From ea03b600c9709ba951fdd47e757f07c522891b16 Mon Sep 17 00:00:00 2001 From: Raphael Jin Date: Mon, 10 Jun 2024 17:21:10 -0700 Subject: [PATCH] [Deploy] Refactor the quick start example, use public ip as default. --- .../examples/deploy/quick_start/config.yaml | 21 +- .../examples/deploy/quick_start/main_entry.py | 27 ++ .../deploy/quick_start/src/__init__.py | 0 .../deploy/quick_start/src/app/__init__.py | 0 .../quick_start/src/app/pipe/__init__.py | 0 .../quick_start/src/app/pipe/constants.py | 68 ----- .../src/app/pipe/instruct_pipeline.py | 261 ------------------ .../quick_start/src/config/bootstrap.sh | 14 - .../deploy/quick_start/src/main_entry.py | 67 ----- python/fedml/api/modules/model.py | 3 + .../device_client_runner_deprecated.py} | 0 .../model_scheduler/sample_model/README.md | 57 ---- .../sample_model/fedml_model.bin | Bin 1476451 -> 0 bytes .../sample_model/fedml_model_config.yaml | 20 -- .../scheduler_core/general_constants.py | 16 +- 15 files changed, 42 insertions(+), 512 deletions(-) create mode 100644 python/examples/deploy/quick_start/main_entry.py delete mode 100644 python/examples/deploy/quick_start/src/__init__.py delete mode 100644 python/examples/deploy/quick_start/src/app/__init__.py delete mode 100644 python/examples/deploy/quick_start/src/app/pipe/__init__.py delete mode 100644 python/examples/deploy/quick_start/src/app/pipe/constants.py delete mode 100644 python/examples/deploy/quick_start/src/app/pipe/instruct_pipeline.py delete mode 100644 python/examples/deploy/quick_start/src/config/bootstrap.sh delete mode 100644 python/examples/deploy/quick_start/src/main_entry.py rename python/{examples/deploy/quick_start/__init__.py => fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py} (100%) mode change 100644 => 100755 delete mode 100644 python/fedml/computing/scheduler/model_scheduler/sample_model/README.md delete mode 100644 python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model.bin delete mode 100644 python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model_config.yaml diff --git a/python/examples/deploy/quick_start/config.yaml b/python/examples/deploy/quick_start/config.yaml index 83479068e6..880ea92d2d 100644 --- a/python/examples/deploy/quick_start/config.yaml +++ b/python/examples/deploy/quick_start/config.yaml @@ -1,21 +1,8 @@ -workspace: "./src" +workspace: "." entry_point: "main_entry.py" + # If you want to install some packages # Please write the command in the bootstrap.sh bootstrap: | - echo "Bootstrap start..." - sh ./config/bootstrap.sh - echo "Bootstrap finished" - -# If you do not have any GPU resource but want to serve the model -# Try FedML® Nexus AI Platform, and Uncomment the following lines. -# ------------------------------------------------------------ -computing: - minimum_num_gpus: 1 # minimum # of GPUs to provision - maximum_cost_per_hour: $3000 # max cost per hour for your job per gpu card - #allow_cross_cloud_resources: true # true, false - #device_type: CPU # options: GPU, CPU, hybrid - resource_type: A100-80G # e.g., A100-80G, - # please check the resource type list by "fedml show-resource-type" - # or visiting URL: https://open.fedml.ai/accelerator_resource_type -# ------------------------------------------------------------ + echo "Install some packages..." + echo "Install finished!" diff --git a/python/examples/deploy/quick_start/main_entry.py b/python/examples/deploy/quick_start/main_entry.py new file mode 100644 index 0000000000..7c4fb910b0 --- /dev/null +++ b/python/examples/deploy/quick_start/main_entry.py @@ -0,0 +1,27 @@ +from fedml.serving import FedMLPredictor +from fedml.serving import FedMLInferenceRunner + + +class Bot(FedMLPredictor): # Inherit FedMLClientPredictor + def __init__(self): + super().