICCvECoAZLmEICAIOD8Cc+d8jD3/7YYXKYM1adpMKSaycmIrUk6sR4WH2RtmS3NFb1iPG3qiOnkY+97a3yJ0qSkpeHvGdKxd8w169+mLl15+1eJx0igIOAICqTm5xhpXPB4vD9uEKBc117y0VMSPvg/+tz8A3153FHWYxfbEmWM5thrBL061uN+RGrmemN7iMrL1m7IuCDgVAkLEnOp2yWAFAUHAXgh8/sUixMbGKFGOfXsN4hwb169HSkoy/Km2WIsWLRU5u/32u6xeZ8wVvWF8H+sTgeWPJftv978YP+5VJCYmYPKb0xURs3SctAkCjoJAfGaBbD2PycvNlDA4yjgtjcMjspYiYpb2OVqbp9lLL3PcHW28Mh5BoDgEhIgVh47sEwQEAUFAh0BERCTl6kSi5403q9Y8klI+efIE1n77DZZ8uQi7du5Q++rAunLKrugN08FqspqTk4NPyfs4//N5aN+hI+bM+1xhzgdxWGhSYiJCw8JMznH1jZg4yRFzhnuckm1KxJypkHPAfU85A8RqjOaOxhT6nSEmCDgrAkLEnPXOybgFAUHArghw2NyWzZvw7TerlHw9i0wMuu0OE1VFawzQVb1hlrA5dfIkxlENsaNHDuN/o57FfQ88CCa7G9avBeeTRR0/rrbr1a+PuwcPwT1DhlrqRtoEAbsgkEm1tPRmFkGn31Xu9ZwzUUhbuwzZUYeQl5sD76s6wbeP5XpzWYf3In3rN8g5eZTcdN7wrN8EfgOGQnnB8keSsmyu8ogFDH1CtSR/NhOedRvBs3FLpK3/CtnHD8E9KBje1/WEX5+7qHpzgbcv58wJpG1eieyjB6h/L3i3ux7e7TsjdflnCBz+DNyrRZZ7vvoO3FFwbW43x11/rKwLAo6OgBAxR79DMj5BQBBwKAQ4ZO6b1avw3dbNCAoOxoABgzBl6ltgdT9bWGXxhnGhbJapZxwXLFqi8vAyMjLw4vPP4s8/fset/QdgJJGz6tUisGfPf/jko9kqVHTUM6NtAbtD9BlZvRp2gx5uxZwCAVMaZrshZx/dj6TZk+Dm6wfvjl2J/Pgg678/kfxh4Vp7GX/8gJRFs+EeVh0+XW5BXmYGMv76CVnTXkDQyNcU0eKR5pw9aRKamHP6OHIT4hWB82rdEb49+yHz7+1IW7OECFkV+HQ1lOzIPnEESe9PyB9LF+rJDZnUf/bB3cg+dYyuZ3t5eXpXIyYIOC0CQsSc9tbJwAUBQaCiELh06RI2rFtD3q/VqpZY1249MHX62+h8fRebinRUJm9Y9YgIjJ8wCd179KAaYj7q1r7y0vOUk7cP8+YvxFVXtTHe7mbNm6NDx454+MH70aVLN1rvZNwnK4KAvRDw1HmJeAy24gepqxcqz1bwS9PhHmoo55B3691Ieu914Hy0cfp56alI/Xo+PGrURfALU4gs+at9vjcNQMKbzyOFPFZVxrxt4t0ynkwr2Uf2IejJV+HVxvDz5dv7Dlx+aTgyd203ErE0Gosb/bwGvzwD7lWqGvon0pbwpu1ekOSZIcv12sQEAWdFwLYyX86KioxbEBAEBIF8BNhT06/PzXhv1jtofdVVWLthM2bMfBddunazKQnjy1cWb5j2ZePC2BoJ43y7X3/5GTPffc+EhGnHNmjQEP3JG7ls6RKtSZaCgF0R8Pc0faTKtYGrJvdCrAoT9Ol8o5GE8aTdfHzhd+tgk/ln/bcDeSlJ8Ot/j5GE8QFM3piMsdeLQxyLMo/I2kYSxscw4fJs0gq5ly+pU3IvnQeHPfp0vcVIwngH9+9z7Q3qGFv8k2PGcP3N5OxtcU3pUxCwFQLiEbMVstKvICAIuAQCrIrYrl177N+/H+vWrsH3328zKiSyfH1Lkq+vUaOm1edambxhlsDbumWzKqDd9up2lnartlatWuP337cXuV92CAIViUCwp4fJ5bLNcsZMdpZxI+d8jDqT87fMjXO/9JYTd05tetYzbedGz3qN1b6cuGh41Gmo1s3/sZTb5UY5ZsgXx9D696hV3/xUWGordFAZG3LMCG4VL1Pcy9itnCYI2AUBIWJ2gV0uKggIAs6CQIOGjfDRnHlKJIIVEvfv2wuWr/9n1y58Rd6YzMxMhIaGksJfJwpXnGG1aVU2b5g5cCmpKShK2l479vTpU/D3D9A2XW4ZEWEIO4uNu+Byc3PFCVX19gDXuMrKJ2Da0qpzzc4ydOdNhMjcmCTpLC/DkJ/l5mMI9dXtIu+Wr9osLofLzaP4R0TON1NmJidv6DjXsM8G/5rjGu5T/DhtMATpUhCwGgLy7bUalNKRICAIuCICK79enu8Ba62IAZODW/sNUFPNzs7GEVL4Y3LGsurWssruDWMc69Wth127dhYJaWJCAr5athT3PzCsyGNkhyBQ0QjU8PXCqdRMdVlbqPm5h0eovnNjzxWaWm6+B0zb4VGthlrNoWM9GzTVmg1t+cd6hJdd0dAjrOixKPEPkytabyOTCmfrjTEXEwScFQHTgGZnnYWMWxAQBAQBGyHAsukXLhR4JM6cOQ2WWWfz9PRUJO3OuwZj+MOPWm0Eld0bxkDecefdYIXKHTv+KoQri6eMfGoEfHx9MPiee9X+3NxcVcet0MHSIAhUIAL1Agq8UhlmhMEaw/CIqKkUEDN++84YIqj1m/7LFm1VLT2bXUUJW+5I/3G9STu595Hx0wa4BQTBw0KIo+nBRW9xDhnng2X88SMpe+R76ujwvOREZOz4pegTy7kn3QzXev4FmJezazldEKhwBISIVTjkckFBQBBwFgQuX76M6HPn0Kp1a+OQv1q2BAsXzDdu88r0qZPxzz9/m7SVdUO8YQbkuGjzS6+8itHPjFTFsqOOH1Oex/mfzcPddw7C5YTLmDtvPvwDAhATE40nRzyqPgf27ysr9HKeIFBuBJoEFoQBMhEzz2cq9wXcPUh8YwhyYs6ShP1EZO3/h2qJHUbqyvlKNl7fv0dELSVZn/nXz0j58iN1XNbhPUj6cDJJyx+HXz8S8SCRjzKbB41lwL3IiT6l+szc+Ssytm9F4qzxcA8JLXO3VzpRT8QaE94imnglxGS/IyMgoYmOfHdkbIKAIGBXBDjkkGXVw8LCjePg/LABAwcZt9kTs37dWmq7zdhWnhXxhhWgxwWyAwICMXfOx3h3piH/LjAwUHnB7rt/GIKrVMGmDesxfdoU0g/IUR7KSBsIpxSMyD5rMbHn7XNhuWqpEWgR7GdyTmp2LoKsLCbhQ0WVuaBy2upFSPpgoroehywGPT0Oie+MM7l+wL0jFClK/34tMn7dajg2JAwBD46C6sfk6NJvcB+sppi2YQWS578LN/9AKuZ8Pbyat0Xy3OnGXLTS91z0GYypZi2Dy0EktU5kKQjYEQEhYnYEXy4tCAgCjo3Avr17wMqImvHD/uFDB9Gy5VitCVHHj4NzxZo0bWZsK+uKeMMKI8eS9vxhr5c3iRFUJWEUN3oITUpKwtgxL1Fh7S0YNvxhChc9AQ8SF6ha1VDLqHBPztkSSYIdQsSc5961rWJKxFKyc6xOxBgNlofnT+7liwB977nIMlvox6vV0vgP/aywrD1/WG4e9DOkHWs8hlaCn5+i30SVCR+abGsbgY+/pK0al0y8+JOXlQU3Ctdmkpi+eZVaanXOjAeXc4Xx1JcFaBtiqI1Wzm7ldEHAbggIEbMb9HJhQUAQcHQE9lOYm14+/djRI5xegcZNCuSg9+3bo0iYl1f5E8bFG1b0NyIy0iA8wEdw3tiE8WOJeHlgDoUnsoCKqvX2wUdFdyB7BIEKQKBDVVNikJyVA5hyM6uOwp28WyU1a5Mivm7C5OfgHhyCoP+Nh1v+70DOEUv/eRO8qOYY/ZCWdHglOk7hqTuyoxneul2yKgg4BQJCxJziNskgBQFBwB4IcGhiTHQ04kkcoiXVrGKvS7PmzRUB0Mazf98+VUtM2y7rUrxhV0aOSwV8/OEH+HLxQlKu7I8XXx6jQhcXL/wCkTVqoGOna67ciRwhCNgQgQAq6twpNAA7LqWoqyRmZtvwavbv2q/XbUj+4j0kTn8JXi3bIS8tBZm7/0Jeehr87hxu9QEmZhKx1VnnsADdlqwKAs6HgBAx57tnMmJBQBCoAAQ4DPGRx0YogYjftv+KpUsWq1pifn5+GPX0E4qYMTnb/e8/4Hyl8pp4w66M4NQ3J+Hnn37Em9NmqHBFPiOPXJSrV6/E7XfcZdLBsaNHsWL5MiX44W6pzpHJ0bIhCFgPgR7VAo1EjGtesRcn0Mp5YtYbbfl68r6mB4JJFCTzjx+QffIo3Hz94NP5Rvj06Ks8ZeXrvfDZCVkFxPY6ImHBLopr4ZlLi6siIETMVe+szEsQEATKhQCHvWnS6NxRSkoy9u/fr4gZe8rWrV2Dz+bNVdfQqyqW5aLiDSsZao8//iSeenoUqlWrZjxh184dymupCagwMVu29EvMfn8Wrm7XHqmpqWCBD2e1iOrhKkcslgQ7tALPzjqXyjLumyOC8PahWON048kr5qpEjCfpWa+x+hgnbKOVy4RjTn6xbL7ELRHBNrqSdCsIVBwCQsQqDmu5kiAgCDgRAixHn0YP8ez1CgkJUSFwnSj0jT+aXbp4Efv270U9ylEqj4k3rGTo1ahZs9CBq1Z+jRtvvgVVqoTgfFwcJrw+Dv/SvRs56lkMufc+JexR6CRpEARsiMAN1YIQ7uOJCxkG7018RhbqBBTI2pf20nkZ6Yh/9l74DRwKv753l/b0Mh+fdYik7kmKPmjUBHi1aFvmfqx1Ynw+nlp/fSOFiGlYyNJ5ERAi5rz3TkYuCAgCNkSA8444DI6NCQCrJ7Zq1UoRs+YtWsLf3x9c66pbtx7qmLL+I96wsiIHxMfH48cftuHDTz4l9cTNmDplEnmNIrFw8TI0atxYdcy14IKCqHCtlUUDyj5qObMyIDCoZgg+izIUgk8jufUkCk+0toy9rXHkMEP2dvHS3kYaSbiYXlA0ugXJ1rcXoQ573xa5vhUQECJmBRClC0FAEHA9BPhB/tdfflYCEOwFO3ToEFas+ArnZr0Dzjmq36CBImccEteufYcyAyDesDJDh7VrvkEIydV/s2olNm5Yh/seeJBCF/8HVrC8ePECxrz0giq07ePjg+EPP4pHHn1cPGRlh1vOLAUCd9UuIGJ82vn0TCJi9ic0pZiCImHBr8wozSk2O/YCkTB9cey7artWmQqbAScdOzwCQsQc/hbJAAUBQcAeCHAuUvcePTFl4gRs3LgeY8e9jjZtrwZ7WDhHjD/76HPhAtXmKaOJN6yMwOWf9g2JdHA4IueJfUReMb1q4lvT3sSZM6fxydzPkEX1jVjoIzU1BaOeGV2+i9rp7Jg4yRGzE/RlumzP6kFgr82BxHR1/vm0LNQN8IWnu1uZ+tNOytj+HTJ+2Yyc2LPwqFkX3h27wbdnP203sg7uRtraZQgc/gzcq0Ua23MvxFLB5Vnw638PhRlerdozd/yM9O/WICf6NEBFmdn75dd/CDwbNFX7s08dQ+pX8+A/+JEKyQEzDtbCyvm0TJPWe+sKETMBRDacFgF3px25DFwQEAQEARsj0Lr1VVi05Cv06dMPTz/5OKZPnUzeFk9c36UrHn38Cbz73mzc0qtPmUch3rAyQ6dOvPba69C7T18sXb5SkbB9e/caOzx39qy6Nx06dsJ1na/HWzPexZeLFoLVFMUEgYpA4MH6pjW+Ys3IRGnHkLnzV6Su+EwRMN+bBqrTU5fPA380y0tKRPbxg8jLNBBAY3tmhqGd9rNl/v0bkj9/lypD+yry5dutF3LORCHpgzeQG28IqcxLSzWcQy8w7GkJJNLBoZ2aDaoVgoblyLnT+pGlIOAICAgRc4S7IGMQBAQBh0XA09MTDz3yKJYs+xonTpzA3XcMUnlJ5R2weMPKiyCojtirmPzmdJUDtmXzJjw8/H5EnzunOm7eogUOHz5kvAjXf2vUqLHKJTM2OsFKZPUChUgnGK4MUYfAIw3C4OdR8JgVQ0RMJ/qnO7Jkq+y5Cnp6HAKG/U+Rp+AXpsK7Uzek/7iBvFqnStZJ/lGZ//wONyJhwc9OhG+v2+E36H5VlJnJV9beXaXqy9YHM256Y1zFBAFXQaDgN4SrzEjmIQgIAoKADRCoU7cuPp4zD088NRKTKFzxxeefVWFxZb2UeMPKilzBefr6YMuoztuddw1Wwip8RJ++/fD3rp1GYsZt/gEBSs6e18UEAVsjEOTpgacaFxDpbGJh0akZZb4sF0z2bNKq4Hw3N/gPvI+L6SHj9x8K2kuw5h5aHazGmE5hjqCaiWwetRsg5K0vwLXBHMXYG3ZZp5bYJTwQvUS23lFuj4zDCggIEbMCiNKFICAIuDYCGRkZOELela1bNiEuNhbNmjUnr9j3+HLxwjJNXLxhZYKt2JPOnz+vcvi0gzgkkcnzmm9Xq6bff/8Ne/7bjZ433awdIktBwOYIjCIips8LO0tELLOMbjHP+gYlUP2g3cMj4BYYjNzzMfrmK65zKKJHjTpIXTYX8S8OQ9LHbyL9h/VKzIY9ZY5ijJfeRjUpILb6dlkXBJwVARHrcNY7J+MWBAQBmyCQmJAADnM7efIEhSJG4SSFI8bERJNcfQDVC6uP+lQzjEUh7rr7Hlx9dbsyjUG8YWWCrdiTOPTwwP595Am71XjcLbf0VoR5xJNPo0OHDnhz2owy3zNjp3ZaiY0z5O3Y6fJy2TIiEOHrhReaRmDaQQNRIucVzqSko2FQGRQUPb0tjsKNVELzMtIs7jM28oV1xgQueMxMCkPciaw99KGaYVn/7UDat4sR+MQr8Gpu/7phcRSSmJRZkBt2IwmgcFkAMUHAlRAQIuZKd1PmIggIAuVGYPv2XzB92hSEhoaiV+++GDb8YTQgqfrwcOu8iRVvWLlvkcUOhj34EEY+NUIRZPaEsVWhQtzR0dFq3ZtU4W6iws/OZhER1vneOdu8XWm8rzSPxIKTFxFNyolsrKAY6uOFEO/SPYLlxhm+y3psOLwwN+EyvFq1NzRTuKIyU96F3MTL+tNUOKIb1dbzbtdZfXgn54YlfTIVaasXwotImj2NwzhPp5h6w8YQjmKCgKshIKGJrnZHZT6CgCBQLgQCAgLBYW3pFI64bOmXeHH0sxg/dgxmvz8LP3z/HWJjSxcCZD4Y8YaZI2KdbS4tcPfgIXhixCMqhJRDRxcumE/hivZ/s2+dGUovzoqAr4cbXm9Zw2T4p5LTYcaVTPZb2sj85zfkpSSZ7Mr86ycgNwdezdqodg5TZMs+aaoOmvnv76pd+ydh6vNImj1R21RLr9Yd4NmwOXKTTa9hchBdC9lEKHmpN2N7rr7VcJw63qzd9KhCWycJHyZjmj3SIBzdqgVqm7IUBFwGgdK9jnGZactEBAFBQBCwjED3HjdQ/bAbkJubS2GJUVQrbB999mDHjr+w5MtFqiZVWFg4CUPcjcdGPGm5kyJaxRtWBDBWah456llEREZi5oy3EB9/CZ2v74LX35hspd6lG0Gg7AgMJyn7NecSsCE6QXWSlp2LE0npaBBUinysnGwkzhwHvwFD4EGhhVlHDyB11QKV6+XdoYvq17NOQ6WGmL51tVpyCGLmP38g68Buk8F7t+mEtI1fI3X1IvC5HN6YfXgvsqMOw+ea7ibH6jfSNqxA2vqvlGqjX797jLvS1i9H2oblNLZ74Xfr4IL2dV/RdVbAj0RF/PreZWwvbuU8FW/mAs6aVfPxxKTWpkRW2ydLQcDZERAi5ux3UMYvCAgCNkGAFfkaNGykPv0HGGr2cGFgFu3YT+QsvFp4qa8r3rBSQ1aqE9woLGvwPfeqT2ZmJry9LefUlKpTOVgQsBIC066qha2xicjK9/RwDlSglweqUR5ZSYxrh+VcPI/keW+Tp4k8TFRaw7tle5KzHwlS2VBduJFHP+D+p5Hy5cdI/ozCC6nds0EzBD05BgkTnzFehgs356WnIePH9UjfsspwLpExn45d4X/PY8bjKnolJTsHUUmm+W5vtamF0FKGcVb0uOV6gkBZEXBLzzLL4CxrT3KeICAICAKCQJEIsDeMiVjbq1rQp2WRx8kOQcAcgYVLVqqmYUPvNN8l206GwLyoCxj592mTUbesGoAgImQlNa71lZtwibxilDNFZMyiEVHLvRinFBXd/PwtHqIaKWwwhws4Ezl0rxoGN8qltJfl0uPovvhUpBIZ0+whqhn2cfu62qYsBQGXQ0ByxFzulsqEBAFBwBEREG+YI94V5xhTpAh2OMeNKsEoH6VcJw5T1NuxRPJM5ZQ8h4qJlUdk7aJJGHdOHn33apEoloTxcZ5e8KhWAx4RNe1KwngoRwkHPQlrG+KHD9rV4V1igoDLIiBEzGVvrUxMEBAEHAUByQ1zlDthGEds7HkSXTnvWIOS0VQaBD7pUBfXhgYY58skjElITiUOUIqifLl4XeFmb3c3ME6emgqkES1ZEQRcCwEhYq51P2U2goAg4IAIiDfMsW5KTNx5bN72M4WK7nesgcloKg0Cn3Wqh0hdblhyVg4OJ6RRhGCBUmBlAYMVEjlfTm/zO9VHu5BiQir1B8u6IODECAgRc+KbJ0MXBAQBx0dAe9jn3DAxx0BAy9FjguxMnjFnGqtj3GnHHUXjQB8surY+PHQen8TMbBwiMpZdicgYK0fGpJqSsJlta+PO2lK42XG/vTIyayIgRMyaaEpfgoAg4DIIaOFr5X34FW+YY34lNGK8e+8BxxygblQR1Uuv0Kk7XVYdFIFu4YH4qnMDk9ExGTt4ORXppcgZM+nAiTY4Ny7WzBP2eqsaeLqxFDF3otsoQy0nAkLEygmgnC4ICAKuiYAWvlae2Yk3rDzo2fZc9ooxGYuhXDHtPtn2itK7IFAYgf41qmDZdaZkLIXCFA/Ep4BJmStaJik0HiCyqa8VxvMcR0WvxzQnJUgxQaASISBErBLdbJmqICAIFEagKM9XbBxJOpfTxBtWTgBtfLo+RFHImI3Blu6LROC2WiFY3aURfEigQjONrMSYeYy0/c66TCByuc8CyZzcuibGtRAS5qz3VcZddgSKKEBR9g7lTEFAEBAEnAmBCJIG1+o08bjNpcJ5f1lMe7DXQuDK0oecY3sE+P4wYeaPRsxsf1W5giBgikDfyGBs6t4Ew/46gdO6nKmTlEPFHrJ6Qb5OryB4NiUDZ+hjbh9RnbCHqV6YmCBQGRHwGDd+woTKOHGZsyAgCAgCegQ0D1hySiq0D+/nB3Tel5ySAn5fnUL7AwMLpKf1fejXt5AqH1vvm3vom2XdwRDQiLd2/7VtRxpmLKk88vgCA/0LvShwpHHKWMqHQB1/b9xO3rHdJNhxUkfGUrNzcTEjC95UG8zfs+SFn8s3GuudzYqQR5PSCoUiRpBqJIdl3lW7qvUuJj0JAk6GgIQmOtkNk+EKAoJAxSNgyCM6YJA8L4G4g3jDKv4eleeK7AljAmbwjDmepH1k9bJ5ZcuDiZxrHwSYjG0hz9hTZoIVmTl5qtbYkYRUk6LH9hllya7KddFOkTQ9hyImZeaYnHRzRBB+6dkUt0QEm7TLhiBQ2RAQIlbZ7rjMVxAQBAohUJqQtF43dTeez14vjXQZG2mFH+jZStOvOkH+sRsC2n3V7p3dBiIXFgQIgXdIwv2La+oj1Ns0g+QSFT3ecykFLPvOhaAd0bgSWjR59P69mKyW5mMc1zIS67o2Rl0inWKCQGVHQIhYZf8GyPwFAUGgxAj01pEw7SR+cOccM42QaUvJDdMQcp6lds+0sFLnGbmM1BURGFKnKv6+pTnurRtaaHos+85EJ4oIWWq2qbep0MEV1JBFaoicB/bPhSTlCcumbb1dGxaAbT2akChHDX2zrAsClRoBt/SsSlQ5sFLfapm8ICAIFIcAE6jivCH8kG7u4eIHdg5btGTDht5pqVnaHBwB7Xtg6X5X1ND1teu4jAIbfzc5fFKrKabltPG2+feyosYp16k4BL49dxmT9sdgL+WPWbIq5DkLp5yrMPoUaC9aOtL6bayEyFL05nL02pWCvDyUIuIzTaprTbIUBASBfARMfd4CiyAgCAgCgkAhBMryUM4P9PKAXAhKh2/ge8akRxEfys0qq2pmWSeqEUFL5zPpNyf+bVu3sHSotLkYAoNqhoA/s4+ex8zDsYhOyzKZIZMh/rCHrKqPJ0KImDE589JJ4pucUI4N9nPxtS7TJ55CJTOLCZFk8vVS8wiEmYVYluPycqog4FIIiEfMpW6nTEYQEATKg4Bexl7rh70QWv6Q1qYti/OIaceUhcRp58rSPgjoyZA9PJv6618JAXuM70pjkv22RYCJ0AdH4vDJ8Qs4nlxYDl5/dVZZZI9UAH38PNzh5+kOD7eS+8z4Wumk2piak4PUrFwkUxhkEhEw06BD/RUBX7rOiIbhGEmCIyw+IiYICAJFIyAesaKxkT2CgCBQyRBg0mVtj4PmXeH8sor2rlSy22e16WqeTMO9q3jPpuaVu9KE+PsqVvkQYBo1ijxN/Fl+Oh6LTl7C1thEi0Bw/pjKIdNFNLKXzIuk8HnJpIydZoqbEbti+Q9WO+T8Li4qXZy3y/yCbUL8cD/lsz1ENcGCnFBm33w+si0IVAQCQsQqAmW5hiAgCDgFApxvoydi1iJP1urHKUB0kUFqZIjJGJtGzipqeuxJ1a5d1DUlLLEoZCpP+2AS9ODPMfKMrTx7GWspl2zHpdRiAWBRjaxc6wh8NAz0Qf8aVVT9s84kxiEmCAgCpUNAiFjp8JKjBQFBwIER4De4hylHIiolE2dJVSyW8hfiqX4Nh9PwPpYm8qTXv34ebqhCoTrhlLdQw89LySg3pgcKrte0G9qDd4tye7CKC2t0YBhlaPkIMIHerEoUHLADETPkqhV1M/i7JR7WotCpfO2N6PfXS80i1OcUScdvi0vCL+eT8fvFFPp9WHz4YmnQCqP8s+tCA9A1PBA9qwfhavKCiQkCgkDZERAiVnbs5ExBQBCwMwL7EtPw64UU/EV1df6JT8X+xPRyjSicHjKqe9XAtfSQUb1abTShJHR/yncoi0luWFlQc6xzmOhonil7iK9o17aEinjDLKEibYwA1+d6qH6Y+vB2LCka/kdqiwfpJdWx5EwwUYumtov0oiqRXlKl0e85qhcN/k3nQ7/vgiiPjOuXRfh6oo6fN9jr1SzIB62D/cCET0wQEASsh4CIdVgPS+lJEBAEKgCBTTGJWB+dgM205AcKW9tNEUHoG1kFA2tWKVSAtCixDglFtPVdqdj+tftcFLm2JUmzJNwhntaKvf9yNUFAEBAEbIVA2V712mo00q8gIAgIAhYQ+PdyGl787yzqrd+D27Yfw6ekFlYaEsaJ6JyU7kFhie4qK93CRYpo2habhBd2n0HTjfvQ/9ej+PLUpSKOhKrzJCSsSHicdoemmmkpZ8sSUbL1RMUbZmuEpX9BQBAQBCoGAfGIVQzOchVBQBAoAwJrziVgLpGu74pQBNN3ybLMAaTUxUuWT+YQG1YG8yTixapg5pZNCWOctM6qYBn04fCcNJJp5nyyHGovzqpTyM6jDcLR/NxRpMfFqUOL8pYU14/scx4ENMKleaO0bW0GtiTg5tcSyXoNdVkKAoKAIODcCAgRc+77J6MXBFwSgdWk/vXu4TiV+1XUBH2JcHHR0mAvTwR5eyjCVdSxpW1nuedEEvlIyKKipZRHUZRxSMHNOQmYdn0LtKwdUdRh0u4iCGiEiMmYXl2Tp2drIq7VuLP1dVzkVsk0BAFBQBBwCgRErMMpbpMMUhCoHAhsv5CMKQdi8D0pflky9nKF+XghlDxS7P2ylXERVP5EwlvV1LlEZOwSJbdfpkKmeuOaO1s8quCXnTF4NQV4kVTLxFwfAXMSZu0Zp5N39gAJKxwlSfJTpAB6jr57R6o1RvTlZATGUs2nS0fATl4fcvUG0guJqvRCojoJzdQiYYUGAd5oGuSrltYel/QnCAgCgoAgYF0ExCNmXTylN0FAECgDAqn04Dl2zzl8fOy8xbOr8IMmycyHEgmzp/EDchzJ4selZSmCZj6WVlX8MLl1TRL3CDbfJdtOjoDmDStqGuXxVB0nefGf86XGd5L65z5SuCuvcXmGdlX9cU2oP7qEBeIGkhpn4iYmCAgCgoAg4DgICBFznHshIxEEKiUCnAf24n9ncJLe/JtbVXrLX8PfB0H0UOlIlkv5ZTFEyKJJtTHbQj7ZE42qYWbbWkogxJHGLWMpPQKxseexe++BQqGI5j1puWPm7UVtc9HdNVR8dyOpf+61AvEq6jpaO4vV9IoMQr/84rth9HJDTBAQBAQBQcC+CAgRsy/+cnVBoFIjMGbPWZULZg4CE69aAT5gT5gjWw4RsnMcOpZauGBqa/KOfdCuDjqHBTjyFGRsxSDAJIwLOpfUriSicYFCXBedvIQlpLy5p5Tki9U+uRg55yVqwp/8DoC/g5ZeBlxpzHfUCsF99UIVMbvSsbJfEBAEBAFBwDYICBGzDa7SqyAgCBSDwBnyJj228xR+MMsF44fNulQwNIJyXZzJUklt8XRKukVhDyZjjzUMd6bpyFjNELhSWKJ2eFFEbB8VGuew23mkAHol09Q/OUeR131I+dPbg0ovaOyriA5YAZTVPzl8lhVAU7NylALolUhai2BfPE7fzyfJiysmCAgCgoAgULEICBGrWLzlaoJApUeABTke3HESZ8yKMXMYYr1AXyU776wgxRLBPJmcDnJSmNhzTatj6lW1TNpkw7kQKEmIormE/UES3Jh5KFZ5wYqarRd5uULou8/eX1YA5W1rWoqmAEpCMwlmYjP669SiHMznmkZgZGMhZHpcZF0QEAQEAVsiIETMluhK34KAIGCCwDckSz/0zyiYp1XVJQJWw9+5vGAmE9NtsHfsRFIaksgjobd764Zifqd6+iZZd0IEiiNkmmBHCn0HJu6PxntHDDXmzKfJzq1wEp4J8/Wq0PBb9ppdysjCRVJhNP9+amNsRoqL41pG4u7aVbUmWQoCgoAgIAjYCAEhYjYCVroVBAQBUwQ4L+Zh8oTpzZve/jcM9qvQh1H99W25HkXeEFZY1NuAmlXw//bOBL6N6lrjx1qsxbuzx9kTshAIlJ0AZS8ta2lYS0sfS8sL0PcehUJZSltaaIGylAJtoZQdSlnKkkCAUiBAgFAggYRA9t1JHNvxJsmyJL9zrj3SnZHsSJbkyNJ3+Cma5c6duf+ZBJ05537n6YMn6JuwPEAJJHLIxBFbUjGcrluymWpZWdNqUmh8KKfdigLozlINrcdmer2VXxRsCwSpLsF1yrlO4TlkN7IC6CROFYaBAAiAAAhkhwAcsexwRa8gAAIagUROWCkLckxiJ0xqg+WriYjHBq4FpZuo1j07E86YzmQgLxsO2cptDTRv0Hh6rUWqy5lNio+P8LiUA2bes+vXZF6ZqH9KWq3VJE3y1hmj6L8nYo6jlQ3WQQAEQCATBOCIZYIi+gABEOiRwAss0X3m+2tM+2U+zGRWFRRxjnw3iYpJdEy3WZz29fiB4/RNWB7ABF6ubaKLFq6hupB5cqBEvUT9cyCk3fo5nXITvziQtEWrnTm6iu7ZZ4wqHm3dh3UQAAEQAIG+E4Aj1nd2OBIEQGAnBN6vb6Nj568wyWuLKMfkCu9Ojsyv3dv5x+2qZnOR3os4yvCHvUfn10ALcDS3sBjH9ZyKaLUhPP9rNM99zLT4hvU8mV5vZIn99awAGmDHTLeprK54/75jaP9qlGPQuWAZBEAABNIhAEcsHXo4FgRAoEcCtex8HPHWclOhZomETa0sLCfMAFTHPFZbnLHf8BycK6YMM5rge4ARmP3JenpwTb3pqsXxGsuCF4NYjGOgmsT11nEU15quKBG+hw8YS6dByGOg3lpcNwiAQI4RgCOWYzcElwMC+ULgpHdX0etbm6PDKeE5YdPYCdvVIgXRC9oFC1t4Lo7I2+sm88Vk3hhs4BAQ9cGzPlhDczklUTeRoR9f5iERockHkzRFSauVotG63bm3zBuDzL3OBMsgAAIg0BcC+TtLvi80cAwIgEBGCPycU7V0J0yiBCLMUchOmIAdzhL91vlCF3+ygSR6CBsYBFo5Ze/Ed1fGOWEjvS6awim3+eKEyd0Qef3pVSUkL1F0+79FG+n25Vv1TVgGARAAARDoAwE4Yn2AhkNAAAR6JjBvSzPdyvNmdBOJepHuhhFJzTSZJ2fYVnbCLuMftrDcJxBghcFTF6yit+taTRc7jlMRR+epzLuHFR93rywxPbMy+Gs+30x3LE9cJ80EBysgAAIgAAI9EsAvox7RYAcIgECqBCSF6aefmZ0K+YFayXPDYDECKn3NHktfk0LX96/eHmuApZwkcCanI75jccImsfrnMK4Nls8mmZYisDOE65/pdvXnm+g+PLc6EiyDAAiAQEoE4IilhAuNQQAEeiNwLackrmiJ1c2SOTOSsgUzE5BUzXGlHtPGa5jdFqQompjk0soFXIz8VY726ibOyUAW5dDHkszyBJ7/JgWpdfufTzeQvEiAgQAIgAAIpE4AjljqzHAECIBAAgIfN/roTi1VSUqEjeU0PFhiApKeqEdSWjrC9MultYkbY+suJfCrL2rp8fUNpmvYjSNheoqpaWcer4znNMzBPHdMt/PYSV28w1yeQd+PZRAAARAAgcQE4Igl5oKtIAACKRK4cdkW0xGjS9yYF2YiEr8iaZu6uMNDa+vpve3m+UfxR2FLfxL4x4ZG+q3l2ZY5j9UDWJ4+XX4TefwS7TbMz3PnfvTxOgpZ1BWN/fgGARAAARBITACOWGIu2AoCIJACARHoeFmT8haVNas6YArdFUxTUZEcZYkaWoVOCgZGDg50VWs7XcKpd7rVlLhIijUXuokKqtcRU1OUiNilrAAKAwEQAAEQSJ4AHLHkWaElCIBADwSs6mk1mBfWA6n4zfKjvkyTBxen9o1tLfENsaXfCVy2eCNJyqhhIuc+ih0xGKlSFBPK3SQpyIZJRPfRdeYUTmMfvkEABEAABOIJwBGLZ4ItIAACKRD419YWlvOOOQ4VrJBYiHNnUkAW13SExXG9d2VdXBts6F8Cd63YRq9p4hxSfkHmR8FiBEo4IiYKoLqJaipEZ3QiWAYBEACBngnAEeuZDfaAAAgkQeD+NWbZdSlaDEuNgDiuelRsLqd5LtrhS60TtM4YgbVtQRIFUN2kVlihFyTXeRjLEtHVZe13BMN0nYWd0RbfIAACIAACZgJwxMw8sAYCIJACgZU8h+YFTbpanAnUDEsBoNZ0mMWBfWgtUrw0PP26KCqJHZHO6DllvqNEemGJCYg6qi468xinJ1ql/hMfia0gAAIgUNgE4IgV9v3H6EEgLQJWSe8hlhpDaXVeYAdLPSpJfzPssXX1UKEzYPTj95s8P+9JTarexfdktEVQpR8vZ0CcSiKFVkY3fWlWUR0QA8FFggAIgEA/E4j9X7+fT4zTgQAIDHwCIu1tmIOLFENNzqDRt2+9PlNrKEJPa3z71iOOSpWAVbVSxDk0PYpUuyuY9vLs6tHwD+vb4mqvFQwMDBQEQAAEkiQARyxJUGgGAiBgJrCgvpVE3tswiejA0iMgqny6Pb+5SV/FcpYJiGLlvzXFynJOR9Sd4yyffsB3P9KiKKkXeB/wg8MAQAAEQCALBOCIZQEqugSBQiAwt7bZNMxqrcCraQdWkiYgqYllxbHaTHPYEWvX5iol3REa9omAVa0StfBSwyhzRPWXCZ83+enpjbGoeWq9oTUIgAAI5D8BOGL5f48xQhDICgFd2tvJaYkSPYClTqD5tmup+darowdWFceiYuHOTpOEerQRFjJO4KMGH722NfZyQZ5nPdUu4yfM0w6HW+aJ3r/arKqap8PGsEAABECgTwTgiPUJGw4CgcImsNEfJHnbbRh+sBok0v+2snxLq9GWfu/ooScCD7M4im7DLA6Fvg/LPRMoFeVULTo+v66VxMmFgQAIgAAIxBOAIxbPBFtAAAR2QuC97W2mFoiGmXCkteJx2EiU+gx7b3ursYjvLBGQ9M/HWXLdMDffA6TaGjRS/x7qNtcSfEJToUy9NxwBAiAAAvlLALlE+XtvMTIQyBqBhQ1mR0wvRhxa/SX5nn2YvGf+kIIfvU0dn39MkZYmcozbjdzHnELOaXuZrquzo4P8Lz1BHUu4XfMOck6eTq6vf5OcU2PtWv50ExXv/jVyzjiA/HP/Th1ffU6Vv/4zRZoayf/C49Sx9GPq9LdRUeUgcu3/dfKccAaRLTbXKtJQR74XHqPQmuXU6Wslx5iJ5DrieCrm/gwLvPZPCm1cQyVnXEhtTz9Aoa+WqF1yvd4zLqAiT4laT7adNE5mbKpTyx/Csz0cUVsX7fCTKCiWsnMAyw6BZ3gek7+bt5wB6p/pcZYC5fIywXiGZZ7YHXuPSq9THA0CIAACeUgA/2fPw5uKIYFAtgmIc2BYMf/g0iM4nb42Emes7ZG7KPjxe+Scvg+5DjmGwts2U8vdv6bgf941DiUKdVDLbddQ+/x57HjNIDc7R+Gt3O6eG6njy8XRduF1K/n4Wmq9/xYKLpxPtpIy9nI6qeWPv6Lgpwuo+AB2vk76LjlGjCH/y/8g33OPxI7dtJaabrqcOj77iIr5WtyHH0+RHY3U+uffUeCNF2Pt6rZQaOUy1WdnWwu5DjuO7MNqqP2DN6ntyb+k3C7ZsUU71hZK2BHTbdEOpHbpPDK9/LxWlFz6hgJo+oR1tcnt7SF6uRYKoOlTRQ8gAAL5RgCOWL7dUYwHBPqBwBJtfpi3h0iNOGTlV91K3tPPJ+93fkAVP/s92YcOZyfpIXbAQuoqA2/OVVGo8it+y1GnC8lz4llUcfVt5Bg7kXxPxJwfadz+3utUVFpOlTc/SOU/u1U5bOFN68jzrdPIO+s8ch/7bSqdfTUV73UAR+Lmq/7lD9+zDxEF2/lablFROs9JZ1P51b8nx6TdVTRNomqGRRq3k2PyHlR26fUqqlZ22Q3knLInBT9ZoBy/VNqlMjajX+O7xGF2xJY2B4xd+M4wAYmEzdWcBEmz1V8sZPh0BdOdRMV0e5lLA8BAAARAAATMBOCImXlgDQRAYCcENvs7qKkjHG3l0eYzRTfygkS3bBVV0U1FJaXk/sZ3KNJYH412tb/3L04P3J/so8ZF25HDQZ7jZlG4rlZF1qI7Ipyed+6POUXQqzbZKrlvu52jbgtUW6NdyXmXUfl1d6pVda5li8l16LFkHx5LjSpyOsl78jmcOhjkCN07xqHqW65RN4noUThMkVbzD8mdtUtpbPoJednKdHkLHDELooyt/mtrC+kVAgyxlPDGtRTmVFVY3wjIywSZ72jY65oipbEN3yAAAiBQ6ATMr6wKnQbGDwIgsFMCa31BUxupfZXI7OMmxW2WeWJiYU4DdLJzI98yt6v55itNbcVBUu1qN5BjwlS1bOdjJSJmWJHbyxG0s8n/4uPUdP3FZK8Zp+aXOfc6UEWx1PF1m1Vz47zGsfJt56gbFRWplEdje5HLTbayCmO169vZLTwQ7oriycadtktxbOYTEjm4HIB8Qt0ewto2M3Nre6z3nYBVldJwxNr+8Vd+UENU/tPf9b3zAj+ygqOL/lDXs7uOn+FlHNmdVu4ucCoYPgiAAAjECMARi7HAEgiAQBIENrF0vW49pXEVOczKaXJMkaOrRlZnu586eX4YRcJUxPO9bENH6F2qZXvNWI6oVUe369E1Y6Pnm7OoeO+DeJ7Y+9SxbBEF3nmNJCXQyVG2souuIgp0R5KKXcYh0W91LTZ2Itu1aJM9yX8Sd9Iu1bFFL0pbcPG1hZiPmJQLgGWHgK4AKs+yHsXJzhkLp9dyp4O2UOzZfa++FY5Y4dx+jBQEQCAJAkn+6kiiJzQBARAoCALbArHIkAxYijknMhHncEyYYtol6YZi9kFDu6JKrHIo0aoSTjk0GQtxSFTMcNxkn76s2nIb4nRF+/AaNU9M5op1cvpg22P3UnDxh6zC+EnUwYvwtVgtUr9NpRzahsQ7gda2qa6riFkKY0vUv9POXLtRb2OxA1jmCbSxGqUuhCI1sESR0//iExTevJ5P2KmKbTv32IefsdPVBSSjhNn6AM9zZGVOx8Sp5J/zFIXWruD5kSPIc8o5Sg3U/9KTSmQmsqOB201S8yiN9NzA268oQZvSH11Jvr/fR6FVX7ICKEdIJ04j7ynfI9vgYSYQySiC+uf8nQVqGqjkuxeRj8fW8cUipQ7qmDRNpf+KwE1o3SrqDPjIVjWYXDOPJs83T4ueJ5FqqbwACa9fRWU//gWHcM0/JUQIxzZkOJWd+oNoH7Ig9cQuHG/ahBUQAAEQKGgCiXOKChoJBg8CINAbgUZtfpi0kxS6RNb+7msmgQtp0/7+v9W8LvlRKSZKicHPFqofgGpD9x/+V5+lxv89yzT3S98vy8FFH1LDpaeZ5pFJ6qLrqBNV00hbs/rxa2OnTyJlhkCI0Y9EzsSscvrG/nS/0xmbnNvBaZOGNQZjc/KMbfhOn8BiTXRGeivleU1FnIpqqx7c5Vxw5FOWbd7SrpMlq/K5YbVSDFUOCTtO7qNPUo5Q619vo9a//p4C819RDpk4PB2rWamTyzOICqiYvCAIreJtd1xPke1blOKozFOUFwtNnMIb3rKp61r4z/CmJBVB+UWEtPX98xEKvP48UUc7z7X08By4tdRy5y+U8I1rv0PJy0I2trJKJWIjcxwNS6RaKhHrjuVL2Kn71GimvkVAR16EyJxM+bdBjzDqTq/pIKyAAAiAQIESML/GKlAIGDYIgEDyBNpCZqfArjkMei/ytrz1vltUTTCJEImSoUjXiyy8rXqIauo5/gxq523Nt12rFBPlbXxo+VIKvPocOblumMjH92TO3XZXtb3aHr2HPKd+n52uGo6INVHglWeUs+fcbQ9VS0yiCK1/u52a/8BKiMefSUXeEiXQEXhzDhV/7aC4qF1P50t1ezpjk3PpXAOs7BfiH+q6c5bq9aB9PIGlFkfM67Sp56GUI7nNt1+n5oiVXnB59EBDCVMpgHYLzEjkqPnOnyuVz4ob7o22DXHJhbLLfs3zFvk5ZHOMn8ylEW5goZrPqPK3f6Wi7nTZIpeH/K88zU7XVhVFUo35XtuqB1HZ7GvVsyzb3Id/ix2xq1TNvdIf/lQ1iyqCXnNbVIzGzdcjZR2kvl7xfodFBXPCPN9S5mNW/PwP0ba+Zx5UKcLls68h+8gxqk/3USdT45U/UE6WlJ0wTFRLRVFUVEtFMKeTU3olYtcuJSo4Fdiw4KIPlDNbvO9MtcnLzq2fI49iUP9UGPAHCIAACEQJwBGLosACCIBAMgSCusQcH1DE/yUy79kXUTtHolru+qXaLeIa7m+cqtKrjPaSvlRx1c3U9ujd1PqXm1VUQFK4ROVQHJneTKJfZZdcR238Y7D1T7+NNpVUxbL/vjqawlW8/2FUyj96fc/8zXwtR59supZoBxlaSGdscglW/1a4OyRdEZYxAita2019eViFszfrTQmz5d4bVXTWEJeRtFzDCZM+DUeneJ+ZUSdM3x5hJ0lPUREhGlEFNcw+ajxJ1Kr9w7eUEyTlITpYEdR95AlRx0raGoqgzVyfTxRB3fyci4nj5D3tfFNb+bvhnLFf9Nq62nGNQAnOBWK1AmW7pAHrqqXycqV430O4tMP7qh4gdc//lPmaTi4hIX/fxXQFUBGfWcnMJ5W61D78AQIgAAKFTgCOWKE/ARg/CKRIoCuBaucH2YeMpPIrbyap09UZDJB98PB474K7kR+Y5Vw7TH4oSiFlI1qmn6Hyd3/TV6PLMgen4trbKdLSRJ38UcIfmmS+0VBqi8mns61VRQUkXdHq6ZScM5vkYzWR4ZePYcm2k/bJjK388huNrvHdzwTWtsUcMTun0fU031FdVopKmLbyWOkGdXy3wIu8KDCZ5mxFt7NQi2PMhOiqsSDzKaXAuKQsRvjviliyiqDyvDt339voquvYsZP474NPRaplHpukFYbWLudUYYsTxq2tqqXSgWvmMdS+4A0KLv1U/f2SouvSh1ebG2YV8xEFUDhiCj/+AAEQAAGCI4aHAARAICUC1vQ4ltXg43uO1CRSO0x0QnnDLp++mJKct8rOJ+hIapnJp7+tL2PrnjIUvVQr9+gOLPSZwCauiWeYqFT2ZplQwuytf9M+G0fC5GO1qOooK32mqAiq/o5Y+uxgB6r1gd8rIRzH1L049XA6uY/7Dvmeus965miKo75DXoRIBDv48bvKEevgaJitvNI079LqiFlVV/X+sAwCIAAChUYAjlih3XGMFwTSJOC11A2TTEVkzKUJNcHhEeXgdu0Q0YNi/sAyS6BOU6PsNRrGpxVn2pamEmbSV8+iIKKGaI0OG6qjEtHt7BYQSVoRtNuJ06+h7fF7lUqi1Eorcntiu6xvAWT8CY6XA4oPPpoC857pKo7OjljxAYezExlzaq1cdeaxE2IJBEAABAqTQOxfy8IcP0YNAiCQIoEKlvjWTUQkdBN5bfex3+YfrbEaYPp+LCdHwCjmLK2tzJPrAa12RkBXAE2o/ml5ttNVwtzZ9ej72995VV9lR6dDpRAw9ITUAAAcuklEQVRKaqOkPUokKh1FUEk/jDRuJ+du001OmIiGhOu2ms7d24rroCM49bid2ufPYwn8lSTrulkjuTssqqt6WyyDAAiAQKERQESs0O44xgsCaRIY4jL/s9HBITGP5puJbLX3Oz9I8yw4XLgaZmVubMd3egSkjphh1oCjjdU1O1Z8QSLQIUIbonqYrhKmca5kvgNvvKSaOWfw3EYW8gi8/oKStlcqjqLkUmRXYjN9VQSVCJikEYrKoXPP/dWLE5nf5ZvzJEe/HBRmGX1x1ETJtDeTKKHMPZPaa1ILzV4zztRcV/+UHTpzU0OsgAAIgEABEjD/oipAABgyCIBAagRGeJymA4IsrU6WKJmpAVb6REBXpxzpNjPvU4c4yEQgzNGuiBbxslnmOUqZhRCXYJD0PdeBRyhHLF0lTNMF9LbCqX2lF11FbU/8mfzznlUtbRXVVHr+T1iS/tDokekqgpb84H+o7ZE/UsvdN3Sdgx0z75k/5FplG1km/0nyPf8olZ53WfR8PS24OD1R5pu5DjwyrolV/VN/wRDXGBtAAARAoMAIFAU6tP8TFdjgMVwQAIHUCYj89B6vfhE9cFSJi2r4A8scAQmGfVTXHO3wvPGD6E/7jImuYyF9AuKIlTy3KNrRME8xjStLXiymN5XPaKd9WPA99zAF3niRqu9hB4yvMcwKiVJzTByx3qw3RdDejqNImCNtdTzZS84RU3pUc9RE+ZGjYzsziRq2PfEnro/2gIqyWdt/uC32LF8wfjDds89oaxOsgwAIgEBBEtj5v7AFiQWDBgEQ6ImASE9LupH8kBXzS0QMllECfpZK1w1y3zqNzCzLMyzpiEYGaJf6Z/J990UJM/neu1vyNdqHjEjqsD4rgrKSokT6rGYVCrHuj66zIxd4ay4VS3ojR9SsZn3VaxXvsLbHOgiAAAgUEgE4YoV0tzFWEMgQgd3L3fR5U1etIV/I7DRk6BRJdSNv7X0vPEahNVz7yNfKtZcmkotrfhXzvJqBbD5t7pKMY/dyTdFuIA8sx65dFEBbu1mHY1Pycuwqc/dyWu+7hSN2LO5Ru0EVe050pcYLG2NfiQMaYQYLfIMACIAAHDE8AyAAAikT2KvSE3XE/PxDVn5sWSflp9xpigeEN62l5juuJwqHWKntSCpiOe/gpx9Q659/R95Z/0Xuo09Oscfcad5mUZabUQFHLBt3p7LYwY5YUHWtq1Rm41zJ9ikqhtZi48ke29/tJCpo52ia99RzyT56QsLTW1VVKzGfNCEnbAQBEChMAnDECvO+Y9QgkBaB/atK6LF1DdE+WthxkB+1/Wm+Zx8iYtns8mtuI1FqFHN/8zRq+eOvyP/C4yxqcJhpzotqMED+aNUcsbElxVRjEUgZIMPI+csUNcqNvi5HLFdEJJx77scqhvvlPDu5QBH72JlZuQ62qK7u7HjsBwEQAIF8JoAcgXy+uxgbCGSJwMGDS0w9NwdDpvVsr0Qa66lj2WJyHXps1AmTcxY5neQ9+Zyu4rL/eSfbl5GV/uWHa5uW7jlzUGlWzoNOyeTgBiOFMdex46vPqWH2qervT388A0pVVTsRXipoMLAIAiBQ8ATgiBX8IwAAIJA6AUmV02Xsm4L9O08sXLdZXbRj3G5xF28fO1GldoW31cbtGwgbmixO7eFD4Ihl676N9RZHu5bUxFxJT4xeVBYWpH6YY+wkUxHnLJwm2mXAIuajM482wgIIgAAIFCgBOGIFeuMxbBBIl8AxQ8uiXYhgR7+qJwYCXedmyW2rFTm45hbXYaL27jbWBjm+vsPiiB2tcc7xSx9wl7dbqVmuvl+f4V1ES5yw8p/dquqi9ccl6I6Y1BSzMu+Pa8A5QAAEQCBXCfTvpI5cpYDrAgEQSJnA8SMq6FFtnlhDewfVeOMdo5Q7TuIA29AuSe/Itq7ImH5IpH4bC3iEWZI7Odlv/dhdvSwFhoWjYQcNKqHRWtTG2I7vzBAQ9U/d5IVC2U7EJNo/eJPa33qZwls3sfZ9hOwjRpP7W6dR8V4H6l2xcMz7JPW1RNHTPngYuY46kSjUobaXXcoiM90maba+fz5MoZVcm4+l5J1TZ5DnuFlRSfnOthZqufcm8px0NkV21FP7O69SePN69Xx7TjiTz2tWCO1Y8jEF/j2HQutWUhHXAHNMnKaOlesUkyLVvqf+St4zLlCRse7LyNqXnmYr6p9SMgAGAiAAAiDQRQARMTwJIAACfSJw8sgK0qWoGwIxB6JPHaZwkJ0dMdugoRR45zX+ZWmenxZ4c67qyTltr1iPXOtIfgRL8VqTRbdb5gf1tN10cOZX6ttDUsM3aqeMjK/LFN2JhbQJiPqnbrpIir7dWG5/93Vqe/gulfrqPuIEch/7bYq0NJGScRfHrNvEWWq9/1bq5H3uI49nRcHx5HvyLxR4ex47ZiuMZhTesomabvqJctZcBx5JxV87mIKLF1LznddTpHG7atfJz3do9Zfs/M0l/z8fIce4yeSaeYza3/qX36k+jA7b332NnbYbKdLUQK7DvkHO6ftQx5eLqfm2aynCMvNinX6f6q/T12YclrVvUVMVVVXD9rbwNrbjGwRAAAQKlQAcsUK98xg3CKRJQOTqTxtVFe1Fal/1m2gHRw68p3xP/bhs/sP1SnhAIgC+Zx+kwJtz+AftQeSYMCV6bf6Xn6aGH59B/leeiW6TBf/cf3Rtn2fZPuepru2vPmdqn+2V7RZndtYoOGLZZF7B0a89tNIAO3PEAvPnka1yEJVfzhGqU84hz4lnUekPf6oiY6FVX6pL7Qz4yffcw+ScMoPKr7yZ25xNJd+7hEovvILCG1abhiPPq81TQhXX3qH6k7ILFawCKn345z5latvx1RJOKfw9eU8/X33KLrqKvapOjrAtiJ33n4+q515SD73f/j6VnPtjKpt9DUlULfD2K6b++mNF1FR127/Kq69iGQRAAAQKngBSEwv+EQAAEOg7gbPHVNHDa+ujHWxjR6K8n2Tsi/c/jEp5jpjvmb9Ry12/VNdQ5Paq+mHipA00EydAd2RP5IjjGKQlZv02zuT0zyXdxcllPpN83FzoOZF52fmyVVYTccqfYZ0Bn1rs9HdFmIKLPlCOlPtbs4jsdqMZOffYl+yjxlGkoSvSFWneQZJG6D3jQpNwhq1qMEe8jiaJbnnP/FH0+OIDvk62qkHRdUk5FOGNyI6uMhIdny1URc0lrbHIGRMhcXBdstLzf0JFnv53gvTnWS585uDS6PVjAQRAAARAgP93AgggAAIg0FcCRwwpo334LfcnjV0/RuvZEaspcZGnhx+yfT1PT8fJ/Bj5dLa1cspVm0pXTFQMVyIX8rGazLuRj9U8J3+X5NOfttXfVc/KOOe5Y2M/uo1t+M48gcP5Gb5vdZdzJL2LaqXbE3Nk9DNKqp/MQWyf/yqFOLolRcUlEqubMW/RUTNO36yWZVuw2xELb9motrW//TIFF75taivpjhIVi+zYTkXFXfPYpHCyyUT5ws7/C+f5kGKGSqidxTisJi8tdoXpaqqisorC5LviLuCcIAACuUwg8Wu/XL5iXBsIgEBOEbhw/GDT9WzpLpBr2pjllaKSUrKxIEIiJyzLp85I9zKPRk9LlHQ5mYMHyz6BY4bF1D/lbI08T68n87/yNO24fjaLYbykmhTvewiV/fgXpuadwW6HWpQ7LdbZGZsvJcXIxWwV1STiM/rHMXEqSQSsqEjrQ5yuXkzmfokVJVAS7eWwrO2SZ1rETww7dli5sYhvEAABEACBbgK9/8sOTCAAAiCwEwLnjx9EN3+1hda1df0A3caRneEcUfA4tB+RO+mj0Hdv9nX9KDc4zJ5odm6N7fjOPAGZJ3bc8HJ6dUuz6lwiYkGuKVZskfeL1G0h/4tPkOvgo6jk+5dGnf5IU6PpokRERiy8dTMLa5jr3IU3rY+2tQ0bqZaLuT/XQUdGt6sFFosRkQ5xqqz9mxvG1ozzRuq3kp0jb7qJgE0RO3Kurx+nb87qsq7+KSc6nhnDQAAEQAAEzATwS8nMA2sgAAJ9IPC/u3X9+DQO3WRxLIzt+I4nIIIGejRscpmbLrBEGeOPwpZMErCqU0qKrdXCtRvUJpnrpUdeRZVQN+e0vdV+meOlW2jFUpXKaGyzDxqmorjBD81pibK/5e5fU9P1FysxDqP9zr4liiYm8vq6SQqk7+kHKFwbcwL1/Wo5qhIai2CZt2uRPNkRbW/Zrg7q+qNeK8MgJQFOqYHwjIYHiyAAAiCgCMARw4MAAiCQNoGLJw6hKexAGCY/ZK2FiY19+DYT2NhmjoZdMcXs1JpbYy0bBE5jdUo9AKY7xsb5bEO7IlgBriEmtcFkbljg9edZLOZB5XiJoyZzFe3DazjCdZSqIdb2xJ+VHL20a2GpeVOkilMXvSd9V8nLi9R9x5efcS2xZew0/U2pgLqPPMHk8BnX0dO3FGounnEABd54ifwvPamuT+T2Wx+4XYl3uA4/vqdDKdOqohJV1GXrT2e+PKMNBgIgAAIgYCGA1EQLEKyCAAj0jcDV04bTfy1cGz14Q2s7VVbjn5gokAQLItChK8sdwqpyEOlIACrLm8o5YvPdMdX0WHeBcpnbJC8SKjUFUHGwRBJeZOWbb2HpeDb7qPFU/n+/Us6TFG92jOcaX4ccy6mLl5CtehC1c7RLaorJ/EVR8pS6YYbcvBwv88BK2SHzPfcQBT9hGXoW4JB0Ru+s81j98yRpkpKVXMDqiM8+RIF/vcDO1T/UsXZOgSydfbVyEFPqLI3Gop6qm7CFgQAIgAAIxBMoCnTo5UPjG2ALCIAACCRL4NQFq+iV2q65NnLMCK+LxpS6kj28oNqJTPpnDaz2qBVwfvXru9HhQyDxvSsehHe2t9Kxb8eKLVe6HDSlIl7yvZNFNkSC3lZRFZOEj0R4W11C1c7OjmBUTr7ljzeQHF9++Y1xQ+xsbeZnoZNsZRkQaeF+wtu3sLy9NzP9xV1tzxuknuDn/FwbdkB1Cc0/crKxim8QAAEQAAGNAFITNRhYBAEQSI/Ab6Z3pW8ZvdTyXDGkKBo0zN9rWwImJ+ziSUPghJkR9evaYRyNPExzgnewemKzpSCxXJAIaEh0zFSXi6Nahmqn1Adr/B8uHt4dkTJqekk6Y8eyReScOiPhuIpKyzPnNHFkzT6ElRgz4dQlvNqeN27xm1NtL5wA4ZmeaWEPCIBAoRNA3lChPwEYPwhkkMB0ll2/ac+RdM3nm6O9rmGHY3pVSZwKXbRBAS7IvDCZR2PY5DIX3bxnjbGK711EQOY6vlMXi+bIi4TyBFGx3i7PVl5JxQceSf45f6fw+lVkHz2BItu3qtRD+7Aach9zSm+HD+h9bZzSWeePpSVO4mj4uWORljigbyouHgRAIKsEkJqYVbzoHAQKk8CpC1ZzimJTdPAVPNdmamV8mle0QQEtiBDEqma/acRzDp1E1npWpgZY6TcCR7y1nD6ob4uebzd2xKo5TTEl49TA4GcLqWPRh1yUuYFsldXKIXOLfLzDmVJXA6nxiiY/6bL1d+8zmqx1BgfSeHCtIAACIJBtAnDEsk0Y/YNAARLYxG/FZ/77K9qqTdofyrXFxmvKigWIhUSq/ovG2I98YfDL6SPoZ1OHFyKOnBzzXH6BMItfJBjm5Xp4e1Zj3p7Bo6dvccDEETNsT46Of3RMl6S+sQ3fIAACIAACZgKYI2bmgTUQAIEMEKjxOOm+fceYepJCzxssUu2mBnm+IiIGK5p8plGeMboKTpiJyK5fOWFEBZ08MlbzSu6btcTArr/K3LoC0ZsRlVTdrmEVVRgIgAAIgEDvBOCI9c4He0EABPpI4Ljh5XTH3qNMR29mR2xTATpjopC4nJ2wjkhMIlHU5B7Yb6yJD1Zyg8AvOEqpmzyzEs2EJSawrjVA8owbdioXb5YPDARAAARAoHcCcMR654O9IAACaRCYzeIH1rQ7iS4UUoRBalJ9ucNH7doP1fElLnr0wHHk1KsIp8EZh2aWwPRyN1mdMVG5hMUTkJTErb5gdIc807/Zw6yeGt2JBRAAARAAARMBOGImHFgBARDINAGZA3UJS7PrJhEGeYue79bcEYpzwoa5nfTUweNprLc434c/oMd3Nc/bkwLbholDLQqgsBgBiYJZmdwyo4YmonZgDBKWQAAEQKAXAnDEeoGDXSAAApkhcNteo0iiY7pt4bfoMrk/olc01hsM8GVRR1zWaE5HHM5O2HMzJ9AMFjKA5T4BSa3lklxRk3mOtVr0J7qjQBdWs/pnSEu3PW1UVdzf8wJFg2GDAAiAQFIE4IglhQmNQAAE0iUgP2p/MnmoqRtJa1rKzkprns2/EeECq0S9RAleOnQi7VsFGX/TQ5DDK+Iw3/M1s+jMeo7k1vNzW+i2kp0wfd7cBH6+//i10YWOBeMHARAAgZQIwBFLCRcagwAIpEPgJi5afCMXfNZNUr6WsqS7RMgGuvlZYW8ZzwfbzIWAdTtoUAnNO2wSiaQ3bGAROH/8ILrUklq7kiO5ekHugTWi9K9W0orrtdIU0qOopFYV29PvHD2AAAiAQAERQB2xArrZGCoI5AqBpzc00oUfr2MBi5iKoFxbJRfOHVPiJg/XbhpoJilrEi2x2tljqpU6InQ5rGQG1vrp76+mlzbHipTbOGdxSqWHyp0pFnseWMOOu1p5xq3pmfez+uf3x1bHtcUGEAABEACB3gnAEeudD/aCAAhkicDnHFW4+JMN9FGDucCxnK6GVQXlo03PydJVpN+tREZEBTJReqWox10xZVj6J0EPu5xAO8+F+ub8FfR+fex5FWdsMkc5K4oLwxmTSJg1co1nfJc/mrgAEACBAUwAjtgAvnm4dBDIBwJXfraJ7lqxLW4oxXYbjWRlwWGe3FQXFMerlsUbGiwpWjKQ6fzj/HYWKDl8SEx1L26A2DDgCGxrD9FJ766kxTv8pmufVO6hQSzEks8mwhx1lmf9ai7a/IvdzTXX8pkBxgYCIAACmSYARyzTRNEfCIBAygTmbWmma5dsoqVN8al9LnbIxBkb6nGSXZewS/ksmTmgmSNgW/0dJEIjiewyFiT5Lc+Fg+Ungc1877+zYDUt4rmAuo1msYqRXpe+KS+WgxwJFCfMOifuKpb3/5Wl8HVeDBiDAAEQAIF+JABHrB9h41QgAAK9E7hp2Rb6zbJalrSPbyc+2BB3sYo8lDv7VxSggy9IlPJEkr6tB4XHo4eV0c+njSAR5oDlN4HtHBk764M19O72VtNAB3NUbHyZmyRlMR9sB790kDphQa0YuYxLHDBxxGAgAAIgAALpEYAjlh4/HA0CIJBhAhtY9OKWr7bS/au399izRMmqWNijkufmlPMnGz97pVitRAEa+Ue3NRqgX9heLNhwxeRhdProKn0zlvOcQIjr3527cC09t3GHaaRuFpoZV+oe8PPGZN6jFF632t37jKYLxw+2bsY6CIAACIBAHwjAEesDNBwCAiCQfQLL+U38Pavq6C+renbI5Cok+FDGynWlDjt5nTby2O3kZkctFZVCSb8KsIy+j+Xn2/hb6iO1W6IA1hEfUF3CxWsHk6giwgqXwLVLNtNt/OLAasN5fuNoVgBN5Tm09rEr1uXZF2VEq/iMFCO/f78xdOyw8l1xWTgnCIAACOQlAThieXlbMSgQyB8Ckgb24Np6enx9A33ZHD+HrKeROvkXsAh+ONhTk7ll4rBJ5EyyHsMczQiz8yUph+KERXg9WTtrTBWdO3YQHTW0LNlD0C7PCTzBz+aln25Qjrw+VHn+RrH655ABIOQhfxckAraVBWis9o3h5XQvR8JG5ahwjvV6sQ4CIAACA4UAHLGBcqdwnSAAAjS/rpWe37yD5nA9p/X9WABaogCn1FTQrJoqFK3Fc5iQwIrWdrp88UZ6jYVnrFbKcxpHsJBHNafT5prJSwmpC1bLRcjZF4uzX/J8sJ9hPlgcF2wAARAAgUwQgCOWCYroAwRAoN8JfNLoozfrWlgwoY0Wci2yeo6cZcpk3tfBg0qV/PzRHPnqb3GQTI0D/fQ/gbtX1tF1nK4ocwytJg7ZUI4q5UKETK5vG0e/RAE0UUT4MC69cNMeNbR/tdc6DKyDAAiAAAhkiAAcsQyBRDcgAAK7lsAqjkgs5dTFr3hu2RpOsdrIPzC3scphI895aeW5XzLnS174S6qih1PGKvhH8WCOUIzktLExJcU0ieXHp7Hi3Qx2wry8HwYCfSWwkR2cG77YQo9wSm0ik7RZUViU2mMlPLexv0yefym7UC9/L3p4cTHU7aBrWf3zogkQ5Oiv+4LzgAAIFC4BOGKFe+8xchAAARAAgSwSWFDfpoQ85tY29XgWD6ssVhU7WWXRTmVZUAANRkT9MxxVAE0U/ZKLk5cTUgPvp1OGqeUeLxg7QAAEQAAEMkYAjljGUKIjEAABEAABEIgn8A7XG/sTK4Bape6tLUVQRtIXJUrm5Y84R6IA6khCelGiXVLvS1IORf3Tx+qfrfwJ8HJvJhGwH00YQpdMHIL5j72Bwj4QAAEQyAIBOGJZgIouQQAEQAAEQMBKQEoyPLKugURlcTOnziZrUiBa0hlF/dMu6p/8EcdLxD5FbCPEKhuiepiKHc5zwM4ZW60UQFM5Dm1BAARAAAQyRwCOWOZYoicQAAEQAAEQSIrAPFZXfJEVQF+ubaYtPGerP0xq350wooK+zQqgU3g+JAwEQAAEQGDXEoAjtmv54+wgAAIgAAIFTuAjVv18m0szvM9zyv7DaqBbM+SY7VHhoQNY9fCQwaV05JAyGulxFjhpDB8EQAAEcosAHLHcuh+4GhAAARAAgQInIKqLy1gBdCUrga6TGl+cxrg9GKImVgCV+V8hzkKU4uQuTlcsUWIfDhrGc71qWBp/PCuATi510/QKN9Q/C/w5wvBBAARynwAcsdy/R7hCEAABEAABEAABEAABEACBPCOAYjl5dkMxHBAAARAAARAAARAAARAAgdwnAEcs9+8RrhAEQAAEQAAEQAAEQAAEQCDPCMARy7MbiuGAAAiAAAiAAAiAAAiAAAjkPgE4Yrl/j3CFIAACIAACIAACIAACIAACeUYAjlie3VAMBwRAAARAAARAAARAAARAIPcJwBHL/XuEKwQBEAABEAABEAABEAABEMgzAnDE8uyGYjggAAIgAAIgAAIgAAIgAAK5TwCOWO7fI1whCIAACIAACIAACIAACIBAnhGAI5ZnNxTDAQEQAAEQAAEQAAEQAAEQyH0CcMRy/x7hCkEABEAABEAABEAABEAABPKMAByxPLuhGA4IgAAIgAAIgAAIgAAIgEDuE4Ajlvv3CFcIAiAAAiAAAiAAAiAAAiCQZwTgiOXZDcVwQAAEQAAEQAAEQAAEQAAEcp8AHLHcv0e4QhAAARAAARAAARAAARAAgTwjAEcsz24ohgMCIAACIAACIAACIAACIJD7BOCI5f49whWCAAiAAAiAAAiAAAiAAAjkGQE4Ynl2QzEcEAABEAABEAABEAABEACB3CcARyz37xGuEARAAARAAARAAARAAARAIM8IwBHLsxuK4YAACIAACIAACIAACIAACOQ+AThiuX+PcIUgAAIgAAIgAAIgAAIgAAJ5RgCOWJ7dUAwHBEAABEAABEAABEAABEAg9wnAEcv9e4QrBAEQAAEQAAEQAAEQAAEQyDMCcMTy7IZiOCAAAiAAAiAAAiAAAiAAArlPAI5Y7t8jXCEIgAAIgAAIgAAIgAAIgECeEYAjlmc3FMMBARAAARAAARAAARAAARDIfQJwxHL/HuEKQQAEQAAEQAAEQAAEQAAE8owAHLE8u6EYDgiAAAiAAAiAAAiAAAiAQO4T+H8S7u5R0AIY4QAAAABJRU5ErkJggg=="
+ "image/png": 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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Checking the property graph, we'll find a new policy (entity) called Equal Employment Opportunity Policy is added\n",
+ "Checking the property graph, we'll find a different Equal Employment Opportunity Policy Node\n",
"\n",
""
]
@@ -387,41 +633,57 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n",
+ "\u001B[33muser_proxy\u001B[0m (to rag_agent):\n",
"\n",
"What is Equal Employment Opportunity Policy at BUZZ?\n",
"\n",
"--------------------------------------------------------------------------------\n",
- "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n",
+ "\u001B[33mrag_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "The Equal Employment Opportunity (EEO) and Anti-Discrimination Policy at BUZZ Co. is designed to ensure full compliance with all applicable anti-discrimination laws and regulations. BUZZ Co. is an equal opportunity employer and strictly prohibits any form of discrimination or harassment. The policy ensures equal employment opportunities for all employees and applicants, regardless of race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other legally protected characteristic.\n",
"\n",
- "The Equal Employment Opportunity Policy at BUZZ Co. ensures non-discrimination and equal employment opportunities for all employees and applicants. It prohibits discrimination or harassment based on various factors and covers aspects such as recruitment, promotion, training, working conditions, wages, and benefits. The policy is disseminated by BUZZ Co. officers, implemented by directors, managers, and supervisors, and enforced by the Human Resources department to ensure company-wide compliance with anti-discrimination laws and regulations.\n",
+ "The policy applies to all aspects of the employment relationship, including recruitment, employment, promotion, transfer, training, working conditions, wages and salary administration, and employee benefits. It also extends to the selection and treatment of independent contractors and temporary agency personnel working on BUZZ Co. premises.\n",
+ "\n",
+ "BUZZ Co. enforces this policy by posting required notices, including EEO statements in job advertisements, posting job openings with state agencies, and prohibiting retaliation against individuals who report discrimination or harassment. Employees are required to report incidents of discrimination or harassment, which are promptly investigated, and appropriate corrective action is taken.\n",
"\n",
"--------------------------------------------------------------------------------\n",
- "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n",
+ "\u001B[33muser_proxy\u001B[0m (to rag_agent):\n",
"\n",
- "What's the scope of the Equal Employment Opportunity and Anti-Discrimination Policy?\n",
+ "What is prohibited sexual harassment stated in the policy?\n",
"\n",
"--------------------------------------------------------------------------------\n",
- "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n",
+ "\u001B[33mrag_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "Prohibited sexual harassment, as stated in BUZZ Co.'s policy, includes unwelcome sexual advances, requests for sexual favors, and other verbal or physical conduct of a sexual nature. Such conduct is considered prohibited sexual harassment when:\n",