__init__() + + # --- Your model initialization code here --- + + # ------------------------------------------- + + def predict(self, request: dict): + input_dict = request + question: str = input_dict.get("text", "").strip() + + # --- Your model inference code here --- + response = "I do not know the answer to your question." + # --------------------------------------- + + return {"generated_text": f"The answer to your question {question} is: {response}"} + + +if __name__ == "__main__": + chatbot = Bot() + fedml_inference_runner = FedMLInferenceRunner(chatbot) + fedml_inference_runner.run() diff --git a/python/examples/deploy/quick_start/src/__init__.py b/python/examples/deploy/quick_start/src/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/python/examples/deploy/quick_start/src/app/__init__.py b/python/examples/deploy/quick_start/src/app/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/python/examples/deploy/quick_start/src/app/pipe/__init__.py b/python/examples/deploy/quick_start/src/app/pipe/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/python/examples/deploy/quick_start/src/app/pipe/constants.py b/python/examples/deploy/quick_start/src/app/pipe/constants.py deleted file mode 100644 index 811418bf5e..0000000000 --- a/python/examples/deploy/quick_start/src/app/pipe/constants.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -Adapted from https://github.com/databrickslabs/dolly/blob/master/training/consts.py -""" - -# ----------------------------------------------------------------- -DEFAULT_MAX_SEQ_LENGTH = 1024 -IGNORE_INDEX = -100 - -# ----------------------------------------------------------------- -MODEL_NAMES = [ - "EleutherAI/pythia-70m", - "EleutherAI/pythia-160m", - "EleutherAI/pythia-2.8b", - "EleutherAI/pythia-6.9b", - "EleutherAI/pythia-12b", - "EleutherAI/gpt-j-6B", - "databricks/dolly-v2-3b", - "databricks/dolly-v2-7b", - "databricks/dolly-v2-12b", -] - -# ----------------------------------------------------------------- -INTRO_BLURB = ( - "Below is an instruction that describes a task. Write a response that appropriately completes the request." -) - -INSTRUCTION_KEY = "### Instruction:" -INPUT_KEY = "Input:" -RESPONSE_KEY = "### Response:" -END_KEY = "### End" -RESPONSE_KEY_NL = f"{RESPONSE_KEY}\n" - -# This is a training prompt that does not contain an input string. The instruction by itself has enough information -# to respond.For example, the instruction might ask for the year a historic figure was born. -PROMPT_NO_INPUT_FORMAT = f"""{INTRO_BLURB} - -{INSTRUCTION_KEY} -{{instruction}} - -{RESPONSE_KEY} -{{response}} - -{END_KEY}""" - -# This is a training prompt that contains an input string that serves as context for the instruction. For example, -# the input might be a passage from Wikipedia and the instruction is to extract some information from it. -PROMPT_WITH_INPUT_FORMAT = f"""{INTRO_BLURB} - -{INSTRUCTION_KEY} -{{instruction}} - -{INPUT_KEY} -{{input}} - -{RESPONSE_KEY} -{{response}} - -{END_KEY}""" - -# This is the prompt that is used for generating responses using an already trained model. It ends with the response -# key, where the job of the model is to provide the completion that follows it (i.e. the response itself). -PROMPT_FOR_GENERATION_FORMAT = f"""{INTRO_BLURB} - -{INSTRUCTION_KEY} -{{instruction}} - -{RESPONSE_KEY} -""" \ No newline at end of file diff --git a/python/examples/deploy/quick_start/src/app/pipe/instruct_pipeline.py b/python/examples/deploy/quick_start/src/app/pipe/instruct_pipeline.py deleted file mode 100644 index edcc1a643b..0000000000 --- a/python/examples/deploy/quick_start/src/app/pipe/instruct_pipeline.py +++ /dev/null @@ -1,261 +0,0 @@ -""" -Adapted from https://github.com/databrickslabs/dolly/blob/master/training/generate.py -""" -from typing import List, Optional, Tuple - -import logging -import re - -import torch -from transformers import ( - AutoModelForCausalLM, - AutoTokenizer, - Pipeline, - PreTrainedModel, - PreTrainedTokenizer, -) -from transformers.utils import is_tf_available - -if is_tf_available(): - import tensorflow as tf - -from .constants import END_KEY, PROMPT_FOR_GENERATION_FORMAT, RESPONSE_KEY - -logger = logging.getLogger(__name__) - - -def load_model_tokenizer_for_generate( - pretrained_model_name_or_path: str, -) -> Tuple[PreTrainedModel, PreTrainedTokenizer]: - """Loads the model and tokenizer so that it can be used for generating responses. - - Args: - pretrained_model_name_or_path (str): name or path for model - - Returns: - Tuple[PreTrainedModel, PreTrainedTokenizer]: model and tokenizer - """ - tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, padding_side="left") - model = AutoModelForCausalLM.from_pretrained( - pretrained_model_name_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True - ) - return model, tokenizer - - -def get_special_token_id(tokenizer: PreTrainedTokenizer, key: str) -> int: - """Gets the token ID for a given string that has been added to the tokenizer as a special token. - - When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are - treated specially and converted to a single, new token. This retrieves the token ID each of these keys map to. - - Args: - tokenizer (PreTrainedTokenizer): the tokenizer - key (str): the key to convert to a single token - - Raises: - ValueError: if more than one ID was generated - - Returns: - int: the token ID for the given key - """ - token_ids = tokenizer.encode(key) - if len(token_ids) > 1: - raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}") - return token_ids[0] - - -class InstructionTextGenerationPipeline(Pipeline): - def __init__( - self, - *args, - do_sample: bool = True, - max_new_tokens: int = 256, - top_p: float = 0.92, - top_k: int = 0, - **kwargs - ): - """Initialize the pipeline - - Args: - do_sample (bool, optional): Whether to use sampling. Defaults to True. - max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128. - top_p (float, optional): If set to float < 1, only the smallest set of most probable tokens with - probabilities that add up to top_p or higher are kept for generation. Defaults to 0.92. - top_k (int, optional): The number of highest probability vocabulary tokens to keep for top-k-filtering. - Defaults to 0. - """ - super().__init__( - *args, - do_sample=do_sample, - max_new_tokens=max_new_tokens, - top_p=top_p, - top_k=top_k, - **kwargs - ) - - def _sanitize_parameters( - self, - return_full_text: bool = None, - **generate_kwargs - ): - preprocess_params = {} - - # newer versions of the tokenizer configure the response key as a special token. newer versions still may - # append a newline to yield a single token. find whatever token is configured for the response key. - tokenizer_response_key = next( - (token for token in self.tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None - ) - - response_key_token_id = None - end_key_token_id = None - if tokenizer_response_key: - try: - response_key_token_id = get_special_token_id(self.tokenizer, tokenizer_response_key) - end_key_token_id = get_special_token_id(self.tokenizer, END_KEY) - - # Ensure generation stops once it generates "### End" - generate_kwargs["eos_token_id"] = end_key_token_id - except ValueError: - pass - - forward_params = generate_kwargs - postprocess_params = { - "response_key_token_id": response_key_token_id, - "end_key_token_id": end_key_token_id - } - - if return_full_text is not None: - postprocess_params["return_full_text"] = return_full_text - - return preprocess_params, forward_params, postprocess_params - - def preprocess(self, instruction_text, **generate_kwargs): - prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction_text) - inputs = self.tokenizer( - prompt_text, - return_tensors="pt", - ) - inputs["prompt_text"] = prompt_text - inputs["instruction_text"] = instruction_text - return inputs - - def _forward(self, model_inputs, **generate_kwargs): - input_ids = model_inputs["input_ids"] - attention_mask = model_inputs.get("attention_mask", None) - - if input_ids.shape[1] == 0: - input_ids = None - attention_mask = None - in_b = 1 - else: - in_b = input_ids.shape[0] - - generated_sequence = self.model.generate( - input_ids=input_ids, - attention_mask=attention_mask, - pad_token_id=self.tokenizer.pad_token_id, - **generate_kwargs, - ) - - out_b = generated_sequence.shape[0] - if self.framework == "pt": - generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:]) - elif self.framework == "tf": - generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:])) - - instruction_text = model_inputs.pop("instruction_text") - return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text} - - def postprocess( - self, - model_outputs, - response_key_token_id: Optional[int] = None, - end_key_token_id: Optional[int] = None, - return_full_text: bool = False - ): - generated_sequence: torch.Tensor = model_outputs["generated_sequence"][0] - instruction_text = model_outputs["instruction_text"] - - generated_sequence: List[List[int]] = generated_sequence.tolist() - records = [] - for sequence in generated_sequence: - - # The response will be set to this variable if we can identify it. - decoded = None - - # If we have token IDs for the response and end, then we can find the tokens and only decode between them. - if response_key_token_id and end_key_token_id: - # Find where "### Response:" is first found in the generated tokens. Considering this is part of the - # prompt, we should definitely find it. We will return the tokens found after this token. - try: - response_pos = sequence.index(response_key_token_id) - except ValueError: - logger.warning(f"Could not find response key {response_key_token_id} in: {sequence}") - response_pos = None - - if response_pos: - # Next find where "### End" is located. The model has been trained to end its responses with this - # sequence (or actually, the token ID it maps to, since it is a special token). We may not find - # this token, as the response could be truncated. If we don't find it then just return everything - # to the end. Note that even though we set eos_token_id, we still see this token at the end. - try: - end_pos = sequence.index(end_key_token_id) - except ValueError: - end_pos = None - - decoded = self.tokenizer.decode(sequence[response_pos + 1: end_pos]).strip() - - if not decoded: - # Otherwise we'll decode everything and use a regex to find the response and end. - - fully_decoded = self.tokenizer.decode(sequence) - - # The response appears after "### Response:". The model has been trained to append "### End" at the - # end. - m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL) - - if m: - decoded = m.group(1).strip() - else: - # The model might not generate the "### End" sequence before reaching the max tokens. In this case, - # return everything after "### Response:". - m = re.search(r"#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL) - if