"\n",
- "The scope of the Equal Employment Opportunity and Anti-Discrimination Policy includes all aspects of the employment relationship at BUZZ Co., such as recruitment, employment, promotion, transfer, training, working conditions, wages and salary administration, and employee benefits. This policy also extends to the selection and treatment of independent contractors, temporary agency personnel working on BUZZ Co. premises, and any other individuals or firms conducting business for or with BUZZ Co.\n",
+ "1. Submission to such conduct is explicitly or implicitly made a term or condition of employment.\n",
+ "2. Submission to or rejection of such conduct is used as the basis for employment decisions.\n",
+ "3. The conduct has the purpose or effect of unreasonably interfering with an individual’s work performance or creating an intimidating, hostile, or offensive work environment.\n",
+ "\n",
+ "The policy also encompasses any unwelcome conduct based on other protected characteristics, such as race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, or protected veteran status. Such conduct becomes unlawful when continued employment is made contingent upon the employee's toleration of the offensive conduct, or when the conduct is so severe or pervasive that it creates a work environment that would be considered intimidating, hostile, or abusive by a reasonable person.\n",
"\n",
"--------------------------------------------------------------------------------\n",
- "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n",
+ "\u001B[33muser_proxy\u001B[0m (to paul_graham_agent):\n",
"\n",
- "Is Promotion part of the scope of the Equal Employment Opportunity and Anti-Discrimination Policy?\n",
+ "List the name of 5 other policies at Buzz.\n",
"\n",
"--------------------------------------------------------------------------------\n",
- "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n",
+ "\u001B[33mpaul_graham_agent\u001B[0m (to user_proxy):\n",
+ "\n",
+ "Certainly! Here are five other policies at BUZZ Co.:\n",
"\n",
- "Yes.\n",
+ "1. **Voluntary At-Will Employment**\n",
+ "2. **Confidentiality Policy**\n",
+ "3. **Solicitation Policy**\n",
+ "4. **Overtime Policy**\n",
+ "5. **Internet Acceptable Use Policy**\n",
"\n",
"--------------------------------------------------------------------------------\n"
]
@@ -429,10 +691,10 @@
{
"data": {
"text/plain": [
- "ChatResult(chat_id=None, chat_history=[{'content': 'What is Equal Employment Opportunity Policy at BUZZ?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The Equal Employment Opportunity Policy at BUZZ Co. ensures non-discrimination and equal employment opportunities for all employees and applicants. It prohibits discrimination or harassment based on various factors and covers aspects such as recruitment, promotion, training, working conditions, wages, and benefits. The policy is disseminated by BUZZ Co. officers, implemented by directors, managers, and supervisors, and enforced by the Human Resources department to ensure company-wide compliance with anti-discrimination laws and regulations.', 'role': 'user', 'name': 'rag_agent'}, {'content': \"What's the scope of the Equal Employment Opportunity and Anti-Discrimination Policy?\", 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The scope of the Equal Employment Opportunity and Anti-Discrimination Policy includes all aspects of the employment relationship at BUZZ Co., such as recruitment, employment, promotion, transfer, training, working conditions, wages and salary administration, and employee benefits. This policy also extends to the selection and treatment of independent contractors, temporary agency personnel working on BUZZ Co. premises, and any other individuals or firms conducting business for or with BUZZ Co.', 'role': 'user', 'name': 'rag_agent'}, {'content': 'Is Promotion part of the scope of the Equal Employment Opportunity and Anti-Discrimination Policy?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'Yes.', 'role': 'user', 'name': 'rag_agent'}], summary='Yes.', cost={'usage_including_cached_inference': {'total_cost': 0}, 'usage_excluding_cached_inference': {'total_cost': 0}}, human_input=[\"What's the scope of the Equal Employment Opportunity and Anti-Discrimination Policy?\", 'Is Promotion part of the scope of the Equal Employment Opportunity and Anti-Discrimination Policy?', 'exit'])"
+ "ChatResult(chat_id=None, chat_history=[{'content': 'What is Equal Employment Opportunity Policy at BUZZ?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The Equal Employment Opportunity (EEO) and Anti-Discrimination Policy at BUZZ Co. is designed to ensure full compliance with all applicable anti-discrimination laws and regulations. BUZZ Co. is an equal opportunity employer and strictly prohibits any form of discrimination or harassment. The policy ensures equal employment opportunities for all employees and applicants, regardless of race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other legally protected characteristic.\\n\\nThe policy applies to all aspects of the employment relationship, including recruitment, employment, promotion, transfer, training, working conditions, wages and salary administration, and employee benefits. It also extends to the selection and treatment of independent contractors and temporary agency personnel working on BUZZ Co. premises.\\n\\nBUZZ Co. enforces this policy by posting required notices, including EEO statements in job advertisements, posting job openings with state agencies, and prohibiting retaliation against individuals who report discrimination or harassment. Employees are required to report incidents of discrimination or harassment, which are promptly investigated, and appropriate corrective action is taken.', 'role': 'user', 'name': 'paul_graham_agent'}, {'content': 'What is prohibited sexual harassment stated in the policy?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': \"Prohibited sexual harassment, as stated in BUZZ Co.'s policy, includes unwelcome sexual advances, requests for sexual favors, and other verbal or physical conduct of a sexual nature. Such conduct is considered prohibited sexual harassment when:\\n\\n1. Submission to such conduct is explicitly or implicitly made a term or condition of employment.\\n2. Submission to or rejection of such conduct is used as the basis for employment decisions.\\n3. The conduct has the purpose or effect of unreasonably interfering with an individual’s work performance or creating an intimidating, hostile, or offensive work environment.\\n\\nThe policy also encompasses any unwelcome conduct based on other protected characteristics, such as race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, or protected veteran status. Such conduct becomes unlawful when continued employment is made contingent upon the employee's toleration of the offensive conduct, or when the conduct is so severe or pervasive that it creates a work environment that would be considered intimidating, hostile, or abusive by a reasonable person.\", 'role': 'user', 'name': 'paul_graham_agent'}, {'content': 'List the name of 5 other policies at Buzz.', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'Certainly! Here are five other policies at BUZZ Co.:\\n\\n1. **Voluntary At-Will Employment**\\n2. **Confidentiality Policy**\\n3. **Solicitation Policy**\\n4. **Overtime Policy**\\n5. **Internet Acceptable Use Policy**', 'role': 'user', 'name': 'paul_graham_agent'}], summary='Certainly! Here are five other policies at BUZZ Co.:\\n\\n1. **Voluntary At-Will Employment**\\n2. **Confidentiality Policy**\\n3. **Solicitation Policy**\\n4. **Overtime Policy**\\n5. **Internet Acceptable Use Policy**', cost={'usage_including_cached_inference': {'total_cost': 0}, 'usage_excluding_cached_inference': {'total_cost': 0}}, human_input=['What is prohibited sexual harassment stated in the policy?', 'List the name of 5 other policies at Buzz.', 'exit'])"
]
},
- "execution_count": 10,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/notebook/neo4j_property_graph_1.png b/notebook/neo4j_property_graph_1.png
index 8d47e2f593..9aaad82191 100644
--- a/notebook/neo4j_property_graph_1.png
+++ b/notebook/neo4j_property_graph_1.png
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:05ce4f905086901f9206e466e75f3cd9819d8f678aa121d2df98a67b4f3bce76
-size 58206
+oid sha256:d068a2b561d35b5d65d207a7865ad9068fce4d19398df96f62a0d23662f237c4
+size 185770
diff --git a/notebook/neo4j_property_graph_2.png b/notebook/neo4j_property_graph_2.png
index 925423aff2..395cbdccb1 100644
--- a/notebook/neo4j_property_graph_2.png
+++ b/notebook/neo4j_property_graph_2.png
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:69567c06b986448cba73d8a4886f7be74363635d4b5f5bb704319ea044429f02
-size 524567
+oid sha256:d1f4ece3fb82f1ed074e7b2cf9b93028bc0c1c255e76895015c27c20e63726eb
+size 153756
diff --git a/notebook/neo4j_property_graph_3.png b/notebook/neo4j_property_graph_3.png
index 25c0b94bbe..67b5f0c7b8 100644
--- a/notebook/neo4j_property_graph_3.png
+++ b/notebook/neo4j_property_graph_3.png
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:f21978433e78b92f59d06c86d37563bf28e10c0a5c8b639620e83bb616737940
-size 139519
+oid sha256:209f9cb9d34e08f282c7449f007dc1065cb6c2dffc4e322783829b0986b6ce7c
+size 78515
diff --git a/test/agentchat/contrib/agent_eval/test_agent_eval.py b/test/agentchat/contrib/agent_eval/test_agent_eval.py
index 65e03af36e..d5f7306528 100644
--- a/test/agentchat/contrib/agent_eval/test_agent_eval.py
+++ b/test/agentchat/contrib/agent_eval/test_agent_eval.py
@@ -54,9 +54,9 @@ def remove_ground_truth(test_case: str):
filter_dict={"api_type": ["azure"]},
)
- success_str = open("test/test_files/agenteval-in-out/samples/sample_math_response_successful.txt", "r").read()
+ success_str = open("test/test_files/agenteval-in-out/samples/sample_math_response_successful.txt").read()
response_successful = remove_ground_truth(success_str)[0]
- failed_str = open("test/test_files/agenteval-in-out/samples/sample_math_response_failed.txt", "r").read()
+ failed_str = open("test/test_files/agenteval-in-out/samples/sample_math_response_failed.txt").read()
response_failed = remove_ground_truth(failed_str)[0]
task = Task(
**{
@@ -87,10 +87,10 @@ def test_generate_criteria():
)
def test_quantify_criteria():
criteria_file = "test/test_files/agenteval-in-out/samples/sample_math_criteria.json"
- criteria = open(criteria_file, "r").read()
+ criteria = open(criteria_file).read()
criteria = Criterion.parse_json_str(criteria)
- test_case = open("test/test_files/agenteval-in-out/samples/sample_test_case.json", "r").read()
+ test_case = open("test/test_files/agenteval-in-out/samples/sample_test_case.json").read()
test_case, ground_truth = remove_ground_truth(test_case)
quantified = quantify_criteria(
diff --git a/test/agentchat/contrib/agent_eval/test_criterion.py b/test/agentchat/contrib/agent_eval/test_criterion.py
index f36ccdfd24..af0600cd3c 100644
--- a/test/agentchat/contrib/agent_eval/test_criterion.py
+++ b/test/agentchat/contrib/agent_eval/test_criterion.py
@@ -11,7 +11,7 @@
def test_parse_json_str():
criteria_file = "test/test_files/agenteval-in-out/samples/sample_math_criteria.json"
- criteria = open(criteria_file, "r").read()
+ criteria = open(criteria_file).read()
criteria = Criterion.parse_json_str(criteria)
assert criteria
assert len(criteria) == 6
diff --git a/test/agentchat/contrib/capabilities/test_image_generation_capability.py b/test/agentchat/contrib/capabilities/test_image_generation_capability.py
index c050a775af..39f5e4daf9 100644
--- a/test/agentchat/contrib/capabilities/test_image_generation_capability.py
+++ b/test/agentchat/contrib/capabilities/test_image_generation_capability.py
@@ -57,7 +57,7 @@ def create_test_agent(name: str = "test_agent", default_auto_reply: str = "") ->
return ConversableAgent(name=name, llm_config=False, default_auto_reply=default_auto_reply)
-def dalle_image_generator(dalle_config: Dict[str, Any], resolution: str, quality: str):
+def dalle_image_generator(dalle_config: dict[str, Any], resolution: str, quality: str):
return generate_images.DalleImageGenerator(dalle_config, resolution=resolution, quality=quality, num_images=1)
@@ -66,7 +66,7 @@ def api_key():
@pytest.fixture
-def dalle_config() -> Dict[str, Any]:
+def dalle_config() -> dict[str, Any]:
config_list = openai_utils.config_list_from_models(model_list=["dall-e-3"], exclude="aoai")
if not config_list:
config_list = [{"model": "dall-e-3", "api_key": api_key()}]
@@ -74,7 +74,7 @@ def dalle_config() -> Dict[str, Any]:
@pytest.fixture
-def gpt4_config() -> Dict[str, Any]:
+def gpt4_config() -> dict[str, Any]:
config_list = [
{
"model": "gpt-4o-mini",
@@ -96,7 +96,7 @@ def image_gen_capability():
@pytest.mark.skipif(skip_openai, reason="Requested to skip.")
@pytest.mark.skipif(skip_requirement, reason="Dependencies are not installed.")
-def test_dalle_image_generator(dalle_config: Dict[str, Any]):
+def test_dalle_image_generator(dalle_config: dict[str, Any]):
"""Tests DalleImageGenerator capability to generate images by calling the OpenAI API."""
dalle_generator = dalle_image_generator(dalle_config, RESOLUTIONS[0], QUALITIES[0])
image = dalle_generator.generate_image(PROMPTS[0])
@@ -109,7 +109,7 @@ def test_dalle_image_generator(dalle_config: Dict[str, Any]):
@pytest.mark.parametrize("gen_config_2", itertools.product(RESOLUTIONS, QUALITIES, PROMPTS))
@pytest.mark.skipif(skip_requirement, reason="Dependencies are not installed.")
def test_dalle_image_generator_cache_key(
- dalle_config: Dict[str, Any], gen_config_1: Tuple[str, str, str], gen_config_2: Tuple[str, str, str]
+ dalle_config: dict[str, Any], gen_config_1: tuple[str, str, str], gen_config_2: tuple[str, str, str]
):
"""Tests if DalleImageGenerator creates unique cache keys.
diff --git a/test/agentchat/contrib/capabilities/test_teachable_agent.py b/test/agentchat/contrib/capabilities/test_teachable_agent.py
index 82252f07f6..b5ed584e3e 100755
--- a/test/agentchat/contrib/capabilities/test_teachable_agent.py
+++ b/test/agentchat/contrib/capabilities/test_teachable_agent.py
@@ -202,7 +202,7 @@ def test_teachability_accuracy():
return
# All trials failed.
- assert False, "test_teachability_accuracy() failed on all {} trials.".format(num_trials)
+ assert False, f"test_teachability_accuracy() failed on all {num_trials} trials."
if __name__ == "__main__":
diff --git a/test/agentchat/contrib/capabilities/test_transforms.py b/test/agentchat/contrib/capabilities/test_transforms.py
index 744727f65e..2038991bfc 100644
--- a/test/agentchat/contrib/capabilities/test_transforms.py
+++ b/test/agentchat/contrib/capabilities/test_transforms.py
@@ -21,11 +21,11 @@
class _MockTextCompressor:
- def compress_text(self, text: str, **compression_params) -> Dict[str, Any]:
+ def compress_text(self, text: str, **compression_params) -> dict[str, Any]:
return {"compressed_prompt": ""}
-def get_long_messages() -> List[Dict]:
+def get_long_messages() -> list[dict]:
return [
{"role": "assistant", "content": [{"type": "text", "text": "are you doing?"}]},
{"role": "user", "content": "very very very very very very long string"},
@@ -35,7 +35,7 @@ def get_long_messages() -> List[Dict]:
]
-def get_short_messages() -> List[Dict]:
+def get_short_messages() -> list[dict]:
return [
{"role": "user", "content": "hello"},
{"role": "assistant", "content": [{"type": "text", "text": "there"}]},
@@ -43,11 +43,11 @@ def get_short_messages() -> List[Dict]:
]
-def get_no_content_messages() -> List[Dict]:
+def get_no_content_messages() -> list[dict]:
return [{"role": "user", "function_call": "example"}, {"role": "assistant", "content": None}]
-def get_tool_messages() -> List[Dict]:
+def get_tool_messages() -> list[dict]:
return [
{"role": "user", "content": "hello"},
{"role": "tool_calls", "content": "calling_tool"},
@@ -57,7 +57,7 @@ def get_tool_messages() -> List[Dict]:
]
-def get_tool_messages_kept() -> List[Dict]:
+def get_tool_messages_kept() -> list[dict]:
return [
{"role": "user", "content": "hello"},
{"role": "tool_calls", "content": "calling_tool"},
@@ -67,7 +67,7 @@ def get_tool_messages_kept() -> List[Dict]:
]
-def get_messages_with_names() -> List[Dict]:
+def get_messages_with_names() -> list[dict]:
return [
{"role": "system", "content": "I am the system."},
{"role": "user", "name": "charlie", "content": "I think the sky is blue."},
@@ -76,7 +76,7 @@ def get_messages_with_names() -> List[Dict]:
]
-def get_messages_with_names_post_start() -> List[Dict]:
+def get_messages_with_names_post_start() -> list[dict]:
return [
{"role": "system", "content": "I am the system."},
{"role": "user", "name": "charlie", "content": "'charlie' said:\nI think the sky is blue."},
@@ -85,7 +85,7 @@ def get_messages_with_names_post_start() -> List[Dict]:
]
-def get_messages_with_names_post_end() -> List[Dict]:
+def get_messages_with_names_post_end() -> list[dict]:
return [
{"role": "system", "content": "I am the system."},
{"role": "user", "name": "charlie", "content": "I think the sky is blue.\n(said 'charlie')"},
@@ -94,7 +94,7 @@ def get_messages_with_names_post_end() -> List[Dict]:
]
-def get_messages_with_names_post_filtered() -> List[Dict]:
+def get_messages_with_names_post_filtered() -> list[dict]:
return [
{"role": "system", "content": "I am the system."},
{"role": "user", "name": "charlie", "content": "I think the sky is blue."},
@@ -103,8 +103,8 @@ def get_messages_with_names_post_filtered() -> List[Dict]:
]
-def get_text_compressors() -> List[TextCompressor]:
- compressors: List[TextCompressor] = [_MockTextCompressor()]
+def get_text_compressors() -> list[TextCompressor]:
+ compressors: list[TextCompressor] = [_MockTextCompressor()]
try:
from autogen.agentchat.contrib.capabilities.text_compressors import LLMLingua
@@ -136,7 +136,7 @@ def message_token_limiter_with_threshold() -> MessageTokenLimiter:
def _filter_dict_test(
- post_transformed_message: Dict, pre_transformed_messages: Dict, roles: List[str], exclude_filter: bool
+ post_transformed_message: dict, pre_transformed_messages: dict, roles: list[str], exclude_filter: bool
) -> bool:
is_role = post_transformed_message["role"] in roles
if exclude_filter:
diff --git a/test/agentchat/contrib/capabilities/test_transforms_util.py b/test/agentchat/contrib/capabilities/test_transforms_util.py
index 31c5ac223e..6647226f0b 100644
--- a/test/agentchat/contrib/capabilities/test_transforms_util.py
+++ b/test/agentchat/contrib/capabilities/test_transforms_util.py
@@ -28,7 +28,7 @@
@pytest.mark.parametrize("message", MESSAGES.values())
-def test_cache_content(message: Dict[str, MessageContentType]) -> None:
+def test_cache_content(message: dict[str, MessageContentType]) -> None:
with tempfile.TemporaryDirectory() as tmpdirname:
cache = Cache.disk(tmpdirname)
cache_key_1 = "test_string"
@@ -51,7 +51,7 @@ def test_cache_content(message: Dict[str, MessageContentType]) -> None:
@pytest.mark.parametrize("messages", itertools.product(MESSAGES.values(), MESSAGES.values()))
-def test_cache_key(messages: Tuple[Dict[str, MessageContentType], Dict[str, MessageContentType]]) -> None:
+def test_cache_key(messages: tuple[dict[str, MessageContentType], dict[str, MessageContentType]]) -> None:
message_1, message_2 = messages
cache_1 = transforms_util.cache_key(message_1["content"], 10)
cache_2 = transforms_util.cache_key(message_2["content"], 10)
@@ -62,17 +62,17 @@ def test_cache_key(messages: Tuple[Dict[str, MessageContentType], Dict[str, Mess
@pytest.mark.parametrize("message", MESSAGES.values())
-def test_min_tokens_reached(message: Dict[str, MessageContentType]):
+def test_min_tokens_reached(message: dict[str, MessageContentType]):
assert transforms_util.min_tokens_reached([message], None)
assert transforms_util.min_tokens_reached([message], 0)
assert not transforms_util.min_tokens_reached([message], message["text_tokens"] + 1)
@pytest.mark.parametrize("message", MESSAGES.values())
-def test_count_text_tokens(message: Dict[str, MessageContentType]):
+def test_count_text_tokens(message: dict[str, MessageContentType]):
assert transforms_util.count_text_tokens(message["content"]) == message["text_tokens"]
@pytest.mark.parametrize("message", MESSAGES.values())
-def test_is_content_text_empty(message: Dict[str, MessageContentType]):
+def test_is_content_text_empty(message: dict[str, MessageContentType]):
assert transforms_util.is_content_text_empty(message["content"]) == (message["text_tokens"] == 0)
diff --git a/test/agentchat/contrib/graph_rag/test_neo4j_graph_rag.py b/test/agentchat/contrib/graph_rag/test_neo4j_graph_rag.py
index 6b8f73122f..f7e891b975 100644
--- a/test/agentchat/contrib/graph_rag/test_neo4j_graph_rag.py
+++ b/test/agentchat/contrib/graph_rag/test_neo4j_graph_rag.py
@@ -49,7 +49,7 @@ def neo4j_query_engine():
]
# define which entities can have which relations
- validation_schema = {
+ schema = {
"EMPLOYEE": ["FOLLOWS", "APPLIES_TO", "ASSIGNED_TO", "ENTITLED_TO", "REPORTS_TO"],
"EMPLOYER": ["PROVIDES", "DEFINED_AS", "MANAGES", "REQUIRES"],
"POLICY": ["APPLIES_TO", "DEFINED_AS", "REQUIRES"],
@@ -69,7 +69,7 @@ def neo4j_query_engine():
database="neo4j", # Change if you want to store the graphh in your custom database
entities=entities, # possible entities
relations=relations, # possible relations
- validation_schema=validation_schema, # schema to validate the extracted triplets
+ schema=schema,
strict=True, # enofrce the extracted triplets to be in the schema
)
@@ -78,6 +78,23 @@ def neo4j_query_engine():
return query_engine
+# Test fixture to test auto-generation without given schema
+@pytest.fixture(scope="module")
+def neo4j_query_engine_auto():
+ """
+ Test the engine with auto-generated property graph
+ """
+ query_engine = Neo4jGraphQueryEngine(
+ username="neo4j",
+ password="password",
+ host="bolt://172.17.0.3",
+ port=7687,
+ database="neo4j",
+ )
+ query_engine.connect_db() # Connect to the existing graph
+ return query_engine
+
+
@pytest.mark.skipif(
sys.platform in ["darwin", "win32"] or skip or skip_openai,
reason=reason,
@@ -117,3 +134,18 @@ def test_neo4j_add_records(neo4j_query_engine):
print(query_result.answer)
assert query_result.answer.find("Keanu Reeves") >= 0
+
+
+@pytest.mark.skipif(
+ sys.platform in ["darwin", "win32"] or skip or skip_openai,
+ reason=reason,
+)
+def test_neo4j_auto(neo4j_query_engine_auto):
+ """
+ Test querying with auto-generated property graph
+ """
+ question = "Which company is the employer?"