m: - decoded = m.group(1).strip() - else: - logger.warning(f"Failed to find response in:\n{fully_decoded}") - - # If the full text is requested, then append the decoded text to the original instruction. - # This technically isn't the full text, as we format the instruction in the prompt the model has been - # trained on, but to the client it will appear to be the full text. - if return_full_text: - decoded = f"{instruction_text}\n{decoded}" - - rec = {"generated_text": decoded} - - records.append(rec) - - return records - - -def generate_response( - instruction: str, - *, - model: PreTrainedModel, - tokenizer: PreTrainedTokenizer, - **kwargs, -) -> str: - """Given an instruction, uses the model and tokenizer to generate a response. This formats the instruction in - the instruction format that the model was fine-tuned on. - - Args: - instruction (str): _description_ - model (PreTrainedModel): the model to use - tokenizer (PreTrainedTokenizer): the tokenizer to use - - Returns: - str: response - """ - - generation_pipeline = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer, **kwargs) - return generation_pipeline(instruction)[0]["generated_text"] \ No newline at end of file diff --git a/python/examples/deploy/quick_start/src/config/bootstrap.sh b/python/examples/deploy/quick_start/src/config/bootstrap.sh deleted file mode 100644 index 950b749792..0000000000 --- a/python/examples/deploy/quick_start/src/config/bootstrap.sh +++ /dev/null @@ -1,14 +0,0 @@ -### don't modify this part ### -set -x -############################## - - -### please customize your script in this region #### -pip install langchain -pip install transformers -pip install accelerate -pip install "pydantic>=1.8.0,<2.0.0" - -### don't modify this part ### -exit 0 -############################## \ No newline at end of file diff --git a/python/examples/deploy/quick_start/src/main_entry.py b/python/examples/deploy/quick_start/src/main_entry.py deleted file mode 100644 index 82ff90155e..0000000000 --- a/python/examples/deploy/quick_start/src/main_entry.py +++ /dev/null @@ -1,67 +0,0 @@ -import os -from fedml.serving import FedMLPredictor -from fedml.serving import FedMLInferenceRunner -from langchain import PromptTemplate, LLMChain -from langchain.llms import HuggingFacePipeline -import torch -from transformers import ( - AutoConfig, - AutoModelForCausalLM, - AutoTokenizer, - TextGenerationPipeline, -) - -class Chatbot(FedMLPredictor): # Inherit FedMLClientPredictor - def __init__(self): - super().__init__() - PROMPT_FOR_GENERATION_FORMAT = f""""Below is an instruction that describes a task. Write a response that appropriately completes the request." - - ### Instruction: - {{instruction}} - - ### Response: - """ - - prompt = PromptTemplate( - input_variables=["instruction"], - template=PROMPT_FOR_GENERATION_FORMAT - ) - - config = AutoConfig.from_pretrained("EleutherAI/pythia-70m") - model = AutoModelForCausalLM.from_pretrained( - "EleutherAI/pythia-70m", - torch_dtype=torch.float32, # float 16 not supported on CPU - trust_remote_code=True, - device_map="auto" - ) - tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-70m", device_map="auto") - - hf_pipeline = HuggingFacePipeline( - pipeline=TextGenerationPipeline( - model=model, - tokenizer=tokenizer, - return_full_text=True, - task="text-generation", - do_sample=True, - max_new_tokens=256, - top_p=0.92, - top_k=0 - ) - ) - self.chatbot = LLMChain(llm=hf_pipeline, prompt=prompt, verbose=True) - - def predict(self, request:dict): - input_dict = request - question: str = input_dict.get("text", "").strip() - - if len(question) == 0: - response_text = "" - else: - response_text = self.chatbot.predict(instruction=question) - - return {"generated_text": str(response_text)} - -if __name__ == "__main__": - chatbot = Chatbot() - fedml_inference_runner = FedMLInferenceRunner(chatbot) - fedml_inference_runner.run() \ No newline at end of file diff --git a/python/fedml/api/modules/model.py b/python/fedml/api/modules/model.py index ca5d0b95c1..a02e674f47 100644 --- a/python/fedml/api/modules/model.py +++ b/python/fedml/api/modules/model.py @@ -21,6 +21,9 @@ def create(name: str, model: str = None, model_config: str = None) -> bool: return True else: return False + elif model.startswith("tutorial:quick_start"): + # ../../../python/examples/deploy/quick_start + return False else: # TODO: Support arbitrary model creation from GitHub / Nexus AI Job Store click.echo("Model {} is not supported yet.".format(model)) diff --git a/python/examples/deploy/quick_start/__init__.py b/python/fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py old mode 100644 new mode 100755 similarity index 100% rename from python/examples/deploy/quick_start/__init__.py rename to python/fedml/computing/scheduler/model_scheduler/device_client_runner_deprecated.py diff --git a/python/fedml/computing/scheduler/model_scheduler/sample_model/README.md b/python/fedml/computing/scheduler/model_scheduler/sample_model/README.md deleted file mode 100644 index fcb51bd792..0000000000 --- a/python/fedml/computing/scheduler/model_scheduler/sample_model/README.md +++ /dev/null @@ -1,57 +0,0 @@ -## 1 Device Login: -Login as fedml cloud device: -```fedml model device login $user_id_or_api_key -c``` - -Login as on premise device: -```fedml model device login $user_id_or_api_key -p``` - - -## 2. Model Card: -Create local model repository: -```fedml model create -n $model_name``` - -Delete local model repository: -```fedml model delete -n $model_name -f $model_file_name``` - -Add file to local model repository: -```fedml model add -n $model_name -p $model_file_path``` - -Remove file from local model repository: -```fedml model remove -n $model_name -f $model_file_name``` - -List model in the local model repository: -```fedml model list -n $model_name``` - -Build local model repository as zip model package: -```fedml model package -n $model_name``` - -Push local model repository to ModelOps(open.fedml.ai): -```fedml model push -n $model_name -u $user_id_or_api_key``` - -Pull remote model(ModelOps) to local model repository: -```fedml model pull -n $model_name -u $user_id_or_api_key``` - - -## 3. Model Package: -Create local model repository: -```fedml model create -n $model_name``` - -Delete local model repository: -```fedml model delete -n $model_name -f $model_file_name``` - -Add file to local model repository: -```fedml model add -n $model_name -p $model_file_path``` - -Remove file from local model repository: -```fedml model remove -n $model_name -f $model_file_name``` - -List model in the local model repository: -```fedml model list -n $model_name``` - -Build local model repository as zip model package: -```fedml model package -n $model_name``` - -## 4. Model Deploy: -``` -fedml model deploy -n $model_name -dt $device_type(md.on_premise_device/md.fedml_cloud_device) -d $master_device_id -u $user_id_or_api_key -p $deployment_extra_params -``` diff --git a/python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model.bin b/python/fedml/computing/scheduler/model_scheduler/sample_model/fedml_model.bin deleted file mode 100644 index d98296eb617c99fdf257707704fa9c61c28bb308..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1476451 zcmZUadq7P4_x>lM5C%ylj3gsr5Nh5FgCv9yg=El&B9$Z|jBdKA=|0kBbiX&cPW!