+ query_result: GraphStoreQueryResult = neo4j_query_engine_auto.query(question=question)
+
+ print(query_result.answer)
+ assert query_result.answer.find("BUZZ") >= 0
diff --git a/test/agentchat/contrib/test_agent_builder.py b/test/agentchat/contrib/test_agent_builder.py
index cab4a051b5..e16468ad62 100755
--- a/test/agentchat/contrib/test_agent_builder.py
+++ b/test/agentchat/contrib/test_agent_builder.py
@@ -180,7 +180,7 @@ def test_load():
)
config_save_path = f"{here}/example_test_agent_builder_config.json"
- json.load(open(config_save_path, "r"))
+ json.load(open(config_save_path))
agent_list, loaded_agent_configs = builder.load(
config_save_path,
diff --git a/test/agentchat/contrib/test_society_of_mind_agent.py b/test/agentchat/contrib/test_society_of_mind_agent.py
index 376bddfd70..1e70ebf47f 100755
--- a/test/agentchat/contrib/test_society_of_mind_agent.py
+++ b/test/agentchat/contrib/test_society_of_mind_agent.py
@@ -8,9 +8,9 @@
import os
import sys
+from typing import Annotated
import pytest
-from typing_extensions import Annotated
import autogen
from autogen.agentchat.contrib.society_of_mind_agent import SocietyOfMindAgent
diff --git a/test/agentchat/contrib/test_swarm.py b/test/agentchat/contrib/test_swarm.py
index 55fae1826d..85c24110a0 100644
--- a/test/agentchat/contrib/test_swarm.py
+++ b/test/agentchat/contrib/test_swarm.py
@@ -306,12 +306,12 @@ def test_context_variables_updating_multi_tools():
test_context_variables = {"my_key": 0}
# Increment the context variable
- def test_func_1(context_variables: Dict[str, Any], param1: str) -> str:
+ def test_func_1(context_variables: dict[str, Any], param1: str) -> str:
context_variables["my_key"] += 1
return SwarmResult(values=f"Test 1 {param1}", context_variables=context_variables, agent=agent1)
# Increment the context variable
- def test_func_2(context_variables: Dict[str, Any], param2: str) -> str:
+ def test_func_2(context_variables: dict[str, Any], param2: str) -> str:
context_variables["my_key"] += 100
return SwarmResult(values=f"Test 2 {param2}", context_variables=context_variables, agent=agent1)
@@ -367,7 +367,7 @@ def test_function_transfer():
test_context_variables = {"my_key": 0}
# Increment the context variable
- def test_func_1(context_variables: Dict[str, Any], param1: str) -> str:
+ def test_func_1(context_variables: dict[str, Any], param1: str) -> str:
context_variables["my_key"] += 1
return SwarmResult(values=f"Test 1 {param1}", context_variables=context_variables, agent=agent1)
@@ -474,7 +474,7 @@ def __init__(self):
message_container = MessageContainer()
# 1. Test with a callable function
- def custom_update_function(agent: ConversableAgent, messages: List[Dict]) -> str:
+ def custom_update_function(agent: ConversableAgent, messages: list[dict]) -> str:
return f"System message with {agent.get_context('test_var')} and {len(messages)} messages"
# 2. Test with a string template
@@ -537,7 +537,7 @@ def invalid_return_function(context_variables, messages) -> dict:
SwarmAgent("agent5", update_agent_state_before_reply=UPDATE_SYSTEM_MESSAGE(invalid_return_function))
# Test multiple update functions
- def another_update_function(context_variables: Dict[str, Any], messages: List[Dict]) -> str:
+ def another_update_function(context_variables: dict[str, Any], messages: list[dict]) -> str:
return "Another update"
agent6 = SwarmAgent(
@@ -673,17 +673,17 @@ def test_after_work_callable():
agent3 = SwarmAgent("agent3", llm_config=testing_llm_config)
def return_agent(
- last_speaker: SwarmAgent, messages: List[Dict[str, Any]], groupchat: GroupChat
+ last_speaker: SwarmAgent, messages: list[dict[str, Any]], groupchat: GroupChat
) -> Union[AfterWorkOption, SwarmAgent, str]:
return agent2
def return_agent_str(
- last_speaker: SwarmAgent, messages: List[Dict[str, Any]], groupchat: GroupChat
+ last_speaker: SwarmAgent, messages: list[dict[str, Any]], groupchat: GroupChat
) -> Union[AfterWorkOption, SwarmAgent, str]:
return "agent3"
def return_after_work_option(
- last_speaker: SwarmAgent, messages: List[Dict[str, Any]], groupchat: GroupChat
+ last_speaker: SwarmAgent, messages: list[dict[str, Any]], groupchat: GroupChat
) -> Union[AfterWorkOption, SwarmAgent, str]:
return AfterWorkOption.TERMINATE
diff --git a/test/agentchat/contrib/vectordb/test_mongodb.py b/test/agentchat/contrib/vectordb/test_mongodb.py
index 536da417fc..055ec22b5f 100644
--- a/test/agentchat/contrib/vectordb/test_mongodb.py
+++ b/test/agentchat/contrib/vectordb/test_mongodb.py
@@ -103,7 +103,7 @@ def db():
@pytest.fixture
-def example_documents() -> List[Document]:
+def example_documents() -> list[Document]:
"""Note mix of integers and strings as ids"""
return [
Document(id=1, content="Dogs are tough.", metadata={"a": 1}),
diff --git a/test/agentchat/contrib/vectordb/test_pgvectordb.py b/test/agentchat/contrib/vectordb/test_pgvectordb.py
index e158cf1678..15f96809b8 100644
--- a/test/agentchat/contrib/vectordb/test_pgvectordb.py
+++ b/test/agentchat/contrib/vectordb/test_pgvectordb.py
@@ -133,7 +133,7 @@ def test_pgvector():
res = db.get_docs_by_ids(["1", "2"], collection_name)
assert [r["id"] for r in res] == ["2"] # "1" has been deleted
res = db.get_docs_by_ids(collection_name=collection_name)
- assert set([r["id"] for r in res]) == set(["2", "3"]) # All Docs returned
+ assert {r["id"] for r in res} == {"2", "3"} # All Docs returned
if __name__ == "__main__":
diff --git a/test/agentchat/test_agent_file_logging.py b/test/agentchat/test_agent_file_logging.py
index d68c5dea9c..78755ab270 100644
--- a/test/agentchat/test_agent_file_logging.py
+++ b/test/agentchat/test_agent_file_logging.py
@@ -74,7 +74,7 @@ def test_log_chat_completion(logger: FileLogger):
source=agent,
)
- with open(logger.log_file, "r") as f:
+ with open(logger.log_file) as f:
lines = f.readlines()
assert len(lines) == 1
log_data = json.loads(lines[0])
@@ -98,7 +98,7 @@ def test_log_function_use(logger: FileLogger):
logger.log_function_use(source=source, function=func, args=args, returns=returns)
- with open(logger.log_file, "r") as f:
+ with open(logger.log_file) as f:
lines = f.readlines()
assert len(lines) == 1
log_data = json.loads(lines[0])
@@ -118,7 +118,7 @@ def test_log_new_agent(logger: FileLogger):
agent = autogen.UserProxyAgent(name="user_proxy", code_execution_config=False)
logger.log_new_agent(agent)
- with open(logger.log_file, "r") as f:
+ with open(logger.log_file) as f:
lines = f.readlines()
log_data = json.loads(lines[0]) # the first line is the session id
assert log_data["agent_name"] == "user_proxy"
@@ -131,7 +131,7 @@ def test_log_event(logger: FileLogger):
kwargs = {"key": "value"}
logger.log_event(source, name, **kwargs)
- with open(logger.log_file, "r") as f:
+ with open(logger.log_file) as f:
lines = f.readlines()
log_data = json.loads(lines[0])
assert log_data["source_name"] == "TestAgent"
@@ -145,7 +145,7 @@ def test_log_new_wrapper(logger: FileLogger):
wrapper = TestWrapper(init_args={"foo": "bar"})
logger.log_new_wrapper(wrapper, wrapper.init_args)
- with open(logger.log_file, "r") as f:
+ with open(logger.log_file) as f:
lines = f.readlines()
log_data = json.loads(lines[0])
assert log_data["wrapper_id"] == id(wrapper)
@@ -160,7 +160,7 @@ def test_log_new_client(logger: FileLogger):
init_args = {"foo": "bar"}
logger.log_new_client(client, wrapper, init_args)
- with open(logger.log_file, "r") as f:
+ with open(logger.log_file) as f:
lines = f.readlines()
log_data = json.loads(lines[0])
assert log_data["client_id"] == id(client)
diff --git a/test/agentchat/test_agentchat_utils.py b/test/agentchat/test_agentchat_utils.py
index 805411f9c2..8c38a6855e 100644
--- a/test/agentchat/test_agentchat_utils.py
+++ b/test/agentchat/test_agentchat_utils.py
@@ -48,13 +48,13 @@
]
-def _delete_unused_keys(d: Dict) -> None:
+def _delete_unused_keys(d: dict) -> None:
if "match" in d:
del d["match"]
@pytest.mark.parametrize("test_case", TAG_PARSING_TESTS)
-def test_tag_parsing(test_case: Dict[str, Union[str, List[Dict[str, Union[str, Dict[str, str]]]]]]) -> None:
+def test_tag_parsing(test_case: dict[str, Union[str, list[dict[str, Union[str, dict[str, str]]]]]]) -> None:
"""Test the tag_parsing function."""
message = test_case["message"]
expected = test_case["expected"]
diff --git a/test/agentchat/test_assistant_agent.py b/test/agentchat/test_assistant_agent.py
index ee7f5b88bd..3e96ecee14 100755
--- a/test/agentchat/test_assistant_agent.py
+++ b/test/agentchat/test_assistant_agent.py
@@ -181,7 +181,7 @@ def test_tsp(human_input_mode="NEVER", max_consecutive_auto_reply=2):
def tsp_message(sender, recipient, context):
filename = context.get("prompt_filename", "")
- with open(filename, "r") as f:
+ with open(filename) as f:
prompt = f.read()
question = context.get("question", "")
return prompt.format(question=question)
diff --git a/test/agentchat/test_chats.py b/test/agentchat/test_chats.py
index a39162debf..6f3504bbdb 100755
--- a/test/agentchat/test_chats.py
+++ b/test/agentchat/test_chats.py
@@ -8,11 +8,10 @@
import os
import sys
-from typing import Literal
+from typing import Annotated, Literal
import pytest
from test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST
-from typing_extensions import Annotated
import autogen
from autogen import AssistantAgent, GroupChat, GroupChatManager, UserProxyAgent, filter_config, initiate_chats
@@ -568,7 +567,7 @@ def my_writing_task(sender, recipient, context):
try:
filename = context.get("work_dir", "") + "/stock_prices.md"
- with open(filename, "r") as file:
+ with open(filename) as file:
data = file.read()
except Exception as e:
data = f"An error occurred while reading the file: {e}"
diff --git a/test/agentchat/test_conversable_agent.py b/test/agentchat/test_conversable_agent.py
index 93866c81a0..d43f2dba3f 100755
--- a/test/agentchat/test_conversable_agent.py
+++ b/test/agentchat/test_conversable_agent.py
@@ -13,13 +13,12 @@
import sys
import time
import unittest
-from typing import Any, Callable, Dict, Literal
+from typing import Annotated, Any, Callable, Dict, Literal
from unittest.mock import MagicMock
import pytest
from pydantic import BaseModel, Field
from test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST
-from typing_extensions import Annotated
import autogen
from autogen.agentchat import ConversableAgent, UserProxyAgent
@@ -660,7 +659,7 @@ async def currency_calculator(
assert inspect.iscoroutinefunction(currency_calculator)
-def get_origin(d: Dict[str, Callable[..., Any]]) -> Dict[str, Callable[..., Any]]:
+def get_origin(d: dict[str, Callable[..., Any]]) -> dict[str, Callable[..., Any]]:
return {k: v._origin for k, v in d.items()}
diff --git a/test/agentchat/test_function_and_tool_calling.py b/test/agentchat/test_function_and_tool_calling.py
index eaaea6a8a9..d3776ce206 100644
--- a/test/agentchat/test_function_and_tool_calling.py
+++ b/test/agentchat/test_function_and_tool_calling.py
@@ -203,7 +203,7 @@ async def _a_tool_func_error(arg1: str, arg2: str) -> str:
_text_message = {"content": "Hi!", "role": "user"}
-def _get_function_map(is_function_async: bool, drop_tool_2: bool = False) -> Dict[str, Callable[..., Any]]:
+def _get_function_map(is_function_async: bool, drop_tool_2: bool = False) -> dict[str, Callable[..., Any]]:
if is_function_async:
return (
{
@@ -230,7 +230,7 @@ def _get_function_map(is_function_async: bool, drop_tool_2: bool = False) -> Dic
def _get_error_function_map(
is_function_async: bool, error_on_tool_func_2: bool = True
-) -> Dict[str, Callable[..., Any]]:
+) -> dict[str, Callable[..., Any]]:
if is_function_async:
return {
"_tool_func_1": _a_tool_func_1 if error_on_tool_func_2 else _a_tool_func_error,
@@ -280,7 +280,7 @@ def test_generate_function_call_reply_on_function_call_message(is_function_async
assert (finished, retval) == (False, None)
# text message
- messages: List[Dict[str, str]] = [_text_message]
+ messages: list[dict[str, str]] = [_text_message]
finished, retval = agent.generate_function_call_reply(messages)
assert (finished, retval) == (False, None)
@@ -329,7 +329,7 @@ async def test_a_generate_function_call_reply_on_function_call_message(is_functi
assert (finished, retval) == (False, None)
# text message
- messages: List[Dict[str, str]] = [_text_message]
+ messages: list[dict[str, str]] = [_text_message]
finished, retval = await agent.a_generate_function_call_reply(messages)
assert (finished, retval) == (False, None)
@@ -377,7 +377,7 @@ def test_generate_tool_calls_reply_on_function_call_message(is_function_async: b
assert (finished, retval) == (False, None)
# text message
- messages: List[Dict[str, str]] = [_text_message]
+ messages: list[dict[str, str]] = [_text_message]
finished, retval = agent.generate_tool_calls_reply(messages)
assert (finished, retval) == (False, None)
@@ -426,7 +426,7 @@ async def test_a_generate_tool_calls_reply_on_function_call_message(is_function_
assert (finished, retval) == (False, None)
# text message
- messages: List[Dict[str, str]] = [_text_message]
+ messages: list[dict[str, str]] = [_text_message]
finished, retval = await agent.a_generate_tool_calls_reply(messages)
assert (finished, retval) == (False, None)
diff --git a/test/agentchat/test_groupchat.py b/test/agentchat/test_groupchat.py
index de5f8bab11..a3063011a9 100755
--- a/test/agentchat/test_groupchat.py
+++ b/test/agentchat/test_groupchat.py
@@ -602,9 +602,7 @@ def test_init_default_parameters():
agents = [autogen.ConversableAgent(name=f"Agent{i}", llm_config=False) for i in range(3)]
group_chat = GroupChat(agents=agents, messages=[], max_round=3)
for agent in agents:
- assert set([a.name for a in group_chat.allowed_speaker_transitions_dict[agent]]) == set(
- [a.name for a in agents]
- )
+ assert {a.name for a in group_chat.allowed_speaker_transitions_dict[agent]} == {a.name for a in agents}
def test_graph_parameters():
@@ -889,7 +887,7 @@ def agent(name: str) -> autogen.ConversableAgent:
llm_config=False,
)
- def team(members: List[autogen.Agent], name: str) -> autogen.Agent:
+ def team(members: list[autogen.Agent], name: str) -> autogen.Agent:
gc = autogen.GroupChat(agents=members, messages=[])
return autogen.GroupChatManager(groupchat=gc, name=name, llm_config=False)
@@ -963,7 +961,7 @@ def test_nested_teams_chat():
team1_msg = {"content": "Hello from team 1"}
team2_msg = {"content": "Hello from team 2"}
- def agent(name: str, auto_reply: Optional[Dict[str, Any]] = None) -> autogen.ConversableAgent:
+ def agent(name: str, auto_reply: Optional[dict[str, Any]] = None) -> autogen.ConversableAgent:
return autogen.ConversableAgent(
name=name,
max_consecutive_auto_reply=10,
@@ -972,7 +970,7 @@ def agent(name: str, auto_reply: Optional[Dict[str, Any]] = None) -> autogen.Con
default_auto_reply=auto_reply,
)
- def team(name: str, auto_reply: Optional[Dict[str, Any]] = None) -> autogen.ConversableAgent:
+ def team(name: str, auto_reply: Optional[dict[str, Any]] = None) -> autogen.ConversableAgent:
member1 = agent(f"member1_{name}", auto_reply=auto_reply)
member2 = agent(f"member2_{name}", auto_reply=auto_reply)
diff --git a/test/agentchat/test_nested.py b/test/agentchat/test_nested.py
index 24c86a7fed..f47371628f 100755
--- a/test/agentchat/test_nested.py
+++ b/test/agentchat/test_nested.py
@@ -22,7 +22,7 @@
class MockAgentReplies(AgentCapability):
- def __init__(self, mock_messages: List[str]):
+ def __init__(self, mock_messages: list[str]):
self.mock_messages = mock_messages
self.mock_message_index = 0
diff --git a/test/agentchat/test_structured_output.py b/test/agentchat/test_structured_output.py
index d99b2a63d6..4c1c671cb7 100644
--- a/test/agentchat/test_structured_output.py
+++ b/test/agentchat/test_structured_output.py
@@ -73,7 +73,7 @@ class Step(BaseModel):
class MathReasoning(BaseModel):
- steps: List[Step]
+ steps: list[Step]
final_answer: str
def format(self) -> str:
diff --git a/test/coding/test_embedded_ipython_code_executor.py b/test/coding/test_embedded_ipython_code_executor.py
index e009981779..df0d315161 100644
--- a/test/coding/test_embedded_ipython_code_executor.py
+++ b/test/coding/test_embedded_ipython_code_executor.py
@@ -61,7 +61,7 @@ def test_is_code_executor(cls) -> None:
@pytest.mark.skipif(skip, reason=skip_reason)
def test_create_dict() -> None:
- config: Dict[str, Union[str, CodeExecutor]] = {"executor": "ipython-embedded"}
+ config: dict[str, Union[str, CodeExecutor]] = {"executor": "ipython-embedded"}
executor = CodeExecutorFactory.create(config)
assert isinstance(executor, EmbeddedIPythonCodeExecutor)
@@ -190,7 +190,7 @@ def test_save_image(cls) -> None:
@pytest.mark.skipif(skip, reason=skip_reason)
@pytest.mark.parametrize("cls", classes_to_test)
-def test_timeout_preserves_kernel_state(cls: Type[CodeExecutor]) -> None:
+def test_timeout_preserves_kernel_state(cls: type[CodeExecutor]) -> None:
executor = cls(timeout=1)
code_blocks = [CodeBlock(code="x = 123", language="python")]
code_result = executor.execute_code_blocks(code_blocks)
diff --git a/test/interop/langchain/test_langchain.py b/test/interop/langchain/test_langchain.py
index be0a2f6bfc..fe129a6e1c 100644
--- a/test/interop/langchain/test_langchain.py
+++ b/test/interop/langchain/test_langchain.py
@@ -8,16 +8,11 @@
import pytest
from conftest import reason, skip_openai
+from langchain.tools import tool as langchain_tool
from pydantic import BaseModel, Field
from autogen import AssistantAgent, UserProxyAgent
from autogen.interop import Interoperable
-
-if sys.version_info >= (3, 9):
- from langchain.tools import tool as langchain_tool
-else:
- langchain_tool = unittest.mock.MagicMock()
-
from autogen.interop.langchain import LangChainInteroperability
diff --git a/test/interop/pydantic_ai/test_pydantic_ai.py b/test/interop/pydantic_ai/test_pydantic_ai.py
index 2840cdbc9a..605e40923b 100644
--- a/test/interop/pydantic_ai/test_pydantic_ai.py
+++ b/test/interop/pydantic_ai/test_pydantic_ai.py
@@ -12,18 +12,11 @@
import pytest
from conftest import reason, skip_openai
from pydantic import BaseModel
+from pydantic_ai import RunContext
+from pydantic_ai.tools import Tool as PydanticAITool
from autogen import AssistantAgent, UserProxyAgent
from autogen.interop import Interoperable
-
-if sys.version_info >= (3, 9):
- from pydantic_ai import RunContext
- from pydantic_ai.tools import Tool as PydanticAITool
-
-else:
- RunContext = unittest.mock.MagicMock()
- PydanticAITool = unittest.mock.MagicMock()
-
from autogen.interop.pydantic_ai import PydanticAIInteroperability
@@ -104,7 +97,7 @@ def f(
tool=pydantic_ai_tool,
)
assert list(signature(g).parameters.keys()) == ["city", "date"]
- kwargs: Dict[str, Any] = {"city": "Zagreb", "date": "2021-01-01"}
+ kwargs: dict[str, Any] = {"city": "Zagreb", "date": "2021-01-01"}
assert g(**kwargs) == "Zagreb 2021-01-01 123"
def test_dependency_injection_with_retry(self) -> None:
diff --git a/test/interop/pydantic_ai/test_pydantic_ai_tool.py b/test/interop/pydantic_ai/test_pydantic_ai_tool.py
index f1ae38389e..0f4eb92577 100644
--- a/test/interop/pydantic_ai/test_pydantic_ai_tool.py
+++ b/test/interop/pydantic_ai/test_pydantic_ai_tool.py
@@ -6,15 +6,9 @@
import unittest
import pytest
+from pydantic_ai.tools import Tool as PydanticAITool
from autogen import AssistantAgent
-
-if sys.version_info >= (3, 9):
- from pydantic_ai.tools import Tool as PydanticAITool
-
-else:
- PydanticAITool = unittest.mock.MagicMock()
-
from autogen.interop.pydantic_ai.pydantic_ai_tool import PydanticAITool as AG2PydanticAITool
diff --git a/test/io/test_base.py b/test/io/test_base.py
index 8083f0d811..c4c77d8f66 100644
--- a/test/io/test_base.py
+++ b/test/io/test_base.py
@@ -35,9 +35,9 @@ def input(self, prompt: str = "", *, password: bool = False) -> str:
assert isinstance(IOStream.get_default(), IOConsole)
def test_get_default_on_new_thread(self) -> None:
- exceptions: List[Exception] = []
+ exceptions: list[Exception] = []
- def on_new_thread(exceptions: List[Exception] = exceptions) -> None:
+ def on_new_thread(exceptions: list[Exception] = exceptions) -> None:
try:
assert isinstance(IOStream.get_default(), IOConsole)
except Exception as e:
diff --git a/test/io/test_websockets.py b/test/io/test_websockets.py
index 6c4b4662e3..1c84eebc79 100644
--- a/test/io/test_websockets.py
+++ b/test/io/test_websockets.py
@@ -92,7 +92,7 @@ def test_chat(self) -> None:
success_dict = {"success": False}
- def on_connect(iostream: IOWebsockets, success_dict: Dict[str, bool] = success_dict) -> None:
+ def on_connect(iostream: IOWebsockets, success_dict: dict[str, bool] = success_dict) -> None:
print(f" - on_connect(): Connected to client using IOWebsockets {iostream}", flush=True)
print(" - on_connect(): Receiving message from client.", flush=True)
diff --git a/test/oai/test_client_stream.py b/test/oai/test_client_stream.py
index abb7e18c72..1ce72e0e77 100755
--- a/test/oai/test_client_stream.py
+++ b/test/oai/test_client_stream.py
@@ -68,13 +68,13 @@ def test_chat_completion_stream() -> None:
def test__update_dict_from_chunk() -> None:
# dictionaries and lists are not supported
mock = MagicMock()
- empty_collections: List[Union[List[Any], Dict[str, Any]]] = [{}, []]
+ empty_collections: list[Union[list[Any], dict[str, Any]]] = [{}, []]
for c in empty_collections:
mock.c = c
with pytest.raises(NotImplementedError):
OpenAIWrapper._update_dict_from_chunk(mock, {}, "c")
- org_d: Dict[str, Any] = {}
+ org_d: dict[str, Any] = {}
for i, v in enumerate([0, 1, False, True, 0.0, 1.0]):
field = "abcedfghijklmnopqrstuvwxyz"[i]
setattr(mock, field, v)
@@ -186,7 +186,7 @@ def test__update_tool_calls_from_chunk() -> None:
),
]
- full_tool_calls: List[Optional[Dict[str, Any]]] = [None, None]
+ full_tool_calls: list[Optional[dict[str, Any]]] = [None, None]
completion_tokens = 0
for tool_calls_chunk in tool_calls_chunks:
index = tool_calls_chunk.index
diff --git a/test/oai/test_custom_client.py b/test/oai/test_custom_client.py
index 5976b7a46f..0e7fea224d 100644
--- a/test/oai/test_custom_client.py
+++ b/test/oai/test_custom_client.py
@@ -28,7 +28,7 @@ def test_custom_model_client():
TEST_MAX_LENGTH = 1000
class CustomModel:
- def __init__(self, config: Dict, test_hook):
+ def __init__(self, config: dict, test_hook):
self.test_hook = test_hook
self.device = config["device"]
self.model = config["model"]
@@ -63,7 +63,7 @@ def cost(self, response) -> float:
return TEST_COST
@staticmethod
- def get_usage(response) -> Dict:
+ def get_usage(response) -> dict:
return {}
config_list = [
@@ -96,7 +96,7 @@ def get_usage(response) -> Dict:
def test_registering_with_wrong_class_name_raises_error():
class CustomModel:
- def __init__(self, config: Dict):
+ def __init__(self, config: dict):
pass
def create(self, params):
@@ -109,7 +109,7 @@ def cost(self, response) -> float:
return 0
@staticmethod
- def get_usage(response) -> Dict:
+ def get_usage(response) -> dict:
return {}
config_list = [
@@ -126,7 +126,7 @@ def get_usage(response) -> Dict:
def test_not_all_clients_registered_raises_error():
class CustomModel:
- def __init__(self, config: Dict):
+ def __init__(self, config: dict):
pass
def create(self, params):
@@ -139,7 +139,7 @@ def cost(self, response) -> float:
return 0
@staticmethod
- def get_usage(response) -> Dict:
+ def get_usage(response) -> dict:
return {}
config_list = [
@@ -173,7 +173,7 @@ def get_usage(response) -> Dict:
def test_registering_with_extra_config_args():
class CustomModel:
- def __init__(self, config: Dict, test_hook):
+ def __init__(self, config: dict, test_hook):
self.test_hook = test_hook
self.test_hook["called"] = True
@@ -192,7 +192,7 @@ def cost(self, response) -> float:
return 0
@staticmethod
- def get_usage(response) -> Dict:
+ def get_usage(response) -> dict:
return {}
config_list = [
diff --git a/test/oai/test_utils.py b/test/oai/test_utils.py
index 599254b47d..1f8d1f4855 100755
--- a/test/oai/test_utils.py
+++ b/test/oai/test_utils.py
@@ -96,7 +96,7 @@
]
-def _compare_lists_of_dicts(list1: List[Dict], list2: List[Dict]) -> bool:
+def _compare_lists_of_dicts(list1: list[dict], list2: list[dict]) -> bool:
dump1 = sorted(json.dumps(d, sort_keys=True) for d in list1)
dump2 = sorted(json.dumps(d, sort_keys=True) for d in list2)
return dump1 == dump2
diff --git a/test/test_function_utils.py b/test/test_function_utils.py
index fce7e819b8..7563979d57 100644
--- a/test/test_function_utils.py
+++ b/test/test_function_utils.py
@@ -7,11 +7,10 @@
import asyncio
import inspect
import unittest.mock
-from typing import Any, Dict, List, Literal, Optional, Tuple
+from typing import Annotated, Any, Dict, List, Literal, Optional, Tuple
import pytest
from pydantic import BaseModel, Field
-from typing_extensions import Annotated
from autogen._pydantic import PYDANTIC_V1, model_dump
from autogen.function_utils import (
@@ -40,7 +39,7 @@ def g( # type: ignore[empty-body]
b: int = 2,
c: Annotated[float, "Parameter c"] = 0.1,
*,
- d: Dict[str, Tuple[Optional[int], List[float]]],
+ d: dict[str, tuple[Optional[int], list[float]]],
) -> str:
pass
@@ -50,7 +49,7 @@ async def a_g( # type: ignore[empty-body]
b: int = 2,
c: Annotated[float, "Parameter c"] = 0.1,
*,
- d: Dict[str, Tuple[Optional[int], List[float]]],
+ d: dict[str, tuple[Optional[int], list[float]]],
) -> str:
pass
@@ -89,7 +88,7 @@ class B(BaseModel):
b: float
c: str
- expected: Dict[str, Any] = {
+ expected: dict[str, Any] = {
"description": "b",
"properties": {"b": {"title": "B", "type": "number"}, "c": {"title": "C", "type": "string"}},
"required": ["b", "c"],
@@ -367,7 +366,7 @@ def test_load_basemodels_if_needed_sync() -> None:
def f(
base: Annotated[Currency, "Base currency"],
quote_currency: Annotated[CurrencySymbol, "Quote currency"] = "EUR",
- ) -> Tuple[Currency, CurrencySymbol]:
+ ) -> tuple[Currency, CurrencySymbol]:
return base, quote_currency
assert not inspect.iscoroutinefunction(f)
@@ -385,7 +384,7 @@ async def test_load_basemodels_if_needed_async() -> None:
async def f(
base: Annotated[Currency, "Base currency"],
quote_currency: Annotated[CurrencySymbol, "Quote currency"] = "EUR",
- ) -> Tuple[Currency, CurrencySymbol]:
+ ) -> tuple[Currency, CurrencySymbol]:
return base, quote_currency
assert inspect.iscoroutinefunction(f)
diff --git a/test/test_logging.py b/test/test_logging.py
index ca2db497ee..f055850953 100644
--- a/test/test_logging.py
+++ b/test/test_logging.py
@@ -264,7 +264,7 @@ def __init__(self):
self.extra_key = "remove this key"
self.path = Path("/to/something")
- class Bar(object):
+ class Bar:
def init(self):
pass
diff --git a/test/test_pydantic.py b/test/test_pydantic.py
index 256b30e335..0006a605b0 100644
--- a/test/test_pydantic.py
+++ b/test/test_pydantic.py
@@ -4,10 +4,9 @@
#
# Portions derived from https://github.com/microsoft/autogen are under the MIT License.
# SPDX-License-Identifier: MIT
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Annotated, Dict, List, Optional, Tuple, Union
from pydantic import BaseModel, Field
-from typing_extensions import Annotated
from autogen._pydantic import model_dump, model_dump_json, type2schema
@@ -19,14 +18,14 @@ def test_type2schema() -> None:
assert type2schema(bool) == {"type": "boolean"}
assert type2schema(None) == {"type": "null"}
assert type2schema(Optional[int]) == {"anyOf": [{"type": "integer"}, {"type": "null"}]}
- assert type2schema(List[int]) == {"items": {"type": "integer"}, "type": "array"}
- assert type2schema(Tuple[int, float, str]) == {
+ assert type2schema(list[int]) == {"items": {"type": "integer"}, "type": "array"}
+ assert type2schema(tuple[int, float, str]) == {
"maxItems": 3,
"minItems": 3,
"prefixItems": [{"type": "integer"}, {"type": "number"}, {"type": "string"}],
"type": "array",
}
- assert type2schema(Dict[str, int]) == {"additionalProperties": {"type": "integer"}, "type": "object"}
+ assert type2schema(dict[str, int]) == {"additionalProperties": {"type": "integer"}, "type": "object"}
assert type2schema(Annotated[str, "some text"]) == {"type": "string"}
assert type2schema(Union[int, float]) == {"anyOf": [{"type": "integer"}, {"type": "number"}]}
diff --git a/website/README.md b/website/README.md
index 22a4e10d6a..8bf386b0f2 100644
--- a/website/README.md
+++ b/website/README.md
@@ -9,7 +9,7 @@ To build and test documentation locally, begin by downloading and installing [No
## Installation
```console
-pip install pydoc-markdown pyyaml colored
+pip install pydoc-markdown pyyaml termcolor nbconvert
```
### Install Quarto
@@ -25,7 +25,7 @@ Install it [here](https://github.com/quarto-dev/quarto-cli/releases).
Navigate to the `website` folder and run:
```console
-pydoc-markdown
+python ./process_api_reference.py
python ./process_notebooks.py render
npm install
```
diff --git a/website/blog/2023-04-21-LLM-tuning-math/index.mdx b/website/blog/2023-04-21-LLM-tuning-math/index.mdx
index a5378af6cf..c8c97f9308 100644
--- a/website/blog/2023-04-21-LLM-tuning-math/index.mdx
+++ b/website/blog/2023-04-21-LLM-tuning-math/index.mdx
@@ -32,7 +32,7 @@ We adapt the models using 20 examples in the train set, using the problem statem
- top_p: The parameter that controls the probability mass of the output tokens. Only tokens with a cumulative probability less than or equal to top-p are considered. A lower top-p means more diversity but less coherence. We search for the optimal top-p in the range of [0, 1].
- max_tokens: The maximum number of tokens that can be generated for each output. We search for the optimal max length in the range of [50, 1000].
- n: The number of responses to generate. We search for the optimal n in the range of [1, 100].
-- prompt: We use the template: "{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\boxed{{}}." where {problem} will be replaced by the math problem instance.
+- prompt: We use the template: "\{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\boxed\{{}}." where \{problem} will be replaced by the math problem instance.
In this experiment, when n > 1, we find the answer with highest votes among all the responses and then select it as the final answer to compare with the ground truth. For example, if n = 5 and 3 of the responses contain a final answer 301 while 2 of the responses contain a final answer 159, we choose 301 as the final answer. This can help with resolving potential errors due to randomness. We use the average accuracy and average inference cost as the metric to evaluate the performance over a dataset. The inference cost of a particular instance is measured by the price per 1K tokens and the number of tokens consumed.
diff --git a/website/blog/2024-12-18-RealtimeAgent/img/1_service_running.png b/website/blog/2024-12-20-RealtimeAgent/img/1_service_running.png
similarity index 100%
rename from website/blog/2024-12-18-RealtimeAgent/img/1_service_running.png
rename to website/blog/2024-12-20-RealtimeAgent/img/1_service_running.png
diff --git a/website/blog/2024-12-18-RealtimeAgent/img/2_incoming_call.png b/website/blog/2024-12-20-RealtimeAgent/img/2_incoming_call.png
similarity index 100%
rename from website/blog/2024-12-18-RealtimeAgent/img/2_incoming_call.png
rename to website/blog/2024-12-20-RealtimeAgent/img/2_incoming_call.png
diff --git a/website/blog/2024-12-18-RealtimeAgent/img/3_request_for_flight_cancellation.png b/website/blog/2024-12-20-RealtimeAgent/img/3_request_for_flight_cancellation.png
similarity index 100%
rename from website/blog/2024-12-18-RealtimeAgent/img/3_request_for_flight_cancellation.png
rename to website/blog/2024-12-20-RealtimeAgent/img/3_request_for_flight_cancellation.png
diff --git a/website/blog/2024-12-18-RealtimeAgent/img/4_flight_number_name.png b/website/blog/2024-12-20-RealtimeAgent/img/4_flight_number_name.png
similarity index 100%
rename from website/blog/2024-12-18-RealtimeAgent/img/4_flight_number_name.png
rename to website/blog/2024-12-20-RealtimeAgent/img/4_flight_number_name.png
diff --git a/website/blog/2024-12-18-RealtimeAgent/img/5_refund_policy.png b/website/blog/2024-12-20-RealtimeAgent/img/5_refund_policy.png
similarity index 100%
rename from website/blog/2024-12-18-RealtimeAgent/img/5_refund_policy.png
rename to website/blog/2024-12-20-RealtimeAgent/img/5_refund_policy.png
diff --git a/website/blog/2024-12-18-RealtimeAgent/img/6_flight_refunded.png b/website/blog/2024-12-20-RealtimeAgent/img/6_flight_refunded.png
similarity index 100%
rename from website/blog/2024-12-18-RealtimeAgent/img/6_flight_refunded.png
rename to website/blog/2024-12-20-RealtimeAgent/img/6_flight_refunded.png
diff --git a/website/blog/2024-12-18-RealtimeAgent/img/realtime_agent_swarm.png b/website/blog/2024-12-20-RealtimeAgent/img/realtime_agent_swarm.png
similarity index 100%
rename from website/blog/2024-12-18-RealtimeAgent/img/realtime_agent_swarm.png
rename to website/blog/2024-12-20-RealtimeAgent/img/realtime_agent_swarm.png
diff --git a/website/blog/2024-12-18-RealtimeAgent/index.mdx b/website/blog/2024-12-20-RealtimeAgent/index.mdx
similarity index 100%
rename from website/blog/2024-12-18-RealtimeAgent/index.mdx
rename to website/blog/2024-12-20-RealtimeAgent/index.mdx
diff --git a/website/blog/2024-12-18-Reasoning-Update/img/reasoningagent_1.png b/website/blog/2024-12-20-Reasoning-Update/img/reasoningagent_1.png
similarity index 100%
rename from website/blog/2024-12-18-Reasoning-Update/img/reasoningagent_1.png
rename to website/blog/2024-12-20-Reasoning-Update/img/reasoningagent_1.png
diff --git a/website/blog/2024-12-18-Reasoning-Update/index.mdx b/website/blog/2024-12-20-Reasoning-Update/index.mdx
similarity index 100%
rename from website/blog/2024-12-18-Reasoning-Update/index.mdx
rename to website/blog/2024-12-20-Reasoning-Update/index.mdx
diff --git a/website/blog/2024-12-18-Tools-interoperability/index.mdx b/website/blog/2024-12-20-Tools-interoperability/index.mdx
similarity index 100%
rename from website/blog/2024-12-18-Tools-interoperability/index.mdx
rename to website/blog/2024-12-20-Tools-interoperability/index.mdx
diff --git a/website/docs/Use-Cases/enhanced_inference.mdx b/website/docs/Use-Cases/enhanced_inference.mdx
index 0b838a92f9..f2bba10f01 100644
--- a/website/docs/Use-Cases/enhanced_inference.mdx
+++ b/website/docs/Use-Cases/enhanced_inference.mdx
@@ -241,7 +241,7 @@ response = client.create(
The example above will try to use text-ada-001, gpt-3.5-turbo-instruct, and text-davinci-003 iteratively, until a valid json string is returned or the last config is used. One can also repeat the same model in the list for multiple times (with different seeds) to try one model multiple times for increasing the robustness of the final response.
-*Advanced use case: Check this [blogpost](/blog/2023/05/18/GPT-adaptive-humaneval) to find how to improve GPT-4's coding performance from 68% to 90% while reducing the inference cost.*
+*Advanced use case: Check this [blogpost](/blog/2023-05-18-GPT-adaptive-humaneval/index) to find how to improve GPT-4's coding performance from 68% to 90% while reducing the inference cost.*
## Templating
diff --git a/website/docs/installation/Optional-Dependencies.mdx b/website/docs/installation/Optional-Dependencies.mdx
index f7b776fbc0..33c6891d4c 100644
--- a/website/docs/installation/Optional-Dependencies.mdx
+++ b/website/docs/installation/Optional-Dependencies.mdx
@@ -7,7 +7,7 @@ pip install autogen[gemini,anthropic,mistral,together,groq,cohere]
```
Check out the [notebook](/notebooks/autogen_uniformed_api_calling) and
-[blogpost](/blog/2024/06/24/AltModels-Classes) for more details.
+[blogpost](/blog/2024-06-24-AltModels-Classes/index) for more details.
## LLM Caching
diff --git a/website/docs/topics/non-openai-models/about-using-nonopenai-models.mdx b/website/docs/topics/non-openai-models/about-using-nonopenai-models.mdx
index c71f11183b..7c966973b7 100644
--- a/website/docs/topics/non-openai-models/about-using-nonopenai-models.mdx
+++ b/website/docs/topics/non-openai-models/about-using-nonopenai-models.mdx
@@ -77,5 +77,5 @@ to assign specific models to agents.
For more advanced users, you can create your own custom model client class, enabling
you to define and load your own models.
-See the [AutoGen with Custom Models: Empowering Users to Use Their Own Inference Mechanism](/blog/2024/01/26/Custom-Models)
-blog post and [this notebook](/docs/notebooks/agentchat_custom_model/) for a guide to creating custom model client classes.
+See the [AutoGen with Custom Models: Empowering Users to Use Their Own Inference Mechanism](/blog/2024-01-26-Custom-Models/index)
+blog post and [this notebook](/notebooks/agentchat_custom_model/) for a guide to creating custom model client classes.
diff --git a/website/docs/tutorial/introduction.ipynb b/website/docs/tutorial/introduction.ipynb
index 92876d2554..cb95acf1d4 100644
--- a/website/docs/tutorial/introduction.ipynb
+++ b/website/docs/tutorial/introduction.ipynb
@@ -65,7 +65,7 @@
"\n",
"You can switch each component on or off and customize it to suit the need of \n",
"your application. For advanced users, you can add additional components to the agent\n",
- "by using [`registered_reply`](../reference/agentchat/conversable_agent/#register_reply). "
+ "by using [`registered_reply`](../reference/agentchat/conversable_agent/#register-reply). "
]
},
{
diff --git a/website/docs/tutorial/tool-use.ipynb b/website/docs/tutorial/tool-use.ipynb
index 1659029c32..0dc305b865 100644
--- a/website/docs/tutorial/tool-use.ipynb
+++ b/website/docs/tutorial/tool-use.ipynb
@@ -163,10 +163,10 @@
"for it to be useful in conversation. \n",
"The agent registered with the tool's signature\n",
"through \n",
- "[`register_for_llm`](/docs/reference/agentchat/conversable_agent#register_for_llm)\n",
+ "[`register_for_llm`](/docs/reference/agentchat/conversable_agent#register-for-llm)\n",
"can call the tool;\n",
"the agent registered with the tool's function object through \n",
- "[`register_for_execution`](/docs/reference/agentchat/conversable_agent#register_for_execution)\n",
+ "[`register_for_execution`](/docs/reference/agentchat/conversable_agent#register-for-execution)\n",
"can execute the tool's function."