#8 zNl6GHP6%;aLg=JJhw$6qzkmH(|FrkoYd!0EUdwjfV08{hQ zd5`y=xdN*dV>vtizaR0(Gs1PcPS}0Fm7KJXM&*oyxb$EwsP+g^IZqSZUY`Qj!!qpn znvcBGHBeYy3_WN4K+U-T%Rjn9I?*h*QC3>3(qEo`B_PfUp-cqar-Z70fCi9j3}T&tS__DLzI`J@0` z##GYs>H?glX^o0I^PsEa3{e!FAjW5^z~yc?Dg2oX+0TN|{Hz1vl!Yoar^LhSnjX5j zCJL-|y&+!97deZ4goC;YL9j$0IAcyQS_c)-ad8O@cVB}Cw#VQ}Wg;kRheM-J2s$%z z;(Nmgb1w`BE!R5eY+4ITs29TF7o_#|J7On&Ncy!o*s1iSg5FBv`M{Tm9jqA*?LZVg zu^=r=lc;cTE^z;SOmh<=k(ZW-a~8-U{EY~8>+C3RsRD9NjmEtj%rN;x9xA`AMqjK2 z!Q*R;^~_SznsgNes<*_E69XLgGr}X$X~<0+4{hmO*L%{dgWN_M-3x57^sI%=f|=;@YY|zPJ`rt(3{cVXT4>ctpztXg?!-mo zt_OP9d|L~xerBVsU?2Rw%LiKrYK0HN_0g!ToZ3#whE1(2@mh@^&YiD~t!J(f&DtRl z_qq^uN7PgK*4Z>TZZ$S*=7a6nQlVn-UJxl_X|S>hdcL?*PS-U$>2?jCE>&QcXc4CF zzXm4t-k47sF>+}#s8*L)nE%Wmb)n@rFhPdlt4mNVFC8U6H&Wk+(Qxsa7~6{f2M(NB z;A=UL&KZ9NP7hAQa^pBO+o>W$Wd||vsV=^bN~TLjoWR;2f!KfZJQ3eC!0yyjAZv&s zvVap1QFs;{+AHX{X=TuT*$+OvtAH&&mKe4!8f7|Gv^$^(?2;YP>T?JvZ4t*@$U~c_ zg*aQh9z(vl0!PghHv2_@^*`R&-cU$;oKhLlr|TAct`8~hH$`RJAR6KnPCFmf2yu2E zRV1s!hP+g4^?XG}(E?aIo&jsrg5UpM25xk+u)pFm+8vt+oak*JwRa)rudImf+|`6L z=%9JeEFOHgSqhSAJeqnZ4g}XssM8%CeAaUX@3luG$72r-8Dt5(8;_`X%MNHM%mdF^ z1VJ|)>Yp6LEnkn~s@!d;8giGp05K@uU`%Y!eJ1T+5}+%5DS7l>h57B5p)>v`txS`_ zfjKj=?shIZMEDTN(x>Fk;qxGI3$HGHFT$;}vXJj&go^jiNqd+ldE1)=n*V9g7Kd<< zde;bJ`j%nXs3??{?F9E?F>Zf(0OJ;ogMqtGsrYjZomeyzx&O7(T+wN0o_vnjI0b-P zSTdbrQirfFk`DB}qPDkmV1;ud=uY1V`zCKeL;rP{QYAs_zdjLO(ilc1w-ml!a~`^k ztBBE*f2nzlF$SKugyu7UlVqz{kbLoDTBgK;`G4ogkK7RGIC-7?cXABcY`RKdMGl%@ zcLwQ#JYl!@dC{zwV!eMA>ABvJO zM^O+myE;%(#hCwFb9GZQvCfc3PyD{ zfb%deh}_wCzGNy&;(rU3|0d98tx&YpSb&Efu0&}lpnBgL=$Ud~*rB@!UJXxz_HQMy z>fSVLcHIKo{tiK>Q;DGIFq~BFNJhTiAyPUr2trDVsrI%DpnKY$s7FVUn14%QbfpkA zbCQWh_yIH@s{zT+i@?^Rgnk&80ipg;nA9nQ)~8p6PdsuEUWI|RNj7S3i-eB1f%G*u zp1Qb3Vz*fxDG&944gHDuO>!RStq|lV{Gc&gZ7}(d5IkEOsk})GX1wu6us#bO&yJ$f zY!xXywjM+gxg>v+Extdo72hjYqv_ZH?6R^TOL^D8=g1&@eOf{G88Ucxbrg14nxePI z9uQ{{ALxzW9acm|eqMnH0OC7tr?1n}qkd9zqf<&>$xanXo9(o@NYEk9grg@>mT1 zUo5m=oJ15h56IB3V-SZ+NX!BWa&8iH{hFO<>0FN?MwIUVRR!XS|08mpr;Jxt3}|!f z;h?b@n!5#&?3G!dUVZ>Af0;ths6s|{sKBCYg&r7PsUZWCD4ISzhmBXP^&coH7zS>m(?ujsjUH^>n zQajreaEMn!$HO)DZyG9x-aBmVU7S)CC0S-&kFDfD#4Q_HSoT*0K3|4z-Egt z$m6zQt|}Wcx2;0Iqx-?>^Hxlq7XXirXM<(eO=4HqMwOzopnwr1x9Bl3vB<{#%c7Cj zt%3Zf^T-|peRN5EL(MPN60@OlkoG!4rerHv?OTlUC;KS+4~IoFEU{1T40NXs0qx;& zpyV_Xc#%$fY(1!p(OPQa?uk27U9r)okb-(L92{MNXgY7k;-BV&*I% zs2>{%oHJ8N*63rn=N|rIvOI=GNCSiF=~41;PJn+5Efa0!o-ZNYkzR@ zFaw2=2W6YoPk}oIYJtf)kRiqHdKh2+S-%vo{hneY!~c_fmM5T#jzX z!>F&;JJP;(GF#IF!obodkgmGTcse9wf!#HXD{dlwF(a_>YzP{EsDq7z^--|5kKD1J zi*YMzNavvqD0#dETBpmPw=D?O-%kOjdkS#sXu#V3gSd0`c5GkgO3HV}LfcsnIQr~7 zs`l4HTy-SEi|wHGBNP1ph%tSf58fLdk8WSC!Ry(Y(BGq?kGD8u+<7PPU7trpyF+p4 zHVyRr%Lja?-Xs%Mr*lLUm4Mk-VLO=4y_vn zp!+t5n%dP;{z3!#?LXr><7Z zuRN#HgamNl-X$(^Qff1)3|?ab?AFUg-F;T1Cx>G1#thKRpF$e9>VxAg5eQ~;Vem>B zuFSVUb+Z%{pagn`gfZhb?ZCp-bwrV&N5)14pyO^QxStY--R5V(%-h5}Sf?j!~p zLDb~EB^GnTQ88i~9b1`*X0b`sYknZeZ+xS(PlVznpOxs=a0s-=JEKA*hEePQp8byr zmf!kGPYp9eP5X)T>B~kG->F8Iz6k9ED^inA&n>)Zyq%IN=wHk)JpiU$X=s z`4nNdQ!btF6bXDj5@*k9>hh(W4E#@w?=_dO^>~BH^i2a!^Pa^Yre=Y8RRi$0KV;VL zP~y$GBhf3S4A=e+NA8C#bo)CS6W3C7njQ$;PuJZ>hdV{o`K1B0JkL(iE%iRbFuB&|Uk z6F=Ew$lg%UvAhC}(UDMoI|f?G2f`2E3@HI?Q0nuQF>km{&sJ?h(V`Hln06AyOS*-n z>OtVSpoDTh#+V1`T)@9FLa=d4G-Mh1;A{UmpmeYW{)lqq`NflF+x>9!axLD_&cHcc zB2-K?0|5tV{tyZ5In#iP61Jn!cMXWx%*DP%abW6Kg`208;O>v*ST;l#p&cAaKtaI9nEqx%=)?^o5&;&C+ktT( zeWB}r)nGGwJn-{G)Mz=o!M41o~v@XypeZKCw zpy?hDiuLD6prQoy_9^kkEqCOv_(DAw3qdflnd!Q$1Ah5xcqM8%&NolQi>*rZ3se!a zt#9b4xtb_Z=26EHodj%^F#W^?w7XmkT5mGoWl01oK9w;7W)?MHw-t<1{6IC(%Sa{& zsyQ=H(T!&H*oozYQ`IDl3(tn6Iw?H*ht(`s;uyt0VUX>*9(7IrM@GKW$EJ_fn0!%; zi=H=tbkTYCnVbg8UCBh1`;SFP-bNbss}?0vGuop%0byhD{@F^k zN^)S2Pbn5A#zOGpD-c$&7F&0o1W|RI#j}!8=wZJGduBhU#UYDP;3fkjn<8k{TMV{? 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