]
},
@@ -175,7 +175,7 @@
"metadata": {},
"source": [
"Alternatively, you can use \n",
- "[`autogen.register_function`](/docs/reference/agentchat/conversable_agent#register_function-1)\n",
+ "[`autogen.register_function`](/docs/reference/agentchat/conversable_agent#register-function)\n",
"function to register a tool with both agents at once."
]
},
diff --git a/website/docs/tutorial/what-next.mdx b/website/docs/tutorial/what-next.mdx
index d73fc615db..194e507fa6 100644
--- a/website/docs/tutorial/what-next.mdx
+++ b/website/docs/tutorial/what-next.mdx
@@ -28,7 +28,7 @@ topics:
## Dig Deeper
- Read the [user guide](/docs/topics) to learn more
-- Read the examples and guides in the [notebooks section](/docs/notebooks)
+- Read the examples and guides in the [notebooks section](/notebooks)
- Check [research](/docs/Research) and [blog](/blog)
## Get Help
diff --git a/website/mint-style.css b/website/mint-style.css
index 25e8388dce..7cc309c825 100644
--- a/website/mint-style.css
+++ b/website/mint-style.css
@@ -38,7 +38,7 @@ h4 {
h1 {
font-size: 3rem !important;
-
+ word-break: break-all;
code {
font-size: 2rem !important;
}
diff --git a/website/mint.json b/website/mint.json
index 5ee80c8390..f1ff688628 100644
--- a/website/mint.json
+++ b/website/mint.json
@@ -300,6 +300,17 @@
"docs/reference/agentchat/contrib/web_surfer"
]
},
+ {
+ "group": "agentchat.realtime_agent",
+ "pages": [
+ "docs/reference/agentchat/realtime_agent/client",
+ "docs/reference/agentchat/realtime_agent/function_observer",
+ "docs/reference/agentchat/realtime_agent/realtime_agent",
+ "docs/reference/agentchat/realtime_agent/realtime_observer",
+ "docs/reference/agentchat/realtime_agent/twilio_observer",
+ "docs/reference/agentchat/realtime_agent/websocket_observer"
+ ]
+ },
"docs/reference/agentchat/agent",
"docs/reference/agentchat/assistant_agent",
"docs/reference/agentchat/chat",
@@ -344,6 +355,33 @@
"docs/reference/coding/utils"
]
},
+ {
+ "group": "interop",
+ "pages": [
+ {
+ "group": "interop.crewai",
+ "pages": [
+ "docs/reference/interop/crewai/crewai"
+ ]
+ },
+ {
+ "group": "interop.langchain",
+ "pages": [
+ "docs/reference/interop/langchain/langchain"
+ ]
+ },
+ {
+ "group": "interop.pydantic_ai",
+ "pages": [
+ "docs/reference/interop/pydantic_ai/pydantic_ai",
+ "docs/reference/interop/pydantic_ai/pydantic_ai_tool"
+ ]
+ },
+ "docs/reference/interop/interoperability",
+ "docs/reference/interop/interoperable",
+ "docs/reference/interop/registry"
+ ]
+ },
{
"group": "io",
"pages": [
@@ -377,6 +415,12 @@
"docs/reference/oai/together"
]
},
+ {
+ "group": "tools",
+ "pages": [
+ "docs/reference/tools/tool"
+ ]
+ },
"docs/reference/browser_utils",
"docs/reference/code_utils",
"docs/reference/exception_utils",
@@ -450,6 +494,9 @@
{
"group": "Recent posts",
"pages": [
+ "blog/2024-12-20-RealtimeAgent/index",
+ "blog/2024-12-20-Tools-interoperability/index",
+ "blog/2024-12-20-Reasoning-Update/index",
"blog/2024-12-06-FalkorDB-Structured/index",
"blog/2024-12-02-ReasoningAgent2/index",
"blog/2024-11-27-Prompt-Leakage-Probing/index",
@@ -571,7 +618,9 @@
"notebooks/JSON_mode_example",
"notebooks/agentchat_RetrieveChat",
"notebooks/agentchat_graph_rag_neo4j",
- "notebooks/agentchat_swarm_enhanced"
+ "notebooks/agentchat_swarm_enhanced",
+ "notebooks/agentchat_realtime_swarm",
+ "notebooks/agentchat_reasoning_agent"
]
},
"notebooks/Gallery"
diff --git a/website/process_api_reference.py b/website/process_api_reference.py
index 9652b8d7fe..3405f360fc 100644
--- a/website/process_api_reference.py
+++ b/website/process_api_reference.py
@@ -55,7 +55,7 @@ def read_file_content(file_path: str) -> str:
Returns:
str: Content of the file
"""
- with open(file_path, "r", encoding="utf-8") as f:
+ with open(file_path, encoding="utf-8") as f:
return f.read()
@@ -100,18 +100,18 @@ def convert_md_to_mdx(input_dir: Path) -> None:
print(f"Converted: {md_file} -> {mdx_file}")
-def get_mdx_files(directory: Path) -> List[str]:
+def get_mdx_files(directory: Path) -> list[str]:
"""Get all MDX files in directory and subdirectories."""
return [f"{str(p.relative_to(directory).with_suffix(''))}".replace("\\", "/") for p in directory.rglob("*.mdx")]
-def add_prefix(path: str, parent_groups: List[str] = None) -> str:
+def add_prefix(path: str, parent_groups: list[str] = None) -> str:
"""Create full path with prefix and parent groups."""
groups = parent_groups or []
return f"docs/reference/{'/'.join(groups + [path])}"
-def create_nav_structure(paths: List[str], parent_groups: List[str] = None) -> List[Any]:
+def create_nav_structure(paths: list[str], parent_groups: list[str] = None) -> list[Any]:
"""Convert list of file paths into nested navigation structure."""
groups = {}
pages = []
@@ -142,7 +142,7 @@ def create_nav_structure(paths: List[str], parent_groups: List[str] = None) -> L
return sorted_groups + sorted_pages
-def update_nav(mint_json_path: Path, new_nav_pages: List[Any]) -> None:
+def update_nav(mint_json_path: Path, new_nav_pages: list[Any]) -> None:
"""
Update the 'API Reference' section in mint.json navigation with new pages.
@@ -152,7 +152,7 @@ def update_nav(mint_json_path: Path, new_nav_pages: List[Any]) -> None:
"""
try:
# Read the current mint.json
- with open(mint_json_path, "r") as f:
+ with open(mint_json_path) as f:
mint_config = json.load(f)
# Find and update the API Reference section
diff --git a/website/process_notebooks.py b/website/process_notebooks.py
index 0e5b903f77..797ced4d91 100755
--- a/website/process_notebooks.py
+++ b/website/process_notebooks.py
@@ -81,12 +81,12 @@ def notebooks_target_dir(website_directory: Path) -> Path:
return website_directory / "notebooks"
-def load_metadata(notebook: Path) -> typing.Dict:
+def load_metadata(notebook: Path) -> dict:
content = json.load(notebook.open(encoding="utf-8"))
return content["metadata"]
-def skip_reason_or_none_if_ok(notebook: Path) -> typing.Optional[str]:
+def skip_reason_or_none_if_ok(notebook: Path) -> str | None:
"""Return a reason to skip the notebook, or None if it should not be skipped."""
if notebook.suffix != ".ipynb":
@@ -99,7 +99,7 @@ def skip_reason_or_none_if_ok(notebook: Path) -> typing.Optional[str]:
if "notebook" not in notebook.parts:
return None
- with open(notebook, "r", encoding="utf-8") as f:
+ with open(notebook, encoding="utf-8") as f:
content = f.read()
# Load the json and get the first cell
@@ -139,9 +139,9 @@ def skip_reason_or_none_if_ok(notebook: Path) -> typing.Optional[str]:
return None
-def extract_title(notebook: Path) -> Optional[str]:
+def extract_title(notebook: Path) -> str | None:
"""Extract the title of the notebook."""
- with open(notebook, "r", encoding="utf-8") as f:
+ with open(notebook, encoding="utf-8") as f:
content = f.read()
# Load the json and get the first cell
@@ -202,9 +202,7 @@ def process_notebook(src_notebook: Path, website_dir: Path, notebook_dir: Path,
shutil.copy(src_notebook.parent / file, dest_dir / file)
# Capture output
- result = subprocess.run(
- [quarto_bin, "render", intermediate_notebook], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
- )
+ result = subprocess.run([quarto_bin, "render", intermediate_notebook], capture_output=True, text=True)
if result.returncode != 0:
return fmt_error(
src_notebook, f"Failed to render {src_notebook}\n\nstderr:\n{result.stderr}\nstdout:\n{result.stdout}"
@@ -223,9 +221,7 @@ def process_notebook(src_notebook: Path, website_dir: Path, notebook_dir: Path,
if dry_run:
return colored(f"Would process {src_notebook.name}", "green")
- result = subprocess.run(
- [quarto_bin, "render", src_notebook], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
- )
+ result = subprocess.run([quarto_bin, "render", src_notebook], capture_output=True, text=True)
if result.returncode != 0:
return fmt_error(
src_notebook, f"Failed to render {src_notebook}\n\nstderr:\n{result.stderr}\nstdout:\n{result.stdout}"
@@ -240,7 +236,7 @@ def process_notebook(src_notebook: Path, website_dir: Path, notebook_dir: Path,
@dataclass
class NotebookError:
error_name: str
- error_value: Optional[str]
+ error_value: str | None
traceback: str
cell_source: str
@@ -253,7 +249,7 @@ class NotebookSkip:
NB_VERSION = 4
-def test_notebook(notebook_path: Path, timeout: int = 300) -> Tuple[Path, Optional[Union[NotebookError, NotebookSkip]]]:
+def test_notebook(notebook_path: Path, timeout: int = 300) -> tuple[Path, NotebookError | NotebookSkip | None]:
nb = nbformat.read(str(notebook_path), NB_VERSION)
if "skip_test" in nb.metadata:
@@ -285,7 +281,7 @@ def test_notebook(notebook_path: Path, timeout: int = 300) -> Tuple[Path, Option
# Find the first code cell which did not complete.
def get_timeout_info(
nb: NotebookNode,
-) -> Optional[NotebookError]:
+) -> NotebookError | None:
for i, cell in enumerate(nb.cells):
if cell.cell_type != "code":
continue
@@ -300,7 +296,7 @@ def get_timeout_info(
return None
-def get_error_info(nb: NotebookNode) -> Optional[NotebookError]:
+def get_error_info(nb: NotebookNode) -> NotebookError | None:
for cell in nb["cells"]: # get LAST error
if cell["cell_type"] != "code":
continue
@@ -318,13 +314,13 @@ def get_error_info(nb: NotebookNode) -> Optional[NotebookError]:
def add_front_matter_to_metadata_mdx(
- front_matter: Dict[str, Union[str, List[str]]], website_dir: Path, rendered_mdx: Path
+ front_matter: dict[str, str | list[str]], website_dir: Path, rendered_mdx: Path
) -> None:
metadata_mdx = website_dir / "snippets" / "data" / "NotebooksMetadata.mdx"
metadata = []
if metadata_mdx.exists():
- with open(metadata_mdx, "r", encoding="utf-8") as f:
+ with open(metadata_mdx, encoding="utf-8") as f:
content = f.read()
if content:
start = content.find("export const notebooksMetadata = [")
@@ -384,8 +380,8 @@ def resolve_path(match):
# rendered_notebook is the final mdx file
-def post_process_mdx(rendered_mdx: Path, source_notebooks: Path, front_matter: Dict, website_dir: Path) -> None:
- with open(rendered_mdx, "r", encoding="utf-8") as f:
+def post_process_mdx(rendered_mdx: Path, source_notebooks: Path, front_matter: dict, website_dir: Path) -> None:
+ with open(rendered_mdx, encoding="utf-8") as f:
content = f.read()
# If there is front matter in the mdx file, we need to remove it
@@ -465,7 +461,7 @@ def path(path_str: str) -> Path:
return Path(path_str)
-def collect_notebooks(notebook_directory: Path, website_directory: Path) -> typing.List[Path]:
+def collect_notebooks(notebook_directory: Path, website_directory: Path) -> list[Path]:
notebooks = list(notebook_directory.glob("*.ipynb"))
notebooks.extend(list(website_directory.glob("docs/**/*.ipynb")))
return notebooks
@@ -479,7 +475,7 @@ def fmt_ok(notebook: Path) -> str:
return f"{colored('[OK]', 'green')} {colored(notebook.name, 'blue')} ✅"
-def fmt_error(notebook: Path, error: Union[NotebookError, str]) -> str:
+def fmt_error(notebook: Path, error: NotebookError | str) -> str:
if isinstance(error, str):
return f"{colored('[Error]', 'red')} {colored(notebook.name, 'blue')}: {error}"
elif isinstance(error, NotebookError):
@@ -538,11 +534,11 @@ def update_navigation_with_notebooks(website_dir: Path) -> None:
return
# Read mint.json
- with open(mint_json_path, "r", encoding="utf-8") as f:
+ with open(mint_json_path, encoding="utf-8") as f:
mint_config = json.load(f)
# Read NotebooksMetadata.mdx and extract metadata links
- with open(metadata_path, "r", encoding="utf-8") as f:
+ with open(metadata_path, encoding="utf-8") as f:
content = f.read()
# Extract the array between the brackets
start = content.find("export const notebooksMetadata = [")
@@ -622,7 +618,7 @@ def fix_internal_references_in_mdx_files(website_dir: Path) -> None:
"""Process all MDX files in directory to fix internal references."""
for file_path in website_dir.glob("**/*.mdx"):
try:
- with open(file_path, "r", encoding="utf-8") as f:
+ with open(file_path, encoding="utf-8") as f:
content = f.read()
fixed_content = fix_internal_references(content, website_dir, file_path)
diff --git a/website/snippets/data/NotebooksMetadata.mdx b/website/snippets/data/NotebooksMetadata.mdx
index 36cc6b28ae..51783730a4 100644
--- a/website/snippets/data/NotebooksMetadata.mdx
+++ b/website/snippets/data/NotebooksMetadata.mdx
@@ -1003,5 +1003,212 @@ export const notebooksMetadata = [
"image": null,
"tags": [],
"source": "/website/docs/topics/non-openai-models/cloud-cerebras.ipynb"
+ },
+ {
+ "title": "RealtimeAgent in a Swarm Orchestration",
+ "link": "/notebooks/agentchat_realtime_swarm",
+ "description": "Swarm Ochestration",
+ "image": null,
+ "tags": [
+ "orchestration",
+ "group chat",
+ "swarm"
+ ],
+ "source": "/notebook/agentchat_realtime_swarm.ipynb"
+ },
+ {
+ "title": "ReasoningAgent - Advanced LLM Reasoning with Multiple Search Strategies",
+ "link": "/notebooks/agentchat_reasoning_agent",
+ "description": "Use ReasoningAgent for o1 style reasoning in Agentic workflows with LLMs using AG2",
+ "image": null,
+ "tags": [
+ "reasoning agent",
+ "tree of thoughts"
+ ],
+ "source": "/notebook/agentchat_reasoning_agent.ipynb"
+ },
+ {
+ "title": "LLM Configuration",
+ "link": "/notebooks/llm_configuration",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/llm_configuration.ipynb"
+ },
+ {
+ "title": "Command Line Code Executor",
+ "link": "/notebooks/cli-code-executor",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/code-execution/cli-code-executor.ipynb"
+ },
+ {
+ "title": "Custom Code Executor",
+ "link": "/notebooks/custom-executor",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/code-execution/custom-executor.ipynb"
+ },
+ {
+ "title": "Jupyter Code Executor",
+ "link": "/notebooks/jupyter-code-executor",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/code-execution/jupyter-code-executor.ipynb"
+ },
+ {
+ "title": "User Defined Functions",
+ "link": "/notebooks/user-defined-functions",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/code-execution/user-defined-functions.ipynb"
+ },
+ {
+ "title": "Customize Speaker Selection",
+ "link": "/notebooks/customized_speaker_selection",
+ "description": "Custom Speaker Selection Function",
+ "image": null,
+ "tags": [
+ "orchestration",
+ "group chat"
+ ],
+ "source": "/website/docs/topics/groupchat/customized_speaker_selection.ipynb"
+ },
+ {
+ "title": "Resuming a GroupChat",
+ "link": "/notebooks/resuming_groupchat",
+ "description": "Resume Group Chat",
+ "image": null,
+ "tags": [
+ "resume",
+ "orchestration",
+ "group chat"
+ ],
+ "source": "/website/docs/topics/groupchat/resuming_groupchat.ipynb"
+ },
+ {
+ "title": "Using Transform Messages during Speaker Selection",
+ "link": "/notebooks/transform_messages_speaker_selection",
+ "description": "Custom Speaker Selection Function",
+ "image": null,
+ "tags": [
+ "orchestration",
+ "long context handling",
+ "group chat"
+ ],
+ "source": "/website/docs/topics/groupchat/transform_messages_speaker_selection.ipynb"
+ },
+ {
+ "title": "Using Custom Model Client classes with Auto Speaker Selection",
+ "link": "/notebooks/using_custom_model_client_classes",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/groupchat/using_custom_model_client_classes.ipynb"
+ },
+ {
+ "title": "Cohere",
+ "link": "/notebooks/cloud-cohere",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/non-openai-models/cloud-cohere.ipynb"
+ },
+ {
+ "title": "Using Gemini in AutoGen with Other LLMs",
+ "link": "/notebooks/cloud-gemini",
+ "description": "Using Gemini with AutoGen",
+ "image": null,
+ "tags": [
+ "gemini"
+ ],
+ "source": "/website/docs/topics/non-openai-models/cloud-gemini.ipynb"
+ },
+ {
+ "title": "Use AutoGen with Gemini via VertexAI",
+ "link": "/notebooks/cloud-gemini_vertexai",
+ "description": "Using Gemini with AutoGen via VertexAI",
+ "image": null,
+ "tags": [
+ "gemini",
+ "vertexai"
+ ],
+ "source": "/website/docs/topics/non-openai-models/cloud-gemini_vertexai.ipynb"
+ },
+ {
+ "title": "Groq",
+ "link": "/notebooks/cloud-groq",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/non-openai-models/cloud-groq.ipynb"
+ },
+ {
+ "title": "Mistral AI",
+ "link": "/notebooks/cloud-mistralai",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/non-openai-models/cloud-mistralai.ipynb"
+ },
+ {
+ "title": "Together.AI",
+ "link": "/notebooks/cloud-togetherai",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/non-openai-models/cloud-togetherai.ipynb"
+ },
+ {
+ "title": "LiteLLM with Ollama",
+ "link": "/notebooks/local-litellm-ollama",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/non-openai-models/local-litellm-ollama.ipynb"
+ },
+ {
+ "title": "LM Studio",
+ "link": "/notebooks/local-lm-studio",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/non-openai-models/local-lm-studio.ipynb"
+ },
+ {
+ "title": "Ollama",
+ "link": "/notebooks/local-ollama",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/non-openai-models/local-ollama.ipynb"
+ },
+ {
+ "title": "LLM Reflection",
+ "link": "/notebooks/reflection",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/topics/prompting-and-reasoning/reflection.ipynb"
+ },
+ {
+ "title": "Terminating Conversations Between Agents",
+ "link": "/notebooks/chat-termination",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/tutorial/chat-termination.ipynb"
+ },
+ {
+ "title": "Code Executors",
+ "link": "/notebooks/code-executors",
+ "description": "",
+ "image": null,
+ "tags": [],
+ "source": "/website/docs/tutorial/code-executors.ipynb"
}
];